Kitchener, S. L., Meritt, & Rosenthal, M. (1975). Observations on the breeding and husbandry of snow leopards, Panthera uncia. Int.Zoo Yearbook, 15, 212–217.
Abstract: Describes adult care and breeding biology, and the care, growth, and mortality factors of young snow leopards in a successful breeding program in the Lincon Park Zoo, Chicago, Illinois.
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Jackson, R., & Wangchuk, R. (2004). A Community-Based Approach to Mitigating Livestock Depredation by Snow Leopards (Vol. 9).
Abstract: Livestock depredation by the endangered snow leopard (Panthera uncia) _is an increasingly contentious issue in Himalayan villages, especially in or near protected areas. Mass attacks in which as many as 100 sheep and goats are killed in a single incident inevitably result in retaliation by local villagers. This article describes a community-based conservation initiative to address this problem in Hemis National Park, India. Human-wildlife conflict is alleviated by predator-proofing villagers' nighttime livestock pens and by enhancing household incomes in environmentally sensitive and culturally compatible ways. The authors have found that the highly participatory strategy described here (Appreciative Participatory Planning and Action-APPA) leads to a sense of project ownership by local stakeholders, communal empowerment, self-reliance, and willingness to co-exist with
snow leopards. The most significant conservation outcome of this process is the protection from retaliatory poaching of up to five snow leopards for every village's livestock pens that are made predator-proof._
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Alexander, J. S., Murali, R., Mijiddorj, T. N., Agvaantseren, B., Lhamo, C., Sharma, D., Suryawanshi, K. R., Zhi, L., Sharma, K., Young, J. C. (2023). Applying a gender lens to biodiversity conservation in High Asia. Frontiers in Conservation Science, , 1–8.
Abstract: Community-based conservation efforts represent an important approach to facilitate the coexistence of people and wildlife. A concern, however, is that these efforts build on existing community structures and social norms, which are commonly dominated by men. Some biodiversity conservation approaches may consequently neglect women’s voices and deepen existing inequalities and inequities. This paper presents two community case studies that draw upon the knowledge and experience gained in our snow leopard conservation practice in pastoral and agro-pastoral settings in Mongolia and India to better understand women’s roles and responsibilities. In these settings, roles and responsibilities in livestock management and agriculture are strongly differentiated along gender lines, and significant gaps remain in women’s decision-making power about natural resources at the community level. We argue that context-specific and gender-responsive approaches are needed to build community support for conservation actions and leverage women’s potential contributions to conservation outcomes.
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Gajurel, D. (2006). Snow Leopards Found in Nepal's Langtang National Park (Editor-in-Chief Sunny Lewis and Managing Editor Jim Crabtree, Ed.). Environment News Service.
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Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection. Ecological Informatics, 77(102214), 1–14.
Abstract: Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone.
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Subbotin, A. E., & Istomov, S. V. (2009). The population status of snow leopards Uncia uncia (Felidae, Carnivora) in the western Sayan Mountain Ridge. Doklady Biologicl Sciences, 425, 183–186.
Abstract: The snow leopard (Uncia uncial Schreber, 1776) is the most poorly studied species of the cat family in the world and, in particular, in Russia, where the northern periphery of the species area (no more than 3% of it) is located in the Altai-Hangai-Sayan range [1]. It is generally known that the existing data on the Russian part of the snow leopard population have never been a result of targeted studies; at best, they have been based on recording the traces of the snow leopard vital activity [2]. This is explained by the snow leopard's elusive behavior, inaccessibility of its habitats for humans, and its naturally small total numbers in the entire species area. All published data on the population status of the snow leopard in Russia, from the first descriptions of the species [3-6] to the latest studies [7, 8] are subjective, often speculative, and are not confirmed by
quantitative estimates. It is obvious, however, that every accurate observation of this animal is of particular interest [9]. The purpose of our study was to determine the structure and size of the population group presumably inhabiting the Western Sayan mountain ridge at the northern boundary of the species area
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Ming, M., Munkhtsog, B., Xu, F., Turghan, M., Yin, S. -jing, & Wei, S. - D. (2005). Markings as Indicator of Snow Leopard in Field Survey, in Xinjiang.
Abstract: The Snow Leopard (Uncia uncia) was a very rare species in China. The survey on the markings of Snow Leopard in Ahay and Tianshan Mountains is the major activity of the Project of Snow Leopard in Xinjiang, supported by International Snow Leopard Trust(ISLT)and Xinjiang Conservation Fund(XCF). During the field work from Sep to Nov 2004 the Xinjiang Snow Leopard Group(XSLG) set 67 transects of a total length of 47 776 m with mean transect length is 7 1 3 m at 9 locations.Total of 1 l 8 markings of Snow Leopards were found in 27 transects the mean density is 247km. The markings of Snow Leopard included the pug marks or footprints, scrapes, feces, bloodstain, scent spray, urine, hair or fur, claw rake, remains of prey corpse, sleep site, roar and others. From the quantity and locations of marks the XSLG got the information on habitat selection distribution region and relative abundance of the Snow Leopard in the study areas. The survey also provided knowledge on distribution and abundance of major prey potential conservation problems and human attitudes to Snow Leopards by taking 200 questionnaires in the study areas.
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Jackson, R., Roe, J., Wangchuk, R., & Hunter, D. (2005). Camera-Trapping of Snow Leopards. Cat News, 42(Spring), 19–21.
Abstract: Solitary felids like tigers and snow leopards are notoriously difficult to enumerate, and indirect techniques like pugmark surveys often produce ambiguous information that is difficult to interpret because many factors influence marking behavior and frequency (Ahlborn & Jackson 1988). Considering the snow leopard's rugged habitat, it is not surprising then that information on its current status and occupied range is very limited. We adapted the camera-trapping techniques pioneered by Ullas Karanth and his associates for counting Bengal tigers to the census taking of snow leopards in the Rumbak watershed of the India's Hemis High Altitude National Park (HNP), located in Ladakh near Leh (76ø 50' to 77ø 45' East; 33ø 15' to 34ø 20'North).
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Jackson, R. (1999). Snow Leopards, Local People and Livestock Losses: Finding solutions using Appreciative Participatory Planning and Action (APPA) in the Markha Valley of Hemis National Park, Ladakh, October 6-26, 1999. Cat News, 31(Autumn), 22–23.
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.
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McCarthy, T., Khan, J., Ud-Din, J., & McCarthy, K. (2007). First study of snow leopards using GPS-satellite collars underway in Pakistan. Cat News, 46(Spring), 22–23.
Abstract: Snow leopards (Uncia uncia) are highly cryptic and occupy remote inaccessible habitat, making studying the cats difficult in the extreme. Yet sound knowledge of the cat's ecology, behavior and habitat needs is required to intelligently conserve them. This information is lacking for snow leopards, and until recently so was the means to fill that knowledge gap. Two long-term studies of snow leopards using VHF radio collars have been undertaken in Nepal (1980s) and Mongolia (1990s) but logistical and technological constraints made the findings of both studies equivocal. Technological advances in the interim, such as GPS collars which report data via satellite, make studies of snow leopards more promising, at least in theory.
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