Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction
The end-Permian mass extinction occurred alongside a large swathe 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 in...
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ftzenodo:oai:zenodo.org:5729046 2024-09-15T18:28:15+00:00 Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction Foster, William Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas Isson, Terry Mutti, Maria Aberhan, Martin 2021-12-20 https://doi.org/10.5281/zenodo.5729046 unknown Zenodo https://doi.org/10.5061/dryad.hmgqnk9j7 https://zenodo.org/communities/dryad https://doi.org/10.5281/zenodo.5729045 https://doi.org/10.5281/zenodo.5729046 oai:zenodo.org:5729046 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.572904610.5061/dryad.hmgqnk9j710.5281/zenodo.5729045 2024-07-25T16:54:18Z The end-Permian mass extinction occurred alongside a large swathe 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 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, had narrow bathymetric ranges limited to deep-water habitats, had a stationary mode of life, possessed a siliceous skeleton or, less critically, had calcitic skeletons. These selective losses directly link the extinction 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. Funding provided by: Geo.X* Crossref Funder Registry ID: Award Number: SO_087_GeoX Other/Unknown Material Ocean acidification Zenodo |
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The end-Permian mass extinction occurred alongside a large swathe 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 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, had narrow bathymetric ranges limited to deep-water habitats, had a stationary mode of life, possessed a siliceous skeleton or, less critically, had calcitic skeletons. These selective losses directly link the extinction 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. Funding provided by: Geo.X* Crossref Funder Registry ID: Award Number: SO_087_GeoX |
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Other/Unknown Material |
author |
Foster, William Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas Isson, Terry Mutti, Maria Aberhan, Martin |
spellingShingle |
Foster, William Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas Isson, Terry Mutti, Maria Aberhan, Martin Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
author_facet |
Foster, William Ayzel, Georgy Münchmeyer, Jannes Rettelbach, Tabea Kitzmann, Niklas Isson, Terry Mutti, Maria Aberhan, Martin |
author_sort |
Foster, William |
title |
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_short |
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_full |
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_fullStr |
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_full_unstemmed |
Data from: Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction |
title_sort |
data from: machine learning identifies ecological selectivity patterns across the end-permian mass extinction |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://doi.org/10.5281/zenodo.5729046 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_relation |
https://doi.org/10.5061/dryad.hmgqnk9j7 https://zenodo.org/communities/dryad https://doi.org/10.5281/zenodo.5729045 https://doi.org/10.5281/zenodo.5729046 oai:zenodo.org:5729046 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.572904610.5061/dryad.hmgqnk9j710.5281/zenodo.5729045 |
_version_ |
1810469580138610688 |