Dinoflagellate vertical migration fuels an intense red tide.

Harmful algal blooms (HABs) are increasing globally, causing economic, human health, and ecosystem harm. In spite of the frequent occurrence of HABs, the mechanisms responsible for their exceptionally high biomass remain imperfectly understood. A 50-y-old hypothesis posits that some dense blooms derive from dinoflagellate motility: organisms swim upward during the day to photosynthesize and downward at night to access deep nutrients. This allows dinoflagellates to outgrow their nonmotile competitors. We tested this hypothesis with in situ data from an autonomous, ocean-wave-powered vertical profiling system. We showed that the dinoflagellate Lingulodinium polyedra's vertical migration led to depletion of deep nitrate during a 2020 red tide HAB event. Downward migration began at dusk, with the maximum migration depth determined by local nitrate concentrations. Losses of nitrate at depth were balanced by proportional increases in phytoplankton chlorophyll concentrations and suspended particle load, conclusively linking vertical migration to the access and assimilation of deep nitrate in the ocean environment. Vertical migration during the red tide created anomalous biogeochemical conditions compared to 70 y of climatological data, demonstrating the capacity of these events to temporarily reshape the coastal ocean's ecosystem and biogeochemistry. Advances in the understanding of the physiological, behavioral, and metabolic dynamics of HAB-forming organisms from cutting-edge observational techniques will improve our ability to forecast HABs and mitigate their consequences in the future.

Temperature dependence of parasitoid infection and abundance of a diatom revealed by automated imaging and classification.

Diatoms are a group of phytoplankton that contribute disproportionately to global primary production. Traditional paradigms that suggest diatoms are consumed primarily by larger zooplankton are challenged by sporadic parasitic "epidemics" within diatom populations. However, our understanding of diatom parasitism is limited by difficulties in quantifying these interactions. Here, we observe the dynamics of Cryothecomonas aestivalis (a protist) infection of an important diatom on the Northeast U.S. Shelf (NES), Guinardia delicatula, with a combination of automated imaging-in-flow cytometry and a convolutional neural network image classifier. Application of the classifier to >1 billion images from a nearshore time series and >20 survey cruises across the broader NES reveals the spatiotemporal gradients and temperature dependence of G. delicatula abundance and infection dynamics. Suppression of parasitoid infection at temperatures <4 °C drives annual cycles in both G. delicatula infection and abundance, with an annual maximum in infection observed in the fall-winter preceding an annual maximum in host abundance in the winter-spring. This annual cycle likely varies spatially across the NES in response to variable annual cycles in water temperature. We show that infection remains suppressed for ~2 mo following cold periods, possibly due to temperature-induced local extinctions of the C. aestivalis strain(s) that infect G. delicatula. These findings have implications for predicting impacts of a warming NES surface ocean on G. delicatula abundance and infection dynamics and demonstrate the potential of automated plankton imaging and classification to quantify phytoplankton parasitism in nature across unprecedented spatiotemporal scales.

Distinct responses to warming within picoplankton communities across an environmental gradient.

Picophytoplankton are a ubiquitous component of marine plankton communities and are expected to be favored by global increases in seawater temperature and stratification associated with climate change. Eukaryotic and prokaryotic picophytoplankton have distinct ecology, and global models predict that the two groups will respond differently to future climate scenarios. At a nearshore observatory on the Northeast US Shelf, however, decades of year-round monitoring have shown these two groups to be highly synchronized in their responses to environmental variability. To reconcile the differences between regional and global predictions for picophytoplankton dynamics, we here investigate the picophytoplankton community across the continental shelf gradient from the nearshore observatory to the continental slope. We analyze flow cytometry data from 22 research cruises, comparing the response of picoeukaryote and Synechococcus communities to environmental variability across time and space. We find that the mechanisms controlling picophytoplankton abundance differ across taxa, season, and distance from shore. Like the prokaryote, Synechococcus, picoeukaryote division rates are limited nearshore by low temperatures in winter and spring, and higher temperatures offshore lead to an earlier spring bloom. Unlike Synechococcus, picoeukaryote concentration in summer decreases dramatically in offshore surface waters and exhibits deeper subsurface maxima. The offshore picoeukaryote community appears to be nutrient limited in the summer and subject to much greater loss rates than Synechococcus. This work both produces and demonstrates the necessity of taxon- and site-specific knowledge for accurately predicting the responses of picophytoplankton to ongoing environmental change.

Predator switching strength controls stability in diamond-shaped food web models.

In food web models that include more than one prey type for a single predator, it is common for the predator's functional response to include some form of switching-preferential consumption of more abundant prey types. Predator switching promotes coexistence among competing prey types and increases diversity in the prey community. Here, we show how the dynamics of a diamond-shaped food web model of a marine plankton community are sensitive to a parameter that sets the strength of predator switching. Stronger switching destabilizes the model's coexistence equilibrium and leads to the appearance of limit cycles. Stronger switching also increases the evenness of the asymptotic prey community and promotes synchrony in the dynamics of disparate prey types. Given the dependence of model behavior on the strength of predator switching, it is important that modelers carefully consider the parameterization of functional responses that include switching.

A discrete, stochastic model of colonial phytoplankton population size structure: Development and application to in situ imaging-in-flow cytometer observations of Dinobryon.

The scientific community lacks models for the dynamic changes in population size structure that occur in colonial phytoplankton. This is surprising, as size is a key trait affecting many aspects of phytoplankton ecology, and colonial forms are very common. We aim to fill this gap with a new discrete, stochastic model of dynamic changes in phytoplankton colonies' population size structure. We use the colonial phytoplankton Dinobryon as a proof-of-concept organism. The model includes four stochastic functions-division, stomatocyst production, colony breakage, and colony loss-to determine Dinobryon population size structure and populations counts. Although the functions presented here are tailored to Dinobryon, the model is readily adaptable to represent other colonial taxa. We demonstrate how fitting our model to in situ observations of colony population size structure can provide a powerful approach to explore colony size dynamics. Here, we have (1) collected high-frequency in situ observations of Dinobryon in Lac (Lake) Montjoie (Quebec, Canada) in 2013 with a moored Imaging FlowCytobot (IFCB) and (2) fit the model to those observations with a genetic algorithm solver that extracts parameter estimates for each of the four stochastic functions. As an example of the power of this model-data integration, we also highlight ecological insights into Dinobryon colony size and stomatocyst production. The Dinobryon population was enriched in larger, flagellate-rich colonies near bloom initiation and shifted to smaller and emptier colonies toward bloom decline.

Dynamics and functional diversity of the smallest phytoplankton on the Northeast US Shelf.

Picophytoplankton are the most abundant primary producers in the ocean. Knowledge of their community dynamics is key to understanding their role in marine food webs and global biogeochemical cycles. To this end, we analyzed a 16-y time series of observations of a phytoplankton community at a nearshore site on the Northeast US Shelf. We used a size-structured population model to estimate in situ division rates for the picoeukaryote assemblage and compared the dynamics with those of the picocyanobacteria Synechococcus at the same location. We found that the picoeukaryotes divide at roughly twice the rate of the more abundant Synechococcus and are subject to greater loss rates (likely from viral lysis and zooplankton grazing). We describe the dynamics of these groups across short and long timescales and conclude that, despite their taxonomic differences, their populations respond similarly to changes in the biotic and abiotic environment. Both groups appear to be temperature limited in the spring and light limited in the fall and to experience greater mortality during the day than at night. Compared with Synechococcus, the picoeukaryotes are subject to greater top-down control and contribute more to the region's primary productivity than their standing stocks suggest.

Machine learning techniques to characterize functional traits of plankton from image data.

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Seasonal environmental variability drives microdiversity within a coastal Synechococcus population.

Marine microbes often show a high degree of physiological or ecological diversity below the species level. This microdiversity raises questions about the processes that drive diversification and permit coexistence of diverse yet closely related marine microbes, especially given the theoretical efficiency of competitive exclusion. Here, we provide insight with an 8-year time series of diversity within Synechococcus, a widespread and important marine picophytoplankter. The population of Synechococcus on the Northeast U.S. Shelf is comprised of six main types, each of which displays a distinct and consistent seasonal pattern. With compositional data analysis, we show that these patterns can be reproduced with a simple model that couples differential responses to temperature and light with the seasonal cycle of the physical environment. These observations support the hypothesis that temporal variability in environmental factors can maintain microdiversity in marine microbial populations. We also identify how seasonal diversity patterns directly determine overarching Synechococcus population abundance features.

Seasons of Syn.

Synechococcus is a widespread and important marine primary producer. Time series provide critical information for identifying and understanding the factors that determine abundance patterns. Here, we present the results of analysis of a 16-yr hourly time series of Synechococcus at the Martha's Vineyard Coastal Observatory, obtained with an automated, in situ flow cytometer. We focus on understanding seasonal abundance patterns by examining relationships between cell division rate, loss rate, cellular properties (e.g., cell volume, phycoerythrin fluorescence), and environmental variables (e.g., temperature, light). We find that the drivers of cell division vary with season; cells are temperature-limited in winter and spring, but light-limited in the fall. Losses to the population also vary with season. Our results lead to testable hypotheses about Synechococcus ecophysiology and a working framework for understanding the seasonal controls of Synechococcus cell abundance in a temperate coastal system.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

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.

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

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.

Diel size distributions reveal seasonal growth dynamics of a coastal phytoplankter.

Phytoplankton account for roughly half of global primary production; it is vital that we understand the processes that control their abundance. A key process is cell division. We have, however, been unable to estimate division rate in natural populations at the appropriate timescale (hours to days) for extended periods of time (months to years). For phytoplankton, the diel change in cell size distribution is related to division rate, which offers an avenue to obtain estimates from in situ observations. We show that a matrix population model, fit to hourly cell size distributions, accurately estimates division rates of both cultured and natural populations of Synechococcus. Application of the model to Synechococcus at the Martha's Vineyard Coastal Observatory provides an unprecedented view that reveals a distinct seasonality in division rates. This information allows us to separate the effects of growth and loss quantitatively over an entire seasonal cycle. We find that division and loss processes are tightly coupled throughout the year. The large seasonal changes in cell abundance are the result of periods of time (weeks to months) when there are small systematic differences that favor either net growth or loss. We also find that temperature plays a critical role in limiting division rate during the annual spring bloom. This approach opens a path to quantify the role of Synechococcus in ecological and biogeochemical processes in natural systems.

Diversity of Synechococcus at the Martha's Vineyard Coastal Observatory: Insights from Culture Isolations, Clone Libraries, and Flow Cytometry.

The cyanobacterium Synechococcus is a ubiquitous, important phytoplankter across the world's oceans. A high degree of genetic diversity exists within the marine group, which likely contributes to its global success. Over 20 clades with different distribution patterns have been identified. However, we do not fully understand the environmental factors that control clade distributions. These factors are likely to change seasonally, especially in dynamic coastal systems. To investigate how coastal Synechococcus assemblages change temporally, we assessed the diversity of Synechococcus at the Martha's Vineyard Coastal Observatory (MVCO) over three annual cycles with culture-dependent and independent approaches. We further investigated the abundance of both phycoerythrin (PE)-containing and phycocyanin (PC)-only Synechococcus with a flow cytometric setup that distinguishes PC-only Synechococcus from picoeukaryotes. We found that the Synechococcus assemblage at MVCO is diverse (13 different clades identified), but dominated by clade I representatives. Many clades were only isolated during late summer and fall, suggesting more favorable conditions for isolation at this time. PC-only strains from four different clades were isolated, but these cells were only detected by flow cytometry in a few samples over the time series, suggesting they are rare at this site. Within clade I, we identified four distinct subclades. The relative abundances of each subclade varied over the seasonal cycle, and the high Synechococcus cell concentration at MVCO may be maintained by the diversity found within this clade. This study highlights the need to understand how temporal aspects of the environment affect Synechococcus community structure and cell abundance.

Rapid growth and concerted sexual transitions by a bloom of the harmful dinoflagellate Alexandrium fundyense (Dinophyceae).

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.

Inversion of spectral absorption coefficients to infer phytoplankton size classes, chlorophyll concentration, and detrital matter.

Measured spectral absorption coefficients were inverted to infer phytoplankton concentration in three size classes (picoplankton, nanoplankton, and microplankton), chlorophyll concentration [Chl], and both magnitude and spectral shape of absorption by colored detrital matter (CDM). Our algorithm allowed us to solve for the nonlinear factor of CDM absorption slope separately from the other linear factors, thus fully utilizing the additive characteristic inherent in absorption coefficients. We validated the inversion with three datasets: two spatially distributed global datasets, the Laboratoire d'Océanographie de Villefranche dataset and the NASA bio-Optical Marine Algorithm Dataset, and a time series coastal dataset, the Martha's Vineyard Coastal Observatory dataset. Comparison with high performance liquid chromatography analyses showed that the phytoplankton size classes can be retrieved with correlation coefficients (r)>0.7, root mean square errors of 0.2, and median relative errors of 20% in oceanic waters and with similar performance in coastal waters. Much improved agreement was found for the entire phytoplankton population, with r>0.90 for [Chl] and absorption coefficients (aph) for all three datasets. The inferred aCDM(400) and CDM spectral slope agree within ±4% of measurements in both oceanic and coastal waters. The results indicate that the chlorophyll-a specific absorption spectra used as an inversion kernel represent well the global mean states for each of the three phytoplankton size classes. The method can be applied to either bulk or particulate absorption data and is spectrally flexible.

Complexities of bloom dynamics in the toxic dinoflagellate Alexandrium fundyense revealed through DNA measurements by imaging flow cytometry coupled with species-specific rRNA probes.

Measurements of the DNA content of different protist populations can shed light on a variety of processes, including cell division, sex, prey ingestion, and parasite invasion. Here, we modified an Imaging FlowCytobot (IFCB), a custom-built flow cytometer that records images of microplankton, to measure the DNA content of large dinoflagellates and other high-DNA content species. The IFCB was also configured to measure fluorescence from Cy3-labeled rRNA probes, aiding the identification of Alexandrium fundyense (syn. A. tamarense Group I), a photosynthetic dinoflagellate that causes paralytic shellfish poisoning (PSP). The modified IFCB was used to analyze samples from the development, peak and termination phases of an inshore A. fundyense bloom (Salt Pond, Eastham, MA USA), and from a rare A. fundyense 'red tide' that occurred in the western Gulf of Maine, offshore of Portsmouth, NH (USA). Diploid or G2 phase ('2C') A. fundyense cells were frequently enriched at the near-surface, suggesting an important role for aggregation at the air-sea interface during sexual events. Also, our analysis showed that large proportions of A. fundyense cells in both the Salt Pond and red tide blooms were planozygotes during bloom decline, highlighting the importance of sexual fusion to bloom termination. At Salt Pond, bloom decline also coincided with a dramatic rise in infections by the parasite genus Amoebophrya. The samples that were most heavily infected contained many large cells with higher DNA-associated fluorescence than 2C vegetative cells, but these cells' nuclei were also frequently consumed by Amoebophrya trophonts. Neither large cell size nor increased DNA-associated fluorescence could be replicated by infecting an A. fundyense culture of vegetative cells. Therefore we attribute these characteristics of the large Salt Pond cells to planozygote maturation rather than Amoebophrya infection, though an interaction between infection and planozygote maturation may also have contributed. The modified IFCB is a valuable tool for exploring the conditions that promote sexual transitions by dinoflagellate blooms but care is needed when interpreting results from samples in which parasitism is prevalent.

Resonance control of acoustic focusing systems through an environmental reference table and impedance spectroscopy.

Acoustic standing waves can precisely focus flowing particles or cells into tightly positioned streams for interrogation or downstream separations. The efficiency of an acoustic standing wave device is dependent upon operating at a resonance frequency. Small changes in a system's temperature and sample salinity can shift the device's resonance condition, leading to poor focusing. Practical implementation of an acoustic standing wave system requires an automated resonance control system to adjust the standing wave frequency in response to environmental changes. Here we have developed a rigorous approach for quantifying the optimal acoustic focusing frequency at any given environmental condition. We have demonstrated our approach across a wide range of temperature and salinity conditions to provide a robust characterization of how the optimal acoustic focusing resonance frequency shifts across these conditions. To generalize these results, two microfluidic bulk acoustic standing wave systems (a steel capillary and an etched silicon wafer) were examined. Models of these temperature and salinity effects suggest that it is the speed of sound within the liquid sample that dominates the resonance frequency shift. Using these results, a simple reference table can be generated to predict the optimal resonance condition as a function of temperature and salinity. Additionally, we show that there is a local impedance minimum associated with the optimal system resonance. The integration of the environmental results for coarse frequency tuning followed by a local impedance characterization for fine frequency adjustments, yields a highly accurate method of resonance control. Such an approach works across a wide range of environmental conditions, is easy to automate, and could have a significant impact across a wide range of microfluidic acoustic standing wave systems.

Physiological and ecological drivers of early spring blooms of a coastal phytoplankter.

Climate affects the timing and magnitude of phytoplankton blooms that fuel marine food webs and influence global biogeochemical cycles. Changes in bloom timing have been detected in some cases, but the underlying mechanisms remain elusive, contributing to uncertainty in long-term predictions of climate change impacts. Here we describe a 13-year hourly time series from the New England shelf of data on the coastal phytoplankter Synechococcus, during which the timing of its spring bloom varied by 4 weeks. We show that multiyear trends are due to temperature-induced changes in cell division rate, with earlier blooms driven by warmer spring water temperatures. Synechococcus loss rates shift in tandem with division rates, suggesting a balance between growth and loss that has persisted despite phenological shifts and environmental change.

Continuous automated imaging-in-flow cytometry for detection and early warning of Karenia brevis blooms in the Gulf of Mexico.

Monitoring programs for harmful algal blooms (HABs) typically rely on time-consuming manual methods for identification and enumeration of phytoplankton, which make it difficult to obtain results with sufficient temporal resolution for early warning. Continuous automated imaging-in-flow by the Imaging FlowCytobot (IFCB) deployed at Port Aransas, TX has provided early warnings of six HAB events. Here we describe the progress in automating this early warning system for blooms of Karenia brevis. In 2009, manual inspection of IFCB images in mid-August 2009 provided early warning for a Karenia bloom that developed in mid-September. Images from 2009 were used to develop an automated classifier that was employed in 2011. Successful implementation of automated file downloading, processing and image classification allowed results to be available within 4 h after collection and to be sent to state agency representatives by email for early warning of HABs. No human illness (neurotoxic shellfish poisoning) has resulted from these events. In contrast to the common assumption that Karenia blooms are near monospecific, post-bloom analysis of the time series revealed that Karenia cells comprised at most 60-75 % of the total microplankton.

Taxonomic classification of phytoplankton with multivariate optical computing, part III: demonstration.

We describe the automatic analysis of fluorescence tracks of phytoplankton recorded with a fluorescence imaging photometer. The optical components and construction of the photometer were described in Part I and Part II of this series in this issue. An algorithm first isolates tracks corresponding to a single phytoplankter transit in the nominal focal plane of a flow cell. Then, the fluorescence streaks in the track that correspond to individual optical elements on the filter wheel are identified. The fluorescence intensity of each streak is integrated and used to calculate ratios. This approach was tested using 853 fluorescence measurements of the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana. Average intensity ratios for the two classes closely follow those predicted in Part I of this series, with a distribution of ratios in each class that is consistent with the signal-to-noise ratio calculations in Part II for single cells. No overlap of the two class ratios was observed, yielding perfect classification.