Wetland ecological environments, especially turfy swamps, are complex systems affected by the interactions of geological, hydrological, physicochemical and biological factors [1
]; they provide habitats for biota on the earth and play a key role in global carbon cycles [2
]. The high water level and subsequent anaerobic environment lead to the imbalance between primary productivity and microbial decomposition of organic matter, which is the important reason for the formation of the carbon sink function of turfy wetlands. However, because of the rapid development of highways in recent decades in China, many highways inevitably invade turfy wetlands [5
], which has caused many negative impacts on the wetland environment, including soil erosion, water quality deterioration and vegetation destruction [7
]. The sharp increasing of negative effects will lead to the imbalance of wetland self-regulation functions and even cause the functional transformation from “carbon sink” to “carbon source”. Microorganisms are important decomposers of soil organic matter in turfy wetlands, and their responses to environmental changes caused by highways are poorly understood.
In natural turfy swamps, the microbial assemblages are determined by vegetation type, soil nutrient status and degree of flooding, and they change with depth due to redox conditions and substrate availability change [8
]. High microbial activity and diversity occur in nutrient-rich turfy wetlands. In addition, Proteobacteria and Acidobacteria are two main microbial phyla in turfy wetlands, and their relative abundance show the opposite trend when the environmental factors change [11
]. Many studies have found that these natural gradients in microbial assemblages in turfy swamps can be disturbed by anthropogenic activities and local changes in environmental conditions. For example, Sun [12
] found that the tree species and forest management have a strong impact on the bacterial diversity and community structure at a boreal peatland of Central Finland, and Urbanová and Bárta [13
] found that the structure of soil bacteria and archaea community changed after the long-term drainage of a peat wetland. All these findings indicate that soil microbes are extremely sensitive to the interference from external environment caused by human activities in turfy swamps.
Heavy metal pollution is an important environmental problem caused by highway traffic, which has attracted many researchers [14
]. These metals (chromium(Cr), cadmium(Cd), copper(Cu), zinc(Zn), lead(Pd)) are accumulated into the roadside environment through dry and wet deposition, and maintaining high concentrations of these pollutants in soils poses a threat to soil microorganisms due to their low degradation and high toxicity [18
]. Turfy wetlands are also facing the same problem, as previously reported by Wang et al. [6
]; their study indicated that traffic-related metals presented nonpollution to severe pollution levels. Zhao conducted heavy metal content testing and microbial gene sequencing in soil along the Qinghai-Tibet Highway and Qinghai-Tibet Railway. Their study reveals that heavy metal contamination from roads that have been open for more than a decade has affected soil bacterial abundance and bacterial community structure [20
]. By correlation analysis and redundancy analysis, Zhang found that Cr and Cd were the major factors that influenced soil bacterial community changes in East Dongting Lake wetland, China [21
]. However, drainage is another important problem affecting the ecological environment of turfy wetlands [12
]. Turfy swamps are usually located in valleys with perennially accumulated water, and in order to ensure the safe operation of highways, drainage ditches parallel to the highway are built, resulting in a large amount of water loss. Many scholars have reported that wetland drainage can change the physical properties such as density and water content of the original soil [23
] and cause soil nutrient loss [25
]. In any case, there is still a lack of knowledge about how soil microorganisms respond to road drainage and heavy metal pollution in turfy swamps.
A better understanding of soil microbial assemblages after road drainage and heavy metal pollution can provide a theoretical foundation for the management and restoration of turfy swamps and can establish a basis for highway construction decisions in turfy wetlands. In this paper, we took soil samples along the turfy swamp highway and in the control area, tested their physical and chemical properties and measured their bacterial composition using high-throughput sequencing technology. Through comparative analysis of soil physical and chemical properties, heavy metal content and bacterial diversity in the affected area and the control area, it was found that water table (WT) is the main factor affecting the structure of soil bacterial community, and its changes are caused by road drainage. Heavy metal emissions caused by highway traffic will also affect the structure and composition of the bacterial community in the soil. The main purpose of this research is to investigate the composition of the bacterial community in the turfy swamp and the changes in the bacterial community structure caused by the impact of highways on its environment.
2. Materials and Methods
2.1. Site Description and Sample Collection
This study was performed in the Changbai Mountain area, Jilin Province, where there are a large number of turfy swamps. This area has a typical temperate continental monsoon climate with annual temperature and rainfall of 2–6 °C and 400–900 mm, respectively. The main vegetation of the turfy swamp in this area includes Carex meyeriana
, Thelypteris palustris
Fernald and Sanguisorba tenuifolia
. Soil samples were collected from three similar and independent turfy swamps, namely Jiangyuan (JY; N43°7′, E128°1′), Longquan (LQ; N42°25′, E126°36′) and Huangsongdian (HSD; N43°39′, E127°39′) (Figure 1
), which have been seriously disturbed by highway-related activities in recent years [6
Soil sampling was performed in an area adjacent to the highway (<10 m; affected area) and in an area away from the highway (500–1000 m; control area) in July 2016. In each area, three quadrats (4 m × 4 m per quadrat) with 100 m spacing were arranged as triplicate sampling sites (Figure 1
). The upper layer of soil (0–30 cm) was sampled using a corer in each quadrat (each sample including 5 subsamples mixed together). After carefully removing root debris, soil samples were divided into two parts: one was naturally air-dried through a 2 mm nylon sieve to determine physical and chemical properties; the other sample was stored in a cryogenic chamber at −80 °C for later throughput. In addition, a perforated PVC tube was set in each quadrat to monitor the water level during the growing season. The naming rules for soil samples in these three sites were as follows: JY affected area is JY1–JY3, and JY control area is CKJ1–CKJ3; LQ affected area is LQ1–LQ3, and LQ control area is CKL1–CKL3; HSD affected area is HSD1–HSD3, and HSD control area is CKH1–CKH3.
2.2. Physicochemical Properties Analyses
To determine the pH, soil organic carbon (SOC), total nitrogen (TN), total potassium (TK) and total phosphorus (TP) of the soil, the soil was dried naturally at room temperature. Coarse particles and grass roots were removed from the soil, and the soil was ground up and passed through a 2 mm nylon sieve for later use. Soil pH was measured using a pH meter (Model PHS-3C pH meter, INESA, Shanghai, China) at 1:2.5 (soil to water) after 30 min of shaking [26
Soil organic carbon (SOC) was determined by the classical potassium dichromate oxidation–outer heating method according to standards of forestry (LY/T 1237-1999 and LY/T 1228-2015). The main steps are as follows: (1) Weigh 0.1000–0.5000 g (accurate to 0.0001 g) of soil sample into a rigid test tube and add 0.1 g of silver sulfate. (2) Add 5.00 mL of 0.8000 mol/L standard solution of potassium dichromate, and then inject 5.00 mL of sulfuric acid into the syringe and shake well. (3) Put the tubes in an oil bath pan at 170 to 180 °C and keep the solution in them boiling for 5 min. (4) Take out the test tube and let it cool down, then wash the solution into 250 mL conical flask, with the volume of the flask is controlled at 60–80 mL. (5) Add 3–4 drops of o-phenanthroline indicator, titrate the solution with 0.2 mol/L standard solution of ammonium ferrous sulfate to the end of the solution from orange-yellow by blue-green to brown-red.
Soil total nitrogen (TN) was determined using the Kjeldahl method according to standards of forestry (LY/T 1237-1999 and LY/T 1228-2015). The main steps are as follows: (1) Weigh 0.5000 g of soil sample, add it to a dry digestion tube, and add 1.5 g of reducing mixture catalyst. (2) Add 5 mL of concentrated sulfuric acid with a syringe and put it on a digester in a fume hood for 1.5 h until the contents are clear and light blue. (3) Place the triangular bottle under the socket of the condensation tube and submerge the mouth of the tube in boric acid solution (absorbent in triangular bottles: 20 mL of 2% boric acid). (4) When the receiving liquid turns blue after distillation for 5 min, leave the lower end of the condenser tube at the boric acid level, and then flush the outside of the tube with distilled water. (5) Titrate with 0.001 eq. of standard solution of hydrochloric acid until red and record the volume of the consumed standard solution. (An additional set of blanks is required, and the steps are identical except that no soil sample is added.)
Soil total phosphorus (TP) was measured according to Mo-Sb colorimetric method after digestion HF-HClO4-HNO3. HF-HClO4-HNO3 digestion steps: (1) Weigh 0.2–0.5 g (accurate to 0.0001 g) of soil sample and put it into a 50 mL PTFE digestion tube. (2) Rinse the adhered soil on the inner wall to the bottom of the tank carefully with a small amount of deionized water through the bottle washing nozzle. Place the digestion tube in the hole of the graphite block of the digester, add 10 mL of HNO3, boil at 100 °C and maintain at this temperature for 60 min, cool for 10 min. (3) Add 5 mL of hydrofluoric acid and 1 mL HCLO4 and continue to heat to 150 °C; heat for 150 min, then cool to room temperature. (4) Add 0.5 mL of nitric acid to dissolve the residue into a 50 mL volumetric jar. Wash the digestion tube several times with a small amount of deionized water and transfer the wash solution along with it to the volumetric bottle.
The main steps of TP determination are as follows: (1) Absorb 5 mL of solution accurately in a 25 mL volumetric bottle, add 2 mL of ammonium molybdate sulfate solution and 2 drops of antimony potassium tartrate solution, mix well and dilute to the scale line. (2) Add ascorbic acid solids, punch and mix well and leave for 5–20 min. (3) Measure the absorbance in a type 72 spectrophotometer at 680 nm wavelength with water as the reference. Then, find the phosphorus content in the standard curve.
Soil total potassium (TK) was analyzed using flame photometric method after digestion HF-HClO4
. The main steps are as follows: (1) Absorb 5–10 mL solution in 50 mL volumetric bottle, then fix the volume to scale with water. (2) Determine it on flame photometer and record the reading of flow detector. (3) Find the potassium concentration from the standard curve. Soil density was determined by the cutting ring method. The contents of Cr, Zn, Cu, Cd and Pb were extracted using the ICP-MAS (Q/JUTC010-2007) methods described by Wang [6
2.3. DNA Extraction, PCR Amplification and Pyrosequencing
Genomic DNA was extracted from 250 mg of each soil sample using a PowerSoil DNA Isolation Kit (MoBio laboratories Inc., Carlsbad, CA, USA) as recommended by the manufacturer. The extracted DNA was purified using NanoDrop-1000 spectrophotometer (ThermoFisher, Waltham, MA, USA). The V4-V5 region of the bacterial 16S rRNA genes was PCR amplified using the primer sets of 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGTCAATTCMTTTGAGTTT-3′). The specific amplification process was as follows: 2 min at 94 °C; 25 cycles of 30 s at 94 °C for denaturation, 30 s at 56 °C for annealing and 30 s at 72 °C for extension; and the final extension at 72 °C for 5 min. In addition, the PCR amplification system included 10 μL of 5 × buffer, 1 μL of dNTP (10 mm), 1 U of Phusion DNA polymerase, 5–50 ng of template DNA, 1 μL of each F/R medial primer (10 mm) and ddH2O to a total volume of 50 μL. After PCR amplification, the PCR products were determined by analyzing 3 μL of product on 1.2% agarose gel. Next, the amplicons extracted from the 2% agarose gels were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using FTC-3000TM real-time PCR (Funglyn Biotech Inc., Toronto, Canada), and the purified amplicons were pooled on an Illumina MiSeq platform (TinyGene Bio-Tech Co., Ltd., Shanghai, China) with the equimolar amounts and paired-end sequenced (2 × 300 bp) according to standard protocols.
2.4. Statistical and Bioinformatics Analysis
Raw pyrosequencing reads with an average quality score < 20 or the reads < 50 bp were trimmed off at 50 bp sliding window using Trimmomatic. Next, all paired reads with at least 20 base overlaps between forward and reverse reads were merged at a maximum mismatch ratio of 0.2 to form a chimeric sequence. To get more accurate results of bioinformatics analysis, the chimeric sequences need to be filtered by quality control, i.e., removing chimerism, ambiguous, homologous and singletons using Mothur software (v.1.39.5). Finally, the optimization sequences were compared with the RDP and database for species annotation, and the confidence threshold was set to 0.6. The operational taxonomic units (OTUs) with ≥ 97% similarity were clustered using Usearch (version 5.2.236). Based the OTUs, bacterial alpha diversity indices including Ace, Chao, Shannon and Simpson were calculated using Mothur.
The one-way ANOVA and LSD tests were used to identify significant differences in the alpha diversity indices and the relative sequence abundances of bacterial phylum and genus between the affected area and control area (SPSS 21.0 software). The same approach was used for analyzing the significant differences in the environmental factors between the affected and control areas. In addition, correlation and multiple linear regression analyses were conducted using SPSS 21.0 software. Principal component analysis (PCA), redundancy analysis (RDA), ANOSIM test, variation partition analysis and heatmap analysis were executed using R vegan package (version 2.5-2), and surface fitting regression was performed using Origin 9.0 software. Monte Carlo permutation was used to test the significance of interpretation of environmental variables for species.
Many studies have shown that fuel consumption and vehicle wear release many heavy metals, such as Cr, Cd, Cu, Pb and Zn, into the roadside soil, resulting in a high concentration of heavy metals in the soil near the highway [6
]. Our findings are consistent with their results. However, we found no significant difference in the concentration of Pb between the affected and the control area, which may be related to the banning of leaded gasoline. As expected, road drainage did change some physical and chemical properties of the soil in turfy swamps. Some authors have shown that drainage can lead to loss of soil nutrients, resulting in a decline in soil quality [13
]. At the three sites considered in the current study, drainage ditches with a parallel distance of about 3 m from the highway had been constructed to ensure the safe operation of the highway (Figure 1
), which caused a large amount of water to be discharged from the affected area. This is the main reason why WT, SOC and TN values in affected area were significantly lower than those in control area. For soil TP, there was no significant difference between the affected and control area, possibly because phosphate is easily immobilized by the soil and water-soluble phosphorus is scarce. In addition, road drainage is also the main reason for the increase of soil density in affected area, which may be due to the accelerated natural subsidence of the soil by drainage. Therefore, road drainage and pollutant emission are the key factors leading to changes in soil environment in this area.
Human disturbance activities can affect the structure and diversity of soil microbial communities by changing local ecological and environmental factors. For example, Urbanová and Bárta [13
] researched the microbial communities of soils from bog, fen and spruce swamp forests and demonstrated that long-term drainage had substantial effects on soil biochemical properties and microbial community composition. Shi et al. [30
] found that land subsidence due to underground coal mining could affect soil electrical conductivity and water content, thus altering soil microbial community structure and diversity in sandy areas of Western China, and Guo et al. [31
] identified that soil microbial community in mining areas is significantly correlated with Cd, Pb and Zn. In the present study, the sequence data obtained indicated significant differences at the genus level in microbial communities, and the great majority of the affected genera belonged to the phyla Proteobacteria and Acidobacteria.
Proteobacteria and Acidobacteria were the most dominant phyla in soil of turfy swamps, and their relative abundances were susceptible to external environmental changes. For example, Sun et al. [12
] found that the relative abundance of Acidobacteria increased but that of Proteobacteria clearly decreased in drained peatland. Our results were similar in that the relative abundance of Acidobacteria increased and that of Proteobacteria clearly decreased under the dual effects of drainage and heavy metal pollution in turfy swamp. Previous studies have shown that the ratio of Proteobacteria to Acidobacteria can be used as an indicator to reflect changes in soil environmental conditions [11
]. In this study, the ratio values in affected and control areas were significantly different and were in the ranges of 1.12–2.05 and 3.03–4.31, respectively, which suggests that soil environmental conditions in affected area of turfy swamps did change. However, our results are inconsistent with the results previously reported by Smit et al. [11
] showing that the ratio of Proteobacteria to Acidobacteria varied from 0.14 to 0.46, which may be related to different geographical location and nutritional conditions.
However, at the genus level, Geobacter
was the most abundant genus in all of the samples, and its relative abundance was significantly lower in the affected area. Geobacter
is an anaerobic bacterium with extracellular respiration, widely distributed in soil and groundwater sediments, and it plays an important role in anaerobic organic matter degradation and anaerobic methane oxidation [32
]. Road drainage can improve aeration of the surface soil of turfy wetland and reduce substrate availability, which may be the reason why the relative abundance of Geobacter
was lower in the affected area than in the control area. The abundance of Methylobacter
was similarly lower, providing additional support for road drainage increasing methane emission, here by reducing Methylobacter
, a type I methanotroph that assimilates methane-derived C under aerobic conditions and plays an important role in the process of methane emission [34
]. The genera Syntrophus
are Gram-negative, present syntrophism with methanogenic microorganisms and are highly efficient in the degradation of aromatic compounds [36
]. Their relative abundance is significantly related to heavy metals and soil nutrients, indicating that they are sensitive to road drainage and pollutant discharge affected by highways. In addition, several studies have found that different microorganisms respond differently to the toxicity of heavy metals: those microorganisms that are susceptible to toxins abruptly decrease while resistant microorganisms can adapt to environmental changes. For example, considering environmental changes, Candidatus Solibacter
were found to increase in terms of relative abundance in metal-contaminated soil, while the relative abundance of Longilinea
]. Our results were consistent with these observations. However, while few studies have reported that Leptolinea
are associated with heavy metals, both were affected in our study, which may be explained by the variations of soil available nutrients and heavy metals.
Based on the above analysis, we conclude that the road drainage and pollutant discharge can alter soil environmental factors and consequently affect the bacterial community structure in soils of turfy swamps. Many studies have indicated that the microbial assemblages are driven by the combined effects of multiple environmental factors, rather than by a single factor. For example, Guo et al. [31
] found that the environmental factors SOM, pH, Zn, Cd, Pb and H2
O had a significant effect on microbial community structure, and Zeng et al. [39
] observed that pH, SOM and nutrients were the key factors affecting Actinobacteria and Proteobacteria in the Loess Plateau of China. In our study, RDA and correlation analysis were performed to further explore the response of soil bacterial community to environmental factors, with the results showing that SOC, TN, Cd, Cr, Zn, Cu, WT and density were the main factors affecting microbial abundance and community structure. Among them, WT and density were considered to be the most important because they can predict the change characteristics of the two main phyla (Proteobacteria and Acidobacteria) well. WT is an important factor in the wetland ecosystem, and persistent decrease of the WT caused by drainage can affect the anaerobic condition and change the microbial habitats. In addition, many studies have shown that even a short-term drought can change the microbial community in peatland [40
]. SOC and TN are the most important nutrient components for organisms because they are involved in a number of cellular processes; many studies have documented that soil microorganisms are significantly correlated with SOC and TN in different ecosystems [42
Cd, Cr, Zn and Cu are also important environmental factors that can affect microbial communities. High concentrations of heavy metals can alter bacterial community structure by many ways, such as protein denaturation, cell membrane destruction and inhibition of cell division or enzyme activity, and different bacteria respond differently to them [44
]. It was observed that the genera Candidatus Solibacter
showed significantly positive correlations with Cd, Cr, Zn and Cu. Both of these genera are reported to have the ability to protect themselves from metal toxicity. In contrast, the Longilinea
is sensitive to heavy metals, and it was found to be negatively correlated with Cd, Cr, Zn and Cu in the present study. In addition, some other genera, such as Syntrophus
, were negatively correlated with Cd, Cr, Zn and Cu, indicating that heavy metals can inhibit their growth in soil. However, there is a need for further studies in this area. In addition, it is noteworthy that the above microorganisms are not only affected by Cd, Cr, Zn and Cu, but also significantly influenced by the WT, density, SOC and TN, which means that road drainage and traffic-related metals simultaneously determine soil microbial assemblages.
This study investigated the impact of highway-related activities on the composition and diversity of bacterial communities in turf swamp soil. The results indicated that road drainage and heavy metal emission are two main driving factors leading to significant changes in soil microbiota at the genus level, and most of the affected genera belonged to Proteobacteria and Acidobacteria. The construction of highways in wetland areas, especially the installation of drainage projects, can lead to severe deterioration and even degradation of wetland ecosystems along the route. Wetlands have important ecological and environmental effects, and wetland conservation is of great importance. Mark studied the environmental changes in the wetland construction area, including bacterial activity, bacterial community structure, and carbon content, and found that significant increases in bacterial activity occurred in wetlands constructed by installing berms across waterways [45
]. Xie′s research indicates that the addition of exogenous microbial products to wetlands helps remove contaminants from the soil through bioproduction [46
]. Li put forward suggestions and methods for the protection of the wetland water environment in terms of pollution sources, pathways and treatment measures [47
]. Along highways in turfy swamps, berms can be installed near the roadbed to prevent wetland water loss. Continuous wetland ecological environment monitoring to obtain effective wetland environmental status and the introduction of exogenous microorganisms to improve the ecological environment can all be effective means of protecting wetlands.
Subsequent statistical analyses further revealed that the main environmental factors determining the soil bacterial community in the turfy swamp were WT, density, SOC, TN, Cd, Cr, Zn and Cu. Moreover, WT and density played a more important role than the other factors, and they can be used to predict the change trend of soil Proteobacteria and Acidobacteria. In addition, WT is also the most important factor affecting the soil bacterial alpha diversity. These findings might have implications for wetland restoration and highway construction scheme selection in turfy swamp areas. However, long-term monitoring should be done to study the effects of continuous drainage and heavy metal accumulation on soil microorganisms and how specific functional microorganisms, such as methanogens, response to complex environmental factors.