Evaluating the impact of climate change on the black spruce defoliation by spruce budworm
Bibliographic record
Abstract
Current ecological models predict profound climate change-related effects on the natural disturbance regimes of forests. Spruce budworm (Choristoneura fumiferana) (SBW) is the principal insect defoliator in eastern North America, and SBW outbreaks have a major impact on the structure and function of the Canadian boreal forest, as defoliation leads to decreased tree growth, increased mortality, and lower forest productivity. SBW outbreaks have become more severe over the last century with the changing climate; however, little is known about how the integrated effect of climate and SBW alters the growth of host species. Here we evaluate how climate and outbreak severity combined to affect black spruce (Picea mariana) growth during the SBW outbreak that occurred between 1968-1988 and 2006-2017. We compiled dendrochronological series (2271 trees), outbreak severity (estimated by observed aerial defoliation), and climate data for 164 sites in Québec, Canada. We used a linear mixed effect model to determine the impacts of climatic parameters, cumulative defoliation (of the previous five years), and their interaction effect on basal area growth. At maximum outbreak severity, basal area growth of black spruce was reduced by 14%–18% over five years due to SBW effect. This outbreak growth response was affected by climate: warmer previous summer minimum temperatures and a higher previous summer climate moisture index (CMI) further decreased growth by 11% and 4%, respectively. In contrast, the negative effect of defoliation was attenuated by 9% for a warmer minimum temperature in the previous spring and 7% for a warmer maximum temperature in the previous summer. This study improves our understanding of combined insect–climate effects on growth and helps in the predictions of future SBW-related damage to forest stands to bolster sustainable forest management. We also recommend that projections of boreal forest ecosystems include several classes of SBW defoliation severity coupled with multiple climatic scenarios.
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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 | 0.001 | 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".