Forest cover and geographical distance influence fine-scale genetic structure of leaf-toed geckos in the tropical dry forests of western Mexico
Bibliographic record
Abstract
Abstract The biodiversity within tropical dry forests (TDFs) is astounding and yet poorly catalogued due to inadequate sampling and the presence of cryptic species. In the Mexican TDF, endemic species are common, and the landscape has been continually altered by geological and anthropogenic changes. To understand how landscape and environmental variables have shaped the population structure of endemic species, we studied the recently described species of leaf-toed gecko, Phyllodactylus benedettii, in coastal western Mexico. Using double-digest restriction site-associated DNA sequencing data, we first explore population structure and estimate the number of ancestral populations. The results indicate a high degree of genetic structure with little admixture, and patterns corresponding to both latitudinal and altitudinal gradients. We find that genetic structure cannot be explained purely by geographical distance, and that ecological corridors may facilitate dispersal and gene flow. We then model the spatial distribution of P. benedettii in the TDF through time and find that the coastline has been climatically suitable for the species since the Last Glacial Maximum. Landscape genetic analyses suggest that the combination of isolation by distance (IBD) and isolation by resistance (IBR; forest cover) has influenced the spatial genetic structure of the species. Overall, our genomic data demonstrate fine-scale population structure in TDF habitat, a complex colonization history, and spatial patterns consistent with both IBD and other ecological factors. These results further highlight the Mexican TDF as a diversity hotspot and suggest that continued anthropogenic changes are likely to affect native fauna.
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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 | 0.000 |
| 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".