Prediction In Ecology And Evolution

I recently published a paper titled Prediction in Ecology and Evolution in BioScience. I was pretty sure the paper would get a lot of attention as I had six reviewers who provided more than 20 single-spaced pages of comments. In all cases, the reviewers were very interested and sincerely wanted to help improve the paper. Most of the criticisms focused on (1) what I should have paid more attention to, or (2) what I should have excluded. After publishing the paper, I immediately started getting emails making similar suggestions. This inspired me to start an exchange of ideas.

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To start, I provide the abstract of my paper, which is freely accessible on the journal website through the end of 2023. Then we will move to some commentaries/criticisms/suggestions. 

Prediction In Ecology and Evolution - by Andrew P. Hendry   

Prediction is frequently asserted to be the sine qua non of science, but prediction means different things to different people in different contexts. I organize and explain this diversity by addressing five questions. What does it mean to predict something? To answer this question, I describe concepts of prediction as prophecy, diagnosis, history, repeatability, and fate. What are we trying to predict? Here, I describe how predictions vary along several axes: general to specific, qualitative to quantitative, relative to absolute, point to range, and continuous to discontinuous. Where do predictions come from? In this case, I focus on deductive versus inductive reasoning. How do we test predictions? The answer here is not straightforward and I discuss various approaches and difficulties. How good are predictions? Not surprisingly, it depends on what is being predicted and how we judge success. Importantly, I do not espouse a “best”way to approach prediction but, rather, I outline its diverse manifestations so as to help organize practical thinking on the topic.

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1. From Predicting an Eclipse to Predicting Speciation

By Marius Roesti - University of Bern

In a separate post, Marius asks if we can move from a machine predicting an eclipse (use of an orrery to do so in the movie Pitch Black is shown below) a "machine" for predicting speciation in the post is here.

Excerpt from the post: In many life situations, we base our decisions and actions on their predicted consequences. Yet, predictions are also indispensable in fundamental sciences where interests center on understanding how our universe works, rather than assisting with practical life problems. We recently asked some colleagues why we want to predict things in science and often heard something like "because this will tell us whether we got it right or not". – This made us think. If true, what does this mean for a field of research still struggling with making accurate predictions?

The paper on which the post is based.

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2. Assessing Predictability in Ecology and Evolution

Daniel Ortiz-Barrientos, The University of Queensland, d.ortizbarrientos@uq.edu.au

 “We never actually prove any proposition in science; nor do we accept any proposition as ‘true,’ ‘finally true,’ or even ‘probable.’ But we do accept some propositions (or theories) as better tested, or better corroborated, than others.” - Lakatos (1978)

The paper “Predictability in Ecology and Evolution” (PIEE, Hendry 2023) comprehensively frames our understanding of the diverse applications of prediction in ecology and evolution. However, an emphasis on predictive ability can lead us to overestimate foresight given the complex and contingent nature of biological systems (Mayr 1961). Integrating a structured categorization of prediction in ecology and evolution with tailored best practices could add to the paper’s grounded perspective (Tables 1-2).

The taxonomy of PIEE encompasses concepts such as prophecy and repeatability and avoids a one-size-fits-all approach. This approach suits the complex context and aims of prediction in our research fields. However, predictions range on a continuum from universal principles to specific forecasts, each with distinct utilities and limitations (Table 1). This spectrum reflects the trade-offs among realism, precision, and generality (Levins 1966). General principles provide theoretical guides but can oversimplify nonlinear dynamics (Gould and Lewontin 1979), and detailed quantitative predictions often fail when extended beyond their inductive scope.

Practices like replicating studies, comparing models, and providing effect sizes (Table 2) should capture the relevant contexts we need for proper interpretation without demanding impossible generalities. Such a goal can ultimately reflect the role of contingency in evolution (Wiens and Donoghue, 2004). Also, conveying historical context and information on the sensitivity of evolutionary processes to initial conditions can help. In general, categorizing prediction into types that teach us about our own practice can prevent us from presuming universal accuracy while keeping our fundamental goal of finding rules in biology.

In conclusion, while the PIEE paper sheds light on the diverse role of prediction in ecology and evolution, it might overstate the predictive power achievable for researchers, given the complex realities of nature. Combining a structured prediction framework (Table 1) with adaptable methodologies (Table 2, Lakatos, 1978), can strengthen PIEE’s string conceptual foundation. It should also help us embrace unpredictability and sharpen our scientific rigor. Recognizing intrinsic limitations, as highlighted by Mayr (1961), can equip researchers with epistemic humility to navigate ecological and evolutionary complexity pragmatically.

References

Gould, S. J., & Lewontin, R. C. (1979). The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proceedings of the Royal Society of London. Series B. Biological Sciences, 205(1161), 581-598.

Hendry, A. P. (2023). Prediction in ecology and evolution. BioScience, 73(3).

Lakatos, I. (1978). The Methodology of Scientific Research Programmes (Philosophical Papers Volume 1). Cambridge University Press.

Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421-431.

Mayr, E. (1961). Cause and effect in biology. Science, 134(3489), 1501-1506.

Wiens, J. J., & Donoghue, M. J. (2004). Historical biogeography, ecology and species richness. Trends in Ecology & Evolution, 19(12), 639-644.

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