Even low levels of spruce budworm defoliation affect mortality and ingrowth but net growth is more driven by competition
Bibliographic record
Abstract
Defoliation reduces the growth and survival of trees, but this influence can be difficult to evaluate largely because of its interplay with various stand and site factors, especially for the highly dynamic defoliation of spruce–fir (Picea–Abies) forests caused by spruce budworm (SBW; Choristoneura fumiferana (Clem.)), the primary tree defoliator in North America. In this study, we developed statistical models to evaluate the influence of SBW defoliation, while considering its interaction with various stand and site factors, on spruce–fir stand dynamics of annual volume net growth, mortality, and ingrowth. The data were collected at intervals of 1 to 3 years from 560 permanent sample plots during the last SBW outbreak (1970s–1980s) in Maine, USA, and New Brunswick, Canada. These data comprise a wide range of observations of cumulative defoliation, especially at relatively low levels, that have been largely overlooked in previous studies. Our results strongly demonstrated that even relatively low levels of cumulative defoliation were significantly related to stand-level mortality and ingrowth, while net growth was more competition driven. Additionally, these stand dynamics were found to be not significantly affected by any of the site factors evaluated. These findings were consistent for Maine and New Brunswick despite their differences in forest management and SBW outbreak histories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".