Biological barriers to forest pest invasions: A novel host tree slows mountain pine beetle range expansion
Bibliographic record
Abstract
Following widespread outbreaks across western North America, mountain pine beetle recently expanded its range from British Columbia into Alberta. However, mountain pine beetle's eastward expansion across Canada has stalled unexpectedly, defying predictions of rapid spread through jack pine, a novel host tree. This study investigates the underlying causes of this deceleration using an integrative approach combining statistical modeling, simulations, and experimental data. We find that the slow spread is primarily due to mountain pine beetle's difficulty in finding and successfully attacking jack pine trees, rather than issues with reproduction or larval development. The underlying mechanism impeding beetle range expansion has been hypothesized to be lower pine volumes in eastern forests, which are primarily a consequence of lower stem density. However, our analysis suggests that jack pine's phenotype itself is the primary impediment. We propose that jack pine's smaller size, thinner phloem, and lower monoterpene concentrations result in weaker chemical cues during the host-finding and mass-attack stages of MPB's life cycle, ultimately leading to fewer successful attacks. These findings suggest a reduced risk of further eastward spread, but should be interpreted cautiously due to enormous policy implications and the inherent limitations of ecological forecasting.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".