Abstract While a rich history of patchiness research has explored spatial structure in the ocean, there is no consensus over the controls on biological patchiness and how physical-ecological-biogeochemical processes and patchiness relate. The prevailing thought is that physics structures biology, but this has not been tested at basin scale with consistent in situ measurements. Here we use the slope of the relationship between variance vs spatial scale to quantify patchiness and ~650,000 nearly continuous (dx ~ 200 m) measurements - representing the Atlantic, Pacific, and Southern Oceans - and find that patchiness of biological parameters and physical parameters are uncorrelated. We show variance slope is an emergent property with unique patterns in biogeochemical properties distinct from physical tracers, yet correlated with other biological tracers. These results provide context for decades of observations with different interpretations, suggest the use of spatial tests of biogeochemical model parameterizations, and open the way for studies into processes regulating the observed patterns.

Candidate glaciovolcanic landforms have been identified across Mars, suggesting that volcano-ice interactions may have been relatively widespread in areas that once contained extensive surface and near-surface ice deposits. To better constrain the detection of glaciovolcanism in Mars' geological record, this study has investigated and characterized the petrology, geochemistry, and mineralogy of three intraglacial volcanoes and an interglacial volcano in the & THORN;& oacute;risj & ouml;kull area of southwest Iceland. Our results show that glaciovolcanism creates abundant, variably altered hyaloclastite and hyalotuff that is sufficiently geochemically and mineralogically distinctive from subaerially erupted lava for identification using instruments available on Mars rovers and landers. Due to the lower gravity and atmospheric pressure at the surface of Mars, hyaloclastite and hyalotuff are also more likely to form in greater abundance in Martian glaciovolcanoes. Our results support that magmatism following deglaciation likely triggers decompression melting of the shallow mantle beneath Iceland, creating systematic changes in geochemistry and mineralogy. Glaciation can also suppress magmatism at its peak, encouraging the formation of shallow fractionated magma chambers. As such, it is possible for the crustal loading of an ice cap to enhance igneous diversity on a planet without plate tectonism, creating glass-rich, altered, and mineralogically diverse deposits such as those discovered in Gale crater by the Curiosity rover. However, as the eroded products of glaciovolcanism are similar to those formed through hydrovolcanism, the presence of a glaciovolcanic landform at the source is required to confirm whether volcano-ice interactions occurred at the sediment source. The ancient Martian climate has been long debated with scientists hypothesizing that the river channels and lake beds preserved on the surface today formed in either a warm and wet environment or a cold and icy environment, with heating events driving large-scale melting. One such heating event includes volcanism. This study investigated the chemical and mineralogical impact of volcano-ice interactions in Iceland using techniques available to Mars rovers and landers to improve the detection of these deposits on Mars. Our results show that deposits rich in glass and the early products of low temperature alteration (palagonite, clays, zeolites) are likely to be more prevalent on Mars compared to the Earth due to differences in gravity between the two planets. Furthermore, the presence of a thick ice cap on Mars may encourage a diversity of lava compositions to form in shallow magma chambers as the weight of a thick ice cap can suppress volcanism. These types of deposits are identifiable using Mars rover and lander techniques, but due to their similarities to other volcanic units that may have erupted in the presence of water, a candidate glaciovolcanic landform is needed to confirm that these deposits are from volcano-ice interactions. Glaciovolcanism creates large deposits of altered fragmented volcanic glass distinct from subaerial volcanism Glaciation can suppress magmatism, driving geochemical and mineralogical diversity in an area once deglaciation starts Glaciovolcanism may have occurred on Mars with explosive phreatomagmatic units more likely under Mars' planetary conditions

While a rich history of patchiness research has explored spatial structure in the ocean, there is still no consensus over the controls on biological patchiness and how biogeochemical processes and patchiness relate. The prevailing thought is that physics structures biology, but this has not been tested at the basin scale with consistent in situ measurements. Here we show that the patchiness of physics and biology are decoupled at the global scale through analysis of a global dataset of in situ surface optical properties from the R/V Tara and using the slope of spatial scale vs variance to quantify patchiness. Based on analysis of ∼650,000 nearly continuous (dx∼150m) measurements from an underway sampling system - representing five years of consistently collected data across the Atlantic, Pacific, and Southern Oceans - we find the patchiness of physical and biological parameters are uncorrelated. We show that variance slope is an emergent property with unique patterns in biogeochemical properties that are distinct from physical tracers, yet connected to other biological tracers. These results provide context for decades of discrepancy between in situ studies, could support new tests of biogeochemical model parameterizations, and open the way for new insight into processes regulating the observed patterns.### Competing Interest StatementThe authors have declared no competing interest.

Understanding the interplay of ocean physics and biology, particularly at the submesoscale and below (<30km), is an ongoing challenge in oceanography. While poorly constrained, these scales may be of critical importance for understanding how changing ocean dynamics will impact marine ecosystems. Fronts in the ocean, regions where two disparate water masses meet and isopycnals become tilted towards vertical, are considered hotspots for biophysical interaction, but there is limited observational evidence at the appropriate scales to assess their importance. Western boundary currents like the Gulf Stream are of particular interest as these dynamic physical regions are thought to influence both productivity and composition of primary producers; however, how exactly this plays out, and at what scales, is not well known. Using satellite data and two years of detailed in situ observations across the Gulf Stream front near Cape Hatteras, North Carolina, U.S.A., we investigate how submesoscale frontal dynamics could affect biological communities associated with frontal regions and generate hotspots of productivity and export. In this analysis we assess the seasonality and phenology of the region, generalize the kilometer-scale structure of the front, and analyze 69 transects to assess two physical processes of potential biogeochemical importance: cold shelf filament subduction and high salinity Sargasso Sea obduction. We link these processes observationally to the meander phase of the Gulf Stream and discuss how cold filament subduction could be exporting carbon and how obduction of high salinity water from depth often leads to high chlorophyll-a. Finally, we report on phytoplankton community composition in each of these features and integrate these new observations into our understanding of frontal submesoscale dynamics. Plain Language Summary Phytoplankton move with large currents and are stirred by eddies with diameters ranging from 100s of kilometers down to the meter scale. Their growth is impacted by physical factors like light and temperature and also chemical and biological factors like nutrient availability, and their accumulation is also impacted by top down controls (zooplankton grazing, viral lysis) and competition with other phytoplankton. This interplay of physics and biology in determining the biomass and composition of phytoplankton communities is poorly understood and is key to understanding marine ecosystem resilience and structure in a changing ocean. In this work we investigated the impact of physics and biology on phytoplankton across scales focusing on the Gulf Stream front. Fronts in the ocean are where lines of equal density go from being horizontal to having a vertical tile, and because of this can enable nutrients and plankton to move from depth to the surface and vice versa. The objective of this work is to understand how physics might drive important changes in phytoplankton biomass and composition in the Gulf Stream front, which is amongst the sharpest gradients in temperature, density, and current speed in the global ocean. We find two frequent processes at the front, the apparent subduction of cold filaments down along the edge of the Gulf Stream, associated with meander troughs, and obduction of high salinity Sargasso Sea water into the front linked to meander crests. While ephemeral, these processes are frequent and could have a large impact on local phytoplankton biomass, phytoplankton composition, and the export of organic matter to depth. Key Points The frontal zone between the Gulf Stream and the shelf has an interface water mass which appears to have different origins with a range of biogeochemical impacts. Meanders appear to largely control the frontal interface: troughs lead to subduction of shelf filaments and crests lead to obduction of high salinity water. These two processes are common at the front and could lead to ephemeral kilometer- scale export and productivity.

The proliferation of easily accessible machine learning algorithms and their apparent successes at inference and classification in computer vision and the sciences has motivated their increased adoption in ocean remote sensing. Our field, however, runs the risk of developing these models on limited training datasets—with sparse geographical and temporal sampling or ignoring the real data dimensionality—thereby constructing over-fitted or non-generalized algorithms. These models may perform poorly in new regimes or on new, anomalous phenomena that emerge in a changing climate. We highlight these issues and strategies for mitigating them, share a few heuristics to help users develop intuition for machine learning methods, and provide a vision for areas we believe are underexplored at the intersection of machine learning and ocean remote sensing. The ocean is a complex physical-biogeochemical system that we cannot mechanistically model well despite our best efforts. Machine learning has the potential to play an important role in improved process understanding, but we must always ask what we are learning after the model has learned.

Ocean physics and biology can interact in myriad and complex ways. Eddies, features found at many scales in the ocean, can drive substantial changes in physical and biogeochemical fields with major implications for marine ecosystems. Mesoscale eddies are challenging to model and difficult to observe synoptically at sea due to their fine-scale variability yet broad extent. In this work we observed a frontal eddy just north of Cape Hatteras via an intensive hydrographic, biogeochemical, and optical sampling campaign. Frontal eddies occur in western boundary currents around the globe and there are major gaps in our understanding of their ecosystem impacts. In the Gulf Stream, frontal eddies have been studied in the South Atlantic Bight, where they are generally assumed to shear apart passing Cape Hatteras. However, we found that the observed frontal eddy had different physical properties and phytoplankton community composition from adjacent water masses, in addition to continued cyclonic rotation. In this work we first synthesize the overall ecological impacts of frontal eddies in a simple conceptual model. This conceptual model led to the hypothesis that frontal eddies could be well timed to supply zooplankton to secondary consumers off Cape Hatteras where there is a notably high concentration and diversity of top predators. Towards testing this hypothesis and our conceptual model we report on the biogeochemical state of this particular eddy connecting physical and biological dynamics, analyze how it differs from Gulf Stream and shelf waters even in “death”, and refine our initial model with this new data. Key Points In-depth investigation of a frontal eddy in the Gulf Stream off Cape Hatteras, North Carolina Continued physical and biogeochemical differences are observed between the eddy and adjacent water masses even as it begins to shear apart We share a conceptual model of the ecological impact of frontal eddies with a hypothesis that they supply zooplankton to secondary consumers Plain Language Summary Frontal eddies are spinning masses of water (~30km in diameter) that move along western boundary currents like the Gulf Stream. When they form they carry productive coastal water into the Gulf Stream and drive upwelling within their cores. Together this leads to an increase in the amount of phytoplankton within them - much higher compared to surrounding nutrient-limited Gulf Stream water. On the east coast of the United States one common area of frontal eddy formation is just off Charleston, SC. Eddies then travel up the coast and dissipate near Cape Hatteras, NC. In this work we measured a wide range of physical and biological properties of a frontal eddy just north of Cape Hatteras. We compared these properties within the eddy to the coastal water on one side and the Gulf Stream water on the other, finding clear differences in phytoplankton community composition and other physical and chemical properties. Using the results of these observations together with previous studies we share a simple model for how frontal eddies may impact phytoplankton, zooplankton, and fish – hypothesizing that they may contribute to the high diversity and density of top predators off Cape Hatteras.

Abstract The combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state‐of‐the‐art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real‐world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys.

Mesoscale oceanographic features, including eddies, have the potential to alter productivity and other biogeochemical rates in the ocean. Here, we examine the microbiome of a cyclonic, Gulf Stream frontal eddy, with a distinct origin and environmental parameters compared to surrounding waters, in order to better understand the processes dominating microbial community assembly in the dynamic coastal ocean. Our microbiome-based approach identified the eddy as distinct from the surround Gulf Stream waters. The eddy-associated microbial community occupied a larger area than identified by temperature and salinity alone, increasing the predicted extent of eddy-associated biogeochemical processes. While the eddy formed on the continental shelf, after two weeks both environmental parameters and microbiome composition of the eddy were most similar to the Gulf Stream, suggesting the effect of environmental filtering on community assembly or physical mixing with adjacent Gulf Stream waters. In spite of the potential for eddy-driven upwelling to introduce nutrients and stimulate primary production, eddy surface waters exhibit lower chlorophyll a along with a distinct and less even microbial community, compared to the Gulf Stream. At the population level, the eddy microbiome exhibited differences among the cyanobacteria (e.g. lower Trichodesmium and higher Prochlorococcus) and in the heterotrophic alpha Proteobacteria (e.g. lower relative abundances of specific SAR11 phylotypes) versus the Gulf Stream. However, better delineation of the relative roles of processes driving eddy community assembly will likely require following the eddy and surrounding waters since inception. Additionally, sampling throughout the water column could better clarify the contribution of these mesoscale features to primary production and carbon export in the oceans.

Satellites have provided high-resolution (<100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll-a, turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date.

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) chlorophyll a year 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 mu m) 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.

Marine biological communities are dynamic across many scales in both space and time. Such multi-scale complexity complicates efforts to fully characterize these communities. Critical processes unfold on the order of 0.1-10 kilometers and 0.1-10 days, but conventional oceanographic techniques generally do not observe or model at this scale. Small aerial drones conveniently achieve scales of observation between satellite resolutions and in-situ sampling, and effectively diminish the "blind spot" between these established measurement techniques. Despite this promise, drone-based techniques face challenges inherent to optical oceanography, as well as logistical and regulatory barriers relating to both aerial and marine operations. Such obstacles have slowed adoption of drones for marine biological study, but best practices are emerging alongside new techniques that facilitate robust study designs and rigorous data collection. With such advancements, drones promise to complement conventional approaches in biological oceanography to more fully capture the spatiotemporal complexity of the marine environment.

Accurate and robust retrieval of ocean color from remote sensing enables critical observations of aquatic natural systems, from open ocean biological oceanography, coastal biodiversity, and water quality for human health. In the last decade, studies have increasingly highlighted the important role of small‐scale processes in coastal and marine ecology and biogeochemistry, but observation and modeling at these scales remains technologically limited. Unoccupied aircraft systems (UAS, aka drones) can rapidly sample large areas with high spatial and temporal resolution; but the challenge of accurately retrieving ocean color, particularly with common wide field‐of‐view multispectral imagers, has limited the adoption of this technology. As UAS endurance, autonomy, and sensor capabilities continue to increase, so does this technology's potential to observe the ocean at fine scales, but only if proper protocols are followed. The present study provides a guide for achieving (1) ideal viewing geometry of UAS‐borne ocean color sensors, (2) techniques for the removal of sun glint and reflected skylight to derive water‐leaving radiances, (3) characterization of uncertainty in these measurements, and (4) converting water‐leaving radiances to remote‐sensing reflectance for analytic end products such as chlorophyll a estimates. Documented open‐source code facilitates replication of this emerging technique. Using this methodology, we briefly describing fine‐scale variability of the Gulf Stream front off North Carolina alongside synoptic satellite data and in situ measurements for comparison. These results demonstrate how UAS‐based ocean color measurements complement and enhance conventional ocean observations and modeling to resolve fine‐scale variability and close the lacuna between satellite and in situ methods.

AbstractHigh-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.

Data returned from the Cassini–Huygens mission have strengthened Enceladus, a small icy moon of Saturn, as an important target in the search for life in our solar system. Information gathered from Cassini to support this includes the presence of a subsurface liquid water ocean, vapor plumes and ice grains emanating from its south polar region, and the detection of essential elements and organic material that could potentially support life. However, several outstanding questions remain regarding the connectivity of plume material to the ocean and the composition of the complex organic material. Herein we introduce Tiger, a mission concept developed during the 2020 Planetary Science Summer School at NASA’s Jet Propulsion Laboratory. Tiger is a flyby mission that would help further constrain the habitability of Enceladus through two science objectives: (1) determine whether Enceladus’s volatile inventory undergoes synthesis of complex organic species that are evidence for a habitable ocean, and (2) determine whether Enceladus’s plume material is supplied directly from the ocean or if it interfaces with other reservoirs within the ice shell. To address the science goals in a total of eight flybys, Tiger would carry a four-instrument payload, including a mass spectrometer, a single-band ice-penetrating radar, an ultraviolet imaging spectrograph, and an imaging camera. We discuss Tiger's instrument and mission architecture, as well as the trades and challenges associated with a habitability-focused New Frontiers–class flyby mission to Enceladus.

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.

Population monitoring of colonial seabirds is often complicated by the large size of colonies, remote locations, and close inter- and intra-species aggregation. While drones have been successfully used to monitor large inaccessible colonies, the vast amount of imagery collected introduces a data analysis bottleneck. Convolutional neural networks (CNN) are evolving as a prominent means for object detection and can be applied to drone imagery for population monitoring. In this study, we explored the use of these technologies to increase capabilities for seabird monitoring by using CNNs to detect and enumerate Black-browed Albatrosses (Thalassarche melanophris) and Southern Rockhopper Penguins (Eudyptes c. chrysocome) at one of their largest breeding colonies, the Falkland (Malvinas) Islands. Our results showed that these techniques have great potential for seabird monitoring at significant and spatially complex colonies, producing accuracies of correctly detecting and counting birds at 97.66% (Black-browed Albatrosses) and 87.16% (Southern Rockhopper Penguins), with 90% of automated counts being within 5% of manual counts from imagery. The results of this study indicate CNN methods are a viable population assessment tool, providing opportunities to reduce manual labor, cost, and human error.

The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decadal cycles using only a decade or less of data. Understanding these long-term changes in low lying coastal areas, home to a huge proportion of the global population, is of particular importance. Relatively simple deep learning models that extract representative spatiotemporal patterns can lead to major improvements in temporal generalizability. To provide insight into major changes in low lying coastal areas, our study (1) developed a recurrent convolutional neural network that incorporates spectral, spatial, and temporal contexts for predicting land cover class, (2) evaluated this model across time and space and compared this model to conventional Random Forest and Support Vector Machine methods as well as other deep learning approaches, and (3) applied this model to classify land cover across 20 years of Landsat 5 data in the low-lying coastal plain of North Carolina, USA. We observed striking changes related to sea level rise that support evidence on a smaller scale of agricultural land and forests transitioning into wetlands and “ghost forests”. This work demonstrates that recurrent convolutional neural networks should be considered when a model is needed that can generalize across time and that they can help uncover important trends necessary for understanding and responding to climate change in vulnerable coastal regions.

The surface of Mars exhibits vast expanses of mafic sediments and ancient sedimentary rocks that record signals of climate and environment. To decipher the paleoenvironments, the sediment sources and transport histories must be con-strained, but it is not well known how physical fractionation and aqueous alteration affect mafic sediments during glacial, eolian, and fluvial processes. Semi-Autonomous Navigation for Detrital Environments (SAND-E), a NASA Planetary Science and Technology through Analog Research (PSTAR) project, bridges this gap through studies of sediment-grain properties and mineralogy in the glacio-XRD)-derived mineralogies.