Inferring the extinction risk of marine fish to inform global conservation priorities
While extinction risk categorization is fundamental for building robust conservation planning for marine fishes, empirical data on occurrence and vulnerability to disturbances are still lacking for most marine teleost fish species, preventing the assessment of their International Union for the Conservation of Nature (IUCN) status. In this article, we predicted the IUCN status of marine fishes based on two machine learning algorithms, trained with available species occurrences, biological traits, taxonomy, and human uses. We found that extinction risk for marine fish species is higher than initially estimated by the IUCN, increasing from 2.5% to 12.7%. Species predicted as Threatened were mainly characterized by a small geographic range, a relatively large body size, and a low growth rate. Hotspots of predicted Threatened species peaked mainly in the South China Sea, the Philippine Sea, the Celebes Sea, the west coast Australia and North America. We also explored the consequences of including these predicted species' IUCN status in the prioritization of marine protected areas through conservation planning. We found a marked increase in prioritization ranks for subpolar and polar regions despite their low species richness. We suggest to integrate multifactorial ensemble learning to assess species extinction risk and offer a more complete view of endangered taxonomic groups to ultimately reach global conservation targets like the extending coverage of protected areas where species are the most vulnerable.
Authors
- Nicolas Mouquet (ORCID: https://orcid.org/0000-0003-1840-6984)
- Nicolas Casajus (ORCID: https://orcid.org/0000-0002-5537-5294)
- Camille P. Albouy (ORCID: https://orcid.org/0000-0003-1629-2389)
- Juliette Langlois (ORCID: https://orcid.org/0000-0003-2386-5739)
- Laure Velez (ORCID: https://orcid.org/0000-0001-9555-6770)
- Raphaël Seguin (ORCID: https://orcid.org/0000-0001-6946-2981)
- Thomas Claverie (ORCID: https://orcid.org/0000-0002-6258-4991)
- Nicolas Loiseau (ORCID: https://orcid.org/0000-0002-2469-1980)
- Sébastien Villéger (ORCID: https://orcid.org/0000-0002-2362-7178)
- Valentine Fleuré (ORCID: https://orcid.org/0000-0002-6567-1986)
- Camille Coux (ORCID: https://orcid.org/0000-0002-6346-9194)
- Agnès Duhamet
- David Mouillot
Institutions
- Centre National de la Recherche Scientifique (FR)
- Ifremer (FR)
- École Pratique des Hautes Études (FR)
- Institut Universitaire de France (FR)
- Université de Montpellier (FR)
- Université Paris Sciences et Lettres (FR)
- Swiss Federal Institute for Forest, Snow and Landscape Research (CH)
- ETH Zurich (CH)
- Centre d'Écologie Fonctionnelle et Évolutive (FR)
- Centre Hospitalier de Beauvais (FR)
- Marine Biodiversity Exploitation and Conservation (FR)
- Fondation Pour la Recherche Sur la Biodiversité (FR)
- Écologie Marine Tropicale des Océans Pacifique et Indien (RE)
- Institut de Recherche pour le Développement (FR)
Publication Details
- Journal
- PLoS Biology
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1371/journal.pbio.3002773
- Citations
- 7
- Primary Topic
- Identification and Quantification in Food
- Type
- article
- Field-Weighted Citation Impact
- 1.64
Funders
- National Science Foundation
- Biodiversa+
- Deutsche Forschungsgemeinschaft
- Agence Nationale de la Recherche
- Fondation pour la Recherche sur la Biodiversite
- Région Occitanie Pyrénées-Méditerranée
- Natural Sciences and Engineering Research Council of Canada