Drought drives selection for earlier flowering, while pollinators drive selection for larger flowers in annual <i>Brassica rapa</i>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Drought-induced changes in floral traits can disrupt plant–pollinator interactions, influencing pollination and reproductive success. These phenotypic changes likely also affect natural selection on floral traits, yet phenotypic selection studies manipulating drought remain rare. We studied how drought impacts selection to understand the potential evolutionary consequences of drought on floral traits. We used a factorial experiment with potted plants to manipulate both water availability (well-watered and drought) and pollination (open and supplemented). We examined the treatment effects on traits of Brassica rapa and estimated phenotypic selection and whether it was pollinator-mediated in these two abiotic conditions. Drought affected plant phenotypes, leading to plants with fewer flowers and ultimately lower seed production. Flowering time did not show variation with watering, but we found the strongest effect of drought on selection was for flowering time. There was a selection for flowering faster in drought but not well-watered conditions. Pollinators instead were the agents responsible for selection on flower size, but we did not find strong evidence that drought effected pollinator-mediated selection. There was a stronger selection for larger flowers in drought compared to well-watered plants, and it could be attributed to pollinators however, there was no significant difference between watering treatments. Our results show the effects of drought are not limited to phenotypic responses and may alter evolution in plants by changing phenotypic selection on traits. The connection between phenotypic plasticity and selection may be important to understand as we found the most variable trait (display size) was not under selection while the trait with different selection in drought (flowering time) did not change in response to drought. Our study highlights the importance of manipulating potential agents of selection, especially to understand fully the potential impacts of components of climate change such as drought.
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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 | 0.000 |
| 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) | 0.000 | 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 it