Concentrating anthropogenic disturbance to balance ecological and economic values: applications to forest management
Bibliographic record
Abstract
To maintain healthy ecosystems, natural-disturbance-based management aims to minimize differences between unmanaged and managed landscapes. Two related approaches may help accomplish this goal, either applied together or in isolation: (1) concentrating anthropogenic disturbance through zoning (with protected areas and intensive management); and (2) emulating natural disturbances. The purpose of this paper is to examine the effects of these two approaches, applied both in isolation and in combination, on the structure of the forest landscape. To do so, we use a spatially explicit landscape simulation model on a large fire-dominated landscape in eastern Canada. Specifically, we examine the effects of (1) increasing the maximum size of logged stands (cutblocks) to better emulate the full range of fire sizes in a fire-dominated landscape, (2) increasing protected areas, and (3) adding aggregated or dispersed intensive wood production areas to the landscape in addition to protected areas (triad management). We focus on maximizing the amount and minimizing the fragmentation of old-growth forest and on reducing road construction. Increasing maximum cutblock size and adding protected areas led to reduced road construction, while the latter also resulted in less fragmentation and more old growth. Although protected areas led to reduced harvest volume, the addition of an intensive production zone (triad management) counterbalanced this loss and resulted in more old growth than equivalent scenarios with protected areas but no intensive production zone. However, we found no differences between aggregated and dispersed intensive wood production. Our results imply that differences between unmanaged and managed landscapes can be reduced by concentrating logging efforts through a combination of protected areas and intensive wood production, and by creating some larger cutblocks. We conclude that the forest industry and regulators should therefore seek to increase protected areas through triad management and consider increasing maximum cutblock size. These results add to a growing body of literature indicating that intensive management on a small part of the landscape may be better than less intensive management spread out over a much larger part of the landscape, whether this is in the context of forestry, agriculture, or urban development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".