Mapping the Intertidal Microphytobenthos Gross Primary Production, Part II: Merging Remote Sensing and Physical-Biological Coupled Modeling

Type Article
Date 2020-10
Language English
Author(s) Savelli Raphael1, Méléder Vona2, Cugier Philippe3, Polsenaere PierreORCID4, Dupuy Christine1, Lavaud Johann1, 5, Barnett Alexandre1, 2, Le Fouest Vincent1
Affiliation(s) 1 : LIttoral, ENvironnement et Sociétés (LIENSs), Université de La Rochelle, UMR 7266, CNRS-ULR, La Rochelle, France
2 : Mer Molécules Santé (MMS) - EA 21 60, Université de Nantes, Mer Molécules Santé, Nantes, France
3 : Département Dynamiques de l'Environnement Côtier, Laboratoire d'Ecologie Benthique, IFREMER, Plouzané, France
4 : IFREMER, Laboratoire Environnement et Ressources des Pertuis Charentais (LER-PC), BP133, La Tremblade, France
5 : Takuvik Joint International Laboratory UMI 3376, CNRS (France) & ULaval (Canada), Département de Biologie, Pavillon Alexandre-Vachon, Université Laval, Québec City, QC, Canada
Source Frontiers In Marine Science (2296-7745) (Frontiers Media SA), 2020-10 , Vol. 7 , N. 521 , P. 15p.
DOI 10.3389/fmars.2020.00521
Keyword(s) microphytobenthos, intertidal mudflat, gross primary production, remote sensing, physical-biological modeling
Abstract

Microphytobenthos (MPB) at the sediment surface of intertidal mudflats are known to show a high spatial and temporal variability in response to the biotic and abiotic conditions prevailing at the mud surface. It makes long-term and large-scale monitoring of MPB Gross Primary Production (GPP) difficult to set up. In this study, we developed the first 3D physical-biological coupled model (MARS-3D) that explicitly simulates GPP of intertidal MPB at the mudflat scale, and we compared the outputs with in situ and space remote sensing MPB GPP data. We discuss the sources of discrepancies between the modeling and the remote sensing approach in the light of future developments to be done. For instance, the remote sensing algorithm provides a very synoptic view of the mudflat GPP. It is well-suited to achieve diagnostic estimates of MPB GPP at the synoptic spatial and temporal scale. By contrast, the MARS-3D model provides a more dynamic representation of the MPB activity and prognostic estimates of MPB GPP over the mudflat. It is very relevant to resolve the seasonal and inter-annual dynamics of MPB. Getting comparable GPP estimates derived from the remote sensing algorithm and 3D physical-biological coupled model will further require a better convergence in terms of equations structure, biological constants parameterization, and source data used (i.e., vegetation index vs. chlorophyll a). Setting a common parameterization in both the numerical model and remote sensing algorithm might be challenging in a perspective of mapping MPB PP over large mudflats from a synoptic to inter-annual time scale, but it could open the door to a new way of quantifying MPB GPP over large intertidal mudflats.

Full Text
File Pages Size Access
Publisher's official version 15 4 MB Open access
Top of the page

How to cite 

Savelli Raphael, Méléder Vona, Cugier Philippe, Polsenaere Pierre, Dupuy Christine, Lavaud Johann, Barnett Alexandre, Le Fouest Vincent (2020). Mapping the Intertidal Microphytobenthos Gross Primary Production, Part II: Merging Remote Sensing and Physical-Biological Coupled Modeling. Frontiers In Marine Science, 7(521), 15p. Publisher's official version : https://doi.org/10.3389/fmars.2020.00521 , Open Access version : https://archimer.ifremer.fr/doc/00654/76612/