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Supplemental Information for
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Pre-exposure to short-term drought increases the resistance of subtropical forest
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soil bacterial communities to extended drought
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Nicholas J Bouskill1, HsiaoChien Lim1, Sharon Borglin1, Rohit Salve2, Tana E Wood3,4,
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Whendee Silver3 & Eoin L Brodie1
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1Ecology
Department, Earth Sciences Division, Lawrence Berkeley National
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Laboratory, Berkeley, CA, 94720
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2Hydrology
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94720.
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3Department
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California-Berkeley, CA, 94209.
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4International
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00926.
Department, Lawrence Berkeley National Laboratory, Berkeley, CA,
of Environmental Science, Policy and Management. University of
institute of Tropical Forestry, USDA Forest Service, Rio Piedras, PR,
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Supplemental materials and methods
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Site Description
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Bisley receives approximately 3500 mm of annual rainfall with no distinct dry-
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season (Heartsill-Scalley et al., 2007). Annual temperature averages 23° C (Scatena,
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1989) and the site experiences little variability in annual temperature (McDowell et
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al., 2010). The soils are classified as ultisols and are clay-rich and acidic with high Al
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and Fe concentrations (Table 1. Scatena, 1989). The study site was confined to an
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upper ridge top with a closed canopy forest and sparse understory (Wood and
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Silver, in press). The dominant vegetation is Manikara bidentata and Dacyodes
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excelsa (Tabonuco) (at 52 and 40 % basal area, respectively).
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Throughfall exclusion and sampling
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Throughfall was excluded using clear, corrugated plastic panels (approximately 1.6
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m2) that were mounted 1 m above the forest floor plots on PVC pipes at a 17 angle.
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The top-sheet drained into a PVC gutter that removed throughfall away from the
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plots. The plots were open to lateral airflow to minimize the effect of temperature
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and increase the likelihood that changes in throughfall was responsible for any
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observed change in bacterial community structure.
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In June 2008, ten 1.5 m2 plots were established. Throughfall was excluded on
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five of these plots for a period of three months (June 2008 – August 2008). This
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exclusion treatment was equivalent to reducing annual throughfall by 22 – 32 % and
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significantly reduced volumetric soil moisture (30 cm depth) by 36 % (Wood and
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Silver, in press). The following year (June 2009) the throughfall shelters were
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replaced over the five original exclusion plots and were termed pre-excluded. At the
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same time an additional five 1.5 m2 plots were selected. Unlike the pre-excluded
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plots, these five plots had not been subject to previous throughfall exclusion and
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were termed de novo. Of these 15 plots throughfall was excluded from 10 (the five
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pre-excluded plots and the five de novo plots) for a period of 10-months. Soil
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samples were collected from the 15 plots three times over this period (June 2009,
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August 2009, April 2010). The June 2009 sampling was considered the baseline
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measurement. At this time the five pre-excluded had been left under conditions of
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ambient rainfall for 1 year. We collected three samples of approximately 5 – 10 g of
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soil (0 – 10 cm depth) from each plot using an alcohol-sterilized corer (1.3 x 14.1 cm
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width x length). The samples were put into sterile whirl pack bags and were shipped
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overnight at ambient temperature to Berkeley, CA. Cores were gently homogenized
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by hand and large root material removed. Fresh soil was used for pore water
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analyses and the remaining material was stored at -80 oC for molecular analyses.
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Soil Chemistry
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The high soil clay content did not allow for direct filtration-based extraction of
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porewater. Therefore, porewater was extracted from 15 g of soil by adding 15 ml
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ultra-pure water and vortexing for 10 minutes. Samples were filtered through a 0.2
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μm nylon filter by centrifugation for 30 minutes at 2,500 rpm at 4 °C. Porewater
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anions (nitrate, nitrite, sulfate, chloride, lactate, acetate, pyruvate and propionate)
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were quantified by ion chromatography (ICS-2100, Dionex and a IonPac AS11-HC
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column; eluent is 30 mM KOH). For cation analysis, 1 ml of porewater was diluted in
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4 ml of 1 % HNO3 and spiked with an internal standard. Analysis was done using a
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multi-element method on an ICP-MS (SCIEX Elan DRC II, Perkin Elmer). Soil pH and
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porewater pH were determined using an Orion 8303 pH electrode (Thermo
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scientific, Waltham, MA), and electrical conductivity measured using the YSI CST
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instrument (Model 3100, YSI, OH). Aliquots of the extracted porewater were spiked
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with sucrose and sodium bicarbonate (at 6 mg L-1 each) and dissolved organic
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carbon (DOC) concentrations determined using a TIC/TOC analyzer (TOC-VCSH,
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Shimadzu). Soil ammonium concentrations were measured from 1 g of soil using a
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KCL extraction procedure and microplate protocol based on the colorimetric
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reduction of sodium salicylate (Allison et al., 2008). Soil Ψ measurements were
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performed using a WP4 dewpoint potentiameter (Decagon Devices Inc, Pullman,
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WA).
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Phospholipid fatty acid analysis (PLFA)
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Fungal biomass and fungal: bacterial ratios were quantified by extraction and
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analysis of soil PLFA profiles. Samples were extracted by the Bligh-Dyer method
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(Guckert et al., 1985; White and Ringelberg, 1998; Pfiffner et al., 2006). Briefly,
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samples were added to 10 ml of a 10:5:4 mixture of methanol:chloroform:pH 7
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phosphate buffer to which 50µL of 500 mg/L 1,2-dinonadecanoyl-sn-glycero-3-
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phosphocholine (Avanti Polar Lipids, Alabaster, Alabama) was added as an internal
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standard The extract was separated into neutral, glycerol, and phospholipids on a C-
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18 silica column (Sigma Chemicals, St. Louis, MO) by sequential elution with
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chloroform, acetone, and methanol. The methanol fractions that contain
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phospholipids were subjected to a mild alkaline hydrolysis to remove the head
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group and create fatty acid methyl esters (FAME) compounds were detected on an
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Agilent 6890N GC/FID on a HP1 60m column x 0.25 mm ID and quantified by
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comparing to known standards. Peak confirmation was accomplished by Agilent
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6890 GC/MS. Lipid classes were grouped into guilds according to previous studies
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(White and Ringelberg, 1998; Cusack et al., 2011).
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Determination of shifts in the osmotic stress tolerance of culturable soil bacteria
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The likelihood that throughfall exclusion will select for drought-tolerant taxa was
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examined by cultivating isolates from control, pre-excluded and de novo plots across
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a NaCl-gradient agar plates. 1 g of each soil sample was suspended in dilute nutrient
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broth (DNB: 0.08 g L-1 nutrient broth, adjusted to pH 5.0 with 1.0 m HCl) by
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vortexing for 1 minute, prior to serial dilution (10-1 - 10-4) in triplicate. The dilutions
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were plated onto DNB/ NaCl gradient plates. The plates were prepared by a
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previously described method (i.e., Szybalski, 1952) with a high salt agar (0.08 g L-1
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DNB, 1M NaCl, 15 g L-1 noble agar) layer poured at a 45 angle into a petri dish. Once
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solidified, DNB agar with no NaCl was poured over the top and left at room
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temperature to solidify and allow diffusion and subsequent osmotic gradient to
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form (0 to 1.0 M NaCl, or 0 to -5.0 MPa). The same procedure was also used to create
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plates with gradients from 0 to 0.5 M NaCl (0 to -2.5 MPa). Plates were inoculated
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with 50 l soil solution and incubated for 3 – 4 weeks at 27 C. Colony forming units
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were counted at each section of the plate and analyzed as a function of inferred Ψ.
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DNA Extraction
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Total nucleic acids were extracted from 0.5 g of the triplicate soil samples according
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to the protocol described in Ivanov et al (2009). Briefly, samples were resuspended
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in a modified CTAB buffer (10 % CTAB, 250 mM potassium phosphate, 300 mM
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NaCl), transferred to a lysing matrix E tube (MP biomedicals) and an equal volume
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of phenol:chloroform:isoamylalcohol (25:24:1) added. Samples were agitated using
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a FastPrep instrument (MP biomedicals: 2 x 20 seconds, 5.5. m/s) and centrifuged
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(16,000 x g, 5 min, 4 C). The aqueous phase was removed to a new tube and an
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equivalent volume of chlorform:isoamylalcohol (24:1) added to yield a crude nucleic
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acid extract and the solution centrifuged again. The aqueous phase was removed to
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a new tube containing polyethylene glycol (30 % wt/vol), and incubated for 2 hours
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before being centrifuged. The supernatant was discarded and the crude nucleic acid
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pellet washed in 70 % ethanol. The pellet was dissolved in nuclease-free (DEPC-
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treated) water and purified using the DNA/RNA AllPrep kit (Qiagen, Valencia, CA)
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following the manufacturers protocol. Purified DNA was eluted in buffer EB (2 x 30
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l) and quantified in triplicate using the Quant-iT Picogreen assay (Invitrogen,
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Carlsbad, CA).
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Quantitative PCR
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qPCR assays were performed on the extracted DNA from each sample following
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Fierer et al., (2007). Separate qPCR assays were performed to quantify the
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abundance of the Actinobacteria and the total bacteria 16S ribosomal RNA copy
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number. The assays were performed in triplicate for each sample in a BioRad iCycler
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single-color real-time PCR detection system (BioRad, CA, USA) using SYBR Green
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dye (BioRad, CA, USA). All primers, reaction conditions and concentrations were
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identical to Fierer et al. (2007). Standard curves for each assay were generated
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using triplicate 10-fold dilutions of known plasmid standards containing PCR
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products from a Kitasatospora isolate from Puerto Rico soil. A melt curve analysis
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was used to confirm the presence of the target PCR product using the MyIQ software
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(BioRad). The standard curves were used to calculate copy number for each
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reaction.
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Bar-coded Pyrosequencing
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Soil DNA samples were normalized to 10 ng l-1 prior to PCR. Samples were
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amplified using a universal primer pair targeting the SSU V9 region. The forward
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primer was 515F (‘5-GTGCCAGCMGCCGCGGTAA-‘3, Turner et al., 1999) and the
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reverse primer was 907R (‘5-CCGTCAATTCCTTTRAGTTT-‘3, Lane et al., 1991). The
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forward and reverse primers were downstream of the FLX-454 primer adapters and
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the reverse primer also contained a 12-bp barcode unique to a specific sample
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(Hamady et al., 2008). PCR reactions were performed using Takara Ex Taq
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polymerase (Takara, Madison, WI), with the following thermocycling parameters:
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initial denaturation at 95 C for 1 minute followed by 25 cycles of 95 C for 20
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seconds, 30 seconds of annealing at 66 C and extension at 72 C for 1 minute. Final
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product extension was at 72 C for 10 minutes. Reaction primer dimers were
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removed from the PCR products via SPRI bead purification according to the
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manufacturers protocol (AMPure XP, Beckman Coulter Genomics, Danvers, MA)
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before being checked for quality and quantity on a Bioanalyzer 2100 using a DNA
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7500 chip (Agilent Technologies, Santa Clara, CA). Each PCR sample was normalized
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to 30 ng and combined together for multiplex sequencing. Sequencing libraries were
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created using the SV emu-PCR kit (Lib-A, Roche, Indianapolis, IN) and sequencing on
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a GS-FLX sequencer (Roche, Indianapolis, IN) at the Veterans Medical Research
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Foundation, La Jolla, CA.
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Pyrosequencing Data processing and statistical analysis
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Raw 454 sequences were uploaded, denoised and processed using the Quantitative
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Insights Into Microbial Ecology (QIIME) pipeline (Caporaso et al., 2010a). Low
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quality reads were filtered out and the sequences assigned back to the original
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samples via the 12 bp barcodes. Default QIIME parameters were used for processing
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the pyrosequencing data. OTUs were defined at 97 % via uclust (Edgar, 2010), and a
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representative sequence of each OTU used for alignment via PyNAST (Caporaso et
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al., 2010b) and for taxonomy assignment through the RDP database (Cole et al.,
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2009). Phylogenetic trees were created using FastTree under default parameters
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(Price et al., 2009). The trees were used for the derivation of alpha and beta
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diversity metrics, including weighted and unweighted unifrac distance matrices that
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calculate phylogenetic distances between communities through the degree of
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overlap between branch lengths in a phylogenetic tree. The weighted unifrac
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distance matrices were used to visualize the soil community composition over time
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through ordination in a correspondence analysis (vegan package, Okansen et al.,
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2010, in the R statistical environment, www.r-project.org).
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Soil chemistry data were tested for normality by assessing skewness and
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kurtosis using the D’Agostino-Pearson K2 test and data was log transformed if
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necessary. The effects of experimental drought on soil chemistry and enzyme
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activity was assessed comparing the control with exclusion plots using by two-way
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ANOVA (Hoff et al., 1987). The relationship between porewater chemistry and
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changes in either soil moisture or Ψ was further quantified using a regression
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analysis (Draper and Smith, 1998) to identify correlations in the chemical dataset.
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All analyses were performed through the Matlab statistics toolbox (version 7.6,
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Natick, MA).
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A number of variance partitioning approaches was applied to explain any
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phylogenetic changes as a function of treatment. The weighted distance matrix was
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used against categorical data in a permutational multivariate analysis (Adonis), also
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implemented in the vegan package, to explain the dissimilarity between
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communities as a function of different variables (soil moisture, Ψ, pH and sodium
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concentration). To more thoroughly characterize the relationship between
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microbial community composition and physicochemical factors the pairwise
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correlation co-efficients were calculated using different matrices (i.e.
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physicochemical x physicochemical; biological x biological; physicochemical x
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biological) following a previously described method (Bouskill et al., 2011). In this
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case the biological matrix was a rarefied genus level OTU table generated through a
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taxa summary command in QIIME. The pairwise relationships between the
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biological and physicochemical matrices were then projected onto a principal
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component ordination space. This approach allows for a constrained visualization of
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specific relationships between biological and environmental data. The constrained
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nature of the ordination likely provides a more accurate depiction of environment-
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genotype relationships than a simple vector overlay method would afford.
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Supplemental Results
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Phospholipid fatty acid analysis
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Total PLFAs across the treatments ranged from 380 to 708 μg g-1 dry soil, however,
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there was no significant difference in lipid biomass across the plots between June
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2009 and April 2010 (Fig. S2). Fungal lipids contributed a small component of the
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total PLFA (range 4.6 – 6.4 % total lipid); yet, trends in the data suggest that fungal
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biomass fell slightly under reduced throughfall. Similarly, fungal: bacterial ratios at
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the outset of the study were very similar between the control and exclusion plots
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(Control, 0.045; Pre-excluded, 0.039; De-novo, 0.046). Following three and ten
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months of throughfall exclusion there was a slight, but insignificant, decline in
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fungal: bacterial ratio between the onset of the experiment and the 10-month
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sampling point.
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Determination of shifts in the osmotic stress tolerance of culturable soil bacteria
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For quantification of the distribution of isolates (CFUs) across osmotic gradient
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plates, petri dishes were sub-divided into six regions representing increasing
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concentrations of NaCl. Inferred NaCl concentrations across section correspond to 0,
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0.17, 0.33, 0.5, 0.66, 0.83 and 0.99 M. Total CFU counts from the 10-3 dilution plates
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spanning a 0 – 1.0 M NaCl gradient were 120 (± 17) from control sample plates; 128
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(± 34) from pre-excluded plates and 195 (± 13) from the de novo plates (Fig. 1).
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From the control soil plots, CFUs were highest where osmotic stress was lowest (0
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to 0.17 M NaCl), while CFUs from the exclusion plot soils peaked where osmotic
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stress was higher (0.33 to 0.55 M NaCl). The upper limit for colony growth was also
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significantly higher for the pre-excluded and de novo exclusion samples compared to
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the controls. Similarly, across more constrained osmotic gradient plates, more CFUs
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from exclusion plot soils were detected at higher NaCl concentrations than control
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plot soils (Fig 1, insert).
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Quantitative PCR
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Quantification of the actinobacterial 16S rRNA gene showed the abundance to
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increase with treatment. The highest abundances were noted in the de novo plots
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(average = 2.9×109 ± 1.7×109 copies/ g of soil) and was significantly higher than in
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the control (average 7.9×108 ± 4.8×108 copies/ g of soil. ANOVA, p < 0.05). The pre-
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excluded treatment also showed elevated actinobacterial 16S rRNA (2×109 ±
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1.6×109). However, it was not significantly higher than the control (Fig. S8).
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References
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Caporaso J, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. (2010b).
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Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, et al. (2009). The ribosomal
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Table S1. Soil physicochemical variables collected from control and treatment plots
during June, 2009 prior to onset of rainfall exclusion and again at August, 2009
three months following the onset of rainfall exclusion.
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Table S2. Analysis of pairwise dissimilarities. Abbreviations: Df, degrees of freedom;
SS, sum of squares; MS, mean squares; F, F-test statistic. Treatment (categorical),
control vs rainfall exclusion; Ψ, Ψ; Na, porewater sodium concentrations.
Ψ
Df
1
1
SS
0.047
0.016
MS
0.023
0.016
F
1.66
1.07
p
0.16
0.34
Na
Treatment x Na
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2
Treatment x Ψ
2
2
0.039
0.04
0.033
0.039
0.022
0.017
2.88
1.9
1.3
0.02
0.07
0.1
0.044
0.029
3.21
0.01
0.021
0.01
0.7
0.8
Sources of variance
Treatment
Ψ x Na
Treatment x Ψ x Na
2
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Figure S1: Hypothetical response curve depicting the relationship between
microbial community activity and soil moisture. The assumed positioning of the
Puerto Rico control and treatment plots is also shown on the curve (see text for full
description).
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Figure S2: Plot biomass data showing average lipid concentrations (μg g-1 dry soil
weight, ± standard deviation). Bars are grouped by treatment with different years
for each plot given from left to right. Left bar = Prior to rainfall exclusion; Middle = 3
month post exclusion and right bar = 11 months post exclusion.
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Figure S3: Stacked bar charts of phylum/ sub-phylum relative abundances across
the different plots prior to the placement of rainfall exclusion shelters (June, 2009).
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Figure S4: Changes in relative abundance of taxa at the genus level three months
following the onset of rainfall exclusion in the de novo-exclusion plots relative to the
control plots. The graph is the same format as that in Fig. 3.
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Figure S5: Changes in relative abundance of taxa at the phyla level ten months after
the onset of rainfall exclusion in the de novo and pre-excluded plots relative to the
control plots.
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Figure S6: Quantitative-PCR of the actinobacterial 16S rRNA gene. Data shows the
abundance of the gene across the control and treated soil plots (n = 5). The x
denotes significant differences between the treatments relative to the controls
(ANOVA, p < 0.05).
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Figure S7: Phylogenetic groupings present in the control plots lost from the de novo
plots by April 2010.
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Figure S8: Principal components analysis detailing the relationship between plot
community composition and edaphic factors following three months of rainfall
exclusion. Symbols of both biological and physicochemical variables are the same as
in Figure 5.
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