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.
Highlights
Benthic microalgae or microphytobenthos (MPB) inhabiting the sediment surface sustain a high biological production in intertidal mudflats (MacIntyre et al, 1996; Underwood and Kromkamp, 1999)
Combined with a novel space remote sensing approach to assess MPB Gross Primary Production (GPP), it allows for a first comparison of MPB GPP estimates derived from a remote sensing algorithm (GPP-algo) and a regional 3D physicalbiological coupled model
The coupling of the intertidal and pelagic domains in the regional 3D model could be envisaged in the future to assess the fate in the coastal ocean of fresh organic carbon resulting from MPB GPP
Summary
Benthic microalgae or microphytobenthos (MPB) inhabiting the sediment surface sustain a high biological production in intertidal mudflats (MacIntyre et al, 1996; Underwood and Kromkamp, 1999). The spatial and temporal distribution of MPB over mudflats is highly variable, as it is driven by highly variable physical [light, mud surface temperature (MST), tides, waves, and current] and biological (grazing, biostabilization, and bioturbation) conditions (e.g., Admiraal, 1984; Blanchard et al, 1996; MacIntyre et al, 1996; Underwood, 2001; Morris and Kromkamp, 2003; Sahan et al, 2007; Salleh and McMinn, 2011; Kwon et al, 2014; Orvain et al, 2014a,b; Savelli et al, 2019) Such a variability impedes any accurate and robust assessment of the role played by MPB at the scale of the whole mudflat ecosystem and of its contribution to the carbon cycle. Remote sensing and physical-biological coupled modeling are relevant and non-invasive approaches to infer on MPB dynamics (e.g., Guarini et al, 2000; Combe et al, 2005; van der Wal et al, 2010; Brito et al, 2013)
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