Comparing landscape partitioning approaches to protect wildlife habitat in managed forests
Bibliographic record
Abstract
Industrial forestry activities can increase landscape fragmentation, impacting wildlife populations, particularly Canada’s woodland caribou, Rangifer tarandus caribou. To protect caribou in areas with forestry activities, the province of Ontario, Canada, implemented a Dynamic Caribou Harvest Schedule (DCHS). The DCHS spatially aggregates harvest disturbance into regions and distributes them across the landscape to maintain forest patch size–age distributions consistent with a natural variation range. However, the DCHS may negatively impact the cost of timber supply. We compared the DCHS with an alternative zoning approach that assigned the harvest deferral and operational management zones within a large forest area. We compared these approaches using an optimization model that combined harvest scheduling, access road construction, and caribou protection sub-problems. We formulated the protection of caribou habitat and road construction as network flow problems, while the harvesting problem incorporated the ecological constraints prescribed by the forest management plan. We compared the DCHS and zoning approaches in the Wabadowgang Noopming Forest of Ontario, a boreal area within the caribou distribution zone. For the same volume of sustainable harvest, the zoning approach protected less total area but more habitat and old-growth stands over the long term, and yielded lower timber costs by 1.2–2.2 $·m−3 than the DCHS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".