Flight Period of Mountain Pine Beetle (Coleoptera: Curculionidae) in its Recently Expanded Range
Bibliographic record
Abstract
The ability to predict key phenological events, such as the timing of flight periods, is useful for the monitoring and management of insect pests. We used empirical data to describe the flight period of mountain pine beetle, Dendroctonus ponderosae Hopkins, in its recently expanded range east of the Rocky Mountains in Canada and developed a degree-day model based on the number of trapped beetles. Data were collected over four degrees of latitude and six years. The main flight period, when the middle 70% of the total number of beetles were caught, started during the second or third week of July, lasted 26 d, and peaked within 2 wk of starting. The best model accounted for 89% of the variation in the data. Mountain pine beetle's flight tended to start later and be more contracted at higher latitudes. The synchrony of mountain pine beetle's flight period in the expanded range appears to be comparable to the limited reports from the historic range, although it may start earlier. This suggests that conditions in the new range are suitable for a coordinated dispersal flight, which is critical for the beetle's strategy of overwhelming tree defenses by attacking en masse. Forest managers can use the model to support operational decisions, e.g., when to impose hauling restrictions to reduce the risk of spread through the transport of infested material, or the time frame for control programs. Understanding the flight period may also improve our ability to assess the response of mountain pine beetle to novel and changing climates in the future.
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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 | 0.000 | 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 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".