DOI: 10.5061/dryad.3r2280gfx
title: Spatial variation in population-density, movement and detectability of snow leopards in a multiple use landscape in Spiti Valley, Trans-Himalaya

description: 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.
description: We
 deployed Reconyx RM45 camera traps at 30 sites over an area of 953
 km<sup>2 </sup>(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
 km<sup>2</sup> 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.
description: Snow_Leopard_captures_encrypted.scv
 Spatially Explicit Capture Recature Data (SECR) on Snow Leopards
 from Spiti Valley, India 
 Snow_Leopard_mask_encrypted.csv The state
 space/mask used for SECR analysis 
 Snow_Leopard_Traps_encryted.csv The
 locations of camera traps deployed for population-density estimation of
 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.