Extensive Long-Distance Pollen Dispersal in a Fragmented Landscape Maintains Genetic Diversity in White Spruce
Bibliographic record
Abstract
Conifers are among the most genetically diverse plants but show the lowest levels of genetic differentiation, even among geographically distant populations. High gene flow among populations may be one of the most important factors in maintaining these genetic patterns. Here, we provide empirical evidence for extensive pollen-mediated gene dispersal between natural stands of a widespread northern temperate/boreal conifer, Picea glauca. We used 6 polymorphic allozyme loci to quantify the proportion of seeds sired by pollen originating from different sources in a landscape fragmented by agriculture in North Central Ontario, Canada. In 7 stands, a small proportion of seeds were sired by self-pollen or neighboring trees but 87.1% (+/-1.7% standard error [SE]) of seeds were sired by pollen from at least 250 to 3000 m away. In 4 single isolated trees, self-fertilization rates were low and more than 96% (+/-1.3% SE) of seeds were sired by immigrant pollen. The average minimum pollen dispersal distance in outcrossed matings was 619 m. These results provide strong evidence that extensive long-distance pollen dispersal plays a primary role in maintaining low genetic differentiation among natural populations of P. glauca and helps maintain genetic diversity and minimize inbreeding in small stands in a fragmented landscape.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".