Suppression of established invasive <i>Phragmites australis</i> leads to secondary invasion
Bibliographic record
Abstract
Abstract Invasive Phragmites australis (European Common Reed) is rapidly spreading throughout North American wetlands, with negative impacts on wildlife and native plants. The removal or suppression of P. australis is desired to provide an opportunity for native vegetation and wetland fauna to recover. In Ontario, managers applied a glyphosate-based herbicide to >400 ha of P. australis in ecologically significant Great Lakes coastal marshes, representing the first time this tool was used over standing water to suppress P. australis in Canada. Using a replicated Before-After-Control-Impact monitoring design, we 1) evaluated the efficacy of glyphosate-based herbicide at suppression P. australis along a water depth gradient and 2) assessed the recovery of the vegetation community for two years after treatment in relation to local reference conditions. We found that herbicide reduced live P. australis stem densities by over 99% the first year after treatment and about 95% the second year post-treatment. Treatment was equally effective along the entire water depth gradient (10 – 48 cm). The initial ‘suppression’ focused management was successful, but sustained monitoring and ‘containment’ focused follow-up treatment will be required to prevent reinvasion. Two years after treatment, the vegetation community does not resemble reference conditions. Although some treated plots initially increased in similarity to the vegetation communities typical of reference areas, many plots where P. australis was suppressed are on a novel trajectory comprising a vegetation community dominated by non-native Hydrocharis morsus-ranae . Secondary invasions represent a major challenge to effective recovery of native vegetation after P. australis control.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".