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1. Editorial: Advances in monitoring and modelling spatial and temporal dynamics of estuarine ecosystems.

Author

Lurdes Lopes, Carina, Le Fouest, Vincent, Corzo, Alfonso, and Miguel Dias, João

Subjects
ECOSYSTEM dynamics, ECOLOGICAL disturbances, ENVIRONMENTAL sciences, MARINE biodiversity, ECOSYSTEMS, TURBIDITY, ROGUE waves, TERRITORIAL waters
Abstract

This document is an editorial published in the journal Frontiers in Marine Science. It discusses the importance of monitoring and modeling estuarine ecosystems, which are valuable but threatened environments. The editorial highlights the major threats to estuaries, such as pollution and changes in geomorphology, and emphasizes the need for comprehensive monitoring methods to assess the impacts of climate change and human activities. The document also provides a summary of seven peer-reviewed studies that contribute to our understanding of estuarine dynamics and processes through innovative approaches, including remote sensing methods, numerical modeling, and laboratory experiments. Overall, these studies offer valuable insights for researchers and coastal managers worldwide. [Extracted from the article]

Published
2024
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2. Towards carbon neutrality by 2040 in La Rochelle metropolitan area (France): quantifying the role of wetlands and littoral zone in the capture and sequestration of blue carbon

Author

Dupuy, Christine, Agogué, Hélène, Amann, Benjamin, Azémar, Frédéric, Becu, Nicolas, Bergeon, Lauriane, Bertin, Xavier, Bocher, Pierrick, Bout, Emilie, Brenon, Isabelle, Carpentier, Alexandre, Ceaux, Serge, Chaumillon, Eric, Choquet, Catherine, Colin, Béatrice, Deborde, Jonathan, Dubillot, Emmanuel, Claire, Emery, Ferrari, Sylvie, Gaucherel, Cédric, Geairon, Philippe, Gilbert, Stéphane, Jeannin, Marc, Jourde, J., Kalenitchenko, Dimitri, Lachaussée, Nicolas, Lacoue-Labarthe, Thomas, Lanneluc, Isabelle, Lavaud, Laura, Lavaud, Sébastien, Lefrançois, Christel, Le Fouest, Vincent, Le Fur, Inès, Long, Nathalie, Mahieux, Pierre-Yves, Mayen, Jérémy, Marais, Caroline, Metzger, Édouard, Moncelon, Raphaël, Ouisse, Vincent, Péreau, Jean-Christophe, Pétillon, Julien, Philippine, Olivier, Pineau, Philippe, Pignon-Mussaud, Cécilia, Polsenaere, Pierre, Sabot, René, Refait, Philippe, Réveillac, Elodie, Robin, François-Xavier, Rouquette, Hélène, Sablé, Sophie, Sauriau, Pierre-Guy, Tackx, Michèle, Turcry, Philippe, Vagner, Marie, Vincent, Julia, Volto, Natacha, LIttoral ENvironnement et Sociétés (LIENSs), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Communauté d'Agglomération de La Rochelle (CDA La Rochelle), Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Université de Rennes (UR), Mathématiques, Image et Applications - EA 3165 (MIA), La Rochelle Université (ULR), Laboratoire Environnement Ressources des Pertuis Charentais (LERPC), LITTORAL (LITTORAL), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Bordeaux Sciences Economiques (BSE), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Laboratoire des Sciences de l'Ingénieur pour l'Environnement - UMR 7356 (LaSIE), Laboratoire de Planétologie et Géosciences [UMR_C 6112] (LPG), Université d'Angers (UA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Institute for Coastal and Marine Research and Department of Zoology [South Africa], Nelson Mandela University [Port Elizabeth], Union des marais de la Charente-Maritime (UNIMA), DDAF LA ROCHELLE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Communauté d'Agglomération de La Rochelle, Ville de La Rochelle, Banque des Territoires, Le Grand Plan d'InVestissement, Région Nouvelle Aquitaine, Port de Plaisance de La Rochelle, ADEME Agence de la Transition Ecologique, AZTI, and ELSEVIER

Subjects
Vegetated aquatic ecosystems, [SHS.ENVIR]Humanities and Social Sciences/Environmental studies, Climate mitigation, Blue Carbon, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Holistic approach
Abstract

International audience; Rising greenhouse gas emissions are causing increasing worldwide impacts and changes on climate patterns, sea level, food production, human lives and livelihoods. Maintaining or improving the ability of coastal aquatic ecosystems and oceans to remove CO2 from the atmosphere is a crucial aspect for climate mitigation. The vegetated coastal ecosystems are able to catch and to sequestrate carbon, the so-called Blue Carbon. These ecosystems are key exchange zones that mediate the biogeochemical cycles across the continent, the ocean and the atmosphere. Given the importance of these ecosystems in biogeochemical cycles and their sensitivity to natural and anthropogenic pressures, the carbon cycle within and between compartments (e.g. pelagos, benthos…) and across the interfaces (e.g. atmosphere, ocean…) need to be addressed. On a regional scale, in the extended urban area of La Rochelle located on the French Atlantic coast (La Rochelle metropolitan area), lack in situ measurements within the wetlands and littoral zone make very uncertain their role as a sink or a source of CO2 to the atmosphere. We will first present a vast research project “La Rochelle Territoire Zéro Carbone” project (https://www.agglo-larochelle.fr/projet-de-territoire/territoire-zero-carbone), that target the ambition of carbon neutrality in La Rochelle metropolitan area by 2040 through a holistic approach (from measuring CO2 to raising people's awareness and assessing the impact of exogenous natural factors). Second, we will present some first results on the Blue Carbon dynamics within the freshwater and salt marshes, and seagrasses.

Published
2022

3. A Machine-Learning Approach to Intertidal Mudflat Mapping Combining Multispectral Reflectance and Geomorphology from UAV-Based Monitoring.

Author

Brunier, Guillaume, Oiry, Simon, Lachaussée, Nicolas, Barillé, Laurent, Le Fouest, Vincent, and Méléder, Vona

Subjects
TIDAL flats, MACHINE learning, GEOMORPHOLOGY, DIGITAL elevation models, DRONE aircraft, REFLECTANCE
Abstract

Remote sensing is a relevant method to map inaccessible areas, such as intertidal mudflats. However, image classification is challenging due to spectral similarity between microphytobenthos and oyster reefs. Because these elements are strongly related to local geomorphic features, including biogenic structures, a new mapping method has been developed to overcome the current obstacles. This method is based on unmanned aerial vehicles (UAV), RGB, and multispectral (four bands: green, red, red-edge, and near-infrared) surveys that combine high spatial resolution (e.g., 5 cm pixel), geomorphic mapping, and machine learning random forest (RF) classification. A mudflat on the Atlantic coast of France (Marennes-Oléron bay) was surveyed based on this method and by using the structure from motion (SfM) photogrammetric approach to produce orthophotographs and digital surface models (DSM). Eight classes of mudflat surface based on indexes, such as NDVI and spectral bands normalised to NIR, were identified either on the whole image (i.e., standard RF classification) or after segmentation into five geomorphic units mapped from DSM (i.e., geomorphic-based RF classification). The classification accuracy was higher with the geomorphic-based RF classification (93.12%) than with the standard RF classification (73.45%), showing the added value of combining topographic and radiometric data to map soft-bottom intertidal areas and the user-friendly potential of this method in applications to other ecosystems, such as wetlands or peatlands. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Potential Impact of Photoinhibition on Microphytobenthic Primary Production on a Large Intertidal Mudflat.

Author

Savelli, Raphaël, Serôdio, João, Cugier, Philippe, Méléder, Vona, Polsenaere, Pierre, Dupuy, Christine, and Le Fouest, Vincent

Subjects
MICROALGAE, ALGAE photoinhibition, TIDAL flats, INTERTIDAL ecology, BENTHOS
Abstract

Microphytobenthos (MPB) are a key primary producer of intertidal mudflats. MPB face strong variability in incident irradiance during low tides. Despite photoprotection and photoacclimation, such variations can translate into the photoinhibition of MPB cells. This study explores the effect of photoinhibition on MPB primary production (PP) over a large and productive temperate mudflat (Brouage mudflat, NW France). We used a regional and high‐resolution tri‐dimensional hydrodynamic model coupled to an MPB model with or without photoinhibition. Photoinhibition leads to a 20% (−0.79 × 103 t C) decrease of the simulated MPB PP over the entire mudflat. As the upper shore is exposed to light more frequently and longer than the lower shore, the decrease of MPB PP is higher on the upper shore (−29%) than on the lower shore (−5%). With the highest photosynthetically active radiation cumulated over the mudflat, the decrease of MPB PP due to photoinhibition is the highest during spring and spring tides (−22% and −23%, respectively). The model suggests MPB photoinhibition is sensitive to the photoacclimation status of MPB cells through the light saturation parameter. This first modeling attempt to account for MPB photoinhibition is highly constrained by our current theoretical knowledge and limitations on the MPB growth physiology, but it suggests that this process can have a substantial impact on the MPB PP. As such, assessing the MPB photosynthetic response to the highly variable environmental conditions that prevail in large and productive intertidal mudflats is a real challenge for quantifying MPB PP from a synoptic to inter‐annual time scale. Plain Language Summary: Benthic micro‐algae or microphytobenthos (MPB) inhabiting the surficial sediment sustain the high biological production of intertidal mudflats. MPB achieve photosynthesis by aggregating into a dense biofilm at the mud surface during daytime low tides. As MPB can be exposed to short‐term variations and high light levels, they change their short‐term physiology and position within the sediment to protect themselves. However, such strategies can be outbalanced by a too long stressful light exposure. In this study, we explore with a numerical model the impact of photoinhibition on MPB primary production (PP) over a large and very productive mudflat (NW France). The model suggests that photoinhibition can strongly impact MPB PP. With photoinhibition, the yearly PP decreases by 20% over the whole mudflat. The model suggests MPB PP is sensitive to the photoacclimation status of MPB cells, that is, their light use efficiency at a given light level. This first modeling attempt to account for MPB photoinhibition is highly constrained by our current theoretical knowledge and limitations on the MPB growth physiology, but it suggests that this process can have a substantial impact on the MPB PP. Key Points: With or without photoinhibition, simulated yearly MPB PP is higher on the upper and middle shores than on the lower shorePhotoinhibition leads to a 20% (−0.79×103 t C yr−1) decrease of MPB PP over the entire mudflatThe photoinhibition process in the model is sensitive to the photoacclimation status of MPB cells [ABSTRACT FROM AUTHOR]

Published
2021
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5. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Author

Lee, Younjoo J., Matrai, Patricia A., Friedrichs, Marjorie A.M., Saba, Vincent S., Aumount, Olivier, Babin, Marcel, Buitenhuis, Erik T., Chevallier, Matthieu, de Mora, Lee, Dessert, Morgane, Dunne, John P., Ellingsen, Ingrid H., Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorguess, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin G., Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe E., Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina E., Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles A., Tjiputra, Jerry, Tremblay, L. Bruno, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew, Zhang, Jinlun, Bigelow Laboratory for Ocean Sciences, Virginia Institute of Marine Science (VIMS), NOAA National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Takuvik Joint International Laboratory ULAVAL-CNRS, Université Laval [Québec] (ULaval)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), School of Environmental Sciences [Norwich], University of East Anglia [Norwich] (UEA), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory (PML), Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), SINTEF Fisheries and Aquaculture, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Scripps Institution of Oceanography (SIO - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Modelling the Earth Response to Multiple Anthropogenic Interactions and Dynamics (MERMAID), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, International Arctic Research Center (IARC), University of Alaska [Fairbanks] (UAF), National Oceanography Centre [Southampton] (NOC), University of Southampton, PaleoEnvironnements et PaleobioSphere (PEPS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Mercator Océan, Société Civile CNRS Ifremer IRD Météo-France SHOM, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), LIttoral ENvironnement et Sociétés (LIENSs), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Nansen Environmental and Remote Sensing Center [Bergen] (NERSC), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Department of Atmospheric and Oceanic Sciences [Montréal], McGill University = Université McGill [Montréal, Canada], Department of Oceanography [Cape Town], University of Cape Town, Applied Physics Laboratory [Seattle] (APL-UW), University of Washington [Seattle], Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Centre National de la Recherche Scientifique (CNRS)-Université Laval [Québec] (ULaval), Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory, Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Scripps Institution of Oceanography (SIO), University of California-University of California, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), LIttoral ENvironnement et Sociétés - UMRi 7266 (LIENSs), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), Naval Postgraduate School (U.S.), and Oceanography

Subjects
Arctic models underestimated net primary productivity (NPP) but overestimated nitrate, mixed layer depth, and euphotic depth, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, Arctic NPP model skill was greatest in low production regions, Arctic NPP model skill was constrained by different environmental factors in different Arctic Ocean regions, Article, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

The article of record as published may be found at http://dx.doi.org/10.1002/2016JC011993 Upon publication, the in situ data will be available for academic purposes through the NASA SeaWiFS Bio-optical Archive and Storage System (http:// seabass.gsfc.nasa.gov/), including NPP, NO3, and Zeu. The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling. National Aeronautics and Space Agency (NASA) Ocean Biology and Biogeochemistry (OBB) The project ‘‘Green Mercator’’ National Program CNRS/LEFE/INSU. NSF Office of Polar Programs FP7 MyOcean2 PAVE (Polish-Norwegian Research Program) Norwegian Supercomputing Project (NOTUR2) Research Council of Norway funded project ORGANIC NASA Cryosphere program CNRM-CM climate model Météo-France/DSI supercomputing Ocean Biology and Biogeochemistry (OBB) NNX13AE81G NSF Office of Polar Programs PLR- 1417925 NSF Office of Polar Programs PLR-1416920 FP7 MyOcean2 (project number 283367) Research Council of Norway funded project ORGANIC (239965/RU) NASA Cryosphere program (NNX15AG68G).

Published
2016

6. On biotic and abiotic drivers of the microphytobenthos seasonal cycle in a temperate intertidal mudflat: a modelling study.

Author

Savelli, Raphaël, Dupuy, Christine, Barillé, Laurent, Lerouxel, Astrid, Guizien, Katell, Philippe, Anne, Bocher, Pierrick, Polsenaere, Pierre, and Le Fouest, Vincent

Subjects
MARINE ecology, BIOMASS, PHYTOPLANKTON, MICROALGAE, BIOGEOCHEMICAL cycles
Abstract

Microphytobenthos (MPB) from intertidal mudflats are key primary producers at the land–ocean interface. MPB can be more productive than phytoplankton and sustain both benthic and pelagic higher trophic levels. The objective of this study is to assess the contribution of light, mud temperature, and gastropod Peringia ulvae grazing pressure in shaping the seasonal MPB dynamics on the Brouage mudflat (NW France). We use a physical–biological coupled model applied to the sediment first centimetre for the year 2008. The simulated data compare to observations, including time-coincident remotely sensed and in situ data. The model suggests an MPB annual cycle characterised by a main spring bloom, a biomass depression in summer, and a moderate fall bloom. In early spring, simulated photosynthetic rates are high due to mud surface temperature (MST) values close to the MPB temperature optimum for photosynthesis and because increasing solar irradiance triggers the onset of the MPB spring bloom. Simulated peaks of high P. ulvae grazing (11 days during which ingestion rates exceed the primary production rate) mostly contribute to the decline of the MPB bloom along with the temperature limitation for MPB growth. In late spring–summer, the MPB biomass depression is due to the combined effect of thermo-inhibition and a moderate but sustained grazing pressure. The model ability to infer biotic and abiotic mechanisms driving the seasonal MPB dynamics could open the door to a new assessment of the export flux of biogenic matter from the coast to the open ocean and, more generally, of the contribution of productive intertidal biofilms to the coastal carbon cycle. [ABSTRACT FROM AUTHOR]

Published
2018
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7. Towards an assessment of riverine dissolved organic carbon in surface waters of the western Arctic Ocean based on remote sensing and biogeochemical modeling.

Author

Le Fouest, Vincent, Matsuoka, Atsushi, Manizza, Manfredi, Shernetsky, Mona, Tremblay, Bruno, and Babin, Marcel

Subjects
CARBON content of seawater, CARBON compounds, REMOTE sensing, BIOGEOCHEMISTRY
Abstract

Future climate warming of the Arctic could potentially enhance the load of terrigenous dissolved organic carbon (tDOC) of Arctic rivers due to increased carbon mobilization within watersheds. A greater flux of tDOC might impact the biogeochemical processes of the coastal Arctic Ocean (AO) and ultimately its capacity to absorb atmospheric CO2. In this study, we show that sea-surface tDOC concentrations simulated by a physical--biogeochemical coupled model in the Canadian Beaufort Sea for 2003-2011 compare favorably with estimates retrieved by satellite imagery. Our results suggest that, over spring--summer, tDOC of riverine origin contributes to 35% of primary production and that an equivalent of ~10% of tDOC is exported westwards with the potential of fueling the biological production of the eastern Alaskan nearshore waters. The combination of model and satellite data provides promising results to extend this work to the entire AO so as to quantify, in conjunction with in situ data, the expected changes in tDOC fluxes and their potential impact on the AO biogeochemistry at basin scale. [ABSTRACT FROM AUTHOR]

Published
2018
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8. Towards an assessment of riverine dissolved organic carbon in surface waters of the Western Arctic Ocean based on remote sensing and biogeochemical modeling.

Author

Le Fouest, Vincent, Matsuoka, Atsushi, Manizza, Manfredi, Shernetsky, Mona, Tremblay, Bruno, and Babin, Marcel

Subjects
CARBON compounds, WATER, REMOTE sensing, BIOGEOCHEMISTRY, MARINE ecology
Abstract

Future climate warming of the Arctic could potentially enhance the load of riverine dissolved organic carbon (RDOC) of Arctic rivers due to increased carbon mobilization within watersheds. A greater flux of RDOC might thus impact the biogeochemical processes of the coastal Arctic Ocean (AO). In this study, we show that estimates of RDOC concentrations in the surface waters of the Canadian Beaufort Sea computed for 2003-2011 by both optical remote sensing and a physical-biogeochemical coupled model compare favorably. Our results suggest that, over spring-summer, RDOC contributes to 35% of primary production and that an equivalent of ∼ 10% of the riverine RDOC is exported westwards with a potential for fueling the biological production of the eastern Alaskan nearshore waters. The combination of model and satellite data can be extended to the entire AO to quantify the expected changes in RDOC fluxes and their potential impact on AO biogeochemistry. This is left for future work. [ABSTRACT FROM AUTHOR]

Published
2017
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10. On the role of tides and strong wind events in promoting summer primary production in the Barents Sea

Author

Le Fouest, Vincent, Postlethwaite, Clare, Morales Maqueda, Miguel Angel, Bélanger, Simon, and Babin, Marcel

Subjects
*TIDES, *WINDS, *SUMMER, *PRIMARY productivity (Biology), *OCEAN circulation, *PHYTOPLANKTON, *BIOLOGICAL models, *CLIMATE change
Abstract

Abstract: Tides and wind-driven mixing play a major role in promoting post-bloom productivity in subarctic shelf seas. Whether this is also true in the high Arctic remains unknown. This question is particularly relevant in a context of increasing Arctic Ocean stratification in response to global climatic change. We have used a three-dimensional ocean-sea ice-plankton ecosystem model to assess the contribution of tides and strong wind events to summer (June–August 2001) primary production in the Barents Sea. Tides are responsible for 20% (60% locally) of the post-bloom primary production above Svalbard Bank and east of the Kola Peninsula. By contrast, more than 9% of the primary production is due to winds faster than 8ms−1 in the central Barents Sea. Locally, this contribution reaches 25%. In the marginal ice zone, both tides and wind events have only a limited effect on primary production (<2%). Removing tides or winds faster than 8ms−1 promotes a regime more sustained by regenerated production with a f-ratio (i.e. the proportion of nitrate-based “new” primary production in the total primary production) that decreases by up to 26% (east of the Kola Peninsula) or 35% (central Barents Sea), respectively. When integrated over all Barents Sea sub-regions, tides and strong wind events account, respectively, for 6.8% (1.55TgC; 1TgC=1012 gC) and 4.1% (0.93TgC) of the post-bloom primary production (22.6TgC). To put this in context, this contribution to summer primary production is equivalent to the spring bloom integrated over the Svalbard area. Tides and winds are significant drivers of summer plankton productivity in the Barents Sea. [Copyright &y& Elsevier]

Published
2011
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11. Modeling the timing of spring phytoplankton bloom and biological production of the Gulf of St. Lawrence (Canada): Effects of colored dissolved organic matter and temperature

Author

Mei, Zhi-Ping, Saucier, François J., Le Fouest, Vincent, Zakardjian, Bruno, Sennville, Simon, Xie, Huixiang, and Starr, Michel

Subjects
*PHYTOPLANKTON, *BIOLOGICAL productivity, *ORGANIC compound content of seawater, *WATER temperature, *SIMULATION methods & models, *MARINE ecosystem management
Abstract

Abstract: The effects of colored dissolved organic matter (CDOM) from freshwater runoff and seasonal cycle of temperature on the dynamic of phytoplankton and zooplankton biomass and production in the Gulf of St. Lawrence (GSL) are studied using a 3-D coupled physical-plankton ecosystem model. Three simulations are conducted: (1) the reference simulation based on , in which light attenuation by CDOM is not considered and maximum growth rate () of phytoplankton and zooplankton are not temperature-dependent (REF simulation); (2) light attenuation by CDOM is added to REF simulation (CDOM simulation); and (3) in addition to CDOM, the of phytoplankton and zooplankton are regulated by temperature (CDOM+TEMP simulation). CDOM simulation shows that CDOM substantially reduces phytoplankton biomass and production in the Lower St. Lawrence Estuary (LSLE), but slightly reduces overall primary production in the GSL. In the LSLE, the spring phytoplankton bloom is delayed from mid-March to mid-April, resulted from light attenuation by CDOM. The CDOM+TEMP simulation shows that the spring phytoplankton bloom in the LSLE is further delayed to July, which is more consistent with observations. Annual primary production is reduced by 33% in CDOM+TEMP simulation from REF and CDOM simulations. Zooplankton production is the same in all three simulations, and export of organic matter to depth is reduced in CDOM+TEMP simulation, suggesting that temperature controlled growth of phytoplankton and zooplankton enhances the coupling between primary production and zooplankton production under the seasonal temperature cycle of the GSL. [ABSTRACT FROM AUTHOR]

Published
2010
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12. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models.

Author

Lee YJ, Matrai PA, Friedrichs MAM, Saba VS, Aumont O, Babin M, Buitenhuis ET, Chevallier M, de Mora L, Dessert M, Dunne JP, Ellingsen IH, Feldman D, Frouin R, Gehlen M, Gorgues T, Ilyina T, Jin M, John JG, Lawrence J, Manizza M, Menkes CE, Perruche C, Le Fouest V, Popova EE, Romanou A, Samuelsen A, Schwinger J, Séférian R, Stock CA, Tjiputra J, Tremblay LB, Ueyoshi K, Vichi M, Yool A, and Zhang J

Abstract

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO 3 ), mixed layer depth (MLD), euphotic layer depth (Z eu ), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO 3 , MLD, and Z eu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO 3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Published
2016
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