Grizzly bear selection of avalanche chutes: Testing the effectiveness of forest buffer retention
Bibliographic record
Abstract
Abstract In mountainous areas with sufficient snowfall, avalanche chutes are an important component of grizzly bear (Ursus arctos) habitat. Therefore, regional land‐use plans have recommended retaining adjacent forest buffers to maintain security and thus reduce potential impacts of clearcut forest harvesting. Our objective was to determine if forest buffers affected selection of avalanche chutes by grizzly bears, while accounting for factors such as vegetation composition and other physical attributes. We used radio‐location data from 61 grizzly bears collected between 1994 and 2000 in southern British Columbia, mapped a sample of avalanche chutes (1,045), and quantified the amount of forb, shrub, tree, and non‐vegetated cover within each chute. We also measured forested buffer width on each side of the chute, solar radiation, chute size, chute frequency (no. of chutes/km), and the area of clearcut logging adjacent to chutes. Each avalanche chute was the sample unit and the number of grizzly bear radiolocations was the dependent variable. We found that natural biophysical attributes were the strongest factors predicting the level of avalanche chute use by bears. Frequency of large chutes (>100 m wide), chute area, forb content, and solar radiation all positively affected use by bears. Larger avalanche chutes had a higher proportion of forb cover than smaller chutes, and more of these large chutes per unit area provided increased forage opportunities. Based on multivariate analyses, forested buffer width or the amount of clearcut logging were not strong factors predicting the level of use. However, a post hoc univariate analysis revealed that clearcut logging reduced the amount of bear use of the best avalanche chutes (large and abundant chutes). Furthermore, because a portion of our study area contained logging but no vehicle traffic, we concluded that it was the removal of tree cover, rather than displacement by vehicles, that caused the observed pattern. Although our multivariate models did not perform well using independent validation in a different geographic area, 4 factors were consistently important (large and abundant chutes, forb content, with a negative but weaker influence of clearcutting), suggesting broad applicability of these factors in mountainous ecosystems. © 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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".