Population genetics of Himalayan langurs and its taxonomic implications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".