Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta
Bibliographic record
Abstract
Woodland caribou (Rangifer tarandus caribou) populations are declining across most of their range. Predation by wolves (Canis lupus) is believed to be the main proximate cause of these declines. However, it has been hypothesized that recent forestry and energy sector activity in caribou range ultimately might have caused population declines by altering wolf–caribou relationships. We tested the hypothesis that industrial linear features influence wolf movements in woodland caribou range in northeastern Alberta, resulting in increased wolf-caused caribou mortalities close to these features. Using step selection functions (SSF) and observed vs. simulated wolf movement paths, we found that wolf movement was influenced by natural linear features (rivers and streams) throughout the year, possibly because they provide ease of travel and high prey abundance. Wolf movement was further influenced by industrial linear features, but use of these features differed depending on line-type and season. Wolves showed strong selection for steps closer to conventional seismic lines during the snow-free season. Likewise, observed wolf movement paths followed conventional seismic lines more closely than simulated paths during snow-free months. Use of seismic lines as movement corridors might result in wolves hunting in caribou-preferred habitats (bogs and fens) more frequently than they did historically, particularly in the snow-free season when most caribou mortalities occur. However, we found no evidence that caribou mortalities occurred closer to industrial linear features than did live caribou. We conclude that wolf use of seismic lines increases predation risk for caribou close to these features, resulting in caribou avoidance of linear developments and thus functional loss of otherwise suitable habitat for caribou.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".