Comparative habitat selection of black bears, wolves, and boreal caribou in an area of low anthropogenic disturbance
Bibliographic record
Abstract
Across boreal ecosystems, landscape disturbance alters predator–prey dynamics by modifying habitat structure, movement corridors, and spatial overlap among species. For woodland caribou ( Rangifer tarandus caribou ), these shifts are largely driven by anthropogenic disturbance and have contributed to population declines through increased predation risk. However, these dynamics are often studied in heavily disturbed regions. We examined seasonal habitat selection using resource selection functions for black bears ( Ursus americanus ) and wolves ( Canis lupus ) in a fire-dominated boreal landscape with low anthropogenic disturbance (Saskatchewan Boreal Shield). Using latent selection difference modelling, we assessed spatial overlap between each predator and caribou during the calving season, when calves are most vulnerable. Black bears selected mixed coniferous-deciduous forest early in the season, young coniferous stands in fall, burns 0–20 years post-fire, and areas closer to linear features, while avoiding black spruce swamp and burns >30 years. Wolves consistently selected open muskeg, burns 31–40 years post-fire, and areas closer to linear features, while avoiding young jack pine and burns >40 years. During the calving season, black bears and wolves were more likely to select mixed coniferous-deciduous forest and burns 11–40 years old, and less likely to select black spruce swamp, open muskeg, and young-mid jack pine compared to caribou. Both predators were more likely to select areas closer to linear features than caribou. These findings provide a critical benchmark for understanding how disturbance patterns may influence predator–prey habitat selection and highlight the need for management that considers natural and anthropogenic processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".