Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Limnology and Oceanography 60 (2015): 2059–2078, doi:10.1002/lno.10155.

Description: Transitions between life cycle stages by the harmful dinoflagellate Alexandrium fundyense are critical for the initiation and termination of its blooms. To quantify these transitions in a single population, an Imaging FlowCytobot (IFCB), was deployed in Salt Pond (Eastham, Massachusetts), a small, tidally flushed kettle pond that hosts near annual, localized A. fundyense blooms. Machine-based image classifiers differentiating A. fundyense life cycle stages were developed and results were compared to manually corrected IFCB samples, manual microscopy-based estimates of A. fundyense abundance, previously published data describing prevalence of the parasite Amoebophrya, and a continuous culture of A. fundyense infected with Amoebophrya. In Salt Pond, a development phase of sustained vegetative division lasted approximately 3 weeks and was followed by a rapid and near complete conversion to small, gamete cells. The gametic period (∼3 d) coincided with a spike in the frequency of fusing gametes (up to 5% of A. fundyense images) and was followed by a zygotic phase (∼4 d) during which cell sizes returned to their normal range but cell division and diel vertical migration ceased. Cell division during bloom development was strongly phased, enabling estimation of daily rates of division, which were more than twice those predicted from batch cultures grown at similar temperatures in replete medium. Data from the Salt Pond deployment provide the first continuous record of an A. fundyense population through its complete bloom cycle and demonstrate growth and sexual induction rates much higher than are typically observed in culture.

Description: National Science Foundation Grant Number: OCE-0430724, OCE-0911031, and OCE-1314642; National Institutes of Health Grant Number: NIEHS-1P50-ES021923-01; National Park Service (NPS) Cooperative Agreement Grant Number: H238015504; Gordon and Betty Moore Foundation Grant Number: #2649 to HMS; IOF Grant Number: MOHAB PIOF-GA-252260

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Limnology and Oceanography: Methods 15 (2017): 94–102, doi:10.1002/lom3.10145.

Description: Recent advances in plankton ecology have brought to light the importance of variability within populations and have suggested that cell-to-cell differences may influence ecosystem-level processes such as species succession and bloom dynamics. Flow cytometric cell sorting has been used to capture individual plankton cells from natural water samples to investigate variability at the single cell level, but the crude taxonomic resolution afforded by the fluorescence and light scattering measurements of conventional flow cytometers necessitates sorting and analyzing many cells that may not be of interest. Addition of imaging to flow cytometry improves classification capability considerably: Imaging FlowCytobot, which has been deployed at the Martha's Vineyard Coastal Observatory since 2006, allows classification of many kinds of nano- and microplankton to the genus or even species level. We present in this paper a modified bench-top Imaging FlowCytobot (IFCB-Sorter) with the capability to sort both single cells and colonies of phytoplankton and microzooplankton from seawater samples. The cells (or subsets selected based on their images) can then be cultured for further manipulation or processed for analyses such as nucleic acid sequencing. The sorting is carried out in two steps: a fluorescence signal triggers imaging and diversion of the sample flow into a commercially available “catcher tube,” and then a solenoid-based flow control system isolates each sorted cell along with 20 μL of fluid.

Description: NSF Grant Number: OCE-11300140; WHOI internal support; NSERC through a Post-Graduate Masters award

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Melin, F., Moore, T. S., Muller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zuhlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., & Platt, T. An ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI). Sensors, 19(19), (2019): 4285, doi: 10.3390/s19194285.

Description: 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 used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Description: This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation – PORTWIMS.

Description: © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.

Description: The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration 〈2%, relative calibration of 0.2%, polarization sensitivity 〈1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

Description: National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC00006

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Ecology Progress Series 550 (2016): 65-81, doi:10.3354/meps11687.

Description: Protozoa play important roles in grazing and nutrient recycling, but quantifying these roles has been hindered by difficulties in collecting, culturing, and observing these often-delicate cells. During long-term deployments at the Martha’s Vineyard Coastal Observatory (Massachusetts, USA), Imaging FlowCytobot (IFCB) has been shown to be useful for studying live cells in situ without the need to culture or preserve. IFCB records images of cells with chlorophyll fluorescence above a trigger threshold, so to date taxonomically resolved analysis of protozoa has presumably been limited to mixotrophs and herbivores which have eaten recently. To overcome this limitation, we have coupled a broad-application ‘live cell’ fluorescent stain with a modified IFCB so that protozoa which do not contain chlorophyll (such as consumers of unpigmented bacteria and other heterotrophs) can also be recorded. Staining IFCB (IFCB-S) revealed higher abundances of grazers than the original IFCB, as well as some cell types not previously detected. Feeding habits of certain morphotypes could be inferred from their fluorescence properties: grazers with stain fluorescence but without chlorophyll cannot be mixotrophs, but could be either starving or feeding on heterotrophs. Comparisons between cell counts for IFCB-S and manual light microscopy of Lugol’s stained samples showed consistently similar or higher counts from IFCB-S. We show how automated classification through the extraction of image features and application of a machine-learning algorithm can be used to evaluate the large high-resolution data sets collected by IFCBs; the results reveal varying seasonal patterns in abundance among groups of protists.

Description: This research was supported in part by NSF (grants OCE-1130140, OCE-1434440), NASA (grants NNX11AF07G and NNX13AC98G), the Gordon and Betty Moore Foundation (grants 934 and 2649), and the Woods Hole Oceanographic Institution’s Innovative Technology Program.

Description: Author Posting. © Inter-Research, 2014. This article is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 503 (2014): 1-10, doi:10.3354/meps10784.

Description: Plankton images collected by Imaging FlowCytobot from 2006 to 2013 at the Martha’s Vineyard Coastal Observatory (Massachusetts, USA) were used to identify and quantify the occurrence of the diatom Guinardia delicatula and of a parasite that seems specific to this host. We observed infection with morphological stages that appear similar to the parasite Cryothecomonas aestivalis. Our results show that events during which infection rates exceed 10% are recurrent on the New England Shelf and suggest that the parasites are an important source of host mortality. We document a significant negative relationship between bloom magnitude and parasite infection rate, supporting the hypothesis that the parasites play a major role in controlling blooms. While G. delicatula is observed during all seasons, the infecting stages of the parasite are abundant only when water temperature is above 4°C. The anomalously warm water and small G. delicatula bloom during the winter of 2012 provided evidence that parasites can be active through winter if temperatures remain relatively high. As climate change continues, winter periods of water below 4°C may shorten or disappear in this region, suggesting that parasite effects on species such as G. delicatula may increase, with immediate impacts on their population dynamics.

Description: This work was supported by grants from NSF’s Ocean Technology and Interdisciplinary Coordination program, NASA’s Ocean Biology and Biogeochemistry program and Biodiversity and Ecological Forecasting program, the Gordon and Betty Moore Foundation, and the National Ocean Partnership Program.

Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Limnology and Oceanography: Methods 15 (2017): 867–874, doi:10.1002/lom3.10205.

Description: Imaging FlowCytobot, a submersible instrument that measures optical properties and captures images of nano- and microplankton-sized particles, has proved useful in plankton studies, but its sampling rate is limited by the ability of hydrodynamic focusing to accurately position flowing sample particles. We show that IFCB's sampling rate can be increased at least several-fold by implementing in-line acoustic focusing upstream of the flow cell. Particles are forced to the center of flow by acoustic standing waves created by a piezo-electric transducer bonded to the sample capillary and driven at the appropriate frequency. With the particles of interest confined to the center of the sample flow, the increased size of the sample core that accompanies increased sample flow rate no longer degrades image and signal quality as it otherwise would. Temperature affects the optimum frequency (through its effect on the speed of sound in water), so a relationship between sample temperature and optimum frequency for acoustic focusing was determined and utilized to control the transducer. The modified instrument's performance was evaluated through analyses of artificial particles, phytoplankton cultures, and natural seawater samples and through deployments in coastal waters. The results show that large cells, especially dinoflagellates, are acoustically focused extremely effectively (which could enable, for example, 〉 10-fold increased sampling rate of harmful algal bloom species, if smaller cells are ignored), while for nearly all cell types typically monitored by IFCB, threefold faster data accumulation was achieved without any compromises. Further increases are possible with more sophisticated software and/or a faster camera.

Description: NSF Grant Numbers: OCE-1130140 , OCE-1131134