Does signalling mitigate the cost of agonistic interactions? A test in a cricket that has lost its song
Bibliographic record
Abstract
Prevailing models of animal communication assume that signalling during aggressive conflict mitigates the costs of fighting. We tested this assumption by staging dyadic encounters between male field crickets, Teleogryllus oceanicus, under three conditions: (i) both males could sing aggressive songs, (ii) neither male could sing, and (iii) one male could sing but the other could not. We conducted experiments on males from a Hawaiian population from Kauai that has recently evolved signal loss, and males from a Hawaiian population from the Big Island that has not. Among both populations, interactions between two silent males were characterized by higher levels of aggression than interactions involving one or two singing males. Because the level of aggression is strongly related to the cost of fighting, these data demonstrate that signalling mitigates the cost of fighting. In mixed trials, we found no statistically significant differences between the behaviour of calling and non-calling males in either population. We conclude that there is no evidence that the Kauai population exhibits special adaptations to alleviate the costs of signal loss. Finally, we found that males were much more likely to signal after their opponent's retreat than after their own retreat. Aggressive song therefore meets the definition of a 'victory display'.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".