Spatial density estimate of the snow leopard, Panthera uncia, in the Central Tibetan Plateau, China

Abstract

Knowledge of large carnivore population abundance is essential for wildlife management and conservation, but these data are often difficult to obtain in inherently low‐density species. In particular, the snow leopard, Panthera uncia, an enigmatic cat occupying remote mountains in Central Asia, has received insufficient assessments of its population abundance because of logistical and methodological challenges. Here, we aimed to develop a robust density estimation of snow leopards based on 81 days of camera trapping within a contiguous and previously unsurveyed 1950 km2 area of habitat on the Tibetan Plateau (Mayue Township, Shenzha County, Tibet Autonomous Region, China). By applying spatially explicit capture–recapture models, we produced an estimate of 1.40 (95%CI: 1.06–1.84) individuals per 100 km2. Results also suggested sex‐specific variation in the range of movement around activity centers, with male (N = 10, σ = 4.02) movement considerably greater than female (N = 8, σ = 1.84) movement. The findings can serve as a reliable baseline reference for assessing the population trends of this endangered felid species with future estimates. This study will provide context to contribute toward a better understanding of ecological factors shaping the distribution and abundance of snow leopards and the related conservation measures needed to sustain their long‐term survival on the Tibetan Plateau.

First large-scale assessment of snow leopard population in China using existing data from multiple organizations

“…2), thereby ensuring a relatively good representation across different strata of snow leopard habitats in the Sanjiangyuan region. Our modeling framework could be further improved by involving prey density as snow leopard density covariates (Suryawanshi et al 2021), as well as covariates in σ and λ simulation (Bian et al 2023). We tested models adding covariates to explain heterogeneity of sigma and lambda; however, the models did not perform well due to insu cient detection number.…”