Overcoming water quality effects in biological monitoring: a case study of amphipod in situ exposures in Ontario agricultural streams
Bibliographic record
Abstract
Bioindicators add valuable understanding of biological impacts to contaminant monitoring programs. However, attributing effects (e.g., mortality and growth impairment) to contaminant exposures is challenging because of potential confounding by environmental variables. We assessed the influence of four water quality variables (temperature, dissolved oxygen, pH, and conductivity) on contaminant effects assessments during in situ exposures of the amphipod Hyalella azteca in six agricultural and urban watersheds in southern Ontario, Canada (2005-06, 2008-10). We further tested whether sampling in specific months of the growing season would minimize confounding effects. While high toxicity from organophosphate and carbamate pesticides increased mortality and reduced growth in caged amphipods, warmer stream temperatures also affected endpoints, increasing mortality and improving growth. Seasonal patterns indicated early summer (June) as optimal for detecting impacts when: (1) stream pesticide concentrations were highest, (2) acetylcholinesterase (AChE) inhibition (a biomarker of organophosphate/carbamate exposure) was highest, and (3) stream temperature was below its seasonal peak. Specifically, higher correlations among organophosphate pesticide concentrations, AChE inhibition, and mortality indicated better attribution of cause in this month (r = 0.53-0.76, p < 0.05). Ability to discriminate between pesticide-impacted sites and reference sites was also greater than other months (June = 100% correct assignment as high-impact or reference site). Considering sampling times of stream bioindicators that maximize pesticide responses and limit confounding effects of water quality may improve the accuracy and resource-efficiency of biological monitoring programs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".