Spruce budworm outbreaks promote natural regeneration of Eastern white pine
Bibliographic record
Abstract
White pine decline in managed forests has led to the adoption of ecosystem-based management using natural disturbances, mainly surface fire. However, other natural disturbances, such as spruce budworm (SBW), are present in the native range of white pine but are rarely considered in the management of this species. SBW outbreaks are expected to change in frequency and severity under climate change, increasing the need to understand if and how this disturbance will affect the dynamics of white pine regeneration. In this study, we evaluated the impacts of an SBW outbreak on white pine and balsam fir regeneration and identified environmental variables that affect regeneration dynamics. We evaluated six defoliated and six control study plots in white pine stands close to the northern limit of the Québec’s temperate forest. In each plot, we counted white pine and balsam fir seedlings and measured structural (diameter, height), and abiotic (gap fraction, soil type) variables. White pine seedling density was three times greater in defoliated plots than in controls. By contrast, there was no significant difference in balsam fir seedling density between disturbance types. Seedlings of both species were taller in defoliated plots than in control plots. Overall, the SBW outbreak promoted white pine seedling density and height growth. This research contributes to our understanding of the effects of SBW outbreaks on white pine regeneration dynamics, and it will help forest managers to select harvesting methods that emulate SBW-modulated stand characteristics. • SBW outbreaks favor white pine regeneration density and height growth. • White pine seedling density increases during the outbreak phase of the SBW outbreak. • Balsam fir seedling density did not change during an SBW outbreak. • SBW outbreaks favor height growth in balsam fir seedlings. • Other species height is the main element affecting in white pine seedling density.
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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) | 0.001 | 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".