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Photosynthetic carbon (C) fixation by phytoplankton in the Southern Ocean (SO) plays a critical role in regulating air–sea exchange of carbon dioxide and thus global climate. In the SO, photosynthesis (PS) is often constrained by low iron, low temperatures, and low but highly variable light intensities. Recently, proton-pumping rhodopsins (PPRs) were identified in marine phytoplankton, providing an alternate iron-free, light-driven source of cellular energy. These proteins pump protons across cellular membranes through light absorption by the chromophore retinal, and the resulting pH energy gradient can then be used for active membrane transport or for synthesis of adenosine triphosphate. Here, we show that PPR is pervasive in Antarctic phytoplankton, especially in iron-limited regions. In a model SO diatom, we found that it was localized to the vacuolar membrane, making the vacuole a putative alternative phototrophic organelle for light-driven production of cellular energy. Unlike photosynthetic C fixation, which decreases substantially at colder temperatures, the proton transport activity of PPR was unaffected by decreasing temperature. Cellular PPR levels in cultured SO diatoms increased with decreasing iron concentrations and energy production from PPR photochemistry could substantially augment that of PS, especially under high light intensities, where PS is often photoinhibited. PPR gene expression and high retinal concentrations in phytoplankton in SO waters support its widespread use in polar environments. PPRs are an important adaptation of SO phytoplankton to growth and survival in their cold, iron-limited, and variable light environment.

 

The Antarctic marine environment is a dynamic ecosystem where microorganisms play an important role in key biogeochemical cycles. Despite the role that microbes play in this ecosystem, little is known about the genetic and metabolic diversity of Antarctic marine microbes. In this study we leveraged DNA samples collected by the Palmer Long Term Ecological Research (LTER) project to sequence shotgun metagenomes of 48 key samples collected across the marine ecosystem of the western Antarctic Peninsula (wAP). We developed an in silico metagenomics pipeline (iMAGine) for processing metagenomic data and constructing metagenome-assembled genomes (MAGs), identifying a diverse genomic repertoire related to the carbon, sulfur, and nitrogen cycles. A novel analytical approach based on gene coverage was used to understand the differences in microbial community functions across depth and region. Our results showed that microbial community functions were partitioned based on depth. Bacterial members harbored diverse genes for carbohydrate transformation, indicating the availability of processes to convert complex carbons into simpler bioavailable forms. We generated 137 dereplicated MAGs giving us a new perspective on the role of prokaryotes in the coastal wAP. In particular, the presence of mixotrophic prokaryotes capable of autotrophic and heterotrophic lifestyles indicated a metabolically flexible community, which we hypothesize enables survival under rapidly changing conditions. Overall, the study identified key microbial community functions and created a valuable sequence library collection for future Antarctic genomics research. 

Collaborating scientists and storytellers successfully built a university-based science-in-action video storytelling model to test the research question: Can university scientists increase their relatability and public engagement through science-in-action video storytelling? Developed over 14 years, this science storytelling model produced more than a dozen high-visibility narratives that translated science to the public and featured scientists, primarily environmental and climate scientists, who are described in audience surveys as relatable people. This collaborative model, based on long-term trusting partnerships between scientists and video storytellers, documented scientists as they conducted their research and together created narratives intended to humanize scientists as authentic people on journeys of discovery. Unlike traditional documentary filmmaking or journalism, the participatory nature of this translational science model involved scientists in the shared making of narratives to ensure the accuracy of the story's science content. Twelve science and research video story products have reached broad audiences through a variety of venues including television and online streaming platforms such as Public Broadcasting Service (PBS), Netflix, PIVOT TV, iTunes, and Kanopy. With a reach of over 180 million potential public audience viewers, we have demonstrated the effectiveness of this model to produce science and environmental narratives that appeal to the public. Results from post-screening surveys with public, high school, and undergraduate audiences showed perceptions of scientists as relatable. Our data includes feedback from undergraduate and high school students who participated in the video storytelling processes and reported increased relatability to both scientists and science. In 2022, we surveyed undergraduate students using a method that differentiated scientists' potential relatable qualities with scientists' passion for their work, and the scientists' motivation to help others, consistently associated with relatability. The value of this model to scientists is offered throughout this paper as two of our authors are biological scientists who were featured in our original science-in-action videos. Additionally, this model provides a time-saving method for scientists to communicate their research. We propose that translational science stories created using this model may provide audiences with opportunities to vicariously experience scientists' day-to-day choices and challenges and thus may evoke audiences' ability to relate to, and trust in, science. 

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. 

Between 1992 and 2018, the breeding population of Adélie penguins around Anvers Island, Antarctica declined by 98%. In this region, natural climate variability drives five‐year cycling in marine phytoplankton productivity, leading to phase‐offset five‐year cycling in the size of the krill population. We demonstrate that the rate of change of the Adélie breeding population also shows five‐year cycling. We link this population response to cyclical krill scarcity, a phenomenon which appears to have arisen from the interaction between climate variability and climate change trends. Modeling suggests that, since at least 1980, natural climate variability has driven cycling in this marine system. However, anthropogenic climate change has shifted conditions so that fewer years in each cycle now prompt strong krill recruitment, triggering intervals of krill scarcity that result in drastic declines in Adélie penguins. Our results imply that climate change can amplify the impacts of natural climate oscillations across trophic levels, driving cycling across species and disrupting food webs. The findings indicate that climate variability plays an integral role in driving ecosystem dynamics under climate change.

 

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. 

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. Heterotrophic marine bacteria utilize organic carbon for growth and biomass synthesis. Thus, their physiological variability is key to the balancebetween the production and consumption of organic matter and ultimately particle export in the ocean. Here we investigate a potential link betweenbacterial traits and ecosystem functions in the rapidly warming West Antarctic Peninsula (WAP) region based on a bacteria-oriented ecosystemmodel. Using a data assimilation scheme, we utilize the observations of bacterial groups with different physiological traits to constrain thegroup-specific bacterial ecosystem functions in the model. We then examine the association of the modeled bacterial and other key ecosystemfunctions with eight recurrent modes representative of different bacterial taxonomic traits. Both taxonomic and physiological traits reflect thevariability in bacterial carbon demand, net primary production, and particle sinking flux. Numerical experiments under perturbed climate conditionsdemonstrate a potential shift from low nucleic acid bacteria to high nucleic acid bacteria-dominated communities in the coastal WAP. Our studysuggests that bacterial diversity via different taxonomic and physiological traits can guide the modeling of the polar marine ecosystem functionsunder climate change. 

Abstract. The West Antarctic Peninsula (WAP) is a rapidly warming region, withsubstantial ecological and biogeochemical responses to the observed changeand variability for the past decades, revealed by multi-decadal observationsfrom the Palmer Antarctica Long-Term Ecological Research (LTER) program. Thewealth of these long-term observations provides an important resource forecosystem modeling, but there has been a lack of focus on the developmentof numerical models that simulate time-evolving plankton dynamics over theaustral growth season along the coastal WAP. Here, we introduce aone-dimensional variational data assimilation planktonic ecosystem model (i.e., theWAP-1D-VAR v1.0 model) equipped with a modelparameter optimization scheme. We first demonstrate the modified and newlyadded model schemes to the pre-existing food web and biogeochemicalcomponents of the other ecosystem models that WAP-1D-VAR model was adaptedfrom, including diagnostic sea-ice forcing and trophic interactions specificto the WAP region. We then present the results from model experiments wherewe assimilate 11 different data types from an example Palmer LTER growthseason (October 2002–March 2003) directly related to corresponding modelstate variables and flows between these variables. The iterative dataassimilation procedure reduces the misfits between observationsand model results by 58 %, compared to before optimization, via an optimized set of12 parameters out of a total of 72 free parameters. The optimized model resultscapture key WAP ecological features, such as blooms during seasonal sea-iceretreat, the lack of macronutrient limitation, and modeled variables andflows comparable to other studies in the WAP region, as well as severalimportant ecosystem metrics. One exception is that the model slightlyunderestimates particle export flux, for which we discuss potentialunderlying reasons. The data assimilation scheme of the WAP-1D-VAR modelenables the available observational data to constrain previously poorlyunderstood processes, including the partitioning of primary production bydifferent phytoplankton groups, the optimal chlorophyll-to-carbon ratio ofthe WAP phytoplankton community, and the partitioning of dissolved organiccarbon pools with different lability. The WAP-1D-VAR model can besuccessfully employed to link the snapshots collected by the available datasets together to explain and understand the observed dynamics along thecoastal WAP.