Marco Scotti

The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.

The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.