Freshwater Effects of Pesticide Mixtures in Mesocosms of Great Pond Snails (Lymnaea stagnalis)
Bibliographic record
Abstract
Pesticide exposure in the environment accumulates through soil, water, and living organisms. Environmental factors such as weather and water conditions can influence how pesticides can impact an ecosystem. Pesticide mixtures from agricultural runoffs could have complex interactive effects between compounds on organisms in natural waterways. Toxicity of pesticides can be increased through additive or synergistic effects in a mixture. This study assessed the chronic toxicity of a pesticide mixture on the great pond snails (Lymnaea stagnalis) over a 28-day period using semi-natural outdoor mesocosms. We used the mesocosms to house a natural biofilm (periphyton) that included other small invertebrates. This design incorporates trophic exposure of pesticides, and potential community effects of the pesticides on the food web. L. stagnalis is a model organism in ecotoxicology. It has well-studied growth, reproductive, behavioral, and biochemical responses to anthropogenic stress that we utilized for this study. We chose to expose the snails and biofilm to a pesticide mixture commonly found in the water surrounding corn/soybean farming of southern Quebec, Canada, based on a report by the ministère de l’Environnement et de la Lutte contre les changements climatiques de la Faune et des Parcs (MELCCFP). Our final selection included the herbicides atrazine, metolachlor, and glyphosate, as well as the insecticides clothianidin, thiamethoxam, imidacloprid, and chlorantraniliprole. The pesticide mixture varied at three environmentally relevant concentrations (atrazine: 0.5-5 ng/mL; metolachlor: 1-10 ng/mL; glyphosate: 1.5-15 ng/mL; clothianidin, imidacloprid, thiamethoxam, chlorantraniliprole: 0.05-0.5 ng/mL). We showcased physiological effects with respect to nominal reductions in pesticide mixture concentrations. We also confirmed environmental exposure concentrations in the experimental media and tissues to provide evidence for policy makers and agronomists to make decisions about the future of agricultural practices. Results showed that snail survival rates were unaffected by pesticides. Smaller snails had lower survival at the highest mixture concentration compared to larger snails, leading to a larger increase in the remaining 1x mixture treatment population’s average body mass. This increase in body mass could potentially be due to community effects, as shown in other studies. The effect sizes of changes in population body mass did decrease with reduced pesticide mixture concentrations. Clutch sizes increased in 1x treatments at greater rates compared to controls. However, 0.5x treatments exhibited reduced clutch sizes compared to controls. Stress-induced reproduction of L. stagnalis has been observed in other studies. However, it was unclear if the varied reproductive effects are a gradient of toxicity with respect to the pesticide exposure concentration. Snail tissues had higher amounts of s-metolachlor, chlorantraniliprole, glyphosate, and imidacloprid than the water, indicating bioaccumulation. Biofilm tissue bioconcentrated chlorantraniliprole, s-metolachlor, and glyphosate at larger than the water. Concentrations of herbicides (atrazine, s-metolachlor, and glyphosate) in snails was significantly related to the concentrations in biofilm, suggesting possible trophic transfer. A two-fold reduction in pesticide exposure did not reduce the accumulation factors of s-metolachlor and imidacloprid in pond snails. Likewise, the concentration factors of s-metolachlor and chlorantraniliprole in biofilm were not diminished. In conclusion, pesticide mixture effects are complex, with imposed sub-lethal and community effects that can appear to benefit an organism. The costs of body mass or reproduction changes need to be studied further. Additionally, the threat of pesticide bioaccumulation in snails and its potential to move throughout the food web remains an unanswered concern.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".