SPATIOTEMPORAL VARIATION IN LINEAR NATURAL SELECTION ON BODY COLOR IN WILD GUPPIES (POECILIA RETICULATA)
Bibliographic record
Abstract
We conducted 10 mark-recapture experiments in natural populations of Trinidadian guppies to test hypotheses concerning the role of viability selection in geographic patterns of male color variation. Previous work has reported that male guppies are more colorful in low-predation sites than in high-predation sites. This pattern of phenotypic variation has been theorized to reflect differences in the balance between natural (viability) selection that disfavors bright male color (owing to predation) and sexual selection that favors bright color (owing to female choice). Our results support the prediction that male color is disfavored by viability selection in both predation regimes. However, it does not support the prediction that viability selection against male color is weaker in low-predation experiments. Instead, some of the most intense bouts of selection against color occurred in low-predation experiments. Our results illustrate considerable spatiotemporal variation in selection among experiments, but such variation was not generally correlated with local patterns of color diversity. More complex selective interactions, possibly including the indirect effects of predators on variation in mating behavior, as well as other environmental factors, might be required to more fully explain patterns of secondary sexual trait variation in this system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".