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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. 

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).