soil organic carbon fractions and microbial community and functions under changes in vegetation: a...

$: Soil organic carbon fractions and microbial community and functions under changes in vegetation: a case of vegetation succession in karst forest$
ORIGINAL ARTICLE

Soil organic carbon fractions and microbial communityand functions under changes in vegetation: a case of vegetationsuccession in karst forest

Lianqing Li • Dan Wang • Xiaoyu Liu •

Bing Zhang • Yongzhuo Liu • Tian Xie •

Youxin Du • Genxing Pan

Received: 25 December 2012 / Accepted: 26 August 2013 / Published online: 8 September 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract The vegetation community succession influ-

ences soil nutrient cycling, and this process is mediated by

soil microorganisms in the forest ecosystem. A degraded

succession series of karst forests were chosen in which

vegetation community changed from deciduous broad-

leaved trees (FO) toward shrubs (SH), and shrubs–grasses

(SHG) in the southwest China. Soil organic carbon (SOC),

total nitrogen (TN), labile organic carbon (LOC), water

extractable organic matter (WEOM), microbial biomass

carbon and nitrogen (MBC and MBN), bacterial and fungal

diversity, as well as soil enzyme activities were tested. The

results showed that SOC, LOC, MBC, MBN, and enzyme

activities declined with vegetation succession, with the

relatively stronger decrease of microbial biomass and

functions, whereas WEOM was higher in SHG than in

other systems. In addition, soil bacterial and fungal com-

position in FO was different from both SH and SHG.

Despite positive relationship with SOC, LOC, and TN

(p \ 0.01), MBC, MBN appeared to be more significantly

correlated to LOC than to SOC. It suggested that vegeta-

tion conversion resulted in significant changes in carbon

fractions and bioavailability, furthermore, caused the

change in soil microbial community and function in the

forest ecosystem.

Keywords Vegetation succession � Forest �Soil carbon fractions � Microbial diversity �Enzyme activity

Introduction

Soil microbial community and activity play a central role

by driving soil organic matter decomposition and nutrient

cycling in forest ecosystem (Carney and Matson 2005;

Masayuki et al. 2008). Change in microbial functional

diversity and metabolic activity can greatly affect ecosys-

tem process. Since microorganisms are sensitive to varia-

tions in the soil organic substrate composition of soil, soil

organic carbon (SOC) content and availability, as a con-

sequence has a major effect on the cycling and turnover of

nutrients in forest ecosystem.

In general, SOC content and composition are influenced

by the nature of the plant material from which it is derived

in forest ecosystem. Quideau et al. (2005) showed that

vegetation was the factor controlling SOM composition in

granitic-derived soils from California. Soil labile organic

carbon defined as the ease and speed with which it is

decomposed by microbes, plays an essential role in the

short-term of nutrients as an important source of energy for

soil microorganisms (Van Miegroet et al. 2005; Hu et al.

1997). Van Miegroet et al. (2005) observed vegetation type

influenced water-soluble organic carbon (DOC), despite

similar total SOC. Lagomarsino et al. (2006) found that

labile substrates quality is the main driving force of

microbial mineralization activity in a poplar plantation soil

under elevated CO2 and nitrogen fertilization. Moreover,

soil labile carbon pools were influenced by different veg-

etation types due to differences in the quality of organic

input in forest soils (Hu et al. 1997). Therefore, the link

between the SOC fraction and soil microorganisms and

function may have implications for sustainability for forest

ecosystem.

Soil enzymatic activities reflect the functional responses

of the soil microbe community to changes in environmental

L. Li (&) � D. Wang � X. Liu � B. Zhang � Y. Liu � T. Xie �Y. Du � G. Pan

Institute of Resource, Ecosystem and Environment of

Agriculture, Nanjing Agricultural University, 1 Weigang,

Nanjing 210095, China

e-mail: [email protected]

123

Environ Earth Sci (2014) 71:3727–3735

DOI 10.1007/s12665-013-2767-3

factors, and are generally considered to be indices of soil

microbial functional diversity (Nannipieri et al. 2003).

They are directly responsible for the initial processing of

nutrient cycling and vegetation communities’ variation

(Caldwell et al. 1999). Grandy et al. (2007) found that

alpine ecosystems had higher enzyme activities per unit C

than the forest systems. Caldwell et al. (1999) observed

that the relationship between major C and P processing

enzymes changed under different soil and vegetations.

Gloria et al. (2008) also showed that SOM content was

positively correlated with b-glucosidase, acid and alkaline

phosphatase and urease in native mixed-oak forests. To

understand the linkages between resource availability,

microbial community structure and function, soil enzyme

functional diversities are required.

In the karst region, which accounts for approximately

336,000 km2 of China, forest ecosystems are seriously

degraded due to over-cultivation and overgrazing under the

pressures of an expanding human population and social and

economic activities in past several decades vegetation

communities have gradually shifted from broadleaved trees

to shrubs and grasses after deforestation (Yuan 1997; Wang

2003; Pan and Cao 1999). Thus, conversion accompanied

by the significant reduction in plant cover, density and

species number, leads to soil degradation, such as serious

soil erosion, water loss, and the decrease of soil fertility,

further affects ecological functioning (Wang 2003; Pan and

Cao 1999). For resource managers to restore the func-

tioning of degraded karst forest ecosystem, there need a

better understanding of soil microorganism functions

change in the process of vegetation succession (Pan and

Cao 1999). Some works have been shown that SOC,

nitrogen and phosphors contents can remarkably speed up

vegetation restoration (Hu et al. 2009). However, full

understanding of the relevant factors in explaining patterns

of soil carbon pool changes, microbial structures and

activities after vegetation changes is still lacking. The

purpose of this work is to study the variation of SOC

fractions, microbial community and function, and their

interactions during vegetation succession.

Materials and methods

Sites descriptions and soil sampling

Study sites were karst forest in a nearly 1.5 km2 small

watershed in Puding county, southwestern China. The cli-

matic characteristics of this region were annual rainfall

1,315 mm, more than 80 % received from May to October.

The mean annual temperature was 15.1 �C. The three study

sites are located in this watershed with about 0.5 km

interval distances between each other (26816036.2000N,

105846043.0800E) with the similar average elevation

(1,309–1,496 m a.s.l.) and geology background. The three

sites were in different vegetation successional stages. One

was a secondary deciduous broadleaved forest stand eco-

system without human disturbance (FO, about 15 hm2), the

other two adjoining FO were shrubbery stand ecosystem

(SH, about 10 hm2) and shrub-and-grassland stand eco-

system (SHG, about 10 hm2) which were degraded by

extensive deforestation and heavy grazing pressure before

the middle of 80 s. Soil groups were calcici aquic Cambisol

which were originated from limestone. Soils of FO, SH and

SHG had pH of 6.84, 7.15 and 7.26, cation exchange

capacity (CEC) of 16.23, 14.61, and 15.14 cmol/kg, car-

bonate content of 9.6, 12.9 and 13.4 g/kg, clay content of

27.4, 23.5 and 24.1 %, respectively. The predominant tree

species were deciduous broadleaved trees (e.g., Quercus

fabric, Platycarya longipes, Kalopanax septemlobus, and

Cinnamomum glanduliferum) in FO, shrub species (e.g.,

Zanthoxylum planispinum, Pyracantha fortuneana, Rosa

cymosa) in SH, and shrub species and herbaceous plants

(e.g., Elsholtzia rugulosa, Eremochloa ciliaris, Taraxacum

mongolicum) in SHG. The canopy cover in FO, SH and

SHG were 83.5, 70.4, and 30.7 %, and vegetation richness

were 34.2, 35.5, and 21.6 %, respectively.

The samples were collected in August 2009. Survey

plots were established in middle slope position of hills with

the area 100 9 100 m2. Twenty composite surface soil

samples at 15 cm depth were collected randomly with a

5 cm diameter core in each system. The soils were trans-

ported to the laboratory in ice-coolers and sieved to 2 mm.

A portion of each sample was kept in the refrigerator at

4 �C for microbial biomass and enzyme activity analysis.

Parts of them were air-dried for soil chemical properties

analysis. Three composite samples combined six or serve

soil samples into one replicate randomly were kept in a

freezer at -20 �C for DNA analysis.

Microbial biomass and enzymatic analyses

Microbial biomass carbon (MBC) and nitrogen were

measured using the chloroform fumigation extraction

technique of Vance et al. (1987). Organic carbon in filtered

extracts was determined using a TOC analyzer (Multi N/C

2100; Analytik JenaAG, Germany). Microbial C was

determined as the difference between extractable C from

fumigated and unfumigated extracts. TOC values were

divided by 0.45 to convert the chloroform-labile C to the

microbial biomass C.

Invertase activity was determined with sucrose as

substrate in 2.0 M acetate buffer in pH 5.5 (Schinner and

Von Mersi 1990). Urease activity was measured using

0.2 M urea as substrate in 0.1 M Na-phosphate buffer at

pH 7.0 (Gianfreda and Bollag 1994). Cellulase activity

3728 Environ Earth Sci (2014) 71:3727–3735

123

was measured with carboxymethyl-cellulose as a sub-

strate in 7.5 mL of 2.0 M acetate buffer in pH 5.5

(Schinner and Von Mersi 1990). Alkaline phosphatase

activity was measured with p-nitrophenyl phosphate in

4 mL modified universal buffer at pH 11 (Tabatabai and

Bremner 1969). Enzymatic measurements were done in

three replicates added in substrate, two controls with

only buffer.

Soil chemical characteristics analyses

Water extractable organic matter (WEOM) extraction

method was used which consisted of a 2:1 deionized

water to moist soil extraction, 15 min gentle shaking,

centrifugation (Baker et al. 2000) and filtration through a

0.45-lm polycarbonate membrane filter and was deter-

mined using a TOC. Samples of soil containing 15 mg C

were weighed into 30 mL plastic screw top centrifuge

tubes and oxidized by 25 mL of 333 mM KMnO4 for

(labile organic carbon (LOC) analysis (Loginow et al.

1987). The SOC content was determined using a wet

combustion method; total nitrogen (TN) content was

determined by the Kjeldahl method (Stockdale and Rees

1994).

DNA extraction and PCR-DGGE analysis

Total DNA was extracted with a PowerSoilTM DNA Iso-

lation Kit (Mo Bio Laboratories Inc., CA) according to the

manufacturer’s protocol.

PCR for the amplification of bacterial 16S rRNA genes

The primers F338-GC (50CGCCCGCCGCGCGCGGCGG

GCGGGGCGGGGGCACGGGGGGCCTACGGGAGGC

AGCAG30) and R518 (50ATTACCGCGGCTGCTGG30)(Nakatsu et al. 2000) were used to amplify the V3 region of

16S rRNA genes. The size of PCR product is about 250 bp.

The PCR reaction was completed in a Mastercycler gra-

dient (Eppendorf, Germany) in 0.2 mL tubes using a

reaction volume of 25 lL. The reaction mixture contained

1 lL of each primer (20 lM), 12.5 lL Go Taq� Green

Master Mix (Promega, Madison, WI) and 1 lL DNA and

9.5 lL dd H2O. The cycling conditions were initial dena-

turation step of 5 min at 95 �C followed by denaturation at

95 �C for 1 min. The annealing temperature of 65 �C for

1 min was decreased by 1 �C at each of the successive

cycles until the touchdown temperature of 55 �C was

reached and the remaining 20 cycles were accomplished at

55 �C for 1 min. The elongation step was conducted at

72 �C for 1 min. A final chain extension at 72 �C for

10 min was used.

PCR for the amplification of fungal 18S rRNA genes

The primers Fungi-GC: 50CGCCCGCCGCGCCCCGCGC

CCGGCCCGCCGCCCCCGCCCCATTCCCCGTTACCC

GTTG30 and NS1: 50GTAGTCATATGCTTGTCTC30

(May et al. 2001) were used in this study for the amplifi-

cation of soil fungal 18S rRNA genes. The size of PCR

product is about 370 bp. PCR reaction was executed in a

Mastercycler gradient (Eppendorf, Germany) in 0.2 mL

tubes using a reaction volume of 25 lL, which contained:

1 lL (20 lM) of each primer, 12.5 lL Go Taq� Green

Master Mix (Promega, Madison, WI), 1 lL DNA and

9.5 lL ddH2O. Cycling conditions were 95 �C for 15 min,

followed by 35 cycles of 95 �C for 1 min, 57 �C for 1 min,

and 72 �C for 2 min. A final extension period of 68 �C for

10 min was used.

Aliquot of 4 lL of PCR products was checked by

electrophoresis in 1.2 % (w/v) agarose gels stained with

Goldview (SBS Inc. China) prior to denaturing gradient gel

electrophoresis (DGGE).

For DGGE analysis, the PCR products generated from

each sample were separated on an 8 % acrylamide gel with

a linear denaturant gradient ranging from 35 to 60 % (for

bacteria) and 15 to 35 % (for fungi) using the Bio-Rad

DGGE system. DGGE was performed using 14 lL of PCR

products in 1 9 TAE buffer at 60 �C. First run the gel at

200 v for 6 min, and then run 90 v for 9 h for bacteria or

100 v for 7 h for fungi. Gels were stained with silver

staining (Bassam et al. 1991), and then the gels were

photographed with Gel Doc-2000 Image Analysis System

(Bio-Rad, USA).

Analysis of DGGE patterns

Digitized DGGE images were analyzed with Quantity One

image analysis software (Version 4.0, Bio-Rad, USA). This

software was able to identify the bands occupying the same

position in different gel lanes. The Shannon index (H) was

used to estimate soil bacterial and fungal diversity based on

the intensity and number of bands using the following

equation:

Shannon index ðHÞ ¼X

ni=Nð Þ ln ni=Nð Þ

where ni is the peak height of band i, i is the number of

bands in each DGGE gel profile, and N is the sum of peak

heights in a given DGGE gel profile.

Statistical analyses

Data analysis was conducted using Microsoft Excel 2003.

Results were represented as arithmetic means and standard

deviations (SD). Differences in three systems soil were

Environ Earth Sci (2014) 71:3727–3735 3729

123

tested by one-way analysis of variance (ANOVA). The

significance of the difference was defined according to

statistical convention at p \ 0.05. Soil DGGE profiles were

compared using a principal component analysis (PCA) and

a correlation matrix. The principal component data were

analyzed using ANOVA. The similarity or diversity of

microbes was evaluated using the cluster analysis of

Weighted Pair Group Method (WPGAMA).

Results

Variation of soil carbon composition

SOC changed in different ecosystems (Fig. 1). SOC and

LOC significantly decreased by 13 and 26.7 % in SH, and

by 14 and 51.4 % in SHG, respectively, as compared to

FO. TN in SH and SHG was similar and significantly

increased by 25.2 % in FO. On the contrary, WEOC was

similar in FO and SH and significantly higher in SHG soil

about more than 44.5 % of their levels in the other two

systems. In addition, the soil carbon contents showed large

spatial variability within each ecosystem, whereas the

degree of variability with vegetation succession differed in

soil carbon fractions (Fig. 1). Coefficient of variation (CV)

within each system declined following with the order from

FO to SHG and SH, and were higher for LOC and WEOC

than SOC and TN (data not shown). The percentage of

LOC/SOC ranged from 8 to 31 %, in particular, largely

decreased by 33 % in SHG, whereas, there was no signif-

icant difference between FO and SH (Table 1).

Variation of soil microbial biomass and enzyme

activities

Soil microbial biomass markedly declined along vegetation

succession (Fig. 2). MBC and MBN decreased by 57.4, 50.0,

and 73.2, 67.0 % in SH and SHG, respectively, as compared

Table 1 Percentage of LOC/SOC, MBC/SOC, and MBC/LOC (%)

LOC/SOC MBC/SOC MBC/LOC

FO 21.36 ± 4.4.1a 1.645 ± 0.478a 7.656 ± 1.428a

SH 21.12 ± 2.32a 0.809 ± 0.150b 3.853 ± 0.694b

SHG 14.11 ± 3.01b 0.606 ± 0.121c 4.459 ± 1.330b

Different letters in a single column indicate significant difference at

p \ 0.05 between different sites

SHGSHFO

90

80

70

60

50

40

30

5

SHGSHFO

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

60

33

SHGSHFO

25

20

15

10

5

0

TN

(g

kg -1

)

a

b c

LO

C(g

kg

-1)

a

b

c

SOC

(g k

g-1

) a

bc

SHGSHFO

60

50

40

30

20

10

0

18

50

WE

OC

(m

g kg

-1)

a a

b

Fig. 1 SOC, TN, LOC and WEOC contents in FO, SH, and SHO.

The results represent maxima, minima, upper and lower quartiles and

averages of 20 soil samples estimations. Bars with different letters are

significantly different (p \ 0.05)

b


123

to FO. The ratio of MBC to SOC significantly declined fol-

lowing with the order from FO (1.64 %), SH (0.81 %) to

SHG (0.61 %). The ratio of MBC to LOC was higher in FO

(7.65 %) than SH (3.85 %) and SHG (4.46 %) (Table 1).

Urease activities were higher in FO ranged from 29.56 to

76.60 lg NH4?–N g-1 h-1, than in SH (15.54–52.64 lg

NH4?–N g-1 h-1), and SHG (7.11–46.52 lg NH4

?–N g-1

h-1) (Fig. 3). Urease activities significantly decreased by 30.1

and 51.2 % in SH and SHG, respectively, as compared to FO.

Invertase activities were higher in FO ranged from 1.211 to

5.552 mg glucose g-1 h-1, then in SH (1.249–4.348 mg

glucose g-1 h-1), and SHG (0.962–5.236 mg glucose g-1

h-1). The average invertase activity values in FO were

approximately 1.43 and 1.16 times those found in SH and

SHG, respectively, whereas, it was not significantly different

between SH and SHG. CV of urease and invertase activities

ranged from 22–41 to 32–43 %, respectively, and was higher

in SHG than in the other two systems. Variation of microbial

biomass was higher in FO, whereas, enzyme activities were

higher in SHG (data not shown).

Variation of soil microbial communities

When subjecting all the DGGE data to a PCA, the PC1 and

PC2 components together accounted for 86.67 % of soil

bacterial variation (Fig. 4). FO samples were found to the

right, along PC1, which account for 73.37 % of the vari-

ation, clearly separated from the other two sites (PC1

scores, p \ 0.05), whereas, SH samples were not different

from SHG. Along PC2, which encompassed 13.3 % of the

variation, samples were significantly different (PC2 scores,

p \ 0.05) among three sites. In addition, the PC1 and PC2

components together accounted for 54 % of the fungal

variation (Fig. 4). Along PC2, which encompassed

28.85 % of the variation, fungal communities in FO were

significantly different from the other two sites, whereas,

there was no difference among ecosystem along PC1. The

Shannon’s diversity index of bacteria was significantly

lower in FO than in other two sites, but there were no

differences of diversity index of fungal among three eco-

systems (Table 2).

SHGSHFO

300

200

100

0

SHGSHFO

1500

1000

500

0

MB

C (

mg

kg-1

) M

BN

(m

g kg

- 1)

a

b

c

a

b

c

Fig. 2 Microbial biomass values in FO, SH, and SHO. The results

represent maxima, minima, upper and lower quartiles and averages of

20 soil samples estimations. Bars with different letters are signif-

icantly different (p \ 0.05)

SHGSHFO

80

60

40

20

0

SHGSHFO

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0In

vert

ase

(m

g gl

ucos

e g-1

h-1

)

a

b

ab

Ure

ase

(µg

NH

+ -N g

-1h-1

)

a

b

c

4

Fig. 3 Enzyme activities in FO, SH and SHO. The results represent

maxima, minima, upper and lower quartiles and averages of 20 soil

samples estimations. Bars with different letters are significantly

different (p \ 0.05)


123

Discussion

Variation of soil carbon composition under vegetation

succession

Vegetation community types are considered as important

factors for the significant variations in SOC and total N

stocks (Yimer et al. 2006). The results showed SOC and

TN content decreased in the following order: FO, SH, and

SHG. This finding supports the observations of Hu et al.

(2009), who found that SOC content gradually decreased

with the vegetation succession in karst region. It may be

due in part to plants biomass inputs reduction, such as plant

litter and fine root. Du et al. (2010) found that the plant

productivity and litter biomass gradually decreased under

vegetation conversion from broadleaved trees to shrubs and

shrub-grass accompanied by a decline of vegetation density

and species in karst areas. Furthermore, the fine root bio-

mass was significantly higher in FO than in the other two

systems (Du et al. 2010). Another possible explanation may

be caused by soil erosion because the canopy cover of SH

and SHG was very low (Hu et al. 2009). High proportions

of bare ground are prone to loss of soil particles, plant

seeds, nutrients and organic matter when intense rainfall

occurs (Zuazo and Pleguezuelo 2008).

In addition, SOC bioavailability appeared great differ-

ence under different vegetation succession stages as com-

pared to total SOC (Fig. 1). It further indicated that

vegetation communities influenced not only quantity but

also composition of SOC (Waldrop et al. 2000; Balser and

Firestone 2005), and vegetation shifts from broadleaved

trees and shrubs to shrub-and-grass significantly decreased

soil labile carbon content. By contrast, WEOC both in FO

and SH was lower than in SHG. The explanation may be

that soil microbial community in FO and SH may deplete

labile WEOC to a greater degree than in SHG. In this case,

there was negative correlation between MBN and WECO

(Table 3). Although WEOC has been proposed as an indi-

cator of the C available to soil microorganisms (Burford and

Bremner 1975; Boyer and Groffman 1996), the bioavail-

ability of WEOC for microorganisms influences their deg-

radation in soil. Some studies found that a large portion of

WEOC was not degraded even after incubations of

90–134 days (Zsolnay and Steindl 1991; Qualls and Haines

1992). Therefore, higher WEOC content may be partly due

to their lower bioavailability in SHG than in other systems.

Variation in soil microbial biomass and enzyme activity

under vegetation succession

A marked decline in soil MBC was found along vegetation

succession. Soil MBC, MBN and urease appeared to have

more significant positive correlation with LOC (r = 0.824,

0.746, 0.689) than SOC (r = 0.766,0.669,0.586).This

change may be strongly dependent on SOC quantity and

quality in each system (Bastida et al. 2006; Nishiyama

et al. 2001). The results further showed the larger ratios of

MBC–SOC, particularly MBC–LOC in FO (Table 1). It

reflected that higher SOC, especially availability of carbon

content was the important factor that determined the pop-

ulation size of the soil microbial community and function.

However, a lack of correlation between MBC and WEOC

was found in this case. It agreed with Lundquist et al.

(1999) who observed a lack of correspondence between

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

PC1 (31.17%)

PC2

(22.

85%

)Shrub-grasslandShrubForest

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1 0 1 2

-1 -0.5 0 0.5 1 1.5 2

PC1 (73.37%)

PC2

(13.

37%

)

ForestShrubShrub-grassland

A

B

Fig. 4 Principal component analysis of soil foungal (a) and bacterial

(b) communities as determined by denaturing gradient gel electro-

phoresis (DGGE) analysis under different sites. Error bars represent

standard errors (n = 3)

Table 2 Shannon’s diversity indices of bacterial and fungal

communities

Bacteria Fungi

FO 2.483 ± 0.052b 3.41 ± 0.04a

SH 2.963 ± 0.028a 3.54 ± 0.09a

SHG 3.005 ± 0.052a 3.10 ± 0.80a




123

changes in WEOC and in respiration rates or MBC in two

California agricultural soils. It seems that, to some extent,

WEOC may not indicate C availability to soil microbes.

Soil enzyme activities, as biochemical characteristics of

soil quality indicators, were the central role in cycling of C

and N, and sensitive to environmental change (Nannipieri

et al. 1990). Results showed the marked decline in soil

urease activity with vegetation succession, while soil

invertase activity significantly decreased from FO to SH,

and a slight change between SH and SHG. Further, urease

activity was well correlated with SOC, TN and LOC

(Table 3; r = 0.586, 0.720, 0.689; p \ 0.01), and with

MBC and MBN (Table 3; r = 0.665, 0.613; p \ 0.01). It is

well known that enzymatic activities are improved by

microorganisms in the soil (Nayak et al. 2007). As such,

positive correlations between enzymatic activity and soil

microbial biomass have often been reported (Haynes

1999).

On the other hand, the response for vegetation changes

appears differently between soil properties and microbial

characteristic. SOC, TN, and LOC were *1.11–2.06-fold

higher, while MBC and enzyme activities were 1.43–3.72-

fold higher in FO than in SH and SHG. In addition, CV for

microbial biomass and enzyme activities among systems

was higher than that of soil nutrients properties. It reflected

that soil microbial properties were more sensitive to

responses for vegetation succession than soil nutrients

properties.

Variation of soil microbial community under vegetation

succession

Soil bacterial community in FO was significantly different

from SH and SHG on both PC1 and PC2, whereas, fungal

community appeared significantly different only on PC2.

The soil bacterial diversity was lower in OF, whereas, soil

fungi were not different from each system (Table 2). It may

partly be explained by difference in the composition of

SOC because bacteria respond differently to substrate

which could influence the types of bacteria in soil (Zak

et al. 2003, Wardle 2005). Greater plant diversity increases

the range of organic substrates entering soil which is

favorable to a greater array of heterotrophic microorgan-

isms (Hooper et al. 2000; Brodie et al. 2003; Baum et al.

2009). On the other hand, C–N ratio of SOC may be an

important factor to soil microbial community and diversity

(Mona et al. 2007; Myrold 1999). Sterner and Elser (2002)

showed that there was a close relationship between the

C–N ratio of microorganisms and their substrates. In this

case, significant difference of C–N ratio of SOC was found

only between FO and SH, and slight difference in the C–N

ratio of microorganisms appeared among these systems

(Table 4). Moreover, Øvreas and Torsvik (1998) observed

that soil nutrient availability could exert a positive influ-

ence on microbial diversity. In contrast, lower soil bacterial

diversity was found in OF (Table 1), despite relative higher

carbon availability and plant diversity in this system. It

suggested that microbial community may be affected by

environment composition factor.

Conclusion

Vegetation types above ground caused the changes in soil

carbon fractions and microorganism community structures

and functions. SOC, LOC, MBC, MBN, and enzyme

activities declined with vegetation succession, with the

marked decline in soil microbial biomass. Soil bacterial

and fungal composition in FO was different from both SH

and SHG. Soil biological properties such as MBC and

MBN were closer relationship with LOC than SOM. It

Table 3 Correlations of soil carbon fractions and microorganisms properties in forest

MBC MBN WEOC SOC TN LOC Urease

MBN 0.8889**

WEOC -0.2911* -0.2555

SOC 0.7660** 0.6690** -0.1617

TN 0.8093** 0.7581** -0.0108 0.7832**

LOC 0.8243** 0.7459** -0.3412* 0.8012** 0.8158**

Urease 0.6650** 0.6130** -0.2170 0.5862** 0.7203** 0.6891**

Invertase 0.1080 0.2798* -0.0720 0.0350 0.1840 0.0170 0.0970

Statistically significant correlations: * p \ 0.05, ** p \ 0.01

Table 4 Ratio of C to N for SOC and microorganism (MIC)

C/N (SOC) C/N(MIC)

FO 11.5 ± 1.8a 6.6 ± 2.3a

SH 12.5 ± 1.1b 6.1 ± 3.6a

SHG 12.0 ± 1.5ab 5.8 ± 4.4a




123

suggested that vegetation changes from trees to shrubs and

shrubs to grasses might affect SOC contents particularly for

organic carbon fractions, and alter soil microbial biomass,

community structures and enzyme activities. Furthermore,

it impacted soil biological processes, nutrient cycling, and

further caused the change in forest ecosystem functioning.

Acknowledgments This study was supported by the National

Key Basic Research Development Foundation of China (No.

2006CB403205).

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