| Summary: | Predator-prey theory predicts that in the presence of multiple types of predators using a common prey, predator facilitation may result as a consequence of contrasting prey defense mechanisms, where reducing the risk from one predator increases the risk from the other. While predator facilitation is well established in natural predator-prey systems, little attention has been paid to situations where human hunters compete with natural predators for the same prey. Here, we investigate hunting-mediated predator facilitation in a hunter-predator-prey system. We found that hunter avoidance by roe deer (Capreolus capreolus) exposed them to increase predation risk by Eurasian lynx (Lynx lynx). Lynx responded by increasing their activity and predation on deer, providing evidence that superadditive hunting mortality may be occurring through predator facilitation. Our results reveal a new pathway through which human hunters, in their role as top predators, may affect species interactions at lower trophic levels and thus drive ecosystem processes. deer_allDataThis table contains the roe deer steps associated with habitat variables and temporal variables that were used to build the step selection function (SSF) named the all data model in Gehr et al. 2017 (Ecology and Evolution). The table is divided into used (actual) lynx steps and random steps in a ratio of 1:10 (column "use"). The random steps match to their corresponding location id in the used steps (column “loc_id”). Swisstopo in the column headers refers to the source of the environmental variables. Cover swisstopo is a dummy variable for open/cover. Edge_dist_swisstopo refers to the distance to the closest forest edge. Slope_sq and altitude_sq are the squared slope and altitude variables. Aspect_swisstopoS is the southern exposition. Dist2 refers to the step length. Pred_risk_search refers to the chronic predation risk which was derived from a lynx habitat model for active/searching lynx. The 6 temporal variables are time harmonics of a Fourier transform for time ...
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