Morphological traits and machine learning for genetic lineage prediction of two reef-building corals

Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plasticity and convergence. Understanding morphological variation among species is crucial to studying coral distribution, life history, ecology, and evolution. Here, we present an application of Random Forest models for coral species identification based on morphological annotation of the corallum and corallites. We show that the integration of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through genome-wide, genetical hierarchical clustering, and coalescence analyses for the Tara Pacific Expedition. While Porites only included three tentative species, most Pocillopora species were accounted by included specimens from the western Indian Ocean, tropical Southwestern Pacific, and southeast Polynesia. Two Random Forest models per genus were trained on the morphological annotations using the genetic lineage labels. One model was developed for in-situ image identification and used corallum traits measured from in-situ photographs. Another model for integrative species identification combined corallum and corallite data measured on scanning electron micrographs. Random Forest models outperformed traditional dimension reduction methods like PCA and FAMD followed by k-means and hierarchical clustering by classifying the correct genetic lineage despite morphological clusters overlapping. This machine learning approach is reproducible, cost-effective, and accessible, reducing the need for taxonomic expertise. It can complement molecular and phylogenetic studies and support image identification, highlighting its potential to advance a coral integrative taxonomy workflow.

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Publication Details

Journal
PLoS ONE
Published
2025-06-18
DOI
https://doi.org/10.1371/journal.pone.0326095
Citations
3
Primary Topic
Coral and Marine Ecosystems Studies
Type
article
Field-Weighted Citation Impact
5.15

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article

Morphological traits and machine learning for genetic lineage prediction of two reef-building corals

Hélène Magalon, Eric Armstrong, Julie Poulain, Sylvain Agostini et al.
3 citations
PLoS ONE
Coral and Marine Ecosystems Studies
5.15
article

Morphological traits and machine learning for genetic lineage prediction of two reef-building corals

Hélène Magalon, Eric Armstrong, Julie Poulain, Sylvain Agostini, Guinther Mitushasi, Barbara Porro, Guillaume Bourdin, Nicolas Oury, Serge Planes, Didier Forcioli, Clémentine Moulin, Sarah Romac, Guillaume Iwankow, Maggie M. Reddy, Benjamin C. C. Hume, David A. Paz‐García, Denis Allemand, Yuko F. Kitano, Émilie Boissin, Christian Robert Voolstra
article en
3 citations

Abstract

Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plasticity and convergence. Understanding morphological variation among species is crucial to studying coral distribution, life history, ecology, and evolution. Here, we present an application of Random Forest models for coral species identification based on morphological annotation of the corallum and corallites. We show that the integration of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through genome-wide, genetical hierarchical clustering, and coalescence analyses for the Tara Pacific Expedition. While Porites only included three tentative species, most Pocillopora species were accounted by included specimens from the western Indian Ocean, tropical Southwestern Pacific, and southeast Polynesia. Two Random Forest models per genus were trained on the morphological annotations using the genetic lineage labels. One model was developed for in-situ image identification and used corallum traits measured from in-situ photographs. Another model for integrative species identification combined corallum and corallite data measured on scanning electron micrographs. Random Forest models outperformed traditional dimension reduction methods like PCA and FAMD followed by k-means and hierarchical clustering by classifying the correct genetic lineage despite morphological clusters overlapping. This machine learning approach is reproducible, cost-effective, and accessible, reducing the need for taxonomic expertise. It can complement molecular and phylogenetic studies and support image identification, highlighting its potential to advance a coral integrative taxonomy workflow.

PLoS ONEVol. 20(6)
Centre National de la Recherche Scientifique (FR), International University of Monaco (MC), University of Tsukuba (JP), Université de Perpignan (FR), Ifremer (FR), École Pratique des Hautes Études (FR), Ollscoil na Gaillimhe – University of Galway (IE), University of Konstanz (DE), University of Reunion Island (RE), Université Paris Sciences et Lettres (FR), Station Biologique de Roscoff (FR), Sorbonne Université (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Labex Corail (FR), Adaptation et Diversité en Milieu Marin (FR), Systématique, adaptation, évolution (FR), Scientific Centre of Monaco (MC), Centro de Investigaciones Biológicas Margarita Salas (ES), Japan Wildlife Research Center (JP), Institut de Recherche pour le Développement (FR), Instituto Politécnico Nacional (MX), University of Maine (US)
Ocean Foundation, Fondation Tara Océan, Institut National de la Santé et de la Recherche Médicale, Weizmann Institute of Science, Université de Perpignan Via Domitia, Centre National de la Recherche Scientifique, University of Tsukuba, Université Paris-Saclay, Centre Scientifique de Monaco, Université de Recherche Paris Sciences et Lettres, Sorbonne Université, Université de Versailles Saint-Quentin-en-Yvelines
Life below water
Openalex Percentile: Top 5%
Coral and Marine Ecosystems Studies
5.15
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