Artificial Selection Reveals High Genetic Variation in Phenology at the Trailing Edge of a Species Range
Bibliographic record
Abstract
Species responses to climate change depend on the interplay of migration and adaptation, yet we know relatively little about the potential for adaptation. Genetic adaptations to climate change often involve shifts in the timing of phenological events, such as flowering. If populations at the edge of a species range have lower genetic variation in phenological traits than central populations, then their persistence under climate change could be threatened. To test this hypothesis, we performed artificial selection experiments using the scarlet monkeyflower (Mimulus cardinalis) and compared genetic variation in flowering time among populations at the latitudinal center, northern edge, and southern edge of the species range. We also assessed whether selection on flowering time yielded correlated responses in functional traits, potentially representing a cost associated with early or late flowering. Contrary to prediction, southern populations exhibited greater responses to selection on flowering time than central or northern populations. Further, selection for early flowering resulted in correlated increases in specific leaf area and leaf nitrogen, whereas selection for late flowering led to decreases in these traits. These results provide critical insights about how spatial variation in the potential for adaptation may affect population persistence under changing climates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.004 | 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 teacher head, 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".