Genetic Constraints and the Evolution of Display Trait Sexual Dimorphism by Natural and Sexual Selection
Bibliographic record
Abstract
The evolution of sexual dimorphism involves an interaction between sex-specific selection and a breakdown of genetic constraints that arise because the two sexes share a genome. We examined genetic constraints and the effect of sex-specific selection on a suite of sexually dimorphic display traits in Drosophila serrata. Sexual dimorphism varied among nine natural populations covering a substantial portion of the species range. Quantitative genetic analyses showed that intersexual genetic correlations were high because of autosomal genetic variance but that the inclusion of X-linked effects reduced genetic correlations substantially, indicating that sex linkage may be an important mechanism by which intersexual genetic constraints are reduced in this species. We then explored the potential for both natural and sexual selection to influence these traits, using a 12-generation laboratory experiment in which we altered the opportunities for each process as flies adapted to a novel environment. Sexual dimorphism evolved, with natural selection reducing sexual dimorphism, whereas sexual selection tended to increase it overall. To this extent, our results are consistent with the hypothesis that sexual selection favors evolutionary divergence of the sexes. However, sex-specific responses to natural and sexual selection contrasted with the classic model because sexual selection affected females rather than males.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".