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McCarthy, T., Murray, K., Sharma, K., & Johansson, O. (2010). Preliminary results of a long-term study of snow leopards in South Gobi, Mongolia. Cat News, Autumn(53), 15–19.
Abstract: Snow leopards Panthera uncia are under threat across their range and require urgent conservation actions based on sound science. However, their remote habitat and cryptic nature make them inherently difficult to study and past attempts have provided insufficient information upon which to base effective conservation. Further, there has been no statistically-reliable and cost-effective method available to monitor snow leopard populations, focus conservation effort on key populations, or assess conservation impacts. To address these multiple information needs, Panthera, Snow Leopard Trust, and Snow Leopard Conservation Fund, launched an ambitious long-term study in Mongolia’s South Gobi province in 2008. To date, 10 snow leo-pards have been fitted with GPS-satellite collars to provide information on basic snow leopard ecology. Using 2,443 locations we calculated MCP home ranges of 150 – 938 km2, with substantial overlap between individuals. Exploratory movements outside typical snow leopard habitat have been observed. Trials of camera trapping, fecal genetics, and occupancy modeling, have been completed. Each method ex-hibits promise, and limitations, as potential monitoring tools for this elusive species.
Keywords: snow leopard, Mongolia, monitor, population, Panthera, Snow Leopard Trust, Snow Leopard Conservation Fund, South Gobi, ecology, radio collar, GPS-satellite collar, home range, camera trapping, fecal genetics, occupancy modeling
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Ming, M., Yun, G., & Bo, W. (2008). Chinese snow leopard team goes into action. Man & the Biosphere, 54(6), 18–25.
Abstract: China, the world's most populous country, also contains the largest number of Snow Leopards of any country in the world. But the survey and research of the snow leopard had been very little for the second half of the 20th century. Until recent years, the members of Xinjiang Snow Leopards Group (XSLG/SLT/XFC) , the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences have been tracking down the solitary animal. The journal reporter does a face-to-face interview with professor Ma Ming who is a main responsible expert of the survey team. By the account of such conversation, we learn the achievements, advances and difficulty of research of snow leopards in the field, Tianshan and Kunlun, Xinjiang, the far west China, and we also know that why the team adopt the infrared camera to capture the animals. Last but not least professor talked about the survival menace faced by the Snow Leopards in Xinjiang.
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Sharma, R. (2010). Of Men and Mountain Ghosts: Glimpses from the Rooftop of the World. GEO, 3(6), 56–67.
Abstract: Catching a glimpse of a snow leopard is a rare and exciting event for anyone. For researchers, hideen camera traps have become a vital tool in their work.
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Ale, S., Shrestha, B., and Jackson, R. (2014). On the status of Snow Leopard Panthera Uncia (Schreber 1775) in Annapurna, Nepal. Journal of Threatened Taxa, (6(3)), 5534–5543.
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McCarthy, K., Fuller, T., Ming, M., McCarthy, T., Waits, L., & Jumabaev, K. (2008). Assessing Estimators of Snow Leopard Abundance (Vol. 72).
Abstract: The secretive nature of snow leopards (Uncia uncia) makes them difficult to monitor, yet conservation efforts require accurate and precise methods to estimate abundance. We assessed accuracy of Snow Leopard Information Management System (SLIMS) sign surveys by comparing them with 4 methods for estimating snow leopard abundance: predator:prey biomass ratios, capture-recapture density estimation, photo-capture rate, and individual identification through genetic analysis. We recorded snow leopard sign during standardized surveys in the SaryChat Zapovednik, the Jangart hunting reserve, and the Tomur Strictly Protected Area, in the Tien Shan Mountains of Kyrgyzstan and China. During June-December 2005, adjusted sign averaged 46.3 (SaryChat), 94.6 (Jangart), and 150.8 (Tomur) occurrences/km. We used
counts of ibex (Capra ibex) and argali (Ovis ammon) to estimate available prey biomass and subsequent potential snow leopard densities of 8.7 (SaryChat), 1.0 (Jangart), and 1.1 (Tomur) snow leopards/100 km2. Photo capture-recapture density estimates were 0.15 (n = 1 identified individual/1 photo), 0.87 (n = 4/13), and 0.74 (n = 5/6) individuals/100 km2 in SaryChat, Jangart, and Tomur, respectively. Photo-capture rates
(photos/100 trap-nights) were 0.09 (SaryChat), 0.93 (Jangart), and 2.37 (Tomur). Genetic analysis of snow leopard fecal samples provided minimum population sizes of 3 (SaryChat), 5 (Jangart), and 9 (Tomur) snow leopards. These results suggest SLIMS sign surveys may be affected by observer bias and environmental variance. However, when such bias and variation are accounted for, sign surveys indicate relative abundances similar to photo rates and genetic individual identification results. Density or abundance estimates based on capture-recapture or ungulate biomass did not agree with other indices of abundance. Confidence in estimated densities, or even detection of significant changes in abundance of snow leopard, will require more effort and better documentation.
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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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Alexander, J. S., Shi, K., Tallents, L. A., Riordan, P. (2015). On the high trail: examining determinants of site use by the Endangered snow leopard Panthera uncia in Qilianshan, China. Oryx, (Fauna & Flora International), 1–8.
Abstract: Abstract There is a need for simple and robust techniques for assessment and monitoring of populations of the Endangered snow leopard Panthera uncia to inform the de- velopment of action plans for snow leopard conservation. We explored the use of occupancy modelling to evaluate the influence of environmental and anthropogenic features on snow leopard site-use patterns. We conducted a camera trap survey across  km in Gansu Province, China, and used data from  camera traps to estimate probabilities of site use and detection using the single season occupancy model. We assessed the influence of three covariates on site use by snow leopards: elevation, the presence of blue sheep Pseudois nayaur and the presence of human disturb- ance (distance to roads). We recorded  captures of snow leopards over , trap-days, representing a mean capture success of . captures per  trap-days. Elevation had the strongest influence on site use, with the probability of site use increasing with altitude, whereas the influence of presence of prey and distance to roads was relatively weak. Our findings indicate the need for practical and robust tech- niques to appraise determinants of site use by snow leo- pards, especially in the context of the limited resources available for such work.
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Thapa, K., Pradhan, N, M, B., Barker, J., Dhakal, M., Bhandari, A, R., Gurung, G, S., Rai, D, P., Thapa, G, J., Shrestha, S., Singh, G, R. (2013). High elevation record of a leopard cat in the Kangchenjunga Conservation Area, Nepal. Cat News, (No 58), 26–27.
Abstract: During a camera trapping survey in Khambachen valley of Kangchenjunga Conservation
Area KCA from 24 April to 26 May 2012 we camera trapped one leopard cat
Prionailurus bengalensis at an altitude of 4,474 meter. This is probably the highest
altitudinal record for the species in its range. Additionally, one melanistic leopard
Panthera pardus was captured at an altitude of 4,300 m, which is probably as well the
highest documented record in the country. Yet at this stage, no obvious reason can
explain these unusual high records for both species, thus more surveys are recommended
for this region.
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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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