Mapping the functional connectivity of predation by large carnivores in the Canadian Rocky Mountains
Bibliographic record
Abstract
After decades of persecution, large carnivores are returning to human-occupied landscapes in many areas of North America and Europe. To ensure the safety of both people and wildlife, we need to understand how these animals perceive and select habitat as they navigate through developed area. Here, I investigate habitat selection by wolves and cougars in a 20,000 km 2 landscape characterized by a marked gradient of urban and industrial development. I used data from over 15,000 km of wolf and cougar movement pathways, and from over 750 kill sites made by these two predators. These data were collected during the past 22 winters in Banff National Park (Alberta, Canada) and environs. Using GIS software, I created habitat selection models by comparing ‘used’ resources (e.g., topography, habitat type) found along movement pathways and at kill sites, with random points located <500 m from used sites. I then incorporated the results of these habitat selection models into a connectivity analysis to quantify the predicted flow of both movement and predation across the landscape. These results illustrate the extent to which ‘connectivity’ depends on species (e.g., cougars vs. wolves), behavior (movement vs. foraging), and proximity to people. These results are being used to inform policy on land-use planning in areas where large carnivores and people co-exist.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".