Genetic Variation in Male Mate Choice for Large Females in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
ABSTRACT Males in many species show courtship and mating preferences for certain females over others when given the choice. One of the most common targets of male mate choice in insects is female body size, with males preferring to court and mate with larger, higher‐fecundity females and investing more resources in matings with those females. Although this preference is well‐documented at the species level, less is known about how this preference varies within species and whether there is standing genetic variation for male mate choice within populations. We used hemiclonal analysis in the fruit fly, Drosophila melanogaster, to test for heritable genetic variation in pre‐ and postcopulatory components of male mate choice for large females. We found additive genetic variation for both forms of male choice: Males from different hemiclone lines varied in the strength of their courtship preferences for large females and the degree to which they extended matings with large females. Although males from hemiclone lines with stronger courtship preferences for large females were more likely to mate with those females, there was no genetic correlation between pre‐ and postcopulatory components of male mate choice, suggesting that they are under independent genetic control. Genetic variation in male mate choice may be widespread, potentially impacting the fitness of both sexes and the adaptive evolution of populations.
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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 | 0.001 | 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) | 0.002 | 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".