Environment-contingent sexual selection in a colour polymorphic fish
Bibliographic record
Abstract
Sexual selection could be a driving force in the maintenance of intraspecific variation, but supporting observations from nature are limited. Here, we test the hypothesis that spatial heterogeneity of the visual environment can influence sexual selection on colourful male secondary traits such that selective advantage is environment contingent. Using a small fish endemic to Sulawesi, Indonesia (Telmatherina sarasinorum) that has five male colour morphs varying in frequency between two visually distinct mating habitats, we used direct behavioural observations to test the environment-contingent selection hypothesis. These observations were combined with measurements of the visual environment, fish coloration and the sensitivity of visual photopigments to determine whether differential morph conspicuousness was associated with reproductive success across habitats. We found that blue and yellow males are most conspicuous in different habitats, where they also have the highest reproductive fitness. A less conspicuous grey morph also gained high reproductive success in both habitats, raising the possibility that alternative behaviours may also contribute to reproductive success. In a comprehensive analysis, conspicuousness was strongly correlated with reproductive success across morphs and environments. Our results suggest an important role for spatially heterogeneous environments in the maintenance of male colour polymorphism.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".