Data from: Competition for mates and the improvement of nonsexual fitness
Bibliographic record
Abstract
Competition for mates can be a major source of selection, not just on secondary sexual traits but across the genome. Mate competition strengthens selection on males via sexual selection, which typically favours healthy, vigorous individuals and, thus, all genetic variants that increase overall quality. However, recent studies suggest another major effect of mate competition that could influence genome-wide selection: sexual harassment by males can drastically weaken selection on quality in females. Because of these conflicting effects, the net effect of mate competition is uncertain, though perhaps not entirely unpredictable. We propose that the environment in which mate competition occurs mediates the importance of sexual selection relative to sexual conflict, and hence the net effect of mate competition on nonsexual fitness. To test this, we performed experimental evolution with 63 fruit fly populations adapting to novel larval conditions where each population was maintained with or without mate competition. In half the populations with mate competition, adults interacted in simple, high-density environments. In the remainder, adults interacted in more spatially complex environments in which male-induced harm is reduced. Populations evolving with mate competition in the complex environment adapted faster to novel larval environments than did populations evolving without mate competition or with mate competition in the simple environment. Moreover, mate competition in the complex environment caused a substantial reduction in inbreeding depression in egg-to-adult viability relative to the other two mating treatments. These results demonstrate that the mating environment has a substantial and predictable effect on nonsexual fitness through adaptation and purging.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.460 | 0.302 |
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".