Temporal population genetic structure in the pollen pool for flowering time: A field experiment with <i>Brassica rapa</i> (Brassicaceae)
Bibliographic record
Abstract
PREMISE OF THE STUDY: Assortative mating by flowering time can cause temporal genetic structure in species with heritable flowering times. A strong temporal structure, when coupled with a seasonal shift in selection, may lead to adaptive temporal clines. We implemented a prospective and retrospective method to estimate the temporal genetic structure in the pollen pool of Brassica rapa. METHODS: The prospective method uses flowering schedules to estimate the seasonal shift in the pollen donors' phenotype. By examining the offspring generation, we can get a direct estimate of temporal genetic structure, i.e., a retrospective estimate. However, this estimate is problematic because of the phenotypic correlation of the trait of interest, flowering time, between dam and sire. We developed a novel retrospective method that isolates flowering time by holding the maternal contribution constant and sampled the pollen pool in eight open-pollinated field plots throughout the flowering season. KEY RESULTS: We found temporal genetic structure for flowering time in seven of the eight field plots. Interestingly, the direct (retrospective) temporal structure estimate was 35% larger than the prospective estimate based on flowering schedules. Spatial clumping of pollen donors did not affect temporal structure, but structure intensified when heritability was experimentally enhanced. CONCLUSIONS: Temporal genetic structure, especially for flowering time, likely occurs in many plant populations and may be underestimated using a prospective method. We discuss the genome-wide consequences of temporal genetic structure and the potential for adaptive temporal clines in plant populations.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".