The endangered snow leopard Panthera uncia occurs in human use landscapes in the mountains of South and Central Asia. Conservationists generally agree that snow leopards must be conserved through a land-sharing approach, rather than land-sparing in the form of strictly protected areas. Effective conservation through land-sharing requires a good understanding of how snow leopards respond to human use of the landscape. Snow leopard density is expected to show spatial variation within a landscape because of variation in the intensity of human use and the quality of habitat. However, snow leopards have been difficult to enumerate and monitor. Variation in the density of snow leopards remains undocumented, and the impact of human use on their populations is poorly understood. We examined spatial variation in snow leopard density in Spiti Valley, an important snow leopard landscape in India, via spatially explicit capture recapture analysis of camera trap data. We camera trapped an area encompassing a minimum convex polygon of 953 km². We estimated an overall density of 0.49 (95% CI: 0.39-0.73) adult snow leopards per 100 km². Using AIC, our best model showed the density of snow leopards to depend on wild prey density, movement about activity centres to depend on altitude, and the expected number of encounters at the activity centre to depend on topography. Models that also used livestock biomass as a density covariate ranked second, but the effect of livestock was weak. Our results highlight the importance of maintaining high density pockets of wild prey populations in multiple use landscapes to enhance snow leopard conservation.

We deployed Reconyx RM45 camera traps at 30 sites over an area of 953 km² (Minimum Convex Polygon joining the outermost trap locations) with an average inter-trap distance of 4035 m (SE = 374m) (Figure 1). The camera traps were deployed from October 2011 to January 2012 for 80 days with an overall trap density of 3 camera traps per 100 km² following recommendations of placing at least two traps per average home range or at least two traps per average female home range. The camera traps were deployed at sites where we encountered relatively high frequency of snow leopard signs such as scrapes, pugmarks, scats and scent marks, especially around terrain features that snow leopards are known to prefer for marking and movement such as ridgelines, cliffs and gully beds. We used a combination of single (n=14) and double (n=16) camera trap placement to optimise coverage and identification of individuals. Double side camera traps were installed to enable capture of both side flanks of as many snow leopards as possible so they can be used to improve our ability to identify individuals with only single flank captures. Our cameras recorded snow leopards at 25 out of 30 sites without using any baits or scent lures.

Individual snow leopards captured in the images were identified based on their pelage patterns by two independent observers using at least three similarities or differences. There was no discrepancy in the identified individuals reported by the two observers. We only count each set of photographs as a new encounter when it was separated from another set of photographs from the same snow leopard by at least four hours. This was done to prevent the overdispersal of counts in using count detectors for our analysis. The mean time between consecutive encounters of the same animal on the same camera trap was 537 hours (95% CI: 409-665 hours) that ensures the validity of the count detector.  We obtained a total of 2,830 snow leopard images from 124 encounters.  A total of twelve encounters were discarded as the pictures were not good enough to identify the individuals. Using a mix of both side and right side only flanks, we obtained complete identification of 16 individual adult snow leopards.

The camera trap locations are randomized/encrypted to prevent potential misuse of the location information on snow leopard captures.Spatial Capture Recapture analysis requires spatial datasets that are projected to cartesian (metric) system. In our case, we projected our location data using World Mercerator. However given the sensitivity of the data, we have encrypted the data by adding two different constants to the x and y coordinates respectively to each spatial point on the datasets. While this allows the datasets to be reused in other scr analyses, it protects the species from the risk of coordinates being misused.