A comparison of forest structure among old-growth, variable retention harvested, and clearcut peatland black spruce (<i>Picea mariana</i>) forests in boreal northeastern Ontario
Bibliographic record
Abstract
Harvesting techniques that retain structural elements of the original forest may help to preserve the characteristic ecological features and biological diversity of old-growth forests. Harvesting with advance regeneration protection (HARP) is one such technique practised in the Lake Abitibi Model Forest of northeastern Ontario on peatland sites. In this system, winter harvesting operations clear trees in strips about 5–7 m wide and using a minimum diameter limit cut extract trees from the adjacent residual forest rows, 5–9 m wide. In order to assess the effectiveness of HARP in retaining forest structure, we quantified forest and understorey structural features in 24 1.65-ha plots spanning a wide range of forest retention, including clearcuts, three levels of HARP, and the edge and interior of unharvested forest (logging had occurred 2.5–3.5 years before the study). Mean tree age, diameter, and height in HARP forests in all cases exceeded 68% of the mean values found in unharvested forest, which was strikingly higher than the percent retention of basal area (20–43%). Also, unlike clearcuts, HARP forests retained the inverse-J curves between stem density and size class observed in unharvested forests, although stem densities in all size classes were lower. The percentage of black spruce regeneration originating from seed versus vegetative layering was directly related to the amount of forest basal area retained, averaging 2–5% in unharvested forests, 9–38% in HARP forests, and 67% in clearcuts. Many understorey characteristics closely followed the harvest intensity gradient, with amounts of moss, lichen, and Ledum highest at the unharvested end of the gradient and amounts of decaying moss, coarse and fine downed woody debris, exposed soil, and sedge highest at the clearcut end of the gradient. When the structural features were combined into a single composite variable using Principal Components Analysis (PCA) and the scores plotted against basal area, the relationship was curvilinear, with HARP treatments retaining greater amounts of structural characteristics than expected based solely on the basal area of wood harvested. This research suggests that the retention and redevelopment of old-growth features in peatland black spruce forests will be better under a HARP system than under a traditional clearcut system. Longer-term research, specifically on the edge habitats created by the HARP system, is required. Key words: boreal, peatland, black spruce, alternative harvesting methods, forest ecology
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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 | 0.001 | 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".