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Abstract The eastern Indian Ocean is substantially under sampled with respect to the biological carbon pump – the suite of processes that transport the carbon fixed by phytoplankton into the deeper ocean. Using sediment traps and other ecosystem measurements, we quantified sinking organic matter flux and investigated the characteristics of sinking particles in waters overlying the Argo Abyssal Plain directly downstream of the Indonesian Throughflow off northwest Australia. Carbon export from the euphotic zone averaged 7.0 mmol C m^-2d^-1, which equated to an average export efficiency (export / net primary production) of 0.17. Sinking particle flux within the euphotic zone (beneath the mixed layer, but above the deep chlorophyll maximum) averaged slightly higher than flux at the base of the euphotic zone, suggesting that the deep euphotic zone was a depth stratum of net particle remineralization. Carbon flux attenuation continued into the twilight zone with a transfer efficiency (export at euphotic depth + 100m / export at euphotic depth) of 0.62 and an average Martin’sb-value of 1.1. Within the euphotic zone, fresh phytoplankton (chlorophyll associated with sinking particles, possibly contained within appendicularian houses) were an important component of sinking particles, but beneath the euphotic zone the fecal pellets of herbivorous zooplankton (phaeopigments) were more important. Changes in carbon and nitrogen isotopic composition with depth further reflected remineralization processes occurring as particles sank. We show similarities with biological carbon pump functioning in a similar semi-enclosed oligotrophic marginal sea, the Gulf of Mexico, including net remineralization across the deep chlorophyll maximum. Submitted to: Deep-sea Research II HighlightsDespite low productivity, export efficiency was 17% of primary productionFlux attenuation beneath the euphotic zone (EZ) was low for a tropical regionSinking particle flux from the upper to lower EZ exceeded export from lower EZThe deep EZ was a stratum of net particle remineralization (and net heterotrophy) 

Zooplankton plays a major role in ocean food webs and biogeochemical cycles, and provides major ecosystem services as a main driver of the biological carbon pump and in sustaining fish communities. Zooplankton is also sensitive to its environment and reacts to its changes. To better understand the importance of zooplankton, and to inform prognostic models that try to represent them, spatially-resolved biomass estimates of key plankton taxa are desirable. In this study we predict, for the first time, the global biomass distribution of 19 zooplankton taxa (1-50 mm Equivalent Spherical Diameter) using observations with the Underwater Vision Profiler 5, a quantitative in situ imaging instrument. After classification of 466,872 organisms from more than 3,549 profiles (0-500 m) obtained between 2008 and 2019 throughout the globe, we estimated their individual biovolumes and converted them to biomass using taxa-specific conversion factors. We then associated these biomass estimates with climatologies of environmental variables (temperature, salinity, oxygen, etc.), to build habitat models using boosted regression trees. The results reveal maximal zooplankton biomass values around 60°N and 55°S as well as minimal values around the oceanic gyres. An increased zooplankton biomass is also predicted for the equator. Global integrated biomass (0-500 m) was estimated at 0.403 PgC. It was largely dominated by Copepoda (35.7%, mostly in polar regions), followed by Eumalacostraca (26.6%) Rhizaria (16.4%, mostly in the intertropical convergence zone). The machine learning approach used here is sensitive to the size of the training set and generates reliable predictions for abundant groups such as Copepoda (R2 ≈ 20-66%) but not for rare ones (Ctenophora, Cnidaria, R2 < 5%). Still, this study offers a first protocol to estimate global, spatially resolved zooplankton biomass and community composition from in situ imaging observations of individual organisms. The underlying dataset covers a period of 10 years while approaches that rely on net samples utilized datasets gathered since the 1960s. Increased use of digital imaging approaches should enable us to obtain zooplankton biomass distribution estimates at basin to global scales in shorter time frames in the future. 

Abstract The last two decades have shown the importance of Rhizaria in the biogeochemical cycles of carbon and silicon in modern oceans. This eukaryotic supergroup, which includes Radiolaria and Phaeodaria, represents an important part of zooplanktonic carbon biomass and contributes to carbon and silica export. Still, accurate estimations of their carbon biomass are hindered by poor knowledge of their elemental composition, contrasting with well‐established allometric carbon‐to‐volume relationships for smaller protists such as phytoplankton. Here, we directly measured carbon, nitrogen, and biogenic silica content as well as silicon uptake rates of planktonic Rhizaria. We highlight that size can be used as a predictor of elemental content for a broad variety of planktonic Rhizaria ranging from 200 μm to several mm, whereas size is weakly correlated with silicon uptake rates. Our results indicate that the scaling exponent of the carbon‐to‐volume allometry is significantly lower than those for smaller protists, underlining the low carbon strategy of these organisms. Still, we show that carbon and nitrogen densities span over four orders of magnitude, possibly accounting for the differences in depth ranges, nutritional modes and colonial or solitary forms. We estimate Rhizaria sinking speeds by combining carbon, nitrogen, and silica content data and show that great variability exists among the different taxa. Besides giving a better understanding of rhizarian ecology and biogeochemistry, these analyses, at the individual scale, are a first step to subsequent biomass and flux estimations at larger scales. 

Abstract The Rhizaria is a super‐group of amoeboid protists with ubiquitous distributions, from the euphotic zone to the twilight zone and beyond. While rhizarians have been recently described as important contributors to both biogenic silica and carbon fluxes, we lack the most basic information about their ecological habitats and preferences. Here, using in situ imaging (Underwater Vision Profiler 5), we characterize the vertical ecological niches of different test‐bearing pelagic rhizarian taxa in the southernCalifornia Current Ecosystem. We define three vertical layers between 0 and 500 m occupied, respectively, by (1) surface dwelling and mostly symbiont‐bearing rhizarians (Acantharia and Collodaria), (2) flux‐feeding phaeodarians in the lower epipelagic (100–200 m), and (3) Foraminifera and Phaeodaria populations adjacent to the oxygen minimum zone. We then use Generalized Additive Models to analyze the response of each rhizarian category to a suite of environmental variables. The models explain between 9% and 93% of the total variance observed for the different groups. While temperature and the depth of the deep chlorophyll maximum appear as the main abiotic factors influencing populations in the upper 200 m, dissolved silicon concentration is related to the abundance of mesopelagic phaeodarians, though it explains only a portion of the variance. The importance of biotic interactions (e.g., prey availability, predation, parasitism, symbiosis) is still to be considered, in order to fully incorporate the dynamics of test‐bearing pelagic rhizarians in ecological and biogeochemical models. 

Abstract. Marine particles of different nature are found throughout the globalocean. The term “marine particles” describes detritus aggregates andfecal pellets as well as bacterioplankton, phytoplankton, zooplankton andnekton. Here, we present a global particle size distribution datasetobtained with several Underwater Vision Profiler 5 (UVP5) camerasystems. Overall, within the 64 µm to about 50 mm size range coveredby the UVP5, detrital particles are the most abundant component of allmarine particles; thus, measurements of theparticle size distribution with the UVP5 can yield importantinformation on detrital particle dynamics. During deployment, which ispossible down to 6000 m depth, the UVP5 images a volume of about 1 Lat a frequency of 6 to 20 Hz. Each image is segmented in real time, andsize measurements of particles are automatically stored. All UVP5units used to generate the dataset presented here wereinter-calibrated using a UVP5 high-definition unit as reference. Ourconsistent particle size distribution dataset contains 8805 verticalprofiles collected between 19 June 2008 and 23 November 2020. All major ocean basins, as well as the Mediterranean Sea and the Baltic Sea, were sampled. A total of 19 % of all profiles had a maximum sampling depth shallower than 200 dbar, 38 % sampled at least the upper 1000 dbar depth range and 11 % went down to at least 3000 dbar depth. First analysis of the particle size distribution dataset shows that particle abundance is found to be high at high latitudes and in coastal areas where surface productivity or continental inputs are elevated. The lowest values are found in the deep ocean and in the oceanic gyres. Our dataset should be valuable for more in-depth studies that focus on the analysis of regional, temporal and global patterns of particle size distribution and flux as well as for the development and adjustment of regional and global biogeochemical models. The marine particle size distribution dataset (Kiko et al., 2021) is available at https://doi.org/10.1594/PANGAEA.924375. 

Abstract Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab‐based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.