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Predicting the conservation status of data‐deficient species

Predicting the conservation status of data‐deficient species There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Conservation Biology Wiley

Predicting the conservation status of data‐deficient species

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References (57)

Publisher
Wiley
Copyright
© 2015, Society for Conservation Biology
ISSN
0888-8892
eISSN
1523-1739
DOI
10.1111/cobi.12372
pmid
25124400
Publisher site
See Article on Publisher Site

Abstract

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos

Journal

Conservation BiologyWiley

Published: Feb 1, 2015

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