Abstract: Predators have significant ecological impacts on the region's prey-predator dynamic and community structure through their numbers and prey selection. During April-December 2007, I conducted a research in Sagarmatha (Mt. Everest) National Park (SNP) to: i) explore population status and density of wild prey species; Himalayan tahr, musk deer and game birds, ii) investigate diet of the snow leopard and to estimate prey selection by snow leopard, iii) identify the pattern of livestock depredation by snow leopard, its mitigation, and raise awareness through outreach program, and identify the challenge and opportunities on conservation snow leopard and its co-existence with wild ungulates and the human using the areas of the SNP. Methodology of my research included vantage points and regular monitoring from trails for Himalayan tahr, fixed line transect with belt drive method for musk deer and game birds, and microscopic hair identification in snow leopard's scat to investigate diet of snow leopard and to estimate prey selection. Based on available evidence and witness accounts of snow leopard attack on livestock, the patterns of livestock depredation were assessed. I obtained 201 sighting of Himalayan tahr (1760 individuals) and estimated 293 populations in post-parturient period (April-June), 394 in birth period (July -October) and 195 November- December) in rutting period. In average, ratio of male to females was ranged from 0.34 to 0.79 and ratio of kid to female was 0.21-0.35, and yearling to kid was 0.21- 0.47. The encounter rate for musk deer was 1.06 and density was 17.28/km2. For Himalayan monal, the encounter rate was 2.14 and density was 35.66/km2. I obtained 12 sighting of snow cock comprising 69 individual in Gokyo. The ratio of male to female was 1.18 and young to female was 2.18. Twelve species (8 species of wild and 4 species of domestic livestock) were identified in the 120 snow leopard scats examined. In average, snow leopard predated most frequently on Himalayan tahr and it was detected in 26.5% relative frequency of occurrence while occurred in 36.66% of all scats, then it was followed by musk deer (19.87%), yak (12.65%), cow (12.04%), dog (10.24%), unidentified mammal (3.61%), woolly hare (3.01%), rat sp. (2.4%), unidentified bird sp. (1.8%), pika (1.2%), and shrew (0.6%) (Table 5.8 ). Wild species were present in 58.99% of scats whereas domestic livestock with dog were present in 40.95% of scats. Snow leopard predated most frequently on wildlife species in three seasons; spring (61.62%), autumn (61.11%) and winter (65.51%), and most frequently on domestic species including dog in summer season (54.54%). In term of relative biomass consumed, in average, Himalayan tahr was the most important prey species contributed 26.27% of the biomass consumed. This was followed by yak (22.13%), cow (21.06%), musk deer (11.32%), horse (10.53%), wooly hare (1.09%), rat (0.29%), pika (0.14%) and shrew (0.07%). In average, domestic livestock including dog were contributed more biomass in the diet of snow leopard comprising 60.8% of the biomass consumed whilst the wild life species comprising 39.19%. The annual prey consumption by a snow leopard (based on 2 kg/day) was estimated to be three Himalayan tahr, seven musk deer, five wooly hare, four rat sp., two pika, one shrew and four livestock. In the present study, the highest frequency of attack was found during April to June and lowest to July to November. The day of rainy and cloudy was the more vulnerable to livestock depredation. Snow leopard attacks occurred were the highest at near escape cover such as shrub land and cliff. Both predation pressure on tahr and that on livestock suggest that the development of effective conservation strategies for two threatened species (predator and prey) depends on resolving conflicts between people and predators. Recently, direct control of free – ranging livestock, good husbandry and compensation to shepherds may reduce snow leopard – human conflict. In long term solution, the reintroduction of blue sheep at the higher altitudes could also “buffer” predation on livestock.

Abstract: In 2001 the King Mahendra Trust for Nature Conservation (KMTNC), Annapurna Conservation Area (ACAP), Snow Leopard Conservancy (SLC) and WWF-Nepal initiated a collaborative project aimed at enhancing ecotourism in the Manang area, in ways that strengthen benefits to local communities while also protecting the environment and the local culture. Manang is known for its relatively dense snow leopard population, along with supporting good numbers of blue sheep, the endangered cat's principal prey through much of the Himalaya. However, snow leopards periodically kill many livestock, leading to retributive killing by herders along with other associated people-wildlife conflict. In order to encourage the local people to better co-exist with snow leopards and other wildlife, SLC, WWF-Nepal and ACAP agreed to explore ways of providing tourism benefits to local communities as an incentive to protect this rare predator and conserve its alpine habitat. Key in this regard is the possibility of developing locally guided nature treks, and accordingly, this survey was conducted in order to assess existing market opportunities and constraints to such ecotourism enterprise.

Abstract: Describes snow leopard status and field observations from studies in Pakistan and Nepal. Review provides some data on snow leopard marking behavior, social relations, food habits and predator behavior.

Abstract: The snow leopard (Panthera uncia) is a flagship species for conservation in the high mountain regions of central Asia. Data on snow leopard predation, habitat conditions and range of main prey species were gathered along with thematic maps of the study area for elevation, snow cover, sighting data, kill data, blue sheep use areas, and vegetation data. These data were entered into a GIS and used to help delineate surface features from a satellite image. Preliminary results show that general physiographic features of snow leopard habitat can be detected using satellite imagery and that GIS cartographic modeling techniques can improve this delineation. -from Authors