Data from: Previous inter-sexual aggression increases female mating propensity in fruit flies
Bibliographic record
Abstract
Female mate choice is a complex decision making process that involves many context-dependent factors. Understanding the factors that shape variation in female mate choice has important consequences for evolution via sexual selection. In many animals including fruit flies, Drosophila melanogaster, males often use aggressive mating strategies to coerce females into mating, but it is not clear if females’ experience with sexual aggression shapes their future behaviors. Here, we used males derived from lineages that were artificially selected to display either low or high sexual aggression toward females to determine how experience with these males shapes subsequent female mate choice. First, we verified that males from these lineages differed in their sexual behaviors. We found that males from high sexual aggression backgrounds spent more time pursuing virgin females, and had a shorter mating latency but shorter copulation duration compared to males from low sexual aggression backgrounds. Next, we tested how either a harassment by or mating experience with males from either a high or low sexual aggression backgrounds influenced subsequent female mate choice behaviors. We found that in both scenarios, females that interacted with high sexual aggression males were more likely and faster to mate with a novel male one day later, regardless of the male’s aggression level. These results have important implications for understanding the evolution of flexible polyandry as a mechanism that benefits 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".