Integrating the effects of climate change into long-term strategic forest management planning using a process-based stand model
Bibliographic record
Abstract
Integrating climate change into strategic forest management is critical for predicting forest dynamics and maintaining resource availability. However, there is currently no scientific consensus on the best approach for integrating climate change effects into strategic-level forest management planning. In this study, we propose a novel approach to incorporate climate change into a strategic forest planning model, Woodstock, using a parsimonious set of climate-sensitive stand yield tables and transition rules derived from the PICUS stand simulation model, calibrated for the Acadian forest region. Climate-sensitive yield tables were generated for a baseline and two climate forcing scenarios to dynamically capture climate effects on forest growth and composition. Initial forest conditions were grouped into four age classes for each stand type, with future conditions determined by three transition periods. Stand simulations predicted a significant shift toward warm-adapted species, with red maple, white pine, and yellow birch becoming more dominant, while cold-adapted species like balsam fir and spruce declined by 2120. Under high climate forcing, the merchantable wood volume is projected to decrease by 50%, indicating potential shortages and economic risks. The incorporation of climate change uncertainty into strategic forest planning is essential to reduce the risk of overutilization of forest resources. This research offers a novel and practical approach to integrating climate change into forest planning models.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".