Ecological and Landscape Drivers of Neonicotinoid Insecticide Detections and Concentrations in Canada’s Prairie Wetlands
Bibliographic record
Abstract
Neonicotinoids are commonly used seed treatments on Canada's major prairie crops. Transported via surface and subsurface runoff into wetlands, their ultimate aquatic fate remains largely unknown. Biotic and abiotic wetland characteristics likely affect neonicotinoid presence and environmental persistence, but concentrations vary widely between wetlands that appear ecologically (e.g., plant composition) and physically (e.g., depth) similar for reasons that remain unclear. We conducted intensive surveys of 238 wetlands, and documented 59 wetland (e.g., dominant plant species) and landscape (e.g., surrounding crop) characteristics as part of a novel rapid wetland assessment system. We used boosted regression tree (BRT) analysis to predict both probability of neonicotinoid analytical detection and concentration. BRT models effectively predicted the deviance in neonicotinoid detection (62.4%) and concentration (74.7%) from 21 and 23 variables, respectively. Detection was best explained by shallow marsh plant species identity (34.8%) and surrounding crop (13.9%). Neonicotinoid concentration was best explained by shallow marsh plant species identity (14.9%) and wetland depth (14.2%). Our research revealed that plant composition is a key indicator and/or driver of neonicotinoid presence and concentration in Prairie wetlands. We recommend wetland buffers consisting of diverse native vegetation be retained or restored to minimize neonicotinoid transport and retention in wetlands, thereby limiting their potential effects on wetland-dependent organisms.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".