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Golla, T. R., Tensen, L., Vipin, Kumar, K., Kumar, S., Gaur, A. |
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Neutral and adaptive genetic variation in Indian snow leopards, Panthera uncia |
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2023 |
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Current Science |
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125 |
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2 |
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204-209 |
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Adaptive evolution, balancing selection, cap- tive breeding, genetic diversity, major histocompatibility complex. |
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In this study, we reveal patterns of genetic variation in snow leopards (Panthera uncia) by combining neutral (mtDNA, microsatellites) and adaptive (MHC II-DRB) genes. We collected 56 faecal samples from three locations in India. We observed moderate levels of microsatellite diversity (N = 30; A = 5.6; HO = 0.559). Nine unique MHC II-DRB sequences were identified in four snow leopard samples, of which 8 were novel. We found low levels of polymorphism in MHC class II-DRB exon, which was higher in captive (VA = 9.4%) compared to wild individuals (VA = 7.8%), likely as a result of a population bottleneck. |
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SLN @ rakhee @ |
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1722 |
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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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Year |
2020 |
Publication |
Ecology and Evolution |
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1-27 |
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MaxEnt, meta-replication, multi-scale, Panthera uncia, sampling bias, scale selection, snow leopard, species distribution model |
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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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1616 |
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