Mountain pine beetle outbreak duration and pine mortality depend on direct control effort
Bibliographic record
Abstract
The efficacy of direct control methods in bark beetle outbreaks is a disputed topic. While some studies report that control reduces tree mortality, others see little effect. Existing models, linking control rate to beetle population dynamics and tree infestations, give insights, but there is a need to take into account the environment spatial variability and its impact on beetle life cycle. Here, we use natural variability found in a carefully monitored and controlled infestation to simulate outbreak dynamics under different control effort and to explore the impact of control on outbreaks suppression and tree mortality. Our semi-empirical predictive model of the number of infested trees as a function of ecological and environmental variables is coupled to a simulation model for infestation dynamics. We show that even a little control can have a major impact on the number of infested trees after several years of sustained effort. However, a moderate control of 60% is required to reduce the beetle population on the long term. Furthermore, a control rate of 69%-83% is needed to achieve outbreak suppression in under 13 years depending on the abundance of incoming flights from outside sources.
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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 | 0.004 |
| 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 | 0.000 | 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".