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) andFejervarya limnocharis(FL). These two species differ in several key life history traits;PNhas a larger body size and larger clutch size, and reaches sexual maturity later thanFL. MicrosatelliteDNAdata were collected and analyzed using conventional (FST, 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 inFL(FST′ = 0.401) than inPN(FST′ = 0.354) was detected, in addition toFLdisplaying strongIBDpatterns (r = .861) unlikePN(r = .073). A general north–south break inFLpopulations was detected, consistent with theIBDpattern, whilePNexhibited 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 toFL, and elevation the main influence onPN. 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 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 | 0.001 | 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".