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Johansson, O., Kachel, S., Weckworth, B. |
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Title |
Guidelines for Telemetry Studies on Snow Leopards |
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Journal Article |
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2022 |
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Animals |
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12 |
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1663 |
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1-12 |
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animal welfare; capture; collar; felid; GPS; immobilization; Panthera uncia; trapping |
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Animal-borne tracking devices have generated a wealth of new knowledge, allowing us to better understand, manage and conserve species. Fitting such tracking devices requires that animals are captured and often chemically immobilized. Such procedures cause stress and involve the risk of injuries and loss of life even in healthy individuals. For telemetry studies to be justifiable, it is vital that capture operations are planned and executed in an efficient and ethical way. Project objectives must be clearly articulated to address well-defined knowledge gaps, and studies designed to maximize the probability of achieving those goals. We provide guidelines for how to plan, design, and implement telemetry studies with a special emphasis on snow leopards that are typically captured using foot snares. We also describe the necessary steps to ensure that captures are conducted safely, and with minimal stress to animals. |
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SLN @ rakhee @ |
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1712 |
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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 |
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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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Allen, M. L., Rovero, F., Oberosler, V., Augugliaro, C., Krofel, M. |
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Title |
Effects of snow leopards (Panthera uncia) on olfactory communication of Pallas’s cats (Otocolobus manul) in the Altai Mountains, Mongolia |
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Journal Article |
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Year |
2023 |
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Behaviour |
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1-9 |
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behaviour, camera trap, Mongolia, Otocolobus manul, Panthera uncia, scent marking. |
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Olfactory communication is important for many solitary carnivores to delineate territories and communicate with potential mates and competitors. Pallas’s cats (Otocolobus manul) are small felids with little published research on their ecology and behaviour, including if they avoid or change behaviours due to dominant carnivores. We studied their olfactory communication and visitation at scent-marking sites using camera traps in two study areas in Mongolia. We documented four types of olfactory communication behaviours, and olfaction (sniffing) was the most frequent. Pallas’s cats used olfactory communication most frequently at sites that were not visited by snow leopards (Panthera uncia) and when they used communal scent-marking sites, they were more likely to use olfactory communication when a longer time had elapsed since the last visit by a snow leopard. This suggests that Pallas’s cats may reduce advertising their presence in response to occurrence of snow leopards, possibly to limit predation risk. |
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SLN @ rakhee @ |
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1721 |
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Zhang, C., Ma, T., Ma, D. |
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Status of the snow leopard Panthera uncia in the Qilian Mountains, Gansu Province, China |
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2023 |
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Oryx |
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1-6 |
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Camera trapping, China, density, Panthera uncia, Qilian Mountains, snow leopard, spatially explicit capture–recapture |
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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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SLN @ rakhee @ |
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1725 |
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Author |
Sharma, R. |
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Title |
Of Men and Mountain Ghosts: Glimpses from the Rooftop of the World |
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Magazine Article |
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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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Simms, A., Moheb, Z., Salahudin, Ali, H., Ali, I. & Wood, T. |
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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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SLN @ rana @ |
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1347 |
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WWF Russia & Mongolia |
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WWF Altai-Sayan Newsletter |
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Report |
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2010 |
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14 |
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Altai Sayan, WWF, camera trap, Argut River, poaching, conservation, Tuva |
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A Snow Leopard – A Treasure of Tuva. A beautiful animal as a winner of a wide-scale public vote
WWF will train a Scat Detection Dog for snow leopard monitoring project
WWF assessed the possibility to fight illegal helicopter hunting
WWF considers support of antipoaching activities an essential part of wildlife conservation in Altai – Sayan
Snow Leopard Camera Trapping in Argut River Valley
“Stars” of Tuva appeal to Snow Leopard Conservation |
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Communication Staff of Altai - Sayan Programme in Russia and Mongolia |
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WWF |
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October-December 2010. Email: tivanitskaya@wwf.ru |
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1297 |
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McCarthy, T.; Murray, K.; Sharma, K.; Johansson, O. |
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Preliminary results of a long-term study of snow leopards in South Gobi, Mongolia |
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2010 |
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Cat News |
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Autumn |
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53 |
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15-19 |
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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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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. |
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1151 |
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Jackson, R.; Roe, J.; Wangchuk, R.; Hunter, D. |
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Title |
Surveying Snow Leopard Populations with Emphasis on Camera Trapping: A Handbook |
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2005 |
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1-73 |
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snow; snow leopard; snow-leopard; leopard; survey; conservation; populations; population; camera; camera trapping; trapping; Chinese |
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This handbook provides an introduction to snow leopard population survey techniques, followed by a detailed account of camera trapping methods.During the 2002 through 2004 winter field seasons, the Snow Leopard Conservancy experimented with infrared camera trapping techniques to define a methodology suitable for the high altitude environment.
In 2001 and 2002, much of our time was spent familiarizing ourselves with various infrared camera traps, their operation and setup, and comparing the effectiveness of different models and sensor types. We placed infrared camera traps along frequently used travel corridors at or near scent-sprayed rocks (rock scents) and scrape sites within 16 km2 sampling cells between January and March in 2003 and 2004. A total of 66 and 49 captures of snow leopards were tallied during 2003 and 2004, resulting in an overall capture success of 8.91 and 5.63 individuals per 100 trap-nights, respectively. Capture probabilities ranged from 0.33 to 0.46. Density estimates ranged from 8.49 ± 0.22 individuals per 100 km2 in 2003 to 4.45 ± 0.16 in 2004, with the disparity between years largely attributed to different trapping densities. Snow leopard abundance estimates were calculated using the computer program CAPTURE. |
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The Snow Leopard Conservancy |
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Sonoma, California |
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Chinese |
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Chinese translation. |
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SLN @ rana @ 1015 |
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473 |
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Jackson, R.; Roe, J.; Wangchuk, R.; Hunter, D. |
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Title |
Surveying Snow Leopard Populations with Emphasis on Camera Trapping: A Handbook |
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2005 |
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1-73 |
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snow; snow leopard; snow-leopard; leopard; populations; population; camera; camera trapping; trapping |
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This handbook provides an introduction to snow leopard population survey techniques, followed by a detailed account of camera trapping methods.During the 2002 through 2004 winter field seasons, the Snow Leopard Conservancy experimented with infrared camera trapping techniques to define a methodology suitable for the high altitude environment.
In 2001 and 2002, much of our time was spent familiarizing ourselves with various infrared camera traps, their operation and setup, and comparing the effectiveness of different models and sensor types. We placed infrared camera traps along frequently used travel corridors at or near scent-sprayed rocks (rock scents) and scrape sites within 16 km2 sampling cells between January and March in 2003 and 2004. A total of 66 and 49 captures of snow leopards were tallied during 2003 and 2004, resulting in an overall capture success of 8.91 and 5.63 individuals per 100 trap-nights, respectively. Capture probabilities ranged from 0.33 to 0.46. Density estimates ranged from 8.49 ± 0.22 individuals per 100 km2 in 2003 to 4.45 ± 0.16 in 2004, with the disparity between years largely attributed to different trapping densities. Snow leopard abundance estimates were calculated using the computer program CAPTURE. |
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The Snow Leopard Conservancy |
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Sonoma, California |
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English version. |
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SLN @ rana @ 1016 |
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474 |
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