Glyphosate treatments for managing successional dynamics in beech bark disease-affected northern hardwood forests
Bibliographic record
Abstract
The spread of beech bark disease (BBD) in northern tolerant hardwood forests poses a significant forest management challenge. Extensive aboveground mortality in BBD-affected stands often leads to the rapid formation of high-density American beech (Fagus grandifolia Ehrh.) thickets, primarily driven by vegetative regeneration through root sprouting. These thickets can outcompete desirable species such as sugar maple (Acer saccharum L.), and negatively impact long-term forest structure and functions. This study evaluated the efficacy of post-harvest herbicide treatments-specifically the application of glyphosate to recently cut stumps and the use of "hack-and-squirt" application techniques on standing beech-to suppress vegetative beech regeneration. Over five years, beech regeneration was significantly lower in treatment plots, averaging 904 stems ha ⁻ ¹ (95% CI: 433-1,378 stems ha ⁻ ¹), compared to 1,741 stems ha-¹ (95% CI: 1,286-2,193 stems ha ⁻ ¹) in untreated control plots. Additionally, by five years post-harvest, glyphosate-treated plots supported higher densities of desirable tree species such as sugar maple, indicating that the intervention shifted species composition by reducing beech dominance. However this method had no significant effect on overall tree species richness or diversity, so while these treatments effectively suppress beech regeneration and promote successional trajectories in hardwood forests, they do not reduce tree diversity. By alleviating the competitive dominance of beech thickets, this management strategy is likely to mitigate the ecological and economic impacts associated with BBD, while maintaining or enhancing desirable tree species diversity.
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 | 0.000 | 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) | 0.000 | 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".