Aug 28, 2008 - Dynamics of soil respiration in sparse Ulmus pumila woodland under semi-arid climate. Received: 20 January 2008 / Accepted: 26 July 2008 ...
Ecol Res (2009) 24: 731–739 DOI 10.1007/s11284-008-0544-7
O R I GI N A L A R T IC L E
Hong-Mei Jin Æ Osbert Jianxin Sun Æ Zhong-Kui Luo Jin Liu
Dynamics of soil respiration in sparse Ulmus pumila woodland under semi-arid climate
Received: 20 January 2008 / Accepted: 26 July 2008 / Published online: 28 August 2008 The Ecological Society of Japan 2008
Abstract Sparse Ulmus pumila woodlands play an important role in contributing to ecosystem function in semi-arid grassland of northern China. To understand the key attributes of soil carbon cycling in U. pumila woodland, we studied dynamics of soil respiration in the canopy field (i.e., the projected crown cover area) and the open field at locations differing in distance (i.e., at 1– 1.5, 3–4, 10, and >15 m) to tree stems from July through September of 2005, and measured soil biotic factors (e.g., fine root mass, soil microbial biomass, and activity) and abiotic factors [e.g., soil water content (SWC) and organic carbon] in mid-August. Soil respiration was further separated into root component and microbial component at the end of the field measurement in September. Results showed that soil respiration had a significant exponent relationship with soil temperature at 10-cm depth. The temperature sensitivity index of soil respiration, Q10, was lower than the global average of 2.0, and declined significantly (P < 0.05) with distance. The rate of soil respiration was generally greater in the canopy field than in the open field; monthly mean of soil respiration was 305.5–730.8 mg CO2 m2 h1 in the canopy field and 299.6–443.1 mg CO2 m2 h1 in the open field from July through September; basal soil respiration at 10C declined with distance, and varied from 250 mg CO2 m2 h1 near tree stems to 20 cm sandy soil. Some basic information on soil characteristics are listed in Table 1. Vegetation is dominated by sparsely distributed U. pumila trees with understorey consisting of Agropyron cristatum, Carex spp., Leymus chinensis and Stipa krylovii, and in the open field Artemisia intramongolica, A. frigida, Setaria spp. and Xanthium sibiricum are the dominant plant species. U. pumila trees have roots that are highly interwoven below 20 cm of soil layer. Vertically, tree roots within the tree crown cover
Mean air temperature (oC)
25 2005 Mean of 1971-2005 20
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scale (Xu and Qi 2001; Franklin and Mills 2003; Maestre and Cortina 2003; Tang and Baldocchi 2005; Han et al. 2007). Spatial variation of soil respiration at the local scale is often not readily explainable by climatic variables, but is modulated by gradients in soil nutrient and biotic factors. For example, the rate of soil respiration has been found to relate closely to soil organic matter (McCulley et al. 2004) and soil microbial biomass and activity (Borken et al. 2002a; Scott-Denton et al. 2003; McCulley et al. 2004; Wang et al. 2006). Thus variations in soil water content (SWC), temperatures, the type and amount of soil organic matter, and microbial activity may ultimately lead to variations in soil respiration. In recent years, the increasing woody plant abundance in temperate grasslands and tropical savannas has been reported worldwide, especially in areas with arid and semi-arid climate (Grover and Musick 1990; Pugnaire et al. 1996a, 1996b; Brown and Archer 1999; McCulley et al. 2004). This patchy vegetative pattern may promote soil C and N accumulation via ‘fertility islands’ effects (Virginia 1986) and result in increasing spatial heterogeneity in soil respiration (Bolton et al. 1993). The semi-arid grassland of northern China contains patches of Ulmus pumila woodland throughout the broad landscape setting. U. pumila trees have both very deep tap roots and far-reaching lateral roots (Li et al. 2002), and often co-exist with grasses and forbs with shallow rooting depth. Inter-mixing of the sparsely distributed U. pumila trees and patches of herbaceous plants makes the sparse U. pumila woodland more heterogeneous in the spatial features of surface soil water and nutrients than typical grassland (Liu et al. 2003). However, carbon dynamics has been less studied in this ecosystem as compared with other ecosystems. How variable are the soil respiration, and what would be the key drivers of spatio-temporal dynamics of soil respiration within such unique plant communities? Identification of the patterns and drivers of soil respiration dynamics in such structurally complex ecosystems is of great importance for accurately predicting ecosystem carbon balance by using modeling approach. In this study, we investigated the spatio-temporal dynamics of soil respiration and the likely impact of trees in a sparse U. pumila woodland in semi-arid grassland ecosystem of Inner Mongolia, China, by making repeated measurements of soil respiration during main growing season of 2005. We also measured water content, organic carbon, microbial biomass and activity of soils during peak biomass of the local plant communities, and separated the total soil respiration into root and microbial components. The objectives of our study were to (1) examine how much U. pumila trees would affect on spatio-temporal dynamics of soil respiration and (2) assess the key drivers of the dynamics in soil respiration in this tree–grass plant community type. We hypothesized that the existence of U. pumila trees would cause greater variation in soil respiration by facilitating more favorable soil water and nutrient conditions under the canopy field than in the open field.
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Fig. 1 Monthly mean air temperature and precipitation for the period 1971–2005 and in 2005 for Duolun County in Inner Mongolia, China. Vertical bars indicate standard errors of means
733 Table 1 Basic information on soil characteristics in 0–20 cm layer at the study site in a sparse U. pumila woodland of Inner Mongolia, China (n = 6) Location
Bulk density pH (g cm3)
Total N (g kg1)
SOC (g kg1)
C:N ratio
Particle-size distribution (%) >0.25 mm
0.250.1 mm 0.1–0.05 mm 15 m) from the tree stems. For C1 and C2, there were six measurement points at each distance for each tree, which were distributed along six equally spaced directions. Each measurement point was treated as a replicate, hence 18 replications for both C1 and C2 in data analysis. For O1 and O2, only three random measurement points (i.e., three replications) were selected because of the observed homogeneity in ground conditions. Soil respiration was measured with a portable soil CO2 flux system (LI-8100, LI-COR, Lincoln, NE, USA) on pre-installed PVC soil collars over a fortnight period from July through September 2005. Soil temperature at 10-cm depth near each collar was measured simultaneously with a temperature probe attached to LI-8100 for developing plot-specific empirical relationships with soil respiration. Measurements were made every 3 h from 6:00 to 18:00 h (Beijing Standard Time), and the average of 1-day data for each point was used in subsequent data analyses. Total soil respiration was further separated into root (Rr) and microbial (Rh) components following the root separation method of Law et al. (2001) at end of the field experiment in September 2005. Briefly, soils under the sample collars were cored to a depth of 20 cm immediately after making the final set of Rs measurement, and fine roots were hand-picked and measured in a dark chamber for Rr by using LI-8100 soil CO2 flux system. Microbial component of soil respiration was computed as the difference between Rs and Rr. Following the measurements of Rr, root samples were placed in zip-
Soil samples were taken near each soil collar with a cylindrical soil sampler (3.0-cm inner diameter) in midAugust (timing for peak biomass) 2005. Samples in six radial directions at the same distance to the same U. pumila tree stem were mixed as a single composite sample and stored at 0–4C before analysis. In laboratory, fresh soils were processed to pass a 2.0-mm sieve and manually cleaned of visible plant roots and other tissues. A subset of soil samples was used for measurements of SWC and soil organic carbon (SOC), and another for measurements of soil microbial biomass carbon (MBC) and activity based on standard procedures. SWC was measured gravimetrically by oven drying at 105C for 24 h. SOC was determined with dichromate oxidation method. MBC was measured using the chloroform-fumigation extraction method (Vance et al. 1987). Before biochemical analysis, a subset of soil samples was adjusted to 60% of water holding capacity (WHC) and incubated at 25C for a week. Then 25 g (dry weight equivalent) of fumigated and non-fumigated soils, respectively, was extracted with 50 ml 0.5 M K2SO4 solution. A TOC analyzer (Phoenix 8000, Tekmar-Dormann, Cincinnati, OH) was used to determine the organic C (Corg) in the extracts. MBC was calculated as: MBC mg Cmic kg1 dry soil ¼ ðCOF CONF Þ=kec where COF is extracted Corg in fumigated soils, CONF is extracted Corg in non-fumigated soils; kec = 0.45, which is the factor for converting the organic C to MBC. Basal respiration, BR, which represents the microbial activity, was measured by determining CO2 evolution over a 7-day incubation. First, 25 g (dry weight equivalent) of soil was adjusted to 60% of WHC and incubated at 28C for 7 days. Respired CO2 was absorbed in 5-ml 0.1 M NaOH solution suspended inside a Mason jar (Hu and van Bruggen 1997). The NaOH solution containing dissolved CO2 was then titrated with 0.05 M HCl solution to determine the amount of CO2 evolved.
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We calculated the values of MBC/SOC, and determined the metabolic quotient, qCO2, as: qCO2 ¼ BR=MBC
monthly mean of Rs for each soil collar for July, August, and September, respectively, following the method of Campbell et al. (2004). Data analysis
Computation of Q10 and monthly mean of soil respiration Following Van’t Hoff (1898), Rs can be described by an exponential function of Q10 as: ðT 10Þ=10
Rs ¼ Rs10 Q10
where Rs10 is basal soil respiration at 10C, T is soil temperature for a given depth. Q10 can be calculated by solving: Q10 ¼ e10b y ¼ aebx
One-way analysis of variance (ANOVA) was used for evaluating the effects of the distance to the tree stems and measurement time on Q10, Rs10, monthly mean of Rs, SWC, DMFR, SOC, MBC, BR, MBC/SOC and qCO2. Linear regression analyses were used to determine the relationships of Rs with Rr and Rh. These statistical analyses were performed using procedures of SPSS (v. 13.0). Multiple means were compared with LSD test at P = 0.05.
Results
where y represents soil respiration, and x soil temperature. We calculated Q10 for each measurement point (i.e., a soil collar) using all data collected form July through September, and used it as a replication for computing average Q10 at each distance from tree stem. Inverting the exponential function of Van’t Hoff (1898) gives: ðT 10Þ=10
Rs10 ¼ Rs=Q10
We gap-filled Rs for days soil respiration was not measured by using above equations and soil temperature data from a nearby meteorological tower, and computed 1400
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The resulting data showed significant (P < 0.01) and positive exponential relationship of Rs with soil temperature at 10-cm depth (Fig. 2). The value of Q10 declined significantly (P < 0.05) with distance to tree stems (Table 2), but all the values were lower than the global average of 2.0 (Table 2). Evaluation of treatment effects on soil respiration using Rs10 eliminates the influence of incidental temperature at time of measurements. Results showed that Rs10 was greater in the canopy field and declined with distance extending to the open field (Table 2). Monthly mean of Rs generally declined from July through
B
C1 y = 133e0.064x R2 = 0.68
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C2 y = 120e0.059x R2 = 0.85
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Fig. 2 Rate of soil respiration (Rs) as function of soil temperature at 10-cm depth at different distances (C1 1–1.5 m; C2 3–4 m; O1 10 m; O2 >15 m) to the base of three U. pumila trees in a sparse U. pumila woodland of Inner Mongolia, China. Repeated measurements were made from July through September 2005, on 18 points at C1 and C2, respectively (six at each distance for each tree distributed along six equally spaced directions), and on three random points for O1 and O2, respectively
Spatial and temporal variations of soil respiration
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greater than O2; while SOC of C1, C2, and O1 were 48, 35, and 6% greater than O2. Soil biotic factors
Partitioning of soil respiration Rh accounted for more soil respiration than Rr in the canopy field. No significant distance effect was detected in Rr. Rs was significantly (R2 = 0.913; P < 0.001) related with Rh (Fig. 3b), and only marginally (R2 = 0.18; P = 0.084) with Rr (Fig. 3a).
Soil abiotic factors Both SWC and SOC were significantly (P < 0.05) greater in the canopy field than in the open field (Fig. 4A, B), and there were significant (P < 0.05) differences in SOC between the two distances within the canopy field (i.e., C1 vs. C2) and between those in the canopy field (C1 and C2) and in the open field (O1 and O2) (Fig. 4B). SWC of C1, C2 and O1 were 82, 69, 24%
There was no significant distance effect on DMFR (Fig. 4C). MBC, BR and MBC/SOC varied significantly (P < 0.05) with distance, and both displayed a declining trend away from tree stems (Fig. 4d, e). The values of MBC for C1, C2, and O1 were 162, 148, and 69% greater than for O2; BR for C1, C2, and O1 were 78, 61, and 17% greater than for O2; and MBC/SOC for C1, C2 and O1 were 77, 84, and 60% greater than for O2 (Fig. 4F). qCO2 was significantly (P > 0.05) greater only at the most distant location in the open field (Fig. 4G).
Discussion It has been widely demonstrated that the relationship between soil respiration and soil temperature can be best
Table 2 Monthly mean of incidental soil respiration (Rs), soil respiration normalized to 10C (Rs10), and Q10 at different distances (C1 1– 1.5 m; C2 3–4 m; O1 10 m; O2 >15 m) to the U. pumila tree stems in a sparse U. pumila woodland of Inner Mongolia, China Distance category
Month
Rs (mg CO2 m2 h1)
Rs10 (mg CO2 m2 h1)
Q10
C1
July August September July August September July August September July August September
730.8 588.3 308.5 695.7 491.9 305.5 443.1 383.5 350.6 389.4 340.3 299.6
252.3 260.7 255.2 214.3 212.1 215.6 190.1 191.3 191.7 199.9 198.8 186.9
1.93 ± 0.03a
C2 O1 O2
± ± ± ± ± ± ± ± ± ± ± ±
5.1A, a 25.7B, a 17.3C, a 36.1A, a 30.6B, b 19.6C, a 15.5A, b 4.1B, c 12.2B, a 9.7A, b 8.7B, c 7.9C, b
± ± ± ± ± ± ± ± ± ± ± ±
4.2a 4.3a 4.4a 2.3b 2.1b 2.6b 2.5c 2.9c 2.8c 4.0c 5.1c 3.8c
1.86 ± 0.05ab 1.71 ± 0b 1.46 ± 0.03c
Repeated measurements were made on 18 points for C1 and C2, respectively (six at each distance for each tree distributed along six equally spaced directions), and on three random points for O1 and O2, respectively. Values shown are means ± standard errors. Uppercase letters indicate separation of means between months within locations, and lowercase letters between locations within months, respectively, both at P < 0.05
800
Rs (mg CO 2 m -2 h -1 )
Fig. 3 Relationship of soil respiration (Rs) with root (Rr; A) and microbial respiration (Rh; B) in a sparse U. pumila woodland of Inner Mongolia, China. Measurements were made in September 2005, on 18 points at 1–1.5 m (C1) and 3–4 m (C2), respectively, to the base of three U. pumila trees (six at each distance for each tree along six equally spaced directions), and on three random points at 10 m (O1) and >15 m (O2), respectively
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R = 0.182
R = 0.913
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Fig. 4 Changes of biotic and abiotic soil variables with distance to U. pumila tree stems in a sparse U. pumila woodland of Inner Mongolia, China. Vertical bars indicate standard errors of means (n = 3, see ‘‘Materials and methods’’ for explanation). Histogram bars designated by the same letter are not significantly different at P < 0.05. (SWC gravimetric soil water content, SOC soil organic carbon, DMFR fine root dry mass, MBC microbial biomass carbon, BR basal rate, or microbial component, of soil respiration, MBC/SOC ratio of microbial carbon over soil organic carbon, qCO2 metabolic quotient of CO2 evolution)
Distance to tree stem
described by an exponential equation (e.g., Raich and Potter 1995; Davidson et al. 1998; Frank et al. 2002; Wu et al. 2006). Results from our study also supported this exponential relationship. Q10 was found to vary with distance to tree stems in our study in a sparse U. pumila woodland, and its range of 1.46–1.90 was below the global average value of 2.0 and other studies under the same climate (e.g., 2.53 reported by Xiao et al. 2007). This finding has significant implications for more accurately modeling soil carbon dynamics of the region as different Q10 values can result in different predictions of soil carbon dynamics and storage (Hashimoto 2005). We normalized soil respiration to 10C with the exponential equation in order to eliminate the influence of the incidental measurement soil temperatures. Our results revealed that the existence of the U. pumila trees
could cause spatial variability in soil respiration, independent of the effect of soil temperature by shading. The basal soil respiration at 10C, Rs10, decreased with distance to trees and the values in the canopy field were found to be significantly greater than those in the open field during the growing season. The spatial variability in soil respiration has been described in a series of ecosystems, such as forest ecosystem (Wiseman and Seiler 2004), grassland ecosystem (Kieft et al. 1998; Maestre and Cortina 2003; McCulley et al. 2004), and crop field (Han et al. 2007). The patchy vegetative cover of sparse U. pumila woodland is similar with the woody plant encroached grassland (such as shrublands), and the spatial variation of soil respiration within the site of U. pumila woodland is revelatory to other heterogeneous sites.
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The monthly mean of soil respiration in the canopy field of U. pumila trees, i.e., 305.5–730.8 mg CO2 m2 h1 in our study, were much higher than the semi-arid temperature grasslands of China (i.e., 297–331 CO2 m2 h1; Wang et al. 2004) under the same climate and the world’s major temperate grasslands (55.3–347 mg CO2 m2 h1; Raich and Schlesinger 1992). Soil respiration in the canopy field of the U. pumila trees was similar with that in forest ecosystems (Buchmann 2000; Borken et al. 2006). Within the U. pumila woodland, soil respiration in the canopy field but closer to U. pumila trees (i.e., 350–443 mg CO2 m2 h1) appears to be much greater than the semi-arid temperate grasslands of China and the world’s major temperate grasslands. Apparently, the presence of sparse U. pumila woodland in the semi-arid grassland has played an important role in enhancing soil respiration. Soil respiration is a total process of heterotrophic respiration (Rh; mainly the soil C decomposition by microorganisms) and autotrophic respiration (Rr; mainly the root respiration). Although soil temperature and water are considered the most influential environmental factors controlling the temporal variation of soil respiration (Wiseman and Seiler 2004; Han et al. 2007), climatic variables are often not to explain the small-scale spatial variations of soil respiration. The spatial variations in soil abiotic and biotic factors should be taken into account (Maestre and Cortina 2003). Variations in SWC, temperatures, the type and content of soil organic matter, soil microbial biomass and activity and interactions with those factors may affect the productivity of roots and the decomposition rate of soil organic matter by microorganisms, ultimately lead to variations in soil respiration (Wiseman and Seiler 2004; Yan et al. 2006). We found microbial respiration could explain >90% of variance in soil respiration; whereas the relationship of soil respiration with root respiration was weak. Moreover, root biomass did not vary with distance to U. pumila tree stems, indicating that microbial respiration played more important roles in driving the spatial variability of soil respiration than roots in U. pumila woodland. Many studies have demonstrated that under arid climate, trees or shrubs play active roles for sustaining the activities of shallow-rooted plants and associated microbial communities through naturally occurring hydraulic redistribution (Querejeta et al. 2007). The U. pumila trees used in our study were observed to possess the ability of hydraulic redistribution and reversed water-flow could occur in lateral roots. This might be a partial explanation for greater soil respiration under the canopy field than in the open field. The higher SWC under U. pumila trees supports the possibility of hydraulic lift. However, other factors may contribute to the observed pattern of soil respiration on our study site. Better nutrient availability and greater decomposition in terms of ‘‘fertility island’’ effect (Reynolds et al. 1999; Liu et al. 2003) may offer additional explanations.
Our results also showed greater microbial biomass and activity in the canopy field of U. pumila trees than in the open field. More available C for microbial biomass production (i.e., greater MBC/SOC values; Anderson and Domsch 1989) and lower maintenance energy requirement for the growth of microbial community (i.e., lower qCO2; Anderson and Domsch 1990) indicated that substrate C efficiency of microorganisms was higher in the canopy field than in the open field. It means that the crown of U. pumila trees could provide a more favorable microhabitat (e.g., soil water, nutrient and microorganisms) in arid and semiarid areas. Temporally, although the rates of soil respiration varied significantly with time, the basal soil temperature at 10C did not change, suggesting that the temporal dynamics of soil respiration in this sparse U. pumila woodland is predominantly controlled by temperature. The complexity of stand structure due to presence of U. pumila trees may further affect the spatial variability of soil respiration by causing patchiness in thermal conditions of the ground surface. A better understanding of the effects of heterogeneous patches on belowground carbon cycling in semi-arid grassland could help to reduce uncertainties in predictions of C balance in grassland ecosystems. This research was partially supported by the National Natural Science Foundation of China (30470292, 30521002, and 40741006). We thank the Duolun Restoration Ecology Experimentation and Demonstration Station for logistic and technical support to our study and Dr. Meizhen Liu for help with setting up the experiment. We are grateful to two anonymous reviewers for constructive comments over an earlier version of the manuscript.
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