Predominant control of moisture on soil organic carbon mineralization across a broad range of arid and semiarid ecosystems on the Mongolia plateau

Abstract

Soil moisture and temperature are known to be the two environmental constraints regulating mineralization of soil organic carbon (SOC). However, it remains unclear to what extent the moisture, temperature, and other abiotic and biotic factors affect the mineralization of SOC across broad geographic regions. Here, we examined the effects of multiple abiotic and biotic factors on SOC mineralization across 12 widespread arid and semiarid ecosystems on the Mongolia plateau, by using an integrative approach combining short-term laboratory incubations (28-day), field survey, and structure equation modeling (SEM). Our results showed that soil moisture had a predominant control on SOC mineralization across all sites. The average CO2 emissions over all sites increased by 23 % from 30 to 60 % water filled pore space (WFPS) and by 176 % from 60 to 90 % WFPS. Under conditions of 25 °C and 60 % WFPS, the cumulative CO2–C emissions in the topsoil (0–20 cm) diminished in the following order: meadow steppe (227 mg kg−1) > typical steppe (216 mg kg−1) > desert (99 mg kg−1) > desert steppe (72 mg kg−1). The temperature sensitivity of SOC mineralization (Q10), the proportional change in carbon mineralization rate given a 10 °C temperature gradient, was highest under conditions of low temperature and high moisture, but it was lowest under high temperature and low moisture. The SEM analyses demonstrate that the mineralization potential of SOC seems to be directly regulated by microbe activity and substrate availability. Climatic factors (e.g. mean annual precipitation, mean annual temperature), above- and belowground biomass, and soil pH, which regulate SOC and microbial biomass carbon content, also indirectly influence the SOC mineralization. Our results indicate that global climate change, particularly the increase in the frequency of extreme storms and droughts, will substantially affect SOC mineralization and ecosystem carbon cycle in arid and semiarid regions.

Introduction

The stability of soil organic carbon (SOC), which is approximately 1,500 Pg in the upper 100 cm and accounts for two-thirds of the global terrestrial carbon pool (Batjes 1996), is critically important for understanding the feedbacks between climate change and ecosystem carbon cycle (Heimann and Reichstein 2008; Schmidt et al. 2011). Previous studies suggest that even subtle changes in SOC stocks can trigger severe changes in atmospheric CO2 concentrations, and thereby exert an important feedback to climate change (Jenkinson et al. 1991; Alvaro-Fuentes et al. 2012). The temperature dependence of SOC mineralization, a biological process in which organic substances are converted to inorganic substances by soil organisms, has been proposed to be a major determinant of responses of SOC stocks to climate change, e.g. global warming (Kirschbaum 1995; Davidson and Janssens 2006). It has been predicted that the temperature sensitivity of carbon mineralization tends to decrease with increasing temperature, with much greater temperature dependence at lower than higher temperatures (Kirschbaum 1995). After decades of extensive research efforts, however, there is still no consensus on the temperature sensitivity of SOC mineralization, but rather a topic of debate (Davidson and Janssens 2006; Kirschbaum 2006; von Lützow and Kögel-Knabner 2009). The lack of agreement among these studies is largely due to effects of confounding factors on SOC mineralization (Conant et al. 2011). These factors include precipitation, soil water content, plant above- and belowground productivity, SOC availability, microbial biomass and community structure, and soil pH (Davidson and Janssens 2006; Kirschbaum 2006; Conant et al. 2011).

Precipitation and soil moisture, which affect aggregate formation and determine soil water film thickness, are important environmental factors controlling the temperature-dependant mineralization of SOC (Raich and Schlesinger 1992; Rey et al. 2005; Davidson and Janssens 2006; Borken and Matzner 2009; Suseela et al. 2012). This is particularly true in arid and semiarid ecosystems, where plant diversity, above- and belowground biomass, litter production, and thereby SOC and nitrogen stocks are extremely limited by water availability (Bai et al. 2008). Recent studies demonstrated that SOC mineralization increased with precipitation and soil moisture in semiarid grasslands (Liu et al. 2009; Norton et al. 2012; Zhou et al. 2012). However, it remains unclear how the temperature sensitivity of SOC mineralization is affected by precipitation and/or soil moisture in these ecosystems.

Substrate availability, which regulates decomposer activities, is also the main constraint on the temperature-dependent mineralization of SOC (Kirschbaum 2004; Knorr et al. 2005). Changes in the labile carbon pools may counteract the temperature effect on SOC mineralization, particularly for measurements under field conditions (Kirschbaum 2006). This is because both the labile carbon pools and temperature vary widely over the growing season, thereby leading to a biased estimate of the temperature dependence of SOC mineralization (Gu et al. 2004; Kirschbaum 2006). The substrate-depletion hypothesis suggests that the temporal variation in temperature sensitivity of SOC mineralization is attributed to the depletion of the most labile organic carbon pool (Kirschbaum 2004; Hartley and Ineson 2008). Therefore, the temperature dependence of SOC mineralization obtained under laboratory-based incubations has been proposed to be the least-biased estimation of SOC mineralization (Kirschbaum 2006).

Many studies have shown that the amount of microbial biomass and community composition have a dominant influence on SOC mineralization (Strickland et al. 2009; Allison et al. 2010; Schmidt et al. 2011). For example, Fierer et al. (2003) showed that microbial biomass decreased while the temperature sensitivity (Q10) of SOC mineralization increased with soil depth through the soil profile. Strickland et al. (2009) found that microbial community composition alone explained 20 % of the variation in SOC mineralization. The regulatory gate hypothesis, however, proposes that the limiting step of SOC mineralization is regulated by abiological processes that control the conversion of non-bioavailable SOC into bioavailable SOC, independent of microbial biomass and community composition (Kemmitt et al. 2008). Thus, the effects of microbial biomass and composition on SOC mineralization are still controversial. In addition, vegetation (i.e. above- and belowground biomass) and soil physical (clay content), chemical (SOC content, C:N ratio and pH) and biological properties (fungi:bacteria ratio) may also directly or indirectly affect SOC mineralization in arid and semiarid ecosystems (Balogh et al. 2011). Hence, it is critical to understand to what extent these biotic and abiotic factors affect the mineralization of SOC across broad arid and semiarid regions.

In this study, we examine how the temperature-dependent mineralization of SOC is affected by multiple biotic and abiotic factors across a broad precipitation gradient on the Mongolia plateau. An integrative approach combining laboratory incubations, field survey, and structure equation modeling (SEM) was used to identify the key abiotic and biotic factors controlling SOC mineralization. Soil sampling and vegetation measurements were conducted across 12 arid and semiarid ecosystems. These ecosystems cover four major plant community types of the Eurasia steppe region, including meadow steppe, typical steppe, desert steppe, and desert (Bai et al. 2008). This allows us to explore the total effects of multiple biotic and abiotic factors on SOC mineralization and their relative strengths across different community types. To get a least-biased estimation of SOC mineralization, soils from different depths and locations were incubated in the laboratory under different temperature and moisture regimes. The study focuses on the following questions: First, how do temperature and moisture affect SOC mineralization in the arid and semiarid grasslands? Second, how does the temperature sensitivity of SOC mineralization differ across different soil depths (e.g. 0–5, 5–10, and 10–20 cm) and community types (i.e. meadow steppe, typical steppe, desert steppe, and desert)? Third, to what extent the SOC mineralization potential is directly or indirectly constrained by climate conditions [e.g. mean annual precipitation (MAP) and mean annual temperature (MAT)], vegetation (above- and belowground biomass), and soil physical (clay content), chemical (SOC content, C:N ratio, pH) and biological properties [microbial biomass carbon (MBC) content, fungi:bacteria ratio]? To address these questions, we tested two hypotheses, including: (1) soil moisture is a major limiting factor in arid and semi-arid ecosystems, and it regulates the process of SOC mineralization in which organic substances are converted to inorganic substances by soil microorganisms; (2) the responses of SOC mineralization to temperature and moisture are greater in the cold and wet meadow and typical steppes than those in the warm and dry desert steppe and desert.

Materials and methods

Study area

This study was carried out in the Inner Mongolia grassland in northern China, which is representative of the Eurasia steppe region in terms of climate, soils, and vegetation properties (Bai et al. 2008). The study area is located at 39°01′–49°32′N in latitude and 101°37′–120°02′E in longitude, with elevation ranging from 653 m in the east to 1,478 m in the west. The MAT ranges from −2 to 8 °C, with the lowest mean monthly temperature occurring in January (−9 to −26 °C) and the highest in July (19–24 °C). The MAP ranges from 100 to 380 mm, 80 % of which falls in the growing season (May–August) in synchrony with the peak temperature (Bai et al. 2008). Soils of the study area are Mollisols, including chernozems, chestnut, calcic brown, and desert soils (Bai et al. 2008). A total of 12 natural arid and semiarid grassland sites were selected along an east–west transect, covering four plant community types: meadow steppe, typical steppe, desert steppe, and desert with decreasing annual precipitation (Table 1). The meadow steppe, at the eastern part of transect, is dominated by Leymus chinensis, Stipa baicalensis, and Filifolium sibiricum. At the middle part of transect, the typical steppe is dominated by L. chinensis, Stipa grandis, and Artemisia frigid. The desert steppe is dominated by Stipa klemenzii, Allium polyrhizum. The desert, at the western part of transect, is dominated by Salsola passerine, Hololachna songarica, and Nitraria tangutorum. Soil physicochemical properties, such as SOC and total nitrogen contents, C:N ratio, and pH also vary with zonal changes in climate, vegetation and soil types across the transect (Table 1).

Table 1 Locations, community type, soil type, climate conditions, and soil physicochemical properties of the 12 study sites on the Mongolia plateau

Field sampling and measurements

Field sampling was conducted in August 2010, corresponding to annual peak aboveground biomass (Bai et al. 2008, 2012). At each site, aboveground biomass of herbaceous plants was measured by ten 1 × 1 m quadrats located randomly within a 100 × 100 m area. Within each quadrat, live and dead aboveground biomass was clipped at the ground level, and dead parts were removed and combined with litter. Plants in five of the ten quadrats were sorted to species and those other five quadrats were kept as bulk samples. Plant materials were oven-dried at 65 °C for 48 h and weighted. Aboveground biomass of shrubs was sampled by five 5 × 5 m quadrats. After the aboveground biomass sampling, the five bulk biomass qudrats were selected for belowground biomass and soil sampling. Belowground biomass was sampled by randomly taking three 7-cm-diameter soil cores from 0 to 100-cm depths (i.e. 0–5, 5–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm) inside each quadrat using a soil auger. Soil was rinsed out from roots under running water over a 1-mm screen, and visible dead materials were separated from live roots. Belowground biomass was oven-dried at 65 °C for 48 h and weighted. Soil samples were collected by randomly taking three 7-cm soil cores from 0 to 5, 5–10, and 10–20 cm depths inside each quadrat. The three soil cores from each quadrat were mixed in situ as one composite sample. Rocks and plant fragments were removed by hand. Soil bulk density and soil moisture at each depth was obtained by using a cylindrical sampler, and soil cores were dried at 105 °C to constant weight. The soil samples were separated into different particle-size fractions (i.e. >0.25, 0.25–0.05, 0.05–0.02, 0.02–0.002, and <0.002 mm) by processes of low-energy sonication, wet-sieving, and centrifugation (Stemmer et al. 1998). Soil texture categories were generated from the particle-size fractions of the 0–20 cm soil layer, i.e. 0.02–2.0 mm for sand, 0.002–0.02 for silt, and <0.002 mm for clay (Stemmer et al. 1998).

The soil samples were taken to the laboratory and sieved through a 2-mm screen. Soil pH was determined in water suspension (water:soil = 2.5:1) by a pH meter (7065 Kent, Cambridge, UK). Water filled pore space (WFPS) was calculated from bulk density and volumetric soil moisture content. Soil total carbon and total nitrogen contents (g kg−1 dry soil) were analyzed by a CHON analyzer (Elementar VARIO EL III, Hanau, Germany). Inorganic carbon (IC) was analyzed by a Calcimeter (Eijkelkamp, Giesbeek, Netherlands). SOC content was calculated as: SOC = TC − IC.

Laboratory incubation experiment

To explore how SOC mineralization is constrained by soil temperature and moisture across different depths and community types, we carried out a 28 days incubation experiment in the laboratory. For each site, three soil samples were randomly selected from the five composite samples of each soil depth for laboratory incubation experiment. The equivalent of 10-g subsamples of each sieved composite sample were placed in 250 ml flasks and distilled water was added until the target mass (30, 60 and 90 % WFPS) was reached. To make the dried soil samples return approximately to their field soil moisture equilibrium and microbial activity, the samples were pre-incubated at 25 °C in a dark incubator room for 1 week as in previous studies (Haney et al. 2004). The sample flasks were then covered with semi-permeable membrane (to reduce evaporation from the soil, but the membrane was perforated to allow gas to diffuse) and incubated at 5, 15, 25 or 35 °C. The selection of soil temperatures was based on the range of temperatures under the field conditions. Soil moisture was regularly checked by weighing each sample flask every day and adjusting the mass by adding distilled water. Three replicates were set up per temperature and soil moisture treatment of the three soil depths (0–5, 5–10, and 10–20 cm).

Gas sampling and carbon mineralization rate measurements

The CO2 released from the soil was determined 1, 2, 3, 7, 14, and 28 days after incubation by measuring the rate at which CO2 accumulated in the headspace of the sample flasks with a gas chromatograph (GC). Flasks were sealed with rubber stoppers that had two inlet and outlet pinholes. Standard air of known gas composition was injected into the sample flasks until it replaced the gas in the flasks completely (~5 min). The flasks were then sealed by parafilm and incubated. After 3 h, the 10 ml headspace gas was extracted immediately using gastight syringes with a three-way stopcock. The concentrations of CO2 in each syringe were immediately determined after sampling using GC (Agilent HP 5890 SERIES II, USA) with N2 as a carrier gas. The measured CO2 (μl l−1) concentrations of each sample were used to calculate the amount of evolved CO2-C (mg C kg−1 dry soil day−1) based on the flask headspace volume, soil dry weight, standard temperature and pressure, and time of CO2 accumulation (Robertson et al. 1999).

$$\it {\text{C}_m = }{{ ( {\text{Cv}} \times {\text{M}} \times {\text{P)}}} \mathord{\left/ {\vphantom {{ ( {\text{Cv}} \times {\text{M}} \times {\text{P)}}} { ( {\text{R}} \times {\text{T)}}}}} \right. \kern-0pt} { ( {\text{R}} \times {\text{T)}}}}$$
$$\it {\text{CO}}_{ 2} - {\text{C}}_{flux}\;{\text{= C}_m} \times {{\text{V}} \mathord{\left/ {\vphantom {{\text{V}} {\text{W}}}} \right. \kern-0pt} {\text{W}}}$$

where Cm is the change in CO2 concentration over the incubation period, expressed as μg l−1 day−1 of the headspace, Cv is the headspace concentration of CO2 expressed by μl l−1, M is molecular weight of CO2–C (12 μg μmol), P is barometric pressure (in atmospheres), R is universal gas constant (0.0820575 l atm °K mole), T is the incubation temperature, in °K (°K = °C + 273.15), V is headspace volume of flask (L), and W is the dry mass equivalent of soil in flask (g).

Three standard air samples in similar flasks were measured in each run for calibration purposes. Because CO2 evolution was not determined every day, C mineralization rates between sampling intervals were assumed equal to the average of the two C mineralization from the two sampling events. The coefficient of variance (CV) among 12 sites in overall treatments was calculated as:

$$\it {\text{CV = }} \sigma /\mu$$

where σ and μ are the standard deviation and mean, respectively.

Temperature sensitivity of SOC mineralization

Temperature coefficient (Q10) is a widely used index of temperature dependence that describes the proportional change in rate given a 10 °C temperature gradient (Kirschbaum 1995). The following exponential function was used to describe the temperature dependence of C mineralization:

$$\it {\text{C}}_{ \hbox{min} } { = }\alpha { \exp }\left( {\beta {\text{T}}} \right)$$
$$\it {\text{Q}}_{ 1 0} {\text{ = exp}}\left({1 0\beta } \right)$$

where Cmin is the measured C mineralization rate (mg C kg−1 dry soil day−1), α is the basal C mineralization rate at 0 °C, T is the incubation temperature (°C), and β is related to the Q10 (increase rate in CO2 efflux or C mineralization with a 10 °C increase in temperature).

Microbial biomass carbon

Soil MBC was estimated by the chloroform fumigation-K2SO4 extraction method, with a k EC value of 0.45 (Joergensen 1996). A 20-g fresh soil subsample was weighed into a 25 ml beaker and placed in a vacuum drier with 50 ml purified chloroform. After the chloroform was boiled for 1–2 min, the dryer was put into an incubator for 24 h at 25 °C. The soil subsamples were extracted by 50 ml K2SO4 (0.5 M). At the same time, the second subsample was extracted by 50 ml K2SO4 (0.5 M) directly as a control sample. The supernatants were filtered using a 0.45 μm syringe filter, and then the extracts were analyzed. The total organic carbon (TOC) was analyzed using a TOC analyzer (Liqui TOC II Elementar, Analysensystem GmbH, Hanau, Germany). The composition of soil microbial community, i.e. fungal biomass, bacterial biomass, and bacteria: fungi ratio, was determined by phospholipid fatty acid (PLFA) analysis as in Chen et al. (2014).

Meteorological data

Long-term meteorological data were obtained from weather stations within and around the study area. For each site, MAT and MAP were interpolated using a geographical-information-system (GIS)-based multiple regression method (Bai et al. 2007).

Statistical analysis

Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC, USA). Repeated measures analysis for carbon mineralization rate was performed with generalized linear mixed models (GLMMs) of PROC MIXED, using temperature, moisture, community type, soil depth, time and their interactions as fixed effects and sampling site and replicate as random effects. The GLMMs were also carried out for cumulative carbon mineralization. For each community type, ANOVAs followed by a least significant difference (LSD) test for cumulative SOC mineralization were performed to test for differences between temperature and moisture treatments and soil depths. Regression models were used to examine relationships between the cumulative SOC mineralization and various controlling factors. Multivariate non-linear regression analysis was conducted to evaluate the joint effects of soil temperature and moisture on SOC mineralization.

Structural equation modeling (SEM) was used to verify hypothetical pathways that may explain the direct or indirect effects of climate conditions, vegetation, and soil physical, chemical and biological properties on SOC mineralization (Grace 2006). The data under the theoretical optimum condition (60 % WFPS, 25 °C) were used for SEM analysis. The mineralized product from SOC has been considered as potential C availability (respiration potentials) under such condition of soil incubation (Robertson et al. 1999). All abiotic and biotic variables that potentially constrain the SOC mineralization were used in the SEM analysis, including MAP, MAT, above- and belowground biomass, SOC, C:N ratio, MBC, fungi:bacteria ratio, clay content, and pH of soil. These variables were classified into six effect and response groups in the SEMs: (1) substrate (SOC content and C:N ratio); (2) climate (MAT and MAP); (3) plant (above- and belowground biomass); (4) microbe (MBC and fungi:bacteria ratio); (5) soil physicochemical properties (pH and clay content), and (6) Potential C mineralization (cumulative CO2–C mineralized). Prior to the SEM procedure, a principal component analysis (PCA) was performed to reduce the number of variables. The first principal component parameters were subsequently used in the SEM model. In the SEM analyses, via comparing the model-implied variance–covariance matrix against the observed variance–covariance matrix, data were fitted to the models using the maximum likelihood estimation method. All SEM analyses were performed using Amos version 17.0.2 (Amos Development Corporation, Chicago, IL, USA). Several tests were used to assess model fit: the Chi square test, Comparative Fit Index, root square mean error of approximation, and Goodness of Fit Index (Grace 2006).

Results

Carbon mineralization rates across soil depths and community types and over time

Repeated measures analysis with PROC MIXED for carbon mineralization rate showed that the effects of soil temperature and moisture, community type, soil depth, and time were highly significant (Table 2). Most interactions were also highly significant (Table 2). For a given incubation temperature and moisture treatment, the temporal patterns of SOC mineralization were similar among the four community types and three soil depths. The SOC mineralization rate generally increased in the first 2 or 3 days of incubation and then declined throughout the course of incubation (Fig. 1). For all community types, carbon mineralization rate tended to decrease with increasing soil depth. The carbon mineralization rate was greater at the meadow and typical steppe sites than that at the desert steppe and desert sites throughout a 28-d incubation period (Fig. 1). Under conditions of 25 °C and 60 % WFPS, the maximum CO2–C release rates at 0–5 cm soil layer was on average 19.94 mg kg−1 day−1 for the meadow steppe, 18.88 mg kg−1 day−1 for the typical steppe, 6.21 mg kg−1 day−1 for the desert steppe, and 8.92 mg kg−1 day−1 for the desert.

Table 2 Repeated measures analysis with PROC MIXED for SOC mineralization rate using moisture, temperature, community type, soil depth, time and all interactions as fixed effects
Fig. 1
figure1

SOC mineralization rate at 0–5, 5–10 and 10–20 cm soil depths across the 12 sampling sites under laboratory incubation conditions of 25 °C and 60 % water filled pore space (WFPS). For each site, carbon mineralization rate of each soil layer in each time was the average of three replicates (error bars denote SEM). For each community type, carbon mineralization rate was the average of three sampling sites

Variation of cumulative CO2–C under different soil moisture and temperature regimes

The results showed that the effects of soil moisture and temperature, soil depth, community type, and their interactions on cumulative CO2–C from SOC mineralization were all highly significant (Table S1). At 30 % WFPS, the cumulative CO2–C first increased and then decreased with increasing incubation temperature across all sampling sites and soil depths (Fig. 2, Figs. S1, S2). However, no significant differences in cumulative CO2–C were found among community types and soil depths under the conditions of low moisture with either low (30 % WFPS and 5 °C) or high temperature (30 % WFPS and 35 °C). Under conditions of 60 and 90 % WFPS, in contrast, the cumulative CO2–C increased with incubation temperature, although it tended to saturate at high incubation temperature (≥25 °C) in some sampling sites and soil depths (Fig. 2, Figs. S1, S2).

Fig. 2
figure2

Effects of soil temperature and moisture on cumulative carbon mineralization at 0–5 cm soil layer across the 12 sampling sites. For each treatment, cumulative CO2–C was the average of three replicates (error bars denote SEM). Bars with the same letter were not significantly different in the least significant difference (LSD) tests reported from ANOVA. For each community type, the cumulative CO2–C was the average of three sampling sites

For most treatments, the cumulative CO2–C at the 0–5 cm soil depth was greater than that at the 5–10 cm and 10–20 cm soil depths, although exceptions were found at several desert and desert steppe sites (Fig. 2). The magnitude of differences in cumulative CO2–C between soil depths was greater in the meadow and typical steppes than that in the desert steppe and desert (Fig. 2). When the data were summed over the three soil layers, the total CO2–C emissions (28-day) under conditions of 25 °C and 60 % WFPS diminished in the following order: meadow steppe (227 mg kg−1) > typical steppe (216 mg kg−1) > desert (99 mg kg−1) > desert steppe (72 mg kg−1).

The effects of soil temperature and moisture, community type, and their interactions on the temperature sensitivity (Q10) of SOC mineralization were all significant (Table S2). However, no significant effect of soil depth on Q10 of carbon mineralization was found, and the interactions of soil depth with temperature, moisture and community type were also non-significant (Table S2). In general, the average Q10 was greater at the low incubation temperatures (5–15 °C) than that at the moderate (15–25 °C) and high incubation temperatures (25–35 °C) across all community types (Fig. 3). The greatest average Q10 was found at the low soil temperature and high moisture treatment (5–15 °C, 90 % WFPS) across all community types. The lowest average Q10 was observed at the high soil temperature and low moisture treatment (25–35 °C, 30 % WFPS; Fig. 3).

Fig. 3
figure3

Effects of soil moisture on temperature sensitivity of SOC mineralization (Q10) at 0–5 cm soil layer across low (a), moderate (b), and high (c) incubation temperatures. For each community type, the Q10 value was the average of three sampling sites (error bars denote SEM). Bars with the same letter were not significantly different in the least significant difference (LSD) tests reported from ANOVA

Effect of climate, plant biomass, SOC and microbial biomass on SOC mineralization

In most cases, the cumulative SOC mineralization was positively correlated with MAT and negatively correlated with MAP even under the laboratory-based incubations with different temperature and moisture regimes (Table S3; Fig. 4). Relationships of the cumulative SOC mineralization with above- and belowground biomass and SOC were also positive linear, except for the high incubation temperature but low moisture treatments (35 °C and 30 % WFPS; Table S3). In contrast, the relationship between SOC mineralization and microbial biomass was dependent upon the incubation temperature and moisture regimes. Under the conditions of high incubation temperature (≥25 °C) and high moisture (WFPS ≥ 60 %), the cumulative carbon mineralization was positively linearly correlated with microbial biomass (Table S3). Under the conditions of low incubation temperature (q 15 °C), however, the relationship between cumulative SOC mineralization and microbial biomass was significant only at low (30 % WFPS) and high moisture (90 % WFPS) treatments (Table S3; Fig. 4).

Fig. 4
figure4

Relationships of the cumulative carbon mineralization with mean annual temperature (MAT), mean annual precipitation (MAP), above- and belowground biomass, soil organic carbon content (SOC), and microbial biomass carbon content (MBC) under the incubation conditions of 25 °C and 60 % WFPS across the 12 sampling sites

Factors and their pathways controlling SOC mineralization

PCA showed that, among all variables that may potentially constrain the SOC mineralization, MAP, MAT, above- and belowground biomass, SOC, MBC, and pH were the first component parameters, which explained 69–97 % of the total variance (Table S4). SEM analyses, using the first component parameters, illustrated that the model had a high fit (χ2 = 9.583, df = 8, P = 0.296; Fig. 5). The SEM exhibited that the potential carbon mineralization was influenced directly by the substrate (i.e. SOC) and decomposers (e.g. MBC), which together explained 69 % of the total variance in SOC mineralization. The SOC mineralization was also indirectly affected by climatic conditions (MAP, MAT) and soil properties (e.g. soil pH) (Fig. 5). Among these interrelated factors examined, MBC was the most crucial factor controlling the SOC mineralization potential. The SEM also showed that MAP and MAT explained 89 % of the variance in SOC, 86 % of the variance in above- and belowground biomass, and 64 % of the variance in soil pH (Fig. 5). However, no direct effects of above- and belowground biomass on SOC and MBC were detected. This may be caused by the over-riding effect of climatic factors on above- and belowground biomass and thereby SOC and MBC. When, climatic factors were removed from the SEM, above- and belowground biomass together explained 79 % of variance in SOC and 63 % of variance in MBC (χ2 = 5.452, df = 3, P = 0.142; Fig. S3).

Fig. 5
figure5

Results from SEM on the drivers of SOC mineralization potential at the theoretical optimum conditions (25 °C and 60 % WFPS). Rectangular boxes indicate variables in the SEM and specific items were contained within each rectangular box. Solid arrows indicate statistically significant pathways at P = 0.05. Dashed arrows indicate non-significant pathways that were necessary to include for obtaining the most parsimonious model. The climate, plant, substrate, microbe and soil property boxes were substituted in the model by PC1 parameters using principal component analysis. R2 values indicate the proportion of variation explained by the relationships with other variables. Values associated with solid arrows represent standardized paths coefficients. MAT Mean annual temperature, MAP mean annual precipitation, AB aboveground biomass, BB belowground biomass, SOC soil organic carbon content, C/N soil carbon to nitrogen ratio, MBC microbial biomass carbon content, F/B fungi to bacteria ratio, SPCP soil physicochemical properties

Discussion

Effects of community type and soil depth on SOC mineralization

Different community types are often characterized by differences in environmental conditions, plant community and soil properties, which may affect the SOC availability, microbial activity, and thus the soil CO2 effluxes (Kucharik et al. 2000). In this study, the mineralization rate of SOC (largely labile carbon) was greater in the meadow and typical steppes than that in the desert steppe and desert along the regional precipitation gradient. This is likely due to the effect of SOC availability on carbon mineralization as indicated by the positive linear relationship between cumulative carbon mineralization and SOC content. Several studies have also reported a positive correlation between CO2 emission and SOC content (Raich and Schlesinger 1992; Kirschbaum 1995; Alvarez and Alvarez 2000). For individual sites, however, the highest carbon mineralization rate occurred in the typical steppe (SL-9). The amounts of CO2–C mineralized at site SL-9 were similar to those at site SL-11 across three soil layers, but the SOC content in the SL-9 site was only 37 % of that at site SL-11. This contradicts the previous studies but can be explained well by the greater root biomass and microbial biomass at the SL-9. Soil microbes are the executors of SOC mineralization, and the microbial biomass and relative activity are important aspects of microbial properties (Garcia-Pausas and Paterson 2011). Our results demonstrate that the cumulative SOC mineralization increases with microbial biomass content and belowground biomass across all the arid and semiarid sites. A previous study also found a significant positive correlation between the belowground biomass and microbial activity (Grayston et al. 1997). Therefore, the SOC content, microbial biomass and belowground biomass, which affect microbial activity, are likely to be major factors controlling SOC mineralization across the arid and semiarid ecosystems.

The results showed that both the carbon mineralization rate and cumulative carbon mineralization differed substantially among different soil layers. The upper soil layers had a higher carbon mineralization rate than the deeper layers. This is possibly because of the greater SOC content and the higher proportion of labile components in the SOC of upper soil layers (Rovira and Vallejo 2002). Previous studies also suggest that SOC mineralization rate in the upper layers is greater than in the deeper layers due to higher SOC content and more labile carbon in the upper layers (Rey et al. 2005; Wang et al. 2010). Our results, however, only partly agree with these findings. In the present study, SOC mineralization was positively correlated with SOC content across different soil depths in the typical and meadow steppe sites. However, there was no such a relationship in most desert and desert steppe sites. This might be due to higher proportion of labile carbon in the upper soil layers than that in the deeper layers in deserts and desert steppes (Budge et al. 2011). Indeed, the soils of desert and desert steppe, with a low SOC content, exhibited high mineralization to SOC ratios (mg cumulative CO2–C mineralized/100 mg SOC), which inferred a higher proportion of labile SOC. Thus, CO2 emission from labile C mineralization is likely accounted for a large proportion of SOC in the arid systems.

The predominant control of moisture on SOC mineralization

The results suggest that soil moisture has a much greater control on the SOC mineralization than soil temperature or the interactions between moisture and temperature across the arid and semiarid ecosystems. The CO2 emissions were accelerated by increasing soil moisture content in most of the sites. This tendency was more intense from 60 to 90 % WFPS. For all sites, the average CO2 emissions increased by 23 % from 30 to 60 % WFPS and by 176 % from 60 to 90 % WFPS. These results are not consistent with findings of a recent laboratory study (Suh et al. 2009). They found that the reduced soil CO2 emission was in response to dry or wet conditions and the maximum CO2 emission appeared at 60 % of water holding capacity. It has been proposed that soil respiration was restricted as the soil moisture increased after reaching a threshold due to limited oxygen diffusion (Wang et al. 2006). In previous studies, the upper threshold is at 60–80 % WFPS, with the clay content of soil being 20–47 % (Suh et al. 2009; Wang et al. 2010). In this study, however, the upper threshold was not observed even at 90 % WFPS. This is likely because of soil properties, of which soil particle composition (e.g. clay content) is the most considerable in semi-arid grasslands (Kadono et al. 2008). Similar results to this study have been obtained by Wu et al. (2010), where the CO2 emission had not been reduced at 80 % WFPS in soils with a clay content of ~ 5 %. Soil moisture diffusion is slow in high clay content soils, which probably caused partial or short-term oxygen deficiency for soil microbes, and soils with low clay contents alleviated the hypoxic conditions by more rapid diffusion (Balogh et al. 2011).

Based on the microbial reaction kinetics theory, SOC mineralization increases with temperature, which results in more CO2 efflux when the environment warms up (Davidson and Janssens 2006). Nevertheless, there were apparent variations in CO2 emission, depending largely upon soil moisture contents in this study. When soil moisture was adjusted to the theoretical optimum (60 % WFPS), CO2 emission was not stimulated consistently by increasing the temperature incrementally from 15 to 25 °C in most sites. Meanwhile, there were two obvious stimulating phases in the temperature intervals from 5 to 15 °C and 25 to 35 °C. The explanation for the lower temperature interval is a promotion of microbial decomposition caused by the substrates of labile organic C at the optimal soil moisture (Matzner and Borken 2008). It is inferred that the labile organic C could come from microbial C or physically protected organic C (Herrmann and Witter 2002). For the higher temperature interval, thermal stimulation of microbial activity at optimal soil moisture likely occurred (Allison et al. 2010). When soil moisture was low (e.g. 30 % WFPS), the same substrate-induced effect existed from 5 to 15 °C. However, as soil temperature increased, this inductive effect was gradually weakened, and the impacts of water restriction on microbial activity eventually suppressed the impacts of thermal stimulation on it (Liu et al. 2002). Therefore, an extremely low CO2 emission was observed under conditions of 35 °C and 30 % WFPS across all the sampling sites. When soil moisture was no longer the restrictive factor (i.e. 90 % WFPS), the results indicated that increasing temperatures stimulated the soil to release CO2, but the stimulation would be mitigated gradually as the temperature rises from 5 to 35 °C.

When soil temperature and moisture were considered separately according to these results, the Q10 of SOC mineralization was substantially decreased at low moisture and high temperature treatments across all community types. Our results support the notion that substrate availability for mineralization is a key factor in causing changes in Q10, and the proportion of SOC components determines the rate of microbial decomposition and the apparent pattern of temperature sensitivity (Dioumaeva et al. 2002). However, this study highlighted the responses of SOC mineralization with root exclusion to soil temperature and moisture in short-term periods as many ecosystem warming studies concealed a critical rapid process of soil respiration to short-term climatic variation (Boone et al. 1998; Suseela et al. 2012).

The drivers and pathways of the SOC mineralization

Generally, the status of SOC mineralization under theoretical optimal conditions (i.e. 60 % WFPS and 25 °C) was considered to be a reflection of the potential of SOC mineralization (Robertson et al. 1999). The analyses from this study highlighted the relative importance of the drivers and their pathways affecting the potential of SOC mineralization across a broad range of arid and semiarid ecosystems. Our results indicate that the variability in SOC mineralization (largely labile carbon) under laboratory incubations can be mostly explained by MAT, MAP, above- and belowground biomass, SOC, MBC, and pH of soil. These are important direct or indirect factors controlling SOC mineralization, and they have been reported to exhibit a close relationship with CO2 emissions (Raich and Tufekcioglu 2000). The SEM results suggest that the SOC mineralization potential seems to be directly regulated by the microbe and substrate properties, with microorganisms playing a more important role. This is further evidenced by the fact that cumulative carbon mineralization increased linearly with MBC and SOC content. Soil physicochemical properties, such as soil pH and clay content, have also been proposed to be important factors controlling SOC mineralization (Post and Kwon 2000). In this study, soil pH influenced SOC mineralization potential through its effects on soil microbes. Regression analysis revealed that both the SMC and fungi:bacteria ratio decreased with increasing pH of soil. These results are in agreement with previous studies in revealing that shifts in the SMC and dominance of fungal and bacterial among microbial decomposers regulated SOC mineralization along the soil pH gradient (Li et al. 2007; Rousk et al. 2009). No significant effects of clay content on SOC mineralization was found in current study. This might be due to the very low clay content across our study sites (Wu et al. 2010).

Our results indicate that climatic factors, particularly precipitation, which directly regulate plant community biomass production, SOC, and soil pH and clay content, are also be important controls on the SOC mineralization potential in the arid and semiarid ecosystems. It is surprising that no significantly direct effects of above- and belowground biomass (i.e. plant carbon gain) on SOC (carbon storage), MBC, and SOC mineralization (i.e. potential soil carbon loss) were detected in current study. Jonsson and Wardle’s (2010) study came to the similar conclusions on the relationship between biomass production and SOC accumulation. This is possibly because that above- and belowground biomass are predominant controlled by water availability (Bai et al. 2008), which may override the influence of plant biomass on SOC, MBC, and thereby SOC mineralization. Indeed, when climatic factors (i.e. MAP and MAT) were removed from the SEM, above- and belowground biomass became direct regulators of SOC and MBC.

Implications for extreme climate changes and CO2 emission predictions

Our results demonstrate that soil moisture has predominant control on SOC mineralization, and that soil temperature is a secondary factor in the arid and semiarid ecosystems. During the process of global climate change, the short-term changes in average temperature or precipitation may be slow in most conditions. However, even small short-term changes in temperature or precipitation will be accumulated gradually and result in a significant increase in the frequency of extreme weather events, such as severe storms or droughts (Borken and Matzner 2009; Suseela et al. 2012). If these changes frequently occur, the responses of SOC mineralization may vary among community types on the Mongolia plateau. The present incubation studies revealed that the response of SOC mineralization (largely labile carbon) to temperature variations was strongly regulated by soil moisture across all community types. The typical and meadow steppes had much higher temperature sensitivity (Q10) than those of desert steppe and desert under conditions of low temperature (e.g. 5–15 °C) but high moisture (e.g. 90 % WFPS). Therefore, if this happens, the proportional carbon loss will be greater in the typical and meadow steppe sites than that in the desert steppe and desert sites. If the environmental conditions shift towards high temperature and low moisture, similar inhibitions of SOC mineralization (soil carbon loss) may occur in these arid and semi-arid sites. On contrary, climatic warming together with increase in soil moisture may stimulate the SOC mineralization in the arid and semiarid ecosystems. Given that plant growth potential, i.e. the plant carbon gain in response to high temperature and moisture, is much higher in the meadow and typical steppes than that in the desert steppe and desert (Bai et al. 2008). The SOC stocks are projected to increase in the meadow and typical steppe sites and decrease in the desert and desert steppe sites. These findings will contribute to better understanding the feedbacks between climate change and ecosystem carbon cycle in the arid and semiarid regions.

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Acknowledgments

We are grateful to Tao Sang for comments on an early version of this manuscript. We thank Hongwei Wan and Junhui Cheng for their helps with statistical analysis. We also gratefully acknowledge undergraduate students from the Inner Mongolia Agriculture University for their helps with fieldwork. This project was supported by the Natural Science Foundation of China (31030013, 31320103916), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050400), and Land-Cover/Land-Use Program at NASA (Grant No. NNX09AK87G).

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Mi, J., Li, J., Chen, D. et al. Predominant control of moisture on soil organic carbon mineralization across a broad range of arid and semiarid ecosystems on the Mongolia plateau. Landscape Ecol 30, 1683–1699 (2015). https://doi.org/10.1007/s10980-014-0040-0

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Keywords

  • SOC mineralization
  • Precipitation gradient
  • Water filled pore space (WFPS)
  • Temperature sensitivity of SOC mineralization
  • Microbial biomass carbon