Statisticians, meet ecologists

Interesting discussion thread over at statistician Andrew Gelman’s blog, about time series analysis of the lynx-hare cycle. Standard phenomenological statistical models (autoregressive moving average models) don’t fit or predict these data all that well. Andrew links approvingly to a recent statistical paper which does much better by fitting a simple mechanistic model–indeed, a laughably simple model, the original Lotka-Volterra predator-prey model! Ecologists Eric Pedersen, Ben Bolker, and myself then showed up in the comments to point out that ecologists have been mechanistically modeling the lynx-hare cycle and other ecological time series for 20 years now. I think this work–by Royama, Turchin, Wood, McCauley, Ellner, Harrison, Rees, Kendall, Bjornstad, Grenfell, King, Keeling, Ben himself, and numerous others*, is some of the best and most important ecological work of my generation. This work has always involved collaboration between ecologists, applied statisticians, and people who wear both hats, but apparently it’s not widely known among statisticians. Hopefully the linked thread will help to change that, as Andrew’s blog is very widely-read by statisticians.

Which does raise the question, are there other ways to increase cross-talk between statisticians and ecologists? NIMBioS is doing its part, and IIRC the Canadian statistical society had a special session on mechanistic time series models at its annual meeting a couple of years ago. What else could be done? Certainly sounds like there’s an opportunity for somebody (not me) to write some sort of perspectives-type review paper for a stats journal, highlighting the success of mechanistic time series modeling in population and disease ecology.

And while we’re at it, how about more cross-talk between the population and disease ecologists who’ve been doing most of this work, and ecologists working in other areas? Ecologists as a whole are increasingly statistically sophisticated. But often that sophistication is at the service of fitting and testing purely phenomenological statistical models. This is problematic, because translating the predictions of mechanistic ecological models into a form that can be evaluated by phenomenological statistical models often is tricky. Unfortunately, this translation process often seems to be based on nothing more than intuition and arm-waving, leading to an unfortunate trend towards using rigorous, sophisticated methods to test shaky, unsophisticated hypotheses. Why not try to follow the lead of the best population and disease ecology and fit mechanistic models directly to our data? And don’t say “we can’t do that because we don’t have enough data”, because one thing ecologists should not be in this LTER–NCEAS–NutNet–NEON–CIEE era is data-limited.

*My own efforts along these lines are so far quite modest and not without problems. But I hope to do better in future. A long-term ambition of mine is to apply this approach to many-species communities.