Soil microbial community composition under Eucalyptus plantations of different age in subtropical China

Soil microbial community composition under Eucalyptus plantations of different age in subtropical China

European Journal of Soil Biology 46 (2010) 128e135 Contents lists available at ScienceDirect European Journal of Soil Biology journal homepage: http...

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European Journal of Soil Biology 46 (2010) 128e135

Contents lists available at ScienceDirect

European Journal of Soil Biology journal homepage: http://www.elsevier.com/locate/ejsobi

Original article

Soil microbial community composition under Eucalyptus plantations of different age in subtropical China Yusong Cao a, Shenglei Fu a, Xiaoming Zou b, Honglin Cao a, Yuanhu Shao a, Lixia Zhou a, * a b

South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Guangzhou 510650, China Institute for Tropical Ecosystem Studies, University of Puerto Rico, PO Box 363682, San Juan, PR 00936, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 July 2009 Received in revised form 11 December 2009 Accepted 15 December 2009 Available online 29 December 2009 Handling editor: Christoph Tebbe

Eucalyptus is one of the fastest growing woody plants in the world, but few studies have reported the soil microbial community composition in Eucalyptus ecosystems. This study investigated the soil microbial communities in plantations of 3-, 7-, 10- and 13-year-old Eucalyptus in subtropical China based on phospholipid fatty acids (PLFA) analysis. The variation in soil microbial biomass and community compositions were influenced by sampling site and season and the interaction of both, which were consistent with the variation in soil total nitrogen (TN), soil organic carbon (SOC) and soil moisture. The number and abundances of PLFAs, and the amount of soil TN and SOC were higher in plantation of 13year-old Eucalyptus than those in other younger plantations, suggesting that the soil properties and the soil microbial community composition is not negatively affected by the planting of Eucalyptus. The ratio of monounsaturated-to-branched fatty acids, the proportional abundance (mol%) of bacterial PLFA and fungal PLFA varied significantly with Eucalyptus plantations of different age, suggesting that the individual PLFA signatures might be sensitive indicators of soil properties associated with forest plantations. Ó 2010 Elsevier Masson SAS. All rights reserved.

Keywords: Eucalyptus Forest plantation Microbial community Soil properties Subtropical China

1. Introduction Soil microorganisms represent essential components of forest ecosystems. In fact, the change in soil microbial community composition can be among the earliest indicators of soil quality on many ecosystem processes [9,18]. The quantitative description of soil microbial communities by phospholipid fatty acids (PLFA) analysis is a good approach for compositional descriptions because it is independent of cultivation and it encompasses thereby their major constituents [45]. Both microbial biomass and community composition are sensitive to belowground profile which often correlates with the soil organic matter content [1,12,33]. Typically, sites with lower fertility have lower soil microbial biomass compared to those with higher fertility [25]. However, microbial communities are also influenced by different environmental variables such as soil salinity, or other factors which change during ecosystem development [1,33,48]. Eucalyptus is one of the fastest growing woody plants in the world. It adapts well to differences in climate, soil type and water regime [27,34]. Consequently, the cultivation of Eucalyptus species for pulpwood production has received great attention in a wide * Corresponding author. Tel.: þ86 20 37252977; fax: þ86 20 37252592. E-mail address: [email protected] (L. Zhou). 1164-5563/$ e see front matter Ó 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejsobi.2009.12.006

range of tropical and subtropical areas [2,11,27]. However, the planting of Eucalyptus is still a controversial issue and generates criticism concerning understory plant diversity, soil fertility and soil biodiversity. Soil under Eucalyptus plantations is considered to be degraded due to nutrient depletion and plant diversity reduction [31]. Eucalyptus commonly produces litter with low nutrient concentrations which decomposes slowly especially at the early stage of the litter decomposition [16], and promotes undesirable soil biochemical changes [2,15]. Therefore, understanding the potential changes in soil quality is important for the sustainable management and restoration of Eucalyptus plantation soils. However, the studies on the soil microbial community composition associated with Eucalyptus are not well documented [25,29]. Eucalyptus was introduced to China in the late 19th century, and the plantation area in subtropical China reached about 1.7 million ha [43]. Studies have been conducted on plant community composition, plant growth, water utilization and soil properties of Eucalyptus plantations [8,22]. However, little attention was paid on the structure of the soil microbial community. The objectives of our study were: (1) to analyze the composition of the soil microbial community in Eucalyptus plantations of different ages, and (2) to analyze the relationships between the microbial community and specific soil properties, i.e. soil moisture, soil pH, soil organic carbon and total nitrogen.

Y. Cao et al. / European Journal of Soil Biology 46 (2010) 128e135

2. Materials and methods 2.1. Study area and sampling The experimental site is located in Dongguan county, southern China (22 510 e22 550 N and 113 440 e113 530 E). This area is characterized by a subtropical monsoon climate with an average annual temperature of 22.1  C, and highest and lowest average monthly temperatures of 28.2  C (July) and 13.4  C (January). The average annual rainfall is 1800 mm with 80% between April and September. The soil is Acrisol developed from granite. Eucalyptus plantations are widespread in this area, and the predominant species is Eucalyptus urophylla. The sampling sites were selected in the 3-, 7-, 10-, and 13-year-old Eucalyptus plantations (E3, E7, E10 and E13) for this study. The descriptions of sites are shown in Table 1. Each site was sampled once in the dry season (October 2005) and once in the rainy season (June 2006) except that plantation E3 was only sampled in 2006. At each site, we established four 20  20 m2 sampling plots. Soil samples were taken from randomly selected locations in each plot at two soil depths of 0e5 and 5e10 cm. The litter layers were removed before sampling. Five cores of the same depth from each plot were combined to form one composite sample. Visible roots in the soil samples were picked out as quickly as possible and the soils were stored at 4  C for no longer than 2 weeks before analysis.

129

containing 19:0 as an internal standard, and were analyzed using a Hewlett-Packard 6890 Gas Chromatograph equipped with an Ultra 2-methylpolysiloxane column. A 2 ml injection with a 1:50 split was analyzed at an oven temperature of 260  C, a flame ionization detector temperature of 300  C, and pressure 10.7 PSI at a constant flow rate of 0.4 ml min1. Peaks were identified using bacterial fatty acid standards and MIDI peak identification software (MIDI, Inc., Newark, DE). Concentrations of each PLFA were calculated based on the 19:0 internal standard concentrations. The relative abundance of individual fatty acid was expressed as the proportion (mol %) of the sum of all fatty acids. Gram-positive bacteria were identified by the PLFAs: i14:0, i15:0, i16:0, i17:0, a15:0, a17:0, Gram-negative bacteria were identified by the PLFAs: 14:1u5c, 15:1u6c, 16:1u7c, cy17:0, cy19:0, 15:0 3OH, 16:1 2OH, and non-specific bacteria were identified by the saturated straight-chain PLFAs: 14:0, 15:0, 16:0, 18:0 [6,20,40,46]. The fungi were identified by the PLFA 18:2u6c [13], and PLFAs 16:1u5c were used as a marker for arbuscular mycorrhizal fungi (AMF)[23,30]. A ratio of the sum of monounsaturated fatty acids to the sum of branched fatty acids (14:1u5c, 15:1u6c, 16:1u5c, 16:1u7c, 18:1u9c/i14:0, i15:0, i16:0, i17:0, a15:0, a17:0, 15:0 3OH, 16:1 2OH) was used to indicate the relative ratio of aerobic to anaerobic organisms [6], and the ratio of fungal-to-bacterial PLFAs (18:2u6c/ i14:0, i15:0, a15:0, 15:0, i16:0, i17:0, a17:0, 14:1u5c, 15:1u6c, 16:1u7c, cy17:0, cy19:0) was used as an indicator of changes in the relative abundance of these two microbial groups [4,5].

2.2. Soil analysis 2.3. Data analysis Soil moisture was determined by oven-drying fresh soil at 105  C to a constant weight. Soil pH was measured from soilewater suspension (1:2.5 v:v) with a digital pH meter. Soil organic carbon (SOC) was determined by the dichromate oxidation and total N (TN) was measured with an ultraviolet spectrophotometer after Kjeldahl digestion [21]. Soil microbial biomass carbon (MBC) was measured using Chloroform FumigationeExtraction method (CFE) [38]. 20 g dryweight-equivalent soil samples were fumigated with CHCl3 for 48 h at 25  C, and then the fumigated and un-fumigated samples were extracted with 0.5 M K2SO4 after shaking for 30 min with a reciprocal shaker. Extracts were filtered and dissolved organic C was measured by a total organic carbon analyzer (TOC-VCSH, Shimadzu Corp., Japan). MBC was calculated from the difference of extractable organic C in the fumigated and un-fumigated soil samples using a Kc of 0.45 [42]. Phospholipid Fatty Acids (PLFA) analysis was conducted using the method described by Bossio and Scow [5]. Lipids were extracted from 8 g of dry-weight-equivalent fresh soil using a chloroform : methanol : phosphate buffer (1:2:0.8). Phospholipids were then split into neutral, glyco- and phospho- lipids using solid-phase extraction columns by eluting with CHCl3, acetone and methanol, respectively. Subsequently phospholipids were subjected to a mild-alkali methanolysis to recover fatty acid methyl esters. Samples were then re-dissolved in 200 ml hexane

For the measurements of soil microbial biomass, soil moisture, soil pH, TN, SOC and the ratio of soil organic carbon to soil total nitrogen (C/N), analysis of variance was conducted to test for the effects of sampling site, season and soil depth as well as possible interactions. Before analysis, data were natural-log transformed where necessary to improve normality and homogeneity of variance. Significant differences were set as p < 0.05. Analysis for all data was carried out using the SAS software (SAS Institute Inc., Cary, NC, USA) (ANOVA and GLM procedures). When the effects were significant, they were further analyzed using Tukey multiple comparison test (HSD). Additionally, correlations between soil properties and microbial variables were determined using the Pearson correlation coefficients. Microbial biomass was calculated as the sum of the individual PLFAs (n mol g1 soil). The composition of the soil microbial community was summarized using a principle component analysis (PCA) on the relative abundances (mol %) of PLFAs in each sample. PCA was conducted using CANOCO software (Microcomputer Power, Inc., Ithaca, NY). Redundancy Analysis (RDA) as a direct ordination technique based on PCA, was used to test specific hypotheses about the relationships between soil properties and microbial community composition. Soil properties were tested for significant contribution to the explanation of the variation in the PLFA data with the Monte Carlo permutation test (p < 0.05). Soil

Table 1 Location and aboveground characteristics of the sites of this study. Site

Slope

Undergrowth vegetation in Eucalyptus

Coverage (%)

3 7

50 60

SW 25 SE 30

45 70

E10

10

80

SE 35

E13

13

280

SW 25

Herbage Castanopsis fissa, Litsea glutinosa, Aporosa yunnanensis, Schefflera octophylla, Litsea cubeba Evodia meliifolia, Litsea glutinosa, Aporosa yunnanensis, Schefflera octophylla, Litsea cubeba Evodia meliifolia, Cleyera japonica, Schefflera octophylla, Litsea glutinosa, Aporosa yunnanensis, Litsea cubeba, Mallotus paniculatus, Liquidambar formosana, Acronychia pedunculata

E3 E7

Forest ages (a)

Elevation (m)

80 90

4.5 4.4 4.4 4.5

25.3 23.1 15.2 14.8

   

1.0 0.5 0.4 0.3

1.1 1.1 1.3 1.1

   

0.2 0.1 0.0 0.1

20.8 14.2 17.1 13.1

   

0.7 1.0 0.6 1.4

19.1  2.9 13.6  0.7 13.2  0.2 12.3  0.4

0e5 5e10 0e5 5e10

4.3 4.4 4.2 4.3

27.0 21.5 20.9 18.4

   

1.2 0.5 0.7 0.7

1.3 1.2 2.4 1.8

   

0.1 0.1 0.2 0.1

24.3 15.9 26.3 22.8

   

0.9 0.7 0.6 0.7

19.2 13.8 11.2 12.3

E10

Rainy Dry

E13

Rainy Dry

   

3.8 1.8 1.2 1.0

2.0 0.9 0.4 0.8

a

Values are means  standard errors for the sampling plots (n ¼ 4). TN, SOC and C/N refer to soil total nitrogen, soil organic carbon and the ratio of soil organic carbon to soil total nitrogen, respectively. b

properties that were significantly correlated with factors in the RDA were stressed in the plots. Soil properties are represented by vectors. Vectors of greater magnitude and forming smaller angles with an axis are more strongly correlated with that axis [48]. 3. Results 3.1. Soil properties The soil pH, soil moisture, TN, SOC and C/N are given in Table 2. Analysis of variance (p < 0.05) was conducted on the combined data showed sampling site to be the main effects controlling of soil properties (Table 3). Though the interactions of soil depth, sampling season and site were not significant for soil properties, the interactions between season and site (p < 0.01) or season and depth (p < 0.05) were significant for TN and SOC (Table 3). Seasonal effect on soil moisture, TN and C/N were significant, while SOC was significantly higher in the 0e5 cm soil layer (from 17.08 g kg1 to 26.27 g kg1) than in the 5e10 cm soil layer (from 13.08 g kg1 to 22.81 g kg1). Relative to higher soil moisture in the rainy season, TN was higher in the dry season than in the rainy season (p < 0.05). However, TN and SOC were both greater in E13 than in other sites in the corresponding season or depth (Table 2). 3.2. Soil microbial biomass and structural diversity Differences for soil microbial biomass carbon (MBC) and total PLFA (TotPLFA) are shown in Fig. 1. MBC and PLFA profiles were not significantly different between soil depth and season, but the differences among sampling sites were significant when all data considered together (p < 0.05, site by season interaction, p < 0.01) (Table 3). The pattern of variation of TotPLFA was positively correlated with MBC (TotPLFA ¼ 0.6243 MBC þ 127.54, R2 ¼ 0.473, p < 0.01), and was affected by soil TN and SOC (p < 0.05) (Table 4). Soil pH and TotPLFA were positively correlated in the rainy season but negatively correlated in the dry season though we did not find significant correlations between the two when all data considered together (Table 4). The relative abundances of the gram-negative bacteria, grampositive bacteria, saturated fatty acids, fungal PLFAs, AMF PLFAs and the ratios of fungal-to-bacterial PLFAs are shown in Fig. 2. The proportional abundance of bacterial PLFA were affected significantly by season and sampling site (p < 0.05), but not by soil depth (Table 3). The mean abundances of bacterial PLFAs and the

Degrees of freedom: df ¼ 3 for Site, df ¼ 1 for Season and Depth. MBC: microbial biomass carbon.

0e5 5e10 0e5 5e10

Dry

   

a

21.6 13.4 14.3 14.7

b

3.5 1.1 4.8 2.0

(1.44)

   

(2.91) (0.26) (0.66)

21.3 15.7 22.5 18.8

0.018 (0.35) ns 0.039 (0.53) ns 0.004 (0.71) ns ns

0.1 0.1 0.2 0.1

0.004 (0.79) 0.025 (1.05) 0.028 (0.83) ns ns ns ns

   

(1.52) (0.58) (1.06)

1.0 1.2 1.5 1.3

0.000 0.019 0.019 ns 0.001 ns ns

2.2 2.4 3.1 4.2

0.012 (521.0) ns 0.004 (1117.9) ns 0.013 (710.7) ns ns

   

ns ns 0.004 (299 838) ns 0.009 (208 756) ns ns

30.5 26.8 19.6 23.4

ns ns 0.012 (275 814) ns 0.002 (336 525) ns ns

4.3 4.3 4.3 4.3

(0.89) (0.47) (0.70) (0.68)

0e5 5e10 0e5 5e10

0.000 0.004 0.007 0.000 ns ns ns

Rainy

(0.89) (1.35) (0.12) (0.42)

E7

ns 0.000 0.000 0.046 0.002 ns ns

13.7  1.2 10.8  0.7

(1.35)

23.7  1.5 20.6  1.0

0.000 ns 0.000 0.022 0.002 ns ns

1.7  0.1 1.9  0.1

0.000 (750.4) ns 0.000 (329.7) 0.036 (65.05) ns ns ns

29.1  1.7a 29.4  0.7

ns ns 0.000 (0.023) ns ns ns ns

4.2 4.2

Season (S)a Depth (D) Site (F) S*D S*F D*F S*D*F

0e5 5e10

The ratio of fungalto-bacterial PLFA

Rainy

Fungi PLFA

E3

Bacterial PLFA

C/N

Total PLFAs

SOC (g kg1)

MBCb

TNb (g kg1)

C/N

Moisture (%)

SOC

pH

TN

Depth (cm)

Moisture

Season

pH

Site

Source

Table 2 Selected soil properties of the sampling sites.

The ratio of monounsaturated to branched PLFA

Y. Cao et al. / European Journal of Soil Biology 46 (2010) 128e135 Table 3 p values for significant effects on soil properties. Data for soil pH, TN, SOC C/N, and the ratio of fungal-to-bacterial PLFA were log-transformed. Mean squares (type III) from ANOVA are given in parentheses (ns ¼ not significant).

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Y. Cao et al. / European Journal of Soil Biology 46 (2010) 128e135

a

600 0-5 cm

-1

MBC (mg kg )

500 400

5-10 cm a'

a

ab ab

a'

b

300

b'

200 100 0 E13

E10

E7

E3

Rainy season

-1

TotPLFA (n mol g soil)

b

E13

E10

E7

Dry season

600 0-5 cm

500 a

5-10 cm

a'

a'

a

400

a

a

300

b' 200 100 0 E13

E10

E7

E3

Rainy season

E13

E10

E7

Dry season

Fig. 1. Soil microbial biomass carbon (MBC) (a) and Total PLFAs (TotPLFA) (b) (mean  standard errors). A one-way ANOVA and Tukey tests (p < 0.05) were used to compare differences in sampling site for each season separately (Figs. 2 and 3 are the same as Fig. 1. The depth of 0e5 cm and 5e10 cm were integrated because of the difference for depth was not significant). E3, E7, E10 and E13 refer to eucalyptus forests of 3-, 7-, 10- and 13-years-old, respectively.

saturated fatty acids were lower in E7 (9.4  1.83% and 1.5  0.52%, respectively) compared with other sites in the rainy season, whereas the mean abundances of gram-positive bacteria and the saturated fatty acids were significantly higher in E13 (20.0  1.50% and 12.2  1.63%, respectively) compared with other sites in the dry season (Fig. 2aec). The proportional abundance of fungi PLFA (18:2u6c) and the mean values of fungal-to-bacterial PLFAs varied with season and sampling site (p < 0.05), and both were positively correlated with SOC (p < 0.01) (Table 4). However, the proportions of bacteria PLFAs were significantly higher than those of fungi PLFAs in all sampling sites (p < 0.01, Fig. 2). The ratio of monounsaturated-to-branched fatty acids, as an indicator for the ratio of aerobic to anaerobic organisms [6], varied with sampling sites (p < 0.05). The mean ratio of monounsaturated-to-branched fatty acids was lower in E13

Table 4 Pearson correlation coefficients between soil properties and microbial variables. Data for soil TN, SOC C/N, and the ratio of fungal-to-bacterial PLFAs were logtransformed. Total PLFA

Gram-positive Gram-negative Fungi bacteria bacteria

pH 0.007 0.112 Moisture 0.103 0.289** TN 0.264* 0.492*** SOC 0.228* 0.206* C/N 0.085 0.347**

0.011 0.200 0.235* 0.076 0.171

*p < 0.05, **p < 0.01, and ***p < 0.001.

The ratio of fungal-tobacterial PLFA

0.253* 0.173 0.219* 0.084 0.458*** 0.115 0.387*** 0.277** 0.163 0.107

131

(0.24  0.03%) and E10 (0.35  0.09%) than that in E7 (0.69  0.11%) in the dry season, but it was lower in E7 (0.10  0.03%) than E13, E10 and E3 (0.29  0.07%, 0.35  0.08% and 0.46  0.08%, respectively) in the rainy season (Fig. 3). Principal Components Analysis of the microbial community composition, defined by the PLFA profile, the first two axes explained 68.8% and 18.1% of the total variation in microbial communities, respectively. Consistent with the patterns seen in the PCA plot, the first axis corresponded to both the sampling sites and the seasons, even if values on the first axes corresponded to the sampling sites (p < 0.001) stronger than seasons (p < 0.05). The microbial communities in E13 and E7 were separated from each other on the first PCA axis (p < 0.01), and these two sites were distinctly set apart from the sampling season on the origin of PCA axis (p < 0.05). Both the sampling sites (p < 0.001) and the seasons (p < 0.01) corresponded strongly to the second axis. On the second axis, the microbial communities in E13 and E3 were distinctly separated from each other (p < 0.01) with E13 below the origin of PCA axis while E3 above the origin (Fig. 4). The relationships between the microbial community composition and soil properties were analyzed by RDA. The significance of environmental variables (MBC, soil pH, soil moisture, SOC, TN and C/N) present in the ordination was determined by Monte Carlo permutation tests (p < 0.05). The results showed that the variation in PLFA profiles were influenced predominantly by TN (F ¼ 13.63, p ¼ 0.001), soil moisture (F ¼ 8.60, p ¼ 0.001) and SOC (F ¼ 6.88, p ¼ 0.003). Changes in microbial community composition along the axis 1 were associated with both higher values of TN and SOC and lower values of soil moisture (Fig. 5). The RDA performed with the PLFA profiles showed that a 69.3% of total variation could be explained by all canonical axes (F ¼ 2.673, p ¼ 0.028). In the RDA biplot, the greatest amount of variation was explained by the first axis (eigenvalue ¼ 53.2%, F ¼ 7.945, p ¼ 0.034), and soil samples were separated by different sampling sites and seasons. The variables TN and SOC showed a positive association with axis 1, while soil moisture was negatively associated with axis 1. Axis 1 separated E7 soils from the E13, and the microbial communities collected from the rainy season fall to the left of RDA axis 1 and were positively associated with relatively higher soil moisture, while samples collected from the dry season fall to the right of RDA axis 1 and were negatively associated with soil moisture (Fig. 5). However, the second axis could explain 9.7% of the variation, and corresponded to the sampling sites and seasons. 4. Discussion 4.1. PLFA and soil properties In this study, TotPLFA was significantly influenced by sampling sites. Higher TotPLFA in E13 was related to higher SOC and TN, lower TotPLFA in E10 was related to lower SOC and TN (Table 4, Fig. 5), indicating that soil properties might account for the variation of TotPLFA. It was reported that high soil fertility favors microbial growth in forest soils [10,24,25]. Nevertheless, TotPLFA can also negatively correlate with SOC and TN [14]. Soil types and associated soil properties (pH, soil moisture, CEC) varied in different studies, which might contribute to the conflicting correlations of TotPLFA with SOC and TN. In previous studies, microbial community composition was highly correlated with soil pH [3,19]. Increased soil acidity may also increase total fungal abundance [3]. Bacterial PLFA was found to increase with increasing soil pH in a boreal forest, and it was suggested that the soil microbial community composition was mainly controlled by the pH and C-toN ratio of the substrate [19]. In the present study, TotPLFA, bacterial and fungal PLFA were positively correlated with soil pH in the rainy

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Y. Cao et al. / European Journal of Soil Biology 46 (2010) 128e135

20 0-5cm a

15

a

5-10cm a'

ab

a'

a'

5 0-5cm

d

5-10cm

4 3

ab 10

2

b

a'

5

1

ab

a

b'

ab

a'b'

b 0

0 30 0-5cm

25

a'

5 0-5cm

e

5-10cm

4 a'

20 15

b

5-10cm

a

b'

a

ab

a

3

b'

a'b'

ab

ab

b'

2

10

b

b

1

5

0

0 15 0-5cm

5-10cm

c

a'

b' a

10

0-5cm

f

5-10cm

0.09

b'

a

ab

0.12

a' 0.06 a

5 b

E7

a

0.03

0 E13 E10

a'b' a

E3

E13 E10

Rainy season

E7

Dry season

b'

a

0.00 E13 E10

E7

E3

Rainy season

E13 E10

E7

Dry season

Fig. 2. The relative abundances of the individual PLFAs (mol %) in sampling sites. (a) The proportion of gram-negative bacterial PLFAs; (b) The proportion of gram-positive bacterial PLFAs; (c) The proportion of saturated fatty acids; (d) The proportion of fungal PLFAs; (e) The proportion of arbuscular mycorrhizal fungal PLFAs; (f) The ratios of fungal-to-bacterial PLFAs. E3, E7, E10 and E13 are the same as Fig. 1.

Monounsaturated/Branched PLFAs

season but negatively correlated in the dry season, which suggested that soil pH as the constraint on soil microorganisms were restricted by other environmental variables [17]. However, we did not find significant correlations between TotPLFA and soil C-to-N ratio (Table 4).

1.0 0-5cm

5-10cm

a

0.8 a

0.6

Seasonal changes in microbial communities have been reported elsewhere, it is considered that soil microbial communities are likely to be altered with the variation of soil temperature and moisture [26,28]. In the present study, there were shifts in the soil microbial community in response to different season and soil microbial community was coincident with the change of soil moisture (Fig. 5, Table 2). Higher moisture in the rainy season would be propitious to plant growth but might restrict soil microbes through competition for resources [35]. However, TotPLFA was influenced by the interaction of sampling site and season, which were correlated with the variation of soil TN, SOC and soil moisture (Table 3, Figs. 4 and 5).

ab 0.4

b

ab

4.2. Soil microbial community and forest plantation

b b

0.2 0.0 E13

E10

E7

E3

Rainy season

E13

E10

E7

Dry season

Fig. 3. The ratio of the sum of monounsaturated fatty acids to the sum of branched fatty acids. E3, E7, E10 and E13 are the same as Fig. 1.

Plant community plays an important role in affecting soil microbial community composition because the quantity and quality of plant litter and roots determine the composition of soil organic matter that provides resources for soil microbial community [28]. The high abundance and diversity of plant species would enrich the contents of soil organic matter and soil nutrients [1,25,33,39]. In 13-year-old Eucalyptus plantations (E13), the understory plant species were more abundant and diverse with

Y. Cao et al. / European Journal of Soil Biology 46 (2010) 128e135

133

0-5CM

2

E7D

1

E3R E10R 0 E13R

E10D

E7R

E13D

-1

5-10CM 2 Fig. 5. Redundancy Analysis (RDA) results of microbial community composition and soil properties. Soil properties that were significantly correlated with factors in the RDA were stressed in the plots (Monte Carlo permutation tests, p < 0.05). Vectors represent the mean value of the soil properties and the mean abundances of microbial community. The direction of an arrow indicates the steepest increase in the variable, and the length indicates the strength relative to the other variables. For all RDA plots, values on the x and y axes represent the percent variation explained by axis 1 and axis 2 respectively (p < 0.05). E3, E7, E10 and E13 are the same as Fig. 1; R, rainy season sampling; D, dry season sampling; S, soil depth of 0e5 cm; L, soil depth of 5e10 cm; MBC, SOC, TN and C/N refer to soil microbial biomass carbon, soil organic carbon, soil total nitrogen and the ratio of soil organic carbon to soil total nitrogen, respectively.

Axis2 (18.1 )

E7D

1 E3R

E10D

E10R

0

E13R

measured to all PLFA data, what is the more pertinent reason for such phenomenon needs further investigation. Soil is a very heterogeneous medium where soil communities and activities vary throughout the soil profile [1,33].

E13D E7R -1 -2

-1

0

1

2

Axis1 (68.8 ) Fig. 4. Results of principle components analysis (PCA) on PLFA data. Values are means (n ¼ 4) with bidirectional error bars of axis 1 and 2. For all PCA plots, values on the x and y axes represent the percent variation explained by axis 1 and axis 2 respectively. E3, E7, E10 and E13 are the same as Fig. 1; R, rainy season sampling; D, dry season sampling.

higher coverage (Table 1), resulting in a greater accumulation of litter on the forest floor. Therefore, higher TotPLFA in E13 was probably induced by the high abundance of understory vegetation [10,47]. In our study, TotPLFA was low in E3 (the 3-year-old plantation) where SOC was relatively high. We consider that the rapid growth of young Eucalyptus restricted the growth of soil microbes either due to nutrient competition or phyto-chemical inhibition [35,47]. In addition, slash-and-burn, which causes a major disturbance of soil composition and the soil microbial community [37,41], is a traditional practice before planting tree seedlings in our study region. The effect of disturbance might be more pronounced in younger forests because adequate time is necessary for recovery of the soil community. However, the results on PLFA were intriguing in E10 and E7, where the TotPLFA was low in E10 in the dry season but individual PLFAs were low in E7 in the rainy season compared to other sampling sites. It is difficult to relate any soil variables we

4.3. Individual PLFA signatures as indicators of soil conditions Individual PLFA signatures might be sensitive indicators of improvement in soil abiotic conditions [14]. AMF PLFA 16:1u5 and fungal PLFA 18:2u6c were more abundant in E13 in the dry season (Fig. 2), which may be associated with high SOC in the forest because fungi incorporate more soil C into biomass than bacteria and the C turnover is slower in fungal-dominated ecosystems [32,36]. The ratio of fungal-to-bacterial PLFAs is commonly used as a measure of the relative abundance of fungi and bacteria in the microbial communities [7,13,36]. The ratios of fungal-to-bacterial PLFAs were low in the present study compared with the findings in other studies [4,5], indicating that bacteria predominated in our sampling sites [10]. This conclusion is consistent with previous reports of a high bacterial-to-fungal ratio in a similar soil type in southern China [44]. The bacteria predominated over fungi in our soils might be due to the fast decomposition of forest litter in south China where subtropical monsoon prevails with strong coupling of heat and humidity [10,44]. The ratio of monounsaturated-to-branched fatty acids, as an indicator of fast-growing aerobic bacteria to slow-growing anaerobic bacteria [6], decreased with increasing plantation ages (except for E7 in the rainy season), suggesting that soil aeration condition was poorer in E13 compared with E10 and E3. However, the ratio of monounsaturated-to-branched fatty acids was lower in the rainy season and higher in the dry season in E7, suggesting that the ratio

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of aerobic to anaerobic organisms was shifted with seasons in the 7-year-old Eucalyptus. 5. Conclusion The variation in soil microbial biomass and community compositions with Eucalyptus plantations corresponded to the varying of sampling site and the season, which correlated with the amounts of soil TN, SOC and the soil moisture. The number and abundances of PLFAs with the amounts of soil TN and SOC were higher in 13-year-old Eucalyptus plantation compared with some younger plantations, which indicated that the structure diversity of the soil microbial community is not negatively affected by the planting of Eucalyptus. However, the changes of PLFA profiles in the soils of Eucalyptus plantations provided some evidence of changes of soil conditions and therefore further studies should address long-term effects of Eucalyptus plantation on ecologically relevant processes. Acknowledgements We thank Dr. Zhanfeng Liu for data analysis, and Professor Mingmao Ding, Dr. Walter Parham and Dr. Howard Ferris for reviewing and proofreading the manuscript. This work was supported by National Science Foundation of China (30630015, 30771704, 30925010), and Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2eYWe413, KZCX2-YW-Q114-02). References [1] Z.T. Aanderud, M.I. Shuldman, R.E. Drenovsky, J.H. Richards, Shrub-interspace dynamics alter relationships between microbial community composition and belowground ecosystem characteristics. Soil Biol. Biochem. 40 (2008) 2206e2216. [2] R.T. Aggangan, A.M. O'Connell, J.F. McGrath, B. Dell, The effects of Eucalyptus globulus Labill. leaf litter on C and N mineralization in soils from pasture and native forest. Soil Biol. Biochem. 31 (1999) 1481e1487. [3] R.D. Bardgett, J.C. Frankland, J.B. Whittaker, The effects of agricultural practices on the soil biota of some upland grasslands. Agric. Ecosyst. Environ. 45 (1993) 25e45. [4] R.D. Bardgett, P.J. Hobbs, A. Frostegård, Changes in soil fungal:bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biol. Fertil. Soils 22 (1996) 261e264. [5] D.A. Bossio, K.M. Scow, Impacts of carbon and flooding on soil microbial communities: phospholipid fatty acid profiles and substrate utilization patterns. Microb. Ecol. 35 (1998) 265e278. [6] D.A. Bossio, J.A. Fleck, K.M. Scow, R. Fujii, Alteration of soil microbial communities and water quality in restored wetlands. Soil Biol. Biochem. 38 (2006) 1223e1233. [7] S.A. Boyle, R.R. Yarwood, P.J. Bottomley, D.D. Myrold, Bacterial and fungal contributions to soil nitrogen cycling under Douglas fir and red alder at two sites in Oregon. Soil Biol. Biochem. 40 (2008) 443e451. [8] Y.L. Chen, L.H. Kang, B. Dell, Inoculation of Eucalyptus urophylla with spores of Scleroderma in a nursery in south China: comparison of field soil and potting mix. For. Ecol. Manage. 222 (2006) 439e449. [9] O. Dilly, J.C. Munch, Ratios between estimates of microbial biomass content and microbial activity in soils. Biol. Fertil. Soils 27 (1998) 374e379. [10] M.M. Ding, W.M. Yi, L.Y. Liao, R. Martens, H. Insam, Effect of afforestation on microbial biomass and activity in soils of tropical China. Soil Biol. Biochem. 24 (1992) 865e872. [11] A. Fabião, M.C. Martins, C. Cerveira, C. Santos, M. Lousã, M. Madeira, A. Correia, Influence of soil and organic residue management on biomass and biodiversity of understory vegetation in a Eucalyptus globulus Labill. plantation. For. Ecol. Manage. 171 (2002) 87e100. [12] N. Fierer, J.P. Schimel, P.A. Holden, Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35 (2003) 167e176. [13] A. Frostegård, E. Bååth, The use of phospholipid analysis to estimate bacterial and fungal biomass in soils. Biol. Fertil. Soils 22 (1996) 59e65. [14] S.J. Grayston, C.D. Campbell, R.D. Bardgett, J.L. Mawdsley, C.D. Clegg, K. Ritz, B. S. Griffiths, J.S. Rodwell, S.J. Edwards, W.J. Daviesd, D.J. Elston, P. Millard, Assessing shifts in microbial community structure across a range of grasslands of differing management intensity using CLPP, PLFA and community DNA techniques. Appl. Soil Ecol. 25 (2004) 63e84.

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