Cluster investigation results from model-predicted mountain lion feeding sites and non-predicted sites in southwest Wyoming
Clapp, Justin (2022), Cluster investigation results from model-predicted mountain lion feeding sites and non-predicted sites in southwest Wyoming, Dryad, Dataset, https://doi.org/10.5061/dryad.7m0cfxpwk
Global positioning system (GPS) receivers allow researchers to collect location data that provide information about fine-scale animal movements. For large carnivores, these data are routinely processed to identify clusters of GPS locations which are investigated to validate feeding sites, estimate prey species composition, and model the likelihood of predation events based on characteristics of GPS location data within clusters. Although developing predation models entails a high level of field effort, researchers are apprehensive in applying system-specific models to other systems both spatially and temporally. Our objectives were to apply and compare multiple predation models to predict and identify feeding events outside of the geographic areas where models were developed. Using our multi-model approach, we identified feeding sites and estimated kill rates and prey composition of mountain lions (Puma concolor) in southwest Wyoming, USA from 2017 to 2019. Our approach increased our field efficiency by reducing potential field site investigations by 63%. We provide results of estimated diet composition in our study area where mountain lion prey included relatively high proportions of pronghorn (12.3%) and smaller mammals, particularly coyotes (7.6%). We also provide a comparison of the predation models developed across unique ecoregions of North America, and how they performed when applied to our area. We believe a similar approach could be adopted for other large carnivore populations where multiple models have been developed to characterize feeding events.
Data includes model-predicted predation sites that were investigated for feeding events, and non-predicted clusters used to validate multimodel performance.