Great Lakes coastal wetland biodiversity increases following invasive Phragmites australis removal
Bibliographic record
Abstract
Abstract Invasive Phragmites australis subsp. australis is invading Great Lakes coastal wetlands and forming monocultures at an alarming rate. P. australis is thought to reduce wetland biodiversity both directly and indirectly through the acquisition of resources and alteration of habitat. Restoration efforts to manually remove P. australis at Point Pelee National Park began in 2020 and here we assess the effect of P. australis removal on vegetation and emerging aquatic invertebrate communities. We compared emergent vegetation and emerging aquatic invertebrate communities between a P. australis-invaded wetland, a P. australis-treated wetland, and a non-invaded wetland. We found that two-years post-restoration, vegetation species richness and the prevalence of non-Phragmites vegetation were both higher in the treated and non-invaded wetlands than the invaded wetland. The vegetation community of the treated wetland resembled the vegetation community of the non-invaded wetland, and the vegetation community of the invaded wetland was very different from both the treated and non-invaded wetlands. We also found that invertebrate total abundance (measured as density/m2) was highest in the treated and non-invaded wetlands, and that invertebrate abundance differed among all wetland types. Invertebrate community composition also differed among all wetland types. Manual removal of P. australis resulted in significant changes in both the emergent vegetation and emerging invertebrate community composition two-years following restoration.
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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".