Linking satellites to genes with machine ...
Type de document :
Pré-publication ou Document de travail: Autre communication scientifique (congrès sans actes - poster - séminaire...)
Titre :
Linking satellites to genes with machine learning to estimate major phytoplankton groups from space
Auteur(s) :
El Hourany, Roy [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Pierella Karlusich, Juan [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Zinger, Lucie [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Loisel, Hubert [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Lévy, Marina [Auteur]
Processus et interactions de fine échelle océanique [PROTEO]
Bowler, Chris [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Pierella Karlusich, Juan [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Zinger, Lucie [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Loisel, Hubert [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Lévy, Marina [Auteur]
Processus et interactions de fine échelle océanique [PROTEO]
Bowler, Chris [Auteur]
Institut de biologie de l'ENS Paris [IBENS]
Résumé en anglais : [en]
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 ...
Lire la suite >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 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 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 satellitebased 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.Lire moins >
Lire la suite >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 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 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 satellitebased 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.Lire moins >
Langue :
Anglais
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