Supplementary Methods
Description of the BC LTSP study sites
Detailed information about the Long-Term Soil Productivity installations in British Columbia can be found
in the establishment report at http://www.for.gov.bc.ca/hfd/library/FIA/2009/FSP_Y092042b.pdf. Here we
provide a synopsis of the most important characteristics. As shown in Figure 1, the six BC LTSP sites are
located in two different biogeoclimatic zones, i.e. the Sub-Boreal Spruce (SBS) and Interior Douglas-Fir (IDF)
zone respectively. The SBS is characterized by snowy, cold winters, and short, warm, and moist summers
with mean annual temperature ranges from 1.7 to 5 °C. Mean annual precipitation can range from 415 to
1650 mm, with snow accounting for approximately 25-50% of the total precipitation. Climax tree species in
the SBS are hybrid white spruce (Picea engelmannii x glauca) and subalpine fir (Abies lasiocarpa), whereas
lodgepole pine (Pinus contorta) is common in maturing forests in the drier and more southern parts of the
zone. Other seral tree species include Douglas-fir (Pseudotsuga mensiesii), trembling aspen (Populus
tremuloides), and common paper birch (Betula papyrifera). The IDF is characterized by warm, dry summers
and cool winters with mean annual temperatures range from 1.6 to 9.5 °C. Mean annual precipitation is
between 300 and 750 mm but can exceed 1000 mm in the wettest subzones, and 20-50% of the
precipitation falls as snow. Douglas-fir is the most common climax tree species in the IDF, while ponderosa
pine (Pinus ponderosa) or hybrid white spruce and western redcedar (Thuja plicata) can be found on drier
and wetter sites, respectively. Seral species include lodgepole pine, trembling aspen, and paper birch.
SBS2
SBS1
SBS3
IDF2
IDF3
IDF1
Figure 1. Locations of the six LTSP experimental sites examined in this study, i.e., SBS1-3 and IDF1-3. The
Sub-Boreal Spruce (SBS) and Interior Douglas-Fir biogeoclimatic zones are indicated in blue and red,
respectively.
1
Each LTSP site of the respective biogeoclimatic zone represents an experimental block in a randomized
complete block design. For the SBS experiment, three sites were selected in zonal ecosystems (maturing
seral to maturing climax stands) in three different subzones with an average distance of 285 km between
sites. For the IDF experiment, three sites with predominantly zonal ecosystems (maturing seral to maturing
climax stands) were selected in one subzone with an average distance of 9 km between sites. The average
distance between the six sites in both biogeoclimatic zones is 395 km. Uniformity of stand structure as well
as soil and site properties critical to plant growth were important criteria in the site selection process (Table
1, Hope 2006, Kranabetter and Sanborn 2003, Sanborn et al. 2000). All sites have deep (> 1 m), mediumtextured soils and mor humus forms. Within each site, understory vegetation was uniform prior to treatment
application.
PCR amplification and sequencing
The DNA concentration of samples was adjusted to 10 ng DNA µl-1 with ddH2O containing bovine serum
albumin (BSA, New England Biolabs, Ipswich, MA) to yield a final concentration of 1 µg BSA µl-1. These
samples were heated for 5 min at 90°C to bind PCR inhibiting substances like humic acids to the BSA
(Rådström et al. 2004). PCR was performed using 50 ng soil DNA in a total volume of 50 µl containing 1×
PCR-buffer (Qiagen Inc., Mississauga, ON), 2 mM MgCl2, 0.2 µM of each primer, 0.4 mM dNTP (Fermentas,
Burlington, ON), 15 µg BSA and 2 U of HotStar Taq polymerase (Qiagen). Amplification was performed with
initial denaturation for 15 min at 95 oC followed by 30 cycles with denaturation for 40 s at 94 oC, annealing for
40 s at 60 oC (bacterial 16S) or 58 oC (eukaryal ITS) respectively, and extension for 1 min at 72 oC, followed
by a final extension for 10 min at 72 oC. The hypervariable region V1-V3 of the bacterial 16S rRNA gene was
amplified
using
primers
27F
(A-AGAGTTTGATCMTGGCTCAG,
Lane
1991)
and
519R
(B-
GWATTACCGCGGCKGCTG, Amann et al. 1995) where A and B are the two standard FLX primers (Roche
454 Life Sciences). The internal transcribed spacer 2 (ITS2) of the fungal ribosomal operon including parts of
the 5.8S and large subunit rRNA gene was amplified using primers ITS3 (B-GCATCGATGAAGAACGCAGC,
White et al. 1990) and ITS4 (A-TCCTCCGCTTATTGATATGC, White et al. 1990). Each sample was
amplified in triplicate and subsequently pooled prior to purification. Each pooled PCR product was purified
using Illustra GFX™ PCR purification columns (GE Healthcare, Piscataway, NJ). Purified PCR products
were checked for quality and quantity with a NanoDrop ND-1000 Spectrophotometer (NanoDrop
Technologies, Wilmington, DE). Concentrations were adjusted to 40 ng µl-1 with Tris-HCl buffer. Barcodes
and 454 GS FLX Titanium adapters were retrofitted to the amplicons at the Genome Québec Innovation
2
Centre (Montréal, Canada) prior to sequencing. Barcodes were added to FLX primer A and tags were unidirectionally sequenced from that end.
Target extraction and verification
After quality filtering of the raw sequences using MOTHUR (Schloss et al. 2009), the bacterial 16SV1-V2 and
fungal ITS2 regions were verified and extracted using V-XTRACTOR (Hartmann et al. 2010) and its equivalent
tool for fungal ITS (Nilsson et al. 2010), respectively. The ITS primers used (White et al. 1990) amplify the
ITS region for several other groups of eukaryotes in addition to fungi, but the extraction software tool only
targeted and extracted fungal ITS (Nilsson et al. 2010). As for 16S, the V1-V2 region was selected as the
segment of choice within the V1-V3 amplicon. In an initial assessment, the V1-V2 region provided a better
trade-off between number of reads and phylogenetic information when compared to shorter (V1, higher
number of reads but lower success of taxonomic assignment) or to longer segments (V1-V3, higher success
of taxonomic assignment but lower number of reads) of the amplicon. Importantly, these software tools
extract gene segments that are comparable for community analysis and at the same time confirm basic
authenticity of the target (Hartmann et al. 2010).
OTU Clustering with Crunchclust
Sequences were clustered into OTUs using CRUNCHCLUST, a novel scalable open-source software tool
written in C++. CRUNCHCLUST uses a greedy incremental clustering algorithm optimized for large amplicon
data sets derived from 454 pyrosequencing and accounts for frequent homopolymer detection errors
considered critical when using this technology (Behnke et al. 2011, Huse et al. 2010, Quince et al. 2009).
Since pyrotag data sets comprise a very large number of reads and encompass mostly variable domains,
clustering based on multiple sequence alignments are frequently inaccurate (Liu et al. 2010), difficult to
compute as data sets grow (Liu et al. 2010), and artificially inflate OTU richness (Sun et al. 2009, Huse et al.
2010). Aligning pyrotags against well-curated reference alignments can improve accuracy and computational
practicability (Schloss 2009), but does not work well for new deeply branched lineages, domains with high
divergence rates, or sequences with homopolymer errors. Therefore, obtaining reasonable reference
alignments for many hypervariable genetic regions such as the ribosomal internal transcribed spacers is
unlikely. As a result, clustering algorithms independent of multiple sequence alignments have been
developed (Edgar 2010, Huang et al. 2010), which however do not solve the problems associated with the
inherent noise. For this matter, dedicated denoising pipelines have been released that try to eliminate
erroneous reads (Huse et al. 2010, Quince et al. 2009, Quince et al. 2011, Reeder and Knight 2010).
3
Unfortunately, these pipelines are often slow and complex to run.
In one single step, CRUNCHCLUST de-replicates raw reads sorting them according to their abundance and
clusters the de-replicated reads into OTUs using a global exact Needlemann-Wunsch (Needleman and
Wunsch 1970) pairwise alignment to calculate the exact number of differences between two sequences
while ignoring dissimilarities due to homopolymer lengths and terminal gaps when the distal primers are not
reached. Although real variations in homopolymer length will go undetected using the optional homopolymerfilter, this strategy has proven to be an efficient means to reduce artificial inflation of OTU richness caused by
sequencing errors, which have been reported to account for a major part of errors in such datasets (Behnke
et al. 2011). These homopolymer errors by far exceed the true biological variation in homopolymer length
among sequences. CRUNCHCLUST does not require high performance computing resources (although it is
MPI compatible) and runs efficiently on laptop computers using a single CPU core. Memory requirements are
negligible and the software is supported on Linux, Windows, and Mac operating systems. CRUNCHCLUST is
available at http://code.google.com/p/crunchclust/. We benchmarked CRUNCHCLUST against related
“denoising” pipelines (Huse et al. 2010, Quince et al. 2009, Quince et al. 2011, Reeder and Knight 2010) and
USEARCH (Edgar
2010) using 454 data sets of mock communities equivalent to one titanium run. Where other
pipelines required days to process this information, both CRUNCHCLUST and USEARCH required only minutes,
the former using an exact method and the latter a fast heuristic one. However, USEARCH does not account for
homopolymer errors, whereas CRUNCHCLUST does. In conclusion, CRUNCHCLUST represents a rapid algorithm
dedicated to accurately clustering massively parallel pyrotag sequence data sets to produce denoised OTUs
for use in downstream analytic pipelines such as MOTHUR.
Multivariate analysis of community structures and diversity
Data were standardized by dividing the number of reads in each taxonomic unit by the total number of
reads in each sample. Standardized data were square root transformed to downweight the contribution of
quantitatively abundant OTUs to the similarities calculated between samples. A resemblance matrix was
calculated using Bray-Curtis similarities (Bray and Curtis 1957) based on the standardized square root
transformed read abundance data. The Bray-Curtis similarity coefficient is one of the most widely applied
resemblance measures and meets many criteria desired for application to ecological data (Clarke et al. 2006
#10473). Principal Coordinate Analysis (PCO, Gower 2005) implemented in the PRIMER6+ package was used
to display similarities in microbial community structures among all samples. PCO is an unconstrained metric
multidimensional scaling ordination that extracts major variance components from the multivariate data set in
4
order to reduce dimensionality of the data cloud by minimizing the residual variation in the space of any
chosen resemblance measure.
Tests of the multivariate null hypotheses of no differences among a priori defined groups were examined
using analysis of similarities (ANOSIM, Clarke 1993), permutational multivariate analysis of variance
(PERMANOVA, Anderson 2001), and canonical analysis of principal coordinates (CAP, Anderson and Willis
2003) implemented in PRIMER6+. These non-parametric discriminative methods based on permutation tests
do not rely on assumptions that are commonly too stringent for most ecological data sets (Anderson 2001)
and base the analysis on multivariate distance measures that are reasonable for these data (McArdle and
Anderson 2001). ANOSIM is a univariate non-parametric analogue of analysis of variance (ANOVA) and
provides the test statistic R that equals 0 when there are no differences among groups and 1 for the
maximum difference among groups (Clarke 1993). PERMANOVA is analogous to multivariate analysis of
variance (MANOVA), which allows partitioning the variability of the data according to a complex design or
model, and provides F-ratios that are analogous to Fisher's F-ratio in MANOVA (Anderson 2001). CAP uses
PCO followed by canonical discriminant analysis to provide a constrained ordination that maximizes the
differences among a priori groups and reveals patterns that can be cryptic in unconstrained ordinations
(Anderson and Willis 2003). The canonical correlation (δ2) of each CAP axis is an estimate of the amount of
shared variance between the respective canonical variates of dependent and independent variables. CAP
also implements the ‘leave-one-out allocation’ approach to determine the misclassification error or
alternatively the classification success provided by the model (Lachenbruch and Mickey 1968). The ratio
between source (known affiliation) and successfully classified (predicted affiliation) data provides an
quantitative estimate of the degree of discrimination among the groups achieved by the canonical axes or,
more general, a goodness of fit of the model (Anderson and Willis 2003). Ordination and classification
success have to be examined in combination to draw conclusions about separation of a prior groups. Finally,
CAP tests the null hypothesis of no difference among the groups by calculation of a trace statistic
traceQ_m'HQ_m and a P value based on permutations (Anderson and Willis 2003). Permutational analysis of
multivariate dispersions (PERMDISP, Anderson 2006) was used to test for heterogeneity of community
structure in a priori groups. ANOSIM, PERMANOVA, CAP, and PERMDISP analyses were performed with
9,999 permutations and run as routines included in the PRIMER6+ software package. In the PERMANOVA
routine, residuals of raw data were permuted under a reduced model and partitioning was performed using
Type I sums of squares, which is recommended when covariates are defined (Clarke and Gorley 2006).
Location factors such as horizon and site were defined as covariates in order to subtract the variance
5
explained by these factors. Since Type I is sequential and the input order of the variables matters, the
analyses was re-run after swapping the order of the treatment factors. The number of PCO axes included in
the CAP analysis was automatically selected by the diagnostics routine in PRIMER6+, choosing the number of
axis showing the lowest misclassification error. Pearson correlations coefficients between species
abundance and CAP coordinates were calculated in PRIMER6+. All ordinations were plotted with STATISTICA
8.0 (StatSoft, Tulsa, OK).
MOTHUR
was used to calculate Good’s coverage (Good 1953) as well as the Simpson’s index for
evenness (E1/D, Simpson 1949). Simpson’s indices are among the measures that perform the best on
ecological data (Magurran 2004). Importantly, E1/D is largely independent from species richness and
sampling size (Magurran 2004), making it well suited for pyrotag data where the accuracy of richness
estimates is questionable. In order to account for variability among different experimental sites, Simpson’s
evenness was normalized relative to the reference plots within each site. The effect of harvesting was
examined by only including samples at compaction level C0 and using one-way ANOVA followed by Fisher’s
Least Significant Difference (LSD) post-hoc test. The effects of the different treatment levels within the
factorial design was examined by excluding the reference samples and using factorial ANOVA followed by a
desirability approach (Derringer and Suich 1980) to find the combination of OM removal and soil compaction
where evenness and diversity are maximized.
Indicator species analysis
Indicator analysis (i.e., taxon-treatment association analysis) was based on the point biserial correlation
coefficient (De Cáceres and Legendre 2009) and used to determine the association strength (R) of each
taxonomic unit with a priori groups. This analysis was performed at each taxonomic level, from OTU to
phylum. The point biserial correlation coefficient is the analogue of the phi coefficient of association for
abundance data (Chytrý et al. 2002) and is mathematically equivalent to the Pearson correlation between a
binary variable (e.g., species membership) and a quantitative variable (e.g., species abundance) (De
Cáceres and Legendre 2009). Such correlation indices are particularly suited to indicate the degree of
preference for the target group compared to the other groups (De Cáceres et al. 2010). All possible
combinations of a priori groups were analyzed to account for different niche breadths of the taxonomic units
(De Cáceres et al. 2010). Correlation analysis was performed on each taxonomic unit using the diagnostic
species analysis routine implemented in GINKGO (Bouxin 2005) with 99,999 permutations.
When multiple statistical inferences are considered simultaneously, the probability of false rejection of the
6
null hypothesis increases with increasing number of tests; thus, multiple hypothesis testing corrections
(Shaffer 1995) are required to compensate for the experiment-wise error rate (Iglewicz 2005). This is usually
done using the Bonferroni method (Bland and Altman 1995), whose usefulness however has been
questioned (Moran 2003, Nakagawa 2004, Perneger 1998) and which has been shown to be too
conservative when a large number of tests are performed (Storey and Tibshirani 2003). The less
conservative false discovery rate (FDR) has been recommended in order to re-calculate these probabilities
and draw experiment-wide conclusions (Benjamini and Hochberg 1995, Storey 2002). Accurate FDR
calculation is limited in permutation-based tests (Lai 2007, Tusher et al. 2001), since P values obtained
through permutation often do not reach the lowest possible values with a number of permutations that is
computationally feasible for large data sets. In our case, not all tests reached saturation after 99,999
permutations, but further permutations were not computationally feasible. Based on the P value distribution,
Q-values were determined using the software QVALITY (Käll et al. 2009) and associations were considered
significant with an FDR of 10% (q < 0.1). Singletons were excluded from FDR-correction at the OTU level,
since the enormous abundance of singletons (i.e. no indicators by definition) in such datasets otherwise
increase type II errors. The significant associations (R) at each taxonomic level were mapped onto the
taxonomic tree generated from the aggregation file in PRIMER6+ and displayed using ITOL (Letunic and Bork
2007, Letunic and Bork 2011). For this purpose and because negative correlation values cannot be
displayed, the delta R of each value to the minimum R in the whole comparison, e.g. OM removal, was
calculated and mapped onto the tree.
The variability of the abundance of each taxonomic unit among all samples was determined using the
coefficient of variation and plotted with STATISTICA 8.0. The coefficient of variation is a measure of dispersion
of data relative to the mean and is defined as the standard deviation divided by the mean. Since the
coefficient of variation is independent of the magnitude of the data, it is useful for comparing the variability of
multiple variables when the means are very different (Lovie 2005).
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1
0
Table 1. Biogeoclimatic characteristics of the six LTSP installations1.
SBS1
SBS2
SBS3
IDF1
IDF2
IDF3
Biogeoclimatic zone
Sub-boreal Spruce
Sub-boreal Spruce
Sub-boreal Spruce
Interior Douglas-fir
Interior Douglas-fir
Interior Douglas-fir
Biogeoclimatic subzone
wet, cool
moist, cold
dry, warm
dry cool
dry cool
dry cool
Region
Prince George
Houston
Williams Lake
Kamloops
Kamloops
Kamloops
Location name
Log Lake
Topley
Skulow Lake
Dairy Creek
Black Pines
O’Connor Lake
Latitude; Longitude
54° 22’N; 122° 37’W
54° 37’N; 126° 18’W
52° 19’N; 121° 55’W
50° 51’N; 120° 25’W
50° 56’N; 120° 18’W
50° 54’N; 120° 21’W
MAP (mm) 2
743
677
533
446
420
416
2.4 (-11.3, 13.8)
1.8 (-9.7, 12.6)
3.0 (-8.7, 13.8)
4.1 (-7.0, 15.1)
4.3 (-7.1, 15.5)
4.2 (-7.1, 15.3)
780
1100
1050
1150
1180
1075
Aspect
South
West
NA
North
South
South West
Slope (%)
0-3
2-12
level
0-3
level
2-12
Landform
Morainal blanket
Morainal blanket
Morainal blanket
Morainal blanket
Morainal blanket
Morainal blanket
Soil classification
Orthic / Gleyed
Gray Luvisol
Orthic Gray
Luvisol
Brunisolic Gray
Luvisol
Brunisolic Gray
Luvisol
Brunisolic Gray
Luvisol
Soil texture
Gleyed Humo-Ferric
Podzol, Gleyed
Eluviated Dystric
Brunisol
silt loam over loam
loam to clay loam
loam
silt loam
silt loam
silt loam
Coarse fragment (%)
37-41
21-40
30-39
20
17
21
Dominant tree species
Pinus contorta, Abies Pinus contorta, Picea Pseudotsuga
lasiocarpa, Picea
glauca x engelmannii menziesii, Picea
glauca x engelmannii
glauca x engelmannii,
Pinus contorta
140
112
210
Pseudotsuga
menziesii, Pinus
contorta
Pseudotsuga
menziesii
Stand age (years)
Abies lasiocarpa,
Pseudotsuga
mensiesii, Picea
glauca x engelmannii
140
210
180
Year of harvesting
1993
1993
1994
1997
1998
1999
Year of sampling
2008
2008
2009
2008
2009
2010
MAT (MCMT,MWMT) (°C)
Elevation (m)
3
1
table modified from previous publications (Hope 2006, Kranabetter and Sanborn 2003, Sanborn et al. 2000)
2
mean annual precipitation (MAP) for the period 1971 to 2000 estimated using Climate BC (Wang et al. 2006)
3
mean annual temperature (MAT) including mean coldest and warmest month temperature (MCMT, MWMT) for the period 1971 to 2000 estimated using Climate BC (Wang et al.
2006)
1
1