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The effect of environmental change in structuring the phytoplankton communities of the coastal waters of the Eastern English Channel was investigated by applying a trait-based approach on two decades (1996-2019) of monitoring on diatoms and Phaeocystis. We show that phytoplankton species richness in an unbalanced nutrient supply context was influenced by wind-driven processes, ecological specialization for dissolved inorganic phosphorous, temporal niche differentiation, and a competition-defense and/or a growth-defense trade-off, a coexistence mechanism where weak competitors (i.e., slower growing) are better protected against predation. Under the influence of both environmental perturbations (e.g., wind-driven processes, freshwater influence, unbalanced nutrient levels) and biotic interactions (e.g., competition, predation, facilitation), phytoplankton species exhibited specific survival strategies such as investment on growth, adaptation and tolerance of species to environmental stresses, silicification and resource specialization. These strategies have led to more speciose communities, higher productivity, functional redundancy and stability in the last decade. Our results revealed that the unbalanced nutrient reduction facilitated Phaeocystis blooms and that anthropogenic climate warming and nitrate reduction may threaten the diatom communities of the eastern English Channel in a near future. Our results provide strong support for biogeographical historical and niche-based processes in structuring the phytoplankton community in this temperate region. The variety of species responses that we characterized in this region may help to better understand future changes in pelagic ecosystems, and can serve as a basis to consider functional approaches for future ecosystem management.

From a data set of observations of Suspended Particulate Matter (SPM) concentration, Turbidity in Formazin Turbidity Unit (FTU) and fluorescence-derived chlorophyll-a at a mooring station in Liverpool Bay, in the Irish Sea, we investigate the seasonal variation of the SPM:Turbidity ratio. This ratio changes from a value of around 1 in winter (minimum in January–February) to 2 in summer (maximum in May–June). This seasonal change can be understood in terms of the cycle of turbulence and of the phytoplankton population that affects the nature, shape and size of the particles responsible for the Turbidity. The data suggest a direct effect of phytoplankton on the SPM:Turbidity ratio during the spring bloom occurring in April and May and a delayed effect, likely due to aggregation of particles, in July and August. Based on the hypothesis that only SPM concentration varies, but not the mass-specific backscattering coefficient of particles bbp*, semi-analytical algorithms aiming at retrieving SPM from satellite radiance ignore the seasonal variability of bbp* which is likely to be inversely correlated to the SPM:Turbidity ratio. A simple sinusoidal modulation of the relationship between Turbidity and SPM with time helps to correct this effect at the location of the mooring. Without applying a seasonal modulation to bbp*, there is an underestimation of SPM in summer by the Ifremer semi-analytical algorithm (Gohin et al., 2015) we tested. SPM derived from this algorithm, as expected from any semi-analytical algorithm, appears to be more related to in situ Turbidity than to in situ SPM throughout the year.

Numerous ecological problems of continental shelf ecosystems require a refined knowledge of the evolution of suspended sediment concentrations (SSC). The present investigation focuses on the spatial and temporal variabilities of near-surface SSC in coastal waters of the English Channel (western Europe) by exploiting numerical predictions from the Regional Ocean Modeling System ROMS. Extending previous investigations of ROMS performances in the Channel, this analysis refines, with increased spatial and temporal resolutions, the characterization of near-surface SSC patterns revealing areas where concentrations are highly correlated with evolutions of tides and waves. Significant tidal modulations of near-surface concentrations are thus found in the eastern English Channel and the French Dover Strait while a pronounced influence of waves is exhibited in the Channel Islands Gulf. Coastal waters present furthermore strong SSC temporal variations, particularly noticeable during storm events of autumn and winter, with maximum near-surface concentrations exceeding 40 mg l−1 and increase by a factor from 10 to 18 in comparison with time-averaged concentrations. This temporal variability strongly depends on the granulometric distribution of suspended sediments characterized by local bi-modal contributions of silts and sands off coastal irregularities of the Isle of Wight, the Cotentin Peninsula and the southern Dover Strait.

The consistency of satellite and in situ time series of Chlorophyll-a (Chl-a), Turbidity and Total Suspended Matters (TSM) was investigated at 17 coastal stations throughout the year 2017. These stations covered different water types, from relatively clear waters in the Mediterranean Sea to moderately turbid regions in the Bay of Biscay and the southern bight of the North-Sea. Satellite retrievals were derived from MODIS/AQUA, VIIRS/NPP and OLCI-A/Sentinel-3 spectral reflectance. In situ data were obtained from the coastal phytoplankton networks SOMLIT (CNRS), REPHY (Ifremer) and associated networks. Satellite and in situ retrievals of the year 2017 were compared to the historical seasonal cycles and percentiles, 10 and 90, observed in situ. Regarding the sampling frequency in the Mediterranean Sea, a weekly in situ sampling allowed all major peaks in Chl-a caught from space to be recorded at sea, and, conversely, all in situ peaks were observed from space in a frequently cloud-free atmosphere. In waters of the Eastern English Channel, lower levels of Chl-a were observed, both in situ and from space, compared to the historical averages. However, despite a good overall agreement for low to moderate biomass, the satellite method, based on blue and green wavelengths, tends to provide elevated and variable Chl-a in a high biomass environment. Satellite-derived TSM and Turbidity were quite consistent with in situ measurements. Moreover, satellite retrievals of the water clarity parameters often showed a lower range of variability than their in situ counterparts did, being less scattered above and under the seasonal curves of percentiles 10 and 90.

The present study investigates the performances of the three-dimensional multicomponent hydro-sedimentary model ROMS (Regional Ocean Modeling System) to predict near-surface suspended sediment concentrations (SSC) in the English Channel (western Europe). Predictions are assessed against satellite-retrieved observations from raw MODIS and MERIS images for the year 2008 characterized by the highest availability of cloud-free data. Focus is put on improvements obtained with: (1) SSC inputs at the open boundaries; and (2) simple parameterizations of the settling velocity and the critical shear stress. Sensitivity studies confirm the importance of the advection of fine-grained suspended sediments in the central waters of the English Channel exhibiting benefits of refined SSC estimations along the sea boundaries. Improvements obtained with modified formulations of the settling velocity and the critical shear stress finally suggest possible seasonal influences of biological activity and thermal stratification on near-surface SSC.

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( H s and H s 50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( U b a r and V b a r ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— N R M S E of less than 16%) is the first step in demonstrating the robustness of the method.