Supporting Information Methods S1, Table S1, Figs S1–S3
Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness
and biomass
Inga Hiiesalu, Meelis Pärtel, John Davison, Pille Gerhold, Madis Metsis, Mari Moora, Maarja Öpik,
Martti Vasar, Martin Zobel and Scott D. Wilson
Methods S1
The number of reads varied considerably between samples in the AMF and belowground plant data
sets (see start of Results section). Variable sequence counts could indicate that different proportions
of the true diversity were recorded in different samples. This in turn could influence relationships
between diversity and explanatory variables if sequence counts varied systematically, or if they add
noise to the relationships. An approach that is commonly used to overcome this is to rarefy or
otherwise downsample data sets to a uniform or similar sampling depth (de Cárcer et al., 2011).
However the merits of this approach depend on the reasons for between-sample variation.
Rarefaction or other types of downsampling may remove infrequent taxa from a data set, and if
there are natural as opposed to technical explanations for variations in sample depth, then this may
itself bias diversity estimates.
In this study, we inferred the richness of plants above- and belowground from fixed volumes
(10x10x10 cm). Meanwhile, belowground plant and fungal richness was estimated from DNA
samples extracted from a subsample of roots constituting a fixed mass of roots — identical for all
samples (100 mg) — expected to yield representative estimates of the diversity of organisms
present in the root samples. Thus, our approach was based on sampling a constant volume, and
differences in the numbers of sequences were anticipated. Nonetheless, we used rarefaction to test
whether variation in sampling depth within the data sets qualitatively influenced our results and thus
whether our findings could have been influenced by technical artifacts.
We used rarefaction to downsample the AMF and belowground plant data sets using the function
rrarefy() from the R package vegan (Oksanen et al., 2013). We randomly resampled the median
number of sequences per sample (264 sequences for AMF and 64 sequences for plants). Median
sample numbers perform better than the minimum sample numbers for high-throughput sequencing
datasets (de Cárcer et al., 2011). Following this, we used Z-tests to compare the correlation
coefficients derived from analysis of the original and rarefied data sets. Rarefaction did not change
any of the correlations significantly (P values between 0.41 and 1; Table S1), although the
coefficients tended to be slightly lower. We believe this is mostly due to a loss of statistical power,
since the rarefaction resulted in considerable loss of data (35% of sequences in the AMF dataset and
and 70% in the belowground plant dataset).
References
de Cárcer DA de, Denman SE, McSweeney C, Morrison M. 2011. Evaluation of subsamplingbased normalization strategies for tagged high-throughput sequencing data sets from gut
microbiomes. Applied and Environmental Microbiology 77: 8795–8798.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O'Hara RB, Simpson GL,
Solymos P, Stevens MHH, Wagner H. 2013. Vegan: Community ecology package. R package
version 2.0-7. http://CRAN.R project.org/package=vegan
Table S1 Summary of the Z-test which compares the r values of the original (unrarefied) and
rarefied data sets
Original
Rarefied
Difference
of
Correlation
r1
z1
r2
z2
z1-z2
SE
Z-score P of Z
AMF richness vs AG
plant richess
0.28
0.29
0.28
0.29
0
0.18
0
1
AMF richness vs BG
plant richness
0.48
0.51
0.43
0.46
0.06
0.18
0.34
0.73
BG plant richness vs BG
plant biomass
0.32
0.33
0.18
0.18
0.15
0.18
0.82
0.41
BG plant richness vs
total plant biomass
0.31
0.32
0.17
0.17
0.15
0.18
0.81
0.42
AMF richness vs BG
plant biomass
-0.29 0.30
-0.20 0.20
0.1
0.18
0.52
0.60
AMF richness vs total
plant biomass
-0.31 0.32
-0.23 0.23
0.09
0.18
0.47
0.64
AMF, arbuscular mycorrhizal fungi; AG, aboveground; BG, belowground; SE, standard error.
Fig. S1 Partial correlation between arbuscular mycorrhizal fungal (AMF) richness and (a)
aboveground plant richness; and (b) belowground plant richness (the sum of species rooted in a
sample and species detected from root samples using 454-sequencing of the trnL (UAA) gene).
Sequence data were rarefied to median number of sequences prior to analysis. Residuals of the x
and y variables are plotted in order to account for the effects of other measured variables and spatial
autocorrelation.
Fig. S2 Partial correlation between plant (a) aboveground richness and belowground biomass; (b)
belowground richness and belowground biomass; and (c) belowground richness and total biomass.
Belowground plant richness is the sum of species rooted in a sample and species detected from root
samples using 454-sequencing of the trnL (UAA) gene. Sequence data were rarefied to median
number of sequences prior to analysis. Total biomass is the sum of above- and belowground plant
biomass. Residuals of the x and y variables are plotted in order to account for the effects of other
measured variables and spatial autocorrelation.
Fig. S3 Partial correlation between arbuscular mycorrhizal fungal (AMF) richness (number of SSU
rRNA gene based virtual taxa) and (a) belowground plant biomass, and (b) total plant biomass (sum
of above- and belowground plant biomass). Sequence data rarefied to median number of sequences
prior to analysis. Residuals of the x and y variables are plotted in order to account for the effects of
other measured variables and spatial autocorrelation.