Predicting kill sites of an apex predator from GPS data in different multi‐prey 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

Scale‐dependent strategies for coexistence of mesocarnivores in human‐dominated landscapes

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Testing the influence of habitat experienced during the natal phase on habitat selection later in life in Scandinavian wolves

Natal habitat preference induction (NHPI) occurs when characteristics of the natal habitat influence the future habitat selection of an animal. However, the influence of NHPI after the dispersal phase has received remarkably little attention. We tested whether exposure to humans in the natal habitat helps understand why some adult wolves Canis lupus may approach human settlements more than other conspecifics, a question of both ecological and management interest. We quantified habitat selection patterns within home ranges using resource selection functions and GPS data from 21 wolf pairs in Scandinavia. We identified the natal territory of each wolf with genetic parental assignment, and we used human-related characteristics within the natal territory to estimate the degree of anthropogenic influence in the early life of each wolf. When the female of the adult wolf pair was born in an area with a high degree of anthropogenic influence, the wolf pair tended to select areas further away from humans, compared to wolf pairs from natal territories with a low degree of anthropogenic influence. Yet the pattern was statistically weak, we suggest that our methodological approach can be useful in other systems to better understand NHPI and to inform management about human-wildlife interactions

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

<div><p>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.</p></div

Apparent prevalence of sarcoptic mange in red foxes in southeastern Norway during the five years of study.

<p>Red dashed lines and grey area represent the 95% credible intervals.</p

Spatiotemporal clusters of red foxes potentially infected with sarcoptic mange detected by the scan analyses.

<p>Spatiotemporal clusters of red foxes potentially infected with sarcoptic mange detected by the scan analyses.</p

Set of candidate logistic models for the occurrence of red foxes showing mange-compatible lesions in southeastern Norway.

<p>Set of candidate logistic models for the occurrence of red foxes showing mange-compatible lesions in southeastern Norway.</p

An example of two camera trapped red foxes with mange-compatible lesions.

<p>An example of two camera trapped red foxes with mange-compatible lesions.</p

Parameter estimates form the best model (see Table 2) explaining the presence of red foxes with mange-compatible lesions in southeastern Norway.

<p>Parameter estimates form the best model (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176200#pone.0176200.t002" target="_blank">Table 2</a>) explaining the presence of red foxes with mange-compatible lesions in southeastern Norway.</p

Variables used in logistic models of the occurrence of red foxes showing mange-compatible lesions in southeastern Norway.

<p>Variables used in logistic models of the occurrence of red foxes showing mange-compatible lesions in southeastern Norway.</p