One of the first lessons in Logic 101 is classically called “Post hoc, ergo propter hoc” or in plain English, “After that, therefore because of that”. The simplest example of many you can see in the newspapers might be: “The ocean is warming up, salmon populations are going down, it must be another effect of climate change. There is a great deal of literature on the problems associated with these kinds of simple inferences, going back to classics like Romesburg (1981), Cox and Wermuth (2004), Sugihara et al. (2012), and Nichols et al. (2019). My purpose here is only to remind you to examine cause and effect when you make ecological conclusions.
My concern is partly related to news articles on ecological problems. A recent example is the collapse of the snow crab fishery in the Gulf of Alaska which in the last 5 years has gone from a very large and profitable fishery interacting with a very large crab population to, at present, a closed fishery with very few snow crabs. What has happened? Where did the snow crabs go? No one really knows but there are perhaps half a dozen ideas put forward to explain what has happened. Meanwhile the fishery and the local economy are in chaos. Without very many critical data on this oceanic ecosystem we can list several factors that might be involved – climate change warming of the Bering Sea, predators, overfishing, diseases, habitat disturbances because of bottom trawl fishing, natural cycles, and then recognizing that we have no simple way for deciding cause and effect and therefore making management choices.
The simplest solution is to say that many interacting factors are involved and many papers indicate the complexity of populations, communities and ecosystems (e,g, Lidicker 1991, Holmes 1995, Howarth et al. 2014). Everyone would agree with this general idea, “the world is complex”, but the arguments have always been “how do we proceed to investigate ecological processes and solve ecological problems given this complexity?” The search for generality has led mostly into replications in which ‘identical’ populations or communities behave very differently. How can we resolve this problem? A simple answer to all this is to go back to the correlation coefficient and avoid complexity.
Having some idea of what is driving changes in ecological systems is certainly better than having no idea, but it is a problem when only one explanation is pushed without a careful consideration of alternative possibilities. The media and particularly the social media are encumbered with oversimplified views of the causes of ecological problems which receive wide approbation with little detailed consideration of alternative views. Perhaps we will always be exposed to these oversimplified views of complex problems but as scientists we should not follow in these footsteps without hard data.
What kind of data do we need in science? We must embrace the rules of causal inference, and a good start might be the books of Popper (1963) and Pearl and Mackenzie (2018) and for ecologists in particular the review of the use of surrogate variables in ecology by Barton et al. (2015). Ecologists are not going to win public respect for their science until they can avoid weak inference, minimize hand waving, and follow the accepted rules of causal inference. We cannot build a science on the simple hypothesis that the world is complicated or by listing multiple possible causes for changes. Correlation coefficients can be a start to unravelling complexity but only a weak one. We need better methods for resolving complex issues in ecology.
Barton, P.S., Pierson, J.C., Westgate, M.J., Lane, P.W. & Lindenmayer, D.B. (2015) Learning from clinical medicine to improve the use of surrogates in ecology. Oikos, 124, 391-398.doi: 10.1111/oik.02007.
Cox, D.R. and Wermuth, N. (2004). Causality: a statistical view. International Statistical Reviews 72: 285-305.
Holmes, J.C. (1995) Population regulation: a dynamic complex of interactions. Wildlife Research, 22, 11-19.
Howarth, L.M., Roberts, C.M., Thurstan, R.H. & Stewart, B.D. (2014) The unintended consequences of simplifying the sea: making the case for complexity. Fish and Fisheries, 15, 690-711.doi: 10.1111/faf.12041
Lidicker, W.Z., Jr. (1991) In defense of a multifactor perspective in population ecology. Journal of Mammalogy, 72, 631-635.
Nichols, J.D., Kendall, W.L. & Boomer, G.S. (2019) Accumulating evidence in ecology: Once is not enough. Ecology and Evolution, 9, 13991-14004.doi: 10.1002/ece3.5836.
Pearl, J., and Mackenzie, D. 2018. The Book of Why. The New Science of Cause and Effect. Penguin, London, U.K. 432 pp. ISBN: 978-1541698963
Popper, K.R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge and Kegan Paul, London. 608 pp. ISBN: 978-1541698963
Romesburg, H.C. (1981) Wildlife science: gaining reliable knowledge. Journal of Wildlife Management, 45, 293-313.
Sugihara, G., et al. (2012) Detecting causality in complex ecosystems. Science, 338, 496-500.doi: 10.1126/science.1227079.
Thanks Charlie – an important and timely reminder. Researchers studying apex predator roles in trophic cascades have struggled with issue for many years, particularly those studying Australian dingoes, and the vast majority of publications in this area are correlative. We’ve tried to address this deficiency for over a decade now (Allen et al. 2013a; Allen et al. 2017), and have also conducted several cause-and-effect manipulative experiments ourselves (Allen et al. 2013b; Allen et al. 2014; Castle et al. 2021; Castle et al. 2022). Unfortunately, the inferential capability of most studies in this area continues to be limited to correlations, and textbooks, undergraduate degree courses, and wildlife managers continue to act in accordance with false beliefs grounded in tenuous correlations . Hopefully your reminder will help improve the situation.
Allen, B.L., Fleming, P.J.S., Allen, L.R., Engeman, R.M., Ballard, G., Leung, L.K-P. (2013a). As clear as mud: a critical review of evidence for the ecological roles of Australian dingoes. Biological Conservation 159, 158-174.
Allen, B.L., Allen, L.R., Engeman, R.M., Leung, L.K.-P. (2013b). Intraguild relationships between sympatric predators exposed to lethal control: predator manipulation experiments. Frontiers in Zoology 10, 39.
Allen, B.L., Allen, L.R., Engeman, R.M, Leung, L.K.-P. (2014). Sympatric prey responses to lethal top-predator control: predator manipulation experiments. Frontiers in Zoology 11, 56.
Allen, B.L., Allen, L.R., Andrén, H., Ballard, G., Boitani, L., Engeman, R.M., Fleming, P.J.S., Ford, A.T., Haswell, P.M., Kowalczyk, R., Linnell, J.D.C., Mech, L.D., Parker, D.M. (2017). Can we save large carnivores without losing large carnivore science? Food Webs 12, 64-75.
Castle, G., Smith, D., Allen, L.R., Allen, B.L. (2021). Terrestrial mesopredators did not increase after top-predator removal in a large-scale experimental test of mesopredator release theory. Scientific Reports 11, 18205.
Castle, G., Smith, D., Allen, L.R., Carter, J., Elsworth, P., Allen, B.L. (2022). Top-predator removal does not cause trophic cascades in Australian rangeland ecosystems. Food Webs 31, e00229.
Ben – excellent. You raise an important point that in some cases it is most difficult to get beyond the simple correlation point. And your dingo work shows how to improve our understanding when you cannot do all the definitive experiments. But the key is to always remember our understanding is tentative and subject to revision with new data. Perhaps the history of ecology maps directly on to the Covid mess we are going through. Thanks for your comment. Charley