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Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands
RAMA-00343; No of Pages 9 Rangeland Ecology & Management xxx (xxxx) xxx–xxx
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Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands☆ Rui Zhang a,b,c, Yanfu Bai a, Tao Zhang a, Zalmen Henkin d, A. Allan Degen e, Tianhua Jia a, Cancan Guo a, Ruijun Long a, Zhanhuan Shang a,f,g,⁎ a
School of Life Sciences, State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730000, China Urat Desert-grassland Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China University of Chinese Academy of Sciences, Beijing 100049, China d Beef Cattle Section, Newe-Ya’ar Research Center, Department of Agronomy and Natural Resources, Agricultural Research Organization, Ramat Yishay 30095, Israel e Desert Animal Adaptations and Husbandry, Wyler Department of Dryland Agriculture, Blaustein Institutes for Desert Research, Ben-Gurion University of Negev, Beer Sheva 8410500, Israel f Qinghai Provincial Key Laboratory of Restoration Ecology of Cold Area, Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining 810008, China g Rangeland Research Institute, Qinghai Academy of Animal and Veterinary Science, Qinghai University, Xining 8100016, China b c
a r t i c l e
i n f o
Article history: Received 13 February 2018 Received in revised form 27 September 2018 Accepted 1 October 2018 Available online xxxx Key Words: carbon degraded grassland soil sugars structure equation model Tibetan plateau
a b s t r a c t Soil carbon and sugars play key roles in carbon (C) cycling in grassland ecosystems. However, little is known about their changes in quantity and composition in degraded alpine meadows in the Tibetan plateau. We compared vegetation C density, soil organic carbon (SOC) density, and soil sugars in nondegraded (ND), degraded (DA; following artificial restoration), and extremely degraded (ED) grasslands and analyzed the relation among these parameters by redundancy analysis (RDA) and structural equation models (SEMs). Belowground biomass, soil microbial biomass C, soil microbial biomass nitrogen (N), belowground biomass C density, SOC density, and soil sugars were lower in DA and ED grasslands than in ND grasslands. In addition, the ratio of belowground biomass to aboveground biomass (BAR) decreased with an increase in degradation. The ratio of belowground biomass to aboveground biomass was identified as the main indirect driving force of ecosystem C density by affecting total vegetation C and SOC densities. Soil dissolved organic carbon (DOC), microbial biomass carbon (SMBC), neutral sugars (NS), and total nitrogen (TN) were identified as main direct driving forces. The ratio of belowground biomass to aboveground biomass altered DOC, SMBC, NS, and TN and, consequently, was the primary driving force for the alpine meadows’ ecosystem C density. It was concluded that land management in alpine meadows should include practices that maintain a relatively high BAR in order to curb degradation and increase ecosystem C density. © 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Introduction Grasslands, one of the most widespread vegetation types worldwide, are an important part of the terrestrial ecosystem carbon (C) cycle, accounting for approximately 35% of the total terrestrial C (Scurlock and Hall, 1998; Tian et al., 2016). Soil C and nitrogen (N) contents, as
☆ This study was supported by the National Key Research and Development Project (2016YFC0501906), Natural Science Foundation of China (41671508; 31870433), Key R&D and Transformation Program of Qinghai (2017-NK-149-2), The Second Stage’s Research and Technique Extending Project of Sanjiangyuan Ecological Protection and Building of 2017 in Qinghai (2017-S-1-06), and Qinghai Innovation Platform Construction Project (2017-ZJ-Y20). ⁎ Correspondence: Zhanhuan Shang, School of Life Sciences, State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730000, China. Tel.: +86 931 8915650. E-mail address: [email protected] (Z. Shang).
well as their changes, reflect soil quality and ecosystem productivity and influence C and N cycling and storage (Wen et al., 2013). Soil carbohydrates, including neutral sugars (NS), amino sugars (AS), sugar alcohols, and sugar acids, are the most abundant organic compounds in the biosphere and the most important C and energy sources for soil microorganisms (Zhang et al., 2007; Gunina and Kuzyakov, 2015). Neutral sugars are the major components in dissolved total saccharides and form the basic units of plant cells, whereas amino sugars are associated with structural polysaccharides or glycolipids. It has been suggested that amino sugars can be used as biomarkers for organic matter sources (Glaser et al., 2004). The Tibetan plateau, with an average elevation of N 4 000 m, is regarded as the world’s “third pole” and the headwater region of Asia. Alpine meadows constitute 46% of the vegetation on the Tibetan plateau (Xie et al., 2003; Shang et al., 2016) and are used principally for raising yaks and Tibetan sheep (Harris, 2010). The alpine grasslands have a huge capacity for C storage, accounting for approximately 49% of all
https://doi.org/10.1016/j.rama.2018.10.001 1550-7424/© 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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rangeland C storage of the Tibetan plateau (Shang et al., 2016). However, C storage has been affected greatly by grassland degradation due to anthropogenic activities and natural factors such as climate change (Sousa et al., 2012; Li et al., 2014). Today, approximately 90% of the alpine meadows has been degraded, and about one-third has been severely degraded into “black soil land” (Zhou et al., 2005; Shang et al., 2016). Black soil land is exposed without plant cover and has little utilization value and ecological function (Harris, 2010; Li et al., 2013; Shang et al., 2016). Alpine grassland degradation affects both aboveground and belowground organisms (Singh et al., 2010), which in turn affect C and N cycling (Zhang et al., 2016). Dry environment, burrowing by rodents, overgrazing by livestock, and mining have been the dominant causes of grassland degradation (Dong et al., 2013). Restoring degraded alpine grasslands, crucial for the Tibetan plateau, would require proper management. Fencing, reducing livestock numbers, fertilizer application, and establishing artificial grasslands have been attempted with mixed results (Akiyama and Kawamura, 2007; Li et al., 2014). Studies reported that grassland degradation led to a decrease in vegetation cover, biomass, and soil organic carbon (SOC) (Foggin and Smith, 1996; Evans, 1998; Zhou et al., 2005; Dong et al., 2012; Li et al., 2014), which affected ecosystem C density. However, little is known about 1) changes in the quantity and composition of soil C and sugar in degraded alpine meadows in the Tibetan plateau and 2) the critical driving factors (vegetation cover, biomass, and SOC) affecting the ecosystem C density. We hypothesized that vegetation biomass is the main driving factor affecting ecosystem C density. To test this hypothesis and to improve our understanding of the effects of alpine grassland degradation on ecosystem C density, we examined and compared nondegraded (ND), degraded (DA; following artificial restoration) and extremely degraded (ED; black soil land) grasslands on the Tibetan plateau by 1) measuring their biomass and soil properties (C, N, and sugar content); and 2) quantifying their plant and soil properties. Using these data, we used structural equation models (SEMs) to determine the critical driving factors affecting vegetation and soil carbon densities. Materials and Methods Study Sites The research was conducted in the alpine meadows located in the Three Rivers Headwaters Region in Qinghai province of the Tibetan plateau in western China (31°39′N−36°12′N: 89°45′E−102°23′E). The area covers approximately 3.58 × 105 km2, at an elevation between 3 450 m and 6 621 m above sea level. The site is characterized by high solar radiation with long, cold winters and short, cool summers. The region has a typical plateau-continental climate characterized by low air oxygen levels and numerous blizzards in winter. Average annual air temperature ranges from −5.6°C to 3.8°C, and annual precipitation ranges between 262 mm and 773 mm, of which approximately 80% is concentrated in the growing season, from May to September (Yang et al., 2015). The dominant plants include Kobresia pygmaea, Kobresia humilis, Kobresia tibetica, Carex atrofusca, Stipa przewalskyi, Festuca ovina, Poa pratensis, Elymus nutans, Chenopodium glaucum, Astragalus fenzelianus, Polygonum viviparum, Herba potentillae, Ajania tenuifolia, and Saussurea superba. According to the Chinese soil classification system, the soil is alpine meadow (National Soil Survey Office, 1998), which is similar to Inceptisols in the USDA soil taxonomy system (Soil Survey Staff, 1999). The ND, DA, and ED grasslands (Fig. 1) were each identified in Maqin County, Dari County, and Gande County. In total, nine plots (each 100 × 100 m) were selected, one plot of each type in each county. The three grasslands in each county were close to each other to minimize differences due to climate and topography. The distances among the three counties (100 − 120 km) and among the three types of grasslands within one county are shown in Figure S1a (available online at https://doi.org/10.1016/j.rama.2018.10.001). The ND grasslands,
grazed in winter, were dominated by Kobresia and Gramineae species, with little weed incursion. The ED grasslands were bare in winter and, in summer, contained sparse ruderal plants, which are inedible by livestock (Ma et al., 2002; Shang et al., 2010; Dong et al., 2012). The artificial grasslands were established in 2002 to restore ED grasslands. However, after 10 yr, the land was invaded by poisonous plants and weeds, became degraded again, and had little value as grazing land. Sampling and Measurements Vegetation and Soil Sampling All plant species in the plots were identified during the peak of the growing season in August 2013. In each plot, all vegetation was collected to ground level in three randomly selected quadrats (see Fig. S1b) of 50 × 50 cm 2 to determine aboveground biomass. Belowground vegetative biomass was measured by collecting three soil core samples (8-cm diameter) in each quadrat to a depth of 0−20 cm. The soil samples were then washed in a mesh bag, 0.25 mm in size. The aboveground and belowground plants were dried at 70°C for 48 h (Zhao et al., 2016) to determine dry biomass matter (plant biomass in this paper refers to dry matter). In addition, soil cores were collected near each quadrat by using a soil auger (3-cm diameter) after the plant biomass was harvested. Each sample included 20 soil cores from depths of 0 − 10 cm and 10 − 20 cm. Roots and debris were removed, and then each sample was passed through a 2-mm sieve. The sample was then split into two subsamples for further analyses. Carbon and N in soil microbial biomass were measured in one subsample after the sample was kept under fieldmoisture conditions at 4°C for a week. For soil property analysis (pH, total SOC, and N), the second subsample was air-dried and stored at room temperature for 2 months (ISSCAS, 1978). Soil and Vegetation Property Measurements Soil bulk density (SBD) was calculated using the volumetric ring method, soil pH was determined in a soil-to-water ratio of 1:2.5 using a PHS-3C pH meter, and soil water content (SWC) was calculated by drying samples in an oven at 105 oC for 48 h (Li et al., 2014). Vegetation C content and total SOC were determined by the dichromate oxidation method (ISSCAS, 1978), and total soil N (TN) was measured by a flow injection analyzer (Sweden the FIA star 5000 Analyzer) after a digestion procedure. The chloroform fumigation solution (at 0.5 mol L −1 K2SO4) extraction method was used to measure soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) (Wei et al., 2013). About 10 g of air-dried soil were sieved through a 0.25-mm screen to determine soil sugars. Neutral sugars and amino sugars were determined by a gas chromatograph (BEIFEN SP-3420A, Beijing, China) after conversion to aldononitrile acetate derivatives (Zhang and Amelung, 1996; Glaser et al., 2004; Zhang et al., 2007). Statistical Analyses Aboveground and belowground biomass carbon density (AGCD, BGCD; g • m−2) and total soil organic carbon density (SOCD; g • m−2) were calculated using the following formulas (Li et al., 2014; Henkner et al., 2016): AGCD=BGCD ¼ AGB=BGB AGC=BGC ð%Þ
ð1Þ
TVCD ¼ AGCD þ BGCD
ð2Þ
SOCD ¼
n X
Di SBDi SOCi 10
ð3Þ
i
where AGB, BGB, AGC, BGC, TVCD, Di, SBDi, and SOCi denoted aboveground biomass (g • m − 2), belowground biomass (g • m − 2), aboveground biomass carbon content, belowground biomass carbon content (%), total vegetation carbon density (g • m − 2 ), soil
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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Figure 1. Present vegetation status of the extremely degraded (a), degraded following artificial restoration (b), and nondegraded grassland (c) in Maqin, Dari, and Gande County, respectively.
thickness (cm), soil bulk density (g • cm − 3), and soil organic carbon content (g • kg − 1), respectively. i = 1 (0 − 10 cm); 2 (10 − 20 cm). One-way analysis of variance (ANOVA) followed by Duncan’s post hoc tests were used to detect differences in AGB, BGB, C, N, and sugars among the three types of grasslands. Data are presented as means ± standard errors, and a level of P b 0.05 was accepted as significant. Statistical analyses were done with SPSS 17.0. On the basis of the knowledge of major factors influencing the ecosystem C densities, the structural equation model (SEM) was used to analyze the causal relationship among vegetation, soil properties, and C densities of the grasslands (Jonsson and Wardle, 2010; Gutiérrez-Girón et al., 2014). The SEM is useful for evaluating the causal relationships among interacting
variables and for partitioning the direct and indirect effects of one variable on another. Before applying the SEM, the number of variables for vegetation and soil properties was reduced through redundancy analysis (RDA). The ratio of belowground to aboveground biomass (BAR) and soil properties was used as raw data for RDA, which was carried out using the CANOCO version 5 software (TerBraak and
Table 2 Mean values (±s.e., n = 9) of soil properties in the three types of alpine grasslands. Soil properties pH
Table 1 Mean vegetation cover, number of plant species, plants biomass, biomass carbon content, and their carbon density (±s.e., n = 9) of degraded following artificial restoration (DA), extremely degraded (ED), and nondegraded grassland (ND). Grassland
DA
ED
ND
Total cover (%) Species no. AGB (g • m−2) BGB (g • m−2) BAR AGC (%) BGC (%) AGCD (g • m−2) BGCD (g • m−2) TVCD (g • m−2)
55-70 29 ± 2.961 144.6 ± 17.22 1368 ± 266.32 10.5 ± 1.92 47.9 ± 1.01 48.1 ± 0.41 69.4 ± 8.32 656.0 ± 1272 725.4 ± 1302
40-58 32 ± 3.381 256.6 ± 37.91 1093 ± 310.02 5.5 ± 1.42 48.5 ± 1.31 49.8 ± 0.61 125.5 ± 19.21 548.7 ± 1452 710.2 ± 1462
88-100 35 ± 2.081 199.0 ± 39.51,2 4171 ± 947.01 27.7 ± 8.41 50.0 ± 0.91 49.7 ± 1.31 100.3 ± 21.01,2 2148 ± 5301 2248.6 ± 5261
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. AGB indicates aboveground biomass; BGB, belowground biomass; BAR, the ratio of belowground biomass to aboveground biomass; AGC, aboveground biomass carbon content; BGC, belowground biomass carbon content; AGCD, aboveground biomass carbon density; BGCD, belowground biomass carbon density; TVCD, total vegetation carbon density.
Soil layers DA (cm)
0-10 10-20 −3 SBD (g • cm ) 0-10 10-20 SWC (%) 0-10 10-20 SOC (g·kg−1) 0-10 10-20 −1 TN (g • kg ) 0-10 10-20 −1 DOC(mg • kg ) 0-10 10-20 DON(mg • kg−1) 0-10 10-20 NH4-N(mg • kg−1) 0-10 10-20 −1 NO3-N(mg • kg ) 0-10 10-20
ED
6.58 ± 0.251 6.76 ± 0.281 7.07 ± 0.381 6.70 ± 0.251 1.25 ± 0.031 1.20 ± 0.031 1.36 ± 0.091 1.37 ± 0.031 24.2 ± 0.92b 20.49 ± 1.382 23.32 ± 0.982 22.69 ± 0.842 32.98 ± 1.72 29.68 ± 1.452 27.67 ± 3.071 23.84 ± 1.271 2.39 ± 0.122 2.07 ± 0.082 2.10 ± 0.211 1.64 ± 0.081 185. 5 ± 22.52 125. 9 ± 19.32 149. 1 ± 21.91,2 103.9 ± 18.92 82.1 ± 6.722 70. 9 ± 4.272 69.4 ± 3.852 60. 9 ± 1.772 9.9 ± 2.422 7.05 ± 1.102 7.39 ± 1.172 5.47 ± 0.472 10.15 ± 1.251 10.48 ± 0.661 8.60 ± 1.041 9.50 ± 0.761
ND 5.98 ± 0.381 6.32 ± 0.401 1.03 ± 0.082 1.27 ± 0.041 42.87 ± 5.141 33.86 ± 2.991 47.5 ± 6.061 28.65 ± 2.621 3.17 ± 0.411 2.02 ± 0.171 346.4 ± 59.31 190.6 ± 224.71 140.6 ± 16.51 86.3 ± 3.591 19.59 ± 3.861 10.32 ± 1.411 4.78 ± 1.352 4.71 ± 1.102
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. SBD indicates soil bulk density; SWC, soil water content; SOC, soil organic carbon; TN, total nitrogen; DOC, dissolvable organic carbon; DON, dissolvable organic nitrogen; NH4-N, soil ammonium nitrogen; NO3-N, soil nitrate nitrogen.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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Smilauer, 2012). The Kaiser-Meyer-Olkin (KMO = 0.706) and Bartlett’s Test (P b 0.001) indicated that the data were fit for factor analysis. The most parsimonious set of predictors was identified by the maximum likelihood estimation method. All possible interaction paths among the data variables were tested, and those that did not improve the initial model were removed. To assess model fit, χ2-test, P values, normed fit index (NFI), comparative fit index (CFI), and the root square mean error of approximation (RMSEA) were used (Jonsson and Wardle, 2010; Hu et al., 2014). Adequate model fits were indicated by nonsignificant χ 2-tests (P N 0.05), high CFI (0.9 b CFI b 1), and low RMSEA (b 0.05) (Grace et al., 2010). The SEM was done using Amos 24.0 (Amos
Development Corporation, Crawfordville, FL). For each analysis, R2 values, which denoted the variance explained by the model, were determined for each dependent matrix (Grace et al., 2010; Jassey et al., 2013). Results Variation of Vegetation in Alpine Meadow Total vegetation cover declined from ND to DA to ED grasslands; however, there was no significant difference in the number of plant species among grasslands (Table 1). Aboveground biomass in ED grasslands
Figure 2. Soil microbial biomass carbon (a), soil microbial biomass nitrogen (b), soil organic carbon density (c), sum of soil neutral sugars (d), and sum of amino sugars (e) referred to soil organic carbon at depths of 0−10 cm and 10−20 cm in degraded following artificial restoration (DA), extremely degraded (ED), and nondegraded grassland (ND); ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) (f), glucosamine (GlcN) to muramic acid (Mur) (g), mannosamine (ManN) to MurA (h), and galactosamine (GalN) to MurA (i) at depths of 0−10 cm and 10−20 cm. Different letters within one depth indicate a significant difference among grasslands (P b 0.05). Bars indicate s.e. of mean, n = 9.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
was higher than in DA grasslands, whereas belowground biomass of ND grasslands was higher than in ED and DA grasslands. The BAR in ND grasslands was 164% and 404% higher (P = 0.012) than in DA and ED grasslands, respectively (see Table 1).
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NH4-N SBD SWC NS DON
Vegetation and Soil Carbon Density There was no difference in AGC and BGC among the three grasslands (see Table 1). The AGCD in ED grasslands was 81% higher than in DA grasslands while BGCD in ND grasslands was 227% and 291% higher than in DA and ED grasslands, respectively (P = 0.003, see Table 1). The TVCD in ND grasslands was approximately three times higher than in DA and ED grasslands (P b 0.01) (see Table 1). Total soil organic carbon densities at 0−10 cm were 4 087 g • m −2, 3 553 g • m−2, and 4 858 g • m−2 in DA, ED, and ND grasslands, respectively. In general, they were higher than at 10−20 cm (see Fig. 2c). In the ND grasslands, SOCD at 0−10 cm was higher than in ED grasslands (P b 0.05), but there was no difference at 10−20 cm (see Fig. 2c). Soil Sugars in Alpine Meadow Soil Neutral Sugars The neutral sugars’ content ranged from 3.93 to 8.80 g·kg − 1, accounting for 13.5 − 17.8% of the SOC in all grasslands (see Fig. 2d). The contribution of each neutral sugar in the soil was as follows: ribose b fucose b xylose b rhamnose b mannose b arabinose b galactose b glucosamine (Table 3). Each neutral sugar at 0 − 10 cm was higher than at 10−20 cm in all grasslands. Apart from fucose, all neutral sugars in ND grasslands were significantly higher than in DA and ED grasslands
AS SMBN SMBC
NO3-N
DOC
-0.5
Soil pH did not differ among the three types of grasslands at depths of 0−10 cm and 10−20 cm (Table 2). The SBDs in DA and ED grasslands were 21% and 17% higher, respectively, than in ND grasslands at 0- to 10-cm depth (see Table 2). The SWC decreased with grassland degradation at depths of 0−10 cm and 10−20 cm (see Table 2). The SOC and TN in ND grasslands were higher than in DA and ED grasslands at a depth of 0−10 cm (P = 0.006; P = 0.013) (see Table 2). Similar patterns were observed for SMBN (Fig. 2b). Soil dissolved organic carbon (DOC), soil dissolved organic nitrogen (DON) and SMBC (Fig. 2a), as well as soil ammonia nitrogen (NH4-N) (see Table 2), were higher in ND grasslands than in DA and ED grasslands at depths of 0−10 cm and 10−20 cm. In contrast to the other soil properties, soil nitrate nitrogen (NO3-N) in ND grasslands was lower than in DA and ED grasslands (P = 0.001 at 0−10 cm; P = 0.005 at 10−20 cm; see Table 2).
Axis2
Variation of Soil Properties in Alpine Meadow
TN SOC
Axis1
-1.0
BAR
1.0
Figure 3. The first two ordination axes in redundancy analysis of the soil properties and vegetation (BAR) of all plots. Eigen values for the first four axes are 0.5653, 0.0655, 0.0162, and 0.2287. The explanatory variables of the first two axes account for 63.1%.
at a depth of 0 − 10 cm; however, there was no significant difference among the grasslands at 10−20 cm (see Table 3). Soil Amino Sugars The amino sugars’ content ranged from 1.96 to 3.39 g·kg − 1 , accounting for 6.7 − 8.4% of SOC (see Fig. 2e). The contribution of each amino sugar was as follows: mannosamine bmuramic acid bgalactosamine b glucosamine. Amino sugars at 0 − 10 cm were higher than at 10 − 20 cm in all grasslands. Furthermore, the amino sugars, as well as glucosamine and galactosamine, were higher (P b 0.05) in ND grasslands than in DA and ED grasslands at 0−10 cm; however, there was no significant difference in mannosamine and muramic acid among the three grasslands (see Table 3). Ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) ranged from 1.42 to 1.95 in the 0- to 10-cm soil layer and from 1.47 to 1.68 in the 10- to 20-cm soil layer (see Fig. 2f). The ratio of glucosamine to muramic acid (see Fig. 3g) showed a similar trend to that of GalN to Mur (see Fig. 2i) in the different grasslands. The highest ratios of GluN: MurA (60) and GalN: MurA (34) emerged in ND grasslands (Fig. 2); however, the highest ratio of ManN: MurA (3.8) occurred in ED grasslands (see Fig. 2h). Relationship Between Soil Properties and Plant Biomass The RDA ordination diagram (Fig. 3) showed that BAR and SWC were related positively, but SBD was related negatively to Axis 1. The first axis was correlated positively with SOC, TN, DON, AS, SMBC,
Table 3 Neutral and amino sugars contents (g·kg−1) of three grasslands (mean ± s.e., n = 9). Soil sugars
DA
ED
ND
0-10 cm Neutral sugars
Amino sugars
Rib Rha Ara Xyl Fuc Man Glu Gal Total NS GluN ManN GalN MurA Total AS
0.078 ± 0.012 0.765 ± 0.062 0.938 ± 0.101,2 0.464 ± 0.121,2 0.218 ± 0.051 0.822 ± 0.041,2 1.24 ± 0.161,2 0.96 ± 0.082 5.48 ± 0.452 1.37 ± 0.102 0.075 ± 0.021 0.563 ± 0.102 0.337 ± 0.141 2.34 ± 0.182
DA
ED
ND
0.060 ± 0.011 0.549 ± 0.041 0.657 ± 0.081 0.236 ± 0.071 0.243 ± 0.091 0.657 ± 0.041 0.816 ± 0.141 0.709 ± 0.061 3.93 ± 0.401 1.14 ± 0.131 0.060 ± 0.011 0.670 ± 0.081 0.079 ± 0.031 1.96 ± 0.241
0.094 ± 0.021 0.541 ± 0.091 0.800 ± 0.161 0.443 ± 0.131 0.168 ± 0.031 0.784 ± 0.131 1.07 ± 0.261 0.867 ± 0.151 4.76 ± 0.911 1.24 ± 0.171 0.068 ± 0.021 0.809 ± 0.151 0.105 ± 0.031 2.20 ± 0.321
10-20 cm 0.084 ± 0.012 0.643 ± 0.052 0.693 ± 0.092 0.151 ± 0.032 0.136 ± 0.031 0.720 ± 0.052 0.80 ± 0.132 0.74 ± 0.062 3.96 ± 0.382 1.20 ± 0.032 0.082 ± 0.021 0.580 ± 0.032 0.106 ± 0.041 1.97 ± 0.102
0.136 ± 0.021 1.07 ± 0.161 1.51 ± 0.321 1.22 ± 0.501 0.204 ± 0.041 1.09 ± 0.181 1.95 ± 0.441 1.61 ± 0.271 8.80 ± 1.681 2.09 ± 0.271 0.106 ± 0.031 1.04 ± 0.171 0.155 ± 0.061 3.39 ± 0.421
0.067 ± 0.011 0.644 ± 0.071 0.846 ± 0.121 0.215 ± 0.031 0.158 ± 0.031 0.743 ± 0.091 1.03 ± 0.131 0.856 ± 0.101 4.56 ± 0.511 1.13 ± 0.161 0.074 ± 0.021 0.521 ± 0.081 0.152 ± 0.081 1.88 ± 0.221
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. Ribose (Rib), rhamnose (Rha), arabinose (Ara), xylose (Xyl), fucose (Fuc), mannose (Man), glucose (Glu), galactose (Gal), total neutral sugar (NS). Glucosamine (GluN), mannosamine (ManN), galactosamine (GalN), muramic acid (MurA), and total amino sugars (AS).
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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SMBN, NH4-N, DOC, and NS but negatively with NO3-N (see Fig. 3). Consequently, changes in BAR and SWC caused the largest variation in soil chemical and microbial properties. With an increase in grassland degradation, more nitrate was accumulated.
Table 4 Direct and indirect effects of vegetation and soil properties on carbon densities calculated by structural equation modeling (SEM) of path analysis based on the collected data of alpine meadows. Variables
Main Driving Forces of Ecosystem Carbon Density in Alpine Meadow On the basis of the ordination diagram and our prior knowledge, we chose BAR (representing vegetation), TN, AS, SMBC, SMBN, DOC, and NS as variables for the structural equation model (SEM) to determine the main driving forces of C density in the alpine grasslands. Effect of Alpine Meadow Degradation on Vegetation Carbon Density The most parsimonious SEM for predicting total vegetation carbon density (TVCD) emerged when BAR, SMBC, SMBN, NS, AS, and TN were the variables (χ 2 = 12.16, df = 12, P = 0.43; NFI = 0.940; CFI = 0.999; RMSEA = 0.023; standardized path coefficients are given in Fig. 4). The correlation coefficient between type of grasslands and BAR was 0.41 in the final model, which explained 46% of the variance of SMBN, 54% of SMBC, 71% of NS, 36% of AS, 30% of TN, and 53% of TVCD (see Fig. 4). The BAR showed the largest positive contribution to neutral sugars and had an indirect effect on TVCD through several soil properties. Total nitrogen had the largest effect on TVCD, while SMBC and neutral sugars had significant negative effects on TVCD. The details of path coefficients for TVCD of the causal models are presented in Table 4. Effect of Alpine Meadow Degradation on Soil Carbon Density The most parsimonious SEM for predicting soil organic C density (SOCD) emerged when BAR, DOC, SMBC, NS, AS, and TN were the variables (χ 2 = 10.67, df = 10, P = 0.47; NFI = 0.953; CFI = 1.000; RMSEA b 0.0001; standardized path coefficients are given in Fig. 5). The correlation coefficient between type of grasslands and BAR was 0.41 in the final model, which explained 27% of the variance
TVCD
SOCD
Direct Indirect Total Direct Indirect Total
DOC
SMBC
NS
TN
TOG
BAR
0 0 0 0.166 0 0.166
−0.326 0 −0.326 0 0 0
−0.476 0 −0.476 0.328 0 0.328
0.961 0 0.961 0.587 0 0.587
0 −0.128 −0.128 0 0.045 0.045
0 −0.036 −0.036 0 0.653 0.653
TOG indicates type of grasslands; BAR, the ratio of belowground to aboveground biomass; TN, total nitrogen; DOC, dissolvable organic carbon; SMBC, soil microbial biomass carbon; NS, neutral sugars; TVCD, total vegetation carbon density; SOCD, soil organic carbon density.
of DOC, 56% of SMBC, 71% of NS, 36% of AS, 30% of TN, and 85% of soil C density (see Fig. 5). As found for the TVCD model, BAR showed the largest positive effect on neutral sugars and had an indirect effect on soil C density through several soil properties. Total nitrogen had the greatest effect on soil C density, and there was also a significant positive effect of DOC and neutral sugars on soil C density. The details of path coefficients for soil C density of the causal models are presented in Table 4. Discussion Variation of Vegetation and Soil Properties It was reported that BAR can be used as an indicator of plant community stability, as a high value was associated with a stable plant community (Wang et al., 2008; Li et al., 2014). Of the three grasslands in the present study, ND grasslands had the highest BAR and, consequently, the most stable community. Vegetation biomass, especially BGB, is the major source of soil organic matter in the terrestrial ecosystem (Wang et al., 2016). The BGB in the degraded grasslands (DA and ED) was
Figure 4. Final structural equation model for total vegetation carbon density (TVCD). The values on the arrows are the standardized path coefficients, and the values in the rectangles are the R2 values that represent the proportion of the variance explained by each endogenous variable. The chi-square (chi-squared test statistic), DF (degrees of freedom), and P value refer to a model fit test statistic; P values N 0.05 indicate good model fit, asterisks following the numbers mean significant relationships (***P b 0.001, **P b 0.01 and *P b 0.05). TOG indicates type of grasslands (DA, ED, ND); BAR, the ratio of belowground to aboveground biomass; TN, soil total nitrogen; SMBC, soil microbial biomass carbon; SMBN, soil microbial biomass nitrogen; NS, total soil neutral sugars; AS, total soil amino sugars (n = 27).
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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Figure 5. Final structural equation model for soil organic carbon density (SOCD). The values on the arrows are the standardized path coefficients, and the values in the rectangles are the R2 values, representing the proportion of the variance explained by each endogenous variable. The chi-square (chi-squared test statistic), DF (degrees of freedom), and P value refer to a model fit test statistic; P values N0.05 indicate good model fit, asterisks following the numbers mean significant relationships (***P b 0.001, **P b 0.01, and *P b 0.05). TOG indicates type of grasslands (DA, ED, ND); BAR, the ratio of belowground to aboveground biomass; TN, soil total nitrogen; SMBC, soil microbial biomass carbon; DOC, soil dissolvable organic carbon; NS, total soil neutral sugars; AS, total soil amino sugars (n = 27).
significantly lower than in ND grasslands, which indicated that DA and ED grasslands were characterized by lower stability and lower vegetation C storage than ND grasslands. In rangeland ecosystems, AGB determines forage availability and is an important component of the global C cycle (Scurlock et al., 2002; Wen et al., 2013). Aboveground biomass was 29% higher in ED than in ND grasslands and 27% higher in ND than in DA grasslands. The main reason for this finding was that, although there was no change in the number of plant species among grasslands, there was a change in plant species composition. The dominant species of ED grasslands were mainly annual broadleaf, tall weeds, and poisonous plants of little value but of high biomass, whereas in ND grasslands they were mainly Cyperaceae plants, and in DA grasslands they were mainly Gramineae plants of little biomass. Soil bulk density and soil water content in the degraded grasslands (DA and ED) were significantly lower than in ND grasslands, while the difference between DA and ED grasslands was minor. It was reported that microbial processes promoted N availability with higher SWC (Sun et al., 2016; Guo et al., 2017) and, therefore, enhanced plant growth (Plestenjak et al., 2012). Consequently, the higher SWC in ND grasslands indicated a better ability of the soil to retain water and nutrients and to provide more favorable conditions for macrofauna and microfauna than the soil in DA and ED grasslands (Dong et al., 2012). Furthermore, SOC is important for the maintenance of soil structure stability and retention of nutrients (Carter, 2002). As SOC, DOC, DON, SMBC, SMBN, NS, and AS were higher in ND grasslands than in DA and ED grasslands, the soil of ND grasslands was more stable and contained more nutrients for plant growth than the other two grasslands. Variation of Soil Sugars Neutral and amino sugars, accounted for 20.2−26.2% of SOC in the present study, which are similar to the percentages found in previous reports (Zhang et al., 2007). Both the single sugars and the sum of soil sugars decreased with grassland degradation at a depth of 0− 10 cm.
The primary (plant-derived) and secondary (microbially and soil organic matter−derived) sources of carbohydrates in the soil have been based on the ratio of hexoses (Man + Gal) to pentoses (Ara + Xyl) (Gunina and Kuzyakov, 2015). When the ratio was below 0.5, the carbohydrates were derived mainly from plant materials, whereas when the ratio was above 2, the carbohydrates were derived mainly from microbes (Oades, 1984; Zhang et al., 2007). The average ratio of (Man + Gal) to (Ara + Xyl) in ED grasslands was 1.8, indicating that the neutral sugars were mainly microbially derived. The ratio in both DA and ND grasslands, 1.5, was lower than in ED grasslands, which indicated that a greater proportion of the neutral sugars in these grasslands was derived from plant material than in ED grasslands. In addition, the ratio of glucosamine (GluN) to muramic acid (MurA) has been used widely to determine the relative contribution of fungi and bacteria to microbial residues and soil organic matter (Ding et al., 2013), as GluN originates from fungi and MurA originates from bacteria (Chantigny et al., 1997). The ratios of GluN:MurA and GalN:MurA were higher in ND grasslands than in the degraded grasslands, indicating a larger fungal contribution to SOC in ND grasslands than in ED and DA grasslands. These results implied that the microbial community changed with grassland degradation. Amino sugars not only reflect the microbial community at the time of sampling but also monitor medium to long-term changes in the microbial community (Glaser et al., 2004; Griepentrog et al., 2014). Driving Factors of Carbon Density in Alpine Meadows Increasing plant species diversity can contribute to greater C sequestration (Fornara and Tilman, 2008; Saha et al., 2009). Plant species composition, plant diversity, net primary productivity, litter decomposition, and soil microbes were proposed as driving factors of ecosystem C storage (Jonsson and Wardle, 2010). In addition, it was reported that species diversity and composition can change plant community biomass (Grace et al., 2007), which would alter BAR. In this study, BAR differed
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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among grasslands and decreased with an increase in grassland degradation. An increased BAR directly enhanced SMBC, SMBN, NS, AS, TN, and DOC and indirectly enhanced TVCD and SOCD. The change in TVCD and SOCD altered soil properties, which, ultimately, increased ecosystem C sequestration. Therefore, our hypothesis that vegetation biomass (represented by BAR) is the main driving factor affecting ecosystem C density was supported. The ratio of belowground biomass to aboveground biomass, DOC, SMBC, NS, and TN were identified as the main driving factors of C density, with BAR enhancing DOC, SMBC, NS, and TN. The study demonstrated that ecosystem C density in the Tibetan plateau was determined, to a large extent, by the degree of grassland degradation. Grassland degradation led to a reduction in BAR, which reduced the ability of the grasslands to function as sinks of atmospheric CO2. Consequently, BAR was the primary driving factor of ecosystem C density. Conclusion Grassland degradation affected vegetation, soil properties, soil sugars, and C density in the Tibetan plateau. The BGB, BGCD, SMBC, SMBN, SOCD, and soil sugars decreased with an increase in grassland degradation. Grassland degradation changed both the quantity and composition of soil sugars. The ratio of belowground biomass to aboveground biomass was identified as an indirect main driving force affecting both TVCD and SOCD, while DOC, SMBC, NS, and TN were identified as direct driving forces of ecosystem C density in the alpine meadow. The data supported our hypothesis that BAR is the main driving factor on ecosystem C density and that BAR affects ecosystem C density by altering soil properties (DOC, SMBC, NS, and TN). The findings suggest that BAR is the key indicator to predict the ecosystem C density. It was concluded that land management in alpine meadows should include practices that maintain a relatively high BAR in order to curb degradation and increase ecosystem C density. In future studies, we plan to determine the species composition of soil microorganisms in alpine meadows and relate the species to soil sugars. The ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) and GluN:MurA, GalN:MurA, and ManN:MurA may indicate changes in the structure of microbial communities with grassland degradation. Supplementary data to this article can be found online at https://doi. org/10.1016/j.rama.2018.10.001. Acknowledgments We thank two anonymous reviewers for helpful comments on an earlier draft. We thank Professors Tamar Zakai and Zhao Xue-yong for their useful suggestions on a previous draft and Li Yaojun for drawing the map of sampling sites. References Akiyama, T., Kawamura, K., 2007. Grassland degradation in China, methods of monitoring, management and restoration. Grassland Science 53, 1–17. Carter, M.R., 2002. Soil quality for sustainable land management: organic matter and aggregation interactions that maintain soil functions. Agronomy Journal 94, 38–47. Chantigny, M.H., Angers, D.A., Prévost, D., Vézina, L.P., Chalifour, F.P., 1997. Soil aggregation and fungal and bacterial biomass under annual and perennial cropping systems. Soil Science Society of America Journal 61, 262–267. Ding, X.L., Han, X.Z., Zhang, X.D., 2013. Long-term impacts of manure, straw, and fertilizer on amino sugars in a silty clay loam soil under temperate conditions. Biology and Fertility of Soils 49, 949–954. Dong, Q.M., Zhao, X.Q., Wu, G.L., Shi, J.J., Ren, G.H., 2013. A review of formation mechanism and restoration measures of ‘black-soil-type’ degraded grassland in the Qinghai-Tibetan Plateau. Environmental Earth Sciences 70, 2359–2370. Dong, S.K., Wen, L., Li, Y.Y., Wang, X.X., Zhu, L., Li, X.Y., 2012. Soil-quality effects of grassland degradation and restoration on the Qinghai-Tibetan Plateau. Soil Science Society of America Journal 76, 2256–2264. Evans, R., 1998. The erosional impacts of grazing animals. Progress in Physical Geography 22 (2), 251–268. Foggin, M., Smith, A.T., 1996. Grassland utilization and biodiversity on the alpine grasslands of Qinghai Province, People’s Republic of China. In: Peter, J. (Ed.), Conserving China’s biodiversity (II). Environmental Science Press, Beijing, China, pp. 247–258.
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Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands☆ Rui Zhang a,b,c, Yanfu Bai a, Tao Zhang a, Zalmen Henkin d, A. Allan Degen e, Tianhua Jia a, Cancan Guo a, Ruijun Long a, Zhanhuan Shang a,f,g,⁎ a
School of Life Sciences, State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou 730000, China Urat Desert-grassland Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China University of Chinese Academy of Sciences, Beijing 100049, China d Beef Cattle Section, Newe-Ya’ar Research Center, Department of Agronomy and Natural Resources, Agricultural Research Organization, Ramat Yishay 30095, Israel e Desert Animal Adaptations and Husbandry, Wyler Department of Dryland Agriculture, Blaustein Institutes for Desert Research, Ben-Gurion University of Negev, Beer Sheva 8410500, Israel f Qinghai Provincial Key Laboratory of Restoration Ecology of Cold Area, Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining 810008, China g Rangeland Research Institute, Qinghai Academy of Animal and Veterinary Science, Qinghai University, Xining 8100016, China b c
a r t i c l e
i n f o
Article history: Received 13 February 2018 Received in revised form 27 September 2018 Accepted 1 October 2018 Available online xxxx Key Words: carbon degraded grassland soil sugars structure equation model Tibetan plateau
a b s t r a c t Soil carbon and sugars play key roles in carbon (C) cycling in grassland ecosystems. However, little is known about their changes in quantity and composition in degraded alpine meadows in the Tibetan plateau. We compared vegetation C density, soil organic carbon (SOC) density, and soil sugars in nondegraded (ND), degraded (DA; following artificial restoration), and extremely degraded (ED) grasslands and analyzed the relation among these parameters by redundancy analysis (RDA) and structural equation models (SEMs). Belowground biomass, soil microbial biomass C, soil microbial biomass nitrogen (N), belowground biomass C density, SOC density, and soil sugars were lower in DA and ED grasslands than in ND grasslands. In addition, the ratio of belowground biomass to aboveground biomass (BAR) decreased with an increase in degradation. The ratio of belowground biomass to aboveground biomass was identified as the main indirect driving force of ecosystem C density by affecting total vegetation C and SOC densities. Soil dissolved organic carbon (DOC), microbial biomass carbon (SMBC), neutral sugars (NS), and total nitrogen (TN) were identified as main direct driving forces. The ratio of belowground biomass to aboveground biomass altered DOC, SMBC, NS, and TN and, consequently, was the primary driving force for the alpine meadows’ ecosystem C density. It was concluded that land management in alpine meadows should include practices that maintain a relatively high BAR in order to curb degradation and increase ecosystem C density. © 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Introduction Grasslands, one of the most widespread vegetation types worldwide, are an important part of the terrestrial ecosystem carbon (C) cycle, accounting for approximately 35% of the total terrestrial C (Scurlock and Hall, 1998; Tian et al., 2016). Soil C and nitrogen (N) contents, as
☆ This study was supported by the National Key Research and Development Project (2016YFC0501906), Natural Science Foundation of China (41671508; 31870433), Key R&D and Transformation Program of Qinghai (2017-NK-149-2), The Second Stage’s Research and Technique Extending Project of Sanjiangyuan Ecological Protection and Building of 2017 in Qinghai (2017-S-1-06), and Qinghai Innovation Platform Construction Project (2017-ZJ-Y20). ⁎ Correspondence: Zhanhuan Shang, School of Life Sciences, State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730000, China. Tel.: +86 931 8915650. E-mail address: [email protected] (Z. Shang).
well as their changes, reflect soil quality and ecosystem productivity and influence C and N cycling and storage (Wen et al., 2013). Soil carbohydrates, including neutral sugars (NS), amino sugars (AS), sugar alcohols, and sugar acids, are the most abundant organic compounds in the biosphere and the most important C and energy sources for soil microorganisms (Zhang et al., 2007; Gunina and Kuzyakov, 2015). Neutral sugars are the major components in dissolved total saccharides and form the basic units of plant cells, whereas amino sugars are associated with structural polysaccharides or glycolipids. It has been suggested that amino sugars can be used as biomarkers for organic matter sources (Glaser et al., 2004). The Tibetan plateau, with an average elevation of N 4 000 m, is regarded as the world’s “third pole” and the headwater region of Asia. Alpine meadows constitute 46% of the vegetation on the Tibetan plateau (Xie et al., 2003; Shang et al., 2016) and are used principally for raising yaks and Tibetan sheep (Harris, 2010). The alpine grasslands have a huge capacity for C storage, accounting for approximately 49% of all
https://doi.org/10.1016/j.rama.2018.10.001 1550-7424/© 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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rangeland C storage of the Tibetan plateau (Shang et al., 2016). However, C storage has been affected greatly by grassland degradation due to anthropogenic activities and natural factors such as climate change (Sousa et al., 2012; Li et al., 2014). Today, approximately 90% of the alpine meadows has been degraded, and about one-third has been severely degraded into “black soil land” (Zhou et al., 2005; Shang et al., 2016). Black soil land is exposed without plant cover and has little utilization value and ecological function (Harris, 2010; Li et al., 2013; Shang et al., 2016). Alpine grassland degradation affects both aboveground and belowground organisms (Singh et al., 2010), which in turn affect C and N cycling (Zhang et al., 2016). Dry environment, burrowing by rodents, overgrazing by livestock, and mining have been the dominant causes of grassland degradation (Dong et al., 2013). Restoring degraded alpine grasslands, crucial for the Tibetan plateau, would require proper management. Fencing, reducing livestock numbers, fertilizer application, and establishing artificial grasslands have been attempted with mixed results (Akiyama and Kawamura, 2007; Li et al., 2014). Studies reported that grassland degradation led to a decrease in vegetation cover, biomass, and soil organic carbon (SOC) (Foggin and Smith, 1996; Evans, 1998; Zhou et al., 2005; Dong et al., 2012; Li et al., 2014), which affected ecosystem C density. However, little is known about 1) changes in the quantity and composition of soil C and sugar in degraded alpine meadows in the Tibetan plateau and 2) the critical driving factors (vegetation cover, biomass, and SOC) affecting the ecosystem C density. We hypothesized that vegetation biomass is the main driving factor affecting ecosystem C density. To test this hypothesis and to improve our understanding of the effects of alpine grassland degradation on ecosystem C density, we examined and compared nondegraded (ND), degraded (DA; following artificial restoration) and extremely degraded (ED; black soil land) grasslands on the Tibetan plateau by 1) measuring their biomass and soil properties (C, N, and sugar content); and 2) quantifying their plant and soil properties. Using these data, we used structural equation models (SEMs) to determine the critical driving factors affecting vegetation and soil carbon densities. Materials and Methods Study Sites The research was conducted in the alpine meadows located in the Three Rivers Headwaters Region in Qinghai province of the Tibetan plateau in western China (31°39′N−36°12′N: 89°45′E−102°23′E). The area covers approximately 3.58 × 105 km2, at an elevation between 3 450 m and 6 621 m above sea level. The site is characterized by high solar radiation with long, cold winters and short, cool summers. The region has a typical plateau-continental climate characterized by low air oxygen levels and numerous blizzards in winter. Average annual air temperature ranges from −5.6°C to 3.8°C, and annual precipitation ranges between 262 mm and 773 mm, of which approximately 80% is concentrated in the growing season, from May to September (Yang et al., 2015). The dominant plants include Kobresia pygmaea, Kobresia humilis, Kobresia tibetica, Carex atrofusca, Stipa przewalskyi, Festuca ovina, Poa pratensis, Elymus nutans, Chenopodium glaucum, Astragalus fenzelianus, Polygonum viviparum, Herba potentillae, Ajania tenuifolia, and Saussurea superba. According to the Chinese soil classification system, the soil is alpine meadow (National Soil Survey Office, 1998), which is similar to Inceptisols in the USDA soil taxonomy system (Soil Survey Staff, 1999). The ND, DA, and ED grasslands (Fig. 1) were each identified in Maqin County, Dari County, and Gande County. In total, nine plots (each 100 × 100 m) were selected, one plot of each type in each county. The three grasslands in each county were close to each other to minimize differences due to climate and topography. The distances among the three counties (100 − 120 km) and among the three types of grasslands within one county are shown in Figure S1a (available online at https://doi.org/10.1016/j.rama.2018.10.001). The ND grasslands,
grazed in winter, were dominated by Kobresia and Gramineae species, with little weed incursion. The ED grasslands were bare in winter and, in summer, contained sparse ruderal plants, which are inedible by livestock (Ma et al., 2002; Shang et al., 2010; Dong et al., 2012). The artificial grasslands were established in 2002 to restore ED grasslands. However, after 10 yr, the land was invaded by poisonous plants and weeds, became degraded again, and had little value as grazing land. Sampling and Measurements Vegetation and Soil Sampling All plant species in the plots were identified during the peak of the growing season in August 2013. In each plot, all vegetation was collected to ground level in three randomly selected quadrats (see Fig. S1b) of 50 × 50 cm 2 to determine aboveground biomass. Belowground vegetative biomass was measured by collecting three soil core samples (8-cm diameter) in each quadrat to a depth of 0−20 cm. The soil samples were then washed in a mesh bag, 0.25 mm in size. The aboveground and belowground plants were dried at 70°C for 48 h (Zhao et al., 2016) to determine dry biomass matter (plant biomass in this paper refers to dry matter). In addition, soil cores were collected near each quadrat by using a soil auger (3-cm diameter) after the plant biomass was harvested. Each sample included 20 soil cores from depths of 0 − 10 cm and 10 − 20 cm. Roots and debris were removed, and then each sample was passed through a 2-mm sieve. The sample was then split into two subsamples for further analyses. Carbon and N in soil microbial biomass were measured in one subsample after the sample was kept under fieldmoisture conditions at 4°C for a week. For soil property analysis (pH, total SOC, and N), the second subsample was air-dried and stored at room temperature for 2 months (ISSCAS, 1978). Soil and Vegetation Property Measurements Soil bulk density (SBD) was calculated using the volumetric ring method, soil pH was determined in a soil-to-water ratio of 1:2.5 using a PHS-3C pH meter, and soil water content (SWC) was calculated by drying samples in an oven at 105 oC for 48 h (Li et al., 2014). Vegetation C content and total SOC were determined by the dichromate oxidation method (ISSCAS, 1978), and total soil N (TN) was measured by a flow injection analyzer (Sweden the FIA star 5000 Analyzer) after a digestion procedure. The chloroform fumigation solution (at 0.5 mol L −1 K2SO4) extraction method was used to measure soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) (Wei et al., 2013). About 10 g of air-dried soil were sieved through a 0.25-mm screen to determine soil sugars. Neutral sugars and amino sugars were determined by a gas chromatograph (BEIFEN SP-3420A, Beijing, China) after conversion to aldononitrile acetate derivatives (Zhang and Amelung, 1996; Glaser et al., 2004; Zhang et al., 2007). Statistical Analyses Aboveground and belowground biomass carbon density (AGCD, BGCD; g • m−2) and total soil organic carbon density (SOCD; g • m−2) were calculated using the following formulas (Li et al., 2014; Henkner et al., 2016): AGCD=BGCD ¼ AGB=BGB AGC=BGC ð%Þ
ð1Þ
TVCD ¼ AGCD þ BGCD
ð2Þ
SOCD ¼
n X
Di SBDi SOCi 10
ð3Þ
i
where AGB, BGB, AGC, BGC, TVCD, Di, SBDi, and SOCi denoted aboveground biomass (g • m − 2), belowground biomass (g • m − 2), aboveground biomass carbon content, belowground biomass carbon content (%), total vegetation carbon density (g • m − 2 ), soil
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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Figure 1. Present vegetation status of the extremely degraded (a), degraded following artificial restoration (b), and nondegraded grassland (c) in Maqin, Dari, and Gande County, respectively.
thickness (cm), soil bulk density (g • cm − 3), and soil organic carbon content (g • kg − 1), respectively. i = 1 (0 − 10 cm); 2 (10 − 20 cm). One-way analysis of variance (ANOVA) followed by Duncan’s post hoc tests were used to detect differences in AGB, BGB, C, N, and sugars among the three types of grasslands. Data are presented as means ± standard errors, and a level of P b 0.05 was accepted as significant. Statistical analyses were done with SPSS 17.0. On the basis of the knowledge of major factors influencing the ecosystem C densities, the structural equation model (SEM) was used to analyze the causal relationship among vegetation, soil properties, and C densities of the grasslands (Jonsson and Wardle, 2010; Gutiérrez-Girón et al., 2014). The SEM is useful for evaluating the causal relationships among interacting
variables and for partitioning the direct and indirect effects of one variable on another. Before applying the SEM, the number of variables for vegetation and soil properties was reduced through redundancy analysis (RDA). The ratio of belowground to aboveground biomass (BAR) and soil properties was used as raw data for RDA, which was carried out using the CANOCO version 5 software (TerBraak and
Table 2 Mean values (±s.e., n = 9) of soil properties in the three types of alpine grasslands. Soil properties pH
Table 1 Mean vegetation cover, number of plant species, plants biomass, biomass carbon content, and their carbon density (±s.e., n = 9) of degraded following artificial restoration (DA), extremely degraded (ED), and nondegraded grassland (ND). Grassland
DA
ED
ND
Total cover (%) Species no. AGB (g • m−2) BGB (g • m−2) BAR AGC (%) BGC (%) AGCD (g • m−2) BGCD (g • m−2) TVCD (g • m−2)
55-70 29 ± 2.961 144.6 ± 17.22 1368 ± 266.32 10.5 ± 1.92 47.9 ± 1.01 48.1 ± 0.41 69.4 ± 8.32 656.0 ± 1272 725.4 ± 1302
40-58 32 ± 3.381 256.6 ± 37.91 1093 ± 310.02 5.5 ± 1.42 48.5 ± 1.31 49.8 ± 0.61 125.5 ± 19.21 548.7 ± 1452 710.2 ± 1462
88-100 35 ± 2.081 199.0 ± 39.51,2 4171 ± 947.01 27.7 ± 8.41 50.0 ± 0.91 49.7 ± 1.31 100.3 ± 21.01,2 2148 ± 5301 2248.6 ± 5261
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. AGB indicates aboveground biomass; BGB, belowground biomass; BAR, the ratio of belowground biomass to aboveground biomass; AGC, aboveground biomass carbon content; BGC, belowground biomass carbon content; AGCD, aboveground biomass carbon density; BGCD, belowground biomass carbon density; TVCD, total vegetation carbon density.
Soil layers DA (cm)
0-10 10-20 −3 SBD (g • cm ) 0-10 10-20 SWC (%) 0-10 10-20 SOC (g·kg−1) 0-10 10-20 −1 TN (g • kg ) 0-10 10-20 −1 DOC(mg • kg ) 0-10 10-20 DON(mg • kg−1) 0-10 10-20 NH4-N(mg • kg−1) 0-10 10-20 −1 NO3-N(mg • kg ) 0-10 10-20
ED
6.58 ± 0.251 6.76 ± 0.281 7.07 ± 0.381 6.70 ± 0.251 1.25 ± 0.031 1.20 ± 0.031 1.36 ± 0.091 1.37 ± 0.031 24.2 ± 0.92b 20.49 ± 1.382 23.32 ± 0.982 22.69 ± 0.842 32.98 ± 1.72 29.68 ± 1.452 27.67 ± 3.071 23.84 ± 1.271 2.39 ± 0.122 2.07 ± 0.082 2.10 ± 0.211 1.64 ± 0.081 185. 5 ± 22.52 125. 9 ± 19.32 149. 1 ± 21.91,2 103.9 ± 18.92 82.1 ± 6.722 70. 9 ± 4.272 69.4 ± 3.852 60. 9 ± 1.772 9.9 ± 2.422 7.05 ± 1.102 7.39 ± 1.172 5.47 ± 0.472 10.15 ± 1.251 10.48 ± 0.661 8.60 ± 1.041 9.50 ± 0.761
ND 5.98 ± 0.381 6.32 ± 0.401 1.03 ± 0.082 1.27 ± 0.041 42.87 ± 5.141 33.86 ± 2.991 47.5 ± 6.061 28.65 ± 2.621 3.17 ± 0.411 2.02 ± 0.171 346.4 ± 59.31 190.6 ± 224.71 140.6 ± 16.51 86.3 ± 3.591 19.59 ± 3.861 10.32 ± 1.411 4.78 ± 1.352 4.71 ± 1.102
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. SBD indicates soil bulk density; SWC, soil water content; SOC, soil organic carbon; TN, total nitrogen; DOC, dissolvable organic carbon; DON, dissolvable organic nitrogen; NH4-N, soil ammonium nitrogen; NO3-N, soil nitrate nitrogen.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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Smilauer, 2012). The Kaiser-Meyer-Olkin (KMO = 0.706) and Bartlett’s Test (P b 0.001) indicated that the data were fit for factor analysis. The most parsimonious set of predictors was identified by the maximum likelihood estimation method. All possible interaction paths among the data variables were tested, and those that did not improve the initial model were removed. To assess model fit, χ2-test, P values, normed fit index (NFI), comparative fit index (CFI), and the root square mean error of approximation (RMSEA) were used (Jonsson and Wardle, 2010; Hu et al., 2014). Adequate model fits were indicated by nonsignificant χ 2-tests (P N 0.05), high CFI (0.9 b CFI b 1), and low RMSEA (b 0.05) (Grace et al., 2010). The SEM was done using Amos 24.0 (Amos
Development Corporation, Crawfordville, FL). For each analysis, R2 values, which denoted the variance explained by the model, were determined for each dependent matrix (Grace et al., 2010; Jassey et al., 2013). Results Variation of Vegetation in Alpine Meadow Total vegetation cover declined from ND to DA to ED grasslands; however, there was no significant difference in the number of plant species among grasslands (Table 1). Aboveground biomass in ED grasslands
Figure 2. Soil microbial biomass carbon (a), soil microbial biomass nitrogen (b), soil organic carbon density (c), sum of soil neutral sugars (d), and sum of amino sugars (e) referred to soil organic carbon at depths of 0−10 cm and 10−20 cm in degraded following artificial restoration (DA), extremely degraded (ED), and nondegraded grassland (ND); ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) (f), glucosamine (GlcN) to muramic acid (Mur) (g), mannosamine (ManN) to MurA (h), and galactosamine (GalN) to MurA (i) at depths of 0−10 cm and 10−20 cm. Different letters within one depth indicate a significant difference among grasslands (P b 0.05). Bars indicate s.e. of mean, n = 9.
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
was higher than in DA grasslands, whereas belowground biomass of ND grasslands was higher than in ED and DA grasslands. The BAR in ND grasslands was 164% and 404% higher (P = 0.012) than in DA and ED grasslands, respectively (see Table 1).
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NH4-N SBD SWC NS DON
Vegetation and Soil Carbon Density There was no difference in AGC and BGC among the three grasslands (see Table 1). The AGCD in ED grasslands was 81% higher than in DA grasslands while BGCD in ND grasslands was 227% and 291% higher than in DA and ED grasslands, respectively (P = 0.003, see Table 1). The TVCD in ND grasslands was approximately three times higher than in DA and ED grasslands (P b 0.01) (see Table 1). Total soil organic carbon densities at 0−10 cm were 4 087 g • m −2, 3 553 g • m−2, and 4 858 g • m−2 in DA, ED, and ND grasslands, respectively. In general, they were higher than at 10−20 cm (see Fig. 2c). In the ND grasslands, SOCD at 0−10 cm was higher than in ED grasslands (P b 0.05), but there was no difference at 10−20 cm (see Fig. 2c). Soil Sugars in Alpine Meadow Soil Neutral Sugars The neutral sugars’ content ranged from 3.93 to 8.80 g·kg − 1, accounting for 13.5 − 17.8% of the SOC in all grasslands (see Fig. 2d). The contribution of each neutral sugar in the soil was as follows: ribose b fucose b xylose b rhamnose b mannose b arabinose b galactose b glucosamine (Table 3). Each neutral sugar at 0 − 10 cm was higher than at 10−20 cm in all grasslands. Apart from fucose, all neutral sugars in ND grasslands were significantly higher than in DA and ED grasslands
AS SMBN SMBC
NO3-N
DOC
-0.5
Soil pH did not differ among the three types of grasslands at depths of 0−10 cm and 10−20 cm (Table 2). The SBDs in DA and ED grasslands were 21% and 17% higher, respectively, than in ND grasslands at 0- to 10-cm depth (see Table 2). The SWC decreased with grassland degradation at depths of 0−10 cm and 10−20 cm (see Table 2). The SOC and TN in ND grasslands were higher than in DA and ED grasslands at a depth of 0−10 cm (P = 0.006; P = 0.013) (see Table 2). Similar patterns were observed for SMBN (Fig. 2b). Soil dissolved organic carbon (DOC), soil dissolved organic nitrogen (DON) and SMBC (Fig. 2a), as well as soil ammonia nitrogen (NH4-N) (see Table 2), were higher in ND grasslands than in DA and ED grasslands at depths of 0−10 cm and 10−20 cm. In contrast to the other soil properties, soil nitrate nitrogen (NO3-N) in ND grasslands was lower than in DA and ED grasslands (P = 0.001 at 0−10 cm; P = 0.005 at 10−20 cm; see Table 2).
Axis2
Variation of Soil Properties in Alpine Meadow
TN SOC
Axis1
-1.0
BAR
1.0
Figure 3. The first two ordination axes in redundancy analysis of the soil properties and vegetation (BAR) of all plots. Eigen values for the first four axes are 0.5653, 0.0655, 0.0162, and 0.2287. The explanatory variables of the first two axes account for 63.1%.
at a depth of 0 − 10 cm; however, there was no significant difference among the grasslands at 10−20 cm (see Table 3). Soil Amino Sugars The amino sugars’ content ranged from 1.96 to 3.39 g·kg − 1 , accounting for 6.7 − 8.4% of SOC (see Fig. 2e). The contribution of each amino sugar was as follows: mannosamine bmuramic acid bgalactosamine b glucosamine. Amino sugars at 0 − 10 cm were higher than at 10 − 20 cm in all grasslands. Furthermore, the amino sugars, as well as glucosamine and galactosamine, were higher (P b 0.05) in ND grasslands than in DA and ED grasslands at 0−10 cm; however, there was no significant difference in mannosamine and muramic acid among the three grasslands (see Table 3). Ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) ranged from 1.42 to 1.95 in the 0- to 10-cm soil layer and from 1.47 to 1.68 in the 10- to 20-cm soil layer (see Fig. 2f). The ratio of glucosamine to muramic acid (see Fig. 3g) showed a similar trend to that of GalN to Mur (see Fig. 2i) in the different grasslands. The highest ratios of GluN: MurA (60) and GalN: MurA (34) emerged in ND grasslands (Fig. 2); however, the highest ratio of ManN: MurA (3.8) occurred in ED grasslands (see Fig. 2h). Relationship Between Soil Properties and Plant Biomass The RDA ordination diagram (Fig. 3) showed that BAR and SWC were related positively, but SBD was related negatively to Axis 1. The first axis was correlated positively with SOC, TN, DON, AS, SMBC,
Table 3 Neutral and amino sugars contents (g·kg−1) of three grasslands (mean ± s.e., n = 9). Soil sugars
DA
ED
ND
0-10 cm Neutral sugars
Amino sugars
Rib Rha Ara Xyl Fuc Man Glu Gal Total NS GluN ManN GalN MurA Total AS
0.078 ± 0.012 0.765 ± 0.062 0.938 ± 0.101,2 0.464 ± 0.121,2 0.218 ± 0.051 0.822 ± 0.041,2 1.24 ± 0.161,2 0.96 ± 0.082 5.48 ± 0.452 1.37 ± 0.102 0.075 ± 0.021 0.563 ± 0.102 0.337 ± 0.141 2.34 ± 0.182
DA
ED
ND
0.060 ± 0.011 0.549 ± 0.041 0.657 ± 0.081 0.236 ± 0.071 0.243 ± 0.091 0.657 ± 0.041 0.816 ± 0.141 0.709 ± 0.061 3.93 ± 0.401 1.14 ± 0.131 0.060 ± 0.011 0.670 ± 0.081 0.079 ± 0.031 1.96 ± 0.241
0.094 ± 0.021 0.541 ± 0.091 0.800 ± 0.161 0.443 ± 0.131 0.168 ± 0.031 0.784 ± 0.131 1.07 ± 0.261 0.867 ± 0.151 4.76 ± 0.911 1.24 ± 0.171 0.068 ± 0.021 0.809 ± 0.151 0.105 ± 0.031 2.20 ± 0.321
10-20 cm 0.084 ± 0.012 0.643 ± 0.052 0.693 ± 0.092 0.151 ± 0.032 0.136 ± 0.031 0.720 ± 0.052 0.80 ± 0.132 0.74 ± 0.062 3.96 ± 0.382 1.20 ± 0.032 0.082 ± 0.021 0.580 ± 0.032 0.106 ± 0.041 1.97 ± 0.102
0.136 ± 0.021 1.07 ± 0.161 1.51 ± 0.321 1.22 ± 0.501 0.204 ± 0.041 1.09 ± 0.181 1.95 ± 0.441 1.61 ± 0.271 8.80 ± 1.681 2.09 ± 0.271 0.106 ± 0.031 1.04 ± 0.171 0.155 ± 0.061 3.39 ± 0.421
0.067 ± 0.011 0.644 ± 0.071 0.846 ± 0.121 0.215 ± 0.031 0.158 ± 0.031 0.743 ± 0.091 1.03 ± 0.131 0.856 ± 0.101 4.56 ± 0.511 1.13 ± 0.161 0.074 ± 0.021 0.521 ± 0.081 0.152 ± 0.081 1.88 ± 0.221
Different numbers within a row indicate significant differences (by Duncan’s post hoc tests, Pb0.05) among grasslands. Ribose (Rib), rhamnose (Rha), arabinose (Ara), xylose (Xyl), fucose (Fuc), mannose (Man), glucose (Glu), galactose (Gal), total neutral sugar (NS). Glucosamine (GluN), mannosamine (ManN), galactosamine (GalN), muramic acid (MurA), and total amino sugars (AS).
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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SMBN, NH4-N, DOC, and NS but negatively with NO3-N (see Fig. 3). Consequently, changes in BAR and SWC caused the largest variation in soil chemical and microbial properties. With an increase in grassland degradation, more nitrate was accumulated.
Table 4 Direct and indirect effects of vegetation and soil properties on carbon densities calculated by structural equation modeling (SEM) of path analysis based on the collected data of alpine meadows. Variables
Main Driving Forces of Ecosystem Carbon Density in Alpine Meadow On the basis of the ordination diagram and our prior knowledge, we chose BAR (representing vegetation), TN, AS, SMBC, SMBN, DOC, and NS as variables for the structural equation model (SEM) to determine the main driving forces of C density in the alpine grasslands. Effect of Alpine Meadow Degradation on Vegetation Carbon Density The most parsimonious SEM for predicting total vegetation carbon density (TVCD) emerged when BAR, SMBC, SMBN, NS, AS, and TN were the variables (χ 2 = 12.16, df = 12, P = 0.43; NFI = 0.940; CFI = 0.999; RMSEA = 0.023; standardized path coefficients are given in Fig. 4). The correlation coefficient between type of grasslands and BAR was 0.41 in the final model, which explained 46% of the variance of SMBN, 54% of SMBC, 71% of NS, 36% of AS, 30% of TN, and 53% of TVCD (see Fig. 4). The BAR showed the largest positive contribution to neutral sugars and had an indirect effect on TVCD through several soil properties. Total nitrogen had the largest effect on TVCD, while SMBC and neutral sugars had significant negative effects on TVCD. The details of path coefficients for TVCD of the causal models are presented in Table 4. Effect of Alpine Meadow Degradation on Soil Carbon Density The most parsimonious SEM for predicting soil organic C density (SOCD) emerged when BAR, DOC, SMBC, NS, AS, and TN were the variables (χ 2 = 10.67, df = 10, P = 0.47; NFI = 0.953; CFI = 1.000; RMSEA b 0.0001; standardized path coefficients are given in Fig. 5). The correlation coefficient between type of grasslands and BAR was 0.41 in the final model, which explained 27% of the variance
TVCD
SOCD
Direct Indirect Total Direct Indirect Total
DOC
SMBC
NS
TN
TOG
BAR
0 0 0 0.166 0 0.166
−0.326 0 −0.326 0 0 0
−0.476 0 −0.476 0.328 0 0.328
0.961 0 0.961 0.587 0 0.587
0 −0.128 −0.128 0 0.045 0.045
0 −0.036 −0.036 0 0.653 0.653
TOG indicates type of grasslands; BAR, the ratio of belowground to aboveground biomass; TN, total nitrogen; DOC, dissolvable organic carbon; SMBC, soil microbial biomass carbon; NS, neutral sugars; TVCD, total vegetation carbon density; SOCD, soil organic carbon density.
of DOC, 56% of SMBC, 71% of NS, 36% of AS, 30% of TN, and 85% of soil C density (see Fig. 5). As found for the TVCD model, BAR showed the largest positive effect on neutral sugars and had an indirect effect on soil C density through several soil properties. Total nitrogen had the greatest effect on soil C density, and there was also a significant positive effect of DOC and neutral sugars on soil C density. The details of path coefficients for soil C density of the causal models are presented in Table 4. Discussion Variation of Vegetation and Soil Properties It was reported that BAR can be used as an indicator of plant community stability, as a high value was associated with a stable plant community (Wang et al., 2008; Li et al., 2014). Of the three grasslands in the present study, ND grasslands had the highest BAR and, consequently, the most stable community. Vegetation biomass, especially BGB, is the major source of soil organic matter in the terrestrial ecosystem (Wang et al., 2016). The BGB in the degraded grasslands (DA and ED) was
Figure 4. Final structural equation model for total vegetation carbon density (TVCD). The values on the arrows are the standardized path coefficients, and the values in the rectangles are the R2 values that represent the proportion of the variance explained by each endogenous variable. The chi-square (chi-squared test statistic), DF (degrees of freedom), and P value refer to a model fit test statistic; P values N 0.05 indicate good model fit, asterisks following the numbers mean significant relationships (***P b 0.001, **P b 0.01 and *P b 0.05). TOG indicates type of grasslands (DA, ED, ND); BAR, the ratio of belowground to aboveground biomass; TN, soil total nitrogen; SMBC, soil microbial biomass carbon; SMBN, soil microbial biomass nitrogen; NS, total soil neutral sugars; AS, total soil amino sugars (n = 27).
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
R. Zhang et al. / Rangeland Ecology & Management xxx (xxxx) xxx–xxx
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Figure 5. Final structural equation model for soil organic carbon density (SOCD). The values on the arrows are the standardized path coefficients, and the values in the rectangles are the R2 values, representing the proportion of the variance explained by each endogenous variable. The chi-square (chi-squared test statistic), DF (degrees of freedom), and P value refer to a model fit test statistic; P values N0.05 indicate good model fit, asterisks following the numbers mean significant relationships (***P b 0.001, **P b 0.01, and *P b 0.05). TOG indicates type of grasslands (DA, ED, ND); BAR, the ratio of belowground to aboveground biomass; TN, soil total nitrogen; SMBC, soil microbial biomass carbon; DOC, soil dissolvable organic carbon; NS, total soil neutral sugars; AS, total soil amino sugars (n = 27).
significantly lower than in ND grasslands, which indicated that DA and ED grasslands were characterized by lower stability and lower vegetation C storage than ND grasslands. In rangeland ecosystems, AGB determines forage availability and is an important component of the global C cycle (Scurlock et al., 2002; Wen et al., 2013). Aboveground biomass was 29% higher in ED than in ND grasslands and 27% higher in ND than in DA grasslands. The main reason for this finding was that, although there was no change in the number of plant species among grasslands, there was a change in plant species composition. The dominant species of ED grasslands were mainly annual broadleaf, tall weeds, and poisonous plants of little value but of high biomass, whereas in ND grasslands they were mainly Cyperaceae plants, and in DA grasslands they were mainly Gramineae plants of little biomass. Soil bulk density and soil water content in the degraded grasslands (DA and ED) were significantly lower than in ND grasslands, while the difference between DA and ED grasslands was minor. It was reported that microbial processes promoted N availability with higher SWC (Sun et al., 2016; Guo et al., 2017) and, therefore, enhanced plant growth (Plestenjak et al., 2012). Consequently, the higher SWC in ND grasslands indicated a better ability of the soil to retain water and nutrients and to provide more favorable conditions for macrofauna and microfauna than the soil in DA and ED grasslands (Dong et al., 2012). Furthermore, SOC is important for the maintenance of soil structure stability and retention of nutrients (Carter, 2002). As SOC, DOC, DON, SMBC, SMBN, NS, and AS were higher in ND grasslands than in DA and ED grasslands, the soil of ND grasslands was more stable and contained more nutrients for plant growth than the other two grasslands. Variation of Soil Sugars Neutral and amino sugars, accounted for 20.2−26.2% of SOC in the present study, which are similar to the percentages found in previous reports (Zhang et al., 2007). Both the single sugars and the sum of soil sugars decreased with grassland degradation at a depth of 0− 10 cm.
The primary (plant-derived) and secondary (microbially and soil organic matter−derived) sources of carbohydrates in the soil have been based on the ratio of hexoses (Man + Gal) to pentoses (Ara + Xyl) (Gunina and Kuzyakov, 2015). When the ratio was below 0.5, the carbohydrates were derived mainly from plant materials, whereas when the ratio was above 2, the carbohydrates were derived mainly from microbes (Oades, 1984; Zhang et al., 2007). The average ratio of (Man + Gal) to (Ara + Xyl) in ED grasslands was 1.8, indicating that the neutral sugars were mainly microbially derived. The ratio in both DA and ND grasslands, 1.5, was lower than in ED grasslands, which indicated that a greater proportion of the neutral sugars in these grasslands was derived from plant material than in ED grasslands. In addition, the ratio of glucosamine (GluN) to muramic acid (MurA) has been used widely to determine the relative contribution of fungi and bacteria to microbial residues and soil organic matter (Ding et al., 2013), as GluN originates from fungi and MurA originates from bacteria (Chantigny et al., 1997). The ratios of GluN:MurA and GalN:MurA were higher in ND grasslands than in the degraded grasslands, indicating a larger fungal contribution to SOC in ND grasslands than in ED and DA grasslands. These results implied that the microbial community changed with grassland degradation. Amino sugars not only reflect the microbial community at the time of sampling but also monitor medium to long-term changes in the microbial community (Glaser et al., 2004; Griepentrog et al., 2014). Driving Factors of Carbon Density in Alpine Meadows Increasing plant species diversity can contribute to greater C sequestration (Fornara and Tilman, 2008; Saha et al., 2009). Plant species composition, plant diversity, net primary productivity, litter decomposition, and soil microbes were proposed as driving factors of ecosystem C storage (Jonsson and Wardle, 2010). In addition, it was reported that species diversity and composition can change plant community biomass (Grace et al., 2007), which would alter BAR. In this study, BAR differed
Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001
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among grasslands and decreased with an increase in grassland degradation. An increased BAR directly enhanced SMBC, SMBN, NS, AS, TN, and DOC and indirectly enhanced TVCD and SOCD. The change in TVCD and SOCD altered soil properties, which, ultimately, increased ecosystem C sequestration. Therefore, our hypothesis that vegetation biomass (represented by BAR) is the main driving factor affecting ecosystem C density was supported. The ratio of belowground biomass to aboveground biomass, DOC, SMBC, NS, and TN were identified as the main driving factors of C density, with BAR enhancing DOC, SMBC, NS, and TN. The study demonstrated that ecosystem C density in the Tibetan plateau was determined, to a large extent, by the degree of grassland degradation. Grassland degradation led to a reduction in BAR, which reduced the ability of the grasslands to function as sinks of atmospheric CO2. Consequently, BAR was the primary driving factor of ecosystem C density. Conclusion Grassland degradation affected vegetation, soil properties, soil sugars, and C density in the Tibetan plateau. The BGB, BGCD, SMBC, SMBN, SOCD, and soil sugars decreased with an increase in grassland degradation. Grassland degradation changed both the quantity and composition of soil sugars. The ratio of belowground biomass to aboveground biomass was identified as an indirect main driving force affecting both TVCD and SOCD, while DOC, SMBC, NS, and TN were identified as direct driving forces of ecosystem C density in the alpine meadow. The data supported our hypothesis that BAR is the main driving factor on ecosystem C density and that BAR affects ecosystem C density by altering soil properties (DOC, SMBC, NS, and TN). The findings suggest that BAR is the key indicator to predict the ecosystem C density. It was concluded that land management in alpine meadows should include practices that maintain a relatively high BAR in order to curb degradation and increase ecosystem C density. In future studies, we plan to determine the species composition of soil microorganisms in alpine meadows and relate the species to soil sugars. The ratios of hexoses (Man + Gal) to pentoses (Ara + Xyl) and GluN:MurA, GalN:MurA, and ManN:MurA may indicate changes in the structure of microbial communities with grassland degradation. Supplementary data to this article can be found online at https://doi. org/10.1016/j.rama.2018.10.001. Acknowledgments We thank two anonymous reviewers for helpful comments on an earlier draft. We thank Professors Tamar Zakai and Zhao Xue-yong for their useful suggestions on a previous draft and Li Yaojun for drawing the map of sampling sites. References Akiyama, T., Kawamura, K., 2007. Grassland degradation in China, methods of monitoring, management and restoration. Grassland Science 53, 1–17. Carter, M.R., 2002. Soil quality for sustainable land management: organic matter and aggregation interactions that maintain soil functions. Agronomy Journal 94, 38–47. Chantigny, M.H., Angers, D.A., Prévost, D., Vézina, L.P., Chalifour, F.P., 1997. Soil aggregation and fungal and bacterial biomass under annual and perennial cropping systems. Soil Science Society of America Journal 61, 262–267. Ding, X.L., Han, X.Z., Zhang, X.D., 2013. Long-term impacts of manure, straw, and fertilizer on amino sugars in a silty clay loam soil under temperate conditions. Biology and Fertility of Soils 49, 949–954. Dong, Q.M., Zhao, X.Q., Wu, G.L., Shi, J.J., Ren, G.H., 2013. A review of formation mechanism and restoration measures of ‘black-soil-type’ degraded grassland in the Qinghai-Tibetan Plateau. Environmental Earth Sciences 70, 2359–2370. Dong, S.K., Wen, L., Li, Y.Y., Wang, X.X., Zhu, L., Li, X.Y., 2012. Soil-quality effects of grassland degradation and restoration on the Qinghai-Tibetan Plateau. Soil Science Society of America Journal 76, 2256–2264. Evans, R., 1998. The erosional impacts of grazing animals. Progress in Physical Geography 22 (2), 251–268. Foggin, M., Smith, A.T., 1996. Grassland utilization and biodiversity on the alpine grasslands of Qinghai Province, People’s Republic of China. In: Peter, J. (Ed.), Conserving China’s biodiversity (II). Environmental Science Press, Beijing, China, pp. 247–258.
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Please cite this article as: Zhang, R., et al., Driving Factors That Reduce Soil Carbon, Sugar, and Microbial Biomass in Degraded Alpine Grasslands, Rangeland Ecology & Management (2018), https://doi.org/10.1016/j.rama.2018.10.001