DOI: 10.5061/dryad.866t1g1tn
title: Eurasian lynx GLCs' characteristics for classification with random forest algorithm

description: 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.
description: 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
description: 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