No evidence for external genital morphology affecting cryptic female choice and reproductive isolation in<i>Drosophila</i>
Bibliographic record
Abstract
Genitalia are one of the most rapidly diverging morphological features in animals. The evolution of genital morphology is proposed to be driven by sexual selection via cryptic female choice, whereby a female selectively uptakes and uses a particular male's sperm on the basis of male genital morphology. The resulting shifts in genital morphology within a species can lead to divergence in genitalia between species, and consequently to reproductive isolation and speciation. Although this conceptual framework is supported by correlative data, there is little direct empirical evidence. Here, we used a microdissection laser to alter the morphology of the external male genitalia in Drosophila, a widely used genetic model for both genital shape and cryptic female choice. We evaluate the effect of precision alterations to lobe morphology on both interspecific and intraspecific mating, and demonstrate experimentally that the male genital lobes do not affect copulation duration or cryptic female choice, contrary to long-standing assumptions regarding the role of the lobes in this model system. Rather, we demonstrate that the lobes are essential for copulation to occur. Moreover, slight alterations to the lobes significantly reduced copulatory success only in competitive environments, identifying precopulatory sexual selection as a potential contributing force behind genital diversification.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".