Biological Barriers to Forest Pest Invasions: A Novel Host Tree Slows Mountain Pine Beetle Range Expansion
Bibliographic record
Abstract
Mountain pine beetle breached the Canadian Rocky Mountains-a former geographic barrier-initiating an eastward range expansion that threatens pine forests across North America. However, mountain pine beetle's expansion stalled unexpectedly in eastern Alberta, defying predictions of rapid spread through jack pine, a novel host tree. We investigated the mechanisms behind this slowed spread using an integrated methodology combining helicopter survey data, statistical modeling, simulations, and a consideration of experimental data. While previous hypotheses attributed the slowed spread to lower pine volumes and stem densities in eastern Alberta's forests, our findings indicate that jack pine's inherent phenotypic characteristics-specifically its smaller size, thinner phloem, and lower monoterpene concentrations-are the main factors limiting beetle success. Mountain pine beetle's limited spread is primarily caused by difficulties in locating and successfully attacking jack pine trees, rather than challenges with reproduction or larval survival within jack pine. Jack pine's traits appear to provide natural resistance against mountain pine beetle invasion, suggesting a lower risk of continued eastward spread than previously assumed. However, given the significant implications for forest management policy and the uncertainties inherent in ecological forecasting, we recommend maintaining beetle monitoring programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".