Irregular forest structures originating after fire: An opportunity to promote alternatives to even‐aged management in boreal forests
Bibliographic record
Abstract
Abstract Even‐aged silviculture based on short‐rotation clearcuts had severely altered boreal forests. Silvicultural alternatives (e.g. continuous cover or retention forestry) have the potential to restore and protect the habitats and functions of boreal forests. These alternatives are however often restricted to structurally complex old‐growth forest, which are particularly threatened by anthropogenic disturbances. Increasing the use of alternatives to even‐aged silviculture in early‐successional stands could help recruit more structurally complex forests, with characteristics closer to the old‐growth. In this article, we therefore evaluate the potential for silvicultural alternatives to even‐aged management in boreal forests that burned less than a century ago. We analysed 1085 field plots in a 243,000 km2area situated in the boreal forest of eastern Canada. These plots burned 30–100 years before the survey and had not been subjected to previous or subsequent anthropogenic disturbance; they hence represent young primary forests. The main patterns of tree diameter distribution variation within the plots were identified usingk‐means clustering. Stand structure, tree species composition and environmental variables that most explained the differences among the clusters were identified with a random forest model, and then compared using Kruskal–Wallis and Fisher's exact tests. The majority (>75%) of the plots presented an irregular structure of stem diameters (i.e. non‐normally distributed, with many small diameter trees). The understorey was generally dominated by black spruce (Picea mariana[Mill.] BSP), a shade‐tolerant species. Irregular structures were observed in both forests of high and low productivity, implying that different processes (e.g. early regeneration, variable tree growth) can lead to observed early irregular structure. Regular structures were generally characterized by a higher productivity and abundance in hardwood species compared to the irregular structures. Synthesis and applications. Many boreal forests of eastern Canada progress towards an irregular structure in the decades following the last stand‐replacing fire. A substantial part of these early‐successional forests may be suitable for alternatives to even‐aged silviculture that better maintains habitats and functions of preindustrial boreal forests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".