Changing grizzly bear space use and functional connectivity in response to human disturbance in the southern Canadian Rocky Mountains
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Understanding wildlife responses to human disturbance is essential for developing effective conservation and management strategies. Grizzly bears in the southern Canadian Rocky Mountains face increasing habitat alteration from roads, forest harvest, human settlements, and mining, which can alter the way animals move through the landscape. Deleterious effects on genetic exchange, demographic connectivity, and access to key resources can occur if movements are dramatically altered. We used integrated step‐selection functions (iSSF) to model movement and habitat selection for 109 GPS‐collared grizzly bears across an 85,000 km 2 multi‐use landscape in southeastern British Columbia and southwestern Alberta. We then simulated individual grizzly bear movements from fitted iSSFs to predict changes in population‐level space use and functional connectivity under the following scenarios: (1) without current levels of human disturbance, (2) under current conditions, and (3) with a defined increase in human disturbance. Bears avoided crossing highways but were attracted to areas alongside highways in areas with relatively low forage availability at a broad spatial scale, such as in Banff National Park and the Kananaskis region. Females generally avoided moving through towns in spring and summer, while males were more likely to do so. Additional footprints of proposed mines and expanded human settlements in a potential future scenario were predicted to further decrease functional connectivity for grizzly bears on top of prior connectivity losses from existing human disturbance. Our study builds upon existing work simulating animal space use from fitted iSSFs by incorporating individual‐level variation into population‐level simulations and by fitting functional responses that help capture broad‐scale variation in behavior and improve model transferability to new areas. Our results provide insights into grizzly bear movement and connectivity in an area of high conservation importance, and our predictive maps can be used to directly inform transboundary management actions and conservation planning.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it