Abstract
Marine ecosystems are increasingly shaped by the combined effects of multiple environmental and anthropogenic stressors, generating non-linear, spatially heterogeneous patterns of risks to ecosystem functioning. The Baltic Sea represents a paradigmatic system where geographical constraints intersect with intense human pressures. This study presents a data-driven framework for ecosystem risk assessment, conceptualising risk as an emergent property of interacting environmental, anthropogenic, and biological stressors. The approach integrates complementary unsupervised models: a clustering-based technique (Multi K-means) and a deep learning model based on a Variational Autoencoder, to capture recurrent and anomalous stressor patterns. Model-generated risk maps are statistically analysed within an ensembled risk characterisation to ensure ecological interpretability and comparability. The framework is applied to 2020 data from the central and western Baltic Sea, integrating multi-source data with abundance information on 145 marine species, commercially relevant species, and cod. The models identify spatially concentrated ecosystem risk hotspots in species-abundant areas characterised by the concurrence of bottom-oxygen depletion, depth-related constraints, and fishing pressure, consistent with expert studies. Cross-model concordance analysis reveals consistency and complementarity between the models. By reducing subjectivity while maintaining ecological interpretability, the framework provides a scalable and transferable tool for ecosystem risk assessment and ecosystem-based management.
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Acknowledgements
The authors wish to thank the staff of the Ecosystem Modelling Working Group at the GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, as well as Pasquale Bove and Gian Luca Vannini of the Natural and Cognitive System Simulation Research Group at CNR-ISTI for their scientific support. The publication has been funded by EU – Next Generation EU Mission 4, Component 2 – CUP B53C22002150006 – Project IR0000032 – ITINERIS – Italian Integrated Environmental Research Infrastructures System. MS acknowledges financial support from the German Federal Agency for Nature Conservation (Bundesamt für Naturschutz), funded by the Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety (BMUKN), under the project AWZ Fisch 4 (grant agreement FKZ 3524520800). MS and LMN are grateful for the financial support from the German Federal Ministry of Education and Research (BMBF) through the German Alliance for Marine Research (DAM), within the framework of the SpaCeParti 2 project (Coastal Fishery, Biodiversity, Spatial Use and Climate Change: A Participative Approach to Navigate the Western Baltic Sea into a Sustainable Future, grant number 03F0914D).
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Pavirani, L., Scotti, M., MacNeil, L. et al. Unsupervised ecosystem risk assessment in the Baltic Sea reveals bottom-oxygen depletion and fishing pressure as key threats in species-abundant hotspots.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-73517-4
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DOI: https://doi.org/10.1038/s41598-026-73517-4
Keywords
- Ecosystem risk assessment
- Baltic Sea
- Cluster analysis
- Deep learning
- FAIR data
Source: Ecology - nature.com
