No abstract

Integrated multi-trophic aquaculture (IMTA) has the potential of reducing open-cage fish farming impacts on the environment while also introducing new value chains. The aim of this study was to investigate the growth and composition of the kelp Saccharina latissima in salmon-driven IMTA, and to assess the spatial extent of the influence of salmon derived nitrogen in order to evaluate the upscaling potential for IMTA. S. latissima was cultivated 100, 200, and 1,000 m east and 1,000 m west of a 5,000 tons salmon farm in Western Norway from February to September 2013. The proportion of salmon derived nitrogen available for the kelp showed a clear decline with distance from the farm. Accordingly, the kelp cultivated near the salmon cages grew faster during the spring season, and growth rate decreased with increasing distance from the farm. A spatially explicit numerical model system (SINMOD), including compartments for dissolved nutrients and kelp growth, was tuned to the field data and used to investigate the potential for upscaling IMTA production. The model was used to introduce a new metric-the impacted area IA-for the areal effects of IMTA in terms of the increase in production by IMTA. The model showed that a 25 hectare kelp farm in the vicinity of the studied salmon farm could take up 1.6 of the 13.5 tons of dissolved inorganic nitrogen released during kelp cultivation, amounting to almost 12% of the ammonia released during the cultivation period from February to June. The 25 hectare kelp farm would have a production yield of 1,125 tons fresh weight (FW), being 60% more than that of a non-IMTA kelp farm, while a 20% increase of kelp FW could be obtained over a 110 hectar area in salmon-driven IMTA. To achieve an even mass balance, an area of approximately 220 ha −1 would be needed to cultivate enough kelp to fix an equivalent of the nitrogen released by the fish.

Sea lice Lepeophtheirus salmonis (Krøyer) are a major ectoparasite affecting farmed Atlantic salmon in most major salmon producing regions. Substantial resources are applied to sea lice control and the development of new technologies towards this end. Identifying and understanding how sea lice population patterns vary among cages on a salmon farm can be an important step in the design and analysis of any sea lice control strategy. Norway’s intense monitoring efforts have provided salmon farmers and researchers with a wealth of sea lice infestation data. A frequently registered parameter is the number of adult female sea lice per cage. These time-series data can be analysed descriptively, the similarity between time-series quantified, so that groups and patterns can be identified among cages, using clustering algorithms capable of handling such dynamic data. We apply such algorithms to investigate the pattern of female sea lice counts among cages for three Atlantic salmon farms in Norway. A series of strategies involving a combination of distance measures and prototypes were explored and cluster evaluation was performed using cluster validity indices. Repeated agreement on cluster membership for different combinations of distance and centroids was taken to be a strong indicator of clustering while the stability of these results reinforced this likelihood. Though drivers behind clustering are not thoroughly investigated here, it appeared that fish weight at time of stocking and other management practices were strongly related to cluster membership. In addition to these internally driven factors it is also possible that external sources of infestation may drive patterns of sea lice infestation in groups of cages; for example, those most proximal to an external source. This exploratory method proved useful as a pattern discovery tool for cages in salmon farms.