Plant diversity effect on water quality in wetlands: a meta‐analysis based on experimental systems
Bibliographic record
Abstract
The ecological literature reports little empirical evidence from biodiversity-ecosystem functioning (BEF) experiments in wetland systems, even though wetlands are widely known for their water filtering capacity. Experiments comparing the effect of plant monocultures and mixtures on water quality to improve pollutant removal efficiency in treatment wetlands share the characteristics of classical BEF experiments, and so could provide insights for wetland management. To add to our understanding of BEF relationships in wetlands, we evaluated plant diversity effects on water purification through a meta-analysis of freshwater experimental wetlands comparing monocultures to mixtures. We found 28 studies that matched our criteria for BEF analysis, for a total of 561 diversity effects on pollutant removal. Overall, the meta-analysis shows no significant effect of plant richness on removal of total suspended solids, but a positive effect on chemical oxygen demand and total nitrogen removal, and a marginal effect on phosphorus removal. Thus, the results of this meta-analysis are consistent with reports of an overall positive biodiversity effect on ecosystem properties. An analysis of moderator variables shows that the experimental context (size of the experimental units, nutrient load, duration of the experiment) does not explain much of the residual variance. For pollutants that benefit from a positive plant richness effects on removal, mixtures do not perform better than the best monoculture. We found no evidence that plant richness effects are due to functional complementarity among species rather than to the presence of particularly efficient species. Complementarity effects may be less prevalent in highly productive, nutrient-rich wetlands, compared to nutrient-limited environments such as natural grasslands. Although findings must be confirmed by long-term field experiments under natural conditions, result from experimental wetland systems may contribute to a better understanding of biodiversity effect on ecosystem functions in wetlands, in addition to guide practices in natural wetland restoration and the use of constructed wetlands for water treatment.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.068 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".