Impacts of forest fragmentation on the reproductive success of white spruce (<i>Picea glauca</i>)
Bibliographic record
Abstract
The fragmentation of forests into small, isolated remnants may reduce pollen quantity and quality in natural plant populations. The reproductive success of white spruce ( Picea glauca (Moench) Voss) was assessed in a landscape fragmented by agriculture in northern Ontario, Canada. We sampled a total of 23 stands and 104 white spruce trees from three different stand size classes. Each sampled stand was separated by 250–3000 m from the nearest neighbouring stand. Reproductive success, measured as the number of filled seeds per cone, increased with stand size. The total number of seeds per cone, a measure that includes both filled and aborted seeds, also increased with stand size, suggesting that pollen receipt limits the number of seeds in a cone. The proportion of empty seeds (postzygotic abortions) was highest in the two smallest stand size classes, suggesting that inbreeding levels were also highest in these stands. We detected no difference in germination success, seedling growth, and growth of trees up to 10 years from seeds produced by trees from different stand size classes. These results suggest that inbred individuals are largely eliminated during the seed development stage. We estimated that a threshold population size of 180 trees is needed to reduce the negative effects of pollen limitation and inbreeding and maintain seed yields observed in large contiguous stands.
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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 | 0.000 |
| 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".