Ecological Impact of Spartina alterniflora Control Methods on Tiaozini Wetland Against the Background of Carbon Neutrality

Abstract

The Tiaozini wetland is an important part of the Yancheng Coastal Wetland, which is a World Natural Heritage Site. With the invasion of Spartina alterniflora, the ecology of the wetland has been severely damaged. The local government has carried out an ecological project to remove Spartina alterniflora, but the long-term influence of ecological projects is unknown. In order to explore the overall impact of ecological restoration projects, the soil at different depths (0~20 cm, 20~40 cm, 40~60 cm) was collected in the plowing area, flooding area, and suaeda area of the Tiaozini wetland. Then, the physicochemical properties and the microbial community of the soil were comprehensively analyzed. The Tiaozini wetland has made satisfactory progress in controlling Spartina alterniflora. And the results show that Tiaozini wetland still plays an important role in carbon sequestration, with the soil organic carbon density ranging from 34.23 ± 0.02 kg/m² to 56.07 ± 0.04 kg/m², which makes it an important blue carbon sink. The high salinity and invasion of Spartina alterniflora inhibit soil nitrogen, phosphorus cycling, and soil enzyme activities. In addition, plowing destroys the microbial structure and reduces the biodiversity of the soil. While the integrated management method has little negative impact on the microbial communities of soil, the invasion of Spartina alterniflora can lead to the accumulation of heavy metals in the environment. Accordingly, this paper further reveals that regional heavy metals are all lower than the background value, but the $E_r$ (potential ecological risk factor of heavy metals) of Cd reached 21.35, indicating a high risk. Furthermore, this paper provides a scientific basis for the government to control Spartina alterniflora, as well as focusing on the overall impact of treatment methods on environmental factors and microorganisms.

1. Introduction

Coastal wetlands, an integral part of marine ecosystems, play a pivotal role in carbon sequestration [1]. In the face of global climate change, China is committed to peaking its carbon emissions before 2030 and attaining carbon neutrality before 2060. Enhancing carbon sinks and curtailing carbon emissions are vital approaches to realizing carbon neutrality [2]. Consequently, when confronted with issues like the deterioration of coastal wetlands and the decline in their carbon sequestration capabilities, it is essential to resolve these issues promptly and foster ecologically sustainable development. The Yancheng Coastal wetland in Jiangsu Province is an important part of the EAAF (East Asian–Australasian Flyway), which was listed as a World Heritage Site for Migratory Bird Habitats in China in 2019. With the wetland being listed as a UNESCO World Heritage Site, the ecological protection of Yancheng coastal wetland has gradually received attention [3]. As an important part of the Yancheng Coastal wetland, a World Natural Heritage Site, the Tiaozini wetland has played an important role in the improvement of water quality, climate regulation, and biodiversity conservation in the surrounding areas, and many rare migratory birds have stopped, bred, and wintered here.

Due to the impact of human activities, the invasion of non-native plants has gradually become one of the most common ecological problems in coastal wetlands around the world [4]. Spartina alterniflora is a wetland plant native to North America. Due to its salt tolerance and strong adaptability, it has been introduced to coastal areas in Asia, Africa, Europe, Oceania, etc. [5]. While this plant brings certain ecological benefits, it has also caused serious ecological problems. The invasion of Spartina alterniflora destroys the habitats of fish and migratory birds and also alters the structure of coastal ecosystems, resulting in a significant decline in biodiversity [6]. China has also been severely affected: with the invasion and expansion of Spartina alterniflora, the ecology of the Dongtai Tiaozini wetland in Jiangsu Province has been seriously damaged, including the change in the tidal wetland landscape, the threat of biodiversity, and the degradation of ecosystem functions. The encroachment of Spartina alterniflora also further intensifies the accumulation of heavy metals in coastal wetlands. These heavy metals accumulate in the plants of coastal wetlands and subsequently pose a severe threat to human health via the food chain [7].

Therefore, the problem of Spartina alterniflora invasion urgently needs to be addressed. There are mainly three methods for controlling Spartina alterniflora: physical, chemical, and biological methods. Abroad, the prevention and control of Spartina alterniflora mainly relies on chemical methods. In China, physical control is the primary approach, and in some regions, the introduction of natural enemies is also adopted for management [8]. The China Forestry and Grassland Administration and four other ministries issued the Special Action Plan for the Control of Spartina alterniflora (2022–2025) [9]. The restoration of the Tiaozini wetland focuses on the restoration of mudflats, the protection of habitats of precious migratory birds, the restoration of local plants, the improvement of ecosystem stability, and the function of wetland carbon sinks. To better promote the ecological protection and ecological construction of the World Heritage Site, a series of Spartina alterniflora control projects have been carried out in the Tiaozini wetland, such as clipping and flooding, and the treatment effect is remarkable. Methods such as plowing and flooding are widely used across the country, but the long-term ecological impact on the Tiaozini wetland is unknown.

Therefore, this paper sampled the soil in different areas of the Tiaozini wetland after ecological restoration and clarified the effects of treatment methods on soil physical and chemical properties, their heavy metal content, soil enzymes, and microbial communities of the Tiaozini wetland. This further revealed the interrelationship between environmental factors. The aim of this study is to comprehensively analyze the environmental factors of the Tiaozini wetland after ecological restoration, evaluate the impact of ecological restoration on carbon sink and soil environments from multiple perspectives, and also find problems and prevent the re-invasion of Spartina alterniflora.

2. Materials and Methods

2.1. Overview of the Study Area

Tiaozini wetland (32°42′ N–32°53′ N, 120°53′ E–120°58′ E) is located in the east of Qiongang Town, Dongtai City, Jiangsu Province, with an area of about 7000 hm². This region is a typical monsoon climate area in China, with concentrated rainfall and rain and heat in the same season. The average temperature throughout the year is 15 °C. The average amount of precipitation is 1060 mm. The soil particle composition is mainly silt and very fine sand [10]. With the progress of the Spartina alterniflora control project, the presence of Spartina alterniflora was cleared, followed by plowing and flooding. The sampling points in this study were located along the Tiaozini wetland, as shown in Figure 1.

2.2. Sample Collection

During the low-tide period on 22 March 2024 (The meteorological conditions during sampling on the same day are presented in Table S1), soil samples were collected by the three-stage soil sampler and the plum blossom distribution method; soil samples at three depths of 0~20 cm, 20~40 cm, and 40~60 cm were collected, respectively. After the samples were collected, they were packed in a polyethylene-sealed bag, stored in an ice pack, and brought back to the laboratory to use the quartile method; one sample was used for soil microbial testing (stored at −80 °C), one was used to detect the physical and chemical properties of the soil samples, one was used to detect heavy metals in the soil samples, and the rest were stored as backup. The collection, storage, and transportation of samples were carried out in accordance with the relevant requirements of “The specification for marine monitoring” [11].

2.3. Determination of Physical and Chemical Properties

The determination of soil pH was carried out according to potentiometry [12]. Meanwhile, the electrical conductivity (EC) was measured using the electrode method [13]. Total nitrogen (TN) was determined by an alkaline potassium persulfate oxide ultraviolet spectrophotometer, and total phosphorus (TP) was determined by potassium persulfate oxidation spectrophotometry [14]. Soil total organic carbon (SOC) was determined by muti NC 3100 (Jena, Germany) [15]. Soil-dissolved organic carbon (DOC) was determined by the colorimetric method [16]. Divalent iron (Fe²⁺) was determined by the ortho phenanthroline colorimetric method [17]. The reducing species (RS) were determined by potassium dichromate oxidation [18].

2.4. Computation of Soil Organic Carbon Density

In order to explore the role of the Tiaozini wetland in carbon sequestration, the soil organic carbon density at the sampling sites was calculated based on the soil organic carbon content value measured by sampling; the calculation formula is as follows [19]:

$SOCD=SOC\times BD\times H\times 0.01$

where SOCD is the soil organic carbon density (kg/m²), SOC is the soil organic carbon (g/kg), BD is the bulk density of the soil (g/cm³), and H is the thickness of the soil layer (cm).

2.5. Detection and Risk Assessment of Heavy Metals

Cr, Cd, Cu, Zn, and Ni were detected following “Flame atomic absorption spectrophotometry” [20]. Employing GBW07556 (GSS-65) as the soil standard reference material in quality control, we ensured that the error was within 5%. Firstly, 0.2 g of the dried soil sample was weighed into a 50 mL PTFE crucible before adding 10 mL of hydrochloric acid, which was heated and decomposed on the electric hot plate. When about 3 mL of the digestion solution remained, we added 9 mL of nitric acid. We then covered the solution and heated it until there were no obvious particles, added 5 mL of hydrofluoric acid, opened the lid, and heated the flying silicon for 30 min. Then, we added 1 mL of perchloric acid until white smoke was emitted. The acid was heated to ensure the remaining object took the shape of non-flowing liquid beads, and added 3 mL of nitric acid solution to dissolve the residue warmly. Finally, we transferred it to a 25 mL volumetric flask, used nitric acid solution on the reticle line, and let it stand for the measurement. The contents of Cr, Cu, Zn, Cd, and Ni were measured by the Atomic Absorption Spectrometer (Z-2000, HITACHI, Tokyo, Japan) in the digestion solution.

Based on the values of each heavy metal at the sampling point and the potential ecological risk index method, the impact of heavy metal elements in sediments on ecological pollution is reflected from the perspective of biological toxicity [21], which can reflect the comprehensive ecological risk degree of several heavy metal elements in the region, and the calculation formula is as follows:

$C_f^i=C_0^i/C_n^i$

$E_r^i=T_r^i\times C_f^i$

$RI=\sum _{i=1}^m{E}_r^i$

where $C_0^i$ is the measured content of the heavy metals in the sediment (mg/kg); $C_n^i$ is the reference value of the heavy metal background of the sediment (mg/kg); $C_f^i$ is the pollution index of the ith heavy metal; $T_r^i$ is the toxicity response coefficient of the ith heavy metal (Table S2); and $E_r^i$ is the potential ecological risk factor of the ith heavy metal. $RI$ is a comprehensive potential ecological risk index for a variety of heavy metals.

2.6. Soil Microbial Determination

Soil microorganisms were subjected to high-throughput sequencing using dry ice and sent to Majorbio for determination. The V3–V4 hypervariable region of 16S rDNA in soil samples was PCR-amplified, sequenced by high-throughput sequencing methods, and finally sequenced by the bioinformatics fraction method [12].

2.7. Soil Enzyme Activity Assay

The soil enzyme activity was determined by colorimetry [22]. Peroxidase activity was measured using the colorimetric method and expressed in milligrams of purple theogallin produced in 1 g of soil after 2 h. A total of 1 g of soil was placed in a 50 mL triangular flask with 10 mL of pyrogallol solution, and 2 mL of hydrogen peroxide solution was added and incubated at 30 °C for 2 h after shaking. In total, 4 mL of citric acid-phosphate buffer at pH 4.5 and 35 mL of ether were added after extraction, and the colorimetric was performed at a wavelength of 430 nm after extraction for 30 min. Urease activity was expressed in milligrams of NH₃-N contained in 1 g of soil after 24 h. We took 5 g of soil and put it in a 50 mL triangular flask, added 1 mL of toluene, left it for 15 min, added 10 mL of urea solution and 20 mL of pH 6.7 citrate buffer, shook it, and incubated at 37 °C for 24 h. Then, we took the sample out, filtered it and took 3 mL of filtrate into a 50 mL volumetric flask, added 4 mL of sodium phenol solution, and 3 mL of sodium hypochlorite solution at a constant volume, shook it, observed the color that developed after 20 min, and compared it to the color at 578 nm. Sucrase activity was expressed as milligrams of 1 g of soil glucose after 24 h. A total of 5 g of soil was placed in a 50 mL triangular flask, 15 mL of sucrose solution, 5 mL of pH 5.5 phosphate buffer, and 5 drops of toluene. This was shaken and incubated at 37 °C for 24 h; 1 mL of filtrate was taken into a 50 mL volumetric flask after filtration and 3 mL of 3,5-dinitrosalicylic acid was added. This was then heated in a boiling water bath for 5 min, the volume was fixed to 50 mL, and the wavelength was 508 nm for colorimetry.

2.8. Data Analysis

Data processing was performed with Microsoft Excel and graphs and analysis were produced with Origin 2022. The data from high-throughput sequencing were analyzed using the Shanghai Majorbio Biological Cloud Platform. Environmental factors such as soil physical and chemical parameters, heavy metals, and soil enzymes were uploaded to the cloud platform for correlation analysis.

3. Results and Discussion

3.1. pH and Conductivity of Soil at Different Sampling Sites

The pH value of different sampling points is shown in Figure 2a. The soil was generally weakly alkaline, with a pH value between 8.09 and 8.59. Soil electrical conductivity (EC) was positively correlated with soil total salinity; when the EC value was greater than 4.0 ms/cm, the soil was saline–alkaline. As shown in Figure 2b, the surface soil at the sampling site was generally saline–alkaline and the EC value decreased with the increase in soil depth. that the study in ref. [23] shows, in subtropical areas, the surface EC value of tidal wetlands is affected by environmental evaporation, which explains the high surface salinity of the sampling site. The salinity of the soil in the Tiaozini wetland may be caused by seawater; the salinity of the plowing area (SA, SB, SC, PL, MP points) was high, and the salinity of the surface soil at the MP point was the highest, reaching 7.87 ± 0.05 ms/cm. The PL point is a new plowing point with a low EC value at deep depths because deep tillage forms clods in the subsoil and cuts off the capillary pipes rising from the groundwater, thus reducing the salinity of the deep layers of the soil [24]. The surface layer of the flooding area (FL point) has the lowest EC value since it improves the salinity of highly saline soil by changing the soil permeability [13]. The salinity of the suaeda area (SO and SS points) is low, which may be due to the improvement effect of plants on saline–alkali soil.

3.2. Organic Carbon Density of Soil at Different Sampling Sites

Coastal saline wetlands are one of the important types of coastal wetland ecosystems, with high carbon burial and sedimentation rates, which are important carbon sinks [25]. Studies have shown that the average carbon sequestration rate of coastal wetlands around the world is 218 gC/(m²·a), compared with the carbon sequestration rate of terrestrial forest ecosystems, which is only about 4~5 gC/(m²·a) [26]. Many factors affect the carbon sequestration of coastal wetland soil, such as salinity, tides, and human activities. About 50% of blue carbon ecosystems are rapidly disappearing due to the direct and indirect impacts of human disturbances and climate change [27].

Figure 3a shows that the organic carbon density of the suaeda area (SO, SS points) is higher than that of the plowing area (SA, SB, SC, PL, MP) and flooding area (FL point) because plant coverage is an important contributor to soil organic carbon production. Plant coverage can delay the degradation of lignin by inhibiting peroxidase activity so that plant-derived carbon can accumulate in soil organic carbon [28]. In the plowing area (SA, SB, SC, PL, MP) and flooding area (FL point), the soil bulk density was changed by the invasion of Spartina alterniflora; the soil carbon sequestration function was also weakened by tillage or inundation. The coastal wetland of Yancheng in Jiangsu Province is an important coastal wetland resource in China, and it is also an effective carbon sink for mitigating global warming in Jiangsu Province and even East China. The carbon cycle of coastal wetlands is mainly carbon input, carbon output, and carbon deposition. Vegetation is the main contributor to the input, and carbon decomposition depends on the role of microorganisms [29]. Although the invasion of Spartina alterniflora increases the organic carbon content of salt marshes for a short period of time, Spartina alterniflora reduces SOC by decreasing soil bulk density over time [30]. Therefore, the invasion of Spartina alterniflora can weaken the role of carbon sequestration in coastal wetlands. Comparing the soil organic carbon density of other coastal wetlands in China, the soil organic carbon density (0~30 cm) of coastal wetlands in the Tianjin–Hebei region [31] ranged from 2.920 kg/m² to 3.678 kg/m². The soil organic carbon density (0~100 cm) of the wetlands of the Minjiang River estuary [32] ranged from 63.21 kg/m² to 98.30 kg/m². The organic carbon density of the wetland soil (0~30 cm) in the eastern part of Hainan Island [33] ranged from 3.73 kg/m² to 14.01 kg/m². The organic carbon density in this study area (0~60 cm) ranged from 34.23 ± 0.02 kg/m² to 56.07 ± 0.04 kg/m², indicating that the organic carbon density of Tiaozini wetland is relatively high in coastal wetlands, and it is also an important blue carbon sink.

Figure 3b shows that soil organic carbon density is highest at the surface, accounting for more than 40% of the sampling depth. The correlation coefficient between soil organic carbon density and physicochemical properties was obtained (Figure 3c), which showed that soil organic carbon density was significantly positively correlated with total nitrogen (TN) (p < 0.01) with a correlation coefficient of 0.73, positively correlated with Fe²⁺ (p < 0.05) and a correlation coefficient of 0.47, negatively correlated with pH (p < 0.05) and a correlation coefficient of −0.48. The carbon cycle in soil is influenced by a combination of factors; for example, changes in soil nitrogen content can alter soil organic carbon levels by affecting plants and soil microorganisms. As one of the most abundant elements in the earth’s crust, iron plays a key role in soil nutrient cycling, in which free iron combines with soil-soluble organic carbon (DOC) to form organic–inorganic complexes, which then promote the formation of soil organic carbon [34]. The pH value is an important factor affecting soil organic carbon content; pH also changes the ability of soil organic matter to participate in the adsorption process by affecting its protonation [35].

3.3. Total Nitrogen and Total Phosphorus in Soil at Different Sampling Sites

Figure 4a shows that the total nitrogen (TN) in the soil surface layer was relatively high, with 0.18~0.38 g/kg in the plowing area (SA, SB, SC, PL, MP points), 0.22 g/kg in the flooding area (FL point), and 0.24~0.29 g/kg in suaeda area (SO, SS points). The results of Figure 4c show that total soil nitrogen (TN) was positively correlated with soil organic carbon (SOC), electrical conductivity (EC), and Fe²⁺ and negatively correlated with pH. Soil nitrogen is mainly stabilized by microorganisms; it is affected by many factors, such as the increase in soil organic matter, which can improve nitrogen fixation, and the redox reaction of iron, which also affects the carbon and nitrogen cycle in the soil. Soil nitrogen and pH were inversely correlated; because soil nitrogen is mainly composed of protein substances composed of amino acids, the adsorption of amino acids is pH-dependent [36], while a high pH leads to a decrease in the proportion of amino acids adsorbed by the soil. The TN content in the surface soil of the Tiaozini wetland is relatively low, at 0.2~0.4 g/kg. Maybe the soil of the Tiaozini wetland is saline–alkaline soil, and the soil nutrient deficiency is caused by excessive soil salinity. High salinity inhibits the decomposition of organic matter by soil nitrogen and the production of nutrients needed to maintain soil [37], resulting in low soil fertility and affecting crop growth. The invasion of Spartina alterniflora has crowded out the growth space of native plants, and the monolithic ecosystem has led to a further reduction in soil fertility, forming a vicious circle.

Figure 4b shows that the total phosphorus of the surface soil in the plowing area (SA, SB, SC, PL, MP points) was 0.12~0.22 g/kg; the flooding area (FL point) was 0.09 g/kg; and the suaeda area (SO; SS points) was 0.07~0.08 g/kg. Figure 4c shows a positive correlation between soil total phosphorus, reducing substances, and electrical conductivity. Phosphorus is one of the main nutrients in wetland ecosystems and is important for maintaining soil productivity and function. Soil phosphorus is affected by soil-reducing substances; the redox reaction of reducing substances changes the transformation and migration of substances in the process of soil formation. Thereby, it affects the morphology and availability of soil elements, such as the reduction in iron to Fe²⁺ driven by microorganisms, which is released under anaerobic conditions ${\mathrm{P}\mathrm{O}}_4^{3-}$ [38]. Soil salinity also affects soil phosphorus content, and high-salinity inputs can disrupt the phosphorus cycle and inhibit plant growth, leading to the accumulation and mineralization of soil organic phosphorus [39]. It affects phosphorus conversion and hinders the uptake of phosphorus by crops, as the abundant presence of Na⁺ and Cl⁻ in the soil has toxic effects on plants, inhibiting the growth of crop roots and nutrient uptake [40]. The total phosphorus of the surface soil in the plowing area (SA, SB, SC, PL, MP) was higher than that in the flooding area (FL point) and suaeda area (SO, SS point); the total phosphorus in the suaeda area (SO, SS point) was lower than that in the suaeda area at the senescence stage, which may be due to the phosphorus uptake effect of plant growth. The surface content of total phosphorus in the plowing area (SA, SB, SC, PL, MP points) was high, and there was an accumulation of soil phosphorus, which may be caused by the lack of vegetation after the treatment of Spartina alterniflora and the high salinity of the location.

3.4. Enzyme Activity in Soil at Different Sampling Sites

Soil enzymes can convert soil organic matter into usable nutrients, promote soil carbon, nitrogen, and phosphorus cycling, reflect the intensity of soil biochemical reactions, and also play a very important role in maintaining the health, stability, and resilience of ecosystems [41]. Therefore, soil enzyme activity is an important biological indicator of soil health and quality. Figure 5a shows that the catalase in the soil surface layer is 0.70~1.40 mg/kg in the plowing area (SA, SB, SC, PL, MP points), 0.49 mg/kg in the flooding area (FL point), and 0.77~0.95 mg/kg in suaeda area (SO and SS points). The soil at the sampling site decreased with the increase in soil depth, but the flooding area (FL point) increased with the increase in depth, which may be caused by the inundation project. As can be seen from Figure 4c, soil catalase activity was positively correlated with TN, TP, SOC, and Fe²⁺. Catalase is very stable in soil and can promote the oxidation of soil compounds [42]. While the effect of EC on catalase is less studied, high salinity can make catalase more unstable [43]. Figure 5b shows that urease has high surface activity in suaeda salsa (SO, SS points), and there is little difference between other sites. Figure 4c shows that urease activity is positively correlated with carbon and nitrogen, while pH is negatively correlated with soil urease; pH affects the structure and function of microbial communities, while alkaline soil urease activity is generally low. As a key enzyme in the global nitrogen cycle, urease is widely distributed in nature, which can indicate the intensity of soil nitrogen conversion and the plant availability of different nitrogen compounds [44]. Figure 5c shows that the activity of sucrase in the plowing area (SA, SB, SC, PL, MP points) and suaeda area (SO, SS points) was generally higher than that in the flooding area (FL point). As can be seen from Figure 4c, sucrase activity was positively correlated with phosphorus and negatively correlated with Fe²⁺ and nitrogen, and it can also be seen that soil nitrogen and phosphorus had an effect on soil sucrase activity. Soil sucrase reflects the transformation of carbon in the soil; the activity of sucrase can reflect the accumulation and decomposition of soil organic carbon, and it can characterize the intensity of soil biological activity, which is closely related to soil nutrients and microbial numbers.

3.5. Comparison of Soil Microbial Communities at Different Sampling Sites

The valid sequences of all samples were clustered with 97% agreement, and a total of 25,428 OTUs were obtained. The analysis of the soil samples at different points in the Tiaozini wetland (Figure 6a) showed that the number of PL points in the surface layer was the highest; the number of middle layer SO points was the highest; and the number of deep SO points was the highest. The number of OTUs in different levels of the same habitat was the highest in the deep layer of the SA point, PL point surface, FL point depth, MP point depth, SO point depth, and SS point middle layer. According to the number of microorganisms specific to each site, there were more microorganisms in the suaeda area (SS and SO points) than in the flooding area (FL point) and plowing area (SA, PL, and MP points). Comparing the abundance of microbial samples at the sampling sites (Figure 6b), the dominant bacterial phyla were Proteobacteria, Desulfobacteria, Chloroflexi, Bacteroidota, and Acidobacteriota.

The abundance of Proteobacteria (relative abundance 18.32–43.40%), Desulfobacteria (relative abundance 6.63–18.58%), and Campylobacter (relative abundance 0.31–16.49%) was highest in the plowing areas (SA, PL, and MP sites). Proteobacteria have metabolic versatility, including chemoautotrophic, chemoheterotrophic, and phototrophic microorganisms [45]. By contrast, Desthiobacteria are associated with anaerobic fermentation, methanogens, and sulfur oxidants [46]. This study has shown that Campylobacter may be involved in sulfur disproportionation. Sulfur disproportionation may be a non-negligible microbial catabolism that plays an important role in the cycling of carbon, nitrogen, sulfur, and metals [47]. These phyla are the main phyla in many types of soil, and the abundance of these phyla in the tillage area is high, which shows that the structure of the bacteria in the tillage area is more homogeneous, and the diversity of bacteria is low. The abundance of Brylobacterium viruginosa (relative abundance 2.44–21.33%) and nitrifying bacteria (relative abundance 0.39–4.64%) was the highest in suaeda salsa (SO and SS points). Brylocyte chlorocystis immobilizes inorganic CO₂ and oxidizes carbon monoxide and nitrite while reducing nitrate and iron [48]. In addition, nitrifying bacteria have the potential for carbon dioxide fixation; they may use manganese oxidation to obtain carbon fixation energy [49]. These phyla are all related to carbon cycling, indicating that the abundance of carbon sequestration bacteria in the suaeda salsa area is high, and it can be seen that vegetation has a positive effect on soil carbon sequestration. Among them, the abundance of bacteria promoting plant growth in suaeda salsa SS at the seedling stage was higher than that in suaeda salsa at the senescence stage. Acidobacteria (relative abundance 1.23–11.65%) were the highest in the flooding area (FL point). Acidobacteria are found in various environments, such as soil, freshwater, and marine ecosystems [50]. The Spartina alterniflora environment in the inundation area is similar to that of the offshore area, which may lead to similarities in the microbial phylum structure of microorganisms in the sediment and microorganisms in the marine ecosystem. Overall, the invasion of Spartina alterniflora reduced the biodiversity of the Tiaozini wetland; plowing would cause certain damage to the tidal flats and aggravate the reduction in microbial diversity, which is consistent with the findings of previous research [51]. Because in the process of managing Spartina alterniflora, the destruction of roots can greatly change the physical environment of the soil on which native plants and macrofauna species depend, this leads to the loss of biodiversity in the soil ecosystem [5]. Therefore, the removal of Spartina alterniflora does not necessarily contribute to the increase in native biodiversity; in some cases, this may lead to further biodiversity loss. Integrated management is the most appropriate method to control Spartina alterniflora with minimal negative impact on biodiversity, for instance, integrating the physical removal approach with the biological substitution method [52]. Ecological restoration projects should not only remove invasive species but also focus on how to restore local biodiversity after restoration.

3.6. Heavy Metal Values of the Soil at Different Sampling Sites

Figure 7a shows that the contents of heavy metals Cd, Cr, Pb, Cu, and Zn in the sediments of the plowing area (SA, SB, SC, PL, MP points), suaeda area (SO, SS points) and flooding areas (FL points) all meet the national marine sediment quality class I standards, which are lower than the background values (Table S3), indicating that the regional heavy metals may be more closely related to the topography and wetland soil types and geological background conditions. It has been demonstrated that the intrusion of Spartina alterniflora contributes to the elevated accumulation of heavy metals in coastal wetlands [20]. Nevertheless, following the implementation of wetland restoration initiatives, the concentrations of these heavy metals are considerably below the background levels. Compared with other coastal wetlands, such as the Bohai coastal wetland [53], the Huanghai Sea [54], and the Zhuhai Delta [55], the Tiaozini wetland has a low heavy metal value and may be less polluted by industry and humans because it is located in an ecological reserve. The heavy metal values at the SA, SB, and SC points were higher than those at other points, which may be because these three points are closer to the habitat of migratory birds, and the heavy metal values are easily affected by migratory bird activities. Because the life activities of migratory birds in the habitat will affect the ecological risk value of heavy metals, the excrement produced by migratory birds will cause heavy metals to enter wetland sediments and produce certain ecological hazards [56].

Figure 7b shows that the potential ecological risk factors ($E_r^i$) associated with Cd are markedly higher than those of other heavy metals, indicating that Cd is the main ecological risk factor in this study area. This finding aligns with the results obtained from previous research [11]. The heavy metal pollution index ($C_f^i$ < 1), potential ecological risk factors ($E_r^i$ < 40), and regional potential ecological risk index ($RI$ < 150) were all at the low ecological risk level (Table S4). The order of the risk factors and value of each heavy metal was Cd > Cu > Pb > Cr > Ni. Research in the literature indicates that Cd is also a high ecological risk element in many of our other coastal wetlands [57,58]. Cd is highly toxic, easy to accumulate, and also has a long residual time, which can affect soil microbial function and community composition, harm the soil ecosystem function, and even affect animals throughout the food chain [59]. The Tiaozini wetland is also an important part of the World Natural Heritage Site, where many rare migratory birds stop, such as the spoon-billed sandpiper, the little greenshank, and the Oriental white stork. So, in the process of the construction and protection of coastal wetlands, we should pay more attention to and improve the ecotoxicity and environmental behavior of Cd.

4. Conclusions

The results show that the soil organic carbon density in the Tiaozini wetland ranges from 34.23 ± 0.02 kg/m² to 56.07 ± 0.04 kg/m², and its carbon sink function is significant. The suaeda area has outstanding carbon sequestration effects, indicating that plant coverage is conducive to the exertion of the wetland’s carbon sink function. High salinity and alkalinity inhibit soil nitrogen and phosphorus cycling and soil enzyme activity. The invasion of Spartina alterniflora disrupts soil material cycling, and it takes a long time for the soil to recover after its removal. The management of Spartina alterniflora in the Tiaozini wetland has achieved good results, and comprehensive control methods are more appropriate. In addition, the heavy metal values at each point in the Tiaozini wetland are lower than the background values, with the $E_r$ (potential ecological risk factor of heavy metals) of Cd reaching up to 21.35, indicating a relatively high risk. Therefore, the removal of invasive species should be carried out in a manner tailored to local conditions to prevent re-invasion.

Author Contributions

Conceptualization, X.W. and X.C. (Xin Cao); Data Curation, X.W.; Data Analysis, X.W.; Investigation, X.W., X.C. (Xiao Chen) and X.Z.; Methodology, X.W., Q.H. and X.Z.; Software, X.W.; Visualization, X.W.; Writing—Original Draft, X.W.; Supervision, Q.H., X.C. (Xiao Chen), X.L., X.S. and X.C. (Xin Cao); Validation, X.L. and X.C. (Xin Cao); Resources, X.C. (Xin Cao); Review and Editing, X.C. (Xin Cao). All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1. Schematic diagram of the sampling points of the Dongtai Tiaozini wetland. The plowing area (SA, SB, SC, PL, MP points), SA, SB, SC, and PL used to be Spartina alterniflora patches, MP used to be Spartina alterniflora patches, and PL represents a newly cultivated area. The suaeda area (SO, SS points), SO, is the senescent suaeda spot; SS is the point of suaeda at the seedling stage. The flooding area (FL point), FL, used to be Spartina alterniflora patches.

Figure 2. (a) pH of the soil at different sampling sites; (b) electrical conductivity (EC) of soil at different sampling points.

Figure 3. (a) Organic carbon density of soil at different sampling sites and depths; (b) proportion of soil organic carbon density at different depths at each point; and (c) correlation coefficient between soil organic carbon density and other physical and chemical properties of soil (‘*’ indicates a significant difference at the 0.05 level (p [less than] 0.05), and ‘**’ indicates a highly significant difference at the 0.01 level (p [less than] 0.01)).

Figure 4. (a) Total nitrogen (TN) in soil at different sampling sites and at different depths; (b) total phosphorus (TP) in soil at different sampling sites and depths; and (c) PCA analysis of soil physicochemical prorties and soil enzyme activities.

Figure 5. Soil enzyme activity at different sampling sites and at different depths: (a) catalase activity; (b) urease activity; and (c) sucrase activity.

Figure 6. (a) Venn diagram of OTUs at different depths and locations of sampling points; (b) comparison of soil microbial community phylum at different sampling sites (1 is 0~20 cm, 2 is 20~40 cm, and 3 is 40~60 cm).

Figure 7. (a) Heavy metal values of soil at different sampling sites; (b) pollution index ([Forumla omitted. See PDF.]) and potential ecological risk factors ([Forumla omitted. See PDF.]) of each heavy metal in soil at different sampling sites.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17060877/s1, Table S1: Meteorological conditions on the day of sampling; Table S2: Toxicity response coefficients of each heavy metal element; Table S3: The first class standard value of heavy metal marine sediments and the standard value of soil in Jiangsu Province; Table S4: The level of potential risk of heavy metals in sediments.

References

1. Guan, Y.; Tian, X.; Bai, J.; Zhou, H.; Wen, L. Assessing the Carbon Neutrality Capacity of Wetland and Non-Wetland Ecosystems in a Typical Coastal Region. Sustain. Prod. Consum.; 2025; 53, pp. 17-27. [DOI: https://dx.doi.org/10.1016/j.spc.2024.11.028]

2. Friedlingstein, P.; O’Sullivan, M.; Jones, M.W.; Andrew, R.M.; Hauck, J.; Olsen, A.; Peters, G.P.; Peters, W.; Pongratz, J.; Sitch, S. et al. Global Carbon Budget 2020. Earth Syst. Sci. Data; 2020; 12, pp. 3269-3340. [DOI: https://dx.doi.org/10.5194/essd-12-3269-2020]

3. Sun, X. Habitat suitability and potential biological corridors for waterbirds in Yancheng coastal wetland of China. Ecol. Indic.; 2023; 148, 110090. [DOI: https://dx.doi.org/10.1016/j.ecolind.2023.110090]

4. Dawei, W.; Zhenqi, W.; Wei, H.; Changhu, L.; Pan, C. The Native Reed-specific Bird, Reed Parrotbill, Has Been Detected in Exotic Smooth Cordgrass. Ecol. Evol.; 2023; 13, e10417. [DOI: https://dx.doi.org/10.1002/ece3.10417] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37575595]

5. Wang, S.; Martin, P.A.; Hao, Y.; Sutherland, W.J.; Shackelford, G.E.; Wu, J.; Ju, R.; Zhou, W.; Li, B. A global synthesis of the effectiveness and ecological impacts of management interventions for Spartina species. Front. Environ. Sci. Eng.; 2023; 17, 141. [DOI: https://dx.doi.org/10.1007/s11783-023-1741-x]

6. Mo, X.; Zhang, Z.; Li, Y.; Chen, X.; Zhou, S.; Liu, J.; Wu, B.; Chen, S.; Zhang, M. Inhibition of Spartina alterniflora Growth Alters Soil Bacteria and Their Regulation of Carbon Metabolism. Environ. Res.; 2023; 236, 116771. [DOI: https://dx.doi.org/10.1016/j.envres.2023.116771]

7. Sabet Aghlidi, P.; Cheraghi, M.; Lorestani, B.; Sobhanardakani, S.; Merrikhpour, H. Analysis, spatial distribution and ecological risk assessment of arsenic and some heavy metals of agricultural soils, case study: South of Iran. J. Environ. Health Sci. Eng.; 2020; 18, pp. 665-676. [DOI: https://dx.doi.org/10.1007/s40201-020-00492-x]

8. Grevstad, F.S.; Strong, D.R.; Garcia-Rossi, D.; Switzer, R.W.; Wecker, M.S. Biological control of Spartina alterniflora in Willapa Bay, Washington using the planthopper Prokelisia marginata: Agent specificity and early results. Biol. Control; 2003; 27, pp. 32-42. [DOI: https://dx.doi.org/10.1016/S1049-9644(02)00181-0]

9. Yang, L.; Chi, Y.; Lu, H.; Sun, G.; Lu, Y.; Li, H.; Luo, Y. Effects of the Comprehensive Elimination of Spartina alterniflora along China’s Coast on Blue Carbon and Scenario Prediction after Ecological Restoration. J. Environ. Manag.; 2024; 369, 122283. [DOI: https://dx.doi.org/10.1016/j.jenvman.2024.122283]

10. Gu, W.; Yang, Z.; Yang, Q.; Zhang, H.; Wang, Y.; He, Q.; Ge, C. Wind-Driven Mechanisms for Allometric Growth of Spartina alterniflora Leaves: Implications for Adaptive Management. Ocean Coast. Manag.; 2025; 262, 107556. [DOI: https://dx.doi.org/10.1016/j.ocecoaman.2025.107556]

11. Cai, Z.; Li, G.; Guang, Z.; Wang, S. Content and ecological risk assessment of heavy metals in surface sediments of Tiaozini migratory bird habitat and its adjacent intertidal zone in Dongtai. J. Appl. Oceanogr.; 2023; 42, pp. 656-666.

12. Ren, Y.; Wang, Y.; Zhang, Q.; Yang, X.; Liu, M. Characteristics of soil microbial diversity in the embankment area of Tiaozini wetland. J. Zhejiang Agric. Sci.; 2023; 64, pp. 1283-1291. (In Chinese) [DOI: https://dx.doi.org/10.16178/j.issn.0528-9017.20220465]

13. Dai, Y.; Senge, M.; Ito, K.; Onishi, T.; Yoshiyama, K. Experimental Evaluation of Irrigation Methods for Soil Desalinization. Paddy Water Environ.; 2015; 13, pp. 159-165. [DOI: https://dx.doi.org/10.1007/s10333-014-0417-3]

14. Wang, S. Sediment-Water Interface Process of Lakes: Nitrogen and Phosphorus Biogeochemistry; China Science Press: Beijing, China, 2013; pp. 60-74. (In Chinese)

15. Qian, B.; Liu, L.; Xiao, X. Comparative tests on different methods for content of soil organic matter. J. Hohai Univ. (Nat. Sci.); 2011; 39, pp. 34-38. (In Chinese) [DOI: https://dx.doi.org/10.3876/j.issn.1000-1980.2011.01.008]

16. Zhan, X.; Zhou, L. Colorimetric determination of dissolved organic carbon in soil solution and water environment. China Environ. Sci.; 2002; 22, pp. 433-437. (In Chinese) [DOI: https://dx.doi.org/10.3321/j.issn:1000-6923.2002.05.013]

17. Yang, J.; Zhou, X.; Hu, L.; Zhang, Y. Optimizing Conditions for Soil Available Iron Determination with Method of Phenanthroline Colorimetry. Soils; 2013; 45, pp. 718-721. (In Chinese) [DOI: https://dx.doi.org/10.13758/j.cnki.tr.2013.04.024]

18. Lu, R. Methods of Soil Agricultural Chemical Analysis; China Agriculture Press: Beijing, China, 2000; 79p. (In Chinese)

19. Han, L.; Wan, Z.; Guo, Y.; Song, C.; Jin, S.; Zuo, Y. Estimation of Soil Organic Carbon Storage in Palustrine Wetlands, China. Int. J. Environ. Res. Public Health; 2020; 17, 4646. [DOI: https://dx.doi.org/10.3390/ijerph17134646]

20. Li, J.; Leng, Z.; Jia, H.; Wei, L.; Yuguda, T.K.; Du, D. Effect of Seawall Embankment Reclamation on the Distribution of Cr, Cu, Pb and Zn Pollution in Invasive Spartina alterniflora and Native Phragmites Australis Coastal Saltmarshes of East China. Biology; 2023; 12, 253. [DOI: https://dx.doi.org/10.3390/biology12020253]

21. Hakanson, L. An Ecological Risk Index for Aquatic Pollution Control. A Sedimentological Approach. Water Res.; 1980; 14, pp. 975-1001. [DOI: https://dx.doi.org/10.1016/0043-1354(80)90143-8]

22. Guan, S. Soil Enzymes and Their Research Methods; Agriculture Press: Beijing, China, 1986; pp. 294-297. (In Chinese)

23. Liu, Y.; Zhang, C.; Liu, X.; Li, C.; Sheuermann, A.; Xin, P.; Li, L.; Lockington, D.A. Salt Transport Under Tide and Evaporation in a Subtropical Wetland: Field Monitoring and Numerical Simulation. Water Resour. Res.; 2022; 58, e2021WR031530. [DOI: https://dx.doi.org/10.1029/2021WR031530]

24. Xong, X.; Zhang, H.; Araya, K.; Teramoto, C.; Ohmiya, K.; Zhu, B.; Sun, Q.; Wang, W. Improvement of Salt-Affected Soils by Deep Ploughing—Part 1: Plot Field Tests in a Saline Soil (Solonchak) Region. Eng. Agric. Environ. Food; 2011; 4, pp. 18-24. [DOI: https://dx.doi.org/10.1016/S1881-8366(11)80004-2]

25. Callaway, J.C.; Borgnis, E.L.; Turner, R.E.; Milan, C.S. Carbon Sequestration and Sediment Accretion in San Francisco Bay Tidal Wetlands. Estuaries Coasts; 2012; 35, pp. 1163-1181. [DOI: https://dx.doi.org/10.1007/s12237-012-9508-9]

26. Mcleod, E.; Chmura, G.L.; Bouillon, S.; Salm, R.; Björk, M.; Duarte, C.M.; Lovelock, C.E.; Schlesinger, W.H.; Silliman, B.R. A Blueprint for Blue Carbon: Toward an Improved Understanding of the Role of Vegetated Coastal Habitats in Sequestering CO₂. Front. Ecol. Environ.; 2011; 9, pp. 552-560. [DOI: https://dx.doi.org/10.1890/110004]

27. He, L.; Ye, S.; Yuan, H.; Yu, C.; Ding, X.; Zhao, G.; Pei, S.; Wang, J.; Yang, S.; Yu, X. et al. Enhanced Sequestration of Carbon in Ocean Sediments as a Means to Reduce Global Emissions: A Case Study from a Coastal Wetland Restoration Project in the Liaohe Delta, Northeast China. Palaeogeogr. Palaeoclimatol. Palaeoecol.; 2024; 648, 112286. [DOI: https://dx.doi.org/10.1016/j.palaeo.2024.112286]

28. Zheng, H.; Liu, D.; Yuan, J.; Li, Y.; Li, J.; Miao, Y.; Chen, Z.; He, T.; Ding, W. Wetland Restoration after Agricultural Abandonment Enhances Soil Organic Carbon Efficiently by Stimulating Plant- Rather than Microbial-Derived Carbon Accumulation in Northeast China. CATENA; 2024; 241, 108077. [DOI: https://dx.doi.org/10.1016/j.catena.2024.108077]

29. Hao, Q.; Song, Z.; Zhang, X.; He, D.; Guo, L.; van Zwieten, L.; Yu, C.; Wang, Y.; Wang, W.; Fang, Y. et al. Organic Blue Carbon Sequestration in Vegetated Coastal Wetlands: Processes and Influencing Factors. Earth-Sci. Rev.; 2024; 255, 104853. [DOI: https://dx.doi.org/10.1016/j.earscirev.2024.104853]

30. Xu, X.; Wei, S.; Chen, H.; Li, B.; Nie, M. Effects of Spartina Invasion on the Soil Organic Carbon Content in Salt Marsh and Mangrove Ecosystems in China. J. Appl. Ecol.; 2022; 59, pp. 1937-1946. [DOI: https://dx.doi.org/10.1111/1365-2664.14202]

31. Wang, Q.; Song, J.; Cao, L.; Li, X.; Yuan, H.; Li, N. Distribution and Storage of Soil Organic Carbon in a Coastal Wetland under the Pressure of Human Activities. J. Soils Sediments; 2017; 17, pp. 11-22. [DOI: https://dx.doi.org/10.1007/s11368-016-1475-5]

32. Chen, Z.; Yang, Y.; Wu, Z.; Chen, X.; Liu, Y.; Tong, C.; Zhang, Y. Changes of Soil Organic Carbon Density and Stock in the Minjiang River Estuarine Wetland under Natural Salinity Gradient. Wetl. Sci.; 2024; 22, pp. 218-226. (In Chinese) [DOI: https://dx.doi.org/10.13248/j.cnki.wetlandsci.2024.02.006]

33. Zhang, G.; Fu, Y.; He, Y.; Yang, Y.; Guo, Y.; Zhang, J.; Ma, R.; Li, S. Distribution and Storage of Soil Organic Carbon in Hainan lsland. Trop. Geogr.; 2011; 31, pp. 554-558. (In Chinese) [DOI: https://dx.doi.org/10.3969/j.issn.1001-5221.2011.06.003]

34. Li, M.; Li, J.; Zhao, L.; Liu, S.; Wang, Y.; Bian, H. Effects of Exogenous Fe Addition on Soil Respiration Rate and Dissolved Organic Carbon Structure in Temperate Forest Swamps of Northeastern China. Environ. Res.; 2023; 216, 114800. [DOI: https://dx.doi.org/10.1016/j.envres.2022.114800] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36379233]

35. Spohn, M.; Stendahl, J. Soil Carbon and Nitrogen Contents in Forest Soils Are Related to Soil Texture in Interaction with pH and Metal Cations. Geoderma; 2024; 441, 116746. [DOI: https://dx.doi.org/10.1016/j.geoderma.2023.116746]

36. Strahm, B.D.; Harrison, R.B. Controls on the Sorption, Desorption and Mineralization of Low-Molecular-Weight Organic Acids in Variable-Charge Soils. Soil Sci. Soc. Am. J.; 2008; 72, pp. 1653-1664. [DOI: https://dx.doi.org/10.2136/sssaj2007.0318]

37. Dendooven, L.; Alcántara-Hernández, R.J.; Valenzuela-Encinas, C.; Luna-Guido, M.; Perez-Guevara, F.; Marsch, R. Dynamics of Carbon and Nitrogen in an Extreme Alkaline Saline Soil: A Review. Soil Biol. Biochem.; 2010; 42, pp. 865-877. [DOI: https://dx.doi.org/10.1016/j.soilbio.2010.02.014]

38. Hurst, N.R.; VanZomeren, C.M.; Berkowitz, J.F. Temperature, Redox, and Amendments Alter Wetland Soil Inorganic Phosphorus Retention Dynamics in a Laurentian Great Lakes Priority Watershed. J. Great Lakes Res.; 2022; 48, pp. 935-943. [DOI: https://dx.doi.org/10.1016/j.jglr.2022.05.010]

39. Jia, J.; Huang, C.; Bai, J.; Zhang, G.; Zhao, Q.; Wen, X. Effects of Drought and Salt Stresses on Growth Characteristics of Euhalophyte Suaeda Salsa in Coastal Wetlands. Phys. Chem. Earth Parts A/B/C; 2018; 103, pp. 68-74. [DOI: https://dx.doi.org/10.1016/j.pce.2017.01.002]

40. Xie, W.; Yang, J.; Gao, S.; Yao, R.; Wang, X. The Effect and Influence Mechanism of Soil Salinity on Phosphorus Availability in Coastal Salt-Affected Soils. Water; 2022; 14, 2804. [DOI: https://dx.doi.org/10.3390/w14182804]

41. Wang, L.; Hamel, C.; Lu, P.; Wang, J.; Sun, D.; Wang, Y.; Lee, S.-J.; Gan, G.Y. Using enzyme activities as an indicator of soil fertility in grassland—An academic dilemma. Front. Plant Sci.; 2023; 14, 1175946. [DOI: https://dx.doi.org/10.3389/fpls.2023.1175946]

42. Guwy, A.J.; Martin, S.R.; Hawkes, F.R.; Hawkes, D.L. Catalase Activity Measurements in Suspended Aerobic Biomass and Soil Samples. Enzym. Microb. Technol.; 1999; 25, pp. 669-676. [DOI: https://dx.doi.org/10.1016/S0141-0229(99)00115-5]

43. Yang, F.; Liu, Y.; Zhang, X.; Liu, X.; Wang, G.; Jing, X.; Wang, X.-F.; Zhang, Z.; Hao, G.-F.; Zhang, S. et al. Oxidative Post-Translational Modification of Catalase Confers Salt Stress Acclimatization by Regulating H₂O₂ Homeostasis in Malus hupehensis. J. Plant Physiol.; 2023; 287, 154037. [DOI: https://dx.doi.org/10.1016/j.jplph.2023.154037]

44. Liu, J.; Li, X.; Zhu, Q.; Zhou, J.; Shi, L.; Lu, W.; Bao, L.; Meng, L.; Wu, L.; Zhang, N. et al. Differences in the Activities of Six Soil Enzymes in Response to Cadmium Contamination of Paddy Soils in High Geological Background Areas. Environ. Pollut.; 2024; 346, 123704. [DOI: https://dx.doi.org/10.1016/j.envpol.2024.123704] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38442823]

45. Wang, Z.; Zhang, S.; Li, C.; Geng, Z.; Sun, L.; Zhang, L.; Cao, Z. Restoration via the Aggregate Spray-Seeding Technique Affected the Soil Proteobacteria on an Uninhabited Island: Community Structure, Metabolic Function, Nutritional Type, and Life Strategy. Glob. Ecol. Conserv.; 2024; 53, e03009. [DOI: https://dx.doi.org/10.1016/j.gecco.2024.e03009]

46. Rincón-Tomás, B.; Lanzén, A.; Sánchez, P.; Estupiñán, M.; Sanz-Sáez, I.; Bilbao, M.E.; Rojo, D.; Mendibil, I.; Pérez-Cruz, C.; Ferri, M. et al. Revisiting the Mercury Cycle in Marine Sediments: A Potential Multifaceted Role for Desulfobacterota. J. Hazard. Mater.; 2024; 465, 133120. [DOI: https://dx.doi.org/10.1016/j.jhazmat.2023.133120] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38101011]

47. Wu, B.; Liu, F.; Fang, W.; Yang, T.; Chen, G.-H.; He, Z.; Wang, S. Microbial Sulfur Metabolism and Environmental Implications. Sci. Total Environ.; 2021; 778, 146085. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.146085]

48. Narsing Rao, M.P.; Luo, Z.-H.; Dong, Z.-Y.; Li, Q.; Liu, B.-B.; Guo, S.-X.; Nie, G.-X.; Li, W.-J. Metagenomic Analysis Further Extends the Role of Chloroflexi in Fundamental Biogeochemical Cycles. Environ. Res.; 2022; 209, 112888. [DOI: https://dx.doi.org/10.1016/j.envres.2022.112888]

49. Ding, Z.-H.; Wu, Y.-H. Genomic Characteristics of Nine Nitrospirota Metagenome-Assembled Genomes in Deep-Sea Sediments from East Pacific Polymetallic Nodules Zone. Mar. Genom.; 2024; 75, 101107. [DOI: https://dx.doi.org/10.1016/j.margen.2024.101107]

50. Gonçalves, O.S.; Fernandes, A.S.; Tupy, S.M.; Ferreira, T.G.; Almeida, L.N.; Creevey, C.J.; Santana, M.F. Insights into Plant Interactions and the Biogeochemical Role of the Globally Widespread Acidobacteriota Phylum. Soil Biol. Biochem.; 2024; 192, 109369. [DOI: https://dx.doi.org/10.1016/j.soilbio.2024.109369]

51. Song, L.; Wang, Q.; Wang, P.; Wu, J. Benthic Bacterial Communities and Bacteria–Environment Interactions after Kandelia obovata Introduction and Spartina alterniflora Invasion in Yueqing Bay, China. Reg. Stud. Mar. Sci.; 2023; 58, 102787. [DOI: https://dx.doi.org/10.1016/j.rsma.2022.102787]

52. Zhao, C.; Li, J.; Zhao, X. Mowing plus Shading as an Effective Method to Control the Invasive Plant Spartina alterniflora. Flora; 2019; 257, 151408. [DOI: https://dx.doi.org/10.1016/j.flora.2019.05.007]

53. Lv, Y.; Wang, M.; Tian, W.; Hui, H. The Content and Potential Ecological Risk Assessment of Heavy Metals in Coastal Wetlands around the Bohai Sea. Plant Soil Environ.; 2024; 70, pp. 356-365. [DOI: https://dx.doi.org/10.17221/24/2024-PSE]

54. Chen, H.; Wang, J.; Chen, J.; Lin, H.; Lin, C. Assessment of Heavy Metal Contamination in the Surface Sediments: A Reexamination into the Offshore Environment in China. Mar. Pollut. Bull.; 2016; 113, pp. 132-140. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2016.08.079] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27600270]

55. Lu, Q.; Bai, J.; Zhang, G.; Wu, J. Effects of Coastal Reclamation History on Heavy Metals in Different Types of Wetland Soils in the Pearl River Delta: Levels, Sources and Ecological Risks. J. Clean. Prod.; 2020; 272, 122668. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122668]

56. Åsa, M.M. Berglund Evaluating Blood and Excrement as Bioindicators for Metal Accumulation in Birds. Environ. Pollut.; 2018; 233, 1198. [DOI: https://dx.doi.org/10.1016/j.envpol.2017.10.031]

57. Gan, H.; Lin, J.; Liang, K.; Xia, Z. Selected Trace Metals (As, Cd and Hg) Distribution and Contamination in the Coastal Wetland Sediment of the Northern Beibu Gulf, South China Sea. Mar. Pollut. Bull.; 2013; 66, pp. 252-258. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2012.09.020]

58. Ma, X.; Xu, H.; Wang, L. Pollution characteristics and risk assessment of heavy metals in Tianjin Lingang Coastal Wetland Park. J. Environ. Eng. Technol.; 2021; 11, pp. 908-918. [DOI: https://dx.doi.org/10.12153/j.issn.1674-991X.20200281]

59. Lorenz, N.; Hintemann, T.; Kramarewa, T.; Katayama, A.; Yasuta, T.; Marschner, P.; Kandeler, E. Response of Microbial Activity and Microbial Community Composition in Soils to Long-Term Arsenic and Cadmium Exposure. Soil Biol. Biochem.; 2006; 38, pp. 1430-1437. [DOI: https://dx.doi.org/10.1016/j.soilbio.2005.10.020]