Spatiotemporal Patterns of Distribution and Drivers of Neonicotinoid Insecticide Fate in Canadian Prairie Pothole Wetlands
Bibliographic record
Abstract
Designed for the protection of major agricultural crops, neonicotinoids are the fastest-growing class of insecticides used against a broad spectrum of insect pests. Although neonicotinoid toxicity toward non-target organisms (e.g., bees, aquatic insects) has been well-studied, less is known about their distribution of use, transport, and fate in North American agroecosystems. This is especially true of neonicotinoid interactions with wetlands in the Canadian Prairies. Between 2009 and 2012, neonicotinoid use as a seed treatment increased by 30% across the Canadian Prairies. During spring 2012 to spring 2013, I sampled water and sediment from 136 wetlands situated in a range of crop types across central Saskatchewan to determine the extent of neonicotinoid contamination. Wetlands situated in oat, canola, and barley fields consistently contained higher neonicotinoid concentrations in water than in grasslands, but no single crop influenced overall detections. Neonicotinoid detections in water varied from 16% (fall 2012) to 91% (spring 2013) with peak concentrations up to 3110 ng/L found in summer. I found numerous detections of neonicotinoids in spring, after ice-off, but before seeding. Through sampling snow, snow meltwater, and soil particulates from previously treated (clothianidin) and untreated fields, meltwater showed the strongest relationship to initial spring concentrations in wetland water. Neonicotinoid concentrations increased with time in shallow temporary wetlands which appeared most at risk for annual contamination. While snowmelt contamination influenced water concentrations in spring, peak concentrations in wetlands were consistently found during summer sampling completed in 2012-2014. Rapid wetland assessments completed on 238 wetlands (summer of 2012 and 2013) revealed key ecological, hydrological and landscape features that influenced neonicotinoid detections and peak concentrations in Prairie wetlands. The results of my exploratory analysis indicated that plant community composition is a key indicator and/or driver of both detection and concentration of neonicotinoids in Prairie wetlands. In particular, specific shallow marsh plants were commonly associated with either higher (e.g., Scirpus validus) or lower (e.g., Mentha arvensis) neonicotinoid concentrations in natural wetlands suggesting wetland macrophytes in this zone may be either indicators of agricultural disturbance intensity or differentially capable of accumulating the insecticide in its tissue. Therefore, in 2014, I conducted an outdoor microcosm experiment to evaluate thiamethoxam uptake from water by Typha latifolia and Alisma triviale using two concentrations over a 7-day period. Experimental results found some trace positive detections but no quantifiable accumulation of the insecticide in plant tissues. This is despite the fact that results of my 2015 field study found species of Typha, Alisma and Equisetum had neonicotinoids more frequently detected in their tissues, at concentrations ranging from 1.01-8.44 ug/kg. My findings demonstrate that neonicotinoid distribution and fate in Canadian Prairie agroecosystems is driven by interactions between ecological, hydrological, and landscape characteristics. Consequently, these drivers regulate neonicotinoid exposure and persistence in ecologically important regional wetlands. In order to effectively conserve these critical waterbodies, conservation planning should consider the importance of maintaining naturally diverse vegetation zones to mitigate insecticide exposure to wetland-dependant 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 | 0.001 | 0.001 |
| 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".