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Author |
Holt, C. D. S., Nevin, O. T., Smith, D., Convery, I. |
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Title |
Environmental niche overlap between snow leopard and four prey species in Kazakhstan |
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Journal Article |
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2018 |
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Elsevier |
Abbreviated Journal |
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48 |
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97-103 |
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Keywords |
SDM, MaxEnt, Management, Conservation, Dispersal, Distribution |
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Abstract |
The snow leopard Panthera uncia has declined due to habitat loss, habitat fragmentation and human persecution. Predator distribution is heavily dependent on prey species availability and distribution. With increasing pressures from farming practices encroaching into native species range and persecution of snow leopards in response to livestock depredation, it is vital to assess current predator and prey species distribution to highlight sensitive areas of overlap for protection. This study uses MaxEnt, a presence-only Species Distribution Model (SDM) to assess snow leopard and four prey species habitat suitability along
the southern and eastern borders of Kazakhstan using environmental data. This area is considered an important corridor between snow leopard populations in the north and south of their range. Each of the five SDM's produced models of �good� discriminating abilities. We then compared the potential niche overlap between snow leopard and four prey species using ENMTools to highlight areas of important niche overlap within the corridor. The results indicated a very high degree of overlap between snow leopard and Siberian ibex and high degrees Capra sibirica with red deer Cervus elaphus, argali Ovis ammon and urial Ovis orientalis. The snow leopard population in this region is also found to be using forested areas below 2500 m, much lower than recorded in other areas of their range. The results highlight areas needed for protection but also pose additional conservation questions regarding the importance of prey species to transitory individuals. |
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SLN @ rakhee @ |
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1472 |
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Karnaukhov, A. S., Malykh, S. V., Korablev, M. P., Kalashnikova, Y. M., Poyarkov, A. D., Rozhnov, V. V. |
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Title |
Current Status of the Eastern Sayan Snow Leopard (Panthera uncia) Grouping and Its Nutritive Base |
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Journal Article |
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2018 |
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Biology Bulletin |
Abbreviated Journal |
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45 |
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9 |
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1106-1115 |
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Keywords |
Panthera uncia, Bol� shoi Sayan, Munku-Sardyk, Tunkinskie Gol� tsy, distribution area, molecular genetic analysis, modeling of potential habitats |
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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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1482 |
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Singh, R., Krausman, P. R., Pandey, P., Maheshwari, A., Rawal,
R. S., Sharma, S., Shekhar, S. |
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Title |
Predicting Habitat Suitability of Snow Leopards in the Western
Himalayan Mountains, India |
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2020 |
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Biology bulletin |
Abbreviated Journal |
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47 |
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6 |
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655-664 |
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Keywords |
biogeographic distribution, climate, endangered cat, MaxEnt, snow leopard |
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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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1629 |
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Atzeni, L., Cushman, S. A., Bai, D., Wang, J., Chen, P., Shi,
K., Riordan, P. |
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Title |
Meta-replication, sampling bias, and multi-scale model selection:
A case study on snow leopard (Panthera uncia) in western China. |
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Journal Article |
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2020 |
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Ecology and Evolution |
Abbreviated Journal |
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1-27 |
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Keywords |
MaxEnt, meta-replication, multi-scale, Panthera uncia, sampling bias, scale selection, snow leopard, species distribution model |
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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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1616 |
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