Generative artificial intelligence and marine ecological monitoring
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. • GenAI architectures enhance marine data fusion, augmentation, and imaging. • Generative models boost autonomy and adaptive control in marine robotics. • GenAI fills spatiotemporal gaps, improving ecological forecasts and mapping. • Synthetic datasets enable species detection and ecological indicator training. • GenAI powers Digital Twins for real-time ecosystem monitoring and prediction.
Authors
- Simona Violino (ORCID: https://orcid.org/0000-0003-1853-7642)
- Matteo Gallici (ORCID: https://orcid.org/0000-0002-4898-2766)
- Damianos Chatzievangelou (ORCID: https://orcid.org/0000-0001-7512-5105)
- Simone Figorilli (ORCID: https://orcid.org/0000-0003-4035-4199)
- Nixon Bahamón (ORCID: https://orcid.org/0000-0002-5802-7367)
- Morane Clavel‐Henry (ORCID: https://orcid.org/0000-0002-2491-6203)
- Lavinia Moscovini (ORCID: https://orcid.org/0000-0001-9886-3194)
- Corrado Costa (ORCID: https://orcid.org/0000-0003-3711-1399)
- Ivan Masmitjà Rusiñol (ORCID: https://orcid.org/0000-0001-6355-7955)
- Alessandro Mei (ORCID: https://orcid.org/0000-0003-1215-9644)
- Nathan J. Robinson (ORCID: https://orcid.org/0000-0001-7384-3576)
- Jacopo Aguzzi (ORCID: https://orcid.org/0000-0002-1484-8219)
- Francesca Antonucci (ORCID: https://orcid.org/0000-0002-8584-6018)
- Luciano Ortenzi (ORCID: https://orcid.org/0000-0002-1245-8882)
- Giacomo Picardi (ORCID: https://orcid.org/0000-0001-9066-692X)
- Eugenio Nerio Nemmi (ORCID: https://orcid.org/0000-0001-6518-7863)
- Maria Garcia Ferrari (ORCID: https://orcid.org/0009-0002-2004-2294)
- Paula Espina (ORCID: https://orcid.org/0009-0002-0995-3900)
- Riccardo De Angelis (ORCID: https://orcid.org/0009-0005-7536-4196)
Institutions
- Università degli Studi della Tuscia (IT)
- GEOMAR Helmholtz Centre for Ocean Research Kiel (DE)
- Centro di Ricerca per l’Orticoltura (IT)
- Institut de Ciències del Mar (ES)
- Sapienza University of Rome (IT)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- Environmental Modelling & Software
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1016/j.envsoft.2025.106789
- Primary Topic
- Species Distribution and Climate Change
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Horizon 2020 Framework Programme
- HORIZON EUROPE Framework Programme
- Agencia Estatal de Investigación