Predicting kill sites of an apex predator from GPS data in different multiprey systems

Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a 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 nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affected the detection of GLC-indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per 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 semidomestic 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 that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.

A Standardized Method for Experimental Human Approach Trials on Wild Wolves

As wolves recolonize areas of Europe ranging from moderate to high anthropogenic impact, fear of wolves is a recurring source of conflict. Shared tools for evaluating wolf responses to humans, and comparing such responses across their range, can be valuable. Experiments in which humans approach wild wolves can increase our understanding of how wolves respond to humans, facilitating human-wolf coexistence. We have developed the first standardized protocol for evaluating wolf responses to approaching humans using high-resolution GPS data, and tested it on wild wolves. We present a field protocol for experimentally approaching GPS-collared wolves, a descriptive comparison of two statistical methods for detecting a measurable flight response, a tutorial for identifying wolf flight initiation and resettling positions, and an evaluation of the method when reducing GPS positioning frequency. The field protocol, a data collection form, and the tutorial with R code for extracting flight parameters are provided. This protocol will facilitate studies of wolf responses to approaching humans, applicable at a local, national, and international level. Data compiled in a standardized way from multiple study areas can be used to quantify the variation in wolf responses to humans within and between populations, and in relation to predictors such as social status, landscape factors, or human population density, and to establish a baseline distribution of wolf response patterns given a number of known predictors. The variation in wolf responses can be used to assess the degree to which results can be generalized to areas where GPS studies are not feasible, e.g., for predicting the range of likely wolf behaviors, assessing the likelihood of wolf-human encounters, and complementing existing tools for evaluating reports of bold wolves. Showing how wolves respond to human encounters should help demystify the behavior of wild wolves toward humans in their shared habitat.

The range of the mange: Spatiotemporal patterns of sarcoptic mange in red foxes (Vulpes vulpes) as revealed by camera trapping

Sarcoptic mange is a widely distributed disease that affects numerous mammalian species. We used camera traps to investigate the apparent prevalence and spatiotemporal dynamics of sarcoptic mange in a red fox population in southeastern Norway. We monitored red foxes for five years using 305 camera traps distributed across an 18000 km² area. A total of 6581 fox events were examined to visually identify mange compatible lesions. We investigated factors associated with the occurrence of mange by using logistic models within a Bayesian framework, whereas the spatiotemporal dynamics of the disease were analysed with space-time scan statistics. The apparent prevalence of the disease fluctuated over the study period with a mean of 3.15% and credible interval [1.25, 6.37], and our best logistic model explaining the presence of red foxes with mange-compatible lesions included time since the beginning of the study and the interaction between distance to settlement and season as explanatory variables. The scan analyses detected several potential clusters of the disease that varied in persistence and size, and the locations in the cluster with the highest probability were closer to human settlements than the other survey locations. Our results indicate that red foxes in an advanced stage of the disease are most likely found closer to human settlements during periods of low wild prey availability (winter). We discuss different potential causes. Furthermore, the disease appears to follow a pattern of small localized outbreaks rather than sporadic isolated events.