The diversification of mate preferences by natural and sexual selection
Bibliographic record
Abstract
The evolution of sexual display traits or preferences for them in response to divergent natural selection will alter sexual selection within populations, yet the role of sexual selection in ecological speciation has received little empirical attention. We evolved 12 populations of Drosophila serrata in a two-way factorial design to investigate the roles of natural and sexual selection in the evolution of female mate preferences for male cuticular hydrocarbons (CHCs). Mate preferences weakened in populations evolving under natural selection alone, implying a cost in the absence of their expression. Comparison of the vectors of linear sexual selection revealed that the populations diverged in the combination of male CHCs that females found most attractive, although this was not significant using a mixed modelling approach. Changes in preference direction tended to evolve when natural and sexual selection were unconstrained, suggesting that both processes may be the key to initial stages of ecological speciation. Determining the generality of this result will require data from various species across a range of novel environments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".