Aquaculture Reports · 2021 · 32 citations · 51 references
Natural ResourcesAnimal SciencesZoologyAquacultureFish BiologyFish PhysiologyAquaculture MonitoringAquaculture NutritionAquatic Animal WelfareFish GrowthFish HealthSwimming PhysiologyAccelerometer CalibrationAcoustic Monitoring Of FishAquaculture WelfareCritical SpeedGrowth Performance AssessmentMonitoring Welfare IndicatorsOrganic AquaculturePhysiological Stress Markers
Both the conventional and organic aquaculture sectors have grown rapidly over the past few years. Moreover, welfare has attracted increased attention on the part of both consumers and governments. However, fish welfare assessment is complex and thus needs to adapt measurements that are easily applicable to aquaculture conditions. In this study, in addition to classical welfare indicator measurements (physiological stress indicators and growth performance), we recorded the swimming activity data using acoustic transmitters to evaluate the welfare of European sea bass (Dicentrarchus labrax) fed a conventional vs. an organic diet. Prior feeding trial, the swimming activity recorded by tag has been calibrated with water speed during critical swimming speed (Ucrit) tests. This calibration allowed us to increase the power of the recorded data, providing information on swimming activity with respect to the Ucrit value and on the metabolic cost of swimming. After a four-month experimental period, physiological stress indicators and growth performance did not differ significantly between the two diet groups. However, we observed a subtle difference in swimming activity: the fish in the organic diet group were more active during the feeding period in the morning. All indicators considered, our results suggest that an organic diet does not incur higher metabolic costs and does not affect the welfare of the European sea bass. Moreover, this study shows that the use of acoustic transmitters previously calibrated with physiological indicators, such as Ucrit, is a promising tool for welfare monitoring in aquaculture conditions.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text