Understory community dynamics 12 years after commercial thinning and gap creation in naturally regenerated and planted stands
Bibliographic record
Abstract
New commercial thinning methods, combined with small gap creation, were introduced to better achieve ecosystem-based management objectives and reduce structural differences between unmanaged and planted stands. However, some aspects of the understory response to these silvicultural treatments remains unknown. Here we evaluate the effect of commercial thinning (from below, crop-tree release) and gap creation on the understory communities in naturally regenerated balsam fir ( Abies balsamea ) stands and white spruce ( Picea glauca ) plantations in eastern Canada. Understory communities were surveyed before thinning and gap creation, and then again 1, 2, and 12 years after treatment. The split-split-plot experimental design included four levels of thinning (none, thinning from below, and thinning by the release of 50 or 100 crop trees per hectare) combined with three gap sizes (none, 100 m², and 500 m²). Among the plots, we compared three understory characteristics: taxonomic composition and diversity, trait assemblage and diversity, and vertical structure. We found differences in trait assemblage and vertical structure between the naturally regenerated stands and plantations 12 years after treatment. Our results show that thinning has negligible effect on understory communities, whereas gaps influence understory structure and composition, and differences remained 12 years after treatment. Large gaps (500 m²) produced the most conspicuous change in the understory communities. Relative to small gaps (100 m²), large gaps favoured shade-intolerant, ruderal species (e.g., Hieracium spp., Carex spp., Rubus idaeus , Chamaenerion angustifolium ), and the development of dense shrub and forb layers. The effect of large gaps was greater in planted stands than in naturally regenerated ones. Commercial thinning did not significantly affect understory communities. Gaps should be used sparingly, especially in plantations with site preparation, to avoid the development of a recalcitrant vegetation layer.
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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) | 0.000 | 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".