Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill site identification, such as failing to detect GLCs that are kill sites and misclassifying the generated GLCs (e.g. kill for non-kill) that were not field-checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx, an apex predator of conservation concern in Europe, in three multi-prey systems, with different combinations of wild, semi-domestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affect the detection of GLCs indicating kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as non-kills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced to from 7 to 3 fixes/night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24-h decreased the probability of detecting GLCs connected with kill sites, particularly those of semi-domestic or domestic prey, and small prey. Random forest successfully predicted between 73%-90% of ungulate kills but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm’s overall accuracy. We provide a set of recommendations for studies focusing on kill site detection, which can be considered for other large carnivore species besides the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.

We used GPS and kill-sites data from three multi-prey systems. Data were collected through the EUROLYNX network, a collaborative bottom-up platform of lynx researchers across Europe for sharing data and expertise (Heurich et al, 2021). We pooled all GPS data available from all schedules (Appendix S2: Table S1) and generated GLCs using the parameters that provided the best results in the previous section for kill site detection through GLCs of all prey. We then associated GLCs to confirmed kill sites (classified as either small prey or ungulates) or to field-checked non-kills.

For more details, please see the README document ("README_dataset_randomforest_classification.txt") and the accompanying published article. Oliveira et al., 2022. Predicting kill sites from an apex predator from GPS data in different multi-prey systems. Ecological Applications. Accepted

Please see the README document ("README_dataset_randomforest_classification.txt") and the accompanying published article. Oliveira et al., 2022. Predicting kill sites from an apex predator from GPS data in different multi-prey systems. Ecological Applications. Accepted