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Marine ecosystems, ranging from coastal seas and wetlands to the open ocean, accommodate a wealth of biological diversity from small microorganisms to large mammals. This biodiversity and its associated ecosystem function occurs across complex spatial and temporal scales and is not yet fully understood. Given the wide range of external pressures on the marine environment, this knowledge is crucial for enabling effective conservation measures and defining the limits of sustainable use. The development and application of omics-based approaches to biodiversity research has helped overcome hurdles, such as allowing the previously hidden community of microbial life to be identified, thereby enabling a holistic view of an entire ecosystem’s biodiversity and functioning. The potential of omics-based approaches for marine ecosystems observation is enormous and their added value to ecosystem monitoring, management, and conservation is widely acknowledged. Despite these encouraging prospects, most omics-based studies are short-termed and typically cover only small spatial scales which therefore fail to include the full spatio-temporal complexity and dynamics of the system. To date, few attempts have been made to establish standardised, coordinated, broad scaled, and long-term omics observation networks. Here we outline the creation of an omics-based marine observation network at the European scale, the European Marine Omics Biodiversity Observation Network (EMO BON). We illustrate how linking multiple existing individual observation efforts increases the observational power in large-scale assessments of status and change in biodiversity in the oceans. Such large-scale observation efforts have the added value of cross-border cooperation, are characterised by shared costs through economies of scale, and produce structured, comparable data. The key components required to compile reference environmental datasets and how these should be linked are major challenges that we address.

A unique collection of oceanic samples was gathered by the Tara Oceans expeditions (2009–2013), targeting plankton organisms ranging from viruses to metazoans, and providing rich environmental context measurements. Thanks to recent advances in the field of genomics, extensive sequencing has been performed for a deep genomic analysis of this huge collection of samples. A strategy based on different approaches, such as metabarcoding, metagenomics, single-cell genomics and metatranscriptomics, has been chosen for analysis of size-fractionated plankton communities. Here, we provide detailed procedures applied for genomic data generation, from nucleic acids extraction to sequence production, and we describe registries of genomics datasets available at the European Nucleotide Archive (ENA, www.ebi.ac.uk/ena ). The association of these metadata to the experimental procedures applied for their generation will help the scientific community to access these data and facilitate their analysis. This paper complements other efforts to provide a full description of experiments and open science resources generated from the Tara Oceans project, further extending their value for the study of the world’s planktonic ecosystems. Design Type(s) observation design • global survey • biodiversity assessment objective Measurement Type(s) metagenomics analysis • rRNA_gene • whole genome sequencing assay • metatranscriptomic data Technology Type(s) sequencing assay • amplicon sequencing • DNA sequencing • RNA sequencing Factor Type(s) protocol • environmental factor • particle size Sample Characteristic(s) Strait of Gibraltar • Mediterranean Sea • Mediterranean Sea, Western Basin • Ligurian Sea • Tyrrhenian Sea • Ionian Sea • Adriatic Sea • Mediterranean Sea, Eastern Basin • Red Sea • Arabian Sea • Indian Ocean • Mozambique Channel • Southeast Atlantic Ocean • South Atlantic Ocean • Southwest Atlantic Ocean • Drake Passage • South Pacific Ocean • Equatorial Pacific Ocean • North East Pacific Ocean • Gulf of Mexico • Florida Straits • NW Atlantic Ocean • North Atlantic Ocean • NE Atlantic Ocean • deep chlorophyll maximum layer • surface water layer • marine water layer • marine mesopelagic zone • sea water • marine wind mixed layer Machine-accessible metadata file describing the reported data (ISA-Tab format)

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.

Satellite remote sensing from space is a powerful way to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here we present an approach to identify representative communities from a global plankton network that included both zooplankton and phytoplankton and using global satellite observations to predict their biogeography. Six representative plankton communities were identified from a global co-occurrence network inferred using a novel rDNA 18S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to train a model that predicted these representative communities from satellite data. The model showed an overall 67% accuracy in the prediction of the representative communities. The prediction based on 17 satellite-derived parameters showed better performance than based only on temperature and/or the concentration of chlorophyll a. The trained model allowed to predict the global spatiotemporal distribution of communities over 19-years. Our model exhibited strong seasonal changes in the community compositions in the subarctic-subtropical boundary regions, which were consistent with previous field observations. This network-oriented approach can easily be extended to more comprehensive models including prokaryotes as well as viruses.

Coral reefs are of paramount importance in marine ecosystems, where they provide support for a large part of the biodiversity. Being quite sensitive to global changes, they are therefore the prime targets for biodiversity conservation policies. However, such conservation goals require accurate species identification, which are notoriously difficult to get in these highly morphologically variable organisms, rich in cryptic species. There is an acute need for easy-to-use and resolutive species diagnostic molecular markers. The present study builds on the huge sequencing effort developed during the TARA Pacific expedition to develop a genotyping strategy to assign coral samples to the correct species within two coral genera (Porites and Pocillopora). For this purpose, we developed a technique that we called \"Divergent Fragment\" based on the sequencing of a less than 2kb long diagnostic genomic fragment determined from the metagenomic data of a subset of the corals collected. This method has proven to be rapid, resolvable and cost-effective. Sequencing of PCR fragments nested along the species diagnostic fragment allowed us to assign 232 individuals of the genus Pocillopora and 247 individuals of the genus Porites to previously identified independent genetic lineages (i.e. species). This genotyping method will allow to fully analyze the coral samples collected across the Pacific during the Tara Pacific expedition and opens technological perspectives in the field of population genomics-guided conservation. Competing Interest Statement The authors have declared no competing interest.

Oxford Nanopore Technologies Ltd (Oxford, UK) have recently commercialized MinION, a small and low-cost single-molecule nanopore sequencer, that offers the possibility of sequencing long DNA fragments. The Oxford Nanopore technology is truly disruptive and can sequence small genomes in a matter of seconds. It has the potential to revolutionize genomic applications due to its portability, low-cost, and ease of use compared with existing long reads sequencing technologies. The MinION sequencer enables the rapid sequencing of small eukaryotic genomes, such as the yeast genome. Combined with existing assembler algorithms, near complete genome assemblies can be generated and comprehensive population genomic analyses can be performed. Here, we resequenced the genome of the Saccharomyces cerevisiae S288C strain to evaluate the performance of nanopore-only assemblers. Then we de novo sequenced and assembled the genomes of 21 isolates representative of the S. cerevisiae genetic diversity using the MinION platform. The contiguity of our assemblies was 14 times higher than the Illumina-only assemblies and we obtained one or two long contigs for 65% of the chromosomes. This high continuity allowed us to accurately detect large structural variations across the 21 studied genomes. Moreover, because of the high completeness of the nanopore assemblies, we were able to produce a complete cartography of transposable elements insertions and inspect structural variants that are generally missed using a short-read sequencing strategy.