Abstract: When densities of large carnivores fall below certain thresholds, dramatic ecological effects
can follow, leading to oversimplified ecosystems. Understanding the population status of
such species remains a major challenge as they occur in low densities and their ranges are
wide. This paper describes the use of non-invasive data collection techniques combined
with recent spatial capture-recapture methods to estimate the density of snow leopards
Panthera uncia. It also investigates the influence of environmental and human activity indicators
on their spatial distribution. A total of 60 camera traps were systematically set up during
a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve,
Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trapdays,
representing an average capture success of 2.62 captures/100 trap-days. We identified
a total number of 20 unique individuals from photographs and estimated snow leopard
density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate
that our estimates from the Spatial Capture Recapture models were not optimal to
respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our
results underline the critical challenge in achieving sufficient sample sizes of snow leopard
captures and recaptures. Possible performance improvements are discussed, principally by
optimising effective camera capture and photographic data quality.