Abstract: Home ranges for five radio-tagged snow leopards (Uncia uncia) inhabiting prime habitat in Nepal Himalaya varied in size from 11-37 km2. These solitary felids were crepuscular in activity, and although highly mobile, nearly 90% of all consecutive day movements involved a straight line distance of 2km or less. No seasonal difference in daily movement or home range boundry was detected. While home ranges overlapped substancially, use of common core spaces was temporally seperated, with tagged animals being located 1.9 km or more apart during the smae day. Spatial analysis indicated that 47-55% of use occured within only 6-15% of total home area. The snow leopards shared a common core use area, which was located at a major stream confuence in an area where topography, habitat and prey abundance appeared to be more favorable. A young female used her core area least, a female with two cubs to the greatest extent. the core area was marked significantly more with scrapes, Faeces and other sighn than non-core sites, suggesting that social marking plays an important role in spacing individuals. Snow leopards showed a strong preference for bedding in steep, rocky or broken terrain, on or close to a natural vegetation or landform edge. linear landform features, such as a cliff or major ridgeline, were preferred for travelling and day time resting. This behavior would tend to place a snow leopard close to its preferred prey, blue sheep (Psuedois nayaur), which uses the same habitat at night. Marking was concetrated along commonly travelled routes, particularly river bluffs, cliff ledges and well defined ridgelines bordering stream confluences--features that were most abundant within the core area. Such marking may facilitate mutual avoidance, help maintain the species' solitary social structure, and also enable a relatively high density of snow leopard, especially within high-quality habitat.

Abstract: Impacts on households from large carnivores are frequently reported in the conservation literature, but conflicts between households and large carnivore conservation are not. Employing a human-wildlife coexistence framework that distinguishes between human-wildlife impacts on one hand, and human-conservation conflicts on the other, this paper presents data from Annapurna Conservation Area and Sagarmatha (Everest) National Park, Nepal, each with different models of conservation governance. Using systematic sampling, quantitative information from 705 households was collected via questionnaires, while 70 semi-structured interviews were conducted with key informants for cross-methods triangulation. 7.7% of households reported conflicts with snow leopard conservation in the previous 12 months, primarily due to damage to livelihoods; these were significantly higher in the Annapurna region. 373 livestock were reported lost by households to snow leopards in the previous 12 months, representing 3.4% of total livestock owned and US$ 132,450 in financial value. Livestock losses were significantly lower in the Everest area. In linear regression models, total household livestock losses to all sources best explained conflicts with snow leopard conservation and household livestock losses to snow leopards but the models for the former dependent variable had very low explanatory power. Conservation in general, and large carnivore conservation in particular, should distinguish carefully between impacts caused by coexistence with these species and conflicts with conservation actors and over the methods and interventions used to conserve carnivores, especially where these negatively impact local livelihoods. In addition, livestock husbandry standards are highlighted again as an important factor in the success of carnivore conservation programmes.

Abstract: The snow leopard (Panthera uncia) inhabits a human-altered alpine landscape and is often tolerated by residents in regions where the dominant religion is Tibetan Buddhism, including in Qomolangma NNR on the northern side of the Chinese Himalayas. Despite these positive attitudes, many decades of rapid economic development and population growth can cause increasing disturbance to the snow leopards, altering their habitat use patterns and ultimately impacting their conservation. We adopted a dynamic landscape ecology perspective and used multi-scale technique and occupancy model to better understand snow leopard habitat use and coexistence with humans in an 825 km2 communal landscape. We ranked eight hypothetical models containing potential natural and anthropogenic drivers of habitat use and compared them between summer and winter seasons within a year. HABITAT was the optimal model in winter, whereas ANTHROPOGENIC INFLUENCE was the top ranking in summer (AICcw≤2). Overall, model performance was better in the winter than in the summer, suggesting that perhaps some latent summer covariates were not measured. Among the individual variables, terrain ruggedness strongly affected snow leopard habitat use in the winter, but not in the summer. Univariate modeling suggested snow leopards prefer to use rugged land in winter with a broad scale (4000 m focal radius) but with a lesser scale in summer (30 m); Snow leopards preferred habitat with a slope of 22° at a scale of 1000 m throughout both seasons, which is possibly correlated with prey occurrence. Furthermore, all covariates mentioned above showed inextricable ties with human activities (presence of settlements and grazing intensity). Our findings show that multiple sources of anthropogenic activity have complex connections with snow leopard habitat use, even under low human density when anthropogenic activities are sparsely distributed across a vast landscape. This study is also valuable for habitat use research in the future, especially regarding covariate selection for finite sample sizes in inaccessible terrain.

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.