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Zhang, C., Ma, T., Ma, D. (2023). Status of the snow leopard Panthera uncia in the Qilian Mountains, Gansu Province, China. Oryx, , 1–6.
Abstract: Population density estimation is integral to the effective conservation and management of wildlife. The snow leopard Panthera uncia is categorized as Vulnerable on the IUCN Red List, and reliable information on its density is a prerequisite for its conservation and management. Little is known about the status of the snow leopard in the central and eastern Qilian Mountains, China. To address this, we estimated the population density of the snow leopard using a spatially explicit capture–recapture model based on camera trapping in Machang in the central and eastern Qilian Mountains during January–March 2019. We set up
40 camera traps and recorded 84 separate snow leopard captures over 3,024 trap-days. We identified 18 individual snow leopards and estimated their density to be 2.26/100 km. Our study provides baseline information on the snow leopard and the first population estimate for the species in the central and eastern Qilian Mountains.
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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. (2020). Fast, flexible alternatives to regular grid designs for spatial
capture–recapture..
Abstract: Spatial capture–recapture (SCR) methods use the location of
detectors (camera traps, hair snares and live-capture traps) and the
locations at which animals were detected (their spatial capture
histories) to estimate animal density. Despite the often large expense
and effort involved in placing detectors in a landscape, there has been
relatively little work on how detectors should be located. A natural
criterion is to place traps so as to maximize the precision of density
estimators, but the lack of a closed-form expression for precision has
made optimizing this criterion computationally demanding. 2. Recent
results by Efford and Boulanger (2019) show that precision can be well
approximated by a function of the expected number of detected
individuals and expected number of recapture events, both of which can
be evaluated at low computational cost. We use these results to develop
a method for obtaining survey designs that optimize this approximate
precision for SCR studies using count or binary proximity detectors, or
multi-catch traps. 3. We show how the basic design protocol can be
extended to incorporate spatially varying distributions of activity
centres and animal detectability. We illustrate our approach by
simulating from a camera trap study of snow leopards in Mongolia and
comparing estimates from our designs to those generated by regular or
optimized grid designs. Optimizing detector placement increased the
number of detected individuals and recaptures, but this did not always
lead to more precise density estimators due to less precise estimation
of the effective sampling area. In most cases, the precision of density
estimators was comparable to that obtained with grid designs, with
improvement in some scenarios where approximate CV(¬D) < 20% and density
varied spatially. 4. Designs generated using our approach are
transparent and statistically grounded. They can be produced for survey
regions of any shape, adapt to known information about animal density
and detectability, and are potentially easier and less costly to
implement. We recommend their use as good, flexible candidate designs
for SCR surveys when reasonable knowledge of model parameters exists. We
provide software for researchers to construct their own designs, in the
form of updates to design functions in the r package oSCR.
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Lama, R. P., Ghale, T. R., Suwal, M. K., Ranabhat, R., Regmi, G. R. (2018). First photographic evidence of Snow Leopard Panthera uncia (Mammalia: Carnivora: Felidae) outside current protected areas network in Nepal Himalaya. Journal of Threatened Taxa, , 12086–12090.
Abstract: 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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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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Simms, A., Moheb, Z., Salahudin, Ali, H., Ali, I. & Wood, T. (2011). Saving threatened species in Afghanistan: snow leopards in the Wakhan Corridor. International Journal of Environmental Studies, 68(3), 299–312.
Abstract: 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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Jackson, R., Ahlborn, G., & Shah, K. B. (1990). Capture and Immobilization of wild snow leopards. Int.Ped.Book of Snow Leopards, 6, 93–102.
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Sokov, A. I. (1990). The present status of the snow leopard population in the south western Pamir-Altai Mountains (Tadzhikistan). Int.Ped.Book of Snow Leopards, 6, 33–36.
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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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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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