Back in the Stone Age when science students took philosophy courses, a logic course was a common choice for students majoring in science. Among the many logical fallacies one of the most common was the Post Hoc Fallacy, or in full “Post hoc, ergo propter hoc”, “After this, therefore because of this.” The Post Hoc Fallacy has the following general form:

1. A occurs before B.
2. Therefore A is the cause of B.

Many examples of this fallacy are given in the newspapers every day. “I lost my pencil this morning and an earthquake occurred in California this afternoon.” Therefore….. Of course, we are certain that this sort of error could never occur in the 21^st century, but I would like to suggest to the contrary that its frequency is probably on the rise in ecology and evolutionary biology, and the culprit (A) is most often climate change.

Hilborn and Stearns (1982) pointed out many years ago that most ecological and evolutionary changes have multiple causes, and thus we must learn to deal with multiple causation in which a variety of factors combine and interact to produce an observed outcome. This point of view places an immediate dichotomy between the two extremes of ecological thinking – single factor experiments to determine causation cleanly versus the “many factors are involved” world view. There are a variety of intermediate views of ecological causality between these two extremes, leading in part to the flow chart syndrome of boxes and arrows aptly described by my CSIRO colleague Kent Williams as “horrendograms”. If you are a natural resource manager you will prefer the simple end of the spectrum to answer the management question of ‘what can I possibly manipulate to change an undesirable outcome for this population or community?’

Many ecological changes are going on today in the world, populations are declining or increasing, species are disappearing, geographical distributions are moving toward the poles or to higher altitudes, and novel diseases are appearing in populations of plants and animals. The simplest explanation of all these changes is that climate change is the major cause because in every part of the Earth some aspect of winter or summer climate is changing. This might be correct, or it might be an example of the Post Hoc Fallacy. How can we determine which explanation is correct?

First, for any ecological change it is important to identify a mechanism of change. Climate, or more properly weather, is itself a complex factor of temperature, humidity, and rainfall, and for climate to be considered a proper cause you must advance some information on physiology or behaviour or genetics that would link some specific climate parameter to the changes observed. Information on possible mechanisms makes the potential explanation more feasible. A second step is to make some specific predictions that can be tested either by experiments or by further observational data. Berteaux et al. (2006) provided a careful list of suggestions on how to proceed in this manner, and Tavecchia et al. (2016) have illustrated how one traditional approach to studying the impact of climate change on population dynamics could lead to forecasting errors.

A second critical focus must be on long-term studies of the population or community of interest. In particular, 3-4 year studies common in Ph.D. theses must make the assumption that the results are a random sample of annual ecological changes. Often this is not the case and this can be recognized when longer term studies are completed or more easily if an experimental manipulation can be carried out on the mechanisms involved.

The retort to these complaints about ecological and evolutionary inference is that all investigated problems are complex and multifactorial, so that after much investigation one can conclude only that “many factors are involved”. The application of AIC analysis attempts to blunt this criticism by taking the approach that, given the data (the evidence), what hypothesis is best supported? Hobbs and Hilborn (2006) provide a guide to the different methods of inference that can improve on the standard statistical approach. The AIC approach has always carried with it the awareness of the possibility that the correct hypothesis is not present in the list being evaluated, or that some combination of relevant factors cannot be tested because the available data does not cover a wide enough range of variation. Burnham et al. (2011) provide an excellent checklist for the use of AIC measures to discriminate among hypotheses. Guthery et al. (2005) and Stephens et al. (2005) carry the discussion in interesting ways. Cade (2015) discusses an interesting case in which inappropriate AIC methods lead to questionable conclusions about habitat distribution preferences and use by sage-grouse in Colorado.

If there is a simple message in all this it is to think very carefully about what the problem is in any investigation, what the possible solutions or hypotheses are that could explain the problem, and then utilize the best statistical methods to answer that question. Older statistical methods are not necessarily bad, and newer statistical methods not automatically better for solving problems. The key lies in good data, relevant to the problem being investigated. And if you are a beginning investigator, read some of these papers.

Berteaux, D., et al. 2006. Constraints to projecting the effects of climate change on mammals. Climate Research 32(2): 151-158. doi: 10.3354/cr032151.

Burnham, K.P., Anderson, D.R., and Huyvaert, K.P. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65(1): 23-35. doi: 10.1007/s00265-010-1029-6.

Guthery, F.S., Brennan, L.A., Peterson, M.J., and Lusk, J.J. 2005. Information theory in wildlife science: Critique and viewpoint. Journal of Wildlife Management 69(2): 457-465. doi: 10.1890/04-0645.

Hilborn, R., and Stearns, S.C. 1982. On inference in ecology and evolutionary biology: the problem of multiple causes. Acta Biotheoretica 31: 145-164. doi: 10.1007/BF01857238

Hobbs, N.T., and Hilborn, R. 2006. Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. Ecological Applications 16(1): 5-19. doi: 10.1890/04-0645

Stephens, P.A., Buskirk, S.W., Hayward, G.D., and Del Rio, C.M. 2005. Information theory and hypothesis testing: a call for pluralism. Journal of Applied Ecology 42(1): 4-12. doi: 10.1111/j.1365-2664.2005.01002.x

Tavecchia, G., et al. 2016. Climate-driven vital rates do not always mean climate-driven population. Global Change Biology 22(12): 3960-3966. doi: 10.1111/gcb.13330.