Determining the influence factors of soil organic carbon stock in opencast coal-mine dumps based on complex network theory

Catena 173 (2019) 433–444

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Determining the influence factors of soil organic carbon stock in opencast coal-mine dumps based on complex network theory

T

⁎

Zhaotong Zhanga, Jinman Wanga,b, , Bo Lia a b

College of Land Science and Technology, China University of Geosciences, 29 Xueyuanlu, Haidian District, 100083 Beijing, People's Republic of China Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Land and Resources, 100035 Beijing, People's Republic of China

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil organic carbon stock Influence factors Complex networks Coal mining area Land reclamation

The soil organic carbon (SOC) stock is severely affected by the exploitation of opencast coal mines, and the relationships of factors influencing SOC stock are complex. The influence factors of SOC stock in reclaimed lands in opencast coal mines are unclear, and the existing models can not characterize the complex relationship of SOC stock sufficiently. This paper analyzed the influence factors of SOC stock using complex network theory in the Antaibao opencast coal-mine in Shanxi province of China. An investigation of the soil, topography and vegetation in 50 reclaimed plots was performed. Soil factors, i.e., rock content (RC), total nitrogen (TN), available phosphorus (AP), available potassium (AK), soil bulk density (BD), soil water content (SWC), SOC stock, electrical conductivity (EC), clay content, silt content, sand content and pH, vegetation factors, i.e., above-ground biomass (AGB), tree volume (TV), herb coverage (HC), canopy density (CD), and topography factors, i.e., slope, slope aspect (SA) and slope position (SP), were selected as the nodes, and the relationship among the various factors were considered as the edges to construct a complex network using Gephi. The network characteristics, including degree, betweenness and average shortest path so on, were calculated. SOC stock, SWC and BD played an important role in the complex network of SOC stock. The SOC stock was affected by three clusters: soil texture cluster, soil physicochemical property cluster, and vegetation-topography cluster. SOC stock network was not stable and was sensitive when some important nodes were disturbed. The complex network theory could be used to analyze the influence factors of SOC stock. This study provided a reference for selecting rational land reclamation measures to increase soil carbon stock.

1. Introduction Soil organic carbon (SOC) is an important component of solid phase and plays a critical role in the process of soil development, especially the fertility improvement (Bodlak et al., 2012; Hoogsteen et al., 2015). Opencast coal mining can trigger violent disturbance to the soil and vegetation in coal mining areas, and severely affects the local eco-environment. Land reclamation can increase SOC stock and improve soil fertility, and is an effective measure to restore the destroyed eco-environment in opencast coal mining areas. However, the relationships of factors influencing SOC stock are complex, and the differences in topography and soil properties should be considered when performing soil reconstruction and selecting vegetation types during land

reclamation (Morohosi, 2010; Wang et al., 2014). To select a rational reclamation method in opencast coal mining areas, many scholars had studied the dynamic succession of SOC stock and other soil physicochemical properties to evaluate the change of reclaimed soil quality (Vinduskova and Frouz, 2013; Wang et al., 2013; Ding et al., 2007; Wu et al., 2013). Scholars evaluated the effects of reclamation mode on SOC stock by comparing the relationship between SOC and other soil physicochemical properties (Wang et al., 2013; Marland et al., 2004). Moreover, many soil physicochemical properties, such as SOC stock, rock content (RC), total nitrogen (TN), available phosphorus (AP), available potassium (AK), soil bulk density (BD), soil water content (SWC), soil electrical conductivity (EC), clay content, silt content, sand content and pH, strongly interacted in reclaimed soils. In

Abbreviations: AGB, above-ground biomass; AK, available potassium; AP, available phosphorus; BD, soil bulk density; CD, canopy density; CV, coefficient of variation; DBH, diameter at breast height; EC, electrical conductivity; GPS, Global Positioning System; HC, herb coverage; LUCC, land use and land cover change; RC, rock content; SA, slope aspect; SOC, soil organic carbon; SOM, soil organic matter; SP, slope position; SPSS, Statistical Product and Service Solutions; SWC, soil water content; TDS, total dissolved solids; TN, total nitrogen; TV, tree volume; USA, the United States of America ⁎ Corresponding author at: College of Land Science and Technology, China University of Geosciences, 29 Xueyuanlu, Haidian District, 100083 Beijing, People's Republic of China. E-mail address: [email protected] (J. Wang). https://doi.org/10.1016/j.catena.2018.10.030 Received 2 February 2018; Received in revised form 29 September 2018; Accepted 23 October 2018 0341-8162/ © 2018 Elsevier B.V. All rights reserved.

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precipitation. The specific study areas are located in the South Dump, West Dump and Internal Dump of the Antaibao mine. The South Dump was reclaimed in 1992, and the West Dump and Internal Dump were reclaimed in 1995. The areas of the South Dump, West Dump and Internal Dump are 180.5 hm2, 280.16 hm2 and 264.4 hm2, respectively, and the peak elevations are 1465 m, 1520 m and 1500 m, respectively. These three dumps had formed a stepped terrain due to the stacking process, the platform and slope presented alternative distribution. The ecological environments of the three dumps have been effectively restored by a multi-level and multi-type forest-shrub-grass plant structure, which is presently composed of elms, black locusts, pines, willows and sea buckthorns.

previous research, the correlation analysis between SOC stock and other influence factors was mainly analyzed using sorting function; however, this method lacked systematic analysis and could not sufficiently disclose the mechanisms of SOC stock in coal mining areas (Wang et al., 2014; Gao et al., 2014; Shrestha and Lal, 2011). Therefore, it is necessary to find a scientific and effective method to determine the influence factors of SOC stock to select better reclamation measures and vegetation types. The complex network theory is an appropriate tool to analyze multidimensional soil data based on holistic thinking and understand the complex interactions among the factors controlling SOC stock. Complex network is a theoretical approach quantifying system features based on graph theory (Buibas et al., 2009), and it can determine the interaction of individual factors and the status of influence factors by analyzing whole network system characteristics (Buibas et al., 2009; Cardenas et al., 2010). Complex network approach possesses the characteristics of self-organizing, self-similarity, small-world and scalefree (Ducruet and Beauguitte, 2014). The complexity of complex network is mainly reflected in two aspects: (i) the number of nodes in complex network is enough; (ii) the relationships of nodes are complicated and uncertain. Compared to other methods, complex network approach can conduct a holistic and systematic analysis. The stability of SOC stock after reclamation in coal mining areas can be understand by analyzing the overall characteristics of the complex network, and the effects of various factors on SOC stock can be disclosed by analyzing individual network indices and impact paths. The numerous subsystems in a network are extracted as nodes, and the interaction between these subsystems is regarded as edges to construct a complex network using complex topological structure and dynamic behavior. The comprehensive study is conducted integrating physical method, social analysis, and computer simulation (Durland and Fredericks, 2005). The complex network theory has been applied to a variety of complex real systems, such as ecosystem, transportation system, and economic system, and it focused on the network structure, network characteristics and evolution kinetics (Zaveri et al., 2003; Cai et al., 2006). The application of complex networks in land use field is mainly focused on land use and land cover change (LUCC), and the researchers studied the change law and stability of land use by analyzing the network indices (Zhang et al., 2016; Wu et al., 2012). In soil science field, complex network has been used to study the spatial distribution patterns and diffusion evolutionary mechanisms of soil heavy metal pollution (Li et al., 2012). The complexity of pore structure of soil system also has been researched using complex network theory (Samec et al., 2013; Martin and Reyes, 2008). Although complex network theory has been employed in soil science, few attempts have been conducted to analyze the factors affecting SOC stock, especially mined soils. Therefore, the objectives of our study were to (i) analyze the influence factors of SOC stock in mined soils using complex network, (ii) to explore the relationships among influence factors, and (iii) disclose the paths and process of various influence factors on SOC stock.

2.2. Plot survey and sampling A field survey was conducted at the three dumps of the Antaibao opencast coal-mine in the summer from July 7–12, 2014. Field measurements of the vegetation were performed in 50 sampling plots in the three dumps (Fig. 2). The number of sampling plots in the South Dump, West Dump and Internal Dump were 22, 15 and 13, respectively. South Dump included two belts transects, both West Dump and Internal Dump consisted of three belts transects. To include different topographic factors in the different sampling plots, the belt transects were laid in the northwest-southeast and northeast-southwest directions. One tree quadrat (10 m × 10 m) and three herb quadrats (1 m × 1 m) were set up in representative areas of each sampling plot. At each tree quadrat, the height, number, diameter at breast height (DBH), and canopy density (CD) were measured directly. The tree volume (TV) was calculated based on the number, height and DBH. The calculation formula of TV as follows:

M = π (D /2)2HN

(1) 3

where, M is TV of the unit area in m , D is the average DBH in m, H is the average height in m, N is individual number of tree of the unit area. At each herb quadrat, the herbage coverage (HC) was measured directly. In addition, all of the above-ground herbage (AGB) was clipped, and any dead branches were removed. The remaining herbs were placed into a package. The AGB was measured in the laboratory. Moreover, each quadrat was labeled with a nail stake at the corner and located using the coordinates of the quadrat center using GPS, and the longitude, latitude, elevation, slope, slope position (SP) and slope aspect (SA) were recorded. Soil samples were collected at the 50 sampling plots in the three dumps after removal of plant litter when present any. At each sampling plot, three soil samples were randomly collected using a cutting ring from the surface layer (0–20 cm) for assessment of the soil physical properties, including the SWC, BD and RC. One composite soil sample was also collected from the surface layer (0–20 cm) to determine the soil properties at each sampling plot, including the soil particle size distribution, TN, soil organic matter (SOM), AP and AK. All of the samples were serially numbered and stored in soil-bags for further analysis. The SWC and BD were measured by the oven-drying method and the RC was measured by the gravimetric method. The soil particle size distribution was divided into three classifications based on American systems: clay (< 0.002 mm), silt (0.002–0.05 mm), and sand (0.05–2 mm). Soil particles of the samples were analyzed using a Longbench Mastersizer 2000 laser particle-size analyzer (Malvern Instruments, Malvern, England). The SOM was determined by the thermal potassium dichromate oxidation colorimetric method, and SOC was determined based on the Bemmelen conversion factor and it is 0.58 times that of SOM (Nelson et al., 1996). The TN was measured by Kjeldahl digestion, distillation and titration (Flowers and Bremner, 1991). The AP was determined by the molybdate colorimetric method after perchloric acid digestion and ascorbic acid reduction (Hedley et al., 1982). The AK was analyzed using a flame atomic absorption

2. Materials and methods 2.1. Study area The study area is located at the Antaibao opencast coal-mine, specifically at the geographical coordinates of 112°11′–113°30′ E, 39°23′–30°27′ N in Pingshuo, Shanxi Province. The area of the Antaibao opencast coal mine is 375.12 km2, and it is the largest opencast coalmine in China (Fig. 1). The coal mining area has a typical temperate arid to semiarid continental monsoon climate and a fragile ecological environment with low coverage and high soil erosion. The annual mean temperature range is 6.2 °C, and the annual precipitation averages is 426.7 mm, with 75%–90% occurring during the rainy season (June–September) (Li et al., 2013). The average annual effective evaporation is approximately 2160 mm, almost five-fold greater than the amount of 434

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Fig. 1. Schematic diagram of geographical location.

spectrophotometer. The soil EC was determined using a soil TDS meter TDS11 (Lovibond, Germany). The soil pH was determined using the glass electrode method. In this study, the 0–20 cm SOC stock of each sampling was estimated using soil type method. The calculation formula as follows:

the estimates of central tendency. The mean and median for one parameter were similar, the measures of central tendency is not dominated by the outliers in its distribution (Cambardella and Elliott, 1994).

SOCD = C × D × E × (1 − G )/10

2.3.2. Correlation analysis The correlation analysis among soil, topography and vegetation properties was conducted using SPSS22.0 (SPSS Inc., Chicago, USA). The Pearson correlation coefficient was used in this study, and significance test was conducted at the confidence level of 95%. Correlation relationship was considered to be statistically significant at p < 0.05. The correlation analysis results among different influence factors are presented in Table 3.

(2)

where, SOCD is SOC stock in each sampling plot in tC ha−1, C is the SOC content in g kg−1, D is soil BD in g cm−3, E is soil thickness in cm, G is the percentage of RC with diameter > 2 mm. The SP and SA were converted into a coded scale through the establishment of a membership function according to an empirical formula in Table 1 (Belkhiri and Narany, 2015). The SA was classified as follows: a platform was given a value of 0, a sunny slope was given a value of 0.3, a half-sunny slope was given a value of 0.5, a half-shaded slope was given a value of 0.8 and a shaded slope was given a value of 1 (Liu et al., 1996; Liu and Zhang, 2003). The SP was classified as follows: a platform was given a value of 0, an upper slope was given a value of 0.4, a middle slope was given a value of 1 and a lower slope was given a value of 0.8 (Liu et al., 1996; Liu and Zhang, 2003).

2.4. Complex network of SOC stock 2.4.1. Complex network construction The factors with significant correlation relationship were defined as network nodes, and the correlation relationships among these factors were regarded as network edges of nodes to construct the complex network of SOC stock in the coal mining area using Gephi0.8.2. Complex network characterized the nature of the network nodes and the network topology characteristics based on the degree distribution (Min et al., 2014; Poisot and Gravel, 2014; Zhang et al., 2012; Ni et al., 2011; Purbosari, 2015), path length (Ni et al., 2011; Purbosari, 2015; Mao and Zhang, 2014), clustering coefficient (Chen et al., 2007; Meghanathan, 2015; Yen et al., 2013) and other indices. These network nodes were selected to construct the complex sourcetarget relationships among these influence factors based on the correlation coefficients, then calculated the attribute indices of the network, including the degree, eccentricity, closeness centrality, betweenness centrality, cluster, clustering coefficient, number of triangles, and eigenvector centrality (Ni et al., 2011; Purbosari, 2015). These attribute indices of the network were presented in Table 4, and the descriptive statistical analysis of these indices, including maximum, minimum, mean, median, standard deviation, and CV, were conducted to provide a reference for the subsequent analysis of structural properties in the

2.3. Statistical analysis 2.3.1. Descriptive statistical analysis Descriptive statistical analysis was used to analyze the statistical regularity and examine the reliability of the data, and eliminate the outlier in this study. These data with the values whose deviation from means being greater than twice standard deviation were defined as outliers. The descriptive statistical analysis on influence factor data (i.e., soil, vegetation and topography) and complex network indices was conducted using SPSS22.0 (SPSS Inc., Chicago, USA) to get the maximum, minimum, mean, median, coefficient of variation (CV), and standard deviation (Table 2). Minimum, maximum, and CV were used as estimates of the variability in influence factors and complex network indices. Variability was presented by ranking the CV into three levels in this study, and which were low (< 15%), moderate (15%–35%) and high (> 35%) (Wang et al., 2016). The mean and median were used as 435

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Fig. 2. Layout of the sampling points at the Antaibao opencast coal-mine dumps, and it was processed using arcgis software based on SPOT satellite image obtained in 2014.

directly reflects not only the connectivity and accessibility between two nodes but also the stability of the entire network (Brandes, 2015; Newman, 2005). The average path length is the mean of the shortest path between any two nodes in the network, reflecting the speed of information transmission in complex network. These two indices can measure the cohesive relations in the network; the smaller the index is, the stronger connection is (Ni et al., 2011). The length dij of the shortest path that links nodes i and j can be calculated using various algorithm. The average path was calculated using following formula:

network (Li et al., 2013). 2.4.2. Degree distribution In this study, the important nodes were determined using degree. The degree ki of node i is the number that other nodes link to it (Dorogovtsev and Mendes, 2002; Newman, 2003). ki can be obtained from the adjacency matrix A as follows:

ki =

∑ Aij j∈N

(3)

L=

where, Aij is the correlation matrix of network indices. When node i is related to node j, Aij = 1; otherwise, Aij = 0. The higher degree of the node is, the more important node is in the network, meaning it shares more links with other nodes (Min et al., 2014; Poisot and Gravel, 2014; Zhang et al., 2012; Ni et al., 2011; Purbosari, 2015).

1 N (N − 1)

∑ dij i≠j

(4)

where, dij is the shortest path between nodes i and j, N is the number of nodes, and L is the average path length. 2.4.4. Betweenness centrality Betweenness reflects the influence of nodes in the overall network information exchange process. In general, the betweenness centrality is used to measure the betweenness of the node. The betweenness centrality of node i is defined as the ratio of the number of shortest paths passing through node i to the number of all shortest paths. The

2.4.3. Path length Path length mainly characterizes the possibility and difficulty of connection between two nodes, and two key indices are the shortest path length and the average path length (Milo et al., 2002). The shortest path is defined as the shortest distance between any two nodes, and it 436

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2.4.5. Cluster coefficient Cluster coefficient can display the clustering characteristics of a complex network. If the link between node i and its adjacent node is stronger than that between two adjacent nodes, the node i is located in the center of the cluster (Saramaki et al., 2007). The higher the cluster coefficient is, the closer link with other nodes is. The cluster coefficient was determined using following formula:

Table 1 Topography data of each sampling point in the study area. Abbreviations of topographic variables: SA, slope aspect; SP, slope position. Sample

Slope

SA

SP

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25

0.00 34.00 0.00 21.00 40.00 0.00 41.00 15.00 36.00 0.00 9.00 19.00 31.00 0.00 42.00 0.00 12.00 31.00 0.00 0.00 23.00 45.00 0.00 0.00 23.00

0.10 0.50 0.50 0.10 0.50 0.10 1.00 0.10 1.00 0.10 1.00 0.50 0.80 0.10 0.50 0.10 0.80 0.30 0.10 0.30 0.30 0.30 1.00 0.80 1.00

0.10 0.80 0.10 0.40 1.00 0.10 0.80 0.40 0.40 0.10 0.40 0.40 0.40 0.10 0.40 0.10 0.80 0.80 0.10 0.10 0.40 0.80 0.10 0.10 0.40

Sample

Slope

SA

SP

S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50

0.00 0.00 0.00 19.00 32.00 0.00 0.00 43.00 39.00 0.00 0.00 24.00 41.00 35.00 0.00 0.00 41.00 0.00 0.00 25.00 0.00 42.00 23.00 9.00 0.00

0.80 0.50 0.10 0.50 0.30 0.30 0.30 0.50 0.30 0.30 0.30 0.30 1.00 1.00 0.10 0.80 0.30 0.30 0.50 0.50 0.50 0.30 0.50 0.50 0.50

0.10 0.10 0.10 0.40 0.40 0.10 0.10 0.40 0.40 0.10 0.10 0.80 0.80 0.40 0.10 0.10 0.40 0.10 0.10 0.40 0.10 0.40 0.40 0.40 0.10

Ci = Ei/[ki (ki − 1)/2]

where, node i has ki adjacent node. k(ki − 1)/2 is the theoretical links among the adjacent nodes of node i, and Ei is the actual links among these nodes. The cluster coefficient is equal to the actual links divided by the theoretical links. 2.4.6. Robustness and number of triangles Robustness is the main index of stability in complex network analysis, reflecting the ability of a network to maintain its dynamical activity when a fraction of the dynamical components are deteriorated or functionally depressed but not removed. It is determined based on the change of main indices after the network is disturbed (Morohosi, 2010; Min et al., 2014; Beygelzimer et al., 2005; Cao et al., 2013). The number of stable triangles of the network can also be used as a measure of network stability. After removing some nodes, some stable triangles will be destroyed and the number of the stable triangles will change. 3. Results 3.1. Descriptive statistical analysis of complex network indices

Table 2 Descriptive statistics analysis results of the soil, vegetation and topography data. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC, soil organic carbon; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, above-ground biomass; TV, tree volume; EC, electrical conductivity. Indices

N

Minimum

Maximum

Mean

Median

CV

Slope SA SP BD (g cm−3) SWC (g g−1) RC (%) TN (%) SOC stock (tC ha−1) AP (mg kg−1) AK (mg kg−1) Clay (%) Silt (%) Sand (%) CD (%) HC (%) AGB (kg m−2) TV (m3) pH EC (μs cm−1)

50 50 50 50 50 50 50 50

0.00 0.10 0.10 1.01 3.44 0.00 0.03 5.45

45.00 1.00 1.00 1.20 8.74 0.58 0.30 58.26

15.90 0.46 0.33 1.35 6.45 0.25 0.09 38.88

10.50 0.50 0.40 1.35 6.44 0.30 0.05 36.67

1.06 0.65 0.79 0.12 0.13 0.77 0.57 0.59

16.92 0.30 0.26 0.16 0.84 0.19 0.05 22.94

50 50 50 50 50 50 50 50 50 50 50

2.42 56.00 0.00 12.10 42.30 0.05 0.02 0.00 0.03 7.70 110.00

11.63 274.00 25.10 51.17 85.49 0.85 0.97 0.09 2.37 8.30 211.00

4.91 151.30 8.39 28.63 62.98 0.36 0.48 0.03 0.82 8.00 143.00

4.29 147.50 1.53 28.22 64.39 0.31 0.29 0.022 0.77 7.90 131.50

0.44 0.33 1.09 0.34 0.16 0.50 0.45 0.70 0.72 0.03 0.19

2.16 49.93 9.15 9.73 10.08 0.18 0.61 0.02 0.59 0.21 26.91

Complex network indices can reflect the interaction among soil, vegetation and topography after reclamation. Descriptive statistical analysis of network indices can disclose the statistical regularity of network nodes, and can also evaluate the reclamation effects from overall perspective. Descriptive statistical analysis results of complex network indices were presented in Table 5. There was a significant difference between the minimum and maximum values of the complex network indices, especially the degree, the number of triangles and the betweenness centrality, indicating that the distribution of nodes was decentralized, the hierarchy structure of nodes was clear, and the difference of importance was quite significant in the network. The mean and median values were mostly similar and the median was smaller than the mean for most complex network indices, demonstrating that the outliers had no effects on the central tendency and overall characteristics of the network. The eccentricity and closeness centrality showed moderate CV values (15%–35%) and the degree, clustering coefficient, cluster and eigenvector centrality showed high CV values (> 35%), especially the number of triangles (CV = 107%) and betweenness centrality (CV = 116%) showed sky-high CV values, indicating that the variability of network indices was high and the robustness of network nodes was quite different.

Standard deviation

3.2. Important node and path analysis The degree reflects the importance of nodes in the network. Degree showed a high CV value (> 35%) in network and had a significant difference between the minimum and maximum values (Table 5). This indicated that the importance of each node was quite different. Nodes directly connected to the SOC stock had a direct influence on the SOC stock (Fig. 3). Eight indices had the direct influence on SOC stock. BD, SWC, AP, AK, EC, RC, pH and clay content had the most important effect on the SOC stock, with various differences. The degrees of SOC stock, BD, SWC and TN were 8, 8, 8, and 7, respectively (Table 4), which were at the core of network and played a key role on the SOC stock. Both of the degrees of AK and TV were 6, these two nodes had a certain degree of influence on the SOC stock. The degrees of BD, SWC, TN, AK and TV were higher than those of other nodes and had a close

betweenness centrality was determined using following formula:

Cb (i) =

∑ gjk (i)/gjk j≠k

(6)

(5)

where, Cb(i) is the betweenness centrality of node i, gjk is the number of the shortest path between nodes j and k, and gjk(i) is the number of the shortest paths connecting nodes j and k, as well as passing through node i.

437

438

⁎

1 0.179 −0.027 0.378⁎⁎ 0.080 0.099 0.171 0.139 0.209 −0.142 0.135 0.001 −0.459⁎⁎ 0.140 −0.545⁎⁎ 0.324⁎ 0.067 −0.060

SA

1 0.070 0.115 0.170 0.189 0.013 −0.114 0.093 −0.108 −0.127 0.206 0.000 −0.101 −0.415⁎⁎ 0.329⁎ 0.001 0.027

SP

1 −0.545⁎⁎ −0.314⁎ 0.609⁎⁎ 0.474⁎⁎ 0.611⁎⁎ 0.455⁎⁎ −0.174 0.034 0.124 0.144 0.026 −0.045 0.178 0.322⁎ −0.486⁎⁎

BD

Significant different at the 0.01 level (p < 0.01). Significant different at the 0.05 level (p < 0.05).

1 0.124 0.122 −0.089 −0.528⁎⁎ 0.154 0.078 −0.050 −0.216 −0.107 0.177 −0.223 0.051 −0.208 −0.323⁎ 0.574⁎⁎ −0.449⁎⁎ 0.077 0.070

Slope SA SP BD SWC RC TN SOC stock AP AK Clay Silt Sand CD HC AGB TV pH EC

⁎⁎

Slope

Indices

1 0.094 0.401⁎⁎ 0.684⁎⁎ 0.111 0.137 0.544⁎⁎ 0.217 −0.201 0.070 −0.147 0.074 0.160 −0.406⁎⁎ −0.591⁎⁎

SWC

1 0.493⁎⁎ −0.380⁎⁎ −0.016 0.119 0.358⁎ −0.212 0.057 0.021 −0.210 −0.044 0.349⁎ −0.095 −0.073

RC

1 0.124 0.500⁎⁎ 0.390⁎⁎ −0.020 −0.045 0.109 0.216 −0.166 −0.178 0.228 −0.309⁎ −0.448⁎⁎

TN

1 −0.471⁎⁎ 0.581⁎⁎ −0.717⁎⁎ 0.132 0.140 0.080 −0.199 −0.213 0.020 0.369⁎ 0.422⁎⁎

SOC stock

1 −0.036 0.108 −0.087 −0.194 −0.165 0.147 −0.071 −0.109 0.172 0.300⁎

AP

1 −0.124 0.120 −0.002 0.094 −0.455⁎⁎ −0.006 0.358⁎⁎ 0.339⁎ 0.030

AK

1 −0.408⁎⁎ −0.713⁎⁎ −0.149 0.033 0.108 −0.184 0.111 0.139

Clay

1 −0.174 0.144 0.127 −0.121 0.082 −0.119 −0.209

Silt

1 0.089 −0.149 0.016 0.088 0.013 0.137

Sand

1 −0.156 −0.031 0.487⁎⁎ −0.104 −0.195

CD

1 0.414⁎⁎ −0.196 0.151 0.009

HC

1 −0.011 0.072 0.038

AGB

1 −0.229 −0.214

TV

1 0.098

pH

1

EC

Table 3 Correlation of influence factors in an opencast coal-mine dump. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC, soil organic carbon; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, aboveground biomass; TV, tree volume; EC, electrical conductivity.

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Table 4 Attribute indices of each factor in SOC stock network. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC, soil organic carbon; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, above-ground biomass; TV, tree volume; EC, electrical conductivity. Indices

Degree

Clustering coefficient

Number of triangles

Community

Eccentricity

Closeness centrality

Betweenness centrality

Eigenvector centrality

5 7 8 8 8 4 6 5 5 5 3 6 4 4 1 1 4 2 2

0.80 0.48 0.32 0.43 0.57 0.83 0.33 0.80 0.00 0.20 0.33 1.00 0.17 0.17 0.30 0.00 0.17 0.07 0.00

8 10 9 12 16 5 5 8 3 2 1 1 1 1 0 0 1 1 0

0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 2 2 2

4 3 3 3 3 4 3 3 3 3 4 3 3 3 4 4 4 4 4

2.17 1.61 1.89 1.72 1.83 2.44 1.78 2.00 1.83 1.94 2.33 1.89 1.94 1.94 2.89 2.89 2.50 2.61 2.67

1.17 38.42 6.22 17.72 6.37 0.14 19.11 0.39 16.12 33.25 3.11 27.05 12.92 9.97 0.00 0.00 4.37 0.00 0.67

0.71 0.87 0.88 0.94 1.00 0.58 0.69 0.73 0.61 0.44 0.21 0.37 0.28 0.29 0.08 0.08 0.17 0.12 0.10

EC TN SWC SOC stock BD AP AK pH RC Clay HC TV SA Slope Sand Silt AGB CD SP

link with other nodes, therefore these five nodes constituted the skeleton frame of SOC stock network. The degrees of EC, pH, RC and clay content were 5 and the degrees of AP, SA, AGB and Slope were 4. These eight nodes formed the foundation of SOC stock network. The other nodes with smaller degree had less impact on SOC stock. The path analysis of the network mainly included the length of average path, closeness centrality and betweenness centrality. The length of average path was 2.152, indicating that the length of average path was short in SOC stock network, the speed of information transmission among nodes was fast, and SOC stock was relatively easy to be achieved. The closeness centrality and betweenness centrality are two vital indices reflecting the closeness of each node in the network. The closeness centrality of TN was 38.42 and the betweenness centrality was 1.61, indicating that TN occupied the most central position in the network and had the maximum connectivity and information transmission efficiency. The closeness centrality and betweenness centrality of SOC stock, AK, RC, clay content, TV were 17.72, 19.11, 16.12, 33.25, 27.05, and 1.72, 1.78, 1.83, 1.94, 1.89, respectively, showing that the five nodes were at the core of the network.

physicochemical factors related to the SOC stock. The soil texture cluster contained clay content, silt content, sand content and RC, with the average clustering coefficient of 0.13, which affected the SOC stock through the respiration of soil microbes. The vegetation-topography cluster contained CD, HC, AGB, TV, slope, SA and SP, with an average clustering coefficient of 0.27, which could affect the surface vegetation condition and increase the surface vegetation litter. The clustering coefficient reflected the tightness and stability of the connections among the clusters. The average clustering coefficient of soil physicochemical property cluster was largest, indicating that the cluster structure was relatively stable and the interaction among the nodes of the cluster was intensive. The AP, TV and sand content were the peak nodes of soil physicochemical property cluster, vegetation-topography cluster and soil texture cluster, these three peak nodes played a major role in mutual influence of clusters. The connection among the clusters would be impeded and the structure of entire network would be altered when these three peak nodes changed.

3.3. Hierarchy structure of the network

The average number of triangles of the soil physicochemical property cluster was 9.125 and was larger than the other clusters (Fig. 5), indicating that the soil physicochemical property cluster was more stable. In addition, the number of triangles of BD, SOC stock, TN, and SWC was 16, 12, 10, and 9, respectively. It indicated that these four indices played a key role in the soil physicochemical property cluster. The indices of soil, vegetation and topography would change when the reclamation area was disturbed. When removing any node with high degree, e.g., SWC, BD, TN, AK, and TV, the average degree and

3.4. The stability analysis of SOC stock network

In the present research, the considered factors formed different clusters. The relations among factors in same cluster were stronger than in different clusters. This SOC stock network was divided into three main clusters, i.e., soil texture cluster, soil physicochemical property cluster, and vegetation-topography cluster (Fig. 4). The soil physicochemical property cluster contained EC, TN, AK, AP, pH, SWC, SOC stock, BD, with the average clustering coefficient of 0.57, reflecting the Table 5 Descriptive statistics analysis results of network indices. Network indices

N

Minimum

Maximum

Mean

Median

Degree Clustering coefficient Number of triangles Community Eccentricity Closeness centrality Betweenness centrality Eigenvector centrality Effective N

19 19 19 19 19 19 19 19 19

1.00 0.00 0.00 0.00 3.00 1.61 0.00 0.08

8.00 1.00 16.00 2.00 4.00 2.89 38.42 1.00

4.63 0.37 4.42 0.95 3.42 2.15 10.37 0.48

5.00 0.32 2.00 1.00 3.00 1.94 6.22 0.44 –

439

CV 48% 84% 107% 96% 15% 19% 116% 67%

Standard deviation 2.22 0.31 4.75 0.91 0.51 0.40 11.99 0.32

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Fig. 3. Degree distributions of vegetation, soil and topography factors. Different colour represents different degree. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC stock, soil organic carbon stock; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, aboveground biomass; TV, tree volume; EC, electrical conductivity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

average clustering coefficient would decrease and the average path length would increase (Table 6 and Fig. 6). It indicated that the stability of network became worse, and the connection of nodes became weaker. The average degree and average clustering coefficient would increase, and the average path length would decrease when removing clay content, silt content, or sand content. It indicated that the network became more stable, and the connection of nodes became stronger. Removing slope, HC and CD would not result in the significant change of the average degree, average clustering coefficient and average path length, demonstrating these nodes had relatively low impact on the stability of SOC stock network.

4.1. Mechanism of SOC in the coal mining areas SOC is regarded as an important indicator of soil physicochemical properties and it plays an important role in the soil fertility characteristic and soil stability. However, the mechanisms of SOC accumulation are complex because of the numerous influence factors. SOC stock is in connection with the input of organic matter and the loss of organic matter mainly resulting from soil microbial decomposition. Among them, the SOC stock depends largely on soil moisture status, soil nutrient availability, vegetation growth and other factors, while the decomposition of SOC is subjected to the chemical composition of organic matter, soil water and temperature conditions and physicochemical characteristics factors. In addition, vegetation, soil quality and Fig. 4. Communities of SOC accumulation network. Different colour represents different clusters. Colour red represents the soil texture cluster, colour green represents the soil physicochemical property cluster, and colour yellow represents the vegetation-topography cluster. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC stock, soil organic carbon stock; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, aboveground biomass; TV, tree volume; EC, electrical conductivity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. Number of triangles of the network. Different colour represents the different number of triangles. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC stock, soil organic carbon stock; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, above-ground biomass; TV, tree volume; EC, electrical conductivity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

the correlation characteristics among these factors and demonstrated the mechanisms of SOC stock in coal mining areas.

Table 6 Network robustness indices after removing different nodes. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC, soil organic carbon; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, above-ground biomass; TV, tree volume; EC, electrical conductivity. Removing nodes

Original indices RC TN SOC stock Clay Slope SWC AGB TV HC SP SA CD pH EC BD AP AK Silt Sand

Average Degree 2.474 2.333 2.222 2.111 2.625 2.389 2.167 2.389 2.278 2.444 2.389 2.389 2.500 2.333 2.278 2.111 2.389 2.222 2.556 2.556

Average path length 2.035 2.078 2.059 2.150 1.800 2.052 2.209 2.013 2.118 2.013 2.072 2.059 1.967 2.046 2.052 2.085 2.007 2.124 1.941 1.941

4.2. Mechanisms of effects of soil properties on the SOC stock In the research, SOC stock exhibited the highest degree and had relatively close connection with the other soil nodes. Soil physicochemical properties dominate the process of SOC accumulation, and the physical properties and soil nutrients affect the SOC stock. Therefore, it is necessary to identify the relationship among soil physicochemical properties indices for selecting more useful measures to enhance the SOC stock. SWC had relatively high degree and was directly related to the SOC stock, and it could not only provide appropriate environment for vegetation growth but also provided suitable soil microorganism environment (Liu et al., 2010; She et al., 2010). Microbes decompose plant litter, making nutrients available for plant growth, but also for microbial biomass. Partly decomposed litter, and microbial debris (dead microbial biomass, microbial exudates, dead cell walls, etc.), are in intimate contact with the mineral particles, and can result in stabilized SOC (Sherman and Steinberger, 2012). BD played the most important role in the network and had close connection with other factors. BD can reflect soil development degree, affect soil pore condition and soil aeration, and in turn affect the growth of plant root system and the SOC stock (Hamonts et al., 2013; Paciullo et al., 2010). Soil microbial activity is strongly influenced by acidity and alkalinity of soil, and pH received the influence from N, P, K, carbon and SWC. Therefore, there remains a dynamic balance between soil pH and SOC through the activity of soil microorganisms (Viani et al., 2014; Zhalnina et al., 2015). Many scholars had researched the dynamic balance of TN and SOC and confirmed that there was a strong connection between them (L. Zhang et al., 2015; J.H. Zhang et al., 2015; Wang et al., 2015). TN had the largest betweenness centrality, was the information exchange center of the network and had a relatively great impact on SOC stocks. Increasing soil nitrogen content can enhance the activity of soil microorganism and then enhance the decomposition rate of SOC. Soil texture can be divided into silt, sand and clay, and the percentage of these contents have a strongly influence on the SOC stock because they determine the water and air conditions (Ceacero et al., 2012). Clay has a strong ability to absorb SOC decreasing the decomposition and increasing the stock. RC directly affects the growth of reclaimed vegetation and has an important influence on BD. Moreover, it can contribute to the circulation

Average clustering coefficient 0.415 0.438 0.411 0.381 0.436 0.408 0.404 0.420 0.403 0.408 0.454 0.406 0.414 0.386 0.371 0.331 0.402 0.408 0.422 0.422

other natural factors as well as reclamation management measures, reclamation patterns and other human disturbance will have an important impact on the stock and transformation of SOC. The stock and decomposition of SOC is mainly dominant by soil microbial activity (Eisenhauer et al., 2010; Lomax et al., 2012; Geisseler and Scow, 2014). Plant litter and soil environments provide raw materials and locations for microbial activities, respectively. There is a linear positive correlation between SOC stock and plant litter and AGB (Min and Kim, 2000). The present research using complex network indicated that the SOC stock is affected by the vegetation restoration, soil physicochemical properties and topographical condition (Bodlak et al., 2012; Li et al., 2009; Fettweis et al., 2005). The graph of complex network reflected 441

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Fig. 6. Network robustness indices after removing different nodes. Abbreviations of vegetation, soil, and topographic variables: SA, slope aspect; SP, slope position; BD, soil bulk density; SWC, soil water content; RC, rock content; TN, total nitrogen; SOC stock, soil organic carbon stock; AP, available phosphorus; AK, available potassium; Clay, clay content; Silt, silt content; Sand, sand content; CD, canopy density; HC, herb coverage; AGB, above-ground biomass; TV, tree volume; EC, electrical conductivity.

of SOC. HC and TV are the connecting factors between the physicochemical property cluster and vegetation-topography cluster, and they have an important impact on SOC stock. The AGB, TV, HC and CD can affect the litter vegetation, therefore influence the SOC stock. In addition, the TV and CD also improve microclimate environment in the area, influence the SWC and BD, then affect the activity of soil microorganism and the SOC stock.

of air and moisture migration and provide appropriate environment for soil microbial activity when it is reconstructed. AK and AP have a direct impact on the growth of plants, and then affect the input of vegetation litter to influence the humification and decomposition process of SOC. 4.3. Mechanisms of effects of topography on the SOC stock Topography factors own an important influence on plant community structure and species distribution, thus influence the SOC stock indirectly (Ma et al., 2010; Hedberg et al., 2012). The shortest path between the SA and SOC stock was established through SWC. There is a significant different for water resources allocation under different SAs. SA affects the growth of vegetation and the activity of microorganism by affecting SWC, then influences the SOC stock. The slope indirectly affects the SOC stock by affecting the degree of soil erosion (L. Zhang et al., 2015; J.H. Zhang et al., 2015). In this research, slope was directly connected with SWC, and it affected the accumulation of SOC by affecting the infiltration and retention of water. In addition, slope was directly connected with HC, TV and AGB. The growth of vegetation is affected and soil fertility reduces under large slope, then influences the SOC stock. The results of previous studies showed that the SOC content was relatively high in the lower slope and was relatively low in the upper slope. In the upper slope, the water evaporation is relatively large and the SWC is relatively low due to the opulent sunshine, and promote the decomposition of SOC (Sariyildiz et al., 2005; Lamparter et al., 2009). In addition, the soil erosion is easy to occur in the upper slope, then the eroded soil were stacked in the lower slope. Therefore, the solum in the low slope is relatively thick and the SOC stock increased.

5. Conclusions This study constructed a complex network of SOC stock using the soil, vegetation and topography data of Pingshuo coal-mine based on the correlation relationships among different factors. Through the complex network chart, the most influential factors of SOC stock and the optimal path of each factor can be found. SOC stock was the centralization of the soil property, and had strong connection with other factors. The degree of SOC stock, SWC and BD were relatively high, indicating that these factors played an important role in the complex network. The BD, SWC, TN, RC, and clay content were the most important indices affecting the SOC stock. In addition, the network was divided into three clusters (soil physicochemical property cluster, soil texture cluster and vegetation-topography cluster) according to the connection among these factors. The soil physicochemical property cluster with higher average clustering coefficient and average number of triangles was more stable and the interaction among the nodes inside the cluster was intensive. The SOC stock network was not stable, when SWC, BD, TN, AK, and TV were changed, the stability of network became worse, and the connection of nodes became weaker. When clay content, silt content, or sand content were changed, the network became more stable, and the connection of nodes became stronger.

4.4. Mechanisms of effects of vegetation on the SOC stock

Acknowledgments

The effects of vegetation on SOC stock is mainly from the increase of the root biomass and plant residues. Reclamation vegetation type directly determines the amount of litter, root distribution of residues, and microorganisms (Kane et al., 2007; DeBeer and Sharp, 2009). In this study area, the main vegetation type is elms, black locusts, pines, willows and sea buckthorns. The previous study showed that vegetation litter and underground root excretion were one of the important sources of SOC. The quantity and quality of vegetation litter influence the quantity and quality of SOC, and developed roots can strengthen soil aggregation and improve SOC accumulation. Zhao et al. (2013) reported that, in the plot with sea-buckthorn vegetation reclaimed for 5 years, the contents of aggregates > 0.25 mm were even higher than in the plot reclaimed for 13 years with plant species mix (locust, Chinese pine, elm, locust, and korshinsk peashrub) in present study area. Soil microbes not only promote the turnover of SOC, but also directly involves in the stabilization

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