Toward the integration of Graph Neural Networks and Digital Twins: Transforming marine ecosystem management and coastal resilience

Marine and coastal ecosystems (MCEs) are vital to human well-being, playing a significant role in climate regulation, carbon sequestration, while protecting coastal areas from sea level rise and erosion. However, these ecosystems are increasingly threatened by the combined effects of anthropogenic stressors (e.g., pollution) and climate change-related pressures (e.g., rising sea temperatures and ocean acidification).  Cumulative impacts arising from this complex interplay threaten MCEs' ability to deliver critical ecosystem services, compromising their health and resilience.

Machine Learning (ML) has emerged as a valuable tool for assessing ecological conditions under multiple pressures. Algorithms like Random Forest (RF) and Support Vector Machine (SVM) have demonstrated their effectiveness in identifying patterns and predicting changes in ecosystem health. However, these models often fail to account for spatial dependencies between data points, which are crucial for understanding the interconnected nature of marine environments. Graph Neural Networks (GNNs), a more recent advancement in ML, overcome this limitation by explicitly modelling spatial relationships, making them highly suitable for analysing complex MCE dynamics.

This study explores the application of GNN-based models to assess the impact of multiple pressures on seagrass ecosystems in the Italian coastal areas. To this aim, a comprehensive dataset was constructed, including key variables influencing seagrass health, such as nutrient concentrations, temperature, and salinity, derived from open-source platforms (e.g., Copernicus CMEMS, EMODnet). Data were synthesized into a 4km raster grid, with each pixel representing seagrass presence or absence. GNNs were constructed by considering each pixel as a node and connecting it to neighbouring pixels to capture spatial relationships. Experiments evaluated different GNN architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), alongside traditional ML models like RF, SVM, and Multi-Layer Perceptron.

The results showed that GNNs outperformed traditional models in terms of F1-score and accuracy, particularly in spatially complex scenarios. Traditional models often misclassified regions with intricate spatial dependencies, such as boundaries between seagrass patches, whereas GNNs demonstrated superior capability in leveraging spatial context. Despite these advantages, the study faced challenges due to the limited availability of high-resolution, temporal datasets, constraining the full exploration of dynamic ecosystem processes. However, by addressing the challenge of spatial resolution in ecological data, GNNs represents a transformative approach to understanding ocean dynamics. Their integration into a Digital Twin of the Ocean has the potential to transform ecosystem management and significantly advance coastal resilience efforts. This framework would enable detailed simulations and predictions of processes like ocean currents, extreme weather events, and the cumulative impacts of climate change and human activities. Moreover, the combination of GNNs and Digital Twins would provide deeper insights into the complex interplay of factors shaping marine and coastal ecosystems ecological state and processes and their resilience overall. This synergy empowers scientists and policymakers with actionable intelligence, fostering effective decision-making and the development of strategies to mitigate ocean hazards, while safeguarding biodiversity and enhancing the resilience of coastal communities. As future efforts move towards incorporating high-resolution data, this integrated approach holds promise for advancing the sustainable management of MCEs globally.