In August 2018, the harmful algae species Margalefidinium polykrikoides bloomed to levels previously unobserved in the open waters of Narragansett Bay, Rhode Island, in a transient but intense bloom. Detected by an Imaging FlowCytobot providing hourly data, it is characterized by a time span of less than a week and patchiness with sub-daily oscillations in concentration. The highest concentrations are recorded at lower salinity and higher temperature, suggesting the bloom may have developed in the upper bay and was transported south. The proportion of chains increased during the height of the bloom, and many of the images contained 4-cells per chain. The development of the bloom was favored by optimal temperature and salinity conditions as well as increased nitrogen coincident with greater precipitation and river flow. The period preceding bloom formation also saw a sharp decrease in the dominating large chain-forming diatom Eucampia sp. and highly abundant Skeletonema spp., thus reducing competition over resources for the slow-growing M. polykrikoides. The height of the bloom was reached during the lowest tidal range of the month when the turbulence and water displacement were lower. This time series highlights an out-of-the-ordinary bloom's environmental and biological conditions and the importance of frequent sampling during known favorable conditions.

Abstract Characterizing marine phytoplankton community variability is crucial to designing sampling strategies and interpreting time series. Satellite remote sensing, microscopy sampling, and flow through imaging systems have widely different resolutions: from weekly or monthly with microscopy sampling to daily when no cloud cover or glint is present with polar‐orbiting satellites, and hourly for autonomous imaging instruments. To improve our understanding of data robustness against sampling resolution at different taxonomic levels, we analyze 2 yr of data from an Imaging FlowCytobot with hourly resolution and resample it to daily, satellite‐temporal, and weekly microscopy sampling resolution. We show that weekly and satellite‐temporal resolutions are sufficient to resolve general community composition but that the randomness of satellite‐temporal resolution can result in overrepresenting or underrepresenting certain categories. While the yearly phytoplankton biomass bloom is detected in late winter by all four resolutions, category‐specific yearly blooms are generally consistent in timing but often underestimated or missed by the weekly and satellite‐temporal resolutions, introducing a bias in year‐to‐year comparisons. A minimum of biweekly sampling, particularly during known bloom periods, would lower the bias in such categories. Similarly, sampling time should be considered as daily variations are category‐specific. Overall, morning and low tide sampling tended to have higher biomass. We provide tables for categories detected by the IFCB in Narragansett Bay with their major bloom characteristics and recorded daily variability to inform future sampling designs. These results provide tools to interpret past and future time series, including possible detection of specific taxonomic groups with targeted satellite algorithms.

Phytoplankton populations in the natural environment interact with each other. Despite rising global concern with Pseudo-nitzschia blooms, which can produce the potent neurotoxin domoic acid, we still do not fully understand how other phytoplankton genera respond to the presence of Pseudo-nitzschia. Here, we used a 4-year high-resolution imaging dataset for 9 commonly found phytoplankton genera in Narragansett Bay, alongside environmental data, to identify potential interactions between phytoplankton genera and their response to elevated Pseudo-nitzschia abundance. Our results indicate that Pseudo-nitzschia tends to bloom either concurrently with or right after other phytoplankton genera. Such bloom periods coincide with higher water temperatures and lower salinity. Pseudo-nitzschia image abundance tends to increase the most from March-May and peaks during May-Jun, whereas the image-derived biovolume and width of Pseudo-nitzschia chains increase the most during Jan-Feb. For most phytoplankton genera, their relationship with Pseudo-nitzschia abundance is noticeably different from their relationship with Pseudo-nitzschia image features. Despite the complexity in the phytoplankton community, our analysis suggests several ecological indicators that may be used to determine the risk of harmful algal blooms.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

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 streams 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 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 other aquatic or terrestrial organisms.