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Phytoplankton communities in the open ocean are high‐dimensional, sparse, and spatiotemporally heterogeneous. The advent of automated imaging systems has enabled high‐resolution observation of these communities, but the amounts of data and their statistical properties make analysis with traditional approaches challenging. Spatiotemporal topic models offer an unsupervised and interpretable approach to dimensionality reduction of sparse, high‐dimensional categorical data. Here we use topic modeling to analyze neural‐network‐classified phytoplankton imagery taken in and around a retentive eddy during the 2021 North Atlantic EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We investigate the role physical‐biological interactions play in altering plankton community composition within the eddy. Analysis of a water mass mixing framework suggests that storm‐driven surface advection and stirring were major drivers of the progression of the eddy plankton community away from a diatom bloom over the course of the cruise. 

Abstract 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 andSynechococcuscommunities 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. UnlikeSynechococcus, 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 thanSynechococcus. 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. 

These data include abundance and carbon concentration of the diatom Hemiaulus on the Northeast U.S. Shelf during 81 research cruises from 2013 to 2023 as part of Long-Term Ecological Research (NES-LTER). Abundances are determined from Imaging FlowCytobot (IFCB) deployed in three different sampling schemes: underway mode (sampling near-surface seawater) on NOAA EcoMon, HAB Cyst, and AMAPPS broadscale survey cruises from 2013 to 2023; in underway mode (sampling near-surface seawater) on NES-LTER transect cruises from 2017 to 2023, and in discrete mode (CTD rosette discrete samples from depth) on NES-LTER transect cruises. Results are based on machine learning image classification, with one data table provided per sampling scheme (broadscale underway, transect underway, and transect discrete). Hemiaulus data are provided in abundance per milliliter and micrograms of carbon per liter. 

These data include abundances of the diatom, Guinardia delicatula (= Rhizosolenia delicatula), on the Northeast U.S. Shelf from 2006 to 2022 as part of Long-Term Ecological Research (NES-LTER). Abundances are determined from Imaging FlowCytobot (IFCB) deployed in-situ at ~4m depth at the nearshore Martha’s Vineyard Coastal Observatory (MVCO) from 2006 to 2022 and in underway mode (sampling near-surface seawater) on 24 NOAA EcoMon survey cruises from 2013 to 2022. Abundances based on both human and machine learning image classification are provided. Total G. delicatula abundances are divided into two categories based on whether G. delicatula exhibited current or recent infection by the protistan parasitoid, Cryothecomonas aestivalis. Four data tables are provided with abundance values separated by sampling scheme (time series or survey cruise) and image classification approach (human or machine learning). 

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. 

Abstract The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms. 

Imaging FlowCytobot (IFCB) deployments have been conducted since June 2006 at the Martha’s Vineyard Coastal Observatory (MVCO; 41° 19.5’ N, 70° 34.0’ W). IFCB, an automated submersible imaging-in-flow cytometer, is specially designed to operate in the ocean and image plankton and other particulate material approximately 5 to 200 micrometers in length. In conjunction with image acquisition, IFCB also uses a diode laser to measure the chlorophyll fluorescence and light scattering associated each imaged target. IFCB typically produces thousands of photomicrographs and associated laser signals each hour. The web-based IFCB dashboard provides browse capability and access to the entire image data set.