The capacity for adaptation to climate warming in an annual plant ( <i>Brassica rapa</i> )
Bibliographic record
Abstract
ABSTRACT The persistence of a declining population in the face of environmental change may depend on how fast natural selection restores fitness, a process called “evolutionary rescue”. In turn, evolutionary rescue depends on a population’s adaptive potential. Fisher’s theorem states that a population’s adaptive potential equals the additive genetic variance for fitness ( V A ( W )) divided by mean fitness . Both the numerator and denominator of this rate can differ across environments even when holding allele frequencies constant. However, little is known about how these rates change in wild populations during adaptation, including changes in additive and dominance variance. We assessed the change in adaptive potential and dominance variance in fitness ( V D ( W )) for a Québec population of wild mustard ( Brassica rapa ) under climate warming. We also assessed adaptive constraints that could arise from negative genetic correlations across environments. We grew a pedigreed population of 7000 plants under ambient and heated (+4°C) temperatures and estimated the change in , V A ( W ), V D ( W ), and the cross-environment genetic correlations ( r A ). As predicted, estimates of V A ( W ) and adaptive potentials were higher under heated conditions but non-significantly so. This is perhaps because, surprisingly, plants exposed to a warmer climate exhibited greater . Nevertheless, increased fitness in the warmer environment suggests a plasticity-based short-term potential for adaptation, and that weak but non-significant genetic correlations across environments will enable slow on-going adaptation to warming. Overall, this population of B. rapa harbours existing genetic architecture to persist under warmer temperatures through pre-adaptation but not through evolutionary rescue.
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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) | 0.001 | 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".