Data from: Antagonistic natural and sexual selection on wing shape in a scrambling damselfly
Bibliographic record
Abstract
Wings are a key trait underlying the evolutionary success of birds, bats, and insects. For over a century, researchers have studied the form and function of wings to understand the determinants of flight performance. However, to understand the evolution of flight, we must comprehend not only how morphology affects performance, but also how morphology and performance affect fitness. Natural and sexual selection can either reinforce or oppose each other, but their role in flight evolution remains poorly understood. Here, we show that wing shape is under antagonistic selection with regard to sexual and natural selection in a scrambling damselfly. In a field setting, natural selection (survival) favored individuals with long and slender forewings and short and broad hindwings. In contrast, sexual selection (mating success) favored individuals with short and broad forewings and narrow-based hindwings. Both types of selection favored individuals of intermediate size. These results suggest that individuals face a trade-off between flight energetics and maneuverability and demonstrate how natural and sexual selection can operate in similar directions for some wing traits, that is, wing size, but antagonistically for others, that is, wing shape. Furthermore, they highlight the need to study flight evolution within the context of species’ mating systems and mating behaviors.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".