Habitat selection by black bears in an intensively logged boreal forest
Bibliographic record
Abstract
Extensive logging of the boreal forest rejuvenates landscapes once dominated by old-growth stands. As black bear ( Ursus americanus Pallas, 1780) fitness and behavior are known to be primarily related to the abundance of shade-intolerant soft mast species in northern forests, we hypothesized that logging will influence habitat and space use patterns of black bears. We used VHF telemetry on 12 female black bears in the Réserve faunique des Laurentides (Quebec, Canada) to investigate seasonal patterns of habitat selection in an exploited heterogeneous boreal landscape at different spatial scales. Habitat characterization based on seven forest cover types allowed us to compare the productivity of key forage species in various post-logging age classes. Regenerating stands (6–20 years old) had the uppermost ground vegetation cover, providing both the highest density and a high biomass of berries. Black bears preferred regenerating stands (6–20 years old) and avoided mature coniferous forests inside their home range. Home-range size was inversely related to the proportion of regenerating stands (6–20 years old). Intensive logging influenced black bear habitat and space use patterns, and presumably their population dynamics. An increase in the habitat quality of an opportunistic predator poses great concern for conservation, especially for forest-dwelling woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".