Evolution of flowering time due to variation in the onset of pollen dispersal among individuals
Bibliographic record
Abstract
The evolution of flowering time is often attributed to variations in pollinator rates over time. This study proposes that flowering time can evolve through siring success variation among individuals caused by differential pollen dispersal timing (a result of flowering time variation). By building quantitative genetic models, I show that flowering time evolves to be earlier when the pollen removal rate is low and pollen deposition rate is high, and the fertilization ability of removed pollen declines slowly. Using evolutionary game theory, I show that the evolutionarily stable variance of flowering time is large when the pollen removal rate is either low or high, the pollen deposition rate is moderate, and the fertilization ability of removed pollen declines rapidly. Investigation of the coevolution of flower longevity and flowering time shows that under constant pollination rates, late flowering will be correlated with long-lived flowers due to nonrandom mating, which suggests that the observed correlation between late flowering and short-lived flowers is caused by other factors, such as declining pollination rates during late-stage flowering. I discuss how altered pollination rates under climate change will influence flowering time evolution and the importance of distinguishing between pollen removal and deposition rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".