Population genetics of Himalayan langurs and its taxonomic implications
M R, S.; Arekar, K.; Karanth, P.
Show abstract
Himalayan langurs (Semnopithecus schistaceus) are one of the most widely distributed colobine monkeys found in the Himalayas from Pakistan in the west to Bhutan in the east. Further, their distribution encompasses a wide range of elevation (from the foothills of the Himalayas to 4,270 m above sea level) and is interspersed with numerous deep river valleys. In this study, we investigate the role of riverine barriers and elevational gradients in shaping the population genetic structure in these langurs. Previous mitochondrial marker-based broad scale studies suggested limited role of river valleys in shaping the phylogeography of these langurs. Here we have utilized nuclear microsatellites and a more fine-scale sampling to further explore this issue. Fecal samples were non-invasively collected from two Indian Himalayan states Himachal Pradesh and Uttarakhand based on distribution records from past studies. A total of 7 microsatellite markers were genotyped for these samples. The data were subjected to various analyses, including Neighbor-joining tree, PCoA, AMOVA, STRUCTURE, and paired Mantel test. The results show an overall lack of population genetic structure and a much higher geneflow along elevational gradient than across river valleys. Significant isolation by distance was also observed. Additionally, our results do not support splitting the Himalayan langurs into multiple species/subspecies based on elevational gradient.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Hybridization and Low Genetic Diversity in the Endangered Alabama Red-Bellied Turtle (Pseudemys alabamensis) 95%
- Changes over a 10-year Period in the Distribution Ranges and Genetic Hybridization of Three Pelophylax Pond Frogs in Central Japan 95%
- The unseen invaders: tracking phylogeographic dynamics and genetic diversity of cryptic Pomacea canaliculata and P. maculata (Golden Apple Snails) across Taiwan 95%
Similar papers in this journal
- Functional connectivity in northern swamp deer (Rucervus duvaucelii duvaucelii) population across a fragmented, human-dominated landscape along Gangetic Plains of north India: Implications for conservation in non-protected areas 96%
- Conservation genomics of an Australian orchid complex with implications for the taxonomic and conservation status of Corybas dowlingii 95%
- Genotyping-by-sequencing reveals the effects of riverscape, climate and interspecific introgression on the genetic diversity and local adaptation of the endangered Mexican golden trout (Oncorhynchus chrysogaster) 94%
Similar papers in this journal
- Comparison of genetic variation between rare and common congeners of Dipodomys with estimates of contemporary and historical effective population size 94%
- Evaluation of intron-1 of odorant-binding protein-1 of Anopheles stephensi as a marker for the identification of biological forms or putative sibling species 94%
- Characterizing the genetic diversity of the Andean blueberry (Vaccinium floribundum Kunth.) across the Ecuadorian Highlands 94%
Similar papers in this journal
- Genetic analyses reveal population structure and recent decline in leopards (Panthera pardus fusca) across Indian subcontinent 97%
- Application of High Resolution Melt analysis (HRM) for screening haplotype variation in non-model plants: a case study of Honeybush (Cyclopia Vent.) 95%
- Fast sequence-based microsatellite genotyping development workflow. 94%
Similar papers in this journal
- Phylogeography and population genetic structure of red muntjacs: Evidence of enigmatic Himalayan red muntjac from India 97%
- Consideration of genetic variation and evolutionary history in future conservation of Indian one-horned rhinoceros (Rhinoceros unicornis) 97%
- Spatially heterogeneous selection and inter-varietal differentiation maintain population structure and local adaptation in a widespread conifer 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.