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Submitted 7 November 2014
Accepted 15 November 2014
Published 16 December 2014
Corresponding author
Peter Kennedy, kennedyp@umn.edu
Academic editor
Francis Martin
Additional Information and
Declarations can be found on
page 16
DOI 10.7717/peerj.686
Copyright
2014 Kennedy et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Missing checkerboards? An absence of
competitive signal in Alnus-associated
ectomycorrhizal fungal communities
Peter Kennedy1,2, Nhu Nguyen1, Hannah Cohen2and Kabir Peay3
1Department of Plant Biology, University of Minnesota, St. Paul, MN, USA
2Department of Biology, Lewis & Clark College, Portland, OR, USA
3Department of Biology, Stanford University, Palo Alto, CA, USA
ABSTRACT
A number of recent studies suggest that interspecific competition plays a key role in
determining the structure of ectomycorrhizal (ECM) fungal communities. Despite
this growing consensus, there has been limited study of ECM fungal community dy-
namics in abiotically stressful environments, which are often dominated by positive
rather than antagonistic interactions. In this study, we examined the ECM fungal
communities associated with the host genus Alnus, which live in soils high in both
nitrate and acidity. The nature of ECM fungal species interactions (i.e., antagonistic,
neutral, or positive) was assessed using taxon co-occurrence and DNA sequence
abundance correlational analyses. ECM fungal communities were sampled from
root tips or mesh in-growth bags in three monodominant A. rubra plots at a site in
Oregon, USA and identified using Illumina-based amplification of the ITS1 gene
region. We found a total of 175 ECM fungal taxa; 16 of which were closely related
to known Alnus-associated ECM fungi. Contrary to previous studies of ECM fungal
communities, taxon co-occurrence analyses on both the total and Alnus-associated
ECM datasets indicated that the ECM fungal communities in this system were not
structured by interspecific competition. Instead, the co-occurrence patterns were
consistent with either random assembly or significant positive interactions. Pair-wise
correlational analyses were also more consistent with neutral or positive interactions.
Taken together, our results suggest that interspecific competition does not appear to
determine the structure of all ECM fungal communities and that abiotic conditions
may be important in determining the specific type of interaction occurring among
ECM fungi.
Subjects Ecology, Mycology
Keywords Interspecific competition, Next-generation sequencing, Checkerboard analysis,
Species interactions, Co-occurrence patterns, Fungi
INTRODUCTION
A common way to assess the role of interspecific competition or facilitation in determining
community structure is experimental manipulation involving the removal of neighboring
individuals. This approach has been widely used in ecological studies examining biotic
determinants of plant and animal communities (Connell, 1983;Schoener, 1983), but
carrying out similar manipulations in field-based studies of soil microbial communities
How to cite this article Kennedy et al. (2014), Missing checkerboards? An absence of competitive signal in Alnus-associated ectomycor-
rhizal fungal communities. PeerJ 2:e686;DOI 10.7717/peerj.686
is less feasible due to the inability to selectively manipulate species-level neighborhood
composition. A widely proposed alternative is to look at species distribution patterns,
with Diamond’s (1975) study of bird distributions in the New Guinea archipelago being
one of most well-recognized examples. In that study, the presence of certain bird species
on a given island was associated with the absence of other species (and vice versa on
other islands), resulting in a series of ‘forbidden species combinations’ or ‘checkerboard
distributions’, which were posited to be the result of competitive exclusion (Diamond,
1975). This technique provided an important step forward in assessing the role of species
interactions in field-based studies at the community level, but it has been frequently noted
that analyses of species co-occurrence patterns need to include comparisons with patterns
generated from communities assembled randomly to maximize inference (Connor &
Simberloff, 1979;Gotelli & Graves, 1996).
Since the 1970s, species co-occurrence analyses have been used to assess the possibility
of species interactions in a wide range of organisms, including both macro- and
microorganisms (Gotelli & McCabe, 2002;Horner-Devine et al., 2007). Plant-associated
fungal communities, which have diverse ecological roles in ecosystems (Smith & Read,
2008;Rodriguez et al., 2009), have shown a full range of co-occurrence patterns, including
those consistent with both positive and antagonistic interactions (Koide et al., 2005;Pan
& May, 2009;Gorzelak, Hambleton & Massicotte, 2012;Ovaskainen, Hottola & Siitonen,
2010;Pickles et al., 2012;Toju et al., 2013). For ectomycorrhizal (ECM) fungi, the dominant
microbial eukaryotes in many temperate and some tropical forest soils (Smith & Read,
2008), these analyses have consistently found evidence of less species co-occurrence than
expected by chance (Koide et al., 2005;Pickles et al., 2010;Pickles et al., 2012). This suggests
that competitive interactions may play a significant role in structuring the communities
of this fungal guild (Kennedy, 2010). The initial studies of species co-occurrence patterns
in ECM fungal communities looked only in forests dominated by conifer hosts, but a
recent study in Fagus sylvatica forests in Europe also found evidence of significantly lower
than expected co-occurrence patterns (Wubet et al., 2012). This latter result indicates that
the predominance of antagonistic interactions in determining ECM fungal community
structure may be a common, host-lineage independent phenomenon. However, other
ecological and evolutionary factors aside from species interactions can also be responsible
for non-random species co-occurrence patterns (Gotelli & McCabe, 2002;Ovaskainen,
Hottola & Siitonen, 2010), so caution must be applied in inferring underlying mechanisms.
In this study, we focused on assessing the community co-occurrence distributions of
ECM fungi associated with the host genus Alnus. Unlike other ECM host genera with
large geographical distributions, the ECM fungal communities associated with Alnus trees
have been consistently found to be both species poor and highly host specific (Tedersoo
et al., 2009;Kennedy & Hill, 2010;Kennedy et al., 2011;Bogar & Kennedy, 2013;P˜
olme et
al., 2013;Roy et al., 2013). The mechanisms driving this atypical structure have long been
thought to be related to the co-presence of nitrogen-fixing Frankia bacteria, which can have
strong biotic and abiotic effects on Alnus-associated ECM fungal communities (Walker
et al., 2014). In particular, the high rates of nitrification present in Alnus forest soils (due
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 2/21
to the high inputs and decomposition of nitrogen-rich leaf litter) results in significantly
higher nitrate and acidity levels than those present in most other ECM-dominated forest
soils Dani`
ere, Capellano & Moiroud, 1986;Miller, Koo & Molina, 1992;Martin, Posavatz
& Myrold, 2003;Walker et al., 2014. Elevated levels of both of these abiotic factors have
been shown to inhibit the growth of many ECM fungi (Hung & Trappe, 1983;Lilleskov
et al., 2002) and, using an experimental pure culture approach, Huggins et al. (in press)
recently demonstrated that Alnus-associated ECM fungi have a greater ability to tolerate
high nitrate and acidity conditions compared to non-Alnus-associated ECM fungi.
Given the ability of Alnus-associated ECM fungi to grow in conditions that are generally
considered abiotically stressful, we hypothesized that ECM fungal species co-occurrence
patterns in Alnus forests may differ from those present in forests dominated by other ECM
hosts. Specifically, we speculated that competitive interactions would be less prevalent
in this study system, based on the fact that many studies of vascular plants have shown
that the nature of species interactions often changes from antagonistic to positive with
increasing levels of abiotic stress (Bertness & Callaway, 1994;G´
omez-Aparicio et al., 2004,
but see Michalet et al., 2006). To examine this hypothesis, we examined the co-occurrence
patterns of the ECM fungal communities present in three mono-dominant plots of Alnus
rubra in the western United States. ECM fungal communities were sampled on root tips
and in soil. For the latter, we used sand-filled mesh in-growth bags, which allow for
efficient, well-replicated community sampling of fungal hyphae growing in soil (Wallander
et al., 2001;Branco, Bruns & Singleton, 2013). To identify the ECM fungi present in the
study, we used high throughput Illumina sequencing, which has been increasingly used to
profile ECM fungal community composition (McGuire et al., 2013;Smith & Peay, 2014).
MATERIALS & METHODS
Study location
The study site was located on the eastern side of the Coast Range mountains in
northwestern Oregon, U.S.A. (latitude: N 45.820 W 123.05376, elevation: 462 m). Tem-
peratures at the site are moderate (mean annual temperature =8.7◦C, min = −1.2◦C,
max =23.8◦C), with significant precipitation between October and May followed by drier
summer months (total =1742 mm). The specific study location is part of a long-term
research project examining the effects of different forest management practices on A. rubra
growth (see the Hardwood Silvicultural Cooperative (HSC) website for details, http://
www.cof.orst.edu/coops/hsc). The HSC site used, Scappoose (HSC 3209), was established
in 1995. Prior to the implementation of the HSC work, the site was a second-growth
coniferous forest, which was clear-cut and replanted with a series of monodominant A.
rubra plots. A. rubra seedlings were planted from nursery stock (Brooks Tree Farm, Brooks,
OR) during the beginning of their second year of growth. Seedling ECM status at the
time of planting was not assessed (Frankia nodules were noted to be absent), but nursery
fumigation practices indicate colonization was unlikely (A Bluhm, pers. comm., 2009).
Our experiment was conducted in three 1,600 m2plots at HSC 3209. The plots,
which were located approximately 100 m apart, differed in initial A. rubra stem density
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 3/21
(Plot 2 =628, Plot 4 =1,557, and Plot 8 =3,559 stems/ha), but had no other forest
management practices applied. Despite the differences in stem density, A. rubra fine root
density did not differ significantly among the three plots (Fig. S1). The understories in
all three plots were colonized by arbuscular mycorrhizal plants (dominated by Mahonia
nervosa and Claytonia perfoliata), with no other ECM hosts besides A. rubra present. Soils
were classified as well-drained Tolamy loams (USDA Soil Survey, Columbia County, OR).
Within each plot, we located a 9 ×9 m subplot and overlaid a 100 point grid, with each
point being separated by 1 m. We chose this subplot size to avoid any dead stems in the
canopy immediately above the sampling area, while at the same time maximizing the
number of samples taken per subplot. At each point in Plot 4, which was sampled for ECM
root tips, a 5 cm diameter ×10 cm deep soil core was taken on May 31, 2013. In Plots 2
and 8, which were sampled for ECM communities present in soil, a 5 ×5 cm mesh bag
was buried at each point 5 cm below the soil surface. The bags were made of anti-static
polyester fabric with 300 µm diameter pores. This pore size allowed fungal hyphae to
grow into the bags, but prevented penetration of plant roots. We filled the bags with twice
autoclaved #3 grade Monterey aquarium sand (Cemex, Marina, CA, USA). Aluminum tags
on fluorescent string were added to facilitate bag recovery. The mesh bags at Plot 2 were
buried on February 1, 2013 and at Plot 8 on February 22. They were left undisturbed in the
soil until May 31, when all were harvested. After removal from the soil, we placed the mesh
bags into individual plastic bags and then onto ice for transport back to the laboratory. Soil
cores and bags were stored at 4 ◦C for <96 h before further processing.
Molecular analyses
We processed the root tip samples by gently washing all roots away from the soil and
removing all ECM colonized root tips from each core under a 10X dissecting scope
(∼10–50 root tips/core). All roots from each core were extracted using individual MoBio
PowerSoil kits (Hercules, CA, USA), following manufacturer’s instructions for maximum
DNA yields. For the mesh bags, we followed the protocol outlined in Branco, Bruns &
Singleton (2013), which provided a cheaper and quicker protocol compared to direct DNA
extraction from the sand within the mesh bags. Briefly, each bag (including a negative
control that was taken to the field, but not buried) was emptied into a sterile 50 ml
centrifuge tube. We added 10 ml of sterile deionized water and vortexed each tube for two
minutes, followed by a five minute settling period (hyphae have been previously observed
to float to the water surface). We then transferred the top two ml top of water to a new
2 ml centrifuge tube and contents were pelleted via centrifugation. On the same day, we
extracted total genomic DNA from the pellets using the Sigma REDExtract-N-Amp kit
(Sigma-Aldrich, St, Louis, MO, USA) following manufacturer’s instructions. Root tips and
extracts were stored for one week at −20 ◦C prior to PCR amplification.
For the root tip samples, we combined equal quantity aliquots from all 97 DNA
extractions (three cores contained no roots) into a single template for PCR. In contrast,
we conducted individual PCR reactions for each mesh bag sample as well as extraction
controls. We processed these two types of samples differently because we were primarily
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 4/21
interested in the spatial co-occurrence patterns in the soil hyphal ECM fungal communities
and therefore only used the root tip samples to create a local sequence reference set of
known Alnus-associated ECM taxa against which the mesh bag data could be compared.
For all PCR reactions, we used the barcoded ITS1F and ITS2 primer set of Smith &
Peay (2014), with each sample run in triplicate and pooled to minimize heterogeneity.
Successful PCR products were determined by gel electrophoresis and magnetically cleaned
using the Agencourt AMPure XP kit (Beckman Coulter, Brea, CA, USA) according to
manufacturer’s instructions. Final product concentrations were quantified using a Qubit
dsDNA HS Fluorometer (Life Technologies, Carlsbad, CA, USA). Root tip and bag samples
were run at different sequencing facilities under the same general conditions. For the root
tips, the single PCR product was run at the University of Minnesota Genomics Center us-
ing 250 bp paired-end sequencing on the MiSeq Illumina platform. For the bags, we pooled
the 192 successfully amplified bag samples at equimolar concentration and ran them on
the same platform at the Stanford Functional Genomics Facility using 250 bp paired-end
sequencing on the MiSeq Illumina platform. A spike of 20% and 30% PhiX was added to
the runs to achieve sufficient sample heterogeneity, respectively. Raw sequence data and
associated metadata from both the root tip and bag samples were deposited at MG-RAST
(http://metagenomics.anl.gov/) under project #1080.
Bioinformatic analyses
We used the software packages QIIME (Caporaso et al., 2010) and MOTHUR (Schloss et
al., 2009) to process the sample sequences. Raw sequences were demultiplexed, quality
filtered using Phred =20, trimmed to 178 base pairs, and ends were paired, followed by
filtering out of sequences that had any ambiguous bases or a homopolymer run of 9 bp.
Following the guidelines discussed in Nguyen et al. (in press), we employed a multi-step
operational taxonomic unit (OTU) picking strategy by first clustering with reference
USEARCH (including de novo chimera checking) at 97% sequence similarity, followed by
UCLUST at 97% sequence similarity. We used a 97% similarity threshold because it was
the most commonly employed in community-level ECM fungal studies, although some
lineages, including Alnicola, may have greater sequence similarity among species (Tedersoo
et al., 2009;Rochet et al., 2011). To assess the validity of the 97% threshold for sequences
based on only ITS1 versus the full ITS region (i.e., ITS1, 5.8S, and ITS2), we examined
seven known Alnus-associated Tomentella taxa (i.e., those present in Kennedy et al., 2011)
and found that that threshold resulted in the same number of OTUs in both cases (data
not shown). The UNITE database (K˜
oljalg et al., 2013) was used in both chimera checking
and OTU clustering, with singleton OTUs discarded to minimize the effects of artifactual
sequences (Tedersoo et al., 2010). We assigned taxonomic data to each OTU with NCBI
BLAST+v2.2.29 (Altschul et al., 1990), using a custom fungal ITS database containing the
curated UNITE SH database (v6) (http://unite.ut.ee/repository.php,K˜
oljalg et al., 2013)
and more than 600 vouchered fungal specimens, including 46 representative sequences
from Alnus forests at other HSC locations in Oregon (Kennedy & Hill, 2010) and Mexico
(Kennedy et al., 2011). Since sequences that had low subject length:query length matches
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 5/21
were typically non-fungal, we further filtered out sequences with matches ≤90% to BLAST
(i.e., at least 90% of the bases in the input sequence matches to another sequence in the
database at some identity level).
Using the remaining sequence dataset, we rarefied all samples to 12946 sequences,
which was the lowest number of sequences obtained across the 192 samples. Since there
has recently been a question raised about the validity of rarefaction in next generation
sequencing analyses (McMurdie & Holmes, 2014), we also analyzed the data without
rarefaction. We obtained very similar results (Table S1), so present the data based on
rarefied samples only. ECM OTUs within each sample were parsed out using a python
script that searches for genera names from a list of 189 known ECM genera and their
synonyms (Branco, Bruns & Singleton, 2013, appended from Tedersoo, May & Smith, 2010).
While this script provides a strong general filter for sorting the data by fungal lifestyle,
some taxa belonging to clades that are polyphyletic for the ECM habit (e.g., Lyophyllum,
Sebacinales) as well as taxa with low matches to Genbank (e.g., Uncultured Fungus) can
be of questionable trophic status. For each of these groups, we carefully checked both the
sequence matches and placement of our OTUs within phylogenetic trees of the clades to
determine whether these taxa were properly classified at ECM. The resulting sample x
OTU matrix contained 190 ECM taxa represented by at least one sequence per sample
(min =1, median =34, mean =1,334, max =209,187). We found that 15 of the 190
OTUs present were highly similar (>97% similar) to ECM fungi present in the dipterocarp
rainforests of Malaysia, which were concurrently being studied in the Peay lab using the
same next-generation sequencing approach (Fig. 1). Because these OTUs represented
accidental contamination probably during library construction, they were eliminated
from the final analyses. Although an additional 80 OTUs had >97% similarity to taxa
found in the Borneo study, because their closest BLAST match was not from Borneo, we
conservatively considered these taxa as having cosmopolitan distributions and included
them in the final analyses. The final OTU ×sample matrix, including taxonomic matches
and representative of sequences for each OTU, can be found in Table S2.
Statistical analyses
Taxon co-occurrence patterns of the ECM fungal communities present in bag samples were
assessed using the program EcoSim (Gotelli & Entsminger, 2009), with presence-absence
matrices for Plots 2 and 8 being analyzed separately. (The root data from Plot 4 could
not be analyzed for sample-level co-occurrence due to the pooled sequencing approach
for those samples). We utilized the C-score algorithm (Stone & Roberts, 1990), which
compares the number of checkerboard units (i.e., 1,0 ×0,1) between all pairs of
species in the observed matrix (Cobserved)to that based in random permutations of
the same matrix (Cexpected, i.e., the null models). Since randomized permutations of
a matrix can be achieved in multiple ways (see Gotelli & Entsminger, 2009 for details),
we analyzed our datasets using both the ‘fixed-fixed’ and ‘fixed-equiprobable’ options
(which are recommended by the program guide and used in the previous ECM fungal
co-occurrence analyses). In both options, the row (i.e., taxon) totals were fixed, so that the
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 6/21
Figure 1 Rank-abundance plot of all 190 (inset) and top 20 ectomycorrhizal (ECM) fungal taxa
sampled in this study. The top 20 ECM fungal taxa are color coded by whether they are known to be
associated with Alnus hosts (black), of unknown host origin (grey), or laboratory contaminants (white).
total abundances of each taxon in the observed and null matrices were identical. In the
‘fixed-equiprobable’ option, however, the column (i.e., sample) totals in the null matrices
were no longer equivalent to those in the observed matrix. Instead, all samples in the null
matrices had an equal probability of being colonized by any of the taxa in the observed
matrix, which effectively eliminates differences in taxon richness among samples.
Of the ECM fungal taxa present in the final root tip and bag datasets, over 90%
(167/175) belonged to species never previously encountered with Alnus (Table S2,
AlnusMatch =No). Unlike other ECM host systems with large geographic ranges, the
ECM fungal community associated with Alnus hosts is remarkably well characterized at
local (Tedersoo et al., 2009;Kennedy & Hill, 2010;Walker et al., 2014), regional (Kennedy
et al., 2011;Roy et al., 2013), and global scales (P˜
olme et al., 2013). As such, it is highly
likely the majority of the novel OTUs encountered were not part of the active ECM
community in our plots, but rather present simply either as spores or additional lab
contaminants. To account for this issue, we divided our checkerboard analyses into five
different input matrices for the bag dataset (Plots 2 and 8). The first matrix included all
175 ECM fungal taxa (referred to as “All”). The second matrix included the 16 taxa that
had >97% similarity matches to ECM samples from Alnus forests (referred to as Alnus).
The third matrix included only the 8 taxa that were encountered on ECM root tips in
Plot 4 (referred to as AlnusRootOnly). To assess the robustness of the results generated
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 7/21
using the larger Alnus matrix, the fourth matrix excluded the three most frequent and
abundant species (Tomentella3, Alnicola1, Tomentella2) (referred to as AlnusMinusTop3).
Finally, the fifth matrix included just the 10 taxa in the genus Tomentella (from the larger
Alnus matrix) to look for evidence of species interactions among this subset of closely
related taxa (referred to as AlnusTomentellaOnly). For all of the aforementioned C-score
analyses, taxa present in less than 5 bag samples were removed, as low frequency taxa are
generally considered non-informative (Koide et al., 2005). The observed input matrices
were compared to 5000 null matrices. Significant differences between the observed matrix
C-score and that of the null matrices were determined along with standardized effect sizes
(SES). Observed C-scores significantly higher than those generated from the null matrices
are consistent with a community being structured by competitive interactions, whereas
Cobserved significantly lower than the Cexpected is consistent with positive interactions.
To further assess the degree of association among known Alnus ECM fungal taxa, we also
used an abundance-based approach (as opposed to the co-occurrence analyses, which
are based on binary presence/absence data). Specifically, we calculated the pair-wise
Spearman rank correlation coefficients among all pairs of the 16 Alnus-associated taxa
using the cor function in R (R Core Team, 2013). Coefficients >0.30 were tested for
significance with the cor.test function. To account for multiple tests (n=13), we used
a Bonferroni-corrected Pvalue of 0.003. With the same data set, we also tested for the
presence of spatial autocorrelation using the mgram function in the ECODIST package
in R. We first converted the sequence abundance datasets in both Plots 2 and 8 into
dissimilarity matrices using the Bray-Curtis Index and then compared those to a Euclidean
distance matrix of sampling points for each plot. For the Mantel correlogram tests, we used
the n.class =0option, which uses Sturge’s equation to determine the appropriate number
of distance classes.
RESULTS
We found 175 total ECM fungal taxa in the study (Table S2); 16 of which matched closely
to known Alnus-associated ECM fungi. In the mesh bags, Alnus-associated ECM fungal
taxa represented six of the ten most abundant OTUs present, including the dominant ECM
fungal taxon, Tomentella3, which was present in all the bag samples in both plots and had
sequence abundances nearly ten-fold higher than any other taxon (Figs. 2A and 2B). Two
other Alnus-associated fungal taxa, Alnicola1 and Tomentella2, were also present in all
samples, whereas the remaining Alnus-associated ECM fungal taxa had frequencies varying
from 2 to 96% (Plot 2 mean =25%, Plot 8 mean =31%) and lower sequence abundances.
Eight of the 16 Alnus-associated ECM fungal taxa were present on both roots and in the
bags, with abundances that were very similar (Fig. 1A). Of the eight ECM fungal taxa found
on root tips, all were previously encountered on A. rubra root tips at other sites in Oregon,
while the eight fungal taxa found exclusively in bags had not been previously documented
(Kennedy & Hill, 2010).
ECM fungal taxon co-occurrence patterns were largely consistent between plots, but
different between null models. Of the ten tests (i.e., 5 matrix types ×2 plots) using the
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 8/21
Figure 2 Rank-abundance (A) and rank-frequency (B) plots of Alnus-associated ectomycorrhizal
fungal taxa sampled in mesh bags and root tips.
‘fixed-fixed’ permutation option, nine indicated that the observed ECM fungal community
did not differ significantly from random assembly (Table 1). In one case, Plot 2 All, the
observed ECM fungal community had significantly more co-occurrence than expected
by chance. In contrast, in the ten tests using the ‘fixed-equiprobable’ permutation option,
three indicated that the observed ECM fungal community did not differ significantly
from random assembly, while seven found that the observed ECM fungal community had
significantly more co-occurrence than expected by chance. Results remained the same for
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 9/21
Table 1 C-score taxon occurrence analyses of ECM fungal communities in Plots 2 and 8. See methods
for details about datasets and null matrix type definitions.
Dataset Plot Null matrix type C observed C expected Pvalue SES
All 2 Fixed–Fixed 173.2 173.8 0.00 −3.35
Fixed-Equiprobable 188.1 0.00 −21.2
All 8 Fixed–Fixed 164.4 164.7 0.73 −0.75
Fixed-Equiprobable 172.1 0.00 −11.8
Alnus 2 Fixed–Fixed 93.5 92.5 0.21 0.76
Fixed-Equiprobable 106.7 0.04 −1.75
Alnus 8 Fixed–Fixed 103.1 103.2 0.47 −0.07
Fixed-Equiprobable 114.5 0.04 −1.82
AlnusRootOnly 2 Fixed–Fixed 77.4 76.7 0.74 0.54
Fixed-Equiprobable 82.5 0.27 −0.59
AlnusRootOnly 8 Fixed–Fixed 61.2 61.6 0.45 −0.26
Fixed-Equiprobable 78.4 0.13 −1.15
AlnusMinusTop3 2 Fixed–Fixed 200.3 198.4 0.27 0.59
Fixed-Equiprobable 228.25 0.04 −1.72
AlnusMinusTop3 8 Fixed–Fixed 178.7 179.3 0.47 −0.20
Fixed-Equiprobable 198.6 0.03 −1.88
AlnusTomentellaOnly 2 Fixed–Fixed 61.6 62.6 0.77 −0.55
Fixed-Equiprobable 88.7 0.02 −1.99
AlnusTomentellaOnly 8 Fixed–Fixed 108.3 107.6 0.64 0.31
Fixed-Equiprobable 109.1 0.47 −0.09
Notes.
SES, Standardized Effect Size.
Alnus ECM fungal communities whether the top three taxa were removed or not. The
Alnus and AlnusRootOnly analyses did differ under the ‘fixed-equiprobable’ option, with
the former showing greater than expected co-occurrence and the latter having a pattern no
different than one based on random assembly. Additionally, in the AlnusTomentellaOnly
analysis, the ECM fungal community showed greater than expected co-occurrence in Plot 2
but not in Plot 8. In all of these cases, significant antagonistic patterns were not observed.
Spearman rank analyses revealed that pair-wise sequence abundances of some of the
16 Alnus ECM fungal taxa were significantly positively correlated (Table 2). The specific
significant combinations varied between plots, with only one taxon pair (Alnicola1 &
Tomentella9) showing significant positive correlations in both plots. Although a number
of pair-wise correlations had negative values (suggesting negative rather than positive
interactions), none of them were significant, even when considered at a Pvalue of
0.05. In addition, the Mantel correlogram analyses found no clear evidence of spatial
autocorrelation in the Alnus-associated ECM fungal communities. In Plot 2, there was no
significant autocorrelation at any distance, while in Plot 8 there was a single significant
positive correlation between samples located 1–2 m apart (Figs. S2 and S3).
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 10/21
Table 2 Spearman rank correlation coefficient matrices for ECM fungal communities in Plots 2 and 8. Significant correlations are indicated in
bold. Numbers over the columns of both matrices correspond to the number of the ECM fungal taxon identified in the first row.
Plot 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Tomentella3 1.00
2. Alnicola1 0.00 1.00
3. Tomentella2 −0.02 0.00 1.00
4. Cortinarius1 −0.05 −0.07 0.01 1.00
5. Lactarius1 −0.07 −0.07 −0.02 0.28 1.00
6. Tomentella1 −0.08 0.09 0.00 −0.13 −0.06 1.00
7. Cortinarius2 −0.13 0.11 0.01 −0.05 0.00 0.26 1.00
8. Tomentella7 0.16 −0.08 0.65 −0.03 0.08 −0.04 0.10 1.00
9. Tomentella9 0.11 0.48 −0.06 −0.05 0.00 0.12 0.06 −0.02 1.00
10. Alnicola2 0.07 0.42 0.07 −0.05 −0.04 −0.02 −0.10 0.02 0.09 1.00
11. Tomentella4 −0.08 −0.04 0.40 −0.01 0.18 0.01 0.00 0.26 0.00 0.04 1.00
12. Tomentella5 0.15 −0.07 −0.04 −0.06 −0.04 −0.03 0.00 −0.10 −0.01 −0.06 −0.05 1.00
13. Tomentella10 0.06 0.01 −0.02 −0.03 0.00 0.08 −0.10 0.01 −0.05 −0.06 −0.05 −0.06 1.00
14. Tomentella8 −0.07 −0.03 0.40 0.01 −0.03 −0.06 0.03 0.25 −0.04 0.04 0.60 −0.04 −0.04 1.00
15. Alnicola3 0.39 −0.06 0.27 −0.04 −0.04 −0.11 0.07 0.50 0.03 −0.04 −0.03 0.29 −0.03 −0.02 1.00
16. Tomentella6 0.37 −0.06 −0.03 −0.03 −0.02 −0.11 −0.08 0.02 0.03 −0.04 −0.03 −0.03 −0.03 −0.02 −0.02 1.00
Plot8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Tomentella3 1.00
2. Alnicola1 0.13 1.00
3. Tomentella2 −0.17 −0.12 1.00
4. Cortinarius1 0.03 −0.03 0.26 1.00
5. Lactarius1 −0.16 −0.09 0.14 0.14 1.00
6. Tomentella1 −0.14 0.03 0.04 0.04 0.46 1.00
7. Cortinarius2 0.06 0.45 −0.04 −0.02 −0.05 −0.06 1.00
8. Tomentella7 −0.09 −0.06 −0.06 0.14 0.11 0.02 0.05 1.00
9. Tomentella9 −0.12 0.47 0.02 −0.01 0.10 0.16 0.28 0.06 1.00
10. Alnicola2 −0.06 0.15 0.02 0.12 −0.04 0.03 0.08 0.13 0.07 1.00
11. Tomentella4 −0.05 −0.07 0.15 0.12 0.14 −0.02 −0.09 0.18 0.02 0.07 1.00
12. Tomentella5 −0.05 0.05 0.07 −0.07 0.14 −0.01 −0.08 −0.08 0.08 −0.10 0.22 1.00
13. Tomentella10 0.08 −0.04 −0.05 0.02 0.02 0.03 0.13 −0.05 −0.12 0.12 −0.04 −0.07 1.00
14. Tomentella8 0.16 −0.07 0.01 0.06 −0.04 0.00 −0.06 0.06 −0.06 0.26 0.14 −0.06 −0.05 1.00
15. Alnicola3 0.28 0.24 −0.10 −0.11 0.00 −0.06 0.21 −0.10 0.14 −0.08 −0.09 −0.07 0.07 −0.05 1.00
16. Tomentella6 −0.02 −0.04 0.18 −0.04 −0.03 −0.04 0.06 −0.10 0.33 −0.05 −0.06 −0.04 −0.04 −0.03 −0.03 1.00
DISCUSSION
We found that the ECM fungal communities in A. rubra forests displayed a different
pattern of taxon co-occurrence compared to those seen for other ECM fungi. Unlike
the consistent previous findings of less co-occurrence among species than expected by
chance (Koide et al., 2005;Pickles et al., 2012;Wubet et al., 2012), we observed no evidence
of spatial patterns consistent with interspecific competition in Alnus-associated ECM
fungal communities. In contrast, we consistently found co-occurrence patterns that
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 11/21
were either no different from random assembly or consistent with positive interactions.
Although we did not measure soil nitrate and acidity conditions in this study (see Martin,
Posavatz & Myrold (2003) and Walker et al. (2014) for values from comparable age A.
rubra forests at other sites in Oregon), Alnus soils are consistently characterized by abiotic
conditions are generally considered stressful to ECM fungi. The results we obtained are
thus consistent with the ‘stress gradient hypothesis’, which posits that species interactions
shift from negative to positive as environmental conditions become harsher (Bertness &
Callaway, 1994). Although we emphasize that the patterns we found in this study are based
solely on correlative inference, there is some experimental evidence that may support
the stress gradient hypothesis for ECM fungal community dynamics. Koide et al. (2005)
found a shift from significant negative co-occurrence patterns in their control plots to
non-significant co-occurrence patterns in plots where either tannins or nitrogen were
added experimentally. While they did not explicitly analyze these manipulations in terms
of stress, both increased tannin and nitrogen levels have been shown to inhibit the growth
of multiple ECM fungal taxa (Koide et al., 1998;Cox et al., 2010). The direction of the
response in the Koide et al. (2005) study is consistent with greater abiotic stress resulting
in a decrease in antagonistic ECM fungal interactions. At the same time, it is plausible
that resource limitation was eliminated with the addition of nitrogen, which could have
allowed for greater spatial co-existence among ECM fungi. Since the Alnus system has
naturally higher nitrogen availability than most ECM forests due to the co-presence of
nitrogen-fixing Frankia bacteria, it is also possible that greater resource abundance could
drive the co-occurrence patterns we observed. Given the fact that the pattern could be
explained by either increasing stress or resource availability, additional studies are needed
to distinguish among these explanations. One promising approach would be to examine
the taxon co-occurrence patterns in younger and older Alnus forests, since soil nitrate and
acidity concentrations increase in these forests over time (Dani`
ere, Capellano & Moiroud,
1986;Martin, Posavatz & Myrold, 2003). If the stress gradient hypothesis were the most
plausible explanation, then we would expect to see competitive and facilitative interactions
to be dominant, respectively.
The presence of co-occurrence patterns consistent with significant negative species
interactions was also missing in our analysis of more closely related ECM fungal taxa.
For the ten Alnus-associated members of the genus Tomentella, co-occurrence patterns
either did not differ significantly from random assembly or reflected an effect of positive
interactions. Like the larger community analyses, this result also differs from previous
experimental studies, where strong antagonistic interactions among closely related ECM
fungal taxa have been observed (Kennedy, 2010). In a similarly designed study that also
assessed ECM fungi with taxon co-occurrence analyses, Pickles et al. (2012) found patterns
consistent with strong interspecific competition among a suite of Cortinarius species in a
Scottish Pinus sylvestris forest. Although it has long been assumed that competition may
be stronger in more closely related species due to greater overlap in resource utilization,
a meta-analysis by Cahill et al. (2008) found little consistent evidence to support this
supposition. Mayfield & Levine (2010) further questioned the validity of phylogenetic
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 12/21
relatedness as a good proxy for competitive strength by showing that in certain abiotic
environments competition may actually select for more closely related taxa than expected
by chance (i.e., phylogenetic clustering). The Alnus ECM system is particularly interesting
in this respect because while the fungal communities associated with Alnus hosts are both
species poor and highly host specific, they include taxa from a number of distantly related
lineages (Rochet et al., 2011). Although explanations for this higher-level phylogenetic
patterning are still lacking, our current results suggest that competitive processes among
both closely and more distantly related taxa are not a key factor generating the atypical
structure of Alnus ECM fungal communities.
Some positive spatial associations have been observed in other studies of ECM fungal
communities (Agerer, Grote & Raidl, 2002;Koide et al., 2005;Pickles et al., 2012), and
have been suggested to be due to complementary resource acquisition abilities of among
individual taxa (Jones et al., 2010). We speculate that in Alnus forests positive associations
among ECM fungi could also reflect possible amelioration of local abiotic conditions.
Huggins et al. (in press) found that Alnus-associated ECM fungi could more effectively
buffer changes in local pH environments than non-Alnus ECM fungi, which may be
key to persistence in the high acidity soils present in Alnus forests. While the exact
buffering mechanism is not yet known, if it involves the release of molecules into the
external environment, growing directly adjacent to another ECM fungus may result in
greater buffering of local pH conditions than when growing in isolation. We believe it is
important to note, however, that the patterning of positive associations were patchy and
not consistent between plots, so it is hard to determine if local pH buffering is actually
significant without local measurements of pH for each sample. Furthermore, sequence
abundance of individual taxa has been shown not to correlate linearly with initial fungal
tissue or DNA abundance in other studies using NGS techniques (Amend, Seifert & Bruns,
2010;Nguyen et al., in press), so caution must be applied in using sequence abundance as an
accurate ecological proxy.
Like the co-occurrence and correlation-based patterns, we found that spatial auto-
correlation patterns observed in Alnus ECM fungal communities were also anomalous
relative to other studies. The specific distance of spatial autocorrelation appears to vary
among systems, but there is typically strong spatial autocorrelation among community
samples located less than 5 m apart (e.g., Lilleskov et al., 2004;Bahram et al., 2013).
While the spatial extent of our study was very limited (the most distant samples within
plots were only ∼12 m apart), the absence of spatial signal was not surprising, based
on previous studies of Alnus ECM fungal communities. Both Pritsch et al. (2010) and
Kennedy et al. (2011) found individual Alnus ECM fungal taxa that were almost identical
genetically (at least in the ITS region) in forests located thousands of kilometers apart
and, in a global scale analysis, P˜
olme et al. (2013) found many Alnus ECM OTUs were
distributed across geographically distant samples. Theoretically, the absence of dispersal
limitation should make the detection of non-random distribution patterns more likely
if biotic interactions (either negative or positive) are strong determinants of community
structure. The classic work of Diamond (1975) is a good example, as the bird populations
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 13/21
studied across the New Guinea archipelago were not dispersal limited, yet exhibited many
checkerboard distribution patterns. As such, we do not think the atypical nature of the
taxon co-occurrence patterns in Alnus ECM fungal communities that we observed were
driven by the also atypical spatial correlation patterns.
As the results observed in this study differed in multiple ways from those found
previously, we had some concern they were caused by an artifact of our identification
or sampling methodology. Unlike previous examinations of taxon co-occurrence for
ECM fungi, we used next-generation sequencing (NGS) to identify the communities
present. NGS methods provide much greater sequencing depth per sample (Smith &
Peay, 2014), which may have allowed us to more effectively document the ECM fungal
communities present in each sample compared to previous studies. We found that the
three most abundant Alnus-associated ECM fungi were present in every bag sample in both
plots, which has not been observed in other systems. Although the presence of spatially
ubiquitous taxa will result in a lower total number of checkerboard units observed (as
1,0 is possible but not 0,1), it has the same effect on both the observed and null matrices
and therefore should not bias statistical comparisons of Cobserved versus Cexpected. We
checked this by eliminating the three ECM fungal taxa present in every sample and
found functionally identical results to those when those taxa were included (Table 2). A
second difference between this and related studies was the sampling of ECM fungal hyphal
communities in mesh bags. Previous studies assessing co-occurrence patterns have largely
focused on ECM root tips, but Koide et al. (2005) found very similar taxon co-occurrence
patterns for root-tip and soil-based analyses of ECM fungal communities in the same Pinus
resinosa forest. Based on that result, and the fact that the sequence abundances of all the
ECM fungi present on A. rubra root tips and the mesh bags showed highly similar patterns
(Table S2), we do not believe assessing ECM hyphal communities was the source of our
incongruous results either (however, in hindsight, a better experimental design would
have been to sample the mesh bags and the ECM root tips directly around them within
each plot). A third difference is the restricted taxonomic richness of Alnus ECM fungal
communities. This explanation, however, also seems unwarranted, as Pickles et al. (2012)
showed highly significant negative co-occurrence patterns in matrices of equivalent sizes.
Finally, it is also possible that variation in soil nutrient availability could drive Alnus ECM
fungal community structure and, because it was relatively homogenous in our small-sized
plots, the resulting taxon distribution patterns were largely random. While we reiterate that
we did not directly measure soil nutrient availability in this study, other studies of Alnus
ECM fungi have shown some significant correlations between community structure and
soil organic matter and nutrients such as K and Ca (Becerra et al., 2005;Tedersoo et al.,
2009;Roy et al., 2013,P˜
olme et al., 2013; see Richard (1968) for a possible mechanism). In
those studies, however, the percent of variance explained by soil nutrients was generally
low, so we believe it is unlikely that variation in resource availability was the primary
determinant of the distribution patterns observed. We recognize that additional differences
likely exist, but feel confident that the co-occurrence results we observed are ecologically
accurate and not generated by methodological or sampling artifact.
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 14/21
NGS techniques clearly represent a powerful and efficient way to assess the richness
and dynamics of fungal communities (Smith & Peay, 2014), but we found that additional
data quality control analyses beyond the standard sequence quality thresholds and chimera
checking were needed to properly characterize ECM fungal community composition.
Specifically, we found that a relatively high number of ECM fungal taxa present appeared to
be the result of PCR contamination. The PCR reactions of our extraction and PCR controls
produced no bands indicating positive product, but the sensitivity of NGS techniques
and the Illumina platform in particular makes the amplification of single DNA molecules
highly probable (Tedersoo et al., 2010;Peay, Baraloto & Fine, 2013). Fortunately, the atypical
and well-described nature of Alnus ECM fungal communities made it relatively easy to
identify the most obvious non-Alnus associated fungal taxa and remove them prior to
the final analyses. For taxa that belonged to ECM fungal lineages known to associate
with Alnus hosts but which had not been previously documented, it was more difficult to
determine their status (i.e., whether they represented PCR contaminants, were present in
A. rubra soils as spores, or actually colonizing A. rubra root tips). In particular, the status of
Thelephoraceae1, which had the third highest sequence abundance in the full dataset, was
interesting because the closest BLAST match to Thelephoraceae1 was an ECM fungal root
tip sample from Betula occidentalis in British Columbia, Canada. Bogar & Kennedy (2013)
found that ECM fungal communities present on Alnus and Betula hosts can overlap, so it is
possible this taxon was overlooked in previous surveys of Alnus ECM fungal communities
that used less sensitive methods. However, the absence of this taxon from any the root tip
samples in Plot 4 suggests that it was most likely present simply as spores rather than an
active member of the Alnus-associated ECM fungal community. Despite the unclear status
of this taxon as well as many others with lower abundance, the co-occurrence patterns
showed the same general results whether taxa of unknown status were included or not,
suggesting the overall results were robust. In less well-characterized ECM fungal and other
microbial systems, however, the potential for inclusion of spurious taxa is sufficiently high
that we strongly recommend the sequencing of negative extraction and PCR controls to
help try to account for any lab-based contamination (Nguyen et al., in press).
Taken together, our results suggest that while many ECM fungal communities appear
to be strongly affected by competitive interactions, those present in Alnus forests are not.
Although the reasons for this difference are not fully resolved in this study, the possibility
of greater abiotic stress changing the way in which species interact in Alnus forests is likely
an important factor. The application of ecological theories such as the stress gradient
hypothesis to better understand the factors driving ECM fungal community structure
has grown rapidly in recent years (Peay, Kennedy & Bruns, 2008;Koide, Fernandez &
Malcolm, 2014) and new technologies such as next generation sequencing continue to
make the study of ECM fungi increasingly tractable for ecologists. While we welcome this
synergy, we stress the importance of a solid foundation in fungal biology as well as a critical
awareness of the limitations of molecular-based identification techniques to successfully
integrate ECM fungi into the ecological mainstream.
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 15/21
ACKNOWLEDGEMENTS
We thank A Bluhm and D Hibbs for assistance using the HSC study location, L Bogar,
V Engebretson, J Huggins, P King for assistance with experiment implementation and
harvest, J Walker for assistance with DNA extractions, D Smith for assistance with NGS
processing, and members of the Peay Lab, C Fernandez, R Koide, M Gardes and one
anonymous reviewer for critical comments on a previous version of this manuscript.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Support for this work came from NSF DEB Grant #1030275 to Peter Kennedy. The
funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
NSF DEB: #1030275.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Peter Kennedy conceived and designed the experiments, analyzed the data, wrote the
paper, prepared figures and/or tables.
•Nhu Nguyen analyzed the data, contributed reagents/materials/analysis tools, reviewed
drafts of the paper.
•Hannah Cohen performed the experiments, reviewed drafts of the paper.
•Kabir Peay contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Field Study Permissions
The following information was supplied relating to field study approvals (i.e., approving
body and any reference numbers):
No permit was needed as the study was conducted on private land.
DNA Deposition
The following information was supplied regarding the deposition of DNA sequences:
We have provided access to the raw sequence reads with the MG-RAST (http://
metagenomics.anl.gov/) under project #1080.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/
10.7717/peerj.686#supplemental-information.
Kennedy et al. (2014), PeerJ, DOI 10.7717/peerj.686 16/21
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