Sources and coastal distribution of dissolved organic matter in the Gulf of Cadiz

Science of the Total Environment 630 (2018) 1583–1595

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Sources and coastal distribution of dissolved organic matter in the Gulf of Cadiz E. González-Ortegón a,b,⁎, V. Amaral c,d, F. Baldó e, Ricardo F. Sánchez-Leal e, María J. Bellanco e, María P. Jiménez e, J. Forja c, César Vilas f, A. Tovar-Sanchez a a

Instituto de Ciencias Marinas de Andalucía (CSIC), Campus Universitario Río San Pedro, 11519 Puerto Real, Cádiz, Spain CEI-MAR International Campus of Excellence of the Sea, Spain Departamento de Química-Física, Facultad de Ciencias del Mar y Ambientales, Universidad de Cádiz, Campus Río San Pedro s/n, Puerto Real, Cádiz 11510, Spain d Ecología Funcional de Sistemas Acuáticos, Centro Universitario Regional Este, Universidad de la República, Rocha, Uruguay e Instituto Español de Oceanografía, Centro Oceanográfico de Cádiz, Puerto Pesquero, Muelle de Levante s/n, 11006 Cádiz, Spain f IFAPA Centro El Toruño, Camino Tiro de Pichón s/n, 11500 El Puerto de Santa María, Spain b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Terrestrially derived OM from wetland discharge as major source of DOM in the GoC • DOC transport from the GuadianaGuadalquivir estuaries to the Mediterranean Sea • Humic-like FDOM production as a byproduct of DOM metabolism by plankton organisms • FDOM as a proxy for tracing physical circulation in the Gulf of Cadiz

a r t i c l e

i n f o

Article history: Received 21 December 2017 Received in revised form 23 February 2018 Accepted 25 February 2018 Available online xxxx Editor: D. Barcelo Keywords: Organic matter FDOM Field-deployable sensor Gulf of Cadiz Water masses Estuaries Salinity gradient Spatial distribution Biogeochemical patterns

a b s t r a c t Dissolved organic matter (DOM) is a major component of the organic matter pool, playing a key role in the global ocean functioning. However, studies on DOM in waters of many ocean regions, such as the Gulf of Cadiz (GoC), are poorly known. Advanced aquatic sensors enable autonomous for long-term deployments in situ collection of high frequency DOM data using fluorescent dissolved organic matter (FDOM) as a proxy. The present study evaluates the relevance of FDOM, the estuarine influence and the environmental factors that determine its spatial distribution in the GoC. Our results suggest that the GoC water mass, under the estuarine influence of three main rivers, is receiving large amounts of DOM transported mainly by Guadalquivir and Guadiana rivers and much less from TintoOdiel. Salinity is the main factor explaining the FDOM variability within the Guadalquivir and Guadiana rivers and in the inner shelf of the GoC. In the outer shelf of the GoC, plankton-produced DOM could explain the persistent spatial pattern of FDOM, playing an important role in the dynamics of FDOM from the North area of the GoC through the persistent low-salinity Eastern North Atlantic Central Water. The oceanographic dynamics and the spatial pattern of FDOM concentration in the continental shelf of the GoC suggest a net transport of FDOM through the GCC (Gulf of Cadiz Current) to the Mediterranean Sea. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Instituto de Ciencias Marinas de Andalucía (CSIC), Campus Universitario Río San Pedro, 11519 Puerto Real, Cádiz, Spain. E-mail address: [email protected] (E. González-Ortegón).

https://doi.org/10.1016/j.scitotenv.2018.02.293 0048-9697/© 2018 Elsevier B.V. All rights reserved.

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1. Introduction Dissolved organic matter (DOM) is an important component in regional and global biogeochemical cycles, as it is an important pathway for carbon, nitrogen, and phosphorus transport from land to sea (Harrison et al., 2005). The Gulf of Cadiz (GoC hereinafter) is the basin that connects the North Atlantic Ocean and the Mediterranean Sea and may play a key role in the DOM exchanges between the two basins through the Strait of Gibraltar. A fraction of DOM can absorb light at the ultraviolet and visible range; this portion is called colored dissolved organic matter (CDOM; Coble, 2007). A subfraction of this CDOM can emit the absorbed radiation in the form of fluorescence and is called fluorescent DOM (FDOM; Coble, 1996, 2007). This optically active fraction is widely used to trace the dynamics of DOM in a wide range of aquatic environments (Coble, 2007; Stedmon et al., 2011; Yamashita et al., 2013). Advancements in sonde technology enable autonomous in situ collection of high frequency DOM data using FDOM as a proxy. The use of an FDOM autonomous sonde in the field combined with fluorescence analysis of water samples in the laboratory (spectrofluorometer) to validate field samples could be useful to characterize rapid changes in DOM (Downing et al., 2012). Recent studies have shown the importance of autochthonous production by plankton of humic-like FDOM components that have similar humic-like fluorescence to that associated with a terrestrial origin in the shelf waters (Romera-Castillo et al., 2011; Amaral et al., 2016). Biological activity in the coastal areas can result in the production of newer humic-like material, commonly referred to as microbial humic peak M (Coble, 1996), with a distinct blue-shifted relative to the analogous for type C humic-like fluorescence (Coble et al., 2014). CDOM produced by plankton through sloppy feeding (e.g. Jumars et al., 1989), excretion (e.g. Nagata and Kirchman, 1992; Steinberg et al., 2002), and fecal pellet dissolution (Urban-Rich et al., 2006) is suggested to be labile and consumed rapidly via bleaching and/or microbial consumption (Steinberg et al., 2002, 2004). Bacterial production is supported by the tight coupling between DOM productions mechanisms such as algal release and zooplankton excretion, rather than from standing stocks of semilabile dissolved organic carbon (DOC, Steinberg et al., 2004). Therefore, the production of DOM by zooplankton (Steinberg et al., 2002) and its subsequent remineralization by bacteria could be an important source of persistent CDOM in oceanic systems, which accounts for local origin and significant dynamics (Nelson et al., 2004). Terrestrially derived organic matter, introduced into aquatic ecosystems mainly through the discharge of rivers and wetlands, is the major source of DOM in nearshore waters (Moran et al., 1991; Tzortziou et al., 2008). In most estuaries and coastal areas, FDOM constitutes a dominant fraction of the DOM pool which is highly correlated with the concentration of DOC (Oestreich et al., 2016; Romera-Castillo et al., 2010). The freshwater discharges on the continental shelf of the GoC - between cape Santa María (Portugal) and cape Trafalgar (Spain) - are directly influenced by salt-marshes and three main estuarine systems (Guadiana, Tinto-Odiel and Guadalquivir). These river basins are located within the Mediterranean-climate region (southwest of Iberian Peninsula) but the river basin characteristics, the connection of the salt-marshes with the main channel and the climatological particularities of each basin can lead to flow out a different DOM concentration into the waters of the GoC. In addition, water management usually causes a decrease in the freshwater input to estuaries and an increase in the residence time of suspended matter (González-Ortegón et al., 2010), altering estuarine natural hydrological regime (Fernández-Delgado et al., 2007). River inflow to these estuaries, located in a semiarid environment and regulated by dams, has an average annual discharge of the Tinto-Odiel, Guadalquivir and Guadiana rivers of 100–473, 1987 and 2522 hm3 year−1, respectively (Elbaz-Poulichet et al., 2001; González-Ortegón et al., 2015; Vasconcelos et al., 2007). The Tinto-Odiel estuary is an industrial port where the low flow rate of freshwater and its great draft make this

ecosystem be shown as a sea-arm. On the other hand, the Guadiana and Guadalquivir estuaries show a salinity gradient, although the recurrent drought events and high demand of freshwater for irrigation usually lead to a high water residence (i.e. 12 and 18 days, respectively) (De la Paz et al., 2007; Vasconcelos et al., 2007). The Guadalquivir estuary Basin shows high sediment loads whilst the Guadiana is a rockbound estuary (González-Ortegón et al., 2010; Garel et al., 2009, 2016). These differences about the water transparency affect the cycling and remineralization rates, through both photochemical and biological processes (Hansell and Carlson, 2014). Recently, a robust time series based on N10 years of near-bottom CTD observations define the spatial distribution and intra-annual variability of seabed hydrography in the eastern GoC (Bellanco and Sánchez-Leal, 2016). Two well-defined water masses with intraannual hydrographic differences are sorted in the continental shelf of the GoC: i) inner shelf waters (b60 m depth) influenced by coastal and atmospheric conditions and ii) outer shelf water (between 100 and 250 m depth) influenced by low-salinity Eastern North Atlantic Central Waters (ENACW). At the moment, we do not know the contributions of each ecosystem through river discharges or from biological process as potential source of DOM to the GoC. It is still unknown how this DOM is transported under the oceanographic dynamics of the water masses in this continental shelf and the interacting effects of environmental factors. Spatial modelling should provide sufficient knowledge about its transport on the CG continental shelf and its implications for the structure and functioning of the marine communities in the basin. The objective of this paper is twofold. First, to identify the sources and the contribution of each one on the platform sea. We hypothesized that the river basin characteristics and the climatological particularities of each studied estuary should explain the baseline concentration and its relation with spatial and salinity gradients. Second, to study the spatial variability of the DOM concentration in the platform sea of the GoC and to evaluate the influence of different sources on its spatial distribution. Our hypothesis is that the spatial variability of the DOM concentration in the GoC is determined by the influence of the estuaries. For that purpose, we modelled the effects of environmental factors on the spatiotemporal distribution of DOM concentrations in the Gulf of Cadiz during the four seasons. 2. Material and methods 2.1. Study area The GoC (southwest Spain) is a semi-enclosed basin whose oceanographic dynamics is mainly controlled by the exchanges between the environmental sub-basins: the Mediterranean and Atlantic basins and the coastal system (Bellanco and Sánchez-Leal, 2016). The basins of the Guadiana, Tinto-Odiel and Guadalquivir rivers are located within the Gulf of Cadiz, where they discharge freshwater into the Atlantic Ocean (Fig. 1). Occasionally, during episodes of heavy rainfall, freshwater discharges from the dams (Alqueva and Alcalá del Río dams in the Guadiana and Guadalquivir rivers, respectively) may be enough to become fluvial-dominated systems (Díez-Minguito et al., 2012). 2.2. Sampling and analysis Recent advancements in sensor technology enable autonomous in situ collection of high frequency DOM data using FDOM as a proxy. We used a YSI multiparameter sonde (EXO2; Yellow Springs, OH) which uses a combination of electrical and optical sensors for specific conductivity (0 to 200 ± 0.001 mS cm−1), water temperature (−5 to 50 °C: ±0.05 °C), pH (0 to 14 ± 0.1 pH), dissolved oxygen (0–50: ± 0.5 mg L−1), turbidity (0 to 4000 FNU), chlorophyll a fluorescence and

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Fig. 1. Map of Gulf of Cadiz showing the location of sampling sites. Sampling locations for the estuaries GU0, TO0 and GD0 and for the continental shelf sampled in different oceanographic cruises. Radials from right to left: TF Trafalgar, SP Sancti Petri, GD Guadalquivir, TO Tinto-Odiel, and GU Guadiana.

FDOM (0 to 300 ± 0.01 μg L−1 QSU). The EXO FDOM sensor was designed to measure humic-like substances from terrestrial sources. This sonde has allowed characterizing rapid changes in DOM both in the column of water of GoC and even in the remote three main estuaries of GoC. The deployment in the GoC was done during March 2016 from the R/V Ramon Margalef and during June, September, and December 2016 from the R/V Ángeles Alvariño (Table 1). The EXO2 sonde was incorporated into the rosette with the Seabird 911+ CTD, the measurements were taken between 3 and 150 m deep and the water column profiles of the downcast were used. In the estuaries, the multiparametric sonde was anchored 1 m above the bottom nearby by the mouth of the river in March and June 2016 (see stations in Fig. 1 and Table 1: GU0, TO0 and GD0 in the Guadiana, Tinto-Odiel and Guadalquivir estuaries, respectively). In June, a current meter (Aquapro Nortek) was coupled to the EXO2 sonde to estimate the tidal distance and model the spatial variability of the FDOM concentration in the three estuaries. In the upper stretch of the Tinto-Odiel estuary three more samples were collected (TO01, TO02 and TO03). Our physical

data set includes temperature, salinity, dissolved oxygen, turbidity, chlorophyll a fluorescence and pH. 2.2.1. Sensor and calibration High-frequency data were collected in situ using an EXO-2 water quality sonde (Yellow Springs Instrument Company (YSI), Ohio). The sensor measuring FDOM had excitation and emission wavelength ranges of 365 ± 5 and 480 ± 40 nm, respectively, with a range of 0–300 μg L−1 quinine sulfate equivalents (QSU) and resolution of 0.01 μg L−1 QSU reported by the manufacturer. The LED bandwidth determines the maximum range on the excitation beam and the error emission range is ±40 nm to give us a better signal to noise ratio for the sensor. The sonde was calibrated according to manufacturer protocols. Calibration solutions for FDOM fluorescence were made from quinine sulfate dehydrate diluted with 0.05 M H2SO4, with calibration errors lower than 5%. Quinine hemisulfate salt monohydrate (N98%) CAS 207671-44-1 was purchased from Sigma–Aldrich Chemical Co. Fluorescence of FDOM is reported in quinine sulfate units (QSU). Chlorophyll a

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Table 1 Cruises with date, research vessels (RV), ecosystem, sampled station and number of observation. Date

RV

Ecosystem

Stations

n

March 2016

Pelagia

Guadiana Guadalquivir Tinto-Odiel Tinto-Odiel Tinto-Odiel Tinto-Odiel Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Guadiana Guadalquivir Tinto-Odiel Tinto-Odiel Tinto-Odiel Tinto-Odiel Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz Gulf of Cadiz

GU0 GD0 TO0 TO01 TO02 TO03 GU(1–4) GD(1–4) TO(1–4) SP(1–4) TF(1–4) GU0 GD0 TO0 TO01 TO02 TO03 GU(1–4) GD(1–4) TO(1–4) SP(1–4) TF(1–3) GU(1–3) GD(1–4) TO(1–4) SP(1–4) TF(1–2) GU(1, 3, 4) GD(1–2, 4) TO(1–4) SP(1–4) TF(1–4)

222 330 415 13 13 13 185 190 179 216 153 386 342 343 13 13 13 205 197 165 234 147 125 238 199 266 73 193 168 181 271 319

Margalef

June 2016

Pelagia

Alvariño

September 2016

Alvariño

December 2016

Alvariño

fluorescence and physical parameters of the EXO2 sonde were highly correlated with the CTD. However, sensor drift was detected in 2 out of the 80 time-series and therefore CTD data were used in the shelf. Temperature, turbidity, and inner filter effects (IFEs) have been shown to alter fluorescence measurements (Downing et al., 2012). For this reason, we corrected fluorescence measurements to account for temperature, turbidity, and IFEs, according to Downing et al. (2012). 2.2.2. DOC and FDOM relationships 2.2.2.1. Laboratory (on board) measurements: DOC and fluorescence. Discrete water samples for DOC and fluorescence from two stations (GD4 and SP4) were taken on the four cruises at five depths between 5 and 125 m depth (n = 40) and then cross-calibrated with the in situ FDOM. The samples were filter through pre-combusted Whatman GF/ F glass fiber filter. Samples for DOC were stored frozen until analysis and DOC concentrations were determined by high temperature catalytic oxidation using a Multi N/C 3100 Analytik Jena analyzer. Potassium hydrogen phthalate was used as standard. Deep seawater and low carbon references waters (Hansell CRM Program, 42–45 μM) were measured to assess instrument variability (n = 5, 43 ± 1.8 μM). The samples for fluorescence analysis were immediately measured on board in a spectrofluorometer (JASCO FP-8300) connected to a Peltier Thermostatted Cell Holder with Stirrer accessory (EHC-813) for temperature control (20 °C). Excitation Emission matrixes (EEMs) were obtained for emission wavelengths from 300 nm to 560 nm (1 nm steps) and excitation wavelengths from 240 nm to 450 nm (5 nm steps) with an integration time of 0.25 s. The drEEM 0.2.0 toolbox was used to standardize the EEMs (Murphy et al., 2013). Briefly, the spectra were corrected using the instrument correction factors, innerfilter correction was based on absorbance measurements (measured in a spectrophotometer JASCO V-750) and Milli-Q water was subtracted

from sample EEMs. Data were then normalized to the area under the water Raman peak at 350 nm (measured daily) and converted to quinine sulfate units (QSU) using calibration curve with quinine sulfate monohydrate (Sigma) in H2SO4. Commonly referenced peaks for components of EEMs obtained for aquatic humic substances, humic-like Peak C, A and M were calculated as the intensities of individual excitation–emission pairs (Ex 350/Em 450, Ex 250/Em 434, Ex 320/Em 410, respectively) according to Coble (1996) using an approach commonly referred to as “peak picking”. Spearman correlations (r) were used to determine the relationship between in situ FDOM with on board FDOM and DOC concentration. 2.3. Data analysis Temporal and spatial differences of FDOM between samples were analyzed by ANOVA, and post hoc Tukey multiple comparisons of means 95% family-wise confidence level. We were interested in testing for effects of season and spatial (radial and distance to the coast) variation. Data were analyzed separately in the three studied estuaries and in each water mass of the GoC (GCC and ENACW) first through GAM models (frequentist approach) with a Gaussian (normal) distribution. The data from the three studied estuaries were combined in a full GAM model. In the case of the GoC, two depth strata were considered to know the effects of water masses and depth (which co-variate). In the GoC, stations 1 and 2 correspond to the GCC water mass with a maximum depth of 60 m and the stations 3 and 4 to the ENACW water mass with a maximum depth of 150 m (Bellanco and Sánchez-Leal, 2016). Namely, surface water masses (b60 m) at each sampling station (Sup1 and Sup2 for the GCC water mass, and Sup3 and Sup4 for the ENACW) and the deepest ones (N60 m) at stations 3 and 4 (Deep3 and Deep4, the deep water in the ENACW) were analyzed. We tested our hypotheses by fitting different statistical models in the GoC. In “Model 1” the GCC water mass (Sup1-Sup2). In “Model 2” the water mass ENACW between surface and 60 m (Sup3-Sup4). In “Model 3”, the water mass ENACW between 60 m of depth and the sea bottom (Deep3-Deep4). The response variable was FDOM. The physical parameters of the CTD salinity, temperature, transmissivity, chlorophyll a fluorescence and oxygen concentration were used as explanatory variables in the models. Statistical analysis was firstly accomplished by fitting a generalized additive mixed model GAM: mgcv 1.8 package (Wood, 2006) in R 3.3.2 (R Development Core Team, 2016). The model selection process started with a model that (i) contains as many explanatory variables as possible, (ii) finds the optimal random structure, and (iii) the optimal fixed structure (Zuur et al., 2009).The model included the fixed factors of Month (March, June, September and December), temperature (°C), salinity PSU, depth (m), transmissivity, oxygen (mg L−1), pH, latitude (dec), longitude (dec), number of each station within radial as proxy of distance to the coast and radial along a north–south latitudinal transect. Multicollinearity was assed using the variance inflation factor (VIF) following the procedure described in Zuur et al. (2010). The function to assess multicollinearity was corvif from the Highstatlib 6.0 package and adopted a threshold of 3 (Zuur et al., 2009). The statistical method used here for the estuaries is additive modelling, which is a generalized additive model (GAM) with a Gaussian (normal) distribution. In the GoC, the two-dimensional smoother (latitude-longitude) was fitted with a tensor product spline. Since the observations in the field showed that the spatial patterns of FDOM differ from month to month, our model allowed a different spatial pattern for each month. AIC values indicate that the model with four smoothers (one per month) is better than the model with one smoother. Therefore, we assumed a model with four two-dimensional smoothers. The results indicated that there is a non-linear depth effect and this was dependent on month. In some months, the profiles among stations within a radial and among radials were similar. However, in other months the profiles changed (see Fig. 5). FDOM along a depth gradient in each station

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indicated heterogeneity along the depth gradient since the variation decreases for larger fitted values. We add the depth gradient in the correlation structure to the model. We use the “Variogram” function from the nlme package (Pinheiro et al., 2007), which indicate that there is spatial correlation in the residuals up to about 30 m. We used mixed models to take spatial correlation as well as heterogeneity patterns into account (Zuur et al., 2009). The random effect of the model performed better using the identity of the twenty stations as a random factor and the varPower functions to allow for different spread along the variance covariate depth (Zuur et al., 2009). Our models in the GoC allow heterogeneity between stations and within stations along the depth. We also tested the correlation structure: Rational quadratic correlation using the function corRatio, Gaussian correlation using the function corGaus and Exponential correlation using the function corExp available in the R package nlme (Pinheiro et al., 2007). The models with the corRatio, corGaus, and corExp correlation structures showed considerable lower AIC values. The plots of the (normalized) residuals versus the pool of covariates do not show residual patterns. Although the models tested were improving the spatial dependence with the correlation structure and adding covariates to the fixed structure, these final models in the GoC still showed in the experimental variogram of the normalized residuals a subtle spatial correlation. In addition, the computational cost of the frequentist statistical approach used in this study ran about 27 h on 64-bit platform with 2.5 GHz Intel Core i7-4710 MQ Processor. Currently, the availability of computation methods based on Bayesian inference in spatial statistics have successfully tackled the computational costs of the frequentist statistical approach (e.g. GAMM used in this study) and the expensive Markov Chain Monte Carlo methods on complex spatio-temporal geostatistical model (Bivand et al., 2015). The Integrated Nested Laplace Approximation (INLA, Rue et al., 2009) avoids such computational issues and provides accurate numerical approximations to the posterior distributions of the parameters involved in the spatio-temporal model (Blangiardo and Cameletti, 2015). The explanatory variables in the frequentist models selected for the GoC were included in the Bayesian hierarchical models. The parameters are treated as random variables, and prior knowledge is incorporated via prior distributions. We used the default and recommended settings for priors (Held et al., 2010). These priors are vague priors or approximations of “non-informative” priors, which have little influence on the posterior distributions; hence, results are mostly derived from the data (similar to a frequentist approach). Approaches designed for latent Gaussian models are applied to fit Gaussian hierarchical spatiotemporal models through INLA approach and stochastic partial differential equations (SPDE) (Rue et al., 2009; Lindgren et al., 2011). The SPDE approach proposed by Lindgren et al. (2011) consists of representing a continuous spatial process (i.e., a GF) using a discretely indexed spatial random process (i.e., a GMRF). This approach has been used for modelling geostatistical patterns of FDOM through four months and space in the platform sea of the GoC. The fundamental building block of such Gaussian Markov random (GMRF) models, as implemented in R-INLA, is a high-dimensional basis representation, with simple local basis functions. SPDE approach provides an explicit link between GF and GMRF. The aim is to include a continuous spatial term (with Matérn covariance) in the model and that then INLA is used to approximate the desired model. The R-INLA interface to the SPDE models require a mesh construction to create the triangulated mesh on top of which the SPDE/GMRF representation (Lindgren et al., 2011). Because the SPDE model is defined on the mesh, we need to link the random field values to the observed values (projector matrix Ap) and to set up the Matérn correlation on the mesh. The mesh design considered the physical boundaries of the study area of the GoC and the sampling points. This random field is a stochastic process indexed in space that essentially represents all spatially explicit processes that may have an effect on the FDOM pattern. In the A matrix, we specify indices for the months. SPDE model object was defined specifying the prior distribution for the parameters and also without specifying the parameterisation.

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FDOM at a geolocation and time, is assumed to have a distribution that belongs to the exponential family, and the parameters of the family (ᴓ) are linked to a structured additive predictor η through a link function g (·) such that g(ᴓ) = η. We define the linear predictor through the following formula:

η ¼ −1 þ β o þ þ þ

k X k¼1 k X

k X

f k ðTemperatureÞwk þ

k¼1

f k ðTransmisivityÞwk þ

k X

k X

f k ðSalinityÞwk þ

k¼1

k X

f k ðOxygenÞwk

k¼1

f k ðChlorophyll aÞwk

k¼1

f k ðDepthÞwk þ f ðÞ

k¼1

where β is the intercept, and the function ƒk is the sum of smooth functions defining the random effect of temperature (°C), salinity (PSU), oxygen concentration (mg L−1), transmissivity, depth (m), where regression coefficients vary with the values of each covariate (K values), and for each covariate wk. is a vector of the known values defined for each of the geolocations. The −1 removes the automatic intercept that doesn't play well with our most common way of handling the spatial models. ƒ (·) is a semiparametric function defining the spatiotemporal random effect included in the model. The inla call family “gaussian” provides the observation nugget component. Best candidate models were selected based on the marginal likelihood (Hubin and Storvik, 2016). In addition, deviance information criterion (DIC; Spiegelhalter et al., 2002), the cross validated logarithmic score (LCPO) (Roos and Held, 2011) and Watanabe Akaike information criterion (WAIC) (Watanabe, 2010) were checked. In the case of WAIC, DIC and CPO they performed better when the models have more covariates, or adopting similar values between the full model with 6 covariates and 5 selected covariates. However, mlik for model selection seems to reduce the models to the core of the covariates that explain mainly the variance of the models. All criteria showed that the models should include the spatial random field. 3. Results 3.1. Relationship between in situ FDOM with DOC and on board FDOM FDOM in situ was strongly and significantly correlated with the DOC concentration when analyzing the overall data set (r = 0.84, p b 0.0001, Fig. 2A), supporting FDOM data corrections (following Downing et al., 2012). Regarding humic peaks, FDOM in situ was related to peak A (r = 0.48, p b 0.01) and no significant or strong relationship was observed with peak C. However, when analyzing samples at different strata of depths different patterns were found: (a) between surface and 25 m, there was poor relationships between any humic peaks (A, C and M) and FDOM values; (b) between 50 and 150 m, FDOM in situ was highly related to humic peak C (r = 0.74, p b 0.01) and to the sum of humic peaks (A + C + M, r = 0.76, p b 0.001 Fig. 2B), supporting the use of this kind of in situ equipment to assess the FDOM distribution in coastal areas. 3.2. FDOM variability in the estuaries of the GoC The high variability of FDOM concentrations found in the Guadalquivir and Guadiana estuaries in both March and June during a tidal period were strongly correlated with the salinity gradient (Fig. 3). In March, during high and low tides ranged from 5.1 QSU (35.6 PSU) to 31.9 QSU (21.8 PSU) in the Guadalquivir estuary (r = −0.99, p b 0.01), and from 5.3 to 16.1 QSU (33.6–21.6 PSU) in the Guadiana estuary (r = −0.97, p b 0.01). In June, during high and low tides, ranged from 5.3 QSU (35.6 PSU) to 31.2 QSU (28.1 PSU) in the Guadalquivir estuary (r = −0.99, p b 0.01), and from 5.5 to 16.5 QSU (35.7–28.9 PSU) in the Guadiana estuary (r = −0.86, p b 0.01). It is remarkable that FDOM at

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A FDOM (QSU)

FDOM (QSU)

7 6 5 4

r=0.84, p<0.0001 60

70

80

90

6

B

5

4 2.5

r=0.76, p<0.001 3.5

4.5

5.5

Humik like (QSU)

DOC (µM)

Fig. 2. Relationship between in situ FDOM (in QSU) and A: Dissolved organic carbon and B: the sum of humic peaks A, C and M (in QSU), estimated from on board fluorescence measurements at two stations (GD4 and SP4) on the four cruises at five depths between 5 and 125 m depth (n = 40).

the marine endmember of the Guadiana estuary (31.8–35.8 PSU) was particularly different in June with a considerable increase from 3.6 to 7.3 QSU, coinciding with an increase in chlorophyll a (Fig. 3). However, FDOM concentrations in the mouth of the Tinto-Odiel estuary in March ranged between 5.0 and 5.7 QSU (35.8–36.3 PSU) and in June between 5.4 and 7.5 QSU (36.12–36.11 PSU) with a positive correlation with the salinity range (r = 0.67, p b 0.01). Results of the correlation analyses of FDOM and others environmental factor also highlighted a different performance between the Tinto-Odiel estuary and the GuadianaGuadalquivir estuaries: negative correlations with oxygen and pH in the Guadiana (r = −0.74 and −0.5, p b 0.01) and Guadalquivir (r = −0.85 and −0.76, p b 0.01) estuaries, but positive correlations (r = 0.59 and 0.78, p b 0.01) in the Tinto-Odiel estuary. In the Tinto-Odiel estuary, samples situated upper from the river mouth at 10 (TO01), 14 (TO02) and 17 (TO03) km (see Fig. 1 for locations) were collected considering the low values of FDOM concentration found in the mouth of this estuary during the sampling in both months compared to the other two estuaries. Salinity kept high values at 10 and 14 km upper its mouth with low FDOM concentrations (35.8 PSU in both stations and 5.6 and 5.2 QSU, respectively) but only in the station situated 17 km (TO03 in Fig. 1) the salinity decreased lightly (34.7 PSU) and the average FDOM concentration was 23.4 QSU. June_GD0 June_GU0 June_TO0

Estuaries March_GD0 March_GU0 March_TO0

The individual models per estuary were fit to the physical data to explain the variability of the FDOM concentration in June. These models indicated that the covariate salinity predicted highly FDOM concentrations: Guadiana (92%), Guadalquivir in combination with turbidity (99%) and in the case of the Tinto-Odiel estuary with DO (62%). A predicted model of the three estuaries were fit to the distance from the mouth of each estuary to compare how the partial effects of this covariate explain its spatial distribution of FDOM among estuaries (Fig. 4 and Table 2). The reduced model includes distance from the mouth, turbidity, chlorophyll, DO, the factor estuaries and the interaction of the covariate distance and this factor which explain 98% of the null deviance. 3.3. Spatio-temporal distribution of FDOM in the GoC The FDOM in the platform sea of the GoC had an average value of 5.2 QSU (DOC 71.9 μM) and ranged from 4.1 to 6.9 QSU (DOC 61.8–87.6 μM) with clear seasonal and spatial (distance to the coast) effects (Fig. 5 and Table 3). In most stations, the highest values of FDOM were in June in both surface waters (Sup1–4: 5.9 ± 0.3 QSU or DOC 78.4 ± 26.8 μM) and the deepest ones (Deep3–4: 5.7 ± 0.1 QSU or DOC 78.9 ± 24.9 μM). The effects of the season mainly consisted of an

TO01 TO02 TO03

fDOMQSU

30

20

10

25

30

35

Salinity

Fig. 3. Relationship between FDOM concentration and salinity for all sampling campaigns at the three main estuaries of the Gulf of Cádiz (GU: Guadiana, TO: Tinto-Odiel and GD: Guadalquivir).

Fig. 4. Estimated smoothers of FDOM concentration (QSU) from the river mouth to the upper part of each estuary in June (GU: Guadiana, TO: Tinto-Odiel and GD: Guadalquivir).

E. González-Ortegón et al. / Science of the Total Environment 630 (2018) 1583–1595 Table 2 Numerical outputs of the optimal GAM model to evaluate the effect of environmental factors on FDOM values in the three main estuaries of the Gulf of Cádiz. S(Distance), smoother of distance measured from the mouth of each studied estuary; Guadiana (GU), Tinto-Odiel (TO) and Guadalquivir (GD) estuaries.

Month s(Distance) s(Distance):GU s(Distance):TO s(Distance):GD s(Turbidity) s(Chlorophyllμg.L−1) s(Dissolved oxygen mg.L−1)

df

F-value

p-Value

2 5.547 8.601 0.750 8.750 2.547 2.973 3.000

718 2.944 64.287 0.124 37.737 16.062 47.055 52.215

b2e−16 0.00884 b2e−16 0.76061 b2e−16 2e−09 b2e−16 b2e−16

increased FDOM from March (4.5) to June (5.9) and then decreased until September, at most stations, or in December at stations 1 (Fig. 5). The decrease of FDOM values from June to December occurred in stations 1, except when salinity decreased in the GD and GU radials of these stations. Spatially, significant variations were found within each radial especially between stations 1 and 4 showing a subtle pattern with the distance to the coast (Table 3). Overall, the levels of FDOM showed a high variation in the surface water masses, especially in the TO, GD and SP radials, which may explain the lack

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of a strong relation with on board humic peaks. The main significant differences of FDOM values showed between the coastal water station 1 (0–60 m) and the surface water masses at the stations 3 and 4 (0–60 m). However, the lesser differences of FDOM values were found between the coastal water mass in station 1 and the deepest ones in stations 3 and 4. In addition, significant differences of FDOM values were also found between the surface (0–60 m) and the deepest water masses (60–150 m) in stations 3 and 4 (Table 3). Differences in levels of FDOM among radials were very low and seem to show that the water masses corresponding to the inner radials of the GoC (TO and GD) were more homogenous but different with the rest (Table 3). The FDOM-Depth relationship showed a seasonal change in vertical profiles with a not clear linear pattern, especially in June and September (Fig. 6). Overall, the levels of FDOM decreased in the first 50 m of depth, although they showed a high variation depending on the distance to the coast. The FDOM values, between this depth and the bottom (stations 3 and 4) in June and especially in September, gradually tended to increase with depth to the bottom. In December, and especially in March, the vertical profiles were more similar and relatively constant with depth. Only at stations 1 and 2 of the Trafalgar radial in March occurred an inverted vertical profile of FDOM - values increased with depth in these shallowest stations (60 m of maximum depth) - which may be caused by an upwelling effect.

Fig. 5. Boxplot of FDOM conditional on month and sampling site. FDOM at sampling sites 3 and 4 is shown both at the surficial (e.g. TO3, TO4) and deepest strata (e.g. TO3F, TO4F), from surface to 60 m of depth and from 60 m to the sea bottom, respectively. Each box goes from the first quartile to the third quartile and the black point is the median.

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Table 3 ANOVA and pairwise test to evaluate differences of FDOM values among months, radials and at the different studied water masses for all campaigns. The surface water masses at each station (Sup 1, Sup 2, Sup 3 and Sup 4) and the deepest water at the stations 3 and 4 (Deep 3 and Deep 4). Factors

edf

F-value

p-Value

Month Radial Water masses

4 3 5

3322 72 209

b2e−16 b2e−16 b2e−16

Pairs-wise Month

Radial

Water masses

March–June March–Sept March–Dec June–Sept June–Dec Sept–Dec GU-TO GU-GD GU-SP GU-TF TO-GD TO-SP TO-TF GD-SP GD-TF SP-TF Sup3-Sup1 Sup4-Sup1 Sup3-Sup2 Sup4-Sup2 Sup4-Sup3 Sup1-Deep3 Sup2-Deep3 Sup3-Deep3 Sup4-Deep3 Sup1-Deep4 Sup2-Deep4 Sup3-Deep4 Sup4-Deep4 Deep 4-Deep3

Diff

Lwr

Upr

p-Value

1.32 0.51 0.82 −0.81 −0.50 0.31 −0.16 −0.15 0.03 −0.07 0.01 0.19 0.09 0.19 0.08 −0.10 0.32 0.47 0.10 0.24 0.14 −0.08 0.14 0.24 0.38 −0.10 0.13 0.22 0.37 0.02

1.28 0.47 0.78 −0.85 −0.54 0.27 −0.26 −0.25 −0.06 −0.17 −0.09 0.10 −0.01 0.10 −0.01 −0.20 0.22 0.36 0.01 0.15 0.06 −0.20 0.04 0.14 0.28 −0.20 0.05 0.15 0.29 −0.08

1.36 0.55 0.86 −0.77 −0.46 0.35 −0.06 −0.06 0.13 0.03 0.10 0.29 0.19 0.28 0.18 −0.01 0.43 0.57 0.18 0.32 0.22 0.04 0.25 0.34 0.48 0.00 0.21 0.30 0.44 0.11

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.77 0.13 1.00 0.00 0.03 0.00 0.04 0.00 0.00 0.00 0.01 0.00 0.00 0.35 0.01 0.00 0.00 0.06 0.00 0.00 0.00 0.99

of FDOM in surface water are close to the Guadiana river, mainly in June, and a subtle increase close to the Guadalquivir river occur in September and December, coinciding with the lowest salinity values (Fig. S5 for DOC concentration, Supporting information). In the deep-water mass, the highest values occurred in June (in the Guadiana and Guadalquivir radials) and in December (in the Guadiana, and between Guadalquivir and Trafalgar radials) (Fig. S1, Supporting information). The standard deviation patterns for the spatial random field are driven by the amount of information (Figs. S2–S3, Supporting information). The predicted values were plotted versus observed values showing the goodness of the fit and not overfitting (Fig. S4, Supporting information). Considering that in situ FDOM was strongly and significantly correlated to the DOC concentration with the overall data set, the posterior mean of the spatial random effect for DOC concentration (μM) in the surface waters of the Gulf Cádiz shelf was also modelled in order to visualize the spatial effect variation of DOC (Figs. S5–S6, Supporting information). As expected, a similar spatial pattern of the concentrations of DOC and FDOM was found. The shape of the smoother depth at each water mass varied within the months. FDOM values increase with depth in March at the inner shelf of the surface water, and in December, the highest values were found at about 20 meter depth. In the outer stations (Sup 3 and Sup 4), the variability with depth was poor. However, in the deep water of the stations 3 and 4 (Deep 3 and Deep 4), the range of variability was important highlighting the increase of FDOM with depth in June and in September at about 80 meter depth. 4. Discussion Few environmental studies have considered a complete spatial sampling design to study FDOM concentration between the potential sources (estuaries) and the surroundings waters of the coastal shelf. In situ FDOM validated in the laboratory shows that it could be used as a proxy for the DOC distribution in the GoC. 4.1. Influence of major estuaries on FDOM inputs to the GoC

3.3.1. Modelling Three different statistical models were fit to compare the different water masses (Table 4). “Model 1” for the GCC water mass (the inner shelf water mass between the surface and the sea bottom at 60 m), “Model 2” for the ENACW water mass between the surface and 60 m, and “Model 3”, for the ENACW water mass between 60 m of depth and the sea bottom (150 m). Overall, the most parsimonious models describing FDOM at each water mass in the GoC included mainly the influence of temperature and transmissivity (Table 4 and Fig. 7). In addition, the model 1 - GCC inner shelf had a higher performed including the covariates salinity and in the case of the model 2 – ENACW (Sup 3–Sup 4) the covariate chlorophyll. The shape of the smoothers for temperature, transmissivity and chlorophyll (this latest in the selected model 2) indicated an increase of FDOM in the range of measured values for each covariate at each water mass (Fig. 7). However, the shape of the smother for the covariate salinity varied depends on the range of salinity values found at the inner shelf water mass. Namely, at the inner shelf water (Sup 1 and Sup 2), the highest FDOM values occurred at the lowest salinity 33.4 PSU (in the stations 1 and 2 of the Guadalquivir radial); from the area with the lowest salinity value, FDOM reduces up to approximately 35.5–36 PSU, and then increases lightly up to the highest observed salinity value of 36.5 PSU (Fig. 7). The spatial random field effect (longitude and latitude) explained mainly FDOM variability through the studied months in the GoC (Figs. 8, S1 and S5). Overall, the FDOM values at each water mass increase from the coastline to the outer stations with the lowest FDOM values associated with the TO radial (Figs. 8 and S1). The highest values

Terrestrial inflow of DOM by rivers is one of the dominant sources of oceanic FDOM in coastal regions, which is highly correlated with DOC (e.g. Ludwig et al., 1996; Klinkhammer et al., 2000). The present study points that the high FDOM concentration found in the Guadiana and Guadalquivir estuaries (ca. 6 times the maximum concentration recorded on the continental shelf) constitutes an important pathway for the transport of organic dissolved components to the GoC. The inverse relationships between FDOM and salinity observed for these two estuaries and in the inner shelf waters of the GoC (Model 1: b60 m, stations 1 and 2) are consistent with other studies at coastal areas (Clark et al., 2016; Oestreich et al., 2016; Klinkhammer et al., 2000), indicating a predominance of terrestrial sources of DOM (e.g. Yamashita and Tanoue, 2008). The influence of the Guadalquivir and Guadiana rivers in the GoC were reflected in an increase in the concentration of DOM accompanied by significant decreases in salinity values in the coastal area of the GoC. The Guadalquivir estuary is the largest river in the GoC and it is possible to detect its direct influence in the open ocean by satellite study of suspended solids (Caballero et al., 2014). The relatively higher freshwater discharges in the Guadalquivir river in December (8.4 hm3 day−1; the other months 1.5 hm3 day−1) decreased the salinity and provided a high FDOM concentration (probably terrestrial DOM) into the inner shelf (around the Guadalquivir area) and even the deepest water mass ENACW in the Gulf Cádiz shelf (see Fig. S1). Therefore, these two estuaries could be the main sources of humic-like FDOM into the GoC, which during high freshwater discharge events would increase the concentration of nutrients and DOM (Prieto et al., 2009; McKee et al., 2004). In the case of the Tinto-Odiel estuary compared to the other two estuaries, the lowest freshwater flux could explain the lowest FDOM concentrations in its water. The fact that the high FDOM

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Fig. 6. Depth profiles of FDOM concentration at the 20 sampling sites along the GoC shelf over 4 studied months.

values of the Tinto-Odiel estuary (in a similar range values to those of the other two estuaries) were found much upper (TO03 see Fig. 1) from its mouth could be related to the transformation of this estuary in an industrial port where the marine influences dominate mostly in its lower part. This transformed estuary could be retaining high amount of DOM and thus a low river export to its coastal zone (low permanent values in the GoC shelf close to this estuary during the studied period). Differences in the salinity-FDOM relationships among the studied estuaries, especially the highest values of FDOM in the Guadalquivir

estuary for a similar salinity range, could be due to differences in the nature of each river basin (i.e. geological, physical, chemical and biological characteristics). The Guadalquivir estuary shows a dense-flowered cordgrass Spartina densiflora (Gallego-Fernández and García-Novo, 2007) whereas at the Guadiana and Tinto-Odiel estuaries these are much less developed. Spartina marshes have shown to be a major factor regulating bacterial biomass and production in estuaries (Wright and Coffin, 1983). In addition, wetland-estuary exchange influences DOM exports to the coastal area (Harrison et al., 2005; Regier et al., 2016).

Table 4 Marginal likelihood values for selected models at the different studied water masses for all campaigns. Model 1 corresponds to the surface water masses at stations 1 (Sup 1) and 2 (Sup 2); Model 2 to the surface water at the stations 3 and 4 (Sup 3 and Sup 4, respectively); and deepest water at the stations 3 and 4 (Deep 3 and Deep 4). Number of covariates

Models Sup1–Sup2

MLIK

Models Sup 3–Sup4

MLIK

Models Deep 3–Deep 4

MLIK

Random field(s) Tem + s Tem + Sal + s Tem + Tra + s Tem + Tra + Sal + s Dep + Tra + Sal + s Tem + Tra + Sal + Dep + s

−18 490 722 781 917 925 1017

5

Tem + Tra + Sal + Dep + Oxy + s Tem + Tra + Sal + Dep + Chla + s

958 996

Random field(s) Tem + s Tem + Sal + s Tem + Dep + s Tem + Tra + Oxy + s Tem + Dep + Tra + s Tem + Tra + Sal + Chla + s Tem + Tra + Sal + Dep + s Tem + Tra + Sal + Oxy + s Tem + Tra + Dep + Chla + s Tem + Tra + Sal + Chla + Oxy + s Tem + Tra + Dep + Chla + Sal + s Tem + Tra + Sal + Dep + Oxy + s

−181 1392 1385 1543 1295 1583 1466 1566 1433 1603 1583 1567 1531

6

Tem + Tra + Sal + Dep + Oxy + Chla + s

939

Tem + Tra + Sal + Dep + Oxy + Chla + s

1526

Random field(s) Tem + s Tem + Sal + s Tem + Dep + s Tem + Dep + Sal + s Tem + Dep + Tra + s Tem + Dep + Sal + Oxy + s Tem + Dep + Sal + Chla + s Tem + Dep + Tra + Oxy + s Tem + Dep + Tra + Chla + s Tem + Dep + Sal + Oxy + Tra + s Tem + Dep + Sal + Oxy + Chla + s Tem + Sal + Dep + Chla + Tra + s Tem + Tra + Dep + Chla + Oxy + s Tem + Sal + Dep + Oxy + Tra + Chla + s

1009 1600 1552 1879 1785 1890 1710 1745 1836 1866 1725 1658 1761 1796 1680

1 2 3 4

Dep = depth, Chla = chlorophyll, Oxy = oxygen, s = random field, Sal = salinity, Tem = temperature, Tra = transmissivity. In bold, the optimal models plotted in Fig. 7.

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Fig. 7. Mean (solid line) and the 2.5% and 97.5% quantiles (dashed lines) for the posterior distribution of temperature, salinity, transmissivity, oxygen and depth smoothed regression effect per month.

The Guadalquivir river basin shows one of the largest wetland areas in Europe which could contribute to net annual source of organic matter (Huertas et al., 2017), despite most of them have been transformed into cropland or are isolated. On the other hand, the high turbid nature of the Guadalquivir estuary could support its highest values of FDOM for a similar salinity range considering that the water transparency could affect the cycling and remineralization rates or organic particles through photochemical processes (Hansell and Carlson, 2014). 4.2. Temporal and spatial autochthonous production of FDOM in the GoC by planktonic communities Humic-like fluorescence production varies across environments and can reflect the presence of terrestrial substances (Walker et al., 2009). However, recent studies have shown the importance of autochthonous production of humic-like FDOM components in the shelf waters by phytoplankton (Romera-Castillo et al., 2010) and as a by-product of DOM metabolism, mainly by bacteria (Kramer and Herndl, 2004). In the GoC, outer shelf waters (ENACW, between 60 and 150 m) showed higher humic-like FDOM components, which have similar humic-like fluorescence to that associated with a terrestrial origin, than the inner shelf waters. That is, the biological activity in the coastal areas of the GoC could results in the production of newer humic-like material, commonly refers as microbial humic peak M (Coble, 1996), with a distinct blue-shifted relative to the analogous for type C humic-like fluorescence (Coble et al., 2014). This consistent subtle spatial pattern (an increase of FDOM concentration from the coastline to the central continental shelf of the GoC during the studied period) strongly matches with a recent study in the GoC where the proportion of chlorophyll a from pico- and nanophytoplankton increases from coast towards the open ocean (González-García et al., 2018). In this study, two groups of cyanobacteria, Synechococcus and Prochlorococcus, are the most

abundant picophytoplankton groups in the GoC, which accounts for almost the total deep chlorophyll maximum levels, and increase with distance from the coast. Specifically, Prochlorococcus is the most abundant population of picophytoplankton at deep chlorophyll maxima and also in waters where the chlorophyll maximum was found at deeper levels. Moreover, a recent work shows that cultured picocyanobacteria, Synechococcus and Prochlorococcus, release FDOM that closely match the typical fluorescent signals found in oceanic environments (Zhao et al., 2017). Thus, bacteria and probably these groups of cyanobacteria could have a significant role as producers of oceanic humic-like FDOM in the GoC with characteristics comparable to oceanic refractory DOM (Shimotori et al., 2012). On the other hand, the importance of other process, such as photo degradation in FDOM characteristic, has been related to a more intense Peak M than C in surface irradiated samples (Helms et al., 2013). Higher photo degradation in surface water in the GoC (the first 20 m), after exposure to sunlight that decrease the emission intensity and blue shift of fluorescence to shorter wavelengths (Coble, 1996), could explain the lack of relationship with humic-like material compared to deeper water. In temperate coastal waters, this production usually displays clear temperature-dependent seasonal patterns. High FDOM was associated with high temperature, oxygen and chlorophyll, which suggest autochthonous primary production by these groups of cyanobacteria mainly in June (Models 2 and 3, stations 3 and 4). In this way, the presence of a relative important FDOM concentration values in the Guadiana estuary associated with high chlorophyll levels at its marine endmember in June could suggest DOM produced by phytoplankton in the warmest period from the wetland area situated on the coastline. The fact that the outer part of this estuary is highly dominated by opened and conserved saltmarshes could explain an export of DOM from the surrounding coast to the open ocean and import of DOM to the estuary during the flood tide produced by plankton. In the same way, the high secondary

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Fig. 8. The posterior mean of the spatial random effect (Gaussian random field) for FDOM concentration in the Gulf Cádiz shelf every studied month. Arrows indicate the direction of the flow. GCCC represents the Gulf of Cadiz Coastal Counter Current and GCC, the Gulf of Cadiz Current (see Bellanco and Sánchez-Leal, 2016 for more details in water masses).

production in the very high turbid estuary of Guadalquivir river during the warmer season (González-Ortegón and Drake, 2012) could support a complex DOM production (around 30% more compared with March at the mid-estuary) based on heterotrophic bacterial growth and mesozooplanktonic organisms (Moran et al., 2016; Ortega-Retuerta et al., 2009; Romera-Castillo et al., 2010), indicating that multiple biological sources of DOM could prevail in the GoC. Our results are in agreement with the idea of humic-like FDOM production by bacteria in coastal environments. Therefore, the relationships among humic-like FDOM productivity and bacterial phylogenetic groups need to be examined in further studies to elucidate the precise production mechanism of FDOM in the GoC (Shimotori et al., 2012). 4.3. Tracing physical circulation in the GoC shelf The optical properties of FDOM can be used as a proxy for tracing physical circulation and water-mass history (Catalá et al., 2015; Tzortziou et al., 2015). Under a year of low flow river, mesoscale processes by the circulation pattern of water masses could dominate the biogeochemical pattern in the continental shelf (Harrison et al., 2005). The establishment of stratification and increasing solar radiation in the warm period (from June to September) in the GoC (Sánchez and Relvas, 2003) could lead to subsequent decreases of FDOM concentration during this warm period (Hansell and Carlson, 2014; Moran et al., 2000). This effect accompanied by a flow from the Portuguese

upwelling zone to the oligotrophic regions of the eastern GoC (Bellanco and Sánchez-Leal, 2016; Sánchez and Relvas, 2003) would explain the spatial variability pattern of FDOM in the GoC. The persistent spatial pattern in the shelf of GoC during the four studied months suggests an important southward transport process of DOM in the study area under a low freshwater influence of the rivers across the shelf during the studied period, which is supported by few salinity values below 35.5 PSU. In the present study, the effects of the circulation of the water masses in the continental shelf of GoC (Bellanco and Sánchez-Leal, 2016; Criado-Aldeanueva et al., 2006) are a clear “mirror” of the spatial pattern of FDOM where this autochthonous biological production could be also supplied by the North wetland area of the GoC. Although reductions in FDOM concentration by the inner westward flow in the GoC were strong enough to explain the FDOM spatial pattern in the four studied months, a greater FDOM contribution from the North wetland area of the GoC between the Guadiana estuary and Santa Maria Cape could be enhanced by the persistent low-salinity ENACW along the central shelf (Bellanco and Sánchez-Leal, 2016) and thus explain the modelling spatial pattern of FDOM on the shelf of GoC. Additionally, internal tides and tidal-induced upwelling provide an alternate mechanism that explains the upwelling spot located off Cape Trafalgar (Vargas-Yáñez et al., 2002) and consequently an increase in FDOM values in the inner shelf of the Trafalgar area as it occurred in March. The Strait of Gibraltar plays a key role in the water exchange between the Mediterranean Sea and the Atlantic Ocean (HernándezMolina et al., 2015). The oceanographic dynamics and the spatial

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pattern of FDOM concentration in the continental shelf of the GoC point to a net transport of FDOM through the GCC to the Mediterranean Sea. The surface circulation in the GoC by the ENACW from the Portuguese Coastal Transition Zone and the Azores Current (Sánchez and Relvas, 2003) would allow not just increasing FDOM level concentrations in the central shelf of the GoC but also a net flow towards the Mediterranean Sea. Thus, under a scenario of low flow river we can tentatively hypothesize that the FDOM from the Portuguese domain would be mostly transported to the Mediterranean Sea. 5. Conclusions We found how important are the connection of wetland areas with the adjacent ecosystems (estuaries or coastline) and the role of plankton as DOM sources to the continental shelf of the GoC. The importance of the wetland area as a source of FDOM to the shelf are reflected in the Northwest domain of the GoC and within the Tinto-Odiel rivers basin, where the high levels of FDOM are found much upper in its main channel but close to salt-marshes. The studied year of low flow river is dominated by the role of plankton as DOM sources and the circulation pattern of water masses in the GoC. By contrast, it would be expected that under high flow river the high FDOM production from the studied estuaries could change the spatial pattern of FDOM found in the GoC. This study suggests a net transport of DOC from the complex Guadiana-Guadalquivir estuaries, highly influenced by water management, and from the outer shelf of the GoC to the Mediterranean Sea through the Eastern North Atlantic Central Waters (ENACW). Acknowledgements Financial support to EGO was given by JdC (FPDI-2013-17708) and CEI-MAR and others were financial supported by the projects OCAL MICCIN grants (CTM2014-59244-C3-1-R, CTM2014-59244-C3-2-R and CTM2014-59244-C3-3-R) and STOCA 2016 (Spanish Institute of Oceanography). The authors thank David Roque, Joaquin Pampin, Antonio Moreno and the crews of the R/V Angeles Alvariño and Ramon Margalef for technical assistance on the field; Finn Lindgren, Aurelie CosandeyGodin and Elias T. Krainski for statistics advice in the INLA package; and Neil Ganju and Bryan Downing for FDOM corrections. Conflict of interest None declared. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.02.293. References Amaral, V., Graeber, D., Calliari, D., Alonso, C., 2016. Strong linkages between DOM optical properties and main clades of aquatic bacteria. Limnol. Oceanogr. 61, 906–918. Bellanco, M.J., Sánchez-Leal, R.F., 2016. Spatial distribution and intra-annual variability of water masses on the Eastern Gulf of Cádiz seabed. Cont. Shelf Res. 128, 26–35. Bivand, R.S., Gómez-Rubio, V., Rue, H., 2015. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31. Blangiardo, M., Cameletti, M., 2015. Spatial and Spatio-temporal Bayesian Models With RINLA. John Wiley & Sons. Caballero, I., Morris, E.P., Ruiz, J., Navarro, G., 2014. Assessment of suspended solids in the Guadalquivir estuary using new DEIMOS-1 medium spatial resolution imagery. Remote Sens. Environ. 146:148–158. https://doi.org/10.1016/j.rse.2013.08.047. Catalá, T.S., Reche, I., Fuentes-Lema, A., Romera-Castillo, C., Nieto-Cid, M., Ortega-Retuerta, E., Calvo, E., Alvarez, M., Marrasé, C., Stedmon, C.A., Alvarez-Salgado, X.A., 2015. Turnover time of fluorescent dissolved organic matter in the dark global ocean. Nat. Commun. 6, 5986. https://doi.org/10.1038/ncomms6986. Clark, C.D., De Bruyn, W.J., Aiona, P.D., 2016. Temporal variation in optical properties of chromophoric dissolved organic matter (CDOM) in Southern California coastal waters with nearshore kelp and seagrass. Limnol. Oceanogr. 61, 32–46.

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