Sustainability of Fisheries and Aquaculture at the Interface of Climate Change and Emerging Infectious Diseases: What Aquatic Epidemiology Has to Offer?
Bibliographic record
Abstract
The global captured fishery is at an all-time low, however, the rising world population and the increase in demand for seafood have led to the rapid growth of the aquaculture industry, including net-pen based salmon production. The aquaculture and fishing industries are challenged by changing ecosystems due to climate change as well as from an increase in the emergence, severity, and prevalence of infectious diseases in the aquatic ecosystem. The potential consequences of farming fish in the vicinity of native wild sympatric fish species is an ongoing debate and has led to the closure of some farm sites in Canada and can have ramifications for other farming regions and species in the absence of a social license. Climate change and infectious diseases are altering the population dynamics of many commercially and culturally important fisheries such as Atlantic and Pacific salmon, lobsters, etc. Many aquatic food animal diseases are associated with pronounced shifts in microbial community structures or genetic and functional changes in reservoir non-virulent progenitor variants. The long-term goal of my research program is to understand the effects of changing ecosystem, and the interaction of farmed and wild fish, by utilizing big data along with appropriate quantitative and epidemiologic tools, on infectious diseases of aquatic food animal species and ultimately to enhance the aquatic epidemiology research program for sustainable aquaculture and fishery. The short-term goal is to conduct prospective and retrospective on-farm (field) or in-silico studies to elucidate the interactions between host, pathogen(s), and environment to understand the occurrence, transmission, and risk factors associated with emerging or likely to emerging infectious diseases in salmon aquaculture and lobster fishery in Canada. Some of the key questions my current research is trying to answer are: what are the spatio-temporal trends in emergence and reemergence of infectious diseases of aquatic food animals, how abundant are non-virulent strains of infectious agents, and how likely are they to convert to virulent strains and result in clinical outbreaks? To what extent do the environmental and other factors interact with these microbial and genetic shifts and result in clinical disease? Are there factors that can be managed to reduce the impact of such diseases? My research uses molecular epidemiology (including microbiome analyses) to evaluate genetic differences between variants and strains of the infectious agents, and profile differences in microbial communities between healthy and clinical fish, applies epidemiological methods to identify the component causes/risk factors (specific agents, genotypes, variants, and strains) involved in the clinical manifestation of these diseases to help understand the complex causal pathway/s of the disease, investigates the association of environmental (water temperature, salinity, dissolved oxygen, and plankton), host (immune system, stress markers), and management factors with outbreaks of the diseases and employs simulation models to evaluate the effectiveness of different control and mitigation measures on the potential spread of infectious diseases between aquaculture sites. I will discuss and present some of my recent studies using these methods that will highlight the importance of epidemiologic research in addressing infectious disease and productivity issues in farmed and wild salmon, lobster, and shrimp.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".