Wetland buffers are no substitute for landscape‐scale conservation
Bibliographic record
Abstract
Abstract Wetlands in farmland are at risk of contamination by fertilizers and pesticides. One recommendation for reducing wetland contamination is to maintain a buffer of contiguous uncropped land around the wetland (a wetland buffer). Many agricultural water protection policies around the world recommend 5 to 50‐m wide uncropped buffers around water bodies, but it is unclear how large wetland buffers must be to effectively protect against these chemicals. In addition, it is unclear whether wetland buffers have similar—or stronger—effects on fertilizer and pesticide contamination than reducing the amount of cropped land within the larger landscape context around wetlands. Our study, conducted across 37 wetlands in eastern Ontario, Canada, addressed the following questions: (1) Does increasing buffer width, or increasing the amount of contiguous uncropped land within recommended buffer width guidelines, reduce nutrient and pesticide levels in agricultural wetlands? (2) Does increasing uncropped land cover in the broader landscape reduce nutrient and pesticide levels in agricultural wetlands? and (3) What is the relative importance of buffer size and landscape‐scale uncropped cover for reducing nutrient and pesticide levels in agricultural wetlands? A rigorous site selection process was employed to minimize the correlation between buffer size and landscape‐scale uncropped cover, minimize spatial gradients in these predictor variables, and minimize variation in potentially confounding variables. We obtained nutrient and pesticide data by collecting water samples from each wetland under similar weather conditions in June–July 2015. Nitrate concentrations were measured using ion chromatography, and atrazine and neonicotinoid (pesticide) concentrations using a combination of high‐performance liquid chromatography and mass spectrometry. We found that nitrate, atrazine, and neonicotinoid concentrations in study wetlands were unaffected by wetland buffer size. However, concentrations of each chemical decreased with uncropped land cover in the surrounding 150 to 300‐m radius landscapes. To effectively protect water in agricultural wetlands from contamination by nitrate‐based fertilizers and atrazine or neonicotinoid pesticides, we recommend either increasing the policy‐recommended width of wetland buffers to at least 150 m, or abandoning the buffer paradigm in favor of landscape‐scale conservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".