Inferring the extinction risk of marine fish to inform global conservation priorities

PLoS Biol. 2024 Aug 29;22(8):e3002773. doi: 10.1371/journal.pbio.3002773. eCollection 2024 Aug.

Abstract

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.

MeSH terms

  • Animals
  • Aquatic Organisms
  • Biodiversity
  • Conservation of Natural Resources* / methods
  • Endangered Species*
  • Extinction, Biological*
  • Fishes*
  • Machine Learning
  • Oceans and Seas
  • Risk Assessment

Grants and funding

NL is supported by the Fondation pour la Recherche sur la Biodiversité (FRB) and La region Occitanie (BiodivOc) in the context of the CESAB project ‘Creating a global database of fish functional traits: integrating physiology and ecology across aquatic ecosystems’ (PHENOFISH). DM was partly funded through the 2017–2018 Belmont Forum and BiodivERsA REEF-FUTURES project under the BiodivScen ERA-Net COFUND programme and with funding from ANR, DFG, NSF, Royal Society, ERC and NSERC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.