The genetics of mating recognition between <i>Drosophila simulans</i> and <i>D. sechellia</i>
Bibliographic record
Abstract
During courtship, visual and chemical signals are often exchanged between the sexes. The proper exchange of such signals ensures intraspecific recognition. We have examined the genetic basis of interspecific differences in male mating behaviour and pheromone concentration between Drosophila simulans and D. sechellia by using Drosophila simulans/D. sechellia introgression lines. Our results show a majority of quantitative trait loci (QTLs) explaining variation in both male mating behaviour and pheromone concentration to be located on the third chromosome. One QTL found on the third chromosome explains variation in time needed to start courtship and copulation as well as time spent courting. The position of such QTL (approximately 84A-88B) with effects on courtship and copulation aspects of mating includes the candidate sex determination gene doublesex (84E5-6) and Voila (86E1-2), a gene that affects male courtship in D. melanogaster. One additional third chromosome QTL explained variation in 7-tricosene pheromone concentrations among males. The interval mapping position of this QTL (approximately 68E-76E) did not overlap with the position detected for differences in mating behaviour and the intervals did not include candidate genes previously identified as having an effect on D. melanogaster cuticular hydrocarbon production. We did not detect any directionality of the effect of Drosophila sechellia allele introgressions in male mating recognition.
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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".