Abstract: Between May 1990 and January 1991, behavioral observations were made of two captive pumas (Felis concolor Linnaeus), and two captive snow leopards (Panthera uncia Schreber) in their outdoor exhibits at the Lincoln Park Zoological Gardens, Chicago, Illinois. Behaviors compared within and between species included: 1) time spend in the different habitat types; 2) time budgets for the different behaviors: laying, moving, sitting, standing, crouching, in the tree, drinking, urinating, defecating, within their inside dens, and “behavior not determined” when the identity or behavior of the individuals could not be determined; and 3) mobility of the animals within their exhibits. Also examined were: 4) preferences for different habitat types; 5) recommendations for future exhibit designs. Both species located themselves within their exhibits in a non-random manner. The majority of cats' time was spent in elevated locations (i.e., gunite ledges approximately 1-5.5 m above ground-level). Snow leopards exhibited this tendency to a greater extent than did the pumas. Both species also spent the majority of their time in the lying-down behavior; again snow leopards displayed this tendency significantly more than the pumas. Pumas were highly mobile and changed locations and behaviors in their exhibit significantly more than the snow leopards. No significant differences were noted between conspecifics in regard to habitat type preference, or mobility within the exhibit. Suggestions for future exhibit design include elevated locations for the cats to lay and look around within and outside their exhibits, caves for access to shade or relief from inclement weather, and ground surfaces to move about on. Features for exhibit design should take into consideration the natural habitat of the cat to occupy the exhibit.

Abstract: Background: Snow leopards, Panthera uncia, are a threatened apex predator, scattered across the mountains of Central and South Asia. Disease threats to wild snow leopards have not been investigated.
Methods and Results: Between 2008 and 2015, twenty snow leopards in the South Gobi desert of Mongolia were captured and immobilised for health screening and radio-collaring. Blood samples and external parasites were collected for pathogen analyses using enzyme- linked immunosorbent assay (ELISA), microscopic agglutination test (MAT), and next- generation sequencing (NGS) techniques. The animals showed no clinical signs of disease, however, serum antibodies to significant zoonotic pathogens were detected. These patho- gens included, Coxiella burnetii, (25% prevalence), Leptospira spp., (20%), and Toxoplasma gondii (20%). Ticks collected from snow leopards contained potentially zoonotic bacteria from the genera Bacillus, Bacteroides, Campylobacter, Coxiella, Rickettsia, Staphylococcus and Streptococcus.
Conclusions: The zoonotic pathogens identified in this study, in the short-term did not appear to cause illness in the snow leopards, but have caused illness in other wild felids. Therefore, surveillance for pathogens should be implemented to monitor for potential longer- term disease impacts on this snow leopard population.

Abstract: Presented is history of the Hissar nature reserve's establishment, physic and geographic description, types of soils, flora and fauna The 28 species of mammals, 103 nested birds, 19 amphibians and reptiles and 2 fishes are presented in nature reserve. Number of snow leopard assessed as 2-3 families, bear 130 individuals, wild boar 460, Turkestan lynx 90,ibex 1700 individuals.

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: We examined predation activity throughout Bhutan by tiger (Panthera tigris), common leopard (Panthera pardus), snow leopard (Uncia uncia) and Himalayan black bear (Ursus thibetanus) on a variety of livestock types using data gathered over the first two years (2003-2005) of a compensation scheme for livestock losses. One thousand three hundred and seventy five kills were documented, with leopards killing significantly more livestock (70% of all kills),
than tigers (19%), bears (8%) and snow leopards (2%). About 50% of livestock killing were of cattle, and about 33% were of horses, with tigers, leopards and snow leopards killing a significantly greater proportion of horses than predicted from availability. Examination of cattle kills showed that leopards killed a significantly greater proportion of smaller prey (e.g., calves), whereas tigers killed a significantly greater proportion of larger prey (e.g., bulls). Overall, livestock predation was greatest in summer and autumn which corresponded with a peak in cropping agriculture; livestock are turned out to pasture and forest during the cropping season, and subsequently, are less well guarded than at other times. Across Bhutan, high horse density and low cattle and yak density were associated with high rates of livestock attack, but no relationship was found with forest cover or human population density. Several northern districts were identified as 'predation hotspots', where proportions of livestock lost to predation were considerable, and the ratio of reported kills to relative abundance of livestock was high. Implications of our findings for mitigating livestock losses and for conserving large carnivores in Bhutan are discussed.