Differential responses of woodland caribou to fire and forestry across boreal and montane ecosystems—a literature review
Bibliographic record
Abstract
Abstract Ecosystem-based management (EBM) is a landscape-level management and planning process that is common across North America. A primary tenet of EBM is that the area and intensity of anthropogenic disturbance should mimic the historical natural disturbance of the focal ecosystem. Biodiversity should persist, at least at a coarse scale, where anthropogenic disturbance, such as forest harvesting, matches natural disturbance. However, EBM is failing some species, particularly those that are dependent on old forest. Across many areas of Canada, woodland caribou (Rangifer tarandus caribou) are declining because of the direct and indirect effects of habitat loss and fragmentation. This is even though forest management often follows the principles of EBM. We conducted a qualitative comparison of the responses of woodland caribou to wildfire and forest harvesting, considering a broad range of responses, including habitat selection and distribution, forage, movement patterns, and population dynamics. We found that while wildfire and forest harvesting both influence caribou, the negative effects are generally greater following forest harvesting. For example, wildfire and forest harvesting result in the loss of habitat, but caribou are more likely to shift, abandon or contract their range in response to harvest. The literature also suggested a stronger negative population response of caribou to forest harvest when compared to wildfire. This difference could be the result of greater residual forest structure associated with wildfire as well as the extensive resource roads that are necessary for forestry operations. Although there is sound theoretical support for EBM, the practice, as implemented, may not be effective for maintaining the habitat and ultimately populations of woodland caribou.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".