Bacterial communities are more dependent on soil type than fertilizer type, but the reverse is true for fungal communities

Soil Science and Plant Nutrition (2009) 55, 80–90 doi: 10.1111/j.1747-0765.2008.00344.x

© 2009 Japanese Society of Soil Science and Plant Nutrition

Blackwell Publishing LtdFertilization impacts fungal communitiesC. Suzuki et al.ORIGINAL ARTICLE

Bacterial communities are more dependent on soil type than fertilizer type, but the reverse is true for fungal communities

Chika SUZUKI1, Kazunari NAGAOKA1, Atuyuki SHIMADA2 and Makoto TAKENAKA1

1Soil Microbial Research Team, National Agricultural Research Center, Ibaraki 305-8666, Japan and 2Kagawa Prefecture Agricultural Experiment Station, Kagawa 761-8078, Japan

Abstract

The soil microbial community is strongly influenced by a wide variety of factors, such as soil characteristicsand field management systems. In order to use biological indicators based on microbial community structure,it is very important to know whether or not these factors can be controlled. The present study aimed todetermine whether soil type or fertilization has a greater influence on the soil microbial community basedon denaturing gradient gel electrophoresis (DGGE) analysis of 12 experimental field plots containing fourdifferent soil types, Cumulic Andosol, Low-humic Andosol, Yellow Soil and Gray Lowland Soil, keptunder three different fertilizer management systems since 2001 (the application of chemical fertilizer, theapplication of rice husk and cow manure, and the application of pig manure). Bacterial DGGE analysisusing 16S rRNA genes and fungal DGGE analysis using 18S rRNA genes revealed that the bacterial com-munity was related to the soil type more than the fertilization; however, the fungal community was relatedto the fertilization more than the soil type. These results might suggest that the fungal community is easierto control by fertilization than the bacterial community. Thus, we propose that indicators based on thefungal community might be more suitable as microbial indicators for soil quality.

Key words: fertilization, microbial community, microbial indicators, soil quality, soil type.

INTRODUCTION

Soil quality is one of the significant agroecosystemcomponents for which management efforts must inten-sify to achieve sustainability. This has been definedas “the capacity of a specific kind of soil to function,within natural or managed ecosystem boundaries, tosustain plant and animal productivity, maintain orenhance water and air quality, and support humanhealth and habitation” (Karlen et al. 1997). In general,soil quality has been estimated in terms of the soilphysical, chemical and biological factors (Karlen et al.2001, 2003). Various biological indicators are knownto be related to soil quality, including microbial biomass,soil respiration, soil enzyme activities, earthworm numberand nematode number (Andrews and Carroll 2001;Andrews et al. 2003; Karlen et al. 2001; Liebig and

Doran 1999). In addition, microbial diversity can directlyinfluence plant growth and development, plant compe-tition, and nutrient and water uptake (Kennedy 1999).Microbial diversity is also sensitive to the phytopatho-logical status of soils (Janvier et al. 2007). Thus, micro-bial diversity based on microbial community structureneeds to be considered in investigations of soil quality(Janvier et al. 2007; Kennedy 1999).

In a soil community, most microorganisms cannot becultivated, and not more than 1% of the total microbescan currently be identified (Anderson 2003; Bengtsson1998; Giller et al. 1997). To overcome these problems,various methods have been developed to identify andstudy these microorganisms, including fatty acid analysisand numerous DNA-based and RNA-based methods(Kirk et al. 2004; Tiedje et al. 1999). Through the useof these methods, it has become clear that the soilmicrobial community is strongly influenced by a widevariety of factors. Some studies have reported that thefactors most affecting soil microbial communities arethe soil characteristics or environmental factors, suchas soil type (Buyer et al. 1999; Girvan et al. 2003;Honeycutt et al. 2005), soil particle size (Marhan et al.

Correspondence: C. SUZUKI, Soil Microbial Research Team,National Agricultural Research Center, 3-1-1 Kannondai,Tsukuba, Ibaraki 305-8666, Japan. Email: [email protected] 25 August 2008.Accepted for publication 20 October 2008.

Fertilization impacts fungal communities 81


2007; Sessitsch et al. 2001), soil air composition(Ludemann et al. 2000; Ovreas et al. 1998) and season(Blume et al. 2002; Girvan et al. 2004). However, otherstudies have shown significant effects of field manage-ment systems, such as cropping systems (Acosta-Martinez et al. 2007; Johnson et al. 2003; Kuske et al.2002), tillage systems (Kennedy and Schillinger 2006;Peixoto et al. 2006), fertilization (Hartmann et al.2006; Marschner et al. 2003; Suzuki et al. 2005),pesticide or herbicide application (Seghers et al. 2003;Yang et al. 2000) and fumigation (Ibekwe et al. 2001).To use biological indicators based on microbial com-munity structure in soil quality, it is very important toknow whether or not these factors can be controlled. Inthe present study, to determine whether soil character-istics or field management systems have a greater influ-ence on the soil microbial community, we analyzed 12experimental field plots containing four different soiltypes, Cumulic Andosol, Low-humic Andosol, YellowSoil and Gray Lowland Soil, kept under three differentfertilizer management systems since 2001, that is, theapplication of chemical fertilizer, the application of ricehusk and cow manure, and the application of pig manure.The field plots were all located in the same region and hadbeen managed using the same tillage and cropping systems.

MATERIALS AND METHODS

Field managementThe experimental field was located at the NationalAgricultural Research Center in Tsukuba, Ibaraki Pre-fecture, Japan. The field experiment had 4 × 3 factorial

plots with two replicates. Each plot was placed up toa depth of 80 cm in the artificial concrete enclosures(3.85 m × 4.35 m; without a bottom). The four differentsoil types were Cumulic Andosol (CA) from Mito,Ibaraki Prefecture, Low-humic Andosol (LHA) fromYatabe, Ibaraki Prefecture, Yellow Soil (YS) from Tahara,Aichi Prefecture, and Gray Lowland Soil (GLS) fromMoriya, Ibaraki Prefecture. The three soil fertilizermanagement systems were the application of chemicalfertilizer (CF), the application of rice husk and cowmanure (HC) and the application of pig manure (PM).These management treatments had been maintainedsince 2001. The management systems of each plot areshown in Table 1. The rice husk and cow manure waspurchased from the Higashimachi Compost Center,Inashiki, Ibaraki Prefecture, and the pig manure waspurchased from the Research Institute of Pig Farming,Ibaraki Prefectural Livestock Research Center, Inashiki,Ibaraki Prefecture. The rice husk and cow manurecontained N at 0.86%, P at 1.03% and K at 1.18%.The pig manure contained N at 1.96%, P at 7.17%and K at 2.4%. The N source was ammonium sulfate[(NH4)2SO4], the P source was amended phosphatefertilizer made from Ca3(PO4)2 and MgO, and the Ksource was potassium sulfate (K2SO4). The field has beencropped once per year in the summer season (fromMay to October). In 2002, 2004 and 2005, the culti-vated crop was carrot, and in 2003 the crop was maize.

Soil sampling

Soil samples were collected from all 24 plots on 16May 2006, just before the next fertilizer treatment.

Table 1 The management system in each plot

Soil typeFertilizer

management

Organic fertilizer†

(kg N ha–1)

Chemical fertilizerFirst additional

fertilization‡

Second additional fertilization§

N(kg N ha–1)

P2O5

(kg P2O5 ha–1)K2O

(kg K2O ha–1)N

(kg N ha–1)N

(kg N ha–1)

CA CF – 80 200 250 60 60HC 60 140 140 140 – –PM 120 80 80 80 – –

LHA CF – 80 200 250 60 60HC 60 140 140 140 – –PM 120 80 80 80 – –

YS CF – 80 200 250 60 60HC 60 140 140 140 – –PM 120 80 80 80 – –

GLS CF – 80 200 250 60 60HC 60 140 140 140 – –PM 120 80 80 80 – –

†Nitrogen rate equivalent. ‡Additional fertilization was carried out twice by chemical fertilizer N. The first was carried out at approximately 30 days after sowing. §Second additional fertilization was carried out at approximately 60 days after sowing. CA, Cumulic Andosol; LHA, Low-humic Andosol; YS, Yellow Soil; GLS, Gray Lowland Soil; CF, chemical fertilizer; HC, rice husk and cow manure; PM, pig manure.

82 C. Suzuki et al.


Surface soil (0–10 cm) was randomly collected fromnine points in each plot and mixed together in a plasticbag. All samples were sieved (2 mm) and the microbialbiomass was immediately analyzed. For nucleic acidextraction, all samples were frozen and stored at –20°C,and for soil analyses the samples were air dried for1 week and stored at room temperature.

Soil analysesThe total C, total N, pH, available P, microbial biomassC and exchangeable potassium (K), magnesium (Mg),calcium (Ca) and sodium (Na) were analyzed. Total Cand total N were measured using an NC-Analyzer(Sumigraph NC-95A; Sumika Chemical AnalysisService, Tokyo, Japan). Soil pH was estimated from asoil : water suspension of 1:2.5. Available P wasmeasured using the Truog method (Nanjyo 1997).Exchangeable K, Mg, Ca and Na were extracted with a0.05 mol L–1 ammonium acetate/0.0114 mol L–1 stron-tium chloride solution and the element concentrationswere measured with an atomic absorption spectropho-tometer (Shimadzu AA6700; Shimadzu, Kyoto, Japan).Biomass C was measured using the chloroform fumiga-tion–extraction method (Brookes et al. 1985), and theamount of total C dissolved in 0.5 mol L–1 potassiumsulfate solution was determined with a total organic Canalyzer (TOC-VCPH; Shimadzu).

DNA extractionThe genomic community DNA was extracted from0.4 g soil samples using a FastDNA SPIN Kit for Soil(Q-BIOgene, Carlsbad, CA, USA) following the manu-facturer’s instructions with a slight modification. As itwas difficult to extract DNA from the CA and LHAsoils, autoclaved 20% skimmed milk solution wasadded at 80 μL or 120 μL, respectively, at the first step(Hoshino and Matsumoto 2004). DNA extraction wasconducted in triplicate for each plot sample.

Denaturing gradient gel electrophoresis analysisThe bacterial community analysis and fungal communityanalysis were carried out with denaturing gradient gelelectrophoresis (DGGE). The DGGE analysis wascarried out using a previously published procedure(Morimoto and Hoshino 2008). Bacterial 16S rRNAgenes in the extracted DNA were amplified using theprimers 968f-GC (5′-CGC CCG GGG CGC GCC CCGGGC GGG GCG GGG GCA CGG GGG GAA CGCGAA GAA CCT TAC-3′) and 1378r (5′-CGG TGTGTA CAA GGC CCG GGA ACG-3′). The reactionswere carried out with a TaKaRa polymerase chainreaction (PCR) Thermal Cycler GP (Takara Biomedicals,Otsu, Japan), applying an initial denaturation step of94°C for 2 min followed by 34 cycles consisting of

denaturation at 94°C for 15 s, primer annealing at55°C for 30 s, and elongation at 68°C for 30 s. ThePCR mixtures (50 μL) contained 1× reaction buffer(Toyobo, Osaka, Japan), 0.2 mmol L–1 dNTPs (Toyobo),1 mmol L–1 MgSO4 (Toyobo), 0.02 mg bovine serumalbumin (TaKaRa Bio), 0.2 μmol L–1 of each primer,1 U of KOD-Plus– DNA polymerase (Toyobo) and 1 μLof extracted DNA solution. Fungal 18S rRNA geneswere amplified using the primers NS1 (5′-GTA GTCATA TGC TTG TCT C-3′) and GCFung (5′-CGC CCGCCG CGC CCC GCG CCC GGC CCG CCG CCCCCG CCC CAT TCC CCG TTA CCC GTT G-3′)(Hoshino and Morimoto 2008). The PCR programconsisted of an initial denaturing step at 94°C for2 min, followed by 30 cycles of denaturation at 94°Cfor 15 s, primer annealing at 50°C for 30 s, and elongationat 68°C for 30 s. The PCR mixture (50 μL) contained1× reaction buffer (Toyobo), 0.2 mmol L–1 dNTPs(Toyobo), 1 mmol L–1 MgSO4 (Toyobo), 0.02 mg bovineserum albumin (TaKaRa Bio), 0.3 μmol L–1 of eachprimer, 1 U of KOD-Plus– DNA polymerase (Toyobo)and 1 μL of extracted DNA solution. The PCR productswere purified with a QIAquick PCR purification kit(Qiagen, Valencia, CA, USA).

The marker for the bacterial DGGE analysis wasmade by amplifying a template DNA mixture that con-tained extracted DNA from Clostridium chartatabium,uncultured Delta proteobacteria, two different speciesof uncultured Acidobacteria, two different species ofuncultured Beta proteobacteria, Arthorobacter pascens,uncultured Gamma proteobacteria, Soliubrobacter sp.and uncultured Chloroflexi bacteria, with the primers968f-GC and 1378r. The PCR program consisted of aninitial denaturing step of 94°C for 2 min, followedby 28 cycles of denaturation at 94°C for 15 s, primerannealing at 55°C for 30 s, and elongation at 68°C for30 s. The PCR mixture (100 μL) contained 1× reactionbuffer (Toyobo), 0.2 mmol L–1 dNTPs (Toyobo),1 mmol L–1 MgSO4 (Toyobo), 0.2 μmol L–1 of eachprimer, 2 U of KOD-Plus– DNA polymerase (Toyobo)and 1 μL of the template DNA mixture. The PCRproduct was purified with a QIAquick PCR purificationkit (Qiagen), adjusted to 20 ng μL–1 and mixed withloading dye. For the fungal DGGE analysis, the markerswere made by amplifying DNA extracted from unculturedCercozoan, Mortierella alpina, Pythium monospermum,Dendryphiella arenaria, Herpotrichia parasitica, Lecy-thophora hoffmannii and two different species ofFolsomia candida using an NS1 and GCFung primer set,and mixed all at the same rate after each PCR productwas adjusted to 20 ng μL–1. The PCR program consistedof an initial denaturing step of 94°C for 2 min, followedby 24 cycles of denaturation at 94°C for 15 s, primerannealing at 50°C for 30 s, and elongation at 68°C for



30 s. The PCR mixture (50 μL) contained 1× reactionbuffer (Toyobo), 0.2 mmol L–1 dNTPs (Toyobo),1 mmol L–1 MgSO4 (Toyobo), 0.3 μmol L–1 of eachprimer, 1 U of KOD-Plus– DNA polymerase (Toyobo) and1 μL of the template DNA. The DGGE was carried outusing a DCode system (BioRad Laboratories, Hercules,CA, USA). For the bacterial analysis, the 6% polyacry-lamide gels were made with a denaturing gradientranging from 50 to 70% and were run at 50 V and 58°Cfor 18 h in TAE buffer. Purified PCR products wereapplied in a quantity of 200 ng and marker DNA wereapplied at 10 ng per band in the gel. For the fungalanalysis, the 7% polyacrylamide gels were made with adenaturing gradient ranging from 20 to 45% and therunning condition was set at 50 V and 60°C for 20 h inTAE buffer. Purified PCR products were applied in aquantity of 200 ng and marker DNA were applied at10 ng per band in the gel. After electrophoresis, thegels were stained with SYBR Green I nucleic acid gelstain (Cambrex Bio Science, Rockland, ME, USA)and scanned with a BioRad ChemiDoc XRS system(BioRad Laboratories). Gel images were analyzedusing Fingerprinting II software (BioRad Laboratories).These measurements were done in triplicate for each plotsample.

Statistical analysis

Non-parametric statistics (Kruskall–Wallis test) were usedto explain the significant differences between the soiltypes. The DGGE band profiles were analyzed usingprincipal component analysis (PCA) and cluster analysisto determine any differences in the microbial communitiesamong fertilizations or soil types. Correlation analysiswas conducted among soil chemical parameters, andbetween bacterial and fungal DGGE bands. All analyseswas carried out with STATISTICA software (StatSoft,Tokyo, Japan).

RESULTS

Soil chemical characteristics, biomass and yieldIn the comparison of the different fertilizer treatments,available P, exchangeable Mg, Ca and soil pH werehigher in the plots treated with pig manure (Table 2).Soil pH had a high positive correlation with exchange-able Mg and exchangeable Ca (r = 0.90, P < 0.05 andr = 0.73, P < 0.05, respectively), and applying pigmanure might have a higher concentration of P, Ca andMg than applying rice husk and cow manure everyyear (Miura and Nishio 2004; results from the analysisof applying pig manure, rice husk and cow manure in2006 and 2007 [data not shown]). Thus, these resultsappear reasonable. There were no significant differences

in carrot yield among the different fertilization treat-ments (Table 2).

Each soil type has some specific characteristics;Andosol has a high humus content, high C/N ratio,high phosphate absorption coefficient and low degreeof base saturation (Kosaki and Kyuma 2000; TheResearch Committee of Soil Conservation 1991a);Yellow Soil has a low phosphate absorption coefficient,low concentration of base elements and low organicmatter content (Konno 2000; The Research Committeeof Soil Conservation 1991b); and Gray Lowland Soilhas a low humus content and low base element content(The Research Committee of Soil Conservation 1991c).Most of the differences between the soil types dependedon these specific characteristics in the comparison ofsoil types (Table 2). Carrot yields were higher in theorder GLS > YS > CA and LHA (P < 0.05).

Bacterial denaturing gradient gel electrophoresis analysisBacterial DGGE profiles based on 16S rRNA geneswere analyzed by PCA and cluster analysis (Fig. 1a,b).In the PCA, all YS plots were located in the lower rightcorner, the GLS plots tended to be on the left side, andthe CA and LHA plots tended to be in the right uppercorner (Fig. 1a). PC1 was positively related to the B25,B29, B31, B35, B37 and B40 bands (the number of eachband indicates its order from the top in the bacterialDGGE analysis), and negatively related to the B3, B6,B55, B58, B65 and B66 bands. PC2 was positively relatedto the B24, B32, B34, B35, B38 and B40 bands, andnegatively related to the B28, B30, B33, B37, B39 andB43 bands. In the cluster analysis, the dendrogram hadthree clusters (Fig. 1b). Cluster I included all the YS plotsand CA–CF1, cluster II included all the LHA plots andall the CA plots with the exception of CA–CF1, andcluster III consisted of all the GLS plots (Fig. 1b). Theseresults suggested that the bacterial community wasrelated to the soil types more than the fertilization.

Fungal denaturing gradient gel electrophoresis analysisFungal DGGE profiles based on 18S rRNA genes werealso analyzed by PCA and cluster analysis (Fig. 2a,b).In the PCA, the plots treated with pig manure werelocated in the lower left corner and the plots treatedwith chemical fertilizer tended to be located in theopposite corner (Fig. 2a). The plots treated with ricehusk and cow manure were located in the upper half ofFig. 2a. PC1 was positively related to the F6, F13, F16,F20 and F22 bands (the number of each band indicatesits order from the top in the fungal DGGE analysis),and negatively related to the F2, F8, F29, F30 and F34bands. PC2 was positively related to the F6, F15, F19,

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© 2009 Japanese Society of Soil Science and Plant N

utrition

Table 2 Soil chemical characteristics, biomass and carrot yield

Soil typeFertilizer

managementReplicate number pH

Total N(%)

Total C(%)

Available P(mg l00 g–1)

Ex. K(mg l00 g–1)

Ex. Mg(mg l00 g–1)

Ex. Ca(mg l00 g–1)

Ex. Na(mg l00 g–1)

Biomass(mg C kg–1)

Carrot yield†

(kg ha–1)

CA CF 1 5.5 0.34 4.38 27.3 26 10 264 5 75 37,275CF 2 5.3 0.36 4.72 32.7 34 6 235 8 116 37,505HC 1 5.7 0.39 4.82 32.6 35 25 308 6 113 44,037HC 2 5.3 0.39 4.95 33.3 36 15 258 6 90 35,749PM 1 5.9 0.39 4.85 65.8 30 41 352 6 133 42,769PM 2 5.6 0.40 4.90 62.9 32 26 279 9 128 49,480

LHA CF 1 6.2 0.26 3.26 28.1 49 32 349 6 133 50,378CF 2 5.6 0.28 3.42 23.8 42 17 266 5 156 53,267HC 1 6.2 0.32 3.82 36.3 56 44 379 6 176 32,206HC 2 5.8 0.31 3.68 29.2 51 28 275 6 187 39,543PM 1 6.6 0.32 3.69 66.5 52 56 413 6 187 34,703PM 2 5.8 0.33 3.77 55.5 48 37 285 5 205 34,617

YS CF 1 5.4 0.10 1.21 64.9 40 9 112 10 55 35,404CF 2 5.2 0.11 1.25 66.0 34 7 106 5 54 49,060HC 1 5.4 0.14 1.63 72.6 34 17 118 6 87 47,364HC 2 5.3 0.14 1.70 70.4 33 13 120 7 78 63,898PM 1 5.9 0.13 1.48 119.8 37 27 150 7 96 57,862PM 2 5.4 0.14 1.53 99.8 34 19 127 7 106 56,986

GLS CF 1 5.7 0.10 1.17 60.0 30 18 185 8 48 54,234CF 2 5.6 0.10 1.19 60.4 30 13 170 8 97 68,572HC 1 5.8 0.14 1.55 72.5 35 27 199 8 135 45,198HC 2 5.5 0.13 1.47 65.4 32 19 165 7 112 67,811PM 1 6.0 0.14 1.45 114.0 37 32 213 8 134 66,571PM 2 5.8 0.15 1.53 119.5 41 30 204 6 161 58,099

†Carrot yield in 2004. Ex, exchangeable; CA, Cumulic Andosol; LHA, Low-humic Andosol; YS, Yellow Soil; GLS, Gray Lowland Soil; CF, chemical fertilizer; HC, rice husk and cow manure; PM, pig manure.



F20 and F22 bands, and negatively related to the F4,F16, F18, F23 and F28 bands. In the cluster analysis,the dendrogram had some small clusters that weredependent on the fertilization treatments and not thesoil type (Fig. 2b). These results suggested that the fungalcommunity was related to fertilization more than soiltype.

DISCUSSION

To determine whether soil bacterial and fungal commu-nities can be controlled, it is important to establishwhat has the greater influence, the soil characteristicsor the field management systems. To compare theeffects of soil characteristics and field management

Figure 1 Principal component analysis (PCA) and cluster analysis using the 16S rRNA gene denaturing gradient gelelectrophoresis profiles. (a) PCA plot bounded by each soil type. Abbreviated names are shown near each plot. The differenttreatments are represented as follows: white, chemical fertilizer application (CF); black, rice husk and cow manure application(HC); gray, pig manure application (PM). The different soil types are represented as follows: �, Cumulic Andosol (CA); ,� Low-humic Andosol (LHA); �, Yellow Soil (YS); �, Gray Lowland Soil (GLS). (b) A dendrogram using Ward’s clustering method.

86 C. Suzuki et al.


systems accurately, other conditions must be made asequal as possible. In the present study, the 12 experi-mental plots, which consisted of four different soiltypes kept under three different fertilizer managementsystems since 2001, were located in the same place and

had been managed using the same tillage and croppingsystems. Thus, we analyzed these plots to compare theinfluence of soil type as a representative of soil charac-teristics and by fertilization as a representative of fieldmanagement systems.

Figure 2 Principal component analysis (PCA) and cluster analysis using the 18S rRNA gene denaturing gradient gelelectrophoresis profiles. (a) PCA plot bounded by each soil. Abbreviated names are shown near each plot. The differenttreatments are represented as follows: white, chemical fertilizer application (CF); black, rice husk and cow manure application(HC); gray, pig manure application (PM). The different soil types are represented as follows: �, Cumulic Andosol (CA); �, Low-humic Andosol (LHA); �, Yellow Soil (YS); �, Gray Lowland Soil (GLS). (b) A dendrogram using Ward’s clustering method.



The microbial community was characterized by DGGEanalysis. Bacterial DGGE band profiles were analyzedby PCA and cluster analysis (Fig. 1a,b). In the PCA,along the PC1 axis, the GLS plots tended to be on thenegative side compared with the other plots (Fig. 1a).As PC1 has a high contribution ratio (63.47%) and thePC1 score of each plot were very close, it could beexpected that negatively related bands had littleinfluence. The B34 band, which was positively relatedto PC1, was present in all GLS plots, one LHA–CF plotand one LHA–PM plot, but the strongest positivelyrelated bands (B25, B29, B31, B35, B37 and B40) wereseen in almost all plots. The presence of a species thathad 16S rRNA genes located at the 34th position byDGGE analysis might have helped to separate the GLSplots from the other plots. Along the PC2 axis, all YSplots, CA–CF1 plots and GLS–CF2 plots were separatedfrom the other plots (Fig. 1). The B28, B30, B33, B37,B39 and B43 bands, which were negatively related toPC2, were seen in all YS plots, the B30, B33, B37, B39and B43 bands were seen in the CA–CF1 plots, and theB28, B30, B37, B39 and B43 bands were seen in theGLS–CF2 plots (data not shown). And this explainswhy all the YS plots, CA–CF1 plots and GLS-CF2 plotswere separated from the other plots. These resultsmight suggest that each soil type had a unique bacterialspecies. In the cluster analysis, the plots were dividedinto three clusters: cluster I contained all of the YSplots and the CA–CF1 plot; cluster II contained all ofthe LHA plots and all of the CA plots except the CA–CF1 plot; and cluster III contained all of the GLSplots (Fig. 1b). These separations were clearly causedby soil type and not by fertilization. Thus, consideringthe results of the PCA and cluster analysis, it could besuggested that the bacterial community was moreinfluenced by soil type than fertilization.

Fungal DGGE bands profiles were also analyzed byPCA and cluster analysis (Fig. 2a,b). In the cluster anal-ysis, the individual plots were not clearly divided, butthey tended to be separated by fertilization and not bysoil type (Fig. 2b). In the PCA, the plots treated withmanure tended to be on the negative side of the PC1axis (Fig. 2a). The bands negatively related to PC1 (F2,F8, F29, F30 and F34) were unique bands in a fewplots, but the bands positively related to PC1 (F6, F13,F16, F20 and F22) were present in almost all plots.Along the PC2 axis, the pig manure applied plots wereseparated from the other plots (Fig. 2a). The bandsnegatively related to PC2 (F4, F16, F18, F23 and F28)were strong in the plots treated with pig manure. Theseresults indicated that the application of manure mightconvey external fungal species, and pig manure appli-cation might stimulate particular fungal species.Considering the results of the PCA and cluster analysis,

it could be suggested that the fungal community wasmore influenced by fertilization than by soil type. Aprevious study showed that arbuscular mycorrhizalfungal communities were more influenced by soil pHthan by geographical distance (Gi-Hong et al. 2008);thus, fungal communities might be more sensitive tosoil environmental changes than soil type.

Bacteria and fungi inhabit various spaces in the soil,such as macro-aggregates and micro-aggregates (Mummeyet al. 2006; Simpson et al. 2004; Six et al. 2004), soilparticles (Chiu et al. 2006; Kirchmann et al. 2004;Kotani-Tanoi et al. 2007; Marhan et al. 2007; Sessitschet al. 2001), plant rhizoplane (Kurakov and Kostina2001; Satoh et al. 2007; Singh et al. 2008) and plantresidues (Matsuyama et al. 2007; Perez et al. 2008).Several studies have reported that bacterial biomassand diversities were higher in smaller size fractionsthan in larger size fractions (Chiu et al. 2006; Fall et al.2004; Kanazawa and Filip 1986; Monrozier et al. 1991;Sessitsch et al. 2001; Van Gestel et al. 1996); however,fungal biomass tends to be higher and fungal activity ishighest in larger size fractions (Chiu et al. 2006;Hattori and Miyashita 1996; Kimura 2000; Kirchmannet al. 2004). Soil environmental factors change morequickly in larger spaces than in smaller spaces (Hattoriand Miyashita 1996; Kandeled et al. 2000). Thus, theinfluence of fertilization appears to be more dominanton the fungal community than the bacterial community.

In soil, bacteria and fungi have an interesting rela-tionship (Antunes et al. 2006; Frey-Klett et al. 2007;Garbaye 1994; Murray and Woodward 2003). In thepresent study, 14 reasonably strong correlations werefound between bacterial DGGE bands and fungal DGGEbands (data not shown). In particular, four groups, theF2 band and B58 band, the F8 band and B15 band, theF4 band and B2 band, and the F28 band and B2 band,tended to be higher in the plots treated with organicfertilizer. This suggests that these groups of species,which had 18S rRNA genes or 16S rRNA genes locatedat each position by DGGE analysis, worked together todegrade external organic compounds. Further studieswill be needed to elucidate the relationship betweenfungi and bacteria, and to reveal their functions inthese soils.

In conclusion, the present study revealed that thebacterial community was related to soil type more thanto fertilization, but the fungal community was relatedto fertilization more than soil type. These results sug-gest that the fungal community can be controlled moreeasily with fertilization than the bacterial community.Thus, we suggest that indicators based on the fungalcommunity might be more suitable as microbial indica-tors for soil quality. Finally, because the concept offield management incorporates not only fertilization,

88 C. Suzuki et al.


but also cropping, tillage systems and fumigation, itwill be necessary to study the effects of these compo-nents of field management systems on bacterial andfungal communities in the future.

ACKNOWLEDGMENTS

We thank Dr Toshihiko Karasawa for his helpful sug-gestions and comments on the manuscript and Ms EmiDoi for her excellent technical assistance. This studywas supported by a Grant-in-Aid (Soil eDNA) from theministry of Agriculture, Forestry and Fisheries of Japan.

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Bacterial communities are more dependent on soil type than fertilizer type, but the reverse is true for fungal communities