Our paper on modeling food webs was just published in PLoS Computational Biology. Here, I was happy to bring the statistics I've learned from phylogenetic analysis to an entirely different field. I advised Ed Baskerville in implementing MCMC and marginal likelihood estimation for network data. In this case, the data is a matrix of predator-prey relationships, which can be thought of as a network of directed edges specifying who-eats-whom. We investigated structure in the Serengeti food web through a model in which groups of species behave similarly to one another in terms of what species they eat and what species they are eaten by. The inferred model shows a high degree of trophic and spatial clustering in which a number of spatially distinct plant groups are fed upon by a few wider-ranging herbivore groups, which are in turn fed upon by just a couple of predator groups.

Also of possible interest, the supporting appendix provides a nice overview of the use Bayesian methods for inference on network data. The model we present here really should be useful in a variety of biological contexts; genetic regulatory networks and protein interaction networks immediately come to mind. Photo by Andy Dobson.