A framework to optimize the restoration and retention of large mature forest tracts in managed boreal landscapes
Bibliographic record
Abstract
The decreasing abundance of mature forests and their fragmentation have been identified as major threats for the preservation of biodiversity in managed landscapes. In this study, we developed a multi-level framework to coordinate forest harvestings so as to optimize the retention or restoration of large mature forest tracts in managed forests. We used mixed-integer programming for this optimization, and integrated realistic management assumptions regarding stand yield and operational harvest constraints. The model was parameterized for eastern Canadian boreal forests, where clear-cutting is the main silvicultural system, and is used to examine two hypotheses. First, we tested if mature forest tract targets had more negative impacts on wood supplies when implemented in landscapes that are very different from targeted conditions. Second, we tested the hypothesis that using more partial cuts can be useful to attenuate the negative impacts of mature forest targets on wood supplies. The results indicate that without the integration of an explicit mature forest tract target, the optimization leads to relatively high fragmentation levels. Forcing the retention or restoration of large mature forest tracts on 40% of the landscapes had negative impacts on wood supplies in all types of landscapes, but these impacts were less important in landscapes that were initially fragmented. This counter-intuitive result is explained by the presence in the models of an operational constraint that forbids diffuse patterns of harvestings, which are more costly. Once this constraint is applied, the residual impact of the mature forest tract target is low. The results also indicate that partial cuts are of very limited use to attenuate the impacts of mature forest tract targets on wood supplies in highly fragmented landscapes. Partial cuts are somewhat more useful in landscapes that are less fragmented, but they have to be well coordinated with clearcut schedules in order to contribute efficiently to conservation objectives. This modeling framework could easily be adapted and parameterized to test hypotheses or to optimize restoration schedules in landscapes where issues such as forest fragmentation and the abundance of mature or old-growth forests are a concern.
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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".