On the expression of male harm in <i>Drosophila melanogaster</i> : impacts of density and structural complexity of the mating environment
Bibliographic record
Abstract
Male harm occurs when traits in males that increase their reproductive success incidentally reduce female fitness. In Drosophila melanogaster, many lab studies have revealed the presence of male harm, but recent work has shown that its expression can be dramatically reduced, even eliminated, when sexual interactions and mating occur in an environment that differs from traditional lab rearing vials. Here, we follow up on this to separately test the effect of fly density and structural complexity of the mating environment in mediating the expression of male harm. We performed separate two-way factorial assays that measured the fitness of females while manipulating their exposure to males and the density of flies or the structural complexity of the environment during exposure. Male harm, quantified as the relative reduction in female fitness under increased male exposure, was not affected by density, but was significantly reduced-essentially eliminated-by increased structural complexity. Our results demonstrate that seemingly simple choices, such as the environment used in a laboratory model system, can have profound impacts on the expression of harm and hence views on the prevalence of sexual conflict. This is noteworthy because conflict can shape other fundamental evolutionary processes, including adaptation, purging, and speciation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".