Exploring Factors Influencing Species Natural Regeneration Response Following Harvesting in the Acadian Forests of New Brunswick
Bibliographic record
Abstract
In the Acadian Forest Region of northeastern North America, forest managers are under increasing public pressure to restore the forest to a more historic, natural condition by reducing in clearcutting and promoting partial-cut treatments that more closely emulate historic, local natural disturbance regimes. However, although numerous studies on the effects of partial-cutting on forest regeneration response have been conducted in surrounding temperate and boreal forest ecosystems, there are few studies that directly explore responses to various forms of harvesting within the Acadian Forest ecosystem, with its unique mixture of northern hardwoods and boreal forest species. Here, we conducted one of the first retrospective studies on forest regeneration following a variety of harvesting methods in the Acadian Forest using univariate and multivariate regression trees to assess regeneration response in 50 naturally-regenerating, harvested forest sites in New Brunswick, Canada. Our study shows that regeneration was highly influenced by harvest type, overstory composition, and environmental conditions as reflected by ecoregion classification. Canopy opening size (as controlled by harvest method) significantly influenced the dominance of regenerating species. The presence of conspecific overstory trees increased the likelihood of their regeneration following disturbance, supporting the direct-regeneration hypothesis, especially for species with limited seed dispersal (e.g., sugar maple (Acer saccharum Marsh.) and American beech (Fagus grandifolia Ehrh.). Despite reported problems elsewhere in eastern North America, neither American beech nor balsam fir (Abies balsamea (L.) Mill.) constituted significant competition for the desired species on a broad scale, but the presence of beech was a significant deterrent for yellow birch (Betula alleghaniensis Britt.).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".