REGULAR ARTICLE

Effects of historical logging on soil microbial communitiesin a subtropical forest in southern China

Piao Song & Haibao Ren & Qi Jia & Jixun Guo &

Naili Zhang & Keping Ma

Received: 27 September 2014 /Accepted: 3 June 2015 /Published online: 26 July 2015# Springer International Publishing Switzerland 2015

AbstractBackground and Aims Gaining a better understandingof the legacy effects of logging and forest restoration onsoil microbial communities could improve our ability toconserve biodiversity and promote ecosystem sustain-ability. Herein, we investigated how soil microbial com-munity is linked to natural, restored, and planted forestsand the legacies of historical forest.Methods Soil microbial biomass and composition weremeasured in four forest types (i.e., primary forest, once-clearcut forest, twice-logged forest, and plantation for-est) and related to physico-chemical soil properties andforest community structure data by using analysis ofcovariance.Results Fungal, bacterial, and total microbial biomassmeasured by phospholipid fatty acid profiles were sig-nificantly lower in the two secondary forests and theplantation than in the primary forest. The conversion of

vegetation and soil regimes due to forest logging alteredmicrobial communities.Conclusions Our findings elucidate the correlation ofplant communities and soil characteristics to soil micro-bial communities in the context of subtropical forestmanagement. Naturally restored and planted forestsmay affect soil microorganisms largely by directly mod-ifying the soil labile C and N fractions of organic matter.

Keywords Clear-cutting .Microbial communities .

Plant diversity . Plantation forestry . Selective logging .

Subtropical forest

Introduction

Forest managements such as selective logging, clear-cutting, and plantation forestry may markedly alter for-est structure, composition, and diversity (Putz et al.2001; Naficy et al. 2010; Souza et al. 2012), as well asforest soil properties (Ballard 2000). Moreover, the im-pacts of logging and plantation forestry on the structureand diversity of aboveground vegetation may be longlasting (Hall et al. 2003; Souza et al. 2012), which willin turn affect soil biotic and abiotic conditions (Aberet al. 1997). Thus far, considerable number of studieshas focused on the effects of historical logging andplantation forestry on forest growth, structure, and di-versity (Eyre et al. 2010; Naficy et al. 2010; Souza et al.2012). However, much uncertainty remains regardingthe impacts of forest management on soil communities,

Plant Soil (2015) 397:115–126DOI 10.1007/s11104-015-2553-y

Responsible Editor: Sven Marhan .

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11104-015-2553-y) contains supplementarymaterial, which is available to authorized users.

P. Song :H. Ren :Q. Jia :N. Zhang (*) :K. MaState Key Laboratory of Vegetation and EnvironmentalChange, Institute of Botany, the Chinese Academy ofSciences, 20 NanxincunXiangshanBeijing 100093, Chinae-mail: zhangnl@ibcas.ac.cn

P. Song : J. Guo (*)Key Laboratory of Vegetation Ecology of Ministry ofEducation, Northeast Normal University, 5268Renmin Street,Changchun 130024, Chinae-mail: gjixun@nenu.edu.cn

whose a l t e r a t i on may th rea t en ecosys t emmultifunctionality and sustainability (Wagg et al. 2014).

Soil microorganisms play a critical role in regulatingecological processes relevant to carbon and nutrientcycling (Quideau et al. 2001; Carney and Matson2006; Eisenhauer et al. 2010). Logging typically setsin motion a series of legacy effects. Large amounts ofaboveground plants are removed and changed, whichmodifies the physico-chemical properties of the soil,including the carbon (C) (Ballard 2000; Jandl et al.2007; van der Heijden et al. 2008), and consequentlyaffects the soil microbial community (Kowalchuk et al.2002; Wardle et al. 2004; Nilsson et al. 2008; Jesus et al.2009). Among all the C sources derived from above-ground plants, the light fraction C is the most responsiveto environmental changes on a short-term basis (Partonet al. 1994). This means that the light fraction C tends tobe the most strongly influenced by changes in above-ground plants, including those that occur followinglogging.

The light fraction of the soil organic matter is com-posed of partially decomposed plant and animal matter(Trumbore 1993; Garten et al. 1999) and plays a keyrole in the microbial immobilization of nitrogen andecosystem productivity on a short-term basis (Zaket al. 1993; Compton and Boone 2002). Bremer et al.(1995) found that changes in the light fraction of organicmatter could serve as an early indicator of future trendsin the total soil organic matter pool. The light fractioncarbon was determined to be more sensitive to alter-ations in moisture, temperature, and plant species thanthe heavy fraction carbon (Neff et al. 2002), which mayfurther affect microbial communities (Sparling and Ross1993). However, few studies have taken different frac-tions of organic matter into account when studying thenexus of plant and soil microbial communities.

In our study, we compared four different forest types:primary forest, once-clearcut forest, twice-logged forest,and plantation forest, to elucidate whether and howlegacies of logging and current forests affect soil micro-bial communities. The experiment was conducted intwelve 1 ha plots, for which we had obtained detaileddata on the plant community and measured soil micro-bial properties, along with the light and heavy fractionsof organic matter and other physico-chemical soil pa-rameters. Specifically, we investigated (i) whether thechanges in forests after logging and plantation forestryaffected the soil microbial biomass and communitycomposition and (ii) which fractions of organic matter

play a role in linking aboveground plants and soil mi-crobial communities.

Materials and methods

Site description

The study was conducted in the Gutianshan NationalNature Reserve (29°10′19″–29°17′41″ N, 118°03′50″–118°11′12″ E), which is located in Kaihua county, Zhe-jiang province, southern China. The nature reserve,established in 1975, is 81 km2 in area. It is characterizedby a subtropical monsoon climate. Temperature andprecipitation data have been monitored at the reservesince 1958. According to these records, the annual meanair temperature is 16.5 °C, the monthly mean tempera-ture of the coldest month (January) is 4.7 °C, and that ofthe warmest month (July) is 27.6 °C. Annual meanprecipitation is 1793 mm, while the driest period (Sep-tember–January) has a monthly mean precipitation of<100 mm (Fig. S1). The vegetation in the nature reserveis a typical subtropical evergreen broadleaved forest.The natural old-growth forest in the core of the reserveis over 100 years old (Legendre et al. 2009).

Experimental design, soil sampling, and plant dataselection

On the basis of data of logging events and forest age, Yuet al. (2001) identified four disturbance regimes withinthe forests of the Gutianshan Nature Reserve: (1) pri-mary forest with minimal human disturbance during atleast the last 100 years ; (2) once-clearcut forest natural-ly restored after a clear-cutting event 50 years ago (oncedisturbance, SF1); (3) twice-logged forest restored aftera clear-cutting event 50 years ago, plus selective logging20 years ago (twice disturbances, SF2); and (4)Cunninghamia lanceolata (Chinese fir) planted afterclear-cutting 20 years ago (PL). In 2008, three 1 ha(100×100 m) plots were randomly selected andestablished under each of these four disturbance re-gimes, for a total of 12 plots aligned with the cardinaldirections (Jia 2011). No two plots were closer than200 m. Each plot was divided into 25 20×20 msubplots.

Soil samples were collected at the intersections of the20×20 m subplots, with the exception of those at theedge of each plot (total 12 per plot). Three soil cores

116 Plant Soil (2015) 397:115–126

(3 cm in diameter and 15 cm in depth) were collectedfrom each intersection point and homogenized into onecomposite sample. Plant roots and large stones wereremoved using a 2-mm-mesh sieve. Soil samples werepacked in an ice-cooled insulated box, and transportedfrom the field to the laboratory within 48 h of collection.One part of each of the fresh soil samples was stored at4 °C, in preparation for measuring the physico-chemicalsoil characteristics and microbial biomass properties.The rest of each of the soil samples was separated andstored at −20 °C for 40 days prior to the measurement ofmicrobial community composition (Schnecker et al.2002). The protocol was repeated in both the rainy(July 2012) and dry season (January 2013).

When the twelve 1 ha plots were first established in2008, all woody plants with a diameter at breast height(DBH) ≥1 cm were tagged, georeferenced, measured,and identified (Jia 2011). A total of 81,659 individualtrees belonging to 53 families and 191 species wereidentified and recorded in all plots. Because all woodyplants with DBH ≥1 cm were spatially mapped, woodyplants could be grouped into specific cells. In each plot,woody plants were grouped into 12 circular regions, 5 min diameter, and centered on the soil core samplingpositions. These were used to estimate the relationshipbetween the plants and soil microbial communities.

Measurements

The elevation of each plot was measured when the plotswere established in 2008. Jia (2011) recorded multipleelevation measurements per plot, with more fine-grained measurements in areas with rugged topography.The mean elevation, terrain convexity, slope, and aspectof each plot were calculated.

Soil microenvironmental variables, i.e., soil temper-ature (ST), soil moisture (SM), and pHwere determined.Soil temperature at 10 cm soil depth was recorded usinga thermometer probe (Campbell Scientific, Logan, UT,USA). Soil gravimetric water content was determinedby oven-drying soil samples at 105 °C for 48 h. Soil pHwas potentiometrically measured in a slurry systemwitha 1:2.5 soil to water ratio, using a glass electrode (Ther-mo Orion T20, USA).

Soil organic carbon (TC) was estimated via the di-chromate oxidation and titration method (Kalembasaand Jenkinson 1973). To determine total soil nitrogen(TN), soil samples were digested using the Kjeldahlacid-digestion method, and further analyzed on an

autoanalyzer (Kjeltec 2200 Auto Distillation Unit,FOSS, Sweden). The light and heavy fractions of soilorganic matter were separated using the density frac-tionation method (Lützowa et al. 2007). Briefly, 15 gair-dried soil was mixed with 50 ml NaI solution (1.7 g·cm−3) and shaken for 30 min to disrupt soil aggregates.The homogenized solution was centrifuged at 3000 rpmfor 10 min. The supernatant was decanted onto a fiber-glass filter in a Buchner funnel, to collect the floatinglight fraction. The processes above were repeated twice,in order to achieve total separation. The light fractionorganic matter on the fiberglass filter was thoroughlyrinsed with 0.01 M CaCl2 and deionized water. Theheavy fraction, which remained in the centrifuge tube,was also rinsed using deionized water to remove theNaI. After oven-drying at 60 °C for 48 h, the separatedlight and heavy fractions were weighed. The content ofthe light fraction carbon (LFC) and nitrogen (LFN), andthe content of the heavy fraction carbon (HFC) andnitrogen (HFN) were measured using the methods de-tailed above, to determine the total soil C and N. Thelight fraction phosphorus (LFP) and the heavy fractionphosphorus (HFP) were measured using an iCAP 6000Series ICP-OES spectrometer (Thermo Electron Corpo-ration, USA).

Phospholipid fatty acid (PLFA) analysis was used toestimate soil microbial community composition anddiversity. Fresh soils were extracted, fractionated, andquantified according to the procedure described byBossio and Scow (1998). Fresh soil equivalent to 8 gdry soil was extracted using a mixture of chloroform,methanol, and phosphate buffer (proportions 1:2:0.8)for 2 h, and extracted again for 30 min after decantingthe supernatant. The two supernatants were mixed withphosphate buffer and CHCl3, and decanted into a sepa-ration funnel. The organic phase (CHCl3 layer) wasobtained after the combined supernatants were allowedto settle overnight. The organic phase containing PLFAswas dried under nitrogen gas. To separate the polarlipids from the neutral and glycolipids, isolated polarlipids were converted to fatty acid methyl ester usingmild alkaline methanolysis. A 2 μl sample of each fattyacid methyl ester extract was injected and analyzedusing an Agilent 6850 N gas chromatograph with aflame ionization detector and an HP-1 Ultra 2 capillarycolumn (Agilent Technologies, Inc., Santa Clara, CA,USA). Gas chromatography was performed as recom-mended by the MIDI standard protocol (Microbial IDInc., Newark, DE). Each resulting fatty acidmethyl ester

Plant Soil (2015) 397:115–126 117

was identified using a microbial identification system(Microbial ID. Inc., Newark, DE) based on chromato-graphic retention time and a series of standards rangingfrom C9 to C30. The sum of the nanogram lipid pergram dry soil for the total or specific lipids was used asan index of microbial PLFA biomass for the total andspecific groups, respectively. Individual PLFAs wereused to identify broad classes of soil microbial commu-nities: the PLFAs (15:0, 17:0, i15:0, a15:0, i16:0, a17:0,i17:0, 16:1ω7c, 18:1ω5c, cy17:0, and cy19:0) for bac-teria (Frostegård and Bååth 1996; Zak et al. 1996; Zelles1997; Zogg et al. 1997), and the unsaturated PLFAs(18:1ω9c, 18:2ω6c, and 18:3ω6) for fungi (Madanet al. 2002; Pinkart et al. 2002).

Data analysis

All statistical analyses were carried out using the Rsoftware program (version 2.11.1). We used an ANOVAof covariance to estimate the effects of forest type,sampling time, and other factors on soil microbial bio-mass. The Mantel test was used to test the relationshipbetween these factors and the composition of the plant(species abundance by site) and microbial communities(permutation=999). Principal component analysis(PCA) was used to analyze the microbial communityby sample site, using the PLFA profiles of the microbialcommunities. The PCAwas conducted using the Brda^function of the vegan package (Oksanen et al. 2008). Toreduce noise in the PCA, only 18 PLFAs with a mol%(moles individual lipid/mol total lipids) >0.5 were used.The Least Significant Difference (LSD) analysis wasused for the post-hoc comparison of the soil physico-chemical parameters, microbial biomass, plant commu-nity, geographical factors, monthly air temperature, andprecipitation among the four forest types.

Results

Soil parameters in primary forest, once-clearcut forest,twice-logged forest, and plantations

Geographic characteristics were similar among the fourforest types (Table S1), while soil microenvironmentsand soil nutrient content varied (Table 1). There weresignificant differences in soil moisture and temperatureamong the different forest types in both the rainy anddry seasons, but no difference in soil pH among the

forest types in the two seasons (Table 1). Soil moisturewas significantly higher in the PF than in the other threeforest types in both seasons (p<0.05, LSD test, Table 1).In both seasons, soil temperature was highest in theonce-clearcut secondary forest (SF1) and lowest in thePF (Table 1).

Both total organic carbon and nitrogen in the PF werehigher than those in the SF1, SF2, and PL in bothseasons, but no differences were found among SF1,SF2, and PL (Table 1). The ratio of carbon to nitrogenwas significantly higher in SF2 than in SF1 in bothseasons, but did not differ between any other two foresttypes (Table 1). The light and heavy fractions of carbon,nitrogen, and phosphorus were all significantly higher inPF than in the other three forest types, in both the rainyand dry seasons (Table 1). The ratios of light fraction Cto N, light fraction C to P, and light fraction N to P wereall greater in SF2 than in the other three forest types inboth the rainy and dry seasons (Table 1).

Soil microbial biomass, community composition,and plant community structure

Total microbial, fungal, and bacterial biomass were allsignificantly higher in PF than in the other three foresttypes in both the rainy and dry seasons (Fig. 1, leftpanel). The total microbial, fungal and bacterial bio-masses in each forest type were all significantly greaterin the dry season than in the rainy season (Fig. 1, leftpanel and Table 2). The ratio of fungi to bacteria wassignificantly different among the four forest types, as thefungal/bacterial PLFA ratios in SF1, SF2, and PL wereall higher than those of PF, in both the rainy and dryseasons (Fig. 1, right panel).

The soil microbial community composition was de-termined using the PLFA profiles. In the dry season, thePCA biplot showed a separation of microbial PLFAcomposition in PL from that of the other three foresttypes (Fig. 2, right panel); however, the separation byforest type was less clear in the rainy season, when onlythe microbial PLFA composition of the SF1 and PLseparated from that in the PF (Fig. 2, left panel). More-over, based on the Multiple Response Permutation Pro-cedure (MRPP) analysis, forest type had a significanteffect on microbial composition in both the rainy (p=0.004, based on 999 permutations) and dry seasons (p=0.001, based on 999 permutations).

Plant diversity in SF2 was highest among the fourforest types, whereas the Shannon–Wiener diversity in

118 Plant Soil (2015) 397:115–126

Tab

le1

Soilphysicochemicalparametersintherainyanddryseasonsinprim

aryforest(PF),once-clearcutforest(SF

1),twice-logged

forest(SF2)andplantatio

n(PL).SM

soilmoisture,ST

soiltemperature,and

pHsoilpH

,TCtotalorganiccarbon,T

Ntotalsoilnitrogen,C

/Nratio

oftotalorganiccarbon

andnitrogen,L

FClig

htfractio

ncarbon,L

FNlig

htfractio

nnitrogen,L

FP

lightfractio

nphosphorus,H

FCheavyfractio

ncarbon,H

FNheavyfractio

nnitrogen,H

FPheavyfractio

nphosphorus.D

ifferentlettersacrossrowsdenotestatisticallysignificantdifferences

amongforesttypesperseason

(p<0.05,L

SDtest)

Mean±S.E.

Rainy

season

Dry

season

PF

SF1

SF2

PL

PF

SF1

SF2

PL

SM32.85±1.45

a22.88±0.83

b23.59±0.56

b25.16±0.78

b38.39±2.24

a23.07±0.87

b21.70±0.69

b24.31±1.02

b

ST24.64±0.29

c26.72±0.26

a25.30±0.10

bc

25.54±0.20

b5.49

±0.10

c6.79

±0.06

a6.63

±0.09

a6.21

±0.08

b

pH4.76

±0.05

a4.89

±0.03

a4.78

±0.03

a5.00

±0.03

a4.70

±0.03

a6.03

±1.23

a4.63

±0.02

a4.97

±0.04

a

TC

48.12±2.93

a22.32±0.72

b27.06±1.14

b22.22±1.07

b81.80±6.73

a38.64±1.81

b40.15±1.76

b38.03±1.64

b

TN

2.83

±0.19

a1.41

±0.05

b1.35

±0.05

b1.25

±0.04

b2.67

±0.26

a1.28

±0.04

b1.15

±0.04

b1.24

±0.05

b

C/N

17.51±0.62

ab16.12±0.45

b20.62±0.85

a17.85±0.62

ab32.07±1.16

ab30.09±0.80

b35.17±1.10

a30.89±0.76

ab

LFC

15.26±1.34

a6.36

±0.82

b7.09

±0.57

b5.80

±0.50

b14.36±1.65

a6.15

±0.37

c9.29

±0.65

b6.62

±0.41

c

LFN

0.68

±0.08

a0.21

±0.02

b0.23

±0.02

b0.20

±0.02

b0.66

±0.08

a0.23

±0.01

b0.31

±0.02

b0.23

±0.02

b

LFP

0.05

±0.01

a0.02

±0.00

bc

0.01

±0.00

c0.03

±0.00

b0.03

±0.00

a0.01

±0.00

b0.01

±0.00

b0.01

±0.00

b

HFC

32.22±1.62

a17.76±0.63

b20.33±0.87

b20.68±2.77

b32.99±2.93

a18.88±0.86

b17.87±0.75

b17.30±0.64

b

HFN

1.58

±0.13

a1.13

±0.04

b1.24

±0.14

ab1.14

±0.07

b2.20

±0.21

a1.13

±0.04

b1.07

±0.09

b1.09

±0.04

b

HFP

0.14

±0.01

a0.06

±0.00

b0.06

±0.00

b0.09

±0.01

b0.14

±0.02

a0.06

±0.00

b0.06

±0.00

b0.08

±0.01

b

LFC

/LFP

348.91

±22.85b

446.55

±61.66b

868.00

±86.37a

422.29

±63.41b

659.58

±105.19

b797.68

±54.15a

b1277.27±259.98

a554.01

±41.09b

LFC

/LFN

25.12±0.98

b30.03±1.52

ab32.08±2.34

a32.21±1.57

ab23.77±1.09

b26.87±1.04

ab30.63±0.78

a30.03±1.14

a

LFN

/LFP

14.05±0.82

b14.98±1.64

b27.45±2.60

a12.22±1.58

b27.34±3.92

ab30.13±2.02

ab43.85±11.06a

18.04±0.98

b

Plant Soil (2015) 397:115–126 119

PL was the lowest (Fig. 3, left panel). There was nodifference in Shannon–Wiener diversity between SF1and PF (Fig. 3, left panel). Similarly, plant speciesrichness was highest in SF2 but lowest in PL (Fig. 3,right panel). In addition, plant community evenness inPF, SF1, and SF2 was significantly higher than that inPL, but there was no difference among the plant com-munity evennesses of PF, SF1, and SF2 (Fig. 3, leftpanel).

Associations of microbial communities with soiland plant properties

The analysis of covariance showed that total microbialbiomass was significantly related to soil pH, soil mois-ture, total soil nitrogen, the ratio of the light fraction C toN, and the Shannon–Wiener diversity of the above-ground plant communities (Table 2). Fungal biomasswas significantly correlated with soil pH and heavyfraction nitrogen (Table 2). Bacterial biomass was sig-nificantly affected by soil moisture, the ratio of lightfraction C to N, and the Shannon–Wiener diversity ofthe plant communities (Table 2). The ratio of fungi to

bacteria was related to the soil pH, soil moisture, totalsoil nitrogen, and the quantities and ratio of light frac-tion C and N (Table 2). In addition, soil microbial PLFAcomposition was significantly correlated with plantcommunity composition at the level of respective foresttype and all plots based on the Mantel test (Table 3).

Discussion

Differentiating the effect of naturally restoredand planted forests in the context of the legacyof historical forest

The primary objective of this research was to investigatehow forest restoration and plantation influences soilmicrobial communities. Generally, it is difficult to clear-ly differentiate between the effects of current forest onsoil microorganisms and the legacy effects of removed,historical forest in natural ecosystems, because of themultifaceted manner in which forest logging affectssoils and plants (Ballard 2000; Hall et al. 2003) andthe long-term legacy effects of historical forest (Wall

Fig. 1 Microbial communitybiomass in different forest typesduring the dry and rainy seasons.Bacteria bacterial PLFA biomass,Fungi fungal PLFA biomass, F/Bthe ratio of fungi to bacteria. PFprimary forest, SF1 once-clearcutforest, SF2 twice-logged forest,PL plantation. Different lettersdenote statistically significantdifferences across rows amongforest types (p<0.05, LSD test)

120 Plant Soil (2015) 397:115–126

Tab

le2

Analysisofcovariance

forestim

atingtheeffectsofforesttype,sam

plingtim

eandotherpropertieson

soilmicrobialbiom

ass.pH

soilpH

,SM

soilmoisture(SM),ST

soiltemperature

at0–10

cmdepth,TCsoiltotalorganiccarbon

(TC),TN

totalsoilnitrogen

(TN),C/N

theratio

ofsoilorganiccarbon

tonitrogen,LFClig

htfractio

ncarbon,L

FNlig

htfractio

nnitrogen,L

FP

lightfractio

nphosphorus,H

FCheavyfractio

ncarbon,H

FNheavyfractio

nnitrogen,H

FPheavyfractio

nphosphorus,LFC/LFNtheratio

ofcarbon

tonitrogen

oflig

htfractio

n,LF

C/LFPthe

ratio

ofcarbon

tophosphorus

oflig

htfractio

n,LFN/LFPtheratio

ofnitrogen

tophosphorus

oflig

htfractio

n,andplantcom

munity

compositio

nindex:Sh

annon–Weinerd

iversity,evenness

andrichness

d.f.

Microbialbiom

ass

Bacteria

Fungi

F/B

F-value

p-value

F-value

p-value

F-value

p-value

F-value

p-value

Foresttype

317.56

<0.001

8.73

<0.001

0.87

0.46

6.68

<0.0001

Samplingtim

e1

60.25

<0.001

32.66

<0.001

54.03

<0.001

6.51

0.01

pH1

4.11

0.04

1.32

0.25

12.02

<0.001

6.46

0.01

SM1

28.87

<0.001

21.47

<0.001

0.42

0.52

11.32

0.00

ST

12.62

0.11

2.60

0.11

2.93

0.09

0.13

0.72

TC

10.67

0.41

0.20

0.66

3.26

0.07

2.11

0.15

TN

18.01

0.005

3.51

0.06

0.91

0.34

6.79

0.01

C/N

10.45

0.50

0.94

0.33

0.31

0.58

0.07

0.80

LFC

12.12

0.15

1.35

0.25

2.24

0.14

0.27

0.61

LFN

10.07

0.79

0.92

0.34

1.41

0.24

4.21

0.04

LFP

10.80

0.37

1.79

0.18

2.81

0.10

8.29

0.004

HFC

10.80

0.37

0.41

0.52

0.28

0.60

0.00

0.99

HFN

10.43

0.51

0.63

0.43

6.27

0.01

3.51

0.06

HFP

10.70

0.40

0.02

0.90

1.34

0.25

1.16

0.28

LFC

/LFN

19.80

0.002

8.21

0.01

0.00

0.99

6.24

0.01

LFC

/LFP

10.20

0.66

1.32

0.25

0.02

0.88

1.31

0.25

LFN

/LFP

10.53

0.47

1.47

0.23

0.08

0.78

0.59

0.45

Shannon–Weinerdiversity

19.02

0.003

6.08

0.01

0.24

0.62

2.67

0.10

Plant

evenness

11.20

0.28

0.83

0.36

1.05

0.31

3.37

0.07

Plant

richness

10.48

0.49

0.71

0.40

0.67

0.42

2.43

0.12

Plant Soil (2015) 397:115–126 121

and Hytönen 2011; Souza et al. 2012). The removal oflogging residue may have immediate effects on soilstructure, hydrothermal regimes, litter inputs, and con-sequent available nutrients (Ballard 2000). It is difficultto estimate the time span across which these changeswill be continuous. Moreover, the continuous effects ofhistorical forest and logging residue removal wouldlikely be intertwined with the effects of restored andplanted forests.

In this study, we attempted to elucidate the effect ofrestored and planted forests after historical loggingabout 50 years ago on soil microbial communities, bymeasuring the labile fractions of organic matter. Thelabile fractions of organic matter can be quickly miner-alized due to their nature and lack of colloids (Jandl et al.2007), and consequently, are much more dependent onshort-term litter input by current plant growth

(Trumbore 1993; Gaudinski et al. 2000; Neff et al.2002). Labile, low-density organic matter generallyhas a much more rapid turnover rate in both the O andA horizons than high density and mineral-associatedorganic matter (Gaudinski et al. 2000). Moreover, thelabile fraction of the organic matter in the O and Ahorizon soils of tropical forest ecosystems may haveresidence times of 10 years or less, and contribute themajority of the annual C flux into and out of these soils(Trumbore 1993). These results support the view thatlabile organic matter, to some extent, could represent theeffects of current forest on soil microbial communitiesof the subtropical forest 50 years after logging.

N limitation of soil microbial biomass as characterizedby legacies of removed, historical, and current forests

The association between plant and microbial communi-ties is of considerable ecological interest (van der

Fig. 3 Plant diversity (Shannon–Wiener index), evenness, andrichness of primary forest (PF), once-clearcut forest (SF1),twice-logged forest (SF2), and plantation (PL). Letters denotestatistically significant differences across rows among the foresttypes (p<0.05, LSD test)

Table 3 Association between plant and microbial communitycomposition for each forest type and across all plots of the fourforest types. PF primary forest, SF1 once-clearcut forest, SF2twice-logged forest, and PL plantation

Site Mantel partial statistic r p-value

PF 0.23 0.001

SF1 0.20 0.002

SF2 0.16 0.003

PL 0.27 0.001

All plots 0.18 0.001

Fig. 2 The principal component analyses (PCA) of soil microbialcommunity composition, as indicated by PLFA profiles in dry andrainy seasons. The first and second principal components (PC 1and PC 2) accounted for 16.8 and 12.1 % of the total variance,respectively. The PCA ordination plot for all 12 surveyed plots,

separated for visual comparison by each season, showing the fourforest types, PF (solid diamond), SF1 (solid triangle), SF2 (opensquare) and PL (open circle).PF primary forest, SF1 once-clearcutforest, SF2 twice-logged forest, PL plantation

122 Plant Soil (2015) 397:115–126

Heijden et al. 2008; Bardgett and Wardle 2010). Agrowing body of evidence shows that changes in plantdiversity, species richness, and evenness may be associ-ated with the dynamics of soil microbial biomass(Orwin and Wardle 2005; Berg and Smalla 2009; Lambet al. 2011). Our results show that soil microbial bio-mass was significantly influenced by the diversity ofrestored and planted forests, indicating that current for-est plays a critical role in regulating soil microbialgrowth in the subtropical forests of southern China.

Increases in the diversity and species richness ofnaturally restored forest after historical logging wereprobably able to stimulate plant productivity andlitter input (Tilman et al. 2001; Zak et al. 2003);however, they may not neutralize the effects of theremoval of large amounts of total organic matter thatoccurred during historical forest logging. The reduc-tion of organic matter often leads to the C limitationof soil microbial growth, and consequently de-creases soil microbial biomass (Holden and Treseder2013). However, there was no pronounced relation-ship between total organic C and microbial biomassin the subtropical forest surveyed in this study. The-se results demonstrate that C limitation cannot ex-plain the observed decline in microbial biomass. Analternative explanation is that microbial growth maybe N-limited (Gallardo and Schlesinger 1992). Ourresults suggest that the decreases in total organic Ncoupled with significant reductions in the total mi-crobial biomass and marginal declines in the bacte-rial biomass support the postulation that N limitationplays a critical role in affecting microbial biomass.Moreover, current forest structure may be, to a largeextent, responsible for the decreases in the lightdensity and labile fractions of organic matter in thetwo secondary forests and plantations. Total micro-bial biomass and bacterial biomass were both alsocontrolled by the observed proportion of light frac-tion N to light fraction C. This highlights the im-portant role played by restored and planted forests inthe control of microbial biomass through modifica-tions of the light fractions of organic matter in thissubtropical forest.

Legacy effects of historical forest and current foreston soil microbial community structure

Historical logging may alter the richness (Kataja-ahoet al. 2011; Souza et al. 2012) and biomass (Mazzei

et al. 2010) of plant communities, which may in turnmodify the composition of soil microbial communi-ties (Bardgett and Wardle 2010). Our results indicatethat there is a substantial link between soil microbialPLFA composition and the composition of the cur-rent forest. Moreover, the fungal/bacterial ratio thathas been considered to be an indicator of microbialcommunity structure (Grogan and Cronan 1997;Pinkart et al. 2002) is marginally correlated with theevenness of the current forest. Even plant communitiesmay accelerate increases in bacterial abundance (Lambet al. 2011). However, this cannot explain the higherfungal/bacterial ratio observed in the two secondaryforests with higher plant evenness, suggesting that plantevenness, as well as richness and diversity, cannot di-rectly account for the effects of plant community com-position on the soil microbial communities in this sub-tropical forest ecosystem (Grayston et al. 1997).

Soil microorganisms can be affected by multipleaspects of plant communities, such as the diversity ofroot exudates, litter mass, and litter quality (Usher2006), all of which are related to the input of organicmatter into the soil. Soil microbial community compo-sition is more directly related to the quality of availableorganic matter (Grayston et al. 2001). The conversion offorest structure due to logging may be accompanied byreductions in total organic matter (Richards et al. 2007)and labile fractions (Chen et al. 2004; Yang et al. 2009).The decreases in the total N and light fraction C and Nplaced much stronger constraints on bacterial growththan on that of fungi. This led to the presence of higherfungal/bacterial PLFA ratios in the two secondary for-ests and plantations than in the primary forest. Thecontribution of the higher ratio of light fraction C rela-tive to N to the relative dominance of fungi in the soils ofthese secondary and plantation forests further demon-strates the significance of the effect of light fractionorganic matter, modified by current vegetation, on mi-crobial community composition. Fungi and bacteria arethe main components of microbial biomass, but thereare fundamental differences between them, such as thehigher C/N ratio of the fungi (De Ruiter et al. 1993).Previous studies showed that fungal biomass typicallyincreased with the ratio of C to N (Grayston and Prescott2005; Högberg et al. 2007). Our results provide aninsight into the increase of fungal biomass not with thetotal organic C/N ratio, but with the increase in the lightfraction C/N ratio, which is ultimately driven by thecomposition of current forests.

Plant Soil (2015) 397:115–126 123

Conclusions

Our results indicate that the conversion of forest struc-ture and consequent changes in soil properties afterlogging 50 years ago may significantly affect soil mi-crobial biomass and community composition. Microbialtotal biomass and bacterial biomass may be limited bythe quantity of soil N in this subtropical forest. The twosecondary forests and the plantation had lower microbialbiomass than the primary forest, which can be attributedto the decrease in soil N, and the decrease in the soillight fraction N after logging and the subsequent con-version of forest structure. Moreover, the main microbi-al groups, i.e., fungi and bacteria, displayed differentdegrees of sensitivity to logging-associated vegetationconversion. An increase in fungal dominance over bac-teria was observed in the soil communities of areas withnaturally restored and planted forest. The association ofnaturally restored and planted forests with microbialcommunity biomass and composition suggests that thecurrent forest play critical roles in the control of soilmicrobial communities, whichmay largely depend uponthe indirect impacts of these changes on soil labilefractions.

Acknowledgments We would like to extend our thanks to theNational Natural Science Foundation of China (31270559), theState Key Laboratory of Vegetation and Environmental Change(LVEC), and the Ministry of Education Laboratory for EarthSurface Processes of Peking University for funding this study.We thank Dr. Dunmei Lin, Xingxing Man, and Dr. Bo Yang fortheir suggestions on data analysis. We gratefully acknowledge Dr.Yu Liang, Dr. Jihong Huang, and Dr. Jiangshan Lai for theirvaluable advice. We appreciate the language assistance providedby Dr. Jeremy Miller, Dr. G.F. (Ciska) Veen, and Dr. G.WKorthals. We also would like to thank the staff of the GutianshanResearch Station of Forest Biodiversity and Climate Change fortheir assistance in the experimental establishment and sampling.

Ethical statement Permission to conduct this research and ob-tain soil samples for analysis was granted by the GutianshanNational Nature Reserve.

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