Journal of aquatic animal health · 2024
Predicting the abundance of Lepeophtheirus salmonis in the Bay of Fundy, New Brunswick.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Parent Marianne I, Stryhn Henrik, Hammell K Larry, Fast Mark D, Vanderstichel Raphaël
Аннотация
OBJECTIVE: The primary objective was to construct a time series model for the abundance of the adult female (AF) sea lice Lepeophtheirus salmonis in Atlantic Salmon Salmo salar farms in the Bay of Fundy, New Brunswick, Canada, for the period 2016-2021 and to illustrate its short-term predictive capabilities.
METHODS: Sea lice are routinely counted for monitoring purposes, and these data are recorded in the Fish-iTrends database. A multivariable autoregressive linear mixed-effects model (second-order autoregressive structure) was generated with the outcome of the abundance of AF sea lice and included treatments, infestation pressures (a measure that represents the dose of exposure of sea louse parasitic stages to potential fish hosts) within sites (internal) and among sites (external), and other predictors. The treatments were categorized by duration and type.
RESULT: The effect of mechanical treatments decreased with increasing sea surface temperature. In-sample predictions had good accuracy. A one-standard-deviation increase in the external infestation pressures (EIPAF) produced a significant relative increase in the abundance of AF sea lice by 5% when other model predictors were kept constant. Sites separated by short seaway distances had stronger EIPAF than sites with more considerable distances.
CONCLUSION: This model may be helpful for managers and farmers in implementing sea lice mitigation strategies on salmon farms in the Bay of Fundy.
METHODS: Sea lice are routinely counted for monitoring purposes, and these data are recorded in the Fish-iTrends database. A multivariable autoregressive linear mixed-effects model (second-order autoregressive structure) was generated with the outcome of the abundance of AF sea lice and included treatments, infestation pressures (a measure that represents the dose of exposure of sea louse parasitic stages to potential fish hosts) within sites (internal) and among sites (external), and other predictors. The treatments were categorized by duration and type.
RESULT: The effect of mechanical treatments decreased with increasing sea surface temperature. In-sample predictions had good accuracy. A one-standard-deviation increase in the external infestation pressures (EIPAF) produced a significant relative increase in the abundance of AF sea lice by 5% when other model predictors were kept constant. Sites separated by short seaway distances had stronger EIPAF than sites with more considerable distances.
CONCLUSION: This model may be helpful for managers and farmers in implementing sea lice mitigation strategies on salmon farms in the Bay of Fundy.