Supplementary Information
Supplemental Methods
All soils were transported to the laboratory within 2 hours of sampling. Subsamples (35 g) from
each core were set aside for the laboratory incubation experiment; the remaining soil was stored
at 4C until analysis. Microbial biomass assays were conducted with 24 h of collection, and
fungal abundance was measured within three days of collection. Soils for enzyme assays were
stored at -20C for one month prior to analysis, and subsamples for DNA extraction were
immediately archived at -80C.
Biogeochemical assays
Soil moisture and inorganic P concentrations were quantified as indices of soil water and nutrient
availability, respectively. Soil moisture was measured gravimetrically by drying soil samples in a
105C oven until they achieved constant weight. We focused on P specifically because there is
evidence that P is a limiting nutrient in this forest (McGlynn et al. 2007, Wood et al. 2005), and
work in similar Neotropical moist forests has shown that microbial respiration rates are strongly
constrained by P availability (Cleveland et al. 2002). Inorganic P was quantified by shaking the
resin bags in 0.5M HCl for 1 hour and filtering the extracts through pre-leached Whatman #1
filter paper. Available PO4- in the extracts was quantified colorimetrically using the malachite
green microplate method (D’Angelo et al. 2001).
Respiration rates and soil enzyme activities were measured as indices of microbial community
function. To quantify microbial respiration rates, a gastight syringe was used to take a headspace
sample from PVC cores or sealed glass microcosms. In the field, an airtight seal was created on
the PVC cores using flat metal lids equipped with rubber septa and lined with vacuum grease
(Dow Corning.) Air was sampled after 15 minutes. To account for temporal variability in
respiration rates, each field core was sampled twice over a 24-hour period, and the resulting
respiration values were averaged for the final analysis. Because the mesh cores were not airtight,
it was not possible to sample these cores for respiration rates in the field. In the laboratory,
microcosms were sealed for 24 hours before sampling the headspace. All gas samples were
stored in 12-ml borosilicate vials (Whatman, Kent, UK) sealed with gas-impermeable butyl
rubber septa (Geo-Microbial Technologies Inc., Ochelata, OK) placed in screw-top lids until
analysis on a SRI 8610C gas chromatograph (SRI Instruments, Santa Monica, CA). Cumulative
respiration over the course of the microcosm experiment was calculated using the formula
n
R T
i
i 0
i
where n is the duration (days) of the microcosm experiment, Ri is the mean respiration
rate (µmol CO2 g-1 h-1) between two successive respiration measurements, and Ti is the time

(hours) between the two respiration measurements (Liu et al. 2009). We also calculated massspecific respiration (an index of microbial carbon use efficiency) by dividing respiration rate
(µmol CO2 g-1 h-1 by microbial biomass (µg C g-1) at each timepoint.
Enzyme assays were performed on 5-g soil samples according to standard colorimetric protocols
(Sinsabaugh et al. 1993). Activities of acid phosphatase (AP) and beta-glucosidase (BG), two
enzymes involved in carbon and phosphorus cycling, respectively, were quantified by incubating
litter with p-nitrophenol labeled substrates for 1-2 hour and measuring concentration of released
p-nitrophenol on a spectrophotometer at 410 nm.
Total microbial biomass and fungal hyphal abundance were quantified as indices of microbial
abundance. Fungal hyphal lengths were counted by mixing 5 g of fresh soil with 500 ml of 5%
sodium hexametaphosphate, filtering 15-ml aliquots through two replicate 0.45-m
nitrocellulose filters, and staining with acid fuchsin (Brundrett et al. 1994). Hyphal abundance
was quantified using the magnified grid-intersection method (McGonigle et al. 1990) at 160x
with 100 fields of view per sample. Microbial biomass was measured using the chloroform
fumigation and direct extraction method (Brookes et al. 1985, Scott-Denton et al. 2006),
whereby organic C was extracted from 5-g unfumigated and 5-g chloroform-fumigated soils with
25 ml of 0.5M K2SO4. The filtered extracts were frozen until analysis on an Apollo 9000 TOC
Analyzer (Teledyne Tekmar, Mason, OH). Microbial biomass was calculated by dividing the
difference in carbon concentrations between fumigated and unfumigated extracts by a conversion
factor of 0.45.
Quantification of microbial community structure via 454 pyrosequencing
DNA was extracted from archived subsamples of each core using a MoBio PowerSoil extraction
kit (MOBIO Laboratories, Carlsbad, CA). DNA extracts from each plot were quantified on a
NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE) and combined within each
individual combination of block, precipitation treatment, and core treatment, for a total of 24
pooled DNA samples. The pooled samples were amplified in triplicate with Invitrogen PCR
Supermix (Invitrogen, Carlsbad, CA) and the primer pairs 27F/338R and EF4/fung5, which
amplify variable regions of the bacterial and fungal small subunit rDNA, respectively (Nemergut
et al. 2010, Smit et al. 1999). Amplicons were purified with a MoBio UltraClean PCR Clean-Up
kit (MoBio Laboratories, Carlsbad, CA). To allow for multiplexing, each unique sample was
ligated to a different RapidLibrary Multiplex Identifier (MID barcode; Roche, Basel,
Switzerland) using the NEBNext 454 Rapid Library Kit (New England Biolabs, Ipswitch, MA).
The barcoded amplicons were then purified of fragments < 300 bp using Agencourt AmPure XP
magnetic beads (Beckman Coulter, Brea, CA). The concentration of DNA in each sample was
quantified using Qubit fluorometric assays (Life Technologies, Grand Island, NY), and bacterial
and fungal amplicons were subsequently pooled in equimolar concentrations. Finally, amplicons
were sequenced on a Roche 454 FLX sequencer with titanium chemistry (Roche 454 Life
Sciences, Branford, CT) at the University of Texas at Austin Genome Sequencing and Analysis
Facility.
Sequence data were analyzed using the QIIME pipeline (Caporaso et al. 2010). The data were
quality filtered to exclude any sequences with quality scores below 25, sequences with fewer
than 150 bp, or sequences with an anomalous barcode (either mismatches or incorrect length).
All singletons unique to the entire dataset were removed, as these are likely to represent
sequence errors (Kunin et al. 2010), and chimeras were removed with USEARCH 6.1 (Edgar
2010). Operational taxonomic units (OTUs) were defined at 97% sequence similarity using the
UCLUST algorithm (Edgar 2010). Representative sequences from each OTU were searched
against the RDP (Wang et al. 2007) and SILVA (Quast et al. 2013) databases to identify any
non-bacterial or non-fungal data (‘unclassified’ or metazoan sequences), which were eliminated
from the dataset. All samples were rarefied before analysis in order to avoid bias in sampling
effort among samples. Because soils from one block (Block 6, the driest block) did not yield
more than 500 bacterial or fungal sequences, even after re-amplification and re-sequencing, all
samples were rarefied to 500 sequences. Recently, McMurdie and Homes (2013) suggest that
rarefaction may bias analyses of community sequence data; however, all statistical results
reported in this manuscript were consistent when performed on rarefied data vs. samples with
unequal library sizes. Representative sequences from each OTU were used to build phylogenetic
trees using the default settings in the Simultaneous Alignment and Tree Estimation program
(SATé II v 2.2.7, Liu et al. 2012).
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Table S1. Spearman correlation matrix for relationships among bacterial and fungal NMS
coordinates, in situ soil moisture (‘GSM’), fungal hyphal abundance, microbial biomass carbon
(‘MBC’), acid phosphatase (AP) and beta-glucosidase (BG) activities, respiration rate (‘CO2’),
and soil inorganic P. Significant ρ values (P ≤ 0.05) are shown in bold.
Bac2 Fung1 Fung2 GSM hyphae MBC AP
BG
CO2 Pinorg
-0.16 0.06
0.55
-0.43
Bac1
0.85
-0.73
-0.59 -0.69 -0.46 0.09
Bac2
Fung1
-0.18
-0.36
-0.65
0.10
-0.50
0.25
0.17
0.11
-0.05
-0.05
-0.65 -0.44 -0.09
0.46
0.66
-0.69
-0.32
-0.66 -0.69 -0.03
0.22
-0.11
0.06
-0.47 -0.34 0.11
0.55
0.43
0.61
0.66
0.41
-0.10
0.38
0.51
0.44
0.34
0.79
0.26
-0.47
0.41
-0.22
Fung2
GSM
Hyphae
MBC
AP
BG
CO2
0.10
-0.23
-0.85
-0.05
Table S2. Summaries of regressions between phylum-level relative abundance of bacteria and
gravimetric soil moisture across plots. Only significant relationships are shown.
Phylum
Actinobacteria
Elusimicrobia
FCPU426 (Candidate division)
Planctomycetes
-Proteobacteria
-Proteobacteria
WPS (Candidate division)
Slope
-0.07266
0.00796
0.00555
0.00426
0.02393
0.03645
-0.04554
P
0.021
<0.001
0.010
0.011
0.003
0.044
0.039
Table S3. Repeated-measures ANOVA for the effects of field precipitation treatment (Precip),
laboratory soil moisture treatment (Moisture), and their interaction on microbial biomass and soil
respiration rate, measured over time. P-values are corrected to account for violations of
sphericity in the respiration data (Greenhouse-Geisser ε = 0.74).
Respiration
Microbial Biomass
Factor
df MS
F
P
df MS
F
P
Between subjects
Precip
1
0.006624 8.53 0.007
1 286957
0.78 0.386
Moisture
2
0.001935
2.49
0.099
2
25552675
Precip*Moisture
2
0.002653
3.42
0.046
Error
Within subjects
Time
30
0.000776
2
30
557732
370214
9988924
47.03 <0.001
3
6
6
90
0.032
0.736
0.001
0.076
1
Time*Precip
Time*Moisture
Time*Precip*Moisture
Error
3.48
0.34
6.28
2.16
1
2
2
30
263395
2076472
44203
212404
1.24 0.274
9.77 <0.001
0.21 0.813
3
0.001095
0.000106
0.001972
0.000679
0.000314
69.02 <0.001
1.51
0.238
Figure S1. Percent change in the relative abundance of each bacterial phylum in the precipitation
exclusion treatment relative to control. Asterisks indicate a significant difference between
treatments in planned comparison tests. Phylum Chlorobi (not shown) exhibited an 89%
decrease in a relative abundance, but this was driven entirely by reductions in Block 3.