Climate change adaptation and sustainable forest management in the boreal forest
Bibliographic record
Abstract
Climate change will pose increasing challenges to forest managers working to achieve sustainable forest management in the boreal forest. A logical starting point for climate change adaptation is to proactively identify management practices and policies that have a higher likelihood of achieving management objectives across a wide range of potential climate futures. This research implemented an approach to identifying such measures by tapping into the experiential knowledge base of local forest practitioners. The assessment was organized according to a structured decision-making (SDM) approach. Northern forest practitioners consider the goals of climate change adaptation to be synonymous with those of sustainable forest management indicating that the criteria for the conservation and sustainable management of boreal forests as defined by the Montréal Process are suitable objectives against which the performance of alternative adaptation options can be assessed. The case study area for this research was the Champagne and Aishihik Traditional Territory of southwest Yukon where a climatically-driven, large-scale spruce bark beetle disturbance has been driving forest management planning yet climate change considerations have not been directly addressed in the planning process. Twenty-four adaptation options were identified as being important to implement in forest development areas to achieve regional goals and objectives of forest management across three scenarios of climate change. In addition, the performance of alternative strategies to re-establish forests was assessed. Results indicate that the applicability of alternative forest renewal adaptation strategies is strongly related to the objectives of forest management which differed across the forest management planning area. However, since none of the strategies were judged to perform highly across any of the scenarios of climate change, additional work is needed to explore whether a threshold of acceptability can be met even with the adoption of adjustments to forest management policies and practices. If not, management objectives themselves may need to be revised. An extensive list of research and monitoring needs were also identified, an indication that climate change is providing the imperative for a more comprehensive research and monitoring program to support the sustainable management of forest resources in this region. The next steps in a SDM approach are to implement adaptation options and strategies deemed appropriate and to monitor their performance in achieving management objectives within an adaptive management context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".