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


Contact
Tabea.Rettelbach [ at ] awi.de

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.



Item Type
Article
Authors
Divisions
Primary Division
Programs
Primary Topic
Helmholtz Cross Cutting Activity (2021-2027)
N/A
Publication Status
Published
Eprint ID
56657
DOI 10.1017/pab.2022.1

Cite as
Foster, W. J. , Ayzel, G. , Münchmeyer, J. , Rettelbach, T. , Kitzmann, N. H. , Isson, T. T. , Mutti, M. and Aberhan, M. (2022): Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction , Paleobiology, pp. 1-15 . doi: 10.1017/pab.2022.1


Share


Citation

Research Platforms
N/A

Campaigns
N/A


Actions
Edit Item Edit Item