Data from: Winners have higher pre-copulatory mating success but losers have better post-copulatory outcomes
Bibliographic record
Abstract
In many animals, the outcomes of competitive interactions can have lasting effects that influence an individual’s reproductive success and have important consequences for the strength and direction of evolution via sexual selection. In the fruit fly, Drosophila melanogaster, males that have won previous contests are more likely to win in subsequent conflicts and losers are more likely to lose (winner-loser effects), but the direct fitness consequences and genetic underpinnings of this plasticity are poorly understood. Here, we tested how male genotype and the outcomes of previous male-male conflicts influence male pre- and post-copulatory success. We quantified pre-copulatory success in a choice and no-choice context, and post-copulatory success by quantifying ejaculate offensive and defensive ability. We found that winners have higher reproductive success compared to losers in both pre-copulatory scenarios. However, losers consistently mated for a longer duration, boosted female fecundity, and had an increased paternity share when they were the first males to mate, suggesting increased investment into post-copulatory mechanisms. Finally, by using clonal hybrids from the Drosophila genetic reference panel, we quantified the proportion of phenotypic variance in the plasticity between winners and losers that was due to genetic differences. Our results place the behavioural data on winner-loser effects in an evolutionary context by documenting the potential fitness gain to males from altering their reproductive strategy based on fighting experience. Our data may also explain the presence and maintenance of trade-offs between different male reproductive strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.343 | 0.196 |
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".