Contrasting Patterns of Pollen and Seed Flow Influence the Spatial Genetic Structure of Sweet Vernal Grass (Anthoxanthum odoratum) Populations
Bibliographic record
Abstract
The spatial genetic structure of plant populations is determined by a combination of gene flow, genetic drift, and natural selection. Gene flow in most plants can result from either seed or pollen dispersal, but detailed investigations of pollen and seed flow among populations that have diverged following local adaptation are lacking. In this study, we compared pollen and seed flow among 10 populations of sweet vernal grass (Anthoxanthum odoratum) on the Park Grass Experiment. Overall, estimates of genetic differentiation that were based on chloroplast DNA (cpDNA) and, which therefore resulted primarily from seed flow, were lower (average F(ST) = 0.058) than previously published estimates that were based on nuclear DNA (average F(ST) = 0.095). Unlike nuclear DNA, cpDNA showed no pattern of isolation by adaptation; cpDNA differentiation was, however, inversely correlated with the number of additions (nutrients and lime) that each plot had received. We suggest that natural selection is restricting pollen flow among plots, whereas nutrient additions are increasing seed flow and genetic diversity by facilitating the successful germination and growth of immigrant seeds. This study highlights the importance of considering all potential gene flow mechanisms when investigating determinants of spatial genetic structure, and cautions against the widespread assumption that pollen flow is more important than seed flow for population connectivity in wind-pollinated species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".