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More than half of data deficient species predicted to be threatened by extinction
The IUCN Red List of Threatened Species is essential for practical and theoretical efforts to protect biodiversity. However, species classified as “Data Deficient” (DD) regularly mislead practitioners due to their uncertain extinction risk. Here we present machine learning-derived probabilities of b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352662/ https://www.ncbi.nlm.nih.gov/pubmed/35927327 http://dx.doi.org/10.1038/s42003-022-03638-9 |
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author | Borgelt, Jan Dorber, Martin Høiberg, Marthe Alnes Verones, Francesca |
author_facet | Borgelt, Jan Dorber, Martin Høiberg, Marthe Alnes Verones, Francesca |
author_sort | Borgelt, Jan |
collection | PubMed |
description | The IUCN Red List of Threatened Species is essential for practical and theoretical efforts to protect biodiversity. However, species classified as “Data Deficient” (DD) regularly mislead practitioners due to their uncertain extinction risk. Here we present machine learning-derived probabilities of being threatened by extinction for 7699 DD species, comprising 17% of the entire IUCN spatial datasets. Our predictions suggest that DD species as a group may in fact be more threatened than data-sufficient species. We found that 85% of DD amphibians are likely to be threatened by extinction, as well as more than half of DD species in many other taxonomic groups, such as mammals and reptiles. Consequently, our predictions indicate that, amongst others, the conservation relevance of biodiversity hotspots in South America may be boosted by up to 20% if DD species were acknowledged. The predicted probabilities for DD species are highly variable across taxa and regions, implying current Red List-derived indices and priorities may be biased. |
format | Online Article Text |
id | pubmed-9352662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93526622022-08-06 More than half of data deficient species predicted to be threatened by extinction Borgelt, Jan Dorber, Martin Høiberg, Marthe Alnes Verones, Francesca Commun Biol Article The IUCN Red List of Threatened Species is essential for practical and theoretical efforts to protect biodiversity. However, species classified as “Data Deficient” (DD) regularly mislead practitioners due to their uncertain extinction risk. Here we present machine learning-derived probabilities of being threatened by extinction for 7699 DD species, comprising 17% of the entire IUCN spatial datasets. Our predictions suggest that DD species as a group may in fact be more threatened than data-sufficient species. We found that 85% of DD amphibians are likely to be threatened by extinction, as well as more than half of DD species in many other taxonomic groups, such as mammals and reptiles. Consequently, our predictions indicate that, amongst others, the conservation relevance of biodiversity hotspots in South America may be boosted by up to 20% if DD species were acknowledged. The predicted probabilities for DD species are highly variable across taxa and regions, implying current Red List-derived indices and priorities may be biased. Nature Publishing Group UK 2022-08-04 /pmc/articles/PMC9352662/ /pubmed/35927327 http://dx.doi.org/10.1038/s42003-022-03638-9 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Borgelt, Jan Dorber, Martin Høiberg, Marthe Alnes Verones, Francesca More than half of data deficient species predicted to be threatened by extinction |
title | More than half of data deficient species predicted to be threatened by extinction |
title_full | More than half of data deficient species predicted to be threatened by extinction |
title_fullStr | More than half of data deficient species predicted to be threatened by extinction |
title_full_unstemmed | More than half of data deficient species predicted to be threatened by extinction |
title_short | More than half of data deficient species predicted to be threatened by extinction |
title_sort | more than half of data deficient species predicted to be threatened by extinction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352662/ https://www.ncbi.nlm.nih.gov/pubmed/35927327 http://dx.doi.org/10.1038/s42003-022-03638-9 |
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