Mountain pine beetle dispersal: spatiotemporal patterns and role in the spread and expansion of the present outbreak
Bibliographic record
Abstract
Dispersal has been least understood in mountain pine beetle ecology. We developed a novel regional dynamic conceptual model of mountain pine beetle infestation using the tree mortality estimated from the British Columbia annual aerial overview survey to quantitatively determine short-distance dispersal (SDD) and long-distance dispersal (LDD) at local (forest district) and regional (provincial) scales. The dispersal patterns were characterized based on distances between a sink patch to its nearest source patch. At the regional scale, SDD accounted for 85.3% of mountain pine beetle dispersal to non-infested areas and 96.8% of beetle dispersal to infested areas. Although SDD was a dominant dispersal mode, LDD played a more important role in the early stage of the current mountain pine beetle outbreak. At the local scale, three patterns of dispersal to non-infested areas were identified. First, LDD dominated in the forest districts where only sparse infestations occurred. Second, LDD was a dominant or important factor in the early stages of the infestations in some districts. Third, SDD dominated throughout the infestations in more severely infested forest districts. However, for dispersal to infested areas, SDD was a dominant mode in most of the forest districts. We conclude from the spatiotemporal patterns of dispersal observed at local and regional scales that LDD is a key factor in the initiation and early stage of the infestations in new remote areas, and SDD dominate in the spread and expansion of the outbreak as the infestations intensify and reach epidemic levels. However, it should be conscious that there is uncertainty that LDD might have been over emphasized in the dispersal before local dynamics is fully taken into account.
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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 | 0.001 |
| 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 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".