Applying Resilience Concepts in Forest Management: A Retrospective Simulation Approach
Bibliographic record
Abstract
Increasing the resilience of ecological and sociological systems has been proposed as an option to adapt to changing future climatic conditions. However, few studies test the applicability of those strategies to forest management. This paper uses a real forest health incident to assess the ability of forest management strategies to affect ecological and economic resilience of the forest. Two landscape scale strategies are compared to business as usual management for their ability to increase resilience to a climate-change induced mountain pine beetle outbreak in the Kamloops Timber Supply Area, British Columbia, Canada for the period 1980 to 2060. Proactive management to reduce high risk species while maintaining or increasing diversity through reforestation was found to be more resilient in terms of the metrics: post-disturbance growing stock, improved volume and stability of timber flow, and net revenue. However, landscape-scale indicators of diversity were little affected by management. Our results were robust to uncertainty in tree growth rates and timber value and show that adapting to climate change through improving the resilience of forested landscapes is an economically viable option.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".