Seasonal habitat use and selection by grizzly bears in Northern British Columbia
Bibliographic record
Abstract
Abstract We defined patterns of habitat use and selection by female grizzly bears (Ursus arctos) in the Besa‐Prophet watershed of northern British Columbia. We fitted 13 adult females with Geographic Positioning System (GPS) radio‐collars and monitored them between 2001 and 2004. We examined patterns of habitat selection by grizzly bears relative to topographical attributes and 3 potential surrogates of food availability: land‐cover class, vegetation biomass or quality (as measured by the Normalized Difference Vegetation Index), and selection value for prey species themselves (moose [Alces alces], elk [Cervus elaphus], woodland caribou [Rangifer tarandus], Stone's sheep [Ovis dalli stonei]). Although vegetation biomass and quality, and selection values for prey were important in seasonal selection by some individual bears, land‐cover class, elevation, aspect, and vegetation diversity most influenced patterns of habitat selection across grizzly bears, which rely on availability of plant foods and encounters with ungulate prey. Grizzly bears as a group avoided conifer stands and areas of low vegetation diversity, and selected for burned land‐cover classes and high vegetation diversity across seasons. They also selected mid elevations from what was available within seasonal ranges. Quantifying relative use of different attributes helped place selection patterns within the context of the landscape. Grizzly bears used higher elevations (1,595 ± 31 m SE) in spring and lower elevations (1,436 ± 27 m) in fall; the range of average elevations used among individuals was highest (500 m) during the summer. During all seasons, grizzly bears most frequented aspects with high solar gain. Use was distributed across 10 land‐cover classes and depended on season. Management and conservation actions must maintain a diverse habitat matrix distributed across a large elevational gradient to ensure persistence of grizzly bears as levels of human access increase in the northern Rocky Mountains. © 2011 The Wildlife Society.
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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 | 0.001 | 0.001 |
| 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".