Source or sink? Meta-analysis reveals diverging controls of phosphorus retention and release in restored and constructed wetlands
Bibliographic record
Abstract
Abstract Wetland restoration is a popular nutrient management strategy for improving water quality in agricultural catchments. However, a wetland’s ability to retain phosphorus is highly variable and wetlands can sometimes be a source of phosphorus to downstream ecosystems. Here, we used a meta-analysis approach to explore the source and sink capacity of 139 wetlands for both total phosphorus (TP) and the more bioavailable form, phosphate (PO4 3−), at seasonal and annual timescales. Median retention efficiency across all studies is 32% for TP and 28% for PO4 3−, however the range is extremely broad. We found that wetlands are often sinks for TP (84% of site-years) and PO4 3− (75% of site years). The median TP retention within wetlands that are sinks (2.0 g·m−2·yr−1) is greater than release by wetlands that are sources (−0.5 g·m−2·yr−1). In contrast, for PO4 3−, median retention within wetlands that are phosphorus sinks (0.8 g·m−2·yr−1) is of similar magnitude to that released by wetlands that are phosphorus sources (−0.7 g·m−2·yr−1). We found that phosphorus release from wetlands coincides with higher hydraulic loading rates, lower influent phosphorus concentration, and legacy soil/sediment phosphorus. Phosphate releases were especially common in wetlands used for treating municipal wastewater, as well as restored and constructed wetlands with flashy, precipitation-driven flow. We found that experimental design may inherently bias our understanding of wetland performance for phosphorus retention as studies conducted in mesocosms outperform other wetland types. Analysis of monthly data demonstrated significant temporal variability in wetland phosphorus dynamics, often switching from retention to release many times within a year, but with no generalizable seasonal trends. Our results highlight the value of restoring wetlands for phosphorus retention and point to ways of furthering their utility towards improving water quality by simultaneously targeting retention enhancing measures and release avoidance.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".