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ORIGINAL RESEARCH
published: 12 July 2021
doi: 10.3389/fmicb.2021.656269
Frontiers in Microbiology | www.frontiersin.org 1July 2021 | Volume 12 | Article 656269
Edited by:
Markus Puschenreiter,
University of Natural Resources and
Life Sciences Vienna, Austria
Reviewed by:
Yosef Steinberger,
Bar Ilan University, Israel
Upendra Kumar,
National Rice Research Institute
(ICAR), India
*Correspondence:
Ashraf Al Ashhab
ashraf@adssc.org
Specialty section:
This article was submitted to
Terrestrial Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 20 January 2021
Accepted: 31 May 2021
Published: 12 July 2021
Citation:
Al Ashhab A, Meshner S,
Alexander-Shani R, Dimerets H,
Brandwein M, Bar-Lavan Y and
Winters G (2021) Temporal and
Spatial Changes in Phyllosphere
Microbiome of Acacia Trees Growing
in Arid Environments.
Front. Microbiol. 12:656269.
doi: 10.3389/fmicb.2021.656269
Temporal and Spatial Changes in
Phyllosphere Microbiome of Acacia
Trees Growing in Arid Environments
Ashraf Al Ashhab 1,2
*, Shiri Meshner 1, Rivka Alexander-Shani1, Hana Dimerets 1,
Michael Brandwein 1,3 , Yael Bar-Lavan 1and Gidon Winters 1,2
1Dead Sea and Arava Science Center, Masada, Israel, 2Ben-Gurion University of the Negev, Eilat Campus, Be’er Sheva,
Israel, 3Biofilm Research Laboratory, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
Background: The evolutionary relationships between plants and their microbiomes
are of high importance to the survival of plants in general and even more in extreme
conditions. Changes in the plant’s microbiome can affect plant development, growth,
fitness, and health. Along the arid Arava, southern Israel, acacia trees (Acacia raddiana
and Acacia tortilis) are considered keystone species. In this study, we investigated
the ecological effects of plant species, microclimate, phenology, and seasonality on
the epiphytic and endophytic microbiome of acacia trees. One hundred thirty-nine leaf
samples were collected throughout the sampling year and were assessed using 16S
rDNA gene amplified with five different primers (targeting different gene regions) and
sequenced (150 bp paired-end) on an Illumina MiSeq sequencing platform.
Results: Epiphytic bacterial diversity indices (Shannon–Wiener, Chao1, Simpson, and
observed number of operational taxonomic units) were found to be nearly double
compared to endophyte counterparts. Epiphyte and endophyte communities were
significantly different from each other in terms of the composition of the microbial
associations. Interestingly, the epiphytic bacterial diversity was similar in the two
acacia species, but the canopy sides and sample months exhibited different diversity,
whereas the endophytic bacterial communities were different in the two acacia
species but similar throughout the year. Abiotic factors, such as air temperature
and precipitation, were shown to significantly affect both epiphyte and endophytes
communities. Bacterial community compositions showed that Firmicutes dominate A.
raddiana, and Proteobacteria dominate A. tortilis; these bacterial communities consisted
of only a small number of bacterial families, mainly Bacillaceae and Comamonadaceae in
the endophyte for A. raddiana and A. tortilis, respectively, and Geodematophilaceae and
Micrococcaceae for epiphyte bacterial communities, respectively. Interestingly, ∼60% of
the obtained bacterial classifications were unclassified below family level (i.e., “new”).
Conclusions: These results shed light on the unique desert phyllosphere microbiome
highlighting the importance of multiple genotypic and abiotic factors in shaping the
epiphytic and endophytic microbial communities. This study also shows that only a few
bacterial families dominate both epiphyte and endophyte communities, highlighting the
importance of climate change (precipitation, air temperature, and humidity) in affecting
arid land ecosystems where acacia trees are considered keystone species.
Keywords: Acacia raddiana,Acacia tortilis, phyllosphere, desert plants, microbiome, endophytes, epiphytes
Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
INTRODUCTION
The aboveground surfaces of plants (the phyllosphere) harbor
a diverse variety of microorganisms, including bacteria (Stone
et al., 2018). The microbiome of the plant phyllosphere has been
shown to play an important role in the adaptation of the plant
host to different environmental stressors by enhancing tolerance
to heat, cold, drought, and salinity (Whipps et al., 2008; Kembel
et al., 2014; Martirosyan and Steinberger, 2014; Agler et al.,
2016; Saleem et al., 2017). Several studies have suggested that
the ecophysiological adaptation of desert plants to their harsh
habitat is, at least partially, via microbial functional diversity
(Redford et al., 2010; Martirosyan and Steinberger, 2014). While
the exact correlation of phyllosphere microbial communities
and these unique adaptations is yet to be clarified, a growing
number of studies indicate that each plant species provides a
unique microenvironment that is suitable for its specific bacterial
communities (Camarena-Pozos et al., 2019; Flores-Núñez et al.,
2019; Chaudhry et al., 2021).
Plant phyllosphere microbes were found to differ among
different habitat and climate conditions when compared among
arid, semiarid, and temperate habitats. For instance, Martirosyan
et al. (2016) investigated the adaptation of three Negev desert
plant species and found gram-negative Bacteroidetes to dominate
the leaves of Hammada scoparia while practically absent in
other neighboring desert plant species (Martirosyan et al.,
2016). Plants desert microbiomes were also found to correlate
with high temperature, droughts, and UV radiation (Qvit-Raz
et al., 2008; Carvalho and Castillo, 2018), regardless of their
geographical location (Finkel et al., 2012). In this context, desert
phyllosphere microbes were shown to mediate plant growth
and the metabolism of some nutrients by fixing nitrogen from
atmospheric sources (Lambais et al., 2017), utilizing phosphorus
through solubilizing enzymes (Mwajita et al., 2013; Batool et al.,
2016) and producing siderophores to bind iron (Scavino and
Pedraza, 2013; Fu et al., 2016), and even increasing plant
resistance against pathogens such as Botrytis fungal infection (i.e.,
blight disease) (Li et al., 2012; Kefi et al., 2015).
Alongside the effects of seasonality (Redford and Fierer, 2009;
Redford et al., 2010; Copeland et al., 2015) and canopy structure
(Leff et al., 2015) on plant phyllosphere microbiome, other
studies have shown that abiotic (climate-related) and biotic (plant
genotype) factors also play an important role in structuring
the phyllosphere microbial communities (Rastogi et al., 2013).
Epiphytic (outside, on the surface the leaves) and endophytic
(inside the leaf tissue) microbial communities were shown to
be different in composition: epiphytic bacterial communities
found to be more diverse and more abundant compared with
the endophytic bacterial communities; moreover, abiotic factors
were shown to have different effects on epiphytic and endophytic
bacterial communities within the same plant host. The season
was the major driver of community composition of epiphytes,
whereas wind speed, rainfall, and temperature were the major
drivers shaping endophytic composition (Gomes et al., 2018).
These complex interactions between the plant microbiome
(both epiphytic and endophytic) and different biotic and
abiotic conditions within arid ecosystems are of particular
interest considering the current scenarios of climate change
and desertification (Stringer et al., 2009). Additionally, studies
on microbiomes in arid plants could shed new light on
important key microbial groups that might be of potential use in
arid agricultural practices, biotechnology, and plant adaptation
strategies to climate change (Vacher et al., 2016).
In this study, we investigated the epiphytic and endophytic
microbiomes associated with the phyllosphere (leaves) of Acacia
raddiana (Savi) and Acacia tortilis (Forssk) (Figure 1B) in the
Arava Desert (Figure 1A). These two tree species grow in some
of the hottest and driest places on Earth, such as the arid
Arava Valley along the Dead Sea Transform (also known as
Dead Sea Rift) in southern Israel and Jordan. In these arid
habitats, A. raddiana and A. tortilis are the most abundant, and
sometimes the only tree species present (Danin, 1983); they are
mostly found growing in the channels of ephemeral river beds
(Munzbergova and Ward, 2002). Both A. raddiana and A. tortilis
are considered keystone species as they support the local plant
and animal communities surrounding them and locally improve
soil conditions for other plant species (Milton, 1995; Ward and
Rohner, 1997; Munzbergova and Ward, 2002; Rodger et al.,
2018; Winters et al., 2018). We hypothesized that variations in
the bacterial communities of phyllosphere would be associated
not only with the host species (A. raddiana and A. tortilis),
but also with sampling season (temporal variations) and tree
microclimate (leaves growing on the shade facing north side of
the tree vs. leaves growing on the south side of the tree that are
exposed to direct sun radiation; spatial variations) (Figure 1D).
MATERIALS AND METHODS
Study Area and Sampling Scheme
This study was conducted in the Arava Valley, a hyperarid region
along the Dead Sea Transform in southern Israel and Jordan. The
elevation of the area ranges from 230 m above sea level to 419 m
below sea level (Figure 1A). The climate in the Arava Valley is
hot and dry: 30-year averages of minimum, mean, and maximum
air temperatures of the hottest months were 26.2, 33.2, and
40.2◦C, respectively; average minimum, mean, and maximum air
temperature of the coldest months were recorded as 9.1, 14.4,
and 19.6◦C, respectively. Annual precipitation ranges between 20
and 70 mm/year and is restricted to the period between October
and May (Winters et al., 2018) with large temporal (year-to-
year) and spatial variations (Ginat et al., 2011). The combination
of the very high air temperatures and the very low relative
humidity values of 6% can cause summer midday vapor-pressure
deficit (VPD) to reach 9 kPa (Winters et al., 2018). Vegetation
in the region is usually confined to “Wadis” (ephemeral river
beds; Ward, 2010), where the main water supply comes from
underground aquifers (Sher et al., 2010; Winters et al., 2015) and
winter flash floods (Shrestha et al., 2003). Multiple individual
A. raddiana and A. tortilis trees are scattered throughout Wadi
Shizaf (Figure 1A), but never form a continuous canopy. Wadi
Shizaf is a dry sandy streambed at the northern edge of the Arava
Valley, Israel (Figure 1A; 30◦44′N, 35◦14′E; elevation −137 m).
Meteorological data (air temperature and humidity logged every
3 h) for this site were obtained from the Israeli Meteorological
Service station 340528, located at Hatzeva only 7 km north of
Wadi Shizaf (Supplementary Figure 1).
Frontiers in Microbiology | www.frontiersin.org 2July 2021 | Volume 12 | Article 656269
Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
FIGURE 1 | (A) Southern Israel topography map showing the study site, Wadi Shizaf, and (B) acacia trees (A. raddiana and A. tortilis) sampled monthly during 2015.
In each month, (C) leaf samples were collected from the (D) north, center, and south sides of the canopies.
To sample bacteria from acacia trees in Wadi Shizaf, leaves
from two neighboring A. tortilis trees (>20 m away from each
other) (T023 and T300) and two neighboring A. raddiana trees
(R284 and R286) were sampled monthly between January and
December 2015 on their north, south, and central canopy sides
(Figure 1D,Supplementary Table 1). This sampling scheme was
chosen to enable us to investigate the effect of two different
host (tree) species, in addition to the variation caused by the
sampling season and the microclimate effect (different canopy
sites—north, central, and south-facing sides of tree) on the
phyllosphere microbiome.
During all sampling months, samples were collected from
trees using sterile gloves (changed between each sample) between
9:00 and 11:00 A.M. Leaves (20–25 g fresh weight) were collected
monthly (see Supplementary Table 1 for exact dates) and
inserted into 15-mL sterile tubes placed on ice. Upon reaching
the laboratory (within <2 h), samples were moved to freezers
(−20◦C) where they were kept until subjected to DNA extraction.
DNA Extraction
All DNA extractions were performed using the MO BIO 96-
well-plate PowerSoil DNA Isolation Kits (MO BIO Laboratories,
Carlsbad, CA, USA). For epiphyte (the outer surface of the
tree’s leaves) microbial community extractions, 0.15 g (FW) of
frozen leaves was weighed and placed in 1.5-mL Eppendorf
tubes filled with 500 µL MO BIO Powerbead Solution and
sonicated (DG-1300 Ultrasonic cleaner; MRC LAB, Israel) for
5 min. The solution was then transferred to the Powerbead Tubes,
and the remaining steps for DNA extraction were carried out
following the manufacturer’s protocol. For the extraction of
endophytic (inside the leaf tissue) microbial communities, leaf
samples following epiphytic microbial extraction were washed
using 1 mL of DNA/RNA-free water three times to eliminate
the epiphyte microbiome fraction. Leaves were washed using
1 mL of DNA/RNA-free water three times to eliminate the
epiphyte microbiome fraction. The washed leaves were then cut
into small pieces using a sterile scalpel and placed into the
MO BIO 96-well-Powerbead plate for DNA extraction following
the manufacturer’s protocol. All steps of DNA extraction were
carried out in a sterile UV-hood (DNA/RNA UV-cleaner
box, UVT-S-AR bioSan; Ornat, Israel) to reduce external
contaminations. In every DNA extraction, using a 96-well-plate,
DNA extraction negative controls were added by placing 200
µL of RNase-free water (Sigma–Aldrich, Israel). All samples
were placed randomly in the DNA extraction plate to exclude
any bias.
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
Multiplex Polymerase Chain Reaction for
Targeted Amplicon Sequencing of the 16S
Ribosomal RNA Gene—Polymerase Chain
Reaction I
To obtain a better phylogenetic resolution and diversity
estimate, a multiplex polymerase chain reaction (PCR) using
five different sets of the 16S rDNA genes was applied
to cover approximately 1,000 bp of the 16S rRNA gene
(Supplementary Table 2). First PCR (PCR-I) reactions were
performed in triplicates, where each PCR-I reaction (total 25
µL) contained 12.5 µL of KAPA HiFi HotStart ReadyMix
(Biosystems, Israel), 0.4 µL of equal vol/vol mixed primers
forward and reverse primers (Supplementary Table 2), 10 µL
of molecular graded DDW (Sigma, Israel), and 2 µL of (2–
100 ng/µL) DNA template. PCR-I reactions were performed in
a Biometra thermal cycler (Biometra, TGradient 48, Göttingen,
Germany) with the following routine: initial denaturation
95◦C for 2 min, followed by 35 cycles of 98◦C for 10 s,
61◦C for 15 s, and 72◦C for 7 s. Ending the PCR-I routine
was a final extension at 72◦C for 1 min. Upon completion
of PCR-I, an electrophoresis gel was run to verify that
all the samples were amplified successfully. Following this
quality control step, triplicate samples were pooled together
and were cleaned using Agencourt R
AMPure XP (Beckman
Coulter, Inc, Indianapolis, IN, USA) bead solution based on
manufacturer’s protocol.
Library Preparation and Next-Generation
Sequencing
Library preparation began with performing a second PCR (PCR-
II) in order to connect the Illumina linker, adapter, and unique
8-bp barcode for each sample (Fuks et al., 2018). The PCR-II
reactions were prepared by mixing 21 µL of KAPA HiFi HotStart
ReadyMix (Biosystems, Israel), 2 µL of mixed primers with the
Illumina adapter (Supplementary Table 3), 12.6 µL of RNase-
free water (Sigma, Israel), and 4 µL of each sample from the
first PCR (PCR-I) product with 2 µL of barcoded reverse primer.
These reactions were run in the thermal cycler with the following
routine: initial denaturation at 98◦C for 2 min, followed by,
eight cycles of 98◦C for 10 s, 64◦C for 15 s, and 72◦C for 25 s,
these cycles were followed by final extension of 5 min at 72◦C.
Then, all PCR-II products were pooled together and subjected to
cleaning using Agencourt R
AMPure XP bead solution Beckman
Coulter, Inc, Indianapolis, IN, USA) based on the manufacturer’s
protocol, where 50 µL of pooled PCR-II product was cleaned
using 1:1 ratio with the bead solution for more conservative size
exclusion of fragments <200 bp. As a final step, 50 µL of DDW
with 10 mM Tris (pH 8.5) was added to each sample. This was
followed by aliquoting 48 µL of the supernatant to sterile PCR
tubes and storing in −80◦C, while an additional 15 µL of the
final product was sent to the Center for Genomic Technologies
at the Hebrew University of Jerusalem (Jerusalem, Israel) where
samples were sequenced on a full lane of 150-bp paired-end (to
correct for sequencing error and enhance total read quality) reads
using an Illumina MiSeq platform.
Sequence Analysis and Quality Control
A series of sequence quality control (QC) steps were applied
before data analysis. These included filtering PhiX contamination
using Bowtie2 (Langmead and Salzberg, 2012), removing
incomplete and low-quality sequences (phred Q threshold 33)
by pairing the two reads using PEAR (Zhang et al., 2014),
and identifying ambiguous bases and miss-merged sequences
using mothur V.1.36.1 (Schloss et al., 2009). Following these
QC steps, QIIME-1 (Caporaso et al., 2010) was used. Sequences
were aligned, checked for chimeric sequences, and clustered to
different operational taxonomic units (OTUs) based on 97%
sequence similarity. Sequences were then classified based on the
Greengenes database V13.8 (DeSantis et al., 2006), and an OTU
table was generated. All sequences classified as f__mitochondria,
c__Chloroplast, k__Archaea, and K__Unclassified were removed
from the OTU table.
OTU Richness and Diversity Estimates
For each sample, four diversity estimates were measured: (i)
observed number of OTUs, (ii) Chao1 species’ diversity estimate
(Hill et al., 2003), (iii) Simpson diversity index (Keylock, 2005),
and (iv) Shannon bacterial communities’ diversity (Haegeman
et al., 2013). All diversity metrics were calculated within QIIME-
1 (Kuczynski et al., 2012) using the parallel_alpha_diversity.py
command on the rarefaction subsamples set to 10,000 sequences
using the multiple_rarefactions.py command.
Assessment of Community Composition
From the obtained QIIME classified OTU table, each taxonomic
group was allocated down to the genus level using the
summarize_taxa.py command in QIIME and relative abundance
was set as the number of sequences affiliated with that taxonomic
level divided by the total number of sequences. Relative
abundances were plotted using R statistical software (R Core
Team, 2013), where each phylum was assigned a distinguished
color, and all genera under the same phylum were assigned to
different shades of the same color.
Statistical Analysis
Using the VEGAN package (Oksanen et al., 2018) in R,
nonparametric multidimensional scaling (NMDS) was used to
produce ordination based on Bray–Curtis distance matrix based
on the total sum transformed matrix for the raw OTU table
(Sinclair et al., 2015). Canonical correspondence analysis (CCA)
was used to plot abiotic factors and to show how they explain
variance in the microbial communities (Gonzalez et al., 2008).
Permutational multivariate analysis of variance (PERMANOVA)
was used to test the effects of tree species, microclimate (different
areas within the tree canopy), and seasonality and tree phenology
on the epiphytic and endophytic microbiomes associated with
these two trees using the adonis2 function in R (McArdle and
Anderson, 2001); relative abundances of different bacterial phyla
were tested using analysis of variance (ANOVA) with post-hoc
Tukey honestly significant difference test for group significance.
Frontiers in Microbiology | www.frontiersin.org 4July 2021 | Volume 12 | Article 656269
Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
RESULTS
A total of 139 acacia leaf samples [two tree species ×two
replicate trees from each species ×3 canopy locations for
epiphytes (but only the south canopy for endophytes) ×9
months] were collected for both epiphytic and endophytic
microbial communities (Supplementary Table 1; notice that a
total of seven samples were lost during sample processing before
sequencing) and sequenced for their 16S rRNA genes using our
five different primer sets (Supplementary Table 2). The average
sequence number per each primer set varied significantly for
the different regions of amplification (Supplementary Table 2).
Results showed that the third primer set (F649 and R889)
obtained the highest number of raw sequences with an average
raw sequence number of 38,683 ±18,723; thus, we based all
further analysis on the F649-R889 primer set. This primer set
retained its rank among all other primer sets even after the QC
procedure, with 15,424 ±13,784 high-quality sequences.
Bacterial Community Composition of
Epiphytic vs. Endophytic Assemblages
For comparing epiphytic and endophytic bacterial community
structures, we used the leaf samples that were collected
only at the south (“S”) canopy side of the acacia trees
(Supplementary Table 1). Diversity estimates, including
observed number of OTUs, Chao1, Simpson diversity index,
and Shannon–Wiener, were calculated for both A. raddiana
and A. tortilis (Table 1). Epiphytic bacterial diversity was higher
when compared to endophytic bacterial communities (Table 1),
indicating a different bacterial structure in the external vs.
internal parts of the leaves.
To compare the diversities of epiphytic and endophytic
bacterial communities extracted from leaf samples, acacia
samples from south-facing canopies were analyzed and plotted
using NMDS, based on the Bray–Curtis distance matrix
(Figure 2). The NMDS showed two separate clusters of epiphytic
and endophytic bacterial communities, statistical analysis Using
PERMANOVA found endophytic and epiphytic microbial
communities to be significantly different (p=0.001; Figure 2A).
While the epiphytic bacterial communities from the two acacia
species (A. raddiana and A. tortilis) did not demonstrate separate
clusters (p=0.134, Table 2,Figure 2A), the endophytic bacterial
communities were found to be significantly different for both
acacia species (p=0.021, Table 3,Figure 2B). To illustrate
these differences, we plotted the bacterial phylum with more
than 5% of the total community composition (Figure 3) and
performed Tukey test of significance on the log-transformed
abundances to normalize the variance. Results showed a higher
median abundance of Actinobacteria in A. raddiana and A.
tortilis, when comparing epiphytic to endophytic bacterial
communities. However, these differences were only significant
for A. tortilis (45.2 ±17.7% and 9.0% ±5.9%, p<0.05)
and not for A. raddiana (44.5 ±19.2% and 5.7 ±10.0%, p
>0.05; Figure 3). Similarly, Cyanobacteria median abundance
was higher in epiphytic compared to endophytic bacterial
communities and was significant for A. tortilis (2.6% ±5.8%
and 0.4 ±0.4%, p<0.05) and not for A. raddiana (2.5 ±5.2%
and 0.5 ±0.4%, p>0.05; Figure 3). On the other hand, the
abundance of both Firmicutes and Proteobacteria was lower in
epiphytic compared to endophytic bacterial communities and
was significantly different among the two acacia species (p<
0.05). The median abundance of Firmicutes in A. raddiana
epiphytes was 21.4 ±10.1% compared to 76.3 ±32.4% in
endophytes and in A. tortilis was 15.8 ±12.2% and 25.1 ±27.6%,
respectively (Figure 3). The median abundance of Proteobacteria
in epiphytes and endophytes of A. raddiana was 13.5 ±12.1% and
19.1 ±21.3%, and that of A. tortilis was 13.2 ±8.4% and 65.5 ±
26.2%, respectively (Figure 3).
Temporal, Seasonality, Phenology, and
Spatial Variation (Canopy Side) of
Phyllosphere Bacterial Communities
To test the temporal effect (sampling month), seasonality,
tree phylogeny (leaves shedding time), and canopy variation
on the epiphytic bacterial communities of both A. raddiana
and A. tortilis, we have performed PERMANOVA analysis of
independent variables and nested models (Table 2). Table 2
shows both seasonality (winter, spring, summer, and autumn)
and different sampling month had a significant effect of the
epiphytic microbial communities (p=0.001), whereas the
effect of leaves shedding period (tree phenology), Acacia species
(A. raddiana and A. tortilis), Tree (2 individual tree for each
acacia species), and canopy side (north, center, and south)
had no significant effect of the epiphytic bacterial diversity
(p>0.05). To better illustrate the microbial communities at
different sampling months and seasonality, we plotted NMDS
(Figure 4A) and dispersion (variance) to the centroid for
the different months and seasons (Supplementary Figure 4),
showing different clusters based on sampling month and season.
We have also plotted bar plots illustrating the bacterial families
composition for OTU >1% at different canopy sides over the
sampling months showing bacterial families to not significantly
change between the different canopy sides, whereas main
changes in the different sampling months were related to higher
abundance of Bacillaceae in January, Enterobacteriaceae in March
and April, and Xanthomonadaceae in May, June, and July
(Supplementary Figure 5).
For endophytic bacterial communities, we have also checked
the effect of sampling months, seasonality, tree phylogeny,
and acacia species on endophytic bacterial diversity using
PERMANOVA analysis (Table 3). Table 3 shows tree phenology
(leaves shedding period) and acacia species (A. raddiana and
A. tortilis) have a significant effect on the endophytic bacterial
diversity (p=0.029 and 0.021, respectively), whereas the effect
of different sampling seasons, months, individual trees within
each species had no significant effect (p>0.05). To better
illustrate the significant effect of tree phylogeny (shedding
period) and acacia species, we plotted NMDS (Figure 4B) and
dispersion (variance) to the centroid (Supplementary Figure 6)
to show the different clusters following tree phenology and
acacia species. When bar plots were plotted illustrating the
bacterial families composition for ASV >1% at different
phylogeny (shedding period vs. non-shedding period) and
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
TABLE 1 | Average diversity estimates (±SD) of microbial communities of A. raddiana and A. tortilis measured across the entire sampling months for the epiphyte at north
(N) and center (C) canopy sides and for both epiphytes and endophytes at south (S) canopy side.
Species Canopy Observed no. of OTUs Chao1 Simpson diversity
index
Shannon–Wiener
diversity index
A. raddiana N-epiphyte 460.5 ±273.1 716.8 ±367.6 0.8 ±0.2 3.5 ±1.1
C-epiphyte 387.3 ±228.1 614.7 ±327.1 0.9 ±0.1 3.4 ±1.0
S-epiphyte 445.5 ±210.2 680.9 ±300.3 0.9 ±0.1 3.6 ±0.8
S-endophytes 236.5 ±42.6 368.5 ±89.4 0.6 ±0.2 1.9 ±0.7
A. tortilis N-epiphyte 607.8 ±290.7 902.7 ±387.5 0.9 ±0.1 3.8 ±0.8
C-epiphyte 519.7 ±254.4 811.0 ±373.2 0.8 ±0.2 3.3 ±1.0
S-epiphyte 512.5 ±262.7 754.2 ±349.6 0.9 ±0.1 3.4 ±0.7
S-endophytes 148.3 ±46.9 242.7 ±80.8 0.6 ±0.2 1.8 ±0.6
FIGURE 2 | NMDS illustrating the phyllosphere bacterial community; separate clusters of bacterial communities are evident for (A) the epiphytic (red) and the
endophytic (blue) bacterial communities from leaves sampled from south side canopy areas; and (B) unique clusters of endophytic bacterial communities observed in
A. raddiana (blue) and A. tortilis leaves.
for different acacia species (Supplementary Figure 7), higher
abundance of Bacillaceae was observed during non-shedding
period “new,” whereas Comamonadaceae showed a higher
abundance at leaves shedding period relative to each other
(Supplementary Figure 7A). Similarly, higher abundance of
Bacillaceae relative to Comamonadaceae was observed for A.
raddiana compared to A. tortilis (Supplementary Figure 7B).
Effect of Changes in Abiotic Factors on
Variation in the Microbial Communities
To test abiotic factors’ effect on the microbial communities,
CCA (ter Braak, 1986) was performed for the epiphytic
(Figure 5A) and endophytic (Figure 5B) bacterial communities
of A. raddiana and A. tortilis. Only those abiotic factors with
significant values (p≤0.05) were plotted. Results show that air
temperature, VPD, humidity, and precipitation had a significant
effect on the variability of epiphytic bacterial communities and
were able to explain up to 49% (30.4 and 18.6% on CCA axes
1 and 2, respectively) of total epiphytic community variability
(Figure 5A). In comparison, temperature and precipitation (but
not VPD) had a significant but slightly weaker effect on the
endophytic bacterial communities, explaining up to 23.9% (16.6
and 7.3% on CCA axes 1 and 2, respectively) (Figure 5B) of total
endophytic community variability.
Bacterial Family Abundances
To test for the major changes in bacterial family abundances,
a heatmap was made to show epiphytic and endophytic
bacterial diversities at the family level (Figure 6). Results
show that only a few bacterial OTUs were differentially
abundant comparing epiphyte and endophytes, or when
comparing within the endophytic communities between A.
raddiana and A. tortilis. Epiphytic bacterial communities were
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
TABLE 2 | PERMANOVA analysis showing statistical significance of epiphytic
microbial communities across sampling months, season, tree phenology (leaves
shedding period), and canopy sides.
Df SumOfSqs F. Model R2Pr(>F)
Season 3 2.5079 4.3842 0.10296 0.001
Month 5 3.3865 3.5520 0.13903 0.001
Shedding 1 0.1722 0.9031 0.00707 0.552
Acacia_species 1 0.2570 1.3479 0.01055 0.134
Month:Acacia_species 7 1.5159 1.1357 0.06223 0.139
Acacia_species:Tree 2 0.4866 1.2759 0.01998 0.114
Acacia_species:Canopy 4 0.7775 1.0194 0.03192 0.410
Residuals 80 15.2542 0.62626
Total 103 24.3578 1.00000
df, degrees of freedom; SumOfSqs, sum of squares.
TABLE 3 | PERMANOVA analysis showing statistical significance of epiphytic and
endophytic microbial communities across sampling months.
Df SumOfSqs F. Model R2Pr(>F)
Shedding 1 0.5609 3.9882 0.10529 0.029
Acacia_species 1 0.6189 4.4006 0.11629 0.021
Season 3 0.4261 1.0100 0.08007 0.418
Month 5 1.0376 1.4756 0.19497 0.206
Acacia_species:Tree 2 0.1807 0.6425 0.03396 0.594
Acacia_species:Season 3 0.5998 1.4217 0.11271 0.219
Acacia_species:Month 2 0.2101 0.7470 0.03948 0.513
Residuals 12 1.6876 0.31712
Total 29 5.3217 1.00000
df, degrees of freedom; SumOfSqs, sum of squares.
mainly dominated by Geodermatophilaceae,Micrococcaceae,
Comamonadaceae, and Bacillaceae bacterial families, whereas
endophytic bacterial communities were dominated only by
alternating abundances of the Bacillaceae,Comamonadaceae,
and Moraxellaceae families (Figure 6, Supplementary Figure 8).
In the endophytic bacterial communities, these changes in
abundance correlated with different months of the year (Figure 6,
Supplementary Figure 5).
DISCUSSION
To improve our understanding of the microbial structure of
the phyllosphere microbiome of plants growing in extreme arid
environments, we applied a high temporal and spatial resolution
sampling scheme in two desert keystone trees (A. raddiana and
A. tortilis). We investigated both the epiphytic and endophytic
bacterial communities to understand the (i) intraindividual and
interindividual spatial variation of the microbial communities
within a tree—the spatial variation within the same tree caused
by sun exposure (only for epiphytic microbial communities)
and the variation between neighboring trees of the same species
sampled at the same time and site (ii) variation of the microbial
community caused by the host (tree) species (i.e., A. raddiana
compared with neighboring A. tortilis), (iii) temporal variation of
the microbial communities within the same tree species, canopy
side, and individual trees sampled during different seasons.
Our results demonstrate that the epiphytic bacterial
communities were more sensitive to changes in environmental
abiotic conditions, compared with the endophytic bacterial
communities that were more stable between different
environmental conditions (e.g., seasons) but varied among
host tree species. Surprisingly, up to 60% of the total bacterial
communities (the combined epiphytic and endophytic
microbiome populations) were unclassified below family
level, highlighting the uniqueness of the microbiome associated
with acacia trees in the arid environment of the Arava. When
actinobacterial differences were compared in tree grove, shrub,
meadow, desert, and farm soil ecosystems, the majority of
unclassified actinobacterial sequences were found in desert
ecosystems, accounting for ∼50% of total Actinobacteria
phylum (Zhang et al., 2019). Similar results from a study
of desert soil in Pakistan indicate that a bulk portion of
the OTUs were assigned to unclassified taxa (Amin et al.,
2020).
In terms of the overall observed number of OTUs, Chao1,
Simpson, and Shannon–Wiener diversity indices, the diversity
of the epiphytic bacterial community was shown to be double
that of its endophytic bacterial community counterpart (Table 1).
While the average number of classified bacteria sequences for
epiphytes was slightly higher (16,752) compared to endophytic
(14,857) bacterial communities, the sequence number in each
sample had no effect on the obtained diversity indices
(Supplementary Figure 2). The higher abundance and richer
microbial communities in epiphytes compared to endophytes
were also observed in young and mature leaves of Origanum
vulgare, where the total number of colony-forming units (CFUs)
of epiphytic bacterial communities (5.0 ±0.2) was more than
double the CFUs of the associated endophytic communities (1.8
±0.1) (Pontonio et al., 2018). However, our results contradict
previous work on microbiomes associated with Arabidopsis
thaliana where epiphytic bacterial diversity indices were found
to be lower than those measured for the associated endophytic
bacterial communities (Bodenhausen et al., 2013). Like the work
shown here, a recent study on the epiphytic and endophytic
fungal diversity in leaves of olive trees (Olea europaea) growing
in Mediterranean environments also showed that the epiphytic
fungal communities had higher diversity indices compared
to the endophytic diversity estimates (Gomes et al., 2018);
similarly, bacterial endophytic diversity was lower compared
to epiphytic diversity in tomato plants (Dong et al., 2019).
The fact that our epiphytic OTU diversity was higher than
the endophytic diversity is particularly surprising, considering
previous work by Thapa and Prasanna (2018) that suggested
that the conditions inside the plant might be more favorable
compared with the more hostile conditions on the outside
(Thapa and Prasanna, 2018). This might explain finding where
diversity was higher for endophytic microbiomes (Bodenhausen
et al., 2013). In our case, however, both A. raddiana and A.
tortilis had a lower endophytic bacterial diversity compared to
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
FIGURE 3 | Box plot illustrating the percent abundance of epiphyte and endophyte major bacterial phyla.
epiphytic bacterial diversity throughout the sampling period
(Supplementary Table 4); the lower diversity of endophytic
microbial communities compared to epiphytic communities may
be a result of plant stress and physiological conditions regulated
by stomatal opening (Arndt et al., 2004; Chaudhry et al., 2021).
In fact, Chao1 richness in epiphytic microbial communities in
A. raddiana and A. tortilis was higher in January, March, and
November compared to summer months and corresponded to
plant water VPD, indicating the importance of the physiological
state of the plant in shaping endophytic bacterial communities
(Supplementary Figure 3,Supplementary Table 4). These plant
responses were shown to reduce the entry of epiphytes to the
endosphere, thus affecting the plant’s microbiome colonization
(Pontonio et al., 2018; Remus-Emsermann and Schlechter, 2018;
Schlechter et al., 2019).
Our results demonstrate that the epiphytic and endophytic
bacterial communities are significantly unique (Figures 2A,3,
Supplementary Figure 8). We also found that the endophytic
(but not the epiphytic) bacteria communities differed
between the two acacia species (Figures 2A,B,Tables 2,3,
Supplementary Figures 6B,8), with each host being associated
with specific endophytic communities. In fact, many studies have
indicated that the composition and abundance of endophytes
in plants are synergistically determined by plant genotype
and environmental factors (Terhonen et al., 2019). Plant
tissue characteristics highly affect microbial abundance; thus,
endophyte enrichment varies widely in different tissues within
a plant (Baldrian, 2017). The significant effect of genotype
on composition of endophytic microbial communities has
been documented (Bodenhausen et al., 2014; Hardoim et al.,
2015; Müller et al., 2015), and it is more severely affected by
genotypic factors than by abiotic factors (Whipps et al., 2008;
Rastogi et al., 2013; Agler et al., 2016). On the other hand,
tree phenology may play an important role in phyllosphere
microbiome; recent work by Winters et al. (2018) followed the
dynamics in stem diameter, leaf phenology, and sap flow of
A. raddiana and A. tortilis trees growing at the same sheizaf
site as the trees presented here, during 3 consecutive years.
While it was expected that stem growth and other tree activities
would be synchronized with and limited to single rainfall or
flash flood events that occur in the winter, Winters et al. (2018)
found that cambial growth of both Acacia species actually
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
FIGURE 4 | NMDS illustrates (A) the epiphytic phyllosphere bacterial communities at different sampling seasons and moths and (B) endophytic for different acacia
species and tree phenology.
FIGURE 5 | CCA ordination illustrating (A) epiphytic bacterial community at north (red), south (green), and center (blue) canopy sides and (B) endophytic bacterial
communities, for A. raddiana (blue circles) and A. tortilis (red triangles) with significant abiotic factors affecting the bacterial communities.
stopped during the wet season and occurred during most of
the dry season, coinciding with maximum daily temperatures
as high as 45◦C and VPD of up to 9 kPa. Summer growth was
correlated with peak sap flow in June, indicating that trees
relied year-round on deep soil water (Winters et al., 2018).
Particularly relevant to the microbiome results presented here
were the phenology changes demonstrated by the 3-year study
by Winters et al. (2018) that showed that in the two acacia
species, new leaves emerged twice a year, in early March and
again in late October. The leaf-shedding period was relatively
short (July–August) for A. raddiana and slightly earlier and
longer (May–September) for A. tortilis. In this study, we applied
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
FIGURE 6 | Heatmap showing the abundance of bacterial family abundances (xaxis) for each of the sampled epiphytic and endophytic bacterial communities at the
south canopy side at different sampling months (January–November).
those findings on our data, and there seems to be a strong
link between the seasonal leaf phenology and the monthly
diversity indices (Supplementary Table 4). The highest diversity
was seen in older leaves, leaves that have been on the tree the
longest time (i.e., June and July, Supplementary Figure 3).
When we statistically checked the effect of tree phylogeny
in both epiphytic and endophytic microbial community, we
found endophytes but not epiphytes to be affected by tree
phenology (leaves shedding period) (Tables 2,3;Figure 4B,
Supplementary Figures 6A, 7A).
Like other findings indicating the changes in bacterial
communities in the phyllosphere due to different environmental
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Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
and biotic factors (Remus-Emsermann and Schlechter, 2018;
Schlechter et al., 2019), our results show that seasonality
(temperature and month) is the major driver of community
composition in epiphytic bacteria (Figures 4A,5A;Table 2).
Humidity, temperature, precipitation, and VPD were shown to
have a strong effect explaining 30.4 and 18.6% in CCA axes 1
and 2, respectively, accounting for the total variance in microbial
community composition (Figure 5A). Studies found that leaves’
microbial communities can be significantly affected by leaves’
moisture (Beattie, 2002; Lindow, 2006), bacteria also found to
produce surfactants to increase the wettability of the leaf and
lessen the ability of the cuticle to limit water accumulation
(Schreiber et al., 2005). Although the effect of moisture from dew
on the leaf community has not been explored, it is expedited to
act similarly to rain (Stone et al., 2018).
In the endophytic bacterial communities, temperature and
precipitation explained 14.6 and 7.3%, respectively, of the total
microbial community (Figure 5B). It was suggested that abiotic
regulatory factors affect the physicochemical properties of the
leaves; in addition, these abiotic factors can affect external
biotic factors (e.g., insects and pathogens), which in turn
affect the plant’s immunity and biology and therefore affect
plant-associated microbiome (Liu et al., 2020). Nonetheless,
plants undergo remarkable physiological changes in relation
to abiotic factors; such changes can affect the availability of
nutrients, water, and a wide range of secondary metabolites on
the leaf surface and therefore significantly affect the epiphytic
microbial communities (Liu et al., 2020). These findings can also
be related to dust accumulation on leaves; dust is considered
an important source of nutrients and essential metals in arid
ecosystems (Reynolds et al., 2001, 2006). Aeolian dust also
emerged as a significant vehicle for long-range transport of
microorganisms (Maki et al., 2019), affecting plant microbiomes
(Brown and Hovmøller, 2002; Banchi et al., 2019). Although
in this study dust samples were not collected, in summer 2018
we have collected Aeolian dust in a pocket nearby the current
study’s sampled acacia trees; we also collected samples for
endophytes and epiphytes from both A. raddiana and A. tortilis.
The dust microbiome showed to cluster separately and closer to
epiphyte bacterial communities (Supplementary Figure 9). The
results also showed that dust samples are composed of two main
bacterial families Pseudomonadaceae and Halomonadaceae,
which were also dominant in the epiphytic microbial
communities of both A. raddiana and tortilis; nonetheless,
acacia epiphytes also showed a significant abundance of other
bacterial families that were not presented in the collected dust
samples (Supplementary Figure 9). These results suggest that
desert dust microbiomes also can play an important role in
desert epiphytes.
The effect of microclimate (i.e., the spatial variation
caused by the different canopy sides) on the epiphytic
bacterial diversity (Tables 1,2) and community composition
(Supplementary Figure 5) was shown to be significant. However,
we only tested the effect of exposure to irradiance on epiphytes.
A recent study investigated the endophytes of roots and
leaves of Oxyria digyna showing the strong impact of tissue
type on the endophytic bacterial community structure (Given
et al., 2020). Assessing the different canopy sides showed that
exposure to the sun significantly affects the physiological state
of leaves (Hussain et al., 2020) and forms distinct epiphytic
microbial communities; therefore, the effect of the canopy side
on the endophytic phyllosphere microbial communities needs
further investigation.
Our microbial community compositions also differ from
those found in the phyllosphere of plants from subtropical
and temperate regions, which are mostly dominated by
Alphaproteobacteria (72%) (Laforest-Lapointe et al., 2016),
Bacteroidetes (8%), and Acidobacteria (17%) (Kim et al., 2012),
whereas ours were dominated by Firmicutes, Proteobacteria
(mainly Betaproteobacteria, Figure 6), and Actinobacteria
(Figure 3), shown to agree with other findings in arid system–
and desert-adapted plant species (Citlali et al., 2018). The
major differences between epiphytic and endophytic bacterial
communities were due to the differential abundance of four
major unclassified OTUs belonging the bacterial families
of Bacillaceae (Firmicutes phylum) and Comamonadaceae
(Betaproteobacteria phylum) for the endophyte of A. raddiana
and A. tortilis, respectively (Figure 6). Other unclassified OTUs
belonging to the bacterial families of Geodematophilaceae and
Micrococcaceae (both belonging to Actinobacteria phylum) were
found in the epiphyte bacterial communities (Figure 6). These
bacterial families were also found in other extreme-condition
studies that investigated the metagenomic signatures of the
phyllosphere of Tamarix aphylla (Finkel et al., 2011, 2012, 2016)
and other desert shrubs (Martirosyan et al., 2016), highlighting
the relationships between these bacterial communities and
their importance in the adaptation of desert plants to arid
environments. However, the exact link between these different
bacterial groups and their functional diversity is still to be
investigated; such studies could shed light on the specific
metabolites and enzymes that these adaptive bacterial groups
exhibit in arid environments. With the growing interest in
manipulating and inoculating food crops with particular
microbial communities to extend shelf life and improve plant
resilience and product taste, the long-coevolved microbiome
of desert plants might have biotechnological potential. Desert
plant microbiome may enhance the resilience of crop plants
during ongoing processes of desertification and soil salinization
expected to affect vast regions of the world in coming decades.
CONCLUSIONS
The evolutionary relationships and interactions between plants
and their microbiome are important for their adaptation to
extreme conditions. In this study and based on 16S rDNA
sequencing, we explored the spatiotemporal relationships
between naturally occurring desert plants and their microbiome.
While changes in the plant microbiome can affect plant
development, growth, and health, we presented the effects
of plant physiological conditions, temporal changes, canopy
structure, abiotic parameters, and plant genotype and phenology
on both epiphytic and endophytic bacterial communities of
desert plant phyllosphere. Moreover, we showed how the
Frontiers in Microbiology | www.frontiersin.org 11 July 2021 | Volume 12 | Article 656269
Al Ashhab et al. Microbiome of Desert Plants Phyllosphere
desert plant phyllosphere is inhabited by distinct microbial
communities compared to temperate and humid regions,
stressing that a large portion of these microbial communities
is not classified below family level. Our results shed light
on the specific bacterial families and diversity patterns in
relation to desert phyllosphere epiphyte and endophytes
associated with extreme environmental conditions. The
agritech (agritechnology) potential of these unique microbial
communities calls for more research on the functionality of these
epiphytic and endophytic microbial communities.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are publicly
available. This data can be found here: All curated sequences
were joined into a single fasta file and submitted to MG-RAST
under project link (https://www.mg-rast.org/linkin.cgi?project=
mgp92155). All codes for quaration steps, quality control and
sequence analysis uploaded to GitHub repository, including the
metadata files and made publicly available (https://github.com/
ashrafashhab/Desert-plant-microbiome).
AUTHOR CONTRIBUTIONS
AA was involved in the project conceptualization, data
curation, formal analysis, methodology, project administration,
resources, visualization, and MS writing. SM, MB, and GW
were involved in funding acquisition, project conceptualization,
and project investigation. In addition, both GW and YB-L
took an active part in MS and graphics revision and
editing. RA-S and HD helped in laboratory and field work.
All authors contributed to the article and approved the
submitted version.
FUNDING
This research was supported by Israel Charitable Association
funding agency, Grant No. 03-16-06A.
ACKNOWLEDGMENTS
We also thank Dr. Noam Shental from the Department of
Computer Science at the Open University of Israel, for his
generous support in preliminary data analysis, selection of 16s
Primes and his insight in experimental design. Special thanks
to Ms. Tal Galker from Arava Studio for developing Figure 1
in the manuscript and we also thank Michelle Finzi for English
language editing.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2021.656269/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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