Differential predation patterns of free‐ranging cats among continents
Philippe‐lesaffre, Martin | Bradshaw, Corey, J A | Castañeda González, Irene | Llewelyn, John | Dickman, Christopher, R | Lepczyk, Christopher, A | Fantle-Lepczyk, Jean | Marino, Clara | Courchamp, Franck | Bonnaud, Elsa | Ecologie, Société et Evolution (ex-Ecologie, Systématique et Evolution) (ESE) ; AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) | Flinders University [Adelaide, Australia] | Biodiversité, Gènes & Communautés (BioGeCo) ; Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | The University of Sydney | Auburn University (AU)
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Показать больше [+] Меньше [-]Английский. <div><p>Co-evolutionary relationships associated with biogeographical context mediate the response of native prey to introduced predators, but this effect has not yet been demonstrated for domestic cats. We investigated the main factors influencing the vulnerability of prey species to domestic cat Felis catus predation across Australia, Europe and North America, where domestic cats are introduced. In addition to prey data from empirical records, we used machine-learning models to compensate for unobserved prey in the diet of cats. We found continent-specific patterns of predation: birds were more frequently depredated by cats in Europe and North America, while mammals were favoured in Australia. Bird prey traits were consistent across continents, but those of mammalian prey diverged, notably in Australia. Differences between prey and non-prey species included mass, distribution, and reproductive traits, except in Australian mammals where there was no evidence for a relationship between mass and the probability of being prey. Many Australian mammal prey also have a high extinction risk, emphasizing their vulnerability compared to European and North American counterparts. Our findings highlight the role of eco-evolutionary context in assessing predation impacts and also demonstrate the potential for machine learning to identify at-risk species, thereby aiding global conservation efforts to reduce the negative impacts of introduced predators.</p></div>
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