Coastal phytoplankton bloom dynamics in the Tyrrhenian Sea: Advantage of integrating in situ observations, large-scale analysis and forecast systems

https://doi.org/10.1016/j.jmarsys.2021.103528Get rights and content

Highlights

  • Best practices methods are provided for integration of multiplatform data streams .

  • Data streams are combined from L-term in situ measurements, CMEMS models and optical satellite data.

  • Combined data streams are used to examine phytoplankton interannual and spatial variability.

  • CMEMS model accuracy is assessed at local and coastal scales.

Abstract

Coastal systems represent the most dynamic natural systems on Earth, making their study particularly challenging. A holistic approach that integrates a set of monitoring tools for data collection (i.e., satellite imagery, numerical models and in situ observations) may provide different information about coastal ecosystems at different spatial and temporal scales. Of course, none of these tools are perfect, being that each is characterized by intrinsic errors and therefore specific uncertainties, which is also an important subject of investigation.

Long-term high-resolution in situ observations of the phytoplankton biomass at a coastal site (Civitavecchia, Tyrrhenian Sea), provided by the Civitavecchia Coastal Environment Monitoring System (C-CEMS) observational platform, are presented, discussed and integrated with data from the Copernicus Marine Environment Monitoring Services (CMEMS) for the Mediterranean Sea, which are generated by the MedBFM model system, and satellite observations from the CMEMS Ocean Colour database. The analysis of the time series of phytoplankton provided by in situ, satellite and model data show the typical dynamics of coastal temperate systems, which are characterized by spring and autumn blooms and significant interannual variability. The empirical orthogonal function (EOF) analysis highlights the consistency among the multiplatform datasets, whereas integrating the local in situ time series with a spatial analysis from model and satellite data provides information about the extent of coastal bloom phenomena and the relevance of the observation location with respect to surroundings. Our study of the dynamics of coastal blooms in the Civitavecchia coastal system allows us to propose a best practice framework that may be of general interest, and potentially applied to any multiplatform monitoring system (MPMS). The MPMS approach allows us to investigate the interannual variability over different horizontal and vertical scales, reflecting the variability in natural drivers (i.e., atmospheric forcings, coastal currents, upwelling, and land inputs), as typically expected in coastal areas.

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.

References (85)

  • B. Anselmi et al.

    Classificazione geomorfologica delle coste italiane come base per l’impostazione di studi sulla contaminazione marina

  • M. Astraldi et al.

    The seasonal characteristics of the circulation in the Tyrrhenian Sea

  • M. Astraldi et al.

    Climatic fluctuations, current variability and marine species distribution a case study in the Ligurian Sea (north-west Mediterranean)

    Oceanol. Acta

    (1995)
  • A. Bakun et al.

    Seasonal patterns of wind-induced upwelling/downwelling in the Mediterranean Sea

    Sci. Mar.

    (2001)
  • M. Bernhard et al.

    La distribuzione del Fitoplancton nel Mar Ligure

    Pubblicazioni Stazione Zoologica Naploli

    (1969)
  • H. Bjornsson et al.

    A manual for EOF and SVD analyses of climate data

  • S. Bonamano et al.

    The Civitavecchia Coastal Environment Monitoring System (C-CEMS) a new tool to analyse the conflicts between coastal pressures and sensitivity areas

    Ocean Sci.

    (2015)
  • S. Bonamano et al.

    Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy)

    Mar. Pollut. Bull.

    (2016)
  • A. Brondi et al.

    Analisi granulometriche e mineralogiche dei sedimenti fluviali e costieri del territorio italiano

    Boll. Soc. Geo. It.

    (1979)
  • F.L. Chiocci et al.

    Analisi sismostratigrafica della piattaforma continentale

  • E. Clementi et al.

    Coupling hydrodynamic and wave models first step and sensitivity experiments in the Mediterranean Sea

    Ocean Dyn.

    (2017)
  • J.E. Cloern

    Phytoplankton bloom dynamics in coastal ecosystems a review with some general lessons from sustained investigation of San Francisco bay, California

    Rev. Geophys.

    (1996)
  • J.E. Cloern et al.

    Patterns and scales of phytoplankton variability in estuarine–coastal ecosystems

    Estuar. Coasts

    (2010)
  • G. Cossarini et al.

    Downscaling experiment for the Venice lagoon. II. Effects of changes in precipitation on biogeochemical properties

    Clim. Res.

    (2008)
  • A. Crise et al.

    A conceptual framework for developing the next generation of marine OBservatories (MOBs) for science and society

    Front. Mar. Sci.

    (2018)
  • C.M. De Angelis

    Ciclo del Fitoplancton del Golfo di Napoli

    Boll. Pesca Piscic. Idrobio.

    (1956)
  • T.D. Dickey et al.

    Interdisciplinary oceanographic observations: the wave of the future

    Sci. Mar.

    (2005)
  • H.W. Ducklow et al.

    The biogeochemistry of carbon dioxide in the coastal oceans

  • A.J. Elliot

    Low frequency current variability off the West coast of Italy

    Oceanol. Acta

    (1981)
  • P.J.S. Franks

    Phytoplankton blooms at fronts fronts patterns, scales, and physical forcing mechanisms

    Rev. Aquat. Sci.

    (1992)
  • P.J.S. Franks et al.

    Plankton production in tidal fronts a model of Georges Bank in summer

    J. Mar. Res.

    (1996)
  • P.J.S. Franks et al.

    Plankton patches at fronts a model of formation and response to wind events

    J. Mar. Res.

    (1997)
  • J.P. Gattuso et al.

    Carbon and carbonate metabolism in coastal aquatic ecosystems

    Annu. Rev. Ecol. Evol. Syst.

    (1998)
  • G.C. Hays et al.

    Climate change and marine plankton

    TRENDS Ecol. Evol.

    (2005)
  • T.S. Hopkins

    Recent observation on the intermediate and deep water circulation in the southern Tyrrhenian Sea

    Oceanol. Acta

    (1988)
  • R. Iacono et al.

    Seasonal variability of the Tyrrhenian Sea surface geostrophic circulation as assessed by altimeter data

    J. Phys. Oceanogr.

    (2013)
  • M. Innamorati et al.

    Biomassa fitoplanctonica e condizioni idrologiche nell'Alto Tirreno Toscano

  • M. Innamorati et al.

    Il fitoplancton dell’alto Tirreno condizioni trofiche e produttive

  • T. Kiørboe et al.

    Effects of wind stress on vertical water column structure, phytoplankton growth, and productivity of planktonic copepods, in Trophic relationships in the marine environment

  • G.B. La Monica et al.

    Morfologia e sedimentologia della spiaggia e della piattaforma continentale interna

  • L. Lazzara et al.

    Pigmenti clorofilliani. Nova Thalassia

    (1990)
  • P. Lazzari et al.

    Seasonal and interannual variability of plankton chlorophyll and primary production in the Mediterranean Sea: a modelling approach

    Biogeosciences

    (2012)
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