Effects of uncertainties about stand-replacing natural disturbances on forest-management projections
Bibliographic record
Abstract
Designing policies for long-term forest management is difficult, in part because ecological processes that drive forest structure and composition interact strongly, both spatially and temporally, with the many values we want to obtain from the forest. Using the Robson Valley in east-central British Columbia as a study area, we developed a spatio-temporal landscape model to assess the effects of uncertainties about stand-replacing natural disturbance regimes on indicators related to the sustainability of forest harvesting and biodiversity. Results show that key timber policy indicators were relatively less sensitive to natural disturbance regime parameters than were the biodiversity indicators of seral stage distribution and tree species composition. The other biodiversity indicator we examined, structural connectivity among old-forest patches, was among those indicators least sensitive to any of the parameters we varied. Other timber supply indicators— including non-recoverable losses, and volumes and areas disturbed—were the most sensitive to both the particular natural disturbance agent chosen and to the parameters describing its behaviour. Projections of a range of scenarios for present and alternative natural disturbance and management regimes for the study area show that most indicators varied from less than 1% up to 93% from the value of the present management/disturbance regime. Generally, three alternative management policies had weak-to-moderate capabilities of reducing effects of natural disturbances. Despite the range of uncertainties explored, the results provided little indication that, at the scale of the whole study area, current timber-harvesting targets are not sustainable over the long term. However, our findings highlight the lack of knowledge about the future, particularly about changes in climate, resulting in significant uncertainty about the future condition of the forest and about future forest-management opportunities.
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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 | 0.000 |
| 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 | 0.000 |
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 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".