Abstract: We explore the role of community-based conservation (CBC) in the sustainable management of conservation conflicts by examining the experiences of conservation practitioners trying to address conflicts between snow leopard conservation and pastoralism in Asian mountains. Practitioner experiences are examined through the lens of the PARTNERS principles for CBC (Presence, Aptness, Respect, Transparency, Negotiation, Empathy, Responsiveness, and Strategic Support) that represent an inclusive conservation framework for effective and ethical engagement with local communities. Case studies from India, Kyrgyzstan, Mongolia, and Pakistan show that resilient relationships arising from respectful engagement and negotiation with local communities can provide a strong platform for robust conflict management. We highlight the heuristic value of documenting practitioner experiences in on-the-ground conflict management and community-based conservation efforts.

Abstract: 1. Camera trapping is a widely employed tool in wildlife
research, used to estimate animal abundances, understand animal
movement, assess species richness and under- stand animal behaviour. In
addition to images of wild animals, research cameras often record human
images, inadvertently capturing behaviours ranging from innocuous
actions to potentially serious crimes.
2. With the increasing use of camera traps, there is an urgent need to
reflect on how researchers should deal with human images caught on
cameras. On the one hand, it is important to respect the privacy of
individuals caught on cameras, while, on the other hand, there is a
larger public duty to report illegal activity. This creates ethical
dilemmas for researchers.
3. Here, based on our camera-trap research on snow leopards Panthera
uncia, we outline a general code of conduct to help improve the practice
of camera trap based research and help researchers better navigate the
ethical-legal tightrope of this important research tool.

Abstract: The protection of Snow Leopards in the remote and economically disadvantaged Northern Areas of Pakistan needs local people equipped with the skills to gather and present information on the number and range of individual animals in their area. It is important for the success of a conservation campaign that the people living in the area are engaged in the conservation process. Snow Leopards are elusive and range through inhospitable terrain so direct study is difficult. Consequently the major goals for this project were twofold, to gather information on snow leopard distribution in this area and to train local university students and conservation management professionals in the techniques used for locating snow leopards without the need to capture or even see the animals. This project pioneered the use of DNA testing of field samples collected in Pakistan to determine the distribution of snow leopards and to attempt to identify individuals. These were collected in and around that country's most northerly national park, the Kunjurab National Park, which sits on the Pakistan China border. Though the Northern Areas is not a well developed part of Pakistan, it does possess a number of institutions that can work together to strengthen snow leopard conservation. The first of these is a newly established University with students ready to be trained in the skills needed. Secondly WWF-Pakistan has an office in the main town and a state of the art GIS laboratory in Lahore and already works closely with the Forest Department who manage the national park. All three institutions worked together in this project with WWF providing GIS expertise, the FD rangers, and the university students carrying out the laboratory work. In addition in the course of the project the University of the Punjab in Lahore also joined the effort, providing laboratory facilities for the students. As a result of this project maps have been produced showing the location of snow leopards in
two areas. Preliminary DNA evidence indicates that there is more than one animal in this
relatively small area, but the greatest achievement of this project is the training and
experience gained by the local students. For one student this has been life changing. Due to
the opportunities provided by this study the student, Nelofar gained significant scientific
training and as a consequence she is now working as a lecturer and research officer for the
Center for Integrated Mountain Research, New Campus University of the Punjab, Lahore
Pakistan

Abstract: In the semi-arid ecosystems of Asia, where pastoralism is a main subsistence occupation, grazing competition from domestic stock is believed to displace the wild ungulates. We studied the habitat relationships among sympatric naur and domestic yak and smallstock in Phu valley in upper Manang district, Nepal, on the basis of their distribution on vegetation types, elevation and slope. To control for the disturbance effect by humans, we collected the data on naur from those ranges where domestic stock were not being attended by herders. We applied correspondence analysis to explore habitat associations among animal groups (n ¬ 1415) within and across-seasons. Within each association, interspecific habitat overlaps and species habitat preferences were calculated. Naur was strongly associated with free-ranging yak as they used similar altitudinal ranges in all seasons, except in spring. Their distributions on vegetation types and slopes were also quite similar, except for a stronger preference for alpine meadows by naur during summer and winter. Naur and smallstock did not form temporal associations as the latter consistently used lower elevations. In autumn and spring, however, naur spatially overlapped with the summer range of smallstock, and both preferred the alpine meadow habitat during these periods. Alpine meadow was the least abundant vegetation type but was consistently and preferentially used by all animal groups across seasons. At high stocking densities, all three animals groups are therefore likely to compete for this vegetation type. The role of spatio-temporal heterogeneity for interpreting the interspecific relationships among ungulates in the semi-arid rangelands of the trans-Himalaya is discussed.

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.