Coexistence between species with similar ecological niches implies species must segregate along one or more niche axes to survive. Space, time, and trophic resources are regarded as the principal axes upon which species segregate. We examined segregation along these niche axes to determine mechanisms underlying coexistence between the two main predators, puma (Puma concolor) and culpeo foxes (Lycalopex culpaeus) in the Andes of Central Chile. We used occupancy modeling to examine space use and overlap, Kernel Density Estimation to determine temporal activity patterns and overlap, and analysis of prey remains in feces to assess diet breadth and similarity. We found high spatial overlap and positive associations between detection of the carnivores lending little support for spatial segregation. Similarly, we found high nocturnal, temporal overlap between pumas and foxes that matched peaks in activity of prey. In contrast, we found relatively low dietary overlap indicating niche segregation likely occurs along the dietary axis. The Puma diet was dominated by introduced, exotic hares and foxes appeared to shift away from hares to rabbits, small mammals, and seeds. Given that lagomorphs are the main dietary resource for pumas in particular, management decisions regarding the control or eradication of such exotic species could negatively affected puma survival.

Knowledge about the puma Puma concolor in the Mediterranean Andes of South America is scarce, with little information available about its distribution and ecology. The species has been hunted in the region for centuries and seems to be relegated to remote areas, where it still comes into conflict with rural communities. Camera-trap surveys have been used to estimate puma density across the species' distribution, and this is a relatively cost-effective approach to generate information about species abundance. We tested the performance of this method in the Andes of central Chile, where the rugged topography, unknown detection probability, and an expected low density make abundance estimation by other methods unreliable or expensive. Using a rotation of  camera-trap stations, for a total of  camera-days, we obtained  records of pumas and were able to identify four individuals in an effective sampling area of  km  (/ mean maximum distance moved) or , km  (mean maximum distance moved). Capturerecapture models estimated a minimum density of . ± SE . to . ± SE . adult pumas per  km  . This is the first estimate of the density of the puma in the Mediterranean Andes, and one of the lowest reported for the Neotropics. Although this low abundance does not necessarily imply a threat to the species, it suggests that the population is potentially vulnerable to threats such as illegal hunting. Our findings demonstrate that camera trapping is an effective technique in difficult field conditions and may be the most appropriate method to assess puma densities in this region.

To analyze the spatial distribution and validate the minimum distance between camera sites, we applied L-function (Besag's transformation of Ripley's K-function). This analysis identifies whether the spatial distribution of the camera sites follows a random, clustered or dispersed process, and identifies the average distance at which the respective process begins. In summary, the algorithm determines when the distribution process of camera sites passes from scattered to clustered considering a theoretical curve of a Poisson process. Given the special distribution of the 80 camera sites in this experimental design, the L-function yielded an average distance of 2,503.374 meters (Appendix Fig. S1.1). Below this distance, it is considered that the spatial process of camera site locations follows a random distribution.