Moose refugia from predation by wolves near mines in the Athabasca oil sands
Bibliographic record
Abstract
Areas near human disturbance may become prey refugia when predators avoid human activities more than their prey leading to decreased predation rates and/or increased prey population growth. Alberta’s Athabasca oil sands region (AOSR) is home to moose ( Alces alces ) and wolf ( Canis lupus ) populations and is characterized by extensive human disturbance including open pit mines, tailings ponds and industrial facilities. We examined the extent to which moose could be released from predation near Alberta’s Athabasca oil sands due to wolf avoidance of mining infrastructure. Using moose and wolves GPS telemetry, we compared the use of natural habitats and distance to mining features to the availability of these variables. We split mining features into high human-use facilities and low human-use pit mines and tailings ponds. We binned distance to mining features variables into distance buffers covering the range of moose home range diameters resulting in buffers of < 2.5 km, 2.5-5 km and 5-10 km. Moose models included an interaction between distance to mining features buffers and the distribution of wolves to assess whether moose exposure to wolves varies with proximity to human activity. We compared a habitat model including forest cover type, streams and rivers to a disturbance model using AIC. The model fitting habitat and distance to facilities was top-ranked for both species. Moose selection for areas near facilities was higher than wolves. Wolves avoided areas within 10 and 5 km of facilities but exhibited an equivocal response within 2.5 km. Moose exposure to wolves increased with distance to mines indicating that use of areas in proximity to human disturbance releases moose from predation by wolves. Human induced prey refugia could increase moose population growth and increase human-moose conflict. Additionally, moose dispersal out of the refuge areas could produce subsequent increases in the wolf population.
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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 | 0.000 | 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) | 0.001 | 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".