Natural regeneration 18 years after experimental silvicultural treatments in Canadian boreal forests
Bibliographic record
Abstract
In Canada, clearcutting is the most widely used silvicultural system in boreal forests despite potential impacts on forest simplification and biodiversity loss. Retaining mature trees is suggested to maintain stand structure and biodiversity, especially for promoting the regeneration of shade-tolerant species. Partial harvesting is considered a promising alternative to the clearcutting system as a means of integrating ecological, economic, and social objectives into silvicultural planning; however, this approach must be developed for use in natural boreal forests. Here, we evaluate the effects of silvicultural treatments on natural regeneration in stands of natural even-aged mature black spruce ( Picea mariana (Mill.) B.S.P.), 18 years after cutting. In 2003, in the Saguenay and North Shore regions of Quebec, an experimental design of fully randomized blocks was established across six sites, each containing two forest stand types (younger and older stands) and six silvicultural treatments. In 1 512 microplots, we categorized all tree seedlings by species and height class and assessed a dominant seedling for growth-related variables, and microenvironment. We found that 18 years after treatment, mini-strip shelterwood harvesting produced the highest black spruce seedling density (39 765 seedlings/ha). In contrast, clearcutting produced a seedling density that was three times lower than uniform shelterwood harvesting but demonstrated a twofold increase in seedling terminal shoot length growth. Mineral soil, spot scarification, moss cover with Polytrichum spp., and distance from residual strips positively correlated with black spruce seedling density. Our study highlights the potential of shelterwood systems as a silvicultural alternative to clearcutting for promoting black spruce regeneration in Canadian boreal forests. • Partial harvest enhances the black spruce regeneration in Canadian boreal forests. • Shelterwood was the most effective treatment for promoting natural regeneration. • Seedling density correlated positively with scarification and skid trail. • Ericaceous shrubs negatively influenced seedling density of black spruce after harvest. • Partial harvest could be a promising silvicultural option.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".