Effects of the Aquatic Herbicide Diquat on Non-Target Aquatic Biota: A Mesocosm Study
Bibliographic record
Abstract
Abstract Invasive aquatic plants threaten the health of aquatic ecosystems, and demand for chemical control is likely to increase as nuisance levels are reached. Diquat is a contact herbicide registered in many countries to control invasive aquatic plants. The objective of our study was to assess the effects of the aquatic herbicide diquat (Reward®) on North American native and non-native plants, algal communities, an amphipod and an amphibian using outdoor mesocosms to simulate natural systems. Our experimental design included a control and five nominal concentrations of diquat ranging from 100% (18.3 L/ha; 1153 µg/L) to 6.4% (1.2 L/ha; 74 µg/L) of the label rate of a single diquat application. Effects of diquat were found to vary among study organisms. All four plant species were negatively affected at all concentrations, exhibiting either mortality or severe reductions in dry biomass (< 1% the biomass of the controls). In contrast, phytoplankton biomass increased 7 d following diquat application concomitant with significant changes in algal community structure. A concentration–response relationship was observed for amphipod survival (LC 50 at 6 weeks = 155 µg/L) with 100% mortality in the highest treatment after two weeks. In contrast, diquat had a significant positive effect on tadpole survival, growth and development, possibly because of the higher algal biomass and decaying plant tissues. A lower label rate than currently recommended, at least in waterbodies with low turbidity, could provide effective control of target species while reducing effects on non-target biota.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".