Mechanical scarification can reduce competitive traits of boreal ericaceous shrubs and improve nutritional site quality
Bibliographic record
Abstract
Abstract Ericaceous shrubs often interfere with the growth of black spruce seedlings on regenerating forest sites in Eastern Canada. Mechanical site preparation such as scarification may improve this situation, but it is uncertain whether this is solely due to a reduction in direct competition from the shrubs, or also from a sustained improvement in nutritional site quality. We sampled experimental plots in two boreal climate regions (i.e. warmer-drier Abitibi vs. cooler-wetter Côte-Nord) where scarification, performed 18 years earlier, had increased the growth of black spruce relative to non-scarified plots. Trees of scarified plots had closed the canopy more than trees of non-scarified plots in Côte-Nord, but not in Abitibi. Total ground cover of ericaceous shrubs was lower in scarified plots at both sites, the main species being Kalmia angustifolia (i.e. Kalmia) in Abitibi and Rhododendron groenlandicum (i.e. Labrador tea) in Côte-Nord. Scarified plots at both sites had significantly shorter current-year ericaceous rhizomes than non-scarified plots, but the difference between treatments was significantly greater in Côte-Nord than in Abitibi. In Côte-Nord, ericaceous shrubs on scarified plots had a lower specific rhizome mass, higher specific leaf area, lower tannin and higher N concentrations in leaves and litter, and lower N use efficiency than on non-scarified plots. By comparison, scarification in Abitibi affected only one foliar property, namely a reduction in the C:N ratio of Kalmia leaf litter. Forest floor N mineralization rates and black spruce needle N concentrations were higher in scarified than non-scarified plots across both sites. Taken collectively, results suggest that mechanical scarification on ericaceous shrub-dominated cutovers can reduce competitive traits of boreal ericaceous shrubs and improve nutritional site quality, especially in cooler-wetter climates. Highlights
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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 | 0.000 | 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) | 0.001 | 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".