Identifying bobcat Lynx rufus kill sites using a global positioning system

The role of predation in ecological systems has received considerable attention in scientific literature and is one of the most important, yet least understood aspects of carnivore ecology. Knowledge of factors that improve our ability to detect predation events using animal telemetry data could be used to develop strategies to reduce time and resources required to obtain reliable kill estimates. Using Global Positioning System telemetry-collars, we investigated 246 bobcat Lynx rufus location clusters to identify white-tailed deer Odocoileus virginianus kill sites in the Upper Peninsula of Michigan, USA, during May-August, 2009-2011. We documented kills of white-tailed deer at 42 location clusters. We used logistic regression and Akaike Information Criterion for small samples to identify factors (i.e. number of locations in cluster, time from cluster formation to investigation, time of day and land cover) that may influence bobcat behaviour and our ability to detect white-tailed deer kill sites. Clusters with more locations and the search of clusters within 14 days after cluster formation increased odds of detecting bobcat kill sites. The best-performing model was 67% accurate overall and identified 34% of kill sites and 75% of non-kill sites. Applying our best-performing model with the optimal cut-off value would result in a twofold increase in the identification of white-tailed deer kill sites reducing time and effort to find a similar number of kill sites without models by half. Identifying factors that improve our ability to identify bobcat kill sites can reduce field effort and search time.