Vulnerable caribou and moose populations display contrasting responses to mountain pine beetle outbreaks and management.
Bibliographic record
Abstract
Rising global temperatures and changing landscape conditions have led to widespread mountain pine beetle (MPB) outbreaks in western North America. Extensive management has been implemented in response, via forest harvesting and prescribed burning. However, the impacts of MPB and MPB-management on ungulate populations, particularly caribou and moose, remain poorly studied. Given the differing specialisations of these species (caribou are habitat specialists, moose are generalists), their responses to these disturbances may vary. This could, in turn, lead to unintended, indirect effects (e.g., disturbance-mediated apparent competition, whereby increased moose presence results in increased predator presence, with resultant caribou declines). Unravelling these responses is crucial for informing MPB management and ensuring that applied actions do not exacerbate adverse effects. We assessed the effects of early-stage MPB-infestation, harvest, and fire, on habitat selection by caribou (boreal and central mountain designatable units) and moose in west-central Alberta. We built resource selection functions and functional response models using GPS collar data collected 3-5 years after MPB infestation and found that responses varied between species. Caribou had complex seasonal responses to MPB, generally avoiding areas with more MPB disturbance in winter but using them in summer. Caribou typically avoided harvested and burned areas, though this was dependent on the overall degree of disturbance within their ranges. In contrast, moose had positive responses to both MPB and burned areas year-round. These findings suggest that MPB may negatively impact long-term winter habitat availability for threatened caribou populations, but may have positive impacts on moose habitat. However, moose use of MPB-impacted areas could further affect caribou by contributing to disturbance-mediated apparent competition. Synthesis and applications: The negative impacts of forest harvest and burning on caribou suggests that less intensive actions are required for MPB management within caribou ranges. While moose selectively used harvested and burned areas, it is possible that they may have adverse responses to cumulative disturbance over time. As caribou and moose responded to MPB soon after infestation, ongoing monitoring is required to detect MPB early and facilitate proactive management, though further study is needed to determine the most effective actions.
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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) | 0.001 | 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".