Untangling the roles of parasites in food webs with generative network models

Abstract

Food webs represent the set of consumer-resource interactions among a set of species that co-occur in a habitat, but most food web studies have omitted parasites and their interactions. Recent studies have provided conflicting evidence on whether including parasites changes food web structure, with some suggesting that parasitic interactions are structurally distinct from those among free-living species while others claim the opposite. Here, we describe a principled method for understanding food web structure that combines an efficient optimization algorithm from statistical physics called parallel tempering with a probabilistic generalization of the empirically well-supported food web niche model. This generative model approach allows us to rigorously estimate the degree to which interactions that involve parasites are statistically distinguishable from interactions among freeliving species, whether parasite niches behave similarly to free-living niches, and the degree to which existing hypotheses about food web structure are naturally recovered. We apply this method to the well-studied Flensburg Fjord food web and show that while predation on parasites, concomitant predation of parasites, and parasitic intraguild trophic interactions are largely indistinguishable from free-living predation interactions, parasite-host interactions are different. These results provide a powerful new tool for evaluating the impact of classes of species and interactions on food web structure to shed new light on the roles of parasites in food webs.

A physical model for efficient ranking in networks

“…These models typically have complicated posterior distributions with many local optima, requiring Monte Carlo methods (e.g. [33]) that do not scale efficiently to large networks. In our case, f (s i − s j ) is a Gaussian centered at 1, and the posterior converges to the multivariate Gaussian Eq.…”

“…Finally, there are fully generative models such as the probabilistic niche model of ecology (31)(32)(33), models of friendship based on social status (9), and, more generally, latent space models (34), which assign probabilities to the existence and direction of edges based on real-valued positions in social space. However, inference of these models tends to be difficult, with many local optima.…”

“…This model belongs to a larger family of generative models considered in ecology and network theory (9,31,32), and, more generally, the class of latent space models (34), where an edge points from i to j with probability f(s i − s j ) for some function f. These models typically have complicated posterior distributions with many local optima, requiring Monte Carlo methods [for example, (33)] that do not scale efficiently to large networks. In our case, f(s i − s j ) is a Gaussian centered at 1, and the posterior converges to the multivariate Gaussian Eq.…”