Abstract: Snow leopards (Panthera uncia) have long co-existed with livestock herding people across Asia’s high mountains. Multiple use landscapes however imply potential competition for shared resources, livestock predation, and the risk of retaliatory killing of predators. Community-based conservation is a central pillar for supporting people’s livelihoods and safeguarding predators and their habitat. Based on the theory of planned behavior, we examined the factors that shape herders’ tolerance of snow leopards. Our questionnaire-based study was conducted in the Sanjiangyuan Region, China, encompassing four communities with varying livelihoods, experiences with live- stock depredation and levels of exposure to community conservation interventions. Our results showed that respondents generally held positive views towards snow leopards, although women tended to have relatively more negative views towards snow leopards compared with men. Current household income was largely dependent on caterpillar fungus rather than livestock. Social norms around religion and the role of community leaders in our study area seemed to be the main determinants of the generally benign association of people with wildlife, overshadowing potential influences of community-based conservation interventions. Our work suggests that conservations programs will be aided through collaborations with communities and religious institutions, and that conservationists must proactively engage with women as significant actors in conservation.

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: Understanding landscape connectivity and population genetic parameters is imperative for threatened species management. However, such information is lacking for the snow leopard (Panthera uncia). This study sought to explore hierarchical snow leopard gene flow patterns and drivers of genetic structure in Mongolia and China. A total of 97 individuals from across Mongolia and from the north-eastern edge of the Qinghai-Tibetan Plateau in Gansu Province to the middle of Qinghai Province in China were genotyped across 24 microsatellite loci. Distance-based frameworks were used to determine a landscape scenario best explaining observed genetic structure. Spatial and non-spatial methods were used to investigate fine-scale autocorrelation and similarity patterns as well as genetic structure and admixture. A genetic macro-division between populations in China and Mongolia was observed, suggesting that the Gobi Desert is a substantial barrier to gene flow. However, admixture and support for a resistance-based mode of isolation suggests connective routes that could facilitate movement. Populations in Mongolia had greater connectivity, indicative of more continuous habitat. Drivers of genetic structure in China were difficult to discern, and fine-scale sampling is needed. This study elucidates snow leopard landscape connectivity and helps to prioritize conservation areas. Although contact zones may have existed and occasional crossings can occur, establishing corridors to connect these areas should not be a priority. Focus should be placed on maintaining the relatively high connectivity for snow leopard populations within Mongolia and increasing research efforts in China.

Abstract: We surveyed the urial Ovis vignei and Siberian ibex Capra sibirica in the Hindu Kush mountain range of Wakhan National Park in north-eastern Afghanistan to determine their population status and identify potential drivers of population change. We conducted two double- observer ground surveys, in April–May 2015 and 2018, in 10 areas (total = 288 km2). Urial herds were mostly com- posed of both sexes (78% of observed herds), the mean adult sex ratio (females:males) was 100:70, and the mean female:juvenile ratio was 100:53. In 2018 we calculated a urial density of 35/100 km2, compared to 72/100 km2 in 2015. Ibex herds were mostly (79%) composed of both sexes, the mean adult sex ratio (females:males) was 100:103, and the mean female:juvenile ratio was 100:58. Ibex density estimates were similar in 2015 and 2018 (c. 250/100 km2). We discuss the usefulness of the double-observer methods for ungulate surveys, highlight the value of viewshed calculations and discuss the possible causes of urial population decline. To ensure the conservation of these ungulate populations, we recommend continued regular monitoring, measures to address poaching and research to clarify the taxonomical status of urials in Wakhan.

Abstract: Olfactory communication is important for many solitary carnivores to delineate territories and communicate with potential mates and competitors. Pallas’s cats (Otocolobus manul) are small felids with little published research on their ecology and behaviour, including if they avoid or change behaviours due to dominant carnivores. We studied their olfactory communication and visitation at scent-marking sites using camera traps in two study areas in Mongolia. We documented four types of olfactory communication behaviours, and olfaction (sniffing) was the most frequent. Pallas’s cats used olfactory communication most frequently at sites that were not visited by snow leopards (Panthera uncia) and when they used communal scent-marking sites, they were more likely to use olfactory communication when a longer time had elapsed since the last visit by a snow leopard. This suggests that Pallas’s cats may reduce advertising their presence in response to occurrence of snow leopards, possibly to limit predation risk.