Forest dynamics and the application of a natural disturbance-based management model in Duck Mountain Provincial Forest, Manitoba
Bibliographic record
Abstract
The forest industry has been moving towards the adoption of ecosystem-based forest management techniques to achieve sustainable forest management. Different models have been developed in an attempt to incorporate increasingly diverse management goals. This study utilizes data from a pilot Forest Land Inventory (FLI) i) to identify major successional pathways in Duck Mountain Provincial Forest of western Manitoba, ii) to analyze tree species cover variability within each pathway, iii) to assess the successional variability of forest stands originating from large, catastrophic fires that occurred in the 1880s and 1890s, and iv) to assess the applicability of the three structural cohort natural disturbance based management (NDBM) model, developed for the mixed boreal forest of Quebec. Cluster analyses were performed to classify the upper forest canopy into 3 major successional pathways: Trembling aspen-white spruce, jack pine-black spruce, and black spruce-eastern larch. Ordination analysis was used to determine the relationship among vegetation, environmental, and structural variables within each major pathway. Results suggested that the FLI environmental variables distinguish well among the three major pathways primarily along a moisture/slope gradient, but explain little of the species and structural variability within each pathway. Forest stands originating from large fires in the 1880s and 1890s were analyzed To assess successional variability within each major pathway. Results showed that despite being of similar age, there was a large amount of variability in structural development, but 2-layered canopies with a continuous to discontinuous upper canopy tended to dominate in these stands. The landscape was then classified into structural cohorts. Globally, 59.5%, 34.5%, and 6.0% of the landscape was in cohort, 1, 2, and 3, respectively. The three structural cohort management model was applied to the forest under three different scenarios: (1) the current distribution of cohorts, (2) a 110-year fire cycle (current), and (3) a 60-year fire cycle (pre-European settlement). Results suggest that, in terms of forest structure, the current distribution of cohorts in DMPF is closest to the expected distribution under a 60-year fire cycle. The results are compared to findings in other regions of the mixed boral forest, and the implications for forest management in DMPF are discussed.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".