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Augugliaro, C., Paniccia, C., Janchivlamdan, C., Monti, I. E., Boldbaatar, T., Munkhtsog, B. |
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Title |
Mammal inventory in the Mongolian Gobi, with the southeasternmost documented record of the Snow Leopard, Panthera uncia (Schreber, 1775), in the country |
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2019 |
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Check List |
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15 |
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4 |
Pages |
575-578 |
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Camera trapping, desert, live trapping, mammal checklist, species richness, threatened species. |
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Studies on mammal diversity and distribution are an important source to develop conservation and management strategies.
The area located in southern Mongolia, encompassing the Alashan Plateau Semi-Desert and the Eastern Gobi Desert-Steppe ecoregions, is considered strategic for the conservation of threatened species. We surveyed the non-volant mammals in the Small Gobi-A Strictly Protected Area (SPA) and its surroundings, by using camera trapping, live trapping, and occasional sightings. We recorded 18 mammal species belonging to 9 families and 6 orders. Among them, 4 are globally threatened or near-threatened, 2 are included in the CITES Appendix I, and 2 are listed in the Appendix II. Moreover, we provide the southeasternmost record for the Snow Leopard (Panthera uncia) in Mongolia, supported by photographic evidence. Our study highlights the importance of this protected area to preserve rare, threatened, and elusive species. |
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SLN @ rakhee @ |
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1486 |
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Sharma, K., Fiechter, M., George, T., Young, J., Alexander, J.
S., Bijoor, Suryawanshi, K., Mishra, C. |
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Title |
Conservation and people: Towards an ethical code of conduct for
the use of camera traps in wildlife research |
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Journal Article |
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2020 |
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Ecological Solutions and Evidence |
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1-6 |
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camera trap, code of conduct, ethics, human rights, law, PARTNERS principles for community- based conservation, privacy, snow leopard |
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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. |
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1626 |
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Sharma, R. |
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Title |
Of Men and Mountain Ghosts: Glimpses from the Rooftop of the World |
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2010 |
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GEO |
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3 |
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6 |
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56-67 |
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snow leopard, camera trap, Spiti, India, Sharma |
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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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GEO International |
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India |
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SLN @ rana @ |
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1139 |
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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. |
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Title |
Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies |
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Journal Article |
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Year |
2023 |
Publication |
Global Ecology & Conservation |
Abbreviated Journal |
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41 |
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e02350 |
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1-13 |
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Snow leopard, Artificial intelligence, Camera trap misclassification, individual ID, HotSpotter |
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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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SLN @ rakhee @ |
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1715 |
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Rode, J., Lambert, C., Marescot, L., Chaix, B., Beesau, J., Bastian, S., Kyrbashev, J., Cabanat, A.L. |
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Population monitoring of snow leopards using camera trapping in Naryn State Nature Reserve, Kyrgyzstan, between 2016 and 2019 |
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Journal Article |
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2021 |
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Global Ecology and Conservation |
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31 |
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e01850 |
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1-6 |
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Snow leopard, camera trapping, Citizen science, Kyrgyzstan |
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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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1660 |
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Author |
Khanyari, M., Sanyal, O., Chandan, P., Bajaj, D., Sharma, C., Rana, M., Sharma, N., Bashir, T., Suryawanshi, K. |
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Title |
A new dawn? Population baselines of snow leopards and other mammals of the Kishtwar High Altitude National Park, India |
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Journal Article |
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Year |
2024 |
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Integrative Conservation |
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1-10 |
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camera trapping, Himalaya, Kishtwar, mammal, SECR, Snow leopard |
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Accurately assessing the status of threatened species requires reliable population estimates. Despite this necessity, only a small proportion of the global distribution range of the vulnerable snow leopard (Panthera uncia) has been systematically sampled. The Indian section of the Greater Himalayas, which includes Kishtwar High Altitude National Park (KHANP), harbours potential snow leopard habitat. Nevertheless, there has been limited ecological and conservation research focusing on species that are specific to KHANP, as well as limited research on the broader biodiversity of the Greater Himalayas. We used Spatially Explicit Capture‐Recapture (SECR) models to provide—to our knowledge—the first robust snow leopard population density and abundance estimates from KHANP. We also provide a Relative Abundance Index (RAI) for non‐volant mammals (excluding small rodents). Our study sampled three catchments within the Dachhan region of KHANP—Kibber, Nanth and Kiyar—using 44 cameras over a 45‐day period between May and June 2023. We identified four unique snow leopard individuals across 15 detections in nine camera locations. SECR analysis estimated a density of 0.50 snow leopards per 100 km2 (95% confidence interval: 0.13–1.86), corresponding to an abundance of four individual (4–9) adults. Camera trapping revealed a total of 16 mammal species, including the endangered Kashmir musk deer (Moschus cupreus). Marmots (Marmota caudata) had the highest RAI of 21.3 (±0.2). Although the estimated density and abundance of snow leopards in our study area had relatively wide 95% confidence intervals, our combined results of snow leopard densities and RAIs of prey species such as ibex and marmots indicate that KHANP is a potentially important area for snow leopards. Given the geopolitical history of Jammu and Kashmir in India, the region where KHANP is located, wildlife research remains a low priority. We hope our study encourages authorities to support further research. This study is an initial step towards evaluating the potential of KHANP as a conservation landscape under the Government of India's Project Snow Leopard. |
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1755 |
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Simms, A., Moheb, Z., Salahudin, Ali, H., Ali, I. & Wood, T. |
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Title |
Saving threatened species in Afghanistan: snow leopards in the Wakhan Corridor |
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2011 |
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International Journal of Environmental Studies |
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68 |
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3 |
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299-312 |
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Snow leopard; Camera trap; Governance; Rangers; Corral; Insurance |
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The Wakhan Corridor in northeast Afghanistan is an area known for relatively abundant wildlife and it appears to represent Afghanistan’s most important snow leopard landscape. The Wildlife Conservation Society (WCS) has been working in Wakhan since 2006. Recent camera trap surveys have documented the presence of snow leopards at 16 different locations in the landscape. These are the first camera trap records of snow leopards in Afghanistan. Threats to snow leopards in the region include the fur trade, retaliatory killing by shepherds and the capture of live animals for pets. WCS is developing an integrated management approach for this species, involving local governance, protection by a cadre of rangers, education, construction of predator-proof livestock corrals, a livestock insurance program, tourism and research activities. This management approach is expected to contribute significantly to the conservation of snow leopards and other wildlife species in the Wakhan. |
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Wildlife Conservation Society, International Programs, 2300 Southern Blvd, New York 10460, USA |
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Wildlife Conservation Society |
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1347 |
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Ming, M., Munkhtsog, B., McCarthy, T., McCarthy, K. |
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Title |
Monitor ing of Population Density of Snow Leopard in X injiang |
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Journal Article |
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2011 |
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Journal of Ecology and Rural Environment |
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27 |
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1 |
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79-83 |
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Uncia uncia; snow leopard; monitoring method; trace; infrared camera; relative intensity |
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The snow leopard (Uncia uncia) is a very rare species in China. The survey of traces of snow leopard in Kunlun, Altay and Tianshan is them a instep of the Project of Snow Leopard in X injiang supported by the International Snow Leopard Trust ( SLT) and the Xinjiang Conservation Fund (XCF). During the field survey from 2004 to 2010, the Xinjiang Snow Leopard Group ( XSLG) spent about 270 days in over 20 different places, covering over 150 transects totaling nearly 190 km, and found 1- 3 traces per kilometer. The traces of snow leopard recorded include dung, odor, chains of footprints, scraping, paw nail marks, lying mark, fur, urine, bloodstain, leftover of prey corpse, roaring and others. Based on tracer image analyses, the XSLG got to know primarily scopes of the domains, distribution and relative density of the snow leopard in these areas. Then the group began to take infrared photos, conducted survey of food sources of the leopards, investigated fur market and paths of trading, and cases of killing, and carry out civil survey through questionnaire, non government organization community service and research on conflicts between grazing and wild life protection. A total of 36 infrared came ras were laid out, working a total of about 2 094 days or 50 256 hours. A total 71 rolls of film were collected and developed, includ ing 32 clear pictures of snow leopards, thus making up a shooting rate or capture rate of 1.53%. It was ascertained that in Tomur Peak area, there were 5- 8 snow leopards roaming within a range of 250 km2, forming a population density of 2��0- 3��2 per 100 km2. After compar ing the various monitoring results, the advantages and limitations of different monitoring methods have been discussed. |
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Chinese |
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1303 |
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Lama, R. P., Ghale, T. R., Suwal, M. K., Ranabhat, R., Regmi, G. R. |
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First photographic evidence of Snow Leopard Panthera uncia (Mammalia: Carnivora: Felidae) outside current protected areas network in Nepal Himalaya |
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Journal Article |
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2018 |
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Journal of Threatened Taxa |
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12086-12090 |
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Camera-trapping, conservation, Humla, livestock depredation, monasteries, non-timber forest products, retaliatory killing, Tibetan Buddhism. |
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The Snow Leopard Panthera uncia is a rare top predator of high-altitude ecosystems and insufficiently surveyed outside of protected areas in Nepal. We conducted a rapid camera-trapping survey to assess the presence of Snow Leopard in the Limi valley of Humla District. Three individuals were recorded in two camera locations offering the first photographic evidence of this elusive cat outside the protected area network of Nepal. In addition to Snow Leopard, the Blue Sheep Pseudois nayaur, Beech Marten Martes foina, Pika Ochotona spp. and different species of birds were also detected by camera-traps. More extensive surveys and monitoring are needed for reliably estimating the population size of Snow Leopard in the area. The most urgent needs are community-based conservation activities aimed at mitigating immediate threats of poaching, retaliatory killing, and rapid prey depletion to ensure the survival of this top predator in the Himalaya. |
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1470 |
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Ale, S., Shrestha, B., and Jackson, R. |
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On the status of Snow Leopard Panthera Uncia (Schreber 1775) in Annapurna, Nepal |
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2014 |
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Journal of Threatened Taxa |
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6(3) |
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5534-5543 |
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Annapurna, Blue Sheep, Buddhism, camera-trapping, Himalayas, Mustang, sign-survey, Snow Leopard. |
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1407 |
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