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Karnaukhov, A. S., Malykh, S. V., Korablev, M. P., Kalashnikova, Y. M., Poyarkov, A. D., Rozhnov, V. V. (2018). Current Status of the Eastern Sayan Snow Leopard (Panthera uncia) Grouping and Its Nutritive Base. Biology Bulletin, 45(9), 1106–1115.
Abstract: A field survey of snow leopard (Panthera uncia) habitats was carried out in the southeastern part of
the Eastern Sayan Mountains (Okinskii and Tunkinskii districts of the Republic of Buryatia and the Kaa-
Khemskii district of Tuva Republic). Seven or eight adult snow leopards were observed as constant inhabitants
of the Tunkinskie Gol'tsy, Munku-Sardyk, and Bol'shoi Sayan mountain ridges. The presence of eight
snow leopards was confirmed using DNA-based analyses of scats collected in 2014 – 2016. The main prey species
of the snow leopard in Eastern Sayan is the Siberian ibex (Capra sibirica), but its abundance has steadily
decreased over the past 20 years. The red deer (Cervus elaphus) and the wild boar (Sus scrofa), which were
some of the most numerous ungulates in the survey area, are replacing the Siberian ibex in the snow leopard's
diet. In addition, the mountain hare (Lepus timidus) is also of importance to the snow leopard's diet.
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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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