Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-217-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-217-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2024
Research article |
| 21 Feb 2024
| 21 Feb 2024
Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
Roy El Hourany
CORRESPONDING AUTHOR
Univ. Littoral Côte d’Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Juan Pierella Karlusich
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
FAS Division of Science, Harvard University, Cambridge, MA, USA
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
Lucie Zinger
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
Naturalis Biodiversity Center, 2300 RA Leiden, the Netherlands
Hubert Loisel
Univ. Littoral Côte d’Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d’Océanologie et de Géosciences, 62930 Wimereux, France
Sorbonne Université, LOCEAN-IPSL, Laboratoire d'Océanographie et du Climat; Expérimentations et Approches Numériques, CNRS, IRD, MNHN, 75005 Paris, France
Chris Bowler
CORRESPONDING AUTHOR
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016 Paris, France
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André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Malcolm Taberner, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Yngve Borsheim, Astrid Bracher, Vittorio Brando, Elisabetta Canuti, Francisco Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Richard Crout, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Richard Gould, Stanford B. Hooker, Mati Kahru, Milton Kampel, Holger Klein, Susanne Kratzer, Raphael Kudela, Jesus Ledesma, Hubert Loisel, Patricia Matrai, David McKee, Brian G. Mitchell, Tiffany Moisan, Frank Muller-Karger, Leonie O'Dowd, Michael Ondrusek, Trevor Platt, Alex J. Poulton, Michel Repecaud, Thomas Schroeder, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Michael Twardowski, Vincenzo Vellucci, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Simon Wright, and Giuseppe Zibordi
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Zouhair Lachkar, Marina Lévy, and Shafer Smith
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Madhavan Girijakumari Keerthi, Matthieu Lengaigne, Marina Levy, Jerome Vialard, Vallivattathillam Parvathi, Clément de Boyer Montégut, Christian Ethé, Olivier Aumont, Iyyappan Suresh, Valiya Parambil Akhil, and Pillathu Moolayil Muraleedharan
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The northern Arabian Sea hosts a winter chlorophyll bloom, which exhibits strong interannual variability. The processes responsible for this interannual variation of the bloom are investigated using observations and a model. The interannual fluctuations of the winter bloom are largely related to the interannual mixed-layer depth (MLD) anomalies, which are driven by net heat flux anomalies. MLD controls the bloom amplitude through a modulation of nutrient turbulent fluxes into the mixed layer.
Parvathi Vallivattathillam, Suresh Iyyappan, Matthieu Lengaigne, Christian Ethé, Jérôme Vialard, Marina Levy, Neetu Suresh, Olivier Aumont, Laure Resplandy, Hema Naik, and Wajih Naqvi
Biogeosciences, 14, 1541–1559, https://doi.org/10.5194/bg-14-1541-2017, https://doi.org/10.5194/bg-14-1541-2017, 2017
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During late boreal summer and fall, the west coast of India (WCI) experiences hypoxia, which turns into anoxia during some years. We analyze a coupled physical–biogeochemical simulation over the 1960–2012 period to investigate the physical processes influencing oxycline interannual variability off the WCI. We show that fall WCI oxycline fluctuations are strongly related to Indian Ocean Dipole (IOD), with positive IODs preventing anoxia, while negative IODs do not necessarily result in anoxia.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Malcolm Taberner, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Vittorio Brando, Elisabetta Canuti, Francisco Chavez, Hervé Claustre, Richard Crout, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Richard Gould, Stanford Hooker, Mati Kahru, Holger Klein, Susanne Kratzer, Hubert Loisel, David McKee, Brian G. Mitchell, Tiffany Moisan, Frank Muller-Karger, Leonie O'Dowd, Michael Ondrusek, Alex J. Poulton, Michel Repecaud, Timothy Smyth, Heidi M. Sosik, Michael Twardowski, Kenneth Voss, Jeremy Werdell, Marcel Wernand, and Giuseppe Zibordi
Earth Syst. Sci. Data, 8, 235–252, https://doi.org/10.5194/essd-8-235-2016, https://doi.org/10.5194/essd-8-235-2016, 2016
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A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite data records. Here we describe the compilation of global bio-optical in situ data (spanning from 1997 to 2012) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
P. R. Renosh, F. G. Schmitt, and H. Loisel
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Intermittent dynamics of particle size distribution in coastal waters is studied. Particle sizes are separated into four size classes: silt, fine, coarse and macro particles. The time series of each size class is derived, and their multiscaling properties studied. Similar analysis has been done for suspended particulate matter and total volume concentration. All quantities display a nonlinear moment function and a negative Hurst scaling exponent.
Related subject area
Approach: Remote Sensing | Properties and processes: Biological oceanography and marine ecology
Analyses of sea surface chlorophyll a trends and variability from 1998 to 2020 in the German Bight (North Sea)
Biophysical coupling of seasonal chlorophyll-a bloom variations and phytoplankton assemblages across the Peninsula Front in the Bransfield Strait
Felipe de Luca Lopes de Amorim, Areti Balkoni, Vera Sidorenko, and Karen Helen Wiltshire
Ocean Sci., 20, 1247–1265, https://doi.org/10.5194/os-20-1247-2024, https://doi.org/10.5194/os-20-1247-2024, 2024
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We studied the increasing or decreasing of chlorophyll a abundance in the German Bight. Chlorophyll a is the pigment present in algae that allows them to capture energy from the sun and indicates both the growth of the algae and the health of the environment. Most of the German Bight has decreasing chlorophyll a concentration in the analysed period. In addition, about 45 % of the changes happening in chlorophyll a were connected with changes in temperature.
Marta Veny, Borja Aguiar-González, Ángeles Marrero-Díaz, Tania Pereira-Vázquez, and Ángel Rodríguez-Santana
Ocean Sci., 20, 389–415, https://doi.org/10.5194/os-20-389-2024, https://doi.org/10.5194/os-20-389-2024, 2024
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This study examines the seasonal patterns of chlorophyll-a (chl-a) blooms in the Bransfield Strait using remote sensing data supported by novel and historical in situ observations. Through satellite data we show that we can identify two distinct phytoplankton niches along a thermal front known as the Peninsula Front: the Transitional Bellingshausen Water and Transitional Weddell Water pools. These findings enable the first climatological description of the chl-a blooms in the Bransfield Strait.
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Short summary
Satellite observations offer valuable information on phytoplankton abundance and community structure. Here, we employ satellite observations to infer seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. The link has been established using machine learning approaches. The output of this work provides excellent tools to collect essential biodiversity variables and a foundation to monitor the evolution of marine biodiversity.
Satellite observations offer valuable information on phytoplankton abundance and community...
