Exotic plant species of the St Lawrence River wetlands: a spatial and historical analysis
Bibliographic record
Abstract
Abstract Aim To evaluate the importance (number of species, plant cover) of the exotic flora in seven well‐defined sectors of one of the most important transportation waterways in North America. To determine the impact of exotic species on wetland plant diversity and reconstruct the spread of some invasive species. Location St Lawrence River, southern Québec. Methods The exotic flora (vascular plants) of wetlands bordering the St Lawrence River was studied using 713 sampling stations (25 m 2 ) along a 560‐km long corridor. Results Exotic species represent 13.7% of the vascular flora of the St Lawrence wetlands. The relative plant cover occupied by exotic species is high in some of the fluvial sectors (42–44%), but low (6–10%) in the estuarine sectors. Wetlands (marshes) surrounding islands were particularly susceptible to invasion by exotic plants. Historical, abiotic and landscape factors may explain the differences observed between sites. Purple loosestrife ( Lythrum salicaria L.) is the most common exotic species of the St Lawrence wetlands, but other species, namely flowering‐rush ( Butomus umbellatus L.) and reed canary grass ( Phalaris arundinacea L.) are much more invasive. There is no linear relationship between the exotic species cover and the diversity of wetland plants; low diversity sites can be dominated by either exotic or native plant species. In the other sites, exotic species generally have little impact on plant communities and can contribute to increase diversity. Common reed ( Phragmites australis (Cav.) Trin. ex Steudel) and reed canary grass, both considered as exotic species in this study, clearly have a stronger impact on plant diversity than flowering‐rush and purple loosestrife. Main conclusions This study shows that the global impact of an invader cannot be adequately evaluated with only a few highly invaded sites. While nationwide strategies have been developed to control exotic species, large surveys are essential to adapt them to regional particularities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".