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Abstract The Antarctic krillEuphausia superbais often considered an herbivore but is notable for its trophic flexibility, which includes feeding on protistan and metazoan zooplankton. Characterizing krill trophic position (TP) is important for understanding carbon and energy flow from phytoplankton to vertebrate predators and to the deep ocean, especially as plankton composition is sensitive to changing climate. We used repeated field sampling and experiments to study feeding by juvenile krill during three austral summers in waters near Palmer Station, Antarctica. Our approach was to combine seasonal carbon budgets, gut fluorescence measurements, imaging flow cytometry, and compound‐specific isotope analysis of amino acids. Field measurements coupled to experimentally derived grazing functional response curves suggest that phytoplankton grazing alone was insufficient to support the growth and basal metabolism of juvenile krill. Phytoplankton consumption by juvenile krill was limited due to inefficient feeding on nanoplankton (2–20 μm), which constituted the majority of autotrophic prey. Mean krill TP and the metazoan dietary fraction increased in years with higher mesozooplankton biomass, which was not coupled to phytoplankton biomass. Comparing TP estimates using δ¹⁵N of different amino acids indicated a substantial and consistent food‐web contribution from heterotrophic protists. Phytoplankton, metazoans, and heterotrophic protists all were important contributors to a diverse krill diet that changed substantially among years. Juvenile krill fed mostly on heterotrophic prey during summer near Palmer Station, and this food web complexity should be considered more broadly throughout the changing Southern Ocean. 

Abstract High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated classification systems such as convolutional neural networks (CNNs) are often developed to identify species within the immense number of images (e.g., millions per month) collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A relatively small CNN (~2 million parameters) was developed and trained using a subset of manually identified images, resulting in an overall test accuracy, recall, and f1-score of 93.8, 93.7, and 93.7%, respectively, on a balanced dataset. However, the f1-score dropped to 46.5% when tested on a dataset of 10,269 new images drawn from the natural environment without balancing classes. This decrease is likely due to highly imbalanced class distributions dominated by smaller, less differentiable cells, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. As a case study to illustrate the value of the model, it was used to predict taxonomic classifications (ranging from genus to class) of phytoplankton at Palmer Station, Antarctica, from late austral spring to early autumn in 2017‐2018 and 2018‐2019. The CNN was generally able to identify important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both years, which is thought to be driven by increases in glacial meltwater from January to March. This shift in particle size distribution has significant implications for the ecology and biogeochemistry of these waters. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions. 

High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated detection systems such as convolutional neural networks (CNN) are often developed to identify the immense number of images collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A medium complexity CNN was developed using a subset of manually-identified images, resulting in an overall accuracy, recall, and f1-score of 93.8%, 93.7%, and 93.7%, respectively. The f1-score dropped to 46.5% when tested on a new random subset of 10,269 images, likely due to highly imbalanced class distributions, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. Our model was then used to predict taxonomic classifications of phytoplankton at Palmer Station, Antarctica over 2017-2018 and 2018-2019 summer field seasons. The CNN was generally able to capture important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both seasons, which is thought to be driven by increases in glacial meltwater from January to March. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions. 

Abstract In coastal West Antarctic Peninsula (WAP) waters, large phytoplankton blooms in late austral spring fuel a highly productive marine ecosystem. However, WAP atmospheric and oceanic temperatures are rising, winter sea ice extent and duration are decreasing, and summer phytoplankton biomass in the northern WAP has decreased and shifted toward smaller cells. To better understand these relationships, an Imaging FlowCytobot was used to characterize seasonal (spring to autumn) phytoplankton community composition and cell size during a low (2017–2018) and high (2018–2019) chlorophyllayear in relation to physical drivers (e.g., sea ice and meteoric water) at Palmer Station, Antarctica. A shorter sea ice season with early rapid retreat resulted in low phytoplankton biomass with a low proportion of diatoms (2017–2018), while a longer sea ice season with late protracted retreat resulted in the opposite (2018–2019). Despite these differences, phytoplankton seasonal succession was similar in both years: (1) a large‐celled centric diatom bloom during spring sea ice retreat; (2) a peak summer phase comprised of mixotrophic cryptophytes with increases in light and postbloom organic matter; and (3) a late summer phase comprised of small (< 20 μm) diatoms and mixed flagellates with increases in wind‐driven nutrient resuspension. In addition, cell diameter decreased from November to April with increases in meteoric water in both years. The tight coupling between sea ice, meltwater, and phytoplankton species composition suggests that continued warming in the WAP will affect phytoplankton seasonal dynamics, and subsequently seasonal food web dynamics. 

Abstract The Palmer Deep canyon along the West Antarctic Peninsula is a biological hotspot with abundant phytoplankton and krill supporting Adélie and gentoo penguin rookeries at the canyon head. Nearshore studies have focused on physical mechanisms driving primary production and penguin foraging, but less is known about finer‐scale krill distribution and density. We designed two acoustic survey grids paired with conductivity–temperature–depth profiles within adjacent Adélie and gentoo penguin foraging regions near Palmer Station, Antarctica. The grids were sampled from January to March 2019 to assess variability in krill availability and associations with oceanographic properties. Krill density was similar in the two regions, but krill swarms were longer and larger in the gentoo foraging region, which was also less stratified and had lower chlorophyll concentrations. In the inshore zone near penguin colonies, depth‐integrated krill density increased from summer to autumn (January–March) independent of chlorophyll concentration, suggesting a life history‐driven adult krill migration rather than a resource‐driven biomass increase. The daytime depth of krill biomass deepened through the summer and became decoupled from the chlorophyll maximum in March as diel vertical migration magnitude likely increased. Penguins near Palmer Station did not appear to be limited by krill availability during our study, and regional differences in krill depth match the foraging behaviors of the two penguin species. Understanding fine‐scale physical forcing and ecological interactions in coastal Antarctic hotspots is critical for predicting how environmental change will impact these ecosystems.