Allometry constrains the evolution of sexual dimorphism in Drosophila across 33 million years of divergence
Bibliographic record
Abstract
Sexual dimorphism is widely viewed as adaptive, reflecting the evolution of males and females towards divergent fitness optima. Its evolution, however, may often be constrained by the shared genetic architecture of the sexes, and by allometry. Here, we investigated the evolution of sexual size dimorphism, shape dimorphism, and their allometric relationship in the wings of 82 taxa in the family Drosophilidae that have been diverging for at least 33 million years. Shape dimorphism among species was remarkably similar, with males characterized by longer thinner wings than females. There was quantitative variation among species, however, in both size and shape dimorphism, and evidence that they have adapted to different evolutionary optima in different clades on timescales of about 10 million years. Within species, shape dimorphism was predicted by size, and among species, there was a strong relationship between size dimorphism and shape dimorphism. Allometry constrained the evolution of shape dimorphism for the two most variable traits we studied but dimorphism was evolutionary labile in other traits. The keys for disentangling alternative explanations for dimorphism evolution are studies of natural and sexual selection, together with a deeper understanding of how microevolutionary parameters of evolvability relate to macroevolutionary patterns of divergence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".