Spruce budworm defoliation patterns during outbreak rise are influenced by tree species, insecticide spraying, and spatial autocorrelation
Bibliographic record
Abstract
Spruce budworm (SBW; Choristoneura fumiferana Clem.) outbreaks are an important natural disturbance in North America, killing trees over millions of hectares. We related 11 years of SBW defoliation in 87 plots in Gaspé Peninsula, Québec, to 23 stand, site, and climate variables. Defoliation was consistently ordered among host species: balsam fir > white spruce > black spruce. Within the relatively small 200 km2 study area, cluster analyses resulted in four and 10 clusters for balsam fir cumulative and current defoliation, respectively; variation in cumulative defoliation converged over 11 years. Current defoliation was significantly spatially autocorrelated among plots within stands, but autocorrelation weakened at distances >2500 m. Cumulative defoliation was significantly related to insecticide spraying, minimum and maximum summer temperature, and interactions between SBW larvae per branch versus hardwood and white spruce basal area. Tree species, insecticide spraying, and number of defoliating SBW larvae were the main determinants of defoliation. Results showed much higher local spatial variability in current defoliation patterns than previous studies, but over the course of an outbreak, cumulative defoliation patterns converged. Cumulative defoliation patterns similar to these, assigned based on local defoliation severity, can be input into defoliation-based growth models to predict impacts on growth and survival.
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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 | 0.001 | 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) | 0.001 | 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".