Non-native plant invasion of boreal forest gaps : implications for stand regeneration in a protected area shaped by hyperabundant herbivores
Bibliographic record
Abstract
While Canada thistle (Cirsium arvense) is notorious as an aggressive, invasive non-native weed in agricultural fields, grasslands, and roadsides throughout North America, it has not typically posed a threat to boreal forests. However, in the balsam fir (Abies balsamea) -dominated lowland boreal forests in Gros Morne National Park (GMNP- Newfoundland, Canada), Canada thistle has recently invaded natural areas on a large landscape scale, occurring in 42% to 55% of anthropogenic and natural forest gaps, respectively, and frequently forming dense monocultures. It is important to determine if and how Canada thistle invasion will affect regeneration of native trees, particularly since regeneration of gaps in GMNP is already threatened by non-native, hyperabundant moose (Alces alces) populations, which exert extreme browsing pressure on forests. This study assessed the condition of forest gaps to support conifer regeneration by describing the current level of balsam fir regeneration, quality of seedbeds, and degree of Canada thistle invasion. Balsam fir seed and seedling addition experiments were performed in gaps to determine the effect of thistle presence on emergence, growth, and survival of balsam fir. Finally, the potential for allelopathic impacts on native conifers from Canada thistle was assessed in greenhouse experiments. Results revealed that gaps are not regenerating, contain poor seedbeds for conifer recruitment, and are heavily disturbed by moose browsing. Canada thistle invasion further threatens balsam fir emergence and early seedling survival. However, older, transplanted fir seedlings were not negatively affected by thistle, suggesting that seedling planting may be an effective management strategy to encourage fir regeneration in thistle-invaded gaps, and potentially even phase out shade-intolerant thistle plants over time.
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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 | 0.001 | 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".