Within‐plant variation in reproductive investment: consequences for selection on flowering time
Bibliographic record
Abstract
Variation among the leaves, flowers or fruit produced by a plant is often regarded as a nuisance to the experimenter and an impediment to selection. Here, we suggest that within-plant variation can drive selection on other plant-level traits. We examine within-plant variation in floral sex allocation and in fruit set and predict that such variation generates variation in male success among plants, thereby driving selection on flowering time. We tested this prediction in a simulation model estimating selection on flowering time through male fitness when floral sex allocation and/or fruit set vary directionally among flowers on plants. We parameterized the model through a quantitative literature survey of within-plant change in sex allocation. As predicted, within-plant variation in floral sex allocation and in fruit set probability can generate selection on flowering time through male fitness. Declining fruit set from first to last flowers on plants, as occurs in many species, selected for early flowering onset through male fitness. This result was robust to self-incompatibility and to varying returns on male versus female investment. Selection caused by declining fruit set was strong enough to reverse the selection for late flowering that can be caused by intrafloral protandry. Our model provides testable predictions regarding selection on flowering time through male fitness. The model also establishes the intriguing possibility that within-plant variation may influence selection on other traits, regardless of whether that variation is under selection itself.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".