Contemporary changes in phenotypic variation, and the potential consequences for eco‐evolutionary dynamics
Bibliographic record
Abstract
Most studies assessing rates of phenotypic change focus on population mean trait values, whereas a largely overlooked additional component is changes in population trait variation. Theoretically, eco-evolutionary dynamics mediated by such changes in trait variation could be as important as those mediated by changes in trait means. To date, however, no study has comprehensively summarised how phenotypic variation is changing in contemporary populations. Here, we explore four questions using a large database: How do changes in trait variances compare to changes in trait means? Do different human disturbances have different effects on trait variance? Do different trait types have different effects on changes in trait variance? Do studies that established a genetic basis for trait change show different patterns from those that did not? We find that changes in variation are typically small; yet we also see some very large changes associated with particular disturbances or trait types. We close by interpreting and discussing the implications of our findings in the context of eco-evolutionary studies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".