Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species

Predicting responses of plankton to variations in essential nutrients is hampered by limited in situ measurements, a poor understanding of community composition, and the lack of reference gene catalogs for key taxa. Iron is a key driver of plankton dynamics and, therefore, of global biogeochemical cycles and climate. To assess the impact of iron availability on plankton communities, we explored the comprehensive bio‐oceanographic and bio‐omics data sets from Tara Oceans in the context of the iron products from two state‐of‐the‐art global scale biogeochemical models. We obtained novel information about adaptation and acclimation toward iron in a range of phytoplankton, including picocyanobacteria and diatoms, and identified whole subcommunities covarying with iron. Many of the observed global patterns were recapitulated in the Marquesas archipelago, where frequent plankton blooms are believed to be caused by natural iron fertilization, although they are not captured in large‐scale biogeochemical models. This work provides a proof of concept that integrative analyses, spanning from genes to ecosystems and viruses to zooplankton, can disentangle the complexity of plankton communities and can lead to more accurate formulations of resource bioavailability in biogeochemical models, thus improving our understanding of plankton resilience in a changing environment.

In contemporary oceans diatoms are an important group of eukaryotic phytoplankton that typically dominate in upwelling regions and at high latitudes. They also make significant contributions to sporadic blooms that often occur in springtime. Recent surveys have revealed global information about their abundance and diversity, as well as their contributions to biogeochemical cycles, both as primary producers of organic material and as conduits facilitating the export of carbon and silicon to the ocean interior. Sequencing of diatom genomes is revealing the evolutionary underpinnings of their ecological success by examination of their gene repertoires and the mechanisms they use to adapt to environmental changes. The rise of the diatoms over the last hundred million years is similarly being explored through analysis of microfossils and biomarkers that can be traced through geological time, as well as their contributions to seafloor sediments and fossil fuel reserves. The current review aims to synthesize current information about the evolution and biogeochemical functions of diatoms as they rose to prominence in the global ocean.This article is part of the themed issue 'The peculiar carbon metabolism in diatoms'.

Mixotrophy, or the ability to acquire carbon from both auto-and heterotrophy, is a widespread ecological trait in marine protists. Using a metabarcoding dataset of marine plankton from the global ocean, 318,054 mixotrophic metabarcodes represented by 89,951,866 sequences and belonging to 133 taxonomic lineages were identified and classified into four mixotrophic functional types: constitutive mixotrophs (CM), generalist non-constitutive mixotrophs (GNCM), endo-symbiotic specialist non-constitutive mixotrophs (eSNCM), and plastidic specialist non-constitutive mixotrophs (pSNCM). Mixotrophy appeared ubiquitous, and the distributions of the four mixotypes were analyzed to identify the abiotic factors shaping their biogeographies. Kleptoplastidic mixotrophs (GNCM and pSNCM) were detected in new zones compared to previous morphological studies. Constitutive and non-constitutive mixotrophs had similar ranges of distributions. Most lineages were evenly found in the samples, yet some of them displayed strongly contrasted distributions, both across and within mixotypes. Particularly divergent biogeographies were found within endo-symbiotic mixotrophs, depending on the ability to form colonies or the mode of symbiosis. We showed how metabarcoding can be used in a complementary way with previous morphological observations to study the biogeography of mixotrophic protists and to identify key drivers of their biogeography.

With global climate change altering marine ecosystems, research on plankton ecology is likely to navigate uncharted seas. Yet, a staggering wealth of new plankton observations, integrated with recent advances in marine ecosystem modeling, may shed light on marine ecosystem structure and functioning. A EuroMarine foresight workshop on the "Impact of climate change on the distribution of plankton functional and phylogenetic diversity" (PlankDiv) identified five grand challenges for future plankton diversity and macroecology research: (1) What can we learn about plankton communities from the new wealth of high-throughput "omics" data? (2) What is the link between plankton diversity and ecosystem function? (3) How can species distribution models be adapted to represent plankton biogeography? (4) How will plankton biogeography be altered due to anthropogenic climate change? and (5) Can a new unifying theory of macroecology be developed based on plankton ecology studies? In this review, we discuss potential future avenues to address these questions, and challenges that need to be tackled along the way.

DNA metabarcoding is becoming the tool of choice for biodiversity assessment across taxa and environments. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines to remove experimental noise exist. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and chosen filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools. Here, we present metabaR, an r package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, that is, contaminants and tag‐jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitates assessment of the appropriateness of data curation filtering thresholds. metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre‐analysed with any bioinformatic pipeline. Its outputs are easily usable for downstream analyses with any ecological r package. metabaR complements existing bioinformatics pipelines by providing scientists with a variety of functions to effectively clean DNA metabarcoding data and avoid serious misinterpretations. It thus offers a promising platform for automatised data quality assessments of DNA metabarcoding data for environmental research and biomonitoring.

DNA metabarcoding is becoming the tool of choice for biodiversity studies across taxa and large-scale environmental gradients. Yet, the artefacts present in metabarcoding datasets often preclude a proper interpretation of ecological patterns. Bioinformatic pipelines removing experimental noise have been designed to address this issue. However, these often only partially target produced artefacts, or are marker specific. In addition, assessments of data curation quality and the appropriateness of filtering thresholds are seldom available in existing pipelines, partly due to the lack of appropriate visualisation tools.Here, we present metabaR, an R package that provides a comprehensive suite of tools to effectively curate DNA metabarcoding data after basic bioinformatic analyses. In particular, metabaR uses experimental negative or positive controls to identify different types of artefactual sequences, i.e. reagent contaminants and tag-jumps. It also flags potentially dysfunctional PCRs based on PCR replicate similarities when those are available. Finally, metabaR provides tools to visualise DNA metabarcoding data characteristics in their experimental context as well as their distribution, and facilitate assessment of the appropriateness of data curation filtering thresholds.metabaR is applicable to any DNA metabarcoding experimental design but is most powerful when the design includes experimental controls and replicates. More generally, the simplicity and flexibility of the package makes it applicable any DNA marker, and data generated with any sequencing platform, and pre-analysed with any bioinformatic pipeline. Its outputs are easily usable for downstream analyses with any ecological R package.metabaR complements existing bioinformatics pipelines by providing scientists with a variety of functions with customisable methods that will allow the user to effectively clean DNA metabarcoding data and avoid serious misinterpretations. It thus offers a promising platform for automatised data quality assessments of DNA metabarcoding data for environmental research and biomonitoring.

No abstract