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Record W7066328154

The Influence of Aquaculture on Marine Microbiota and Pathogen Communities in a British Columbia Coastal Ecosystem

2022· dissertation· W7066328154 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsnot available
FundersUniversity of TorontoDavid Suzuki Foundation
KeywordsAquacultureBiological dispersalMaricultureFishingMarine ecosystemBiosecurity
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.239
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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