Quantifying ecological impacts of mass extinctions with network analysis of fossil communities
Abstract
Mass extinctions documented by the fossil record provide critical benchmarks for assessing changes through time in biodiversity and ecology. Efforts to compare biotic crises of the past and present, however, encounter difficulty because taxonomic and ecological changes are decoupled, and although various metrics exist for describing taxonomic turnover, no methods have yet been proposed to quantify the ecological impacts of extinction events. To address this issue, we apply a network-based approach to exploring the evolution of marine animal communities over the Phanerozoic Eon. Network analysis of fossil co-occurrence data enables us to identify nonrandom associations of interrelated paleocommunities. These associations, or evolutionary paleocommunities, dominated total diversity during successive intervals of relative community stasis. Community turnover occurred largely during mass extinctions and radiations, when ecological reorganization resulted in the decline of one association and the rise of another. Altogether, we identify five evolutionary paleocommunities at the generic and familial levels in addition to three ordinal associations that correspond to Sepkoski's Cambrian, Paleozoic, and Modern evolutionary faunas. In this context, we quantify magnitudes of ecological change by measuring shifts in the representation of evolutionary paleocommunities over geologic time. Our work shows that the Great Ordovician Biodiversification Event had the largest effect on ecology, followed in descending order by the Permian-Triassic, Cretaceous-Paleogene, Devonian, and Triassic-Jurassic mass extinctions. Despite its taxonomic severity, the Ordovician extinction did not strongly affect co-occurrences of taxa, affirming its limited ecological impact. Network paleoecology offers promising approaches to exploring ecological consequences of extinctions and radiations.
- Publication:
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Proceedings of the National Academy of Science
- Pub Date:
- May 2018
- DOI:
- 10.1073/pnas.1719976115
- Bibcode:
- 2018PNAS..115.5217M