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Sandeep Sharma

Researcher at University of Göttingen

Publications -  19
Citations -  732

Sandeep Sharma is an academic researcher from University of Göttingen. The author has contributed to research in topics: Population & Tiger. The author has an hindex of 12, co-authored 17 publications receiving 562 citations. Previous affiliations of Sandeep Sharma include Wildlife Institute of India & Smithsonian Conservation Biology Institute.

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Connecting the dots: mapping habitat connectivity for tigers in central India

TL;DR: In this article, the authors identified and prioritized movement routes using a combination of least-cost corridor modeling and circuit theory, and mapped a total of thirty-five linkages in the region and calculated metrics to estimate their quality and importance.
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Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India

TL;DR: It is found that the tiger metapopulation in central India has high rates of historical and contemporary gene flow, and the highest rates ofemporary gene flow in populations that are connected by forest corridors.
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Identification of individual tigers (Panthera tigris) from their pugmarks

TL;DR: It is suggested that tigers can be individually identified from their pugmarks with a high level of accuracy and pugmark-sets could be used for population estimation of tigers within a statistically designed mark–recapture framework.
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Fine-scale population genetic structure in a wide-ranging carnivore, the leopard (Panthera pardus fusca) in central India

TL;DR: This study demonstrates that the leopard, an adaptable and vagile species, can become genetically differentiated with increased habitat fragmentation, and concludes that habitat fragmentation and corridors are of immense value in maintaining genetic connectivity in this landscape.
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Spatial genetic analysis reveals high connectivity of tiger (Panthera tigris) populations in the Satpura–Maikal landscape of Central India

TL;DR: It is found that the tiger meta-population in the Satpura–Maikal landscape has high genetic variation and very low genetic subdivision, and individual-based Bayesian clustering algorithms reveal two highly admixed genetic populations.