Australia's tropical waters represent predicted 'hotspots' for nitrogen (N 2 ) fixation based on empirical and modelled data. However, the identity, activity and ecology of diazotrophs within this region are virtually unknown. By coupling DNA and cDNA sequencing of nitrogenase genes (nifH) with sizefractionated N 2 fixation rate measurements, we elucidated diazotroph dynamics across the shelf region of the Arafura and Timor Seas (ATS) and oceanic Coral Sea during Austral spring and winter. During spring, Trichodesmium dominated ATS assemblages, comprising 60% of nifH DNA sequences, while Candidatus Atelocyanobacterium thalassa (UCYN-A) comprised 42% in the Coral Sea. In contrast, during winter the relative abundance of heterotrophic unicellular diazotrophs (δ-proteobacteria and γ-24774A11) increased in both regions, concomitant with a marked decline in UCYN-A sequences, whereby this clade effectively disappeared in the Coral Sea. Conservative estimates of N 2 fixation rates ranged from o1 to 91 nmol l − 1 day − 1 , and size fractionation indicated that unicellular organisms dominated N 2 fixation during both spring and winter, but average unicellular rates were up to 10-fold higher in winter than in spring. Relative abundances of UCYN-A1 and γ-24774A11 nifH transcripts negatively correlated to silicate and phosphate, suggesting an affinity for oligotrophy. Our results indicate that Australia's tropical waters are indeed hotspots for N 2 fixation and that regional physicochemical characteristics drive differential contributions of cyanobacterial and heterotrophic phylotypes to N 2 fixation.

Capturing the variability of primary productivity in highly dynamic coastal ecosystems remains a major challenge to marine scientists. To test the suitability of Fast Repetition Rate fluorometry (FRRf) for rapid assessment of primary productivity in estuarine and coastal locations, we conducted a series of paired analyses estimating 14 C carbon fixation and primary productivity from electron transport rates with a Fast Repetition Rate fluorometer MkII, from waters on the Australian east coast. Samples were collected from two locations with contrasting optical properties and we compared the relative magnitude of photosynthetic traits, such as the maximum rate of photosynthesis (P max ), light utilization efficiency (α) and minimum saturating irradiance (E K ) estimated using both methods. In the case of FRRf, we applied recent algorithm developments that enabled electron transport rates to be determined free from the need for assumed constants, as in most previous studies. Differences in the concentration and relative proportion of optically active substances at the two locations were evident in the contrasting attenuation of PAR (400 -700 nm), blue (431 nm), green (531 nm) and red (669 nm) wavelengths. FRRF-derived estimates of photosynthetic parameters were positively correlated with independent estimates of 14 C carbon fixation (P max : n = 19, R 2 = 0.66; α : n = 21, R 2 = 0.77; E K : n = 19, R 2 = 0.45; all p < 0.05 ), however primary productivity was frequently underestimated by the FRRf method. Up to 81% of the variation in the relationship between FRRf and 14 C estimates was explained by the presence of picocyanobacteria, chlorophyll-a biomass, and the proportion of photoprotective pigments, that appeared to be linked to turbidity. We discuss the potential importance of cyanobacteria in influencing the underestimations of FRRf productivity and steps to overcome this potential limitation.

How the functional traits (FTs) of phytoplankton change with temperature is important for understanding the impacts of ocean warming on phytoplankton mediated biogeochemical fluxes. This study quantifies the thermal performance curves (TPCs) of FTs in the cosmopolitan model diatom, Thalassiosira pseudonana, to advance understanding of trade-offs between physiological (photoacclimation, carbon fixation, nitrate, phosphate, and silicate uptake) and morphological traits (cell volume and frustule silicification). We show that each FT has substantial phenotypic plasticity and exhibits a unique TPC, varying in both shape and thermal optimum, and diverging from the growth response. The TPC for growth was symmetric with a thermal optimum (T opt ) of 18 • C. In comparison, the TPC for primary productivity was warm-skewed with a T opt around 21 • C, whereas frustule silicification decreased linearly with increasing temperature. Together, this suggests that the optimal temperature for overall fitness is a balance of trade-offs in the underlying functional traits. Moreover, these results demonstrate that growth is not necessarily an accurate estimate of overall biogeochemical performance and that temperature change will likely influence elemental fluxes such as carbon and silicon. Finally, we show that temperature-driven changes in individual traits e.g., photoacclimation, can mimic responses experienced under other environmental stressors (high light) and so a multi-trait assessment is essential for accurate interpretation of the cellular impact of warming. This study also reveals that multi-trait analysis, in the context of TPCs, provides insight into the cellular physiology regulating the whole cell response and has the potential to provide better estimates of how diatom-mediated biogeochemical fluxes are likely to be impacted in the context of ocean warming. Analyzing the response of multiple traits more comprehensively over other environmental gradients may therefore provide a useful framework to advance understanding of how taxon-specific functional traits will respond to multifaceted ocean change.

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Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

Isoprene produced by marine phytoplankton acts as a precursor of secondary organic aerosol and thereby affects cloud formation and brightness over the remote oceans. Yet the marine isoprene emission is poorly constrained, with discrepancies among estimates that reach 2 orders of magnitude. Here we present ISOREMS, the first satellite‐only based algorithm for the retrieval of isoprene concentration in the Southern Ocean. Sea surface concentrations from six cruises were matched with remotely sensed variables from MODIS Aqua, and isoprene was best predicted by multiple linear regression with chlorophyll a and sea surface temperature. Climatological (2002–2018) isoprene distributions computed with ISOREMS revealed high concentrations in coastal and near‐island waters, and within the 40–50°S latitudinal band. Isoprene seasonality paralleled phytoplankton productivity, with annual maxima in summer. The annual Southern Ocean emission of isoprene was estimated at 63 Gg C yr−1. The algorithm can provide spatially and temporally realistic inputs to atmospheric and climate models.

Abstract. The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.

Isoprene is a biogenic trace gas produced by terrestrial vegetation and marine phytoplankton. In the remote oceans, where secondary aerosols are mostly biogenic, marine isoprene emissions affect atmospheric chemistry and influence cloud formation and brightness. Here, we present the first compilation of new and published measurements of isoprene concentrations in the Southern Ocean and explore their distribution patterns. Surface ocean isoprene concentrations in November through April span 1 to 94 pM. A band of higher concentrations is observed around a latitude of ≈40 ∘ S and a surface sea temperature of 15 ∘ C. High isoprene also occurs in high productivity waters near islands and continental coasts. We use concurrent measurements of physical, chemical, and biological variables to explore the main potential drivers of isoprene concentration by means of paired regressions and multivariate analysis. Isoprene is best explained by phytoplankton-related variables like the concentrations of chlorophyll-a, photoprotective pigments and particulate organic matter, photosynthetic efficiency (influenced by iron availability), and the chlorophyll-a shares of most phytoplankton groups, and not by macronutrients or bacterial abundance. A simple statistical model based on chlorophyll-a concentration and a sea surface temperature discontinuity accounts for half of the variance of isoprene concentrations in surface waters of the Southern Ocean.