Quantifying grizzly bear (Ursus arctos) habitat selection for a seasonal resource, the Canadian buffaloberry (Sheperdia canadensis) in southern British Columbia
Bibliographic record
Abstract
Wildlife conservation requires timely information on the availability and use of key habitats and resources by a species. Among large terrestrial carnivores in North America, grizzly bears (Ursus arctos) are experiencing substantial reductions in range and population size due to habitat loss and anthropogenic activities. To support grizzly bear conservation, this research will quantify the impacts of anthropogenic disturbance and habitat characteristics on grizzly bear habitat selection for an essential seasonal resource, buffaloberry (Sheperdia canadensis). Using grizzly bear telemetry data across southern British Columbia, Canada, this research first develops a resource selection function to predict buffaloberry selection based on the influence of disturbance and habitat characteristics. Grizzly bear recursive movements were then quantified using a revisitation analysis to test competing hypotheses related to the influence of buffaloberry availability, resource availability and disturbance conditions on foraging behaviour during the buffaloberry ripe period. The probability of selection for habitat with buffaloberry was widely distributed throughout southern BC, with notable clusters of high probabilities. Six variables influenced the probability of selection: available kilocalories of buffaloberry, elevation, distance to roads, aspect, terrain ruggedness index, and canopy height. Selection for habitat with buffaloberry generally increased as available kilocalories increased, between 400 – 1500m and 2500 – 2700m elevation, occurred near roads but increased as the distance from a road increased, was highest on northern and southern aspects, in habitat with low terrain ruggedness, and moderate canopy height. The number of revisits to a site increased as the percent cover of fruiting buffaloberry increased. This work has several direct and indirect applications to the management of grizzly bears in southern BC. Our research identified that the most important factors influencing grizzly bear habitat selection for buffaloberry was iv buffaloberry productivity (i.e., moderate to high available kilocalories of buffaloberry and high percent cover of fruiting buffaloberry), highlighting the need to create more areas that foster understory growth and encourage buffaloberry production. Analyzing the drivers of grizzly bear habitat selection for buffaloberry provides a better understanding of the impacts of anthropogenic disturbance and habitat quality on behaviour helping to inform pro-active and adaptive grizzly bear conservation.
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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".