Wildlife, fire, and forestry: Understanding the spatial and temporal relationships between caribou habitat and disturbance
Bibliographic record
Abstract
Across the boreal forest, timber harvesting and wildfire convert mature forests to early seral stands resulting in habitat loss for specialists like woodland caribou ( Rangifer tarandus caribou ) and habitat gain for generalists like moose ( Alces americanus ) and bears (black bears: Ursus americanus , grizzly bears: Ursus arctos ). However, there have been few studies on how post-disturbance vegetation communities differ in their value as habitat for these large wildlife species and whether differences vary among disturbance and ecosystem types. We investigated the differential effects of clearcut harvest and wildfire on the habitat of caribou, moose, and bears across the boreal and foothills forests of Alberta, Canada. During 2021 and 2022, we collected tree and understory data from 251 harvested and 264 burned stands (0–40 years post-disturbance), as well as 256 stands with recent caribou use (>40 years post-disturbance). We used generalized linear models to quantify availability of caribou, moose, and bear forage as a function of forest attributes (e.g., basal area, coarse woody debris, soil depth), and assessed differences among harvest, wildfire, and caribou use sites. We found that forest attributes that promoted forage for one species limited forage of another. For example, basal area of deciduous trees was positively related to moose forage and negatively related to caribou winter forage. Our results demonstrate that regardless of disturbance type, regenerating forests can provide seasonal forage for caribou, moose, and bears. Effective habitat management will need to consider not only the dynamic availability of forage following disturbance, but also how these changes in forage influence the spatial interactions herbivores and predators.
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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 | 0.001 | 0.001 |
| 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".