Ecologists, especially community ecologists, are always looking for ways to infer process from pattern, cause from effect. Ideally, they’d like some way to do this that:
- Is based on previously-collected or easily-obtained observational data
- Is “off the shelf”, meaning that it can be implemented in a routine, “crank the handle” way, without the need for much customization or even thought from the user.
- Can be used in any system
Examples of previously- or currently-prominent ways to infer process from pattern in ecology include:
- randomization of species x site matrices to infer interspecific competition
- plotting coexisting species onto a phylogeny to infer contemporary coexistence mechanisms
- plotting local vs. regional species richness to infer whether local communities are closed to invasion, or whether local species richness and composition is just a random draw from the regional “species pool”
- using the shape of the species-abundance distribution to infer whether communities have neutral dynamics
- using ordination to infer the process dominating metacommunity dynamics
- the use of power law distributions of movement lengths to infer whether foraging animals follow Levy walks
- using body size ratios of co-occurring species to test for limiting similarity
- attractor reconstruction and convergent cross-mapping
The above approaches to inferring process from pattern all have something in common: none of them work, either in theory or practice. Which leads to the my question:
Has any widely applicable “off the shelf” method to infer process from pattern in ecology ever worked? Can anyone name one?
It doesn’t count if a method is merely “suggestive”. “Suggestive” is cheap; just eyeballing your data is suggestive! It doesn’t count if a method is potentially useful when combined with many other lines of evidence, because then those other lines of evidence are doing most of the work. And it doesn’t count if a method isn’t “off the shelf”. For instance, “develop and parameterize a dynamical model of your study system and then validate it with independent data” isn’t an “off the shelf” method. I’m talking about methods that were sold and used as a powerful way to cut through the complexity of ecology and provide a rigorous yet easy-to-follow path from pattern to process.
Another way to pose the question is to ask: what would have to be the case for a method based solely on observational data to allow more-or-less reliable inferences about underlying causal processes? Is it likely that any such method exists in ecology? If it did, is it possible that it would be off-the-shelf, broadly-applicable, and yet powerful? Based on the example of successful observation-based sciences like astronomy, which I’ve discussed previously, I doubt that any such method exists in ecology.
Note that I do think there are cases outside the physical sciences where such methods exist. I’m thinking of methods like the HKA test and its derivatives, used to test for selection in population and evolutionary genetics just based on gene sequence data (here is a brief review). Notably, much as with the case of astronomy, this is a case where we can write down a more or less complete list of the underlying processes that affect the observational data of interest, where we have a rigorous, quantitative theory of how those processes will affect the observed data, and (crucially) where different processes (here, selection vs. drift) are predicted to affect the data in quite different ways, leaving quite different “signatures”. In contrast, one common failing of putative methods for inferring process from pattern in ecology is that many processes or combinations of processes produce the same pattern.
My hope with this post is to raise the bar for any future proposal to infer process from pattern in ecology. Any such proposed method should have to meet a very high burden of proof that it works before it is widely adopted.
Note: The above is an edited and retitled version of a post that first ran in 2012. Sorry for the rerun. Meg, Brian, and I are all busy right now.