Fine‐scale genetic analysis of species‐specific female preference in <i>Drosophila simulans</i>
Bibliographic record
Abstract
Behavioural differences are thought to be the first components to contribute to species isolation, yet the precise genetic basis of behavioural isolation remains poorly understood. Here, we used a combination of behaviour assays and genetic mapping to provide the first refined map locating candidate genes for interspecific female preference isolating Drosophila simulans from D. melanogaster. First, we tested whether two genes identified as affecting D. melanogaster female intraspecific mate choice also affect interspecific mate choice; neither of these genes was found to contribute to species-specific female preference. Next, we used deficiency mapping to locate genes on the right arm of the third chromosome for species-specific female preference and identified five small significant regions that contain candidate genes contributing to behavioural isolation. All five regions were located in areas that would have low interspecific recombination, which mirrors the results of other behavioural isolation studies that used quantitative trait locus (QTL) mapping, but without the potential concern of bias towards regions of low recombination that QTL mapping may have. As this model system may be refined to the individual gene level using the same methodology, this initial map we provide may potentially serve as a ready template for the identification and characterization of the first behavioural isolation genes.
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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".