Appendix S2: Experimental Procedures
1. DNA Extraction
DNA was extracted from a 0.25 g subsample of each soil using PowerSoil® DNA Isolation Kits (MoBio
Laboratories Inc., Carlsbad, USA).
2. Automated Ribosomal Intergenic Spacer Analysis (ARISA)
ARISA profiles were generated according to the method of Lear et al. (2009). PCR was undertaken on
the extracted DNA using GoTaq® Green DNA polymerase master mix (Invitro Technologies Ltd.,
Auckland, New Zealand) supplemented with Bovine Serum Albumin (BSA; Invitrogen Ltd., New
Zealand) to a final concentration of 0.8 μg μl-1. The primers SDBact (5’-TGC GGC TGG ATC CCC TCC
TT-3’) and LDBact (5’-CCG GGT TTC CCC ATT CGG)(Ranjard et al., 2001) were added to a final
concentration of 0.4 μM to amplify an intergenic region, between bacterial 16S and 23S rRNA genes.
The primer SDBact was labelled at the 5’ end with the HEX fluorochrome (carboxyhexafluorescein;
Invitrogen Molecular Probes, New Zealand) to enable subsequent analysis by ARISA. PCRs were
performed using the following temperature cycling conditions : (i) 95 oC for 5 min; (ii) 30 cycles of 95
o
C for 30 s, 61.5 oC for 30 s, 72 oC for 90 s and then (iii) 72 oC for 10 min.
Following amplification, PCR products were purified using Zymo DNA Clean and Concentrator kits
(Ngaio Diagnostics Ltd., Nelson, New Zealand) and 1 μl combined with 10 μl Hi Di formamide and an
internal LIZ1200 standard (ABI Ltd., Melbourne, Australia) before being heat treated (95oC, 5 min)
and then cooled on ice. To generate ARISA profiles of bacterial community structure, these samples
were run on a 3130XL Capillary Genetic Analyser (Applied Biosystems Ltd.) using a 50 cm capillary
and standard genemapper protocol [but with an increased run time(15 kV, 65 000 s)] to record the
fluorescent intensity of different sized PCR products within each sample. Genemapper software
(version 3.7; ABI Ltd., Melbourne, Australia) was then used to assign a fragment length (in nucleotide
base pairs) to ARISA peaks, via comparison with the LIZ1200 standard ladder. The protocol of
Ramette et al (2009) was then used to identify ‘true peaks’ (i.e., removing background ‘noise’
generated during automated analysis) and bin fragments of similar size. Using this approach we
detected 343 different sized PCR products among all samples, which we use as the operational
taxonomic units of bacteria in our study. A maximum of 153 different ARISA peaks (or bacterial taxa)
were detected in any single sample.
3. Quantitative PCR of bacterial and archaeal amoA genes
The abundance of genes encoding bacterial ammonia oxidation (bacterial amoA) and archaeal
ammonia oxidation (archaeal amoA) were quantified with qPCR. Bacterial amoA genes were
amplified using the primers amoA1F (5’-GGG GTT TCT ACT GGT GGT-3’) and amoA2R (5’-CCC CTC
KGS AAA GCC TTC TTC-3’)(Rotthauwe et al. 1997) and the following temperature cycling conditions:
(i) 94 oC for 2 min; (ii) 40 cycles of 94 oC for 20 s, 57 oC for 30 s, 72 oC for 30 s, as described in Di et al
(2009). Archael amoA genes were amplified using the primers amoA1F (5’-STA ATG GTC TGG CTT
AGA CG-3’) and amoA2R (5’-GCG GCC ATC CAT CTG TAT GT-3’)(Francis et al. 2005) and the following
temperature cycling conditions: (i) 94 oC for 2 min; (ii) 40 cycles of 94 oC for 20 s, 55 oC for 30 s, 72 oC
for 30 s, as described in Di et al (2009). qPCR was undertaken on every DNA extract, using SYBR®
Premix Ex Taq™ (TaKaRa, Japan) in which the final 24 μl mix contained 0.2 μM of each primer and 1.5
μg μl-1 of BSA (Invitrogen, New Zealand). For each PCR, tenfold serial dilutions of a known copy
number of target DNA (provided by Jei Lei, Lincoln University) were also amplified to check reaction
efficiencies. R2 values ranged from 0.981 to 0.991 for bacterial amoA, and from 0.985 to 0.992 for
archaeal amoA. Melting curve analysis was performed to ensure PCR product specificity after
amplification, by continuously measuring fluorescence as the amplification temperature was raised
from 50oC to 99oC. All qPCR reactions were run on an ABI Prism 7000 sequence detection system
(ABI Ltd.) and analyses of qPCR data were performed using ABI PRISM® 7000 SDS software (version
1.2.3, ABI Ltd.).
4. Quantitative Analyses
To visualise multivariate patterns in bacterial community ARISA data, multidimensional scaling
(MDS) was performed on a Bray Curtis distance matrix. Using this procedure, points located closer
together on the MDS plot are presumed to represent datasets which are more similar than those
located further apart.
ARISA data were analysed using permutational multivariate analysis of variance (PERMANOVA;
Anderson, 2001; McArdle & Anderson, 2001) on the basis of Bray Curtis distances among ARISA
profiles. The overall experimental design consisted of two factors: (i) Mowing/Biomass removal (four
levels; [M0] never mown, [MIBL] mown irregularly (when the sward reaches 30 cm in height) with
mown biomass (clippings) left on the plot, [MIBR] mown irregularly with mown biomass removed on
the plot and [MRBR] mown regularly when the sward reaches 15 cm in height, with mown biomass
removed from the plot, and (ii) N-fertiliser (two levels, [N0] no fertiliser added and [N1] 50 kg urea-N
ha-1 yr-1 applied in spring). P-values were obtained using 9999 permutations under a reduced model
(Anderson & ter Braak, 2003). In addition to the PERMANOVA partitioning, multivariate analogues to
the usual univariate ANOVA estimators for variance components were used to quantify the
variability associated with each source of variation in the model. These were expressed in terms of
their square root (i.e., as a ‘pseudo’ standard deviation). ARISA datasets were also compared using
multivariate dispersion index values in PRIMER (MVDISP; based on Bray Curtis distances among
samples) which provide a ‘relative’ score of the multivariate variability within each of the treatment
groups in a single ordination.
In addition, the relationship between the ARISA data and measured environmental variables was
investigated using distance-based multivariate multiple regression on the basis of Bray Curtis
distance using the DISTLM routine (McArdle & Anderson, 2001). The model was built using forward
selection and P-values obtained using 9999 permutations.
All multivariate analyses were performed using the PRIMER v.6 computer program (Clarke & Gorley,
2006) with the add-on package PERMANOVA+ (Anderson et al., 2008). Tukey’s honest significant
difference test (SPSS 20) was used to test whether univariate soil chemistry parameters differed
between treatments.
References
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Anderson, M. J., Gorley, R. N. & Clarke, K. R. (2008). PERMANOVA+ for PRIMER: guide to software
and statistical methods. PRIMER-E Ltd., Plymouth, United Kingdom.
Clarke, K. R. & Gorley, R. N. (2006). PRIMER v.6: User Manual/Tutorial. PRIMER-E, Plymouth, UK.
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