Plankton networks driving carbon export in the oligotrophic ocean

The biological carbon pump is the process by which CO 2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions

Globally consistent quantitative observations of planktonic ecosystems

In this paper we review the technologies available to make globally quantitative observations of particles in general—and plankton in particular—in the world oceans, and for sizes varying from sub-microns to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical and acoustical methods as well as analysis using particle counters, flow cytometers and quantitative imaging devices. We follow by highlighting the requirements necessary for a plankton observing system, the approach to achieve it and associated challenges. We conclude with ranked action-item recommendations for the next 10 years to move toward our vision of a holistic ocean-wide plankton observing system. Particularly, we suggest to begin with a demonstration project on a GO-SHIP line and/or a long-term observation site and expand from there, ensuring that issues associated with methods, observation tools, data analysis, quality assessment and curation are addressed early in the implementation. Global coordination is key for the success of this vision and will bring new insights on processes associated with nutrient regeneration, ocean production, fisheries and carbon sequestration

Assimilation de données dans un modèle couplé physique-biogéochimie de l'Atlantique Nord

This work aims at studying how the representation of physical processes of the surface layers, known to be critical for biogeochemistry, can be improved by SSH and SST data assimilation with a reduced order Kalman filter into a coupled physical-biogeochemical model of the North Atlantic. First, it is shown that the original assimilation scheme is not adapted to the problem, as hydrostatic stability, dynamic stability and temporal continuity are not respected. Two new schemes enforcing hydrostatic stability, and a continuous correction scheme are proposed and evaluated through annual simulations over 1998. The nutrient input in the euphotic zone is used to quantify the coupling quality. Diffusive inputs are improved, but advective inputs are overestimated in the subtropics. Annual primary productivity is improved, but its annual cycle remains unchanged. The biogeochemical model flaws limit improvements of the biogeochemical response at high latitudes and in the subtropical gyre centre.Ce travail examine comment l'assimilation de données de SSH et de SST par un filtre de Kalman de rang réduit dans un modèle couplé physique-biogéochimie de l'Atlantique Nord peut améliorer la représentation des processus physiques des couches superficielles importants pour la biogéochimie. On montre tout d'abord que le schéma d'assimilation original est inadapté, car l'estimation d'état ne respecte pas les contraintes de stabilité hydrostatique, stabilité dynamique et de continuité temporelle. Deux nouveaux schémas imposant la stabilité hydrostatique, et un schéma de correction continue sont proposés, et évalués par des simulations annuelles en 1998. L'apport en nutriment dans la zone euphotique permet de quantifier la qualité du couplage. Les apports diffusifs sont améliorés, tandis que les apports advectifs sont surestimés. La production primaire annuelle est plus réaliste, mais son cycle annuel reste mal représenté. Les écarts aux observations dans les hautes latitudes et le centre du gyre subtropical sont liés aux défauts du modèle biogéochimique

Assimilation de données dans un modèle couplé physique-biogéochimie de l'Atlantique Nord

Ce travail examine comment l'assimilation de données de SSH et de SST par un filtre de Kalman de rang réduit dans un modèle couplé physique-biogéochimie de l'Atlantique Nord peut améliorer la représentation des processus physiques des couches superficielles importants pour la biogéochimie. On montre tout d'abord que le schéma d'assimilation original est inadapté, car l'estimation d'état ne respecte pas les contraintes de stabilité hydrostatique, stabilité dynamique et de continuité temporelle. Deux nouveaux schémas imposant la stabilité hydrostatique, et un schéma de correction continue sont proposés, et évalués par des simulations annuelles en 1998. L'apport en nutriment dans la zone euphotique permet de quantifier la qualité du couplage. Les apports diffusifs sont améliorés, tandis que les apports advectifs sont surestimés. La production primaire annuelle est plus réaliste, mais son cycle annuel reste mal représenté. Les écarts aux observations dans les hautes latitudes et le centre du gyre subtropical sont liés aux défauts du modèle biogéochimique.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Sargassum observations from MODIS: using aggregations context to filter false detections

International audienceSince 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only alternative to monitor such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and others phenomena. All together, they lead to false detections that cannot be discriminated with classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model based on spatial features to filter false detections. More specifically, Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum detections, five spatial indices were extracted for describing their shape and surrounding context and then used by a random forest binary classifier. Contextual features were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the classifier performs the filtering of daily false detections with an accuracy of 90%. This leads to a reduction of detected Sargassum pixels of 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution on 2016-2020 is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. In particular, it retrieves the two areas of consolidation in the western and eastern part of the Tropical Atlantic Ocean associated with distinct temporal dynamics. At full resolution, the dataset allowed us to semi-automatically extract Sargassum aggregations trajectories from successive filtered images. Using those trajectories will help to better quantify the drift of aggregations with respect to the currents, the wind and sea state. Overall, this new dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models

Revisited Estimation of Moderate Resolution Sargassum Fractional Coverage Using Decametric Satellite Data (S2-MSI)

International audienceSince 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil and West Africa, leading to significant environmental and socioeconomic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA's 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus's 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state

Filtering out false Sargassum detections using context features

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only way to monitor regularly such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and other phenomena. All together, they lead to false detections that can hardly be discriminated by classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model base exclusively on spatial features to filter out false detections after the detection process. Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum aggregations, five contextual indices were extracted (number of neighbours, surface of neighbours, temporal persistence, distance to the coast and aggregation texture) then used by a random forest binary classifier. Contextual features at large-scale were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the model performs the filtering of daily false detections with an accuracy of ~ 90%. This leads to a reduction of detected Sargassum pixels of ~ 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. This dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models. The methodology used here demonstrates the usefulness of contextual features for complementing classical remote sensing approaches. Our model could easily be adapted to other datasets containing erroneous detections

Assessing biases in computing size spectra of automatically classified zooplankton from imaging systems: A case study with the ZooScan integrated system

International audienceBody size constrains prey-predator interactions and physiology, therefore plankton size spectra have been appointed as synthetic descriptors of plankton community structure and functioning. Recently developed imaging systems and supervised classification tools provide size measurements of any object in situ or in net samples and automatically classify them into previously defined categories. But because the nature of objects detected by these imaging systems is diverse, from non-living detritus to organisms of different plankton taxa, and because the steps in the analysis could introduce specific biases, a careful analysis of such plankton size spectra is needed before going deeper into ecological considerations. Using a WP2 net time series, we propose a general framework to analyze and validate zooplankton size spectra collected with nets and analyzed with the ZooScan integrated system that includes supervised classification. Size spectra were controlled, at each step of the procedure, to assess the modification of their shape due to several possible biases: (i) the effect of objects touching each other during the image acquisition, (ii) the error of the automatic classification differing among size classes and (iii) the choice of model to estimate body biovolume

Skillful Seasonal Forecast of Sargassum Proliferation in the Tropical Atlantic

International audienceAbstract The large‐scale proliferation of pelagic Sargassum in the Tropical Atlantic from 2011 has been the subject of increasing attention because of its negative consequences on the environment, fishing, and socioeconomic activities when stranding on coastal areas. This recurrent phenomenon presents strong seasonal and year to year variations. Anticipating the abundances and stranding on seasonal scale poses important challenges in terms of observations and modeling. We show that skillful seasonal forecast of Sargassum distribution can be achieved with up to 7 months in advance over the Tropical Atlantic, by integrating both transport and current knowledge on physiology of Sargassum . This forecast is designed to help marine stakeholders to develop mitigation and resilience strategies through anticipatory decision‐making