Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning

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

Community-Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Predicting responses of plankton to variations in essential nutrients is hampered by limited in situ measurements, a poor understanding of community composition, and the lack of reference gene catalogs for key taxa. Iron is a key driver of plankton dynamics and, therefore, of global biogeochemical cycles and climate. To assess the impact of iron availability on plankton communities, we explored the comprehensive bio-oceanographic and bio-omics data sets from Tara Oceans in the context of the iron products from two state-of-the-art global scale biogeochemical models. We obtained novel information about adaptation and acclimation toward iron in a range of phytoplankton, including picocyanobacteria and diatoms, and identified whole subcommunities covarying with iron. Many of the observed global patterns were recapitulated in the Marquesas archipelago, where frequent plankton blooms are believed to be caused by natural iron fertilization, although they are not captured in large-scale biogeochemical models. This work provides a proof of concept that integrative analyses, spanning from genes to ecosystems and viruses to zooplankton, can disentangle the complexity of plankton communities and can lead to more accurate formulations of resource bioavailability in biogeochemical models, thus improving our understanding of plankton resilience in a changing environment

Machine learning techniques to characterize functional traits of plankton from image data

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

Determination of the Transport Properties of Liquid Electrolytes Using a Multi-Electrode Cell

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Investigation of the Oxidation Reaction of LiFePO 4 Cathode Material using Environmental TEM

International audienceThe last decades LiFePO4 (triphylite) has been extensively studied due to interesting electrochemical properties that make it an attractive positive electrode candidate for Li-ion batteries. LiFePO4 used as a cathode material exhibits a high retention in cycling, a structural stability of the delithiated phase FePO4 and a low cost of its components. Recent developments show the possibility of significantly increasing power densities. However, the LiFePO4 nanoparticles show significant amounts of structural defects according to the synthetic route used. For instance, the presence of Fe and vacancies in Li crystallographic sites (Pnma space group) has a significant effect on electrochemical behavior by hindering cation diffusion. LiFePO4 also has a high reactivity to O2 at moderate temperatures (300-500°C depending on the size) leading to the gradual diffusion of Fe from the core to the surface of the material accompanied by the formation of Fe2O3 nanoparticles. Therefore, the composition material LixFeyPO4 exhibits very high degree of crystalline defects. These transformations were evidenced by X-ray diffraction and electron diffraction at different temperatures. These olivine compounds outside stoichiometry have an order of defects leading to the formation of a superstructure. However, in order to get a better insight into the mechanisms related to these transformations, in situ investigation in a real-time is necessary. Environmental ETEM, in which a pression of O2 can be injected, coupled with a heating holder is a perfect characterization platform to monitor oxidation reactivity of LiFePO4 up to 700°C. This project focuses on the study of the structural mechanisms associated with the temperature reactivity of LiFePO4 under an oxidizing atmosphere using environmental TEM (TITAN). The aim is to quantify the kinetics of FeyOx nanoparticle formation, and the appearance of superstructures induced by Fe diffusion from the core to the surface of the material

Mass Spectrometry Analysis of NMC622/Graphite Li-Ion Cells Electrolyte Degradation Products after Storage and Cycling

International audienceWith the aim of establishing a data simultaneous comparison, the Principal Component Analysis (PCA) statistical tool was applied to LiNi0.6Mn0.2Co0.2O2/graphite Li-ion cells electrolyte's decomposition products detected by UHPLC-ESI-HRMS. Herein, we illustrate how the chemometric tool associated with mass spectrometry data can be relevant to provide information about the presence of unusual molecules. Indeed, pristine Triton X-100 surfactant molecules used in the electrode elaboration process were detected after the impregnation stage. However, as they chemically react and oxidize at a potential lower than 4.5 V vs Li/Li+, only surfactant derivatives and classical ageing molecules were observed, respectively, after storage and cycling stages at 55 degrees C, leading to a triangle-type correlation circle. On the other hand, global schemes of LiPF6-based electrolyte degradation pathways were elaborated from a comparative study with literature to help interpret results in future electrolyte ageing studies

Enzymatically demethylated pectins: from fruit waste to an outstanding polymer binder for silicon-based anodes of Li-ion batteries

International audiencePectin is a polysaccharide frequently found in large amounts in the peel and seeds of many fruits and therefore represents very common food industry waste. In this study, we investigate demethylated citrus and apple pectins with a methylation degree ranging from 76% to 3%, as polymer binders for silicon-based anodes of lithium-ion batteries. Both chemical and enzymatic pectin demethylations were considered. First, the usual aggressive saponification reaction was carried out leading to 24% and 28% methylesterified pectins from citrus and apple, respectively, but leading at the same time to unavoidable strong pectin depolymerization, as shown by SEC studies. In a second approach, the methylesterase enzyme was used to catalyze citrus pectin demethylation leading to a similar methylesterification degree (24%) but drastically minimizing polymer chain degradation. Our best-demethylated pectin was compared with the standard polymer binder for Li-ion batteries i.e. carboxymethyl cellulose (CMC) inside a composite silicon anode for their effect on silicon electrochemical capacity retention. The 24% enzymatically demethylated citrus pectin achieved here a remarkable capacity of 2275 mA h g-1 after 49 cycles with a load of 1.6 mg cm-2 compared to 245 mA h g-1 measured for CMC. These demethylated pectins have a buffering effect on the silicon particles' volume change during discharge/charge cycles. The increase of interactions between silicon and pectin, probably due to the presence of numerous carboxylic acid functions in this demethylated pectin, is hypothesized to be, at least partly, responsible for these enhanced electrochemical performances. In addition, the existing type of glycosidic linkage (& alpha; in pectins and & beta; in CMC) can also be responsible for these enhanced results.Enzymatically demethylated citrus pectins: an efficient polymer binder in Si-based anodes of Li-ion batteries

Operando X-ray diffraction in transmission geometry « at home » from tape casted electrodes to all-solid-state battery

International audienceThe ever-growing field of energy storage needs the development of fast cycling rate and high energy density batteries and substantial research efforts are required to characterize them for improved performances. Operando X-ray diffraction is the most effective and convenient laboratory technique to get a deep insight into structural and electrochemical changes during operating conditions of batteries. In this work, we presented our newly developed operando LeRiChe'S Cell v2 which has been used to study NMC electrode materials not only with liquid electrolyte but also in solid-state batteries. The high brilliance of our laboratory diffractometer combined with our newly developed operando LeRiChe'S Cell v2 allowed us to investigate electrode materials at high C rates in a very short span of time

The formulation of a CMC binder/silicon composite anode for Li-ion batteries: from molecular effects of ball milling on polymer chains to consequences on electrochemical performances

The semi-synthetic polysaccharide carboxymethylcellulose (CMC) is one of the most studied and effective polymer binders for silicon-based anodes in Li-ion batteries

The formulation of a CMC binder/silicon composite anode for Li-ion batteries: from molecular effects of ball milling on polymer chains to consequences on electrochemical performances

The semi-synthetic polysaccharide carboxymethylcellulose (CMC) is one of the most studied and effective polymer binders for silicon-based anodes in Li-ion batteries