Machine learning, eDNA and citizen science in monitoring and assessing biodiversity and invasive alien species at sea - Angel Borja; Mihailo Azhar; Lisandro Benedetti-Cecchi; Rein Brys; Berta Companys; Alice Estrela; José A. Fernandes-Salvador; Igor Granado; Stelios Katsanevakis; Agnese Marchini; Jaume Piera; Lauriane Ribas-Deulofeu; et al. (Frontiers in Marine Science, 2026)
This research outlines the integration of machine learning, citizen science, environmental DNA (eDNA), and other emerging technologies to enhance marine biodiversity and invasive alien species monitoring, discussed during a summer school organized by several European projects in June 2025. Furthermore, monitoring approaches are increasingly complemented by remote sensing, drones, and machine learning to expand spatial coverage and improve data processing. Machine learning supports species identification across taxa through deep learning and other methods, underpinned by robust validation protocols. Frameworks such as Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs) standardize data collection and interpretation, enabling hypothesis-driven monitoring and global synthesis. Computer vision models (e.g., YOLO, Mask R-CNN) and transfer learning further facilitate species identification required by EBVs. Citizen science initiatives such as iNaturalist and MINKA combine machine learning-assisted identification with expert validation, substantially broadening biodiversity monitoring. However, spatial biases and limitations persist, particularly for cryptic and deep-sea taxa. For invasive species, technologies like autonomous vehicles and eDNA sampling enhance early detection. The CIMPAL+ framework supports ecosystem-based management by assessing cumulative impacts of non-native species. Overall, these tools enhance monitoring efficiency and inclusivity but require ethical oversight, standardized protocols, and interdisciplinary collaboration to ensure scientific integrity and policy relevance.