Landscape characteristics determine the gene exchange pattern of Opisthopappus species in the Taihang Mountains
Bibliographic record
Abstract
Landscape features are effective geographical barriers resulting inpopulation differentiation of plant species. Taihang Mountains in China possess complexly geographical topology and specific landscape characteristics. Two closely related syntopic Opisthopappus species, mainly distribute in different areas of Taihang Mountains, respectively. How the landscape of Taihang Mountains affects the gene exchange between these two species still unclear. Combined SNP data from restriction-site associated DNA sequencing (RAD-seq) and recently developed landscape genetic methods (EEMS, Samβada, LFMM), we conducted a landscape genetic analysis of these two species. It found that the diversity of O. longilobus was higher than that of O. taihangensis, the gene flow was mostly from north to south along Taihang Mountains. However, a general north–south gene exchange barrier between O. longilobus and O. taihangensis was detected. Among the landscape factors of Taihang Mountains, eight was found to be the important ones, including average precipitation in August, October, and November, solar radiation in August, soil PH, built-up land, rain-fed cultivated land, and workability. And these eight factors were closely related to the occurred barriers, indicating that climatic conditions and human activities rather than geographical environment resulted in these barriers. Twenty-nine selected SNPs were identified to be significant correlated with the eight factors, especially average precipitation in November. Thus, the average precipitation in November could be regarded as an ecological indicator for O. longilobus and O. taihangensis. The results revealed the effect of landscape features on two species and the adaption on the landscape environment of Taihang Mountains during the long-term evolution.
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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 | 0.001 |
| 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".