A change in climate causes rapid evolution of multiple life‐history traits and their interactions in an annual plant
Bibliographic record
Abstract
Climate change is likely to spur rapid evolution, potentially altering integrated suites of life-history traits. We examined evolutionary change in multiple life-history traits of the annual plant Brassica rapa collected before and after a recent 5-year drought in southern California. We used a direct approach to examining evolutionary change by comparing ancestors and descendants. Collections were made from two populations varying in average soil moisture levels, and lines propagated from the collected seeds were grown in a greenhouse and experimentally subjected to conditions simulating either drought (short growing season) or high precipitation (long growing season) years. Comparing ancestors and descendants, we found that the drought caused many changes in life-history traits, including a shift to earlier flowering, longer duration of flowering, reduced peak flowering and greater skew of the flowering schedule. Descendants had thinner stems and fewer leaf nodes at the time of flowering than ancestors, indicating that the drought selected for plants that flowered at a smaller size and earlier ontogenetic stage rather than selecting for plants to develop more rapidly. Thus, there was not evidence for absolute developmental constraints to flowering time evolution. Common principal component analyses showed substantial differences in the matrix of trait covariances both between short and long growing season treatments and between populations. Although the covariances matrices were generally similar between ancestors and descendants, there was evidence for complex evolutionary changes in the relationships among the traits, and these changes depended on the population and treatment. These results show that a full appreciation of the impacts of global change on phenotypic evolution will entail an understanding of how changes in climatic conditions affect trait values and the structure of relationships among traits.
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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".