# Effect of vegetation cover and sediment type on 3D subsurface structure and shear strength in saltmarshes

Clementine Chirol, Kate Spencer, Simon Carr, Iris Möller, Ben Evans, Jason

Lynch, Helen Brooks, Katherine Royse

► To cite this version:

Clementine Chirol, Kate Spencer, Simon Carr, Iris Möller, Ben Evans, et al.. Effect of vegetation cover and sediment type on 3D subsurface structure and shear strength in saltmarshes. Earth Surface Processes and Landforms, 2021, 46 (11), pp.2279-2297. 10.1002/esp.5174. hal-04004284

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# Effect of vegetation cover and sediment type on 3D subsurface structure and shear strength in saltmarshes

Clementine Chirol<fnref n="1" /> ID
Ben Evans<fnref n="4" /> ID

Kate L. Spencer<fnref n="1" />
Jason Lynch<fnref n="5" />

Simon J. Carr<fnref n="2" />
Helen Brooks<fnref n="4" /> ID

Iris Möller<fnref n="3" />
Katherine R. Royse<fnref n="6" />

**Correspondence**

Clementine Chirol, Université de Lorraine, INRAE, LSE, F-54000 Nancy, France.

Email: clementine.chirol@univ-lorraine.fr; clementine.chir@hotmail.fr

**Funding information**

Queen Mary

**Abstract**

The vulnerability of saltmarshes to lateral erosion at their margin depends on the local biogeomorphological properties of the substrate. In particular, the 3D architecture of pore and root systems is expected to influence shear strength, with repercussions for the wider-scale stability of saltmarshes. We apply X-ray computed microtomography (µCT) to visualize and quantify subsurface structures in two UK saltmarshes at Tillingham Farm, Essex (silt/clay rich substrate) and Warton Sands (sand-rich substrate), with four types of ground cover: bare ground, *Spartina spp*, *Salicornia spp* and *Puccinellia spp*. We extracted µCT structural parameters that characterize pore and root morphologies at each station, and compared them with field measurements of shear strength using a principal component analysis and correlation tests. The 3D volumes show that species-dependent variations in root structures, plant colonization events and bioturbation activity control the morphology of macropores, while sediment cohesivity determines the structural stability and persistence of these pore structures over time, even after the vegetation has died. Areas of high porosity and high mean pore thickness were correlated to lower values of shear strength, especially at Tillingham Farm, where well-connected vertical systems of macropores were associated with current or previous colonization by *Spartina spp*. However, while well-connected systems of macropores may lower the local deformation threshold of the sediment, they also encourage drainage, promote vegetation growth and reduce the marsh vulnerability to hydrodynamic forces. The highest values of shear strength at both sites were found under *Puccinellia spp*, and were associated with a high density of mesh-like root structures that bind the sediment and resist deformation. Future studies of marsh stability should ideally consider time series of vegetation cover, especially in silt/clay-dominated saltmarshes, in order to consider the potential effect of preserved buried networks of macropores on water circulation, marsh functioning and cliff-face erosion.

**KEYWORDS**

porosity, root architecture, saltmarsh, shear strength, X-ray computed microtomography

## 1 | INTRODUCTION

Saltmarshes provide key ecosystem services such as carbon storage and water purification (Barbier et al., 2011), and are important buffer habitats between the sea and the land: because of their capacity to

accumulate sediment and keep pace with rising sea levels, they have the potential to contribute towards long-term, sustainable coastal defence across the world, provided that sufficient sediment input is available (Leonardi et al., 2018). However, while these habitats are efficient at dissipating wave action during marsh surface inundation

<footnote n="1">¹School of Geography, Queen Mary University London, Mile End Road, London, E1 4NS, UK</footnote>

<footnote n="2">²University of Cumbria, Rydal Road, Ambleside, LA22 9BB, UK</footnote>

<footnote n="3">³Department of Geography, Trinity College Dublin, Museum Building, Dublin 2, Ireland</footnote>

<footnote n="4">⁴Department of Geography, University of Cambridge, Downing Place, Cambridge, CB2 3EN, UK</footnote>

<footnote n="5">⁵Department of Geography, University College London & Zoological Society of London, Gower Street, London, WC1E 6BT, UK</footnote>

<footnote n="6">⁶British Geological Survey, Nicker Hill, Keyworth, NG12 5GG, UK</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2021 The Authors. *Earth Surface Processes and Landforms* published by John Wiley & Sons Ltd.</footnote>
(Möller et al., 2014), they have been shown to be vulnerable to lateral erosion at the margin (Bendoni et al., 2016). It has been argued that the destruction and rejuvenation of saltmarshes is a natural process occurring over an order of a few hundred or thousand years, dominated by sedimentological processes (Chauhan, 2009; Fagherazzi, 2013; Van de Koppel et al., 2005). However, in the context of anthropological pressure on coastal environments, sediment starvation and increased wave impact and storm frequency accompanying sea-level rise, trends of net saltmarsh loss have been observed around the world (Allen, 2000; Gedan et al., 2009; Gu et al., 2018; Schwimmer, 2001). While wind-waves play a primary role on long-term marsh edge erosion at the landscape scale, local marsh characteristics such as vegetation cover are also important (Finotello et al., 2020). Therefore, rates of saltmarsh erosion from wave action are variable from marsh to marsh (Ford et al., 2016; Wang et al., 2017), and even over small spatial scales within the same marsh (Bernik et al., 2018; Priestas et al., 2015; Van de Koppel et al., 2005; Wang et al., 2017). Since local vulnerabilities in the marsh structure can have broader implications for the whole marsh and lead to widespread erosion (Bendoni et al., 2016; Leonardi & Fagherazzi, 2015), better understanding of what causes these local changes in stability or susceptibility to erosion is needed to more accurately project potential future losses and efficiently mitigate against these in the context of a changing climate.

The intrinsic capacity of saltmarsh substrates to resist hydrodynamic erosive forces at the local scale is often measured as localized shear strength. While wave flume experiments can help us understand the specific effect of wave thrust on the erosion of ‘transposed’ marsh cliffs, in-situ measurements have the advantage of preventing disturbance during sampling, transport and storage (Grabowski, 2014). At the local scale, this resistance to deformation depends on bulk sediment properties such as the grain size, cohesivity and water retention properties of the sediment (Crooks & Pye, 2000; Grabowski et al., 2011), but also on biogeomorphological factors such as the presence and morphology of vegetation, roots and pores (Brooks et al., 2020; Wang et al., 2017). While influences on surface shear strength, such as the presence of vegetation and biofilm, have been extensively studied (Feagin et al., 2009; Gedan et al., 2011), the impacts of subsurface structures and processes on shear strength remain challenging to observe and quantify (Brooks et al., 2020). Structural pores or macropores, caused by cracks, burrows and decaying roots (as opposed to micropores or matrix pores which are formed by the space between sediment particles; Rabot et al., 2018), can create areas of structural vulnerability in the soil (Vu et al., 2017). They can also promote vertical water movement in the subsurface environment, improve drainage (Tempest et al., 2015) and facilitate root growth (Li et al., 2005). Roots are another important architectural component of the marsh substrate. The tensile strength provided by the roots complements the sediment, which is naturally strong in compression (Gyssels et al., 2005), and thus helps to prevent block failure (Brooks et al., 2020; Wang et al., 2017). However, the roots’ exact role in substrate stability depends on species- and environment-specific structural characteristics (Gyssels et al., 2005). A particularly understudied aspect is how the 3D architectures of roots and pore networks interact within different types of substrates, and influence the internal shear strength of a saltmarsh (Brooks et al., 2020).

X-ray computed microtomography (µCT) combines the penetrating capacity of X-rays with 3D volume reconstruction to observe the internal 3D structure of objects in a non-destructive manner (Cnudde & Boone, 2013). µCT has been applied extensively to agricultural soils to investigate the impact of subsurface structures on crucial soil functions such as water infiltration (Jarvis et al., 2017; Katuwal et al., 2015; Müller et al., 2018; Pot et al., 2020; Tracy et al., 2015), root-pore interactions and patterns of plant growth (Hu et al., 2020; Lucas et al., 2019; Pulido-Moncada et al., 2020). In recent years, the technique has been extended to saltmarsh substrates (Dale et al., 2019; Spencer et al., 2017; Van Putte et al., 2019); however, distinguishing roots from pores is challenging because their greyscale values overlap due to the partial volume effect (Cnudde & Boone, 2013; Helliwell et al., 2013), especially in these complex heterogeneous substrates. Indeed, saltmarshes are transitional habitats formed by a constant interplay of sediment deposition and erosional processes, and where ground cover and other soil characteristics can vary rapidly both in space and time. Episodes of storm surges, colonization by burrowing organisms, or colonization and die-off of plants may be recorded as sedimentary features, and therefore subsurface features may be critical to interpreting surface information and marsh response. Recent studies have developed new approaches for root analysis (Chirol et al., 2021), which allow us to capture the complexity of heterogeneous saltmarsh substrates with unprecedented precision.

This study applies µCT to the 3D structural analysis of roots and pores in two UK saltmarshes of contrasting sediment type and under four contrasting ground covers (bare ground, *Spartina spp*, *Salicornia spp*, *Puccinellia spp*). We provide a detailed analysis, both visual and quantitative, of saltmarsh below-ground structures, and discuss the interplay of root and pore systems under different ground covers and sediment types. We select parameters that best capture the structural variability of roots and pores, and explore correlations between substrate morphology and internal shear strength using a principal component analysis. Taking into consideration other geochemical factors of erodibility such as the proportion of clay-sized particles, the organic matter content and the dispersibility of the clay, we then discuss the wider implications in terms of how sediment properties and morphology contribute to marsh stability at different spatial scales, and provide recommendations for further study.

## 2 | METHODS

We analysed below-ground structure and shear strength at two mine-rogenic saltmarshes in the UK. In order to compare the effect of vegetation and substrate on the subsurface structure, we considered two sediment types (sand-rich at Warton Sands and silt/clay-rich at Tillingham) and four types of ground cover (bare ground, *Spartina spp*, *Salicornia spp*, *Puccinellia spp*), for a total of eight stations (Figure 1). The ground cover choices reflect the zonation of vegetation in the saltmarshes, with bare ground mudflats fronting the marsh, then pioneer species *Spartina spp* and *Salicornia spp*, and lower marsh species *Puccinellia spp* (Figure 2). This may inform us on how a saltmarsh inherits structural features as it accretes vertically from fronting mud-flat to an inner marsh. The plant species were also chosen for their contrasting root structures: *Spartina spp* have long stems with internal
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** ArcGIS map of the sediment core sample stations. Left: Warton Sands (WS, 2°47′27.17″W, 54°7′43.003″N). Right: Tillingham Farm (TF, 0°56′31.148″E, 51°41′50.697″N). BG = bare ground. PUC = Puccinellia. SAL = Salicornia. SPA = Spartina [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

voids as an adaptation strategy to anoxic conditions (Mitsch & Gosselink, 1986); *Salicornia spp* have a shallow and sparse root system and *Puccinellia spp* have a dense system of thin roots (Chapman, 1960).

Three replicate sediment cores (15 cm depth and 15 cm diameter) were collected at each station in January 2019. The replicates were spaced within 0.5–2 m from one another to ensure that all replicates are independent but have similar substrates. The sediment cores were collected to minimize disturbance of structural features, as summarized in Carr et al. (2020). After extraction, the cores were stored upright in a cooling box filled with bubble wrap to minimize disturbance during transport, and stored at 4°C until required.

The whole, intact cores were scanned using a Nikon Metrology XT H 225 µCT system at 205 kV and 46 µA (9.4 W). The exposure time was 500 ms at 36 dB gain. A Cu 1 mm copper filter was used to reduce beam hardening artefacts. 4486 projections were acquired with four frames per projection, for a scan time of 4.5 h. The effective voxel size is 61.79 µm, downscaled to 62.5 µm during volume reconstruction. The scanned volumes were cropped to an 8.75 × 8.75 cm square base to reduce edge effects and remove any disturbance from sampling. All 24 scanned volumes were processed following the method detailed in Chirol et al. (2021) to segment the µCT data into three phases: pore space, organic matter elements (including roots and degraded organic matter) and finally the bulk inorganic mineral phase. All elements larger than 5000 voxels (1.22 mm³) were removed as noise, and the minimal thickness of elements at any point is twice

the resolution, so 125 µm. This method was developed to distinguish live and decayed (necromass) roots from pores in heterogeneous saltmarsh soils, which makes it highly relevant here.

Each phase was visualized in 3D using the volume-rendering software Drishti (Limaye, 2012), and a detailed morphological analysis was performed using the automated software plugin 'Particle Analysis' for ImageJ (Schindelin et al., 2012) to extract a number of shape parameters (Table 1). Out of these parameters we selected those that best represent the structural differences between vegetation and sediment types, basing ourselves on previous studies (Rabot et al., 2018; Spencer et al., 2017) and on our own observation of the dataset. Each selected variable was then normalized within the interval [0, 1] to visualize variations across stations for all variables, and plotted as spider plots.

While the main focus of the paper is to establish relationships between substrate structure and shear strength, other parameters commonly associated with soil stability or vulnerability to erosion were also considered. These include the proportion of clay-sized particles, which influences the cohesivity of the sediment, the proportion of organic matter as determined by loss on ignition and the sodium adsorption ratio of the sediment. The latter considers how high content of exchangeable Na⁺ in the soil can lead to the formation of thick water films around the clay particles and to slow rates of sediment consolidation, thus making the marsh more prone to erosion (Crooks & Pye, 2000). Three replicate cores per station were taken for the analysis of the sodium adsorption ratio at two depths (0–1 and
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** Plant zonation in NW Europe saltmarshes (Redelstein et al., 2018) [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

7–8 cm from the surface): samples were freeze-dried, sieved at 2 mm, then mixed with a recorded mass of distilled water until the obtention of a saturation paste as outlined by Rowell (1994), and left overnight for the cations to equilibrate. The samples were then centrifuged to retrieve the extracts, and the exchanged cations were measured in the extract using inductively coupled plasma optical emission spectrometry (ICP-OES). The sodium adsorption ratio (SAR) was then calculated as $SAR = [Na⁺]/([Ca²⁺] + [Mg²⁺])^{0.5}$, with [ ] the concentration in mmol⁻¹. Of the three replicate cores, one core per station was also processed for particle size analysis by laser granulometry and organic matter content every 1–2 cm from the surface to 15 cm deep (see Table 2 for details). Measurements of organic matter content, including both particulate organic carbon and roots, were obtained by loss on ignition following the method of Rowell (1994): soil samples were first air-dried, heated overnight at 105°C, then weighed and combusted at 500°C overnight.

Finally, shear vane data were collected in August and September 2019 from a distributed survey across a large area of the Tillingham Farm and Warton Sands saltmarshes. The shear vane measures pressure applied at failure point at a depth of 7.5 cm from the surface by rotating a handle against the vane head, and quantifies the undrained geotechnical shear strength of the sediment, that is to say its resistance to deformation and fracture at a very local scale (Grabowski, 2014). While there is a spatial and temporal mismatch

between the shear strength measurements and the position of our sediment cores, the survey was designed to capture the characteristic shear strength for each station, with measurements taken at a frequency of 150 per sediment and vegetation type. A summary of the sampling procedure for each data type is provided in Table 2.

Due to the small number of measurements for all considered parameters except the shear strength, the normality hypothesis cannot be assumed to distinguish between groups using analysis of variance (ANOVA) tests. We used the non-parametric tests of Kolmogorov-Smirnov for normality and Bartlett-Levene for homoscedasticity. When the conditions of normal distribution and homogeneous variances were not met, we relied on the non-parametric Kruskal-Wallis test, which is less sensitive to outliers.

Finally, we performed a principal component analysis (PCA) to compare the µCT morphological characteristics to the shear strength in order to estimate which structural parameters are the main drivers of variability between locations. Since we want to focus on the role of soil structure on shear strength, sedimentological and geochemical properties were not included in the PCA; instead, their specific contributions to shear strength were studied using linear regression. PCA transforms the variables in a dataset into a set of principal components in order to reduce the dimensionality while retaining as much of the variation as possible (Jolliffe, 2002). PCA assumes that all considered variables follow a normal distribution, and that the
<table><caption>TABLE 1 List of variables considered when interpreting the µCT data, with their definition and corresponding structural parameters when applied to pores and organic matter elements</caption><thead><tr><th>Variable</th><th>Definition</th><th>Pore parameter</th><th>Organic matter parameter</th></tr></thead><tbody><tr><td>Total phase fraction (%)</td><td>Fraction of the number of voxels belonging to a phase by the total number of voxels in the volume</td><td>Pore fraction (porosity as a percentage of the total sample volume)</td><td>Organic fraction</td></tr><tr><td>Number of objects/particles</td><td>Number of individual connected clusters of voxels (= objects or particles) in a phase, detected using a 26-voxel connectivity with the 'Particle Analysis' plugin in ImageJ</td><td>Number of connected pore systems</td><td>Number of connected organic matter elements</td></tr><tr><td>Connectivity (%)</td><td>Volume of the largest connected particle divided by the total volume of the studied phase (how much of the total phase belongs to a single connected system)</td><td>Connectivity of the pore system</td><td>Connectivity of the root system</td></tr><tr><td>Emptiness (mm)</td><td>Mean distance between voxels of the same phase</td><td>Mean distance between pores</td><td>Mean distance between organic matter elements</td></tr><tr><td>Euler-Poincaré characteristic</td><td>Topological invariant that describes the shape or structure of a topological space (Vogel, 1997), calculated using the 'Particle Analysis' plugin in ImageJ. A value of 0 means perfectly simple (i.e. one single pore/root); the further the value deviates from 0, the greater the topological complexity of the phase</td><td>Complexity of the pore system</td><td>Complexity of the root system</td></tr><tr><td>Mean thickness (µm)</td><td>Mean value of the local thickness ('Particle Analysis' plugin in ImageJ), measured at each point in a particle as the diameter of the greatest sphere that fits within the particle and which contains the point</td><td>Pore mean thickness</td><td>Mean thickness of organic matter elements</td></tr></tbody></table>

<table><caption>TABLE 2 Summary table for all datasets</caption><thead><tr><th>Variable:</th><th>CT morphological parameters</th><th>Organic matter (%)</th><th>% Clay material</th><th>Sodium adsorption ratio</th><th>Shear strength (kPa)</th></tr></thead><tbody><tr><td>Sample type:</td><td>Undisturbed sediment core</td><td>Disturbed sediment core</td><td></td><td></td><td>Distributed survey across a large area of the marsh</td></tr><tr><td>Date collected:</td><td>Jan 2019</td><td>Jan 2019</td><td></td><td></td><td>Aug–Sept 2019</td></tr><tr><td>Method:</td><td>Computed tomography</td><td>Loss on ignition</td><td>Particle size analysis</td><td>Exchanged cation analysis</td><td>Shear vane</td></tr><tr><td>Sampling frequency and depth per station:</td><td>Three to four replicate cores (15 cm)</td><td>One core, 15 measurements (every 1 cm)</td><td>One core, 7–8 measurements (every 2 cm)</td><td>Two measurements (at 0.5 and 7.5 cm) for each three replicates</td><td>150 measurements (at 7.5 cm)</td></tr></tbody></table>

variables considered fully represent the statistical variation in the dataset; however, even if these conditions are not met, as is the case in our dataset according to the Kolmogorov–Smirnov test, PCA is a robust analytical tool that still provides a useful means to group intercorrelated parameters as a function of their contribution to the overall variability of the dataset (Chirol et al., 2018; Jolliffe, 2002; Reid & Spencer, 2009; Steel, 1996). It is therefore well suited to the analysis of novel parameters such as µCT structural indicators, because their relations with one another and with shear strength are still poorly understood. For this step, we subsampled the shear strength dataset to three or four data points per location while remaining representative of the mean and spread of the sample. We calculated the 10th, 50th and 90th percentiles for all stations with three replicates, and the 10th, 40th, 60th and 90th percentiles for

the station TF PUC where four replicates had been selected. All percentiles were sorted randomly to not skew the dataset. All datasets presented in the paper can be found online in the Supporting Information.

## 3 | RESULTS

### 3.1 | Subsurface structural properties

The 3D volumes highlight the complexity of pore and root networks under the surface of a saltmarsh (Figure 3; see also Figure A1 in the Appendix); the main structures observed at each station are summarized in Table 3. Three main types of macropores are observed in our
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** 3D visualization of pores and organic matter elements for all stations. Sample size = 8.75 × 8.75 × 14.5 cm (see Figure A1 in the Appendix for a visualization of replicates) [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<table><caption>**TABLE 3** Description of pores and organic matter elements appearance at each station (TF = Tillingham Farm; WS = Warton Sands; BG = bare ground; PUC = Puccinellia spp; SAL = Salicornia spp; SPA = Spartina spp; PORES = pores; ORGS = organic matter elements)</caption><thead><tr><td></td><td>PORES appearance</td><td>ORGS appearance</td></tr></thead><tbody><tr><td>TF BG</td><td>Large and complex pore system through the whole core, dominantly vertical, highly connected. The diameter of the pores matches that of <i>Spartina</i> spp roots in other samples</td><td>Small number of scattered, small and roundish organic particles. No live root system</td></tr><tr><td>WS BG</td><td>Regularly spaced small vertical tubular pores at the bottom of the sample, characteristic of a buried bioturbation horizon</td><td>Few scattered tubular organic particles. No live root system</td></tr><tr><td>TF PUC</td><td>Internal cracks with a preferential horizontal orientation</td><td>Dense mat of thin roots with a preferential horizontal orientation. Live root system highly fibrous</td></tr><tr><td>WS PUC</td><td>Internal cracks with no preferential orientation; thin tubular voids</td><td>Dense mat of thin roots with no preferential orientation. Live root system highly fibrous</td></tr><tr><td>TF SAL</td><td>Thick, tubular, interconnected and complex pore network with a preferential vertical orientation. The diameter of the pores matches that of <i>Spartina</i> spp roots in other samples</td><td>Combination of thicker roots with a preferential horizontal orientation, and of a loose mesh of thinner roots with no preferential orientation. Live root system appears fibrous. Several species are likely to coexist (most <i>Salicornia</i>-dominated spots at Tillingham also contained some <i>Puccinellia</i> spp)</td></tr><tr><td>WS SAL</td><td>Buried horizons of extremely regular vertical or looping burrows, horizontal cracks in the sediment</td><td>Thin roots or buried stems joining at depth into one tap root with a vertical orientation, surrounded by a very loose mesh of thinner roots. Root system type: tap root</td></tr><tr><td>TF SPA</td><td>Thick, tubular, interconnected and complex pore network with a preferential vertical orientation; round holes caused by hollow shells</td><td>Thick curved roots, with a preferential vertical orientation, abundant at the surface but growing sparser with depth, surrounded by a loose mesh of thinner roots with a preferential vertical orientation. Root system type: tap root</td></tr><tr><td>WS SPA</td><td>Straight tubular pores with a strong preferential vertical orientation through the whole core; small elongated vertical pores in the lower half</td><td>Thick straight roots piercing vertically through the whole core, surrounded by small elongated vertical organic matter elements in the lower half. Root system type: tap root</td></tr></tbody></table>

samples: (1) highly connected vertical pore systems; (2) sub-horizontal sheets of porosity corresponding to internal cracks (the internal cracks observed during sampling were surrounded by iron precipitates, confirming them as pre-existing structures rather than a product of disturbance during core sampling); and (3) disconnected pores ordered along a horizontal plane, corresponding to bioturbation horizons. We find a highly connected network of macropores with a vertical orientation at Tillingham Farm at the bare ground, *Salicornia spp* and *Spartina spp* stations, and a sparser network at Warton Sands under the

*Spartina spp* station. Internal cracks in the cores with a more horizontal orientation are found under *Puccinellia spp* at both Warton Sands and Tillingham Farm. Finally, bioturbation horizons with characteristic straight or looping burrows are found at Warton Sands under the bare ground and *Salicornia spp* stations. Large round porosity elements are also found under the *Spartina spp* station at Tillingham Farm, corresponding to empty shells found in the field.

The organic matter elements detected by µCT belong either to disconnected fragments corresponding to the necromass or to a live
<table><caption>TABLE 4 Topology of the macropores at all stations. Mean values of all core replicates (standard deviation in brackets). Spider plot values have been normalized to the interval [0, 1]</caption><thead><tr><th>PORES</th><th>Total phase fraction (%)</th><th>Number of particles</th><th>Connectivity (%)</th><th>Emptiness (mm)</th><th>Euler-Poincaré characteristic (absolute value)</th><th>Mean thickness (μm)</th></tr></thead><tbody><tr><td>TF BG</td><td>8.06 (0.79)</td><td>1140 (228)</td><td>46.4 (18.6)</td><td>1.07 (0.05)</td><td>2298 (2094)</td><td>1108 (59)</td></tr><tr><td>TF PUC</td><td>3.94 (1.55)</td><td>614 (96)</td><td>52.0 (30.0)</td><td>2.50 (0.43)</td><td>8967 (5689)</td><td>643 (14)</td></tr><tr><td>TF SAL</td><td>5.73 (0.67)</td><td>1330 (267)</td><td>63.1 (9.0)</td><td>1.51 (0.37)</td><td>3744 (261)</td><td>729 (55)</td></tr><tr><td>TF SPA</td><td>7.09 (0.94)</td><td>817 (34)</td><td>73.2 (4.3)</td><td>1.33 (0.15)</td><td>17 136 (4034)</td><td>876 (111)</td></tr><tr><td>WS BG</td><td>0.60 (0.28)</td><td>401 (97)</td><td>3.9 (0.6)</td><td>14.95 (6.45)</td><td>66 (34)</td><td>852 (74)</td></tr><tr><td>WS PUC</td><td>1.12 (0.80)</td><td>959 (285)</td><td>51.1 (24.4)</td><td>3.18 (1.40)</td><td>4637 (3241)</td><td>410 (81)</td></tr><tr><td>WS SAL</td><td>3.14 (1.13)</td><td>703 (112)</td><td>36.9 (13.3)</td><td>3.32 (0.62)</td><td>5288 (2438)</td><td>698 (50)</td></tr><tr><td>WS SPA</td><td>2.64 (0.70)</td><td>725 (244)</td><td>38.5 (15.7)</td><td>3.01 (0.54)</td><td>14 967 (4884)</td><td>705 (43)</td></tr></tbody></table>

<table><caption>TABLE 5 Topology of the organic matter elements at all stations. Mean values of all core replicates (standard deviation in brackets). Spider plot values have been normalized to the interval [0, 1]</caption><thead><tr><th>ORGS</th><th>Total phase fraction (%)</th><th>Number of particles</th><th>Connectivity (%)</th><th>Emptiness (mm)</th><th>Euler-Poincaré characteristic (absolute value)</th><th>Mean thickness (μm)</th></tr></thead><tbody><tr><td>TF BG</td><td>0.18 (0.05)</td><td>701 (163)</td><td>27.83 (15.00)</td><td>3.48 (0.80)</td><td>300 (161)</td><td>509 (20)</td></tr><tr><td>TF PUC</td><td>2.72 (0.55)</td><td>3617 (537)</td><td>31.71 (16.99)</td><td>0.80 (0.16)</td><td>9942 (9167)</td><td>534 (17)</td></tr><tr><td>TF SAL</td><td>1.06 (0.48)</td><td>2501 (763)</td><td>20.48 (9.48)</td><td>1.26 (0.25)</td><td>3271 (3924)</td><td>514 (18)</td></tr><tr><td>TF SPA</td><td>2.92 (1.20)</td><td>1853 (664)</td><td>73.47 (5.95)</td><td>1.31 (0.47)</td><td>26 360 (3632)</td><td>502 (15)</td></tr><tr><td>WS BG</td><td>0.05 (0.03)</td><td>221 (132)</td><td>5.31 (3.48)</td><td>17.30 (5.24)</td><td>191 (208)</td><td>441 (43)</td></tr><tr><td>WS PUC</td><td>2.32 (1.33)</td><td>3465 (278)</td><td>24.44 (32.37)</td><td>0.96 (0.26)</td><td>24 396 (33 884)</td><td>523 (38)</td></tr><tr><td>WS SAL</td><td>0.44 (0.27)</td><td>781 (410)</td><td>20.89 (14.37)</td><td>4.79 (2.91)</td><td>4540 (4115)</td><td>467 (16)</td></tr><tr><td>WS SPA</td><td>2.30 (0.49)</td><td>1559 (280)</td><td>64.54 (6.04)</td><td>1.75 (0.19)</td><td>34 258 (6403)</td><td>558 (51)</td></tr></tbody></table>

PORES NOMENCLATURE

<figure><img src="image_5.png" /><figcaption>FIGURE 4 Spider plot representation of the topology of the macropores at all stations. Spider plot values have been normalized to the interval [0, 1] [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

root system. Necromass is detected under all stations at both Warton Sands and Tillingham Farm, but is particularly conspicuous at the bare ground stations where live root systems are absent. In the vegetated

cores, the organic phase is dominated by the live root system. While root system architecture is challenging to visualize in the more diverse Tillingham cores, key differences between the root systems of the
ORGANICS NOMENCLATURE

<figure><img src="image_6.png" /><figcaption>**FIGURE 5** Spider plot representation of the topology of the organic matter elements at all stations. Spider plot values have been normalized to the interval [0, 1] [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

three vegetation types can be distinguished at Warton Sands. *Puccinellia spp* plants have a highly fibrous root system, while *Salicornia spp* and *Spartina spp* are closer to a tap root morphology according to the classification system of Delory et al. (2018). The tap root belonging to *Salicornia spp* is shorter, thinner and shallower than that of *Spartina spp*. Other classification systems distinguish herringbone from dichotomous root patterns based on their branching configuration (Lupini et al., 2018); however, the complexity of the root networks and the proximity of the roots to one another make them appear interconnected in µCT, which masks the exact branching pattern and makes it difficult to resolve the different networks.

Tables 4 and 5 show the mean morphological parameters that characterize the pore and root systems at each station. The pore fraction is systematically higher at Tillingham Farm (4–8%) than at Warton Sands (1–3%). The mean distance between pores is lower (1–3 mm vs. 3–15 mm) and the pore systems are better connected (46–73% vs. 4–51%). Furthermore, the spider plots show structural differences between pore systems that look similar in the 3D volumes, such as those found under the bare ground, *Salicornia spp* and *Spartina spp* stations at Tillingham Farm: the pores under *Spartina spp* and under the bare ground station are thicker than under *Salicornia spp*, while the *Spartina spp* station has the smallest number of pores due to having the best connectivity, and has the highest complexity according to the Euler-Poincaré characteristic (~17 000, see Figure 4). The porosity at the Warton Sands stations has a lower structural complexity than at Tillingham Farm, except under *Spartina spp*, where the pore complexity is similar to that of the *Spartina spp* station at Tillingham Farm. The bare ground stations have contrasting pore structures: at Tillingham Farm, the bare ground station has the most and the thickest macropores, while at Warton Sands the bare ground has the lowest fraction of macropores. Out of the vegetation covers considered, the *Puccinellia spp* stations have the fewest macropores at both

Warton Sands and Tillingham Farm. The *Salicornia spp* and *Spartina spp* stations have similar fractions of macropores, but the macropores at the *Spartina spp* stations have a higher level of internal complexity.

The morphological characteristics of the root systems under the *Spartina spp*, *Salicornia spp* and *Puccinellia spp* stations depend more on vegetation species than on sediment type (Figure 5). The ground cover types can be ordered according to their organic fraction and number of connected organic matter elements, respectively, from lowest to highest: bare ground (<0.2%, 200–700), *Salicornia spp* (0.4–1%, 800–2500), *Spartina spp* (2–3%, 1600–1900) and *Puccinellia spp* (2–3%, 3500–3600). The root systems can also be ordered according to their connectivity and complexity, again from lowest to highest: bare ground (5–28%, 200–300), *Salicornia spp* (20–21%, 3000–4000), *Puccinellia spp* (24–32%, 10 000–24 000) and *Spartina spp* (65–73%, 26 000–32 000). The mean distance between organic matter elements is within the range 1–2 mm for most vegetation types except *Salicornia spp* at Warton Sands (5 mm); predictably, this mean distance is greater in the bare ground stations (4–17 mm).

## 3.2 | Geotechnical, sedimentological and geochemical properties

Since the conditions of normal distribution and homogeneous variances are not met for the geotechnical, sedimentological and geochemical properties considered (see Table A1 in the Appendix), we rely instead on visual observation and on the Kruskal-Wallis test to interpret differences between the locations; the *p*-values for these tests are shown in Table 6. Based on a visual observation of the boxplots, compared to Warton Sands, core samples collected from Tillingham Farm have a higher proportion of clay-sized particles (84–96% vs. 44–58%) and of organic matter content (6–15% vs.
<table><caption>TABLE 6 Comparison of p-values for Kruskal-Wallis tests for the organic matter content, percentage of particles below 63 µm, sodium adsorption ratio and shear strength for each ground cover and sediment type (p < 0.05 means two groups are significantly different, with blue and yellow highlights denoting significant or non-significant differences)</caption><thead><tr><th>Organic matter</th><th>TFPUC</th><th>TFSAL</th><th>TFSPA</th><th>WSBG</th><th>WSPUC</th><th>WSSAL</th><th>WSSPA</th></tr></thead><tbody><tr><td>TFBG</td><td>0.041949</td><td>0.856539</td><td>0.963948</td><td>0.00433</td><td>0.007617</td><td>0.248141</td><td>0.135231</td></tr><tr><td>TFPUC</td><td></td><td>0.695476</td><td>0.464693</td><td>6.01E-08</td><td>6.04E-08</td><td>1.26E-06</td><td>3.05E-07</td></tr><tr><td>TFSAL</td><td></td><td></td><td>0.999976</td><td>7.02E-06</td><td>1.55E-05</td><td>0.003787</td><td>0.001262</td></tr><tr><td>TFSPA</td><td></td><td></td><td></td><td>3.96E-05</td><td>8.31E-05</td><td>0.013041</td><td>0.004837</td></tr><tr><td>WSBG</td><td></td><td></td><td></td><td></td><td>1</td><td>0.872087</td><td>0.958265</td></tr><tr><td>WSPUC</td><td></td><td></td><td></td><td></td><td></td><td>0.927422</td><td>0.981847</td></tr><tr><td>WSSAL</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.999994</td></tr><tr><th>% &lt; 63 µm</th><th>TFPUC</th><th>TFSAL</th><th>TFSPA</th><th>WSBG</th><th>WSPUC</th><th>WSSAL</th><th>WSSPA</th></tr><tr><td>TFBG</td><td>0.790961</td><td>0.767856</td><td>0.775679</td><td>4.57E-06</td><td>0.000119</td><td>0.003262</td><td>0.000222</td></tr><tr><td>TFPUC</td><td></td><td>1</td><td>1</td><td>0.005185</td><td>0.045695</td><td>0.295596</td><td>0.067284</td></tr><tr><td>TFSAL</td><td></td><td></td><td>1</td><td>0.006029</td><td>0.051449</td><td>0.318447</td><td>0.07521</td></tr><tr><td>TFSPA</td><td></td><td></td><td></td><td>0.005734</td><td>0.049466</td><td>0.310726</td><td>0.072487</td></tr><tr><td>WSBG</td><td></td><td></td><td></td><td></td><td>0.998237</td><td>0.853102</td><td>0.994203</td></tr><tr><td>WSPUC</td><td></td><td></td><td></td><td></td><td></td><td>0.994793</td><td>1</td></tr><tr><td>WSSAL</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.99846</td></tr><tr><th>SAR</th><th>TFPUC</th><th>TFSAL</th><th>TFSPA</th><th>WSBG</th><th>WSPUC</th><th>WSSAL</th><th>WSSPA</th></tr><tr><td>TFBG</td><td>0.99995</td><td>0.805</td><td>1</td><td>0.03229</td><td>0.000362</td><td>0.01245</td><td>0.5243</td></tr><tr><td>TFPUC</td><td></td><td>0.9494</td><td>0.99969</td><td>0.09384</td><td>0.001772</td><td>0.04158</td><td>0.77013</td></tr><tr><td>TFSAL</td><td></td><td></td><td>0.73279</td><td>0.70671</td><td>0.084119</td><td>0.51004</td><td>0.99987</td></tr><tr><td>TFSPA</td><td></td><td></td><td></td><td>0.02174</td><td>0.000206</td><td>0.00802</td><td>0.4399</td></tr><tr><td>WSBG</td><td></td><td></td><td></td><td></td><td>0.939059</td><td>0.99999</td><td>0.92092</td></tr><tr><td>WSPUC</td><td></td><td></td><td></td><td></td><td></td><td>0.98732</td><td>0.23505</td></tr><tr><td>WSSAL</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.79366</td></tr><tr><th>Shear strength</th><th>TFPUC</th><th>TFSAL</th><th>TFSPA</th><th>WSBG</th><th>WSPUC</th><th>WSSAL</th><th>WSSPA</th></tr><tr><td>TFBG</td><td>1.71E-06</td><td>0.983641</td><td>0.838997</td><td>2.70E-07</td><td>5.99E-08</td><td>5.99E-08</td><td>5.99E-08</td></tr><tr><td>TFPUC</td><td></td><td>0.00016</td><td>0.001428</td><td>0.999951</td><td>5.99E-08</td><td>5.99E-08</td><td>7.73E-07</td></tr><tr><td>TFSAL</td><td></td><td></td><td>0.999598</td><td>2.78E-05</td><td>5.99E-08</td><td>5.99E-08</td><td>5.99E-08</td></tr><tr><td>TFSPA</td><td></td><td></td><td></td><td>0.000295</td><td>5.99E-08</td><td>5.99E-08</td><td>5.99E-08</td></tr><tr><td>WSBG</td><td></td><td></td><td></td><td></td><td>5.99E-08</td><td>5.99E-08</td><td>5.81E-06</td></tr><tr><td>WSPUC</td><td></td><td></td><td></td><td></td><td></td><td>2.74E-07</td><td>5.99E-08</td></tr><tr><td>WSSAL</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.394279</td></tr></tbody></table>

<table><caption>TABLE 7 Mean results per ground cover and sediment type for percentage of clay material, percentage of organic matter, sodium adsorption ratio and shear strength (standard deviation in brackets)</caption><thead><tr><th></th><th>% &lt; 63 µm</th><th>Organic matter</th><th>SAR</th><th>Shear strength</th></tr></thead><tbody><tr><td>TF BG</td><td>96.5 (2.9)</td><td>6.7 (1.0)</td><td>55.8 (8.3)</td><td>20.6 (4.9)</td></tr><tr><td>TF PUC</td><td>88.1 (7.9)</td><td>15.0 (1.9)</td><td>49.5 (1.7)</td><td>27.3 (6.0)</td></tr><tr><td>TF SAL</td><td>86.0 (6.1)</td><td>9.3 (1.5)</td><td>45.7 (1.7)</td><td>22.5 (7.0)</td></tr><tr><td>TF SPA</td><td>89.8 (2.4)</td><td>7.7 (1.2)</td><td>58.7 (13.7)</td><td>22.4 (6.7)</td></tr><tr><td>WS BG</td><td>46.8 (9.5)</td><td>2.3 (0.3)</td><td>29.5 (3.7)</td><td>31.1 (17.4)</td></tr><tr><td>WS PUC</td><td>57.1 (7.5)</td><td>2.5 (0.8)</td><td>21.5 (1.5)</td><td>68.2 (16.8)</td></tr><tr><td>WS SAL</td><td>65.3 (10.0)</td><td>2.9 (0.6)</td><td>24.6 (1.5)</td><td>47.4 (17.6)</td></tr><tr><td>WS SPA</td><td>51.7 (6.9)</td><td>3.1 (0.5)</td><td>37.1 (7.8)</td><td>42.3 (18.4)</td></tr></tbody></table>

2–3%) for all vegetation types (Table 7, Figures 6A and B). According to Kruskal-Wallis, TF BG and WS BG have statistically different median clay fractions, and the median organic matter concentration is significantly different between Tillingham Farm and Warton Sands,

except for TF BG, which is not significantly different from WS SAL and WS SPA. The difference in organic matter concentration between Warton Sands and Tillingham Farm is more pronounced at the *Puccinellia spp* stations. This confirms that the two saltmarshes
<figure><img src="image_7.png" /><figcaption>**FIGURE 6** Boxplots showing the distribution of (A) organic matter content (%); (B) percentage of particles below 63 µm (%); (C) sodium adsorption ratio (no unit); (D) shear strength (kPa) for each ground cover and sediment type. Black circles represent the individual measurements for each boxplot (see Table 2 for the sampling strategy and number of data points for each data type) [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

considered in this study are characterized by contrasting sediment types. The Tillingham Farm samples also have a statistically higher SAR according to Kruskal-Wallis, compared to those from Warton Sands (46-59 vs. 21-30, Table 7, Figure 6D), with a few exceptions, such as WS SPA and TF SAL not being statistically different from either group, and TF PUC not being significantly different from WS BG.

Greater mean values of shear strength, as well as a higher spread in the measurements, are found at Warton Sands. Out of the vegetation covers considered, the *Puccinellia spp* plots have the greatest measured shear strength (Figure 6D). According to both Kruskal-Wallis results and ANOVA tests and *p*-values, no statistical difference is found between the shear strength at TF BG, TF SAL and TF SPA. TF PUC has significantly higher shear strength values than the other ground covers at Tillingham Farm, but not significantly different from WS BG. All Warton Sand ground covers are significantly different from one another and are ordered as follows in ascending shear strength: WS BG, WS SPA, WS SAL, WS PUC. Across all ground covers and sediment types, shear strength is negatively linearly correlated with SAR (R = -0.83, *p* = 0.01) and has a non-significant correlation with the clay fraction (R = -0.67, *p* = 0.07). There is a very strong positive correlation between SAR and the clay fraction (R = 0.86, *p* = 0.01). No significant correlation is found between shear strength and organic matter content across the whole dataset (R = -0.59, *p* = 0.12), but a significant positive correlation exists between the two variables at Tillingham Farm (see Figure A2 in the Appendix). Interestingly, no significant correlation is found between organic matter content from loss on ignition and the organic fraction

obtained from the µCT data (R = 0.39, *p* = 0.34), probably because loss on ignition includes particulate organic matter and not just the live root system and the necromass.

## 3.3 | Correlations between subsurface morphological properties and erosion resistance

We conduct a PCA to explore the controls on variability in shear strength and in the morphological characteristics of the organic matter elements and macropores under each ground cover and sediment type. The Kaiser-Meyer-Olkin (KMO) test yields a measure of sampling adequacy of 0.63, which corresponds to an acceptable but mediocre degree of common variance class. The low KMO reflects the small sample size available for the PCA (three or four cores per ground cover type, 25 data points in total). Another limitation of PCA is the assumption that the variables selected fully represent the statistical variation of the dataset (Jolliffe, 2002), which is unlikely in a complex saltmarsh substrate. Therefore, the interpretations should be treated with caution, but graphical observation of the principal components (PCs) using biplots offers an indication of the relative importance of each considered variable (Figure 7). In order to increase the interpretability of the PC loadings, we use a varimax rotation to rotate the orthogonal axis so that it aligns with the data points in a way that maximizes the degree of variance in the data (Steel, 1996). Following varimax rotation, the first three PCs explain over 73% of the total variation. At each PC the variables are considered important determinants of the variability in the dataset if their loadings exceed ±0.40 (Williams et al., 2010), shaded red in Table 8.
<figure><img src="image_8.png" /><figcaption>FIGURE 7 Biplot of the PCA: (A) PC 1 and 2; (B) PC 2 and 3. Pore parameters are represented in red, organic parameters in green and the shear strength in black [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

PC1 opposes the pore and organics emptiness with the number of pore particles: the presence/absence of vegetation is the most significant distinguisher between the cores, with the Warton Sands samples typically having fewer pores and roots than the more structurally complex core samples at Tillingham Farm, and WS BG far apart from all the other locations.

PC2 opposes shear strength and the number of organic matter elements with pore thickness and pore fraction. The clearest

correlation is the negative correlation between pore thickness and shear strength. PC2 opposes WS PUC and TF BG as end members.

PC3 is dominated by variables of organic matter elements abundance and complexity (organic fraction, connectivity and Euler complexity) and by the Euler complexity of the pores. This highlights the structural differences between the *Spartina spp* cores and the bare ground cores at both Warton Sands and Tillingham Farm. *Spartina spp* have large stems and roots with a complex internal structure and internal voids, which is shown
<table><caption>TABLE 8 Principal component loadings (variables whose loadings exceed ±0.40 are considered important determinants of the variability in the dataset and shaded in red)</caption><thead><tr><th></th><th>PC1</th><th>PC2</th><th>PC3</th></tr></thead><tbody><tr><td>'PORE Fraction (%)'</td><td>-0.38</td><td>0.42</td><td>-0.01</td></tr><tr><td>'ORGS Fraction (%)'</td><td>-0.05</td><td>-0.18</td><td>0.42</td></tr><tr><td>PORE nb particles</td><td>-0.43</td><td>-0.03</td><td>-0.20</td></tr><tr><td>ORGS nb particles</td><td>-0.28</td><td>-0.41</td><td>0.00</td></tr><tr><td>'PORE Emptiness'</td><td>0.46</td><td>-0.03</td><td>-0.02</td></tr><tr><td>'ORGS Emptiness'</td><td>0.42</td><td>0.14</td><td>-0.07</td></tr><tr><td>PORE % Connectivity</td><td>-0.39</td><td>0.10</td><td>0.06</td></tr><tr><td>ORGS % Connectivity</td><td>0.01</td><td>0.19</td><td>0.55</td></tr><tr><td>PORE Euler</td><td>-0.06</td><td>0.08</td><td>0.45</td></tr><tr><td>ORGS Euler</td><td>0.09</td><td>-0.10</td><td>0.49</td></tr><tr><td>'PORE thickness'</td><td>0.02</td><td>0.57</td><td>-0.04</td></tr><tr><td>'ORGS thickness'</td><td>-0.18</td><td>-0.11</td><td>0.14</td></tr><tr><td>'Shear Strength'</td><td>0.01</td><td>-0.45</td><td>-0.04</td></tr></tbody></table>

in the parameters by a more connected root system, and a higher Euler complexity of both pores and organic matter elements.

# 4 | DISCUSSION

## 4.1 | Influence of vegetation, bioturbation and sediment cohesivity on the formation and structural stability of macropores

Compared with previous studies, the development of a novel segmentation technique combining local adaptive thresholding and tubular shape detection gives us greater confidence in our capacity to capture the complexity of a saltmarsh subsurface environment: ground referencing tests confirmed that the µCT data accurately capture regions of dense roots as well as the position and structures of macropores (Chirol et al., 2021). Using µCT morphological data under various types of ground cover and under different sediment types allows us to explore the potential role of subsurface structure on shear strength with an unprecedented perspective on the 3D structure and interplay of pores and roots.

One notable observation is how rare it is to find buried roots in unvegetated regions of the marsh: only small fragments of organic matter are found under the bare ground stations at Tillingham Farm and Warton Sands. However, at Tillingham Farm, macropores are found in the shape of *Spartina spp* roots, possibly from a previous episode of colonization and die-off. The persistence of the pores even after the roots have fully decayed suggests that the above-ground plant breakage and removal occurred without causing widespread erosion of the bed or infill of the macropores, which signifies that the substrate around the pores has enough internal cohesion to retain its shape even under tidal inundation. Since no such pore system is found under the Warton Sands stations, it is probable that these complex, highly connected and vertical pore systems are less structurally stable in sandier, less-cohesive sediment types. This would explain why the distance between pores dominates the morphological differences

between the Warton Sands and Tillingham Farm samples according to the first PC of the PCA. We do find evidence of pore structures being preserved at Warton Sands, but these are thin, horizontal bioturbation horizons, with characteristic I- and U-shaped burrow structures observable in 3D (Figure 3). Burrowing organisms tend to consolidate their burrow structures by coating the walls with secretions (Kristensen & Kostka, 2005; Pagenkemper et al., 2015), which might explain why these horizons have been so well preserved.

Among the vegetated cores, *Spartina spp* and *Puccinellia spp* cores have similar volumes of organic matter elements, but very distinct root morphologies. *Spartina spp* stems have a greater connectivity and internal complexity due to their internal air spaces, giving them a perforated shaft structure, while *Puccinellia spp* root systems are detected as a fragmented mesh structure. The complexity and connectivity of the *Puccinellia spp* root structure is likely to be underestimated due to fine roots falling below the detection threshold of µCT, while tap root morphologies like those of *Spartina spp* or *Salicornia spp* have reduced branching and fewer lateral roots (Vannoppen et al., 2015), making them easier to detect in µCT than fibrous morphologies. Vegetation type also has an impact on the morphology of macropores: at both Warton Sands and Tillingham Farm, we find vertically oriented tubular pores under the *Spartina spp* stations due to the aerenchyma, and horizontally oriented internal cracks in the sediment under the *Puccinellia spp* stations. The *Puccinellia spp* stations have a smaller volume of macropores overall, in accordance with previous observations on fibrous root systems (Vannoppen et al., 2015). The second PC also shows correlations between the complexity of the root system and the complexity and connectivity of the pore system. These observations indicate that vegetation cover (and burrowing organisms) control the type of macropores that form within the substrate, and that the sediment type controls how well these macropores will be preserved.

## 4.2 | Influence of substrate internal structure and geochemistry on shear strength

Higher values of shear strength are found at Warton Sands compared to Tillingham Farm, even though the sandier sediment at Warton Sands is more erosion-prone according to previous remote sensing and flume experiments (Ford et al., 2016; Pringle, 1995). This discrepancy is explained by the very localized properties measured by the shear vane, arguably at the sub-root-layer scale: while still cohesive, the higher sand content of the Warton Sand sediment might make it less deformable by the shear vane blades than the more clay-rich sediment of Tillingham Farm. The shear strength is also significantly negatively correlated to pore fraction and mean pore thickness: large macropores offer no resistance to the shear vane, which would explain the low shear strength values recorded at TF BG compared to WS BG, even though the cohesive clay is in fact more resistant to hydrodynamic forces—as is evidenced by the persistence of the below-ground *Spartina*-root-like pore structures under the bare ground cover at Tillingham Farm. The exact role of macropores on marsh stability is difficult to parse out: while previous studies have associated porosity with greater erodibility in tidal flats (Wiberg et al., 2012), vertical connected systems of macropores
promote drainage, which not only reduces the water's erosive capacity at the surface (Tempest et al., 2015), but also improves sediment aeration, biogeochemical cycling, plant growth and the overall productivity of the saltmarsh (Xin et al., 2009). Therefore, the instantaneous, localized weakening effect of macropores may be compensated by their indirect contribution to marsh stability. Our results highlight complex interactions between substrate structure, potential water flow and erosion vulnerability, which occur at different spatial and temporal scales. The 3D volumes of pore systems obtainable in µCT could provide a framework for water infiltration models in different types of saltmarsh substrates, and help us understand these feedback processes in future studies.

Links between shear strength and root system morphology are harder to decipher in the PCA. According to Brooks et al. (2020), in the upper 15 cm of the marsh, resistance to erosion should be controlled by both the root mat and the sediment properties. Because the binding action of fine root meshes is considered to have an impact on shear strength as measured by the shear vanes (Grabowski, 2014), we hypothesized that either the Euler-Poincaré characteristic or the mean distance to root elements could be used as a descriptor of the 3D mesh-like structure and to quantify this structure's contribution to marsh strength. Here, however, only the number of organic matter elements is grouped with the shear strength in the second PC's loadings. At present, while our method allows us to visualize this mesh-like structure in the 3D volumes, the resolution limit of µCT means that this mesh is too disconnected to

be correctly described with quantitative parameters. The *Puccinellia spp* stations are characterized by both the highest number of root elements (Figure 5) and the highest shear strength for both Warton Sands and Tillingham Farm (Figure 6), indicating that the mesh-like root structure does have an impact on bed/soil stability. The impact of vegetation type on shear strength appears greater in the sand-rich than in the silt/clay-rich substrate, in accordance with previous studies (De Battisti et al., 2019; Ford et al., 2016). This could be due to a facilitated root penetration in coarser sediments, which exacerbates the structural differences between tap root and mesh root traits: the observed effects of sediment type and root morphology on macropore fraction and shear strength are schematized in Figure 8. The lack of a significant relationship between shear strength and organic matter content from loss on ignition also suggests that the binding action of roots has more impact on shear strength than their contribution to organic matter content in the substrate, at least within the root zone.

Finally, we found higher SAR values at Tillingham Farm compared to Warton Sands, despite higher SAR normally being associated with a more erosion-prone sediment. This occurs because the difference in soil properties between the two sites affects the relationships between geotechnical and sedimentological properties (see Figure A2 in the Appendix): since Warton Sands is not clay-dominated (<60% clay), the dispersibility of the clay material present is unlikely to significantly affect the overall erodibility of the sediment. The 0.88 correlation between SAR and shear

<figure><img src="image_9.png" /><figcaption>**FIGURE 8** Schematic representation of root and macropore characteristics in different substrates and associated shear strength [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
strength reported across the whole dataset is probably not, there-
fore, a function of SAR but of the other factors that co-vary with
SAR between the two sites and have a greater impact on shear
strength. At Tillingham Farm, both the shear strength and SAR
values are within the range obtained by Crooks and Pye (2000) for
active saltmarshes in Essex, with low proportions of calcium
carbonates (SAR = 53.4–66.9; shear strength = 10.5–27.8 kPa).
Within this clay-rich site, SAR values are more likely to have an
impact on erodibility; however, we see no significant difference in
SAR values between the locations, which suggests that there is
minimal mineralogical variation at this spatial scale and that
vegetation and pore structures are a greater determinant of
variations in erodibility.

4.3 | Future perspectives

µCT has the potential to capture the whole 3D structural complexity of the saltmarsh: future studies could also incorporate shell deposits, or refine the method for smaller scales to resolve fine roots. Whilst we focused on monospecific locations to describe the root structure of common saltmarsh species, the impact of species richness should also be explored: root structure depends not only on the growth strategy specific to each plant species, but can also change as a function of nutrient availability, redox potential and competition with other species (Bardgett et al., 2014; Bouma et al., 2001; De Battisti et al., 2019). Enhanced biodiversity has been found to exacerbate competition strategies between species and lead to greater root biomass and greater sediment cohesivity (Ford et al., 2016).

In order to better correlate these structures to marsh stability
in future studies, further geotechnical tests and flume experiments
are required to better understand the effect of different ground
covers on substrate resistance to deformation and to hydrodynamic
forcing, so that we may capture the different processes that
contribute to marsh resistance at different scales. Indeed, the
effect of local, centimetre-scale pore and root structures on
erosion resistance depends not only on shear strength, but also on
the position and orientation of these features relative to the
dominant wind direction, water depth and tidal regime (Brooks
et al., 2020; Schwimmer, 2001).

Consideration of the marsh topography and foreshore morphol-
ogy will therefore be necessary to fully understand saltmarsh
morphodynamics at the landscape scale. To that end, remote sens-
ing data are frequently used to map vegetation distribution and
erosion patterns (Van der Wal et al., 2008). This paper will assist
in the interpretation of vegetation maps in terms of what dominant
structures might be found below-ground, and their effects on
marsh strength. Our results also suggest that vegetation maps
should ideally consider time series of vegetation covers over the
years, since porosity structures inherited from certain vegetation
types can be preserved underground even after the vegetation
has died, at least in cohesive sediments. These buried and
preserved pore networks might lower hydrodynamic forcing at the
surface by facilitating water infiltration and drainage, but might also
contribute to cliff-face erosion by providing areas of structural
weakness.

5 | CONCLUSION

In this study, we compared morphological parameters of
macroporosity and root structure from µCT with shear strength data
obtained in the field in saltmarshes of contrasting sediment types and
in four contrasting ground cover types, in order to explore links
between marsh subsurface structure and marsh strength. Our results
show that a combination of ecological factors (different root struc-
tures create different porosity elements) and sedimentological factors
(the soil cohesivity controls its capacity to preserve these pore struc-
tures even after the above-ground vegetation has died) play a signifi-
cant role in determining the macropore structures in saltmarshes.
Large, vertically connected systems of macropores were found at
Tillingham Farm under all ground covers except Puccinellia spp: these
macropores reduce the internal shear strength, but may facilitate
water infiltration and drainage and reduce erosive forces at the
surface. The mesh-like root structure characteristic of Puccinellia spp
contrasts with the tap root morphology at Salicornia spp and Spartina
spp and was found to be the most efficient at increasing the shear
strength due to its binding action, at least when looking at monospe-
cific locations. At the scale considered, vegetation type was a better
predictor of shear strength than sodium adsorption ratio, which did
not change significantly from location to location at Tillingham Farm.
The subsurface structure and strength of saltmarshes results from a
complex balance between the marsh history (succession of ground
covers and species over time, storm events and other variations in
sedimentation rates leading to different sedimentary horizons) and
the capacity of the marsh substrate to preserve its internal structure,
which depends on the cohesivity of the sediment but also on consoli-
dation by living organisms and plants.

ACKNOWLEDGEMENTS

We acknowledge funding from the National Environment Research Council (NERC) for the research project ‘Response of Ecologically-mediated Shallow Intertidal Shores and their Transitions to extreme hydrodynamic forcing in UK settings’ (RESIST-UK) (Grant Ref. No. NE/R01082X/1). We would like to thank the School of Geography laboratory manager Michelle Day at Queen Mary University London for the use of their Nikon Metrology XT H 225 X-ray Computed Tomography system and laboratory instrument technician Dr Natalie Ludgate for her assistance with the sediment analysis, contribution to method development for the sodium adsorption ratio analysis and operation of the ICP-OES.

CONFLICT OF INTEREST

The authors have no conflict of interest to declare.

DATA AVAILABILITY STATEMENT

The dataset used in this study will be made openly available through
the NERC Grants Data Management Plan at the National Geoscience
Data Centre (NGDC), after a 2-year embargo period.

ORCID

Clementine Chirol ID https://orcid.org/0000-0003-0932-4725

Ben Evans ID https://orcid.org/0000-0003-0643-526X

Helen Brooks ID https://orcid.org/0000-0002-8291-4070
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## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Chirol, C., Spencer, K.L., Carr, S.J., Möller, I., Evans, B., Lynch, J. et al. (2021) Effect of vegetation cover and sediment type on 3D subsurface structure and shear strength in saltmarshes. *Earth Surface Processes and Landforms*, 46(11), 2279–2297. Available from: https://doi.org/10.1002/esp.5174
APPENDIX A.

<figure><img src="image_10.png" /><figcaption>**FIGURE A1** 3D visualization of pores and organic matter elements for all eight stations, showing the differences between replicate cores. Pore features are represented in grey and organic matter elements in green. Left to right: Pores + organic matter elements, pores, organic matter elements [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
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**FIGURE A2** Visualization of the correlations between geotechnical and sedimentological properties at the two saltmarshes. Linear correlation tests were done over the whole dataset (dashed line) and for each study site (solid lines). Linear correlations were tested between (A) organic matter content from loss on ignition and the organic fraction obtained from the µCT data; (B) shear strength and organic matter content from loss on ignition; (C) shear strength and clay fraction; (D) shear strength and sodium adsorption ratio; (E) sodium adsorption ratio and clay fraction [Color figure can be viewed at wileyonlinelibrary.com]
<table><caption>TABLE A1 p-Values for Kolmogorov-Smirnov test for normality, Bartlett-Levene for homoscedasticity for the clay fraction, organic matter content, sodium adsorption ratio and shear strength (p < 0.05 means two groups are significantly different, with blue and yellow highlights denoting significant or non-significant differences)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Kolmogorov-Smirnov test p-value</th></tr><tr><th>% &lt; 63 µm</th><th>OM</th><th>SAR</th><th>Shear strength</th></tr></thead><tbody><tr><td>TFBG</td><td>0.657</td><td>0.454</td><td>0.764</td><td>0.245</td></tr><tr><td>TFPUC</td><td>0.644</td><td>0.515</td><td>0.980</td><td>0.093</td></tr><tr><td>TFSAL</td><td>0.690</td><td>0.708</td><td>0.954</td><td>0.172</td></tr><tr><td>TFSPA</td><td>0.456</td><td>0.689</td><td>0.276</td><td>0.188</td></tr><tr><td>WSBG</td><td>0.472</td><td>0.898</td><td>0.197</td><td>0.004</td></tr><tr><td>WSPUC</td><td>0.932</td><td>0.582</td><td>0.985</td><td>0.041</td></tr><tr><td>WSSAL</td><td>0.808</td><td>0.655</td><td>0.560</td><td>0.317</td></tr><tr><td>WSSPA</td><td>0.943</td><td>0.042</td><td>0.509</td><td>0.143</td></tr><tr><td>Bartlett test p-value</td><td>0.006</td><td>1.68E-09</td><td>1.10E-06</td><td>6.84E-88</td></tr></tbody></table>