Positive Results of an Early Intervention Strategy to Suppress a Spruce Budworm Outbreak after Five Years of Trials
Bibliographic record
Abstract
Spruce budworm (Choristoneura fumiferana Clem.; SBW) outbreaks are one of the dominant natural disturbances in North America, having killed balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea sp.) trees over tens of millions of hectares. Responses to past SBW outbreaks have included the aerial application of insecticides to limit defoliation and keep trees alive, salvage harvesting of dead and dying trees, or doing nothing and accepting the resulting timber losses. We tested a new ‘early intervention strategy’ (EIS) focused on suppressing rising SBW populations before major defoliation occurs, from 2014 to 2018 in New Brunswick, Canada. The EIS approach included: (1) intensive monitoring of overwintering SBW to detect ‘hot spots’ of low but rising populations; (2) targeted insecticide treatment to prevent spread; and (3) proactive public communications and engagement on project activities and results. This is the first attempt of area-wide (all areas within the jurisdiction of the province of New Brunswick) management of a native forest insect population. The project was conducted by a consortium of government, forest industry, researchers, and other partners. We developed a treatment priority and blocking model to optimize planning and efficacy of EIS SBW insecticide treatment programs. Following 5 years of over 420,000 ha of EIS treatments of low but increasing SBW populations, second instar larvae (L2) SBW levels across northern New Brunswick were found to be considerably lower than populations in adjacent Québec. Treatments increased from 4500 ha in 2014, to 56,600 ha in 2016, and to 199,000 ha in 2018. SBW populations in blocks treated with Bacillus thuringiensis or tebufenozide insecticide were consistently reduced, and generally did not require treatment in the subsequent year. Areas requiring treatment increased up to 2018, but SBW L2 populations showed over 90% reductions in that year. Although this may be a temporary annual decline in SBW population increases, it is counter to continued increases in Québec. Following 5 years of tests, the EIS appears to be effective in reducing the SBW outbreak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".