Enzyme assays conducted on soil samples collected from the open top chamber in this study.
1. Introduction
Owing to fossil fuel combustion, deforestation, and intense agriculture, the concentrations of atmospheric CO2 [CO2] has risen by 100ppm since the mid 1800s [1], and it has been predicted to double until the end of this century compared to the pre-industrial value [2]. Numerous studies have shown a greater biomass gain of plants, higher fine root and leaf litter C/N in some species under elevated CO2 condition [3−7]. Moreover, the rising CO2 also could alter litter chemistry (e.g., total N, lignin and starch content) and fine root turnover. Because microbial growth is limited by the type and amount of organic substrates entering the soil [8, 9], the changes in above- and below-ground plant input under elevated CO2 could potentially alter both the substrate availability and microbial activity. Although the effect of elevated CO2 via plants on soil microorganisms has been few studies investigated [10−12], the detailed plant-mediated effects still are unclear because of the complexity of microbial processes.
Soil microorganisms play an important role in nutrient cycling, CO2 emission and in formation of soil total organic carbon (TOC) pool. Therefore, any effect of the rising [CO2] on soil microorganisms might in turn feedback on the response of terrestrial ecosystem to atmospheric CO2 and the sequestration of extra carbon [9]. Soil enzymes drive soil organic matter decomposition and nutrient transformations. Soil enzyme also was considered as a sensitive indicator, which could be significantly affected by temporal variability [13]. It is evident that the seasonal patterns of temperature and moisture of north temperate ecosystems can affect the activity of soil enzymes [14]. Although several studies have investigated the effects of increased CO2 on the soil microbial biomass and activity, to our knowledge, only relative few studies have measured the seasonal fluctuations of microbial biomass and soil enzyme activity under higher CO2 levels [10,11]. At the Oak Ridge FACE site, Sinsabaugh
Temperate forest ecosystems, which occupy much of the earth’s terrestrial surface area, have been considered as the most important C sink for sequestering the increasing atmospheric CO2 [15]. To understand elevated CO2 effects on temperate forest ecosystems, a growing number of free air CO2 enrichment (FACE) and open-top chamber (OTC) research project have been initiated throughout the world. In 1998, we began a long term CO2-enrichment experiment using open top chamber (OTC) growing three species of trees, Korean pine (
The objectives of this study were (a) to investigate effects of elevated CO2 on microbial biomass C/N and the variations in activities of various enzymes throughout the growing season, and (b) to compare the CO2 response of activities of C, N and P cycling related enzymes in the soils under three different tree species.
2. Materials and methods
The experimental fields were located at Changbai Mountain in Jilin province, northeastern China (42º24´N, 128º06´E, and 738 m elevation). The soil is a dark-brown soil developed from volcanic ash. The topography is basaltic mesa, and the parent rock is loose volcanic ash sand. The mechanical composition of the soil is approximately 29% sand (20
Soil samples were collected seven times: May, June, August and September in 2006, and May, July and September in 2007. At each sampling date, five soil cores (3 cm in diameter and 0–10 cm at deep) were collected within each chamber. The pooled samples were homogenized and roots removed by passing the soil through a 2-mm sieve. The samples used for measurements of soil enzyme activity were kept frozen (-70°C) and microbial biomass were kept cool (4°C) until analysis within 1 week after sampling.
Subsamples of soil were dried at 105°C for 12 h to determine gravimetric water content. Soil pH was measured in solutions of 50 ml water and 10 g air-dry soil. Microbial biomasses C (Cmin) and N (Nmic) were measured by fumigation-extraction method [20]. Subsamples of sieved soil were fumigated with alcohol-free CHCl3 for 24 h, and then extracted with 0.5 M K2SO4 solution. The K2SO4 soil extract was analysed for total dissolved organic C (DOC) and total dissolved N (TDN) using a Total Organic Carbon Analyzer (multi N/C 3000, Jena, Germany). Microbial biomass C and N were calculated as differences in extractable DOC and TDN between fumigated and unfumigated soils using a correction factor (Kc) of 0.38 for Cmin and 0.54 for Nmic [20, 21].
Enzyme activities were determined following Freeman
1,4-β-glucosidase | bG | 3.2.1.21 | 4-MUB-β-D-glucoside |
1,4-α-glucosidase | aG | 3.2.1.20 | 4-MUB-α-D-glucoside |
1,4-β-Acetylglucosaminidase | NAG (1,4-β-NAG) | 3.1.6.1 | 4-MUB- |
Cellobiohydrolase | CBH | 3.2.1.91 | 4-MUB-β-D-cellobioside |
1,4-β-xylosidase | bX | 3.2.1.37 | 4-MUB-β-D-xyloside |
Phosphatase | PA | 3.1.3.2 | 4-MUB-phosphate |
Phenol oxidase | PPO | 1.10.3.2 | L-3,4-Dihydroxyphenylalanine |
Peroxidase | PO | 1.11.17 | L-3,4-Dihydroxyphenylalanine |
EC: enzyme commission number; MUB: 4-methylumbelliferyl; Abbreviation: used in this article |
For the phenol oxidase and peroxidase activities assay, l-3,4-dihydroxyphenylalanine (L-DOPA) was used as substrate [24]. We prepared soil slurry solutions of 5.0 g soil in 100 ml of 50 mmol l-1 acetate buffer (pH 5.0.) The reaction mixture for the phenol oxidase assay, containing 2 ml, 5 mM l-3,4-dihydroxyphenylalanine (L-DOPA) solution and 2 ml of soil slurry in 5 ml tube, was vortexed for exactly 60 min at 20°C in a shaking incubator and was centrifuged for 5 min at 4°C (6000 r.p.m.). The absorbance of the filtrate was read at 460 nm. For peroxidase activities assay, at the beginning of the incubation, were processed in the same way as phenol oxidase, with L-DOPA substrate and the addition of 200 μl of 0.3 % H2O2 [25]. Phenol oxidase activity was subtracted from peroxidase activity to calculate the net peroxidase activities.
Data were analyzed using repeated measures analysis of variance (RM-ANOVA) with CO2 treatment, plant species and their interaction as explaining variables. Prior to analysis, the data was checked for heterogeneity of variance, and when necessary, the variable was transformed to improve normality. RM-ANOVA analyses were performed with SPSS 13.0 statistical software package (SPSS Inc.). Correlation analysis was used to test for correlations between microbial biomass C, N and soil enzyme evaluated as significant at
3. Results
Elevated CO2 significantly decreased the activities of 1,4-β-NAG, 1,4-β-xylosidase, phosphatase, 1,4-β-glucosedase, and phenol oxidase in soil under Changbai pine in 2007 (Fig. 1). On all sampling dates, there was no CO2 main effect on soil enzyme activity across three tree species (Table 2). Time was a significant factor affecting the activities of hydrolotic enzymes and the two oxidase enzymes in soil (Fig. 1, Table 2). The activity of 1,4-β-NAG and phenol oxidase over the two years for all sample dates showed an interaction between tree species and CO2 (
Tests of Between-Subjects Effects | ||||||||||
CO2 | 2.69NS | 0.70 NS | 4.06 NS | 0.63 NS | 0.06 NS | 2.59 NS | 1.75 NS | <0.01 NS | ||
Species | 10.97** | 4.03 NS | 35.05*** | 2.45 NS | 120.13*** | 32.09*** | 88.93*** | 410.14*** | ||
Species ×CO2 | 1.90NS | 1.04 NS | 5.28* | 2.10 NS | 0.39 NS | 2.94 NS | 6.69* | 3.72 NS | ||
Tests of Within-Subjects Effects | ||||||||||
Time | 8.89*** | 2.92* | 1.90 NS | 4.11** | 15.19*** | 5.85*** | 12.65*** | 37.86*** | ||
Time × Species | 7.43*** | 2.97* | 5.84*** | 2.97** | 7.33*** | 5.06*** | 10.88*** | 33.40*** | ||
Time × CO2 | 1.99 NS | 0.85 NS | 2.02 NS | 2.53* | 0.82 NS | 1.52 NS | 6.72*** | 6.02*** | ||
Time ×species × CO2 | 5.69*** | 1.16 NS | 2.66* | 3.21** | 0.60 NS | 2.19* | 1.91* | 5.03*** | ||
Note: |
Correlation analysis indicated that soil moisture was positively correlated with 1,4-α-glucosidase. The TDN content under Korean pine was negatively correlated with DOC (r = -0.6166,
Mean enzyme activity decreased from highest to lowest in the following order: PPO > AP > βG > βX > CBH > NAG > PO > αG in the Korean pine soil. Mean enzyme activity in the Changbai pine soil decreased from highest to lowest in a very similar order with Korean pine: PPO > AP > βG > βX > NAG > CBH > PO > αG, therefore, tree species is a important factor influencing soil enzyme activities, there were significant interactive effects between species and sampling time.
Soil pH was slightly acidic (5.4–6.1) and did not vary significantly between treatment or sampling dates, and varied among species. Multivariate statistics were used to assess the functional diversity and temporal vary of the soil microbial enzyme. In the principal component analysis of the data of all eight enzyme activities, PC1 and PC2 explained 20.25% and 34.73% of the total variance, respectively (Fig. 2). The first factor (PC1) appears to be associated with labile nutrient acquisition, the second factor (PC2) appears to be associated with lignocelluloses degradation. For the canonical discriminant analysis of the soils under three trees sampled in 2006 and 2007, the following variables were determined: 1,4-β-xylosidase, 1,4-α-glucosidase, 1,4-β-NAG, cellobiohydrolase, 1,4-β-xylosidase, phosphatase, phenol oxidase, peroxidase. Canonical discriminant analysis also showed that the effect of the tree species on soil enzymes during the two experimental years (Fig. 3). Root 1 seems to discriminate mostly between pines and oak (means of the canonical variables: -2.005 and 4.010, respectively). In the vertical direction (Root 2), seems to discriminate the two pines (means of the canonical variables: Korean pine -1.005, Changbai pine 1.264). Correlation analysis indicated that soil moisture was positively correlated strongly with 1,4-α-glucosidase, microbial biomass C and N across three species of tree (Table 3). Peroxidase activity showed strong correlations with Nmic (r = 0.54,
4. Discussioin
Elevated atmospheric CO2 can lead to an increase in the size of the substrate pool utilized by soil microbes and to stimulate the activities of soil enzyme [26]. For example, Larson
1,4-β-NAG is one of the enzymes regulating nitrogen availability in soil, the activity is often used as an indicator of N demand by microbes. Its enhancement under elevated CO2 (av. +18%) in our study of Korean pine reflected a microbial demand for N nutrient. It is interesting to note that the activities of cellobiohydrolase, 1,4-β-NAG, 1,4-β-xylosidase, phenol oxidase and microbial biomass were decreased significantly in soil of Changbai pine under elevated CO2 in summer 2007, which can be observed also from mean activity of the enzymes and microbial biomass in 2007. Ebersberger
The ecophysiological responses of the three species to elevated CO2 were significant different (Zhou and Han, unpublished data). The fast-growing Changbai pine tends to show larger growth increases under elevated CO2 than the slow-growing Korean pine, but the biomass was stored in stem and branch. Many studies showed that above-ground cover plant could determine the composition of the soil microbial community structure and function [33 −35]. In our study, discrimination between the pines and the oak was observed (Fig. 3), but these results must be careful explained, because of variation in duration of elevated CO2 between the pines and oak. Changbai pine tends to possess characteristics promoting rapid growth generally associated with high competitive ability. This can explain why elevated CO2 caused a decrease in microbial biomass and extracellular enzyme activity under Changbai pine (Fig. 1), due to the increase in competition for mineral nutrients with microorganisms. We speculate that fast-growing plant may be strongly affect soil N and P cycling compared to the slow-growing plant under elevated CO2 condition, contributing disadvantage to soil microbial community as well as significant declining the activities of microbial enzyme. These results also imply that tree species might also differ in their influence on soil microbial activity that in turn affects soil properties under elevated atmospheric CO2 concentration.
Due to Korean pine has a slower growth rate and related physiological characteristics, the tree shows a narrower response to resource levels; whereas Changbai pine has a faster growth rate related to Korean pine shows a broad response to N and P nutrient. Hungate
Temporal fluctuation of soil moisture, soil temperature, and C input from tree roots, rhizosphere products (e.g., root exudates), and tree residues can have large effects on soil microbial biomass and activity [38]. Most of the enzymes measured in our study (table 2), were significantly affected by the temporal variability, and the interaction between time and CO2 level significantly influenced CHB, phenol oxidase and peroxidase activities across the three species. Our data also revealed a strong positive correlation between soil moisture and microbial biomass or soil enzyme activity (Table 3), especially the activity of 1,4-α-glucosidase that correlated with soil moisture independent on tree species. These results are consistent with Devi and Yadava (2006), who reported that microbial biomass showed a positive significant correlation with soil moisture [39]. Soil moisture may be a master variable controlling microbial biomass and mineralization of starch (catalyzed by 1,4-α-glucosidase) in this temperate volcanic soil (Table 3).
1,4-β-glucosidase | 0.0993 |
|
0.3779 | |||
1,4-α-glucosidase |
|
|
|
|||
1,4-β-NAGase | 0.0551 | 0.3233 | 0.5305 | |||
Cellobiohydrolase | 0.4239 | 0.3061 |
|
|||
1,4-β-xylosidase | 0.2733 | 0.2320 |
|
|||
Phosphatase |
|
0.1812 | 0.3672 | |||
Phenol oxidase | 0.4411 | 0.2139 | 0.2976 | |||
Peroxidase | 0.2568 | -0.2690 | -0.1960 | |||
Microbial biomass C | 0.5843 |
|
0.5018 | |||
Microbial biomass N | 0.4207 |
|
0.6033 | |||
Levels of significance of the coefficients are indicated for P < 0.05* or P < 0.01**. |
Tree growth often stimulates an increase in the size of microbial biomass during the growing season [40, 41]. The seasonal variations in 1,4-β-glucosidase of Changbai pine we observed were consistent with the seasonal variations in 1,4-α-glucosidase, 1,4-β-NAG, and cellobiohydrodase activity (e.g., greatest activity in spring), which were in agreement with a report of study in a oak forest in USA [42]. Seasonal fluctuations of these enzymes might be strongly influenced by life cycle of Changbai pine. Because spring and early summer is the fast-growing period of the pine, the developing root system could provide enough of easily mineralizable substrate to microorganisms and exhibited a positive effect on their activities. So for Changbai pine, the peak activity of soil microbial community was detected in spring. Seasonal patterns of soil enzyme were controlled by growth also observed from oak, which growth began from mid of May to end of October. This indicates that the activity of soil enzyme may be concomitantly controlled by plant seasonal growth.
In addition, differently seasonal pattern of identical enzyme from elevated and ambient CO2 was also found, e.g., cellobiohydrolase from Korean pine, 1,4-β-NAG from Changbai pine, 1,4-β-glucosidase and 1,4-β-NAG from oak. This may be due to soil extracellular enzymes can be either induced by the substrate [43], which are often altered by elevated CO2 [44], and influenced by soil moisture, or controlled by combine effect of there factors and/or other environmental factors [13], e.g., tree species, duration of CO2 enrichment. Differences between the two coniferous pine and the broadleaf oak results from OTC may be best accounted for by the different seasonal patterns of soil microbial biomass and enzyme in respond to tree species and elevated CO2. So our results confirm the importance of taking into account the seasonal variation of biochemical parameters when these are used as indicators of soil ecosystem in response to elevated CO2.
In conclusion, seasonal variations are the factor mostly affecting soil biological properties and nutrients availability in Changbai mountain forest ecosystem. Long-term exposure of elevated CO2 can alter microbial biomass and the production of enzymes, but the effects are always detectable at specific times and are closely linked to plant processes, soil moisture and aboveground vegetations. Hence, a single sampling of the soil may not fully reveal its response to prolonged elevated atmospheric CO2. We proposed that it is imperative to assess microbial function for soil ecosystem with one or two year-round sampling regime.
Acknowledgments
We thank XiuXiu Wang, ShiFu Meng, LiHua Niu and GuoZheng Song for their assistance in the open top chamber, and LiHua Xin for help with soil biochemical property analysis. This research was supported by the National Natural Science Foundation of China (NSFC).
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