An assessment of Alberta's strategy for controlling mountain pine beetle outbreaks
Bibliographic record
Abstract
Abstract Managing forest pests at landscape scales presents one of the greatest challenges in applied ecology. Since 2004, the Canadian province of Alberta spent more than 500 million dollars managing mountain pine beetle (MPB) populations—primarily by detecting and burning infested trees—yet the effectiveness of this intervention remains uncertain. Using a statistical modelling framework coupled with long‐term field data, we examined how direct control measures, severe winters and host‐tree depletion shaped the trajectory of Alberta's MPB outbreak between 2009 and 2020. Control efforts reduced cumulative tree mortality by 79% (95% predictive interval: 55%–89%), preventing approximately 1.8 (0.77–3.8) trees per hectare from being killed during the study period. Cold winters had minimal direct impact on tree mortality, but worked synergistically with control efforts to collapse beetle populations around 2020. Host‐tree depletion played a negligible role. Each infestation that was removed (cost: 320 CAD) prevented the loss of approximately seven additional trees in the long term (95% predictive interval: 2.4–14), demonstrating potential cost‐effectiveness. Model projections show high uncertainty in future outbreak severity, with potential tree mortality ranging from 0.37 to 8.6 trees per hectare over an 11‐year period under a no‐control scenario, and an alternative model suggesting even wider variation (~1–40 trees per hectare). Synthesis and applications. Our findings tentatively support a ‘wait it out’ management strategy for forest pests with strong Allee effects, where small populations face experience negative growth. Moderate but sustained control efforts prevent explosive population growth until extreme winter conditions deliver a final blow, offering a cost‐effective approach that enables complete outbreak suppression without indefinite intervention.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".