Summary
Tuna, a cornerstone of global fisheries, representing 10% of marine catches and sustaining numerous jobs, confronts pressing challenges from overfishing and climate change. To address these issues comprehensively, our proposal outlines a three-fold strategy: 1 - Advanced Spatial Modeling: We will employ Bayesian spatial models, specifically INLA (Integrated Nested Laplace Approximation), to predict tuna catch and price dynamics while considering vital environmental factors. This approach will unveil intricate relationships between catch, prices, and environmental variables, providing a deeper understanding of the complex interplay; 2 - Climate-Based Predictions: Building upon the insights gained from our spatial models, we will integrate predictions from the Norwegian Climate Prediction Model (NorCPM). This integration will allow us to provide skillful forecasts for tuna catch and price fluctuations over the next decade. These forecasts are invaluable for anticipating and responding to dynamic changes in the tuna industry, enhancing its resilience; 3 - User-Friendly Web Application: In collaboration with experts from ICES (International Council for the Exploration of the Sea) and the Norwegian IMR (Institute of Marine Research), we will develop a user-friendly web application. By developing this tool, we aim to make our scientific insights accessible to a wide audience, including researchers, managers, and policymakers. This project not only enhances our understanding of tuna fisheries and climate vulnerabilities but also empowers coastal communities, supports sustainability, and aligns with the Sustainable Development Goals. Our commitment to open-science practices ensures lasting impact through readily available project code and results.
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| Web resources: | https://cordis.europa.eu/project/id/101153695 |
| Start date: | 01-01-2025 |
| End date: | 31-12-2026 |
| Total budget - Public funding: | - 226 751,00 Euro |
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Original description
Tuna, a cornerstone of global fisheries, representing 10% of marine catches and sustaining numerous jobs, confronts pressing challenges from overfishing and climate change. To address these issues comprehensively, our proposal outlines a three-fold strategy: 1 - Advanced Spatial Modeling: We will employ Bayesian spatial models, specifically INLA (Integrated Nested Laplace Approximation), to predict tuna catch and price dynamics while considering vital environmental factors. This approach will unveil intricate relationships between catch, prices, and environmental variables, providing a deeper understanding of the complex interplay; 2 - Climate-Based Predictions: Building upon the insights gained from our spatial models, we will integrate predictions from the Norwegian Climate Prediction Model (NorCPM). This integration will allow us to provide skillful forecasts for tuna catch and price fluctuations over the next decade. These forecasts are invaluable for anticipating and responding to dynamic changes in the tuna industry, enhancing its resilience; 3 - User-Friendly Web Application: In collaboration with experts from ICES (International Council for the Exploration of the Sea) and the Norwegian IMR (Institute of Marine Research), we will develop a user-friendly web application. By developing this tool, we aim to make our scientific insights accessible to a wide audience, including researchers, managers, and policymakers. This project not only enhances our understanding of tuna fisheries and climate vulnerabilities but also empowers coastal communities, supports sustainability, and aligns with the Sustainable Development Goals. Our commitment to open-science practices ensures lasting impact through readily available project code and results.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
12-03-2024
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