Syntopic frogs reveal different patterns of interaction with the landscape: A comparative landscape genetic study of<i>Pelophylax nigromaculatus</i>and<i>Fejervarya limnocharis</i>from central China
Bibliographic record
Abstract
Abstract Amphibians are often considered excellent environmental indicator species. Natural and man‐made landscape features are known to form effective genetic barriers to amphibian populations; however, amphibians with different characteristics may have different species–landscape interaction patterns. We conducted a comparative landscape genetic analysis of two closely related syntopic frog species from central China, Pelophylax nigromaculatus ( PN ) and Fejervarya limnocharis ( FL ). These two species differ in several key life history traits; PN has a larger body size and larger clutch size, and reaches sexual maturity later than FL . Microsatellite DNA data were collected and analyzed using conventional ( F ST , isolation by distance ( IBD ), AMOVA ) and recently developed (Bayesian assignment test, isolation by resistance) landscape genetic methods. As predicted, a higher level of population structure in FL ( F ST ′ = 0.401) than in PN ( F ST ′ = 0.354) was detected, in addition to FL displaying strong IBD patterns ( r = .861) unlike PN ( r = .073). A general north–south break in FL populations was detected, consistent with the IBD pattern, while PN exhibited clustering of northern‐ and southern‐most populations, suggestive of altered dispersal patterns. Species‐specific resistant landscape features were also identified, with roads and land cover the main cause of resistance to FL , and elevation the main influence on PN . These different species–landscape interactions can be explained mostly by their life history traits, revealing that closely related and ecologically similar species have different responses to the same landscape features. Comparative landscape genetic studies are important in detecting such differences and refining generalizations about amphibians in monitoring environmental changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".