Industrial development alters wolf spatial distribution mediated by prey availability
Bibliographic record
Abstract
Abstract Increasing resource extraction and human activity are reshaping species' spatial distributions in human‐altered landscape and consequently shaping the dynamics of interspecific interactions, such as between predators and prey. To evaluate the effects of industrial features and human activity on the occurrence of wolves (Canis lupus), we used wildlife detection data collected in 2014 from an array of 122 remote wildlife camera traps in Alberta's Rocky Mountains and foothills near Hinton, Canada. Using generalized linear models, we compared the occurrence frequency of wolves at camera sites to natural land cover, industrial disturbance (forestry and oil/gas exploration), human activity (motorized and non‐motorized), and prey availability (moose, Alces alces; elk, Cervus elaphus; mule deer, Odocoileus hemionus; and white‐tailed deer, Odocoileus virginianus). Industrial block features (well sites and cutblocks) and prey (elk or mule deer) availability interacted to influence wolf occurrence, but models including motorized and non‐motorized human activity were not strongly supported. Wolves occurred infrequently at sites with high densities of well sites and cutblocks, except when elk or mule deer were frequently detected. Our results suggest that wolves risk using industrial block features when prey occur frequently to increase predation opportunities, but otherwise avoid them due to risk of human encounters. Effective management of wolves in anthropogenically altered landscapes thus requires the simultaneous consideration of industrial block features and populations of elk and mule deer.
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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 | 0.000 |
| 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) | 0.002 | 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 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".