Supporting Information Methods S1, Tables S1–S3 and Figs S1–S5
Methods S1
MCA OTU delineation
Quality-filtered sequence data was pre-clustered at 97% identity using CD-HIT. Then, a
multiple sequence alignment of cluster centroid sequences was performed using the
program MAFFT, version 6.925 (Katoh & Standley, 2013), with the FFT-NS-2 strategy
assuming multiple conserved regions and long gaps. This setting works with large data
sets. The aligned data set was then split into subsets based on branching patterns and
reproducibility of the clusters for more detailed analyses. One or two groups often
dominate, and these dominant sequence types were analyzed separately. The new datasets
were aligned again with MAFFT, and each of the data sets was clustered using a neighbor
net analysis (Huson et al., 2011). Neighbor net is an algorithm for constructing
phylogenetic networks and is based on the neighbor joining method. Similar to neighbor
joining, the method uses a distance matrix as input and agglomerating clustering. The
neighbor net algorithm can make overlapping clusters, which do not form a hierarchy.
The result is presented as a phylogenetic network referred to as a split network.
Monophyletic clades (OTUs) were identified in these split networks as terminal clades
without reticulations (see example of a reticulation in Fig. S1b). From these terminal
clades representative sequences were selected to construct a neighbor net or a
phylogenetic tree to illustrate the distance between the OTUs. OTUs represented by only
one (Site and Regional) or fewer than five (Local) sequence(s) in the data set were
removed prior to analysis.
97% OTU delineation using QIIME
All sequence analysis was done using QIIME 1.6.0 (Caporaso et al., 2010a; QIIME script
available upon request) using the same sequences as in the MCA clustering. OTUs were
picked de novo using 97% similarity (90-99% similarity was used for the supervised
learning approach), using the UCLUST algorithm (QIIME script: pick_otus.py)
according to Edgar (2010). Cluster centroids for OTUs were chosen as the OTU
representative sequences. Abundance matrices of OTUs across sample locations (OTU
tables) were constructed, and OTUs represented by only one (Site and Regional) or fewer
than five (Local) sequence in the data set were removed prior to analysis. Sequence
alignments were run using MUSCLE (align_seqs.py) according to Caporaso et al.
(2010b). QIIME was used to calculated all diversity metrics and statistics
(alpha_diversity.py and beta_diversity.py) according to Caporaso et al. (2010a).
Testing the relationships between OTU distributions and environmental and spatial
variables
All statistical analyses were performed using QIIME (Caporaso et al., 2010a). For
categorical variables, we used Analysis of Similarities (ANOSIM) to determine
significance (compare_categories.py). ANOSIM is a non-parametric (permutation-based)
test that is similar to Nonmetric Multidimensional Scaling (NMDS) ordination in that it
uses the rank order of dissimilarity values (from a distance matrix) across metadata
categories (see vegan package in R). Mantel tests were used for numerical variables
(compare_distance_matrices.py), and are equivalent to a multivariate Pearson’s
correlation. In order to construct the optimal n-parameter model of all available
quantitative (numerical) variables, we use BEST analysis (compare_categories.py).
BEST will rank variables that explain the largest amount of variance in the data set for a
1-parameter, 2-parameter…n-parameter model (where n is equal to the number of
variables provided), and provide a rho-statistic that quantifies the goodness of fit (see
‘bioenv’ in the vegan package for R). Distance-based Redundancy Analysis (db-RDA;
constrained ordination) was used in conjunction with forward selection (ordistep function
the vegan package for R) to obtain a set of metadata variables that independently (and
significantly at P<0.1) explained a portion of the community variance. This subset of
non-autocorrelated metadata parameters was used to constrain our Mantel-r comparison
in Fig. 1. Procrustes analysis, as implemented in QIIME, was used to compare
distributions of samples in PCoA space (Caporaso et al., 2012).
References
Caporaso JG, Lauber CH, Walters WA, Berg-Lyons D, Huntley J, Fierer N et al.
2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq
and MiSeq platforms. The ISME Journal 6: 1621-1624.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK et
al. 2010a. QIIME allows analysis of high-throughput community sequencing data.
Nature Methods 7: 335-336.
Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R.
2010b. PyNAST: a flexible tool for aligning sequences to a template alignment.
Bioinformatics 26: 266-267.
Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics 26: 2460-2461.
Huson DH, Rupp R, Scornavacca C. 2011. Phylogenetic networks: Concepts,
algorithms and applications. Cambridge University Press, Cambridge.
Katoh K, Standley DM. 2013. MAFFT Multiple Sequence Alignment Software Version
7: Improvements in Performance and Usability. Molecular Biology and Evolution 30:
772-780
Table S1 Comparisons of Mantel-r and P-values from Mantel tests (based on 1000
permutations on β-diversity distance matrices using Hellinger distances) across the three
datasets when OTUs were delineated using the monophyletic clade approach (MCA) or
97% universal threshold.
Dataset
Variable
Site
NO3
pH
PO4
Soil moisture
Spatial coordinate X
Spatial coordinate Y
Spatial coordinate Y2
Carex arenaria
Dianthus deltoides
Aspect
Elevation
pH
SOM
CEC
PO4
K
NO3
Mg
Ca
Spatial coordinates
Coarse sand
Fine sand
Silt
Clay
Conductivity
pH
NO3
SOM
PO4
S
K
Mg
Ca
Na
Invasive cover
Native cover
Spatial coordinates
Local
Regional
MCA
Mantel-r P-value
0.079
0.27
0.036
0.48
0.000
0.99
0.073
0.34
-0.039
0.46
0.13
0.010
0.13
0.012
0.17
0.012
0.19
0.023
-0.13
0.24
-0.130
0.38
-0.040
0.74
0.001
0.99
0.011
0.92
0.099
0.30
0.19
0.039
0.11
0.41
-0.081
0.53
-0.068
0.55
0.066
0.44
0.57
0.005
0.38
0.055
0.42
0.032
0.20
0.12
0.17
0.36
0.32
0.025
-0.031
0.85
0.006
0.98
0.25
0.13
0.21
0.014
0.37
0.014
0.32
0.021
0.026
0.91
0.50
0.010
-0.16
0.34
-0.11
0.58
0.55
0.002
97%
Mantel-r
P-value
0.091
0.24
0.033
0.50
0.020
0.80
0.084
0.38
-0.039
0.47
0.16
0.006
0.16
0.006
0.19
0.014
0.25
0.009
-0.12
0.21
-0.15
0.24
0.019
0.86
0.050
0.59
0.008
0.93
0.09
0.33
0.25
0.008
0.14
0.24
-0.035
0.75
0.003
0.98
0.057
0.46
0.42
0.025
0.33
0.065
0.36
0.045
0.12
0.32
0.26
0.095
0.24
0.054
-0.047
0.76
-0.033
0.81
0.25
0.089
0.12
0.18
0.30
0.029
0.23
0.067
0.023
0.88
0.41
0.023
-0.098
0.52
-0.058
0.73
0.43
0.008
Table S2 Results from ANOSIM and BEST analyses for the three datasets using either
the monophyletic clade approach (MCA) or the 97% universal threshold to delineate
OTUs.
Dataset
Analysis
Response
MCA
97%
Site
ANOSIMTreatment
R-value
P-value
BEST-rho
Parameters
-0.0004
0.48
0.12
Soil moisture
NO3, pH
0.53
0.0001
0.20
PO4, K
0.068
0.09
0.67
0.001
0.63
0.001
0.59
S, silt,
coarse sand
-0.0149
0.61
0.14
Soil moisture
NO3, pH
0.62
0.0001
0.27
PO4, K
0.15
0.029
0.49
0.008
0.43
0.003
0.49
pH, clay, silt,
coarse sand
BEST
Local
ANOSIMCover type
BEST
Regional
ANOSIMPlant species
ANOSIMRegion
ANOSIMSite
BEST
R-value
P-value
BEST-rho
Parameters
R-value
P-value
R-value
P-value
R-value
P-value
BEST-rho
Parameters
Table S3 Correlations of the beta-diversity distance matrices (Hellinger distances) and
Procrustes analyses of MCA versus 97% OTU delineation approach for the three
datasets.
Dataset
Analysis
Parameter
Value
Site
Correlation
Mantel-r
P-value
M2
P-value
Mantel-r
P-value
M2
P-value
Mantel-r
P-value
M2
P-value
0.98
0.001
0.000
0.008
0.94
0.001
0.089
<0.0001
0.77
0.001
0.42
<0.001
Procrustes analysis
Local
Correlation
Procrustes analysis
Regional
Correlation
Procrustes analysis
Supporting Information Figs S1–S5
Fig. S1 NeighborNet split network on the Site dataset based on the MCA approach that
identified 33 OTUs and a 97% universal threshold that identified 76 OTUs (b).
Fig. S2 NeighborNet split network based on the MCA approach that identified 46 OTUs
(a, as in Lekberg et al. 2013) and a 97% universal threshold that identified 1083 OTUs
(b).
Fig. S3 NeighborNet split network on the Regional data set based on the MCA approach
that identified 30 OTUs (a) and a 97% universal threshold that identified 278 OTUs (b).
Fig. S4 Procrustes analysis of AMF community samples for the Site (a) and Regional (b)
datasets. Duplicate points represent data processed through the MCA and 97% OTU
pipelines. The lines between points highlight the Euclidean distance between points that
represent the two different methods.
Fig. S5 Supervised learning results for the Local dataset, showing an exponential increase
in OTU numbers and varying classification error over a 90–99% OTU threshold range.
The error indicates the likelihood of assigning an unknown AMF community to the
incorrect aboveground plant communities (cheatgrass, knapweed, leafy spurge, and
native) after training the learning algorithm on a subset of the data. Error bars for the
classification errors were determined via 10-fold cross validation. The 98% threshold
differed from the 95% (F = 4.77, P = 0.043) and 97% (F = 5.09, P = 0.037), but these
differences disappeared after controlling for multiple comparisons.
Fig. S1
Fig. S2
Fig. S3
Fig. S4
Fig. S5