Landscape Genetics of Hume's Pheasant Syrmaticus humiae: Rivers Act as Potential Genetic Barriers
Bibliographic record
Abstract
Landscape features, such as rivers, can act as geographical barriers to dispersal and gene flow and thus influence the population structure of some species. In this study, tissue samples were collected from 73 Hume's Pheasant Syrmaticus humiae, from six localities in Guangxi and Guizhou provinces, China, to examine the influence of rivers in landscape structure on genetic diversity and structure based on 12 microsatellite loci. Results indicated a high genetic diversity in Hume's Pheasant. Individuals from populations in Tianlin, Longlin and Xilin counties (TXL) (three geographically proximate populations) tended to form a genetic cluster, distinct from three other geographically proximate populations 100 km to the west in Pojie town (PJ), Luodian county (LD) and Leye county (LX), which showed more mixing and were less genetically distinct. Using simulated Markov-switching VAR (MSVAR), we found that the median population sizes of the posterior distributions were approximately 3,715 individuals for N0, and approximately 100,000 for N1, indicating that Hume's Pheasant experienced a significant genetic bottleneck 4,800 years ago, possibly due to human activity. Hume's Pheasant shows female-biased dispersal. The results of STRUCTURE and GENELAND indicate that Nanpan River, Hongshui River and national road G324 act as potential genetic barriers for Hume's Pheasant in Guangxi and Guizhou provinces. In addition, genetic distinctiveness has persisted despite population declines of the Hume's Pheasant due to the bottleneck approximately 5,000 years ago and population declines in the last 100 years.
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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".