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Carbon quality mediates the temperature sensitivity of soil organic carbon decomposition in managed ecosystems
Agriculture, Ecosystems and Environment 250 (2017) 44–50
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Research Paper
Carbon quality mediates the temperature sensitivity of soil organic carbon decomposition in managed ecosystems ⁎
Jinquan Lia, Junmin Peia, Jun Cuia, Xueping Chenb, Bo Lia, Ming Niea, , Changming Fanga,
MARK
⁎
a Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, The Institute of Biodiversity Science, Fudan University, 2005 Songhu Road, Shanghai 200438, China b School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Soil incubation Sequential temperature change Q10 value The carbon quality-temperature hypothesis
The carbon quality-temperature (CQT) hypothesis suggests that the temperature sensitivity (Q10) of soil organic carbon (SOC) decomposition is negatively related to soil C quality. This hypothesis was widely tested in various natural ecosystems, but the effect of soil C quality on the temperature response of SOC decomposition has not been well addressed in managed ecosystems. In this study, Q10 values of SOC decomposition were estimated in three adjacent managed ecosystems (planted forest (PF), paddy (PA), and upland (UL)) within the topsoil (0–5 cm) and subsoil (30–35 cm) layers of six sites across different climate zones in northeast China. The results suggested that the soil C quality differed significantly among the managed ecosystems (PF > PA > UL; P < 0.05) or soil types (P < 0.05), and decreased with soil depth (P < 0.001). Overall, Q10 values differed significantly across the ecosystems (PF < PA < UL) and soil types (P < 0.05), but were significantly greater in the subsoil than those in the topsoil (P < 0.001). The negative relationship between Q10 value and soil C quality suggested that the CQT hypothesis was applicable to ecosystem types, soil types, and soil profiles of the managed ecosystems. In addition, the Q10 value had a positive correlation with soil pH (P < 0.001). The results suggested that incorporating soil C quality and soil pH into models would help us to predict the feedbacks between soil C dynamics and global warming in managed ecosystems.
1. Introduction The decomposition of soil organic carbon (SOC), which is the largest carbon (C) pool among the terrestrial ecosystems, is intrinsically sensitive to temperature change (Davidson and Janssens, 2006). Although an increase in temperature is considered to accelerate SOC decomposition (Friedlingstein et al., 2001), global C cycle models show large uncertainties in predicting soil C dynamics and their feedbacks to global change (Cox et al., 2000; Friedlingstein et al., 2001). To reduce these uncertainties, it is necessary to estimate the temperature sensitivity (Q10) of SOC decomposition accurately. The Q10 is a measure of the rate of change of a biological or chemical system as a consequence of increasing the temperature by 10 °C, and it is the underlying feedback mechanisms in the global C cycle (Conant et al., 2011). Many factors affect the Q10, such as soil C quality (Hartley and Ineson, 2008b), plant type (Gutierrez-Giron et al., 2015), soil matrix (Arevalo et al., 2012), soil pH (Craine et al., 2010), C to N ratio (Haddix et al., 2011), and soil microbes (Thiessen et al., 2013). Among them, soil C quality is considered as one of the most important factors affecting Q10 (Fierer et al., 2006; Conant et al., 2008a), because SOC ⁎
consists of various C components (Arevalo et al., 2012), which may have different Q10 values (Leifeld and Fuhrer, 2005; Conant et al., 2008a; Wetterstedt et al., 2010). However, in most terrestrial C models, the effects of soil C quality on Q10 have not been well considered (Parton et al., 1987; Cox et al., 2000; Jones et al., 2003). The understanding of the effects of soil C quality on Q10 will improve the development and parameterization of terrestrial C models and help in predicting the climate change – terrestrial C cycle feedbacks (Post and Kwon, 2000; Davidson and Janssens, 2006). The C quality-temperature (CQT) hypothesis suggests the lowquality SOC is more stable than the high-quality SOC under increasing temperature (Bosatta and Ågren, 1999; Fierer et al., 2005; Davidson and Janssens, 2006). However, most of the previous studies on the effects of soil C quality on Q10 have focused primarily on natural ecosystems (Craine et al., 2010; Zimmermann and Bird, 2012). For example, Ding et al. (2016) evaluated the temperature sensitivity of SOC decomposition using the soils from 156 sites across the Tibetan alpine grasslands and found that Q10 value of SOC decomposition was negatively correlated with soil C quality. Likewise, Fierer et al. (2006) found a significantly negative correlation between Q10 value and soil C quality
Corresponding authors at: School of Life Science, Fudan University, 2005 Songhu Road, Shanghai 200438, China. E-mail addresses: [email protected] (M. Nie), [email protected] (C. Fang).
http://dx.doi.org/10.1016/j.agee.2017.09.001 Received 4 June 2017; Received in revised form 5 September 2017; Accepted 6 September 2017 Available online 20 September 2017 0167-8809/ © 2017 Elsevier B.V. All rights reserved.
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2.2. Soil chemical analyses
by incubating 77 unique soils from main natural ecosystems of the United States. Further, Q10 value has been found negatively related to the C quality in 24 different litter types varying in chemical composition (Fierer et al., 2005). However, only a few studies have been conducted to test the CQT hypothesis in managed sites or ecosystem types (Conant et al., 2008a; Hartley and Ineson, 2008a) and study on the CQT hypothesis in broad managed ecosystems is still scarce. Managed ecosystems such as croplands and planted forests play important roles in C cycle and C budget in terrestrial ecosystems (Houghton and Hackler, 2003). At present, approximately 8 and 5% of the Earth’s land surfaces are managed for croplands and planted forests, respectively (Hansen et al., 2000; Berthrong et al., 2009), which is highly important to human well-being (Beier et al., 2008). The area of other land uses change to croplands and planted forests take up more than 80% of the total area of land use change on the earth (Lal et al., 1998). In addition, land use change as one of the major drivers of global climate change has led to release 1.1 ± 0.7 Pg C per year in the first decade of the 2000s worldwide (Poeplau and Don, 2013). Because of the human activities such as fertilization and irrigation, there are differences in soil C quality between managed and natural ecosystems, leading to diverse SOC decomposition processes in these ecosystems (Guo and Gifford, 2002; Celik, 2005; Beheshti et al., 2012). In addition, soil C quality differs at soil depths. The topsoil layer often has a higher C quality than the subsoil layer (Omonode and Vyn, 2006). Some studies have been carried out on the variation in the temperature sensitivity of SOC decomposition with soil depths in managed ecosystems (e.g. Lomander et al., 1998; Gillabel et al., 2010; Pang et al., 2015). However, to our knowledge, whether the CQT hypothesis can be applied to soil profiles across various managed ecosystems is still unknown. Northeast China, located in the temperate region, is the main food and timber producing areas in China. The global circulation models predict that middle latitude regions are more likely to experience a warming climate than the low latitude regions in the future (Raisanen et al., 2004), and therefore Northeast China would be highly vulnerable to climate change. In this study, the temperature sensitivity of SOC decomposition was estimated in six sites with different climate conditions in northeast China. At each site, soils from the three adjacent managed ecosystems (i.e. planted forest (PF), paddy (PA), and upland (UL)) were collected to test whether the CQT hypothesis could be applied to various managed ecosystems and soil profiles.
SMBC was determined by a CHCl3 fumigation-extraction method (Vance et al., 1987). The soil dissolved C (DOC) concentrations in nonfumigated and fumigated soils were determined by a TOC analyzer (Multi N/C 3100, Germany). Soil pH was measured in a supernatant of the deionized water solution (soil:solution = 1:2.5) using a pH meter (METTLER TOLEDO FiveEasy). Total C (TC) and total nitrogen (TN) were measured in an NC analyzer (FlashEA 1112 Series, Italy). The SOC was determined using a TOC analyzer (vario TOC cube, Germany) after the removal of carbonates with 0.1 M HCl. Soil texture was measured by using a particle size analyzer (Laser Particle Sizer, LS-CWM(2), OMEC, China) after the removal of organic matter (using 30% hydrogen peroxide) and carbonates (using 30% hydrochloric acid) (Ding et al., 2016). Soil water holding capacity (WHC) was gravimetrically determined (Chen et al., 2010). 2.3. Soil incubation and respiration measurement Soil samples were incubated in the laboratory using a sequential temperature change method following Chen et al. (2010) and Ding et al. (2016). Briefly, each soil sample with four replicates (equivalent to 50 g dry-weight with 60% WHC in a 250 mL jar) was pre-incubated at 20 °C for 7 d to minimize the disturbances from soil sieving, packing, and rewetting. A water bath (DC0530; Bilang Instrument Corp. Ltd., Shanghai) was then used for the sequential incubation. The temperature was changed from 4 °C up to 28 °C and then down to 4 °C with a changing step of 4 °C. After changing the temperature, soil samples were allowed to stand for two hours to achieve a new equilibrium (Koch et al., 2007; Chen et al., 2010; Ding et al., 2016). The entire sequential temperature incubation lasted for seven to ten days. Fresh air was continuously passed, at a flow rate of 0.75 L min−1, through the head space of incubation jars by an air distribution system. After an equilibrium was achieved at the target temperature, i.e. two hours after the temperature was changed, all the incubation jars were sealed and 5 mL of headspace gas samples were collected by syringes. Five milliliters of CO2-free air were immediately injected into each incubation jar to avoid changes in the headspace air pressure. After being sealed for 1 to 24 h (depending on headspace CO2 concentrations that were set at no more than 0.3% for minimizing the inhibitory effects of CO2 on soil respiration), 5 mL of the headspace gas sample was collected. The CO2 concentrations were determined by a gas chromatograph (Agilent 6890; Agilent Corp., USA). The SOC decomposition rate was calculated as below (Yan et al., 2017):
2. Materials and methods 2.1. Soil sampling
R=
Soil samples were collected from the three adjacent managed ecosystems at six sites across northeast China (Table 1; Fig. S1 in the Supplementary material). Six sites have gradients of the mean annual temperature (MAT) from 1.1 to 8.1 °C and the mean annual precipitation (MAP) from 479 to 776 mm. Each site consists of three adjacent managed ecosystems, including PF, PA, and UL. The vegetation of the six PF was planted with the coniferous species with a history of more than 20 years. The six PA and six UL fields were cultivated with rice and corn for more than 15 years, respectively. The land use types of the six sampling sites were forest or grassland ecosystems before changing them to the managed ecosystems. In each managed ecosystem, at each site, four sampling locations were randomly chosen. At each location, two layers per transect (i.e. topsoil 0–5 cm and subsoil 30–35 cm) were sampled by using an 8-cm diameter core sampler. Soil samples were sieved (mesh size < 2 mm) to remove stones and plant materials. The sampled soils from the four locations of each site were mixed together to merge the soil samples by soil depth. Approximately 20 g of mixed soil samples were air-dried for analyzing soil properties and the rest was stored at 4 °C for incubation and soil microbial biomass C (SMBC).
M P T0 ΔC V 22.4 P0 T Δt m
(1) −1
−1
where R is SOC decomposition rate (μg CO2-C g SOC h ), M is the molar mass of CO2-C (g mol−1), 22.4 is the molar mass of gas in the standard conditions (273 K, 1013 hPa) (1 mol−1), T0 and P0 are the temperature (K) and pressure (hPa) of the air in standard conditions, respectively, T and P are the air temperature (K) and pressure (hPa) while gas sampling, respectively, ΔC/Δt is the change of CO2 concentrations (ppm) in the jar by the time (h), V is the headspace volume of jar (l), and m is the SOC content of the incubated soil (g). 2.4. Calculations and statistical analysis An exponential function was used to plot SOC decomposition rates against temperatures (all fitting coefficient R2 > 0.95; Fig. S2) by Eq. (2), and the Q10 value of SOC decomposition was calculated by Eq. (3) as below (Fierer et al., 2006). R = BekT Q10 = e 45
10k
(2) (3)
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Table 1 Information for the sampling sites. Sampling Site
Location
MAT (°C)
MAP (mm)
Altitude (m)
Soil Typea
Ecosystem Type
Texture Class
ShenYang (SY)
41°40′22′′–41°40′50′′N, 123°42′36′′–123°42′52′′E
8.1
680
81–125
Leptosols
TieLing (TL)
42°15′26′′–42°16′6′′N, 123°47′7′′–123°47′24′′E
6.3
700
64–93
Leptosols
YanJi (YJ)
42°50′36′′–42°50′55′′N, 129°28′16′′–129°28′52′′E
5.6
479
277–314
Luvisols
BinXian (BX)
45°44′37′′–45°44′45′′N, 127°23′31′′–129°24′5′′E
3.4
578
182–214
Phaeozems
SuiHua (SH)
46°55′47′′–46°56′24′′N, 127°24′22′′–127°24′55′′E
2.8
492
113–157
Phaeozems
YiChun (YC)
47°43′28′′–47°43′46′′N, 128°42′18′′–128°42′46′′E
1.1
776
222–269
Phaeozems
PF PA UL PF PA UL PF PA UL PF PA UL PF PA UL PF PA UL
Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Loam Silt loam Loam Loam Sandy loam Sandy loam Sandy loam Sandy loam Loam Loam Sandy loam Loam Loam
PF, PA, and UL represent planted forest, paddy, and upland, respectively. a Based on the World Reference Base for Soil Resources (IUSS Working Group WRB, 2015) and the soil map of China (http://www.gifex.com/fullsize-en/2011-07-27-14149/Soil-mapof-China-1990.html).
where R is the SOC decomposition rate (μg CO2-C g−1 SOC h−1), T is the temperature (°C), and B and k are model fitting parameters. SOC quality is a functional concept and commonly refers to how easy SOC is decomposed under given conditions of biotic and abiotic environments. Based on this understanding, DOC and SMBC are commonly used as indexes of labile SOC since DOC is widely considered to be the direct substrates for decomposition and SMBC is an active soil C with a high turnover rate (Haynes, 2000; Neff and Asner, 2001; Zou et al., 2005). Indeed, both DOC and SMBC are the basic parameters of SOC quality and cannot fully reflect SOC quality in either natural or managed ecosystem. Therefore, the parameter B in Eq. (2), defining the rate of SOC decomposition by microbes at 0 °C, was also used to quantify the SOC quality in this study. The parameter B can be defined as the decomposability of SOC, quantifying the fraction of SOC that can be decomposed over a given period of time (i.e. SOC quality/lability) (Fierer et al., 2005), and is being widely used as an index of SOC quality in short-term incubation studies (e.g. Fierer et al., 2003, 2006; Ding et al., 2016). To determine the effects of the ecosystem type and soil type on soil properties, C quality, and Q10 value, a two-way ANOVA with ecosystem type and soil type as fixed effect and sampling site as covariance was used for each soil layer. Paired t-tests were used to compare the differences in soil properties, C quality, and Q10 value between the topsoil and subsoil. Person correlation analyses were performed to show the relationships between Q10 value and soil C quality or soil properties. Statistical analyses and correlations were performed using IBM SPSS Statistics 22 and the curve fittings were performed by using SigmaPlot 12.5.
between soil layers (Fig. S3 and Table S1). In addition, the soil type had significant effects on C quality (i.e. B, SMBC, and DOC) (Fig. 2a–c) and other soil properties (e.g. SOC and C:N) at each soil layer (Fig. S4). 3.2. Q10 of SOC decomposition Q10 values varied significantly among the managed ecosystems (P < 0.05; Fig. 1d), soil types (P < 0.05; Fig. 2d) or the sampling sites (Figs. S5 and S6). At both soil layers, Q10 values were in the order of UL > PA > PF (Fig. 1d). Across all the soils, Q10 values ranged from 2.90 to 4.63 with a mean value of 3.32 ± 0.08 (mean ± SE) and 3.60 ± 0.10 at topsoil and subsoil layers, respectively. In addition, subsoil Q10 value was significantly higher than that of the topsoil (P < 0.001; Table 2). 3.3. Relationships between Q10 and soil C quality or soil properties Q10 values were negatively correlated with SMBC, DOC, and parameter B, respectively, across all the studied ecosystems (Table 3). Within each type of ecosystem, the parameter B rather than SMBC or DOC was negatively correlated with Q10 values (Table 3). In general, soil type with higher C quality had lower Q10 value. In addition, soil pH was positively correlated with Q10 value across all the ecosystems or in each type of ecosystem (Table 3). However, soil textures were not significantly correlated with Q10 value across all the ecosystems or in each type of ecosystem except for PA (Table S2). 4. Discussion
3. Results
4.1. The CQT hypothesis in managed ecosystems
3.1. Soil properties and C quality
In this study, the temperature sensitivity of SOC decomposition differed significantly across the managed ecosystems, varying from 2.90 to 4.63 in an order of UL > PA > PF. The estimated Q10 value from the six sites was consistent with previous studies in the managed ecosystems (Conant et al., 2008a) and the natural ecosystems (Fierer et al., 2006). The common range of Q10 value of SOC decomposition in managed soils was 1.5–4.8 (Hamdi et al., 2013). A similar pattern of Q10 value was reported by Arevalo et al. (2012) in cropland (Q10 = 2.27) and PF soils (Q10 = 1.73). The variations of Q10 values among different managed ecosystems could be well explained by conceptual SOC quality in these soils. For example, B, SMBC, and DOC in
Soil C quality (as indicated by SMBC, DOC, and B) at both the topand sub-soil depths differed significantly among the managed ecosystems (P < 0.05), except for SMBC in the subsoil (Fig. 1a–c). Overall, soil C quality was the highest in the PF and the lowest in the UL (Fig. 1a–c). Across all the soils, topsoil C quality was significantly higher than that of the subsoil (Table 2). The type of managed ecosystem and soil depth also had significant effects on SOC, TN, and pH (Fig. S3 and Table S1). However, soil textures (i.e. clay, silt, and sand content) were not significantly different among ecosystem types and 46
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Fig. 1. Soil C quality and Q10 values of the managed ecosystems. B obtained from the Eq. (2) is an index of C quality. Error bars represent standard errors of the means. PF, PA, and UL represent planted forest, paddy, and upland, respectively.
indicators of soil C quality (Filep et al., 2015), the significant correlation between Q10 value and SMBC or DOC was only found across the ecosystems rather than in individual ecosystem. By the definition in Eq. (2), parameter B reflected the overall quality of SOC along with the decomposition process mediated by soil microbes. SMBC and DOC as two labile pools of SOC may partly involve in SOC decomposition during short-term incubation. Gutierrez-Giron et al. (2015) also found that Q10 value was not significantly correlated with SMBC or DOC in a grassland. The study suggested that SMBC and DOC were helpful to explain the effects of soil C quality on the temperature sensitivity of SOC decomposition, but parameter B was more useful for understanding the relationship between Q10 value and soil C quality in the managed ecosystems. Soil depth also had a significant effect on Q10 value (P < 0.001; Table 2), which could probably be explained by the significantly higher values of B and labile C (SMBC and DOC) in the topsoil layer. In general, SOC quality indexed by the decomposability (parameter B) or lability (DOC or SMBC) decreases with soil depth (Fierer et al., 2003) due to the physical protection of soil minerals (Hassink and Whitmore, 1997) or chemical resistance of SOC to microbial metabolism (Bosatta and Ågren, 1999). This pattern of SOC quality decreasing with soil depth was found in natural ecosystems such as natural forest (Karhu et al., 2010), grassland (Craine et al., 2010), and peatland (Hilasvuori et al., 2013), and was used to explain the increase of Q10 value with soil depth (Karhu et al., 2010). This study suggested that the CQT hypothesis could also be applied to the managed ecosystems with similar underlying mechanisms. In addition, soil type was responsible for the variations of Q10 values (P < 0.05; Fig. 2d), because soil type could synthetically represent C quality (Fig. 2a–c) and some soil properties (Fig. S4). However, no
Table 2 Comparisons of soil C quality and Q10 values of the 0–5 cm and the 30–35 cm soil layers by the paired t-test.
−1
SMBC (mg kg ) DOC (mg kg−1) B (μg CO2-C g−1 SOC h−1) Q10 value
Difference
t
P
269.97 ± 143.63 60.64 ± 22.32 0.63 ± 0.37 0.28 ± 0.11
7.974 11.528 7.317 −10.810
< 0.001 < 0.001 < 0.001 < 0.001
PA soils were approximately 1.6, 1.2, and 1.4 times higher than those in the UL soils, respectively, suggesting that C quality of the PA soils was higher than that of the UL soils. The findings supported the CQT hypothesis in predicting SOC dynamics and their feedbacks to global warming. The parameter B (in Eq. (2)) was widely used to indicate C quality in previous short-term studies (Fierer et al., 2006). In this study, the significantly negative correlations were found between Q10 value and B either across ecosystems or soil layers in the individual ecosystem (Table 3), suggesting that the soils with more recalcitrant C, as indexed by relatively lower B, were more temperature sensitivity than those with more labile C. Likewise, B is negatively related to Q10 value across 156 sites in Tibetan alpine grasslands with correlation coefficients (r) of −0.61 and −0.63 for alpine steppe and alpine meadow, respectively (Ding et al., 2016). Such pattern has also been reported in the main natural ecosystems of the United States, where B explained 44% of the variation in Q10 value (Fierer et al., 2006). These results demonstrate that B, an indicator of soil C quality, could explain most of the variations of Q10 values in both natural and managed ecosystems. Despite that SMBC and DOC were considered to be important 47
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Fig. 2. Soil C quality and Q10 values of the different soil types. B obtained from the Eq. (2) is an index of C quality. Error bars represent standard errors of the means.
temperature than multicellular fungi (Barcenas-Moreno et al., 2009). As a result, SOC decomposition at lower pH may have lower temperature sensitivity. However, one must bear in mind that this pattern may only occur in near neutral soils, such as in soils of this study (pH 5.6–7.9). The variation in Q10 value of SOC decomposition with pH in extreme acid or alkaline soil is warranted a further study in order to predict the response of terrestrial C pools to global warming.
Table 3 Person correlation coefficients (r) between Q10 values and soil properties. B
PF PA UL All
SMBC
DOC
pH
r
P
r
P
r
P
r
P
−0.74 −0.69 −0.79 −0.73
0.006 0.013 0.002 0.000
−0.37 −0.31 −0.12 −0.39
0.236 0.331 0.723 0.021
−0.27 −0.30 −0.46 −0.53
0.404 0.344 0.113 0.001
0.86 0.91 0.92 0.91
< 0.001 < 0.001 < 0.001 < 0.001
4.3. Limitations of incubation method
PF, PA, and UL represent planted forest, paddy, and upland, respectively.
Large variations or uncertainties in observed Q10 values have been reported for both in situ observations and laboratory incubations (Chen et al., 2010). Soil incubation has been considered as an effective complementary method to field observation in studying the temperature sensitivity of SOC decomposition. Generally, there are two types of methods for laboratory soil incubation, i.e. parallel and sequential incubation approaches. For the parallel incubation, soils are incubated at different while constant temperatures throughout the entire experimental duration (Conant et al., 2008b; Wetterstedt et al., 2010; Arevalo et al., 2012). Due to the differences in C substrates depletion at different temperatures, soils incubated at relatively high temperatures deplete more labile fractions of SOC and sooner than those at lower temperatures, resulting in the underestimation of Q10 value by parallel incubation (Leifeld and Fuhrer, 2005). In addition, the parallel approach often needs a relatively long-term incubation. Soil microbes driving SOC decomposition may, therefore, adapt to different but constant temperatures during the long-term incubation, leading to inauthentic results (Davidson and Janssens, 2006). The sequential approach used in this study can ensure the same soil conditions for estimating Q10 value before increasing the incubation temperature and minimize the different exhaustion of SOC and the
significant relationship was found between Q10 value and soil texture across all the managed ecosystems (Table S2), suggesting that soil particle size was not a good indicator of the variations of Q10 values in the managed ecosystems. This result was not consistent with previous studies, which showed that Q10 value decreased in the order clay > silt > sand in an artificial forest soil and a permanent grassland (Ding et al., 2014). 4.2. Q10 value and soil pH Strong positive relationships were found between Q10 value and soil pH either across or within the managed ecosystems. These results were consistent with previous studies that found a positive correlation between the temperature sensitivity of SOC decomposition and the soil pH along a gradient of altitude (Gutierrez-Giron et al., 2015) and among 71 grassland sites in the North America (Craine et al., 2010). A possible explanation for this relationship was that soil fungi relative to bacteria often dominate in the acid soil (Blagodatskaya and Anderson, 1998) and the unicellular bacteria have higher responses to changes in 48
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Temperature sensitivity of organic matter decomposition in two boreal forest soil profiles. Soil Biol. Biochem. 42, 72–82. Koch, O., Tscherko, D., Kandeler, E., 2007. Temperature sensitivity of microbial respiration, nitrogen mineralization, and potential soil enzyme activities in organic alpine soils. Glob. Biogeochem. Cycles 21, 11. Lal, R., Kimble, J., Follett, R., 1998. Land Use and Soil C Pools in Terrestrial Ecosystems. CRC Press-Taylor & Francis Group, Boca Raton. Leifeld, J., Fuhrer, J., 2005. The temperature response of CO2 production from bulk soils and soil fractions is related to soil organic matter quality. Biogeochemistry 75, 433–453. Lomander, A., Kätterer, T., Andrén, O., 1998. Carbon dioxide evolution from top- and subsoil as affected by moisture and constant and fluctuating temperature. Soil Biol. Biochem. 30, 2017–2022. Neff, J.C., Asner, G.P., 2001. Dissolved organic carbon in terrestrial ecosystems: synthesis and a model. Ecosystems 4, 29–48. Omonode, R.A., Vyn, T.J., 2006. Vertical distribution of soil organic carbon and nitrogen under warm-season native grasses relative to croplands in west-central Indiana USA. Agric. Ecosyst. Environ. 117, 159–170.
microbial adaptation caused by different long-term constant temperatures. However, the short-duration sequential method might only reflect the temperature sensitivity of more labile component of SOC (Fierer et al., 2006). Despite the limitation of the short-term sequential method, it has been increasingly used to compare the temperature sensitivity of SOC decomposition across a wide variety of soils (e.g. Fang et al., 2005; Koch et al., 2007; Chen et al., 2010; Ding et al., 2016). 5. Conclusions Results of this study showed that the temperature sensitivity of SOC decomposition differed significantly across the managed ecosystems, soil types, and soil depth. Soil C quality and pH were two important factors explaining the changes in the temperature sensitivity of SOC decomposition in the managed ecosystems, supporting the carbon quality-temperature (CQT) hypothesis. Thus, soil C quality and pH regulating the response of SOC decomposition to temperature change should be potentially incorporated into the biogeochemical models to better predict SOC dynamics in managed ecosystems in the context of global warming and land use changes. Acknowledgements We are grateful to Yong Li and two anonymous reviewers for their insightful comments and suggestions on an earlier version of this MS. This research was financially supported by the National Basic Research Program of China (Grant No. 2013CB430404), the National Science Foundation of China (Grant No. 41630528 and 31670491), the Science and Technology Commission of Shanghai Municipality (Grant No. 14DZ1206003), and the Shanghai Pujiang Scholar Program (Grant No. 16PJ1400900). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2017.09.001. References Arevalo, C.B.M., Chang, S.X., Bhatti, J.S., Sidders, D., 2012. Mineralization potential and temperature sensitivity of soil organic carbon under different land uses in the parkland region of Alberta, Canada. Soil Sci. Soc. Am. J. 76, 241–251. Barcenas-Moreno, G., Gomez-Brandon, M., Rousk, J., Baath, E., 2009. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957. Beheshti, A., Raiesi, F., Golchin, A., 2012. Soil properties, C fractions and their dynamics in land use conversion from native forests to croplands in northern Iran. Agric. Ecosyst. Environ. 148, 121–133. Beier, C.M., Patterson, T.M., Chapin, F.S., 2008. Ecosystem services and emergent vulnerability in managed ecosystems: a geospatial decision-support tool. Ecosystems 11, 923–938. Berthrong, S.T., Jobbagy, E.G., Jackson, R.B., 2009. A global meta-analysis of soil exchangeable cations, pH, carbon, and nitrogen with afforestation. Ecol. Appl. 19, 2228–2241. Blagodatskaya, E.V., Anderson, T.H., 1998. Interactive effects of pH and substrate quality on the fungal-to-bacterial ratio and qCO2 of microbial communities in forest soils. Soil Biol. Biochem. 30, 1269–1274. Bosatta, E., Ågren, G.I., 1999. Soil organic matter quality interpreted thermodynamically. Soil Biol. Biochem. 31, 1889–1891. Celik, I., 2005. Land-use effects on organic matter and physical properties of soil in a southern Mediterranean highland of Turkey. Soil Tillage Res. 83, 270–277. Chen, X.P., Tang, J., Jiang, L.F., Li, B., Chen, J.K., Fang, C.M., 2010. Evaluating the impacts of incubation procedures on estimated Q10 values of soil respiration. Soil Biol. Biochem. 42, 2282–2288. Conant, R.T., Drijber, R.A., Haddix, M.L., Parton, W.J., Paul, E.A., Plante, A.F., Six, J., Steinweg, J.M., 2008a. Sensitivity of organic matter decomposition to warming varies with its quality. Glob. Change Biol. 14, 868–877. Conant, R.T., Steinweg, J.M., Haddix, M.L., Paul, E.A., Plante, A.F., Six, J., 2008b. Experimental warming shows that decomposition temperature sensitivity increases with soil organic matter recalcitrance. Ecology 89, 2384–2391. Conant, R.T., Ryan, M.G., Agren, G.I., Birge, H.E., Davidson, E.A., Eliasson, P.E., Evans, S.E., Frey, S.D., Giardina, C.P., Hopkins, F.M., Hyvonen, R., Kirschbaum, M.U.F., Lavallee, J.M., Leifeld, J., Parton, W.J., Steinweg, J.M., Wallenstein, M.D., Wetterstedt, J.A.M., Bradford, M.A., 2011. Temperature and soil organic matter
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biomass—an incubation study. Soil Biol. Biochem. 57, 739–748. Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring microbial biomass C. Soil Biol. Biochem. 19, 703–707. Wetterstedt, J.A.M., Persson, T., Agren, G.I., 2010. Temperature sensitivity and substrate quality in soil organic matter decomposition: results of an incubation study with three substrates. Glob. Change Biol. 16, 1806–1819. Yan, D., Li, J.Q., Pei, J.M., Cui, J., Nie, M., Fang, C.M., 2017. The temperature sensitivity of soil organic carbon decomposition is greater in subsoil than in topsoil during laboratory incubation. Sci. Rep. 7, 9. Zimmermann, M., Bird, M.I., 2012. Temperature sensitivity of tropical forest soil respiration increase along an altitudinal gradient with ongoing decomposition. Geoderma 187, 8–15. Zou, X.M., Ruan, H.H., Fu, Y., Yang, X.D., Sha, L.Q., 2005. Estimating soil labile organic carbon and potential turnover rates using a sequential fumigation-incubation procedure. Soil Biol. Biochem. 37, 1923–1928.
Pang, X.Y., Zhu, B.A., Lu, X.T., Cheng, W.X., 2015. Labile substrate availability controls temperature sensitivity of organic carbon decomposition at different soil depths. Biogeochemistry 126, 85–98. Parton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S., 1987. Analysis of factors controlling soil organic matter levels in great plains grasslands. Soil Sci. Soc. Am. J. 51, 1173–1179. Poeplau, C., Don, A., 2013. Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. Geoderma 192, 189–201. Post, W.M., Kwon, K.C., 2000. Soil carbon sequestration and land-use change: processes and potential. Glob. Change Biol. 6, 317–327. Raisanen, J., Hansson, U., Ullerstig, A., Doscher, R., Graham, L.P., Jones, C., Meier, H.E.M., Samuelsson, P., Willen, U., 2004. European climate in the late twenty-first century: regional simulations with two driving global models and two forcing scenarios. Clim. Dyn. 22, 13–31. Thiessen, S., Gleixner, G., Wutzler, T., Reichstein, M., 2013. Both priming and temperature sensitivity of soil organic matter decomposition depend on microbial
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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee
Research Paper
Carbon quality mediates the temperature sensitivity of soil organic carbon decomposition in managed ecosystems ⁎
Jinquan Lia, Junmin Peia, Jun Cuia, Xueping Chenb, Bo Lia, Ming Niea, , Changming Fanga,
MARK
⁎
a Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, The Institute of Biodiversity Science, Fudan University, 2005 Songhu Road, Shanghai 200438, China b School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Soil incubation Sequential temperature change Q10 value The carbon quality-temperature hypothesis
The carbon quality-temperature (CQT) hypothesis suggests that the temperature sensitivity (Q10) of soil organic carbon (SOC) decomposition is negatively related to soil C quality. This hypothesis was widely tested in various natural ecosystems, but the effect of soil C quality on the temperature response of SOC decomposition has not been well addressed in managed ecosystems. In this study, Q10 values of SOC decomposition were estimated in three adjacent managed ecosystems (planted forest (PF), paddy (PA), and upland (UL)) within the topsoil (0–5 cm) and subsoil (30–35 cm) layers of six sites across different climate zones in northeast China. The results suggested that the soil C quality differed significantly among the managed ecosystems (PF > PA > UL; P < 0.05) or soil types (P < 0.05), and decreased with soil depth (P < 0.001). Overall, Q10 values differed significantly across the ecosystems (PF < PA < UL) and soil types (P < 0.05), but were significantly greater in the subsoil than those in the topsoil (P < 0.001). The negative relationship between Q10 value and soil C quality suggested that the CQT hypothesis was applicable to ecosystem types, soil types, and soil profiles of the managed ecosystems. In addition, the Q10 value had a positive correlation with soil pH (P < 0.001). The results suggested that incorporating soil C quality and soil pH into models would help us to predict the feedbacks between soil C dynamics and global warming in managed ecosystems.
1. Introduction The decomposition of soil organic carbon (SOC), which is the largest carbon (C) pool among the terrestrial ecosystems, is intrinsically sensitive to temperature change (Davidson and Janssens, 2006). Although an increase in temperature is considered to accelerate SOC decomposition (Friedlingstein et al., 2001), global C cycle models show large uncertainties in predicting soil C dynamics and their feedbacks to global change (Cox et al., 2000; Friedlingstein et al., 2001). To reduce these uncertainties, it is necessary to estimate the temperature sensitivity (Q10) of SOC decomposition accurately. The Q10 is a measure of the rate of change of a biological or chemical system as a consequence of increasing the temperature by 10 °C, and it is the underlying feedback mechanisms in the global C cycle (Conant et al., 2011). Many factors affect the Q10, such as soil C quality (Hartley and Ineson, 2008b), plant type (Gutierrez-Giron et al., 2015), soil matrix (Arevalo et al., 2012), soil pH (Craine et al., 2010), C to N ratio (Haddix et al., 2011), and soil microbes (Thiessen et al., 2013). Among them, soil C quality is considered as one of the most important factors affecting Q10 (Fierer et al., 2006; Conant et al., 2008a), because SOC ⁎
consists of various C components (Arevalo et al., 2012), which may have different Q10 values (Leifeld and Fuhrer, 2005; Conant et al., 2008a; Wetterstedt et al., 2010). However, in most terrestrial C models, the effects of soil C quality on Q10 have not been well considered (Parton et al., 1987; Cox et al., 2000; Jones et al., 2003). The understanding of the effects of soil C quality on Q10 will improve the development and parameterization of terrestrial C models and help in predicting the climate change – terrestrial C cycle feedbacks (Post and Kwon, 2000; Davidson and Janssens, 2006). The C quality-temperature (CQT) hypothesis suggests the lowquality SOC is more stable than the high-quality SOC under increasing temperature (Bosatta and Ågren, 1999; Fierer et al., 2005; Davidson and Janssens, 2006). However, most of the previous studies on the effects of soil C quality on Q10 have focused primarily on natural ecosystems (Craine et al., 2010; Zimmermann and Bird, 2012). For example, Ding et al. (2016) evaluated the temperature sensitivity of SOC decomposition using the soils from 156 sites across the Tibetan alpine grasslands and found that Q10 value of SOC decomposition was negatively correlated with soil C quality. Likewise, Fierer et al. (2006) found a significantly negative correlation between Q10 value and soil C quality
Corresponding authors at: School of Life Science, Fudan University, 2005 Songhu Road, Shanghai 200438, China. E-mail addresses: [email protected] (M. Nie), [email protected] (C. Fang).
http://dx.doi.org/10.1016/j.agee.2017.09.001 Received 4 June 2017; Received in revised form 5 September 2017; Accepted 6 September 2017 Available online 20 September 2017 0167-8809/ © 2017 Elsevier B.V. All rights reserved.
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2.2. Soil chemical analyses
by incubating 77 unique soils from main natural ecosystems of the United States. Further, Q10 value has been found negatively related to the C quality in 24 different litter types varying in chemical composition (Fierer et al., 2005). However, only a few studies have been conducted to test the CQT hypothesis in managed sites or ecosystem types (Conant et al., 2008a; Hartley and Ineson, 2008a) and study on the CQT hypothesis in broad managed ecosystems is still scarce. Managed ecosystems such as croplands and planted forests play important roles in C cycle and C budget in terrestrial ecosystems (Houghton and Hackler, 2003). At present, approximately 8 and 5% of the Earth’s land surfaces are managed for croplands and planted forests, respectively (Hansen et al., 2000; Berthrong et al., 2009), which is highly important to human well-being (Beier et al., 2008). The area of other land uses change to croplands and planted forests take up more than 80% of the total area of land use change on the earth (Lal et al., 1998). In addition, land use change as one of the major drivers of global climate change has led to release 1.1 ± 0.7 Pg C per year in the first decade of the 2000s worldwide (Poeplau and Don, 2013). Because of the human activities such as fertilization and irrigation, there are differences in soil C quality between managed and natural ecosystems, leading to diverse SOC decomposition processes in these ecosystems (Guo and Gifford, 2002; Celik, 2005; Beheshti et al., 2012). In addition, soil C quality differs at soil depths. The topsoil layer often has a higher C quality than the subsoil layer (Omonode and Vyn, 2006). Some studies have been carried out on the variation in the temperature sensitivity of SOC decomposition with soil depths in managed ecosystems (e.g. Lomander et al., 1998; Gillabel et al., 2010; Pang et al., 2015). However, to our knowledge, whether the CQT hypothesis can be applied to soil profiles across various managed ecosystems is still unknown. Northeast China, located in the temperate region, is the main food and timber producing areas in China. The global circulation models predict that middle latitude regions are more likely to experience a warming climate than the low latitude regions in the future (Raisanen et al., 2004), and therefore Northeast China would be highly vulnerable to climate change. In this study, the temperature sensitivity of SOC decomposition was estimated in six sites with different climate conditions in northeast China. At each site, soils from the three adjacent managed ecosystems (i.e. planted forest (PF), paddy (PA), and upland (UL)) were collected to test whether the CQT hypothesis could be applied to various managed ecosystems and soil profiles.
SMBC was determined by a CHCl3 fumigation-extraction method (Vance et al., 1987). The soil dissolved C (DOC) concentrations in nonfumigated and fumigated soils were determined by a TOC analyzer (Multi N/C 3100, Germany). Soil pH was measured in a supernatant of the deionized water solution (soil:solution = 1:2.5) using a pH meter (METTLER TOLEDO FiveEasy). Total C (TC) and total nitrogen (TN) were measured in an NC analyzer (FlashEA 1112 Series, Italy). The SOC was determined using a TOC analyzer (vario TOC cube, Germany) after the removal of carbonates with 0.1 M HCl. Soil texture was measured by using a particle size analyzer (Laser Particle Sizer, LS-CWM(2), OMEC, China) after the removal of organic matter (using 30% hydrogen peroxide) and carbonates (using 30% hydrochloric acid) (Ding et al., 2016). Soil water holding capacity (WHC) was gravimetrically determined (Chen et al., 2010). 2.3. Soil incubation and respiration measurement Soil samples were incubated in the laboratory using a sequential temperature change method following Chen et al. (2010) and Ding et al. (2016). Briefly, each soil sample with four replicates (equivalent to 50 g dry-weight with 60% WHC in a 250 mL jar) was pre-incubated at 20 °C for 7 d to minimize the disturbances from soil sieving, packing, and rewetting. A water bath (DC0530; Bilang Instrument Corp. Ltd., Shanghai) was then used for the sequential incubation. The temperature was changed from 4 °C up to 28 °C and then down to 4 °C with a changing step of 4 °C. After changing the temperature, soil samples were allowed to stand for two hours to achieve a new equilibrium (Koch et al., 2007; Chen et al., 2010; Ding et al., 2016). The entire sequential temperature incubation lasted for seven to ten days. Fresh air was continuously passed, at a flow rate of 0.75 L min−1, through the head space of incubation jars by an air distribution system. After an equilibrium was achieved at the target temperature, i.e. two hours after the temperature was changed, all the incubation jars were sealed and 5 mL of headspace gas samples were collected by syringes. Five milliliters of CO2-free air were immediately injected into each incubation jar to avoid changes in the headspace air pressure. After being sealed for 1 to 24 h (depending on headspace CO2 concentrations that were set at no more than 0.3% for minimizing the inhibitory effects of CO2 on soil respiration), 5 mL of the headspace gas sample was collected. The CO2 concentrations were determined by a gas chromatograph (Agilent 6890; Agilent Corp., USA). The SOC decomposition rate was calculated as below (Yan et al., 2017):
2. Materials and methods 2.1. Soil sampling
R=
Soil samples were collected from the three adjacent managed ecosystems at six sites across northeast China (Table 1; Fig. S1 in the Supplementary material). Six sites have gradients of the mean annual temperature (MAT) from 1.1 to 8.1 °C and the mean annual precipitation (MAP) from 479 to 776 mm. Each site consists of three adjacent managed ecosystems, including PF, PA, and UL. The vegetation of the six PF was planted with the coniferous species with a history of more than 20 years. The six PA and six UL fields were cultivated with rice and corn for more than 15 years, respectively. The land use types of the six sampling sites were forest or grassland ecosystems before changing them to the managed ecosystems. In each managed ecosystem, at each site, four sampling locations were randomly chosen. At each location, two layers per transect (i.e. topsoil 0–5 cm and subsoil 30–35 cm) were sampled by using an 8-cm diameter core sampler. Soil samples were sieved (mesh size < 2 mm) to remove stones and plant materials. The sampled soils from the four locations of each site were mixed together to merge the soil samples by soil depth. Approximately 20 g of mixed soil samples were air-dried for analyzing soil properties and the rest was stored at 4 °C for incubation and soil microbial biomass C (SMBC).
M P T0 ΔC V 22.4 P0 T Δt m
(1) −1
−1
where R is SOC decomposition rate (μg CO2-C g SOC h ), M is the molar mass of CO2-C (g mol−1), 22.4 is the molar mass of gas in the standard conditions (273 K, 1013 hPa) (1 mol−1), T0 and P0 are the temperature (K) and pressure (hPa) of the air in standard conditions, respectively, T and P are the air temperature (K) and pressure (hPa) while gas sampling, respectively, ΔC/Δt is the change of CO2 concentrations (ppm) in the jar by the time (h), V is the headspace volume of jar (l), and m is the SOC content of the incubated soil (g). 2.4. Calculations and statistical analysis An exponential function was used to plot SOC decomposition rates against temperatures (all fitting coefficient R2 > 0.95; Fig. S2) by Eq. (2), and the Q10 value of SOC decomposition was calculated by Eq. (3) as below (Fierer et al., 2006). R = BekT Q10 = e 45
10k
(2) (3)
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Table 1 Information for the sampling sites. Sampling Site
Location
MAT (°C)
MAP (mm)
Altitude (m)
Soil Typea
Ecosystem Type
Texture Class
ShenYang (SY)
41°40′22′′–41°40′50′′N, 123°42′36′′–123°42′52′′E
8.1
680
81–125
Leptosols
TieLing (TL)
42°15′26′′–42°16′6′′N, 123°47′7′′–123°47′24′′E
6.3
700
64–93
Leptosols
YanJi (YJ)
42°50′36′′–42°50′55′′N, 129°28′16′′–129°28′52′′E
5.6
479
277–314
Luvisols
BinXian (BX)
45°44′37′′–45°44′45′′N, 127°23′31′′–129°24′5′′E
3.4
578
182–214
Phaeozems
SuiHua (SH)
46°55′47′′–46°56′24′′N, 127°24′22′′–127°24′55′′E
2.8
492
113–157
Phaeozems
YiChun (YC)
47°43′28′′–47°43′46′′N, 128°42′18′′–128°42′46′′E
1.1
776
222–269
Phaeozems
PF PA UL PF PA UL PF PA UL PF PA UL PF PA UL PF PA UL
Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Loam Silt loam Loam Loam Sandy loam Sandy loam Sandy loam Sandy loam Loam Loam Sandy loam Loam Loam
PF, PA, and UL represent planted forest, paddy, and upland, respectively. a Based on the World Reference Base for Soil Resources (IUSS Working Group WRB, 2015) and the soil map of China (http://www.gifex.com/fullsize-en/2011-07-27-14149/Soil-mapof-China-1990.html).
where R is the SOC decomposition rate (μg CO2-C g−1 SOC h−1), T is the temperature (°C), and B and k are model fitting parameters. SOC quality is a functional concept and commonly refers to how easy SOC is decomposed under given conditions of biotic and abiotic environments. Based on this understanding, DOC and SMBC are commonly used as indexes of labile SOC since DOC is widely considered to be the direct substrates for decomposition and SMBC is an active soil C with a high turnover rate (Haynes, 2000; Neff and Asner, 2001; Zou et al., 2005). Indeed, both DOC and SMBC are the basic parameters of SOC quality and cannot fully reflect SOC quality in either natural or managed ecosystem. Therefore, the parameter B in Eq. (2), defining the rate of SOC decomposition by microbes at 0 °C, was also used to quantify the SOC quality in this study. The parameter B can be defined as the decomposability of SOC, quantifying the fraction of SOC that can be decomposed over a given period of time (i.e. SOC quality/lability) (Fierer et al., 2005), and is being widely used as an index of SOC quality in short-term incubation studies (e.g. Fierer et al., 2003, 2006; Ding et al., 2016). To determine the effects of the ecosystem type and soil type on soil properties, C quality, and Q10 value, a two-way ANOVA with ecosystem type and soil type as fixed effect and sampling site as covariance was used for each soil layer. Paired t-tests were used to compare the differences in soil properties, C quality, and Q10 value between the topsoil and subsoil. Person correlation analyses were performed to show the relationships between Q10 value and soil C quality or soil properties. Statistical analyses and correlations were performed using IBM SPSS Statistics 22 and the curve fittings were performed by using SigmaPlot 12.5.
between soil layers (Fig. S3 and Table S1). In addition, the soil type had significant effects on C quality (i.e. B, SMBC, and DOC) (Fig. 2a–c) and other soil properties (e.g. SOC and C:N) at each soil layer (Fig. S4). 3.2. Q10 of SOC decomposition Q10 values varied significantly among the managed ecosystems (P < 0.05; Fig. 1d), soil types (P < 0.05; Fig. 2d) or the sampling sites (Figs. S5 and S6). At both soil layers, Q10 values were in the order of UL > PA > PF (Fig. 1d). Across all the soils, Q10 values ranged from 2.90 to 4.63 with a mean value of 3.32 ± 0.08 (mean ± SE) and 3.60 ± 0.10 at topsoil and subsoil layers, respectively. In addition, subsoil Q10 value was significantly higher than that of the topsoil (P < 0.001; Table 2). 3.3. Relationships between Q10 and soil C quality or soil properties Q10 values were negatively correlated with SMBC, DOC, and parameter B, respectively, across all the studied ecosystems (Table 3). Within each type of ecosystem, the parameter B rather than SMBC or DOC was negatively correlated with Q10 values (Table 3). In general, soil type with higher C quality had lower Q10 value. In addition, soil pH was positively correlated with Q10 value across all the ecosystems or in each type of ecosystem (Table 3). However, soil textures were not significantly correlated with Q10 value across all the ecosystems or in each type of ecosystem except for PA (Table S2). 4. Discussion
3. Results
4.1. The CQT hypothesis in managed ecosystems
3.1. Soil properties and C quality
In this study, the temperature sensitivity of SOC decomposition differed significantly across the managed ecosystems, varying from 2.90 to 4.63 in an order of UL > PA > PF. The estimated Q10 value from the six sites was consistent with previous studies in the managed ecosystems (Conant et al., 2008a) and the natural ecosystems (Fierer et al., 2006). The common range of Q10 value of SOC decomposition in managed soils was 1.5–4.8 (Hamdi et al., 2013). A similar pattern of Q10 value was reported by Arevalo et al. (2012) in cropland (Q10 = 2.27) and PF soils (Q10 = 1.73). The variations of Q10 values among different managed ecosystems could be well explained by conceptual SOC quality in these soils. For example, B, SMBC, and DOC in
Soil C quality (as indicated by SMBC, DOC, and B) at both the topand sub-soil depths differed significantly among the managed ecosystems (P < 0.05), except for SMBC in the subsoil (Fig. 1a–c). Overall, soil C quality was the highest in the PF and the lowest in the UL (Fig. 1a–c). Across all the soils, topsoil C quality was significantly higher than that of the subsoil (Table 2). The type of managed ecosystem and soil depth also had significant effects on SOC, TN, and pH (Fig. S3 and Table S1). However, soil textures (i.e. clay, silt, and sand content) were not significantly different among ecosystem types and 46
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Fig. 1. Soil C quality and Q10 values of the managed ecosystems. B obtained from the Eq. (2) is an index of C quality. Error bars represent standard errors of the means. PF, PA, and UL represent planted forest, paddy, and upland, respectively.
indicators of soil C quality (Filep et al., 2015), the significant correlation between Q10 value and SMBC or DOC was only found across the ecosystems rather than in individual ecosystem. By the definition in Eq. (2), parameter B reflected the overall quality of SOC along with the decomposition process mediated by soil microbes. SMBC and DOC as two labile pools of SOC may partly involve in SOC decomposition during short-term incubation. Gutierrez-Giron et al. (2015) also found that Q10 value was not significantly correlated with SMBC or DOC in a grassland. The study suggested that SMBC and DOC were helpful to explain the effects of soil C quality on the temperature sensitivity of SOC decomposition, but parameter B was more useful for understanding the relationship between Q10 value and soil C quality in the managed ecosystems. Soil depth also had a significant effect on Q10 value (P < 0.001; Table 2), which could probably be explained by the significantly higher values of B and labile C (SMBC and DOC) in the topsoil layer. In general, SOC quality indexed by the decomposability (parameter B) or lability (DOC or SMBC) decreases with soil depth (Fierer et al., 2003) due to the physical protection of soil minerals (Hassink and Whitmore, 1997) or chemical resistance of SOC to microbial metabolism (Bosatta and Ågren, 1999). This pattern of SOC quality decreasing with soil depth was found in natural ecosystems such as natural forest (Karhu et al., 2010), grassland (Craine et al., 2010), and peatland (Hilasvuori et al., 2013), and was used to explain the increase of Q10 value with soil depth (Karhu et al., 2010). This study suggested that the CQT hypothesis could also be applied to the managed ecosystems with similar underlying mechanisms. In addition, soil type was responsible for the variations of Q10 values (P < 0.05; Fig. 2d), because soil type could synthetically represent C quality (Fig. 2a–c) and some soil properties (Fig. S4). However, no
Table 2 Comparisons of soil C quality and Q10 values of the 0–5 cm and the 30–35 cm soil layers by the paired t-test.
−1
SMBC (mg kg ) DOC (mg kg−1) B (μg CO2-C g−1 SOC h−1) Q10 value
Difference
t
P
269.97 ± 143.63 60.64 ± 22.32 0.63 ± 0.37 0.28 ± 0.11
7.974 11.528 7.317 −10.810
< 0.001 < 0.001 < 0.001 < 0.001
PA soils were approximately 1.6, 1.2, and 1.4 times higher than those in the UL soils, respectively, suggesting that C quality of the PA soils was higher than that of the UL soils. The findings supported the CQT hypothesis in predicting SOC dynamics and their feedbacks to global warming. The parameter B (in Eq. (2)) was widely used to indicate C quality in previous short-term studies (Fierer et al., 2006). In this study, the significantly negative correlations were found between Q10 value and B either across ecosystems or soil layers in the individual ecosystem (Table 3), suggesting that the soils with more recalcitrant C, as indexed by relatively lower B, were more temperature sensitivity than those with more labile C. Likewise, B is negatively related to Q10 value across 156 sites in Tibetan alpine grasslands with correlation coefficients (r) of −0.61 and −0.63 for alpine steppe and alpine meadow, respectively (Ding et al., 2016). Such pattern has also been reported in the main natural ecosystems of the United States, where B explained 44% of the variation in Q10 value (Fierer et al., 2006). These results demonstrate that B, an indicator of soil C quality, could explain most of the variations of Q10 values in both natural and managed ecosystems. Despite that SMBC and DOC were considered to be important 47
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Fig. 2. Soil C quality and Q10 values of the different soil types. B obtained from the Eq. (2) is an index of C quality. Error bars represent standard errors of the means.
temperature than multicellular fungi (Barcenas-Moreno et al., 2009). As a result, SOC decomposition at lower pH may have lower temperature sensitivity. However, one must bear in mind that this pattern may only occur in near neutral soils, such as in soils of this study (pH 5.6–7.9). The variation in Q10 value of SOC decomposition with pH in extreme acid or alkaline soil is warranted a further study in order to predict the response of terrestrial C pools to global warming.
Table 3 Person correlation coefficients (r) between Q10 values and soil properties. B
PF PA UL All
SMBC
DOC
pH
r
P
r
P
r
P
r
P
−0.74 −0.69 −0.79 −0.73
0.006 0.013 0.002 0.000
−0.37 −0.31 −0.12 −0.39
0.236 0.331 0.723 0.021
−0.27 −0.30 −0.46 −0.53
0.404 0.344 0.113 0.001
0.86 0.91 0.92 0.91
< 0.001 < 0.001 < 0.001 < 0.001
4.3. Limitations of incubation method
PF, PA, and UL represent planted forest, paddy, and upland, respectively.
Large variations or uncertainties in observed Q10 values have been reported for both in situ observations and laboratory incubations (Chen et al., 2010). Soil incubation has been considered as an effective complementary method to field observation in studying the temperature sensitivity of SOC decomposition. Generally, there are two types of methods for laboratory soil incubation, i.e. parallel and sequential incubation approaches. For the parallel incubation, soils are incubated at different while constant temperatures throughout the entire experimental duration (Conant et al., 2008b; Wetterstedt et al., 2010; Arevalo et al., 2012). Due to the differences in C substrates depletion at different temperatures, soils incubated at relatively high temperatures deplete more labile fractions of SOC and sooner than those at lower temperatures, resulting in the underestimation of Q10 value by parallel incubation (Leifeld and Fuhrer, 2005). In addition, the parallel approach often needs a relatively long-term incubation. Soil microbes driving SOC decomposition may, therefore, adapt to different but constant temperatures during the long-term incubation, leading to inauthentic results (Davidson and Janssens, 2006). The sequential approach used in this study can ensure the same soil conditions for estimating Q10 value before increasing the incubation temperature and minimize the different exhaustion of SOC and the
significant relationship was found between Q10 value and soil texture across all the managed ecosystems (Table S2), suggesting that soil particle size was not a good indicator of the variations of Q10 values in the managed ecosystems. This result was not consistent with previous studies, which showed that Q10 value decreased in the order clay > silt > sand in an artificial forest soil and a permanent grassland (Ding et al., 2014). 4.2. Q10 value and soil pH Strong positive relationships were found between Q10 value and soil pH either across or within the managed ecosystems. These results were consistent with previous studies that found a positive correlation between the temperature sensitivity of SOC decomposition and the soil pH along a gradient of altitude (Gutierrez-Giron et al., 2015) and among 71 grassland sites in the North America (Craine et al., 2010). A possible explanation for this relationship was that soil fungi relative to bacteria often dominate in the acid soil (Blagodatskaya and Anderson, 1998) and the unicellular bacteria have higher responses to changes in 48
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microbial adaptation caused by different long-term constant temperatures. However, the short-duration sequential method might only reflect the temperature sensitivity of more labile component of SOC (Fierer et al., 2006). Despite the limitation of the short-term sequential method, it has been increasingly used to compare the temperature sensitivity of SOC decomposition across a wide variety of soils (e.g. Fang et al., 2005; Koch et al., 2007; Chen et al., 2010; Ding et al., 2016). 5. Conclusions Results of this study showed that the temperature sensitivity of SOC decomposition differed significantly across the managed ecosystems, soil types, and soil depth. Soil C quality and pH were two important factors explaining the changes in the temperature sensitivity of SOC decomposition in the managed ecosystems, supporting the carbon quality-temperature (CQT) hypothesis. Thus, soil C quality and pH regulating the response of SOC decomposition to temperature change should be potentially incorporated into the biogeochemical models to better predict SOC dynamics in managed ecosystems in the context of global warming and land use changes. Acknowledgements We are grateful to Yong Li and two anonymous reviewers for their insightful comments and suggestions on an earlier version of this MS. This research was financially supported by the National Basic Research Program of China (Grant No. 2013CB430404), the National Science Foundation of China (Grant No. 41630528 and 31670491), the Science and Technology Commission of Shanghai Municipality (Grant No. 14DZ1206003), and the Shanghai Pujiang Scholar Program (Grant No. 16PJ1400900). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2017.09.001. References Arevalo, C.B.M., Chang, S.X., Bhatti, J.S., Sidders, D., 2012. Mineralization potential and temperature sensitivity of soil organic carbon under different land uses in the parkland region of Alberta, Canada. Soil Sci. Soc. Am. J. 76, 241–251. Barcenas-Moreno, G., Gomez-Brandon, M., Rousk, J., Baath, E., 2009. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957. Beheshti, A., Raiesi, F., Golchin, A., 2012. Soil properties, C fractions and their dynamics in land use conversion from native forests to croplands in northern Iran. Agric. Ecosyst. Environ. 148, 121–133. Beier, C.M., Patterson, T.M., Chapin, F.S., 2008. Ecosystem services and emergent vulnerability in managed ecosystems: a geospatial decision-support tool. Ecosystems 11, 923–938. Berthrong, S.T., Jobbagy, E.G., Jackson, R.B., 2009. A global meta-analysis of soil exchangeable cations, pH, carbon, and nitrogen with afforestation. Ecol. Appl. 19, 2228–2241. Blagodatskaya, E.V., Anderson, T.H., 1998. Interactive effects of pH and substrate quality on the fungal-to-bacterial ratio and qCO2 of microbial communities in forest soils. Soil Biol. Biochem. 30, 1269–1274. Bosatta, E., Ågren, G.I., 1999. Soil organic matter quality interpreted thermodynamically. Soil Biol. Biochem. 31, 1889–1891. Celik, I., 2005. Land-use effects on organic matter and physical properties of soil in a southern Mediterranean highland of Turkey. Soil Tillage Res. 83, 270–277. Chen, X.P., Tang, J., Jiang, L.F., Li, B., Chen, J.K., Fang, C.M., 2010. Evaluating the impacts of incubation procedures on estimated Q10 values of soil respiration. Soil Biol. Biochem. 42, 2282–2288. Conant, R.T., Drijber, R.A., Haddix, M.L., Parton, W.J., Paul, E.A., Plante, A.F., Six, J., Steinweg, J.M., 2008a. Sensitivity of organic matter decomposition to warming varies with its quality. Glob. Change Biol. 14, 868–877. Conant, R.T., Steinweg, J.M., Haddix, M.L., Paul, E.A., Plante, A.F., Six, J., 2008b. Experimental warming shows that decomposition temperature sensitivity increases with soil organic matter recalcitrance. Ecology 89, 2384–2391. Conant, R.T., Ryan, M.G., Agren, G.I., Birge, H.E., Davidson, E.A., Eliasson, P.E., Evans, S.E., Frey, S.D., Giardina, C.P., Hopkins, F.M., Hyvonen, R., Kirschbaum, M.U.F., Lavallee, J.M., Leifeld, J., Parton, W.J., Steinweg, J.M., Wallenstein, M.D., Wetterstedt, J.A.M., Bradford, M.A., 2011. Temperature and soil organic matter
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