Low Light Availability Associated with American Beech Is the Main Factor for Reduced Sugar Maple Seedling Survival and Growth Rates in a Hardwood Forest of Southern Quebec
Bibliographic record
Abstract
Several recent studies have reported a marked increase in American beech dominance (Fagus grandifolia Ehrh.) relative to sugar maple (Acer saccharum Marsh.) in late successional forests of North America. However, many factors have been proposed to explain this sudden shift in tree species composition. We investigated the microsite factors responsible for maple regeneration failure under maple-beech stands, focusing on both light availability and soil conditions. The survival and growth of maple seedlings planted in the natural soil and in pots with enriched soil were monitored for two years, as well as foliar nutrition and herbivory damages of natural seedlings. The results indicate that low light availability associated with the presence of beech is the primary factor leading to maple regeneration failures. Soil nutrient availability and foliar nutrition of natural seedlings did not differ between forest types. Yet, the results indicate that factors such as allelopathy and preferential herbivory on maple seedlings under beech could be superimposed effects that hinder maple regeneration. Under similar forests, a control of beech sapling abundance in the understory followed by selection cutting could be one way to promote and maintain maple populations in the longer term.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".