Exotic Invasive Shrub Glossy Buckthorn Reduces Restoration Potential for Native Forest Herbs
Bibliographic record
Abstract
Invasive glossy buckthorn could reduce restoration potential for understory native forest herbs by compromising their growth and biodiversity. Few studies of glossy buckthorn’s effects on forest herbs exist, and none were done in early-successional, partially open hardwood forests. This study was conducted in a mature hybrid poplar plantation invaded by buckthorn, located in southeastern Québec. We tested the effect of buckthorn removal on the growth of three forest herb species, whether this effect varied among species, and if canopy type (two poplar clones) influenced this effect. Forest herbs were planted in herbicide (buckthorn removed) and control treatments in the plantation understory, an environment similar to that of early-successional hardwood forests. Over the first two growing seasons, species showed specific reactions to buckthorn cover. Mean relative growth rate (RGR) for Asarum canadense and Polygonatum pubescens was increased in the herbicide treatment (48% and 33%, respectively) and decreased in the control treatment (−35% and −33%, respectively). Sanguinaria canadensis growth was the highest among species, with no difference between treatments. No effects of canopy type were detected. Results suggest that planting forest herbs for restoration purposes may be unsuccessful if buckthorn is present. Important changes in understory flora biodiversity are likely to occur over the long term in forests invaded by buckthorn.
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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".