Microplastics (MPs) are a contaminant of growing concern due to
their widespread distribution and interactions with marine species, such as
filter feeders. To investigate the MPs accumulation in wild and cultured
mussels, a dynamic energy budget (DEB) model was developed and validated
with the available field data of Mytilus edulis (M. edulis, wild) from the North Sea and Mytilus galloprovincialis (M. galloprovincialis,
cultured) from the northern Ionian Sea. Towards a generic DEB model, the
site-specific model parameter, half-saturation coefficient (Xk), was
applied as a power function of food density for the cultured mussel, while
for the wild mussel it was calibrated to a constant value. The
DEB-accumulation model simulated the uptake and excretion rate of MPs,
taking into account environmental characteristics (temperature and
chlorophyll a). An accumulation of MPs equal to 0.53 particles per individual (fresh tissue mass 1.9 g) and 0.91 particles per individual (fresh tissue mass 3.3 g) was simulated for the wild and
cultured mussel after 4 and 1 years respectively, in agreement with the
field data. The inverse experiments investigating the depuration time of the
wild and cultured mussel in a clean-from-MPs environment showed a 90 %
removal of MPs load after 2.5 and 12 d respectively. Furthermore,
sensitivity tests on model parameters and forcing functions highlighted that
besides MPs concentration, the accumulation is highly dependent on
temperature and chlorophyll a of the surrounding environment. For this
reason, an empirical equation was found, directly relating the environmental
concentration of MPs, with the seawater temperature, chlorophyll a, and the
mussel's soft tissue MPs load.
Introduction
Microplastic (MP) particles are synthetic organic polymers with size below
5 mm (Arthur et al., 2009) that originate from a variety of sources
including the following: those particles that are manufactured for particular household or
industrial activities, such as facial scrubs, toothpastes, and resin pellets
used in the plastic industry (primary MPs), and those formed from the
fragmentation of larger plastic items (secondary MPs) (GESAMP, 2015).
Eriksen et al. (2014) estimated that more than 5 trillion microplastic
particles, weighing over 250 000 t, float in the oceans. Due to their
composition, density, and shape, MPs are highly persistent in the environment
and are, therefore, accumulating at different rates in different marine compartments: surface and deeper layers in the water column, as well as
at the seafloor and within the sediments (Moore et al., 2001; Lattin et al.,
2004; Thompson, 2004; Lusher, 2015). Since the majority of MPs entering the
marine environment originate from the land (i.e. landfills, littering of
beaches and coastal areas, rivers, floodwaters, untreated municipal
sewerage, and industrial emissions), the threat of MPs pollution in the coastal
zone puts considerable pressure on the coastal ecosystems (Cole et al.,
2011; Andrady, 2011). In recent years, initiatives under various projects
(i.e. CLAIM, DeFishGear) aim at evaluating the threat and impact of
marine litter pollution; the European framework of JERICO-RI focuses on a
sustainable research infrastructure in the coastal area to support the
monitoring, science, and management of coastal marine areas
(http://www.jerico-ri.eu/, last access: 27 July 2020). In the framework of JERICO-NEXT, a recent study
addressed the environmental threats and gaps with monitoring programmes in
European coastal waters, including the marine litter (i.e. MPs), as one of
the most commonly identified threats to the marine environment and
highlighted the need for improved monitoring of the MPs distribution and
their impacts in European coastal environments (Painting et al., 2020).
Numerous studies have revealed that MPs are ingested either directly or
through lower trophic prey by animals at all levels of the food web – from
zooplankton (Cole et al., 2013), small pelagic fish, and mussels (Digka et
al., 2018a) to mesopelagic fish (Wieczorek et al., 2018) and large predators
like tuna and swordfish (Romeo et al., 2015). Microplastic ingestion by
marine animals can potentially affect animal health and raises toxicity
concerns, since plastics can facilitate the transfer of chemical additives
and/or hydrophobic organic contaminants to biota (Mato et al., 2001; Rios et
al., 2007; Teuten et al., 2007, 2009; Hirai et al., 2011). Humans, as top
predators, are also contaminated by MPs (Schwabl et al., 2019). Mussels and
small fish that are commonly consumed whole, without removing digestive
tracts, where MPs are concentrated, are among the most likely pathways for
MPs to embed in the human diet (Smith et al., 2018). Especially regarding
marine organisms (i.e. mussels), it is notable that the levels of their
contamination have been added to the European database
(http://www.ecsafeseafooddbase.eu, last access: 27 July 2020) as an environmental variable of growing concern,
reflecting the health status (Marine Strategy Framework Directive, MSFD,
Descriptor 10 – Marine Litter; Decision 2017/848/EU) (De Witte et al.,
2014; Vandermeersch et al., 2015; Digka et al., 2018a). Today, a series of
studies have noted the presence of MPs in mussels' tissue intended for
human consumption (Van Cauwenberghe and Janssen, 2014; Mathalon and Hill,
2014; Li et al., 2016, 2018; Hantoro et al., 2019). For instance, in a
recent study, Li et al. (2018) sampled mussels from coastal waters and
supermarkets in the UK and estimated that a plate of 100 g mussels contains
70 MPs that will be ingested by the consumer. The presence of MPs in mussels
has been also demonstrated during laboratory trials in their faeces,
intestinal tract (Von Moos et al., 2012; Van Cauwenberghe et al., 2015;
Wegner et al., 2012; Khan and Prezant, 2018), and
circulatory system (Browne et al., 2008). Other laboratory studies showed
several effects of microplastic ingestion in laboratory-exposed mussels,
including histological changes, inflammatory responses, immunological
alterations, lysosomal membrane destabilization, reduced filtering activity,
neurotoxic effects, oxidative stress effects, increase in haemocyte
mortality, dysplasia, genotoxicity, and transcriptional responses (reviewed
by Li et al., 2019). However, the tested concentrations of MPs in laboratory
experiments are frequently unrealistic, being several orders of magnitude
higher (2 to 7 orders of magnitude) than the observed seawater
concentrations (Van Cauwenberge et al., 2015; Lenz et al., 2016).
Mussels, through their extensive filtering activity, feed on planktonic
organisms that have a similar size to MPs (Browne et al., 2007), and
considering also their inability to select particles with high energy value
(i.e. phytoplankton) during filtration (Vahl, 1972; Saraiva et al., 2011a),
they are directly exposed to MPs contamination. Recent studies suggest a
positive linear correlation between MPs concentration in mussels and
surrounding waters (Capolupo et al., 2018; Qu et al., 2018; Li et al., 2019).
The filtering activity of mussels, which directly affects the resulting MPs
accumulation, is a complicated process that is controlled by other factors
(food availability, temperature, tides etc.).
The purpose of the present work is to study the accumulation of MPs in
mussels and reveal relations between the accumulated concentrations in
mussels' soft parts and environmental features. In this context, an
accumulation model was developed based on dynamic energy budget theory (DEB;
Kooijman, 2000) and applied in two different regions, in two different
modes of life (wild and cultivated): in the North Sea (Mytilus edulis (M. edulis), wild) and in the
northern Ionian Sea (Mytilus galloprovincialis (M. galloprovincialis), cultivated). DEB theory provides all the necessary
detail to model the feeding processes and aspects of the mussel metabolism,
taking into account the impact of the environmental variability on the
simulated individual. Apart from modelling the growth of bivalves (Rosland
et al., 2009; Sarà et al., 2012; Thomas et al., 2011; Saraiva et al., 2012;
Hatzonikolakis et al., 2017; Monaco and McQuaid, 2018), DEB models have
been used to study other processes as well, such as bioaccumulation of PCBs
(polychlorinated biphenyls) and POPs (persistent organic compounds)
(Zaldivar, 2008), trace metals (Casas and Bacher, 2006), and the impact of
climate change on individual's physiology (Sarà et al., 2014). However, to
our knowledge this is the first time that a DEB-based model is used to
assess the uptake and excretion rates of MPs in mussels.
Materials and methodsStudy areas and field data
The North Sea is a marginal sea on the continental shelf of north-western
Europe with a total surface area of 850 000 km2 and is bounded by the
coastlines of nine countries. The sea is shallow (mean depth 90 m), getting
deeper towards the north (up to 725 m), and the semidiurnal tide (tidal
range 0–5 m) is the dominant feature of the region (Otto et al., 1990).
Major rivers, such as the Rhine, Elbe, Weser, Ems, and Thames discharge into the
southern part of the sea (Lacroix et al., 2004), making this area a
productive ecosystem. In this study, the area is limited to along the French,
Belgian, and Dutch North Sea coast (50.98–51.46∘ N,
1.75–3.54∘ W). This is located close to harbours, where shipping,
industrial, and agricultural activity is high, putting considerable pressure
on the ecological systems of the region (Van Cauwenberghe et al., 2015).
The MPs concentration in mussels' tissue and seawater that were used to
validate and force the model respectively at its North Sea implementation
were derived from Van Cauwenberghe et al. (2015). Van Cauwenberghe et al. (2015) examined the presence of MPs in wild mussels (M. edulis) and thus collected
both biota and water at six sampling stations along the French, Belgian, and
Dutch North Sea coast in late summer of 2011. M. edulis (mean shell length: 4±0.5 cm and wet weight (w.w.): 2±0.7 g) and water samples were
randomly collected in the local breakwaters, in order to assess the MPs
concentration in the organisms and their habitat. MPs were present in all
analysed samples, both organisms and water. Seawater samples (N=12) had
MPs (<1 mm) on average 0.4±0.3 particles L-1 (range:
0.0–0.8 particles L-1), and M. edulis contained on average 0.2±0.3 particles g-1 w.w. (or 0.4±0.3 particles per individual) (Van
Cauwenberghe et al., 2015). The size range of MPs found within the mussels
was 20–90 µm.
The northern Ionian Sea is located in the transition zone between the
Adriatic and Ionian Sea. The long and complex coastline presents a high
diversity of hydrodynamic and sedimentary features. Rivers discharging into
the northern Ionian Sea include Kalamas/Thyamis (Greece) and Butrinto
(Albania) (Skoulikidis et al., 2009; Vlachogianni et al., 2017), making the area suitable for
aquaculture. Small farming sites and shellfish grounds are operating in
Thesprotia (northwestern Ionian Sea) (Theodorou et al., 2011). The main
source of marine litter inputs in the area originates from anthropogenic
activities that mainly include shoreline tourism and recreational
activities, poor wastewater management, agriculture, fisheries, aquacultures,
and shipping (Vlachogianni et al., 2017; Digka et al., 2018a). According to
Politikos et al. (2020), the area around the Corfu island (northern Ionian
Sea) is characterized as a retention area of litter particles probably due
to the prevailing weak coastal circulation. Furthermore, a northward current
on the east Ionian Sea facilitates the transfer of litter particles towards
the Adriatic Sea, which has been characterized as a hotspot of marine litter
and one of the most affected areas in the Mediterranean Sea (Pasquini et
al., 2016; Vlachogianni et al., 2017; Liubartseva et al., 2018; Politikos et
al., 2020).
The field data used to validate the model output in the N Ionian Sea were
obtained from Digka et al. (2018a, b). In the framework of the
“DeFishGear” project, mussels (M. galloprovincialis) were collected by hand from a longline-type mussel culture farm in Thesprotia (39.606567∘ N, 20.149421∘ E), in summer 2015 (end of June) at a sampling depth up to 3 m (Digka et
al., 2018a). The average MPs accumulation was calculated from a total
population of 40 mussels originated from the farm, with 18 of them were found to be
contaminated with MPs (46.25 %). The average load of MPs (size
<1 mm) per mussel (mean shell length 5.0±0.3 cm) was 0.9±0.2 particles per individual, and the size of MPs found in the
mussel's tissue ranged from 55 to 620 µm. Both clean and contaminated
mussels were included in the calculated mean value in order to represent the
mean state of the contamination level for the individual inhabiting the
study area.
The seawater concentration of MPs for the N Ionian Sea implementation was
obtained from Digka et al. (2018b) and the DeFishGear project results
(http://www.defishgear.net/project/main-lines-of-activities, last access: 27 July 2020). In total, 12
manta net tows were conducted in the region, collecting a total number of
n1=2027 particles on October 2014 and n2=1332 on April 2015, leading
to an average of 280 particles per tow with size <1 mm and
>330µm (Digka et al., 2018b). In order to estimate the
mean MPs concentration in the region, expressed as particles per volume, the
dimensions of the manta net (W: 60 cm, H: 24 cm, rectangular frame opening;
mesh size 330 µm) and the sampling distance of each tow
(∼2 km) were used by multiplying the sample surface of the
net by the trawled distance in metres (Maes et al., 2017), which resulted in
a mean MPs concentration of 1.17 particles m-3 (233 333 particles km-2). Moreover, in the wider region of the Adriatic Sea, Zeri et al. (2018) found a mean density of 315 009±568 578 particles km-2 (1.58±2.84 particles m-3), out of which 34 % were sized
<1 mm. A relatively high value of standard deviation (1 order of
magnitude higher than the mean value) is adopted (0.0012±0.024 particles L-1), considering that the mussel farm is established in an
enclosed gulf and close to the coast, since, according to Zeri et al. (2018), the abundance of MPs is 1 order of magnitude higher in inshore
(<4 km) compared to offshore waters (>4 km).
Furthermore, it may be assumed that the adopted range (standard deviation is
also multiplied by a factor of 2) includes also the smaller particles sized
between 50 and <330µm, which have been found in
mussel tissue (Digka et al., 2018a) but were overlooked during the
seawater sampling due to the manta net's mesh size (>330µm). According to Enders et al. (2015) the relative abundance of small
particles (50–300 µm) compared to particles larger than 300 µm is
approximately 50 %.
DEB model description
In the present study, a DEB (Kooijman, 2000, 2010) model is used as basis to
simulate the accumulation of MPs by mussels. In DEB theory (Kooijman, 2000),
the energy assimilated through food by the simulated individual is stored in
a reserve compartment from where a fixed energy fraction κ is
allocated for growth and somatic maintenance, with a priority for
maintenance. The remaining energy (1-κ) is spent on maturity
maintenance and reproduction. The individual's condition is defined by the
dynamics of three state variables: energy reserves E (joules), structural
volume V (cm3), and energy allocated to reproduction R (joules). The
energy flow through the organism is controlled by the fluctuations of the
available food density and temperature characterizing the surrounding environment.
The DEB model implemented here is an extended version of the model described
in Hatzonikolakis et al. (2017), where the growth of the Mediterranean
mussel is simulated taking into account only the assimilation rate of the
individual. Since the present study focuses on the MPs accumulation, it is
crucial to include a detailed representation of the mussel's feeding
mechanism. In this context, the DEB model was extended by including the
clearance (Cr), filtration (p´XiF), and ingestion (p´XiI) rates of the mussel, following Saraiva et al. (2011a), assuming
that all parameters referred to as silt (or inedible particles) are applicable
also to MP particles. In this approach, a pre-ingestive selection occurs
between filtration and ingestion, returning the rejected material in the
water through pseudofaeces (Jpfi). Consequently, energy is assimilated
through food, and the non-assimilated particles are excreted through the
faeces production (Jf). The model's equations, variables, and parameters
are shown in Tables 1, 2, and 3 respectively. The scaled functional response
f (Eq. 5, Table 1), which regulates the assimilation rate, is modified
following Kooijman (2006) to include an inorganic term representing the
non-digestible matter, i.e. microplastics: f=X/X+Ky and Ky=XK⋅(1+Y/YK), where Y and Yk are the
concentration of MPs, converted from
particles per litre to grams per cubic metre
(Everaert et al., 2018) and the half-saturation coefficient of inorganic
particles here represented by MPs (g m-3) respectively. Thus, the
assimilation rate that is regulated by f is decreasing when the concentration
of MPs is increased. The same approach is followed by other authors who
considered inedible particles in the mussel's diet (Ren, 2009; Troost et
al., 2010). During the filtration process the same clearance rate for all
particles is used (C˙R), representing the same
searching rate for food that depends on the organism maximum capacity
(C˙Rm) and environmental particle concentrations
(Vahl, 1972; Widdows et al., 1979; Cucci et al., 1989). During the ingestion
process the mussel is able to selectively ingest food particles and reject
inedible material, in order to increase the organic content of the ingested
material (Kiørboe and Møhlenberg, 1981; Jørgensen et al., 1990;
Prins et al., 1991; Maire et al., 2007; Ren, 2009; Saraiva et al., 2011a).
This selection is reflected by the different binding probabilities adopted
for each type of particle (ρ1 for algae particles and ρ2 for inorganic particles, i.e. MPs; see Eq. 14 and Table 3). The
equations representing the feeding processes handle each type of particle
separately, while there is interference between the simultaneous handling of
different particle types (Eqs. 12–14, Table 1) (Saraiva et al., 2011a).
Finally, during the assimilation process, suspended matter (i.e. MPs) that
the mussel is not able to assimilate due to its different chemical
composition from the reserve compartment (Saraiva et al., 2011a) or
incipient saturation at high algal concentrations (Riisgård et al., 2011)
results in the faeces production (Eq. 16, Table 1).
Dynamic energy budget model: equations.
See Table 2 for model variables, Table 3 for parameters, and Table 5 for initial values.
dEdt=p´a-p´c(1)dVdt=k⋅p´c-p´M⋅VEg(2)dRdt=1-k⋅p´c-1-kk⋅minV,Vp⋅p´M(3)p´a=p´Am⋅f⋅kT⋅V23(4)f=XX+Ky, where Ky=XK⋅(1+YYK)(5)p´c=EEg+k⋅E⋅Eg⋅p´Am⋅kT⋅V23Em+p´M⋅V(6)E=EV(7)p´M=kT⋅p´Mm(8)kT=expTATI-TAT1+expTALT-TALTL+expTAHTH-TAHT(9)L=V13δm(10)W=d⋅V+EEg+RμE(11)C´R=C´Rm1+∑inXi⋅C˙Rmp˙XiFm⋅k(T)⋅V23,i=1 for CHLa2 for MPs(12)*p´XiF=C´R⋅Xi(13)*p´XiI=ρXi⋅p´XiF1+∑inρXi⋅p´XiFp˙XiIm(14)*p´pfi=p´XiF-p´XiI(15)*f´f=p´X1I-p´A(16)GSI=RμEd⋅V+EEg+RμE(17)
* Notation refers to feeding equations handling each type of suspended
matter separately (i=1 for algae and i=2 for microplastics) where unit
transformation is applied when it is necessary (see Table 3).
Microplastics accumulation submodel
With the DEB model as a basis, a submodel describing the MPs accumulation
by the mussel was developed, assuming that the presence of MPs in the
ambient water does not cause a significant adverse effect on the organisms'
overall energy budget, in accordance with laboratory experiments, conducted
in mussel species (Van Cauwenberghe et al., 2015, for Mytilus edulis; Santana et al., 2018, for the mussel Perna perna). Additionally, it was assumed that the mussel filtrates MPs present
in the water, without the ability of selecting between the high-energy-valued particles and the MPs during the filtration process (Van Cauwenberghe
et al., 2015; Von Moos et al., 2012; Browne et al., 2008; Digka et al.,
2018a among others). The uptake of MPs from the environment is taken into
account through the process of clearance/filtration rate, while the
excretion of the contaminant is derived from two processes: (i) pseudofaeces
production and (ii) faeces production. The resulting MPs accumulation is
influenced by external environmental factors (MPs concentration, food
availability, temperature) and internal biological processes (clearance,
filtration, ingestion, growth). The following differential equation
describes the change of the individual MPs accumulation (C, particles per individual), taking into account the processes mentioned above:
dCdt=Cenv⋅C´R-J´pf2-kf⋅J´fpX1I⋅C,
where C´R is the clearance rate for water (L h-1), containing a
concentration of MPs Cenv (particles L-1). The terms of
J´pf2 and J´fpX1I represent the elimination rate of MPs through pseudofaeces
(particles h-1) and the nondimensional rate of faeces production with
respect to the ingestion rate respectively (see Table 1, Eqs. 15–16). The
parameter kf represents the post-ingestive selection mechanism utilized
by the mussel to incorporate indigestible material (i.e. MPs) into faeces
and was calibrated using the available field data of mussel MPs
accumulation from both study areas (Table 3). A mussel is able to discriminate
among particles in the gut based on size, density and chemical properties of
the particles (i.e. between microalgae and inorganic material) and thus to
eliminate them through faeces (Ward et al., 2019a, and references therein).
In this context, the pseudofaeces production incorporates the rejected MPs
prior to the ingestion, while the faeces production includes MPs that are
rejected along with the food particles that are not assimilated by the
mussel. The model's time step has been set to 1 h in order to capture
the dynamics of the rapidly changing processes, such as feeding and
excretion.
Dynamic energy budget model: variables
VariableDescriptionUnitsVStructural volumecm3EEnergy reservesJREnergy allocated to development and reproductionJCMicroplastics accumulationparticles per individualp´aAssimilation energy rateJ d-1p´cUtilization energy rateJ d-1C´RClearance ratem3 d-1CenvMicroplastics concentrationparticles L-1p´XiFFiltration rateJ d-1 or g d-1p´XiIIngestion rateJ d-1 or g d-1J´pfiPseudofaeces production rateJ d-1 or g d-1J´fFaeces production rateJ d-1fFunctional response function–XiFood or MPs densitymg m-3 or g m-3p´MMaintenance costsJ cm-3 d-1TTemperatureKk(T)Temperature dependence–LShell lengthcmWFresh tissue massgGSIGonado-somatic index–
Dynamic energy budget model: parameters.
ParameterUnitsDescriptionValueReferencep´AmJ cm-2 d-1Maximum surface area-specific assimilation rate147.6Van der Veer et al. (2006)C´Rmm3 cm-2 d-1Maximum surface area-specific clearance rate0.096Saraiva et al. (2011a)p´X1Fmmg cm-2 d-1Algal maximum surface area-specific filtration rate*0.1152Rosland et al. (2009)p´X2Fmg cm-2 d-1Silt maximum surface area-specific filtration rate3.5Saraiva et al. (2011a)p´X1Immg d-1Algae maximum ingestion rate*3.12×106Saraiva et al. (2011b)p´X2Img d-1Silt maximum ingestion rate0.11Saraiva et al. (2011b)ρ1–Algae binding probability0.99Saraiva et al. (2011a)ρ2–Inorganic material binding probability0.45Saraiva et al. (2011a)kfd-1Post-ingestive losses through faecesCalibrated–XKmg m-3Half-saturation coefficientCalibrated–TAKArrhenius temperature5800Van der Veer et al. (2006)TIKReference temperature293Van der Veer et al. (2006)TLKLower boundary of tolerance rate275Van der Veer et al. (2006)THKUpper boundary of tolerance rate296Van der Veer et al. (2006)TALKRate of decrease in upper boundary45 430Van der Veer et al. (2006)TAHKRate of decrease in lower boundary31 376Van der Veer et al. (2006)p´MmJ cm-3 d-1Volume specific maintenance costs24Van der Veer et al. (2006)EGJ cm-3Volume specific growth costs1900Van der Veer et al. (2006)EmJ cm-3Maximum energy density2190Van der Veer et al. (2006)k–Fraction of utilized energy spent on maintenance/growth0.7Van der Veer et al. (2006)Vpcm3Volume at start of reproductive stage0.06Van der Veer et al. (2006)GSIth–Gonado-somatic index triggering spawning0.28Van der Veer et al. (2006)δm–Shape coefficient0.25Casas and Bacher (2006)dg cm-3Specific density1.0Kooijman (2000)μEJ g-1Energy content of reserves6750Casas and Bacher (2006)λJ mg-1Conversion factor2387.73Rosland et al. (2009)
* Units of moles of carbon (mol C) converted to milligrams of CHL a (mg CHL a) by multiplying with the factor
12×10350 assuming carbon:CHLa ratio of 50
(Hatzonikolakis et al., 2017).
Environmental drivers
Besides MPs concentration in the seawater, the DEB model is forced by sea
surface temperature (SST) and food availability, represented by
chlorophyll a concentrations (CHL a, an index of phytoplankton biomass). M. edulis
has been demonstrated to filter suspended particles greater than 1 µm, a
size class that includes all of the phytoplankton, zooplankton, and much of
the detritus (Vahl, 1972; Møhlenberg and Riisgård, 1978; Saraiva et al.,
2011a; Strohmeier et al., 2012), including even aggregated picoplankton-size
particles (i.e. marine snow) (Kach and Ward, 2007; Ward and Kach, 2009).
CHL a has been considered the most reliable food quantifier for the
calculation of DEB shellfish parameters (Pouvreau et al., 2006; Sarà et al.,
2012; Hatzonikolakis et al., 2017, and references therein). Hatzonikolakis et
al. (2017) have tested the performance of the model, considering also
particulate organic carbon (POC) in the mussel's diet, which, however, did
not have an important impact on the model's skill against field data in the
Mediterranean Sea study areas. This outcome agrees with Troost et al. (2010)
demonstration that POC contributes to the mussel's diet when CHL a
concentrations are low in the southwest of Netherlands. Thus, in the present
study, only CHL a is considered as the available food source for mussels
originated from the southern North Sea and the northern Ionian Sea. For
both study areas SST and CHL a are derived from daily satellite data, a
method also used by other authors (i.e. Thomas et al., 2011; Monaco and
McQuaid, 2018).
In the North Sea, SST data were obtained from daily satellite images
provided by Copernicus Marine Environmental Monitoring Service (CMEMS) at
0.04∘ spatial resolution. CHL a data obtained from the Globcolour
daily multi-sensor product provided by CMEMS at 1 km spatial resolution,
based on the OC5 algorithm of Gohin et al. (2002)
(http://marine.copernicus.eu/, last access: 27 July 2020, generated using CMEMS Products, production
centre ACRI-ST). The environmental forcing data (SST, CHL a) were averaged
over the study area (51.08–51.44∘ N, 2.19–3.45∘ E),
covering the period 2007–2011 (5 years), in order to realistically simulate
the wild mussel's growth harvested in late summer 2011 (Van Cauwenberghe et
al., 2015). It is notable that the study area of the North Sea belongs to
Case II waters (coastal region), where algorithms tend to overestimate CHL a
concentrations. In optically complex Case II waters, CHL a cannot readily be
distinguished from particulate matter and/or yellow substances (dissolved
organic matter), and so global chlorophyll algorithms are less reliable
(IOCCG, 2000). However, the CHL a dataset that was used was found in good
agreement with available in situ data from the ICES database
(https://www.ices.dk/data/Pages/default.aspx, last access: 27 July 2020) for the specific study area
and time period (Fig. 1), showing a relatively smaller bias and better
time–space coverage, as compared with other tested remote sensing datasets
(not shown) (i.e. regional chlorophyll product available for the North West
Shelf Seas in the CMEMS catalogue, http://marine.copernicus.eu/, last access: 27 July 2020).
Environmental data used for the forcing of the dynamic energy budget model (DEB) in the North Sea simulation, showing (a) temperature and (b) chlorophyll a concentration against in situ data from the ICES database.
In the northern Ionian Sea, daily satellite SST data were also obtained from
the CMEMS database for the Mediterranean Sea with 0.04∘ spatial
resolution, while CHL a daily data were derived from the Globcolour
multi-sensor (i.e. SeaWiFS, MERIS, MODIS, VIIRS, and OLCI a) merged product
(http://globcolour.info last access: 27 July 2020) at 1 km spatial resolution based on
the OC5 algorithm suitable for coastal regions (Gohin et al., 2002). The
forcing data were averaged over the study area (39.49–39.65∘ N,
20.09–20.23∘ E) covering the period 2014–2015 (2 years), when the
cultured mussel is ready for the market. The chosen CHL a dataset was found
preferable, as compared with other available remote sensing datasets (i.e.
CMEMS chlorophyll product for Mediterranean Sea), since it presented a
better spatial and temporal coverage (El Hourany et al., 2019; Garnesson et
al., 2019) and a slightly lower error, as compared with the very few
available in situ data in the study area (not shown). Unfortunately, these
were very scarce, and therefore an extended comparison between remote and in
situ data could not be conducted. Satellite data have facilitated large-scale ecological studies by providing maps of phytoplankton functional types
and sea surface temperature (Raitsos et al., 2005, 2008, 2012, 2014; Palacz
et al., 2013; Di Cicco et al., 2017; Brewin et al., 2017). The daily
environmental forcing data are shown in Figs. 1 and 2 for the North Sea
and the N Ionian Sea respectively. The two coastal environments present
some important differences regarding both CHL a and SST. Specifically, in
the N Ionian Sea, CHL a is relatively low (annual mean ∼0.88 mg m-3) and peaks during winter (maximum ∼2.64 mg m-3 at December 2014), while in the North Sea CHL a is about 4 times
higher (annual mean 4.25 mg m-3), peaking in April every year (maximum
range 29.44–33.38 mg m-3), as soon as light availability reaches a
critical level (Van Beusekom et al., 2009). The higher productivity during the
spring season in the North Sea is related with the nutrient inputs from the
English Channel, the North Atlantic, and particularly the river discharge of
nutrient-rich waters along the Belgian–French–Dutch coastline, which peaks
earlier, during winter period (Van Beusekom et al., 2009). The SST peaks
during August in both areas (Figs. 1 and 2) but is significantly higher
in the N Ionian Sea (maximum 28.8 ∘C), as compared to the North Sea
(maximum 18–19.3 ∘C).
Environmental data used for the forcing of the dynamic energy budget model in the northern Ionian Sea simulation, showing (a) temperature and (b) chlorophyll a concentration.
The environmental concentration of MPs, Cenv (particles L-1), was
also obtained at a daily time step as randomly generated values of the
Gaussian distribution that is determined by the mean value and standard
deviation of the observed field data (0.4±0.3 particles L-1,
North Sea; Van Cauwenberghe et al., 2015; 0.0012±0.024 particles L-1, N Ionian Sea, Digka et al., 2018a). Considering that these values
originate from surface waters and that mussels live in the near-surface
layer (0–5 m), Cenv is estimated as a mean value of the upper layer with
the methods described by Kooi et al. (2016), which studied the vertical
distribution of MPs, considering an exponential decrease with depth.
Specifically, in the N Ionian Sea, mussels were collected from a depth up
to 3 m (Digka et al., 2018a), while in the North Sea (Van Cauwenberghe et
al., 2015), there is no information, and thus a maximum depth of 5 m is
adopted.
In the North Sea simulation, the effect of tides is taken into account by
considering that the mussel originated from the intertidal zone is
submerged 12 h during the day (Van Cauwenberghe et al., 2015). In the N
Ionian Sea simulation, tides are not considered, given the very small tide
amplitude (few centimetres) in the Mediterranean (i.e. Sarà et al., 2011;
Hatzonikolakis et al., 2017), and thus the cultured mussel is assumed
permanently submerged. In situ hourly tide data (2007–2011) from the
coastal zone of the region (Dunkerque station 51.04820∘ N,
2.36650∘ E) obtained from Coriolis and Copernicus data provider
(http://marine.copernicus.eu, http://www.coriolis.eu.org, last access: 28 July 2020) showed that
mussels experience alternating periods of aerial exposure and submergence at
approximately every 6 h (two high and two low tides). During aerial exposure,
the model suspends the feeding processes (Sarà et al., 2011) and simulates
metabolic depression (Monaco and McQuaid, 2018) where the Arrhenius
thermal sensitivity equation (Eq. 9) is corrected by a metabolic depression
constant (Md=0.15), a value representative for M. galloprovincialis and here applied
also for M. edulis. In the present study, the mussel's body temperature change during
low tide is ignored, inducing a model error. The mussel's body temperature
(i.e. surrounding water temperature for submerged mussels) during air
exposure depends on many factors, such as solar radiation, air
temperature, wind speed and wave height, according to studies investigating
the temperature effect on intertidal mussels (Kearney et al., 2010; Sarà et
al., 2011). However, the present study aims to primarily examine the MPs
accumulation, and thus the intertidal mussel's body temperature was not
thoroughly examined. Nonetheless, the time that the mussel is able to
filter, ingest, and excrete the suspended matter (i.e. food and MP
particles) and the effect on the mussel's growth through the modified
relation of k(T) are included, since the assimilation process occurs whether the
mussel is submerged or not (Kearney et al., 2010).
Parameter values
Most of the DEB model parameters were obtained from Van der Veer et al. (2006) and refer to the blue mussel M. edulis in the northeast Atlantic (see
Table 3 for the exceptions). This assumption has also been adopted in
previous studies which showed that this parameter set for M. edulis applies also for
M. galloprovincialis (i.e. Casas and Bacher, 2006; Hatzonikolakis et al., 2017). The half-saturation coefficient Xk represents the density of food at which the
food uptake rate reaches half of its maximum value and should be treated as
a site-specific parameter (Troost et al., 2010; Pouvreau et al., 2006).
In order to estimate the value of Xk, a different approach was followed
for each study area.
For the North Sea simulation, Xk was tuned so that the simulated
individual has the recorded size at the corresponding estimated age (Van
Cauwenberghe et al., 2015) growing with the representative growth rates of
wild M. edulis at the region (Saraiva et al., 2012; Sukhotin et al., 2007). For the
N Ionian Sea simulation, an alternative method was adopted, aiming to
generalize the DEB model to overcome the problem of site-specific
parameterization. The DEB model was tuned against literature field data for
cultured mussels originated from different areas in the Mediterranean and
Black seas, where the average CHL a concentration ranged between 1.0 and 5.0 mg m-3, and one Xk value was found for each area. The four areas
used, their characteristics, and the corresponding value of Xk adopted
are shown in Table 4. These values of Xk are related to the prevailing
CHL a concentration of each area ([CHL a]) through three different
functions: linear: fx=a⋅CHLa+b;
exponential: fx=a⋅exp(b⋅CHLa); and power: fx=a⋅CHLab+c.
The curve fitting app of MATLAB (MATLAB R2015a) was used for the
determination of a,b, and c of each function, taking into account the
95 % confidence level. The score of each function regarding the
somatic/mussel growth simulation in all four regions is tested through
target diagrams (Jolliff et al., 2009) by computing the bias and unbiased
root-mean-square deviation (RMSD) between field and simulated data of all four
regions, and the function with the best score is adopted. A similar approach
was followed by Alunno-Bruscia et al. (2011) for the oyster Crassostrea gigas in six Atlantic
ecosystems, expressing the Xk as a linear function of food density
(e.g. phytoplankton). Unfortunately, the approach described for the N
Ionian Sea simulation could not be applied in the North Sea, as the limited
amount of growth data from the literature for wild M. edulis in similar environments
did not permit a statistically significant fit of a similar function, Xk=fchla.
Half-saturation tuned values (Xk) and mussel growth data
(length) in different areas of the Mediterranean and Black seas.
AreaXk valueCHL a rangeCHL a meanTemperature rangeLength after 1Reference(mg m-3)(mg m-3)(mg m-3)(∘C)year ± SD (cm)Maliakos Gulf0.720.87–5.591.8012.0–26.07.06±0.46Hatzonikolakis et al. (2017)Thermaikos Gulf0.561.04–2.761.8911.5–24.57.0±0.47Hatzonikolakis et al. (2017)Black SeaCalibrated: 0.960.53–16.303.076.5–25.07.5±0.1Karayücel et al. (2010)Bizerte Lagoon3.8294.00–7.705.2012.0–28.07.26±0.46Béjaoui-Omri et al. (2014)Simulation of reproduction–initialization of the model
The reproductive buffer (R) is assumed to be completely emptied at spawning
(R=0) (Sprung, 1983; Van Haren et al., 1994). In order to simulate
mussel spawning, the gonado-somatic index (GSI) defined as gonad dry mass
over total dry flesh mass was computed at every model's time step (Eq. 17
Table 1; the water content of the fresh tissue mass was assumed 80 %
according to Thomas et al., 2011). Spawning was induced by a critical value
of GSI (GSIth, Table 3) and a minimum temperature threshold (Tth)
at each study area, obtained from the literature. In the North Sea
implementation, Tth was set at 9.6 ∘C (Saraiva et al., 2012),
while in the N Ionian Sea, at 15 ∘C (Honkoop and Van der Meer,
1998). This kind of formulation for the spawning event in bivalves has been
used in previous studies (i.e. Pouvreau et al., 2006; Troost et al., 2010;
Thomas et al., 2011; Monaco and McQuaid, 2018). The simulated abrupt losses
of the mussel's tissue mass correspond to spawning events, and the model's
prediction was compared with the available literature data regarding the
spawning period in each study area. Theodorou et al. (2011) demonstrated
that the spawning events occur during winter for M. galloprovincialis in the mussel farms of
Greece, while in the North Sea the spawning period for M. edulis is extended from the
end of April until the end of June (Sprung, 1983; Cardoso et al., 2007).
In both areas, the model was initialized so that the simulated individual is
in the juvenile phase (V<Vp; Table 3) and the reproductive
buffer can be considered to be empty (R=0) (Thomas et al., 2011). As
stated by Jacobs et al. (2015) amongst others, juvenile mussels (M. edulis) range
between 1.5 and 25 mm in size. Specifically, in the North Sea the settlement of
mussel larvae (M. edulis) takes place in June and the juveniles grow to a maximum
size of 25 mm within 4 months (Jacobs et al., 2014). In the N Ionian Sea,
the operating mussel farms follow the life cycle of M. galloprovincialis, starting the
operational cycle each year by dropping seed collectors from late November
until March, and the juvenile mussels grow up to 6–6.5 cm after approximately
1 year according to the information obtained from the local farms in the
region and Theodorou et al. (2011). The initial fresh tissue mass was
distributed between the structural volume (V) and reserves energy (E).
Energy allocated to those two compartments was firstly constrained by the
initial length (L), and then energy allocated to V was in Eq. (10) (Table 1).
The initial value of E was set so that the simulated individual has an
initial weight that corresponds to the juvenile phase (V<Vp)
(Table 5). Finally, for both model implementations, the initial accumulation
of MPs in the mussel's tissue (C) was set to zero.
Dynamic energy budget-accumulation model: initial values. L: shell length; W: fresh tissue mass; V: structural volume; E: energy reserves; R: energy
allocated to reproduction; C: microplastics accumulation.
The DEB-accumulation model simulates at an hourly basis the growth and MPs
accumulation of the wild mussel from the North Sea and the cultured mussel
from the N Ionian Sea. Initially, a model run is performed at each study
area during the periods from July 2007 to August 2011 (4 years) for the North Sea
simulation and late November 2014 to January 2016 (∼1 year)
for the N Ionian Sea simulation. Additionally, the inverse simulations were
performed in order to evaluate the depuration phase of both cultured and
wild mussel, by setting the environmental MPs concentration equal to zero
(Cenv=0), after a period of 1 year simulation at the N Ionian Sea,
when the cultured mussel has the appropriate size for market, and after 4 years in the North Sea, when literature field data are available (Van
Cauwenberghe et al., 2015). In this simulation, the mussel's gut clearance
is achieved by the excretion of MPs through faeces (third term of Eq. 18), and thus it is necessary to maintain the existence of food in the
mussel's environment in order to ensure that the feeding–excretion processes
will occur.
Furthermore, to examine the model's uncertainty related to the environmental
MPs concentration, a series of 15 and 13 simulations were performed in the
North Sea and N Ionian Sea respectively, adopting different constant values
of Cenv within the observed range of each area. Finally, the effect of
the environmental forcing data and some model parameters on the resulting
MPs accumulation by both mussels was explored through sensitivity
experiments. These were used to derive a new function that predicts the
level of MPs pollution in the environment.
Sensitivity tests and regression analysis
The effect of the environmental data (CHL a, temperature, Cenv) and two
parameters representative of mussel's growth (Xk, Yk) on the MPs
accumulation by the mussel for each study area was examined through
sensitivity experiments with the DEB-accumulation model. Each variable
(CHL a, T, Cenv) and parameter (Xk, Yk) was perturbed by ±10 % to examine its effect on the simulated MPs accumulation, and the
results of each run were analysed using a sensitivity index (SI). SI
calculates the percentage change in the mussel's MPs accumulation;
SI=1n∑t=1nCt1-Ct0Ct0⋅100 (%), where n is the simulated time steps,
Ct0 is the MPs accumulation predicted with the standard simulation
at time t, and Ct1 is the MPs accumulation with a perturbed
variable/parameter at time t; for details see Bacher and Gangnery (2006).
The same method has been also applied to other studies, which examined the
model's sensitivity for specific variables/parameters regarding the mussel
growth (Casas and Bacher, 2006; Rosland et al., 2009; Béjaoui-Omri et
al., 2014; Hatzonikolakis et al., 2017). In order to also examine the effect
of tides, in the North Sea implementation, the sensitivity experiments were
conducted twice as follows: the first time assuming that the mussel is permanently
submerged and the second time assuming that the mussel is periodically
exposed to the air.
Preliminary sensitivity experiments showed that the MPs accumulation is
highly dependent on the prevailing conditions regarding the CHL a,
temperature, and Cenv and the mussel's growth that is regulated by the
half-saturation coefficient (Xk). Therefore an attempt was made using
the model's output to describe the MPs accumulation as a function of these
variables through a custom regression model as follows:
y=b1⋅W+b2⋅exp1T+b3⋅1CHLa+b4⋅Cenv,
where y (particles per individual) is the response variable and represent the
predicted MPs accumulation by the mussel; W (g) is the mussel's fresh tissue
mass, T (K) is the sea surface temperature; CHL a and Cenv are the
concentrations of chlorophyll a and MPs in the water respectively, which are
the predictor variables. The values of coefficients b1, b2,
b3, and b4 are calculated using the nonlinear regression function
(nlinfit, MATLAB R2015a), which attempts to find values of the parameters b that
minimize the least-squared differences between the model's MPs accumulation
output C and the predictions of the regression model y=f(W,T, [CHL a], Cenv, b).
The ultimate aim of this analysis, once coefficients are determined, is to
use Eq. (19) to obtain the environmental MPs concentration:
Cenv=1b4⋅C-b1⋅W-b2⋅exp1T-b3⋅1CHLa,
which could be a very useful tool to predict the MPs concentration in the
environment, when all involved variables are known (mussel's accumulated MPs, C; wet weight, W; temperature, T; and CHL a), using the mussel as a potential
bioindicator (Li et al., 2016, 2019). The score of this custom
model was tested by applying Eq. (20) in our study areas and six more areas
around the UK, where information on a mussel's wet weight and both the mussels'
and environment's MPs load is available (Li et al., 2018). CHL a and
temperature, which were not included in Li et al. (2018), were obtained from
daily satellite images (same source as in the North Sea; see Sect. 2.4),
covering the period that the mussels were harvested (Li et al., 2018).
ResultsGrowth simulations
The growth simulations of M. edulis and M. galloprovincialis for the North Sea and the N Ionian Sea are
shown in Figs. 3 and 4 respectively. In the North Sea implementation,
Xk was tuned to a constant value: Xk=8 mg m-3 (±1.5 mg m-3). The fitted value was higher, as compared to the one
(Xk=3.88 mg m-3) used by Casas and Bacher (2006) in productive
areas of the French Mediterranean shoreline (average CHL a concentration:
1.45 mg m-3; maximum peak at 20 mg m-3), as a consequence of the
higher productivity in the North Sea (average CHL a concentration: 4.25 mg m-3; maximum peak at ∼33.40 mg m-3). The high value
of Xk could also be explained by the presence of inedible particles
(i.e. MPs) that led to lower-quality food in the mussel's diet compared
with an assumed clean-from-inedible-particles environment (Kooijman, 2006;
Ren, 2009). In the present study the inedible particles (i.e. MPs) have been
incorporated in the mussel's diet through the modified relation of the
functional response f (Eq. 5, Table 1), which regulates the assimilation rate
and thus the mussel's growth. However, the DEB model applied at the French
site, did not account for inedible particles in the mussel's food. Furthermore,
it has been reported that wild mussels grow considerably slower than farmed
mussels (∼1.7 times) (Sukhotin and Kulakowski, 1992), and
thus, a higher value of Xk promotes less mussel growth, which is the
case for the North Sea mussel. The simulated mussel shell length after 4 years, in August, is 4.35 cm, and the fresh tissue mass is 1.87 g, in
agreement with Van Cauwenberghe et al. (2015) and other studies conducted on
wild mussels (Sukhotin et al., 2007; Saraiva et al., 2012; MarLIN, 2016). In
particular, Saraiva et al. (2012) found that after 16 years of simulation,
the wild mussel of the Wadden Sea (North Sea) is 7 cm long, while according
to Bayne and Worral (1980) a mussel with shell length 4 cm corresponds to
the age of 4 years, in agreement with the current study. The simulated
growth presents a strong seasonal pattern, being higher during the spring and
summer season, as compared to autumn and winter, which is consistent with
the seasonal cycle of temperature and CHL a concentration, for a typical
year in the region (Fig. 1). The increase in food availability and
temperature during spring (April) results in high mussel growth for a
4-month period, while the decrease in CHL a from summer until the end of the
year, in conjunction with the temperature decrease in autumn, results in a
less mussel growth. Spawning events that occurred in late April–early May
(30 April–2 May) each year are responsible for the sharp decline in a mussel's
fresh tissue mass, shown in Fig. 4 (Handa et al., 2011; Zaldivar, 2008), which is
in agreement with the literature (Sprung, 1983; Cardoso et al., 2007;
Saraiva et al., 2012). The predicted weight loss due to spawning was around
7 % at the first year of simulation, while the second, third, and fourth
year the percentage of weight loss increased gradually to 8.3 %, 12.6 %,
and 14.4 % respectively. Bayne and Worral (1980) demonstrated that the
weight losses on spawning for individuals of 1 g weight vary between 2.1 %
and 39.8 %, presenting a weight-specific increase with size.
(a) Simulated mussel shell length (L) and (b) fresh tissue mass (W) against North Sea data (red star: mean ± SD), using chlorophyll a, X=[CHLa], in the mussel diet.
(a) Simulated mussel shell length (L) and (b) fresh tissue mass (W) against North Sea data (red star: mean ± SD), using chlorophyll a, X=[CHLa], in the mussel diet.
In the N Ionian Sea implementation, Xk is applied as a function of
CHL a concentration through the method described in Section 2.5. The target
diagram showing the performance of each tested function (linear: fx=a⋅CHLa+b, where a=0.959 and b=-1.420;
exponential: fx=a⋅exp(b⋅CHLa), where a=0.2 and b=0.567; power: fx=a⋅CHLab+c, where a=0.01, b=3.529 and c=0.480) is
shown in Fig. 5. The linear and power function of Xk present good
skill, with the power function leading to the most successful simulation of
the cultured mussel's growth in all four areas (diagram marks for mussel
length and fresh tissue mass are closer to the target's centre). The power
function applied in the N Ionian Sea resulted in a mussel's shell length of 5.8 cm and fresh tissue mass of 5.92 g after 1 year of simulation, in agreement
with Theodorou et al. (2011). The spawning event occurred at the beginning
of December (Theodorou et al., 2011) and was illustrated by a 12.6 %
tissue mass decline.
Target diagram of simulated shell length (L) and fresh mass tissue weight (W) against field data from the Thermaikos and Maliakos gulfs (eastern Mediterranean Sea), Black Sea, and Bizerte Lagoon (southwestern Mediterranean Sea), using the power (L1,W1), exponential (L2,W2), and linear (L3,W3) functions of the half-saturation coefficient. The model bias is indicated on the y axis, while the unbiased root-mean-square deviation (RMSD) is indicated on the x axis.
Microplastics accumulation and depuration phase
The hourly simulated MPs accumulation by the mussel in the North Sea and N
Ionian Sea are shown in Figs. 6 and 7 respectively. Calibration of the
parameter kf (1.2 d-1) led to a model which was well fitted to the
observed MPs accumulation in the mussel of both study areas. In the North
Sea, a 4-year-old wild mussel (L=4.35 cm, W=1.87 g) contains 0.53 particles per individual in August, within the range value found by Van
Cauwenberghe et al. (2015) (0.4±0.3 particles per individual),
although the model overestimated the data range, reproducing a seasonal
increase that was not observed. This is most likely due to the fact that Van
Cauwenberghe et al. (2015) allowed a 24 h clearance period before analysing
the mussels' tissue for MPs, resulting in slightly lower MPs accumulation
than the model's prediction. The MPs egested through faeces by the 4-year-old mussel after 24 h were 0.2±0.2 particles per individual (Van
Cauwenberghe et al., 2015), which agree also with model's output (0.3 particles per individual, Fig. 8) regarding the depuration phase and
could compensate for the observed difference in the mussel's MPs load between
the simulated and field data. In the N Ionian Sea, the simulated MPs
accumulation by the cultured mussel with L=4.85 cm and W=3.33 g was
0.91 particles per individual at the end of June, in agreement with field
observations obtained from Digka et al. (2018a) (0.9±0.2 particles per individual). Overall, the developed model simulated the MPs
accumulation by both mussels in the two different areas, using the same
parameter set (see Table 3 for the exceptions), under the assumption that
parameters referred to as silt particles (i.e. inedible particles) may also be used
to describe the MPs accumulation. Both simulations were in good
agreement with the available field data, with a small deviation for the
North Sea. This may lead to the assumption that mussels present a common
behaviour against all inedible particles. In the model's results, based on the
uptake and excretion rates of MPs by the mussels in both study areas, the
majority of MPs are rejected through pseudofaeces and fewer through faeces
production (not shown). This is in agreement with Woods et al. (2018), which
found that most microplastic fibres (71 %) were quickly rejected as
pseudofaeces and <1 % excreted in faeces.
Microplastics (MPs) accumulation by the mussel (blue line) against field data (red star: mean ± SD), using daily environmental concentration of MPs (Cenv mean value ± SD: 0.4±0.3 particles L-1) in the North Sea.
Microplastics (MPs) accumulation by the mussel (blue line) against field data (red star: mean value ± SD), using daily environmental concentration of MPs (Cenv mean value ± SD: 0.0012±0.024 particles L-1) in the northern Ionian Sea.
The small-scale (daily) fluctuations of MPs in the mussel (wild and
cultivated) reflect the adopted random variability in the environmental MPs
concentration Cenv and the daily fluctuations of the environmental
forcing (CHL a, temperature). The large-scale (seasonal) variability follows
mainly the variability of the clearance rate. The seasonal variability in
the CHL a concentration and temperature greatly determines the variability
in the clearance rate and hence the variability in MPs in the individual.
Moreover, the model predicts that mussel's energy needs are increased as it
grows, and therefore the clearance rate is increased, resulting in higher MPs
accumulation.
The simulated time needed to clean the mussel's gut from the MPs load for
both areas is shown in Fig. 8. In both areas, the cleaning follows an
exponential decay, in agreement with laboratory experiments by Woods et al. (2018). In particular, the model predicts a 90 % mussel's cleaning after
284 h (∼12 d) and 56 h (∼2.5 d)
for the N Ionian Sea and North Sea respectively. The cleaning process is
more rapid in the North Sea simulation, which can be attributed to the
higher CHL a concentration found in this area, leading to increased
production of faeces by the mussel and hence faster excretion of the
accumulated MPs. In the N Ionian Sea, on the other hand, the rate of the
mussel's cleaning is slower, due to the limited food availability.
Depuration phase of the cultured Mytilus galloprovincialis (red line) and wild Mytilus edulis (black line) using zero environmental concentration of microplastics (Cenv=0) after 1 and 4 years of simulation time in the northern Ionian Sea and North Sea respectively.
Model's uncertainty regarding the environmental microplastics concentration
The MPs concentration in the environment presents a strong variability in
both temporal and spatial scales. To examine the model's uncertainty related
to the environmental MPs concentration (Cenv), a series of 15 and 13
simulations were performed in the North Sea and N Ionian Sea respectively,
adopting different values of Cenv within the observed range of each
area. In the North Sea, the adopted Cenv ranged between 0.1 and 0.8 particles L-1 with a step of 0.05 (15 runs), while in the N Ionian Sea
Cenv ranged between 0.0012 and 0.0252 particles L-1 with a step of
0.002 (13 runs). The mean seasonal values and standard deviation of the 15
simulations in the North Sea and the mean monthly values and standard
deviation of the 13 simulations in the N Ionian Sea were computed and
plotted in Figs. 9 and 10 respectively. Each error bar represents the
uncertainty in the simulated accumulation at the specific time, related to
the environmental MPs concentration.
Mean seasonally values and standard deviation of microplastics (MPs) accumulation (red error bars: mean value ± SD) by the mussel in North Sea derived from 15 model runs with different constant values of environmental MPs concentration (Cenv range: 0.1–0.8 particles L-1); Mean hourly simulated data (black line) and standard deviation (blue lines) of microplastics accumulation derived from three model runs with stochastic sequences of daily random Cenv values.
Mean monthly values and standard deviation of microplastics accumulation (red error bars: mean value ± SD) by the mussel in northern Ionian Sea derived from 13 model runs with different constant values of environmental MPs concentration (Cenv range: 0.0012–0.024 particles L-1); Mean hourly simulated data (back line) and standard deviation (blue lines) of microplastics accumulation derived from three model runs with stochastic sequences of daily random Cenv values.
In both case studies, the uncertainty of the model appears to increase as
the MPs accumulation is increased. As the mussel grows in the North Sea, the
mean value and standard deviation of MPs accumulation is increased during
the same season every year, illustrating the effect of the mussel's weight.
Moreover, the seasonal variability in the MPs accumulation appears to be
related with the seasonality of CHL a concentration. This is apparent during
each year's spring, when CHL a concentration peaks at its maximum value
(∼30 mg m-3; see Fig. 1); the filtration rate is
decreased (Riisgård et al., 2003, 2011), leading to lower MPs accumulation
by the mussel and thus lower model uncertainty. In the N Ionian Sea, the
effect of the mussel's weight is more apparent in the early months
(∼6 months), resulting in higher MPs accumulation and model
uncertainty as the mussel grows. Afterwards, the seasonality of both CHL a
concentration and temperature plays the major role. During summer, when the
CHL a concentration is progressively decreased, reaching minimum values
(∼0.7 mg m-3), and temperature is increased (>20∘C), the filtration rate is significantly decreased or stopped,
resulting in lower MPs accumulation and lower model uncertainty. This is
in line with studies reporting that the mussel suspends the filtering
activity and thus closes its valves until better conditions occur (Pascoe et
al., 2009; Riisgård et al., 2011). Overall, the available field data lie
within the model's uncertainty, apart from the North Sea case, where the
range of field data variability and model uncertainty dot not overlap
significantly at the time of the observations.
Moreover, to evaluate the scenario adopted with the set-up of the previous
experiments (random Cenv at a daily time step) three additional model runs
are performed in each study area, adopting each time different stochastic
sequences of daily random Cenv values within the observed range, which
is considered to reflect the high spatial and temporal variability in the
environmental MPs concentration. The mean value and standard deviation of
these “stochastic” runs lie most of the time within the standard deviation
of the overall model uncertainty in both case study areas (Figs. 9 and
10).
Sensitivity and regression analysis results
The results of the sensitivity experiments regarding the MPs accumulation by
the mussels are shown in Figs. 11 and 12 for the North Sea and N Ionian Sea
respectively. The comparison between the intertidal and subtidal mussel of
the North Sea revealed that both +10 % and -10 % perturbation of
CHL a and Xk have a slightly lower effect on the MPs accumulation by the
intertidal mussel, which is probably attributed to the intermittent feeding
periods experienced by the individual due to the tide effect. As far as the
temperature effect, both a +10 % and -10 % perturbed value led to
higher sensitivity to the MPs accumulation by the intertidal mussel, due to
the adopted modified temperature relation during low tide. Especially, if
the mussel's body temperature change during air exposure would be
considered, the perturbed temperature will probably affect the MPs
accumulation even more for the intertidal than the subtidal mussel. The sensitivity of
the Cenv to the MPs accumulation when perturbed either +10 % or -10 % is almost the same for the intertidal and subtidal mussel, indicating
that the environmental MPs concentration affects similarly both mussels,
regardless the continuous or intermittent feeding–excretion process.
Sensitivity index of MPs accumulation for the wild mussel of the North Sea when variables (CHL a, temperature, Cenv) and parameters (Xk,Yk) are perturbed ±10 %. The notation (s) refers to the permanently submerged mussel.
Sensitivity index of MPs accumulation for the cultured mussel of the northern Ionian Sea when variables (CHL a, temperature, Cenv) and parameters (Xk,Yk) are perturbed ±10 %.
The comparison between the mussel sensitivity indexes in the N Ionian and
the North Sea (in conditions of submergence) study areas reveals some
important differences. Generally, most of the perturbed (either +10 %
or -10 %) variables and parameters (i.e. CHL a, temperature, Xk)
present higher sensitivity to the MPs accumulation by the mussel from the N
Ionian Sea. This is attributed to the prevailing environmental conditions
and specifically the lower food availability (CHL a) and the higher
temperature range in the N Ionian Sea compared to the North Sea, which
greatly determine the feeding processes, the mussel's growth, and hence the
MPs accumulation. The perturbed Cenv in both study areas appears to
affect similarly the MPs accumulation for both mussels (∼10 %), with the small difference (<2 %) probably attributed to
the higher abundance of seawater MPs present in the North Sea compared to
the N Ionian Sea. Finally, the half-saturation coefficient for the
inorganic particles (Yk) has no effect on the MPs accumulation of both
North Sea and N Ionian Sea mussels, indicating that the amount of inedible
particles (i.e. MPs) is relatively low in both areas, and thus the Yk
does not affect the way that the organic particles are being ingested
(Kooijman, 2006). According to Ren (2009), when the inorganic matter is low,
the K(y) (Eq. 5; Table 1) is approximately equal to Xk, and then
Yk is the least sensitive parameter for the ingestion rate and thus
growth.
The DEB-accumulation model output was used to determine the coefficients in
Eq. (19) by the nonlinear regression analysis: b1=0.1909 (±0.0006),
b2=0.0412 (±0.0019), b3=0.1315 (±0.0021), and b4=1.1060
(±0.0253). The accurate estimation of the coefficients (b1,
b2, b3, b4) is indicated by the low confidence intervals,
while the mean squared error of the regression model appears also
sufficiently small (MSE =0.0523). Subsequently, as shown in Fig. 13, Eq. (20) may be used to predict the MPs concentration of the environment where
mussels live. In most cases, the predicted MPs concentration is found within
the standard deviation of the field data. Two exceptions are shown in
Hastings-A and Plymouth areas. The reasons behind these discrepancies may be
related to the environmental conditions prevailing in each area at the
sampling time. For example, Eq. (20) does not take into account the impact of
tides that may affected the mussel's MPs load (C), and the lack of information
on the exact sampling date led to using a mean SST and CHL a value
representative of the given sampling time period (Li et al., 2018).
Although Eq. (20) does not account for the tide effect, the
sensitivity analysis (Fig. 11) showed that the effect of Cenv on the
mussel's MPs accumulation was the same for both the intertidal and subtidal
mussel in the North Sea. This result may also apply at the two exceptions
areas, leading to the assumption that the discrepancies are due to the lack
of the ambient temperature and CHL a information during the sampling date.
In any case, this is a first rough demonstration of the method and should be
implemented in more environments in order to be further validated.
Prediction of seawater microplastics concentration by applying Eq. (20) in the northern Ionian Sea, North Sea (present study), and six areas around the UK (Filey, Hastings-A, Hastings-B, Brighton, Plymouth, and Wallasey; Li et al., 2018).
Discussion
A DEB-accumulation model was developed and validated with data available
from the North Sea and the N Ionian Sea, to study the MPs accumulation by
wild M. edulis and cultured M. galloprovincialis, grown in different, representative environments. Although
the study is limited by scarce validation data, it should be noted the MPs
accumulation model parameter set, except one tuning parameter (kf), was
extracted from the literature (Table 3), assuming that mussels adopt a
common defensive mechanism against inedible particles (i.e. silt, MPs).
Thus, the theoretical background constructed by Saraiva et al. (2011a)
(based on Kooijman, 2010) regarding the feeding and excretion processes of
the mussel remains unspoiled. Through the strong theoretical background of
DEB theory, this study highlights that the accumulation of MPs by the mussel
is highly dependent on the prevailing environmental conditions which control
the amount of MPs that the mussel filtrates and excretes.
Towards a generic DEB model, the applied function of the half-saturation
coefficient, fx=a⋅CHLab+c,
successfully captures the physiological responses and thus the growth rate
of the cultured mussel at the N Ionian Sea implementation. In the current
study, this method led to a robust and generic DEB growth model able to
simulate the mussel growth in representative mussel habitats of the
Mediterranean Sea, covering a range of productivity and sea surface
temperature. This approach supports and takes one step further by using the Bourlès et al. (2008) suggestion about a seasonally varied half-saturation coefficient,
demonstrating an improvement of the food quantifier. The applied function of
Xkconsiders the daily CHL a fluctuations and, thus, the seasonal
variation of the seawater composition. As more field data become available
from various environments, the applied approach could result to more generic
formulations for the site-specific parameter Xk, so that the model could
be applied in several areas of interest, where field growth data are absent
and/or to simulate the potential mussel growth in the 2D space.
The simulation of MPs accumulation by the mussels, using the
DEB-accumulation model, is in good agreement with the available field data
(Figs. 3 and 4). The simulated values lie within the observed field data
range (mean ± SD), although the seasonal increase reproduced by the
model in the North Sea implementation did not exactly overlap with the field
data at the time of observations. This could be attributed to the clearance
period (24 h) that allowed mussels to excrete MPs through faeces (0.2±0.2 particles per individual) before the mussel's tissue analysis
(Van Cauwenberghe et al., 2015). The measured loss of the mussel's MPs is in
agreement with the model's result for the depuration experiment after 24 h.
The MPs accumulation by the cultivated mussel (fresh tissue mass 3.33 g)
originated from the N Ionian Sea with mean Cenv=0.0012±0.024 particles L-1 is 0.91 particles per individual and by the wild
mussel (fresh tissue mass 1.87 g) from the North Sea with mean
Cenv=0.4±0.3 particles L-1 is 0.53 particles per individual. If these concentrations are expressed per gram of wet
tissue of mussel, the cultivated mussel contamination (0.27 particles g-1 w.w.) is comparable with the wild mussel (0.28 particles g-1 w.w.), despite the much lower environmental MPs concentration
(Cenv) in the N Ionian Sea than in the North Sea. This comparison aims to
highlight the significant impact of the prevailing environmental conditions
(CHL a and temperature) on the MPs accumulation by the mussels, although
they originate from different areas and lived during different time periods. The
generally high abundance of CHL a in the North Sea simulation contributes
to a reduction of the filtering activity and hence of the MPs accumulation.
The threshold algal concentration for reduction of the mussel's filtration
rate (incipient saturation) has been found to lie between 6.3 and 10.0 mg m-3 (Riisgård et al., 2011), which is a range comparable to the CHL a
concentrations in the North Sea. Van Cauwenberghe and Janssen (2014) found
that cultivated M. edulis from the North Sea contained on average 0.36±0.07 particles g-1 w.w., a slightly higher value than that found in the
present study for the wild mussel of the North Sea (0.28 particles g-1 w.w.). This could be attributed to mussel farms acting as a
potential source of MPs contamination for the mussels due to plastic
materials (i.e. plastic sock nets and polypropylene long lines) used during
cultivation (Mathalon and Hill, 2014; Santana et al., 2018). Moreover, the
intertidal wild mussel (present study) is assumed to filter and excrete MPs
half of the time in comparison with the submerged cultured mussel in the
North Sea, resulting though in similar accumulation level. The model also
predicts the time needed for the 90 % gut clearance of both cultured (N
Ionian Sea) and wild (North Sea) mussels to be almost 284 and 56 h
(equivalent to 12 and 2.5 d) respectively, when MPs contamination is
removed from their habitat. This is in line with a series of studies which
demonstrated that the depuration time varies between 6–72 h and can last
up to 40 d depending on several factors such as species, environmental
conditions (Bayne et al., 1987), size, and type of MPs (Browne et al., 2008;
Ward and Kach, 2009; Woods et al., 2018; Birnstiel et al., 2019).
The strong dependence of food (CHL a), temperature, and seawater MPs
concentration on the MPs accumulation by the mussel, regarding its wet
weight, is demonstrated through sensitivity experiments that were used to
derive a rather simple nonlinear regression model (Eq. 19). The comparison
of the regression model's with the DEB model's output resulted in a quite
accurate estimation of the coefficients, which in turn sparked the idea of a
“new” relationship (Eq. 20) that could potentially predict the MPs
concentration in the environment (Cenv) when certain conditions are
known (CHL a, T, C, W). The latter equation was applied in eight areas in total
(two from the present study areas and six from Li et al., 2018), with
relatively good results since there is general overlapping of regressed and
observed MPs concentration in the environment (Cenv), except for
Hastings-A and Plymouth areas, probably due to missing information on the
environmental conditions (CHL a, SST) during the sampling, suggesting that
the mussels can be used as potential bioindicators. Mussels have been
previously proposed as bioindicators for marine microplastic pollution
(<1 mm), although the efficient gut clearance and selective feeding
behaviour limit their quantitative ability (Lusher et al., 2017; Bråte et
al., 2018; Beyer et al., 2017; Fossi et al., 2018; Li et al., 2019).The
recent study by Ward et al. (2019b) demonstrated that bivalves are poor
bioindicators of MPs pollution due to the particle selection during feeding
and excretion processes that is based on the physical characteristics of the
MPs. Considering that the MPs accumulation is site-dependent and that
sampling of mussels is usually easier than seawater (Karlsson et al., 2017;
Bråte et al., 2018), models like the one described in Eq. (20), besides the
MPs accumulation, take into account also characteristics of the environment
that are crucial for the way that mussels accumulate MPs. This method could
be possibly used at a global level and allow comparisons between various
environments. However, the method described should be validated in more
environments with more frequent field data to be able to provide secure
results.
In addition to the scarce validation data regarding the MPs accumulation in
mussels, this study has some more limitations. First of all, the data
regarding the concentration of MPs in the mussels' environment are also
scarce; since MPs are a relatively recent subject of study, the existing
knowledge of the spatial and temporal distribution is still quite limited
(Law and Thompson, 2014; Browne, 2015; Anderson et al., 2016; de Sá et al.,
2018; Smith et al., 2018; Troost et al., 2018). To overcome the lack of
environmental MPs time series, a function of randomly generated values
within the observed range of each area was applied, and its uncertainty was
examined through an ensemble forecasting. Specifically, the model's
uncertainty due to the environmental MPs concentration (Cenv) was tested
by performing a series of model runs forced by an envelope of representative
values of Cenv. The results (Sect. 3.3) showed that the adopted
stochastic scenario simulated quite satisfactorily the MPs accumulation by
the mussels, lying within the observed field range, although a slight
overestimation was found in the North Sea. The approach used is assumed to
represent the natural variability since it has been reported that tides,
wind, wave action, ocean currents, river inputs, and hydrodynamic features
lead to high spatial and temporal variability in MPs distribution even on
very small scales (Messinetti et al., 2018; Goldstein et al., 2013). In
addition, the nature of the variable Cenv makes it difficult to
estimate, presenting large observational errors, not only due to the intense
physical variation but also due to different sampling and analysis
techniques that were used. In a future work the DEB-accumulation model could
be coupled with a high-resolution MPs distribution model (Kalaroni et al.,
2019), being extensively validated against field data that will have been
collected and processed according to a common scientifically defined
protocol, to overcome this limitation. Moreover, the approach followed in
calculating the value of MPs concentration in the near-surface layer (0–5 m
depth) (Kooi et al., 2016) resulted in a representative value of the upper-ocean layer. In-depth knowledge of the MPs distribution, both horizontally
and vertically, is essential to understand and mitigate their impact not
only on the various marine compartments but also on the organisms inhabiting
those compartments (Van Sebille et al., 2015; Kooi et al., 2016). For that
reason, it is important to enhance the monitoring activity especially in the
vulnerable coastal environments, adopting integrated cross-disciplinary
approaches and monitoring of biological, physical, and chemical parameters
which provide information on the ecosystem function, in order to improve the
assessment of emerging pollutants (i.e. MPs) and their impacts on biota
(objective of JERICO-RI framework).
Our assumption that the mussel has the same filtration rate for all
particles, independent of their chemical composition, size, and shape, is a
simplification and an open theme of discussion (see Saraiva et al., 2011a
for details). However, in our model application, a pre-ingestive particle
selection by the mussel is implied based on the organic–inorganic content of
the suspended matter illustrating the different binding probabilities
applied for algal and MP particles during the ingestion process. Through an
investigation of wild mussel's faeces and pseudofaeces production in
laboratory conditions, Zhao et al. (2018) found that the length of MPs was
significantly longer in pseudofaeces than in the digestive gland and faeces.
Furthermore, Van Cauwenberghe et al. (2015) demonstrated that mussel's
faeces contained larger MPs (15–500 µm) compared to the mussel's
tissue (20–90 µm). Apparently, smaller-sized MPs seem to be dominant
within the mussels in comparison with the size of the MPs in the ambient
environment (Li et al., 2018; Qu et al., 2018; Digka et al., 2018b),
implying that the mussel is more prone to ingest and retain smaller-sized
MPs. As an example, Digka et al. (2018b) confirmed that the smaller MPs
(<1 mm) occupy 62.3 %, 96.9 %, and 100 % of the total MPs
in seawater, sediments, and mussels from the N Ionian Sea respectively. In a
future work this selection pattern regarding size could be simulated by
suitable preference weights among different MPs sizes. This will improve the
knowledge of the feeding and excretion mechanisms used by the mussels
against MPs pollution and the assessment of the ecological footprint (Rist
et al., 2019).
Our assumption that the contamination by MPs does not affect the energy
budget in terms of growth might also be a simplification as this is a
subject currently under investigation. Van Cauwenberghe et al. (2015) found
that although mussels M. edulis exposed to MPs increased their energy consumption,
the energy reserves were not affected compared to the control organisms,
implying that mussels are able to adopt a defensive mechanism against the
suspended inorganic particles (i.e. MPs) (Ward and Shumway, 2004).
Furthermore, MPs exposure showed no significant effect on a mussel's (Perna perna) energy
budget, despite its long duration and relatively realistic intensity,
leading to the hypothesis that mussels can acclimate to the MPs exposure to
maintain their health (Santana et al., 2018). On the contrary, other authors
suggested a significant energy shift from reproduction to structural growth
and elevated maintenance costs, probably attributed to the reduced energy
intake, when the organisms (i.e. oyster Crassostrea gigas) were contaminated with high and
unrealistic concentration of MPs (Sussarellu et al., 2016). Moreover, Gardon
et al. (2018) showed that the overall energy balance of oyster Pinctada margaritifera was
significantly impacted by the reduced assimilation efficiency in correlation
with the exposed dose of MPs, and for that reason energy had to be withdrawn
from reproduction to compensate for the energy loss. In the future, dedicated
experiments exploring the effects on all components of a DEB model should be
carried out considering long-term realistic MPs exposure.
Our use of the tide data led to some model bias, since the model does not
take into account the mussel's body temperature change when this is
exposed to air. Assessing the mussel's body temperature requires extended
experiments in field conditions (Tagliarolo and McQuaid, 2015; Monaco and
McQuaid, 2018). The study by Seuront et al. (2019) along the French coast of
the eastern English Channel found no significant correlation between air and
a mussel's body temperature but demonstrated a significant positive
correlation between the body temperature and the hard substrate (i.e. rocks)
temperature. However, in the present study the tide effect on processes that
are affected by the thermal equation, k(T), is considered indirectly through the
metabolic depression (details in Sect. 2.4). Sarà et al. (2011) coupled a
DEB model with a biophysical model (Kearney et al., 2010), incorporating the
change of mussel's body temperature during emersion by using information of
various climatological variables (i.e. solar radiation, air temperature,
wind speed, wave height), but ignored the temperature sensitivity for the
physiological processes. In a future study, a combined approach of coupling
the present DEB-accumulation model with a biophysical model, which includes
both the tide effect on the physiological processes and the mussel's body
temperature respectively, could be followed and lead to a more detailed
simulation of the intertidal mussel.
Conclusions
In a future study the model should be corroborated further by using a larger
dataset of MPs accumulation, with sampling of mussels of various sizes and
life stages. Currently, the model is mainly limited by the insufficient
validation, as a larger dataset could be also used for a better model
calibration. However, this study provides a new approach in studying the
accumulation of MPs by filter feeders and reveals the relations between
characteristics of the mussel's surrounding environment and the MPs
accumulation, which is presented with high seasonal fluctuations.
Additionally, in a future study the DEB-accumulation model will be coupled
to a hydrodynamic–biochemical model (e.g. Petihakis et al., 2002, 2012;
Triantafyllou et al., 2003; Tsiaras et al., 2014; Ciavatta et al., 2019;
Kalaroni et al., 2020) and a MPs distribution model (Kalaroni et al., 2019)
that will provide fields of temperature, food availability, and MPs
concentration respectively at the Mediterranean scale and eventually lead
to an integrated representation of the MPs accumulation by mussels (Daewel
et al., 2008). This fully coupled model will be downscaled to the Cretan Sea
SuperSite (a marine observatory dedicated to multiple in situ observations at appropriate spatio-temporal resolution, in a restricted geographical region, maintained over long timescales, and designed to address interdisciplinary objectives, driven by science and society needs), while the parameterization of important biological processes will
be redesigned based on the new data which will be acquired in the framework
of the JERICO S3 project (http://www.jerico-ri.eu, last access: 28 July 2020). The present study
highlights the urgent need for adopting a multidisciplinary monitoring
activity by measuring physical, biological, and chemical parameters that are
crucial for mapping the MPs distribution, assessing the contamination level
of the marine organisms, and investigating the impact on the health status.
Overall, despite the limitations mentioned, taking into account that plastics
are one of the global hot issues, this particular study could help design
next efforts, since it provides indications of the future related priority
issues.
Data availability
The CMEMS Globcolour chlorophyll products are available on the CMEMS web portal (http://marine.copernicus.eu/, last access: 28 July 2020). The dataset used is available as daily-filled (spatial and temporal interpolation) product with a 1 km spatial resolution for the European North West Shelf Seas: OCEANCOLOUR_ATL_CHL_L4_REP_OBSERVATIONS_009_098 (Atlantic and daily-filled).
The Globcolour chlorophyll product is available as a daily product with 1 km resolution for Europe, and access is free after registration at http://www.globcolour.info/ (last access: 28 July 2020). GlobColour data (http://globcolour.info) used in this study have been developed, validated, and distributed by ACRI-ST, France.
The CMEMS SST products used include daily gap-free maps of sea surface temperature, referred to as L4 product, at 0.04∘ spatial resolution for the European North West Shelf Seas and for the Mediterranean Sea: SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 and SST_MED_SST_L4_REP_OBSERVATIONS_010_021, respectively.
Access to all products from CMEMS is granted after free registration at http://marine.copernicus.eu/ (last access: 28 July 2020).
The research data regarding the MPs accumulation in the mussels and the ambient environment are publicly accessible and obtained from published research for both study areas (already cited in the paper).
Author contributions
GT conceived the basic idea of the present study and was responsible for
the management and coordination of the research planning and execution. NS
and YH developed the model code with a contribution from KT. NS
collected the existing information on the subject and performed the
simulations of the present study with the help of YH when needed. GT,
GP, KT, YH, and NS contributed to the interpretation of the results.
CT provided the field data of the mussel's microplastic accumulation in
the northern Ionian Sea. NS prepared the paper, with critical review,
commentary, and revision contributed from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Coastal marine infrastructure in support of monitoring, science, and policy strategies”. It is not associated with a conference.
Acknowledgements
This study has been conducted
using E.U. Copernicus Marine Service Information
(http://marine.copernicus.eu/, last access: 28 July 2020).
Financial support
This work was partially funded by the project Blue Growth with Innovation and application in the Greek Seas – GLAFKI (MIS 5002438) funded by national and EU funds under National Strategic Reference Framework 2014–2020 and the EC H2020 CLAIM project (grant agreement no. 774586). Part of this research has been supported by the JERICO-NEXT project (grant agreement no. 654410).
Review statement
This paper was edited by Ingrid Puillat and reviewed by two anonymous referees.
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