Natural Disturbance-Based Forest Management: Moving Beyond Retention and Continuous-Cover Forestry
Bibliographic record
Abstract
Global forest area is declining rapidly, along with degradation of the ecological condition of remaining forests. Hence it is necessary to adopt forest management approaches that can achieve a balance between (1) human management designs based on homogenization of forest structure to efficiently deliver economic values and (2) naturally emerging self-organized ecosystem dynamics that foster heterogeneity, biodiversity, resilience and adaptive capacity. Natural disturbance-based management is suggested to provide such an approach. It is grounded on the premise that disturbance is a key process maintaining diversity of ecosystem structures, species and functions, and adaptive and evolutionary potential, which functionally link to sustainability of ecosystem services supporting human well-being. We review the development, ecological and evolutionary foundations and applications of natural disturbance-based forest management. With emphasis on boreal forests, we compare this approach with two mainstream approaches to sustainable forest management, retention and continuous-cover forestry. Compared with these approaches, natural disturbance-based management provides a more comprehensive framework, which is compatible with current understanding of multiple-scale ecological processes and structures, which underlie biodiversity, resilience and adaptive potential of forest ecosystems. We conclude that natural disturbance-based management provides a comprehensive ecosystem-based framework for managing forests for human needs of commodity production and immaterial values, while maintaining forest health in the rapidly changing global environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".