Data from: Using adaptive traits to consider potential consequences of temporal variation in selection: male guppy colour through time and space
Bibliographic record
Abstract
Temporal variation in selection is typically evaluated by estimating and comparing selection coefficients in natural populations. Meta-analyses of these coefficients have yielded important insights, but selection coefficients are limited in several respects, including low statistical power, imperfect fitness surrogates, and uncertainty regarding consequences for trait change. A complementary approach without these limitations is to examine temporal variation in adaptive traits themselves, which is mechanistically easier and more directly relevant to evolutionary consequences. We illustrate this approach by analyzing the colour patterns of male guppies, Poecilia reticulata, from each of six sites in Trinidad in each of 6 years. This system is particularly appropriate for our study because key aspects of colour variation are genetically-based and responsive to selection. However, although spatial patterns of colour variation have been extensively considered in this system, no study has yet formally assessed annual temporal variation in non-manipulated populations. Matching previous conclusions for the guppy system, we find that guppies from different sites manifest different colour patterns in association with different predation regimes. We here add the new finding that, although some temporal variation is present, spatial patterns of colour variation are generally consistent across years. These results suggest that, when considering adaptive traits, spatial variation is more important than temporal variation, although our study system might be exceptional in this regard. Additional studies examining spatiotemporal variation in adaptive traits could help to improve our understanding of the role that spatiotemporal variation in selection plays in the evolutionary process.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".