Modelling growth variability in longline mussel farms as a function of stocking density and farm design

Mussels (Mytilus edulis) are commonly cultivated on artificial structures like rafts, poles or longlines to facilitate farming operations. Farm structures and dense mussel populations may result in water flow reduction and seston depletion and thus reduced individual mussel growth and spatial growth variability inside a farm. One of the challenges in mussel farming is thus to scale and configure farms in order to optimise total mussel production and individual mussel quality under different environmental regimes. Here we present a spatially resolved model for simulation of flow reduction, seston depletion and individual mussel growth inside a longline farm based on information about farm configuration (spacing between longlines, farm length and stocking density) and background environmental conditions (current speed, seston concentration and temperature). The model simulations are forced by environmental data from two fjords in south-western Norway and the farm configurations are defined within operational ranges. The simulations demonstrate spatial growth patterns at longlines under environmental settings and farm configurations where flow reduction and seston depletion have significant impacts on individual mussel growth. Longline spacing has a strong impact on the spatial distribution of individual growth, and the spacing is characterised by a threshold value. Below the threshold growth reduction and spatial growth variability increase rapidly as a consequence of reduced water flow and seston supply rate, but increased filtration due to higher mussel densities also contributes to the growth reduction. The spacing threshold is moderated by other farm configuration factors and environmental conditions. Comparisons with seston depletion reported from other farm sites show that the model simulations are within observed ranges. A demonstration is provided on how the model can guide farm configuration with the aim of optimising total farm biomass and individual mussel quality (shell length, flesh mass, spatial flesh mass variability) under different environmental settings. The model has a potential as a decision support tool in mussel farm management and will be incorporated into a GIS-based toolbox for spatial aquaculture planning and management.