We estimated pelagic primary production (PP) in the
coastal (<200 m depth) Mediterranean Sea from satellite-borne data,
its contribution to basin-scale carbon fixation, its variability, and
long-term trends during the period 2002–2016. Annual coastal PP was
estimated at 0.041 Gt C, which approximately represents 12 % of total
carbon fixation in the Mediterranean Sea. About 51 % of this production
occurs in the eastern basin, whereas the western and Adriatic shelves
contribute with ∼25 % each of total coastal production.
Strong regional variability is revealed in coastal PP, from high-production
areas (>300gCm-2) associated with major river
discharges to less productive provinces (<50gCm-2) located
in the southeastern Mediterranean. PP variability in the Mediterranean Sea
is dominated by interannual variations, but a notable basin-scale decline (17 %) has been observed since 2012 concurring with a period of increasing sea
surface temperatures in the Mediterranean Sea and positive North Atlantic
Oscillation and Mediterranean Oscillation climate indices. Long-term trends
in PP reveal slight declines in most coastal areas (-0.05 to -0.1gCm-2 per decade) except in the Adriatic where PP increases at +0.1gCm-2 per decade. Regionalization of coastal waters based on PP seasonal
patterns reveals the importance of river effluents in determining PP in
coastal waters that can regionally increase up to 5-fold. Our study
provides insight into the contribution of coastal waters to basin-scale carbon
balances in the Mediterranean Sea while highlighting the importance of the
different temporal and spatial scales of variability.
Introduction
Coastal ocean waters (i.e., <200 m depth) are an important link
between the land and the open ocean. They act as a buffer between
terrestrial and human influences and the open ocean (Liu
et al., 2000). Despite their relatively reduced extension (∼7 % of ocean surface area; Gattuso et
al., 1998), they behold some of the most productive habitats on the planet.
Therefore, they have a disproportionate importance in many basin-scale
biogeochemical and ecological processes, including carbon and nitrogen
cycling, and in the maintenance of marine diversity
(Cebrian, 2002; Coll et al., 2010; Dunne et al.,
2007). Besides, biological production of continental shelves supports over
90 % of global fish catches (Pauly et al., 2002; Pauly and
Christensen, 1995).
Coastal seawaters support high primary production (PP), contributing to some
10 % of global ocean PP and up to 30 % if estuarine and benthic
production is considered (Ducklow et al., 2001;
Muller-Karger et al., 2005). These high rates of organic productivity occur
in the coastal oceans due to the rapid turnover of the large inputs of
nutrients and organic carbon from land. PP drives a significant carbon sink
in the ocean (Field et al.,
1998; Laws et al., 2000) and is a key regulator of ecological processes
such as elemental cycling, trophic structure variabilities, and climate
change (Bauer et al., 2013; Chavez et al., 2011). In
coastal waters, physical and biological processes enhance the carbon
transport out of the continental margins into the deep layers of the oceans,
thus connecting terrestrial with deep oceanic systems
(Cai, 2011; Carlson et
al., 2001; Cole et al., 2007). The productivity of coastal sea areas is also
of strategic socio-economic importance for many countries considering that
PP constrains the amount of fish and invertebrates available to expanding
fisheries, a primary resource for many coastal human communities
(Chassot et al., 2010). The estimation and understanding
of PP evolution and trends in the coastal seas is therefore essential to
improve our knowledge of the oceanic carbon cycle.
Scaling up local measurements to estimate the contribution of coastal
regions to global carbon fluxes has been hindered by the high spatial and
temporal heterogeneity of these waters. Global models of oceanic systems
produce carbon fixation estimates with a high degree of uncertainty in
coastal regions (Muller-Karger et al., 2005). Coastal waters
are complex because of the tight connection between terrestrial and oceanic
systems. Terrestrial uploads of nutrients and organic matter originating
from groundwater discharges, flash floods, or river runoff as well as
exchanges with the seafloor strongly control the productivity of these waters
(Basterretxea et al., 2010; Woodson and Litvin, 2014). The amplitude of seasonal variation in
surface chlorophyll (chl) and surface temperature is often higher in coastal
waters compared to the open ocean (Cloern and
Jassby, 2008). Furthermore, coastal topography and its interaction with
winds, waves, and currents generates a high variety of physicochemical niches
for phytoplankton growth. Likewise, benthic–pelagic coupling allows the
remineralization of nutrients present in shelf sediments during most intense
storms. These episodic variations may constitute an important contribution
to the overall productivity of shelf waters. Because of the high
spatiotemporal heterogeneity in the main coastal subsystems and the
concomitant lack of data, most estimated carbon fluxes in these subsystems
have relatively high uncertainties (Bauer et al., 2013). In
addition, direct human activities and climate change lead to a long-term
variation in terrestrial fluxes and coastal biogeochemistry that can
potentially have important consequences for the global carbon cycle
(Gregg et al., 2003).
In the Mediterranean Sea, coastal and shelf areas represent about 21 % of
the global basin (259 000 km2), which is a higher contribution than for
the global ocean (Pinardi
et al., 2006). Although the Mediterranean Sea is included amongst the most
oligotrophic areas of the world oceans, it can display marked spatial
productivity variations related to the variety of regional climate and
oceanographic conditions as well as related to the multiple land-derived fluxes that
locally fertilize the coastal waters
(Goffart et al., 2002). Nutrient-rich
effluents from human activities on the coast (domestic wastewater,
fertilizers, industry, etc.) and natural river discharges affect
continental shelf productivity in this sea, sustaining locally enhanced
pelagic and benthic biomass. Nevertheless, the influence of some river flows
has been notably reduced by damming affecting water chemistry and sediment
loads and, thereby, the productivity of coastal waters at local and regional
scales (Ludwig et al., 2009; Tovar-Sánchez et al., 2016). Moreover, intensive
agricultural practices and urbanization have brought unprecedented use and
contamination of coastal groundwater
(Basterretxea et al., 2010; Tovar-Sánchez et al., 2014). For example, the use of
fertilizers has resulted in higher nutrient flow into the Adriatic and in
the lagoons of the Nile River, which has led to eutrophication
(Turley, 1999). However, the impact of this
anthropogenic nutrient enrichment may vary between regions, and modeling
projections suggest spatial variations in PP as a result of climate change.
Accurate quantification of the coastal PP is fundamental for assessment of
global carbon cycling in the Mediterranean Sea. Changes in PP have important
effects on fish stocks that are socially relevant because of the economical
dependency of many Mediterranean coastal communities on marine food
products. Several studies have assessed PP at the scale of the entire
Mediterranean Sea from satellite remote sensing data
(Bosc et al., 2004; Bricaud et al., 2002; Lazzari et al., 2012). However, coastal
areas were generally ignored in such studies, so that their contribution to
basin-scale budgets is still largely unknown. Most coastal studies have a
focus on specific regions and/or times
(Estrada, 1996; Marty et al., 2002; Moutin and Raimbault, 2002; Rahav et al., 2013).
Observed rates of climate change in the Mediterranean basin exceed global
trends (Cramer et al., 2018) and future warming in the
Mediterranean region is expected to be above global rates by 25 %
(Lionello and Scarascia, 2018).
Long-term responses of PP in coastal areas to climate forcing remain uncertain however
because of the scarcity of adequate field datasets
(Gasol et al., 2016).
In this study, we present major characteristics of pelagic PP in
Mediterranean coastal waters based on satellite-borne observations for the
period 2002–2016. First, we provide global estimations of PP in coastal
waters, and we assess their contribution to basin-scale PP, their interannual
variability, and long-term trends. Then, we regionalize the coastal waters
based on their temporal patterns of pelagic PP using self-organizing maps
(SOMs), and we analyze the contribution of each region to total coastal PP.
Materials and methodsRemote sensing data
We used the Mediterranean Sea Level-3 reprocessed surface chlorophyll
concentration product (Chl L3) obtained from the EU Copernicus Marine
Environment Monitoring Service (CMEMS). This product merges multi-satellite
observations, and it is available at 1 d and 1 km resolution
(https://resources.marine.copernicus.eu/product-detail/OCEANCOLOUR_MED_CHL_L3_REP_OBSERVATIONS_009_073/, last access: 2 August 2019). Specifically, the
dataset used is “dataset-oc-med-chl-multi-l3-chl_1km_daily-rep-v02”, and the variable name used is
“mass_concentration_of_chlorophyll_a_in_sea_water (Chl)” obtainable in a NetCDF-4 file format. This
Chl L3 dataset is derived with an updated version of the regional algorithm
MedOC4 (Mediterranean Ocean-Colour 4 bands MedOC4;
Volpe et al., 2019) for deep pelagic Case-1 waters
and the AD4 algorithm (ADriatic 4 band;
Berthon and Zibordi, 2004;
D'Alimonte and Zibordi, 2003) for Case-2 coastal waters (generally shallow
and turbid waters).
Level-2 sea surface temperature (SST, ∘C) at 1 d and 1 km
was obtained from every available orbit from the Moderate Resolution Imaging
Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. Data were
downloaded from the National Aeronautics and Space Administration (NASA)
archive website (http://oceancolor.gsfc.nasa.gov/, last access: 18 July 2017). Only nighttime orbits
were selected to avoid problems with skin temperature during daylight.
Orbits with quality flags 0 (best), 1 (good), and 2 (questionable) in SST
were included after checking their validity and accuracy in order to have a
more complete dataset. Daily (24 h averaged) photosynthetically active
radiation (PAR, in Em-2) was obtained as a Level-3 product at 9 km and
1 d resolution. This is the best available resolution at the NASA archive of
MODIS and Medium Resolution Imaging Spectrometer (MERIS) data
(https://oceancolor.gsfc.nasa.gov/l3/, last access: 30 December 2017).
All satellite-derived variables were remapped onto a regular 1 km spatial
grid over the study area, by averaging all available pixels within each grid
cell. For each parameter, outliers were removed whenever they exceeded
3 times the mean ± SD (standard deviation) of the time series. For the
purpose of this study, coastal areas were defined as the waters lying
between 5 and 200 m depth. Only values at depths exceeding 5 m depth were
considered in order to reduce the possible influence of seafloor vegetation
reflectance in chlorophyll concentration values (chl, mgm-3) at
shallow waters. The analyzed time series covers the period from January 2002
to December 2016 for the Mediterranean Sea (30 to 46∘ N and
6∘ W to 37∘ E, Fig. 1).
Map of the Mediterranean Sea showing the main basins, sea regions,
surrounding countries and major rivers. Bathymetric data were obtained from
ETOPO1 (Amante and Eakins, 2009). The black
contour indicates the 200 m isobath, the limit of coastal waters as defined
in the present study.
Primary production estimates
PP was estimated from satellite-derived chl, SST, and PAR values using the
time, depth, and wavelength-resolved light-photosynthesis model of
Morel (1991). This
model was previously used for estimating PP in the Mediterranean Sea
(Antoine and André, 1995) and at global scale
(Antoine et al., 1996; Antoine and Morel, 1996) and
performs well when compared to in situ measurements
(Campbell et al., 2002; Friedrichs et al.,
2009) or when compared to other similar algorithms designed for use with
satellite observations
(Carr et al., 2006; Saba et al., 2011). Instantaneous production at depth z (m) of
the water column, time t of the day, and for absorption of irradiance at
wavelength λ, P(λ,z,t) is calculated as
P(λ,z,t)=E(λ,z,t)chl(z)a∗(λ,z)Φ(gCm-3s-1),
where E(λ,z,t) is the spectral scalar irradiance for wavelength
λ, depth z, and time t of the day (molphotonsm-2s-1);
a∗(λ,z) is the spectral chlorophyll-specific absorption
coefficient of phytoplankton (m2mgchl-1); and Φ is the
quantum yield of photosynthesis for carbon fixation (molCmol per photon; its possible spectral changes are ignored). Note that chl, a∗, and Φ are not made variable with time.
The triple integration of (1) with respect to wavelength, depth, and time gives the
daily column-integrated primary production, PP:
daily PP=12∫0D∫0min(Zp/Zb)∫400700P(λ,z,t)dλdzdt(gCm-2),
where the factor 12 is the conversion from moles to grams of carbon, D is the
day length or hours of daylight (h), Zp (m) is the depth where the
photosynthetically available radiation (PAR) falls to 0.1 % of its value
just below the sea surface (so approximately 1.5 times the euphotic depth),
and Zb is the bottom depth taken from the ETOPO1 database
(Amante and Eakins, 2009). The time
integration used intervals equal to 1/30 of the day length (about 20 to 30 min depending on season). The depth integration used intervals equal to 1/50
of Zp and goes down to whichever is shallower between Zp and
Zb. The spectral integration is performed over the visible range (400 to
700 nm) with a 5 nm resolution.
The spectral irradiance at a given depth z, E(λ,z,t), is calculated as
(starting from just below the sea surface)
E(λ,z,t)=E(λ,z-dz,t)e[-Kd(λ,z)dz],
where the diffuse attenuation coefficient for downward irradiance,
Kd(λ,z) (m-1), is computed as a function of chlorophyll following
Morel and Maritorena (2001):
Kd(λ,z)=Kw(λ,z)+χ(λ)chl(z)e(λ).
Details about how values are assigned to the parameters a∗ and
Φ, their dependence on temperature, and other features of this net
primary production (NPP) model are to be found in
Morel (1991) and
Morel et al. (1996).
Daily PP calculation was performed every 7 d (starting at day 4), using
the averaged chl for a 7 d window from day -3 to day +3 and for one grid
point out of three. Therefore, the PP model was run with 8 d resolution and for one
pixel out of three pixels. Later, daily PP data were interpolated to a 1 d by 1 km
grid. Monthly PP data and their anomalies were derived from the
abovementioned dataset.
The model was operated both for clear-sky conditions and for the actual
MODIS PAR values, in which case a reduction of the clear-sky irradiance is
uniformly applied across the entire day, as being the ratio of the daily satellite
to clear-sky PAR values. Chl is assumed to be uniformly distributed
with depth and equal to the satellite-derived value. This simplification
was considered more appropriate for the generally shallow and well-mixed
waters of coastal areas than the use of global parameterization of the shape
of the vertical profile as a function of the surface chl value
(e.g.,
Morel and Berthon, 1989; Uitz et al., 2006), whose validity outside of open
ocean waters is not established.
From PP estimates, new (PPnew) and regenerated (PPreg) production
were calculated using the ratio of export production (PPexp) to total
production (PP)
(i.e., ef-ratio) (Laws et al., 2000, 2011). Indeed, assuming a steady state, the
export production must equal the new production fueled by new nutrients
brought to the surface layers. The ef-ratio as a function of satellite-derived
temperature and production can be obtained from the empirical relationship
obtained by Laws et al. (2011):
5ef=(0.5857-0.0165T)PP(51.7+PP),6PPexp=PPnew=PP×ef,7PPreg=PP-PPnew,
where T is temperature in degrees Celsius (∘C) and PP is the daily
production (mgCm-2).
As shown in Table 1, we report PP as vertically integrated values
(PP, in gCm-2), spatially integrated estimates for certain basins or
regions (ΣPP, in Gt C), or mean volumetric values (PPVOL, in gCm-3). The coefficient of variation (CV) has been estimated for
PP as the ratio of the standard deviation to the mean. While some authors
include the Adriatic in the eastern basin (e.g.,
Bosc et al., 2004), we
treated this region separately because its peculiarities (i.e., bathymetry,
influence of rivers, eutrophic character) differentiate it from the rest of
the Mediterranean Sea (Cushman-Roisin et al., 2001). Most of
the Adriatic Sea has a shallow (<200 m) bathymetry, and it collects
some 30 % of the freshwater flowing into the Mediterranean, acting as a
dilution basin for the nutrients discharged by the Po and other Adriatic
rivers and becoming one of the most human-impacted regions of the
Mediterranean Sea
(Ludwig
et al., 2009; Micheli et al., 2013; Raicich et al., 2013a).
Primary production acronyms used in this study, their units, and their definitions.
VariableUnitsDefinitionPgCm-3s-1Instantaneous production at each depth (z) of the water columnPPgCm-2Daily primary production per surface unit; integration of P over depth and day lengthPPannualgCm-2Annual mean production per surface unitΣPPGt CTotal carbon fixation per year within a basin or specific regionPPVOLgCm-3Mean volumetric primary production; column-integrated PP divided by whichever is shallowerof the bottom depth or the productive layerPPnewgCm-2New production (i.e., from allochthonous sources)PPreggCm-2Regenerated productionCoastal regionalization
We used a two-step classification procedure to define coastal regions along
the Mediterranean based on their temporal PP patterns. First, nine regions (R1
to R9) were identified using a classification technique based on an
unsupervised learning neural network (self-organizing maps or SOMs;
Kohonen, 1982, 2001). Then, 18 alongshore marine
ecoregions were obtained considering the most relevant cross-shore limits of
the SOM-derived regions (Z1 to Z18).
SOM is an unsupervised neural network method that reduces the high-dimensional feature space of the input data to a lower-dimensional network
of units called neurons. SOM is especially suited to extract patterns in
large datasets of satellite data
(Ben
Mustapha et al., 2014; Charantonis et al., 2015; Farikou et al., 2015).
Unlike other classification methods, like k-means, SOM tends to preserve data
topology (i.e., preserves neighboring regions), and, therefore, it is
particularly suited for pattern recognition
(Liu and Weisberg, 2005). It allows
adequate classification of areas with high spatial complexity and strong
gradients. Similar neurons are mapped close together on the network,
facilitating the visualization of patterns and a topological ordination of
the classified areas, and the relative distance among neurons is obtained as a
result of the analysis.
For typical satellite imagery, SOM can be applied to both space and time
domains. Here, we have addressed the analysis in the time domain of the
datasets, which allows regionalization of the studied area on the basis of
similitudes in the time variation in PP. We chose a map size of (3×3),
with nine neurons (for further details, see Basterretxea
et al., 2018). We used a hexagonal map lattice in order to have equidistant
neighbors and to avoid introducing anisotropy artifacts. For the algorithm
initialization, we opted for linear mode, batch training algorithm, and
ep, or Epanechnikov function, type neighborhood function since this parameter configuration produces
the lower quantitative and topological error and computational cost
(Liu et al., 2006). This methodology was applied in Basterretxea et al. (2018) for satellite-derived chlorophyll time series. For further details of the methodology used, see Hernández-Carrasco and Orfila (2018). These SOM computations were
performed using the MATLAB toolbox of SOM v.2.0
(Vesanto et al., 2000a, b) provided by the
Helsinki University of Technology
(http://www.cis.hut.fi/somtoolbox/, last access: 12 April 2019).
Climate data
To identify possible drivers of long-term PP variability, we searched for
correlations with two climate indices, the North Atlantic Oscillation (NAO) index
and the Mediterranean Oscillation Index (MOI). The corresponding data
were downloaded from the Climate Research Unit at the University of East
Anglia (https://crudata.uea.ac.uk/cru/data/, last access: 21 August 2018) in monthly resolution. Climate
indices are defined either as anomalies of a climate variable, using the
difference between two geographical points, or as principal components
(Hurrell, 1995).
NAO is the central mode of climate variability of the Northern Hemisphere
atmosphere. It is based on the pressure difference between the middle of the
North Atlantic Ocean and Iceland, which affects winter conditions in the
North Hemisphere (Hurrell and Van Loon,
1997; Marshall et al., 2001). Positive NAO results in a relatively dry
winter in the Mediterranean but a warmer and wetter winter in northern
Europe, and vice versa. Because of its influence on precipitation, Mediterranean
river inflows are generally anti-correlated with the NAO
(Trigo et al., 2006).
MOI is the most widely used teleconnection index for the Mediterranean
basin. It reflects differences in temperature, precipitation, circulation,
evaporation, and other parameters between the eastern and western basin.
There are different versions depending on the points of reference
(Criado-Aldeanueva and Soto-Navarro, 2013). We
used the version obtained as the normalized pressure difference between
Gibraltar and Israel (Palutikof, 2003). Positive MOI phases
are associated with increased atmospheric pressure over the Mediterranean
Sea that promotes a shift of the wind trajectories toward lower latitudes
leading to milder winters (Criado-Aldeanueva
and Soto-Navarro, 2013). Under these conditions, reduced precipitation is
observed in the southeastern Mediterranean region
(Törnros, 2013). With some regional differences,
NAO and MOI express relatively similar climate patterns over the
Mediterranean Sea. They are highly positively correlated in winter and
weakly but still significantly correlated in summer
(Efthymiadis
et al., 2011; Martínez-Asensio et al., 2014).
Statistical analyses
Linear temporal trends in the PP series were calculated using Theil–Sen
slope adjustment (Sen, 1968) of the residuals of the
de-seasonalized series. Time series have been de-seasonalized by removing
the 8 d climatological mean for the original time series. Time series with
>12 % of missing values were excluded from the analysis, and
the analysis was calculated pixel by pixel. A Mann–Kendall statistics test
is applied to each pixel, and only pixels with a trend statistically
significant at the 95 % level were considered (Salmi
et al., 2002). The use of this non-parametrical test is suitable for
non-normally distributed data and has been previously used in the trend
examination of remote-sensing chl time series
(Colella
et al., 2016; Kahru et al., 2011; Salgado-Hernanz et al., 2019).
ResultsCoastal primary production
Annual primary production in coastal waters of the Mediterranean Sea
(ΣPPCoast) is estimated to be 0.041±0.004 Gt C, which
represents some 12 % of total carbon fixation in the Mediterranean Sea
(see Tables 2 and 3). Approximately, 80 % of this ΣPPCoast is
sustained by recycling processes, and, the rest, PPnew, is exported to
the seafloor or to nearby areas. Although average surface chl concentration
is 3-fold higher in coastal areas (0.3 mgm-3) than in open areas (0.11 mgm-3), the annual carbon fixation per surface area (PPannual)
over the shelf is, on average, 26 % lower than in the open ocean
(100±91 and 136±40gCm-2, respectively; see Tables 2 and
3). The lower PPannual values on the coast are due to depth
integration quite often being stopped at shallow depths, i.e.,
Zb<Zp.
Figure 2 reveals the differences in PPannual between the more productive
shelf waters in the western basin and those in the eastern basin (90±39 and 73±86gCm-2, p<0.001). The
Adriatic shelf is by far the most productive, particularly if mean values
are considered (124±76gCm-2, Table 2). Annual carbon fixation
is 97 % higher in the eastern (ΣPPeast=0.021±0.002 Gt C) than in the western shelf (ΣPPwest=0.011±0.001 Gt C), which is due to greater eastern surface area (about twice the western
surface area; Table 2). PPannual varies spatially from 90 to 250 gCm-2 in the western shelf and from 50 to 400 gCm-2 in the
eastern basin. Contrastingly, PPannual exceeds 100 gCm-2 in the
Adriatic basin, reaching values above 400 gCm-2 on the northwestern
Adriatic coast (Fig. 2b). The most productive coastal regions (>150gCm-2) are located in regions enriched by major river outflows.
Indeed, the highest values of the coefficient of variation in primary
production (CVPP) are observed at the mouth of the Nile, Ebro, Rhône,
Tiber, Po, Neretva, or Nestos–Evros rivers. Nevertheless, in some coastal
regions of the eastern basin like the Gulf of Gabès primary production is
also outstandingly high (>300gCm-2). Along the western
North African coast, PPannual also displays values >150gCm-2; however, since the shelf is narrow, its contribution to ΣPPCoast is marginal (Figs. 1 and 2). The annual volumetric
productivity follows a similar pattern than the annual integrated production,
with most values varying between 1–3 gCm-3 but reaching up to
>10gCm-3 in the most productive coastal regions of the
Adriatic Sea and the Gulf of Gabès (Fig. 3a).
Surface area and the corresponding basin percentage to
surface area, (chl) ± standard deviation (SD), ΣPP, the
corresponding basin percentage to ΣPPCoast (% ΣPPCoast), PPannual median ± SD (PPannual mean), and
PPVOL median ± SD (PPVOL mean) for the
Mediterranean Sea, open ocean waters, and coastal waters during the period
2002–2016. For ΣPP and PPannual, Mediterranean Sea
values were obtained summing open ocean water values and coastal water
values.
a Mean surface chl values obtained by averaging the 8 d and 4 km
resolution of surface satellite chl values obtained from CMEMS
(Salgado-Hernanz et al., 2019). b PP estimated by averaging published satellite data shown in Table 3.
cΣPP estimated adding coastal water data from this study to open
ocean water data obtained from Table 3.
Compilation of published values of PPannual and
ΣPP for the different Mediterranean
basins.
RegionPeriodPPannualΣPPMethodReference(years)(gCm-2)(Gt C)Whole–80–90In situ (14C method)Sournia (1973)Mediterranean198194±60–117.5±75aSatellite (CZCS)Morel and André (1991)estimation1979–1983125–156a0.308–0.385aSatellite (CZCS)Antoine and André (1995)1997–1998190Satellite (SeaWiFS)Bricaud et al. (2002)1998–200179.1–88.4Satellite (SeaWiFS)Colella et al. (2003)1998–2001130–140Satellite (SeaWiFS)Bosc et al. (2004)1998–20070.5Satellite (SeaWiFS)Uitz et al. (2010)1998–2013116Satellite (five sensors)O'Reilly and Sherman (2016)Western1980–1985120In situ (Oxygen)Bethoux (1989)basin1981157.7Satellite (CZCS)Morel and André (1991)1979–1983157–197aSatellite (CZCS)Antoine and André (1995)1996140–150In situ (14C data)Conan et al. (1998)1997–1998198Satellite (SeaWiFS)Bricaud et al. (2002)1991–199983–235In situ (14C method)Marty et al. (2002)1996175In situ (14C method)Moutin and Raimbault (2002)1997–1998123In situ (14C method)Van Wambeke et al. (2004)1997–1998152In situ (14C method)Lefèvre unpubl. (2001)∗1998–200193.8–98.8Satellite (SeaWiFS)Colella et al. (2003)1998–2001163±7Satellite (SeaWiFS)Bosc et al. (2004)2006–2007858c,dIn situ (dark–light method)Regaudie-De-Gioux et al. (2009)Eastern1980–1985137–150bIn situ (Phosphorous)Béthoux et al. (1998)basin1981109.4Satellite (CZCS)Morel and André (1991)(including1979–1983110–137aSatellite (CZCS)Antoine and André (1995)Adriatic)1997–1998183Satellite (SeaWiFS)Bricaud et al. (2002)199696In situ (14C data)Moutin and Raimbault (2002)1998–200169.1–81.5Satellite (SeaWiFS)Colella et al. (2003)1998–2001121±5Satellite (SeaWiFS)Bosc et al. (2004)2006–2007521c,dIn situ (dark–light method)Regaudie-De-Gioux et al. (2009)Adriatic1978–1983241–301a0.0235Satellite (CZCS)Antoine and André (1995)basin1998–200192.4–104.4Satellite (SeaWiFS)Colella et al., (2003)
a The estimates from Antoine et al. (1995) and
Morel and André (1991) have been
corrected by a factor of 1.25 as recommended by
Morel et al. (1996).
b From Colella et al. (2003), who
estimated it using f-ratios (the ratio between new and total PP) obtained
from Boldrin et al. (2002).
c Daily PP (mgCm-2d-1) converted to annual PP
(mgCm-2yr-1) multiplied by 365.
d Conversion to carbon unit using photosynthetic quotient PQ=1.
∗ Data included in the Data Published for Earth and Environmental Science PANGAEA data collection.
Mean distribution of (a) chlorophyll (chlmean, in mgm-3), (b) annual PP (PPannual, in gCm-2), and (c) coefficient
of variation in PP values (CVPP).
Mean distribution of (a) volumetric PP (PPVOL, in gCm-3) and (b) coefficient of variation in volumetric PP values
(CVPP).
Long-term variability and trends
As shown in Fig. 4, variability in annual PP is dominated by short-scale
variations (i.e., sub-decadal). The interannual variability is indicated by
the low-frequency signal of the monthly mean anomalies. The filtered low-frequency signal to the anomalies of ΣPP and chl has been calculated
using the MATLAB smooth function and applying the sgolay filter, which uses the
Savitzky–Golay method, with a polynomial span degree of 17. This degree was
therefore filtering about 8 months before and after every time step (about
1.5 years), then showing interannual variability. ΣPPCoastal
exhibits moderate interannual variability (up to 25 %) whereas basin-scale interannual variations range from 26 % in the Adriatic basin up to
28 % in the western basin and 29 % in the eastern basin. This value of
interannual variability was calculated subtracting the year with the minimum
annual PP from the year with the maximum annual PP and then dividing this
value by the mean annual PP. Positive anomalies in ΣPPCoastal extended between 2004 and 2011 (mean ΣPPCoastal=0.044±0.001GtCyr-1 for the whole basin between those years,
Fig. 4a and Appendix Fig. A1a). Conversely, the year 2012 was particularly
unproductive in all three basins (0.037 for the whole
basin, 0.010 for the western basin, 0.019 for the eastern basin, and 0.009 GtCyr-1 for the Adriatic basin). This
negative anomaly marked the beginning of a less productive period,
particularly noticeable in the eastern basin (Figs. 4, A1 and
A2).
PPannual (gCm-2) and ΣPP (Gt C) in every year are shown
in Figs. A1 and A2.
PP variability and trends for coastal waters in (a) the whole
Mediterranean Sea, (b) western basin, (c) eastern basin, and (d) Adriatic
coast. Black solid lines indicate the original monthly ΣPPCoastal anomalies, and the filtered low-frequency signal is
overlaid in blue (16-month width sgolay filter). Green solid lines indicate the
filtered low-frequency signal for chl anomalies (mgm-3). The red line
indicates the PP trend during the analyzed period (2002–2016), and the gray
band indicates the year 2012.
As revealed by Fig. 5a, some regionally coherent patches of significant
trend in PP are observed along the coast. Most of these regions presented
declining PP trends, particularly along the North African coast where SST is
increasing at a higher rate (Fig. 5b). Typical PP trend magnitudes observed
along the Spanish Mediterranean and the North African coast from the Gulf of
Gabès range from -0.05 to +0.05gCm-2 per decade. Some positive PP
trends, exceeding +0.1gCm-2 per decade, can be determined in some
coastal regions north of the Adriatic Sea.
Trends in primary production and sea surface temperature. Values
correspond to the change per decade. (a) Theil–Sen trend in pelagic primary
production (PP trend) estimated from daily values for the 2002–2016 period.
(b) Trend in sea surface temperature (SST trend). Only significant trends
(p<0.05) are shown.
A significant negative correlation was observed between monthly coastal
anomalies of ΣPP and SST at the whole Mediterranean basin scale
(r=-0.66, p<0.001; Fig. 6a, Table A1), revealing a decrease in
phytoplankton biomass as the sea warms up. In addition, we observed evidence
of an inverse relationship between PP variability and the phase of the climate
indices NAO and MOI (r=-0.46, p<0.001, and r=-0.24, p<0.001,
respectively; Fig. 6b and c, Table A1). A similar relationship is observed
between monthly coastal chl anomalies with SST (r=-0.64, p<0.001)
and climate indices NAO and MOI (r=-0.60, p<0.001, and r=-0.35,
p<0.001, respectively; see Appendix Fig. A3) at the whole
Mediterranean basin scale.
Additionally, the response of PP to climate variations varied seasonally.
Indeed, NAO influenced coastal PP in summer, both in the western and in the
eastern basin (r=0.25, p=0.06; r=0.22, p=0.08, respectively) and MOI
variations were better correlated with PP global variations in spring
(r=0.28, p=0.04), showing a higher impact in the Adriatic basin
(r=0.37, p=0.02). No significant correlation was found during the winter
or fall season for any index (see Table A1 in Appendix). Correlation
analyses were performed using the Pearson product moment correlation.
Differences between means were tested using the Kolmogorov–Smirnov test
(Massey et al., 1951).
Relationship between coastal pelagic primary production (ΣPP anomalies, blue lines) and (a) SST anomalies, (b) NAO index, and (c) MOI
index (red lines).
Coastal regionalization
The nine characteristic temporal PP patterns, their corresponding spatial
distribution obtained from the SOM analysis, and the 18 zones in which the
coastal region was classified are shown in Fig. 7. Generally, wider shelves
present higher spatial complexity manifested as a larger number of SOM
patterns. About 78 % of the shelf waters include R1, R2, R3, and R4
patterns. In particular, R1 and R2 characterize areas of low production, with
scarce seasonality (PPannual=44±17 and 69±22gCm-2, respectively; Table 4) typically occurring in the southern and
eastern Mediterranean (12 % and 29 % of the total surface area). They are
representative of the productivity patterns in vast shelf regions in the
Gulf of Gabès and Sirte and in the central Aegean (Z17, Z16, Z14 and Z11).
R3 and R4 correspond to higher production and a wider range of variation
(PPannual=90±32 and 98±39gCm-2; Table 4 and
Fig. 7). While R3 (18.6 % of total coastal surface) is frequent in shelf
regions of the western basin (Z2, Z3, Z5, and Z7), R4 extends over the
deepest areas of the Adriatic Sea (Z9 and Z10). Both patterns are
representative of variations observed in 36.9 % of the coastal waters.
Regionalization of the coastal waters in the Mediterranean Sea
based on their temporal patterns of pelagic primary production. (a)
Characteristic temporal patterns of PP obtained from SOM classification (R1
to R9) and (b) coastal regions defined from alongshore variations in the SOM
– regions (Z1 to Z18).
Extension and primary production for each of the
SOM-defined regions (R1 and R9). Mean annual PP is estimated by averaging
mean daily PP and then multiplying it by the number of days of the year, i.e.,
365.
As shown in Table 4, areas of low production with seasonal patterns R1 to R4
contribute to more than 62 % of total pelagic carbon fixation in
Mediterranean coastal areas. In contrast, systems of higher production
(PPannual>280gCm-2d-1) barely contribute to
17 % of total production. These regions of enhanced production are
generally constrained to regions of freshwater influence
(ROFIs; Simpson, 1997) where terrestrial
nutrients fuel coastal production. Indeed, R8 is almost exclusively
restricted to the river mouths, and it presents elevated PPannual values
(1.29±0.50gCm-2d-1) and a wide range of variation of
0.67 to 2.14 gCm-2d-1. An exception is the R7 pattern, which is
exclusively located in the shallowest inner shelf of the Gulf of Gabès, and
it is bounded by R5, a transition region between the inner and outer shelf.
Unlike the other regions, where PP peaks in late winter–spring, maximum PP
in R7 occurs in fall. Finally, R5 and R6 patterns correspond to transition
regions accounting for 20.4 % of the total production and 14.8 % of
the Mediterranean coast. While R5 mainly occurs in deltas, R6 is
characteristic of the western Mediterranean shelf, including the North
African coast (0.36–0.08 gCm-2d-1; Fig. 7).
The SOM-based regionalization reveals two groups of coastal waters: those
with low cross-shore variability and including only one or two SOM regions
(i.e., Z1, Z13, Z16, and Z18) and those with strong cross-shore gradients
including several SOM regions (i.e., Z4, Z9, Z12, Z15). The first pattern is
typically observed in narrow continental shelf areas with low influence of
river inputs whereas the second group is found in regions with a wider
continental shelf such as ROFIs (the Rhône delta, the north and western
coastline of the Adriatic Sea, and the Nile Delta) and in the Gulf of Gabès.
The western Adriatic (Z9) and the Gulf of Gabès (Z17) are the largest
contributors to ΣPPCoast, together contributing 35.9 % of
shelf production in the Mediterranean Sea, but, in the case of Z17, it is
mainly due to its large extension (Table 5). PP is also high in the northern
Alboran Sea (Z1), Nile delta (Z15), the western Adriatic (Z9), and Gulf of
Lion (Z4; Table 5). With the exception of Z1, influenced by the entrance of
waters from the Atlantic Ocean and by local coastal upwelling, these zones
receive important riverine fluxes (Q).
DiscussionCoastal primary production
In this study we focused on the contribution of coastal waters to the
overall pelagic PP in the Mediterranean Sea. While the mean coastal values
for the Mediterranean (100±91gCm-2) are somewhat lower than
the mean values over the continental shelves of the world ocean (160±40gCm-2; Smith and Hollibaugh, 1993), the impact of
coastal pelagic PP on total basin production (12 %) is in the high range
of the estimations for other seas (Muller-Karger et al.,
2005). This estimation is subject to the uncertainties inherent to using
satellite ocean color, which is limited to the upper ocean (down to 20 m at
best in clear waters) and has poor performance in some areas (i.e., Case-2
waters). It nevertheless provides an assessment of net rates of carbon
fixation in coastal areas that is consistent with global estimations of the
contribution of coastal areas to oceanic production
(Gattuso et al., 1998; Ducklow and
McCallister, 2004;
Muller-Karger et al., 2005). Bias in coastal chl
estimations is mainly due to the presence of non-phytoplankton components
such as colored dissolved organic matter (CDOM) or other terrestrial
substances (Morel et al., 2006). These
compounds originate from coastal erosion, resuspension in shallow areas,
river inputs, or anthropogenic effluents. Likewise, they affect the
propagation of photosynthetic radiation through the water column
(Morel, 1991).
However, the possible uncertainties and biases caused by chl estimation
through satellite data might very weakly alter our estimation of coastal PP.
Indeed, previous studies agreed that Case-1 waters are largely predominant
in the coastal Mediterranean regions whereas Case-2 waters are reduced to
less than 5 % of the whole basin
(Antoine
and André, 1995; Bosc et al., 2004; Bricaud et al., 2002). This
constitutes some 23 % of the coastal waters with prevalence in the north
Adriatic Sea, Gulf of Gabès, and around the Nile delta. In particular, they are
confined to the north Adriatic Sea, Gulf of Gabès, and around the Nile delta
where our PP estimations may present larger uncertainties (Antoine
and André, 1995). However, PPannual values off the Nile River
delta, >100gCm-2 estimated here, are only slightly
higher than those reported by Antoine et al. (1995) (80–100 gCm-2). The highest values have been reported for this region (>300gCm-2), but, as shown in Fig. 2, they are restricted to a narrow
coastal band. In the case of the Adriatic Sea,
Umani (1996) reported values of PP from 50 to 200 gCm-2yr-1, while
Zoppini et al. (1995) estimated
PP rates from 210 to 260 gCm-2yr-1 in the northern coastal
areas. Our estimations range between 100 and >350gCm-2
(with mean values of 123±106gCm-2).
Surface, river discharge flow (Q), annual mean PP
(PPannual), annual integrated PP (ΣPP), and its contribution with
respect to the total coastal Mediterranean Sea PP for each of the 18
alongshore zones characterized in the Mediterranean Sea. Mean and standard
deviation (SD) are calculated from 15-year averages (2002–2016). River discharge flow was extracted from
Tockner et al. (2008).
Contrary to what we would have expected, we observed that the eastern basin
contributes more than the western basin to overall coastal production (51 % and 25 %, respectively; Table 2). Its great extension (twice higher
than the western basin) and the increased productivity in regions like
Gabès, the Nile, and the northern Aegean Sea may explain greater coastal PP
in the eastern basin than in the western basin. Additionally, due to the
lack of large shallow and productive areas in the western basin, we observed
few volumetric PP values above 30 gCm-3 in the western Mediterranean
whereas high volumetric PP is more frequent in the Adriatic Sea and in the
eastern Mediterranean, in shallow waters of the Gulf of Gabès, and in the Nile
delta. Furthermore, carbon fixation from the Adriatic Sea represents 24 %
of total coastal production, which is significant considering the area of
this sea (19 % of Mediterranean coastal waters). The relevance of the
contribution of the Adriatic Sea in overall coastal PP lies in two main
characteristics: (1) coastal waters (<200 m) constitute a large
part of the Adriatic Sea, and (2) about one-third of the river discharge in
the Mediterranean is concentrated in the Adriatic Sea (see Table 5). Indeed,
patterns in the northern Adriatic Sea reflect a variation in the drivers of
PP with respect to other regions. For example, while internal processes
(i.e., vertical diffusion and mixing) and atmospheric deposition drive PP
in most coastal waters, production in the north Adriatic is mainly
driven by fluvial sources of carbon and regeneration through bacterial
pathways
(Powley
et al., 2016; Rodellas et al., 2015; Umani et al., 2007). Moreover,
distinctive dynamics in this sea is driven by the influence of river
outflows on stratification and general circulation patterns
(Djakovac et al.,
2012; Giani et al., 2012).
Regionally, the Alboran Sea, the Gulf of Lion, the Adriatic Sea, the Aegean
Sea, the Nile delta, and particularly the Gulf of Gabès (up to 23 % of
total coastal production) are the most productive zones. While some of these
productive (i.e., PPVOL>3gCm-3) coastal regions are
located in areas affected by river outflows, this type of production rapidly
decreases in the offshore direction (see Fig. 3). Exceptions are the Rhône and
the Po and less so the Nile, the influence of which extends far along the shelf.
Other processes like mesoscale circulation are also important in some of
these regions (i.e., Gulf of Lion), as demonstrated by
Macias et al. (2017). Additionally, the exchanges
with the more productive Atlantic and Black Sea waters in Alboran and in the
northern Aegean and local enrichment processes in the Gulf of Gabès are
major contributors to coastal productivity. Indeed, the Gulf of Gabès
constitutes an anomaly in the eastern Mediterranean basin. Its shallowness
(<50 m depth at 110 km off the coast), unique tidal range (maximum
>2 m) promoting vertical mixing, and the lack of summer nutrient
exhaustion undoubtedly contribute to its high productivity
(Béjaoui et al., 2019).
Notwithstanding the importance of land inputs in the production of coastal
Mediterranean waters, from ef-ratios, we estimated that on average only
22%±20 % of the production is new, and the rest is sustained via
regenerated sources (Fig. 8a–c). This PPnew value (Fig. 8b) is
comparable to the mean organic carbon that sinks to the sea floor (28 %)
estimated from Muller-Karger et al. (2005) and
Pace et al. (1987) but higher than PPnew estimations provided by
Vidussi et al. (2001) for oceanic waters in the eastern
basin (15 % of total production). Contrarily to Vidussi
et al. (2001), who estimated PPnew in the eastern basin, the coastal
PPnew average here includes both eastern and western basins of the
Mediterranean, but also the highly productive areas in the northern
Adriatic. This could explain why the PPnew observed here is higher
than the one observed for oceanic waters in the eastern basins.
Additionally, high ef-ratios (>0.3) are observed in our case in
the areas where nutrient inputs from the Atlantic and river effluents
significantly enhance PPnew (Fig. 8a). Furthermore, ef-ratios present
significant seasonality, varying between 0.26±0.04 in the most
productive winter–spring season and 0.15±0.02 in summer, when the
water column is strongly stratified and the food web shifts to a more recycling-dominated system.
(a)ef-ratio in coastal waters (<200 m) of the Mediterranean
Sea and estimated values of (b) new (PPnew) and (c) regenerated
production (PPreg). Mean values for the period 2002–2016.
Long-term variability and trends
Available satellite ocean color data span about 20 years, so that temporal
trends derived from their analysis highly depend on decadal variability
(Henson et al., 2010). Despite
these limitations, satellite observations of ocean color over the past 2
decades suggest a relationship between warming and reduced productivity in
permanently stratified areas
(Behrenfeld et al., 2006). Since
clear tendencies of warming are observed in the Mediterranean Sea
(Nykjaer, 2009; Pastor et al., 2018),
intensification of stratification would decrease nutrient supply to
phytoplankton and, thus, decrease PP
(Behrenfeld et al., 2006;
Stambler, 2014).
Barale et al. (2008), using chl
anomalies derived from SeaWiFS data, observed a general decrease in chl
biomass in the Mediterranean Sea over the period 1998–2003. However, some
coastal areas in their study displayed the opposite tendency.
Macias et al. (2015) anticipated
no future global changes of integrated PP in the Mediterranean Sea from
modeling results. They predicted a tendency to oligotrophication in the
western basin and increase in the productivity of the eastern basin. Our
study reveals that ΣPPCoastal in the Mediterranean Sea varies
nonlinearly, and a reduction of carbon fixation rates has been observed since 2012
(Fig. 4a). Overall negative coastal PP trends are reported here in both the
western and in the eastern basin (-10.70 and -25.39 TC per decade;
Fig. 4b and c). A spatial analysis of the long-term decadal variability reveals weak
but spatially coherent and significant tendencies (p<0.05; Fig. 5).
In particular, PP declines along the coasts of Spain and Africa. Conversely,
trends in some areas of the Adriatic Sea are markedly positive (>0.1gCm-2 per decade), mainly in the proximity of the Po River. While
negative tendencies seem to fit with the assumed model of PP limitation
associated with increasing temperatures, the origin of the positive trend in
the Adriatic basin is more uncertain. A plausible explanation is the
variation in the flux and loads of the northern Adriatic rivers. For
example, Giani et al. (2012) observed an
increase in the Po River flow with increasing phosphate and dissolved
nitrogen concentrations in the Po delta and its surrounding shelf waters.
Alternatively, the Bimodal Oscillating System (BiOS), i.e., the feedback
mechanism between the Adriatic and Ionian
(Civitarese et al., 2010) peaking
between 2004 and 2006, could have affected mass and nutrient exchanges
between the Adriatic and the north Ionian Sea (Font et al., 2007; Schroeder
et al., 2008; Šolić et al., 2008; Viličić et al., 2012).
Long-term decadal variations in the eastern and western basins are mostly
coupled, suggesting that they share the same PP drivers at this basin scale
(Fig. 4b and c). A major feature in the interannual pattern is a global decrease
in production in 2012 that is extended to the following years in the eastern
basin. Durrieu de
Madron et al. (2013) reported peculiar atmospheric conditions in the
Mediterranean Sea during 2012 that triggered a massive formation of dense
water on the continental shelf and in the deep basin of the Gulf of Lion. A
similar anomaly was described in the Adriatic shelf where unprecedented
dense water generation was preconditioned by a dry and warm year, resulting
in a significant reduction of coastal freshwaters and basin-wide salinity
increase
(Mihanović
et al., 2013; Raicich et al., 2013b). Additionally,
Pastor et al. (2018) observed an anomalous
temperature increase in the Mediterranean Sea during summer 2012. From our
analysis, we infer that this climate-related event had a strong influence on
the global coastal PP of the Mediterranean Sea.
Several studies have reported influence of climate variations on the coast
(Belgrano
et al., 2008; Cloern et al., 2007; Tiselius et al., 2016). In agreement, we
observed an influence of climate-scale variability on coastal productivity
as suggested by the inverse correlations between ΣPP and SST and,
more loosely, by NAO and MOI (Fig. 6). While these correlations emphasize
the pre-eminent role of climate variability in the regulation of interannual
to decadal-scale coastal productivity, the pathways through which this
control of the atmosphere over coastal productivity is exerted are complex
and may regionally differ (Grbec et al.,
2009). Climate can influence phytoplankton growth by the direct effect of
temperature on algal metabolism, by changes in basin-scale circulation
(including exchanges with adjacent seas), by regulating nutrient supply
through variations in the thermocline intensity, by changes in wind patterns
affecting mixing and dust deposition pathways, or through changes in
precipitation that have direct influence on wet deposition and on river
runoff. These effects are modulated by changes in the biota and in the
interaction between organisms (e.g.,
Molinero et al., 2005). The relative importance of climate-driven processes
relative to other productivity-enhancing processes depends on regional
characteristics and may be seasonally varying. For example, variations in
dust deposition, which may sustain up to 50 % of new production in the
Levantine basin
(Kress
and Herut, 2001; Herut et al., 2002), are expected to be more important on
the eastern and southern Mediterranean coasts because of their proximity to
the Saharan dust sources. Likewise, variations in cooling and vertical
mixing are expected to be more effective during late winter when PP peaks
and when diatoms dominate in the Mediterranean Sea
(Lacroix
and Nival, 1998; Marty, 2002; Marty and Chiavérini, 2010).
Our results reveal that, in contrast to other regions like the North Sea
(Capuzzo et al., 2018) or the Arctic Ocean
(Gregg et al., 2003), the coastal Mediterranean Sea
did not globally display a marked decline in PP during the last decades. We
suggest that in some coastal areas, a decrease in vertical nutrient supply
though the thermocline may be compensated for by other nutrient sources.
Variations in atmospheric deposition, groundwater and river outflows,
and the influence of human activities through changes in landscape
use and domestic wastewater management are important sources of nutrients in
the ecosystem and thus act as major drivers of PP in these waters
(e.g., Paerl et al., 1999;
Powley et al., 2016). As a consequence of human activities, both terrestrial
and coastal ecosystems have experienced progressive nutrient enrichment
(Conley et al., 2009; Deegan et al.,
2012). However, while this effect is evidenced in shallow nearshore waters,
its influence in the ocean is estimated to be minimal
(Wang et al., 2018). In the Mediterranean
Sea, high coast population growth rates and concomitant food demand have
resulted in a dramatic increase in water demand for irrigation farming and
fertilizer use (Ryan, 2008). Indeed, while the
freshwater discharge of Mediterranean rivers has significantly reduced
during recent decades (∼20 %), the corresponding total
nitrogen inputs to coastal seas are estimated to have increased by a factor
of up to 5, fueling PP in river-influenced areas
(Ludwig et al., 2009). While the
importance of groundwater in the Mediterranean Sea could be comparable to
that of rivers (Rodellas et al., 2015)
and generalized nitrification of Mediterranean coastal aquifers is
acknowledged (EEA, 1995; Zalidis et al.,
2002), general trends in groundwater discharges remain largely unknown.
Coastal regionalization
Coastal regionalization reveals marked differences in coastal water PP in
the Mediterranean Sea. Annual values range from 215±124gCm-2
in the north Alboran Sea (Z1) to 48±17gCm-2 along the coasts
of Egypt and Libya (Z16). These values are globally lower than published
data; yet, literature values in coastal waters are highly variable depending
on methodology, depth, and/or sampling date. For example,
García-Gorriz and Carr (2001)
estimated annual PP of 300–900 gCm-2 for Z1, but
Morán
and Estrada (2001) narrowed this range to mean values between 121 and 366 gCm-2 depending on distance from the coast.
Pugnetti et al. (2008)
reported mean values of 150 gCm-2 that are almost 2 times higher than
our values at Z8. In the lower range, Sournia (1973)
estimated 30–60 gCm-2 in Z16, which is in accordance with our values
for this zone. In this sense, despite the limitations inherent to
satellite data, the present work provides estimations based on a long data
record (14 years) and a homogeneous methodology.
The nine characteristic temporal patterns obtained from the SOM analysis
(Fig. 7) reveal small differences in PP among the different regions. Most
variations are due to changes in the magnitude of annual carbon fixation,
although seasonality varies little. Exceptions are R7, R8, and R9 which
represent the dynamics in coastal regions regulated by terrestrial inputs.
Likewise, interannual variations are highly coherent among regions,
following the basin-scale pattern shown in Fig. 4, including the remarkable
decline in productivity during 2012. Exceptions are R1, representing the
dynamics in the Gulf of Sirte and R7 in the Gulf of Gabès where a different
interannual variability suggests alternative sources of PP variability in
this region. Indeed, the Gulf of Gabès is a region displaying consistently
high chl and PP in most studies
(e.g.,
Bosc et al., 2004; Barale et al., 2008). Drira et al.
(2008) reported high biomass and toxic dinoflagellate blooms in the inner
shelf of the Gulf of Gabès where surface nitrate concentration often
exceeded 1 µM. This enrichment is associated with degradation of the
water quality attributed to industrial and urban activities
(Hamza-Chaffai et
al., 1997; Zairi and Rouis, 1999). However, even though these waters may
suffer from eutrophication, satellite-borne data overestimate chl within
these waters, as revealed by Katlane
et al. (2011), who observed constant high turbidity and suspended matter of
industrial origin affecting these waters but also reflection from the
bottom affecting MODIS data. This suggests that general chl algorithms may
be particularly inaccurate in this region.
The magnitude of coastal PP has often been related to both shelf width and
magnitude of river discharge (Liu et al., 2010). Our data do not display a
general relationship between shelf width and PPannual (Table 5 and
Fig. 9). Indeed, wide shelves with important river discharge flux from the
Po, the Rhône, and the Nile rivers display high productivity (Z4, Z9, and Z15
>170gCm-2) whereas production is low in the narrowest
shelves like Z2, Z5, and Z16. However, PPannual in some regions with
important river inflows, like Z10, are significantly lower (89±37gCm-2). In other regions like Z1 and Z8 PP is high despite the lack of
important freshwater sources.
The role of river discharges depends both on Q and on nutrient loads. Most
Mediterranean rivers have lost their natural flows, and their discharges to
the sea are strongly regulated by dams and water abstractions. Consequently,
their outflow to the Mediterranean Sea is highly uncoupled from weather and
climate variability. For instance, some rivers flowing into the Adriatic and
Ionian seas, like the Acheloos, Nestos, or Aliákmon, today present high to
maximum discharge in July due to peak hydropower production
(Skoulikidis et al., 2009).
Relation between primary production, shelf width, and river
discharge flow (Q). Bubble colors indicate the PPannual (gCm-2)
for each of the 18 defined zones (see Fig. 7 and Table 5).
Nutrient and organic matter loads have increased globally during the last
century (Beusen et al., 2016).
Concentrations exported to nearby seas depend on the combined effects of
lithology, urban effluents, industry, and agriculture in catchment basins
that are often difficult to quantify. The land use and land cover (LULC)
data collection provides indices of the threat of potential development for
setting land and water quality policies. Rivers like the Rhône and Po with
important influence on coastal productivity flow through extensive areas and
therefore accumulate the impact from anthropogenic activities. Agricultural
practices and urban effluents can strongly determine the concentration and
molar ratios of the nutrients flowing into coastal waters. For example,
despite the flux of the Nile River having been drastically reduced after the
operation of the Aswan Dam (from 47 to 17 km3yr-1,
Ludwig et al., 2009), the
coastal region is still highly productive. A remarkable increase in the
concentrations of nitrate derived from fertilizers and sewage is responsible
for this sustained productivity
(Turley, 1999; Nixon, 2003,
2004). Conversely, pollution pressures in the western Balkan basins are
relatively low, and the Neretva (Z10), running through a karstic region in
Croatia, displays low nutrient levels
(Ludwig et al., 2009;
Skoulikidis et al., 2009).
Finally, other oceanographic processes determine the productivity of coastal
regions. In particular, Z1 and Z18 in the Alboran Sea are comparatively
more productive than other areas. The influence of winds and circulation
patterns favoring subsurface water upwelling and higher productivity in the
northern Alboran Sea were described by
García-Gorriz and Carr (2001). Also,
localized patterns of relatively high primary production were found in
persistent deep water density fronts resulting from the interaction of
Modified Atlantic Water (MAW) and Mediterranean water by
Lohrenz et al.
(1988).
Conclusions
In summary, pelagic PP in coastal shelves of the Mediterranean Sea during
the period 2002–2016 was estimated in this study for the first time using
available satellite ocean color products. We estimated that 12 % of PP of
the Mediterranean Sea is attributable to coastal pelagic production, and from
that about 80 % of this carbon fixation is sustained by regenerated
pathways. High PP spatial variations were observed among the different
regions, as mainly driven by major river effluents, exchanges with nearby
seas (i.e., Black Sea and the Atlantic Ocean), and local processes. Our
study shows that some coastal areas are indeed highly productive
(>400gCm-2) and sustain a large percentage of overall
coastal production. Indeed, their temporal variability could be of paramount
importance to understand variations in higher trophic levels
(e.g., Piroddi et al., 2017). Despite
that temporal variability being dominated by interannual and sub-decadal
variations, our analysis reveals a weak negative global PP trend in the
Mediterranean Sea related to climate-driven patterns (i.e., temperature
increase). Nevertheless, long-term effects can be regionally variable (i.e.,
PP trends in the Adriatic Sea are positive), and variations in fluvial
nutrient inputs, together with other processes such as ocean warming in
coastal regions, including heat waves, deserve a closer look as longer ocean
color databases become available. Finally, we identify 18 along-shelf zones
based on their temporal PP patterns. Two main PP groups were observed: zones
with strong cross-shore gradients, typically found in wider estuarine
regions, and homogeneous zones within narrow continental shelf areas. These
two types of coastal waters clearly characterize the coastal area of a sea
where coastal waters are otherwise strongly influenced by ocean conditions.
Annual coastal variations in ΣPP (GtCyr-1) for (a)
the whole Mediterranean Sea, (b) the western basin, (c) the eastern basin, and
(d) the Adriatic basin. Dashed black lines indicate the mean ΣPPCoastal for the period 2002–2016 at each study region. Red and
blue bars indicate years above and below average values, respectively.
Annual mean coastal variations, PPannual (gCm-2yr-1), for (a) the whole Mediterranean Sea, (b) the western basin, (c) the eastern basin, and (d) the Adriatic basin. Dashed black lines indicate the coastal mean PPannual at each study region. Red and blue bars indicate years above and below average values, respectively.
Relationship between coastal pelagic chlorophyll (chl anomalies,
green lines) and (a) SST anomalies, (b) NAO index, and (c) MOI index (red
lines).
Correlations between yearly and seasonal total carbon
fixation anomalies (ΣPP) for the coastal Mediterranean waters and
its sub-basins with the climatic indices NAO and MOI.
NAO index MOI index AnnualSpringSummerFallWinterAnnualSpringSummerFallWinterCoastal watersr-0.460.020.020.000.00-0.240.280.020.020.02P value<0.0010.600.580.960.970.000.040.620.630.60Western coastr-0.400.000.250.120.02-0.220.080.000.000.00P value<0.0010.880.060.200.590.010.310.800.850.96Eastern coastr-0.420.020.220.020.00-0.110.020.020.010.04P value<0.0010.630.080.590.890.150.620.650.710.46Adriatic coastr-0.310.030.080.000.00-0.380.370.010.000.01P value<0.0010.560.300.890.89<0.0010.020.740.930.70Code availability
Software codes would be available upon request to the contact author.
Data availability
Datasets and their sources are fully detailed in the manuscript. Remote sensing products and climate change indices are publicly accessible from their original providers.
Author contributions
GB, PMSH, and ARdG designed the study in collaboration with DA. PMSH and DA conducted most of the analyses. PMSH wrote the paper, with contributions from all co-authors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We are grateful to the National Aeronautics and Space Administration, NASA
(https://oceancolour.gsfc.nasa.gov/, last access: 18 July 2017), and EU Copernicus Marine
Environment Monitoring Service, CMEMS (http://marine.copernicus.eu/, last access: 2 August 2019), for the freely available ocean-color
remotely sensed data. Climate indices were obtained from the Climate
Research Unit at the University of East Anglia
(https://crudata.uea.ac.uk/cru/data/, last access: 21 August 2018). The authors would like to acknowledge the three anonymous reviewers and the editor Stefano Ciavatta for providing constructive and insightful comments on our paper.
Financial support
This article is a result of the Ministry of Economy and Competitiveness
(MINECO) of Spain Project Fine-scale structure of cross-shore GRADIENTS
along the Mediterranean coast (CTM2012-39476) and SifoMED (CTM2017-83774-P).
P. M. Salgado-Hernanz was supported by a Ph.D. doctoral research fellowship
FPI (Formación Personal Investigación) BES-2013-067305 from MINECO.
Review statement
This paper was edited by Stefano Ciavatta and reviewed by three anonymous referees.
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