| Summary: | Apex predators exert suppressive effects on mesocarnivores; however, they also provide important carrion subsidies. Optimal foraging theory predicts that individuals respond to resource competition by using high value resources, while competition theory predicts that individuals respond by partitioning resources. This study investigated how the return of wolves (Canis lupus Linneas, 1758) to Washington state impacted the diet of a subordinate carnivore - the coyote (Canis latrans Say, 1823). We collected coyote scats from two areas of northern Washington with differing wolf densities and used traditional analysis of undigested remains to infer diet. We tested for differences in the volumes of prey categories, the proportion of ungulate prey that was scavenged, and diet diversity between seasons, study sites, and inside and outside of wolf pack territories. Coyote scats contained more adult ungulate remains inside of wolf pack territories (27%) compared to outside (14%), while seeds and berries were more commonly consumed outside of wolf pack territories (23%) than inside of wolf pack territories (4%). These findings suggest that coyotes are taking advantage of wolf kills to increase ungulate carrion consumption, as predicted by optimal foraging theory, which may substantially affect plant and wildlife communities as wolves continue to recover and coyote diets shift in response. Funding provided by: National Science Foundation Crossref Funder Registry ID: https://ror.org/021nxhr62 Award Number: DEB-1652420 Coyote ( Canis latrans ) scats were collected from two sites in northern Washington, USA. Scats were primarily collected from transects that were surveyed monthly, with additional scats collected opportunistically at both sites during winter (January to March) 2018, summer (mid-June to mid-September) 2018, and winter (January to March) 2019. Scats were stored frozen, sterilized at -80 degrees Celsius for two weeks, and then species of origin was determined by molecular analysis of mitochondrial DNA. Scats ...
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