How predictable are mass extinction events?

Foster, William J.; Allen, Bethany J.; Kitzmann, Niklas H.; Münchmeyer, Jannes; Rettelbach, Tabea; Witts, James D.; Whittle, Rowan J. ORCID: https://orcid.org/0000-0001-6953-5829; Larina, Ekaterina; Clapham, Matthew E.; Dunhill, Alexander M.. 2023 How predictable are mass extinction events? Royal Society Open Science, 10 (3), 221507. 18, pp. 10.1098/rsos.221507

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Official URL: https://royalsocietypublishing.org/doi/10.1098/rso...

Abstract/Summary

Many modern extinction drivers are shared with past mass extinction events, such as rapid climate warming, habitat loss, pollution and invasive species. This commonality presents a key question: can the extinction risk of species during past mass extinction events inform our predictions for a modern biodiversity crisis? To investigate if it is possible to establish which species were more likely to go extinct during mass extinctions, we applied a functional trait-based model of extinction risk using a machine learning algorithm to datasets of marine fossils for the end-Permian, end-Triassic and end-Cretaceous mass extinctions. Extinction selectivity was inferred across each individual mass extinction event, before testing whether the selectivity patterns obtained could be used to ‘predict’ the extinction selectivity exhibited during the other mass extinctions. Our analyses show that, despite some similarities in extinction selectivity patterns between ancient crises, the selectivity of mass extinction events is inconsistent, which leads to a poor predictive performance. This lack of predictability is attributed to evolution in marine ecosystems, particularly during the Mesozoic Marine Revolution, associated with shifts in community structure alongside coincident Earth system changes. Our results suggest that past extinctions are unlikely to be informative for predicting extinction risk during a projected mass extinction.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1098/rsos.221507
ISSN:	2054-5703
Additional Keywords:	mass extinction, machine learning, fossil, end-Permian, end-Triassic, end-Cretaceous
Date made live:	16 Mar 2023 14:16 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534236

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1098/rsos.221507

ISSN:

2054-5703

Additional Keywords:

mass extinction, machine learning, fossil, end-Permian, end-Triassic, end-Cretaceous

Date made live:

16 Mar 2023 14:16 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/534236