Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny

by Valeriano Parravicini, Jordan M. Casey, Nina M. D. Schiettekatte, Simon J. Brandl, Chlo é Pozas-Schacre, Jérémy Carlot, Graham J. Edgar, Nicholas A. J. Graham, Mireille Harmelin-Vivien, Michel Kulbicki, Giovanni Strona, Rick D. Stuart-Smith Understanding species’ roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, a nd reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high accuracy. We synthesize data from community-wide gut content analyses of tropical coral reef fishes worldwide, resulting in diet informa tion from 13,961 individuals belonging to 615 reef fish. We then use network analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. Finally, we use machine learning to test whether pairwise troph ic interactions can be predicted with accuracy. Our...
Source: PLoS Biology: Archived Table of Contents - Category: Biology Authors: Source Type: research