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| Author | Ale, S., Shrestha, B., and Jackson, R. | ||||
| Title | On the status of Snow Leopard Panthera Uncia (Schreber 1775) in Annapurna, Nepal | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Threatened Taxa | Abbreviated Journal | |
| Volume | Issue | 6(3) | Pages | 5534-5543 | |
| Keywords | Annapurna, Blue Sheep, Buddhism, camera-trapping, Himalayas, Mustang, sign-survey, Snow Leopard. | ||||
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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1407 | ||
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| Author | Alexander, J. S., Gopalswamy, A. M., Shi, K., Riordan, P. | ||||
| Title | Face Value: Towards Robust Estimates of Snow Leopard Densities | Type | Journal Article | ||
| Year | 2015 | Publication | Plos One | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Densities, Snow Leopard, Camera traps, Spatial Capture Recapture models | ||||
| Abstract | When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trapdays, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality. |
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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1431 | ||
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| Author | Alexander, J. S., Shi, K., Tallents, L. A., Riordan, P. | ||||
| Title | On the high trail: examining determinants of site use by the Endangered snow leopard Panthera uncia in Qilianshan, China | Type | Journal Article | ||
| Year | 2015 | Publication | Oryx | Abbreviated Journal | |
| Volume | Issue | Fauna & Flora International | Pages | 1-8 | |
| Keywords | Camera Trap, China, Detection Probability, Occupancy Modelling, Panthera Uncia, Site use, Snow Leopard | ||||
| 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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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1433 | ||
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| Author | Alexander, S., A., Zhang, C., Shi, K., Riordan, P. | ||||
| Title | A granular view of a snow leopard population using camera traps in Central China | Type | Journal Article | ||
| Year | 2016 | Publication | Biological Conservation | Abbreviated Journal | |
| Volume | Issue | 197 | Pages | 27-31 | |
| Keywords | Snow leopard Camera trap China Density Monitoring | ||||
| 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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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1436 | ||
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| Author | Allen, M. L., Rovero, F., Oberosler, V., Augugliaro, C., Krofel, M. | ||||
| Title | Effects of snow leopards (Panthera uncia) on olfactory communication of Pallas’s cats (Otocolobus manul) in the Altai Mountains, Mongolia | Type | Journal Article | ||
| Year | 2023 | Publication | Behaviour | Abbreviated Journal | |
| Volume | Issue | Pages | 1-9 | ||
| Keywords | behaviour, camera trap, Mongolia, Otocolobus manul, Panthera uncia, scent marking. | ||||
| Abstract | 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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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1721 | ||
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| Author | Anonymous | ||||
| Title | International Specialists Discuss China's Threatened Cats | Type | Miscellaneous | ||
| Year | 1992 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | |||
| Keywords | China; Iucn; habitat; predator; prey; livestock; herders; conflict; hunting; poaching; trapping; bones; medicine; trade; development; Slims; Cites; protected-areas; parks; preserves; reserves; refuge; browse; protected; areas; 3990 | ||||
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| Notes | Full text at URL | Approved | no | ||
| Call Number | SLN @ rana @ 186 | Serial | 80 | ||
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| Author | Augugliaro, C., Paniccia, C., Janchivlamdan, C., Monti, I. E., Boldbaatar, T., Munkhtsog, B. | ||||
| Title | Mammal inventory in the Mongolian Gobi, with the southeasternmost documented record of the Snow Leopard, Panthera uncia (Schreber, 1775), in the country | Type | Journal Article | ||
| Year | 2019 | Publication | Check List | Abbreviated Journal | |
| Volume | 15 | Issue | 4 | Pages | 575-578 |
| Keywords | Camera trapping, desert, live trapping, mammal checklist, species richness, threatened species. | ||||
| Abstract | 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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| Notes | Approved | no | |||
| Call Number | SLN @ rakhee @ | Serial | 1486 | ||
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| Author | Bo, W. | ||||
| Title | Illegal Trade of Snow Leopards in China: An Overview | Type | Conference Article | ||
| Year | 2002 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | |||
| Keywords | zoo; collection; illegal; poaching; snow; leopard; trading; pelts; pelt; skin; bone; meat; trapping; 5110 | ||||
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| Publisher | Islt | Place of Publication | Islt | Editor | |
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| Notes | Title, Monographic: Proceedings of the Snow Leopard Survival SummitPlace of Meeting: Seattle,WA | Approved | no | ||
| Call Number | SLN @ rana @ 497 | Serial | 177 | ||
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| Author | 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. | ||||
| Title | Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies | Type | Journal Article | ||
| Year | 2023 | Publication | Global Ecology & Conservation | Abbreviated Journal | |
| Volume | 41 | Issue | e02350 | Pages | 1-13 |
| Keywords | Snow leopard, Artificial intelligence, Camera trap misclassification, individual ID, HotSpotter | ||||
| 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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| Call Number | SLN @ rakhee @ | Serial | 1715 | ||
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| Author | Durbach, I., Borchers, D., Sutherland, C., Sharma, K. | ||||
| Title | Fast, flexible alternatives to regular grid designs for spatial capture–recapture. | Type | Research Article | ||
| Year | 2020 | Publication | Methods in Ecology and Evolution | Abbreviated Journal | |
| Volume | Issue | Pages | 1-13 | ||
| Keywords | camera trap, population ecology,sampling, spatial capture-recapture, surveys | ||||
| 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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| Call Number | Serial | 1618 | |||
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