Patterns, Mechanisms and Genetics of Speciation in Reptiles and Amphibians
Bibliographic record
Abstract
In this contribution, the aspects of reptile and amphibian speciation that emerged from research performed over the past decade are reviewed. First, this study assesses how patterns and processes of speciation depend on knowing the taxonomy of the group in question, and discuss how integrative taxonomy has contributed to speciation research in these groups. This study then reviews the research on different aspects of speciation in reptiles and amphibians, including biogeography and climatic niches, ecological speciation, the relationship between speciation rates and phenotypic traits, and genetics and genomics. Further, several case studies of speciation in reptiles and amphibians that exemplify many of these themes are discussed. These include studies of integrative taxonomy and biogeography in South American lizards, ecological speciation in European salamanders, speciation and phenotypic evolution in frogs and lizards. The final case study combines genomics and biogeography in tortoises. The field of amphibian and reptile speciation research has steadily moved forward from the assessment of geographic and ecological aspects, to incorporating other dimensions of speciation, such as genetic mechanisms and evolutionary forces. A higher degree of integration among all these dimensions emerges as a goal for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".