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 Wakhan Corridor in northeast Afghanistan is an area known for relatively abundant wildlife and it appears to represent Afghanistan’s most important snow leopard landscape. The Wildlife Conservation Society (WCS) has been working in Wakhan since 2006. Recent camera trap surveys have documented the presence of snow leopards at 16 different locations in the landscape. These are the first camera trap records of snow leopards in Afghanistan. Threats to snow leopards in the region include the fur trade, retaliatory killing by shepherds and the capture of live animals for pets. WCS is developing an integrated management approach for this species, involving local governance, protection by a cadre of rangers, education, construction of predator-proof livestock corrals, a livestock insurance program, tourism and research activities. This management approach is expected to contribute significantly to the conservation of snow leopards and other wildlife species in the Wakhan.

Abstract: The main purpose of this work was to study the use of infrared trapping cameras to estimate snow leopard Uncia uncia population size in a specific study area. This is the first time a study of this nature has taken place in China. During 71 days of field work, a total of 36 cameras were set up in five different small vales of the Muzat Valley adjacent to the Tomur Nature Reserve in Xinjiang Province, E80ø35' – 81ø00' and N42ø00' – 42ø10', elevation 2'300 – 3'000 m, from 18th October to 27th December 2005. We expended approximately 2094 trap days and nights total (c. 50'256 hours). At least 32 pictures of snow leopards, 22 pictures of other wild species (e.g. chukor, wild pig, ibex, red fox, cape hare) and 72 pictures of livestock were taken by the passive Cam Trakker (CT) train monitor in about 16 points of the Muzat Valley. The movement distance of snow leopard was 3-10 km/day. And the capture rate or photographic rate of snow leopard was 1.53%. Meanwhile, 20 transects were run and 31 feces sample were collected. According to 32 photos, photographic rate and sign survey after snowing on the spot, were about 5-8 individuals of snow leopards in the research area, and the minimum density of snow leopard in Muzat Valley was 2.0 – 3.2 individuals/100 km2. We observed the behavior of ibex for 77.3 hours, and found about 20 groups and a total of approximately 264 ibexes in the research area.

Abstract: Solitary felids like tigers and snow leopards are notoriously difficult to enumerate, and indirect techniques like pugmark surveys often produce ambiguous information that is difficult to interpret because many factors influence marking behavior and frequency (Ahlborn & Jackson 1988). Considering the snow leopard's rugged habitat, it is not surprising then that information on its current status and occupied range is very limited. We adapted the camera-trapping techniques pioneered by Ullas Karanth and his associates for counting Bengal tigers to the census taking of snow leopards in the Rumbak watershed of the India's Hemis High Altitude National Park (HNP), located in Ladakh near Leh (76ø 50' to 77ø 45' East; 33ø 15' to 34ø 20'North).

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.