Prescribed fire does not promote outbreaks of a primary bark beetle at low‐density populations
Bibliographic record
Abstract
Summary The causes of bark beetle outbreaks – particularly the role of disturbances – are poorly understood. Stand‐scale disturbances, like fires, can suddenly improve local host susceptibility and may attract beetles; however, whether such increases can lead to outbreaks in post‐disturbance stands is unclear. Using low‐densityDendroctonus ponderosaemountain pine beetle populations inPinus contortalodgepole pine forests in western Canada, we investigated whether prescribed fires promote outbreaks or provide only short‐term resources. Proportionally more burned than non‐burned trees were attacked. At one site, beetle attacks increased in response to a resource pulse, but the proportions of attacked trees and numbers of attacks per tree declined over four years after fire. Elsewhere, beetle attacks remained very low. As the resource (phloem) quality of burned trees remained high three years after fire, we propose that post‐fire mortality, resulting in fewer available host trees, can at least partially explain whyD. ponderosaedid not build up populations in burned stands. Synthesis and applications. Our study emphasizes the importance of examining long‐term trends in fire–bark beetle interactions, and of understanding low‐density beetle populations. Because fire does not seem to promote mountain pine beetle outbreaks, we recommend the continued use of prescribed fire for the general management ofP. contortaforests with low‐density beetle populations.
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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 | 0.000 |
| 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) | 0.002 | 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".