The capacity for adaptation to climate warming in a naturalized annual plant (<i>Brassica rapa</i>)
Bibliographic record
Abstract
The persistence of a declining population under 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 capacity, which can be defined as the ratio between additive genetic variance for fitness (VA(W)) and mean fitness (W-), or represented by ΔevolW-. However, little is known about how both VA(W and (W-) change in wild populations during environmental change, including changes in dominance variance for fitness (VD(W)). We assessed the change in ΔevolW- and VD(W) for a Québec population of wild mustard (Brassica rapa) under climate warming. We also evaluated adaptive constraints that could arise from negative genetic correlations for fitness across environments. We grew a pedigreed population of 7000 plants under ambient and heated (+4°C) temperatures and estimated the change in mean survival and fecundity (W-), VA(W), and VD(W), plus cross-environment genetic correlations (rA). VA for fecundity non-significantly increased under heated conditions, mean fecundity (evolW-) increased significantly, and ΔevolW- was unchanged. We also detected no significant rA for survival and fecundity, suggesting little antagonistic constraint to adaptation. Overall, while this B. rapa population may feature some adaptive plasticity via fecundity, its adaptive capacity to warming seems limited.
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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 | 0.000 |
| 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".