Lizards as model organisms for linking phylogeographic and speciation studies
Bibliographic record
Abstract
Lizards have been model organisms for ecological and evolutionary studies from individual to community levels at multiple spatial and temporal scales. Here we highlight lizards as models for phylogeographic studies, review the published population genetics/phylogeography literature to summarize general patterns and trends and describe some studies that have contributed to conceptual advances. Our review includes 426 references and 452 case studies: this literature reflects a general trend of exponential growth associated with the theoretical and empirical expansions of the discipline. We describe recent lizard studies that have contributed to advances in understanding of several aspects of phylogeography, emphasize some linkages between phylogeography and speciation and suggest ways to expand phylogeographic studies to test alternative pattern-based modes of speciation. Allopatric speciation patterns can be tested by phylogeographic approaches if these are designed to discriminate among four alternatives based on the role of selection in driving divergence between populations, including: (i) passive divergence by genetic drift; (ii) adaptive divergence by natural selection (niche conservatism or ecological speciation); and (iii) socially-mediated speciation. Here we propose an expanded approach to compare patterns of variation in phylogeographic data sets that, when coupled with morphological and environmental data, can be used to to discriminate among these alternative speciation patterns. [Correction made after online publication (28/07/2010): (minor deletion in the last line of the abstract)].
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".