Understanding Yellowmouth: Reeling in the Root of Antimicrobial Use in British Columbia Salmonid Production
Bibliographic record
Abstract
Food insecurity is a pressing global issue that has been further exacerbated by challenges such as a burgeoning global population and unforeseen crises like the COVID-19 pandemic. Salmonid aquaculture could mitigate these challenges by contributing to sustainable food systems. In British Columbia, Canada, the salmonid aquaculture industry is facing critical sustainability and economic challenges due to bacterial diseases, with yellowmouth disease caused by Tenacibaculum maritimum posing substantial concerns. This disease necessitates antimicrobial use (AMU), thereby increasing the risk of antimicrobial resistance (AMR), a problem that spans the One Health sectors of environmental, human, and animal health. The current research delves into understanding and managing yellowmouth disease in salmonid farming operations to reduce AMU and mitigate AMR risks. By synthesizing existing research through a comprehensive scoping review and analyzing extensive data from an Atlantic salmon producer in British Columbia, this thesis aims to identify the management, production, and environmental factors that contribute to the incidence of yellowmouth disease. Understanding these factors is crucial for developing targeted interventions that can reduce disease. Chapter 2 of the thesis is a scoping review aimed at synthesizing the available literature on factors associated with T. maritimum infection in both farmed and wild salmonids. The review highlighted the multifactorial nature of the disease, emphasizing the interplay of host biology, environmental factors, and pathogen characteristics. Key findings included the importance of considering fish age and size at sea entry, stocking density, minimizing physical abrasions, and reducing stress-inducing conditions to manage and prevent outbreaks effectively. The chapter also identified an important gap in previous research regarding multivariable analyses of T. maritimum infections, underscoring a need for comprehensive studies that incorporate multiple interacting factors. Chapter 3 examined data from a British Columbia Atlantic salmon producer (2015-2021) using random forest models and multivariable linear regression to identify factors associated with antimicrobial use (AMU), a proxy for yellowmouth disease incidence. Key findings included a significant protective effect of higher salinity levels, reducing AMU by 0.09 mg/kg biomass (p < 0.05). Temperature also acted as a confounding variable. The findings further underscored the importance of broodstock and days between weights of 100-500 grams (DBW) after placement at sea on AMU. Specific interactions between broodstock types and DBW showed differential impacts on AMU, indicating that genetic factors and growth rates are critical in disease management. There were seasonal and year-to-year trends that differed in magnitude and shape depending on the area of placement. Antimicrobial use was generally the greatest when fish were placed in the middle of the year (week 20), falling when fish were placed towards the end of the year, but these trends depended on the area of production. Additionally, site-level clustering was significant, with an intraclass correlation coefficient (ICC) of 0.30 (p < 0.001), emphasizing the need for site-specific management. The study also highlighted interactions between area and temporal variables, indicating the importance of regional and seasonal considerations in disease management strategies. The findings of this thesis provide valuable insights into the factors influencing antimicrobial use (AMU) in the Atlantic salmon aquaculture industry in BC. As the industry faces increasing public pressure to adopt sustainable practices, understanding the specific roles of environmental conditions, broodstock genetics, growth rates (DBW), and site-specific management is essential. Continued research is necessary, and the complexity of our findings highlight the necessity for targeted approaches to meet public demand for responsible aquaculture practices. Integrating the findings in this thesis and future research will support the health of both aquaculture systems and the broader environmental context in which they operate. Preserving and improving aquatic health through informed management practices in BC finfish farms is crucial for reducing antimicrobial use (AMU) and slowing the development of antimicrobial resistance (AMR). This study embodies the One Health approach by exploring how management practices and environmental factors affect AMU in aquaculture. This highlights the idea that the management of Tenacibaculum maritimum impacts not only salmon health but also the broader marine ecosystem and human health by potentially contributing to the development of AMR, emphasizing the interconnected impact on animal, human, and environmental health.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".