نبذة مختصرة : DATA AVAILABILITY : Data used in this paper are stored in https://doi.org/10.5061/ dryad.xgxd254k9. ; The Coyote (Canis latrans) is one of the most studied species in North America with at least 445 papers on its diet alone. While this research has yielded excellent reviews of what coyotes eat, it has been inadequate to draw deeper conclusions because no synthesis to date has considered prey availability. We accounted for prey availability by investigating the prey selection of coyotes across its distribution using the traditional Jacobs’ index method, as well as the new iterative preference averaging (IPA) method on scats and biomass. We found that coyotes selected for Dall’s Sheep (Ovis dalli), White-tailed Deer (Odocoileus virginianus), Eastern Cottontail Rabbit (Sylvilagus floridanus), and California Vole (Microtus californicus), which yielded a predator-to-preferred prey mass ratio of 1:2. We also found that coyotes avoided preying on other small mammals, including carnivorans and arboreal species. There was strong concordance between the traditional and IPA method on scats, but this pattern was weakened when biomass was considered. General linear models revealed that coyotes preferred to prey upon larger species that were riskier to hunt, reflecting their ability to hunt in groups, and were least likely to hunt solitary species. Coyotes increasingly selected Mule Deer (O. hemionus) and Snowshoe Hare (Lepus americanus) at higher latitudes, whereas Black-tailed Jackrabbit (L. californicus) were increasingly selected toward the tropics. Mule Deer were increasingly selected at higher coyote densities, while Black-tailed Jackrabbit were increasingly avoided at higher coyote densities. Coyote predation could constrain the realized niche of prey species at the distributional limits of the predator through their increased efficiency of predation reflected in increased prey selection values. These results are integral to improved understandings of Coyote ecology and can inform predictive analyses allowing for ...
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