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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. |
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Abstract |
Replicated multiple scale species distribution models (SDMs)
have become increasingly important to identify the correct variables
determining species distribution and their influences on ecological
responses. This study explores multi-scale habitat relationships of the
snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan
Plateau of western China. Our primary objectives were to evaluate the
degree to which snow leopard habitat relationships, expressed by
predictors, scales of response, and magnitude of effects, were
consistent across study areas or locally landcape-specific. We coupled
univariate scale optimization and the maximum entropy algorithm to
produce multivariate SDMs, inferring the relative suitability for the
species by ensembling top performing models. We optimized the SDMs based
on average omission rate across the top models and ensembles’ overlap
with a simulated reference model. Comparison of SDMs in the two study
areas highlighted landscape-specific responses to limiting factors.
These were dependent on the effects of the hydrological network,
anthropogenic features, topographic complexity, and the heterogeneity of
the landcover patch mosaic. Overall, even accounting for specific local
differences, we found general landscape attributes associated with snow
leopard ecological requirements, consisting of a positive association
with uplands and ridges, aggregated low-contrast landscapes, and large
extents of grassy and herbaceous vegetation. As a means to evaluate the
performance of two bias correction methods, we explored their effects on
three datasets showing a range of bias intensities. The performance of
corrections depends on the bias intensity; however, density kernels
offered a reliable correction strategy under all circumstances. This
study reveals the multi-scale response of snow leopards to environmental
attributes and confirms the role of meta-replicated study designs for
the identification of spatially varying limiting factors. Furthermore,
this study makes important contributions to the ongoing discussion about
the best approaches for sampling bias correction. |
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