Evolution of dorsal pattern variation in Greater Antillean<i>Anolis</i>lizards
Bibliographic record
Abstract
Dorsal patterning in animals can serve as an antipredator defence and may be involved in sexual selection, and is thus likely to be the target of multiple selective forces. Intraspecific variation in dorsal patterning is not rare, but the reasons behind it are poorly understood. Anolis lizards offer an ideal system to test for a role of ecological factors in driving variation in dorsal pattern. Anoles show a high degree of variation in dorsal pattern not only among species, but also between and within sexes. We use a comparative framework to explore whether ecological variables such as habitat use and perch height can explain the evolution of sexual dimorphism in dorsal pattern and the presence of female pattern polymorphism (FPP) in 36 Greater Antillean Anolis species. We provide evidence that anoles that perch closer to the ground are more likely to exhibit sexual dimorphism in dorsal pattern, and we suggest that habitat-use differences between sexes in ground-affiliated ecomorphs may drive the evolution of dorsal pattern dimorphism. In contrast, the ecological variables we investigated cannot explain the presence of FPP. Our results demonstrate that niche-associated diversification can generate phenotypic diversity within as well as among species, but the factors responsible for intrasexual polymorphism in some anole species remain cryptic.
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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".