Data from: Estimating the impact of divergent mating phenology between residents and migrants on the potential for gene flow
Bibliographic record
Abstract
Gene flow between populations can allow the spread of beneficial alleles and genetic diversity between populations, with importance to conservation, invasion biology, and agriculture. Levels of gene flow between populations vary not only with distance, but also with divergence in reproductive phenology. Since phenology is often locally adapted, arriving migrants may be reproductively out of synch with residents, which can depress realized gene flow. In flowering plants, the potential impact of phenological divergence on hybridization between populations can be predicted from overlap in flowering schedules—the daily count of flowers capable of pollen exchange—between a resident and migrant population. The accuracy of this prospective hybridization estimate, based on parental phenotypes, rests upon the assumptions of unbiased pollen transfer between resident and migrant active flowers. We tested the impact of phenological divergence on resident–migrant mating frequencies in experiments that mimicked a single large gene flow event. We first prospectively estimated mating frequencies two lines of Brassica rapaselected or early and late flowering. We then estimated realized mating frequencies retrospectively through progeny testing. The two estimates strongly agreed in a greenhouse experiment, where procedures ensured saturating, unbiased pollination. Under natural pollination in the field, the rate of resident–migrant mating, was lower than estimated by phenological divergence alone, although prospective and retrospective estimates were correlated. In both experiments, differences between residents and migrants in flowering schedule shape led to asymmetric hybridization. Results suggest that a prospective estimate of hybridization based on mating schedules can be a useful, although imperfect, tool for evaluating potential gene flow. They also illustrate the impact of mating phenology on the magnitude and symmetry of reproductive isolation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".