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.

Abstract: The Woolly Flying Squirrel (Eupetaurus cinereus), considered native to Northern Pakistan and North- Western Himalaya has remained an elusive species because of its presence in high mountain rocky cliffs near the tree line. These montane habitats have remained inaccessible for research thus making it difficult to study their distribution. Here, we present the first photographic evidence of the Woolly Flying Squirrel from the Kishtwar region of Jammu and Kashmir obtained from camera-traps placed to detect snow leopard presence and distribution. This record adds valuable presence information to the existing distribution range of the species from the North-Western Himalaya.

Abstract: While the study of carnivore ecology has made significant progress, our knowledge of small cats remains limited. For some species, their distribution remains largely unknown. We report the first photographic evidence of the Pallas’s cat in Himachal Pradesh, India. Of the 56 camera traps placed for snow leopard population estimation across Kinnaur region between March-May 2024, we recorded Pallas’s cat at three camera trap sites with 19 images from three instances during morning hours. These captures were at an elevation of 3900–4100 meters in rocky habitats largely dominated by boulders and cliffs. Sympatric carnivores captured were snow leopard (Panthera uncia), red fox (Vulpes vulpes), stone marten (Martes foina) and free-ranging dogs. This discovery not only extends the known distribution of Pallas’s cat but also underscores the urgent need for focused conservation research and action in this region, especially given the presence of free-ranging dogs. This can be achieved through coordinated, landscape level and trans-boundary efforts.

Abstract: The interaction between two predators in overlapping habitats with distinct habitat requirements poses critical challenges for conservation and management strategies. This study explores the ecological dynamics and coexistence of common leopards (Panthera pardus) and snow leopards (Panthera uncia) in the Himalayan regions, explicitly focusing on their spatial and temporal distribution as well as factors influencing their habitat overlap. Utilizing historical accounts dating back to the 1950s and our winter sighting data from trail cameras from 2016 to 2023 in the trans-Himalayan part of Annapurna Conservation Area of Nepal (3100–4696 m), we provide substantial evidence of the common leopard’s established residency at high altitudes traditionally associated with snow leopards. This challenges the notion that the co-occurrence of these species is a recent
phenomenon attributable to climate change. Instead, our findings suggest a long-term adaptation of common leopards to high-altitude environments, demonstrated by their ability to survive and breed in cold temperatures as low as − 18◦C. We show that the likelihood of co-occurrence of the two leopard species increases with proximity to open forest canopy, farmland and greater distances from snowline. In contrast, steep terrain negatively impacted the likelihood of co-occurrence, highlighting the snow leopard’s specialized adaptations to rugged landscapes. Our analysis reveals a considerable overlap between common and snow leopards in sub-alpine mixed forest and alpine scrub. This overlap extends to dietary habits along the altitudinal range, with both species preying on similar wild and domestic animals, highlighting the potential for interspecies competition, especially during the winter months when prey availability decreases.