Abstract
To assess the potential ecological effects of marine renewable energy (MRE) devices, it is essential to establish a baseline of ecosystem conditions prior to disturbance and to monitor throughout the installation, operation, maintenance, and decommissioning phases. The design of mitigation and compensation measures depends on the difference between baseline conditions and the impacted ecosystem. In this study, we develop and evaluate a semi-automated methodological proof-of-concept using computer vision to assess environmental changes around MRE devices. Using fish monitoring as a case study, we reduced image processing time by eliminating empty frames and detecting fish in the remaining frames. Species identification was carried out by experts using a public database for comparative analysis. We also conducted an identification exercise for a single fish species and tested a monocular depth-estimation method (3D reconstruction from 2D images) to measure distances between organisms and devices. Finally, we propose a potential methodological framework for integrating computer vision across the life cycle of MRE devices to monitor both natural and anthropogenic processes, including space colonization, trophic interactions, pollution, and the effects of underwater structures. The merits and drawbacks of the approach are discussed.
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Subjects
- Ecology
- Environmental sciences
- Ocean sciences
Acknowledgements
We want to thank Trevor Mendelow and Fathom Ocean for facilitating the use of the underwater camera for this analysis. To Edgar Escalante Mancera, Miguel Ángel Gómez Reali, and José Antonio López Portillo at the “Servicio Académico de Monitoreo Meteorológico y Oceanográfico” laboratory (SAMMO) in Puerto Morelos, Q. Roo, Mexico, for helping install the equipment. To Carlos Echeverría Arjonilla and José Antonio López Portillo for helping program and set up the camera. To Roberto Alexander Martínez Lagos for his valuable help in identifying aquatic species.
Funding
The authors acknowledge that the research reported in this publication was financially supported by the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) through the Doctoral fellowship (CVU: 488940) and by the Secretaría de Ciencia, Humanidades, Tecnología e Innovación Secihti, project CF-2023-G-1497.
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Alamillo-Paredes, A., Lagunes-Díaz, E.G., Pérez-Maqueo, O. et al. A computer vision-based approach to monitor changes in ecosystems associated with marine renewable energy projects.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-62922-4
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DOI: https://doi.org/10.1038/s41598-026-62922-4
Source: Ecology - nature.com
