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1. Introduction
Eutrophication is the process by which nutrients, mostly nitrogen (N) and phosphorus (P), accumulate in a body of water, as defined by Smith et al. [1999]. A typical result of coastal eutrophication is the growth of dense macroalgal mats observed in shallow waters worldwide near industrial, agricultural and urban areas [Gladyshev and Gubelit 2019; Morand and Briand 1996; Valiela et al. 1997]. This phenomenon was given the evocative name “green tides” because of the visible proliferation of Ulva sp. in both estuarine and coastal marine ecosystems [Perrot et al. 2014; Pinay et al. 2018]. The subsequent decay of the macroalgal biomass has harmful environmental consequences through changes in the microbial and macrofaunal food web in the sediment [Davoult et al. 2017; García-Robledo et al. 2008; Hardison et al. 2013; Valiela et al. 1997] and an accumulation of toxic hydrogen sulfide [Nedergaard et al. 2002]. Even though river nutrient loading, especially N, is the main cause of green tides [e.g. Perrot et al. 2014], coastal sediments could be an additional source of nutrients for macroalgae, thus contributing to eutrophication [Sundbäck et al. 2003; Engelsen et al. 2008; Robertson and Savage 2018]. For example, it has been shown that benthic nutrient effluxes could supply up to 55–100% and 30–70% of the N and P requirement, respectively, needed to initiate the growth of filamentous green algal mats in shallow microtidal embayments on the west coast of Sweden [Sundbäck et al. 2003].
In order to improve our understanding of coastal eutrophication, mathematical models have been developed by coupling hydro-biogeochemical and ecological mechanisms [Le Moal et al. 2019 and references therein]. Models such as the “Mars-Ulves” [Perrot et al. 2014] and “ECO-MARS3D” models [Ménesguen et al. 2019] predict fine-scale algal blooms based on nutrient loading from rivers, pelagic nutrient cycling and the primary production of a coastal system, whereas the benthic compartment is represented in less detail. Currently, a constant benthic flux of N and P is used to take the nutrient exchanges between the sediment and bottom waters into account, but this remains limited due to the lack of suitable data for a given area. Determining the spatial variability of the benthic nutrient fluxes requires a large analytical effort, and may be high according the variations of environmental factors (e.g. hydrodynamism, anthropogenic pressures) of coastal ecosystems. This could be facilitated by using sedimentary proxies, which are easily measurable and directly related to benthic fluxes.
Benthic N and P fluxes, through the regeneration of ammonium () and phosphate (PO4) in the sediment, are driven by chemical (e.g. adsorption), biological (e.g. mineralization) and physical (e.g. diffusion) processes. To a large extent, they depend on (1) the redox conditions, (2) the deposition rate and the composition of the sedimentary organic matter (SOM), and (3) the physical properties of the sediment [e.g. Ait Ballagh et al. 2020; Arndt et al. 2013; Dauwe et al. 2001 and references therein; Khalil et al. 2018; Middelburg et al. 1996]. Redox conditions, driven by electron acceptor availability in the porewater, control the microbially mediated processes in N and P cycling [Capone et al. 2008 and references therein; Paytan and McLaughlin 2007; Sundby et al. 1992] and the adsorption–desorption processes of PO4 onto iron oxides [Andrieux-Loyer et al. 2008, 2014; Krom and Berner 1980; Rozan et al. 2002].
The composition of SOM is influenced by a mixture of organic matter (OM) inputs from both autochthonous (e.g. microphytobenthos and sediment-attached bacteria) and allochthonous sources (e.g. algal and vascular plant detritus from terrestrial and marine origins). It is generally accepted that a high contribution of algal biomass relative to terrestrial detritus in the SOM composition enhances its biodegradability [e.g. Arndt et al. 2013]. Two markers can be used to assess the labile OM flux in sediment: the chla and phaeopigment contents in surface sediment. They are good tracers of OM produced by photosynthetic organisms [Dell’Anno et al. 2002]. Whereas the chla content in surface sediment would be mainly related to the microphytobenthos biomass, the content of its breakdown product, phaeopigment, would indicate the sedimentation of phytoplankton and macroalgal detritus from the water column [Therkildsen and Lomstein 1993].
The SOM composition may also be characterized by the elemental C:N:P ratios. The C:N ratio is widely used to discriminate the sources of OM in the surface sediment [e.g. Dubois et al. 2012; Galois et al. 2000; Gu et al. 2017]. Typically, C:N values > 20 can be used to distinguish terrestrial higher plants from macrophytes (from 10 to 20) and phytoplankton (from 6 to 10) [Dubois et al. 2012; Liénart et al. 2017; Meyers 1994]. Therefore, it is assumed that when the C:N ratio is lower, the organic matter is more easily biodegradable [Enríquez et al. 1993]. Conversely, the N:P ratio in SOM is not commonly used to describe the origin and biodegradability of SOM. Nevertheless, an increase in the N:P ratio in the surface sediment could reflect the enrichment of OM via the decay of macroalgae, as observed in the eutrophic lagoon of Venice [Sfriso et al. 1988].
Physical sediment properties, such as the grain-size distribution and porosity, also affect SOM mineralization and the transport processes of solutes, and thus benthic fluxes. Fine-grained sediment, reflecting a high proportion of clay minerals, may prevent the microbially mediated OM degradation through a physical protection of the OM [e.g. Arndt et al. 2013 and references therein; Rasheed et al. 2003]. Porosity, which is generally inversely related to grain-size [Meade 1966], is a key parameter driving the sediment–water exchanges of solutes, including diffusion, advection and bio-irrigation [Boudreau 1996, 1997]. Therefore, a high porosity could, for example, enhance the diffusive fluxes across the sediment–water interface.
The present study was based on the hypothesis that benthic nutrient fluxes may be directly related to the sedimentary characteristics. Two previous studies carried out in temperate estuaries showed relationships between fluxes and markers of the SOM composition [Clavero et al. 2000; Cowan and Boynton 1996]. The nutrient fluxes were positively correlated with the chla content along the axis of Chesapeake Bay, USA [Cowan and Boynton 1996], and negatively correlated with the C:N ratio in the seasonal study carried out in the Palmones River estuary in southern Spain [Clavero et al. 2000]. Therefore, the C:N ratio and chla content may be effective proxies of the and PO4 fluxes. However, the sedimentary characteristics were not ranked in these previous studies, and the effects of the environmental conditions (e.g. the temperature of bottom water), known to affect both the diffusion and metabolic activities of benthic bacteria [Arndt et al. 2013 and references therein], were not discerned from those of the SOM composition [Clavero et al. 2000].
The aim of the present study was to investigate the relationships between benthic nutrient fluxes and the surface sediment characteristics from intertidal mudflats. Our objective was to answer the following questions: (1) What are the main drivers of benthic nutrient fluxes? (2) Are these drivers similar with regards to the and PO4 fluxes? (3) Does the SOM composition have a predominant role compared to the physical properties of the sediment, and (4) Can the sedimentary properties be used as efficient tools to predict and PO4 fluxes?
To this end, a broad sediment sampling campaign was carried out in the spring of 2019 on the Brittany coast (France). Since the 1970s, the Brittany coast has been particularly affected by green tides; this timeline coincides with changes in agricultural practices and an increase in anthropogenic nitrogen loading in the watersheds [Morand and Briand 1996]. Many surface and subsurface waters in Brittany have nitrate concentrations which exceed the European Community 50 mg⋅L−1 drinking standard.
A total of 200 sediment samples collected from 45 sites were analyzed for their and PO4 fluxes as well as their physical properties and chemical composition. This sampling strategy allowed to investigate a range of sedimentary characteristics while maintaining similar climatic conditions. The surface sediments were characterized by their porosity and grain-size, as well as their carbon, nitrogen, total phosphorus, chla and phaeopigment contents. In addition, organic phosphorus (Org-P) and iron oxide-bound phosphorus (Fe-P) were distinguished from the total phosphorus pool.
Location of mudflats (n = 12) and sites of sampling (n = 45) in spring (April until June) 2019. See the list of sites and sampling plan in Supplementary annex Table 1.
2. Materials and methods
2.1. Study sites and sampling
The study sites were located in macrotidal mudflats in Brittany (north-western France) under eutrophic conditions where green algae mats are observed (https://bretagne-environnement.fr/donnees-algues-vertes-bretagne). The mudflats that we selected for our study are the mudflats that are regularly monitored as part of the green tide monitoring programs set up by the Loire-Bretagne Water Agency (CEVA], final report, 2015]). Over the sampling period (between mid-April to mid-June 2019), the tidal range fluctuated from 2 to 9 m. Overall, 200 sediment samples were collected from 45 sites (Figure 1, Table S1) at mid–low tide. Sediment cores were sampled with a PVC core (diameter = 6 cm,h = 20 cm) in the upper 10 cm sediment layer for the benthic flux measurements, and another core (diameter = 9 cm, h = 5 cm) was sampled in order to analyze the surface sediment characteristics (Figure S1). For the benthic flux measurements, the cores were incubated in the dark directly on site in a mobile laboratory under controlled temperature (19 ± 2 °C) within one hour following sampling (see below). Based on the sample collected using the PVC core with a height of 5 cm, an aliquot of the wet sediment, with a known volume and weight, was maintained at 4 °C and used to determine the porosity. The remaining sediment was frozen at −20 °C in order to analyze the pigment content (chla and phaeopigment), phosphorus speciation, elemental composition and grain-size.
2.2. Benthic fluxes
The cores were incubated in the dark during 4 hours for the and PO4 efflux measurements. The overlying water was replaced by 150 mL of nutrient-free artificial seawater ([NaCl] = 33 g⋅L−1, [NaHCO3] = 0.2 g⋅L−1, pH ≈ 8) and gently aerated by bubbling in order to preserve the redox conditions of the sediment. Two 0.2 μm-filtered water samples were collected in the overlying water after 2 h and 4 h of incubation and stored at refrigerator temperature (4 °C) for less than three days prior to the nutrient analysis. The , PO4, and concentrations in the overlying water were measured with the colorimetric method using an automated photometric analyzer Gallery™, with a detection limit of 0.9, 0.2, 0.07 and 3.6 μM, respectively. All of the and concentration measurements were below the detection limit.
The and PO4 fluxes across the sediment–water interface were accessed by using the change in the molar concentration of the solute in the known volume of overlying water as a function of incubation time and the surface area of the sediment core [Aller et al. 1985]. If the rate of nutrient release from the sediment did not follow a linear trend over the incubation period, only the first sample was considered in the flux estimation. This was the case for PO4 when the exchanges between the sediment and water reached an equilibrium state after two hours of incubation, in general, due to the re-adsorption onto particles [Sundby et al. 1992]. Note that these fluxes were conducted under standardized conditions (temperature, aeration) and therefore considered as potential fluxes.
All of the values are reported in Supplementary Table S1.
2.3. Analysis of the sedimentary characteristics
The porosity was calculated by drying a previously weighed wet aliquot sediment at 60 °C. The water loss, determined by mass difference, and the sediment density set at 2.55 were used to calculate the porosity [Krom and Berner 1980]. The particle-size distribution (<2 mm) was measured using a laser diffraction instrument (Malvern Mastersizer). The particles were classified as either sand (63 −−2000 μm), silt (3 −−63 μm) or clay (<3 μm) fractions [e.g. Keil and Hedges 1993; Pye and Blott 2004]. “Mud” is defined as the sum of the clay and silt particles. The percentage of mud was used as a sedimentary parameter thereafter.
The pigment content (chla and phaeopigment) was measured in freeze-dried sediment (0.5–1 g) extracted in 90% acetone (10 mL) in the dark (18–20 h) at 4 °C. Each sample was previously gently ground using an agate pestle and mortar. After centrifugation, the chla and phaeopigment contents were measured in the supernatant using the spectrophotometric method of Lorenzen [1967] at 665 and 750 nm (Uvikon spectrophotometer), and expressed in μg⋅g−1 of dry sediment. The detection limits of the chla and phaeopigment contents were 2.7 and 12.9 μg⋅g−1, respectively.
The total organic carbon and nitrogen (TOC and TN) contents were determined using an element analyzer (FLASH™ 2000 OEA). An aliquot of freeze-dried and crushed sediment was acid-treated with 2N HCl to remove the carbonate and was subsequently rinsed with deionized water. After centrifugation, the carbonate-free sample was dried at 60 °C, and ground before being placed into a tin capsule for the TOC analysis. A second aliquot without an acidification treatment was used to determine the TN analysis.
The iron oxide-bound P (Fe-P) content was determined using a Dithionite-Bicarbonate solution [Ruttenberg 1992], as described in Andrieux-Loyer et al. [2008]. The total P (Ptot) content was determined using a 1 mol⋅L−1 HCl treatment overnight after sediment ignition at 550 °C (4 h), while the inorganic P content refers to the sum of the P-forms (Fe-bound P, Ca-bound P and detrital P), that was extracted with 1 mol⋅L−1 HCl before sediment ignition [Aspila et al. 1976]. The organic P (Org-P) content was then quantified by calculating the difference between the total P and inorganic P contents [Andrieux-Loyer et al. 2008]. The phosphorus-form extracts were subsequently analyzed using segmented flow analysis (SFA) [Aminot and Kérouel 2007]. The Org-P and Fe-P represented the pool of potentially bioavailable P.
The TOC, TN, Ptot, Org-P and Fe-P contents were expressed as the mass of the carbon, nitrogen and phosphorus in the total dry mass of the sediment.
The C:N and TN:Org-P ratios (mol:mol) in SOM were calculated from the TOC, TN and Org-P contents.
All of the values are reported in Supplementary Table S1.
Boxplot of (A) and PO4 (B) effluxes, and effluxes versus PO4 effluxes (C) in μmol⋅m−2⋅h−1. Within the boxes, medians are the black lines. First and third quartiles are hinges, outliers are black dots.
2.4. Statistical analysis
Pearson’s correlation matrix was calculated to establish the pairwise correlations between the sedimentary characteristics of the sediment. The relationships between the benthic nutrient fluxes and the sedimentary characteristics were assessed through multiple linear regressions. To better predict and PO4 fluxes, two models were built by multiple linear regression (MLR) using a stepwise selection procedure based on the Akaike information criterion (AIC). To ensure that the multi-collinearity did not skew our results, the variance inflation factors (VIF) were measured. None of the VIF values were higher than 3 (a threshold value was set at 5). In addition, when the selected variables had a Pearson’s correlation higher than 0.7 between them, we checked that there was no interaction effect in the model. To identify the main drivers of the benthic nutrient fluxes by MLR, the data were normalized beforehand, and a p-value < 0.05 for the F-test was used to consider a significant effect of one predictor variable on the model. In addition to the calculation of AIC, the K-fold cross validation was run to evaluate the performance of each model built by linear regressions. The data set was split into 10 folds. In order to compare two means, a Student’s t-test was performed when the sample size (n) was higher than 30. Each statistical analysis was performed using the R-studio software. The “aod”, “car” and “caret” packages were used to calculate AIC and VIF and to perform the k-fold cross validation, respectively. The “lm”, “step” and “scale” functions were used to perform the linear regressions, the stepwise selection procedure by MLR and the database normalization, respectively.
3. Results
3.1. Benthic nutrient fluxes
Taking the entire data set into account, the average and PO4 fluxes were 101 ± 117 μmol⋅m−2⋅h−1 and 17 ± 20 μmol⋅m−2⋅h−1, respectively. Over 50% of the fluxes ranged from 19 and 151 μmol⋅m−2⋅h−1, with values up to 526 μmol⋅m−2⋅h−1 (Figure 2A). With regards to the PO4 fluxes, 50% of the data ranged from 3 and 24 μmol⋅m−2⋅h−1, with a maximum flux of 172 μmol⋅m−2⋅h−1 (Figure 2B). In general, the PO4 fluxes were lower than those of . Over 50% of the N:P flux ratios ranged from 2 to 12, with values up to 765. The large variability in the ratios of the N:P fluxes reflects a weak correlation between the and PO4 fluxes (Pearson’s coefficient = 0.31, p-value < 0.05) (Figure 2C). The lowest N:P flux ratios (N:P ⩽ 2) were significantly explained by high PO4 fluxes (mean = 25 μmol⋅m−2⋅h−1) and low fluxes (31 μmol⋅m−2⋅h−1) (Student’s t-test for one sample, p-value < 0.05). The highest ratios for the N:P fluxes (N:P ⩾ 12) were significantly explained by low PO4 fluxes (mean = 7 μmol⋅m−2⋅h−1) and high fluxes (192 μmol⋅m−2⋅h−1) (Student’s t-test for one sample, p-value < 0.05) (Figure S2).
The variability of both and PO4 fluxes at the regional scale allowed to distinguish the locations where the sediment presented the highest average effluxes of nutrient. For the effluxes, they were Gulf of Morbihan (mean: 185 μmol⋅m−2⋅h−1), Vannes Estuary (mean: 173 μmol⋅m−2⋅h−1) and Auray River (mean: 162 μmol⋅m−2⋅h−1) (Figure 3). For the PO4 effluxes, they were Lorient Bay (mean: 34 μmol⋅m−2⋅h−1), Pont L’Abbé (mean: 34 μmol⋅m−2⋅h−1) and Rance Estuary (mean: 30 μmol⋅m−2⋅h−1) (Figure 4). By contrast, the three locations which presented the lowest average effluxes of were Aber Wrac’h (mean: 19 μmol⋅m−2⋅h−1), Morlaix Bay (mean: 48 μmol⋅m−2⋅h−1) and Port La Forêt (mean: 79 μmol⋅m−2⋅h−1) (Figure 3), and those with the lowest average effluxes of were Ria Etel (mean: 3 μmol⋅m−2⋅h−1), Gulf of Morbihan (mean: 4 μmol⋅m−2⋅h−1) and Port la Forêt (mean: 7 μmol⋅m−2⋅h−1) (Figure 4). A high spatial variability of these fluxes was also observed between the sampling sites of the same location (Figures 3 and 4, Table S1). For example, in the Gulf of Morbihan, the lowest effluxes of were measured at the site #45 (7.3 ± 1.4 μmol⋅m−2⋅h−1), whereas the site #44 presented large effluxes of (388.6 ± 189.5 μmol⋅m−2⋅h−1) (Figure 3).
Spatial variability of the effluxes (μmol⋅m−2⋅h−1) from the sediment collected on 45 sites in Brittany (France). The values represent the average flux of for each sampling site and for each intertidal area as a whole (n = 12).
Spatial variability of the PO4 effluxes (μmol⋅m−2⋅h−1) from the sediment collected on 45 sites in Brittany (France). The values represent the average flux of PO4 for each sampling site and for each intertidal area as a whole (n = 12).
3.2. Surface sediment characteristics
The distribution and pairwise correlations with Pearson’s coefficient for the surface sediment characteristics measured in the present study are presented in Tables 1 and 2.
Sediment properties of all samples (n = 200)
| Mean (±sd) | Median | Min–Max | Q1–Q3 | |
|---|---|---|---|---|
| Mud [clay + silt] (%) | 68.3 (±17.2) | 72.6 | 15.4–92.8 | 56.2–81.9 |
| Porosity (%) | 69.8 (±10.3) | 70.1 | 46.1–86.5 | 62.2–78.8 |
| Chla (μg⋅g−1) | 5.8 (±4.8) | 4.9 | 0–27.8 | 3.1–8.4 |
| Phaeopigment (μg⋅g−1) | 28.0 (±18.7) | 26.1 | 0–107.0 | 15.2–39.7 |
| TN (%) | 0.24 (±0.12) | 0.22 | 0.06–0.64 | 0.15–0.32 |
| TOC (%) | 2.2 (±1.2) | 2.1 | 0.2–7.7 | 1.1–3.0 |
| C:N (mol⋅mol−1) | 10.0 (±2.4) | 9.8 | 1.0–21.9 | 8.7–10.9 |
| Ptot (%) | 0.055 (±0.022) | 0.051 | 0.011–0.131 | 0.038–0.070 |
| Org-P (%) | 0.026 (±0.014) | 0.023 | 0.004–0.065 | 0.014–0.034 |
| Fe-P (%) | 0.008 (±0.005) | 0.007 | 0.001–0.033 | 0.004–0.010 |
| TN:Org-P (mol⋅mol−1) | 23.1 (±6.9) | 22.9 | 5.0–65.9 | 19.1–25.3 |
Molar C:N and TN:Org-P ratios were calculated from TOC, TN and Org-P content. Q1 and Q3 represent the 25th and 75th percentile respectively.
Pearson’s correlation matrix
| Porosity | chla | Phaeo | TN | TOC | C:N | Org-P | Fe-P | TN:Org-P | |
|---|---|---|---|---|---|---|---|---|---|
| Mud | 0.82 | 0.44 | 0.70 | 0.73 | 0.64 | 0.30 | 0.72 | 0.54 | −0.20 |
| Porosity | 0.57 | 0.76 | 0.87 | 0.78 | 0.35 | 0.73 | 0.54 | −0.04 | |
| chla | 0.64 | 0.66 | 0.62 | 0.26 | 0.60 | 0.45 | −0.04 | ||
| Phaeo | 0.86 | 0.79 | 0.31 | 0.70 | 0.53 | 0.04 | |||
| TN | 0.91 | 0.32 | 0.82 | 0.58 | −0.02 | ||||
| TOC | 0.66 | 0.75 | 0.58 | −0.02 | |||||
| C:N | 0.31 | 0.33 | −0.05 | ||||||
| Org-P | 0.65 | −0.48 | |||||||
| Fe-P | −0.23 | ||||||||
Bold values were coefficients ⩾ 0.70. “mud” represents the percentage contribution of clay and silt particles (diameter < 0.63 μm). “chla”, “phaeo”, “TOC”, “TN”, “Org-P” and “Fe-P” corresponds to the content of chlorophyll a, phaeopigment, total organic carbon, total nitrogen, total phosphorus, organic phosphorus and phosphorus associated with iron oxides respectively in the upper 5 cm of sediment. Molar C:N and TN:Org-P ratios were calculated from TOC, TN and Org-P content.
The particle-size distribution of the samples indicates that the median grain-size (D50) ranged from 16 to 519 μm. With an average mud content of 68.3 ± 17.2% for all of the samples, the clay and silt particles represented 4.7 ± 2.1 and 63.6 ± 15.4% of the particle-size distribution, respectively. According to the ternary diagram based on the sand/mud ratios proposed by Flemming [2000], a large proportion of the surface sediments collected in the present study were classified as sandy mud (38%) and slightly sandy mud (49%). As expected, the mud content was positively correlated with the porosity of the sediment (Pearson’s coefficient = 0.82, p-value < 0.05). The average porosity was 69.8 ± 10.3% with values that ranged from 46 to 87%.
The mud content was also positively correlated with the TOC (Pearson’s coefficient = 0.64, p-value < 0.05), TN (Pearson’s coefficient = 0.73, p-value < 0.05) and Org-P contents (Pearson’s coefficient = 0.72, p-value < 0.05) in the surface sediment. The TOC, TN and Org-P contents averaged 2.2 ± 1.2%, 0.24 ± 0.12% and 0.026 ± 0.014%, respectively. With an average Ptot content of 0.055 ± 0.022%, the amount of Org-P was 45 ± 12% of the Ptot content compared to 14 ± 6% for the Fe-P (average content of 0.008 ± 0.005%).
The C:N and TN:Org-P ratios in the SOM averaged 10.0 ± 2.4 and 23.1 ± 6.9, respectively. The C:N ratios reached up to 21.9 with half of the values ranging from 8.7 to 10.9. The highest values corresponded to the sediment samples collected in the Goulven Bay (mean = 17.8 ± 3.9).
The accumulation of OM from the algal biomass (which included microphytobenthos, pelagic phytoplankton, macroalgae) in the surface sediment was described by the chla and phaeopigment contents, with a mean value of 5.8 ± 4.8 and 28 ± 18.7μg⋅g−1 of dry sediment, respectively. The chla and phaeopigment contents were positively correlated with the TOC, TN and Org-P contents (Pearson’s coefficient > 0.6, p-value < 0.05).
3.3. Relationships between the sedimentary parameters and benthic nutrient fluxes
The stepwise multiple linear regression (MLR) allowed to select the best combination of sedimentary characteristics to predict the and PO4 fluxes. With regards to the PO4 flux, the parameters selected by the final MLR model were the phaeopigment, Fe-P and Org-P contents, and the porosity (Figure 5). Whereas the PO4 flux was positively and significantly correlated with the phaeopigment and Fe-P contents (p-value < 0.05), it was negatively and significantly correlated with the porosity (p-value < 0.05). In addition, a positive relationship was observed between the Org-P content and the PO4 flux, despite a less significant effect (p-value = 0.079). To predict the flux, the porosity, chla content and C:N and TN:Org-P ratios in the SOM were selected by the final MLR model (Figure 6). The porosity, as well as the TN:Org-P ratio and chla content, were positively correlated with the flux. In contrast, a negative relationship was observed between the C:N ratio and the flux. The parameter with the highest significant effect was porosity (p-value < 0.001), followed by the TN:Org-P ratio (p-value = 0.01). The chla content and C:N ratio presented a less significant relationship with the flux (p-value > 0.05).
Parameters selected during the stepwise selection procedure by multiple linear regression (MLR) to predict PO4 flux (A). The effect of each parameter on the model was characterized by the standardized coefficient and its significance by the pvalue of F-test. The fitted values of PO4 flux by the model were plotted versus PO4 fluxes measured by core incubations (μmol⋅m−2⋅h−1) (B). The straight line represents the slope 1:1.
Parameters selected during the stepwise selection procedure by multiple linear regression (MLR) to predict flux (A). The effect of each parameter on the model was characterized by the standardized coefficient and its significance by the pvalue of F-test. The fitted values of flux by the model were plotted versus fluxes measured by core incubations (μmol⋅m−2⋅h−1) (B). The straight line represents the slope 1:1.
The accuracy of the prediction remained low for both models, with only 18 and 24% of the and PO4 flux variations explained, respectively.
4. Discussion
In the present study, 75% of the and PO4 fluxes were below 151 and 24 μmol⋅m−2⋅h−1, respectively. These measurements were within the range found in previous studies carried out in Brittany and other European intertidal mudflats during the spring period, from either incubation in the dark or porewater nutrient profiles (Table 3). Higher values were also measured in our work, reaching 526 μmol⋅m−2⋅h−1 for and 172 μmol⋅m−2⋅h−1 for PO4. Significant differences in the benthic nutrient fluxes were observed between the sampling locations (Table S1). For example, the mouth of the Penzé River (sites #18 and #19) presented an average flux of 25 ± 16 μmol⋅m−2⋅h−1, whereas this value was 162 ± 132 μmol⋅m−2⋅h−1 in the Auray River (sites #37, #38 and #39) (Figure 3). These results are in agreement with the fluxes observed in the same areas by Lerat et al. [1990] and Andrieux-Loyer et al. [2014] (Table 3). Thus, our broad sampling program emphasized the variability of and PO4 fluxes at the regional scale. Considering these large variations in and PO4 fluxes, using an average benthic flux on the regional scale does not seem appropriate for environmental modeling and management at a local scale. This requires the availability of data over a given area [Le Moal et al. 2019], which may be facilitated by the use of proxies. In this work, we explored the possibility of using sediment characteristics for the quantification of and PO4 fluxes. These parameters were identified via a modeling approach using a stepwise Multiple Linear Regression (MLR). One of the questions asked here was whether or not the benthic nutrient fluxes were controlled by the same sedimentary parameters. The present study showed no correlation between and PO4 fluxes from sediments covering a range of 200 sampling sites. The main sedimentary parameters driving the fluxes were different from those driving PO4 fluxes.
Estimations of and PO4 fluxes at sediment–water interface in the present study and from other European intertidal mudflats
| Location | Method | Period | flux | PO4 flux | Reference |
|---|---|---|---|---|---|
| Brittany mudflats, France | Core incubation | Spring 2019 | 101 ± 117 | 17 ± 20 | Present study |
| Aber Benoît, Brittany, France | Calculation from porewater profiles | May 2008 | 39 ± 16 | <6 | Andrieux-Loyer et al. [2014] |
| The Auray River, Brittany, France | Calculation from porewater profiles | May 2009 | 206 ± 47 | <6 | Andrieux-Loyer et al. [2014] |
| The Penzé River, Brittany, France | Core incubation | March 1985 July 1985 | 48 21 | 5 2 | Lerat [1990]; Lerat et al. [1990] |
| Marennes-Oléron Bay, France | Core incubation | March 1999 June 1999 | 138 ± 75∗ 58 ± 25∗ | Laima et al. [2002] | |
| Rågårdsvik and Bassholmen, Sweden | Core incubation | April–June 2000 | <70 | <5 | Sundbäck and Miles [2002] |
| Palmones River, Spain | Opaque PVC benthic chamber | May 1997 | 187 ± 18∗ | 1 ± 2∗ | Clavero et al. [2000] |
All incubations were carried out in the dark. and PO4 fluxes are expressed as μmol⋅m−2⋅h−1. *When daily flux was converted into hour assuming a 24 h-period.
4.1. What are the main parameters of surface sediment driving the PO4 fluxes?
Four parameters were selected via the final MLR model in order to predict the PO4 fluxes: porosity, phaeopigment content, Fe-P content and Org-P content. Among the four selected parameters, the correlation between the Org-P content and the PO4 flux was not significant (F-test p-value > 0.05). Therefore, the porosity, phaeopigment and Fe-P contents were considered as the key variables for predicting PO4 fluxes.
The parameter presenting the most significant positive correlation for the PO4 fluxes was the phaeopigment content (F-test p-value = 0.002). The phaeopigment content is an indicator of algal biomass detritus in the SOM composition [Dell’Anno et al. 2002; Therkildsen and Lomstein 1993]. In the present study, the significant and positive correlations between the phaeopigment content and that of the TOC, TN and Org-P contents indicated that the organic matter was enriched in the surface sediment due to the accumulation of algal detritus. In the Auray River (Brittany, France), Andrieux-Loyer et al. [2014] also observed that the increase in the phaeopigment, TOC, TN and Org-P contents in the surface sediment was fueled by the occurrence of green macroalgae. An accumulation of algal OM most likely results in intense SOM mineralization, leading to both oxygen depletion in porewater and a shift in anaerobic mineralization from iron to sulfate reduction thereby enabling iron-sulfide formation [Lehtoranta et al. 2009]. Under these anoxic conditions, the adsorption of PO4 onto iron oxides would be less efficient, increasing the upward diffusion of PO4 from the sediment [Ekholm and Lehtoranta 2012 and references therein; Lehtoranta et al. 2009; Rozan et al. 2002]. Thus, surface sediment that is rich in algal detritus would be a more effective source of PO4 due to the creation of anoxic conditions and the subsequent desorption of P (Figure 7A). The solubilization of Fe-P has been shown to be a main process in PO4 release in overlying water from sulfidic sediments during macroalgal blooms [Rozan et al. 2002]. Consequently, sediment that is rich in Fe-P would represent a potentially bioavailable source of P under anoxic conditions. This is in good agreement with our results which identify the Fe-P content as the second main driver, and which was positively correlated with the PO4 flux.
Stimulation of PO4 effluxes by an increase in phaeopigment and Fe-P content, and a decrease in porosity of surface sediment. A high algal biomass detritus and a low porosity decrease the oxic surface layer of sediment, and modify the anaerobic microbial reduction. This enhances the solubilization of Fe(III)-bound P, and avoids the re-adsorption of PO4 on Fe(III) oxides at the interface sediment–water (A). Stimulation of effluxes by an increase in TN:Org-P and porosity of surface sediment. Mineralization of SOM rich in N enhances the regeneration of in sediment. is released to overlying water by diffusion during the core incubation, which largely depends on porosity of sediment. Other changes in microbial N transformations in sediment cannot be stated here (B). Bottom water: nutrient-free and oxygenated artificial seawater used during the core incubations.
In contrast to the previous variables, porosity, the third main driver, was negatively correlated with the PO4 flux. An increase in porosity favors the diffusion of solutes through the sediment [Boudreau 1996], and as a result, a positive effect on the PO4 fluxes could be expected due to an upward diffusion from the sediment. On the other hand, high porosity may also improve the oxygen diffusion across the sediment–water interface [House 2003]. The ferrous ion (Fe2+) oxidation followed by the re-precipitation of iron oxides at the oxygenated interface, would increase the adsorption sites for PO4 [e.g. Krom and Berner 1980; Gunnars et al. 2002; Mayer and Jarrell 2000; Zhang et al. 2010]. This may limit the release of PO4 into the overlying water, thereby explaining the negative correlation between the porosity and the PO4 flux observed in the present study.
4.2. What are the main parameters of surface sediment driving the fluxes?
The parameters selected by the final MLR model for predicting the fluxes were the porosity, the TN:Org-P ratio, the chla content and the C:N ratio. Only the porosity and TN:Org-P ratio had a significant effect (F-test p-value < 0.05), and were thus considered as the key variables for predicting fluxes.
Contrary to the PO4 flux, the flux was positively correlated with the porosity. When sediment has a high porosity, this likely enhances the diffusion of solutes across the sediment–water interface [Boudreau 1996] including that of . As discussed above, an increase in porosity could also enhance the oxygen diffusion at the sediment–water interface. This may favor the oxidation of by nitrifying microorganisms, and could therefore increase the effluxes of oxidized nitrogen species (NOx) at the expense of [Capone et al. 2008 and references therein]. In the present study, the impact of nitrification on the fluxes seemed to be negligible as there was neither a negative effect of the porosity on the fluxes nor a release of NOx in the overlying water ([NOx] < detection limit) (Figure 7B). Consequently, the enhanced oxygen diffusion in the upper layer of the sediment due to the increase in porosity would poorly impact the efflux of in contrast to that of PO4. This could be explained by the fact that abiotic Fe2+ oxidation, fueling the precipitation of Fe-P, would be faster and less oxygen-limited compared to oxidation [Canavan et al. 2006].
The positive correlation observed between the porosity and the flux might also be explained by the fact that the sediments with higher porosity would be also associated with higher OM input flux, through the positive and significant correlation observed between the porosity and e.g. the TOC content (Table 2). Indeed, an increase in the deposition of OM in the sediment, supported by the river export of fine mud particles (with higher porosity, Table 2), can improve the mineralization rates of SOM, as observed in the Elorn and Aulne estuaries in Brittany [Khalil et al. 2018], and thus lead to a larger regeneration in porewaters of sediment. Thereby, higher recycling rates combined with higher sediment–water exchanges could enhance the diffusion of to the overlying water.
In addition to the porosity, the flux was significantly and positively correlated with the TN:Org-P ratio in the SOM. This suggests that the fluxes were controlled by the elemental composition of the SOM. SOM that is rich in both N and easily biodegradable compounds enhances the production of in the sediment via SOM mineralization [Capone et al. 2008 and references therein], releasing into the overlying water (Figure 7B). In coastal systems, the SOM composition depends on a large variability of marine and terrigenous sources of OM [e.g. Carlier et al. 2008; Cook et al. 2004; Dubois et al. 2012]. As a result, the variability of the TN:Org-P ratio in the SOM observed in the present study may be controlled by different OM sources. An increase in the TN:Org-P ratio in the SOM ratio could indicate an accumulation of macroalgae in coastal systems. Such a relationship has been observed in the Venetian lagoon during the spring and summer [Sfriso et al. 1988]. An increase in this ratio could also reflect an anthropogenic N input from riverine particulate matter. This was based on imbalances in the N:P ratio observed in watersheds in previous studies, which depend on industrial and agricultural activities, and the management policy [Arbuckle and Downing 2001; Bouwman et al. 2017; Carpenter et al. 1998; Guenther et al. 2015; Sardans et al. 2012].
4.3. Sedimentary proxies: a good tool to predict and PO4 fluxes?
We hypothesized that the chla content and the C:N ratio, i.e. tracers of labile OM input fluxes explain the spatial variability in benthic nutrient fluxes and therefore allowing them to be used as proxies. Such correlations between these two parameters and the and PO4 fluxes were shown by Cowan and Boynton [1996] and Clavero et al. [2000]. In the present study, the chla content and the C:N ratio were selected by the final MLR model of the flux, but without significant correlations (F-test p-value > 0.05). With regard to the PO4 fluxes, the modeling approach did not identify the chla content and the C:N ratio as main drivers. However, other parameters related to the SOM composition presented significant correlations with the benthic nutrient fluxes: i.e. the TN:Org-P ratio for the flux, and the phaeopigment content, a chla breakdown product, for the PO4 flux. The characterization of SOM composition, largely dependent on the spatial variability of OM sources, may require more specific tools (e.g. isotopic or biomolecular markers) to better relate the benthic nutrient fluxes to the sedimentary composition.
In the present study, the stepwise MLR allowed to select the best combinations of parameters to predict and PO4 fluxes. Nevertheless, only 18 and 24% of and PO4 flux variations respectively were explained by our modeling approach. This highlights the difficulty to determine the controls on the benthic nutrient fluxes by sedimentary proxies in the intertidal mudflats at the regional scale where the rate deposition of particulate matter is influenced by river discharges, tidal fluctuations and anthropogenic pressures [Bauer et al. 2013].
In addition, the contribution of biological parameters was not considered in the present work, which could explain the weak accuracy of prediction for both models of and PO4 fluxes. The and PO4 fluxes may be impacted by bioturbation, through particle reworking and burrow ventilation by the benthic macrofauna [Kristensen et al. 2012]. Bioturbation enhances the solute exchanges between the porewater and the overlying water, affects the remobilization of burial OM, and modifies the redox conditions [Graf and Rosenberg 1997; Welsh 2003; Kristensen et al. 2012]. The stimulation of and PO4 fluxes varies according to the density, the burrowing depth, the ventilation of the benthic macrofauna, and the specific-site conditions (e.g. sediment components, nutrient concentrations in porewater). The study carried out in four European estuaries by Nizzoli et al. [2007] has shown that the early stage of sediment colonization by the polychaete Nereis spp. stimulated the effluxes in all geographical areas (from 1.5 to 4-fold higher) and promoted the sediment to act either as a sink or a source of PO4 according to the site-specific conditions. This thus leads to shifts in the magnitude of effluxes and ratios of N:P fluxes which depend on the spatial variability of environmental conditions.
5. Conclusion
The broad sediment sampling carried out at the regional scale in the present study has allowed to determine the main parameters of the surface sediment driving the and PO4 fluxes. The results have shown that (1) high phaeopigment and iron-bound phosphorus (Fe-P) contents and a low porosity were related to a high PO4 flux, and (2) a high porosity and TN:Org-P ratio in the SOM were related to a high flux. The PO4 fluxes would be more driven by the redox status of the sediment through the desorption of Fe-P under specific anoxic conditions during the algal decomposition. The fluxes would be more driven by high recycling rates from SOM mineralization and high sediment–water exchanges, enhancing the diffusion of to the overlying water. The present work has highlighted that the interactions between the SOM composition and physical properties in the sediment exert a control on the benthic fluxes. This reflects the connections between the microbial (SOM mineralization), chemical (adsorption–desorption) and physical (diffusion) processes (Figure 7). We focussed on the surface sediment characteristics in order to assess the pertinence of these parameters for use as proxies of the and PO4 fluxes. It has underlined that the and PO4 fluxes are partially explained by these sedimentary parameters, and we suggest that the spatial variability of the biological parameters should be considered in future investigations. In addition, in order to better understand the effect of SOM on benthic nutrient fluxes, the sources of OM in the sediment need to be better discerned in a future work using stable isotope and biomolecular analysis.
Acknowledgments
This work was funded by Loire-Bretagne Water Agency and the Regional Council of Brittany (France). It was carried out as a part of the IMPRO research project. The authors would like to thank O. Jambon and C. Roose-Amsaleg for their assistance in the field. The CEVA, the Syndicat Mixte EPTB Rance-Fremur, the Pays de Guingamp, the Syndicat Mixte des Bassins du Haut-Léon, the Syndicat des Eaux du Bas-Léon, Lorient-Agglomération, the Syndicat Mixte du SAGE Ouest-Cornouaille (OUESCO), Concarneau-Cornouaille-Agglomération, the Syndicat Mixte de la Ria d’Etel (SMRE) and the Syndicat Mixte du Loc’h et du Sal (SMLS) are acknowledged for their help in preparing the field campaign. The authors would also like to thank O. Lebeau (Plateforme Isotopes Stables, IUEM) and Marie-Claire Perello (EPOC, University of Bordeaux) for the elemental and particle-size analyses, respectively. S. Mullin is acknowledged for carefully checking the English content. The authors would also like to thank C. Rabouille for his constructive comments and suggestions in order to improve the quality of the paper.