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

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 expe...

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Published inPLoS biology Vol. 18; no. 12; p. e3000702
Main Authors 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.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 28.12.2020
Public Library of Science (PLoS)
Subjects
Agreements
Animals
Bayes Theorem
Biodiversity
Biodiversity and Ecology
Biology and Life Sciences
Body Size
Classification
Classification schemes
Computer and Information Sciences
Coral reef ecology
Coral Reefs
Datasets
Diet
Earth Sciences
Ecology
Ecology and Environmental Sciences
Ecosystem
Ecosystems
Environmental Sciences
Fish
Fishes - metabolism
Fishes - microbiology
Food Chain
Functional groups
Gastrointestinal Microbiome - physiology
Go/no-go discrimination learning
Guilds
Herbivores
Learning algorithms
Life history
Machine learning
Medicine and Health Sciences
Methods and Resources
Models, Theoretical
Niche overlap
Ontogeny
Phylogeny
Physiological aspects
Prey
Redundancy
Reef fish
Reproducibility of Results
Species richness
Taxonomy
Trophic levels
Trophic relationships
Tropical fish
France
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Abstract 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.
AbstractList 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.
[...]the degree of niche overlap among species can be a major determinant of relationships among species richness [5], ecosystem productivity [6–8], and vulnerability [9] since limited functional redundancy can make ecosystems more prone to lose entire energetic pathways [10–12]. [...]the use of functional groups enables the quantification of functional metrics (e.g., functional richness and functional redundancy) from a standard community data matrix without complex manipulative experiments [25–27]. [...]dietary data are multidimensional (i.e., various prey items can be recorded across individuals), influenced by ontogenetic and spatio-temporal variables (i.e., life history, time, and location can incur dietary shifts), and prone to methodological differences and thus observer bias. [...]while some exceptions exist [30,33], our capacity to define coral reef trophic interactions still largely depends on discrete trophic categories defined by expert opinion [27]. Here, we quantify expert agreement in the definition of coral reef fish trophic guilds and propose a novel, quantitative framework to delineate trophic guilds. [...]we test whether machine learning allows us to go beyond the definition of discrete categories, accurately predicting individual trophic interactions in hyperdiverse ecosystems.
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.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.
[...]the degree of niche overlap among species can be a major determinant of relationships among species richness [5], ecosystem productivity [6–8], and vulnerability [9] since limited functional redundancy can make ecosystems more prone to lose entire energetic pathways [10–12]. [...]the use of functional groups enables the quantification of functional metrics (e.g., functional richness and functional redundancy) from a standard community data matrix without complex manipulative experiments [25–27]. [...]dietary data are multidimensional (i.e., various prey items can be recorded across individuals), influenced by ontogenetic and spatio-temporal variables (i.e., life history, time, and location can incur dietary shifts), and prone to methodological differences and thus observer bias. [...]while some exceptions exist [30,33], our capacity to define coral reef trophic interactions still largely depends on discrete trophic categories defined by expert opinion [27]. Here, we quantify expert agreement in the definition of coral reef fish trophic guilds and propose a novel, quantitative framework to delineate trophic guilds. [...]we test whether machine learning allows us to go beyond the definition of discrete categories, accurately predicting individual trophic interactions in hyperdiverse ecosystems.
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. The diversity of life on our planet has produced a remarkable variety of biological traits that characterize different species, and such traits are widely considered an alternative to taxonomy to increase our understanding of biodiversity and ecosystem functioning. This study presents an unbiased and fully reproducible framework to delineate trophic guilds in reef fishes.
Audience Academic
Author Kulbicki, Michel
Brandl, Simon J.
Casey, Jordan M.
Graham, Nicholas A. J.
Edgar, Graham J.
Schiettekatte, Nina M. D.
Harmelin-Vivien, Mireille
Pozas-Schacre, Chloé
Strona, Giovanni
Carlot, Jérémy
Stuart-Smith, Rick D.
Parravicini, Valeriano
AuthorAffiliation 1 PSL Université Paris: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, Perpignan, France
9 University of Helsinki, Department of Bioscience, Helsinki, Finland
Estacion Biologica de Doñana CSIC, SPAIN
2 Laboratoire d’Excellence “CORAIL,” Perpignan, France
4 Centre for the Synthesis and Analysis of Biodiversity (CESAB), Institut Bouisson Bertrand, Montpellier, France
5 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
8 UMR Entropie, LabEx Corail, IRD, Université de Perpignan, Perpignan, France
3 Department of Marine Science, University of Texas at Austin, Marine Science Institute, Port Aransas, Texas, United States of America
6 Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
7 Aix-Marseille Université, Institut Méditerranéen d’Océanologie, CNRS/INSU, Marseille, France
AuthorAffiliation_xml – name: 8 UMR Entropie, LabEx Corail, IRD, Université de Perpignan, Perpignan, France
– name: 5 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
– name: Estacion Biologica de Doñana CSIC, SPAIN
– name: 7 Aix-Marseille Université, Institut Méditerranéen d’Océanologie, CNRS/INSU, Marseille, France
– name: 2 Laboratoire d’Excellence “CORAIL,” Perpignan, France
– name: 4 Centre for the Synthesis and Analysis of Biodiversity (CESAB), Institut Bouisson Bertrand, Montpellier, France
– name: 3 Department of Marine Science, University of Texas at Austin, Marine Science Institute, Port Aransas, Texas, United States of America
– name: 6 Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
– name: 1 PSL Université Paris: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, Perpignan, France
– name: 9 University of Helsinki, Department of Bioscience, Helsinki, Finland
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  orcidid: 0000-0002-3408-1625
  surname: Parravicini
  fullname: Parravicini, Valeriano
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  surname: Stuart-Smith
  fullname: Stuart-Smith, Rick D.
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33370276$$D View this record in MEDLINE/PubMed
https://amu.hal.science/hal-03105771$$DView record in HAL
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ContentType Journal Article
Copyright COPYRIGHT 2020 Public Library of Science
2020 Parravicini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Copyright_xml – notice: COPYRIGHT 2020 Public Library of Science
– notice: 2020 Parravicini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet Understanding species’ roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic...
Understanding species' roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic...
[...]the degree of niche overlap among species can be a major determinant of relationships among species richness [5], ecosystem productivity [6–8], and...
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SubjectTerms Agreements
Animals
Bayes Theorem
Biodiversity
Biodiversity and Ecology
Biology and Life Sciences
Body Size
Classification
Classification schemes
Computer and Information Sciences
Coral reef ecology
Coral Reefs
Datasets
Diet
Earth Sciences
Ecology
Ecology and Environmental Sciences
Ecosystem
Ecosystems
Environmental Sciences
Fish
Fishes - metabolism
Fishes - microbiology
Food Chain
Functional groups
Gastrointestinal Microbiome - physiology
Go/no-go discrimination learning
Guilds
Herbivores
Learning algorithms
Life history
Machine learning
Medicine and Health Sciences
Methods and Resources
Models, Theoretical
Niche overlap
Ontogeny
Phylogeny
Physiological aspects
Prey
Redundancy
Reef fish
Reproducibility of Results
Species richness
Taxonomy
Trophic levels
Trophic relationships
Tropical fish
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Title Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny
URI https://www.ncbi.nlm.nih.gov/pubmed/33370276
https://www.proquest.com/docview/2479139716
https://www.proquest.com/docview/2473747447
https://amu.hal.science/hal-03105771
https://pubmed.ncbi.nlm.nih.gov/PMC7793298
https://doaj.org/article/66a5a48013de401d99c12ef71d274652
http://dx.doi.org/10.1371/journal.pbio.3000702
Volume 18
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