Description: Aim: The distribution of mesoplankton communities has been poorly studied at global scale, especially from in situ instruments. This study aims to (1) describe the global distribution of mesoplankton communities in relation to their environment and (2) as-sess the ability of various environmental- based ocean regionalizations to explain the distribution of these communities. Location: Global ocean, 0–500 m depth. Time Period: 2008–2019. Major Taxa Studied: Twenty-eight groups of large mesoplanktonic and macroplank-tonic organisms, covering Metazoa, Rhizaria and Cyanobacteria. Methods: From a global data set of 2500 vertical profiles making use of the Underwater Vision Profiler 5 (UVP5), an in situ imaging instrument, we studied the global distribu-tion of large (〉600 μm) mesoplanktonic organisms. Among the 6.8 million imaged ob-jects, 330,000 were large zooplanktonic organisms and phytoplankton colonies, the rest consisting of marine snow particles. Multivariate ordination (PCA) and clustering were used to describe patterns in community composition, while comparison with existing regionalizations was performed with regression methods (RDA). Results: Within the observed size range, epipelagic plankton communities were Trichodesmium- enriched in the intertropical Atlantic, Copepoda- enriched at high latitudes and in upwelling areas, and Rhizaria-enriched in oligotrophic areas. In the mesopelagic layer, Copepoda-enriched communities were also found at high lati-tudes and in the Atlantic Ocean, while Rhizaria-enriched communities prevailed in the Peruvian upwelling system and a few mixed communities were found elsewhere. The comparison between the distribution of these communities and a set of existing regionalizations of the ocean suggested that the structure of plankton communities described above is mostly driven by basin- level environmental conditions. Main Conclusions: In both layers, three types of plankton communities emerged and seemed to be mostly driven by regional environmental conditions. This work sheds light on the role not only of metazoans, but also of unexpected large protists and cy-anobacteria in structuring large mesoplankton communities.

Description: Marine particles of different nature are found throughout the global ocean. The term "marine particles"describes detritus aggregates and fecal pellets as well as bacterioplankton, phytoplankton, zooplankton and nekton. Here, we present a global particle size distribution dataset obtained with several Underwater Vision Profiler 5 (UVP5) camera systems. Overall, within the 64 μm to about 50 mm size range covered by the UVP5, detrital particles are the most abundant component of all marine particles; thus, measurements of the particle size distribution with the UVP5 can yield important information on detrital particle dynamics. During deployment, which is possible down to 6000 m depth, the UVP5 images a volume of about 1 L at a frequency of 6 to 20 Hz. Each image is segmented in real time, and size measurements of particles are automatically stored. All UVP5 units used to generate the dataset presented here were inter-calibrated using a UVP5 high-definition unit as reference. Our consistent particle size distribution dataset contains 8805 vertical profiles 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 is available at 10.1594/PANGAEA.924375.

Description: 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.