Effective suppression of established invasive <i>Phragmites australis</i> leads to secondary invasion in a coastal marsh
Bibliographic record
Abstract
Abstract Invasive species negatively impact vegetation communities. Invasive European common reed [ Phragmites australis (Cav.) Trin. ex Steud. ssp. australis ] is rapidly spreading throughout North American wetlands. As such, the suppression of P. australis populations is a goal of many managers, as its removal should provide an opportunity to restore native vegetation communities. In Ontario, managers applied a glyphosate-based herbicide to more than 400 ha of P. australis in ecologically significant coastal marshes, representing the first time this tool was used over standing water to suppress an invasive species in Canada. Using a before–after–control–impact monitoring design, we evaluated the efficacy of glyphosate-based herbicide at removing P. australis along a water-depth gradient and assessed the recovery of the vegetation community for 2 yr after treatment in relation to reference conditions. We found that herbicide suppressed more than 99% of P. australis 1 yr after treatment and worked effectively along the entire water-depth gradient (10 to 48 cm). However, the post-treatment vegetation community remains distinctive from reference marsh 2 yr after treatment. In many plots where P. australis was removed, nonnative European frog-bit ( Hydrocharis morsus-ranae L.) is now dominant, likely aided by high lake-water levels.
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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".