Socio-ecological Models
Socio-ecological models to manage coastal marine systems.
EcoTwin is creating new socio-ecological models that use both quantitative and qualitative data to help manage coastal marine systems. These models will be developed with input from various stakeholders, allowing for the exploration of different scenarios and aiding decision-making.
A comprehensive EcoTwin database will be established, integrating data from multiple European sources and supplementing it with data from ecological simulations and social workshops. These models aim to provide a better understanding of complex socio-ecological systems, improve the management of human activities, and inform policy decisions. The models will be tested and refined through real-world applications, ensuring they are reliable and effective for future use.
Quantitative Causal Socio-Ecological Models
These models integrate quantitative ecological and social data into graphical networks. Initially, ecological data is modelled analytically, and stakeholder input is used for social modelling. These networks are combined using a transdisciplinary and multi-actor approach. The framework, which includes spectral theory, causal theory, and entropy, will be enhanced and validated through various use cases, achieving higher levels of technological readiness.
Qualitative Causal Socio-Ecological Models
These models utilise qualitative relationships to develop graphical networks for ecological and social systems. The models assess stability and sustainability and are validated through specific use cases. The approach progresses from conceptualization to proof of concept, enhancing the models’ readiness and integrating them with broader digital twin platforms.
Participatory Feedback Models
This methodology emphasises dynamic socio-ecological modelling with active stakeholder engagement. Workshops gather stakeholder feedback, which is then incorporated into model development. These dynamic models are validated through specific use cases, achieving higher levels of readiness and integration with digital twin platforms.
Parallel Generative AI Tools
Leveraging pretrained AI models, these tools enhance the accessibility and analysis of digital twin data. They integrate data from various sources to identify causal connections and address user challenges. This approach ensures the models are generalizable across different scenarios and validated through multiple use cases, with integration into broader digital twin frameworks.