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Jackson, R.; Roe, J. |
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Title |
Preliminary Observations On Non-Invasive Techniques for Identifying Individual Snow Leopards and Monitoring Populations |
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2002 |
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snow; leopard; monitoring; population; uncia; non; invasive; photo; camera; trap; traps; 4970 |
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Abastract OnlyTitle, Monographic: Proceedings of the Snow Leopard Survival SummitPlace of Meeting: Seattle,WA |
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SLN @ rana @ 483 |
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469 |
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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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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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Zhang, L., Lian, X., Yang, X |
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Population density of snow leopards (Panthera Uncia) in the Yage Valley Region of the Sanjiangyuan National Park: Conservation Implications and future directions |
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Journal Article |
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2020 |
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Artic, Antartic and Alpine Research |
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52 |
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1 |
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541-550 |
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Snow leopard; population density; camera trapping; Tibetan Plateau; alpine ecosystem |
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Population-based studies on snow leopard (Panthera uncia) are of theoretical and practical sig- nificance for the conservation of alpine ecosystems, though geographic remoteness and isolation hinder surveys in many promising regions. The Sanjiangyuan National Park on the Tibetan Plateau is acknowledged as a main snow leopard habitat, but most of the region remains unexplored and unknown. We adopted a combined approach of route survey and camera trapping survey to explore the population density of snow leopard in the Yage Valley region of the Sanjiangyuan National Park. Results indicated that (1) large populations of blue sheep contributed to the major food supply for snow leopards, along with diverse prey species as dietary supplementations, and (2) a population density of four to six snow leopards per 100 km2 on the north bank was estimated, and nine to fourteen individuals within the valley core areas were identified. We also argue that under the potential impacts of hydropower dams, this valley ecosystem should be symbolized as a conservation hotspot and therefore merits prioritized conservation. We recommend further surveys combined with novel methods/techniques and advocate a sustainable ecotourism model for the first V-shaped valley along the Yangtze mainstream. |
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1619 |
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Author |
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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Alexander, J. S., Shi, K., Tallents, L. A., Riordan, P. |
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On the high trail: examining determinants of site use by the Endangered snow leopard Panthera uncia in Qilianshan, China |
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2015 |
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Oryx |
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Fauna & Flora International |
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1-8 |
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Camera Trap, China, Detection Probability, Occupancy Modelling, Panthera Uncia, Site use, Snow Leopard |
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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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1433 |
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Sharma, R. |
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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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1139 |
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Ming, M., Munkhtsog, B., McCarthy, T., McCarthy, K. |
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Monitor ing of Population Density of Snow Leopard in X injiang |
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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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1303 |
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Augugliaro, C., Paniccia, C., Janchivlamdan, C., Monti, I. E., Boldbaatar, T., Munkhtsog, B. |
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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 |
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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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1486 |
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Henschel, P.; Ray, J. |
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Title |
Leopards in African Rainforests: Survey and Monitoring Techniques |
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Miscellaneous |
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2003 |
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forest leopards; african rainforests; survey; monitoring techniques; lope reserve; gabon; central africa; congo; zaire; field testing; populations; wild meat; relative abundance; density; live-trapping; presence and absense surveys; ad-hoc survey; bushmeat; systematic survey; monitoring; individual identification; tracks; Discriminant Function Analysis; genotyping; scat; Hair; Dna; remote photography; camera trapping; capture rates; Trailmaster; Camtrakker; bait; duikers; pigs; elephant; bongo; okapi; human hunters; 5300 |
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Monitoring Techniques Forest leopards have never been systematically surveyed in African forests, in spite of their potentially vital ecological role as the sole large mammalian predators in these systems. Because leopards are rarely seen in this habitat, and are difficult to survey using the most common techniques for assessing relative abundances of forest mammals, baseline knowledge of leopard ecology and responses to human disturbance in African forests remain largely unknown. This technical handbook sums up the experience gained during a two-year study of leopards by Philipp Henschel in the Lop‚ Reserve in Gabon, Central Africa, in 2001/2002, supplemented by additional experience from carnivore studies conducted by Justina Ray in southwestern Central African Republic and eastern Congo (Zaire) . The main focus of this effort has been to develop a protocol that can be used by fieldworkers across west and central Africa to estimate leopard densities in various forest types. In developing this manual, Henschel tested several indirect methods to assess leopard numbers in both logged and unlogged forests, with the main effort devoted to testing remote photography survey methods developed for tigers by Karanth (e.g., Karanth 1995, Karanth & Nichols 1998; 2000; 2002), and modifying them for the specific conditions characterizing African forest environments. This handbook summarizes the results of the field testing, and provides recommendations for techniques to assess leopard presence/absence, relative abundance, and densities in African forest sites. We briefly review the suitability of various methods for different study objectives and go into particular detail on remote photography survey methodology, adapting previously developed methods and sampling considerations specifically to the African forest environment. Finally, we briefly discuss how camera trapping may be used as a tool to survey other forest mammals. Developing a survey protocol for African leopards is a necessary first step towards a regional assessment and priority setting exercise targeted at forest leopards, similar to those carried out on large carnivores in Asian and South American forests. |
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Wildlife Conservation Society |
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SLN @ rana @ 515 |
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382 |
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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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Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies |
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2023 |
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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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