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Abstract. Ocean color remote sensing offers two decades-long time series of information on phytoplankton abundance. However, determining the structure of the phytoplankton community from this signal is not straightforward, and many uncertainties remain to be evaluated, despite multiple intercomparison efforts of the different available algorithms. Here, we use remote sensing and machine learning to infer the abundance of seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. Our dataset is to our knowledge the most comprehensive and complete, available to describe phytoplankton community structure at a global scale using a molecular marker that defines relative abundances of all phytoplankton groups simultaneously. The methodology shows satisfying performances to provide robust estimates of phytoplankton groups using satellite data, with few limitations regarding the global generalization of the method. Furthermore, this new satellite-based methodology allows a valuable global intercomparison with the pigment-based approach used in in-situ and satellite data to identify phytoplankton groups. Nevertheless, these datasets show different, yet coherent information on the phytoplankton, valuable for the understanding of community structure. This makes remote sensing observations excellent tools to collect Essential Biodiversity Variables and provide a foundation for developing marine biodiversity forecasts.

Detailed knowledge of the shape of the seafloor is crucial to humankind. Bathymetry data is critical for safety of navigation and is used for many other applications. In an era of ongoing environmental degradation worldwide, bathymetry data (and the knowledge derived from it) play a pivotal role in using and managing the world’s oceans in a way that is in accordance with the United Nations Sustainable Development Goal 14 – conserve and sustainably use the oceans, seas and marine resources for sustainable development. However, the vast majority of our oceans is still virtually unmapped, unobserved, and unexplored. Only a small fraction of the seafloor has been systematically mapped by direct measurement. The remaining bathymetry is predicted from satellite altimeter data, providing only an approximate estimation of the shape of the seafloor. Several global and regional initiatives are underway to change this situation. This paper presents a selection of these initiatives as best practice examples for bathymetry data collection, compilation and open data sharing as well as the Nippon Foundation-GEBCO (The General Bathymetric Chart of the Oceans) Seabed 2030 Project that complements and leverages these initiatives and promotes international collaboration and partnership. Several non-traditional data collection opportunities are looked at that are currently gaining momentum as well as new and innovative technologies that can increase the efficiency of collecting bathymetric data. Finally, recommendations are given toward a possible way forward into the future of seafloor mapping and toward achieving the goal of a truly global ocean bathymetry.

In addition to indirect support to fisheries, marine habitats also provide non-use benefits often overlooked in most bioeconomic models. We expand a dynamic bioeconomic fisheries model where presence of natural habitats reduces fishing cost via aggregation effects and supplies non-use benefits. The theoretical model is illustrated with an application to cold-water corals in Norway where two fishing methods are considered–destructive bottom trawl and non-destructive coastal gear. Non-use values of cold-water corals in Norway are estimated using a discrete choice experiment. Both the theoretical model and its empirical applications demonstrate how non-use values impact optimal fishing practices.

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying “just enough” Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article11 https://w3id.org/ro/doi/10.5281/zenodo.5146227 is archived at https://doi.org/10.5281/zenodo.5146227.

The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles.

AbstractSince January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly high-spatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period (2009–12). The skills of the different HF radar products are evaluated within a 48-h window. The mean distances between real trajectories and their radar-derived counterparts range from 4 to 5 km for real-time and forecast currents after 12 hours of simulations. The forecast model improves persistence (i.e., the simulations obtained by using the last available OMA fields as a constant variable) after 6 hours of simulation and improves the estimation of trajectories up to 28% after 48 hours. The performance of the forecast is observed to be variable in space and time, related to the different ocean processes governing the local ocean circulation.