Floral trait variation and links to climate in the mixed-mating annual <i>Clarkia pulchella</i>
Bibliographic record
Abstract
The benefits of self-fertilization can vary across environments, leading to selection for different reproductive strategies and influencing the evolution of floral traits. Although stressful conditions have been suggested to favour self-pollination, the role of climate as a driver of mating-system variation is generally not well understood. Here, we investigate the contributions of local climate to intraspecific differences in mating-system traits in Clarkia pulchella Pursh in a common-garden growth chamber experiment. We also tested for plastic responses to soil moisture with watering treatments. Herkogamy (anther–stigma spacing) correlated positively with dichogamy (timing of anther–stigma receptivity) and date of first flower, and northern populations had smaller petals and flowered earlier in response to experimental drought. Watering treatment alone had little effect on traits, and dichogamy unexpectedly decreased with annual precipitation. Populations also differed in phenological response to watering treatment, based on precipitation and winter temperature of their origin, indicating that populations from cool and dry sites have greater plasticity under different levels of moisture stress. While some variation in floral traits is attributable to climate, further investigation into variation in pollinator communities and the indirect effects of climate on mating system can improve our understanding of the evolution of plant mating.
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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 | 0.001 | 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".