The evolution of weaponry and aggressive behaviour in field crickets
Bibliographic record
Abstract
Abstract Weapons are among the most extravagant sexually selected traits known, yet the evolution of weapon diversity remains understudied. We used field crickets (Orthoptera, Gryllinae) to test two hypotheses explaining interspecific diversity in weaponry. We raised eight species of Gryllus field crickets under common garden conditions and staged interactions between conspecific males. We measured body size and weapon shape (relative head and mouthpart size) to determine weapon allometry in both males and females, quantified the intensity of male-male aggression for each species, analyzed the effects of both body size and weapon shape on contest outcome, and tested comparative relationships between morphology and behaviour using phylogenetic least squares regression. We found that larger males won more contests than smaller males in seven of eight species, and weapon shape predicted contest success in only one species. Contrary to the fighting advantage hypothesis, body size was not related to aggressiveness across species, but weaponry was. Additionally, the most aggressive species had the most elaborate weaponry, contrary to the weapon-signal continuum hypothesis. Our results highlight the complexity of weaponry evolution in a group of organisms that has been a model system for the observation and study of aggression for approximately 1000 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".