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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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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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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, S., A., Zhang, C., Shi, K., Riordan, P. (2016). A granular view of a snow leopard population using camera traps in Central China. Biological Conservation, (197), 27–31.
Abstract: Successful conservation of the endangered snow leopard (Panthera uncia) relies on the effectiveness of monitoring programmes. We present the results of a 19-month camera trap survey effort, conducted as part of a longterm study of the snow leopard population in Qilianshan National Nature Reserve of Gansu Province, China. Weassessed the minimumnumber of individual snowleopards and population density across different sampling periods using spatial capture–recapture methods. Between 2013–2014, we deployed 34 camera traps across an area of 375 km2, investing a total of 7133 trap-days effort. Weidentified a total number of 17–19 unique individuals
from photographs (10–12 adults, five sub-adults and two cubs). The total number of individuals identified and estimated density varied across sampling periods, between 10–15 individuals and 1.46–3.29 snow leopards per 100 km2 respectively. We demonstrate that snow leopard surveys of limited scale and conducted over short sampling periods only present partial views of a dynamic and transient system.We also underline the challenges in achieving a sufficient sample size of captures and recaptures to assess trends in snow leopard population size and/or density for policy and conservation decision-making
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Jackson, R., Zongyi, W., Xuedong, L., & Yun, C. (1994). Snow Leopards in the Qomolangma Nature Preserve of Tibet Autonomous Region. In J.L.Fox, & D.Jizeng (Eds.), (pp. 85–95). Usa: Islt.
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Joslin, P. (1988). A Phototrapline for Cold Temperatures. In H.Freeman (Ed.), (pp. 121–128). India: International Snow Leopard Trust and WIldlife Institute of India.
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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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Jackson, R. M., & Ahlborn, G. (1988). Observations on the Ecology of Snow Leopard in West Nepal. In H.Freeman (Ed.), (pp. 65–87). India: Snow Leopard Trust and Wildlife Institute of India.
Abstract: This summary of a four year field study by Jackson and Ahlborn begging in 1982 and concluding in 1985, discusses behaviour, trapping and tracking techniques, home range, activity patterns, prey and habitat and survey methods.
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Schaller, G. B., Tserendeleg, J., & Amarsana, G. (1994). Observations on snow leopards in Mongolia. In J.Fox, & D.Jizeng (Eds.), (pp. 33–42). Usa: Islt.
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McCarthy, T. (1999). Snow Leopard Conservation Plan for the Republic of Mongolia.
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