Particularly in wetlands, air-water CH4 fluxes are affected by several environmental variables, such as the water table level, temperature, and salinity. The water table directly influences methane release through its effect on vascular plant species (Sun et al., 2013) and the redox potential (Yang, 2013). Low temperature negatively affects methane emissions, as both organic matter oxidation and plant photosynthesis decrease in response to reduced temperature (Ding and Cai, 2007). Similarly, high salinity reduces methane generation, as sulfate reducing bacteria compit with methanogens for substrate (Bartlett et al., 1987; Poffenbarger et al., 2011). Therefore, CH4 emissions are routinely higher in freshwater wetlands and inland waters in comparison with coastal salt marshes and mangroves (Ding and Cai, 2007; Sun et al., 2013; Tong et al., 2013). Nevertheless, the influence of salinity on methane production is complicated by local conditions, and methane release in saline marshes still persists despite the inhibitory effects of sulfate (Weston et al., 2011). In addition, site-specific conditions are usually quite variable in coastal wetlands, especially in temperate regions, resulting in a large spatio-temporal heterogeneity of methane fluxes in these ecosystems (Poffenbarger et al., 2011). Therefore, because coastal wetland types vary in methane release and controlling processes, they need to be considered individually

to yield reliable estimates of global wetland methane emissions (Turetsky et al., 2014) and to assess the potential evolution with the advent of environmental stressors.

As a contribution to the Frontiers Research Topic Structure, Functioning, and Conservation of Coastal Wetlands, we report a data set obtained in the salt marshes of the Doñana National Park (DNP, **Figure 1**) during a number of samplings conducted between March 2016 and March 2018, of biogeochemical variables related to CH4 production pathways in aquatic systems. Located at the mouth of the Guadalquivir River estuary (South Western Iberian Peninsula, **Figure 1**), the ecosystem complex jointly formed by the Doñana salt marshes and the estuary covers a surface of 3,560 km2 , representing the largest coastal wetland in Southern Europe. In particular, DNP (Ramsar Site since 1982 and UNESCO World Heritage Site since 1995) is considered the most relevant sanctuary for migratory birds in Western Europe (Cížková et al., 2013), as it hosts more than 500,000 waterfowl each year during winter and also includes globally threatened water bird species.

Historically, the Doñana salt marshes were flooded by estuarine waters. However, in April 1998, a massive toxic spill from storage ponds at the Aznalcollar mine reached the Guadiamar River (the major tributary of the Guadalquivir) and 2,650 ha of land adjacent to DNP became contaminated (Taggart et al., 2006). The release of toxic sludge led to the construction of dykes and floodgates in order to protect the marshes against the advance of the contaminated tailings. These prevention actions completely isolated the marshland and impeded any connection with their main draining fluvial basin. Therefore, over almost two decades, hydrology in the Doñana wetlands mostly depended on the annual rainfall regime, small streams situated in the northern side of the Park (e.g., La Rocina, **Figure 1**) and to a lesser extent, groundwater from the Almonte-Marismas aquifer (Olías et al., 2008). As a result, inundation cycles in the marshland became highly variable (Díaz-Delgado et al., 2016; Huertas et al., 2017). The scientific community and policy-makers designed an ambitious restoration program, the so called Doñana 2005, aimed at restoring partial connectivity between the estuary and the marshes and since 2014, estuarine waters have inundated the Doñana salt marshes again.

Prior to connection, biogeochemistry in aquatic systems of the DNP was examined in relation to the patterns of the variable hydrological cycle occurring in the region (Morris et al., 2013; Huertas et al., 2017). Under wet cycles, prolonged flooding resulted in a marked heterotrophy and a significant CO2 outgassing (Morris et al., 2013) whereas during drynormal hydrological cycles, air-water CO2 fluxes decreased, with permanent floodplains and ponds of the marshland even behaving as mild sinks for atmospheric CO2 for short periods (Huertas et al., 2017). Alternatively, aquaculture lagoons with man-managed water supply from the estuary continuously behaved as carbon sinks, which highlighted the importance of water management in the Doñana, not only for wetland conservation, but also for its role in the atmospheric CO2 exchange. Nevertheless, regardless of the hydroperiod, net primary production of the surrounding terrestrial vegetation largely offset aquatic CO2 effluxes and hence, considering the net ecosystem exchange, the Doñana wetlands were categorized as a large carbon sequestrator (Huertas et al., 2017).

In contrast, previous studies have identified the Guadalquivir estuary as a potent CO2 source in the atmosphere (de la Paz et al., 2007; Flecha et al., 2015). Higher CO2 emissions are mainly associated to the net heterotrophy occurring upstream of the estuary which is less influenced by the tidal intrusion (salinity <5 psu) and characterized by a considerable turbidity, which leads to a strong CO2 supersaturation (Flecha et al., 2015). Recent work has also shown that the estuary functions as a small CH4 emitter and as a moderate N2O source, with the trace gas dynamics being highly dependent on the tidal-fluvial interaction (Huertas et al., 2018).

To our knowledge, no previous assessment of methane dynamics in the Doñana marshland has been performed, neither during the period of isolation from the estuary, nor after restoring connectivity. The aim of our study was therefore to provide a general description of the current methane dynamics in the salt marshes. Our findings can be used as a base-line to evaluate future trends in response to changes in hydrology, related to direct human management interventions (control of estuarine inputs through dykes and floodgates), or those associated to climate change (draft, sea level rise).

# MATERIALS AND METHODS

#### Sampling Sites and Strategy

Samples were taken at seven sites (**Figure 1**) that represent salt marshes inundated by the Guadalquivir estuary during the 16 field campaigns conducted between March 2016 and March 2018. Sites were selected to account for the salinity gradient present in the estuary, which can range from 34 psu in the river mouth down to near 0 psu in the upper limit of the land strip adjacent to the riverside considered in our study (Huertas et al., 2018). Vegetation in the marshes is characterized by pioneer halophilous species like Sarcocornia fruticosa, Halimione portulacoides (Chenopodiaceae), and Limoniastrum monopetalum (Plumbaginaceae) (García-Murillo et al., 2014). During the monitoring period, we also identified Arthrocnemum macrostachyum, Plantago coronopus, Cotula coronopifolia, Taraxacum spp, Centaurium pulchellum, Polypogon maritimus, and Juncus sp., although plant biodiversity exhibited a marked spatio-temporal variability, which is higher during spring periods (Romagnoli, 2018).

All samplings were carried out when the estuary was under the influence of spring and rising tides. Some studies in marshes and other coastal wetlands, such as mangroves, have shown that there is a strong tidal signal in CH4 and water chemistry (Call et al., 2015). Our sampling design might therefore have affected gas dynamics, as sample collection occurred along with the net water flux into the system. However, those tidal conditions ensured overflow in the mudflat and the presence of a significant water level (a minimum water height of 0.1 m) allowing sampling, which was conducted on foot. This strategy may have also had implications for gas distribution due to sediment dynamics disturbance. Nonetheless, it was the only way to access the flooded marshes in certain sites. Considering this caveat, we tried to minimize sediment disturbance as much as possible.

The floodplain is highly variable depending on annual rainfall (Huertas et al., 2017), and is also presently affected by the estuarine intrusion, that in turn is controlled by the tide amplitude. In fact, water levels were too low for sampling during the summer months and early fall. Therefore, data are not available for all sites during the dry season, and temporal variability of aquatic parameters has been examined between periods in which measurements could be performed.

Even though surface water coverage in the marshland changes temporarily, the mudflat is a relatively shallow system. Considering past and current records of wind speed in the area and the average depth of the water column, the Richardson number was calculated, which indicated that at each site, the flooded area was a well-mixed water body (not shown). Due to mixing conditions, methane levels measured at each sampling station were assumed to be representative of each inundated zone.

In all sites, conductivity, temperature and pH (National Bureau of Standards, NBS scale) were obtained with a Yellow Spring (YSI Incorporate) portable multiparameter probe YS6820v2. The pH probe was calibrated before samplings using the United States National Bureau of Standards buffer solutions (4 and 7). Discrete surface water samples were taken

to determine dissolved CH4 concentrations and the rest of the aquatic biogeochemical variables considered in our study such as chlorophyll (Chl a), dissolved oxygen (DO), total alkalinity (TA), inorganic nutrients (NH+ 4 , NO− 2 , NO− 3 , PO3− 4 , and SiO4− 4 ), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), and suspended particulate matter (TSM).

#### Data

#### Meteorology

Hourly measurements of rainfall (mm), air temperature (◦C), humidity and wind speed at height z (uz, m s−1 ) were obtained from a meteorological station located in Lebrija (36◦ 58′ 35′′ N, 06◦ 07′ 34′′ W) which is situated 20 Kms south east of S3 and belongs to the Junta de Andalucía network (www.juntadeandalucia.es/agriculturaypesca/ifapa/ria/ servlet/FrontController).

#### Aquatic Biogeochemical Variables

Samples for CH4 measurements were collected in duplicate 120 mL serum vials, poisoned with HgCl2, sealed and stored upside down in the dark. Analysis was performed by statichead space equilibration gas chromatography (GC) using an Agilent 7890 GC, equipped with a Flame Ionization Detector (FID) for CH4 (de la Paz et al., 2015). Before analysis, 20 mL of N2 headspace were added to each sample, vigorously shaken and equilibrated for at least 12 h. The gas cromatographer was calibrated using three different standard gas mixtures, a certified NOAA primary standard with composition similar to the atmosphere (1863.4 ± 0.3 ppb of CH4), and two additional standard gas mixtures of CH4 in a N2 matrix, provided by Air Liquide (France) with certified concentrations (3,000 and 5,000 ppb of CH4). The precision of the method estimated from the coefficient of variation, based on replicate analysis, was 0.6%.

The response of the FID was very stable and highly lineal for CH4 concentrations within the calibration range. Using daily calibrations during the measurement period, a mean regression coefficient was calculated, yielding a value of 0.9997 ± 0.0002. For samples above the calibration range, linearity was assumed based on a perturbation test of the calibration equation that gave a relative error of 0.32%. In terms of concentration, the linearity assumption resulted in errors of 0.1 and 3 nmol l−1 for CH4 concentrations of 50 nmol and 1,000 nmol l−1 , respectively, which allowed considering that the uncertainty associated with the measurements was not significant in the overall database.

Saturation values expressed as percentage (%) of CH4 were computed as the ratio between the gas concentration measured and the calculated equilibrium concentration for the gas. The equilibrium concentration of CH4 was calculated using the solubility expression proposed by (Wiesenburg and Guinasso, 1979) and the annual averaged atmospheric mixing ratio CH4 (×CH4atm) at the nearest station, provided by the World Data Center for Greenhouse Gases (http://ds.data.jma.go.jp/gmd/ wdcgg). As CH4 data at the nearest station were not available for the entire study period, a constant value of 1,866 ppb was used, which corresponds to the last annual averaged value available for 2015.

Chl a analysis was performed by filtering known volumes of water through Whatman GF/F glass fiber filters, dipping in 90% acetone overnight in the dark, and measuring by fluorometry using a Turner Designs 10-AU Model fluorometer that was calibrated with a pure standard from Anacystis nidulans (Sigma Chemical Company). DO concentrations were determined by the Winkler method through potentiometric titration using a Metrohm 794 Titroprocessor. Saturation values of DO were calculated according to Benson and Krause (1984) and the Apparent Oxygen Utilization (AOU) was estimated as the difference between the oxygen concentration at saturation and that measured in the samples. Samples for TA determinations were collected in borosilicate bottles (500 ml) preserved with 100 µl of a HgCl2 saturated aqueous solution and stored until their analysis in the laboratory by titration according to Mintrop et al. (2000). Accuracy (±5 µmolkg−1 ) was regularly checked by measurements of Certificate Reference Material provided by Prof. Andrew Dickson, the Scripps Institution of Oceanography, La Jolla, CA, USA (Batch 147). Partial pressure of CO2 (pCO2) was calculated from TA and pHNBS applying the CO2SYS.xls program (Lewis et al., 1998), with the Cai and Wang (1998) and Dickson (1990) constants for carbon and sulfate, respectively. The propagated error for pCO2 data was ±3%, using an estimated error for pH measurements of ±0.01 pH units, ±2 µmolkg−1 for TA measurements and ±0.1◦C for temperature.

Inorganic nutrients were obtained from filtered (Whatman GF/F glass fiber filters) and frozen (−20◦C) water samples (5 mL) that were measured in a Continuous Flow Analyzer, following the protocols described by (Grasshoff et al., 1983). DOC and TDN concentrations were obtained with a Shimadzu TOC-VCPH analyzer, by catalytic oxidation, at high temperature (720◦C) and chemiluminescence, respectively, according to Álvarez-Salgado and Miller (1998). TSM was obtained by the loss on ignition method on known volumes of water filtered through pre-weighed and pre-combusted 450◦C Whatman GF/F glass fiber filters, which were subsequently desiccated at 60◦C for 48 h and totally combusted at 450◦C during 5 h.

#### Air-Water CH4 Fluxes

The air-water CH4 flux (FCH4, µmol m−2 d −1 ) was computed as:

$$\mathbf{F} = \mathbf{k(Cw - Ca)}\tag{1}$$

Where Cw and Ca are the dissolved gas concentration and the equilibrium concentration in water, based on the molar atmospheric ratio, and k (cm h−1 ) is the gas transfer rate as a function of wind speed at 10 m height, that was calculated from k and normalized to a Schmidt number (Sc) of 600 (k600) according to:

$$k = k\_{600} \sqrt{\frac{600}{\text{Sc}}} \tag{2}$$

Sc was obtained using the formulations given by Wanninkhof (1992) interpolated at the in-situ salinity from the freshwater and seawater equations and k600 was calculated from U10 according to Cole and Caraco (1998).

$$k\_{600} = 2.07 \, + 0.215 \, \text{U}\_{10}^{1.7} \tag{3}$$

where U10 was calculated from uz measured at the meteorological station according to Smith (1988).

We acknowledge that the empirical relationship chosen to adjust k600 values may not be ideal for representing wind– enhancement effects in the flooded marshes (shallow water bodies with substantial variations in extent) but it was proven to be suitable to compute air-water CO2 fluxes in relation to other parameterizations (Morris et al., 2013). Nevertheless, to give an indication of the uncertainty this choice introduces, air-water CH4 fluxes were also predicted considering a 0.67 exponent for the Schmidt number. Despite minor differences obtained at high wind speeds (<10%), the mean flux remained unchanged when annual values were averaged.

#### Statistics

The program language MATLAB was used to perform statistics analysis. Probability distributions of variables were examined through a Shapiro-Wilk test. Pearson's product-moment correlation (PPMC) was used to test for significant correlations between variables. Significance levels were set at p < 0.05.

All data contained in this work are available for download from Digital.CSIC, the Institutional Repository of the Spanish National Research Council (CSIC), http://hdl.handle.net/10261/ 173204, http://dx.doi.org/10.20350/digitalCSIC/8588).

# RESULTS

#### Meteorological Conditions

Monthly averaged air temperature during the samplings ranged between a minimum of about 8 and a maximum of 24.5◦C in January 2017 and July 2017, respectively (**Figure 2A**). Rainfall was quite variable during the study period (**Figure 2B**), typical of the Mediterranean climatic region that is characterized by a rainy season (usually extending from October to March) and a dry season (from June to September) when rainfall is almost absent. The inter-annual precipitation pattern in this geographical area also fluctuates often, which affects the flooding regime in the wetlands (Huertas et al., 2017). Particularly, between March 2016 and March 2018 which corresponds to 3 hydrological years (starting 1 September 2015), total annual rainfall was 493 mm from September 2015 to August 2016, 539 mm from September 2016 to August 2017 and 500 mm from September 2017 to March 2018. These values are very close to the mean annual precipitation in the region, equivalent to 550 mm (Serrano Martín et al., 2008), thereby representing normal hydrological cycles. Monthly averaged precipitations during the sampling period varied from a minimum of 0 mm, registered during the months of June and September 2017, to maximum of about 150 mm in spring of 2016 and 2018 and autumn 2016 (**Figure 2B**). The tidal coefficient at the mouth of the estuary during the sampling dates and defined as the amplitude of the tide forecast, oscillated between 41 and 111, as observed in January 2017 and November 2016, respectively (**Figure 2B**). Monthly averaged horizontal wind velocity at 10 m above the surface ranged between 1.8 in January 2017 and 4.5 m s −1 in March 2018 with a median value of 2.5 m s−1 (**Figure 2C**).

#### Biogeochemical Conditions

Water temperature during samplings oscillated between 11.4 and 32.3◦C (**Figure 2A**), with a clear seasonal pattern that was similar at all sites (not shown for sites individually). The range of variation of other aquatic biogeochemical variables is summarized in **Table 1**.

Because of meteorology and tide variations, salinity markedly changed across the marshes during the study period. It is worth noting that the Guadalquivir estuary is a mesotidal system, in which tidal influence can be noticeable up to 100 km upstream of the river mouth. Nevertheless, high freshwater discharges from an upstream dam during the rainy season interferes with the tidal effect and causes drastic changes in salinity (Díez-Minguito et al., 2013). Therefore, under heavy and prolonged rainfall events, salinity along the estuary experiences large decreases, although during the dry period (70% of the hydrological year) low discharges regularly occur to compensate for evaporation losses and the estuary is tidally dominated.

Consequently, salt marshes located closer to the river mouth (S1:S4) generally had higher salinities compared to those located on the land strip upstream of the estuary (S5:S7). With the exception of a single value of 33.6 psu in March 2016, salinity always remained below 5 psu in Site 7. Based on salinity, S1:S4 could be considered polyhaline tidal marshes whereas S5:S7 formed a group of oligo-mesohaline marshes. Clear variations for most other variables were also observed in all sites during the different samplings (**Table 1**).

According to Chl a, the salt marshes may be categorized as meso to eutrophic systems, with the higher pigment concentrations mostly found in the mesohaline sites (**Table 1**). This was also the case for DOC and nutrients (PO3− 4 and DIN), which appeared at higher levels in S5:S7. Possibly related to the presence of high phytoplankton biomass, the flooded marshes were always well-oxygenated, and hypoxia was not detected during the samplings, with DO levels keeping values above 5 mg l −1 (**Table 1**). Results of the Pearson correlations performed with the complete dataset of the variables measured (**Table 2**), confirmed that the chlorophyll concentration was negatively and significantly (p < 0.05) correlated with AOU and was therefore, directly related to the dissolved oxygen concentration.

Besides direct precipitation, the only water source to the Doñana marshland is the adjacent estuary and thus, the increased salinity in the flooded marshes reflected the effective entry of estuarine waters through the tides. Salinity was significantly (p < 0.05) and positively correlated with TSM, DOC and TDN, indicating that the dissolved and suspended matter measured in the marshes could partially have a fluvial origin.

# Spatio-Temporal Variation in Dissolved CH4

Dissolved methane ranged between 5.7 and 12,290 nmol l−1 and generally increased in the mesohaline marshes (S5:S7, **Table 1**). The spatio-temporal variability of CH4 in all sites is shown in **Figure 3** where salinity has also been plotted. Methane values at

site S1 were below 18 nmol l−1 during the entire sampling period, except for a single value of 22 nmol l−1 observed in March 2018, coinciding with a drop in salinity (∼4 psu) due to heavy monthly precipitations and a moderate estuarine intrusion (marked by the tidal coefficient) during the sampling date (**Figure 2B**). The highest methane levels in this site were observed when salinity exhibited values below 15 psu, as it occurred in May and December of 2016, March 2017 and March 2018 (**Figure 3**).

In S2, CH4 was invariably higher than in S1 with a median value of 278 nmol l−1 (**Table 1** and **Figure 3**). A minimum of around 50 nmol l−1 was measured in March, April and December 2016 when salinity was above 14 psu. A second minimum of 100 nmol l−1 was observed in March 2018 despite the low salinity present in the flooded marshes, which may be attributable to a dilution effect caused by the high monthly rainfall (**Figure 2B**). The maximum concentration of dissolved methane in this site (617 nmol l−1 ) was detected in the summer of 2017 at a salinity of 19 psu. Therefore, even though salinity was likely to affect methane production, other processes were involved in CH4 dynamics in this particular sector of the marshes.

The range of variation of dissolved methane in S3 decreased with respect to that in S2, oscillating between 13.1 and 319.3 nmol l−1 (**Table 1**) and with an average concentration of 104 nmol l−1 . Apparent relationship with salinity was not evidenced in this site, as maximum levels of methane were not associated with decreased salinity and vice versa (**Figure 3**).

In S4, the average concentration of methane during the sampling period was 65 nmol l−1 , with a maximum value of 114 nmol l−1 found in December 2016 at a salinity of 4 psu (**Figure 3**). A second maximum was also observed in March 2018 at a similar salinity. However, high CH4 concentrations (∼90) were still measured under significant estuarine intrusions, such as those occurring in June 2016 and May 2017 which can be evidenced by the salinity rises (∼35 and ∼24 psu, respectively, **Figure 3**).

In the mesohaline marshes (S5:S7) that were characterized by salinities below 10, except in March 2016, methane concentration clearly increased in relation to the polyhaline sites (**Table 1** and **Figure 3**). Average concentrations of CH4 were 338.5, 261.2, and 431.3 nmol l−1 in S5, S6, and S7, respectively. However, it must

TABLE 2 | Pearson correlation matrix for biogeochemical variables measured in Doñana salt marshes.

The values were obtained with the complete database collected during the 16 samplings conducted between March 2016 and March 2018. Abbreviations are indicated in the text. \*Correlations significant at p < 0.05.

be noted that an exceptionally high methane level, equivalent to nearly 12,300 nmol l−1 , was found in S5 during July 2017, which has been excluded of the average local computation. As a general trend, methane concentration rose in these marshes during the summer of 2017 when water temperatures reached 32◦C (**Figure 2A**).

Overall, the influence of temperature on dissolved CH4 was observed in all sites, as the gas levels increased during the spring and summer months, compared to the levels measured over the winters (**Figure 3**). The temperature dependence was confirmed by the significant (p<0.05) and positive correlation obtained between this variable and CH4 (**Table 2**). When methane concentration was plotted against temperature (**Figure 4A**), the thermal effect on CH4 levels was especially obvious in the mesohaline marshes, where sulfate inhibition is likely to be suppressed.

In contrast, no statistical correlation between methane and salinity was found (**Table 2**). Nonetheless, when the relationship between the gas saturation levels in the salt marshes and salinity was more closely examined, separately considering the polyhaline (S1:S4) and mesohaline sites (S5:S7), two different behaviors of CH4 dynamics and their relation to this variable were distinguished (**Figure 4B**). Polyhaline marshes were characterized by a CH4 saturation range of 252–36,735% (average 5,170%) and a scattered distribution with salinity (**Figure 4B**). On the contrary, mesohaline marshes exhibited a larger CH4 saturation range (374–620,007%, average 31,541%) and a positive relationship with salinity was noticeable (**Figure 4B**).

In order to better elucidate the effect of ecosystem metabolism on the dynamics of CH4 in the salt marshes, the relationship between CH4 saturation and the oxygen availability was also considered. As shown in **Figure 4C**, log10%CH4 vs. %O2 displayed a scattered distribution but a positive relationship, indicating that high levels of CH4 were present in welloxygenated inundated marshes.

Moreover, dissolved CH4 concentration was shown to be strong and significantly (p < 0.05) correlated with Chl a (**Table 2**). The increase of methane with the phytoplankton biomass was evident in both the polyhaline and mesotrophic sites (**Figure 5A**) and in the mesohaline and eutrophic sites (**Figure 5B**). The trend found between DOC and Chl a in the salt marshes (**Figures 5C**,**D**) suggests that (**Figures 5C**,**D**) organic matter in the water column may have originated partially

from phytoplankton productivity. Nonetheless, the fact that high values of DOC (>10 mg l−1 ) were still present under a low Chl a concentration (<1.5 µg l−1 ) in the two groups of sites, indicate the contribution of external sources to the organic matter content in the marshes. Increases in organic and suspended matter were accompanied by rises in dissolved CH4 (**Figures 5E–H**). Significant and positive correlations with salinity were found for both variables (**Table 2**) and between salinity and TDN, supporting the hypothesis that estuarine inputs also contribute to organic matter accumulation and suspended matter entry in salt marshes.

The pCO2 levels in the marshes were also well-correlated to the log of %CH4 and to %O2 (**Figures 6A,B**). pCO2 values spanned three orders of magnitude, ranging between 0.5 and 4,427µatm in S3 and S5, respectively (**Table 1**). Extremely low pCO2 levels, could be attributed to the high pH values measured in the marshes (∼10). The inundated mudflat remained undersaturated with respect to atmospheric CO2

polyhaline marshes (black symbols) and mesohaline marshes (open symbols) of Doñana National Park. (average concentration of ∼405.4µatm during the sampling

period estimated with the Mauna Loa trends, https://www.esrl. noaa.gov/gmd/ccgg/trends/) half of the time. In particular, S3 always behaved as a sink for atmospheric CO2 (**Table 2**).

# CH4 Emission Fluxes

CH4 emissions exhibited a marked variability during the sampling period in all sites, varying from ∼3 µmol m−2 d −1 in site 1 in March 2016, to a maximum of ∼12,700 µmol m−2 d −1 in site 5 in July 2017 (**Figure 7**). Overall, a tendency of higher methane effluxes was observed during the summer months, although emissions increased in the mesohaline marshes (from 5.6 to 12,715 µmol m−2d −1 ,average 637 µmol m−2 d −1 ) in relation to the polyhaline marshes (from 2.6 to 720 µmol m−2d −1 , average 104 µmol m−2 d −1 ).

#### DISCUSSION

# Sources of CH4 in Doñana Salt Marshes

Coastal wetlands, and particularly estuarine systems are intense sources of CH4 to the atmosphere (Borges and Abril, 2011; Upstill-Goddard and Barnes, 2016) where emissions are sustained by fluvial inputs and sedimentary methanogenesis, fueled by a high organic matter deposition (Borges et al., 2017; Rosentreter et al., 2018a).

We recently reported a data set of dissolved CH4 concentrations in the Guadalquivir estuary transect that flooded the Doñana salt marshes from March 2016 to March 2017 (Huertas et al., 2018). For this period, that partially encompasses the one used in this study, the CH4 concentration ranged from 14 nmol l−1 in the river mouth (average salinity during the study >25) to 750 nmol l−1 upstream to the limit of site 5 (average salinity ∼5). These levels of dissolved methane corresponded to a saturation range of 520–30,800% (average 2,285%) (Huertas et al., 2018), which is comparable to that measured in the Doñana polyhaline marshes (S1:S4) corresponding to 252– 36,735% (average 5,170%) but much lower than that observed in the mesohaline marshes (374–620,007%, average 31,541%). This indicates that even though estuarine inputs of CH4 in the salt marshes may occur by lateral transport, through riverine overflow, a local additional source of CH4 must contribute to the observed high CH4 values. The methane over-saturations and concentrations measured in Doñana fall within the range reported in temperate estuarine systems characterized by a salinity gradient (Upstill-Goddard and Barnes, 2016; Borges et al., 2018b) and in mangroves (Rosentreter et al., 2018b and references therein).

The most likely additional source of CH4 to the Doñana tidal flat would be sedimentary methanogenesis. Warm and waterlogged soils provide ideal conditions for methanogenesis (Roehm, 2005). Deep standing water enriched in dissolved organic matter and covered by plant litter creates an anaerobic environment that triggers CH4 production (Ding and Cai, 2007), with this process being favored by temperature increases. Our data show a direct and strong relationship between the levels of dissolved methane, organic matter content and water temperature, as it has been observed in other brackish coastal wetlands (Welti et al., 2017). Methanogenesis is substantially driven by temperature (Martin and Moseman-Valtierra, 2017) and it is therefore the most dominant environmental driver of CH4 emissions in wetlands (Yvon-Durocher et al., 2014) specifically in coastal salt marshes (Abdul-Aziz et al., 2018). The overall pattern found here therefore suggests a methanogenesis control by temperature in the sediment, which has been described as muddy with numerous stems and roots and scarce micro-fauna (Ruiz et al., 2004). In addition, vegetation in the

FIGURE 5 | Dissolved methane concentration vs. Chl a (A,B), DOC (E,F), and TSM (G,H) in polyhaline marshes (black symbols) and mesohaline marshes (open symbols) of the Doñana National Park. The relationship between Chl a and DOC in the marshes is also shown in panels (C,D). Abbreviations are indicated in the text.

line is the average atmospheric pCO2 during the sampling period.

marshes may have supported a methanogens population that could contribiute to an increase of CH4 concentrations during warmer conditions, which has been found in Spartina dominated wetlands (Burke et al., 2002; Abdul-Aziz et al., 2018). The organic-matter rich sediment in conjunction with the organic inputs from the heterotrophic Guadalquivir estuary (Flecha et al., 2015) and those generated in situ by the phytoplankton productivity, as indicated by the association found between Chl a and DOC, provide optimal conditions for sedimentary methanogenesis to proceed, with temperature acting as a major regulating driver. The higher temperatures during the late spring and summer months possibly resulted in increased microbial activity and greater primary productivity (Chl a and temperature were significantly and positively correlated), leading to high organic inputs, sediment carbon contents, and subsequently higher substrate availability. Large suspended matter loads that are characteristic in the turbid Guadalquivir River (Flecha et al., 2015) would also provide higher silt deposition and enhance methane generation, in agreement with what has been observed in shallow coastal areas (Borges et al., 2017). The pattern between TSM and dissolved CH4 in the salt marshes supports this hypothesis, particularly in the mesohaline marshes where very high levels of dissolved methane were measured at a high Chl a concentration, DOC content and TSM.

The couplings between pCO2, CH4, DOC, and %O2 also indicate that the dynamics of dissolved gases in the tidal flat were driven by the oxidation of the organic matter produced by either phytoplankton, as described elsewhere (Borges et al., 2017; Welti et al., 2017) or from allochthonous sources (Upstill-Goddard et al., 2017). Here, the relationship observed between pCO2 and CH4 is quite frequent (Borges et al., 2015, 2018a; Rosentreter et al., 2018b) and denotes a common source and similar production dynamics, as both gases are produced through organic matter respiration.

In the polyhaline marshes, large CH4 concentrations were measured when DOC levels were high, but this association was not as clear as in the mesohaline sites. This was probably due to the general inhibitory effects of abundant sulfate electron acceptors and reducing bacteria generally found in saline environments (Alongi et al., 2001; vanDijk et al., 2015). In fact, even though we did not observe a linear relationship between salinity and CH4 levels, coinciding with the trend described in other brackish systems (Chuang et al., 2017; Welti et al., 2017), the lower CH4 levels in the Doñana salt marshes were mainly associated to high salinity values, consistent with existing literature on coastal wetlands (Bartlett et al., 1987; Poffenbarger et al., 2011). Nevertheless, large CH4 levels can still occur in wetlands even when sulfate reduction is relevant (Lee et al., 2008; Alongi and Brinkman, 2011).

Similarly, distinct associations between methane and oxygen have been reported in aquatic systems. It is certain that methanogens are obligate anaerobes, and thus a negative relationship %CH4 vs. %O2 would be expected, as previously shown in other coastal wetlands (Livesley and Andrusiak, 2012), including the Guadalquivir estuary (Huertas et al., 2018). But alternatively, positive associations have been described in Amazon floodplain lakes (Devol et al., 1990), in freshwater lakes (Bogard et al., 2014), African savannah rivers (Upstill-Goddard et al., 2017) and shallow coastal zones (Borges et al., 2017). This feature has been attributed to several causes. First, the diffusion of CH4 produced in the underlying sediment into the aerated water column under low levels of turbulence since the CH4 diffusion term in these conditions, exceeds combined CH4 losses via oxidation, and water-to-air exchange (Upstill-Goddard et al., 2017). The contribution of high macrophyte-related productivity has also been suggested as a cause for positive relationships with dissolved oxygen due to direct CH4 production (Stanley et al., 2016). Additionally, methanogens have a higher sensitivity to temperature than methanotrophs do (Dunfield et al., 1993), which leads to higher rates of CH4 production in relation to consumption at elevated temperatures within a certain range of salinity values. Furthermore, methanogenesis in aerobic conditions by methanogen archaea fixed at the surface of phytoplankton cells has also been reported (Grossart et al., 2011). Therefore, peaks of dissolved CH4 that correlate positively with oxygen, phytoplankton biomass, and productivity are a recurrent feature in aquatic ecosystems (Bogard et al., 2014 and references therein).

Considering the shallowness of the tidal flat in the Doñana, it is plausible to assume that benthic productivity could have been decoupled from CH4 production (Borges et al., 2015). Moreover, methane could also have been transported from the creek onto the marshes during the flood tide as it occurs in estuarine systems (Rosentreter et al., 2018b),

whereas the DO signal would be related to the primary productivity occurring in the mudflat. Unfortunately, tidal pumping cannot be evaluated due to the lack of measurements in the creek, but it could be an important mechanism for CH4 import to the marshes (Rosentreter et al., 2018b). The correlation of methane with a Chl a concentration may support a strong benthic CH4 source, fueled by sinking phytoplankton material (Borges et al., 2017) rather than an aerobic CH4 production.

Therefore, according to the patterns found in our study, temperature, and phytoplankton activity seem to be the main drivers of CH4 formation when salinity does not exceed 17 psu (see for instance temporal changes in methane levels in site 5 and the overall relationship %sat CH4 vs. salinity). Above this salinity threshold, sulfate inhibition would constrain methane formation in the marshes.

Nevertheless, our findings also indicate that besides sedimentary methanogenesis, inflowing estuarine inputs would provide an additional source of methane to the salt marshes. Offsetting these sources, losses by microbial oxidation, lateral advection towards the estuary and dilution by heavy rain events, must also be incorporated as mechanisms affecting temporal variability of methane in the system. Unfortunately, the relative importance of all sources and sinks of dissolved methane cannot presently be estimated.

# CH4 Emissions From the Doñana Salt Marshes

The mean air-water CH4 flux during the sampling period was 324 µmol m−2 d −1 , although a high variation in the emissions was observed (±1,394 µmol m−2 d −1 ). This was a consequence of the different ranges of emissions found between the polyhaline and mesohaline marshes, as the methane fluxes were generally lower in the former compared to the latter, in agreement with what has been observed in systems characterized by marked salinity gradients (Poffenbarger et al., 2011; Welti et al., 2017). Nevertheless, the mean flux provided here falls within the ranges of CH4 emissions reported in European estuarine zones (Upstill-Goddard and Barnes, 2016), in temperate fluvial catchments (Upstill-Goddard et al., 2017) and in mid-latitude mangroves (Rosentreter et al., 2018a). Few studies have quantified and investigated air-water CH4 exchange and dynamics in tidal flats (Dean et al., 2018). The fluxes measured in our study are slightly lower than those observed in subtropical tidal wetlands (Tong et al., 2013; Welti et al., 2017), coherent with a temperature control on methanogenesis (Abdul-Aziz et al., 2018). Lower emissions can therefore be expected from temperate coastal wetlands compared to tropical and subtropical coastal wetlands. In fact, the higher methane fluxes in the Doñana were found during the summer months in all sites, as increasing temperature increases microbial activity. Nevertheless, methane emissions from the Doñana salt marshes are still of the same order of magnitude than those measured in Indian and Australian mangroves (Linto et al., 2014; Call et al., 2015; Rosentreter et al., 2018b).

The average air-water CH4 flux estimated here is almost 5 fold higher than the mean value computed in the Guadalquivir estuary transect that floods Doñana and equivalent to 66.2 µmol m−2d −1 (Huertas et al., 2018). The biogeochemistry in the estuary is totally different than that in the marshes where large loads of inorganic suspended particulate matter cause a considerable turbidity that limits phytoplankton growth (Ruiz et al., 2013). The low primary productivity in the estuarine waters would constrain labile organic matter supply for sedimentary methanogenesis, whereas in the shallow and lower turbulent salt marshes organic inputs from in situ photosynthesis, or from external sources, would fuel the process. This hypothesis however, remains to be tested.

The Doñana salt marshes behaved as a moderate CH4 emitter under the environmental conditions present during the sampling period, which corresponds to a normal hydrological cycle for this geographical area. Cycles characterized by heavy precipitations, which are not so uncommon (Díaz-Delgado et al., 2016; Huertas et al., 2017), are expected to affect methane dynamics and increase the emissions due to the overall reduction in salinity over the inundated marshes. This effect has been observed in tropical mangrove-dominated estuaries (Rosentreter et al., 2018b). Moreover, it is important to acknowledge that the approach used here to compute methane fluxes, does not quantify CH4 ebullition fluxes, whose contribution to total CH4 emissions may be significant (Baulch et al., 2011), particularly during low water periods. Furthermore, our sampling regime did not account for the potential importance of lateral tidal pumping as a source of CH4 and higher concentrations (and fluxes) during low tide, as it has been reported in other intertidal systems (Call et al., 2015; Maher et al., 2015). In fact, tidal differences may account 5-fold in the estimated CH4 diffusive water to air fluxes (Maher et al., 2015). CH4 may also be transferred directly from sediments to the atmosphere (Borges and Abril, 2011). At low tide, this can take place through diffusion or ebullition and in vegetated tidal flats, such as Doñana, plants may act as both active and passive conduits of the CH4 (Foster-Martinez and Variano, 2016). All these pathways were not determined in our study and total CH4 emissions must be seen as a lower limit.

Our study is, however, foundational because it underlines the drivers controlling CH4 dynamics in the Doñana marshes. These wetlands are threatened by climate change and many local stressors associated to anthropogenic activities (Green et al., 2017). In the Mediterranean climatic region, higher minimum temperatures, more extreme high temperature events in summer and less precipitation are projected to occur by the end of the 21st century (Giorgi and Lionello, 2008). How the expected warming will affect methane dynamics in the ecosystem complex formed by the estuary and the marshes cannot be anticipated. Increased temperatures can cause methane to be produced at a faster rate but a rise in the saline intrusion associated to the projected sea level rise may offset methane release. Sustained observations would then be required to constrain the gas budget in this iconic coastal wetland.

#### AUTHOR CONTRIBUTIONS

IEH conceived the study, contributed to data analysis and interpretation, and drafted the manuscript. GN and FFP contributed to data analysis and interpretation, and critical discussion. SF and MdP contributed to the analytical design, data calculation and discussion.

# FUNDING

This research was funded by the Spanish Ministry for Agriculture, Food and Environment, project 1539/2015. Mercedes de la Paz would like to acknowledge the support of the Spanish Ministry of Economy and Competitiveness (CTM2015-74510-JIN).

# ACKNOWLEDGMENTS

The authors are indebted to María Ferrer-Marco, Marta Riera and Antonio Moreno for their support in the field work and samples analysis and Manuel Arjonilla for nutrient analysis.

#### REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Huertas, de la Paz, Perez, Navarro and Flecha. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Creek Dynamics Determine Pond Subsurface Geochemical Heterogeneity in East Anglian (UK) Salt Marshes

Alec M. Hutchings1 \*, Gilad Antler2,3, Jean V. Wilkening4 , Anirban Basu5 , Harold J. Bradbury1 , Josephine A. Clegg1 , Marton Gorka1 , Chin Yik Lin1,6 , Jennifer V. Mills7 , Andre Pellerin8 , Kelly R. Redeker9 , Xiaole Sun10 and Alexandra V. Turchyn1

1 Department of Earth Sciences, University of Cambridge, Cambridge, United Kingdom, 2 Department of Geological and Environmental Sciences, Ben-Gurion University of the Negev, Beersheba, Israel, 3 The Interuniversity Institute for Marine Sciences in Eilat, Eilat, Israel, 4 Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, United States, 5 Department of Earth Sciences, Royal Holloway, University of London, Egham, United Kingdom, 6 Department of Geology, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia, 7 Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, United States, 8 Department of Bioscience – Microbiology, University of Aarhus, Aarhus, Denmark, 9 Department of Biology, The University of York, York, United Kingdom, 10 Baltic Sea Center, Stockholm University, Stockholm, Sweden

#### Edited by:

Alberto Vieira Borges, University of Liège, Belgium

#### Reviewed by:

Filip Meysman, University of Antwerp, Belgium Perran Cook, Monash University, Australia

> \*Correspondence: Alec M. Hutchings<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Biogeoscience, a section of the journal Frontiers in Earth Science

Received: 14 November 2018 Accepted: 20 February 2019 Published: 14 March 2019

#### Citation:

Hutchings AM, Antler G, Wilkening JV, Basu A, Bradbury HJ, Clegg JA, Gorka M, Lin CY, Mills JV, Pellerin A, Redeker KR, Sun X and Turchyn AV (2019) Creek Dynamics Determine Pond Subsurface Geochemical Heterogeneity in East Anglian (UK) Salt Marshes. Front. Earth Sci. 7:41. doi: 10.3389/feart.2019.00041 Salt marshes are complex systems comprising ephemerally flooded, vegetated platforms hydraulically fed by tidal creeks. Where drainage is poor, formation of saline-water ponds can occur. Within East Anglian (UK) salt marshes, two types of sediment chemistries can be found beneath these ponds; iron-rich sediment, which is characterized by high ferrous iron concentration in subsurface porewaters (up to 2 mM in the upper 30 cm); and sulfide-rich sediment, which is characterized by high porewater sulfide concentrations (up to 8 mM). We present 5 years of push-core sampling to explore the geochemistry of the porewater in these two types of sediment. We suggest that when organic carbon is present in quantities sufficient to exhaust the oxygen and iron content within pond sediments, conditions favor the presence of sulfide-rich sediments. In contrast, in pond sediments where oxygen is present, primarily through bioirrigation, reduced iron can be reoxidised and thus recycled for further reduction, favoring the perpetuation of iron-rich sedimentary conditions. We find these pond sediments can alter significantly over an annual timescale. We carried out a drone survey, with ground-truthed measurements, to explore the spatial distribution of geochemistry in these ponds. Our results suggest that a pond's proximity to a creek partially determines the pond subsurface geochemistry, with iron-rich ponds tending to be closer to large creeks than sulfide-rich ponds. We suggest differences in surface delivery of organic carbon, due to differences in the energy of the ebb flow, or the surface/subsurface delivery of iron may control the distribution. This could be amplified

if tidal inundations flush ponds closer to creeks more frequently, removing carbon and flushing with oxygen. These results suggest that anthropogenic creation of drainage ditches could shift the distribution of iron- and sulfide-rich ponds and thus have an impact on nutrient, trace metal and carbon cycling in salt marsh ecosystems.

Keywords: biogeochemistry, salt marsh, iron–sulfur, carbon, redox, diagenesis

#### INTRODUCTION

Salt marshes are highly productive coastal wetlands that serve a critical role in carbon sequestration and nutrient trapping (Valiela et al., 1978; Barbier et al., 2011; Mcleod et al., 2011). As a marginal environment poised between the terrestrial and marine realms, salt marshes are extremely vulnerable to changes in environmental conditions such as anthropogenic eutrophication/drainage, climate change and sea level rise (Deegan et al., 2012; Kirwan and Megonigal, 2013; Kennish, 2016). The delicate interplay between the redox cycles of sulfur, carbon and iron present in salt marsh sediments could potentially become unbalanced through anthropogenic change (Koretsky et al., 2006). This could have consequences for the overall storage of carbon in marginal marine environments; the production and release of methane from these environments; and the efficiency of nutrient capture which impacts the overall productivity of these ecosystems (Mcleod et al., 2011).

 In the areas furthest from these creeks, where drainage is poor, ponds (also commonly referred to as 'salt pans') exist on the surface. These ponds can exist for long periods of time (1000+ years) (Pethick, 1980) and are sporadically flushed by tidal events which flood the vegetated platforms during high tides or storm surges (Santos et al., 2009). The formation mechanism of these ponds is uncertain, although it has been proposed that either physical processes (e.g., algal debris or heterogeneous development of the salt marsh) or biogeochemical processes (e.g., build-up of salinity in standing water or decay of deposited algal matter) are responsible for the initial formation of primary (wellrounded) ponds and senescence of creeks isolated by vegetation forms secondary (elongate-shaped) ponds, both of which can be present in the same salt marsh (Redfield, 1972; Pethick, 1974; Wilson et al., 2014). Ponds are more common where drainage is poorest (Wilson et al., 2014). As brackish water sits on the surface of these ponds, vegetation is prevented from colonizing, resulting in a set of feedbacks which allow these ponds to remain semi-permanent and respond to sea level change and other larger perturbations (Wilson et al., 2014).

The sediment beneath these ponds, and indeed throughout the salt marsh beneath the rhizosphere, is largely anoxic (Mills et al., 2016). In the ponds, the presence of a standing water column (1–50 cm) and the lack of surface vegetation limits the supply of oxygen into the sediment. In the absence of processes which would disturb the sediment (e.g., bioturbation or a rhizosphere), aerobic respiration is limited to a small boundary layer at the sediment surface (<1 cm) (Nealson, 1997). In the absence of sufficient oxygen, microbial life will use other electron acceptors roughly in order of decreasing free energy yield: nitrate, manganese, iron, sulfur; and finally, in the absence of other electron acceptors, organic carbon can be converted to methane through methanogenesis (Froelich et al., 1979). Which redox couple dominates in any anoxic sedimentary environment is not always as simple as the conventional redox ladder would suggest, instead depending on a myriad of environmental conditions such as pH and the availability and speciation of both electron donors and electron acceptors (Lovley and Chapelle, 1995; Bethke et al., 2011). The convergence of the accessible free energies of iron reduction, sulfate reduction and methanogenesis to similar values at the circumneutral pH conditions observed in these salt marshes means that small changes in these conditions can alter the dominant microbial community and thus the sediment geochemistry (Postma and Jakobsen, 1996; Bethke et al., 2011). Furthermore, interplay among the three redox cycles—iron, sulfur, and carbon—is common; various iron species can be used to reoxidise sulfide to intermediate valence state sulfur species, allowing for processes such as cryptic sulfur cycling (Holmkvist et al., 2011; Hansel et al., 2015; Mills et al., 2016; Blonder et al., 2017). This geochemical complexity is combined with periodic tidal flushing events which supply a range of different elemental species to the water column and sediment (Santos et al., 2009).

Here we investigate the controls on subsurface geochemistry and redox poising in salt marshes from East Anglia, United Kingdom (**Figure 1A**). We present data from two geographic areas—north Norfolk salt marshes found along the North Sea coast (**Figure 1B**) and an estuarine salt marsh on the River Blackwater in Essex named Abbotts Hall Farm (**Figure 1F**). These salt marsh systems contain two types of pond sediment porewater chemistry; sulfide-rich pond sediments, characterized by high sulfide concentrations (up to 6 mM); and iron-rich pond sediments, characterized by high ferrous iron concentrations (up to 1.5 mM) (Antler et al., unpublished). Previous work has shown that iron-rich sediments can be converted to sulfide-rich sediments in the laboratory through the addition of labile organic carbon, such as lactate, implying that the geochemical and redox conditions within these sediments may be susceptible to perturbation (Koretsky et al., 2006; Mills et al., 2016). Understanding the distribution of the different types of ponds will provide insight into the carbon budget in these salt marshes and could illustrate how future changes to these environments (e.g., sea level rise or land use change) might change the sedimentary geochemistry, leading to impacts on regional nutrient and carbon cycling.

We describe the geochemical nature of these pond sediments using multiple push cores taken over a 5-year period and an in situ sampler from which samples were collected over the

course of 1 year. A drone survey was carried out to map the distributions of the types of ponds to understand the mechanisms governing the pond sediment geochemistry. We offer potential hypotheses and areas of investigation for the observed spatial and temporal geochemical patterns. We test the distribution of overlying water depth on the pond sediment, the distribution of carbon within surface sediments and we look for evidence of groundwater fluxes in the salt marsh sediment for preliminary insights into the system.

# MATERIALS AND METHODS

#### Field Locations

This study discusses fieldwork undertaken in salt marsh systems across East Anglia, both in Norfolk, United Kingdom and at Abbotts Hall Farm, Essex, United Kingdom (**Figure 1**). On the Norfolk coastline, protection offered by migrating barrier islands, spits and intertidal sand flats have allowed salt marsh systems to form since the start of the Holocene (Pethick, 1980). In Norfolk, our field locations are three developed areas of stable, upper salt marsh near the small towns of Blakeney, Warham, and Stiffkey (**Figure 1B**). Areas 1 and 2 for the drone mapping are locations in the Blakeney salt marsh area (**Figure 1C**). In these sites, the lower marsh is more recent (post-medieval) whereas the upper, stable, marsh is Romano-British (2000+ years) (Pethick, 1980). The upper marsh sediment consists of grayish-brown, silty sands and clayey silts, likely accumulated by vegetative capture of finer grained sediment, which sits upon a northward dipping boulder clay (Pye et al., 1990). Abbotts Hall Farm is a nature reserve on an estuarine portion of the River Blackwater (**Figure 1F**). Area 3 (**Figure 1G**) lies on the edge of this tributary (defined as fringed-estuarine) where low energy environments are protected from direct wave action (Allen, 2000). Sediment in this location was previously not well studied; we observe it to be similar to that in the Norfolk marshes, likely reflecting similar provenance of sediment.

#### Porewater Collection

Push core liners made of polyvinyl chloride (PVC) were used to extract vertical columns of sediment from ponds at irregular

intervals over the period 2013–2018. One core, taken in May 2018, was pushed horizontally into the side of a creek at 20 cm depth. Porewater was extracted from the cores either in the field or within 24 h of being collected using Rhizons (syringe-based samplers with an inert polymer membrane). Samples for each core were taken at variable depth resolutions (between 1 and 4 cm; see data table in **Supplementary Information**). Typically, between 2 and 10 mL of porewater was extracted from each depth sampled, depending on the sediment porosity and what was needed for analysis. Aliquots of porewater (50–2000 µL) for ferrous iron analysis were fixed with 100 µL ferrozine reagent; the precise amount of porewater depended on the amount of iron present. Aliquots (50–2000 µL) for aqueous sulfide were fixed with excess quantities—dependent on sulfide concentration—of either 5 or 20% zinc acetate.

#### Analytical Measurements

All analytical measurements were carried out at the University of Cambridge. Dissolved iron concentrations [Fe(II)] were determined spectrophotometrically (Thermo Aquamate UV-Vis) according to the method described by Stookey (1970) with an error of 0.4%. Dissolved sulfide concentrations were measured spectrophotometrically using the methylene blue method with an error of 2% and a detection limit of 1 µM (Cline, 1969). Samples were diluted with ultrapure water to fit within a calibrated range. Major anion (sulfate and chloride) concentrations were measured by ion chromatography (Thermo Scientific Dionex ICS5000+) with an error up to 2% based on repeat analysis of standards. Samples measured for alkalinity were filtered using a cellulose nitrate membrane syringe filter (0.2 µM, 47 mm). Alkalinity was determined using the inflection point method by titrating 1–1.2 mL aliquots of filtered samples with 0.0166 M analytical grade hydrochloric acid (HCl). The pH was measured at 25◦C on the NBS scale using an Orion 3 Star meter with a ROSS micro-electrode (ORION 8220 BNWP PerpHect ROSS).

Samples for sulfur isotope analysis were separated into vials and a supersaturated barium chloride solution was added, precipitating barite (BaSO4). The barite was cleaned using 10% HCl, triple washed with ultrapure water, and dried. The δ 34SSO4 was determined through combustion on a Flash Element Analyzer (Thermo Scientific) coupled with continuous helium flow to a Delta V Plus mass spectrometer. Samples were corrected to NBS-127 barite standards (δ 34S = 20.3h) which were run before and after sets of 20 samples. Based on blind replicates and repeat running of the standard, the data had precision of 0.2h (1σ). Sulfur isotopic compositions are reported in standard

FIGURE 2 | Typical method of characterization of an individual pond based on features visible and/or easily testable from the surface. Flowchart is constructed in order of most diagnostic and most easily characterized (e.g., the presence of Beggiatoaceae and surface color are far quicker to differentiate than the sediment texture). The presence of white microbial mats, presumed to be Beggiatoaceae and resulting oxidized sulfur products present on the sediment surface, was decided to be sufficient enough evidence that sulfate reducing conditions were present in these ponds (Supplementary Figure S3). The color of the sediment surface was typically well differentiated; ponds were either a very dark gray color or had a red/brown tint to them (similar to iron oxide coloration). If the pond sediment surface was a different color to this or was a shade between the classifications, this category would be ignored for the classification. The presence of bioturbation was ascertained by worm casts seen on the sediment surface (Supplementary Figures S1, S2 for evidence). The presence of a single worm cast was deemed sufficient to assign a positive result to this category. The sulfidic smell property relates to a strong sulfidic odor when the sediment was removed from the water and handled. Sediment texture was determined by handling of the top 5 cm of sediment. When touched, certain sediments were clearly very soft and fluffy at the surface, whilst others had a stiffer clay texture. These would be defined as soft and hard respectively. Some characteristics were occasionally present but in spatially isolated zones on the pond (e.g., signs of bioturbation on one side of the pond and white microbial mats on the other). This is defined as 'zoned' on the diagram and ponds of this nature were classified as intermediate. This method is preferred to geochemical analysis as it is quicker than sampling and allows for rapid characterization of the salt marsh system.

delta notation as per mil (h) deviations from the Vienna Cañon Diablo Troilite (VCDT).

### Mapping of Ponds

feart-07-00041 March 13, 2019 Time: 16:32 # 5

We used a drone (Phantom 3 Standard) to create an aerial survey at a height of 150 m in May 2018. The orthophotos were assembled using the MapsMadeEasy website and the projection used for uploading to QGIS was WGS 84 31N. We were unable to drone map Area 3, in Abbotts Hall, so we used Google Earth satellite imagery of the area. Two ground-truth campaigns—in May and July 2018—were undertaken to characterize pond sediment types from the drone images. Using the different surface characteristics typical of an iron-rich pond and a sulfide-rich pond (described in results), we were able to classify 350 ponds using the flowchart presented in **Figure 2** (see **Supplementary Table S1** and **Supplementary Figures S1**–**S5** for full observed attributes and methods of characterization). For each pond visible in the aerial images, we would classify it as an iron-rich pond sediment, a sulfide-rich pond sediment or as 'intermediate' between iron-rich and sulfide-rich. The third category was necessary given that some ponds contained attributes of both iron- and sulfide-rich sediments. We attempted to characterize ponds directly from the drone imagery but this proved impossible given the variable surface colors and the small scale of the distinguishing features. At each pond, we measured the depth of the overlying water column using a tape measure. We also recorded the following characteristics for 267 of the 350 ponds for statistical analysis: the presence of algae (**Supplementary Figure S5**), signs of bioturbation (worm casts) (**Supplementary Figures S1**, **S2**), the presence of sulfur oxidizing bacteria (Beggiatoaceae) (**Supplementary Figure S3**) and signs of desiccation cracks (**Supplementary Figure S6**).

### In situ Sampling

In order to monitor geochemical changes in individual pond sediments over time, in situ samplers were designed to allow repeated sampling of pond sediment pore fluids without disturbing the sediment. The submerged samplers consist of plastic frames to which Rhizon filters are attached at 2 cm intervals starting from the sediment-water interface to a depth of 34 cm (see **Supplementary Figure S7** for construction). Outlets from the Rhizons were connected to sampling ports via airtight, double-walled, (<60 cm) tubes (polyethylene inner and polyvinylchloride outer, ID = 1mm) to the surface to which 10 mL syringes were attached for extraction. We selected spacing of 2 cm so that sampling volume from separate

Rhizons remains discrete and does not result in artificial drawdown of surface water, based on tracer and modeling studies (Seeberg-Elverfeldt et al., 2005).

In situ samplers were installed in two Warham salt marsh ponds (termed Pond A and Pond B) in January 2017 (**Figures 1D,E**). Pond A appeared to contain sulfide-rich pond sediment—using the above criteria—whilst the other, Pond B, an elongate pond in shape, was defined as intermediate. Samples of pore fluids from sediment in both ponds were collected in March, April, May, July, and November 2017, and May 2018. During each sampling, the stagnant fluid in the tubing (roughly 0.6 mL for 60 cm of 1 mm ID tubing) was first removed by extracting and discarding 1–2 mL of pore fluid from each outlet. Another 4–5 mL of pore fluid

FIGURE 4 | (A) Fe(II) concentrations in iron-rich pond sediment porewaters, with increasing Fe(II) with depth, sampled in November 2013 (Warham), November 2013 (Stiffkey), January 2014 (Warham), May 2014 (Warham), October 2015 (Antler et al., unpublished), October 2015 (Stiffkey) and October 2017 (Blakeney). (B) Fe(II) concentrations in iron-rich sediment porewaters, with decreasing Fe(II) with depth, sampled in November 2013 (Stiffkey), November 2013 (Stiffkey), March 2014 (Warham), December 2017 (Blakeney) and December 2017 (Blakeney). (C) Fe(II) concentrations in sulfide-rich pond sediment porewaters. Only three cores [October 2015 (Antler et al., unpublished), October 2015 (Blakeney) and December 2017 (Blakeney)] are presented here since Fe(II) concentrations are typically below detection limit using our analytical method. (D) Fe(II) concentrations in sediment porewaters in cores taken from the vegetated platform in November 2013 (Warham) and October 2015 (Warham). (E) δ 34SSO4 profiles in iron-rich pond sediment porewaters sampled in November 2013 (Warham), November 2013 (Stiffkey), January 2014 (Warham), May 2014 (Warham), October 2015 (Antler et al., unpublished) and October 2015 (Stiffkey) (Note: Not all cores present in subplot A are present here). (F) δ 34SSO4 profiles in iron-rich pond sediment porewaters sampled in November 2013 (Stiffkey), November 2013 (Stiffkey) and March 2014 (Warham) (Note: Not all cores present in subplot B are present here). (G) δ 34SSO4 profiles taken from sulfide-rich cores sampled in November 2013 (Blakeney), November 2013 (Blakeney), November 2013 (Blakeney), October 2015 (Antler et al., unpublished) and October 2015 (Blakeney). (H) δ 34SSO4 profiles from cores taken from vegetated platforms in November 2013 (Warham) and October 2015 (Warham). Vegetated cores are taken from the sediment surface whereas iron-rich and sulfide-rich pond cores have an undefined column of water on the surface.

was then extracted for geochemical analysis. All depths were sampled simultaneously.

# Carbon Content Analysis

feart-07-00041 March 13, 2019 Time: 16:32 # 7

In May 2018, sediment was taken from the surface of 30 ponds in Abbotts Hall (Area 3 on **Figure 1G**) using a 1 mL syringe with a cut edge. Sediments were dried and analyzed for total carbon content using a Costech element analyzer coupled via continuous flow to a Delta V mass spectrometer. Five samples were tested for the presence of carbonate minerals using a duplicate washed in 0.1 M HCl and subsequently triple washed with ultrapure water. As there were no carbonate minerals found, total carbon content is assumed to come from the organic carbon fraction.

# RESULTS

# Sediment Cores Taken Over the 2013–2018 Period

The subsurface geochemistry of ponds in the East Anglian salt marshes is reflected in characteristics observed at the sediment surface; iron-rich pond sediments are often stained red at the surface, contain worm casts and iron-oxide films are occasionally visible on the water surface; whereas sulfide-rich pond sediments are typically gray/black, commonly contain sulfide oxidizing bacteria on the sediment surface (Beggiatoaceae) and exude a strong sulfidic odor. Sulfide-rich pond sediments have a higher porewater methane concentration, in contrast, iron-rich pond sediments have no methane present (Mills et al., 2016).

**Figure 3** shows porewater data collected from two cores; an iron-rich core taken from Stiffkey, August 2015, and a sulfide-rich core taken from Blakeney, August 2015. We note the key features of iron-rich versus sulfide-rich pond sediment porewater in **Figure 3**. In the iron-rich sediment there are high concentrations of ferrous iron, little change in the δ 34SSO4, near-zero aqueous sulfide, near seawater [SO4]/[Cl] ratios, lower overall pH and low alkalinity (**Figure 3**, red). In contrast, in the sulfide-rich pond sediment there is no measurable ferrous iron, an increase in δ 34SSO4 by 40h, sulfide concentrations above 4 mM, lower [SO4]/[Cl] ratios relative to seawater, higher pH and higher pore fluid alkalinity (**Figure 3**, black). The concentration of both chloride and sulfate in the overlying pond water are lower than typical seawater. In particular, the surface 20 cm of the ironrich pond contains very low porewater chloride concentrations (200–400 mM, 30–60% lower than seawater) (**Figures 3E,F**). We present these cores, collected in August 2015, separately (**Figure 3**) as they were the only two cores on which pH and alkalinity were measured in addition to other geochemical parameters. **Figure 4** presents a more limited geochemical dataset collected from far more cores taken over the three field sites shown in **Figure 1B** between 2013 and 2017. Cores taken through the vegetated platform surrounding these ponds are included (denoted as 'vegetated') for an understanding of the porewater profiles in the rest of the marsh where there is an absence of ponds. In one case, we took multiple cores from the same sulfiderich pond and found near identical geochemistry across the pond (**Supplementary Figure S8**), thus we conclude each core accurately represents the geochemistry of the whole pond from which it was sampled.

Dissolved ferrous iron (Fe2+) concentrations of porewater in our compilation of cores indicate that there are two broad types of pond sediments (**Figures 3A**, **4A,B**). Ferrous iron in ironrich sediment porewater is present at millimolar concentrations, whereas concentrations are below the detection limit (3 µM) in sulfide-rich sediment porewater (**Figures 3A**, **4C**). We find, however, that there are variations in the general pattern of ferrous iron concentrations within pond sediments broadly classified as 'iron-rich.' In some cores, porewater ferrous iron concentrations increase with depth, sometimes reaching as high as 2 mM at 35 cm depth (**Figure 4A**). In these cores, lower ferrous iron concentrations are observed from 5 to 15 cm, but iron and manganese oxides are visible, giving the sediments an orange hue in the core within this depth range. Conversely, in the other subset of 'iron-rich cores' there is a decrease in ferrous iron concentration with depth (**Figure 4B**). Ferrous iron concentrations in cores taken from the vegetated platform in Warham are near zero up to 8–10 cm from the surface (this coincides with the depth of root penetration) (**Figure 4D**). Below this rooting zone, porewater ferrous iron concentrations increase up to 3 mM—the highest observed in any of the cores.

Both types of iron-rich core have similar porewater δ 34SSO4, either a minor increase with depth or a constant δ 34SSO4 near that of seawater (**Figures 3B**, **4E,F**). In sulfide-rich cores (**Figures 3B**, **4C,G**), δ 34SSO4 increases up to 70h by 15–25 cm depth below the sediment-water interface. This is coincident with an increase in sulfide concentrations and decrease in sulfate concentrations (**Figures 3C,D**). In vegetated cores (**Figure 4H**), there is an increase of approximately 5–15h in δ 34SSO4 from 0 to 35 cm.

It should be noted that absolute depths cannot be accurately compared among the sampled ponds because water depth was not always recorded in ponds where sediment cores were taken and we do not have elevation data for the salt marsh platforms. For the full compilation of geochemical data acquired on cores from 2013 to 2018, see the **Supplementary Material**.

In the core extracted from the creek wall edge, all geochemical measurements show large changes within 5 cm from the creeksediment boundary (**Figure 5**). In this zone, a threefold decrease in iron concentration is observed toward the creek edge. This is accompanied by higher concentrations of chlorine and sulfate and lower δ 34SSO4. From 5 cm inward, the geochemistry is much more stable. Ferrous iron concentrations are significantly higher (6 mM) than those seen in the other cores taken.

# In situ Sampling From March 2017 – May 2018

Two ponds were sampled using in situ samplers over the year from March 2017 - May 2018. Pond A (sulfide-rich pond) shows small seasonal variations in δ 34SSO4 of porewater sulfate with higher δ 34SSO4 observed in the summer and autumn months (**Figure 6A**). These variations are more pronounced in the upper 15 cm and values become more uniform (within 10h) below this. Changes in δ 34SSO4 in Pond B (an intermediate pond) are far greater (**Figure 6B**); in March 2017, the δ 34SSO4 is constant with

FIGURE 5 | Core horizontally extracted from creek wall (see Figure 1 for location). Depth is 20 cm from the surface of the salt marsh. (A) Fe(II) concentrations (mM) (B) Cl concentrations (M) (C) δ 34SSO4 (D) [SO4]/[Cl]. Blue dashed line corresponds with seawater values. Location shown in Figure 1.

depth and only slightly elevated from the values seen in a 'typical' iron-rich pond. Ferrous iron concentrations were negligible in both ponds for months March-July and aqueous sulfide is present in both ponds in May 2018, albeit it at higher concentrations in Pond A (**Supplementary Figure S9**).

Whilst the pond sediment appears to become more sulfiderich with time, there is a decoupling between the shallow (<20 cm) and the deep zone (>20 cm). The shallow zone displays greatest 34S enrichment - δ 34SSO4 up to 55h—during July 2017 before falling to lower δ 34SSO4 in November 2017 and May 2018.

In contrast, the deep zone shows a consistent increase in δ 34SSO4 through time—up to 35h in May 2018.

#### Mapping of Pond Distributions

Two areas from the Blakeney salt marsh system were mapped using an aerial drone survey and an area in the Abbotts Hall salt marsh system (**Figures 1F,G**) was mapped using Google Earth satellite imagery (**Figures 7–9**). Area 1, from Blakeney (**Figure 7**) consists of two groups of ponds separated by a tidal creek. Ponds containing sulfide-rich sediment tend to be situated further from large creek networks than ponds containing iron-rich sediment (**Figure 7B**). The presence of sulfide-rich ponds is often accompanied by a region of standing water on the surrounding vegetated platform (visible in **Figure 7A**). Ponds of a similar geochemical and geomorphological nature appear to cluster in groups. Intermediate ponds, displaying neither or both criteria for iron-rich or sulfide-rich ponds, often lie at the boundary between iron-rich and sulfide-rich pond clusters. There are also clear differences in the vegetation over the marsh. The center of the platform is characterized by salt marsh 'lawn' species (typically some combination of Puccinellia spp., Salicornia spp., and Sesuvium portulacastrum) whereas the areas closest to the creek have larger species (Atriplex spp. and Suaeda spp.).

In an area of upper marsh (the oldest, most inland marsh) (**Figure 1C**), ponds with sulfide-rich sediment concentrate at the point furthest inland and iron-rich or intermediate ponds are located more toward the littoral zone (**Figure 8**). Drone imagery shows vegetation debris on the vegetated platform with sulfide-rich ponds (south of **Figure 8A**) and larger vegetation (likely Spartina patens) around the edges of sulfide-rich ponds (seen as gray patches surrounding ponds in south of **Figure 8A**). Proximity to larger creeks appears to favor the presence of ponds with iron-rich sediment (north in **Figure 8A**).

At Abbotts Hall, we observe a similar pattern of internal salt marsh ponds being more sulfide-rich (**Figure 9B**), albeit with more exceptions than in Areas 1 and 2 (from Norfolk). Ironrich vs. sulfide-rich pond sediment distribution does not seem to correlate with the type of vegetation observed on satellite imagery (**Figure 9A**). An artificial drainage ditch (north east of area) is proximal to an area of what we have defined as 'intermediate ponds.'

### Pond Characteristics and Water Depths

There is a correlation between water depth and the type of pond sediment geochemistry; the average pond water depth in sulfidic pond sediments is deeper than that of iron-rich pond sediments [Area 1—mean iron-rich pond water depth = 20.2 ± 5.0 cm

salt marsh area 2 (see Figure 1C) overlain by geochemical classification observed over period from May 2018. (B) Geochemical classification with hypothetical, hand-drawn zones of sulfide-rich pond clustering shown in gray.

(n = 60), mean sulfide-rich pond water depth = 24.1 ± 4.7 cm (n = 50) (two sample t-test, p < 0.05); Area 2—mean iron-rich pond water depth = 19.1 ± 5.6 cm (n = 40), mean sulfide-rich pond water = 22.4 ± 5.5 cm (n = 64) (two sample t-test, p < 0.05); Area 3—mean iron-rich pond water depth = 11.8 ± 4.0 cm (n = 40), mean sulfide-rich pond water depth = 15.2 ± 4.6 cm (n = 40) (two sample t-test, p < 0.05) (**Figures 10B–E**)].

After identifying the presence of certain features notable from the pond surface, (bioturbation evidence, presence of algae in the surface water, presence of Beggiatoaceae and the presence of desiccation cracks) we observe that cracks, evidence of a transient drying effect, are less prevalent in sulfide-rich ponds compared to iron-rich ponds and intermediate ponds (**Figure 10A**). This is consistent with the observation that sulfide-rich ponds in general have greater water depths and thus dry out less frequently (**Figure 10E**). Conversely, the presence of algae is common in sulfide-rich (90%) and intermediate ponds (80%) and yet is only present in 30% of iron-rich ponds. Since we used both the presence of white microbial mats (Beggiatoaceae) and bioturbation as tools for characterizing ponds, it is unsurprising that we find bioturbation to be common in iron-rich ponds and sulfide oxidizing bacteria to be negligible. Intermediate ponds contain any combination of characteristics; in particular, the presence of both algae and bioturbation was common (>70%).

#### DISCUSSION

#### Salt Marsh Pond Geochemistry

In the absence of microbial data on the pond sediments, we use the porewater geochemistry, that is the concentrations of various redox-sensitive species, to infer the biogeochemical processes occurring within the sedimentary environment. For example, the presence of ferrous iron [Fe(II)] suggests that reduction of ferric iron is occurring, either through bacterial iron reduction (Equation 1) or the reduction of ferric iron coupled to sulfide oxidation (Equation 2). Similarly, the presence of aqueous sulfide would suggest the presence of either microbial sulfate reduction (Equation 3) or dissolution of sulfide-containing minerals in the environment. The δ 34SSO4 of the porewater sulfate allows us to identify the occurrence of microbial sulfate reduction because during microbial sulfate reduction, bacteria preferentially utilize sulfate with the lighter 32S isotope over the heavier 34S isotope. This leads an isotopically lower sulfide pool and a residual porewater sulfate pool enriched in the heavier isotope; the opposite would be true if there were dissolution of sulfide minerals. In this way we use the geochemistry that we have measured to infer the microbially driven processes that are occurring in the salt marsh sediment.

$$4Fe(OH)\_3 + 7H^+ + CH\_2O \rightarrow HCO\_3^- + 4Fe^{2+} + 10H\_2O \quad \text{(1)}$$

$$\text{HS}^- + 2\text{Fe(OH)}\_3 + 5\text{H}^+ \rightarrow 2\text{Fe}^{2+} + \text{S}^\circ + 6\text{H}\_2\text{O} \qquad \text{(2)}$$

$$2\text{CH}\_2\text{O} + \text{SO}\_4^{2-} \rightarrow \text{H}\_2\text{S} + 2\text{HCO}\_3^- \tag{3}$$

Our data, along with pH and alkalinity, demonstrate that there are two broad classifications of pond sediment in the studied East Anglian salt marshes. Some pond sediments contain porewater with high δ 34SSO4, high quantities of aqueous sulfide and declining SO4/Cl ratios indicative of sulfate consumption; these pond sediments are interpreted to sustain high levels of microbial sulfate reduction. The other group of pond sediments contain porewater with near seawater δ 34SSO4, seawater SO4/Cl and high quantities of ferrous iron. These ponds are likely dominated by bacterial iron reduction, potentially in addition to a cryptic sulfur cycle in which reduced sulfur species are nearly quantitatively recycled back to sulfate (Mills et al., 2016). These pond sediments are referred to as sulfide-rich and iron-rich, respectively.

This broad dichotomy arises due to the titration reaction between sulfide and ferrous iron or iron monosulfides (Equations 4 and 5) which means only one of the species (the one in excess) can be present in significant quantities (Drobner et al., 1990). The pond sediment 'type' therefore reflects environmental conditions which favor production of either reduced iron or aqueous sulfide. The aim of this study is to understand why such contrasting sediment redox states can be seen separated by less than five meters and yet two sulfide-rich ponds, for

corresponds to landward extent of the salt marsh.

example, can be almost identical across hundreds of kilometers over East Anglia.

$$Fe^{2+} + H\text{S}^- \rightarrow Fe\text{S} + H^+ \tag{4}$$

$$FeS + H\_2S \to FeS\_2 + H\_2 \tag{5}$$

The conditions which favor certain microbial metabolisms are complex. In contrast to the canonical view of the redox ladder (Froelich et al., 1979), when pH is circumneutral there is a convergence of the useable energies for iron reducing bacteria, sulfate reducing bacteria, and even methanogenic bacteria (Bethke et al., 2011). Furthermore, multiple studies have seen interplay among redox cycles (Mortimer et al., 2011; Hansel et al., 2015; Antler et al., unpublished).

We find evidence of this interplay; despite changes in alkalinity that are more consistent with iron reduction or sulfate reduction dominating a sediment, the fact that pH decreases with depth in the iron-rich core (**Figure 4E**) is counter-intuitive because bacterial iron reduction consumes protons and thus raises pH (Soetaert et al., 2007). We suggest other redox reactions are working in tandem with bacterial iron reduction, causing the decrease in pH. One example could be Equation (4) or the regeneration of ferric iron (Soetaert et al., 2007). The net result

FIGURE 10 | (A) Characteristics [bioturbation, algae, desiccation cracks and sulfide oxidizing bacteria (e.g., Beggiatoaceae)] observed in each category of pond taken in the three areas. (B) Water depth measurements taken in Area 1 for each type of pond (17/05/18). P-value taken from t-tests testing the null hypothesis that there is no difference in water depth between pond classfications. (C) Water depth measurements taken in Area 2 for each type of pond (17/05/18). (D) Water depth measurements taken in Area 3 for each type of pond (19/06/18). (E) Composite made of all depth measurements taken from Area's 1, 2, and 3.

of the reactions present are not well understood currently, and characterization of the fundamental chemical reactions involved is necessary to understand changes in sedimentary pH. This complexity and closeness of energies among metabolisms means even subtle differences in the speciation and quantity of the electron acceptor; the speciation and quantity of the electron donor; and the influence of other redox cycles within the sediment could alter which metabolism, and therefore which geochemistry, dominates.

One observable difference is that 50% of iron-rich ponds contain evidence of bioturbation on the sediment surface (**Supplementary Figures S1**, **S2**), a result of species such as polychaete worms (Nereis spp.) and lugworm (Arenicola spp.) burrowing between 5 and 15 cm depth (Antler et al., unpublished). The amount of worm casts in iron-rich ponds and worm density in sediment varies substantially, although the 5–15 cm depth increment contains the highest density of worms (Antler et al., unpublished). This process would introduce pathways for oxic waters into an otherwise diffusion-dominated, anoxic system, potentially providing redox conditions which could regenerate ferric phase minerals [likely poorly ordered iron (oxyhydr)oxides] which become available for further bacterial iron reduction. Although sulfide produced through microbial sulfate reduction, thought to be active in iron-rich sediments due to the presence of reduced sulfide minerals, could titrate some of this iron away (Antler et al., unpublished), the ongoing regeneration of Fe(III) phases creates favorable conditions for iron reduction over other microbial metabolisms, resulting in high ferrous iron concentrations within the sedimentary porewater. Conversely, in sulfide-rich pond sediment, no worm casts are seen, implying there is no bioturbation. This is likely due to the build-up of aqueous sulfide, generating toxic conditions which preclude the presence of bioturbating organisms. A lack of bioturbation limits mixing of the surface sediment, and hence oxygen penetration within the system. This leads to a positive feedback mechanism where ferrous iron would react with sulfide instead of being regenerated into ferric iron phases. Microbial sulfate reduction would become the dominant reduction pathway, using seawater sulfate as the electron acceptor.

Within the broad categories of iron-rich and sulfide-rich sediment, we find pond sediment chemistry can vary in terms of absolute concentrations of either iron or sulfide. All ironrich cores have a depletion in ferrous iron concentrations from 5 to 15 cm; as suggested above, bioturbation in this region introduces oxygen to the system and reoxidises most aqueous Fe(II). However, beneath this zone, ferrous iron concentrations in porewaters can either increase from 15 to 35 cm in some cores (**Figure 4A**) or continue to decrease to negligible concentrations below 15 cm (**Figure 4B**). No major difference is observed in the corresponding δ 34SSO4 (**Figures 4E,F**). This discrepancy among profiles of ferrous iron could be due to different iron minerals present at depth, though it is not intuitive why there would be significant differences in mineralogy or concentration of iron minerals over a salt marsh platform. Another potential reason for the increase in Fe(II) is reduction of Fe(III), coupled to deep sulfide oxidation in some cores (Mortimer et al., 2011; Hansel et al., 2015). This would be undetectable in sulfide or sulfate concentrations or δ 34SSO4 if it were near quantitative, implying some kind of 'cryptic' sulfur cycling was occurring (Holmkvist et al., 2011; Mills et al., 2016; Blonder et al., 2017). Further studies of the interconnectedness of the iron and sulfur cycles in these pond sediments are needed to refine this hypothesis.

There are also observed differences among porewater characteristics in sulfide-rich cores (**Figure 4G**); the maximum concentration of aqueous sulfide varies from 1 to 8 mM among cores. The maximum porewater δ 34SSO4 also varies between sulfide-rich cores, from 50 to 75h. These variations suggest that there are differences in the amount of microbial sulfate reduction compared to the sulfate reservoir or how close to isotopic equilibrium this reduction is occurring (Johnston et al., 2007). Overall, we suggest that, within the two broad groups of the pond sediment geochemistry, there is a continuum on which the pond sediment porewater geochemistry may exist between a concentrated sulfide-rich pond and a concentrated iron-rich pond. Consequently, what we have defined as 'intermediate ponds' would likely have characteristics consistent with both iron-rich and sulfide-rich porewater geochemistry. For example, if free sulfide is present, but in low quantities or only present in concentrated microenvironments, bioturbation may still be possible, which may drive some reoxidation of aqueous Fe(II).

Furthermore, we suggest that iron-rich pond sediment can transition to sulfide-rich pond sediment over relatively short timescales. In Pond B (**Figure 6B**), whilst March and April 2017 samples show similar δ 34SSO4 with depth to iron-rich ponds, later months (July 2017 onward) gradually exhibit higher δ 34SSO4, up to 35h higher than seawater. This is a phenomenon not observed in the wide array of cores (**Figure 4**) which either show constant near-seawater δ 34SSO4 (iron-rich cores) or a clear sulfate reducing zone with sulfate up to 75h, 55h over seawater δ 34SSO4 (sulfide-rich cores). Given that we sampled the cores from **Figure 4** at different points throughout the year, we would expect at least some iron-rich cores or sulfide-rich cores to show evidence of larger seasonal variations. Furthermore, Pond A (**Figure 6A**), a sulfide-rich pond, shows seasonal variation of a smaller magnitude. Whilst there is clearly some cyclicity in the δ 34SSO4 profile (for example the two May samples), there is also some longer timescale transition toward more microbial sulfate reduction, particularly at depths greater than 30 cm. Unfortunately, the paucity of longer term data prevents us from drawing conclusions on the speed of these transitions and thus we merely speculate that there are both seasonal and non-seasonal changes taking place. A natural extension also exists that the mechanism determining spatial distribution (as discussed below) may alter how extensive sulfate reduction is within a given pond.

#### Hypotheses for Spatial Distributions

Prior to this study, few studies have tried to understand what might drive a pond sediment toward iron- or sulfiderich conditions. Antler et al. (unpublished) proposed the idea that a transient flux of iron may be delivered to certain ponds, stimulating a sulfide-rich to iron-rich transition through bioturbation-related feedbacks. Mills et al. (2016) showed

that addition of labile organic carbon was able to alter pond sediment geochemistry from iron-rich to sulfide-rich. However, no environmental study has so far attempted to relate the geochemistry of the pond sediments with their spatial distribution on a salt marsh. Of the 350 ponds, we classified 140 as iron-rich, 154 as sulfide-rich and 56 as intermediate. Whilst this suggests a similar number of iron- and sulfiderich ponds in salt marsh systems, we observed no sulfiderich ponds in some areas specifically in the Stiffkey area and we find the quantity of the three types of pond to vary from area to area. Our results suggest that ponds classified as containing sulfide-rich sediment are more likely to be located away from larger creek networks. This result was consistent across two sites; Area 1, in Blakeney (**Figure 7**), Norfolk; and Area 3, Abbotts Hall, Essex (**Figure 9**). Given the spatial separation of over 100 km between these sites, we believe that this correlation is likely not a local phenomenon and instead is a result of more widespread, pervasive, processes. We suggest that Area 2, where iron-rich pond sediments seem to be present moving into the littoral zone, is merely an extension of this creek-pond relationship but on a larger scale. In other words, sulfide-rich pond sediment is more common in areas which we would assume to be less frequently flooded (either further inland or further from the creeks which provide seawater inundation at high tide events). We find that the distribution of pond sediments adheres to this relationship with regards to modern drainage ditches. For example, in Abbotts Hall (**Figure 9**), pond sediments to the north of a small drainage creek (linear ditch) are all classified as intermediate or iron-rich. This, combined with evidence from our in situ sampling—that pond sediments are capable of geochemical 'switching'—may be an indication that the building of ditches could change pond sediment geochemistry. This observed distribution is not absolute and small-scale exceptions are seen where sulfide-rich pond sediment can be next to a creek. We attribute this to the complex hydrology and geochemistry in these salt marshes.

Almost all ponds appear to exist on lawn vegetation (as described above) as opposed to within the taller vegetation closer to the creeks. This is likely simply because standing water, and subsequent formation of these ponds, is more likely to exist in the lower energy zone where lawn vegetation is present. We have not established if the vegetation type of the adjacent platform surrounding a pond affects pond sediment geochemistry.

We offer three possible hypotheses for the distribution of pond sediment geochemistry in salt marsh ponds we observe: (1) heterogeneity in water depths overlying the pond sediment over a platform could produce differences in the supply of oxygen, relative to other electron acceptors available in the sediment, allowing different metabolisms to dominate; (2) differences in the source or type of organic carbon supplied to the sediment could vary based on a pond's position on a platform; and (3) subsurface water flows could deliver reactants in varying quantities to ponds based on their position on a platform. We will consider each of these in turn.

# Water Depth Controls

The depth of the water column overlying the pond sediments gives an indication of how deep the hollow containing a pond is from the vegetated surface to the sediment-water interface. This is because the major input of water is the tidal inundation events which periodically cover the salt marsh and the output is largely through evaporation. Though there is likely some aspect of groundwater flow contributing water to the salt marsh system, the hydraulic conductivity within this setting is very low; a 2 m head difference is observed between a pond and a creek horizontally less than 20 cm apart. As such, unless tidal inundation events vary over a platform (discussed later), the depth of the sediment-water interface compared to the vegetated surface likely controls the water column depth.

The mean water depth of the sulfide-rich ponds is greater than for iron-rich ponds in all three areas (p < 0.05). There is, however, no threshold value where it can be said that if a pond is deeper by this amount, it will have a sulfide-rich subsurface geochemistry. We do observe, however, that there is greater disparity when ponds are either very shallow (<10 cm) or very deep (>30 cm) (**Figure 10E**). The correlation between water depth and subsurface geochemistry is weaker in the depth range between 16 and 28 cm (75% of the classified ponds) in Norfolk (Areas 1 and 2) and between 11 and 19 cm (67% of the classified ponds) in Essex (Area 3)—where the average water depth is shallower. We suggest the relationship between water depth and sediment geochemistry is coincidental. Given that photosynthetic and wind driven sources of oxygen should be largely independent of water depth, small changes in water depth are unlikely to generate large differences in oxygen fluxes to the sediment. Instead, we speculate that ponds further from creeks are simply more likely to be older (as standing water is more likely to exist at this point). The depth of the sediment water interface increases with this increase in the age of a pond because vegetated capture of sediment causes the platform to accrete faster relative to the pond (Spivak et al., 2017) and because continual decomposition of organic matter (which has high porosity) leaves a residual mud layer (which has low porosity), decreasing the volume of sediment (Van Huissteden and van de Plassche, 1998). We suggest, due to the lack of a threshold depth dictating pond chemistry, that some other factor is driving the pond sediment geochemistry distribution.

If, however, ponds are sufficiently shallow that they become perennially exposed to the atmosphere by evaporation, desiccation cracks extending as deep as 15 cm can form (**Supplementary Figure S6**). Evidence of desiccation cracks is observed far more in iron-rich pond sediments (50%) than sulfide-rich pond sediments (5%)—particularly in Abbotts Hall (**Supplementary Figure S10**)—which suggests this may be an important 'perturbing' factor capable of favoring iron-rich environments (**Figure 10A**). We suggest that these cracks act as conduits allowing atmospheric oxygen and, upon re-submergence, oxygen-rich floodwater into a previously anoxic environment. This could regenerate Fe(III) (oxyhydr)oxides and allow bioturbating organisms into the

sediment (as seen in **Supplementary Figures S11**, **S12**) leading to favorable conditions for bacterial iron reduction (Antler et al., unpublished). Conversely, very deep ponds will likely favor sulfide-rich sediment conditions. As the water column is less likely to be fully evaporated, in situ growth of vegetation can provide a larger source of carbon to the sediment (discussed below).

#### Carbon Controls

Our second hypothesis suggests differences in the form and quantity of available organic carbon (i.e., the electron donor) delivered to pond sediment may play a significant role in influencing the subsurface heterogeneity. We postulate that the balance of the distribution of oxygen and carbon plays a key role in the redox dynamics of the sediment. It is possible that there is heterogeneity in the delivery of sulfate or iron, however, we dispute this as both are in excess in this local system. There is a high sulfate flux to ponds from the tidal inundation events which periodically flood the platform with seawater. As such, sulfate concentrations in the ponds do not approach zero, even in sulfide-rich pond sediments with extensive sulfate reduction (**Figure 3**). This suggests that sulfate reduction rate is not limited by sulfate concentration in the water column. The iron content in the fine-grained sediment across East Anglia, as is present in these salt marshes, is supplied by cliff erosion (McCave, 1987). The nearby Crag formation, the likely provenance of the majority of the sediment, contains high iron concentrations (originally from glauconite) which will cause the geochemical composition of the sediment to be iron-rich (Hamblin et al., 1997). This is further supported by the overall high iron content (>1 mM) seen in the vegetated platform sediment porewater (**Figure 4D**). The majority of the pond sediments, upon formation, are therefore unlikely to be limited by iron or sulfate supply. The supply of organic carbon and dissolved oxygen (DO) over the salt marsh is somewhat more complicated; carbon from organic matter is supplied by a combination of terrestrial inputs, in situ growth, and erosion of the vegetated edges of the ponds. Whilst we do not have water column DO measurements, we would expect large variations over diurnal cycles, from possible supersaturation in the day to significant undersaturation at night.

A higher amount of organic carbon relative to ferrous iron would favor the onset of microbial sulfate reduction in the sediment. The presence of bioturbating organisms introduces oxygen to the system through mixing of the upper 15 cm of sediment and this reoxidises the reduced Fe(II) pool, keeping the concentration of Fe(III) high relative to the content of organic carbon. Over time, if random events or organic matter accumulation lead to a condition where the Fe(III) pool is sufficiently exhausted to allow the onset of microbial sulfate reduction, this could cause a switch to sulfide-rich geochemical conditions. This will happen if the sulfide concentration builds up to where it can titrate all Fe(II) as iron monosulfides, essentially removing the possibility of iron regeneration. Sulfide will then be in excess in the system; the toxicity imparted by free sulfide prevents bioturbation and acts as a positive feedback mechanism which enforces the 'switch.' This also explains the characteristic black color observed in sulfide-rich sediments.

The importance of the amount of organic carbon in 'tipping' the system has been shown previously in laboratory incubations with lactate (Koretsky et al., 2006; Mills et al., 2016). The measurement of organic carbon content in the upper 5 cm in sediment in ponds at Abbotts Hall (Area 3) allows insight into the quantity of carbon delivered to the ponds. We find sulfide ponds to have significantly greater organic carbon content in the upper 5 cm (p < 0.05) (one outlier was excluded from this which was 2 SD from the mean). We also observe that ponds near the creeks have lower organic carbon content (**Figure 11**).

We hypothesize that if a pond is proximal to the creeks, it is susceptible to more flooding events and thus any organic carbon, present as dissolved organic carbon, in situ vegetation or marine algae, would be periodically flushed more regularly than ponds located centrally (Spivak et al., 2017). Additionally, the drag imparted by the rough, vegetated surfaces mean that particulate organic carbon is more likely to be deposited in the center of the platform, as the water loses energy during the ebb flow back to the creeks (**Figure 12B**). This mechanism may help explain why sulfide-rich ponds are located further from creeks (**Figures 7**, **9**). On larger scales, more inland salt marsh areas would also be less affected by these flooding events due to the loss of energy of water as it travels up the platform (**Figure 12B**). This correlates with what we show in **Figure 8**, where ponds with sulfide-rich sediment are located further inland. The presence of algal debris on the surface in drone imagery (**Figure 8A**) reinforces this.

This effect of carbon flushing will likely be convolved with the probability that more frequent flooding events add more oxygenated seawater to ponds proximal to creeks (i.e., oxygenrich water gets added to water which has been stripped of oxygen by aerobic respiration). This would enhance aerobic respiration of organic carbon, lowering the amount of organic carbon available to sustain subsurface microbial activity (**Figure 12C**). More central ponds, however, are more hydrologically isolated and are less regularly flushed (**Figure 12C**). This allows for the in situ growth of vegetation, causing a greater organic carbon supply to the sediment (while the oxygen produced through

FIGURE 12 | Schematic of potential mechanisms driving pond chemistry heterogeneity over a salt marsh platform. Vertical scale is exaggerated (likely elevation differences are within a meter). (A) Represents an earlier stage of development [T (time) = 0] in a salt marsh platform whereas (B–D) represent changes over time [T (time) > 0] (e.g., the effects of deeper, older ponds and the potential transitioning of pond sediment geochemistry). (A) Due to the high iron content of the sediment, all ponds should, in theory, have an iron-rich pond sediment geochemistry. The reaction shows bacterial iron reduction (BIR) reducing organic matter, with oxygen supplied from bioturbative-mixing and oxic flood waters, which acts to regenerate the ferrous iron. Fe(II) efflux to creek demonstrates the unknown diffusive flux at the creek wall boundary (Figure 5). (B) Periods of extremely high flooding (storm events or very high tides) from tidal creeks. All ponds are flushed with seawater and organic carbon will be deposited. As the energy of the ebb flow decreases during retreat (due to friction with the vegetated surface), the suspended organic carbon load will decrease as the flow returns to the creeks. If the central pond has sufficient organic matter to exhaust the ferric iron pool, microbial sulfate reduction can occur (see reaction alongside diagram), using seawater sulfate as the electron acceptor. This will slowly act to titrate ferrous iron as iron monosulfides, possibly acting as a switch to sulfide-rich conditions (C). (C) Low energy flooding events which may only flood ponds closer to the creeks introduces oxic water. The interior pond is assumed to have negligible available ferric iron supply so sulfide-rich pond sediment conditions are achieved. (D) Periods of excessive dry weather on the salt marsh platform (as observed in July 2018). Differential water head is attained due to the low hydraulic conductivity of the clay-rich sediment. More central ponds, having a deeper water column initially, are less susceptible to full evaporation. When the water column is fully evaporated, deep (<15 cm) desiccation cracks can occur, allowing atmospheric oxygen into the sediment. Further oxygen may be added upon submergence of the pond sediment with oxic seawater and potential bioturbating organisms.

this in situ growth would escape to the atmosphere), favoring sulfide-rich conditions. We suggest that the quantity and type of organic carbon being metabolized in different ponds and temporal studies on carbon input onto the salt marsh platform surface need to be established before we can properly understand this delivery process.

# Subsurface and Surface Hydrological Flow Controls

The final hypothesis to explain the spatial distribution in the pond geochemistry is that subsurface flow, or surface flow, may play a role in distributing reactants, such as ferrous iron, to different parts of the salt marsh (see red arrows on **Figure 12**). This mechanism is discussed further in Antler et al. (unpublished), whereby a transient flux of ferrous iron might allow for titration of free sulfide and thus allow bioturbation to occur within pond sediments.

The large variation in ferrous iron concentrations among different iron-rich pond sediments, vegetated sediment (**Figure 4**) and the core taken through the creek wall (**Figure 5**) indicates there is heterogeneous concentration of ferrous iron over the salt marsh platform. The 3 mM decrease in ferrous iron over the 5 cm boundary layer in the creek core also suggests there is an efflux of iron out of the sediment, into the creek water. Burrowing of crabs and other organisms appears to intensify

this effect. The chloride and [SO4]/[Cl] data suggests this is hydrological mixing and not simply a zone of iron reduction. This efflux can be seen in creek water which contains up to 21 µM ferrous iron (**Supplementary Table S2**). From this observation, we believe that there is subsurface movement of ferrous iron over the platform, though we cannot offer insight into the direction of these flows. Hydrological flow paths in salt marshes are complex to predict; a catchment area is typically dictated over long timescales by hydrological equilibrium attained by the flood and ebb tides (Allen, 2000). This complexity could be compounded if the ponds interact with these flow paths. Additionally, surface flow likely plays some role in distributing ferrous iron leached from the sediment and deposited closer to the creeks with ebb tides. Evidence of an iron-oxide rich layer on the surface would suggest that at least some portion of this is captured upon diffusion out, though we cannot quantify this effect. Further work would be needed to test the potential of these fluxes to 'switch' pond sediment.

#### A Potential Mechanism

The mechanism which actually causes pond sediment to become sulfide-rich or iron-rich is complex. It is likely some combination of the three effects described above which, together, create conditions favorable for a certain dominant redox condition (**Figure 12**). Water column depth could play a role, particularly where pond sediments can become sub-aerially exposed, or where the water is so deep that atmospheric oxygen delivery to the sediment surface is greatly curtailed (**Figure 12D**). The correlation between water depth and classification of the pond could simply be a result of both factors being controlled by creek dynamics independently. If sea level rise causes salt marsh accretion through vegetation-induced sediment capture, then we would expect ponds being impacted by fewer flood tides—the ponds distally located from creeks—to have a lower sedimentation rate to keep pace. As such, depth distribution over the salt marsh favors deeper ponds in the center of the salt marsh platform as the system ages (**Figures 12A–D**). Since our data suggests a higher organic carbon content in sediments located far from creeks, there is also likely some feedback associated with organic matter degradation and a porosity increase (Spivak et al., 2017). The relative abundance of the surface fluxes of oxygen and carbon compared to the subsurface fluxes of Fe(II) and other redox available elements is important to understand whether geochemical changes are mainly imparted from the surface water or from the platform. It appears more likely based on the data presented here that the surface coupling of organic carbon and other electron acceptors acts to alter an originally iron-rich pond sediment. This is exemplified by the in situ sampler recording a change from iron-rich conditions to sulfiderich conditions as opposed to the reverse. If this were the only mechanism, we would expect clearer boundaries of iron- and sulfide-rich pond sediment zonation. In view of this, it is likely that other processes are capable of influencing the geochemical characteristics. We suggest spatially heterogeneous transient groundwater and surface water introduction of chemical species and the evaporation of surface water introducing desiccation cracks as such processes for further exploration.

The fact that we see a zonal distribution of pond sediment chemistry implies that the change in pond chemistry is faster than rates of creek migration at present. Whilst it is thought that creek migration is actually quite slow (due to the inability of the flow to carry eroded sediment away) (Allen, 2000), if drainage ditches are emplaced, as in the northern part of Abbotts Hall, the sediment chemistry is likely to be more susceptible to change.

# CONCLUSION

Sulfide-rich and iron-rich sediments are found throughout the East Anglian salt marshes. There is a continuum of observational evidence and porewater geochemical signatures which suggest that these classifications can encapsulate a range of geochemical behaviors. The distribution and in situ sampler data indicate that pond sediment geochemistry can be readily altered in terms of chemical classification over as short a timescale as a year. Drone based mapping of the salt marshes has shown that ponds containing sulfide-rich sediments tend to be congregated in the center of a salt marsh aggregate of ponds, further away from larger creek networks. We hypothesize that this is due to a combination of factors, including delivery of organic carbon across the salt marsh platform, variation in physical characteristics based on the position of the pond (i.e., water depth and the propensity for total evaporation), and a possible sub-surface groundwater flow distributing reactants based on position on the salt marsh. This study suggests that some combination of these effects will result in chemical heterogeneity of salt marsh pond sediment geochemistry and that no single mechanism appears dominant. The results suggest that artificial drainage ditches, common in these East Anglian salt marshes, could alter the geochemistry of these pond sediments on short timescales. This has consequences for the carbon budget, and for nutrient and trace metal capture within salt marsh ecosystems.

# DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the **Supplementary Files**.

# AUTHOR CONTRIBUTIONS

AH wrote the manuscript. All authors contributed feedback on said manuscript. GA had the initial idea behind the study. GA and CYL collected cores for **Figure 3** and provided a number of cores for **Figure 4**. HB, XS, and JM collected cores for **Figure 4** and contributed pioneering fieldwork toward the study. MG, AH, HB, AP, and GA undertook the drone survey and subsequent characterization. JW and JC provided data for in situ sampling and AH finished the final 2 months of data analysis. AB and AH

worked in the field collecting cores and undertaking subsequent analysis. KR offered insight into the Essex salt marsh system where he had worked previously. AVT is the supervisor behind all of the work involved in the salt marshes and was the first to suggest work there.

### FUNDING

This work was funded partially by an ERC starting investigator grant (CARBONSINK – 307582) to AVT as well as NERC RG94667 to AVT. Funding for AH was provided by a NERC DTP grant (LBAG/199.02.RG91292).

#### ACKNOWLEDGMENTS

The data table for this study is supplied in the **Supplementary Material**. This work has evolved over the

#### REFERENCES

course of many years and has involved the contribution of many students and scientists who have worked in the Turchyn lab and have contributed to data collection. We acknowledge in particular the help of Jiarui Lui, Katherine Halloran, and Paul Hutchings for help with fieldwork, B. Lawrence and F. Llewellyn-Beard who helped with the initial design of the in situ sampler and James Rolfe who performed carbon analysis. We acknowledge Essex Wildlife Trust for their permission for us to sample in Abbotts Hall Farm.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart. 2019.00041/full#supplementary-material

nutrient exchange. Limnol. Oceanogr. 23, 798–812. doi: 10.4319/lo.1978.23.4. 0798

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Hutchings, Antler, Wilkening, Basu, Bradbury, Clegg, Gorka, Lin, Mills, Pellerin, Redeker, Sun and Turchyn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# No Detectable Broad-Scale Effect of Livestock Grazing on Soil Blue-Carbon Stock in Salt Marshes

Rachel J. Harvey 1 \*, Angus Garbutt 2 , Stephen J. Hawkins 1,3,4 and Martin W. Skov 1

1 School of Ocean Sciences, Bangor University, Bangor, United Kingdom, 2 Centre for Ecology and Hydrology, Bangor, United Kingdom, 3 Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, United Kingdom, 4 Marine Biological Association, Plymouth, United Kingdom

Grassland carbon capturing and storage (CCS) is thought to benefit from regulation of grazing. The impact is likely to depend on livestock density. Yet, few studies have tested this principle or evaluated the consistency of grazer-carbon relationships across multiple sites. We sampled four intertidal zones across 22 salt marshes along a 650 km stretch of coast in the UK to examine the impact of livestock density on globally important saltmarsh "blue carbon" stocks. Although there were marked impacts of grazing pressure on above ground vegetation composition, structure and biomass, there was no detectable relationship between grazing intensity and soil organic carbon, irrespective of tidal zone in the marsh or soil depth-layer analyzed. A substantial spatial variation in soil carbon was instead explained by contextual environmental variables. There was evidence that compensatory responses by vegetation, such as increased root growth, countered carbon loss from grazing impacts. Our work suggests that grazing effects on carbon stocks are minimal on broader scales in comparison with the influence of environmental context. The benefits of grazing management to carbon stores are likely to be highly context dependent.

#### Edited by:

Divya Karnad, Ashoka University, India

#### Reviewed by:

Peter Mueller, Universität Hamburg, Germany Reza Erfanzadeh, Tarbiat Modares University, Iran

#### \*Correspondence:

Rachel J. Harvey<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Conservation, a section of the journal Frontiers in Ecology and Evolution

> Received: 31 October 2018 Accepted: 17 April 2019 Published: 10 May 2019

#### Citation:

Harvey RJ, Garbutt A, Hawkins SJ and Skov MW (2019) No Detectable Broad-Scale Effect of Livestock Grazing on Soil Blue-Carbon Stock in Salt Marshes. Front. Ecol. Evol. 7:151. doi: 10.3389/fevo.2019.00151 Keywords: blue carbon, grazing, saltmarsh, broad-scale, environmental context

# INTRODUCTION

Environmental policies and management seek to optimize natural carbon capture and storage (CCS) across natural landscapes (Richards, 2004; Beaumont et al., 2014). Natural landscapes that are of most interest for carbon storage, such as peat bogs, upland heathland, mangroves and salt marshes, are often subject to livestock grazing that can influence CCS (Reeder and Schuman, 2002; Ostle et al., 2009; Donato et al., 2011; McSherry and Ritchie, 2013). Effective management of CCS relies both on understanding the nature of the relationship between CCS and intensity of land-use, and the consistency of this relationship across environmentally variable landscapes. While there are several ways to enhance the CCS potential of natural landscapes (Wallage et al., 2006; Ostle et al., 2009; Dung et al., 2016), adjustment of livestock grazing pressure is perhaps one of the easiest to manage and implement.

Natural and semi-natural grasslands, such as prairies, steppes and plains provide a valuable insight into the impact of livestock grazing on above- and below-ground processes (Kang et al., 2007). Globally, more than 40% of grasslands are grazed by livestock (Reid et al., 2008); thus, the regulation of stocking density provides an opportunity to positively influence carbon sequestration (Tanentzap and Coomes, 2012). While the sum of direct emissions of carbon dioxide and methane associated with livestock respiration, digestion and fecal decomposition increase with stocking density (Murray et al., 2001; Pinares-Patino et al., 2007), grazers also have strong above ground impacts on vegetation (**Figures 1A,B**) (McNaughton, 1979; Jensen, 1985; Facelli and Pickett, 1991; Olofsson and Oksanen, 2002; Cao et al., 2004), indirectly linking to below ground processes associated with CCS (De Deyn et al., 2008). The direction and magnitude of the impacts of livestock grazing are dictated by its intensity. Given the relationship between above and below ground processes (Bardgett and Wardle, 2003), it might be expected that an increase in grazing intensity will lead to a decrease in below ground soil carbon stocks. However, plant compensatory responses, such as increased root growth and increased carbon allocation to the roots with elevation of grazing pressure, can sometimes counter the above ground effects of grazers on carbon storage (Sollins et al., 1996; Derner et al., 2006; Tanentzap and Coomes, 2012). For example, light or moderate grazing intensity results in a fast growing, diverse plant community, increased root growth, and increased carbon allocation to the roots. In contrast, heavy grazing intensity results in minimal above ground biomass, low species richness and stunted root growth (Schuster, 1964; McNaughton, 1979; Jensen, 1985; Kiehl et al., 1996).

There is the expectation that regulation of livestock density will benefit below ground carbon stocks (Frank et al., 1995; Conant et al., 2000; Derner et al., 2006; Yu and Chmura, 2010; Ward et al., 2016). Here we employ a multi-site study to explore whether grazing has detectable broad-scale impacts on aboveand below-ground carbon stores in salt marshes, an ecosystem that is reputedly rich in carbon stocks (Duarte et al., 2013). So far, saltmarsh grazer-carbon relationships have only been explored by small scale studies and these have generated conflicting results from increases in soil C (Schuman et al., 1999; Derner et al., 2006; Yu and Chmura, 2010; Elschot et al., 2015a) to decreases in soil C (Morris and Jensen, 1998; Reader and Craft, 1999; Klumpp et al., 2009; Tanentzap and Coomes, 2012; Di Bella et al., 2015) or varied/no impacts (Meyer et al., 1995; Ford et al., 2012; Chen et al., 2015; Schipper et al., 2017) Large-scale studies are needed given that the likely cause for conflicting evidence is that grazing imapct on cabon storage is context dependent as suggested by recent systematic reviews (Conant and Paustian, 2002; McSherry and Ritchie, 2013; Davidson et al., 2017). In this study, we aim to empirically determine whether livestock grazing shows a significant impact on above- and below-ground salt marsh bluecarbon stocks in relation to several environmental parameters across a large biogeographical region.

Salt marshes are characterized by halophytic herbs, grasses and low shrubs that are periodically inundated with saline water, forming distinct plant communities zoned along the vertical stress-gradient of tidal inundation (Adam, 1990a) (**Figure 1B**). Salt marshes, along with mangroves and seagrass beds, are thought to provide longer-lasting and far denser "blue" carbon stores than most terrestrial systems, although salt marsh carbon stocks do vary considerably with marsh maturity, geomorphology and environmental setting (Sousa et al., 2010; Mcleud et al., 2011; Hayes et al., 2017; Himes-Cornell et al., 2018). Carbon stores are rich and long-lasting in marshes because they have high plant productivity (Middelburg et al., 1997) and sulfate rich, anaerobic sediments with slow organic deposition rates (Howarth, 1984; Valiela et al., 1985; Mueller et al., 2017) The low-energy, depositional characteristics of salt marshes also make them excellent carbon sinks (Chmura et al., 2003; Andersen et al., 2010) as much of the particulate material trapped by marshes is rich in externally produced organic matter (Van de Broek et al., 2018; Mueller et al., 2019).

Globally, many salt marshes are grazed by livestock for meat production and conservation management (Gedan et al., 2009). In un-grazed marshes the main inputs to soil carbon are sedimentation (Stumpf, 1983), degradation of root matter and plant litter deposition (Saunders et al., 2006), while the main outputs are gas emissions of carbon dioxide and methane (Morris and Whiting, 1986; Bartlett et al., 1987; Ford et al., 2012; Kingham, 2013) and sediment loss from wave and tidal erosion (Chalmers et al., 1985; Kingham, 2013). Introducing livestock is likely to have conflicting effects on saltmarsh properties and processes that affect CCS (**Figure 1C**) (Schuster, 1964; Jensen, 1985; Kiehl et al., 1996; McNaughton et al., 1998; Milotic´ et al., 2010). For example, above ground biomass and plant litter are likely to decrease under all grazing regimes (Bakker, 1985; Jensen, 1985; Kiehl et al., 1996). Consequently, less sediment, and associated carbon, would be trapped by vegetation during immersion (Neuhaus et al., 1999; Mueller et al., 2017). Conversely, soil compaction by livestock reduces soil pore size and induces anaerobic conditions (Tanner and Mamaril, 1958; Mueller et al., 2017), which in turn diminishes decomposition and carbon dioxide emission rates (Scanlon and Moore, 2000; Hussein and Rabenhorst, 2002; Elschot et al., 2015b). Grazer introduction of terrestrial sources of organic matter in to marshes may alter microbial community composition, which in turn can cause microbially-controlled pathways to allocate more carbon to medium- and long-term storage pools (Olsen et al., 2011; Mueller et al., 2017). In terrestrial wetlands anaerobic conditions result in microbial communities that increase methane emissions (Wang et al., 1996), but in salt marshes methane emissions are limited by abundant sulfate-reducing bacteria (Winfrey and Ward, 1983); therefore soil compaction at high stocking densities would be expected to reduce carbon loss by gas efflux in salt marshes.