The snow leopard in China 257


pugmarks, scats and scent marks or around terrain features such as narrow ridgelines, bottoms, saddles and outcrops that are connection points between areas). We placed cam- eras in rock piles 45–50 cm above the ground. The cameras were triggered by heat in motion and programmed to capture three consecutive photographs and 10 s of video per trigger, with a 1 min delay between triggers. Two authors (CZ and DM) independently identified in-


dividual snow leopards in photographs, aided by video re- cords if available, and jointly reviewed any discrepancies in identification (which occurred for six capture events). A third author (TM) resolved any disagreement, to achieve final consensus on the identity of each snow leopard. Following concerns raised previously (Johansson et al., 2020), we assessed the accuracy level for snow leopard individual identification using the Snow Leopard Training and Evaluation Toolkit (Snow Leopard Network, 2019). The accuracy levels for our three observers were 94.48%, 89.68% and 86.78%, respectively, from 30 blind, independ- ent trials in which each observer identified 100 photographs of known identity. We assumed this level of accuracy was adequate for abundance estimates. We defined an inde- pendent detection event as all photographs and videos of an individual that we obtained at the same camera-trap station within a 30-min period. We constructed individual capture histories using a 24-h sampling occasion length.


Data analysis


Using a maximum-likelihood spatially explicit capture– recapture framework we estimated snow leopard density using the secr package (Efford, 2017)in R 4.2.1 (R Core Team, 2022). Spatially explicit capture–recapture is a spa- tially explicit hierarchical modelling process that combines a state model and an observation model. We used a half- normal detection function for the observation model. We thus assumed that capture probability declined mono- tonically with distance. We ignored habitat effects on snow leopard density and instead assumed a homogeneous Poisson distribution for latent individual activity centres (Borchers & Efford, 2008). We used a homogeneous dis- tribution because we were primarily interested in density rather than landscape ecology, and we used a Poisson dis- tribution because snow leopards are territorial and solitary (Darmaraj, 2012). We assumed there were no temporal effects on the detection probability of snow leopards during the 3-month survey period. We used a 24-km buffer around aminimumconvex pol-


ygon of the survey area to generate the model state space, with potential activity centres spaced at regular intervals of 1.4 km, based on previous recommendations (Alexander et al., 2015). Based on our field knowledge, we excluded areas outside nature reserves. These areas are unsuitable habitat for snow leopard because of dense human activity.


We conducted a closure test (Otis et al., 1978), using the secr package, to evaluate whether the closed population assumption was violated. We fitted three models for the detection functions, including a learned response model (b, where snow leopard detection probability changes depending on previous captures), a site learned effective- ness model (k, where site effectiveness changes once a snow leopard is caught on camera) and a site learned response model (bk, where snow leopard detection prob- ability changes at a particular site once a snow leopard is caught on camera). We used the best-supported model to estimate snow leopard density (D), detection prob- ability (g0) and the spatial scale over which the detection probability declines as the distance between the activity cen- tre of an individual and the camera-trap station increases (σ), and the 95% confidence intervals (CIs) of these para- meters. We assessed relative model support using the Akaike information criterion corrected for small sample sizes (AICc; Burnham & Anderson, 2002).


Results


We recorded 87 snow leopard captures, of which we dis- carded three because the captures were within 24 hof a previous capture. We therefore retained 84 captures over 3,024 trap-days, a mean capture success rate of 2.78 cap- tures/100 trap-days. A total of 50 (60%) capture incidences were of insufficient quality to allow individual recogni- tion. The remaining 34 captures were suitable for further analysis, from which we identified 18 individual snow leopards. Ten identified individuals were captured once, four captured twice, two captured three times, one captured four times and one captured six times. We detected the 18 individuals amean of 1.89 times (range 1–6), and recorded individuals detected more than once at a mean of 2.80 locations (range 1–8). The 18 individual snow leopards captured included a group of three individuals, of which we assumed two to be subadults. We considered these as separate individuals in the analysis. The closure test supported the assumption that the pop-


ulation was closed during the survey period (z =−0.28, P=0.34). A constant capture probability (g0) model was


the best-fitting model based on AICc (Table 1). The esti- mated snow leopard density was 2.26 snow leopards/ 100 km2 (95%CI 1.24–4.18)over an areaof 5,484 km2. The detection probability at the activity centre (g0)was 0.04 (95%CI 0.01–0.08) and the scale parameter of the space use submodel (σ) was 5,642 (95%CI 4,835–7,058).


Discussion


Our study provides the first estimate of snow leopard pop- ulation density in the central and eastern Qilian Mountains,


Oryx, 2024, 58(2), 255–260 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323000340


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