Preprints
https://doi.org/10.5194/egusphere-2022-1421
https://doi.org/10.5194/egusphere-2022-1421
14 Dec 2022
 | 14 Dec 2022

Linking satellites to genes with machine learning to estimate major phytoplankton groups from space

Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler

Abstract. Ocean color remote sensing offers two decades-long time series of information on phytoplankton abundance. However, determining the structure of the phytoplankton community from this signal is not straightforward, and many uncertainties remain to be evaluated, despite multiple intercomparison efforts of the different available algorithms. Here, we use remote sensing and machine learning to infer the abundance of seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. Our dataset is to our knowledge the most comprehensive and complete, available to describe phytoplankton community structure at a global scale using a molecular marker that defines relative abundances of all phytoplankton groups simultaneously. The methodology shows satisfying performances to provide robust estimates of phytoplankton groups using satellite data, with few limitations regarding the global generalization of the method. Furthermore, this new satellite-based methodology allows a valuable global intercomparison with the pigment-based approach used in in-situ and satellite data to identify phytoplankton groups. Nevertheless, these datasets show different, yet coherent information on the phytoplankton, valuable for the understanding of community structure. This makes remote sensing observations excellent tools to collect Essential Biodiversity Variables and provide a foundation for developing marine biodiversity forecasts.

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Journal article(s) based on this preprint

21 Feb 2024
Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024,https://doi.org/10.5194/os-20-217-2024, 2024
Short summary
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1421', Anonymous Referee #1, 11 Feb 2023
    • AC1: 'Reply on RC1', Roy El Hourany, 10 Jun 2023
  • RC2: 'Comment on egusphere-2022-1421', Anonymous Referee #2, 19 Mar 2023
    • AC2: 'Reply on RC2', Roy El Hourany, 10 Jun 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1421', Anonymous Referee #1, 11 Feb 2023
    • AC1: 'Reply on RC1', Roy El Hourany, 10 Jun 2023
  • RC2: 'Comment on egusphere-2022-1421', Anonymous Referee #2, 19 Mar 2023
    • AC2: 'Reply on RC2', Roy El Hourany, 10 Jun 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Roy El Hourany on behalf of the Authors (11 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Jul 2023) by Jochen Wollschlaeger
RR by Anonymous Referee #3 (17 Sep 2023)
RR by Alison Chase (04 Oct 2023)
ED: Reconsider after major revisions (11 Oct 2023) by Jochen Wollschlaeger
AR by Roy El Hourany on behalf of the Authors (09 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (10 Nov 2023) by Jochen Wollschlaeger
RR by Anonymous Referee #3 (29 Nov 2023)
RR by Alison Chase (29 Nov 2023)
ED: Publish subject to technical corrections (05 Dec 2023) by Jochen Wollschlaeger
AR by Roy El Hourany on behalf of the Authors (15 Dec 2023)  Author's response   Manuscript 

Journal article(s) based on this preprint

21 Feb 2024
Linking satellites to genes with machine learning to estimate phytoplankton community structure from space
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024,https://doi.org/10.5194/os-20-217-2024, 2024
Short summary
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler

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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 provide a foundation to monitor the evolution of marine biodiversity.