Climate change could alter the distribution of mountain pine beetle outbreaks in western Canada
Bibliographic record
Abstract
Climate change can markedly impact biology, population ecology, and spatial patterns of eruptive insects due to the direct influence of temperature on insect development and population success. The mountain pine beetle Dendroctonus ponderosae (Coleoptera: Curculionidae), is a landscape‐altering insect that infests forests of North America. Abundant availability of host trees due to altered disturbance regimes has facilitated an unprecedented, landscape‐wide outbreak of this pest in British Columbia and Alberta, Canada, during the past decade. A previous outbreak in the 1980s, in central British Columbia, collapsed due to host depletion and extreme cold weather events. Despite the importance of such extreme weather events and other temperature‐related signals in modulating an outbreak, few landscape‐level models have studied the associations of extreme cold events with outbreak occurrences. We studied the individual associations of several biologically‐relevant cold temperature variables, and other temperature/degree‐day terms, with outbreak occurrences in a spatial‐temporal logistic regression model using data from the current outbreak. Timing, frequency, and duration of cold snaps had a severe negative association with occurrence of an outbreak in a given area. Large drops in temperature (>10°C) or extreme winter minimum temperatures reduced the outbreak probability. We then used the model to apply eight different climate change scenarios to the peak year of the current outbreak. Our scenarios involved combinations of increasing annual temperature and different variances about this trend. Our goal was to examine how spatial outbreak pattern would have changed in the face of changing thermal regime if the underlying outbreak behaviour remained consistent. We demonstrate that increases in mean temperature by 1°C to 4°C profoundly increased the risk of outbreaks with effects first being manifested at higher elevations and then at increasing latitudes. However, increasing the variance associated with a mean temperature increase did not change the overall trend in outbreak potential.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".