Past climate change and recent anthropogenic activities affect genetic structure and population demography of the greater long‐tailed hamster in northern China
Bibliographic record
Abstract
The genetic diversity and the spatial structure of a species are likely consequences of both past and recent evolutionary processes, but relevant studies are still rare in East Asia where the Pleistocene climate has unique influences. In this study, we examined the impact of past climate change and recent anthropogenic activities on the genetic structure and population size of the greater long-tailed hamster (Tscherskia triton), an agricultural rodent pest species in northern China. DNA sequence data of 2 mitochondrial genes and genotypic data of 11 microsatellite DNA loci from 41 populations (545 individuals) were gathered. Phylogenetic and population genetic analyses, as well as species distribution modeling and coalescent simulations, were conducted to infer its historical and demographic patterns and processes. Two deeply diverged mitochondrial clades were recovered. A small one was restricted to the Shandong Peninsula while the main clade was further divided into 3 geographic clusters by their microsatellite DNA genotypes: Northwest, North-center and Northeast. Divergence dating indicated a Middle-to-Late Pleistocene divergence between the 2 clades. Demographic analysis indicated that all 3 and pooled populations showed consistent long-period expansions during last glacial period; but not during the Holocene, probably due to the impact of climate warming and human disturbances. Conflicting patterns between mtDNA and microsatellite markers imply an anthropogenic impact on North-center populations due to intensified agricultural cultivation in this region. Our study demonstrated that the impact of past glaciation on organisms in East Asia significantly differs from that of Europe and North America, and human activity is an important factor in determining the genetic diversity of a species, as well as its spatial structure.
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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 | 0.000 | 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) | 0.000 | 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".