Bibliographic record
Abstract
Sexual conflict can lead to rapid and continuous coevolution between females and males, without any inputs from varying ecology. Yet both the degree of conflict and selection on antagonistic traits are known to be sensitive to local ecological conditions. This leads to the longstanding question: to what extent does variation in ecological context drive sexually antagonistic coevolution? In water striders, there is much information about the impacts of ecological factors on conflict, and about patterns of antagonistic coevolution. However, the connection between the two is poorly understood. Here, we first review the multiple ways in which ecological context might affect the coevolutionary trajectory of the sexes. We then review ecological and coevolutionary patterns in water striders, and connections between them, in light of theory and new data. Our analysis suggests that ecological variation does impact observed patterns of antagonistic coevolution, but highlights significant uncertainty due to the multiple pathways by which ecological factors can influence conflict and its evolutionary outcome. To the extent that water striders are a reasonable reflection of other systems, this observation serves as both an opportunity and a warning: there is much to learn, but gaining insight may be a daunting process in many systems.This article is part of the theme issue 'Linking local adaptation with the evolution of sex differences'.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".