Native Wetland Plant Recovery Following Phragmites australis Invasion
Bibliographic record
Abstract
Invasive common reed (Phragmites australis ssp. australis) has negatively affected 25% of all Species at Risk in Ontario since its arrival in the twentieth century. This is of particular concern at Long Point and Rondeau, two wetland complexes located on the northern shore of Lake Erie. To combat the negative effects of P. australis, over 1500 ha of invaded marsh was treated with a glyphosate-based herbicide, beginning in 2016. We monitored the vegetation communities in these wetland complexes over five years to track changes in the wetland vegetation following herbicide application. In the two years following herbicide application, a secondary invasion by Hydrocharis morsus-ranae was observed but was short-lived. Three to five years following treatment, treated plots shifted towards native-dominated vegetation communities. However, with lower Lake Erie water levels predicted in the next five years, these communities are expected to change, as more seedlings will emerge from the wetland seedbank. To predict what may return to treated areas, and to determine the effects of herbicide treatment on viable P. australis seeds in the seedbank, we performed a greenhouse emergence experiment with seedbank samples collected from invaded, herbicide-treated, and native reference marsh. We determined that while a high abundance of viable P. australis remain in the seedbanks of invaded \nareas, treatment followed by flooding for a minimum of one year effectively reduced the number of viable P. australis seeds. The seedbanks of all three vegetation types contained many native seeds but contained many non-native seeds as well. Further monitoring of the vegetation communities that emerge as Lake Erie water levels decline is recommended to ensure that the \nvegetation communities at Long Point remain native-dominated, and that low water levels do not facilitate the reinvasion of P. australis.
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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".