Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

Marine hard-bottom communities are undergoing severe change under the influence of multiple drivers, notably climate change, extraction of natural resources, pollution and eutrophication, habitat degradation, and invasive species. Monitoring marine biodiversity in such habitats is, however, challenging as it typically involves expensive, non-standardized, and often destructive sampling methods that limit its scalability. Differences in monitoring approaches furthermore hinders inter-comparison among monitoring programs. Here, we announce a Marine Biodiversity Observation Network (MBON) consisting of Autonomous Reef Monitoring Structures (ARMS) with the aim to assess the status and changes in benthic fauna with genomic-based methods, notably DNA metabarcoding, in combination with image-based identifications. This article presents the results of a 30-month pilot phase in which we established an operational and geographically expansive ARMS-MBON. The network currently consists of 20 observatories distributed across European coastal waters and the polar regions, in which 134 ARMS have been deployed to date. Sampling takes place annually, either as short-term deployments during the summer or as long-term deployments starting in spring. The pilot phase was used to establish a common set of standards for field sampling, genetic analysis, data management, and legal compliance, which are presented here. We also tested the potential of ARMS for combining genetic and image-based identification methods in comparative studies of benthic diversity, as well as for detecting non-indigenous species. Results show that ARMS are suitable for monitoring hard-bottom environments as they provide genetic data that can be continuously enriched, re-analyzed, and integrated with conventional data to document benthic community composition and detect non-indigenous species. Finally, we provide guidelines to expand the network and present a sustainability plan as part of the European Marine Biological Resource Centre (www.embrc.eu).

Through regular sampling surveys, the Flanders Marine Institute is generating long term data series for the Belgian coastal water and sand bank systems, a designated site in the Long Term Ecological Research (LTER) network. The data series is built on sampling activities initiated in 2002, but gradually upgraded and extended in the framework of the LifeWatch marine observatory and the Integrated Carbon Observation System (ICOS) participation. Nine nearshore stations are sampled monthly, with additional seasonal sampling of eight offshore stations. This paper presents the generated data series for nutrients, pigments, suspended matter and turbidity. The collection, methodology and processing of the 2002–2018 dataset is described, along with its data curation, integration and quality control. Yearly versions of the data are published online in a standardized format, accompanied with extensive metadata description and labelled with digital identifiers for traceability. Data is published under a CC-BY license, allowing use of the data under the condition of providing reference to the original source.

Empidoidea is one of the largest extant lineages of flies, but phylogenetic relationships among species of this group are poorly investigated and global diversity remains scarcely assessed. In this context, one of the most enigmatic empidoid families is Hybotidae. Within the framework of a pilot study, we barcoded 339 specimens of Old World hybotids belonging to 164 species and 22 genera (plus two Empis as outgroups) and attempted to evaluate whether patterns of intra- and interspecific divergences match the current taxonomy. We used a large sampling of diverse Hybotidae. The material came from the Palaearctic (Belgium, France, Portugal and Russian Caucasus), the Afrotropic (Democratic Republic of the Congo) and the Oriental realms (Singapore and Thailand). Thereby, we optimized lab protocols for barcoding hybotids. Although DNA barcodes generally well distinguished recognized taxa, the study also revealed a number of unexpected phenomena: e.g., undescribed taxa found within morphologically very similar or identical specimens, especially when geographic distance was large; some morphologically distinct species showed no genetic divergence; or different pattern of intraspecific divergence between populations or closely related species. Using COI sequences and simple Neighbour-Joining tree reconstructions, the monophyly of many species- and genus-level taxa was well supported, but more inclusive taxonomical levels did not receive significant bootstrap support. We conclude that in hybotids DNA barcoding might be well used to identify species, when two main constraints are considered. First, incomplete barcoding libraries hinder efficient (correct) identification. Therefore, extra efforts are needed to increase the representation of hybotids in these databases. Second, the spatial scale of sampling has to be taken into account, and especially for widespread species or species complexes with unclear taxonomy, an integrative approach has to be used to clarify species boundaries and identities.

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This paper describes a phytoplankton data series generated through systematic observations in the Belgian Part of the North Sea (BPNS). Phytoplankton samples were collected during multidisciplinary sampling campaigns, visiting nine nearshore stations with monthly frequency and an additional eight offshore stations on a seasonal basis. The data series contain taxon-specific phytoplankton densities determined by analysis with the Flow Cytometer And Microscope (FlowCAM®) and associated image-based classification. The classification is performed by two separate semi-automated classification systems, followed by manual validation by taxonomic experts. To date, 637,819 biological particles have been collected and identified, yielding a large dataset of validated phytoplankton images. The collection and processing of the 2017–2018 dataset are described, along with its data curation, quality control and data storage. In addition, the classification of images using image classification algorithms, based on convolutional neural networks (CNN) from 2019 onwards, is also described. Data are published in a standardised format together with environmental parameters, accompanied by extensive metadata descriptions and finally labelled with digital identifiers for traceability. The data are published under a CC‐BY 4.0 licence, allowing the use of the data under the condition of providing the reference to the source.

Climate driven changes and anthropogenic pressures on the marine environment have been shown to favor the increase in certain potentially harmful species. Among them, Noctiluca scintillans, a common dinoflagellate, often blooms during warm summers and is known to affect plankton communities. In this study, we assessed the dynamics in abundance and cell size of N. scintillans as well as the relationship between N. scintillans and small soft-bodied zooplankton in the Belgian part of the North Sea (BPNS), since negative correlations between these plankton groups have been previously reported for nearby regions. This study is the first to present consistently counted N. scintillans cell numbers and measured cell lengths, through the analysis of ZooScan images from samples taken monthly at stations throughout the coastal zone of the BPNS. The results show that N. scintillans demonstrated clear seasonal dynamics with both high densities and large cell sizes in spring/summer (May-July). The occurrence of N. scintillans in the analyzed plankton samples and the abundance of N. scintillans at the observed peak intensities nearly tripled over a period of 5 years. A zero-inflated model showed a correlation of N. scintillans abundance with temperature as well as with phosphate concentrations, suggesting that anthropogenic influences such as climate change and riverine nutrient inputs could affect the temporal dynamics of the species. The results, on the other hand, did not show any negative impact of N. scintillans on the soft-bodied plankton community.