Fish kills and insecticides: historical water quality patterns in 10 agricultural watersheds in Prince Edward Island, Canada (2002–2022)
Bibliographic record
Abstract
Global pesticide use has resulted in widespread environmental degradation, persistent contamination of surface and ground waters, bioaccumulation of these contaminants in food webs, and unintended impacts on non-target species (e.g., fish kills). Mixtures of multiple pesticides are commonly found in the environment, yet these chemicals are rarely studied in combination. Insecticides are of particular concern as these chemicals are designed to target terrestrial insect pests but also impact aquatic macroinvertebrates. In the following study, we explore the regional context of surface water concentrations of four insecticides in 10 study watersheds over a 20-year period (2002 to 2022) in Prince Edward Island, a region of Canada with continuous agricultural activity since the 1720s. These agroecosystems have been subject to generations of restoration, conservation, and more recently, implementation of various beneficial or best management practices (BMPs). The changing climate significantly adds to the complexity of monitoring these systems as the regional rate of change is exceptionally high (e.g., a 0.70°C to 1.14°C increase in air temperature and 5–8% decrease in precipitation in the last 3 decades). The results of this study highlight that efforts in this area would benefit from a more collaborative, transdisciplinary approach that integrates local, regional, national, and global perspectives while respecting the needs of growers, consumers, and the immense natural capital in the aquatic ecosystems draining these landscapes - such as Atlantic salmon, that can draw both anglers and ecotourists alike. A pivot toward a One-Health Framework is a logical next step for the province as, at present, efforts to integrate observational and monitoring efforts are already conducted by multiple federal, provincial, Indigenous rightsholders, as well as non-government stakeholders. Finally, in recognition that financial resources for these efforts are finite, we would recommend the implementation of a standardized, seasonal water quality sampling scheme that includes flow-weighted sampling and automated samplers to better capture and predict rapidly changing conditions in the region in response to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".