Reference - Detail
| RRC ID | 89644 |
|---|---|
| Author | Murphy CA, Olivos JA, Arismendi I, García-Berthou E, Johnson SL, Dunham J. |
| Title | Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes. |
| Journal | Nat Commun |
| Abstract |
Freshwater fishes are among the most threatened taxa, yet conservation assessments remain incomplete for many species. Freshwater fishes provide essential ecosystem services such as food security, recreational opportunities, and cultural significance. Despite heavy alterations to freshwater ecosystems, the reasons for species' sensitivity and resistance to imperilment are unclear. To address this need, we develop a machine learning framework to predict global imperilment status for 10,631 freshwater fish species using a comprehensive set of environmental, socioeconomic, and intrinsic species-level predictors. Using updated IUCN Red List data, we train and validate Random Forest classifiers to distinguish imperiled (Vulnerable, Endangered, Critically Endangered) from non-imperiled species. We examine the relative influence of 52 variables derived from 12 global sources describing extrinsic environmental and socioeconomic factors and intrinsic species-specific characteristics. Our models achieve higher accuracy for non-imperiled species (90.1%) compared to imperiled species (81.8%), reflecting the greater heterogeneity of threats and conditions driving imperilment. Across models, key predictors include habitat variables, taxonomic order, hydrological characteristics, and disturbance indicators, underscoring the interplay between ecology, geography, and human pressures. This integrative, reproducible approach demonstrates the utility of machine learning for guiding proactive conservation and provides a scalable framework for global biodiversity risk assessment. |
| Volume | 17(1) |
| Pages | 1661 |
| Published | 2026-2-16 |
| DOI | 10.1038/s41467-025-68154-w |
| PII | 10.1038/s41467-025-68154-w |
| Description | NIES data were referenced. |
| PMID | 41698904 |
| PMC | PMC12909873 |
| MeSH | Animals Biodiversity Classification Algorithms Conservation of Natural Resources* Ecosystem Endangered Species Fishes* / classification Fishes* / physiology Fresh Water* Machine Learning Predictive Learning Models Random Forest Socioeconomic Factors |
| Resource | |
| GBIF | Long-term fauna and flora records of the experimental forests of the Forest Research Station of Hokkaido University, Japan Biodiversity monitoring in Nakaikemi-shicchi Fish specimens of Bihoro Museum Fish Specimens of Water Environmental Field Station, Global and Local Environment Co-creation Institute, Ibaraki University Fish specimens of Natural History Museum, Kishiwada City Fish monitoring data in Lake Kasumigaura Fish collection of the Kagoshima University Museum Fish collection of the Museum of Nature and Human Activities, Hyogo Fish Collection of Yokosuka City Museum Fish Collection of Hokkaido University Fish Specimens of Graduate School of Bioresources Mie University Fish Collection of Natural History Museum and Institute, Chiba Zoological specimens of Gifu Prefectural Museum Underwater video and still-image dataset of fishes and other aquatic animals in Lake Biwa, Japan, observed via carp-mounted video loggers. Fish Collection of Coastal Branch of Natural History Museum and Institute, Chiba Fish specimens of Ryukyu University Museum (Fujukan) Fish specimens of Toyama Science Museum |