FN Archimer Export Format
PT J
TI Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny
BT 
AF 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.
AS 1:1;2:1,2;3:1;4:1,2,3;5:1;6:1;7:4;8:5;9:6;10:7;11:8;12:3;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;11:;12:;
C1 PSL Université Paris: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, Perpignan, France, Laboratoire d’Excellence “CORAIL,” Perpignan, France
   Department of Marine Science, University of Texas at Austin, Marine Science Institute, Port Aransas, Texas, United States of America
   Centre for the Synthesis and Analysis of Biodiversity (CESAB), Institut Bouisson Bertrand, Montpellier, France
   Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
   Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
   Aix-Marseille Université, Institut Méditerranéen d’Océanologie, CNRS/INSU, Marseille, France
   UMR Entropie, LabEx Corail, IRD, Université de Perpignan, Perpignan, France
   University of Helsinki, Department of Bioscience, Helsinki, Finland
C2 UNIV PERPIGNAN, FRANCE
   UNIV TEXAS AUSTIN, USA
   CESAB, FRANCE
   UNIV TASMANIA, AUSTRALIA
   UNIV LANCASTER, UK
   UNIV AIX MARSEILLE, FRANCE
   UNIV PERPIGNAN, FRANCE
   UNIV HELSINKI, FINLAND
UM ENTROPIE
IN WOS Cotutelle UMR
   DOAJ
   copubli-france
   copubli-europe
   copubli-univ-france
   copubli-int-hors-europe
IF 8.029
TC 35
UR https://archimer.ifremer.fr/doc/00688/79980/82934.pdf
   https://archimer.ifremer.fr/doc/00688/79980/82935.png
   https://archimer.ifremer.fr/doc/00688/79980/82936.csv
   https://archimer.ifremer.fr/doc/00688/79980/82937.pdf
   https://archimer.ifremer.fr/doc/00688/79980/82938.csv
   https://archimer.ifremer.fr/doc/00688/79980/82939.csv
LA English
DT Article
AB 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. 
PY 2020
PD DEC
SO Plos Biology
SN 1544-9173
PU Public Library of Science (PLoS)
VL 18
IS 12
UT 000603611300002
DI 10.1371/journal.pbio.3000702
ID 79980
ER

EF