Female mating experience and genetic background independently influence male mating success in fruit flies
Bibliographic record
Abstract
When the reproductive interests of males and females conflict, males can evolve traits that are harmful to females, and females can coevolve traits to resist this harm. In the fruit fly, Drosophila melanogaster, there is genetic variation in female resistance traits, which can affect the pre- and post-mating success of males that try to mate with them. However, it is not clear to what extent the expression of these phenotypes can be modified by environmental factors such as sociosexual experience. Here, we tested how the genetic background of a female and her previous mating experience interact to affect the mating success of focal males. In the experience phase, we placed females from 28 distinct genetic backgrounds individually either with a single male (low conflict) or with three males (high conflict) for 48 hr. In the subsequent test phase, we measured the mating and post-mating fertilization success of focal males paired individually with each female. We found that focal males paired with females from the high-conflict treatment were less successful at mating, took longer to mate when they were successful, and had a lower proportion of paternity share. Furthermore, we identified significant female genetic variation associated with male mating success. These results indicate that female experience, along with intrinsic genetic factors, can independently influence different fitness components of her subsequent mates and has implications for our understanding of plastic female mating strategies and the evolution of sexually antagonistic traits in males and females.
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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 | 0.000 |
| 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".