Abstract: The high altitudes of Arunachal Pradesh,India, located in the Eastern Himalaya biodiversity hotspot, remain zoologically unexplored and unprotected. We report results of recent mammal surveys in the high altitude habitats of western Arunachal Pradesh. A total of 35 mammal species (including 12 carnivores, 10 ungulates and 5 primates) were recorded, of which 13 are categorized as Endangered or Vulnerable on the IUCN Red List. One species of primate, the Arunachal macaque Macaca munzala, is new to science and the Chinese goral Nemorhaedus caudatus is a new addition to the ungulate fauna of the Indian subcontinent. We documented peoples' dependence on natural resources for grazing and extraction of timber and medicinal plants. The region's mammals are threatened by widespread hunting. The snow leopard Uncia uncia and dhole Cuon alpinus are also persecuted in retaliation for livestock depredation. The tiger Panthera tigris, earlier reported from the lower valleys, is now apparently extinct there, and range reductions over the last two decades are reported for bharal Pseudois nayaur and musk deer Moschus sp.. Based on mammal species richness, extent of high altitude habitat, and levels of anthropogenic disturbance, we identified a potential site for the creation of Arunachal's first high altitude wildlife reserve (815 km2). Community-based efforts that provide incentives for conservation-friendly practices could work in this area, and conservation awareness programmes are required, not just amongst the local communities and schools but for politicians, bureaucrats and the army.

Abstract: China, the world's most populous country, also contains the largest number of Snow Leopards of any country in the world. But the survey and research of the snow leopard had been very little for the second half of the 20th century. Until recent years, the members of Xinjiang Snow Leopards Group (XSLG/SLT/XFC) , the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences have been tracking down the solitary animal. The journal reporter does a face-to-face interview with professor Ma Ming who is a main responsible expert of the survey team. By the account of such conversation, we learn the achievements, advances and difficulty of research of snow leopards in the field, Tianshan and Kunlun, Xinjiang, the far west China, and we also know that why the team adopt the infrared camera to capture the animals. Last but not least professor talked about the survival menace faced by the Snow Leopards in Xinjiang.

Abstract: The Greater and Trans Himalayan regions of Uttarakhand and Himachal Pradesh have great potential in terms of wildlife (flora and fauna). This survey was the first ever survey for the snow leopard in Uttarakhand and some of the areas of Himachal Pradesh till date. It confirms the presence of snow leopard in Uttarakhand on the basis of indirect evidence. We could not find any evidence of snow leopard from surveyed areas in Himachal Pradesh – but it certainly does not mean that there are no snow leopards in the surveyed areas.
Areas above 3000m elevation were selected for this survey in 10 protected areas of both the states. Status and distribution of snow leopard was assessed through indirect evidence (n=13) found between 3190 and 4115m. On average, one indirect evidence of snow leopard was found for every 39km walked. About 39% of the evidence was found on the hill-slope followed by valley floor (30%), cliff (15%) and 8% from both stream bed and scree slope. Preferred mean slope was 28° (maximum 60°). Snow leopard-human conflicts were assessed through questionnaire surveys from Govind Pashu Vihar, Askot Wild Life Sanctuary and Dung (Munsiari) areas. They revealed that livestock depredation is the only component of conflict and contributed to 36% of the total diet (mule, goat and sheep) of snow leopard. Blue sheep and rodents together comprised 36.4% of the total diet.

Abstract: The secretive nature of snow leopards (Uncia uncia) makes them difficult to monitor, yet conservation efforts require accurate and precise methods to estimate abundance. We assessed accuracy of Snow Leopard Information Management System (SLIMS) sign surveys by comparing them with 4 methods for estimating snow leopard abundance: predator:prey biomass ratios, capture-recapture density estimation, photo-capture rate, and individual identification through genetic analysis. We recorded snow leopard sign during standardized surveys in the SaryChat Zapovednik, the Jangart hunting reserve, and the Tomur Strictly Protected Area, in the Tien Shan Mountains of Kyrgyzstan and China. During June-December 2005, adjusted sign averaged 46.3 (SaryChat), 94.6 (Jangart), and 150.8 (Tomur) occurrences/km. We used
counts of ibex (Capra ibex) and argali (Ovis ammon) to estimate available prey biomass and subsequent potential snow leopard densities of 8.7 (SaryChat), 1.0 (Jangart), and 1.1 (Tomur) snow leopards/100 km2. Photo capture-recapture density estimates were 0.15 (n = 1 identified individual/1 photo), 0.87 (n = 4/13), and 0.74 (n = 5/6) individuals/100 km2 in SaryChat, Jangart, and Tomur, respectively. Photo-capture rates
(photos/100 trap-nights) were 0.09 (SaryChat), 0.93 (Jangart), and 2.37 (Tomur). Genetic analysis of snow leopard fecal samples provided minimum population sizes of 3 (SaryChat), 5 (Jangart), and 9 (Tomur) snow leopards. These results suggest SLIMS sign surveys may be affected by observer bias and environmental variance. However, when such bias and variation are accounted for, sign surveys indicate relative abundances similar to photo rates and genetic individual identification results. Density or abundance estimates based on capture-recapture or ungulate biomass did not agree with other indices of abundance. Confidence in estimated densities, or even detection of significant changes in abundance of snow leopard, will require more effort and better documentation.