Consequences of various landscape-scale ecosystem management strategies and fire cycles on age-class structure and harvest in boreal forests
Bibliographic record
Abstract
At the landscape scale, one of the key indicators of sustainable forest management is the age-class distribution of stands, since it provides a coarse synopsis of habitat potential, structural complexity, and stand volume, and it is directly modified by timber extraction and wildfire. To explore the consequences of several landscape-scale boreal forest management strategies on age-class structure in the Mauricie region of Quebec, we used spatially explicit simulation modelling. Our study investigated three different harvesting strategies (the one currently practiced and two different strategies to maintain late seral stands) and interactions between fire and harvesting on stand age-class distribution. We found that the legacy of initial forested age structure and its spatial configuration can pose short- (<50 years) to medium-term (150300 years) challenges to balancing wood supply and ecological objectives. Also, ongoing disturbance by fire, even at relatively long cycles in relation to historic levels, can further constrain the achievement of both timber and biodiversity goals. For example, when fire was combined with management, harvest shortfalls occurred in all scenarios with a fire cycle of 100 years and most scenarios with a fire cycle of 150 years. Even a fire cycle of 500 years led to a reduction in older forest when its maintenance was not a primary constraint. Our results highlight the need to consider the broad-scale effects of natural disturbance when developing ecosystem management policies and the importance of prioritizing objectives when planning for multiple resource use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".