The impacts of forest management strategies for woodland caribou vary across biogeographic gradients
Bibliographic record
Abstract
Loss or alteration of forest ecosystems due to anthropogenic activities has prompted the need for mitigation measures aimed at protecting habitat for forest-dependent wildlife. Understanding how wildlife respond to such management efforts is essential for achieving conservation targets. Boreal caribou are a species of conservation concern due to the impacts of human induced habitat alteration; however the effects of habitat management activities are poorly understood. We assessed the relationship between large scale patterns in forest harvesting and caribou spatial behaviours over a 20-year period, spanning a change in forest management intended to protect caribou habitat. Caribou range size, fidelity, and proximity to forest harvests were assessed in relation to change in harvest patterns through time and across two landscapes that varied widely in natural disturbance and community dynamics. We observed up to 89% declines in total area harvested within our study areas, with declining harvest size and aggregation. These landscape outcomes were coincident with caribou exhibiting greater fidelity and spacing farther away from disturbances at smaller scales, hypothesized to be beneficial for acquiring food and avoiding predators. Contrary to our expectation that the large scale maintenance of habitat patches would permit caribou to space away from disturbance, their proximity to harvest blocks at the population range scale did not decrease through time, suggesting that movement toward landscape recovery for caribou in previously harvested regions will likely stretch over multiple decades. Caribou spatial behaviours varied across the two landscapes independently of forest management. Our study underlines the importance of understanding both changes in industry demands, as well as natural landscape variation in habitat when managing wildlife.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".