Generative artificial intelligence and marine ecological monitoring

Abstract

To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management