Evaluating the effects of alternative forest management strategies on spruce budworm outbreaks
Bibliographic record
Abstract
Abstract Context Forest insect outbreaks are major landscape-scale disturbances that shape forest composition and dynamics. In eastern North America, the spruce budworm (SBW) is a key defoliator driving these patterns. Understanding how local forest management interacts with outbreak dynamics at broad spatial extents is crucial for designing sustainable management strategies under climate change. Objective We evaluated whether forest management strategies can modify SBW outbreak development across broad spatial extents and over long time frames. Methods We developed a spatially explicit forest landscape model to simulate SBW outbreaks in Quebec, Canada, over 80 years. Simulations were used to compare scenarios with different harvesting rates and regeneration strategies under current and future climate conditions. Results Outbreaks affected from 60,000 to 160,000 km 2 (11–31% of forest area), with climate change driving a gradual northward shift in maximum outbreak impacts. Annual harvesting of 1% of the forest and promoting non-host regeneration (e.g., trembling aspen) reduced SBW-induced forest mortality by up to 30%, particularly when combined with higher harvest intensities that accelerated species turnover and advanced dampening effects by one to two decades. In contrast, prioritizing host species (e.g., black spruce) had little effect on outbreak patterns. Conclusions Local management strategies can substantially influence large-extent outbreak dynamics if applied consistently and intensively. However, our results highlight that even proactive measures will only partially offset future risks, as climate-driven northward shifts may reduce management effectiveness. Spatially explicit models provide valuable insights to design adaptive forest management that mitigates insect disturbances under climate change. Graphical Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".