Fig 1.
Location of camera traps in QNNR, Gansu Province, China.
Table 1.
Factors hypothesized to influence patterns of snow leopard density in QNNR, with the corresponding index used, predicted direction of effect, source of data, and range of values across sampled state space (480 km2).
Fig 2.
The mean pixel-specific abundance plotted against standardized covariates.
Fig 3.
Snow leopard individual identification.
B and C are photos of the same individual from different camera traps with C taken at night with infrared. A is a photo of a different individual. Identification is based on distinct spot patterns on the face.
Table 2.
Posterior summaries from Bayesian spatially explicit capture-recapture (SECR) of the model parameters implemented in SPACECAP.
(Density is presented per 100 km2).
Fig 4.
The map of the spatial distribution of snow leopards across the study area.
A pixelated density map produced in SPACECAP showing estimated snow leopard densities per pixel of size 1.96 km2.
Table 3.
Simulation results showing the bias and precision of the posterior mean, mode and median for the density and psi parameter.
Root-mean-squared-error (RMSE) and % coverage rates for the 95% highest posterior density (HPD) intervals are reported.
Table 4.
Negative Binomial Models quantifying the influence of factors on estimates of snow leopard abundance.
Rankings are based on Akaike’s Information Criterion (AIC). Also includes relative parameter importance with summed AIC weights. (K = Number of parameters in the model; AIC wt = AIC model weight; AIC cum wt = AIC cumulative model weight).
Table 5.
Negative Binomial top two models (delta-AIC < 2) quantifying the influence of covariates on estimates of snow leopard abundance.