Feng, X., Peng, Q., Chen, Y., Li, W. (2022). A Case Study of the Snow Leopard in Sanjiangyuan National Park Boundaries regarding Park Boundary Divergence. Land, 11(813), 1–15.
Abstract: This paper uses remote sensing data from the Sanjiangyuan National Park (SNP) to explore the divergence between the boundaries of national parks and the distribution of natural habitats. Results are used to argue that these discrepancies evolve along with the potential impact of global warming. Using the example of the habitat change of snow leopards and the conflicts between local people and snow leopards, we reflect on the consequences of this divergence. Results show that divergence between the political boundaries and natural habitats as well as the consequent influence on the living conditions of local people are strikingly visible, and the effects of global warming on such conflicts are apparent. The authors conclude that both notions of ‘political boundaries’ and ‘natural habitats’ are expected to come together as the SNP region is spatially configured, while ‘global warming’ seems to be relevant as an essential reference when delimiting the region in the future. Finally, the proposal for the establishment of cooperative conservation areas is presented,
emphasizing the role of cooperative governance in/around national parks.
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Blomqvist, L. (2008). International Pedigree Book for Snow Leopards, Uncia uncia. Helsinki: Helsinki Zoo.
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Darehshuri, B. F. (1978). Threatened cats of Asia. Wildlife, 20(9), 396–400.
Abstract: Man's hand is turned against the wild cats wherever they occur, often due to the value of their fur, but also because of the danger they sometimes pose to domestic stock and even human beings. All the larger Asian cats are threatened, and on this and the following pages we look at three of them – the Asiatic cheetah, the Siberian tiger, and the snow leopard.
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Chalise, M. K. (2011). Snow Leopard (Uncia uncia), Prey Species and Outreach in Langtang National, Park, Nepal. Our Nature, (9), 138–145.
Abstract: Presence of snow leopard (Uncia uncia) in Langtang National Park was obscure till 2003. It was confirmed by a
research team trained for the wildlife biology in the field. Along with the study of ecology and behavior of snow leopard sufficient effort were made to generate data on pre species. The study also dealt with threat perceived for the leopard survival while basic unit of conservation- local outreach programs were also initiated.
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Suryawanshi, K., K. (2011). Sunshine and the Shadow. Hornbill, (April-June), 34–37.
Abstract: Kulbhushansingh Suryawanshi shares an update on his blog which describes snow leopard sightings in Spiti, Himachal Pradesh, while studying the foraging behavior and eating habits of blue sheep (Pseudois nayaur).
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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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Ale, S., Thapa, K., Jackson, R., Smith, J.L.D. (2010). The fate of snow leopards in and around Mt. Everest. Cat News, 53(Autumn), 19–21.
Abstract: Since the early 2000s snow leopards Panthera uncia have re-colonized the southern slopes of Mt. Everest after several decades of extirpation. Are they now beginning to disperse to the adjoining valleys that may serve as habitat corridors linking the Everest region to other protected areas in Nepal? We conducted a cursory survey in autumn 2009 in Rolwaling lying west of Mt. Everest and detected snow leopard presence. We conclude that in these remote valleys snow leopards must rely upon livestock given the low abundance of natural prey, Himalayan tahr. Livestock-rearing is unfortunately declining in the region. Rolwaling requires immediate conservation attention for the continued survival of the endangered snow leopard and other high altitude flora and fauna.
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Mallon, D. (2003). An early record of snow leopard in Myanmar. Cat News, 39(Autumn), 24.
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Rode, J., Lambert, C., Marescot, L., Chaix, B., Beesau, J., Bastian, S., Kyrbashev, J., Cabanat, A.L. (2021). Population monitoring of snow leopards using camera trapping in Naryn State Nature Reserve, Kyrgyzstan, between 2016 and 2019. Global Ecology and Conservation, 31(e01850), 1–6.
Abstract: Four field seasons of snow leopard (Panthera uncia) camera trapping inside Naryn State Nature Reserve, Kyrgyzstan, performed thanks to citizen science expeditions, allowed detecting a minimal population of five adults, caught every year with an equilibrated sex ratio (1.5:1) and reproduction: five cubs or subadults have been identified from three litters of two different females. Crossings were observed one to three times a year, in front of most camera traps, and several times a month in front of one of them. Overlap of adults’ minimal territories was observed in front of several camera traps, regardless of their sex. Significant snow leopard presence was detected in the buffer area and at Ulan area which is situated at the reserve border. To avoid poaching on this apex predator and its preys, extending the more stringent protection measures of the core zone to both the Southern buffer area and land adjacent to Ulan is recommended.
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Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S. (2023). Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies. Global Ecology & Conservation, 41(e02350), 1–13.
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.
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