Leveraging artificial intelligence methods to map seagrass ecosystems in Italian Seas: Tackling human impact and climate change

Marine coastal ecosystems (MCEs) are crucial for human health, playing a key role in climate change adaptation. However, MCEs are globally threatened by environmental and human pressures. This study applies Graph Neural Networks (GNNs) to model seagrass distribution in the Italian Seas using a dataset of 2244 spatial units with environmental, climatic, and anthropogenic factors harmonised at 4 km resolution. GNN models, including Graph Convolutional and Attention Networks, were benchmarked against traditional machine learning methods: Random Forest, Support Vector Machine, and Multi-Layer Perceptron. GNNs achieved comparable overall accuracy (91%) but delivered more spatially consistent predictions and higher F1-scores (0.89) for the minority class (seagrass presence). Sensitivity analysis identified climatic and human variables as key drivers of seagrass distribution. These insights support the implementation of blue Nature-based Solutions (NbS) to protect and restore seagrass habitats, aiding biodiversity conservation and climate change mitigation while guiding effective policymaking. • Apply Graph Neural Networks to model seagrass distribution in Italian Seas. • GNNs outperform ML models in presence class detection (F1-score: 0.89). • Leverage spatial data in graphs to capture ecosystem relationships effectively. • Identify climate and human pressures as main drivers via sensitivity analysis.