Significant Increase in Nutrient Stocks Following Phragmites australis Invasion of Freshwater Meadow Marsh but Not of Cattail Marsh
Bibliographic record
Abstract
Invasive species are a threat to biodiversity and can cause ecological degradation, however, well-established invasive species may serve valuable ecological functions. For example, in the Laurentian Great Lakes, where nutrient pollution is a major issue, highly productive Phragmites australis (European Common Reed) may provide a nutrient retention service. Yet there is a lack of research comparing carbon and macronutrient stocks in P. australis with resident plant communities, such as cattail and meadow marsh. We quantified the effect of P. australis invasion on carbon and macronutrient standing stocks in a freshwater coastal marsh by comparing the above- and belowground biomass, tissue nutrient concentrations, and annual nutrient standing stocks in marsh invaded by P. australis with cattail marsh (dominated by Typha spp.) and meadow marsh (dominated by Calamagrostis canadensis), which are being displaced by P. australis in Lake Erie coastal marsh. We conclude that the effect of P. australis invasion on carbon and macronutrient standing stocks is dependent on the plant community being replaced. The annual standing stock of carbon, nitrogen, phosphorus, and potassium are consistent between cattail marsh and P. australis-invaded marsh, though cattail marsh contains more magnesium (112 % mean increase) and calcium (364 % mean increase). In contrast, when P. australis replaces meadow marsh, the standing stocks of all measured nutrients and carbon increase significantly (103 - 188 % mean increase). Our study highlights that plant invasions may create trade-offs between ecosystem services. In our case, the increase in nutrient standing stocks when P. australis invades meadow marsh should be weighed against the documented reductions in biodiversity and habitat value.
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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".