Inferring predator–prey interactions in food webs 

Justin P. F. Pomeranz Ross M. Thompson Timothée Poisot Jon S. Harding

1. Food webs are a powerful way to represent the diversity, structure, and function of ecological systems. However, the accurate description of food webs requires significant effort in time and resources, limiting their widespread use in ecological studies. Newly published methods allow for the inference of feeding interactions using proxy variables. Here, we compare the accuracy of two recently described methods, as well as describe a composite model of the two, for the inference of feeding interactions using a large, well‐described dataset.
2. Both niche and neutral processes are involved in determining whether or not two species will form a feeding link in communities. Three different models for determining niche constraints of feeding interactions are compared, and all three models are extended by incorporating neutral processes, based on relative abundances. The three models compared here infer niche processes through (a) phylogenetic relationships, (b) local species trait distributions (e.g., body size), and (c) a composite of phylogeny and local traits.
3. We show that all three methods perform well at predicting individual species interactions, and that these individual predictions scale up to the network level, resulting in food web structure of inferred networks being similar to their empirical counterparts.
4. Our results indicate that inferring food web structure using phylogenies can be an efficient way of getting summary webs with minimal data, and offers a conservative test of changes in food web structure, particularly when there is low species turnover between sites. Inferences made using traits require more data, but allows for greater understanding of the mechanisms underlying trophic interactions. A composite model of the two methods provides a framework for investigating the importance of how phylogeny, trait distributions, and relative abundances, affect species interactions, and network structure.

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https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13125

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