Comparison of soil bacterial communities between coastal and inland forests in a subtropical area

Applied Soil Ecology 60 (2012) 49–55

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Comparison of soil bacterial communities between coastal and inland forests in a subtropical area Yu-Te Lin a , William B. Whitman b , David C. Coleman c , Chih-Yu Chiu a,∗ a b c

Biodiversity Research Center, Academia Sinica, Nankang, Taipei, Taiwan Department of Microbiology, University of Georgia, Athens, GA 30602-2605, USA Odum School of Ecology, University of Georgia, Athens, GA 30602-2602, USA

a r t i c l e

i n f o

Article history: Received 30 July 2011 Received in revised form 1 March 2012 Accepted 2 March 2012 Keywords: Bacterial community Coastal forest soil 16S rRNA genes

a b s t r a c t The diversity and composition of soil bacterial communities in four subtropical coastal forest ecosystems were examined using 16S rRNA gene clone libraries. The communities were collected from forests in two islets, Green Island (GI) and Orchid Island (OI), and two coastal forests in Chenggong (CG) and Shitoushan (ST) in southeastern Taiwan. At the elevation ranges from 60 to 340 m, the mean annual precipitation is >2200 mm, the mean annual temperature is about 22 ◦ C, and the soil pH is about 5–6. These forests were compared to an inland natural low montane forest ecosystem with less humidity and more acidic soils. The phyla Acidobacteria and Proteobacteria predominated among these forest soil communities. Within the Proteobacteria, the ␣-Proteobacteria was the most abundant group. The proportion of Verrucomicrobia at one OI study site was significantly higher than that in other communities. Based on the richness and the rarefaction curve analysis, the GI community was the most diverse. Analysis of molecular variance revealed that the communities at two islet soils and coastal soils were similar, although these islets are isolated ecosystems. Most of the abundant operational taxonomic units (OTUs) did not differ significantly among the coastal forest soils. Compared to coastal forest soil communities, the inland natural forest soil community was less diverse and Proteobacteria accounted for more than half of the community. In contrast to the coastal communities, ␥-Proteobacteria was the most abundant proteobacterial class in the inland community, and the most abundant OTU only existed in inland soils. These results suggest that climate conditions and soil characteristics affect the bacterial community composition in coastal and inland forest soils. Disturbance by human activity is another factor that may influence the diversity of the coastal forest soil community. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Soil bacteria play an essential role in nutrient cycling, organic matter decomposition, and soil fertility (Ehrlich, 1996), and they account for a major portion of the community diversity in soils (Roesch et al., 2007; Fulthorpe et al., 2008). The soil bacterial community and activity can be influenced by several environmental factors, including vegetation type (Chan et al., 2008), climate conditions (Lipson, 2007; Sowerby et al., 2005) and soil properties (Brockett et al., 2012; Lauber et al., 2009). Based on the advent of the DNA-analysis methods, the full extent of microbial communities and the biogeography can be described in different ecosystems (Ge et al., 2008; Griffiths et al., 2011).

∗ Corresponding author at: Biodiversity Research Center, Academia Sinica, Nankang, Taipei 11529, Taiwan. Tel.: +886 2 2787 1068. E-mail address: [email protected] (C.-Y. Chiu). 0929-1393/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.apsoil.2012.03.001

To determine if geographic location played a role in the distribution of bacteria, we compared the diversity and composition of the indigenous bacterial communities in forest soils on two islets, two coastal regions in southeastern Taiwan and an inland low mountain in central Taiwan. They are all broad-leaved forests and receive >2000 mm annual precipitation. Previously, we found that the proportion of Proteobacteria accounted for more than 50% of the soil bacterial community in a low mountain (at around 500 m a.s.l.), subtropical forest located in central Taiwan (Lin et al., 2011a). This inland forest ecosystem is distant from coastal forests. Hence, we assumed that the composition and diversity of the soil bacterial community in coastal forests would differ from communities in inland ecosystems. Libraries of the 16S ribosomal RNA (rRNA) genes were prepared from these soils to elucidate the bacterial community. These libraries provide an unequivocal identification of the organisms in soil (Lasher et al., 2009) and facilitate the analysis of the composition of the soil bacterial community and comparisons with the communities from other soils. These results improve our knowledge of bacterial

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communities inhabiting coastal forest soils and the key determinants that influence bacterial communities in inland and coastal forest soils. 2. Materials and methods 2.1. Study sites and soil sampling This study was conducted in tropical forest ecosystems in two islands and coastal region in the southeastern part of Taiwan. Two islets, Green and Orchid Islands, are small island townships of Taitung County, southeastern Taiwan. Green Island (GI; 22◦ 39 N, 121◦ 29 E) is a 16 km2 island and located about 33 km east of Taitung. The mean temperature is 23.5 ◦ C. It rains year round and the mean precipitation is about 2500 mm. Orchid Island (OI; 22◦ 01 N, 121◦ 34 E), with an area of approximately 45 km2 , is located about 90 km southeast of Taitung. Rain falls year round and the mean precipitation is >3000 mm. The annual average temperature is 22.6 ◦ C. The coastal forests at Chenggong (CG; 23◦ 07 N, 121◦ 23 E) and Shitoushan (ST; 22◦ 47 N, 121◦ 09 E) are also located in Taitung County, with elevation 60–180 m a.s.l. The mean annual precipitation is about 2200 mm, and the average annual temperature is 23.8 ◦ C. The pH values of the surface soils were in the range between 5.1 and 6.6. Other characteristics of all the soil are shown in Table 1. Among these four study sites of coastal forests, OI is natural preserve area with least disturbance, followed by GI. Comparatively, CG and ST are located near a town and received more human activities. As the most pristine region in this study, three study sites were selected in OI. In GI, due to the booming tourism activity, only one site was selected near an old forest path in the center of island. For similar reasons, soils were collected at single sites at both CG and ST. Sequences data from a native hardwood subtropical low montane forest soils in Lienhuachi (LHC) Experimental Forest (Lin et al., 2011a) were used for comparison in the present study. For each of the study sites, three replicates were collected from three 50 m × 50 m plots. Soil samples 8 cm in diameter and 10 cm deep were collected with use of a soil auger. Three subsamples collected in each plot were combined. Visible detrital material, such as roots and litter, was manually removed before material was passed through a 2-mm sieve. Soils were then stored at −20 ◦ C for a few days before DNA extraction. 2.2. 16S rRNA gene clone library construction The construction of 16S rRNA clone libraries was performed as described previously (Lin et al., 2010). In brief, soil community DNA was extracted by use of the PowerSoilTM DNA Isolation kit (MoBio Industries) in accordance with the manufacturer’s instructions. The bacterial 16S rRNA genes were amplified by PCR with the primer set 27F and 1492R (Lane, 1991). The PCR products were cloned by use of the pGEM® -T Easy Vector System (Promega). White colonies on selective Luria-Bertani (LB) agar plates were placed into 96-well blocks containing 1 ml LB broth plus kanamycin (50 ␮g ml−1 ) and grown overnight. Sterile glycerol was added to a final concentration of 10%, and an aliquot of this was transferred to a 96-well sequencing block. Both the sequencing and original culture blocks for inoculation of cultures for plasmid isolation and sequencing were stored at −80 ◦ C. 2.3. DNA sequencing and taxonomic assessment Bacterial clones were partially sequenced with the primer 27F. Sequence analysis involved use of an ABI PRISM Big Dye Terminator cycle sequencing ready reaction kit (Applied Biosystems)

and an ABI 3730 Genetic Analyzer (Applied Biosystems) following the manufacturer’s instructions. Sequences were analyzed by use of the Mallard (Ashelford et al., 2006) and Pintail programs (Ashelford et al., 2005) to test for chimeras. The sequences were taxonomically identified by use of the naive Bayesian rRNA classifier (Wang et al., 2007) in the Ribosomal Database Project (http://rdp.cme.msu.edu/index.jsp). Sequences were named as follows. For instance, the sequence GI101 comprised a five-character code that signified the source of forest soils (OI, GI, CG or ST), the sample replicate in the second number (1 in this case), and a two-digit unique indicator of the sequence (01 in this case). The sequences were submitted to the GenBank database under the accession numbers HQ663936–HQ664087, HQ684227–HQ684703, JF440368–JF440524 and JF701280–JF701433. The hardwood soil bacterial community in LHC forest was described previously (Lin et al., 2011a) and was included for comparison purposes. 2.4. Phylogenetic dendrogram construction The sequences were screened against those in the NCBI GenBank database by use of the BLAST program. Phylogenetic relationships were analyzed as described (Lin et al., 2010). Phylogenetic tree was constructed with PHYLIP v3.6 (Felsenstein, 2005) as described previously (Lin et al., 2011b). 2.5. Diversity estimates, library comparison and statistical analyses Distance matrices calculated by use of DNADIST in PHYLIP were used as the input file for the program DOTUR (Schloss and Handelsman, 2005) to derive ␣-diversity parameters within each clone library. Rarefaction analyses, richness, evenness, Shannon diversity index (H) and Chao 1 estimates were calculated for operational taxonomic units (OTUs) with an evolutionary distance (D) of 0.03 (or about 97% 16S rRNA gene sequence similarity). ␥Diversity was also estimated using the Shannon diversity index (H) calculated by DOTUR. To evaluate the similarity between communities from different soils (␤-diversity), we used Mothur program (Schloss et al., 2009) to compute abundance-based Jaccard similarities among communities at sequence similarity threshold of 97%. Pairwise similarity values were converted to distances and used to construct dendrogram. To analyze the distribution of abundant taxa within libraries, groups were constructed by use of DOTUR at a distance of ≤0.03. These groups were then analyzed by the Fisher exact test (Agresti, 1992). UniFrac (Lozupone et al., 2006) analysis was used to compare the clone libraries based on the phylogenetic information. The UniFrac significance test option with 100 permutations was used to test the significant difference of each pair of samples. The Jackknife Environment Clusters were used with the weighted algorithm (which considers relative abundance of OTUs) and the normalization step. Relationships between bacterial community phylogenetic distances and soil properties were assessed using Mantel tests as implemented in the PRIMER V6 software (PRIMER-E, Lutton, Ivybridge, UK). 3. Results 3.1. Phylogenetic groups represented in the clone libraries About 50–60 clones of 16S rRNA genes were sequenced from each replicate sample collected from these coastal forest soils. Each site was represented by three replicate samples, and the sequences from replicates of each site were then combined for further analyses. We obtained 161 clones for the OI-1 site, 163 for OI-2, 153 for OI-3, 152 for GI, 157 for CG and 156 for ST (Table 2). All clones were

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Table 1 Soil chemical and physical properties of study sites. Study site

Elevation (m)

pH

Organic C (g kg−1 )

Total N (g kg−1 )

C:N

Soil group

OI-1 OI-2 OI-3 GI CG ST LHCa

340 320 190 198 60 180 680

5.1 5.3 6.5 6.0 5.6 6.6 4.0

85.8 47.6 71.0 78.0 27.9 26.1 38.1

8.8 4.7 6.8 6.4 2.4 2.4 2.7

9.8 10.1 10.4 12.2 11.6 10.9 14.0

Paleudult Dystrudults Eutrudepts Paleudalf Dystrudept Dystrudept Dystrudept

a

Data from Lin et al. (2011a).

classified into 11 phylogenetic groups (Table 2). Across all soils, the most abundant groups were Acidobacteria and Proteobacteria, comprising more than 60% of all clones. Verrucomicrobia was the third most abundant group. The remaining phyla present within the libraries, such as Actinobacteria and Planctomycetes, all comprised less than 6% of the clones. In addition, about 8% of clones from these forest soils were only distantly related to cultured bacteria and were designated as the unclassified bacterial group. These clones included a representative of the deeply branching but only recently uncultured group, Genera incertae sedis BRC1. In the library from LHC soils, more than half of clones were Proteobacteria (Table 2). In the phylum Proteobacteria, ␣-Proteobacteria was the most abundant class. However, in the OI-2 and GI communities, the proportions of ␣- and ␤-Proteobacteria were similar. Among the Acidobacteria, clones were further clustered into subdivisions 1–7, 11, 13, 17 and 22 according to the studies of Hugenholtz et al. (1998) and Barns et al. (2007). Most of the clones were in subdivision 1 and 2. In OI-1 community, the proportion of Verrucomicrobia was significantly higher than that in other communities (Table 2). Although they are in the same islet, the relative abundances of clones clustered into various phyla differed significantly among the three soil communities from OI. The proportion of Acidobacteria in OI-3 community was significantly lower than that in other two OI communities. Actinobacteria accounted for 13% in OI-3 soils, while represented less than 6% in OI-1 and OI-2 soils. Within the Proteobacteria, the abundance of ␤- and ␦-Proteobacteria in OI-1 were less than 7%, while those in OI-2 and OI-3 were all >10%.

3.2. Diversity of soil bacterial communities In order to calculate diversity indices, OTUs were formed at D ≤ 0.03 (about 97% sequence similarity). Based on the richness, the soil bacterial community of GI showed the highest diversity, while OI-1 had the least. The diversity of OI-2 and OI-3 communities was intermediate (Table 3). The rarefaction curves analysis also showed that the diversity of GI community was higher than communities in the other coastal forest soils (Fig. 1). The failure of the rarefaction curves to plateau indicated that these communities were incompletely sampled (Fig. 1). Nevertheless, the slopes of curves for GI soil community were steeper than those in other coastal soils. These coastal communities were all more diverse than that in LHC soils (Table 3; Fig. 1). ␥-Diversity of the coastal region (H/Hmax = 0.95) was also higher than that in inland LHC soils (H/Hmax = 0.94). Examination of the sizes of abundant OTUs supported above results. In the GI soils, 56% of the clones were in single-member OTUs or singletons. By comparison, in the OI-1 soils, 43% of the clones were in singletons. For inland forests, less than 40% of clones were in singletons. In addition, the LHC community was dominated by an abundant OTU, with 28 sequences, not found in the other sites (Table 4). Thus, the lower diversity of the inland forest soil community resulted from the presence of this abundant OTU. 3.3. Abundant OTUs in soil bacterial communities Analyses of composition differences of communities provided additional evidence for differences among the bacterial

Table 2 Phylotype distribution of clone in 16S rRNA gene libraries (site abbreviations: OI-1, Orchid Island-1; OI-2, Orchid-Island-2; OI-3, Orchid-Island-3; GI, Green Island; CG, Chenggong; ST, Shitoushan; LHC, Lienhuachi.). Phylogenetic group

Acidobacteria Actinobacteria Armatimonadetes Bacteroidetes Chloroflexi Firmicutes Gemmatimonadetes Nitrospira Planctomycetes Proteobacteria ␣-Proteobacteria ␤-Proteobacteria ␥-Proteobacteria ␦-Proteobacteria Unclassified Proteobacteria Verrucomicrobia Unclassified bacteria Total clone numbers a b

Clone library (% of clones)a

Total

OI-1

OI-2

OI-3

GI

CG

ST

LHCb

28.0a 5.6b 0.0 0.6b 1.9 0.0b 0.0b 0.0b 5.6ab 33.5b 22.4a 2.5c 2.5b 6.2ab 0.0 20.5a 4.3bc 161

25.8a 2.5b 0.6 1.2a 0.0 0.0b 2.5ab 0.0b 2.5ab 45.4a 14.7ab 14.1a 3.7b 12.9a 0.0 8.0b 11.7a 163

15.7b 13.1a 0.0 3.3a 2.6 1.3ab 0.0b 0.0b 3.3b 44.4a 17.6ab 13.1a 2.6b 11.1a 0.0 5.2bc 11.1a 153

19.7ab 2.6b 0.0 2.0a 1.3 0.0b 0.7ab 1.3ab 9.2a 44.7a 17.1ab 17.1a 4.6b 5.3b 0.7 3.9c 14.5a 152

36.3a 7.0ab 0.0 5.1a 0.6 5.1a 3.8a 1.3ab 0.0c 26.8b 15.9ab 4.5bc 2.5b 3.8b 0.0 11.5b 2.5c 157

34.6a 3.2b 0.0 3.2a 0.0 2.6a 0.0b 3.2a 1.3b 37.2b 18.6a 7.1bc 5.8b 4.5b 0.6 9.0b 6.4abc 156

16.0b 12.7a 0.7 4.7a 0.7 1.3ab 0.0b 0.0b 1.3b 53.3a 10.0b 10.7b 30.7a 2.0b 0.0 2.0c 8.0ab 150

Data with the same letter in each row indicates no significant difference according to LSD-test at P < 0.05. Data from Lin et al. (2011a).

25.3 6.6 0.2 2.8 1.0 1.5 1.0 0.8 3.3 40.7 16.7 9.8 7.3 6.6 0.2 8.7 8.3 1092

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Table 3 Diversity indices for the soil bacterial communities as represented by the 16S rRNA gene librariesa (site abbreviations are same as in Table 2.). Index

OI-1

c

99 161 0.94 0.43 4.33 222 163–336

S Nd Evennesse Richnessf Shannong Chao 1 95% Chao 1 a b c d e f g

OI-2

OI-3 117 163 0.97 0.55 4.62 340 238–527

GI 104 153 0.96 0.51 4.46 304 208–490

CG 114 152 0.98 0.56 4.65 284 207–424

LHCb

ST 104 157 0.97 0.44 4.50 204 158–292

103 156 0.96 0.49 4.40 328 217–547

76 150 0.88 0.35 3.80 168 120–267

Calculations were based on OTUs formed at an evolutionary distance of <0.03 (or about 97% similarity). Data from Lin et al. (2011a). S defined as the number of OTUs observed. N defined as the number of sequences. Evenness defined as H/Hmax (Pielou, 1966). Richness = (number of singleton OTUs-1)/log N. The maximum value is (N − 1)/log N. The observed/maximum possible value is reported. Shannon diversity index (H).

Table 4 Distribution of the most abundant OTUs in 16S rRNA gene clone librariesa (site abbreviations are same as in Table 2.). Clone nameb

Taxonomic affiliation

OI-1 LHC201* OI1113* OI3145 ST112 CG303 OI1201 OI3119* OI1207 OI1116

Nc

Clone library

␥-Proteobacteria (FJ894817) Acidobacteria (EU445201) ␣-Proteobacteria (FJ478493) ␣-Proteobacteria (EU881284) ␣-Proteobacteria (EF019976) ␣-Proteobacteria (AY425765) ␦-Proteobacteria (FJ479291) Verrucomicrobia (FJ479439) ␣-Proteobacteria (HM125059)

OI-2

0b 14a 1 2 0 3 1b 4 3

0b 4b 2 1 2 2 0b 0 2

OI-3 0b 0b 5 1 2 2 8a 0 3

GI

CG

0b 2b 1 2 0 2 0b 1 0

0b 0b 2 2 4 0 1b 4 1

d

ST

LHC

0b 1b 3 3 3 2 0b 2 1

28a 0b 0 1 0 0 0b 0 0

28 21 14 12 11 11 11 11 10

a OTUs formed at an evolutionary distance ≤0.03 (or about >97% similarity). Data with the same letter in each row indicates no significant difference according to LSD-test at P < 0.05. b Representative clone for each OTU. OTUs with nonrandom distribution are marked by an asterisk. c Total number of clones in an OTU. d Data were calculated from Lin et al. (2011a).

communities in the coastal forest soils. UniFrac significance analyses were used to compare the bacterial communities on the basis of the phylogenetic information (Fig. 2). Although in the same island, the OI-3 community did not cluster with the OI-1 and OI-2 communities. Instead, OI-3 community was in the cluster formed by the GI, CG and ST soil communities. The UniFrac significance and cluster analyses all revealed that inland forest soil community at LHC differed significantly from those of coastal forests (Fig. 2, Table 5). Similar clustering were also observed using Jaccard similarity analyses, which also clearly separated the coastal soil communities from LHC soils at sequence similarity threshold of 97% (Fig. 3). The differences in composition between coastal and inland forest soils were also identified by examination of the abundant OTUs with sizes larger than 10 (Table 4). Because representatives of each OTU were obtained from independent replicates in multiple sampling locations and the representation was similar in the

different sample replicates (data not shown), their abundance was not due to PCR or cloning artifacts or to a single unusual sample. Only 13% of clones were in abundant OTUs with sizes ≥10 (Table 4). These clones were representative of Acidobacteria, ␣-, ␥and ␦-Proteobacteria and Verrucomicrobia. The most abundant OTU, affiliated with ␥-proteobacterial Stenotrophomonas spp., was only found in LHC forest with more acidic soils (Table 4; Fig. 4). The other two abundant Acidobacteria and ␦-Proteobacteria-affiliated OTUs, represented by clones OI1113 and OI3119, respectively, were distributed significantly differently among these communities (Table 4). Other abundant OTUs were distributed equally between coastal and inland forest soils. Many of the abundant OTUs affiliated to the Acidobacteria, ␦-Proteobacteria and Verrucomicrobia were not closely related to organisms that have been isolated and studied in pure culture (Fig. 4). This result suggests that parts of community diversity remains to be characterized.

Table 5 Statistical significance (P-values) of differences among the forest soil communities (site abbreviations are same as in Table 2.).

OI-1 OI-2 OI-3 GI CG ST LHC a b

OI-1

OI-2

OI-3

–

0.17b –

<0.001 0.3 –

GI

CG

ST

LHCa

0.02 0.18 0.44 –

0.41 0.02 0.18 0.03 –

0.04 0.12 0.73 0.18 0.81 –

0.01 0.04 0.02 0.01 0.04 0.02 –

Values were calculated from Lin et al. (2011a). P-values of UniFrac significance test were based on 100 permutations. Bold values indicate significant difference at P < 0.05.

Number of OTUs Observed

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120

4. Discussion

100

Our results reveal that the diversity and composition of coastal forest soil bacterial communities differed from that in an inland forest at LHC. In coastal forest soils, Proteobacteria and Acidobacteria were the major phyla in the clone libraries, and ␣-Proteobacteria was the most abundant group in the Proteobacteria. In contrast, more than half of clones were Proteobacteria, and ␥-Proteobacteria predominated in the inland forest soil community. Moreover, the coastal soil communities were more diverse than inland soils. Compared to other different types of forests, including temperate hardwood forest (Lauber et al., 2008), subtropical evergreen broadleaved forest (Chan et al., 2008) and tropical peat swamp forest (Kanokratana et al., 2011), similar phyla also predominated in these communities. In a large scale assessment of soil bacterial community profiles across great Britain, acidic soils, similar to those in LHC forest (Lin et al., 2011a), were also dominated by Acidobacteria and ␣-Proteobacteria (Griffiths et al., 2011). Other portions of communities, e.g., Verrucomicrobia, did not account for as high proportion as that in OI-1 soils (Kanokratana et al., 2011). Differences in climate conditions, including temperature and precipitation, could affect soil microbial community composition (Stres et al., 2008; Zhang et al., 2005). In this study, compared to the inland region, coastal forests were warmer and subjected to year around rains. The relatively constant weather might facilitate the development of a characteristic soil community. Moreover, soils in LHC forest were more acidic than those in coastal forests. Soil pH was reported to be a key factor to influence the soil bacterial community (Lauber et al., 2009). Thus, the distinct soil bacterial communities between coastal and inland forests could result from differences in climate conditions as well as soil characteristics. Disturbance might be another important factor to affect the soil bacterial community diversity. Our previous study reported that the disturbance of forest soils could increase their diversity (Lin et al., 2011a,b). Studies conducted in Georgia, USA, also revealed that bacterial communities in frequently tilled cultural soils were more diverse than in less disturbed forest soils (Jangid et al., 2008; Upchurch et al., 2008). In these forest ecosystems, LHC and OI1 are subjected to fewer disturbances than the other forests and have less community diversity among these soils. Comparing to OI-1 community, OI-2 and OI-3 communities are more diverse. OI2 is located near a weather station, where it could be disturbed by human activity. Moreover, boom of eco-tour at OI might make human disturbance inevitable. The similar condition might happen in the GI forest, and result the highly diverse soil community. In other coastal forests, also subjected to the disturbance from human activity, all have highly diverse communities. The differences in bacterial community compositions between sites also showed that the geographic distance could be a parameter influencing the structure of the soil bacterial communities. Martiny et al. (2006) proposed the biogeography of microorganisms affected community composition and structure. Influences included both contemporary environmental variations (local) and historical events (spatial) factors. Some studies have found the significant environmental effects at small spatial scales of a few kilometers (Kuske et al., 2002; Horner-Devine et al., 2004). At intermediate scales (10–3000 km), some studies found a significant distance effect (Reche et al., 2005; Yannarell and Triplett, 2005), and environmental conditions also seemed to affect the composition as this spatial scale (Green et al., 2004; Yannarell and Triplett, 2005). Hence, both distance and environmental conditions could be the important factors driving variation in microbial communities at this intermediate spatial scale (Ge et al., 2008). In this study, the geographic distances between inland and coastal forests were all more than 100 km or at the intermediate spatial scale. The geographic distance and the environment variability could have

80 60 40 20 0 0

30

60

90

120

150

180

Number of Sequences Sampled Fig. 1. Rarefaction curve analysis for OI-1 (䊉), OI-2 (), OI-3 (), GI (), CG (), ST () and LHC () forest soil libraries. OTUs were defined as sequences sharing 97% nucleotide sequence similarity. Values of LHC were calculated from data in Lin et al. (2011a).

Fig. 2. A dendrogram from UniFrac Jackknife Environment Clusters analysis of 16S rRNA gene clone libraries. Analysis involved weighted data. Numbers at nodes indicate the frequency with which nodes were supported by Jackknife analysis.

3.4. Relationship between community composition and soil properties Mantel tests between community assemblage matrix and pairwise environmental matrices indicated significant correlations (P < 0.05) between community and elevation, pH and C/N ratios (R = 0.763, 0.622 and 0.698, respectively). The other two parameters, Organic C and total N, had no significant correlations (R = −0.117 and −0.152, respectively) with bacterial community structure in coastal and inland forest soils.

Fig. 3. A dendrogram of the pairwise Jaccard similarity values among communities at sequence similarity threshold of 97%.

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Fig. 4. Phylogeny of clone for abundant operational taxonomic units (OTUs) derived from forest soil clone libraries. Clones analyzed in this study are in boldface. The number of sequences for each OTU is in parentheses following the clone name. Numbers in the nodes represent bootstrap confidence values obtained with 100 resamplings and values below 70 are not shown. The scale bar indicates 0.1 substitutions per nucleotide position.

different extents in shaping the different soil communities. In the three OI sites, apart less than 10 km, the environmental conditions are probably the dominant factors to cause the variability in community structure. Furthermore, the isolation between islands and coastal sites, could have been enhanced the differences among coastal CG and ST forests and island soil communities. Molecular surveys have found Acidobacteria in a wide variety of environments (Barns et al., 1999; Janssen, 2006; Zimmermann et al., 2005). Other forest soils, including temperate pine and hardwood forests (Lauber et al., 2008), temperate loblolly pine (Jangid et al., 2008) and subtropical evergreen broad-leaved forest (Chan et al., 2008), Acidobacteria comprise more than half of libraries. In this study, Acidobacteria was also an abundant phylum in the community, but the proportion was all less than 40%, especially that in OI-3 and GI soils. Their abundance could be related to the higher pH values in these soils (Jones et al., 2009). However, these clones are not closely related to known species of this phylum, Acidobacterium capsulatum (Kishimoto et al., 1991), Geothrix fermentans (Coates et al., 1999) and Holophaga foetida (Liesack et al., 1994). Based on the analyses of complete genomes of three Acidobacteria strains, they are long-lived, divide slowly and exhibit slow metabolic rates under low-nutrient conditions (Ward et al., 2009). However, the described strains are not so closely related to the clone sequences obtained from this study. Hence, predicting function from their properties is not possible. Due to the abundance and distinctive physiological characteristics of Acidobacteria, examination of clones, especially representatives of abundant OTUs in pure culture, should have very high priority.

In conclusion, differences in climate conditions and soil characteristics influence the soil bacterial community composition in coastal and inland forest soils. Disturbance due to human activity could also influence the community diversity. Geographic distance could be another factor that shapes the variability in bacterial community between inland and coastal forest soils. These findings significantly improve on our understanding of coastal forest soil bacterial communities under geographic isolation. Further investigation of the functional diversity of soil bacterial communities in these forest ecosystems is needed to address the impact of geographic isolation on ecosystem function. Acknowledgment This work was supported by the Taiwan National Science Council, Taiwan (NSC 100-2621-B-001-002). References Agresti, A., 1992. A survey of exact inference for contingency tables. Statist. Sci. 7, 131–153. Ashelford, K.E., Chuzhanova, N.A., Fry, J.C., Jones, A.J., Weightman, A.J., 2005. At least 1 in 20 16S rRNA sequence records currently held in public repositories is estimated to contain substantial anomalies. Appl. Environ. Microbiol. 71, 7724–7736. Ashelford, K.E., Chuzhanova, N.A., Fry, J.C., Jones, A.J., Weightman, A.J., 2006. New screening software shows that most recent large 16S rRNA gene clone libraries contain chimeras. Appl. Environ. Microbiol. 72, 5734–5741. Barns, S.M., Takala, S.L., Kuske, C.R., 1999. Wide distribution and diversity of members of the bacterial kingdom Acidobacterium in the environment. Appl. Environ. Microbiol. 65, 1731–1737.

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