The physical environment mediates male harm and its effect on selection in females
Bibliographic record
Abstract
Recent experiments indicate that male preferential harassment of high-quality females reduces the variance in female fitness, thereby weakening natural selection through females and hampering adaptation and purging. We propose that this phenomenon, which results from a combination of male choice and male-induced harm, should be mediated by the physical environment in which intersexual interactions occur. UsingDrosophila melanogaster, we examined intersexual interactions in small and simple (standard fly vials) versus slightly more realistic (small cages with spatial structure) environments. We show that in these more realistic environments, sexual interactions are less frequent, are no longer biased towards high-quality females, and that overall male harm is reduced. Next, we examine the selective advantage of high- over low-quality females while manipulating the opportunity for male choice. Male choice weakens the viability advantage of high-quality females in the simple environment, consistent with previous work, but strengthens selection on females in the more realistic environment. Laboratory studies in simple environments have strongly shaped our understanding of sexual conflict but may provide biased insight. Our results suggest that the physical environment plays a key role in the evolutionary consequences of sexual interactions and ultimately the alignment of natural and sexual selection.
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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 | 0.000 | 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) | 0.003 | 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".