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Blower, J. H. (1986). Nature Conservation in Bhutan: Project Findings and Recommendations.
Abstract: Snow leopard is relatively common, but there is some destruction of its habitat in Northern Bhutan
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Jackson, R. (1997). Bhutan Workshop: Thimpu, Land of the Thunder Dragon (Vol. xv). Seattle, Wa: Islt.
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Norbu, U. P. (1997). Status and Conservation of Snow Leopard In Bhutan. In R.Jackson, & A.Ahmad (Eds.), (pp. 28–34). Lahore, India: International Snow Leopard Trust.
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Wangchuk, T. R. (1992). Snow Leopard: Its Management with Emphasis on Bhutan.
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Mongar, T. B. (1992). Protected Area System Network: A Strategy for Managing Biodiversity in Bhutan..
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Gee, E. P. (1967). Occurrence of the snow leopard Panthera uncia (Schreber) in Bhutan. Journal of the Natural History Museum Society, 30, 634–636.
Abstract: Indicates that snow leopard range includes all of Northern Bhutan
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Fox, J. L. (1989). An Annotated Bibliography of Literature on the Snow Leopard. Usa: Islt.
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Islam, M., Sahana, M., Areendran, G., Jamir, C., Raj, K., Sajjad, H. (2023). Prediction of potential habitat suitability of snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) and niche overlap in the parts of western Himalayan region. Geo: Geography and Environment, 10(e00121), 1–15.
Abstract: The snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) are the inhabitants of remote areas at higher altitudes with extreme geographic and climatic conditions. The habitats of these least-studied species are crucial for sustaining the Himalayan ecosystem. We employed the Maximum Entropy (MaxEnt) species distribution model to predict the potential habitat suitability of snow leopards and blue sheep and extracted common overlapped niches. For this, we utilised presence location, bio-climatic and environmental variables, and correlation analysis was applied to reduce the negative impact of multicollinearity. A total of 134 presence locations of snow leopards and 64 for blue sheep were selected from the Global Biodiversity Information Facility (GBIF). The annual mean temperature (Bio1) was found to be the most useful and highly influential factor to predict the potential habitat suitability of snow leopards. Annual mean temperature, annual precipitation and isothermality were the major influencing factors for blue sheep habitat suitability. Highly influential bio-climatic, topographic and environmental variables were integrated to construct the model for predicting habitat suitability. The area under the curve (AUC) values for snow leopard (0.87) and blue sheep (0.82) showed that the models are under good representation. Of the total area investigated, 47% was suitable for the blue sheep and 38% for the snow leopards. Spatial habitat assessment revealed that nearly 11% area from the predicted suitable habitat class of both species was spatially matched (overlapped), 48.6% area was unsuitable under niche overlap and 40.5% area was spatially mismatched niche. The presence of snow leopards and blue sheep in some highly suitable areas was not observed, yet such areas have the potential to sustain these elusive species. The other geographical regions interested in exploring habitat suitability may find the methodological framework adopted in this study useful for formulating an effective conservation policy and management strategy.
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Ale, S., & Whelan, C. (2008). Reappraisal of the role of big, fierce predators.
Abstract: The suggestion in the early 20th century that top predators were a necessary component of ecosystems because they hold herbivore populations in check and promote biodiversity was at Wrst accepted and then largely rejected. With the advent of Evolutionary Ecology and a more full appreciation of direct and indirect effects of top predators, this role of top predators is again gaining acceptance. The previous views were predicated upon lethal effects of predators but largely overlooked their non-lethal effects. We suggest that
conceptual advances coupled with an increased use of experiments have convincingly demonstrated that prey experience costs that transcend the obvious cost of death. Prey species use adaptive behaviours to avoid predators, and these behaviours are not cost-free. With predation risk, prey species greatly restrict their use of available habitats and consumption of available food resources. Effects of top predators consequently cascade down to the trophic levels below them. Top predators, the biggies, are thus both the targets of and the means for conservation at the landscape scale.
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Singh, R., Krausman, P. R., Pandey, P., Maheshwari, A., Rawal,
R. S., Sharma, S., Shekhar, S. (2020). Predicting Habitat Suitability of Snow Leopards in the Western
Himalayan Mountains, India. Biology bulletin, 47(6), 655–664.
Abstract: The population of snow leopard (Panthera uncia) is declining
across their range, due to poaching, habitat fragmentation, retaliatory
killing, and a decrease of wild prey species. Obtaining information on
rare and cryptic predators living in remote and rugged terrain is
important for making conservation and management strategies. We used the
Maximum Entropy (MaxEnt) ecological niche modeling framework to predict
the potential habitat of snow leopards across the western Himalayan
region, India. The model was developed using 34 spatial species
occurrence points in the western Himalaya, and 26 parameters including,
prey species distribution, temperature, precipitation, land use and land
cover (LULC), slope, aspect, terrain ruggedness and altitude. Thirteen
variables contributed 98.6% towards predicting the distribution of snow
leopards. The area under the curve (AUC) score was high (0.994) for the
training data from our model, which indicates pre- dictive ability of
the model. The model predicted that there was 42432 km2 of potential
habitat for snow leop- ards in the western Himalaya region. Protected
status was available for 11247 km2 (26.5%), but the other 31185 km2
(73.5%) of potential habitat did not have any protected status. Thus,
our approach is useful for predicting the distribution and suitable
habitats and can focus field surveys in selected areas to save
resources, increase survey success, and improve conservation efforts for
snow leopards.
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