Litter decomposition and nutrient dynamics of four macrophytes in intact, restored, and constructed freshwater marshes of Canada
Bibliographic record
Abstract
The restoration and construction of wetlands offer opportunities to rewet soils, inhibit decomposition, and enhance nutrient retention in decomposing litters. Here, we report the decomposition rates and nutrient dynamics of macrophyte litters in intact, restored, and constructed wetlands. A 2.1‐year litterbag experiment of four common freshwater macrophytes (Phalaris arundinacea, Phragmites australis, Scirpus cyperinus, and Typha latifolia) was conducted in eight freshwater marshes (three intact, four restored, and one constructed) within three sites in Manitoba and Ontario, Canada, which varied in restoration age, inundation periods, and surrounding land uses. Litter mass loss and N and P dynamics were measured. Litter decomposition rates (k) followed the order of P. arundinacea (0.42 ± 0.03 year−1) > T. latifolia (0.31 ± 0.03 year−1) > P. australis (0.19 ± 0.01 year−1) > S. cyperinus (0.13 ± 0.01 year−1) in most wetlands and were positively correlated to the initial litter N concentration. Litters decomposed fastest under seasonally inundated conditions rather than permanent inundation. N and P retention in litters were significantly affected by both initial litter N and P concentration and wetland surrounding land uses. After 2.1 years of decomposition, the N:P ratio of all litters converged to 20 to 28:1, regardless of the initial litter N:P ratio or N or P concentrations. The effectiveness of wetland restoration in slowing decomposition and enhancing nutrient accumulation depends on the quality of the input litters and wetland characteristics, including inundated periods and surrounding anthropogenic disturbances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".