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Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction

Urheber*innen

Foster,  William J.
External Organizations;

Ayzel,  Georgy
External Organizations;

Münchmeyer,  Jannes
External Organizations;

Rettelbach,  Tabea
External Organizations;

/persons/resource/Niklas.Kitzmann

Kitzmann,  Niklas
Potsdam Institute for Climate Impact Research;

Isson,  Terry T.
External Organizations;

Mutti,  Maria
External Organizations;

Aberhan,  Martin
External Organizations;

Externe Ressourcen

https://doi.org/10.5061/dryad.hmgqnk9j7
(Ergänzendes Material)

Volltexte (frei zugänglich)

27592oa.pdf
(Verlagsversion), 949KB

Ergänzendes Material (frei zugänglich)
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Zitation

Foster, W. J., Ayzel, G., Münchmeyer, J., Rettelbach, T., Kitzmann, N., Isson, T. T., Mutti, M., Aberhan, M. (2022): Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. - Paleobiology, 48, 3, 357-371.
https://doi.org/10.1017/pab.2022.1


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_27592
Zusammenfassung
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.