The Influence of Aquaculture on Marine Microbiota and Pathogen Communities in a British Columbia Coastal Ecosystem
Bibliographic record
Abstract
The productivity of wild-capture fisheries has long plateaued and global dependence on aquaculture is progressively increasing. Despite the perception that substituting wild caught seafood for domestic production benefits the conservation of wild populations through decreased fishing pressure, this is frequently not the case. Aquaculture can challenge the conservation of wild populations via the harvest of wild fish for feed, degradation of wild habitat, and by facilitating the spread of infectious disease. This thesis examines how aquaculture facilities influence microparasite transmission dynamics and the potential consequences for co-occurring wild species. I investigate these topics in coastal British Columbia, Canada, where wild Pacific salmon populations encounter open-net salmon farms along their migration routes. In chapter 2, I use seawater filtration and quantitative PCR (qPCR) to quantify the presence of DNA for 39 viral, bacterial, and eukaryotic microparasite species in the marine environment in relation to active Atlantic salmon farms. I show that the odds of encountering an infectious agent are 2.72 times higher at active salmon farms relative to inactive control sites. In chapter 3, I evaluate Atlantic salmon eDNA dispersal at 2m and 8m depths throughout 55 km of narrow migratory channels, in relation to four Atlantic salmon farms. I show that long distance dispersal (1.5 km – 3.9 km) occurs, and the spatial extent of environmental eDNA dispersal depends primarily on current velocity and depth. In chapter 4, I evaluate environmental microbe communities using deep amplicon sequencing to evaluate variation in community structure among 57 salmon farm tenures in relation to aquaculture activity, temperature, salinity, turbidity, and multiple scales of spatial predictors. The results from chapter 4 show that the effect of salmon farm activity on the marine microbiota was detectable but small relative to environmental variation and spatial structure although three bacterial Orders: Flavobacteriales, Rhodobacterales, and Alteromonadales, stood out as strongly positively correlated with salmon farm activity. The sustainability of aquaculture and the persistence of many wild populations will depend on our understanding of the local and regional influences of domestic reservoirs on microparasite transmission and our ability to develop integrated control measures to limit transmission among wild populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".