Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.

Phytoplankton blooms over Arctic Ocean continental shelves are thought to be restricted to waters free of sea ice. Here, we document a massive phytoplankton bloom beneath fully consolidated pack ice far from the ice edge in the Chukchi Sea, where light transmission has increased in recent decades because of thinning ice cover and proliferation of melt ponds. The bloom was characterized by high diatom biomass and rates of growth and primary production. Evidence suggests that under-ice phytoplankton blooms may be more widespread over nutrient-rich Arctic continental shelves and that satellite-based estimates of annual primary production in these waters may be underestimated by up to 10-fold.

High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imagingin-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

In this paper we review the technologies available to make globally quantitative observations of particles in general-and plankton in particular-in the world oceans, and for sizes varying from sub-microns to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical and acoustical methods as well

A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to >100 μm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano-and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long-term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.

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Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were u...

Our understanding of the dynamics of phytoplankton communities has been limited by the space and timescales associated with traditional monitoring approaches. To overcome some of these limitations, we have developed a submersible flow cytometer (FlowCytobot) that is designed for extended autonomous monitoring of phytoplankton abundance, cell size, and pigmentation. FlowCytobot was moored on the seafloor from late July to October 2001 at the Long-term Environmental Observatory study site off the coast of New Jersey, and water samples from 5 m depth were pumped continuously through the instrument. Analysis of cells' optical properties revealed distinct populations of Synechococcus and cryptophytes, as well as an assemblage of other pico-and nanophytoplankton of mixed taxonomy. For each of these groups, dramatic variations in cell concentration were observed within the sampling period. Diel variations in cell scattering, which are indicative of changes in cell size, were consistent with patterns of cell growth during the light period and cell division late in the day. We developed a size-structured matrix population model that accommodates simultaneous growth and division and then used the model and size distribution data from FlowCytobot to estimate daily specific growth rates for Synechococcus; these estimates are independent of cell concentration and do not include mortality. The results show that a dramatic autumn decline in the concentration of Synechococcus can be attributed to a decrease in the specific growth rate rather than to effects of physical transport processes or trophic interactions.The distributions of marine phytoplankton are highly variable in space and time. Evidence for this fact has come from a variety of sampling approaches, ranging from shipboardor mooring-based measurement of in vivo chlorophyll fluorescence to satellite-based assessment of ocean color (e.g., Dickey 1991Dickey , 2001). Nevertheless, our knowledge of the factors regulating phytoplankton populations at and below the mesoscale remains limited by inadequate sampling and our inability to measure the species composition, size distribution, and growth rate of the phytoplankton community. Recently developed automated flow cytometers (Dubelaar et al. 1999;Olson et al. 2003) and cell imaging systems (Sieracki et al. 1998) are aimed at resolving some of these limitations. With these instruments, we can continuously monitor the phytoplankton at the individual cell level and document changes in the taxonomic and size structure of the phytoplankton community at a wider range of scales than has been possible with traditional techniques.