Bacteria in the Tropical Rainforests ___________

Introduction

Ecologists are fascinated by patterns in the biodiversity of tropical rainforests, including how and why communities differ from 1 another, and why variety is greater in some places than others. In terms of known biodiversity, Southeast Asian tropical rainforests are one of the most diverse terrestrial ecosystems on Earth (Corlett, 2014), with numerous poorly studied habitat types within them. The major equatorial rainforest types in Southeast Asia are mixed dipterocarp woods (MDF), heath forest and peat swamp forest (Whitmore, 1984). Heath and peat swamp forests occur on acidic sandy and wet peaty soils, respectively, and support lower plant multifariousness compared to MDF (Bruenig and Droste, 1995; Davies and Becker, 1996; Slik et al., 2009). However, compared to macro-organisms, the community composition and diversity of microorganisms in these rainforest habitats is largely unknown.

Soil microorganisms constitute the largest proportion of the world’s biodiversity and are important to terrestrial ecosystem functioning (Prosser, 2012). Thus, understanding their biodiversity patterns and the major divers of these patterns in natural habitats may exist of import for prediction of ecosystem responses to a irresolute environment (Jing et al., 2015). Previous studies of tropical soils accept indicated that country use changes in tropical regions influence soil microbial communities, which are mainly driven by changes in soil chemical properties (Jesus et al., 2009; Tripathi et al., 2012; Lee-Cruz et al., 2013; Rodrigues et al., 2013; Kerfahi et al., 2014). Soil pH is becoming recognized as one of the most import drivers of microbial community structure and diversity in tropical soils at various scales (Jesus et al., 2009; Tripathi et al., 2014). At that place have also been some studies which compared the soil microbial community composition and diversity in different rainforest habitat types (Satrio et al., 2009; Araujo et al., 2012; Miyashita et al., 2013; Pacchioni et al., 2014; Pupin and Nahas, 2014). However, most of these studies were concentrated in Neotropical regions.

The present report concentrates on variation in soil bacterial and fungal community composition and diverseness in several unlike types of lowland tropical rainforest habitat within Brunei Darussalam, Northwest Kalimantan, in Southeast Asia. The MDF forests boss the lowland forests of Borneo (Ashton, 1988; Slik et al., 2003, 2009), whereas tropical heath and peat swamp forests are relatively distinctive habitats compared to MDF forests in Borneo in terms of species composition and diverseness (Brünig, 1974; MacKinnon, 1996; Cannon and Leighton, 2004). Though the to a higher place ground diversity is well-studied in these various rainforest types, it is however unclear whether different rainforest habitats take singled-out microbial community composition and multifariousness, analogous to the distinct plant community composition and diversity levels of these habitats (Bruenig and Droste, 1995; Davies and Becker, 1996).

The present written report was conducted in Brunei Darussalam, Northwest Borneo, in Southeast Asia. Across Brunei, the major rainforest types are MDF master, MDF secondary and peat swamp forests, with smaller scattered areas of heath forests (Whitmore, 1984). This concentration of a range of different rainforest types in close proximity provides an opportunity to study the soil microbial customs composition and diversity under common climatic atmospheric condition while also diminishing the potential effect of dispersal limitation, meaning that detected differences tin most likely be ascribed to differences in the soil and found community only. Nosotros used 16S rRNA gene and ITS1 region amplicon sequencing using Illumina MiSeq platform to accost the post-obit questions:

(1) How do different rainforest habitats influence the OTU composition of soil bacteria and fungi, and what are the major soil backdrop linked to bacterial and fungal customs construction?

(2) What are the dominant higher level bacterial and fungal taxa in each rainforest habitat type, and how does their relative abundance vary with respect to different rainforest habitats?

(3) How does the alpha and beta-diversity of bacteria and fungi vary across different rainforest habitats?

We hypothesized that white sand heath, inland heath and peat swamp forests would show lower alpha- and beta-diverseness of bacteria and fungi, with relatively distinct microbial communities compared to MDF primary and secondary forests due to distinctive weather condition of these environments.

Materials and Methods

Site Description and Sample Collection

Five unlike lowland tropical rainforest types in Negara brunei darussalam Darussalam, Northwest Borneo were selected for this study (Figure 1). These woods types were MDF primary, mixed dipterocarp secondary, white sand heath, inland heath, and peat swamp forests. The MDF primary forest is dominated by large tree species in the family Dipterocarpaceae and the forest structure is circuitous and multi-layered. The sampled MDF secondary forests were aged around 60 years (Davies and Becker, 1996). Previously, the secondary forest sampling area was covered with primary forest (Davies and Becker, 1996). MDF secondary forest is characterized by similar plant species composition to the MDF principal woods, merely differing by the dominance of pioneer tree species such as
Macaranga,
Vitex, and
Dillenia
species. The secondary MDF is also has a more open up structure, consisting of a circuitous mosaic of virtually- mature and regenerating forest patches with contrasting found compositions and micro-climates. The white sand and inland heath forests differ considerably from MDF forest both in plant species and structure (having a depression and uniform unmarried-layered canopy with dumbo undergrowth full of shrubs, herbs, pitcher plants, etc.). The main difference between the two heath forest types we sampled is that inland heath forest has low drainage capacity compared to white sand heath forest, which means that the white sand heath forest is beingness more than susceptible to drought, while the inland heath forest tin can be flooded for part of the year. The peat swamp forest sampled in this study is dominated by a single canopy species of fifty-fifty aged/sized trees of
Shorea albida
(Dipterocarpaceae), while general plant diversity is much lower than in MDF, although the overall forest structure tin be quite similar.

FIGURE ane

Effigy 1.
(A)
Soil sample locations of dissimilar woods types in Brunei.
(B)
Sampling scheme, iii clusters of samples (designated every bit A–C) were taken in each wood blazon within a 3 km transect. Within each cluster, three quadrats (10 1000 × 10 m in size) were nerveless at least thirty m apart forth a smaller calibration linear transect. Soil collected from the four corners and center of the each quadrat was pooled to make one samples for DNA extraction and soil holding analysis.

Field sampling was carried out during the month of June 2014, during a time with characteristic climate atmospheric condition in which afternoon rainstorms occurred nearly every other day (Becker, 1992). Brunei has a seasonal climate, with two drier periods Feb/March and July/August (Becker, 1992), and a mean almanac rainfall above 2300 mm (David and Sidup, 1996). Three clusters of samples were taken in each wood type within a 3 km transect (Figure 1). Within each cluster, three quadrats (10 m × 10 chiliad in size) were collected at least thirty m autonomously along a smaller scale linear transect (Figure 1). Each private sample consisted of v pooled samples (each approximately 50 1000 from the four corners and one center point of the quadrat). The top 10 cm of soil was nerveless in a sterile sampling handbag afterwards removing the litter layer. A total of 45 samples were collected from five different forest types (ix samples from each forest type). The nerveless soil samples were homogenized past sieving (ii mm sieve), and stored at -20°C until DNA extraction.

Soil Backdrop Analysis

Geographical co-ordinates and soil temperature at 5 cm depth were measured using a GPS device and a soil thermometer at each sampling quadrat during field sampling. Soil pH, gravimetric water content, organic affair content, full nitrogen and available phosphorus concentrations, and soil texture were measured at Universiti Brunei Darussalam using the standard methods (Allen, 1989). Total nitrogen content was determined past Kjeldahl method. Soil available phosphorus was extracted using Bray’s reagent (0.025 M hydrochloric acid and 0.03 M ammonium fluoride), and the phosphorus concentration in the extracts was and then determined using a UV-spectrophotometer (UV-1800, Shimadzu, Kyoto, Japan). Soil organic matter content was determined after incineration in a muffle furnace at 550°C for 2 h, according to the methodology described by Allen (1989).

DNA Extraction, PCR, and Illumina Sequencing of 16S rRNA Gene and ITS1 Region

Soil Dna was extracted from each of the collected samples using the PowerSoil DNA extraction kit (MO BIO Laboratories, Carlsbad, CA, USA) post-obit manufacturer’southward instructions, and Dna samples were sent to Macrogen Incorporated (Seoul, Korea) for PCR distension and sequencing. The extracted DNA samples were amplified for V3 and V4 region of 16S rRNA cistron using the primer pairs Bakt_341F (5′-CCTACGGGNGGCWGCAG-iii′) and Bakt_805R (5′-GACTACHVGGGTATCTAATCC-3′) for characterizing the bacterial communities (Herlemann et al., 2011). The fungal internal transcribed spacer (ITS) region one was amplified using ITS1F (v′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-iii′) primer pairs (White et al., 1990; Gardes and Bruns, 1993). The resulting 16S rRNA gene and ITS1 amplicons were sequenced using paired-terminate (two × 300 nt) Illumina Miseq system (Illumina, Us).

Sequence Processing

The paired-end sequences of 16S rRNA gene and ITS1 amplicons were assembled using PANDAseq assembler (Masella et al., 2012). The initial sequence processing steps such as quality filtering and sequence alignment were performed using mothur (Schloss et al., 2009). The 16S rRNA gene sequences were aligned confronting a SILVA alignmenti. Chimeric 16S rRNA gene and ITS1 sequences were identified using ‘chimera.uchime’ command implemented in mothur in
de novo
mode (Edgar et al., 2011), and removed. Taxonomic assignments of all the high quality 16S rRNA gene and ITS1 sequences were performed in mothur using the EzTaxon-e database2
(Kim et al., 2012), and UNITE database (Abarenkov et al., 2010), respectively. To make up one’s mind the ectomycorrhizal (EcM) fungi we matched the fungal taxonomic assignments with known EcM lineages (Tedersoo et al., 2010). The operational taxonomic units (OTUs) were assigned for 16S rRNA cistron and ITS one sequences using mothur and QIIME implementation of UCLUST (Caporaso et al., 2010; Edgar, 2010), respectively, with a threshold of ≥97% sequence similarity. The entire singleton OTUs were removed prior to analysis. All the 16S rRNA gene and ITS1 sequences used in this study are deposited to metagenomic-RAST server (Meyer et al., 2008) under the project ID 148753.

Statistical Analysis

Prior to statistical assay, a random subset of 3,352 and iv,207 sequences per sample was generated for 16S rRNA factor and ITS 1 sequences, respectively, to right for the differences in number of reads. To assess the differences in soil properties among different forest types, we used analysis of variance (ANOVA) or Kruskal–Wallis tests for normal and non-normal data, respectively. Furthermore, parametric (Tukey’due south HSD test) or non-parametric (pairwise Wilcox test)
post hoc
tests were used in case of significant results of ANOVA or Kruskal–Wallis tests, respectively. Nosotros used the Benjamini-Hochberg correction to assess pairwise comparisons (P
< 0.05; Benjamini and Hochberg, 1995). A principal components analysis (PCA) was performed on the correlation matrix of soil backdrop data of each sample in Canoco 5.0 (Biometrics, Wageningen, The Netherlands). We used permutational multivariate analysis of variance (PerMANOVA, ‘adonis’ function in vegan R package) to test the effect of forest blazon on a Euclidean altitude matrix of normalized soil backdrop data with 9999 random permutations.

Popular:   Agonists Bind to ________ and Antagonists Bind to ________

Cluster analysis was performed on Bray–Curtis distance matrices of bacterial and fungal OTUs by using an unweighted pair grouping mean (UPGMA) algorithm implemented in the ‘hclust’ function of vegan R package (Oksanen et al., 2007). As 16S rRNA genes are suitable for phylogenetic analysis, a unweighted UniFrac distance matrix was also generated for bacteria (Lozupone et al., 2011). Bray–Curtis and unweighted UniFrac distance matrices were farther visualized by non-metric multidimensional scaling (NMDS) plots. Furthermore, PerMANOVA was used to evaluate the upshot of forest type on Bray–Curtis and unweighted UniFrac distance matrices with 9999 random permutations. To detect possible associations between bacterial and fungal community structure and soil properties, the vectors of meaning soil properties (P
< 0.05) were fitted onto ordination space using the ‘envfit’ function of the vegan R package with 999 random permutations.

The significant differences in composition and diversity of bacterial and fungal taxa in unlike woods types were analyzed past ANOVA or Kruskal–Wallis tests as described in a higher place. To exam the human relationship betwixt soil properties and the relative abundance of dominant bacterial and fungal phyla, we used the Spearman rank correlation test. We performed linear regression analysis to test for differences in alpha-diversity (Shannon index) in relation to soil properties. We used the betadisper function of ‘vegan’ R package to assess the differences in beta-diversity amongst different forest types, and significance of this examination was determined using 999 permutations.

Results

Soil Properties amongst Forest Types

All the measured soil properties varied significantly among unlike forest types, except for total nitrogen and silt concentrations (Tabular array 1). PCA of the different soil backdrop measured indicated that peat swamp forest sites were clearly singled-out from other forest types (Supplementary Figure S1); even so, sites from other woods types were non well-separated from each other (Supplementary Effigy S1). The starting time ii axis of the PCA explained almost 71% of the total variance, with axis 1 and 2 explaining 51.6 and 19.4% of the total variance, respectively. The PerMANOVA analysis revealed a statistically significant effect of forest type on soil properties (P
< 0.001, 9999 permutations).

Tabular array 1

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TABLE 1.
Values of (mean ± SD) of soil properties in unlike forest types.

Bacterial and Fungal Community Composition among Forest Types

The UPGMA clustering analysis based on Bray–Curtis distance showed that bacterial and fungal customs compositions were largely separated past forest types (Supplementary Effigy S2). The bacterial and fungal communities in white sand heath forest were most distinct from all other woods types (Supplementary Figure S2). Whereas, MDF primary and secondary forests had virtually similar bacterial and fungal community composition. However, in the MDF secondary forest there were two and four extreme bacterial and fungal communities, respectively. The bacterial communities of inland heath and peat swamp forests were at the same altitude level to the MDF primary and secondary forest communities, whereas, bacterial communities in these forests were in turn at the same distance level to those in the white sand heath forest. In the case of fungi, inland heath forest communities are closer in composition to MDF forests than to peat swamp and white sand heath forests communities. The UPGMA clustering results were farther corroborated by the NMDS ordination plot, which besides showed that bacterial and fungal community compositions were segregated by forest type (Figures 2A,B). The PerMANOVA analyses indicated that forest type explained 36.one and 37.8% variation in bacterial and fungal community composition, respectively (P
< 0.001, 9999 permutations). The phylogenetic community limerick of bacteria, based on unweighted UniFrac distance also displayed similar pattern as that of bacterial OTU composition (Supplementary Figure S3), and also significantly influenced by forest type (PerMANOVA,
P
< 0.001, 9999 permutations).

Effigy 2

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Figure two.
Non-metric multidimensional scaling plot of
(A)
bacterial, and
(B)
fungal communities based on pairwise Bray–Curtis distances.

A vector overlay of the significantly correlated variables is shown on the plot. GWC, gravimetric h2o content and OM, organic matter content.

To further investigate the event of soil properties on bacterial and fungal community structure, the vectors of environmental variables were fitted onto ordination space. The environmental plumbing fixtures analysis indicated that of the measured soil properties, soil pH, organic matter content, gravimetric water content, bachelor phosphorus, temperature, sand and clay content were strongly correlated with bacterial and fungal customs structure (Supplementary Effigy S3 and Figures 4A,B).

Dominant Bacterial and Fungal Taxa

A full of 150,840 good quality bacterial 16S rRNA gene sequences were obtained (3,352 randomly selected reads per sample).
Proteobacteria
was the most dominant bacterial phylum (40.6% of all bacterial sequences) followed past
Acidobacteria
(37.2%),
Planctomycetes
(7.1%),
Actinobacteria
(3.5%),
Verrucomicrobia
(3.iv%), and
Chloroflexi
(2.ix%; Figure 3A). Except
Planctomycetes, the relative abundance of these phyla varied significantly (P
< 0.05) among forest types (Table two). For fungal ITS1 sequences, a total of 189,315 loftier quality sequences were obtained from 45 samples (4,207 randomly selected reads per sample). The most abundant fungal phylum detected across all samples was Ascomycota (54.ane% of all fungal sequences) followed by Basidiomycota (15.4%), and 30.1% of the detected sequences were unclassified (Figure 3B). The relative affluence of these most abundant fungal phyla varied significantly in relation to dissimilar woods types (Tabular array two). The relative abundance of Ascomycota was higher in white sand and inland heath forests (Table 2), whereas the relative affluence of Basidiomycota was higher in MDF principal and secondary forests (Table 2).

FIGURE 3

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Figure 3.
Relative affluence of dominant
(A)
bacterial phyla observed in 16S rRNA factor sequences and
(B)
fungal phyla in ITS1 sequences in different forest types.

Tabular array 2

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Table 2.
Comparing of relative abundance (mean ± SD) of dominant bacterial and fungal phyla, and EcM fungi among forest types.

A total of 18,460 sequences belonged to known EcM fungal genera, representing around nine.vii% of the total detected fungal sequences. The relative abundance of the detected EcM fungal genera varied significantly between woods types, with highest and everyman relative affluence observed in MDF primary forest and white sand heath forest, respectively (P
< 0.0001; Tabular array 2). The most abundant EcM fungal genus was
Russula
(78% of total EcM sequences), followed past
Amanita,
Thelephora, and
Tomentella. The relative abundance of
Russula
too varied significantly betwixt forest types, and showed similar design to that of total EcM fungi (Supplementary Tabular array S1).

The relative abundance of
Proteobacteria,
Acidobacteria, and
Chloroflexi
was significantly correlated with gravimetric h2o content, soil pH, and organic matter content (Tabular array 3). Whereas, the reactive abundance of
Planctomycetes
and
Actinobacteria
correlated with clay content (Table three). Additionally, soil temperature and sand content was also found to exist correlated with the relative affluence of
Acidobacteria
and
Actinobacteria, respectively. The relative abundance of
Verrucomicrobia
was not correlated with any of the environmental variables measured (Table 3). The relative abundance of the most dominant fungal phylum Ascomycota was significantly correlated only with dirt content, whereas the relative abundance of Basidiomycota the other dominant fungal phylum was significantly correlated with gravimetric water content and organic matter content.

TABLE 3

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TABLE 3.
Spearman rank correlations between soil properties and the relative abundance of ascendant bacterial and fungal phyla, and alpha diversity indices.

The Blastoff and Beta-Diversity of Leaner and Fungi

The blastoff-diversity index (OTU richness and Shannon index) of both bacteria and fungi also varied significantly among forest types (Table 4). The everyman average bacterial alpha diversity was observed in inland heath and peat swamp forest, whereas fungal blastoff diversity was everyman in peat swamp woods just, although due to high variation in variety values, considerable overlap in diversity existed between some forest types (Tabular array iv). Bacterial Shannon multifariousness alphabetize correlated positively with soil temperature and pH (Effigy 4A), whereas gravimetric water content and organic matter content of the soils displayed negative correlation with bacterial diversity indices (Table 3). Available phosphorus and soil dirt content were negatively correlated with the Shannon index of the fungi (Figure 4B), while sand content was establish to exist positively correlated with fungal Shannon alphabetize (Table 3). The Whittaker beta-diversity of bacterial and fungal communities, measured as the average altitude of all samples to the centroid in each forest blazon varied significantly among woods types (Figure 5). The MDF secondary and white sand heath forests having highest bacterial beta-variety, whereas MDF primary and secondary forests had highest fungal beta-diversity (Figure v).

Table 4

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TABLE 4.
The alpha diverseness indices (OTU richness and Shannon index) of bacteria and fungi in different forest types.

FIGURE 4

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Figure 4.
The relationships between
(A)
bacterial, and
(B)
fungal Shannon alphabetize and soil properties with symbols coded past forest types
. Linear regressions were used to test the correlation between Shannon index and soil properties.

Figure 5

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Figure five.
The Whittaker beta-variety of
(A)
bacterial customs and
(B)
fungal community in five different wood types in Brunei, Borneo.

Pregnant differences (P
< 0.05) between forest types, when nowadays, are indicated past different letters.

Give-and-take

Distinct Soil Conditions amongst the Dissimilar Woods Types

The soil conditions varied significantly among unlike woods types (Table 1), reflecting the wide scale mosaic of environments inside the lowland forests of Negara brunei darussalam (Moran et al., 2000; Din et al., 2015). The primary dipterocarp forest was toward the more acidic stop of the normal range of pH for lowland terra firme rainforest (about pH 3.7–5.5), with typical bachelor P and total N content (Sukri et al., 2012). The secondary wood had somewhat higher pH, but similar available P and total North levels. Soil temperature in the secondary woods at the time of sampling was somewhat higher on average, likely due to the more open up canopy assuasive greater daytime heating of the soil surface. Organic matter content was higher in both white sand and inland heath forests compared to MDF principal and secondary wood. The water logged surroundings of heath forests with limited oxygen levels might lead to accumulation of organic matter content (Moran et al., 2000). The sand content of both heath woods types was similar to the MDF main and secondary forests. Our ain examination of these soils before analyses showed that the white quartz grain component of the soils in both these heath woods types was unusually fine grade at these sites, and probably it ended up classified as silt course. The inland heath forest was very acidic and high in soil gravimetric h2o content, showing swampy conditions. Swampy areas in heath forests are quite common in this region (Moran et al., 2000; Din et al., 2015). The peat swamp forest soils were very acidic, with college organic matter and bachelor P contents than the other forest types, and its gravimetric water content was much higher – reflecting the high water table and the abundant spongy peat. The higher level of nutrients in peat swamp forest is not unusual, as the peat soil is generated from the aggregating of partially decayed organic matter due to water logged conditions with express oxygen supply (Andriesse, 1988; Satrio et al., 2009).

Popular:   Just Nintey Sense for a Bag

Are There Singled-out Microbial Communities on Different Rainforest Types?

The results of our comparison of soil bacterial and fungal communities across Negara brunei darussalam forests revealed that there are singled-out community types in different types of rainforest. The community composition of fungi and bacteria in each wood type was significantly different from all of the others, just the nearly distinct community is that of the peat swamp woods. The singled-out nature of these communities suggests that there is strong environmental and perhaps evolutionary selection for both bacterial and fungal OTUs better adapted in each environment. The clustering patterns of bacterial and fungal communities were very similar to one some other, and influenced past soil pH, organic affair content, gravimetric water content, available phosphorus, temperature, sand and dirt content. Previous studies on tropical soils accept too shown that microbial customs composition is influenced by variations in underlying soil properties due to land use change (Jesus et al., 2009; Tripathi et al., 2012, 2013; Lee-Cruz et al., 2013; Kerfahi et al., 2014).

The most arable bacterial phyla detected across the samples were
Proteobacteria
and
Acidobacteria, which is consequent with the results of previous studies on rainforest soils (Kanokratana et al., 2011; Tripathi et al., 2012; Lee-Cruz et al., 2013). The relative abundance of
Proteobacteria
was significantly lower in white sand heath and peat swamp forests, and this result could be due to restricted nutrient availability in oxygen-limited waterlogged environments of white sand heath peat swamp forests (Moran et al., 2000; Page et al., 2006). It has been shown that the relative abundance of major proteobacterial subphyla increases with food additions (Leff et al., 2015). However, the relative abundance of
Acidobacteria
was significantly higher in peat swamp forests and negatively correlated with soil pH – a event which is to be expected every bit peat soils had very low pH, and most of the acidobacterial lineages are shown to dominate acidic soil environments (Jones et al., 2009). The relative abundance of
Actinobacteria
was highest in white sand heath forest, and positively correlated with the sand content of soil. These results are in agreement with previous observations that the members of phylum
Actinobacteria
are generally abundant in sandy wood soils (Russo et al., 2012; Pacchioni et al., 2014). The dominance of photosynthetic bacterial phylum
Chloroflexi
in secondary forests could be explained by that, due to more open canopy secondary forest soils are exposed to sunlight to a greater extent than other forest soils (Nacke et al., 2014).

At the broad taxonomic level, the relative abundance of fungal taxa detected in this written report is similar to the soils of other tropical regions, where Ascomycota and Basidiomycota are too the most predominant phyla (Kerfahi et al., 2014; McGuire et al., 2014). Compared to MDF main forest, there is an increased proportion of Ascomycota in other forest types. Ascomycota are often found at higher affluence in stressful environments (De Beeck et al., 2015), and the communities hither appears to reflect this blueprint. However, the lower relative abundance of Basidiomycota in white sand heath forest, inland heath forest and peat swamp woods reflect the distinctive conditions in these woods types compared to MDF forests, as numerous Basidiomycota fungi tend to be slow-growing, belatedly-successional fungi that are sensitive to concrete and chemical perturbations (Frankland, 1998; Osono, 2007).

Are Distinctive Conditions Associated with Lower Fungal and Bacterial Diversity?

We expected to discover lower alpha- and beta-multifariousness of leaner and fungi in the more distinctive environments of the heath and peat swamp forests. This would exist due to a combination of the low likelihood of lineages acquiring the evolutionary adaptations necessary to live in the conditions of low pH and water logged environments with limited oxygen supply. However, the observed variety patterns did non follow these predictions. Alpha diversity of bacteria was college in the white sand heath forest than in MDF chief forest, and similar to MDF secondary forest, while inland heath and peat swamp wood had almost similar level of blastoff diverseness to the MDF primary woods. When samples across all the forest types were compared in relation to soil parameters, pH emerged as overwhelmingly the strongest predictor of bacterial alpha diversity (Figure 4A). This result gives farther confirmation of the generality of the pattern observed in other contexts around the earth (Fierer and Jackson, 2006; Lauber et al., 2009; Tripathi et al., 2012), that bacterial alpha multifariousness increases toward neutral pH. Bacterial beta-diversity was highest in the white sand heath forest and in the MDF secondary forest – the same blueprint every bit for bacterial alpha diversity. The other woods types had near similar levels of bacterial beta multifariousness to one some other. It appears that in this case tree species diversity has no bearing on the beta-diverseness, possibly reflecting the by and large looser relationships between soil bacterial diversity and particular tree hosts (Millard and Singh, 2010).

Fungal alpha variety was the same in all of the wood types except peat swamp forest. Thus, despite the apparently extreme conditions of two types of heath forests, fungal alpha diversity is no lower than in MDF principal or secondary forests. Only the peat swamp forest, possibly because of waterlogged weather, had lower fungal diversity. Beta variety of fungi was, notwithstanding, greater in the MDF primary and secondary forests than in the heath and peat swamp forests. This might be explicable in terms of the lower plant species diversity of these other not-terra firme wood types (Davies and Becker, 1996). Fungi often are involved in direct interactions with plants (Broeckling et al., 2008; Millard and Singh, 2010), and mycorrhizal fungi are specialized to grow nether straight symbiotic relationships with plants (Gao et al., 2013). The greater beta diversity in the two terra firme wood types might and so reverberate the greater tree species diverseness of these, with different samples able to reflect the range of host-tree-specific fungal communities that are present. An important role of the woody establish cover is too supported past the dominance of EcM fungi in MDF forests, EcM fungal groups are often dominant in Southeast Asian dipterocarp forests (Peay et al., 2010; Brearley, 2012; McGuire et al., 2014). Also importance might be a greater range of different saprotrophic fungal communities resulting from the input of unlike litter types from a more than various assemblage of tree species.

Overall, this study confirmed our expectations that inside the tropical rainforest, there is a potent degree of ecological differentiation in soil bacterial and fungal communities. Withal, the patterns in soil microbial diversity that nosotros found amidst the various forest types in Brunei practice not closely conform to our predictions that distinctive environments would prove lower alpha- and beta-multifariousness of bacteria and fungi. There is need for further theoretical consideration to endeavor to explain why the obviously distinctive and geologically ‘imperceptible’ environments of heath and peat swamp forests are about as various, or more diverse, than terra firme MDF chief and secondary forests.

Writer Contributions

BT, JS, RS, and JA designed the study, BT, WS, JS, RS, and SJ completed fieldwork in Brunei, BT, WS, and SJ processed samples in the laboratory, BT and KD completed data processing and analysis, BT and JA produced the commencement typhoon of the manuscript, and all authors edited the manuscript.

Funding

This work was supported past a grant from the National Research Foundation (NRF) funded by the Korean regime, Ministry building of Education, Science and Engineering science (MEST; NRF-0409-20150076).

Conflict of Involvement Statement

The authors declare that the enquiry was conducted in the absence of whatsoever commercial or fiscal relationships that could be construed as a potential disharmonize of involvement.

Acknowledgments

Nosotros give thanks the Negara brunei darussalam Forestry Department and the Biodiversity Research and Innovation Centre for entry and export permits respectively, and Universiti Negara brunei darussalam Darussalam for permission to comport research.

Supplementary Material

The Supplementary Fabric for this article tin exist found online at: https://www.frontiersin.org/article/x.3389/fmicb.2016.00376

Footnotes

  1. ^http://www.arb-silva.de/
  2. ^http://eztaxon-e.ezbiocloud.net/
  3. ^http://metagenomics.anl.gov/linkin.cgi?project=14875

References

Abarenkov, 1000., Henrik Nilsson, R., Larsson, K. H., Alexander, I. J., Eberhardt, U., Erland, Due south., et al. (2010). The UNITE database for molecular identification of fungi–recent updates and future perspectives.
New Phytol.
186, 281–285. doi: 10.1111/j.1469-8137.2009.03160.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen, S. E. (1989).
Chemic Assay of Ecological Materials.
Oxford: Blackwell Scientific Publications.

Google Scholar

Andriesse, J. (1988).
Nature and Management of Tropical Peat Soils.
Rome: Nutrient and Agriculture Organization of the Un Soils Bulletin. 59, 165.

Google Scholar

Araujo, J. F., de Castro, A. P., Costa, M. M., Togawa, R. C., Júnior, Yard. J. P., Quirino, B. F., et al. (2012). Characterization of soil bacterial assemblies in Brazilian savanna-like vegetation reveals acidobacteria dominance.
Microb. Ecol.
64, 760–770.

PubMed Abstract | Google Scholar

Ashton, P. S. (1988). Dipterocarp biology every bit a window to the understanding of tropical wood structure.
Ann. Rev. Ecol. Syst.
19, 347–370. doi: ten.1146/annurev.es.19.110188.002023

CrossRef Full Text

Becker, P. (1992). Seasonality of rainfall and drought in Brunei Darussalam.
Brunei Mus. J.
7, 99–109.

Google Scholar

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing.
J. R. Stat. Soc. Series B Stat. Methodol.
57, 289–300.

Google Scholar

Brearley, F. Q. (2012). Ectomycorrhizal associations of the Dipterocarpaceae.
Biotropica
44, 637–648. doi: ten.1111/j.1744-7429.2012.00862.10

CrossRef Total Text | Google Scholar

Broeckling, C. D., Broz, A. K., Bergelson, J., Manter, D. Thousand., and Vivanco, J. M. (2008). Root exudates regulate soil fungal customs limerick and multifariousness.
Appl. Environ. Microbiol.
74, 738–744. doi: 10.1128/AEM.02188-07

PubMed Abstract | CrossRef Total Text | Google Scholar

Bruenig, E., and Droste, H. (1995). “Structure, dynamics and direction of rainforests on food-deficient soils in Sarawak,” in
Ecology, Conservation, and Direction of Southeast Asian rainforests, eds R. B. Primack and T. Eastward. Lovejoy (London: Yale Academy Printing), 41–53.

Google Scholar

Brünig, E. F. (1974).
Ecological Studies in the Kerangas Forests of Sarawak and Negara brunei darussalam.
Kuching: Borneo Literature Bureau for Sarawak Woods Department.

Google Scholar

Cannon, C. H., and Leighton, G. (2004). Tree species distributions beyond five habitats in a Bornean pelting forest.
J. Veg. Sci.
15, 257–266. doi: x.1111/j.1654-1103.2004.tb02260.x

CrossRef Total Text | Google Scholar

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, One thousand., Bushman, F. D., Costello, E. K., et al. (2010). QIIME allows assay of loftier-throughput community sequencing data.
Nat. Methods
7, 335–336. doi: 10.1038/nmeth.f.303

PubMed Abstract | CrossRef Total Text | Google Scholar

Corlett, R. T. (2014).
The Ecology of Tropical East asia.
Oxford: Oxford University Printing.

Google Scholar

De Beeck, M. O., Ruytinx, J., Smits, Thousand. M., Vangronsveld, J., Colpaert, J. Five., and Rineau, F. (2015). Belowground fungal communities in pioneer Scots pine stands growing on heavy metal polluted and non-polluted soils.
Soil Biol. Biochem.
86, 58–66. doi: 10.1016/j.soilbio.2015.03.007

CrossRef Full Text | Google Scholar

Din, H., Metali, F., and Sukri, R. S. (2015). Tree diversity and community composition of the Tutong white sands, Brunei Darussalam: a rare tropical heath forest ecosystem.
Int. J. Ecol.
2015:2015. doi: 10.1155/2015/807876

CrossRef Full Text | Google Scholar

Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., and Knight, R. (2011). UCHIME improves sensitivity and speed of chimera detection.
Bioinformatics
27, 2194–2200. doi: 10.1093/bioinformatics/btr381

PubMed Abstract | CrossRef Full Text | Google Scholar

Frankland, J. C. (1998). Fungal succession—unravelling the unpredictable.
Mycol. Res.
102, 1–fifteen. doi: x.1017/S0953756297005364

CrossRef Full Text | Google Scholar

Gao, C., Shi, Northward. Due north., Liu, Y. X., Peay, K. G., Zheng, Y., Ding, Q., et al. (2013). Host found genus-level diversity is the best predictor of ectomycorrhizal fungal diversity in a Chinese subtropical wood.
Mol. Ecol.
22, 3403–3414. doi: 10.1111/mec.12297

PubMed Abstract | CrossRef Full Text | Google Scholar

Gardes, M., and Bruns, T. D. (1993). ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts.
Mol. Ecol.
2, 113–118. doi: 10.1111/j.1365-294X.1993.tb00005.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Herlemann, D. P., Labrenz, M., Jürgens, Thou., Bertilsson, Southward., Waniek, J. J., and Andersson, A. F. (2011). Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea.
ISME J.
5, 1571–1579. doi: 10.1038/ismej.2011.41

PubMed Abstruse | CrossRef Full Text | Google Scholar

Jesus, Due east. D., Marsh, T. L., Tiedje, J. M., and Moreira, F. M. D. (2009). Changes in land use alter the structure of bacterial communities in Western Amazon soils.
ISME J.
3, 1004–1011. doi: ten.1038/ismej.2009.47

PubMed Abstract | CrossRef Full Text | Google Scholar

Jing, X., Sanders, N. J., Shi, Y., Chu, H., Classen, A. T., Zhao, M., et al. (2015). The links between ecosystem multifunctionality and higher up-and belowground biodiversity are mediated by climate.
Nat. Commun.
vi:8159. doi: x.1038/ncomms9159

PubMed Abstruse | CrossRef Total Text | Google Scholar

Jones, R. T., Robeson, M. S., Lauber, C. L., Hamady, M., Knight, R., and Fierer, North. (2009). A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses.
ISME J.
3, 442–453. doi: ten.1038/ismej.2008.127

PubMed Abstract | CrossRef Full Text | Google Scholar

Kanokratana, P., Uengwetwanit, T., Rattanachomsri, U., Bunterngsook, B., Nimchua, T., Tangphatsornruang, Due south., et al. (2011). Insights into the phylogeny and metabolic potential of a chief tropical peat swamp woods microbial community by metagenomic analysis.
Microb. Ecol.
61, 518–528. doi: x.1007/s00248-010-9766-7

PubMed Abstruse | CrossRef Full Text | Google Scholar

Kerfahi, D., Tripathi, B. M., Lee, J., Edwards, D. P., and Adams, J. Yard. (2014). The impact of selective-logging and forest clearance for oil palm on fungal communities in Kalimantan.
PLoS ONE
nine:e111525. doi: 10.1371/journal.pone.0111525

PubMed Abstruse | CrossRef Full Text | Google Scholar

Kim, O. S., Cho, Y. J., Lee, K., Yoon, South. H., Kim, Yard., Na, H., et al. (2012). Introducing EzTaxon-e: a prokaryotic 16S rRNA factor sequence database with phylotypes that represent uncultured species.
Int. J. Syst. Evol. Microbiol.
62, 716–721. doi: 10.1099/ijs.0.038075-0

PubMed Abstract | CrossRef Total Text | Google Scholar

Lauber, C. 50., Hamady, 1000., Knight, R., and Fierer, N. (2009). Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale.
Appl. Environ. Microbiol.
75, 5111–5120. doi: x.1128/AEM.00335-09

PubMed Abstruse | CrossRef Full Text | Google Scholar

Lee-Cruz, L., Edwards, D. P., Tripathi, B. Grand., and Adams, J. M. (2013). Touch of logging and forest conversion to oil palm plantations on soil bacterial communities in Borneo.
Appl. Environ. Microbiol.
79, 7290–7297. doi: ten.1128/AEM.02541-13

PubMed Abstruse | CrossRef Full Text | Google Scholar

Leff, J. W., Jones, South. E., Prober, Due south. M., Barberán, A., Borer, E. T., Firn, J. Fifty., et al. (2015). Consistent responses of soil microbial communities to elevated food inputs in grasslands beyond the earth.
Proc. Natl. Acad. Sci. U.s.a.A.
112, 10967–10972. doi: 10.1073/pnas.1508382112

PubMed Abstract | CrossRef Full Text | Google Scholar

Lozupone, C., Lladser, M. Due east., Knights, D., Stombaugh, J., and Knight, R. (2011). UniFrac: an constructive distance metric for microbial community comparison.
ISME J.
five, 169–172. doi: 10.1038/ismej.2010.133

PubMed Abstract | CrossRef Full Text | Google Scholar

MacKinnon, K. (1996).
The Ecology of Kalimantan.
Oxford: Oxford University Press.

Google Scholar

Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G., and Neufeld, J. D. (2012). PANDAseq: paired-end assembler for illumina sequences.
BMC Bioinformatics
13:31. doi: 10.1186/1471-2105-13-31

PubMed Abstract | CrossRef Full Text | Google Scholar

McGuire, M., D’Angelo, H., Brearley, F., Gedallovich, S., Babar, Due north., Yang, N., et al. (2014). Responses of soil fungi to logging and oil palm agriculture in Southeast Asian tropical forests.
Microb. Ecol.
69, 733–747. doi: 10.1007/s00248-014-0468-4

PubMed Abstract | CrossRef Total Text | Google Scholar

Meyer, F., Paarmann, D., D’Souza, One thousand., Olson, R., Glass, E. M., Kubal, Thou., et al. (2008). The metagenomics RAST server–a public resource for the automatic phylogenetic and functional assay of metagenomes.
BMC Bioinformatics
9:386. doi: 10.1186/1471-2105-9-386

PubMed Abstruse | CrossRef Full Text | Google Scholar

Millard, P., and Singh, B. (2010). Does grassland vegetation bulldoze soil microbial diversity?
Nutr. Cycl. Agroecosys.
88, 147–158. doi: 10.1007/s10705-009-9314-iii

CrossRef Total Text | Google Scholar

Miyashita, N. T., Iwanaga, H., Charles, S., Diway, B., Sabang, J., and Chong, L. (2013). Soil bacterial community construction in v tropical forests in Malaysia and i temperate wood in Japan revealed past pyrosequencing analyses of 16S rRNA gene sequence variation.
Genes Genet. Syst.
88, 93–103. doi: 10.1266/ggs.88.93

PubMed Abstract | CrossRef Total Text | Google Scholar

Moran, J. A., Barker, M. G., Moran, A. J., Becker, P., and Ross, S. Chiliad. (2000). A comparison of the soil water, nutrient status, and litterfall characteristics of tropical heath and mixed-dipterocarp wood sites in Brunei1.
Biotropica
32, 2–13. doi: 10.1646/0006-3606(2000)032[0002:ACOTSW]ii.0.CO;2

CrossRef Total Text | Google Scholar

Nacke, H., Fischer, C., Thürmer, A., Meinicke, P., and Daniel, R. (2014). Land utilise blazon significantly affects microbial gene transcription in soil.
Microb. Ecol.
67, 919–930. doi: 10.1007/s00248-014-0377-6

PubMed Abstruse | CrossRef Full Text | Google Scholar

Oksanen, J., Kindt, R., Legendre, P., O’Hara, B., Stevens, M., Oksanen, M., et al. (2007).
The Vegan Bundle. Community Ecology Package 2.0-vii.

Google Scholar

Pacchioni, R. G., Carvalho, F. 1000., Thompson, C. East., Faustino, A. Fifty., Nicolini, F., Pereira, T. S., et al. (2014). Taxonomic and functional profiles of soil samples from Atlantic woods and Caatinga biomes in northeastern Brazil.
Microbiologyopen
3, 299–315. doi: x.1002/mbo3.169

PubMed Abstruse | CrossRef Full Text | Google Scholar

Folio, South., Rieley, J., and Wüst, R. (2006). Lowland tropical peatlands of Southeast Asia.
Dev Globe Surf. Proc.
9, 145–172. doi: 10.1016/S0928-2025(06)09007-ix

CrossRef Full Text | Google Scholar

Peay, K. Thou., Kennedy, P. Chiliad., Davies, S. J., Tan, S., and Bruns, T. D. (2010). Potential link between institute and fungal distributions in a dipterocarp rainforest: customs and phylogenetic construction of tropical ectomycorrhizal fungi beyond a plant and soil ecotone.
New Phytol.
185, 529–542. doi: 10.1111/j.1469-8137.2009.03075.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Pupin, B., and Nahas, E. (2014). Microbial populations and activities of mangrove, restinga and Atlantic forest soils from Cardoso Isle, Brazil.
J. Appl. Microbiol.
116, 851–864. doi: 10.1111/jam.12413

PubMed Abstract | CrossRef Full Text | Google Scholar

Rodrigues, J. L., Pellizari, Five. H., Mueller, R., Baek, Yard., Jesus Eda, C., Paula, F. S., et al. (2013). Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities.
Proc. Natl. Acad. Sci. U.S.A.
110, 988–993. doi: 10.1073/pnas.1220608110

PubMed Abstract | CrossRef Total Text | Google Scholar

Russo, S. E., Legge, R., Weber, K. A., Brodie, E. Fifty., Goldfarb, K. C., Benson, A. K., et al. (2012). Bacterial community structure of contrasting soils underlying Bornean rain forests: inferences from microarray and next-generation sequencing methods.
Soil Biol. Biochem.
55, 48–59. doi: 10.1016/j.soilbio.2012.05.021

CrossRef Full Text | Google Scholar

Satrio, A. East., Gandaseca, Due south., Ahmed, O. H., and Nik Muhamad, A. (2009). Result of precipitation fluctuation on soil carbon storage of a tropical peat swamp forest.
Am. J. Appl. Sci.
6, 1484–1488. doi: 10.3844/ajassp.2009.1484.1488

CrossRef Full Text | Google Scholar

Schloss, P. D., Westcott, South. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., et al. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.
Appl. Environ. Microbiol.
75, 7537–7541. doi: x.1128/AEM.01541-09

PubMed Abstruse | CrossRef Full Text | Google Scholar

Slik, J. W. F., Poulsen, A., Ashton, P., Cannon, C., Eichhorn, One thousand., Kartawinata, K., et al. (2003). A floristic analysis of the lowland dipterocarp forests of Borneo.
J. Biogeogr.
30, 1517–1531. doi: 10.1046/j.1365-2699.2003.00967.x

CrossRef Total Text | Google Scholar

Slik, J. West. F., Raes, Due north., Aiba, S. I., Brearley, F. Q., Cannon, C. H., Meijaard, E., et al. (2009). Ecology correlates for tropical tree diversity and distribution patterns in Borneo.
Divers. Distrib.
xv, 523–532. doi: 10.1111/j.1472-4642.2009.00557.x

CrossRef Full Text | Google Scholar

Sukri, R. S., Wahab, R. A., Salim, K. A., and Burslem, D. F. (2012). Habitat associations and community construction of dipterocarps in response to environment and soil weather condition in Brunei Darussalam, northwest Borneo.
Biotropica
44, 595–605. doi: 10.1111/j.1744-7429.2011.00837.x

CrossRef Full Text | Google Scholar

Tedersoo, L., May, T. W., and Smith, M. E. (2010). Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages.
Mycorrhiza
20, 217–263. doi: ten.1007/s00572-009-0274-x

PubMed Abstract | CrossRef Total Text | Google Scholar

Tripathi, B. One thousand., Kim, Thou., Lai-Hoe, A., Shukor, N. A. A., Rahim, R. A., Go, R., et al. (2013). pH dominates variation in tropical soil archaeal diversity and community structure.
FEMS Microbiol. Ecol.
86, 303–311. doi: ten.1111/1574-6941.12163

PubMed Abstract | CrossRef Full Text | Google Scholar

Tripathi, B. M., Kim, M., Singh, D., Lee-Cruz, 50., Lai-Hoe, A., Ainuddin, A., et al. (2012). Tropical soil bacterial communities in Malaysia: pH dominates in the equatorial torrid zone too.
Microb. Ecol.
64, 474–484. doi: 10.1007/s00248-012-0028-8

PubMed Abstruse | CrossRef Full Text | Google Scholar

Tripathi, B. M., Lee-Cruz, 50., Kim, G., Singh, D., Become, R., Shukor, North. A., et al. (2014). Spatial scaling furnishings on soil bacterial communities in Malaysian tropical forests.
Microb. Ecol.
68, 247–258. doi: 10.1007/s00248-014-0404-vii

PubMed Abstruse | CrossRef Total Text | Google Scholar

White, T. J., Bruns, T., Lee, S., and Taylor, J. (1990). “Distension and directly sequencing of fungal ribosomal RNA genes for phylogenetics,” in
PCR Protocols: A Guide to Methods and Applications, eds Thousand. A. Innis, D. H. Gelfand, J. J. Sninsky, and T. J. White (San Diego, CA: Academic Press), 315–322.

Google Scholar

Whitmore, T. (1984).
Tropical Rain Forests of the Far Eastward, 2nd Edn. Oxford: Oxford University Press.

Google Scholar

Bacteria in the Tropical Rainforests ___________

Source: https://www.frontiersin.org/articles/10.3389/fmicb.2016.00376/full