Characteristics of soil organic carbon and total nitrogen along various vegetation types in Hongqipao reservoir , Northeast China

This study investigated the spatial variability of soil organic carbon (SOC), total nitrogen (TN), soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) in Hongqipao reservoir dominated by different vegetation types and the possible relationships with other soil properties. Top 0–50cm soil samples were collected in sites dominated by different vegetation types within the reservoir littoral zone. There was high spatial variability for SOC, TN, SMBC and SMBN in the Hongqipao reservoir. In addition, the SOC, TN, SMBC and SMBN contents decreased with increasing soil depth. This could be attributed by the fact that when plants detritus decompose, most of their organic matter is mineralized and a new soil layer which contains a greater amount of organic carbon is formed at the top. According to Pearson's correlation values and redundancy analysis (RDA) results, SOC was significantly and positively correlated with TN likely because the vegetation organic matter and liter could be the main nitrogen sources. Similarly, soil moisture content (MC) was significant positive correlated with SOC and TN. Conversely, BD was significant negative correlated with SOC and TN contents in the 0-50 cm soil profiles. However, no significant correlations were observed between SOC, TN, SMBC and SMBN contents Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 July 2020 doi:10.20944/preprints202007.0539.v1 © 2020 by the author(s). Distributed under a Creative Commons CC BY license. and soil pH values. SMBN was significantly and positive correlated with C:N ratio and BD and negative related with MC. Multiple linear regression model revealed that all measures soil properties in this study could explain higher significant variability of the response variables (SOC, TN, SMBC and SMBN contents). This implies that all the measured soil variables within the different vegetation types in the reservoir played a crucial role in determining the contents of SOC, TN, SMBC and SMBN. This study further suggests that vegetation types play a major role in determining the spatial characteristics of SOC and TN. Any changes in the vegetation types in the reservoir may influence the distribution of SOC and TN. This may affect the global carbon budget and the atmospheric greenhouse gas concentration significantly.


Introduction
Soil carbon and nitrogen are essential elements of sustainable soil fertility and productivity and can substantially affect climate change through carbon and nitrogen emissions (including carbon dioxide, methane and nitrous oxide) [1][2][3] . As one important pool of soil organic matter in wetland ecosystems, wetland soils serve as sources, sinks and transfers of nutrients and chemical pollutants [4][5] . It is estimated that 202-377 Gt carbon is stored in the top 100 cm of wetland soil since wetlands provide an optimum environmental conditions for the sequestration and long-term storage of carbon [6][7] . Reservoirs are an important type of wetland, whose combined area across the world has increased in recent years to now occupy approximately 5 ×10 5 km 2 , which is 1/3 the size of all natural lakes 8 . Among the reasons of large development of reservoirs is their potential as a clean energy source, although uncertainties in their role in greenhouse gas contribution have led some scientists to question whether they are as clean as people believe 9 . Since reservoirs have major impacts on biogeochemical cycles by acting as a storage of soil organic carbon at the local and global scales, several researchers have insisted that the reservoirs requires more scientific study 2,[10][11] . The Ministry of Water Resources of the People's Republic of China has stated that China has over 80,000 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 July 2020 doi:10.20944/preprints202007.0539.v1 reservoirs, large and small 12 . However, limited studies have been carried out to determine the role of reservoirs in storage of soil organic carbon and nitrogen 2,13 .
Studies have revealed that the spatial variation, accumulation and distribution characteristics of organic carbon and nitrogen in wetland soil is influenced by the vegetation types, hydrology (water level fluctuation), soil microbial community, pH, salinity, temperature, soil moisture among many others may have a strong influence on soil organic carbon and nitrogen storage [14][15] . Changes in the hydrological regime can have substantial effects on soil properties, particularly carbon and nitrogen accumulation and release due to alterations in their chemical forms and spatial movements [16][17] .
Wetlands littoral zones with abundant vegetation and soil microbes has been reported to have higher capacity of carbon deposition than other land types 18 . Bahn, et al. 19 noted that different types of vegetation communities and their development will have obvious influence on soil organic carbon contents, and soils with high primary productivity have high organic carbon storage. Soil mechanical composition, bulk density, salinity, and nutritional status will influence the capacity of vegetation directly and affect the input and output of soil carbon 20

Soil sampling and laboratory analyses
Soil samples were collected from 7 georeferenced sites (coded as A-G). These sites were selected based on their dominant plant species and whether the site is inundation or dry ( Table 1). The soil profiles with three replicates for each sampling site were sectioned into five depths at 10 cm intervals (0-10, 10-20, 20-30, 30-40, and 40-50 cm) and mixed with the same soil layers to form composite samples. Moreover, soil samples for analysis of soil bulk densities (BD) and moistures content (MC) were collected at each sampling site. The collected soil samples were portioned into two for every soil layer. One part of the sample for each soil layer was stored at 4 °C in sealed plastic bags to limit microorganism activity for the determination of soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN). The second portion of the sample of each soil layer were air-dried at room temperature in the laboratory for three weeks, and stones, roots and coarse debris were removed. The dried soil samples were ground to fine power using a mill (FW100, Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 July 2020 doi:10.20944/preprints202007.0539.v1 Taisite in Tianjin, China), and then sieved through a 100-mesh sieve. The Sieved soil samples were treated with 2 M HCl for 24 h at room temperature to remove carbonates [25][26] . The soil was then washed to pH>5 with distilled water and dried at 40 °C. The SOC and TN were determined using an automatic elemental analyzer (Flash EA 1112, Italy). Soil pH was measured using a digital pH meter in in the supernatants of 1:5 soil:water mixtures. The soil BD and MC were calculated on a dry weight basis. Soil MC was determined by the oven drying method (weighing before and after drying at 105 °C for 24 h). Soil BD was calculated by dividing the total dry weight of the soil sample by the volume of the core 27 . All results were expressed on a dry gram basis.
The soil microbial biomass carbon (MBC) and the soil microbial biomass nitrogen (MBN) were determined using the soil microbial biomass chloroform fumigation extraction method [28][29][30]

Determination of vegetation aboveground biomass
Three quadrants (1 m × 1 m) were established at each sampling sites. The vegetation in the quadrants were manually cut close to the soil surface, harvested and weighed immediately while fresh. The weighed samples were then taken to the laboratory and oven dried at 80°C, to a constant weight. The dry biomass was calculated by multiplying the fresh weight of the harvested vegetation and the dry/wet ratios of the samples.
Data were statistically analysed using SPSS (version 19.0). A one-way analysis of variance (ANOVA) was conducted to test the spatial variation of SOC, TN, SMBC, SMBN and other soil properties.
Correlation analyses were carried out to evaluate the relationships between SOC, TN, SMBC, SMBN and soil properties. Multivariable linear regressions were used to build regression models for SOC, TN, SMBC and SMBN. The figures were drawn using Origin 9 and Canoco 5 softwares.

Spatial variation of SOC, TN, SMBC, SMBN and other soil properties
The spatial soil characteristics from the seven sites sampled are presented in (Table 2 and Figure 2).
In all the sites, SOC decreased with increasing soil depth ( Figure 2). One-way ANOVA revealed that

Total SOC, TN, SMBC and SMBN Storage
In the 0～50 cm depth, total SOC ranged from 12.98 to 17.39 kg C m -2 across the sampled sites dominated by different vegetation types in Hongqipao reservoir. TSOC differed significantly among the sites (P < 0.05) (

Aboveground biomass production
The results of aboveground plant biomass are presented on Table 6. In site C which was dominated by Tamarix chinensis Lour. and Polygonum amphibium L. the biomass was 3.74 ±1.73 kg m -2 which was about ten times higher than that of site A of 0.38 ±0.21 kg m -2 which was the lowest. Site E which was dominated by Phragmites australis had the second relative high value of 0.91 ±0.08 kg m -2 which was about four times lower than that of site C. The aboveground biomass production of most of the sites did not exceed 1 kg m -2 ( Table 6).

Soil Organic Carbon (SOC) and TN variations among vegetation types and soil depths
In to the fact that soil BD is considered to be the basic property that varies with the soil structural conditions, and increases with soil depth, due to changes in porosity, soil texture, and organic matter content 35 . The mean BD among sites differed significantly with sampling site A and B having remarkable high values. This could have been likely due to mineral sedimentation since these sites were located close to reservoir shoreline where the inflowing rivers pour water into the reservoir. Wang, et al. 36 observed relative high BD over a depth of 24 cm from site located near influent of a river flowing in a freshwater marsh. These authors attributed their findings to rates of mineral sediment accumulation which tend to increase towards the lake/open water in a wetland than in the interior marshes 37 . Another possible reason for the relative high soil BD in sites A and B could be due to sediment settling out of the water column which is then transformed into a soil layer that bonds with the preexisting surface, thus decreasing pore space, resulting in increased BD 38 .
Unlike BD, the SOC and TN decreased with depth from the soil surface to the bottom of the soil profile, reflecting the variations in growth and distribution of roots and rhizome, and the decomposition with depth. According to Huang, et al. 39  reported that the liter falls of Phragmites australis were important reservoirs of organic carbon which were mineralized into the soil. Zhang, et al. 43 and Gower, et al. 44 noted in their studies that accumulation of organic carbon in soil and the proportion of carbon fixed to soil carbon pool with different turnover rates tend to vary with vegetation types. Secondly, since sites A, F and G were located on dry places within the littoral zone of the reservoir, then their relative low SOC concentration could probably because mineralization was favoured since the soil was dry most of the time. Scores of studies have shown that hydrological processes (such as groundwater level, depth, duration and frequency of flooding) affect the decomposition of litter hence soil SOC and TN 45 .
Soil microbial biomass (SMB), plays an important role in SOC mineralization and nutrient cycling [46][47] . The composition of vegetation species and soil water availability in wetlands greatly influences the soil microbial biomass [48][49] . In this study, the overall mean SMBC did not differ significantly among the sites dominated by different vegetation types. However, relative high mean values were observed in site B which was mainly dominated by Phragmites australis and Mongolian wormwood.
Sturz and Christie 50 and Kyambadde, et al. 51 noted in their study that the vigorous root system of Phragmites community vegetation increase the level of soil microbial activity hence its biomass.
These authors further noted that higher organic matter from Phragmites community increases the carbon enrichment of the soil, which then helped maintain the microbial activities in the soil. More importantly, the mean SMBN which differ significantly among the sites was relatively higher at site B.
Although Correspondingly, the relative high SMBC and SMBN at the top soil profiles indicate higher microbial metabolic activities in these profiles.

Soil Organic Carbon (SOC) and TN relationships with other soil properties
According to Bi, et al. 53 the carbon cycle is closely linked with nitrogen cycle through production and decomposition. Our study also revealed that the soil TN concentration was significant positive related with SOC concentration in the 0-50cm soil layer of the different vegetation types (Table 3 and Figure 3). This observation could be because the main nitrogen sources were the vegetation organic matter, litter, and biological nitrogen fixation 54 . The Pearson correlation analysis and RDA also showed that soil MC was positive correlated with SOC and TN. This concurs with finding of other studies [55][56] . Under high soil MC conditions, the anoxic decomposition of soil organic matter tends to be inhibited resulting in the accumulation of SOC. Importantly, soil MC also affects nitrogen since higher soil MC can hinder soil microbial activities, creating environment which is not conducive to the mineralization and decomposition of soil organic nitrogen 57 . This therefore implies a high soil MC can lead to a high soil TN concentration.
Unlike soil MC which was significantly positively correlated with SOC and TN, the soil BD was significant negative correlated with SOC and TN concentration in the 0-50 cm soil profiles (Table 3 and Figure 3). While assessing stock and thresholds detection of SOC and nitrogen along an altitude gradient in an east Africa mountain ecosystem, Njeru, et al. 58

Conclusions
Some conclusions could be drawn from this study: There is high spatial variability for SOC, TN,