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TRENDS in Ecology and Evolution
Vol.22 No.4
Why evolutionary biologists should be
demographers
C. Jessica E. Metcalf1,2 and Samuel Pavard1
1
2
Max Planck Institute for Demographic Research, Konrad-Zuse Str. 1, D-18057, Rostock, Germany
Department of Biology, 139 Biological Sciences Dr, Duke University, Durham, NC 27707, USA
Evolution is driven by the propagation of genes, traits
and individuals within and between populations. This
propagation depends on the survival, fertility and dispersal of individuals at each age or stage during their life
history, as well as on population growth and (st)age
structure. Demography is therefore central to understanding evolution. Recent demographic research provides new perspectives on fitness, the spread of
mutations within populations and the establishment
of life histories in a phylogenetic context. New challenges resulting from individual heterogeneity, and
instances where survival and reproduction are linked
across generations are being recognized. Evolutionary
demography is a field of exciting developments through
both methodological and empirical advances. Here, we
review these developments and outline two emergent
research questions.
Introduction
Demography, the study of survival, fertility and population
dynamics, is a crucial tool for evolutionary biologists. In
particular, survival and fertility at each age or life-history
stage determine offspring production, which defines fitness. However, the situation is complex: fitness can be
estimated for a single individual, a subpopulation of individuals sharing a genotype or phenotype, or an entire
population, in constant or fluctuating environments and
in density-dependent or independent contexts (Table 1).
This diversity makes the appropriate fitness measure a
difficult topic. It also complicates the ultimate goal of
evolutionary ecology, that is, to understand the establishment of a given life history and life-history diversity across
species. Although more rarely considered, complications
also emerge, because spatial structure (dispersal) and
genetic drift might also affect life-history evolution. Recent
work also indicates that the heterogeneity of survival and
fertility among individuals and across generations can
profoundly modify estimates of individual fitness.
This brief overview makes understanding life-history
evolution seem dauntingly complex. However, the different sources of complexity are theoretically and empirically
exciting and shed new light on where and why demography matters in evolution. The recent availability of large
long-term data sets, enabling estimation of survival and
fertility across (st)ages in several species [i.e. (st)age
Corresponding author: Metcalf, C.J.E. (cjm29@duke.edu).
Available online 13 December 2006.
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trajectories of survival and fertility], has led to a flurry
of demographic modelling and empirical exploration of
several evolutionary theories, addressing, for example,
how variation in stage trajectories of survival and fertility
across individuals and between years drives selection on
timing of reproduction in monocarpic plants [1]; when
variation in stage trajectories between years selects for
buffering of individual variation in stage trajectories in a
perennial plant [2]; when covariation between fitness
components affects selection on age trajectories in red
Glossary
Adaptive dynamics: extends invasibility to consider the long-term outcome of
a selective process [the evolutionarily stable strategy (EES)] and the process
involved in attaining this outcome (mutation and iterative invasion).
Effective population size (Ne): the size of an ‘ideal’ (stable, random mating)
population that results in the same degree of genetic drift or inbreeding as
observed in the actual population.
Ergodic: the dynamic of a population when, after an interval of time, the
population aysmptotically reaches a stable (st)age distribution and a
correspondingly constant population growth rate, which is independent of
the initial (st)age distribution.
Hierarchical Bayes: a set of statistical tools using relationships between
conditional probabilities defined by Bayes Rule to break complex statistical
distributions into sets of interdependent distributions for which parameters
can be obtained iteratively using Markov Chain Monte Carlo approaches. It is
often used to model data resulting from complex underlying processes, such
as interdependence in demographic rates.
Integral projection models: analogous to matrix population models, but can be
based on a continuous rather than discrete population structure (such as size
rather than life-history stage). Population structure is expressed as a density
function, so that inclusion of variance within or across individuals is
straightforward and can be directly related to standard statistical techniques.
All population measures available from matrix population models can also be
obtained from integral projection models.
Invasibility analysis: resembles optimization, except that instead of comparing
fitness measures, a population context is modelled. Repeated invasion of a
chosen resident strategy (which sets some component of the environment for
the invader) is used to identify the strategy that cannot be invaded by any
other, or the ESS. Invasion is considered successful for strategies that can
‘invade when rare’. Successful invasion is identified using invasion exponents
(Table 1).
Matrix population models: can be stage or age-based and enable calculation of
R0, l, ls, or invasion exponents (Table 1), as well as their stage- or age-specific
sensitivities or elasticities; or sensitivity or elasticity to underlying parameters
[56,7]. Other population characteristics, such as the stable (st)age structure or
reproductive value, are available.
Optimization: involves defining a fitness measure and then locating the
strategy or trait that maximizes it given a set of constraints.
Perturbation analysis: involves calculating changes in fitness corresponding to
a relative (i.e. elasticity) or absolute (i.e. sensitivity) change in a life-cycle
component. This enables quantification of the relative or absolute contributions of a life-cycle component or life-cycle path to population growth rate and
thereby fitness.
‘Transient’: term used to define population dynamics experienced by
populations when the (st)age distribution fluctuates unstably. Transient
fluctuations in (st)age structure can persist indefinitely in non-ergodic systems.
The appropriate fitness measure for transient phases of population dynamics
is still unclear.
0169-5347/$ – see front matter ß 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tree.2006.12.001
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TRENDS in Ecology and Evolution Vol.22 No.4
deer [3]; and how individual contributions to population
growth rate from survival and fecundity can be estimated
in Soay sheep [4].
Such theoretical and empirical developments make it
timely to review how new demographic research clarifies
evolutionary questions. In particular, we address here how
changes in (st)age trajectories of survival and fertility
affect (i) fitness; (ii) the spread of a mutation within a
population; and (iii) the dynamic interplay between age
trajectories and selection pressures that lead to the estab-
lishment of a particular life history over many generations.
We then choose to discuss two major challenges in evolutionary biology where a demographic perspective is essential. First, (st)age trajectories vary across individuals and
environments, which hampers the accurate inference of
(st)age trajectory parameters and can alter evolutionary
outcomes. Second, survival and reproduction might be
linked across generations, which has major implications
for the evolution of parental care. Both cases call for
progress in the development of more complex models.
Table 1. Different fitness measures, both for direct comparison and in a population context (invasion)
Fitness measure Formula
I. Stable population theory (based on ergodic assumptions)
Constant environments
X
Comparison Net
R0 ¼
l mx a,b
x x
X
reproductive
T ¼
xl mx a,b
x x
rate, R0, and
generation
time, T
Root of the Euler–Lotka equation:
Finite rate of
X
increase, l
lx l x mx ¼ 1
Invasion
Invasion
exponent, W
Fluctuating environments
Comparison Stochastic
growth rate, lS
Invasion
Invasion
exponent, W
Similar to l but where lx or mx
might be functions of population
size N or population size in a
(st)age class
loglS ¼ lim
t !1
# ¼ lim
t !1
1
E ½logN f
t
1
E ½logN i f
t
II. Unstable population theory (no ergodic assumptions)
N tþ1 ztðiÞ h
Comparison De-lifing
P ti ¼ w t Nt 1
Transient
sensitivities i
Invasion
Transient
sensitivities i
Where appropriate
Refs
Average number of offspring born to
individuals during their lifetime; or
factor by which the size of the
population is multiplied after one
generation of length T
Finite increase in population size per
unit of absolute time
Constant environments
[43,56]
As above, but where A also
depends on some aspect of
population size N
Non-overlapping
generations
Rate insensitive c
Constant environments
Overlapping or nonoverlapping generations
Rate sensitive c
As for l d
[43,56]
Stochastic or periodic equivalent of l,
obtained numerically by iteration of
matrix multiplication, where matrices
for every time-step t are defined
by environmental conditions
Rate of invasion of a rare invader
into an environment set by both
fluctuations in the environment,
and the dynamics of a resident
strategy (acting through frequency
or density dependence)g
Fluctuating environments
[2,9,
45–
46,56]
Fluctuating environments
[56]
Statistical estimate of the
contribution of an individual
to population growth
Accounts for non-ergodic [4]
population dynamics in
fluctuating environments
Assumes individuals are
independent (i.e. cannot
include frequency or
density dependence)
Accounts for non-ergodic [7]
population dynamics in
fluctuating environments
Requires, and is dependent
on, a definition of
population structure at t0
Has no single fitness
definition, or synthetic
measure
As above, but where the
[7]
population context is
important
Finite rate of increase of an invader
[56,59,
into an environment set by a resident
60]
strategy affecting the invader through When density or frequency
density or frequency dependence
dependence are operating e
dvecA j Captures the response of a chosen
dnðt þ 1Þ
dnðt Þ T
¼A
þ n ðt ÞI S
fitness measure (population size at t,
duT
duT
duT
cumulative density at t, etc.) to
transient dynamics
Individual measures of fitness (not based on population averages)
X
Individual finite
F ðl Þ ðlI Þ1 ¼ 1 k
x x
rate of increase,
lI
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Description
Captures the response of a chosen
fitness measure to transient
dynamics in the case of density
or frequency dependence
Equivalent to l but based on
individual life-history parameters
(i.e. lx is replaced by ’1’ for each
age x at which the individual
survived, and zero otherwise)l,m
When density or frequency
dependence are operating
Where individual-level
covariation is expected to
bias population-level
averages
Problematic, owing to
biases inherent in making
predictions based on a
sample size of 1 l
[57,58]
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Table 1 (Continued )
Fitness measure Formula
X
Lifetime
LRS I ¼
F ðI Þ k
x x
reproductive
success, LRSI
Description
Total number of offspring
produced by each individualm
Where appropriate
If the population is stable,
E(LRSI) = R0
Refs
[13,61]
a
x, age; lx, probability of surviving to x; mx, fertility at x.
See also Ref. [56].
c
R0 is rate insensitive, so does not capture the importance in differences in relative timing of life-history events; l is rate sensitive and does.
d
If a density-dependent population has a stable equilibrium, the sensitivity of W to underlying parameters will be identical to the sensitivity of l [59].
e
A key result for invasion analysis in constant environments is that if density dependence acts on offspring establishment, the strategy that maximizes R0 is the EES (i.e. cannot
be invaded); if density dependence acts on survival at all ages, the strategy that maximizes l is the ESS [60].
f
E(y), expected value of y; N, population size; Ni, invader population size.
g
For example, if density dependence acts on offspring establishment, the offspring number produced by the invader at each t will be determined by the environmental
conditions at t, and the offspring production of the resident at t [1].
h
pti, contribution of individual i to pop growth during time step t; wt, population size at t + 1 minus population size at t; jt(i), performance of individual i (number surviving
offspring + 1 over time-step t).
i
No direct fitness measure is available but sensitivities can be calculated.
j
A, matrix of population transitions; u, lower level parameter to which sensitivity is being estimated; Is, identity matrix; vec, operator that stacks matrix columns into a column
vector.
k
Fx, fertility at age x of individual I.
l
See Ref. [58] for more complex Bayesian approaches.
m
See Ref. [13] for a comparison between LRSI and lI and Ref. [61] for comparison with the long-term genetic contribution.
b
Estimating fitness
When will a mutation affect individual fitness?
Even if a mutation changes the phenotype of an individual
in a given environment (through action at any level:
molecular, cellular, physiological or behavioral), it will
not alter fitness unless it changes how the individual
survives and reproduces across (st)ages. Furthermore, if
the mutation acts at a specific age, the degree to which it
alters fitness depends on the survival and fertility of the
affected individual at all other ages. For example, if mortality is such that few individuals are alive beyond a
certain age, and the contribution of these individuals to
lifetime reproduction is negligible, mutations acting after
this age are effectively neutral. Consequently, late-acting
deleterious mutations are subject to less selection and
their resulting accumulation is one possible explanation
for the evolution of senescence.
How do researchers explore fitness in natural
populations?
Empirical work often focuses on the relationship between
a trait and a single fitness component, such as mean age at
first reproduction, life expectancy, total fertility rate,
clutch size, offspring survival, length of the juvenile period, adult survival, viability, and so on (reviewed in Ref.
[5]). However, traits can be negatively or positively correlated with more than one fitness component. For example,
Coulson et al. [3] showed that birth weight in red deer was
affected by selection on several fitness components,
including birth rate and winter survival of male calves.
Using a single fitness component as a proxy for fitness can,
consequently, be problematic. It is particularly advisable
to avoid using the offspring number of breeding individuals as a fitness measure. Tradeoffs between juvenile
survival and adult fertility are probable, and a trait that
impacts positively on this fitness measure (i.e. adult fertility) might also impact negatively on juvenile survival.
This is referred to by Grafen [6] as the problem of the
‘invisible fraction’; that is, the fraction of individuals that
die before the trait is measured. Accurate measures of
fitness should therefore (where possible) include both
survival and fertility.
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How can we quantify fitness?
To compare the fitness of phenotypes or genotypes, a range
of different fitness measures that summarize the full (st)age
trajectories of survival and fertility is available, including
novel techniques allied to recent approaches in demographic
modeling, reviewed in Table 1. These fitness measures
can be based on individual values or population averages,
in constant or fluctuating environments, stable population
contexts or during transient dynamics. Each fitness measure has advantages and drawbacks. Determining the
appropriate fitness measure is a complex and controversial
topic, but one that is of considerable importance, as different
fitness measures can lead to different predictions. For example, the predicted evolutionarily stable flowering size for the
plant Onopordum illyricum is different in a stochastic
environment compared to that in a constant environment
and is closer to the observed value [1]. Until recently, most
fitness measures have been defined within the framework of
stable population theory (Table 1), but it is increasingly
recognized that transient dynamics might be important.
This is an area of active research [4,7,8].
Once a fitness measure has been chosen, the impact of
changes in fitness components (traits) on the fitness measure can be analyzed using perturbation analysis (see
Glossary). This basic framework has been considerably
extended in recent years: covariation between life-cycle
components can now be incorporated into elasticity analysis (e.g. Ref. [3]); the calculation of elasticity in changing
environments has recently been elucidated [9]; and even
more recently, methods for the calculation of elasticity
during transient phases have been developed [7]
(Table 1). However, interpretation of perturbation analysis
is still debated [9] and, again, can vary with the context
considered. One key complication that we now address is
the population context itself [59].
Short-term evolutionary outcomes: the spread of genes
within populations
Earlier, we reviewed the list of classic and recently
developed fitness measures (Table 1). However, to
determine whether a genotype will spread to fixation,
coexist, or disappear when in competition with other
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genotypes requires more than a comparison of fitness
values. Short-term evolution is driven by selection, migration and drift. The dynamic interplay among the survival
and fertility of different individuals (e.g. through
density dependence) will impact selection; but also has
implications for drift and migration (more rarely
addressed). Given accumulating empirical evidence for
complex interactions among individuals and populations
in the demography of natural populations [10] it is timely
to consider integrating such complexity into an evolutionary perspective.
results are robust to this assumption [18]. However,
techniques for calculating Ne for overlapping generations
are available (reviewed in Ref. [19]) and resulting estimates of the magnitude of genetic drift can double [20].
Sophisticated approaches have recently been developed to
incorporate the effect of individual differences in survival
and fertility on Ne via resulting fluctuations in age-structure and population size (e.g. Ref. [21]). A key question for
future research, generally neglected by evolutionary ecology, is to what degree the evolution of age trajectories is
determined by genetic drift.
Selection
The population context affects selection because the fitness
of an individual is contingent on what other individuals in
the population are doing, through: (i) density dependence:
if a mutation improves the fertility of a plant, but only
when it is not shaded by its neighbors, the mutation is
unlikely to spread successfully through a high-density
population (e.g. Ref. [11]); (ii) frequency dependence: if
predators always choose prey of the most common color,
then a mutation for a new prey color will increase survival
and will therefore spread until it becomes common enough
that predators prefer it (e.g. Ref. [12]); (iii) timing effects: if
a population is growing, mutations for earlier reproduction
are successful because the earlier offspring are produced,
the more descendants they leave (discussed in Ref. [13]). In
all three cases, the (st)age trajectories of the dominant
strategy of a population determine population dynamics
and, thus, whether a new mutation can spread.
Although theoretical tools are available to handle
dependence of fitness on the population context (invasibility analysis, Table 1), we are only beginning to understand how such dependencies operate in natural systems.
For example, methods for estimating density dependence
in stage-structured populations have only just become
available [14] and more empirical work is a crucial next
step. Furthermore, any interdependence between population- and individual-level outcomes leads to an inherently
dynamic situation (e.g. Ref. [15], reviewed in Ref. [10]).
Although adaptive dynamics approaches [16] have provided us with a novel and intuitive solution to analyzing
this problem, the invasion criteria most commonly used is
‘invasion when rare’. The fate of an invader in a population
once it has successfully passed the status of ‘rare’ is rarely
discussed, but might have complex implications for population dynamics and evolutionary outcomes (e.g. Ref. [17]),
and be important for progress in the field.
Dispersal
The spatial structure of populations might be as important
as (st)age structure, because selection pressures can
change across space. Combined with dispersal rates, this
might significantly alter evolutionary outcomes [22], for
example, enhancing [23], or diminishing [24] the coexistence of different traits. Spatial connectivity is often poorly
understood, but increasingly sophisticated capture–recapture techniques can provide sufficient information even for
linking alleles to demographic patterns [25]. (St)age specific dispersal is also likely to be the norm for most species,
particularly where juveniles establish new territories on
attaining maturity. For example, different passerine species [26] and humans [27] migrate at different ages; (st)age
specific dispersal might modify population dynamics by
altering (st)age structure and affecting population growth,
with repercussions for selection and drift. Although optimality models have been applied to the evolution of dispersal
(e.g. Ref. [28]), the (st)age specific dimension is rarely
considered and is ripe for development.
Drift
Drift refers to the fact that, purely by chance, some
mutations will become ubiquitous in a population even if
they increase neither fertility nor survival, and even
slightly decrease them. The population context matters
for drift because the key variable driving this stochastic
process is the effective population size, Ne, which is influenced by fluctuations in total population size and (st)agestructure, as well as by variance in reproductive success.
These are accurately captured only by (st)age-trajectories
of survival and fertility. Theoretical work often makes the
assumption that generations do not overlap and many
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Long-term evolutionary outcomes: the diversity of
(st)age trajectories across species
Species display a variety of age trajectories. Life span
varies from a few hours (mayflies) to hundreds of years
(trees). Fertility also varies considerably (Table 2). Certain
broad-scale patterns, such as the ‘slow–fast continuum’ of
mammals (after removing the effect of size, mammals can
be categorized along a continuum from species with late
maturity, few offspring per reproductive event and a long
generation time, to species that reproduce early, have large
litters and a short generation time), are relatively well
understood [29]. However, many species deviate from these
general trends: for example, the small precocial mammal
Cavia magna produces few juveniles with high survival
when they are expected to produce large litters of
altricial juveniles [30].
Although the foundations of a cross-species
understanding of demography have been laid [31], several
major aspects remain unclear. For example, mortality in
many species increases with age (senescence), although it
can also remain constant (e.g. hydra [32]) or decrease (e.g. a
monocarpic plant [1]). Recent models have begun to
address such patterns. For example, Baudisch [33] showed
that the force of selection does not necessarily decrease
with age if fertility and survival increase sufficiently with
age. This might explain why some species do not senesce.
More generally, advances in mechanistically based theoretical demography (e.g. Ref. [34]) provide a framework for
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Table 2. The diversity of age trajectories
Organism
Humans
Average population Comment
trajectory of survival
and fertility a
Mortality is high for infants and is then low until maturity,
corresponding to the initial drop in survival. In late
adulthood, mortality increases gradually (senescence)
and survival continues to falls away. Maximum recorded
age is 122. Fertility peaks at 30 and then falls to zero
at 50 (the age of menopause)
Ongoing research areas
Refs
What causes senescence
(, mutation accumulation
or antagonistic pleiotropy
or optimization of resource
allocation)?
Why do humans have the
highest fertility among
primates?
Why do menopause and
post-reproductive life exist?
[52,53]
[32]
Hydra (lab
population)
Over a period of four years, mortality was low and
showed no sign of increase with age; consequently,
survival diminished gradually. Fertility (both asexual and
sexual reproduction) remained approximately constant
How can a species escape
senescence even though
(presumably) mortality in
natural populations is high?
Species with
indeterminate growth
Juvenile mortality is high; mortality then slowly
decreases in association with increasing size, so that
mortality can continue to fall even after the start of
reproduction. Fertility is also associated with increasing
size. Theoretically, growth could continue and size could
increase indefinitely, but practically, growth is limited by
resource availability, usually driven by density
dependence
Mortality decreases with increasing size. Once the age
at flowering is reached, individuals flower and die
How can mortality continue
[34] b
to decrease (negative
senescence)?
In the absence of density
dependence, can growth
and therefore fertility continue
to increase indefinitely?
Monocarpic plantsc
Why do many monocarpic
plants delay reproduction?
How can broad ranges of
different flowering sizes or
ages coexist?
[1]
a
Survival (red curve; i.e. the proportion of individuals surviving to each age) and fertility (blue curve; i.e. the mean number of offspring produced by surviving individuals at
each age) on an arbitrary scale.
b
See Ref. [34] for a model including such dynamics based around rockfish, a group of species whose longevity varies between 12 and at least 200 years.
c
Image reproduced with permission from Barry Rice.
compiling data across the range of species to clarify the
underlying processes driving age-trajectory evolution. An
interesting direction for future research is collection of
demographic data on more species across the tree of life.
This would provide a deeper understanding of the dynamic
interplay between age trajectories and selection, and
would clarify the extent to which age trajectories are
constrained by phylogeny [35] (as yet unclear).
Going beyond aggregate (st)age trajectories
The most widely used fitness measures are based on
population averages (e.g. the net reproductive rate, R0;
the finite rate of increase, l; and the stochastic growth
rate, ls, defined in Table 1). However, in most populations, even in a constant environment, individuals differ
from one another, leading to variance in the number of
offspring produced over the life course. This variance has
two components: (i) the difference between individuals
that survive to reproduce and those that do not (this
variance will exist even if every individual in the population is strictly identical and has an identical risk of
dying, and can be estimated using mean age trajectories); and (ii) individual differences in fertility and
survival (some individuals might be more fecund, or
more robust than others).
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The second type of variation can arise for several
reasons: (i) differences might be a direct outcome of environmental variation. Some seeds might land in patches that
are more resource-rich than are others, entraining differences in age trajectories of survival and fertility. Such
variation will not have a genetic component in that
offspring might not at all resemble their parents; (ii)
individuals might respond adaptively to environmental
variation [36], altering their (st)age trajectories to best
match environmental conditions. In Drosophila [37] and
other species, lifespan can respond plastically to caloric
restriction: if resources become scarce, individuals reduce
metabolism, allocating resources preferentially to survival; (iii) variation might persist because different (st)age
trajectories have equivalent fitness, enabling the longterm coexistence of different genotypes. For example, some
individuals might allocate more to survival at the expense
of competitive ability, whereas others might suffer higher
mortality but monopolize resources and reproduce more
frequently (e.g. Ref. [34]); (iv) variation might persist
because, over the timescales considered, new mutations
have not had enough time to go to fixation or be driven to
extinction.
As a result of all four aspects listed above, natural
populations will be aggregates of individuals with different
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Box 1. Human demography and evolutionary biology
Demography is central to evolution, and it is therefore unsurprising
that human demography contributes to the study of evolutionary
biology. A first major asset is data availability: more information is
available on humans than on any other species. Data sets often
contain thousands of records, repeated measures on individuals,
considerable environmental information and even complete genealogies. Such data can be used to test evolutionary hypotheses, such
as the role of parental care in the evolution of post-reproductive life,
that could not be tested on other species. Considerable genetic data
are also available. Coupled with knowledge of human demographic
history (bottle-necks, migrations, etc.) genetic data enable the
quantification of gene propagation (e.g. Ref. [62]). Conversely,
genetic polymorphism and evolutionary models inform our understanding of human demographic and social history (e.g. Refs.
[63,64]).
Theoretical advances in classical demography in fields such as
period and cohort effects, unstable populations, migration, hidden
heterogeneity and so on, will also contribute as greatly to
evolutionary biology in the future as they have in the past. For
example, the demographic theory of unstable population dynamics
[65] might clarify evolution in non-stationary populations, an area
where ecological models are recognized as being inadequate [66].
Contributions might also be methodological, informing estimates of
age trajectories in unstable populations via information from two
census points [67] and so on.
If demography serves evolutionary biology, evolutionary biology
also contributes to demography. Failure to forecast population
patterns resulting from increasing longevity [68] led demographers
to turn to biology. Evolutionary biology provides specific predictions on aging [33] and also contributes to our understanding of
diseases whose rampant effects are linked to rapid evolution, such
as influenza (e.g. Ref. [69]). Demographers and biologists are also
increasingly working together (e.g. Ref. [70]): a synthesis of
techniques and discoveries made independently in both fields will
successfully inform both in the future.
characteristics of survival and fertility. Why is this variation challenging? First, fitness outcomes might change
substantially. For example, Rees et al. [1] showed that
individual variation in growth changes the predicted optimal flowering size in monocarpic plants (i.e. the fitness
estimate corresponding to the mean growth curve does not
accurately portray fitness for individuals within the population). However, this challenge can be met if individual
variation in growth is specifically estimated. A more complex example learnt from human demography (Box 1) is
how aggregate measures of survival can obscure individual-level patterns. Average population survival curves
flatten off at advanced ages. However, this flattening might
not accurately reflect the survival trajectory of an individual, but might be observed only because ‘frail’ individuals
die early, so that only individuals with overall lower mortality are present at late ages [38,39]. Understanding the
evolution of aging requires information about the relative
fitness of these two types of individual. However the frailty
attribute constitutes a ‘hidden heterogeneity’ among individuals, which can never be directly measured, but is
important because survival is a dynamic process.
More generally, individual age trajectories of survival
and fertility can (co)vary in complex ways as a result of
direct connections between fitness components (reproduction can decrease survival) or indirect connections (growth
can affect both survival and fertility; e.g. Ref. [1]). Because
of the dynamic aspect of survival outlined earlier, a
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demographic perspective is essential to understand the
implications of this (co)variation for evolution. Recent
methodological advances, such as Hierarchical Bayes
approaches, can be used to partition and structure models
of variation in aggregates in a demographic framework
(e.g. Refs [40,41]). Advances in pedigree analysis also
enable heritable variation in (st)age trajectories to be
distinguished from environmental noise [42], but sufficient
data are only just becoming available. Allying such estimation of individual demographic (co)variation with models for predictions of evolutionary outcomes is an area in
considerable development (e.g. integral projection models
[43]; or indirect approaches [44]), with many unanswered
questions, even at the most basic level. For example, it is
still not clear, even for humans, what fraction of variation
in age trajectories of survival is heritable, what fraction is
environmentally imposed and what fraction is due to
plasticity.
Environmental variation through time might not only
generate variation in the age trajectories of individuals,
but also impose selection pressures on this variation. For
example, in some cases, the more survival and fertility
vary through time, the more ls is decreased [45] (this
breaks down if environments are autocorrelated [46]).
Vital rates with the biggest effects on ls (as identified by
perturbation analysis) should therefore be ‘buffered’
against variation, as a result of selection, so that we might
expect fewer differences between individuals at any given
time. This is the case in some natural populations (e.g.
Refs. [2,47]). Another complication connected to heterogeneity and currently opening up a new field of investigation,
is that environmental conditions can lead negative
covariation between selection pressures and the genetic
variance that they will act on, so that little evolutionary
change is possible [48].
Having outlined challenges arising both from estimation
of individual (st)age trajectories and modeling heterogeneous populations, we now turn to cases where even accurate estimates of individual (st)age trajectories might be
insufficient, another current key challenge.
Going beyond trajectories of survival and fertility
Within the framework of stable population theory, age
trajectories of survival and fertility enable calculation of
the (st)age-specific contribution of one (st)age class to the
total number of descendants. This provides us with the
tools to dissect evolutionary processes at the individual,
population and phylogenetic levels. However, these tools
are not sufficient if age trajectories of survival and fertility
are linked across generations. For example, offspring survival often depends on the age of the parents at childbirth.
This occurs in humans owing to an increase in germline
mutation rates with age in males [49]; or to increased risk
of a child with Down’s Syndrome with age for females (of up
to 35% for ages 45) [50]; and has also been recorded for
Drosophila [51]. Generally, the result is a decrease in
offspring quality with age of parents.
The issue of linkage across generations also arises
where parental care is required for offspring to survive
and reproduce successfully. Adults can increase fitness by
investing time and energy in existing offspring instead of
Review
TRENDS in Ecology and Evolution
current reproduction. They thereby contribute to fitness
without reproducing, even during post-reproductive periods [52,53]. Although the age trajectory of this investment
towards offspring, and its relationship with offspring fitness, are difficult to measure empirically and difficult to
model theoretically, the way that it impacts population
dynamics and evolution is the subject of increasing
research interest, and recent theoretical and empirical
advances [15,54,55].
To conclude, simple measures and models of fertility
are often not adequate to measure gene transmission:
distinctions must be made between offspring of parents
of different ages. Calculating the (st)age specific contribution to fitness of survivors at one (st)age class is therefore
complex and both perturbation analysis and optimization
are extremely challenging. This challenge is only starting
to be met.
Conclusions
Evolutionary outcomes result from an interplay between
ultimate and proximate determinants of variation in the
trajectories of survival and fertility across (st)ages, within
and between species, and owing to phylogenetic history.
Complexities, covariation and feedbacks abound at each
level, and must be addressed, as they can have broad
implications. A demographic framework is the only way
to do this. Demographic research has recently made giant
strides towards developing statistical models of heterogeneity across individuals. Much exciting work is resulting
from the integration of this progress into evolutionary
analysis. Every month, new tools appear, such as methods
enabling empirical quantification of density dependence
from time series of (st)age structured populations [14], and
new properties are clarified, such as those of stochastic
environment elasticities [9]. However, new questions
appear with equal regularity, such as the demographic
implications of parental care (e.g. Ref. [53]), or how adaptation at the individual level can interact with population
dynamics (e.g. Refs. [17,25]). Evolutionary demography is
in a period of intense activity and the current productive
focus on (st)age trajectories might improve the understanding for both evolutionary biologists and human demographers of key questions, such as the evolution of
senescence.
Acknowledgements
We thank the evolutionary demography group at Rostock, T. Coulson, H.
Caswell, D. Koons, A. Baudisch, and E. Cam for discussion and comments
on earlier drafts. We also thank S. Munch, B. Rice and the demography
group at Rostock for providing images.
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