Fear of large carnivores amplifies human-caused mortality for mesopredators.

The challenge that large carnivores face in coexisting with humans calls into question their ability to carry out critical ecosystem functions such as mesopredator suppression outside protected areas. In this study, we examined the movements and fates of mesopredators and large carnivores across rural landscapes characterized by substantial human influences. Mesopredators shifted their movements toward areas with twofold-greater human influence in regions occupied by large carnivores, indicating that they perceived humans to be less of a threat. However, rather than shielding mesopredators, human-caused mortality was more than three times higher than large carnivore-caused mortality. Mesopredator suppression by apex predators may thus be amplified, rather than dampened, outside protected areas, because fear of large carnivores drives mesopredators into areas of even greater risk from human super predators.

Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.