Coastal phytoplankton bloom dynamics in the Tyrrhenian Sea: Advantage of integrating in situ observations, large-scale analysis and forecast systems
Graphical abstract
Introduction
Coastal systems are among the most dynamic natural systems, where chemical, physical and biological processes interact at different spatial and temporal scales (Walsh, 1988, Walsh, 1991; Gattuso et al., 1998; Liu et al., 2000; Ducklow and McCallister, 2005; Muller-Karger et al., 2005). Approximately 40% of the world's population lives in land-sea transition zones (Seibert et al., 2020), increasing human pressure, modifying the normal characteristics of coastal systems and causing a series of detrimental effects (e.g., eutrophication and harmful algal blooms). Quantification of the state of the marine environment (sensu MSFD; Oesterwind et al., 2016) is necessary to understand anthropogenic impacts and separate them from natural fluctuations (Hardman-Mountford et al., 2005; Allen et al., 2007).
The new and emerging ecosystem-based management method proposed by the Marine Strategy Framework Directive (2008/56/EC) suggests implementing the monitoring of coastal systems by using different observation platforms, which would provide information on the space-time distribution of major parameters related to water quality (Oinonen et al., 2016).
Following such an approach, we aim to understand the spatial and temporal distributions of phytoplankton biomasses in coastal waters to evaluate the evolution of phytoplankton dynamics in a coastal polluted area located in the northern Tyrrhenian Sea. Indeed, as also suggested by the MSFD (e.g., descriptors 1, 4 and 5), chlorophyll a concentrations and the consequent phytoplankton blooms enable us to understand the ecosystem status (Hays et al., 2005; Cloern and Jassby, 2010).
It is well known that phytoplankton growth and the related increase in biomass (i.e., blooms) are controlled by light energy availability and nutrient supply (Cloern, 1996; Lévy et al., 1998). Moreover, phytoplankton growth is strictly related to the physical forcing (Legendre and Demers, 1984; Kiørboe and Nielsen, 1990) that drives coastal current and runoff dynamics, which will influence primary producer succession in marine environments (Margalef, 1978; Lennert-Cody and Franks, 2002). In coastal waters, environmental forcings (e.g., wind, rain, rivers, waves, and tides) present the highest variability, and phytoplankton growth strictly depends on this variability (Franks and Walstad, 1997), making an understanding of phytoplankton dynamics extremely difficult to obtain.
The focus of this work is twofold: first, we are interested in analysing the phytoplankton bloom dynamics of the Civitavecchia coastal ecosystem by adopting a multiplatform approach that integrates the Copernicus Marine Environment Monitoring Services (CMEMS) products and the C-CEMS (Civitavecchia Coastal Environment Monitoring System; Bonamano et al., 2016) in situ data. Second, we aim to propose best practices to integrate multiplatform data streams that may also be adopted in other similar contexts of coastal ecosystems.
CMEMS is part of the EU Copernicus programme services (www.copernicus.eu) and is devoted to the monitoring and forecasting of the marine environment, operationally providing free-access, standardized, quality-validated data and information on ocean state (physics, biogeochemistry and sea ice). CMEMS products are available to users through a digital catalogue (http://marine.copernicus.eu/services-portfolio/access-to-products/), which includes near real time and reprocessed observations from satellite and reanalysis, analysis and short-term forecasts from global and regional models. An exhaustive description of the CMEMS architecture and further details on the service can be found in Le Traon et al. (2019).
Integrating multi-sensor and multiplatform data streams requires a preliminary analysis of their consistency with respect to the spatial and temporal scales of the investigated dynamics. As a reference, in Mozetič et al. (2010), satellite and in situ chlorophyll are compared for consistency at several representative stations before investigating the chlorophyll multidecadal trend in the Northern Adriatic Sea.
The paper is organized as follows: Section 2 describes the study area and the multiplatform datasets, and Section 3 shows the results concerning phytoplankton dynamics. A discussion is provided in Section 4, and concluding remarks are provided in Section 5.
Section snippets
Study area
The coastal area of Civitavecchia (Fig. 1c) is in the northeastern part of the Tyrrhenian Sea (Fig. 1a and b). The Tyrrhenian Sea is affected by mesoscale circulation and significant seasonal variability (Hopkins, 1988; Pinardi and Navarra, 1993; Marcelli et al., 2005; Vetrano et al., 2010; Poulain et al., 2012; Iacono et al., 2013; de la Vara et al., 2019) and can be considered the most isolated basin in the western Mediterranean (Astraldi and Gasparini, 1994) due to its morphodynamic
Physical and biological characterization of the study area
Sea temperature vertical profile (Fig. 2) time series show definite seasonal variations characterized by cold and vertically mixed water columns, from mid-autumn to late spring, and thermally stratified water columns during the summer months. Both the duration and the intensity of mixing and stratification vary among different years. The lowest temperature occurs during the 2015 and 2017 winter seasons (14 °C), while during the 2016 winter, the water column presents a temperature higher than
Impact of external forcing on phytoplankton annual cycles
In marine process studies, the knowledge of a phenomenon depends on observations, which are usually extremely complex because of the intrinsic difficulty of the type of measurements that have to be made (Crise et al., 2018). These issues are specifically urgent and strategic for coastal areas that present extremely high spatial-temporal variability. In this work, we aim to overcome the difficulties of the approach based on a limited set of observational tools by applying a multiplatform
Conclusions
The analysis of the time series of phytoplankton provided by in situ, satellite and model data for the Civitavecchia MPMS shows the typical dynamics of the coastal temperate systems, which are characterized by spring and autumn blooms and significant interannual variability. Following our data integration approach, the EOF analysis has extensively shown consistency among the multiplatform datasets. Notwithstanding the incongruences related to model representativeness error (i.e., river nutrient
Declaration of Competing Interest
None.
Acknowledgements
This study has been conducted using EU Copernicus Marine Service Information. The authors thank to the Civitavecchia Port Authority for funding the implementation of C-CEMS, and in particular to Calogero Burgio, Giorgio Fersini and Maurizio Marini. We would like to thank Dr. Alberto Pierattini for data acquisition, Dr. Cristiano Melchiorri for data analysis and the scientific illustrator Dr. Matteo Oliverio for graphical abstract and Fig. 8.
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