Mitigating the Effects of Human Activity on Grizzly Bears (Ursus arctos) in Southwestern Alberta.
Bibliographic record
Abstract
Anthropogenic habitat loss and alteration, as well as human-caused mortalities associated with increasing access, threaten grizzly bear populations across much of their North American range. This research investigates strategies for mitigating the negative effects of human activities on grizzly bears in southwestern Alberta. First, an optimization approach was used to prioritize sites for both protection and restriction while also considering landscape composition. Seasonal habitats where bears forage were balanced against proximity to roads, which are associated with mortality risk, to identify priority source- (high quality, low risk) and sink-like (high quality, high risk) habitats. Most sink-like sites (63%) were associated with unimproved roads or truck trails and are the best candidates for decommissioning and restoration efforts. Approximately 75% of priority source-like sites are currently unprotected, and overlap between protected areas and source-like sites was geographically biased. Second, the viability of using wildlife habitat enhancements to increase local food supply for grizzly bears in clearcuts was assessed. Specifically, I conducted planting trials of seedlings (plugs) for three important late-season fruiting shrubs and monitored their survival and growth over two growing seasons. The effects of soil nutrient amendments, exclosures, initial seedling condition, and environmental factors (elevation and terrain) on seedling growth were considered. A. alnifolia had the highest survival rate, although may not be as effective as S. canadensis and V. membranaceum in the long term due to browse preferences. Soil nutrient amendments reduced survival rates, whereas exclosures increased survival rates. Survival rates for S. canadensis and A. alnifolia along elevation gradients were inconsistent with expected niche spaces for both species, suggesting that knowledge of their natural niche spaces along the elevation gradient alone may not be sufficient to identify sites where they have the greatest chances of success. Management of sustainable grizzly bear populations should include measures that reduce the negative effects of human activities. Access management will be a critical component of this, and should be prioritized to areas where conflicts are most likely to occur, or to proactively protect secure, high quality habitats. As the prevalence of natural forest openings continues to decline, wildlife habitat enhancements in disturbed areas with open canopies, including forest harvests, have the potential to locally increase late-season food supply for grizzly bears and should be further explored.
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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) | 0.000 | 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 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".