Calculating kill intervals for a specific prey from GPS location cluster data of a predator

Abstract

Abstract An increasing number of GPS telemetry studies have helped to gain important insights into predator-prey relationships in the last years. However, considerable time and effort have to be invested to evaluate whether GPS location clusters (GLCs) reflect predation events. To reduce field effort, predictive models are being developed to calculate predator kill intervals, but few studies have attempted to do this for one specific prey species. Between 2013 and 2018, we studied predation of 13 GPS-collared Eurasian lynx (Lynx lynx) on Alpine chamois (Rupicapra rupicapra) in the Northwestern Swiss Alps. Our objectives were to predict the total number of killed chamois, including potential kills in unchecked GLCs, and to evaluate if model predictions were accurate enough for practical use. We built a set of generalized linear models (GLM) predicting the occurrence of GLCs containing killed chamois versus GLCs containing other prey types or no prey and compared their predictive performance by means of k-fold cross-validation. We found that model performance was very similar for all candidate models, with the full model yielding the best cross-validation result (accuracy = 0.83, sensitivity = 0.43, specificity = 0.94). Female lynx killed on average one chamois every 11.9 days (10.6–13.0 days, 95% CI); male lynx killed one chamois every 7.2 days (6.7–7.6 days, 95% CI). We conclude that our modelling results were sufficient for practical application. However, this approach does not replace extensive fieldwork but depends both on fieldwork and thorough knowledge of the predator’s ecology and prey community. It may provide useful results only for binary classifications in rather simple predator-prey systems, and results cannot easily be extrapolated from one study area to another.