• Energy Research

Phytoplankton blooms are usually dominated by chain-forming diatom species that can alter food pathways from primary producers to predators by reducing the interactions between intermediate trophic levels. The food-web modifications are determined by the length of the chains; however, the estimation is biased because traditional sampling strategies damage the chains and, therefore, change the phytoplankton size structure. Sedimentological studies around oceanic fronts have shown high concentrations of giant diatom mats (>1 cm in length), suggesting that the size of diatom chains is underestimated in the pelagic realm. Here, we investigate the variability in size and abundance of phytoplankton chains at the Ushant tidal front (NW France) using the Video Fluorescence Analyzer (VFA), a novel and non-invasive system. CTD and Scanfish profiling characterized a strong temperature and chlorophyll front, separating mixed coastal waters from the oceanic-stratified domain. In order to elucidate spring-neap variations in the front, vertical microstructure profiler was used to estimate the turbulence and vertical nitrate flux. Key findings were: (1) the VFA system recorded large diatom chains up to 10.7 mm in length; (2) chains were mainly distributed in the frontal region, with maximum values above the pycnocline in coincidence with the maximum chlorophyll; (3) the diapycnal fluxes of nitrate enabled the maintenance of the bloom in the frontal area throughout the spring-neap tidal cycle; (4) from spring to neap tide the chains length was significantly reduced; (5) during neap tide, the less intense vertical diffusion of nutrients, as well as the lower turbulence around the chains, intensified nutrient-depleted conditions and, thus, very large chains became disadvantageous. To explain this pattern, we suggest that size plasticity is an important ecological trait driving phytoplankton species competition. Although this plasticity behavior is well known from experiments in the laboratory, it has never been reported from observations in the field.

This data publication provides two datasets of turbulent kinetic energy dissipation rates sampled during the MomarSat 2022 cruise. One dataset was gathered with a deep autonomous Vertical Microstructure Profiler (VMP-6000). The second dataset was gathered with the MicroRiYo mooring as described in the reference paper (Ferron et al. 2024). The two datasets, one for each instrument, are available as tar files. Each tar file contains fourteen NetCDF files. Each NetCDF file contains the dissipation rate profile, the time (UTC) of the profile start, the geographical position (deployment of the VMP or mooring position), and the mean pressure for each dissipation rate estimate (two estimates at each pressure level from the two shear sensors). Each dissipation rate comes with a quality control matrix QC (14 x 4) that characterizes how the associated mean shear spectrum fitted the expected theoretical Nasmyth spectrum: QC( 1:10, 1 ) : Value of the 10 criteria used (see reference paper) for the dissipation rates of shear 1. QC( 1:10, 2 ): Criteria met (=1) or not met (=0) for shear 1 dissipation rates. QC(11,1): Same criteria as QC(10,1) expressed in terms of mean absolute deviation (MAD) instead of variance (see Lueck et al. 2022) (shear 1). QC(11,2): state whether criteria QC(11,1) is met (=1) or not met (=0) (shear 1). QC(12,1): Number of shear spectra averaged to compute one dissipation rate estimate (shear 1). QC(12,2): Number of accelerometer used to remove vibrations (Goodman et al. 2006; Lueck et al. 2022; Ferron et al. 2023) (shear 1) QC(13,1): MAD (shear 1) QC(13,2): unused QC(14,1): index of first used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(14,2): index of last used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(:,3): same as QC(:,1) for shear 2. QC(:,4): same as QC(:,2) for shear 2. Shear data were processed following the processing flow chart of the Atomix SCOR Working Group 160 (https://wiki.app.uib.no/atomix/index.php?title=Flow_chart_for_shear_probes). References: Ferron, B., S. Leizour, M. Hamon, O. Peden, 2024: MicroRiYo : An observing system for deep repeated profiles of kinetic energy dissipation rates from shear-microstructure turbulence along a mooring line, submitted to J. Atmos. Ocean. Tech. Lueck, R. G., 2022: The Statistics of Oceanic Turbulence Measurements. Part II: Shear Spectra and a New Spectral Model. J. Atmos. Oceanic Technol., 39, 1273–1282, https://doi.org/10.1175/JTECH-D-21-0050.1.