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Record W7029065567

Investigating the role of Vibrio aestuarianus in summer mortality of farmed Crassostrea gigas in Baynes Sound, British Columbia

2021· dissertation· en· W7029065567 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCrassostreaPacific oysterAquacultureOysterShellfishAbundance (ecology)Vibrio
DOInot available

Abstract

fetched live from OpenAlex

Marine aquaculture is already vital to global food security and will continue to become more important in the coming years. Crassostrea gigas (Pacific oysters) is the primary oyster species cultivated worldwide. The FAO and IPCC predict that climate change will create uncertainty and challenges for marine aquaculture. Baynes Sound, British Columbia, is a productive region for aquaculture, producing >50% of British Columbia’s total annual bivalve production by live weight and value. Major summer mortality events have been documented in farmed Crassostrea gigas globally since the 1950’s. These events are believed to be caused by a multiplicity of factors including changes induced by anthropogenic climate change. One of the major contributors to summer mortality is the proliferation of Vibrio bacteria, specifically Vibrio aestuarianus, which has been shown to increase in abundance and virulence when seawater temperatures rise. Despite this connection and the economic importance of oyster farming in the region, little is known about the presence of V. aestuarianus in Baynes Sound. Our 17-month study sampled 7 sites in Baynes Sound on 33 occasions from May 2019 to September 2020. We found a positive correlation between seawater temperature and total Vibrio detected in water samples in Baynes Sound, an association that was stronger when the overall temperature regime was warmer. We found no significant correlation between any of the bacterial assays tested and salinity, pH, or Ωarag saturation. We also did not identify a geographic pattern to bacterial abundance or virulence amongst test C. gigas in the field. Understanding that flagellates are the predominant type of microalgae present in Baynes Sound when summer mortality events occur, in lab trials, we found that incorporating V. aestuarianus into marine aggregates with flagellate microalgae caused higher mortality than aggregates with diatoms or planktonic V. aestuarianus. These results were not statistically significant but led us to look at how exposure to husbandry stress pre and post inoculation with V. aestuarianus incorporated into marine aggregates affects mortality. We found that stress was a significant driver of mortality, particularly when administered 24h post inoculation, suggesting that farmers should avoid sorting or tumbling their oysters in the summer, and particularly immediately after a marine heatwave. Oysters lack adaptive immune systems and are grown in an open ocean environment where it is not possible to eliminate their exposure to pathogens. These factors make it impossible to use vaccines or antibacterial disinfectants to combat diseases. Therefore, breeding genetic resistance to V. aestuarianus may be the most effective way to fight summer mortality. Creating a repeatable and accurate protocol for inoculating oysters with marine bacteria is key to accurate heritability measurements and the estimation of breeding values of different families. Key factors include controlling for dose per animal, laboratory efficiency, and inoculation via a mechanism that mimics real-world infection and does not bypass the animal’s immune defenses. We designed a protocol which controls for these factors, separating each animal into individual containers and adding a controlled dose of planktonic bacteria to each. Previously used methods of injection or using an infected “donor” animal in a group tank do not control for dose or bypass parts of the oysters’ natural immune system, potentially creating inaccuracies in survival data generated with these methods. After designing this protocol, we tested 32 full-sib families and estimated the heritability of survival to V. aestuarianus on the observed and underlying liability scales to be 0.095 (SE = 0.043), and 0.15 (SE = 0.068) respectively. We also found a strong negative correlation between oyster size and survival, with a gram of additional weight creating a 73% increase in the risk of death. Fast growth and large size are two traits which have been explicitly bred into C. gigas stock. Our work suggests that breeding a slower-growing, smaller animal may reduce summer mortality.

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 score0.092
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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