Does the type of silvicultural practice influence spruce budworm defoliation of seedlings?
Bibliographic record
Abstract
Abstract Spruce budworm (Choristoneura fumiferana(Clem)) is the main defoliator in the boreal forest of North America, and its outbreaks have major ecological and economic consequences and represent a challenge for forest management. Numerous studies have addressed the effects of this defoliator on mature trees, whereas the effects of spruce budworm on regeneration remain elusive. Furthermore, intensive exploitation practices during the last decades have left a large area of the Canadian boreal forest in an early development stage. In this context, it becomes vital to understand those factors affecting the severity of spruce budworm‐related defoliation on regeneration. Here, we determine the defoliation severity of black spruce and balsam fir seedlings in both mature pure black spruce and black spruce–balsam fir stands subjected to two different silvicultural treatments (clear‐cutting and partial cutting). Defoliation intensity varied between stand types, silvicultural treatments, species, and height classes. Seedlings in black spruce–balsam fir stands experienced twice the defoliation of those in pure black spruce stands (black spruce seedlings 10% vs. 23%; balsam fir seedlings 29% vs. 47%, respectively). Harvesting methods also influenced seedling defoliation. Under clear‐cutting, black spruce seedlings (24%) were three times as defoliated as black spruce seedlings in partial cutting stands (8%), whereas balsam fir seedlings in clear‐cutting plots experienced twice the defoliation (42%) of balsam fir seedlings in partial cutting plots (20%). The level of defoliation also increased with seedling height. This study will help silvicultural strategies adapt to the effects of natural disturbance regimes. As the intensity and severity of defoliator outbreaks are expected to increase under climate change, these results will help guide forest management strategies to select harvesting methods that will limit the effects of defoliation on conifer regeneration.
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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".