Search

Your search keyword '"Martinez, Elodie"' showing total 200 results
Start Over Author "Martinez, Elodie"
200 results on '"Martinez, Elodie"'

1. Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Author

Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J, Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G, Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R, Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sébastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima‐Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, Maria Grazia, Néou, Mario B, Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean‐Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, Remo, Speich, Sabrina, Sprovieri, Mario, Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban, Follows, Michael J, Grimsley, Nigel, Guidi, Lionel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B, Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, d'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, d'Alcalà, Maurizio Ribera, Wincker, Patrick, Bowler, Chris, Iudicone, Daniele, Gorsky, Gabriel, and Jaillon, Olivier

Subjects
Genetics, Life Below Water, Atmospheric Sciences, Geochemistry, Oceanography, Meteorology & Atmospheric Sciences
Abstract

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.

Published
2019

11. Contrasted Trends in Chlorophyll‐a Satellite Products.

Author

Pauthenet, Etienne, Martinez, Elodie, Gorgues, Thomas, Roussillon, Joana, Drumetz, Lucas, Fablet, Ronan, and Roux, Maïlys

Subjects
*CONVOLUTIONAL neural networks, *CARBON cycle, *MARINE biomass, *THEMATIC mapper satellite, *MACHINE learning, *MARINE phytoplankton, *ECOSYSTEMS, *MARINE ecology
Abstract

Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems. Plain Language Summary: This study investigates trends in surface concentration of chlorophyll‐a (Schl), a proxy of phytoplankton biomass in the ocean. Our study reveals different trends according to the considered satellite products, challenging previous assumptions relating some Schl negative trends to the anthropogenic signal. To investigate the reliability of these trends, a machine learning approach is applied, showing that the observed changes in Schl trends may not always be related to changes in some physical ocean properties but sometimes to sensor drift. These findings underscore the importance of considering multiple factors when interpreting Schl trends and highlight the potential of advanced statistical tools in understanding marine ecosystems. Key Points: An analysis of remotely sensed surface chlorophyll‐a concentration reveals contrasted trends between available merged productsA convolutional neural network can effectively reconstruct surface chlorophyll‐a concentration from the properties of the ocean surfaceRecent trends in surface chlorophyll‐a concentration merged products are not all supported by changes in properties of the ocean surface [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

12. Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.

Author

Martinez, Elodie, Gorgues, Thomas, Lengaigne, Matthieu, Sauzède, Raphaëlle, Menkes, Christophe, Uitz, Julia, Di Lorenzo, Emanuele, and Fablet, Ronan

Subjects
EL Nino, OCEAN color, SPATIO-temporal variation, CHLOROPHYLL in water, COLORIMETRY, MACHINE learning, OCEAN
Abstract

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979--2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

13. Adverse Events in Intensive Care and Continuing Care Units During Bed-Bath Procedures: The Prospective Observational NURSIng during critical carE (NURSIE) Study

Author

Decormeille, Guillaume, Maurer-Maouchi, Valerie, Mercier, Gwennaelle, Debock, Sylvie, Lebrun, Cindy, Rouhier, Maud, Martinez, Elodie, Lise Faure, Anne, Duviver, Julien, Hultet-Midelton, Samia, De Freitas Pereira, Francoise, Tuaillon, Catherine, Soulisse, Lea, Demont, Gregoire, Youssouf, Atika, Dauve, Virginie, Negrel, Julie, Sauvage, Brice, Morand, Celina, Pedrono, Kristell, L’Hotellier, Sylvie, Nicolas, Francoise, Robquin, Nadine, Michel, Philippe, Muller, Gregoire, Aissaoui, Nadia, Davide Barbar, Saber, Boissier, Florence, Grimaldi, David, Hraiech, Sami, Piton, Gael, Jacq, Gwenaelle, and Lascarrou, Jean Baptiste

Published
2021
Full Text
View/download PDF

14. A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers

Author

Roussillon, Joana, Fablet, Ronan, Gorgues, Thomas, Drumetz, Lucas, Littaye, Jean, Martinez, Elodie, Roussillon, Joana, Fablet, Ronan, Gorgues, Thomas, Drumetz, Lucas, Littaye, Jean, and Martinez, Elodie

Abstract

Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability of phytoplankton biomass (e.g. decadal variability). Machine learning models such as Support Vector Regression (SVR) or Multi-Layer Perceptron (MLP) have recently proven to be an alternative approach to mechanistic ones to reconstruct Chl synoptic past time-series before the satellite era from physical predictors. Nevertheless, the relationships between phytoplankton and its physical surrounding environment were implicitly considered homogeneous in space, and training such models on a global scale does not allow one to consider known regional mechanisms. Indeed, the global ocean is commonly partitioned into biogeochemical provinces (BGCPs) into which phytoplankton growth is supposed to be governed by regionally-”homogeneous” processes. The time-evolving nature of those provinces prevents imposing a priori spatially-fixed boundary constraints to restrict the learning phase. Here, we propose to use a multi-mode Convolutional Neural Network (CNN), which can spatially learn and combine different modes, to globally account for interregional variabilities. Each mode is associated with a CNN submodel, standing for a mode-specific response of phytoplankton biomass to the physical forcing. Beyond improving performance reconstruction, we show that the different modes appear regionally consistent with the ocean dynamics and that they may help to get new insights into physical-biogeochemical processes controlling phytoplankton spatio-temporal variability at global scale.

Published
2023
Full Text
View/download PDF

20. On the Fate of Floating Marine Debris Carried to the Sea through the Main Rivers of Indonesia

Author

Dobler, Delphine, Maes, Christophe, Martinez, Elodie, Rahmania, Rinny, Gautama, Budhi Gunadharma, Farhan, Aulia Riza, Dounias, Edmond, Dobler, Delphine, Maes, Christophe, Martinez, Elodie, Rahmania, Rinny, Gautama, Budhi Gunadharma, Farhan, Aulia Riza, and Dounias, Edmond

Abstract

Plastic debris has become an acute marine pollution concern worldwide in modern times. Indonesia is particularly impacted because of its high population density, heavy rainfall rate and numerous coastlines. A Lagrangian analysis was performed to simulate the fate of fictive marine debris drifting along surface currents, including tides and Stokes drift. The fictive particles were released according to the discharge rate of 21 Indonesian rivers and advected over 4 years. Most of the particles were stranded along Indonesian coasts (60%), before 6 months had elapsed (84%) and within a range of 1000 km (76%). The time variability exhibited two seasonal peaks, one centered on January-February and one on June-July, consistent with in situ observations. However, the results underline the complexity of performing direct comparisons between in situ observations and numerical simulations for stranded waste due to limited measurements and the heterogeneity of field methods and protocols.

Published
2022
Full Text
View/download PDF

24. Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean.

Author

Martinez, Elodie, Brini, Anouar, Gorgues, Thomas, Drumetz, Lucas, Roussillon, Joana, Tandeo, Pierre, Maze, Guillaume, and Fablet, Ronan

Subjects
*OCEAN color, *MACHINE learning, *MULTILAYER perceptrons, *CHLOROPHYLL in water, *PHYTOPLANKTON, *DEEP learning, *CARBON cycle
Abstract

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

26. Floating marine debris along Indonesian coasts : an atlas of strandings based on Lagrangian modelling

Author

Dobler, Delphine, Martinez, Elodie, Rahmania, R., Gautama, B.D., Riza Farhan, A., Maes, Christophe, Dounias, Edmond, HORIZON, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), and 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)

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, [SDV.EE] Life Sciences [q-bio]/Ecology, environment, OCEAN INDIEN, INDONESIE, PACIFIQUE, [SDU.STU.OC] Sciences of the Universe [physics]/Earth Sciences/Oceanography, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, MER DE CHINE
Abstract

The book Floating Marine Debris along Indonesian Coasts. An Atlas of Strandings based on Lagrangian Modelling was conceived within the framework of a collaborative research project conducted jointly by the Indonesian Ministry of Marine Affairs and Fisheries (KKP), Collecte Localisation Satellite (CLS), and the French National Research Institute for Sustainable Development (IRD), and funded, since early 2020, by the Agence Française de Développement (AFD). The project aims to sustain the monitoring and modelling of marine debris in the Indonesian seas and beyond. The final goal is to strengthen Indonesian agencies in their efforts to rise public awareness about marine pollution by plastics, and to implement adapted mitigation strategies. Due to their high durability and relatively low-cost production, plastics constitute by far the predominant fraction of solid waste products, a substantial proportion of which is eventually discharged into the oceans. Emissions and circulation of plastic and marine debris follow complex pathways that vary locally according to waste management, population density and hydrology, but the majority of litter runs off from rivers. A major priority is to understand the sources of floating plastic debris and their eventual beaching locations and to identify potential connectivity patterns at the regional and basin ocean scales. A Lagrangian assessment of the debris dispersion originating from 21 major rivers in Indonesia was carried out as a means to identify the concentration at sea, as well as the geographic and temporal distribution of stranding along Indonesian coastlines based on a state-of-art ocean modelling. The present atlas detailing the results of this assessment at the regional scales should be viewed as a first effort to establish the river connectivity implied in the marine dispersion of surface plastic debris in Indonesia.

Published
2021

28. Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Author

Martinez, Elodie, Brini, A., Gorgues, Thomas, Drumetz, L., Roussillon, J., Tandeo, P., Maeze, G., Fablet, R., 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)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Interdisciplinary Graduate School for the Blue planet, ANR-17-EURE-0015,ISBlue,Interdisciplinary Graduate School for the Blue planet(2017), 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), IMT Atlantique (IMT Atlantique), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique)

Subjects
ocean color, multi-layer perceptron, physical predictors, [SDE.IE]Environmental Sciences/Environmental Engineering, Science, [SDE]Environmental Sciences, phytoplankton time-series reconstruction, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], support vector regression, neural networks, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

International audience; Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP outperforms the SVR to capture satellite Chl (correlation of 0.6 vs. 0.17 on a global scale, respectively) along with its seasonal and interannual variability, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Published
2020
Full Text
View/download PDF

31. Adverse Events in Intensive Care and Continuing Care Units During Bed-Bath Procedures: The Prospective Observational NURSIng during critical carE (NURSIE) Study

Author

Decormeille, Guillaume, primary, Maurer-Maouchi, Valerie, additional, Mercier, Gwennaelle, additional, Debock, Sylvie, additional, Lebrun, Cindy, additional, Rouhier, Maud, additional, Martinez, Elodie, additional, Lise Faure, Anne, additional, Duviver, Julien, additional, Hultet-Midelton, Samia, additional, De Freitas Pereira, Francoise, additional, Tuaillon, Catherine, additional, Soulisse, Lea, additional, Demont, Gregoire, additional, Youssouf, Atika, additional, Dauve, Virginie, additional, Negrel, Julie, additional, Sauvage, Brice, additional, Morand, Celina, additional, Pedrono, Kristell, additional, L’Hotellier, Sylvie, additional, Nicolas, Francoise, additional, Robquin, Nadine, additional, Michel, Philippe, additional, Muller, Gregoire, additional, Aissaoui, Nadia, additional, Davide Barbar, Saber, additional, Boissier, Florence, additional, Grimaldi, David, additional, Hraiech, Sami, additional, Piton, Gael, additional, Jacq, Gwenaelle, additional, and Lascarrou, Jean Baptiste, additional

Published
2020
Full Text
View/download PDF

34. WORKSHOP ON AI FOR OCEAN, ATMOSPHERE AND CLIMATE SCIENCE, BREST, JANUARY 2020: ENVISIONED METHODOLOGICAL AND TOPICAL CHALLENGES AND ASSOCIATED ACTIONS *

Author

Fablet, Ronan, Ailliot, Pierre, Beauchamp, Maxime, Chapron, Bertrand, Cosme, Emmanuel, Debreu, Laurent, Drumetz, Lucas, Frézat, Hugo, Gallinari, Patrick, Gula, Jonathan, Sommer, Julien Le, Lguensat, Redouane, Memery, Laurent, Martinez, Elodie, Naveau, Philippe, Platzer, Paul, Pannekoucke, Olivier, Papadakis, Nicolas, Rousseau, François, Tissot, Gilles, and Ubelman, Clément

Published
2020
Full Text
View/download PDF

35. Monitoring the Influence of the Mesoscale Ocean Dynamics on Phytoplanktonic Plumes around the Marquesas Islands Using Multi-Satellite Missions

Author

Cassianides, Angelina, Martinez, Elodie, Maes, Christophe, Carton, Xavier, Gorgues, Thomas, Cassianides, Angelina, Martinez, Elodie, Maes, Christophe, Carton, Xavier, and Gorgues, Thomas

Abstract

The Marquesas islands are a place of strong phytoplanktonic enhancement, whose original mechanisms have not been explained yet. Several mechanisms such as current−bathymetry interactions or island run-off can fertilize waters in the immediate vicinity or downstream of the islands, allowing phytoplankton enhancement. Here, we took the opportunity of an oceanographic cruise carried out at the end of 2018, to combine in situ and satellite observations to investigate two phytoplanktonic blooms occurring north and south of the archipelago. First, Lagrangian diagnostics show that both chlorophyll-a concentrations (Chl) plumes are advected from the islands. Second, the use of Finite-size Lyaponov Exponent and frontogenesis diagnostics reveal how the Chl plumes are shaped by the passage of a mesoscale cyclonic eddy in the south and by a converging front and finer-scale dynamic activity in the north. Our results based on these observations provide clues to the hypothesis of a fertilization from the islands themselves allowing phytoplankton to thrive. They also highlight the role of advection to disperse and shape the Chl plumes in two regions with contrasting dynamical regimes

Published
2020
Full Text
View/download PDF

36. Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

Author

Martinez, Elodie, Gorgues, Thomas, Lengaigne, Matthieu, Fontana, Clement, Sauzède, Raphaëlle, Menkes, Christophe, Uitz, Julia, Di Lorenzo, Emanuele, Fablet, Ronan, Martinez, Elodie, Gorgues, Thomas, Lengaigne, Matthieu, Fontana, Clement, Sauzède, Raphaëlle, Menkes, Christophe, Uitz, Julia, Di Lorenzo, Emanuele, and Fablet, Ronan

Abstract

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demo

Published
2020
Full Text
View/download PDF

37. Plankton spatial variability within the Marquesas archipelago, South Pacific

Author

Martinez, Elodie, Rodier, Martine, Pagano, M., Sauzede, R., Martinez, Elodie, Rodier, Martine, Pagano, M., and Sauzede, R.

Abstract

The Marquesas Islands, in the central South Pacific, are a place of an outstanding phytoplankton enrichment visible from space and is a hotspot of endemism and biodiversity; however, it has been poorly studied. In situ physical-chemical-biological concomitant observations are almost non-existent and all located close to the main northern island, while ocean dynamics based on satellite observations and numerical modeling show contrasting north/south patterns within the archipelago. Thus, we took the opportunity of the Pakahi I te Moana cruise conducted in 2012 to collect hydrological and plankton samples and investigate biogeochemical spatial patterns, especially north/south, over the archipelago. These data provide the first description of the spatial and vertical distribution of physical, chemical and plankton characteristics over the entire Marquesas archipelago, and gave a first hint to improve our understanding of this planktonic enrichment. The whole archipelago appeared to be a macronutrient-rich environment. Different patterns in the physical, chemical and biological vertical distributions were observed between the northern vs. southern part of the archipelago, and offshore vs. nearshore stations. Phytoplankton biomasses were higher in the north where stratification was weaker, compared to the south and higher close to the islands than offshore. Phytoplankton all over the archipelago was largely dominated by picophytoplankton; specifically, Prochlorococcus presented a more widespread distribution than previously thought and were present over a large range of nutrient concentrations. Copepods were always the most abundant taxa in the archipelago but showed higher mean relative abundances near the islands. The importance of suspension-feeding zooplankton in the northern islands coincided with the highest biomasses of phytoplankton with a predominance of nano- and micro-phytoplankton. This was consistent with a young community responding to nutrient enrichment b

Published
2020
Full Text
View/download PDF

38. The Delayed Island Mass Effect: how islands can remotely trigger blooms in the oligotrophic ocean

Author

Messié, M., Petrenko, A., Doglioli, A.m., Aldebert, C., Martinez, Elodie, Koenig, G., Bonnet, S., Moutin, T., Messié, M., Petrenko, A., Doglioli, A.m., Aldebert, C., Martinez, Elodie, Koenig, G., Bonnet, S., and Moutin, T.

Abstract

In oligotrophic gyres of the tropical ocean, islands can enhance phytoplankton biomass and create hotspots of productivity and biodiversity. This “Island Mass Effect” (IME) is typically identified by increased chlorophyll concentrations next to an island. Here we use a simple plankton model in a Lagrangian framework to represent an unexplained open ocean bloom, demonstrating how islands could have triggered it remotely. This new type of IME, termed “delayed IME”, occurs when nitrate is limiting, N:P ratios are low, and excess phosphate and iron remain in water masses after an initial bloom associated to a “classical” IME. Nitrogen fixers then slowly utilize leftover phosphate and iron while water masses get advected away, resulting in a bloom decoupled in time (several weeks) and space (hundreds of km) from island‐driven nutrient supply. This study suggests that the fertilizing effect of islands on phytoplankton may have been largely underestimated. Plain language summary In the poor and nutrient‐depleted waters of the tropical Pacific, islands act as sources of nutrients fertilizing nearby waters. These nutrients are consumed by microscopic photosynthesizing algae, the phytoplankton. The resulting phytoplankton enrichments (blooms) in turn support productive ecosystems. This phenomenon, termed the “island mass effect”, has been known for sixty years and is classically defined by increased chlorophyll (representing phytoplankton biomass) next to an island. Blooms also occur in the open ocean and are usually attributed to vertical processes such as mixing or uplifting that locally supply nutrients from subsurface reservoirs. In this paper, we demonstrate that a different type of island mass effect exists, where the phytoplankton response is delayed because they grow very slowly. These blooms are supported by the nitrogen fixer Trichodesmium. Since phytoplankton get carried away from islands by oceanic currents while they grow, this can lead to a bloom located hundred

Published
2020
Full Text
View/download PDF

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

Author

Karp‐Boss, Lee, Wincker, Patrick, Kandels‐Lewis, Stefanie, Jaillon, Olivier, Iudicone, Daniele, Hingamp, Pascal, Grimsley, Nigel, Gorsky, Gabriel, Follows, Michael J., Vargas, Colomban, Bowler, Chris, Boss, Emmanuel, Bork, Peer, Acinas, Silvia G., Karsenti, Eric, Weissenbach, Jean, Velayoudon, Didier, Sunagawa, Shinichi, Sullivan, Matthew B., Stemmann, Lars, Speich, Sabrina, Sieracki, Mike, Sardet, Christian, Reynaud, Emmanuel G., Raes, Jeroen, Pesant, Stéphane, Ogata, Hiroyuki, Not, Fabrice, Krzic, Uros, Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J., Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R., Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sébastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima‐Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, Maria Grazia, Néou, Mario B., Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean‐Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, Remo, Sprovieri, Mario, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Searson, Sarah, Dimier, Céline, Picheral, Marc, Guidi, Lionel, Sordino, Paolo, Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, d'Ortenzio, Fabrizio, Testor, Pierre, d'Alcalà, Maurizio Ribera, Stazione Zoologica Anton Dohrn (SZN), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Combinatoire et Bioinformatique (LS2N - équipe COMBI), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-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)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Diversité et Interactions au sein du Plancton Océanique (DIPO), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] (ICM), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Istituto per la Microelettronica e Microsistemi [Catania] (IMM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Department of Oceanography and Coastal Sciences, Louisiana State University (LSU), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Instituto de Engenharia de Sistemas e Computadores (INESC), Departament de Genetica, Facultat de Biologia, Universitat de Barcelona (UB), Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), Information génomique et structurale (IGS), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Czech Academy of Sciences [Prague] (CAS), Laboratoire Microorganismes : Génome et Environnement (LMGE), Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Istituto per l'Ambiente Marino Costiero, Sezione Ecologia Marina Integrata, European Molecular Biology Laboratory [Heidelberg] (EMBL), National Cardiovascular Center, Biologie intégrative des organismes marins (BIOM), Observatoire océanologique de Banyuls (OOB), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Oceanography [Honolulu], University of Hawai‘i [Mānoa] (UHM), University of Maine, Department of Earth and Environment [Boston], Boston University [Boston] (BU), Department of Earth Ocean and Ecological Sciences [Liverpool], University of Liverpool, Variabilité de l'Océan et de la Glace de mer (VOG), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-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)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Data Publisher for Earth and Environmental Science (PANGAEA), University of Bremen, National Science Foundation [Arlington] (NSF), DVIPC, ANR-17-CE02-0014,CINNAMON,Analyse multi-échelle de l'adaptation à la carence en Fer chez un organisme clé du phytoplancton marin, dans un contexte de changement global(2017), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Combinatoire et Bioinformatique (COMBI), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-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)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Adaptation et diversité en milieu marin (AD2M), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lausanne (UNIL), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UMR237-Aix Marseille Université (AMU)-Avignon Université (AU), Consiglio Nazionale delle Ricerche [Roma] (CNR), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-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)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-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)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), ECOlogy of MArine Plankton (ECOMAP), Vrije Universiteit Brussel (VUB), Department of Structural Biology, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), UCL - SST/LIBST - Louvain Institute of Biomolecular Science and Technology, Gordon and Betty Moore Foundation, Fondation Prince Albert II de Monaco, National Science Foundation (US), Stazione Zoologica Anton Dohrn, National Fund for Scientific Research (Belgium), Centre National de la Recherche Scientifique (France), Région Bretagne, and JAMET, Jean-Louis

Subjects
0106 biological sciences, Atmospheric Science, PROCHLOROCOCCUS, 010504 meteorology & atmospheric sciences, Iron fertilization, species networks, DIVERSITY, PROTEIN, Oceanography, 01 natural sciences, CARBON, Settore BIO/13 - Biologia Applicata, oxidative stress, Meteorology & Atmospheric Sciences, Geosciences, Multidisciplinary, OXIDATIVE STRESS, ComputingMilieux_MISCELLANEOUS, GENE-EXPRESSION, General Environmental Science, Global and Planetary Change, LIMITATION, Ecology, IN-SITU, system biology, Geology, Plankton, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], iron response, meta-omics, MARINE-PHYTOPLANKTON, in-situ, limitation, Physical Sciences, Life Sciences & Biomedicine, Biogeochemical cycle, PACIFIC, Environmental Sciences & Ecology, Context (language use), prochlorococcus, Biology, Zooplankton, marine-phytoplankton, diversity, Atmospheric Sciences, Phytoplankton, pacific, Genetics, Environmental Chemistry, Ecosystem, 14. Life underwater, Life Below Water, 0105 earth and related environmental sciences, Science & Technology, 010604 marine biology & hydrobiology, carbon, fungi, Pelagic zone, gene-expression, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Geochemistry, Cardiovascular and Metabolic Diseases, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDE.BE]Environmental Sciences/Biodiversity and Ecology, protein, Environmental Sciences, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

29 pages, 9 figures, supporting information https://doi.org/10.1029/2018GB006022, 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, We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS (in particular Groupement de Recherche GDR3280, the Mission Pour l'Interdisciplinarité – Project MEGALODOM, and the Fédération de Recherche GO‐SEE FR2022), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, the French Government “Investissements d'Avenir” programs Oceanomics (ANR‐11‐BTBR‐0008), MEMO LIFE (ANR‐10‐LABX‐54), PSL* Research University (ANR‐11‐IDEX‐0001‐02), and FRANCE GENOMIQUE (ANR‐10‐INBS‐09), Fund for Scientific Research – Flanders, VIB, Stazione Zoologica Anton Dohrn, UNIMIB, ANR (projects “PHYTBACK/ANR‐2010‐1709‐01,” POSEIDON/ANR‐09‐BLAN‐0348, PROMETHEUS/ANR‐09‐PCS‐GENM‐217, TARA‐GIRUS/ANR‐09‐PCS‐GENM‐218, SAMOSA/ANR‐13‐ADAP‐0010, CINNAMON/ANR‐17‐CE02‐0014‐01), EU FP7 (MicroB3/No. 287589), ERC Advanced Grant Award (Diatomite: 294823), the LouisD foundation of the Institut de France, a Radcliffe Institute Fellowship from Harvard University to C. B., JSPS/MEXT KAKENHI (26430184, 16H06437, and 16KT0020), The Canon Foundation (203143100025), Gordon and Betty Moore Foundation (award #3790) and the US National Science Foundation (awards OCE#1536989 and OCE#1829831) to MBS, agnès b., the Veolia Environment Foundation, Region Bretagne, World Courier, Illumina, Cap L'Orient, the EDF Foundation EDF Diversiterre, FRB, the Prince Albert II de Monaco Foundation, Etienne Bourgois, the Fonds Français pour l'Environnement Mondial, the TARA schooner and its captain and crew.

Published
2019
Full Text
View/download PDF

40. Toward the Integrated Marine Debris Observing System

Author

Maximenko, Nikolai, Corradi, Paolo, Law, Kara Lavender, Van Sebille, Erik, Garaba, Shungudzemwoyo P., Lampitt, Richard Stephen, Galgani, Francois, Martinez-Vicente, Victor, Goddijn-Murphy, Lonneke, Veiga, Joana Mira, Thompson, Richard C., Maes, Christophe, Moller, Delwyn, Loscher, Carolin Regina, Addamo, Anna Maria, Lamson, Megan R., Centurioni, Luca R., Posth, Nicole R., Lumpkin, Rick, Vinci, Matteo, Martins, Ana Maria, Pieper, Catharina Diogo, Isobe, Atsuhiko, Hanke, Georg, Edwards, Margo, Chubarenko, Irina P., Rodriguez, Ernesto, Aliani, Stefano, Arias, Manuel, Asner, Gregory P., Brosich, Alberto, Carlton, James T., Chao, Yi, Cook, Anna-Marie, Cundy, Andrew B., Galloway, Tamara S., Giorgetti, Alessandra, Goni, Gustavo Jorge, Guichoux, Yann, Haram, Linsey E., Hardesty, Britta Denise, Holdsworth, Neil, Lebreton, Laurent, Leslie, Heather A., Macadam-Somer, Ilan, Mace, Thomas, Manuel, Mark, Marsh, Robert, Martinez, Elodie, Mayor, Daniel J., Le Moigne, Morgan, Jack, Maria Eugenia Molina, Mowlem, Matt Charles, Obbard, Rachel W., Pabortsava, Katsiaryna, Robberson, Bill, Rotaru, Amelia-Elena, Ruiz, Gregory M., Teresa Spedicato, Maria, Thiel, Martin, Turra, Alexander, Wilcox, Chris, Sub Physical Oceanography, Marine and Atmospheric Research, European Research Council, Sub Physical Oceanography, and Marine and Atmospheric Research

Subjects
0106 biological sciences, LITTER, Marine litter, 010504 meteorology & atmospheric sciences, lcsh:QH1-199.5, Microplastics, marine debris, sensor development, Oceanography, 01 natural sciences, NILE RED, maritime safety, Marine debris, Citizen science, SENSORS, Marine & Freshwater Biology, lcsh:Science, Seabed, Water Science and Technology, Global and Planetary Change, geography.geographical_feature_category, NORTH, FLOW-CYTOMETRY, ecosystemstressors, Plastic pollution, REMOTE, plastics, Life Sciences & Biomedicine, Antropogenic debris, ecosystem stressors, Ocean Engineering, Environmental Sciences & Ecology, Aquatic Science, PELAGIC SARGASSUM, lcsh:General. Including nature conservation, geographical distribution, Marine debris drift, observing network, Sea ice, 14. Life underwater, SDG 14 - Life Below Water, 0105 earth and related environmental sciences, Remote sensing, Observing network design, Shore, geography, Science & Technology, Buoy, MICROPLASTICS, 010604 marine biology & hydrobiology, Ocean current, PLASTIC DEBRIS, Debris, 13. Climate action, SEA-FLOOR, Environmental science, lcsh:Q, Environmental Sciences
Abstract

Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS) that is required to provide long-term monitoring of the state of this anthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi- and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multi-disciplinary research and serve a diverse community of users.

Published
2019
Full Text
View/download PDF

42. Influence of Tropical Instability Waves on Phytoplankton Biomass near the Marquesas Islands

Author

Martinez, Elodie, Raapoto, Hirohiti, Maes, Christophe, Maamaatuaihutapu, Keitapu, Laboratoire Terre-Ocean, Université de la Polynésie Française (UPF), 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)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Géopôle du Pacifique Sud (GePaSUD), and 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)

Subjects
tropical instability waves, La Niña, Science, chlorophyll-a concentration variability, satellite observations, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, island mass effect, La Nina, Marquesas islands, oceanography, ocean colour
Abstract

The Marquesas form an isolated group of small islands in the Central South Pacific where quasi-permanent biological activity is observed. During La Niña events, this biological activity, shown by a net increase of chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), is particularly strong. It has been hypothesized that this strong activity is due to iron-rich waters advected from the equatorial region to the Marquesas by tropical instability waves (TIWs). Here we investigate this hypothesis over 18 years by combining satellite observations, re-analyses of ocean data, and Lagrangian diagnostics. Four La Niña events ranging from moderate to strong intensity occurred during this period, and our results show that the Chl plume within the archipelago can be indeed influenced by such equatorial advection, but this was observed during the strong 1998 and 2010 La Niña conditions only. Chl spatio-temporal patterns during the occurrence of other TIWs rather suggest the interaction of large-scale forcing events such as an uplift of the thermocline or the enhancement of coastal upwelling induced by the tropical strengthening of the trades with the islands leading to enhancement of phytoplankton biomass within the surface waters. Overall, whatever the conditions, our analyses suggest that the influence of the TIWs is to disperse, stir, and, therefore, modulate the shape of the existing phytoplankton plume.

Published
2018
Full Text
View/download PDF

43. Role of Iron in the Marquesas Island Mass Effect

Author

Raapoto, Hirohiti, primary, Martinez, Elodie, additional, Petrenko, Anne, additional, Doglioli, Andrea, additional, Gorgues, Thomas, additional, Sauzède, Raphaëlle, additional, Maamaatuaiahutapu, Keitapu, additional, Maes, Christophe, additional, Menkes, Christophe, additional, and Lefèvre, Jérôme, additional

Published
2019
Full Text
View/download PDF

44. Role of Iron in the Marquesas Island Mass Effect

Author

Raapoto, Hirohiti, Martinez, Elodie, Petrenko, Anne, Doglioli, Andrea, Gorgues, Thomas, Sauzède, Raphaëlle, Maamaatuaiahutapu, Keitapu, Maes, Christophe, Menkes, Christophe, Lefèvre, Jérôme, Raapoto, Hirohiti, Martinez, Elodie, Petrenko, Anne, Doglioli, Andrea, Gorgues, Thomas, Sauzède, Raphaëlle, Maamaatuaiahutapu, Keitapu, Maes, Christophe, Menkes, Christophe, and Lefèvre, Jérôme

Abstract

A remarkable chlorophyll‐a concentration (Chl, a proxy of phytoplankton biomass) plume can be noticed on remotely sensed ocean color observations at the boundary separating the equatorial mesotrophic from the subtropical oligotrophic waters in the central South Pacific Ocean. This prominent biological feature is known as the island mass effect of the Marquesas archipelago. Waters surrounding these islands present high macronutrient concentrations but an iron depletion. In this study, the origin of Chl enhancement is investigated using a modeling approach. Four simulations based on identical physical and biogeochemical forcings but with different iron sources are conducted and analyzed. Only simulations considering an iron input from the island sediments present similar patterns (despite being too weak) of vertical and horizontal Chl distributions as compared to biogeochemical‐Argo profiling float and satellite observations. In addition, simulations with no other iron input than the boundary forcings reveal the relative importance of remote processes in modulating the seasonal pattern of Chl around the archipelago through horizontal advection of nutrient‐rich waters from the equator toward the archipelago and vertical mixing uplifting deep nutrient‐rich waters toward the upper lit layer.

Published
2019
Full Text
View/download PDF

45. Toward the Integrated Marine Debris Observing System

Author

Sub Physical Oceanography, Marine and Atmospheric Research, Maximenko, Nikolai, Corradi, Paolo, Law, Kara Lavender, Van Sebille, Erik, Garaba, Shungudzemwoyo P., Lampitt, Richard Stephen, Galgani, Francois, Martinez-Vicente, Victor, Goddijn-Murphy, Lonneke, Veiga, Joana Mira, Thompson, Richard C., Maes, Christophe, Moller, Delwyn, Loscher, Carolin Regina, Addamo, Anna Maria, Lamson, Megan R., Centurioni, Luca R., Posth, Nicole R., Lumpkin, Rick, Vinci, Matteo, Martins, Ana Maria, Pieper, Catharina Diogo, Isobe, Atsuhiko, Hanke, Georg, Edwards, Margo, Chubarenko, Irina P., Rodriguez, Ernesto, Aliani, Stefano, Arias, Manuel, Asner, Gregory P., Brosich, Alberto, Carlton, James T., Chao, Yi, Cook, Anna-Marie, Cundy, Andrew B., Galloway, Tamara S., Giorgetti, Alessandra, Goni, Gustavo Jorge, Guichoux, Yann, Haram, Linsey E., Hardesty, Britta Denise, Holdsworth, Neil, Lebreton, Laurent, Leslie, Heather A., Macadam-Somer, Ilan, Mace, Thomas, Manuel, Mark, Marsh, Robert, Martinez, Elodie, Mayor, Daniel J., Le Moigne, Morgan, Jack, Maria Eugenia Molina, Mowlem, Matt Charles, Obbard, Rachel W., Pabortsava, Katsiaryna, Robberson, Bill, Rotaru, Amelia-Elena, Ruiz, Gregory M., Teresa Spedicato, Maria, Thiel, Martin, Turra, Alexander, Wilcox, Chris, Sub Physical Oceanography, Marine and Atmospheric Research, Maximenko, Nikolai, Corradi, Paolo, Law, Kara Lavender, Van Sebille, Erik, Garaba, Shungudzemwoyo P., Lampitt, Richard Stephen, Galgani, Francois, Martinez-Vicente, Victor, Goddijn-Murphy, Lonneke, Veiga, Joana Mira, Thompson, Richard C., Maes, Christophe, Moller, Delwyn, Loscher, Carolin Regina, Addamo, Anna Maria, Lamson, Megan R., Centurioni, Luca R., Posth, Nicole R., Lumpkin, Rick, Vinci, Matteo, Martins, Ana Maria, Pieper, Catharina Diogo, Isobe, Atsuhiko, Hanke, Georg, Edwards, Margo, Chubarenko, Irina P., Rodriguez, Ernesto, Aliani, Stefano, Arias, Manuel, Asner, Gregory P., Brosich, Alberto, Carlton, James T., Chao, Yi, Cook, Anna-Marie, Cundy, Andrew B., Galloway, Tamara S., Giorgetti, Alessandra, Goni, Gustavo Jorge, Guichoux, Yann, Haram, Linsey E., Hardesty, Britta Denise, Holdsworth, Neil, Lebreton, Laurent, Leslie, Heather A., Macadam-Somer, Ilan, Mace, Thomas, Manuel, Mark, Marsh, Robert, Martinez, Elodie, Mayor, Daniel J., Le Moigne, Morgan, Jack, Maria Eugenia Molina, Mowlem, Matt Charles, Obbard, Rachel W., Pabortsava, Katsiaryna, Robberson, Bill, Rotaru, Amelia-Elena, Ruiz, Gregory M., Teresa Spedicato, Maria, Thiel, Martin, Turra, Alexander, and Wilcox, Chris

Published
2019

46. Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Author

UCL - SST/LIBST - Louvain Institute of Biomolecular Science and Technology, Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J., Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G., Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R., Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sébastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, Maria Grazia, Néou, Mario B., Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean‐Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, Remo, Speich, Sabrina, Sprovieri, Mario, Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban, Follows, Michael J., Grimsley, Nigel, Guidi, Lionel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B., Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, d'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, d'Alcalà, Maurizio Ribera, Wincker, Patrick, Bowler, Chris, Iudicone, Daniele, Gorsky, Gabriel, Jaillon, Olivier, Karp‐Boss, Lee, Krzic, Uros, Ogata, Hiroyuki, Pesant, Stéphane, Raes, Jeroen, Reynaud, Emmanuel G., Sardet, Christian, Sieracki, Mike, Velayoudon, Didier, Weissenbach, Jean, UCL - SST/LIBST - Louvain Institute of Biomolecular Science and Technology, Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J., Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G., Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R., Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sébastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, Maria Grazia, Néou, Mario B., Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean‐Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, Remo, Speich, Sabrina, Sprovieri, Mario, Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban, Follows, Michael J., Grimsley, Nigel, Guidi, Lionel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B., Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, d'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, d'Alcalà, Maurizio Ribera, Wincker, Patrick, Bowler, Chris, Iudicone, Daniele, Gorsky, Gabriel, Jaillon, Olivier, Karp‐Boss, Lee, Krzic, Uros, Ogata, Hiroyuki, Pesant, Stéphane, Raes, Jeroen, Reynaud, Emmanuel G., Sardet, Christian, Sieracki, Mike, Velayoudon, Didier, and Weissenbach, Jean

Abstract

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. © 2019. American Geophysical Union. All Rights Reserved.

Published
2019

47. Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Author

Gordon and Betty Moore Foundation, Fondation Prince Albert II de Monaco, National Science Foundation (US), Stazione Zoologica Anton Dohrn, National Fund for Scientific Research (Belgium), Centre National de la Recherche Scientifique (France), Région Bretagne, Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J., Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G., Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R., Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sebastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima-Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, M. G., Néou, Mario B., Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean-Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, R., Speich, Sabrina, Sprovieri, M., Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban de, Follows, Michael J., Grimsley, Nigel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B., Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, D'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, Ribera d’Alcalà, Maurizio, Wincker, Patrick, Bowler, Chris, Guidi, Lionel, Iudicone, Daniele, Gordon and Betty Moore Foundation, Fondation Prince Albert II de Monaco, National Science Foundation (US), Stazione Zoologica Anton Dohrn, National Fund for Scientific Research (Belgium), Centre National de la Recherche Scientifique (France), Région Bretagne, Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J., Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G., Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R., Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sebastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima-Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, M. G., Néou, Mario B., Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean-Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, R., Speich, Sabrina, Sprovieri, M., Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban de, Follows, Michael J., Grimsley, Nigel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B., Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, D'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, Ribera d’Alcalà, Maurizio, Wincker, Patrick, Bowler, Chris, Guidi, Lionel, and Iudicone, Daniele

Abstract

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

Published
2019

48. Space-time variability of albacore (Thunnus alalunga) in French Polynesia

Author

Raapoto, Hirohiti, Maamaatuaiahutapu, Keitapu, MARTINEZ, Elodie, Ponsonnet, Cedric, Benson-Rumiz, Alicia, Géopôle du Pacifique Sud (GePaSUD), Université de la Polynésie Française (UPF), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.BA] Life Sciences [q-bio]/Animal biology, [SDV.BA]Life Sciences [q-bio]/Animal biology, [SDV.SA.STP]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery, French Polynesia, [SDV.SA.STP] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery, Space-time variability, Thunnus alalunga, Albacore, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

49. Contrasted Trends in Chlorophyll‐aSatellite Products

Author

Pauthenet, Etienne, Martinez, Elodie, Gorgues, Thomas, Roussillon, Joana, Drumetz, Lucas, Fablet, Ronan, and Roux, Maïlys

Abstract

Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐aconcentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems. This study investigates trends in surface concentration of chlorophyll‐a(Schl), a proxy of phytoplankton biomass in the ocean. Our study reveals different trends according to the considered satellite products, challenging previous assumptions relating some Schl negative trends to the anthropogenic signal. To investigate the reliability of these trends, a machine learning approach is applied, showing that the observed changes in Schl trends may not always be related to changes in some physical ocean properties but sometimes to sensor drift. These findings underscore the importance of considering multiple factors when interpreting Schl trends and highlight the potential of advanced statistical tools in understanding marine ecosystems. An analysis of remotely sensed surface chlorophyll‐aconcentration reveals contrasted trends between available merged productsA convolutional neural network can effectively reconstruct surface chlorophyll‐aconcentration from the properties of the ocean surfaceRecent trends in surface chlorophyll‐aconcentration merged products are not all supported by changes in properties of the ocean surface An analysis of remotely sensed surface chlorophyll‐aconcentration reveals contrasted trends between available merged products A convolutional neural network can effectively reconstruct surface chlorophyll‐aconcentration from the properties of the ocean surface Recent trends in surface chlorophyll‐aconcentration merged products are not all supported by changes in properties of the ocean surface

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results