Study Area

Dongzhaigang National Mangrove Nature Reserve is located around the bay of NE Hainan Island, with a total area of about 1578.2 ha (Li and Lee, 1997). It is a semienclosed, muddy-bottom estuary formed by continental sink during the Great Qiongzhou Earthquake in 1605 and fed by four small rivers (Liao et al., 2009; Wang et al., 2009). The climate is tropical marine monsoon, with an average annual temperature of 23.3 to 23.8°C and rainfall of approximately 1700 mm. The reserve has an irregular semidiurnal tide, with an average tidal range of 1.1 m (Zhang et al., 1997). Dominant species in Dongzhaigang mangrove reserve include Rhizophora stylosa Griff., Bruguiera sexangula (Lour.) Poir., Avicennia marina (Forsk) Vierh., Ceriops tagal (Perr.) C. B. Rob., Aegiceras corniculatum (Linn.) Blanco., Sonneratia apetala Buch.-Ham., Bruguiera gymnorrhiza (Linn.) Sav., B. sexangula var. rhynchopetala Ko, Kandelia obovata Sheue et al., Excoecaria agallocha Linn., and Acanthus lilcifolius Linn., among a total of 35 species (Li and Lee, 1997).

Sampling

Based on remotely sensed data and a field survey, six mangrove communities dominated by A. marina (Figure 1), B. sexangula, C. tagal, R. stylosa, A. corniculatum, or S. apetala (Figure 2) were identified.

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From September to December 2013, six quadrats for six mangrove communities of Dongzhaigang were established (Figure 3). Quadrat size for arbors was 10 × 10 m and shrubs was 5 × 5 m. Within each quadrat, point information, species, number of each species, and tree height and diameter at the breast height 1.3 m (DBH) were recorded. Three core soil samples were collected from each quadrat with a depth of 0 to 100 cm using a stainless-steel soil sampler (inner diameter is 8 cm, and depth is 1.15 m) during ebb tide. Then, for each core soil sample, three parallel samples were collected from the layers of 0 to 20, 20 to 40, 40 to 60, 60 to 80, and 80 to 100 cm using a cutting ring. The samples were stored in clean, dry, labeled polyethylene plastic bags, sealed, and transported to the laboratory at normal temperature on the same day. The samples were dried in the open air in the laboratory.

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The air-dried soil samples were mixed and sieved (0.15 mm) after sorting out the plant and animal residues and stones. Before laboratory analysis, all samples were stored in glass desiccators with desiccants. The soil properties analyzed included bulk density, water content, pH, conductivity, SOC content, total nitrogen (TN), total phosphorus (TP), and total potassium (TK). Soil bulk density was measured as the ratio of soil dry weight to the soil cutting ring's volume. Water content was measured using a drying oven (at 105°C). pH was measured with an IQ150 pH meter (IQ Scientific Instruments Inc., U.S.A.). Conductivity was measured with a Hydra Probe (Stevens Water Monitoring Systems Inc., U.S.A.). The SOC content was measured using the potassium dichromate outside heating method (Zhang et al., 2014; Precision: ±0.001). The TN was measured with a Kjeltec 8400 system (Foss, Denmark). The TP was measured with the molybdenum-antimony antispectrophotometric method, and the TK was measured with flame photometry (Lu, 2000).

Data Analysis

SOC density was calculated using the following formula:

where, SOCD is SOC density (in kilograms per square meter), C_i is SOC content (in grams per kilogram), D_i is soil bulk density (in grams per cubic centimeter), E_i is soil depth (in centimeters), and G_i is the volume percentage of gravel with a size greater than 2 mm.

All data of the SOC and the environmental factors passed the nonparametric one-sample Kolmogorov-Smirnov test, which indicated that all data followed a normal distribution (p > 0.05, n = 90). The SOC content in different depths and soil physicochemical properties of mangrove communities was analyzed with the least significant difference method in one-way analysis of variance (ANOVA), and the significance was conducted at p < 0.05. Pearson's correlation analysis was used to delineate the pairwise relationships between SOC and soil physicochemical properties (Cheng and Wang, 2004). Regression analysis was then employed to deduce the regression equation describing the relationship between SOC and soil factors. All of these tests were accomplished using Statistical Product and Service Solutions (SPSS) version 18.0 software.

Moreover, the impact of soil physicochemical properties on the SOC content and density was determined by multivariate ordination analysis to sort the orders of the correlation degree using Canoco version 4.5 software. Detrended correspondence analysis indicated the use of redundancy analysis (RDA) was applicable because of predominantly linear relationships within all data according to the value of lengths of gradient. Significance of the first and other ordination axes was calculated with the Monte-Carlo significance test (ter Braak and Šmilauer, 2002).

Soil Physicochemical Properties

In the six vegetation communities, the content of SOC ranged from 2.60 to 89.51 g kg⁻¹ with an average of 18.35 g kg⁻¹, while the SOC density ranged from 0.81 to 27.68 kg m⁻² with a mean value of 5.01 kg m⁻². The contents of SOC at different depths and communities are shown in Table 1.

Table 1 Parameters of mangrove communities at Dongzhaigang, Hainan Island.

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Other physicochemical properties of different mangrove communities are shown in Table 2. Overall, the contents of TN and TP in the B. sexangula community were significantly higher than in the other communities except in the C. tagal community, while the C/N ratio, bulk density, and conductivity of B. sexangula community was significantly lower than the other communities. The significantly higher content of TK was found in the R. stylosa community, which also had the significantly highest value of pH. For the water content, it was found that A. marina, A. corniculatum, and C. tagal communities were significantly higher than R. stylosa, B. sexangula, and S. apetala communities.

Table 2 Physicochemical properties of mangrove communities at Dongzhaigang, Hainan Island.

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Vertical Distribution of SOC

In R. stylosa (Figure 4b), A. marina (Figure 4d), S. apetala (Figure 4a), and C. tagal (Figure 4e), the highest content of SOC was found with a depth of 0 to 20 cm, which occurred in the following order: C. tagal (51.9 ± 10.2 g kg⁻¹) > R. stylosa (45.0 ± 1.9 g kg⁻¹) > A. marina (40.7 ± 1.2 g kg⁻¹) > S. apetala (21.2 ± 3.6 g kg⁻¹). In general, the SOC content decreased as the soil depth increased in the R. stylosa, A. marina, S. apetala, and C. tagal, but this trend was not remarkable in B. sexangula and A. corniculatum. Besides, as shown in Figure 4, SOC content had great variation in different communities within 0 to 100 cm. Overall, SOC content of R. stylosa, A. marina, and C. tagal communities from 0 to 20 cm was significantly higher than that at other depths. There were no significant differences among 0 to 20, 20 to 40, and 80 to 100 cm in the SOC content of S. apetala community, but it was significantly higher than that with 40 to 60 and 60 to 80 cm. For the B. sexangula community (Figure 4c), the maximum SOC content was 29.7 ± 2.6 g kg⁻¹, which was observed with a depth of 60 to 80 cm and was significantly higher than that with the other depths except 40 to 60 cm. For the A. corniculatum community (Figure 4f), the maximum SOC content was observed from 20 to 40 cm with an average of 52.4 g kg⁻¹, which was significantly higher than that with the other depths of 40 to 60, 60 to 80, and 80 to 100 cm except 0 to 20 cm.

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The vertical distribution of SOC density of six communities in Dongzhaigang also had great variation within 0 to 100 cm, as shown in Table 1 and Figure 5. In general, there is no obvious trend for the SOC density of all mangrove communities with soil depth. In R. stylosa (Figure 5b), A. marina (Figure 5d), C. tagal (Figure 5e), and A. corniculatum (Figure 5f) communities, the SOC densities decreased with increasing soil depth. The highest SOC densities in the R. stylosa (9.6 kg m⁻²), A. marina (10.4 kg m⁻²), and C. tagal (17.7 kg m⁻²) communities were found with depths of 0 to 20 cm, while those of the A. corniculatum community (16.8 kg m⁻²) appeared from 20 to 40 cm. But in B. sexangula (Figure 5c), SOC density increased with increasing soil depth, with the highest value found with a depth of 60 to 80 cm (5.7 kg m⁻²) and the minimum value with a depth of 0 to 20 cm (3.4 kg m⁻²).

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Horizontal Distribution of SOC

The average SOC content for the whole (0–100 cm) soil layer of the six communities is as follows: B. sexangula community (20.89 g kg⁻¹) > R. stylosa community (20.26 g kg⁻¹) > A. corniculatum community (19.65 g kg⁻¹) > A. marina community (17.39 g kg⁻¹) > C. tagal community (17.03 g kg⁻¹) > S. apetala community (14.89 g kg⁻¹). The order for the SOC density was slightly different: A. corniculatum community (5.98 kg m⁻²) > C. tagal community (5.46 kg m⁻²) > R. stylosa community (5.23 kg m⁻²) > A. marina community (4.99 kg m⁻²) > B. sexangula community (4.40 kg m⁻²) > S. apetala community (4.11 kg m⁻²). As shown in Figure 6, SOC content and density decreased with the tidal gradient from the high intertidal zone to the low intertidal zone for the three mangrove shrubs, with the overall levels occurring as follows: A. marina community < C. tagal community < A. corniculatum community. The SOC content showed the same trend for arbors, with levels occurring as follows: S. apetala community < R. stylosa community < B. sexangula community. However, SOC density in arbors showed little difference with SOC content, with levels occurring as follows: S. apetala community < B. sexangula community < R. stylosa community.

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Relationships between SOC and Soil Physicochemical Properties

Figure 7 shows the ordination diagram of a RDA, which was calculated for the environmental variables (physicochemical properties of soils) and the SOC content and density as species variables. Eigenvalues of the RDA showed that 40 and 1.2% of the total variance within the SOC content and density were explained by the first and second ordination axes, respectively. High species–environment relationships indicated a strong relationship (species–environment correlation = 0.64) between physicochemical properties of soils and SOC. The result of RDA (Figure 7) indicated that SOC content and density were positively correlated to TN, TP, pH, C/N ratio, and bulk density and negatively correlated to TK, conductivity, and water content. But the p values between SOC and TN, TP, TK, and water content were less than 0.05, while the p values between SOC and C/N ratio, pH, bulk density, and conductivity were higher than 0.05. The result of RDA also showed that the total variance of TN, TP, TK, C/N ratio, and water content was 36.7% on the first ordination axis, which explained most information of environmental variables. Besides, the results of Pearson's correlation analysis indicated that SOC content had a significantly positive correlation with TN, TP, and C/N ratio but a significantly negative correlation with TK and water content. No significant correlation was found between SOC and pH, bulk density, or conductivity. Therefore, it could be considered that the SOC content was more closely related to soil nutrient elements than to other environmental factors.

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The regression equation was further developed with regression analysis. With a 0- to 20-cm depth, the regression equations between SOC and physicochemical properties fit better than in other depths. Thus, the data with depths of 0 to 20 cm were chosen to establish the regression equation between SOC and physicochemical properties. To find a general model, five groups of data from five communities were used to establish the regressions, while the data from the remaining community were used for verification. Regression equations are shown in Table 3.

Table 3 The regression equation between SOC and soil physicochemical properties with a depth of 0 to 20 cm.

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For C/N ratio, the content of N had been calculated with TN, while for water content, it had been used in calculating bulk density. Therefore, C/N ratio and bulk density were not used to avoid increasing the weight values of N and water content in the regression equations. From the seven equations in Table 3, a universal regression model between SOC and soil physicochemical properties can be established:

where, X₁ to X₆ represent TN, TK, TP, conductivity, pH, and water content, respectively; k ranges from −9.11 to 142.64; a is from 10.74 to 57.72; b is from 5.84 to 10.12; c is from 20.37 to 69.70; d is from 0.03 to 0.04; e is from 14.76 to 21.05; and f is from 10.27 to 122.59. In these equations, all values of R² were less than 0.95, indicating that other factors influenced the distribution of SOC in mangrove soils besides the factors presented in the equations.

Distribution of SOC

In this study, the content of SOC was lower than that in Yingluo Bay, Guangdong Province, China (Wang et al., 2013). Compared with other ecosystems in Hainan Island, the average SOC content of Dongzhaigang mangrove wetland in this study is 6.8 times, 3.8 times, and 1.1 times higher than that of the tropical rubber plantations (Zhang, Ding, and Meng, 2010), the mango plantations (Zheng et al., 2013), and the tropical rain forest on the same island (Zhang et al., 2014), respectively. The density of SOC in this study was similar to that of the Sanya mangrove of China (5.6 kg m⁻²; Ren et al., 2010), which was higher than that in the mangrove in Sundarbans of India (0.61–1.55 kg m⁻²; Mitra, Banerjee, and Sett, 2012), the K. obovata community of the Jiulong River Estuary of China (1.51–1.70 kg m⁻²; Alongi et al., 2005), and the Manko wetland of Japan (0.57 kg m⁻²; Khan, Suwa, and Hagihara, 2007) but lower than that in the Hinchinbrook Channel mangroves of Australia (29.6 kg m⁻²; Matsui, 1998). These differences of SOC content and density in different mangrove ecosystems were mainly caused by differences of biomass, which is further influenced by site condition, species composition, and primary productivity of each species (Lovelock et al., 2010; Sebastian and Chacko, 2006). Wang et al. (2013) found that biomass and SOC concentration had significantly positive correlation in the Yingluo Bay mangrove forest of South China. Ren et al. (2008) found that the high amount of dead roots resulted in the large vegetation biomass input and caused the high SOC density in the Leizhou Bay mangrove forest of Southern China.

SOC entering mangroves can be derived from autochthonous carbon, such as litter from trees and roots, or imported by tides or rivers from outside (Bouillon et al., 2003; Kristensen et al., 2008). Mangroves are shallow-root plants, with root systems mainly within a depth of 0 to 90 cm and usually not more than 120-cm deep (Boto and Wellington, 1984; Duke, Ball, and Ellison, 1998; Youssef and Saenger, 1996). Different communities may have different root structures; for example, R. stylosa has stilt roots, A. marina and S. stylosa have pneumatophores, and B. sexangula has knee roots (Srikanth, Lum, and Chen, 2016; Youssef and Saenger, 1996). This may affect the differences of the distribution of SOC in different mangrove communities. In this study, it was found that SOC content and density in different mangrove communities decreased with increasing depth in most sampling sites generally, which was found to be similar in the Yap state of the Federated States of Micronesia, the Republic of Palau, and the Manko wetlands of Okinawa Island, Japan (Donato et al., 2012; Khan, Suwa, and Hagihara, 2007).

In the horizontal direction, SOC content and density varied with the tidal gradient. The SOC content and density for the three mangrove shrubs and SOC content for the three mangrove arbors in Dongzhaigang decreased with the tidal gradient from the high intertidal zone to the low intertidal zone. A similar result was found in Yingluo Bay, Guangdong, Southern China, which was attributed to the diversities of the vegetation biomass and net primary production in different mangrove communities (Wang et al., 2013). In other studies, Zhang et al. (2009) reported that in the mangrove of the Pearl River Estuary, the SOC content near the estuary was significantly higher than that in the more salinized coast, and Tam and Wong (1998) found that the content of OC in sediments decreased as the distance from land to the sea decreased. These differences of SOC in different mangrove communities indicated that the distributions of SOC was influenced by various factors, such as location, species composition, primary productivity, and stages of forest (Lovelock et al., 2010; Sebastian and Chacko, 2006). Therefore, it is necessary to conduct further investigation on the aboveground and underground biomass of mangrove communities at different locations and forest ages in Dongzhaigang.

Besides, in the process of afforestation practices, some mangrove plants, such as R. stylosa, A. corniculatum, A. marina, and S. apetala, are commonly used in China. R. stylosa, A. corniculatum, and A. marina have strong adaptability to temperature, salinity, and tide. They also have developed strong root systems that can resist wind and waves (He et al., 2007). S. apetala is a highly adaptive and fast-growing exotic species introduced into Dongzhaigang in 1985 from Bangladesh (Wang et al., 2013). However, according to the results of carbon sequestration ability in this study, native species R. stylosa and A. corniculatum should be highly prioritized during mangrove restoration.

Relationship between SOC and Soil Physicochemical Properties

In this study, the correlations between mangrove SOC content and soil nutrient elements were found to be higher than those between SOC and other environmental factors. The SOC showed positive correlations with TN, TP, and C/N ratio, which was consistent with the results of previous studies (Boto and Wellington, 1984; Bouillon et al., 2003; Soto-Jiménez, Páez-Osuna, and Ruiz-Fernández, 2003), indicating that N, P, and K were the major elements influencing plant growth and standing biomass (Boto and Wellington, 1984). Bouillon et al. (2003) found that a lower C/N ratio is associated with a lower SOC content in mangroves, because high N content could stimulate the decomposition of soil organic matter and thus lower the content of SOC (Batjes, 1996; Soto-Jiménez, Páez-Osuna, and Ruiz-Fernández, 2003). The SOC displayed a negative correlation with TK in this study. According to the results by Wang and Hong (2005), the number of bacteria was decreased when TK was high, while bacteria can process most of the energy flow and nutrients and provide more organic matter in soils (Holguin, Vazquez, and Bashan, 2001).

For pH, it was found that pH and SOC have a significantly negative correlation in C. tagal of this study, which was similar to the results presented for the mangroves of Hong Kong and Hainan Island (Tam and Wong, 1998; Xin et al., 2014). This might be related to the microbial activity under different pH values; when extreme pH values of more than 8.5 or less than 5.5 inhibited microbial activity, the decomposition rate of OC would be affected (Alongi, Boto, and Tirendi, 1989; Holguin, Vazquez, and Bashan, 2001). Usually, when the pH is close to neutral, the microbial activity is the strongest (Xin et al., 2014). Bulk density has the great influence on soil solute transport, water-holding capacity, permeability, and mineralization rate, which affect the accumulation of SOC (Avnimelech et al., 2001). The total SOC content of the six mangrove communities tended to decrease as bulk density increases significantly in A. marina, B. sexangula, and R. stylosa. Avnimelech et al. (2001) also found that SOC content is inversely proportional to soil bulk density in Israel, Alabama, Egypt, and three other areas. Although SOC was not significantly correlated with conductivity in this study, the influence of conductivity on SOC should not be ignored, because it is the index of soil water-soluble salt, while salinity has a significant impact on plant growth, biomass, and soil microbial activities (Lacerda, Ittekkot, and Patchineelam, 1995; Li et al., 2010; Mitra, 2011). In mangrove wetlands, the excessive salinity would inhibit mangrove growth, biomass, and carbon accumulation (Mitra, 2011). There was a significantly negative correlation between SOC and water content (50–82%, p < 0.05). Based on the control experiment, Wang et al. (2010) found that the mineralization rate of peat wetland increased with increasing water content, with the maximum value being observed at a water content of 60%. The average water content of Dongzhaigang was 71% (>60%), because mangrove communities are growing under long-term flooding.

These aforementioned relationships between SOC and soil physicochemical properties indicated that the impact factors of SOC were complicated and heterogeneous because of the special growth environment of mangroves, according to the RDA and Pearson's correlation analysis in this study. Besides, many other factors might influence the SOC, such as temperature (Melillo et al., 2002), tidal differences (Bouillon et al., 2003; Lallier-Verges et al., 1998; Yuan et al., 2014), microorganisms (Holguin, Vazquez, and Bashan, 2001), enzyme activity (Acosta-Martínez et al., 2007), and human disturbance (Matsui et al., 2010). Therefore, to better understand mangrove OC storage, further study about the influence of other factors on the OC should be conducted in the future.

Moreover, vegetation litter and root systems are not only the main source of SOC but also the main components of vegetation biomass (Kristensen et al., 2008; Lawson et al., 2015). Foody et al. (2001) found that normalized difference vegetation index (NDVI) was highly correlated with vegetation biomass. However, in this study, when NDVI was added into the SOC regression equation, R² values did not improve significantly. NDVI can only reflect the information provided by the vegetation canopy; however, canopy litter can be easily transported to other places by the tide, while the trunk and underground biomass cannot be reflected by the NDVI (Donato et al., 2011; Li et al., 2007). Therefore, it is necessary to explore other methods to reveal the relationship between vegetation biomass and SOC in mangroves.

Climate warming could stimulate plant growth, promote nutrient absorption, and increase the absorption of carbon dioxide released from soils (Lal, 2005). Thus, global warming caused by the influence of greenhouse gases may lead to a change of carbon storage in wetland soils (Chmura et al., 2003). Sea-level rise is an important consequence of global climate change, while in mangrove coast, Alongi (2008) found that the sediment deposition rate was approximately equivalent to the rate of sea-level rise and the carbon sequestration capacity of mangrove ecosystem was important for global carbon cycling.