Courtship and genetic quality: asymmetric males show their best side
Bibliographic record
Abstract
Fluctuating asymmetry (FA), the small random deviations from perfect morphological symmetry that result during development, is ubiquitous throughout the animal kingdom. In many species, FA seems to play a role in mate choice, perhaps because it signals an individual's genetic quality and health. However, the relationship between an individual's FA and behaviour is generally unknown: what do more asymmetric individuals do about their own asymmetry? We now show for the first time that individuals respond behaviourally to their own morphological FA in what appears to be an adaptive manner. During courtship, male guppies exhibiting high FA in ornamental colour, bias their displays towards their more colourful body side, thus potentially increasing their attractiveness by exaggerating the quantity of their orange signal. This appears to be a strictly behavioural male response to cues provided by females, as it does not occur when males court a non-reactive model female. Whether inferior males realize any mating advantage remains uncertain, but our study clearly demonstrates a behavioural response to random morphological asymmetries that appears to be adaptive. We propose that the tendency to show or otherwise use a 'best side' is common in nature, with implications for sexual signalling and the evolution of more pronounced asymmetries.
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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 | 0.000 | 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 | 0.000 |
| 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".