Vulnerable caribou and moose populations display varying responses to mountain pine beetle outbreaks and management
Bibliographic record
Abstract
Abstract Rising global temperatures and changing landscape conditions have led to widespread mountain pine beetle (Dendroctonus ponderosae) outbreaks in western North America. Pine beetle management is typically implemented to mitigate economic losses, but its effects on wildlife, particularly ecologically important species like caribou (Rangifer tarandus) and moose (Alces alces), warrant greater attention. We assessed the effects of early‐stage pine beetle infestation, timber harvest, and fire on habitat selection by caribou (boreal and central mountain designatable units) and moose in west‐central Alberta, Canada. Using global positioning system (GPS) collar data collected 3–5 years after infestation, we developed resource selection functions and functional response models. Caribou exhibited seasonally variable responses, generally avoiding pine beetle‐affected areas in winter but selecting them in summer. They also avoided harvested and burned areas, though this avoidance depended on overall disturbance levels within their ranges. Moose displayed sex‐specific responses to pine beetle infestations and associated management: females avoided pine beetle‐affected areas but selected burned sites year‐round, while males showed the opposite pattern. These findings suggest that pine beetle disturbances may negatively affect caribou and female moose winter habitat availability while simultaneously enhancing conditions for male moose. Further research is needed to disentangle the individual and cumulative effects of pine beetle management actions versus general timber harvests and wildfires, as these disturbances may be compounding rather than acting in isolation.
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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 | 0.000 | 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".