Life-history evolution under artificial selection in a clonal plant
Bibliographic record
Abstract
ABSTRACT The response of natural populations to selection and the role of genetic correlations in constraining or facilitating evolutionary change is fundamental to adaptation. We use artificial selection to investigate the evolutionary response of clonal reproduction in the common monkeyflower ( Mimulus guttatus ), a species with extensive life history variation. We first characterize the standing genetic variation in a single perennial population, and then conduct four generations of divergent artificial selection on stolon number—the mechanism of clonal reproduction in this species. To start, stolon number had moderate heritability ( H ²=0.25) and was negatively genetically correlated with reproductive traits. Artificial selection produced a clear but asymmetrical response. High selection lines made significantly more stolons, while low lines diverged less from controls. Analyses of G matrices revealed that selection not only changed trait means but also genetic correlations, with high lines diverging more in multivariate genetic architecture. Our results demonstrate that single populations harbor sufficient genetic variation to respond rapidly to selection on clonality, and the response is shaped by existing patterns of genetic covariation. The capacity for rapid evolution of clonal traits is particularly relevant as climate change alters selection and shifts the relative advantages of sexual versus clonal life-history strategies.
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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 | 0.001 | 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".