Isolation by resistance analysis reveals major barrier effect imposed by the Tsinling Mountains on the Chinese wood frog
Bibliographic record
Abstract
Abstract Amphibians are excellent model systems for studying how heterogeneous landscapes drive population differentiation. Though mountain ridges have been widely implicated as barriers to amphibian dispersal, a landscape genetic study by Zhan et al. (BMC Genet, 10, 2009, 17) indicated that the Tsinling Mountains of northwestern China did not significantly impede gene flow of the Chinese wood frog ( Rana chensinensis ). Using their published genetic data, we re‐assessed the impact of elevation, land cover, and roads on the Chinese wood frog using an isolation by resistance (IBR) approach, which allows for the modeling of complex and heterogeneous landscapes unavailable in classical population genetic analysis. We developed a novel method for optimization of IBR analysis using Circuitscape involving initial permutation‐based optimization of the correlation coefficient obtained through partial Mantel tests, followed by subsequent sensitivity analysis to improve upon the model’s biological relevance. Our results indicated that mountain ridges indeed functioned as barriers to dispersal of Chinese wood frogs, and that previous conclusions were based on methodological limitations. Furthermore, a prominent threshold effect at elevations from 1500–2000 m was evident, with elevations below this range minimally impeding gene flow and higher elevations having a significant barrier effect. We suggest that a combination of expert opinion, sensitivity analysis, and permutation‐based optimization procedures in landscape genetic studies will likely generate models that are both highly explanatory and biologically relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".