Protecting wildlife habitat in managed forest landscapes—How can network connectivity models help?
Bibliographic record
Abstract
Abstract Industrial forestry in boreal regions increases fragmentation and may decrease the viability of some wildlife populations, particularly the woodland caribou, Rangifer tarandus caribou . Caribou protection often calls for changes in forestry practices, which may increase the cost and reduce the available timber supply. We present a linear programming model that assesses the trade‐off between habitat protection and harvesting objectives by combining harvest scheduling and optimal habitat connectivity problems. We formulate the habitat connectivity model as a network flow problem that maximizes the amount of habitat connected over a desired time span in a forested landscape, while the forestry objective maximizes net undiscounted revenues from timber harvest subject to even harvest flow and environmental sustainability constraints. We applied the approach to explore the trade‐off between caribou habitat protection and harvesting goals in the Armstrong‐Whitesand Forest, Ontario, Canada, a boreal forest area with prime caribou habitat. Our model also incorporates Dynamic Caribou Harvesting Scheduling (DCHS), a harvest policy currently in a place in Ontario that aims to balance the forest management and caribou protection goals in northern boreal regions. In our study area, the implementation of DCHS appears to have relatively minor impact on timber supply cost. By comparison, maximizing the protection of caribou habitat would lead to a noticeable increase of the mill gate timber cost by $3.3 m −3 on average, while enabling habitat protection in an additional 5.0%–9.5% of the range area. Our model is generalizable and can be adapted for assessing habitat recovery and harvest goals in other regions. Recommendations for Resource Managers: Incorporating the concept of long‐term habitat connectivity into forest planning can help reduce the negative impacts of harvest activities on caribou populations. Prioritizing habitat connectivity leads to a small increase in the overall harvest area because harvest has to be allocated to less productive and more geographically isolated sites to protect prime wildlife habitat containing old conifer stands. Maximizing the habitat protection would lead to a noticeable increase of the timber supply cost (by $3.3 m −3 on average), while enabling moderate increase of the protected habitat area (i.e., an additional 5.0%–9.5% of the range area). Implementation of Dynamic Caribou Harvest Schedules, which is the current harvesting policy in Ontario's boreal forests when caribou populations are present, causes only a minor increase of the timber supply cost in our study area.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".