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Article

Effects of Spartina Alterniflora Invasion on Soil Organic Carbon Dynamics and Potential Sequestration Mechanisms in Coastal Wetlands, Eastern China

by
Qi Cai
1,†,
Zhuyuan Yao
1,†,
Xuefeng Xie
1,2,*,
Lijie Pu
3,
Lingyue Zhu
1,
Zhenyi Jia
1,2,
Shuntao Chen
1,
Fei Xu
4 and
Tao Wu
1,2,*
1
College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
2
Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Hangzhou 310012, China
3
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
4
School of Public Management, Zhejiang University of Finance & Economics, Hangzhou 310018, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2025, 17(19), 8638; https://doi.org/10.3390/su17198638
Submission received: 12 July 2025 / Revised: 21 September 2025 / Accepted: 24 September 2025 / Published: 25 September 2025
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Abstract

Coastal wetlands play a crucial role in carbon sequestration, yet the invasion of Spartina alterniflora (SA) significantly alters the cycling and sequestration of soil organic carbon (SOC) in coastal wetlands. Nevertheless, the potential underlying mechanisms governing the dynamics of SOC in coastal wetlands following SA invasion remain poorly understood. Here, we investigated the impacts of SA invasion on the dynamics and potential sequestration mechanisms of SOC in the Hangzhou Bay Estuary Wetland, China. Compared to the bare flat (BF), SOC and its fractions in 0–20 cm increased by 1.37–2.24 times after 8 years of SA invasion. Variance partitioning analysis indicated that the combined effects of soil physicochemical properties, soil carbon cycle-related enzymes, and vegetation type were the primary drivers of SOC and its fractions. Redundancy analysis revealed significant positive correlations between SOC and key soil physicochemical properties and enzymes, including sucrase, clay particles, total nitrogen, ammonium nitrogen, and β-glucosidase. Structural equation modeling demonstrated that SA invasion was associated with significant alterations in soil physicochemical properties and positively correlated with both stable and labile carbon fractions, or indirectly linked to these fractions through carbon cycle-related enzymes, thereby substantially positively contributing to SOC. This study supports the hypothesis that the invasion of SA affects the linkage pathway of SOC sequestration and offers valuable guidance for carbon sequestration strategies of coastal wetlands.

1. Introduction

Coastal wetlands, situated at the land–sea interface, are distinguished by their high primary productivity, sedimentation rates, and carbon burial rates, driven by unique geographical and hydrological conditions [1,2]. These ecosystems, often termed blue carbon systems, play a pivotal role in enhancing carbon sequestration and mitigating climate change [3,4,5]. Studies indicate that coastal wetlands bury carbon at a rate 15 times higher than terrestrial ecosystems and 50 times higher than marine ecosystems [6]. Notably, soil organic carbon (SOC) constitutes 50–90% of the total carbon stored in these wetlands, underscoring its critical role as the primary component of the carbon sink [7].
However, coastal wetlands are among the most vulnerable ecosystems globally [8]. The invasion of alien species has introduced significant uncertainty into the carbon dynamics of coastal wetlands [9,10]. For instance, Spartina alterniflora (SA), a C4 plant originally from the Atlantic coast of North America, has rapidly expanded along China’s eastern coast and established itself as a dominant vegetation type since its introduction to China in 1979 [11]. Its high photosynthetic rate and primary productivity exert a profound influence on the cycling and sequestration of SOC in coastal wetlands [12]. Studies in regions such as Yancheng coastal wetlands and Yueqing Bay have demonstrated that SA invasion significantly increases the content of SOC and enhances carbon sequestration capacity [13]. Conversely, Xu et al. reported that SOC content exhibits a transient increase following SA invasion, peaking within a decade before declining [4]. Additionally, research has highlighted that SA invasion alters both stable and labile fractions of soil SOC [4,12]. For instance, Yang et al. observed a marked increase in mineral-associated organic carbon (MAOC) under short-term SA invasion [12]. Similarly, Jiang et al. documented elevated levels of microbial biomass carbon (MBC), dissolved organic carbon (DOC), and easily oxidizable organic carbon (EOC) in soils following the invasion of SA in the Minjiang Estuary [14]. Furthermore, Zhang et al. demonstrated the significant impacts of SA invasion on soil particulate organic carbon (POC) dynamics [15].
The invasion of SA can alter inputs of humus and plant litter, subsequently modifying the soil’s physicochemical properties and enzyme activities [16,17]. These changes, in turn, regulate the formation and transformation of SOC and its fractions. Numerous studies have demonstrated that SA invasion significantly impacts soil properties, driving shifts in SOC dynamics [18,19]. For example, soil pH influences SOC decomposition and stability by modulating microbial metabolism, enzyme activity, and organic-mineral interactions [20]. Additionally, Yin et al. highlighted that total nitrogen (TN) content is a critical determinant of SOC, POC, and MBC levels [21]. Soil enzyme activities also play a pivotal role in SOC cycling. Several studies have demonstrated that the activity of soil carbon cycle-related enzymes is closely linked to the accumulation and decomposition of SOC [17,22]. For instance, amylase and sucrase, which are involved in carbohydrate transformation, are positively correlated with SOC content [23]. Similarly, β-glucosidase activity, which releases glucose during cellulose decomposition, indirectly promotes microbial growth and increases MBC [6]. Conversely, polyphenol oxidase activity accelerates the degradation of complex organic matter, releasing labile compounds that enhance SOC turnover [24]. This enzymatic action contributes to a negative correlation between polyphenol oxidase activity and SOC, underscoring the nuanced interplay between enzymatic processes and carbon sequestration.
Hangzhou Bay, a vital estuarine embayment in China, is characterized by unique tidal dynamics, sediment deposition patterns, and salt marsh ecosystems. It serves not only as a critical habitat along the East Asian–Australasian Flyway for migratory waterbirds but also represents a heavily affected area by the SA invasion. Within this context, the bay constitutes an ideal model system for assessing regional heterogeneity in the ecological effects of SA invasion across coastal wetlands. Despite extensive research on carbon dynamics, organic carbon storage, and the sequestration capacity of Hangzhou Bay [10], the role and underlying potential path relationships of soil physicochemical properties and enzymatic activities in shaping SOC and its fractions under SA invasion remain poorly understood. This knowledge gap prompts our hypothesis that SA invasion may increase SOC content and fractions, mediated by environmental factors such as soil physicochemical properties and carbon cycle-related enzyme activities. Therefore, the main objectives of this study were to (1) explore the changes in soil physicochemical properties and carbon cycle-related enzymes; (2) characterize the changes and distribution patterns of SOC and its fractions; and (3) identify the dominant environmental drivers and underlying mechanisms that control SOC dynamics. By addressing these objectives, our work seeks to advance the understanding of how SA invasion influences soil carbon cycling and sequestration and offers insights relevant to wetland management and restoration efforts.

2. Materials and Methods

2.1. Study Area

The study area is situated to the east of Hangzhou Bay National Wetland Park (121°26′ E, 30°38′ N) in Ningbo City, Zhejiang Province (Figure 1). This region experiences a subtropical monsoon climate, characterized by distinct seasonal variations, with an average annual temperature of 16.0 °C and an average annual precipitation of 1345 mm. The study area is significantly influenced by tides, characterized by an irregular semidiurnal tidal pattern. The flood tide lasts for approximately 6 h, while the ebb tide persists for 6.4 h. The sedimentation rate in this region is relatively high and is subject to minimal influence from human activities. The sediments primarily consist of modern marine and fluvial deposits, characterized by elevated salinity, high water content, relatively low organic matter, and a predominantly sandy and silty texture. Coastal wetland vegetation communities in the study area follow a successional sequence of Bare flat (BF), Scirpus mariqueter (SM) community, Phragmites australis (PA) community, with the invasive SA forming a transitional mosaic-dominant community between the SM and PA stages.

2.2. Soil Sampling

Sampling was conducted during the low tide period of the neap tide in August 2022 to minimize the influence of daily tidal fluctuations. This timing was selected as it occurs after the peak of the plant growing season, when environmental conditions are relatively stable. Four wetland community sites, including SM (with average plant height of 0.5 m and average canopy coverage of 72%), 2-year invasion of SA (SA2, with average plant height of 1.7 m and average canopy coverage of 62%), 8-year invasion of SA (SA8, with average plant height of 2.5 m and average canopy coverage of 86%), and PA (with average plant height of 2.8 m and average canopy coverage of 89%), were selected in the study area. The bare flat (BF) was selected as a control to provide a vegetation-free baseline state for evaluating the vegetation effects resulting from SA invasion. The invasion duration of SA was primarily identified and determined using high-resolution remote sensing imagery. Three parallel sampling belts were established, each approximately 3 km long, with a spacing of at least 300 m between adjacent belts to minimize mutual interference. Each belt covered the five communities, forming 15 plots (1 m × 1 m). To ensure the representativeness and independence of the samples, the distance between different community plots within the same belt was set to be greater than 500 m. All sampling across the sites was completed within two days to further minimize the impact of short-term seasonal variations on comparative analysis. Soil samples were collected using a plum blossom sampling method at five points in the center of each plot, with soil cores taken at 0–10 cm and 10–20 cm depths, as these layers represent the most active zone of plant–soil interaction and are the most sensitive to short-term invasion responses. A total of 30 soil samples were collected. Soil samples were thoroughly mixed after removing roots, gravel, and other non-soil fractions to ensure sample homogeneity and purity. The collected soils were separated into two parts: one was air-dried for the determination of soil physicochemical properties, organic carbon, and fractions, and the other was stored at 4 °C for the determination of soil carbon cycle-related enzyme activities, DOC, and MBC for less than 5 days.

2.3. Laboratory Analysis

2.3.1. Determination of Soil Physicochemical Properties and Organic Carbon Fractions

Soil physicochemical properties and organic carbon fractions were determined as described by Bao [25]. Briefly, soil moisture content (MC) was measured by the drying method. Particle size was measured by a laser particle size analyzer (Malvern Mastersizer 3000, Malvern, UK). Electrical conductivity (EC) and pH were measured by conductivity (Mettler Toledo FE20, Zurich, Switzerland) and pH meter (Mettler Toledo FE38, Zurich, Switzerland), with soil-water ratios of 1:2.5 and 1:5, respectively. Total nitrogen (TN) and alkali hydrolyzable nitrogen (AN) were measured by an automatic Kjeldahl nitrogen analyzer (Hanon K1160, Qingdao, China). Total phosphorus (TP) was measured by the molybdenum blue colorimetric method after acid digestion. SOC was measured by the dichromate oxidation method; DOC was measured by the 0.45 μm filtration and dichromate oxidation method; EOC was measured by the potassium permanganate oxidation method; MBC was measured by the fumigation-extraction-dichromate oxidation method; POC and MAOC were quantified by the wet sieving-dichromate oxidation method.

2.3.2. Determination of Soil Carbon Cycle-Related Enzyme Activities

Soil carbon cycle-related enzyme activities were determined as described by Guan [26]. In brief, soil sucrase (SUC) and amylase (AMY) activities were measured by the 3,5-dinitrosalicylic acid colorimetric method using sucrose and starch as substrates; polyphenol oxidase (PPO) activity was measured by the colorimetric method based on the purple pyrogallus produced from pyrogallol; β-glucosidase (BG) was measured by the nitrophenol colorimetric method using p-nitrophenyl-β-D-glucopyranoside as the substrate.

2.4. Data Processing and Statistical Analysis

The One-way analysis of variance was used to explore the impact of SA invasion on SOC and its fractions, physicochemical properties, and carbon cycle-related enzyme activities. Further, redundancy analysis (RDA) was employed to dissect the intricate relationships between soil physicochemical properties, carbon cycle-related enzyme activities, and SOC and its fractions. The variance partitioning analysis (VPA) was conducted to determine the contribution of soil physicochemical properties, carbon cycle-related enzyme activities, and vegetation communities to the variation in SOC and its fractions. The Mantel test was implemented to identify the dominant ecological and environmental factors shaping the SOC and its fractions. To assess how plant communities, soil physicochemical properties, and carbon cycle-related enzyme activities affect soil C sequestration, we constructed a structural equation model (SEM). Data were preprocessed and statistically analyzed using SPSS 27.0 software. The VPA, Mantel test, and SEM were implemented in R 4.4.1 using the dplyr, linkET, tidyverse, ggplot2, nlme, lme4, and piecewiseSEM packages, respectively.

3. Results

3.1. Soil Physicochemical Properties and Carbon Cycle-Related Enzymes

As presented in Table 1, soil MC in BF exhibited the highest value in 0–20 cm, notably higher than in SM. Soil clay and silt particles in SA8 and PA were substantially higher, whereas soil sand particles were markedly lower than in BF, SM, and SA2 in 0–20 cm. Soil pH observed the highest value in PA and the lowest value in SA8, while soil EC exhibited the lowest value in PA and the highest value in SM in 0–20 cm. Soil TN content in 0–20 cm reached the highest in PA and was significantly higher than in BF, SM, and SA2. Soil AN content achieved the highest value in SA8, considerably exceeding that in BF and SM. Soil TP content in 0–10 cm attained the highest value in PA, significantly higher than in BF. However, in 10–20 cm, no significant differences were observed among different vegetation types in TP content.
As shown in Table 2, the activity of SUC was highest in SA8, with values significantly exceeding those in BF, SM, and SA2. The highest value of AMY activity was observed in PA and SA8 in 0–10 cm and 10–20 cm, respectively, and was significantly higher than in BF, SM, and SA2. The activity of BG in 0–20 cm reached the highest value in PA, significantly surpassing values in BF, SM, and SA2. Furthermore, no significant differences were observed in soil PPO activity across different vegetation communities in 0–10 cm. However, in 10–20 cm, PPO activity in SM and SA2 was significantly higher than in BF, SA8, and PA.

3.2. Distribution Characteristics of SOC and Its Fractions

As depicted in Figure 2, SOC, DOC, POC, and MAOC content reached their highest values in PA in 0–10 cm. In contrast, EOC and MBC content were highest in the SA8. Notably, the content of SOC and its fractions did not differ significantly between the PA and SA8 but were notably higher than in the BF, SM, and SA2. In 10–20 cm, the content of SOC and its fractions were observed to be highest in the SA8, with values significantly exceeding those in the BF, SM, and SA2, except for POC.

3.3. Relationship Between Environmental Factors and SOC and Its Fractions

VPA (Figure 3a) revealed that vegetation type, soil physicochemical properties, and carbon cycling enzyme activities collectively explained 79% of the variability in SOC and its fractions. Specifically, the interactive effects among soil physicochemical properties, carbon cycling enzyme activities, and vegetation type contributed the most to this variability, accounting for 52%. This was followed by the interaction between soil physicochemical properties and vegetation type (9%), the individual effect of soil physicochemical properties (9%), and vegetation type (6%). RDA indicated (Figure 3b) that environmental factors cumulatively explained 90.61% of the variability in SOC and its fractions. The RDA and Mantel test (Figure 3c) demonstrated that soil clay particles, TN, SUC, and AMY exhibited highly significant positive correlations with SOC and its fractions (p < 0.01). In addition, soil AN was highly significant positive correlated with SOC, DOC, MBC, EOC, and MAOC (p < 0.01), and significantly positive correlated with POC (p < 0.05); BG was highly significant positive correlated with SOC, DOC, EOC, and MAOC (p < 0.01), and significantly positive correlated with MBC and POC (p < 0.05); TP was highly significant positive correlated with SOC, DOC, and POC (p < 0.01), and significantly positive correlated with MBC and EOC (p < 0.05); EC was highly significant negative correlated with SOC, DOC, and MAOC (p < 0.01), silt particles was highly significant negative correlated with MBC (p < 0.01), and significantly negative correlated with EOC and POC (p < 0.05); MC and PPO were highly significant positive and significantly negative correlated with MAOC, respectively.
The SEM elucidates the pathways through which plant types exert direct or indirect impacts on SOC via multiple mediating factors, including soil physicochemical properties, carbon cycle-related enzymes, and stable and labile carbon fractions (Figure 4). The model fits the data well (Fisher’s C = 15.38, p = 0.222, df = 12, AIC = 53.38, BIC = 80.00), indicating no significant inconsistency between the data and the model. Although the overall model fit is acceptable, the p-value being close to 0.2 suggests that there may be some uncaptured structure or mild specification errors in the model. On one hand, SA invasion was associated with significant alterations in soil physicochemical properties (path coefficient = 0.89, p < 0.001), which, in turn, directly exerts a positive relationships with soil stable (path coefficient = 0.73, p < 0.001) and labile carbon fractions (path coefficient = 0.56, p < 0.001), thus positive contribute to the accumulation of SOC. Simultaneously, soil physicochemical properties are positively and indirectly linked to soil stable (path coefficient = 0.27, p < 0.05) and labile carbon fractions (path coefficient = 0.44, p < 0.001), which are highly correlated with carbon cycle-related enzymes (path coefficient = 0.56, p < 0.05), thereby substantially positive contributing to the accumulation of SOC.

4. Discussion

4.1. Effects of Spartina Alterniflora Invasion on SOC and Its Fractions in Coastal Wetlands

This study found that the 2-year invasion of SA did not exert a significant impact on the accumulation of SOC, whereas SOC content increased significantly compared to BF after 8-year invasion of SA. This suggests that with the increasing years of SA invasion, the higher net photosynthetic rate of SA can produce greater net primary productivity, enhance the net primary productivity, and improve soil carbon sequestration capacity [27]. In 0–10 cm, the content of SOC in SA8 remained lower than that of the PA, consistent with studies conducted in the Yellow River Estuary, which may be due to the relatively short-term invasion years (<5 years) of SA in the study area [28], indicating that its carbon sequestration capacity has not yet peaked [28,29]. Zhang et al. and Jin et al. found that SOC content continued to rise after 10 years of SA invasion [20,30], while Yang et al. observed that SOC content peaked after 17 years of invasion [9]. Furthermore, studies indicate that compared to PA, SA vegetation contains lower levels of cellulose and lignin, and its litter decomposes more readily [16,31]. Additionally, organic carbon derived from plant decay is retained in the soil for a shorter duration, with more being lost to the atmosphere through soil respiration [18].
The active fraction of SOC primarily originates from plant residues, root exudates, and microbial debris, rendering it highly responsive to environmental changes [32]. In Quanzhou Bay, Cui et al. reported that the content of the active SOC fraction increased with the duration of SA invasion [33], a trend consistent with the findings of this study. Specifically, the active SOC fractions in SA8 and PA communities within the 0–20 cm layer were significantly higher than in other communities. This observation is likely attributable to the higher biomass of SA8 and PA communities, which enhances inputs of root exudates and dead fine roots. These inputs accelerate humus decomposition and provide abundant nutrients and substrates for microbial activity, thereby elevating the content of active organic carbon in the soil [17,34]. In this study, the trend of MAOC mirrored that of SOC, potentially due to the greater surface biomass in the SA8 and PA communities. This biomass generates more plant litter, creating a favorable environment for microbial activity and thereby promoting MAOC accumulation [35]. Haddix et al. used isotope tracing to demonstrate that high-quality, labile plant inputs can rapidly and effectively convert MAOC [36]. Additionally, the subtropical climate of the study area stimulates high microbial metabolic activity, which not only increases microbial carbon input but also amplifies the positive impact of microbially derived MAOC on soil carbon sequestration [37].

4.2. Potential Mechanism of Spartina Alterniflora Invasion on SOC Sequestration in Coastal Wetlands

Under natural conditions, the input of fine-grained sediments, high sedimentation rates, and the efficient carbon capture and sequestration capacity of salt marsh vegetation collectively contribute to making the southern coast of Hangzhou Bay an important “blue carbon sink”. The strong tidal dynamics characteristic of the southern Hangzhou Bay brings abundant allochthonous organic materials (such as algae and suspended particles) and influence the distribution and resuspension of sediments [38]. Furthermore, the weakened hydrodynamic environment in salt marsh wetlands favors the settlement of fine particles and organic matter. Additionally, SA has a longer growing season and a higher net photosynthetic rate, enabling greater net primary production. Its invasion can increase the net primary productivity of invaded areas, thereby enhancing carbon sequestration capacity [39]. The invasion of SA alters soil physicochemical properties, which significantly impacts SOC and its fractions [40]. This study demonstrated that TP, TN, and AN promote the formation of SOC and its fractions. Duan et al. indicated that nitrogen is an essential nutrient for the decomposition of organic matter, and higher soil TN and AN levels facilitate litter decomposition and enhance vegetation productivity [41]. This can also stimulate chlorophyll synthesis in plant leaves, thus improving photosynthesis efficiency and markedly increasing organic matter input [42]. This observation also elucidates the significant positive correlation between soil TN and AN with soil SOC found in this study. Soil MAOC is primarily made up of nitrogen-rich microbial products, which persist in the soil due to mineral chemical bonds and the physical protection offered by small aggregates. Therefore, the increase in soil TN and AN supports MAOC accumulation [43]. Furthermore, Yang et al. reported that elevated soil TN can drive increases in MBC, DOC, and other active fractions of SOC [44]. This study revealed that soil TP significantly influences SOC and its fractions, potentially because warmer regions typically demand higher phosphorus levels and experience lower nitrogen restrictions for plant growth. In these warm regions, soil TP can lead to increased biomass production and plant input, thereby stimulating organic carbon levels [45]. However, the variation range of soil TP across different vegetation communities in this study was minimal. Additionally, some research suggests that soil TP has a negligible effect on the slow cycling of MAOC [46]. Soil particle size influences soil aeration and water retention, altering nutrient fixation and transformation. Notably, an increase in soil clay content contributes to a greater specific surface area of the soil, allowing for more organic carbon adsorption [47]. This finding confirms the observed rise in soil clay content due to SA invasion, which further enhances SOC levels. Moreover, soil clay and silt can promote MBC production and MAOC accumulation by increasing soil moisture content and microbial habitability (i.e., surface area and microporosity) [48].
Some studies have suggested that soil enzyme activities are sensitive to surface vegetation cover, leading to changes in SOC fractions and impacting the sequestration of SOC [49]. Our study demonstrated (Figure 4) that the combined effect of soil sucrose (SUC), β-glucosidase (BG), and amylase (AMY) significantly influences SOC fractions. For instance, SUC decomposes glucose and sucrose derived from soil organic matter, promoting plant residue input and enhancing microbial activity, which provides sources for increasing POC, DOC, and MBC content, thus elevating SOC levels [50]. BG encourages the accumulation of soil organic matter, which stimulates vegetation growth and boosts biomass, consequently leading to an increase in MBC content [51]. Additionally, Wang et al. found a significant correlation between soil AMY and SOC, MBC, and EOC, paralleling our study’s findings [52]. The primary roles of soil carbon cycle-related enzyme activities include enhancing organic matter decomposition and nutrient mineralization, which may interact with soil minerals to form MAOC [53].
The structural equation modeling (SEM) results indicated (Figure 4) that different vegetation communities in the Hangzhou Bay Wetland exert a significant indirect contribution to SOC through positive links with soil physicochemical properties, enzyme activities, and carbon fractions. Organic carbon fractions play a crucial role in regulating changes in SOC, with soil MAOC acting as a key driving force for SOC fluctuations. MAOC is intricately associated with minerals or microaggregates, allowing for a quicker cycling process and serving as a nutrient reserve for plants [37]. Moreover, the turnover time of MAOC is thousands of times longer than that of POC, suggesting that increases in MAOC content might be crucial for long-term carbon sequestration in the soil [54]. MAOC is safeguarded by adsorption to iron oxides and clay minerals, and clay provides a significant specific surface area for the accumulation of SOC [55]. The active fraction of SOC is essential for shifting SOC into more stable forms [56]. It is particularly responsive to changes in vegetation dynamics and is often considered an indicator of SOC variations. This study found that relative to MAOC, the active fraction of SOC exerted a more pronounced effect on SOC. Among these, soil DOC is abundant in various soluble organic residues, serving as a primary energy source for soil microorganisms and a potential reserve for soil carbon sequestration [57]. EOC arises from microbial leaching of plant residues, root exudates, and organic matter decomposition, closely linked to the accumulation and decomposition mechanisms of SOC [58]. Furthermore, MBC plays a significant role in forming soil humus, facilitating nutrient transformation and cycling, and is a key source of available soil nutrients [56]. POC predominantly originates from partially decomposed plant residues, remains unprotected by minerals, and is readily utilized by microorganisms. The invasion of SA significantly enhances the short-term burial of POC in sediments, potentially altering the transport pathways and degradation-transformation efficiency of POC through its high productivity, strong sediment trapping effect, and physical modification of habitats [57].
This study found that 8 years of SA invasion significantly enhanced carbon sequestration capacity. However, numerous studies have also demonstrated that its invasion leads to the loss of critical ecosystem services, such as biodiversity maintenance [59,60]. Decision-makers need to weigh the trade-offs between invasion control and carbon sequestration enhancement, e.g., in coastal wetland restoration, selecting native salt marsh plants with high carbon sequestration potential could synergistically achieve both biodiversity recovery and blue carbon augmentation goals. However, our study has several limitations. Since soil sampling was conducted only in summer, the findings may not fully capture seasonal dynamics, representing a constraint in the temporal scope of this research. It is worth noting that the observed patterns of SOC dynamics may be driven not only by the invasion of SA itself but also by site-specific historical factors (such as variations in sedimentation rates and past disturbances). These unquantified historical influences introduce contextual uncertainties that should be considered when interpreting the results. Furthermore, this study used BF areas as a proxy for pre-invasion conditions. Although BF is a common non-vegetated state in intertidal zones, it may not fully represent the initial soil conditions—such as SOC baselines under native salt marsh vegetation—that existed before replacement by SA. Additionally, the application of multivariate methods (e.g., VPA and RDA) carried a potential risk of overfitting, given the relatively high number of variables compared to the sample size. Thus, the robustness of these analytical outcomes should be validated in future independent studies.
Future research should prioritize the following directions: (i) implementing long-term continuous monitoring and expanding to broader spatial scales to elucidate the spatiotemporal dynamics of SOC under SA invasion; (ii) integrating molecular biology techniques to deeply investigate the response mechanisms of microbial community structure and function to the invasion process and SOC transformation; (iii) accurately quantifying the relative contributions of different carbon sources (such as SA, native plants, and marine-derived carbon) to the SOC pool to clarify carbon sequestration pathways; and (iv) systematically evaluating the effects of different management measures (e.g., mowing, flooding, and replacement planting) on the stability of the SOC pool and carbon sequestration function in invaded wetlands, thereby providing a theoretical basis and practical strategies for ecological restoration.

5. Conclusions

After 8 years of SA invasion, SOC and its fractions significantly increased compared with BF. The interactive effects of soil physicochemical properties, carbon cycle-related enzyme activities, and vegetation type may be the most influential factors affecting SOC and its fractions, while soil SUC, clay particles, TN, AN, and BG were observed to have significant positive correlations with SOC and its fractions. SEM further revealed that the succession of vegetation type had a high indirect correlation with SOC, whereas soil carbon cycle-related enzymes and soil physicochemical properties are significantly directly linked with SOC fractions, thereby significantly associated with SOC sequestration. Our findings highlight the importance of SA invasion in enhancing SOC sequestration and provide insights into the mechanisms underlying SOC changes, offering valuable guidance for coastal wetland restoration and carbon sequestration strategies.

Author Contributions

Conceptualization, X.X.; methodology, Z.Y. and T.W.; software, Q.C.; investigation, Q.C., Z.Y., Z.J. and S.C.; data curation, Q.C. and Z.J.; writing—original draft preparation, Q.C.; writing—review and editing, X.X., L.Z. and T.W.; supervision, L.P., S.C. and F.X.; project administration, X.X.; funding acquisition, X.X., L.P. and F.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42571070, 42101068), the Open Fund Project of Key Laboratory of Ocean Space Resource Management Technology, MNR, China (KF-2022-106), and the Natural Science Foundation of Zhejiang Province, China (LMS25D010001, LQN25D010006).

Data Availability Statement

Data will be available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Duarte, C.M.; Middelburg, J.J.; Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2005, 2, 1–8. [Google Scholar] [CrossRef]
  2. Wei, S.; Zhu, Z.; Wang, S. Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China. Sci. Total Environ. 2024, 953, 176099. [Google Scholar] [CrossRef]
  3. Bertram, C.; Quaas, M.; Reusch, T.B.H.; Vafeidis, A.T.; Wolff, C.; Rickels, W. The blue carbon wealth of nations. Nat. Clim. Change 2021, 11, 704–709. [Google Scholar] [CrossRef]
  4. Xu, C.; Xue, Z.; Jiang, M.; Lyu, X.; Zou, Y.; Gao, Y.; Sun, X.; Wang, D.; Li, R. Simulating potential impacts of climate change on the habitats and carbon benefits of mangroves in China. Glob. Ecol. Conserv. 2024, 54, e03048. [Google Scholar] [CrossRef]
  5. Lee, Y.; Lee, J. Blue carbon ecosystems for hypoxia solution: How to maximize their carbon sequestration potential. Mar. Env. Res. 2025, 209, 107202. [Google Scholar] [CrossRef] [PubMed]
  6. Wang, F.; Sanders, C.; Santos, I.; Tang, J.; Schuerch, M.; Kirwan, M.; Kopp, R.; Zhu, K.; Li, X.; Yuan, J.; et al. Global blue carbon accumulation in tidal wetlands increases with climate change. Natl. Sci. Rev. 2021, 8, nwaa296. [Google Scholar] [CrossRef] [PubMed]
  7. Tang, Z.; Wang, D.; Tian, X.; Bi, X.; Zhou, Z.; Luo, F.; Ning, R.; Li, J. Exploring the factors influencing the carbon sink function of coastal wetlands in the Yellow River Delta. Sci. Rep. 2024, 14, 28938. [Google Scholar] [CrossRef]
  8. Zhao, W.; Li, X.; Costa, M.; Wartman, M.; Lin, S.; Wang, J.; Yuan, L.; Wang, T.; Yang, H.; Qin, Y.; et al. Modelling the spatiotemporal dynamics of blue carbon stocks in tidal marsh under Spartina alterniflora invasion. Ecol. Indic. 2024, 166, 112426. [Google Scholar] [CrossRef]
  9. Xia, S.; Song, Z.; Singh, B.; Guo, L.; Bolan, N.; Wang, W.; Lin, G.; Fang, Y.; Wen, X.; Wang, J.; et al. Contrasting patterns and controls of soil carbon and nitrogen isotope compositions in coastal wetlands of China. Plant Soil 2023, 489, 483–505. [Google Scholar] [CrossRef]
  10. Yuan, H.; Chen, J.; Ye, Y.; Lou, Z.; Jin, A.; Chen, X.; Jiang, Z.; Lin, Y.; Chen, C.; Loh, P. Sources and distribution of sedimentary organic matter along the Andong salt marsh, Hangzhou Bay. J. Mar. Syst. 2017, 174, 78–88. [Google Scholar] [CrossRef]
  11. Bernik, B.M.; Li, H.; Blum, M.J. Genetic variation of Spartina alterniflora intentionally introduced to China. Biol. Invasions 2016, 18, 1485–1498. [Google Scholar] [CrossRef]
  12. Yang, W.; Zhao, H.; Leng, X.; Cheng, X.; An, S. Soil organic carbon and nitrogen dynamics following Spartina alterniflora invasion in a coastal wetland of eastern China. Catena 2017, 156, 281–289. [Google Scholar] [CrossRef]
  13. Huang, R.; He, J.; Wang, N.; Christakos, G.; Gu, J.; Song, L.; Luo, J.; Agusti, S.; Duarte, C.; Wu, J. Carbon sequestration potential of transplanted mangroves and exotic saltmarsh plants in the sediments of subtropical wetlands. Sci. Total Environ. 2023, 904, 166185. [Google Scholar] [CrossRef]
  14. Jiang, Y.; Yang, D.; Huang, J.; Wen, Y.; Tang, H.; Xu, J.; Lin, X.; Sun, D.; Gao, D. Invasive Spartina alterniflora alters sediment organic carbon mineralization dynamics in a coastal wetland of Southeastern China. Front. Mar. Sci. 2024, 11, 1400381. [Google Scholar] [CrossRef]
  15. Zhang, Y.; Li, M.; Zhang, J.; Cui, J.; Wang, X.; Song, M.; Qiao, Q.; Long, X. Spatial soil heterogeneity rather than the invasion of Spartina alterniflora drives soil bacterial community assembly in an Eastern Chinese intertidal zone along an estuary coastline. Catena 2024, 237, 107784. [Google Scholar] [CrossRef]
  16. Yan, J.; Wang, L.; Hu, Y.; Tsang, Y.; Zhang, Y.; Wu, J.; Fu, X.; Sun, Y. Plant litter composition selects different soil microbial structures and in turn drives different litter decomposition pattern and soil carbon sequestration capability. Geoderma 2018, 319, 194–203. [Google Scholar] [CrossRef]
  17. Cui, Q.; Yang, H.; Wang, G.; Ma, J.; Feng, L.; Liu, J. Response of soil carbon fractions and enzyme activities to mowing management on in a coastal wetland of the Yellow River Delta. Front. Mar. Sci. 2022, 9, 993181. [Google Scholar] [CrossRef]
  18. Yan, J.; Wang, L.; Tsang, Y.F.; Qian, L.; Fu, X.; Sun, Y.; Wu, P. Conversion of organic carbon from decayed native and invasive plant litter in Jiuduansha wetland and its implications for SOC formation and sequestration. J. Soil Sediments 2019, 20, 675–689. [Google Scholar] [CrossRef]
  19. Yang, R. Interacting effects of plant invasion, climate, and soils on soil organic carbon storage in coastal wetlands. J. Geophys. Res. Biogeosci. 2019, 124, 2554–2564. [Google Scholar] [CrossRef]
  20. Wang, F.; Zhang, N.; Yang, S.; Li, Y.; Yang, L.; Cao, W. Source and stability of soil organic carbon jointly regulate soil carbon pool, but source alteration is more effective in mangrove ecosystem following Spartina alterniflora invasion. Catena 2024, 235, 107681. [Google Scholar] [CrossRef]
  21. Yin, S.; Wang, J.; Yu, T.; Wang, M.; Wu, Y.; Zeng, H. Constraints on the spatial variations of soil carbon fractions in a mangrove forest in Southeast China. Catena 2023, 222, 106889. [Google Scholar] [CrossRef]
  22. Ma, W.; Li, G.; Wu, J.; Xu, G.; Wu, J. Response of soil labile organic carbon fractions and carbon-cycle enzyme activities to vegetation degradation in a wet meadow on the Qinghai–Tibet Plateau. Geoderma 2020, 377, 114565. [Google Scholar] [CrossRef]
  23. Xie, X.F.; Wu, T.; Zhu, M.; Jiang, G.; Xu, Y.; Wang, X.; Pu, L. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol. Indic. 2021, 120, 106925. [Google Scholar] [CrossRef]
  24. Duan, J.; Yuan, M.; Jian, S. Soil extracellular oxidases mediated nitrogen fertilization effects on soil organic carbon sequestration in bioenergy croplands. GCB Bioenergy 2021, 13, 1303–1318. [Google Scholar] [CrossRef]
  25. Bao, S.D. Soil Agrochemical Analysis, 3rd ed.; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  26. Guan, S.Y.; Zhang, D.S.; Zhang, Z.M. Soil Enzyme and Its Research Methods; China Agriculture Press: Beijing, China, 1986. [Google Scholar]
  27. Qiu, S.; Wang, Z.; Xu, J.; Cui, E.; Yan, L. Influence of vegetation dynamics on soil organic carbon and its fractions in a coastal wetland. Ecosyst. Health Sustain. 2023, 9, 0016. [Google Scholar] [CrossRef]
  28. Zhang, X.; Zhang, Z.; Li, Z.; Li, M.; Wu, H.; Jiang, M. Impacts of Spartina alterniflora invasion on soil carbon contents and stability in the Yellow River Delta, China. Sci. Total Environ. 2021, 775, 145188. [Google Scholar]
  29. Zhang, Y.; Ding, W.; Luo, J.; Donnison, A. Changes in soil organic carbon dynamics in an Eastern Chinese coastal wetland following invasion by a C4 plant Spartina alterniflora. Soil Biol. Biochem. 2010, 42, 1712–1720. [Google Scholar] [CrossRef]
  30. Jin, B.; Lai, D.; Gao, D.; Tong, C.; Zeng, C. Changes in soil organic carbon dynamics in a native C4 plant-dominated tidal marsh following Spartina alterniflora invasion. Pedosphere 2017, 27, 856–867. [Google Scholar] [CrossRef]
  31. He, S.; Lin, J.; Liu, X.; Jia, S.; Chen, S. Cordgrass Spartina alterniflora acts as a key carbon source to support macrozoobenthos in the salt marsh and nearby mudflat communities. Ecol. Indic. 2023, 148, 110052. [Google Scholar] [CrossRef]
  32. Cheng, H.; Zhou, X.; Dong, R.; Wang, X.; Liu, G.; Li, Q. Natural vegetation regeneration facilitated soil organic carbon sequestration and microbial community stability in the degraded karst ecosystem. Catena 2023, 222, 106856. [Google Scholar] [CrossRef]
  33. Cui, L.; Sun, H.; Du, X.; Feng, W.; Wang, Y.; Zhang, J.; Jiang, J. Dynamics of labile soil organic carbon during the development of mangrove and salt marsh ecosystems. Ecol. Indic. 2021, 129, 107875. [Google Scholar] [CrossRef]
  34. Han, X.; Liu, X.; Li, Z.; Li, J.; Yuan, Y.; Li, H.; Zhang, L.; Liu, S.; Wang, L.; You, C.; et al. Characteristics of soil organic carbon fractions and stability along a chronosequence of Cryptomeria japonica var. sinensis plantation in the rainy area of Western China. Forests 2022, 13, 1663. [Google Scholar] [CrossRef]
  35. Angst, G.; Mueller, K.E.; Castellano, M.J.; Vogel, C.; Wiesmeier, M.; Mueller, C.W. Unlocking complex soil systems as carbon sinks: Multi-pool management as the key. Nat. Commun. 2023, 14, 2967. [Google Scholar] [CrossRef]
  36. Haddix, M.L.; Paul, E.A.; Cotrufo, M.F. Dual, differential isotope labeling shows the preferential movement of labile plant constituents into mineral—Bonded soil organic matter. Glob. Change Biol. 2016, 22, 2301–2312. [Google Scholar] [CrossRef]
  37. Chen, R.; Yin, L.; Wang, X.; Chen, T.; Jia, L.; Jiang, Q.; Lyu, M.; Yao, X.; Chen, G. Mineral-associated organic carbon predicts the variations in microbial biomass and specific enzyme activities in a subtropical forest. Geoderma 2023, 439, 116671. [Google Scholar] [CrossRef]
  38. Gao, S.; Du, Y.F.; Xie, W.J.; Gao, W.H.; Wang, D.D.; Wu, X.D. Environment-ecosystem dynamic processes of Spartina alterniflora salt-marshes along the eastern China coastlines. Sci. China Earth Sci. 2014, 57, 2567–2586. [Google Scholar] [CrossRef]
  39. Yang, W.; An, S.Q.; Zhao, H.; Xu, L.Q.; Qiao, Y.J.; Chen, X.L. Impacts of Spartina alterniflora invasion on soil organic carbon and nitrogen pools sizes, stability, and turnover in a coastal salt marsh of eastern China. Ecol. Eng. 2016, 86, 174–182. [Google Scholar] [CrossRef]
  40. Zhang, N.; Huang, S.; Lei, H.; Lei, X.; Liu, P.; Yan, J. Changes in soil quality over time focusing on organic acid content in restoration areas following coal mining. Catena 2022, 218, 106567. [Google Scholar] [CrossRef]
  41. Duan, H.; Wang, L.; Zhang, Y.; Fu, X.; Tsang, Y.; Wu, J.; Le, Y. Variable decomposition of two plant litters and their effects on the carbon sequestration ability of wetland soil in the Yangtze River estuary. Geoderma 2018, 319, 230–238. [Google Scholar] [CrossRef]
  42. Li, P.; Jia, L.; Chen, Q.; Zhang, H.; Deng, J.; Lu, J.; Xu, L.; Li, H.; Hu, F.; Jiao, J. Adaptive evaluation for agricultural sustainability of different fertilizer management options for a green manure-maize rotation system: Impacts on crop yield, soil biochemical properties and organic carbon fractions. Sci. Total Environ. 2024, 908, 168170. [Google Scholar] [CrossRef]
  43. Cotrufo, M.F.; Ranalli, M.G.; Haddix, M.L. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 2019, 12, 989–994. [Google Scholar] [CrossRef]
  44. Yang, Y.; Chen, X.; Liu, L.; Li, T.; Dou, Y.; Qiao, J.; Wang, Y.; An, S.; Chang, S. Nitrogen fertilization weakens the linkage between soil carbon and microbial diversity: A global meta-analysis. Glob. Change Biol. 2022, 28, 6446–6461. [Google Scholar] [CrossRef]
  45. Feng, J.; Song, Y.; Zhu, B. Ecosystem—Dependent responses of soil carbon storage to phosphorus enrichment. New Phytol. 2023, 238, 2363–2374. [Google Scholar] [CrossRef] [PubMed]
  46. Luo, R.; Kuzyakov, Y.; Zhu, B.; Qiang, W.; Zhang, Y.; Pang, X. Phosphorus addition decreases plant lignin but increases microbial necromass contribution to soil organic carbon in a subalpine forest. Glob. Change Biol. 2022, 28, 4194–4210. [Google Scholar] [CrossRef] [PubMed]
  47. Tran, C.T.K.; Watts-Williams, S.J.; Smernik, R.; Cavagnaro, T. Root and arbuscular mycorrhizal effects on soil nutrient loss are modulated by soil texture. Appl. Soil Ecol. 2021, 167, 104097. [Google Scholar] [CrossRef]
  48. Mao, H.R.; Cotrufo, M.F.; Hart, S.C.; Sullivan, B.; Zhu, X.; Zhang, J.; Liang, C.; Zhu, M. Dual role of silt and clay in the formation and accrual of stabilized soil organic carbon. Soil Biol. Biochem. 2024, 192, 109390. [Google Scholar] [CrossRef]
  49. Li, J.; Wang, S.; Tang, Y.; Du, Y.; Xu, L.; Hu, J.; Zhu, C. Coastal reclamation alters soil organic carbon dynamics: A meta-analysis in China. Catena 2024, 240, 107975. [Google Scholar] [CrossRef]
  50. Wang, G.; Pan, J.; Yu, J.; Yan, W.; Gu, D.; Du, J. Soil organic carbon storage in Liaohe River Estuary Wetlands under restoration and multiple management strategies, based on landscape patterns. Front. Mar. Sci. 2023, 10, 1100208. [Google Scholar] [CrossRef]
  51. Zhang, Q.; Ma, J.; Gonzalez-Ollauri, A.; Yang, Y.; Chen, F. Soil microbes-mediated enzymes promoted the secondary succession in post-mining plantations on the Loess Plateau, China. Soil Ecol. Lett. 2022, 5, 79–93. [Google Scholar] [CrossRef]
  52. Wang, L.; Luo, N.; Shi, Q.; Sheng, M. Responses of soil labile organic carbon fractions and enzyme activities to long-term vegetation restorations in the karst ecosystems, Southwest China. Ecol. Eng. 2023, 194, 107034. [Google Scholar] [CrossRef]
  53. Zuccarini, P.; Sardans, J.; Asensio, L.; Peñuelas, J. Altered activities of extracellular soil enzymes by the interacting global environmental changes. Glob. Change Biol. 2023, 29, 2067–2091. [Google Scholar] [CrossRef]
  54. Georgiou, K.; Jackson, R.B.; Vindušková, O.; Abramoff, R.; Ahlström, A.; Feng, W.; Harden, J.W.; Pellegrini, A.F.A.; Polley, H.W.; Soong, J.L.; et al. Global stocks and capacity of mineral-associated soil organic carbon. Nat. Commun. 2022, 13, 3797. [Google Scholar] [CrossRef] [PubMed]
  55. Zhang, F.; Chen, X.; Yao, S.; Ye, Y.; Zhang, B. Responses of soil mineral-associated and particulate organic carbon to carbon input: A meta-analysis. Sci. Total Environ. 2022, 829, 154626. [Google Scholar] [CrossRef]
  56. Cotrufo, M.F.; Wallenstein, M.D.; Boot, C.M.; Denef, K.; Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? Glob. Change Biol. 2013, 19, 988–995. [Google Scholar] [CrossRef] [PubMed]
  57. Deng, W.; Wang, X.; Hu, H.; Zhu, M.; Chen, J.; Zhang, S.; Cheng, C.; Zhu, Z.; Wu, C.; Zhu, L. Variation characteristics of soil organic carbon storage and fractions with stand age in north subtropical Quercus acutissima Carruth. Forest in China. Forests 2022, 13, 1649. [Google Scholar] [CrossRef]
  58. Wang, M.; Wang, S.; Cao, Y.; Jiang, M.; Wang, G.; Dong, Y. The effects of hummock-hollow microtopography on soil organic carbon stocks and soil labile organic carbon fractions in a sedge peatland in Changbai Mountain, China. Catena 2021, 201, 105204. [Google Scholar] [CrossRef]
  59. Tang, L.; Gao, Y.; Li, B.; Wang, Q.; Wang, C.; Zhao, B. Spartina alterniflora with high tolerance to salt stress changes vegetation pattern by outcompeting native species. Ecosphere 2014, 5, 1–18. [Google Scholar] [CrossRef]
  60. Fu, S.; Cai, L.; Cao, J.; Chen, X. Nematode responses to the invasion of exotic Spartina in mangrove wetlands in southern China. Estuaries Coasts 2017, 40, 1437–1449. [Google Scholar] [CrossRef]
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Figure 1. Distribution map of transects and quadrats in study area.
Figure 1. Distribution map of transects and quadrats in study area.
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Figure 2. Variation in (a) SOC, (b) EOC, (c) DOC, (d) MBC, (e) POC, and (f) MAOC across different vegetation types. Different lowercase letters indicate significant differences between different vegetation types at the same layer (p < 0.05).
Figure 2. Variation in (a) SOC, (b) EOC, (c) DOC, (d) MBC, (e) POC, and (f) MAOC across different vegetation types. Different lowercase letters indicate significant differences between different vegetation types at the same layer (p < 0.05).
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Figure 3. The explanatory power of plant types, soil properties, and enzyme activities on organic carbon fractions (a), along with redundancy analysis (b) and the Mantel test (c) of soil environmental factors and organic carbon fractions. MC, moisture content; SAND, sand particles; SILT, silt particles; CLAY, clay particles; EC, electrical conductivity; TN, total nitrogen; AN, alkali hydrolyzable nitrogen; TP, total phosphorus; SUC, sucrase; AMY, amylase; BG, β-glucosidase; PPO, polyphenol oxidase; SOC, soil organic carbon; DOC, dissolved organic carbon; MBC, microbial biomass carbon; EOC, easily oxidizable organic carbon; POC, particulate organic carbon; MAOC, mineral-associated organic carbon. The larger size of square, the higher value of Pearson’s r.
Figure 3. The explanatory power of plant types, soil properties, and enzyme activities on organic carbon fractions (a), along with redundancy analysis (b) and the Mantel test (c) of soil environmental factors and organic carbon fractions. MC, moisture content; SAND, sand particles; SILT, silt particles; CLAY, clay particles; EC, electrical conductivity; TN, total nitrogen; AN, alkali hydrolyzable nitrogen; TP, total phosphorus; SUC, sucrase; AMY, amylase; BG, β-glucosidase; PPO, polyphenol oxidase; SOC, soil organic carbon; DOC, dissolved organic carbon; MBC, microbial biomass carbon; EOC, easily oxidizable organic carbon; POC, particulate organic carbon; MAOC, mineral-associated organic carbon. The larger size of square, the higher value of Pearson’s r.
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Figure 4. Structural equation modeling of the effects of environmental factors on SOC (a), and its direct and indirect standardized effect (b). Red arrows represent significant correlations, blue arrows represent non-significant correlations, and the numbers on the arrows represent standardized path coefficients. * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001.
Figure 4. Structural equation modeling of the effects of environmental factors on SOC (a), and its direct and indirect standardized effect (b). Red arrows represent significant correlations, blue arrows represent non-significant correlations, and the numbers on the arrows represent standardized path coefficients. * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001.
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Table 1. Variation in soil properties among different vegetation types in 0–20 cm.
Table 1. Variation in soil properties among different vegetation types in 0–20 cm.
Depth
(cm)		Soil PropertiesVegetation Types
BFSMSA2SA8PA
0–10MC (%)33.3 ± 2.2 a25.9 ± 2.5 b30.2 ± 1.5 ab32.7 ± 3.2 a32.8 ± 0.4 a
Clay (%)0.9 ± 0.4 b0.7 ± 0.5 b0.9 ± 0.3 b2.7 ± 0.5 a3.4 ± 0.3 a
Silt (%)46.1 ± 8.5 b42.4 ± 12.4 b46.7 ± 7.7 b62.8 ± 2.4 a69.2 ± 8.6 a
Sand (%)53.0 ± 8.9 a56.9 ± 12.7 a52.4 ± 7.7 a34.5 ± 2.9 b27.4 ± 8.3 b
pH8.9 ± 0.2 ab8.8 ± 0.1 ab8.9 ± 0.3 ab8.5 ± 0.1 b9.1 ± 0.3 a
EC (dS m−1)3.0 ± 0.2 b4.7 ± 0.5 a3.4 ± 0.7 b3.1 ± 0.3 b1.5 ± 0.1 c
TN (g kg−1)0.6 ± 0.0 c0.5 ± 0.1 cd0.4 ± 0.1 d0.8 ± 0.1 b1.2 ± 0.1 a
AN (mg kg−1)29.3 ± 8.1 c23.1 ± 1.2 c44.0 ± 25.4 bc70.7 ± 13.8 a61.3 ± 9.3 ab
TP (g kg−1)0.7 ± 0.0 b0.7 ± 0.1 ab0.7 ± 0.0 ab0.8 ± 0.0 a0.8 ± 0.1 a
10–20MC (%)31.2 ± 0.6 a27.1 ± 0.9 b29.8 ± 0.4 ab29.8 ± 1.3 ab28.6 ± 11.0 ab
Clay (%)1.0 ± 0.5 b0.4 ± 0.0 b0.8 ± 0.2 b2.4 ± 0.6 a3.0 ± 0.5 a
Silt (%)49.1 ± 3.4 bc39.3 ± 4.9 c40.0 ± 15.9 c62.2 ± 2.2 ab66.4 ± 2.9 a
Sand (%)50.0 ± 3.9 a59.1 ± 2.8 a59.3 ± 16.0 a35.4 ± 2.1 b30.6 ± 3.1 b
pH9.0 ± 0.1 a8.7 ± 0.2 b8.6 ± 0.1 b8.5 ± 0.1 b9.1 ± 0.3 a
EC (dS m−1)2.0 ± 0.2 bc3.9 ± 0.35 a3.8 ± 1.1 a3.1 ± 0.4 b1.4 ± 0.2 c
TN (g kg−1)0.6 ± 0.1 b0.5 ± 0.1 c0.4 ± 0.0 c0.8 ± 0.1 a0.9 ± 0.1 a
AN (mg kg−1)40.1 ± 12.4 b23.1 ± 9.3 b48.2 ± 29.6 ab70.7 ± 13.8 a43.9 ± 4.2 ab
TP (g kg−1)0.7 ± 0.0 a0.7 ± 0.0 a0.7 ± 0.0 a0.8 ± 0.0 a0.7 ± 0.1 a
Different lowercase letters indicate significant differences between different vegetation types at the same layer (p < 0.05).
Table 2. Variation in soil carbon-related enzymes among different vegetation types in 0–20 cm.
Table 2. Variation in soil carbon-related enzymes among different vegetation types in 0–20 cm.
Depth
(cm)		Soil Carbon-Related EnzymesVegetation Types
BFSMSA2SA8PA
0–10SUC (mg g 24 h−1)1.2 ± 0.7 b0.7 ± 0.8 b0.7 ± 0.7 b39.5 ± 4.3 a34.5 ± 7.7 a
AMY (mg g 24 h−1)3.4 ± 0.2 b8.9 ± 3.8 b9.6 ± 3.1 b23.3 ± 6.6 a25.4 ± 7.1 a
BG (μg g h−1)18.6 ± 3.9 c107.4 ± 18.9 bc204.9 ± 125.3 b242.1 ± 18.8 b515.6 ± 141.6 a
PPO (μg g 2 h−1)97.6 ± 18.9 a87.7 ± 27.8 a71.5 ± 11.4 a71.5 ± 8.1 a83.5 ± 24.4 a
10–20SUC (mg g 24 h−1)0.3 ± 0.3 c0.6 ± 0.1 c1.9 ± 1.1 c16.4 ± 3.4 a10.5 ± 2.1 b
AMY (mg g 24 h−1)2.3 ± 0.6 c8.7 ± 5.6 bc9.6 ± 5.0 bc21.7 ± 5.9 a20.0 ± 10.4 ab
BG (μg g h−1)23.4 ± 8.1 c81.8 ± 25.7 bc120.3 ± 51.9 b194.0 ± 44.3 a202.9 ± 44.0 a
PPO (μg g 2 h−1)85.1 ± 4.0 b127.6 ± 20.2 a108.4 ± 14.2 a65.0 ± 3.4 bc57.1 ± 3.9 c
Different lowercase letters indicate significant differences between different vegetation types at the same layer (p < 0.05).
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Cai, Q.; Yao, Z.; Xie, X.; Pu, L.; Zhu, L.; Jia, Z.; Chen, S.; Xu, F.; Wu, T. Effects of Spartina Alterniflora Invasion on Soil Organic Carbon Dynamics and Potential Sequestration Mechanisms in Coastal Wetlands, Eastern China. Sustainability 2025, 17, 8638. https://doi.org/10.3390/su17198638

AMA Style

Cai Q, Yao Z, Xie X, Pu L, Zhu L, Jia Z, Chen S, Xu F, Wu T. Effects of Spartina Alterniflora Invasion on Soil Organic Carbon Dynamics and Potential Sequestration Mechanisms in Coastal Wetlands, Eastern China. Sustainability. 2025; 17(19):8638. https://doi.org/10.3390/su17198638

Chicago/Turabian Style

Cai, Qi, Zhuyuan Yao, Xuefeng Xie, Lijie Pu, Lingyue Zhu, Zhenyi Jia, Shuntao Chen, Fei Xu, and Tao Wu. 2025. "Effects of Spartina Alterniflora Invasion on Soil Organic Carbon Dynamics and Potential Sequestration Mechanisms in Coastal Wetlands, Eastern China" Sustainability 17, no. 19: 8638. https://doi.org/10.3390/su17198638

APA Style

Cai, Q., Yao, Z., Xie, X., Pu, L., Zhu, L., Jia, Z., Chen, S., Xu, F., & Wu, T. (2025). Effects of Spartina Alterniflora Invasion on Soil Organic Carbon Dynamics and Potential Sequestration Mechanisms in Coastal Wetlands, Eastern China. Sustainability, 17(19), 8638. https://doi.org/10.3390/su17198638

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