Can sugar maple establish into the boreal forest? Insights from seedlings under various canopies in southern Quebec
Bibliographic record
Abstract
Abstract Understanding tree recruitment dynamics in various growth environments is essential for a better assessment of tree species’ adaptive capacity to climate change. We investigated the microsite factors influencing survival, growth, and foliar nutrition of natural and planted sugar maple seedlings ( Acer saccharum ) along a gradient of tree species that reflect the change in composition from temperate hardwoods to boreal forests of eastern Canada. We specifically tested whether the increasing abundance of conifers in the forest and its modifications on soil properties negatively affects foliar nutrition of natural seedlings as well as the survival and growth of seedlings planted directly in the natural soil and in pots filled with enriched soil. Results of natural seedlings indicate that under conifer‐dominated stands, lower soil pH, accelerated dissolution of some minerals, lower temperature and moisture, and higher levels of phenolic compounds have created microsites that are less suitable for sugar maple foliar nutrition and regeneration. These conditions were omnipresent under hemlock. The growth of seedlings planted in the natural soil was negatively impacted by the overall low soil quality under all forest types (as compared to seedlings planted in pots with enriched soil). However, survival and growth of the seedlings were not negatively affected by conifers, regardless of planting type, likely because of stored nutrients from the nursery. Also, lower survival was found under maple–birch stands for seedlings planted both in the natural soil and in pots with enriched soil due to higher shading. This study has identified key microsite factors created by specific conifers that may impede or benefit the potential of sugar maple to maintain its current range or expand its range northward under climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".