Data products for sustainable fisheries V1. ARCFISH Deliverable D3.1

Project co-funded by the European Union under the SBEP 2023 First Joint Transnational Co-Funded Call Project Acronym: ARCFISH Project Full Title: Digital Twin of the Ocean for Arctic Fisheries Project Coordinator: Nansen Environmental and Remote Sensing Center (NERSC) Deliverable 3.1 Data products for sustainable fisheries V1 Work-package WP3 New data products and regional database Task Task 3.1: Ecological indices from modelling Task 3.2: Time-series of environmental data Task 3.3: Fishery-dependent data products Task 3.4: Regional database Deliverable leader AU Authors Marie Maar (AU), Vibe Schourup-Kristensen (AU), Janus Larsen (AU), Arne Johan Hestnes (KD), Ilona Goszczko (IOPAN), Agnieszka Beszczyńska-Möller (IOPAN), Valur N. Gunnlaugsson (MATIS) Reviewers Ruth Higgins, Torill Hamre Version 1.0 Due date 30.09.2025 Submission date 20.10.2015 Revision date NA Dissemination level PU - Public Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 2 Revision history Version Date Change record Responsible 0.1 07.08.2025 Document outline AU 0.2 09.10.2025 First draft ready for internal review AU 0.3 15.10.2025 Draft edited by internal reviewers EurOcean, NERSC 0.4 17.10.2025 Submitted version AU 1.0 20.10.2025 Final QA and approved for delivery NERSC Copyright © ARCFISH Consortium. Copies of this deliverable – also of extracts thereof – may only be made with reference to this deliverable. Citation Maar, M., Schourup-Kristensen, V., Larsen, J., Hestnes, A. J., Goszczko, I., Beszczynska-Möller, A., & Gunnlaugsson, V. N. (2025). Data products for sustainable fisheries V1. ARCFISH Deliverable D3.1. Zenodo. https://doi.org/10.5281/zenodo.17348384 Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 3 Executive Summary This document describes the first version of new data products for sustainable fisheries developed in the ARCFISH project. This project is funded by the Research Councils of Norway (Research Council of Norway (RCN)), Portugal (Fundação para a Ciência e a Tecnologia (FCT)), Poland (The National Centre for Research and Development, Poland (NCBR)), Denmark (Innovation Fund Denmark (IFD)) and Iceland (The Icelandic Centre for Research (RANNIS)), after successful evaluation of the proposals for the 2023 First Sustainable Blue Economy Partnership Joint Transnational Co-Funded Call. ARCFISH will develop a pilot Digital Twin of the Ocean (DTO) supporting sustainable fisheries in the Arctic. The DTO will be based on the existing Blue Insight platform that seamlessly supports the market segments of the ocean observing value chain of the New Blue Economy (Heslop et al., 2022). The New Blue Economy, also known as the Sustainable Blue Economy (SBE), is a concept that promotes sustainable development and conservation of ocean resources for economic, social, and environmental benefits. In the Arctic, sustainable fisheries focusing on achieving long-term sustainability and resilience of ocean ecosystems and communities is the key to uphold good living conditions for its residents. However, the negative impacts of traditional economic activities such as overfishing, marine pollution, habitat destruction, and climate change have threatened the health and sustainability of the ocean, leading to declining fish stocks, loss of biodiversity, and increased coastal vulnerability to natural disasters. New data products and services, co-developed with relevant stakeholders, are needed to provide a better basis for decisionmaking in fisheries planning and management. For example, for Disko Bay, western Greenland, ecosystem indices based on an improved ecosystem model could include monthly spatial maps of zooplankton biomass and production, benthos biomass, shrimp biomass, and upwelling areas. Data collected around Iceland towards the Barents Sea onboard fishing vessels and in seafood processing could be implemented in the DTO to support relevant stakeholders. For the Arctic in general, potential new services could include advanced eLogbooks and spatio-temporal pattern detection and visualisation. For cost-efficiency these services must leverage novel digital technologies such as DTOs to acquire, process, analyse and visualise heterogeneous data from a wide range of sources. More information on the project can be found at: https://www.arcfish.eu/. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 4 Contents Executive Summary ....................................................................................................................................... 3 List of Acronyms and Abbreviations ............................................................................................................. 6 1. Introduction ......................................................................................................................................... 7 1.1. Background .................................................................................................................................. 7 1.2. Organisation of the document .................................................................................................... 7 2. Short introduction on the new data products and regional database................................................. 8 3. Ecological indices from modelling ........................................................................................................ 8 3.1. Disko Bay ecosystem model ........................................................................................................ 8 3.2. Spatio-temporal patterns of zooplankton biomass and production ......................................... 10 3.3. Shrimp biophysical model ......................................................................................................... 13 3.4. Ecological indices for fisheries ................................................................................................... 14 4. Time-series of environmental data .................................................................................................... 15 4.1. Time-series data collection ........................................................................................................ 15 4.2. Spatio-temporal patterns of key variables for fisheries ............................................................ 26 5. Fishery-dependent data products ...................................................................................................... 26 5.1. Fisheries-dependent data (FDD), collection and data utilization. ............................................. 26 6. Creation of a Regional Database by Kongsberg Discovery ................................................................. 30 7. Summary and next steps .................................................................................................................... 31 8. References .......................................................................................................................................... 33 Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 5 List of Figures Figure 1: FlexSem ecosystem model. Copernicus data is used to force and validate the model, and Greenland Ecosystem Monitoring (GEM) program has local data from Disko Bay. The 3D model uses an unstructured grid and is coupled to a biogeochem. ...........................................9 Figure 2: a) Depth of the model domain. The dotted and solid lines mark the 150and 350-meter contour lines, respectively. B) Resolution of the model mesh (from 1.8 km to 16 km). ......... 10 Figure 3: Modeled surface chlorophyll concentration on the 23rd of May 2004 from a) previous hydrodynamical model setup, and b) current model setup with new advection scheme. The white line marks an ice concentration of 10%. ........................................................................ 11 Figure 4: Average) Mean surface concentration of zooplankton in Disko Bay from 2004 to 2017. Years) Bias from the long-term mean in surface zooplankton concentration for each year. ............. 12 Figure 5: Mean ice-free period (days) from 2004 to 2018....................................................................... 12 Figure 6: a) Start position of particles, blue colour marks particles released in Disko Bay. b) Density plot of 90-day tracks for all start positions c) Density plot of 90-day tracks for Disko Bay start-positions. .......................................................................................................................... 13 Figure 7: Shrimp habitat model . ............................................................................................................. 14 Figure 8: Four locations chosen to display time series from BIOMER4V2R1 and BIO4GLO12 models with monthly data spanning from December 2021 to August 2025, and from December 2023 to August 2025, respectively. .......................................................................................... 16 Figure 9: Maps of the outputs from BIOMER4V2R1 and BIO4GLO12 models: a) phosphorus, b) chlorophyll, phytoplankton and d) zooplankton concentrations on 1st May 2025 at depth of 25 m. ..................................................................................................................................... 17 Figure 10: Time series of nitrate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. ..................................................................................... 18 Figure 11: Time series of phosphate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. ..................................................................................... 19 Figure 12: Time series of silicate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. ..................................................................................... 20 Figure 13: Time series of chlorophyll concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. ............................................... 21 Figure 14: Time series of phytoplankton concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. ............................................... 22 Figure 15: Time series of zooplankton concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIO4GLO12 model with monthly data spanning from December 2023 to August 2025. ............................................................. 23 Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 6 Figure 16: Trajectories of all BGC Argo floats active in 2025 in the regions (or nearby) of a) West Greenland and b) Iceland shelves. ........................................................................................... 24 Figure 17: Data retrieved from the selected BGC Argo float active in 2025 south of Iceland: a) trajectory of the instrument, b) adjusted temperature, and c) adjusted oxygen concentration along the float’s pathway. ................................................................................ 25 Figure 18: Example of information that are or can be reported in an TrackWell logbook system. ........ 28 Figure 19: Vessel movement and trawl data from Brim pelagic fisheries, displayed in Blue Insight. ..... 30 Figure 20: The regional database, as it will be integrated into the Blue Insight platform. ..................... 31 List of Acronyms and Abbreviations Acronym/Abbreviation Definition AIS Automatic Identification System BGC Biogeochemical CMEMS Copernicus Marine Service CTD Conductivity, Temperature, and Depth DITTO Digital Twins of the Ocean program of the UN Decade of Ocean Science for Sustainable Development DOI Digital Object Identifier DTO Digital Twin of the Ocean eCUDO Electronic Center for Sharing Oceanographic Data ERGOM Ecological ReGional Ocean Model ERS Electronic Reporting System EMODNET European Marine Observation and Data Network EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FDD Fisheries-dependent data EDITO European Digital Twin Ocean GEBCO General Bathymetric Chart of the Oceans GEM Greenland Ecosystem Monitoring ICES International Council for the Exploration of the Sea ILIAD Integrated digital solution facilitates access to oceanic data and boosts decisionmaking INTAROS Integrated Arctic observation system MCS Monitoring, control and surveillance NMDC Norwegian Marine Data Centre OGC Open Geospatial Consortium OSISAF Ocean and Sea Ice Satellite Application Facility RF Recreational fisheries SBE Sustainable Blue Economy SBEP Sustainable Blue Economy Partnership SSF Small-scale fisheries VMS Vessel Monitoring System Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 7 1. Introduction 1.1. Background ARCFISH will develop a pilot Digital Twin of the Ocean (DTO) Platform delivering new data products and services in support of sustainable Arctic Fisheries. These data products and services will be co-designed with stakeholders in the fisheries sector and used to create products such as ecosystem indices that can be applied in fisheries planning and management. Available data sources and gaps will be analysed to fulfil user needs and ingest relevant data into the Blue Insight DTO Platform. They include (1) oceanographic data from research vessels, autonomous mobile platforms, fixed buoys, and ships of opportunity such as fishing vessels, (2) met-ice-ocean forecasts and reanalysis from models (e.g., from CMEMS and INTAROS), (3) fisheries management data (e.g., fisheries stocks, species) from ICES and national sources, and (4) reference data (e.g., bathymetry, economic zones, AIS data). Based on stakeholder needs, a use case for sustainable fisheries will be implemented using the ingested data and tools for generating customised products. The use case will address two geographic regions, the west coast of Greenland centred around the rich fishing grounds surrounding Disko Bay, and the region around Iceland, northwards to the Svalbard archipelago and the Barents Sea. Using the compiled data and developed tools, a regional database of climate, environmental, and fisheries data will be created and made available through an open data repository to support sustainable Arctic Fisheries. This important asset will contribute to the Sustainable Blue Economy Partnership (SBEP) program by providing new data products that can be utilised in DTO platforms to support decision-making in fisheries management. The Blue Insight platform developed by Kongsberg Discovery, Norway, will be part of the Digital Twins of the Ocean (DITTO) program of the UN Decade of Ocean Science for Sustainable Development. As part of DITTO and through engagement with other running DTO projects and initiatives such as EDITO, ILIAD, Blue-Cloud2026 and EOSC, ARCFISH development will follow standards for data exchange and implementation of services and tools in the EU DTO. ARCFISH will prepare training material for using the Blue Insight DTO platform and organise capacity building events for stakeholders and other SBE projects. Training will be organised in conjunction with project meetings and SBEP events. Furthermore, the regional database, developed services and tools, training and promotional material will be promoted through the iAOS portal from INTAROS, a public project website linked to relevant DTO sites, through the ARCFISH website, and dedicated social media channels. ARCFISH results will also be promoted through the partners’ extensive network of Arctic observing and data management, digital technologies, capacity building in ocean literacy, stakeholder interaction and fisheries. 1.2. Organisation of the document The structure of this document is as follows. Chapter 2 provides a short introduction on the new data products and regional database that will be ingested into the Blue Insight DTO platform. Chapter 3 presents the first version of the modelling products from Disko Bay, Greenland, model system. Chapters 4 and 5 present environmental time-series data and fishery-dependent data from research projects, operational services, databases, in-situ data and monitoring programmes in the regions addressed by the two case studies. Chapter 6 showcase the regional database for the two use cases. The final chapter, Chapter 7, summarizes the work to prepare a first set of new data products for ARCFISH and plans for enhancing the products offered through the Blue Insight DTO platform for the ARCFISH community. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 8 2. Short introduction on the new data products and regional database Based on stakeholder needs from WP1 (multi-stakeholder co-creation of the Arctic DTO) and data ingested in WP2 (Data ingestion services), partners will develop new data products for sustainable fisheries. The products will be available in the Blue Insight DTO platform operated by Kongsberg Discovery and used to create a public regional database. New data products will be developed either from measurements made by the project partners, from in-situ sources or from models developed by the project partners in this or other projects. Extending ecosystem models with a richer representation of the complex interactions in the marine food web will facilitate new products tailored for sound management and utilisation of fisheries resources under today’s rapidly changing climate conditions. Time-series of water column physical and if/where available, biogeochemical properties from in situ platforms, augmented with selected outputs of reanalysis models will be produced to provide comprehensive information on environmental background for fishery data. Fishery-dependent data will be coupled with fishery-independent data and then integrated into the DTO. Fishery-dependent data can include fishing pressure, gear, catch composition, size distribution, condition factor, yield, nematodes etc. Fishery-independent information includes oceanographic-, meteorological-, social, and economic data. Further, a collection of the data integrated for the use cases will be gathered into a regional database. Integrating new model projections, available observations and powerful analysis tools in a DTO platform will open for novel ways of investigating and visualising trends and patterns in species distribution and abundance. Enhancing scientific analysis tools with new algorithms (e.g., machine learning, big data analytics) integrated in the powerful DTO infrastructure will allow more efficient exploitation of the vast amount of data currently available from a broad range of sources (including real-time). This can in turn foster innovations in predicting future scenarios for better informed decision-making and policymaking for a sustainable Blue Economy in Arctic regions. 3. Ecological indices from modelling 3.1. Disko Bay ecosystem model About 60,000 people live in Greenland and most of them along the West coast. They are traditionally highly dependent on the marine ecosystem and Greenland’s economy is presently strongly related to the productivity of the marine waters. With a changing climate regime, i.e. reduction of ice thickness and increasing temperature, an increase in commercial fisheries is likely. Disko Bay is located on the west coast of Greenland at the southern border of the Arctic Sea ice and is influenced by both sub-Arctic waters from southwestern Greenland and Arctic waters from Baffin Bay. Disko Bay is an important “hot spot” for biodiversity and fisheries, and one of the best studied areas in Greenland. The Greenland Ecosystem Monitoring (GEM) program provides data on oceanography, nutrients, phytoplankton, and zooplankton. Fish stocks are monitored by the Greenland Institute of Natural Resources. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 9 The 3D dynamic model of Disko Bay was developed in the EU INTAROS project using local measurements and Copernicus data for model forcing, calibration, and validation (Figure 1). The model was set up using the FlexSem model system (Larsen et al. 2020). FlexSem is an open-source modular framework for 3D unstructured marine modelling. The system contains modules for hydrostatic and non-hydrostatic hydrodynamics, 3D pelagic and 3D benthic models, sediment transport, and agent-based models. The FlexSem source code and precompiled source code for Windows (GNU General Public License) can be downloaded at https://marweb.bios.au.dk/Flexsem. The specific code for the Disko set-up can be downloaded on https://zenodo.org/ (Larsen, 2025; Maar et al., 2025). Bathymetry was obtained from the 150x150 m resolved IceBridge BedMachine Greenland, Version 3 (https://nsidc.org/data/IDBMG4) and interpolated to the FlexSem computational mesh using linear interpolation. The 96,300 km2 large computational mesh for the Disko Bay area was constructed using the mesh generator JigSaw (https://github.com/dengwirda/jigsaw) (Figure 2). It consists of 6,349 elements and 34 depth z-layers with a total of 105,678 computational cells. The horizontal resolution varies from 1.8 km in the Disko Bay proper, 4.7 km in Strait of Vaigat and 16 km towards the semi-circular Baffin Bay open boundary. In the deepest layers, the vertical resolution is 50 m, decreasing towards the surface, where the top 5 layers are 3.5, 1.5, 2.0, 2.0 and 2.0 meters thick, respectively. The surface layer thickness is flexible, allowing changes in water level, e.g. due to tidal elevations. The model time step is 300 seconds, and it has been run for the period from 2004 to 2018. The MAR and RACMO regional climate model (RCM) runoff field was used to compute freshwater discharge. The long-term sea ice cover within Disko Bay was extracted from daily sea ice concentration data provided by the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSISAF, www.osi-saf.org, Lavergne et al., 2019). More detailed information of the model configuration can be found in Møller et al. (2023). The biogeochemical model in the FlexSem framework was based on a modification of the ERGOM model that originally was applied to the Baltic Sea and the North Sea (Møller et al. 2023). In the Disko Bay version, 11 state Figure 1: FlexSem ecosystem model. Copernicus data is used to force and validate the model, and Greenland Ecosystem Monitoring (GEM) program has local data from Disko Bay. The 3D model uses an unstructured grid and is coupled to a biogeochem. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 16 of OCEANCOLOUR_GLO_BGC_L4_NRT_009_102. The PISCES biogeochemical model is available on the NEMO (https://www.nemo-ocean.eu/) modelling platform. To illustrate the recent changes in the upper, productive layers of the ocean, four chosen locations of similar water depth (two west of Greenland and two north-west of Iceland, Figure 8), representing various domains of the North Atlantic, as shown in the example spatial distribution at 25 m in May 2025 of four variables retrieved from biogeochemical models (Figure 9). Further, time series of nitrate, phosphate, silicate, chlorophyll, phytoplankton (Dec 2021 – Aug 2025), and zooplankton concentrations (Dec 2023 – Aug 2025) from ocean surface to bottom are plotted (Figures 10-15). Figure 8: Four locations chosen to display time series from BIOMER4V2R1 and BIO4GLO12 models with monthly data spanning from December 2021 to August 2025, and from December 2023 to August 2025, respectively. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 17 Figure 9: Maps of the outputs from BIOMER4V2R1 and BIO4GLO12 models: a) phosphorus, b) chlorophyll, phytoplankton and d) zooplankton concentrations on 1st May 2025 at depth of 25 m. An important question is the prediction of nutrient limitation. For this, the Redfield ratio (C: N: P ~= 106: 16: 1 in the ocean) is used to determine whether nitrogen or phosphorus is the limiting nutrient for phytoplankton growth. Monitoring changes in ocean chemistry by analyzing deviations from the Redfield ratio can indicate shifts in ocean conditions, such as changes in nutrient availability or the influence of human activities. The new collection of biogeochemical time series and outputs of biogeochemical reanalysis and forecasts allows the development of new products for sustainable fisheries in the subpolar and polar regions. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 18 . Figure 10: Time series of nitrate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 19 Figure 11: Time series of phosphate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 20 Figure 12: Time series of silicate from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 21 Figure 13: Time series of chlorophyll concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 22 Figure 14: Time series of phytoplankton concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIOMER4V2R1 model with monthly data spanning from December 2021 to August 2025. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 23 Figure 15: Time series of zooplankton concentration from four locations: a) Disko Bay, b) West Greenland, c) west of Iceland and d) north of Iceland, from BIO4GLO12 model with monthly data spanning from December 2023 to August 2025. Currently, the Argo Program (Argo, 2025) is an important source of observational data on ocean biogeochemistry. Nevertheless, the data coverage is sparse in the shallow seas and where the sea ice is Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 24 present. For the West Greenland and Iceland shelves, it is particularly challenging. All available BGC Argo floats in these regions, or nearby, are plotted in Figure 16, showing large, unsurveyed areas closer to the coastline. An example of valuable float data is shown in Figure 17, where the development of the deep mixed layer (ML) during winter 2024/2025 is depicted with its maximum reaching 600 m in the end of March 2025, with the highest concentration of oxygen concentration at the base of the newly formed ML, and its further restratification and depletion of the oxygen concentration during the following spring/summer. Figure 16: Trajectories of all BGC Argo floats active in 2025 in the regions (or nearby) of a) West Greenland and b) Iceland shelves. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 25 Figure 17: Data retrieved from the selected BGC Argo float active in 2025 south of Iceland: a) trajectory of the instrument, b) adjusted temperature, and c) adjusted oxygen concentration along the float’s pathway. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System (GOOS). Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 32 dependent data, further with fisheries-independent data and integrate that into the ARCFISH DTO. This could support new model projections and powerful analysis tools in a DTO platform, that could open for novel ways of investigating and visualising trends and patterns in species distribution and abundance, supporting sustainable fisheries and resource management, and providing valuable tools to bolster the Blue Economy. A developed toolbox/GUI dedicated to a better understanding of the spatio-temporal patterns of key environmental variables will be developed, allowing for calculation and further plotting of maps in the Blue Insight DTO platform. Here is an example set of key variables: • Mixed layer depth – the stratification of the upper layer of the ocean • Upper ocean (50 m) temperature and salinity • Subsurface ocean (50-100 m) temperature and salinity • Ocean chemistry and nutrient distribution • Depth of thermocline, halocline and pycnocline • Depth of chlorophyll maximum • Upper ocean (100 m) zooplankton concentration Kongsberg Discovery will proceed with developing the Blue Insight solutions, focusing on visualizations, automatic data processing solutions, integrations, and progress to work on the regional database. By integrating ecological models, environmental time-series, and fisheries-dependent data, Blue Insight will support informed decision-making for sustainable Arctic fisheries. Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 33 8. References Argo (2025). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182. Brownscombe J. W., et al. (2019). The future of recreational fisheries: Advances in science, monitoring, management, and practice. DOI:10.1016/j.fishres.2018.10.019 Duke University and WorldFish (2022). Small-scale fisheries and sustainable development. https://openknowledge.fao.org/server/api/core/bitstreams/df776156-23d4-4712-869e8cf088ce5091/content European Commission (2023), Project ECOTIP, Deliverable No. 4.3: The economic importance of fisheries in Greenland, with special emphasis on the coastal fishery for Greenland halibut. Global Ocean Biogeochemistry Analysis and Forecast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi00015 (Accessed on 26-SEP-2025). Larsen J, Mohn, C, Pastor AR, Maar, M (2020). A versatile marine modelling tool applied to arctic, temperate and tropical waters. PLoS One 15(4): e0231193. Larsen J. (2025). FlexSem source code. Zenodo. https://doi.org/10.5281/zenodo.15695485 Le Corre, N., Pepin, P., Han, G., Ma, Z., & Snelgrove, P. V. (2019). Assessing connectivity patterns among management units of the Newfoundland and Labrador shrimp population. Fisheries Oceanography, 28(2), 183-202. Møller EF, Christensen A, Larsen J, Mankoff KD, Ribergaard MH, Sejr M, Wallhead P, Maar M. (2023). The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem to changes in freshwater discharge and sea ice cover. Ocean Science 19: 403-420. Marie Maar, Eva Friis Møller, Janus Larsen, & Schourup-Kristensen, V. (2025). FlexSem Biogeochemical model for Disko Bay, Greenland. Zenodo. https://doi.org/10.5281/zenodo.15728647 Thakur, M., & Gunnlaugsson, V. N. (2018). Information Sharing Strategies in Whitefish Supply Chains in Norway vs. Iceland: Impact on Supply Chain Decision Making. International Journal on Food System Dynamics, 9(3), 240-252. https://doi.org/10.18461/ijfsd.v9i3.933 Deliverable 3.1 Data products for sustainable fisheries V1 Version 1.0 20 October 2025 34 Co-funded by the European Union through the Sustainable Blue Economy Partnership, The Research Council of Norway (RCN), Innovation Fund Denmark, The National Centre for Research and Development, Poland (NCBR); The Icelandic Centre for Research, Iceland (RANNIS); and Fundação para a Ciência e a Tecnologia (FCT). Views and opinions expressed, however, are those of the author(s) only and do not necessarily reflect those of the European Union or the national research agencies. Neither the European Union nor the granting authorities can be held responsible for them. Endorsed by: