Using forestry archives to assess long-term changes in forest landscape age structure and tree composition (1950–2020) in eastern Canada
Bibliographic record
Abstract
Global forest landscapes are undergoing profound changes driven by the influence of multiple interacting factors, including forestry, natural disturbances, and climate change. Monitoring and understanding these complex dynamics is challenging due to the lack of data at the spatiotemporal scale at which changes occur (i.e., millions of ha over decades). In this study, we analyzed forest management plans from the 1950s alongside contemporary forest inventories to track changes in age structure and tree species composition across 18 large landscapes covering 3.8 million hectares of eastern Canada's forests. Using cluster analysis, we grouped the 18 studied landscapes into four broad ecological regions (i.e., northern and southern boreal and western and eastern temperate mixed forests) characterized by similar forest composition in the 1950s and subsequent disturbance regimes from 1950 to 2020. The boreal regions transitioned from old-growth-dominated landscapes to those dominated by young stands, mainly due to clearcutting. This transformation was associated with declines in spruces and paper birch and increases in poplars, balsam fir, and jack pine. In contrast, the temperate regions—already logged before the 1950s—experienced subtler age structure changes. Birches and black spruce declined in those forests, while maples, balsam fir, and white pine became more prevalent. We discuss the potential interactive effects of forestry practices, natural disturbances, and climate change on these changes. We conclude that forestry archives are valuable long-term ecological data that should be systematically analyzed to assess global long-term forest change.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".