Range-wide variation in bud break phenology

Climate-driven divergence in plant-microbiome
interactions generates range-wide variation in
bud break phenology
Ian M. Ware 1,4✉, Michael E. Van Nuland 1,5, Zamin K. Yang2, Christopher W. Schadt 2,3,
Jennifer A. Schweitzer1 & Joseph K. Bailey1
Soil microbiomes are rapidly becoming known as an important driver of plant phenotypic
variation and may mediate plant responses to environmental factors. However, integrating
spatial scales relevant to climate change with plant intraspecific genetic variation and soil
microbial ecology is difficult, making studies of broad inference rare. Here we hypothesize
and show: 1) the degree to which tree genotypes condition their soil microbiomes varies by
population across the geographic distribution of a widespread riparian tree, Populus angustifolia; 2) geographic dissimilarity in soil microbiomes among populations is influenced by
both abiotic and biotic environmental variation; and 3) soil microbiomes that vary in response
to abiotic and biotic factors can change plant foliar phenology. We show soil microbiomes
respond to intraspecific variation at the tree genotype and population level, and geographic
variation in soil characteristics and climate. Using a fully reciprocal plant population by soil
location feedback experiment, we identified a climate-based soil microbiome effect that
advanced and delayed bud break phenology by approximately 10 days. These results
demonstrate a landscape-level feedback between tree populations and associated soil
microbial communities and suggest soil microbes may play important roles in mediating and
buffering bud break phenology with climate warming, with whole ecosystem implications.
https://doi.org/10.1038/s42003-021-02244-5 OPEN
1Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA. 2 Biosciences Division, Oak Ridge National Laboratory, Oak
Ridge, TN, USA. 3Department of Microbiology, University of Tennessee, Knoxville, TN, USA. 4
Present address: Pacific Southwest Research Station, Institute
of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, USA. 5
Present address: Department of Biology, Stanford University, Stanford, CA, USA. ✉email: [email protected]
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At large geographic scales, understanding how abiotic and
biotic factors alter plant phenological traits (i.e., the
timing of life cycle events such as leaf bud break) is
important for predicting species, community, and ecosystem
responses to global change1–3. Bud break phenology in temperate
deciduous trees is a complex trait widely considered to be driven
by the interaction of temperature, photoperiod, and plant genetic
architecture4–6. Spring leaf-out initiates the growing season and
represents a major driver of productivity, resource acquisition,
and carbon dynamics, and is thus a critical trait that has farreaching consequences on the whole ecosystem7,8. Alterations to
spring bud break can cause asynchrony in associated community
interactions, directly influencing food web dynamics and even
spatiotemporal patterns in species migration9–11. However, in
addition to temperature, photoperiod, and plant genetic architecture, recent controlled studies have linked soil microbial
communities with changes in phenological variation12,13,
revealing the surprising importance of biotic interactions in
determining phenological events.
Rhizosphere soil microbes can shift plant phenological traits,
such as the timing of flowering and leaf-out, leaf longevity, and
nutrient acquisition14–16, as well as mediate plant growth and
fitness17–20, highlighting the magnitude of biotic regulation of
plant performance and life history events. For example, Wagner
et al.12 have shown that selection intensity on flowering time in
Boechera stricta varies depending on the soil microbiome and on
abiotic factors as well. Similarly, Lu et al.13 mechanistically
determined that microbially derived indole acetic acid production
delayed flowering in Arabidopsis thaliana by downregulating
genes responsible for flowering. In addition to microbially
mediated phenological changes, microbial symbionts and whole
communities have been shown to confer tolerance and fitness
advantages to environmental stress18,21,22. Gehring et al.22, e.g.,
found that growth in drought-tolerant and drought-intolerant
Pinus edulis seedlings was similar when given sterile ectomycorrhizal fungi (EMF) inoculum but that drought-tolerant seedlings
grew 25% larger than drought-intolerant seedlings under dry
conditions when seedlings were allowed to develop in their own
distinct EMF communities. Cumulatively, these studies highlight
a few of the many roles soil microbial communities play in
determining plant phenotypes and plant responses to climatic
stressors. However, the roles and subsequent effects of the soil
microbiome on plant phenological events across large spatial scale
gradients in plant population genetic variation and environmental conditions remain largely unknown23,24.
To improve understanding of how plant–microbiome–
environment interactions may alter the timing of spring phenology, we combined field observations and a reciprocal
population-level greenhouse soil inoculation experiment using
soil from across the geographic range of a keystone riparian tree
species. In previous research, we showed that climate-driven
evolution in bud break phenology created population-level differences in the degree trees condition soil microbial communities
and nutrient pools across the range of Populus angustifolia25 (i.e.,
plant phenotypes influenced the soil microbiome). In this study,
we use modern genomic sequencing and community-level analysis to further examine the interactions between trees and their
soil microbiomes with improved resolution. We also experimentally determined whether tree-conditioned soil microbiomes
from warm and cool sites differentially influence bud break
phenology (i.e., the soil microbiome alters plant phenotype)
across a species range. Our overarching hypothesis is that soil
microbial communities vary across the geographic range of P.
angustifolia along strong environmental gradients, and that this
variation predictably alters plant phenology in a reciprocal
inoculation experiment. Specifically, we hypothesize the
following: (1) the degree tree genotypes condition their soil
microbiomes varies by population; (2) geographic dissimilarity in
soil microbiomes among populations is influenced by both abiotic
and biotic environmental variation; and (3) soil microbiomes that
vary in response to abiotic and biotic factors can change plant
foliar phenology. Consistent with these hypotheses, we show that
soil microbiomes vary along a geographic climate gradient,
respond to intraspecific variation at the tree genotype and
population level, and experimentally show that this variation
influences leaf bud break phenology, all of which can have large
consequences for ecosystem productivity.
Soil microbiomes vary in response to trees, climate, and
soil chemistry. We examined soil microbial communities associated with trees and adjacent interspace soils—to separate the
environmental effects from tree-driven conditioning on soil
communities—from 15 populations between Arizona and
Montana (please see Fig. 1 and Supplemental Table 6 for population descriptions). We identified a total of 3486 bacterial
amplicon sequence variants (ASVs) and 2523 fungal ASVs. These
ASV’s contributed to soil microbial community composition,
taxonomic variation, and species turnover associated with treeconditioned and adjacent interspace soils across the landscape.
Bacterial ASVs could be assigned to 18 phyla, 50 classes, 76
orders, 126 families, and 279 genera (Supplemental Data 1), and
fungal ASVs could be assigned to 10 phyla, 30 classes, 63 orders,
108 families, 178 genera, and 215 species (Supplemental Data 2).
Overall, our results are consistent with the hypothesis that soil
microbiomes respond to tree conditioning across all abiotic
conditions, relative to interspace soils not associated with
trees (Hypothesis 1). Independent one-sample t-tests indicated
that three separate metrics of soil microbial community turnover
(q = 0, S (Richness); q = 1, exp(H’) (exponential of Shannon’s
Entropy Index); q = 2, 1/γ (reciprocal of Simpson’s Concentration Index γ) between tree and interspace soils across the geographic extent of the study was significantly different than 0
for both soil bacteria and soil fungal communities (Fig. 2a, b,
Supplemental Table 1, and Supplemental Data 3 and 4). A
turnover = 0 would mean that tree and interspace communities
were identical, and a turnover = 1 would mean tree and interspace communities did not share any community members.
Observed differences in community turnover suggest that soil
bacterial communities underneath trees are on average 46% and
33% different for richness (q = 0) and estimates accounting for
rare members (q = 2), respectively (Fig. 2a). Soil fungal communities underneath trees are on average 57% and 46% different
for richness (q = 0) and estimates accounting for rare members
(q = 2), respectively (Fig. 2b). Total soil carbon (C), total soil
nitrogen (N), and soil pH also varied between paired tree and
interspace soil samples (Fig. 2c and Supplemental Table 2). We
also show that the soil microbiome response to trees varied by
population, indicating that the soil microbial community turnover in response to tree conditioning varied across the geographic
distribution and genetic composition of P. angustifolia (Fig. 2c,
Supplemental Table 3, and Supplemental Data 5). As the soil
microbiome varies in response to tree conditioning locally and
across sites, these results provide a foundation for further studies
separating the biotic and abiotic factors that drive soil microbiome structure and function across these landscapes in the
western United States.
We used distance-based redundancy analysis (dbRDA) to test
the hypothesis that tree-associated soil microbiomes were
responding to different environmental factors relative to interspace soil microbiomes across the range of the tree populations
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(Hypothesis 2). To examine potential biotic environmental
drivers of observed variation in the soil microbiome, we measured
plant traits in the field and in a common garden. The dbRDA
analysis contained data from ~147 replicated genotypes from 15
populations growing under common conditions. Measured field
traits included diameter at breast height (DBH), specific leaf area
(SLA), and foliar carbon to nitrogen ratios (C : N). In the
common garden, we measured bud break phenology, with daily
assessments for the date when leaves emerged from the bud,
which initiated the growing season. Second, to examine potential
abiotic drivers of microbiome community change, we included
latitude, longitude, mean annual temperature (MAT), annual
precipitation, total soil C, total soil N, and soil pH in our analyses.
The dbRDA analysis showed that genetic variation in plant traits
and abiotic environmental factors both explain significant
variation in tree-associated bacterial and fungal communities
(Supplemental Table 4). As expected, there was no relationship
between plant traits and interspace soils, as only abiotic factors
explained interspace soil microbial community composition.
Similar to the dbRDA, generalized dissimilarity models (GDMs)
identify significant drivers of community dissimilarity, but also
provide additional information on where community dissimilarity changes occur and allow for comparisons among normalized predictors. Our GDMs show that variation in plant
phenotypes, tree-associated soil chemistry, and environmental
gradients are important in driving community dissimilarity in
tree-associated soil bacterial and fungal communities (Fig. 3a, c,
Supplemental Table 5, and Supplemental Data 6 and 7). Likewise,
geography, interspace soil chemistry, and climate are the most
influential drivers of interspace soil bacterial and fungal
communities (Fig. 3b, d, Supplemental Table 5, and Supplemental
Data 8 and 9). Such consistent patterns and results using
powerful multivariate approaches combined with general dissimilarity modeling indicate that: (1) soil microbiomes under trees
are different than those in adjacent interspaces and (2) the
environmental factors structuring those two community
types vary.
To further examine hypothesis 2, we performed redundancy
analysis (RDA) to determine whether tree-associated microbial
community composition differed among the five warmest and
five coolest populations, to examine the potential consequences of
warming on the soil microbiome. Based on MAT, the five
warmest and five coolest populations span the extremes of the
landscape-level temperature gradient and represent climate origin
comparisons. We found soil microbial community composition
differs for both soil bacteria (bacteria: χ2 = 166.4, Pr(>χ2)
<< 0.0001) and soil fungi (fungi: χ2 = 4.245, Pr(>χ2) << 0.00001)
between the warm and cool sites regardless of other abiotic
variation. Climatic origin RDA models, for both soil bacteria and
soil fungi communities, were significantly different than a null
Fig. 1 Map of P. angustifolia collections and soil inoculation experimental design. a Idealized distribution of P. angustifolia and collection sites where
interspace and tree-conditioned soils and tree genotypes were collected for study. Red symbols represent the five warm sites and blue symbols represent
the five cool sites where soils were recollected in 2015 for the soil inoculation experiment. Soil samples for microbial sequencing were collected from all
sites (red, blue, and white) in 2012. b, c 2012 and 2015 field sampling schematic and outline greenhouse soil inoculation experiment to test the hypothesis
that soil microbes may mediate variation in bud break phenology. Colored gradient bars match the map in a and represent variation in mean annual
temperature within five warmest and five coolest tree populations.
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model where climatic origin was not included, (bacteria:
p < 0.001; fungi: p = 0.001). Separation and variation in soil
microbial community composition among warm and cool
habitats is visualized in Supplemental Fig. 1. To provide
information on specific taxa and functional groups that reflect
the determined differences among the warm and cool site soil
microbiomes, we performed indicator species analysis (ISA) on
our soil bacterial and fungal community ASV tables. We found
large, diverse sets of indicator taxa, of both soil bacteria and fungi,
to be strongly associated with either warm or cool habitats (please
see Supplemental Data 3–5). From this analysis, we were able to
identify 893 bacterial and 553 fungal ASVs significantly
associated with cool habitats, and 287 bacterial and 325 fungal
ASVs significantly associated with warm habitats (i.e., ASVs
assigned a p < 0.05). Full indicator species lists and results are
displayed in.xls files associated with Supplemental Data 10–12. To
gain information on potential functional variation, fungal
indicator species lists were analyzed with FUNGuild to assign
functional profiles to each fungal assemblage26. Of the 325 warmassociated, fungal ASVs, 70 ASVs were assigned to a functional
guild with “probable” or “highly probable” confidence rankings.
Of the 553 cool-associated, fungal ASVs, 100 ASVs were assigned
to a guild with “probable” or “highly probable” confidence
rankings. Functional profiles of each fungal assemblage are
displayed in Supplemental Fig. 2 and Supplemental Data 11 and
12. These results provide evidence of compositional and
functional variation in tree-associated microbiomes between
warm and cool populations.
Role of the tree-associated soil microbiome on bud break
phenology. We experimentally manipulated soil climatic origin
(i.e., soil from warm and cool populations) and soil microbial
presence (i.e., live and sterile) to understand how environmentally
driven variation in the soil microbiome can change bud break
phenology across the geographic range of P. angustifolia
(Hypothesis 3). In a previous study, we found genetic divergence
in bud break phenology in greenhouse common garden conditions, as tree populations from cool locales were breaking bud
later than those originating from warm locales25. Having previously examined the genetic basis to tree bud break phenology,
we then aimed to understand whether tree-conditioned soil
microbial communities collected from warm and cool sites have
different functional effects on bud break phenology (Supplemental Fig. 3). Consistent with our previous study, there were
population-level differences in bud break phenology, irrespective
of soil microbiomes (see Supplemental Tables 7 and 8). We also
found a significant interaction effect between live/sterile soil
microbiomes and soil climatic origin (Supplemental Table 9). As
a significant interaction was detected, we used a reduced model
including live/sterile as a fixed effect and population as a random
effect for both warm and cool soil origin data sets. Adding live
microbial inoculations from warm habitats advanced bud break
phenology by ~6 days across all populations when compared to
sterile inoculations (Fig. 4 and Supplemental Table 9). Further,
live microbial inoculations from cool habitats delayed bud break
phenology ~4 days, compared to sterile inoculations (Fig. 4,
Supplemental Table 9, and Supplemental Data 13). We also
examined the effects of soil microbes and soil climatic origin on
plant phenology in a continuous framework. We determined the
difference between the tree’s climatic origin and the soil inoculum’s climatic origin to provide a “temperature transfer distance.”
We show that for every 1° Δ°C in the origin of the live soil
treatment there was advancement of bud break phenology in the
Fig. 2 Tree-driven differences in soil microbial communities and soil chemistry. a Mean bacterial community turnover for diversity orders q = 0–2 (when
turnover/β-diversity = 0 tree and interspace communities are identical; when turnover/β-diversity = 1 tree and interspace communities did not share any
members). b Mean fungal community turnover for diversity orders q = 0–2. c Mean differences in soil carbon (C), soil nitrogen (N), and soil pH between
each tree-interspace pair. Error bars in each panel represent 95% confidence interval of the mean. d Population-level differences in bacterial community
turnover (q = 2; 1/γ (reciprocal of Simpson’s γ)) between tree-interspace pairs. e Population-level differences in fungal community turnover (q = 2)
between tree-interspace pairs. P. angustifolia populations across the 15 watersheds are arranged from coolest to warmest. Center line in each boxplot
represents the median turnover for each tree population, end lines represent lower and upper quartiles, and whiskers represent the minimum and
maximum turnover estimates within each tree population. Blue and red boxplots match Fig. 1 and represent populations re-sampled in 2015 for soil
inoculation experiment. *The soil microbiome was not sequenced for the Gros Ventre River population but was included in the inoculation experiment.
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greenhouse by ~1 day (Supplemental Fig. 4 and Supplemental
Table 10). The sterile soil treatment shows no significant pattern
(Supplemental Fig. 4 and Supplemental Table 10). Mortality
within the experiment was not statistically different among live
and sterile inoculation treatments (χ2 = 0.22, df = 1, p = 0.6376).
Together, these results provide evidence that soil biota and soil
climatic origin (i.e., warm or cool microbiomes) interact to
mediate the expression of bud break phenology across large
spatial scales and plant host genetic backgrounds.
Our results are consistent with the hypotheses that: (1) soil
microbiomes respond to tree conditioning and tree-driven community turnover varies by tree population; (2) geographic variation in tree-associated soil microbiomes are related to biotic and
abiotic environmental variation; variation in interspace soil
microbiomes is only related to abiotic environmental variation;
and (3) experimentally, tree-conditioned microbial communities
function differently along a MAT gradient by mediating variation
in the timing of bud break in P. angustifolia. Together, our
findings establish a landscape-level feedback between tree populations and their associated soil microbial counterparts, and
suggest that soil microbes may play important roles in mediating
and buffering bud break phenology with climate warming.
In this study, we highlighted taxonomic and functional variation in soil microbial communities associated with riparian forests across a large portion of the geographic range of P.
angustifolia. We find evidence that trees influence their associated
soil microbiomes locally and range wide, as suggested by
differences in community turnover at both tree and population
levels. Determining plant populations condition their soil
microbiome differently reinforces the importance of examining
the relationship between intraspecific variation and patterns of
biodiversity; specifically, in the case presented above, that
intraspecific genetic variation can be an important driver of largescale patterns of soil microbial diversity. Second, we show that
both climatic and soil chemistry variation are linked to patterns
of soil bacterial and fungal community dissimilarity in interspace
soils (Fig. 3 and Supplemental Tables 2 and 4). It is important to
note that plant intraspecific variation did not influence interspace
soil microbial community variation. In contrast, we identified
intraspecific variation in plant phenology, biomass, and foliar
chemistry as important drivers of tree-associated bacterial and
fungal community dissimilarity, although dominant climate and
soil chemistry characteristics were also identified as important
(Fig. 3 and Supplemental Tables 2 and 4). Together, these results
highlight that intraspecific genetic variation and tree-associated
soil microbiomes are responding to similar environmental gradients. Moreover, linkages between intraspecific variation, soil
microbial community dissimilarity, and ecosystem characteristics
consistently support our previous findings that population
divergence and genetic variation alters landscape-level patterns in
soil chemistry, soil microbiomes, and eco-evolutionary plant–soil
feedbacks20,25. On the landscape, plant hosts and associated
microbial communities experience similar environmental gradients and may have similar environmental constraints27. Similar
to plants, there is also evidence that soil microbial diversity,
composition, and function can be influenced by climatic, geographic, edaphic, and biotic variation27–32. There is also now a
Fig. 3 Landscape-level variation in climate, edaphic characteristics, and plant phenotypes drive turnover in soil bacterial and fungal communities.
a The plotted generalized dissimilarity model (GDM) for tree soil bacterial community dissimilarity. b The plotted GDM for interspace soil bacterial
community dissimilarity. c The plotted GDM for tree soil fungal community dissimilarity. d The plotted GDM for interspace soil fungal community
dissimilarity. X-axes are normalized to allow for direct comparison of biotic and abiotic environmental gradients included in the analysis.
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large body of evidence that variation in plant community diversity and composition, plant traits, and plant genetics can influence soil microbial diversity and composition31,33–36. Although a
growing number of studies have linked plant traits and ecosystem
characteristics to geographic patterns in soil microbial diversity,
few have exclusively linked intraspecific genetic variation to
patterns of soil microbial diversity and composition at the landscape level.
We also provide experimental evidence that the geographical
variation in the soil microbial community mediated variation in
foliar bud break phenology. Adding soil inoculum from either
warm or cool populations had the effect of altering bud break
phenology by ~10 days when averaged by soil climatic origin
(Fig. 4) and up to 16 days when using the temperature transfer
distance (Supplemental Fig. 4; b1 = −0.94; range in soil climatic
origin = 17.67 °C). A 10- to 16-day range in microbially mediated
phenology corresponds to 36–57% of the total quantitative difference due to genetic variation within P. angustifolia (~28 days,
based on population-level averages25) and is two to three times
greater than historical (previous half century) and future model
projections of change in temperate tree phenology
(5–9.2 days37,38). Therefore, plant–microbe interactions could be
integral in generating intraspecific variation in response to the
climatic variation tree populations experience. The rates of evolutionary response or range shifts in tree species may be outpaced
by contemporary (i.e., twentieth century) climatic change39,40.
However, soil microbes will respond more quickly to changes in
climate than tree populations due to differences in generation
times and dispersal abilities41,42. Therefore, soil microbial mediation of plant phenotypes may represent a mechanism for plant
adaptation and persistence, and buffer responses to global
Mechanistically, ISA and FUNGuild results suggest that the
observed change in bud break timing due to the soil microbiome
origin could be due to altered species interactions. Our ISA
showed a diverse list of soil bacteria and soil fungi under both
warm and cool climatic origins (Supplemental Data 3–5). The
most apparent difference in warm and cool soil microbial communities from these analyses is the relative frequency of ECM and
saprotrophic fungal phyla. We can only speculate what relative
differences in the frequency of ectomycorrhizas and saprotrophic
fungi could mean in this case. Interactions and contrasts between
ECM and saprotrophic fungi have received much attention in
regard to soil organic matter decomposition and nutrient
cycling44, which could certainly influence plant–microbe–soil
interactions. We found that warm habitat soils tend to have
higher proportion of ectomycorrhizas than cooler soils (Supplemental Fig. 2b), which could suggest a higher probability of
plant–fungal symbioses in more stressful environments, as warm
sites are warmer and dryer22,43,45. In contrast, saprotrophic fungi
had a larger proportional frequency in cool soils than in warm
soils, and cool habitats are on average much cooler and wetter
(Supplemental Fig. 2a). Increasing diversity of saprotrophic fungi
may promote the rate of decomposition of soil organic matter46
and influence soil nutrient availability. Further exploration of
functional variation in soil microbiomes is needed to improve our
understanding of direct and indirect consequences of global
change scenarios on soil microbial functional variation and
related ecosystem functions47–49. Likewise, unraveling how plant
form and function may be related to soil microbe compositional
variation and associated ecosystem processes will yield important
context regarding the factors that govern intraspecific genetic
variation, patterns of biodiversity, and divergence in ecosystem
function in a changing world50–52.
Results from this landscape-level field and soil inoculation
experiment show plant–soil microbiome feedbacks operate at
scales relevant to climate change (i.e., among populations across a
species distributional range). Our results demonstrate that soil
microbiomes vary in response to the presence of a keystone tree
species, and that response varies among tree populations and
along ecologically important gradients spanning the western
United States. Our results also demonstrate that variation in the
soil microbiome due to conditioning by trees can feedback to
affect geographic patterns of bud break phenology, which is a
critical indicator of the whole ecosystem productivity. Although
the soil microbiome is not a panacea, it may provide a 10- to 16-
day phenological buffer to climate warming and enhance the
ability of plants to respond to global change. Whether or not
these results represent a pattern of local adaptation is unclear, but
soil microbiomes respond to population-level differences in tree
conditioning and trees respond differently to tree-conditioned
soil microbiomes across the western United States, a pattern that
is consistent with co-adaptation. As important genetically based
biotic interactions change in response to climate change, we must
continue to examine the role intraspecific variation and associated
patterns of biodiversity play in mediating plant phenotypes and
variation in ecosystem functions.
Study species and site selection. P. angustifolia James is a dominant tree species
distributed throughout high elevation riparian zones (900–2500 m) along the
Rocky Mountains from southern Alberta, through the intermountain United States,
and into northern Mexico53. During May and June 2012, 17 distinct P. angustifolia
populations were surveyed collectively from three different genetic provenances
(Arizona, Eastern, and Northern/Wasatch Clusters54) across a gradient of ~1700
km latitude from southeastern Arizona to south central Montana. Only 15 of which
are used in the observational portion of the study. All trees used in the study were
GPS located in the field and 18 bioclimatic traits were determined for the collection
Cool Warm
Soil Climatic Origin
Bud Break Time (day)
Fig. 4 Tree-associated soil microbiomes and their climatic origin mediate
bud break phenology. The reaction norm depicts mean soil inoculation
effects on bud break phenology (Julian day) across all plant populations
when grown in a common environment. Error bars represent ±1 SE from the
mean. The blue and red points represent the mean effect of soil
inoculations from cool and warm climatic origins, respectively. The white
dots represent the mean effect of sterilized inoculations from cool and
warm climatic origins. Green and gray dots represent individual data points
from live and sterile inoculations, respectively.
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sites along each river (via WorldClim55) See Ware et al.25 for details regarding
established greenhouse common garden and methods for climatic data extraction.
Characterizing soil microbial communities. To understand the range-wide variation in the P. angustifolia-associated soil environment, paired conditioned (i.e.,
tree associated) and unconditioned soils were collected for each tree surveyed
across the range of P. angustifolia at the same time cuttings were collected. To
separate the conditioning effects of P. angustifolia from underlying site differences,
tree-conditioned soils were collected at the base of each trunk (within 0.25 m) and
unconditioned interspace soils were collected from a random location away from
the tree canopy, no less than 5 m from the trunk and consistently outside of the
drip line of each tree canopy. In 2012, soil samples were collected with a 2.5 cm
diameter Oatfield soil core to a vertical depth of 15 cm, placed in a plastic bag,
transported cold from the field, and stored at 4 °C in the lab until analysis. Fieldcollected wet soils were sieved to 2 mm and then sub-sampled and preserved for
various analyses25. Soil DNA was extracted from a 0.25 g frozen sub-sample of each
sieved soil by using the Power Soil DNA isolation kit (MoBio, Carlsbad, CA USA)
according to the manufacturer’s instructions. Quantitative PCR reactions to assess
bacterial and fungal abundance in each field soil sample were performed after
Castro et al.56 and Wilson et al.57 in 96-well plates on a CFX96 real-time PCR
detection system (Bio-Rad Laboratories, Hercules, CA, USA). Samples were
amplified for the 16S v4 region using primers 515F/806R, and for the ITS2 region
using primers ITS9F/ITS4R from a subset of the total field collections (~270 samples; across 15 populations). Samples were sent to the Department of Energy Joint
Genome Institute for sequencing on an Illumina MiSeq (2 × 300 bp; Illumina, Inc.,
San Diego, CA). The resultant demultiplexed samples underwent initial preprocessing using BBTools. Specifically, adapters were trimmed and contaminants
were filtered from reads using BBDuk. Paired-end reads were then merged with
BBmerge before further processing.
Soil inoculation experiment. To address whether plant genetic variation, soil
biota, and soil climatic origin interact to influence plant bud break phenology, we
established a greenhouse soil inoculation experiment. Replicated tree genotypes
were collected in 2012 and grown in a common greenhouse environment. The
greenhouse common garden is located at the University of Tennessee in a glass
greenhouse allowed to follow seasonal changes in temperature. Saplings grew for 3
years in ambient light with weekly water and monthly fertilizer during growing
season for maintenance (a water-soluble, balanced 20 : 20 : 20 of N, P, K). During
establishment period (prior to experiment), Ultra‐Pure Oil Horticultural Miticide/
Insecticide/Fungicide treatments were applied before bud break, after leaf senescence and as needed to control foliar fungal and pest outbreaks. In May 2015, treeconditioned soils were recollected from ten of the P. angustifolia populations
(previously surveyed in 2012) for experimental inoculations (Fig. 1a). Based on
MAT, the five warmest and five coolest populations (referred throughout as warm
vs. cool or climatic origin comparisons) from the original 2012 survey were
included in the inoculation experiment to span the extremes of the landscape-level
temperature gradient (see Supplemental Table 6). For each of the ten populations
re-sampled, we surveyed five tree genotypes and collected tree-associated soils from
each of the five genotypes. Previous work in these same P. angustifolia populations
shows dominant taxonomic groups of soil microbes did not differ between years
from re-sampled populations, indicating that the identity of dominant taxa were
generally consistent across years58. Half of each soil sample collected was sterilized
using γ-irradiation (exposed to a radioisotope cobalt 60 radiation field and irradiated at ~25–30 kGy; STERIS Corporation; Spartanburg, SC), to specifically test
the influence of a live soil microbiome on bud break phenology and plant growth
traits expressed in the common garden trial. Each tree in the experiment was
extracted from their original pots and the soil was carefully removed from their
roots. Trees were placed into new pots that were filled to ~80% capacity with
general potting mix before receiving soil inoculum. Approximately 20 g of either
live or sterile soil inoculum was added directly to the upper portion of each tree’s
root system and then covered with general potting mix in an effort to minimize
cross-contamination among pots in the experiment. Each replicated genotype was
inoculated with live and sterile soil from the site where the tree was collected
(hereafter “home” soil), as well as a random soil from a site represented by climate
(hereafter referred to as “away” warm or “away” cool soil). For example, one
genotype replicate from a warm population was inoculated with live (i.e., microbes
present) and another replicate of the same genotype was inoculated with sterile soil
from its home soil, live and sterile soil from a random warm population’s soil, and
live and sterile soil from a random cool population’s soil. This was replicated for all
genotypes and populations (~250 trees survived). Following the 2015 growing
season, all trees in the inoculation experiment senesced and entered dormancy.
Starting in February 2016 (i.e., the first spring after experiment was established),
foliar bud break phenology was measured every 48 h until all trees had flushed. Bud
break was recorded as the ordinal day when new leaves unfurl during spring
emergence, representing the onset of annual aboveground biomass production59,60.
Statistics and reproducibility
Soil microbial community assessments. To address Hypotheses 1, we processed
iTags from soils collected in 2012 from beneath trees, paired with an interspace
soil, to identify ASVs, using DADA2 version 1.6.061. Reads were truncated to 280
bp to remove low-quality nucleotides at the tails and were quality filtered by
removing PhiX contamination and allowing a maximum of 1 expected errors
(maxEE = 1). A parametric error model was learned from the data and identical
sequences were dereplicated before ASVs were identified and an ASV table (analogous to an operational taxonomic unit (OTU) table) was constructed. This
workflow was performed for each of the three plates, resulting in three ASV tables.
As DADA2 identifies ASVs (rather than clustering OTUs based on similarity), the
three ASV tables were merged into a single table from which chimeras were
removed. Taxonomy was assigned for each unique ASV using Ribosomal Database
Project (RDP) training set 16 (16S) and the UNITE 28/06/2017 general release
(ITS2). In total, 48,686 bacterial/archaeal and 50,630 fungal ASVs were identified
from 147 samples. To focus on the most prevalent taxa, we filtered bacterial ASVs
not seen more than three times in at least 10% of samples and fungal ASVs not
seen more than three times in at least 5% of samples (different filtering criteria used
to account for sparser fungal ASV tables). This resulted in a total of 3486 bacterial
ASVs and 2523 fungal ASVs that were used to analyze soil microbial community
variation and taxonomic composition. Archaea accounted for <1% of the filtered
ASVs (26 out of 3486 ASVs). This is expected given the 16S primers the Joint
Genome Institute (JGI) used for sequencing are known to underrepresent Archaeal
To understand whether trees directly influence soil microbial communities
(Hypothesis 1), we calculated community turnover (pairwise β-diversity) between
tree and interspace samples. Turnover was calculated for both bacterial (16S) and
fungal (ITS2) communities using diversity orders q = 0–2 [q = 0, S (Richness); q =
1, exp(H’) (exponential of Shannon’s Entropy Index); q = 2, 1/γ (reciprocal of
Simpson’s Concentration Index γ)] (i.e., Hill numbers63–65). Hill numbers (1) are
all expressed in units of effective numbers of species and incorporate relative
abundances; (2) provide a direct link with differentiation (i.e, compositional
similarity) among assemblages uniting diversity and similarity; (3) account for rare
community members as the order of q increases; and (4) are being increasingly
used to characterize taxonomic diversity within and among assemblages of
interest63,64. Each tree-interspace pair was rarefied to the sample with the lowest
number of reads. Relative abundances for each community member were
determined and β-diversity was calculated for each order of q (0–2) using the
“vegetarian” R package. A t-test was used to test whether estimates of turnover
differed from 0. A turnover = 0 would mean that tree and interspace communities
were identical and a turnover = 1 would mean tree and interspace communities did
not share any community members. A generalized linear model was used to
explore among population-level variation in both bacterial and fungal community
turnover for each order of q (glm function).
To identify the environmental drivers of soil microbial community composition
at the landscape-scale, we used dbRDA in the vegan R package using soil and
climatic data previously reported in Ware et al.25. Individual dbRDAs were
completed separately for tree-associated bacteria, interspace bacteria, treeassociated fungi, and interspace fungi. Jaccard distance was used to determine
dissimilarity among samples and dissimilarity matrices were included in the
dbRDA as the response variable. Biotic and abiotic environmental variables were
included in each dbRDA model including the following: latitude, longitude, MAT,
annual precipitation, total soil C, total soil N, soil pH, field DBH, field SLA, field
Foliar C : N, and common garden genetic variation in bud break phenology. Each
dbRDA model was analyzed using anova.cca() with resampling (permutations =
10,000) to identify significant environmental predictors. Including field and
common garden traits allows for exploring the importance of genetic variation in
phenology and productivity in determining soil microbial community composition.
The same model was run for each of the four community matrices (i.e., treeassociated bacteria, interspace bacteria, tree-associated fungi, and interspace fungi).
This approach allows us to understand if plant traits are of any importance to
interspace soil microbial communities, providing further evidence that intraspecific
variation among individual trees are conditioning their associated soil microbial
community. To compliment dbRDA, we explored soil bacterial and fungal
community dissimilarity using GDM66,67 (gdm R package). GDM models
biological variation as a function of environment and geography using distance
matrices—specifically by relating dissimilarity in species composition. The same
variables used in the dbRDA were included in individual GDMs for tree-associated
bacteria and tree-associated fungi. If plant traits are determined to be unimportant
in the dbRDA framework, they will be excluded from GDM models for interspace
bacteria and interspace fungi. Similar to dbRDA, the GDMs identify significant
drivers of community dissimilarity, but will also provide additional information on
where community dissimilarity changes and allows for comparisons among
individual predictors.
Reciprocal soil microbiome experiment. We developed multiple statistical models to
look for patterns of local adaptation or phenotypic plasticity in plant phenological
responses to soil microbial inoculations and origin (Hypothesis 2). Four models
were constructed to look for potential patterns of local adaptation (i.e., a significant
genetic by environment interaction—G × E) to both population-level and soil climatic origin-level variation in soil microbiomes. The first was a fully factorial
generalized linear model with tree population (i.e., background tree genetics), soil
source population (i.e., the population where the soil inoculum was collected), and
their interaction term as fixed effects. The second was a fully factorial generalized
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linear model with tree population home or away (i.e., whether tree was inoculated
with soil from its home population or an away population), and their interaction
term as fixed effects. The third was a fully factorial generalized linear model with
tree population, soil climatic origin (i.e., climatic origin: warm or cool origin), and
their interaction as fixed effects. The fourth was a fully factorial mixed effects
model with tree climatic origin (i.e., warm or cool), soil climatic origin (i.e., warm
or cool origin), and their interaction as fixed effects, and tree population included
as a random effect (lme4 package). Population was included as a random effect, to
account for variance in plant traits. A significant interaction term in either of the
three models would suggest local adaptation to the soil microbiome at different
geographic scales. Each of the four models above was run on both live and sterile
data sets to directly test the effect of soil microbial communities on plant phenotypes. Bonferonni corrections were applied to account for multiple comparisons.
If no significant genetic × environment effects were discovered, then a fully factorial
linear mixed effects model was constructed with live/sterile (i.e., with and without
microbes) and soil climatic origin (i.e., from warm or cool habitats), and subsequent interactions were included as fixed effects to explore patterns of phenotypic
plasticity. Population was included as a random effect, to account for populationlevel variance in plant traits. If the full model yields a significant interaction term,
the model will be reduced to specifically examine individual fixed effects. We also
examined the effects of soil microbes and soil climatic origin of plant phenology in
a continuous framework. The difference between the tree’s climatic origin and the
soil inoculum’s climatic origin was determined to provide a “temperature transfer
distance” and is referred to as Δ°C in any accompanying figure or table. This
experimental Δ°C represents a hypothetical, temperature-based change to where
the tree is rooted. As soil microbial generation times are likely orders of magnitude
quicker than P. angustifolia genotypes, this experimental “temperature transfer
distance” manipulation serves as a useful tool to examine how warming may
indirectly influence the plant phenotypes as climate alters the soil environment in
which longer lived plants occupy. Mortality within the experiment was recorded
and analyzed using a χ2-test to determine whether there were non-random effects
of live vs. sterile soil inoculation on plant mortality.
To explore compositional differences and provide information on specific taxa
and functional groups that reflect the determined differences between warm–cool
soil microbiomes implemented in the soil inoculation experiment, we took a twostep approach. First, we performed RDA (vegan R package) on both tree-associated
soil bacterial and soil fungal communities to ask whether soil microbial
communities differed among warm and cool populations (i.e., climatic origin). The
anova.cca() function with resampling (permutations = 10,000) was used to test the
significance of climatic origin. Analysis of variance was used to compare each RDA
model to a null model. Second, we performed ISA (indicspecies R package) on our
tree-associated soil bacterial and fungal community ASV tables, to identify specific
taxa and functional groups that were strongly associated with warm or cool tree
populations/habitats. Indicator fungal taxa lists were assigned to functional guilds,
using an open annotation tool (FUNGuild26). Only the guild assignments with
“probable” and “highly probable” confidence rankings were accepted. All analyses
were performed in R68 unless otherwise noted.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
Amplicon sequences are archived in the National Center for Biotechnology Information
SRA database (BioProject accession number: PRJNA726831). Observational and
experimental data generated during and/or analyzed during the current study can be
found as Supplementary Data 1–13.
Code availability
All codes were written and performed in R (version 3.5.2). Codes generated during this
study are available from the corresponding author upon reasonable request.
Received: 20 February 2020; Accepted: 12 May 2021;
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We extend gratitude to Stephanie Kivlin, James Fordyce, and Liam Mueller for invaluable
discussion on this manuscript. We thank Shannon Bayliss, Kendall Beals, Phil Patterson,
Ken McFarland, Jeff Martin, Alex Neild, Dailee Metts, Kassie Hollabaugh, Kelsey Greiff,
Parker Wilson, Kaleb Menzel, Dylan Johnson, Blake Scalf, Katie Baer, and Alex Gifford
for lab and greenhouse support. DNA sequencing for the work presented was provided
by the DOE Joint Genome Institute. Funding support for I.M.W., M.V.N., J.A.S., and J.K.
B. was from the University of Tennessee, Knoxville, and Z.K.Y. and C.W.S. are supported
by the Genomic Science Program, US Department of Energy, as part of the Plant
Microbe Interfaces Scientific Focus Area (http://pmi.ornl.gov). The work conducted by
the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User
Facility, is supported by the Office of Science of the U.S. Department of Energy under
Contract No. DE-AC02-05CH11231.
Author contributions
I.M.W., J.A.S., and J.K.B. conceptualized the study. I.M.W. performed the field work,
data collection, statistical analysis, and drafted the manuscript. I.M.W., M.V.N., Z.K.Y.,
and C.W.S. completed soil DNA extractions and sample preparation prior to sequencing.
All authors discussed the results and equally contributed to subsequent drafts of the
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s42003-021-02244-5.
Correspondence and requests for materials should be addressed to I.M.W.
Peer review information Communications Biology thanks the anonymous reviewers for
their contribution to the peer review of this work.
Reprints and permission information is available at http://www.nature.com/reprints
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What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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