Sustainability of Fisheries and Aquaculture at the Interface of Climate Change and Emerging Infectious Diseases: What Aquatic Epidemiology Has to Offer?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».