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Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach

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Abstract

This paper presents a high spatial and temporal resolution microphytoplankton long-term biodiversity assessment for the southern bight of the North Sea obtained by FlowCam imaging. We describe the extension of the time series with the release of over six years of new quality-controlled data as well as a taxonomic revision of previously published data leading to 92 newly recognized groups. We also describe the latest fine-tuning of sampling and laboratory processing protocols leading to a more robust methodological framework while maintaining time series continuity. The implementation of semi-automated data pipelines, leveraging convolutional neural networks, allows to deal with the high influx of biodiversity imaging data and metadata. Data and provenance metadata are annually published under a CC-BY license in trusted repositories. This current open access, high-resolution 7 year-long dataset serves as a valuable tool for studying phytoplankton communities in the Belgian Part of the North Sea.

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Data availability

The FlowCam biodiversity occurrence and density data are available in the global and Euopean node of the Ocean Biogeographic Information System (OBIS and EurOBIS) and in the Marine Data Archive (MDA). The data in these archives is stored in the Darwin Core Archive (DwC-A) format for sample-based biodiversity data. Sampling time and spatial information are stored in a single “Event Core” text file while the “Occurrence Core” holds the occurrence data, with all taxon names matched to the WoRMS taxonomic backbone38. Sampling descriptions and measured values are stored in the “Extended MeasurementOrFactExtension” or “eMoF” text file. Associated parameters on water quality, and essentially temperature, salinity, are also included in this extension file. The FlowCam dataset is available in OBIS via https://doi.org/10.14284/760 and EurOBIS via https://doi.org/10.14284/76040 and in the Marine Data Archive via https://doi.org/10.14284/71041. The full annotated image library, i.e. the ROIs and their quality controlled labels, is published in the Marine Data Archive and available via https://doi.org/10.14284/680 as well as Zenodo via https://doi.org/10.14284/68043. The training dataset sampled from this image library and used in our latest CNN training is available through Zenodo via https://doi.org/10.5281/zenodo.1055484544. All datasets described above are linked to each other in the Integrated Marine Information System (IMIS) catalogue for metadata discovery catalogue via https://doi.org/10.14284/65042.

Code availability

Code to process raw FlowCam data is not openly accessible as it is fully tailored to and dependent on VLIZ internal data systems (BioSense MongoDB, MIDAS RV information system), the LifeWatch monitoring framework and methodological which makes it not applicable for other FlowCam users. It is a means of processing large amounts of raw data and linking to VLIZ internal data systems, but is in no way imperative for processing FlowCam data. A very similar flow can easily be archived via the EcoTaxa platform for instance (Picheral M. et al., https://ecotaxa.obs-vlfr.fr/). The internal pipeline was developed at a time where the EcoTaxa platform was not suitable yet for processing and classifying FlowCam data. As part of the Imaging data and services for aquatic science (iMagine) Horizon 2020 project, we are committed to publishing the full FlowCam human annotated image library comprising over 2 million images, training data splits and trained CNN. Currently, a training dataset is openly available via Zenodo44 and the trained CNN is available for external use via the iMagine platform at https://dashboard.cloud.imagine-ai.eu/marketplace/modules/uc-lifewatch-deep-oc-phyto-plankton-classification. Source code of the classification service is publicly available via https://github.com/lifewatch/phyto-plankton-classification under an Apache 2.0 license. As the project is still ongoing, no static identifiers can be assigned to the model code yet. By October 2025, a DOI will be assigned and code will be static.

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Acknowledgements

Funding is provided by the Research Foundation – Flanders (FWO) in the framework of the Flemish contribution to LifeWatch, which is a landmark European Research Infrastructure on the European Strategy Forum on Research (ESFRI) roadmap. We thank scientists and crew of RV Simon Stevin joining the monthly sampling campaigns for their practical support and the Flemish Ministry of Mobility and Public Works (DAB VLOOT) for operating the RV Simon Stevin and facilitating the surveys.

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Authors and Affiliations

  1. Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400, Oostende, Belgium

    Rune Lagaisse, Nick Dillen, Dias Bakeev, Wout Decrop, Paul Focke, Jonas Mortelmans, Julie Muyle & Klaas Deneudt

  2. Ghent University, Department of Biology, Laboratory of Protistology & Aquatic Ecology, Ghent, Belgium

    Nick Dillen

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  1. Rune Lagaisse
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Contributions

R.L. wrote the manuscript, fine-tuned sampling and lab SOPs, runs data pipelines since June 2022 and has been doing image validations for 2019, 2020, 2021, 2022, 2023 and 2024, for 2017 and 2018 image data taxonomic review of previously published data, aids in regular processing of samples in the lab. N.D. set-up data processing pipelines, developed tools and Python packages for processing of raw FlowCam output data, maintained data pipelines prior to June 2022, aided in laboratory testing for fine-tuning protocols. W.D supports internal processing database operations and data pipelines, and leads developments under the iMagine project. D.B maintains the QC tool for upload to the processing MongoDB database, and maintains BioSens MongoDB. P.F. maintains the VLIZ labelling tool. J. Muyle conducts sampling and performs laboratory processing of samples. J. Mortelmans organises multidisciplinary sampling campaigns, conducts sampling and handles submission of data to European and global data aggregators. K.D. has built and manages the Marine Observation Centre team and facilitates LifeWatch EFRI and iMagine project.

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Correspondence to
Rune Lagaisse.

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Supplementary table 1

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Lagaisse, R., Dillen, N., Bakeev, D. et al. Advancing long-term phytoplankton biodiversity assessment in the North Sea using an imaging approach.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06278-w

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