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

Type Article
Date 2020-12
Language English
Author(s) Parravicini ValerianoORCID1, Casey Jordan M.ORCID1, 2, Schiettekatte Nina M. D.ORCID1, Brandl Simon J.ORCID1, 2, 3, Pozas-Schacre ChloéORCID1, Carlot JérémyORCID1, Edgar Graham J.ORCID4, Graham Nicholas A. J.ORCID5, Harmelin-Vivien Mireille6, Kulbicki Michel7, Strona GiovanniORCID8, Stuart-Smith Rick D.3
Affiliation(s) 1 : PSL Université Paris: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, Perpignan, France, Laboratoire d’Excellence “CORAIL,” Perpignan, France
2 : Department of Marine Science, University of Texas at Austin, Marine Science Institute, Port Aransas, Texas, United States of America
3 : Centre for the Synthesis and Analysis of Biodiversity (CESAB), Institut Bouisson Bertrand, Montpellier, France
4 : Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
5 : Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
6 : Aix-Marseille Université, Institut Méditerranéen d’Océanologie, CNRS/INSU, Marseille, France
7 : UMR Entropie, LabEx Corail, IRD, Université de Perpignan, Perpignan, France
8 : University of Helsinki, Department of Bioscience, Helsinki, Finland
Source Plos Biology (1544-9173) (Public Library of Science (PLoS)), 2020-12 , Vol. 18 , N. 12 , P. e3000702 (20p.)
DOI 10.1371/journal.pbio.3000702
WOS© Times Cited 32

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, and 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 information 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 trophic interactions can be predicted with accuracy. Our models achieved a misclassification error of less than 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, as well as high-resolution probabilities of trophic interactions. By applying our framework to the most diverse vertebrate consumer group, we show that it can be applied to other organismal groups to advance reproducibility in trait-based ecology. Our work thus provides a viable approach to account for the complexity of predator–prey interactions in highly diverse ecosystems.

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Publisher's official version 20 2 MB Open access
S1 Fig. Confusion matrix showcasing the accuracy of the 8 trophic guild predictions from the leave-one-out cross validation based on the extrapolation of the Bayesian phylogenetic model. 385 KB Open access
S1 Table. Prey categories used to define the trophic guilds of coral reef fishes. 4 KB Open access
S2 Table. Summary of the 33 papers used to evaluate expert agreement on reef fish trophic guilds. 1 9 KB Open access
S3 Table. Global extrapolation to infer the probability of each of the 4554 reef fish species to belong to the 8 trophic guilds. 1 MB Open access
S4 Table. Probability of trophic interaction between the 4554 reef fish species and the 38 prey categories according to the extrapolation performed by the machine learning approach. 842 KB Open access
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Parravicini Valeriano, Casey Jordan M., Schiettekatte Nina M. D., Brandl Simon J., Pozas-Schacre Chloé, Carlot Jérémy, Edgar Graham J., Graham Nicholas A. J., Harmelin-Vivien Mireille, Kulbicki Michel, Strona Giovanni, Stuart-Smith Rick D. (2020). Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny. Plos Biology, 18(12), e3000702 (20p.). Publisher's official version : , Open Access version :