TY - JOUR

T1 - Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny

AU - Parravicini, V.

AU - Casey, J.M.

AU - Schiettekatte, N.M.D.

AU - Brandl, S.J.

AU - Pozas-Schacre, C.

AU - Carlot, J.

AU - Edgar, G.J.

AU - Graham, N.A.J.

AU - Harmelin-Vivien, M.

AU - Kulbicki, M.

AU - Strona, G.

AU - Stuart-Smith, R.D.

PY - 2020/12/28

Y1 - 2020/12/28

N2 - 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. Copyright:

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. Copyright:

KW - adult

KW - article

KW - body size

KW - consumer

KW - content analysis

KW - coral reef

KW - data synthesis

KW - diet

KW - ecology

KW - female

KW - fish

KW - gastrointestinal tract

KW - human

KW - human experiment

KW - machine learning

KW - major clinical study

KW - male

KW - nonhuman

KW - phylogeny

KW - predator prey interaction

KW - probability

KW - quantitative analysis

KW - reproducibility

U2 - 10.1371/journal.pbio.3000702

DO - 10.1371/journal.pbio.3000702

M3 - Journal article

VL - 18

JO - Plos Biology

JF - Plos Biology

SN - 1544-9173

IS - 12 December

M1 - e3000702

ER -