Spatial variability of organic carbon storage and sources in China’s subtropical Halophila beccarii seagrass meadow

Spatial variability of organic carbon storage and sources in China’s subtropical Halophila beccarii seagrass meadow | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial variability of organic carbon storage and sources in China’s subtropical Halophila beccarii seagrass meadow Xiaomei Shen, Yiguo Hong, Fei Ye, Jiapeng Wu, Yu Wang, Fen Guo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7456384/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Seagrass meadows are crucial in marine blue carbon storage. However, blue carbon storage of seagrass meadows in subtropical regions dominated by small-sized species may be overestimated, and the primary factors regulating organic carbon (C org ) variability remain uncertain. Here we investigated spatial patterns in blue carbon storage and sediment C org sources in China's subtropical estuarine meadows of the small seagrass, Halophila beccarii , and identified key environmental drivers influencing its spatial heterogeneity. The results revealed that these species may store less blue carbon than estimated, with low carbon stocks revealed in China’s estuarine meadows. Sediment carbon varied spatially, influenced by moisture, salinity, CaCO₃, and bulk density. Terrigenous sources contributed most sediment carbon, followed by seagrass, and phytoplankton, exhibiting distinct spatial variation along the transect. These findings highlight the need for refined blue carbon assessments in subtropical regions and suggest managing environmental factors to enhance seagrass carbon storage as a climate solution. Earth and environmental sciences/Ocean sciences/Marine biology Earth and environmental sciences/Solid Earth sciences/Sedimentology Earth and environmental sciences/Climate sciences/Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The increasing emission of greenhouse gases, particularly CO₂, has driven global warming to critical levels, with the global monthly mean CO₂ concentration reaching 427 ppm in 2025 1 . This alarming trend highlights the urgency of addressing one of the most unrelenting global environmental challenges. In this context, quantifying the carbon sequestration capacity of natural ecosystems has emerged as a key strategy for mitigating atmospheric CO₂ levels and combating climate change 2 , 3 . The concept of "blue carbon" was formally introduced in 2009 through a landmark report by the United Nations Environment Programme (UNEP), highlighting the critical role of marine ecosystems in global carbon cycling. Seagrass ecosystems represent one of the most efficient carbon sinks within marine environments and global carbon pools 4 , 5 , 6 . Despite occupying less than 0.2% of the ocean's area (approximately 330,000 km² globally), seagrass meadows store an estimated 19.9 Pg of organic carbon (C org ), accounting for 10–18% of the total oceanic carbon burial 7 . The remarkable carbon sequestration capacity of seagrass meadows originates from three key mechanisms: (1) high primary productivity, (2) efficient particle trapping by seagrass canopies, and (3) slow organic matter decomposition in underlying anaerobic sediments. Importantly, over 90% of this blue carbon is stored in sediments 8 , 9 , making sediment C org pools the dominant component of seagrass carbon stocks. Research on seagrass meadows as blue carbon sinks originated in the 1980s, when Smith (1981) 10 first identified the significant carbon sequestration potential of marine macrophytes. Subsequent studies have systematically quantified blue carbon storage across global seagrass ecosystems. Comprehensive analysis by Fourqurean et al. (2012) 7 revealed substantial regional variation, with sediment carbon stocks ranging from 23.6 Mg C ha⁻¹ in Indo-Pacific meadows to 372.4 Mg C ha⁻¹ in Mediterranean systems. Their global synthesis established mean carbon stocks of 2.52 ± 0.48 Mg C ha⁻¹ in living biomass and 194.2 Mg C ha⁻¹ in sediments (0–1 m depth). Notably, Fourqurean et al. (2012) 7 standardised sediment sampling to 1 m depth, as this surface layer represents the most labile carbon pool susceptible to mineralisation. Although recent studies have enhanced spatial resolution of blue carbon storage data 9 , 11 , 12 , 13 , 14 , 15 , global estimates of seagrass C org stocks remain largely derived from limited field sampling. This is particularly evident in West Pacific subtropical monsoon regions, where data gaps continue to hinder the integration of seagrass meadows into policy frameworks such as carbon finance mechanisms and international climate agreements 16 . Existing research has demonstrated significant spatial variation in seagrass carbon stocks, occurring both regionally and locally. This variability complicates stock assessments, as shown by Posidonia oceanica meadows, where 2 m depth sites contain 14–16 times more carbon than 32 m depth sites 17 . Similarly, along Kalimantan Island's coastal gradient, sediment carbon stocks (0–1 m depth) demonstrate substantial spatial variation, with values increasing from 80.37 Mg C ha⁻¹ in near-shore zones to 121.84 and 243.33 Mg C ha⁻¹ in mid- and far-shore zones, respectively 18 . Ricart et al. (2020) 19 documented a pronounced estuarine gradient in Port Curtis, Australia, where C org stocks decreased from 52.16 mg cm⁻³ in upper estuary meadows to 1.06 mg cm⁻³ at the estuary mouth, reflecting landscape-driven variations in sediment dynamics and allochthonous carbon inputs. Similarly, in China's Liusha Bay, Shen et al. (2024) 9 reported C org stocks ranging 39.74 to 157.12 Mg ha⁻¹, with maximum values occurring near deep-water channels in far-shore zones. Even within the same species, significant regional differences exist— Zostera marina meadows store 23.1 Mg ha⁻¹ in Baltic Sea sediments versus 351.7 Mg ha⁻¹ (15× higher) in Mediterranean systems 20 . While seagrasses provide critical carbon sequestration services, small-bodied species like Halophila beccarii (33.9 ± 7.7 Mg ha⁻¹ in Zanzibar) 21 remain understudied due to their diminutive morphology and restricted intertidal distributions 22 . Spatial variability of C org stocks in seagrass meadows is influenced by multiple interacting environmental factors 23 , 24 . Key drivers include: (1) seagrass species composition and coverage 25 ; (2) hydrodynamic conditions 26 , 27 ; (3) sediment properties (e.g., particle size, often mediated by hydrodynamics) 28 , 29 ; (4) climate zone 30 ; (5) carbon sources proximity 31 ; (6) biogeochemical processes 32 ; and (7) landscape configuration (e.g., patchy vs. continuous beds) 31 , 33 . However, the relative importance of these factors in driving blue carbon variability remains poorly understood, particularly for specific seagrass meadows. While seagrasses are widely recognized as important carbon sinks, site-specific studies are required to elucidate the influence of local environmental conditions on C org stocks across spatial scales 34 . High-resolution assessments of blue carbon stocks are critical for improving multi-scale carbon accounting 35 . Hence, this study aims to: (1) quantify C org storage in intertidal Halophila beccarii ( H. beccarii ) meadows of the Yifengxi Estuary, southern China; (2) identify the key drivers of spatial variability in C org stocks; and (3) characterise sources of sediment C org . As a comprehensive assessment of C org storage and sources in subtropical H. beccarii meadows of the Yifengxi Estuary, the study findings will enhance the accuracy of blue carbon accounting and inform conservation strategies for seagrass meadows in the Pacific subtropical monsoon region. 2. Materials and methods 2.1 Study area and sampling methods The study was conducted in the Yifengxi seagrass meadow (418 ha), located in the subtropical monsoon-influenced Yifengxi Estuary of Shantou City, southern China (116°53′-116°56′E, 23°31′-23°33′N; Fig. 1 ). This area represents the largest seagrass meadow in eastern Guangdong Province and the most extensive Halophila beccarii habitat in the region 36 , 37 . The area experiences an annual mean temperature of 21.9°C and receives 1098 mm of precipitation, predominantly between May and September. Notably, H. beccarii is ecologically significant as: (1) one of the only two extant seagrass species considered evolutionarily ancient and (2) one of ten seagrass species globally classified as vulnerable (VL) on the International Union for Conservation of Nature (IUCN) Red List 38 , highlighting its conservation priority. Field surveys and sample collection were conducted during spring tide low tides from April 12–17, 2022. Seven transects were systematically distributed across the meadow to ensure representative coverage of the seagrass habitat. At each transect, 2–3 replicate quadrats were established (0.5 m × 0.5 m) and spaced at ≥ 50 m apart (Fig. 1 ). In-situ water quality parameters were recorded (temperature, pH, conductivity, and dissolved oxygen) using a calibrated multi-parameter meter (YSI 30M-25; YSI, Yellow Springs, OH, USA). A total of 16 living plant samples and 20 sediment cores were collected across the seven established transects (Supplementary Table 1). Three plant samples were collected from each of transects Y1 through Y5, and a single sample from transect Y6. Due to challenging silty substrate conditions that necessitated boat access during high tide periods, no plant samples were collected from transect Y7. Plant sampling was conducted using a 10 cm diameter PVC corer cautiously inserted vertically to a depth of 20 cm to ensure complete capture of both above- and below-ground biomass components. Samples were placed in pre-labelled monofilament mesh transport bags immediately after collection to maintain sample integrity during transfer from field to laboratory. In the laboratory, plant samples underwent a standardized processing protocol. First, residual sediments were removed through sequential washing, beginning with seawater rinses in the field followed by additional freshwater rinses under controlled laboratory conditions. Epiphytes were then gently removed from leaf surfaces using soft plastic scrapers to avoid tissue damage, after which shoot density was recorded for each sample. The cleaned samples were subsequently separated into their constituent above-ground (leaves and stems) and below-ground (rhizomes and roots) components. Each component was individually wrapped in acid-washed aluminium foil and stored at 4°C to preserve sample integrity until further analysis, following established protocols 39 . Sediment cores were systematically collected from the upper 1 m profile using a Russian Peat Corer (1 m length, 0.07 m sector radius) to minimise compaction during sampling. Three replicate cores were obtained at each of transects Y1–Y5 and Y7, while two cores were collected at transect Y6 (Table 1). Surface litter layer and any living biomass were removed to ensure clean sediment sampling. Following extraction, each intact core was immediately sectioned into seven depth intervals (0–10, 10–20, 20–30, 30–40, 40–50, 50–70, and 70–100 cm) in the field. From each stratified subsample, a 15 mL vertical aliquot was collected using a sterile 20 mL syringe for detailed analysis. The remaining sediment from each depth interval was placed in pre-labelled, self-sealing bags. All samples were immediately cryopreserved using dry ice in the field and maintained frozen during transport to the laboratory for subsequent analysis. This stratified sampling approach allowed for comprehensive characterisation of sediment properties and carbon distribution throughout the entire 1 m profile while preserving the vertical integrity of each core segment. 2.2 Seagrass traits, sediment characteristics, and C org stock measurements For each sampling quadrat, key morphological characteristics of H. beccarii , including leaf length, leaf width, and shoot height, were measured from five randomly selected shoots using digital callipers with 0.1 mm precision. The plants were then carefully separated into above-ground (stem, leaf sheath, and leaf) and below-ground (root and rhizome) components after the measurements. All plant tissues were oven-dried at 60°C for 72 hours until constant weight was achieved to determine dry biomass. C org stocks in living seagrass biomass were calculated using the following approach: first, above- and below-ground biomass values (g m⁻²) were multiplied by the standardized carbon conversion factor of 0.34 40 . These values were then scaled to per hectare units (Mg C ha⁻¹). Total living biomass carbon stocks were derived by summing carbon stocks across all sampling regions, weighted by their respective spatial coverage 9 , 39 , 41 . The 140 sediment subsamples were oven-dried at 60℃ until constant weight was achieved. Moisture content and dry bulk density (DBD) were measured and calculated. C org content was measured using an elemental analyzer (Vario EL Ⅲ, Germany). The C org density (mg cm - 3 ) in each sediment layer was calculated by multiplying C org content by the corresponding DBD (g cm - 3 ) using Eq. ( 1 ). $$\:{\text{C}}_{\text{org}}\text{ density = DBD×}{\text{C}}_{\text{org}}\text{×1000}$$ 1 where DBD is the dry bulk density of the sediment subsample (g cm − 3 ) and C org is the organic carbon content (g/g). Sediment C org stocks per layer were calculated by multiplying C org density by layer thickness (10, 20, or 30 cm). The total stock for the upper 1 m was derived by summing all layers (Mg C ha⁻¹). Total blue carbon storage (Mg C ha⁻¹) comprised of sediment and living biomass C org stocks 9 , 39 , 41 . The g C cm − 2 was converted to the standard Mg C ha − 1 using the following: 1 Mg = 10 6 g; 1 ha = 10 9 cm 2 . The dried subsamples were ground and sieved (100-mesh). To remove inorganic carbon, the samples were treated with 1 mol L⁻¹ HCl for 24 h. For salinity analysis, a 5:1 water-to-sediment extract was stirred (30 min), filtered, and measured using an HQ4300 meter (Hach, Loveland, CO, USA). For pH, 2.5:1 extracts were stirred (10 min), settled (1–3 h), and analysed using the HQ4300 meter. Organic matter and carbonate (CO 3 2- ) contents were measured using the sequential loss on ignition (LOI) method, with furnacing steps at 550°C and 950°C, respectively 41 , 42 . Organic matter content was calculated as per Eq. 2 : $$\:\text{%LO}{\text{I}}_{\text{550}}\text{= }\frac{\left({\text{m}}_{\text{0}}\text{-}{\text{m}}_{\text{1}}\right)\text{×100}}{{\text{m}}_{\text{0}}}$$ 2 where %LOI 550 is the organic matter content, m 0 (g) is the initial dry weight, and m 1 (g) is the remaining weight after 550°C furnacing. The CO 3 2− in sediments typically exists as carbonate (CaCO 3 ) 41 . CaCO 3 content in sediments was calculated as per Eq. 3 : $$\:\text{%LO}{\text{I}}_{\text{950}}\text{=}\frac{\left({\text{m}}_{\text{1}}\text{-}{\text{m}}_{\text{2}}\right)\text{×100}}{{\text{m}}_{\text{0}}}$$ 3 where m 0 (g) is the initial dry weight, m 1 (g) is the remaining weight after furnacing at 550°C, and m 2 (g) is the remaining weight after furnacing at 950°C. The weight loss at 950°C (LOI) was multiplied by 1.36 estimated CO₃²⁻ content, based on molecular weights of CO₂ (44 g mol⁻¹) and carbonate (60 g mol⁻¹) 42 , 43 . A linear regression (%LOI₅₅₀ vs. %Cₒ r g ) was established using Eq. ( 4 ). $$\:\text{%}{\text{C}}_{\text{org}}\text{ = a×%LO}{\text{I}}_{\text{550}}\text{-b}$$ 4 where %C org is the organic carbon content, %LOI 550 is the organic matter content, and fitted coefficients, a and b , were determined from the measured values of %C org and %LOI, respectively. 2.3 Determination for stable carbon isotopic compositions and C org sources To quantify the relative contributions of different C org sources to sediment C org in the estuarine seagrass meadows, three potential end-members were considered: terrigenous, seagrass-derived, and phytoplankton organic matter. The proportional contributions of these sources were estimated based on their stable carbon isotope (δ 13 C) signatures using the Bayesian mixing model MixSIAR 44 . The δ 13 C end-member values were established as follows: H. beccarii seagrass (-21.767‰) and phytoplankton (-20.000‰) values were obtained from Kennedy et al. (2010) 45 , while the terrigenous organic matter signature (-28.640‰) was determined by integrating data from global terrigenous organic matter and seasonal measurements of suspended organic matter from 20 reservoirs in Guangdong Province 45 , 46 . For δ 13 C analysis, air-dried sediment subsamples were finely ground using an agate mortar and pestle, and measured by an elemental analyzer-isotope ratio mass spectrometry system (EA-IRMS; Manufacturer, City, Abb. State, Country), with values reported in per mil (‰) relative to the Vienna Pee Dee Belemnite (V-PDB) standard. 2.4 Statistical analysis method We conducted comprehensive statistical analyses to examine patterns in the carbon storage data. All datasets were rigorously evaluated for normality and variance homogeneity before the analysis using Shapiro–Wilk and Levene's tests, respectively. Appropriate data transformations, including log10 for strictly positive values and log10(x + 1) for datasets containing zeros, were applied when parametric assumptions were not met. For normally distributed data, one-way analysis of variance (ANOVA) was performed, followed by Dunn's post-hoc pairwise comparisons, with statistical significance set at p < 0.05. Correlation analyses, principal component analysis (PCA), and linear fitting were conducted to identify key drivers of C org variability and explore multivariate relationships among environmental factors and carbon stocks. All parametric tests and regression analyses were performed using SPSS Statistics 27 (IBM Corp., Armonk, NY, USA), while multivariate analyses were implemented in R version 4.3.3. 3. Results 3.1 Environmental conditions, seagrass characteristics, and C org stocks Environmental conditions and seagrass characteristics were monitored in the H. beccarii meadow at Yifengxi Estuary. During the study period, water temperature averaged 23 ± 2.03°C, with dissolved oxygen (7.11 ± 1.09 mg L⁻¹), pH (8.02 ± 0.33), and salinity (5.55 ± 1.36‰) recorded. The meadow occurred in shallow waters (0.1–0.5 m depth) with variable seagrass coverage (45–85%). Morphometric measurements revealed average leaf dimensions of 8.37 ± 1.82 mm (length) and 2.64 ± 0.56 mm (width), with shoots reaching 15.67 ± 3.82 mm in height (Supplementary Table 2). Shoot density showed substantial spatial variation (255–39,852 shoots m⁻²), averaging 17,844 ± 12,639 shoots m⁻² across the meadow. Significant differences were observed among transects (P < 0.01, F = 13.57), with the lowest density at Y4 (7,639 ± 5,729 shoots m⁻²) and the highest at Y3 (34,717 ± 6,432 shoots m⁻²) (Table 2). The carbon storage characteristics of H. beccarii showed significant spatial variation across the study area. Above-ground biomass C org stocks averaged 1.22 ± 0.93 g C m⁻², exhibiting marked differences among transects (P < 0.01, F = 10.39). The values ranged from 0.17 ± 0.13 g C m⁻² at transect Y5 to 2.27 ± 0.35 g C m⁻² at transect Y3 (Complementary Table 2; Fig. 2 a). Below-ground biomass displayed similar variability, with mean stocks of 1.12 ± 0.91 g C m⁻² (P < 0.05, F = 7.41), reaching minimum (0.20 g C m⁻² at Y6) and maximum (2.53 ± 0.47 g C m⁻² at Y3) values. Total biomass C org stocks demonstrated a 60-fold variation across transects, from 0.40 ± 0.33 g C m⁻² at Y5 to 4.80 ± 0.30 g C m⁻² at Y3. The above-to-below-ground biomass ratio averaged 1.28 ± 0.88, with statistically significant differences among transects (P < 0.01, F = 25.80), indicating substantial spatial heterogeneity in biomass allocation patterns. Figure 2 The total biomass carbon storage averaged 0.024 ± 0.018 Mg C ha⁻¹, corresponding to 10.03 ± 7.52 Mg C across the entire 418 ha meadow. Contrastingly, sediment carbon stocks (0–1 m depth) showed substantially higher values, averaging 82.13 ± 28.82 Mg C ha⁻¹ (34,333 ± 12,049 Mg C) and ranging 33.57–125.29 Mg C ha − 1 , with significant spatial variation among transects (P < 0.001, F = 10.54). Notably, transect Y5 contained the highest sediment stocks (114.57 ± 10.71 Mg C ha⁻¹), representing 2.36-fold greater storage than transect Y3 (48.52 ± 11.17 Mg C ha⁻¹). The combined carbon pool (biomass + sediment) totalled 34,343 ± 12,049 Mg C, with sediments accounting for > 99.99% of the stored carbon (Fig. 2 b). The significant disparity highlights the predominant role of sediment carbon sequestration in the seagrass ecosystem, while living biomass insignificantly contributed to the total carbon storage. 3.2 Spatial variability analysis in sediment characteristics The sediment characteristics exhibited pronounced spatial heterogeneity across the study area (Fig. 3 ). Moisture content averaged 29.00 ± 8.53% and ranged from 16.20–55.30%, with the minimum and maximum values at transects Y3 and Y7, respectively (Fig. 3 a). Salinity displayed an average of 1.15 ± 0.53‰, varying between 0.37‰ and 2.75‰, where transect Y3 recorded the lowest mean salinity (0.77 ± 0.31‰) and Y7 the highest (2.08 ± 0.32‰) (Fig. 3 b). The pH values averaged 7.72 ± 0.84, with transect Y3 exhibiting the lowest mean pH (7.29 ± 0.27), while Y1 (7.77 ± 0.23) and Y7 (7.75 ± 0.22) displayed the highest values (Fig. 3 c). DBD ranged 0.36–2.13 g cm − 3 (mean: 1.30 ± 0.39 g cm − 3 ), with the minimum at Y7 (0.94 ± 0.34 g cm − 3 ) and maximum at Y4 (1.59 ± 0.37 g cm − 3 ) (Fig. 3 d). C org density varied considerably from 1.85 to 19.13 mg C cm − 3 (mean: 8.07 ± 4.00 mg C cm − 3 ), peaking at Y7 (11.47 ± 3.38 mg cm − 3 ) and Y5 (10.29 ± 4.68 mg cm − 3 ), while reaching a minimum at Y3 (4.76 ± 3.20 mg C cm-3) and Y2 (5.10 ± 1.85 mg cm-3) (Fig. 3 e). Organic carbon percentage (%C org ) ranged 0.13–1.85% (mean: 0.67 ± 0.37%), with Y7 (1.29 ± 0.22%) and Y3 (0.39 ± 0.18%) representing the extremes (Fig. 3 f). Additional parameters showed the following patterns: %LOI averaged 0.04 ± 0.02% (range: 0.01–0.10%; Fig. 3 g); %CaCO 3 ranged 0.50–3.92% (mean: 1.52 ± 0.80%), being lowest at Y3 (0.82 ± 0.28%) and highest at Y7 (3.97 ± 0.39%; Fig. 3 h); and δ 13 C varied between − 27.71‰ and − 22.65‰ (mean: -26.00 ± 1.50‰), with the most depleted values at Y5 and Y6 and the most enriched at Y1 (Fig. 3 i). Figure 3 The sediment characteristics exhibited distinct vertical variations across depth profiles (Fig. 4 ). Moisture content displayed a gradual decreasing trend with depth at transect Y7 (Fig. 4 a). Salinity showed a consistent reduction from 0 cm to 30 cm depth across all seven transects (Fig. 4 b). DBD demonstrated a progressive increase from surface layers to 40 cm depth, with particularly notable increasing trends observed at transects Y1, Y3, and Y7 (Fig. 4 d). %CaCO 3 exhibited a more complex vertical pattern, initially decreasing from 0 cm to 40 cm before increasing again between 50 cm and 100 cm at transects Y2, Y3, and Y5 (Fig. 4 h). The remaining sediment parameters showed unclear or inconsistent vertical distribution patterns. Figure 4 3.3 Analysis of driving factors that influence C org stocks variations Relationships between biomass C org stocks and vegetation characteristics: biomass C org stocks exhibited strong positive correlations with above-ground biomass C org (r = 0.97, p < 0.01), shoot density (r = 0.92, p < 0.01), and below-ground biomass C org (r = 0.98, p < 0.01). Additionally, significant positive associations were observed with shoot height (r = 0.55, p < 0.05) and leaf length (r = 0.52, p < 0.05) (Fig. 5 a). Negative correlations with sediment and blue carbon parameters: biomass C org stocks showed significant inverse relationships with sediment C org stocks (r = -0.73, p < 0.01), blue carbon storage (r = -0.73, p < 0.01), C org density (r = -0.74, p < 0.01), %C org (r = -0.68, p < 0.01), %LOI (r = -0.67, p < 0.01), and %CaCO₃ (r = -0.55, p < 0.05) (Fig. 5 a). Sediment %C org relationships: sediment %C org was negatively correlated with DBD (r = -0.56, p < 0.01). Conversely, sediment %C org displayed strong positive correlations with sediment C org stocks (r = 0.78, p < 0.01), blue carbon storage (r = 0.78, p < 0.01), C org density (r = 0.80, p < 0.01), organic matter content (r = 0.97, p < 0.01), %CaCO₃ (r = 0.92, p < 0.01), salinity (r = 0.89, p < 0.01), and moisture content (r = 0.92, p < 0.01) (Fig. 5 a, N = 20). Consistent patterns were observed across all sediment subsamples (Fig. 5 b, N = 140). Figure 5 Figure 6 Additionally, linear regression analysis also revealed significant positive correlations between %C org and several environmental variables, including moisture content ( y = 0.036 x + 0.364, R 2 = 0.69), salinity ( y = 0.531 x + 0.064, R 2 = 0.60), CaCO₃ content ( y = 0.397 x + 0.069, R 2 = 0.74) and organic matter (%LOI, y = 0.148 x + 0.012, R 2 = 0.60) (Fig. 6 a, c, d, and f, respectively). A weaker but still significant negative correlation was observed between %C org and DBD, δ 13 C, and shoot density (Fig. 6 a, g, and h, respectively). The first two principal components (PC1 and PC2) explained 78.65% of the total variance (PC1: 51.41%; PC2: 27.23%) (Fig. 7 a, Supplementary Table 3). Both components were strongly associated with seagrass traits and sediment characteristics. PC1 displayed negative loadings for seagrass traits (e.g., coverage, biomass C org , shoot density) but positive loadings for sediment characteristics, including moisture content, salinity, %CaCO 3 content, %LOI, %C org , C org density, and blue carbon storage. In contrast, PC2 was predominantly associated with positive loadings of the above-ground biomass C org , leaf length, and shoot density, but negative loading for DBD. Similarly, for sediment characteristics alone, the first two PCs explained 78.50% of the total variance (PC1: 58.16%; PC2: 20.34%; Fig. 7 b, Supplementary Table 3). PC1 also demonstrated significant influence from sediment properties, with negative loadings for DBD and δ 13 C, and positive loadings for sediment C org density, %C org , salinity, %CaCO 3 , %LOI, and moisture content. Figure 7 3.4 Sediment C org sources at different transects The sediment sub-samples exhibited an average δ 13 C value of -26.00 ± 1.50‰. The Bayesian mixing model results of δ 13 C showed that the C org of the upper 1 m sediment mainly originated from terrigenous C org (49.84 ± 23.57%), seagrass C org (26.83 ± 21.43%), and phytoplankton C org (23.33 ± 19.98%) (Supplementary Table 4). Vertical distributions of C org sources displayed opposing trends: terrigenous inputs dominated at depth (53.10 ± 23.00% at 50–70 cm) but decreased by approximately 7% toward surface layers (46.00 ± 20.00% at 0–10 cm). Seagrass C org (24.90 ± 18.50% to 28.90 ± 20.40%) and phytoplankton C org (22.0 ± 18.5% to 25.10 ± 18.40%) showed proportional increases in surface sediments (Fig. 8 a). Sediment C org sources also exhibited distinct spatial variation along the transect, with seagrass- and phytoplankton-derived C org decreasing and terrigenous C org increasing from Y1 to Y7 (Fig. 8 b). Figure 8 4. Discussion 4.1 Comparison of blue carbon storage among different seagrass meadows The living biomass carbon stock (0.028 ± 0.017 Mg C ha⁻¹) in the Yifengxi Estuary's H. beccarii meadow represents only 1.1% of the global seagrass average (2.52 ± 0.48 Mg C ha⁻¹), though it falls within the documented range for this species (0.001–5.54 Mg C ha⁻¹) 7 . The biomass C org stock was evidently influenced by the seagrass characteristics, e.g. shoot density, shoot height, and leaf length. This limited biomass accumulation reflects H. beccarii 's ecological strategy as a pioneer species—its small morphological structure, coupled with rapid growth rates and high turnover, facilitates efficient organic matter decomposition in sediments 29 , 47 , contrasting with the greater carbon retention in larger, longer-lived seagrass species. Sediment carbon stocks (0–1 m depth) in the study area measured 82.13 ± 28.82 Mg C ha⁻¹, comparable to H. beccarii meadows at similar latitudes in Guangxi, China (83.98 Mg C ha⁻¹) 48 . However, these values are substantially lower than both the global median (139.7 Mg C ha⁻¹) and mean (194.2 Mg C ha⁻¹) for seagrass ecosystems 7 . Regional comparisons further highlight this disparity, with H. beccarii meadows in Singapore (138 ± 8.6 Mg C ha⁻¹) 49 and Beibu Gulf, Guangxi (112 ± 33.3 Mg C ha⁻¹, obtained by extrapolating from the carbon storage data ranging 0–60 cm) 50 demonstrating 68–37% greater carbon storage capacity. The blue carbon storage capacity of H. beccarii meadows was comparatively lower than other dominant seagrass genera, including Zostera (mean: 108.9 Mg C ha⁻¹) and Posidonia (mean: 155 Mg C ha⁻¹) 20 , but higher than Halophila ovalis in Liusha Bay (64.93 ± 22.31 Mg C ha − 1 ) in the coastal area of south China’s Zhanjiang City 9 . This disparity reflects fundamental differences in carbon sequestration efficiency among seagrass species, which scales positively with both morphological characteristics (e.g., leaf size and canopy structure) and habitat-forming capacity (e.g., habitat configuration in bay, estuarine, or open-water environments) 48 , 51 . 4.2 Key factors affecting the variability of sediment C org stocks The spatial distribution of sediment C org stocks in seagrass meadows is governed by a complex interplay of biotic and abiotic factors, including seagrass species traits, topographic features, hydrodynamic conditions, water depth, and sediment physicochemical properties 23 , 27 . Synergistic interactions among these environmental variables can significantly enhance the carbon sequestration capacity of seagrass ecosystems 20 . Generally, larger-bodied seagrass species with more extensive canopies and high shoot density demonstrate enhanced sediment trapping efficiency, facilitating greater accumulation of allochthonous carbon inputs. This relationship likely stems from two key mechanisms: (1) taller canopy structure associated with higher aboveground biomass reduces near-bed flow velocities, and (2) greater three-dimensional habitat complexity enhances particle trapping efficiency, thereby increasing the deposition of suspended organic matter 51 . However, the shoot density and biomass C org stocks showed significant inverse relationships with sediment C org stocks in the study (Figs. 2 and 5 a). This suggests that the sediment characteristics, along with other key factors (e.g., topographic features, hydrodynamic conditions, discharge and tide flow), are critical determinants of the spatial distribution patterns of C org stocks. The sediment %C org and C org density exhibited significant positive correlations with moisture, salinity, and CaCO 3 content, and a significant negative correlation with DBD, indicating their synergistic role in promoting long-term carbon sequestration. These factors enhance C org storage through key mechanisms below. Moisture-Oxygen Dynamics: High moisture content was associated with reduced oxygen permeability due to its close relationship with DBD and sediment grain size 8 . The resulting hypoxic conditions suppressed organic matter decomposition 52 , 53 , thereby promoting C org accumulation. Salinity-Microbial Regulation Elevated salinity reduced microbial biomass and extracellular enzyme activity 54 , limiting organic matter microbial mineralisation. This salinity-induced suppression of degradation further contributed to higher sedimentary C org retention. Carbonate-Associated Preservation: CaCO 3 adsorption physically protected C org from oxidation while restricting microbial access, synergistically enhancing long-term storage through two key processes: (1) physical protection of organic matter through adsorption and (2) reduced exposure to oxidative degradation, thereby decreasing mineralisation rates 55 . This carbonate-mediated preservation mechanism is further supported by Ingalls et al. (2004) 56 , who identified that CaCO 3 matrices provide both intra- and inter-crystalline spaces for organic matter storage, while surface adsorption creates a protective barrier that limits microbial access and subsequent oxidation of C org . Arina et al. (2020) 57 also found that CaCO 3 deposition by calcifying algae in disturbed seagrass beds enhances organic carbon stabilisation for long-term storage. The inverse relationship between DBD and %C org may reflect a dynamic equilibrium among three key factors: organic matter input, sediment physical structure, and microbial decomposition. High organic matter input from sources like dense vegetation can reduce DBD (e.g., the lowest DBD, the highest %C org , and 55% terrigenous C org source at transect Y7) by increasing sediment porosity, while the resulting low-density, high-porosity structure in turn protects C org by limiting oxygen diffusion and suppressing microbial mineralisation 58 . This self-reinforcing mechanism creates a positive feedback loop that sustains elevated C org stocks in low-DBD sediments in seagrass meadows. Estuarine ecosystems serve as critical zones for processing, transporting, and sequestering terrestrial and marine-derived organic matter 59 . The sediment C org source order was terrigenous C org >seagrass C org >phytoplankton C org in the H. beccarii seagrass meadow (Fig. 8 a). As terrigenous C org usually has higher C org content and low δ 13 C (due to the presence of recalcitrant lignin), its reduction will lead to a decrease in the total %C org and an increase in δ 13 C 60 , 61 . The distinct spatial variation occurred along the transect, with seagrass- and phytoplankton-derived C org decreasing and terrigenous C org increasing from Y1 to Y7 (Fig. 8 a). A significant negative correlation was also observed between %C org and δ 13 C (Fig. 6 g). Therefore, the sediment C org source can influence sediment C org stocks variation. Additionally, for the Yifengxi Estuary, characterised by a low flow rate, the mid-reach of the river mouth (transects Y4 to Y7) exhibits a more favourable geomorphological setting for the accumulation of terrestrial organic matter, with terrigenous C org content exceeding 55%. This depositional hotspot likely results from the interplay of reduced hydrodynamic energy, localised topographic traps, and proximity to terrestrial organic inputs (e.g., mangroves). These findings suggest that targeted management of environmental parameters (e.g., sediment composition through substrate amendments) could potentially optimise the carbon storage function of seagrass meadows as a nature-based climate solution. The substantial variability in carbon stocks observed even within the same genus highlights the importance of site-specific combinations of environmental characteristics, including salinity gradients, sediment moisture, CaCO 3 content, and DBD, in determining ultimate sequestration capacity. Further analysis can include long-term monitoring to assess seasonal and interannual C org dynamics and comparative studies with other seagrass species to broaden the study findings. 5. Conclusions Our study investigated the spatial variation of blue carbon storage and the sources of sedimentary C org in H. beccarii seagrass meadows, using multi-site sampling and stable isotope analysis. Focusing on the Yifengxi Estuary in a subtropical monsoon climate zone—where reliable blue carbon data for seagrass ecosystems remain scarce—both the biological and sedimentary carbon stocks were quantified. The study results revealed a relatively low living biomass carbon stock (0.028 ± 0.017 Mg C ha⁻¹), consistent with H. beccarii 's pioneer species characteristics, and a substantial sediment carbon stock (82.41 ± 29.99 Mg C ha⁻¹ in the upper 1 m), demonstrating significant below-ground sequestration potential. Key environmental drivers of C org preservation included high moisture content and elevated salinity, which reduced sediment oxygen permeability and microbial activity and CaCO₃ enrichment, which enhanced C org adsorption and physical protection from mineralisation. High organic matter input from terrigenous sources like dense vegetation reduces DBD by increasing sediment porosity, while the resulting low-density, high-porosity structure in turn protects C org by limiting oxygen diffusion and suppressing microbial mineralization. These factors collectively created reducing conditions that suppressed extracellular enzyme activity and microbial degradation rates, thereby promoting long-term carbon storage. Terrigenous sources contributed most sediment carbon (49.84 ± 23.57%), followed by seagrass (26.83 ± 21.43%), and phytoplankton (23.33 ± 19.98%), exhibiting distinct spatial variation along the transect. The study findings provide critical baseline data for blue carbon assessments in understudied subtropical monsoon regions and recommend that targeted management of environmental parameters could potentially optimise the carbon storage function of seagrass meadows as a nature-based climate solution. Declarations Acknowledgement This work was financially supported by the Guangdong Basic and Applied Basic Research Foundation (2025A1515012007), National Natural Science Foundation of China (42071030 and 52379067) and Nansha Key Scientific and Technological Project, Guangdong Province (2023ZD012). We would like to thank Editage (www.editage.com) for English language editing. References CO 2 Earth, 2025. https://www.co2.earth/earths-CO2-main-page . Gouldsmith, V. & Cooper, A. Consideration of the carbon sequestration potential of seagrass to inform recovery and restoration projects within the Essex Estuaries Special Area of Conservation (SAC), United Kingdom. J. Coast. Conserv. 26, 36 (2022). Schaefer, R., Colarusso, P., Simpson, J. C., Novak, A. & Nepf, H. Continual migration of patches within a Massachusetts seagrass meadow limits carbon accretion and storage. Commun Earth Environ. 6, 129 (2025). Himes-Cornell, A., Pendleton, L. & Atiyah, P. 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Fu, C. et al. Substantial blue carbon sequestration in the world’s largest seagrass meadow. Commun. Earth Environ . 4:474 (2023). Tesi, T. et al. Composition and fate of terrigenous organic matter along the Arctic land-ocean continuum in East Siberia: insights from biomarkers and carbon isotopes. Geochim. Cosmochim. Acta. 133, 235–256 (2014). Wu S, Liu J, Chu H, et al. 2025. Sources and influencing mechanisms of organic carbon in the western Bohai Sea over the past century. Mar. Geol. Quat. Geol. 45, 1–17 (2025). (In Chinese) Additional Declarations There is NO Competing Interest. Supplementary Files SubSupplementaryTables.docx Supplementary Table1, Supplementary Table2, Supplementary Table3, Supplementary Table4， Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7456384","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509210933,"identity":"a8d7bba6-5166-46ae-b93a-0b8e2e2f9e97","order_by":0,"name":"Xiaomei Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACCTBpA2WwEa8ljXQth0nQIj+7+dhj3pzzdvNn9xgwfCg7zMA/uwG/FoM7x9INZ267ndw454wB44xzhxkk7hwgoEUix0ziI1ALs0SOATNv22GgSAIBh83I/yaRuO1cMhtIy19itDDcyGED2nLAjgekhZEYLQY30swkZ25LTpCQSCs42HMunUfiBkGHJT+T5t1mZw9kbHzwo8xajn8GIYdBQWIDkDgAxDzEqQcCe6JVjoJRMApGwcgDAOPSP6BUydeCAAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Shen","suffix":""},{"id":509210934,"identity":"4d1d3641-5069-4394-8aa2-9f2746ae3ded","order_by":1,"name":"Yiguo Hong","email":"","orcid":"https://orcid.org/0000-0002-6255-4100","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yiguo","middleName":"","lastName":"Hong","suffix":""},{"id":509210935,"identity":"5b7c0207-0a3c-41d6-94f3-518721bb9a37","order_by":2,"name":"Fei Ye","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Ye","suffix":""},{"id":509210936,"identity":"dc193094-3b5c-4b62-ba8d-197915c8fbce","order_by":3,"name":"Jiapeng Wu","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiapeng","middleName":"","lastName":"Wu","suffix":""},{"id":509210937,"identity":"3a8763d3-0e88-4dc9-aa6d-cda1fe515ecc","order_by":4,"name":"Yu Wang","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":509210938,"identity":"ef1b0bb9-a879-418f-8baf-fb3ae2f8290a","order_by":5,"name":"Fen Guo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Guo","suffix":""},{"id":509210939,"identity":"fc7e8105-09a2-45c9-a3a1-6810f446097b","order_by":6,"name":"Hang Wan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Wan","suffix":""},{"id":509210940,"identity":"188a990d-3d98-4a33-8ba9-b218243a67d0","order_by":7,"name":"Hongbin Liu","email":"","orcid":"https://orcid.org/0000-0002-3184-2898","institution":"Hong Kong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongbin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-25 18:40:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7456384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7456384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90490253,"identity":"e816fdf6-8f05-4564-92ce-502628be7549","added_by":"auto","created_at":"2025-09-03 09:31:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":625420,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of seven sampling transects at the \u003cem\u003eHalophila beccarii\u003c/em\u003e seagrass meadow in Yifengxi Estuary of Guangdong Province, southern China.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/20d4686d053261692dcb826d.jpeg"},{"id":90491124,"identity":"7ca43762-ea3e-4514-8623-ed78b701ee1e","added_by":"auto","created_at":"2025-09-03 09:39:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":482807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHalophila beccarii\u003c/em\u003e (\u003cem\u003eH. beccarii)\u003c/em\u003e carbon storage characteristics. a) Spatial variation in above- and below-ground biomass C\u003csub\u003eorg\u003c/sub\u003e stocks at monitoring transects of \u003cem\u003eH. beccarii\u003c/em\u003e seagrass meadows; b) total biomass and sediment C\u003csub\u003eorg\u003c/sub\u003e stocks of the upper 1 m at each transect.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/5ad6a60dec14d4a00fef3764.png"},{"id":90489102,"identity":"37eec0ae-b9c7-437b-b1c4-5a66d95fb304","added_by":"auto","created_at":"2025-09-03 09:23:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":375684,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability in sediment environmental characteristics and C\u003csub\u003eorg\u003c/sub\u003e at monitoring transects. a) Moisture; b) Salinity; c) pH; d) Dry bulk density; e) C\u003csub\u003eorg\u003c/sub\u003e density; f) C\u003csub\u003eorg\u003c/sub\u003e; g) Loss on ignition (LOI); h) CaCO\u003csub\u003e3\u003c/sub\u003e; and i) δ\u003csup\u003e13\u003c/sup\u003eC. Different letters indicate significant differences at p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/3e66e310fac44a9bf8dd115b.png"},{"id":90489099,"identity":"c55662cc-4a6f-47be-ac46-7dd4a1436da2","added_by":"auto","created_at":"2025-09-03 09:23:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":539974,"visible":true,"origin":"","legend":"\u003cp\u003eVertical variations of the sediment characteristics across depth profiles. a) Moisture; b) Salinity; c) pH; d) Dry bulk density; e) C\u003csub\u003eorg\u003c/sub\u003e density; f) C\u003csub\u003eorg\u003c/sub\u003e; g) LOI; h) CaCO\u003csub\u003e3\u003c/sub\u003e; and i) δ\u003csup\u003e13\u003c/sup\u003eC.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/29d8efc5c66f8bfa7215e5da.png"},{"id":90489103,"identity":"b8871ee2-61d6-465e-8c4a-0c6e07cdc39e","added_by":"auto","created_at":"2025-09-03 09:23:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1139876,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation diagrams illustrating the relationship between biological characteristics, living biomass, biomass C\u003csub\u003eorg\u003c/sub\u003e stocks, sediment characteristics, and sediment C\u003csub\u003eorg\u003c/sub\u003e stocks in \u003cem\u003eH. beccarii\u003c/em\u003e seagrass meadow. a) \u003cem\u003eN\u003c/em\u003e = 20 and b) \u003cem\u003eN \u003c/em\u003e= 140.\u003c/p\u003e\n\u003cp\u003eAbbreviations: Above-ground biomass C\u003csub\u003eorg\u003c/sub\u003e (A.ground.C\u003csub\u003eorg\u003c/sub\u003e), Below-ground biomass C\u003csub\u003eorg\u003c/sub\u003e (B.ground C\u003csub\u003eorg\u003c/sub\u003e), Aboveground-to-belowground biomass ratio of seagrass (A/B), Shoot height (ShootH.), Leaf length (LeafL.), Shoots density (ShootD.), Leaf width (LeafW.), Coverage (Cov.), dry bulk density (DBD), sediment C\u003csub\u003eorg \u003c/sub\u003estock (Sed. C\u003csub\u003eorg\u003c/sub\u003e), blue carbon storage of biomass and sediment (BC), sediment C\u003csub\u003eorg\u003c/sub\u003e density (C\u003csub\u003eorg\u003c/sub\u003e den.), sediment organic carbon content (%C\u003csub\u003eorg\u003c/sub\u003e), organic matter content (%LOI), CaCO\u003csub\u003e3\u003c/sub\u003e content (%CaCO\u003csub\u003e3\u003c/sub\u003e), salinity (Sal.), and moisture content (Moi.)\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/6e9f2bf025e97fe1e545d2e7.jpeg"},{"id":90490256,"identity":"ebb369ef-5b79-4731-8b3d-e8681d7692c2","added_by":"auto","created_at":"2025-09-03 09:31:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1400007,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis of sediment characteristics a) Dry bulk density; b) Moisture; c) Salinity; d) CaCO\u003csub\u003e3\u003c/sub\u003e; e) pH; f) LOI; g) δ\u003csup\u003e13\u003c/sup\u003eC; and h) shoot density of seagrass.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/aaa218f04e78629aefdb403e.png"},{"id":90489118,"identity":"4ae14780-091e-4f42-b1ba-e0cdf6557444","added_by":"auto","created_at":"2025-09-03 09:23:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":629758,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) results. PCA among seagrass characteristics, sediment characteristics, and C\u003csub\u003eorg\u003c/sub\u003e density at \u003cem\u003eH. beccarii\u003c/em\u003e seagrass meadow (a), \u003cem\u003eN = \u003c/em\u003e20) and PCA among sediment characteristics and C\u003csub\u003eorg\u003c/sub\u003e density (b), \u003cem\u003eN\u003c/em\u003e = 140). Refer to Figure 5 for abbreviations.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/e071a4af7b6ae2ff6b1cb0b1.png"},{"id":90491127,"identity":"f4f82f5b-7c6c-4e26-9129-29f19390d98f","added_by":"auto","created_at":"2025-09-03 09:39:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":609717,"visible":true,"origin":"","legend":"\u003cp\u003eRelative contribution (%) of source to sediment C\u003csub\u003eorg\u003c/sub\u003e across seven transects. a) Depth profiles and b) Transects.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/ed78aae96aca6bfd3c73cbea.png"},{"id":92069826,"identity":"fe8e7ba3-334b-46e3-a3ba-0408953c37e6","added_by":"auto","created_at":"2025-09-24 09:35:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8445214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/a089ba3e-b4a6-44d8-aaa6-40d609bd2cd3.pdf"},{"id":90489097,"identity":"64aeea26-eb24-4a79-8e37-646f33f07d54","added_by":"auto","created_at":"2025-09-03 09:23:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40841,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table1, Supplementary Table2, Supplementary Table3, Supplementary Table4，\u003c/p\u003e","description":"","filename":"SubSupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7456384/v1/25bf36b8766713934f04630d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eSpatial variability of organic carbon storage and sources in China’s subtropical\u003cem\u003e Halophila beccarii\u003c/em\u003e seagrass meadow\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increasing emission of greenhouse gases, particularly CO₂, has driven global warming to critical levels, with the global monthly mean CO₂ concentration reaching 427 ppm in 2025\u003csup\u003e1\u003c/sup\u003e. This alarming trend highlights the urgency of addressing one of the most unrelenting global environmental challenges. In this context, quantifying the carbon sequestration capacity of natural ecosystems has emerged as a key strategy for mitigating atmospheric CO₂ levels and combating climate change\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The concept of \"blue carbon\" was formally introduced in 2009 through a landmark report by the United Nations Environment Programme (UNEP), highlighting the critical role of marine ecosystems in global carbon cycling. Seagrass ecosystems represent one of the most efficient carbon sinks within marine environments and global carbon pools\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite occupying less than 0.2% of the ocean's area (approximately 330,000 km\u0026sup2; globally), seagrass meadows store an estimated 19.9 Pg of organic carbon (C\u003csub\u003eorg\u003c/sub\u003e), accounting for 10\u0026ndash;18% of the total oceanic carbon burial\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The remarkable carbon sequestration capacity of seagrass meadows originates from three key mechanisms: (1) high primary productivity, (2) efficient particle trapping by seagrass canopies, and (3) slow organic matter decomposition in underlying anaerobic sediments. Importantly, over 90% of this blue carbon is stored in sediments\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, making sediment C\u003csub\u003eorg\u003c/sub\u003e pools the dominant component of seagrass carbon stocks.\u003c/p\u003e\u003cp\u003eResearch on seagrass meadows as blue carbon sinks originated in the 1980s, when Smith (1981)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e first identified the significant carbon sequestration potential of marine macrophytes. Subsequent studies have systematically quantified blue carbon storage across global seagrass ecosystems. Comprehensive analysis by Fourqurean et al. (2012)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e revealed substantial regional variation, with sediment carbon stocks ranging from 23.6 Mg C ha⁻\u0026sup1; in Indo-Pacific meadows to 372.4 Mg C ha⁻\u0026sup1; in Mediterranean systems. Their global synthesis established mean carbon stocks of 2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 Mg C ha⁻\u0026sup1; in living biomass and 194.2 Mg C ha⁻\u0026sup1; in sediments (0\u0026ndash;1 m depth). Notably, Fourqurean et al. (2012)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e standardised sediment sampling to 1 m depth, as this surface layer represents the most labile carbon pool susceptible to mineralisation. Although recent studies have enhanced spatial resolution of blue carbon storage data\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, global estimates of seagrass C\u003csub\u003eorg\u003c/sub\u003e stocks remain largely derived from limited field sampling. This is particularly evident in West Pacific subtropical monsoon regions, where data gaps continue to hinder the integration of seagrass meadows into policy frameworks such as carbon finance mechanisms and international climate agreements\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting research has demonstrated significant spatial variation in seagrass carbon stocks, occurring both regionally and locally. This variability complicates stock assessments, as shown by \u003cem\u003ePosidonia oceanica\u003c/em\u003e meadows, where 2 m depth sites contain 14\u0026ndash;16 times more carbon than 32 m depth sites\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Similarly, along Kalimantan Island's coastal gradient, sediment carbon stocks (0\u0026ndash;1 m depth) demonstrate substantial spatial variation, with values increasing from 80.37 Mg C ha⁻\u0026sup1; in near-shore zones to 121.84 and 243.33 Mg C ha⁻\u0026sup1; in mid- and far-shore zones, respectively\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Ricart et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e documented a pronounced estuarine gradient in Port Curtis, Australia, where C\u003csub\u003eorg\u003c/sub\u003e stocks decreased from 52.16 mg cm⁻\u0026sup3; in upper estuary meadows to 1.06 mg cm⁻\u0026sup3; at the estuary mouth, reflecting landscape-driven variations in sediment dynamics and allochthonous carbon inputs. Similarly, in China's Liusha Bay, Shen et al. (2024)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e reported C\u003csub\u003eorg\u003c/sub\u003e stocks ranging 39.74 to 157.12 Mg ha⁻\u0026sup1;, with maximum values occurring near deep-water channels in far-shore zones. Even within the same species, significant regional differences exist\u0026mdash;\u003cem\u003eZostera marina\u003c/em\u003e meadows store 23.1 Mg ha⁻\u0026sup1; in Baltic Sea sediments versus 351.7 Mg ha⁻\u0026sup1; (15\u0026times; higher) in Mediterranean systems\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. While seagrasses provide critical carbon sequestration services, small-bodied species like \u003cem\u003eHalophila beccarii\u003c/em\u003e (33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 Mg ha⁻\u0026sup1; in Zanzibar)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e remain understudied due to their diminutive morphology and restricted intertidal distributions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpatial variability of C\u003csub\u003eorg\u003c/sub\u003e stocks in seagrass meadows is influenced by multiple interacting environmental factors\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Key drivers include: (1) seagrass species composition and coverage\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e; (2) hydrodynamic conditions\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; (3) sediment properties (e.g., particle size, often mediated by hydrodynamics)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e; (4) climate zone\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e; (5) carbon sources proximity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e; (6) biogeochemical processes\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e; and (7) landscape configuration (e.g., patchy vs. continuous beds)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, the relative importance of these factors in driving blue carbon variability remains poorly understood, particularly for specific seagrass meadows.\u003c/p\u003e\u003cp\u003eWhile seagrasses are widely recognized as important carbon sinks, site-specific studies are required to elucidate the influence of local environmental conditions on C\u003csub\u003eorg\u003c/sub\u003e stocks across spatial scales\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. High-resolution assessments of blue carbon stocks are critical for improving multi-scale carbon accounting\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Hence, this study aims to: (1) quantify C\u003csub\u003eorg\u003c/sub\u003e storage in intertidal \u003cem\u003eHalophila beccarii\u003c/em\u003e (\u003cem\u003eH. beccarii\u003c/em\u003e) meadows of the Yifengxi Estuary, southern China; (2) identify the key drivers of spatial variability in C\u003csub\u003eorg\u003c/sub\u003e stocks; and (3) characterise sources of sediment C\u003csub\u003eorg\u003c/sub\u003e. As a comprehensive assessment of C\u003csub\u003eorg\u003c/sub\u003e storage and sources in subtropical \u003cem\u003eH. beccarii\u003c/em\u003e meadows of the Yifengxi Estuary, the study findings will enhance the accuracy of blue carbon accounting and inform conservation strategies for seagrass meadows in the Pacific subtropical monsoon region.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study area and sampling methods\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in the Yifengxi seagrass meadow (418 ha), located in the subtropical monsoon-influenced Yifengxi Estuary of Shantou City, southern China (116\u0026deg;53\u0026prime;-116\u0026deg;56\u0026prime;E, 23\u0026deg;31\u0026prime;-23\u0026deg;33\u0026prime;N; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This area represents the largest seagrass meadow in eastern Guangdong Province and the most extensive \u003cem\u003eHalophila beccarii\u003c/em\u003e habitat in the region\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The area experiences an annual mean temperature of 21.9\u0026deg;C and receives 1098 mm of precipitation, predominantly between May and September. Notably, \u003cem\u003eH. beccarii\u003c/em\u003e is ecologically significant as: (1) one of the only two extant seagrass species considered evolutionarily ancient and (2) one of ten seagrass species globally classified as vulnerable (VL) on the International Union for Conservation of Nature (IUCN) Red List\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, highlighting its conservation priority.\u003c/p\u003e\n\u003cp\u003eField surveys and sample collection were conducted during spring tide low tides from April 12\u0026ndash;17, 2022. Seven transects were systematically distributed across the meadow to ensure representative coverage of the seagrass habitat. At each transect, 2\u0026ndash;3 replicate quadrats were established (0.5 m \u0026times; 0.5 m) and spaced at \u0026ge;\u0026thinsp;50 m apart (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In-situ water quality parameters were recorded (temperature, pH, conductivity, and dissolved oxygen) using a calibrated multi-parameter meter (YSI 30M-25; YSI, Yellow Springs, OH, USA).\u003c/p\u003e\n\u003cp\u003eA total of 16 living plant samples and 20 sediment cores were collected across the seven established transects (Supplementary Table\u0026nbsp;1). Three plant samples were collected from each of transects Y1 through Y5, and a single sample from transect Y6. Due to challenging silty substrate conditions that necessitated boat access during high tide periods, no plant samples were collected from transect Y7. Plant sampling was conducted using a 10 cm diameter PVC corer cautiously inserted vertically to a depth of 20 cm to ensure complete capture of both above- and below-ground biomass components. Samples were placed in pre-labelled monofilament mesh transport bags immediately after collection to maintain sample integrity during transfer from field to laboratory.\u003c/p\u003e\n\u003cp\u003eIn the laboratory, plant samples underwent a standardized processing protocol. First, residual sediments were removed through sequential washing, beginning with seawater rinses in the field followed by additional freshwater rinses under controlled laboratory conditions. Epiphytes were then gently removed from leaf surfaces using soft plastic scrapers to avoid tissue damage, after which shoot density was recorded for each sample. The cleaned samples were subsequently separated into their constituent above-ground (leaves and stems) and below-ground (rhizomes and roots) components. Each component was individually wrapped in acid-washed aluminium foil and stored at 4\u0026deg;C to preserve sample integrity until further analysis, following established protocols\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSediment cores were systematically collected from the upper 1 m profile using a Russian Peat Corer (1 m length, 0.07 m sector radius) to minimise compaction during sampling. Three replicate cores were obtained at each of transects Y1\u0026ndash;Y5 and Y7, while two cores were collected at transect Y6 (Table\u0026nbsp;1). Surface litter layer and any living biomass were removed to ensure clean sediment sampling. Following extraction, each intact core was immediately sectioned into seven depth intervals (0\u0026ndash;10, 10\u0026ndash;20, 20\u0026ndash;30, 30\u0026ndash;40, 40\u0026ndash;50, 50\u0026ndash;70, and 70\u0026ndash;100 cm) in the field. From each stratified subsample, a 15 mL vertical aliquot was collected using a sterile 20 mL syringe for detailed analysis. The remaining sediment from each depth interval was placed in pre-labelled, self-sealing bags. All samples were immediately cryopreserved using dry ice in the field and maintained frozen during transport to the laboratory for subsequent analysis. This stratified sampling approach allowed for comprehensive characterisation of sediment properties and carbon distribution throughout the entire 1 m profile while preserving the vertical integrity of each core segment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Seagrass traits, sediment characteristics, and C\u003csub\u003eorg\u003c/sub\u003e stock measurements\u003c/h2\u003e\n\u003cp\u003eFor each sampling quadrat, key morphological characteristics of \u003cem\u003eH. beccarii\u003c/em\u003e, including leaf length, leaf width, and shoot height, were measured from five randomly selected shoots using digital callipers with 0.1 mm precision. The plants were then carefully separated into above-ground (stem, leaf sheath, and leaf) and below-ground (root and rhizome) components after the measurements. All plant tissues were oven-dried at 60\u0026deg;C for 72 hours until constant weight was achieved to determine dry biomass. C\u003csub\u003eorg\u003c/sub\u003e stocks in living seagrass biomass were calculated using the following approach: first, above- and below-ground biomass values (g m⁻\u0026sup2;) were multiplied by the standardized carbon conversion factor of 0.34\u003csup\u003e40\u003c/sup\u003e. These values were then scaled to per hectare units (Mg C ha⁻\u0026sup1;). Total living biomass carbon stocks were derived by summing carbon stocks across all sampling regions, weighted by their respective spatial coverage\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe 140 sediment subsamples were oven-dried at 60℃ until constant weight was achieved. Moisture content and dry bulk density (DBD) were measured and calculated. C\u003csub\u003eorg\u003c/sub\u003e content was measured using an elemental analyzer (Vario EL Ⅲ, Germany). The C\u003csub\u003eorg\u003c/sub\u003e density (mg cm\u003csup\u003e-\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) in each sediment layer was calculated by multiplying C\u003csub\u003eorg\u003c/sub\u003e content by the corresponding DBD (g cm\u003csup\u003e-\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e) using Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:{\\text{C}}_{\\text{org}}\\text{ density = DBD\u0026times;}{\\text{C}}_{\\text{org}}\\text{\u0026times;1000}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere DBD is the dry bulk density of the sediment subsample (g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and C\u003csub\u003eorg\u003c/sub\u003e is the organic carbon content (g/g). Sediment C\u003csub\u003eorg\u003c/sub\u003e stocks per layer were calculated by multiplying C\u003csub\u003eorg\u003c/sub\u003e density by layer thickness (10, 20, or 30 cm). The total stock for the upper 1 m was derived by summing all layers (Mg C ha⁻\u0026sup1;). Total blue carbon storage (Mg C ha⁻\u0026sup1;) comprised of sediment and living biomass C\u003csub\u003eorg\u003c/sub\u003e stocks\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The g C cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e was converted to the standard Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e using the following: 1 Mg\u0026thinsp;=\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e g; 1 ha\u0026thinsp;=\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe dried subsamples were ground and sieved (100-mesh). To remove inorganic carbon, the samples were treated with 1 mol L⁻\u0026sup1; HCl for 24 h. For salinity analysis, a 5:1 water-to-sediment extract was stirred (30 min), filtered, and measured using an HQ4300 meter (Hach, Loveland, CO, USA). For pH, 2.5:1 extracts were stirred (10 min), settled (1\u0026ndash;3 h), and analysed using the HQ4300 meter. Organic matter and carbonate (CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) contents were measured using the sequential loss on ignition (LOI) method, with furnacing steps at 550\u0026deg;C and 950\u0026deg;C, respectively\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Organic matter content was calculated as per Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:\\text{%LO}{\\text{I}}_{\\text{550}}\\text{= }\\frac{\\left({\\text{m}}_{\\text{0}}\\text{-}{\\text{m}}_{\\text{1}}\\right)\\text{\u0026times;100}}{{\\text{m}}_{\\text{0}}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere %LOI\u003csub\u003e550\u003c/sub\u003e is the organic matter content, m\u003csub\u003e0\u003c/sub\u003e (g) is the initial dry weight, and m\u003csub\u003e1\u003c/sub\u003e (g) is the remaining weight after 550\u0026deg;C furnacing. The CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e in sediments typically exists as carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. CaCO\u003csub\u003e3\u003c/sub\u003e content in sediments was calculated as per Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:\\text{%LO}{\\text{I}}_{\\text{950}}\\text{=}\\frac{\\left({\\text{m}}_{\\text{1}}\\text{-}{\\text{m}}_{\\text{2}}\\right)\\text{\u0026times;100}}{{\\text{m}}_{\\text{0}}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere m\u003csub\u003e0\u003c/sub\u003e (g) is the initial dry weight, m\u003csub\u003e1\u003c/sub\u003e (g) is the remaining weight after furnacing at 550\u0026deg;C, and m\u003csub\u003e2\u003c/sub\u003e (g) is the remaining weight after furnacing at 950\u0026deg;C. The weight loss at 950\u0026deg;C (LOI) was multiplied by 1.36 estimated CO₃\u0026sup2;⁻ content, based on molecular weights of CO₂ (44 g mol⁻\u0026sup1;) and carbonate (60 g mol⁻\u0026sup1;)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. A linear regression (%LOI₅₅₀ vs. %Cₒ\u003csub\u003er\u003c/sub\u003e\u003csub\u003eg\u003c/sub\u003e) was established using Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\:\\text{%}{\\text{C}}_{\\text{org}}\\text{ = a\u0026times;%LO}{\\text{I}}_{\\text{550}}\\text{-b}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere %C\u003csub\u003eorg\u003c/sub\u003e is the organic carbon content, %LOI\u003csub\u003e550\u003c/sub\u003e is the organic matter content, and fitted coefficients, \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e, were determined from the measured values of %C\u003csub\u003eorg\u003c/sub\u003e and %LOI, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Determination for stable carbon isotopic compositions and C\u003csub\u003eorg\u003c/sub\u003e sources\u003c/h2\u003e\n\u003cp\u003eTo quantify the relative contributions of different C\u003csub\u003eorg\u003c/sub\u003e sources to sediment C\u003csub\u003eorg\u003c/sub\u003e in the estuarine seagrass meadows, three potential end-members were considered: terrigenous, seagrass-derived, and phytoplankton organic matter. The proportional contributions of these sources were estimated based on their stable carbon isotope (\u0026delta;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC) signatures using the Bayesian mixing model MixSIAR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The \u0026delta;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC end-member values were established as follows: \u003cem\u003eH. beccarii\u003c/em\u003e seagrass (-21.767\u0026permil;) and phytoplankton (-20.000\u0026permil;) values were obtained from Kennedy et al. (2010)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while the terrigenous organic matter signature (-28.640\u0026permil;) was determined by integrating data from global terrigenous organic matter and seasonal measurements of suspended organic matter from 20 reservoirs in Guangdong Province\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. For \u0026delta;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC analysis, air-dried sediment subsamples were finely ground using an agate mortar and pestle, and measured by an elemental analyzer-isotope ratio mass spectrometry system (EA-IRMS; Manufacturer, City, Abb. State, Country), with values reported in per mil (\u0026permil;) relative to the Vienna Pee Dee Belemnite (V-PDB) standard.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Statistical analysis method\u003c/h2\u003e\n\u003cp\u003eWe conducted comprehensive statistical analyses to examine patterns in the carbon storage data. All datasets were rigorously evaluated for normality and variance homogeneity before the analysis using Shapiro\u0026ndash;Wilk and Levene's tests, respectively. Appropriate data transformations, including log10 for strictly positive values and log10(x\u0026thinsp;+\u0026thinsp;1) for datasets containing zeros, were applied when parametric assumptions were not met. For normally distributed data, one-way analysis of variance (ANOVA) was performed, followed by Dunn's post-hoc pairwise comparisons, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Correlation analyses, principal component analysis (PCA), and linear fitting were conducted to identify key drivers of C\u003csub\u003eorg\u003c/sub\u003e variability and explore multivariate relationships among environmental factors and carbon stocks. All parametric tests and regression analyses were performed using SPSS Statistics 27 (IBM Corp., Armonk, NY, USA), while multivariate analyses were implemented in R version 4.3.3.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Environmental conditions, seagrass characteristics, and C\u003csub\u003eorg\u003c/sub\u003e stocks\u003c/h2\u003e\u003cp\u003eEnvironmental conditions and seagrass characteristics were monitored in the \u003cem\u003eH. beccarii\u003c/em\u003e meadow at Yifengxi Estuary. During the study period, water temperature averaged 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u0026deg;C, with dissolved oxygen (7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 mg L⁻\u0026sup1;), pH (8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33), and salinity (5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u0026permil;) recorded. The meadow occurred in shallow waters (0.1\u0026ndash;0.5 m depth) with variable seagrass coverage (45\u0026ndash;85%). Morphometric measurements revealed average leaf dimensions of 8.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82 mm (length) and 2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 mm (width), with shoots reaching 15.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82 mm in height (Supplementary Table\u0026nbsp;2). Shoot density showed substantial spatial variation (255\u0026ndash;39,852 shoots m⁻\u0026sup2;), averaging 17,844\u0026thinsp;\u0026plusmn;\u0026thinsp;12,639 shoots m⁻\u0026sup2; across the meadow. Significant differences were observed among transects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, F\u0026thinsp;=\u0026thinsp;13.57), with the lowest density at Y4 (7,639\u0026thinsp;\u0026plusmn;\u0026thinsp;5,729 shoots m⁻\u0026sup2;) and the highest at Y3 (34,717\u0026thinsp;\u0026plusmn;\u0026thinsp;6,432 shoots m⁻\u0026sup2;) (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe carbon storage characteristics of \u003cem\u003eH. beccarii\u003c/em\u003e showed significant spatial variation across the study area. Above-ground biomass C\u003csub\u003eorg\u003c/sub\u003e stocks averaged 1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 g C m⁻\u0026sup2;, exhibiting marked differences among transects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, F\u0026thinsp;=\u0026thinsp;10.39). The values ranged from 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 g C m⁻\u0026sup2; at transect Y5 to 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 g C m⁻\u0026sup2; at transect Y3 (Complementary Table\u0026nbsp;2; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Below-ground biomass displayed similar variability, with mean stocks of 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 g C m⁻\u0026sup2; (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, F\u0026thinsp;=\u0026thinsp;7.41), reaching minimum (0.20 g C m⁻\u0026sup2; at Y6) and maximum (2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 g C m⁻\u0026sup2; at Y3) values. Total biomass C\u003csub\u003eorg\u003c/sub\u003e stocks demonstrated a 60-fold variation across transects, from 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 g C m⁻\u0026sup2; at Y5 to 4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 g C m⁻\u0026sup2; at Y3. The above-to-below-ground biomass ratio averaged 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88, with statistically significant differences among transects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, F\u0026thinsp;=\u0026thinsp;25.80), indicating substantial spatial heterogeneity in biomass allocation patterns.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe total biomass carbon storage averaged 0.024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018 Mg C ha⁻\u0026sup1;, corresponding to 10.03\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52 Mg C across the entire 418 ha meadow. Contrastingly, sediment carbon stocks (0\u0026ndash;1 m depth) showed substantially higher values, averaging 82.13\u0026thinsp;\u0026plusmn;\u0026thinsp;28.82 Mg C ha⁻\u0026sup1; (34,333\u0026thinsp;\u0026plusmn;\u0026thinsp;12,049 Mg C) and ranging 33.57\u0026ndash;125.29 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with significant spatial variation among transects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, F\u0026thinsp;=\u0026thinsp;10.54). Notably, transect Y5 contained the highest sediment stocks (114.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71 Mg C ha⁻\u0026sup1;), representing 2.36-fold greater storage than transect Y3 (48.52\u0026thinsp;\u0026plusmn;\u0026thinsp;11.17 Mg C ha⁻\u0026sup1;). The combined carbon pool (biomass\u0026thinsp;+\u0026thinsp;sediment) totalled 34,343\u0026thinsp;\u0026plusmn;\u0026thinsp;12,049 Mg C, with sediments accounting for \u0026gt;\u0026thinsp;99.99% of the stored carbon (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The significant disparity highlights the predominant role of sediment carbon sequestration in the seagrass ecosystem, while living biomass insignificantly contributed to the total carbon storage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spatial variability analysis in sediment characteristics\u003c/h2\u003e\u003cp\u003eThe sediment characteristics exhibited pronounced spatial heterogeneity across the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moisture content averaged 29.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53% and ranged from 16.20\u0026ndash;55.30%, with the minimum and maximum values at transects Y3 and Y7, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Salinity displayed an average of 1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u0026permil;, varying between 0.37\u0026permil; and 2.75\u0026permil;, where transect Y3 recorded the lowest mean salinity (0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u0026permil;) and Y7 the highest (2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u0026permil;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The pH values averaged 7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84, with transect Y3 exhibiting the lowest mean pH (7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27), while Y1 (7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23) and Y7 (7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22) displayed the highest values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). DBD ranged 0.36\u0026ndash;2.13 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (mean: 1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), with the minimum at Y7 (0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and maximum at Y4 (1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eC\u003csub\u003eorg\u003c/sub\u003e density varied considerably from 1.85 to 19.13 mg C cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (mean: 8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00 mg C cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), peaking at Y7 (11.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38 mg cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and Y5 (10.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68 mg cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), while reaching a minimum at Y3 (4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20 mg C cm-3) and Y2 (5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 mg cm-3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Organic carbon percentage (%C\u003csub\u003eorg\u003c/sub\u003e) ranged 0.13\u0026ndash;1.85% (mean: 0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37%), with Y7 (1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22%) and Y3 (0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18%) representing the extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Additional parameters showed the following patterns: %LOI averaged 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02% (range: 0.01\u0026ndash;0.10%; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eg); %CaCO\u003csub\u003e3\u003c/sub\u003e ranged 0.50\u0026ndash;3.92% (mean: 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80%), being lowest at Y3 (0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28%) and highest at Y7 (3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39%; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eh); and δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC varied between \u0026minus;\u0026thinsp;27.71\u0026permil; and \u0026minus;\u0026thinsp;22.65\u0026permil; (mean: -26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u0026permil;), with the most depleted values at Y5 and Y6 and the most enriched at Y1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ei).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe sediment characteristics exhibited distinct vertical variations across depth profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moisture content displayed a gradual decreasing trend with depth at transect Y7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Salinity showed a consistent reduction from 0 cm to 30 cm depth across all seven transects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). DBD demonstrated a progressive increase from surface layers to 40 cm depth, with particularly notable increasing trends observed at transects Y1, Y3, and Y7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). %CaCO\u003csub\u003e3\u003c/sub\u003e exhibited a more complex vertical pattern, initially decreasing from 0 cm to 40 cm before increasing again between 50 cm and 100 cm at transects Y2, Y3, and Y5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). The remaining sediment parameters showed unclear or inconsistent vertical distribution patterns.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of driving factors that influence C\u003csub\u003eorg\u003c/sub\u003e stocks variations\u003c/h2\u003e\u003cp\u003eRelationships between biomass C\u003csub\u003eorg\u003c/sub\u003e stocks and vegetation characteristics: biomass C\u003csub\u003eorg\u003c/sub\u003e stocks exhibited strong positive correlations with above-ground biomass C\u003csub\u003eorg\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), shoot density (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and below-ground biomass C\u003csub\u003eorg\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, significant positive associations were observed with shoot height (r\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and leaf length (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eNegative correlations with sediment and blue carbon parameters: biomass C\u003csub\u003eorg\u003c/sub\u003e stocks showed significant inverse relationships with sediment C\u003csub\u003eorg\u003c/sub\u003e stocks (r = -0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), blue carbon storage (r = -0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), C\u003csub\u003eorg\u003c/sub\u003e density (r = -0.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), %C\u003csub\u003eorg\u003c/sub\u003e (r = -0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), %LOI (r = -0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and %CaCO₃ (r = -0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eSediment %C\u003csub\u003eorg\u003c/sub\u003e relationships: sediment %C\u003csub\u003eorg\u003c/sub\u003e was negatively correlated with DBD (r = -0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, sediment %C\u003csub\u003eorg\u003c/sub\u003e displayed strong positive correlations with sediment C\u003csub\u003eorg\u003c/sub\u003e stocks (r\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), blue carbon storage (r\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), C\u003csub\u003eorg\u003c/sub\u003e density (r\u0026thinsp;=\u0026thinsp;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), organic matter content (r\u0026thinsp;=\u0026thinsp;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), %CaCO₃ (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), salinity (r\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and moisture content (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, N\u0026thinsp;=\u0026thinsp;20). Consistent patterns were observed across all sediment subsamples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, N\u0026thinsp;=\u0026thinsp;140).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\u003cp\u003eAdditionally, linear regression analysis also revealed significant positive correlations between %C\u003csub\u003eorg\u003c/sub\u003e and several environmental variables, including moisture content (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.364, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.69), salinity (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.531\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.064, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.60), CaCO₃ content (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.397\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.069, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.74) and organic matter (%LOI, \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148\u003cem\u003ex\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.012, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.60) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, c, d, and f, respectively). A weaker but still significant negative correlation was observed between %C\u003csub\u003eorg\u003c/sub\u003e and DBD, δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC, and shoot density (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, g, and h, respectively).\u003c/p\u003e\u003cp\u003eThe first two principal components (PC1 and PC2) explained 78.65% of the total variance (PC1: 51.41%; PC2: 27.23%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, Supplementary Table\u0026nbsp;3). Both components were strongly associated with seagrass traits and sediment characteristics. PC1 displayed negative loadings for seagrass traits (e.g., coverage, biomass C\u003csub\u003eorg\u003c/sub\u003e, shoot density) but positive loadings for sediment characteristics, including moisture content, salinity, %CaCO\u003csub\u003e3\u003c/sub\u003e content, %LOI, %C\u003csub\u003eorg\u003c/sub\u003e, C\u003csub\u003eorg\u003c/sub\u003e density, and blue carbon storage. In contrast, PC2 was predominantly associated with positive loadings of the above-ground biomass C\u003csub\u003eorg\u003c/sub\u003e, leaf length, and shoot density, but negative loading for DBD. Similarly, for sediment characteristics alone, the first two PCs explained 78.50% of the total variance (PC1: 58.16%; PC2: 20.34%; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, Supplementary Table\u0026nbsp;3). PC1 also demonstrated significant influence from sediment properties, with negative loadings for DBD and δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC, and positive loadings for sediment C\u003csub\u003eorg\u003c/sub\u003e density, %C\u003csub\u003eorg\u003c/sub\u003e, salinity, %CaCO\u003csub\u003e3\u003c/sub\u003e, %LOI, and moisture content.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Sediment C\u003csub\u003eorg\u003c/sub\u003e sources at different transects\u003c/h2\u003e\u003cp\u003eThe sediment sub-samples exhibited an average δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC value of -26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u0026permil;. The Bayesian mixing model results of δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC showed that the C\u003csub\u003eorg\u003c/sub\u003e of the upper 1 m sediment mainly originated from terrigenous C\u003csub\u003eorg\u003c/sub\u003e (49.84\u0026thinsp;\u0026plusmn;\u0026thinsp;23.57%), seagrass C\u003csub\u003eorg\u003c/sub\u003e (26.83\u0026thinsp;\u0026plusmn;\u0026thinsp;21.43%), and phytoplankton C\u003csub\u003eorg\u003c/sub\u003e (23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98%) (Supplementary Table\u0026nbsp;4). Vertical distributions of C\u003csub\u003eorg\u003c/sub\u003e sources displayed opposing trends: terrigenous inputs dominated at depth (53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;23.00% at 50\u0026ndash;70 cm) but decreased by approximately 7% toward surface layers (46.00\u0026thinsp;\u0026plusmn;\u0026thinsp;20.00% at 0\u0026ndash;10 cm). Seagrass C\u003csub\u003eorg\u003c/sub\u003e (24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;18.50% to 28.90\u0026thinsp;\u0026plusmn;\u0026thinsp;20.40%) and phytoplankton C\u003csub\u003eorg\u003c/sub\u003e (22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5% to 25.10\u0026thinsp;\u0026plusmn;\u0026thinsp;18.40%) showed proportional increases in surface sediments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Sediment C\u003csub\u003eorg\u003c/sub\u003e sources also exhibited distinct spatial variation along the transect, with seagrass- and phytoplankton-derived C\u003csub\u003eorg\u003c/sub\u003e decreasing and terrigenous C\u003csub\u003eorg\u003c/sub\u003e increasing from Y1 to Y7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e8\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Comparison of blue carbon storage among different seagrass meadows\u003c/h2\u003e\u003cp\u003eThe living biomass carbon stock (0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017 Mg C ha⁻\u0026sup1;) in the Yifengxi Estuary's \u003cem\u003eH. beccarii\u003c/em\u003e meadow represents only 1.1% of the global seagrass average (2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 Mg C ha⁻\u0026sup1;), though it falls within the documented range for this species (0.001\u0026ndash;5.54 Mg C ha⁻\u0026sup1;)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The biomass C\u003csub\u003eorg\u003c/sub\u003e stock was evidently influenced by the seagrass characteristics, e.g. shoot density, shoot height, and leaf length. This limited biomass accumulation reflects \u003cem\u003eH. beccarii\u003c/em\u003e's ecological strategy as a pioneer species\u0026mdash;its small morphological structure, coupled with rapid growth rates and high turnover, facilitates efficient organic matter decomposition in sediments\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, contrasting with the greater carbon retention in larger, longer-lived seagrass species.\u003c/p\u003e\u003cp\u003eSediment carbon stocks (0\u0026ndash;1 m depth) in the study area measured 82.13\u0026thinsp;\u0026plusmn;\u0026thinsp;28.82 Mg C ha⁻\u0026sup1;, comparable to \u003cem\u003eH. beccarii\u003c/em\u003e meadows at similar latitudes in Guangxi, China (83.98 Mg C ha⁻\u0026sup1;)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, these values are substantially lower than both the global median (139.7 Mg C ha⁻\u0026sup1;) and mean (194.2 Mg C ha⁻\u0026sup1;) for seagrass ecosystems\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Regional comparisons further highlight this disparity, with \u003cem\u003eH. beccarii\u003c/em\u003e meadows in Singapore (138\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 Mg C ha⁻\u0026sup1;)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and Beibu Gulf, Guangxi (112\u0026thinsp;\u0026plusmn;\u0026thinsp;33.3 Mg C ha⁻\u0026sup1;, obtained by extrapolating from the carbon storage data ranging 0\u0026ndash;60 cm)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e demonstrating 68\u0026ndash;37% greater carbon storage capacity. The blue carbon storage capacity of \u003cem\u003eH. beccarii\u003c/em\u003e meadows was comparatively lower than other dominant seagrass genera, including \u003cem\u003eZostera\u003c/em\u003e (mean: 108.9 Mg C ha⁻\u0026sup1;) and \u003cem\u003ePosidonia\u003c/em\u003e (mean: 155 Mg C ha⁻\u0026sup1;)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, but higher than Halophila ovalis in Liusha Bay (64.93\u0026thinsp;\u0026plusmn;\u0026thinsp;22.31 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in the coastal area of south China\u0026rsquo;s Zhanjiang City\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This disparity reflects fundamental differences in carbon sequestration efficiency among seagrass species, which scales positively with both morphological characteristics (e.g., leaf size and canopy structure) and habitat-forming capacity (e.g., habitat configuration in bay, estuarine, or open-water environments)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Key factors affecting the variability of sediment C\u003csub\u003eorg\u003c/sub\u003e stocks\u003c/h2\u003e\u003cp\u003eThe spatial distribution of sediment C\u003csub\u003eorg\u003c/sub\u003e stocks in seagrass meadows is governed by a complex interplay of biotic and abiotic factors, including seagrass species traits, topographic features, hydrodynamic conditions, water depth, and sediment physicochemical properties\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Synergistic interactions among these environmental variables can significantly enhance the carbon sequestration capacity of seagrass ecosystems\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Generally, larger-bodied seagrass species with more extensive canopies and high shoot density demonstrate enhanced sediment trapping efficiency, facilitating greater accumulation of allochthonous carbon inputs. This relationship likely stems from two key mechanisms: (1) taller canopy structure associated with higher aboveground biomass reduces near-bed flow velocities, and (2) greater three-dimensional habitat complexity enhances particle trapping efficiency, thereby increasing the deposition of suspended organic matter\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, the shoot density and biomass C\u003csub\u003eorg\u003c/sub\u003e stocks showed significant inverse relationships with sediment C\u003csub\u003eorg\u003c/sub\u003e stocks in the study (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This suggests that the sediment characteristics, along with other key factors (e.g., topographic features, hydrodynamic conditions, discharge and tide flow), are critical determinants of the spatial distribution patterns of C\u003csub\u003eorg\u003c/sub\u003e stocks.\u003c/p\u003e\u003cp\u003eThe sediment %C\u003csub\u003eorg\u003c/sub\u003e and C\u003csub\u003eorg\u003c/sub\u003e density exhibited significant positive correlations with moisture, salinity, and CaCO\u003csub\u003e3\u003c/sub\u003e content, and a significant negative correlation with DBD, indicating their synergistic role in promoting long-term carbon sequestration. These factors enhance C\u003csub\u003eorg\u003c/sub\u003e storage through key mechanisms below.\u003c/p\u003e\u003cp\u003eMoisture-Oxygen Dynamics: High moisture content was associated with reduced oxygen permeability due to its close relationship with DBD and sediment grain size\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The resulting hypoxic conditions suppressed organic matter decomposition\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, thereby promoting C\u003csub\u003eorg\u003c/sub\u003e accumulation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSalinity-Microbial Regulation\u003c/strong\u003e\u003cp\u003eElevated salinity reduced microbial biomass and extracellular enzyme activity\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, limiting organic matter microbial mineralisation. This salinity-induced suppression of degradation further contributed to higher sedimentary C\u003csub\u003eorg\u003c/sub\u003e retention.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eCarbonate-Associated Preservation: CaCO\u003csub\u003e3\u003c/sub\u003e adsorption physically protected C\u003csub\u003eorg\u003c/sub\u003e from oxidation while restricting microbial access, synergistically enhancing long-term storage through two key processes: (1) physical protection of organic matter through adsorption and (2) reduced exposure to oxidative degradation, thereby decreasing mineralisation rates\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This carbonate-mediated preservation mechanism is further supported by Ingalls et al. (2004)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, who identified that CaCO\u003csub\u003e3\u003c/sub\u003e matrices provide both intra- and inter-crystalline spaces for organic matter storage, while surface adsorption creates a protective barrier that limits microbial access and subsequent oxidation of C\u003csub\u003eorg\u003c/sub\u003e. Arina et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e also found that CaCO\u003csub\u003e3\u003c/sub\u003e deposition by calcifying algae in disturbed seagrass beds enhances organic carbon stabilisation for long-term storage.\u003c/p\u003e\u003cp\u003eThe inverse relationship between DBD and %C\u003csub\u003eorg\u003c/sub\u003e may reflect a dynamic equilibrium among three key factors: organic matter input, sediment physical structure, and microbial decomposition. High organic matter input from sources like dense vegetation can reduce DBD (e.g., the lowest DBD, the highest %C\u003csub\u003eorg\u003c/sub\u003e, and 55% terrigenous C\u003csub\u003eorg\u003c/sub\u003e source at transect Y7) by increasing sediment porosity, while the resulting low-density, high-porosity structure in turn protects C\u003csub\u003eorg\u003c/sub\u003e by limiting oxygen diffusion and suppressing microbial mineralisation\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This self-reinforcing mechanism creates a positive feedback loop that sustains elevated C\u003csub\u003eorg\u003c/sub\u003e stocks in low-DBD sediments in seagrass meadows.\u003c/p\u003e\u003cp\u003eEstuarine ecosystems serve as critical zones for processing, transporting, and sequestering terrestrial and marine-derived organic matter\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The sediment C\u003csub\u003eorg\u003c/sub\u003e source order was terrigenous C\u003csub\u003eorg\u003c/sub\u003e \u0026gt;seagrass C\u003csub\u003eorg\u003c/sub\u003e \u0026gt;phytoplankton C\u003csub\u003eorg\u003c/sub\u003e in the \u003cem\u003eH. beccarii\u003c/em\u003e seagrass meadow (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). As terrigenous C\u003csub\u003eorg\u003c/sub\u003e usually has higher C\u003csub\u003eorg\u003c/sub\u003e content and low δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC (due to the presence of recalcitrant lignin), its reduction will lead to a decrease in the total %C\u003csub\u003eorg\u003c/sub\u003e and an increase in δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The distinct spatial variation occurred along the transect, with seagrass- and phytoplankton-derived C\u003csub\u003eorg\u003c/sub\u003e decreasing and terrigenous C\u003csub\u003eorg\u003c/sub\u003e increasing from Y1 to Y7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). A significant negative correlation was also observed between %C\u003csub\u003eorg\u003c/sub\u003e and δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). Therefore, the sediment C\u003csub\u003eorg\u003c/sub\u003e source can influence sediment C\u003csub\u003eorg\u003c/sub\u003e stocks variation. Additionally, for the Yifengxi Estuary, characterised by a low flow rate, the mid-reach of the river mouth (transects Y4 to Y7) exhibits a more favourable geomorphological setting for the accumulation of terrestrial organic matter, with terrigenous C\u003csub\u003eorg\u003c/sub\u003e content exceeding 55%. This depositional hotspot likely results from the interplay of reduced hydrodynamic energy, localised topographic traps, and proximity to terrestrial organic inputs (e.g., mangroves).\u003c/p\u003e\u003cp\u003eThese findings suggest that targeted management of environmental parameters (e.g., sediment composition through substrate amendments) could potentially optimise the carbon storage function of seagrass meadows as a nature-based climate solution. The substantial variability in carbon stocks observed even within the same genus highlights the importance of site-specific combinations of environmental characteristics, including salinity gradients, sediment moisture, CaCO\u003csub\u003e3\u003c/sub\u003e content, and DBD, in determining ultimate sequestration capacity. Further analysis can include long-term monitoring to assess seasonal and interannual C\u003csub\u003eorg\u003c/sub\u003e dynamics and comparative studies with other seagrass species to broaden the study findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study investigated the spatial variation of blue carbon storage and the sources of sedimentary C\u003csub\u003eorg\u003c/sub\u003e in \u003cem\u003eH. beccarii\u003c/em\u003e seagrass meadows, using multi-site sampling and stable isotope analysis. Focusing on the Yifengxi Estuary in a subtropical monsoon climate zone\u0026mdash;where reliable blue carbon data for seagrass ecosystems remain scarce\u0026mdash;both the biological and sedimentary carbon stocks were quantified. The study results revealed a relatively low living biomass carbon stock (0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017 Mg C ha⁻\u0026sup1;), consistent with \u003cem\u003eH. beccarii\u003c/em\u003e's pioneer species characteristics, and a substantial sediment carbon stock (82.41\u0026thinsp;\u0026plusmn;\u0026thinsp;29.99 Mg C ha⁻\u0026sup1; in the upper 1 m), demonstrating significant below-ground sequestration potential. Key environmental drivers of C\u003csub\u003eorg\u003c/sub\u003e preservation included high moisture content and elevated salinity, which reduced sediment oxygen permeability and microbial activity and CaCO₃ enrichment, which enhanced C\u003csub\u003eorg\u003c/sub\u003e adsorption and physical protection from mineralisation. High organic matter input from terrigenous sources like dense vegetation reduces DBD by increasing sediment porosity, while the resulting low-density, high-porosity structure in turn protects C\u003csub\u003eorg\u003c/sub\u003e by limiting oxygen diffusion and suppressing microbial mineralization. These factors collectively created reducing conditions that suppressed extracellular enzyme activity and microbial degradation rates, thereby promoting long-term carbon storage. Terrigenous sources contributed most sediment carbon (49.84\u0026thinsp;\u0026plusmn;\u0026thinsp;23.57%), followed by seagrass (26.83\u0026thinsp;\u0026plusmn;\u0026thinsp;21.43%), and phytoplankton (23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98%), exhibiting distinct spatial variation along the transect. The study findings provide critical baseline data for blue carbon assessments in understudied subtropical monsoon regions and recommend that targeted management of environmental parameters could potentially optimise the carbon storage function of seagrass meadows as a nature-based climate solution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was financially supported by the Guangdong Basic and Applied Basic Research Foundation (2025A1515012007), National Natural Science Foundation of China (42071030 and 52379067) and Nansha Key Scientific and Technological Project, Guangdong Province (2023ZD012). 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(In Chinese)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7456384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7456384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeagrass meadows are crucial in marine blue carbon storage. However, blue carbon storage of seagrass meadows in subtropical regions dominated by small-sized species may be overestimated, and the primary factors regulating organic carbon (C\u003csub\u003eorg\u003c/sub\u003e) variability remain uncertain. Here we investigated spatial patterns in blue carbon storage and sediment C\u003csub\u003eorg\u003c/sub\u003e sources in China's subtropical estuarine meadows of the small seagrass, \u003cem\u003eHalophila beccarii\u003c/em\u003e, and identified key environmental drivers influencing its spatial heterogeneity. The results revealed that these species may store less blue carbon than estimated, with low carbon stocks revealed in China\u0026rsquo;s estuarine meadows. Sediment carbon varied spatially, influenced by moisture, salinity, CaCO₃, and bulk density. Terrigenous sources contributed most sediment carbon, followed by seagrass, and phytoplankton, exhibiting distinct spatial variation along the transect. These findings highlight the need for refined blue carbon assessments in subtropical regions and suggest managing environmental factors to enhance seagrass carbon storage as a climate solution.\u003c/p\u003e","manuscriptTitle":"Spatial variability of organic carbon storage and sources in China’s subtropical Halophila beccarii seagrass meadow","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 09:22:55","doi":"10.21203/rs.3.rs-7456384/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd1f6886-3f77-4bd9-80ae-4c4a421400f9","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54120940,"name":"Earth and environmental sciences/Ocean sciences/Marine biology"},{"id":54120941,"name":"Earth and environmental sciences/Solid Earth sciences/Sedimentology"},{"id":54120942,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2025-09-03T09:22:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 09:22:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7456384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7456384","identity":"rs-7456384","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}