ALBERT

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) assess 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 macroplanktonic 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 distribution of large (〉600 μm) mesoplanktonic organisms. Among the 6.8 million imaged objects, 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 latitudes 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 cyanobacteria in structuring large mesoplankton communities.

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 degrees N and 55 degrees 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 approximate to 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.

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: 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.