evolution
FIFTH EDITION
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evolution
FIFTH EDITION
DOUGLAS J. FUTUYMA
Stony Brook University
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MARK KIRKPATRICK
University of Texas at Austin
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About the cover
Atlantic horseshoe crabs (Limulus
polyphemus) laying eggs at low
tide. These relatives of scorpions
and spiders are living fossils:
they closely resemble fossils
in the Ordovician period, 480
million years ago — long before
the evolution of dinosaurs.
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Library of Congress Cataloging-in-Publication Data
Names: Futuyma, Douglas J., 1942- author. | Kirkpatrick, Mark, 1956- author.
Title: Evolution / Douglas J. Futuyma, Stony Brook University, Mark Kirkpatrick,
University of Texas at Austin.
Description: Fifth edition. | New York, NY : Oxford University Press, [2023] | Includes
bibliographical references and index. | Summary: “Evolution 5e addresses major
themes, including the history of evolution, evolutionary processes, adaptation, and evolution as an explanatory framework-at levels of biological organization ranging from
genomes to ecological communities. Extensively revised for clarity and currency, this
new edition of Evolution presents this field of evolution as a living, breathing science.
Updated coverage in evolutionary genetics and genomics illustrates the rapidly moving
science of evolution and emphasizes the interplay between theory and empirical tests of
hypotheses, acquainting students with the process of science. Written for undergraduate
students in Psychology and Biology, the text is available in a dynamic and interactive
Enhanced eBook that allows student to hone their problem solving and data analysis
skills while seeing Evolution in the context of their life through video, animations and
more”-- Provided by publisher.
Identifiers: LCCN 2022030795 (print) | LCCN 2022030796 (ebook) | ISBN 9780197619612
(hardcover) | ISBN 9780197619636 (epub)
Subjects: LCSH: Evolution (Biology)
Classification: LCC QH366.2 .F87 2023 (print) | LCC QH366.2 (ebook) | DDC 576.8--dc23/
eng/20220719
LC record available at https://lccn.loc.gov/2022030795
LC ebook record available at https://lccn.loc.gov/2022030796
987654321
Printed in the United States of America
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To Phoebe, Audrey, Annabelle, and Matthew:
may your lives live up to your dreams
DJF
To Sharon, who kept the ship afloat, and to Don,
who first pointed the prow in this direction.
MK
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Brief Contents
UNIT I An Idea that Changed
the World 1
CHAPTER 1 Evolutionary Biology 3
CHAPTER 2 The Tree of Life 29
CHAPTER 3Natural Selection
and Adaptation 59
UNIT II How Evolution Works 81
CHAPTER 4 Mutation and Variation 83
CHAPTER 5The Genetical Theory of
Natural Selection 107
CHAPTER 6Genetic Drift: Evolution
at Random 139
CHAPTER 7The Evolution of
Quantitative Traits 167
CHAPTER 8 Evolution in Space 199
UNIT III The Products of Evolution 223
UNIT IV Interactions Between
Individuals and Species 315
CHAPTER 12 All About Sex 317
CHAPTER 13 Cooperation and Conflict 345
CHAPTER 14Evolutionary Ecology:
How to Be Fit 373
CHAPTER 15Evolutionary Ecology:
Interactions among
Species 397
UNIT V The History of Life 423
CHAPTER 16Phylogeny: The Unity and
Diversity of Life 425
CHAPTER 17 The History of Life 451
CHAPTER 18The Evolution of Biological
Diversity 487
CHAPTER 19Macroevolution: Evolution above
the Species Level 519
CHAPTER 9 Species and Speciation 225
UNIT VI Evolution of Homo sapiens 547
CHAPTER 10The Evolution of Genes
CHAPTER 20The Evolutionary Story of
and Genomes 261
Homo sapiens 549
CHAPTER 11Evolution and Development 285
CHAPTER 21 Evolution and Society 575
UNIT VII A Statistics Primer 601
CHAPTER 22 A Statistics Primer
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Contents
UNIT I An Idea that Changed the World 1
1
Evolutionary Biology 3
1.1 The Evolution of SARS-CoV-2
Evolutionary biology since the synthesis
4
1.2 “Nothing in Biology Makes Sense Except in
the Light of Evolution”
8
1.3 What Is Evolution? Is It Fact or Theory?
9
1.4 The Evolution of Evolutionary Biology
10
Before Darwin 11
Charles Darwin
15
1.6 Evolutionary Biology after Darwin
The evolutionary synthesis
1.8 Philosophical Issues
21
22
Ethics, religion, and evolution
23
16
17
2.1 The Tree of Life, from Darwin to Today
29
30
■ BOX 2.1 Classification, Taxonomic Practice,
and Nomenclature 33
37
■ BOX 2.2 Models in Evolutionary Biology
40
Branches of a phylogenetic tree
sometimes rejoin 41
45
45
Inferring the history of character evolution
Estimating time of divergence
Inferring phylogenies: An introduction
Not only organisms have “phylogenies”
2.4 How Phylogenies are Useful
Classification
34
2.3 Variations on the Phylogenetic Theme
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1.7 How Evolution Is Studied
SUMMARY 26
The Tree of Life
2.2 Phylogenetic Trees
■ BOX 1.1 Fundamental Principles of
Biological Evolution 20
How to Learn Evolutionary Biology
(Read This!) 24
13
1.5 Darwin’s Evolutionary Theory
2
19
Patterns of evolution
46
47
■ BOX 2.3 Evidence for Evolution
41
45
48
Phylogenies describe patterns of
diversification 54
SUMMARY 56
42
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VIII Contents
3
Natural Selection and Adaptation
3.1 Adaptive Evolution Observed
3.2 Natural Selection
61
3.4 The Nature of Adaptations
70
Selection of and selection for
70
64
Recognizing adaptations
The meaning of natural selection 64
Natural selection and chance
Selection of organisms and groups
75
3.6 Natural Selection and the Evolution
of Diversity
66
Selfish genes and unselfish behaviors
Species selection
71
3.5 Imperfections and Constraints
65
The effective environment depends on
the organism 66
3.3 Levels of Selection
59
67
68
76
3.7 What Not to Expect of
Natural Selection
77
SUMMARY 78
69
UNIT II How Evolution Works 81
4
Mutation and Variation 83
4.1 The Machinery of Inheritance
84
4.2 The Inheritance of Variation
86
Gene mixing by segregation
Mutation rates
93
93
SUMMARY 104
94
Whole genome duplication
5
96
The Genetical Theory of Natural Selection
5.1 We Can Watch Natural Selection and
Evolution Happening in Real Time
108
5.2 Evolution by Natural Selection and
Inheritance
111
Fitness: The unit we use to measure
selection 111
5.3 Positive Selection and the Spread of
Beneficial Mutations
112
■ BOX 5.1 Evolution by Positive Selection
on a Single Locus 113
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102
What We Don’t Know 104
94
Structural mutations
100
100
4.6 Nongenetic Inheritance
93
Point mutations
Germ line mutations and somatic mutations
4.5 Is Mutation Random?
4.3 Mutation: The Ultimate Source
of Variation
97
■ BOX 4.1 Estimating Mutation Rates 98
90
Gene mixing with asexual inheritance
96
97
Effects of mutations
88
Gene mixing by recombination
Gene mixing by hybridization
4.4 Rates and Effects of Mutations
The rate of evolution
107
114
Chance and adaptation: The probability that
a beneficial mutation spreads 119
5.4 Natural Selection Has Side Effects
120
Hitchhiking: When one allele goes for a ride
with another 121
5.5 Balancing Selection Preserves
Genetic Variation
Overdominance
124
125
Other kinds of balancing selection
126
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Contents
5.6 Selection That Favors the Spread of
Common Alleles
5.8 Many Mutations Are Harmful
128
A mutation-selection balance
Underdominance: When heterozygotes
suffer 129
The mutation load
Positive frequency-dependent selection
130
IX
133
133
134
SUMMARY 135
5.7 The Evolution of a Population’s
Mean Fitness 130
The fundamental theorem of natural selection
and the adaptive landscape 131
6
Genetic Drift: Evolution at Random
6.1 What Is Random Genetic Drift?
6.2 How Strong Is Genetic Drift?
Populations that change in size
6.3 The Genealogy of Genes
140
6.6 The Neutral Theory of Molecular
Evolution
144
of Adaptation
147
The MK test
151
Crossing an adaptive valley by drift
153
156
What We Don’t Know 163
The Evolution of Quantitative Traits
168
Quantitative traits evolve by changes in
allele frequencies 170
7.2 Selection on Quantitative Traits
171
Measuring the strength of selection
175
176
Adaptation from standing genetic variation
and from new mutations 179
Can adaptation rescue species from
extinction? 180
7.4 Connecting Variation in Genes to Variation
181
167
7.5 Artificial Selection
7.3 Evolution by Directional Selection
162
SUMMARY 163
The fate of beneficial mutations in
large populations 156
in Phenotypes
161
Divergence among populations
6.5 Genetic Drift and Natural Selection
7.1 Genotypes and Phenotypes
159
Synonymous versus nonsynonymous
differences 160
within Species 149
7
157
6.7 Searching the Genes for Signatures
145
6.4 Genetic Drift and Genetic Variation
Estimating population size
139
182
7.6 The Evolution of Correlated Traits
184
Evolutionary constraints and trade-offs
185
The causes of genetic correlations
7.7 Phenotypic Plasticity
7.8 The Genetic Basis of
188
188
Quantitative Traits
189
Quantitative trait loci
189
What We Don’t Know 194
SUMMARY 194
When genes interact: Dominance and
epistasis 182
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X Contents
8 Evolution in Space 199
8.1 Patterns in Space
8.2 Genes in Motion
200
8.4 Gene Flow, Genetic Drift,
and Detecting Selection
204
How is gene flow measured?
8.3 Gene Flow versus Selection
213
Gene flow, local adaptation, and drift
208
A migration-selection balance
Clines
The effects of history
206
212
8.5 The Evolution of Dispersal
208
214
214
8.6 The Evolution of a Species’ Range
209
217
What We Don’t Know 219
Tension zones 211
SUMMARY 220
UNIT III The Products of Evolution 223
9
Species and Speciation
9.1 What Are Species?
227
9.4 The Origins of Reproductive Isolation
■ BOX 9.1 Diagnosis of a New Species
9.2 Reproductive Isolation
225
232
Ecological speciation
249
Mutation order speciation
232
250
Prezygotic barriers
235
Speciation by genetic conflict
251
Postzygotic barriers
236
Speciation by sexual selection
252
Speciation by polyploidy
How fast does reproductive
isolation evolve? 238
Allopatric speciation
241
Sympatric speciation
242
Speciation by hybridization
240
9.6 Speciation and Diversification
246
254
255
9.5 The Genomics of Speciation
Speciation with limited gene flow
Secondary contact
252
Speciation by random genetic drift
9.3 The Geography of Speciation
249
256
256
What We Don’t Know 257
246
SUMMARY 258
10 The Evolution of Genes and Genomes 261
10.1 The Birth and Death of Genes
Gene families
10.3 The Evolution of Gene and
263
Chromosome Structure
267
The death of a gene
Chromosome evolution
269
10.2 Evolution of Protein-Coding Genes
269
Evolution of coding regions by genetic drift
275
10.4 Evolution of Genome Size and Content
Genomes large and small
277
277
Genetic parasites and transposable elements 278
Evolution of coding regions by
positive selection 270
Evolution of gene expression
270
274
272
Routes to the evolution of the smallest and
largest genomes 280
What We Don’t Know 281
SUMMARY 282
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XI
Contents
11
Evolution and Development 285
11.1 Comparative Development
and Evolution
11.4 How Development Affects
287
11.2 Gene Regulation
Morphological Evolution 301
Facilitation
292
Developmental bias
■ BOX 11.1 Some Methods in
Developmental Genetics 293
304
Constraints on adaptive evolution
Hox genes and the genetic tool kit
294
305
11.5 Phenotypic Plasticity, Canalization,
11.3 Developmental Bases of
Phenotypic Evolution
302
and Genetic Assimilation
297
307
Does phenotypic plasticity contribute
to evolution? 309
A study in developmental evolution:
Tetrapod limbs 299
What We Don’t Know 311
A study in developmental evolution:
Flower symmetry 300
SUMMARY 312
UNIT IV Interactions Between Individuals and Species 315
12 All About Sex 317
12.1 The Astonishing Diversity of Ways That
Organisms Have Sex 318
What are females and males?
Microbial sex
The evolution of sex ratios
319
12.6 Why Is There Sex?
320
330
331
333
Advantages to sex in changing environments 335
12.2 What Is Sexual Selection?
320
Why are males sexually selected?
Selective interference favors sex and
recombination 336
323
The evolution of recombination in sexual
populations 338
12.3 Sexual Selection by Male-Male
Competition
12.5 Sex Determination and Sex Ratios
325
Sexual selection in flowering plants
12.7 Selfing and Outcrossing
327
12.4 Sexual Selection by Female Choice
327
339
What We Don’t Know 341
SUMMARY 342
13 Cooperation and Conflict 345
13.1 The Evolution of Social Interactions
346
13.2 Shared Genes and the Evolution
of Altruism 347
■ BOX 13.1 Calculating Relatedness
349
■ BOX 13.2 Altruistic Mating Displays in Turkeys 350
Spite
Unrelated Individuals
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352
■ BOX 13.3 Evolutionarily Stable Strategies
The Family
355
Conflict between mates
355
357
Parent-offspring conflict
352
354
13.4 Conflict and Cooperation in Close Quarters:
Murder in the family
352
13.3 Cooperation among
Reciprocity
357
Eusocial animals: The ultimate families
359
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XII Contents
13.5 Levels of Selection
Selfish DNA
360
13.6 Cooperation and Major Evolutionary
363
What We Don’t Know 368
Transitions
361
Selfish mitochondria
Selfish symbionts
Group selection
364
SUMMARY 368
364
Species selection
366
14
Evolutionary Ecology: How to Be Fit 373
Life histories, fast and slow
14.1 Life History Traits as Components
of Fitness 375
Life histories and mating strategies
14.4 Phenology
14.2 The Evolution of Life History
Characteristics
Number of offspring
390
How specialization evolves
381
391
Experiments on niche evolution
382
the Population Growth Rate
387
388
Generalists
14.3 Density and the Evolution of
385
14.5 Ecological Niches: Specialists and
377
378
Age at maturity
384
Semelparous and iteroparous reproduction
Fitness in populations with
overlapping generations 376
Senescence
366
393
What We Don’t Know 393
SUMMARY 394
383
15 Evolutionary Ecology: Interactions among Species 397
15.1 Coevolution and Interactions
among Species
15.3 Mutualisms
498
15.2 The Evolution of Enemies and Victims
Aposematism and mimicry
Plants and herbivores
401
404
15.4 The Evolution of Competitive
Interactions
414
15.5 Evolution and Community Structure
405
416
What We Don’t Know 419
Parasite-host interactions and
infectious disease 406
The evolution of virulence
409
SUMMARY 419
407
UNIT V The History of Life 423
16
Phylogeny: The Unity and Diversity of Life
16.1 Estimating Phylogenies
Reconstructing ancestors
426
Why estimating phylogenies can be hard
16.2 Methods for Estimating Phylogenies
16.3 How Do We Use Phylogenies?
Dating evolutionary events
428
434
437
437
Discovering the history of genes and cultures 438
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425
16.4 Studying Adaptations:
The Comparative Method
16.5 Classification
439
441
443
What We Don’t Know 446
SUMMARY 447
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Contents
XIII
17 The History of Life 451
17.1 Some Geological Fundamentals
The fossil record
452
454
17.6 Mesozoic Life
470
17.7 The Cenozoic Era
477
17.2 The Origin of Life
454
The modern world takes shape
17.3 Precambrian Life
457
The adaptive radiation of mammals
17.4 The Origins of Animal Diversity
17.5 Paleozoic Life
462
The colonization of land
Paleozoic life on land
18
Pleistocene events
482
What We Don’t Know 484
467
469
The Evolution of Biological Diversity
487
A model of changes in diversity
489
18.2 Historical Explanations of
Geographic Distributions
499
Studying diversity in the fossil record
493
Rates of extinction and origination
Dispersal
493
Mass extinctions
Vicariance
494
504
The shapes of phylogenies
Geographic Range 497
Diversity
498
Biological Diversity
508
509
18.8 Geographic Variation in Species
497
18.4 Studying Changes in
505
18.7 Has Species Diversity Reached a Limit?
497
Interacting species
501
502
18.6 Phylogenetic Studies of Diversity
18.3 Ecological Limits on
Ecological niches
500
18.5 Diversity through the Phanerozoic
492
Extinction
Dispersal
479
SUMMARY 484
464
The end-Permian mass extinction
18.1 Biogeography
459
477
513
What We Don’t Know 515
SUMMARY 516
499
19 Macroevolution: Evolution above the Species Level 519
19.1 The Origin of Major Forms of Life
The origin of mammals
520
19.2 Gradualism and Saltation
Complex characteristics
19.3 The Evolution of Novelty
523
525
527
Incipient and novel features:
Permissive conditions and natural
selection 527
Homology and the emergence of novel
characters 529
520
19.4 From Microevolution to
Macroevolution 533
Rates of evolution
533
Speciation and character evolution
19.5 Is Evolution Predictable?
19.6 Trends and Progress
535
536
538
Trends: Kinds and causes 538
Are there major trends in the history of life?
The question of progress
540
543
What We Don’t Know 543
SUMMARY 544
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XIV Contents
UNIT VI Evolution of Homo sapiens 547
20
The Evolutionary Story of Homo sapiens 549
20.1 Where Did We Come From?
Our closest living relatives
20.5 Diet and Agriculture: A Revolution in
550
Our World
550
How we differ from other apes
■ BOX 20.1 Domesticated Plants and Animals 564
552
20.2 Our Ancestry: Hominins through Time
553
20.6 Natural Selection, Past and Present
Our genetic loads
20.3 The Appearance of Modern
Homo sapiens
The human history of hybridization
558
The diversity of human populations
559
565
566
Natural selection and evolution in
our lifetimes 567
557
20.4 Brain and Language
562
Evolutionary mismatches
20.7 The Evolution of Culture
560
568
569
SUMMARY 571
21 Evolution and Society 575
21.1 Science and Skeptics
How science works
Creationism
Agriculture and natural resources
576
Conservation
576
585
586
■ BOX 21.2 The Current Extinction Crisis 587
577
The evidence for evolution
Health and medicine
578
21.3 Evolution and Human Behavior
■ BOX 21.1 Refuting Anti-evolutionary
Arguments 579
594
Variation in cognitive and behavioral traits
21.2 The Uses and Implications of
Evolutionary Science
590
Human behavior: Evolution and culture
584
Understanding nature and humanity
Practical applications of
evolutionary science 584
594
596
597
SUMMARY 598
UNIT VII A Statistics Primer 601
22
A Statistics Primer
22.1 Probability Distributions
22.2 Descriptive Statistics
604
22.4 Likelihood
613
SUMMARY 614
605
22.3 Estimation and Testing Hypotheses
603
610
Appendix A-1
Glossary G-2
Literature Cited LC-2
Index I-2
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Preface
Evolution is the most comprehensive unifying theme in
biology. All organisms, from bacteria to humans, descended
from a single ancient common ancestor. Hence, all their characteristics—their DNA sequences, biochemical pathways,
physiological functions, anatomy, life span, behavior, ecological way of life, geographic distribution—have evolved.
Whatever your interest in living things might be—biochemistry, genetics, brain science, behavior, ecology, the diversity
of life—it will be enlarged and illuminated by an evolutionary perspective. For example, if you are interested in a medical profession, you will benefit from knowing about the
evolution of the human body and of organisms that cause
disease. You will find in Chapter 1, for instance, how critical it is to understand the rapid, ongoing evolution of the
coronavirus that has killed or sickened countless people and
is wreaking economic turmoil as we write these words. And
beyond the central place of evolution in biology, it has been
a key concept in the social sciences and in philosophy: when
Darwin published The Origin of Species in 1859, he moved
the explanation of living beings—including humans—from
the realm of theology into the realm of science.
We the authors have been fascinated by the diversity of
living things all our lives, and have been fortunate in being
able both to contribute to understanding the evolution of
diversity and to share this wonderful world of knowledge
with students. We have written this book partly because
writing it brings the pleasure of organizing and expanding
our own knowledge, but mostly because we want to share
it more widely. We treat the two great questions that biologists ask about evolution: What has happened? (What has
been the history of life?) And how has it happened? (What
are its causes?)
Organization
Unit I, An Idea that Changed the World, begins with our
conviction that to understand evolution (or any other area
of study), it is important to know something of its history—
how the questions and answers have changed (evolved!)
00_EVOL5E_FM.indd 15
over time. We then sketch how the history of life on Earth
and the mechanisms of evolution are studied. In Unit II,
How Evolution Works, we explore the fundamental processes by which species evolve. Natural selection and genetics are prominent in these chapters. Unit III is devoted to
The Products of Evolution: how new species, their genomes,
and their developmental processes evolve. In Unit IV, Interactions between Individuals and Species, we delve into the
particular processes by which diverse characteristics have
evolved. We recount some wonderful insights into the evolution of sexual reproduction, cooperation within species, life
span and reproductive rates, and interactions between species, such as parasitism and mutualistic symbiosis. Unit V,
The History of Life, brings us to the “tree of life”—how
living things are related to each other. We also survey the
grand story of what has happened in the four billion years
since the origin of life, and how biological diversity has
changed in time and space. In Unit VI, Evolution and Homo
sapiens, we turn to our own species, with a chapter on the
evolutionary history of humans, and a chapter on the uses
and implications of evolutionary science for society today.
Unit VII, provides a short introduction to statistics, a field
that is essential to the study of evolution.
New to this Edition
Driven in part by the genomics revolution, evolutionary
biology is now one of the most rapidly advancing areas of
science. Changes have been made throughout the book that
reflect facts discovered in the last five years. The most extensive updates are in the discussion of the last million years of
evolution of humans and our close relatives (Chapter 20).
In this chapter, we describe the exciting research of Svante
Pääbo in the field of paleogenomics. Pääbo was awarded the
2022 Nobel Prize in Physiology or Medicine for his research
in sequencing the Neanderthal's genome and discovery of
the new hominin, Denisova.
The artwork has been extensively revised with the goal
of making the figures conceptually clear and graphically
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XVI Preface
compelling. Some chapters (e.g. Chapters 11 and 19) have
been almost completely redrawn. In addition, each section of this new edition now opens with Learning Objectives
that focus the reader’s attention on the key concepts. Many
chapters now include a short section, What We Don’t Know.
This both identifies future points of growth in evolutionary
biology and reinforces the key point that evolutionary biology is an active and dynamic field of science.
Emphasis on quantitative material has been added in
two ways. Chapter 2 introduces the crucial concept of a
mathematical model. These are important throughout evolutionary biology, and they make appearances at several
points in the book (e.g. Chapters 4, 5, 6, 7, and 13). Second,
the Problems and Discussion Topics at the end of each chapter,
which have been extensively overhauled, now include more
quantitative exercises.
The biggest single innovation in this edition is the new
enhanced e-book. Each chapter incudes an opening video or
audio vignette narrated by the authors that offers intriguing
facts and images to capture the reader’s imagination. End of
section practice lets students test themselves on content covered in the section. Chapters also include interactive figures
with quizzes that promote active learning.
For this edition, we sought to emphasize contributions
to evolutionary biology by researchers and scholars from
marginalized groups. Throughout the text, we cite the
research of women and biologists of color, with particular
attention to the work of emerging scholars.
Acknowledgements
The authors are most grateful to the many colleagues and
friends who generously contributed their knowledge and
time to improve this book. David Hall authored most of the
Problems and Discussion Topics and the on-line exercises.
This book was substantially improved by comments from
and discussions with Anurag Agrawal, Diego Ayala, Claudia
Bank, Dean Bobo, Deborah Bolnick, Andrew Brower, Roger
Butlin, Shana Caro, Andrius Dagilis, Charles Davis, Erika
Deinert, Anthony Di Fiori, Lawrence Gilbert, Shyamalika
Gopalan, Fred Grine, Matthew Hahn, Arbel Harpak, Brenna
Henn, David Hillis, Robin Hopkins, David Houle, David
Jablonski, Nina Jablonski, Daniel Jeffries, Matheiu Joron,
Thomas Juenger, Peter Keightley, Marcus Kronfrost, Curtis
Lively, James Mallet, Mikhail Matz, Richard Merrill, Sharon
Miller, Miriam Miyagi, Nancy Moran, Vagheesh Narasimhan,
Fred Nijhout, Patrik Nosil, Howard Ochman, Kenneth Olson,
Sarah Otto, David Pfennig, Karin Pfennig, Davin Presgraves,
Trevor Price, Fernando Racimo, Michael Ryan, Sara Sawyer,
Dolph Schluter, Michael Shapiro, Ruth Shaw, Montgomery
Slatkin, Krishna Veeramah, Michael Wade, Günter Wagner,
Stuart West, Mary Jane West-Eberhard, Noah Whiteman,
Michael Whitlock, and Harold Zakon.
00_EVOL5E_FM.indd 16
The authors are very appreciative of the reviews provided by:
David Begun
University of California, Davis
Alyssa BellCalifornia State Polytechnic University,
Pomona
Mirjana Milosevic
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Digital Resources
Evolution, Fifth Edition
For the Instructor
(Available at oup.com/he/futuyma5e)
Instructors using Evolution, Fifth Edition, have access to a
wide variety of resources to aid in course planning, lecture
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to explore many of the dynamic processes of evolution
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These are assignable activities that can pass grades to an
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• Test Bank—NEW to the Fifth Edition, the Test Bank consists of a broad range of questions covering key facts and
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00_EVOL5E_FM.indd 17
• Chapter Quizzes—Assignable quizzes that cover all the
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UNIT I
An Idea
that Changed
the World
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1
Evolutionary Biology
I
n late 2019, a virus that had never before been seen began to spread rapidly
in Wuhan, a large city in central China. The SARS-CoV-2 virus is only 100 nm
in diameter (1000 times smaller than the width of a human hair) and has a
genome with only a dozen genes. But this tiny object has changed the course
of human history. The virus causes the deadly disease COVID-19. Within just
2 years, the virus had killed over 5 million people, infected some 400 million
more, and was continuing to spread ever more rapidly.
Many of the key facts that we know about this terrible virus are thanks
to evolutionary biology, and they touch on major topics that we’ll explore
throughout this book.
A brilliant caterpillar, the larva of the Old World swallowtail butterfly (Papilio polyxenes).
Although the larvae of some butterflies eat a wide variety of plants, this species eats the
leaves only of plants in the carrot family (Apiaceae). This animal poses many questions:
why does it have such a complex, bright color pattern? Why is its diet so restricted? Why
does this insect, and many others, have such astonishingly different juvenile and adult
stages? Research on insect evolution can answer these questions and many more.
© Thomas Marent/Minden Pictures
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Chapter 1
Evolutionary Biology
1.1 The Evolution of SARS-CoV-2
Learning objectives
■ Understand how evolutionary trees are used to learn about where diseases
originated and how they evolve.
■ Explain how the evolutionary tree of SARS-CoV-2 illustrates evolution by
natural selection.
Like most viruses that are new to humans, SARS-CoV-2 originated in another
animal. Perhaps just a year before the first human was infected, the virus was
carried in bats. We know this because we have constructed evolutionary trees
from the genome sequences of viruses sampled from humans and other animals.
These trees show that the human SARS-CoV-2 is very closely related to viruses
in bats and other mammals, which tells us that viruses in these species had a
common ancestor in the very recent past. SARS-CoV-2 is also closely related to
the SARS-CoV-1 virus that infected some 8000 people in the early 2000s (FIGURE
1.1). We’ll see how evolutionary trees like these are made in Chapter 2.
The virus then jumped from bats into another animal, perhaps a pangolin or a
raccoon dog [23]. The pangolin is an Asian mammal that eats ants and has many
features in common with the distantly related giant anteater of South America. Both are highly adapted to eating ants and termites, with powerful front
claws for excavating the insects’ nests and extremely long tongues for probing
into their tunnels. The raccoon dog, which is so named because of its strong
resemblance to North American raccoons, is in fact a member of the dog family.
H
SARS-CoV-2
B
B
B
FIGURE 1.1
SARS-CoV-2 is the virus responsible for the global
pandemic that began in 2019. The evolutionary tree
revealed by the sequences of viral genomes shows that
the human virus (H) is very closely related to viruses
in bats (B) and pangolins (P). That virus is also closely
related to SARS-CoV-1, which caused a much smaller
epidemic in 2002–2004. (After B. Hu et al. 2021. Nat Rev
Microbiol 19: 141–154.)
01_EVOL5E_01.indd 4
B
The family tree of the SARS viruses.
H
SARS-CoV-1
H
Human, © Dean Drobot/Shutterstock.com; Bat, © Rudmer Zwerver/
Shutterstock.com; Pangolin, © Binturong-tonoscarpe/Shutterstock.com
P
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1.1 The Evolution of SARS-CoV-2 5
(B)
(A)
© Vickey Chauhan/Shutterstock.com
© Kirill Skorobogatko/Shutterstock.com
Raccoon dog
Pangolin
© Azahara Perez/Shutterstock.com
© FotoRequest/Shutterstock.com
Raccoon
Giant anteater
FIGURE 1.2
Genetic evidence shows that humans acquired SARS-CoV-2 from
another animal, and two of the possible species happen to provide striking
examples of convergent evolution. (A) The raccoon dog (Nyctereutes procyonoides)
in Asia bears a striking resemblance to the raccoon (Procyon lotor) in North America.
(B) The Java pangolin (Manis javanica) from Asia, which is also called a scaly anteater,
has many similarities to the giant anteater (Myrmecophaga tridactyla) from South
America. Both of them have several evolutionary adaptations that allow them to dig
up colonies of the ants and termites that make up their diets.
Pangolins
andEvolution,
anteaters,
Futuyma
Kirkpatrick
5e and raccoon dogs and raccoons, are wonderful examOUP/SInauer/Troutt
Visual evolution,
Services
ples of convergent
which occurs when natural selection acts on unreEvolution5e_01.02.ai 05-11-2022
lated species in similar ways, causing them to evolve to become similar (FIGURE
1.2). We’ll see more fascinating examples of convergent evolution in Chapter 3.
The first human was infected with SARS-CoV-2 in the late fall of 2019. The
infection almost certainly occurred in a market in Wuhan, China, that sold live
animals (including pangolins and raccoon dogs) [52]. But by the time the virus
was first detected in Wuhan, it was far too late: evolutionary trees reveal that the
virus had already spread across Asia, Europe, and North America [29].
While the pandemic that followed was horrifying, one can hardly imagine a
more graphic example of evolution by natural selection (FIGURE 1.3). In the first
year, the original virus evolved into a variety of lineages, each with a distinctive
set of mutations. Then, in early 2021, a new lineage emerged that was very different genetically from any other that had been previously seen. This lineage,
named the Alpha variant, was evolutionarily superior. It spread more rapidly
than the early lineages, and it quickly drove them to near extinction. Six months
later, this story repeated itself when the Delta variant appeared with a set of
mutations that made it spread even more rapidly. Later that year, the Omicron
variant was first detected. This was the most infectious human virus ever seen,
an astounding four times more contagious and 30 times more able to evade our
immune system’s antibodies [6]. Omicron spread throughout the world in just a
few months, driving Delta and almost all other lineages to near extinction [38].
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6
Chapter 1
1
Evolutionary Biology
First human
infection
2
Early strains
3
4
Alpha spreads,
outcompeting
early strains
Delta spreads,
outcompeting
Alpha
Delta
Alpha
Omicron
Frequency
Omicron
Delta
Alpha
2020
5
2021
2021
2020
Omicron
spreads,
outcompeting
Delta
2022
Date
FIGURE 1.3 An evolutionary tree of SARS-CoV-2 based
on viral genomic sequences shows how later lineages
replaced earlier ones by natural selection. Time flows
from the left to the right. The tree is based on thousands of
genomes sampled from across the Earth. The inset shows the
frequencies of the major variants in the USA during the first
2 years of the pandemic. (After E. Hodcroft. 2022. CoVariants.
https://covariants.org/per-country; Nextstrain. 2022. https://
nextstrain.org/ncov/gisaid/global.)
As these variants spread and outcompeted other variants, the virus gave us a
dramatic example of evolution driven by natural selection. If one kind of individual leaves more descendants than other kinds, that kind will become more common through time. This evolutionary process happens with astounding speed in
viruses because they reproduce themselves many times within an infected person before infecting others within just a few days [17]. As we will see, evolution
is a universal feature of life, although usually it plays out much more slowly. We
will begin our deep exploration of the principle of natural selection in Chapter 3.
When the Alpha, then Delta, and then Omicron variants first appeared, their
genome sequences revealed a surprise. Somehow, they had acquired not just
two or three new mutations, but dozens of them. Since individual mutations
are rare, it was puzzling how these variants could have accumulated so many,
so quickly. The most widely accepted hypothesis is that these variants originated in individuals with compromised immune systems [26]. In most cases,
when a virus infects someone, the patient either recovers or dies in the span
of a few weeks. But in someone who is immunocompromised, the virus can
linger for many months, mutating to many genotypes that almost always die
out but very occasionally are very successful. (We will discuss the positive and
negative effects of new mutations in Chapter 4.) Later variants of the virus
had combinations of mutations in the gene for the virus’s “spike” protein that
is critical for its entry into the victim’s cells. Individually, these mutations do
not have large effects, but their combination gives the spike protein powerful
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1.1 The Evolution of SARS-CoV-2 7
new capabilities. (These kinds of interactions between mutations are called
epistasis, which we will discuss in Chapter 5.) Mutation is not the only way
that SARS-CoV-2 can acquire new combinations of mutations. It can also gain
them by a critical process of genetic mixing called recombination [50], which
we will delve into in Chapter 12.
A remarkable fact is that typically fewer than ten viral particles of SARS-CoV-2
are needed to infect someone [34]. Since experimental infections of people are not
considered ethical, how could that number possibly be known? When the virus
is transmitted, a sample of the infected person’s viruses is passed on. This causes
random changes in the frequencies of the mutations carried by the original host,
an evolutionary process called random genetic drift. Evolutionary biologists
have deduced the number of viral particles needed for infection by measuring
the genetic drift involved in transmission. Chapter 6 explores the key concept
of genetic drift.
The story of the SARS-CoV-2 pandemic also touches on human evolution. All
of us are genetically different, and some of those differences affect the chance
of infection. Differences among us in a small segment of chromosome 3 make a
60% difference in the probability that infection ends in hospitalization. The chromosome variant that makes some of us much more vulnerable has a fascinating
evolutionary history: it entered some (but not all) human populations following
hybridization with Neanderthals, our very close relatives who went extinct about
40,000 years ago [53]. The astounding story of hybridization between humans
and Neanderthals is told in Chapter 20.
These words are being written in March 2022. By the time you read them,
much more will be known about the evolution and biology of this virus. It’s virtually certain that some of the statements made above will have been modified
or disproven by more and better data that have been gathered more recently.
Updates to our understanding are a universal feature of all fields of science,
including evolutionary biology.
The SARS-CoV-2 pandemic is by no means the first time evolutionary methods had been used to trace the origin of an infectious disease. This approach has
been routine ever since the origin of the human immunodeficiency virus (HIV),
which causes AIDS, was determined in 1989. Two distinct HIVs (HIV-1 and
HIV-2) infect humans; the pandemic is caused by HIV-1. Both HIVs are lentiviruses, a group of retroviruses that infect diverse mammals. In monkeys and other
nonhuman primates, the viruses are called simian immunodeficiency viruses,
or SIVs. An evolutionary analysis showed that HIV-2 recently evolved from an
SIV carried by sooty mangabey monkeys; HIV-1 evolved from SIVcpz, the virus
that infects wild chimpanzees [20, 45]. The evolutionary analysis showed, moreover, that HIV-1 entered the human population near the beginning of the twentieth century, decades before it spread beyond Africa. It is thought that humans
became infected with SIVs by contact with the blood of chimpanzees and mangabeys that were killed for food.
Microbial adaptation to drugs is the same, in principle, as the countless adaptations of all species to their environments, so it is very familiar to evolutionary
biologists. The principles and methods of evolutionary biology have provided
some answers to questions about antibiotic resistance and have shed light on
many other problems that affect society. Evolutionary biologists have studied
the evolution of insecticide resistance in disease-carrying and crop-destroying
insects. They have helped devise methods of nonchemical pest control, and they
have laid the foundations for transferring genetic resistance against diseases and
insects from wild plants to crop plants. Evolutionary principles and knowledge
are being used in biotechnology, to design new drugs and other useful products,
and in medical genetics, to identify and analyze inherited diseases as well as variation in susceptibility to infectious diseases. In the fields of computer science and
artificial intelligence, “evolutionary computation” uses principles taken directly
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Chapter 1
Evolutionary Biology
from evolutionary theory to solve mathematically difficult practical problems,
such as constructing complex timetables and processing radar data.
The importance of evolutionary biology goes far beyond its practical uses.
An evolutionary framework provides answers to many questions about ourselves. How do we account for human variation—the fact that almost everyone
is genetically and phenotypically unique? What accounts for behavioral differences between men and women? How did exquisitely complex, useful features
such as our hands and our eyes come to exist? What about apparently useless or
even potentially harmful characteristics such as our wisdom teeth and appendix? What causes us to age, senesce, and eventually die? Evolution raises still
larger questions. As soon as Darwin published On the Origin of Species in 1859,
the evolutionary perspective was perceived to bear on long-standing questions
in philosophy [8]. If humans, with all their mental and emotional complexity,
originated by natural processes, where do ethics and moral precepts find a foundation and origin? What, if anything, does evolution imply about the meaning
and purpose of life? Must one choose between evolution and religious belief?
1.2 “Nothing in Biology Makes Sense Except in
the Light of Evolution”
Learning objective
■ Distinguish proximate from ultimate causes of biological phenomena.
If you suppose that scientists study evolution by analyzing fossils, you are
right—but as the analyses of infectious diseases show, students of evolution also
employ many other approaches and address a wide range of questions. Evolutionary biology is concerned with explaining and understanding the diversity of
living things and their characteristics: what has been the history that produced
this diversity, and what have been the causes of this history? Some evolutionary scientists try to elucidate the history of viruses, how they became capable
of infecting diverse species of animals, and how antibiotic resistance evolves.
Others ask similar questions about the origin of humans and human characteristics—or of mammals, plants, beetles, or dinosaurs. And because all features of
all organisms have evolved, evolutionary biologists study the evolution of DNA
sequences, proteins, biochemical pathways, embryological development, anatomical features, behaviors, life histories, interactions among different species:
all of biology. Facing such an overwhelming profusion of subjects, evolutionary
scientists aim to develop broad principles and to document common patterns
of evolution—to arrive at general principles that apply to diverse organisms and
diverse kinds of characteristics. Most of this book attempts to convey these general principles, although we illustrate the principles with studies of particular
organisms and characteristics.
Evolutionary biology extends and amplifies the explanation of biological phenomena. It complements studies of the proximate causes (immediate, mechanical causes) of biological phenomena—the subject of cell biology, neurobiology,
and many other biological disciplines—with analysis of the ultimate causes of
those phenomena: their historical causes, especially the action of natural selection. If we ask what causes a male bird to sing, the proximate causes include the
action of testosterone or other hormones, the structure and action of the singing
apparatus (syrinx), and the operation of certain centers in the brain (FIGURE 1.4).
The ultimate causes lie in the history of events that led to the evolution of singing in the bird’s remote ancestors. For example, past individuals whose genes
inclined them to sing may have been more successful in attracting females or
in driving away competing males, and thus they may have transmitted their
genes to more descendants than did their less vocal competitors. Proximate and
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1.3 What Is Evolution? 9
kHz
Marsh warbler
4
0
0
1
Time (s)
kHz
Grasshopper warbler
4
0
0
1
Time (s)
© Wolfgang Brandmeier/Minden Pictures
8
© Friedhelm Adam/Getty Images
8
FIGURE 1.4 The song of a male marsh
warbler (Acrocephalus palustris) is much
more complex than the song of a male
grasshopper warbler (Locustella naevia),
which is a simple buzz. The sonograms
(diagrams of the song) show frequency in
relation to time. The song nucleus in the
brain is larger in the marsh warbler than in
the grasshopper warbler. Female marsh
warblers prefer males with more complex
songs. The proximate causes of the song
difference include the brain structure; the
ultimate causes include natural selection
owing to the reproductive success of males
whose songs attract more females. (Sonograms from T. Székely et al. 1996. Proc R
Soc London B 263: 607–610.)
ultimate explanations may interact [29] and together provide more complete
understanding than either does alone. As the great evolutionary biologist Theodosius Dobzhansky [14] wrote, “Nothing in biology makes sense except in the
Futuyma
Kirkpatrick
Evolution, 5e
light of
evolution.”
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1.3 What Is Evolution? Is It Fact or Theory?
Learning objectives
■ Define evolution.
■ Distinguish among theory, hypothesis, evidence, and fact.
The word evolution comes from the Latin evolvere, “to unfold or unroll”—to
reveal or manifest hidden potentialities. Today evolution has come to mean, simply, “change.” But changes in individual organisms, such as those that transpire
in development (ontogeny), are not considered evolution. Biological evolution
(or organic evolution) is inherited change in the properties of groups of organisms
over the course of generations. As Darwin elegantly phrased it, evolution is descent
with modification.
As SARS-CoV2 illustrates, a single group, or population, of organisms may be
modified over the course of time (e.g., becoming more infectious). A population
may become subdivided in such a way that several populations are descended
from a common ancestral population. If different changes transpire in the several
populations, the populations diverge—that is, they become different from each
other (e.g., as the various HIVs and SIVs have done). Over great spans of time,
more and greater differences may accrue, so that the descendants of the ancestral
population come to be very different from their ancestor and from each other.
Is evolution a fact, a theory, or a hypothesis? Biologists often speak of the “theory of evolution,” but they usually mean something quite different from what
most nonscientists take that phrase to mean. Biologists talk about the “theory of
evolution” in the same way that physicists talk about the “theory of gravitation.”
Scientists are as confident about the reality of evolution as they are about the
reality of gravity.
In science, a hypothesis is an informed conjecture or statement of what might
be true. Most philosophers (and scientists) hold that we do not know anything
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Chapter 1
Evolutionary Biology
with absolute certainty. What we call “facts” are in some cases simple, confirmed
observations; in other cases, a “fact” is a hypothesis that has acquired so much
supporting evidence that we can consider it to be correct. A hypothesis may be
poorly supported at first, but it can gain support to the point that it is effectively a
fact. For Copernicus, the revolution of Earth around the sun was a hypothesis with
modest support; for us, this hypothesis has such strong support that we consider
it a fact. Occasionally, an accepted “fact” may need to be revised in the face of new
evidence; for example, humans have 46 chromosomes, not 48 as once thought.
In everyday use, theory refers to an unsupported speculation. Like many words,
however, this term has a different meaning in science. Strictly speaking, a scientific
theory is a comprehensive, coherent body of interconnected statements, based on
reasoning and evidence, that explain some aspect of nature—usually many aspects.
Physicists refer to gravity as a “theory,” but nobody doubts the reality of gravity. In
just the same way, biologists talk about the “theory” of evolution, without doubting its reality. Theory is a term of honor in science; the greatest accomplishment a
scientist can aspire to is to develop a valid, successful new theory.
In The Origin of Species, Darwin propounded two major hypotheses: that organisms have descended, with modification, from common ancestors and that the
chief cause of modification is natural selection acting on hereditary variation.
Darwin provided abundant evidence for descent with modification; since then,
hundreds of thousands of observations from paleontology, geographic distributions of species, comparative anatomy, embryology, genetics, biochemistry,
and molecular biology have confirmed that all known species are related to one
another through a history of common ancestry. Thus the hypothesis of descent
with modification from common ancestors has long had the status of a scientific
fact. (We will describe some of the evidence in Chapters 2 and 21.)
The explanation of how modification occurs and how ancestors give rise to
diverse descendants constitutes the scientific theory of evolution. We now know
that Darwin’s hypothesis that evolution occurs by natural selection acting on
hereditary variation was correct. We also know that there are more causes of evolution than Darwin realized and that natural selection and hereditary variation
are more complex than he imagined. A body of ideas about the causes of evolution, including mutation, recombination, gene flow, isolation, random genetic
drift, the several forms of natural selection, and other factors, constitutes our
current theory of evolution, or “evolutionary theory.” Like all theories in science,
it is a work in progress, for we do not entirely know the causes of all of evolution,
or of all the biological phenomena that evolutionary biology will have to explain.
In evolutionary biology, as in every other scientific discipline, there are “core”
principles that have withstood skeptical challenges and are highly unlikely to
require revision, and there are “frontier” areas in which research actively continues. Some widely held ideas about frontier subjects may prove to be wrong,
but the uncertainty at the frontier does not undermine the core. The main tenets
of evolutionary theory—descent with modification from a common ancestor, in
part caused by natural selection—are so well supported that almost all biologists
confidently accept evolutionary theory as the foundation of the science of life.
1.4 The Evolution of Evolutionary Biology
Learning objectives
■ Describe Darwin’s two major hypotheses about evolution, and explain how
they differ from earlier theories of the origin of diverse organisms.
■ Describe how Darwin’s concept of populational change was novel.
■ Describe how Mendel’s work helped to fill a gap in Darwin’s theory.
That the past is often the key to the present may be a cliché, but it happens to
be true. Just as evolutionary history has shaped today’s organisms, and just as
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1.4 The Evolution of Evolutionary Biology 11
social and political history is the key to understanding today’s nations and conflicts, so the content of any science or other intellectual discipline cannot be fully
understood without reference to its history.
Before Darwin
Carolus Linnaeus
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Portrait by Charles Thévenin
Darwin’s theory of biological evolution is one of the most revolutionary ideas in
Western thought, perhaps rivaled only by Newton’s and Einstein’s theories of
physics. It profoundly challenged the prevailing worldview, which had originated
largely with Plato and Aristotle, who developed the notion that species have fixed
properties. Later, Christians interpreted the biblical account of Genesis literally
and concluded that each species had been created individually by God in the same
form it has today. (This belief is known as special creation.) Christian theologians
and philosophers argued that since existence is good and God’s benevolence is
complete, He must have bestowed existence on every creature of which He could
conceive. Because order is superior to disorder, God’s creation must follow a plan:
specifically, a gradation from inanimate objects and barely animate forms of life
through plants and invertebrates and up through ever “higher” forms of life.
Humankind, being both physical and spiritual in nature, formed the link between
animals and angels. This “great chain of being,” or scala naturae (“the scale, or ladder, of nature”), must be permanent and unchanging, since change would imply
that there had been imperfection in the original creation [33].
As late as the nineteenth century, natural history was justified partly as a way
to reveal the plan of creation so that we might appreciate God’s wisdom. Carolus
Linnaeus (1707–1778), who established the framework of modern taxonomy in
his Systema Naturae (1758), won worldwide fame for his exhaustive classification
of plants and animals, undertaken in the hope of discovering the pattern of the
creation. Linnaeus classified “related” species into genera, “related” genera into
families, and so on. To him, “relatedness” meant propinquity in the Creator’s
design [31].
Belief in the literal truth of the biblical story of creation started to give way
in the eighteenth century, when a philosophical movement called the Enlightenment, largely inspired by Newton’s explanations of physical phenomena,
adopted reason as the major basis of authority and marked the emergence of
science. The foundations for evolutionary thought were laid by astronomers,
who developed theories of the origin of stars and planets, and by geologists,
who amassed evidence that Earth had undergone profound changes, that it had
been populated by many creatures now extinct, and that it was very old. The
geologists James Hutton and Charles Lyell expounded the principle of uniformitarianism, holding that the same processes operated in the past as in the present
and that the data of geology should therefore be explained by causes that we can
now observe. Darwin was greatly influenced by Lyell, and he adopted uniformitarianism in his thinking about evolution.
In the eighteenth century, several French philosophers and naturalists
suggested that species had arisen by natural causes. The most significant
pre-Darwinian evolutionary hypothesis was proposed by the Chevalier de
Lamarck in his Philosophie Zoologique (1809) [30]. Lamarck hypothesized that
different organisms originated separately by spontaneous generation from nonliving matter, starting at the bottom of the chain of being. A “nervous fluid” acts
within each species, he said, causing it to progress up the chain; species originated at different times, so we now see a hierarchy of species because they differ
in age (FIGURE 1.5A).
Lamarck argued that species differ from one another because they have different needs and so use certain of their organs and appendages more than others.
Just as muscles become strengthened by work, more strongly exercised organs
attract and become enlarged by the “nervous fluid.” Lamarck, like most people at
the time, believed that such alterations, acquired during an individual’s lifetime,
Jean-Baptiste Pierre Antoine de Monet,
Chevalier de Lamarck
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Evolutionary Biology
FIGURE 1.5 Lamarck’s and
Darwin’s hypotheses of the
history of evolution. (A) Under
Lamarck’s hypothesis, life has originated many times (the red dots along
the x-axis). Each lineage that descends
from one of these origins becomes
more complex. Thus, organisms range
from recently originated, simple forms
of life to older, more complex forms.
(B) Darwin’s theory of descent with
modification, represented by a phylogenetic tree. From a single ancestor
(the red dot), different lineages arise by
speciating (splitting) from existing lineages. Some (such as the more central
lineages) may undergo less modification from the ancestral condition than
others. Darwin supposed that species
become different from each other
in various features, not necessarily
becoming more complex. (A after P. J.
Bowler. 1989. Evolution: The History
of an Idea. Berkeley, CA: University of
California Press.)
(A) Lamarck’s hypothesis
(B) Darwin’s hypothesis
Form
Chapter 1
Complexity
12
Time
Time
are inherited—a principle called inheritance of acquired characteristics. The theory
of evolution based on this principle is called Lamarckism. In the most famous
example
Lamarck’s
Futuyma
KirkpatrickofEvolution,
5e theory, giraffes must have stretched their necks to reach
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Visualthem,
Services
foliage above
and so their necks were lengthened. The longer necks were
Evolution5e_01.05.ai 04-12-2022
inherited, and over the course of generations, this process was repeated and their
necks got longer and longer. This could happen to any and all giraffes, so the
entire species could have acquired longer necks because it was composed of individual organisms that changed during their lifetimes (FIGURE 1.6A). Lamarck’s
ideas of how evolution works were wrong, but he deserves credit for being the
first to advance a coherent and testable theory of evolution.
(A) Lamarck’s hypothesis
Generation 1
Young adults
Older adults
Generation 2
Young adults
Surviving adults
Generation 2
Young adults
FIGURE 1.6
Contrast between
Lamarck’s and Darwin’s hypotheses
for how characteristics evolve, shown
across two generations. (A) Under
Lamarck’s hypothesis, traits change
within the lifetime of individuals because
of their needs, illustrated here by giraffes
that need longer necks to reach high
leaves. Changes that are acquired during
this generation are passed on to the next
generation. (B) Under Darwin’s hypothesis,
there is variation among individuals at
the start of each generation. Individuals
with certain traits (e.g., a longer neck)
have a greater chance of surviving. The
variation is inherited, so survivors pass on
their traits to the next generation. Darwin
was right, but about 50 years would pass
before scientists would understand how
the inherited variations arise.
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(B) Darwin’s hypothesis
Generation 1
Young adults
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1.4 The Evolution of Evolutionary Biology 13
Courtesy of Frank Sulloway
Charles Robert Darwin (February 12, 1809–April 19, 1882) was the son of an
English physician. He briefly studied medicine in Edinburgh, then turned to
studying for a career in the clergy at Cambridge University. He believed in the
literal truth of the Bible as a young man. He was passionately interested in natural history. In 1831, at the age of 22, his life was forever changed when he was
invited to serve as a naturalist and captain’s companion on the British navy ship
HMS Beagle, tasked with charting the coast of South America.
The voyage of the Beagle lasted from December 27, 1831, to October 2, 1836. The
ship spent several years traveling along the coast of South America, where Darwin
observed the natural history of the Brazilian rainforest and the Argentine Pampas,
then stopped in the Galápagos Islands, which lie on the equator off the coast of
Ecuador. In the course of the voyage, Darwin became an accomplished naturalist,
collected specimens, made innumerable geological and biological observations,
and conceived a new (and correct) theory about the formation of coral atolls.
Soon after Darwin returned, the ornithologist John Gould pointed out that
Darwin’s specimens of mockingbirds from the Galápagos Islands were so different from one island to another that they represented different species (FIGURE
1.7). Darwin then recalled that the giant tortoises, too, differed from one island to
the next (FIGURE 1.8). These facts, and the similarities between fossil and living
mammals that he had found in South America, triggered his conviction that
different species had evolved from common ancestors.
Courtesy of Frank Sulloway
Pinta
Genovesa
Marchena
Santiago
Baltra
Fernandina
Santa
Cruz
Isabela
Equador
San
Cristóbal
Santa Fe
Galápagos
Islands
(Equador)
Española
Courtesy of Frank Sulloway
Courtesy of Frank Sulloway
Floreana mockingbird
M. trifasciatus
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Atlantic
Ocean
San Cristóbal mockingbird
M. melanotis
Floréana
20 km
Charles Robert Darwin
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Mimus spp.
Galápagos mockingbird
M. parvulus
Portrait by G. Richmond
Charles Darwin
Española mockingbird
M. macdonaldi
South
America
Pacific
Ocean
FIGURE 1.7 Four species of mockingbirds
(Mimus) on different islands in the Galápagos
archipelago. They were among the observations that led Darwin to suspect that different
species evolve from a common ancestor.
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14
Chapter 1
Evolutionary Biology
(B)
© Tui De Roy/Minden Pictures
© imageBROKER/Alamy Stock Photo
(A)
FIGURE 1.8 Galápagos giant tortoises (Chelonoidis nigra) differ in shell shape
among islands. Some subspecies, especially those that occupy humid highlands
From My Life by Alfred Russel Wallace
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Alfred Russel Wallace
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with low vegetation, have a domed shell (A), whereas those in dry lowland habitats
tend to have a “saddleback” shell (B) that enables the animal to extend its long neck
to reach vegetation higher above the ground.
Darwin’s comfortable finances enabled him to devote the rest of his life exclusively to his scientific work (although he was chronically ill for most of his life after
the voyage). He set about amassing evidence of evolution and trying to conceive
of its causes. In 1838, at the age of 29, Darwin read an essay by the economist
Thomas Malthus. Malthus argued that the rate of human population growth is
greater than the rate of increase in the food supply, so unchecked growth must lead
to famine. This essay was the inspiration for Darwin’s great idea, one of the most
important ideas in the history of thought: natural selection. Darwin wrote in his
autobiography that “being well prepared to appreciate the struggle for existence
which everywhere goes on from long-continued observation of the habits of animals and plants, it at once struck me that under these circumstances favourable
variations would tend to be preserved and unfavourable ones to be destroyed”
[9]. In other words, of the many individuals that are born, not all survive; if certain
individuals with superior features survived and reproduced more successfully
than individuals with inferior features, and if these differences were inherited, the
average character of the species would be altered over the course of generations.
Mindful of how controversial the subject would be, Darwin then spent 20
years developing his theory, amassing evidence, and pursuing other research
before publishing his ideas. In 1844 he wrote a private essay outlining his theory, and in 1856 he finally began a book he intended to call Natural Selection.
He never completed it, for in June 1858 he received a manuscript from a young
naturalist, Alfred Russel Wallace (1823–1913). Wallace, who was collecting
specimens in the Malay Archipelago, had independently conceived of natural
selection. Darwin’s scientific colleagues presented extracts from his 1844 essay,
along with Wallace’s manuscript, at a meeting of the major scientific society in
London. Darwin immediately set about writing an “abstract” of the book he
had intended. The 490-page result, titled On the Origin of Species by Means of
Natural Selection, or The Preservation of Favoured Races in the Struggle for Life, was
published on November 24, 1859; it instantly made Darwin, by now 50 years
old, both a celebrity and a figure of controversy.
For the rest of his life, Darwin continued to read and correspond on an
immense range of subjects, to revise The Origin of Species (the initial On was
deleted from the title of later editions), to perform experiments of all sorts
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1.5 Darwin’s Evolutionary Theory 15
(especially on plants), and to publish many more articles and books, of which
The Descent of Man is the most renowned. Darwin’s books reveal an irrepressibly inquisitive man, fascinated with all aspects of nature, creative in devising
hypotheses and in bringing evidence to bear on them, and deeply aware that
every biological fact, no matter how seemingly trivial, must fit into a coherent,
unified understanding of the world. Wallace made significant further contributions to biology, especially about biogeography, the geographic distribution of
species. He always gave credit to Darwin for the concept of natural selection,
referring to it as “Mr. Darwin’s theory.”
1.5 Darwin’s Evolutionary Theory
Learning objectives
■ Describe the two major theories in The Origin of Species.
■ Describe how Darwin’s idea of common descent differs from Lamarck’s
concept of the origin of diverse organisms.
■ Describe the difference between Lamarck’s and Darwin’s hypotheses for
the cause of evolutionary change.
The Origin of Species contains two major theories. The first is Darwin’s idea
of descent with modification. It holds that all species, living and extinct, have
descended, without interruption, from one or a few original forms of life (Figure 1.5B). Species that diverge from a common ancestor are at first very similar
but accumulate differences over great spans of time, so they may come to differ
radically from one another. Darwin’s conception of the course of evolution is
profoundly different from Lamarck’s, in which the concept of common ancestry
plays almost no role.
The second major theory in The Origin of Species is natural selection, which Darwin proposed is the chief cause of evolutionary change. He summarized it in the
following way: “If variations useful to any organic being ever occur, assuredly
individuals thus characterized will have the best chance of being preserved in the
struggle for life; and from the strong principle of inheritance, these will tend to
produce offspring similarly characterized. This principle of preservation, or the
survival of the fittest, I have called natural selection.” Unlike Lamarck’s transformational theory, in which individual organisms change, Darwin’s is a variational
theory of change, in which the frequency of a variant form (i.e., the proportion of
individuals with that variant feature) increases within a population from generation to generation (Figure 1.6B). Darwin proposed (as did Wallace) that fitter
individuals differ only slightly from the norm of the population but that a feature
such as body size gradually evolves to become more and more different because
new, slightly more extreme, advantageous variants continue to arise.
Darwin’s theory of evolution includes five distinct components [36]:
1. Evolution as such is the simple proposition that the characteristics of organisms change over time. Darwin was not the first to have this idea, but he
so convincingly marshaled the evidence for evolution that most scientists
soon accepted that it has indeed occurred.
2. Common descent: Differing radically from Lamarck, Darwin was the first to
argue that species had diverged from common ancestors and that species
could be portrayed as one great family tree representing actual ancestry
(see Figure 1.5B).
3. Gradualism is Darwin’s proposition that the differences between even radically different organisms have evolved by small steps through intermediate forms, not by large leaps (“saltations”).
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Evolutionary Biology
4. Populational change is Darwin’s hypothesis that evolution occurs by
changes in the proportions (frequencies) of different variant kinds of individuals within a population (see Figure 1.6B). This profoundly important,
completely original idea contrasts with the sudden origin of new species
by saltation and with Lamarckian transformation of individuals. For
Darwin, the average was a statistical abstraction; there exist only varied
individuals, and there are no fixed limits to the variation that a species
may undergo [21, 36].
5. Natural selection was Darwin’s brilliant hypothesis, independently conceived by Wallace, that accounts for adaptations, features that enhance
survival or reproduction and often appear “designed” to fit organisms
to their environment. Because it provided an entirely natural, mechanistic
explanation for adaptive design that had previously been attributed to a divine
intelligence, the concept of natural selection revolutionized not only biology, but
Western thought as a whole.
Darwin proposed that the various species that descend from a common ancestor evolve different features because those features are adaptive under different “conditions of life”—different habitats or habits. Moreover, the pressure of
competition favors the use of different foods or habitats by different species. He
believed that no matter how extensively a species has diverged from its ancestor,
new hereditary variations continue to arise, so given enough time, there is no
evident limit to the amount of divergence that can occur.
Where, though, do these hereditary variations come from? This was the
great gap in Darwin’s theory, and he never filled it. The problem was serious
because according to the prevailing belief in blending inheritance, variation should
decrease, not increase. Because offspring are often intermediate between their
parents in features such as color or size, it was widely believed that characteristics are inherited like fluids, such as paints of different colors. (This notion
persists today when people speak of having Italian or Indian “blood.”) Blending
white and black paints produces gray, but mixing two gray paints yields more
gray, not black or white. Darwin never knew that Gregor Mendel had solved the
problem in a paper that was published in 1866 but not widely noticed until 1900.
Mendel’s theory of particulate inheritance proposed that inheritance is based not
on blending fluids, but on particles that pass unaltered from generation to generation—thus variation can persist. The concept of “mutation” in such particles
(later called genes) was developed only after 1900 and was not clarified until
considerably later.
The Origin of Species is extraordinarily rich in insights and implications. Darwin
supported his hypotheses with an astonishingly broad variety of information,
from variation in domesticated species to embryology to geographic patterns in
the distribution of species. And he showed, or at least glimpsed, how research in
every biological subject—taxonomy, paleontology, anatomy, embryology, physiology, behavior, biogeography, ecology—could be advanced and reinterpreted
in the light of evolution.
1.6 Evolutionary Biology after Darwin
Learning objectives
■ Describe the relationship between genetics and natural selection in
the synthetic theory.
■ Describe what is meant by microevolution and macroevolution.
■ State the relationship between microevolution and macroevolution,
as maintained during the evolutionary synthesis.
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1.6 Evolutionary Biology after Darwin 17
The evolutionary synthesis
J. G. Millais
Although The Origin of Species raised enormous controversy, by the 1870s most
scientists accepted the historical reality of evolution by descent, with modification, from common ancestors. (Some of the evidence will be described in
subsequent chapters and will be briefly summarized in Chapter 19.) This theory provided a new framework for exploring and interpreting the history and
diversification of life, a project that was especially promoted by the German
zoologist Ernst Haeckel. Thus the late nineteenth and early twentieth centuries
were a “golden age” of paleontology, comparative morphology, and comparative embryology, during which a great deal of information on evolution in the
fossil record and on relationships among organisms was amassed [3]. But the
consensus did not extend to Darwin’s theory of the cause of evolution, natural
selection. For about 60 years after the publication of The Origin of Species, all but
a few faithful Darwinians rejected natural selection, and numerous theories were
proposed in its stead. These theories included neo-Lamarckian, orthogenetic, and
mutationist theories [2].
Neo-Lamarckism includes several theories based on the old idea of inheritance
of modifications acquired during an organism’s lifetime. In a famous experiment,
the German biologist August Weismann cut off the tails of mice for many generations and showed that this mutilation had no effect on the tail length of their
descendants. Extensive subsequent research has provided no evidence that specific mutations can be induced by environmental conditions under which they
would be advantageous. The DNA of an individual
that is reared in a hot environment is not more likely
to undergo mutations that make its offspring more
heat tolerant (see Chapter 4).
Theories of orthogenesis, or “straight-line evolution,” held that the variation that arises is directed
toward fixed goals, so a species evolves in a predetermined direction by some kind of internal drive,
without the aid of natural selection. Some paleontologists held that such trends need not be adaptive and
could even drive species toward extinction (FIGURE
1.9). None of the proponents of orthogenesis ever proposed a mechanism for it.
Mutationist theories were advanced by some geneticists who observed that discretely different new phenotypes (e.g., white versus red eyes in fruit flies) can
arise by a process of mutation. They supposed that
such mutant forms constituted new species and thus
believed that natural selection was not necessary to
account for the origin of species. The last influential
mutationist was Richard Goldschmidt [19], an accomplished geneticist who nevertheless erroneously
argued that the origin of new species and higher taxa
is entirely different in kind from evolutionary change
within species. New species or genera, he said, originate by sudden, drastic changes that reorganize the
whole genome. Although most such reorganizations
would be deleterious, a few “hopeful monsters”
would be the progenitors of new forms of life.
FIGURE 1.9 The extinct Irish elk (Megaloceros giganteus)
had such enormous antlers that it was cited as an example of
orthogenetic “momentum” that drove the species to evolve a
maladaptive feature that caused its extinction. Since the 1940s,
Evolution,
5e
These anti-Darwinian ideas were refuted in the 1930sFutuyma Kirkpatrick
evolutionary
biologists
have rejected this idea. The huge antlers
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probably
resulted
from the animal’s overall large size and from natuEvolution5e_01.09.ai 04-12-2022
ral selection caused by competition among males for females.
ontologists who reconciled Darwin’s theory with the
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Evolutionary Biology
Courtesy of Dr. K. Patau
Courtesy of Doris Marie Provine
Chapter 1
Courtesy of Joan Fisher Box
18
J. B. S. Haldane
Ronald Fisher
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Sewall Wright
facts of genetics [37, 48]. The consensus they forged is known as the evolutionary
synthesis, or modern synthesis, and its chief principle, that adaptive evolution is
caused by natural selection acting on particulate (Mendelian) genetic variation,
is often referred to as neo-Darwinism1 but is more properly called the synthetic
theory. Ronald A. Fisher and John B. S. Haldane, in England, and Sewall Wright,
in the United States, developed a mathematical theory of population genetics,
which showed that mutation and natural selection together cause adaptive
evolution: mutation is not an alternative to natural selection but is, rather, its
raw material. The study of genetic variation and change in natural populations
was pioneered in Russia by Sergei Chetverikov and continued by Theodosius
Dobzhansky, who moved from Russia to the United States. In his influential
book Genetics and the Origin of Species (1937 [13]), Dobzhansky conveyed the
ideas of the population geneticists to other biologists, thus influencing their
appreciation of the genetic basis of evolution. Other major contributors to the
synthesis included the zoologists Ernst Mayr, in Systematics and the Origin of
Species (1942 [35]), Julian Huxley, in Evolution: The Modern Synthesis (1942), and
Bernhard Rensch, in Evolution above the Species Level (1959 [41]); the botanist
G. Ledyard Stebbins, in Variation and Evolution in Plants (1950 [49]); and the
paleontologist George Gaylord Simpson, in Tempo and Mode in Evolution (1944
[46]) and its successor, The Major Features of Evolution (1953 [47]). These authors
argued persuasively that mutation, gene flow or migration, natural selection,
and genetic drift are the major causes of evolution within species (which Dobzhansky called microevolution)—and that, continued over long periods of time,
these same causes account for the origin of new species and for macroevolution:
the evolution of the major alterations that distinguish higher taxa (genera, families, orders, and classes). The principal claims of the evolutionary synthesis are
the foundations of modern evolutionary biology.
Although some of these principles have been extended, clarified, or modified
since the 1940s, most evolutionary biologists today accept them as substantially
valid. They are summarized in BOX 1.1.
1
“Neo-Darwinism” properly refers to Weismann’s strict version of Darwin’s theory of evolution
by natural selection. Darwin had admitted a role for inheritance of acquired characteristics, but
Weismann rejected this. Today, “neo-Darwinism” is often used to refer to the theory articulated in
the evolutionary synthesis.
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From Levit, Simunek & Hoßfeld. 2008.
Theory Biosci 197: 297–322. By permission of Springer
Dutch National Archives/CC BY-SA 3.0
© Realy Easy Star/Alamy Stock Photo
1.6 Evolutionary Biology after Darwin 19
Ernst Mayr
Julian Huxley
Bernhard Rensch
Evolutionary biology since the synthesis
Courtesy of G. L. Stebbins
Since the evolutionary synthesis, a great deal of research has tested and elaborated its basic principles. These principles have largely been supported. Progress in evolutionary biology has modified some of these ideas and has extended
many, and it has spurred additional theory to account for new phenomena as
they Kirkpatrick
were discovered.
Futuyma
Evolution, 5eSince James Watson and Francis Crick established the
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structure of DNA
1953, advances in genetics, molecular biology, and molecular
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and information technology
have revolutionized the study of evolution.
New molecular and computational technology has allowed new fields of evolutionary study to develop, among them molecular evolution (analysis of the processes
and history of changes in genes). The leaders of the evolutionary synthesis had
maintained that almost all features of organisms are adaptive and are evolved
by natural selection. But this principle was challenged by the neutral theory of
molecular evolution, developed by Motoo Kimura [27], who argued that most of
the evolution of DNA sequences occurs by chance (genetic drift) rather than by
natural selection. Evolutionary developmental biology, growing out of comparative
embryology and based partly on molecular genetics, is devoted to understanding how the evolution of developmental processes underlies the evolution of
morphological features at all levels, from cells to whole organisms. Because the
entire genome—the full DNA complement of an organism—can now be sequenced,
G. Ledyard Stebbins, George Gaylord Simpson, and Theodosius Dobzhansky
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Chapter 1
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BOX 1.1
Fundamental Principles of Biological Evolution
1. These are fundamental principles about evolution
that emerged from the modern synthesis. Much of
the rest of this book is devoted to explaining and
building on them. Some statements, marked by
asterisks (*), have to be qualified to some degree, in
light of later research.
2. An individual’s phenotype (its observed traits) is distinct from its genotype (its DNA). Phenotypic differences among individuals are caused by both genetic
differences and environmental effects.
3. Acquired characteristics are not inherited.*
4. Hereditary variations are based on particles—the
genes.* This is true for traits with continuous variation (e.g., body size) as well as those with discrete
variation (e.g., eye color).
5. Genetic variation arises by random mutation. Mutations do not arise in response to need. Variation that
arises by mutation is amplified by recombination of
alleles at different loci.
6. Evolution is a change of a population, not of an
individual. The elementary process of evolution is
a change across generations in the frequencies of
alleles or genotypes, which can change the frequencies of phenotypes.
7. Changes in allele frequencies may be random or nonrandom. Natural selection results from differences
among individuals in survival and reproduction, and
it causes nonrandom changes. Genetic drift causes
random changes.
8. Natural selection can account for both slight and
great differences among species. Even a low intensity of natural selection can cause substantial
evolutionary change over time. Adaptations are
traits that have been shaped by natural selection.
9. Natural selection can alter populations beyond the
original range of variation when changes in allele
frequencies generate new combinations of genes.
10. Populations usually have considerable genetic
variation. Many populations evolve rapidly, to
some degree, when environmental conditions
change; they do not have to wait for new favorable
mutations.
11. The differences between species evolve by rather
small steps and are often based on differences
at many genes that accumulated over many
generations.*
12. Species are groups of interbreeding or potentially
interbreeding individuals that do not exchange genes
with other such groups.* Species are not defined
simply by phenotypic differences. Rather, they
represent separately evolving “gene pools.”
13. Speciation (the origin of two species from a single
ancestor species) usually occurs by the genetic differentiation of geographically isolated populations.*
Species have genetic differences that prevent
interbreeding if they are no longer geographically
separated.
14. Higher taxa arise by the sequential accumulation of
small differences, rather than by the sudden appearance of drastically new types by mutation.
15. All organisms form a great tree of life (or phylogeny)
that evolved by the branching of common ancestors
into diverse lineages, chiefly by speciation. All forms
of life descended from a single common ancestor
that lived in the remote past.
molecular evolutionary studies have expanded into evolutionary genomics, which is
concerned with variation and evolution in multiple genes or even entire genomes.
Inferring phylogenetic relationships from DNA sequences was pioneered in the 1970s
and 1980s by Walter Fitch [16], Allan Wilson [51], and others but depended on statistical methods of analyzing the data, such as those introduced by Joseph Felsenstein [15]. Today, genome sequences enable biologists to determine phylogenetic
relationships with ever-greater confidence; they are revealing the genetic bases
of adaptive characteristics of species and how and when they were modified by
natural selection, and they are revealing the history of populations and their distributions over the globe. The histories of species are written in their genes.
The advances in these new fields are complemented by vigorous research, new
discoveries, and new ideas about long-standing topics in evolutionary biology,
such as the evolution of adaptations and of new species. Since the mid-1960s,
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evolutionary theory has expanded into areas such as ecology, animal behavior,
and reproductive biology. Detailed theories that explain the evolution of particular
kinds of characteristics such as life-span, ecological distribution, and social behavior were pioneered by the evolutionary theoreticians William Hamilton and John
Maynard Smith in England and by George Williams in the United States. The study
of macroevolution has been renewed by provocative interpretations of the fossil
record and by new methods for using phylogenies to reconstruct evolutionary
histories. Research in evolutionary biology is progressing more rapidly than ever
before, carried out by thousands of people, worldwide, with specialized expertise
in mathematics, molecular biology, ecology, taxonomy, paleontology, and more.
Modern evolutionary research often is carried out by teams of specialists in several
of these areas, who are often located in different countries.
Research since Darwin’s time has radically transformed evolutionary biology. Were Darwin to reappear today, he would understand very few scientific
papers about evolution. Modern evolutionary biology does not equal Darwinism, and
any antievolutionary critiques of Darwin that do not take into account modern
research are irrelevant to our understanding of evolution today.
1.7 How Evolution Is Studied
Learning objectives
■ Describe the roles of observation and inference in biology,
Courtesy of W. Provine
1.7 How Evolution Is Studied 21
Motoo Kimura
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and in evolutionary biology in particular.
Evolutionary biology is a more historical science than most other biological disciplines, for one of its goals is to determine what the history of life has been
and what has caused those historical events. Also, understanding the form and
function of species’ characteristics often depends on knowing their history. For
example, whales and seals obtain oxygen from air instead of water because they
evolved from land-living ancestors.
Occasionally we can document an evolutionary change as it occurs or piece
together records to reconstruct a recent change, just as we do when studying
human history (see Chapter 3). Usually, however, we must infer evolutionary
history and its causes by interpreting less-direct evidence. Some historical events
are inferred from fossils, the province of paleontology (see Chapters 17 and 19).
Other evolutionary events are inferred from comparisons among living organisms or by studying their phylogenetic relationships, which provide a framework
that enables us to draw conclusions about the historical evolution of their phenotypic characteristics and even their genes (see Chapters 2 and 16).
The causes of evolution, such as genetic drift and natural selection, are often
studied by comparing data, such as patterns of variation in genes, with what we
would expect, based on theoretical models (see Chapters 4, 5, 6, 7, and 8). This is
sometimes called the hypothetico-deductive method of science. Causes of evolution are also studied by the methods of experimental evolution, in which laboratory
populations of rapidly reproducing organisms adapt to an environment (e.g., a
stressful temperature) designed by an investigator (see Chapter 7). The adaptive
reasons for certain characteristics may be inferred from experimental and other
functional studies, from their “fit” to theoretical designs (e.g., the heart fits a
“pump” design), or by comparing many populations or species to see if their
characteristics are correlated with specific environmental factors or ways of life
(see Chapters 12, 13, 14, and 15). Certain patterns of variation in DNA sequences
can tell us if natural selection has affected evolutionary changes in genes (see
Chapters 5, 6, and 10).
When we make inferences about history, or about past causes of change such
as natural selection, we do not see the changes occurring, nor do we observe the
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Courtesy of the University of Oxford
■ Describe the role of hypotheses in biological science.
William D. Hamilton
Futuyma Kirkpatrick Evolution, 5e
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Chapter 1
Evolutionary Biology
causes in action. But throughout science, causes are not seen; rather, they are inferred.
All of chemistry, for example, concerns invisible atoms and orbitals that govern
the association of atoms into molecules. These theoretically postulated entities
and their behavior have been confirmed because the theory that employs them
makes predictions (hypotheses) that are matched by observed data. We know that DNA
replicates semiconservatively, not because anyone has ever seen DNA do that,
but because the outcome of a famous experiment (and of later ones) matched the
prediction made by the hypothesis.
This hypothetico-deductive method has been a powerful tool throughout the
sciences and is the basis of much evolutionary research. For example, would
you predict that the DNA in mitochondria carries more mutations that are
harmful to males than to females? There is no obvious biochemical reason to
expect this, but evolutionary theory makes such a prediction. The mitochondria
of both males and females are inherited from the mother; the mitochondria in
males are not inherited via sperm and are thus at a “dead end.” If a mutation
in mitochondrial DNA reduces the survival or reproduction of females, it is
unlikely to be transmitted to subsequent generations, but the transmission of
a mutation will not be affected if it is similarly harmful only to males, because
males do not transmit the DNA anyway. So, male-deleterious mitochondrial
mutations are expected to accumulate. This prediction, from the theory of natural selection at the level of the gene, has been verified: mitochondrial variants
commonly affect male, but not female, fertility in humans and other animals,
and they cause variation in reproductive gene expression in male fruit flies [18,
24]. This example illustrates how evolutionary hypotheses can be tested, and
it also shows how they can predict and reveal aspects of biology we would not
otherwise have expected.
1.8 Philosophical Issues
Learning objectives
■ Explain the difference between “function” and “purpose,” in describing
a characteristic such as eyes.
■ Define the “naturalistic fallacy.”
Thousands of pages have been written about the philosophical and social implications of evolution. Darwin argued that every characteristic of a species can vary
and can be altered radically, given enough time. Thus he rejected the emphasis on
distinct “types” that Western philosophy had inherited from Plato and Aristotle,
and he put variation in its place. Darwin also helped replace a static conception of
the world—one virtually identical to the Creator’s perfect creation—with a world
of ceaseless change. It was Darwin who extended to living things, including the
human species, the principle that change, not stasis, is the natural order. In contrast to traditional views that elevated the human species to a special position,
distinct from other living things, Darwin began the trend to see humans as part
of the natural world, a species of animal (though a very remarkable species, to
be sure!) subject to the same processes as others, including natural selection.
Darwin has been credited with making biology a science, for he proposed to
replace supernatural explanations in biology with purely natural causes. His
theory of random, purposeless variation acted on by blind, purposeless natural
selection provided a revolutionary new kind of answer to almost all questions in
biology that begin with “Why?” Before Darwin, both philosophers and people
in general answered questions such as “Why do plants have flowers?” or “Why
are there apple trees?”—or diseases, or sexual reproduction—by imagining the
possible purpose that God could have had in creating them. “Purpose” implies
forethought: envisioning a goal and acting to achieve it. This kind of explanation
was made completely superfluous by Darwin’s theory: natural selection has no
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1.8 Philosophical Issues 23
thought at all. The adaptations of organisms—long cited as the most conspicuous
evidence of intelligent design in the universe—could be explained by purely
mechanistic causes. For evolutionary biologists, the yellow petals of a buttercup’s flower have a function (attracting pollinating insects) but not a purpose. The
flower was not designed in order to propagate the species, much less to delight
us with its beauty, but instead evolved because buttercups with brightly colored
flowers reproduced more prolifically than those with duller flowers. The unsettling implication of this purely material explanation is that, except in the case
of human behavior, we need not invoke, nor can we find any evidence for, any
design, goal, or purpose anywhere in the natural world.
All of modern science employs the way of thought that Darwin applied to
biology. Geologists do not seek the purpose of earthquakes or plate tectonics,
nor chemists the purpose of hydrogen bonds. The concept of purpose plays no
part in scientific explanation.
Ethics, religion, and evolution
In the world of science, the reality of evolution has not been in doubt for more
than 100 years, but evolution remains an exceedingly controversial subject in the
United States and a few other countries. The creationist movement opposes the
teaching of evolution in public schools, or at least it demands “equal time” for
creationist beliefs. Such opposition arises from the fear that evolutionary science
denies the existence of God and, consequently, denies any basis for rules of moral
or ethical conduct.
Science, including evolutionary biology, is silent on the existence of a supernatural being or a human soul, because these hypotheses cannot be tested. Many
people, including some priests, ministers, rabbis, and evolutionary biologists,
hold both religious beliefs and belief in evolution (see Chapter 21). But to explain
phenomena in the natural world, science must assume that only natural causes
operate, just as most people do in everyday affairs: we assume that there is a
material cause when our car or computer or heart malfunctions. Supernatural
explanations for observable phenomena often do conflict with naturalistic, scientific explanation. A literal reading of some passages in the Bible is incompatible
with the principles of physics, geology, and other natural sciences. Our knowledge of the history and mechanisms of evolution is certainly incompatible with
a literal reading of the creation stories in the Bible’s book of Genesis—just as it is
incompatible with hundreds of other creation myths people have devised.
Wherever ethical and moral principles are to be found, it is probably not in
science and surely not in evolutionary biology. Opponents of evolution have
charged that evolution by natural selection justifies the principle that “might
makes right.” But evolutionary theory cannot provide any such precept for
behavior. Like any other science, evolutionary biology describes how the world
is, not how it should be. The supposition that what is “natural” is “good” is called
by philosophers the naturalistic fallacy.
Various animals have evolved behaviors that we give names such as cooperation, monogamy, competition, infanticide, and the like. Whether or not these
behaviors ought to be—and whether or not they are—moral is not a scientific
question. The natural world is amoral—the concepts of “moral” and “immoral”
simply do not apply outside the realm of human behavior. Despite this, the concepts of natural selection and evolutionary progress were taken as a “law of
nature” by which Karl Marx justified class struggle, by which the social Darwinists of the late eighteenth and early nineteenth centuries justified economic
competition and imperialism, and by which the biologist Julian Huxley justified
humanitarianism [22]. These justifications are instances of the naturalistic fallacy.
Infanticide by lions and langur monkeys does not justify infanticide in humans;
monogamy in penguins does not imply that humans should do the same. Evolution provides no basis for human ethics.
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24
Chapter 1
Evolutionary Biology
How to Learn Evolutionary Biology (Read This!)
As we have described, evolution provides an indispensable framework for
understanding phenomena that range from genomes to the ecological interactions among different species. And it has many philosophical implications and
practical applications, ranging from understanding human diversity and behavior to health and medicine, food production, and environmental science. How
can you hope to learn such a gigantic subject?
Your course on evolution is likely to differ from almost any other course in biology
you may have had, and it may present an unfamiliar challenge. In a course in
cell biology, you are expected to learn many factual aspects of cell structure and
function, which apply very broadly to various types of cells in almost all organisms. But courses in evolution generally do not emphasize the factual details
of the evolution of particular groups of organisms—the amount of information
would be impossibly overwhelming. There certainly are some important facts—
for example, you should learn about the mass extinctions and other major events
in the history of life. But for the most part, your instructor is likely to emphasize
(1) the general principles of evolution, especially the processes of evolutionary change
that apply to most or all organisms; (2) how we can learn what has happened in the
evolutionary past; and (3) the most common patterns of change, those that have characterized many different groups of organisms.
For example, you will learn that natural selection is a consistent, statistical difference between groups of reproducing entities (such as large versus small individuals of a species) in the number of descendants they have. By understanding how
a characteristic can affect survival or reproduction, we can arrive at generalizations
about how certain characteristics are likely to evolve. For instance, a feature would
be likely to evolve if it made males more attractive to females so that they would
have more offspring. But evolution by natural selection equally well explains why
about half of the human genome consists of repeated DNA sequences that do nothing of value to the human organism! (The reason is that any DNA sequence that
can make more copies of itself will automatically increase more than a sequence
that makes fewer copies. This is the essence of natural selection.) So the abstract
concept of natural selection has a great range of applications and implications that
will make up much of what you will want to learn about evolution. You will want
to learn the basic processes of natural selection (in Chapter 5), how it is likely to
account for certain features of one sex or both (Chapter 12), and the ways in which
it affects the size and structure of genomes—whether of bacteria, plants, or mammals (Chapter 10). (You might find a parallel in, say, cell biology: certain features
characterize all cells; some features, like nerve cells, are specific to animals while
others, like stomate cells, are specific to plants; and there are still other properties
that are specific to each cell type.)
It is important to learn how evolutionary hypotheses have been tested, in other
words, how we can obtain evidence for (or against) postulated histories and
causes of evolutionary change. For example, if you hypothesize that the long tail
feathers of males in a species of bird evolved because such males attract more
females and have more offspring, you might predict that if you lengthen males’
tail feathers, they will succeed in mating with more females. In Chapter 12, we
describe how this experiment has been done with long-tailed widowbirds, with
exactly this outcome. Your instructor will probably not require you to remember
anything about widowbirds but might expect you to be able to design a test for
sexual selection in crickets or fish (given what you need to know about the species). You will find that throughout this book, we develop an idea or hypothesis
theoretically and then present one or two examples of empirical (i.e., real-world)
studies that biologists have done, which provide evidence supporting the idea.
Understanding the theoretical ideas, and how and why the empirical study provides
evidence for them, is the key to learning evolutionary biology.
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1.8 Philosophical Issues 25
It is also the key to understanding how science works. Science isn’t merely
accumulating facts. Scientists try to develop general principles that explain how
natural phenomena work. Often, there are several conceivable explanations. The
community of scientists in a field develops fuller understanding by devising
alternative hypotheses and thinking of what kind of data would support one
while refuting another. There is a competition of ideas (and competition among
scientists) that results in a closer approach to reality. We cannot prove that a
scientific hypothesis is absolutely true, but we can hope for very high confidence—and no other method of knowing can be shown to come as close. You
can have very high confidence that DNA is the basis of inheritance, that human
consumption of fossil fuels causes global climate change, and that humans have
evolved from the same ancestor as all other animals and from a much older
ancestor of all the living things we know of.
In every field of science, the unknown greatly exceeds the known. Thousands
of research papers on evolutionary topics are published each year, and many
of them raise new questions even as they attempt to answer old ones. No one,
least of all a scientist, should be afraid to say “I don’t know” or “I’m not sure.”
To recognize where our knowledge and understanding are uncertain or lacking
is to see where research may be warranted or where exciting new research trails
might be blazed. We hope that some readers will find evolution so rich a subject,
so intellectually challenging, so fertile in insights, and so deep in its implications
that they will adopt our subject as a career. But all readers, we hope, will find in
evolutionary biology the thrill of understanding and the excitement of finding
both answers and intriguing new questions about the living world, including
ourselves. Felix, qui potuit rerum cognoscere causas, wrote Virgil: happy is the person who could learn the causes of things.
One more message. You will find that we cite studies of a great many species
of animals, plants, and microbes. This is largely because researchers have studied diverse species to see how broadly an evolutionary principle applies; we
want to know, for example, if new species arise by the same processes in insects,
fishes, and plants. Another reason is that species have diverse features that cry
out for explanation: why (for example) are the young cared for by females in
some species, but by males in others? Yet another reason is that many biologists
are enchanted by the diversity of life. Some evolutionary biologists found their
way into the field through looking at birds or sea life or plants (natural history),
some through molecular or developmental biology, some through mathematics
or engineering or other paths. We authors took the naturalist route and were
hooked when we found that evolutionary biology provided so many insights
into so much that we wanted to know about various organisms. Likewise, students choose to take a course in evolution (or in many other areas of biology and
other disciplines) for various reasons, ranging from requirements to scheduling
to mild curiosity to passionate interest in animals or plants, or fascination with
the ideas and implications of evolution. You need not remember the names and
details of all the organisms we cite, but some other readers will respond exactly
to that diversity as they do to the exquisite diversity of living things that have
emerged in the history of life.
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26
Chapter 1
Evolutionary Biology
SUMMARY
■ Evolution is the unifying theory of the biological
sciences. Evolutionary biology aims to discover
the history of life, the causes of the diversity and characteristics of organisms, and the
mechanisms that underlie evolutionary change.
■ Charles Darwin’s major work, On the Origin of
Species, published in 1859, contains two major
hypotheses: first, that all organisms have descended, with modification, from common ancestral forms of life; and second, that the chief
agent of modification is natural selection [8].
■ Darwin’s hypothesis that all species have
descended with modification from common
ancestors is supported by so much evidence
that it has become as well established a fact as
any in biology. His theory of natural selection
as the chief cause of evolution was not broadly
supported until the evolutionary synthesis that
occurred in the 1930s and 1940s.
■ Modern evolutionary biology is based on the
evolutionary synthesis, which united Darwin’s
ideas with Mendelian genetics. The major
causes of evolution within species change the
frequencies of alleles and hence the frequencies of the phenotypes they may affect. Differ-
ent populations of a species may experience
different genetic changes, and ultimately they
may become different species. Over long time
periods, many slight changes accumulate to
yield large genetic and phenotypic differences
among species and between them and their
ancient ancestors.
■ Evolutionary biology makes important contributions to other biological disciplines and
to social concerns in areas such as medicine,
agriculture, computer science, and our understanding of ourselves.
■ The implications of Darwin’s theory, which revolutionized Western thought, include the ideas
that change, rather than stasis, is the natural
order; that biological phenomena, including
those seemingly designed, can be explained
by purely material causes rather than by divine
creation; and that no evidence for purpose or
goals can be found in the living world, other
than in actions of intelligent beings.
■ Like other sciences, evolutionary biology
cannot be used to justify beliefs about
ethics or morality.
SUGGESTIONS FOR FURTHER READING
The readings at the end of each chapter include
major works that provide a comprehensive
treatment and an entry into the professional literature. The references cited in each
chapter also serve this important function.
The Princeton Guide to Evolution (ed. Losos,
2014) is a collection of 107 short essays by
prominent evolutionary biologists that treats
all the major topics in evolutionary biology
and its relation to modern society [32].
No one should fail to read at least part of Darwin’s On the Origin of Species by Means of
Natural Selection, or The Preservation of
Favoured Races in the Struggle for Life in either the first edition (1859) [8] or the sixth edition (1872), in which Darwin deleted “On” from
the title. After some adjustment to the Victorian prose, you will be enthralled by the craft,
detail, completeness, and insight in Darwin’s
arguments. It is an astonishing book.
The Cambridge Encyclopedia of Darwin and
Evolutionary Thought (Ruse, 2013) is a set of
outstanding essays on the history of evolutionary thought and on the influence of
Darwin and of evolution on science and other
areas such as literature, theology, and philosophy [43].
01_EVOL5E_01.indd 26
Among biographies of Darwin, the best include Darwin, by Desmond and Moore (1991)
[12], and Janet Browne’s two-volume work,
Charles Darwin: Voyaging and Charles Darwin:
The Power of Place (1995 and 2002) [4, 5]. An
enjoyable popular biography is The Reluctant
Mr. Darwin: An Intimate Portrait of Charles
Darwin and the Making of His Theory of Evolution by D. Quammen (2006) [40].
Important works on the history of evolutionary
biology include P. J. Bowler, Evolution:
The History of an Idea (1989) [2]; E. Mayr,
The Growth of Biological Thought: Diversity,
Evolution, and Inheritance (1982) [36]; and
E. Mayr and W. B. Provine (eds.), The Evolutionary Synthesis: Perspectives on the
Unification of Biology (1980), which contains
essays by historians and biologists, including
some of the major contributors to the evolutionary synthesis [37].
A few works treating the philosophical and
practical implications of evolution are D. C.
Dennett, Darwin’s Dangerous Idea: Evolution
and the Meanings of Life (1995) [11]; M. Ruse,
The Philosophy of Human Evolution (2012)
[42]; and N. A. Johnson, Darwin’s Reach: 21st
Century Applications of Evolutionary Biology
(2022) [25].
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Summary
E. C. Scott, in Evolution versus Creationism: An
Introduction (2009), describes the fallacies
of creationism [44]. The evidence for evolution is presented in two outstanding books:
Why Evolution Is True, by J. A. Coyne (2009)
[7], and The Greatest Show on Earth: The Evidence for Evolution, by R. Dawkins (2009) [10].
Darwin’s birth, in 1809, was marked in 2009 by
many bicentennial celebrations and books.
Among these is Evolution Since Darwin: The
First 150 Years, edited by M. A. Bell et al.
(2010) [1].
Several excellent websites provide good introductions to evolution; most of them also
include material on teaching evolution and
on creationism.
27
The National Center for Science Education
(ncse.com) is devoted to defending the
teaching of evolution and climate science.
Understanding Evolution (http://evolution.
berkeley.edu) is an excellent site developed
by the Museum of Paleontology at the University of California, Berkeley.
Outstanding videos on evolution are at www.
hhmi.org/biointeractive/evolution-collection.
Darwin Online (http://darwin-online.org.uk),
compiled by John van Wyhe, provides all of
Darwin’s writings, including many translations of The Origin of Species and his other
books into other languages. Van Wyhe has
also created Wallace Online (http://wallaceonline.org), a similar website on Alfred Russel Wallace.
PROBLEMS AND DISCUSSION TOPICS
1. Theodosius Dobzhansky wrote, “Nothing in
biology makes sense except in the light of
evolution.” What did he mean by this? How
does evolution unify the biological sciences?
What other principles might do so?
2. Analyze this Ralph Waldo Emerson couplet:
Striving to be man, the worm
Mounts through all the spires of form.
What pre-Darwinian concepts does it express?
What fault in it would a Darwinian find?
3. The two revolutionary hypotheses proposed
by Darwin in On the Origin of Species were descent with modification, and natural selection
as the main mechanism of evolution. How did
Darwin’s ideas contrast with the prevailing notions of the origins of species at the time?
4. Some scientists vigorously rejected Darwin’s
ideas when On the Origin of Species was published. Richard Owen (1860) [39], perhaps the
01_EVOL5E_01.indd 27
most respected biologist in England, wrote
(among many other objections): “Are all the
recognised organic forms of the present date,
so differentiated, so complex, so superior
to conceivable primordial simplicity of form
and structure, as to testify to the effects of
Natural Selection continuously operating
through untold time? Unquestionably not. The
most numerous living beings … are precisely
those which offer such simplicity of form and
structure, as best agrees … with that ideal
prototype from which … vegetable and animal
life might have diverged.” How might Darwin,
or you, argue against Owen’s logic?
5. Drawing on sources available in a good library,
discuss how the “Darwinian revolution” affected
one of the following fields: philosophy,
literature, psychology, or economics.
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2
The Tree of Life
I
n 1915, Mildred Hoge, studying the genetics of the fruit fly Drosophila
melanogaster, found a mutation that she designated eyeless because it
reduced or eliminated the flies’ eyes [13]. The normal form of the eyeless
gene, then, must be necessary for eye development. Years later, similar
mutations were found in mice and humans, and eventually it was discovered
that the DNA sequences of the mammals’ genes were similar to that of the
fly’s gene. In 1995, Georg Halder and his colleagues reported a remarkable
experiment [12]. By inserting an extra copy of the normal form of a fly’s
eyeless gene into Drosophila larvae, they induced the development of almost
perfect miniature extra eyes—on the wings, legs, and elsewhere on the body
of the adult fly. But more astonishingly, the researchers obtained exactly the
same effect when they inserted the mouse version of the gene. The mouse
gene (now called Pax6) caused the flies to develop eyes. Not mouse eyes,
however, but almost perfect fly eyes (FIGURE 2.1). The same result was later
obtained with the human gene.
Halder and his colleagues noted that more than 2000 different genes are
thought to contribute to developing a Drosophila eye. The normal eyeless gene
is near the start of a chain of command: it activates other genes, which activate
yet others, and so on, to produce all the details of the eye. This experiment
shows that even though insect and vertebrate eyes are radically different in
structure, and are produced by somewhat different sets of genes, the system
that activates these genes is very similar in insects and vertebrates. The fly
genome responds to the signal from the mammalian Pax6 gene just as it does
A mammal that lays eggs. Monotreme mammals—spiny anteaters and this Australian
platypus (Ornithorhynchus anatinus)—are one of the two oldest branches of the phylogeny
of mammals. Monotremes lay eggs, while all other mammals have live birth.
© Doug Gimesy/Minden Pictures
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30
Chapter 2
The Tree of Life
to the equivalent insect gene. This is only one of many systems of interacting
genes that are now known to be shared by insects and vertebrates—and indeed
by all animals.
In a more extreme variation on this theme, more than one-third of the genes
in the genome of yeast (a fungus) have recognizable similarity to human genes.
In one experiment, biologists studied 414 genes that are necessary for yeast to
survive, replacing them with the equivalent human genes [15]. Almost half of
the human gene replacements enabled the yeast to survive!
What accounts for these amazing results? These organisms share fundamental
characteristics because they—and their genes—have descended from a common
ancestor in the distant past. Throughout biology and health science, common
ancestry between humans and all other living things is a fundamental principle
that explains countless facts. Common ancestry explains why nonhuman species
can be used as models in human biomedical research. Descent from common
ancestors is one of the two great themes in Darwin’s On the Origin of Species [7].
Along with natural selection, it is one of the two greatest principles of evolution.
(A)
(B)
2.1 The Tree of Life, from Darwin to Today
Courtesy of W. Gehring and G. Halder
FIGURE 2.1 Evidence of deep
genetic similarity between very
distantly related species. Eyes were
induced to develop in abnormal places
in fruit flies by inserting extra copies
Futuyma
Kirkpatrick
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of either
(A) the fly
eyeless5egene or (B)
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the corresponding
mouse
gene, Pax6.
Evolution5e_02.01ALT.ai 10-03-2022
Photo (A) shows extra eyes on the fly’s
mouthparts (arrows). Photo (B) shows eye
induced by expression of the mouse gene
in the fly’s leg region. (From G. Halder et
al. 1995. Science 267: 1788–1792.)
Learning objectives
■ Describe what aspect of evolution is represented by a phylogenetic tree.
■ Learn the relationships among Bacteria, Archaea, green algae, plants,
fungi, and animals, and the origin of mitochondria and of chloroplasts.
When Darwin recounted his visit to the Galápagos Islands in The Voyage of the
Beagle (1845) [6], he wrote about the finches (FIGURE 2.2): “Seeing this gradation and variety of structure in one small, intimately related group of birds, one
might really fancy that from an original paucity of birds in this archipelago, one
species has been taken and modified for different ends.” He came to suspect
that different forms of mockingbirds, also, had come from a common ancestor,
and the same for the various tortoises that inhabited the different islands. But
this thought, logically extended, suggested that those mockingbirds’ ancestor
itself had descended from an older ancestor that could have given rise to yet
other descendants—the South American mockingbirds, for instance. In 1837, a
year after he returned from the voyage, Darwin sketched a branching diagram
representing the idea of descent from common ancestors (FIGURE 2.3). By the
time he published On the Origin of Species (1859) [7], he could write, “I doubt not
that the theory of descent with modification embraces all the members of the
same class.” Extending the logic, he went on: “Analogy would lead me one step
further, namely to the belief that all animals and plants have descended from
some one prototype.” And finally, in one of the most daring thoughts anyone has
ever had: “I should infer from analogy that probably all the organic beings which
have ever lived on this earth have descended from some one primordial form.”
Extending his early sketch into a great metaphor, Darwin proposed that all
species, extant and extinct, form a great “Tree of Life,” or phylogenetic tree. Closely
adjacent twigs represent living species derived only recently from their common
ancestors (shared ancestors). Twigs on more distant branches represent species
derived from more ancient common ancestors. For readers who enjoy Dickens
and other Victorian authors, we offer Darwin’s metaphor, at his most poetic:
The affinities of all the beings of the same class have sometimes been represented
by a great tree. I believe this simile largely speaks the truth. The green and
budding twigs may represent existing species; and those produced during former
years may represent the long succession of extinct species. At each period of
growth all the growing twigs have tried to branch out on all sides, and to overtop
and kill the surrounding twigs and branches, in the same manner as species and
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2.1 The Tree of Life, from Darwin to Today 31
FIGURE 2.2
Four species of finches
in the Galápagos Islands, drawn from
Darwin’s specimens. The bills are
C. Darwin. 1845. Pictorial Press Ltd / Alamy Stock Photo
adapted for the species’ differences in
diet. The warbler finch (4) feeds mostly
on insects; the three species of ground
finches (Geospiza, 1–3) feed mostly on
seeds of different sizes; the huge bill of
G. magnirostris (1) is used for cracking
large, hard seeds. The species in this
group are now commonly known as
“Darwin’s finches.”
groups of species have at all times overmastered other species in the great battle
for life. The limbs divided into great branches, and these into lesser and lesser
branches, were themselves once, when the tree was young, budding twigs; and
this connection of the former and present buds by ramifying branches may well
represent the classification of all extinct and living species in groups subordinate
to groups. Of the many twigs which flourished when the tree was a mere bush,
two orEvolution,
three, now
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Kirkpatrick
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the species which lived during long-past geological periods,
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very few have left
living and modified descendants. From the very first growth
of the tree, many a limb and branch has decayed and dropped off; and these fallen
branches of various sizes may represent those whole orders, families, and genera
which have now no living representatives, and which are known to us only in a
fossil state. As we here and there see a thin, straggling branch springing from a
fork low down in a tree, and which by some chance has been favoured and is still
alive on its summit, so we occasionally see an animal like the Ornithorhynchus
or Lepidosiren,1 which in some small degree connects by its affinities two large
branches of life, and which has apparently been saved from fatal competition by
having inhabited a protected station. As buds give rise by growth to fresh buds,
and these, if vigorous, branch out and overtop on all sides many a feebler branch,
so by generation I believe it has been with the great Tree of Life, which fills with
its dead and broken branches the crust of the earth, and covers the surface with
its ever-branching and beautiful ramifications.
Today, biologists agree that Darwin was right: all the organisms we know of
have descended from a single ancestor that lived between 4 and 3.7 billion years
ago. (A billion is a thousand million.) And thanks to research by hundreds of
biologists, we can draw an increasingly complete picture of the history by which
the millions of living species, and a great many extinct ones, evolved from common ancestors. We will return to this amazing history in Chapter 17, but here
we sketch some highlights in FIGURE 2.4. The first cellular organisms that we
know of were prokaryotes that evolved into two great groups (A in Figure 2.4),
the Bacteria and Archaea. Eukaryotes evolved from a symbiotic association (B)
1
Ornithorhynchus, the duck-billed platypus, is a primitive, egg-laying mammal. Lepidosiren is a
genus of living lungfishes, a group that is closely related to the ancestor of the tetrapod (four-legged)
vertebrates and that is known from ancient fossils.
02_EVOL5E_02.indd 31
FIGURE 2.3 Darwin’s first speculative
diagram of a phylogenetic tree, in an 1837
notebook. The numeral 1 represented the
ancestor of groups A–D.
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32
Chapter 2
The Tree of Life
Proteobacteria
(A)
Mitochondria
Cyanobacteria
Chloroplasts
LUCA
A
Archaea
Green algae
C
D
Plants
E
Brown algae
Other protists
B
Fungi
F
no
ba
cte
ria
G
a
pl
L
J
Coelenterates (jellyfishes)
K
Echinoderms (sea urchins)
O
Bony fishes
Birds
M
Crocodiles
o
or
at
l
Ch
es
N
aea
de
h
Arc
nts
Hippopotamuses
Whales
Rodents
Primates
Whale
Green
algae
s
LUCA
Hippopotamuses
Plants
Bro
w
alga n
e
iles
d
roco
C
s
st
Cy
a
Mito
chon
dria
s
opod
Arthr
im
Ro
Sponges
cs
s
llu
Mo
Pr
Proteobacteria
(B)
Choanoflagellates
I
H
s
rd
Coelente
rates
(jellyfishe
s)
Ech
i
(se noder
au
rch ms
ins
)
he
fis
ny
ooan s
Ch ellate
flag
Sponges
Bo
Ot
pr her
ot
ist
s
i
ng
Fu
s
Bi
Molluscs
P
Arthropods
FIGURE 2.4 A phylogeny of some major groups
of organisms. (A) A standard treelike representa-
tion. LUCA means “last universal common ancestor.”
The letters are keyed to descriptions of some interesting events in the history of life described in the
text. The dashed arrows show the origin of symbiotic
mitochondria and chloroplasts from two lineages of
bacteria. (B) The same tree, with branches arranged
to form a circular diagram so that many branches can
be displayed in a small space.
between an archaean and a bacterium that evolved into the mitochondrion. So
the mitochondria in each of our cells are more closely related to E. coli bacteria
than they are to the nuclei in the same cells! Early eukaryotes evolved into diverse
lineages. One of these became the green algae, which acquired symbiotic photosynthetic cyanobacteria that evolved into chloroplasts (C). Green algae gave rise
to the true plants (D).
Remarkably, complex multicellular life-forms evolved many times. Among these
groups are plants, brown algae (E), some fungi (F), and animals (G). Fungi and
animals stem from a single ancestor (H), so we are more closely related to mushrooms than to plants. The closest relatives of animals are single-celled choanoflagellates (I), which closely resemble some of the cells in sponges (see Figure 17.8).
Several groups of animals have no heads. Some, such as jellyfishes, never had
one in their evolutionary past (J). Others had a head but then lost it. The echinoderms, such as starfishes, evolved radial symmetry from ancestors that were
bilaterally symmetric and, in doing so, became headless (K).
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2.1 The Tree of Life, from Darwin to Today 33
The vertebrates (L) are most closely related to echinoderms (K) in this tree.
Among the tetrapod (four-limbed) vertebrates, research using fossils, comparative anatomy, and DNA has revealed some surprising relations. Birds are more
closely related to crocodiles than to any other living animals (M). Primates—
including people—are more closely related to rodents than to most other orders
of mammals. Whales are clearly mammals rather than fishes, and it turns out
that they are related to hippopotamuses (N).
The diversity of species within different groups is wildly uneven. With about
33,000 species, bony fishes (O) are the most diverse group of vertebrates. All the
other vertebrates combined sum to about 30,000 known species. But the diversity
prize goes to the single insect order Coleoptera: there are about 350,000 known
species of beetles (P). Among the families of flowering plants (angiosperms), the
sunflowers (23,000 species) and orchids (19,500 species) are fantastically diverse. At
the other end of the diversity spectrum, the most ancient lineage of all living angiosperms has only a single living species, Amborella trichopoda, which survives only
in remote rainforests on the South Pacific island of New Caledonia. (Notice that
here and elsewhere we use order, family, and other terms from taxonomy, the classification of organisms. BOX 2.1 reviews some important aspects of classification.)
BOX 2.1
Classification, Taxonomic Practice, and Nomenclature
The scheme of classification that is used today was
developed by the Swedish botanist Carolus Linnaeus
(1707–1778). Linnaeus introduced binomial nomenclature,
a system of two-part names consisting of a genus name
and a specific epithet (such as Homo sapiens). The names
are in Latin, the language of most scholarly discourse in
the eighteenth century. Linnaeus proposed a system of
grouping species in a hierarchical classification of groups
nested within larger groups (such as genera nested within
families). The levels of classification—such as kingdom,
phylum, class, order, family, genus, and species—are
referred to as taxonomic categories, whereas a particular
group of organisms assigned to any of these levels is
a taxon (plural: taxa). Higher taxa are those above the
species level. Thus the rhesus monkey species (Macaca
mulatta) is placed in the genus Macaca, in the family
Cercopithecidae; Macaca and Cercopithecidae are higher
taxa that exemplify the taxonomic categories genus (plural: genera) and family, respectively. Several intermediate
taxonomic categories, such as superfamily and subspecies, are sometimes used in addition to the more familiar
and universal ones.
To ensure that names are standardized, taxonomy has
developed rules of procedure. For example, since names
are in Latin, and Latin nouns have gender (as in most
European languages), the genus name and specific epithet
must agree in gender: Rattus norvegicus, not Rattus
norvegica, for the brown rat. (Masculine nouns often end
02_EVOL5E_02.indd 33
in –us, feminine nouns in –a, and neuter nouns in –um.)
No two species of animals, or of plants, can bear the same
name. The valid name of a taxon is the oldest available
name that has been applied to it. It sometimes happens
that two authors independently describe the same species
under different names; in this case, the valid name is the
older one. Sometimes two or more species have masqueraded under one name; in this case, the name is applied to
the species that the author used in his or her description.
To prevent ambiguity, it is standard practice for the author
to designate a single specimen (the type specimen, or
holotype) as the “name bearer” so that later workers can
determine which of several similar species rightfully bears
the name. The holotype, usually with other specimens
(paratypes) that show the range of variation, is deposited
and carefully preserved in a museum or herbarium.
The rules for naming higher taxa are not all as strict as
those for species and genera. Names of subfamilies, families, and sometimes orders are formed from the name of
the type genus (the first genus described). Family names
of plants end in -aceae. In zoology, subfamily names end
in -inae and family names in -idae. Thus Columba (Latin
for “pigeon”), the genus of the familiar pigeon, is the
type genus of the family Columbidae and the subfamily
Columbinae; Rosa (rose) is the type genus of the family
Rosaceae. Names of genera and species are always italicized; taxa above the genus level are not italicized but are
always capitalized.
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34
Chapter 2
The Tree of Life
These are only a few of the fascinating glimpses into the history of life that
phylogenetic studies have revealed. We must ask, though, how the relationships
among diverse species can be determined; how events in evolutionary history,
such as the echinoderms’ losing their heads, can be inferred from these relationships; and what else we can learn from phylogenetic studies. This chapter and
Chapter 16 delve into these questions.
2.2 Phylogenetic Trees
Learning objectives
■ Learn the terminology used in discussing phylogenetic trees.
■ Explain what information is conveyed by the shape of a phylogeny,
and what isn’t.
There are three major processes in the evolution of a higher taxon, which is a
named group of organisms above the level of species (e.g., genus, family, order).
These processes include anagenesis, which is evolutionary change of features
within a single lineage (species), and cladogenesis, or branching of a lineage into
two or more descendant lineages. In addition, extinction affects the diversity of
a higher taxon. Following cladogenesis, anagenesis in each of the descendant
lineages results in their becoming more different from each other (divergence,
or divergent evolution). A phylogeny is the history of the cladogenetic events by
which species or other taxa have successively arisen from common ancestors.
The branching diagram that portrays this history is called a phylogenetic tree
(FIGURE 2.5). Other kinds of evolutionary events are sometimes also represented
in such diagrams, such as extinction (e.g., taxon F in Figure 2.5) and reticulation,
which occurs when two lineages merge or form a hybrid descendant, giving the
tree a netlike structure. We will focus on branching trees here.
The phylogenetic tree in Figure 2.5 shows three living species: human (H), chimpanzee (C), and bonobo (B). (A similar tree could also represent three higher taxa,
such as lizards [A], crocodiles [B], and birds [C].) Each segment in the tree is a
lineage, or branch, which may split at an internal branch point or node (such as D),
H
C
B
Extinct
lineage
most phylogenetic trees is a relative one,
but the tree always implies the passage
of time from the root of the tree toward
the tips of the branches.
Time
Names of terminal
taxa (e.g., species)
Terminal node
F
FIGURE 2.5 A phylogenetic tree of
three taxa, human (H), chimpanzee (C),
and bonobo (B), illustrating major
phylogenetic terms. The time scale in
© corlaffra/
Shutterstock.com
© iStock.com/leonp69
© ESB Essentials/
Shutterstock.com
Present
Lineage, or branch
D
E
Internal nodes
(common ancestors)
G
MRCA (most recent common ancestor)
Past
Root of tree
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2.2 Phylogenetic Trees 35
representing the formation of two descendant lineages (C and B) by speciation from
their common ancestor. All the descendants of any one ancestor form a clade (also
called a monophyletic group); thus C and B form a clade that is “nested” within the
larger clade H + C + B. Two clades that originate from a common ancestor are called
sister groups. (Here, because C and B are species, they are called sister species.)
The tree in Figure 2.5 represents the genealogical relationships among the taxa,
meaning the temporal order of branching by which they have originated from the
common ancestor (G in this case). The lineage leading to the most recent common
ancestor (MRCA) of all the species in the phylogeny is called the root of the tree.
Thus a tree has an implicit time scale from past (at the root) to more recent time
(e.g., the present). This time scale (which is often omitted from phylogenetic
diagrams) is often relative, implying only the order of branching: it simply says
that along any path from the root to a tip, nodes closer to the root are older than
those closer to the tips. In some cases, however, an absolute time scale is used,
and branch points are drawn to match the dates at which the branching events
are thought to have occurred. As we will see, phylogenetic trees can convey
information not only about the relationships among species and their time of
divergence, but also about evolutionary changes in phenotypic and genetic characteristics and geographic distributions.
The order of branching in a phylogenetic tree defines which species are more
closely and which are more distantly related. Two species are more closely related
to each other than to a third species if they are derived from a more recent common ancestor. By analogy, two siblings are more closely related to each other
than they are to a cousin because they share more recent common ancestors (their
parents) with each other than with their cousin (a grandparent).
A phylogenetic tree may be drawn in any of several equivalent ways. The
nodes may be angular or rectangular (FIGURE 2.6A) and with time flowing
upward, downward, or sideways. The two trees at the left in FIGURE 2.6B show
that the clades arising from a branch point may be rotated without any change in
the diagram’s meaning: species C and B are each other’s closest relative in both
diagrams. However, the other two trees in Figure 2.6B show different relationships, with the closest relatives being H and B or H and C. These diagrams show
that relationships among the taxa are defined by the sequence of branching, not by the
linear order of the tips of the tree.
FIGURE 2.6
A phylogeny may be
depicted in several ways. (A) These
four trees all show that species C and B
are the closest relatives. The tree may
be drawn with angular or rectangular
branches (two trees at left) and with different orientations (two trees at right);
in all cases, time flows from the root
toward the tips. (B) The two trees at left
are equivalent: both show species B and
C as closest relatives. They differ from
the two other trees, at right, which show
different pairs of species (H and B, or H
and C) as closest relatives.
(A)
H
C
B
H
C
H
B
H Human
C Chimpanzee
B Bonobo
t
t
t
C
B
H
C
B
B
H
C
t
(B)
H
C
B
B
Same
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C
H
C
H
B
Different
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36
Chapter 2
The Tree of Life
(A)
(B)
(C)
A
A
B
B
C
C
D
D
4
Time (relative)
3
2
1
0 Ma
Time (absolute, Ma)
FIGURE 2.7
The lengths of the branches of
a phylogenetic tree may express different
information. (A) Commonly, the scale expresses the
relative sequence of branching events: taxa A and B
diverged more recently than their common ancestor diverged from lineage C. (B) Here, the time scale
expresses absolute geological time: A and B diverged
A
B
C
D
400
300 200 100
0
Base pair substitutions
(branch length)
about 1.3 million years ago (Ma). (C) Occasionally,
branch lengths portray the number of base pair substitutions in a DNA sequence. Note, for example, that
taxa A, B, and C all have exactly the same time back to
their common ancestor, but more substitutions have
occurred in B than in C or (especially) A.
The lengths of the branches in a phylogenetic tree may or may not have
any meaning (FIGURE 2.7). If the tree conveys only branching order, the relative lengths of branches have no significance. If the tree is accompanied by an
absolute time scale, however, the positions of branch points indicate when those
branching events occurred. In some phylogenies, the length of a branch indicates
the number of evolutionary changes (e.g., DNA nucleotide substitutions) that
occurred on that branch.
Closeness of relationship is not the same as similarity. A person might more closely
resemble her cousin than her sister with respect to eye color or many other features, but she is still more closely related to her sister. Likewise, two closely
related species may be less similar to each other than one is to a more distantly
related species (FIGURE 2.8). For instance, dolphins are more closely related to
hippopotamuses and humans than they are to sharks, even though they resemble
(A)
Rodent, © nelik/Shutterstock.com; Chimp, © Eric Isselee/
Shutterstock.com; Macaque, © Eric Isselee/Shutterstock.
com; Shark, © Triduza Studio/Shutterstock.com; Dolphin,
© SiiKA Photo/Shutterstock.com; Hippo, © J. Lekavicius/
Shutterstock.com; Lizard, © Eric Isselee/Shutterstock.com;
Crocodile, © nattanan726/Shutterstock.com; Bird, © Charles
Brutlag/Shutterstock.com
(B)
(C)
FIGURE 2.8 Similarity versus relationship. (A) Chimpanzees and monkeys are
more closely related to each other than to rodents, and they are also more similar.
(B) Dolphins are closely related to hippopotamuses and other mammals, even though
they superficially look more like sharks. (C) Crocodiles and birds share a more recent
common ancestor than either does with lizards, but birds look very different because
they have undergone more evolutionary changes than crocodiles.
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2.2 Phylogenetic Trees 37
sharks in some ways. Dolphins have independently evolved fins and a body
form adapted for swimming. Crocodiles and lizards are superficially more similar to each other than they are to birds, but crocodiles are more closely related
to birds than they are to lizards. The MRCA of lizards, crocodiles, and birds
certainly had a lizard-like body form, but birds evolved more differences from
that ancestral form than crocodiles did. Even though certain aspects of similarity
may be used as data to determine the relationships among species, a phylogeny portrays
relationship (common ancestry), not similarity.
Inferring phylogenies: An introduction
It can be difficult to determine phylogenetic relationships, and so evolutionary
biologists are developing increasingly sophisticated methods. We will touch on
some of the difficulties and methods in Chapter 16. At this point we consider one
simplified approach, in order to convey the basic ideas.
Our estimate of how taxa are related to one another is based on characteristics that are homologous among the taxa, such as the forelimb bones of tetrapod
(four-legged) vertebrates (FIGURE 2.9). Features are homologous among species
if they have been inherited from common ancestors. Homology describes not
only morphological and other phenotypic features, but also DNA sequences. For
example, the α hemoglobin gene can be aligned across all species of mammals,
having been inherited from their common ancestor.
Adapted for swimming
Adapted for running
Seal
Horse
Adapted for grasping
Humerus
Radius
Ulna
Carpals
Metacarpals
Phalanges
1
Human
2
5
3 4
Early tetrapod
5
1
23
1
2
4
3
1
Adapted for flying
S
2 3 4 5
3
2
4
1
1
2
3
3
4
Pterosaur (extinct)
FIGURE 2.9
Forelimb skeletons of some tetrapod
vertebrates, showing homologous bones, those derived in
evolution from those in the tetrapods’ common ancestor.
Compared with the “ground plan,” as seen in the early tetrapod,
bones have been lost or fused (e.g., horse, bird) or modified in
relative size and shape. Modifications for swimming evolved in
Futuyma
Kirkpatrick
Evolution,
5e horse, for grasping in the human, and
the seal,
for running
in the
Bird
Bat
5
for flight in the bird, bat, and pterosaur. All the bones shown are
homologous among these organisms except for the sesamoid
bone (S) in the pterosaur; this bone has a different developmental origin from the rest of the limb skeleton. (After D. J. Futuyma.
1995. Science on Trial: The Case for Evolution. Sunderland, MA:
Oxford University Press/Sinauer.)
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38
Chapter 2
The Tree of Life
clade (right) is marked by one or two derived
morphological features (left) that mark
monophyletic groups. Gymnosperms and
angiosperms (flowering plants) together are
called seed plants, and they, together with
ferns (and some groups not shown), are
vascular plants.
Green algae
Mosses
Cellulose
Angiosperms
Flowers (magnolias, grasses,
roses, many others)
SABENCIA
Guillermo César
Ruiz/CC BY-SA 4.0
Seeds
© aga7ta
Shutterstock.com
Gymnosperms
(pines, others)
Vascular (waterconducting)
tissues
© Todd Boland/
Shutterstock.com
Ferns
Embryo
© iStock.com/ © Ihor Hvozdetskyi/
buccaneershi Shutterstock.com
FIGURE 2.10 Phylogenetic relationships
among some major groups of plants. Each
In some cases, fossils provide very important information about the evolutionary history of a group, including relationships among its members (see Chapter
16). But many groups of organisms have left no fossil record at all, and even in
the best cases, the fossil record is incomplete. We will therefore concern ourselves
mostly with how to infer phylogenies from data on living organisms. Each trait
of an organism (e.g., the number of toes on a hind limb) is called a character,
which may have various character states (e.g., five toes in humans, three in rhinoceroses, one in horses). All kinds of phenotypic characters have been used,
especially morphological features (FIGURE 2.10). Phylogenies of fossilized extinct
taxa are usually based on morphological traits.
Phylogenetic study of living organisms has been revolutionized by DNA
sequencing, which reveals variation at thousands or even millions of base pair
positions in homologous DNA sequences. Each position (“site”) on one strand of
the double helix represents a character, and the identity of its nucleotide base (A, T,
C, or G) represents a character state. Homologous character states that are shared
among species provide evidence of common ancestry if they evolved only once.
Using DNA sequence data, we begin our discussion of how phylogenies are
estimated with an imaginary example. Suppose that we want to find the evolutionary relations among three species of squirrels in the genus Sciurus. We have
sequenced part of the hemoglobin gene from an individual of each of four species:
the eastern gray squirrel (Sciurus carolinensis), western gray squirrel (S. griseus),
fox squirrel (S. niger), and a ground squirrel (Spermophilus citellus) (FIGURE 2.11A).
The homologous sequence fragments from the hemoglobin gene are shown in
FIGURE 2.11B.
The ground squirrel serves as an outgroup. This is a taxon that we are quite
sure (based on prior evidence) is more distantly related to the three species of
interest than they are to each other. Those three species are the ingroup. The
outgroup/ingroup distinction immediately gives us a basic framework for the
phylogenetic tree: the outgroup and the ingroup form two branches from the root
of the tree. Given this framework, there are three possible evolutionary trees for
these species (FIGURE 2.11C).
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2.2 Phylogenetic Trees 39
(A)
(B)
© iStock.com/leekris
© Martin Viazanko/
Shutterstock.com
Ground 1
Fox 2
2
3
ATT AAT GAA
ATT AAT GAA
Fox 2
ATT AAT GAT
Eastern gray 3
ATA AAA GAA
Western gray 4
ATA AAT GAA
Western gray 4
Tree 1
1
Ground 1
Fish Demon/CC BY 3.0
© iStock.com/McKinneMike
Eastern gray 3
(C)
Site: 123 456 789
Tree 2
4
1
3
ATT AAT GAA
2
Tree 3
4
1
4
2
3
ATT AAT GAA
FIGURE 2.11 A simple example inferring a phylogeny by parsimony. (A) The aim
is to determine relationships among three species of Sciurus (species 2, 3, and 4). The
ground squirrel is an outgroup. (B) Sequences of a small part of a hemoglobin gene in
Futuyma
Kirkpatrick
Evolution,
5edifferences among the species at sites 3 and 9. (C) There
the four
species.
Note the
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are three possible relationships (trees 1, 2, and 3) among the three ingroup taxa (fox,
Evolution5e_02.11.ai 08-01-2022
eastern gray, and western gray squirrels). In tree 1, the red bar indicates the single
evolutionary change at site 3 from T to A in the ancestor of species 3 and 4 (eastern
and western gray squirrels). The blue bar in the species 2 lineage marks evolution from
A to T at site 9. Trees 2 and 3 would require us to suppose that evolutionary changes
happened twice at site 3, shown by two red bars. Based on the assumption that the
most likely tree is that with the smallest number of changes, tree 1 is the correct tree.
We know from many studies of the hemoglobin gene that changes to the
sequence are rare over short evolutionary time spans. This means that if we
compare possible phylogenies, those that require fewer evolutionary changes are more
likely to reflect actual relationships than are those with more changes. That logic makes
it simple to find the evolutionary tree that most likely represents the history of
the four DNA sequences and hence the four species of squirrels.
Look at Figure 2.11B and tree 1 in Figure 2.11C. At site 3, the eastern and
western gray squirrels share an A, and they differ from the other two species,
which share a T. Starting with the DNA sequence at the root of the tree, the evolution from T to A (shown by the red bar on the tree) happened in the common
ancestor of these two species. At site 9, the evolution from A to T occurred in the
ancestor of the fox squirrel (shown by the blue bar). Tree 1 therefore involves two
evolutionary changes.
Now consider tree 2. At site 3, there were two changes from T to A (shown
by the two red bars) and again one change at site 9 (blue bar), for a total of three
changes. The same conclusion applies to tree 3: at least three changes must have
occurred to produce the data at the tips of the tree, that is, the DNA sequences
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40
Chapter 2
The Tree of Life
from the four species. (We can imagine other scenarios for where and when
changes occurred on the tree, but they all require at least three changes.)
To sum up, the phylogeny that requires the fewest evolutionary changes is tree 1.
Given our assumption that evolutionary changes to the hemoglobin sequence are
rare, this is the most likely phylogeny. This logic for estimating phylogenies is
called parsimony. A final question you may have is how we could possibly have
known the DNA sequence of the ancestor at the root of the tree. For example,
that species could have had an A rather than a T at site 3. But if it did, all three
phylogenies require at least three evolutionary changes. (Convince yourself by
trying it out!) So the most likely hypothesis is that the ancestor had the sequence
shown at the root, and that species then gave rise to four living species by the
phylogeny shown in tree 1.
This example touches on two points that we will explore in detail in Chapter
16. First, the logic behind our approach here is to find the most likely tree. In this
example, it is the tree in which a change at any given base happens only once.
With other cases, however, that is no longer true. (The same mutation is likely
to happen more than once if mutation rates are very high, the evolutionary time
scale is very long, or there are many species in the phylogeny.) Second, the evolutionary tree of the hemoglobin gene probably reflects the evolutionary tree of
the squirrel species, but there are situations in which it will not. For example,
if two distantly related squirrel species hybridized in the past, the hemoglobin
gene from one species might have spread through the other. That would cause
the sequences of their hemoglobin genes to make the species seem more closely
related than they really are.
Sometimes the information about DNA sequences or other characteristics
simply is insufficient to resolve the relationships. (Often the tree will then be
shown with a polytomy, a node from which three or more lineages emerge.) That
often is the case if successive speciation events happened so rapidly that there
was not enough time for many mutations to become fixed in between successive
branching points.
As you can tell from these last three paragraphs, estimating the phylogeny
for a group of species requires that we make assumptions about how they have
evolved (e.g., the assumption that each mutation occurred only once). Phylogeny
is one of many areas of evolutionary biology in which data are analyzed using
methods that depend on a model, or series of assumptions, which are often represented mathematically. The concept and application of models in evolutionary
biology is explored in BOX 2.2.
BOX 2.2 Models in Evolutionary Biology
Much research in evolutionary biology involves comparing
data with what we expect to see, based on models of how
we think evolution might work. By model we mean a set of
deductions based on assumptions about the processes
that produced the data we are studying and the logical
implications of these assumptions. Our exercise in simple phylogeny building is based on a conceptual model
of evolution, in which we logically deduce a most likely
evolutionary history if certain assumptions are made. We
assume that lineages split and do not later rejoin and that a
particular evolutionary change in a DNA site is so rare that
it probably would have happened only once in the history of
02_EVOL5E_02.indd 40
the species under study. Applying this model to some data,
we find support for one of several possible hypotheses
about the sequence of splits in this group of animals.
In other cases, other assumptions will be more appropriate. For example, if the phylogeny extends very
far back in time, we will likely want to use a model that
assumes the same mutation can occur more than once.
This use of models is an example of one important use
of models in all of science: in order to estimate things that
we want to know about nature, we need both data and
models that allow us to interpret the data quantitatively.
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2.3 Variations on the Phylogenetic Theme 41
2.3 Variations on the Phylogenetic Theme
Learning objectives
■ Define hybridization and horizontal gene transfer, and describe
how they are represented in a phylogenetic tree.
■ Define orthologous, paralogous, and homologous.
The diversity of life, from viruses to animals and plants, is so great, and has
evolved over so long a time, that we should be surprised if all phylogenetic
histories conform to the assumptions of our simple model. And not only species
have branching histories; to a greater or lesser degree, so do DNA sequences,
cells, and even human languages and elements of culture.
Branches of a phylogenetic tree sometimes rejoin
The results of phylogenetic studies are often consistent with the assumption that
the various lineages that arise from common ancestors remain separate and diverge
from each other—that the tree consists only of bifurcations. But branches sometimes rejoin, in whole or in part, and relationships among organisms may form a
network rather than just a branching tree. For example, some species have evolved
from hybrid crosses between two different ancestors, a pattern that is especially
common in plants (FIGURE 2.12). In these cases of hybrid speciation, various phenotypic features and DNA markers throughout the genome reveal two ancestors.
More commonly, analysis of one or a few genes suggests a radically different
phylogeny than do analyses of most other genes. For example, aphids are obviously insects, based on both their morphology and almost all DNA sequences.
A few species of aphids, unlike almost all other animals, can synthesize carotenoid pigments. A phylogenetic analysis of the genes that enable this biosynthesis placed these aphids among the fungi—clear evidence that they acquired
these genes from a fungus (FIGURE 2.13). In contrast to “vertical” inheritance of
genes by offspring from parents, such nonreproductive passage of genes among
organisms is horizontal gene transfer (HGT) (see Chapter 4). The genomes of most
eukaryotes, including humans, include at least a few genes that have been horizontally acquired [5, 9].
Among prokaryotes, both during the early evolution of life and among living
bacteria, HGT has played a major evolutionary role [10]. Bacteria acquire genes
from other species by many mechanisms, including transfer of plasmids, and
natural transformation: uptake of DNA that has been released into the environment by the death of other bacterial cells. HGT among some bacteria can be so
H. neglectus
Photo by Jason Rick,
courtesy of Loren Rieseberg
H. niveus
H. petiolaris
H. anomalus (sand dune)
H. annuus
H. deserticola (desert floor)
H. argophyllus
H. paradoxus (salt marsh)
FIGURE 2.12 Hybrid origin of some diploid species of sunflowers. The phylogeny,
based on sequences of chloroplast DNA and nuclear ribosomal DNA, shows that Helianthus
anomalus, H. paradoxus, and H. deserticola have arisen from hybrids between H. annuus
and H. petiolaris. (After B. L. Gross and L. H. Rieseberg. 2005. J Hered 96: 241–252.)
Futuyma Kirkpatrick Evolution, 5e
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Chapter 2
The Tree of Life
Courtesy of Nancy Moran,
University of Texas at Austin
Xanthophyllomyces dendrorhous
Rhodosporidium sp.
Phycomyces blakesleeanus
Fungus genes
Blakeslea trispora
Mucor circinelloides
Acyrthosiphon pisum
Acyrthosiphon pisum
Acyrthosiphon pisum
Aphid genes
Myzus persicae
Ustilago maydis
Pyrenophora tritici-repentis
Phaeosphaeria nodorum
Neurospora crassa
Podospora anserina
Fungus genes
Aspergillus oryzae
Nectria haematococca
Gibberella fujikuroi
FIGURE 2.13 Genes that encode the enzymes that synthesize carotenoid
compounds are found in one group of aphids, but not in any other animal.
This gene tree shows that the ancestor of these aphids acquired the gene from
Futuyma Kirkpatrick Evolution, 5e
a fungus. The photo shows pea aphids that have this gene. (After N. A. Moran
OUP/SInauer/Troutt Visual Services
Evolution5e_02.13.ai 03-16-2022and T. Jarvik. 2010. Science 328: 624–627.)
frequent that their relationships may look more netlike than treelike. HGT has
enabled some bacteria to metabolize new nutritional substrates and to adapt
to toxic environments, including antibiotics. Methicillin-resistant Staphylococcus aureus (MRSA) is a dangerous pathogen, especially prevalent in hospitals,
that has evolved resistance to almost all antibiotics, partly by HGT from other
bacteria. Pathogenic Clostridium difficile and Escherichia coli are among the other
species that have acquired antibiotic resistance by HGT [14]. A major concern is
that human pathogens can acquire antibiotic resistance from the bacteria that
inhabit cattle and other livestock that are routinely treated with antibiotics in
order to promote rapid growth [17, 23].
Not only organisms have “phylogenies”
So far we have been concerned with inferring phylogenetic trees of species. But
the same methods can shed light on the history of any diverse objects that have
arisen by a history of divergence from common ancestors. For example, different copies of a gene, whether within a single species or in more than one species, have a history of descent from common ancestral genes. (We already have
encountered this notion in the example of squirrels.) A branching tree that portrays the history of DNA sequences of a gene is often called a gene tree or a gene
genealogy (see Chapter 7). For example, the tree that portrays a gene acquired by
aphids from fungi (see Figure 2.13) is a gene tree. A more common kind of gene
tree is illustrated for a mitochondrial gene in MacGillivray’s warbler (FIGURE
2.14). It shows that most of the DNA sequences in Mexican populations are more
closely related to one another than they are to the sequences in birds from the
United States. For several reasons (such as HGT), different genes sometimes have
had different phylogenetic histories—different gene trees (see Chapter 16). Thus,
a gene tree can differ from the species tree, the phylogeny of the species from
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2.3 Variations on the Phylogenetic Theme 43
FIGURE 2.14
A gene tree showing the
relationships among DNA sequences of the
mitochondrial cytochrome b gene in MacGillivray’s
warbler, using a sequence from the mourning
warbler as an outgroup. Sequences are more closely
related within a region than between regions, implying
that there is little mixture between warbler populations
in Mexico and the United States. (After B. Milá et al.
2000. Proc R Soc London B 267: 1033–1040.)
M
P
C
D
O
N
© Tim Zurowski/
All Canada Photos/
Alamy Stock Photo
Mexico
Q
E
L
F
which the genes are sampled. For this reason
(among others), it is best to use many genes
K
in order to find the phylogenetic relationships
H
among species.
G
Organisms vary greatly in the number of
J
functional genes in their genomes. One of the
USA
most important processes by which genomes
A
have increased in size is gene duplication (see
I
Chapters 4 and 10). A new copy of a locus
B
(say, β) arises by duplication of a preexisting
gene (α), so a single gene locus in an ancestor
G. philadelphia (outgroup)
is represented by two loci in the descendant.
These two genes will subsequently undergo
different evolutionary changes in sequence
Futuyma Kirkpatrick Evolution, 5e
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(FIGURE 2.15). This process may occur repeatedlyOUP/SInauer/Troutt
over evoSpecies 1
Species 2
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lutionary time, generating a gene family. The history
of gene
β2
β1
α1
α2
duplication and sequence divergence can be determined by
standard phylogenetic methods. In the human genome, for
example, the 12 members of the globin gene family include
genes that encode myoglobin and several α and β hemoglobin chains (FIGURE 2.16). The origin of myoglobin and hemoglobin by duplication of an ancestral globin gene occurred
during the ancestry of the vertebrates, all of which have both
genes. The α and β hemoglobins arose by gene duplication in
Speciation
the ancestor of jawed vertebrates, whereas the jawless vertebrates (e.g., lampreys) have only a single hemoglobin chain.
α
β
The origin of the other globin genes can be similarly traced
based on their sequences and phylogenetic distribution. A
more extended description of the evolution of gene families,
and of genome size, is provided in Chapter 10.
Cells give rise to lineages of cells by division, and these linDuplication
eages can be traced by the somatic mutations that arise and are
α
inherited by descendant cells. Biologists are beginning to use
the “phylogeny” of cells to trace the developmental history of
FIGURE 2.15 When gene duplication is followed by
the brain and other organs [16], and phylogenies of tumor cells
speciation, two types of relationship exist among the
are important for studying the source and spread of cancers
four copies
of the
locus.
Futuyma
Kirkpatrick
Evolution,
5e The loci in different species that
[18]. A comprehensive study used genome sequences of indidescended from
the same
locus in their most recent ancesOUP/SInauer/Troutt
Visual
Services
vidual human cells to trace cell lineages that are marked by
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tor (α1 and α2, shown
in red, or β1 and β2, shown in green)
somatic mutations [3]. Lineages that stem from early cell diviare called orthologous genes. Loci in the same species or in
sions generally contribute to a number of tissues, and a tissue
different species that descended from different duplicate
such as the exocrine tissue in the pancreas includes cells from
genes in the ancestral species are shown as differing in
various lineages (FIGURE 2.17). The cell phylogeny confirms
color, such as α1 and β2. They are called paralogous genes.
that the germ line, represented by seminiferous tubules in the
Homologous loci in different species are more closely
related than are paralogous loci within the same species.
testes, separates quite early from the somatic tissues, as they
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44
Chapter 2
The Tree of Life
FIGURE 2.16
Phylogeny of genes in
the globin family in the human genome.
Myoglobin consists of a single protein unit,
whereas mammalian hemoglobins consist
of four subunits, two each from the α and β
subfamilies. Each branch point on the tree
denotes a gene duplication event; some of
these events are marked with estimates of
when the duplication occurred. The origin of
hemoglobin and myoglobin from a common
ancestral gene occurred in the ancestor of all
vertebrates, but the α and β hemoglobin subfamilies originated by duplication in an ancestor of the jawed vertebrates. The duplication
of the β hemoglobin into two genes occurred in
the ancestor of placental mammals, since the
A γ , G γ , and ε genes are lacking in monotremes
and marsupials. In some instances, one of
the pairs of genes that formed by duplication
became a nonfunctional pseudogene, symbolized by ψ. (After L. H. Hartwell et al. 2000.
Genetics: From Genes to Genomes. Boston, MA:
McGraw-Hill Higher Education; W. H. Li et al.
1996. Mol Phyl Evol 5: 182–187.)
β subfamily
α subfamily
Hemoglobin β subfamily
β
δ
ψβ1
Aγ
Gγ
Hemoglobin α subfamily
ε
ζ
ψζ1
Primates
α2
α1
Myoglobin
(active in muscle)
40–50 Ma
100–140 Ma
100–140
Ma
ψα1
Placental
mammals
>300 Ma
150–200 Ma
Common ancestor
of α and β hemoglobin
subfamilies
Jawed
vertebrates
Common ancestor of hemoglobin
and myoglobin, 600–800 Ma
Futuyma Kirkpatrick Evolution, 5e
have some unique
lineages. Nevertheless, several cell lineages contributed to
OUP/SInauer/Troutt
Visualcell
Services
both the seminiferous
tubules and other tissues. There is some variation among
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people in the percentages of various cell lineages that may contribute to a tissue.
Phylogenetic methods are used even beyond biology: French, Spanish, and
the other Romance languages evolved from Latin, an example of nongenetic
cultural evolution. Students of cultural evolution are increasingly using phylogenetic methods, borrowed from evolutionary biology, to analyze the history of
languages and other cultural traits (see Chapter 16).
Seminiferous tubules
FIGURE 2.17
A phylogeny of cells in some of the tissues of a single human.
Symbols represent different tissues. The cell lineages that predominate in the seminiferous tubules (in blue, at left) have few members in other tissues, showing that this
tissue separates from other tissues quite early in development. (After T. H. H. Coorens
et al. 2021. Nature 597: 387–392.)
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2.4 How Phylogenies are Useful 45
2.4 How Phylogenies are Useful
Learning objectives
■ Define monophyletic, and describe how phylogenies can be
expressed as monophyletic taxa in classifications.
■ Describe how the evolution of characteristics is inferred from
phylogenetic information.
■ Describe how rates of sequence evolution are estimated and
how they are used as “molecular clocks.”
■ Describe how phylogenies are used to provide evidence about
evolutionary patterns such as gradual evolution, mosaic evolution,
homoplasy, and convergence.
Classification
Classifying diverse organisms into named categories is indispensable: we simply can’t speak biology without referring to “mammals” or “Escherichia,” and
we need the objects in these categories to have some unifying property. Box 2.1
describes some aspects of biological classifications. Darwin wrote that when his
view should become widely accepted, “our classifications will become, as far as
they can be so made, genealogies,” reflecting the actual historical relationships
among species. Systematists today rely on phylogeny when classifying organisms.
A monophyletic taxon is one that includes all the named descendants of a particular common ancestor (e.g., the traditional class Aves, which includes all birds,
is monophyletic). Most systematists today hold the opinion that classifications
should consist of monophyletic taxa only and thus reflect phylogenetic relationships. A paraphyletic taxon includes some, but not all, of the descendants from
a particular ancestor. (The traditional class Reptilia is paraphyletic because it
did not include the birds, which share a common ancestor with dinosaurs and
crocodiles. Reptilia would be monophyletic if we abolished the class Aves and
included the birds in the class Reptilia.) A polyphyletic taxon includes species that
do not exclusively share a common ancestor. (The falcons, hawks, and eagles
used to be combined in a single order, Falconiformes. We now know, from DNA
evidence, that falcons are more closely related to parrots and songbirds. The
order Falconiformes was polyphyletic and had been defined by features we now
know evolved convergently, such as a short, hooked bill and strong feet with
sharp, curved claws for grasping prey. The hawks and eagles are now classified
as a separate order from the falcons.) Systematists today aim to rid classifications
of paraphyletic and polyphyletic taxa.
Inferring the history of character evolution
Phylogenetic studies, sometimes in concert with information from the fossil
record, enable biologists to piece together the evolutionary history of organisms
and their characteristics, ranging from DNA sequences to geographic distributions. They document patterns of evolution—aspects of change that are common
to many groups of organisms. Some of these patterns were already known to
Darwin but have been studied in depth using phylogenetic methods.
One of the most important uses of phylogenetic information is to reconstruct
the history of evolutionary change in interesting characteristics by inferring the
state in each common ancestor, right back to the root of the entire tree. In the simplest methods, we assign to ancestors those character states that require us to
postulate the fewest evolutionary changes for which we lack independent evidence. This method enables us to infer when (i.e., on which branch of a phylogeny) changes in characters occurred, and thus to trace their history.
The orangutan, gorilla, and chimpanzee have opposable first toes (like our
thumbs), while we do not. In FIGURE 2.18 we consider two possible evolutionary
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46
Chapter 2
The Tree of Life
FIGURE 2.18
Inferring ancestral character
states. Two possible histories of the evolu-
tion of opposable (O) versus nonopposable
(N) toes in the Hominoidea (O, orangutan;
G, gorilla; C, chimpanzee; H, human) are
shown. At left, if nonopposable toes (open
circles) are hypothesized for A 3, the common
ancestor of chimpanzee and human, two state
changes must be postulated. At right, opposable toes are hypothesized for A 3, and only one
change need be postulated. Assuming that
character state changes are rare, the more
likely hypothesis is that humans evolved from
an ancestor with opposable toes.
O
G
C
H
N
O
O
N
O
G
C
H
O
A3
A3
A2
A1
N
A2
A1
histories. The common ancestors are labeled A1, A2, and A3, from older to younger.
If we assume that A1 and A2 had opposable toes and that A3, the immediate common
ancestor
ofEvolution,
chimpanzees
and humans, had nonopposable toes, we have to
Futuyma
Kirkpatrick
5e
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Visual Services
postulate two changes,
with the chimpanzee reverting to the ancestral state (left
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side of figure). If, however,
we assume that A3, like A1 and A2, had opposable toes,
we need to infer only one evolutionary change, namely the shift to nonopposable
toes in the human lineage that is shown on the right side of the figure. If we assume
that changes between these states are very rare, the tree with the fewest changes is
the most likely. This leads to the conclusion that the common ancestor of humans
and chimpanzees had opposable toes. This is a very simple example, but many
studies are more complicated; for example, researchers often analyze how two or
more features may have evolved in relation to each other (e.g., Figure 16.21).
Estimating time of divergence
In the 1970s, when DNA sequencing was very difficult, researchers compared
the amino acid sequences of proteins in pairs of species that were known to have
diverged from their common ancestors at various times in the past. For example,
pigs and cows belong to groups that are first recorded as fossils in the Eocene,
about 50 million years ago (Ma), so they diverged from their common ancestor
at least that long ago. When a few proteins were sequenced from different species, and the corresponding DNA sequence differences were plotted against such
estimated divergence times, a close relationship was found (FIGURE 2.19). That is,
the proportion of base pairs that differ between homologous DNA sequences in
two species increases with the amount of time that has elapsed since the species
originated from their common ancestor. This discovery had a huge impact on the
subsequent development of evolutionary biology.
As long as the increase is constant with time (as shown in Figure 2.19), the difference in
sequence can serve as a molecular clock. The figure shows that if you were to sequence
the same genes for two species of mammals and find 45 nucleotide differences,
you could read horizontally across to the best-fit line and then down to the time
axis. Even if the mammal species belong to lineages that lack a fossil record, you
might estimate that they diverged about 74 Ma—as long as you assume that the
genes in these lineages have evolved at the same rate as in the mammals with fossil
records that were used to determine the original correlation of sequence difference
with time. The fossils have been used to calibrate the rate, r, at which these genes
have evolved in mammals. If r is roughly constant within a clade of organisms,
the expected difference D between two species, each evolving at rate r, is D = 2rt,
where t is time since they split from their common ancestor; hence t is estimated as
t = D/2r. (The factor 2 appears because the genes have evolved along each of the
two lineages that descended from the most recent common ancestor.)
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2.4 How Phylogenies are Useful 47
FIGURE 2.19 Some genes evolve at approximately constant rates,
and they can be used as “molecular clocks.” Each point represents
Nucleotide differences
a pair of living mammal species whose most recent common ancestor,
based on fossil evidence, occurred at the time indicated on the x-axis.
(The fossil would indicate the minimal age of the lineage to which a
living species belongs.) The y-axis shows the number of base pair differences between the species, inferred from the amino acid sequences
of seven proteins. The three green circles represent pairs of hominoid
primate species, which have diverged more slowly than other mammal
groups. The arrows show how we would estimate that a pair of species
with 45 base pair differences shared a common ancestor about 74 Ma.
(After C. H. Langley and W. M. Fitch. 1974. J Mol Evol 3: 161–177.)
75
50
25
0
25
50
75
100
Ma (millions of years ago)
125
Rates of evolution differ among the different positions in codons and among
different genes in the genome (see Chapter 7). Rates of sequence evolution also
differ among groups of organisms, especially distantly related taxa: there is not
a universal molecular clock. For example, sequence evolution among hominoid
Futuyma Kirkpatrick Evolution, 5e
primates (apes, including humans) has been slower than among other
primates Visual Services
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and mammals (see Figure 2.19). Differences in generation time and
in mutaEvolution5e_02.19.ai
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tion rate are among the factors that have been proposed to explain why rates of
sequence evolution vary among taxa [11].
Patterns of evolution
A phylogenetic perspective enables biologists to trace patterns of evolution of
various characteristics. The following patterns are typical of many, or even most,
groups of multicellular organisms. These patterns provide massive evidence that
diverse organisms have evolved from common ancestors—evidence for the fact
of evolution (BOX 2.3).
MOST FEATURES OF ORGANISMS HAVE BEEN MODIFIED FROM PREEXISTING
FEATURES. Phylogenetic analysis is based on homologous features, those
derived from common ancestors. It is made possible by one of the most important principles of evolution: the features of organisms almost always evolve from
preexisting features of their ancestors. (We will consider the origin of novel features in Chapters 11 and 19.) By analyzing homologous characters, biologists have
documented many fascinating evolutionary changes in form and function. The
middle-ear bones of mammals evolved from jaw bones of reptiles (see Chapter 19).
The wings of birds, bats, and pterosaurs are highly modified forelimbs (see Figure
2.9); they do not arise from the shoulders (as in angels and dragons), presumably
because the ancestors of these animals had no shoulder structures that could be
modified for flight. Homologous morphological characters in different species
generally have similar genetic and developmental underpinnings, although these
foundations have sometimes undergone greater divergence than have the finished
products. Likewise, the majority of proteins have been modified from ancestral
proteins (see Chapter 10).
A character may be homologous among species (e.g., toes), but a given character
state may not be (e.g., a certain number of toes). The five-toed state is homologous in humans and crocodiles: both have an unbroken history of pentadactyly
as far back as their common ancestor. But the three-toed state in guinea pigs and
rhinoceroses is not homologous, for this condition has evolved independently in
these animals by modification from a five-toed ancestral state.
Determining whether or not characters of two species are homologous can be
difficult. The most common criteria for hypothesizing homology of anatomical
characters are correspondence of position relative to other parts of the body and
correspondence of structure (the parts of which a complex feature is composed).
Correspondence of shape or function is not a useful criterion for homology
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48
Chapter 2
The Tree of Life
BOX 2.3 Evidence for Evolution
Both before and since Darwin, systematists have classified organisms by comparing characteristics among
them. Darwin drew on much of this information as evidence for his theory of descent from common ancestors.
Since Darwin’s time, the amount of comparative information has increased greatly, and today it includes data
not only from the traditional realms of morphology and
embryology, but also from cell biology, biochemistry, and
molecular biology.
All of this information is consistent with Darwin’s hypothesis that living organisms have descended from common
ancestors. Indeed, innumerable biological observations
are hard to reconcile with the alternative hypothesis, that
species have been individually created by a supernatural
being, unless that being is credited with arbitrariness,
whimsy, or a devious intent to make organisms look as if
they have evolved. From the comparative data amassed
by systematists, we can identify several patterns that
confirm the historical reality of evolution and that make
sense only if evolution has occurred:
1. Homology. Similarity of structure despite differences in function follows from the hypothesis
that the characteristics of organisms have been
modified from the characteristics of their ancestors, but it is hard to reconcile with the hypothesis
of intelligent design. Design does not require that
the same bony elements form the frame of the
hands of primates; the digging forelimbs of moles;
the wings of bats, birds, and pterosaurs; and the
flippers of whales and penguins (see Figure 2.9).
Modification of preexisting structures, not design,
explains why the “stingers” → “stings” of wasps and
bees are modified egg-laying tools (ovipositors)
and why only females possess them. All proteins
are composed of “left-handed” (l) amino acids,
even though the “right-handed” (d) optical isomers
would work just as well if proteins were composed
only of those. But once the ancestors of all living
things adopted l-amino acids, their descendants
were committed to them; introducing d-amino
acids would be as disadvantageous as driving on
the right in the United Kingdom or on the left in
the United States. Likewise, the nearly universal,
arbitrary genetic code makes sense only as a consequence of common ancestry.
2. Embryological similarities. Homologous characters include some features that appear during
development but would be unnecessary if the
development of an organism were not a modification of its ancestors’ ontogeny. For example, tooth
primordia appear and then are lost in the jaws of
fetal anteaters. The leg bones of a bird develop just
like a crocodile’s, but then several of them fuse to
form the adult leg. Early in development, human
embryos briefly display branchial pouches similar
to the gill slits of fish embryos, and they have a long
tail that mostly undergoes cell death and is lost.
3. Vestigial characters. Many plants have flowers that are functionally either female or male
but have a rudiment of the other sex’s organ.
Cave-dwelling fishes and other animals display
eyes in every stage of degeneration. Flightless
beetles retain rudimentary wings, concealed in
some species beneath fused wing covers that
would not permit the wings to be spread even if
there were reason for the beetles to spread them.
In The Descent of Man (1871) [8], Darwin listed
a dozen vestigial features in the human body,
including the appendix, the coccyx (four fused tail
vertebrae), and the posterior molars, or wisdom
(consider the forelimbs of a horse and an eagle). Embryological studies are often
important for hypothesizing homology. For example, the structural correspondence between the hind limbs of birds and crocodiles is more evident in the
embryo than in the adult because many of the bird’s bones become fused as
development proceeds. Homology between DNA sequences is determined by
finding an alignment that maximizes the match between nucleotides; often there
are “gaps” caused by past deletions or duplications (see Chapter 16).
RATES OF CHARACTER EVOLUTION DIFFER. Like DNA sequences, different
phenotypic characters evolve at different rates, as is evident from the simple
observation that any two species differ in some features but not in others. Some
characters, often called conservative characters, are retained with little or no
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2.4 How Phylogenies are Useful 49
BOX 2.3 (continued)
teeth, that fail to erupt, or do so aberrantly,
in many people. At the molecular level, every
eukaryote’s genome contains numerous nonfunctional DNA sequences, including pseudogenes—
sequences that retain some similarity to the functional genes from which they have been derived
(see Chapter 10). The only scientific explanation of
these and many other useless features is that they
are derived by evolutionary change from adaptively useful characteristics.
4. Convergence. There are many examples, such as
the eyes of vertebrates and squids, in which functionally similar features actually differ profoundly in
structure. Such differences are expected if structures are modified from very different ancestral
features. Likewise, evolutionary history is a logical
explanation (and creation is not) for cases in which
different organisms use very different structures
for the same function, such as the various modified
structures that enable different vines to climb.
5. Suboptimal design. Evolutionary history explains
many features that no intelligent engineer would
be expected to design. For example, the paths
followed by food and air cross in the human pharynx, so we risk choking on food. The human eye
has a “blind spot,” which you can find at about 15°
to the right or left of your line of sight. It is caused
by the functionally nonsensical arrangement of
the axons of the retinal cells, which run forward
into the eye and then converge to form the optic
nerve, which interrupts the retina by extending
back through it toward the brain.
6. Geographic distributions. The study of systematics includes the geographic distributions of
species and higher taxa. This subject, known as
biogeography, is treated in Chapter 18. Suffice it to
say that the distributions of many taxa make sense
only if they have arisen from common ancestors.
For example, like many other oceanic islands, the
Galápagos archipelago has birds that resemble
continental species but lacks amphibians and
most groups of mammals, which are less likely to
cross open ocean. Many continental species thrive
on islands to which humans have inadvertently
carried them. They must have originated on their
native continent but failed to colonize the islands
without human aid.
7. Intermediate forms. The hypothesis of evolution
by successive small changes predicts the innumerable cases in which characteristics vary by
degrees among species and higher taxa. Among
living species of birds, we see gradations in beaks;
among snakes, basal phylogenetic lineages retain
a vestige of a pelvic girdle, while most other
snakes have lost it altogether. At the molecular
level, the difference in DNA sequences for the
same protein may be minimal among very closely
related species and increase as we compare more
remotely related taxa.
For each of these patterns, hundreds or thousands
of examples could be cited from studies of living species. The fossil record, of course, also provides massive
evidence (see Chapter 17), but even if there were no fossil
record, the evidence from living species would be more
than sufficient to demonstrate the historical reality of
evolution: all organisms have descended, with modification, from common ancestors. We can be even more
confident than Darwin and assert that all organisms we
know of are descended from a single original form of life.
Evolution is a fact.
change over long periods among the many descendants of an ancestor. For
example, all amphibians and reptiles have paired arteries that carry blood from
the heart, and all mammals have only the left artery. Body size, in contrast,
evolves more rapidly; within orders of mammals, it may vary at least 100-fold.
Evolution of different characters at different rates within a lineage is called
mosaic evolution (FIGURE 2.20). It is one of the most important principles of evolution, for it says that a species evolves not as a whole, but piecemeal: many of its
features evolve more or less independently. Every species is a mosaic of plesiomorphic
(ancestral, or “primitive”) and apomorphic (derived, or “advanced”) features. For
example, the amphibian lineage leading to frogs split from the lineage leading
to mammals before the mammalian orders diversified, so in terms of order of
branching, frogs are an older branch than cows or humans. In that sense, frogs
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50
Chapter 2
(A)
Ancestral features include
short ribs, small cranium,
and multiple bones in the
lower jaw (not visible).
(B)
Derived features
include large cranial size
and single jaw bone.
© Photosani/Shutterstock.com
© photowind/Shutterstock.com
The Tree of Life
Derived features include
loss of teeth in lower jaw and
reduction of 5 to 4 fingers.
© The Science Picture Company/Alamy Stock Photo
Ancestral features include
5 fingers on the hand.
FIGURE 2.20
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Mosaic evolution. (A)The skeleton of a frog shows ancestral (“primitive”) characters such as short ribs, a small cranium, and multiple bones in the lower
jaw (not visible in the photo). (B) In mammals such as humans, the ribs connect to a
breastbone, the lower jaw has a single bone, and the enlarged cranium houses a large
brain—derived characters. Characters that are more derived in frogs than in humans
include loss of teeth in the lower jaw, reduction from five to four fingers, fused tibia
and fibula in the hind leg, and fusion of tail bones into a rod (urostyle).
might be said to be more ancestral. Frogs do have some ancestral features (e.g.,
five toes on the hind foot, multiple bones in the lower jaw), but they also have
some features (e.g., four fingers, lack of teeth in the lower jaw) that are more
derived than those of many mammals, in that they have changed further from
the ancestral state. Overall, frogs and mammals both differ greatly from their
common ancestors, the early land-living tetrapods (see Figure 17.16). Moreover,
numerous differences among frog species have evolved in the recent past, just
as in mammals. For example, some frogs give birth to live young. Humans also
have both ancestral characters (e.g., five fingers; teeth in the lower jaw) and
derived characters compared with those of frogs (e.g., a single lower jawbone,
a much more complex brain). Because of mosaic evolution, it is inaccurate or even
wrong to consider one living species more “advanced” than another.
EVOLUTION IS OFTEN GRADUAL. Darwin argued that evolution proceeds by
small successive changes (gradualism) rather than by large “leaps” (saltations).
How often phenotypes evolve by discrete rather than gradual change is debated.
Many higher taxa that diverged in the distant past (e.g., the animal phyla; many
orders of insects and of mammals) are very different and are not bridged by
intermediate forms, either among living species or in the fossil record. In some
cases, that is surely because of gaps in the record: the intermediates weren’t
fossilized, or haven’t been found. The fossil record does document intermediates in the evolution of some higher taxa (see Chapters 17 and 19). Gradations
among living species are very common, as we would expect if characters evolve
gradually. For example, the length and shape of the bill differ greatly among
species of sandpipers, but the most extreme forms are bridged by species with
intermediate bills (FIGURE 2.21).
HOMOPLASY IS COMMON Homoplasy—the independent evolution of a character
or character state in different taxa—includes convergent evolution (convergence),
parallel evolution (parallelism), and evolutionary reversal.
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2.4 How Phylogenies are Useful 51
(A)
(B)
26–35
(C)
54–99
70–95
(D)
123–192
38–48
183–219
120–201
(A) © All Canada Photos/Alamy Stock Photo; (B) © McPhoto/
Schaef/Alamy Stock Photo; (C) © Robert Pickett/Getty Images;
(D) © David Cantrille/Alamy Stock Photo
FIGURE 2.21 Graded differences in bill length in these closely related members
of the sandpiper family suggest that evolution has been gradual. The range of
bill length (in centimeters) within a species is given at the tip of each branch for the
four species shown and for three other species of Numenius. (A) Upland sandpiper.
(B) Whimbrel. (C) Eurasian curlew. (D) Long-billed curlew. (Data from P. Heyman et al.
1986. Shorebirds: An Identification Guide to the Waders of the World. Boston, MA: Houghton Mifflin; phylogeny after R. Gibson and A. Baker. 2012. Mol Phy Evol 64: 66–72.)
We have already encountered an example of convergent evolution, in the body
forms of dolphins and some sharks (Figure 2.8). The eyes of vertebrates and of
cephalopod molluscs (such as squids and octopuses) are a more intricate example
of convergence. Both have a lens and a retina, but their many profound differences and their distant phylogenetic relationship (Figures 2.4 and 17.7) indicate
that they evolved independently. For example, the axons arise from the back of
the retinal cells in cephalopods but from the front in vertebrates (FIGURE 2.22).
Convergent features are often adaptations by different lineages to similar
environmental conditions. In fact, a correlation between a particular convergent
character in different groups and a feature of those organisms’ environment or
niche is often the best initial evidence of the feature’s adaptive significance. For
example, long, tubular flowers have evolved independently in many families of
plants (FIGURE 2.23). These are adaptions for pollination by animals with long,
slender mouthparts, such as hummingbirds and sunbirds.
Convergence is also observed at the molecular level. Cardiac glycosides (CGs)
are toxic compounds that are synthesized and used for defense by several lineages of plants (e.g., milkweeds, family Apocynaceae) and animals (e.g., toads,
family Bufonidae). They inhibit the sodium-potassium pump protein and upset
cell membrane potentials by disrupting ion transport. Many insects that feed on
plants with CGs are resistant to them and actually achieve protection by storing
them in their own tissues (e. g., the monarch butterfy, Danaus plexippus). Toads
are resistant to their own toxin, and resistance has also evolved independently
in various animals, including some rodents, hedgehogs, snakes, and lizards, that
eat toads or CG-containing insects (FIGURE 2.24). In all of these cases, only four
of the amino acids that compose the pump protein have been replaced, with
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Chapter 2
The Tree of Life
(A) Octopus
© Stubblefield
Photography/
Shutterstock.com
© Kletr/
Shutterstock.com
(B) Fish
The nerve cells that convey
visual signals from the retinal
receptors to the brain leave
the eye directly in multiple
optic nerves.
Muscle
The nerve cells leaving
the retina gather into a
single optic nerve.
Muscle
Retina
Lens
Lens
Light
Light
Cornea
Cornea
Iris
Photoreceptor
Retina
Iris
To
brain
To
brain
Integrating
neurons
Photoreceptor
Light
Light
The photoreceptors point
toward the incoming light.
Retinal nerve cells form networks
that extensively process visual
information before signals go
to the brain.
The photoreceptors point away
from the incoming light,
so the light must pass through the
retinal tissue to stimulate the
photoreceptors.
FIGURE 2.22
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An extraordinary example of convergent evolution. The eyes of
(A) an octopus (cephalopod mollusc) and (B) a vertebrate have many similarities, but
note the several differences, including interruption of the retina by the optic nerve in
the vertebrate but not in the cephalopod. (A after M. J. Wells. 1966. In K. M. Wilbur and
C. M. Yonge [eds.], Physiology of Mollusca, vol. 2, pp. 523–545. New York: Academic
Press; J. Z. Young. 1971. The Anatomy of the Nervous System of Octopus vulgaris.
London: Oxford University Press; B after G. J. Walls. 1942. The Vertebrate Eye and
Its Adaptive Radiation. Oxford: Oxford University Press.)
exactly the same amino acid substitutions in some cases. The Australasian lineage of monitor lizards, including the giant Komodo dragon, has lost resistance,
and the two amino acid positions have reverted precisely to their ancestral states
(glutamine and glycine) [25].
Parallel evolution is a term that has been used to describe cases in which independent evolution of a character state is thought to have similar genetic and
developmental bases, especially in closely related species. Mutations in a specific
gene, Pitx1, are the basis of independent loss of the pelvic girdle and fins in many
freshwater populations of a small fish, the three-spined stickleback (see Figure
11.15). But the distinction between parallel evolution and convergent evolution
may not be very meaningful because, as we will see, the same gene often contributes to similar evolutionary changes in distantly related organisms.
Evolutionary reversals constitute a return from a derived character state to a
more ancestral state [21]. Winged insects evolved from wingless ancestors, but
many lineages of insects have lost their wings in the course of subsequent evolution.
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2.4 How Phylogenies are Useful 53
(A)
(B)
(C)
(E)
(F)
© Jason Gallier/Alamy Stock Photo
© imageBROKER/Alamy Stock Photo
Mark Marathon/CC BY-SA 4.0
FIGURE 2.23
© Ivan Kuzmin/Alamy Stock Photo
StanShebs/CC BY-SA 3.0
Courtesy of Douglas Futuyma
(D)
Examples of convergent evolution.
(Onagraceae) attracts hummingbirds in cloud forest at high
altitude in Central America. (C) A tropical American hummingbird, the violet sabrewing. (D) Gastrolobium, in the pea
family, is pollinated by honeyeaters in Australia. (E) Many
species of Aloe (Asphodelaceae) are visited by sunbirds in
Africa and the Middle East. (F) A greater double-collared
sunbird, native to Africa.
Many groups of birds have independently evolved long,
slender bills for feeding on nectar produced at the base
of long, tubular flowers. A long, tubular flower, often red
or orange, has evolved independently in many groups of
bird-pollinated plants. (A) Erythrina, a tropical American
member of the pea family, Fabaceae. (B) Fuchsia splendens
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Glutamine (Q) changed
independently to arginine (R)
in these four lineages.
FIGURE 2.24
R
R
E
R
D
D
Rampant
convergent evolution in
a protein. This diagram of part
R
R
R
D
L
D
L
Extracellular
Q
H
G
Q
N
NH2
COOH
Membrane
Cytoplasmic
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Visual Services
of the sodium-potassium pump
protein shows the four positions
of amino acids in the extracellular
domain (circles containing letters)
where there have been independent
substitutions that make the animals
resistant to CG. For example, a
change from glutamine (Q) to arginine (R) occurred independently
in four lineages (dashed arrows).
Amino acids are designated by their
single-letter abbreviations. (After
B. Ujvari et al. 2015. Proc Natl Acad
Sci USA 112: 11911–11916.)
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Chapter 2
The Tree of Life
FIGURE 2.25
A violation of Dollo’s law is illustrated
by the repeated re-evolution of wings in stick insects
(order Phasmida). Phasmids are derived from winged
ancestors, as shown by the outgroup (a web-spinner).
Many basal lineages of Phasmida, such as Timema and
Diapheromera, are wingless. From among the wingless
lineages, several clades, such as the genera Phyllium and
Extatosoma, have re-evolved wings. (Phylogeny after
M. F. Whiting et al. 2003. Nature 421: 264–267.)
Order
Embioptera
Web-spinners
Timema
Order
Phasmida
Diapheromera
Phyllium
Loss of wings
Gain of wings
Dimorphodes
Extatosoma
Web spinner, © Apurv Jadhav/Shutterstock.com; Timema, © Stuart
Wilson/Science Source; Diapheromera, © Brian Lasenby/Science Source;
Phyllium, Drägüs; Extatosoma, © Maximilian Weinzierl/Alamy Stock Photo
It was long assumed that complex characters, once lost, are unlikely to be regained,
a principle known as Dollo’s law. But there are exceptions [2]. Stick insects are
derived from winged ancestors. Their phylogeny, based on DNA sequences, shows
that ancestral stick insects became wingless (FIGURE 2.25). Many basal lineages in
the phylogeny are wingless, but wings have re-evolved several times. Evidently
the genetic and developmental potential for expressing wings was retained for
millions of years in wingless lineages.
Phylogenies describe patterns of diversification
If the time of each branching point in a phylogeny has been estimated by
a molecular clock, the phylogeny may suggest whether new lineages arose
steadily over a long period or arose episodically, in one or more bursts of
diversification. A common pattern is adaptive radiation: divergent evolution
of numerous related lineages within a relatively short time that are adapted
to many different ways of life [24]. The characteristics of the members of an
adaptive radiation usually do not show a trend in any one direction. In many
groups of birds, species differ in the morphology of the bill, which provides
adaptation to different diets. Darwin’s finches, in the Galápagos Islands, are a
Futuyma
Kirkpatrick
5e vangas, a diverse bird family in Madafamous example
(see
FigureEvolution,
2.2). The
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gascar, provide
an
even
more
dramatic
example, in which species differ greatly
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in bill morphology, foraging behavior, diet, and habitat (FIGURE 2.26) [22].
Another example of adaptive radiation is the Hawaiian silverswords and their
close relatives. These members of the sunflower family occupy habitats ranging
from exposed lava rock to wet forest, and their growth forms include shrubs,
vines, trees, and creeping mats (FIGURE 2.27).
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Le
os
er
yc
Sc
pt
op
te
ru
s
bias
udo
Vanga
Pse
r
Eu
Cya
nola
nius
2.4 How Phylogenies are Useful 55
is
orn
ac
yst
he
tba
M
Xenopir
ta
ostris
Hyposit
Oriolia
Tylas
Ca
ella
lica
licu
s
ia
Fa
on
wt
lcu
Ne
lea
am
Art
FIGURE 2.26
A spectacular adaptive radiation, the vangas of Madagascar.
All the species eat mostly insects, but their various bill shapes, resembling those of
diverse other birds outside Madagascar, are adapted for different ways of foraging.
For example, Hypositta creeps on tree trunks, Xenopirostris strips loose bark from
trees, and the spectacular sickle-billed vanga, Falculea, probes deep into tree holes
and crevices (After S. Reddy et al. 2012. Proc R Soc London B 279: 2062–2071; bird
illustrations by Velizar Simeonovski.)
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(A) Argyroxiphium sandwicense
(B) Wilkesia gymnoxiphium
(C) Dubautia menziesii
© Noble Proctor/The National Audubon
Society Collection/Science Source
© Dani Carlo/Prisma/AGE Fotostock
© Brenda Smith DVM/Shutterstock.com
FIGURE 2.27
Some of the Hawaiian silverswords showing closely related
species with very different growth forms. (A) Argyroxiphium sandwicense, a
rosette plant that lacks a stem except when flowering (as it is here). (B) Wilkesia
gymnoxiphium, a stemmed rosette plant. (C) Dubautia menziesii, a small shrub.
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Chapter 2
The Tree of Life
SUMMARY
■ Modern biology has affirmed Darwin’s hypoth-
esis that all the organisms we know of, present
and past, have descended from one ancient
common ancestor. Current understanding of
the history by which diverse groups have originated reveals fascinating events, such as the symbiotic origin of eukaryotic cells and several origins
of multicellular organisms from single-celled
ancestors.
■ A phylogeny is the history of the events by
which species or other taxa have successively
originated from common ancestors. It may be
depicted by a phylogenetic tree, in which each
branch point (node) represents the division of
an ancestral lineage into two or more lineages.
Closely related species have more recent common ancestors than distantly related species.
The group of species descended from a particular common ancestor is a monophyletic group,
or clade; a phylogenetic tree portrays nested
sets of monophyletic groups.
■ The phylogeny of a focal group of species can
be estimated by using characters that change
so rarely that those species that share a derived (“advanced”) character state can safely be
assumed to have inherited it from their common ancestor. A character state that occurs
within the group of species can be judged to
be derived rather than ancestral if it does not
occur among other lineages (outgroups) that
are related to the focal group. The characters
used in most modern phylogenetic studies are
DNA sequences.
■ In some cases, a phylogeny is not strictly
dichotomous (branching) but may include
reticulation (joining of separate lineages into
one). This can occur if some species have
originated by hybridization between different
ancestral species or if genes have moved
“horizontally” between organisms.
■ Phylogenetic methods can be used to describe
the history not only of species, but also of DNA
sequences, gene families, tumors and
02_EVOL5E_02.indd 56
other cell lineages, and cultural traits such
as languages.
■ Phylogenetic analyses have many uses. An important one is inferring the history of evolution
of interesting characters by “mapping” changes
in a character onto a phylogeny that has been
derived from other data. Such systematic
studies have yielded information on common
patterns and principles of character evolution.
■ The rate of evolution of DNA sequences in many
cases is fairly constant (providing an approximate molecular clock), such that sequences in
different lineages diverge at a roughly constant
rate. The absolute rate of sequence evolution
can sometimes be calibrated if the ages of
fossils of some lineages are known. The rate
of sequence evolution can then be used to
estimate the absolute age of some evolutionary events, such as the origin of other taxa and
their characteristics.
■ New features almost always evolve from preexisting characters. Homologous characters in
different organisms are those that have been
inherited from their common ancestors, with or
without evolutionary change.
■ Different characters commonly evolve at
different rates (mosaic evolution).
■ Homoplasy, including convergent evolution and
reversal, is often a result of similar adaptations
in different lineages.
■ In an adaptive radiation, numerous related lineages arise in a relatively short time and evolve
in many different directions as they adapt to
different habitats or ways of life. Radiation,
rather than directional trends, is perhaps the
most common pattern of long-term evolution.
■ Comparisons of the characteristics of diverse
organisms provide abundant evidence, of many
kinds, that species have evolved from common
ancestors—that evolution is true.
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Summary
57
SUGGESTIONS FOR FURTHER READING
Tree Thinking: An Introduction to Phylogenetic
Biology, by D. A. Baum and S. D. Smith is a
comprehensive introduction to the concepts, methods, and uses of phylogenetics
in biology, for nonspecialists. [1]
Why Evolution Is True, by Jerry A. Coyne, is a
masterful summary of the diverse kinds
of evidence for evolution, in an enjoyable
style. [4]
These journal articles provide introductions to some of the topics in this chapter:
Omland, K. E., et al. 2008. Tree thinking for all
Pagel, M. 1999. Inferring the historical patterns
of biological evolution. Philos. Trans. R. Soc.
biology: The problem with reading phylogenies as ladders of progress. BioEssays
B 352:519–529. [20]
30:854–867. [19]
PROBLEMS AND DISCUSSION TOPICS
1. Suppose species 1, 2, and 3 are endemic to a
group of islands and are all descended from
species 4 on the mainland (which will serve
as an outgroup; its very large population size
means that no new mutations have become
fixed in its population in the time since the
islands were colonized). We sequence a gene
and find ten nucleotide sites that differ among
the four species (among many others that do
not vary). The nucleotide bases at these sites
are the following:
Species 1: GCTGATGAGT
Species 2: ATCAATGAGT
Species 3: GTTGCAACGT
Species 4: GTCAATGACA
Using species 4 as an outgroup, estimate
the phylogeny of these taxa by plotting the
changes on each of the three possible trees
and determining which tree requires the
fewest evolutionary changes.
2. Suppose the species in the previous question
are birds that differ in diet: species 1 and 3
eat insects, and species 2 and 4 eat fruit. We
also happen to know that another fruit-eating species, species 5, is a mainland relative
of species 4. Given your best estimate of
the phylogenetic history, what has been the
probable history of the evolution of diet in this
clade of birds?
3. Phylogenetic information is the basis for
describing patterns of evolution, yet some
examples of patterns were presented in the
text without phylogenetic trees. Consider
the following examples and discuss what
02_EVOL5E_02.indd 57
phylogenetic evidence or inference was
left unstated:
a. The fusion of hind limb bones during
embryonic development of birds is
a derived trait, not an ancestral trait,
relative to the unfused condition in
crocodiles.
b. P
entadactyly (five digits) is homologous
in humans and crocodiles.
c. F
rogs have some “primitive” traits that
are very similar to those of their deep
ancestors (five toes on the hind limb,
multiple bones in the lower jaw) but
others that are relatively advanced
(lack of teeth in the lower jaw).
4. There is evidence that some differences in
DNA sequence among species are adaptive
and that many are not. Do you think adaptive
and nonadaptive variations differ in their usefulness for phylogenetic inference? Why or
why not?
5. It is possible for two different genes to imply
different phylogenetic relationships among a
group of species? What are the possible reasons for this? Is it possible for both gene trees
to be accurate even if there has been only one
history of species divergence?
6. Refer to Figure 2.19. Suppose you have the
DNA sequences, for two species of rodents,
of the same set of genes that the data in this
figure portray. You find 20 base pair differences between the species. Assuming the rate of
sequence evolution in this group of rodents is
about the same as in the mammals the figure
is based on, approximately how long ago did
these two species lineages diverge from their
common ancestor?
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3
Natural Selection
and Adaptation
S
ome people are thrilled by snakes, while others are repelled and fearful,
but no one can deny that snakes are fascinating animals. Depending on
the species, they can crawl, burrow, swim, climb trees, and even glide,
all without benefit of arms or legs. Perhaps most amazing, they can swallow
prey, whole, that are much larger than their heads (FIGURE 3.1A). Snakes can
do this because, unlike humans, they have movable skull bones. Each lower
jawbone (mandible) is joined to a long, movable quadrate bone, so it can drop
away from the skull, and the mandibles’ front ends are not fused but joined by
a stretchable ligament. The tooth-bearing maxilla bones of the upper jaw can
be flexed outward, further increasing the mouth opening. The upper and lower
jaw bones on both sides can be independently moved forward and backward
to pull the prey into the throat (FIGURE 3.1B). Rattlesnakes and other vipers
take this apparatus a step further: their maxilla is short and bears only a long,
hollow fang—a natural hypodermic needle—to which a duct leads from the
massive poison gland (a modified salivary gland). The fang lies against the roof
of the mouth when the mouth is closed. When the snake opens its mouth, the
short maxilla is rotated 90° so that the fang is fully erected (FIGURE 3.1C,D).
Snakes’ skulls, like many anatomical features, are complex mechanisms that
look as if they had been designed by engineers to perform a specified function.
They are said to be adapted to the animal’s way of life: swallowing large prey
whole. Every species has features—adaptations—that enhance survival or
reproduction. Many features are adaptations to specific environments. Cacti
A marvel of adaptation: the sword-billed hummingbird (Ensifera ensifera) is adapted to
reach nectar deep in the long, tubular flowers of passion vines (Passifloraceae) and other
plants. Its extraordinary bill evolved by natural selection.
© Rolf Nussbaumer/Minden Pictures
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Chapter 3
FIGURE 3.1
Natural Selection and Adaptation
Amazing adaptations.
(A) Egg-eating snake
(B) Rattlesnake
© Tom McHugh/Science Source
© Helmut Göthel Symbiosis/
Alamy Stock Photo
(A) Most snakes, such as this egg-eating
snake, can eat prey much larger than
their heads. This ability is enabled by
loose connections among many skull
bones. The movable upper jaw bones are
shown in blue. (B) The head of a red diamondback rattlesnake is in strike mode.
In vipers such as rattlesnakes, rotation
of the shortened maxilla erects a single
tooth, a hollow fang, on each side of its
head. (Illustrations after K. R. Porter.
1972. Herpetology. Philadelphia, PA:
W. B. Saunders.)
Quadrate
Maxilla
Quadrate
Maxilla
that grow in arid environments lack leaves and have thick, sometimes globular
stems with a low ratio of surface area to volume, reducing water loss by evapFutuyma
orationKirkpatrick
(FIGUREEvolution,
3.2). In 5e
some cacti, a coat of hair that reflects sunlight reduces
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the
plant’s
temperature.
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An adaptation is a characteristic that enhances the survival or reproduction
of organisms that bear it, relative to alternative character states. Adaptations
have evolved by natural selection, which is the centerpiece of Darwin’s On the
Origin of Species and of evolutionary theory and is perhaps the most important
idea in biology. It is also one of the most important ideas in the history of human
thought, for it explains the apparent design of the living world without recourse
to a supernatural, omnipotent designer [9].
(A) Barrel cactus
(B) Cholla
Many plants that live in arid environments have adaptations
to reduce water loss, as in these cacti, which lack leaves. The stems, where
photosynthesis takes place, are thick, with a low ratio of surface area to volume.
(A) This adaptation is taken to an extreme in almost globular barrel cacti. (B) Some
cacti, such as this cholla, have hairs that reflect light and so reduce the temperature of the plant body.
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© Susan E. Degginger/Alamy Stock Photo
© Organica/Alamy Stock Photo
FIGURE 3.2
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3.1 Adaptive Evolution Observed 61
For hundreds of years, it seemed that adaptive design could be explained
only by an intelligent designer. In fact, this “argument from design” was considered one of the strongest proofs of the existence of God. The Reverend William
Paley wrote in Natural Theology that, just as the intricacy of a watch implies an
intelligent, purposeful watchmaker, so every aspect of living nature, such as the
human eye, displays “every indication of contrivance, every manifestation of
design, which exists in the watch” and must, likewise, have had a designer [31].
When Darwin offered a purely natural, materialistic alternative to the argument
from design, he not only shook the foundations of theology and philosophy, but
also brought every aspect of the study of life into the realm of science. His alternative
to intelligent design was design by the completely mindless process of natural
selection. This process cannot have a goal, any more than erosion has the goal
of forming canyons, for the future cannot cause material events in the present. Thus
the concepts of goals or purposes have no place in biology (or in any of the other
natural sciences), except in studies of some animals’ behavior.
Adaptive biological processes appear to have goals: animals engage in many
adaptive behaviors, and a morphological feature, such as a flower, develops
toward a suitable shape and stops developing when that shape is attained. We
may loosely describe such features by teleological statements, which express goals
(e.g., “She studied in order to pass the exam”; “Wasps sting to defend themselves
from predators”). But no conscious anticipation of the future resides in the cell
divisions that shape a flower or, as far as we can tell, in the behavior of wasps.
Rather, the apparent goal-directedness is caused by the operation of a program—
coded or prearranged information, residing in DNA sequences—that, when activated by external or internal stimuli, controls a process [27]. A program likewise
resides in a computer chip, but whereas that program has been shaped by an
intelligent designer, the information in DNA has been shaped by a historical
process of natural selection. Modern biology views the development, physiology, and behavior of organisms as the results of purely mechanical processes,
resulting from interactions between programmed instructions and environmental conditions or triggers.
3.1 Adaptive Evolution Observed
Learning objective
■ Describe a case in which the process of adaptive evolution has been
observed, and explain its cause.
Darwin could not point to any cases in which evolutionary change of a population or species had actually been observed, and he supposed that evolution
was much too slow for us to see it in action. But today we can cite hundreds of
cases of adaptive evolution of morphological, physiological, and behavioral traits
that have been directly observed. Adaptive evolution can be rapid, especially
in species that have been introduced into new regions or subjected to human
alterations of their environment [30]. For instance, several species of insects, such
as the soapberry bug (Jadera haematoloma) [5], have adapted rapidly to new food
plants. The bug feeds on seeds of plants in the soapberry family (Sapindaceae)
by piercing the enveloping seedpod with its slender beak. In the last 50 years,
related species of Asian trees have been so abundantly planted in Texas and
Florida that the bug populations feed mostly on these species. Compared with
the original native host plants, the Asian tree species in Texas has a larger pod,
and the Asian tree species in Florida has a much smaller pod. Corresponding to
this difference, soapberry bug populations in Texas have evolved a longer beak,
and those in Florida, a shorter beak (FIGURE 3.3).
We have already seen (in Chapter 1) that bacteria can evolve resistance to
antibiotics very rapidly. Similarly, resistance to chemical pesticides has evolved
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Beak
Florida
Texas
Native
Introduced
9
Beak length (mm)
9
Beak length (mm)
Introduced
8
7
6
2
4
6
8
10
Pod radius (mm)
8
7
6
Native
2
12
4
6
8
10
12
Pod radius (mm)
FIGURE 3.3 Soapberry bugs and the seedpods of their native and introduced
host plants in Texas and Florida, drawn to scale. The average pod radius of each
host species is plotted against the average beak length of associated Jadera populations. Beak length has evolved rapidly as an adaptation to the new host plants. (After
Futuyma Kirkpatrick Evolution, 5e
S. B. Carroll and C. Boyd. 1992. Evolution 46: 1052–1069.)
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in hundreds of species of insects (FIGURE 3.4), and many species of weeds have
evolved resistance to herbicides within 10 to 20 years of field exposure. Copper,
zinc, and other heavy metals are toxic to plants, but in several species of grasses
and other plants, metal-tolerant populations have evolved where soils have been
contaminated by mine works less than 100 years old. When tolerant and nontolerant genotypes of a species are grown in competition with other plant species
in the absence of the metal, the growth of the tolerant genotypes is often much
lower than that of the nontolerant genotypes, implying that adaptation has costly
side effects [1, 26].
Commercial overexploitation has severely depleted populations of many species of fish and has resulted in evolutionary changes as well [24]. In many species
there has been a trend toward earlier sexual maturation at a smaller size, as we
predict will happen when larger fish are more subject to predation (see Chapter
14). In some species, such as Atlantic cod (Gadus morhua), these changes clearly
have a genetic basis (FIGURE 3.5A). Similarly, trophy hunting for those bighorn
sheep (Ovis canadensis) with the largest horns has resulted in the evolution of
smaller horns (FIGURE 3.5B). In both instances, the very quality that adds value
to the resource has been diminished by the response to selection.
FIGURE 3.4
Cumulative numbers
of insect pest species known to have
evolved resistance to five classes of
insecticides. The upper curve shows the
total number of insecticide-resistant species. The number has increased since these
data were tabulated. (After R. L. Metcalf and
W. H. Luckmann. 1994. Introduction to Insect
Pest Management, 3rd ed. New York: Wiley.)
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Number of resistant pest species
600
Insecticide class:
Total
500
400
300
Cyclodienes
DDT
© Tim UR/Shutterstock.com
Organophosphates
200
100
0
1940
Carbamates
Pyrethroids
1950
1960
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1970
1980
1990
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3.1 Adaptive Evolution Observed 63
© B. Wilson/Shutterstock.com
(A) Atlantic cod
(B) Bighorn sheep
© blickwinkel/Alamy Stock Photo
6
Body length
60
50
5
40
Fishery closed
1992
1980
1985
Maturity
1990
Year
1995
2000
30
Mean horn length (cm)
70
1975
80
80
Body length at 5 years (cm)
50% maturity (age in years)
7
2005
FIGURE 3.5 Evolutionary changes caused by human
harvesting. (A) The age at which 50% of Atlantic cod reached
maturity declined until 1994, 2 years after the fishery in Canadian waters was closed because of overfishing. Body length at
70
60
50
40
1975
1980
1985
1990
Year
1995
2000
2005
5 years of age also declined. (B) Mean horn length of 4-year-old
bighorn sheep rams declined because of selection imposed by
hunting. (A after E. M. Olsen et al. 2004. Nature 428: 932–935;
B after D. W. Coltman et al. 2003. Nature 426: 655–658.)
Futuyma Kirkpatrick Evolution, 5e
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Some species show
adaptation to the ongoing climate change caused by
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human production of CO2 and other greenhouse gases. In many insects, day
length is the cue for entering diapause, a state of low metabolic activity that is
necessary for surviving the winter. Northern populations are genetically programmed to enter diapause at a longer day length than southern populations
because cold weather arrives at northern latitudes sooner, when days are still
long. William Bradshaw and Christina Holzapfel [3] sampled populations of
the pitcher plant mosquito (Wyeomyia smithii) from southern Canada to Florida
four times between 1972 and 1996 and experimentally measured the day length
at which the insects entered diapause. They found that during this time, the critical photoperiod became shorter: the insects became programmed to enter diapause later in autumn (FIGURE 3.6). The change was greatest in the most northern
1988
1993
1.24
1988
1.21
1993
1.18
1.15
1.12
30 35 40 45 50
Altitude-corrected latitude
55
Pitcher plant
Josh Emm/CC BY-SA 4.0
© Arend Trent/Shutterstock.com
Critical photoperiod (log hours)
1.27
Mosquito larva
FIGURE 3.6
In North America, the critical photoperiod for entering diapause,
in relation to latitude, has decreased in the pitcher plant mosquito. This change
is an adaptation to climate change. The larvae of this mosquito develop only in the
water that collects in the tubular leaves of the pitcher plant. (After W. E. Bradshaw
and C. M. Holzapfel. 2001. Proc Natl Acad Sci USA 98: 14509–14511.)
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populations, as expected because the increase in temperature, as climate changes,
has been greater at higher latitudes. The speed of evolution was amazing, having
taken as little as 5 years. (Do not suppose, though, that all species threatened
by climate change will adapt successfully. We return to this topic in Chapter 7.)
These evolutionary changes can be so rapid because populations in altered
environments, especially those altered by human activities, can experience stringent natural selection and because they contain genetic variation in many characteristics—a necessary ingredient of evolution.
3.2 Natural Selection
Learning objectives
■ Define natural selection and fitness.
■ Define frequency as it is applied to a genotype or an allele.
■ Distinguish between natural selection and genetic drift, and describe
how each is related to the concept of “chance” or “randomness.”
The meaning of natural selection
In The Origin of Species, Darwin introduced natural selection with these words:
Can it … be thought improbable, seeing that variations useful to man have
undoubtedly occurred [in domesticated animals and plants], that other variations
useful in some way to each being in the great and complex battle of life, should
sometimes occur in the course of thousands of generations? If such do occur, can
we doubt (remembering that many more individuals are born than can possibly
survive), that individuals having any advantage, however slight, over others,
would have the best chance of surviving and procreating their kind? On the other
hand, we may feel sure that any variation in the least degree injurious would be
rigidly destroyed. This preservation of favoured variations and the rejection of
injurious variations, I call Natural Selection.
Among several slightly different definitions of natural selection used by biologists today [11], we use this one: natural selection is any consistent difference in
fitness among different classes of biological entities. A simple way to think of fitness is
as the number of offspring an individual leaves in the next generation. Suppose
that in a species of annual plant, only 1 of every 1000 seeds survives to reproductive age and that those that survive produce an average of 3000 seeds. The
average fitness of that type of individual is 0.001 × 3000 = 3. The components of
fitness are survival and reproduction. Fitness is sometimes called reproductive
success, which includes survival because organisms do not reproduce when
they are dead.
If evolution by natural selection is to occur, there must be a change in the population across generations, and this requires that the phenotypic differences among
the entities be inherited. Thus, evolution by natural selection occurs if (1) there is
a correlation between an individual’s phenotype and its fitness and (2) variation
in the phenotype is correlated between parents and their offspring.
Suppose, for example, that in an asexually reproducing annual plant, genotypes A and B differ in a characteristic that affects their fitness (e.g., susceptibility vs. resistance to an herbicide) and that their average fitnesses are 3
and 4, respectively. If these values are constant from generation to generation,
genotype B increases in number much faster than A and will make up the great
majority of the population within a few generations (FIGURE 3.7). We say that
the frequency (proportion) of genotype B has increased (and that the frequency
of A has declined). In sexually reproducing organisms, fitness is more complicated. Males compete for mating success, which Darwin called sexual selection.
(In some species, females also experience sexual selection.) In sexual species,
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3.2 Natural Selection 65
Frequency
Numbers of individuals
(A)
moreover, individuals’ genes replicate, but their genotypes do not because they are broken up by recombina20,000
tion and segregation during meiosis. So it can be useful
to think about the fitness of an allele, and consequently
15,000
of selection among alleles, even though the “entities”
that differ in survival and reproduction in most dis10,000
Genotype B
course about evolution are individual organisms with
different phenotypes (individual selection). Evolution by
5000
natural selection in sexually reproducing populations
Genotype A
entails changes in the frequencies of alleles at the locus
(or loci) underlying variation in the phenotypic charac0
1
2
3
4
5
6
7
teristic that influences fitness.
Generation
We will almost always discuss natural selection
(B)
among alleles and among heritable individual phenotypes because selection has no evolutionary effect with1.0
out inheritance. Most of our discussion will assume that
inheritance of a trait is based on genes. However, many
0.75
Genotype B
of the principles of evolution by natural selection also
apply if inheritance is epigenetic (due to differences in
0.5
DNA methylation or other causes; see Chapter 4). Inheritance can also be based on cultural transmission of
Genotype A
0.25
learned traits such as language, especially from parents
to offspring [34].
We must be very careful to understand that natural
0
1
2
3
4
5
6
7
selection is not an agent or active power, and certainly
Generation
not a purposeful one, even though the language we use
often seems to personify it or to suggest that it is an
FIGURE 3.7 Two genotypes of a plant are growing together.
agent. Darwin coined the term natural selection to parGenotype A has a fitness of 3, while genotype B has a fitness of 4.
Both genotypes start with 10 individuals. (A) The population size of
allel the selection that breeders of crops and domestic
Futuyma Kirkpatrick Evolution, 5e
genotype B grows
much
more rapidly. (B) The frequency of genoanimals use to improve desirable characteristics. In later
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the frequency of genotype A decreases. The
Evolution5e_03.07.ai
editions of The Origin of Species, he wrote that “it has
result is that, while both genotypes start with a frequency of 0.5,
been said that I speak of natural selection as an active
genotype B makes up almost 90% of the population just seven
power or Deity; but who objects to an author speaking of
generations later.
the attraction of gravity as ruling the movements of the
planets? Every one knows what is meant and is implied
by such metaphorical expressions; and they are almost necessary for brevity.”
Likewise, evolutionary biologists often say that selection “favors” a certain characteristic, or they refer to selection as a “force.” This is metaphorical language,
used for brevity. Natural selection is a name for statistical differences in reproductive
success among genes, organisms, or populations—and nothing more.
Natural selection and chance
Natural selection is not synonymous with evolution. Natural selection can occur
without any evolutionary change, as when natural selection maintains the status
quo by eliminating deviants from the optimal phenotype. And processes other
than natural selection can cause evolution.
One of those processes is genetic drift: random fluctuations in the frequencies of genotypes within a population. (Genetic drift is the subject of Chapter
6.) Neutral alleles are those that do not alter fitness: the average reproductive
success does not differ between individuals that carry one neutral allele (say,
A1) or another (A2). Purely by random accidents, a higher percentage of A1
individuals may be killed before they reproduce in one generation, but a higher
percentage of A2 individuals might not survive in the following generation. The
frequency of A1 declines slightly in one generation and increases slightly in the
next, but the increase need not exactly equal the decrease. So the frequencies
of these neutral alleles fluctuate: they change by genetic drift, and eventually
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(A) Army ant
(B) Blind snake
© FLPA/Alamy Stock Photo
© Mark Moffett/Minden Pictures
FIGURE 3.8
The evolutionary histories of some animals have made them
less reliant on vision, so selection for
visual acuity has been relaxed.
one allele may completely replace the other. If this occurs, the bearers of one
allele have had a greater rate of increase than the bearers of the other allele.
But natural selection has not occurred, because the genotypes do not differ
(A) Army ants (genus Eciton) rely almost
consistently in fitness: the alternative allele could just as well have been the one
Futuyma
Kirkpatrick Evolution,
5e
entirely on chemical
information.
In
OUP/SInauer/Troutt Visual Services to increase. There is no average difference between the alleles, no bias toward the
these ants, the compound eyes have
Evolution5e_03.08.ai 03-31-2022increase of one relative to the other. Fitness differences, in contrast, are average
been highly reduced, consisting of a
differences, biases, differences in the probability of reproductive success. Natural
single unit (ommatidium) rather than the
selection is the antithesis of chance. In practice, we can ascribe genetic changes to
many that compose most insects’ eyes.
natural selection rather than random genetic drift only if we measure numerous
(B) Similarly, burrowing blind snakes
individuals of each genotype or phenotype and find an average difference in
(Typhlopidae) have highly reduced eyes
reproductive success.
that perceive light but cannot form an
image. These species experience their
environments in very different ways
than we do.
The effective environment depends on the organism
The environmental factors that impose natural selection on a species are greatly
influenced by the characteristics of the species itself: the evolutionary history of
a species affects its relationship to the environment [25]. The branching structure
of trees in a forest is important for many tree-nesting birds, such as orioles, but
almost irrelevant for ground-nesting species, such as partridges. The viscosity
of water, which varies with temperature, is much more important for a ciliate
than for a fish because the ciliate is so small. Many species of ants, rodents, and
other animals have become so reliant on chemical signals that they have become
blind because natural selection for sight has become reduced or even negative:
well-developed eyes may be disadvantageous if they conflict with other important functions (FIGURE 3.8). Colors and distant forms are not part of these animals’
environment. Likewise, humans have lost the function of many olfactory receptor genes, having become so much more reliant on vision than smell.
3.3 Levels of Selection
Learning objectives
■ Describe what is meant by levels of selection, and specify them.
■ Describe how selection can occur but not increase adaptation.
■ Distinguish group selection from individual selection, and describe
why group selection may have a smaller effect.
■ Describe an example of a selfish gene.
■ Define kin selection.
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3.3 Levels of Selection 67
By natural selection, both Darwin and present-day evolutionary biologists usually mean consistent differences in fitness among phenotypically and genetically
different individual organisms within populations. But our definition of natural
selection applies to any classes of variable entities that can change in number.
Selection can occur among genes, cell types, individual organisms, populations,
or species. These entities form a hierarchy of levels of selection.
Natural selection at the level of the gene is illustrated by transposable
elements, which replicate and proliferate within the genome, irrespective of
whether their proliferation affects the organisms for good or ill. Transposable
elements are among the many kinds of selfish genetic elements, which are transmitted at a higher rate than the rest of an individual’s genome and may be detrimental (or at least not advantageous) to the organism [4]. Some selfish alleles
exhibit segregation distortion and are passed to more than 50% of a heterozygous
individual’s gametes. Segregation distortion can result from meiotic drive (in
which meiosis does not follow Mendel’s laws; see Chapter 10) and from other
processes that happen after the gametes are formed.
An example of a selfish genetic element that exhibits segregation distortion
is the t locus of the house mouse (Mus musculus). In a male heterozygous for a t
allele and the normal allele T, the t allele kills sperm that carry the normal allele.
As a result, more than 90% of the male’s functional sperm carry t. Embryos that
are tt homozygotes, however, die or are sterile. Despite these disadvantages to
the individual, segregation distortion is so great that the harmful t allele reaches
a high frequency in many populations of mice.
Selfish genetic elements forcefully illustrate the nature of natural selection:
it is nothing more than differential reproductive success, which need not result
in adaptation or improvement in any sense. Selection among individuals is at a
“higher level” than selection among genes [29]. Selection at the gene level may
act in opposition to individual selection: it may be harmful to individual organisms, and it might even cause the extinction of populations or species.
Selfish genes and unselfish behaviors
Evolutionary geneticists have long recognized that natural selection will cause
an allele to increase in frequency if it consistently leaves more copies of itself to
subsequent generations, no matter how it causes its greater success. For example,
plants that produce more pollen are likely to fertilize more ovules, so any allele
that increases pollen production is likely to spread. J. B. S. Haldane wrote, “No
sufferer from hay fever will doubt that more pollen is produced than is needed
to assure that almost every ovule should be fertilised” [19]. In the same book,
he wrote that “in a beehive the workers [which do not reproduce] and young
queens are samples of the same set of genotypes, so any form of behaviour in
the former (however suicidal it may be) which is of advantage to the hive will
promote the survival of the latter, and thus tend to spread through the species.”
That is, by helping queens, workers promote the survival of copies of their own
genes, which are shared with the queens.
The key issue is that it is often useful to think of selection among genes, based
on the effects that change their frequencies—whether these effects are on the
number of pollen grains, behavior that enhances the survival of relatives that
share the same gene, or many other biological features. In a sense, then, any gene
that has successfully increased in frequency is a selfish gene, as biologist Richard
Dawkins has famously written [7]. The evolution of many puzzling traits of
organisms can be understood by considering the rates at which different variants of a gene that affects the trait would change in frequency over the course
of generations.
An important example of this approach is the topic that Haldane addressed
in accounting for the behavior of worker bees: what he called “socially valuable
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but individually disadvantageous characters.” Many such altruistic traits are best
explained by the principle Haldane described, which we now call kin selection
(see Chapter 13). An allele for altruistic behavior can increase in frequency in a
population if the beneficiaries of the behavior are usually related to the individual who performs it. Since the altruist’s relatives are more likely to carry copies
of the altruistic allele than are members of the population at large, when the
altruist enhances the fitness of its relatives, even at some cost to its own fitness,
it can increase the frequency of the allele. We may therefore define kin selection
as a form of selection in which alleles differ in fitness by influencing the effect of
their bearers on the reproductive success of individuals (kin) who carry the same
allele by common descent. The simplest example of a trait that has evolved by
kin selection is parental care: alleles that enhance a parent’s caregiving behavior
have increased in frequency because they promote the survival of identical copies
of those same alleles that the offspring carry.
Selection of organisms and groups
Do oysters have a high reproductive rate “to ensure the survival of the species,”
a phrase that we often hear? Do antelopes with sharp horns refrain from physical
combat because combat would lead to the species’ extinction? Is there any truth
to the myth that lemmings (small Arctic rodents) commit suicide by drowning,
in order to relieve the pressure of high population density on the food supply?
If traits evolve by individual selection—the replacement of less-fit by more-fit
individuals, generation by generation—the possibility of future extinction cannot possibly affect the course of evolution. The process of natural selection lacks
forethought (or any thought at all): the future cannot affect the present. Suppose
an individual’s behavior—such as committing suicide—is due to a certain allele,
and suppose that it benefits the population by making more food available. If
the benefit flows to the individual’s relatives, the behavior might increase in
frequency, due to kin selection. But if the benefit is likely to be enjoyed equally
by related and nonrelated individuals, the allele decreases in frequency because
its carriers die without having increased the survival of copies of that allele compared with other alleles. Conversely, in a population of altruistic genotypes, a
selfish mutant—a “cheater”—would increase and replace the altruistic genotype,
even if a population of such selfish organisms had a higher risk of extinction.
So it would seem impossible that a trait could evolve that benefits the population
at a cost to the individual. However, there is one conceivable way it might evolve,
namely by group selection: differential production or survival of groups that differ in
genetic composition. For instance, populations made up of selfish genotypes, such
as those with high reproductive rates that exhaust their food supply, might have a
higher extinction rate than populations made up of altruistic genotypes that have
lower reproductive rates. If so, then the species as a whole might evolve altruism
through the greater survival of groups of altruistic individuals, even though individual selection within each group would act in the opposite direction.
This hypothesis of group selection was criticized by George Williams in his influential book Adaptation and Natural Selection [39]. Williams argued that supposed
adaptations that benefit the population or species, rather than the individual, can
be plausibly explained by benefit to the individual or the individual’s genes, or they
may not be adaptations at all. For instance, females of many species lay fewer eggs
when population densities are high and food is scarce, not to ensure a sufficient food
supply for the good of the species, but simply because they cannot form as many
eggs. Williams based his opposition to group selection on a simple argument. Individual organisms are much more numerous than the populations into which they
are aggregated, and they turn over—are born and die—much more rapidly than
populations are formed or become extinct. Thus the rate of replacement of less-fit
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3.3 Levels of Selection 69
t2
Increasing
mean size
Time
(altruistic) by more-fit (selfish) individuals is potentially much greater than the rate
of replacement of less-fit by more-fit populations, so individual selection will generally prevail over group selection. Among evolutionary biologists, the majority view
is that few characteristics have evolved because they benefit the population or species and
that cooperation and apparent altruism are most likely to have evolved by other
causes, especially kin selection.
t1
Species selection
Selection among groups of organisms is called species selection when the groups
involved are species. This occurs when the groups involved are species and there
is a correlation between some characteristic of the species and the rate of speciation or extinction [16, 21, 33]. Species selection does not shape adaptations
of organisms, but it does affect the diversity of biological characteristics of the
world’s organisms. The result of species selection is that the proportion of species that have one character state rather than another changes over time (FIGURE
3.9). A likely example of the effects of species selection is the prevalence of sexual
species compared with closely related asexual forms. Many groups of plants and
animals have given rise to asexually reproducing lineages, but with some interesting exceptions, asexual lineages tend to be young, as indicated by their close
genetic similarity to sexual forms. This observation implies that asexual forms
have a higher rate of extinction than sexual populations, since few asexual forms
that arose long ago have persisted (see Chapter 12) [28]. In another example, 15 of
19 families of plants that have bilaterally symmetrical flowers have more species
than their sister groups with radially symmetrical flowers (for example, violets
vs. passionflowers, FIGURE 3.10) [36]. The time that has elapsed since the sister
groups’ common ancestor is the same, so a difference in the number of species
must be due to different rates of speciation, extinction, or both. There is evidence
that new species evolve more often in bilaterally symmetrical plants because
slight differences in flower structure among populations are more likely to cause
them to be pollinated by different species of insects—leading the populations to
become different species.
Bilaterally symmetric
1000 species
Passionflowers
Violets
FIGURE 3.9
Species selection
caused by a correlation between
speciation rate and a morphological
character, such as body size. Larger-
bodied species persist longer before
becoming extinct, and so they give rise
to other large-bodied species more
often than small-bodied species give
rise to other
small-bodied
species. The
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lower extinction rate
lineages with
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large body sizes is analogous
to a lower
mortality rate of individual organisms
in individual selection. The character
value, averaged across species (dots),
increases from time t 1 to time t 2 . (After
S. J. Gould. 1982. In Perspectives on
Evolution, R. Milkman [ed.], pp. 83–104.
Sunderland, MA: Oxford University
Press/Sinauer Associates.)
Salicyna/CC BY-SA 4.0
© Vankich1/Shutterstock.com
Radially symmetric
750 species
Body size
FIGURE 3.10
There has been species selection of flower symmetry.
Bilaterally (left-to-right) symmetrical flowers appear to increase speciation
rates, compared with radially (clocklike) symmetrical flowers. One example
is the comparison between passionflowers (Passifloraceae and others),
which are radially symmetric, and their sister group the violets (Violaceae),
which are bilaterally symmetric (After R. D. Sargent. 2004. Proc R Soc LonFutuyma
Kirkpatrick
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don B 271:
603–608.)
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3.4 The Nature of Adaptations
Learning objectives
■ Describe how a characteristic may evolve if it fortuitously serves
a new function.
■ Describe how the distinction between selection for and selection of
characteristics applies to side effects of advantageous features.
■ Describe several kinds of evidence that may indicate whether or
not a feature is an adaptation.
Adaptation is a central concept in biology. The word has two related meanings.
Adaptation means the evolutionary process by which, over the course of generations, organisms are altered to become improved with respect to features that
affect survival or reproduction. “An adaptation” is a characteristic of an organism
that evolved by natural selection. Both meanings are difficult to define precisely
[22]. Most evolutionary biologists think that for a character to be regarded as an
adaptation, it must be a derived character that conferred higher fitness than the
ancestral character state from which it evolved [20].
A preadaptation is a feature that fortuitously serves a new function. For
instance, parrots have strong, sharp beaks, used for feeding on fruits and seeds.
When domesticated sheep were introduced into New Zealand, some were
attacked by an indigenous parrot, the kea (Nestor notabilis), which pierced the
sheeps’ skin and fed on their fat (FIGURE 3.11). The kea’s beak happened to be
useful for this new activity. Such a feature, if co-opted for a new function during
evolution, is sometimes called an exaptation [17]. For example, the wings of auks
are used for swimming: these birds “fly” underwater as well as in air (FIGURE
3.12A). Such features frequently become further modified by selection for the
feature’s new function. The wings of penguins have been modified into flippers
that enhance swimming but cannot support flight in air (FIGURE 3.12B).
Kea
© Rolf Hicker Photography/Alamy Stock Photo
Selection of and selection for
FIGURE 3.11
An example of a preadaptation.
The long, sharp bill of the kea evolved for functions such as cracking seeds, but it can be used
for many other things, such as slicing into sheep
skin and ripping windshield wipers, rubber gaskets, and other removable pieces from parked
automobiles. At several sites in New Zealand,
tourists are warned to protect their cars against
keas. Why
keas Evolution,
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To say that a feature is adaptive is unsatisfying unless we have some idea
of what it is adaptive for: by what mechanism did it increase fitness? What
is its function?
In the child’s “selection toy” pictured in FIGURE 3.13, the holes in each
partition are smaller than in the one above. Balls of several sizes, when
placed in the top compartment, fall through the holes in the partitions. If the
smallest balls in the toy are all red, and the larger ones are all other colors,
the toy will select the small, red balls. Thus we must distinguish selection
of objects from selection for properties [37]. Balls are selected for the property
of small size—that is, because of their small size. They are not selected for
their color, or because of their color; nonetheless, there is selection of red
balls. Natural selection may similarly be considered a sieve that selects for
a certain body size, mating behavior, or other feature. There may be incidental selection of other features that are correlated with that feature. We
will return several times in the book to the theme that selection on one trait
has side effects on others.
The importance of this semantic point is that when we speak of the function of a feature, we imply that there has been natural selection for the feature
itself: that the feature caused its bearers to have higher fitness. The feature
may have side effects, other consequences that were not its function and for
which there was no selection. For instance, a fish species may be selected for
coloration that makes it less conspicuous to predators. The function of the
coloration, then, is predator avoidance. An effect of this evolutionary change
might well be a lower likelihood that the population will become extinct, but
avoidance of extinction is not a cause of evolution of the coloration.
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3.4 The Nature of Adaptations 71
(A) Atlantic puffin
(B) Humboldt penguin
© Christian Musat/Shutterstock.com
© Alex Mustard/Minden Pictures
FIGURE 3.12
Exaptation and adaptation. (A) The wing might be called an exaptation for underwater “flight” in members of the auk family, such as this Atlantic puffin.
(B) The modifications of the wing for efficient underwater locomotion in penguins
(these are Humboldt penguins) may be considered adaptations.
Recognizing adaptations
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Not all
traits are
adaptations.
There are at least four other possible explanations
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of
organisms’
characteristics.
First, a trait may be a necessary consequence of
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physics or chemistry. Hemoglobin gives blood a red color, but the redness is not
an adaptation; it is a by-product of the protein’s structure. There has been selection of the red color of blood, but not selection for red color.
Second, the trait may have evolved by other mechanisms (such as random
genetic drift) rather than by natural selection (see Chapter 6).
Third, the feature may have evolved not because it conferred an adaptive
advantage, but because it was correlated with another feature that did. That was
the case of color of the balls in the selection machine example in Figure 3.13. As
we will see, genetic linkage and pleiotropy—the phenotypic effect of a gene on
multiple characters—are important causes of such correlations.
Fourth, a character state may be a consequence of phylogenetic history. Darwin
saw clearly that a feature might be beneficial yet not have evolved for the function
it serves today, or for any function at all: “The sutures in the skulls of young mammals have been advanced as a beautiful adaptation for aiding parturition [birth],
and no doubt they facilitate, or may be indispensable for this act; but as sutures
occur in the skulls of young birds and reptiles, which have only to escape from a
broken egg, we may infer that this structure has arisen from the laws of growth,
and has been taken advantage of in the parturition of the higher animals” (On the
Origin of Species, chapter 6). Whether or not we should postulate that a trait is an
adaptation depends on such insights. We know that pigmentation varies in many
species of birds, so it makes sense to ask whether there is an adaptive reason for
color differences among closely related species. But it is not sensible to ask whether
it is adaptive for a hummingbird to have four toes rather than five, because the
ancestor of birds lost the fifth toe, and it has never been regained in any bird since.
Five toes are probably not an option for hummingbirds.
For all these reasons, many authors hold that we should not assume that a
feature is an adaptation unless the evidence favors this interpretation [39]. This
is not to deny that a great many of an organism’s features are adaptations. Several methods are used to infer that a feature is an adaptation for some particular
function. We will note these methods only briefly and incompletely at this point,
exemplifying them more extensively in later chapters. The approaches described
here apply to phenotypic characters; in Chapter 6 we will describe how selection
can be inferred from DNA sequence data.
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FIGURE 3.13
A child’s toy that
selects small balls, which drop
through smaller and smaller holes
from top to bottom. In this case
there is selection of red balls, which
happen to be the smallest, but
selection is for small size. (After E.
Futuyma
Sober. Kirkpatrick
1984. TheEvolution,
Nature of5eSelection:
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Philosophical
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Focus. Cambridge, MA: MIT Press.)
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FIGURE 3.14 The pecten of a bird’s eye, shown
in sagittal section. About 30 hypotheses were proposed for the pecten’s function. It was finally shown
to supply oxygen to the retina. (After F. B. Gill. 1995.
Ornithology, 2nd ed. New York: W. H. Freeman.)
Retina
Iris
Vitreous
body
Cornea
Pupil
Pecten
Lens
The pecten is a highly
vascularized structure
that projects into the
vitreous body.
Optic
nerve
COMPLEXITY Even if we cannot immediately guess the function of a feature, we
often suspect it has an adaptive function if it is complex, for complexity is unlikely to
evolve except by natural selection. A peculiar, highly vascularized structure called
a pecten projects
in front Evolution,
of the retina
in the eyes of birds (FIGURE 3.14). Only
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recently has
evidence beenVisual
developed
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but it has always been
assumed to play some important functional role
because of its complexity and because it is ubiquitous among bird species.
DESIGN The function of a character is often inferred from its correspondence with
the design an engineer might use to accomplish some task or from its correspondence with the predictions of a model about its function. For instance, many plants
that grow in hot environments have leaves that are finely divided into leaflets or
that tear along fracture lines (FIGURE 3.15). These features conform to a model in
which the thin, hot boundary layer of air at the surface of a leaf is more readily
dissipated by wind passing over a small than a large surface, so a divided leaf is
(B) Banana
© Pepbaix/Alamy Stock Photo
© imageBROKER/Alamy Stock Photo
(A) Acacia
FIGURE 3.15 Functional studies have shown that small surfaces shed the hot boundary
layer of air that forms around them more readily than do large surfaces. Many tropical and
desert-dwelling plants have large leaves that are divided into leaflets, as in (A) the sweet thorn
(Acacia karroo), or split into small sections, as in (B) the banana (genus Musa). The form of these
leaves is therefore believed to be an adaptation for reducing leaf temperature.
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3.4 The Nature of Adaptations 73
more effectively cooled. The fields of functional
morphology and ecological physiology are concerned with analyses of this kind.
(B) Normal flower
Lip
Liz Makings/SEINet/CC BY-SA 3.0
Penstemon strictus
(C) Lower lip removed
(D) Projecting anthers
(E) Narrow corolla
David McIntyre
EXPERIMENTS Experiments may show that
a feature enhances survival or reproduction
or enhances performance (e.g., locomotion or
defense) in a way that is likely to increase fitness,
relative to individuals with other features. For
example, several floral characters have evolved
convergently in the many plant lineages that have
shifted from insect pollination to bird pollination
(FIGURE 3.16A). Maria Castellanos and colleagues
tested the hypothesis that some of these features
are advantageous because they facilitate bird
pollination, and others because they discourage
bees, which are less effective pollinators because
they comb much of the pollen into a mass that
they feed to their larvae [6]. The researchers
surgically altered several features of flowers on
a bee-pollinated species to resemble those of
related hummingbird-pollinated species (FIGURE 3.16B–E). They then measured pollen transfer from the altered flowers by bumblebees and
hummingbirds. The researchers concluded that
the lower “lip” that bees use as a landing platform (see Figure 3.16B) has been reduced or lost in
some bird-pollinated species because its absence
discourages bees (see Figure 3.16C). The projecting anthers of bird-pollinated plants also seem
to be an “anti-bee” adaptation (see Figure 3.16D),
and the narrowly constricted corolla tube (see
Figure 3.16E) is both “pro-bird” and “anti-bee”: it
forces hummingbirds to remove more pollen but
prevents bees from easily obtaining nectar.
(A)
P. barbatus
FIGURE 3.16
Experimental test of a hypothesis of adaptation.
(A) Photos show the bee-pollinated Penstemon strictus and hummingbirdpollinated P. barbatus. (B) The normal flower of P. strictus. (C–E) Experimental modifications of the bee-pollinated P. strictus were made to mimic
features of hummingbird-pollinated species. Modifications included
(C) removal
of the
lower lip
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The first two changes discouraged bees; the third discouraged bees and attracted hummingbirds. (B–E after M. C. Castellanos
et al. 2004. J Evol Biol 17: 876–885.)
THE COMPARATIVE METHOD A powerful
means of inferring the adaptive significance
of a feature is the comparative method, which
consists of comparing sets of species to pose or test
hypotheses on adaptation and other evolutionary
phenomena [12]. This method takes advantage of
natural evolutionary experiments provided by
convergent evolution. If a feature evolves independently in many lineages because of a similar selection pressure, we can often
infer the function of that feature by determining the ecological or other selective
factor with which it is correlated. Among fishes, open-water, fast-moving predators in many families commonly have a streamlined shape, a forked tail fin,
and a slender tail base (caudal peduncle), whereas fishes that live in complicated
environments, such as among corals or vegetation, have a deep, compressed
body that enables them to change direction rapidly (FIGURE 3.17).
Biologists often predict such correlations by postulating, perhaps on the basis of
a model, the adaptive features we would expect to evolve repeatedly in response
to a given selective factor. In some species of animals, a female typically mates
with multiple males, whose sperm may compete to fertilize eggs. Males that
produce more abundant sperm should therefore have a reproductive advantage
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Chapter 3
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© Reinhard Dirscherl/Getty Images
© Michael Patrick O’Neill/Alamy Stock Photo
Open water:
Jacks
Swordfish
Slender penduncle
Deep body
Coral reefs:
Courtesy of Michael D. Bryant
Courtesy of Michael D. Bryant
Moorish idol
Angelfish
FIGURE 3.17
Body forms of fishes are adapted for different
modes of swimming. Open-water, fast-moving predators such
as jacks (Carangidae) and swordfishes (Xiphiidae) have a slender
body, narrow caudal peduncle, and narrow, forked tail fin. Fishes
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that maneuver in small spaces have a deep body, as in angelfishes (Pomacanthidae) and the Moorish idol (Zanclidae),
both inhabitants of coral reefs.
(like plants that are selected to produce more pollen). In primates, the quantity
of sperm produced is correlated with the size of the testes, so large testes should
be expected to provide a greater reproductive advantage in polygamous than in
monogamous species. Paul Harvey and Mark Pagel compiled data from earlier
publications on the mating behavior and testes size of various primates [20]. They
confirmed that, as predicted, the weight of the testes, relative to body weight, is
significantly higher among polygamous than monogamous species (FIGURE 3.18).
An important aspect of this example is that although all the data needed to test
this hypothesis already existed, the relationship between the two variables was
not known until Harvey and Pagel compiled the data, because no one had had
any reason to do so until an adaptive hypothesis had been formulated. Hypotheses about adaptation can be fruitful because they suggest investigations that
would not otherwise occur to us.
Also, notice that because the consistent relationship between testes size and
mating system was not known a priori, the hypothesis generated a prediction.
The predictions made by evolutionary theory, like those in many other scientific
disciplines, are usually predictions of what we will find when we collect data.
(Prediction in evolutionary theory does not usually mean that we predict the
future course of evolution of a species.) Predictions of what we will find, deduced
from hypotheses, constitute the hypothetico-deductive method, of which Darwin
was one of the first effective exponents [15, 35].
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3.5 Imperfections and Constraints 75
Polygamous
Monogamous
200
Testes weight (g)
100
10
Monogamous
1
Relationship between weight of the testes
and body weight in polygamous and monogamous primate
taxa. The data support the prediction, based on the theory of
natural selection, that males in polygamous species have relatively larger testes, which produce more sperm, than do males
in monogamous species. The photos show polygamous mating
bonobos and a monogamous pair of yellow-cheeked gibbons.
(After P. H. Harvey and M. D. Pagel. 1991. The Comparative Method
in Evolutionary Biology. Oxford: Oxford University Press.)
Polygamous
1
FIGURE 3.18
10
Body weight (kg)
Bonobo, © Gallo Images/Getty Images; Gibbons, © Rüdiger Katterwe/EyeEm/Getty Images
100 200
3.5 Imperfections and Constraints
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■ Describe two
possible constraints on rapid adaptation by natural selection.
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From Owen, 1866. London: Longman, Green, and Co.
Darwin noted that “natural selection will not produce absolute perfection, nor do
we always meet, as far as we can judge, with this high standard in nature” (On
the Origin of Species, chapter 6). Selection can fix only those genetic variants with a
higher fitness than other genetic variants in a particular population at a particular
time. It cannot fix the best of all conceivable variants if they do not arise, or have
not yet arisen, and the best possible variants often fall short of perfection because
of various constraints [13]. Among these constraints are trade-offs (following the
maxim “there is no such thing as a free lunch”). With a fixed amount of available
energy or nutrients, a plant species might evolve higher
Dugong
seed numbers, but only by reducing the size of its seeds
or its further growth. If genotypes differ in reproductive
output, one would expect to see a negative correlation
between seed number and seed size: the greater the number, the lower the average size (see Chapter 11).
In some cases, it appears that adaptations have not
evolved because of a shortage of suitable mutations [2].
Giraffe
For example, although some species of grasses have rapidly evolved tolerance to heavy metals in the vicinity of
mines, other species have not. In large samples of seeds
collected from grass populations on normal soils, far
from copper mines, a small percentage of seeds produced
copper-tolerant seedlings in every one of eight species
that have evolved copper tolerance near mines, but in
none of seven species that have failed to do so. These species apparently lack the genetic variation in tolerance that
would be necessary for adaptive evolution. Lack of suitable genetic variation may explain cases of so-called phylogenetic constraints, in which species retain nonadaptive
features or are unable to evolve adaptive traits. It makes
adaptive sense that birds such as swans have more neck
vertebrae than birds with shorter necks. But almost all
mammals have seven neck vertebrae, including giraffes,
FIGURE 3.19 Phylogenetic constraint Almost all mammals,
the aquatic dugongs, and whales, despite the extreme
including the long-necked giraffe and the short-necked aquatic
difference in the lengths of their necks (FIGURE 3.19).
dugong, have seven neck vertebrae, a likely example of a phylogenetic constraint.
Individual mice and humans with an aberrant number
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of cervical vertebrae show various skeletal abnormalities and a high incidence of
embryonic cancer—harmful side effects that probably prevented the evolution,
in other mammals, of what might otherwise have been advantageous changes in
vertebral number [14, 38].
3.6 Natural Selection and the Evolution of Diversity
Learning objective
■ Describe at least two factors that could cause related species to evolve
different characteristics by natural selection.
A mechanic uses a variety of different wrenches because each is suited to a different task. Likewise, any characteristic of an organism is likely to be advantageous under some circumstances but not others. That is, the optimal feature, the
character that maximizes fitness, depends on the context in which it functions.
A simple example is provided by many instances of cryptic coloration (camouflage) in animals, whereby colors and patterns that match the background lower
the likelihood that an animal will be detected by predators. In many species of
animals, darker individuals tend to be more common in areas with darker substrates (see Figure 8.2).
Both on the land and in the sea, the variety of different environments that
organisms face is immense. There are major differences in physical conditions
among geographic regions and over even short distances, in which a species
may encounter different sets of prey, predators, parasites, and competitors. A tree
experiences a different environment than does a shrub that grows in its shade.
Different parts of the human body are different environments for bacteria and
support very different, diverse bacterial communities. Any of these variables
may be relevant to a particular species and impose natural selection on many of
its features, so natural selection is the ultimate cause of divergence among populations and species: it is the source of the immense diversity of life.
Darwin, in considering why the various species that descended from a common ancestor should become different from one another, drew special attention
to the role of competition for limiting resources, such as food. He postulated that
if different closely related species coexist, those individuals that use different
resources from the other species will suffer less competition and have higher fitness. Consequently, the species will diverge from the others. It took nearly a century for biologists to show that Darwin was right. Today we know many cases of
what is now called character displacement: divergence of species because of their
interaction (see Chapter 15). Peter and Rosemary Grant and their collaborators
have studied certain ground finches in the Galápagos Islands for almost 40 years
(see Figure 2.2) [18]. Among the seed-eating ground finches, those with larger,
deeper bills feed more efficiently on larger, harder seeds. Species with different
bill depth differ accordingly in diet, and the species that coexist on any island
differ, matching the availability of different seeds (FIGURE 3.20A). In a population of one species, Geospiza fortis, there was high mortality of individuals with
smaller bills during a drought, in 1977, that caused a dearth of plants with small
seeds. The result was an evolved increase in average bill size (FIGURE 3.20B). A
few years later, Geospiza magnirostris, with the large bill described by its name,
invaded the island and slowly grew in numbers until, in 2004, it depleted the
supply of large seeds. The G. fortis population then evolved smaller average bill
size, as Darwin would have predicted.
The finch example shows the first stages of the evolution of diversity that is
seen in adaptive radiations, such as those described in Chapter 2 (see Figures
2.2, 2.26, and 2.27). In each of those cases, the morphological differences are
associated with using different resources. The huge diversity seen among higher
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3.7 What Not to Expect of Natural Selection 77
(A)
Wolf
Marchena
Expected population density
Daphne
1.7
2.2
2.7
3.2
2.2
2.7
Log bill depth (mm)
(B)
1.0
Relative in bill size
1.7
0.5
Arrival of G. magnirostris
Drought
0
–0.5
–1.0
–1.5
1975
1980
1985
1990
Year
1995
2000
3.2
1.7
2.2
2.7
3.2
FIGURE 3.20
Bill size in Galápagos ground finches
(Geospiza) is adapted to feeding on seeds, but competition
among species affects what kinds of seeds a species eats.
(A) The curves show the expected numbers of finches that could
live on three islands, based on the sizes of seeds present and the bill
sizes of hypothetical finches. The actual bill sizes of finches are well
matched to peaks of those curves. On islands with more than one
species of finch, the species occupy different peaks as a result of
competition. (B) On the island of Daphne Major, the average bill size
of G. fortis increased after a 1977 drought that made smaller seeds
less abundant than large seeds. Bill size then evolved back to its
original level until the population of the large ground finch (G. magnirostris) became large enough to deplete the supply of large seeds.
Bill size then evolved back to its original range of variation (blue bar).
(A after D. Schluter and P. R. Grant. 1984. Am Nat 123: 175–196; B after
P. R. Grant and B. R. Grant. 2006. Science 313: 224–226.)
taxa, such as the immense variety of flowers among plant families and of bills,
legs, and wings among the families and orders of birds, may be ascribed partly
to the same principle.
3.7 What Not to Expect of Natural Selection
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objectives
■ Describe why concepts of ethics or morality do not apply outside the realm
of human behavior and cannot be derived from natural selection.
■ Describe why species do not evolve features for the benefit of other
species or for the ecosystem they inhabit.
Selection at the level of genes and individual organisms is inherently “selfish”:
the gene or genotype with the highest rate of increase spreads at the expense
of others. The variety of selfish behaviors that organisms inflict on conspecific
individuals, ranging from territory defense to parasitism and infanticide, is
truly stunning. Indeed, cooperation among organisms requires special explanations, such as kin selection (see Chapter 13). Natural selection—or simply
“nature”—has often been invoked to justify codes of human behavior that we
might agree are admirable, as well as others that are pernicious. But natural
selection is just a name for differences among organisms or genes in reproductive success. It cannot be described as moral or immoral, just or unjust, kind or
cruel, any more than wind, erosion, or entropy can. Hence it cannot be used as a
justification or model for human morality or ethics.
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FIGURE 3.21 An example
of how a species may trick
another species into providing
a benefit. (A) An orchid in the
(A) Orchid
(B) Bee attempting copulation
© Perennou Nuridsany/Science Source
© Richard Becker/Alamy Stock Photo
Mediterranean region that is
pollinated by “pseudocopulation.”
(B) Male bees of certain species
are attracted to the flower by its
scent (which mimics a female
bee’s sex pheromone) and color
pattern (which imperfectly mimics a bee), and they “mate” with it.
The male bee shown here carries
the yellow pollen mass of a previously visited orchid flower on
its forehead.
Because the principle of kin selection cannot operate across species, “natural
selection cannot possibly produce any modification in a species exclusively for
the good of another species” (Darwin, On the Origin of Species, chapter 6). If a
species exhibits behavior that benefits another species, either the behavior is
profitable to the individuals performing it, as in bees that obtain food from the
flowers they pollinate, or else one species is duped by another, as are insects that
copulate with sexually deceptive orchids (FIGURE 3.21).
The
equilibrium
we5emay observe in ecological communities—the so-called
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balance of nature—likewise
does not reflect any striving for harmony [40].
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We observe coexistence
of predators and prey not because predators restrain
themselves, but because prey species are well enough defended to persist or
because the abundance of predators is limited by some factor other than food
supply. Nitrogen and mineral nutrients are rapidly and “efficiently” recycled
within tropical rainforests not because ecosystems are selected for or strive for
efficiency, but because under competition for sparse nutrients, microorganisms
have evolved to decompose litter rapidly, while plants have evolved to capture
nutrients released by decomposition. Selection of individual organisms for their
ability to capture nutrients has the effect, in aggregate, of producing a dynamic
that we measure as ecosystem “efficiency.”
SUMMARY
■ A feature is an adaptation for a particular
function if it has evolved by natural selection
for that function by enhancing the relative rate
of increase—the fitness—of biological entities
with that feature.
■ Because many characteristics are genetically
variable, they may evolve rapidly if selection
pressures change. Especially because humans drastically alter environments and move
species into new environments, many historical
examples of rapid adaptive evolution have
been documented, often within much less
than a century.
03_EVOL5E_03.indd 78
■ Natural selection is a consistent difference in
fitness among phenotypically different biological entities. It is the antithesis of chance.
Natural selection may occur at different levels,
such as genes, individual organisms, populations, and species.
■ Selection at the level of genes or organisms
is likely to be the most important because the
numbers and turnover rates of these entities
are greater than those of populations or species. Therefore most features are unlikely to
have evolved by group selection, the one form
of selection that could in theory promote the
10/5/22 3:03 PM
Summary
evolution of features that benefit the species
even though they are disadvantageous to the
individual organism. Both genetic and individual selection can be viewed as fitness differences among genes, with “selfish genes” being
those that prevail.
■ Species selection is a correlation between a
trait and the rate of speciation or extinction. It
can result in variation among clades in diversity
of species.
■ Not all features are adaptations. Methods for
identifying and elucidating adaptations include
studies of function and design, experimental
studies of the correspondence between fitness
and variation within species, and correlations
between the traits of species and environmental or other features (the comparative method).
79
Phylogenetic information may be necessary for
proper use of the comparative method.
■ Organisms may not have perfect adaptations,
because of functional compromises or tradeoffs or because mutations enabling perfect
adaptation have not been available.
■ As a consequence of adaptation of species to
different environments and ways of life, natural
selection is the basis of adaptive radiations and
adaptive diversity. Competition for resources is
one of many factors that can select for differences among species.
■ Natural selection need not promote harmony
or balance in nature; utterly lacking any moral
content, it provides no foundation for morality
or ethics in human behavior.
SUGGESTIONS FOR FURTHER READING
Adaptation and Natural Selection by G. C. Williams is a classic and is still worth reading for
its clear, insightful analysis of individual and
group selection [39].
Eldakar, O. T., and D. S. Wilson. 2011. “Eight
criticisms not to make of group selection”
is a position paper by advocates of group
selection [10].
The Selfish Gene and The Blind Watchmaker,
both by R. Dawkins, explore the nature of
natural selection in depth, as well as treating
many other topics in a vivid style for general
audiences [7, 8].
“Adaptation, speciation and extinction in the Anthropocene” by S. P. Otto is a comprehensive
but concise review and analysis of how
human alterations of the environment affect
ongoing evolution [30]. This is also the topic
of The Evolution Explosion: How Humans
Cause Rapid Evolutionary Change by S. R.
Palumbi, written for a general audience [32].
Levels of selection and related topics are
treated at an advanced level in two books by
philosophers of science: The Nature of Selection: Evolutionary Theory in Philosophical
Focus by E. Sober [37] and Evolution and the
Levels of Selection by S. Okasha [29]. Biologists treat this topic in Levels of Selection in
Evolution, edited by L. Keller [23].
PROBLEMS AND DISCUSSION TOPICS
1. Consider the first copy of an allele for insecticide resistance that arises by mutation
in a population of insects exposed to an
insecticide.
a. Is this mutation an adaptation?
b. If, after some generations, we find that
most of the population is resistant, is the
resistance an adaptation?
c. If we discover genetic variation for
insecticide resistance in a population
that has had no experience of
insecticides, is the variation an
adaptation?
2. It is often proposed that a feature that is
advantageous to individual organisms is the
reason for the great number of species in
03_EVOL5E_03.indd 79
certain clades. For example, wings have been
postulated to be a cause of the great diversity
of winged insects, compared with the few
species of primitively wingless insects.
a. How could an individually advantageous
feature cause greater species diversity?
b. How can one test a hypothesis that a
certain feature has caused the great
diversity of certain groups of organisms?
3. Provide an adaptive and a nonadaptive hypothesis for the evolutionary loss of useless
organs, such as eyes in many cave-dwelling
animals.
4. Could natural selection, at any level of organization, ever cause a population or species to
become extinct?
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UNIT II
How
Evolution
Works
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4
Mutation and
Variation
T
he people around you differ in the color of their hair, the shape of their
noses, the length of their fingers, and countless other ways. If you knew
their DNA sequences, you would also see that they differ from you at
millions of places in their genomes. Variation among individuals is universal
across all species on Earth (FIGURE 4.1).
Unlike physics and chemistry, evolution depends on variation. All electrons
are identical, but no two living organisms are. Without that variation,
evolution—and so life itself—would not be possible.
Understanding how traits and genes vary and how this variation is inherited
is fundamental to understanding evolution. Genetics also provides us with
a vast trove of information about the history of life on Earth and about the
evolutionary factors acting on living species. Before delving into those
topics, this chapter starts with a short review of key concepts in genetics
that we hope are familiar to you from earlier courses in biology.
The zigzag nerite (Neritina communis), a marine snail that lives among mangroves in
the Western Pacific, has extremely variable shells.
Courtesy of David McIntyre
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84
Chapter 4
Mutation and Variation
© Dirk Ercken Photography/Getty Images
biology include, What maintains variation?,
and How is variation is shaped by selection
and other evolutionary factors?
I. J. Wang and H. B. Shaffer. 2008. Evolution 62: 2742–2759
FIGURE 4.1 The strawberry poison dart
frog (Oophaga pumilio) has conspicuous
coloration that warns predators it is
toxic. Central questions in evolutionary
4.1 The Machinery of Inheritance
Learning objectives
■ Summarize the role that DNA plays in inheritance and how it
codes for
proteins.
Futuyma Kirkpatrick
Evolution,
5e
OUP/SInauer/Troutt
Visual
■ Describe
theServices
difference between coding and noncoding DNA.
Evolution5e_04.01.ai 09-21-2022
The genetic material of almost all organisms on Earth is DNA (deoxyribonucleic
acid). This is a very long molecule made up of pairs of bases. Each base takes
one of four forms: adenine (A), guanine (G), cytosine (C), or thymine (T). In
each pair of bases, an A is matched with a T, or a C is matched with a G. The
average chromosome in humans has more than 100 million base pairs (abbreviated bp), and the entire human genome consists of 3.2 billion bp. Our genome is
neither exceptionally big or small. Some bacteria have genomes that are thousands of times smaller, with less than 200,000 bp. At the other extreme are some
plants, salamanders, and even protozoa that have genomes that are hundreds of
times larger than ours. A surprising observation is that there is little correlation
between the complexity of an organism and the size of its genome. We’ll return
to this puzzling fact in Chapter 10.
Some viruses use RNA (ribonucleic acid) rather than DNA for their genetic
material. To replicate themselves, these viruses convert their genome into DNA
by a process called reverse transcription. This DNA is then inserted into the
genome of the host cell that the virus has infected, and offspring viruses are
made using that cell’s biochemical machinery. Thus, despite the difference in
the genetic material of RNA- and DNA-based life-forms, they share much of the
machinery that expresses their genes.
An organism’s genetic material is carried by one or more chromosomes.
Chromosomes in eukaryotes are long strings of DNA bases bound together with
proteins, and each chromosome is linear. In diploid species such as humans,
chromosomes come in pairs, one inherited from each parent. In prokaryotes,
chromosomes are unpaired (haploid) and circular. Genes are segments of chromosomes that perform a function. Many code for proteins that compose tissues and
catalyze reactions. A smaller number have other functions, for example, coding for
the RNA of ribosomes and the microRNAs that are important to gene regulation.
The human genome has roughly 20,000 protein-coding genes, some plants and
fish have many more, and some bacteria have hundreds of times fewer.
To make a protein, the cellular machinery reads a gene’s DNA in sets of three
bases. These sets, called codons, represent the amino acids that make up the
protein. The genetic code is a set of rules that relates the codons to the amino
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4.1 The Machinery of Inheritance 85
FIGURE 4.2
Second letter
T
TTT PhenylTTC alanine
C
TTA Leucine
TTG
TCT
TCC Serine
TCA
TCG
C
CTT
CTC Leucine
CTA
CTG
CCT
CCC Proline
CCA
CCG
A
ATT
ATC Isoleucine
ATA
Methionine;
ATG start codon
ACT
ACC
Threonine
ACA
ACG
First letter
T
GTT
GTC
G
Valine
GTA
GTG
GCT
GCC
Alanine
GCA
GCG
A
G
TAT Tyrosine
TAC
TGT Cysteine
TGC
TAA Stop codon
TAG Stop codon
TGA Stop codon
TGG Tryptophan
CAT Histidine
CAC
CAA
Glutamine
CAG
CGT
CGC Arginine
CGA
CGG
AAT Asparagine
AAC
AGT Serine
AGC
AAA
Lysine
AAG
AGA
Arginine
AGG
GAT Aspartic
GAC acid
GGT
GGC Glycine
GGA
GGG
GAA Glutamic
GAG acid
The universal genetic
code relates the three DNA bases in a
codon to the amino acid in the protein
made by the gene. All organisms on
Earth use this code or a minor variant of it.
Evolution5e_04.03.ai
04_EVOL5E_04.indd 85
© Sebastian Kaulitzki/Getty Images
acids they represent (FIGURE 4.2). A profound and wonderful fact is that the
genetic code is shared by virtually all life on Earth, from viruses to bacteria to
pineapples to humans. This is powerful evidence that all life evolved from a
single common ancestor.
Futuyma
Kirkpatrick
Since
there Evolution,
are four5etypes of DNA bases, there are 4 × 4 × 4 = 64 different
OUP/SInauer/Troutt Visual Services
codons. But because there are only 20 types of amino acids, most amino acids are
Evolution5e_04.02.ai 04-28-2022
represented by more than one codon. For example,
the codons CCT, CCC, CCA, and CCG all specify the
amino acid proline. Changes to a codon that do not
Codon:
5
6
7
alter an amino acid, for example, from GAG to GAA,
DNA sequence:
CCT
GAG
GAG
are called synonymous (or “silent”). Looking again at
β hemoglobin:
Pro
Glu
Glu
Figure 4.2, you see that next to the name of each
amino acid is a set of between two and four codons.
These are synonymous codons. In contrast, changes
Synonymous Nonsynonymous
to a codon that do alter an amino acid, such as GAG
to GTG, are called nonsynonymous (or “replacement”). The contrast between these two types of
GAA
GAG
CCT
GTG
GAG
CCT
changes is illustrated with the β hemoglobin gene
from humans in FIGURE 4.3. We’ll see shortly that
Glu
Val
Pro
Glu
Pro
Glu
the nonsynonymous change in β hemoglobin leads
to interesting evolutionary consequences.
Proteins are synthesized in three steps (FIGURE
Normal red
Sickled red
blood cell
blood cell
4.4). A cell first transcribes the DNA from a gene
into RNA. The rate at which a gene is transcribed is
called its expression level. This first form of the gene’s
message is called pre-mRNA. That molecule is then
FIGURE 4.3 Synonymous changes to a DNA sequence do not alter
spliced so that parts of the molecule are removed to
the amino acids in a protein, but nonsynonymous changes do. At top
form a mature mRNA. Finally, the message in the
is the DNA sequence for three codons of the A allele of the β hemoglobin
mRNA is translated into the string of amino acids
gene in humans. Beneath each codon is an abbreviation for the amino acid
that make up the protein.
it codes for. The sixth codon, GAG, codes for glutamic acid (Glu). A change
In most eukaryotic genes, the DNA that codes
in that codon from GAG to GAA does not alter the amino acid, so this is a
for a protein is broken into several segments along
synonymous change. A change from GAG to GTG replaces the glutamic acid
Evolution,
the chromosome. These segments are called exons, Futuyma
withKirkpatrick
valine (Val),
so this5e
is a nonsynonymous change. The change to the GTG
Visual
codon produces
theServices
S allele, which results in the sickle cell condition.
while the noncoding segments between the exons OUP/SInauer/Troutt
06-02-2022
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86
Chapter 4
Mutation and Variation
FIGURE 4.4 Proteins are synthesized
in three steps. In the first step of assem-
Exon
A
5′
Intron Exon
B
Intron
Exon
C
3′
1 Transcription
Pre-mRNA
A
B
C
2 Alternative splicing
mRNA 1
A
B
A
C
mRNA 2
3 Translation
Protein 1
Protein 2
Protein 1, Data from PDB 4Z2X, Madej et al. 2014. Nucleic
Acid Res 42: D297; Protein 2, Data from PDB 5UOJ,
Wang et al. 1997. Proc Nat Acad Sci USA 94: 23237
bling a protein, a gene’s DNA sequence is
transcribed into pre-mRNA. This molecule is
spliced to remove the introns (and often some
of the exons) to produce mature mRNA. Many
pre-mRNAs are spliced in more than one way,
yielding different mRNAs. The mRNA is then
translated into the protein.
Promoter
DNA
are called introns. Many eukaryotic genes are spliced in more than one way,
removing some of the exons (as well as the introns) from the pre-mRNA. This
alternative splicing allows a single gene to code for more than one protein.
Introns and exons evolve quite differently, which gives us important cues about
how selection and other factors cause genes and genomes to evolve (see Chapters
Futuyma Kirkpatrick Evolution, 5e
5 and
10). In humans, a typical gene has seven introns and 1400 bp in its exons,
OUP/SInauer/Troutt Visual Services
andEvolution5e_04.04.ai
on average the introns
are 17 times larger than the exons. Squids and octo09-22-2022
puses have an extra trick: they can alter individual bases in the messenger RNA
to make yet more different proteins from a single gene. Many of the messages
that are edited this way are expressed in the nervous system, suggesting that
the editing may be a key to the famously high intelligence of those animals [20].
Like the genetic code, the cellular machinery for transcribing DNA and translating mRNA is almost universal across life on Earth. The DNA or mRNA from
a sea urchin is translated into a protein if it is injected into a bacterium. This universality is the basis of genetic engineering. The “golden rice” strain was developed by inserting a bacterial gene that allows the plant to synthesize a precursor
to vitamin A [36]. A deficiency of that vitamin kills 2 million people each year,
many of whom live in countries where rice is a major part of the diet. Golden rice
has the potential to relieve much of that suffering. This advance is only possible
because rice and bacteria share genetic machinery that they both inherited from
a common ancestor that lived more than 1.5 billion years ago (see Chapter 2).
Genes make up only a small part of the genome in eukaryotes. In humans, for
example, 98% of the DNA does not code for any gene product. A small fraction
of this noncoding DNA affects how coding genes are expressed. The vast bulk of
noncoding DNA, however, does not have an obvious function. The genomes of
prokaryotes are very different, and typically only about 20% of their genome is
noncoding. Chapter 10 will return to the fascinating observation that so much of
the eukaryotic genome is noncoding DNA.
4.2 The Inheritance of Variation
Learning objectives
■ Differentiate between phenotypes and genotypes.
■ Describe genetic segregation and recombination.
■ Explain the Hardy-Weinberg equilibrium and linkage disequilibrium,
and discuss how they evolve.
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4.2 The Inheritance of Variation 87
All species are variable: individuals differ in their physical (morphological) feaCCT
GAG
GAG
tures, physiology, biochemistry, and behavior. The differences among you, your
family, and your friends are caused partly by differences in genes and partly by
CCA
GGA
GAG
different environmental conditions, such as nutrition. In this chapter, we will
concentrate on the genetic component of this variation, which is necessary for
CCT
GAA
GAG
evolution to happen.
In all populations, there is variation in the DNA sequences. A specific DNA
π = [(2 × 23 ) + (7 × 0)]
base in the genome that varies among individuals is called a single nucleotide
= 0.148
polymorphism, or SNP (pronounced “snip”). The most common measurement of
9
genetic variation in DNA sequences is nucleotide diversity, which is symbolized
FIGURE 4.5 Nucleotide diversity
by π. The nucleotide diversity at a given site in the genome is the chance that two
(π) is the most common measure of
chromosomes from the population will have different DNA bases (A, G, C, or T)
variation in DNA sequences. Here
at that site (FIGURE 4.5). This measure of variation is closely related to another
there are three sequences that are 9 bp
one called heterozygosity, which is the frequency of heterozygotes in the populong. At seven sites, there is no variation
lation. You can think of π as the heterozygosity that we would expect at that site
among the sequences, and so at those
if the chromosomes were paired up at random. Often π is given as the average
sites π
= 0. At the
two sites
Futuyma
Kirkpatrick
Evolution,
5e with bases
value across many sites, or even the whole genome.
in red text, the sequences
differ in two
OUP/SInauer/Troutt
Visual Services
out of the three possible
pairwise comThere are dramatic differences between species, and between different parts of
Evolution5e_04.05.ai
06-06-2022
parisons, and so at those sites π = 0.666.
the genome within a species, in the nucleotide diversity. Given how people from
When the values are averaged over all
different parts of Earth appear to us, you may be surprised to learn that humans are
nine sites, we find that π = 0.148 for these
among the least genetically variable species on Earth (FIGURE 4.6). In the populasequences. This value is much higher
tions with the most variation (who are in western Africa), there are about 5 million
than is found in natural populations.
SNPs in their genomes, or 1 every 640 bp [33]. Many species of clams and butterflies are ten times more variable in their DNA sequences. Several factors affect the
amount of genetic variation in a species (as we’ll delve into in Chapter 6). One is
population size: species with small populations tend to have less genetic variation.
While there are many people alive today, not long ago in our evolutionary past we
were far less numerous. The low genetic variation in living humans is a legacy of
our small population size in the past.
The variations we see among individuals of a species (such
as our own) are differences in phenotypes, or observable characteristics. Recall from the Chapter 3 that fitness is the number
of offspring that an individual, gene, or phenotype leaves to
the next generation. Differences in fitness between different
Prokaryotes
phenotypes result in natural selection, but that only results
in evolution if at least some of the variation in phenotypes is
Unicellular eukaryotes
transmitted between generations. We resemble our parents
more than we do passing strangers on the street. The familiar patterns of inheritance are the result of differences in the
Invertebrates
genotypes, encoded by DNA. To understand how evolution
works, it is crucial to understand how this remarkable mechanism of inheritance works.
Land plants
The basic unit of genetic inheritance is called a locus (plural:
loci), which is the more formal term for what we sometimes
Vertebrates
call a gene. A locus is a section of chromosome, often one that
produces a gene product such as a protein. The DNA sequence
Humans
at a given locus often varies among the chromosomes carried
by different individuals, and if it does, we say that the locus
0.001
0.01
0.1
1.0
is polymorphic.
Nucleotide diversity (π)
The different variants at a locus are called alleles. The allele
frequency is how often a variant occurs at a locus or a DNA base
FIGURE 4.6 Nucleotide diversity varies greatly
Prokaryote, © species.
SciePro/Shutterstock.com;
Unicellular
eukaryote,
© Lebendkulturen.
in a population (FIGURE 4.7). Some alleles are very rare. For
The averages
(dots)
and ranges
(bars)
between
de/Shutterstock.com; Insect, © Cosmin Manci/Shutterstock.com; Flower, © Nancy
of
π at synonymous Fish,
sites
in several
groups of organisms.
example, one of the alleles that causes albinism (a condition in
Kennedy/Shutterstock.com;
©Porntanapat
Jaisamarnmit/Shutterstock.com
(After M. Lynch. 2006. Mol Biol Evol 23: 450.)
which melanic skin pigments are missing) occurs at a frequency
Prokaryote, © SciePro/Shutterstock.com; Unicellular eukaryote, © Lebendkulturen.
of about 0.0002 in Europeans. Other alleles occur at much higher
de/Shutterstock.com; Insect, © Cosmin Manci/Shutterstock.com; Flower, © Nancy
frequencies. A person’s blood type depends on the alleles that
Kennedy/Shutterstock.com; Fish, ©Porntanapat Jaisamarnmit/Shutterstock.com
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88
Chapter 4
Mutation and Variation
A1 A1
A1 A1
A1 A1
A1 A2
A2 A2
A1 A1
A1 A2
A2 A2
FIGURE 4.7 Allele frequencies and
genotype frequencies. In this popula-
tion, the frequency of the A 1 A 1 homozygote genotype is 0.5, the frequency of
the A 1 A 2 heterozygote
genotype is 0.25,
Futuyma Kirkpatrick
Evolution, 5e
and the frequency
the A 2 A 2 homozyOUP/SInauer/Troutt
Visual of
Services
gote genotype is 04-28-2022
0.25. Six of the 16 copEvolution5e_04.07.ai
ies of the gene are the A2 allele, so its
allele frequency is p = 6/16 = 0.375.
affect the surface of the red blood cells. In the United States, the frequency of the
A blood type allele is about 0.4 (i.e., about 40% of chromosomes carry that allele).
How is this variation transmitted from one generation to the next? Most
eukaryotic species reproduce sexually: it takes both a mother and a father to
produce an offspring. Sex mixes the genes of the parents (gametes) to produce
genetic combinations in the offspring not found in the parents. In organisms with
meiosis (such as ourselves), this mixing involves two basic genetic processes,
segregation and recombination. Organisms without meiosis, such as bacteria
and viruses, do not have segregation, but most of them still mix their genes by
some form of recombination.
Gene mixing by segregation
Segregation is the transmission of one of the two copies of a locus when a gamete
is made during meiosis. The fusion of an egg and sperm brings together the copy
from the mother with that from the father. A result is that the offspring can have
a genotype unlike either of its parents. A mother with genotype A 1 A 1 (homo­
zygous for the A1 allele) and a father with genotype A 2 A 2 (homozygous for the
A2 allele) will have entirely heterozygous A1 A2 offspring.
The mixing of genes caused by segregation changes the proportions of genotypes in a population. Think of a population in which half the males and females
are A1A1 homozygotes and the other half are A 2 A 2 homozygotes. When sperm
and eggs are produced by meiosis, half will carry the A1 allele, and half will carry
the A 2 allele. If sperm and eggs meet at random, the chance that an A1A1 offspring
is produced equals the chance that an egg carrying an A1 (= 0.5) is fertilized by
a sperm that also carries an A1 (= 0.5). Thus the chance that an A1A1 offspring is
produced is 0.5 × 0.5 = 0.25. Likewise, the frequency of A 2 A 2 homozygotes in the
offspring is 0.25. To find the frequency of A1 A 2 heterozygotes in the offspring,
we add up the chance that a sperm with an A1 fertilizes an egg with an A2 (= 0.5
× 0.5 = 0.25) and the chance that a sperm with an A2 fertilizes an egg with an A1
(= 0.5 × 0.5 = 0.25). The frequency of A1 A 2 offspring is therefore 0.5.
Looking at these numbers, we see that the frequency of genotypes changed
from one generation to the next. Heterozygotes are absent in the parents but
make up half of the offspring. What has not changed, however, are the frequencies of the A1 and A2 alleles, which are equal to 0.5 in both generations. As a
result, when the offspring mate with each other to produce a third generation,
the frequencies of the A 1 A 1 , A1 A 2, and A 2 A 2 genotypes will again be 0.25, 0.5,
and 0.25. Thus the population is at an equilibrium: once the population reaches
that state, no further change in genotype frequencies will happen.
This example is a special case of the Hardy-Weinberg equilibrium, which tells us
the relative proportions of genotypes in a population when segregation is the
only factor that changes genotype frequencies. We just looked at the situation
in which the frequencies of the two alleles are equal to 0.5. The more general
situation is illustrated in FIGURE 4.8. We now let the frequency of allele A2 be
any number between 0 and 1, and we represent that frequency by the symbol
p. Since there are only two alleles at the locus in this example, their frequencies
must sum to 1, and so the frequency of allele A1 is (1 – p). Following the logic
used in the earlier example, we find that the chance of an A 2 A 2 offspring being
produced is equal to the probability that a sperm carries the A2 allele times the
probability that an egg also carries the A2 allele, or p2. Likewise, the frequency of
A 1 A 1 offspring is (1 – p) 2, and the frequency of A 1 A 2 offspring is 2p(1 – p).
Putting those results together gives us the Hardy-Weinberg proportions:
Genotype:
Frequency:
A 1A 1
(1 – p)
2
A 1A 2
A2A2
2p(1 – p)
p2
An example with p = 0.7 is shown in Figure 4.8.
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4.2 The Inheritance of Variation 89
(A) Adults
0.25
0
0.7
0.75
p = 0.7
Frequency
Frequency
0.75
0.50
FIGURE 4.8 Hardy-Weinberg equilibrium results from the
random union of gametes. (A) In the adults, 30% of the individu-
(B) Gametes
0.3
A1A1
A1A2
0.7
als are A 1 A 1 homozygotes and 70% are A 2 A 2 homozygotes. The
frequency of allele A 2 is p = 0.7, and the frequency of allele A1 is
1 – p = 0.3. The population is not in Hardy-Weinberg equilibrium:
there are too few heterozygotes. (B) In the sperm and eggs, the
allele frequencies are the same as in the adults. (C) The numbers
inside the box are the frequencies of genotypes in the offspring
that are formed by random fertilization between sperm and eggs.
(D) In the offspring, genotype frequencies are found by adding up
the cells inside the box: the frequency of A 1 A 1 homozygotes is
(1 – p) 2 = 0.09, the frequency of A 1 A 2 heterozygotes is 2 p (1 – p) =
0.42, and the frequency of A 2 A 2 homozygotes is p 2 = 0.49. The
genotype frequencies are different than they were in the parents,
and they are now in Hardy-Weinberg equilibrium. The allele
frequencies, however, have not changed.
0.50
0.25
0
A2 A2
0.3
A1
A2
Sperm
(C) Gametes
(D) Offspring
A1
A2
0.3
0.7
A1 0.3 0.09
0.21
A2 0.7 0.21
0.49
0.75
0.50
0.42
0.49
Eggs
Frequency
p = 0.7
0.25
0.09
0
A1A1
A1A2
A2 A2
The key conditions for the Hardy-Weinberg equilibrium are these:
An infinite population size
No natural selection
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● No mutation
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● No movement
between populations
● Random mating
●
●
No real population meets these conditions: populations cannot have infinite
size, and mutation always happens. But if the population is very large, and
the mutation rate is very small, the genotype frequencies will often be so
close to these predicted frequencies that the difference doesn’t matter. The
Hardy-Weinberg equilibrium plays a key role in evolutionary biology. It is the
foundation for mathematical models of evolution, and it provides a baseline
for analyzing data. If a locus is not in Hardy-Weinberg equilibrium, then one of
the key conditions just listed has been violated; for example, natural selection
might be at work.
The β hemoglobin locus in humans has two alleles called A and S. They differ
by a single base in the sixth codon (see Figure 4.3). Here are the frequencies of
the genotypes at this locus among 654 adults in western Africa, as well as the
frequencies expected if the population were at Hardy-Weinberg equilibrium [2]:
Genotype:
AA
AS
SS
Observed frequencies:
0.612
0.381
0.008
Observed numbers:
400
249
5
Hardy-Weinberg frequencies:
0.643
0.317
0.039
Hardy-Weinberg numbers:
421
207
26
The observed number of heterozygotes is higher, and the observed numbers of
both homozygotes are lower, than what the Hardy-Weinberg equilibrium predicts. The discrepancies are small but statistically significant. They result from
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FIGURE 4.9 Recombination randomizes the combinations of
alleles at two loci. The two loci may be on the same chromosome or
on different chromosomes. The offspring makes two kinds of gametes
(sperm): those that have recombined, and those that have not.
Parents
A1
A1
A2 A2
B1
B1
B2 B2
A1 A2
Offspring
B1
Recombination
Probability = r
Gametes
Chromosome 1
Chromosome 2
r = 0.5
B2
No recombination
Probability = (1 – r )
A1
A2
A1
A2
B2
B1
B1
B2
differences in survival: the AA and SS genotypes do not survive as well as the
AS genotype. This is one of the most famous examples of natural selection acting on our own species, and we will discuss it further in Chapter 5. It illustrates
how the Hardy-Weinberg equilibrium is used to detect evolutionary processes
acting on a locus.
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Gene mixing by recombination
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r = 0.4
r = 0.03
FIGURE 4.10 The recombination
rates between pairs of loci. A pair
of loci that are close together on the
same chromosome have a low recombination rate (here, r = 0.03). A pair that
is far apart on the same chromosome
Futuyma
Kirkpatrick
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has a high
recombination
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approaches 0.5 (here,
= 0.4). A pair
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of loci on different chromosomes
has
the maximum possible recombination
rate, r = 0.5.
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The second type of genetic mixing results from recombination. This is the process
that combines in a gamete a gene copy at one locus that was inherited from one
parent with a gene copy at a second locus that was inherited from the other parent (FIGURE 4.9). In eukaryotes, recombination occurs during meiosis. Loci that
are carried on different chromosomes recombine by the independent assortment
of those chromosomes. Recombination happens between loci on the same chromosome by crossing over, which joins together two pieces of a chromosome that
were inherited from different parents.
The recombination rate, symbolized by r, is the probability that recombination
occurs between a given pair of loci (FIGURE 4.10). If the two loci are on different
chromosomes, then when an individual makes a gamete, there is a chance of 0.5
that one of the chromosomes the gamete carries will be from the mother and the
other from the father. Here r = 0.5, which is the maximum possible value for the
recombination rate. At the other extreme, DNA bases that are adjacent on a chromosome have an extremely low chance of recombining. The smallest possible
value for the recombination rate is r = 0.
When an allele at one locus is found together in a population more often than
expected by chance with an allele at a second locus, we say the loci are in linkage
disequilibrium. (This is a confusing term: genes that are not physically linked, i.e.,
on the same chromosome, can be in linkage disequilibrium, while genes that are
physically linked can be in linkage equilibrium. Unfortunately, it is the only term
we have for this important concept.) To take an extreme case, let’s consider a
locus A with alleles A1 and A2, and a locus B with alleles B1 and B2. Imagine that
gametes carrying A1 always carry B1, while gametes carrying A2 always carry B2.
The two other kinds of gametes (A1 with B2 , and A2 with B1 ) never occur. Then
these two loci are in linkage disequilibrium.
The key effect of recombination is to break down linkage disequilibrium.
Recombination moves the population toward a state where there is no statistical
association between the alleles at the two loci, a situation called linkage equilibrium. This is analogous to the Hardy-Weinberg equilibrium at a single locus,
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4.2 The Inheritance of Variation 91
(A) D = 0.25
(B) D = –0.25
FIGURE 4.11
(C) D = 0
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
B1
B1
B1
B1
B2
B2
B2
Bb2
B1
B1
B2
B2
A2
A2
A2
A2
A2
A2
A2
A2
A2
A2
A2
A2
B2
B2
B2
B2
B1
B1
B1
B1
B1
B1
B2
B2
Three groups of gametes
that have the same allele frequencies
(pA = p B = 0.5) but have different values
of linkage disequilibrium. (A) When the
disequilibrium (D) between alleles A2 and
B2 is positive, they are found together
more often than by chance. (B) When D is
negative, A2 and B2 are found together less
often than by chance. (C) When D = 0 (linkage equilibrium), there is no association
between alleles at the two loci.
where two alleles at the same locus are uncorrelated with each other. Unlike the
Hardy-Weinberg equilibrium, linkage equilibrium takes more than one generation to reach. How long it takes depends on the rate of recombination between
the loci. Less recombination (smaller r) means the genes at a pair of loci mix more
slowly, so linkage equilibrium between them takes longer to reach.
We
can be more
specific
about linkage disequilibrium by introducing a way to
Futuyma
Kirkpatrick
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5e
measure it. Consider
two loci, one with alleles A1 and A2, the other with alleles
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B1 and B2. We will
use PAB to represent the frequency of gametes carrying both
the A2 and the B2 alleles, pA to represent the frequency of gametes with the A2
allele (no matter which B allele they have), and pB to represent the frequency of
gametes with the B2 allele (no matter which A allele they have). The measure of
linkage disequilibrium is symbolized by D and is defined as
(4.1)
(The term pA pB simply means pA times pB.) The population is in linkage equilibrium when D = 0. If alleles A2 and B2 appear together more often than expected
by chance, then D is positive. If A2 and B2 occur less often than expected, then
D is negative.
Three groups of gametes are compared in FIGURE 4.11. Although the allele
frequencies are the same in all three (pA = pB = 0.5), the populations clearly differ.
The differences reflect the effects of linkage disequilibrium. When D is positive,
knowing that a gamete carries allele A2 tells us that it is more likely to carry allele
B2 than expected by chance. When D is negative, a gamete that carries allele
A2 is less likely to carry allele B2. Last, when a population is in linkage equilibrium (D = 0), knowing that a gamete carries allele A2 tells us nothing about
which allele it carries at locus B. Linkage disequilibrium is therefore a statistic
that measures a property of the population. It has the same meaning whether
the two loci are on the same chromosome or on different chromosomes.
The most important role that recombination plays in evolution is through its effects
on linkage disequilibrium. If Mendelian inheritance is the only factor at work,
the value of D in the next generation is decreased by a proportion r from its
value in the current generation. Thus recombination causes the population
to evolve toward linkage equilibrium with D = 0. It does so quickly if r is
large (near 0.5) and slowly if r is small (near 0). The evolution of D with three
different values of r is shown in FIGURE 4.12. On average, there is less recombination between pairs of DNA bases on a chromosome when they are closer
to each other than when pairs are farther apart. For that reason, D tends to be
higher between pairs that are closer (FIGURE 4.13).
Linkage disequilibrium can be produced by natural selection. Epistasis is
the situation in which the effect on a phenotype (or on fitness) of an allele at
Linkage disequilibrium, D
D = PAB – pA pB.
0.25
r = 0.01
0.20
r = 0.1
0.15
0.10
0.05
r = 0.5
0
2
4
6
Generation
8
10
FIGURE 4.12 Recombination causes
linkage disequilibrium to decrease.
The value of D declines toward 0 rapidly
when the recombination rate is large
(r = 0.5), and slowly when it is small (r = 0.01).
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Chapter 4
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FIGURE 4.13
Linkage disequilibrium
tends to be higher between pairs
of DNA bases that are very close to
each other on a chromosome. Shown
is a region of 500 thousand base pairs
(kb) of a chromosome sampled from
89 humans in eastern Asia. Sites A and
B are relatively far apart (200 kb), and
they have low linkage disequilibrium.
Sites C and D are much closer (50 kb),
and they have high linkage disequilibrium. (Based on The International
HapMap Consortium. 2005. Nature 437:
1299–1320.)
Low disequilibrium
High disequilibrium
Low disequilibrium
High disequilibrium
0
A
100
200
B
300
C
D 400
500
Position (kb)
one locus depends on the alleles at another locus. If some combinations of alleles
have high fitness, selection will generate linkage disequilibrium between them.
The primrose (Primula vulgaris) has an interesting mechanism to avoid fertilizing
itself. Populations of this plant have mixtures of individuals with flowers that
have either the “pin” or the “thrum” phenotype (FIGURE 4.14). In pin plants, the
anthers (which produce pollen) are low in the flower, while the stigma (which
receives pollen) is high. In thrum plants, this arrangement is reversed. Because
the anthers and stigma are separated in both pin and thrum plants, pollen is
rarely transferred between the anthers and stigma of the same flower, and so
they rarely self-fertilize. The height of the anthers is determined by one locus,
and the height of the stigma by another. These loci are in linkage disequilibrium:
the allele for low anthers is most often with the allele for a high stigma, and the
allele for high anthers is most often with the allele for a low stigma. Plants with
the “wrong” combination of alleles have a stigma and anthers close to each other.
They self-fertilize, which produces offspring that survive poorly. Natural selection therefore maintains the linkage disequilibrium.
A second important cause of linkage disequilibrium is the mixing of populations
that have different allele frequencies. This situation is conspicuous in countries that
The height of the anthers is
controlled by alleles a and A.
FIGURE 4.14
Flowers of the
primrose (Primula vulgaris) show
how linkage disequilibrium can
result from selection. Allele A
04_EVOL5E_04.indd 92
Stigma
The length of the style is
controlled by alleles g and G.
Pin flower
ga/ga
Style
Thrum flower
GA/ga
Rare individuals with
the gA/ga genotype
have both anthers
and stigma high.
Rare gA/ga form
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produces anthers that are high in
the flower and is on chromosomes
most often with allele G, which produces a stigma that is low. Allele a
produces low anthers and is most
often on chromosomes with allele
g, which produces a high stigma.
Chromosomes with the Ag or the
aG allele combinations produce
plants that self-fertilize, resulting
in offspring that survive poorly.
(After E. B. Ford. 1971. Ecological
Genetics. London: Chapman & Hall.)
Anthers
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4.3 Mutation: The Ultimate Source of Variation 93
have people with ancestries from different geographical regions. For example, the
shape of the eyes and the curliness of the hair are determined by different loci. In
places that have people of both Asian and African ancestry, seeing the texture of a
person’s hair tells you what the shape of their eyes is likely to be.
Linkage disequilibrium is important because it affects how genes evolve. In
Chapter 5 we will see that selection on one locus can cause a second locus to evolve
if the two loci are in linkage disequilibrium. Linkage disequilibrium is useful to
geneticists because it allows them to find genes that affect traits of interest. We will
see in Chapter 7 how this idea is used to study the genetic basis of traits as diverse
as the size of a tomato and adaptation to high elevation in humans.
Gene mixing with asexual inheritance
Almost all eukaryotes reproduce sexually. A small fraction of eukaryotes, all
prokaryotes, and all viruses reproduce asexually (without meiosis). But even
these organisms have mechanisms that mix their genes. While the mechanisms
are very different, their evolutionary consequences are largely the same.
Horizontal gene transfer (HGT) is the movement of DNA between different individuals without help from sexual reproduction. It is particularly common in prokaryotes (see Chapter 2), and several mechanisms are involved [24, 8]. HGT can
move genes between individuals of the same species and sometimes even between
different species. Some bacteria exchange DNA by conjugation, in which two cells
exchange DNA while in direct contact. Other bacteria simply take up naked DNA
that was left behind by bacteria that died. When a virus replicates inside a bacterium, its offspring viruses sometimes integrate a piece of the host’s genome into
their own. When those viruses then infect another bacterium (or less commonly, a
eukaryote), the virus can insert the gene from the previous host into the DNA of the
new host. As we saw in Chapter 2, HGT is important to the evolution of antibiotic
resistance in bacteria that are responsible for major human diseases.
Gene mixing by hybridization
Eukaryotes can also gain genes from other species. The most common way for
this to happen is when they hybridize. Hybrids often have low fitness (see Chapter 9), and so they are evolutionary dead ends. But rarely, an allele in one species
is beneficial in another, and it spreads through the second species by natural
selection. This event is called introgression.
Snowshoe hares (Lepus americanus) are a beautiful relative of rabbits; they live
in the northern USA and Canada. In the winter, their fur is white, making them
wonderfully camouflaged on the snow. In the summer, they molt that fur and
grow a brown coat that is good camouflage in vegetation. But some populations
of snowshoe hare that live in regions with little snow remain brown all year. The
loss of the ability to grow white fur in the winter results from an allele that originated in a closely related species (the black-tailed jackrabbit, Lepus californicus)
that is also brown all year. That allele spread into snowshoe hares after the two
species hybridized several thousand years ago [13].
4.3 Mutation: The Ultimate Source of Variation
Learning objectives
■ Define point mutation and whole genome duplication.
■ Contrast three different kinds of structural mutations that
occur on chromosomes.
The replication of DNA is an exquisitely precise affair, but it is not perfect—
errors are made. These mutations are the ultimate source of genetic variation in all
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organisms. Without these accidents, there would be no variation, no evolution,
and no life. Fundamentally, mutation can be thought of as an inevitable consequence of the second law of thermodynamics, which implies that no process can
occur without mistakes.
Mutations come in a variety of forms that differ in how much of the genome
they affect. We begin with point mutations, which affect only a single DNA
base, and end with whole genome duplication, which affects every DNA base in
the genome.
Point mutations
The simplest type of mutation is a point mutation. It occurs when a single DNA
base is changed from one to another of its four possible states (A, G, C, or T).
We have seen that changes to some codons are synonymous (they do not alter
an amino acid in a protein) while others are nonsynonymous (they do alter an
amino acid). A nonsynonymous mutation in the β hemoglobin gene results in the
S allele that appeared earlier in the discussion of the Hardy-Weinberg equilibrium (see Figure 4.3). When homozygous, this single microscopic change to the
genome has sweeping effects on development and physiology, causing a medical
condition called sickle cell anemia.
As with the S allele in the β hemoglobin gene, any change to the second position
of a codon is nonsynonymous. Most (but not all) changes to the first position are
also nonsynonymous. In contrast, most changes to the third base of a codon are
synonymous. (You can verify this from Figure 4.2.) We will see in Chapter 10 that
as a result, the three positions of codons evolve quite differently from one another.
Much of the eukaryotic genome does not code for any gene product, and
the same is true for some of the prokaryotic genome. Point mutations in some
noncoding DNA can, however, affect an organism by altering how genes are
expressed (see Chapter 11). When a species evolves by natural selection, the
genetic changes can involve coding DNA, noncoding DNA, or both.
Structural mutations
Some kinds of mutations affect more than one DNA base. These are structural
mutations of chromosomes, which can be as small as a few bases or as large as
billions of bases. Most happen as errors when chromosomes are replicated. Different kinds of structural mutations are illustrated in FIGURE 4.15.
Deletions are a common type of structural mutation that occurs when a segment of a chromosome is left out during replication. A deletion of only three
base pairs in a sodium channel gene causes cystic fibrosis, one of the most common human genetic disorders in Europe and North America. Other deletions
are much larger. The first genome-wide survey of deletions in humans made the
surprising discovery that individuals typically have about a dozen rare deletions
Deletion
Duplication
Inversion
Fission
Fusion
FIGURE 4.15
Five types of structural
mutations that alter chromosomes.
The DNA at a locus is transcribed into preRNA. Introns are spliced out of the pre-RNA
to make a mature messenger RNA transcript. Finally, the transcript is translated
into the protein.
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4.3 Mutation: The Ultimate Source of Variation 95
that average 465,000 bp in size [28]. Although some are in noncoding regions,
many of them eliminate several genes. Most (but not all) deletions that knock
out genes are harmful. Insertions are the opposite situation, in which a segment
of DNA is added to a chromosome, either from nearby on the same chromosome
or elsewhere in the genome. Some insertions cause genetic disease. Huntington’s
disease is a neurological disorder whose most famous victim was the American
folk singer Woody Guthrie. The disease is caused by multiple insertions of three
DNA bases (CAG) into the huntingtin gene. Other insertions have played important roles in adaptation.
In most eukaryotes, a large fraction of the genome consists of transposable
elements (also called transposons, or mobile genetic elements). Almost half of the
human genome is made up of millions of copies of these short DNA sequences.
Transposable elements are molecular parasites that duplicate themselves in different ways. Sometimes a new copy will insert itself into a gene. Most often, these
mutations are deleterious, but occasionally they have a beneficial effect. We’ll discuss an example from the famous peppered moth (Biston betularia) in Chapter 5.
A duplication is a mutation in which a second copy of a gene is added to the
genome. This process can be repeated, giving rise to a gene family with several
copies of the original locus. In some cases, the DNA sequences of the duplicate
loci diverge and lead to the evolution of new biological functions, as happened
with the hemoglobin genes we discussed in Chapter 2 (Figure 2.16). Less often,
duplicates retain the same sequence. This outcome can result from gene conversion, which is an unusual type of mutation in which the DNA sequence of one
duplicate in a gene family is replaced by the sequence of another.
Inversions are structural mutations that occur when a chromosome breaks
in two places and the middle segment is reattached in the reverse orientation.
Inversions played an important role in the development of evolutionary genetics
in the first half of the twentieth century when it was discovered that inversions
in fruit flies (Drosophila) can be seen under the light microscope. The geneticist
Theodosius Dobzhansky, who was one of the leaders of the modern synthesis
(see Chapter 1), pioneered the use of inversions to study the evolution of genetic
differences within and between species [9]. With the advent of powerful DNA
sequencing technologies in the twenty-first century, it has become clear that
inversions are a common feature in the evolution of many species. The genomes
of humans and chimpanzees differ in about 1500 chromosome inversions that
became fixed in one lineage or the other since our last common ancestor roughly
7 million years ago (Ma) [12]. Inversions have an important effect on recombination. When they are heterozygous in an individual, crossing over is prevented
in that region of the chromosome. As a result, chromosomes with and without
the inversion can carry different sets of alleles at several sites, and these sets are
prevented from intermixing. This allows those two kinds of chromosomes to
adapt to different environmental conditions.
A reciprocal translocation is the exchange of chromosome segments between
two nonhomologous chromosomes. Translocation heterozygotes can have
reduced fertility, which contributes to the genetic isolation between some closely
related species (see Chapter 9).
Fusions are structural changes in which two nonhomologous chromosomes
are joined. Fissions are the opposite type of mutation, in which one chromosome
breaks into two. Fissions and fusions are responsible for changes in the number
of chromosomes in the genome. Organisms show a bewildering range of chromosome numbers (FIGURE 4.16). A highly venomous ant from Australia called
the jack jumper has only a single chromosome [7]. At the opposite extreme, a
fern has 630 pairs of chromosomes [15], while a ciliate has about 16,000 pairs of
very small chromosomes [32]! This variation results from different histories of
fissions and fusions. We still understand little about how and why these differences evolved. Surprisingly, changes in chromosome number and structure often
have no obvious phenotypic effects.
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FIGURE 4.16 Variation in haploid
chromosome numbers. Shown here
© Brooke Whatnall/Getty Images
(B)
© Kirsty Wigglesworth/PA Images/Alamy Stock Photo
are (A) a jackjumper ant (Myrmecia
pilosula), (B) a human, (C) a fern, and
(D) a ciliate.
(A)
Jackjumper ant
n=1
(C)
Human
n = 23
(D)
Velela
Courtesy of the National Human
Genome Research Institute
Fern
n = 630
Ciliate
n = 16,000
Whole genome duplication
The final
and most extreme type of mutation is whole genome duplication. OccaFutuyma Kirkpatrick Evolution, 5e
sionally
meiosis
produces
gamete that carries the entire diploid genome, rather
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haploid with just one
of each pair of chromosomes. If two of these unreduced gametes meet and fertilize each other, an offspring is produced that has
four copies of each chromosome. This genetic result, called tetraploidy, happens
much more frequently in plants than animals [26]. Later rounds of genome duplication can lead to even more complicated complements of chromosomes. One of
the interesting consequences is that the offspring typically cannot interbreed with
their parental population. Thus whole genome duplication can produce a new
species with a single mutation, as we’ll discuss further in Chapter 9.
4.4 Rates and Effects of Mutations
Learning objectives
■ Learn the typical mutation rate for a DNA base and for a typical
protein-coding gene in humans.
■ Define deleterious mutation and beneficial mutation.
■ Contrast germ line and somatic mutations.
Since there are so many kinds of mutations, it is not surprising that their rates
and effects vary greatly [30, 10].
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4.4 Rates and Effects of Mutations 97
Mutation rates
10–7
Mutation rate (per bp)
DNA replication is extremely accurate in eukaryotes and bacteria. Each time an Escherichia coli cell divides, there is roughly
a chance of only 1 out of 2 × 1010 that a given DNA base in a
Mammals
daughter cell will carry a new point mutation [18]. (Can you
Plants
10–8
think of any human action that is so accurate?) The probability
that an offspring carries a new mutation is called the mutation
Invertebrates
rate, which is symbolized by μ. The mutation rate at a single base
in E. coli is therefore μ = 1 / (2 × 1010) = 5 × 10-11 per bp. MutaBacteria
tion rates can also be expressed in terms of how often mutations
10–9
Unicellular
occur within a single gene or throughout the entire genome.
eukaryotes
Mutation rates vary greatly among species (FIGURE 4.17). Each
8
time a person makes an egg or sperm, roughly 1 out of 10 of
the DNA bases carries a new mutation (and so μ = 10–8 per bp)
[14]. Like all other properties of life, mutation rates evolve. A
10–10
Archaea
surprising recent discovery by Kelley Harris is that the mutation
rate in some human populations has changed subtly in the last
107
108
109
several thousand years [4]. The mutation rates in RNA viruses,
Genome size (bp)
like those responsible for AIDS, Ebola, and influenza, are thouFIGURE 4.17 Mutation rates in different groups of
sands of times higher (μ = 10–3 to 10–5 per bp). These extremely
organisms. Species with larger genomes tend to have
high mutation rates may result in part from natural selection that
higher mutation rates per base pair per generation. (That
favors rapid evolution of viruses to evade host defenses [30]. BOX
pattern does not hold for viruses, however.) The points for
4.1 describes how mutation rates are estimated.
Archaea and Bacteria represent averages of many species,
The concept of a mutation rate applies not just to a single
and there is considerable variation within those groups. ChapDNA base but also to an entire gene. Per-locus mutation rates are
ter 10 discusses an intriguing hypothesis to explain the corhigher than rates for single bases, simply because the locus carrelation between genome size and mutation rate seen here.
ries a mutation if any of its many base pairs mutates. These rates
vary greatly, both between loci within a species and betweenFutuyma Kirkpatrick Evolution, 5e
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species. Mutation rates for protein-coding loci in eukaryotes such
as humans and
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flies are typically in the range of μ = 10–5 to 10–7. We can also consider
the muta- 05-10-2022
tion rate across the entire genome. Although the human mutation rate per DNA
base is very small (μ = 10–8), our genomes have large numbers of bases (3 × 109).
As a result, every time we make a gamete, it carries roughly 30 new mutations
scattered throughout the genome, with more new mutations in sperm than in eggs
[14]. There are almost 8 billion people alive on Earth, and among us we carry some
480 billion new mutations. The amount of new genetic variation that is introduced
in each generation into humans and other species is vast.
Effects of mutations
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© Album/Alamy Stock Photo
Mutations affect virtually all aspects of an organism, ranging from the ability
of a bacterium to metabolize a new substrate, to the ability of a person to learn
language. Despite the huge range of effects that mutations have, they show two
general features.
The first is pleiotropy, which occurs when a single mutation affects multiple
traits. An extreme example is a type of dwarfism called achondroplasia (FIGURE
4.18), which results from a mutation in the gene FGFR3 that interferes with the
conversion of cartilage to bone during development. This decreases the size of
many bones in the body, particularly in the arms and legs. It also decreases longevity and affects many physiological traits.
Virtually all mutations that have any phenotypic effects show pleiotropy. Pleiotropy therefore plays a key role in evolution: genetic changes that alter one trait
invariably have side effects on other traits. Frogs in the genus Leptodactylus feed
on toads that have toxins in their skin that disrupt nerve function. To survive
the toxins, the frogs have evolved 12 changes to the amino acid sequence of the
enzyme ATP1A1R. A team of scientists led by Peter Andolfatto engineered proteins
Peter Dinklage
FIGURE 4.18 The actor Peter Dinklage
has achondroplasia. This condition
results from a mutation with dramatic
pleiotropic effects throughout the body.
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BOX 4.1
Estimating Mutation Rates
Mutation is fundamental to genetics and evolution, and
generations of scientists have devoted their careers
to its study. Several strategies have been invented to
estimate mutation rates [16, 14]. The first approach used
was phenotype screening. Here the investigator examines
(“screens”) a large number of individuals in a laboratory
population, looking for changes in a character caused by
a new mutation (e.g., changes in the eye color of the fruit
fly Drosophila melanogaster). The mutation rate, μ, is simply estimated by counting the number of new mutations
found by the total number of individuals. This method has
several limitations. Among them, we cannot estimate mutation rates at individual DNA bases, and only some kinds
of organisms can be used.
Another strategy became possible with the advent of
DNA sequencing. The phylogenetic method exploits the
fact that if a segment of chromosome has no effect on
fitness, it will accumulate mutations at a rate of μ per
generation (as we will see in Chapter 6). Two species
that last shared a common ancestor t generations ago
are therefore expected to differ in that segment by 2μt
mutations. (The factor of 2 appears because mutations
have accumulated in each of the two species since their
common ancestor.) By sequencing a piece of chromosome that has no effect on fitness in two species, we can
count the number of mutations by which they differ. If we
know when their last common ancestor lived (e.g., from
fossils) and the number of generations per year, we can
then calculate μ. Weaknesses of this approach are that it
can be applied only to regions of the genome that are free
from selection, and it requires that we know the number
of generations since the common ancestor.
A related method is called mutation accumulation. Here
several laboratory populations are established from a
single founding population (or individual, in the case of
asexual species). Each population is maintained under
conditions that largely eliminate natural selection so
that mutation is the only evolutionary process at work.
After many generations, individuals from each population
are sequenced, and mutation rate is estimated by the
same calculation used in the phylogenetic method. An
advantage of this approach is that we can be much more
confident about the number of generations since the
common ancestor and the assumption of neutral evolution. Disadvantages are that there are fewer mutations
to study (because they accumulate over a much shorter
time) and that only some species are suitable.
The conceptually simplest method for estimating mutation rates became possible recently as DNA sequencing
became relatively inexpensive. In the direct method, we
sequence the DNA of parents and offspring and look for
differences caused by mutations. This approach is free
of the assumptions that are important weaknesses of the
other two methods. But it too has limitations. The chance
of a mutation in any specific gene is extremely small, and
so at present the direct method can only give estimates
for mutation rates that are averages over large regions of
the genome.
with different combinations of these changes, then analyzed how they affected the
enzyme’s activity and its resistance to the toxins [22]. Two changes had the positive
effect of greatly increasing the enzyme’s resistance to the toxin, but they also had
the negative side effect of decreasing the enzyme’s activity (FIGURE 4.19). Ten other
changes did not have much effect on toxin resistance, but they did recover most of
the loss in enzyme activity. This example of pleiotropy also illustrates a common
pattern: mutations that increase fitness in one way often decrease fitness in another.
A second general feature of mutations involves their effects on an organism’s
fitness. While many mutations have no detectable effect on survival or reproduction, most of those that do are deleterious (i.e., harmful to survival or reproduction). On average, each human gamete carries one new deleterious mutation in
addition to many that were inherited from the previous generation [14]. Much less
often, mutations are beneficial, meaning that they increase fitness (FIGURE 4.20).
It is easy to understand why this is so by thinking about an enzyme that catalyzes
a biochemical reaction. Enzymes are remarkable molecules; each type is able to
bind to a very specific substrate and trigger a precise biochemical reaction. Most
changes to an enzyme’s amino acid sequence alter its chemical properties, reducing
or destroying the enzyme’s ability to perform its function. In humans, mutations
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4.4 Rates and Effects of Mutations 99
FIGURE 4.19 Leptodactylus frogs have evolved resistance to toxins in the toads
that they eat by 12 amino acid changes in the enzyme ATP1A1R. Shown is a plau-
Frog eating poisonous toad
(A)
Sensitive
Resistant
First
change
Ten more
changes
Second
change
Low
High
Low
Resistance to toxin
(B) Distribution of fitness effects
Stop codons
Synonymous mutations
Nonsynonymous mutations
0.4
–0.10
–0.05
Selection coefficient
Relative frequency
FIGURE 4.20
Most new mutations that affect fitness are
deleterious. (A) The effects of 560 mutations in the heat shock
protein Hsp90 in the yeast. A selection coefficient of –0.1, for
example, means that the mutation decreases fitness by 10%.
Effects of three kinds of mutations are shown. A very small fraction of mutations has beneficial (positive) effects. (B) Selection
coefficients for nonsynonymous mutations in humans. Mutations
0
0.3
Frequency
Beneficial mutations
Deleterious mutations
< –0.15 –0.15
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High
Enzyme activity
that change a protein typically have deleterious effects that decrease fitness by less
than 1%, but occasionally their effects are much more severe [11]. Most deleterious
mutations are partly recessive, meaning that their negative effects are more than
twice as harmful when they are homozygous as when heterozygous.
Because most mutations are deleterious, natural selection favors lower mutation rates (at least in organisms with sexual reproduction, like almost all eukaryotes). Several factors apparently prevent mutation rates from evolving to even
lower levels than they already are [21]. So it is a strange and wonderful fact that
life itself, which is the glorious product of natural selection, only exists because
natural selection is not efficient enough to drive mutation rates to zero. Were that
to happen, life on Earth would be choked off from the fuel needed for adaptation,
leading inevitably to its extinction.
Fortunately, a small fraction of mutations are beneficial. These are the mutations that spread by natural selection and that allow organisms to adapt. These
changes are therefore much more common as differences among species than
they are among new mutations that appear within a species. We will discuss
many examples in Chapter 5.
A homeotic mutation, a particularly dramatic type of mutation, transforms one
body part into another. Homeotic mutations reduce survival, which is not surprising, because they disrupt complex developmental pathways that evolved
through a large number of much smaller evolutionary changes (see Chapter 11).
Mutations do occasionally produce large beneficial effects, but there is no evidence that complex structures (like a leg) originate by single mutations. Goldschmidt’s idea of “hopeful monsters” that we discussed in Chapter 1 was an
evolutionary hypothesis that has been tested and proved wrong.
O. Rojas-Padilla et al. 2018.
Herpetol Notes 11, 831–834.
Photo by Caio V. Mira-Mendes
sible evolutionary sequence in which they evolved. The first two changes together
increased toxin resistance by more than 100 times, but they lowered enzyme activity by
40%. Ten further changes restored almost all of the activity loss. (After S. Mohammadi
et al. 2021. Curr Biol 31: 2530–2538.)
0.2
0.1
0
–1.0
–0.1
–0.01
–0.001 –0.0001
Selection coefficient
0
that are more deleterious are farther to the left. Roughly 12%
have large deleterious effects that decrease fitness by 10% (i.e.,
a selection coefficient of –0.1) or more. The method used in this
study cannot detect Futuyma
beneficial
mutations.
(A after
Kirkpatrick
Evolution,
5e C. Bank et al.
2014. Genetics 196: 841–852;
B after A.Visual
Eyre-Walker
OUP/SInauer/Troutt
Services and P. D.
Evolution5e_04.19.ai
Keightley. 2007. Nat Rev
Genet 8: 610–618.)06-02-2022
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Mutation and Variation
Germ line mutations and somatic mutations
Exposure to sunlight causes mutations in skin cells that can lead to cancer. Those
mutations, however, cannot be transmitted to the next generation. Early in development, most groups of animals set aside a small group of cells to form the germ
line, which then produces the gametes when the individual is sexually mature.
The rest of the cells in the early embryo go on to form the soma, consisting of all
the other tissues in the organism. These somatic cells leave no descendants to the
next generation. Somatic mutations, like those responsible for skin cancer, are not
transmitted to the next generation.
Somatic mutations still have important evolutionary consequences in animals that have a germ line. Cancer decreases an organism’s chance of surviving
and reproducing. Natural selection can favor mutations in the germ line that
improve DNA repair because they decrease the chance that mutations will occur
in somatic cells and produce a cancer. Elephants live longer and have vastly more
cells in their body, so you might expect they would be much more prone to cancer
than are humans. But the reverse is true: elephants die of cancer less than half as
often as we do. Elephants have 20 copies of the TP53 gene, which is involved in
repairing damaged DNA [1]. Like the rates of germ line mutations, the rates of
somatic mutations can evolve.
Plants and some animals (e.g., sponges and corals) do not have a germ line.
Instead, gametes are made from somatic cells that divided many times as an
individual was growing. In these species, somatic mutations can be passed to a
gamete and then on to the next generation.
4.5 Is Mutation Random?
Learning objective
■ Describe the design and the result of an experiment showing that
the effects of mutations are random with respect to what the
environment favors.
The word random often appears with the word mutation. But are mutations in fact
random, and if so, in what sense? This is an important question in evolution. But
it is also a tricky one, because the word random has different meanings.
There is substantial variation in the mutation rates of different regions of the
genome, and different DNA bases. Transition mutations are those between A
and G, and between C and T. Transversion mutations are all the other kinds (e.g.,
between A and C). Even though there are twice as many possible transversion
mutations, transition mutations are more common. In short, not all mutations
are equally likely. In that sense, mutation is not random.
Mutation is random, however, in a different and fundamental sense. Mutations are random with respect to what will improve survival and reproduction.
We could imagine a world in which organisms respond to increasing temperatures (for example) by increasing the occurrence of mutations that make them
more heat tolerant. This kind of “directed mutation” would allow organisms to
adapt more rapidly to the changing conditions even without the action of natural
selection. But new conditions do not generally make it more likely that mutations
beneficial in those conditions will occur.
An elegant test of the directed mutation hypothesis is the famed replica plate
experiment done by Esther and Joshua Lederberg [17]. The bacterium Escherichia coli
is attacked by a virus called T1, but some cells carry a mutation that allows them to
resist the attack. The Lederbergs began by inoculating a petri plate with a culture
of E. coli that they started from a single cell that was not resistant to the virus. After
incubation, this “master plate” had a few hundred colonies on its surface, each of
which grew from a single cell in the original culture. A disk covered in velvet was
gently pressed to the surface of the master plate, then gently pressed to the surface
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4.5 Is Mutation Random? 101
of several other “replica plates” that had been sterilized. The velvet picked up a
few cells from each colony on the master plate, then transferred them to the replica
plates. The brilliance of this experimental design is that the spatial positions of the
colonies were the same on the master and the replica plates.
The next step of the experiment was to treat all of the plates with a culture of
the T1 virus. As expected, the virus killed almost all of the colonies. The Lederbergs showed that the colonies that survived carried a mutation for phage resistance: after they were transferred to new plates and attacked again with T1 virus,
those colonies still survived.
What can this experiment tell us about the randomness of mutation? Consider
the two hypotheses shown in FIGURE 4.21. Under hypothesis H1, the addition of
the virus induces resistance mutations in some of the bacteria. In that case, some
colonies should survive and some should not, depending on whether they are able
to make the adaptive mutation before they are killed. But crucially, there is no reason
that the particular colonies that survive on the replica plates should be the same.
We predict that the positions of the surviving colonies will vary between the plates.
Joshua and Esther Lederburg
(A)
Virus added
H1: Virus induces
resistance mutations
Mutations in
different places
Master plate
Replica plates
(B)
Virus added
H2: Mutations occur
at random before
exposure to virus
Mutations in
same places
Master plate
Replica plates
FIGURE 4.21 The replica plate experiment shows that
mutations in E. coli for resistance to a virus occur at
random, before exposure to the virus. The experiment begins
with a master agar plate that has numerous colonies of E. coli
that were all derived from a single cell. Replica agar plates are
made by
first pressing
a velvet-covered
disk against the master
Futuyma
Kirkpatrick
Evolution,
5e
plate, then against
sterilized
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Visual
Services plates. This transfers some cells
from each colony to05-10-2022
each replica plate in the same spatial conEvolution5e_04.21.ai
figuration as they are on the master plate. A culture of T1 viruses
is then added to the replica plates. (A) Under the hypothesis of
directed mutation, exposure to the virus induces the bacteria to
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generate resistance mutations. This hypothesis predicts that
those colonies that are successful in generating these beneficial
mutations will appear at different places on the replica plates.
(B) The alternative hypothesis is that resistance mutations occur
spontaneously before the virus is added. In this case, when the
virus is added, the colonies that already have resistance mutations will appear in the same places on the replica plates. The
experiment confirms the second hypothesis: mutations appear
at random with respect to what the environment favors. (Inset)
Joshua and Esther Lederberg also made several other breakthroughs in genetics.
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Mutation and Variation
Under hypothesis H2, resistance mutations occur randomly, whether the virus
is present or not. In this case, some of the colonies on the master plate will have
the mutation, even though they have never been in contact with the virus. Those
colonies will appear in the same locations on the replica plates. When the virus
is added to the replica plates, we predict, the colonies that survive will be in the
same locations.
The results of the Lederbergs’ experiment were clear: the same colonies survived the viral attacks. This shows clearly that the mutation for resistance to the
virus occurred in colonies on the master plate, before the mutation was favorable
to the bacteria. This experiment, and many more like it, give strong support to the
hypothesis that mutation is random with respect to what the environment favors.
A final point is that the environment can (and does) have important effects on
mutation rates. Exposure to radiation can increase a person’s mutation rate. But
the mutations that result will not be particularly good at improving the person’s
chance of surviving the effects of radiation. A species becomes better at surviving
in its environment because natural selection picks out the favorable mutations
and causes them to spread. How that happens is the key topic of Chapter 5.
4.6 Nongenetic Inheritance
Learning objective
■ Describe how three kinds of nongenetic inheritance work.
The vast majority of inherited changes involve alterations of the DNA (or RNA)
sequence of a genome. In some organisms, however, other mechanisms also contribute to inheritance and so can play a role in evolution [3].
Epigenetic inheritance occurs when offspring inherit phenotypic traits from their
parents by some means that does not involve a change in their DNA sequences.
One kind of mechanism involves chemical changes to DNA [5]. Two of the four
DNA bases (cytosine and adenine) can be methylated, which is a biochemical change
to their structure. In another mechanism, the histones that bind to DNA to form
eukaryotic chromosomes can be biochemically modified. Both kinds of changes
alter how genes are expressed, and both can be transmitted across generations.
Epigenetic inheritance has generated excitement among some evolutionary
biologists because it might allow for the inheritance of acquired characters. One
can imagine that a change in the environment (e.g., increasing temperatures)
could change methylation patterns in the genome in ways that would allow
individuals to survive better in the new conditions. They could then pass these
epigenetic changes to their offspring, thus accelerating adaptation. Epigenetic
inheritance is more common in plants than animals [27] and can contribute to
short-term adaptation. Experiments show that when the wild radish (Raphanus
sativus) is attacked by caterpillars, chemical defenses are induced in the plant
and, further, that epigenetic changes cause induced defenses in its offspring [31].
Existing evidence, however, does not support the idea that epigenetic inheritance
makes major contributions to long-term evolutionary change [6]. One kind of evidence leading to that conclusion comes from studies of inbred laboratory populations of animals and plants. They have very little genetic variation, but they are still
capable of epigenetic modifications to their chromosomes. When artificial selection
acts on these populations, they do not evolve, which shows that genetic variation
rather than epigenetic variation is necessary for adaptation. In the following chapters, we will look at some of the hundreds of cases in which we know the specific
genetic changes that are responsible for adaptations. A basic limitation of epigenetic
inheritance is that epigenetic changes are not permanent—they are lost after one
or several generations. Thus epigenetic inheritance can be important in the short
term, but it does not make major contributions to long-term evolutionary change.
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4.6 Nongenetic Inheritance 103
White-throated sparrow
FIGURE 4.22 A new song in the
white-throated sparrows spread
through most of the species’ range by
cultural evolution in just 20 years. The
sonograms, which are a plot of a song’s
frequency over 1 second, show the
difference between the old song with
triplets and the new song with doublets.
(Sonograms and maps after K. A. Otter
et al. 2020. Curr Biol 30: 3231–3235.)
Before 2000
Frequency
Genes are not the only way that mothers affect their offspring. Maternal effects
occur when the genotype or phenotype of the mother directly influences the phenotype of her offspring. In mammals, including humans, the amount and quality
of milk that a mother produces has important effects on many phenotypes of
her offspring, such as their growth rate and immune system [35, 23]. In turn,
the mother’s milk is affected by her physiological condition as well as her genotype. Maternal effects like these are transmitted across only a few generations.
They can contribute to the resemblance between mothers and their offspring but
(unlike genetic inheritance) do not contribute to long-term evolutionary change.
Traits with cultural inheritance are transmitted by behavior and learning. In
humans, many traits, including language, religion, and dietary preferences, are
strongly influenced by cultural inheritance (see Chapter 21), but some other species also show cultural inheritance. The white-throated sparrow (Zonotrichia albicollis) learns its song from other individuals. For many years, that song sounded
like “Oh sweet Canada, Canada, Canada,” with the syllables of each “Canada”
forming a triplet (three notes). Sometime around 2000, a new song appeared in
western Canada that had doublets (two notes) in place of the triplets (FIGURE
4.22). Over the next 20 years, the new doublet song rapidly swept eastward, and
as of 2020 it had replaced the old song over most of the species’ range [25]. Why
the doublet song spread is not certain, but females might be attracted to males
that sing it simply because it is novel. The parallels with genetic evolution by
natural selection are clear: a “mutant” song appears that for some reason “reproduces” more rapidly, causing it to spread.
An important difference between cultural inheritance and other forms of
nongenetic inheritance is that traits can be transmitted between unrelated individuals (as well as between parents and offspring). That fact is exploited by the
advertising industry and politicians in their efforts to persuade us to buy their
products and to vote in certain ways.
Nongenetic inheritance has been crucial to the evolution of some traits in some
species. Human society would not be possible without cultural inheritance. For
the vast majority of life on Earth, however, evolutionary change results only from
genetic inheritance, so we concentrate on genetic change in this book. There are,
however, many fascinating aspects of nongenetic inheritance
in humans that are studied in fields that include linguistics,
Triplets (old)
psychology, and behavior.
Time
After 2015
Doublets (new)
Frequency
© Mircea Costina/Shutterstock.com
Time
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Mutation and Variation
What We Don’t Know
For over a century, evolutionary biologists have studied variation, within and
between species, in the structure of the chromosomes, for example, differences
in chromosome numbers caused by fusions and fissions. But despite the vast
amount of data gathered, we have only hypotheses but no clear evidence for
how and why these changes have evolved.
● Although meiosis is central to reproduction in almost all species of eukaryotes,
it has many features that have no simple explanation. For example, it is unclear
why when eggs are made three of the four products of meiosis are discarded in
polar bodies. These and other mysteries of meiosis are discussed by Lenormand
and colleagues [19].
●
SUMMARY
■ DNA is the genetic material of almost all life on
Earth. Genes consist of sequences made up of
four bases (A, G, C, and T) that are read in sets
of three that make up each codon.
■ Phenotypes are traits, or characteristics, of
individuals that can be observed, while geno­
types are their DNA sequences. Variation
among individuals in their genotypes contributes to variation in their phenotypes. Alleles are
the different DNA sequences found at a particular gene (locus).
■ Most species reproduce sexually: parents
transmit DNA to their offspring through their
gametes. The mixing of DNA sequences by
segregation and recombination during meiosis
causes offspring to carry new combinations of
alleles not found in either parent.
■ Segregation causes the frequencies of geno­
types at an individual locus to evolve to the
Hardy-Weinberg equilibrium under certain
idealized conditions: an infinite population size,
no natural selection, no mutation, no movement of individuals between populations, and
random mating. These conditions are nearly
met in many real populations, so loci are often
in approximate Hardy-Weinberg equilibrium.
■ Recombination between loci decreases
the correlations between alleles (linkage
disequilibrium).
■ In species that reproduce asexually, offspring
are nearly genetically identical to their parents.
Rare differences result from mutation and the
horizontal transmission of small pieces of DNA
between individuals.
■ Genetic variation can be introduced to eukaryotic species by introgression, which can occur
when two species hybridize.
■ The ultimate source of all genetic variation is
mutation. Mutations can affect a single DNA
base (point mutations) or change many
DNA bases along a chromosome at once
(structural mutations). Very rarely, the entire
genome is duplicated.
■ Any specific mutation is very rare: in humans,
for example, only about one base pair (bp) per
billion mutates when a gamete is made.
■ Many mutations do not affect the phenotype,
but mutations that do have effects typically
show pleiotropy and are deleterious. A small
minority of mutations are advantageous.
■ Experiments show that the effects of mutations on phenotypes are random with respect
to what is favored by the environment.
■ Genetic inheritance is by far the most important mechanism of inheritance, but not the
only one. Some traits are affected by epigenetic inheritance, maternal effects, or cultural
inheritance.
SUGGESTIONS FOR FURTHER READING
The chapter introduced some of the basic
concepts of population genetics that we
will build on in the next few chapters. A very
good introduction to this field is Graham
Coop’s book, Population and Quantitative Genetics, which is available as a free PDF available for download at github.com/cooplab/
popgen-notes/releases/tag/v1.1.
04_EVOL5E_04.indd 104
The large literature on mutation is nicely summarized in a 2010 review by F. A. Kondrashov and A. S. Kondrashov, “Measurements of
spontaneous rates of mutations in the recent
past and the near future” [16]. P. D. Keightley’s
2012 “Rates and fitness consequences of new
mutations in humans” focuses on the rates
and effects of new mutations in humans [14].
10/5/22 3:10 PM
Summary
The interesting question of how mutation
rates evolve was explored in 2000 by P. D.
Sniegowski and colleagues in “The evolution
of mutation rates: Separating causes from
consequences” [30] and by M. Lynch in “Evolution of the mutation rate” [21].
The causes, consequences, and uses of linkage
disequilibrium in humans and other species
105
are reviewed by M. Slatkin in “Linkage disequilibrium—understanding the evolutionary
past and mapping the medical future” [29].
Cultural evolution is a fascinating topic studied
in several areas of science, including animal
behavior, psychology, and linguistics. A recent review by A. Whiten will introduce you
to the topic [34].
PROBLEMS AND DISCUSSION TOPICS
1. The replica plate experiment shows that mutations are random. However, certain environmental stresses (e.g., high temperature, high
salt, low pH) can increase the mutation rate.
a. Does this indicate that mutations are
nonrandom, since they increase in
response to cell stressors?
b. Does increasing the mutation rate
increase the probability that an individual
mutation will be adaptive?
c. Does increasing the mutation rate
increase the probability that a cell will
experience an adaptive mutation?
2. Almost all genes in eukaryotes have introns
and exons, while almost no genes in prokaryotes do. How will that difference affect the
number of different proteins that can be
produced from a single gene?
3. Below are the DNA sequences that encode
the first eight amino acids for five alleles of
the Adh protein in Drosophila pseudoobscura.
Nucleotides that differ from the first sequence
are shown by a lowercase letter.
(1) ATG TCT CTC ACC AAC AAG AAC GTC
(2) ATG gCT CTC ACC AAC AAG AAC GTC
(3) ATG TCg CTC ACC AAC AAG AAC GTC
(4) ATG TCT tTg ACC AAC AAG AAC GTC
(5) ATG TCT CTC ACC AAC AAG AAC GTg
a. What are the first eight amino acids for
each of these five DNA sequences?
b. For each of the changes in sequences
(2) to (5), indicate whether it represents a
synonymous or nonsynonymous mutation
compared to sequence (1).
4. A sample of 100 individuals is genotyped
at loci A and B. The following numbers of
two-locus genotypes are obtained:
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Genotype
Numbers of individuals
A1 A1B 1B 1
36
A1 A 2B 1B 2
48
A 2 A 2B 2B 2
16
a. What is surprising about the observed
numbers of two-locus genotypes? What
is a possible explanation?
b. Determine whether each locus is at
Hardy-Weinberg equilibrium.
c. Do you think the two loci are close to
one another, or far apart in the genome?
Why?
5. From Figure 4.13, say whether linkage disequilibrium is high or low between sites at
the following positions:
a. 400 kb and 450 kb
b. 300 kb and 350 kb
c. 200 kb and 400 kb
d. 290 kb and 300 kb
6. The replicate plate experiment tested two
hypotheses and falsified one of them. Imagine
that the false hypothesis was actually true.
a. What would the result of the experiment
be?
b. What would that tell us about mutation?
c. How would that change our view of how
adaptation works?
7. Cancers result from mutations in somatic
cells, and these mutations therefore are not
passed on to gametes. However, some families have much higher rates of cancer than
average, showing that there are heritable factors that contribute to the risk of developing
cancer. Discuss the roles that somatic mutations and germ line mutations play in producing cancer.
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5
The Genetical Theory
of Natural Selection
N
atural selection explains many of the most fascinating things in nature,
from why there is sex and death to the complexities of the human
brain. Natural selection is fundamentally a simple concept. But its
explanatory power expands when we see how it acts and how the evolutionary
outcome depends on genetics and development. When we understand
these sides of the evolutionary process, we can begin to answer a cascade
of fascinating questions: How do cooperative and selfish behaviors evolve?
Why do some species reproduce sexually and others asexually? Are different
populations of a species likely to evolve the same way if they experience
the same environmental challenge? What explains the extraordinary display
of the peacock, the immense fecundity of oysters, the brevity of a mayfly’s
life, the pregnancy of the male sea horse, the abundance of transposable
elements in our own genome?
Darwin realized that a complete understanding of evolution requires
understanding the mechanism of inheritance. One of the biggest frustrations
of his life was that he never was able to learn that mechanism. The field of
genetics began after Darwin died, when Mendel’s work was rediscovered in
1900. Modern evolutionary genetics started to develop with the synthesis of
Mendelian genetics and Darwin’s theory of natural selection, and the discovery
of other causes of evolutionary change. The keystone to understanding how
evolution works is the “genetical theory of natural selection,” to quote the title
of a famous 1930 book by the pioneering population geneticist R. A. Fisher.
A mating pair of the tropical butterfly Heliconius melpomene. They have evolved bright
colors by natural selection that warn predators they are distasteful.
© Loic Poidevin/Minden Pictures
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Chapter 5
The Genetical Theory of Natural Selection
5.1 We Can Watch Natural Selection and Evolution
Happening in Real Time
Learning objectives
■ Describe an example of natural selection and evolution that was
directly observed.
■ Explain a hypothesis about evolution that was tested by an experiment
and the conclusion that was drawn.
The English have long had a passion for butterflies and moths. For hundreds of
years, amateur collectors have roamed the countryside in search of specimens.
They were surprised when a melanic (dark) color form of the peppered moth
(Biston betularia) was first discovered in 1848 and then rapidly spread in central
England (FIGURE 5.1). Recent research shows that the dark coloration results from
a single mutation that likely occurred around 1819 [20].
(A)
(B)
Unpolluted
Polluted
Up to 1860
Up to 1870
Melanic
Up to
1880
Up to
1890
Up to
1900
© Bill Coster/Alamy Stock Photo
© Bill Coster/Alamy Stock Photo
Typical
1.0
100 km
Location 1
Location 2
Location 3
Frequency of melanic form
0.9
0.8
0.7
0.5
Up to
1880
England
(C)
0.6
1848
Frequencies at
three locations
0.4
0.3
0.2
FIGURE 5.1 Industrial melanism
in the peppered moth is the most
famous example of evolution by
natural selection directly observed
in the wild. (A) The “typical” (ances-
tral) form and the melanic form of
the moth on tree trunks in unpolluted
and polluted areas. (B) Dates at which
melanic moths first appeared in
England. (C) The decline of the melanic
form as air pollution decreased during
the late twentieth century. (B after
R. J. Berry. 1990. Biol J Linn Soc 39:
301; C after L. M. Cook. 2003. Q Rev
Biol 78: 399–417.)
0.1
0
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1960
1970
1980
Year
1990
2000
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5.1 We Can Watch Natural Selection and Evolution Happening in Real Time 109
Why did the melanic mutation in the peppered moth suddenly spread?
During the mid-1800s, England was transformed by the Industrial Revolution,
which was powered by burning vast quantities of coal. Before then, peppered
moths had been well camouflaged: their light coloration was a beautiful match
to the bark and lichen on the trees where the moths rest during the day. But the
burning of coal produced soot that killed the lichens and blackened the tree
trunks, making moths with the typical coloration very conspicuous. Moths with
the melanic allele, however, were camouflaged in the new environment. That
increased their survival, and it caused the melanic allele to almost completely
replace the typical coloration by the late 1800s [45, 46]. In 1924, J. B. S. Haldane
calculated from the speed of its spread that the melanic mutation must have
given moths a 50% survival advantage [17]. Experiments done by Bernard Kettlewell in the 1950s suggested that predation by birds was responsible.
Starting in the 1960s, coal burning decreased dramatically and trees slowly
lost their coatings of soot. The change provided an unintended experiment that
verified the hypothesis that pollution gave melanic moths a survival advantage.
Between 1960 and 2000, the frequency of melanic moths fell from nearly 100% to
nearly 0% throughout much of central England (Figure 5.1C) [6].
Several scientists raised questions about whether bird predation was really
the cause of the melanic mutation’s rise and fall. Michael Majerus took up the
challenge of answering this question in the 2000s. Over 7 years, he did a series
of experiments involving nearly 5000 moths. Tragically, Majerus died before the
work was published, but his colleagues finished the job [7]. The results decisively
confirmed Kettlewell’s conclusions: bird predation was the overriding reason
for the rapid evolution of melanism in the 1800s and its decline 100 years later.
The spread of melanism in the peppered moth was the first evidence of evolution that had been documented in real time, and Haldane’s calculations showed
that natural selection acting on the melanic allele can explain its rapid spread.
The dozens of studies that have been done on the genetics and ecology of the
peppered moth give us one of the most detailed understandings of evolution
by natural selection in any species. The peppered moth story also shows that
evolutionary biology, like physics and chemistry, is a rigorous science in which
scientists propose and test hypotheses.
While the peppered moth is the most famous example of evolution by natural selection that has been directly observed, hundreds of other cases have
also been studied. One is close to home. If you’ve ever had cockroaches in your
kitchen, you may be familiar with the traps used to control them. Cockroaches
are attracted to the traps by glucose that is laced with poison. Some populations
of the German cockroach (Blattella germanica), which is found in kitchens around
the world, have evolved an aversion to glucose in just a few years. This change
is caused by a single mutation that rewires the neural receptors that cockroaches
use to taste their food [40, 47]. One type of receptor fires when a cockroach tastes
something bitter, causing the cockroach to avoid it. In cockroaches that carry the
mutation, the receptor for bitter taste also fires when it is exposed to glucose
(FIGURE 5.2), effectively repelling the cockroaches from the traps.
Further evidence of the power of selection to cause rapid evolution can be seen
at your local supermarket. Almost all the food there results from the remarkable
evolutionary changes resulting from artificial selection, the selective breeding by
humans of animals and plants. When prehistoric farmers harvested their crops, the
plants that were most productive contributed the most seeds to the harvest. Some
of those seeds were used to plant the next year’s crop, resulting in plants that were
more productive than the previous generation. Selective breeding of domesticated
animals and crops was likely unconscious at first, but it is now practiced with
sophisticated statistical methods, and it results in spectacularly rapid evolution.
FIGURE 5.3 shows the results of artificial selection for increased weight in chickens.
After just 48 years, the weight of chickens at 56 days of age was increased more
than four times.
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Chapter 5
The Genetical Theory of Natural Selection
FIGURE 5.2
German cockroaches
have evolved an aversion to the
glucose used to trap them. Cock-
Caffeine
Bitter
Sweet
Bitter
Glucose
Sweet
Bitter
Sweet
Bitter receptor
not firing
Time
Glucose-averse
Fructose
Caffeine
Bitter
Sweet
Bitter
Glucose
Sweet
Bitter
Sweet
© 7th Son Studio/
Shutterstock.com
Amplitude
roaches have four types of taste receptors that respond to different tastes.
These neural impulses show how two of
the receptors respond to two kinds of
sugar (fructose and glucose) and to
a bitter stimulus (caffeine). In wild-type
cockroaches, the bitter receptor is
triggered by caffeine but not by either
sugar. In cockroaches that have the
glucose-aversion mutation, the bitter
receptor is also triggered by glucose (but
not fructose). (After A. Wada-Katsumata
et al. 2013. Science 340: 972–975.)
Wild type
Fructose
Amplitude
110
Time
Bitter receptor fires
21 years
of selection
48 years
of selection
34 g
42 g
44 g
316 g
632 g
1396 g
Starting
population
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0 days
Age
28 days
56 days
FIGURE 5.3
Broiler chickens rapidly evolved
to be heavier in response to artificial selection.
In the starting population, artificial selection was
stopped in 1957, in the second it stopped after
21 more years of selection, and in the third it
stopped after 48 more years of selection. (From
M. J. Zuidhof et al. 2014. Poult Sci 93: 2970–2982.)
05_EVOL5E_05.indd 110
Four-fold increase
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5.2 Evolution by Natural Selection and Inheritance 111
5.2 Evolution by Natural Selection and Inheritance
Learning objectives
■ State the two conditions that must be met for a population to evolve
by natural selection.
■ Explain what absolute and relative fitness are.
As we stressed in Chapter 3, selection results when there is a consistent relation
between a phenotype and fitness. Evolution by selection also requires a second
ingredient: inheritance. In the simplest terms, if
there is a correlation between a phenotypic trait and the number of
offspring that individuals leave to the next generation, and
● there is a correlation between the phenotype of a trait in parents and
in their offspring,
●
then:
● the trait will evolve.
The first condition means that selection on the trait is occurring, favoring individuals with more extreme phenotypes than average. The second condition means
that at least some of the phenotypic variation is inherited, causing the individuals
with those more extreme phenotypes to pass on their traits to their offspring. The
term correlation is used here in the statistical sense described in Chapter 22.
A key point is that natural selection and evolution are not the same thing.
If selection on a trait occurs but the trait is not inherited, then evolution will
not happen. You will see in later chapters that natural selection is not the only
factor that can cause evolution, and so evolution can occur even if selection
does not. But if the twin conditions listed above are met, then the population
will change across generations. To make that basic idea more quantitative, let’s
now consider how to measure selection, account for inheritance, and predict
the outcome of evolution.
Fitness: The unit we use to measure selection
To understand how evolution by selection works, we need a way to measure
selection. An individual’s absolute fitness is the number of zygotes (offspring) produced over its lifetime. If an immature butterfly is eaten by a bird, the butterfly’s
fitness is 0. If an oak tree survives many years, its fitness could easily be in the
millions. (Just think of the number of acorns and pollen grains produced by
a big tree.) It is also useful to consider the fitness of an allele, a genotype, or a
phenotype. In those cases, fitness is the average of the fitnesses of the individuals with that allele, genotype, or phenotype. We use the symbol W to represent
absolute fitness.
Over the life cycle of any organism, many events affect fitness. An embryo
may develop to sexual maturity or die before that happens. If it survives to
maturity, the individual may or may not be able to mate. If it does successfully
mate, the individual’s gametes may or may not be successful at fertilization.
These various events are called fitness components (FIGURE 5.4). They can be
divided even more finely, or grouped together, depending on the situation.
Often it is useful to simplify our thinking by thinking of fitness as the product
of just two fitness components:
W = (probability that the individual survives to maturity)
(5.1)
× (expected number of offspring if the individual does survive)
The strength of selection is determined by fitness differences. It is the relative (or proportional) differences that matter. To see why this is so, consider an
Zygotes
Fertilization
success
Viability
Fecundity
Mating
success
Adults
Females Males
Gametes
Parents
FIGURE 5.4
Selection can affect fitness
at different points during the life cycle.
This sketch shows the life cycle of a sexually
reproducing species with four fitness com­
ponents that affect the number of descendants that an individual leaves to the next
generation. (After F. B. Christiansen. 1984.
In B. Shorrocks [ed.], Evolutionary Ecology,
pp. 65–79. Blackwell Scientific, Oxford.)
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Chapter 5
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individual who leaves two offspring to the next generation (a fitness of 2). That
individual enjoys a huge fitness advantage if all others in the population have
a fitness of 1, but it is at a selective disadvantage if all others have a fitness of 4.
We therefore often work with relative fitness, which is the absolute fitness
divided by a fitness reference that we agree on. The choice of the fitness reference
is a matter of convenience, and it changes depending on the situation under
consideration. Say there are two alleles, A1 and A 2 , at a locus. We use the symbols W11 to represent the absolute fitness of A1A1 homozygotes, W12 for that of
A1 A2 heterozygotes, and W22 for that of A2 A2 homozygotes. We write the relative
fitnesses of the three genotypes the same way, but using a lowercase w (e.g., the
relative fitness of A1 A2 is w12 ). If we agree to use the A1 A1 homozygote as our
fitness reference, then the relative fitnesses are
w11 = W11/W11 = 1
w12 = W12/W11(5.2)
w22 = W22/W11
Relative fitnesses play a critical role in determining the speed and outcome of
evolution by natural selection, as you will now see.
5.3 P
ositive Selection and the Spread of
Beneficial Mutations
Learning objectives
■ Predict how fast the frequency of a beneficial allele will change.
■ Explain why beneficial mutations sometimes do not spread.
FIGURE 5.5
Positive selection
causes the allele with higher fitness
to increase in frequency. Following
one generation of selection, the allele frequency increases from p = 0.5 to p = 0.58.
Whenever one allele has higher fitness than another, natural selection will favor it
to spread through the population. This is called positive selection. A hypothetical
example is shown in FIGURE 5.5. A population of mice has two alleles that affect
the coat color. Allele A 2 causes darker fur than allele A1 , and it is initially present
at a frequency of 0.5. Hawks prey on the mice and kill half of the light-colored
A 1 A 1 and one-fourth of the intermediate-colored A1 A2 individuals. The dark
A 2 A 2 homozygotes are well camouflaged, and all of them survive. Selection has
increased the frequency of A 2 to 0.58. When the survivors reproduce, this new
allele frequency is passed to the next generation.
Surviving adults
Juveniles
Offspring
A2 A2
A1A1
A1 A2
A1 A2
A2 A2
X
X
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A 2
A1A1
A1 A2
A1 A2
A2 A2
X
X
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A 2
A1A1
A1 A2
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A 2
A1A1
A1 A2
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A2
A1A1
A1 A2
A1 A2
A2 A 2
Selection
Reproduction
A1A1
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A1 A2
A2 A2
Frequency
p = 0.58
Frequency
p = 0.58
Frequency
p = 0.5
A1A1
A1 A2
A2 A2
A1A1
A1 A2
A2 A2
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5.3 Positive Selection and the Spread of Beneficial Mutations 113
BOX 5.1
Evolution by Positive Selection on a Single Locus
This box explores the evolutionary change that results
from positive selection on a single locus with two alleles,
A1 and A 2. We will look at the simplest case, in which the
heterozygote has intermediate fitness, which is often
the case. The following will illustrate one of the uses of
models in evolutionary biology: a set of assumptions is
translated into a mathematical representation that is
then used to discover implications of the assumptions, in
this case the rate that allele frequencies change as the
result of natural selection.
The A1 A 1 genotype will be our fitness reference. The A 2
allele increases survival, and for each copy of A 2 that an
individual carries, its fitness increases by a proportion s.
The frequency of A 2 is p, and we assume the population is
in Hardy-Weinberg equilibrium. The relative fitnesses and
frequencies of the three genotypes are therefore
the following:
Genotype
A1 A 1
A1 A 2
A 2A 2
Relative fitness
1
1+s
1 + 2s
Frequency at birth
(1 – p) 2
2p(1 – p)
p2
Our goal is to find the frequency of A 2 in the next generation. The first step is to calculate the frequencies of the
three genotypes among surviving adults after selection
has acted, as follows:
Genotype
A1 A 1
A1 A 2
(1 – p) 2 2p(1 – p)(1 + s)
Frequency in adults
w
w
A 2A 2
p2 (1 + 2s)
w
In each numerator, we see the product of the genotype’s frequency at birth and its relative fitness. In the
denominators is —
w , a factor that is needed to ensure that
all the frequencies sum to 1 (which is required by the
definition of frequencies). That number has an important
interpretation: it is the mean fitness of the population. It
is given by the sum of the three numerators:
— = (1 – p) 2 + 2p(1 – p) (1 + s) + p 2 (1 + 2s) = 1 + 2sp (A1)
w
We now have what we need to find the allele frequencies at the start of the next generation. Using the logic
sketched in Figure 4.7, the frequency of allele A 2 is given
by the frequency among surviving adults of A 2 A 2 individuals plus half the frequency of A 1 A 2 individuals. After a bit
of algebra, we find the answer is
pʹ = [1 + s (1 + p)] p/ —
w
(A2)
We can learn more by asking how much allele frequencies change from one generation to the next. Using our
results from above, we find that the change in the frequency of allele A 2 caused by one generation of selection is
∆p = pʹ– p = sp(1 – p) / —
w ≈ sp(1 – p)
(A3)
The first two expressions for ∆p are exact. The last one
is an approximation that is very accurate when s is less
than 0.1. It is useful because it is so simple.
We’ve now seen how to calculate the change in allele
frequencies for the particular case of the relative fitnesses shown above. Fitnesses that follow that pattern are
common for alleles with small fitness effects. For other
situations, including cases in which heterozygotes have
a fitness higher or lower than both homozygotes, we calculate the allele frequency change using the same logic
used here. The results, however, are a bit more complicated than Equation A3. For more details, see [8].
The rate at which an allele’s frequency changes—that is, the speed of evolution—is determined by the relative fitness advantage of the favored allele. The
mathematical theory behind that fact is explained in BOX 5.1.
FIGURE 5.6 shows examples of beneficial mutations spreading through a population. The population is initially fixed for allele A1, which means that the allele
is at a frequency of 1.0. We will use the A1 A1 homozygote for the fitness reference.
Mutation then produces a new allele, A 2, which is beneficial. Each copy of this
allele that an individual carries increases fitness by a fraction s. This means that
A 1 A 2 heterozygotes have relative fitness w12 = (1 + s). The A 2 A 2 genotype has
the highest fitness of all, and its fitness is increased by a factor of (1 + 2s) relative
to the A1 A1 homozygotes. In the mouse example of Figure 5.5, the selection coefficient favoring the A 2 allele is s = 0.5: each copy of that allele increases survival
by 50%. That is extremely strong selection compared with what is typically seen
in nature, where fitness effects of alleles are typically many times smaller.
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114
Chapter 5
The Genetical Theory of Natural Selection
FIGURE 5.6 The spread of three beneficial mutations
that have different selection coefficients, s. Stronger
1.0
selection leads to faster evolution.
s = 0.05
Frequency of A2
0.8
0.6
s = 0.02
0.4
0.2
0
s = 0.01
100
200
300
Generation
400
500
The number s is called the selection coefficient. It is a natural measure of the
strength of selection that favors the beneficial allele. For the moment we are
assuming selection coefficients are constant in time and that the heterozygote’s
fitness is intermediate between the two homozygotes’, but we will see shortly
what happens when those assumptions are not met. Note that selection coefficients depend on which genotype is chosen to be the fitness reference. In the current situation, it is most convenient to use the A1 A1 homozygotes as the standard,
but later in this chapter we will use other genotypes as the reference.
It is important
to remember
that a selection
coefficient is not a fixed property
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in the peppered
moth did during 04-29-2022
the Industrial Revolution (Figure 5.1). Selection
coefficients also depend on what other alleles are present in the genome. Recall
from Chapter 4 that epistasis is the situation when the effects of alleles at different loci (or different sites within the same locus) interact. As a result, mutations
that were deleterious in the past can be beneficial now because of changes that
happened elsewhere in the genome. Many plants have toxic compounds that discourage insects from feeding on them. When an insect evolves to feed on them,
it must acquire mutations to detoxify the compounds as well as mutations that
attract it to the plants. Neither kind of mutation is beneficial without the other.
Remarkably, recent advances in technology now make it possible to reconstruct the ancestral forms of proteins and make changes in them to explore the
effects that different mutations would have had in the past. Heat shock protein
90 (Hsp90) is an ancient protein found across the eukaryotes. In the billion years
during which it evolved in the lineage leading to baker’s yeast (Saccharomyces
cerevisiae), there were 98 changes to its amino acid sequence. Joseph Thornton
and colleagues [41] asked if those changes could have been fixed in any possible order, which would mean a vast number of possible evolutionary trajectories. Using phylogenetic analysis to estimate the ancestral DNA sequence of the
Hsp90 gene, they synthesized the ancestral protein and the variations of that
protein that might have appeared later in its evolution. By measuring the functional properties of these proteins, they found that many of the changes only
became beneficial when other changes had already evolved. Going forward in
time, the options open to adaptation can depend on the evolutionary path that
was taken in the past.
The rate of evolution
We can predict the course of evolution if we know the current state of the population and the strength of selection. We will use p to represent the frequency
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5.3 Positive Selection and the Spread of Beneficial Mutations 115
of the A2 allele, and so the frequency of the A1 allele is 1 – p. The evolutionary
change per generation is measured by the change in the frequency of A 2 from
the beginning of the current generation to the beginning of the next. Using Δp to
represent that change, Box 5.1 shows that
Δp ≈ s p (1 – p) (5.3)
Equation 5.3 is beautiful in its simplicity, and it carries important messages.
On the left side is Δp, which is the rate at which the allele frequency evolves. The
right side shows that the rate is the product of two quantities. The first is the
selection coefficient s, which measures the strength of selection. When s = 0, there
is no selection acting, Δp = 0, and there is no evolution. The second quantity on
the right side of Equation 5.3 is p(1 – p), which is a natural measure of genetic
variation. If A2 is absent from the population, then p = 0, while if A1 is absent,
then (1 – p) = 0. In either case, there is no genetic variation at this locus. Consistent
with that fact, genetic variation as measured by p(1 – p) equals 0. The opposite
situation is when variation is maximized, which happens when p = (1 – p) = 0.5
(FIGURE 5.7).
The key conclusion is this: the rate of evolution is proportional to the strength of
selection and the amount of genetic variation. If either of those two ingredients is
missing, there can be no evolution by selection.
Knowing Δp tells us what the population will look like in the next generation.
The frequency of A2 in the next generation is equal to its current frequency plus
the evolutionary change. Using pʹ to represent the frequency of A2 at the start of
generation 2,
p′ = p + Δp (5.4)
Now let’s look further into the future. We can take the frequency of A2 at the
start of generation 2 and substitute that number for p on the right side of Equations 5.3 and 5.4 to find the allele frequency at the start of generation 3. Repeating
that process let us trace the trajectories of the allele frequency through time (Figure
5.6). The trajectories are S shaped. They change slowly when p is near 0 or near 1
p = 0.95
Low variation
A1A1
A1A1
A1A1
A1 A2
A1A1
A1A1
A1A1
A1A1
A1 A2
A2 A2
A2 A2
A2 A2
A2 A2
A1A1
A1 A2
A2 A2
A2 A2
A1 A2
A1 A2
A1A1
A1A1
A1 A2
A2 A2
A2 A2
A1A1
A1A1
A1A1
A2 A2
A1A1
A1 A2
A1A1
A1 A2
A1 A2
A2 A2
A2 A2
A2 A2
A2 A2
A1 A2
A2 A2
A1 A2
A2 A2
A1A1
A1A1
A1 A2
A2 A 2
A1A1
A1A1
p = 0.05
Low variation
A1 A2
A1A1
A1A1
greatest when allele frequencies are intermediate. There is little variation when the
frequency p of allele A2 is near 0 or near 1.
Variation is maximized when p = 0.5.
p = 0.5
High variation
A1A1
A1A1
FIGURE 5.7 The amount of genetic
variation at a locus depends on the
allele frequencies. Genetic variation is
A2 A2
A2 A2
A1 A2
A2 A2
A2 A2
A2 A2
A2 A2
A2 A2
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Chapter 5
(A)
1.0
The Genetical Theory of Natural Selection
Stronger selection
Faster fixation
(B)
1.0
s = 0.05
s = 0.02
0.8
Frequency of A2
Frequency of A2
0.8
0.6
88 generations
0.4
0.6
220 generations
0.4
0.2
0.2
0
Weaker selection
Slower fixation
100
200
300
Generation
400
500
0
100
200
300
Generation
400
500
FIGURE 5.8 The time needed for a beneficial allele to spread through
a population is inversely proportional to the strength of selection. (A) With
s = 0.05, the time needed will be about 4/s = 80 generations. The exact time
taken is a bit longer: 88 generations. (B) With s = 0.02, the time needed will be
about 4/s = 200 generations. The exact time needed is 220 generations.
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05_EVOL5E_05.indd 116
and change most rapidly when p is near 0.5. Although the strength of selection is
constant, the genetic variation is not: variation is small when either allele is rare
and is large when the alleles are about equally frequent (see Figure 5.7).
When a beneficial allele spreads by selection, the final outcome is that it
becomes fixed. That is, it reaches a frequency of 1, and the other allele is eliminated. The conclusion is that positive selection ultimately eliminates genetic
variation. That means other evolutionary factors must be responsible for maintaining all the genetic variation that we see in nature. Mutation is one factor, and
we will see shortly that there are also others.
A beneficial allele spreads through a population more quickly if it is more
strongly selected. There is a simple rule of thumb that tells us the time needed
for an allele to spread most of the way through the population when heterozygotes have intermediate fitness. A beneficial allele will increase in frequency from
10% to 90% in roughly 4/s generations (FIGURE 5.8). For example, if A 2 increases
fitness by 0.1%, then s = 0.001, and it will take about 4000 generations for A2 to
spread through the population. This shows that a minute fitness advantage will
cause large evolutionary change in a geologically short period of time. If selection is 100 times stronger, then the beneficial allele has a 10% fitness advantage
and s = 0.1. The allele frequency changes 100 times faster, and the favorable
allele spreads through most of the population in only about 40 generations. To
show that there is a fitness difference of 10% would require measuring fitness on
hundreds of individuals. Yet even a difference of that size can cause evolution
fast enough to be directly observed. For the peppered moth, with a generation
time of 1 year, a noticeable change in the population took just a few years. For a
bacterium, whose generation time might be measured in hours, that change can
take just a few days.
The rate at which positive selection causes an allele frequency to evolve also
depends on dominance. An allele is dominant if it causes the same phenotypic
effect when heterozygous as when homozygous. (Dominance does not refer to
an allele’s frequency in a population.) Up to this point we have been discussing
alleles that do not have dominance: the heterozygote’s fitness has been intermediate between the fitnesses of the two homozygotes. If allele A2 is dominant and
beneficial, it will initially spread even more quickly than what we saw previously
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5.3 Positive Selection and the Spread of Beneficial Mutations 117
FIGURE 5.9 Dominance affects the
evolutionary trajectories of beneficial
alleles. In these three cases, the fitness of
Fitness
Dominant
A1A1
the A2 A2 homozygote is 1.01 relative to that
of the A1 A1 homozygote. The three cases
differ only in the relative fitness of the
heterozygote.
A1 A2 A2 A2
1.0
0.4
Recessive
A1A1 A1 A2 A2 A2
0.2
0
200
400
Fitness
0.6
No dominance
Fitness
Frequency of A2
0.8
A1A1
A1 A2 A2 A2
600
800
Generation
1000
1200
(FIGURE 5.9). When A 2 is rare (as when it first appears by mutation), almost all
copies of it are in heterozygotes. That fact follows from the Hardy-Weinberg
ratios. For example, with an allele frequency of 0.01, the frequency of heterozygotes is 2 × 0.01 × 0.99 = 0.0198, while homozygotes are almost 200 times
more rare: (0.01) 2 = 0.0001. A dominant beneficial mutation spreads more rapidly
Futuyma Kirkpatrick Evolution, 5e
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situation reverses, however, when allele A 2 is common. Now
Evolution5e_05.09.ai
the low-fitness A1A1 homozygotes are rare, and almost all individuals have high
fitness. Because there is so little variation in fitness, selection has little power to
increase the frequency of A 2 further, and the rate of evolution slows dramatically.
The opposite situation occurs when the beneficial A 2 allele is recessive. In that
case, when A2 is rare, its frequency increases very slowly: only the exceedingly
rare A 2 A 2 homozygotes enjoy the fitness advantage. But when the allele is common, selection is now much more effective than it is for a common dominant
allele (Figure 5.9).
Thus far, we have focused on positive selection favoring the spread of a beneficial mutation. The same logic and same equations (Equations 5.3 and 5.4)
also apply to deleterious mutations (those that decrease fitness). In this case, the
selection coefficient s is negative, and the change in allele frequency Δp is negative (from Equation 5.3). Many genetic diseases in humans are caused by mutations that are nearly or completely recessive. Because they are at low frequencies,
almost all copies are in heterozygotes that have fitness close or equal to that of
individuals who do not carry the mutations. Selection is therefore very ineffective
at removing these disease-causing mutations from the population.
We’ve now seen that if we know the fitnesses of the genotypes, we can predict
how many generations it takes for a beneficial mutation to spread. We can turn
the tables around by asking, If we know how many generations it took for an
allele to spread, how strong was selection? This was the strategy used by J. B. S.
Haldane to estimate that the melanic allele of the peppered moth had a selection
coefficient of s = 0.5.
The peppered moth is not the only example of rapid evolution in response to
intense selection. Many other animals have evolved melanic forms in polluted
urban environments—so many that the phenomenon has a name: industrial melanism [32]. Other cases of strong natural selection and rapid evolutionary change
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include bacteria evolving resistance to antibiotics, insects evolving resistance to
pesticides, and plants evolving resistance to herbicides. You may have noticed
a theme here: in these examples, selection results from a change to the environment caused by humans. With antibiotic resistance in bacteria, the change was
intentional (when we administered the drugs), while with industrial melanism
it was not. But whether we intend to or not, our activity is rapidly changing
the environment of many, in fact probably most, organisms on the planet. This
is causing strong selection and rapid evolution in a wide range of species [35].
While adaptation to changes to the environment caused by humans may help
some species avoid extinction (see Chapter 7), most are likely to become extinct
(see Chapter 17).
Many genes in humans have also evolved in response to strong natural selection. When humans domesticated cattle, cows’ milk suddenly offered a rich
source of energy that previously had been available only to infants nursing on
their mothers. Milk contains a sugar called lactose. Children and other young
mammals derive energy from lactose by digesting it with the enzyme lactase. In
our ancestors, lactase was produced during infancy to digest the mothers’ milk,
but it was not produced in adults, because there was no lactose in their diet.
Things changed with the domestication of livestock in Africa, the Middle East,
and Europe starting about 10,000 years ago. Suddenly, natural selection favored
lactase in adults so they could digest the milk of cows and other domesticated
animals. This led to the spread of mutations that cause lactase persistence, in which
lactase is produced throughout life rather than only being expressed in infants.
Sarah Tishkoff and collaborators [44, 38, 11] discovered that at least five mutations were independently selected in different populations—a striking example
FIGURE 5.10 A map shows the
frequency of lactase persistence.
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0.8–0.9
0.6–0.7
0.4–0.5
0.2–0.3
0.0–0.1
© Thierry PRAT/
Sygma/Getty Images
At least five different mutations
spread in different regions where
cattle were domesticated in Africa,
the Middle East, and Europe [38].
(After C. J. E. Ingram et al. 2009.
Hum Genet 124: 579–591.)
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5.3 Positive Selection and the Spread of Beneficial Mutations 119
FIGURE 5.11
Courtesy of Mark Kirkpatrick
The shaded portion of
each pie diagram
shows the frequency
of the SLC24A5
allele for light skin color.
A nonsynonymous mutation
at the locus SLC24A5 contributes to light
skin color in European and West Asian
populations. It spread by strong positive
selection when humans colonized the Middle
East from Africa starting about 100,000 years
ago. (Inset) The arm of this book’s second
author just after the removal of a basal cell
carcinoma. Increased risk of skin cancer is a
negative side effect of light skin. (After G. Coop
et al. 2009. PLOS Genet 5: e1000500.)
of parallel evolution, the situation in which the same trait evolves independently
in different populations or species. They used patterns of genetic variation to
estimate the selection coefficients that favored these mutations and found that
they were very large, perhaps s = 0.05 or even larger. In northern Europe today,
about 98% of adults show lactase persistence (FIGURE 5.10). The remaining 2% of
Futuyma
Kirkpatrick Evolution,
5e intolerant: they do not produce the enzyme as adults
the population
are lactose
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and so have trouble
digesting milk and cheese. In parts of the world where dairy
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products have rarely been consumed, lactase persistence is uncommon (e.g., less
than 10% in Southeast Asia).
Light skin color is a second example of strong natural selection causing parallel adaptation in humans. The dark skin of people living in tropical regions is an
adaptation that protects them from damaging effects of strong ultraviolet (UV)
light [24]. When modern humans spread out of Africa roughly 70,000 years ago
(see Chapter 20), they colonized regions with reduced sunlight, which generated
new selective pressures. The synthesis of vitamin D, which is critical to calcium
metabolism, requires UV. Mutations that decrease skin pigmentation likely had
a selective advantage in the north because they enhanced vitamin D synthesis.
Mutations at several loci that lighten the color of the skin appeared and spread to
high frequency under strong selection (FIGURE 5.11). Different alleles at different
loci were selected in different populations. For example, the alleles that cause
light skin color in Asia are different from those that cause it in Europe. It is no
accident that human populations far from the equator have very light skin: it is
an adaptation in their habitats. But like many adaptations, light skin comes with
a cost. The increased pigmentation in African skin provides an increased sun
protection factor (SPF) of 10 compared with light skin. People with light skin
suffer higher rates of skin cancer.
Chance and adaptation:
The probability that a beneficial mutation spreads
Our discussion so far might lead you to think that if a beneficial mutation appears
in a population, it is certain to spread by natural selection and become fixed. But
that is not so: chance also plays a role. Consider an adult who is heterozygous
for a mutation that increases the probability of survival. If he or she has two
offspring, there is a 50% chance that the mutation will not be passed to the first
offspring and a 50% chance that it won’t be passed to the second offspring. No
matter how large a survival benefit the mutation gives, there is a 25% chance that
it will leave no copies of itself to the next generation. This example shows how
an allele can be lost through the random segregation of alleles during meiosis.
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Chapter 5
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Other factors also contribute to the chance that the allele will be lost. Even if it
increases survival on average, any particular individual who carries the allele
might not survive.
This example shows just one aspect of an important evolutionary process
called random genetic drift. As you will see in Chapter 6, drift is particularly
important in small populations, but it plays a role in very large ones as well.
When an allele first appears in a population by mutation, it is represented by
only a single copy. It may be lost by chance, either then or in a later generation
while it is still rare. If it does survive, it will spread to 100% frequency, and we
then say that the allele has reached fixation. Population genetic theory shows
that the probability that a single copy of a new beneficial mutation will become
fixed in the population is
Pr(fixation) = 2s(5.5)
where s is the selection coefficient that gives the relative fitness advantage of the
mutant heterozygote relative to the homozygote with the original allele.1
To clarify these ideas, consider a situation in which A1 A1 individuals again
have a relative fitness of w11 = 1. A new mutation A2 appears that gives heterozygotes a fitness w12 = 1.01, and so s = 0.01. Beneficial mutations that increase
fitness this much are rare—most have even smaller effects. Nevertheless, the
mutation has only a 2% chance of becoming established. The conclusion is that
even an allele with a large fitness advantage has an overwhelming chance of
being lost by chance when it first appears. If it is lucky enough to survive the
first few generations, however, then it is much more likely to survive and spread
throughout the population.
The conclusion is that even when a mutation increases fitness, it is not certain
that natural selection will cause it to spread to fixation. For a type of mutation
that recurs again and again, chance will have little impact in the long run because
eventually one of the mutations will survive and spread. But some kinds of
mutations are exceedingly rare and may occur only once in very long periods
of evolutionary time. In those cases, the chance loss of beneficial mutations is a
major limitation to adaptation.
5.4 Natural Selection Has Side Effects
Learning objectives
■ Explain what a genetic correlation is.
■ Describe how linkage disequilibrium causes alleles to hitchhike.
Often when natural selection acts on a trait (or a gene), it causes other traits (or
genes) to also evolve. These side effects result from genetic correlations, which
occur when two traits tend to be inherited together. One cause of genetic correlations is pleiotropy (see Chapter 4). If you are taller than average, you are also
likely to have feet that are larger than average. Height and foot size are genetically correlated because alleles that make individuals large for one trait tend also
to make them large for the other.
You can see immediately how this kind of genetic correlation will cause evolutionary side effects. Imagine, for example, that natural selection favors shorter
individuals. As alleles for short height spread, height will decrease—and so will
foot size, which might or might not be advantageous. Early in the evolution
of mammals, selection fixed alleles that enabled females to nurse their young.
Among them were alleles for nipples that are used to deliver the milk. Those
1
Equation 5.5 is an approximation that is very accurate when s is smaller than 0.1 and the size of
the population is much larger than 1/s individuals. Chapter 7 discusses what happens when the
population size is smaller than that.
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5.4 Natural Selection Has Side Effects 121
alleles, however, are also expressed in males. Nipples
in males evolved as a pleiotropic side effect of natural
selection acting on females.
An allele that increases fitness through its effect
on one trait sometimes decreases fitness because
of its effect on another trait. We then say there is an
evolutionary trade-off. When there is a trade-off, natural selection favors the allele that has the highest
fitness overall. As that allele spreads, it will increase
some fitness components (e.g., early reproduction) but
have negative effects on others (e.g., survival). Recall
that the alleles for light skin color that are common in
northern populations of humans are beneficial (they
increase vitamin D production) but also have trade-offs
(they make skin cancer more likely). You will see in
Chapter 14 that trade-offs are an important cause of
senescence in humans and other species.
Soay sheep are an ancient breed that lives wild on a
remote island off the coast of Scotland. Most males have
large, curled horns that are important to their mating
success. About 13% of males, though, develop tiny vestigial horns (FIGURE 5.12). This variation results from
polymorphism at a single locus. Many of the males that
are homozygous for an allele called HoP grow vestigial
horns, while heterozygotes and homozygotes for the
Ho+ allele grow normal horns. These alleles have strong
pleiotropic effects on fitness components [26]. Vestigial
horns decrease mating success but increase survival. As
a result of this trade-off, heterozygous males have the
highest overall fitness. We will explore the evolutionary
outcome of this situation shortly.
Hitchhiking: When one allele goes for a ride
with another
Normal horns
Ho+Ho+
1.3
Mating success
1.0
0.7
0.4
0.1
Survival
3.0
Vestigial horns
HoPHoP
2.5
2.0
1.5
1.0
Fitness
1.2
Both photos from S. E. Johnston et al. 2013.
Nature 502: 93–95
1.1
1.0
0.9
Ho+Ho+
Ho+HoP
HoPHoP
FIGURE 5.12 Genetic trade-offs between survival and mating
success maintain a dramatic polymorphism in the horns in Soay
sheep. Males homozygous for the Ho+ allele have normal horns. They
have high mating success but low survival. Males homozygous for the
Futuyma
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HoP allele
have vestigial
horns.
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overall fitness. (From S. E. Johnston et al. 2013. Nature 502: 93–95.)
You saw in Chapter 4 that an allele at one locus is sometimes found together with an allele at a second locus
more often than expected by chance. When that happens, we say that the two loci
are in linkage disequilibrium. Recall from Chapter 4 that linkage disequilibrium is a
statistical property of the population, like an allele frequency, and that it can occur
whether or not the loci are physically linked.
An important consequence of linkage disequilibrium is hitchhiking. This happens when an allele at one locus spreads by natural selection acting on a second
locus that is in linkage disequilibrium with the first. FIGURE 5.13 shows a hypothetical population of the grove snail, which has separate loci that affect color and
banding pattern on its shell. There is linkage disequilibrium in this population.
The allele for an unbanded shell is correlated with the allele for a pink shell: all of
the yellow shells are banded, but only half of the pink shells are. If all the banded
snails die (e.g., because birds prey on them), all the survivors will be pink as well
as unbanded. Thus the frequencies of the shell color alleles changed because of
selection on the banding alleles (see Figure 3.13). While it sounds paradoxical,
one implication of hitchhiking is that an allele that does not affect fitness can
spread by natural selection.
Population genetic theory quantifies how much an allele’s frequency will
change by hitchhiking. The simplest situation is where only locus A is under
selection. Allele A 2 has a selection coefficient sA such that the relative fitnesses
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122
Chapter 5
The Genetical Theory of Natural Selection
Only half the pink
shells are banded
Striped and unstriped
Only unstriped
David McIntyre
Selection
on color
All yellow shells
are banded
FIGURE 5.13 An allele can spread by hitchhiking if it is associated with another
allele that is a target of selection. In this population of the grove snail, alleles for
banding of the shell are in linkage disequilibrium with alleles for shell color. Selection
against banded snails also eliminates yellow snails.
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are w11 = 1, w12 = (1 + sA ), and w22 = (1 + 2sA ). The two alleles at locus B have no
Evolution5e_05.13.ai 05-11-2022
fitness effects. The change in the frequency of allele B2 in one generation is then
Δp B ≈ sA D(5.6)
where D is the linkage disequilibrium between allele A 2 and allele B2. (Recall
from Chapter 4 that D = PAB – pA pB , where PAB is the frequency of gametes that
carry both alleles A 2 and B2 , pA is the frequency of allele A 2 , and pB is the frequency of allele B2.)
Equation 5.6 resembles Equation 5.3. The rate of evolution at locus B depends
on two quantities. The first is the strength of selection acting on locus A, which is
measured by the selection coefficient sA. The second quantity is the linkage disequilibrium, D, which plays a role analogous to the term p(1 – p) that represents genetic
variation in Equation 5.3. Equation 5.6 tells us that hitchhiking will happen at locus
B only if it is in disequilibrium with locus A (i.e., D does not equal 0).2
Hitchhiking is responsible for the evolution of genes that themselves do not
impact survival or fecundity but that do have other effects. In some environments, mutations spread in bacterial populations that drastically increase mutation rates throughout the genome. They do so because they generate mutations
at other loci that are beneficial, and as those mutations spread, the mutator allele
hitchhikes along with them.
Evolutionary biologists exploit hitchhiking to find genes that have recently
evolved by positive selection. When a beneficial mutation first appears, it is in
perfect linkage disequilibrium with all the other alleles on the same chromosome
(FIGURE 5.14). As the mutation spreads, recombination breaks down the disequilibrium. The breakdown is most rapid between the selected locus and distant sites
on the chromosome (because there is more recombination between the selected
site and the distant sites). Sites close to the selected locus have little chance to
recombine before the mutation becomes fixed. As a result, those sites also become
2
Equation 5.6, like Equation 5.3, is an approximation that is very accurate for selection coefficients
less than 0.1.
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5.4 Natural Selection Has Side Effects 123
Beneficial mutation appears
Neutral
alleles
Beneficial
mutation
Beneficial mutation spreading
Beneficial mutation fixed
Neutral mutations
hitchiking
FIGURE 5.14
When a beneficial mutation spreads to
fixation, hitchhiking eliminates polymorphism at selectively
neutral sites nearby on the chromosome. The beneficial muta-
tion first appears on a chromosome that has selectively neutral
variants in its DNA sequence at two sites nearby. As the mutation
spreads to higher frequency, the neutral variants hitchhike with it
Neutral mutations
fixed
to higher frequency. When the mutation becomes fixed, genetic
variation is eliminated in the region nearby. Regions of the chromosome farther from the beneficial mutation retain variation
because recombination joins together chromosomes that carry
the beneficial mutation with chromosomes that carry different
neutral variants.
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Heterozygosity
fixed for some alleles that were on the original chromosome
where the mutation appeared. All variation at those sites is
eliminated. Polymorphism remains, however, at regions
farther away on the chromosome. This distinctive pattern,
which is called a selective sweep, is one of the telltale signs
used by evolutionary geneticists to find evidence of recent
adaptation [16].
This pattern is exactly what we see in corn (maize; Zea
mays mays) (FIGURE 5.15). Starting a few thousand years
ago, this crop was domesticated in Central America from
0.5
a wild plant called teosinte. Early farmers bred the teosinte plants that were most productive and easiest to harvest, which generated strong selection on several traits.
Teosinte
Among them was the plant’s growth form. Teosinte is
bushy, while corn grows as a single tall stalk. This major
difference is largely the result of a single mutation near
Selective
Corn
sweep
a gene called tb1 that ancient farmers unconsciously
in corn
selected and that is now fixed in corn [48]. The selective sweep seen in the figure shows where in the corn’s
genome that mutation lies.
0
The pattern of polymorphism along the chromosomes
we see in Figures 5.14 and 5.15 results when a mutation
Exon 1 Intron
Exon 2
is favored by selection when it first appears. In some sittb1
Locus
uations, an allele that is present in the population is ini500 bp
tially not favored but then suddenly becomes beneficial
FIGURE 5.15 A selective sweep that occurred during the
when conditions change. In this case, we say selection is
domestication of corn. The ancestor of corn, which looked
acting on standing genetic variation. The population can
very much like the living teosinte plant, was short and bushy.
evolve immediately, without waiting for a new mutation
Domestication in Central America roughly 10,000 thousand years
to occur. That is what happened in the selection experiment
ago selected for corn plants that branched less. This favored a
on chickens we saw earlier (Figure 5.3). Another example
mutation in a region of chromosome that regulates expression
comes from the ACKR1 locus in humans, which is one of
of the gene tb1, which controls shoot branching. As the mutation
the genes that has evolved to give resistance to malaria in
spread, heterozygosity nearby on the chromosome was drastiseveral populations in the tropics. In Africa, when malaria
cally reduced. Other evidence pinpoints the mutation in a nonfirst appeared about 40,000 years ago, an allele that was
coding region about 60 thousand base pairs upstream (to the left)
Futuyma Kirkpatrick
Evolution,
5e
of tb1. (After
R. L. Wang
et al. 1999. Nature 398: 236–239.)
already present suddenly gained a very large fitness
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Chapter 5
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(A) Before selection
(B) Selection acting
(C) Beneficial alleles fixed
1
1
1
Neutral
alleles
FIGURE 5.16
1
1
1
2
2
2
2
2
2
3
3
3
3
3
Standing
variation
Beneficial alleles
increasing by selection
Selection on standing genetic variation.
Changes in the environment can suddenly give a fitness
advantage to an allele that is already present in a population—
a situation called selection on standing genetic variation.
(A) Before selection acts, there are three copies of the allele
(yellow, orange, and red) that have already recombined onto
Polymorphism remains
at nearby sites
chromosomes with different neutral alleles at other sites.
(B) When selection starts acting, the neutral alleles linked
to each of them hitchhike to higher frequency. (C) When the
beneficial alleles become fixed, selectively neutral sites
nearby remain polymorphic (compare with Figure 5.14).
Futuyma Kirkpatrick Evolution, 5e advantage of s = 0.04 and swept to fixation [34]. In contrast, there was a lag of
OUP/SInauer/Troutt Visual Services many generations before mutations giving lactose tolerance appeared in humans
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after cattle were domesticated (Figure 5.10).
Selection that acts on standing genetic variation leaves a telltale signal in DNA
sequences. Before the selection pressure changes, different copies of the mutation
that will be favored have already recombined onto chromosomes with different
combinations of alleles at other sites. As a result, when the favored allele reaches
fixation, only a very small region of the chromosome around the selected site
shows reduced polymorphism (FIGURE 5.16). Evolutionary geneticists use the
contrast between the patterns seen in Figures 5.14 and 5.16 to determine whether
or not selection acted on standing genetic variation in loci that have recently
experienced adaptive evolution.
Many other methods are also used to find genes that recently experienced or
are under ongoing selection. A key to many of those methods is that they let us
distinguish the action of selection from random genetic drift. We will therefore
put off exploring those approaches to Chapter 6, where we discuss random factors and how they interact with selection.
5.5 Balancing Selection Preserves Genetic Variation
Learning objective
■ Understand how overdominance and frequency-dependent selection
can maintain genetic variation, and give examples of each.
Chapter 4 introduced the β hemoglobin locus. Most humans are homozygous for
the A allele. Populations in some regions of Africa and Asia also have appreciable
frequencies of the S allele. Individuals who are SS homozygotes suffer from a
debilitating condition called sickle cell anemia, which drastically decreases survival. But the S allele has not been eliminated by natural selection. Why?
The β hemoglobin locus is under balancing selection, which is selection that
maintains genetic variation within a population. Balancing selection is fundamentally different from selection on beneficial and deleterious alleles, which
eliminates genetic variation. It is rare in the genomes of most species. To understand how selection can preserve variation, we will now explore the biology of
the β hemoglobin locus further.
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5.5 Balancing Selection Preserves Genetic Variation 125
Frequency of S allele
Incidence of malaria
FIGURE 5.17 An allele that protects
humans against malaria is most
frequent where the disease is common.
The frequency of the hemoglobin S allele
in Africa is highly correlated with the incidence of malaria. (After F. B. Piel et al.
2010. Nat Comm 1: 104.)
>15%
7–14%
0.6–6%
<0.5%
High
Moderate
Low
Malaria free
Overdominance
FIGURE 5.17 shows that the S allele has high frequency in regions where malaria
is common. Malaria is a disease caused by a protozoan parasite (Plasmodium)
that is transmitted by mosquitoes in tropical regions around the world. There
are over 200 million cases and 400,000 deaths each year from malaria, making it
Futuyma Kirkpatrick Evolution, 5e
one of the most
deadly
infectious diseases worldwide.
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Alleles at several
loci make people partially resistant to malaria. One of those
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is the β hemoglobin locus that we discussed in Chapter 4. Recall that in central
Africa, the frequencies of the three genotypes were not in Hardy-Weinberg equilibrium. The reason was that the AA homozygotes survived the parasite poorly,
while SS homozygotes had severe effects of sickle cell anemia. Heterozygotes
survived best. From the data in Chapter 4 and using the heterozygotes as the
fitness reference, we find the relative fitnesses of the three genotypes:
Genotype
AA
AS
SS
Relative survival
0.79
1
0.17
This is the most famous example of overdominance, which occurs when the
heterozygote has a greater value of some trait (survival, in this case) than both
homozygotes. Another example comes from the Soay sheep discussed earlier
(Figure 5.12).
Overdominance in fitness leads to a kind of evolutionary outcome that we
have not yet discussed. The population evolves to a stable polymorphic equilibrium, which means that both alleles are maintained. Given the relative fitnesses of
the AA and SS homozygotes (and again using the AS heterozygote as the fitness
reference), a mathematical model like that in Box 5.1 tells us that any population
that has genetic variation will evolve to the same final allele frequency (FIGURE
5.18). Ultimately the population reaches an equilibrium at which the frequency
of the S allele is
1 – wAA
pˆ =
(5.7)
2 – wAA – wSS
This formula holds for any values of wAA and wSS that lie between 0 and 1.
We can use that formula and the data on survivorship to predict how the allele
frequencies will evolve. Plugging into Equation 5.7 the values for relative fitness
shown above (wAA = 0.79, wSS = 0.17), we predict that the equilibrium frequency
of the S allele will be p̂ = 0.20. That agrees reasonably well with the frequency of
the S allele in West Africa near the equator (Figure 5.17).
Natural selection acting on traits that affect survival causes deaths. Given the
relative fitnesses shown above, the average survival rate will be 17% lower than
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The Genetical Theory of Natural Selection
Positive
Frequency of A2
1.0
selection here favors allele A 2, which increases
fitness by s = 0.05 (the relative fitnesses are
w 11 = 1, w 12 = 1.05, and w 22 = 1.1). With overdominance, the A 1 A 2 heterozygote has highest
fitness. Starting at any allele frequency, A 2
evolves to a polymorphic equilibrium. Selection
preserves genetic variation. In this example,
the relative fitnesses are w 11 = 0.9, w 12 = 1, and
w 22 = 0.8, and the final frequency of allele A 2
is 0.33 (dashed line). With underdominance,
the A 1 A 2 heterozygote has the lowest fitness.
Populations evolve to different final allele
frequencies depending on where they start.
In this example, the relative fitnesses are w 11
= 1.1, w 12 = 1, and w 22 = 1.2. If allele A 2 starts at
a frequency below 0.33 (dashed line), it is lost,
but if it starts above that frequency, it spreads
to fixation.
0.8
0.6
Beneficial allele
fixed
0.4
0.2
0
w11 w12 w22
20
40
60
Generation
80
100
Overdominance
1.0
Frequency of A2
FIGURE 5.18 Allele frequency trajectories
under three kinds of selection. Positive
Fitness
Chapter 5
Fitness
126
0.8
0.6
0.4
0.2
0
w11 w12 w22
Polymorphic
equilibrium
20
40
60
Generation
80
100
Underdominance
Fitness
Frequency of A2
1.0
w11 w12 w22
0.8
Common
allele fixed
0.6
0.4
0.2
0
20
40
60
Generation
80
100
a hypothetical population that consists only of the high-fitness heterozygotes.
When overdominance is caused by selection acting on survival, there is a mortality cost paid by the low-fitness homozygotes.
The evolutionary genetics of sickle cell anemia have three general messages
Futuyma Kirkpatrick Evolution, 5e
about
evolution: overdominance
maintains genetic variation, population genetic
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makes testable
predictions about evolution, and humans (like all other
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species) are still evolving by natural selection.
Other kinds of balancing selection
Overdominance is one form of balancing selection. A second form can result from
frequency-dependent selection, which occurs when the relative fitnesses of alleles
change depending on their own frequencies. In some cases, an allele gets a fitness
advantage when it is rare, a situation called negative frequency dependence. This can
maintain polymorphism because when one allele becomes rare, selection causes its
frequency to increase. The scale-eating cichlid in Lake Tanganyika has a remarkable polymorphism: in some fish the jaws open to the right, and in others they
open to the left (FIGURE 5.19). Right-jawed individuals swim along the left side
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5.5 Balancing Selection Preserves Genetic Variation 127
(A)
(B)
Both photos© Ad Konings/
www.cichlidpress.com
Jaws open to right
Prey species
(Interchromis looki)
Jaws open to left
(D)
Frequency of left-jawed individuals
“Right-jawed” Perissodus
attack prey from the
left rear side
“Left-jawed” Perissodus
attack prey from the
right rear side
FIGURE 5.19
Negative frequency-dependent selection
maintains a remarkable polymorphism in a fish that feeds
on the scales of other fishes. (A) The scale-eater and one
of the species that it preys on. (B) X-rays looking down at two
heads of scale-eaters show they have jaws that open either
to the right or the left. (C) A sketch showing how left-jawed
and right-jawed fish attack their prey. (D) Negative frequencydependent selection maintained both morphs in the population over 10 years. (B after A. Indermaur et al. 2018. Evolution
72: 1963–1969; C from D. R. Rubenstein. 2023. Animal Behavior,
12th ed. Sunderland, MA: Oxford University Press/Sinauer;
D after M. Hori. 1993. Science 260: 216–219.)
1.0
0.5
0
Both photos from A. Indermaur et al. 2018. Evolution 72: 1962–1969
Scale-eating cichlid
(Perissodus microlepis)
(C)
1980
1982
1984
1986
Sample year
1988
1990
of a potential victim, then suddenly scrape off scales from that side of its body.
Experiments under natural conditions show that when either the right-jawed or
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left-jawed fish are much more common than fish of the other morph, their attacks
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become less successful,
probably because the prey learn better how to escape. The
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result is negative frequency-dependent selection that maintains left-jawed and
right-jawed individuals in the population at equal frequencies [21, 22].
A third form of balancing selection results when different genotypes specialize
on different ecological niches. In effect, each genotype is partly shielded from
competition with other genotypes, and so its population size is regulated independently. Genetic variation that is maintained this way is called multiple niche
polymorphism. Many herbivorous insects have host races that specialize on different species of host plants. The pea aphid (Acyrthosiphon pisum) has host races
adapted to different crop plants. Each host selects for different alleles at several
loci, which maintains polymorphism in the aphid [25].
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Chapter 5
The Genetical Theory of Natural Selection
FIGURE 5.20
A mosquito is
polymorphic for a chromosome
inversion in Cameroon (Africa).
Elevation
4000 m
Savannah
0m
Cameroon
SS
SI
II
Highlands
The mosquito Anopheles funestus
is one of the species that transmits
malaria. Pie diagrams show the
frequencies of the three genotypes
in villages along a highway. In the
savannah (north), the inversion
is almost entirely absent. In the
rainforest (south), the inversion is
at almost 100% frequency. In the
mountainous highlands (center), the
inversion is at intermediate frequencies. (Both maps from D. Ayala
et al. 2013. Evolution 67: 946–958.)
(B)
Courtesy of James
Gathany/Centers for
Disease Control
(A) Mosquito
Rainforest
0 50 100
200
km
300
A final type of selection that can maintain genetic variation occurs when fitnesses vary in space. This situation is similar to multiple niche polymorphism.
It can maintain different alleles in different populations, and if individuals move
between those populations, it can maintain polymorphism within the populations. FIGURE 5.20 shows a map of the frequency of a chromosome inversion
that occurs in a mosquito that lives in West Africa. The inversion is near fixation
in the south, nearly absent in the north, and present at intermediate frequencies
in the central region. Analysis of these data suggests that fitnesses vary along
a north–south axis: in the south, chromosomes with the inversion have higher
fitness than those without it, heterozygotes have highest fitness in the central
region, and chromosomes with the inversion have low fitness in the north [3].
Chapter 8 explores in detail selection that varies in space.
Perhaps surprisingly, selection that fluctuates in time does not generally maintain genetic variation. If selection favors one allele in some generations and a
different allele in others, in many cases the allele that has the highest fitness on
average will spread to fixation. Likewise, the evolutionary trade-offs discussed
earlier in the chapter do not typically maintain polymorphism. The horn polymorphism in Soay sheep is unusual. More commonly, despite trade-offs, one
allele is on average more fit than the other, and it will spread to fixation.
5.6 Selection That Favors the Spread of
Common Alleles
Learning objective
■ Explain how allele frequencies to evolve when there is underdominance or
positive frequency dependence.
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5.6 Selection That Favors the Spread of Common Alleles 129
H. argophyllus
Chromosome 12
© Bob Gibbons/Alamy Stock Photo
FIGURE 5.21
H. annuus
Chromosome 16
Inversion
Fusion
H. argophyllus
Closely related sunflowers differ in chromosome rearrangements.
Several rearrangements became fixed in Helianthus argophyllus after it speciated from
H. annuus. Pieces of chromosomes 12 and 16 fused. The piece from chromosome 16 was
also inverted. (After J. G. Barb et al. 2014. Genetics 197: 969–979.)
Balancing selection preserves genetic variation, and in most cases the population
Futuyma
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evolve to
the same
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ent picture emerges
from two kinds of selection that favor whichever allele is
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most common. Here variation is eliminated, and which allele spreads to fixation
depends on the initial allele frequency in the population. These next two kinds
of selection therefore lead to historical contingency: the outcome of evolution is
determined by where the population begins.
Underdominance: When heterozygotes suffer
The familiar sunflower is one of about 70 species of sunflowers found across
North America. They differ in many ways, for example, in their growth forms
and the habitats where they grow. Their chromosomes also show differences:
closely related species differ in the number of chromosomes they have and in
the order of genes along the chromosomes (FIGURE 5.21). These differences are
the result of chromosomal rearrangements (discussed in Chapter 4) that became
fixed during the evolution of these species.
Did these chromosomal changes become established because they increased
fitness and spread as advantageous mutations? Surprisingly, many of them
decreased fitness when they first appeared. Sunflower species can be hybridized,
which allows measurement of the fitness of individuals with different combinations of chromosomes. Heterozygotes for some chromosome rearrangements
have lower fertility than either homozygote because the chromosomes fail to
pair correctly during meiosis [28]. When a new rearrangement is still at low frequency, almost all of its copies are in these low-fitness heterozygotes. Selection
acts to eliminate a new chromosome rearrangement when it is still rare.
The situation in which heterozygotes have lowest value for a trait (e.g., fitness)
is called underdominance. As its name implies, underdominance is the opposite of
overdominance. When fitnesses are underdominant, selection eliminates rather
than preserves genetic variation. The evolutionary outcome is shown in Figure
5.18. The key feature is that if an allele’s frequency is below a threshold value,
then selection tends to drive it out of the population. However, if the allele’s frequency is above the threshold, selection favors it to increase further. The value
of the threshold is determined by the relative fitnesses of the two homozygotes.3
3
This type of threshold is called an unstable equilibrium. If the allele frequency lies exactly at this
equilibrium, it will not change, but the slightest deviation will cause the allele frequency to evolve
either to 0 or to 1. In contrast, overdominance produces a stable equilibrium toward which allele
frequencies converge.
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Chapter 5
The Genetical Theory of Natural Selection
In each pair of butterflies,
H. melpomene is shown
on top, with H. erato below.
For example, if both homozygotes have the same fitness and it is higher than that of the heterozygotes,
the threshold allele frequency is 0.5. At any other frequency, the more common allele becomes fixed.
If some chromosome rearrangements are underdominant, how did they become fixed in different species?
Since selection is clearly not the answer, something else
must be responsible. We will return to this question
when we discuss random genetic drift (see Chapter 6)
and meiotic drive (see Chapter 13).
Positive frequency-dependent selection
Courtesy of Andrew Brower
Earlier we discussed negative frequency-dependent
selection, which preserves variation by favoring an
allele when it is rare. Frequency-dependent selection
can also favor the most common allele, a situation
called positive frequency dependence. The butterfly Heliconius erato is a single species with many populations that
differ dramatically in coloration (FIGURE 5.22). This
species is distasteful, and birds that eat one of these
butterflies quickly learn to avoid other butterflies with
FIGURE 5.22 Positive frequency-dependent selection favors
the same color pattern. In field experiments, butterflies
whatever color pattern is locally most common in populations of
distasteful butterflies. The butterflies Heliconius melpomene and
were marked and released with two color patterns: the
H. erato both show extraordinary geographical variation in coloration,
local pattern and a pattern from one of the neighboring
and their colors vary in parallel. Positive frequency-dependent selecraces [33]. Butterflies with the local pattern survived
tion eliminates variation in color within populations of both species.
more than twice as well as those with the foreign color
pattern, which generates a selection coefficient of about
Futuyma Kirkpatrick Evolution, 5e
s = 0.17 at each of the three major loci that control the
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differences in color pattern among the races. Other data show that these survival
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differences result from bird predation: the birds avoid the color pattern they
know, but they attack butterflies that have unfamiliar colors. As a result, strong
selection favors whatever color is locally common. Within a population there is
typically little variation for color: positive frequency-dependent selection eliminates polymorphism. The same form of selection occurs on the closely related
Heliconius melpomene, which mimics and co-occurs with H. erato.
5.7 The Evolution of a Population’s Mean Fitness
Learning objectives
■ Explain what Fisher’s fundamental theorem says.
■ Describe the adaptive landscape, and understand how it predicts
how allele frequencies will change.
Many of our questions about evolution concern adaptations such as the feathers of
birds and the brains of humans. But we can also ask, How does fitness itself evolve?
—, is
The mean fitness of a population, which is represented by the symbol w
simply the average of the fitnesses of the individuals in it. We can easily calculate
— if we know the fitnesses and the frequencies of the three genotypes at a locus.
w
(Mean fitness can be calculated using either relative or absolute fitnesses, but in
—.) As selection causes the allele frequency
both cases we will use the symbol w
—
— follows simple
p to evolve, the mean fitness w also changes. The evolution of w
but important rules when fitnesses are constant in time and other evolutionary
factors (such as mutation) are weak relative to selection. These principles were
discovered by R. A. Fisher and Sewall Wright, two of the founders of population
genetics (see Chapter 1).
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5.7 The Evolution of a Population’s Mean Fitness 131
The fundamental theorem of natural selection and
the adaptive landscape
–­ to
Fisher showed mathematically that evolution by natural selection causes w
–
increase through time. He found that when fitness is normalized so that w
­ = 1,
the increase in mean fitness per generation is equal to the genetic variance for
fitness itself.4 (“Variance” is a specific measure of variation that is discussed in
Chapter 22.) Fisher named this principle the fundamental theorem of natural selection. Because a variance can never be negative, the important message from this
result is that natural selection causes populations to evolve so that they become
better adapted: the average survival and reproduction of individuals increase
through time.
During the spread of a beneficial allele, its frequency increases slowly when
the allele is rare, then accelerates as the allele frequency nears 0.5, and then slows
again as the allele nears fixation (see Figures 5.6 and 5.9). We can understand
that rhythm of change in terms of the fundamental theorem. When the beneficial
allele A2 is rare, almost all of the population is homozygous for the A1 allele (see
— increases
Figure 5.7). There is little variation among individuals in fitness, so w
slowly. The variance for fitness among individuals is maximized when p = 0.5,
— per generation is greatest. The variance in fitness is again
and so the change in w
— is again small.
small as A2 nears fixation, so the rate of increase in w
Fisher’s fundamental theorem leads to the question of just how much genetic
variation for fitness exists in natural populations. The answer depends on the
species, the time, and the environment, but it seems that the genetic variance in
relative fitness may often be a few percent [18], although in some cases it may
be much larger [29]. Based on the limited data available, the fundamental theorem would lead us to expect that in a constant environment the mean fitness
of species should increase by a few percent per generation. But the world is
not constant. The gains in mean fitness made by selection are also affected by
environments that change in space and time, deleterious mutations, and other
events. Increased fitness can also be offset by increased competition: a mutation
that spreads because it increases survival, for example, could lead to more competition for food and as a result decreased reproduction.
A complementary perspective on the evolution of fitness was developed by
—, against the frequency of the A
Sewall Wright. He plotted the mean fitness, w
2
allele, p. This plot, which Wright called the adaptive landscape, tells us how the
population will evolve. His key insight was that selection causes populations to
evolve uphill on the landscape (FIGURE 5.23). Wright proved mathematically that
the allele frequency will change at a rate
1
d
–
∆p = p(1 – p) ln(w)
2
dp
(5.8)
On the right side of this equation, you will recognize p(1 – p) as a measure
of genetic variation; it equals 0 when there is only one allele in the population,
that is, p = 0 or p = 1; it is maximized when the two alleles are equally common,
that is, p = (1 – p) = 0.5. The last term on the right is a derivative that is equal to
the slope of the adaptive landscape. This measures the direction and strength of
selection. When the slope is positive, selection causes A 2 to spread, and when the
slope is negative, selection causes A2 to decrease in frequency. When the slope
of the landscape is 0, selection does not favor either allele, and the population is
at equilibrium.
The adaptive landscape explains the differences in the allele frequency trajectories that we saw earlier among positive, overdominant, and underdominant selection (see Figure 5.18). With positive selection, the slope of the adaptive
landscape is always positive, and selection causes the advantageous allele to
4
More specifically, it is the “additive” genetic variance for fitness that matters. Additive genetic
variance is discussed in Chapter 7.
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Mean fitness, w
Relative fitness, w
(A) Positive
(B) Overdominance
(C) Underdominance
1.0
1.0
1.0
0.5
0.5
0.5
0
A1A1
A1A2
0
A2A2
A1A1
A1A2
0
A2A2
1.0
1.0
1.0
0.8
0.8
0.8
0.6
0.6
0.4
Frequency evolves
to peak at right
0.4
0
0.2
0.4
0.6
0.8
1
FIGURE 5.23
0
A1A2
A2A2
0.6
Evolves to
peak at middle
Evolves to peak
at left or right
0.4
0.2
0.2
A1A1
0.2
0.2 0.4 0.6 0.8
Frequency of A2
1
0
0.2
0.4
0.6
0.8
1
Wright’s adaptive landscape under three kinds of selection.
With positive selection favoring allele A2, the population evolves to the fitness peak
at p = 1. With overdominance, the population evolves to a peak that produces a stable
polymorphic equilibrium. With underdominance, there is a fitness valley, and the frequency of A2 evolves to p = 0 or p = 1 depending on whether the allele frequency is initially to the left or right of the valley.
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(Figure 5.23A). With overdominance, the landscape has a peak at an intermediate allele frequency. No matter what the initial allele frequency is, selection
pushes the population to the adaptive peak, where it reaches the polymorphic
equilibrium (Figure 5.23B). Underdominant selection produces an adaptive valley. If the allele frequency starts to the left of the low point, selection drives the
allele frequency toward 0. However, if the allele frequency starts to the right of
the valley, it evolves uphill toward 1 (Figure 5.23C). This visualization of how
selection causes gene frequencies to evolve is one of the most famous images in
evolutionary biology.
The fundamental theorem and the adaptive landscape make assumptions that
do not apply exactly to any natural populations. In many cases, though, they give
very good approximations that are useful to guide our thinking about evolution.
In other cases, the assumptions are violated in ways that make evolution behave
very differently.
A particularly important situation where the fundamental theorem and adaptive landscape do not apply is when selection is frequency‑dependent. In some
cases, this can cause the mean fitness of a population to decline [51]. FIGURE 5.24
shows a cartoon of an example. In a population of sheep, a mutation appears that
gives males horns. They have higher fitness because they can prevent other males
from mating, but the horns also decrease male survival. The mutation spreads to
fixation, causing the population to decline. Later mutations that make the horns
even larger give males even higher mating success, but they also decrease the
population size further.
Many animals and plants have traits that enhance their abilities to compete
with others. Even microbes have these kinds of nasty traits. Some bacteria secrete
toxins called bacteriocins that kill neighboring cells, giving them access to more
nutrients [49]. The bacteria that make bacteriocins also have genes that protect
them from the toxin, but both the poison and the antidote are expensive to make.
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5.8 Many Mutations Are Harmful 133
(A)
(B)
FIGURE 5.24 Frequency-dependent selection can cause
the mean fitness of a population to decline. (A) A mutation
for male horns increases mating success, and so it spreads,
but it also decreases male survival and the size of the population.
(C)
(B) A second mutation makes the horns larger, which increases mating success and causes males with the first mutation to have low
fitness. (C) Fixation of the second mutation. Later mutations that
increase horn size can cause the population size to decline further.
A benign population of microbes that does not make bacteriocins does not waste
that energy. Nevertheless, individuals that make bacteriocins become more frequent because their individual fitness is higher. As they spread, however, the
mean fitness of the population and the size of the population decline.
5.8 Many Mutations Are Harmful
Learning objective
■ Understand what a mutation-selection balance and a mutation load are.
Most mutations have no effect on fitness. A small fraction of mutations increase
fitness by making individuals more resistant to a disease, say, or better able to
digest a new food resource. But among the mutations that affect fitness, the vast
majority are deleterious (harmful). Studies of the fitness effects of mutations
suggest that deleterious mutations are at least ten times more common than
beneficial mutations (see Figure 4.20). The injection of deleterious alleles into
populations by mutation has important evolutionary consequences.
A mutation-selection balance
Deleterious mutation is an important cause of genetic disease in humans and
other organisms. The famous American musician Woody Guthrie died at the age
of 55 of Huntington’s disease. This results from a dominant mutation that causes
degeneration of the central nervous system. In the United Kingdom, the mutation is present in about 12 out of 100,000 people. More than 4000 genes have been
identified in humans that, when mutated, cause diseases such as cystic fibrosis,
color blindness, and hemophilia [2]. Selection that acts to remove deleterious
mutations from a population is called purifying selection.
Why hasn’t purifying selection eradicated deleterious mutations that cause
these diseases? The answer is that they are being constantly reintroduced. This
flow of new mutations into the population is offset by natural selection that
removes them. This situation leads to a mutation-selection balance. Here we will
consider the typical situation where the mutation is not completely recessive. We
will use the mutation-free homozygote as the fitness reference and will write the
fitness of the mutant heterozygote as (1 – s). When the input of the deleterious
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allele by mutation balances its removal by selection, the deleterious mutation
reaches an equilibrium frequency of
μ
(5.9)
pˆ ≈
s
On the right side of the equation, μ is the mutation rate, that is, the probability
that a copy of the normal allele mutates to a deleterious allele in a given generation. In the denominator is the selection coefficient s, which is the proportional
decrease in relative fitness caused by carrying a copy of the mutation.5
Equation 5.9 carries the simple and intuitive message that a deleterious mutation will be more common if it appears at a higher rate (larger μ) and has weaker
deleterious effects (smaller s). While both numbers vary tremendously among
loci and organisms, to make the ideas clear, consider the gene responsible for
Huntington’s disease. Its mutation rate is μ = 1.4 × 10-5 [15], and it occurs in
roughly 1 in 10,000 newborns [50]. By rearranging Equation 5.9, we find that the
data suggest the mutation reduces fitness by about 14%.
The mutation load
Deleterious mutations decrease survival and/or fecundity, and so they decrease
a population’s mean fitness. Surprisingly, the impact on mean fitness of a mutation is independent of whether the deleterious effect is strong or weak. Equation
5.9 shows that very harmful mutations will only persist at very low frequencies,
while mutations with milder effects will reach equilibrium at higher frequencies. The net effect is that highly deleterious and weakly deleterious mutations
decrease the population’s mean fitness by the same amount.
The mutation load, represented by L, is the proportion by which the mean
fitness of individuals in the population is reduced by deleterious mutations compared with a hypothetical population without mutations. The load caused by
deleterious mutations at a single locus is simply equal to the rate at which they
occur: L = 2μ. For example, with a typical mutation rate of μ = 10–6, the mean
fitness of the population is reduced by a very small amount: L = 0.000002.
But while mutation rates at individual loci are typically very small, eukaryotes (including humans) have a great many loci. We use U to represent the total
mutation rate across the genome for deleterious alleles. That is, U is the average
number of new deleterious mutations that are added to an individual’s genome
each generation. Recent studies suggest that U in humans is about two new
deleterious mutations per genome per generation [27]. A result is that each of us
carries hundreds of deleterious mutations at various loci scattered throughout
the genome [10, 1]. Their combined impact on mean population fitness (i.e., the
total mutational load) depends on how the effects of deleterious mutations at different loci combine to determine overall fitness, which is very difficult to estimate
accurately. If mutations have independent effects on fitness, the mutation load is
L = 1 – e–U (5.10)
where e is a mathematical constant approximately equal to 2.7.
The genome-wide mutation rate estimated for humans is about U = 2, so this
equation says that the load is L = 0.86. This implies that 86% of the maximum
possible mean fitness in humans is lost to the effects of deleterious mutations, by
either death or reduced fertility. This conclusion, however, is based on a comparison between an individual who carries the average number of mutations and a
5
Equation 5.9 is an approximation that applies when μ << s and the mutation is not completely
recessive, which is typically the case. The fitness of the mutant homozygote does not matter because
it is so rare in this case. A more complex equation applies when those conditions do not hold.
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Summary
135
hypothetical individual who carries none. But no real organism is mutation-free,
so this may be an inappropriate comparison [14].
Do deleterious mutations really have a big impact on a population’s mean
fitness in many species, including humans? The actual effect is likely much
smaller than Equation 5.10 suggests, for several reasons. When there is competition for resources such as food, the death of one individual frees up some
of those resources for others, which can increase their survival and reproduction. Further, the assumption that mutations have independent fitness effects
may not be correct [30]. Nevertheless, deleterious mutations do contribute to
senescence and genetic diseases (see Chapter 14). In humans, less than half
of eggs that are fertilized lead to a successful birth [31]. It is possible that this
high rate of mortality at the earliest stages of development is partly caused by
deleterious mutations.
What is the impact of modern health care on deleterious mutations? By saving
individuals from genetic diseases, medical intervention does allow mutations
to be passed on that would have been eliminated by natural selection earlier in
human history. But population genetic theory tells us that the frequency of these
mutations will increase slowly (because mutation rates are very small), and many
generations will pass before they become much more common. Whether the end
result will have a major impact on human health is controversial [27, 19].
SUMMARY
■ Natural selection is any consistent difference
in fitness among different phenotypes or genotypes. Evolution caused by natural selection
has been observed directly many times.
■ Fitness is the number of offspring that an
individual leaves to the next generation, or the
average number that an allele, genotype, or
phenotype leaves. Selection causes evolution
when there is a correlation between a phenotype and fitness, and also a correlation for that
phenotype between parents and offspring.
■ The rate at which a beneficial allele spreads
through a population is determined by how
strongly it is favored, measured by its selection
coefficient, and by the amount of genetic variation at that locus in the population. Even if it is
favored by selection, a new beneficial mutation
can be lost by chance when it is still rare.
■ An allele that has no effect on fitness can
spread if it is associated (in linkage disequilibrium) with an allele at another locus that is
favored by selection. One result is that genetic
variation is reduced in the region of a chromosome near the selected locus, a pattern that is
called a selective sweep.
■ Several kinds of selection can act to maintain
genetic variation. One is when fitness is overdominant, which is the situation in which heterozygotes have highest fitness. Other kinds
are negative frequency dependence, multiple
05_EVOL5E_05.indd 135
niche polymorphism, and spatial variation
in selection.
■ Positive frequency-dependent selection occurs when fitness increases with the frequencies of genotypes or phenotypes in a population. Unlike balancing selection, this situation
eliminates variation.
■ When fitness is underdominant, heterozygotes
have lower fitness than both homozygotes. This
situation does not preserve genetic variation,
and one of the alleles will either be lost or
spread to fixation, depending on its initial frequency. An allele can spread when it is rare only
if some evolutionary factor other than selection
is at work.
■ The mean fitness of a population evolves as
allele frequencies change. Fisher’s fundamental theorem of natural selection states that
selection causes the mean fitness to increase.
In Wright’s adaptive landscape, allele frequencies change in the direction that increases
mean fitness. These conclusions hold only
under certain conditions. Evolution can cause
a population’s mean fitness to decrease when
fitnesses are frequency‑dependent.
■ Deleterious mutations occur frequently. They
are maintained in populations by mutation even
though selection acts to remove them. Their
effects cause genetic diseases in humans and
other organisms.
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SUGGESTIONS FOR FURTHER READING
Many of the concepts discussed in this chapter
were first explored by the three people who
pioneered the subject of population genetics
in the early twentieth century: R. A. Fisher,
Sewell Wright, and J. B. S. Haldane. The title
of this chapter is taken from that of a book
written by Fisher in 1930 [13]. It is a brilliant
but difficult work that is still an inspiration
to researchers. W. B. Provine’s The Origins of
Theoretical Population Genetics is a wonderful history of the period leading up to the rise
of population genetics [37].
An outstanding introduction to population
genetics is Graham Coop’s Population and
Quantitative Genetics, which is available
for free as a PDF at github.com/cooplab/
popgen-notes/releases/tag/v1.1 [8]. A more
detailed (and also free) introduction to the
theory is Joseph Felsenstein’s Theoretical
Evolutionary Genetics, available in PDF at
felsenst.github.io/pgbook/pgbook.html [12].
Brian and Deborah Charlesworth’s Elements
of Evolutionary Genetics is an advanced but
definitive summary of the field of evolutionary genetics [5].
Molecular evolution is among the fastest-moving
areas of biology. The data, analyses, and data
collection technologies change so rapidly
that any review we suggest here will soon be
dated. Given that caveat, we recommend
A. J. Stern and R. Nielsen’s 2019 review “Detecting natural selection” for those interested
in learning more about how we use genetic data to find evidence of selection [42].
“Strength in small numbers” by S. Tishkoff
[43] and “Signals of recent positive selection
in a worldwide sample of human populations”
by J. K. Pickrell and colleagues give nice
summaries of adaptation in humans [36].
M. Dehasque et al. give a nice introduction to
how DNA sequences from the past (ancient
DNA) are revolutionizing the study of selection in humans [9].
For a broader perspective on what genetic
data can tell us about adaptation, “Molecular spandrels: Tests of adaptation at the
genetic level” by R. D. H. Barrett and H. E.
Hoekstra [4] and “Commentary: When does
understanding phenotypic evolution require
identification of the underlying genes?” by
M. D. Rausher and L. F. Delph give thoughtful
perspectives on a complex topic [39].
Skin color has emotional, social, and political
effects in societies around the world. It also
has very interesting evolutionary stories to
tell us. Nina Jablonski’s book Living Color:
The Biological and Social Meaning of Color
explores all of these topics [23].
PROBLEMS AND DISCUSSION TOPICS
1. The absolute fitnesses of three genotypes at a
locus are W11 = 0.88, W12 = 2, and W22 = 0.14.
a. What are their relative fitnesses?
b. What will be the frequency of the A2
allele when the population reaches an
equilibrium? Please give a quantitative
answer accurate to two significant digits.
2. These are the frequencies of genotypes in
a population of gametes:
Genotype
Frequency
A1B1
0.5
A1B2
0.3
A 2B 1
0.2
a. What is the linkage disequilibrium, D?
b. If allele A2 has a selection coefficient
of sA = 0.1 and locus B does not affect
fitness, what will be the frequency
of allele B2 at the start of the next
generation?
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3. a. How rapidly would a large population adapt
to an environmental change if an advantageous allele were already present at low
frequency (say, 1%), compared with the
situation where it adapts by a new mutation
that appears at that locus after the environmental change?
b. After the beneficial allele has become
fixed, how could we distinguish between
the two situations, using variation in DNA
sequences near the locus under selection?
4. Imagine that changes in Earth’s atmosphere
cause an increase in UV at the surface, and
this change leads to an increase in the somatic mutation rate but not the germ line mutation rate.
a. How will that affect the mutation load?
b. What if the increase in UV also increased
the germ line mutation rate?
5. In the popular press, natural selection is often
described as benefitting species. If by “benefit” the authors mean increase mean fitness,
10/5/22 3:13 PM
Summary
is that an accurate way to think about evolution by natural selection? In general, do you
expect natural selection to benefit
a species?
6. Discuss different hypotheses that could
account for the observation that more
genes have experienced recent adaptive
evolution in the chimpanzee genome than
in the human genome.
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137
7. a. Describe a situation in which you expect
natural selection to cause the population
size of a species to increase.
b. Describe another in which you expect there
would be no change in population size.
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6
Genetic Drift:
Evolution at Random
I
magine that you could travel in time and space to a remote island in the
Arctic Ocean some 4000 years ago. There you would see a remarkable
sight: the last of the woolly mammoths, just before the warming climate
after the last ice age drove them to extinction. These animals were relatives
of the African elephant and about as big. Mammoths had many adaptations
to the extreme cold of the far north, including very long hair, and they carried
huge curved tusks up to 4 m long. But if that wasn’t enough to inspire awe,
you would see something even more astounding about the last survivors of
the species: their long hair was not brown, as in other mammoths. It was a
ghostly, translucent white (FIGURE 6.1).
Rebekah Rogers and Montgomery Slatkin made this remarkable discovery
by analyzing the genome sequence of a frozen mammoth [31]. It carried
loss-of-function mutations in a gene responsible for pigment in the hair. The
mammoth had many other mutations that disrupted other important genes.
These included deletions and premature stop codons in genes responsible for
the urinary proteins and olfactory receptors that the mammoths likely used
to mark their territories and choose mates. So it seems that in their final days
before extinction, the mammoths might have descended into social chaos.
How could so many mutations with such bad effects become established?
The last population of woolly mammoths was very small, numbering perhaps
only 300 individuals. In small populations, harmful mutations sometimes spread
The Galápagos tortoise (Chelonoidis nigra) is the largest living tortoise. Populations on
different islands in the Galápagos archipelago have strikingly different shells. These
tortoises also have very little genetic variation, a result of their small population sizes.
© Tui De Roy/Minden Pictures
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Chapter 6
Genetic Drift: Evolution at Random
FIGURE 6.1
The last surviving woolly
mammoths were fixed for many
deleterious mutations. These included
© iStock.com/Aunt_Spray
mutations that knocked out their ability
to make pigments in their hair.
simply by chance. This is one of the effects of random genetic drift, a critical evolutionary process that we have not yet discussed in detail. Random genetic drift
explains features of the living world that natural selection cannot. It also provides
us with tools to estimate population sizes, phylogenies, and other important
features of nature.
This chapter opens by looking at random genetic drift from two perspectives:
forward in time and backward in time. It then turns to the factors that determine
the strength of drift and how it affects genetic variation. The next topic is how
random genetic drift interacts with natural selection. We will then discuss how
Futuyma Kirkpatrick Evolution, 5e
molecular
differences among species accumulate in time. It closes with a look at
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one of the
most exciting subjects of current research in biology: the use of DNA
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sequences to find evidence for adaptive evolution in humans and other species.
6.1 What Is Random Genetic Drift?
Learning objective
■ Describe how random genetic drift happens, and list
several of its key features.
Genetic change between generations—that is, evolution—happens even when
selection is not at work. We have already seen that mutation changes allele frequencies. Evolution also results from chance events of survival, reproduction,
and inheritance. The evolutionary process that results is called random genetic
drift, or simply “drift” for short. It affects every variable gene in the genome in
every generation.
The grove snail (Cepaea nemoralis) is known for its highly polymorphic shells
(see Figure 5.13). This is a terrestrial species that lives in European pastures,
which it often shares with cows and sheep. Thousands of snails die each day
when they are stepped on by livestock that are oblivious to what is beneath their
hooves. In any given field on any given day, snails of some colors will happen
to get crushed more often than snails of other colors. Variation in color is determined by a few loci, and so these random deaths cause changes in the allele
frequencies in that population. This scenario shows that natural selection is not
the only factor that determines whether an individual leaves genes to the next
generation. Chance plays a role too.
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6.1 What Is Random Genetic Drift? 141
1. Each generation starts with N juveniles. This population size is
held constant by the limited space that is available.
2. Every individual survives to become an adult, so there is no
selection on survival.
3. When adults reproduce, each individual makes 1000 sperm and
1000 eggs. (The actual number does not matter so long as it is reaFIGURE 6.2 Genetic variation in northern
sonably large.) Because every individual makes the same number
elephant seals is very low. Only a few males are
of gametes, there is no selection on reproductive success. After
able to mate in each generation, and hunting reduced
reproduction, the adults die.
the total population size of the species to less than
4. Zygotes are formed by random fertilization between the sperm
40 individuals in the 1800s. Both factors result in
strong genetic drift.
and eggs. But since space is limited, only N randomly chosen
zygotes survive to become juveniles. The life cycle then beginsFutuyma Kirkpatrick Evolution, 5e
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again, and the simulation returns to step 1.
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© Don Johnston_MA/Alamy Stock Photo
Another opportunity for chance to influence the fate of genes comes
during meiosis. When an individual is heterozygous at a locus, only one
of the two alleles is passed to each gamete that it makes. If you have two
children, there is a chance of 1/2 that they will both inherit the same allele
from you, and your other allele will leave no descendants in the next
generation. So even if all individuals survive and leave the same number
of offspring, meiosis itself causes random changes in allele frequencies.
These random effects are greatest when only a few individuals
contribute to the next generation. Males of the northern elephant seal
(Mirounga angustirostrus) fight for control of harems of females (FIGURE
6.2). Only a few males obtain almost all of the matings. This situation
results in strong random genetic drift.
We can discover several key features of genetic drift with an experiment. Rather than using living organisms, the experiment simulates
evolution on a computer. The advantage of this strategy is that it can
perfectly control the conditions. The simulation is designed so that
all individuals have exactly the same chance of surviving and leaving offspring. It simulates a diploid species of hermaphrodites (every
individual makes both sperm and eggs) that does not self-fertilize. The
simulation follows the changes in the frequency of two alleles at a locus
that evolves according these rules:
What happens when we run the simulation? FIGURE 6.3 shows how allele
frequencies change over 500 generations in replicate populations that begin with
an allele frequency of p = 0.5. Frequencies fluctuate randomly. In each generation,
allele frequencies change by the random sampling of the genes that start the next
generation (step 4 of the simulation). This is the essence of genetic drift.
Figure 6.3 illustrates five fundamental features of drift. The first is that drift is
unbiased: an allele frequency is as likely to go up as to go down. Natural selection
can favor one allele over another, but genetic drift does not.
Second, the figure shows that the random fluctuations in allele frequency are larger
in smaller populations. That results from a basic law of probability. When you flip a
coin, you expect to get heads half the time and tails half the time. If you flip a coin
only twice, there is a probability of 1/2 that you will get all heads or all tails, rather
than half and half. But if you flip the coin 1000 times, the probability that you will
get all heads or all tails is less than 1 0 – 300. It is much more likely that about half
of the flips will come up heads and half of them tails. Outcomes become more
consistent when averaging over a larger number of random events. This is why
random genetic drift is stronger in small populations and weaker in large ones.
A third basic feature is that drift causes genetic variation to be lost. An allele
frequency that fluctuates randomly up and down will eventually reach either
p = 0 or p = 1. One of the alleles is then fixed (i.e., it reaches a frequency of 100%).
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Chapter 6
Genetic Drift: Evolution at Random
FIGURE 6.3 Computer simulations of
drift. In each graph, five identical pop-
1
ulations start with two alleles at equal
frequencies (p = 0.5). Allele frequencies
change in later generations by random
genetic drift (no selection is acting).
Smaller population sizes cause bigger random changes in allele frequencies in each
generation and lead to quicker fixation
(p = 0 or p = 1) of one allele or the other.
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
1
Allele frequency, p
1
N = 500,000
100
200
300
400
500
0
1
N = 5000
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
1
100
200
300
400
500
0
1
N = 50
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
100
200
300
400
0
500
Generation
N = 50,000
100
200
300
400
500
200
300
400
500
200
300
400
500
N = 500
100
N=5
100
While the allele that is lost can be reintroduced by mutation, drift by itself causes
genetic variation to be lost. The loss is faster in smaller populations because they
have larger allele frequency fluctuations.
A fourth basic feature seen in Figure 6.3 is that drift causes populations that are initially identical to become different. A useful way to think about this point is to consider
the variance
inEvolution,
allele frequencies
among the populations in our experiment. At
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Kirkpatrick
5e
generation 0, allVisual
populations
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Services have an allele frequency of p = 0.5, and so the variance
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among them is zero.
After one generation, however, variation among the populations is generated by drift, and the variation grows with time. In quantitative terms,
starting with an allele frequency p, the average of the allele frequencies across the
replicates in the next generation is also expected to be p, and the variance of allele
frequencies across the replicates will be p(1 – p)/2N. (This quantity is based on the
binomial distribution, which describes how allele frequencies change by genetic
drift.) This confirms our earlier conclusion that the variation among populations
generated by drift grows more slowly in large populations than in small ones.
Fifth, the figure shows that an allele can become fixed without the benefit of natural
selection. If we wait long enough, it is certain that one of the two alleles will become
fixed and the other lost. A simple rule tells us the probabilities of those two outcomes: if an allele’s current frequency is p, then the probability that it becomes fixed
is also p. This result implies that a new mutation that has no effect on fitness has
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6.1 What Is Random Genetic Drift? 143
a probability of 1/2N of ultimately becoming fixed, since it is initially present as a
single copy among 2N copies of the gene in a diploid population.
We can see several key features of drift in an experiment that used the fruit
fly Drosophila melanogaster [6]. Replicate populations were established, each with
eight females and eight males. Each replicate was begun with equal frequencies
of two alleles that do not measurably affect survival or reproduction (at least in
the lab). The replicate populations were propagated by allowing the parents to
reproduce, then choosing eight female and eight male offspring at random to
start the next generation. FIGURE 6.4 shows the results. After one generation, the
107
25
20
15
10
5
0
Number of populations
30
25
20
15
10
5
0
30
25
20
15
10
5
0
30
25
20
15
10
5
0
30
25
20
15
10
5
0
30
25
20
15
10
5
0
© Martin Shields/Alamy Stock Photo
Initial populations
bw homozygote
bw75 homozygote
Generation 1
Generation 5
Generation 10
Generation 15
FIGURE 6.4 Random genetic drift in
experimental populations of Drosophila
melanogaster. At a locus that affects eye
Generation 19
2
4
6
8
10
12 14 16 18 20 22
Number of bw75 alleles
24
26
28
30
32
color, homozygous bw/bw flies have white
eyes, homozygous bw 75 /bw 75 have brown
eyes, and heterozygotes are intermediate.
Each of 107 populations was founded with
16 heterozygotes. Populations were then
propagated with eight males and eight
females per generation. Differences
among populations accumulated by drift as
the experiment went on. By generation 19,
the bw 75 allele was lost from 30 populations
and was fixed in 28 populations. (Data from
P. Buri. 1956. Evolution 10: 367–402.)
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Chapter 6
Genetic Drift: Evolution at Random
FIGURE 6.5 Allele frequency
differences between two
populations of the garden snail.
The pie diagrams show the frequencies of two alleles at the GOT-1 locus
in groups of snails living on two city
blocks. The sizes of the circles are
proportional to the numbers of individuals in each group. The “50” allele
(shown in white) is quite common
in the left block but almost absent
from the right block. That difference almost certainly evolved by
drift. (After R. K. Selander and D. W.
Kaufman. 1975. Evolution 29: 385–401.)
© Eric Isselee/
Shutterstock.com
144
Cornu aspersum
50
100
0
10 20
Meters
allele frequencies varied substantially among the replicates. By ten generations,
the frequencies were distributed evenly between 0% and 100%. After 19 generations, more than half of the replicates had become fixed for one allele or the other.
Figure 6.4 shows one more general feature of genetic drift. Across the populations taken as a whole, there are fewer heterozygotes than predicted by the
Hardy-Weinberg ratios. This deficit is caused by allele frequency differences
among populations. If the Drosophila experiment were continued, one or the other
Futuyma Kirkpatrick Evolution, 5e
alleleServices
would become fixed in every population. At that point, roughly half the
OUP/SInauer/Troutt Visual
populations
Evolution5e_06.05.ai
09-16-2022 would be fixed for one allele, and half for the other allele. Within
each population, allele frequencies would be in Hardy-Weinberg equilibrium, but
the frequency of heterozygotes would be 0 rather than 0.5 as expected if all individuals were in a single population. Thus drift does not cause large departures
from Hardy-Weinberg equilibrium within a population, but it does cause a deficit
of heterozygotes when a set of diverging populations is considered as a whole.
We can see the effects of drift in natural populations. Researchers collected
2218 individuals of the garden snail (Cornu aspersum) living on two adjacent
city blocks in Bryan, Texas, and genotyped them at several loci [33]. FIGURE
6.5 shows the data from one locus. Allele frequencies differ on the two sides
of the street. The street acts as a strong barrier to dispersal between the two
blocks, and the allele frequency differences between them almost certainly are
the result of genetic drift.
6.2 How Strong Is Genetic Drift?
Learning objective
■ Describe what the effective population size (Ne) is and how it affects drift.
Genetic drift is a random process that is always at work. While it is stronger in
small populations than in large populations, only an infinitely large population
would be immune from drift. The most numerous organism on Earth is a bacterium called Pelagibacter ubique that has a population size of 1028 individuals [27],
a number that is more than a million times larger than the number of stars in the
universe. But it is still a finite number, so even Pelagibacter experiences genetic drift.
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6.2 How Strong Is Genetic Drift? 145
The strength of random genetic drift in a population is measured by the
effective population size, represented with the symbol Ne. This number provides
a way to compare the strength of drift in different populations. We have already
seen that the size of a population affects drift—it is stronger in smaller populations. Many other factors also affect drift. If most individuals in a population are
too young or too old to reproduce, then drift is stronger than it would be if all
the individuals were reproductive. Drift is also affected by changes in population
size and unequal numbers of reproducing males and females (see Figure 6.2).
To account for all these factors, we imagine an idealized hermaphroditic population of constant size in which all individuals have an equal chance of leaving
offspring (just as in the simulation). The effective population size is the number of individuals that would give this idealized population the same strength
of random drift as the actual population of interest. For example, a calculation
shows that a population of size 100, with 90 females and 10 males, has an effective population size of Ne = 36. This means drift in this population is as strong
as if there were 18 females and 18 males. A small value of Ne means that drift is
strong, while a large value means that drift is weak.
The value of Ne tells us several useful things about a population. We saw earlier
that any two copies of a gene share a common ancestor at some point in the past.
If the gene is evolving neutrally (i.e., with no selection) in a diploid organism, the
mathematics of probability tell us that the average time back to this common ancestor is 2Ne generations. For Drosophila melanogaster, with an effective population size
of about 1 million, the ancestor of two gene copies typically existed about 2 million
generations ago. For chimpanzees, with an effective population size of about 20,000,
the most recent common ancestor of two copies of a gene typically occurred about
40,000 generations ago [7]. The smallest effective population size that has been measured is smaller than Ne = 20 in the symbiotic bacterium Buchnera aphidicola [28].
In its populations, two copies of a gene typically shared a common ancestor just a
few dozen generations ago. Because of the random nature of drift, different pairs of
gene copies within the same species show a lot of variation around these averages.
Populations that change in size
The evolutionary history of many species includes times when the population size
was reduced to small numbers. Northern elephant seals (Figure 6.2) are one of the
least genetically variable mammals known [1]. As we mentioned earlier, one reason is the small number of males that are able to breed. But there’s also a second
reason. When European settlers arrived on the Pacific coast of North America in
the 1700s, they found elephant seals living from Alaska south to Mexico. The seals
were easily hunted for their blubber and fur, and by the late1800s the population
had been reduced to a few dozen individuals. Fortunately, hunting was banned
before the species was driven to extinction. By 2020, there were more than 200,000
northern elephant seals, and the population continues to expand. This is a dramatic example of a population bottleneck, the situation in which a population is
reduced to a small size for a small number of generations. The term bottleneck is a
metaphor: we visualize a few individuals squeezing through a period of reduced
population size. A population bottleneck causes intense genetic drift for a brief
time, but it leaves a long-lasting impact on genetic variation.
Reduced population size also occurs during a founder event, which is when a
new population is begun from a small number of individuals. Even if that population then grows to a large size, if we look backward in time, we will see a period in
which the ancestors of the present population were few in number. A founder event,
like a bottleneck, reduces genetic variation. The zebra finch (Taeniopygia guttata)
lives in Australia and the nearby Lesser Sunda Islands, which they colonized from
Australia roughly 1 million years ago (Ma). The birds on the Lesser Sundas show
the effects of that founder event: they have much less genetic variation than do
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146
Chapter 6
Genetic Drift: Evolution at Random
© blickwinkel/Alamy
Stock Photo
Zebra finch, Australia
7,000,000
Zebra finch,
Lesser Sundas
26,750
Time
Australian birds. This difference can be quantified as the nucleotide diversity,
symbolized by π. (Recall from Chapter 4 that π is the chance that two chromosomes in a population have different nucleotides at a given site in their
DNA sequences.) The nucleotide diversity in zebra finches on the Lesser
Sundas (π = 0.002) is about five times smaller than it is in Australian birds (π
= 0.010). The reduced diversity on the Lesser Sundas suggests that population was founded by as few as nine birds (FIGURE 6.6) [4].
Founder events have also shaped genetic variation in human populations. Pennsylvania Amish are a religious community that is closed to intermarrying with people from the outside. They now number about 30,000
individuals, but most are descendants of only a few hundred individuals
who arrived in the United States from Europe in the 1700s (i.e., about 12
generations ago). One result of a bottleneck or founder event is to cause
rare alleles at a random set of loci to become more common. A recessive
mutation in the EVC locus causes polydactyly, a condition in which individuals have six fingers or toes. The mutation is at a frequency of about 7%
in Amish populations in the United States, hundreds of times higher than
it is elsewhere in Europe or North America [26]. Genealogies show that all
copies of the mutation in the Amish descend from a single copy carried by
either Mr. or Mrs. Samuel King, who immigrated to America in 1744. By chance,
one of those two was carrying the rare polydactyly mutation. Had that person
accidentally missed the boat to America, the mutation would be absent from the
US Amish population today. Chance is always a factor in evolution.
Modern humans expanded out of Africa starting about 60,000 years ago. Small
numbers of individuals colonized new regions and then expanded still farther
until the entire planet was inhabited. Native populations now living far from
southern Africa therefore experienced many founder events in their past, while
those living closer to Africa experienced fewer. Remarkably, the signature of
those events can still be seen in our genes. Genetic variation (measured by heterozygosity) is highest in Africa and declines as we follow the paths that humans
took as they expanded across Earth (FIGURE 6.7).
~9
1 Ma
18,760
2 Ma
FIGURE 6.6 Estimates of the effective
population sizes for the zebra finch in
Australia and the nearby Lesser Sunda
Islands through time. Analysis of genetic
variation suggests that the Australian
finches are descendants of a population
of roughly 19,000 birds that lived some
2 Ma. Kirkpatrick
The muchEvolution,
smaller and
Futuyma
5e less genetically diverse Lesser
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Visual Sunda
Servicespopulation was
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founded by a small 06-22-2022
number of colonists
(perhaps only 9 individuals) that arrived
from Australia about 1 Ma. (Data from
C. N. Balakrishnan and S. V. Edwards.
2009. Genetics 181: 645–660.)
(A)
(B)
Africa
Middle East
South/central Asia
Europe
East Asia
Oceania
America
0.75
Mean heterozygosity
0.70
0.65
0.60
0.55
0.50
0.45
5000
FIGURE 6.7
10,000
15,000
20,000
Distance to Addis Ababa (km)
25,000
Genetic variation in humans declines with distance from East Africa.
(A) The routes used by humans to colonize the Earth. (B) Heterozygosity in modern native
populations plotted against the distance from Addis Ababa, Ethiopia (shown by the star
in A) along the colonization routes. Colors indicate the geographical origin of the populations. Heterozygosity is here measured for haplotypes, which are stretches of DNA with
multiple SNPs. (A after H. Liu et al. 2006. Am J Hum Genet 79: 230–237; B after J. Z. Li
et al. 2008. Science 319: 1100–1104.)
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6.3 The Genealogy of Genes 147
All species experience fluctuations in population size. The effect of these fluctuations is to make the effective population size much closer to the minimum
than the maximum population size. During an outbreak of the flu, the population
size of the influenza virus grows to an unimaginably large size, but each outbreak
starts from a very small number of viral particles. Estimates based on methods
discussed in the next section suggest that Ne for the influenza virus is only a few
hundred individual virus particles [30].
6.3 The Genealogy of Genes
Learning objectives
■ Describe what a gene tree is.
■ Explain why all copies of a gene that are present now descended from
a single ancestral copy of the gene.
For a modest cost, you and your parents can have your genomes analyzed.
With those data, you can determine which gene copy at a locus you inherited from your mother and which from your father. With the genomes of your
ancestors going back further in time, you could trace the evolutionary history
of every gene in your genome. Now imagine we did that
for every person now alive. The result would reveal a
(A)
0:
remarkable fact: for any given locus in the human
genome, there was a copy of that gene at some time in
the past that was the ancestor of all copies of the gene
(B)
0:
now carried by all living humans.
To make these ideas more clear, let’s look at a second
simulation experiment (FIGURE 6.8). Here we follow the
1:
genealogy of the ten gene copies at a locus in a population of five individuals. The gene copies may or may not
have the same DNA sequence. We again ensure there is
(C)
0:
no selection—all individuals have equal chances of surviving and reproducing. In the first generation of the sim1:
ulation, some gene copies are lost when the individuals
carrying them die by random events, while others are lost
2:
when by chance they fail to be passed to a gamete during
meiosis. Looking at Figure 6.8B, we see that only six of the
ten copies of the gene present in generation 0 left descen(D)
dants to generation 1. By chance alone, four gene copies
0:
present in generation 0 will never be represented in future
generations. When generation 2 is produced by genera1:
tion 1, accidents of survival and reproduction eliminate
one more of the original ten lineages (see Figure 6.8C).
2:
FIGURE 6.8
The evolution of a gene tree. In this example,
there is no selection, and differences in the number of descendants left by different copies of the gene are entirely the result of
random genetic drift. (A–C) The population begins at generation
0 with five diploid individuals, so there are ten copies of the gene
in all. The shapes and colors are to help distinguish the different
copies, which may or may not differ in their DNA sequences. (D) By
generation 7, all of the gene copies are descendants from a single
copy in generation 0. Their gene tree is shown by the dashed lines.
Coalescence events are shown by gene copies with bold outlines.
The most recent common ancestor of all gene copies in generation 7 is in bold at the top of the gene tree in generation 0.
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3:
4:
5:
6:
7:
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Once a lineage goes extinct, it can never come back from the dead, so the total
number of lineages descending from the original population can only decrease
in time.
By the time we reach generation 7, all of the gene copies are the descendants
of just one copy that was present in generation 0 (see Figure 6.8D). Looking from
generation 7 backward in time, we can trace the ancestry of all the gene copies.
That genealogy, shown by the red lines, is called the gene tree.
What happens if we run the simulation again? Genetic drift is random, and
so different copies of the gene will become the lucky winners that become
ancestors of all the future copies. Furthermore, the number of generations that
it takes for one copy to become the ancestor of all living copies will vary randomly between the simulations.
We are now discussing the genealogy of genes: the paths of their inheritance
across generations. Looking backward in time, when the lineages of two gene
copies merge, we say that they coalesce. These events appear in Figure 6.8 as
branch points (nodes) on the gene tree that represent the most recent common
ancestor of the gene copies. As we have seen, if we go back far enough, we are
certain to arrive at the most recent common ancestor of all copies of the gene now
present in a species.1 That typically happens in the recent past if the population
size is small, and in the remote past if it is very large.
Figure 6.8 illustrates another key point. Although all the gene copies in generation 7 descended from a single copy in generation 0, that does not mean
there was only a single gene copy (or single individual) present then. There
were nine other copies in that generation, and it is only by chance that they left
no descendants in the long run.
If we could trace the gene tree of all human mitochondrial genomes backward in time, we would eventually arrive at a single mitochondrial genome,
which was their most recent common ancestor. Since mitochondria are inherited maternally, the person carrying that ancestral mitochondrion must have
been a woman. Analysis of variation in mitochondrial DNA among living
humans suggests that she lived about 125,000 years ago [29]. She is sometimes
called the “mitochondrial Eve.” That name is misleading, however, because
many other females were also alive at that time, and they contributed other
genes to modern humans. Likewise, tracing the ancestry of all Y chromosomes
would lead back to a single male who was alive about 250,000 years ago [18].
There were many other males alive at that time, and they contributed other
parts of the genome to living humans.
We can be certain that the ancestor of all mitochondria in living humans was
carried by a female and that the ancestor of all Y chromosomes was carried by
a male. Thus different genes have different genealogies. They have different
common ancestors that lived in different places and at different times. This is a
general principle that applies throughout the genome of all sexually reproducing
species: each part of the genome has a different genealogy.
This genealogical perspective also extends across species. The mitochondria that each of us carries and the ones carried by all living chimpanzees are
the descendants of a mitochondrion that lived perhaps 8 Ma in an ancestor
of humans and chimps, before those two lineages split apart. Going back yet
further, the common ancestor of the mitochondria in every individual of every
eukaryotic species now alive existed some 2 billion years ago. That single mitochondrion left an extraordinary legacy: the DNA that it passed on is now carried by
every animal, plant, fungus, and alga on Earth!
1
The picture is a bit more complicated for loci that recombine. Any given DNA site in a locus has
a gene tree of the sort we just described. But recombination between different sites within a locus
causes them to have different gene trees. For the purposes of our discussion here, however, we will
ignore this complexity because the central principles remain true. In particular, for any locus there is
a most recent common ancestor for all copies of the locus carried by living individuals.
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6.4 Genetic Drift and Genetic Variation within Species 149
6.4 Genetic Drift and Genetic Variation within Species
Learning objectives
■ Explain how and why Ne and the mutation rate affect variation in DNA.
■ Describe how selective constraint, recombination, selective sweeps, and
background selection affect genetic variation.
Genetically speaking, you are very similar to the person next to you. Of the 3.2
billion DNA bases in the human genome, 99.9% of them are identical in two randomly chosen individuals. But while only 1 out of 1000 DNA bases differs, the
genome is so large that you differ from your neighbor at some 3 million bases.
In humans and other species, polymorphism is spread unevenly across the
genome (FIGURE 6.9). Genetic variation (for example, measured by nucleotide
diversity) is typically high in the regions between genes, and within introns.
Coding regions (exons) are less variable, particularly at the first and second base
of each codon. These patterns are so common across the tree of life that they must
result from very general features of evolution.
The explanation is that much of the polymorphism in DNA within species
results from random genetic drift acting on selectively neutral mutations. On
average, the nucleotide diversity resulting from neutral mutations evolving by
drift in a diploid species is expected to be
π ≈ 4 Ne μ n
(6.1)
where μn is the neutral mutation rate—that is, the chance per generation that the
locus mutates to another allele that does not change the organism’s fitness. For
example, if the total mutation rate at a locus is μ = 10 –6 but only 10% of mutations
are selectively neutral, then the neutral mutation rate is μ n = 0.1 × 10 –6 = 10 –7.
Equation 6.1 is the product of three quantities: the expected number of generations back to the common ancestor of two copies of a gene (2Ne ), the probability that a selectively neutral mutation occurs in a generation (μ n ), and a factor
of 2 that accounts for the fact that mutations could occur in either of the two
lineages leading back to their most recent common ancestor. (Equation 6.1 is an
approximation that is accurate when π is 0.1 or less, which is typically the case.)
To summarize, genetic variation increases with the effective population size (Ne )
and the neutral mutation rate (μ n ).
The effect of Ne on genetic variation can be understood with gene trees. Any
differences between two DNA sequences sampled from the present are the results
of mutations that have occurred in the ancestors of those sequences on the gene
tree that connects them (FIGURE 6.10). Sequences connected by gene trees that
go back farther in time have had more opportunities to accumulate mutations,
so they tend to be more variable.
Introns
5ʹ
3ʹ
Exon 1
0
200
FIGURE 6.9
400
600
Exon 2
800
1000
Exon 3
1200
1400
Exon 4
1600
1800
2000
2200
2400 bp
Nucleotide variation at the Adh locus in Drosophila melanogaster.
Four exons (green) are separated by introns (blue). The vertical lines show the
positions of 43 DNA polymorphisms (SNPs) found in a sample of 11 chromosomes.
Of all the SNPs, 17 are in exons, but only 1 of these (shown by the triangle) causes
an amino acid change in the alcohol dehydrogenase enzyme. (After M. Kreitman.
1983. Nature 304: 412–417.)
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150
Chapter 6
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(A)
Past
AAA
AAT
(B)
TTT
Time
ATA
ATT
TAA
Present
AAT
AAT
ATA
TAA
ATT
ATT
ATT
TTT
FIGURE 6.10
DNA sequences in current populations vary as the result of
mutations that occurred on their gene tree. (A) Three mutations occurred on this
gene tree that changed an A to a T. As a result, all three sites in this short sequence
are variable in the four sequences at the bottom. (B) This gene tree is much shallower,
and so there has been much less time for mutations to occur. Only one mutation happened, and so only one of the three sites in the sequence is variable.
These basic facts explain major patterns in genetic variation seen across the
genome. Most mutations that occur in coding regions of the genome are nonsynonymous (they change an amino acid in the protein), and most changes to
a protein are deleterious (they decrease survival or reproduction) (see Chapter 4). These mutations are weeded out from the population in a process called
purifying selection, and they do not contribute to the heterozygosity we observe.
Loci that experience purifying selection are said to be under selective constraint.
The neutral mutation rate for these loci (μn) is smaller than the total mutation rate
(μ). In contrast, in many noncoding regions, none of the mutations affect fitness,
so in those regions the neutral mutation rate is equal to the total mutation rate.
Noncoding regions therefore typically have higher heterozygosity, as predicted
by Equation 6.1 (see Figure 6.9).
The same logic explains patterns of variation at different sites within a coding
locus. Many mutations at the third positions of codons are synonymous. Most
mutations at the first and second positions, however, are nonsynonymous. They
therefore have a lower neutral mutation rate than do third positions, and as a
result they are less variable. The Adh locus in Figure 6.9 shows this effect: only
one of the 17 polymorphisms found in the exons is nonsynonymous.
In sum, at sites of the genome that are free of selection, all mutations are
selectively neutral. These mutations are free to drift through the population,
and they contribute genetic variation as they do. But at most sites that experience selection, most mutations are deleterious. They are selected out of the
population and so contribute very little to nucleotide diversity. These sites
therefore tend to be less genetically variable.
Levels of polymorphism also vary systematically along chromosomes. Regions
with high recombination rates tend to be more polymorphic (FIGURE 6.11). We
saw in Chapter 5 that a selective sweep of a beneficial mutation eliminates variation in the surrounding region of a chromosome (see Figures 5.14 and 5.15). In a
similar way, when selection eliminates a deleterious mutation, polymorphism is
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6.4 Genetic Drift and Genetic Variation within Species 151
0.012
Recombination
rate
0.004
0.000
Telomere
Centromere
4
3
2
1
0
Chromosome 3
0.012
0.008
0.004
0.000
Telomere
Centromere
Telomere
4
3
2
1
0
Recombination (cM/Mb)
Nucleotide diversity (π)
0.008
Telomere
FIGURE 6.11 Nucleotide diversity along the
left and right arms of chromosomes 2 and 3 of
Drosophila melanogaster. Values of π at different
π
Chromosome 2
genes are shown by the points, and the curves show
the recombination rate (in centimorgans [cM] per
million nucleotides [Mb] of DNA) along the chromosome. Genetic variation is reduced by background
selection and selective sweeps in regions of low
recombination near the centromeres and telomeres.
(After T. F. C. Mackay et al. 2012. Nature 482: 173–178.)
reduced nearby on the chromosome (FIGURE 6.12). This effect is called background
selection. Both selective sweeps and background selection affect larger pieces
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of a chromosome
regions where recombination rates are low, producing the
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pattern seen in Figure
6.11. Selective sweeps and background selection reduce
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the amount of polymorphism at neutral sites of the genome below what Equation
6.1 predicts. In Drosophila, it is estimated that selective sweeps and background
selection reduce the nucleotide diversity (π) at selectively neutral sites to only
about 25% of what it would otherwise be [11].
Estimating population size
The relation between heterozygosity and population size suggests a strategy for
estimating the effective population size of a species. The idea is to estimate π by
sequencing several individuals at sites in the genome that are evolving neutrally.
(Synonymous sites are often used for this purpose.) We can also estimate the
total mutation rate (μ) using the methods discussed in Chapter 4. Because we are
focusing on sites that are selectively neutral, the total mutation rate is equal to the
(A)
1
(B)
2
3 4
5
Neutral allele
FIGURE 6.12
1
(C)
2
3 4
5
1
2
3 4
5
Deleterious mutation
Background selection decreases neutral genetic polymorphism.
(A) A population of chromosomes is polymorphic for neutral mutations at five sites in a
DNA sequence (yellow bands). (B) Deleterious mutations appear on two chromosomes
(red bands). (C) Selection eliminates chromosomes carrying deleterious mutations.
A side effect is that neutral polymorphism is reduced nearby on the chromosome
(at sites 2 and 5); the population is now polymorphic at only three sites.
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152
Chapter 6
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neutral mutation rate (μn ). We find the value of Ne that solves Equation 6.1 for
each DNA site, then average those values. For example, in gray whales neutral
sites in the gene ACTA have molecular diversity π = 0.0015, and the mutation rate
is estimated to be 7.3 × 10 –9 per site per generation [3]. This gives us an estimated
population size of
π
Ne =
= 51,000
4µn
The estimate can be made more accurate by including data from additional
genes. Estimates of Ne based on π are rough, however, as they do not account
for the effects of background selection, selective sweeps, and other factors [13].
Estimates of the effective population sizes have been made for several species
using this approach (FIGURE 6.13). The lowest estimate shown comes from humans:
Ne for our species is roughly 10,000. While there are now more than 7 billion people
on Earth, our numbers were much smaller just a few thousand generations ago.
Our genetic variability has not yet caught up with the current population size.
This approach can be pushed further to do something even more remarkable:
we can estimate a species’ effective population size from just a single diploid
individual [22]. The approach is to sequence its DNA to find the average heterozygosity at a large number of selectively neutral sites, then use the logic just
described to estimate Ne. Another remarkable use of DNA polymorphism is to
estimate population sizes in the past. The zebra finch discussed earlier now has
an effective population size of about 7 million individuals in Australia. A research
group headed by Scott Edwards, an Afro-American who is a leading evolutionary biologist, analyzed their genetic variation and found that the Australian birds
are descendants of a much smaller population of roughly 19,000 birds that lived
some 2 Ma (see Figure 6.6) [4].
There are large differences in the average values of π among different groups
of organisms. Ants and vertebrates tend to have relatively little genetic variation, for example, while butterflies and clams are highly variable. Differences
in effective population size account for some of this variation, but variation in
mutation rates, selective sweeps, background selection, and other factors must
A. thaliana
Gray whale
FIGURE 6.13 Effective population sizes (Ne),
estimated from levels of DNA polymorphism.
The species are ordered by body size; notice that
large organisms tend to have smaller effective population sizes. The horizontal scale is logarithmic, so
each vertical line shows a change of Ne by a factor of
10. Humans have a very small Ne: although there are
now more than 7 billion people, we descended from
only about 10,000 individuals living 100,000 years ago.
Two nematode worms are shown. The effective population size of Caenorhabditis remanei, which does not
self-fertilize, is 20 times larger than that of C. elegans,
which does self-fertilize. The plant Arabidopsis thaliana also self-fertilizes, which explains its very low Ne.
The gut bacterium Escherichia coli has an effective
population size much larger than that of any of the
animals or plants. (Data from B. Charlesworth. 2009.
Nat Rev Genet 10: 195–205; S. Lundemo et al. 2009.
Mol Ecol 18: 2798–2811.)
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Gorilla
Human
Chimpanzee
D. melanogaster
C. remanei
C. elegans
E. coli
1000
10,000
100,000
1,000,000 10,000,000
Effective population size, Ne
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6.5 Genetic Drift and Natural Selection 153
FIGURE 6.14 The heterozygosity averaged across
the genome varies among groups of animals. Dots
are colored according to their nucleotide diversity (π)
100
Propagule size (cm)
0.2
1
4
Nucleotide diversity (π) (%)
1
at synonymous sites in coding regions. Ants and vertebrates have low heterozygosity, typically between
0.2% and 1%. Other groups of animals, including
butterflies and bivalves, have higher heterozygosity,
typically between 1% and 10%. These differences
result from variation in population sizes, as predicted
by Equation 6.1, and from other factors. Fecundity and
propagule size (shown on the x- and y-axes) are also
strongly correlated with levels of genetic variation.
(After H. Ellegren and N. Galtier. 2016. Nat Rev Genet
17: 422–433.)
0.01
0.1
100
100,000
Fecundity (offspring per day)
contribute as well. Intriguingly, species with high fecundity and small propagules tend to have high variability (FIGURE 6.14). Exactly how those life history
factors affect heterozygosity is not yet clear [10].
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6.5 Genetic Drift and Natural Selection
Learning objective
■ Describe when selection will be a much stronger force than drift,
and vice versa.
As an advantageous mutation sweeps through a population, natural selection
is not the only process that determines its fate. Genetic drift adds a random
component to its trajectory. As a result, the mutation’s frequency can increase
more rapidly than is expected from selection alone. But sometimes drift causes
its frequency to increase less rapidly or even to decrease.
We can see drift interacting with selection in FIGURE 6.15. In these simulations,
the favored allele has a selective advantage of s = 0.01. In a population size of Ne =
500,000, the allele frequency increases along a smooth trajectory much like those
back in Chapter 5 (see Figure 5.6) but with small random fluctuations caused
by genetic drift. When the population size is Ne = 5000, the fluctuations become
more obvious. With a population size of Ne = 50, the effects of drift are so strong
that the tendency of the advantageous allele to spread is no longer obvious. In
fact, that allele is completely lost in two of the five simulations.
These examples illustrate a general point about how genes evolve when both
selection and drift are at work. In some cases, the selection is so much stronger
than drift that the effects of drift can be largely ignored. In other cases, drift overwhelms selection. A simple rule of thumb tells us whether an allele is in one situation or the other. A natural measure for the strength of selection is the selection
coefficient s (see Chapter 5). Since large populations have weak drift, a natural
measure for the strength of genetic drift is 1/Ne . We then simply compare these
two numbers. The vast majority of alleles fall into one of two categories. Some
alleles are strongly selected (s is much bigger than 1/Ne ), and selection largely
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Chapter 6
Genetic Drift: Evolution at Random
FIGURE 6.15 Simulations of the spread
of beneficial mutations in populations
with different effective sizes. The
1
mutations start at a frequency of p = 0.25
and have a selective advantage of s = 0.01.
Each graph shows five replicate populations. With Ne = 500,000, allele frequencies follow trajectories like those seen
for an infinite population in Figure 5.6. As
Ne becomes smaller, the effects of drift
become stronger. When the population
size is so small that 1/Ne is much less than
s, there is a high probability that the beneficial allele will be lost by drift, as seen in
the graphs with Ne = 50 and Ne = 5.
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
1
Allele frequency
1
Ne = 500,000
100
200
300
400
500
0
1
Ne = 5000
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
100
1
200
300
400
500
0
1
Ne = 50
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
100
200
300
400
0
500
Generation
Ne = 50,000
100
200
300
400
500
200
300
400
500
200
300
400
500
Ne = 500
100
Ne = 5
100
determines their evolution. Other alleles are nearly neutral (s is much smaller
than 1/Ne), and drift largely determines their evolution. These rules explain the
differences
weEvolution,
see in the
Futuyma Kirkpatrick
5e graphs of Figure 6.15.
There is an Visual
interesting
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selected in one species can evolve in another as if it were selectively neutral
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(FIGURE 6.16). Some organisms have very large effective population sizes. Drosophila melanogaster has an effective population size of about 1 million individuals, and the populations of some bacteria (like the E. coli that live in our guts)
are bigger still (see Figure 6.13). For these species, selection is much more powerful than drift for alleles that have fitness effects as small as s = 10–5. These
species have features that reflect exceedingly precise adaptation. One is codon
bias. Earlier, we said that synonymous mutations are selectively neutral. In fact,
different codons that code for the same amino acid can have minute differences
in fitness because they affect how accurately and efficiently a gene is transcribed
and translated [15]. The genomes of species with very large Ne tend to be biased
toward the codons that are most efficient. Adaptation is less precise in species
with smaller population sizes. Gray whales have an effective population size in
the tens of thousands [3]. Mutations with fitness effects of s = 10–5 evolve in that
species as if they were selectively neutral, a very different situation from what
mutations with the same effect experience in Drosophila.
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6.5 Genetic Drift and Natural Selection 155
(A)
(B)
Ne = 10,000
Beneficial
Frequency
Deleterious
3
Neutral
2
(C)
Frequency
Relative fixation probability
4
1
0
–2
–1
Neutral
mutations
0
Ne s
1
2
Ne = 1000
–10–4
0
10–4
Selection coefficient, s
FIGURE 6.16
The probability that a single copy of a mutation becomes fixed
depends on its selection coefficient (s) and the effective population size (Ne).
(A) Here we assume that the relative fitnesses of A 1 A 1, A 1 A 2, and A 2 A 2 genotypes are 1,
1 + s, and 1 + 2s. The x-axis shows the product of Ne and s. The y-axis shows the fixation
probability relative to that of a neutral mutation (whose fixation probability is 1/2Ne). For
mutations with |Ne s| << 1, the fixation probability is very close to that of a neutral mutation. (B) Hypothetical distribution of fitness effects for new mutations. When Ne = 10,000,
mutations with fitness effects in the range –10–5 < s < 10–5 (shown in gray) evolve almost
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neutrally. (C) With the same distribution of mutation effects as in (B) but with Ne = 1000,
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the range of mutations
that evolve almost neutrally expands to –10–4 < s < 10–4.
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The relative contributions that adaptation and drift make to the differences
among species vary dramatically among groups of organisms [19]. In flies and
bacteria that have very large Ne, about half of the amino acid differences in the
proteins of closely related species evolved by positive selection, that is, by the
fixation of beneficial mutations. The other half of the differences were fixed by
drift. The picture is different for our own species: less than 15% of the amino acid
changes in the last 5 million years (My) resulted from adaptive evolution, and
the rest accumulated by drift [35].
Drift can cause deleterious mutations to spread to fixation. We saw earlier that
human effective population size was roughly 10,000 in our recent evolutionary
past. As a result, many mutations that reduce fitness by s = 10–5 became fixed in
our genomes [2]. The fixation of deleterious mutations is a well-known problem
in the small populations of animals and plants in zoos [12].
The same problem can also contribute to the extinction of natural populations.
The decline of fitness by the fixation of deleterious mutations in a small population is called the inbreeding load. (Note that the inbreeding load is different from
inbreeding depression, which is the loss of fitness of offspring from parents that are
closely related, compared with offspring from unrelated parents; see Chapter 12.)
An isolated population of the adder Vipera berus had fewer than 40 individuals and
was highly homozygous [24]. Females had unusually small litter sizes, and many
of their offspring were deformed or stillborn. Twenty adult males were introduced
from other populations, left to interbreed with the residents for 4 years, and then
removed. Soon after, the population size rebounded dramatically (FIGURE 6.17).
The snakes from the other populations reintroduced the beneficial alleles that had
been lost by drift, leading to increased survival of offspring. In populations that
suffer from inbreeding load, individuals that have lower genetic variation across
their genomes survive less well. This effect was seen in a conservation project with
endangered desert tortoises: individuals that were highly heterozygous had better
success in surviving relocation [32].
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30
All snakes
Number of snakes
25
© Matteo photos/
Shutterstock.com
35
20 males from outside
populations were
introduced into the
inbred Swedish
population in 1992.
20
15
10
New juveniles
In very small populations, the accumulation of deleterious mutations can lead
to a vicious spiral. As the inbreeding load
grows, the population shrinks, causing deleterious mutations to become fixed even
faster. This process is called a mutational
meltdown and can lead to the extinction of a
species. The last of the woolly mammoths,
which we discussed at the beginning of the
chapter, apparently suffered exactly this
fate [31].
Crossing an adaptive valley by drift
Adaptation can sometimes result when
drift acts against selection. Many chromosome rearrangements are underdominant:
heterozygotes have lower fitness than both
0
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999
homozygotes. Recall from Chapter 5 that
Year
in this situation the adaptive landscape
The introduced males
has a valley. When a new rearrangement
were removed in 1996.
FIGURE 6.17 Population decline and
appears by mutation, its frequency is low,
rebound in an inbred population of
and selection will tend to drive it out of the
adders in Sweden. The introduction of
males from elsewhere rescued a small
population. If drift is strong, however, the
and inbred population from extinction.
frequency of the rearrangement can sometimes increase by chance until its fre(After T. Madsen et al. 1999. Nature 402:
quency is past the low point in the valley (FIGURE 6.18). Now selection favors the
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tive valley by drift, an event called a peak shift. Sewall Wright, one of the founders
of modern evolutionary biology (see Chapter 1), believed that peak shifts are
critical in the origin of many evolutionary novelties. That view, however, is not
shared by most evolutionary biologists today.
The chance that drift will cause a population to cross an adaptive valley is highly
sensitive to the depth of the valley, which depends on the strength of selection
against heterozygotes. A fitness reduction in heterozygotes of even 1% is enough
to make this outcome virtually impossible except in extremely small populations.
Chromosome rearrangements, which are often underdominant, evolve more frequently in weedy plants than in long-lived plants [16]. One hypothesis to explain
that pattern is that weeds often self-fertilize and have frequent population bottlenecks caused by founder events, both of which can drastically reduce Ne.
5
The fate of beneficial mutations in large populations
When a beneficial mutation first appears, it is present as only a single copy. The
individual carrying it may leave no offspring, or if it does, the mutation may not
be passed through meiosis to its offspring. A mutation that increases fitness has
a much greater chance of becoming fixed than does a deleterious mutation, but
its fate is still not certain.
A surprising conclusion from population genetics theory is that a single copy of
a new beneficial mutation is almost certain to be lost by drift, even if it has a large
positive effect on fitness. The reason is that when there are only a few copies of the
mutation, there are large random changes in its numbers. Say that a mutant heterozygote has a relative fitness advantage of s. Then even in a large population with
very weak genetic drift, the probability that the mutation will become fixed is only
Pr(fixation) = 2s
(6.2)
This means that a mutation that improves survival by 1%, which is an unusually large advantage, has a 98% chance of being lost in the first few generations
06_EVOL5E_06.indd 156
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6.6 The Neutral Theory of Molecular Evolution 157
1.0
FIGURE 6.18
A peak shift caused by
drift is likely only if the population size is
extremely small and the adaptive valley
is very shallow. (Top) The adaptive land-
Ne = 103
w
Ne = 104
0.999
0
Generation
20,000
40,000
60,000
80,000
100,000
0
0.2
0.4
0.6
0.8
Allele frequency, p
1.0
0
0.2
0.4
0.6
0.8
Allele frequency, p
1.0
scape for an underdominant mutation when
the fitness of the heterozygote is reduced
by s = 0.001 relative to the homozygotes.
—, reaches
The population’s mean fitness, w
its lowest value of 0.9995 at p = 0.5. Arrows
show the direction of change in allele
frequency, p, that is favored by selection
when p is above and below 0.5. Both alleles
mutate at a rate of μ = 10 –4. (Bottom) Simulations of allele frequency trajectories over
100,000 generations. The vertical dashed
lines show the bottom of the adaptive valley. (Bottom left) Ne = 10 4. Here 1/Ne << s,
and selection is stronger than drift. Among
the three populations, only one crosses the
adaptive valley and evolves to an allele frequency greater than p = 0.5. (Bottom right)
Ne = 10 3 . Now s = 1/Ne, so selection and drift
are about equally strong. The allele frequency in this one simulation tends to stay
near the adaptive peaks at p = 0 and p = 1 but
also frequently crosses the adaptive valley.
after it appears. Some kinds of beneficial mutations occur repeatedly, and in
those cases one of the copies will eventually spread. But other kinds of mutations
appear in a population only rarely or perhaps just once. Their loss by drift can
put an evolutionary speed limit on how fast a species can adapt to changing
conditions or evolve a new advantageous trait.
Futuyma Kirkpatrick Evolution, 5e
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Evolution5e_00.00.ai 05-12-2022
6.6 The Neutral Theory of Molecular Evolution
Learning objective
■ Explain why the number of genetic differences between species depends
more strongly on how closely they are related than on where they live and
what they eat.
Like other vertebrates, humans, sharks, and carps use hemoglobin to transport
oxygen in their blood. Humans extract the oxygen from the air, while sharks and
carps extract it from water. You might therefore expect the hemoglobins of sharks
and carps to be more similar to each other than they are to that of a human.
Surprisingly, the data tell another story. FIGURE 6.19 shows the percentage of
amino acids that differ among the α hemoglobins of four species of vertebrates.
The hemoglobin in a carp is more similar to the hemoglobin in a human than
it is to that in a shark. But while the protein’s similarity does not correlate with
the habitat that these animals live in, it does correlate strongly with their phylogenetic relations. Fossils show when the most recent common ancestors of
these animals lived. The number of differences between the hemoglobins of two
species correlates very well with the age of their most recent ancestors. These
data show that changes in hemoglobin molecules accumulate in time at a nearly
constant rate. FIGURE 6.20 shows plots in which differences in DNA and protein sequences between humans and nine other mammals are compared with
the time since our most recent common ancestors. Again, there is a very strong
correlation between divergence time and sequence differences. The figure also
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Chapter 6
Genetic Drift: Evolution at Random
FIGURE 6.19 Differences in the
amino acid sequence of α hemoglobin
are predicted well by phylogenetic
relationships but not by ecological
similarities. (Right) The table shows the
percentage of amino acids in α hemoglo-
bin that differ between pairs of vertebrate
species. The carp is more similar to the
human and dog than to the shark, even
though both carps and sharks are fish.
(Left) A phylogeny showing the dates for
the most recent common ancestors of the
species, which are known from fossils.
The percentage of amino acid differences
between a pair of species is highly correlated with the age of their most recent
common ancestor. (After M. Kimura. 1983.
The Neutral Theory of Molecular Evolution.
Cambridge: Cambridge University Press.)
440 400
70
Ma
Shark
Carp
Dog
Human
Human 53%
49%
16%
—
Dog
57%
48%
—
Carp
59%
—
Shark
—
Shark, © Triduza Studio/Shutterstock.com; Carp, © Nosyrevy/
Shutterstock.com; Dog, © otsphoto/Shutterstock.com; Human,
© Eduardo Munoz/Reuters/Alamy Stock Photo
Substitutions (per site)
shows that different types of changes accumulate at different rates: synonymous
differences build up about five times more rapidly than nonsynonymous differences. But while these two types of changes evolve at different rates, within each
type the
rate isEvolution,
quite constant.
Futuyma
Kirkpatrick
5e
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Services
Genes and Visual
proteins
that evolve at roughly constant rates provide us with
Evolution5e_06.19.ai
06-22-2022
molecular clocks. We
use these molecules to learn about the evolutionary histories
of species that left no fossils. The idea is simply to count up the number of amino
1.6
dS
acid differences between the hemoglobins of two species, then use a plot like that in
9
dA
Figure 6.20 to estimate when their most recent common ancestor lived (see Figure
1.4
8
dN
2.19). This strategy can be used with many proteins other than hemoglobin and
1.2
with DNA sequences as well as amino acid sequences. Extensions of this approach
that use more sophisticated statistical analyses are the main tools used to construct
1.0
7
the phylogenetic tree of life that links all species on Earth (see Chapters 2 and 16).
What could explain the relatively constant evolutionary rates of molecules
0.8
6
that evolve in a clocklike way? Motoo Kimura, one of the giants in the history
of evolutionary biology, proposed the neutral theory of molecular evolution. One
0.6
of the many predictions that flow from that theory is that random genetic drift
4
0.4
will cause genes to evolve at relatively constant rates [20]. Kimura thought that
5
beneficial mutations are so rare that they make only a trivial contribution to
0.2 3
2
the molecular differences among species. Instead, Kimura believed that the vast
1
majority of those differences result from the fixation of neutral mutations (those
0
with s much smaller than 1/Ne ).
400
500
100
200
300
What does this theory imply about molecular clocks? Consider a locus that
Divergence time (My) based
on fossil record
is evolving by drift in two species that split apart t generations ago. We sample
one copy of the gene from each of the species. In the branch of the gene tree that
FIGURE 6.20 Molecular clocks based
leads from the first species back to the most recent common ancestor of the two
on three kinds of data run at different
species, on average μnt mutations will have occurred, where once again μn is
rates. The x-axis shows time since the
the rate at which neutral mutations occur at the locus per generation. Likewise,
most recent common ancestor of humans
and nine other vertebrates, as determined
on average μnt mutations will have occurred on the branch leading back from
from fossils. The y-axis plots the number
the second species. If the amount of time, t, is not too long, there is a negligible
of amino acid substitutions (dA), number
chance that a mutation will happen twice at any single DNA site. We therefore
of synonymous
DNA substitutions
(d S), and
Futuyma
Kirkpatrick Evolution,
5e
expect that the total number of differences between the two copies of the gene,
number of nonsynonymous
OUP/SInauer/Troutt
Visual Services DNA substid, will equal the total number of mutations that have occurred along the two
Evolution5e_06.20.ai
tutions (d N) per site05-02-2022
between humans and
branches (see Figure 6.10). That is simply
those species, based on 4198 loci. Humans
are compared with the following: 1 = chimpanzee, 2 = orangutan, 3 = macaque, 4 =
mouse, 5 = cow, 6 = opossum, 7 = chicken,
8 = western clawed frog, 9 = zebra fish.
My = million years. (After M. Nei et al. 2010.
Annu Rev Genom Hum Genet 11: 265–289.)
06_EVOL5E_06.indd 158
d = 2 μn t
(6.3)
Thus for genes that are accumulating differences by drift, the number of differences between two species is proportional to the time since their most recent
common ancestor. This gives us a molecular clock: a linear relation between time
and divergence, consistent with what we see in Figure 6.20.
10/5/22 3:15 PM
6.7 Searching the Genes for Signatures of Adaptation
159
Recall from earlier in this chapter that μn is greater at sites in the
genome that are free of purifying selection. Equation 6.3 predicts
Pseudogenes
that those sites will accumulate differences more rapidly than will
Intergenic regions
sites that experience strong purifying selection. A natural experiment to test this idea comes from pseudogenes, which are nonIntrons
functional versions of functioning genes. Because pseudogenes
do not produce a gene product, we expect that mutations anyCoding
where in their DNA sequences will be selectively neutral, and they
should therefore evolve rapidly. Consistent with Kimura’s neutral
0
5
10
15
20
theory, pseudogenes are among the fastest-evolving parts of the
Evolutionary rate
genome (FIGURE 6.21). The neutral theory also predicts that inside
FIGURE 6.21 The relative rates of evolution for
coding regions, synonymous differences should accumulate faster
different parts of the genome. These rates are estithan nonsynonymous differences among species. Again, that is
mated from differences between humans and chimpanwhat the data show.
zees and are measured as the number of differences per
The neutral theory does a good job of explaining why parts of
Futuyma
Kirkpatrick
5eby 10 3. (After D. Graur. 2016.
nucleotide
site,Evolution,
multiplied
the genome that experience little or no purifying selection evolve OUP/SInauer/Troutt
Visual
Services
Molecular and Genome Evolution. Sunderland, MA: Oxford
so fast. We now know, however, that adaptive evolution is much Evolution5e_06.21.ai 05-03-2022
University Press/Sinauer.)
more important to molecular evolution than Kimura believed.
We saw earlier that in some organisms, fully half of the amino
acid differences between closely related species result from positive selection rather than drift [19].
If adaptive evolution is so common, how can we explain molecular clocks?
Fluctuating selection pressures, for example, those caused by changing environments, can also cause a locus to evolve in a relatively steady, clocklike way. A
relatively constant appearance of new beneficial mutations in a population will
produce the same result. Clocklike evolution has been seen in experimental populations of E. coli, where it was possible to show that the genetic changes resulted
from positive selection and not drift [5]. Some genes evolve at very uneven rates
and so make poor molecular clocks. But those genes that do evolve at relatively
constant rates, whether by selection or drift, give us valuable tools to estimate
divergence times among species and to build phylogenies.
In sum, the differences in the DNA sequences of species evolve both by drift
and by positive selection. Noncoding regions of the genome that are free of purifying selection accumulate differences quickly, as predicted by the neutral theory of
molecular evolution. In coding regions, many sites are under purifying selection.
Like neutral sites, they accumulate differences by drift but more slowly. Other sites
in coding regions evolve largely by positive selection, not drift. We will now look
at how we can identify which changes occurred by adaptation and which by drift.
6.7 Searching the Genes for Signatures of Adaptation
Learning objectives
■ List three ways that DNA sequences are used to find genes that have
recently adapted.
■ Describe an example of positive selection that has caused DNA sequence
differences between human populations.
Our genes are books that have recorded the evolutionary histories of our ancestors.
Evolutionary biologists have recently made great progress in decoding these books.
One important goal is to find genes that show signs of recent adaptive evolution.
These will help us understand the genetic basis of adaptation, how we became different from other species, and how differences among human populations evolved.
The biggest challenge in finding signs of recent positive selection in DNA
sequences is that other evolutionary processes are also operating. Because genetic
drift is always at work, drift provides a baseline. If we see that the pattern of
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Chapter 6
Genetic Drift: Evolution at Random
polymorphism within a species, or the difference between two species, differs
from what we expect under drift alone, that suggests selection may have played
a role. Biologists have developed a toolbox of methods to distinguish the signals
of selection and drift. We discussed one of the approaches in Chapter 5, where
we saw that a selective sweep leaves a region of depressed polymorphism in the
region of chromosome near where a selective sweep recently occurred (see Figures 5.14 and 5.15). Here we will look at three more methods that are widely used
to study adaptation at the level of the genes. Other approaches are discussed in
the Suggestions for Further Reading.
Synonymous versus nonsynonymous differences
The BRCA1 locus has an important place in human health. Women who carry
certain mutations in BRCA1 have a 72% chance of developing breast cancer and
a 42% chance of developing ovarian cancer before age 70 [21]. Some women who
find they carry this mutation make the courageous decision to have a double
mastectomy and ovariectomy rather than risk developing cancer (FIGURE 6.22).
This locus also has an interesting evolutionary story: it has evolved rapidly by
positive selection in primates, particularly in the last few My of human history
[23]. The signs of positive selection can be seen by comparing the sequences of
this gene from two species. We count the number of differences that are nonsynonymous, then divide that number by the number of DNA sites in the gene
where nonsynonymous mutations could occur. This gives us the number of nonsynonymous differences per nonsynonymous site, which we refer to as dN. We
then repeat this procedure for the synonymous differences to get dS, the number
of synonymous differences per synonymous site. Finally, we divide the two numbers to find the d N /d S ratio. We can calculate this ratio using data from just a single
individual sampled from each of two species, and for that reason it is widely used.
How does this ratio inform us about selection? For many species, the synonymous sites are expected to evolve neutrally because changes to the DNA
sequence at those sites do not alter the protein. Those sites should accumulate
differences between species by random genetic drift (as predicted by Equation
6.3). Now consider the nonsynonymous sites. If positive selection is at work,
they will evolve more quickly than the neutral synonymous sites, and differences between species will build up more rapidly. Then the dN/dS ratio will be
greater than 1. Another possibility is that most nonsynonymous mutations are
(A)
2.79
0.00
0.76
FIGURE 6.22
apes showing the ratio of nonsynonymous to synonymous substitutions, dN/dS , along each branch. Ratios
in bold are significantly greater than 1, suggesting
evolution by positive selection. (B) Schematic of
BRCA1, showing sites of mutations known to cause
disease in humans and sites that have recently
evolved under positive selection. The green bands
are the gene’s introns. (C) The famed actress Angelina
Jolie made the decision to undergo a double mastectomy and oophorectomy after learning she had a high
probability of developing breast cancer as a result of
her genotype at BRCA1. (After D. Lou et al. 2014. BMC
Evol Biol 14: 155.)
06_EVOL5E_06.indd 160
2.66
0.76
0.71
8 Ma
6 Ma
(B)
2 Ma
Human
Bonobo
© Featureflash Photo agency/
Shutterstock.com
The BRCA1 gene shows evidence
of rapid evolution by positive selection in our
recent evolutionary past. (A) A phylogeny of great
(C)
Chimpanzee
Gorilla
Today
Angelina Jolie
Sites with disease-causing mutations
**
**
*
Sites of positive selection
Futuyma Kirkpatrick Evolution, 5e
10/5/22 3:15 PM
6.7 Searching the Genes for Signatures of Adaptation
161
deleterious and are removed by purifying selection. In that case, the dN/dS
ratio will be smaller than 1. These interpretations of the dN/dS ratio are summarized as follows:
dN/dS < 1
dN/dS ≈ 1
dN/dS > 1
Most nonsynonymous mutations are deleterious and
removed by purifying selection
Nonsynonymous and synonymous mutations are
evolving largely neutrally
Many of the nonsynonymous differences between
the species were fixed by positive selection
The dN/dS ratio for the BRCA1 gene is greater than 1 along two branches of the
primate phylogeny, with the highest ratios occurring on the branches leading to
humans and chimps (see Figure 6.22). What caused the positive selection is not certain, but there are tantalizing suggestions. The BRCA1 protein is important to the
repair of damaged DNA, which helps explain why mutations in the gene can cause
cancer. Sara Sawyer and colleagues hypothesize that BRCA1 also helps thwart
infections by interacting with viral DNA or proteins. Perhaps coevolution with
viruses drove the rapid evolution of the BRCA1 gene, with the unfortunate side
effect that it is now more likely to mutate to a form that can trigger a cancer [23].
The dN/dS ratio for BRCA1 in the human lineage is very unusual: most genes
have ratios much smaller than 1. FIGURE 6.23 shows the distribution of dN/dS
ratios in a comparison of 15,350 genes in humans and mice. (The dN/dS ratio for
BRCA1 in the human lineage is so high that it falls beyond the right end of this
distribution.) This distribution suggests that most mutations at nonsynonymous
sites are deleterious and are removed by purifying selection, while beneficial
mutations are much rarer.
The dN/dS ratio is a conservative test for adaptation: it often has a value
less than 1 even when positive selection has occurred. That is because a single
nonsynonymous change caused by positive selection will be missed if many
other nonsynonymous mutations were deleterious and removed by purifying
selection. The next test we discuss is more sensitive to positive selection, but it
requires more data than does the dN/dS ratio.
The MK test
A second strategy to find evidence of positive selection builds on the basic idea
of the dN/dS ratio by exploiting data on polymorphism within one of the species.
3000
Number of genes
2500
Purifying
selection
Positive
selection
2000
FIGURE 6.23 The distribution of the
d N /d S ratios in comparisons of loci in
humans and mice. The data are from
1500
1000
500
0
06_EVOL5E_06.indd 161
0.1
0.2
0.3
0.4
0.5
0.6
0.7 0.8
dN /dS
0.9
1.0
1.1
1.2
1.3
1.4
15,350 loci. The ratio d N /d S is typically
much smaller than 1 (vertical dashed
line), which suggests that most nonsynonymous mutations are deleterious and
removed by purifying selection. Values of
d N /d S greater than 1 are rare and strongly
suggest positive selection has acted on
those genes. (After M. Nei et al. 2010. Annu
Rev Genom Hum Genet 11: 265–289.)
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Chapter 6
Genetic Drift: Evolution at Random
The MK test is named after John McDonald and Martin Kreitman, who first developed the approach [25].
Looking back at Equations 6.1 and 6.3, we see that the ratio of d (the number of
differences between two species) and π (the nucleotide diversity within one of the
species) will be constant for all regions of the genome that are evolving neutrally.
If a gene is evolving by positive selection, however, the d/π ratio will be inflated
because additional differences between species will have accumulated by positive
selection (making d larger). On the other hand, if a site is under purifying selection,
the ratio will be depressed because selection will prevent many deleterious mutations from becoming fixed (making d smaller). The MK test assumes that synonymous changes evolve neutrally. It compares the d/π ratio at nonsynonymous DNA
sites with the d/π ratio at synonymous sites. If the ratio for nonsynonymous sites is
higher, there is evidence of positive selection. If the ratio is lower, there is evidence
of purifying (or balancing) selection. An extension of the MK test was used to make
the estimates cited earlier for the fraction of amino acid differences among species
that result from adaptation and the fraction that result from drift [19].
The MK test has been used to find evidence of positive and purifying selection.
One recent study estimated that almost 15% of changes in human proteins over
the last 5 My resulted from positive selection [35]. Many of these loci fall into
interesting functional groups. Adaptive evolution has been particularly common
in transcription factors and hormone receptor genes. Some of these genetic changes
may have been crucial to the emergence of the unique traits we see in modern
Homo sapiens, including language.
© 2xSamara.com/
Shutterstock.com
Divergence among populations
1
Allele frequency in Han Chinese
10,000
1000
Number of SNPs
100
10
EPAS1
0
0
1
1
Allele frequency in Tibetans
FIGURE 6.24
Tibetans have recently adapted to low oxygen
pressure. The plot compares allele frequencies at SNPs from about
20,000 coding genes in Tibetans and Han Chinese. The numbers of SNPs
with the corresponding frequencies are color-coded according to the
scale at right. At most SNPs, allele frequencies in the two populations are
highly correlated. Alleles at two SNPs in the EPAS1 gene are outliers: they
Futuyma
5e selection in Tibetans (blue arrow). This rapid
have Kirkpatrick
increasedEvolution,
by positive
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Visual Services
evolution is thought
to be an adaptation to low oxygen pressure. (After
Evolution5e_06.24.ai
09-16-2022
X. Yi et al. 2010. Science
329: 75–78.)
06_EVOL5E_06.indd 162
In some cases we suspect there has been adaptation
to a particular environment but don’t know which
gene or genes were involved. In this situation we
can use a genome scan to look across the genome
for signs of positive selection. One type of scan
compares allele frequencies in two or more populations. Sites in the genome that have unusually
high divergence are suggestive of local adaptation
resulting from the increase of different alleles in
different populations (see Chapter 8).
The Tibetan Plateau is one of the harshest environments inhabited by humans. It is very cold and
very high: many Tibetans live above 4000 m elevation, where oxygen pressure is only two-thirds
of what it is at sea level. FIGURE 6.24 shows a plot
comparing frequencies of SNPs (single nucleotide
polymorphisms; see Chapter 4) across the genome
in Tibetans with those of Han Chinese, who live
near sea level [36]. Overall, there is a very strong
correlation: alleles that are common in one population are common in the other. Two exceptions
stand out at the bottom right of the plot. These are
SNPs with alleles that are at about 90% frequency
in Tibetans but only 10% frequency in the Han.
Those alleles are also at low frequency among
Europeans, which shows that the difference
between the Tibetans and the Han resulted from
evolution in the Tibetan population, not the Han.
These data strongly suggest there has been local
adaptation in Tibetans at a site on the chromosome
near to these SNPs.
10/5/22 3:15 PM
Summary
163
The local adaptation hypothesis becomes even stronger when we learn where
the two SNPs are in the genome. Both lie in a locus called EPAS1, which is
expressed in lungs and placenta, and it appears to be involved in the regulation
of red blood cells. Those features suggest that EPAS1 adapted to the low oxygen
pressures at the high elevations of the Tibetan Plateau. Selection must have been
very intense. Tibetans colonized the plateau only 2750 years ago, and so the allele
at EPAS1 that is now common there must have swept almost to fixation in less
than 150 generations. We’ll see in Chapter 21 that the EPAS1 allele carried by
Tibetans has an even more surprising evolutionary story to tell.
What We Don’t Know
In this chapter we discussed how variation at neutral sites in the genome is
reduced by selective sweeps and background selection happening at sites
nearby. Currently there is no general method that corrects for those effects
in order to obtain reliable estimates of population size. Likewise, changes in
population size can generate patterns of variation among DNA sequences that
mimic those produced by selection, making it difficult to get accurate estimates
of positive and background selection. This is an area of active research.
● We saw that there are strong correlations between nucleotide diversity (π) and
life history variables (Figure 6.14). There are several hypotheses that might
explain these patterns, but there is no clear evidence yet showing which are
most likely correct [10].
●
SUMMARY
■ Random genetic drift is the change in allele
frequencies caused by chance events of
survival, reproduction, and meiosis. Unlike
selection, it does not systematically favor
one allele or another.
■ Drift is stronger in smaller populations, causing
faster evolution.
■ Drift tends to erode genetic variation within
a population, and it causes differences among
populations to accumulate.
■ Some effects of drift are best understood by
looking backward in time at the gene tree that
reflects the genealogy of the copies of a gene
that are carried by living individuals. If we look
backward in time along the gene tree, we see
two gene copies that coalesce at their most
recent common ancestor. All the copies of a
gene in any population, species, or group of
species ultimately trace back to a single ancestral gene copy at some point in the past.
■ The strength of drift is determined by the
effective population size, Ne. This is the size
of an idealized population that has the same
strength of drift as the population of interest.
A natural measure for the strength of drift is
1/Ne, which determines the size of the random
fluctuations in allele frequencies going forward in time. It also determines the average
time in the past of the most recent common
06_EVOL5E_06.indd 163
ancestor of two gene copies now carried by
living individuals.
■ Ne is smaller (sometimes much smaller) than
the actual population size, as the result of
factors that include fluctuating population
sizes and unequal sex ratios. Population
bottlenecks are short, severe reductions in
population size.
■ The nucleotide diversity (π) at a DNA site that is
evolving neutrally (with no selection) is expected to be proportional to Ne. This relation can
be used to estimate Ne from genetic data. DNA
bases at which deleterious mutations occur
tend to be less polymorphic. As a result, introns
and regions between genes are typically more
variable than the coding regions of a genome.
For the same reason, the third positions of codons are typically more variable than the first
and second positions.
■ An allele will evolve largely as if selection is
not acting when s is much smaller than 1/Ne ,
while it will evolve largely as if drift is not
acting when s is much larger than 1/Ne.
■ Because the relative importance of selection
and drift depends on the population size, many
of the genetic differences among species with
large Ne are expected to be adaptive, while in
species with small Ne many of the genetic differences result from drift rather than adaptation.
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Chapter 6
Genetic Drift: Evolution at Random
Drift has caused many deleterious mutations to
be fixed in humans and other species.
■ Many (but not all) genes evolve at a relatively
constant rate. A constant rate of molecular
evolution is called a molecular clock, and it can
be used to estimate the time since two species
shared a common ancestor. Constant rates
are expected when genes evolve neutrally, but
relatively constant rates are also seen in some
genes that are evolving by positive selection.
■ Several methods are used to detect positive selection acting on DNA sequences. Recent positive selection can be detected using variation
within and differences between populations
and species. Loci identified this way are of interest because they tell us about the molecular
basis of adaptive evolution.
SUGGESTIONS FOR FURTHER READING
The human brain is not well adapted to thinking
about probability, and so random genetic
drift is a difficult concept to grasp. A lucid
introduction to the theory is found in Graham
Coop’s Population and Quantitative Genetics,
available for free as a PDF at https://github.
com/cooplab/popgen-notes/releases/tag/
v1.1. [9]
The neutral theory of molecular evolution was
wildly controversial when it was first proposed in the 1960s. Since then, a muchtempered version has become woven into
the fabric of modern evolutionary biology.
An overview of the current thinking about the
neutral theory is given by Brian and Deborah
Charlesworth [8] and Jensen et al. [17].
The development of methods for making inferences from DNA sequences is an exciting
and rapidly changing field of research. Aaron
Stern and Rasmus Nielsen’s 2019 review
“Detecting natural selection” covers the
state of the art in that field [34]. A much more
detailed overview is given by Matthew Hahn
(2019) in Molecular Population Genetics [14].
H. Ellegren and N. Galtier review variation between species in levels of DNA polymorphism
in “Determinants of genetic diversity” [10].
PROBLEMS AND DISCUSSION TOPICS
1. Imagine that you have the DNA sequences
from the intron of a gene in three species
called A, B, and C. Species A and B are most
closely related, while C is more distantly
related. The sequences of A and B differ by
18 base pairs, A and C differ by 26 base pairs,
and B and C differ by 28 base pairs. Fossils
show that species A and B diverged about
1.2 Ma, but there is no fossil evidence showing
when the most recent common ancestor of
all three species lived. Use the genetic data
to estimate that date. What assumptions
are you making to get this estimate?
2. In a population of 105 rabbits that has equal
numbers of males and females, how many
copies of a gene are there
a. at a locus that is carried on an autosome,
b. at a locus on the X chromosome, and
c. at a locus on the Y chromosome?
If all else is equal,
d. which loci do you expect to be the most
polymorphic for selectively neutral
mutation, and
e. which the least?
06_EVOL5E_06.indd 164
f. In a sentence, explain the logic behind
your answers.
g. How would your answers change if
mutation rates were much higher in
males than in females (which is the case
in many species)?
3. The logic of the MK test mentioned in the text
is based on the ratio of the number of sites
that are different between two species and
the number of sites that are polymorphic
within one of those species. This ratio is expected to be the same for synonymous and
nonsynonymous changes in a locus that is
evolving neutrally.
A study of a locus in two species of fishes
called A and B obtained the following results:
Sites different
between
A and B
π in A
Synonymous
Nonsynonymous
10
30
2 × 10 -4
1.5 × 10 -3
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Summary
Calculate the ratios of the differences
between species and polymorphism within a
species for synonymous and nonsynonymous
changes. What do your results suggest about
how this locus is evolving? If you had only
measured the nonsynonymous/synonymous
ratio, what would your conclusion about this
locus have been?
4. Two populations of a fly are isolated from
all other populations. Population S lives on a
very small island and has a population size of
103 individuals. Population L lives on a large
continent and has a population size of 108 individuals. There is a beneficial mutation that
increases fitness by 1%, but initially neither
population has a copy of that allele. The mutation rate from the current allele to the beneficial mutation is 5 × 10 –7. Is it likely that the
beneficial mutation will be common in population S or population L after 1000 generations?
5. Consider a species of sparrow that originally
lived only in Alaska but recently expanded its
range through North America, then Central
06_EVOL5E_06.indd 165
165
America, and finally South America. Discuss
how you expect heterozygosity for most loci
to differ among populations in North America,
Central America, and South America. Which
pairs of regions would you expect to have the
most genetically similar populations,
and which the most different? If the range
expansion involved adaptation to new environments, what test might you use to find
the regions of the genome involved?
6. The genomes of prokaryotes and eukaryotes
differ in many important ways. Prokaryotes
usually have very little noncoding DNA (introns, intergenic DNA, pseudogenes, etc.) and
usually have much lower mutation rates than
eukaryotes. Many (but not all) prokaryotes
have larger effective population sizes than
eukaryotes. Prokaryotes often reproduce
asexually (without recombination). Discuss
how these factors might make the average
nucleotide diversity (π) differ between prokaryotes and eukaryotes.
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7
The Evolution of
Quantitative Traits
W
hat was the most important advance in human history? Fire? The
wheel? The computer? A strong case can be made for the origin of
agriculture, which began with the harnessing of evolution by our
ancestors starting some 11,000 years ago. They bred wheat that provided
more grain and cows that produced more milk. Selective breeding produced
domesticated plants and animals that gave the first farmers vastly more food
than their ancestors could have imagined (FIGURE 7.1). For the first time in
history, humans had abundant resources. In order to grow crops, they had
to abandon a nomadic lifestyle and settled in communities that became the
earliest villages, towns, and then cities. Much of civilization—buildings, writing,
commerce—only then became possible. In short, the genetic modification of
plants and animals by selective breeding is the foundation of human civilization.
The discovery of the power of selective breeding is simply the discovery that
selection and inheritance together can produce large evolutionary changes.
And, as Darwin realized, evolutionary changes caused by artificial selection
of domesticated animals and plants illustrate what natural selection can do
in the wild.
This chapter begins by looking at how genes and the environment affect
quantitative traits, how selection acts on them, and how fast they evolve in
response. The next topic is artificial selection, in which people selectively
breed animals and plants to improve them for food production and other
purposes. (For Darwin and many biologists since, artificial selection has
provided insights into the evolution of quantitative traits.) The chapter then
Several species of grasses have been modified by human selection to make them the major
food in many parts of the world. Among them is rice (Oryza sativa), seen here in beautiful
terraces in Vietnam.
© mauritius images GmbH/Alamy Stock Photo
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168
Chapter 7
The Evolution of Quantitative Traits
FIGURE 7.1 Modern corn and its wild ancestor, teosinte, differ in
many ways. The striking differences in the pattern of branching result
from a small number of genetic changes. Other differences, for example,
in the size of the cob, involve changes at many loci.
describes how correlations between traits alter evolutionary trajectories. The final topic is the genetic basis of quantitative traits.
Quantitative genetics is largely based on statistics, and Chapter
22 provides a quick introduction to the key concepts.
7.1 Genotypes and Phenotypes
Learning objectives
■ Explain how phenotypes that vary continuously can
result from discrete genotypes and nongenetic factors.
■ Describe how quantitative traits can evolve beyond
Corn
John Doebley
Teosinte
FIGURE 7.2 Height in humans is a classic
example of a quantitative trait that varies
Futuyma
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students are lined
continuously.
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that Services
show their heights, varying
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Traits such as crop yield in corn, milk production in cows,
and body height in humans are examples of quantitative traits.
These are traits that vary continuously and that are affected by
several, sometimes thousands, of loci (and for that reason they
are also called polygenic traits). Most morphological and physiological characteristics of plants and animals are quantitative
traits. Quantitative genetics is the study of how quantitative
traits are inherited and how they evolve.
Variation in DNA is discrete. At any particular site on a
chromosome, the DNA can be one of four possible bases (A,
C, G, or T). But a quantitative trait like height in humans varies in a continuous way (FIGURE 7.2). What is the connection
between discrete variation in the DNA and continuous variation in height? Height is affected by thousands of loci [42].
It is also affected by environmental (nongenetic) influences,
such as nutrition during early development. Identical twins
(which have the same genotype) have slightly different heights
for that reason. The distinction between phenotypes and genotypes made in Chapter 4 is particularly important for quantitative traits: many phenotypic traits can be seen directly, but
their genotypes cannot.
© Peter Morenus/U. of Connecticut
from short on the left to tall on the right. Women
are dressed in white and men in black.
their original range of variation.
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7.1 Genotypes and Phenotypes 169
The visible variation in quantitative traits like height is measured by the phenotypic
variance. This is simply the variance in the measurements of the trait in the pop-
ulation. (Variance is a key concept in this chapter and is explained in Chapter
22.) The phenotypic variance results from both genetic and environmental (nongenetic) causes. FIGURE 7.3 shows how these factors combine to determine the
distribution of phenotypes in a population. The three rows show the situation
when different numbers of loci contribute variation to the trait. The left-hand
panels show traits that have small amounts of environmental variation, while the
right-hand panels show traits for which the genetic and environmental sources
of variance are about equally large (a situation typical of many traits). When just
a single locus affects the trait and the environmental variation is small, the phenotypic distribution shows distinct categories that reflect the discrete genotypes,
but the distribution is smooth if there is more environmental variation. When ten
loci contribute variation to the trait, the distribution is very smooth even when
there is little environmental variation. The combination of several to many loci
with some environmental variation explains why so many familiar traits such as
body height vary in a smooth, continuous way. Quantitative traits often follow
a normal distribution, which is also called a Gaussian or bell-shaped distribution.
1 locus
Frequency
More loci
Phenotypic distribution for
entire population
Phenotypic distributions for each
genotype that results from the
addition of environmental variance
Phenotypes that would be
produced without any
environmental influence
6
8
10
12
Trait value
14
6
8
10
12
Trait value
14
Frequency
4 loci
6
8
10
12
Trait value
14
6
8
10
12
Trait value
14
6
8
10
12
Trait value
14
Frequency
10 loci
6
8
10
12
Trait value
14
FIGURE 7.3
A phenotypic
distribution becomes smoother
when a trait is affected by many
loci and/or large environmental
influences. The vertical blue bars
show the phenotypes that would be
produced without any environmental
influence. The solid green curves show
the phenotypic distributions for each
of the genotypes that result from the
addition of environmental variance.
The dashed red curve shows the
phenotypic distribution for the entire
population. Each locus has two alleles
with frequencies 0.5. The loci are in
linkage equilibrium, and alleles have
equal and additive effects on the trait
(there is no dominance or epistasis).
The effects of alleles have been made
smaller as the number of loci increases,
so the overall variance is the same in
the three cases.
More environmental variance
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Chapter 7
The Evolution of Quantitative Traits
Frequency
p = 0.25
p = 0.75
Trait value
Frequency
(B) 50 loci
Frequency
(A) 1 locus
Frequency
170
Phenotypic distribution for
entire population
Phenotypic distributions for each
genotype that results from the
addition of environmental variance
p = 0.25
p = 0.75
Trait value
FIGURE 7.4
Large changes in quantitative traits can occur
by the evolution of allele frequencies without the addition
of new mutations. Bars and lines are as in Figure 7.3. (A) The
trait is affected by one locus with two alleles. Changing the allele
frequency from p = 0.25 to p = 0.75 causes the means of the trait
(red triangles) to increase, but the new phenotypic distribution
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Phenotypes that would be produced
without any environmental influence
overlaps substantially with the old one. The evolutionary change
in the mean is shown by the arrow beneath the bottom panel.
(B) The trait is now affected by 50 loci. Increasing the allele frequency from 0.25 to 0.75 causes the new distribution to evolve
so far that there is no overlap between the old and
new distributions.
Quantitative traits evolve by changes in allele frequencies
The mean value of the trait in the population evolves when allele frequencies at
the loci change. The change in the mean can often be so large that the range of
trait values in the population falls entirely outside the range that was present
in the original population. This is because the new allele frequencies produce
significant numbers of some genotypes that previously were rare or absent,
and these new genotypes produce new and extreme phenotypes. FIGURE
7.4A shows how the phenotypic distributions change for a trait affected by one
locus. There are two alleles, A1 and A2. Each copy of the A2 allele that an individual carries increases the value of the trait by 3 units. With the frequency of
allele A2 at p = 0.25, the Hardy-Weinberg ratio tells us that the frequency of the
rarest genotype (the A 2 A2 homozygote) is (0.25) 2, or 6.25%. So even the rarest
genotype is common enough to be seen. When the allele frequency increases
to p = 0.75, the mean of the trait increases, but the distribution of the trait still
overlaps with the original distribution, and the new mean lies within the original range of variation.
What happens if instead there are 50 loci (FIGURE 7.4B)? When the frequency
of the allele that increases the trait is p = 0.25 at all loci, the frequency of the genotype that is homozygous for the “big” allele at all 50 loci is (0.25)100 = 10–60. That
number is so small that this genotype—the one with the largest size—will never
exist. If the allele frequency at all loci increases to p = 0.75, the mean increases
so much that the new distribution of the trait does not overlap at all with the
original distribution. Quantitative traits can therefore evolve to produce entirely
new phenotypes by changes in the frequencies of alleles that are already in the
population, without the introduction of new mutations.
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7.2 Selection on Quantitative Traits 171
7.2 Selection on Quantitative Traits
Learning objectives
■ Explain what fitness functions show.
■ Describe how the strength of directional selection is measured and how
fast it causes the mean of a trait to evolve.
The horned lizards of the American Southwest look like they came from Jurassic
Park (FIGURE 7.5). Some species have dramatic horns projecting from the back
and sides of their heads. The horns help deter predators, such as the fearsome
loggerhead shrike. This bird has a remarkable (and gruesome) behavior. After
catching a horned lizard, the shrike often impales it on a branch or on barbed
wire, and returns to eat it later.
Researchers have exploited that behavior to learn how natural selection acts
on the size of the lizard’s horns [44]. They compared the horn lengths of living
lizards with those of lizards they found impaled by shrikes. From these data, the
researchers were able to estimate how relative survival varies with horn length. A
plot of survival against horn length shows that lizards with longer horns survive
best (see Figure 7.5D). We don’t know exactly how the horns protect the lizard.
Perhaps shrikes have difficulty picking up a lizard with large horns, or perhaps
the lizard stabs the shrike with its horns if it is caught.
The plot of survival against horn length is an example of a fitness function.
This quantifies how selection acts on a quantitative trait. The horizontal axis is
the value of the trait, and the vertical axis gives the average fitness for individuals
with that phenotype.
(A)
(B)
© Arthur Morris/Getty Images
Courtesy of Kevin Young
Horn
(D)
1.0
Expected survival
Horns
Courtesy of E. D. Brodie, Jr.
(C)
0.8
0.6
0.4
0.2
0
–2.5
–1.4 –0.3 –0.8
1.8
Relative horn length
2.9
FIGURE 7.5
The fitness function for horn length in the horned lizard Phrynosoma
mcallii was estimated by comparing horn size in living and dead individuals.
(A) A lizard that was caught and impaled on a thorn by a shrike. (B) The loggerhead shrike
is a major predator of horned lizards. The lizard shown here apparently did not defend
itself successfully. (C) A lizard that has avoided predation. (D) The fitness function
showing how survival varies with the length of the horns. (D after K. V. Young et al. 2004.
Science 304: 65.)
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The Evolution of Quantitative Traits
(A)
Directional
30
30
30
5
0
10
10
0
5
0
10
1
1
0.8
0.8
0.8
0.6
0.6
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Survival
Survival
0
20
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0.4
0
0.4
0.2
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10
Mean changed
50
0
5
0
10
Variance decreased
40
30
30
30
0
20
10
0
5
10
Trait value
FIGURE 7.6 The three modes of
selection on a quantitative trait.
Triangles show the trait means before
and after directional selection. Directional selection causes a change in the
mean. Stabilizing selection decreases
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the variance,
while
disruptive
selection
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increases it. (After
L.Services
L. Cavalli-Sforza
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and W. F. Bodmer 1971.
The Genetics of
Human Populations. San Francisco:
W. H. Freeman.)
0
10
0
5
10
Variance increased
50
40
10
5
0.4
40
20
0
0.2
50
Number
Number
20
10
0.2
07_EVOL5E_07.indd 172
Number
40
20
Disruptive
50
40
0
Trait distribution
after selection
(C)
40
10
Fitness function
Stabilizing
50
Number
Trait distribution
before selection
Number
50
(B)
Survival
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20
10
0
5
10
Trait value
0
0
5
10
Trait value
The horned lizards give an example of directional selection, which favors either
an increase or a decrease in a trait’s mean (FIGURE 7.6A). Many of the phenotypic
differences we see among species are the result of directional selection. The ancestors of today’s horned lizards had smaller horns that were enlarged by directional
selection. In other cases, directional selection favors a decrease in a trait mean.
Earlier we emphasized that selection and evolution are two very different things.
Selection happens within a generation, and it may or may not lead to evolution, which
happens between generations. In 1977 a severe drought hit the Galápagos Islands,
and many plants there failed to set seed. One of the seed-eating Galápagos finches
(Geospiza fortis) was forced to eat a new kind of seed that was larger, and birds with
larger bills had higher survival rates (FIGURE 7.7). The mean size of survivors’ bills
was about 5% larger than the mean size in the population before the drought. The
difference, which is highly statistically significant, shows there was directional
selection. Note that this comparison is between individuals of the same generation,
before and after the drought. At this point, no evolution has yet occurred.
A fitness function can also act on the variance of a trait. Stabilizing selection
favors individuals whose trait values are near the population’s mean. After stabilizing selection acts, the phenotypic variance is reduced (FIGURE 7.6B). This
is a common form of selection because the means of many traits are near the
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7.2 Selection on Quantitative Traits 173
(A)
(B)
0.5
75
0.4
0.3
Number of individuals
Survival rate
0.6
0.2
0.1
0
8
9
10
11
Bill depth (mm)
12
(C)
Before
selection
G. fortis
50
25
0
10
0
After
selection
8
9
10
11
Bill depth (mm)
values that have the highest fitness (often referred to as the optimum phenotype).
In that situation, individuals that are much smaller or much larger than the mean
have lower fitness. The result is that the tails of the phenotypic distribution are
trimmed, so the variance is smaller. Birth weight in humans is a classic example. Babies that weigh much less or much more than the average at birth have a
lower chance of surviving (FIGURE 7.8). Stabilizing and directional selection act
on many traits in modern human societies [30].
The opposite situation is called disruptive selection. Here the smallest and
largest individuals have higher fitness than individuals near the mean. After
selection, the phenotypic variance is greater than it was before (Figure 7.6C). Disruptive selection rarely splits a population into two separate groups, but rather
makes intermediate individuals less common. In the spadefoot toad Spea multiplicata, some individuals develop into normal-looking tadpoles that are omnivorous. Others develop into tadpoles with a biting beak and muscular jaws, and
they feed as predators, often cannibalizing other tadpoles. Both omnivorous and
carnivorous tadpoles grow quickly and have high fitness. Tadpoles that are intermediate between those two forms, however, have low fitness (FIGURE 7.9). The
morphology
ofEvolution,
spadefoot
Futuyma
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FIGURE 7.7 A severe drought
in the Galápagos Islands in 1977
produced very strong directional
selection on bills of the Galápagos
finch Geospiza fortis. Bill depth,
which is the distance from the top to
the bottom of the bill at its base, has
a strong effect on the size of seed the
finches are able to eat. (A) The fitness
function of survival as a function of bill
depth shows there was strong directional selection for deeper bills, estimated from the data in (B) and (C).
(B) Distribution in bill depth of 751 G.
fortis before the drought. (C) Distribution of bill depth of the 90 individuals
that survived the drought. (A after D.
Schluter. 1988. Evolution 42: 849–861;
B and C after P. T. Boag and P. R. Grant.
1984. Biol J Linn Soc 22: 243–287.)
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1
Survival
0.8
0.6
0.4
0.2
0
2
4
6
8
Weight (lbs)
10
© iStock.com/ArtShotPhoto
FIGURE 7.8
Birth weight in humans is under stabilizing selection. Infants with
birth weights much smaller or much larger than average have a lower probability of
surviving to 28 days. Each circle shows the survival rate for a group of infants that
had the birth weight shown and the same gestation time. (Courtesy of Dolph Schluter,
after data from D. Schluter. 1988. Evolution 42: 849–861.)
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174
Chapter 7
The Evolution of Quantitative Traits
FIGURE 7.9 Depending on the conditions, tadpoles of
spadefoot toads Spea multiplicata develop either into
a typical omnivore morph or into a carnivorous morph.
1.2
Individuals that are intermediate between those morphs
have low fitness (estimated by body size), showing that this
trait is under disruptive selection. (After R. A. Martin and
D. W. Pfennig. 2012. BMC Evol Biol 12: 136.)
Body size
1.1
1.0
0.9
Omnivore
(A)
Fitness
© iStock.com/
BirdImages
Hemlock
Directional
Disruptive
Stabilizing
1
2
3
9.0
Bill depth (mm)
9.5
Frequency
(B)
Lodgepole
8.5
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0
1
Morphology
2
Carnivore
Courtesy of David Pfennig
–1
Selection can alter both the mean and the variance of a trait at the same time.
For example, if a trait’s distribution after selection has a larger mean and a larger
variance than it did before, then both directional selection and disruptive selection have acted.
The fitness function and the trait’s distribution together determine whether
selection is directional, stabilizing, or disruptive. FIGURE 7.10 shows the fitness
function for bill depth in the red crossbill (Loxia curvirostra), a bird that specializes
in extracting seeds from the cones of pine trees and other conifers. The fitness
function for bill depth was estimated by the rate at which birds can feed. Birds
with small bills are efficient at opening the cones of western hemlocks, those
with large bills are efficient at opening cones of lodgepole pines, and those with
intermediate-sized bills are poor at feeding on both types of cones. This generates
two peaks in the fitness function for the crossbill. Is selection on bills directional,
stabilizing, or disruptive? The answer depends on the distribution of bill depth
Futuyma
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relative
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fitness Evolution,
function.
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where the fitness function
05-23-2022is increasing or decreasing, then directional
selection acts. If the population lies near a peak in the fitness function, then stabilizing selection acts. Last, if the population lies near a low point, then disruptive
selection acts.
Fitness functions are also used to visualize selection acting on more than one
trait. In these cases, the fitness function tells us which combinations of traits
give high or low fitness. An example is shown in FIGURE 7.11. In juvenile male
song sparrows (Melospiza melodia), birds with low body mass survive best if
they have short wings, while heavy birds survive best if they have long wings.
Selection of this sort, which favors particular combinations of traits, is called
correlational selection.
FIGURE 7.10 The type of selection acting on red crossbills depends on their
phenotypic distribution. Birds whose bills are matched to cones of either hemlock or
lodgepole pine have high fitness. The distributions of bill depth in three hypothetical populations of crossbills shown below the fitness function experience three different modes
of selection. (Based on data from C. W. Benkman. 2003. Evolution 57: 1176–1181.)
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7.2 Selection on Quantitative Traits 175
(A)
(B)
71
Survival
70
Wing length
0.7
High
fitness
69
Cephas/CC BY-SA 4.0
Low
fitness
0.6
0.5
Song sparrow
68
0.4
67
70
24
26
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28
68
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Low
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Survived
Died
66
66
22
26
24
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a
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FIGURE 7.11 Body mass and wing length in song sparrows (Melospiza melodia)
are under correlational selection. Light birds survive best with short wings, while
heavy birds survive best with long wings. (A) A plot of the fitness function in two dimensions showing the data points for the individual birds. (B) A three-dimensional view of
the fitness function. (After D. Schluter and D. Nychka. 1994. Am Nat 143: 597–616.)
Measuring the strength of selection
Many of the questions that evolutionary biologists ask are about how and why
the mean values of traits evolve. (How and why did blue whales become so
large?) Evolutionary changes in means are often caused by directional selection,
and so it is important to be able to quantify its strength.
The selection gradient measures the strength of directional selection acting on
a quantitative trait. It plays a role analogous to that of the selection coefficient for
the alleles at a single locus. The basic recipe for estimating a selection gradient
is illustrated by a study of guppies (Poecilia reticulata), a tropical freshwater fish
that is popular among aquarium enthusiasts because males are very colorful.
Ann Houde [17] was curious about how male coloration affects mating success.
For each male in her study population, she measured the proportion of his body
with orange pigment (a phenotype value) and the proportion of his mating displays that elicited a sexual response from a female (a measure of mating success).
Houde then calculated the relative fitness of each male by dividing his number
of matings by the average number. Last, she fit a regression line to the data, with
the phenotypic value on the x-axis and relative fitness on the y-axis (FIGURE 7.12).
(See Chapter 22 for a brief introduction to regressions.)
The slope of this line estimates the selection gradient, symbolized as β. This is
a natural measure of the strength of directional selection. The units of a selection
gradient are 1/(units of measurement). If the trait is measured in millimeters, for
example, then β is expressed as per millimeter. If the gradient is positive, then directional selection favors the mean to increase. In the guppies, β = 3.8, showing that
selection favors males with more orange. (In this case β has no units because the
trait is measured as a proportion of the body surface.) If they existed, completely
orange males would on average have 3.8 times more matings than males with no
orange at all. A negative β implies that selection favors smaller values of the trait.
Last, a value of β = 0 means that there is no directional selection acting.
Futuyma
Kirkpatrick the
Evolution,
5e
In general,
data needed
to estimate a selection gradient are measurements
OUP/SInauer/Troutt Visual Services
of
the
trait
and
of
fitness
on
a
set of individuals. Ideally, we would like to use
Evolution5e_07.11.ai 05-23-2022
the lifetime fitness. Often that is not possible to measure, so instead we use an
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Chapter 7
The Evolution of Quantitative Traits
important fitness component, such as survival or
mating success. Evolutionary biologists have mea1.6
sured selection acting on hundreds of different
traits in many species of plants and animals [22].
1.4
Directional selection is very common. Sometimes
β = 3.8
it is very strong, sufficient to shift the mean of the
1.2
trait by more than one phenotypic standard deviation within the generation.
1
In Chapter 5 we saw that natural selection
0.8
drives populations uphill on Wright’s adaptive
landscape. This landscape is a plot of the popu0.6
lation’s mean fitness against the frequency of an
allele. The concept of an adaptive landscape also
0.4
applies to quantitative traits [24]. Here the landscape plots the population’s mean fitness against
0.2
the mean value of the trait, rather than the allele
frequency. (It is important to distinguish between
0
0.05
0.1
0.15
0.2
0.25
the fitness function and the adaptive landscape.
Amount of orange
The first shows how the phenotype of an individual affects its fitness. The second shows how the
FIGURE 7.12 Selection gradient on
mean trait value in a population affects the popuorange coloration in male guppies.
lation’s mean fitness.) When the fitnesses of different phenotypes are constant
The horizontal axis shows the proportion
of the body that is orange, and the vertical
in time, natural selection causes populations to evolve uphill on this landscape.
axis shows relative fitness, as measured
The trait’s mean value will stop evolving when either it reaches a peak or the
by attractiveness to females in the lab.
population runs out of genetic variation.
The slope of the regression line gives an
The selection gradient and adaptive landscape are useful tools for visualizing
estimate of the selection gradient: β = 3.8.
how
selection is acting. A second use for them is to test hypotheses about adapFutuyma
Evolution,
5e
(After Kirkpatrick
A. E. Houde.
1987. Evolution
41: 1–10.)
tation. If a trait has reached an optimum favored by natural selection, then the
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Evolution5e_07.12.ai 05-17-2022
population should be at a peak on the adaptive landscape and there should be
no directional selection. That idea has been used to study the evolution of clutch
size (the number of eggs laid) in birds. Many birds are physiologically able to lay
more eggs than they actually do. This seems like an evolutionary paradox: why
doesn’t natural selection favor them to lay more? In fact, the selection gradient
on clutch size is close to zero [33]. Females that lay more eggs than average hatch
more chicks, but many of the chicks starve because their parents are unable to
feed so many mouths. Females that lay the average number of eggs leave the
largest number of surviving offspring to the next generation. We’ll look more
closely at the evolution of clutch size in Chapter 14.
From K. A. Hughes et al. 2013.
Nature 503: 108–110
Relative fitness
1.8
7.3 Evolution by Directional Selection
Learning objectives
■ Explain how the rate of evolution of a trait depends on the selection
gradient, the trait’s heritability, and its additive genetic variance.
■ Describe what additive genetic variance is, how it is measured, and why it
is important to adaptation.
We saw in Chapter 5 that if selection acts on a trait and if that trait is inherited,
then evolution will result. This is a condensed version of the most important
point made by Darwin in The Origin of Species. We can now go further than
Darwin could: we can predict how much evolution will result from natural selection.
Both evolutionary biologists and farmers need to know how much the mean
of a trait will evolve if there is directional selection (caused either by nature or
by selective breeding). Happily, the answer is the simplest mathematical relationship that makes sense, and it can be understood with a diagram (FIGURE
7.13). The evolutionary change in the mean of a trait from a single generation
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7.3 Evolution by Directional Selection 177
Schematic of the breeder’s equation. Each dot represents a family.
The mean size of the two parents is plotted on the x-axis, and the mean size of their
offspring on the y-axis. The regression line shows the mean size of offspring that are
expected from parents of a given size. The slope of this line is equal to the heritability,
h 2 . (A) With no selection, there is no evolutionary change, so the mean size of all offspring in the next generation, z–, is equal to the mean size of all parents in the previous
generation. (B) Directional selection occurs. In this example, only parents whose size
is larger than a threshold survive. The mean size of the surviving parents is *. Now the
mean size of their offspring (z–) is larger than if selection had not acted: the mean of
the population has evolved. (C) The selection differential, S, is the difference in the
mean size of individuals before and after selection. The evolutionary change in the
mean from one generation to the next is = h 2 S.
of selection equals the product of two quantities: the strength of directional
selection and the amount of genetic variation. This is an exact parallel to the
discussion in Chapter 5 of selection acting on a single locus (see Equation 5.3).
To make this point more quantitative, let –z represent the mean of a trait at
the start of a generation. Selection acts on the trait, and the survivors breed to
produce the next generation. Using z–ʹ to represent the mean at the start of that
new generation, the amount of evolutionary change is simply the difference
between z–ʹ and z–, which we symbolize by ∆z–. As shown in Figure 7.13, that
change is predicted to be
∆z– = z–ʹ – z– = h2 S
(7.1)
(A) Before selection
h2
zʹ = z
z
(B) After selection
Mean of offspring
FIGURE 7.13
zʹ
z*
This is the famous breeder’s equation, which is used to predict how much evolutionary change will result from selective breeding.
On the right side of Equation 7.1 is h2, which represents the trait’s heritability.
The heritability is equal to the slope of the regression line that relates the value
of a trait in two parents to its value in their offspring (FIGURE 7.14). The heritability therefore measures the strength of inheritance. If h2 is 0, then there is no
resemblance between offspring and their parents. At the other extreme, if h2 is 1,
then offspring look exactly like the average of their parents.
In this discussion, we assume that resemblance between parents and offspring is caused only by shared genes. Nongenetic factors can also contribute
to that resemblance, as when some groups of individuals live in good environments and others in poor environments. In those situations, a correction is made
to remove the environmental effects from the estimate of heritability. We are
also concentrating here on the genetic component of how populations change in
(C) Evolutionary change
(A)
Bill depth in 1976
Bill depth in 1978
Mean of offspring
h2 = 0
Mean of offspring
(C)
h =1
zʹ
z
∆ z = h2S
h2
S
z
z*
Mean of parents
2
Mean of offspring (mm)
FIGURE 7.14 The plot of the phenotypes
(B)
Futuyma
Kirkpatrick
Evolution, 5e
in parents
and offspring
is used to measure
OUP/SInauer/Troutt Visual Services
heritability. Each point represents the mean
Evolution5e_07.13.ai 05-17-2022
G. fortis
10.0
h2 = 0.9
1978
9.0
1976
8.0
Mean of parents
07_EVOL5E_07.indd 177
11.0
Survive
Die
8.0
9.0
10.0
Mean of parents (mm)
11.0
of all the offspring in a single family, plotted
against the mean of their two parents. (Left)
Two hypothetical cases show what the plot
looks like when (A) the heritability is h2 = 0 and
when (B) it is h2 = 1. (C) The plot of parents and
offspring for bill depth in the Galápagos finch
Geospiza fortis in 1976 and 1978. Although the
offspring’s bills were larger in 1978, the slope
of the regression between offspring and parents was nearly the same for both years. The
heritability, estimated from the slope of the
regression, was h2 = 0.9 in both years. (C after
P. T. Boag. 1983. Evolution 37: 877–894.)
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time. Changes in the environment can also cause traits to change, as when grass
grows taller in a wet year than in a dry one.
The second quantity on the right of Equation 7.1 is S, which is the amount
of change in the mean of the trait caused by selection within a generation. That
is, S equals the difference between the mean of the population after selection,
which is written z– *, and the mean before selection, z– (see Figure 7.7). If smaller
individuals are more likely to survive and reproduce than larger individuals, for
example, then S will be negative. Equation 7.1 is called the “breeder’s equation”
because farmers who breed domesticated plants and animals can decide how
large to make S by the choice of individuals that they allow to mate. A key point
is that S is the change caused by selection within a generation, while ∆ z– is the
evolutionary change between one generation and the next.
The essential message from Equation 7.1 is that the rate of evolution depends
on both the strength of inheritance, measured by h2, and the strength of directional selection, measured by S. The trait will not evolve by natural selection if it
is heritable (h2 > 0) but there is no selection acting on it (S = 0). Likewise, a trait
will not evolve if there is selection (S ≠ 0) but no heritability (h2 = 0).
A second version of the breeder’s equation is mathematically equivalent but
often more useful in evolutionary biology and is called Lande’s equation:
∆ z– = Gβ
(7.2)
Here G is an important quantity called the additive genetic variance. It is the part
of the phenotypic variation that is caused by genetic variation and that contributes to the resemblance between parents and offspring.
The additive genetic variance can be defined as
G = h2 P
(7.3)
Here P is the phenotypic variance of the trait, that is, the variance of the phenotypic measurements. Equation 7.3 can be rearranged to give
h2 = G / P
(7.4)
This shows that the heritability equals the fraction of the phenotypic variance
that is due to additive genetic variation. The rest of the phenotypic variance is
contributed by two other sources. The first (and most important) source is nongenetic factors. These can include nutrition, age, random effects during development, and many other factors. Collectively, they give the trait environmental
variance, which causes individuals with the same genotype to have different
phenotypes (see Figure 7.3). Different environments contribute different amounts
of environmental variance, so a trait’s heritability depends on the environment
in which it is measured. The other source of phenotypic variance is genetic variation that is not additive, which is caused by dominance and epistasis, as we will
discuss shortly. The overall phenotypic variance for a trait is simply the sum of
the additive genetic, environmental, and nonadditive genetic variances.
Studies of many species have shown that most quantitative traits are heritable
and evolve if selection acts on them [16]. Heritabilities vary among traits and
species. The values for morphological traits in vertebrates typically fall in a range
between 0.2 and 0.7. That means that much (and sometime most) of the phenotypic variation we see for quantitative traits is genetic in origin and can respond
to selection. Components of fitness such as fecundity and longevity tend to have
higher additive genetic variances but lower heritabilities than morphological
traits because they often have more environmental variance [18].
We now have everything we need to predict how far and in what direction
the mean of a trait will evolve in one generation. The heritability h2 is estimated
from the regression of the trait measured in offspring plotted against the trait in
their parents (see Figure 7.14). We can also use those measurements to find P, the
phenotypic variance of the trait. With those values in hand, Equation 7.3 gives us
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7.3 Evolution by Directional Selection 179
the additive genetic variance G. The strength of directional selection, measured
by the selection gradient β, is estimated by the regression of relative fitness onto
the trait value (see Figure 7.12). Finally, the evolutionary change in the mean of
the trait, ∆ z–, is simply the product of G and β (Equation 7.2).
Consider this: we can predict the outcome of genetic evolution without knowing
anything about the genes that affect the trait! Thousands of years before genes were
discovered, prehistoric farmers made very large genetic changes as they domesticated animals and plants (Figure 7.17). Equation 7.2 tells us that the rate of evolution is not determined by the number of genes that affect the trait (at least in
the short term). A second insight is that the rate of evolution is not determined by
the population size (again, in the short term). A small population does not evolve
more quickly than a large one if the two have the same additive genetic variance G.
Evolutionary biologists are interested in which traits have the potential to
adapt most quickly, and which most slowly. You can immediately see a couple of
difficulties in answering this question. Traits are measured in different units, so
how could we (for example) compare a rate of change measured in millimeters
with one measured in kilograms? And even if the traits are measured in the same
units, a change (for example) in body mass of 0.01 g would be huge for an ant
but biologically irrelevant for an elephant. A solution is to think in terms of the
proportional rate of evolutionary change in a trait’s mean caused by directional
selection. We can do that with a modification of the Lande’s equation (Equation
7.2) that produces a new quantity, called evolvability:
(7.5)
Evolvability is a natural measure of a trait’s potential for adaptation. Since it has
no units, we can use it to compare different kinds of traits [14]. Using this measurement reveals interesting patterns. For example, life history traits like survival
and fecundity on average have higher evolvability than do morphological traits
like height. (In Chapter 11, we will see that evolvability is also used by developmental biologists to mean something slightly different.)
Adaptation from standing genetic variation and from new mutations
Many traits have standing genetic variation—variation that already exists. These
traits can respond to selection immediately. In a famous breeding experiment
with corn, the selected trait (oil content of the kernels) changed rapidly from the
very first generation that selection was applied (FIGURE 7.15). This shows there
25
Oil content (%)
20
15
10
FIGURE 7.15
The oil content of corn kernels
increased over 107 generations of artificial selection.
5
Range of variation
in starting population
0
07_EVOL5E_07.indd 179
10
20
30
Futuyma Kirkpatrick Evolution, 5e
40
© Maks Narodenko/Shutterstock.com
50 60 70
Generation
80
90
100
The average oil content in the most recent generation
was more than 4.5 times higher than in the initial population, and it continues to increase at a nearly constant
rate. (Data from J. W. Dudley and R. J. Lambert. 2004.
Plant Breed Rev 24: 79–110.)
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was abundant additive genetic variation (large G) already present in the population when selection began. In this experiment, the trait under selection has
evolved at a relatively constant rate for over 100 generations. Selection caused
fixation of some alleles that affect oil content, but new ones generated by mutation have maintained the additive genetic variance for oil content at a nearly
constant value. In other cases, as selection fixes alleles that contribute to genetic
variation, the additive genetic variance and heritability decline. This can slow
and even halt the trait’s evolution.
Other traits do not have genetic variation now and cannot evolve until new
mutations favored by the new environmental conditions appear. Many examples
of this situation come from bacteria when they are exposed to a new antibiotic.
The drug is highly effective at first, but eventually a mutation appears that makes
the bacteria resistant. In some cases, new adaptive mutations appear quickly, but
in other cases they do not appear for long periods of time. This kind of speed limit
to adaptation is particularly common to small populations because fewer new
mutations enter a population when there are fewer copies of the genes to mutate.
Can adaptation rescue species from extinction?
As our atmosphere becomes enriched in CO2, many of Earth’s organisms are
experiencing directional selection caused by changing temperatures, acidified
oceans, and other new conditions. Will they be able to adapt? Some species can
buffer themselves from the environmental change by phenotypic plasticity [8].
But if the plasticity is not sufficient, the population will decline to extinction
unless the population can adapt by genetic change. Although all species now
alive owe their existence to adaptation in the past, the fact that well over 99%
of species that ever lived are extinct tells us that evolution does not guarantee
survival. When conditions change, what determines whether a species can adapt
fast enough to avoid extinction?
We can use mathematical models to explore that question [3]. Imagine that
the mean of a trait is initially at a fitness peak. The environment then changes,
favoring a new value for the trait. The mortality rate can then rise and exceed
the birth rate, causing the population to start shrinking in size. This triggers a
race between adaptation and extinction. If the species can adapt quickly enough,
survival rates will rebound and the species will be rescued. If it cannot, the population’s size can fall below a critical threshold where extinction will occur.
How this race ends depends on several key factors (FIGURE 7.16A). A population is more likely to survive if it has greater standing genetic variation, which
will allow it to adapt more quickly. A large initial population size helps survival
(A)
Population size
Large K
Large G
FIGURE 7.16 Adaptation can rescue some species but not others from extinction.
Danger zone
Environment
changes
Time
(B)
Cell density
1000
100
10
1
0
07_EVOL5E_07.indd 180
50
Time (hours)
100
(A) Simulations of how the size of a population changes in time following an environmental change that suddenly favors a different value of a quantitative trait. The environmental change (vertical dashed line) triggers declines in the population sizes of
three species. The population size of the blue species (dashed line) falls below a critical
threshold and into the “danger zone” (shaded area), leading to its extinction (marked
by the X). The green species (dotted line) has larger genetic variance (G) for the trait,
which allows it to adapt more rapidly to the new adaptive peak and avoid extinction.
The red species (solid line) has a larger carrying capacity (equilibrium population size,
K). It avoids extinction because it has a longer time to adapt before reaching the danger
zone. For simplicity, these simulations assume that the additive genetic variance is
the same in all three cases and does not change in time. (B) Evolutionary rescue allows
laboratory populations of yeast to avoid extinction following the sudden introduction
of salt into their medium at time 0. The trajectory of population size during the decline
and recovery is a good match for the “large K” simulation shown in (A). Note that cell
density (on the y-axis) is plotted logarithmically, so the changes in density are very
large. (B after G. Bell and A. Gonzalez. 2009. Ecol Lett 12: 942–948.)
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7.4 Connecting Variation in Genes to Variation in Phenotypes 181
in several other ways: the population size must decline a long way before it is
at risk of extinction, and more new beneficial mutations enter the population
in each generation.
One approach used to study how these and other factors affect the risk of
extinction is experimental evolution [4]. FIGURE 7.16B shows results from a laboratory study with yeast. Populations were suddenly subjected to high concentrations of salt. Initially, population sizes declined as the salt killed off yeast cells
more rapidly than they were able to divide. As the populations adapted to the
salt, population growth rates became positive. In this case, adaptation happened
quickly enough to prevent extinction. We’ll see later in the chapter an example
in which extinction is the likely outcome.
While we expect that adaptation will allow some species to avert extinction as
humans change the planet, many (perhaps most) will not be so lucky. The climate
changed rapidly many times during the Pleistocene (see inside back cover), with
warm periods interspersed with cold glacial periods. Many species survived by
colonizing new areas where the altered climate matched the climate they were
adapted to. But others did not adapt rapidly enough and became extinct. Currently, global change caused by the burning of fossil fuels is causing the climate to
change at rates more than 100 times greater than during the Pleistocene, probably
too fast for most species to shift their ranges to favorable regions [29].
7.4 C
onnecting Variation in Genes to Variation
in Phenotypes
Learning objectives
■ Describe how variation at a single locus can contribute additive genetic
variance to a trait.
■ Understand the effects that genetic dominance and epistasis have on
genetic variation.
How does variation at individual genes contribute to the additive genetic variance of traits that they affect? The simplest situation is when alleles have independent effects on a trait (i.e., there is no genetic dominance or epistasis). If we
focus on one of the genes, we can write the relationship between the alleles at
that locus and the phenotypic values of the trait as follows:
Genotype
A 1A 1
A 1A 2
A 2 A2
Average trait value
C–a
C
C+a
Here C is the average value of the trait in heterozygotes, and a is the effect that
a single copy of allele A2 has on the trait’s value. For example, for body mass
in a population of mice, C might be 20 g, and a locus that has a large effect on
weight might have a = 0.1 g. Then the average weights of mice with those three
genotypes would be 19.9 g, 20 g, and 20.1 g.
This locus contributes genetic variation to weight in this population. If
the frequency of allele A2 is p, then the variance in a population of mice at
Hardy-Weinberg equilibrium is
2 p (1 – p) a 2
(7.6)
This quantity is the additive genetic variance for mouse weight that is contributed by this locus. If p = 0.5, that variance is 2 × 0.5 × 0.5 × (0.1 g) 2 = 0.005 g 2.
What of the other genes that also affect the weight of these mice? Very often, the
effects of loci are independent, which means that the variances that they contribute can simply be added together. For example, imagine that a second locus has
allele B2 at frequency p = 0.25, and the effect of that allele on mouse weight is
a = 0.2 g. Then the genetic variance contributed by this locus is 2 × 0.25 × 0.75 ×
(0.2 g) 2 = 0.015 g 2, and the total genetic variance is 0.005 g 2 + 0.015 g 2 = 0.02 g 2.
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In many traits, such as height in humans, thousands of loci contribute genetic
variation, each having a very small effect. In other traits, a small number of loci
that have large effects contribute most of the genetic variation.
Expression 7.6 has important implications. As the allele frequency (p) changes
by selection or drift, the amount of genetic variance that the locus contributes
changes. Thus a trait’s genetic variance (G) can change in time. Further, the effect
that the alleles have on the trait (a) can vary depending on environmental conditions. Nevertheless, the genetic variances for some traits remain relatively constant, allowing them to evolve in one direction for many generations (see Figure
7.16). But other traits, particularly those for which genetic variation depends on
a small number of genes, can only evolve a limited amount before they need new
mutations to evolve further (see Figure 7.4A).
When genes interact: Dominance and epistasis
You may be wondering why G is called the “additive” genetic variance. The
answer is that it is important to distinguish additive from nonadditive genetic
variance because only the additive genetic variance contributes directly to evolutionary change.
Imagine that the height of a plant is completely determined by variation at a
single locus, with no environmental variance. This locus is overdominant (see
Chapter 5): both A 1 A 1 homozygotes and A 2 A 2 homozygotes are 20 cm tall, while
A 1 A 2 heterozygotes are 25 cm tall. If both the A1 and A2 alleles have a frequency
of 0.5 and the population is at Hardy-Weinberg equilibrium, then half the plants
will be 20 cm tall, and half will be 25 cm tall. There is lots of variation in this
population, and all of it is caused by genetic differences. Now imagine that there
is strong directional selection favoring tall plants, and all the short plants die.
The tall plants (which are heterozygotes) survive and reproduce. In the next
generation, the population looks exactly like it did before selection acted, with
equal numbers of short and tall plants.
Why didn’t selection cause an evolutionary change? Certainly not because of
a lack of genetic variation—all of the variation was genetic. Rather, it is because
none of it is additive genetic variance. In this example, the genetic variation
is of a form called dominance variance, which results when the phenotype of
heterozygotes is not intermediate between the phenotypes of the homozygotes.
Here the two alleles interact: the effect of an allele on an individual’s phenotype
depends on the other allele that is carried at the same locus. (The whole is not
simply the sum of its parts.) Alleles at different loci can also interact, a situation
called epistasis (see Chapter 4), which generates epistatic variance. Like dominance variance, epistatic variance does not contribute to evolutionary change.
For the great majority of traits, the additive genetic variance is much larger
than the dominance variance and the epistatic variance. (The example of the
plants was made extreme to make the concepts clear, but it is not typical.) In
short, most but not all genetic variation contributes to how fast a population
evolves in response to directional selection.
7.5 Artificial Selection
Learning objective
■ Describe artificial selection and what it has taught us about evolution.
Humans have been selectively breeding animals and plants for millennia. Long
before Equations 7.1 and 7.2 were known, people were genetically improving
animals and plants by breeding together the best individuals in their fields and
flocks. That process is called artificial selection, and it continues to this day as a
critical part of modern agriculture.
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7.5 Artificial Selection 183
Wild wheat, © iStock.com/fotolinchen;
Wild tomato, © jankamenar.com/
Shutterstock.com; Auroch, Dfoidl/
CC BY-SA 3.0; Wheat, © iStock.com/
osoznaniejizni; Tomato, © iStock.com/
AlexeyKonovalenko; Cow, © iStock.com/
GlobalP; Pizza, © iStock.com/dehooks
Wheat
Tomato
Milk
The next time you walk into a supermarket, think about the animals and plants
used to make the food you are about to
Wild ancestor
buy. Virtually all of them have been radically changed by artificial selection, some
so much that they only vaguely resemble
the wild species that were domesticated
by prehistoric farmers. The wheat for a
pizza’s crust, the tomatoes for its sauce,
Domesticated
the cows whose milk makes the cheese
on top—all of these species have been
changed dramatically by human-caused
evolution (FIGURE 7.17).
Despite centuries of selective breeding,
domesticated species continue to evolve in
response to artificial selection. Milk production and poultry growth are increasing at
1% to 2% per year (see Figure 5.3). This is a
critical contribution to society since it allows
us to produce more food with the same or
even fewer resources. Artificial selection on
many species is now done using sophisticated statistical methods that evaluate each
FIGURE 7.17 A familiar food item and the sources for three major ingredients—
individual’s genetic potential.
as they looked in their wild ancestors before domestication, and as they look
In addition to improving domesticated
today. Selective breeding has greatly increased the productivity of hundreds of
species, artificial selection is used by biolospecies of domesticated plants and animals.
gists to study basic questions about evolution. This research often uses model organisms such as Drosophila and E. coli because they
are convenient and well known
Futuyma Kirkpatrick Evolution, 5e
genetically. Several general conclusions haveOUP/SInauer/Troutt
emerged that are
likely
to apply
Visual
Services
Evolution5e_07.17.ai 08-12-2022
to all species [16]:
Almost all traits evolve when selected. Results from hundreds of selection
experiments show that most traits in diverse species immediately respond
to selection based on standing genetic variation. We will see shortly, however, that there are exceptions to this rule that help us understand when
and why there are limits to adaptation.
● Selection can cause a trait to evolve far beyond its original range of variation. We saw early in this chapter that changes in allele frequencies can
cause a quantitative trait to evolve far beyond the range of variation that
was originally present in the population. An example in real organisms
comes from a famous artificial selection experiment on corn that is still
continuing after more than 100 years (Figure 7.15). Early in the experiment, evolutionary change was based on standing genetic variation, but
new mutations contributed in later generations.
● Large populations evolve faster and farther than small populations.
Researchers have used artificial selection to learn what factors affect how
populations adapt. One pattern that emerges is that large populations
tend to evolve faster and farther (FIGURE 7.18). That may be surprising
because there is nothing in Equations 7.1 and 7.2 that suggests population size should have an effect. The explanation is that smaller populations tend to have less genetic variation as the result of genetic drift (see
Chapter 6). Large populations also accumulate beneficial mutations more
quickly because they have more copies of each gene that can mutate.
An important conclusion is that species that are already rare are particularly vulnerable to environmental change because they may not adapt as
quickly as abundant species.
●
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Deviation from controls (mils)
25
●
N
1000
20
15
© iStock.com/rob_lan
200
Strong selection on one trait often has negative side effects on other traits.
Over a span of 50 years, artificial selection on dairy cows increased milk
production by 1% per year, but it also caused fertility to decline at about
the same rate. This is an example of how selection on one trait often
causes evolutionary side effects on other traits, which is our next topic.
7.6 The Evolution of Correlated Traits
10
40
Learning objectives
■ Explain the difference between phenotypic correlations and genetic
5
correlations.
0
10
20
30
40
Generation
50
60
FIGURE 7.18
A selection experiment
for increased wing-tip height in
Drosophila melanogaster shows
evolutionary change in populations
of different sizes. N is the number of
individuals
that Evolution,
are selected
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Kirkpatrick
5e to start each
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generation. (ForVisual
example,
with N = 200
Evolution5e_07.18.ai
flies, a total of 100005-17-2022
flies were measured,
and the 200 flies with the longest wings
were bred to begin the next generation.)
The largest population has evolved the
fastest and farthest. (After K. E. Weber
1990. Genetics 125: 579–584.)
■ Compare the direct response and indirect response to selection.
■ Summarize how evolutionary constraints limit what natural selection
can produce.
Many traits are correlated: if you have long arms, you probably also have long
legs. This kind of correlation is a phenotypic correlation, that is, the correlation
between the phenotypic measurements of two traits. It is in part heritable and
genetic, meaning that individuals with long arms tend to have offspring that
have both long arms and long legs. Genetic correlations such as these cause evolutionary side effects. When directional selection acts on one trait, it will not only
cause the mean of that trait to evolve, it will also change the means of other traits
that are genetically correlated with it.
These evolutionary side effects are described by an expanded version of
Lande’s equation (Equation 7.2). Imagine that directional selection is acting on
two traits. The evolutionary change in trait 1 caused by one generation of selection is
∆z–1 = G1 β1 + G1,2 β2
(7.7)
There are two terms on the right side. The first is just what we saw in Equation
7.2: it is the product of G1, the additive genetic variance for trait 1, and β 1, the
selection gradient acting on trait 1.
The second term in Equation 7.7 is new. It is the product of G1,2 , which is the
genetic covariance between trait 1 and trait 2, and β 2, which is the selection gradient on trait 2. A genetic covariance measures how strongly two traits tend to
be inherited together. A covariance is closely related to a correlation, which is a
covariance that has been rescaled so that it ranges from –1 to 1 (see Chapter 22).
A genetic covariance of 0 means that the traits are inherited independently. A positive covariance means that individuals that are, for example, larger than average
for one trait will tend to have offspring that are larger for both traits. That is typically the case for morphological traits, simply because big individuals tend to be
big for all traits. A negative genetic covariance implies the opposite: individuals
that are bigger than average for one trait will have offspring that are big for that
trait but smaller than average for the second trait. An example of negative genetic
covariance is reproductive rate and longevity in fruit flies. Females that lay many
eggs survive less long than females that lay few eggs.
The two terms on the right side of Equation 7.7 show the rate of a trait’s evolution is determined by two factors. The first is the direct response to selection,
which is evolution caused by selection acting directly on that trait. The second
factor is the indirect response to selection, which is evolution caused by selection
acting on another trait with which it is correlated.
One implication of Equation 7.7 is that a trait can evolve by natural selection
even if selection does not act on that trait. If that statement sounds nonsensical
at first, consider what happens to trait 1 when the selection gradient acting
on that trait is zero. Its mean will nevertheless evolve if directional selection
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7.6 The Evolution of Correlated Traits 185
(A)
After selection
Change in means
Die
FIGURE 7.19
Directional selection
on one trait can cause another trait
to evolve as a correlated response.
Selection acts on one trait (dashed
arrows) but not the second trait.
(A) The two traits are not correlated.
After selection, the mean of the
selected trait has increased, but the
mean of the second trait is unchanged.
(B) The two traits have a strong positive
correlation. After selection, the means
of both traits have increased.
Trait 2
Before selection
Survive
Trait 2
(B)
Die
Selected trait
Survive
Selected trait
Selected trait
Evolutionary constraints and trade-offs
While the great majority of quantitative traits have standing genetic variation,
not all do. Traits that lack variation cannot respond to directional selection, and
07_EVOL5E_07.indd 185
© Paulo de Oliveira/Minden Pictures
acts on trait 2 and there is genetic covariance between the traits (i.e., neither β2
nor G1,2 is 0). The reason becomes clear with a simple example. Say that trait
1 is arm length and trait 2 is leg length. If selection acts to increase leg length,
then alleles that increase that trait will become more frequent. Because those
alleles also increase arm length, that trait will also evolve to a larger mean value
in the next generation. This situation is sketched in FIGURE 7.19. Even more
Futuyma Kirkpatrick Evolution, 5e
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is Visual
that selection
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to what selection 08-12-2022
on that trait favors. For example, if there is weak selection to
Evolution5e_07.19.ai
increase leg length but very strong selection to decrease arm length, both traits
can evolve to be shorter.
Earlier we discussed selection on bill size in one of the Galápagos finches
during an intense drought. Trevor Price and colleagues [28] found that selection
favored finches with narrower beaks, likely because they could better crack open
new seed types. Nevertheless, the average bill width among birds that survived
the drought was larger than it had been before the drought. The explanation for
this counterintuitive result is that bill width has strong positive correlations with
other traits, including body size. Those traits caused indirect selection on beak
width that was stronger than the direct selection on beak width.
Side effects like these may explain some evolutionary enigmas. The Mexican tetra (Astyanax mexicanus) is a fish that has both surface-dwelling and
cave-dwelling populations. Fish from the surface have eyes and can see, but fish
from caves have lost their eyes (FIGURE 7.20). Why does adaptation to the dark
and nutrient-poor environment in caves favor mutations that eliminate sight?
Cave fish find prey in the dark using sensory cells on their heads that respond
to vibrations in the water. Genetic analysis shows that mutations that increase
the responsiveness of this detection system also cause a reduction in the eyes as
a correlated side effect [43]. The blind cave fish also illustrate one of the ways
that natural selection can cause the loss of a complex structure such as the eye.
FIGURE 7.20 Mexican tetra (Astyanax
mexicanus) that live in surface populations have eyes, while many populations
that live in caves have lost them. Eyes
were lost as a side effect of the adaptation of other senses to life in the dark.
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The Evolution of Quantitative Traits
so we say they have an evolutionary constraint that can prevent them from adapting. Species of Drosophila that live only in wet tropical habitats have little or no
genetic variation that would allow them to adapt to cool and dry habitats. This
may explain why their ranges do not expand outward into drier habitats [21].
The cliché that there’s no such thing as a free lunch applies to the evolution
of many quantitative traits. Say that natural selection favors deer that can run
faster. Increased speed puts more stress on the deer’s leg bones, which selects for
stronger bones. If there is genetic variation for growing thicker bones, that trait
can increase. But there’s a catch: bones that are thicker are also heavier, which
decreases speed.
This is an example of an evolutionary trade-off, which occurs when increasing
fitness in one way decreases it in another. Trade-offs can be understood at different levels. The trade-off between the strength and weight of a leg bone results
from simple physics: more bone mass increases both strength and weight. A
complementary perspective comes from genetic correlations. Bone strength and
bone weight are highly correlated, so an evolutionary increase in one necessarily
causes an increase in the other. Genetic correlations can therefore cause evolutionary constraints. Even though individual traits show genetic variation, there
can be combinations of trait values for which there is little or no variation.
Like many plants, the partridge pea (Chamaecrista fasciculata) trades off growing to a larger size, which increases the number of seeds a plant can make, against
reproducing earlier, which reduces the risk of harm from early frosts. Julie Etterson and Ruth Shaw [11] asked how this tradeoff might affect the plant’s ability to
adapt to climate change. They measured the genetic variances and covariances
for leaf number (a measure of size) and reproductive stage. Next, Etterson and
Shaw transplanted individuals from northern populations farther south, into
the hotter and drier climate their native (northern) population will experience in
2050. Finally, they measured selection gradients on the two traits.
FIGURE 7.21 shows their results. There is abundant genetic variation that will
let the population evolve either more leaves and later reproduction, or vice versa.
This is an example of a genetic line of least resistance, which is a combination of
traits for which a population has abundant genetic variation [31]. In contrast,
there is little variation that would allow the population to evolve toward more
leaves and earlier reproduction, which is what selection favors. The transplants
in the hotter environment showed reduced fitness because they did not have the
favored combination of these traits. Further, there may not be enough genetic
dot shows the values of those traits for
a genotype. Climate change is selecting
for the combination of traits indicated
by the bull’s-eye. This population is at
risk of extinction because there is little
genetic variation to evolve in the direction favored by selection. (After J. R.
Etterson and R. G. Shaw. 2001. Science
294: 151–154.)
07_EVOL5E_07.indd 186
Trait combination favored
by climate change
4
Little genetic variation
in this direction
3
2
© Judy M Darby/
Shutterstock.com
FIGURE 7.21 A negative genetic
correlation between leaf number and
reproductive stage in the partridge
pea (Chamaecrista fasciculata) results
in an evolutionary trade-off. Each
Reproductive stage
Genetic line of least resistance
(maximum variation)
3.5
4
4.5
Leaf number
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7.6 The Evolution of Correlated Traits 187
variation in their native population to allow the rapid adaptation needed for
them to survive in the face of the climate change that is currently happening [10].
Genetic correlations can evolve as gene frequencies change (just as genetic
variances do), so correlations like those in the partridge pea will constrain adaptation in the medium to long term only if they remain relatively constant. Many
morphological traits are highly correlated with overall body size, and genetic
correlations between them may be stable over thousands or even millions of
years [24]. Other genetic correlations change over shorter time scales.
The hypothesis that genetic correlations can constrain evolutionary change in
the short term has been tested in a selection experiment. The butterfly Bicyclus
anynana has spots on its wings (FIGURE 7.22). Artificial selection on the two large
spots can change their sizes independently, so they are not constrained. Selection
on the colors of two other eyespots was able to make both become more black or
both become more golden, but it was unsuccessful in making one eyespot black
and the other gold. The color of these two eyespots is constrained to be the same
by a genetic correlation [2].
(A)
(B)
3
Large-small
Large-large
Color composition of eyespot 4
Relative size of anterior eyespot
6
4
2
0
Small-small
Small-large
–2
–4
–6
–6
–4
–2
0
2
4
Relative size of posterior eyespot
6
Black-gold
Black-black
Gold-gold
Gold-black
2
1
0
–1
–2
–3
–3
–2
–1
0
1
2
Color composition of eyespot 6
3
Courtesy of Cerisse Allen
and Paul Brakefield
FIGURE 7.22
Artificial selection experiments showing
different levels of constraint on the evolution of eyespots
in wings of the butterfly Bicyclus anynana. (A) Evolutionary
trajectories that result from selection on the sizes of the two
eyespots
on theEvolution,
dorsal side
Futuyma
Kirkpatrick
5e of the wing. The graph shows the
results of selection
both eyespots to be large or small (largeOUP/SInauer/Troutt
Visualfor
Services
large and small-small,
green trajectories) and for one to be large
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and the other to be small (large-small and small-large, purple
trajectories). Each point represents one generation. Rapid evolution occurred for all combinations of traits that were selected,
07_EVOL5E_07.indd 187
showing there is no evolutionary constraint. (B) Trajectories
resulting from selection on the colors of two other eyespots
(shown by arrows) on the ventral hind wing. Selection to make
both eyespots more black or more gold caused substantial evolutionary change (black-black and gold-gold, blue trajectories).
In contrast, selection for different colors in the eyespots was
largely ineffective (black-gold and gold-black, red trajectories).
This is an example of an evolutionary constraint caused by a
genetic correlation between two traits. (After C. E. Allen et al.
2008. BMC Evol Biol 8: 94.)
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Chapter 7
The Evolution of Quantitative Traits
Arrangement
1
Breakpoint
Arrangement
2
Arrangement
3
Hn23
Hn40
Hn40
Hn24
Hn39
Hn39
Hn39
Hn24
Hn24
Hn23
Hn23
Hn40
Hn41
Hn41
Hn42
Hn42
Hn53
Hn53
Hn53
Hn42
Hn41
FIGURE 7.23
A wing-color polymorphism in the
butterfly Heliconius numata is controlled by a small
segment of chromosome. Genetic analysis shows that
the segment consists of two overlapping inversions that
do not recombine. These inversions carry loci with alleles
that alter the pattern and coloration of the wings. The different color morphs are favored in different parts of the
species’ range because they mimic different species of
toxic butterflies, that are common in those regions.
(Top) Schematics of the chromosomes showing the
changes in gene order produced by the inversions.
(Bottom) The wing-color patterns produced by the
different chromosomes. (After M. Joron et al. 2011.
Nature 477: 203–206.)
The causes of genetic correlations
Genetic correlations have two sources. The first is pleiotropy, the
situation in which a single locus affects more than one trait (see
Chapter 4). Many loci affect body size in humans. These genes
generate correlations among almost all morphological traits, since
individuals who are large for one body part tend to be large for
others. They also generate correlations among other types of traits.
For example, smaller individuals tend to have higher metabolic
rates per body mass. A second source of genetic correlations is
linkage disequilibrium, the nonrandom association between alleles
at different loci (see Chapter 4). If alleles that increase one trait
tend to be inherited together with alleles that increase another
trait, then directional selection on either trait will cause both traits
to evolve. Of these two sources of correlations, pleiotropy is by far
the most common.
An important question is how often the evolutionary limits
seen in short-term experiments like that with the butterfly persist
over longer evolutionary time scales. David Houle and colleagues
[19] applied artificial selection to the wing shape of D. melanogaster. After only 25 generations, they saw evolutionary change
in shape that was as large as the difference seen between species
of flies that have diverged for 40 million years. But when they
stopped the artificial selection, the wings rapidly evolved back to
their original shape. The best hypothesis to explain this outcome
is that the mutations that contributed to the change in wing shape
also had strong pleiotropic effects that were deleterious. It is possible that many evolutionary changes that would be adaptive are
prevented because of these kinds of pleiotropic effects.
In some cases, selection favors genetic correlations. The butterfly Heliconius numata has several wing-color morphs that mimic
different species of model butterflies. (See Chapter 13 for more
on mimicry.) Selection caused by predation favors certain combinations of color elements. Genetic analysis shows that the color
morphs are controlled by a small segment of chromosome with
two loci that affect the wing patterns [20]. The high fitness combinations of alleles at those loci have been locked together by a
series of chromosome inversions (FIGURE 7.23). Recall from Chapter 4 that recombination does not occur when an inversion is heterozygous, so the inversions prevent recombination from producing low-fitness combinations of colors. Selection favored a genetic
correlation between the colors controlled by the two loci, and the
inversions spread because they strengthened that correlation.
7.7 Phenotypic Plasticity
Learning objective
■ Explain the relationship between a reaction norm and
phenotypic plasticity.
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07_EVOL5E_07.indd 188
Water fleas (Daphnia melanica) are small aquatic crustaceans. In some populations,
exposure to ultraviolet (UV) light causes them to develop dark melanic pigment,
much as light-skinned humans become tan (FIGURE 7.24). The melanin protects the
internal organs of the water fleas from the UV, which can damage cells and cause
mutations that lead to cancer. This developmental shift is an example of phenotypic
plasticity, which occurs when an individual’s phenotype changes in response to
the environment it experiences. In some cases, plasticity is irreversible, such as the
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7.8 The Genetic Basis of Quantitative Traits 189
© Joachim Mergeay
Melanin (µg/mm)
Courtesy of Michael Pfrender;
used with permission
stunted height that humans can have as the result of poor nutrition
when very young. Plasticity can also be physiological and reversible,
for example, the shivering we experience when very cold. Plasticity is
seen in a wide range of traits that range from gene expression to mor1.2
Without
phology and physiology to behavior.
predators
1.0
Phenotypic plasticity can be visualized with the reaction norm,
which is a plot showing how environmental conditions affect how a
0.8
phenotype is expressed. Reaction norms can differ among genotypes,
0.6
which means that reaction norms themselves can evolve. Genetic
0.4
variation in a reaction norm is referred to as genotype-environment
interaction (or GxE, for short). Figure 7.24 shows that reaction norms
With
0.2
for increased pigmentation in response to UV light differ between
predators
0
populations of Daphnia. In populations without fish predators, DaphLow
High
nia darken when exposed to UV. But in populations with fish predaIntensity of UV
tors, the water fleas remain transparent, since darkening would make
FIGURE 7.24 Reaction norms for pigmentation in
them more conspicuous and increase their chance of being eaten.
Daphnia differ between two lakes with predators
The difference between the reaction norms in populations with and
and two lakes without them. When Daphnia from
without predators is therefore adaptive.
lakes without predators are exposed to high levels of
Not all phenotypic plasticity is adaptive, however [13]. When
UV, they develop dark melanic pigmentation that propeople who live at sea level ascend to high elevations, physiological
tects their internal organs from the radiation. In conchanges are triggered by the decrease in oxygen pressure. Among
trast, Daphnia
from
lakes 5e
with predatory trout do not
Futuyma
Kirkpatrick
Evolution,
these changes is an increase in the concentration of red blood cells
become pigmented
under
high UV. (After A. G. Scoville
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(RBCs) in the blood. Since RBCs transport oxygen, this change may
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05-17-2022
and M. E. Pfrender. 2010.
Proc Natl Acad Sci USA 107:
sound adaptive, but in fact it is not. Increased concentrations of RBCs
4260–4263.)
make the blood more viscous, which slows oxygen delivery and can
even trigger medical emergencies. Populations of humans adapted
to very high elevations in the Himalayas and Andes have RBC concentrations
similar to those of lowland populations. They have adapted to low oxygen pressures by genetic changes in other traits. Increased RBC density is not favored by
natural selection at high elevation. In summary, people adapted to living at low
elevations show phenotypic plasticity in RBC concentrations when they move
to high elevations, but that is a maladaptive response [35].
7.8 The Genetic Basis of Quantitative Traits
Learning objective
■ Describe what QTL are and how they are mapped in the genome.
We have seen that we can predict the outcome of evolution without knowing
anything about the genes that underlie the traits. While that is a tremendous
strength of quantitative genetics, there are times when it is important to understand the genetic basis of traits. There are several questions we would like to
answer: Are the differences among species caused by many or just a few genes?
Does the variation in quantitative traits result mainly from genetic variation
in the coding or the noncoding regions of the genome? When changing environments generate directional selection, do traits typically respond quickly by
evolving with standing genetic variation, or is there a lag until new beneficial
mutations occur? When the same phenotypic adaptation evolves independently
in different species, are the same or different genes responsible? What are the
functions of the genes that affect different quantitative traits, and do their functions explain patterns of pleiotropy?
Quantitative trait loci
The regions of the genome that affect a quantitative trait are called quantitative
trait loci, abbreviated as QTL. They can range in size from a single nucleotide to a
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Chapter 7
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FIGURE 7.25
AA
AT
TT
CC
CG
GG
AA
AT
TT
Likelihood of QTL
variable sites (SNPs) are shown by the bands on the
chromosome at bottom. Individuals are genotyped at
each SNP, and their phenotypic values (in this example,
heights) are plotted against their genotypes. Examples
are shown for three markers. At markers 1 and 3, there
is no relation between genotype and height. At marker
2, however, individuals with a G at the site are taller.
This suggests that this site, or another site near to it,
contributes genetic variation to the trait. By combining
results across all of the SNPs, a plot is generated that
shows the likelihood of a QTL at each point along the
chromosome (blue curve).
Height
QTL can be mapped by associating
phenotypic variation in a trait with DNA polymorphisms along a chromosome. The positions of
1
2
Position on chromosome
3
segment of chromosome that contains many genes. Several strategies are used to
determine the number, genomic locations, and effects of QTL. Variation in melanism
in the peppered moth and sickle cell anemia in humans are caused almost entirely
by single loci with alleles that have large effects (see Chapter 4). The inheritance
of these two traits was discovered by controlled breeding experiments with the
moth and by studying inheritance of sickle cell disease in human families. But those
research strategies do not work when many genes contribute to the trait and when
controlled breeding experiments are not possible.
The most widely used method for identifying QTL is called a genome-wide
Futuyma
5econsist of phenotypic measurements and the
association
study,Kirkpatrick
or GWAS.Evolution,
The data
OUP/SInauer/Troutt Visual Services
genome sequences
from
a
large
number
of individuals. The idea is then to look
Evolution5e_07.25.ai 05-17-2022
for correlations between the DNA base (A, G, C, or T) at sites in the genome and
the trait values. An association suggests that the trait is affected by either that site
or another site correlated with it (i.e., in linkage disequilibrium; see Chapter 4)
(FIGURE 7.25). Schizophrenia is a mental condition that is common worldwide
and results in symptoms such as hallucinations. It is very strongly influenced
by genetic factors and has a heritability (h2) between 0.7 and 0.8 [37]. Finding
the genes involved could lead to the development of drugs to fight the disease.
The results from one GWAS study of schizophrenia are shown in FIGURE 7.26. It
pinpointed 21 single nucleotide polymorphisms (SNPs) in 19 different genes that
had the largest effects, but even those only slightly affected the odds of becoming schizophrenic. The vast majority of the phenotypic variance is explained
by thousands of genes with much smaller effects, so small that they cannot be
detected by GWAS. When the genome is sliced into small (1 megabase) pieces,
over 2240 (or 70%) of them have significant effects on the chance of becoming
schizophrenic, meaning that each one of these pieces carries one or more SNPs
that contribute to the disease [25]. So schizophrenia is one of the traits in humans
that, like height, is influenced by a very large number of loci [5].
The QTL that underlie traits that differ strongly between populations of a
species can be analyzed by another method. The strategy is to scan the genome
for loci that have large allele frequency differences among populations with
divergent phenotypes. This strategy has discovered QTL that underlie adaptive evolution of human traits that include disease resistance, body height, and
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7.8 The Genetic Basis of Quantitative Traits 191
Combined
Female-specific
Male-specific
Significant
Probability not random
Not significant
1
2
3
4
5
6
7
8
9
10
Position on chromosome
FIGURE 7.26
A GWAS study of schizophrenia in East Asia
found 21 SNPs in 19 genes significantly associated with the
disease. The x-axis represents the genome, with the chromo­
somes numbered and shown in different colors. Each point
represents a SNP, and the y-axis shows the probability that it
is associated with the chance of becoming schizophrenic. (For
11
12
13
14
15
16 17 18 19 20 2122
statistical reasons, this is measured by probability that the SNP
has no effect, with smaller probabilities as we go higher.) Points
above the horizontal line are significant. The diamonds represent
individual SNPs that are very likely to contribute to the disease
[23]. (From A. Ganna et al. 2019. Science 365: eaat7693.)
Futuyma Kirkpatrick Evolution, 5e
OUP/SInauer/Troutt
Servicesand cold (FIGURE 7.27). For example, the Inuit people
tolerance to highVisual
elevation
Evolution5e_07.26.ai
05-17-2022to a cold climate and to a diet with lots of fatty meat.
in Greenland have adapted
Allele frequencies in the Inuit are very different from those in other populations
at several places in the genome. One of these is a gene that is involved in producing body heat, while others are involved in metabolizing fat [12].
To study the genetic changes that occur when animals are domesticated, Russian scientists selectively bred groups of silver foxes (Vulpes vulpes) for either
tame or aggressive behavior [40]. After just 50 generations of breeding, the foxes
bred to be tame eagerly approach people with tails wagging, while foxes bred
to be aggressive snarl and back away (FIGURE 7.28). A comparison of genomes
from those two groups of foxes uncovered more than 150 genes that had evolved.
Among them were genes involved with the mood-altering hormone serotonin.
This example illustrates that QTL can be discovered for traits as complex as
innate behavior and that artificial selection can be coupled with GWAS to study
the genetic basis of interesting traits in nonhuman species.
One basic question that we can ask about quantitative traits is how many
QTL contribute to their variation. The answer is important for several reasons.
Population genetics theory tells us that if many loci contribute, then the trait
can evolve further before genetic variation is exhausted as alleles become fixed
(Figure 7.4). The dramatic responses to artificial selection in the growth rate in
chickens (Figure 5.3) and in the oil content of corn (Figure 7.15) show how far
and how fast traits can evolve over many generations when a large number of
loci are involved. Knowing the QTL underlying diseases is important to health
sciences because this information can lead to new treatments. Last, the QTL for
a given trait can help explain pleiotropic correlations with other traits.
We’ve seen that the vast majority of quantitative traits are heritable, meaning there is standing genetic variation that selection can act on. What maintains
that variation? Mutation is often the most important force. Mutations at QTL
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192
Chapter 7
Marine
diet
The Evolution of Quantitative Traits
Lactose
tolerance
Malaria
resistance
Cholera
Arsenic-rich
resistance environment
Cold
climate
Elevation
Light skin
pigmentation
Short
stature
FIGURE 7.27 QTL involved in adaptive evolution of several traits in humans
have been identified by correlating large differences in allele frequencies
among populations with distinct phenotypes. Several of these traits have
evolved independently more than once: for example, lactose tolerance in Europe
and Africa. (After S. Tishkoff. 2015. Science 349: 1282–1283.)
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07_EVOL5E_07.indd 192
introduce alleles that are typically deleterious, leading to a mutation-selection
balance (see Chapter 5). Although mutation rates at individual loci are usually
very small, a considerable amount of additive genetic variance can be generated
when there are many QTL. Experiments with Drosophila show that mutation typically increases the phenotypic variance of a trait by 0.1% to 1% per generation
[26]. An equilibrium level of standing genetic variation is reached when selection
removes the same amount of variance. In addition to standing variation, new
mutations also contribute to the evolution of quantitative traits in the long term.
The blue whale is the largest animal that has ever lived. Its remote ancestor, a
proto-whale called Pakicetus, was only about the size of a large dog. That enormous change in body size involved many new mutations that appeared as the
whale’s ancestor evolved to larger and larger sizes.
The QTL responsible for the standing genetic variation within a species may
not include the loci responsible for differences between species [41]. A study of
the genetics of flower color found that all of the molecular differences in species
in color intensity that have been studied result from mutations in transcription
factors, which are a type of regulatory gene. By contrast, transcription factors are in
the minority of spontaneous mutations that affect color within a single species [36].
The evolution of a trait’s mean is caused by changes in allele frequencies at
the QTL that affect the trait (Figure 7.4). This suggests that if directional selection
causes a trait to evolve, we should be able to see changes at QTL for the trait.
During the winter of 2013–2014, a very cold mass of air descended from the
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7.8 The Genetic Basis of Quantitative Traits 193
Arctic onto the southern United States. The extreme temperatures killed many
animals and plants, including Anolis lizards. Comparisons of lizards studied
before the cold snap and the survivors afterward showed that in southern
Texas, the lizards had evolved to become more cold tolerant. Further, allele
frequencies in 14 regions of the genome had changed. Several of the genes
involved affect nerve function, and it seems likely that the intense cold tended
to kill lizards who became immobilized because their alleles at these genes
made their nervous system perform poorly [6].
A final question about the genetic basis of quantitative traits is how often
convergent evolution, which occurs when two species independently evolve
similar phenotypes, involves changes at the same genes [34]. When very
different animals independently evolve similar coloration, is that the result
of changes at the same gene or at different genes? Surprisingly, the evolution
of darker and lighter colors in many species of mammals, birds, and reptiles
is caused by changes at just a single locus, the melanocortin-1 receptor gene,
or Mc1r. The protein produced by this gene regulates the production of pigments in hair, feathers, and scales. Mutations at several different nucleotides
in the Mc1r gene have produced dark (melanic) coloration (FIGURE 7.29).
Here convergent phenotypic evolution results from genetic changes that
are also convergent at the level of the gene but not at the level of the nucleotides. The convergent evolution at Mc1r is not as unusual as one might
think: similar selection pressures often result in evolutionary changes in
the same gene, and sometimes even the same amino acid, in even distantly
related species [27].
1959
1970
Aggressive
Tame
From X. Wang et al. 2018. Proc Natl
Acad Sci USA 115: 10398-10403
Snow goose
Farm-bred silver foxes
FIGURE 7.28 Silver foxes rapidly
evolved either tame or aggressive
behavior as the result of selective
breeding. A GWAS analysis found more
Jaguar
than 150 genes that evolved in response
to the artificial selection, including those
in a hormone that affects behavior.
Striped whiptail
NH2
Extracellular
FIGURE 7.29
Many polymorphisms
within species and differences between
species in melanic coloration have
evolved by changes to the Mc1r gene.
Membrane
Cytoplasm
COOH
Amino
acids
White snow goose, © BirdImages/Getty
Images; Blue snow goose, © Richard
Mittleman/Gon2Foto/Alamy Stock Photo,
Jaguars, © Sharon Morris/Shutterstock.
com; Mice, Nachman, et al. 2003. Proc Natl
Acad Sci USA 100: 5268-5273. Courtesy of
Michael Nachman; Lizards, Courtesy of
Erica Bree Rosenblum
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Kirkpatrick Evolution, 5e
Rock pocket mouse
The diagram shows the melanocortinin-1
protein bound to the cell membrane. The
arrows point to positions where selection
has changed amino acids to produce new
coloration in four species of animals. In
these cases, the results are polymorphisms
within species (shown in the photos). (After
N. Mundy. 2005. Proc R Soc London B 272:
1633–1640.)
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What We Don’t Know
Despite decades of research, there is still no consensus about what maintains
the abundant additive genetic variance seen in most quantitative traits. While
mutation clearly plays a key role, detailed analyses of the data lead to conflicting
conclusions about how often other factors are also important. The topic is nicely
reviewed by G. Sella and N. H. Barton [32].
● The strengths of stabilizing and disruptive selection have important effects on
how rapidly traits evolve to new adaptive peaks and the maintenance of genetic
variation for quantitative traits. Despite major efforts, existing estimates are
hampered by sample size and selection on unmeasured correlated traits [22, 32].
● The issue of how often adaptation is based on standing genetic variation and
how often on new mutations is a focus of active research [1]. There isn’t yet a
clear answer to this question that has important implications for several areas of
biology, including conservation.
● Studies using GWAS have estimated that many traits in humans—such as height,
schizophrenia, and diabetes—are influenced by hundreds or thousands of genes.
But a very large number of traits cannot each be influenced by a very large number of independent sets of genes because our genome only has about 20,000
loci. An intriguing solution to this puzzle was recently proposed by Boyle and colleagues [5]. The next few years of research could resolve this fundamental issue.
● Our understanding of the genetic and developmental basis of correlations
between traits is in its infancy. We are still far from being able to predict how these
correlations, and the evolutionary constraints that they impose, will evolve.
●
SUMMARY
■ An individual’s phenotype—the set of its visible
traits—is determined by a combination of its
genotype and environmental factors.
■ Variation in quantitative traits can be caused
by just a few or by a very large number of loci.
When variation results from many genes, the
trait can evolve far past its original range of
variation by changes in allele frequencies,
without contributions from new mutations.
■ A fitness function shows the relation between
the values of a trait and the average fitness
that individuals with each value have. A fitness
function can result in selection that is directional (favoring an increase or decrease of
a trait’s mean), stabilizing (selection against
extreme individuals, which decreases variation in the population), or disruptive (selection
against intermediate individuals, which increases variation).
■ The strength of directional selection on a trait
is measured by the selection gradient, which
is the slope of the regression line that relates
relative fitness to the trait value. The selection gradient can be used to predict the rate at
which a trait will evolve and to test hypotheses
about adaptation.
■ The adaptive landscape for a quantitative trait
plots the trait’s mean (on the x-axis) against the
population’s mean fitness (on the y-axis). The
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trait mean is expected to evolve in the direction
that moves uphill on the landscape.
■ The rate at which the mean value of a trait will
evolve is given by the breeder’s equation; it
depends on the amount of genetic variation
(measured by either the additive genetic variance or the heritability) and the strength
of directional selection.
■ Almost all quantitative traits have standing
genetic variation and will evolve when selection
acts on them. When selection acts on traits
that do not have heritable variation, new mutations must arise before the trait will evolve.
■ Artificial selection has been essential to civilization. Selective breeding has caused many
species of domesticated animals and plants to
evolve dramatically new forms, very different
from those of their wild ancestors. The results
of artificial selection demonstrate that selection can produce very large changes in relatively short periods of time. Natural selection can
do the same in natural populations.
■ Genetic covariance (or correlation) between
traits causes evolutionary side effects: selection on one trait will cause others to evolve. This
can result in trade-offs and constraints, in which
adaptation in one trait has negative fitness effects on other traits. Genetic correlations result
from pleiotropy and linkage disequilibrium.
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Summary
■ Some traits show phenotypic plasticity, the
situation in which the phenotype produced by a
genotype is altered by the environment that an
individual experiences. Plasticity of some traits
has evolved adaptively, but in other cases the
response to the environment is not adaptive.
■ Genetic variation in quantitative traits can be
caused by a small or a large number of quanti-
195
tative trait loci (QTL). These chromosome regions can be localized by QTL mapping, in which
variation at genetic markers is correlated with
a trait’s phenotypic value.
■ The number and types of loci that contribute
to additive genetic variation within populations
may often be quite different from those involved in adaptive differences among species.
SUGGESTIONS FOR FURTHER READING
Graham Coop’s 2020 Population and Quantitative Genetics [9] provides a concise and
readable introduction to quantitative genetics in chapters 7–9. It is available as a free
download at https://github.com/cooplab/
popgen-notes/releases/tag/v1.1. For an
encyclopedic tour of the subject, consult
the two-volume opus (1998 and 2018) from
Michael Lynch and Bruce Walsh [26, 39].
Excellent reviews of what we know about patterns of quantitative genetic variation are the
2008 review by Peter Visscher et al. [38] and
the 2010 review by W. G. Hill [15]. Quantitative
Genetics in the Wild by Anne Charmantier and
colleagues is a collection of interesting articles on diverse species [7].
The question of how genetic variation constrains evolution has been studied and
debated for decades. David Houle and colleagues present fascinating results from
their work on this topic and give an excellent
overview of the topic in their 2019 paper
“Why does allometry evolve so slowly?” [19].
Most evolutionary biologists agree that the current pace of climate change will outstrip the
ability of many species to adapt, and mass
extinction will result. But some lucky species
will be able to survive by adapting. Graham
Bell [3] reviews current research on when
that outcome might or might not happen in
his 2017 article “Evolutionary rescue.”
PROBLEMS AND DISCUSSION TOPICS
1. In a study of selection on the leg length of
migratory locusts, the mean leg length is 18.6
mm, the selection gradient is β = –0.13/mm,
the phenotypic variance is P = 1.4 mm2, and
the heritability is h2 = 0.37.
a. What is the additive genetic variance for
leg length in grasshoppers?
b. Using the additive genetic variance
and selection gradient, calculate the
expected evolutionary change in leg
length after one generation of selection.
c. What do you predict that the average leg
length will be in the next generation?
2. In the same population of locusts, wing length
is 47 mm, the selection gradient on wing
length is β = 0.12/mm, the phenotypic variance
for wing length is P = 3.6 mm2, and the heritability of wing length is h2 = 0.27. In addition,
we know that the additive genetic covariance
between wing and leg length is 0.60 mm2.
a. What will be the indirect response of leg
length due to selection on wings?
b. What will be the overall evolutionary
change in mean leg length due to
selection on both wings and legs?
07_EVOL5E_07.indd 195
c. Repeat these calculations to predict
what will happen to wing length from
the selection on both wings and legs.
d. What do you predict the average of
both wing and leg length will be in
the next generation?
e. Assuming the genetic variances and
covariances do not change, do you think
this grasshopper population will evolve
to the optimal phenotype for both leg
and wing length?
3. Figure 7.6 shows stabilizing selection and
disruptive selection.
a. If the traits that are being selected on
have heritabilities greater than 0, do
you predict that the means in the next
generation will be equal to the means
in the populations after selection?
b. Can selection simultaneously be
directional and stabilizing?
c. Directional and disruptive?
d. Stabilizing and disruptive?
4. In some cases, the effects of a locus will
cause phenotypes to be so different that we
can see which individuals carry which allele.
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The Evolution of Quantitative Traits
(A sex-determining gene is a good example.)
Why is this unusual; that is, why can’t we usually tell at a glance who carries which allele?
5. In Equations 7.1 and 7.2 we wrote the equation
for evolutionary change in the mean of a trait
(∆ z–) two different ways. Using the definitions
of h 2 and β from the text, investigate the differences between these two equations.
a. Can you rewrite Equation 7.1 so that it
involves just P, G, and S?
b. Can you rewrite Equation 7.2 so that
it does not have G?
c. Equations 7.1 and 7.2 suggest that h2 and
G measure inheritance, while β and S
measure selection. Given the ways you
can rewrite these equations, discuss
which of these are the best measures
of inheritance and selection.
6. There are many traits that it seems natural selection should increase every generation, such
as survival from birth to reproduction. In most
cases, when we look for such increases in natural populations, we do not see the predicted
07_EVOL5E_07.indd 196
change. Make a list of reasons that we might
not see a response to directional selection on
such a trait. Include reasons suggested by the
material in this chapter and other reasons you
can think of.
7. When the technology for QTL mapping first
became available, researchers studying
complex human genetic diseases, such as
cancer and heart disease, hoped to discover
common alleles that caused increased risk
for those diseases. Discuss the advantages to
studying the causes of complex diseases that
are caused by common, as opposed to rare,
alleles. Can you describe the advantages to
treating diseases caused by common alleles?
8. The results of QTL mapping studies for human
diseases often show that disease-causing
alleles either are rare or have very small
effects on the risk of contracting the disease.
With this information in mind, discuss the
evolutionary forces that would be most likely
to be responsible for this situation.
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8
Evolution in Space
H
omo sapiens is a remarkably variable species. Populations differ in
height, metabolic rate, the frequencies of genetic diseases, the shape
of the nose, and many, many other traits. But among all traits, skin
color is the one that is most loaded with cultural, emotional, and political
significance. And because skin color triggers those kinds of reactions, it is
easy to overlook the simple biological fact that it is an adaptation [19].
Around the world, humans who are native to tropical regions tend to have
dark skin, while those living closer to the poles have light skin (FIGURE 8.1). In
the tropics, the sun’s ultraviolet (UV) radiation is intense. Folate (vitamin B9)
is essential for the synthesis of DNA and RNA. It is easily degraded by UV, and
folate deficiency can cause birth defects and infertility. Dark skin decreases
the damaging effects of intense sunlight, and it has evolved independently at
least four times (and perhaps many more).
Populations of humans living closer to the poles have a different problem.
UV radiation is necessary for the synthesis of vitamin D, which is critical to
bone development. In European and Asian populations, two different sets
of alleles decrease the amount of pigment in the skin. Lighter skin allows
populations living far from the equator to enhance vitamin D synthesis and
so be adapted to environments with decreased sunlight.
Skin color in humans provides an example of geographic variation, which
is a nearly universal feature of life on Earth. In this case, dark and light skin
correlate with the intensity of UV radiation, and their geographic variation
reflects how natural selection changes in space. Other evolutionary factors
A giant barrel sponge (Xestospongia testudinaria) releases a large cloud of sperm as it
spawns on a coral reef, while a threadfin hawkfish (Cirrhitichthys aprinus) looks on. When
gametes and individuals move, they take their genes with them, which results in gene flow
between populations. Daram Islands, Misool, Raja Ampat, West Papua, Indonesia.
© Alex Mustard/Minden Pictures
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Chapter 8
Evolution in Space
Yves Picq http://veton.
picq.fr/CC BY-SA 3.0
© Pascal Mannaerts/
Alamy Stock Photo
© Ruslana Iurchenko/
Shutterstock.com
200
© robertharding/Alamy Stock Photo
FIGURE 8.1
Skin color reflectance
of peoples who are native to different
parts of the world form smooth
gradients between dark skin in the
tropics and light skin nearer to the
poles. The gradients in the Americas
and the Old World evolved independently,
an example of convergent evolution.
(Map after G. Chaplin. 2004. Am J Phys
Anthropol 125: 292–302.)
also shape the spatial variation in phenotypes. Studying that variation helps us
understand the interplay among selection, random genetic drift, and the movement of individuals. Patterns of variation in space also give insights into variation in time, that is, evolution itself.
8.1 Patterns in Space
Learning objectives
■ Define local adaptation and explain how genes involved with
local adaptation are found in the genome.
■ Understand what FST measures and how it is calculated.
In the central United States large areas covered by light-colored sandy soils are
next to areas covered by dark, rich soils. The deer mouse (Peromyscus maniculatus), which lives in both habitats, has two color forms: one with light fur
on its back, and the other with dark fur (FIGURE 8.2). Hawks and owls prey
on the mice, and as you can imagine, mice survive poorly if the color of their
fur is mismatched to the background. Rowan Barrett, Hopi Hoekstra, and their
Futuyma Kirkpatrick Evolution, 5e
colleagues measured how fur color affects survival [3]. They transplanted light
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Evolution5e_08.01.ai 09-22-2022 and dark mice into several large (50 m × 50 m) enclosures and then monitored
survival. The fitness functions are dramatically different in the two habitats: mice
whose coats matched the habitat survived many times better. The scientists went
further: they scanned the genome for regions that showed significant allele frequency differences between natural populations living on dark and light soils.
The largest genetic difference is in a gene called Agouti, which is well known to
affect the hair color of many mammals, including cats and dogs. One allele in that
gene carries a small deletion of three bases that eliminates an amino acid from the
hormone that the gene produces. Experiments confirmed that mice with the deletion have lighter coat color. This elegant study connects the effects of predation
on phenotypes down to genes that contribute to variation in those phenotypes.
Fur color in deer mice and skin color in humans are examples of local
adaptation, in this case of coat color matching the soil. Local adaptation is the
situation in which organisms are more fit in their local environment than are
individuals from elsewhere [16]. The genome scan that identified the Agouti gene
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8.1 Patterns in Space 201
(A) Light soil
1.0
Probability of survival
0.8
0.6
0.4
0.2
0
(B) Dark soil
–3
–2
–1
0
1
Dorsal brightness
2
3
–3
–2
–1
0
1
Dorsal brightness
2
3
1.0
Probability of survival
0.8
0.6
0.4
0.2
0
All photos from R.. H. Barrett, et al. 2019. Science 363: 499–504.
With permission from AAAS.
(C)
Dorsal brightness
FST
0.3
0.2 0.3 0.4 0.5
0.2
0.1
0
131.4
131.5
Position (Mb)
FIGURE 8.2
The color of the fur of deer mice
(Peromyscus maniculatus) is locally adapted to the
color of the soil they live on. (A and B) Mismatched
mice are conspicuous (insets) and get eaten by predators. At right are the fitness functions for the brightness
of their fur in the two habitats (see Chapter 6). (C) A small
region on chromosome 11 showing a schematic of the
exons of the Agouti locus (top) and the genetic differentiation between light-colored and dark-colored mice
(measured by FST). The vertical line shows the location
of the deletion ΔSer, which makes the mice’s fur lighter
colored. Differentiation is highest at this site, which is
evidence for local adaptation at this gene.
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Chapter 8
Evolution in Space
Population 1
Population 2
FST = 0
A2
A1
A2
A1
A1
A2
A1
A2
A2
A1
A1
A2
A2
A1
A1
A2
A2
A1
A1
A2
A2
A2
A1
A2
FST = 0.36
A1
A2
A1
A1
A1
A1
A1
A1
A1
A2
A2
A2
A2
A1
in the deer mice was based on a statistic called FST, which is very commonly used to measure differences between populations (FIGURE 8.3).
In any set of populations, the total variance in allele frequency can be
divided into a fraction that is due to variation within populations and
a fraction that is due to differences among populations. FST measures
the fraction of the total genetic variance that results from genetic differences between populations, and its value ranges between 0 and 1.
A value of FST = 0 means that the populations have the same allele
frequencies, while a value of FST = 1 means that each population is
fixed for a different allele. For a locus with two alleles, it is defined as
Var ( p )
F ST = –
(8.1)
p ( 1− p– )
A2
A2
where Var(p) is the variance of the allele frequency in two or more populations, and p– is the mean allele frequency across all the populations.
A1
(A more complicated formula that includes sample sizes is used when
A1
A2
A1
A2
A2
FST is estimated from data.) FST varies across the genome, and regions
A1
A2
A1
A2
A1
A1
A2
A2
with high FST may contain genes that contribute to local adaptation.
A2
A2
A1
A1
A2
A1
The Agouti locus in deer mice is in a region showing high FST.
FST is often used to measure how much human populations differ
from each other genetically. The International HapMap Project
FIGURE 8.3 FST is a statistic used
analyzed
differentiation in single nucleotide polymorphisms (SNPs)
to measure genetic differences
across
the
genomes
of East Asians, Europeans, and Yoruba from Nigeria [39].
between two or more populations.
Averaged over the genome, FST is 0.12. This tells us that only 12% of the genetic
In this schematic, two alleles at a locus
are represented by red and blue circles.
variation in the populations combined is caused by genetic differences among
Futuyma Kirkpatrick Evolution, 5e
(Top)
F
=
0
when
allele
frequencies
the populations. A full 88% of all the variation in our species can be found within
ST
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are equal in the populations.
a single African population. The striking phenotypic differences we see among
Evolution5e_08.03.ai
05-20-2022(Middle)
FST = 0.36 when allele frequencies are
human populations are therefore not representative of the genome as a whole.
0.2 and 0.8 in the two populations.
FST in humans increases as the distance between a pair of populations increases
(Bottom) FST = 1 when the populations
(FIGURE 8.4) [31]. This pattern is called isolation-by-distance. In our species, this
are fixed for different alleles.
pattern reflects the effects of random genetic drift that occurred when we spread
out of Africa and colonized different parts of Earth. In other species, the picture
of genetic differentiation is quite different. Several populations of the northern
dusky salamander (Desmognathus fuscus) living near New York City have F ST
values more than four times larger than those between human populations on
different sides of the planet [25].
Geographic differentiation varies across the genome. Marine populations of
the three-spined stickleback (Gasterosteus aculeatus) have invaded thousands
of freshwater streams around the Northern Hemisphere. Adapting to a freshwater environment involves many genetic changes. These changes cause high
FST between freshwater and marine populations to develop in regions of the
genome with loci that are locally adapted to those very different environments
FST = 1
Within a
single region
FST
0.20
Between the Americas
and Oceania
FIGURE 8.4
Isolation-by-distance in human populations.
The horizontal axis has been corrected for large bodies of water
that could not be crossed when humans first spread across
Earth. The vertical axis gives FST estimated from 783 loci. Each
data point represents a comparison between a pair of populations
from the indicated region(s). (After S. Ramachandran et al. 2005.
Proc Natl Acad Sci USA 102: 15942–15947.)
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0.10
Between Africa and Eurasia
0
0
5000
10,000 15,000 20,000 25,000
Distance between populations (km)
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8.1 Patterns in Space 203
From R. D. H. Barrett et al. 2008. Science 322: 255–257. With permission from AAAS
Freshwater
(low-plated)
0.8
1
2
Marine
(completely plated)
Eda
3
4
5
6
7
8
Significant peak
Chromosomes
9 10 11 12
13
14 15 16 17 18
19
20 21 22
Bear Paw
0.6
0.4
0.2
0
0.8
Boot Lake
FST
0.6
0.4
0.2
0
0.8
Mud Lake
0.6
0.4
0.2
0
50
100
150
200
250
300
350
400
Genomic position (Mb)
FIGURE 8.5 Divergence measured by FST between three
independent freshwater populations of three-spined
stickleback (Gasterostreus aculeatus) and the marine
populations from which they evolved. The horizontal axis
represents genomic location along the 22 chromosomes,
which are numbered at top. The vertical axis is FST at points
along the genome. Peaks of significantly great differentiation
are indicated by dots. One of the strongest regions of differentiation in all three comparisons maps to the region on chromosome 4 that carries the EDA locus. This gene controls a major
polymorphism in bony plates that run along the side of the fish.
The three freshwater populations are low-plated, and the marine
populations are high-plated. (After P. A. Hohenlohe et al. 2010.
PLOS Genet 6: e1000862.)
(FIGURE 8.5). Repeated divergence in the same genomic regions in different
freshwater populations is strong evidence for adaptation, and it opens the possibility of finding the genes that are involved [17].
Futuyma
Kirkpatrick Evolution,
5e between populations can be caused by phenotypic
Phenotypic
differences
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plasticity
as
well
as
by
genetic divergence (see Chapter 6). Clausen et al. [11]
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carried out a famous study of yarrow (Achillea millefolium), a plant that grows
from sea level up to the tree line in the mountains of California. In their natural habitat, plants at high elevations are much shorter than those at low elevations. This is likely adaptive because tall plants attract more pollinators and have
greater fecundity at low elevations, while short plants are able to flower before
winter arrives at high elevations. To determine whether the differences in height
are genetic or result from phenotypic plasticity (see Chapter 7), the investigators grew the plants in the same place under uniform conditions in a common
garden experiment. Plants grown from seeds sampled from high elevations grew
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Chapter 8
Plant height (cm)
204
Evolution in Space
75
50
25
Mather
Aspen
Valley
Yosemite
Creek
Tenaya
Lake
Tuolumne
Meadows
Big Horn Lake Timberline
Conway
Summit
Lee Vining
Elevation (m)
3600
2400
1200
Sierra Nevada range
0
20
40
Distance (km)
60
FIGURE 8.6 Yarrow (Achillea millefolium) is tall in populations near sea level
and short in populations at high elevation. Seeds were sampled along a transect
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Evolution5e_08.06.ai 05-20-2022
from coastal California into the high mountains inland (bottom), then grown in a common garden near sea level. The drawings (top) show their heights. Plants grown from
high-elevation seeds were shorter than those from low-elevation seeds. This shows
that some of the difference in height seen in populations growing at different elevations is genetic. (After J. Clausen et al. 1948. Experimental Studies on the Nature of
Species. III: Environmental Responses of Climatic Races of Achillea. Washington, DC:
Carnegie Institute.)
to shorter heights than those from lower elevations, but the differences were not
as great as what is seen in nature (FIGURE 8.6). These results show that in the
yarrow, both genes and plasticity contribute to local adaptation.
8.2 Genes in Motion
Learning objectives
■ Describe two measures of gene flow.
■ Explain how gene flow affects the Hardy-Weinberg equilibrium and
linkage disequilibrium.
In earlier chapters we talked in detail about two key evolutionary processes:
selection and drift. A third process, called gene flow, results from the mixing
of alleles from different populations. Gene flow plays two important roles in
evolution. First, it equalizes allele frequencies, so it works to erode genetic
differences between populations. Natural selection can cause two populations
to become either more similar or less similar, but gene flow can only make
them more similar. The second effect of gene flow is to introduce new alleles
into a population from other populations. Here gene flow plays a role similar
to that of mutation.
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8.2 Genes in Motion 205
Gene flow results from dispersal, which is the movement of individuals and
gametes. Some species disperse their genes passively. The pollen of plants such
as grasses and pines is blown many kilometers by the wind. Many plants have
adaptations for dispersing their seeds (FIGURE 8.7). Coconuts can float thousands
of kilometers on ocean currents. Some plants have seeds with long hairs or thin
wings that allow them to be blown by the wind. Many dry fruits have hooks
or bristles that catch onto mammals’ fur (or our shoelaces). Many families of
plants have evolved fleshy fruits that are adapted to be eaten by birds, mammals
(including bats, rodents, monkeys, and elephants), or even tortoises. In most
cases, the seeds are excreted in the feces, but some are simply dropped wherever the animal carries a fruit and eats it. Some tropical trees have fleshy fruits
with large seeds that may be adapted for dispersal by elephants and other large
mammals that were driven to extinction by some of the early human inhabitants
of the New World [20, 40]. The sugary flesh of the fruits of wild cherries, grapes,
and tomatoes evolved to attract animals, some of which in turn evolved to be
specialized on a diet of fruit. A happy side effect of this evolutionary outcome is
that domesticated versions of these plants now provide us with fruit that we eat.
Some animals also disperse passively. Many spiders can “balloon.” The spider climbs to a high point, such as the top of a plant, and spins out a thread of
(B)
Some of the ways that
plants disperse their seeds. Bota-
nists have given interesting names to
these syndromes. (A) Cottonwood trees
(Populus) disperse by wind (anemochory). (B) The devil’s claw (Proboscidea
parviflora) has seedpods dispersed
by large mammals (epizoochory). The
hooks attach themselves to the legs
of passing animals, including humans.
(C) Pokeweed (Phytolacca americana)
has brightly colored fruits that are
eaten and dispersed by birds (ornithochory). (D) Avocado (Persea americana)
has a large and tasty fruit that carries a
large seed inside (shown in the inset). It
may have been dispersed by very large
mammals that became extinct at the
end of the Pleistocene (endozoochory).
Roger Culos/CC BY-SA 3.0
© Doug Matthews/Shutterstock.com
(A)
FIGURE 8.7
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© Olga Guchek/Shutterstock.com
(D)
© barmalini/Shutterstock.com
© LianeM/Shutterstock.com
(C)
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The solitary and
gregarious forms of the desert
locust (Schistocerca gregaria) differ
in color, morphology, and behavior.
High population densities cause individuals to develop into the gregarious
form and gather in huge swarms that
disperse long distances.
© Papilio/Alamy Stock Photo
FIGURE 8.8
Gregarious
© Dominic Jones/
Alamy Stock Photo
© Alain Beignet/Minden Pictures
Solitary
silk. When it feels the tug of a breeze on the thread, the spider lets go and is carried away. Spiders can reach altitudes of thousands of meters and can be blown
downwind hundreds of kilometers. Corals and other marine animals that do
not move as adults have planktonic larvae that are dispersed hundreds or even
thousands of kilometers by ocean currents.
In other animals, dispersal is active. Desert locusts (Schistocerca gregaria) are
grasshoppers that typically live solitary lives. But when they are crowded and
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are scarce,
in hormones radically transform their behavior
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and morphology
[43].
They form vast swarms of hundreds of millions that fly
Evolution5e_08.08.ai 09-22-2022
long distances in active search of food (FIGURE 8.8). The devastation that locust
swarms wreak on crops has been feared by farmers for millennia.
Evolutionary biologists often use the word migration as a synonym for gene
flow. Both refer to the mixing of genes between populations. In other contexts,
migration means something quite different. Many species of salmon are born in
streams, then swim to the ocean where they live for several years. When they
are ready to reproduce, salmon have the astonishing ability to find their way
back to the very stream in which they were born. To a behavioral biologist, the
salmon migrate twice in their lives, once to the sea and again a second time when
they return to freshwater. But to an evolutionary biologist, there is very little
migration here: almost all salmon return to breed in the very population where
they were born, and there is hardly any mixing of genes between the different
populations that breed in different streams.
How is gene flow measured?
To understand how gene flow acts, we need a way to measure it. The appropriate measure depends on whether the environment is divided into discrete
patches (like islands, lakes, or mountaintops) or is spatially continuous (like a
grassland or an ocean).
With discrete patches, gene flow is quantified with the migration rate. This rate,
symbolized by m, is the fraction of individuals in a population that arrives from
another population in each generation. If there are 120 immigrants in a population of size 1000, then the migration rate is m = 0.12.
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8.2 Genes in Motion 207
The migration rate tells us how quickly gene flow erases the genetic differences between populations. The difference in an allele’s frequency before and
after migration in a given population is given by the equation
Δp = m ( pm – p)(8.2)
where pm is the allele’s frequency in the migrants, and p is its frequency in the
focal population before migration. The right side of Equation 8.2 shows that
the change in allele frequency is proportional to two quantities: the migration
rate, m, and the difference in allele frequencies between the local population
and the migrants, (pm – p). Say that a population has an allele frequency of p
= 0.25, and it receives immigrants at a rate m = 0.1 from another population
whose allele frequency is 0.75. Equation 8.2 tells us that the change in allele
frequency is 0.05, so migration will cause the allele frequency in the focal
population to increase from p = 0.25 to 0.3. The population has become more
genetically similar to the population that is the source of the migrants. The
higher the migration rate between populations, the faster genetic differences
between them are erased.
In populations that are spatially continuous, there are no distinct populations, and so the migration rate cannot be used. Instead, we measure gene flow
with the migration variance, symbolized by σm2. FIGURE 8.9 plots the distribution of where a population of Texas spiny lizards (Sceloporus olivaceus) reproduced relative to where they were born [6]. The variance of this distribution
gives an estimate of σm2. The average distance between the birthplaces of a
parent and its offspring is given by roughly 1.4 σm , where σm is the square root
of the migration variance.
Over the course of many generations, as individuals move and interbreed,
their genes diffuse across a landscape in a way that is similar to how a cloud
of smoke disperses. The migration variance measures the speed of that diffusion. The units of the migration variance depend on how we choose to measure
space. For example, σm2 might be in units of square kilometers if we are studying
moose but in units of square millimeters if we are studying microbes. (Chapter
22 explains variances and how they are calculated.)
The migration rate and migration variance are measured in several ways.
Direct methods measure gene flow by following individuals, as in the study of
the lizard. This approach is useful if we need a snapshot of dispersal over a short
period of time, but it has limitations. The evolutionary effects of gene flow are
typically averaged over many generations, and a short-term estimate may not
be representative. Another problem is that some individuals do not reproduce
0.12
© Design Pics/
Getty Images
0.10
Frequency
0.08
Sceloporus olivaceus
FIGURE 8.9
Distribution of dispersal
of the Texas spiny lizard (Sceloporus
olivaceus) in a population in Austin, Texas.
σm2 = 9800 m2
σm = 99 m
Mean distance = 140 m
0.06
0.04
0.02
0
–500
–250
West
0
Distance (m)
250
500
East
The birthplaces of individuals are centered
at 0, and distribution shows the places where
they established territories and presumably
reproduced. (The original data are reported
as absolute distances. This figure assumes
that half of the individuals dispersed to the
east and half to the west, which is why the
distribution is symmetrical.) (Data from
W. F. Blair. 1960. The Rusty Lizard: A Population
Study. Austin, TX: University of Texas Press.)
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successfully after they disperse, so they do not contribute to genetic mixing. And
if an individual is not found, we don’t know whether it dispersed so far that we
could not find it or simply died.
These considerations motivate indirect methods that use genetic data to
estimate gene flow. The mixing of individuals from populations with different
allele frequencies generates a systematic deviation from the Hardy-Weinberg
ratios. It results in an excess of homozygotes and a deficit of heterozygotes.
You can see this effect with an extreme case: if you sample individuals from
two populations that are fixed for different alleles, your sample will have no
heterozygotes. A second effect of migration is to generate linkage disequilibrium (see Chapter 4). In the United States, males who are bald are more likely
to have white skin than black skin. That is because the US population includes
some people with ancestry from northern Europe and others from Africa. In
northern Europe, alleles for white skin and baldness are common, while in
Africa they are rare. The mixing of people from those two populations has
caused linkage disequilibrium between alleles that affect baldness and those
that affect skin color. Departures from Hardy-Weinberg ratios and excess linkage disequilibrium can be used to estimate gene flow between populations.
Other indirect methods for estimating migration rates from genetic data are
discussed later in this chapter.
8.3 Gene Flow versus Selection
Learning objectives
■ Explain what gene swamping is and when it will happen.
■ Understand the migration-selection balance and
how it can result in clines.
When selection favors different alleles or phenotypes in different places, a tension develops between local selection, which enhances the genetic differences
between populations, and gene flow, which erodes them. Without gene flow,
selection would cause whatever alleles have highest fitness at any place to
become fixed there. Likewise, we saw in Chapter 6 that drift causes allele frequencies usually to diverge between populations. Without selection or genetic
drift, gene flow would equalize allele frequencies everywhere. Many spatial patterns seen in nature result from balances between those opposing forces.
A migration-selection balance
The result of the opposing effects of gene flow and local adaptation depends on
the relative strengths of selection and migration. The simplest situation is when
an island (or other small region) receives migrants from a nearby continent (or
other large region). Imagine that the continent is fixed for allele A1. Different ecological conditions on the island give allele A2 higher fitness there, so the relative
fitnesses of the A1A1, A1A2 , and A 2 A2 genotypes are 1, 1 + s, and 1 + 2s. Migrants
from the continent arrive on the island at a rate m per generation.
How do allele frequencies on the island evolve? The answer depends on
the relative sizes of the selection coefficient s and the migration rate m. If
selection is much stronger than migration (m << s), then gene flow will be
largely overwhelmed, and the locally adapted A2 allele will become nearly
fixed. In the simple case of no dominance, the equilibrium frequency of A2 on
the island is simply
p2 ≈ 1 –
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m
s (8.3)
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8.3 Gene Flow versus Selection 209
This is an approximation that is accurate when m is ten times (or more) smaller
than s. This situation is called a migration-selection balance. It has clear parallels
to the mutation-selection balance we discussed in Chapter 5.
A very different outcome occurs when migration to an island is much stronger
than selection (s << m). The frequency of the locally adapted allele evolves to
0, and the allele is lost entirely. This outcome, in which gene flow overwhelms
local adaptation, is called gene swamping. As in many insects, populations of the
mosquito Culex pipiens have evolved resistance to insecticides. On the island of
Corsica, however, resistance has not evolved, despite the presence of resistance
alleles at low frequency. The best explanation is gene swamping. Patches of habitat on Corsica where insecticide is applied are small. Mosquitoes disperse into
those patches from neighboring populations that are not resistant, swamping the
resistance alleles that are favored inside the patches [22].
An important feature of gene flow is that it is equally strong across the entire
genome. Selection, however, is not. Genes that are locally adapted will tend to
show greater allele frequency differences between populations than regions of
the genome that are not. This was the key insight used to identify genes that are
locally adapted in the studies of deer mice (Figure 8.2) and sticklebacks (Figure
8.5) we discussed earlier.
Clines
Average body mass of moose (kg)
180 190 200 210 220 230
The evolutionary tension between selection and gene flow also plays out in continuous habitats. The result can be a cline, which is a smooth change in space of
a trait mean or an allele frequency. Some clines extend over large geographical
scales. Moose (Alces alces) are more massive in northern Sweden than they are
farther south (FIGURE 8.10). Similar clines are seen in many species of mammals
and birds: populations in colder climates tend to have greater body mass than
those in warmer climates. This pattern is so common that it has a name (Bergmann’s rule). In the moose and other homeothermic animals, larger individuals lose heat less quickly in cold climates (because they have lower surface-tovolume ratios), while smaller individuals can dissipate heat more quickly in
warm climates. These clines are examples of convergent evolution.
Other clines are very much shorter. A grass called the common bent (Agrostis
capillaris) grows on and around an abandoned surface mine in Wales (United
Kingdom). An area of about 100 m × 300 m is heavily contaminated with copper,
which is lethal to most plants. Remarkably, plants growing on the mine have
evolved much higher tolerance to copper than plants growing nearby. Plants
and seeds were sampled from two transects, and their copper tolerance was
measured by the rates at which their roots grew in solutions with controlled
69°
© blickwinkel/Alamy
Stock Photo
Sweden
63°
FIGURE 8.10 Moose (Alces alces) are
larger in the north of Sweden than in
the south. This cline is an example of
60°
Alces alces
0
08_EVOL5E_08.indd 209
66°
250 500 1000
Latitude (km)
57°
54°
200 km
Bergmann’s rule, which says that the body
sizes of mammals and birds tend to increase
with distance from the equator. (After
H. Sand et al. 1995. Oecologia 102: 433–442.)
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Adults
Grown from seed
Copper tolerance
60
50
40
30
20
FIGURE 8.11 The grass Agrostis capillaris grows on and
around an abandoned surface mine in Wales. Soil on the mine
has high concentrations of copper, and plants growing there have
evolved tolerance of this toxic element. Tolerance declines over
very short distances along two transects that run from the mine into
the surrounding meadow. On the mine, tolerance is greater among
adult plants than plants grown from seed, while off the mine the
pattern is reversed. (After T. McNeilly. 1968. Heredity 23: 99–108.)
10
0
Copper mine
Wind
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concentrations of copper. The results revealed clines in tolerance that are only dozens of meters long (FIGURE 8.11).
The clines in this grass result from local adaptation and
gene flow. On the mine, the adults are more tolerant of copper than are plants grown from seed. Far from the mine, the
pattern is reversed: plants grown from seed are more tolerant
than the adults. These differences between seeds and adults
result from very strong selection. Among the plants that ger0 50 100
minate on the mine, only the few that can tolerate very high
m
levels of copper in the soil survive in each generation. Surviving adults therefore have higher tolerance than the seeds,
which have not yet experienced selection in the current generation. Why are
seeds on the mine less tolerant than adult plants? Because gene flow prevents
the population that is growing there from becoming fixed for copper-tolerance
alleles. Each generation, pollen and seeds from the pasture are blown onto the
mine, bringing alleles that are not adapted to high copper concentrations.
Selection works in the opposite direction off the mine. Plants that are tolerant
grow more slowly in soil that has no copper, likely because the alleles that make
plants tolerant have deleterious pleiotropic effects (see Chapter 4). At the same
time, gene flow from the mine prevents alleles for tolerance from being completely
eliminated in the pasture nearby. The hypothesis that the cline results from strong
selection and gene flow is confirmed by comparing the clines from the two transects on the left and the right in Figure 8.11. The cline on the left is very steep: copper tolerance declines quickly just a few meters from the mine. But the cline on the
right is much more gradual. This transect goes downwind from the mine. Pollen
from the plants on the mine is blown far by the prevailing winds. That pollen continually introduces alleles for high tolerance into the populations far downwind.
What determines whether a cline will be short, as in this grass, or long, as in
the moose? To make our discussion concrete, let’s discuss an example of a grass
growing in a meadow that has two kinds of soil that meet at a sharp boundary.
The soil to the west of the boundary favors allele A1, while the soil to the east
favors allele A2. Allele A1 has a relative fitness advantage of s in the west, while
A2 has the same advantage in the east, and there is no dominance. What happens? Without gene flow, A1 will become fixed everywhere to the west of the
boundary, and A2 will be fixed everywhere to the east. But with gene flow, alleles
move across the boundary. This introduces into each habitat the allele that is not
favored there, and a cline develops (FIGURE 8.12). The cline can be short or long,
depending on the relative strengths of gene flow and selection. A mathematical
model shows that the width of the cline will be
w c = 2.5
σm2 s
(8.4)
This is the width of region over which allele A2 increases from a low frequency
(p = 0.1) to a high frequency (p = 0.9). Notice that by taking the square root of σm2,
the units of wc match the units in which space is being measured.
Equation 8.4 shows that if we know the migration variance, we can use the
width of a cline to estimate the strength of selection. Alleles for insecticide
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8.3 Gene Flow versus Selection 211
(A)
Allele A2 favored
Allele A1 favored
1
(B)
Frequency of A2
1
Frequency of A2
0.8
σm2 s
0.6
0.01
0.25
1
4
0.4
–2
West
–1
0
Distance
1
0.6
0.4
wc
0.2
0
Distance
FIGURE 8.12
Clines in allele frequencies
predicted by a mathematical model.
0.2
0
–3
0.8
2
3
East
resistance in the mosquito Culex pipiens form clines in southern France at the
boundary between regions that are treated with insecticide and those that are
not. Combining estimates for the cline width (wc ) and the migration variance
(σm2 ) with a theoretical prediction similar to that in Equation 8.4 suggests that the
insecticide generates very strong selection for the resistance alleles, with values
of s up to 0.33 [23]. If a patch of habitat favors an allele but the width of the cline
wouldKirkpatrick
be wider
than is5ethe patch, that allele is lost by gene swamping.
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Clines also develop
when the transition between two types of habitats is gradEvolution5e_08.12.ai
06-01-2022
ual. In that case, gene
flow causes the shape of the cline to resemble those in
Figure 8.12, even though the clines in that figure result from an abrupt change
in selection. The shape of a cline therefore does not tell us much about whether
selection varies abruptly or in a smooth gradient.
As we saw with skin color in humans and body mass in moose, quantitative
traits also have clines. Many quantitative traits experience stabilizing selection
with an optimum that varies in space. In that situation, the mean value for a trait
evolves to a compromise between what local selection pressures favor and the
homogenizing effect of gene flow. The effect of gene flow is again determined
by the migration rate (in habitats made of patches) or the migration variance
(in a continuous habitat). In places where the environment changes quickly in
space, gene flow will have a stronger role in hindering local adaptation than
where the environment change is extended over a longer distance. This is just
the pattern seen in the common frog (Rana temporaria). The growth rate that
maximizes its survival depends on temperature. Along a 1500 km latitudinal
transect in Europe, growth rate is well adapted to the local climate, but it is less
well matched to an elevational gradient where temperature changes almost 1000
times more rapidly with distance [2].
The horizontal axis is distance along a transect. The vertical axis is the frequency of
allele A2, which has a relative fitness of 1 – s
to the left (west) of x = 0, and fitness 1 + s to
the right (east). The four curves show the
clines for different values of σm2 /s. The
clines become flatter as the strength of
gene flow increases relative to selection.
The inset shows that the cline width, wc, is
measured by the distance over which the
allele frequency changes from 0.1 to 0.9.
Tension zones
We’ve just seen that selection can maintain differences between populations that
are connected by gene flow when fitness varies in space. While it may sound
counterintuitive, in some cases selection can also maintain differences even when
it acts the same way everywhere. A tension zone can occur when fitnesses at one
or more loci are underdominant (see Chapter 4).
High in the Pyrenees Mountains between France and Spain, some populations
of snapdragons (Antirrhinum majus) have flowers that are yellow, and others have
flowers that are purple (FIGURE 8.13). Bees pollinate both kinds of flowers. Once a
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(B)
A–C from H. Tavares et al. 2018. Proc Natl Acad Sci USA 115: 11006–11011
FST
(A)
0
200
400
600
800
1000
kb
(C)
Frequency (p)
1.0
0.8
0.6
0.4
0.2
0
5
FIGURE 8.13 Steep clines can form
when adjacent populations are fixed
for different alleles and selection acts
agains heterozygotes (A) Snapdragons
(Antirrhinum majus) have a tension zone
where populations with yellow and purple flowers meet. (B) One of the genes
responsible for the color difference
shows three regions with high FST
between the two populations. (C) SNPs
within this gene show very sharp clines
in the tension zone.
10
15
Distance (km)
20
bee learns to visit one of the colors, it strongly favors the same color in future visits.
When yellow-flowered and purple-flowered populations meet, interbreeding produces heterozygotes at flower color genes, and their flowers do not look quite like
either of the parents. Bees avoid the heterozygotes, so these snapdragons have low
fitness, which prevents alleles from spreading between the two populations. The
result is that there are very steep clines, just a few hundred meters wide, in allele
frequencies at the genes that determine flower color [38]. These clines do not reflect
local adaptation, as in humans (Figure 8.1), the moose (Figure 8.10), and the grass
(Figure 8.11), but rather the ecological disadvantage suffered by hybrids between
two populations that happened to have evolved differences in flower color.
8.4 Gene Flow, Genetic Drift, and Detecting Selection
Learning objectives
■ Understand how drift and gene flow affect genetic differentiation
between populations when they are at equilibrium.
■ Explain how recent events in a population’s history can
affect differentiation.
■ Describe how scans of FST across the genome can identify genes
that are under selection.
In Chapter 7 we looked at populations of a terrestrial snail whose allele frequencies have diverged by drift (see Figure 7.4). Groups of snails living on the
same city block have similar allele frequencies, but frequencies are quite different
between populations that are separated by a barrier (a street). So drift, as well as
selection, can build up differences between populations, and its potential to do
that is limited by gene flow.
That intuition is confirmed by population genetics theory. Let’s consider two
populations, both with an effective size Ne. Each population receives a fraction m
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8.4 Gene Flow, Genetic Drift, and Detecting Selection 213
of its individuals as migrants from the other population in each generation. For
a locus that is free of selection and that has been evolving long enough to reach
an evolutionary equilibrium, a mathematical model [4] predicts that the genetic
divergence between the populations will be
F ST =
1
1 + 16 N e m (8.5)
This equation has an interesting implication. Since Ne is the population size and
m is the fraction of each population that immigrates from elsewhere, the product Ne m is simply the average number of immigrants that arrive from the other
population in each generation.
Equation 8.5 shows that if that number is 1 or more, then FST will be close to 0,
meaning that the two populations are expected to have similar allele frequencies.
At the other extreme, if the number of immigrants per generation (Ne m) is much
less than 1, then FST will be close to 1, and the two populations will be genetically very different. In short, for a selectively neutral locus, a single migrant per
generation is sufficient to prevent drift from causing two populations to diverge
very much. Remarkably, this conclusion is independent of the population size.
(But please be clear that the picture is very different if selection is at work, as we
saw earlier.)
The relationship shown in Equation 8.5 suggests that we can estimate the number of migrants exchanged between two populations by measuring FST between
them. This approach is one of the indirect methods to estimate migration using
genetic data. These methods enjoy several advantages over direct methods like
those used in the lizard study of Figure 8.9. Indirect methods average gene flow
over many generations and are sensitive only to migrants that actually contribute
to genetic mixing between the populations. A limitation of Equation 8.5 is that it
is based on assumptions that are often violated in nature, but more sophisticated
methods have been developed that relax those assumptions [8].
The effects of history
Drift also causes populations that live in continuous habitats to diverge. This can
result in a pattern of isolation-by-distance, in which genetic differences between
populations (measured, for example, by FST) increase with the distance between
them. Earlier we saw this pattern in humans (Figure 8.4). Notice that FST between
populations living on different sides of the globe is much less than 1, despite the
absence of gene flow before the twentieth century. That is not what we expect
based on Equation 8.5. Many species show similar patterns in which distant
populations are much more genetically similar than would be expected from the
amount of gene flow that they currently experience.
One reason for this disconnect between current rates of gene flow and genetic
differences between populations is that they are not at an equilibrium. That pattern in Figure 8.4 reflects the history of how humans colonized the planet starting
some 70,000 years ago, rather than an equilibrium between current rates of gene
flow and drift. The red-backed salamander (Plethodon cinereus) moves very little.
Populations in the southern part of its range, which has never been glaciated,
show large genetic differences in their mitochondrial DNA. That is expected from
Equation 8.5 because the migration rate is very low. But in the northern part of
its range, which the salamanders rapidly colonized after glaciers melted away
several thousand years ago, there is only a single mitochondrial genotype [30].
The lack of differences between northern populations reflects the recent range
expansion, not the current rates of gene flow and genetic drift.
A second factor that can genetically homogenize a species over large spatial
scales is a history of frequent extinction and recolonization. In some species,
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populations that are wiped out by disturbances (such as fires) are replaced by
colonists that move into the empty habitat. This generates bouts of high gene
flow that decrease divergence and depress FST far below what would be expected
from typical rates of movement [35, 5].
Gene flow, local adaptation, and drift
We have seen that two different evolutionary processes—selection and drift—can
cause populations to diverge. This can make it challenging to decide whether
genetic differences between populations are signs of local adaptation or simply
the result of neutral drift.
It is now possible to sample many genes and even entire genomes from different populations of some species. How can those data be used to hunt for
genes involved in local adaptation? The most basic approach is simply to scan
the genome for regions that show unusually high FST between two populations.
The idea here is that neutrally evolving regions of the genome will show a baseline level of FST caused by drift, and regions that show much higher divergence
may be under spatially divergent selection pressures. Further evidence for local
adaptation at these candidate regions can be gleaned using comparisons with
additional populations and by finding genes within the regions that are plausible targets of selection. In the three-spined stickleback that we discussed earlier,
comparisons between three independent pairs of stream and marine populations
show repeated peaks of high divergence (Figure 8.5). One of the highest peaks is
on chromosome 4 and corresponds to the location of the Eda locus that controls
the striking differences between the populations in lateral bony plates. This is
compelling evidence for local adaptation at both the phenotypic and genomic
levels. In humans, the EPAS1 locus, which contributes to adaptation to high elevations in Tibetans (see Figure 7.23), was also discovered by scanning the genome
for regions of high divergence.
Testing the hypothesis of local adaptation in quantitative traits presents new
challenges. In body size clines like that in moose (Figure 8.10), the evidence for
local adaptation is clear: the correlation between body size and latitude in mammals is repeated across many species and has a functional interpretation. In other
cases, however, the evidence is less clear. One strategy is to compare variation
among populations in a quantitative trait of interest with estimates of FST based
on neutral genes. We can then use FST to predict the amount of variation in the
quantitative trait under the null hypothesis of no local adaptation [44]. An excess
of variation in the trait between populations suggests local adaptation is at play.
8.5 The Evolution of Dispersal
Learning objective
■ Describe factors that can favor the evolution of increased dispersal and
those that can favor the evolution of decreased dispersal.
Gene flow is caused by dispersal. But why do individuals disperse? There are
often benefits to movement, for example, to find food or a mate. But there are
costs as well—dispersing can be risky business.
An important evolutionary advantage to dispersal is that it allows individuals to find habitat that is better now or that will be in the future. When the
environment changes in time and space, natural selection favors dispersal if
individuals that move have higher fitness on average than do those that do not
move [24]. If environmental changes wipe out local populations from time to
time, any genotype that does not disperse will leave no progeny to the evolutionary future. Weeds like dandelions live in ephemeral patches of open habitat
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unable to move to another spring. This species is found only in the aquifer that
feeds a group of springs in Austin, Texas, that provide water to a large swimming pool. The salamander’s entire range is only a few square kilometers.
08_EVOL5E_08.indd 215
© Sinclair Stammers/
Science Source
0.8
Frequent
extinction
0.6
0.4
No extinction
0.2
0
No extinction,
no migration
1
2
3
Generation
4
5
FIGURE 8.14 Dispersal evolved in
response to environmental disturbance
in a laboratory experiment with the
nematode Caenorhabditis elegans.
Mixtures of two genotypes that differ in
their dispersal
were5ecultured on agar
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plates with two Visual
patches
of the bacteria on
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which the worms feed.
In the experimental
treatment, every 3 days nematodes from a
randomly chosen patch were used to inoculate a new plate. The old plate was discarded, which simulated extinction of the
population in the second patch. There were
two control treatments: a single patch with
no extinction, and two patches with no
extinction. (After N. A. Friedenberg. 2003.
Ecol Lett 6: 953–959.)
© Michael Ventura/Alamy Stock Photo
FIGURE 8.15 The Barton Springs salamander (Eurycea sosorum) has
lost the terrestrial phase of its life cycle. It cannot survive on land and so is
1
© Nature’s Images,
Inc./Science Source
that are created by disturbances such as fire, grazing, and human activity.
These patches are eventually overgrown by larger plants (or paved over by
people), driving the population of dandelions living there to extinction. Only
dandelions that disperse out of a patch when they are seeds will leave descendants to the distant future. This has selected for seeds that disperse well in
the ancestors of dandelions. They evolved seeds with a parachute (technically
called a pappus) that carries seeds by wind to newly opened patches.
The evolution of increased dispersal has been studied using experimental
evolution [14]. Replicated microcosms were established of the nematode Caenorhabditis elegans living on agar in petri dishes. Each dish had two patches of food
for the worms. The worms could either feed in their current patch or move
between patches. This setup allowed the investigator to manipulate environmental disturbance. A single patch in each plate was inoculated with a small
number of worms. After the worms had the opportunity to disperse and reproduce, a sample of the offspring was transferred to a new plate. Disturbance was
simulated by transferring worms from only one randomly chosen patch, which
is analogous to extinction of the other patch. In control treatments, there was
no selection favoring dispersal. The experiment was run with a mixture of two
worm genotypes that differ in their propensity to disperse. The high-dispersal
genotype had lower fecundity than the low-dispersal genotype. The experiment’s outcome fits the theoretical expectation (FIGURE 8.14). Despite the fecundity disadvantage, in just five generations the high-dispersal genotype had
become much more frequent in the treatment that simulated patch extinction.
In the control treatments without extinction, it became more rare.
A second factor that can select for dispersal is competition with relatives [32].
To see this, consider a plant that lives in patches of suitable habitat that are so
small that each patch is only big enough for a single individual. If a plant drops
all its seeds within its own patch, then when it dies, many seeds may germinate
but only a single offspring will survive and inherit the patch. But if instead the
plant disperses its seeds to other patches, it has a chance of leaving more than
one surviving offspring. Dispersing individuals experience less competition
than their siblings that stay at home. Genes for dispersal can therefore benefit
from kin selection (a topic we will discuss more in Chapter 13). Competition
with unrelated individuals can also favor dispersal if individuals can move from
regions of high density to low density (where competition is weaker).
Another kind of interaction between individuals can also favor dispersal. Mating between closely related individuals often results in offspring
that suffer low fitness due to inbreeding depression (see Chapter 12).
Many species, including humans, are able to recognize close relatives
and avoid mating with them. But other species do not have that ability.
They can, however, decrease the risk of inbreeding by dispersing far from
where they were born [37].
These benefits to dispersal are offset by costs. Moving is often dangerous. Passive dispersal can land an individual in hostile habitat where
there is little or no chance of survival. Active dispersal is also dangerous
when patches of good habitat are separated by regions of bad habitat.
Several species of salamanders inhabit isolated springs in the hot and
dry southwest of the United States (FIGURE 8.15). Millions of years ago,
the ancestor of these salamanders had a terrestrial phase of the life cycle
that could disperse between springs. As the climate became warmer and
Frequency of the better disperser
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Evolution in Space
FIGURE 8.16
morph with normal wings can disperse long
distances by flying, but it has lower fecundity because of the energy it invests in the
growth and maintenance of flight muscles.
© iStock.com/Ian_Redding
© DP Wildlife Invertebrates/Alamy Stock Photo
Roesel’s bush-cricket
(Metrioptera roeselii) has two morphs,
one with normal wings (left) and one
with highly reduced wings (right). The
drier, life on land became increasingly hostile to these salamanders. In response,
the salamanders lost the terrestrial phase and are now no longer able to disperse
between springs. Ultimately, this may be a form of evolutionary suicide: a species
that is endemic to one spring can be driven to extinction by a single catastrophe,
either natural or caused by humans.
Energetic
trade-offs
can also select against dispersal. Many species of crickets
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Kirkpatrick
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have two morphs,
with functional wings and the other without (FIGURE 8.16).
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Winged individuals
are able to disperse by flying. But they have lower fecundity
than the wingless morph because of the large energetic investment required to
develop functional flight muscles [47].
Habitats that change in time and space create mosaics of shifting selection
pressures, favoring the evolution of increased dispersal at some times and in
some places, and decreased dispersal elsewhere. The Glanville fritillary (Melitaea
cinxia) is a butterfly that lives in meadows that are surrounded by forest. When a
new meadow appears after a fire, it is colonized by butterflies that disperse from
neighboring patches. The colonizers are not a random sample of the population:
they are strong fliers that disperse well. Flight strength is strongly heritable, so
the population that is established in a new patch has strong fliers for the next several generations. But in later generations, the population evolves toward weaker
and weaker flight. Why? Simply because the strongest fliers disperse, leaving the
weaker fliers at home [15].
The Glanville fritillary illustrates an interesting consequence of dispersal: it
sorts individuals according to how well they disperse. As a result, dispersal can
cause evolutionary change even without the help of natural selection. When
a species spreads into a new habitat, individuals that disperse the fastest will
tend to accumulate at the expanding front. Further, mating between the fast
dispersers will sometimes produce new genotypes that disperse even faster
[34]. A dramatic example of these effects comes from the cane toad (Rhinella
marina) [29]. The toad was introduced to Australia from South America in 1935,
and it has been rapidly expanding its range ever since. Because of the sorting
effect, we would predict that toads at the leading edge of the invasion should
disperse better than those from older, established populations. That is exactly
what is seen: toads at the leading edge have longer legs and disperse faster than
the short-legged toads behind them (FIGURE 8.17).
The evolution of dispersal rates and distances has a host of downstream
effects. Dispersers take their genes with them, and so evolutionary changes
to dispersal also affect local adaptation and patterns of geographic variation.
For example, when increased dispersal evolves, local adaptation and inbreeding load will tend to decrease. The evolution of decreased dispersal makes it
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8.6 The Evolution of a Species’ Range 217
(B)
(A)
© Patrick K. Campbell/
Shutterstock.com
60
Rate of spread (km/y)
Relative leg length
1.1
1.0
0.9
1
200
400
600
Order of arrival
800
50
40
30
20
10
0
1945–
1954
1955–
1964
1965–
1974
1980–
1984
2001–
2005
Year
FIGURE 8.17
When a species’ range expands, the most rapidly dispersing
genotypes automatically become more common at the range edge, causing
the rate of spread to increase. (A) During their range expansion in Australia, the
first cane toads (Rhinella marina) to arrive at a research station had the longest legs,
while those that arrived later had shorter legs. Toads with longer legs disperse faster
Futuyma Kirkpatrick Evolution, 5e
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jumpServices
farther. (B) As a result of the sorting process, the advancing front
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of the cane toad invasion
has accelerated since the toad was introduced in 1935.
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(After B. L. Phillips et al. 2006. Nature 439: 803.)
more likely that deleterious mutations will become fixed by drift in isolated
populations, so immigrants that arrive from populations where they are not
fixed have offspring with higher fitness (see Figure 6.16). This effect amplifies
the genetic impact of migration [12] and can sometimes rescue local populations from extinction [33]. Paleontologists have found that marine snails with
planktonic larvae capable of dispersing long distances had larger geographic
ranges and survived longer in the geological record than did those without
planktonic larvae [18].
8.6 The Evolution of a Species’ Range
Learning objective
■ Describe two ways by which a species might respond to
a rapidly changing climate.
No other species lives over a greater expanse of Earth’s surface than humans.
Near the other extreme, the world’s entire population of the Barton Springs salamander (Eurycea sosorum) is restricted to just a few square kilometers (see Figure
8.15). The contrast between these two species illustrates that geographic range of
a species can evolve. This raises the question of why some species have evolved
large ranges and others small ones. In some cases, barriers prevent a species from
moving elsewhere. The Barton Springs salamander is unable to live out of water,
and so it cannot migrate to other springs. On a larger scale, the ranges of many
marine species are defined by the edges of continents, and the ranges of many
terrestrial species by the edges of oceans.
But in other cases, there is no obvious barrier that limits a species’ range. On
several continents, as we move away from the tropics and into the temperate
zones, palm trees become less and less common until they disappear from the
landscape. These range limits usually do not occur at clear geographical features
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Chapter 8
Evolution in Space
but, rather, at places where the climate is too cold for palms to tolerate. From an
evolutionary perspective, these situations are more difficult to understand. If a
tropical plant cannot survive far from the equator because winters are too cold,
why don’t populations at the limits of the range evolve greater cold tolerance and
so cause the range to expand outward? If a barnacle cannot live higher in the tidal
zone because it reaches its tolerance limits for heat and desiccation there, why
doesn’t it evolve greater ability to withstand those stresses? If a bird cannot live
in the same habitat as another species that is a better competitor, why doesn’t it
evolve to feed on different foods?
What limits species’ ranges is an evolutionary puzzle. Several factors are
thought to be in play [1]. Populations may simply lack genetic variation in a
trait necessary for adapting to a new environment. For example, populations of
two species of rainforest-dwelling Drosophila have no detectable genetic variation for desiccation tolerance, which may be preventing them from expanding
into drier habitats [21]. A second situation is when gene swamping caused by
migration from other parts of a species’ range can prevent local adaptation to
the extreme conditions at the range edge and prevent the species from expanding outward [22]. Consistent with that idea, at the northern edge of the range
for the cricket Allenemobius socius, gene flow from southern populations limits
the evolution of eggs that can survive cold winters [13]. Third, the small and
fragmented populations that are often found at the edges of a species’ range are
prone to accumulating deleterious mutations by genetic drift. This can decrease
the populations’ reproductive output to the point that they do not send out colonists. In the plant Arabidopsis lyrata, this inbreeding load is so severe that the
average fitness of individuals is depressed by 80% [28]. High inbreeding load
appears to be a common situation in populations at the poleward limits of their
ranges [7]. Last, ecological interactions can set range boundaries where a species encounters a competitor, predator, or pathogen. The distribution of highly
specialized species is restricted to the distribution of their special resources. For
example, a caterpillar that feeds only on junipers can only live where junipers
grow (see Figure 14.17).
Global climate change provides a very large (if uncontrolled) experiment that
gives insights on how species ranges respond to environmental change. Species
might respond in two ways: by changing where they live or by adapting to the new
conditions. Reviews of ranges for which there are historical data have found that a
large fraction have shifted in the direction expected from climate change [46]. In the
Northern Hemisphere, many northern range limits have expanded farther north,
while southern limits are contracting toward the range centers [27, 10]. Thus, many
species are tracking their niche by shifting where they live, as the environment
to which they are adapted moves northward and to higher elevations. These are
not evolutionary changes, just geographic changes. In fact, few cases of genetic
changes in climate tolerance have been documented [9], probably because the climate is changing so rapidly—faster than any other change in millions of years [26].
Many other species, however, are not able to keep up with climate change, even
by shifting where they live. A variety of environmental challenges prevents them
from maintaining a stable range size while moving within the envelope of suitable
habitat. The most obvious is when the species runs out of suitable habitat. The pika
(Ochotona princeps) is a small (and very cute) relative of the rabbits that lives high
in the mountains of western North America (FIGURE 8.18). As the Earth’s climate
warms, some populations have been able to persist by moving uphill to where the
temperature suits them. But many populations cannot move uphill, because the
terrain is not appropriate or they simply reach the top of the mountain. As a result,
they have gone extinct in some areas where they had been abundant [36]. The hope
that evolutionary adaptation might rescue most of the threatened species from the
challenges of rapid climate change appears to be dim [41].
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8.6 The Evolution of a Species’ Range 219
(A)
Pika populations have
become extinct because of climate
change. (A) Before 1955, pikas were
Pika
Tahoe City
0
2.5 5
Kilometers
From J. A. E. Stewart et al. 2017. PLOS ONE 12: e0181834
abundant on Mt. Pluto (California), as
shown by radiocarbon dating of their
fecal pellets. They are now extinct
on the mountain. (B) The cause of the
extinction is very likely climate change.
Pikas depend on thick snow pack in
winter. Temperatures in the region
have increased by almost 2°C in the last
100 years, causing the snow pack on
Mt. Pluto to decrease. These data are
from Tahoe City.
© Sean R. Stubben/Shutterstock.com
FIGURE 8.18
Pika evidence
Extant
Extinct
10
Mean summer temperature (°C)
(B)
8
Data from Tahoe City
7
6
5
4
1910
1930
1950
1970
1990
2010
Year
What We Don’t Know
Predicting the weather is hard, as is predicting how populations will evolve and
change demographically. So you can imagine how difficult it is to predict how species and even entire ecosystems will respond to Earth’s changing climate. Despite
the challenges, many scientists are now working to do just that. Mark Urban and
colleagues [42] review this issue in their article “Improving the forecast for biodiversity under climate change.” While there have been many advances, the article
points out how much we do not yet know about this critical topic.
● Genes across the landscape as individuals and gametes disperse under normal
conditions. But gene flow can be greatly accelerated by a catastrophe that wipes
out a population, which is then replaced by individuals that recolonize the empty
habitat. We would like to understand the relative importance of these two modes
of gene flow in different types of organisms.
●
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SUMMARY
■ Almost all species show geographic variation in
allele frequencies and the means of phenotypic
traits.
■ Local adaptation, which results when selection
varies in space, is an important cause of genetic
differences between populations. Phenotypic
differences between populations can also result
from phenotypic plasticity.
■ FST is a statistic commonly used to describe
genetic divergence between two or more populations. In many species, FST increases with the distance between two populations, a pattern called
isolation-by-distance. FST between populations
varies across the genome, and regions with high
values may be under spatially varying selection.
■ Gene flow is the mixing of alleles from different
populations. It tends to decrease genetic differences caused by selection and drift. It results
from the dispersal of individuals and their gametes. Dispersal can be either active or passive.
■ Gene flow is measured by the migration rate
(m), when populations are discrete or patchy,
and by the migration variance (σm2), when populations are continuously distributed in space.
These measures can be estimated directly, by
observing dispersal, or indirectly by using patterns of genetic variation between populations.
■ When both gene flow and local selection are
at work, allele frequencies evolve toward a
balance between them. If gene flow is weak
relative to selection, allele frequencies will tend
to evolve to what selection favors in space. If
gene flow is relatively strong, allele frequencies
will tend to be equalized. Strong gene flow can
cause gene swamping, which is the loss of a
locally favored allele because migration overwhelms local selection.
■ A smooth change in a phenotype or allele frequency in space is called a cline. Clines range
tremendously in width, from less than a few
meters to hundreds of kilometers.
■ One cause of clines is selection that varies in
space. The widths of these clines are determined by the ratio of the migration variance
to the strength of local selection.
■ Tension zones are clines in allele frequencies
that result from selection against hetero­
zygotes (underdominance) that acts uniformly
in space.
■ Random genetic drift can cause populations’
allele frequencies at selectively neutral loci to
diverge. Very small rates of migration prevent
divergence at selectively neutral loci. When
two populations are at a genetic equilibrium,
FST will be close to 0 if just a single individual
moves between them, and FST will only be close
to 1 if there is much less migration than that.
These predictions do not hold if the populations
have recently expanded or if alleles are under
selection pressures that vary in space.
■ Dispersal rates evolve. Higher dispersal is
favored by habitat disturbance that reduces survival, by competition between relatives, and by
inbreeding. Lower dispersal can be favored when
movement is risky and energetically expensive.
■ In a species that is expanding its range, alleles
that enhance dispersal automatically tend to
become concentrated at the expanding edge
of the range.
■ Species’ ranges evolve. Factors that prevent
ranges from expanding outward include dispersal
barriers, genetic constraints, gene flow that prevents adaptation to more extreme environments,
and competition with other species that have adjacent ranges. Global climate change is causing
shifts in the ranges of many species, but many or
most of them will likely become extinct because
the change is too rapid for them to adapt.
SUGGESTIONS FOR FURTHER READING
Local adaptation has been studied via many
different approaches. A special 2015 issue
of the American Naturalist is devoted to a
series of reviews on this topic [45]. In a 2016
American Naturalist article Sean Hoban and
colleagues [16] provide an excellent discussion of methods used to identify regions of
the genome that are locally adapted.
Genetic data are now being used to estimate not
just migration rates, but also things such as
the history of range expansions and contractions, how birth and death rates vary in space,
and how selection varies in space. A nice recent review of the concepts and methods is in
08_EVOL5E_08.indd 220
Bradburd and Ralph (2019), “Spatial population
genetics: It’s about time” [8].
The evolution of dispersal involves a fascinating
but complex web of evolutionary forces that
are discussed in the 2007 review “How does
it feel to be like a rolling stone? Ten questions about dispersal evolution” by Ophélie
Ronce [32].
Many evolutionary biologists are now studying
how species ranges evolve, how they are
responding to climate change, and what evolutionary forces are in play at range margins.
Amy Angert and colleagues discuss these
key issues in 2020 and 2021 reviews [1, 7].
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Summary
221
PROBLEMS AND DISCUSSION TOPICS
1. Consider a cricket that has recently arrived
from a continent and colonized a remote
oceanic island.
a. How do you expect the average size
of wings in the island population to
compare with the average size on the
continent? Why?
b. How do you expect wing size in the island
population to evolve over the next several
hundred generations? Why?
2. Clines in body size have been observed in
many species, such as the latitudinal cline in
the size of moose (Figure 8.10).
a. Does a cline in body size necessarily result
from variation in allele frequencies at loci
that affect body size? Why or why not?
b. How might you determine whether a cline
in body size was caused by clines in allele
frequencies?
3. Imagine that you sample 100 plants from five
populations, then sequence several of their
genes. Across the entire sample of all five
populations, there are fewer heterozygotes
than expected under Hardy-Weinberg equilibrium. Within each population, the genes show
linkage disequilibrium. What could explain
those results?
4. If there are two populations each of size N = 105
individuals, how many migrants must move
between the populations in each generation
to prevent large differences from evolving at
selectively neutral loci?
5. In a population of armadillos living on an island,
the frequency of allele A1 is initially p = 0.4. In
each generation, 10% of the individuals in that
population are migrants from the nearby mainland, which has an allele frequency of pm = 0.6.
a. Calculate the frequency of A 1 in each of the
next two generations (generations 1 and 2).
b. Is the change in allele frequency in
generation 2 greater than, less than, or
equal to the change in generation 1? How
can you explain that answer?
c. What will the allele frequency become on
the island after many generations?
6. Equation 8.5 gives the equilibrium value of FST
between two populations for a neutrally evolving locus when the populations are of equal
size and are exchanging equal numbers of migrants. When there is equal migration among
a large number of populations, a different
equation holds:
FST = 1 / (1 + 4 Ne m)
08_EVOL5E_08.indd 221
Suppose that you sample individuals from two
populations, but you do not know whether
these populations exchange migrants only
with one another or whether they are part of
a group of many populations that exchange
migrants. You genotype the individuals in
your samples at several loci and find that the
average FST between the two populations
is 0.1. Using the equation given above and
Equation 8.5, determine the range of plausible
values for the number of migrants that arrive
in each population in each generation.
7. It is now common to score thousands or even
millions of single nucleotide polymorphisms
(SNPs) in many individuals sampled from
several populations. (See, for example, the
results from stickleback fishes shown in Figure 8.5.) Many of these SNPs are neutral and
therefore provide information about rates
of migration among populations. SNPs that
show unusual patterns of genetic variation
may indicate regions of the genome that are
subject to selection.
a. Will loci involved in local adaptation,
such that different alleles are favored in
different populations, show unusually high
or unusually low values of FST between
populations? Explain your answer.
b. What could account for SNPs that show
high heterozygosity within populations
but unusually low values of FST between
populations?
c. Even when thousands of SNPs are
scored, many of the SNPs that are the
actual targets of selection are often not
genotyped. However, even SNPs that are
selectively neutral can be used to detect
regions of the genome that are locally
adapted. Discuss why that might be.
8. A species that has a high rate of longdistance dispersal is more likely to colonize
new habitat. But that species may also be
less likely to adapt to local conditions,
because migration will be stronger than
local selection pressures for many loci.
Given those considerations, discuss when
you expect that increasing dispersal might
result in the evolution of a larger geographic
range, and when it might not.
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UNIT III
The Products
of Evolution
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9
Species and
Speciation
I
n the Rift Valley of eastern Africa, just south of the equator, lie three great
lakes and many smaller ones. Lakes Tanganyika and Malawi are deep and
old, having been formed by the separation (rifting) of two continental
plates. Lake Victoria, in contrast, is broad and shallow, lying in a basin that
was dry only 15,000 years ago. These lakes harbor a few species of catfish,
spiny eels, and other fish families. But more than 90% of all the fish species
are cichlids, a family that includes species well known to fish hobbyists. Lake
Tanganyika has at least 250 species of cichlids, Lake Victoria between 450
and 530 species, and Lake Malawi at least 480 species [84]. (In comparison,
the American Great Lakes have only about 175 species of fish of all kinds.)
These cichlid fishes are extraordinarily diverse in coloration, form, feeding
habits, and habitat use (FIGURE 9.1). Different species eat insects, snails,
detritus, rock-encrusting algae, aquatic plants, phytoplankton, zooplankton,
baby fish, and larger fish. Some species are specialized to feed on the scales
of other fish, and one has the gruesome habit of plucking out their eyes. The
teeth of some closely related species differ more than do those of some
whole families of fish. Many of these habits and morphologies have evolved
convergently in the different lakes [46]. Phylogenetic analyses show that
the 250 cichlids in Lake Tanganyika have evolved from at most 16 original
species. The cichlids of Lake Victoria have multiplied faster than any other
group of vertebrates on Earth: the 450-plus species evolved from just 5
original ancestral species in perhaps only 15,000 years [101].
Fishes in the family Cichlidae are highly diverse in the fresh waters of eastern Africa and South
America. Here, a male blue discus (Symphysodon aequifasciatus) from the Amazon basin
guards his young. In many cichlids, the male broods the eggs in his mouth to protect them.
© Aqua Press/Biosphoto/Minden Pictures
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Chapter 9
Species and Speciation
Lake Tanganyika
Lake Malawi
(A)
Petrochromis
Petrotilapia
(B)
Bathybates
Rhamphochromis
Lobochilotes
Placidochromis
Tropheus
Pseudotropheus
(C)
(D)
(E)
Cyrtocara
(F)
Julidochromis
Melanochromis
All photos © Ad Konings/www.cichlidpress.com
Cyphotilapia
FIGURE 9.1 Examples of the diversity of cichlid fishes in Lakes Tanganyika
(at left) and Malawi (at right). Ecologically and morphologically similar forms have
evolved independently in the two lakes. (A) Rock-dwelling species with rasping
jaws. (B) Open-water fish eaters. (C) Fleshy-lipped species that suck prey from
crevices. (D) Rock dwellers. (E) Hump-headed species. (F) Slender, striped species.
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What caused 08-29-2022
this explosion of diversity? Do the number and ecological variEvolution5e_09.01.ai
ety of species depend only on current ecological conditions, such as how many
different kinds of resources can sustain different species? Or do they reflect the
rate at which new species have arisen? Why should the rate of speciation have
been so high in this fish family, and only in these lakes? Has speciation been
caused by fish mating patterns? By sexual selection on coloration? By adaptation
to different ecological niches? By genetic drift? Fundamentally, what we want to
know is, How do new species form?
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9.1 What Are Species? 227
9.1 What Are Species?
Learning objectives
■ Compare and contrast the “biological” and the “phylogenetic”
species concepts.
■ Define and explain reproductive isolation.
Several definitions of species—which is Latin for “kind”—are used by biologists.
A definition is not true or false, because the definition of a word is a convention.
Because of biological differences between different groups of organisms, no single definition of species suffices for all of them.
Linnaeus and other early taxonomists classified as different species groups
of organisms that they could distinguish, based on features of preserved specimens. But as knowledge of organisms grew, ambiguous cases arose. For example, two kinds of small owls in eastern North America look very different: one
is gray and the other bright reddish brown (FIGURE 9.2A,B). Nevertheless, they
are clearly the same species, eastern screech owl. The two forms sound the same,
they interbreed, and a brood may include both color forms—which are a simple
genetic polymorphism (with rufous dominant over gray). But an owl in the west,
although almost indistinguishable in appearance from the gray eastern owl, has a
very different voice and is recognized as a distinct species—the western screech
owl (FIGURE 9.2C). Birds classified by their call differ slightly, but consistently, in
bill color and the fine streaks on their belly. They also differ in their mitochondrial
DNA sequence [71]. That birds differ consistently in all these features, rather
that showing mixtures, indicates that even though they coexist in Texas, they
seldom (if ever) interbreed. So grouping specimens by a single feature, such as
coloration, doesn’t always distinguish species.
Cases such as the screech owls led to the concept of species as groups of individuals that interbreed. Ernst Mayr [55] (photo, Chapter 1) formalized this idea
in the biological species concept (BSC), which he defined as follows: “Species are
groups of actually or potentially interbreeding populations, which are reproductively isolated from other such groups.” Reproductive isolation (RI) means that
any of several biological differences between the groups substantially reduce
gene exchange between them, even if they are not geographically separated.
(In fact, this definition of reproductive isolation does not include simple spatial separation, such as populations on different islands; that is referred to as
(A)
(B)
© Alan Murphy/BIA/Minden Pictures
Futuyma Kirkpatrick Evolution, 5e
morphs of the eastern screech owl
(Megascops asio). (C) The western
screech owl (Megascops kennicottii).
(C)
© rcyoung/Getty Images
© Dembinsky Photo Associates/Skip Moody/Alamy Stock Photo
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FIGURE 9.2 Can you distinguish
the species? (A) Gray and (B) rufous
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Species and Speciation
geographic isolation.) As we will see, reproductive isolation is not an all-or-none
affair: the degree of RI evolves gradually, so there are various levels of gene
exchange between populations. Consequently, diverging populations usually
become different species gradually, and there is no amount of reproductive isolation that sharply determines whether or not two populations are different
species. Some populations that are classified as different species under the BSC
exchange genes to some extent.
Genetic and phenotypic differences enable us to recognize and distinguish species, but they do not define species according to the BSC. The western and eastern
screech owls are separate species because even where they have an opportunity
to interbreed, they do not (perhaps because they have different vocalizations).
When populations that differ in several features do interbreed, hybrids can often be
identified because they are intermediate and show various mixtures of the features,
due to genetic recombination (FIGURE 9.3) The combination of voice, bill color, and
mitochondrial DNA provides evidence that the owls do not interbreed, because if
they did, we would expect various mixtures of these several features. Notice that
according to the BSC, an inability to form hybrid offspring, or sterility of hybrids,
is not a necessary criterion of species: it is only one of many ways in which gene
exchange may be reduced or prevented.
Individual members of a biological species vary, both within a single population and among different geographic populations, which often show evidence of
interbreeding where they meet. In some cases, such as the gray forms of the two
screech owl species, different biological species are almost identical in appearance but differ in ecology, behavior, chromosomes, or genes. Species that differ
little in appearance are sometimes called sibling species or cryptic species. The discovery that the European mosquito Anopheles maculipennis is actually a cluster
of nine sibling species had great practical importance because some transmit
human malaria and others do not [42].
The biological species concept is the most widely used definition among biologists, but some biologists favor other definitions of species. Some systematists
prefer the phylogenetic species concept (PSC), which emphasizes species as the
outcome of evolution—the products of a history of evolutionary divergence.
Golden-winged warbler
ica, the blue-winged warbler (Vermivora pinus) differs from the
golden-winged warbler (V. chrysoptera) in the color of the throat,
belly, and wing bars. First-generation hybrids and backcross
hybrids occur at low frequency and have different combinations
of these features. Conversely, we would infer that two cooccurring populations are not interbreeding if they differ in several features and if few or no mixed phenotypes can be found.
09_EVOL5E_09.indd 228
© FotoRequest/Shutterstock.com
Hybrids usually show mixtures of several
features that distinguish species. In eastern North Amer-
Dominic Sherony/CC BY-SA 2.0
FIGURE 9.3
© Agami Photo Agency/
Shutterstock.com
Andy Reago & Chrissy McClarren/
CC BY 2.0
Blue-winged warbler
F1
Backcross
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9.1 What Are Species? 229
Under this concept, groups of organisms are different species if they are distinguishable phylogenetic lineages [16]. The PSC can be useful for classification,
because unlike the BSC, it can be applied to geographically isolated populations,
such as those on different islands, in which reproductive isolation is difficult or
impossible to assess. Both the phylogenetic and the biological species concepts
recognize species as sets of populations on separate evolutionary trajectories
[18]. The PSC would name populations as species earlier in the process, when
one or a few differences have evolved, whether or not they affect the organisms’
biological functions. The BSC would usually recognize diverging populations as
species later in the process, when the differences have evolved to the point that
they cause substantial reproductive isolation. Under both definitions, there is no
sharp cutoff at which two populations become two species.
The greatest practical limitation of the BSC is in determining whether populations that are geographically separated (allopatric) belong to the same species
(FIGURE 9.4). The BSC requires that we make a judgment call on whether they
would interbreed if they came into contact under natural conditions. One could
test for reproductive isolation experimentally, for example, in the lab or garden.
But this approach is impractical with many species (e.g., giant squids), and the
results may not reflect what would happen in nature. Commonly, allopatric populations have been classified as species if their differences in phenotype or in DNA
sequence are as great as those usually displayed by species in the same group that
are sympatric (in the same location) [96]. A similar approach is taken with classifying fossils into species, since paleontologists cannot study the mating behavior or
hybrid survival of extinct ammonites and dinosaurs.
The BSC does not apply to organisms that do not reproduce sexually. Bacteria do
not have meiotic sex, but they do exchange genetic material in other ways. Species
of bacteria, such as Escherichia coli and Salmonella typhimurium, were traditionally
FIGURE 9.4
© iStock.com/Veni
© Michael Woodruff/
Shutterstock.com
The geographic ranges of the pygmy
nuthatch and the brown-headed nuthatch. The geo-
Pygmy nuthatch
09_EVOL5E_09.indd 229
Brown-headed nuthatch
graphic ranges of the pygmy nuthatch (Sitta pygmaea),
in western North America, and of the brown-headed
nuthatch (Sitta pusilla), in the southeastern United States,
are separated by hundreds of miles in which neither bird
occurs. They differ in voice and subtly in color pattern.
It is difficult to tell if they are different biological species.
(After BirdLife International. 2017. IUCN Red List for birds.)
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Species and Speciation
recognized by differences in their metabolic capabilities. More recently, genetic
similarity has been used to group individuals into species. Although bacteria
can acquire new genes from even distantly related organisms, most homologous
recombination (“sex”) occurs within traditionally recognized species [5].
Although some systematists use the PSC in classifying organisms, most evolutionary biologists use one or another variant of the BSC, and they define speciation,
in sexually reproducing organisms, as the evolution of reproductive isolation [14]. This
process is the key that enables sexually reproducing lineages to evolve independently and generate biological diversity. Without the evolution of reproductive isolation, the great morphological and ecological diversity of cichlids within
the African lakes could not exist.
No matter which species concept is adopted, some populations of organisms
cannot be unambiguously assigned to one species or another, because the features that distinguish species (by any definition) usually evolve gradually. As
reproductive isolation between populations increases with time, there is no bright
moment in time in which they change from being two populations of one species
to being two distinct species. The distinction between populations and species is
particularly difficult in hybrid zones, where genetically differentiated populations
meet and interbreed to a limited extent (FIGURE 9.5). Hybridization occurs among
species in many groups of plants and animals [52]. Genes are sometimes incorporated into the gene pool of one species from another, a process called introgression
(or introgressive hybridization). Some introgressing genes are adaptive [1]. For
instance, Heliconius butterflies are distasteful to predators and have warning
0.99
09_EVOL5E_09.indd 230
0.1
–30
–20
–10
0
10
20
30
40
Distance from center of cline (km)
(B)
0.99
Morphological score
The two species differ in loci that code
for enzymes and several morphological
features. Red dots and blue squares represent two different 60 km transects in
Poland. (A) Average allele frequency at
six enzyme loci. (B) A morphological score
based on seven characters. The clines in
enzyme allele frequencies and morphological features are coincident, suggesting that this hybrid zone was formed by
contact between two formerly allopatric
populations. (After J. M. Szymura. 1993.
In R. G. Harrison [ed.], Hybrid Zones and
the Evolutionary Process, pp. 261–289.
New York: Oxford University Press.)
Fire-bellied toad
0.5
0.01
FIGURE 9.5
The eastern European
fire-bellied toad (Bombina bombina)
and the western European yellowbellied toad (B. variegata) meet and
interbreed in a narrow hybrid zone.
0.9
Both photos © Boris I.
Timofeev/Pensoft
Allele frequency
(A)
Yellow-bellied toad
0.9
0.5
0.1
0.01
–30
–20
–10
0
10
20
30
Distance from center of cline (km)
Futuyma Kirkpatrick Evolution, 5e
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9.1 What Are Species? 231
H. numata
H. ethilla
H. hecale
H. pardalinus sergestus
H. elevatus
H. pardalinus butleri
H. timareta florencia
H. timareta ssp. nov.
H. heurippa
H. cydno
H. melpomene rosina
H. melpomene melpomene
H. melpomene amaryllis
Large wing images
courtesy of J. Mallet
H. melpomene aglaope/malleti
Rayed
Postman
Heliconius hind wing patterns
FIGURE 9.6
Advantageous alleles have spread by introgression between
distantly related species of Heliconius butterflies in South America. The phylog-
eny is based on many genes. The DNA sequence of two genes that control color
pattern shows that H. timareta ssp. nov. acquired the “postman” pattern in the hind
wing from H. melpomene amaryllis, and H. elevatus acquired the “rayed” hind wing
pattern from H. melpomene aglaope/malleti. (From Heliconius Genome Consortium.
2012. Nature 487: 94–98)
Futuyma Kirkpatrick Evolution, 5e
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Evolution5e_09.06.ai
08-29-2022
coloration: predators
do not attack butterflies with this pattern after one or two
experiences in which they learn to associate the coloration with distastefulness.
Alleles that determine part of the color pattern of the wings of certain Heliconius
species have spread among even distantly related species (FIGURE 9.6).
Biological species are seldom distinguished in practice by directly testing their
propensity to interbreed or their ability to produce fertile offspring. Indeed, this
is usually not necessary. If a sample of sympatric organisms falls into two discrete
clusters that differ in multiple characters, it is likely to represent two species. In
modern studies, genetic markers are often used to reveal the existence of two or
more sympatric species. A polymorphic locus that shows few heterozygotes, and
so departs strongly from Hardy-Weinberg equilibrium, is a signal that there are
probably two or more species. (BOX 9.1 provides an example.) Strong correlations
(linkage disequilibrium) between alleles at many loci would likewise be probable
evidence of different species.
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232
Chapter 9
BOX 9.1
Species and Speciation
Diagnosis of a New Species
Each species in the leaf beetle genus Ophraella feeds on
one species or a few related species of plants. Ophraella
notulata, for example, has been found feeding only on two
species of Iva (marsh elder) along the East Coast of the
United States. This species is most readily distinguished
from other species of Ophraella by the number and pattern of dark stripes on each wing cover.
Some leaf beetles found in Florida closely resembled
O. notulata but were collected on ragweed, Ambrosia
artemisiifolia. This host association suggested the
possibility that these beetles were a different species. In
a broader study of the genus, one of the authors of this
Ophraella slobodkini
book (DJF) collected samples of beetles from both Ambrosia and Iva throughout Florida and examined several
morphological characters, such as the shape of one of the
genetic markers [31]. He found consistent differences
mouthparts and the relative length of the legs. Later
in allele frequencies between samples from Iva and from
studies showed that adults and newly hatched larvae
Ambrosia, even in samples from both plants in the same
strongly prefer their natural host plant (Ambrosia or Iva)
locality. In the most extreme case, one allele had an overwhen given a choice and that the beetles mate preferenall frequency of 0.97 in Ambrosia-derived specimens,
tially with their own species. In laboratory crosses, viable
but it was absent in Iva-derived specimens, in which a
eggs were obtained by mating female Ambrosia beetles
different allele had a frequency of 0.999. No specimens
with males from Iva, but not the reverse. Few of the hybrid
had heterozygous allele profiles that would suggest
larvae survived to adulthood, and none laid viable eggs.
hybridization. Thus these genetic markers were evidence
Based on all of this evidence, the author concluded that
of two reproductively isolated gene pools.
the Ambrosia-associated form is a distinct species, and
A careful examination then revealed average differences
he named it Ophraella slobodkini in honor of the ecologist
Kirkpatrick Evolution, 5e
between Ambrosia- and Iva-associated beetles in a few Futuyma
Lawrence
Slobodkin.
OUP/SInauer/Troutt Visual Services
Evolution5e_BOX 09.01.ai 06-24-2022
9.2 Reproductive Isolation
Learning objectives
■ Describe different kinds of pre- and postzygotic RIBs,
and give examples of each.
■ Define Dobzhansky-Muller incompatibilities.
Gene exchange between biological species is reduced or prevented by differences
between them that the great evolutionary biologist Theodosius Dobzhansky [19]
called isolating mechanisms and that today are called reproductive isolating barriers
(RIBs). Speciation is the origin of two species from a single ancestral species. Two
species that have evolved from each other’s most common ancestor are called
sister species. Under the biological species concept, speciation is the evolution of
reproductive isolating barriers. Understanding the evolution of RIBs tells us how
new species evolve.
The total degree of reproductive isolation between two species may result from
several RIBs that act in sequence—and some potential RIBs may not come into
play. For example, the monkey flower Mimulus lewisii is distributed in the Sierra
Nevada of California at higher elevations than its close relative Mimulus cardinalis,
although they both occur at intermediate elevations [87]. Mimulus lewisii has pink
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9.2 Reproductive Isolation 233
M. lewisii
M. cardinalis
FIGURE 9.7
Pollinator isolation in monkey
flowers. Mimulus lewisii has the broadly splayed
petals characteristic of many bee-pollinated flowers. M. cardinalis has the red coloration and narrow,
tubular form that have evolved independently in
many bird-pollinated flowers. F2 hybrids show tremendous phenotypic variation, which Schemske
and Bradshaw used to analyze the genetic basis of
differences between these two species.
From D. W. Schemske and H. D. Bradshaw.
1999. Proc Natl Acad Sci USA 96: 11910–11915
F2 hybrids
Reproductive barrier
09_EVOL5E_09.indd 233
Both photos courtesy of
D. W. Schemske and H. D. Bradshaw Jr.
flowers with a wide corolla and is pollinated by bees, whereas M. cardinalis has
a narrow, red, tubular corolla and is pollinated by hummingbirds (FIGURE 9.7).
Although almost no hybrids are found where the species occur together, the species can be readily crossed, and they produce viable, fertile hybrids.
FIGURE 9.8 Relative contributions
Douglas Schemske, Toby Bradshaw, and their colleagues were able to quantify
of successively acting isolating
the contribution that different mechanisms make to reproductive isolation, in
mechanisms between the monkey
sequence (FIGURE 9.8). Separation by elevation alone, an ecological difference
flowers Mimulus lewisii and
between the species, reduces gene exchange by 59%. Among plants living at the
M. cardinalis. Elevational separation
same elevation, pollinator fidelity alone is 98% effective: bees almost always visit
and pollinator isolation account for
Futuyma
Kirkpatrick
Evolution, 5e
M. lewisii
and hummingbirds
are faithful to M. cardinalis. If a flower receives both
almost all the reproductive isolation.
OUP/SInauer/Troutt Visual Services
species’ pollen, the conspecific pollen (i.e., pollen from the same species) fertilizes
In places where both species occur,
Evolution5e_09.07.ai 08-29-2022
the ovules at least 70% of the time. The germination of F1 hybrid seeds is reduced
pollinators provided almost complete
by 20% compared with nonhybrids, but a hybrid seed that does germinate is
reproductive isolation. (After J. Ramsey
just as viable. Hybrids produce fewer seeds, however, and
et al. 2003. Evolution 57: 1520–1534.)
they produce much less viable pollen. But because isolation by elevation and pollinator behavior is so great, the
Elevation
later barriers—reduced production, viability, and fertility
of hybrids—hardly come into play at all.
Pollinator
To understand the roles played by different flower feaM. cardinalis
tures in attracting bees versus hummingbirds, the invesPollen precedence
tigators performed a massive field experiment. They bred
Hybrid germination
a large number of F2 hybrids and planted them in an area
where the two species coexist. The F2s have far greater
Hybrid fertility
phenotypic variation than the parental species, and they
have novel combinations of traits (Figure 9.7). By ampli0
0.25
0.5
fying the variation this way, the researchers were able to
Contribution
to
isolation
M. lewisii
determine which of 12 floral traits that distinguish the
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Chapter 9
Species and Speciation
parental species are important to reproductive isolation. They went further by
using a quantitative trait loci (QTL) study (see Chapter 7) to reveal the genes
underlying those traits [7]. At least four traits affect the type of pollinator that
is attracted to a flower, which in turn determines which individuals exchange
genes. The difference between the species in some of these traits is based on
between one and six QTL, so a change to one or a few genes can greatly affect
reproductive isolation.
The monkey flowers illustrate some of the many kinds of RIBs (TABLE 9.1).
Prezygotic barriers reduce the likelihood that hybrids are formed. These include
such factors as separation of the species in different habitats, pollination by different animals, mating at different seasons, mating preferentially with conspecifics, and failure of gametes to unite even if mating occurs. Postzygotic barriers
reduce gene exchange between populations even if hybrid zygotes are produced.
They consist of reduced hybrid viability (survival) or reproduction (fertility).
Both classes of barriers are often asymmetric. For example, females of species
A may be less inclined to mate with males of species B than females of B are to
mate with males of A; or F1 hybrids between the species may differ in viability,
depending on the direction of the cross [97, 105].
Since prezygotic isolating barriers act before postzygotic barriers, they often
have a greater opportunity to restrict gene flow. A second reason why the distinction between prezygotic and postzygotic mechanisms is important is that
different kinds of selection act on them, as we will see shortly.
It is often difficult to tell which isolating barrier was the original cause of speciation. A character difference that contributes to reproductive isolation now may
have evolved partly in geographically isolated populations before they became
different species, partly during the process of speciation, and partly after the
reproductive barriers evolved. Because genetic differences continue to accumulate long after two species achieve complete reproductive isolation, some of the
genes and traits may not have been key to forming the species in the first place.
TABLE 9.1
A classification of isolating barriers
I. Premating barriers: features that impede transfer of gametes to members of other species
A. Ecological isolation: potential mates do not meet
1. Temporal isolation: species breed at different seasons or times of day
2. Habitat isolation: species mate and breed in different habitats
3. Immigrants between divergent populations do not survive long enough to interbreed
B. Potential mates meet but do not mate
1. Sexual isolation in animals: individuals prefer mating with members of their own species
2. Pollinator isolation in plants: pollinators do not transfer pollen between species
II. Postmating prezygotic barriers: mating occurs, but zygotes are not formed
A. Mechanical isolation: reproductive structures of the sexes do not fit
B. Copulatory isolation: female is not stimulated by males of the other species
C. Gametic isolation: failure of fertilization
III. Postzygotic barriers: hybrids are formed but have reduced fitness
A. Extrinsic: hybrids have low fitness for environmental reasons
1. Ecological inviability: hybrids are poorly adapted to both of the parental habitats
2. Behavioral sterility: hybrids are less successful in obtaining mates
B. Intrinsic: low hybrid fitness is independent of environmental context
1. Hybrid inviability: reduced survival is due to genetic incompatibility
2. Hybrid sterility: reduced production of viable gamates
Source: After J. A. Coyne and H. A. Orr. 2004. Speciation. Sunderland, MA: Oxford University Press/Sinauer Associates.
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9.2 Reproductive Isolation 235
Prezygotic barriers
In many plants and animals, prezygotic barriers make the greatest contribution
to reproductive isolation. There are several kinds of barriers, depending on the
biology of the organism (FIGURE 9.9).
Species may be temporally isolated by mating at different times of year, or
even in different years. Ecological isolation results when ecological differences, for
example, habitat preference, contribute to genetic barriers [88, 62]. For example,
two Japanese species of herbivorous ladybird beetles (Henosepilachna) feed on
different genera of host plants (Cirsium and Caulophyllum). Each species mates
exclusively on its own host plant, and this ecological segregation appears to be
the only barrier to gene exchange [43]. Related to this is immigrant inviability: individuals from populations that have become adapted to different environments,
if they disperse into each other’s environment, may not survive long enough to
mate with the local residents [64].
Sexual isolation is a very important barrier to gene flow among sympatric species that frequently encounter each other but do not interbreed [47]. Commonly,
females will not respond to inappropriate male vocalizations or other display
signals, but in some species, such as Heliconius butterflies, males choose among
females. Two kinds of mating systems can prevent individuals of different species from interbreeding. One is assortative mating, according to a matching rule
in which mates are alike in some respect. For example, they might be matched
for size or color, or mate at the same time of the year. The second kind of mating
system is based on signal and response. One sex, most often the female, responds
to (or has a “preference” for) a signal produced by the other sex, usually the male.
These preferences do not involve thought but are simply neural traits that affect
which individuals will be chosen as mates. Mating preferences can play a critical
role in preventing individuals from mating with other species [83]. Many birds
choose mates by visual signals, many frogs choose them based on sounds, and
many insects choose them by sex pheromones. In some groups of insects and
other taxa, the genitalia of related species differ in morphology. Females terminate
(A)
Prezygotic isolation
takes many forms, illustrated by
some species that have been extensively studied. (A–C) Three modes of
prezygotic isolation: (A) Seasonal isolation: the band-rumped storm petrel
(Oceanodroma castro) includes two
genetically different populations that
mate at different times of year [29].
(B) Temporal isolation: related species
of periodical cicadas (Magicicada) have
either 17- or 13-year life cycles, and they
rarely emerge in the same year [9].
(C) Ecological isolation: closely related
species of ladybird beetles (Henosepilachna) feed and mate on different
species of plants [43]. (D–F) Examples
of sexual isolation based on different
sensory modalities: (D) Female túngara
frogs respond almost exclusively to the
calls of conspecific males. A calling
male P. pustulosus is shown here [83].
(E) In moths and many other animals,
sexual isolation is based on different
chemical signals [80]. Two forms of the
European corn borer (Ostrinia nubilalis)
are strongly isolated by responses of
males to different female sex pheromones. (F) Males of Heliconius pachinus
recognize conspecific females by their
wing color pattern [49].
(C)
(B)
© Ivan Kuzmin/Alamy Stock Photo
© Gary Meszaros/Science Source
Courtesy of Sean Sime
(D)
(E)
(F)
Courtesy of Lawrence Gilbert
© Andrew Darrington/Alamy Stock Photo
Courtesy of Ryan Taylor
09_EVOL5E_09.indd 235
FIGURE 9.9
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236
Chapter 9
Species and Speciation
(A)
(B)
Proportion attacked
B–D courtesy of J. R. True
From J. P. Masly. 2012. Int J Evol
Biol 2012: 247352/CC BY 3.0
mating, and prevent transfer of sperm,
if a male’s genitalia do not provide
suitable tactile stimulation (FIGURE
9.10) [21]. In a preference-and-trait system, the male’s trait and the female’s
response are based on different genes.
In contrast, in assortative mating, both
sexes have the same genes for the trait
that determines mate choice.
Several kinds of prezygotic RIBs
can
reduce gene exchange between
100 µm
species of flowering plants. Differ(C)
(D)
ent species often attract different
pollinators, as in the two species of
Mimulus (Figure 9.7). Biochemical
mechanisms often inhibit the pollen
of other species from growing down
the pistil of the flower and fertilizing the ovules (see Figure 12.30). As
in many other kinds of organisms,
related species of plants often flower
at different times of the year.
Gametic isolation occurs when gametes of different species fail to unite.
FIGURE 9.10 Differences in genitalia can contribute to reproductive isolation
It is prominent in many species of
between species if copulation between them occurs. (A) The genital arch in male
marine invertebrates that release their
Drosophila is involved in transferring sperm to females. Its shape differs among closely
eggs and sperm into the water. Difrelated species, as the close-ups show: (B) D. sechellia, (C) D. mauritiana, (D) D. simulans.
ferent proteins on the surface of the
This morphological feature is almost the only one that differs among these species.
sperm and eggs determine whether
the gamete can fuse and cause fertilFutuyma Kirkpatrick Evolution, 5e
ization. These proteins are rapidly evolving in abalones, and differences between
OUP/SInauer/Troutt Visual Services
species in their amino acid sequences prevent hybridization [99].
Evolution5e_09.10.ai 08-29-2022
Some features act as postmating prezygotic barriers: mating occurs, but hybrid
zygotes are not formed. In some insects, such as Allonemobius crickets, conspecific sperm is more likely to fertilize eggs than other species’ sperm, even if the
female mated with both within a very short time [38]. In many plants, similarly,
conspecific pollen grows down the pistil and reaches the ovules faster than pollen from other species.
0.08
Postzygotic barriers
0.06
0.04
0.02
0
melpomene F1 hybrid
cydno
FIGURE 9.11
Model butterflies with
the color pattern of the F 1 hybrid
between Heliconius melpomene
and H. cydno were attacked by birds
significantly more frequently than
those with the pattern of either
parent. The low survival of hybrids is
an example of postzygotic isolation
caused
by an extrinsic
(Based on
Futuyma
Kirkpatrick
Evolution,factor.
5e
data in R. M. Merrill
al. 2012. Proc R Soc
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VisualetServices
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06-03-2022
London B 279: 4907–4913.)
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Postzygotic barriers consist of reduced survival or reproductive rates of hybrids
that would otherwise backcross to the parent populations and transfer genes
between them. These barriers can be classified as either extrinsic or intrinsic,
depending on whether or not their effect depends on the environment.
Extrinsic postzygotic isolation results when hybrids have poor survival because
of ecological factors. An example is provided by hybrids between species of Heliconius butterflies that are distasteful to birds and have different patterns of warning
coloration. Birds learn to associate common color patterns with distastefulness but
are likely to attack butterflies with rare, unfamiliar phenotypes, such as hybrids.
Researchers placed artificial butterflies, with wing patterns of two species and their
F1 hybrid, in a tropical forest, then scored the number that were damaged by attacking birds [58]. Those with hybrid color patterns were more frequently attacked
(FIGURE 9.11). In other cases, the parent species are adapted to different environments, but the hybrid is poorly adapted to both. FIGURE 9.12 shows that populations
of a plant in the sunflower family, in two different habitats (dune and headland),
show lower fitness if they are grown in the other’s habitat. Reciprocal backcross
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9.2 Reproductive Isolation 237
FIGURE 9.12
An experiment showed a genetic basis for extrinsic
reproductive isolation between ecologically different populations.
Krzysztof Golik/CC BY-SA 4.0
Seeds of Senecio lautus from a headland habitat and a dune habitat
were planted in both places, as well as F 1 offspring and both backcrosses of F 1 to parental types. The backcrosses had 75% of their
genomes from one parental species and 25% from the other. Fitness
(the probability the seedlings established and grew) was greater when
a higher proportion of the genome was from the parent that grew in that
habitat. (From T. J. Richards et al. 2016. Evolution 70: 1993–2003.)
Probability of establishment
Dune
hybrids, in which 75% of the genome is
0.5
from one or the other parental population,
grow better in the habitat of the parent that
contributed most of the hybrid’s genome.
0.4
Postzygotic isolation is intrinsic if hy-­
brids suffer high mortality, have low
0.3
mating success, or are partially or entirely
sterile irrespective of environment.
Intrinsic isolation is based on interactions
0.2
between genes from two populations,
and it is often more permanent than
extrinsic isolation. The causes of intrinsic
0.1
postzygotic isolation and its genetic bases
are diverse.
75
100
0
25
50
Many sister species are distinguished
% local genes
by chromosome rearrangements. Two
common rearrangements are inversions
and reciprocal translocations (Chapter 4). Hybrids that are heterozygous for these
rearrangements can have low fertility or viability. Their loss of fitness can be caused
when alternative forms of the rearrangements carry incompatible combinations of
alleles. For example, gene expression in hybrids can be misregulated due to mismatches between regulatory elements (transcription factors, enhancers, and promoters) from the two species [10]. Hybrids can also suffer reduced fertility when
heterozygous rearrangements fail to pair correctly during meiosis, producing gametes that are missing parts of or even entire chromosomes. When populations with
different rearrangements meet and interbreed, selection against the hybrids can
result in steep clines in the frequencies of the rearrangements (FIGURE 9.13). This is
a form of a tension zone (see Chapter 8).
g
k
i
h
n
m
p
(B)
Hybrid
Tomsk
o
g
k
i
h
n
m
p
Frequency of Novosibirsk type
(A)
Novosibirsk
o
1
0.8
0.6
0.4
© Rudmer Zwerver/
Shutterstock.com
0.2
0
0
5
10
15
Transect distance
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20
Headland
0.6
0.4
0.2
0
25
50
75
% local genes
100
FIGURE 9.13
Two “chromosome
races” of the common shrew (Sorex
araneus) form a very narrow hybrid
zone in Siberia. (A) The Novosibirsk and
Tomsk races differ by the fusion of some
single-armed chromosomes (e.g., o and
p in Tomsk) into double-armed chromosomes (e.g., o and g in Novosibirsk). In
hybrids, the multiple rearrangements
cause a chain of nine chromosomes to
form during meiosis, and irregular segregation produces many unbalanced gametes and low fertility. (B) A transect from
Novosibirsk to Tomsk shows a cline in the
frequency of the Novosibirsk chromosome arrangement less than 9 km wide.
The chromosome configuration of either
race cannot increase within populations
of the other race, probably because meiosis in F 1 hybrids produces gametes that
lack some chromosomal regions. (A after
P. D. Polly et al. 2013. PLOS ONE 8: e67455;
B after D. L. Polyakov et al. 2011. J Evol Biol
24: 1393–1402.)
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Ancestor
A1A1B1B1
Geographic
separation
Population 1
Population 2
A1A1B1B1
A1A1B1B1
Time
Genetic
divergence
A2 A2B1B1
F1 hybrids
A1A1B2B2
A1A2B1B2
FIGURE 9.14 Dobzhansky-Muller
incompatibilities (DMIs) can evolve
when geographically separated
populations become fixed for different
alleles at two loci. (After D. C. Presgraves. 2010. Nat Rev Genet 11: 175–180.)
Futuyma Kirkpatrick Evolution, 5e
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X. birchmanni
X. malinche
Tumor
Hybrid
From D. L. Powell et al.
2020. Science 368:
731–736. Used with
permission from AAAS
Caudal
spot
How fast does reproductive isolation evolve?
X. birchmanni
The time required for reproductive isolation to become strong, after it has
started to evolve, varies greatly. The origin of a new species by polyploidy,
which is especially common in plants, requires only one or two generations
(see below). If more than 450 species of cichlid fish evolved from 5 ancestral
species in Lake Victoria in just 15,000 years (see opening of this chapter), the
average time between speciation events was only about 2300 years, which is
Hybrid
population 1
Hybrid
population 2
Hybrid
population 3
FIGURE 9.15 An allele for a black tail spot is present in populations of the
swordtail fish Xiphophorus birchmanni but is absent in X. malinche. Cancer
X. malinche
0.0 0.2 0.4 0.6 0.8 1.0
Spotted caudal frequency
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DOBZHANSKY-MULLER INCOMPATIBILITIES A common and important kind
of genetic incompatibility was postulated by Theodosius Dobzhansky [19] and
by Hermann Muller [59], and so it is often referred to as Dobzhansky-Muller
incompatibility (DMI). These scientists had the insight that an incompatibility between two allopatric populations can originate without ever producing
incompatibility within either population (FIGURE 9.14). Imagine that the ancestor of the two species had genotype A1 A1B1B1. That species was then divided
into two populations, for example, by the rise of a new mountain range. In one
population, allele A 2 spreads to fixation because of adaptation to local conditions. This population is now A 2 A 2 B1B1. In the second population, allele B2
spreads to fixation, so this population becomes A1 A1B2B2. During this period,
alleles A2 and B2 have never been in the same population, so there is no reason
they should have been selected to function well together. If they are incompatible, hybrids between the two populations will have low fitness.
A simple DMI has been described for a cross between strains of Arabidopsis
thaliana from different regions [4]. Both strains have two loci (call them α and β),
formed by duplication. In one strain, the α locus is functional but the β locus is
nonfunctional. In the other strain, the situation is reversed: β is functional but α
is not. The F1 offspring of a cross between the strains are viable, but in the F2 generation, some recombinant offspring are homozygous for nonfunctional alleles of
both genes—a lethal combination. In a more dramatic example, hybrids between
two species of Mexican swordtail fish (Xiphophorus) often develop a malignant
melanoma from a melanic spot on the tail that is a normal polymorphism in
one of the species [69]. Tumor development is triggered in hybrids that carry
the allele to make the melanic spot but not the allele that prevents the spot from
developing into a tumor (FIGURE 9.15). In another example of reduced hybrid
fitness, experimentally produced hybrids among 73 species of Drosophila had
trouble locating food, and the more distantly related their parents were, the less
competent were the hybrid offspring [98].
Two very general patterns about the fitness of hybrids result from DMIs [14]. The
first is Haldane’s rule, which says that hybrid sterility or inviability is often limited
to the heterogametic sex. (The heterogametic sex is the one with two different sex
chromosomes, while the homogametic sex has two sex chromosomes of the same
type.) In mammals and most insects, males are XY and thus are the heterogametic
sex. In birds and butterflies, the situation is reversed: females have two different
kinds of sex chromosomes (the Z and W). Thus male hybrids are frequently sterile in
mammals (e.g., mules), while female hybrids are frequently sterile in birds. The second pattern is that in species with XY sex chromosomes, the X chromosome plays
a disproportionate role in causing hybrid inviability or infertility. These patterns
result because incompatibilities are often recessive and so their effects are masked
in XX females. But they are unmasked in XY males, which is why hybrid males are
often less fit than hybrid females.
(melanoma) often develops in hybrids if they inherit the spot allele but not the allele
that represses tumor growth. The frequency of the tail spot is high in three hybrid
populations (shown in the graph).
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9.2 Reproductive Isolation 239
astonishingly rapid. Molecular clocks (see Chapter 6) can be used to estimate the
time back to the most recent common ancestor of sister species, giving us the age
of their speciation. Based on this approach, sympatric sister species of Drosophila
are estimated to have taken about 200,000 years to evolve, while it requires 1.1 to
2.7 million years for allopatric populations to evolve full reproductive isolation
[13, 14]. Some populations of birds that have been diverging for 1.5 to 3 million
years form hybrid zones, showing that it often takes a long time for birds to
evolve strong reproductive isolation [102].
In many groups of organisms, prezygotic isolation evolves considerably faster
than intrinsic postzygotic isolation (FIGURE 9.16). Consequently, closely related
species are often fully interfertile: many species and genera of birds, even after
more than 5 million years of divergence, can form fully viable, fertile hybrids
(A) Drosophila
(B) Etheostoma
Prezygotic isolation
1.0
Prezygotic isolation appears at
relatively small genetic distances.
0.8
0.6
0.2
0
0.5
1.0
Genetic distance
1.5
0.6
© blickwinkel/Alamy
Stock Photo
0.4
Sexual isolation
0.8
© blickwinkel/Alamy
Stock Photo
Strength of reproductive isolation
1.0
0.4
0.2
0
2.0
0.1
0.2
0.3
0.4
0.5
Genetic distance
0.6
0.7
Postzygotic isolation
1.0
0.8
0.8
Hybrid inviability
Strength of reproductive isolation
1.0
0.6
0.4
0.2
0
0.5
1.0
Genetic distance
1.5
2.0
of prezygotic and postzygotic reproductive isolation between
pairs of populations and species of Drosophila increases with the
amount of time since the lineages split. The time is estimated
by the genetic distance between each pair. The strength of prezygotic isolation was measured by observing mating between
flies in the laboratory. The strength of postzygotic isolation was
measured
by survival
and5efertility of hybrid individuals. ComparFutuyma
Kirkpatrick
Evolution,
ison of the upper
left Services
part of the two graphs reveals that strong
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09_EVOL5E_09.indd 239
0.4
0.2
0
FIGURE 9.16 Prezygotic isolation evolves faster than
postzygotic isolation in flies and fishes. (A) The strength
Evolution5e_09.16.ai
0.6
–0.2
0.1
0.2
0.3
Genetic distance
0.4
0.5
prezygotic isolation evolves shortly after isolation (at small
genetic distances), while strong postzygotic isolation evolves
only later. (B) Similar patterns are seen in a genus of freshwater
fishes, the darters (Etheostoma). Thirteen pairs of allopatric
species were tested for both sexual isolation and the survival
of artificially produced hybrids. For both indices, a value of 0
indicates that the pairs are no more isolated than conspecific
individuals, and a value of 1 indicates complete reproductive
isolation. (A after J. A. Coyne and H. A. Orr. 1997. Evolution 51:
295–303; B after T. C. Mendelson. 2003. Evolution 57: 317–327.)
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Species and Speciation
when crossed [70]. Plants commonly remain interfertile after several million
years of geographic separation, and some trees can still form fertile hybrids after
10 to 50 million years [51]. In these cases (which may be the rule in many kinds
of organisms), intrinsic postzygotic isolation probably plays a minor role in speciation. It may, however, affect the further evolution of prezygotic isolation (see
“Reinforcement”), and it may help keep species separate, because prezygotic barriers such as ecological or sexual isolation may not evolve to completion or can
become weaker if habitats change [81]. For example, increasing turbidity in Lake
Victoria interfered with female cichlids’ ability to see differences in male coloration that are the basis of sexual isolation between some closely related species.
The result is that species that were previously well isolated are now hybridizing
[90]. In contrast, strong postzygotic isolation, such as complete hybrid sterility,
can be irreversible and make speciation permanent.
9.3 The Geography of Speciation
Learning objectives
■ Explain why some amount of geographic isolation is usually important
to speciation.
■ Define speciation with gene flow, and describe factors that make it
more or less likely to happen.
Speciation is the evolution of reproductive isolating barriers. But because these
barriers decrease the chance that some individuals mate or that their offspring
survive, it might seem impossible that they could ever evolve. Something is
needed to restrict interbreeding between two populations, since interbreeding
tends to erase their emerging genetic differences.
The solution to this puzzle is that speciation often starts when populations are
geographically isolated, so gene flow between them is reduced to some extent.
The more complete the separation is, the lower gene flow will be, and the easier
it is for genetic differences that underlie an RIB to evolve. In some cases, a species
with a broad geographic distribution is cleaved by the development of a barrier
to dispersal. This happened to species in the southeastern United States when
they were divided into eastern and western populations during the Pleistocene
glaciations, and those populations evolved genetic differences while they were
separated (FIGURE 9.17). This is an example of allopatric speciation, which occurs
when diverging populations are entirely separated and there is very little or no
gene exchange between them as they evolve reproductive isolation.
Less often, speciation occurs with no spatial isolation, a situation called
sympatric speciation. We will discuss how sympatric speciation can happen
under special conditions. Much more common is a situation that is intermediate
between allopatric and sympatric speciation. In speciation with gene flow, two
Mint
Bowfin
FIGURE 9.17
Gene trees. Gene trees
of the bowfin (Amia calva), the mint
Dicerandra, and many other species
in the southeastern United States
show that eastern and western populations were separated and diverged
in the recent past (the Pleistocene).
(After D. E. Soltis et al. 2006. Mol Evol
15: 4261–4293.)
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Gulf of Mexico
Gulf of Mexico
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9.3 The Geography of Speciation 241
populations evolve reproductive isolation while there is some ongoing gene flow
between them, without a continuous period of complete geographical isolation.
As you might expect, speciation happens more easily in this setting than it does
with sympatry, but less easily than with complete allopatry.
Allopatric speciation
Allopatric speciation is the evolution of genetic barriers between populations that are
geographically isolated by an external barrier, such as a mountain range, river,
or other unsuitable habitat. Allopatry prevents gene flow between the populations, allowing them to evolve independently. In species that disperse little or are
faithful to one habitat, populations can be isolated over even short distances, for
example, between patches of different kinds of habitat along a lakeshore.
Allopatric populations can originate in two ways. One is vicariance, which is
separation that occurs when a barrier appears and divides a population that was
occupying a larger region. When the Isthmus of Panama rose out of the ocean
several million years ago, it divided many marine species into Caribbean and
Pacific populations. Since then, many of these populations have evolved into distinct species (Figure 18.10) [50]. Allopatric populations also originate by dispersal,
when individuals from one population spread to isolated regions. Speciation by
dispersal has happened innumerable times when oceanic islands have been colonized. For instance, almost every island in Melanesia, east of New Guinea, has
a bird called a whistler with a distinct color pattern; many of these are classified
as distinct species (FIGURE 9.18).
Different geographic populations of
many species show partial reproductive
Oriole whistler
isolation when they are put to a test. For
example, when males and females from
different populations of dusky salamanders (Desmognathus ochrophaeus) from
FIGURE 9.18 Allopatric speciation
is illustrated by populations that
are isolated on different islands.
© Lars Petersson
Among many examples are the whistlers (Pachycephala) in the islands east
of New Guinea. Among many are the
Samoan (P. flavifrons), New Caledonian
(P. caledonica), Oriole (P. orioloides), and
Rennell (P. feminina) whistlers.
Samoan whistler
© Lars Petersson
© Lars Petersson
© Serban Bogdan/
Shutterstock.com
© Lars Petersson
Rennell whistler
New Caledonian whistler
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© Robert Hamilton/
Alamy Stock Photo
Sexual isolation index
2.0
1.5
1.0
0.5
0
50 100 150 200 250 300
Geographic distance (km)
FIGURE 9.19 The strength of sexual
isolation between populations of
the salamander Desmognathus
ochrophaeus is correlated with
the geographic distance between
them. The data are based on observa-
tions of mating behavior of pairs of wild
salamanders, taken into the laboratory.
(After S. G. Tilley et al. 1990. Proc Natl
Acad Sci USA 87: 2715–2719.)
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various localities in the eastern United States are placed together, the level of
sexual isolation among them varies. The more geographically distant the populations (and the lower the gene flow between them), the less likely they are to mate
(FIGURE 9.19) [95]. Similarly, sexual isolation has been shown among allopatric
species of darters that occupy different river systems (see Figure 9.16B).
Species on islands provide abundant evidence that spatial separation results
in speciation. Jerry Coyne and Trevor Price [15] reviewed bird distributions
and found that no pairs of sister species of birds occur together on any isolated
island smaller than 10,000 km2. This observation implies that speciation in birds
does not occur on land masses that are too small to provide geographic isolation
between populations. A similar pattern is found in many other taxa [45]. As
expected, taxa in which dispersal and gene flow are high (such as bats) have speciated only on very large islands, while taxa in which gene flow is very limited
(such as snails) have speciated on small islands (FIGURE 9.20).
The role of geographic isolation between islands is obvious, but what kinds
of barriers could have produced the great numbers of species that are found on
continents? Geographic distributions may be fragmented if populations maintain
dependence on specific environmental conditions, such as climate regimes or
habitats. For example, a species that is widely distributed at low elevations in a
mountain range when the climate is cool may move upward and form separate
populations on different mountains when the climate becomes warmer. Exactly
this pattern has been found for allopatric sister species of salamanders, which
are found in locations with similar climate conditions and are absent from intervening regions with different climate conditions [48]. The number of species of
birds, plants, and some other taxa is very high in mountainous regions such as
the Andes, where many species have small ranges and are isolated by valleys
from their sister species [25]. In only 1.76 million years, one Andean lineage of
lupines (members of the pea family) has evolved into 81 species [39].
Minimal island size allowing
speciation (km2 )
Sympatric speciation
Sympatric speciation is the geographical opposite of allopatric speciation. It
occurs when reproductive isolation evolves without any geographic isolation.
Sympatric speciation is difficult because interbreeding between the populations
causes genetic mixing that can prevent them from diverging [6].
To see how sympatric speciation could work,
imagine a bird species that has disruptive selection
on its bill. Birds with long and thin bills eat insects,
and birds with short and stout bills eat seeds (FIGURE
9.21). Birds with intermediate bills, however, have
difficulty finding food and survive poorly. Because
they have different ecological niches, selection
maintains this polymorphism in bills. Now imagine that the birds also vary in color and mate
1000
200
FIGURE 9.20
Speciation is more likely to occur
on larger islands and in species with restricted
gene flow. The minimal island size allowing specia-
50
10
5
2
5
7
10
Increasing gene flow (1/FST)
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20
tion is small in taxa with low rates of gene flow, such
as snails. Islands must be much larger for speciation
to occur in taxa with high rates of gene flow, such as
bats. Gene flow is measured here as 1/FST between
populations ranging from 10 to 100 km apart. FST is a
measure of genetic differentiation that decreases
with greater gene flow (see Chapter 8). (After Y. Kisel
and T. G. Barraclough. 2010. Am Nat 175: 316–334.)
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9.3 The Geography of Speciation 243
Color and beak size correlated
Color and beak size not correlated
FIGURE 9.21
In a hypothetical bird,
prezygotic isolation results from
assortative mating based on color,
while postzygotic isolation results from
selection against birds with intermediate
bill size. When the alleles for color and bill
size are correlated, there is strong isolation
between two populations and sympatric
speciation can result. When the alleles are
uncorrelated, there is much more gene flow
between the populations, which can
prevent sympatric speciation.
assortatively: red birds tend to mate with red birds, and blue birds with blue
birds. This situation provides the ingredients for speciation. If all the birds with
thin bills are blue and all the birds with stout bills are red, the two populations
have both prezygotic isolation (caused by assortative mating based on color)
and postzygotic isolation (caused by selection against hybrids with intermediate bills). In this case, the alleles for color and bill size are correlated: they are in
linkage disequilibrium (see Chapter 4). This causes the prezygotic and postzygotic
reproductive barriers to act to reinforce each other in reducing gene flow between
the two populations.
Why would a correlation develop between alleles for color and alleles for bill
shape? Pairs of birds with the same type of bill (either thin or stout) will tend to have
offspring with the same bill shape rather than an intermediate, disadvantageous
bill. Pairs with the same type of bill are more likely to form if bill shape is correlated
with color, the basis for assortative mating. Pairs that have the same color but different bill shapes will often produce progeny with intermediate bills and lower
survival. Therefore, selection favors correlations between color and bill-shape alleles
(e.g., red and stout bill alleles together, and blue and slender bill alleles together). In
this way, selection can favor linkage disequilibrium between loci that contribute to
prezygotic barriers and those that contribute to postzygotic barriers.
The difficulty for sympatric speciation is that if there is any continued interbreeding between birds with different combinations of alleles for color and bill
shape, the correlations between them tends to be broken down by recombination
[24]. Disruptive selection and assortative mating must be very strong to prevent
recombination from thwarting sympatric speciation. It is particularly unlikely
when there is high recombination between the loci that contribute to isolation or
when the isolating barriers are polygenic [47].
Two situations make sympatric speciation more likely. The first is when
reproductive isolation is caused by a speciation trait (sometimes called a “magic
trait”). This is a trait that causes both adaptive ecological divergence and reproductive isolation between populations [92]. This situation occurs frequently in
insects that feed on a narrow range of host plants [27]. Many herbivorous insects
mate on the plants where they feed, which causes individuals with the same host
Futuyma
Kirkpatrick
Evolution,
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preference
to breed
together.
Natural selection can favor different genotypes that
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prefer different host plants, perhaps because the plants are chemically different
or become suitable at different times of the year. Selection acts against insects
with intermediate preferences, for example, because they have high survival on
one or the other plant but not both. As mutations to prefer one host more strongly
spread in one group of insects, both the prezygotic and postzygotic isolation from
the other group are strengthened. Reproductive isolation can build up this way
to the point that what was a single species becomes two non-interbreeding populations, that is, species. But the more loci affect host preference, the less likely
this process is to complete speciation, because of recombination.
Insect populations that have partial reproductive isolation because they feed
on different plants are called “host races.” For example, the ancestor of the fly
Rhagoletis pomonella laid eggs only in the fruit of hawthorns (FIGURE 9.22). In the
late nineteenth century, the fly started to infest apple trees in the same areas as the
normal host—and it is now known as the apple maggot. Guy Bush [8], and later
Jeffrey Feder and colleagues [22], found that the flies mate on the host plant (assortative mating) and now consist of genetically divergent populations that differ in
host preference and in their mating season: their life cycle is timed to match the
difference between fruit development of the two plants. Some of the difference in
timing probably evolved in distant populations, with those alleles spreading to
populations in apple-growing regions, where they enhance adaptation to apples.
A second situation favorable to sympatric speciation occurs when the spread
of the same allele can increase reproductive isolation in both populations. Imagine a mutation in the Rhagoletis flies that does not affect the average date on
which flies breed, but it decreases the variation around that average. If this mutation first appears in the early-breeding flies, there will be fewer of them breeding
late enough to hybridize with late-breeding flies, so reproductive isolation is
increased. And if the mutation then spreads into the late-breeding flies, they too
will become more isolated. This kind of situation is called one-allele speciation.
© Perutskyi Petro/
Shutterstock.com
© Lukiyanova Natalia frenta/
Shutterstock.com
Courtesy of Joseph Berger,
Bugwood.org/CC BY
FIGURE 9.22
Sympatric speciation
gave rise to an apple-adapted form
of the fly Rhagoletis pomonella from
a hawthorn-feeding ancestor. Mating
occurs only on host plants. Between July
13 and September 11, 1988, counts of the
number of flies on nearby hawthorn and
apple trees showed that the two forms
have evolved different seasonal schedules
and host preferences, which together
greatly reduce gene exchange between
them. (After J. L. Feder et al. 1993.
Entomol Exp Appl 69: 117–135.)
09_EVOL5E_09.indd 244
Number adults captured (1 hr.)
30
25
Apple
Hawthorn
20
15
10
5
0
7.13 7.18 7.21 7.25 7.27 7.29 8.01 8.04 8.08 8.11 8.15 8.19 8.22 8.27 8.29 9.07 9.07 9.11
Date
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9.3 The Geography of Speciation 245
It is favorable to sympatric speciation because the spread of the same allele in
both populations increases their reproductive isolation, so introgression does not
work against this process. In contrast, other ways that isolation can evolve, for
example, by changes in the average breeding date, require that different alleles
spread in the two populations, and introgression can prevent that from happening. We do not yet know how often one-allele speciation has happened.
Some of the best evidence of sympatric speciation comes from cases in which
sister species occupy a small, isolated island that provides no chance for allopatry.
Lord Howe Island is a small (15 km2) island in the southwest Pacific (FIGURE
9.23) [67]. More than one-third of its plant species occur nowhere else in the
world. Among these are the curly palm (Howea belmoreana) and its sister species, the kentia palm (H. forsteriana), which often grow close together [85]. The
key RIB between these palms is a 6-week difference in peak flowering time:
cross-pollination can occur only for a short time, between relatively few plants
(see Figure 9.23D). The two species favor growing on different soils, and this may
have generated disruptive selection that led to sympatric speciation. Similarly,
cichlid fishes have speciated not only in the great lakes of Africa, but also within
tiny crater lakes, on extinct volcanoes in Africa and Nicaragua. Among 13 such
species in Nicaragua, some sister species differ in just a few genomic regions that
are thought to affect coloration and mate choice [44].
How common is sympatric speciation? That question is difficult to answer
because many sister species that today are sympatric have speciated allopatrically and then expanded their ranges. This means that current sympatry, in
itself, is not evidence that speciation occurred sympatrically. Other lines of evidence
(C)
(B)
(A)
(D)
Frequency of flowering
(A) Lord Howe Island is small and remote, lying between
Australia and New Zealand. (B) The kentia palm (Howea
forsteriana). (C) The curly palm (H. belmoreana). (D) The
flowering time of the kentia palm overlaps only slightly
with that of the curly palm. Each plant has a phase when
male flowers open, beginning shortly before the female
flowers begin to open. (D after V. Savolainen et al. 2006.
Nature 441: 210–213.)
Courtesy of W. J. Baker, Royal Botanic Gardens, Kew
Courtesy of W. J. Baker, Royal Botanic Gardens, Kew
© Juergen_Wallstabe/Shutterstock.com
FIGURE 9.23 The palms of Lord Howe Island are
among the best examples of sympatric speciation.
Kentia palm
0.30
Curly palm
Female
flowers
0.25
Male
flowers
0.20
0.15
0.10
Female
flowers
Male
flowers
0.05
0
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have been used to identify cases of sympatric speciation (as in the Lord Howe
Island palms), and the results show that its frequency varies substantially among
groups of organisms [14, 6]. A fair number of possible cases of sympatric speciation exist in herbivorous insects, but only a few examples in birds. One is a
seabird, the band-rumped storm petrel (Oceanodroma castro; see Figure 9.9A),
which has split into sympatric populations with separate breeding seasons [29].
By contrast, perhaps as many as one-third of the endemic species of plants on
Lord Howe Island may have originated there by sympatric speciation [67]. Most
evolutionary biologists believe that allopatric speciation is much more common
than sympatric speciation. Many cases of sympatric speciation may have gone
undetected, however, because it is usually difficult to rule out a past history of
allopatric divergence.
Speciation with limited gene flow
We have now discussed two extreme situations: allopatric speciation, where
reproductive isolation evolves while geographical barriers prevent any gene
flow between the populations, and sympatric speciation, where there is no geographical isolation at all. Speciation often happens in situations between those
extremes, where there is a little bit of ongoing gene flow, or where geographical
barriers come and go in time [93, 23].
One setting in which this occurs is where populations are parapatric, meaning
that they are neighbors to each other. Parapatry occurs if diverging populations
occupy intermingled patches of different habitats, meet at a transition between
two widespread environments, or diverge over a broad environmental gradient.
Many examples have been described in which neighboring populations experience divergent selection and have evolved partial reproductive isolation while
they continue a low level of interbreeding [62]. In the White Sands region of
New Mexico are dunes, formed less than 5000 years ago, that differ starkly from
the surrounding dark soils. In three species of lizards that are distributed across
both soil types, the populations that inhabit the dunes differ in the frequencies
of genetic markers and in head shape, toe length, and most strikingly, in color—
characteristics that are adaptive and are strongly selected (FIGURE 9.24) [81]. In
two of the species, a fence lizard and an earless lizard, genetic markers show that
gene flow has been reduced by the evolution of partial reproductive isolation.
In one species, the RIB involves differences between the two populations in the
color of ventral blotches used in sexual displays [33]. As we would predict, the
level of genetic differentiation between the populations on white sands and dark
soils is lowest in the whiptail lizard (Aspidoscelis inornata), the species that is most
active and shows the highest level of gene flow among populations.
Secondary contact
Often populations are allopatrically separated for long enough to evolve partial
reproductive isolation; then conditions change and they come into secondary
contact. This situation can occur if a geographical barrier (like a region of unsuitable habitat) disappears with a change in the environment or if the range of one
population expands and comes into touch with the other.
There are three possible outcomes to secondary contact. First, the RIB between
the populations can disappear if they hybridize so freely that they meld back into
a single population, and speciation fails. This happened to ecologically distinct
populations of three-spined sticklebacks (Gasterosteus aculeatus) living in the
same lake when the habitat changed and ecological selection against hybrids was
reduced [3]. Second, one population can drive the other to extinction, which occurs
if the ecological differences between them are not large enough to allow coexistence. This seems to be happening in northwestern North America, where barred
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9.3 The Geography of Speciation 247
All photos courtesy of
Erica Bree Rosenblum
White sand
Genetic score
Earless lizard
Dark soil
FIGURE 9.24
Incipient speciation with
gene flow: divergence of lizards on the
white sand and dark soil habitats shown in
the photos. Multiple nuclear genes show that
genotypes differ between habitats strongly in
an earless lizard (Holbrookia maculata) and less
so in a fence lizard (Sceloporus undulatus). Each
bar represents the genotype of one individual.
The proportion of a bar that is white shows the
probability, based on a lizard’s genotype, that
the individual belongs to a distinct white sand
population, and the proportion that is blue, that
it belongs to a distinct dark soil population. Bars
with intermediate amounts of white and blue
indicate that the individual has a mixed genotype.
(Graphs after E. B. Rosenblum and L. J. Harmon.
2011. Evolution 65: 946–960.)
Fence lizard
owls (Strix varia) are expanding their range westward and are reducing the population of the spotted owl (Strix occidentalis) [41]. Third, the populations may remain
distinct, despite ongoing interbreeding. We see evidence of this outcome in hybrid
zones, for example, as seen in the toads in Figure 9.5. Allele frequency clines are
Futuyma
Kirkpatrick
Evolution,
5e between the populations. Genetic differentiation may
produced
as alleles
mix
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the genome become homogenized by gene flow.
REINFORCEMENT When secondary contact occurs between populations that
have partial or complete reproductive isolation, isolating barriers between them
can become strengthened by reinforcement of reproductive isolation. So far, we
have discussed how speciation can result as a side effect of divergent selection.
But natural selection can also directly favor the evolution of prezygotic isolation. Consider two populations whose hybrids have lower survival or fertility,
for example, because of a genetic incompatibility. A female that chooses a male
from her own population will leave more descendants than one that makes the
mistake of mating with a male from the other population. This creates a selective advantage to a mutation for a mating preference that increases the chance
of mating with a male from her own population. This kind of mutation will
be transmitted to more progeny, on average, than an allele that does not bias
which kind of male she mates with. Reinforcement is the result. This process is
very similar to sympatric speciation, but it starts with two populations that are
already strongly differentiated and have some degree of reproductive isolation.
That makes speciation much more likely to occur.
Not all types of isolating barriers can evolve this way. Alleles that strengthen
prezygotic isolation gain an advantage because individuals with them have higher
fitness than do those that hybridize. But stronger postzygotic isolation usually
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cannot evolve by natural selection. An allele that lowers hybrid fitness cannot
increase in frequency, for that would be the antithesis of natural selection. (Exceptions are in organisms such as plants and mammals, in which embryos compete
for the mother’s nutrients. It can be advantageous for a mother to abort hybrid
embryos and allocate resources to nonhybrid offspring that are more likely to
pass on her genes [12].) This is one reason why the distinction between prezygotic and postzygotic mechanisms is important: prezygotic mechanisms can
evolve by reinforcement, but postzygotic mechanisms generally cannot.
Wildflowers in the genus Phlox provide a clear example of reinforcement.
Allopatric populations of the two species Phlox drummondii and P. cuspidata both
have light blue flowers (FIGURE 9.25). Where their ranges overlap, however,
P. drummondii has evolved dark red flowers, a difference in color caused by
changes at two loci. Robin Hopkins [37] found that the fertility of hybrids is up
to 90% lower than that of nonhybrids, and so the difference in color is strongly
favored by selection: pollinators move less pollen between flowers that have
different colors, so P. drummondii with dark red rather than light blue flowers in
the zone of sympatry produces fewer low-fitness hybrids.
This example illustrates how selection against hybrids that have low fitness can
lead to reinforcement. The same outcome can occur even if the two populations in
secondary contact are species that are completely reproductively isolated. Females
waste their reproductive effort if they hybridize and then produce offspring that
are completely inviable or infertile. For instance, male European pied flycatchers
(Ficedula hypoleuca) are black and white except where their range overlaps the very
similar collared flycatcher (Ficedula albicollis). Here, males are brown and white,
and they are preferred by females. There is nearly complete postzygotic isolation:
female hybrids are sterile, and males have very low mating success [72].
If reinforcement is common, we would expect sympatric pairs of species
(which could potentially mate) to show greater prezygotic isolation than allopatric pairs of species (which have no chance of mating). Jerry Coyne and Allen Orr
FIGURE 9.25 Reinforcement of
reproductive isolation by flower
color in Phlox. (A) The geographic
distributions of P. cuspidata and
P. drummondii overlap in Texas. Flowers
in allopatric populations of both species
are light blue, but they are dark red in
P. drummondii where the species are
sympatric. (B) The flower color difference in P. drummondii is based on two
loci. (C) Results of common-garden
field experiments, in which all four color
types of P. drummondii were grown
together with P. cuspidata. Both parental
types of P. drummondi (light blue and
dark red) and hybrid genotypes with light
red and dark blue flowers have equal
fruit production (top graph), but they
differ in how often they hybridize with
P. cuspidata (bottom graph). (From
R. Hopkins. 2013. New Phytol 197:
1095–1103.)
(A)
(C) 60
50
Texas
P. cuspidata
40
Fruit set
100 km
P. drummondii
Austin
P. drummondii
30
20
10
0
(B)
Light red
(iihh)
Dark red
(IIhh)
Light blue
(iiHH)
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Dark red
Light blue
Light red
Dark blue
Flower color genotype
Dark red
50
40
30
20
10
0
Dark blue
(I_H_)
Light red
60
Sympatry
Relative hybridization
Allopatry
Light blue
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[13] compiled data from other workers’ Drosophila experiments and found
exactly the expected pattern. In FIGURE 9.26, the strength of prezygotic isolation between a pair of populations or species is plotted against the genetic
difference (or genetic “distance,” based on molecular differences) between
them. Prezygotic isolation here refers to behavioral isolation when females’
choice between males of both species was observed in laboratory tests. The
genetic distance serves as a molecular clock, indicating time since the species
diverged from their common ancestor (see Chapter 6). Sympatric pairs of
species show strong prezygotic isolation at lower genetic distances than do
allopatric pairs, meaning they evolved mating discrimination faster. This pattern is what we expect if the sympatric pairs tended to hybridize shortly after
speciation, and reinforcement then strengthened their prezygotic isolation.
Further analysis found additional support for the role of reinforcement.
Hybridization between some pairs of sympatric species is asymmetric: the
offspring from a cross between a female of species A and a male of species
B have lower fitness than those from the reciprocal cross (female B × male
A). Roman Yukilevich [104] analyzed studies of speciation in Drosophila
and found that sexual isolation is stronger in the female-male combination that produces less fit hybrids, as we predict from the hypothesis of
reinforcement. He estimated that reinforcement may occur in as many
as 83% of sympatric Drosophila pairs. Also, in pairs of allopatric species,
both male preference and female preference for their own species evolve
at the same rate—but in sympatric pairs, female preference for conspecific males has evolved much faster than male preference for conspecific
females [106]. This is expected, because the cost to females of having unfit
hybrid offspring is greater than the cost to males, which can mate with
many females, some of which may be conspecific.
Strength of prezygotic isolation
9.4 The Origins of Reproductive Isolation 249
1.0
0.8
0.6
Sympatric taxa
Allopatric taxa
0.4
0.2
0
0.5
1.0
Genetic distance
2.0
FIGURE 9.26 In Drosophila, the strength of
prezygotic isolation increases more rapidly
between sympatric pairs of species (open
dots) than it does between allopatric pairs
(solid dots). The genetic distance between
members of a pair is an index of the time since
divergence began. At small genetic distances,
many sympatric
pairs show
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Kirkpatrick Evolution,
5e strong isolation, but
allopatric pairs do
not.
These patterns suggest
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reinforcement is common.
(After J. A. Coyne and
H. A. Orr. 1987. Evolution 51: 295–303.)
9.4 The Origins of Reproductive Isolation
Learning objectives
■ Discuss several processes that contribute to speciation, and provide
evidence for each of them.
■ Describe the evidence for reinforcement of reproductive isolation
and for how it evolves.
We now turn to the question of what causes the evolution of genetic and phenotypic differences between populations that result in reproductive isolation. That
is, what are the causes of speciation? We will break the topic down into different
ways that reproductive isolation can evolve, but please keep in mind that often
several occur at the same time.
Ecological speciation
The two monkey flower species discussed earlier (see Figure 9.7) provide a vivid
example of ecological speciation: reproductive isolation that results from natural
selection, owing to ecological factors, that acts differently on two populations [88,
86, 62]. Based on phylogenetic reconstruction of ancestral characteristics in the
genus, it is likely that the ancestor of these species resembled Mimulus lewisii: it
was bee pollinated and occupied high elevations. The population that gave rise to
M. cardinalis colonized lower elevations, where natural selection favored flower
traits that attract hummingbirds: red pigments, abundant nectar, and extension of
the petals to form a long, tubular corolla that excludes bees but allows hummingbirds to reach the nectar. Those changes to the elevational distribution and flowers
had the effect of strongly decreasing the exchange of pollen (and genes) with the
ancestral population, giving rise to the new species.
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(A)
(B)
Same-host pairs
Different-host pairs
0.4
0.2
–0.2
Habitat
isolation
Immigrant
inviability
Sexual
isolation
Hybrid
inviability
Courtesy of Aaron Comeault
0.0
Courtesy of Aaron Comeault
Reproductive isolation
0.6
Adenostoma ecotype
Ceanothus ecotype
FIGURE 9.27
Ecological speciation
in the stick insect Timema cristinae.
Two forms that differ in color pattern
and shape live and feed on two intermingled shrubs (Adenostoma and Ceanothus) in coastal California. The graphs
show that there are several forms of
reproductive isolation between these
forms, including host preference and
immigrant inviability (low survival if on
the wrong host plant). (Graph after P.
Nosil. 2007. Am Nat 169: 151–162.)
An example of ongoing ecological speciation in animals, extensively studied
by Patrik Nosil [61], is the stick insect Timema cristinae, which has a striped form
that lives on and eats the foliage of the shrub Adenostoma, and an unstriped form
that lives on another shrub, Ceanothus. Each ecotype is more cryptic and more
likely to avoid predation on its usual host plant. These ecotypes show allele
frequency differences at genetic markers, and Nosil has shown that they are partially reproductively isolated by several factors, including host-plant preference,
partial sexual isolation, and immigrant inviability (high predation on individuals
that migrate to the “wrong” host plant) (FIGURE 9.27).
Biologists have also observed the evolution of reproductive isolation by selection in the laboratory. Chewing lice (Mallophaga) are ectoparasites that infest
birds. Males and females must be of the right sizes relative to their mates for
copulation to be successful (FIGURE 9.28). To avoid being removed by a bird
when it preens, they also must be of the right size relative to the feathers of the
bird species they are living on. Lice were taken from feral pigeons of the sort that
you are familiar with (Columba livia) and transferred to a colony of a domesticated pigeon breed that has larger feathers [100]. After 4 years (or about 60 louse
generations), the lice on the domesticated pigeons had adapted to their new host
and evolved to become substantially bigger. Mating trials showed that when a
male and a female come from different hosts, their reproductive success is greatly
reduced because of their mismatch in size (Figure 9.28D). Although speciation
was not yet complete, this elegant experiment shows that a great amount of
reproductive isolation can evolve in a very short time.
These examples illustrate how reproductive isolation can evolve as a side
effect of adaptation to different ecological circumstances. The key point is that the
RIBs evolved by pleiotropy (see Chapter 4). There was no direct natural selection
for isolation between the populations. Rather, selection acted on other traits, such
as body size or host-plant preference, that happened to cause isolation. (Recall
the distinction between “selection for” and “selection of” features, in Chapter
3.) Although speciation, one of the most important elements of evolution, is
commonly a consequence of adaptive changes in organisms’ characteristics, it is
typically not an adaptation itself.
Mutation order speciation
We have just seen that speciation can result when selection acts differently on
different populations. But species can also form when the same selection pressure acts on them. By chance alone, different sets of mutations that enhance the
same adaptation may first appear and spread to fixation in different populations.
Sometimes the mutations fixed in one population are incompatible with those
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9.4 The Origins of Reproductive Isolation 251
(A)
FIGURE 9.28
(B)
After S. M. Villa et al. 2019. Proc Natl
Acad Sci USA 116: 13440–13445
Male
Female
(D)
0.0
6
0.8
5
0.6
Giant runt
0.4
0.2
0.0
Feral pigeon
Number of offspring
Relative louse body size
(C)
–0.2
Feral pigeon
Giant runt
Male F × female G
Male F × female F
Male G × female F
4
3
2
1
0
0 6 12 18 24 30 36 42 48
Time (months)
–800 –700 –600 –500 –400 –300 –200 –100
Body lenth dimorphism ( µm)
(male length–female length)
0
in other populations, for example, when they result in a Dobzhansky-Muller
incompatibility. This situation is mutation-order speciation.
This process has been demonstrated in experiments. Jasmine Ono, Sally Otto,
and their colleagues [65] studied the adaptation of multiple laboratory populations of yeast (Saccharomyces cerevisiae) to a fungicide over a number of generations. By sequencing genomes from each population, these scientists found that
adaptation resulted from mutations in a single biochemical pathway. Selection
fixed different mutations in different populations, and when they were brought
together in a hybrid, fitness was reduced.
Mutation-order speciation may be common. It is expected to be more prevalent in smaller populations because that situation increases the chance that different mutations will become fixed in different populations.
Bird lice evolved
strong reproductive isolation in
an experiment only 4 years long.
(A) Mating between a male (colored red)
and female (colored orange) is only successful if they are the appropriate relative sizes. The male grasps the female
with his antennae (black arrow, inset)
and then inseminates her with the tip of
his abdomen (white arrow). (B) One laboratory population of lice was maintained
on feral pigeons, and a second population was established by moving lice
from that host onto a domesticated
pigeon breed called a “giant runt.”
(C) Lice transferred to the giant runt
rapidly adapted to the new host by
evolving a larger body size. (D) Mating
trials showed that when male and
female lice come from different hosts,
they have greatly reduced reproductive
success compared with mating pairs in
which the male and female both come
from the feral pigeon population. Pairs
have much higher reproductive success
when their relative sizes are appropriate
(shaded region). When one individual
comes from a feral pigeon (F) and the
other from a giant runt (G), they are
mismatched and have low reproductive
success. When both individuals come
from feral pigeons, they are matched in
size and have high success.
Speciation by genetic conflict
Another powerful cause of the evolution of reproductive isolation is genetic
conflict, which occurs when an allele increases its own transmission to the detriment of other alleles at the same or other loci. Many mutations have been found
that transmit more copies of themselves to the next generation, not by increasing survival or reproduction, but by violating the rules of inheritance. They
are transmitted to more than 50% of the gametes (a process called segregation
distortion, see Chapter 13). These mutations increase in frequency in a population even though they often reduce fertility. Selection therefore favors mutations
at other loci that restore full fertility by disabling the segregation distortion
caused by the “selfish” mutation.
When this conflict between a distorter and a restorer has played out in one
population but not another, the populations may be genetically incompatible. This is the basis for strong postzygotic isolation between populations of
Drosophila pseudoobscura in North America and in Bogotá, Colombia: hybrid
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males are almost completely sterile [68]. Sterility is the result of a mutation
that reduces male fertility but that spread by segregation distortion through
the Bogotá population. This population has restorer alleles at other loci that
maintain male fertility, but restoration is inadequate in hybrid males. Genetic
conflict seems to be an important cause of Dobzhansky-Muller incompatibilities in other Drosophila species and perhaps other groups of organisms [17].
Earlier we saw that different species of abalones are reproductively isolated
because proteins on the outside of their eggs and sperm have diverged to the
point where they do not bind to one another. Divergence may have been caused
by sexual conflict: changes in the egg surface that slow down sperm entry are
advantageous because fertilization by more than one sperm kills the egg. Any
such changes in the egg will impose selection for sperm that can beat their competitors by penetrating more quickly [99].
Speciation by sexual selection
Mean pulse rate of male (pulses/s)
3.1
3.0
2.9
2.8
Courtesy of Kerry Shaw
2.7
2.6
2.5
2.4
2.3
2.2
2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1
Mean pulse rate preference of female
(pulses/s)
FIGURE 9.29
An example of sexual
isolation attributed to sexual selection.
The pulse rate of the mating call of male
crickets (Laupala cerasina) and the pulse
rate preferred by females both vary
among local populations. These differFutuyma Kirkpatrick Evolution, 5e
ences are genetically
based. Females of
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the most mismatched
populations do not
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respond to the most divergent males.
(After J. L. Grace and K. L. Shaw. 2011.
Evolution 65: 2184–2196.)
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In many groups of rapidly speciating animals and plants, species differ more
in their secondary sexual traits,1 such as male coloration or vocalization, than
in ecologically important traits (see Figure 9.9). In many cases, one sex (let’s
suppose the female) chooses mates based on variation in these traits. Females
impose strong sexual selection, which can drive the rapid evolution of male
secondary sexual traits (see Chapter 12). Species that differ in sexually selected
male features also commonly differ in female preference, so females recognize
and mate preferentially with males of their own species. These patterns suggest that divergent sexual selection can cause rapid evolution of prezygotic
isolation between populations [83]. (We will see in Chapter 12 that different
populations can evolve different mating preferences for a number of reasons.)
Certain groups of animals, such as cichlid fishes and hummingbirds, have
indeed speciated rapidly and show strong sexual selection. Recent phylogenetic analyses of birds suggest that male coloration patterns associated with
both sexual selection and species recognition evolve fastest in lineages with
high speciation rates [89, 11].
Studies of closely related populations and species provide more direct evidence that sexual selection may cause speciation. For example, Kerry Shaw’s
research team found that male calls and female preferences covary among
populations of a Hawaiian cricket (Laupala cerasina), to the point that females
hardly respond to the calls of the most mismatched population (FIGURE 9.29)
[32]. Tamra Mendelson and her lab study freshwater fishes called darters, and
they concluded that sexual isolation is apparently the sole basis of reproductive
isolation between ecologically indistinguishable species (see Figure 9.16B) [53].
Why then does sexual selection vary among populations? In Chapter 12 we will
consider some of the factors at work. These include direct benefits to mate preferences, selection acting on pleiotropic effects of preference genes, preferences
for mates with “good genes,” and ecological factors that make different courtship
signals more effective in different environments.
Speciation by polyploidy
When a diploid species’ entire genome is doubled (see Chapter 4), the result is
a tetraploid that has four copies of every chromosome. Tetraploids originate by
the union of two “unreduced” gametes—both carrying a full diploid set of chromosomes—that are formed when chromosomes occasionally fail to segregate in
meiosis. The polyploid offspring is autopolyploid if both unreduced gametes come
1
Secondary sexual traits are those that differ between the sexes, other than the gonads and reproductive structures.
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9.4 The Origins of Reproductive Isolation 253
from the same diploid species and is allopolyploid if they come from different
diploid species. If similar events happen in tetraploid species, offspring with
even more sets of chromosomes (e.g., eight in octoploids) result. The increased
number of gene copies in polyploids changes the amount of many gene products
and alters many phenotypic traits [66].
Tetraploids typically have complete reproductive isolation from their diploid
ancestors and so are distinct biological species that have arisen in one step [75].
That is because hybrids between a diploid and a tetraploid are triploid: they
have one set of chromosomes from the diploid parent and two from the tetraploid parent (in which four homologous chromosomes generally segregate two
by two in meiosis). Triploids are largely sterile [40] because their gametes are
unbalanced: they have one copy of certain chromosomes and two copies of other
chromosomes. Genome doubling is a large mutational event: one of the very rare
situations in which mutations of large effect make important contributions to
evolution, in this case the origin of new species.
Speciation by polyploidy is rare in animals, but it is quite common in some
groups of plants. It accounts for about 15% of speciation events in flowering
plants [103], and all plants have a polyploid ancestor somewhere in their evolutionary past [66]. Speciation by polyploidy has occurred even very recently. For
example, hybridization among three species of goatsbeards (Tragopogon) generated new allopolyploid species within the last several centuries, after their
accidental introduction to North America from Europe (FIGURE 9.30).
How can a new tetraploid build up a population? There is a serious obstacle.
The tetraploid species starts out with just one or a few individuals, so often
the diploid ancestor is more abundant in its habitat. This can cause the tetraploids to cross most often with the diploids, producing triploid offspring with
low fitness and pushing the tetraploid toward extinction. A new tetraploid
might increase if hybridization were reduced by self-fertilization, vegetative
propagation, higher fitness than that of the diploid, or
habitat segregation from the diploid [28]. Many tetraploid taxa do indeed have these characteristics. The
phenotypic differences that are an immediate effect of
chromosome doubling may cause separation. In California, tetraploids and hexaploids of the yarrow Achillea
borealis grow in wetter and drier habitats, respectively.
Justin Ramsey planted seedlings of both forms in dry
dunes, as well as “neohexaploids” that had originated
de novo from tetraploid parents that he grew [74]. The
neohexaploids survived better and flowered earlier than
the tetraploids (FIGURE 9.31), showing that they would
be partly isolated from the tetraploids, by habitat and
flowering time, immediately upon their origin.
FIGURE 9.30 Several species of goatsbeards
(Tragopogon) are tetraploids that have formed by
hybridization (allotetraploids). The diploid species
T. dubius and T. pratensis hybridized and produced the
tetraploid species T. miscellus. Next to the pictures of
the flowers are drawings of their chromosomes. The
tetraploid has twice as many chromosomes as either
diploid species. T. dubius has also hybridized with the
diploid T. porrifolius to produce the tetraploid T. mirus.
(After M. Ownbey. 1950. Am J Bot 37: 487–499; J. C. Pires
et al. 2004. Am J Bot 91: 1022–1035.)
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T. miscellus
n = 12
T. pratensis
n=6
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T. dubius
n=6
T. mirus
n = 12
T. porrifolius
n=6
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(A) Survival
1.0
0.8
0.6
0.4
0.2
0.0
(B) Flowering time
10
Frequency
time (B) of a newly originated hexaploid
(neo-6n) yarrow (Achillea borealis), planted in
a dry dune, were intermediate between those
of its tetraploid parent (4n) and an existing
hexaploid (6n) species. Increasing the
chromosome number immediately changes
these characteristics, but the wild hexaploid
(6n) differs even more from the tetraploid
ancestor (4n), so the wild hexaploid must
have undergone some additional evolutionary change. (After J. Ramsey. 2011. Proc Natl
Acad Sci USA 108: 7096–7101.)
Survivorship
Differences between a
newly formed polyploid and its ancestor
may confer ecological differences that
could reduce the opportunity for crossing
between them. Survival (A) and flowering
© H. Mark Weidman Photography
/Alamy Stock Photo
FIGURE 9.31
1
10
neo-6n
6n
neo-6n
1
10
4n
4n
6n
1
May 15
June 1
June 15
July 1
Date of first flowering
Speciation by random genetic drift
Some closely related species differ by chromosomal rearrangements that contribute
postzygotic
isolation
because of low fertility of chromosome heterozyFutuymatoKirkpatrick
Evolution,
5e
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Visual Services
gotes,
which are underdominant
for fitness (see Figure 9.13). How these isolating
Evolution5e_09.31.ai 08-29-2022
mechanisms
evolve is puzzling because underdominant mutations are selected
against when they first appear (see Chapter 5).
One possible answer is random genetic drift. If the population size is so small
that genetic drift is stronger than selection, there is a chance that the new rearrangement will increase in frequency even if heterozygotes have decreased fitness. Several factors, including self-fertilization and large fluctuations in population size, make this more probable. Drift is unlikely to establish individual
rearrangements that cause very strong postzygotic isolation, because the force
of selection against them will be overwhelming.
But even a rearrangement that makes only a small contribution to reproductive
isolation at first may later reduce gene exchange with populations of the other species. For example, heterozygous inversions suppress recombination (see Chapter
4). If a population becomes fixed for a new chromosomal inversion, either by drift
or some other mechanism, hybrids with other populations will be heterozygous
for the inversion, and genes in that chromosome region will not introgress between
the populations. Thus, mutations that differ between the populations accumulate
more rapidly in the inverted region than in other parts of the genome [60].
One hypothesis for how random genetic drift might trigger the origin of new
species is called founder effect or peripatric speciation. Drift can be particularly
strong when a new population is founded by a small number of individuals (see
Chapter 6). Under this hypothesis, proposed by Ernst Mayr [56], drift in a new
population, founded by a few individuals, fixes rare alleles at certain loci. Alleles
at other loci that increase fitness by interacting favorably with these newly fixed
alleles increase, resulting in a new combination of genes that may be genetically
incompatible with the parent population from which the colony was derived.
Mayr suggested that founder effect speciation is an important way that new
species arise at the periphery of species ranges, and he offered potential examples
from island populations of birds.
Several investigators have tested the idea that drift can cause the evolution of
reproductive isolation, by passing laboratory populations of flies through bottlenecks and then measuring reproductive isolation several generations later [54].
Typically a few of the replicate populations display partial sexual isolation from
the parent population, although the change is often temporary. There is some
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9.4 The Origins of Reproductive Isolation 255
disagreement about whether or not to view these results as support for founder
effect speciation [14, 94]. Founder effect speciation is controversial for both theoretical and empirical reasons [54].
Speciation by hybridization
Finally, new species sometimes evolve by hybridization. This is more common in
plants, but some examples are known in fish, birds, and other animals. We saw
earlier that polyploid species of plants sometimes arise from hybrids between
diploid species, but hybrid species can also form without an increase in genome
size [1]. For example, three species of sunflowers have originated from independent hybridization events between Helianthus annuus and H. petiolaris (see Figure
2.12) [77]. All have the same number of chromosomes. The hybrid species live in
drier or saltier habitats than the parents and are genetically incompatible with
them. DNA studies show that all three hybrid species have arisen from the same
two parental species but that they have different combinations of parental genes.
The origin of the hybrids has also been confirmed experimentally, by crossing the
parental species. Their F2 hybrids show some of the same distinctive adaptive
features as the wild hybrid species [79].
A few species of animals are known to have formed by hybridization. The Italian
sparrow (Passer italiae) has plumage features and many genetic markers of both the
Spanish sparrow (P. hispaniolensis) and the widespread house sparrow (P. domesticus) (FIGURE 9.32). It does not interbreed with the Spanish sparrow and is partly
reproductively isolated from the house sparrow where it overlaps these species [36].
A recent and surprising discovery is that hybridization may have been a key
that triggered some of the most dramatic adaptive radiations on Earth. The chapter opening described some of the hundreds of remarkable cichlid fishes that live
in Africa’s Great Rift Valley lakes. Genome analysis shows that the explosion of
Spanish sparrow
Passer hispaniolensis
House sparrow
Passer domesticus
Italian sparrow
Passer italiae
FIGURE 9.32
The Italian sparrow
evolved from hybrids between the
house sparrow and the Spanish
sparrow. The distribution of the house
sparrow is shown in gray, of the Spanish
sparrow in red, and of the Italian sparrow
in yellow. The Italian sparrow overlaps
with the house sparrow in the Alps (shown
in northernmost Italy) and with a small,
disjunct population of the Spanish sparrow in a small peninsula on easternmost
Italy. The Spanish and house sparrows
co-occur in the hatched region. (After
J. S. Hermansen et al. 2011. Mol Ecol 20:
3812–3822.)
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Species and Speciation
ecological diversity in Lake Victoria was enabled when the lakes were colonized by
a small number of ancestral cichlids that had adapted to very different conditions
while in allopatry [57]. They hybridized when they met in the lakes. Many different
combinations of alleles affecting traits such as head shape and vision produced
new phenotypes that then adapted to many different ecological niches in the lake.
9.5 The Genomics of Speciation
Learning objective
■ Describe speciation genes and how they can be located in the genome.
As in all other fields of biology, the genomics revolution has opened up new
perspectives on speciation. Genomics can help us determine the number, identities, and genomic locations of speciation genes, the loci that contributed to the
evolution of reproductive isolation [63].
When two populations or species are partly isolated but continue to hybridize,
alleles introgress between them. The introgression, however, is uneven across the
genome. In parts of the genome that are evolving neutrally, gene flow between
the populations will tend to make them very similar. In addition, if a mutation
appears in one species that is beneficial to both species, it will sweep through
both and homogenize that region of the two genomes. Earlier in this chapter, we
saw that genes for warning colors in Heliconius butterflies have spread among
species this way (see Figure 9.6) [35].
A contrasting picture is expected in genomic regions that carry loci that isolate
hybridizing species. Those regions, sometimes called “genomic islands of speciation,” are expected to show greater genetic divergence than the rest of the genome.
The group of mosquitoes that transmit malaria in Africa (genus Anopheles) includes
six species that diverged recently and that continue to hybridize [26]. Comparisons
of the genomes reveal some regions of the genome (particularly inversions) that are
similar in some species pairs because they have introgressed following hybridization, like the color pattern genes in Heliconius butterflies. But other regions of the
Anopheles genome show unusually high divergence among species—the signature
of speciation genes. In particular, the X chromosome is more genetically different
than the autosomes. It has deep gene trees (see Chapter 6), which strongly suggests
that genes on the X chromosome were among the first to contribute to reproductive
isolation during the speciation process. That pattern is consistent with an observation we discussed earlier: sex chromosomes often play a disproportionately large
role in the evolution of reproductive isolation [14].
Genomic islands of speciation might provide evidence about the geography
of speciation, but this is still uncertain. Genomic islands are predicted to develop
during speciation with gene flow. They will also appear when two populations
that speciated in allopatry come back into contact and then hybridize [76]. Allopatric populations will show different genomic islands at sites where divergent
natural selection fixed different alleles that may or may not make a potential
contribution to reproductive isolation.
9.6 Speciation and Diversification
Learning objectives
■ Define speciation rate and speed of speciation, and describe
the connection between them.
■ Describe factors that affect the rate of speciation.
Each of two species that are formed by speciation may later again undergo speciation; as this process is repeated through time, the number of species in that
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9.6 Speciation and Diversification 257
lineage grows. If two clades each begin with one species, and one has twice the
number of species as the other 10 My later, it has had a higher diversification rate.
That could be because it had a higher speciation rate or a lower extinction
rate (or both). We can determine diversification rates, and sometimes speciation rates, from time-calibrated phylogenies (see Chapter 18). Now recall that
Coyne and Orr found that prezygotic reproductive isolation (RI) in Drosophila
evolves faster between sympatric species than allopatric species, by plotting
RI against genetic distance, used as a molecular clock (see Figure 9.16). They
were measuring the speed of speciation—the rate at which populations evolve
reproductive isolation.
Daniel Rabosky and Daniel Matute [73] asked if the speed of speciation determines the rate of diversification, using data for Drosophila and birds. They found
no correlation between the two rates: speciation may be faster in one clade than
another, but that clade doesn’t necessarily have more species. Among bird clades,
the speed of speciation is correlated with the rate at which local populations of
species become genetically different—but this rate is three times faster than the
long-term rate of speciation [34]. This implies that the great majority of genetically different populations do not become different species that persist until we
see them. This would help to explain the mismatch between the speed of speciation and the long-term rate of diversification.
This mismatch might be caused partly by extinction of many species, but
another cause is also likely: speciation may often begin but not go to completion. Separated populations of an ancestral species start to diverge, but they
may fail to persist, either because of extinction or because reproductive isolation is not completed. Incompletely isolated new species may merge, as in
the stickleback case described earlier. Environmental changes may increase
gene flow between them, or the disruptive selection that can cause ecological
speciation may cease. The rate of speciation, then, depends on the rate of splitting into separate populations, the speed of RI evolution, and the persistence
of the populations despite the possibility of extinction or fusion by gene flow
[20]. Many speciation events are probably ephemeral and collapse before full
reproductive isolation evolves [30, 82, 34].
What We Don’t Know
Many questions about speciation are about percentages. For example, what
percentage of species have evolved via allopatric speciation versus speciation
with gene flow? How does this differ among groups of animals and of plants,
and what features would make sympatric speciation more likely? Likewise, how
often have the various causes of speciation, such as ecological divergence,
genomic conflict, and sexual selection, caused speciation, and how does their
relative impact differ among groups of organisms? Do the factors that influence
or cause speciation differ between terrestrial and marine organisms?
● When sexual selection has played a role, we do not fully understand why the male
traits and female preferences (or vice versa) diverged between populations. For
example, why have the male calls and female preferences diverged among the
cricket populations in Figure 9.29? (See Chapter 12.)
● We know little about how often speciation fails or goes into reverse, due to
increased gene flow between diverging populations. Is there any way to tell how
frequently this will be the fate of species that are recognized under the phylo­
genetic species concept versus the biological species concept?
●
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Species and Speciation
SUMMARY
■ Several definitions of species have been pro-
posed. Most evolutionary biologists use the biological species concept, which defines species
as groups of actually or potentially interbreeding organisms that are reproductively isolated
from other such groups, meaning they do not
exchange genes very much (or would not even
if they encountered each other). Under this
definition, speciation is the evolution of reproductive isolation. Some other biologists favor
the phylogenetic species concept, according
to which species are sets of populations with
character states that distinguish them.
■ Under any definition of species, the defining
qualities (such as reproductive isolation) usually evolve gradually, so some populations cannot
be classified unambiguously as the same or
different species.
■ The biological differences that constitute reproductive isolation include prezygotic barriers
to gene exchange (e.g., ecological or sexual
isolation) and postzygotic barriers (hybrid inviability or sterility), which may depend on the environment or may be intrinsic (independent of
environment). Several potential isolating barriers may be discovered between two species.
Some of them may have evolved before the
others and been the actual basis of speciation.
Some barriers (e.g., postzygotic barriers) may
not come into play because an earlier-acting
difference already prevents gene exchange.
■ Speciation is rapid in some cases, requiring only
a few thousand years or even less. Partial reproductive isolation has evolved even in laboratory
populations. Occasionally, a new species is generated instantly by whole genome duplication.
In other cases, it may take millions of years for
populations to evolve reproductive isolation.
■ The evolution of reproductive isolation is
hindered by ongoing interbreeding (which maintains gene exchange) and recombination (which
opposes the buildup of divergent sets of genes
and characteristics).
■ Evolutionary biologists agree that allopatric
speciation is common. Here a physical barrier
separates populations of an ancestral species,
and evolutionary changes in one or both populations result in biological barriers to gene flow
if the populations come back into contact. One
possible mode of allopatric speciation, peripatric or founder effect speciation, is thought
to be initiated by genetic drift in a small local
population of an ancestral species. This is
generally thought to be rare.
09_EVOL5E_09.indd 258
■ In speciation with gene flow, a species evolves
into two species because of strong divergent
selection, without a complete physical barrier
between populations. Sympatric speciation is
the evolution of reproductive isolation within
an initially randomly mating population. It is the
most extreme instance of speciation with gene
flow, and it requires very strong selection. It is
made more likely if traits that are disruptively
selected because of their ecological function
also automatically reduce gene exchange
(e.g., seasonal timing of reproduction).
■ The causes of the evolution of prezygotic
reproductive isolation include divergent natural selection arising from ecological factors
(ecological speciation) and divergent sexual
selection. It is also possible for two populations
to adapt to similar environments with different
mutations that are incompatible when combined in hybrids (mutation-order speciation).
When hybrids between two divergent populations have low fitness, there is selection for
stronger prezygotic isolation, which may result
in reinforcement of a prezygotic barrier.
■ The causes of evolution of intrinsic postzygotic isolation are less well understood.
Hybrid inviability and sterility are often based
on incompatible interactions between two
or more genetic loci that diverged between
populations by genetic conflict or divergent
selection. Hybrid sterility can also be caused
by differences in the numbers or arrangements of chromosomes. In some cases, these
chromosome differences may have been
established by random genetic drift.
■ New species sometimes evolve from hybrids
between parent species. In many cases, the
hybrid species is polyploid.
■ Hybridization occurs to some degree between
many closely related species. It can homogenize parts of the genome, except in regions
that are under strong divergent selection. It
may often introduce advantageous genetic
variation into the recipient species and so
increase adaptation.
■ An unknown, but probably high, proportion of
diverging populations fail to complete speciation because they may not persist, due to
environmental changes that may extinguish
them or increase gene flow and undo divergence. Perhaps for this reason, there is little
correlation among clades between the speed
of evolution of reproductive isolation and the
phylogenetic rate of species accumulation.
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Summary
259
SUGGESTIONS FOR FURTHER READING
Speciation, by J. A. Coyne and H. A. Orr (2004),
is the most recent comprehensive book
about speciation [14]. Other important books
include Ecological Speciation by P. Nosil
(2012) [62] and The Ecology of Adaptive
Radiation by D. Schluter (2000) [88].
Useful overviews and reviews include “The
origins of reproductive isolation in plants,”
by Baack et al. (2015) [2]; “Plant speciation,”
by L. H., Rieseberg and J. H. Willis (2007)
[78]; Speciation in Birds by T. D. Price, (2008)
[70]; “Genomics and the origin of species,”
by O. Seehausen et al. (2014) [91]; “Sympatric
speciation,” by D. I. Bolnick and B. M. Fitzpatrick (2007) [6]; and “Beyond reproductive isolation: Demographic controls on the
speciation process,” by M. G. Harvey et al.
(2019) [34].
PROBLEMS AND DISCUSSION TOPICS
1. Some degree of genetic exchange occurs in
bacteria, which reproduce mostly asexually.
What evolutionary factors should be considered in debating whether or not the biological species concept (BSC) can be applied to
bacteria?
2. Suppose the phylogenetic species concept
(PSC) were preferred over other species concepts, such as the BSC. What would be the
implications for the following:
a. discourse on the evolutionary
mechanisms of speciation,
b. studies of species diversity in ecological
communities,
c. estimates of species diversity on
a worldwide basis, and
d. conservation practices under such
legal frameworks as the US Endangered
Species Act?
09_EVOL5E_09.indd 259
3. How might the fate of two hybridizing populations—that is, whether or not they persist as
distinct populations—depend on the kinds of
isolating barriers that reduce gene exchange
between them?
4. The heritability of an animal’s preference for
different habitats or host plants might be high
or low. How might heritability affect the likelihood of sympatric speciation by divergence in
habitat or host preference?
5. Can a species originate more than once?
If so, how?
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10
The Evolution of
Genes and Genomes
C
omparing people, plants, and protists reveals a startling disconnect
between our impressions of their complexity and the makeup of
their genomes. The human genome comprises some 3.2 billion base
pairs (bp). Humans are obviously more complex organisms than plants, let
alone protists. So it may seem strange that some pine trees have almost
ten times more DNA than we do [56], while a single-celled amoeba with the
wonderful name Chaos chaos has 400 times more [19]. Another way we can
quantify the size of a genome is by the number of protein-coding genes it
has. That measure also reveals the unexpected. Homo sapiens has about
20,000 protein-coding genes. The nematode worm Caenorhabditis elegans
has about the same number, even though its body has only 1000 cells [26].
By contrast, bread wheat (Triticum aestivum) has about five times as many
protein-coding genes as we do [10], even though it is unable to compose a
song or program a computer.
These examples highlight a strange fact about life on Earth. There is no
simple relation between any measure of the complexity of an organism and
the size of its genome, measured either by DNA content or number of genes
(FIGURES 10.1 and 10.2). One might guess that the genome would be where
adaptation rises to the pinnacle of refinement. You will see shortly that the
reality is quite the opposite: genomes are the messy outcomes of conflict
and cooperation, of selection and random genetic drift, and of all the other
evolutionary ingredients that make life the beautiful tangled mess that we
see around us.
The douc langur (Pygathrix nemaeus) is arguably the world’s most beautiful primate. It has an
unusual adaptation to its diet of leaves: a duplicated gene that allows it to efficiently digest
the bacteria that ferment leaves in the monkey’s gut.
© Tier Und Naturfotografie J und C Sohns/Getty Images
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FIGURE 10.1
The Evolution of Genes and Genomes
Genome size variation.
Prokaryotes
The bars indicate the range of genome sizes
(shown in megabases, Mb; 1 Mb = 10 6 bases)
across species within each group. Some protists (unicellular eukaryotes) have genomes
that are hundreds of times larger than that of
any mammal. (After T. R. Gregory. 2001. Biol
Rev 76: 65–101; A. F. Palazzo and T. R. Gregory.
2014. PLOS Genet 10: e1004351.)
All eukaryotes
Algae
Protists
Fungi
Vascular plants
Arthropods
Chordates
Fishes, amphibians
Birds, mammals,
reptiles
10–1
1
10
10 2
10 3
10 4
Genome size (Mb)
10 5
10 6
In this chapter, we explore the evolution of the genome by first discussing how
new genes originate, and then the different fates of new genes and how genes die.
The next topic is the evolution of protein-coding genes by changes to their sequences
and their expression. We then consider how and why the number and structure of
chromosomes evolve. Finally, we tackle the evolution of genome size and genome
content, including the surprising discovery that much DNA has no obvious function and how that DNA explains much of the variation in the sizes of genomes.
FutuymaUKirkpatrick Evolution, 5e
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U
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U
Diatom (Thalassiosira)
Malaria parasite (Plasmodium)
Red alga (Cyanidioschyzon)
Rice (Oryza)
Common
ancestor
Mouse-ear cress (Arabidopsis)
Plants
Legume (Lotus)
Smut fungus (Ustilago)
Yeast (Schizosaccharomyces)
U
Bread mold (Neurospora)
Fungi
Yeast (Saccharomyces)
U
Nematode (Caenorhabditis)
Mosquito (Anopheles)
Fruit fly (Drosophila)
Silk worm (Bombyx)
Tunicate (Ciona)
Animals
Puffer fish (Fugu)
Chicken (Gallus)
Mouse (Mus)
Human (Homo)
0
FIGURE 10.2 Numbers of protein-coding genes for
several eukaryotes, arranged on a phylogeny. Some
10
20
30
40
50
Number of genes (× 1000)
patterns make intuitive sense: multicellular eukaryotes with
tissue organization (plants and animals) have more proteincoding genes than most unicellular eukaryotes (U). Other
60
patterns are puzzling: rice has more than twice as many protein-coding genes as the other flowering plants shown, and
puffer fishes have almost twice as many as humans. (After
D. E. Sadava et al. 2017. Life: The Science of Biology, 11e.
Sunderland, MA: Macmillan Learning/Sinauer Associates.)
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10.1 The Birth and Death of Genes 263
10.1 The Birth and Death of Genes
Learning objectives
■ Describe the different ways in which genes can originate and be lost.
■ Explain what pseudogenes are and why they evolve so quickly.
■ Define what a gene family is and how different members of a gene family
can evolve.
One of the most fundamental questions we can ask about evolution is, How does
biological novelty originate? The answer must lie partly with the origin of genes
that have new functions. Like much of evolutionary genetics, this research topic
has exploded recently with the arrival of large numbers of genome sequences
from diverse branches of the tree of life.
The vertebrate eye is a remarkable organ. It forms a high-resolution image by
focusing light through a transparent lens made of living tissue. The lens is made
up largely of proteins called crystallins. Where did they originate? It turns out
that many proteins happen to be nearly transparent. Crystallins in the vertebrate
lens are derived from proteins with a variety of other functions, for example, a
small heat-shock protein that protects many tissues from different types of stress
[12]. Crystallins in other animals are derived from different proteins with yet
other functions (FIGURE 10.3). In some instances, crystallins continue to function
in their original roles while also serving in the lens [50].
The crystallins show how novel biochemical functions can originate by a process central to Darwin’s theory: descent with modification. Some of them originated by gene duplication (see Chapter 4), which is the most common way that
new genes arise in eukaryotes. At the molecular level, duplications are caused
by several mechanisms. One is unequal crossing-over, in which recombination happens between different positions on chromosomes that are misaligned
during meiosis. Another is replication slippage, in which the DNA polymerase
loses its place and copies a segment of chromosome twice. In either event, the
resulting chromosomes can carry a gene that is duplicated in tandem with the
parental copy. A third way that gene duplicates arise is by retrotransposition.
α β
γ
γ
ρ
δ τ
δ τ π
Frog
Turtle
Lizard Crocodile
Crystallin
α
β/γ
ρ
δ
τ
π
ε
µ
η
ζ
λ
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δ ε
δ ε
Bird
µ
ζ
ζ
Guinea
Human
pig
η
Kangaroo Camel
λ
Rabbit
Ancestral protein function
Small heat-shock proteins
Related to bacterial stress protein
NADPH-dependent reductase
Argininosuccinate lyase
α-Enolase
Glyceraldehyde phosphate dehydogenase
Lactate dehydrogenase
Similar to bacterial ornithine deaminase
Aldehyde dehydrogenase
Alcohol dehydrogenase
Hydroxyacyl-CoA dehydrogenase
FIGURE 10.3 Crystallin proteins in the lens of vertebrate eyes allow
them to focus light. Different groups of vertebrates have different crystallins derived from a wide variety of proteins with other functions. (After
J. R. True and S. B. Carroll. 2002. Annu Rev Cell Dev Biol 18: 53–80.)
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(A)
(B)
0.30
Biaka
Proportion of individuals
0.25
Low starch
High starch
0.20
0.15
Japanese
0.10
0.05
0
2
3
4
5 6 7 8 9 10 11 12 13 14 15
AMY1 diploid gene copy number
FIGURE 10.4
Copy number variation at the AMY1 locus in humans. (A) Distributions of copy number in populations that have low-starch diets (Biaka and Mbuti huntergatherers) and high-starch diets (Japanese, European Americans, and Hadza). On average, the populations with a high-starch diet have 6.7 copies of AMY1, while those with
a low-starch diet have 5.4 copies. (B) Photos of the two chromosome regions that carry
duplicates of the AMY1 locus. Each copy of the gene is labeled with a red and green dye.
The Biaka individual shown here has 6 copies, while the Japanese individual has 14 copies of AMY1. (After G. H. Perry et al. 2007. Nat Genet 39: 1256–1260.)
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Expanded
Contracted
No change
Human
Primate
Chimpanzee
Mouse
Rodent
Rat
Dog
93 87
17
6
Time (Ma)
FIGURE 10.5
Gains and losses of genes in several
lineages of mammals. The pie charts show the fraction
of gene families that have expanded, contracted, or not
changed. (After J. P. Demuth et al. 2006. PLOS ONE 1: e85.)
Here the messenger RNA from a gene is reverse transcribed to
DNA, which is then integrated into the genome. The result is
a duplicate gene that is far from the parental copy, often on a
different chromosome. Duplicates that originate this way can
be distinguished from their parental genes because they lack
introns, which were spliced out of the mRNA before it was
reverse transcribed.
As a chromosome with a new gene duplicate spreads through
a population, there is polymorphism in the number of copies
of the gene that individuals carry. This situation, called copy
number variation, is found at the AMY1 locus in humans [49].
This gene codes for amylase, an enzyme in saliva that breaks
down starch. Individuals with more copies of AMY1 have more
amylase in their saliva and digest starch more efficiently. The
Central African Biaka, who are hunter-gatherers with a diet that
is rich in protein but low in starch, typically have four or five
copies of the gene. Japanese, whose rice-heavy diet has abundant starch, typically have six to eight copies (FIGURE 10.4).
Gene duplication plays a key role in genome evolution. In
the recent evolutionary past of humans, 1% of our genes have
been duplicated every million years [13]. Some 1400 duplicates
have been fixed in humans or chimpanzees since we shared a
common ancestor roughly 7 million years ago (Ma). More of
the base pair differences between humans and chimpanzees
have resulted from gene duplication than from changes at single nucleotides. Gene duplication has played major roles in the
evolution of other species as well (FIGURE 10.5).
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10.1 The Birth and Death of Genes 265
FIGURE 10.6
Duplication and adaptation of the
RNASE1 gene in the douc langur. The gene tree relates
© Tier Und Naturfotografie
J und C Sohns/Getty Images
Nonsynonymous change
Synonymous change
the RNASE1B duplicate locus in the douc langur to the
RNASE1 locus in the langur, rhesus monkey, and human.
Branch lengths are proportional to the number of substitutions in the coding region. Since the duplication event,
there have been 12 changes to the coding region of the
duplicate, and none to the original copy of the gene. Of
the changes in the duplicate, nine are nonsynonymous
and three are synonymous. The d N /d S ratio (see Chapter 7)
is much greater than 1, which is strong evidence that the
duplicate has evolved under positive selection. (After
M. Long et al. 2003. Nat Rev Genet 4: 865–875, based on
data from J. Z. Zhang et al. 2002. Nat Genet 30: 411–415.)
Duplication
dN /dS = 4.03
Douc langur
RNASE1B
Douc langur RNASE1
Rhesus monkey RNASE1
Human RNASE1
Important ecological adaptations can result from gene duplication. The douc
langur (Pygathrix nemaeus) that we met at the opening of this chapter lives on a
diet of leaves. The leaves are fermented in the gut by symbiotic bacteria, much
as in cows, and the monkeys gain nutrition by digesting the bacteria. One of
the enzymes that digests the bacteria is encoded by the RNASE1B locus, which
originated by gene duplication about 4 Ma. This new enzyme rapidly evolved
nine amino acid changes (FIGURE 10.6) [63]. Those changes allow the enzyme
to work
in theEvolution,
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ferment the leaves.
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Some proteins06-27-2022
have repeated domains that confer part of their function. The
sodium channel that is critical to the firing of vertebrate nerves has four domains
with very similar structure (FIGURE 10.7). They arose by two rounds of duplication of a gene in a remote ancestor that coded for only a single domain [32].
Exon shuffling has played a role in the evolution of many eukaryotic genes
[38]. In this process, a new locus is created when the exons of one gene are duplicated (by retrotransposition) next to the exons of another gene. This results in a
mosaic of exons that can produce a new gene product. An example is the jingwei locus, which is found only in the fruit flies Drosophila teissieri and D. yakuba
(FIGURE 10.8). The first three exons of this gene are duplicates of exons in the
(B) Bacterial sodium channel
(A) Vertebrate sodium channel
Outside cell
II
I
12345
6
12345
6
12345
6
H3N
+
Inside cell
FIGURE 10.7
Vertebrate sodium channels, which are
critical to firing neurons, evolved by duplication. (A) The
α-subunit of the sodium channel protein has four domains
(labeled I–IV). Each domain has six segments that traverse
the cell membrane (numbered in each domain). The corresponding segments in the four domains have very similar
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Outside cell
IV
III
12345
6
12345
6
CO2–
H3N
+
CO2–
Inside cell
protein sequences. The sodium channel protein evolved by
two rounds of duplication of a locus that coded for a single
domain. (B) The ancestor of the vertebrate sodium channel
had a structure similar to this bacterial sodium channel.
(After R. W. Hill et al. 2016. Animal Physiology. Sunderland,
MA: Oxford University Press/Sinauer.)
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FIGURE 10.8 A new Drosophila gene called jingwei
originated by the retrotransposition of one gene into
the intron of another. The Adh gene (with exons shown
in purple) was retrotransposed into the third intron of the
Ymp gene (with exons shown in green) about 2 Ma. A new
stop codon was added at the end of the Adh sequence (red
bar). The name jingwei comes from a Chinese myth in which
a princess metamorphoses into a new form. (After M. Long
et al. 2003. Nat Rev Genet 4: 865–875.)
Adh
Ymp
Exons
jingwei
Exons
A duplicate of the Adh gene
retrotransposes into the Ymp gene
New stop codon
from the Adh gene
yellow-emperor (Ymp) gene found in many Drosophila species. The fourth exon,
however, is a duplicate of the entire alcohol dehydrogenase (Adh) gene that was
retrotransposed into an intron of Ymp. The new jingwei gene shows evidence of
rapid evolution by positive selection after it originated about 2 Ma. It is expressed
in the testes and is thought to be involved in the metabolism of hormones and
pheromones [62].
Gene duplication happens on a massive scale when mutation produces a tetra­
ploid (see Chapter 4). A tetraploid descendant of a diploid has four copies of
each gene rather than two. Tetraploids can arise in two ways (see Chapter 9). The
first is by whole genome duplication. This can occur when the genome of a single
species is
doubled
(resulting
in an
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also occur
when two species
hybridize, and the gametes from both of them are
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with unreduced diploid
genotypes (resulting in an allopolyploid). Further
rounds of duplication can give rise to species with six, eight, and even more copies of each chromosome. Whole genome duplication is much more common in
plants than animals, but it did occur twice in our own remote ancestors, between
650 and 550 Ma [31]. Recent events of polyploidy occurred during the domestication of several important crop plants (including wheat, coffee, and cotton) and
were key to improving some of their economically valuable traits [47].
While recombination usually involves the mixing of genes of the same species,
occasionally genes from other species are mixed into the gene pool. In eukaryotes, this usually occurs through hybridization between closely related species.
Genetic exchange also happens between distantly related organisms by horizontal
gene transfer, or HGT (see Chapter 4). HGT is particularly important to prokaryotes and is the most common way by which they acquire new genes, including
those that confer antibiotic resistance. HGT can vastly speed up adaptation since
a new functional gene is acquired in one fell swoop, rather than evolving through
many mutations.
HGT is much less common in eukaryotes, but it has occurred in many lineages. It has allowed the recipients to feed on new resources, parasitize other
organisms, survive in extreme environments, and protect themselves from attack
by other organisms [29]. Beetles are an extraordinarily diverse lineage that represents about one-quarter of all animal species. A key factor in their diversity is
the ability of many species to feed on plants. Most animals are unable to digest
cellulose and other components of plant cell walls, which are the most abundant
source of carbohydrates on Earth. In the Jurassic Period (roughly 175 Ma), two
lineages of beetles independently acquired genes by HGT from bacteria and
fungi that allow them to digest plant cell walls. This evolutionary innovation
triggered the rapid diversification of those two lineages, which together now
represent nearly half of all species of beetles [44].
Given the intimacy between the genomes of the mitochondria and the nucleus
within a cell, it may not be surprising that extensive HGT has occurred between
them [1]. The vast majority of the transfers have been from the mitochondria to
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10.1 The Birth and Death of Genes 267
Fixation 1
Frequency
Single
copy
Fixation 2
Copy number
variation
Gene
family
A A
A
A B
A A
Duplication 1
Loss 1 Duplication 2
Deletion
Loss 2 New function
Time
the nucleus, which has greatly reduced the size of the mitochondrial genome.
In the most extreme cases, all mitochondrial genes have moved to the nucleus.
Mitochondria have lost their entire genomes in several unicellular eukaryotes
(such as the Microsporidia, a group of intracellular parasites).
It might seem impossible that a new gene could originate from DNA that
previously had no function. After all, the number of possible combinations of
codons is beyond comprehension, and the chance that a random combination
mightKirkpatrick
make a Evolution,
useful protein
seems vanishingly small. But as unlikely as that is,
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hasServices
uncovered new genes in organisms ranging from yeast to
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humans that did indeed
originate from noncoding DNA [36, 59]. These are called
de novo genes, to distinguish them from genes that are born by the much more
common routes of gene duplication and HGT. A fascinating example is the birth
of the genes in the cod (Gadus morhua) responsible for the antifreeze proteins that
prevent the fish from freezing solid as they swim beneath Arctic ice in waters that
are below 0ºC [5]. The origin of de novo genes may be as simple as a mutation
at a single base that by chance turns on transcription of a downstream stretch of
DNA that fortuitously codes for a protein that enhances fitness.
No matter how it is born, a new gene starts its life as a single copy in the population. As with any new mutation, by far the most likely outcome is that it will
be lost by random drift or selection over the next few generations (see Chapter 6).
Very few new genes manage to spread and become fixed in the species (FIGURE
10.9). Even when they do, they usually do not survive very long—they are later
destroyed by mutations, as you will see below. Very few new genes remain functional and become permanent features of the genome. Often these genes survive
because they evolve a new adaptive function, like the RNASE1 duplicate in the
douc langur.
FIGURE 10.9
Fates of gene duplicates.
Duplications produce chromosomes carrying a second copy of locus A. Copy number variation occurs when chromosomes
with different numbers of copies are present in the population. A duplication can be
lost or spread to fixation. A gene that has
been duplicated can later be lost by deletion. When there are two or more copies
of locus A, one of them can evolve a new
function (neofunctionalization), resulting
in a gene family. (After H. Innan and F. Kondrashov. 2010. Nat Rev Genet 11: 97–108.)
Gene families
Back in Chapter 2 we saw that humans have 11 genes that encode hemoglobins,
which are proteins responsible for oxygen transport (Figure 2.16). These loci are
an example of a gene family, which is a set of genes that arose by duplication
and that code for proteins that typically continue to have similar biochemical
functions. Two or more genes that originated by duplication are called paralogs.
A functioning duplicate that becomes fixed in a population can meet one of
several ultimate fates (Figure 10.9) [34, 39]. If the duplicate is redundant and does
not provide a fitness benefit, it will be lost by deletion or the accumulation of
loss-of-function mutations. A second fate is that a duplicate can simply retain its
original function. A locus that has been amplified this way now has two (or more)
copies in the genome. They can all be favored to continue functioning when there
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Scn4aa
Scn4ab
Gasterosteus
FIGURE 10.10
The neofunctionalization
of a sodium channel gene was a key to the
independent origin of electric organs in two
families of fishes. The phylogeny shows repre-
Porichthys
Electric
Eigenmannia
Ictalurus
Danio
Pantodon
Osteoglossum
Electric
Gnathonemus
Xenomystus
From A. Thompson et al. 2014. Mol. Biol. Evol. 31(8):1941–1955
sentatives of ten families of distantly related teleost fishes, and the pie diagrams show the relative
expression levels in muscle tissue of two duplicated sodium channel genes, Scn4aa and Scn4ab,
that trigger muscle contraction. In South America
an electric organ evolved in the knifefishes (represented here by Eigenmannia virescens), while
in Africa the organ evolved in mormyrid fishes
(represented by Gnathonemus petersii). In those
two families, expression of Scn4aa has been lost
completely from the muscle. Instead, the gene is
expressed in the electric organ, which gives these
fishes a remarkable sensory modality that allows
them to hunt and communicate in darkness. (After
A. Thompson et al. 2016. Genetics 202: 765–774.)
Tetraodon
100 My
is selection for increased expression of the gene’s product. We saw earlier that
some human populations have increased numbers of copies of the AMY1 gene,
which is an adaptation to a starch-rich diet (Figure 10.4).
A third fate that can befall a duplicated gene is neofunctionalization. Here
the duplicate evolves a novel biological function. The α-crystallin in the lens of
the vertebrate eye originated by duplication of a heat-shock gene (Figure 10.3).
Another
example
is found
in electric
fishes. All teleost fishes have two duplicates
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of a sodium
channel gene
is expressed in muscles. In most teleost fishes, both
06-22 -2022
genesEvolution5e_10.10.ai
are involved in triggering
muscle contractions. But in two families of fishes
(the knifefishes [Gymnotidae] and elephant-nosed fishes [Mormyridae]), one of
the paralogs has independently evolved an entirely different function (FIGURE
10.10). Here it plays a key role in firing the electric organ, which is a unique
structure derived from muscle tissue that enables these fishes to sense prey and
communicate with each other in darkness [3]. The power of the electric organ is
magnified in the electric eel (Electrophorus electricus), which can generate currents
of hundreds of volts to stun prey.
Fourth, a duplicate gene and its parental copy can subfunctionalize so that each
carries out only some of the roles that the ancestral gene performed. In most vertebrates, the same hemoglobin molecule transports oxygen in the bloodstream at
all stages of life. In mammals, however, a duplicate of the β hemoglobin locus is
expressed in the fetus [57]. The fetal hemoglobin has evolved differences that give
it a higher affinity for oxygen than the adult hemoglobin. This enables the fetus
to strip oxygen from its mother’s blood in the placenta. A few months after birth,
expression of the fetal hemoglobin is shut off, and the adult hemoglobin takes over
the task of oxygen transport. The evolution of the specialized fetal hemoglobin by
subfunctionalization was one key to the origin of live birth in mammals.
The diversification of genes by neofunctionalization and subfunctionalization
can greatly expand the range of biochemical roles that their enzymes can perform.
In turn, that can lead to the invasion of new ecological niches. Earlier we saw that
one key to the enormous diversity of beetles was the acquisition by HGT of genes
to digest plant cell walls. A second key was the diversification of those genes into
large gene families that can degrade a diversity of plant compounds [44].
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10.2 Evolution of Protein-Coding Genes 269
A strange form of inheritance occurs in some gene families. When duplicates
occur in tandem along the chromosome, the DNA replication machinery sometimes gets confused and aligns one copy of a gene with a paralog nearby. Gene
conversion can then happen between the two genes, causing a mutation in one
locus to be copied to the other (see Chapter 4). A mutation at one locus can spread
this way through all the paralogs in the gene family. This process is called concerted
evolution. The ribosomal RNA genes exist in many copies in eukaryote genomes.
Gene conversion between the copies keeps their sequences from diverging very
much within species, even though they diverge between species [21].
The death of a gene
There are many ways that a gene can die. When a gene is duplicated, the new
copy is often dead on arrival. The duplicate may not include the entire gene.
Even if it does, if often lacks the regulatory elements needed to express it at the
right time and in the right places. Duplicates produced by retrotransposition
face an additional challenge. Since they lack introns, there is no opportunity for
posttranscriptional regulation that involves splicing.
When a nonfunctioning duplicate is fixed in the population, or a functioning
gene becomes nonfunctional, the result is a genetic skeleton called a pseudogene.
The genomes of many species are littered with pseudogenes. Our own genome has
almost as many pseudogenes as functional genes [39]. Although they serve no function, pseudogenes are useful to evolutionary biologists as a sort of natural controlled
experiment. The parental gene from which a pseudogene originated continues to
evolve by natural selection. But the pseudogene does not produce a functioning
product, so it is freed from selection. Comparing the sequences of pseudogenes with
those of their parental genes reveals that pseudogenes typically evolve much more
quickly (Figure 6.21). This shows that most mutations to functioning genes reduce
fitness and are removed from the population by purifying selection (i.e., selection
against deleterious mutations). In contrast, mutations in a pseudogene are selectively neutral, and so they are free to drift to fixation. Pseudogenes evolve quickly
because these selectively neutral mutations become fixed relatively often.
Deletions are important in shaping the genome (see Chapter 4). Deletions
are indiscriminate: they will sometimes eliminate part of a gene, all of a gene,
or even a large piece of chromosome that carries many genes. The loss of genes
by deletion and pseudogenization contributes to the divergence in the genetic
content of the genomes of related species (see Figure 10.5).
It may seem surprising that a deletion can sometimes be beneficial. Attached to
the surface of human white blood cells is a protein called CCR5 that plays a role in
inflammatory response. Unfortunately, this protein is also a key to human immunodeficiency virus (HIV) infection. The virus binds to a loop of the CCR5 protein
and then enters the cell. Some people have an allele at the CCR5 locus in which
32 bp are deleted. The missing part of the gene codes for the loop in the protein to
which the virus attaches. People who are homozygous for the deletion are highly
resistant to HIV, while heterozygotes are partly resistant. The deletion has spread in
some populations by positive selection that occurred long before HIV first infected
humans, possibly because it also confers resistance to other diseases [20].
10.2 Evolution of Protein-Coding Genes
Learning objectives
■ Understand what the neutral theory of molecular evolution says about the
genetic differences between species and variation within species.
■ Explain the dN/dS ratio and how it can give evidence for positive selection.
■ Describe evidence showing that changes to gene regulation are important
to adaptation.
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Although coding sequences comprise less than 2% of the human genome, that
slender slice of our genetic material is by far the best understood. Codons provide genetic landmarks that greatly help us to interpret how the genome evolves.
Evolution of coding regions by genetic drift
A strong evolutionary pattern is seen across protein-coding genes. The DNA bases
that appear in the first and second positions of codons tend to evolve slowly.
DNA bases in the third positions of codons and in introns tend to evolve much
more rapidly (see Figures 6.21 and 16.6). Changes to first and second positions
are likely to be nonsynonymous, that is, to change the protein, while changes to
third positions of codons are likely to be synonymous, that is, to have no effect on
the protein (see Chapters 4 and 6). In short, the DNA bases most likely to affect a
gene’s product evolve most slowly. The pattern seems puzzling. If most genetic
change were caused by natural selection, then we would expect exactly the opposite pattern. Changes that alter a protein have a chance of improving fitness, and
so nonsynonymous changes should evolve the most frequently.
But the pattern makes sense if most changes to DNA sequences evolve by
random genetic drift rather than by adaptation. Between 70% and 97% of nonsynonymous mutations are deleterious, depending on the organism [15]. Most of
those mutations are eliminated by purifying selection. In contrast, synonymous
mutations do not change a gene’s product and so are expected to have much
smaller effects on fitness. Those mutations have a chance of spreading through
the species by random genetic drift, contributing to the differences we see among
species. As a result, the DNA bases in a gene that are least likely to have a fitness
effect tend to be those that evolve the most rapidly and that show the largest
numbers of differences among species.
This pattern lies at the heart of the famous neutral theory of molecular evolution developed by Motoo Kimura starting in the 1960s (see Chapter 6). Kimura
argued that the vast majority of differences among species and variation within
species in DNA sequences are due to mutations that are nearly selectively neutral
and have evolved mainly by drift. Purifying selection is constantly at work eliminating deleterious mutations, Kimura thought, but positive selection leading
to adaptive differences among species is so rare that it makes only a negligible
contribution to molecular evolution. We now understand that the neutral theory
is a powerful explanation for some patterns, such as why pseudogenes evolve
so quickly. But Kimura’s claim that positive selection makes only a trivial contribution to the differences between species turns out to have been somewhat
exaggerated, as you will now see.
Evolution of coding regions by positive selection
Random genetic drift is responsible for much of the evolution of DNA sequences—
much, but certainly not all. Natural selection occasionally favors changes to a
protein that enhance adaptation, for example, when a species encounters a new
environment. Then the typical pattern can be reversed, and DNA mutations that
change a protein spread more often than those that do not. In the 4 million years
following the origin of the RNASE1B gene in the douc langur, nine nonsynonymous mutations became fixed, while only three synonymous mutations became
fixed (see Figure 10.6). Several lines of evidence show that most or all of the
changes to the protein produced by RNASE1B were driven by positive selection
that improves its new role in digestion [63].
We can get a rough idea of the relative importance of purifying selection, drift,
and positive selection by using a simple statistic based on a comparison of the
DNA sequences of the same gene from two species. We determine d N, the fraction
of sites that have nonsynonymous differences (those that do change an amino
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10.2 Evolution of Protein-Coding Genes 271
acid) and d S , the fraction of sites that have synonymous differences (those that
do not change an amino acid). Dividing the first fraction by the second gives us
the d N /d S ratio. Imagine that synonymous and nonsynonymous mutations both
have very little effect on fitness. Then both types of mutations will have the same
chance of drifting through the population to fixation, and so we expect d N/d S
to be 1. However, if most nonsynonymous mutations are deleterious and are
removed by purifying selection, then d N/d S will be much less than 1. This is what
we see at the great majority of genes (see Figure 6.20).
Occasionally loci show a dN/d S ratio greater than 1. This means that more nonsynonymous mutations, which are likely to affect fitness, have been fixed than
synonymous mutations, which are likely to be selectively neutral. That suggests
some of the nonsynonymous mutations that were fixed had a boost from positive
selection. The RNASE1B gene in the douc langur discussed earlier shows exactly
this pattern.
So some of the genetic differences we see among species became fixed by
adaptation, and others by random genetic drift. This raises a fundamental question: How much do these two processes contribute to evolutionary change? The
answer depends strongly on the group of organisms [11, 24, 58]. About half of
the differences in protein sequences among species of fruit flies (Drosophila), mice
(Mus), and enteric bacteria (E. coli and Salmonella enterica) evolved by positive
selection and half by drift. In our own species, the picture is very different: only
about 14% of protein evolution in our recent past has been adaptive.1 Many
genes have been found in humans that show evidence of positive selection, but
they represent only a small fraction of all the changes that have evolved in the
last few million years.
This striking difference between the evolutionary patterns of flies and humans
is a consequence of population size. We saw in Chapter 6 that a mutation will
evolve mainly by selection if the product of Ne (the effective population size) and
s (the selection coefficient) is much greater than 1, but it will evolve mainly by
drift if Ne s is much less than 1. This means that selection will be a more important evolutionary factor in species with large populations, while drift will dominate in species with small populations. Drosophila melanogaster has an effective
population size in the millions, while humans have had an effective population
size of only about 10,000 over much of our evolutionary history. Consequently,
genetic drift has been weaker in D. melanogaster and stronger in humans. A deleterious mutation in the fly that decreases fitness by s = 10–5 will be weeded out
by purifying selection. This prevents it from becoming fixed and contributing
to differences between species. In humans, however, a mutation with that same
selection coefficient evolves almost as if it is selectively neutral, and so it can be
fixed by drift. As a result, more evolutionary change is caused by drift and less
by positive selection in humans than in D. melanogaster. Some microbes have
population sizes that are vastly larger than those of fruit flies, and evolution in
those organisms is even more strongly dominated by selection. In each generation, moreover, more beneficial mutations enter a big population than a small
population, which further tilts the balance of molecular evolution away from
drift and toward adaptation.
Earlier we discussed how the evolution of synonymous changes by drift often
makes a big contribution to the divergence between the DNA sequences of species.
In fact, selection can also play a role in the evolution of synonymous mutations.
The different codons that correspond to the same amino acid appear at different
frequencies in the genome, a phenomenon called codon bias. One cause of this
bias is mutation. Mutations from the DNA base G to A and from C to T are more
than twice as common as other types of mutations [25, 40]. This tends to favor the
1
The relative contributions of selection and drift to the evolution of differences among species are
estimated using an extension of the MK test that we discussed in Chapter 6.
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accumulation of codons with A and T bases. A second cause of codon bias is natural selection [51]. Because they do not change the protein, synonymous mutations
were long thought to be selectively neutral. In fact, they can have minute effects
on fitness. The translation of highly expressed genes is most efficient when their
codons correspond to transfer RNAs that are common in the nucleus, so selection
favors those codons. Selection can also favor codons that produce messages that
are less prone to translation errors. The relative strengths of the forces, and so
the direction and degree of codon bias, differ among taxa. Codon bias driven by
selection tends to be stronger in genes that are highly expressed and in species with
very large population sizes, such as free-living microbes. In species with smaller
population sizes (such as vertebrates), drift overwhelms whatever selection acts
on synonymous mutations, and codon bias is very weak or absent.
Evolution of gene expression
The mosquito Aedes aegypti is the vector of Zika virus, dengue fever, yellow
fever, and chikungunya virus—diseases that together kill about 50,000 people
each year. In East Africa, there are two genetically distinct types of mosquitoes
(FIGURE 10.11). The domestic type specializes in biting humans (whom the mosquitoes infect), while the forest type feeds on other animals. A key adaptation
that enables the domestic type to find humans is its ability to detect human
odors [42]. An odorant receptor protein called Or4 that is expressed on the mosquito’s antennae has recently evolved in two ways. Domestic mosquitoes carry
alleles for the receptor with coding differences that enable it to detect sulcatone, a chemical that contributes to human body odor. A second change is that
domestic mosquitoes express roughly twice as much of the receptor on their
antennae. The combined effect of these changes is to make the domestic form
of Aedes aegypti very efficient at feeding on humans—and transmitting disease.
In the previous section you saw how a gene adapts by changes to its coding sequence that alter the biochemistry of the protein made by the gene. The
evolution of the Or4 gene illustrates a second pathway to adaptation: changes
in expression. Selection can alter how often, when, and where a gene is transcribed, how the transcript is spliced and processed, if and how the transcript is
translated into a protein, and how the protein is deployed (see Chapter 11). Several mechanisms are involved. Many evolutionary changes to gene expression
come from changes in sites that bind transcription factors. Gene expression can
Courtesy of James
Gathany/CDC
(A)
FIGURE 10.11
The mosquito Aedes aegypti is the vector
that transmits the Zika virus, dengue fever, yellow fever,
and chikungunya virus. (A) In East Africa the mosquito has a
10_EVOL5E_10.indd 272
Domestic
Forest
(B)
Human
Domestic form
Forest form
Guinea pig
Relative preference
domestic and a forest form. (B) Among its adaptations to feeding on humans, the domestic form is attracted to human body
odor. This adaptation involves changes to both the structure
and the expression level of Or4, a receptor on the mosquito’s
antenna that is sensitive to an odor distinctive to humans. The
graph shows results from an experiment in which mosquitoes
could respond to the odor of either a human or a guinea pig. Each
vertical bar represents the relative preference of a different laboratory colony of mosquitoes that was established from a small
number of mosquitoes sampled from Rabai, Kenya. (Colonies are
arranged from those that most prefer humans to those that most
prefer guinea pigs.) All colonies of the domestic form (open bars)
preferred the human odor, while all but one of the colonies of the
forest form (solid bars) preferred the guinea pig odor. (After
C. S. McBride et al. 2014. Nature 515: 222–227.)
0
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10.2 Evolution of Protein-Coding Genes 273
Coding
(33%)
Regulatory
(26%)
Likely regulatory
(41%)
FIGURE 10.12 Marine populations of three-spined sticklebacks (Gasterosteus
aculeatus) have colonized rivers and lakes many times in the Northern Hemisphere.
Adaptation to freshwater environments involves changes in hundreds of genes. One-third
of these are in coding regions, while the rest are in regions of the genome that are likely
or definitely involved in gene regulation. (After K. Reid et al. 2021. Annu Rev Genom Hum
Genet 22: 357–383.)
also evolve through changes to alternative splicing patterns and by epigenetic
changes to the DNA and the histones that are bound to it (see Chapter 4).
When a population adapts to a new environment, regions of the genome that
are under positive selection will diverge most rapidly from other populations
and closely related species. This fact has been used to study how marine populations of three-spined stickleback fish (Gasterosteus aculeatus) have adapted
in parallel in independent colonizations of rivers and streams (see also Figure
8.5) [53].
Of the
many 5e
places in the genome that show evidence of adaptation,
Futuyma
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Evolution,
only 33% are in
coding
regions, while the rest are involved in gene regulation
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(FIGURE 10.12). Changes
in gene expression have also been very important in
recent human evolution. You or someone you know may be lactose intolerant.
That condition results when a person carries an allele that stops the production of the lactase enzyme, which digests lactose, after childhood is reached.
That situation was adaptive before sheep, goats, and cows were domesticated
starting about 10,000 years ago. In populations that started to consume milk
from those animals, a mutation that causes the expression of lactase to persist
throughout life was favored. Today, almost all people in Britain are lactose tolerant, while in regions that never had a tradition of consuming dairy products,
such as Southeast Asia, almost nobody is lactose tolerant [2]. Looking across
all genes in humans that are adapted to local ecological conditions, the large
majority have evolved by changes in expression and relatively few by changes
in their coding regions [18].
Comparisons among the genomes of distantly related species reveal small
regions of noncoding DNA that are much more similar than the rest of the
genome. These ultraconserved elements are thought to be under strong purifying selection that constrains them from drifting apart [54]. Ultraconserved elements have been used to identify thousands of noncoding regions in mammalian
genomes that may be regulatory elements [37].
Operons are clusters of genes that are transcribed together into a single mRNA.
This message can be translated as a single unit or can be spliced into several
messages that are translated separately. This setup provides an economical way
to regulate expression because all of the genes in an operon are turned on and off
together. The favorite food of the gut bacterium Escherichia coli is glucose. But if
there is no glucose nearby, a set of genes called the lac operon turns on, producing
the enzymes needed to feed on lactose. Expression patterns can evolve by adding and removing individual genes from operons [52]. Operons are particularly
important to bacteria and archaea, in which they are widespread in the genome,
but they are also found in viruses and a small number of eukaryotes.
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10.3 The Evolution of Gene and
Chromosome Structure
Learning objectives
■ Understand how alternative splicing allows eukaryotes to produce many
more types of proteins than the number of genes in their genomes.
■ Explain the processes that favor an increase or a decrease in
the number of chromosomes.
■ Describe how inversions can link together alleles at many loci that
together can make up a complex adaptation.
A shocking discovery was made in 1977, in the early days of DNA sequencing.
The genes that code for many proteins are broken into pieces. These pieces—the
exons—are separated by stretches of noncoding DNA—the introns (see Chapter
4). Further research revealed that most genes in prokaryotes lack introns, while
most genes in eukaryotes have them. In humans, about one-fourth of the entire
genome is made up of introns, which account for more than ten times as many
DNA bases as the coding sequences (FIGURE 10.13).
When a gene with introns is transcribed into messenger RNA, the message is
processed by splicing to produce the final message that is then translated into a
protein. The splicing removes all the segments of the message corresponding to
introns. Splicing can also remove one or more segments that correspond to exons.
Alternative splicing brings together different combinations of exons from the same
locus. As a result, a single gene can produce more than one protein. The current
record holder is the gene Dscam in Drosophila. Alternative splicing of its 95 exons
could potentially produce more than 38,000 kinds of proteins from this single gene
[66]. Alternative splicing is a major mechanism used by eukaryotes to increase
organismal complexity without increasing the size of their genomes, and it may
be as important as amino acid changes in the functional diversification of proteins.
The gene doublesex gives an example of how dramatic the effects of alternative splicing can be. In many
insects, if the mRNA includes exon 4 but not exon 5 of
LTR
the gene, then the individual develops as a female, while
retrotransposons
SINEs
if it includes 5 but not 4, then it develops as a male [28].
DNA transposons
Changes in alternative splicing can evolve quite quickly.
Transposons
Simple
About one-third of alternative splicing events differ
sequence repeats
between the genes of humans and mice [45], while differ8%
13%
3%
Duplications
ences in their coding sequences have evolved only half as
3%
fast. Alternative splicing also contributes to phenotypic
5%
plasticity (see Chapters 7 and 11). The plant Arabidopsis
LINEs
splices some of its genes in different ways depending on
Miscellaneous
8%
20%
the environment in which it grows [64]. In sum, introns
heterochromatin
are essential to the proper function of the eukaryotic
genome. You will see shortly, however, that introns may
have originated for nonadaptive reasons.
12%
Another surprising feature of how genes are put
Miscellaneous
2%
together is that they sometimes overlap such that one
unique sequences
26%
Coding
stretch of chromosome encodes two different proteins
sequences
[30]. (Imagine a string of letters that form two different
(exons)
and useful sentences depending on where you start readIntrons
ing!) Overlapping genes are found in viruses and bacteria,
FIGURE 10.13 The ingredients that make up the human
but
they are much more rare in eukaryotes. The selective
genome. Less than 2% is devoted to protein-coding sequences.
advantage
of overlapping genes is debated. One hypothDifferent kinds of transposable elements (SINEs, LINEs, LTR retroesis
is
that
the
overlapping arrangement allows the genes
transposons, and DNA transposons) together make up almost half
to
share
their
timing and level of expression. A second
the genome, while introns make up more than one-fourth. (After
idea is that they are favored because they allow the
T. R. Gregory. 2005. In The Evolution of the Genome, T. R. Gregory
[ed.], pp. 3–86. Burlington, MA: Elsevier.)
genome to be streamlined.
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10.3 The Evolution of Gene and Chromosome Structure 275
Chromosome evolution
Before the rise of molecular genetics in the late twentieth century, much of what
we knew about evolutionary genetics came from studying chromosomes through
the light microscope. Species differ in their numbers of chromosomes and in how
the genes are arranged on them. The karyotype consists of the number and structure of the chromosomes. How do changes in the karyotype evolve? Although
many questions are not yet answered, several patterns have emerged.
FISSIONS, FUSIONS, AND THE EVOLUTION OF CHROMOSOME NUMBER The
most common way for the number of chromosomes to change is by the fusing of two chromosomes, which reduces the haploid chromosome number by
one, or by their fission, which increases the number by one. Humans have 23
pairs of chromosomes, but all of the other great apes have 24 pairs. At some
point since our lineage split from that of chimpanzees, two chromosomes fused.
That mutant chromosome spread throughout our species, resulting in the second-largest chromosome in the human karyotype. We do not yet know what
caused it to spread: it may have had a boost from positive selection, or it may
have simply drifted to fixation.
We do, however, have a good idea about how fissions and fusions evolve
in the house mouse (Mus musculus). Here karyotype evolution is on the fast
track: changes in chromosome number are evolving at rates hundreds of times
faster than in most other mammals [46]. Populations can have anywhere between
11 and 20 chromosome pairs. What accounts for this chromosomal chaos? The
answer seems to be meiotic drive and selection that has favored selfish genes
rather than the fitness of individuals [48]. During meiosis in females, one haploid
set of chromosomes is transmitted to the egg while the other sets enter the polar
bodies, which die (FIGURE 10.14). In a female mouse that is heterozygous for a
fusion, the fused chromosome competes with the two unfused chromosomes for
transmission to the egg. Whether the fused or unfused chromosomes win this
battle depends on whether the meiotic spindle attached to the egg or the spindle attached to the polar body is stronger. In some mouse populations, unfused
(A)
Polar body has
stronger spindle
Egg
Fused
chromosomes
Egg has
stronger spindle
Spindle
Spindle
Unfused
chromosomes
Polar
body
FIGURE 10.14 Female meiotic drive can favor chromosomes
that are either fused or unfused. (A) In some populations, the
(B)
Number of species
400
Expected
200
0
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Observed
0.25
0.5
0.75
Fraction of unfused chromosomes
1
polar body has a stronger meiotic spindle than the egg (left). In
individuals that are heterozygous for a fused and unfused
chromosome pair, the fused chromosomes are more likely to
attach to the spindle of the egg, giving them an advantage. In
other populations, the egg has a stronger spindle, which favors
the unfused pair of chromosomes (right). (B) Mammals tend to
have either most of their chromosomes fused or most unfused.
The bars show the frequencies of unfused chromosomes in the
genomes of 1170 species of mammals. The dots show the distribution expected if pairs of chromosomes fused independently of
each other, which is the null hypothesis in the absence of meiotic
drive. (A after K. Yoshida and J. Kitano. 2012. Evolution 66: 3198–
3208; B after F. Pardo-Manuel de Villena and C. Sapienza. 2001.
Genetics 159: 1179–1189.)
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chromosomes attach more often to the egg’s spindle, causing the mouse karyotype to evolve rapidly toward 20 pairs of unfused chromosomes. But in other
populations, changes to the machinery of cell division turn the tables, and then
meiosis favors 11 pairs of fused chromosomes. Remarkably, when this happens, it
doesn’t matter which chromosomes fuse—almost every possible pair of unfused
autosomes has been fused in different mouse populations [46]. It appears that the
same shifts between karyotypes made up mainly of fused or of unfused chromosomes are playing out at a slower evolutionary tempo across all mammals. As a
result, species tend to have either most of their autosomes fused or most unfused.
While the details of how meiotic drive causes fusions and fissions to evolve are
a bit complex, the bigger message of this story is simple. Some of the most basic
features of the genome, including the number of chromosomes in the karyotype,
are not refined adaptations that enhance survival and reproduction. Instead, they
are the messy outcomes of competing evolutionary processes that act at different
levels of selection.
INVERSIONS AND THE EVOLUTION OF CHROMOSOME STRUCTURE When geneticists began to study the chromosomes of fruit flies in the 1930s, they saw banding patterns that vary within and among species (FIGURE 10.15). Closer study
revealed that many of these differences result from chromosome inversions (see
Chapter 4). A chromosome with an inversion has the same genes as one without
it, but they are in a different order.
If chromosomes with and without an inversion have the same genetic content,
what could cause an inversion to spread in a species? Several mechanisms are
responsible [33, 16]. First, an inversion is produced when a chromosome breaks
at two places and the middle segment is then reinserted backward. The break
points can disrupt a gene or alter its expression. Occasionally this generates a
beneficial mutation that causes the inversion to spread by positive selection.
Second, some inversions benefit from meiotic drive. When such an inversion
is heterozygous (i.e., paired with a chromosome that lacks the inversion), it is
transmitted to the gamete more than 50% of the time. This favors the inversion
to spread just as if it increased survival.
(A)
(B)
A
B
Chromosome arm
C
D
E
D. melanogaster
D. ananassae
D. pseudoobscura
D. willistoni
D. virilis
© Alex Wild
D. mojavensis
D. grimshawi
FIGURE 10.15
Chromosome rearrangements in fruit flies.
(A) The third chromosome of Drosophila pseudoobscura as seen
under a light microscope. The banding patterns, which result
from staining the DNA, are altered by chromosome rearrangements. Inversion heterozygotes produce loops in their chromosomes, as seen here. (B) Differences in the gene order among
eight Drosophila species that have resulted from rearrangements. The phylogeny for the species is shown at left. For each
10_EVOL5E_10.indd
276 Evolution, 5e
Futuyma Kirkpatrick
From T. Dobzhansky and C. Epling. 1944.
Carnegie Inst Publ 554: 111–183
D. erecta
species, individual genes are indicated by colored vertical lines,
and chromosomal arms are shown in different colors. The lines
connecting the genomes of adjacent species link homologous
genes. Crossing lines show that two species differ in a chromosome inversion. A translocation can be seen in D. pseudoobscura
that moved a segment of arm A to arm D. (A from T. Dobzhansky
and C. Epling. 1944. Carnegie Inst Publ 554: 111–183, B from
A. Bhutkar et al. 2008. Genetics 179: 1657–1680.)
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10.4 Evolution of Genome Size and Content 277
FIGURE 10.16
Chromosome inversions in the ruff (Calidris pugnax) are
responsible for dramatically different male morphs. (A) “Independent”
(A)
Female
Independent male
Inversions can also increase in frequency because of their effects on
recombination. When an inversion is heterozygous, recombination is
blocked in that region of the chromosome. This can bind together a
(B)
Inverted region
favorable combination of alleles at several loci, causing the inversion to
spread. The Ruff (Calidris pugnax) is a bird with different male morphs
that use alternative strategies to obtain mates (FIGURE 10.16). The morphs
Genes
differ dramatically in plumage, behavior, and body size. These phenotypes are determined by chromosome inversions that carry alleles that
code for different combinations of feather colors and reproductive hormones that determine each morph’s mating strategy [35]. (One of the
loci is Mc1r, which is involved in adaptive changes to coloration in many
groups of animals—see Figure 7.29.) Inversions can also be established
when they capture alleles that are beneficial in particular environments.
Last, an inversion can spread by random genetic drift. You saw in Chapter 5 that
some inversions are underdominant: they decrease fitness when heterozygous.
When an underdominant inversion is rare, as when it first appears, selection acts
to eliminate it from the population. But if drift causes it to reach a frequency
greater
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Kirkpatrick Evolution, 5e
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than 50%, then selection favors it to spread to fixation. Inversions established
this
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way in one population will cause interbreeding with other populations
to produce heterozygote offspring with low fitness. This can generate genetic isolation
between the populations and contribute to speciation (see Chapter 9).
Satellite male
© Winfried Wisniewski/
Minden Pictures
males are territorial and dominant, and they display to attract females.
“Satellite” males are nonterritorial and submissive to the independent
males but are more attractive to females. (B) Satellite males are heterozygous for a chromosome inversion that independent males do not carry.
The inversion spans many loci (indicated by the rectangles), some of which
affect plumage and sex hormones. (B after S. Lamichhaney et al. 2016.
Nat Genet 48: 84–88.)
10.4 Evolution of Genome Size and Content
Learning objectives
■ Understand why the size of a genome (in bp) is highly correlated with the
number of genes in prokaryotes but not eukaryotes.
■ Describe how and why very small genomes evolved in symbiotic bacteria
and superabundant marine microbes.
■ Explain why transposable elements are usually harmful.
More than 98% of our genome does not code for a protein or other gene product
(Figure 10.13). In some other eukaryotes, the fraction is even larger. Many scientists think that the noncoding part of a genome is largely “junk DNA” that has
no function that is useful to the organism. Where does all this noncoding DNA
come from? And is it really junk?
Genomes large and small
The staggering diversity seen across the tree of life extends all the way down to
the level of the genome. The record for the smallest known genome is held by a
DNA virus (porcine circovirus type 1), with only 1759 bp that code for just three
proteins [17]. The smallest genome for a bacterium is an insect symbiont called
Nasuia deltocephalinicola, with just 137 coding genes and about 112,000 bp [8]. At
the other end of the spectrum, the prize for the largest genome is held by the
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FIGURE 10.17 The relation across the tree
of life between a genome’s total DNA content
and the amount of its sequence that codes
for proteins. The three dashed lines indicate
the percentage of the genome devoted to coding
sequences. Viruses and prokaryotes have little or
no noncoding DNA, so nearly 100% of their genome
is coding sequence. In contrast, the genomes of
most animals and plants are made up largely of
noncoding DNA, which varies greatly in quantity.
In some species, less than 1% of the genome is
coding sequence. Unicellular eukaryotes show an
intermediate pattern. (After M. Lynch. 2006. Annu
Rev Microbiol 60: 327–349.)
100
Coding sequences (Mb)
278
100%
10
10%
1
Coding fraction
of genome
1%
Land plants
Animals
Unicellular eukaryotes
Prokaryotes
Eukaryote DNA viruses
Bacteriophage
0.1
0.01
0.01
0.1
1
10
Genome size (Mb)
100
1000
amoeba Chaos chaos that you met in this chapter’s introduction—it has 400 times
more DNA than we do [19]. There is substantial variation in a genome’s DNA
content even within some species: it differs by up to 30% in Drosophila melanogaster [9]. The largest number of genes in any organism so far known is in bread
wheat, which has about 110,000 genes [4], which is five times more than we have.
Our species, with 3.2 billion DNA bases and about 20,000 protein-coding genes,
has neither an unusually large nor small genome for a multicellular organism.
These numbers bring into bright relief an apparent disparity between genome
size and any subjective measure of organismal complexity (e.g., number of types
of tissues). Patterns emerge, however, if we divide organisms into major groups
of life and then plot the amount of DNA devoted to coding for proteins against
the genome’s total size (FIGURE 10.17). Across viruses and prokaryotes, there is
nearly a one-to-one relation. In animals and plants, however, the correlation is
much weaker: species with similar amounts of coding sequence can differ dramatically in the sizes of their genomes.
These patterns
begin to5emake sense with a single key insight. The genomes
Futuyma
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ofOUP/SInauer/Troutt
most bacteria and
viruses
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animals
and plants, by
contrast, most DNA does not code for any protein. The
amount of noncoding DNA varies tremendously among species and explains
much of the scatter in the genome size of animals and plants. The different types
of DNA in the human genome are seen in Figure 10.13. Less than 2% codes for
proteins. About 25 times more—nearly half of the genome—is taken up by the
DNA parasites that we will now discuss.
Genetic parasites and transposable elements
Transposable elements (TEs), also called transposons, are short sequences of DNA
that occur in many copies in the genomes of most organisms. They were discovered in corn by Barbara McClintock in 1951, who received a Nobel Prize in 1983
for her breakthrough (FIGURE 10.18). The most numerous TE in the human genome
is Alu, which is about 300 bp long (see Figure 13.14). Each of us has more than a
million copies of Alu that together make up more than 10% of our genome [7]. Alu
proliferates by making copies of itself: the DNA sequence is transcribed into RNA,
which is then reverse transcribed into DNA and inserted elsewhere in the genome.
Alu and other TEs are parasites that work at the molecular level. They are
usually bad news for their host. The insertion of a TE into the host’s genome
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Acc. 90-105 - Science Service, Records,
1920s-1970s, Smithsonian Institution Archives
causes a mutation that can disrupt a coding sequence, alter the
expression of important genes, or cause misregulation that triggers a cancer. A second problem for the host is that two copies
of a TE at different places in the genome can recombine, causing
a chromosomal mutation. (TEs tend to accumulate in regions of
the genome that have low recombination rates, because there
they are less likely to cause this problem and so be selected out
of the population.) These deleterious effects of TEs may contribute a significant fraction of the deleterious genetic material and
its effects in humans [65].
Transposons reproduce not to improve the fitness of their
host, but simply because they can. By the nature of natural
selection, any sequences that copy themselves more prolifically
than others will come to make up more of the genome. For this
reason, TEs are sometimes referred to as “selfish DNA” (see
Chapter 13). The evolutionary origin of TEs is uncertain. One
plausible idea is that they are modified viruses that evolved
Barbara McClintock
the ability to reproduce without leaving the cell. There are two
major classes of TEs that differ in how they replicate, perhaps
FIGURE 10.18 Barbara McClintock was awarded the
pointing to more than one evolutionary origin [61]. The Alu eleNobel Prize in Physiology or Medicine for her work
ment is an example of a retrotransposon. Its DNA is transcribed
showing that genetic elements can transpose, that is,
into RNA, and then that molecule is retrotranscribed into DNA
move their locations in the genome.
and reintegrated into the host’s genome. Like RNA viruses such
as HIV, some retrotransposons code for the reverse transcriptase
that they need to retrotranscribe the RNA intermediate into DNA. But TEs such
as Alu take parasitism to the next level: they don’t make reverse transcriptase
themselves but instead use the transcriptase made by other TEs and viruses. The
second class of TE is DNA transposons, which do not use an RNA intermediate
in their life cycle. They are much rarer in humans, in which they make up about
3% of the genome (see Figure 10.13).
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multiplying Visual Services
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to vast numbers over short periods of evolutionary time. AmongEvolution5e_10.18.ai
species of Drosophila, the fraction of the genome composed of TEs ranges by a factor of 6, from
5% to 31% [55]. The origin of a TE can be dated in two ways. The first approach
uses the divergence among different copies as a molecular clock (FIGURE 10.19).
The second approach uses the age of the most recent common ancestor of the
species in which the TE is found. This phylogenetic approach suggests that the
FIGURE 10.19
Age distributions of transposable elements in the
human genome. The graph shows the fraction of the current genome
2.5
Percent of genome
2.0
1.5
Alu
1.0
L1
0.5
MIR
L2
LTR
DNA
200
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150
100
Age (My)
50
that consists of TEs that inserted at a given time in the past. When a
new copy of a TE inserts, its sequence begins to diverge from that of
other copies. Copies that are more greatly diverged are therefore
older, and their ages can be estimated using a molecular clock (see
Chapter 6). Age distributions are shown for six different TE types:
Alu elements, L1 and L2 long interspersed nuclear elements (LINEs),
mammalian-wide interspersed repeats (MIRs), long terminal repeats
(LTRs), and DNA transposons (DNA). Alu first infected the remote
ancestors of humans about 175 Ma. There was a burst of activity about
45 Ma, and about 2% of our genome is now made up of Alu elements
that inserted at that time. Lower activity after that inserted additional
copies. Taken together, the 1 million copies of Alu now make up about
10% of our genome. By contrast, L1 elements had a protracted phase
of proliferation much earlier, with a second spike roughly 25 Ma. The
total insertion rate of all TEs is indicated by the overall height of
the plot: it reached a peak about 45 Ma and is now much lower. (After
P. L. Deininger and M. A. Batzer. 2002. Genome Res 12: 1455–1465.)
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Alu element infected primates about 65 Ma, because it occurs only in primate
species that last shared a common ancestor at that time [7]. The molecular clock,
in contrast, suggests a much earlier origin. Why the two methods give such different answers is not clear, but it may largely be due to errors in the molecular
clock resulting from variability in rates of molecular evolution (see Chapter 6).
Host genomes have evolved several ways to fight the spread of TEs. When
they succeed, the host genome is left with the aftermath of the battle: dead TEs
that can no longer spread but that still fill up much of the genome. That is the
current state of our own genome. Our ancestors evolved ways to shut down most
of the movement of TEs. Figure 10.19 shows that currently the activity of human
transposons is much lower than in the past.
Although transposable elements are largely harmful to their hosts, they occasionally produce a beneficial mutation. Chapter 5 recounts the evolution of melanic
coloration in the peppered moth (Biston betularia), which is perhaps the most
famous example of evolution observed in action. Recent research has revealed that
the mutation responsible for the melanism is a transposon whose insertion altered
the expression of a gene called cortex [27]. On a much grander scale, TEs may be
responsible for a fundamental feature of the eukaryotic genome. The molecular
machinery used to splice introns out of messenger RNA is also used by TEs. In
fact, some introns contribute to their own splicing. These facts suggest that TEs
may have been responsible for the evolutionary origin of introns, which are now
so essential to gene regulation [39]. If that hypothesis is correct, introns are the
most spectacular example imaginable of a “bug” that was turned into a feature.
Routes to the evolution of the smallest and largest genomes
This chapter opened by looking at the dramatic variation in the sizes of different genomes. While we understand some of the factors responsible, there is
still much uncertainty (and debate) among evolutionary biologists about how
genome size evolves.
The very smallest genomes are found in viruses (see Figure 10.17). These
genomes have very little or no noncoding DNA and lack genes that are essential
to life in free-living organisms. Viruses reproduce by hijacking gene products
produced by their hosts. Many viruses are in a race to replicate, which favors
reducing the genome to a minimal size.
The bacterium Buchnera aphidicola has evolved a small genome for very different reasons. It has one of the smallest genomes of any bacterium, with only
about 500 genes and 600 kb of DNA. Buchnera developed a symbiotic relation
with aphids more than 160 Ma (see Figure 15.4). Since then, it has lived entirely
within its host, and it is transmitted vertically from mother aphids to their offspring. Like viruses, Buchnera uses metabolic products provided by its hosts,
which allows its genome to do without many essential genes. Unlike in viruses,
replication of these symbionts is typically limited by that of their host, so there
is little or no fitness benefit to speed the genome’s replication. The population
sizes of Buchnera are much smaller than those of most free-living prokaryotes,
and they experience very strong drift. If deletion of a gene decreases fitness
but is not lethal, it can become fixed by chance. Deletions are more frequent
than duplications in bacteria, so genome reduction is more likely than expansion. This leads to an evolutionary trajectory in which the genome’s size evolves
downward until it teeters on the brink of its own destruction: the minimal size
necessary for survival [43].
Supercompact genomes have evolved in the world’s most abundant organisms
by yet another route. Every milliliter of seawater near the surface of temperate
and tropical oceans around the world has some million cells of a planktonic bacterium called Pelagibacter ubique. With only 1 megabase of DNA and fewer than 1500
genes, it has the smallest genome of any free-living organism [6]. Nothing about
it is superfluous: it has no introns, no transposons, no pseudogenes. Pelagibacter’s
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10.4 Evolution of Genome Size and Content 281
population size worldwide is some 10 28 cells (more than 1 million times the number of stars in the universe!), so drift in this species is almost nonexistent. In contrast to Buchnera, the streamlined genome of Pelagibacter appears to be the product
of adaptation, likely to the very low nutrient levels in its environment. By paring
down its genome, the bacterium has reduced its need for the nitrogen and phosphorus needed to replicate the DNA. The tiny genome also allows the cell size to
be miniaturized, which may be a further advantage to Pelagibacter.
Animals and plants live at the other end of the genomic spectrum, with large
numbers of genes and vast amounts of DNA. Much of the variation in gene
number in eukaryotes results from whole genome duplication. You saw earlier in
this chapter that bread wheat has about 110,000 genes. The ancestor of wheat had
some 25,000 genes. During domestication, its genome then doubled in size, not
once but twice, by allopolyploidy [10]. If the wheat follows the evolutionary path
taken by many other plants, we can anticipate that most of the duplicate genes
will eventually be lost by deletion or become pseudogenes, and ultimately the
wheat’s gene number will decline to a level more typical of flowering plants. The
total size of the genome can also shrink by deletion. This outcome has occurred
many times independently in the mustard family (Brassicaceae), as in the model
species Arabidopsis thaliana [41].
What explains the large scatter in the DNA content of eukaryotes? As in the
prokaryotes, there is a trend for species with more genes to have larger genomes,
but the correlation is much weaker (see Figure 10.17). Whole genome duplication
is one factor at work. A second is the evolution of noncoding DNA driven by the
proliferation of transposable elements. A third major player in the evolution of
genome size may be random genetic drift. Animals and plants are much bigger
than free-living microbes, and as a result they have much smaller population
sizes. Their reduced population sizes make natural selection less efficient (see
Chapter 6). If the deletion of a segment of noncoding DNA produces a fitness
benefit but the effect is small relative to the strength of drift, selection will be
largely powerless to slim down the genomes of animals and plants [39].
Does that mean the 98% of our own genome that is noncoding is actually
junk? We are still far from having a clear answer to this fundamental question.
Although much of our DNA originated as transposable elements, some of the
resulting “junk” now plays key roles in regulating gene expression, and the cell’s
metabolism has coevolved with the total quantity of DNA in the nucleus. Those
factors may diminish the selective benefit of deletions that eliminate noncoding
DNA. Like an addict and his drug, eukaryotes may not be able to break their
dependence on a bloated genome. But there is also good news in this story. When
ancient eukaryotes acquired large amounts of noncoding DNA, it opened up new
options for the evolution of gene regulation. That, in turn, may have enabled the
origin of complex life-forms, including ourselves.
What We Don’t Know
●
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Despite the vast efforts now being directed to studies of the genomes of diverse
organisms, there are very basic questions about origins and functions that are
still quite unclear. Transposable elements may have originated more than once,
but the number of origins and the timing is debated [61]. A related mystery is the
origin of the genes-in-pieces structure of eukaryotic genes. One leading idea is
that introns evolved from TEs that infected coding regions of genes. Viruses and
some TEs share certain features, suggesting that one may have given rise to the
other, but which came first is not established. Also debated is the fraction of the
eukaryotic genome that has a function. One large-scale study estimated that
80% of the human genome has a function [14]. That estimate, however, has been
criticized as far too high [23], and many evolutionary biologists would agree that
perhaps only about 10% of our genome has a definite function [60].
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The Evolution of Genes and Genomes
SUMMARY
■ New genes are an important source of evo-
lutionary novelty and adaptation. The most
common origin of new genes in eukaryotes is
by gene duplication that happens when there
is an error in DNA replication. New genes also
originate by retrotransposition from mRNA to
DNA, by duplications that bring together pieces
of different genes (exon shuffling), and by mutations that cause expression of DNA that was
previously noncoding (de novo genes).
■ New genes can be acquired from unrelated
species by horizontal gene transfer. HGT is particularly common in prokaryotes, in which it has
enabled the rapid evolution of traits, including
antibiotic resistance.
■ Whole genome duplication is much rarer but has
been a key event in the evolution of genomes in
many groups of organisms, especially plants.
■ Duplicated genes can form a gene family in
which the different members (paralogs) have
evolved different, but often related, functions.
■ Most gene duplicates degenerate into nonfunctional pseudogenes. They typically evolve
rapidly by drift because they are free from
purifying selection.
■ Chromosomal deletions can eliminate functioning genes. Natural selection can cause a
deletion to increase in frequency, for example,
when the deleted gene codes for a protein that
increases the risk of infection.
■ Most mutations that change the amino acid
of a protein (nonsynonymous mutations) are
deleterious and are removed from the population by purifying selection. Mutations in coding
sequences that do not change the protein’s
amino acid sequence (synonymous mutations)
have only very weak effects on fitness and
evolve largely by random drift. Nonsynonymous
mutations that are beneficial are the rarest of
all, but their fixation by positive selection is the
basis of much adaptive evolution.
■ The neutral theory of molecular evolution
proposed that most variation in DNA sequences
within species and differences in the sequences between species is selectively neutral and
evolves by drift.
■ The dN /dS ratio provides a rough measure of the
relative contributions of drift and selection to
the evolution of a gene. The ratio is given by the
frequency of differences, between two species, in the DNA sequences for the gene that
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are nonsynonymous divided by the frequency
of differences that are synonymous. Typically
this ratio is less than 1, which is expected when
most nonsynonymous changes are eliminated
by purifying selection but some synonymous
changes have become fixed by drift. Occasionally genes have a dN/dS ratio greater than 1,
which strongly suggests that nonsynonymous
changes have become fixed by positive
selection.
■ The fraction of protein differences among
species caused by adaptive evolution versus
genetic drift varies greatly among groups of
organisms. About half the differences are
adaptive in species with very large population
sizes (e.g., Drosophila and free-living bacteria).
In species with a small effective population
size, including humans, drift is much stronger
and so the fraction of adaptive differences is
much smaller.
■ Another important route to adaptation comes
from changes in how genes are expressed.
Changes in gene expression have been key to
adaptation to new environments in many species, including humans.
■ In eukaryotes, almost all genes have introns.
These allow the mRNA to be spliced in different
ways to make a variety of proteins.
■ Chromosome numbers change by fusion and
fission. In some cases, fissions and fusions
have become fixed, not because they increase
fitness, but because they benefit from meiotic
drive.
■ Chromosome inversions can spread by several
mechanisms. One is to prevent recombination
from breaking apart beneficial combinations of
alleles at different loci.
■ Genome size varies dramatically among species. In viruses and prokaryotes, almost all of
the genome is coding sequence. In animals and
plants, most of the genome is noncoding, and
the quantity of noncoding DNA differs greatly
among species.
■ Transposable elements, which are genetic parasites, are a major component of the noncoding
DNA and account for much of the variation in
genome size among species of eukaryotes.
■ Whole genome duplication, which is particularly
common in plants, is responsible for the large
differences in gene number seen among some
closely related groups of organisms.
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283
SUGGESTIONS FOR FURTHER READING
Dan Graur’s Molecular and Genome Evolution
(2016) gives an overview of the topics covered by this chapter (and much more) [22].
Michael Lynch’s The Origins of Genome Architecture (2007) is another valuable reference
[39]. He argues that several key aspects of
the eukaryote genome are consequences of
random genetic drift.
T. Ryan Gregory’s website (https://www.
uoguelph.ca/ib/gregory) and his Animal
Genome Size Database (www.genomesize.
com) are fonts of information about
genome size.
PROBLEMS AND DISCUSSION TOPICS
1. The origin of genes that have new functions
often involves the divergence of gene duplicates. Duplicates can arise via several
mechanisms.
a. One mechanism of gene duplication is
retrotransposition, the insertion into
the genome of DNA produced by reverse
transcription of a messenger RNA. These
gene duplicates are often dead on arrival:
they are pseudogenes as soon as they are
formed. Why is that?
b. A second mechanism of gene duplication
occurs via unequal crossing-over during
meiosis. Gene duplicates formed this
way are functional more often than when
they arise by reverse transcription. Why
is that?
c. If a gene duplicate is initially functional,
what are its possible ultimate fates,
which is most likely, and why?
2. The ratio of nonsynonymous differences
per nonsynonymous site, dN , to synonymous
differences per synonymous site, dS , can be
used to test for positive selection (see Chapter 6). Imagine that in a duplicate pair of loci,
one paralog is evolving neutrally while the
other is evolving under strong positive selection. What specific data are needed to detect
that situation using the dN /dS ratio, and what
pattern do you expect to see?
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3. The human genome contains more than a million copies of the Alu transposable element.
Comparative genomics reveals that the Alu
element is found only in the clade of mammals
that includes primates, tree shrews, rodents,
and rabbits.
a. What does the observation that the Alu
transposon is limited to this clade reveal
about its origin?
b. At many sites in the genome, an Alu
element is present in humans but absent
in chimpanzees, while at many other sites
an Alu element is present in chimpanzees
but absent in humans. What are two
hypotheses that could explain this
situation, and for any particular site, how
could the hypotheses be distinguished?
4. Imagine that you have a device that can accurately measure the DNA content of individual
cells of living yeast. This device enables you
to do artificial selection on the genome size
of the yeast. Propose an experiment using
artificial selection that could test the hypothesis that the yeast genome size is optimal for
yeast fitness versus the alternative hypothesis that transposons have caused the genome
to evolve to a size that is larger than what
maximizes yeast fitness.
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11
Evolution and
Development
“I
n my possession are two little embryos in spirit [alcohol], whose names I
have omitted to attach, and at present I am quite unable to say to what class
they belong. They may be lizards or small birds, or very young mammalia, so
complete is the similarity in the mode of formation … in these animals.”
In this passage in On the Origin of Species, Darwin quoted the world’s leading
authority on the embryonic development of animals. Karl Ernst von Baer had
found that the early embryonic stages of the most diverse vertebrates are
nearly indistinguishable, and that the special characteristics that distinguish
mammals from reptiles, or primates from rodents, develop only at later stages.
It is hard to tell if an early embryo is an alligator, a human, or any other mammal
(FIGURE 11.1). In their earliest stages, consisting of a few cells, all vertebrates—
and almost all animals—are indistinguishable. The distinct features of each
species result from developmental processes, so understanding the evolution
of these features should include understanding the evolution of development.
The similarity of diverse embryos that von Baer had discovered is one of
countless examples of organisms’ development that Darwin cited as evidence
for the evolution of diverse species from common ancestors. The embryos of
birds, anteaters, and baleen whales develop incipient teeth that are resorbed
and are lacking in the adults. Human embryos have a tail that usually stops
How did the precisely arranged pattern of stripes of this plains zebra (Equus zebra) evolve?
This is a question for evolutionary developmental biology: accounting for the evolution of
processes that specify which hair follicles will develop to be white or black.
Courtesy of Mark Kirkpatrick
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Chapter 11
Evolution and Development
By Catherine M. May CC BY-SA 2013
© Sinclair Stammers/Science Source
© Omikron/Science Source
FIGURE 11.1 Many species are very similar as embryos, with the distinctive
features of their clade developing only later. Here are embryos of human,
alligator, and mouse. Can you tell which is which? 1
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growing and persists only as three fused vertebrae at the end of our spine; but
infants are occasionally born with an anatomically complete tail. As in other
primates, human fetuses develop a dense coat of hair, which most shed about a
month before birth.
Clearly, the developmental processes by which a fertilized egg becomes a
differentiated organism, with features such as toes and a tail, are shared among
species. These processes result in yet other puzzling similarities. Some plants
have separate female and male flowers, but in many such species, a flower
has the sexual parts of the other sex, in rudimentary form. We see much the
same in humans: men have nipples. The bones in a mammal’s hind limb have
exactly matching counterparts in the forelimb (femur/humerus, tarsals/carpals),
whether in humans or bats (see Figure 2.9). Evidently a developmental process is
shared between the sexes, or between limbs. But just as clearly, the developmental processes have diverged—between sexes, between limbs, and among species.
How do we account for the similarities and differences? At one level, we know
the answer: homologous similarities are often based on shared genes. But that
does not tell us how a change in DNA sequence becomes realized as a change in
an organism’s form and function. Someone trained in architecture can look at a
blueprint and visualize the completed building, but in itself, the blueprint does
not specify how the workers will construct the building.
Some transitions from genes to phenotypes are simple and clearly understood,
such as transcription from DNA to RNA and translation to a phenotype, the protein. The far more complex transition from genes to physical structures such as
cells, tissues, and organs is the province of developmental biology. Understanding how these transitions evolve is the task of evolutionary developmental biology
(often shortened to EDB or evo-devo). Especially since the 1980s, when knowledge of molecular genetic mechanisms of development made major advances,
EDB has been an active research area that has been filling major gaps in biologists’ understanding of organismal diversity and evolution. Research in EDB is
concerned with several large questions [41, 48, 80]: What have been the changes
in developmental mechanisms that give rise to different phenotypes? How do
genetic differences among species map onto phenotypic differences? What is
the role of development in enhancing, channeling, or constraining evolutionary
change in characters? How does developmental information help us identify
homologous characters, or even to define homology? Finally, can understanding
development help us understand the origin of novel characteristics? Much of this
chapter bears on the first three questions; we will save homology and evolutionary novelty for Chapter 19, on macroevolution.
1
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From left to right, they are alligator, human, and mouse.
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11.1 Comparative Development and Evolution
Learning objectives
■ Define allometry, heterochrony, paedomorphosis, heterotopy, modularity,
and individualization.
■ Explain the allometric equation and how it describes shape differences
among individuals and species.
In the nineteenth and early twentieth centuries, biologists described and compared the embryonic development of diverse animals (and plants, to a much
lesser extent) and described how differences in development result in different
adult forms. One of the tasks of EDB is to understand these processes, such
as growth rates and differentiation of body parts, in modern terms of genetic
and cellular processes. We now know that changes in the time, place, and level
of expression (especially transcription) of particular genes produce different
morphologies, depending on influences such as temperature or day length or
sex-determining genes. Physical forces also affect the course of development;
for instance, limb bones and their muscles do not develop normally if the limb
is not exercised.
Developmental causes of phenotypes are proximate causes, mechanisms that
operate within an individual organism. They complement the processes that
caused these phenotypes, and these mechanisms, to evolve and to differ among
species. These processes are the ultimate causes, such as natural selection, that
act at the level of populations across generations (see Chapter 1). These are not
mutually exclusive explanations of phenotypes; instead, they together form a
more complete explanation. For example, embryonic mammals and birds have
webbing between the developing digits. This remains in the wings of bats and
the feet of ducks, but humans and chickens have separated digits because the
webbing cells are eliminated by programmed cell death. This is the proximate
cause of the webbing. This is a simple example of how developmental biology
helps us understand how differences between species are generated. It does not
answer why ducks evolved webbed feet and suppression of the cell death that
occurs in most other birds. A likely answer is that effective swimming enhanced
fitness in the ancestors of ducks. Selection favored mutations in genes that determine the process of cell death and resulted in birds that retained some webbing.
Natural selection is the ultimate cause of the webbing.
Among the first things that scientists learned about development is that
species are often more similar as embryos than as adults. Karl Ernst von Baer
noted in 1828 that the features common to a higher taxon (such as the Vertebrata)
often appear earlier in development than the characters that are restricted to
lower-level taxa (such as various orders of mammals) [75]. This generalization
is now known as von Baer’s law. All tetrapod vertebrate embryos display pharyngeal clefts (gill slits), a notochord, segmentation, and paddle-like limb buds
before the features typical of their class or order become apparent (see Figure
11.1). One of Darwin’s most enthusiastic supporters, the German biologist Ernst
Haeckel, reinterpreted such patterns to mean that “ontogeny recapitulates phylogeny”—that is, that the development of the individual organism (ontogeny)
repeats the evolutionary history of the adult forms of its ancestors and could
indicate its phylogenetic relationships. By the end of the nineteenth century, however, it was already clear that Haeckel’s dictum seldom holds [23]. To give one
instance, the pharyngeal clefts and associated gill arches of embryonic mammals
and reptiles never acquire the form typical of adult fishes. Moreover, embryos
and juvenile stages of many species have stage-specific adaptations of their own.
Ontogeny is not a very useful guide to phylogenetic history.
By the early twentieth century, biologists had identified several common patterns of developmental differences among species—patterns that are now part
of the language of evolutionary developmental biology.
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Allometric growth refers to the differential rate of growth of different parts or dimensions of an organism during its ontogeny. An adult human is not simply a magnified infant: during postnatal growth, the head grows at a slower rate than the
body as a whole, and the legs grow at a faster rate. Allometry thus refers to differences in the shape of organisms or of certain of their parts, such as the dimensions
of a skull or a leaf. These differences in shape may be seen at different stages of an
individual’s growth and, often, among adults that grew to different sizes. Allometric changes account for a great deal of morphological evolution. An increased
rate of elongation of the digits accounts for the shape of a bat’s wing compared
with the forelimbs of other mammals (see Figure 2.9), and an elephant’s tusks are
incisor teeth that have grown faster and for a longer time than the other teeth.
Allometric relationships are often described by the equation
y = bx a
(11.1)
where y and x are measurements of two features. Plots of this equation are shown
in FIGURE 11.2. In many studies, x is a measure of body size, such as weight, and
y is a measure of a part of the body. If a = 1, then the relation between x and y is
linear, and b measures how rapidly y increases with x. In this case, the relative sizes
of x and y remain constant, and we say the two traits are isometric. If a > 1, then y
increases faster than linearly, and we say there is positive allometry. An example is
human leg length (y) relative to height (x) measured during development. If a < 1,
then y increases more slowly than linearly, and we say there is negative allometry.
An example is human head size (y) relative to height (x) measured during development. An allometric relationship can also be represented by taking the logarithm
of both sides of Equation 11.1, yielding the regression equation
log y = log b + a log x
(11.2)
Written this way, the plots now become straight lines, where log b is the elevation
(or y-intercept), and a is the slope of the line.
Allometric variation is seen both within species (as the beetles in Figure 11.2
illustrate) and among species. Species of deer show positive allometry between
antler size and body mass; the largest deer, the extinct Irish elk (Megaloceros
giganteus), had spectacular antlers (see Figure 1.9).
(A) Arithmetic plots
(B) Logarithmic plots
Positive
a = 1.5
300
3
(C) Stag beetles
250
Isometric
a=1
2
Isometric
a=1
150
100
Negative
a = 0.66
1
Negative
a = 0.66
50
0
Log10 y
y
200
20
40
60
x
80
100
0
1
Log10 x
FIGURE 11.2
The allometric relations between the
measurements (x and y) of two body parts. (A) Examples
of three kinds of allometry in an arithmetic plot based on
Equation 11.1. In this kind of plot, isometric allometry appears
as a straight line. (B) The same three examples in a logarithmic
2
Mandible
From H. Gotoh et al. 2014. PLOS Genet 10(1): e1004098.
doi:10.1371/journal.pgen.1004098 / CC BY 4.0
Positive
a = 1.5
plot, based on Equation 11.2. In this kind of plot, all three cases
appear as straight lines. The slope of the line equals a, and isometric allometry has a slope of 1. (C) Male stag beetles (Cyclommatus metallifer) have positive allometry of their mandibles,
relative to body length.
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11.1 Comparative Development and Evolution 289
(A) Tiger salamander
(B) Axolotl
Adult
© Mark Boulton/Alamy Stock Photo
© Chris Mattison/Alamy Stock Photo
© Roberto Nistri/Alamy Stock Photo
Larva
Adult
FIGURE 11.3 Neoteny in salamanders. (A) The tiger salamander (Ambystoma
tigrinum), like most salamanders, undergoes metamorphosis from an aquatic larva,
with gills and tail fin, to a terrestrial adult that lacks both. (B) The adult axolotl
(Ambystoma mexicanum) resembles the larva of its terrestrial relative and remains
aquatic throughout its life.
Heterochrony [47] is broadly defined as an evolutionary change in the timing or
rate of developmental events. One of the best known instances is an evolutionary
Futuyma
Kirkpatrick
5e
change
called Evolution,
paedomorphosis
, in which some characteristics of the adult of a
OUP/SInauer/Troutt
species have aVisual
moreServices
juvenile form than in the species’ ancestor. One way paedoEvolution5e_11.03.ai 06-21-2022
morphosis can happen, called neoteny, is seen in the axolotl, a salamander that
grows to full size but does not undergo metamorphosis, as most salamanders do.
Instead, it reproduces while retaining most of its larval characteristics (FIGURE
11.3). Some human features appear to be neotenic, such as the shape of our head
compared to other apes’ and the prolonged development of our brain. A high
proportion of genes are expressed in the human brain later in life, compared with
the chimpanzee (FIGURE 11.4).
Heterotopy is an evolutionary change in the spatial position of a feature within
an organism. Often, the feature is expressed at an additional, novel position.
As we will see, this can result from the expression of specific genes in novel
parts of the developing body. Heterotopic differences among species are very
common in plants (FIGURE 11.5A,B). In vertebrates, many phylogenetically new
bones have arisen in tendons or other connective tissues subject to stress. Many
dinosaurs had bony tendons to support their long tails, and the giant panda
(Ailuropoda melanoleuca) has a novel “thumb” that is not a true jointed digit
(see Figure 19.11). Bony elements are conspicuous in the skin of armadillos and
crocodiles (FIGURE 11.5C,D).
Chimps
5.8
Humans
5.6
5.4
5.2
Expression more similar
at early and late ages
5.0
0
11_EVOL5E_11.indd 289
© iStock.com/laflor
Expression level
6.0
© LeonP/
Shutterstock.com
6.2
0.5
2
5
10
Age (years)
22
44
FIGURE 11.4 The expression (transcription) of some
human genes is considered neotenic. The red line shows
a gene (EZH1) that is expressed in adult humans at the same
level as in juvenile chimpanzees. The blue line shows that
this gene’s expression in adult chimpanzees is higher than in
juveniles. (After M. Somel et al. 2009. Proc Natl Acad Sci USA
106: 5743–5748.)
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Chapter 11
Evolution and Development
(B)
(A)
© adrian davies/Alamy Stock Photo
Stem
Roots
David McIntyre
© FLPA/Alamy Stock Photo
(A) Haptodus
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(B) Phenacodus
(C) Dolphin
© praisaeng/Getty
Images
(C)
(D)
FIGURE 11.5 Examples of heterotopy. (A) The vine Monstera
deliciosa has evolved exposed roots that grow from an aerial stem.
(B) Entire plants develop on the leaf margins of Bryophyllum; they eventually drop off and take root. (C,D) Platelets of bone develop in the skin
of diverse vertebrates, such as armadillos (C) and crocodiles (D).
The bodies of many organisms consist of modules—distinct units that have
distinct genetic specifications, developmental patterns, locations, and interactions with other modules [57]. Some modules are repeated on the body and are
termed serially homologous. In some cases, serially homologous features lack distinct individual identities (e.g., leaves of many plants, teeth of most amphibians
and reptiles) and may be considered representatives of a single character. An
important evolutionary phenomenon is the acquisition of distinct identities by
such modules, called individualization [50, 77]. For instance, the Permian ancestors
of mammals, more than 290 million years ago (Ma), had simple, conical teeth
(FIGURE 11.6A; please refer to the geological time scale in the Appendix). During
the evolution of mammals, teeth became differentiated into incisors, canines,
premolars, and molars, with different functions—suggesting that some different
genes are active in the developing primordia of different teeth (FIGURE 11.6B).
The evolution of molariform teeth was a key innovation in mammals because
the animals could then chew food, enabling rapid digestion. But distinct tooth
identity was later lost during the evolution of the toothed whales (FIGURE 11.6C).
FIGURE 11.6 The teeth of mammals provide examples of the
acquisition and loss of individualization. (A) The teeth of mammal
ancestors (synapsids), such as Haptodus in the Permian, are uniform.
(B) Teeth became individualized during the evolution of mammals, as
illustrated by Phenacodus, an Eocene relative of the ancestor of horses.
(C) Distinct tooth identity was lost in modern toothed whales, such as
dolphins. (A, B after A. S. Romer 1966. Vertebrate Paleontology. Chicago:
University of Chicago Press; C after T. A. Vaughan. 1986. Mammalogy,
3rd ed. Philadelphia: Saunders College Publishing.)
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11.1 Comparative Development and Evolution 291
Concentration
Concentration
David McIntyre
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Position on organism
(C) Puffer fish
From A. R. Sanderson et al.
2006. J Graphics Tools 11: 47–71.
http://www.tandfonline.com
chemical morphogens shows that regular patterns of a product may
be produced across a part of a developing embryo, which may induce
the development of regular patterns in the distribution of pigments,
hairs, or other features. (B) A cone snail shell (Oliva porphyria) at left,
and at right the pattern produced by a computer simulation based on
Turing’s reaction-diffusion model. (C) A puffer fish (Arothron mappa)
and a simulation of the pattern around its eye. (A after S Kondo and
T. Miura. 2010. Science 329: 1616–1620; B shell simulation from M. Meinhardt. 1998. The Algorhythmic Beauty of Sea Shells. Berlin: Springer.)
© AndamanSE/Getty Images
FIGURE 11.7 Mathematical models of development can
predict patterns (A) Alan Turing’s model of diffusion of interacting
Product (e.g., pigment)
Courtesy of D. Fowler and P. Prusinkiewicz
Before modern molecular biology, biologists made four discoveries about
development that remain important today. First, almost all cells in an organism
have the same set of genes, based on replication of the genome during mitosis.
Second, the differences among cells, tissues, and organs must result from differences in the activity of certain genes. Third, different cells have properties
that affect morphogenesis (the development of form). These include growth of
individual cells, change in cell shape, adhesion to certain other cells, mitosis in
certain dimensions (e.g., forming sheets or masses), cell movement (in animals
but not plants), and programmed cell death (apoptosis). Fourth, many aspects of
growth and differentiation are affected by chemical signals, especially hormones.
Metamorphosis in amphibians is triggered by thyroxine. The axolotl (see Figure
11.3) does not synthesize thyroxine, and it differentiates into a typical adult form
if it is injected with that hormone. Likewise, cell division and differentiation in
plants are controlled by auxins and other hormones.
By performing experiments on embryos, biologists
learned that certain events in an animal’s development
(A)
depend on preceding events and that the differentiation of
Interacting morphogens
one tissue or organ is often influenced by others. Cells in
the posterior region of a vertebrate’s limb bud induce the
formation of limb structures such as digits and muscles
by producing signaling molecules (morphogens). Scientists
developed mathematical models to describe how development could emerge from such chemical interactions. One of
the most important models was developed by the mathemaPosition on organism
tician Alan Turing, who invented the prototype of modern
computers and famously helped the Allies defeat the Nazis
(B) Cone snail
in World War II by breaking the German code. In Turing’s
mathematical model of development, two chemicals diffuse
and interact to produce a morphogen in a regular spatial
pattern that induces a repeated feature such as a structure
or pigment. Changing parameters such as diffusion rates
produce a variety of patterns that match those seen in real
organisms (FIGURE 11.7) [36].
A key advance toward modern developmental genetics was François Jacob and Jacques Monod’s discovery in
1960 of the bacterial operon, a combination of a regulatory
sequence and coregulated protein-coding genes. Based on
this concept, the developmental biologists Roy Britten and
Eric Davidson laid the foundations of the modern view, in
which eukaryotes’ genes have gene regulatory elements, or
binding sites, to which bind proteins that initiate or stop
transcription [9]. Changes in these interactions could result
in evolution of altered phenotypes. These ideas were very
similar to what was later learned about the developmental
transition from genes to phenotypes.
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11.2 Gene Regulation
Learning objective
■ Describe cis- and trans-regulatory elements and their role in the
development of phenotypic traits.
In eukaryotes, transcription of a protein-coding gene is controlled by a promoter
just upstream of the coding region (FIGURE 11.8). The promoter acts as a switch
that is turned on when RNA polymerase binds to it, which in turn occurs when
regulatory proteins called transcription factors (TFs) bind to both the polymerase
and an upstream region called an enhancer. A single gene often has multiple
enhancers, with different sequences that bind different transcription factors. The
gene can be transcribed in specific cell types and at specific times when different
sets of transcription factors are activated. Other upstream binding sites have
the opposite effect: they can block transcription, and they are called repressors.
Enhancers and repressors are referred to as cis-regulatory elements. Cis means
that the element regulates a gene on the same stretch of DNA. Transcription
factors are trans-regulatory elements, meaning that they are encoded by DNA
distant from the genes that they regulate.
Much of the research on the evolution of development concerns enhancers
and transcription factors, but several other mechanisms also affect the expression
of genes and their products. One is DNA methylation. In some organisms, such
as vertebrates and plants, transcription of a gene is repressed by methylation of
certain cytosines that are followed by guanine residues (CpG dinucleotides). The
methylated state may be maintained by a specific enzyme in newly synthesized
DNA during cell division. Eyes fail to develop in cave-dwelling populations of
the Mexican tetra (Figure 7.20) because more than 100 genes related to eye development are highly methylated, especially in the
promoter sequence [22]. Some of these genes,
such as crx, encode transcription factors that are
Transcription
RNA
factors
polymerase
necessary for normal expression of many downstream genes. The crx gene and about 25 other
genes that are repressed in the cave-dwelling
fish are known to cause human eye disorders,
including blindness.
Gene expression can also be affected at
Enhancer 1 Enhancer 2
Promoter
Coding region
the level of translation of mRNA to protein.
Transcription
MicroRNAs, together with proteins, bind
off
to the 3′ untranslated region (UTR) of RNA
messages and prevent translation. And variEnhancer 2
Enhancer 1
ous posttranslational processes can affect the
activity of proteins, such as binding a signaling molecule. In interactions among cells, such
as those that underlie tissue induction, molecules released by one cell type diffuse and are
bound by the extracellular domain of receptor
proteins that span the membranes of other
Promoter
Coding region
cells. This changes the configuration of the
Transcription
protein’s intracellular domain, which sets off a
on
signal transduction cascade of protein interactions in the receiving cell, and that may end by
FIGURE 11.8 A highly simplified sketch of how transcription is
activating transcription factors and changing
controlled. A gene is transcribed when RNA polymerase II binds to its
which genes are transcribed in the cell. BOX 11.1
promoter. That occurs when transcription factors bind to the polymerase
describes some of the methods used to study
and to one of the gene’s enhancers. (From E. A. Foster et al. 2012. Science
337: 1313; after V. S. Cooper and R. E. Lenski. 2000. Nature 407: 736–739.)
gene expression during development.
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11.2 Gene Regulation 293
BOX 11.1
Some Methods in Developmental Genetics
We briefly describe here some methods commonly used
to study genes that underlie development, and their
expression as mRNA transcripts.
Several methods are used to insert a gene from one
organism into the genome of another (often a different
species), creating a transgenic organism. The gene may
be microinjected into a fertilized egg, or it may be
attached to a transposable element or to a retrovirus
vector, which inserts the gene into the host’s genome.
Gene function is also studied by gene knockout (or
knockdown), using RNA interference (RNAi), or by
replacing the normal gene with a mutated sequence,
using CRISPR-Cas targeted mutagenesis (FIGURE A).
Several methods are used to visualize and measure
transcription of specific genes in various cell populations
at different times in development. RNA sequencing (RNAseq) and related methods are used to measure the entire
transcriptome—the expression of all the genes at once. The
transcription patterns of cis-regulated genes can be studied using reporter constructs inserted into cultured cells or
transgenic individuals. Reporter constructs consist of the
regulatory DNA of interest, spliced upstream of a reporter
gene that encodes a protein whose expression can be easily visualized under the microscope. One such protein, from
jellyfish, is green fluorescent protein, GFP, which fluoresces
bright green. FIGURE B shows the nematode Caenorhabditis
briggsae expressing a GFP reporter construct containing
cis-regulatory DNA from the myo-2 gene, which directs the
reporter gene’s expression in the pharynx.
In genetic model species, the integration of genomic
with genetic, developmental, and functional data provides
a powerful base of knowledge for studies in evolutionary
genetics and evolutionary developmental biology. These
data are available in public online databases such as FlyBase (http://flybase.org) for Drosophila melanogaster and
ENCODE (www.encodeproject.org) for humans. As genomic and transcriptomic data from more species are accumulated and compared with the comprehensive information
on model species, more and more investigations of the
evolution of species differences become possible.
(A)
From A. Martin et al. 2016.
Curr Biol 26: 140-026
Some methods of studying gene expression in developing animals.
Photo by Eric Haag, courtesy of
Takao Inoue and Eric Haag
(B)
11_EVOL5E_11.indd 293
GFP expression
(A) CRISPR-Cas has been used to knock out function of various Hox genes
in the crustacean Parhyale, resulting in homeotic transformation of certain
appendages into other appendages. Normal and transformed appendages
are shown in the scanning electron micrographs. Thoracic and abdominal
segments are labeled T and A, respectively. Appendages on segments T6, T7,
and T8 are colored in a wild-type (WT) larva (left photo), and the transformation of these to a T4 or T5 morphology (colored as in the wild type) is shown in
a larva in which the abdA Hox gene was knocked out (right photo). (B) Green
fluorescent protein (GFP) expression (bright green) by a transgenic reporter
construct containing cis-regulatory DNA from a gene that is expressed in
the pharynx of the nematode Caenorhabditis briggsae.
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Hox genes and the genetic tool kit
(A)
Haltere
(B)
Courtesy of E. B. Lewis
FIGURE 11.9 A homeotic mutation.
(A) A wild-type Drosophila melanogaster
has a single pair of wings, borne on
the second thoracic segment, and a
pair of small winglike structures called
halteres, borne on the third thoracic
segment.Evolution,
(B) In a fly
Futuyma Kirkpatrick
5ecarrying mutations
in
the
Ultrabithorax
(Ubx) gene, the third
OUP/SInauer/Troutt Visual Services
thoracic segment
has been transformed
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into another second thoracic segment,
bearing wings instead of halteres.
Long before DNA was identified as the basis of heredity, geneticists described
homeotic mutations in Drosophila and other species. These are mutations that transform a structure into a different structure. For example, the Ultrabithorax (Ubx)
mutation turns halteres (balancers) into wings (FIGURE 11.9).
In arthropods such as crustaceans, the body consists of multiple segments,
almost all of which bear a pair of serially homologous appendages with similar basic structure. Appendages on the most anterior segments are modified
as mouthparts, and those on the trunk serve for locomotion. In insects, which
are descended from crustaceans, three distinct segments make up the thorax,
each with a pair of legs, and the posterior segments are legless and compose the
abdomen. In most insects, the second and third thoracic segments (T2 and T3)
each have a pair of wings. In the order Diptera (true flies), such as Drosophila,
T3 is smaller than T2, and the T3 wings are modified into small structures, the
halteres, that are used for balance rather than flight.
Some of the homeotic mutations in Drosophila change the identity of a segment.
We have seen that mutations in the Ubx locus change the T3 segment into a second
T2 segment and, therefore, the halteres into wings (see Figure 11.9). Mutations of
other genes that affect characteristics of the wing affect the duplicated wing just as
they affect the normal wing on T2. Thus, the normal Ubx gene regulates the transcription of the diverse genes that together produce a T3. If mutation or deletion
of the Ubx gene causes failure of regulation, the segment develops into a default
state, T2. Antp and Ubx are two of eight genes, in two clusters, that control the
anterior–posterior identity of body segments, in the same order as the genes’ positions along the chromosome (FIGURE 11.10). The expression of each of these genes
along the anterior–posterior axis of the developing fly corresponds to the segments
whose identity the gene affects. These genes encode transcription factors. The part
of the genes’ sequence that encodes the DNA-binding domain of the transcription
factor is called the homeobox, and the genes are called homeotic selector genes, or
Hox genes. Research on Hox genes largely initiated modern developmental genetics and evolutionary developmental biology.
A1 A2
T1 T2
T3
Antennapedia complex
lab
FIGURE 11.10
Hox gene expression
in Drosophila. At center are schematics of the chromosome regions that
carry the Antennapedia and Bithorax
gene complexes. Regions where individual loci are expressed in the embryo
are shown in the schematics below,
where the intensity of shading corresponds to the expression level. The
corresponding tissues in the adult are
shown above. (After S. Dessain et al.
1992. EMBO J 11: 991–1002; T. C. Kaufman
et al. 1990. Adv Genet 27: 309–362.)
11_EVOL5E_11.indd 294
Pb
1
Dfd
A3
A4
A5
A6
A7
A8
Bithorax complex
Scr
Antp
Ubx
T1
T2
T3
abdA AbdB
A1–A5
A5–A8
1413121110 9
8
23 4 5 6 7
labial (lab)
Deformed (Dfd)
Abdominal B (AbdB)
abdominal A (abdA)
Sex combs reduced (Scr) Antennapedia (Antp) Ultrabithorax (Ubx)
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11.2 Gene Regulation 295
(A) Maternal effect genes
(B) Gap genes
hunchback
Anterior
Posterior
(C) Pair-rule genes
(D) Segment polarity genes
ftz
engrailed
Courtesy of Drs. S. Carroll
and S. Paddock
Courtesy of Dr. D. W. Knowles and the
Berkeley Drosophila Transcription
Network Project, http://bdtnp.lbl.
gov/Fly-Net/
Krüppel
Courtesy of Drs. C. Rushlow
and M. Levine
Courtesy of C. Nusslein-Volhard
bicoid
FIGURE 11.11 Example of a developmental network: a simplified model of anterior–posterior
pattern formation in the Drosophila embryo. Anterior is to the left, and we view the left side of
the embryo. Colors show gene expression, as concentrations of protein products. (A) Maternal effect
genes, (B) gap genes, (C) pair-rule genes, (D) segment polarity genes.
The Hox genes are part of a gene regulatory network—a set of interacting regulatory genes and the genes they regulate—that specifies the anterior–posterior
Futuyma Kirkpatrick Evolution, 5e
body pattern
(FIGURE
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Visual
Services11.11). The first genes expressed (Figure 11.11A) are maternal genes such09-27-2022
as bicoid whose protein products form concentration gradients
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in the egg. These proteins are transcription factors that activate expression of
(Figure 11.11B) gap genes such as hunchback (orange in the figure) and Krüppel
(green), which define broad territories in the embryo. The gap gene transcription
proteins determine the expression of (Figure 11.11C) pair-rule genes such as ftz,
each of which specifies a region about two segments long. Later in development, the pair-rule genes activate (Figure 11.11D) segment polarity genes such as
engrailed that divide each of these regions into two segment-sized units, each with
anterior–posterior polarity. These several groups of genes determine the region
where each homeotic selector (Hox) gene is transcribed, defining the identity of
each segment and initiating developmental pathways that result in the segment’s
various features, such as antennae, legs, and wings. Complex regulatory pathways of this kind are characteristic of development of most of the morphological
features of multicellular organisms.
A profoundly important and most surprising discovery was that the Hox genes
described in Drosophila are also present in mammals, and some of them occur in
all bilaterian animal phyla. This family of genes evolved in the ancestor of almost
all animals, more than 540 Ma (FIGURE 11.12). In some groups, the entire array of
Hox genes has been duplicated: mammals and other tetrapods have four sets,
although each set lacks one or more of the genes. To a large degree, these genes
play similar developmental roles in the various phyla. They specify features along
the anterior–posterior axis of a mammal embryo just as they do in insects, and
they also specify regions of some other parts of the body, such as the limbs and
the female reproductive tract. This functional role of the Hox genes has been
phylogenetically conserved.
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Chapter 11
Drosophila
Hox genes
lab
Mouse
Hoxb genes
Mouse
embryo
1
Evolution and Development
pb
Dfd
2
3
4
Scr Antp
5
Hindbrain
Midbrain
6
7
Spinal
Cervical
8
9–13
FIGURE 11.12
Segment-specific patterning functions of
Hox genes in the vertebrate hindbrain and spinal cord.
In this schematic diagram of a mouse embryo, the black horizontal bars indicate segmental patterns of Hoxb gene expression, with darker color corresponding to areas of relatively
high gene expression. The double-headed arrows connect
the genes in the Hoxb cluster to the homologous Hox genes in
Drosophila. (After W. McGinnis and R. Krumlauf. 1992. Cell 68:
283–302.)
cord
However, Hox genes and other genes that encode
transcription factors can interact with each other and
Tho
racic
play other roles. Ubx acts with other genes to determine
the length of the middle legs in water striders, insects
that skate on the surface of water [59]. The Ubx transcription factor can play diverse roles because it can bind the
enhancers of diverse genes. Ubx and some other genes
that encode transcription factors are like a hammer or
wrench that can be used for a wide range of different
tasks. Sean Carroll [10] referred to such genes as a genetic tool kit that is shared
widely among animals and can contribute to evolutionary changes in the regulation of diverse genes with diverse developmental roles. In some instances,
genetic pathways, not just single genes, have taken on additional roles [73].
For example, a subset of the Hox genes is expressed in the limbs of tetrapods,
where they determine the proximal–distal differentiation of the limb (e.g.,
humerus to radius to wrist bones). The same principles apply to plants. Most
species in the potato family (Solanaceae) express the MADS16 gene, which
encodes a transcription factor, only in vegetative tissues, where it affects cell
shape and division rate. In the genus Physalis (Chinese lantern plant), the gene
is heterotopically expressed in the sepals after pollination and causes these
flower parts to grow into a “balloon” that envelops the fruit (FIGURE 11.13) [28].
Lu
m
ba
Forebrain
Ubx abdA AbdB
r
296
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(A)
(B)
©Food and Drink Photos/Alamy Stock Photo
© Selwyn/Alamy Stock Photo
Sepals
FIGURE 11.13
An example of heterotopic gene expression. (A) The fruit of a
tomato plant shows the sepals as small structures at the base of the fruit. This is the
condition in most members of the plant family Solanaceae. (B) In the ground cherry
genus, Physalis, a gene that is ordinarily expressed only in vegetative tissues is transcribed in the sepals after the fruit starts to develop. The sepals form the “Chinese
Futuyma
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lantern”Kirkpatrick
that envelops
and5eprotects the fruit.
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11.3 Developmental Bases of Phenotypic Evolution 297
11.3 Developmental Bases of Phenotypic Evolution
Learning objectives
■ Describe an example of an evolutionary change based on change in
cis-regulation and one based on change in a transcription factor.
■ Describe an example of evolutionary change in the effect of
a transcription factor.
■ Describe a real or hypothetical example of a gene
regulatory network and how it may affect the evolution
of a trait.
Because early data showed that the proteins of humans and
chimpanzees are very similar, Mary-Claire King and Allan Wilson [34] suggested that the many morphological differences
between the species are likely to be caused mostly by regulatory differences rather than protein sequence differences. We
now know that differences in gene regulation do indeed underlie much morphological evolution. Differences in beak size and
shape among the Galápagos finches in the genus Geospiza (see
Figure 2.2) are based on growth differences in the prenasal
cartilage and later in the premaxillary bone. The expression of
growth-regulating genes during beak development accounts
for these differences [1, 44]. The transcription factor gene Bmp4
is expressed in the prenasal cartilage earlier and at higher levels
in the embryos of species that have deeper, wider beaks (FIGURE
11.14). Later in development, differences in expression of three
other genes in the premaxilla correlate with differences in several beak dimensions. The expression level of most of these
genes has been experimentally altered in chicken embryos and
produces the same effects that are seen in the finches.
Evolution of both transcription factors and cis-regulatory
sequences has been important [63, 70]. Changes in cisregulatory elements can happen in several ways. The pelvic girdle and fin spines have been reduced independently in
many freshwater populations of the three-spined stickleback
(Gasterosteus aculeatus) that have descended from marine
ancestors. This change is caused by a deletion of an enhancer
of the Pitx1 gene (FIGURE 11.15) [11]. Mutations in an enhancer
of the wingless gene cause dark pigment spots to develop in
the wings of Drosophila guttifera, in contrast to the clear wings
of D. melanogaster [37].
Where do new cis-regulatory elements come from? Some
arise de novo (see Chapter 10, “The Birth and Death of
Genes.”) Others originate by duplication and sequence divergence of ancestral sequences. In mammals, some binding
sites for various transcription factors are embedded within
transposable elements [71]. A dramatic example is in the
endometrial stromal cells of the placenta in humans and
other placental mammals. A gene that encodes the hormone
prolactin is expressed in these cells, in response to cAMP
[42]. The cAMP-responsive enhancer of this gene is derived
from a DNA transposon, MER20. MER20-related sequences
are close to the coding sequences of hundreds of genes that are
expressed in these cells, and these sequences have been experimentally shown to bind several transcription factors that are
important in pregnancy.
11_EVOL5E_11.indd 297
Bmp4 expression
Premaxilla
G. scandens
Low
G. conirostris
Geospiza fuliginosa
G. fortis
High
G. magnirostris
Skull images from R. I. Bowman. 1961. Morphological Differentiation and
Adaptation in the Galápagos Finches. Berkeley, CA: University of California
Press, reproduced with permission of University of California Press
Bmp4 expression images from A. Abzhanov et al. 2004. Science 305: 1462–1465
FIGURE 11.14 Among species of Galápagos finches
(Geospiza), differences in the depth and length of
the premaxilla are determined largely by differences
in the expression of the gene Bmp4 at a critical stage
Darker5estaining in the region indicated by
in development.
Futuyma
Kirkpatrick Evolution,
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red arrows shows
higher
gene expression in species with
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bills that are less slender
and pointed.
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FIGURE 11.15 Expression of Pitx1 affects
development of the dorsal and pelvic spines in
the three-spined stickleback (Gasterosteus
aculeatus). The spines are strongly expressed in
Dorsal spines
Ventral view
Strong
expression
of Pitx1
Pelvic spine
(B) Freshwater
Photos courtesy of D. M. Kingsley
marine populations but not freshwater populations.
Bone and cartilage are stained red. Views of the
ventral regions at left show that Pitx1 is much more
strongly expressed in marine populations. (After
M. D. Shapiro et al. 2004. Nature 428: 717–723.)
(A) Marine
Alterations of genes that encode transcription factors have also proven to play
major roles in phenotypic evolution [43]. Insects, which evolved from a crustacean
lineage, have legs only on the three thoracic segments. In Drosophila embryos, Ubx
is expressed in the abdomen, where it inhibits leg development. When biologists
caused the gene to be expressed in the thorax as well, it suppressed limb development. However, Ubx is expressed throughout the segments in the brine shrimp
Futuyma Kirkpatrick
Evolution,
5ecrustaceans, has appendages on most of the body
(Artemia),
which, like
other
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ServicesUbx was transferred into Drosophila embryos, it did
segments.
When brine
Evolution5e_11.15.ai 06-21-2022
not suppress embryonic limb development [62]. Researchers found the same effect
when they used Ubx from a velvet worm in the phylum Onychophora, the sister
group of the arthropods. The difference between these Ubx effects was traced to a
new polyalanine domain (AAAA…) that evolved in insects, which causes Ubx to
inhibit transcription of a gene (Distal-less) that is necessary for leg development
(FIGURE 11.16) [17]. Thus, when insects evolved, changes in Ubx caused it to repress
leg development.
Fly (Diptera)
WFQNRRMKLKKEIQAIKELNEQEKQAQAQKAAAAAAAAAAVQGGHLDQ
Beetle (Coleoptera)
WFQNRRMKLKKEIQAIKELNEQEKQAQAQKAAAAAAAVAAQVDPN
Brine shrimp (Crustacea)
WFQNRRMKLKKEIQAIKELNEQDKRITPSKLHSNC-SSPTGILVTMKKMK-SFNLITE
Velvet worm (Onychophora)
WFQNRRMKLKKEMQTIKDLNEQEKK---QRDTLSTV
Conserved sequence
Domain that suppresses
legs on abdomen
FIGURE 11.16 The Ubx gene suppresses
development of legs on the abdomen
of insects, but not in crustaceans and
other arthropods or in velvet worms
(the phylum Onychophora). Part of the
Ubx protein sequence is shown for these
species; the letters in the sequences represent amino acids (e.g., A = alanine). One
region of the protein has been conserved
across these species for more than 540 My.
Insects have evolved a novel polyalanine
sequence that is responsible for repression
of legs on their abdomen. (After R. Galant
and S. B. Carroll. 2002. Nature 415: 910–913.)
Fly © iStock.com/rob_lan, beetle courtesy of Peggy Greb/USDA, brine shrimp
© Dan Olsen/Shutterstock.com, worm © Dr. Morley Read/Shutterstock.com
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11.3 Developmental Bases of Phenotypic Evolution 299
Normal
FIGURE 11.17
In contrast to a normal pigeon foot, the feet of “muffed” pigeons
are feathered. This phenotype results from reduced expression of Pitx1, which
determines hind limb identity, and increased expression of Tb5, which determines
forelimb identity. (From F. Petit et al. 2017. Nat Rev Genet 18: 245–258.)
A study in developmental evolution: Tetrapod limbs
© iStock.com/profeta
Tetrapod limbs and flowers illustrate how evolutionary divergence in development underlies morphological diversity. The diverse limbs of tetrapods
(four-legged vertebrates) are a leading example of the developmental basis of
morphological evolution [55, 84]. The limbs develop from a ridge on the sides of
the embryo, at sites specified by Hox genes. The Pitx1 gene encodes a transcription factor that is important in specifying whether a limb develops into a forelimb
or hind limb. When Pitx1 is underexpressed and another gene (Tb5) is overexpressed, feathers that normally develop on the wings (forelimbs) of a pigeon also
develop on the legs (hind limbs) (FIGURE 11.17). Within the developing limb bud,
a network of genes governs differentiation (FIGURE 11.18A). A region (the apical
ectodermal ridge) at the end of the limb bud emanates Fgf signaling proteins that
induce growth in length of the limb. Fdf also upregulates the Shh gene in the zone
of polarizing activity, located at the rear side of the bud. Shh produces a diffusing
protein that elicits the development and affects the identity (form) of the digits.
An evolutionary change that has occurred in many tetrapod lineages is reduction in the number of digits, and of entire legs in snakes and some lizards. This
is largely caused by reduced Shh signaling, and the anterior digits, farthest from
the zone of polarizing activity, tend to be the first to go (FIGURE 11.18B). Pythons
and some other phylogenetically basal lineages of snakes develop a small pelvis
and femur; their failure to develop further, and the loss of even these rudiments
3
2
Anterior
AER
zone
BMP
5
2
Grem
Fgf10
Fgfs
3
4
2
3
1
2
Shh
Proximal
Posterior
ZPA
zone
Distal
1
Hemiergis
quadrilineata
FIGURE 11.18 Developmental evolutionary changes in
tetrapod limbs. (A) Diagram of a limb bud, showing elements
of the genetic regulatory network of limb development. A line
terminating in an arrowhead means the gene upregulates the
gene it points to; a line with a crossbar downregulates it. Note
the effects of genes active in the zone of polarizing activity
(ZPA, red) and the apical ectodermal ridge (AER, green) zones.
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© Nature Picture Library/
Alamy Stock Photo
Hemiergis
peronii
Fewer toes
Tbx4/5
1
Smaller ZPA zone
Hemiergis
peronii
4
1
Hemiergis
initialis
© Joe Blossom/Alamy Stock Photo
(B)
© Nature Picture
Library/Alamy
Stock Photo
(A)
Muffed
(B) Species of lizards (skinks) in the genus Hemiergis have fewer
digits where the ZPA is less active, as shown by red in the limb
bud diagrams. The photos show H. peronii, with four toes, and
H. quadrilineata, with two. The elongate body and reduced legs in
these lizards are associated with their snakelike locomotion, as
they slither through leaf litter. (After J. J. Young and C. J. Tabin.
2017. Dev Biol 429: 401–408.)
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in most snakes, is caused largely by greatly reduced expression of Shh, due to the
degenerated sequence of a key enhancer.
Evolutionary changes in the length of autopod elements (hands and feet) have
several causes. Heightened Fgf signaling from the apical ectodermal ridge causes
the elongated fingers and the pointed shape of a dolphin’s flipper, while the
great length of a bat’s fingers, the framework of its wing, seems to have multiple
causes. Finally, apoptosis—programmed cell death—is a major sculptor of hands
and feet. It produces free instead of webbed toes (as in chickens versus ducks),
and the spatial pattern of apoptosis accounts for the hoof of a cow, dominated
by toes 3 and 4, and for the single toe (3) of a horse.
Ongoing studies of diverse tetrapods illustrate the how of structural diversity:
the causal sequence from gene expression to developmental events to the diversity of legs, arms, and wings. Why lineages have evolved these differences is
another question—one that is answered mostly by natural selection, the relation
between form and advantageous function, and the ecological context that sets
functional needs.
A study in developmental evolution: Flower symmetry
FIGURE 11.19
Flower symmetry.
(A) A radially symmetrical flower of a
cherry. (B) The bilaterally symmetrical
flower of snapdragon. (C) A simplified
diagram of the genetic network determining flower development. Lines with
arrowheads and crossbars show gene
activation and repression, as in Figure
11.18. The protein products of DIV and
DRIF (in ovals) combine to determine
ventral identity. CYC and DICH determine
dorsal identity; they also upregulate
RAD, which inhibits DIV and so suppresses ventral identity in the dorsal
region. DIV upregulates the CUP pathway that determines the form of the
flower’s lip. (After C. A. Wessinger and
L. C. Hileman. 2020. Annu Rev Ecol Evol
Syst 51: 387–408.)
(A)
Angiosperms (flowering plants) vary in floral symmetry [29, 82]. The ancestral
condition is radial symmetry, in which any line through the center divides the flower
into two mirror-image halves, as in a rose or a lily. Bilaterally symmetrical flowers,
as in peas and snapdragons, yield mirror-image halves only along a single line
(like vertebrate bodies). Bilateral floral symmetry has evolved independently in
about 200 lineages and has reverted back to radial symmetry about 70 times [60].
Snapdragons (Antirrhinum majus), which belong to a large clade of species with
five petals and five stamens, have bilaterally symmetrical flowers, with a distinctive
dorsal (top) petal, a lateral petal on each side, and two ventral (bottom) petals that
form a “lip,” a landing platform for bees. The dorsal stamen does not develop fully.
Developmental geneticists discovered that the bilateral symmetry of snapdragon
flowers is largely caused by two genes, CYC and DICH. They encode transcription factors that interact with other genes (FIGURE 11.19). These transcription factors upregulate RAD, which encodes another transcription factor that prevents the
expression of DIV, which controls the development of ventral features. Plants with
mutations in CYC and DICH that prevent their expression fail to develop distinctive
dorsal features, so the entire flower has ventral identity and is radially symmetrical.
Changes in the expression of these genes, especially CYC, have occurred in
parallel in many lineages that independently evolved bilateral symmetry. The
(B)
(C)
CYC and DICH
From C. A. Wessinger and L. C.
Hileman. 2020. Annu Rev Ecol
Evol Syst 51: 387–408
© Bob Gibbons/Alamy Stock Photo
© iStock.com/NaokiKim
Lip
Dorsal
RAD
DIV
DIV
Lip
DRIF
Ventral
ANT
CUP
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11.4 How Development Affects Morphological Evolution 301
a bilaterally symmetrical species (Stigmaphyllon
jatrophifolium) and a radially symmetrical species
(Sphedamnocarpus sp.). (A) Diagram of expression of
the CYC gene expression in bilaterally symmetrical
New World species that are pollinated by oil bees.
(B) Two radially symmetrical Old World species that
are not pollinated by oil bees. CYC is expressed
throughout the flower at left but almost lacking
expression in the flower at right.
Courtesy of C. C. Davis
Photo by Carol Gracie, courtesy of C. C. Davis
FIGURE 11.20 The genetics and evolution of
flower form in Malpighiaceae. The photos show
Stigmaphyllon
Sphedamnocarpus
(B) Reversals to radial symmetry in the Old World
From W. Zhang et al. 2013.
Front Plant Sci 4: 1–13. doi:
10.3389/fpls.2013.00302
(A) New World bilateral symmetry
Banisteriopsis
argyrophylla
CYC2A
Madagasikaria
andersonii
Sphedarppocarpus
sp. (nov.)
Malpighiaceae, a tropical family of trees and vines, is rich with examples [86,
87, 88]. Closely related outgroup families with radial symmetry have a single
CYC gene, expressed in flower primordia only before they begin to develop into
flowers. The basal lineages of Malpighiaceae have bilaterally symmetrical flowers, with a characteristically shaped dorsal petal that their specialized pollinators—oil bees—cling to as they collect oil from special glands. In one clade, the
CYC2 gene has been duplicated (CYC2A, CYC2B) and determines the distinctive
dorsal features, with the CYC2B gene specifically expressed in the modified dorsal petal (FIGURE 11.20A). In a second clade that diversified where there are no
oil bees, four lineages have independently reverted to radial symmetry—and
they have done so in two different ways. In Madagasikaria, CYC2A has expanded
its expression from the dorsal area to the entire flower (FIGURE 11.20B, left); in
Sphedamnocarpus, it is simply not expressed at all (FIGURE 11.20B, right).
These studies of tetrapod limbs and flower form are two examples of how
genetic changes in developmental processes deepen our understanding of how
adaptive diversity can evolve.
11.4 How Development Affects Morphological Evolution
Learning objectives
■ Describe what evolvability measures, and describe factors that influence it.
■ Describe modularity in terms of genetic correlations and how it
affects evolvability.
■ Describe how development can influence the direction of evolution of
a character and how that interacts with natural selection.
■ Describe how developmental constraints and genetic constraints
may be related to each other and how they can constrain the response
to natural
selection.
Futuyma Kirkpatrick
Evolution,
5e
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Development can affect the course of evolution of phenotypic traits in at least
three ways. It can facilitate adaptive evolution; it may bias the direction of change
by making some changes more likely than others; and it may constrain evolution—reduce or prevent some changes despite natural selection. In some cases,
constraint and bias are much the same thing, just as the closure of a road may
leave only one other route. These influences can also be described in terms of
genetics. We may find that a trait responds slowly to directional selection because
it is genetically correlated with another trait that is under strong stabilizing selection (Chapter 7). The genes have this effect via their influence on the development of the traits, so we can equally well call this a genetic constraint or a developmental constraint, an option that is especially appealing if we know something
about the developmental process.
Facilitation
A trait’s evolvability measures the speed at which it can evolve, especially under
directional selection [27, 79]. Characters that differ in additive genetic variance
differ correspondingly in evolvability, all else being equal (see Chapter 7). But
all else may not be equal, because a character may be genetically correlated
with other characters. Genetic correlations may constrain a character’s ability to
respond to directional selection, or they may enhance selection response if the
correlation points toward a new and better combination of character states. In
particular, functionally interacting features may be more evolvable if they are
genetically correlated.
The earliest evidence that functionally interacting structures may be especially strongly correlated was presented by the paleontologists Everett Olson
and Robert Miller, who called such a pattern “morphological integration” [52].
At about the same time, the Russian geneticist Raissa Berg found that correlations among flower structures and among vegetative structures were higher than
correlations between floral and vegetative elements [5]. Such observations led
others, such as the Austrian zoologist Rupert Riedl [61], to propose that natural
selection shapes genetic correlations, so functionally interdependent features
become more strongly integrated. Each combination of highly integrated features is referred to as a module. Population genetics models show that pleiotropic
correlations and modularity can be shaped by natural selection, so evolvability
itself can evolve [15, 26].
Phenotypic integration and modularity are prominent in the flowers of some
animal-pollinated plants [14]. Erin Bissell and Pamela Diggle [7] studied phenotypic and genetic correlations among flower dimensions in two closely related
species of wild tobacco, in which the petals are fused into a corolla tube and are
bent outward at the top into a limb (FIGURE 11.21). Nicotiana alata is pollinated
by long-tongued sphinx moths. N. forgetiana, with a shorter, wider throat of the
corolla tube, is pollinated by hummingbirds. Bissell and Diggle found high phenotypic and genetic correlations among all the measurements of the corolla limb,
but the limb measurements were not correlated with the length of the corolla. The
limb, therefore, is a distinct module, which is thought to attract pollinators. Similarly, the lengths of the corolla tube, pistil, and stamens are correlated, forming
an integrated module that makes the moth or bird deposit and pick up pollen.
The several measurements of the width of the corolla tube were highly correlated
only in the hummingbird-pollinated species.
The genetic correlations among traits are largely caused by pleiotropy, and the
different patterns of integration in different species suggest that pleiotropy can
evolve. This can happen if variation in one gene alters the effect of another locus
on two or more different characters [53]. A change of a transcription factor could
alter another gene’s expression in one developing characteristic but not another. A
mutation at the S locus in the butterfly Bicyclus anynana (see Figure 7.22) reduces
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11.4 How Development Affects Morphological Evolution 303
L1
L2
© Stephen Orsillo/Shutterstock.com
T2
L3
T3
T4
Corolla tube
T1
Limb
L4
Front view
T5
Lateral view
FIGURE 11.21
A flower of a wild tobacco (Nicotiana alata) showing four
measurements of the limb (front view) and five measurements of the corolla
tube (side view). Dimensions of the limb are more highly correlated with each other
than with those of the corolla tube, and vice versa. These correlations identify the
limb and corolla as different modules. (Diagrams after E. K. Bissell and P. K. Diggle.
2010. J Evol Biol 23: 1744–1758.)
colored spots (eyespots) only on the hind wing if the insect has a certain allele at
the R locus, but it has this effect on both the hind wing and the forewing in the
presence of a different R allele (FIGURE 11.22) [49]. The different R alleles affect
whether the forewing and hind wing spots develop and evolve as an integrated
unit or as independent characters.
Many studies of phenotypic variation support the ideas of modularity and
phenotypic integration. We would expect that when new functional relationships
among characteristics evolve, selection would shape new correlation patterns
[3, 35]. Serially homologous organs, such as the front and hind limbs of vertebrates, are based on similar developmental genetic pathways (e.g., the expression
of Hox genes; see Figure 11.12). Nathan Young and colleagues [85] found that
in quadrupedal monkeys, the front and hind limbs are rather similar in length,
and the lengths of corresponding parts are highly correlated. (This is shown in
FIGURE 11.23 by the correlations between different bones.) The corresponding
parts of the front and hind limbs are three modules. But apes use their front
and hind limbs quite differently; chimpanzees have long arms, used for traveling
between branches of trees. The correlations between corresponding bones in the
front and hind limbs are lower in apes than in monkeys: they appear less modular.
The reduced pleiotropic integration of front and hind limbs may have enabled
them to evolve more independently—including the evolution of the unique limbs
of humans, the only species of primate with much longer legs than arms.
(A)
(B)
FIGURE 11.22
Modularity of characters can evolve by
means of changes in pleiotropic effects. Locus S affects
S1
R1
S2
R1
S1
R2
S2
R2
the size of wing spots of a hypothetical butterfly. (A) In the
presence of allele R 1, the alleles S 1 and S2 have pleiotropic
effects on the forewing and hindwing; there appears to be
a single character, “spot size.” (B) The allele R 2 suppresses
the effect of S alleles on the forewing, so they affect only
the hind wing. Thus, the spot sizes are decoupled and are
distinct characters. This diagram is based on a genetic
study of wing spots in the butterfly Bicyclus anynana.
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Chapter 11
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FIGURE 11.23 Developmental
and functional modules in the
limbs of humans, chimpanzees,
and macaque monkeys. (A) Both
forelimbs (arms) and hind limbs
(legs) have three corresponding
sections, marking three developmental modules. (B) The diagrams
above the drawings show correlations in the length of bones in the
forelimb (left boxes) and the hind
limb (right boxes). These correlations are lower in apes (human,
chimpanzee) than in monkeys,
indicating lower modularity. (From
N. M. Young et al. 2010. Proc Natl
Acad Sci USA 107: 3400–3405.)
(A) Corresponding elements in
three developmental modules
Forelimb
(B)
Module
correlations
304
Hindlimb
Humerus/femur
H
H 0.80 F
H 0.81 F
H 0.86 F
R 0.79 T
R 0.68 T
R 0.90 T
MC
0.73 MT
Human
MC
0.77 MT
Chimpanzee
MC
0.91 MT
Macaque
F
Radius/tibia
R
T
MC
MT
Functional modules
Metacarpals,
metatarsals
Developmental bias
Evolutionary biologists have recognized for decades that certain morphological changes have occurred repeatedly, in parallel, within clades of animals and
plants. This may result from similar natural selection, or from development that
makes some phenotypes more likely than others (developmental bias). Pigmented bands or spots have evolved independently in many families of moths
and butterflies, only in certain locations on the wings (FIGURE 11.24). This is at
least partly because the developing wing of Lepidoptera is divided into fields,
to which changes in pattern are restricted [51]. The gene WntA, which encodes a
signaling protein, determines the boundaries of some fields and underlies some
of the diversity of wing patterns among butterflies [46]. Most cases of parallel
evolution probably result from natural selection, but development can affect the
kind of variation on which selection can act and so can make some kinds of
changes more likely than others [74].
Jukka Jernvall’s research group has shown experimentally that development
explains the relative sizes of molar teeth in the rodent family Muridae, which
includes the house mouse [33]. The sizes of the three molars decrease from front
Basal field
Pyralidae
Sphingidae
Geometridae
Nymphalidae
FIGURE 11.24 Distinct developmental
fields, highlighted by colored rectangles,
are thought to determine the locations
of pigmented bands that have evolved
repeatedly in many Lepidoptera (moths
ele- Kirkpatrick Evolution, 5e
and butterflies). The basic patternFutuyma
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of
Lepidoptera are shown.
Noctuidae
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From B. Rensch. 1959. Evolution above the Species
Level. New York: Columbia University Press
Tineidae
Border field
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11.4 How Development Affects Morphological Evolution 305
(M1) to rear (M3), in the same sequence in which the teeth develop. M2 and M3
both develop from the growing rear end of the anterior tooth bud (e.g., M2 from
M1). When researchers severed the end of M1, M2 developed faster and grew
larger, implying that M1 produces an inhibitor of M2 growth and accounts for
this tooth’s smaller size. The relative molar sizes in 29 species of Muridae correspond to the experimental data fairly well, suggesting that the size of their molar
teeth is partly determined by interactions between the teeth during development.
Constraints on adaptive evolution
A developmental constraint is “a bias on the production of various phenotypes
caused by the structure, character, composition, or dynamics of the developmental system” [45]. The two most common causes of developmental constraint are
(1) absence or paucity of genetic variation, including the lack of required genes,
proteins, or developmental pathways, and (2) strong correlations among characters, caused by pleiotropy. Thus, developmental constraint can be considered
a form of genetic constraint (see Chapter 7).
Developmental constraints can be revealed by embryological manipulations
in the laboratory. In a classic experiment, Pere Alberch and Emily Gale used the
mitosis-inhibiting chemical colchicine to inhibit digit development in the limb
buds of frogs (Xenopus) and salamanders (Ambystoma; FIGURE 11.25) [2]. The treatment consistently caused preaxial (front) digits to be lost in the frogs, and postaxial
(rear) digits to be lost in the salamanders. These results correspond to the different
order of digit differentiation in the two taxa: the last digits to form tended to be
the most affected by the colchicine treatment. Furthermore, the results strongly
reflected evolutionary trends: salamanders have often lost postaxial digits during
evolution, and frogs have repeatedly experienced preaxial digit reduction. We do
not know if postaxial reduction would ever have been advantageous to any frogs,
but natural selection would have had to overcome a developmental barrier.
Developmental studies are beginning to shed light on some constraints.
Among birds, swans have longer necks with more vertebrae than do ducks
and most other birds. But almost all mammals—from whales to giraffes—have
seven cervical (neck) vertebrae (see Figure 3.19). One might suppose that a giraffe
Control
Experimental
Four-toed salamander
From P. Alberch and E. A. Gale 1985. Evolution 39:
8–23; photos courtesy of the late P. Alberch
Missing toe
FIGURE 11.25
Evidence for developmental constraints. X-rays compare the
hind feet of two axolotl salamanders (Ambystoma mexicanum). The control shows the
normal five-toed condition. The experimentally treated axolotl received an inhibitor of
mitosis during the limb bud stage, and it lacks a toe. The normal hind foot of the fourtoed salamander (Hemidactyliumscutatum) has the same features as the experimentally treated foot of the axolotl.
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would profit from more. However, abnormal vertebrae in deceased human
fetuses and infants have been homeotically transformed: a cervical vertebra often
has the shape and ribs of a thoracic vertebra. These transformations are typically
associated with malformations of the skull and face and of the heart, lungs, and
other organs, evidently due to pleiotropy [72]. These harmful pleiotropic effects
may have constrained cervical vertebra evolution in mammals.
A remarkable fact, familiar to every entomologist, is that very slight differences in wing veins distinguish certain families of flies, bees, and many other
insects. These features have been constant within some families for more than 50
million years. David Houle and colleagues have described the long-term impact
of genetic developmental constraints on the evolution of shape and of wing vein
structure among all the major clades of Drosophila species, a 40-million-year-old
radiation (FIGURE 11.26) [8, 31, 32]. By scoring over 50,000 wings of D. melanogaster, they estimated the rate at which genetic variation in shape arises by new
mutations. They found that the amount of variation that has evolved among
species in 40 million years can be produced by mutation in only 187 generations.
They selected lab populations of this species for altered wing shape and in 25
(A)
(B)
Second vein
D. melanogaster
From D. Houle et al. 2017. Nature 548: 447–450
D. grimshawi
D. silvestris
(C)
FIGURE 11.26
Developmental genetic
constraint on the evolution of wing
shape in Drosophila. (A) With a wing of
11_EVOL5E_11.indd 306
Vein L2 length (loge mm)
D. melanogaster as a basis for comparison, the position of several landmarks
is shown for 11 species of Drosophila
that diverged from a common ancestor
about 40 Ma. (B) The range of wing shape
differences is illustrated by these three
species. Very little evolutionary change
has occurred. (C) A plot of wing vein length
versus wing size among species of Drosophila: the small lines show the slope
for various species. The frequency distribution shows the allometric slopes for
111 species. Slopes for lines of D. melanogaster that were selected for high and low
allometry are colored. These lab-selected
lines span the range of values that have
evolved among species.
Males
1.5
1.0
0.5
0.0
–0.5
0.8
1.0
1.2
Allometric slope
1.4
Wings from G. H. Bolstad et al.
2015. PNAS 112: 13285–13289
0.0
0.5
Wing size (loge mm)
1.0
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11.5 Phenotypic Plasticity, Canalization, and Genetic Assimilation 307
generations produced changes as great as the entire range of wing shape in the
genus. But when they relaxed (ceased) selection, wing shape evolved back to
the original state in only 16 generations. The alleles for altered wing shape must
have severely harmful pleiotropic effects, through their role in the development
of other, so far unknown, characteristics.
11.5 P
henotypic Plasticity, Canalization, and
Genetic Assimilation
Learning objectives
■ Define canalization and describe its possible causes.
■ Define genetic assimilation, describe how it evolves, and distinguish it
from the “inheritance of acquired characteristics” associated with
Lamarck (see Chapter 1).
■ Learn how to test the hypothesis that an evolutionary change in
a structure began as a nongenetic plastic change that became
genetically assimilated.
The correspondence between genotypic differences and phenotypic differences
depends not only on the effects of genotype, but also on environmental conditions that may affect the developmental expression of the genotype. This phenomenon, called phenotypic plasticity (see Chapter 7), is another way in which
development may affect the evolution of morphology.
If an environmental difference affects all genotypes in a population the same
way, the degree of phenotypic plasticity cannot evolve. In FIGURE 11.27A, each
line shows a different genotype’s reaction norm (see “Phenotypic plasticity,”
Chapter 7): its phenotypic expression (z 1 and z 2) in two environments E 1 and
E 2 (such as temperatures). If the optimal (fittest) phenotype in each environment is as shown by the small arrowheads, this population cannot evolve to
the optimum. When the effect of environmental differences on the phenotype
differs from one genotype to another in a population, as in FIGURE 11.27B, the
reaction norms of the genotypes are not parallel, and the phenotypic variance
Optimal phenotype
in each environment
(C)
Reaction norm
of a genotype
E1
E2
Genotype with the
norm of reaction
closest to the optima
in environments
Phenotype
z2
Phenotype
z1
(B)
E1
11_EVOL5E_11.indd 307
Environment
E2
35
Mean number of bristles
Phenotype
z2
Phenotype
z1
(A)
FIGURE 11.27 Genotype × environment interaction
and the evolution of reaction norms. (A) Each line
30
25
14°
21°
Temperature
represents the phenotypes expressed by a genotype in
environments E 1 and E 2 . All the genotypes are affected
by the difference between environments in the same
way. (B) The effect of environment on phenotype differs
among genotypes. The genotype with the norm of reaction closest to the optima in environments E 1 and
E 2 (dashed line) will be fixed. (C) The number of bristles
on the abdomen of male Drosophila pseudoobscura of
ten genotypes, each reared in environments E 1 (14°C)
and E 2 (21°C). The variation indicates a G × E interaction.
(C after A. P. Gupta and R. C. Lewontin. 1982. Evolution
36: 934–948.)
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includes a component (referred to as VG × E) that is due to genotype × environment
(G × E) interaction. This was found to be the case in Drosophila pseudoobscura: the
effect of temperature on the number of bristles differed among ten genetic strains
(FIGURE 11.27C). In this case, selection could change the average degree of phenotypic plasticity, and the population might evolve toward an optimal phenotype
for each environment (as shown by the red line in Figure 11.27B).
Some environmentally determined phenotypic effects are not adaptive and
may be maladaptive: poikilothermal (“cold-blooded”) organisms grow more
slowly at lower temperatures, and most of us will weigh more if we eat too much.
In many species, however, natural selection has resulted in norms of reaction
that yield nearly optimal phenotypes for the various environments the organism
commonly encounters [65, 83]. Phenotypic plasticity includes rapidly reversible
changes in morphology, physiology, and behavior, as well as “developmental
switches” that cannot be reversed during the organism’s lifetime (FIGURE 11.28).
Plastic changes in phenotype often result from changes in gene regulation. These
changes have various causes; for example, environmental stimuli often alter levels of hormones or other signaling molecules that affect levels of transcription
factors, which in turn alter the expression of downstream genes [25].
For many characteristics, the most adaptive norm of reaction is a constant
phenotype, buffered against alteration by the environment (as shown by the red
line in Figure 11.27B). It may be advantageous for an animal to attain a fixed body
size at maturity or metamorphosis, despite variations in nutrition or temperature
(A)
Spring
Catkins
Nemoria arizonaria that hatch in the spring
resemble the oak flowers (catkins) on which
they feed. Larvae that hatch in the summer
feed on oak leaves and resemble twigs.
(B) The form of a leaf of the water-crowfoot
Ranunculus aquatilis depends on whether
it is submerged, aerial, or at the air-water
interface during development. (B from
C. D. K. Cook. 1968. In Modern Methods in
Plant Taxonomy, V. Heywood [ed.], pp. 97–111.
Academic Press, London.)
11_EVOL5E_11.indd 308
Courtesy of Erick Greene
FIGURE 11.28 Examples of phenotypic
plasticity. (A) Larvae of the geometrid moth
Summer
Caterpillars
(B)
Submerged
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Aerial
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11.5 Phenotypic Plasticity, Canalization, and Genetic Assimilation 309
that affect the rate of growth. The developmental system underlying the character may then evolve such that it resists environmental influences on the phenotype [64]. This idea was developed independently by the Russian biologist
Ivan Schmalhausen [67] and by British developmental biologist Conrad Waddington, who referred to it as canalization [16]. Waddington used the concept of
canalization to interpret some curious experimental results [76]. A crossvein in
the wing of Drosophila sometimes fails to develop if the fly is subjected to heat
shock in the pupal stage. By selecting and propagating flies that developed a
crossveinless condition in response to heat shock, Waddington bred a population
in which most individuals were crossveinless when treated with heat. But after
further selection, a considerable portion of the population lacked the crossvein
even without heat shock, and this condition was heritable. A character state that
initially developed in response to the environment had become genetically determined, a phenomenon that Waddington called genetic assimilation.
Although this result is reminiscent of the discredited theory of inheritance of
acquired characteristics, it has a simple genetic interpretation. Genotypes of flies
differ in their susceptibility to the influence of the environment (in this case, temperature): some are more easily deflected into an aberrant developmental pattern.
Selection for this developmental pattern favors alleles that channel development
into the newly favored pathway. As such alleles accumulate, less environmental
stimulus is required to produce the new phenotype. The finding that genetic assimilation does not occur in inbred populations that lack genetic variation supports this
interpretation [64]. We encountered an example of genetic assimilation in natural
populations in Chapter 7 (see Figure 7.24). In high-elevation lakes, exposed to high
levels of ultraviolet radiation (UV), the crustacean Daphnia melanica can develop
a range of low to high pigmentation, as a protection against UV. But dark color
makes the Daphnia more visible to fish, and two populations in lakes with recently
introduced trout show constitutive (unchanging) expression of the low-melanin
(light) phenotype. The expression of two genes that affect melanin production is
less affected by UV in these populations than in ancestral populations. An environmentally inducible expression has become genetically determined [68].
Constancy of a feature is likely to have evolved by canalizing selection in
many cases. For instance, the floral structures of some animal-pollinated plants,
in which successful pollination often depends on a close fit to the animal’s
mouthparts, are less variable than leaves are [54].
Does phenotypic plasticity contribute to evolution?
In the modern theory of adaptive evolution by natural selection among inherited
phenotypes, reaction norms evolve by selection of mutations (e.g., in regulatory
elements or hormone levels) that differentially affect the expression of the trait in
different environments. Mary Jane West-Eberhard [83] and some other biologists
advocate a somewhat different process. They suggest that a new environment
can often induce the prevalent genotype to express a novel beneficial phenotype,
by phenotypic plasticity. The expression of this phenotype, they suggest, can
subsequently become canalized (i.e., genetically assimilated) by natural selection
[38, 66, 39, 56]. The feature may later be further modified by mutation and natural
selection, which West-Eberhard has called “genetic accommodation.” According
to this “plasticity-first” hypothesis, phenotypic plasticity paves the way for standard evolution by selection of advantageous mutations that affect the feature.
Tadpoles of spadefoot toads appear to be an example of this process [40]. Like
most anuran larvae, spadefoot tadpoles in the genus Scaphiopus eat only a diet of
detritus. But in their sister genus Spea, larvae feed either on detritus or on a carnivorous diet of shrimp and other animal food. Eating shrimp has strong effects
through phenotypic plasticity. Carnivorous tadpoles develop features adapted
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to an animal diet, including larger jaw muscles and fewer toothlike structures
(denticles) that are used to process detritus (FIGURE 11.29). In an experiment,
Scaphiopus tadpoles (which normally eat only detritus) were raised on shrimp.
The number of denticles decreased, as they do in Spea. Thus the plasticity that
enables Spea to develop some traits adapted to carnivory was already present,
even if not used, in the common ancestor of Spea and Scaphiopus. However, not
all features of the carnivore morph developed in Scaphiopus fed shrimp: Figure
11.29 shows that their jaw muscle mass decreased, instead of increasing. So the
developmental reaction of jaw muscles to the carnivorous diet in Spea evolved
after the two lineages diverged.
When a population experiences a new environment, an environmentally
induced alteration of the phenotype may or may not be in the right direction
[18]: shrimp-fed Scaphiopus tadpoles developed smaller rather than larger jaw
muscles. Similarly, most of the changes in gene expression in yeast that are grown
in novel environments are deleterious and are compensated by selection of mutations that revert the phenotype back toward its former state [30]. Genetic compensation for disadvantageous plasticity sometimes produces countergradient
variation among geographic populations of a species: genetic differences opposite
(A)
Jaws of
carnivore
From N. A. Levis and D. W. Pfennig.
2020. Evol Dev 22: 71–87
1 mm
Omnivore (eats detritus)
Carnivore (eats shrimp)
(B)
Scaphiopus
Spea
vore morphs of Spea, which develop
depending on whether they eat mostly
detritus or animal prey such as shrimp.
(B) The traits that develop on a diet of
detritus (D) or shrimp (S) in Scaphiopus,
which normally eats only detritus, and
Spea, which can feed on detritus or
animals such as shrimp. If Scaphiopus
is forced to feed on shrimp, some plastic changes in the head (such as in the
number of denticle rows) are advantageous, but other changes (such as in
jaw muscle size) are not.
11_EVOL5E_11.indd 310
Number of
denticle rows
1 mm
17
15
14
13
10
1 mm
Relative jaw
muscle size
FIGURE 11.29 Ancestral and derived
phenotypic plasticity in spadefoot
toads. (A) The omnivore and carni-
From N. A. Levis et al. 2018. Nat Ecol Evol 2: 289–1297
18
D
11
S
0.02
0.04
0
0
–0.02
D
S
–0.04
D
S
D
S
Diet
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11.5 Phenotypic Plasticity, Canalization, and Genetic Assimilation 311
(A)
0.006
0.004
Montane
0.002
0.001
Lowland
13
18
23
28
33
Temperature (°C)
(B)
0.4
© Sharon Cummings/Dembinsky Photo
Associates/Alamy Stock Photo
Growth rate
0.01
Differentiation rate
0.1
0.06
Montane
FIGURE 11.30 Populations of green
frogs (Rana clamitans) at different
elevations have evolved different
norms of reaction that compensate
for differences in environmental
temperature. The tadpoles’ rate of
growth in size, in relation to the temperature at which they were reared
in the laboratory, for individuals from
a montane population (blue circles)
and from a lowland population (red
squares). (B) The rate of tissue and
organ differentiation in the same
experimental groups. At low temperatures, the montane population has
higher rates of growth and differentiation than the lowland population, compensating to some extent for the effect
of low temperatures on rates of biological processes. (After K. A. Berven et al.
1979. Evolution 33: 609–623.)
0.01
0.006
0.004
Lowland
0.002
0.001
13
18
23
28
33
Temperature (°C)
to the phenotypic effect of environmental differences [12, 24]. For instance, low
temperatures reduce the rate of growth and development in plants and ectothermic animals. The tadpoles of populations of green frogs (Rana clamitans) at cold,
high elevations develop more rapidly than do those from warmer lowlands,
when both are reared at low temperatures in the laboratory (FIGURE 11.30) [6].
The high-elevation populations have genetically faster growth that compensates
for the slower growth caused by their environment. Thus, both advantageous
and disadvantageous phenotypic plasticity can create opportunity for natural
selection and adaptive evolution [19].
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What We Don’t Know
Are similar (convergent) evolutionary changes more often based on changes in
the same or different genetic networks?
● Do pleiotropic genes generally affect characters within the same developmental
pathway? Or characters with the same function? How can we determine whether
patterns of pleiotropy have been shaped by natural selection?
● What developmental processes cause canalization? Can canalization be
reduced, and is this often advantageous?
● What determines whether morphological integration facilitates or
constrains evolution?
● How can phenotypic plasticity retard or enhance response to directional natural
selection? Which effect is more common?
● How commonly do phenotypic changes arise first as plastic reactions to the
environment and later become genetically assimilated and canalized? Are
certain kinds of traits more likely than others to do so?
●
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SUMMARY
■ Evolutionary developmental biology (EDB)
seeks to integrate data from comparative
embryology and developmental genetics
with morphological evolution and population
genetics and to determine how changes
in genes are expressed as changes in
phenotypes. Understanding the molecular
mechanisms by which genes, together with
environment, produce phenotypes helps us
understand how phenotypes evolve. The
mechanistic proximal causes of phenotypes
complement the ultimate causes of allele
frequency change, such as natural selection,
in our understanding of the evolution of form.
■ Many differences among species are due to
heterochronic and allometric changes in the
relative developmental rates of different body
parts or in the rates or durations of different
life history stages. Some characteristics have
evolved by heterotopy, based on gene expression at a novel location on the body. The
modularity of morphogenesis in different body
parts and in different developmental stages
facilitates such changes.
■ The vast diversity of multicellular eukaryotes
is largely due to diverse uses of a tool kit of
genes and developmental pathways that are
conserved across wide phyletic ranges.
■ Morphological variation within and among species may be caused by changes in gene regulation. Among several mechanisms, changes in
regulatory sequences play a large role.
■ A gene may have many cis-regulatory elements
(enhancers) that bind different transcription
factor proteins and can be expressed in diverse
tissues or at different times in development.
Changes in their interactions with transcription
factor genes can alter the time and place of
their activity. Evolution of the coding sequence
of a transcription factor can change its developmental function.
■ During evolution, genes and developmental
pathways have often evolved to serve additional new functions, a process that is probably
responsible for the evolution of many novel
morphological traits. This process results
from evolutionary changes in functional connections between transcription factors and
cis-regulatory elements.
■ Evolutionary changes in tetrapod limbs and
flower symmetry are among the best understood examples of the developmental genetic bases for the evolution of morphological
diversity.
■ Pleiotropic effects of genes that affect functionally interacting characteristics may evolve,
resulting in the evolution of functional modules
(morphological integration). Modularity helps
different parts of the body develop divergent
morphologies (e.g., differences among segments) and can facilitate evolution by directional selection. Genetic and developmental
processes, such as pleiotropic connections
among characters, can bias the direction of
evolutionary change or constrain the response
to selection.
■ A single genotype may be expressed as an array
of different phenotypes, the genotype’s norm
of reaction. Reaction norms are genetically
variable and so can evolve to increase phenotypic plasticity or to develop a constant, canalized phenotype. Genetic assimilation is the
genetic fixation of one of the states of a phenotypically plastic character. In some instances,
adaptation may occur first by a nongenetic
phenotypic change that later becomes genetically assimilated by natural selection. How
common this process has been is not known.
SUGGESTIONS FOR FURTHER READING
An introduction to evolutionary developmental biology, emphasizing regulation of gene
expression, is the second edition (2005) of
From DNA to Diversity: Molecular Genetics
and the Evolution of Animal Design by Sean
B. Carroll et al [10]. David L. Stern provides a
clear exposition of developmental pathways
and their evolution in Evolution, Development,
& the Predictable Genome (2011) [69]. Also see
M. Rebeiz et al. (2015) “Unravelling the tangled skein: The evolution of transcriptional
regulatory networks in development” [58].
One of the leading textbooks on developmental
biology is Developmental Biology by Michael
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J. F. Barresi and Scott F. Gilbert (2020) [4].
In Ecological Developmental Biology (2009),
Scott F. Gilbert and David Epel treat the evolution and ecology of phenotypic plasticity
and related topics [20].
Rudolph A. Raff, one of the founders of modern
EDB, portrayed the field at an early stage in
his influential 1996 book The Shape of Life:
Genes, Development, and the Evolution of
Animal Form [57]. Eric H. Davidson, a developmental biologist whose insights helped shape
the field, did much the same in The Regulatory
Genome: Gene Regulatory Networks in Development and Evolution (2006) [13].
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Summary
An introduction to the evolution of modularity
is the 2007 article by G. P. Wagner et al., “The
road to modularity” [81]. G. P. Wagner’s book
Homology, Genes, and Evolutionary Innovation
313
(2014) describes how developmental genetic
networks may underlie homology of features
among species, and the origin of novel characteristics [78].
PROBLEMS AND DISCUSSION TOPICS
1. Haeckel’s statement that “ontogeny recapitulates phylogeny” differs from what we now
call von Baer’s law—the generalization that
features common to a higher taxon often appear earlier in development than the specific
characters of lower-level taxa. Compare and
contrast these two ways of thinking about
changes in development over the course of
animal evolution.
2. If two allometrically related traits show a
strong correlation both within and among species, what kinds of experiments would you use
to test whether these correlations are due to
natural selection or to developmental genetic
constraints? (Assume the organisms of interest are easily amenable to laboratory study.)
11_EVOL5E_11.indd 313
3. How might differential expression of and
regulation by Hox genes contribute to mosaic evolution in which different segments
of an animal body plan evolve different
morphologies?
4. If mutations such as those of the Ubx gene can
drastically change morphology in a single step,
why do most evolutionary biologists maintain
that modification of existing traits and the
evolution of novel characters have generally
proceeded by successive small steps?
5. Almost all extant animals have Hox genes that
are involved in body patterning. Why do organ­
isms that are incredibly distinct morphologically, with completely different body plans, use
so many of the same patterning genes?
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UNIT IV
Interactions
Between
Individuals
and Species
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12
All About Sex
M
any of nature’s greatest wonders involve sex. It is impossible not to
be astonished by the mating display of a male peacock. Plants are
equally remarkable: the flowers of an orchid are to the plant what the
peacock’s feathers are to the bird.
Our group of animals, the primates, has its own set of sexual curiosities.
Different species have penises that vary dramatically in shape and size.
Several have spines. In fact, our recent evolutionary ancestor had a spiny
penis. That can be inferred from a chromosome deletion found in humans
but not other primates that removes a regulatory element needed for
development of the spines [43]. Across the animal kingdom, male genitalia
are among the most rapidly evolving traits [22].
These examples immediately inspire questions. Why do males and females
often look so different? Why do organisms have such diverse ways of mating?
And since sex isn’t necessary for reproduction—there are asexual fishes,
lizards, and plants (such as dandelions)—why did sex evolve in the first place?
Sexual displays are among the most remarkable sights in nature. Here a male flame bowerbird
(Sericulus ardens) impresses a female with his remarkable display. She is observing from
a bower (not a nest), which is an elaborate structure he built to attract females.
Courtesy of Mark Kirkpatrick
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Chapter 12
All About Sex
12.1 The Astonishing Diversity of Ways That
Organisms Have Sex
Learning objectives
■ Compare and contrast several ways that organisms have sex.
■ Describe what defines females and males and what sexual dimorphism is.
What is sex? To an evolutionary biologist, sex is not just the act of copulation.
Rather, it is what copulation and a variety of other mechanisms do to the genes
that organisms carry. As we discussed in Chapter 4, genes in eukaryotes are
mixed during meiosis by two processes: segregation and recombination. In prokaryotes, genes are mixed by horizontal gene transfer. It is the mixing of genes
that is the essential feature of sex.
Animals and plants accomplish this mixing in a staggering diversity of ways
(FIGURE 12.1). Many species are hermaphroditic: each individual has both male
and female gonads, and mating involves exchanging eggs and sperm (in animals) or mutual pollination (in plants). When a pair of leopard slugs mate, their
intimate moment begins as they hang together from a long thread of mucus. Each
of them extrudes its penis, and the two fertilize each other’s eggs simultaneously.
Species in which individuals have both male and female gonads at the same time
are called simultaneous hermaphrodites.
Most flowering plants are simultaneous hermaphrodites that (like the leopard slugs) use a variety of adaptations to avoid fertilizing themselves. But since
plants cannot go in search of partners, many enlist help from the wind or from
animals such as bees, bats, and birds that pollinate the plants while visiting their
flowers for nectar. Orchids have evolved elaborate features to attract their pollinators, in some cases exploiting those animals’ own sexual instincts. Orchid bees
(B) Clownfish
© Richard Packwood/Getty Images
© Design Pics Inc/Alamy Stock Photo
(A) Leopard slugs
Male
Female
(C) Cactus
(D) E. coli
FIGURE 12.1
© Dr. L. Caro/Science Source
are hermaphrodites—simultaneously male and female.
They are copulating while hanging from a mucus thread.
(B) Clownfishes (genus Amphiprion) are sequential hermaphrodites that first mature as males and later change
sex to become females. (C) The cactus Epithelantha
micromeris has flowers that do not open. It is one of
the few species that reproduce almost entirely by selffertilization. (D) Bacteria, such as these E. coli, use
several mechanisms to exchange DNA.
© farmer images/Getty Images
Sexual reproduction is remarkably
diverse. (A) This pair of leopard slugs (Limax maximus)
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12.1 The Astonishing Diversity of Ways That Organisms Have Sex 319
© Cyril Ruoso/Minden Pictures
Orchid
Bee
turn the tables on the orchids: males collect the flowers’ scents
and use them as their own sex pheromones (chemical signals)
to attract female bees (FIGURE 12.2).
The slipper shell is a snail that lives in a stack of several
individuals (see Figure 14.15). A young snail settles on the top
of a stack and matures into a male. All the snails beneath him
in the stack are also male except the one at the very bottom,
which is female. When she dies, the male immediately above
her, which is the largest and oldest male in the stack, changes
sex to become the new female. Other species that switch sex
include the famous clownfishes (Figure 12.1). Like the slipper
shell, clownfishes live in groups that consist of a single female
and several males. When the female dies, the most dominant
male changes sex to become the group’s female. Species in
which individuals are first one sex, then change later in life to
the other sex are called sequential hermaphrodites.
As you well know, there are many species that are not hermaphrodites. Different ecological conditions favor the evoluFIGURE 12.2 Many animals use
tion of separate sexes (as in mammals) or hemaphroditism.
sex pheromones to attract mates.
An important factor favoring hermaphroditism is reproductive assurance, which
In tropical America, male orchid bees
collects scent from certain flowers
is the increased chance of successful reproduction when potential mates are rare
Futuyma Kirkpatrick Evolution,
5e it as their sex pheromone. Here
and use
(or even absent). When finding a reproductive partner is difficult,
there
is
an
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a
brilliantly
colored Euglossa imperialis
advantage for individuals to be simultaneously male and female. This
condition
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collects
scent
from an orchid flower in
allows them to mate with any other individual they encounter, or (in some speCosta Rica.
cies) even with themselves. The need for reproductive assurance is one reason
why plants, which cannot actively search for a mate, are hermaphroditic more
often than animals and, in many species, can fertilize themselves.
What are females and males?
In plants, animals, and some other eukaryotes, the gametes of a species come
in two sizes. One gamete, the egg, is large and immobile. The other, the sperm,
is small and mobile, either actively or passively. This dimorphism in gamete
size, which is called anisogamy, likely evolved because there are two ways that
a gamete can have high fitness. It can succeed by being large and well provisioned, which inhibits movement. Alternatively, it can succeed by being small
and mobile, which enhances its ability to find and fertilize a large immobile gamete. This difference in size leads to a difference in number: females typically make
far fewer eggs (or ovules) than males make sperm (or pollen). The difference in
gamete size is so basic that we use it to define the two sexes. Males are the sex
that makes small gametes, and females are the sex that makes large ones. This
distinction holds for hermaphrodites as well: the testis (or anther, in plants) is
the gonad that makes sperm (or pollen), and the ovary is the gonad that makes
eggs (or ovules).
In many animals and some plants, individuals are either female or male. The
two sexes of some animals are so similar that the only way they can be distinguished is by their genitalia. But in other species, including humans, many
traits are sexually dimorphic, meaning they are expressed differently in males and
females. One of the most spectacular examples of sexual dimorphisms is found
in fishes that live deep in the world’s oceans [53]. A female anglerfish attracts
her prey with a bioluminescent lure that she dangles in front of her enormous
mouth. But even more bizarre are the small growths attached to the female’s
body (FIGURE 12.3). These are male anglerfish. When young, a male swims freely
until he finds a mature female. He bites onto whatever part of her he can, and
then he never lets go. Their circulatory systems fuse, and almost all of the male’s
organs—eyes, gills, digestive system—degenerate. One organ, however, grows
much larger: when the metamorphosis is complete, the male’s body is almost
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Chapter 12
All About Sex
Anglerfish
FIGURE 12.3
Anglerfishes (suborder Ceratioidei)
have extreme sexual dimorphism. This female has
♀
© Danté Fenolio/Science Source
a male attached to her.
♂
entirely filled by his testes. To mate, the female releases her eggs and the male
his sperm, which fertilize the eggs in the surrounding water.
Sexual dimorphism can evolve when selection favors different expression of
a trait in males than females. This situation is called sexually antagonistic selection
because an increase in the trait’s expression benefits one sex but harms the other.
Genetic constraints (see Chapter 7) sometimes prevent a trait from evolving sexual dimorphism, and so the fitnesses of both sexes are permanently compromised
[55]. For example, we will see later in the book that in some human populations,
males and females are under selection to be even more different in height than
they already are (Figure 20.21). The difference in height is not rapidly increasing,
though, because
almost
all alleles
that
Futuyma
Kirkpatrick
Evolution,
5e affect height either increase it or decrease
it in both sexes.
Sexually antagonistic
selection seems to act on a large number of
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genes in several
organisms, including
humans [18]. In Chapter 13 we’ll see that
males and females have sometimes evolved traits that increase their own fitness
at the expense of greater mortality of their mates.
Microbial sex
Even bacteria and viruses have sex of a sort. Although they lack the form of sex
based on meiosis that is familiar to us from animals and plants, they exchange
genes using a variety of mechanisms that together are called parasexuality [54].
Some bacteria pass DNA between each other by conjugation (Figure 12.1). Others
can absorb naked DNA left in the environment by bacteria that have died. When
several viral particles infect the same host, progeny viruses are produced that
carry mixtures of their genotypes. These different processes result in the genetic
mixing that is the fundamental evolutionary effect of sex.
12.2 What Is Sexual Selection?
Learning objectives
■ Describe experiments that show that male display traits decrease survival
but increase mating success.
■ Discuss why males are more often sexually selected than females,
and give an exception to that rule.
It is easy to understand how some traits have evolved to become sexually dimorphic. An individual without gonads will leave no genes to the next generation.
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12.2 What Is Sexual Selection? 321
Displaying
male widowbird
2
Mean number of nests per male
Gonads and genitalia are called primary sexual traits. But Darwin pointed out that
other kinds of traits, such as the male peacock’s spectacular train of feathers, are
more difficult to explain. These are secondary sexual traits, which differ between
the sexes but do not play a direct role in reproduction. A male peacock’s spectacular feathers make flight more difficult and attract predators, so we might expect
them never to have evolved by natural selection. Interestingly, secondary sexual
traits are often among the most rapidly evolving phenotypic characters. In some
groups of animals, they are the only traits that can reliably distinguish species.
Darwin reasoned that secondary sexual traits must evolve by something other
than selection for survival and the production of gametes. He made several key
observations. Traits with extreme sexual dimorphism tend to be exaggerated
much more often in males than in females. Often these traits are not expressed
in immature males. Most important, these traits are used in the mating season
when males interact aggressively with other males and court females.
These observations lead Darwin [20, 21] to propose that these traits have
evolved by sexual selection, which is selection caused by competition for mates
among individuals of the same sex. A male that prevents other males from mating will leave more copies of his genes to the next generation than they will.
Likewise, a male that attracts many females to mate with him will be more genetically successful than a male that attracts few or none. Sexual selection can cause
the evolution of traits that decrease survival if the reproductive advantage they
produce compensates for that cost. In short, a trait can evolve by sexual selection
if it increases a male’s overall fitness, even if it decreases survival.
Sexual selection is one of Darwin’s most ingenious ideas. He developed it
without the benefit of any data showing whether these traits in fact do decrease
survival but increase male reproductive success. Was Darwin right? Many studies since his time have verified that secondary sexual traits have exactly the
effects that he predicted.
A simple and elegant experiment with the long-tailed widowbird shows that
the male’s extremely long tail attracts females [2]. Males establish territories on
the African savannah. One or more females mate with a male that attracts them,
and then they nest on his territory. In the experiment, males were captured and
divided into four groups. The tail feathers of the first group were cut to half their
original length. The clipped feathers were glued onto the tails of males in the
second group, increasing their tail length by 50%. The third and fourth groups
were controls: in one there was no manipulation, and in the other the tails w