About the EcoTwin Project

Innovative socio-ecological models and tools

The European Digital Twin Ocean (EDITO) and local Digital Twins of the Ocean (DTOs) generate immense amounts of data from various sources like ocean observations, high-resolution modelling, and databases such as Copernicus, EMODnet, and EUROSTAT. This data is enriched with information from ecological, social, economic, legal, and other fields. However, the sheer volume and complexity of this data make it difficult for decision-makers to extract useful insights quickly, limiting accessibility to only a few experts.

Currently, there are no tools that effectively integrate social-ecological (SE) interactions into these digital twins, which are essential for making ocean data easily accessible and useful for citizens, entrepreneurs, scientists, and policymakers. The EcoTwin project aims to bridge this gap by developing innovative socio-ecological models and tools, integrating them with EDITO and local DTOs, and applying them to four specific coastal marine ecosystems in the Southern North Sea, Celtic Sea, Thracian Sea, and Waterford Estuary.

EcoTwin’s Approach

EcoTwin will create four classes of socio-ecological models, each designed to handle different types of data and interactions:

Quantitative Causal Graph Theoretic Models

These models use mathematical and statistical methods to analyze and predict interactions within the ecosystem.

Qualitative Causal Graph Theoretic Models

These models focus on understanding the stability and sustainability of the ecosystem using qualitative data, such as human perceptions and opinions gathered from workshops.

Network Models with Participatory Feedback

These models incorporate dynamic feedback from stakeholders, ensuring that the models remain relevant and accurate over time.

Parallel Generative AI Models

These AI-based tools leverage advanced machine learning techniques to make the vast amounts of data more accessible and actionable for non-experts.

Multi-Actor Approach

The development of these models will involve a multi-actor approach, engaging various stakeholders, including government authorities, NGOs, and representatives from industries like tourism and fishing. This collaborative method ensures that the models are grounded in real-world experiences and needs.

Data Integration and Interoperability

A key part of the project is the creation of an open, reliable, and interoperable ECOTWIN database. This database will integrate data from multiple sources, including ecological simulations and social workshops, making it a comprehensive resource for developing and refining the models.

Use Cases

The models will be tested and validated through four specific use cases:

  1. Southern North Sea: Focusing on spatial closures, ecosystem health, and fisheries.
  2. Celtic & North Seas: Examining renewable energy, job creation, and fishing reduction.
  3. Thracian Sea: Looking at tourism, renewable energy, fishing reduction, and coastal job impacts.
  4. Waterford Estuary: Addressing water quality, conservation, and sustainable tourism.

These use cases will help evaluate the models’ effectiveness in real-world scenarios and ensure they provide valuable insights for managing human activities, implementing policies, and meeting societal needs. Read more about the use cases here.

Generative AI Tools

By developing generative AI tools, EcoTwin aims to simplify the process of extracting insights from complex datasets. These tools will provide a natural language interface, making it easier for non-experts to interact with and benefit from the data.

Collaboration and Knowledge Transfer

EcoTwin will collaborate with other European DTO projects and infrastructures to maximise knowledge transfer and adopt best practices in data management. This will help ensure that the project’s outputs are widely applicable and beneficial.

Expected Outcomes

The project aims to achieve three key outcomes:

1.Solutions to Socio-Ecological Modelling Challenges: Developing models that can handle the complexity of socio-ecological systems.

2.Models Developed Using a Multi-Actor Approach: Ensuring that the models are relevant and useful by involving a wide range of stakeholders.

3.Improved Understanding and Risk Assessment: Enhancing our understanding of socio-ecological systems to support better decision-making and policy implementation.

EcoTwin is set to revolutionise the way we manage and understand marine coastal ecosystems by integrating advanced socio-ecological models with digital twin technology. By leveraging a multi-actor approach and cutting-edge AI tools, the project aims to make complex ocean data accessible and actionable for a wide range of users, ultimately contributing to better ocean health, economic development, and societal well-being.