A digital-twin strategy using robots for marine ecosystem monitoring

Aguzzi, Jacopo; Chatzidouros, Elias; Chatzievangelou, Damianos; Clavel-Henry, Morane; Flögel, Sascha; Bahamon, Nixon; Tangerlini, Michael; Thomsen, Laurenz; Picardi, Giacomo; Navarro, Joan; Masmitja, Ivan; Robinson, Nathan J.; Nattkemper, Tim; Stefanni, Sergio; Quintana, José; Campos, Ricard; García, Rafael; Fanelli, Emanuela; Francescangeli, Marco; Mirimin, Luca; Danovaro, Roberto; Toma, Daniel Mihai; Del Rio-Fernandez, Joaquín; Martinez, Enoc; Baños, Pol; Prat, Oriol; Sarria, David; Carandell, Matias; White, Jonathan; Parissis, Thomas; Panagiotidou, Stavroula; Quevedo, Juliana; Gallegati, Silvia; Grinyó, Jordi; Simon-Lledó, Erik; Company, Joan B.; Doyle, Jennifer

A digital-twin strategy using robots for marine ecosystem monitoring

Aguzzi, Jacopo; Chatzidouros, Elias; Chatzievangelou, Damianos; Clavel-Henry, Morane; Flögel, Sascha; Bahamon, Nixon; Tangerlini, Michael; Thomsen, Laurenz; Picardi, Giacomo; Navarro, Joan; Masmitja, Ivan; Robinson, Nathan J.; Nattkemper, Tim; Stefanni, Sergio; Quintana, José; Campos, Ricard; García, Rafael; Fanelli, Emanuela; Francescangeli, Marco; Mirimin, Luca; Danovaro, Roberto; Toma, Daniel Mihai; Del Rio-Fernandez, Joaquín; Martinez, Enoc; Baños, Pol; Prat, Oriol; Sarria, David; Carandell, Matias; White, Jonathan; Parissis, Thomas; Panagiotidou, Stavroula; Quevedo, Juliana; Gallegati, Silvia; Grinyó, Jordi; Simon-Lledó, Erik ORCID: https://orcid.org/0000-0001-9667-2917; Company, Joan B.; Doyle, Jennifer. 2025 A digital-twin strategy using robots for marine ecosystem monitoring. Ecological Informatics, 91, 103409. 1, pp. 10.1016/j.ecoinf.2025.103409

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Official URL: https://doi.org/10.1016/j.ecoinf.2025.103409

Abstract/Summary

Effective marine conservation and management require ecological monitoring in the form of intensive real-time data collection over large spatial scales. The combined use of fixed platforms (e.g., cabled observatories) and research vessels with platforms of different levels of teleoperated autonomy (e.g., remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) can contribute to the acquisition of large multiparametric biological and environmental data. If those data are spatially combined, sufficient spatial coverage can be achieved for ecological monitoring. A digital twin of the ocean (DTO) approach can then be used as a virtual representation of that monitored space, enabling multiparametric analyses of environmental patterns and processes affecting biodiversity and species distributions, as well as socioeconomic activities. Here, we propose a general architecture for a DTO centred on real-time data collection from local networks on fixed and mobile platforms, such as the physical twin observers (PTO), which is synergistically merged with platforms operating at large geographic scales. We describe a roadmap to achieve this DTO via 4 key steps: (1) acquisition of in situ data with a robotic network of platforms; (2) the application of AI in image processing for extracting biological data; (3) big data management with data bubbles; and (4) development of the resulting DTO framework for providing ecosystem monitoring via the computation of ecological indicators and socioecological modelling.

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.ecoinf.2025.103409

ISSN:

15749541

Additional Keywords:

Marine monitoring, Robotic platforms, Data integration, Ecological indicators, Machine learning, Spatial modelling

NORA Subject Terms:

Marine Sciences

Date made live:

10 Nov 2025 12:03 +0 (UTC)