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  • 1
    Publication Date: 2022-05-25
    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
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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  • 2
    Publication Date: 2022-05-25
    Description: © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 217 (2018): 126-143, doi:10.1016/j.rse.2018.08.010.
    Description: Diatoms dominate global silica production and export production in the ocean; they form the base of productive food webs and fisheries. Thus, a remote sensing algorithm to identify diatoms has great potential to describe ecological and biogeochemical trends and fluctuations in the surface ocean. Despite the importance of detecting diatoms from remote sensing and the demand for reliable methods of diatom identification, there has not been a systematic evaluation of algorithms that are being applied to this end. The efficacy of these models remains difficult to constrain in part due to limited datasets for validation. In this study, we test a bio-optical algorithm developed by Sathyendranath et al. (2004) to identify diatom dominance from the relationship between ratios of remote sensing reflectance and chlorophyll concentration. We evaluate and refine the original model with data collected at the Martha's Vineyard Coastal Observatory (MVCO), a near-shore location on the New England shelf. We then validated the refined model with data collected in Harpswell Sound, Maine, a site with greater optical complexity than MVCO. At both sites, despite relatively large changes in diatom fraction (0.8–82% of chlorophyll concentration), the magnitude of variability in optical properties due to the dominance or non-dominance of diatoms is less than the variability induced by other absorbing and scattering constituents of the water. While the original model performance was improved through successive re-parameterizations and re-formulations of the absorption and backscattering coefficients, we show that even a model originally parameterized for the Northwest Atlantic and re-parameterized for sites such as MVCO and Harpswell Sound performs poorly in discriminating diatom-dominance from optical properties.
    Description: This work was supported by: a Woods Hole Oceanographic Institution Summer Student Fellowship (NSF REU award #1156952) and a Bowdoin College Grua/O'Connell Research Award to SJK; grants to HMS from NASA (Ocean Biology and Biogeochemistry program and Biodiversity and Ecological Forecasting program), NSF (Ocean Sciences), the Gordon and Betty Moore Foundation, the Simons Foundation, and NOAA through the Cooperative Institute for the North Atlantic Region (CINAR) under Cooperative Agreement NA14OAR4320158; and grants to CSR from NASA (Ocean Biology and Biogeochemistry program).
    Keywords: Phytoplankton ; Community structure ; Ocean color ; Diatoms
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2022-05-25
    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
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 4
    Publication Date: 2022-05-25
    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
    Keywords: Aquatic ; Coastal zone ; Ecology ; Essentail biodiversity variables ; H4 imaging ; Hyperspectral ; Remote sensing ; Vegetation ; Wetland
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 5
    Publication Date: 2022-05-26
    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
    Repository Name: Woods Hole Open Access Server
    Type: Article
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