<figure><img src="image_1.png" /></figure>

# Carbon stocks in marine habitats across Jersey's territorial waters

Bede Ffinian Rowe Davies<sup>a,*</sup>, Samantha Blampied<sup>b</sup>, Francis Binney<sup>b</sup>, Graham Epstein<sup>c</sup>, Paul Chambers<sup>d</sup>, Emma V. Sheehan<sup>e</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

**Keywords:**

Carbon

Channel Islands

Marine

Seabed

Territorial Waters

## ABSTRACT

Marine areas have been shown to store varying amounts of carbon. The densities of these stores varies considerably across environmental settings, including the biotic and physical habitat type, the productivity of these habitats, the ephemeral or permanent nature of the habitats as well as the hydrological energy of the area, its depth and its exposure to anthropogenic activities. To appropriately incorporate carbon storage into management plans, baseline information on the carbon held within marine areas is required. Here, an inventory of carbon was carried out for marine habitats found within the territorial waters of Jersey, one of the Channel Islands situated between England and France. Across the habitats surveyed, the biogenic habitats Seagrass and Sand Mason Worm Dominated Sands contained high densities of Organic Carbon with $0.25 \pm 0.047$ (Seagrass) and $0.17 \pm 0.048$ Mg Ha-1 (Sand Mason Worms) respectively, but due to their small spatial area this only accounted for $110 \pm 0.211$ (Seagrass) and $359 \pm 1.02$ Tonnes (Sand Mason Worms) across Jersey's entire territorial waters (cumulatively $1.11 \%$ by area). Less carbon dense habitats, such as Coarse Sediments with $0.12 \pm 0.031$ Mg Ha-1 covered much larger areas and accounted for a much larger proportion of organic carbon stocks ($8450 \pm 21.9$ Tonnes; $30.9 \%$ of Jersey's Territorial waters by area). As nations aim to quantify their biosphere carbon stocks to best inform future management, the need for extensive knowledge of the quantities of carbon found within and across marine habitats is highly important. This work provides a baseline assessment of carbon found within marine habitats in Jersey's territorial waters. The work highlights the importance of not only understanding the density of carbon within specific habitats but also the necessity of accurate spatial information characterising the extents of these habitats.

## 1. Introduction

The global carbon cycle incorporates many different pools of carbon from both marine and terrestrial environments. Benthic marine ecosystems are highly important as both sinks and sources of carbon (Smale et al., 2018). Their role as a sink has been highlighted within the context of climate change mitigation and management. This has led to certain components being included within IPCC reports (Połtner et al., 2022), national greenhouse gas emission objectives (Wedding et al., 2021) and international incentives to demonstrably show increased Carbon sequestration (Clean Development Mechanism of the United Nations Framework Convention on Climate Change: Erickson et al., 2014; Herr and Landis, 2016).

Knowledge of carbon currently stored in marine territorial areas is lacking, with many carbon stock assessments relying on estimates from either historic records or data extrapolated from other geographical areas (Rogers et al., 2019). Furthermore, many carbon stock assessments only focus on specific vegetated coastal habitats, such as seagrass, mangroves or salt marsh. Although these habitats can store and sequester organic carbon at far higher rates than other habitats per unit area (Duarte et al., 2013; Macreadie et al., 2017; Mcleod et al., 2011), they cover only a small total area (~0.2% of surface oceans) and are known to be sensitive to anthropogenic perturbations and pressures, with highly documented decreases in their spatial extent over the last century (Alongi, 2002; Duarte et al., 2013; Unsworth et al., 2015, ≈; Waycott et al., 2009). Therefore, these habitats contain only a very small

<footnote><sup>a</sup> Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR2160, Nantes F-44000, France</footnote>

<footnote><sup>b</sup> Government of Jersey Marine Resources, Natural Environment, Howard Davis Farm, La Route de la Trinitie, Trinity JE3 5JP, Jersey</footnote>

<footnote><sup>c</sup> University of Victoria, Victoria, British Columbia, Canada</footnote>

<footnote><sup>d</sup> Jersey Heritage, Jersey Museum, The Weighbridge JE2 3NG, Jersey</footnote>

<footnote><sup>e</sup> School of Biological and Marine Science, University of Plymouth, Plymouth PL4 8AA, United Kingdom</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: bedeffinian@gmail.com (B.F.R. Davies).</footnote>
proportion of the oceans total carbon stock (Atwood et al., 2020; Duarte et al., 2013; Macreadie et al., 2021). Assessments of carbon stocks across all habitats within territorial waters are needed to move towards effective management of carbon.

It has been suggested that Marine Protected Areas (MPAs) could be an effective management tool to preserve hotspots of stored Carbon (Howard et al., 2017; Roberts et al., 2017). Yet, MPAs have generally been created for biodiversity conservation, fisheries sustainability and tourism (Davies et al., 2021; Epstein and Roberts, 2023; Howard et al., 2017; Renn et al., 2024). To most effectively preserve, maintain and improve carbon stores using MPAs, extensive data will be required pertaining to the extent of carbon stores, the spatial variability of these stores and the effects that specific activities and scenarios will have on these stores (Epstein and Roberts, 2023; Howard et al., 2017; Roberts et al., 2017).

The amount of carbon stored within benthic ecosystems is likely to vary considerably both spatially and temporally depending on a range of influencing factors (Ewers Lewis et al., 2018; Macreadie et al., 2017; Pendleton et al., 2012). Carbon stocks will vary based on local physical, geological, biological and anthropogenic factors; such as wave exposure, tidal influence, substrate type (Hard Ground or Sedimentary etc.), sediment stabilising fauna (Sand Mason Worms etc), anthropogenic disturbance (dredging etc.), presence of calcifying organisms (Bivalse and Molluscs) and primary productivity (Macro and Microalgae: Epstein et al., 2022; Epstein and Roberts, 2023; Howard et al., 2017; Roberts et al., 2017; Sheehan et al., 2015).

The Bailwick of Jersey, a British Crown Dependency, is the largest of the UK Channel islands (102 km²) situated in the southwest of the English Channel. Its territorial waters cover ~ 2455 km² and include multiple offshore reef systems as well as extensive sedimentary habitats

(Fig. 1). These reefs and the habitats they contain have been managed to promote both conservation and local fisheries (Blampied et al., 2022a, 2022b, 2023), yet carbon stocks within these habitats is still lacking. To provide information on the current carbon stock across Jersey's territorial waters and how that carbon stock is distributed, cores and grabs were taken spanning multiple different habitats. The density of carbon (both Inorganic and Organic) across the surveyed habitats was then used to estimate total stocks. The objective of this work was to provide data on carbon stocks and their spatial distribution across Jersey's territory to act as a baseline study and allow the most effective management of these carbon stocks.

## 2. Materials and Methods

### 2.1. Sample Locations

Grabs and Cores were taken across multiple different habitat types in Jersey's territorial waters (Table 1). Habitats were defined from JNCC biotopes (JNCC, 2022) with some grouping applied to similar habitats (Table 1). Cores were taken from subtidal and intertidal Coarse Sediments, Fine Sediments, Sand Mason Worms and Seagrass Beds, while grabs were taken across all the same habitats as well as Maerl Beds and Offshore Mixed Sediments (Table 1 & Fig. 1).

### 2.2. Sampling Methods

#### 2.2.1. Core specific

The corer (45 mm inner diameter, 50 cm long clear PVC tube) was driven into sediment to ~ 40 cm deep. Core sleeves containing viable samples (more than 30 cm of sediment) were then capped at both ends

<figure><img src="image_3.png" /><figcaption>Fig. 1. Jersey's territorial waters with modelled habitat cover (Chambers et al., 2019) and Core (diamonds) and Grab (triangles) sample locations.</figcaption></figure>
<table><caption>**Table 1**
Groupings of Sampled and not Sampled Habitat Types by JNCC biotopes and EUNIS codes.</caption><thead><tr><th>Habitats</th><th>JNCC Groupings</th><th>EUNIS Code</th></tr></thead><tbody><tr><td>Coarse Sediments (2,4)*</td><td>SS.SCS.CCS.Blan,<br>SS.SCS.ICS.Glap,<br>SS.SCS.CCS.MedLumVen,<br>SS.SCS.ICS.MoeVen</td><td>A5.145,<br>A5.135,<br>A5.142,<br>A5.133</td></tr><tr><td>Fine Sediments (3,1)*</td><td>SS.SSa.IFiSa.imoSa</td><td>A5.231</td></tr><tr><td>Maerl Beds (0,4)*</td><td>SS.SMp.Mrl</td><td>A5.51</td></tr><tr><td>Offshore Mixed Sediments (0,3)*</td><td>SS.SMx.OMx.PoVen</td><td>A5.451</td></tr><tr><td>Sand Mason Worms (2,3)*</td><td>SS.SCS.ICS.Slan</td><td>A5.137</td></tr><tr><td>Seagrass (6,9)*</td><td>SS.SMp.SSgr.Zmar</td><td>A5.5331</td></tr><tr><td>Cobbles and Pebbles</td><td>SS.SCS.CCS.PomB</td><td>A5.141</td></tr><tr><td>Hard Ground</td><td>CR.HCR.XFa,<br>IR.HIR.KSed</td><td>A4.13,<br>A3.12</td></tr><tr><td>Kelp Forest</td><td>IR.MIR.KR.Lhyp</td><td>A3.214</td></tr><tr><td>Rocky Reef</td><td>IR.HIR.KSed.XKScrR,<br>IR.MIR.KSed.XKScrR</td><td>A3.125</td></tr><tr><td>Slipper Limpets</td><td>SS.SMx.IME.CreAsAn</td><td>A5.431</td></tr></tbody></table>

and maintained upright. In the laboratory, cores were expelled from the sleeves onto a clean tray while maintaining core integrity, 1 cm thick slices with a 45 mm diameter were taken from along the length of the core, starting at surface (0–1 cm) then proceeding every 5 cm (i.e. 5–6, 10–11, 15–16 etc.). Wet weight of each slice was recorded, its volume was recorded (V), then each slice was oven dried at 60–70°C over night (Schofield, 2015), dry weight of each slice was then also recorded (ω) and the dry bulk density was calculated (DBD: g dry sediment per cm³).

$$DBD = \omega/V$$

### 2.2.2. Grab Specific

Grabs were collected using a VanVeen grab (0.2 m²) deployed from the Jersey Government's research vessel Norman Le Broq. Samples were taken using a 45 mm diameter PVC tube through the middle of grab. In the laboratory, the volume and wet weight of each sample was recorded, then each sample was oven dried at 60–70°C over night (Schofield, 2015). The dry weight of each sample was then recorded and dry bulk density calculated as above.

### 2.2.3. Geochemical Measurements from Cores and Grabs

A random subsample of approximately ~ 6 g was taken from each dried sample or slice and the mass was weighed ($m_0$). The subsample was then ground with mortar and pestle then passed through multiple sieves to finally pass through a 180µm sieve. The mass of the portions of the sample that did ($m_t$) and did not pass through the sieve were weighed and a ratio of successfully ground sample was calculated ($\Delta m$). The ground sample, which passed through the 180µm sieve, was then split into two subsamples. One subsample was digested with 0.1 M HCL to remove all inorganic carbons (Nieuwenhuize et al., 1994; Verardo et al., 1990). Before digestion the subsample was weighed ($s_0$). To digest, 1 ml aliquots of 0.1 M HCL were sequentially added to the subsample until effervescing ceased. Once effervescing ceases, digested samples were then re dried at 60–70°C over night and their new weight recorded ($s_t$). The change in weight during digestion was recorded ($\Delta s$).

$$\Delta m = m_t/m_0$$

$$\Delta s = s_t/s_0$$

To measure the percentage by weight of carbon in each sample, 4–6 mg of each subsample was accurately weighed using a microbalance from both the digested and non digested subsamples. An Elemental Microanalysis CHN Analyser (EA1110, CE Instruments Ltd., Wigan) was used to estimate carbon percentages, with acetanilide ($C_8H_9NO$ 99%) as a standard. For each sample, subsamples were sealed in tin capsules

prior to mineralization and separation in a gas chromatographic system for quantification of percentage carbon, nitrogen and hydrogen. Each run of CHN also used 6 blank samples and 3 standards before samples were measured. Non-digested samples returned the ratio by weight of all carbon present in samples (Total Carbon ratio by weight: $rc_{TC}$), while digested samples returned the ratio by weight of all organic carbon in the samples (Organic Carbon ratio by weight: $rc_{OC}$).

Carbon density ($gcm^{-3}$) was then calculated for Inorganic and Organic Carbon using the ratio of carbon by weight ($rc_{TC}$ and $rc_{OC}$), the dry bulk density (DBD), the ratio of ground subsample ($\Delta m$) and, for the Organic samples, the ratio of digested subsample ($\Delta s$). Inorganic carbon was calculated as the difference between Total and Organic carbon. All carbon density values were converted from $gcm^{-3}$ to $MgHa^{-1}$ using a factor of 1000.

$$TC = rc_{TC} * \Delta m * DBD$$

$$OC = rc_{OC} * \Delta m * \Delta s * DBD$$

$$IC = TC - OC$$

## 2.3. Data Analysis

General Linear Models (GLM) were carried out to assess the differences in Inorganic and Organic Carbon density across Habitats (Coarse Sediments, Fine Sediments, Maerl Beds, Offshore Mixed Sediments, Sand Mason Worms and Seagrass). Models used a Gamma distribution. To assess change in Carbon density with depth, Generalised Linear Mixed Effect Models (GLMMs) were applied to core data using a Gamma distribution. GLMMs modelled Inorganic and Organic Carbon as functions of Habitat (Coarse Sediments, Fine Sediments, Maerl Beds, Offshore Mixed Sediments, Sand Mason Worms and Seagrass) and Depth (0–40 cm) with Core ID as a random factor (1–13). Sample vs fitted residuals, quartile-quartile and autocorrelation of temporally sequential samples were assessed visually, to fit assumptions of the models used. All stated values are model (GLM or GLMM) estimates ± standard error.

## 2.4. Spatial Data

A benthic biotope GIS model covering 2313 km² of Jersey's territorial waters (Chambers et al., 2022) was used to derive data on spatial coverage of specific habitats (Chambers et al., 2019). These data were created using a range of different data sources from regional studies, admiralty charts and oceanographic surveys to provide point data of biological, geological, oceanographic and other parameters. These combinations of parameters were then assessed and used to assign spatial extents of JNCC biotope classifications. These classifications were then further scrutinized using field data (Chambers et al., 2022). For the current research, these JNCC biotopes were then grouped into broader scale habitat types: Coarse Sediments, Cobbles and Pebbles, Fine Sediments, Hard Ground, Kelp Forest, Maerl Beds, Offshore Mixed Sediments, Rocky Reef, Sand Masson Worms, Seagrass and Slipper Limpets (Table 1). To calculate the stock of Inorganic and organic carbon found across Jersey's waters in the top 10 cm of sediment, the spatial extents of habitats were used alongside the quantity of carbon found in their top 10 cm (GLM model estimates). This approach assumes that the distribution and quantity of carbon found in the samples taken will be spatially consistent across each habitat type.

# 3. Results

## 3.1. Carbon Density

Inorganic Carbon in the top 10 cm of Seagrass sediment showed distinctly greater values (3.46 ± 0.842 $MgHa^{-1}$) than Coarse Sediments and Maerl Beds (1.31 ± 0.48 and 1.12 ± 0.409 $MgHa^{-1}$ respectively:

<footnote>* Habitats Sampled across Jersey's Territorial Waters. Numbers in brackets denote amount of samples by cores followed by grabs.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Inorganic (a) and Organic (b) Carbon (MgHa⁻¹ in the top 10 cm) across different habitats in Jersey's territorial waters. Solid points show modelled mean estimates and errorbars show modelled standard error.</figcaption></figure>

Fig. 2a and Table 2). Organic Carbon displayed similar patterns to that of Inorganic Carbon, with Seagrass showed distinctly greater values (24.5 ± 4.69 MgHa⁻¹) than Coarse Sediments and Fine Sediments (11.8 ± 3.06 and 7.03 ± 3.15 MgHa⁻¹ respectively: Fig. 2b and Table 2).

### 3.1.1. Spatial Distribution

The spatial distribution of carbon, shows that the highest density of both organic carbon is mostly inshore and to the east of Jersey, where the majority of Seagrass, Sand Mason Worms and Offshore Mixed Sediments are located, with large areas of lower densities of carbon, where Fine and Coarse Sediment habitats are prevalent, across the north and west of the territorial waters (Fig. 3). Inorganic carbon showed similar patterns of higher densities in seagrass areas across the inshore and east of Jersey, while fine sediments, which showed more variable levels also showed greater values across all regions.

Across all of Jersey's marine area, habitats assessed for their carbon content covered ~ 1050 km² (~ 44.7% of Jersey's territorial waters: Table 3 & Fig. 4a). The majority of this area was split between Coarse and Fine Sediments, which generally contained the lowest amounts of Inorganic and Organic carbon across the habitats surveyed, although fine sediments showed highly variable levels of inorganic carbon.

However, due to their extensive spatial coverage these habitats cumulatively contain an estimated ~ 2068 ± 4 and ~ 9950 ± 6 Tonnes of Inorganic and Organic carbon respectively (around ~ 92% of all habitats surveyed: Table 3 & Fig. 4b). Biogenic habitats, which cover a far smaller area, cumulatively contained ~ 118 ± 2 and ~ 1210 ± 2 Tonnes of Inorganic and Organic carbon in Jersey territorial waters (Table 3 & Fig. 4b). In total, all habitats were estimated to contain 2250 ± 7 and 11,948 ± 7 Tonnes of Inorganic and Organic Carbon (Table 3 & Fig. 4b).

### 3.2. Carbon Density with Depth

As above, the levels of Inorganic and organic carbon showed very similar patterns, both across depth and habitats. Seagrass, Sand Mason Worms and Coarse sediments showed a marginal decrease in carbon with depth, while Fine Sediments showed a marked increase over the depth of core albeit with a strong increase in standard error (Table 4 & Fig. 5). Seagrass and Sand Mason Worms showed consistently greater levels of carbon across all depths compared to both Fine and Coarse sediments (Table 4 & Fig. 5).

<table><caption>**Table 2**

General Linear Model outputs showing the Inorganic and Organic Carbon (MgHa⁻¹ in the top 10 cm of sediment) as a function of Habitat.</caption><thead><tr><th rowspan="2">Terms</th><th colspan="4">Inorganic Carbon</th><th colspan="4">Organic Carbon</th></tr><tr><th>Estimate</th><th>Standard Error</th><th>t value</th><th>p value</th><th>Estimate</th><th>Standard Error</th><th>t value</th><th>p value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>5.850</td><td>0.244</td><td>24.000</td><td>&lt;0.0001***</td><td>7.810</td><td>0.191</td><td>40.800</td><td>&lt;0.0001***</td></tr><tr><td>Seagrass-Sand Mason Worms</td><td>-0.620</td><td>0.487</td><td>-1.270</td><td>0.22</td><td>-0.367</td><td>0.342</td><td>-1.070</td><td>0.29</td></tr><tr><td>Seagrass-Fine Sediments</td><td>0.423</td><td>0.770</td><td>0.549</td><td>0.59</td><td>-1.250</td><td>0.488</td><td>-2.560</td><td><strong>0.017*</strong></td></tr><tr><td>Seagrass-Coarse Sediments</td><td>-0.969</td><td>0.439</td><td>-2.210</td><td><strong>0.041*</strong></td><td>-0.730</td><td>0.322</td><td>-2.270</td><td><strong>0.032*</strong></td></tr><tr><td>Seagrass-Offshore Mixed Sediments</td><td>-0.849</td><td>0.487</td><td>-1.740</td><td>0.099</td><td>-0.283</td><td>0.413</td><td>-0.686</td><td>0.5</td></tr><tr><td>Seagrass-Maerl Beds</td><td>-1.130</td><td>0.439</td><td>-2.570</td><td><strong>0.019*</strong></td><td>-0.631</td><td>0.370</td><td>-1.700</td><td>0.1</td></tr></tbody></table>

Bold values denote significant p values (<0.05) and asterick's define p<0.0001 = '<0.0001***'; p<0.001 = '<***'; p<0.01 = '<**'; p<0.05 = '<*'}}
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Spatial distribution of Inorganic and Organic Carbon (MgHa⁻¹ in top 10 cm) across all habitats in Jersey's Territorial waters. Habitats not assessed in the current work are fill in black.</figcaption></figure>

<table><caption>**Table 3**
Inorganic and Organic Carbon found within different habitats within Jersey's territorial waters.</caption><thead><tr><th rowspan="2">Habitat</th><th rowspan="2">Area of<br>Habitat<br>(km²)</th><th colspan="2">Inorganic Carbon</th><th colspan="2">Organic Carbon</th></tr><tr><th>Density<br>(Mg Ha⁻¹<br>± SE)</th><th>Stock<br>(Tonnes<br>± SE)</th><th>Density<br>(Mg Ha⁻¹<br>± SE)</th><th>Stock<br>(Tonnes<br>± SE)</th></tr></thead><tbody><tr><td>Coarse<br>Sediments</td><td>715.0</td><td>0.0131 ±<br>0.0048</td><td>938 ±<br>3.43</td><td>0.118 ±<br>0.0306</td><td>8450 ±<br>21.9</td></tr><tr><td>Fine<br>Sediments</td><td>213.0</td><td>0.0528 ±<br>0.0386</td><td>1130 ±<br>8.23</td><td>0.0703 ±<br>0.0315</td><td>1500 ±<br>6.73</td></tr><tr><td>Maerl Beds*</td><td>56.8</td><td>0.0112 ±<br>0.00409</td><td>63.6 ±<br>0.232</td><td>0.131 ±<br>0.0414</td><td>741 ±<br>2.35</td></tr><tr><td>Offshore<br>Mixed<br>Sediments</td><td>42.6</td><td>0.0148 ±<br>0.00624</td><td>63.1 ±<br>0.266</td><td>0.185 ±<br>0.0677</td><td>788 ±<br>2.89</td></tr><tr><td>Sand Mason<br>Worms*</td><td>21.1</td><td>0.0186 ±<br>0.00785</td><td>39.3 ±<br>0.166</td><td>0.17 ±<br>0.0482</td><td>359 ±<br>1.02</td></tr><tr><td>Seagrass*</td><td>4.5</td><td>0.0346 ±<br>0.00842</td><td>15.6 ±<br>0.0379</td><td>0.245 ±<br>0.0469</td><td>110 ±<br>0.211</td></tr><tr><td>Hard<br>Ground<br>Kelp Forest<br>Rocky Reef<br>Cobbles and<br>Pebbles<br>Slipper<br>Limpets</td><td>502.0<br>134.0<br>176.0<br>478.0<br>10.1</td><td></td><td></td><td></td><td></td></tr></tbody></table>

## 4. Discussion

Here the density of carbon (Inorganic and Organic) found within habitats across Jersey's territorial waters was measured and these data,

alongside spatial habitat distribution data, were used to estimate the quantity of carbon found within specific habitats. Across all habitats measured there was a cumulative weight of ~ 2250 ± 7 Tonnes of Inorganic Carbon and ~ 11,948 ± 7 Tonnes of Organic Carbon. The density of Inorganic and Organic Carbon showed consistent patterns with Seagrass having the highest density compared to the other habitats surveyed. As Jersey has an extreme tidal regime as well as being generally exposed (especially on the West) to the full reach of the Northern Atlantic, the stabilising effects of the biogenic habitats may be leading to increased storage of carbon. The lowest densities of carbon were found in Fine and Coarse Sediments. However, due to their greater spatial coverage, these low carbon density habitats contained the vast majority of the carbon stock held in habitats surveyed in Jersey's territorial waters. These habitats may be more prone to re-suspension of carbon in high energy events (Epstein et al., 2022). Furthermore, biogenic habitats will also contain higher floral and/or faunal Organic Carbon stocks, both due to the habitat forming species themselves and the higher associated biodiversity (infaunal, epifaunal and benthic: Mcleod et al., 2011).

The high densities of Carbon found within Seagrass habitats is well known (Ricart et al., 2020). Yet, spatial coverage of Seagrass in Jersey is a very small fraction of the total marine area (~ 0.173 %). Sand Mason Worms showed similarly high densities while covering around 5 times as much area (~ 0.908 % of Jersey's marine area). This high density of carbon is most likely due to the stability provided by the Sand Mason Worm tubes, which, similarly to Seagrass, will allow the accumulation of organic matter. In contrast, the largest stocks of carbon were found in coarse sediments, which historically have been overlooked within management plans. Recent research has highlighted the importance of holistic marine management where all habitats within a specific area are equally managed, especially for conservation objectives (Davies et al., 2021; Sheehan et al., 2013; Solandt et al., 2020). The results found here would support this management style specifically for the protection of carbon stocks. However, the effects of different activities on stored

<footnote>* Habitats Sampled across Jersey's Territorial Waters.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 4. The areas of all habitats (a) and the Inorganic and Organic carbon (with error bars showing standard error) found in habits (b) of Jerseys Territorial waters.</figcaption></figure>

<table><caption>**Table 4**

General Linear Mixed Effect Model outputs showing the Inorganic and Organic Carbon (gcm⁻³) as a function of Depth and Habitat.</caption><thead><tr><th rowspan="2">Terms</th><th colspan="4">Inorganic Carbon</th><th colspan="4">Organic Carbon</th></tr><tr><th>Estimate</th><th>Standard Error</th><th>t value</th><th>p value</th><th>Estimate</th><th>Standard Error</th><th>t value</th><th>p value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-5.04000</td><td>0.23500</td><td>-21.4000</td><td><strong>&lt;0.0001***</strong></td><td>-3.770000</td><td>0.22600</td><td>-16.7000</td><td><strong>&lt;0.0001***</strong></td></tr><tr><td>Seagrass-Fine sediment</td><td>-0.55500</td><td>0.40700</td><td>-1.3600</td><td>0.17</td><td>-1.920000</td><td>0.38500</td><td>-4.9900</td><td><strong>&lt;0.0001***</strong></td></tr><tr><td>Seagrass-Coarse sediment</td><td>-1.04000</td><td>0.47200</td><td>-2.2000</td><td><strong>0.028*</strong></td><td>-1.510000</td><td>0.44200</td><td>-3.4100</td><td><strong>&lt;0.0001***</strong></td></tr><tr><td>Seagrass-Sand Mason Worms</td><td>0.04940</td><td>0.46500</td><td>0.1060</td><td>0.92</td><td>-0.010200</td><td>0.44600</td><td>-0.0228</td><td>0.98</td></tr><tr><td>Depth</td><td>-0.01590</td><td>0.00679</td><td>-2.3500</td><td><strong>0.019*</strong></td><td>-0.001520</td><td>0.00454</td><td>-0.3350</td><td>0.74</td></tr><tr><td>Seagrass-Fine sediment:Depth</td><td>0.02540</td><td>0.01410</td><td>1.8100</td><td>0.071</td><td>0.042600</td><td>0.00978</td><td>4.3600</td><td><strong>&lt;0.0001***</strong></td></tr><tr><td>Seagrass-Coarse sediment:Depth</td><td>0.00147</td><td>0.01730</td><td>0.0846</td><td>0.93</td><td>-0.044100</td><td>0.01170</td><td>-3.7800</td><td><strong>&lt;0.0001***</strong></td></tr><tr><td>Seagrass-Sand Mason Worms:Depth</td><td>0.00200</td><td>0.01390</td><td>0.1440</td><td>0.89</td><td>-0.000677</td><td>0.00956</td><td>-0.0708</td><td>0.94</td></tr></tbody></table>

Bold values denote significant p values (<0.05) and asterick's define $p<0.0001 = <0.0001***$; $p<0.001 = <0.001***$; $p<0.01 = <0.01***$; $p<0.05 = <0.05***$

carbon are poorly empirically understood (Epstein et al., 2022; Macreadie et al., 2019), which makes appropriate management of sediment carbon stocks problematic. Furthermore, the current levels of carbon will be influenced by previous management measures, meaning any change to this management would also require continued monitoring to allow detection of stored carbon alterations over time.

When discussed within a global context (Pörtner et al., 2022) there is concern that anthropogenic pressures to stores of carbon will lead to the re-suspension and potential subsequent release of carbon in the form of greenhouse gases (Duarte et al., 2013). Specifically in Jersey, all known areas of seagrass beds are protected from most forms of human activities that would disturb or resuspend carbon (bottom trawl fishing, dredging, drilling/mining etc.), although some areas have high levels of pleasure and commercial boat anchorage, as well as high levels of pot fishing.

Therefore, as a carbon store in Jersey, the highest risk habitat would be Coarse Sediments, as it covers the highest area and is least protected. However, the effects of physical disturbance on different habitats is less well known, especially for non biogenic habitats such as Coarse Sediments (Epstein et al., 2022). For example, the effect of bottom-towed fishing on Seagrass habitats, which are highly vulnerable to disturbance, would change its physical structure (Krause-Jensen et al., 2021), whereas many Coarse and Fine sediment habitats that are less vulnerable to disturbance would not undergo the same structural changes.

There was little evidence in any of the habitats surveyed of variation in carbon with depth (up to 40 cm). This could suggest that the amounts of carbon being accumulated in each habitat has been consistent across time alongside low remineralisation rates, they are experiencing very high burial rates or sediments are continually experiencing high levels of
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Inorganic (a) and Organic (b) Carbon (gcm⁻³) with depth across different habitats in Jersey's territorial waters. Lines show modelled mean estimates and shading show modelled standard error, raw data are displayed by points.</figcaption></figure>

mixing. Jersey's waters experience high levels of mixing, potentially re-suspending sediments down to the depths surveyed here, which is the most likely explanation for these consistencies with depth. If extrapolated to 100 cm depth, the projected organic carbon stocks estimated in seagrass sediments would average 245 Mg C Ha-1 (range 91–567 Mg C Ha-1). This is about twice that for global seagrass estimates (109 Mg C Ha-1: Röhr et al., 2018), along Scotland's coastline (109 Mg C Ha-1: Potouroglou et al., 2021) and South England (141 Mg C Ha-1: Green et al., 2018), and roughly 5 times higher than previously reported for the Western and Eastern Atlantic (54 and 55 Mg C Ha-1 respectively, based on low number of replicates from USA and Irish Sea: Röhr et al., 2018). These comparisons might indicate far greater values of carbon in Jersey's seagrass beds compared to global values, perhaps due to high productivity of these beds, which can lead to high carbon content (Duarte et al., 2010). However, the values found here do fall within the range of those found elsewhere (Röhr et al., 2018). The range of values found both in the current study and across these literature show the very high variability found across studies. Furthermore, assuming consistent carbon content up to 100 cm depth from a 10 cm estimate may considerably over estimate these values, even if the current work found consistent content up to 40 cm depth, especially if this consistency is driven by high rates of re-suspension.

Collection of samples across different spatial and physically influenced areas of Jersey's habitats would allow more understanding of the local and regional biogeochemical processes and the influences of anthropogenic pressures (Epstein et al., 2022; Macreadie et al., 2019). This increased spatial collection of data would also benefit from the

inclusion of extra data pertaining to physical oceanography (Diesing et al., 2021; tidal energy, wave exposure, eddies/currents storm events: Diesing et al., 2017; Luisetti et al., 2019), anthropogenic pressures (dredging, sewage expulsion, recreational uses: Macreadie et al., 2015) and biodiversity (high productivity areas, growth, reduction and age of biogenic habitats: Ricart et al., 2020). Once combined these data would allow for a more realistic spatial assessment of Jersey's marine habitats, whereas here the assumption of consistent carbon content across habitats could be leading to under or over estimation of the Carbon stock across the whole territory (Ricart et al., 2020). Furthermore, the source of carbon found within different habitats could provide information on the influence of inputs from other nearby habitats. For example, currently the effect of Kelp Forests in the marine carbon cycle is relatively unknown, with the scale of lateral transportation and storage of kelp organic carbon into shallow or deep sea areas being poorly understood (Filbee-Dexter and Wernberg, 2020; Krause-Jensen et al., 2018).

Samples were taken across a range of habitats based on JNCC biotopes, with some of these habitats resulting from the grouping of many JNCC biotopes together (See Table 1). Yet, across these grouped habitats (e.g. Coarse Sediments), there was very little variation for the range of the habitat surveyed. Therefore, it is not expected that higher resolution habitat classification would show clear distinctions in carbon values. However, it is expected that the carbon within sediments of similar habitats will vary spatially depending on different influential factors, such as exposure and current flow (Diesing et al., 2017; Ricart et al., 2020). Therefore, it is recommended that sampling take place across as
much of Jersey's territorial waters as possible. This will require samples from within each habitat but with differing local environmental parameters (physical oceanography, chemical processes, biological processes and anthropogenic pressures). These data could provide further detail on the effects of other influential factors relating to carbon storage of the sampled habitats (Diesing et al., 2021, 2017). As discussed these factors will vary greatly across Jersey's territorial waters. The vast majority of core samples were taken from near-shore areas to the south and east of Jersey's mainland, which in comparison to the west coast are far less exposed to wave energy and tidal movement but being near to the largest density of human activity on Jersey (St Helier) are likely to have greater human pressures. Therefore many of the physical drivers likely to influence carbon storage will be relatively similar across the samples taken in this study. A wider range environmental drivers sampled within habitats would allow future modelling projects to estimate more accurately carbon content under different scenarios of changing climate, and the effects of the likely increases in extreme weather events and direct human impacts (Lovelock and Reef, 2020).

## 5. Conclusions

This preliminary assessment of BC in Jersey's territorial waters found, as expected, that Seagrass habitat had the highest densities of Carbon (both Inorganic and Organic). Alongside Seagrass, Sand Mason Worms showed high densities of Carbon as well as Offshore Mixed Sediments. The lowest densities of carbon were found in Fine and Coarse Sediments, yet due to their spatial coverage these habitats contained the greatest totals of carbon in Jersey's territorial Waters. The densities of carbon were also consistent with depth, potentially due to relatively high levels of mixing due to the hydrologically energetic nature of Jersey's waters. These results show that appropriate management of marine carbon stocks will require the consideration of all habitats. This provides yet more evidence that holistic management of marine systems is necessary.

### CRediT authorship contribution statement

**Graham Epstein:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation. **Francis C. T. Binney:** Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Emma V Sheehan:** Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Investigation, Funding acquisition, Data curation. **Paul Chambers:** Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Samantha Robyn Blampied:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. **Bede Ffinian Rowe Davies:** Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data Availability

Data will be made available on request.

### Acknowledgements

This work was supported and funded by the Government of Jersey Marine Resources, Natural Environment, Howard Davis Farm, La Route de la Trinitie, Trinity JE3 5JP, Jersey. Laboratory work was carried out at Howard Davis Farm and at the University of Plymouth with the help of Billy Simmons.

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