Untangling the roles of parasites in food webs with generative network models
Abigail Z. Jacobs, Jennifer A. Dunne, Cristopher Moore, Aaron Clauset

TL;DR
This study introduces a probabilistic generative model combining statistical physics and ecological data to analyze the role of parasites in food web structure, revealing that parasite-host interactions are distinct from other interactions.
Contribution
The paper presents a novel generative modeling approach that rigorously distinguishes parasite-related interactions from free-living species interactions in food webs.
Findings
Parasite-host interactions are statistically different from other interactions.
Predation on parasites and concomitant predation are similar to free-living predation.
The method effectively evaluates the impact of parasites on food web structure.
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 free-living…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Plant and animal studies · Evolution and Genetic Dynamics
