Movement of outbreak populations of mountain pine beetle: influences of spatiotemporal patterns and climate
Why this work is in the frame
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Bibliographic record
Abstract
Insect outbreaks exert landscape‐level influences, yet quantifying the relative contributions of various exogenous and endogenous factors that contribute to their pattern and spread remains elusive. We examine an outbreak of mountain pine beetle covering an 800 thousand ha area on the Chilcotin Plateau of British Columbia, Canada, during the 1970s and early 1980s. We present a model that incorporates the spatial and temporal arrangements of outbreaking insect populations, as well as various climatic factors that influence insect development. Onsets of eruptions of mountain pine beetle demonstrated landscape‐level synchrony. On average, the presence of outbreaking populations was highly correlated with outbreaking populations within the nearest 18 km the same year and local populations within 6 km in the previous two years. After incorporating these spatial and temporal dependencies, we found that increasing temperatures contributed to explaining outbreak probabilities during this 15 yr outbreak. During collapse years, landscape‐level synchrony declined while local synchrony values remained high, suggesting that in some areas host depletion was contributing to population decline. Model forecasts of outbreak propensity one year in advance at a 12 by 12 km scale provided 80% accuracy over the landscape, and never underestimated the occurrence of locally outbreaking populations. This model provides a flexible approach for linking temperature and insect population dynamics to spatial spread, and complements existing decision support tools for resource managers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it