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

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).

FIGURE 1

Figure 1. Salt marsh ecology and impacts of grazing. (A) The contrast between an un-grazed marsh (left) and an intensively grazed marsh (right). (B) A diagrammatic representation of a salt marsh showing zonation according to tidal inundation: extreme high-water spring (EHWS), mean high water spring (MHWS), mean high water (MHW) and mean high water neap (MHWN). Only the pioneer, low, mid and high zones were sampled in this study. Grazer preference is indicated showing decreased grazing activity in the lower zones. (C) Predicted effects of a range of grazing intensities on vegetation height, aboveground biomass, species richness and root biomass according to the literature (Stumpf, 1983; Jensen, 1985; Facelli and Pickett, 1991; McNaughton et al., 1998). Grazing is shown as “Grazing Intensity” category classified according to a Welsh Assembly Grazing Management Scheme (un-grazed, lightly grazed, moderately grazed and intensively grazed) and Livestock Units per hectare per year (LSU ha⁻¹ yr⁻¹). (D) Location of the 22 study sites along the coasts of west Wales and northwest England.

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 above- and 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 blue-carbon 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; Milotić 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.

We surveyed salt marshes across the west coast of the UK, covering a range of livestock densities, to investigate the broad-scale impacts of grazing intensity on salt marsh below-ground carbon stock. We expected grazing would reduce vegetation height and biomass (Figure 1C) and predicted that below ground carbon stock would significantly decrease at the highest stocking density, due to strong coupling between above and below ground processes. The study explored if grazer-induced loss of above-ground carbon input was counteracted by below-ground increased root growth at lower stocking densities (Figure 1C). Light and intense grazing were expected to increase and reduce plant diversity, respectively (Figure 1C). The study collected information on abiotic environmental variables to explore the influence of environmental context on salt marsh soil carbon, and to contrast the relative importance of context against the effects of grazer density.

Materials and Methods

Study Sites

Soil carbon stock was sampled in 22 salt marshes along a 650 km stretch of the west coast of the United Kingdom (Figure 1D; Table 1). Sites were in a single biogeographical saltmarsh region (Adam, 1990a); they were selected to incorporate a wide range of stocking densities with grazing regimes that were known and had been consistent for the past 30 years or more (Table 1). Although there were around 35–40 salt marshes within this region, marshes that were too small (<2 hectares), had a too much freshwater influence, or those where access was denied by landowners were excluded.

TABLE 1

Table 1. Site details.

Livestock Units (LSU ha⁻¹ yr⁻¹) were used as a standardized measure of stocking density, where 1 LSU = 1 cow/6.6 sheep/33 geese (Almunia, 2009; Welsh Government, 2017). Management schemes in the UK stipulate grazing pressure by categorizing sites into a range of grazing intensities. Thus, sites were categorized into four grazing intensities based on Tir Gofal, an agri-environmental scheme in Wales (un-grazed; lightly grazed: <0.3 LSU ha⁻¹ yr⁻¹; moderately grazed: 0.3–0.7 LSU ha⁻¹ yr⁻¹; intensively grazed: >0.7 LSU ha⁻¹ yr⁻¹). Stocking densities (Table 1) and the Tir Gofal grazing categories were comparable to those observed in grazing studies from other salt marshes (Andresen et al., 1990; Berg et al., 1997; Neuhaus et al., 1999) and terrestrial grasslands (Rauzi and Hanson, 1966; Haveren, 1983; Graff et al., 2007; Proensky et al., 2016).

Sampling Design

Sampling of each marsh was stratified using the four marsh vegetation zones at different shore elevations classically recognized in the salt marsh literature: pioneer, low, mid, and high marsh (Figure 1B) (Adam, 1990a). Some sites had fewer than four marsh zones due to embankments at the top end and/or erosion at the seaward margin (Table 1). Each zone was sampled in a 100 m wide area, using 10 randomly located 2 × 2 m quadrats. Above-ground plant measurements were sampled in all 10 quadrats and four of the quadrats were additionally sampled below ground. Table 1 shows the total number of quadrats per site.

Above and Below-Ground Sampling

Ten quadrats per zone were sampled above-ground for the following. Percentage cover per vegetation species was estimated by eye (Rodwell et al., 2000). Vegetation mean (n = 5) and maximum heights (n = 1) were observed according to Stewart et al. (2001). Vegetation biomass and litter biomass were sampled in a 25 × 50 cm area at the bottom left corner of each quadrat: live vegetation was cut at the soil level and dead litter was collected from the soil surface. Vegetation samples were then dried (80°C, 3 d). Four quadrats per zone were sampled below-ground for the following. One soil core (46 mm diameter, 46 cm deep) was taken centrally in the quadrat using a split tube corer with removable PVC liner that held the sediment until analysis. Three bulk density soil samples (4.8 cm diameter, 2.5 cm deep) were taken at the soil surface by each soil core and pooled. The mean (n = 5) soil surface compaction and subsurface soil strength were observed using a Geotest pocket penetrometer and a Pilcon shear vane, respectively. In the laboratory, the soil core was split lengthways into two halves: a half-core for root analysis and a half-core for soil organic carbon (SOC) analysis. The root core was divided into 5 cm depth segments; roots per segment were washed free of sediment over a 0.5 mm meshed metal sieve and dried (80°C, for 3 d). Sediment was extracted from the SOC half-core at five depths: 0–2, 5–7, 11–13, 22–24, and 44–46 cm; roots were manually removed and soil organic matter (SOM) was determined by loss on ignition (Ball, 1964; Schumacher, 2002) of 10 g homogenized sub-samples per depth. SOM was converted to SOC (%) by a quadratic formula (Craft et al., 1991). Bulk density samples (BD) had large roots and stones removed before being dried (105°C, 16 h), where-after SOC density (g C cm⁻³) was calculated (Emmet et al., 2008).

Observations of Environmental Contextual Predictor Variables

Data on abiotic indicators of environmental context were collated to examine the influence of environmental context on patterns of SOC. Sediment grain-size samples were taken alongside SOC samples (core depths: 0–2, 5–7, 11–13, 22–24, and 44–46 cm) and analyzed in a Malvern Mastersizer 2000 laser particle size analyser after H₂O₂ treatment (Robinson, 1927). Marsh geomorphological type was classified according to Allen (2000). Site wave exposure was calculated according to Burrows et al. (2008). Estuarine water quality variables (dissolved inorganic N, Orthophosphate, Silicates, pH, salinity, and suspended solids) were obtained from the June-2009 to May-2012 observational dataset held by the Environment Agency of Wales and north-west England.

Data Analysis

The study ran separate analyses for each of the four marsh zones (Figure 1B), as well as a single analysis with all zones incorporated. Single-zone analyses were included because substantial between-zone variation in environmental context made it difficult to detect grazing effects in the all-data analysis. Carbon in the top 12 cm of soils was assumed accumulated over the 30-years period for which we knew the grazing history of sites (Table 1), given the region's 3–4 mm y⁻¹ vertical accretion rate (Shi, 1992; French and Spencer, 1993; Cundy and Croudace, 1996; Fox et al., 1999; Van der Wal et al., 2002; Shepherd et al., 2007). The grazing history of soils deeper than 12 cm was uncertain. The analysis took into consideration this depth-dependent variation in the temporal coupling of soil SOC with known grazing regimes: separate analyses were done for soil layers from 0 to 2, 0 to 10, and 0 to 50 cm. The top soil profile (0–2 cm) was regarded to be indicative of the present flux of material from above ground biomass (e.g., litter) to the below ground carbon pool. This layer was expected to show a tight coupling between above and below ground processes, and to be representative of the current grazing regime. The middle profile (0–10 cm) encompassed most of the root biomass and reflected the 30-years time scale for which the grazing regimes of marshes were known. This layer was expected to be less representative of the current above ground processes than the 0–2 cm depth profile, but to still show a weak coupling between above and below ground processes. The deepest profile (0–50 cm) was considered a metric of grazing in relation to the broader contextual influences. Some cores lacked the deepest soil layer (44–46 cm); missing points were estimated using regression techniques based on the remaining real-data points from the core.

Analyses explored the effects of grazing intensity on a categorical scale (un-grazed, light, medium and intense grazing), which are used by grazing management schemes, and on a continuous scale (LSU ha⁻¹ yr⁻¹) to look for threshold levels in carbon-grazing relationships. Analyses of Variance (ANOVA) or the non-parametric equivalent (Kruskal-Wallis multiple comparisons) were used to assess the impact of grazing as a categorical factor on plant height and biomass and on the individual depth profiles for root biomass and SOC. A p-value threshold of 0.05 was used throughout the analysis. A multivariate Permutational Analysis of Variance (PERMANOVA) (Anderson, 2005) was used to analyse the impact of grazing as a categorical factor on root biomass and SOC across all soil depth profiles, and an Analysis of Covariance (ANCOVA) was run on SOC with depth as a covariate to analyse the impact of grazing on the rate of SOC decrease with depth. A series of regression analyses were used to determine the impact of grazing as a continuous variable on both the aboveground plant characteristics and the individual depth profiles for both root biomass and SOC. The effects of factors grazing intensity (four categorical levels) and zone (four levels) on plant community composition was analyzed using 2-way PERMANOVA, followed by a similarity percentage test (SIMPER) to identify species that most strongly characterized compositional differences between grazing intensities (Primer statistical package: Clarke, 1993; Clarke and Warwick, 2001). The PERMANOVA was run with 9,999 permutations on a log transformed Bray-Curtis similarity matrix. False Discovery Rate (FDR) control p-values were calculated in Microsot Excel for all analyses to compensate for the large number of tests (Verhoeven et al., 2004) and partial eta squared effect sizes (${\eta }_p^2$) were calculated in Microsoft Excel for all results where ≥0.0099 was a small effect, ≥0.0588 was a medium effect, and ≥0.1379 was a large effect (Cohen, 1988; Richardson, 2011). Finally, a mixed effects model was used to analyse the impact of multiple environmental and contextual factors (including grazing measured as LSU) on soil organic carbon (R Core Team, 2012). The model was run on the overall data set, the combined high marsh zone and mid marsh zone data (zones most used by grazers: Sharps et al., 2017), and the combined low zone and pioneer zone data (zones least likely to be influenced by grazers: Sharps et al., 2017).

Results

Grazing had significant impacts on above ground vegetation, as expected. Plant height (ANOVA, factor Grazing Intensity: F_{3, 9} = 17.67, p < 0001, ${\eta }_p^2$ = 0.494), above ground biomass (ANOVA, factor Grazing Intensity: F_{3, 9} = 6.96, p = 0.040, ${\eta }_p^2$ = 0.181) and plant litter biomass (K-WMC: H₃ = 135.12, p < 0001) all significantly decreased with increasing grazing intensity regardless of whether grazing was treated as a categorical (grazing intensity category) or continuous (LSU) variable (Figure 2). There was no significant effect of grazing on plant species richness (ANOVA, factor Grazing Intensity: F_3,9 = 0.05, p = 0.387, ${\eta }_p^2$ = 0.072), although community composition did change with grazing intensity in the mid marsh [PERMANOVA, factor Grazing Intensity: Pseudo F₍₃₎ = 2.071, P(perm) = 0.029]. A SIMPER analysis found that communities of un-grazed marshes were generally highly variable and dominated by one of several community types (Festuca rubra, Atriplex portulacoides or a diverse Plantago maritima/Puccinellia maritima community). Light and moderately grazed marshes were less variable and were consistently dominated by Puccinellia maritima, a stress-tolerant, low marsh species. Intensively grazed marshes were characterized by a low-diversity Festuca rubra sward, which changed little between each marsh. Grazing also impacted surface soil compaction (ANOVA, factor: Grazing—F_{3, 18} = 6.39, p = 0.003, η_p² = 0.355), with soil compaction increasing with increasing grazing intensity.

FIGURE 2

Figure 2. Grazing impacts on above ground vegetation. Results of the regression analysis showing the relationship between grazing intensity (stocking density, LSU ha⁻¹) and (A) average vegetation height, (B) above ground biomass and (C) litter biomass. All significantly decreased with an increase in stocking density, with litter biomass reducing to almost zero as soon as grazers are introduced.

Grazing had no single or interactive effects on below ground soil organic carbon (SOC) [PERMANOVA, factor Grazing Intensity: Pseudo F = 1.18, P(perm) = 0.327] or root biomass [PERMANOVA, factor Grazing Intensity: Pseudo F = 1.35, P(perm) = 0.235] for any of the sediment depths analyzed, regardless of whether grazing was treated as a categorical or continuous variable (Figure 3) (Supplementary Information S1: Regression Table). The rate of decline in SOC with soil depth did not vary significantly between grazing intensities (ANCOVA, factor Grazing Intensity: F_{3, 1093} = 0.64, p = 0.597, η_p² = 0.009), although SOC did significantly decrease with depth (Figure 4) (ANCOVA, factor Depth: F_{1, 1093} = 219.96, p < 0.001, ${\eta }_p^2$ = 0.166). There was no overall (all zones) relationship between root biomass and stocking density (LSU), except for a significant positive relation of root biomass with LSU in the surface soils of the high marsh, suggesting a possible plant compensatory response in this zone (Supplementary Information S1: regression table).

FIGURE 3

Figure 3. Soil organic carbon density vs. grazing intensity by zone for each of the depths analyzed. Results of the regression analysis showing the relationship between soil organic carbon density and grazing intensity (stocking density, LSU ha⁻¹) for each zone and for each of the depth profiles analyzed: top 2 cm, top 10 cm, and top 50 cm. With the false discovery rate corrections (Verhoeven et al., 2004), there were no significant impacts of grazing on soil carbon (Supplementary Information S1: regression table).

FIGURE 4

Figure 4. Soil organic carbon with depth for four different grazing intensities. The mean ± standard error soil organic carbon vs. soil depth for each marsh within each “Grazing Intensity” category: (A) un-grazed (n = 8 marshes), (B) lightly grazed (n = 5), (C) moderately grazed (n = 3), and (D) intensively grazed (n = 6). Each line represents a single marsh.

SOC (ANOVA, factor: Zone—F_{3, 18} = 5.09, p < 0.001, η_p² = 0.193) and root biomass (ANOVA factor: Zone—F_{3, 18} = 41.47, p < 0.001, η_p² = 0.365) did differ between marsh zones: both were higher in the mid and high zones than in the low marsh and pioneer zone. A Tukey HSD test showed that there was significantly less SOC in the top 10 cm of soil of the pioneer (x ± SD = 0.009 ± 0.007) and low marsh (0.014 ± 0.008) zones than in the mid (0.019 ± 0.008) and high (0.017 ± 0.008) marsh zones. SOC in the top 50 cm of soils was lower in the pioneer zone (0.007 ± 0.005) than in the other three zones (low: 0.012 ± 0.007; mid: 0.014 ± 0.005; high: 0.012 ± 0.005). A similar pattern was found for root biomass where root biomass in the top 10 cm was significantly lower in the pioneer zone (0.003 ± 0.005) than in the other three zones (low: 0.011 ± 0.007; mid: 0.015 ± 0.011; high: 0.021 ± 0.015) and was significantly higher in the high marsh than in the other three zones. This pattern was expected as older, higher marsh zones have had longer to accumulate organic matter.

The substantial spatial variation in SOC (Figure 3) was due to consistent among-marsh differences in SOC (ANOVA, factor Marsh: F_{18, 18} = 7.95, p < 0.001, η_p² = 0.315) and root biomass (ANOVA, factor Marsh: F_{3, 18} = 7.14, p < 0.001, η_p² = 0.392), suggesting that contextual differences among marshes had a greater influence on carbon stocks than grazing. A linear mixed effects model found that SOC in the top 10 cm of soil had a significant association with plant community composition (F_{16, 181} = 3.10, p < 0.001), but this relationship generally did not translate down to the deeper soil layers (F_{16, 181} = 1.17, p = 0.292). SOC in the deeper soil profiles of the higher marsh zones showed a significant association with marsh morphology (F_{5, 12} = 3.92, p = 0.024), tidal range (F_{1, 12} = 11.86, p = 0.005) with more SOC in marshes with higher tidal ranges, and wave fetch (F_{1, 12} = 21.69, p = 0.001) with higher SOC in more sheltered marshes. SOC in the deeper profiles in the lower marsh zones was significantly associated with soil grain size, with less SOC in coarse sediments (F_{1, 69} = 29.38, p < 0.001) (Table 2).

TABLE 2

Table 2. ANOVA output from mixed effects model.

Discussion

Our study shows that clear above ground effects of grazing do not necessarily translate to below ground effects on soil carbon stocks on the broad-scale. We found no detectable effect of grazing on carbon density (g C cm⁻³), irrespective of which soil depth or marsh zones were considered, or whether or not grazing was considered a categorical or continuous predictor of carbon. The expectation of coupled above to below-ground effect of grazing negates the many complex interactions that vegetation and carbon may have with livestock. Grazing has complex influences on grassland abiotic (e.g., compaction) and biotic (e.g., vegetation biomass and structure) conditions that influence carbon capture and storage (Andresen et al., 1990; Bardgett and Wardle, 2003; Bhogal et al., 2010). Increased stocking density can also initiate compensatory responses by the vegetation community (Holland et al., 1996; Tanentzap and Coomes, 2012) and Tanentzap and Coomes (2012) showed that a small reduction of soil carbon storing in the early years after grazer addition tailed off to no effect of grazing when study systems had sufficient time to develop compensatory responses to herbivory. We found that livestock grazing weakly stimulated the root content in high marsh soils, suggesting that there is some compensation by the species in the vegetation community, thereby mitigating any negative impact of grazing on soil carbon stocks (Tanentzap and Coomes, 2012). Similarly, changes in the microbial communities on grazed marshes may also have increased SOC due to the introduction of terrestrial organic matter from the livestock (Mueller et al., 2017). Alternatively, carbon turnover in microbial communities may have been slower, due to lower redox levels under grazed regimes, resulting in reduced microbial exo-enzymes and therefore slower decomposition rates and greater carbon accumulation in medium- and long-term storage pools (Olsen et al., 2011; Mueller et al., 2017).

In salt marshes, above ground plant adaptation to grazing occurs within a few years to a decade of introduction of grazers (Kuijper and Bakker, 2004). Our sites had been exposed to the same grazing regimes for a minimum of 30 years prior to the study—long enough for plant compensation to take place. We do not dismiss that grazers might have diminished the annual carbon storage at some point; for instance, in the early years following grazer introduction, when some studies have found that grazing can impact on carbon storing (Yu and Chmura, 2010; Sammul et al., 2012). However, over time, this early reduction of soil carbon stocks is likely to have been diluted into insignificance by decades of compensation by the plant community (Tanentzap and Coomes, 2012).

Across a large biogeographical region, variation in environmental drivers had a stronger influence on carbon stocks than livestock density. Salt marshes are at the interface between terrestrial and marine environments (Adam, 1990b) and are subjected to greater spatial and temporal variation in environmental conditions, and hence more disturbance, than many grazed terrestrial grasslands (Adam, 1990c). Environmental context determines much of the external carbon inputs to salt marshes. For instance, sedimentation rates are linked to the local tidal regime (Stumpf, 1983) and allochthonous organic matter, that imported with marine sediment rather than created on the salt marsh, can contribute considerably to SOC stocks where tall vegetation traps sediment (Van de Broek et al., 2018; Mueller et al., 2019). Arguably, the impact of grazing on saltmarsh carbon is comparatively weak relative to the influence of sharp background environmental gradients (Grime, 1974; Nolte et al., 2013). The assumption that carbon capture and storage benefits from management of grazing might well be tenuous in naturally variable and disturbed systems, such as salt marshes.

The observed lack of grazer effects on carbon stock is consistent with the findings of a few smaller-scale grazer presence/absence studies (Meyer et al., 1995; Ford et al., 2012), but it disagrees with studies made in North America (Reader and Craft, 1999; Yu and Chmura, 2010). A recent systematic review showed grazer effects on carbon have only been demonstrated in the USA (Davidson et al., 2017), where marshes are structurally and functionally different too much of the rest of the world (Catterjsse and Hampel, 2006; Bakker et al., 2015). In the USA much of SOC is derived from prolific within-marsh plant production, whilst in Europe the majority of carbon is externally derived through sedimentation (Catterjsse and Hampel, 2006; Bakker et al., 2015). Thus, carbon storing in European type marshes may be more strongly influenced by extrinsic processes that regulate sedimentation than intrinsic processes that influence the vegetation, such as grazing pressure. This deduction is corroborated by the observation made here that environmental context explained the majority of spatial variation in carbon stock, whereas the effects of grazing could not be detected.

Our expectations of negative impacts of grazing on soil carbon stocks were based on evidence from a combination of single-site, experimental studies (Schuster, 1964; Klumpp et al., 2009; Yu and Chmura, 2010; Ford et al., 2012) and meta-data analyses from both terrestrial grasslands and coastal wetlands (Chmura et al., 2003; Tanentzap and Coomes, 2012). None of these studies incorporated a full range of grazing intensities, as we did here; nor did they view the influence of grazing in the context of natural variation. Our study does not imply that grazing has no effect on carbon stocks; rather it shows that the effect of grazing was insufficiently strong or consistent to be detectable above the broad-scale influences of environmental variation and compensation by vegetation. This finding implies that management schemes that stipulate grazing regimes across multiple sites are unlikely to generate spatially consistent effects on carbon stocks. Furthermore, our study only looked at carbon stocks and not carbon sequestration rates. The carbon stocks in this study are a product of accumulated inputs and outputs of organic and inorganic carbon from autochthonous and allochthonous sources, and while there was no impact of grazing on the overall carbon stocks, grazing may affect carbon sequestration rates and mechanisms. There are still many good reasons to manage grazing, such as safeguarding the provision of other ecosystem services, including wave attenuation (Möller et al., 1999), sea defense (King and Lester, 1995; Möller et al., 2007); habitat management (Norris et al., 1997) and biodiversity conservation (Adler et al., 2001). It may be that the regulation of grazing intensity is of particular benefit to above-ground functioning of saltmarshes, such as the provisioning of wildlife habitat and the regulation of gaseous fluxes, and less beneficial to below-ground processes, such as carbon storing.

Author Contributions

MS, AG, RH, and SH contributed toward experimental conception design. RH and MS selected the study sites and collected the field data. RH analyzed all laboratory samples and MS and RH conducted the data analysis with inputs from SH. RH wrote the first draft of the manuscript and all authors contributed to revisions.

Funding

Funding was provided from Knowledge Economy Skills Scholarships (KESS) as a European Social Fund, and from the Natural Environmental Research Council C-SIDE grant (NERC: NE/RO10846/1 R28R32).

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.

Acknowledgments

We thank the Centre for Ecology and Hydrology (CEH), Bangor for providing project support and field and laboratory facilities throughout the project. We would like to thank Inma Roberts, Steve Hughes, Hilary Ford, and Jan Hiddink for help and advice on practical and analytical techniques throughout the project. Thanks for fieldwork assistance: Marc Brouard, Gareth Harvey, Jennifer Harvey, Fred Maier, Damian Smith, Caroline Lucien, Kate Batchelor, Cat Spinney, Dan Hewitt, Vicki Foster, and in particular Matt Lundquist, Aoife Ni Neachtain, and Cai Ladd.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2019.00151/full#supplementary-material

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