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Small pigmented eukaryotes (⩽ 5 µm) are an important, but overlooked component of global marine phytoplankton. The Amazon River plume delivers nutrients into the oligotrophic western tropical North Atlantic, shades the deeper waters, and drives the structure of microphytoplankton (> 20 µm) communities. For small pigmented eukaryotes, however, diversity and distribution in the region remain unknown, despite their significant contribution to open ocean primary production and other biogeochemical processes. To investigate how habitats created by the Amazon river plume shape small pigmented eukaryote communities, we used high-throughput sequencing of the 18S ribosomal RNA genes from up to five distinct small pigmented eukaryote cell populations, identified and sorted by flow cytometry. Small pigmented eukaryotes dominated small phytoplankton biomass across all habitat types, but the population abundances varied among stations resulting in a random distribution. Small pigmented eukaryote communities were consistently dominated by Chloropicophyceae (0.8–2 µm) and Bacillariophyceae (0.8–3.5 µm), accompanied by MOCH-5 at the surface or by Dinophyceae at the chlorophyll maximum. Taxonomic composition only displayed differences in the old plume core and at one of the plume margin stations. Such results reflect the dynamic interactions of the plume and offshore oceanic waters and suggest that the resident small pigmented eukaryote diversity was not strongly affected by habitat types at this time of the year.

Monitoring phytoplankton community composition from space is an important challenge in ocean remote sensing. Researchers have proposed several algorithms for this purpose. However, the in-situ data used to train and validate such algorithms at the global scale are often clustered along ship cruise tracks and in some well-studied locations, whereas many large marine regions have no in-situ data at all. Furthermore, oceanographic variables are typically spatially auto-correlated. In this situation, the common practice of validating algorithms with randomly chosen held-out observations can underestimate errors. Based on a global database of in-situ HPLC data, we applied supervised learning methods to train and test empirical algorithms predicting the relative concentrations of eight diagnostic pigments that serve as biomarkers for different phytoplankton types. For each pigment, we trained three types of satellite algorithms distinguished by their input data: abundance-based (using only chlorophyll-a as input), spectral (using remote sensing reflectance), and ecological algorithms (combining reflectance and environmental variables). The algorithms were implemented as statistical models (smoothing splines, polynomials, random forests and boosted regression trees). To address clustering of data and spatial auto-correlation, we tested the algorithms by means of spatial block cross-validation. This provided a less confident picture of the potential for global mapping of diagnostic pigments and hence the associated phytoplankton types using existing satellite data than suggested by some previous research and a 5-fold cross-validation conducted for comparison. Of the eight diagnostic pigments, only two (fucoxanthin and zeaxanthin) could be predicted in marine regions that the algorithms were not trained in with considerably lower errors than a constant null model. Thus, global-scale algorithms based on existing, multi-spectral satellite data and commonly available environmental variables can estimate relative diagnostic pigment concentrations and hence distinguish phytoplankton types in some broad classes, but are likely inaccurate for some classes and in some marine regions. Overall, the ecological algorithms had the lowest prediction errors. Finally, our results suggest that more discussion of the best approaches for training and validating empirical satellite algorithms is needed if the in-situ data are unevenly distributed in the study region and spatially clustered.

Dissolved inorganic nutrient concentrations in the surface waters (0 to 5 m) of the Northern Gulf of Mexico (NGoM) were analyzed from 1985 to 2019 (> 10,000 observations) to determine spatiotemporal trends and their connection to nutrients supplied from the Mississippi/Atchafalaya River (MAR). In the NGoM, annual mean dissolved inorganic P (DIP) concentrations increased significantly over time, while dissolved inorganic N (DIN) concentrations showed no temporal trend. With greater salinity, mean DIN:DIP decreased from above the Redfield ratio of 16 to below it, reflecting DIN losses and the more conservative behavior of DIP with salinity. Over the same time period, annual mean P (total dissolved P, DIP, dissolved organic P) loading from the MAR to the NGoM significantly increased, annual mean DIN and total dissolved N loading showed no temporal trend, and dissolved organic N loading significantly decreased. Though DIP increased in the MAR, MAR DIP alone was insufficient to explain the surface distribution of DIP with salinity. Therefore, increases in surface DIP in the NGoM are not simply a reflection of increasing MAR DIP, pointing to temporal changes in other DIP sources. The increase in NGoM DIP suggests greater N limitation for phytoplankton, with implications for N fixation and nutrient management.

The cyanobacterium Trichodesmium is responsible for approximately half of the ocean's nitrogen input through nitrogen fixation. Although it was first recorded near Australia in the 18th century, the knowledge of where and when large quantity of Trichodesmium around Australia could be found is still lacking. Here, using multi‐band satellite imagery acquired between 2012 and 2021, we fill this knowledge gap through the use of deep learning, designed to recognize both the spectral shapes of individual pixels and spatial morphology of surface aggregations (scums) of Trichodesmium. Trichodesmium scums were found nearly everywhere around Australia, with a cumulative footprint (i.e., where the 10‐year average density is >0.001‰) exceeding 4.6 million km2. Strong seasonality was found, with peak months between September and November. Furthermore, temperature, iron‐rich dust and black carbon aerosols, with the latter being a result of frequent bushfires, play major roles in determining the spatial distributions and seasonality of Trichodesmium. Plain Language Summary Responsible for half of the ocean's nitrogen input through nitrogen fixation, the saltwater cyanobacterium Trichodesmium is ubiquitous in global tropical and subtropical oceans but particularly abundant around Australia. However, although the earliest report goes back to the 18th century, the knowledge of where and when large quantities of Trichodesmium can be found around Australia is still incomplete. Based on satellite imagery and deep learning, we quantified relative abundance of Trichodesmium around Australia for the period of 2012–2021. Surface aggregations of Trichodesmium were found almost everywhere except the southern coast, with a cumulative footprint exceeding 4.6 million km2. Strong seasonality was found, with peak months between September and November. The spatial distributions and seasonality were found to correlate well with water temperature, iron‐rich dust from Australian desert, and black carbon aerosols from frequent bushfires. With the projected ocean warming in the coming century, Trichodesmium may expand further south, making the cumulative footprint even larger. Key Points Deep learning was applied to multi‐band satellite images to detect and quantify Trichodesmium surface scums around Australia Trichodesmium scums were found nearly everywhere around Australia with a seasonality and a cumulative footprint exceeding 4.6 million km2 Distribution and seasonality of Trichodesmium were driven by temperature, iron‐rich dust and black carbon from the mainland bushfires

Profound sea ice loss is rapidly transforming coupled social-ecological Arctic marine systems. However, explicit impacts to harvesting of traditional resources for coastal Indigenous communities remain largely unquantified, particularly where the primary research questions are posed by the Indigenous community as a result of emerging approaches such as knowledge co-production. Here, we directly link reduced sea ice coverage to decreasing harvesting opportunities for ugruk (bearded seal, Erignathus barbatus ) as a component of a partnership among a multidisciplinary team of scientists, Indigenous Elder Advisory Council, and sovereign Indigenous tribe in northwest Alaska, USA. We collaboratively established research questions, coordinated data collection, and interpreted results to understand the causes and consequences of changing ugruk harvests for the community of Qikiqtaġruk (Kotzebue). The duration of spring ugruk hunts by the Qikiqtaġruŋmiut declined significantly during 2003–2019 due to a shift (∼3 weeks earlier) in the timing of regional sea ice breakup. Harvests now cease ∼26 d earlier than in the past decade. Using historical sea ice records, we further demonstrate that ice coverage in May now resembles conditions that were common in July during the mid-20th century. Overall, we show that climate change is constraining hunting opportunities for this traditional marine resource, although Qikiqtaġruŋmiut hunters have so far been able to offset a shortened season with changes in effort. Notwithstanding recent hunting success in unprecedentedly sparse ice conditions, accessibility to traditional resources remains a prominent concern for many Arctic communities. Management and policy decisions related to Arctic marine mammal resources, such as ugruk, are therefore also interwoven with food security, well-being, and culture of Indigenous communities. Hence, research that originates with Indigenous sovereignty over the entire research process, such as demonstrated here, has the potential to also lead to more inclusive, sustainable, and equitable outcomes in the face of rapid and accelerating Arctic change.

Marine remote sensing provides comprehensive characterizations of the ocean surface across space and time. However, cloud cover is a significant challenge in marine satellite monitoring. Researchers have proposed various algorithms to fill data gaps “below the clouds”, but a comparison of algorithm performance across several geographic regions has not yet been conducted. We compared ten basic algorithms, including data-interpolating empirical orthogonal functions (DINEOF), geostatistical interpolation, and supervised learning methods, in two gap-filling tasks: the reconstruction of chlorophyll a in pixels covered by clouds, and the correction of regional mean chlorophyll a concentrations. For this purpose, we combined tens of cloud-free images with hundreds of cloud masks in four study areas, creating thousands of situations in which to test the algorithms. The best algorithm depended on the study area and task, and differences between the best algorithms were small. Ordinary Kriging, spatiotemporal Kriging, and DINEOF worked well across study areas and tasks. Random forests reconstructed individual pixels most accurately. We also found that high levels of cloud cover led to considerable errors in estimated regional mean chlorophyll a concentration. These errors could, however, be reduced by about 50% to 80% (depending on the study area) with prior cloud-filling.

Volcanoes that deposit eruptive products into the ocean can trigger phytoplankton blooms near the deposition area. Phytoplankton blooms impact the global carbon cycle, but the specific conditions and mechanisms that facilitate volcanically triggered blooms are not well understood, especially in low nutrient ocean regions. We use satellite remote sensing to analyze the chlorophyll response to an 8‐month period of explosive and effusive activity from Nishinoshima volcano, Japan. Nishinoshima is an ocean island volcano in a low nutrient low chlorophyll region of the Northern Pacific Ocean. From June to August 2020, during explosive activity, satellite‐derived chlorophyll‐a was detectable with amplitudes significantly above the long‐term climatological value. After the explosive activity ceased in mid‐August 2020, these areas of heightened chlorophyll concentration decreased as well. In addition, we used aerial observations and satellite imagery to demonstrate a spatial correlation between blooms and ash plume direction. Using a sun‐induced chlorophyll‐a fluorescence satellite product, we confirmed that the observed chlorophyll blooms are phytoplankton blooms. Based on an understanding of the nutrients needed to supply blooms, we hypothesize that blooms of nitrogen‐fixing phytoplankton led to a 1010–1012 g drawdown of carbon. Thus, the bloom could have significantly mediated the output of carbon from the explosive phase of the eruption but is a small fraction of anthropogenic CO2 stored in the ocean or the global biological pump. Overall, we provide a case study of fertilization of a nutrient‐poor ocean with volcanic ash and demonstrate a scenario where multi‐month scale deposition triggers continuous phytoplankton blooms across 1,000s of km2. Plain Language Summary Volcanic eruptions can cause organisms known as phytoplankton to multiply and form what is known as a phytoplankton bloom in the ocean. Phytoplankton blooms can impact the life cycle of carbon in the earth system, but it is not always obvious why phytoplankton blooms happen. Using different satellite data, we observe phytoplankton blooms by viewing chlorophyll concentration in the ocean. Nishinoshima is a remote volcano in an area of the Pacific that lacks nutrients necessary for phytoplankton blooms. Nishinoshima erupted in 2019–2020 and deposited lava and ash into the ocean at different times. By looking at the chlorophyll concentration during the time periods lava and ash were deposited into the ocean, we found that chlorophyll concentration increased when ash was deposited into the ocean. These increases in chlorophyll concentration were determined to be phytoplankton blooms. These phytoplankton blooms may utilize nutrients from volcanic ash and the atmosphere, leading to a drawdown of atmospheric carbon. Key Points Ash deposition triggers phytoplankton blooms at Nishinoshima during the explosive phase of the 2019–2020 eruption Phytoplankton blooms were not present during the effusive phase of the 2019–2020 eruption Phytoplankton blooms triggered by ash deposition can lead to carbon drawdown that can mediate the carbon output from the eruption

The inaugural data from the first systematic program of sea-ice observations in Kotzebue Sound, Alaska, in 2018 coincided with the first winter in living memory when the Sound was not choked with ice. The following winter of 2018–19 was even warmer and characterized by even less ice. Here we discuss the mass balance of landfast ice near Kotzebue (Qikiqtaġruk) during these two anomalously warm winters. We use in situ observations and a 1-D thermodynamic model to address three research questions developed in partnership with an Indigenous Advisory Council. In doing so, we improve our understanding of connections between landfast ice mass balance, marine mammals and subsistence hunting. Specifically, we show: (i) ice growth stopped unusually early due to strong vertical ocean heat flux, which also likely contributed to early start to bearded seal hunting; (ii) unusually thin ice contributed to widespread surface flooding. The associated snow ice formation partly offset the reduced ice growth, but the flooding likely had a negative impact on ringed seal habitat; (iii) sea ice near Kotzebue during the winters of 2017–18 and 2018–19 was likely the thinnest since at least 1945, driven by a combination of warm air temperatures and a persistent ocean heat flux.

Marine phytoplankton play a central role in global biogeochemical cycling, carbon export, and the overall functioning of marine ecosystems. While chlorophyll a (Chl a ) is widely used as a proxy for phytoplankton biomass, identifying the proportion of Chl a attributable to different phytoplankton groups remains a major challenge in oceanography, especially for the picophytoplankton groups that often represent the majority of phytoplankton biomass in the open ocean. We describe a method for measuring picophytoplankton per-cell Chl a in field samples using fluorescence-activated cell sorting followed by solvent-based Chl a extraction and fluorescence quantification. Applying this method to surface samples from the Gulf of Mexico, we determined per-cell Chl a to be 0.24 ± 0.07, 0.6 ± 0.33, and 26.36 ± 20.9 fg Chl a cell -1 for Prochlorococcus , Synechococcus , and PPE, respectively (mean ± SD). Measurements of per-cell Chl a using this method are precise to within 1.7, 2.1, and 3.1% for Prochlorococcus , Synechococcus , and PPE, respectively. We demonstrate that this approach can be used to obtain estimates of group-specific Chl a for Prochlorococcus , Synechococcus , and picophytoeukaryotes, the latter two of which cannot be captured by existing methods. We also demonstrate that measurements of per-cell Chl a made using this method in field samples are sufficiently precise to capture relationships between per-cell Chl a and cytometer red fluorescence, providing a bridge between biomass estimates from cell counts and bulk measurements of total Chl a .