Community Ecology
Ecology as a science: hypothesis testing, paradigms and revolutions
Outline:
1. Scientific method
A. Inductive vs. deductive approaches
i. falsifiability a. Type I and II errors
ii. theory
iii. scientific laws / laws in ecology
B. Study design
i. mensurative studies vs. experiments (classic vs. natural) vs. modeling
ii. 3 key aspects of experimental design
a. replication (and pseudoreplication)
b. randomization
c. controls
2. Critical thinking
3. Objectivity vs. subjectivity in science
A. Scientific method
B. Peer review
4. Paradigms are subjective and influence our desire for objectivity
5. Scientific revolutions involve the overthrow of a dominant paradigm
Terms/people: scientific method falsifiable hypothesis (null and alternative)
Karl Popper hypothetico-deductive approach deduction (cf. induction) pseudoreplication (Stuart Hurlbert) randomization replication experiment treatment control Type I and II errors scientific "laws" theory pattern vs. process strong inference science "hard" vs. "soft" sciences classic experiment natural experiment mensurative study model “normal science” conceptual evolution (Paine) paradigm Kuhn scientific revolution
Francis Bacon prediction science = a way of knowing inductive vs. deductive approaches
Science is differentiated from all other ways of knowing (e.g. faith, intuition, pseudoscience, instinct, etc.) because of use of hypothesis testing in the “scientific method.” hypothesis
vs. prediction null and alternative strong inference (Platt 1964)

Scientific method (idealized):
 make observations

 ask question
 form hypotheses (null and alternative(s)) test hypotheses (usually by experimentation)
 evaluate test results
 reject/retain hypothesis
Use of this standardized method provides a common framework among scientists and minimizes variation (and thus, bias). differences in the scientific methods of Francis Bacon and Karl Popper ( inductive vs. deductive )
Karl Popper - hypothetico-deductive method
Importance of falsifiability
Only if the possibility of disproof exists can something be said to be scientific; this lack of certainty can lead people who do not understand the very nature of science to demand a “fair” perspective from multiple sides, but this is applying rhetorical principles to knowledge. This
“marketplace of ideas” implies equality of ideas, but ideas that are not backed up with objectively obtained, empirical evidence and/or critical thinking are NOT equal, and we do a disservice to society to act as if they are, for it generates an atmosphere where ideas that are not based in facts are given equal footing with those that are.
Note that not all questions can be answered using the scientific method
, such as "Is Bach’s music better than Beethoven’s?" Or "Why is there suffering in the world?" Question for students to answer: Why not?
Type I and II errors:
Type I -
Type II -
When you fail to reject your null hypothesis, there may be 4 reasons:
1) the alternative hypothesis is wrong (and you are correct to fail to reject the null)
2) one or more of the background assumptions or initial conditions were not satisfied
3) you measured your variables incorrectly
4) the alternative hypothesis is correct but only for a limited range of conditions that were not present in your setup statistics -
"In biology most phenomena are affected by many casual factors, uncontrollable in their variation and often unidentifiable. Statistics is needed to measure such variable phenomena with a predictable error and to ascertain the reality of minute but important differences. Whether biological phenomena are in fact fundamentally deterministic and only the variety of causal variables and our inability to control these make these phenomena appear probabilistic, or

whether biological processes are truly probabilistic, as postulated in quantum mechanics for elementary particles, is a deep philosophical question." (Sokal and Rohlf 1969:5)
The deductive process allows us to test theory
Theory – a set of principles, derived from observations, experiments, models, and/or proofs, that explain natural events; is subject to peer-review and adaptable to new evidence (i.e., revision)
This is a very commonly misused term--in common parlance, a theory is erroneously equated with a guess that is unproven. The term will be used properly in this class.
A scientific theory is more than mere unproven guesswork. A clear example is given by Sam
Harris ( Letter to a Christian Nation , 2006, Knopf, New York, p. 69):
“Christians who doubt the truth of evolution are apt to say things like ‘Evolution is just a theory, not a fact.’ Such statements betray a serious misunderstanding of the way the term ‘theory’ is used in scientific discourse. In science, facts must be explained with reference to other facts. These larger explanatory models are ‘theories.’ Theories make predictions and can, in principle, be tested. The phrase ‘the theory of evolution’ does not in the least suggest that evolution is not a fact. One can speak about ‘the germ theory of disease’ or ‘the theory of gravitation’ without casting doubt upon disease or gravity as facts of nature.”
Roles of theory in ecology:
 define possibilities
 provide framework around which to organize thoughts
 provide standard for comparison
 indicate explanations
 make predictions
 give satisfaction (humans tend to pigeon-hole/categorize)
Many ecologists are enamored with theory because:
- it provides a route to deriving general laws
- "physics envy"
- intellectual excitement
- it provides a way to use mathematical or computer skills to increase quantitative rigor
- it provides a way to isolate and simplify the essential features of complex systems so that we can get on with making reliable predictions and achieving understanding
Good theory: (a) generates testable hypotheses and (b) explains phenomena that actually occur
(i.e., if the conditions of theory are met, the predicted patterns will in fact be observed).
Developing good theory in ecology, however, is difficult because:
- ecological systems are often "middle-number" systems (i.e., neither atomic nor cosmic)
- ecological systems are sensitive to initial conditions (i.e., history is important)
- critical features are often unmeasurable (e.g. "niche")
- experiments are often unfeasible or unrepeatable

Nonetheless, theory is valuable because it:
- defines what is possible, given certain assumptions
- provides a framework for thinking and generating questions
- provides standards for comparison
- makes predictions (which can be tested)
- suggests explanations of observed patterns
See also Marquet et al. (2014).
"Theoreticians" vs. "naturalists"
Theory has its proponents and its detractors in ecology; consider these quotes:
Roughgarden: “Theory does not substitute for the knowledge of actual systems any more than architectural drawings substitute for a house. Yet we cannot build a house without architectural drawings.”
MacArthur: “Scientists are perennially aware that it is best not to trust theory until it is confirmed by evidence. It is equally true...that it is best not to put too much faith in facts until they have been confirmed by theory.”
Connell: “Ecological theory does not establish or show anything about nature. It simply lays out the consequences of certain assumptions. Only a study of nature itself can tell us whether these assumptions and consequences are true.”
Haldane: “No scientific theory is worth anything unless it enables us to predict something which is actually going on. Until that is done, theories are a mere game of words, and not such a good game as poetry.” predictability
 scientific laws
"hard" vs. "soft" sciences
Are there ecological laws? Are ecological laws possible?
Ecological systems are extremely complex and deal with not only physical variability but also endogenous variability, and may differ in short- vs. long-term responses, making the establishment of universal ecological laws (sensu stricto) nearly impossible (Lawton 1999 - who suggested we “move on,” O’Hara 2005; but see Berryman 2003, Colyvan and Ginzburg
2003, Simberloff 2004)
“Community ecology, perhaps more than other subfields of ecology, seems to be mired in contingency, the answer to more questions being ‘it depends.’ This is frustrating for everyone from undergraduates to seasoned researchers. So, community ecology lacks generality in the sense that a physicist or even a molecular biologist might define the term...” (Chase and Leibold 2003)
Laws sensu lato , on the other hand, as true, general statements, can be found in ecology

see Fig. 1.1 from Ritchie (2010). These may indeed be laws, albeit ones that only hold within limited domains (e.g. Willig and Scheiner 2011).

pattern vs. process: MacArthur 1972: “To do science is to search for repeated patterns, not simply to accumulate facts.”
Study design in community ecology (i.e., ways of implementing the scientific method):
I. Mensurative studies - observational (or natural history ) studies
II. Experiment = treatment vs. control
Two broad types of experiments:
1. classic experiment
2. " natural experiment " (Diamond 1986)
Setting up a controlled (i.e., classic) experiment is only one way of gaining knowledge and testing hypotheses. Predictions (statements about the way things will happen in the future, i.e., forecasts) can also be tested by comparing them against future patterns that unfold naturally with no intervention by a researcher. Astronomers do this every day (indeed, they cannot experiment on their subjects!). But just because they cannot do classic experiments does not mean that they cannot test hypotheses or do science. The same is true for community ecologists.
Basic principles of experimental design (for classic experiments):
1. Replication
-Beware of pseudoreplication (see Hurlbert 1984 )
2. Randomization : randomize whenever possible in order to have a representative (and not idiosyncratic) sample
3. Controls
III. Because of the logistical difficulties with experimentation in ecology, modeling is a key endeavor:
-allow us to explore possibilities before using up time/resources
-allow us to examine alternatives
-allow us to explore future scenarios and test theories critical thinking
Developing your critical thinking skills is one of my key underlying goals in this course.
For a great book on critical thinking, I recommend How to Think about Weird Things: Critical
Thinking for a New Age by T. Schick and L. Vaughn (2002, McGraw-Hill Publishers).
The scientific method helps to minimize bias. One form of bias can come from subjectivity. objectivity vs. subjectivity in science
Why strive for objectivity (i.e., why minimize subjectivity)? Isn’t science inherently objective?
How do we minimize subjectivity that can cloud our judgment?

1.
Use of the scientific method (formulation of testable, falsifiable hypotheses, with robust study design).
2. Peer-review of results. paradigm
Thomas Kuhn 1970 - The Structure of Scientific Revolutions - Kuhn’s book has been described as "Perhaps the best explanation of [the] process of discovery." --William Erwin Thompson, New
York Times Book Review
“ normal science
” - intellectual chauvinism -
“Our beliefs are not automatically updated by the best evidence available. They often have an active life of their own and fight tenaciously for their own survival.” - D. Marks and R.
Kammann 1980 ( The Psychology of the Psychic , Prometheus Books, Buffalo, NY)
“A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” - Max Planck 1948 (as cited in Kuhn)
" scientific revolution " ( paradigm shift ) examples of paradigms and revolutions
Or is it conceptual evolution (Paine 2002)?
So today’s take-home message is to try to step back and objectively view your worldview: is it hindering you? Are you reasoning objectively? What are the alternatives to your paradigm? And what about the studies that we will examine? What are the paradigms under which the studies were conducted?
Why is this important? Scientists are trained to avoid rhetoric, to let the facts speak for themselves. The general public, however, does the opposite, forming frames of reference and values as guides for navigating life, using rhetoric to support these guides. This can be a dangerous practice when evidence is ignored because of one’s paradigms--without objective evidence gained via the scientific method, then only popularity or authority matter. Allowing equal air time in an effort at fairness (due to our contemporary American worldview of justice and fair play) can actually diminish science and lend credence of opinion to unsubstantiated claims, because not all viewpoints are backed up with objective data. Many societal problems aren’t due to knowledge deficit but rather are due to knowledge resistance, because of our paradigms. People may not think of themselves as ignorant or antiscientific, but social identity and messages from authority can induce a suspension of skepticism and appeal to emotion rather than to thought.

Consider this quote from literature’s greatest thinker: “I make a point of never having any prejudices and of following docilely wherever fact may lead me.” – Sherlock Holmes (A.C.
Doyle, “The Reigate Squires”)
References:
Note: The journal Ecology vol. 83 no. 6 from June 2002 has a special feature on paradigms in ecology. A couple of the refs below come from this issue.
Armstrong, D.P. 1991. Levels of cause and effect as organizing principles for research in animal behaviour. Can. J. Zool. 69:823-829.
Berryman, A.A. 2003. On principles, laws and theory in population ecology. Oikos 103:695-701.
Cale, W.G., G.M. Henebry, and J.A. Yeakley. 1989. Inferring process from pattern in natural communities. BioScience 39:600-605.
Caswell, H. 1988. Theory and models in ecology: a different perspective. Ecological Modelling
43:33-44.
Chase, J.M., and M.A. Leibold. 2003. Ecological Niches: Linking Classical and Contemporary
Approaches . Univ. Chicago Press, Chicago, IL.
Colyvan, M., and L.R. Ginzburg. 2003. Laws of nature and laws of ecology. Oikos 101:649-653.
Diamond, J.M. 1986. Overview: laboratory experiments, field experiments, and natural experiments. Pp. 3-22 in: Community Ecology (J. Diamond and T.J. Case, eds.). Harper & Row,
New York, NY.
Dodds, W.K. 2009. Laws, Theories, and Patterns in Ecology . Univ. of California Press,
Berkeley, CA.
Fagerström, T. 1987. On theory, data and mathematics in ecology. Oikos 50:258-261.
Francis, R.G. 1990. Causes, proximate and ultimate. Biology and Philosophy 5:401-415.
Graham, M.H., and P.K. Dayton. 2002. On the evolution of ecological ideas: paradigms and scientific progress. Ecology 83:1481-1489.
Hilborn, R., and M. Mangel. 1997. The Ecological Detective: Confronting Models with Data .
Princeton University Press, Princeton, NJ. [an excellent treatment of hypothesis testing and inference]
Hilborn, R., and S.C. Stearns. 1982. On inference in ecology and evolutionary biology: the problem of multiple causes. Acta Biotheoretica 31:145-164.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol.
Monogr. 54:187-211.
Johnson, D.H. 2002. The role of hypothesis testing in wildlife science. J. Wildl. Manage. 66:272-
276.
Krebs, C.J. 2000. Hypothesis testing in ecology. Pp. 1-14 in: Research Techniques in Animal
Ecology: Controversies and Consequences (L. Boitani and T.K. Fuller, eds.). Columbia
University Press, New York, NY.
Kuhn, T.S. 1970. The Structure of Scientific Revolutions (2nd ed.). University of Chicago Press,
Chicago, IL.
Lawton, J.H. 1999. Are there general laws in ecology? Oikos 84:177-192.
Loehle, C. 1987. Hypothesis testing in ecology: psychological aspects and the importance of theory maturation. Quarterly Review of Biology 62:397-409.
Loehle, C. 1988. Philosophical tools: potential contributions to ecology. Oikos 51:97-104.
Loehle, C. 1990. Philosophical tools: reply to Shrader-Frechette and McCoy. Oikos 58:115- 119.
MacArthur, R.H. 1972. Geographical Ecology . Harper & Row, New York, NY.
Marquet, P.A., A.P. Allen, J.H. Brown, J.A. Dunne, B.J. Enquist, J.F. Gillooly, P.A. Gowaty,
J.L. Green, J. Harte, S.P. Hubbell, J. O’Dwyer, J.G. Okie, A Ostling, M. Ritchie, D. Storch, and
G.B. West. 2014. On theory in ecology. BioScience 64:701-710.
Murray, B.G. 2000. Universal laws and predictive theory in ecology and evolution. Oikos
89:403-408.
O’Hara, R.B. 2005. The anarchist’s guide to ecological theory. Or, we don’t need no stinkin’ laws. Oikos 110:390-393.
Otto, S.L. 2011. Fool Me Twice . Rodale Books, Emmaus, PA.
Paine, R.T. 2002. Advances in ecological understanding: by Kuhnian revolution or conceptual evolution? Ecology 83:1553-1559.
Peters, R.H. 1991. A Critique for Ecology . Cambridge University Press, Cambridge, UK.
Pickett, S.T.A., J. Kolasa, and C.G. Jones. 2007. Ecological Understanding: The Theory of
Nature, and the Nature of Theory , 2 nd ed. Academic Press, San Diego, CA.
Platt, J.R. 1964. Strong inference–certain systematic methods of scientific thinking may produce much more rapid progress than others. Science 146:347-353.

Popper, K.R. 1959. The Logic of Scientific Discovery . Hutchinson, London, UK.
Popper, K.R. 1970. Normal science and its dangers. Pp. 51-59 in: Criticism and the Growth of
Knowledge (I. Lakatos and A. Musgrave, eds.). Cambridge University Press, Cambridge, UK.
Ritchie, M.E. 2010. Scale, Heterogeneity, and the Structure and Diversity of Ecological
Communities . Princeton Univ. Press, Princeton, NJ.
Shrader-Frechette, K.S., and E.D. McCoy. 1990. Theory reduction and explanation in ecology.
Oikos 58:109-114.
Shrader-Frechette, K.S., and E.D. McCoy. 1993. Method in Ecology . Cambridge University
Press, Cambridge, UK.
Simberloff, D. 2004. Community ecology: is it time to move on? Am. Nat. 163:787-799.
Sokal, R.R., and F.J. Rohlf. 1969. Biometry . W.H. Freeman and Co., San Francisco, CA. [a book that should be on every professional ecologist’s shelf]
Taylor, P. 1989. Revising models and generating theory. Oikos 54:121-126.
Willig, M.R., and S.M. Scheiner. 2011. The state of theory in ecology. Pp. 333-347 in: The
Theory of Ecology (S.M. Scheiner and M.R. Willig, eds.). Univ. of Chicago Press, Chicago, IL.