Science of the Total Environment 521–522 (2015) 52–58

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Factors controlling accumulation of soil organic carbon along vegetation succession in a typical karst region in Southwest China Shujuan Liu a,b,c,1, Wei Zhang a,b,1, Kelin Wang a,b,⁎, Fujing Pan a,b,c, Shan Yang d, Shiyan Shu e,f a

Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China Huanjiang Observation and Research Station of Karst Ecosystem, Chinese Academy of Sciences, Huanjiang, Guangxi Zhuang Autonomous Region 547100, China University of Chinese Academy of Sciences, Beijing 100049, China d Changsha university, Changsha 410003, China e Changjiang Project Supervision & Consultancy Co. Ltd, Wuhan 430010, China f Changjiang Ecology (Hubei) Technology Development LLC, Wuhan 430010, China b c

H I G H L I G H T S • Vegetation restoration is conducive to soil carbon sequestration in karst areas. • The factors controlling SOC accumulation differed along vegetation succession. • The interaction effect among significant factors became more and more prominent along succession.

a r t i c l e

i n f o

Article history: Received 14 August 2014 Received in revised form 16 March 2015 Accepted 18 March 2015 Available online 28 March 2015 Editor: Charlotte Poschenrieder Keywords: Karst peak-cluster depression Soil organic carbon Silt content Litter Microbial biomass Enzyme activities

a b s t r a c t Vegetation succession enhances the accumulation of carbon in the soil. However, little is known about the mechanisms underlying soil organic carbon (SOC) accumulation in different vegetation types in the karst region of Southwest China. The goal of this study was to identify and prioritize the effects of environmental parameters, including soil physico-chemical properties, microbial biomass, enzyme activities, and litter characteristics, on SOC accumulation along a vegetation succession sere (grassland, shrubland, secondary forest, and primary forest) in the karst landscape of Southwest China. Relationships between these parameters and SOC were evaluated by redundancy analysis. The results showed that SOC accumulation was significantly different among vegetation types (P b 0.01) and increased with vegetation succession (from 29.10 g·kg−1 in grassland to 73.92 g·kg−1 in primary forest). Soil biochemistry and physical characteristics significantly affected the accumulation of SOC. Soil microbial biomass showed a predominant effect on SOC in each of the four vegetation types. In addition, the soil physical property (especially the silt content) was another controlling factor in the early stages (grassland), and urease activity and saccharase activity were important controlling factors in the early-middle and middle-late stages, respectively. Litter characteristics only showed mild effects on SOC accumulation. Variation partitioning analysis showed that the contribution of sole main factors to SOC variation decreased, while the interaction effect among parameters increased along the succession gradient. © 2015 Elsevier B.V. All rights reserved.

1. Introduction

Abbreviations: SOC, soil organic carbon; SMBC, soil microbial biomass carbon content; SMBN, soil microbial biomass nitrogen content; litter C/N, ratio of carbon to nitrogen in the litter; SAC, saccharase activity; URE, urease activity; RDA, redundancy analysis; N, nitrogen; P, phosphorus. ⁎ Corresponding author at: Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China. E-mail address: [email protected] (K. Wang). 1 Shujuan Liu and Wei Zhang are the first co-authors. These authors contribute equally to this work.

http://dx.doi.org/10.1016/j.scitotenv.2015.03.074 0048-9697/© 2015 Elsevier B.V. All rights reserved.

Soil contains more carbon than does the atmosphere and vegetation of the Earth combined (Tarnocai et al., 2009). Understanding the mechanisms controlling the accumulation of soil carbon is critical to predict patterns of global warming (Jenkinson et al., 1991; Knorr et al., 2005). Recent studies suggest that vegetation succession caused by both natural restoration and managed activity enhances the soil carbon sequestration capacity, which could thereby combat the effects of global climate change (IPCC, Intergovernmental Panel on Climate Change, 2007; Wilson et al., 2003; Deng et al., 2013; Chang et al., 2011). In addition, understanding the relationships between soil organic carbon

S. Liu et al. / Science of the Total Environment 521–522 (2015) 52–58

(SOC) and soil biotic and abiotic characteristics in different vegetation types is essential for carbon management. However, soil carbon sequestration may be affected by different factors in different vegetation types (Zhu et al., 2012). Therefore, it is essential to identify the main drivers that control soil carbon sequestration to achieve effective soil carbon management at the regional or global scale. However, little is known about the factors controlling soil organic carbon in karst areas. Karst is a distinctive topography, created by the action of acidic water on carbonate bedrock, such as limestone, dolomite, or marble. Due to its specific geologic and climate conditions, karst area is characterized by small environment capacity, weak anti-disturbance, low stability and powerless self-adjustment (Chen and Wang, 2004). In the late of the 20th century, an increasing human population and other heavy anthropogenic impacts have seriously damaged the vegetation in the karst region of Southwest China (Yao et al., 2001). In 1999, China launched the ‘Grain-for-Green’ project that converted 9.26 × 106 ha of former croplands to forests or grasslands until 2010, which resulted in significant increases in soil carbon sequestration in China (Zhang et al., 2013). The karst region of Southwest China covers an area of 550,000 km2 (Li et al., 2002), and is one of the main regions involved in the ‘Grain-for-Green’ project. These abandoned agricultural lands are experiencing a change from crop to forest or other secondary vegetation states, which are accompanied by changes in ecosystem structure, processes, and functions (Davidson et al., 2007; Zhang et al., 2010). Since the 1990s, government policies have forced farmers to abandon fields in parts of the karst area where erosion losses were especially high. With the enforcement of the projects, agricultural and rocky land gradually restored to grassland, shrubland, and forest, depending on the time since abandonment. Some studies have reported an increase in SOC content along vegetation successions in many regions (Silver et al., 2000; Chang et al., 2011). In addition, many factors have been reported to affect soil carbon sequestration, such as vegetation type and biodiversity (Wardle et al., 2012), microbe type (Averill et al., 2014), litter quantity and quality (Gentile et al., 2011), and soil physical properties (Marschner et al., 2008). However, these parameters have usually been considered as single or several environmental factors that act independently on soil carbon accumulation. There has been little work done to explore the influences of composite influencing factors on SOC accumulation in different vegetation types along vegetation succession. Although soil carbon accumulation during ecosystem restoration in the karst region has been well documented (Zhu et al., 2012; Zheng et al., 2012), the specific factors that drive this accumulation are poorly known. In this study, SOC was measured along a vegetation succession sere in a karst area in Southwest China, and other soil physicochemical properties, microbial biomass, enzyme activities, and litter characteristics were recorded. Our objectives were to (1) characterize the trend of SOC variation along vegetation succession, (2) identify the critical parameter(s) affecting SOC along vegetation succession, and (3) explain how these critical parameters affect SOC. We hypothesized that the factors controlling SOC accumulation differed along vegetation succession, and the interaction effect among the controlling factors became more and more prominent along succession. 2. Materials and methods 2.1. Study sites This study was conducted at Guzhou catchments (24°54′–24°55′ N, 107°56′–107°57′E) and Mulun National Natural Reserve (25°06′09″– 25°12′25″ N, 107°53′29″–108°05′45″ E) in Huanjiang county, Guangxi province in Southwest China. This region experiences a typical subtropical monsoon climate. Mean annual temperature is 19 °C, and mean annual precipitation is 1380 mm, mostly falling from May to September. Both sites are typical karst landscapes with a gentle valley flanked by steeper hills. The soil is lime soil and the parent rock is limestone. Soil

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pH ranged from 6.29 to 7.85 during the study period. Average soil depth was 50–80 cm in the depression and 10–30 cm on the hillslope. The Guzhou catchments are located in an area of 10.2 km2 with elevation ranging from 375 to 816 m above sea level, and contain 1.01 km2 of farmlands, which are mainly located in the depression. Before the 1980s, the catchments were severely disturbed by deforestation and cultivation. Toward the end of 1996, some residents moved out of the area, and a part of the sloping farmlands was abandoned owing to the “Grain-for-Green” project. Three vegetation types that belong to different succession stages are widespread in the catchments, including grasslands, shrublands, and secondary forest lands. The grasslands were used as farmland but were abandoned in the 1990s, and then naturally recovered to a grass community. The dominant species in the grass community are Miscanthus floridulus, Neyraudia reynaudiana, and Imperata cylindrica (Table 1). Shrublands were deforested and cultivated before the 1980s, but were protected at the end of the 1980s and naturally recovered to a shrub community. The dominant species in the shrubland are Alchornea trewioides, Cipadessa cinerascens, and Rhus chinensis (Table 1). The secondary forests were deforested and cultivated before 1959, and were then protected and naturally recovered to secondary forest. The dominant species in the secondary forests are Toona sinensis, Gleditsia sinensis, Radermachera sinica, Bauhinia brachycarpa var. cavaleriei, Sterculia euosma, and Rapanea kwangsiensis (Table 1). Mulun National Natural Reserve, about 25 km away from the Guzhou catchments and with an area of 108.6 km2, was established in 1991 to protect a remnant of undisturbed mixed evergreen and deciduous broadleaved forests in the karst region. The primary forest at this site has not been disturbed for more than 200 years. The dominant species in the primary forest are Cyclobalanopsis glauca, Miliusa chunii, Cryptocarya chinensis, Cleidion bracteosum, and Aidia cochinchinensis (Table 1). 2.2. Sampling methods and collection All samples were taken from July to August 2009. Plots were designed in 30 × 20 m quadrats, and in at least 30-m intervals from 3 sample lines of uniform vegetation in the whole slope under all four nonadjacent vegetation types. Soil samples were extracted from each plot. Before soil sampling, each plot was divided into four subplots (15 × 10 m). Eight sets (20 × 20 cm) of leaf litter and soil cores (15 cm depth) per subplot in 3-m intervals were taken and thoroughly mixed into one composite sample, respectively. A total of 51 plots included 12 grassland plots, 12 shrubland plots, and 11 secondary forest plots in Guzhou and 16 primary forest plots in Mulun National Natural Reserve. A total of 204 subplots were investigated, and 204 litter samples and 200 topsoil samples (2 shrubland subplots and 2 secondary forest subplots did not cover soil) were collected. The composite samples were placed in polyethylene bags and carried to the laboratory within no more than 10 h. For each subplot, canopy cover, slope, soil depth, rock exposure, and dominant species (determined according to Zhang, 2005) were recorded. In addition, we recorded the plot corner latitude, longitude, and altitude using a geographic positioning system (E640+ MobileMapper), and drew the map by ArcGIS 9.2. Litter samples were oven-dried at 70 °C to constant weight and ground for further analysis. Soil samples were air-dried and sieved with 2-mm mesh for soil physico-chemical analysis. 2.3. Laboratory analysis Soil chemical and physical properties, including SOC, soil pH, and soil texture, were measured according to Liu et al. (1996). SOC was determined by a KCr2O7–H2SO4 oil bath. Soil pH value was measured in a 1:2.5 (w/v) soil to distilled water suspension. Soil texture analysis was determined by a pipette and sieve analysis. According to the USDA classification system, soil particle can be divided into 3 particle fractions:

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Table 1 Plot characteristics of the four vegetation types (mean ± SD). Succession stage

Canopy cover (%)

Community height (m)

Dominant species

Altitude (m)

Slope (°)

Rock exposure (%)

Disturbance history

Grassland

84.83 ± 4.55

1.74 ± 0.43

425–655

35.1 ± 6.6

61.6 ± 15.7

Long tillage history, abandoned in the 1990s, natural recovery to a grass community

Shrubland

87.78 ± 2.06

2.52 ± 0.23

375–627

40.4 ± 9.0

82.4 ± 9.8

Secondary forest

75.00 ± 5.20

6.38 ± 0.37

391–575

42.1 ± 12.9

69.9 ± 21.2

Deforested and cultivated before the 1980s, abandoned at the end of the 1980s, and naturally recovered to a shrub community Deforested and cultivated before 1959, then abandoned and naturally recovered to secondary forest

Primary forest

87.08 ± 1.99

7.37 ± 0.46

Miscanthus floridulus; Neyraudia reynaudiana; Imperata cylindrica Alchornea trewioides; Cipadessa cinerascens; Rhus chinensis Toona sinensis; Gleditsia sinensis; Radermachera sinica; Bauhinia brachycarpa var. cavaleriei; Sterculia euosma; Rapanea kwangsiensis Cyclobalanopsis glauca; Miliusa chunii; Cryptocarya chinensis; Cleidion bracteosum; Aidia cochinchinensis

403–661

35.3 ± 6.1

77.5 ± 11.8

clay content b 0.002 mm, silt content 0.05–0.002 mm, and sand content 2.0–0.05 mm. The soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) analyses were conducted by chloroform fumigation–K2SO4 extraction carbon and nitrogen automatic analysis, respectively (Wu et al., 2006). Soil enzyme activities were assayed in triplicate air-dried samples, as described by Guan (1986). Briefly, saccharase activity (SAC) was determined with the 3,5-dinitrosalicylic acid colorimetric method. The urease activity (URE) was determined using urea as a substrate: the soil mixture was first incubated at 37 °C for 24 h, the produced NH3–N was determined by a colorimetric method, and the URE is expressed as mg NH3–N·g−1·24 h− 1. Three replicates were established for each test. Litter carbon and nitrogen evaluations were conducted with a KCr2O7–H2SO4 oil bath and an H2SO4–H2O2 flow injection analysis instrument (FIAstar 5000), respectively.

2.4. Data analysis The Margalef richness index (Ma) was used to describe the species richness in four vegetation types and their layers. Ma = (S − 1) / lnN, where S is the species number in a quadrat, N is the sum of all species in a quadrat (Zhang, 2005).

Remnant of undisturbed natural mixed evergreen and deciduous broadleaved forest

All data were checked for normality of distributions and homogeneity of variances prior to analysis. Analysis of variance followed by a least square difference multiple comparison test was performed to test the effects of vegetation types on soil and litter parameters. These analyses were performed using SPSS 16.0 for Windows (SPSS Inc.; Chicago, IL, USA). Redundancy analysis (RDA) was applied to determine the significant parameters on the portion of explained SOC variation and to test the contribution of individual significant parameters to the SOC variation for the different vegetation types (Lepš and Šmilauer, 2003). The whole process was based on computations made with Canoco software (version 4.5, Centre for Biometry, Wageningen, The Netherlands). The analytical process was as follows (Fig. 1). Forward selections were performed to test which of the parameters had a significant influence on SOC in each vegetation type. The selection procedures were stopped when there were no further significant parameters to be added. Explanatory variables were added until addition of further parameters failed to significantly improve (P b 0.05) the model's explanatory power. In addition, following the forward selection, variation partitioning was conducted to discriminate the influence of each significant parameter using partial RDA (Lepš and Šmilauer, 2003). One of the significant parameters was used as a constraining variable, while the other significant variables were used as covariates, which allowed us to estimate the

Fig. 1. Flowchart of redundancy analysis.

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3. Results

SMBC, respectively. Up to 85.1% of the variation in SOC was explained by SMBC, litter nitrogen, and URE in the shrubland and SMBC alone explained 60.4% of this variation. Up to 81.5% of the variation in SOC in the secondary forest was explained by SMBC, SAC, URE, pH, and SMBN, and SMBC was once again the greatest contributor (15.6%) to this variation. Up to 85.2% of the variation in SOC in the primary forest was explained by SMBC, SAC, pH, SMBN, and clay (P = 0.002) and SMBC was again the predominant factor (9.0%). The interaction effects of the significant parameters were 19.1%, 19.7%, 49.7%, and 69.0% in the four succession vegetation types, respectively (Fig. 2).

3.1. Soil characteristics

4. Discussion

SOC and soil pH significantly increased along the vegetation succession (Table 2). Moreover, the contents of clay and silt were significantly different among vegetation types (P b 0.01). Clay was lowest in the primary forest, and was highest in the grassland and secondary forest. Silt showed an opposite pattern to that of clay. Contents of sand were not significantly different among vegetation types (P N 0.05). SMBC and SMBN were also significantly different among the four vegetation types (P b 0.05), and were significantly greater in the shrubland and primary forest than in the secondary forest and grassland. In addition, SMBC and SMBN were higher in the secondary forest than the grassland. Litter carbon and nitrogen significantly increased along vegetation succession, and the litter C/N ratio decreased. Soil enzyme activities under different vegetation types did not exhibit a fixed trend: the maximum and minimum values of SAC were found in the secondary forest and primary forest, respectively; URE was lower in the primary forest than in the shrubland, secondary forest, and grassland.

4.1. The main factors controlling SOC accumulation over vegetation succession

proportion of variation that is solely explained by each of the individual variables. The proportion of unexplained variation represents the proportion of variation unexplained by all significant parameters, and the interaction effect is the shared variation of significant parameters. Monte Carlo reduced model tests with 499 unrestricted permutations were used to statistically evaluate the significance of the first canonical axis and of all canonical axes together. Statistical significance was determined at P b 0.05 for all analyses.

3.2. Redundancy analysis (RDA) RDA showed that Monte Carlo tests for the first and all canonical axes were highly significant (P = 0.002), indicating that all of 11 environmental parameters (clay, silt, sand, pH, SMBC, SMBN, litter carbon, litter nitrogen, litter C/N, SAC and URE) might be important in explaining the variation in SOC. The first canonical axes for the grassland, shrubland, secondary forest, and primary forest explained 71.0%, 88.4%, 87.6%, and 86.9% of the variation in SOC, respectively. 3.3. Forward selections and variation partitioning The RDA model showed that up to 65.8% of the variation in SOC in the grassland was explained by silt, SMBC, SMBN, and URE (P b 0.05), and 19.0% and 15.6% of the variation in SOC was explained by silt and

SOC may not be stabilized in the long term if it is not regulated by physical mechanisms (Marschner et al., 2008). In our study, silt and clay particle content showed a significant influence on carbon accumulation in the early and late stages of vegetation succession, respectively. Kiem et al. (2002) assessed the dynamics of SOC associated with different particle sizes using various approaches (e.g., natural 13C abundance technique, mineralization experiments), and showed that the turnover of SOC in silt and clay is slower than that in sand fractions. Moreover, Saggar et al. (1996) concluded that not the clay content itself but rather the total surface area of clay determined the soil's capacity to stabilize SOC. The actual surface area of the soil is controlled by particle-size fractions with high clay content and by the nature of the minerals present in the clay (Saggar et al., 1996). Therefore, the contents of clay and silt appear to be crucial for the long-term preservation of SOC. At the global scale, about 70% of the net primary productivity of the natural ecosystem returns to the soil through litter (Schlesinger, 1997). Plant litter decomposition was shown to be a critical step in the formation of soil organic matter and the carbon balance in terrestrial ecosystems (Austin and Ballaré, 2010). However, in our study, litter C and N only accounted for relatively low proportions of the variation of SOC in each vegetation type, and litter nitrogen only significantly affected the accumulation of SOC in the early succession stage (shrubland) (Fig. 2). Some studies also showed that the SOC increased in the earliest stage of vegetation change (Deng et al., 2013) because biomass input facilitated SOC accumulation (Tang et al., 2010). In addition, litter quality was found to control the short-term dynamics of C decomposition and accumulation in the soil (Gentile et al., 2011). Therefore, litter, as a short-term factor, appears to play only a basic nutrient supply role in carbon accumulation in early vegetation succession.

Table 2 Characteristics of the topsoil (0–15 cm) and litter in the four vegetation types. Variable

n SOC (g·kg−1) Clay (%) Silt (%) Sand (%) pH SMBC (mg·kg−1) SMBN (mg·kg−1) Litter carbon (mg·g−1) Litter nitrogen (mg·g−1) Litter C/N SAC (mg·g−1·24 h−1) URE (mg·g−1·24 h−1)

Vegetation type

F value

Grassland

Shrubland

Secondary forest

Primary forest

48 29.10 (0.75)a 39.62 (1.62)c 52.18 (1.36)a 8.19 (0.94)a 6.85 (0.05)a 551.67 (18.75)a 56.84 (2.24)a 337.47 (42.04)a 9.70 (2.12)a 36.35 (9.17)c 16.88 (1.21)b 0.46 (0.13)c

46 64.05 (2.25)b 32.17 (2.11)b 57.49 (1.47)b 10.35 (1.13)a 6.78 (0.06)a 1365.98 (69.07)c 138.82 (7.82)d 383.98 (44.10)b 16.86 (2.54)b 23.11 (3.21)b 14.15 (6.52)b 0.37 (0.15)b

42 57.57 (3.42)b 39.55 (2.28)c 51.53 (1.62)a 8.92 (1.12)a 7.21 (0.07)b 699.23 (47.52)b 91.50 (8.15)b 401.56 (24.54)c 18.21 (2.20)c 22.34 (2.88)b 26.97 (12.03)c 0.41 (0.23)bc

64 73.92 (3.55)c 20.57 (1.86)a 64.78 (1.10)c 14.65 (0.95)a 7.12 (0.07)b 1322.25 (39.67)c 119.95 (8.69)c 385.64 (30.15)b 21.00 (2.23)d 18.48 (1.68)a 10.03 (7.74)a 0.19 (0.11)a

45.86** 22.88** 22.28** 2.91 10.69** 81.89** 23.71** 27.77** 233.90** 123.68** 31.82** 31.48**

Note: Different letters following values indicate significance at P = 0.05; SOC, soil organic carbon content; Clay, soil clay content (b0.002 mm); silt, soil silt content (0.05–0.002 mm); sand, soil sand content (2.0–0.05 mm); pH, soil pH value; SMBC, soil microbial biomass carbon content; SMBN, soil microbial biomass nitrogen content; Litter carbon, litter carbon content; Litter nitrogen, litter nitrogen content; Litter C/N, ratio of carbon to nitrogen in the litter; SAC, saccharase activity; URE, urease activity; ** means P b 0.01. All values are means within a vegetation type, with the associated standard error given in parentheses.

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S. Liu et al. / Science of the Total Environment 521–522 (2015) 52–58

100% 14.9

Variation partitioning (%) of SOCC

90% 80%

34.2

60% 50% 40% 30%

pH

3.10

Interaction effect

19.10

49.70

Clay

69.00

SAC

8.30 15.60

Unexplained URE

3.80

Litter N SMBN

60.40 8.40

20% 10%

1.40

2.80 3.30

19.70

70%

14.8

18.5 1.90

SMBC 1.70 3.70

19.00

15.60

0.70 1.40

Silt

9.00

0% grassland

shrubland

secondary forest

primary forest

Fig. 2. Individual contribution of each significant parameter to the proportion of variation explained (%) in SOC in the four vegetation types, determined from the partial redundancy analysis. Note: The numbers embedded in each bar represent the variation in SOC that can be explained solely by the corresponding factor; the unexplained portion (white) is the variation that is not attributed to the significant parameters, and the interaction effect (yellow) is the portion of variation shared by significant parameters. Silt, soil silt content (0.05–0.002 mm); URE, urease activity; SMBC, soil microbial biomass carbon content; SMBN, soil microbial biomass nitrogen content; litter N, litter nitrogen content; SAC, saccharase activity; pH, soil pH value; Clay, soil clay content (b0.002 mm). Partial redundancy analyses were based on Monte Carlo permutation (n = 499) keeping only the significant parameters in the models (P b 0.05). For each partial model, the other significant parameters were used as covariates.

Microbial biomass was found to be the most relevant factor influencing SOC across the vegetation succession, and it also played an important role in each of the four vegetation types. Microorganisms play a fundamental role in the biogeochemical cycles of the elements and in the formation of SOC; indeed, it is widely accepted that high microbial density and activity are necessary for the maintenance of soil quality (Kiem and Kögel-Knabner, 2003; Bastida et al., 2007; Anderson et al., 2008). Singh et al. (1989) considered that microorganisms could quickly and effectively provide nutrients to plants. Mineral N is released mainly from dead microbial cells, microbial materials, and microbial metabolites (France, 1982). The turnover of N from dead microbial cells was shown to be approximately five times greater than that of native soil organic N (Marumoto et al., 1982). Microorganisms accumulate and conserve nutrients during the plentiful period of biological activity; when the activity of the plants is low and they are not able to extract nutrients effectively from soil, the microorganisms can rapidly release the conserved nutrients to initiate plant growth during the deficient period (Singh et al., 1989). Conversely, an increase in microbial biomass might lead to soil organic matter immobilization (McGill et al., 1986). Soil enzyme activities were also found to play an important role in carbon accumulation. However, the key enzyme species for carbon accumulation differed along succession. Urease activity was the most important enzyme type that affected SOC in the early stages (grassland and shrubland), both SAC and urease activity played a significant role in the secondary forest stage, and only SAC played a significant role in the primary forest stage. Urease is an important enzyme involved in the process of nitrogen supply, by transforming organic nitrogen to ammonium nitrogen (Zantua and Bremner, 1975; Zhou et al., 1983). Urease-producing bacteria release abundant URE to accelerate the N mineralization rate to meet their mineral N needs in N-poor soils. During vegetation succession, with the accumulation of available soil N, plants can directly obtain enough N from the soil; thus, plants will reduce the URE input to soil (Liu et al., 2011b). In addition, the unexplained variation in SOC may be contributed by some uninvolved factors in this study, including topography, rock exposure and so on. Our previous studies showed that these environmental parameters affected SOC and soil nutrient accumulation (Liu et al., 2011a; Zhang et al., 2007, 2015). Topography affects soil C through erosion and redistribution of fine soil particles and organic matter across landscapes, and through water redistribution induced varying leaching,

infiltration, and runoff potential (Senthilkumar et al., 2009; Wiaux et al., 2014). Aboveground rain-funneling structures formed by the exposed rocks can produce a stoichiometric gradient of N:P ratios from the vicinity of rocks to open soil (Göransson et al., 2014), which definitely affects the heterogeneous accumulation of SOC. This result corresponds well with our previous study, which found that rock exposure ratio positively correlated with SOC and soil nutrients in the cropland in the present research area (Zhang et al., 2007). However, high canopy cover of the vegetation in the present research may complicate the funneling effect of rocks, and cause more complicated spatial heterogeneity of SOC, which in turn may contribute to species diversity in the karst ecosystems (Zhang et al., 2015).

4.2. Contribution of each significant parameter to SOC and their interaction effect The proportion of variance in SOC explained solely by each parameter and the interaction effect clearly decreased and increased, respectively, along vegetation succession. This pattern indicated that over the course of vegetation succession, the effect of single parameters on SOC accumulation gradually weakened, while the effect of the interaction among parameters gradually strengthened. Callaway (1995) proposed that positive interactions among environmental parameters may be fundamental processes in plant communities. Positive interactions may determine community spatial patterns, permit the coexistence of different plant species, enhance diversity and productivity, and drive community dynamics. Wardle et al. (2012) provided observational and experimental evidence that plant diversity is a major driver

Table 3 Species richness of four vegetations types and their layers.

Grassland layer Shrubland layer Tree layer

Grassland

Shrubland

Secondary forest

Primary forest

2.00 – –

1.11 6.36 –

1.73 5.41 5.46

1.86 4.33 5.62

Note: The Margalef richness index (Ma) Ma = (S − 1) / lnN, where S is the species number in a quadrat, N is the sum of all species in a quadrat (Zhang, 2005).

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of ecosystem C storage relative to other biotic and abiotic factors. In our study, both species richness (Table 3) and soil enrichment (Table 2) increased along vegetation succession, indicating that positive interactions of plant communities would also increase gradually. The accumulation of nutrients and organic matter in the topsoil results from complex interactions between biotic processes that are regulated by plants and soil biota as well as by abiotic processes driven by environmental factors (Hooper et al., 2000). However, the mechanism underlying the influence of the interactions between biotic factors and the soil matrix on increasing SOC accumulation over vegetation succession remains unclear. 5. Conclusions Vegetation restoration is conducive to soil carbon sequestration in karst areas. Microbial quantity, enzyme activities, soil texture, and litter fall were found to play important roles in SOC accumulation; however, the factors controlling SOC accumulation differed along vegetation succession. Microbial biomass parameters (SMBC and SMBN) were the main factors contributing to SOC accumulation in all vegetation stages, while URE and SAC were only important controlling factors in the early-middle and middle-late stages, respectively. Litter N only played an important role in the shrubland. In addition, along succession, the interaction effect among significant factors became more and more prominent, indicating that positive interactions among plant communities, soil biotic processes, and the soil matrix were enhanced over vegetation succession. Based on these results, we suggest that managers should increase nitrogen import in the early and middle succession stages including legume species cultivation in the ecosystems, and prevent disruption of the physical structure of the soil and provide a suitable environment for microbe activity to enhance the soil carbon sequestration capacity, which may promote ecological restoration in the ecologically vulnerable karst landscape. Acknowledgments This study was supported by the National Key Basic Research Program of China (2015CB452703), the Chinese Academy of Sciences (Y523101030) through its Hundred Talent Program to Dejun Li, two grants from the National Natural Science Foundation of China (31270555 and 41471445), and a grant from the Western Light Program from the Chinese Academy of Sciences to Wei Zhang. We appreciate Dr. Poschenrieder, Dr. Dejun Li, Dr. Jie Zhao and the anonymous reviewers for their time and constructive comments and suggestions. References Anderson, J.D., Ingram, L.J., Stahl, P.D., 2008. Influence of reclamation management practices on microbial biomass carbon and soil organic carbon accumulation in semiarid mined lands of Wyoming. Appl. Soil Ecol. 40, 387–397. Austin, A.T., Ballaré, C.L., 2010. Dual role of lignin in plant litter decomposition in terrestrial ecosystems. Proc. Natl. Acad. Sci. 107 (10), 4618–4622. Averill, C., Turner, B.L., Finzi, A.C., 2014. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 000, 1–3. Bastida, F., Moreno, J.L., Hernández, T., García, C., 2007. The long-term effects of the management of a forest soil on its carbon content, microbial biomass and activity under a semi-arid climate. Appl. Soil Ecol. 37, 53–62. Callaway, R.M., 1995. Positive interactions among plants. Bot. Rev. 61 (4), 306–349. Chang, R.Y., Fu, B.J., Liu, G.H., Liu, S.G., 2011. Soil carbon sequestration potential for “Grain for Green” project in Loess Plateau, China. Environ. Manag. 48, 1158–1172. Chen, H.S., Wang, K.L., 2004. Characteristics of karst drought and its countermeasures (in Chinese). Res. Agric. Mod. 25, 70–73 (Monog.). Davidson, E.A., Reis de Carvalho, C.J., Figueira, A.M., Ishida, F.Y., Ometto, J.H.B., Nardoto, G.B., Sabá, R.T., Hayashi, S.N., Leal, E.C., Vieira, I.C.G., Martinelli, L.A., 2007. Recuperation of nitrogen cycling in Amazonian forests following agricultural abandonment. Nature 447, 995–998. Deng, L., Wang, K.B., Chen, M.L., Shangguan, Z.P., Sweeney, S., 2013. Soil organic carbon storage capacity positively related to forest succession on the Loess Plateau, China. Catena 110, 1–7. France, B.R., 1982. Biogeochemical cycle of nitrogen in a semi-arid Savanna. Oikos 38 (3), 321–332.

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