Black bear adaptation to low productivity in the boreal forest
Bibliographic record
Abstract
Long snowy winters combined with a short growing season make boreal forests an unproductive environment that challenges black bears (Ursus americanus). We used resource selection functions (based on GPS telemetry of 16 bears), diet analysis, surveys of plant phenology, and vegetation inventories to study adaptations of black bears to boreal forest. Because plants are heavily favoured in bear diets, we expected diet composition to reflect their temporal availability. We anticipated that bears would make choices among land cover types and specific topographic conditions in order to select plants that would fulfil their energetic demands throughout the active period. We also predicted that bears would select habitats modified by insect outbreaks or forest harvesting because these disturbances likely increase resource availability. We found supporting evidence for all of our predictions. (1) Bear diet was closely linked to plant availability. (2) Bears made seasonal altitudinal movements and selected sites according to solar irradiation, tracking the availability of the most digestible plants. Accordingly, bears relied on high-altitude graminoids in spring, a variety of fleshy fruits in summer, and mainly Sorbus americana berries in autumn. (3) Land covers resulting from clearcutting and insect outbreaks increased resource availability for bears and were preferred from summer to autumn. In our study area, black bears are considered predators of a threatened caribou (Rangifer tarandus) population. Even so, we did not find any caribou remains in bear scats. However, our results show that forestry practices, such as clearcutting near the caribou range, could contribute to increased bear presence and thus increase the probability of predation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".