Print Page Explaining the structure of ecosystems is one of the great challenges
of ecology. Simple models for food web structure aim at disentangling
the complexity of ecological interaction networks and detect the main
forces responsible for their shape. Trophic interactions are
influenced by species' traits, which in turn are largely determined by
evolutionary history. Closely related species are more likely to share
similar traits such as body size, feeding mode and habitat preference
than distant ones. Here we test whether evolutionary history --
represented by taxonomic classification -- provides valuable
information on food web structure. In doing so, we measure which
taxonomic ranks better explain species' interactions. Our analysis is
based on the partition of the species into taxonomic units. For each
partition, we compute the probability that a probabilistic model for
food web structure reproduces the data using this information
(likelihood).
Results/Conclusions
We find that taxonomic partitions produce significantly
higher likelihoods than expected at random. Marginal likelihoods
(Bayes factors) are used to perform model selection among taxonomic
ranks. We show that food webs are best explained by the coarser
taxonomic ranks (Kingdom to Class). The methods provide a way to
explicitly include evolutionary history in models for food web
structure.
