@article{fdi:010080580,
  title = {{D}elineating reef fish trophic guilds with global gut content data synthesis and phylogeny},
  author = {{P}arravicini, {V}. and {C}asey, {J}. {M}. and {S}chiettekatte, {N}. {M}. {D}. and {B}randl, {S}. {J}. and {P}ozas-{S}chacre, {C}. and {C}arlot, {J}. and {E}dgar, {G}. {J}. and {G}raham, {N}. {A}. {J}. and {H}armelin-{V}ivien, {M}. and {K}ulbicki, {M}ichel and {S}trona, {G}. and {S}tuart-{S}mith, {R}. {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{U}nderstanding species' roles in food webs requires an accurate assessment of their trophic niche. {H}owever, 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. {U}sing 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. {H}ere, 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. {W}e 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. {W}e then use network analysis to identify 8 trophic guilds and {B}ayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. {F}inally, we use machine learning to test whether pairwise trophic interactions can be predicted with accuracy. {O}ur 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. {B}y 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. {O}ur work thus provides a viable approach to account for the complexity of predator-prey interactions in highly diverse ecosystems.},
  keywords = {{MONDE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{PL}o{S} {B}iology},
  volume = {18},
  numero = {12},
  pages = {e3000702 [20 p.]},
  ISSN = {1544-9173},
  year = {2020},
  DOI = {10.1371/journal.pbio.3000702},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080580},
}