Descriptive analyses of bacterial communities in marine sediment microcosms spiked with fish wastes, emamectin benzoate, and oxytetracycline
Bibliographic record
Abstract
In marine sediments surrounding salmon aquaculture sites, organic matter (OM) enrichment has been shown to influence resident bacterial community composition; however, additional effects on these communities due to combined use of the sea-lice therapeutant emamectin benzoate (EMB) and the widely used antibiotic oxytetracycline (OTC) are unknown. Here, we use sediment microcosms to assess the influence of OM, EMB, and OTC on benthic bacterial communities. Microcosms consisted of mud or sand sediments enriched with OM (fish and feed wastes) and spiked with EMB and OTC at environmentally-relevant concentrations. Samples were collected from initial matrices at the initiation of the trial and after 110 days for 16 S rRNA gene sequencing of the V3-V4 region and microbiome profiling. The addition of OM in both mud and sand sediments reduced alpha diversities; for example, an average of 1106 amplicon sequence variants (ASVs) were detected in mud with no OM addition, while only 729 and 596 ASVs were detected in mud with low OM and high OM, respectively. Sediments enriched with OM had higher relative abundances of Spirochaetota, Firmicutes, and Bacteroidota. For instance, Spirochaetota were detected in sediments with no OM with a relative abundance range of 0.01-1.2%, while in sediments enriched with OM relative abundance varied from 0.16% to 26.1%. In contrast, the addition of EMB (60 ng/g) or OTC (150 ng/g) did not result in distinct taxonomic shifts in the bacterial communities compared to un-spiked sediments during the timeline of this experiment. EMB and OTC concentrations may have been below effective inhibitor concentrations for taxa in these communities; further work should explore gene content and the presence of antibiotic resistance genes (ARGs) in sediment-dwelling bacteria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".