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Supplementary Material and Methods on The ISME Journal website
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Microbial succession in a chronosequence of chalk grasslands
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Eiko E. Kuramae1, 2, Hannes A. Gamper1, Etienne Yergeau1, 3, Yvette M. Piceno4, Eoin L.
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Brodie4, Todd Z. DeSantis4, Gary L. Andersen4, Johannes A. van Veen1,5 and George A.
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Kowalchuk1,2
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Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW),
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Heteren, The Netherlands; 2Institute of Ecological Science, Free University Amsterdam,
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Amsterdam, The Netherlands; 3Biotechnology Research Institute, National Research Council
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of Canada, Montréal, Canada; 4Ecology Department, Lawrence Berkeley National Laboratory,
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Berkeley, USA; 5Institute of Biology, Leiden University, Leiden, The Netherlands
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Site description and soil sampling and chemical analyses
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Two secondary grassland succession series were defined in nature reserves in the province of
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Limburg, The Netherlands. The first series were selected at Wrakelberg (stages: W0-W4), on
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a slope (15-20º inclination) exposed to the SW, and the second series at Gerendal (stages: G0-
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G4) in fields with variable inclinations and exposures. Conventional arable fields with winter
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wheat (Triticum aestivum) represented the arable fields as reference of succession (W0/G0).
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Three grasslands, whose soils were last plowed about 17, 22, and 36 years ago, formed
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intermediate succession stages (W1/G1, W2/G2, W3/G3, respectively). The soils of the oldest
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succession stages were most probably never plowed and never fertilized, which is verified for
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at least the last 66 years.
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Soil samples were taken at each of the 10 sites at the end of winter 2007 along linear
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transects of 20 m (Wrakelberg) or 10 m (Gerendal). At each site, five (A, B, C, D, E
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(Wrakelberg) or three A, C, E (Gerendal) within-field replicate composite soil samples were
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made by combining two cores (10-cm depth, 2cm diameter) taken less than 10 cm apart. This
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yielded a total of 40 composite soil samples (25 Wrakelberg and 15 Gerendal). The soils were
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sieved to < 3mm, thereby excluding larger root fragments and pieces of chalk.
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Chemical soil analyses were performed according to Novozsamsky et al. (1984). Briefly,
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soil pH was determined in 1:2.5 (w/w) suspensions of lyophilized soil:H2O and 0.01 M CaCl2.
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Available P was measured colorimetrically in 0.01 M CaCl2 suspensions by the Molybdenum
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blue method and mineral N as the NH4+ and NO3- concentrations in KCl extracts, using a
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Traacs 800 autoanalyzer .
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Plant species
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A total of 52 plant species were counted per 1m2 in five biological replicates per succession
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stage for plant species richness and diversity (Simpson index and Shanon Weaver index) at
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Wrakelberg site. The plant species were Arhenatherum elatius, Avenula sp., Avenula sp.
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(hard), Brachypodium pinatum, Briza media, Campanula sp., Carex flacca, Carex
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cariophyllaceae, Cirsium sp., Centaurea scabiosa, Convolvulus arvensis, Crataegus sp.,
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Dactylis glomerata, Daucus carota, Euphrasia stricta, Festuca ovina, Galium mollugo,
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Genista tinctoria, Gymnadenia conopsea, Hieracium pilosella, Hypericum perforatum, Inula
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conyza, Knautia arvense, Leontodon hispidus, Leucanthemum vulgare, Linum catharticum,
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Lolium perenne, Lotus corniculatus, Medicago lupulina, Ononis repens, Oreganum vulgare,
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Pimpinella saxifraga, Plantago lanceolata, Poa sp., Poligola comosa, Potentilla sp., Prunella
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vulagris, Prunus avium, Ranunculus sp., Reseda lutea, Rhinanthus minor, Rosa sp., Rubus sp.,
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Sanguisorba minor, Scabiosa columbaria, Senecio sp., Thymus pulegioides, Trifolium
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pratense, Trifolium sp., Triticun aestivum, Vicia sp., Viola sp.
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Nucleic acids extraction and 16S rRNA gene amplification
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Five biological replicates per succession stage of Wrakelberg and three biological replicates
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per succession stages of Gerendal, totalling 40 samples were used for DNA isolation. Each
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sample was DNA extracted from 0.25g of soil, using the Power Soil kit (MoBio, Carlsbad,
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CA, USA) with bead-beating at 5.5 m s-1 for 10 min. Total DNA concentration was quantified
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on a ND-1000 spectrophotometer (Nanodrop Technology, Wilmington, DE, USA). 16S rRNA
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gene amplification was performed by using the bacterial-specific primers, 27F (5’-
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AGAGTTTGATCCTGGCTCAG-3’)
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(Wilson
et
al.,
1990),
and
and
the
1492R
(5’-GGTTACCTTGTTACGACTT-3’)
archaeal-specific
primers,
4fa
(5’-
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TCCGGTTGATCCTGCCRG-3’) and 1492R (5’-AGAGTTTGATCCTGGCTCAG-3’). PCR
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amplifications were carried out with 1× Ex Taq buffer (Takara Bio Inc, Japan), 0.8 mM dNTP,
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0.02 units l-1 Ex Taq polymerase, 0.4 mg ml-1 BSA, and 1.0 M of each primer. Three
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independently PCR amplifications were performed with the annealing temperatures 48C,
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51.9C and 58C for both, bacteria and Archaea, with an initial denaturation 95C (3 min), 25
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cycles amplification cycles with denaturation 95C (30 s), annealing (30 s), and extension
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72C (60 s), followed by a final extension at 72C (7 min). PCR products of bacteria and PCR
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products of archaea were separately pooled, and a 2 l sub sample was quantified on 2%
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agarose by using E-gel Low Range Quantitative DNA ladder (Invitrogen, USA). The volume
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of pooled PCR reactions was reduced to less than 40 l with micrometer YM100 spin filters
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(Millipore, Billerica, MA). Regression analysis confirmed that quantity of PCR amplicon
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applied to the array was not correlated with any organism abundances as estimated by
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fluorescence intensity of hybridization (data not shown).
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Phylochip processing, scanning, and normalization
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The pooled PCR products described above were spiked with known concentrations of
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amplicons derived from yeast (YEL002C_WPB1, YEL024W_RIP1, YER022W_SRB4,
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YER148W_SPT15, YFL039C_ACT1) and prokaryotic metabolic genes (i.e. Bacillus
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thuringiensis, Mesorhizobium loti, Pseudomonas aeruginosa, etc). This amplicon mix was
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fragmented to 50-200 bp using DNase I (0.02 U/ug DNA; Invitrogen) and One-Phor-All
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buffer following Affymetrix’s protocol. The mixture was then incubating at 25C for 20 min,
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98C for 10 min. before labelling. Biotin labelling was carried out by using GeneChip DNA
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labelling reagent following manufacturer’s instructions (Affymetrix). Next the labelled DNA
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was denatured at 99C for 5 min and hybridized to custom-made Affymetrix GeneChips (16S
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PhyloChips) (DeSantis et al., 2007), at 48C and 60 rpm for 16 h in a hybridization chamber.
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PhyloChip washing and staining were performed according to standard Affymetrix protocols
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(Masuda and Church, 2002). Each PhyloChip was scanned and recorded as a pixel image, and
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initial data acquisition and intensity determination were performed using standard Affymetrix
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software (GeneChip microarray analysis suite, version 5.1). To account for scanning intensity
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variation from array to array, the intensities resulting from the internal standard probe sets
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were natural-log-transformed. Adjustment factors for each PhyloChip were calculated by
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fitting a linear model with the least-squares method. A PhyloChip's adjustment factor was
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subtracted from each probe set's natural log of intensity.
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Background subtraction, noise calculation, and detection and quantification were
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essentially as previously reported (Brodie et al., 2006). The probe pair was considered positive
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when the difference in intensity between the perfect match and mismatch probes was at least
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130 times the square noise value (N). A taxon was considered present in the sample when 90%
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or more of its assigned probe pairs for its corresponding probe set were positive (positive
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fraction (PosFrac) >=0.90). OTU richness of a sample was simply the number of OTU
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considered positive for a given sample. Relative OTU intensities were calculated by dividing
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the average signal of the probes aiming at a given OTU by the total average signal for all the
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OTUs that were identified as present. OTUs which PosFrac did not exceed 0.90 were set to
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zero scored as not detected. The relative abundance values were used directly for most
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analyses, and were also summed up to the Phylum level for other analyses.
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Real-time PCR
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Real-time PCR quantifications for Acidobacteria, Actinobacteria, Alphaproteobacteria,
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Bacteroidetes, Betaproteobacteria, Firmicutes and bacteria were performed using primers and
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cycling conditions previous described (Fierer et al., 2005), and fungi were quantified
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according to Lueders et al. (Lueders et al., 2004). Real-time PCR quantifications were carried
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out on soil DNA as previously described (Yergeau et al., 2007). Known template standards
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were made from PCR-amplified full-length 16S or 18S genes recovered from previously
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characterized clones. Most samples, and all standards, were assessed in at least two different
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runs to confirm the reproducibility of the quantification. Results of real-time PCR
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quantifications for the different phyla/classes were calculated as a percentage of total bacteria,
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by dividing the phyla/class 16S rRNA gene abundance by total bacterial 16S rRNA gene
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abundance.
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Statistical Analyses
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In order to evaluate if similarities between samples based on OTU composition, soil factors
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and plant community were significantly related, Mantel test on similarity/distance matrices
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was carried out, based on Mantel’s r (rm) with 999 permutations using P. Legendre’s statistical
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package Casgrain and Legendre (2001). The choice of similarity/distance indices for the
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different datasets followed the rationale detailed in Legendre and Legendre (1998): Bray-
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Curtis distance for OTU relative intensity and plant biomass, Jaccard similarity for OTU
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presence-absence and Gower similarity for soil chemical data.
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Multivariate test of significance for the effects of field age or location on the microbial
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community structure was carried out using db-RDA (Legendre and Anderson, 1999). First, the
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relative intensity matrices coming from image analyses were transformed using principal
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coordinate analysis (PCoA), based on the appropriate distance/similarity as above. All
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resulting axes (representing 100% of the variation in the dataset) were then used as “species”
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data in redundancy analysis (RDA) in Canoco 4.5 (ter Braak and Šmilauer, 2002), with
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treatment variables being the only environmental variable included in the analysis. The
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significances of each treatment were tested with 999 permutations.
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Microbial community structure was related to soil factors using canonical correspondence
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analyses (CCA) in Canoco. Relative OTU intensity was used as “species” data while soil and
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environmental data were included in the analysis as “environmental” variables. Since this
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analysis is sensitive to rare species, only OTUs that were present in more than 4 different
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samples were used in the analyses. Variables having the most significant influence on the
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bacterial community structure were chosen by forward selection with a 0.10 baseline. The
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variable selected this way were then included in a model of which significance was tested with
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999 permutations. Phylum relative abundance data was added as “supplementary” variables,
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not involved in the calculations.
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ANOVA was carried out in Statistica 7.1, following normalising transformations when
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required (mostly log, square or cubic root transformation). Factorial ANOVA was used to test
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the effects of restoration type (Wrakelberg vs Gerendal), succession stage (arable field to
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permanent chalk grassland) and their interaction. When transformation failed to normalise
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data, Kruskal-Wallis ANOVA was used instead of parametric ANOVA. Results were
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considered to be significant at P<0.05. For some data, Tukey-HSD post-hoc tests were also
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carried out following ANOVA to identify significantly different groups. All correlation
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analyses (Spearman rs) were carried out in Statistica 7.0 (StatSoft Inc., Tulsa, OK). Results
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were considered significant at a P<0.05 baseline and to be nearly significant at 0.05<P<0.10.
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Supplementary References
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Application of a high-density oligonucleotide microarray approach to study bacterial
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clone library when sampling the environment. Microb Ecol 53: 371-383.
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