In Marine Science, ecosystem risk assessment is a process that integrates data to estimate the potential impact of harmful and fragile forces (stressors) on the ecosystem. Nowadays, the management of marine ecosystems is increasingly complex due to multiple stressors, including human activities and climate change, and requires robust tools to address challenges effectively. We present big data-driven methods that enable a rapid, simultaneous analysis of multiple stressors using unsupervised learning techniques and statistical analysis to produce prior ecosystem risk assessments. We apply four cluster analysis methods based on Multi K-means, Fuzzy C-means, X-means, and DBSCAN, to identify stressor concurrency areas in Mediterranean Sea data from 2017 to 2021. These data include stressor variables related to environmental, oceanographic, fishing, and biodiversity factors. The methods assess ecosystem risk by detecting high stressor concurrency conditions. Finally, they produce maps that highlight potential high-risk regions. We compare the results of the four methods to examine the similarities and differences in their abilities to detect high-risk areas. From the Mediterranean data, all methods jointly indicate known high-risk areas but differ in the extent of the identified areas. Our comparative analysis highlights the importance of selecting the most appropriate clustering technique based on the balance between precautionary (highlighting broader areas) and conservative (highlighting smaller areas) perspectives. The results provide information that should be used in ecosystem models and marine spatial planning to improve the accuracy and objectivity of ecosystem risk assessment and management strategies.

Keywords: Risk assessment, Cluster analysis, Marine ecosystems, Comparative analysis.

File: https://ieeexplore.ieee.org/abstract/document/11104682