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Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework

Terrestrial mammals face a severe crisis of habitat loss worldwide. Therefore, assessing information on habitat loss throughout different time periods is crucial for assessing species’ conservation statuses based on the IUCN Red List system. To support the national extinction risk assessment in Braz...

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Autores principales: Butti, Mariella, Pacca, Luciana, Santos, Paloma, Alonso, André C., Buss, Gerson, Ludwig, Gabriela, Jerusalinsky, Leandro, Martins, Amely B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753759/
https://www.ncbi.nlm.nih.gov/pubmed/36530404
http://dx.doi.org/10.7717/peerj.14289
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author Butti, Mariella
Pacca, Luciana
Santos, Paloma
Alonso, André C.
Buss, Gerson
Ludwig, Gabriela
Jerusalinsky, Leandro
Martins, Amely B.
author_facet Butti, Mariella
Pacca, Luciana
Santos, Paloma
Alonso, André C.
Buss, Gerson
Ludwig, Gabriela
Jerusalinsky, Leandro
Martins, Amely B.
author_sort Butti, Mariella
collection PubMed
description Terrestrial mammals face a severe crisis of habitat loss worldwide. Therefore, assessing information on habitat loss throughout different time periods is crucial for assessing species’ conservation statuses based on the IUCN Red List system. To support the national extinction risk assessment in Brazil (2016–2022), we developed a script that uses the MapBiomas Project 6.0 data source of land cover and land use (annual maps at 30 m scale) within the Google Earth Engine (GEE) platform to calculate habitat loss. We defined suitable habitats from the MapBiomas Project land cover classification for 190 mammalian taxa, according to each species range map and ecological characteristics. We considered a period of three generation lengths to assess habitat loss in accordance with the Red List assessment criteria. We used the script to estimate changes in available habitat throughout the analyzed period within the species’ known ranges. The results indicated that habitat loss occurred within 94.3% of the analyzed taxa range, with the Carnivora order suffering the greatest habitat loss, followed by the Cingulata order. These analyses may be decisive for applying criteria, defining categories during the assessment of at least 17 species (9%), enriching discussions, and raising new questions for several other species. We considered the outcome of estimating habitat loss for various taxa when applying criterion A, which refers to population reduction, thus supporting more accurate inferences about past population declines.
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spelling pubmed-97537592022-12-16 Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework Butti, Mariella Pacca, Luciana Santos, Paloma Alonso, André C. Buss, Gerson Ludwig, Gabriela Jerusalinsky, Leandro Martins, Amely B. PeerJ Biodiversity Terrestrial mammals face a severe crisis of habitat loss worldwide. Therefore, assessing information on habitat loss throughout different time periods is crucial for assessing species’ conservation statuses based on the IUCN Red List system. To support the national extinction risk assessment in Brazil (2016–2022), we developed a script that uses the MapBiomas Project 6.0 data source of land cover and land use (annual maps at 30 m scale) within the Google Earth Engine (GEE) platform to calculate habitat loss. We defined suitable habitats from the MapBiomas Project land cover classification for 190 mammalian taxa, according to each species range map and ecological characteristics. We considered a period of three generation lengths to assess habitat loss in accordance with the Red List assessment criteria. We used the script to estimate changes in available habitat throughout the analyzed period within the species’ known ranges. The results indicated that habitat loss occurred within 94.3% of the analyzed taxa range, with the Carnivora order suffering the greatest habitat loss, followed by the Cingulata order. These analyses may be decisive for applying criteria, defining categories during the assessment of at least 17 species (9%), enriching discussions, and raising new questions for several other species. We considered the outcome of estimating habitat loss for various taxa when applying criterion A, which refers to population reduction, thus supporting more accurate inferences about past population declines. PeerJ Inc. 2022-12-12 /pmc/articles/PMC9753759/ /pubmed/36530404 http://dx.doi.org/10.7717/peerj.14289 Text en © 2022 Butti et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biodiversity
Butti, Mariella
Pacca, Luciana
Santos, Paloma
Alonso, André C.
Buss, Gerson
Ludwig, Gabriela
Jerusalinsky, Leandro
Martins, Amely B.
Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title_full Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title_fullStr Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title_full_unstemmed Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title_short Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
title_sort habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework
topic Biodiversity
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753759/
https://www.ncbi.nlm.nih.gov/pubmed/36530404
http://dx.doi.org/10.7717/peerj.14289
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