SBEP-A low CO2 smart autonomous multiplatform system to monitor and forecast Calanus stock -a new sustainable climate-neutral blue fish feed

The aquaculture industry is expected to grow, necessitating the production of fish feed from sustainable resources to support the production of farmed fish. One promising approach involves harvesting zooplankton, a natural resource abundant in the oceans, renowned for its richness in marine fat and protein content. The waters surrounding the Lofoten Islands in the Norwegian Sea are particularly rich in zooplankton, especially from March to June. This represents a substantial biological reservoir for harvesting and processing into fish feed. Despite the potential, the zooplankton fishery remains relatively underdeveloped. As of 2024, only three fishing boats in Norway are engaged in zooplankton harvesting, with catch levels significantly below the quotas set by Norwegian authorities. Recognizing the large demand for sustainable fish feed and the untapped potential of zooplankton fisheries, proactive management of zooplankton stocks is imperative to ensure their sustainable utilization. To address this need, we propose leveraging cutting-edge technologies, including autonomous uncrewed vehicles both subsea and surface, equipped with advanced sensors. These vehicles will operate in tandem with a cloud-based data portal, complemented by satellite observation data, to comprehensively map and quantify zooplankton populations. This mapping initiative holds the promise of aligning with fisheries operations, fostering more sustainable practices. The real-time data generated by this initiative will offer an integrated understanding of zooplankton resources across temporal and spatial dimensions empowering authorities to make informed decisions regarding the management of zooplankton stocks. This collaborative project involves partnerships with Poland, Portugal, Germany, and Cyprus, uniting diverse expertise and resources towards a common goal of sustainable aquaculture and marine resource management.

The CliN-BluFeed project ambitions to develop and use a low-CO2 smart autonomous multiplatform system to monitor and forecast Calanus finmarchicus stock which is a new sustainable climate neutral blue fish feed for the growing aquaculture industry. We aim to broaden the spatial, temporal and biological resolution and coverage of Calanus in-situ monitoring by combining optical and acoustic sensors on autonomous surface and underwater vehicles and earth-orbiting satellites. We will use this in-situ data in tandem with ex-situ experimental assays and mechanistic simulation modelling to generate real-time predictions of C. finmarchicus abundance, biomass, population dynamics and the vertical and horizontal distributions of the stock in the Norwegian Sea. The project will: 1) optimize an optical sensor (UVP6) for real-time in-situ identification of C. finmarchicus and other co-occurring plankton and micronekton, and estimation of their abundances and size-structure. 2) characterize the zooplankton and micronekton community characterization and quantification towards identification of potential bycatch composition and reduction of bycatch during harvesting operations using optical and acoustic sensors installed on autonomous vehicles. 3) advance the present understanding of how external environmental variables influence Calanus vertical behavioral and abundance dynamics during the harvesting season. 4) Describe Calanus transcriptomic rhythmicity in the field and determine to what extend different environmental cues and internal rhythm regulators drive diel behavior. 5) Map the three-dimensional spatial distributions of Calanus in the harvesting area using ocean color and LiDAR remote sensing technologies and 6) forecasting of Calanus stock size and 3D spatial distributions. The project will deliver processed data products to multiple stakeholders and end users (regulatory agencies, state owned data portal, repository database, fishery etc).