Abstract: Accurate assessments of the status of threatened species and their conservation
planning require reliable estimation of their global populations and robust monitoring
of local population trends. We assessed the adequacy and suitability of studies
in reliably estimating the global snow leopard (Panthera uncia) population. We
compiled a dataset of all the peer-reviewed published literature on snow leopard
population estimation. Metadata analysis showed estimates of snow leopard density
to be a negative exponential function of area, suggesting that study areas have generally
been too small for accurate density estimation, and sampling has often been
biased towards the best habitats. Published studies are restricted to six of the
12 range countries, covering only 0.3�0.9% of the presumed global range of the
species. Re-sampling of camera trap data from a relatively large study site
(c.1684 km2) showed that small-sized study areas together with a bias towards
good quality habitats in existing studies may have overestimated densities by up to
five times. We conclude that current information is biased and inadequate for generating
a reliable global population estimate of snow leopards. To develop a rigorous
and useful baseline and to avoid pitfalls, there is an urgent need for
(a) refinement of sampling and analytical protocols for population estimation of
snow leopards (b) agreement and coordinated use of standardized sampling protocols
amongst researchers and governments across the range, and (c) sampling
larger and under-represented areas of the snow leopard's global range.

Abstract: In countries such as Mongolia, where globalization of the cashmere market has spurred herders to massively increase their livestock numbers, an important conservation concern is the effect of livestock encroachment on wildlife. This is especially important inside protected areas (PAs), which often represent the last refugia for threatened large mammals. We used camera-traps to sample four areas with different protection status across the Mongolian Altai Mountains, and targeted a predator-prey system composed of livestock, one large herbivore, the Siberian ibex, and two large carnivores, the snow leopard and the wolf. To determine the effect of livestock on habitat use by the wild species and their spatio-temporal co-occurrence we applied an occupancy framework explicitly developed for modelling interacting species. We recorded a widespread presence of domestic animals in the PAs, and observed avoidance of sites used by livestock by snow leopard and ibex, while wolves tended to co-occur with it. Snow leopard and ibex showed clear mutual co-occurrence, indicating a tight predator-prey relationship. Results provide evidence that, at the scale of sites sampled primarily to maximise snow leopard detections, grazing livestock interferes with wild species by inducing avoidance in snow leopards, and attraction in wolves. We suggest that (1) PAs management should enforce real grazing limitations on the ground, especially in the core areas of the parks; (2) new policies incorporating wildlife conservation into government subsidies to pastoralists should be envisaged, to prevent increasing displacement of snow leopards and ibex; (3) as wolves co- occurred with livestock, with the potential for human-wildlife conflicts, we encourage the use of a set of prevention techniques to mitigate livestock depredation.

Abstract: The snow leopard Panthera uncia is an elusive and globally-threatened apex predator occurring in the mountain ranges of central Asia. As with other large carnivores, gaps in data on its distribution and abundance still persist. Moreover, available density estimates are often based on inadequate sampling designs or analytical approaches. Here, we used camera trapping across a vast mountainous area (area of the sampling frame 850 km2; analysed habitat extent 2600 km2) and spatially-explicit capture-recapture (SECR) models to provide, to our knowledge, the first robust snow leopard population density estimate for the Altai Mountains. This region is considered one of the most important conservation areas for snow leopards, representing a vast portion of suitable habitat and a key ecological corridor. We also provide estimates of the scale parameter (σ) that reflects ranging behaviour (activity range) and baseline encounter probability, and investigated potential drivers of density and related parameters by assessing their associations with anthropogenic and environmental factors. Sampling yielded 9729 images of snow leopards corresponding to 224 independent detections that belonged to a minimum of 23 identified adult individuals. SECR analysis resulted in an overall density of 1.31 individuals/100 km2 (1.15%–1.50 95% CI), which was positively correlated with terrain slope. This estimate falls within the mid-values of the range of density estimates for the species globally. We estimated significantly different activity range size for females and males (79 and 329 km2, respectively). Base- line encounter probability was negatively associated with anthropogenic activity. Our study contributes to on-going efforts to produce robust global estimates of population abundance for this top carnivore.

Abstract: Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.

Abstract: Population density estimation is integral to the effective conservation and management of wildlife. The snow leopard Panthera uncia is categorized as Vulnerable on the IUCN Red List, and reliable information on its density is a prerequisite for its conservation and management. Little is known about the status of the snow leopard in the central and eastern Qilian Mountains, China. To address this, we estimated the population density of the snow leopard using a spatially explicit capture–recapture model based on camera trapping in Machang in the central and eastern Qilian Mountains during January–March 2019. We set up
40 camera traps and recorded 84 separate snow leopard captures over 3,024 trap-days. We identified 18 individual snow leopards and estimated their density to be 2.26/100 km. Our study provides baseline information on the snow leopard and the first population estimate for the species in the central and eastern Qilian Mountains.