Microhabitat heterogeneity across leaves and flower organs

FEMS Microbiology Ecology, 91, 2015, fiv097
doi: 10.1093/femsec/fiv097
Advance Access Publication Date: 7 August 2015
Research Article
RESEARCH ARTICLE
Microhabitat heterogeneity across leaves and flower
organs promotes bacterial diversity
Robert R. Junker1,∗ and Alexander Keller2
1
Department of Ecology and Evolution, University of Salzburg, Hellbrunnerstr. 34, 5020 Salzburg, Austria and
Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074
Würzburg, Germany
2
∗ Corresponding author: Department of Ecology and Evolution, University of Salzburg, Hellbrunnerstr. 34, 5020 Salzburg, Austria. Tel: +43-662-8044-5512;
Fax: +43-662-8044-142; E-mail: [email protected]
One sentence summary: Estimates on bacterial diversity associated with plants strongly increase with the number of plant organs sampled, including
leaves and flower tissues, due to the high microhabitat specificity of bacterial communities.
Editor: Angela Sessitsch
ABSTRACT
Eukaryote-associated microbiomes interact with their hosts in multiple manners, thereby affecting the hosts’ phenotype,
physical condition and behaviour. In plants, bacteria have numerous functions, with variable net effects, both in natural
and agricultural systems. However, information about the composition and diversity of the bacterial communities
associated with different aboveground plant organs, particularly flowers, is lacking. In addition, the relative effects of
microhabitat and environmental conditions on community establishment require further attention. Here, using
culture-independent methods, we determine that leaves and three floral microhabitats (nectar, stamina and styles) of
Metrosideros polymorpha (Myrtaceae), a tree endemic to Hawai’i, host unique indicator communities composed of relatively
abundant bacterial taxa. These indicator communities are accompanied by a large number of ubiquitous or rare bacteria
with lower abundances. In our study system, the strong effect of microhabitat filtering on plant-associated community
composition and bacterial richness and diversity strongly exceeds the influence of environmental effects such as
precipitation, altitude, substrate age and geographic distance. Thus, the bacterial richness of aboveground plant organs is
strongly underestimated when only one microhabitat, e.g. leaves, is considered. Our study represents a first step towards a
comprehensive characterization of the distribution, composition and underlying factors, of plant bacterial communities,
with implications for future basic and applied research on plant health, pollination and reproduction.
Keywords: floral microbial ecology; habitat filtering; Hawaii; indicator taxa; Metrosideros polymorpha; next-generation 16S
rRNA gene amplicon sequencing
INTRODUCTION
Bacteria and other microorganisms paved the way for the origin
and evolution of plants and animals. They remain omnipresent
interaction partners with multiple profound effects on their
hosts and far-reaching implications for ecology, evolutionary biology, the economy and human well-being (Biere and Tack 2013;
McFall-Ngai et al. 2013). In response, hosts have evolved physi-
ological or behavioural adaptations that structure the communities of their bacterial associates, and bacteria have also developed strategies to affect the physiology and behaviour of
their hosts (Costello et al. 2009; Ezenwa et al. 2012; Mann et al.
2012; Bodenhausen et al. 2014). The bacterial communities associated with angiosperms and reciprocal effects are best described for the rhizo- and phyllosphere (Andrews and Harris
Received: 30 April 2015; Accepted: 4 August 2015
C FEMS 2015. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 9
2000; Berendsen, Pieterse and Bakker 2012; Vorholt 2012), i.e.
the microbial habitats in and on roots and leaves. For example, it has been shown that aboveground plant tissues (excluding flowers) within plant individuals strongly differ in their bacterial communities (Ottesen et al. 2013) and that also the same
tissues show some systematic variance in their associated microorganisms depending on their position within a plant (Leff
et al. 2015). Although flowers are essential for sexual reproduction, the microbial communities colonizing the anthosphere
(i.e. flowers) and their impacts on pollination and seed set are
less well characterized (Aleklett, Hart and Shade 2014). While
some studies characterized the bacterial communities colonizing whole flowers or parts of flowers, e.g. nectar (Junker et al.
2011; Fridman et al. 2012, Fuernkranz et al. 2012; Shade, McManus
and Handelsman 2013; Aleklett, Hart and Shade 2014), information on the distribution of bacteria within flowers of a single
plant species, i.e. their specificity to floral microhabitats such as
nectar, stamina and styles is absent. In contrast, the abundance
and species composition of yeast in different floral microhabitats has been described in two plant species (Pozo, Lachance and
Herrera 2012). The complexity of flowers, which includes sterile,
male and female whorls of organs and nutrient-rich rewards for
pollinators, offers a number of distinct microhabitats for bacterial colonization that clearly exceed the structural heterogeneity of leaves (Lindow and Brandl 2003; Karamanoli et al. 2005).
The plant-based factors controlling bacterial colonization and
establishments and thus community composition are manifold
including cuticle properties, ethylene signalling, the availability of nutrients and secondary metabolites (Lindow and Brandl
2003; Huang et al. 2012; Junker and Tholl 2013; Bodenhausen
et al. 2014), factors that may differ between floral microhabitats. Indeed, some bacterial strains specifically colonize floral
microhabitats (Junker et al. 2011; Fuernkranz et al. 2012; Ottesen
et al. 2013), including pathogens such as Erwinia amylovora, which
causes fire blight when it proliferates on stigmas (Farkas et al.
2012), and bacteria that disrupt flower–pollinator interactions,
e.g. Gluconobacter sp., which unfavourably alters the properties of
nectar (Vannette, Gauthier and Fukami 2012). For these reasons,
we hypothesized that the structural and chemical diversity of
flowers offers multiple microhabitats with diverging properties
that are colonized by unique bacterial communities distinguishable from each other and from those inhabiting leaves.
The tree Metrosideros polymorpha (Myrtaceae) is endemic to
the Hawaiian Islands and features an ecologically diverse distribution from sea level to the alpine zone over variable precipitation regimes; it is a pioneer on young lava flows but also
builds dense forests. By using next-generation 16S rRNA gene
amplicon sequencing of bacterial communities associated with
leaves, nectar, stamina and styles of several trees growing across
a large altitudinal and ecological gradient, we aimed to quantify
the relative effects of plant-based microhabitats and large-scale
environmental conditions on plant-associated bacterial communities and reveal patterns of bacterial diversity within plants
and across landscapes.
MATERIALS AND METHODS
Study system and field sampling
We collected samples of healthy young leaves, nectar, stamina
and styles of 33 individual M. polymorpha (Myrtaceae) trees from
14 populations (n = 125 total samples, seven samples failed)
on the island of Hawaii, USA (Fig. S1, Table S1, Supporting Information). Most populations (1–13, see Material 1, Supporting
Information, for a map and a table containing information on
the populations) were located in the Hawaii Volcanoes National
Park, one was located along the Saddle road (14) connecting the
east and west coasts of the island. Pieces of leaves (approximately a quarter of a leaf), 5.9 ± 0.83 μL nectar (mean ± SE),
7.23 ± 1.26 stamina (mean number ± SE) and 2.94 ± 0.17 styles
of flowers were sampled from each plant during anthesis. All
flower organs from one plant individual were sampled from the
same inflorescence. Additionally to plant parts of M. polymorpha, we also sampled leaves and whole flowers of six additional
plant species growing in the same habitats. While focusing on
the bacterial communities associated with M. polymoprha plant
parts, these samples were used to test whether bacterial communities specific to M. polymorpha leaves and flowers are also
characteristic for these organs in other plant species. Samples
were collected in the field with sterile forceps or microcapillaries
(nectar samples) and directly placed into lysis tubes filled with
ZR BashingBeads and 750 μL of Xpedition lysis/stabilization solution (Zymo Research Corporation, Irvine, CA, USA). Samples
were further processed (bead beating) with an Xpedition sample
processor (XSP, Zymo Research Corporation, Irvine, CA, USA) directly in the field. Samples were stored at room temperature until further processing in the lab. Environmental factors were either measured in the field (altitude using a GPS device, GPSmap
60SCx, Garmin, Germany) or were obtained from published resources: mean annual precipitation (Giambelluca et al. 2013) and
age of substrate (years since last lava flow) (Sherrod et al. 2007).
Substrate age is in the Hawaii Volcanoes National Park also a surrogate for the density and composition of the surrounding vegetation, which is sparse and mostly herbaceous on young lava
flows and M. polymorpha-dominated forests on older lava flows.
Molecular preparation
Stabilized DNA was isolated using the Xpedition Fungal/Bacterial DNA MiniPrep (also Zymo Research) following
the manufacturer’s instructions. PCR and library preparation
were performed according to a previously published dual indexing approach using primers for the V4 region of the 16S rRNA
gene (Kozich et al. 2013). PCR was performed in triplicate for each
R
samples in 10 μL reactions, each containing 5 μL 2x Phusion
High Fidelity PCR Master Mix (New England Biolabs, Ipswich,
MA, USA), 0.33 μM each of forward and reverse primer (Eurofins
MWG Operon, Huntsville, AL, USA), 3.34 μL PCR grade water and
1 μL template DNA. The complete primer sequences were forward: 5 -AATGATACGGCGACCACCGAGATCTACAC XXXXXXXX
TATGGTAATT GT GTGCCAGCMGCCGCGGTAA-3 and reverse:
5 -CAAGCAGAAGACGGCATACGAGAT XXXXXXXX AGTCAGTCAG
CC GGACTACHVGGGTWTCTAAT-3 , where XXXXXXXX indicates
the index sequences for multiplexing samples (Kozich et al.
2013). PCR conditions comprised an initial denaturation step
at 95◦ C for 4 min, 35 cycles of denaturation at 95◦ C for 40 s,
annealing at 55◦ C for 30 s and elongation at 72◦ C for 1 min,
followed by final extension at 72◦ C for 5 min. Triplicates of
a sample were combined and successful amplification was
verified with an agarose gel using 5 μL.
The remaining 25 μL were cleaned up and normalized between samples in DNA amounts using the SequalPrep Normalization Plate Kit (Invitrogen GmbH, Carlsbad, CA, USA), eluting in
20 μL. Of each sample, 5 μL normalized DNA was taken for pooling 4 × 96 samples (together with samples of other projects and
laboratory control samples with the pure extraction kit) according to Kozich et al. (2013). These pools were verified for library
fragment size with a Bioanalyzer High Sensitivity DNA Chip
Junker and Keller
(Agilent Technologies, Santa Clara, CA, USA) and quantified with
the dsDNA High Sensitivity Assay (Life Technologies GmbH,
Darmstadt, Germany) and merged to a final pool. This was diluted to 2 nM and further prepared for sequencing following the
Illumina Guide for DNA library preparation (lllumina Inc. 2013),
obtaining a final library of 10 pM. PhiX Control Kit V3 (Illumina
Inc., San Diego, CA, USA) was added as a spike-in to ensure highquality reads (10%). Sequencing was performed on the Illumina
R Platform (Illumina Inc., San Diego, CA, USA) using 2 ×
MiSeq
250 bp v2 MiSeq chemistry. The cartridge of the reagent kit was
additionally supplied with 3 μL each of the custom sequencing
and index primers (Kozich et al. 2013).
Bioinformatics processing
Overall quality was assessed using FastQC v0.11.2
(Babraham Bioinformatics 2014). Forward and reverse reads
were joined together with fastq-join v.1.8.0 (https://code.
google.com/p/ea-utils/wiki/FastqJoin) within QIIME v. 1.8.0
(Caporaso et al. 2010), where also quality filtering (phred score
> Q20, sequence length > 200 bp, no ambiguous bases) was
performed. Clusters of operational taxonomic units (OTUs)
were dynamically built and chimera removed using the UCLUST
(Edgar 2010) and UCHIME algorithms (Edgar et al. 2011), as
implemented in USEARCH v.7.0.1090 (Edgar 2010). OTUs were
taxonomically classified with QIIME and the GreenGenes reference database (DeSantis et al. 2006; Caporaso et al. 2010).
With QIIME the data was transferred into the Biom format,
and chloroplast, chlorophyta, mitochondrial as well as OTUs
unassigned at kingdom level were filtered out. A tree of all
OTUs was recalculated using FastTree2 (Price, Dehal and Arkin
2010). For details on parameter settings for all steps above, see
the Shell-script in the Material S2 (Supporting Information).
The data set was imported into R v.3.0.3 using the phyloseq
package (McMurdie and Holmes 2013). Laboratory control
samples suggested 31 OTUs to originate from kits, plasticware
and/or laboratory contamination, which were removed prior
to follow-up analyses (see R-script in Material S3, Supporting
Information, for further details). Samples with less than 1000
reads after filtering were removed from the analysis.
Diversity of communities
The OTU richness and Shannon diversity of the communities
were calculated using the R package vegan (Dixon 2003), and the
phylogenetic species variability was calculated using the picante
package (Helmus et al. 2007). The latter index is statistically independent of species richness and quantifies how relatedness decreases variance of a neutral trait shared by all species in a community. To test for effects of environmental factors on diversity
measures, we fit a linear mixed-effects model using the maximum likelihood with mean annual precipitation, altitude, substrate age (log(x+1)) and their interaction terms as fixed factors,
the populations of M. polymorpha and the microhabitats (plant
organs) as random factors, and OTU richness, Shannon diversity or phylogenetic species variability as the response variable
(Crawley 2005).
Composition of communities
We used random forest analysis to assign bacterial communities to plant organs based on their composition. Random forest
is a machine learning algorithm that assigns samples to predefined groups (here plant organs) in multiple iterations and esti-
3
mates the importance of each compound for a correct classification (Breiman 2001). This bagging method has been successfully
applied in other ecological studies to classify samples based on
community composition, and its advantages and applicability
(i.e. it calculates the importance of each variable for a correct
classification independently of all others but also considers multivariate interactions with the others while not overfitting the
data) have been demonstrated (Prasad, Iverson and Liaw 2006;
Ranganathan and Borges 2010; Junker et al. 2011). For the analysis, ntree = 10 000 bootstrap replicates were drawn with mtry =
48 variables randomly selected at each node. Confusion matrix
(see Table 2 in the main text) shows number of correctly assigned
communities to either leaves, nectar, stamina or styles as well as
proportional class error and total out-of-basket (OOB) estimate
of error rate. To identify indicator communities/OTUs, we used
variable selection using random forest (Diaz-Uriarte 2007) as implemented in the R package varSelRF (Diaz-Uriarte 2007) (ntree =
5000 bootstrap replicates, variable drop fraction = 0.2) with the
OOB error as a minimization criterion.
To identify indicator families characteristic for plant organs,
we pooled abundances of OTUs belonging to the same family and performed random forest analysis based on the family
composition of samples. Families with a variable importance
>7.5 were selected for visualization of community composition
(Fig. 2).
Effects of environmental factors on bacterial community
composition were tested with Mantel statistic based on Pearson’s product-moment correlation. For similarity between communities, we used abundance-weighted UniFrac distances
(Lozupone and Knight 2005) retained through the package phyloseq (McMurdie and Holmes 2013). This metric uses phylogenetic information to calculate dissimilarities between communities, and was weighted by the relative abundance of OTUs
within a sample. For distances based on mean annual precipitation, altitude and substrate age (log(x+1)), we used Euclidean
distances. Additionally, UniFrac distances between communities were correlated to geographic distances that were calculated
based on longitude and latitude of tree populations using the
package fossil for R (Vavrek 2011).
In order to directly compare the effect of microhabitat (i.e.
plant organs) and environmental factors on bacterial community composition within a single model, we fitted environmental
vectors (altitude, substrate age and precipitation) as well as factors (plant organs and populations) onto an ordination (NMDS,
10 000 permutations, complete communities) based on UniFrac
distances, using the R package vegan (Dixon 2003).
Comparison with sympatric plant species
To identify general patterns transferable to other plant species,
i.e. whether bacterial communities specific to M. polymorpha
leaves and flowers are also characteristic for organs of others,
we performed a random forest analysis for flower (f) and leaf (l)
tissues together with samples of Acacia koa (1l), Sophora chrysophylla (2f, 2l), Lantana camara (2f, 2l), Oenothera stricta (1f, 1l),
Chamechrista nicitians (2f, 2l) and Crotolaria retusa (1f, 2l) sympatric
with M. polymorpha. For that, we reclassified all stamina, style
and nectar samples of M. polymorpha as flower samples, to make
them comparable to the sampling design of the other species.
To avoid a sampling bias in M. polymorpha after the reclassification towards flowers (94 samples), these were randomly subsampled to match the sampling size of leaves (31 samples). This
subsampling was performed 1000 times to avoid random effects,
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 9
Figure 1. Bacterial species richness accumulated across four plant microhabitats. Samples of leaves, nectar, stamina and styles of M. polymorpha were collected and
analysed using culture-independent 16S rRNA gene amplicon sequencing (Illumina MiSeq). The species accumulation curves exhibited the expected number of OTUs
and its standard deviation after increasing the number of individual trees sampled (rarefaction). Species accumulation curves are shown for individual organs (listed
in order at the end points of the curves) or for two, three and four organs sampled per tree.
and each time a random forest analysis was performed. Over all
trials, misclassified samples were counted and the mean of all
statistics was analysed (OOB, confusion matrix). All statistical
analyses were performed using the statistical software R (R Development Core Team 2014).
RESULTS
Diversity of bacterial communities
Following quality control and filtering of the sequences, we
obtained 1.41 million sequences for 2274 OTUs associated
with the vegetative and reproductive plant parts of M. polymorpha (study accession number of sequences at the European Nucleotide Archive: PRJEB7828, http://www.ebi.ac.uk/ena/
data/view/PRJEB7828). The OTU number per sample varied
among organs. Styles and stamina hosted the most OTUs on average (mean ± SE: 250.2 ± 9.1 and 247.5 ± 10.8 OTUs per sample, respectively), followed by nectar (228.5 ± 8.8) and (203.6 ±
8.9; ANOVA: F3,121 = 5.15, p < 0.01). However, we observed the
opposite trend for OTU diversity (Shannon’s Index), with leaves
hosting the most diverse communities (3.82 ± 0.09), followed
by styles (3.43 ± 0.11), stamina (3.43 ± 0.09) and nectar (3.37 ±
0.13; ANOVA: F3,121 = 3.62, p = 0.015). Phylogenetic species variability (Helmus et al. 2007) exhibited a trend that was similar to
that of OTU diversity, albeit it was not significant (F3,121 = 2.46,
p = 0.066). All four microhabitats additively contributed to the
overall bacterial OTU richness on M. polymorpha trees (Fig. 1) and
explained a considerable portion of the variation in OTU richness and diversity.
Environmental factors, such as altitude, mean annual precipitation and substrate age (years since last lava flow, which
also corresponds to the degree of vegetation cover), were also
correlated to the diversity of bacterial communities (Table 1).
Linear mixed-effects models revealed a positive relationship between precipitation and OTU richness, and Shannon diversity
increased with age and positively correlated with the interaction
effects between the three environmental factors. Phylogenetic
species variability was predicted by the interaction between precipitation and altitude (Table 1).
Composition of bacterial communities
The communities associated with the microhabitats comprised
a large number of ubiquitous or low-abundance taxa with no
specific affiliation with either vegetative or reproductive plant
parts. However, based on the taxonomic composition of the
samples, random forest analysis (Breiman 2001) correctly assigned nearly 70% of the communities to the plant organ from
which they originated, whereas most of the false assignments
were attributed to the confusion of communities colonizing
stamina and styles (see Table 2; see also the non-metric multidimensional scaling based on abundance-weighted UniFrac
distances (Lozupone and Knight 2005) that is presented in
the Material S4, Supporting Information). When stamina and
styles were considered as a common community, more than
Junker and Keller
Table 1. Effect of environmental factors on the OTU richness, diversity and phylogenetic species variability of bacterial communities.
Stepwise reduction of linear mixed-effects model fit by maximum
likelihood with mean annual precipitation, altitude, substrate age
(log(x+1)) and their interaction terms as fixed factors, populations
of M. polymorpha and microhabitats (plant organs) as random factors and OTU richness, Shannon diversity or phylogenetic species
variability as response variable revealed the significant models explaining variation in diversity measures (excluding factors not significantly explaining variance).
df
F
p
OTU richness:
Precipitation
1, 87
1493.711
<0.0001
Shannon diversity:
Precipitation
Altitude
Age
Precipitation : altitude
Precipitation : agelog
Altitude : agelog
Precipitation : altitude : agelog
1, 22
1, 22
1, 22
1, 22
1, 22
1, 22
1, 22
0.584
1.382
7.218
4.997
4.282
0.221
8.437
0.4528
0.2523
0.0135
0.0359
0.0505
0.6431
0.0082
Phylogenetic species variability:
Precipitation
Altitude
Precipitation : altitude
1, 26
1, 26
1, 26
2.458
0.05
5.949
0.129
0.8257
0.0219
85% of communities were correctly assigned (Table 2). Variable selection using random forest analysis (Diaz-Uriarte 2007)
identified indicator taxa (n = 122 OTUs; see Material S5, Supporting Information, for a complete list) responsible for the successful classification and characterization of leaves, nectar or
stamina/styles. Despite the comparably low number of indicator taxa, they quantitatively dominated the samples, repre-
5
Table 2. Composition of bacterial communities associated with
aboveground plant organs of M. polymorpha. The confusion matrix of
the random forest analysis shows the number of correctly assigned
communities and the proportional class error for each organ and the
total OOB estimate of the error rate. The numbers in bold are correct microhabitat-specific assignments and denote the characteristic
bacterial communities of leaves, nectar and stamina/styles.
Leaves
Nectar
Stamina
Styles
Leaves
Nectar
Stamina
Styles
29
2
4
1
2
28
0
5
0
1
17
12
1
2
9
13
Class error
0.09
0.15
0.43
0.58
OOB estimate of
error rate: 30.95%
senting up to 78.4% of the total number of sequences (mean
± SD: 39.3 ± 12.0%). The indicator community of the stamina
and styles was dominated by Enterobacteriaceae (including
Erwinia strains, see Farkas et al. 2012), whereas nectar and leaves
featured a more even distribution of several bacterial families
(Fig. 2).
Similar to the diversity patterns described above, environmental factors were not as predictive for bacterial community
composition as the microhabitat. Samples obtained from trees
subjected to similar environmental conditions (Euclidean distances based on altitude, precipitation and substrate age) did
not exhibit smaller differences in bacterial composition (UniFrac
distances) than samples obtained from trees from different environments. The exceptions were slight correlations between
the distances between altitudes and community composition
when all samples were considered and the distances between
precipitation and communities of the style samples (Table 3).
Likewise, samples collected in close geographic proximity were
not more similar than those separated by larger distances, with
Figure 2. Proportional composition of bacterial families associated with the aboveground plant organs of M. polymorpha. Bar plots depict the proportional contribution
of bacterial families to the bacterial communities of samples of individual plant organs. Only families (n = 23 families) that strongly contributed to a correct assignment
to plant organs were considered (variable importance > 7.5; range of variable importance considering all bacterial families (n = 232 families) max = 38.3, min = –5.06;
random forest analysis). The Y-axis denotes cumulative proportional abundances. Pie charts represent the mean proportion across all samples isolated from one
microhabitat.
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 9
Table 3. Effects of geographic distance and environmental factors on bacterial community similarity. Mantel statistics testing for correlations
between distances in environmental factors (Euclidean distances) or geographic distances and UniFrac distances between bacterial communities merely suggest effects of mean annual precipitation and altitude on communities isolated from styles or from all organs combined,
respectively. Communities associated with styles were more similar when sampled in close geographic proximity. Significant correlations are
highlighted in bold. Note that effects were no longer significant after Bonferroni correction for multiple testing: α’ = 0.05 / 20 = 0.0025. These
weak effects found in communities comprising all OTUs were not detected as significant when only indicator taxa where considered.
All organs combined
R
p
Leaves
r
Nectar
r
p
p
Stamina
r
p
Styles
r
p
All
OTUs
Geographic distance
Altitude
Mean annual precipitation
Substrate age
0.032
0.083
–0.011
0.017
0.280
0.031
0.592
0.349
–0.086
–0.067
0.045
–0.016
0.756
0.719
0.298
0.574
0.058
0.074
0.007
–0.041
0.269
0.223
0.438
0.681
–0.011
0.060
0.073
0.023
0.516
0.274
0.237
0.340
0.204
0.103
0.170
–0.039
0.040
0.142
0.029
0.664
Indicator
OTUs
Geographic distance
Altitude
Mean annual precipitation
Substrate age
–0.006
0.010
–0.023
–0.033
0.546
0.366
0.773
0.889
–0.090
–0.096
0.016
0.052
0.831
0.869
0.375
0.246
0.068
0.029
0.034
–0.005
0.209
0.334
0.294
0.493
−0.016
–0.059
0.019
0.024
0.553
0.830
0.358
0.312
0.068
0.096
0.064
0.066
0.191
0.082
0.151
0.155
Table 4. Results of fitting of environmental environmental vectors
(altitude, mean annual precipitation and substrate age) and factors
(plant organs and populations) onto an ordination (NMDS, 10 000 permutations) based on UniFrac distances. Significant fittings are highlighted in bold.
r2
p
Altitude
Mean annual precipitation
Substrate age
0.005
0.009
0.056
0.75
0.58
0.04
Factors
Plant organ
Population
0.231
0.135
<0.001
0.09
Vectors
Table 5. Composition of bacterial communities associated with flowers and leaves of M. polymorpha and six further sympatric plant
species. The confusion matrix of the random forest analysis shows
the mean number of correctly assigned communities and the mean
proportional class error for each organ and the mean total OOB estimate of the error rate. Stamina, style and nectar samples of M.
polymorpha were reclassified as flower samples. To avoid a sampling
bias in M. polymorpha after the reclassification towards flowers (94
samples), these were randomly subsampled (n = 1000 subsamples)
to match the sampling size of leaves (31 samples). The numbers in
bold are correct microhabitat-specific assignments and denote the
characteristic bacterial communities of flowers and leaves. Probability of misclassification (%) of leaves and flowers of other plant species
apart from M. polymorpha in n = 1000 subsamples are listed.
Flower
Leaf
the exception of communities isolated from styles, which were
more similar on average when sampled at short distances. In
tests considering only OTUs belonging to the indicator communities, neither environment nor geographic distance between
samples explained the variation in community composition
(Table 3). Thus, both the environment and geographic distance
were of minor importance compared to organ specificity in
structuring the communities in M. polymorpha. Likewise, fitting
of environmental vectors and factors (plant organ and population) onto a ordination based on UniFrac distances of bacterial
communities revealed that the plant organs by far were best
fitted into the ordination compared to populations and environmental factors (Table 4). Among the environmental factors,
only substrate age was significantly fitted onto the ordination
(Table 4).
Comparison with sympatric plant species
The indicator communities characteristic for M. polymorpha
flowers and leaves were also representative for flower and leave
tissues when other plant species are taken into account. Random forest analysis correctly assigned 84.05% of all samples
correctly to either flowers or leaves (Table 5). The probability of misclassification in the n = 1000 subsamples for leaves
and flowers of the six plant species apart from M. polymorpha
was below 3% in all cases (Table 5). Whereas a mean of 39.3%
of the total number of sequences obtained from M. polymorpha
Flower
Leaf
Class error
32.75
6.51
6.25
34.49
0.16
0.16
OOB estimate of error rate: 15.95%
Probability of misclassification (%)
Acacia koa
Leaves
Sophora chrysophylla
Flowers
Leaves
Lantana camara
Flowers
Leaves
Oenothera stricta
Flowers
Leaves
Chamaechrista nicitians
Flowers
Leaves
Crotolaria retusa
Flowers
Leaves
0.00
0.06
0.08
0.00
2.50
0.01
0.00
0.00
2.39
0.00
0.26
samples was assignable to indicator taxa, this percentage was
reduced to 26.7% in the other species.
DISCUSSION
Diversity and composition of bacterial communities
Our results demonstrate that aboveground plant organs in
M. polymorpha host bacterial assemblages that are qualitatively dominated by ubiquitous or rare taxa, which is a common pattern in plant-associated microbial communities (e.g.
Junker and Keller
Redford et al. 2010). This means that most bacterial OTUs are
either not specific to any of the microhabitats investigated or
were detected only once or a few times in all of the samples.
However, the relatively few indicator taxa from communities
characteristic of leaves, nectar, stamina or styles quantitatively dominate the community compositions meaning that
indicator taxa were most abundant in samples. Abundance
is a surrogate for habitat suitability (Fuhrman 2009), suggesting that indicator species are well adapted to the plant organs that provide a niche for their establishment and proliferation. Although stamina, style or nectar sample hosted
on average a higher number of OTUs per sample compared to leaves, the bacterial communities associated with
leaves were more diverse than the floral organs suggesting
that a larger number of bacterial strains can proliferate on
leaves (resulting in a higher evenness of abundances) than
on flower organs. Interestingly, we identified similar indicator
communities on flowers and leaves of M. polymorpha and on
other plant species suggesting that these plant organs provide bacterial habitats with similar conditions across species.
The role of strong habitat filtering by plant characteristics as
a mechanism controlling the composition and diversity of (indicator) bacterial communities is also supported by the weak
responses to environmental conditions such as mean annual
precipitation, altitude or substrate age, which contradict earlier
findings on endophytic fungi isolated from M. polymorpha, too
(Zimmerman and Vitousek 2012). Thus, the strong effect of microhabitat compared to large-scale environmental conditions on
bacterial communities is the foundation for the high species
richness associated with individual plants, which is also supported by a recent study that demonstrated that bacterial classes
are heterogeneously distributed across different aboveground
parts of Ginkgo biloba (Leff et al. 2015). This finding suggests
that not only specific characteristics of plant organs but also
different characteristics of individual plant organs at different
position within a plant increases overall bacterial diversity of
plants. Apart from spatial variation within trees and flowers,
bacterial communities colonizing flowers show a pronounced
successional pattern (Shade, McManus and Handelsman 2013)
additionally contributing to the overall diversity on plants. The
microhabitat specificity and the successional pattern are also
known from other plant species (Ottesen et al. 2013) and other
organisms such as humans (Costello et al. 2009), and thus may
represent a common feature of associations between eukaryotes
and bacteria. Thus, the additive effect of the microhabitat specificity of communities on the overall bacterial diversity associated with plants thus suggests that studies may underestimate
the diversity of plants’ microbiomes when considering individual organs, only. Note, that our study focused on a single plant
species sampled within a short period of time on one oceanic
island, which may limit the generality of our findings. However,
the finding that flowers and leaves of other plant species host
similar indicator taxa suggests that plant organs commonly differ in their bacterial communities, which is also supported by
earlier findings (Junker et al. 2011; Ottesen et al. 2013) and that
some bacterial taxa are specifically adapted to the conditions
commonly encountered on leaf and flower surfaces or nectar.
Potential factors explaining microhabitat specificity
Bacterial communities are shaped by biotic and abiotic parameters such as the availability of nutrients and the presence of
growth inhibiting substances (Whipps et al. 2008; Berendsen,
Pieterse and Bakker 2012; Junker and Tholl 2013), but they are
7
often not limited by the dispersal ability of bacteria (Beisner
et al. 2006; Östman et al. 2010), but see Lindström and Langenheder (2012). For example, it has been shown that only a small
proportion of airborne bacteria overlapped with those isolated
from leaf surfaces (Vokou et al. 2012) suggesting that the majority of bacteria arriving at plant surfaces do not find a suitable
habitat. Thus, characteristics of plant-based habitats can be assumed to be largely responsible for the diversity and taxonomical composition of local bacterial assemblages. This notion is
supported by our results, as neither the distance between trees
nor environmental factors had a strong effect on the similarity
and diversity of bacterial communities. The four plant tissues
sampled in our study strongly differ in their properties: bacteria disseminated to plants either encounter rather ephemeral
flower organs or leaves that offer a substrate for a longer period
of time, they need to attach to surfaces (leaves, stamina, style)
or find a liquid medium (nectar), and finally they are confronted
with a variable quantitative and qualitative composition of primary and secondary metabolites depending on the organ they
are colonizing. Additionally to the deviating features across organs, many factors affecting bacterial proliferation and establishment are also heterogeneously distributed within individual
plants organs. For instance, trichomes found on leaves are preferred microhabitats for some bacterial strains but are hostile
environments for others (Karamanoli et al. 2000; 2012). Stamina
and styles also provide multiple structures that may be suitable
for different sets of bacterial colonists (Aleklett, Hart and Shade
2014).
An additional explanation for the microhabitat-specific communities are animal vectors such as pollinators that specifically
visit flowers but not leaves and thereby facilitate the dissemination of bacteria between the same plant organs. Accordingly,
insects as well as birds, taxa that visit flowers of M. polymorpha (Junker et al. 2010), have been shown to transport microorganisms between flowers (Lachance et al. 2001; Belisle, Peay and
Fukami 2012), and thereby may contribute to the frequent occurrence of some strains on flowers. Our result that bacterial communities isolated from styles were more similar to each other
when sampled in close geographic proximity may support the
notion that flower visitors disseminated the bacteria. However,
further experimental studies clearly are needed to stringently
test the effect of animal vectors on the establishment of bacterial communities. Thus, the structural, physical and chemical diversity within individual plants along with different eukaryotic
interactions partners transporting bacteria to different organs
may explain the microhabitat specificity and the accumulating diversity of bacteria across the plant organs. Future studies
may disentangle these effects towards a better understanding of
the underlying mechanisms shaping plant-associated bacterial
communities.
Conclusions and outlook
In contrast to bacterial assemblages in the phyllosphere, those
colonizing the anthosphere or even those colonizing individual organs of flowers have, so far, been less well characterized. The existing information was restricted either to petals
in comparison to leaves (Junker et al. 2011), nectar (Fridman
et al. 2012) or whole flowers (Shade, McManus and Handelsman
2013), but does not comprise a systematic evaluation of the communities associated with specific organs within flowers of the
same species, a gap which is addressed in our study. Even less
well investigated are the consequences of bacterial colonization
on floral ecology apart from the effects of pathogens (e.g. Farkas
8
FEMS Microbiology Ecology, 2015, Vol. 91, No. 9
et al. 2012). We are just beginning to understand the potential
roles of bacteria associated with flowers in pollination ecology
and plant reproduction (Vannett, Gauthier and Fukami 2012;
Junker et al. 2014). In this context, it is important to note that the
maintenance of diverse bacterial communities is of paramount
importance to the resilience of plants against antagonistic microorganisms that can cause diseases (Berg et al. 2014). Phytopathogens that infect leaves and flower organs can have devastating impacts on both agriculture and natural ecosystems
(Burdon and Thrall 2009; Vorholt 2012; McArt et al. 2014) but
may be mitigated by either individual antagonistic bacterial
strains or diverse, natural bacterial communities (Fuernkranz
et al. 2012; Jousset et al. 2014). In addition to effects related to either causing or preventing plant diseases, non-pathogenic bacteria can interfere with mutualistic or detrimental plant–animal
interactions (Junker 2014), which may influence evolutionary
and ecological trajectories in the interplay between plants
and their biotic environment (Biere and Tack 2013). Given the
multiple interactions between plants and their bacterial communities and their variable effects on plant fitness, detailed
knowledge about bacterial community composition and diversity among different plant parts is a first step towards a comprehensive understanding of bacterial effects on pollination, plant
reproduction and health.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank Robin Gottlieb for assistance in the field, Gudrun
Grimmer for assistance in the lab, Gabriele Schlerka and Anopoli Biomedical Systems for technical support, Curtis C. Daehler
for organizational support and the United States Department
of the Interior (National Park Service) and Rhonda Loh for the
scientific research and collecting permit (HAVO-2013-SCI-0003),
Stefan Dötterl for helpful comments on an earlier version of the
manuscript, and James Jacobi for making the maps and data
available.
FUNDING
The study was supported by the Deutsche Forschungsgemeinschaft (DFG, JU2856/2–2) to RRJ.
Conflict of interest. None declared.
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