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Estimating sea lice infestation pressure on salmon farms: Comparing different methods using multivariate state-space models.
Elghafghuf, Adel; Vanderstichel, Raphael; Hammell, Larry; Stryhn, Henrik.
Affiliation
  • Elghafghuf A; Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada. Electronic address: aelghafghuf@upei.ca.
  • Vanderstichel R; Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
  • Hammell L; Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
  • Stryhn H; Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
Epidemics ; 31: 100394, 2020 06.
Article in En | MEDLINE | ID: mdl-32422519
Sea lice are ectoparasites of salmonids, and are considered to be one of the main threats to Atlantic salmon farming. Sea lice infestation on a farm is usually initiated by attachment of the free-living copepodid stage derived from the surrounding water, frequently originating from adult lice on the same farm or from neighboring salmonid farms, referred to as internal and external sources, respectively. Various approaches have been proposed to quantify sea lice infestation pressure on farms to improve the management of this pest. Here, we review and compare five of these methods based on sea lice data from 20 farms located near Grand Manan island in the Bay of Fundy, New Brunswick, Canada. Internal and external infestation pressures (IIP and EIP, respectively) were estimated using different approaches, and their effects were modeled either by a unique parameter for all production cycles or by different parameters for each production cycle, using a multivariate state-space model. Predictive variables, such as water temperature and sea lice treatments, were included in the model, and their effects across production cycles were estimated along with those of other model parameters. Results showed that models with only EIP explained the variation in the data better than models with only IIP, and that models with unique IIP and unique EIP for all cycles were generally associated with the best model fit. The simplest, fixed lag method for calculating infestation pressure had the best predictive performance in our models among the methods studied.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aquaculture / Salmo salar / Copepoda / Fish Diseases Limits: Animals Country/Region as subject: America do norte Language: En Journal: Epidemics Year: 2020 Document type: Article Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Aquaculture / Salmo salar / Copepoda / Fish Diseases Limits: Animals Country/Region as subject: America do norte Language: En Journal: Epidemics Year: 2020 Document type: Article Country of publication: Netherlands