Abstract: Livestock depredation is emerging as a significant issue across the Himalaya, including the Hemis National Park (HNP) in Ladakh. Some consider that this protected area harbors the best snow leopard population in India, but local herders perceive the endangered snow leopard as a serious threat to their livelihood.

Abstract: 1. Community-based conservation, despite being more inclusive than fortress con- servation, has been criticized for being a top-down implementation of external ideas brought to local communities for conservation's benefit. This is particularly true for Changpas, the pastoral people of Changthang in trans-Himalayan India who live alongside unique wildlife.
2. Our main aim was to co-design conservation interventions through participatory action research. We worked with two Changpa communities, to understand the issues faced by them. Subsequently, we co-designed context-sensitive interventions to facilitate positive human–nature interactions. We did so by integrating the PARTNERS (Presence, Aptness, Respect, Transparency, Empathy, Responsiveness, Strategic Support) principles with the Trinity of Voice (Access, Standing and Influence).
3. In Rupsho, we facilitated focus group discussions (FGDs) led by the community. We found livestock depredation by wildlife was primarily facilitated by the weather. This led to co-designing of a new corral design, which was piloted with seven households, safeguarding 2385 pashmina goats and sheep. Approximating the value of each sheep/goat to be USD125, this intervention amounts to a significant economic protection of USD c. 42,500 for each household. This is along with intangible gains of trust, ownership and improved self-esteem.
4. In Tegazong, a restricted area adjoining the Indo-China border with no previous research records, we worked with 43 Changpa people to co-create research questions of mutual interest. Wildlife presence and reasons for livestock loss were identified as areas of mutual interest. The herders suggested they would record data in a form of their choice, for 6 months, while they live in their winter pastures. This participatory community monitoring revealed nutrition and hypothermia to be a key cause of livestock death. Subsequently, we delimited two previously untested interventions: lamb cribs and provisioning of locally sourced barley as a feed supplement. The wildlife monitoring recorded the first record of Tibetan Gazelle Procapra picticuadata, outside of their known distribution, in Tegazong.
5. We aim to highlight the benefits of co-designing projects with local communities that link research and conservation, while also discussing the challenges faced. Ultimately, such projects are needed to ensure ethical knowledge generation and conservation, which aims to be decolonial and inclusive.

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: 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: Tremendous strides have been made in recent years to broaden our understanding of reproductive processes in nondomestic felid species and further our capacity to use this basic knowledge to control and manipulate reproduction of endangered cats. Much of that progress has culminated from detailed scientific studies conducted in nontraditional laboratory settings, frequently at collaborating zoological parks but also under more primitive conditions, including in the field. A mobile laboratory approach is described, which incorporates a diverse array of disciplines and research techniques. This approach has been extremely useful, especially for conducting gamete characterization and function studies as well as reproductive surveys, and for facilitating the development of assisted reproductive technology. With continuing advances in assisted reproduction in rare felids, more procedures are being conducted primarily as service-related activities, targeted to increase effectiveness of species propagation and population management. It can be a challenge for both investigators and institutional animal care and use committees (IACUCs) to differentiate these service-based procedures from traditional research studies (that require IACUC oversight). For research with rare cat species, multi-institutional collaboration frequently is necessary to gain access to scientifically meaningful numbers of study subjects. Similarly, for service-based efforts, the ability to perform reproductive procedures across institutions under nonstandard laboratory conditions is critical to applying reproductive sciences for managing and preserving threatened cat populations. Reproductive sciences can most effectively assist population management programs (e.g., Species Survival Plans) in addressing conservation priorities if these research and service- related procedures can be conducted “on the road” at distant national and international locales. This mobile laboratory approach has applications beyond endangered species research, notably for other scientific fields (e.g., studies of hereditary disease in domestic cat models) in which bringing the laboratory to the subject is of value.