Negative effects of the sea lice therapeutant emamectin benzoate at low concentrations on benthic communities around Scottish fish farms
Bibliographic record
Abstract
Emamectin benzoate is used as an in-feed treatment for the control of sea lice parasites in all of the main farmed Atlantic salmon (Salmo salar) facilities worldwide (Norway, Chile, Scotland and Canada). Investigations into its effect on non-target benthic fauna resulting from its excretion from farmed fish and uneaten feed have been limited. This paper presents the findings from a study that intended to assess the impact of emamectin benzoate on benthic fauna using a new low detection method for emamectin benzoate. Eight fish farms in the Shetland Isles, Scotland were surveyed, with sediment sampled along transects radiating from the farms analysed for benthic ecology, sediment chemistry and sediment veterinary medicine residues (analysed for emamectin benzoate and teflubenzuron). Canonical Correspondence Analysis (CCA) and Generalised Linear Mixed Modelling (GLMM) were used to assess which environmental parameters observed during the survey had the biggest effect on benthic community composition and abundance, and more specifically crustacean abundance and richness. Emamectin benzoate was found in 97% of samples, demonstrating widespread dispersion in the sediments sampled. The CCA showed that species composition was predominantly ordinated along a gradient of particle size, with a secondary axis dominated by a change in emamectin benzoate and organic carbon enrichment. Peaks in abundance of crustacean species were predicted to be organised along a gradient of emamectin benzoate concentration. The GLMM corroborated this by showing that emamectin benzoate had the strongest negative effect on total crustacean abundance and species richness, though there was some degree of collinearity with organic carbon, that had a smaller effect. Overall, this study shows that, following its use as an in-feed treatment for sea lice, emamectin benzoate residues are more widely distributed in the benthic environment than previously thought, and have a statistically significant effect on benthic ecology at the concentrations observed in this study.
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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 | 0.000 | 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 | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 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 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".