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Late Pleistocene faunal community patterns disrupted by Holocene human impacts
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Abstract
We analysed fossil mammal assemblages from over 350 Late Pleistocene and Holocene sites worldwide to test whether human activities, such as agriculture, domestication and intensified land use, restructured global patterns of mammal co-occurrence. Using presence-absence data, we contrasted a novel iterative ‘chase clustering’ method, which is compositionally driven, against a traditional spatially constrained Ward’s clustering approach. Both methods recovered continental-scale groupings in the Pleistocene consistent with known biogeographic boundaries. In the Holocene, however, domestication and agricultural expansion disrupted these historical patterns, generating novel clusters unbound by geography and traditional evolutionary lineages. Faunal turnover at the local scale varied substantially across regions, being especially pronounced in the Americas, whereas other areas showed relative stability. Even moderate expansion of domesticates altered how communities grouped, highlighting their disproportionate ecological influence. Our findings demonstrate that human-driven niche modification, beyond earlier megafaunal extinctions, profoundly reshaped mammal communities on a global scale. Recognising these anthropogenic legacies provides essential context for anticipating how current and future human pressures might further transform biodiversity.
DOI
https://doi.org/10.32942/X23G9S
Subjects
Biodiversity, Ecology and Evolutionary Biology, Life Sciences, Paleobiology
Keywords
biogeography, Mammal communities, Domestication, Agriculture, Faunal turnover, Clustering, Zooarchaeology, Holocene, Mammal communities, Domestication, agriculture, Faunal turnover, clustering, Zooarchaeology, Holocene
Dates
Published: 2025-03-20 14:18
Last Updated: 2025-03-20 14:18
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None.
Data and Code Availability Statement:
All data and R code required for full replication are provided at https://github.com/bwbrook/chase-clustering, archived and citable via Zenodo (DOI: 10.5281/zenodo.15054872).
Language:
English
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