Supplementing Environmental Assessments with Cumulative Effects Scenario Modeling for Grizzly Bear Connectivity in the Bow Valley, Alberta, Canada
Bibliographic record
Abstract
Persistence of sensitive wildlife in populated regions requires conservation strategies that address gradual expansion of development footprint and human activity. The project-based environmental assessment regime for municipal development is poorly suited to provide necessary strategic perspective, given its focus on local and short-term impacts. We used the ALCES cumulative effects model to strategically assess impacts to grizzly bears (Ursus arctos) in the Bow Valley of Alberta, Canada. Landscape simulation mapped expansion of past and potential future development footprint in the region over multiple decades. Consequences to movement connectivity for grizzly bears were estimated by applying a least cost path analysis to the landscape simulation. An index of recreational activity was derived from fitness tracking data and integrated with the landscape simulation to model change in recreational activity through time. Maps of grizzly bear connectivity and recreational activity were combined to calculate human-bear conflict risk. The analysis suggests that connectivity has been altered through displacement to upslope areas by settlement expansion, such that surrounding natural areas have become important for grizzly bear connectivity. These areas are also popular for outdoor recreation, resulting in elevated human-bear conflict risk which can be expected to increase if development and human activity continue to expand in high connectivity areas. Conservation of wildlife in populated regions will be supported by broadening the scope of environmental assessment to address cumulative effects of development footprint and human activity over large spatial and temporal scales.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".