Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction
Abstract The end-Permian mass extinction occurred alongside a large swath of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity, as it can reveal cr...
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Online Access: | http://dx.doi.org/10.1017/pab.2022.1 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S009483732200001X |
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crcambridgeupr:10.1017/pab.2022.1 2024-06-23T07:55:52+00:00 Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction Foster, William J. Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas H. Isson, Terry T. Mutti, Maria Aberhan, Martin Deutsche Forschungsgemeinschaft 2022 http://dx.doi.org/10.1017/pab.2022.1 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S009483732200001X en eng Cambridge University Press (CUP) https://creativecommons.org/licenses/by/4.0/ Paleobiology volume 48, issue 3, page 357-371 ISSN 0094-8373 1938-5331 journal-article 2022 crcambridgeupr https://doi.org/10.1017/pab.2022.1 2024-05-29T08:06:08Z Abstract The end-Permian mass extinction occurred alongside a large swath of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity, as it can reveal critical information on organismic traits as key determinants of extinction and survival. Here we show that machine learning algorithms, specifically gradient boosted decision trees, can be used to identify determinants of extinction as well as to predict extinction risk. To understand which factors led to the end-Permian mass extinction during an extreme global warming event, we quantified the ecological selectivity of marine extinctions in the well-studied South China region. We find that extinction selectivity varies between different groups of organisms and that a synergy of multiple environmental stressors best explains the overall end-Permian extinction selectivity pattern. Extinction risk was greater for genera that had a low species richness, narrow bathymetric ranges limited to deep-water habitats, a stationary mode of life, a siliceous skeleton, or, less critically, calcitic skeletons. These selective losses directly link the extinctions to the environmental effects of rapid injections of carbon dioxide into the ocean–atmosphere system, specifically the combined effects of expanded oxygen minimum zones, rapid warming, and potentially ocean acidification. Article in Journal/Newspaper Ocean acidification Cambridge University Press Paleobiology 1 15 |
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Cambridge University Press |
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crcambridgeupr |
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English |
description |
Abstract The end-Permian mass extinction occurred alongside a large swath of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity, as it can reveal critical information on organismic traits as key determinants of extinction and survival. Here we show that machine learning algorithms, specifically gradient boosted decision trees, can be used to identify determinants of extinction as well as to predict extinction risk. To understand which factors led to the end-Permian mass extinction during an extreme global warming event, we quantified the ecological selectivity of marine extinctions in the well-studied South China region. We find that extinction selectivity varies between different groups of organisms and that a synergy of multiple environmental stressors best explains the overall end-Permian extinction selectivity pattern. Extinction risk was greater for genera that had a low species richness, narrow bathymetric ranges limited to deep-water habitats, a stationary mode of life, a siliceous skeleton, or, less critically, calcitic skeletons. These selective losses directly link the extinctions to the environmental effects of rapid injections of carbon dioxide into the ocean–atmosphere system, specifically the combined effects of expanded oxygen minimum zones, rapid warming, and potentially ocean acidification. |
author2 |
Deutsche Forschungsgemeinschaft |
format |
Article in Journal/Newspaper |
author |
Foster, William J. Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas H. Isson, Terry T. Mutti, Maria Aberhan, Martin |
spellingShingle |
Foster, William J. Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas H. Isson, Terry T. Mutti, Maria Aberhan, Martin Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
author_facet |
Foster, William J. Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas H. Isson, Terry T. Mutti, Maria Aberhan, Martin |
author_sort |
Foster, William J. |
title |
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_short |
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_full |
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_fullStr |
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_full_unstemmed |
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_sort |
machine learning identifies ecological selectivity patterns across the end-permian mass extinction |
publisher |
Cambridge University Press (CUP) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1017/pab.2022.1 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S009483732200001X |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Paleobiology volume 48, issue 3, page 357-371 ISSN 0094-8373 1938-5331 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/pab.2022.1 |
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Paleobiology |
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1 |
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15 |
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1802648650390700032 |