Sexual size dimorphism as a determinant of biting performance dimorphism in <i>Anolis</i> lizards
Bibliographic record
Abstract
Rensch's rule describes a pattern of interspecific allometry in which sexual size dimorphism (SSD) increases with size among closely related species (i.e., among a group of related species, the largest ones tend to show more male-biased SSD). Sexual selection is often invoked to explain Rensch's rule, as larger male body size is assumed to be favoured by sexual selection for increased fighting performance in contests for mating opportunities. Often, however, the correlation between size and performance is not well described. We studied a sexually selected performance trait, bite force in Anolis lizards, to determine whether patterns of SSD are linked to size-associated patterns of performance dimorphism at the macroevolutionary level, as expected under the sexual selection hypothesis for Rensch's rule. Additionally, we tested whether allometric patterns of performance dimorphism differ between mainland and island species, as the latter have likely evolved under a stronger sexual selection regime. We found that SSD overwhelmingly explains the relationship between performance dimorphism and size in anoles, as expected under a sexual selection model for Rensch's rule. However, residual performance dimorphism was higher in island than in mainland species, suggesting that these groups differ in performance dimorphism for reasons unrelated to size. Head size dimorphism was associated with residual performance dimorphism, but did not fully explain the difference in performance dimorphism between island and mainland species. Together, these findings highlight the need to interpret Rensch's rule patterns of body size evolution cautiously, as allometric patterns of performance dimorphism and size dimorphism might not be equivalent.
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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 | 0.001 |
| 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".