The definition of an ecological niche makes it possible to anticipate the responses of a species to changing environmental conditions. Broad tolerance limits and a paucity of readily observable niches in the pelagic zone make it difficult to anticipate responses of the plankton community related to anthropogenic or environmental changes. Plankton distributions are closely linked to climate change and shape the seascape for higher trophic levels, so monitoring plankton distributions and defining ecological niches will help to understand and predict ecosystem responses. Here we apply a machine learning autoencoder and a density‐based clustering algorithm to high‐frequency datasets sampled with a ROTV Triaxus in the North Sea. The results indicate that in this highly dynamic environment, local hydrography prevents niche‐based separation of plankton species at the sub‐mesoscale, despite the availability of different habitats. Plankton patches were associated with naturally occurring frontal systems and anthropogenically induced upwelling‐downwelling dipoles in the vicinity of offshore wind farms (OWFs).

Physical and biological oceanographic measurements were recorded on different North Sea surveys with the RV Heincke (Knust et al., 2017) using a MacArtney TRIAXUS ROTV, complemented by a Video Plankton Recorder (VPR). The TRIAXUS was towed behind the research vessel in an undulating fashion between the sea surface and bottom. Data was processed using a machine learning Autoencoder and a density-based clustering algorythm HDBSCAN. Analysis and data handling were handled with the statistical software R4.4.0 and Python 3.7. A detailed description can be found in 'Identification of plankton habitats in the North Sea' (the DOI can be found at 'Related works'). Knust, R., Nixdorf, U. and Hirsekorn, M. 2017 'Research vessel HEINCKE operated by the alfred-wegener-institute', Journal of large-scale research facilities JLSRF, 3, pp. A120–A120. doi: 10.17815/jlsrf-3-164.