Research

Natural soil microbiome variation affects spring foliar phenology with consequences for plant productivity and climate-driven range shifts

Michael E. Van Nuland1 , Ian M. Ware2 , Chris W. Schadt3,4 , Zamin Yang3, Joseph K. Bailey5 and

Jennifer A. Schweitzer5

1Department of Biology, Stanford University, Stanford, CA 94305, USA; 2Institute of Pacific Islands Forestry, USDA Forest Service, Pacific Southwest Research Station, Hilo, HI 96720, USA;

3Bioscience Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA; 4Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA; 5Ecology and

Evolutionary Biology Department, University of Tennessee, Knoxville, TN 37996, USA

Summary

Author for correspondence: � Identifying the potential for natural soil microbial communities to predictably affect com-Michael E. Van Nuland plex plant traits is an important frontier in climate change research. Plant phenology varies Email: mvannula@stanford.edu

with environmental and genetic factors, but few studies have examined whether the soil

microbiome interacts with plant population differentiation to affect phenology and ecosystem Received: 11 March 2021 function. Accepted: 25 June 2021 � We compared soil microbial variation in a widespread tree species (Populus angustifolia)

with different soil inoculum treatments in a common garden environment to test how the soil

New Phytologist (2021) microbiome affects spring foliar phenology and subsequent biomass growth. doi: 10.1111/nph.17599 � We hypothesized and show that soil bacterial and fungal communities vary with tree condi-

tioning from different populations and elevations, that this soil community variation influences

patterns of foliar phenology and plant growth across populations and elevation gradients, and Key words: ecosystem ecology, elevation gradients, phenology, plant–soil interactions, that transferring lower elevation plant genotypes to higher elevation soil communities delayed

productivity, range shifts, soil microbiome. foliar phenology, thereby shortening the growing season and reducing annual biomass pro-

duction. � Our findings show the importance of plant–soil interactions that help shape the timing of

tree foliar phenology and productivity. These geographic patterns in plant population 9 mi-

crobiome interactions also broaden our understanding of how soil communities impact plant

phenotypic variation across key climate change gradients, with consequences for ecosystem

functioning.

Introduction

Symbiotic and free-living microorganisms can alter a wide range of plant phenotypes, including growth traits, leaf morphology, and tissue chemistry (van der Heijden et al., 2008; Johnson et al., 2010; Friesen et al., 2011; Henning et al., 2016), with microbial roles increasingly recognized as important causal mechanisms of plant phenotypes. Studies that characterize such microbial effects have mainly focused on plant functional traits related to fitness, productivity, stress tolerance, or nutrient acquisition, and typi-cally isolate specific soil microbial groups (e.g. nitrogen (N)-fixing bacteria or mycorrhizal fungi – Yang et al., 2009; Friesen et al., 2011; Chaudhary et al., 2016; Averill et al., 2019). How-ever, recent experiments indicate that differences among entire soil microbial communities may also influence the timing of important plant life history events such as flowering and leaf emergence (Cleland et al., 2007; Batten et al., 2008; Lau &

Lennon, 2012; CaraDonna et al., 2014; Wagner et al., 2014; Panke-Buisse et al., 2015, 2017).

Direct or indirect effects of the soil microbiome on a host plant’s root system and belowground environment could have multiple effects on the expression of specific plant phenology-related genes. For example, plant roots can be involved in the sys-temic coordination of plant phenology (i.e. genes for flowering time were differentially expressed in Arabidopsis roots when flow-ering was induced; Bouch�e et al., 2016). Using a multigenera-tional experiment with Arabidopsis, soil microbial consortia were selected to consistently induce early or late flowering (Panke-Buisse et al., 2015, 2017), which likely resulted from microbial synthesis of phytohormones (e.g. auxins) or their ability to increase soil N availability through nitrification and disrupt phy-tohormone metabolic networks (Lu et al., 2018). Though highly controlled experiments with model plant species are useful for characterizing the physiological mechanisms that link microbial

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activity and plant phenology, a large gap remains in understand-ing the relative role of the soil microbiome in the phenological variation observed in natural systems (Wagner et al., 2014).

Understanding how the soil microbiome interacts with the expression of plant foliar phenological traits may have important consequences for a wide range of ecological and evolutionary pro-cesses. Spring foliar bud-break phenology is a common adapta-tive feature of temperate deciduous trees, the timing of which balances fitness risks with productivity gains (Lechowicz, 1984; Cooke et al., 2012). Premature foliar bud-break from overwinter-ing dormancy risks spring frost damage and dehydration, whereas late bud-break shortens the growing season and reduces the opportunity for maximum biomass growth (Saxe et al., 2001; McKown et al., 2018). This means that changing the timing of spring leaf emergence could affect plant survivorship and growing-season length and, thus, the overall productivity and functioning of an ecosystem (Polgar & Primack, 2011; McKown et al., 2016). Soil microbial assemblages that reinforce plant phe-nological variation could be driven by greater abiotic stress in cooler climates (promoting later bud-break for spring frost avoid-ance) and stronger plant–plant competition in warmer climates (promoting earlier bud-break to maximize productivity) (Louthan et al., 2015). If the soil microbiome helps cue phenol-ogy across environmental gradients (e.g. elevation), then disrupt-ing these potentially coadapted plant–soil biotic interactions might impact the timing of leaf emergence and subsequent ecosystem processes (van der Putten, 2012; Classen et al., 2015; Sugden, 2018).

Phenology is also influenced, in part, by plant genetic variation (Ernst & Fechner, 1981; Rathcke & Lacey, 1985; Polgar & Pri-mack, 2011; McKown et al., 2014), and quantitative genetic divergence of phenological traits is common among populations from different latitudes and elevations (Evans et al., 2016; Halbritter et al., 2018; Ware et al., 2019b). However, phenology can exhibit plastic responses to environmental stimuli, including temperature (Aikawa et al., 2010), water availability (Crimmins et al., 2013), aboveground biotic interactions (Korves & Bergel-son, 2003; Brys et al., 2011), and soil chemistry (Ryser & Sauder, 2006). Because variation in soil microbial communities can affect the strength and direction of selection acting on phenology (Lau & Lennon, 2011; Wagner et al., 2014) and the expression of plant flowering or foliar phenology genes through geno-type 9 environment interactions (Batten et al., 2008; Van Nuland et al., 2016; Ware et al., 2019a,b), plant–microbe inter-actions might be important drivers of phenological variation that could be harnessed for climate change mitigation and restoration (Gehring et al., 2017; Lau et al., 2017).

We examined the role of the soil microbiome on the timing of spring foliar bud-break and plant biomass growth across elevation transects from each of seven populations of the dominant tree species Populus angustifolia (James). First, we used high-throughput amplicon sequencing of field-collected bulk soils to characterize how bacterial and fungal communities that naturally associate with trees vary across populations and elevations, rela-tive to adjacent interspace soils that are not influenced by trees. We then created a glasshouse experiment that incorporated

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different field soil inocula treatments applied to clonally repli-cated stem cuttings in a common environment. Plants were grown with live or sterilized (gamma irradiated) field soil inocu-lum that was collected beneath trees (Home), outside the zone of tree influence (Away) at the same elevation site, and from beneath trees at the next highest site along elevation transects (Higher) to simulate a disruption of plant–microbe interactions caused by upward range shifts. We combined evidence from the field obser-vations and glasshouse experiment to test three hypotheses: (1) P. angustifolia trees associate with distinct soil bacterial and fun-gal communities among populations and across elevation gradi-ents; (2) tree-associated soil microbial communities interact with plant population and elevational differences to influence the tim-ing of spring foliar bud-break and productivity; and (3) disrupt-ing plant–microbe linkages across elevation gradients affects phenology and ecosystem function, such that higher elevation microbial communities delay foliar bud-break and reduce growth in lower elevation plants.

Methods

Field sampling approach

We collected stem cuttings and soil samples in June 2012 from three to five sites along elevation transects from seven distinct P. angustifolia populations (Fig. 1a; Supporting Information Table S1). We define populations as geographically distinct river systems based on the species’ natural history characteristics (Braatne et al., 1996) and previous genetic marker studies (Evans et al., 2015; Ware et al., 2019b). We use the term population to refer to these areas where plant and soil material was collected, which are designated as follows: BL, Blue River, AZ (1793– 2238 m); OC, Oak Creek, AZ (1683–1982 m); OGC, Ogden Canyon, UT (1625–2192 m); SJ, San Juan River, CO (2178– 2663 m); SMIG, San Miguel River, CO (1961–2749 m); SNR, Snake River, WY (1695–2209 m); WR, Weber River, UT (1414–2168 m). Cuttings were collected from 10 mature P. an-gustifolia trees at each elevation site (30–50 replicated genotypes per population that were later confirmed by microsatellite analy-sis; Ware et al., 2019b), treated with rooting hormone (Hor-modin 2; 0.3% indole-3-butryic acid), and planted in a common glasshouse environment where they received regular water and fertilizer as needed to establish for 1 yr in standard potting soil (equal parts peat, vermiculite, and perlite). Bulk soils were col-lected for microbial community sequencing directly beneath P. angustifolia trees and from paired interspace positions that were c. 5 m away from the direct influence of a sampled tree (i.e. c. 5 m outside the dripline) to a depth of 15 cm (Fig. 1b,d). This paired sampling design in the field allowed us to separate below-ground communities based on P. angustifolia soil conditioning effects while accounting for soil type and other environmental variation across populations and elevation within each transect (Madritch & Lindroth, 2011; Van Nuland et al., 2017; Ware et al., 2019b). Soil samples were transported from the field to the University of Tennessee at 4°C, where a subsample was stored at �40°C until DNA extraction and amplicon sequencing.

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(a) (b) (c) Experimental design

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Fig. 1 Field sampling, experimental design, and spring foliar phenology. (a) Locations of sampled Populus angustifolia populations and elevation transects. BL, Blue River, AZ; OC, Oak Creek, AZ; OGC, Ogden Canyon, UT; SJ, San Juan River, CO; SNR, Snake River, WY; SMIG, San Miguel River, CO; WR, Weber River, UT. (b) Clonal replicate stem cuttings were collected from mature trees and soils were collected from two positions: tree soil (used as ‘Home’ soil inoculum) and interspace soil (used as ‘Away’ soil inoculum). (c) Cuttings from each population source were grown with live or sterilized (gamma irradiated) soil inoculum from Home or Away soil positions at their elevation transect site, as well as Home soil inoculum from beneath trees at the next highest elevation transect site (‘Higher’) and areas beyond current range limits (not pictured). Photographs show examples of (d) paired tree–interspace soil sampling positions, (e) spring foliar bud-break, and (f) the range of bud-break phenotypes on 13 April 2014 (day of year 103).

Soil amplicon sequencing and bioinformatic workflow

We isolated DNA using PowerSoil Isolation kits (Mo Bio Labo-ratories, Carlsbad, CA, USA) from 166 field-collected soils across both tree and interspace sampling positions and all populations (BL: 30; OC: 18; OGC: 12; SJ: 24; SMIG: 30; SNR: 28; WR: 24; a subset of sites from which cuttings were collected in the field). These DNA samples were sent to the Department of Energy’s Joint Genome Institute for sequencing using 16S v4 and ITS2 ribosomal RNA (rRNA) gene region primers with the JGI iTag sequencing standard operating procedures (jgi.doe.gov/ wp-content/uploads/2016/06/DOE-JGI-iTagger-methods.pdf). We used a DADA2 workflow to quality filter (maxEE = 2, phiX removed), denoise, remove chimeras, and process reads to iden-tify amplicon sequence variants (ASVs) (Callahan et al., 2016), which yielded a total of 8727 844 high-quality 16S reads (mean of 52 577 � 2239 reads/sample) and 20 199 859 high-quality ITS reads (mean of 123 170 � 3865 reads/sample). Bacterial/ar-chaeal taxonomy was assigned using the RDP naive Bayesian clas-sifier (RDP trainset 16, 11.5 release; Wang et al., 2007), and

fungal taxonomy was assigned using the UNITE database (28 June 2017 release; Abarenkov et al., 2010). To focus on the most prevalent taxa, we filtered bacterial/archaeal ASVs seen less than three times in at least 10% of samples, and fungal ASVs seen less than three times in at least 5% of samples (lower percentage-sample prevalence cutoff because of relatively sparser coverage of fungal versus bacterial/archaeal taxa in the respective ASV tables). Though archaeal taxa were present in most soil samples, propor-tional abundances were low (likely from known biases in the JGI primers; Tremblay et al., 2015) and did not meet criteria for inclusion in the subsequent analyses. This resulted in a total of 3199 bacterial ASVs and 2168 fungal ASVs used to analyze soil microbial community variation and taxonomic composition among populations and across elevation gradients.

Soil inoculum experiments and plant trait measurements

In June 2013, we revisited the same elevation transect sites within each population where cuttings and soils were previously sampled and collected soil from beneath five individual trees (Home soil)

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and from paired interspace soils (Away soil), as noted earlier. These Home and Away soil samples were separately pooled at each elevation site (totaling c. 3.8 l each) and transported at 4°C in a cooler to the University of Tennessee. The pooled soil sam-ples were thoroughly mixed and divided for live or sterile inocula treatments (gamma irradiated at 48 kGy; Steris Isomedix, Spar-tanburg, SC, USA) and subsequently stored at 4°C until soil treatments were applied. We note that the sterilized soil inocu-lum treatments were likely recolonized by the natural and homo-geneous glasshouse microbial pool and could be considered ‘community uniform’, but we still refer to these as sterile treat-ments for simplicity. Our previous work in this system has showed that the dominance of major soil microbial groups is rela-tively consistent across years (Van Nuland et al., 2017, 2019), which suggests that microbiome characterizations from 2012 field soil samples may generally match the microbial composition of experimental inoculum from 2013 field soils.

In September 2013, we selected 211 P. angustifolia genotypes (and clonal replicates) from the previously collected genotype pool at each elevation site within populations to use with experi-mental soil inoculum treatments (number of unique genotypes was 41 at BL, 14 at OC, 17 at OGC, 35 at SJ, 40 at SMIG, 37 at SNR, and 27 at WR; Fig. S1). This subsampling approach was used to include a high amount of genotypic diversity in the experiment with replication across multiple soil treatments. After allowing the cuttings to establish and grow for 1 yr, soil inoculum was applied by adding c. 20 g of live or sterile field soil directly on top of c. 200 g potting mix (equal parts peat, perlite, and ver-miculite) and added to individual pots (D60 deepots; Stuewe & Sons Inc., Tangent, OR, USA). The inocula represented c. 10% of total soil volume to isolate microbial effects and likely do not introduce nutrient variation among sites (Van Nuland et al., 2017; Crawford et al., 2019). Five cuttings per soil treatment were transplanted from their prior containers into deepots con-taining the inocula treatments such that the previous soil around their root systems was carefully removed and placed in direct contact with the soil inoculum and new potting soil. We used this soil inoculum procedure to create multiple types of treatment combinations in a large, fully randomized glasshouse experiment with clonal replicate cuttings of P. angustifolia genotypes. We focus on two groups of treatments (within the same larger experi-ment) to test our hypotheses on how soil communities might affect plant phenotypes (Fig. 1c):

Home–Away group: plants were grown with live and sterile inoculum from Home and Away soils collected in 2013 that came from the same elevation sites as the 1-yr-old plants. This paired Home vs Away inoculum design can tease apart the tree-specific soil microbiome effects on foliar phenology and plant growth compared with the natural variation in back-ground soil communities (Madritch & Lindroth, 2011; Van Nuland et al., 2017; Ware et al., 2019b). Range Shift group: plants were grown with live and sterile soil inoculum from Home soils collected beneath trees at the next highest elevation site relative to their original elevation. This inoculum design simulates upward plant range shifts in a step-wise manner to compare elevational differences in soil

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microbial effects on foliar phenology and plant growth (Van Nuland et al., 2017). Our design initially encompassed 870 plant cuttings: five cut-

tings 9 three soil inoculum categories (Home–Away–Higher) 9

two sterilization treatments 9 three to five elevation sites 9 seven populations. We measured spring foliar bud-break on 695 total plants surviving in the experiment between March and June 2014, 6 months after the soil inoculum treatments were applied (mortality occurred randomly across treatments, often caused by Venturia or other pathogens). Individual plants were surveyed every 48 h for evidence of leaf emergence from buds (character-ized as the onset of new leaf unfolding; Fig. 1e,f), and recorded dates were converted to sequential day number of the year for analysis. We estimated total aboveground biomass at the begin-ning and end of the experiment using an allometric equa-tion derived from diameter and height measurements that explains > 98% of harvested biomass variation (Van Nuland et al., 2017). We calculated plant biomass growth as the final minus initial difference in aboveground biomass estimates (nega-tive growth measures were removed from the data set). Collec-tively, this experiment allowed us to measure how intraspecific P. angustifolia variation interacts with the soil microbiome to influence spring foliar phenology and biomass growth across pop-ulations and elevation gradients.

Statistical analyses

Soil microbial conditioning We tested our first hypothesis, that P. angustifolia trees associate with or select distinct soil communi-ties, by comparing soil microbiome differences beneath and adja-cent to trees among populations and across elevation. Specifically, we created a fully factorial permutational multivari-ate ANOVA model with soil position (tree vs interspace), eleva-tion, population, and all interaction effects to predict community composition using the ‘adonis’ function in the VEGAN package (Oksanen et al., 2019), and visualized community structure with nonmetric multidimensional scaling. Because we expected trees might associate with a subset of the total soil microbial diversity present in a given area (Nemergut et al., 2013), we used the ‘be-tadisper’ function to measure multivariate homogeneity of group dispersions (i.e. variances) to test whether the within-group vari-ance of microbial community structure differed by soil position. We also created a generalized dissimilarity model (GDM) using the GDM package in R (Manion et al., 2018) to examine the effect of elevation on soil microbial community composition. In the GDM model, the maximum I-spline height (which is the sum of the I-spline coefficients) estimates the magnitude of composi-tional turnover predicted by elevation, and the I-spline slope shows the rate of compositional turnover across elevation gradi-ents (Ferrier et al., 2007; Fitzpatrick et al., 2013). To analyze changes in the most prevalent taxonomic groups, we created a fully factorial linear model testing how soil position, elevation, population, and their interactions affect the relative abundance of the 11 most dominant bacterial phyla and fungal classes, com-prising > 95% and > 91% of the total relative abundance per population, respectively (no archaeal classes were included in

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these analyses based on these criteria; see the Soil amplicon sequencing and bioinformatic workflow section ). The same anal-ysis was also run with the top 25 fungal families (based on pro-portional abundance) to examine finer-scale taxonomic patterns. Differences in community composition and taxa relative abun-dances between soil positions would be broadly consistent with P. angustifolia trees conditioning their soil biotic environment.

Soil community effects on foliar phenology and plant growth After assessing differences in field soil communities, we tested the second hypothesis that the P. angustifolia soil micro-biome influences phenological and growth variation among pop-ulations and across elevation transects (Home–Away group). We created a mixed-effects model with foliar bud-break (day of the year) as the response, plant population, elevation site (where plant cuttings and soils were collected), sterilization treatment (live/sterile), soil position (Home soil beneath trees vs Away soil from interspaces), and all interaction terms as fixed effects, and genotype (IDs implicitly nested within population) and soil posi-tion nested within elevation site per population as random effects (on the intercept). An identical model was created using plant biomass growth as the response, and a similar model was used to assess the relationship between foliar phenology and plant biomass growth (see Supporting Information). We also measured soil community conditioning effect sizes on plant traits using log response ratios between Home and Away soil inoculum (calcu-lated from the average phenology or plant growth values at each elevation site where cuttings and soils were collected). To test whether soil conditioning effect sizes differ among populations, we created a linear model with log(PhenologyHome/Phenol-ogyAway) or log(GrowthHome/GrowthAway) as the response, steril-ization treatment, population, and sterilization 9 population as fixed effects. This was followed by a post hoc test of pairwise steril-ization treatment comparisons within populations using a Tukey adjustment for multiple comparisons. To test how the response ratios varied with elevation, we created a mixed-effects model with log ratios as the response, sterilization treatment, elevation, and sterilization 9 elevation as fixed effects and population as a random effect. Because all cuttings were grown in a common environment, significant population and elevation source effects would indicate a plant genetic basis to phenological or growth differences. Moreover, significant sterilization treatment effects would suggest that microbial communities also help determine the timing of foliar phenology or plant biomass growth, poten-tially caused by plant–soil conditioning (i.e. significant soil posi-tion effects or log response ratio patterns), and may interact with differences among plant populations or across elevations.

Phenology and growth responses to simulated range shifts We tested our third hypothesis, that disrupting plant–soil interactions across elevation gradients will affect foliar phenology and plant growth, by simulating plant range shifts towards higher elevations (Range Shift group). We analyzed foliar bud-break and plant growth between cuttings grown with P. angustifolia-conditioned soil inoculum from Higher sites versus their original Home site at lower elevations in two ways. First, we created an analogous

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mixed-effects model, as before, with foliar phenology or growth as the response, range shift position (Home vs Higher soil inocu-lum), sterilization treatment, elevation, population, and all inter-action terms as fixed effects, and genotype and range shift position nested within elevation site per population as random effects. Second, to examine whether soil community effects vary with ele-vation in this context, we compared plant responses based on the elevational difference occurring in Home vs Higher soil inoculum sampling locations. Average bud-break date and plant growth val-ues were calculated at each elevation site where soils were pooled for cuttings with their Home soil community and cuttings with the higher elevation communities. We then calculated the differ-ence in average foliar phenology and growth occurring in Higher vs Home soil inocula treatments and tested for a relationship with the elevation difference between the two soil inoculum sources (i.e. a proxy for upward plant range shift distance). To account for population effects, we quantified partial phenology and growth differences from the residuals of a linear model with population as a fixed effect. We then created an analysis of covariance (ANCOVA) with partial foliar phenology or growth differences as the response and elevation difference between field soil inoculum (Higher vs Home sites), sterilization treatment, and their interac-tion as fixed effects. Additionally, plants from the highest eleva-tion site were grown with soil inoculum from beyond current range limits. We used these treatments to test the alternative hypothesis that the effect of higher elevation microbes on phenol-ogy or growth is not host specific (i.e. that plant responses to higher elevation soil inoculum result from a change in soil com-munity regardless of soil-conditioning plant species identity). No difference in phenology or growth for plants at upper range limits between Home and beyond range limit soil inoculum would be consistent with host-specific soil microbiome effects on plant responses in this study. Lastly, we tested whether foliar phenology differences correlated with plant growth differences, which would indicate that plasticity in spring bud-break responses to simulated range shifts ultimately affects plant productivity.

Results

Soil microbial conditioning

Soil microbial communities differ among populations, across ele-vation gradients, and from plant conditioning effects underneath vs away from trees (in support of Hypothesis 1). Bacterial com-munity composition varied by population (F6,133 = 5.4, P = 0.001), along elevation transects (F1,133 = 3.7, P = 0.002), and between tree and interspace soil positions (F1,133 = 3.4, P = 0.002) (Figs 2a, S2; Table S2). Population explained 17% of variation in bacterial community structure, followed by elevation (1.9%) and soil position (1.7%). Similarly, population source predicted the greatest amount (11%) of variation in soil fungal community composition (F6,136 = 3.3, P = 0.001; Figs 2a, S2). Elevation (1.3% variance explained; F1,136 = 2.6, P = 0.001) and soil position (1% variance explained, F1,136 = 1.8, P = 0.001) showed significant but weaker effects on the overall fungal com-munity structure (Table S2). Both bacterial and fungal

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Fig. 2 Variation in soil microbial communities associated with Populus angustifolia trees. (a) Soil bacterial and fungal community composition differs by population and soil position (tree-associated vs interspace; ellipses are 95% confidence intervals). (b) Generalized dissimilarity models show total estimates of bacterial and fungal community turnover (summed I-spline coefficients) across elevation gradients for each plant population and between soil positions. Horizontal lines are population averages for each soil position. BL, Blue River, AZ; OC, Oak Creek, AZ; OGC, Ogden Canyon, UT; SJ, San Juan River, CO; SNR, Snake River, WY; SMIG, San Miguel River, CO; WR, Weber River, UT. (c) Relative abundances of the 11 most dominant bacterial phyla and Proteobacteria classes (comprising > 95% of total relative abundance per population) between tree vs interspace soil (visualized as mean log response ratios (RR), where positive values indicate greater relative abundance in tree soil). Changes in bacterial relative abundance across elevation were estimated by standardized slope values (b1), with positive values indicate an increase in abundance at higher elevations. Taxa plots show population-level differences in relative abundances separated by soil position. (d) Identical analyses were performed for the 11 most dominant fungal classes (comprising > 91% of total relative abundance per population). Points are mean values � 1 SE.

communities in soil directly beneath P. angustifolia trees were less F1,159 = 4.2, P = 0.04; fungi: F1,162 = 7.8, P = 0.01). The amount variable relative to interspace soil in their immediate surround- of bacterial and fungal community change across elevation gradi-ings (beta-dispersion test for homogeneity of variance; bacteria: ents also varied by population source (significant

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population 9 elevation interactions; Figs 2b, S3; Table S2). Fun-gal communities showed greater compositional changes across elevation overall than bacterial communities did (bacterial mean I-spline coefficients: Home, 0.30 � 0.10; Away, 0.39 � 0.16; fungal mean I-spline coefficients: tree, 0.75 � 0.15; interspace, 0.81 � 0.11). When considered together, the most bacterial and fungal community turnover occurred within the highest (SJ, 2178–2663 m) and most northern (SNR, latitude 43.59°N) ele-vation transects (Table S3).

Tree-associated soil bacterial communities had more Actinobac-teria (+7.1%), Planctomycetes (+14.7%), and Gammaproteobac-teria (+23.2%) and less Acidobacteria (�7.0%,) and Firmicutes (�62.7%) (Fig. 2c). Most bacterial taxa analyzed responded posi-tively or negatively to elevation (Table S2), with predominant changes of certain classes in either tree (Firmicutes) or interspace (Verrucomicrobia) soil. Fungal communities beneath trees had rel-atively more Eurotiomycetes (+43.3%), Mortierrellomycotina (+28.5), and Pezizomycetes (+25.8) and less Agaricomycetes (�90.2%) and Dothideomycetes (�29%) (Fig. 2d). Two fungal classes responded strongly to elevation overall (though six of the top 25 fungal families significantly varied with elevation; Fig. S4; Table S4), and there tended to be more variability in how the same classes responded to elevation in tree vs interspace soil than was observed for bacteria (Fig. 2d; Table S2). Only two of the 11 most dominant bacterial taxa we examined showed no significant varia-tion among P. angustifolia populations (Betaproteobacteria and Gammaproteobacteria; Fig. 2c; Table S2). Six of 11 fungal classes (and 14 of 25 fungal families; Table S4) showed significant population-level variation: Agaricomycetes, Leotiomycetes, Microbotryomycetes, Mortierrellomycotina, Pezizomycetes, and Tremellomycetes (Fig. 2d; Table S2).

Soil community effects on foliar phenology and plant growth

The soil microbiome, in combination with plant population dif-ferences, influenced foliar phenology variation in P. angustifolia and across elevation (support for Hypothesis 2). When grown in a common environment, plants from different populations varied by nearly 3 wk in spring bud-break (F6,376 = 22.6, P < 0.001; Fig. 3a; Table 1). Phenology responses in the Home–Away group resulted in a significant interaction between population, steriliza-tion treatment, and soil position (Home vs Away inoculum) (F6,397 = 2.7, P = 0.01; Table 1; Fig. S5). We quantified soil con-ditioning effect sizes on plant phenology by calculating standard-ized log response ratios between Home and Away soil inoculum. Here, there was a significant population 9 sterilization interac-tion on phenology response ratios (F6,43 = 2.7, P = 0.03; Fig. 3b). Specifically, live soil community conditioning effects caused ear-lier bud-break in three ‘early’ phenology populations compared with sterile treatments (post hoc tests of sterilization effects by population with Tukey’s multiplicity adjustments: BL, P = 0.08; OC, P = 0.008; OGC, P = 0.01). Populus angustifolia soil microbial communities help shape the

phenological cline from early to late spring bud-break with increasing elevation. Foliar phenology was marginally affected by

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the interaction between elevation origin (based on where stem cuttings and soils were collected along elevation transects in the field), soil position, and sterilization treatment (F1,397 = 3.2, P = 0.08; Table 1). When analyzed separately by Home or Away soil position, phenology responses show a significant eleva-tion 9 treatment interaction in Home soil treatments (F1,198 = 4.5, P = 0.04) but not in Away soil treatments (Fig. 3c; Table S4). Soil conditioning effect sizes on phenology responses also showed a significant interaction between sterilization and ele-vation (F1,53 = 6.3, P = 0.01). Specifically, live soil community conditioning effects on foliar phenology response ratios were strongest (most negative) at low elevations, promoting earlier bud-break, gradually becoming more neutral/positive towards higher elevation (r 2 = 0.13; Fig. 3d). By contrast, sterile treat-ments had largely neutral effects on phenology log response ratios that did not vary with elevation.

Population source was a strong predictor of plant biomass growth, indicating genetic differences in productivity (Table 1; Fig. S6), with BL plants showing the greatest average growth (1.93 � 0.13 g) and SNR plants the least (0.64 � 0.05 g). Live soil communities improved plant growth by an average of 17% (0.19 g) in Home vs Away soils (marginal interaction effect of soil position 9 sterilization; F1,376 = 3.4, P = 0.07; Fig. S6), but plant growth did not vary based on elevation origin or soil community differences across the elevation transects (Table 1; Fig. S6; Table S5). Analysis of biomass growth log response ratios between Home and Away soil inoculum did not show any effects of steril-ization, elevation, or their interaction (Fig. S6; Table S6). How-ever, variation in biomass production was significantly related to the onset of spring leaf emergence (F1,370 = 47.1, P < 0.001). Specifically, a 10 d difference in spring bud-break corresponded with a 22% change in aboveground biomass growth (r 2 = 0.09; Fig. 4), showing the connection between foliar phenology and total biomass production. This relationship was unaffected by soil inoculum position or sterilization treatment (Table S7).

Phenology and growth responses to simulated range shifts

An elevation increase in the P. angustifolia soil microbiome tended to delay bud-break, which reduced plant growth, provid-ing evidence that phenological and productivity responses to soil microbial variation are plastic in the context of climate-driven range shifts (support for Hypothesis 3). When analyzing plant responses with an analogous mixed-effects model similar to Hypothesis 2, there were no sterilization treatment 9 range shift position (Home vs Higher soil inoculum) interaction effects with population or elevation (Table S8). Based on the experimental design, we were also able to test whether phenology and growth differences measured between Home and Higher soil inoculum varied with the elevational difference between transect collection sites (i.e. shorter vs steeper range shift simulations; Table 2). This ANCOVA approach showed a significant sterilization effect (F1,36 = 10.2, P = 0.003) and a marginally significant steriliza-tion 9 elevation difference interaction (F1,36 = 3.3, P = 0.08). The average spring bud-break difference from simulated range shifts was 4.2 d later with live inoculum (mean 3 � 1.3 d) than

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(a) (b)

Live Away Home120

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Fig. 3 Soil inoculum effects on the timing of spring foliar phenology among populations and across elevation gradients. (a) Populus angustifolia populations genetically differ in their timing of spring foliar bud-break when grown in a common environment (broad-sense heritability: 0.27). Boxplots show the

0.00

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110

100

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median (middle line) and upper to lower 25th SMIG SJ percentile range, with box whiskers 0.10

WR extending to the maximum and minimum SNR

values excluding outliers (points beyond OC OGC whiskers). BL, Blue River, AZ; OC, Oak 70 BLA HH A H A H A H 0.15A H A H A Creek, AZ; OGC, Ogden Canyon, UT; SJ,

San Juan River, CO; SNR, Snake River, WY; BL OGC OC SNR WR SJ SMIG Live Sterile Population Sterilization treatment SMIG, San Miguel River, CO; WR, Weber

River, UT. (b) Population-level variation in (c) (d) phenology is partly influenced by plant-

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130 Live Sterile Home 120

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(log

(Phe

nolo

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) conditioned soil communities. The reaction norm shows standardized phenology 0.1 responses among populations (different colors) and soil inoculum sterilization treatments using log response ratios (RR) (log(PhenologyHome/PhenologyAway). Phenology response ratios greater or less

0.0 than zero indicate soil conditioning effects (i.e. Home vs Away soil position effects) with negative values representing earlier spring bud-break responses. Points are mean values

SMIG � 1 SE. (c) Plants from higher elevations SJ0.1 WR generally show later spring phenology than

SNR OC

plants from lower elevations do. (d) Soil

Live OGC community conditioning effects are strongest Sterile BL at lower elevations, promoting earlier spring

bud-break that helps define the positive 1500 2000 2500 1500 2000 2500 relationship between foliar phenology and

Elevation origin (m) Elevation origin (m) elevation.

with sterile treatments (mean �0.2 � 1.1 d) (Fig. 5a). Larger ele-vation differences between Home and Higher soil collection sites increasingly delayed phenology with live inoculum (r 2 = 0.13) but not sterile inoculum (r 2 = 0.03), such that a 100 m increase in P. angustifolia soil communities delayed bud-break by 3.4 d (Fig. 5a). Although plant growth differences did not show similar responses (Table 2; Fig. S7), there was a significant negative cor-relation between phenology and growth responses to the simu-lated range shifts overall (F1,36 = 4.8, P = 0.04). Delaying spring bud-break by 5 d corresponded with a 11% productivity loss across both live and sterile treatments (Fig. 5b; Table S9). These phenology and growth responses appear to be specific to soil communities associated with P. angustifolia trees as opposed to higher elevation heterospecific tree species (Fig. S8).

Discussion

Overall, the soil microbiome affects plant foliar phenology and productivity, and such plant–microbiome relationships differ

across populations and elevation gradients. Our results show that P. angustifolia trees associate with a distinct soil microbiome that interacts with population-level variation and affects phenotypic divergence in spring foliar bud-break phenology. This is likely due to underlying genetic variation in plant responses to soil microbes (as determined by the tree–interspace comparison), microbial community variation across source populations and elevation, and the combination of plant effects with the environ-mentally determined microbiome. Plant growth correlated with earlier spring bud-break overall but showed less evidence of pop-ulation 9 soil community interactions. Moreover, soil communi-ties at higher elevations generally acted to delay foliar bud-break in plants from lower elevations, indicating that phenotypic plas-ticity could be influenced by plant–soil–microbial relationships that become disrupted as plant ranges shift towards higher eleva-tions. These phenology delays also impacted ecosystem function by reducing plant productivity due to the shortened growing sea-son. Our study provides evidence of plant–microbiome relation-ships that cue P. angustifolia phenology and drive ecosystem

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Table 1 Results of foliar bud-break phenology and plant biomass growth variation across Populus angustifolia populations, elevation, and soil inoculum treatments (Home–Away group).

Foliar phenology Plant biomass growth

Factor F-statistic P-value F-statistic P-value

Population source 22.6 < 0.001*** 14.0 < 0.001*** Elevation source 0.5 0.48 0 0.87 Soil position 0 0.85 0.4 0.51 (Home/Away)

Sterilization treatment 2.8 0.09† 0.6 0.44 (live/sterile)

Population 9 Elevation 2.6 0.03* 1.4 0.3 Population 9 Position 0.4 0.90 0.9 0.48 Elevation 9 Position 0.3 0.59 0.2 0.62 Population 9 1.2 0.33 0.9 0.48 Sterilization

Elevation 9 Sterilization 1.3 0.26 0.2 0.66 Position 9 Sterilization 1.2 0.26 3.4 0.07†

Population 9 Elevation 0.3 0.91 0.6 0.71 9 Position

Population 9 Elevation 0.8 0.58 3.8 0.001** 9 Sterilization

Population 9 Position 9 2.7 0.01* 0.6 0.73 Sterilization

Elevation 9 Position 9 3.2 0.08† 0 0.89 Sterilization

Population 9 Elevation 0.9 0.52 0.9 0.50 9 Position 9

Sterilization

Plant genotype within Population (random effect). Position within Elevation source within Population (random effect). † , P < 0.1; *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.

function among populations and across key climate change gradi-ents covering large spatial scales.

Soil microbial conditioning and biogeography

Characterizing patterns of microbial diversity is important for understanding how soil communities regulate ecosystem pro-cesses and influence plant phenotypes. At large scales, microbial communities are expected to change with habitat differences and spatial distances between sites (Nemergut et al., 2013), which we find here in the strong population effects on soil microbiome structure. Because ‘population’ here refers to the soil sampling areas, this effect captures some combination of plant genetic and environmental influence on microbial composition. However, at this scale, plant genetic effects are likely dwarfed by the environ-mental differences and spatial distances between sites. At small scales, trees alter their immediate soil abiotic and biotic environ-ments (Waring et al., 2015). We find that P. angustifolia trees associate with a unique soil microbiome relative to their nearby surroundings (i.e. adjacent interspace soils), and this varies by population and elevation. By analyzing multivariate group dis-persion, we found less variance in microbial composition in tree-associated vs interspace soil samples, indicating that bacterial and fungal beta diversity beneath trees is a subset of the total riparian soil microbial diversity measured in this study (gamma diversity).

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Pla

nt b

iom

ass

grow

th (

g)

Home inoculum Live

5.0 Sterile

Away inoculum 4.0 Live Sterile

3.0

Population BL2.0 OGC OC SNR1.0 WR SJ

0.0 SMIG 70 80 90 100 110 120

Foliar phenology (spring bud-break)

Fig. 4 Foliar phenology correlation with plant biomass growth. The effect of phenology variation on growth did not differ by Populus angustifolia population, soil position, or sterilization treatment. Linear regression line shows overall trend. BL, Blue River, AZ; OC, Oak Creek, AZ; OGC, Ogden Canyon, UT; SJ, San Juan River, CO; SNR, Snake River, WY; SMIG, San Miguel River, CO; WR, Weber River, UT.

Table 2 Foliar phenology and plant growth differencesa relative to soil inoculum elevation differencesb between ‘Home’ and ‘Higher’ range shift positions (Range Shift group).

Foliar phenology difference

Plant growth difference

Factor F-statistic P-value

F-statistic

P-value

Elevation difference 0.8 0.38 0.5 0.47 Sterilization treatment 10.2 0.003** 0.1 0.80 Elevation difference 9 3.3 0.08† 0.0 0.94 Sterilization

aPhenology and growth differences were calculated as the difference between the elevation site-average trait value for each Populus angustifolia population when stem cuttings were grown with soil inoculum from Higher vs lower (Home) elevation sites. Analysis of covariance results show partial phenology and partial growth differences after accounting for plant population (residuals) († , P < 0.1; **, P ≤ 0.01). bThe average elevational difference between field sites where soil inoculum was collected for simulating upwards shifts in the Range Shift group.

This pattern is expected if trees condition soil environments that are only habitable by a portion of the total microbial species pool (e.g. microbial traits underlying Populus–soil interactions help fil-ter community diversity from a regional to local scale; Nemergut et al., 2013; Vellend, 2016). One clear example is the greater pro-portional dominance of Pezizomycetes beneath trees, which includes ectomycorrhizal fungal genera Helvella and Tuber. Field surveys and bioassay experiments have shown Populus to enrich Ascomycota rather than Basidiomycota fungi compared with pine and oak trees (Bonito et al., 2014, 2016), potentially explaining why Agaricomycetes were less abundant in tree-associated soils relative to surrounding environments where Pinus and Quercus species were often nearby. Interestingly, soils beneath trees had

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(a) Elevation gradients are at the center of an increased focus on Population BL OGC OC SNR WR SJ SMIG

finding the fundamental rules that control how microbial diversity

Par

tial f

olia

r ph

enol

ogy

diffe

renc

e (d

)

10

5

0

5

Live Sterile

is shaped across terrestrial systems (Bryant et al., 2008; Fierer10

et al., 2011; Hendershot et al., 2017; Kivlin et al., 2017; Peay et al., 2017; Kivlin et al., 2019). We found significant soil micro-

5 bial compositional turnover across the elevational transects, and the magnitude and rate of turnover differed by population. This microbial variation likely relates to the significant popula-

0 tion 9 elevation 9 sterilization treatment effect on plant biomass growth, indicating that not all elevation gradients are the same and highlighting how more care should be given to understanding how and why they differ. Phylogenetic patterns of microbial dis-

5

tributions across habitats can reflect the evolutionary conservation of ecological strategies (Treseder et al., 2014; Peay et al., 2017). In 10 10

Live Sterile 100 150 200 250 our study, bacterial and fungal responses to elevation likely result Sterilization Elevation difference of from a combination of dispersal limitation among transect sites treatment field soil inoculum (m)

(Peay et al., 2010) and taxonomic differences in niche breadth(b)

Par

tial p

lant

bio

mas

s gr

owth

diff

eren

ce (

g)

0.6

0.3

0.0

0.3

across the environmental gradients that covary with elevation (e.g. 0.6 climate, soil pH; Treseder et al., 2014; Hendershot et al., 2017;

Peay et al., 2017). Although the specific site-level mechanisms for each taxon are beyond the scope of our study to explore in great

0.3 detail, instances where microbial responses to elevation are less pronounced in tree-associated vs interspace soil suggest that plant–soil interactions might be dampening abiotic elevational

0.0 effects on microbial biodiversity.

Soil microbiome and plant variation affect the timing of 0.3 spring bud-break

Live Sterile

As expected, we find broad genetic clines in spring bud-break 0.6 0.6 among the seven P. angustifolia populations throughout the west-

Live Sterile 10 5 0 5 10 ern USA and across elevations when plants are grown in common conditions. However, these population effects varied depending

Sterilization Partial foliar phenology difference (d) treatment

Fig. 5 Higher elevation soil inoculum effects on foliar phenology and consequences for plant biomass growth. We simulated future aboveground–belowground interactions with upward range shifts by growing Populus angustifolia plants with soil inocula from higher elevations. We then compared the elevation difference between the Home and Higher sampling sites with the phenology and growth differences when the same plants were grown with the two inocula types. (a) Phenology differences varied by sterilization treatment overall. In live soils, increasing elevational differences between Home and Higher soil inoculum sources positively correlate with phenology differences (no relationship was found with sterile soil). Boxplots show the median (middle line) and upper to lower 25th percentile range, with box whiskers extending to the maximum and minimum values. (b) Plant biomass growth differences did not vary by sterilization treatment, though foliar phenology differences predict plant biomass growth differences overall. Residuals are used to show partial phenology and growth differences after accounting for plant population effects. BL, Blue River, AZ; OC, Oak Creek, AZ; OGC, Ogden Canyon, UT; SJ, San Juan River, CO; SNR, Snake River, WY; SMIG, San Miguel River, CO; WR, Weber River, UT.

relatively less Dothideomycetes – which includes many known plant pathogens, like Pleosporales, that cause leaf and shoot blight in poplars (Newcombe, 1996; Busby et al., 2012) – suggesting that healthy tree defenses might suppress the abundance of these potential pathogens relative to background levels.

on soil sterilization and whether microbial communities were associated with trees or not, indicating that the timing of spring bud-break is partially determined by host-specific plant–micro-biome interactions. Specifically, three ‘early’ phenology popula-tions (BL, OC and OGC) showed strong soil conditioning effects that advanced bud-bud break with Home vs Away soil communities. Such patterns might be caused, in part, by genetic interactions, as previous work has shown that P. angustifolia pop-ulations have evolved distinct rhizosphere metabolic profiles (Mueller et al., 2020) that likely affect soil microbial community structure, and different populations show inconsistent patterns of local adaptation to their soil biotic environments (Van Nuland et al., 2019). Though further work is needed to confirm the genetic basis of these patterns, the result that population-level trait differentiation is tied to the diversity and activity of the plant microbiome is consistent with past work on the importance of plant–soil interactions in an evolutionary framework (Kylafis & Loreau, 2008; Lau & Lennon, 2011; Schweitzer et al., 2014; Wagner et al., 2014; Van Nuland et al., 2016). Previous studies that show microbial effects on phenology have often used small, fast-growing species (e.g. Arabidopsis and Brassica), and here we show some of the first empirical evidence that this process can occur in a widespread, long-lived tree species across replicate

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elevation gradients. If this is true of other dominant tree species, natural variation in plant–microbial interactions might influence the ecotype divergence of spring phenology at larger geographic scales, and with broader community and ecosystem-level conse-quences, than is currently understood.

Though soil microfauna transferred in the field inoculum could have influenced plant phenology, the evidence to date sug-gests that microbes may be primarily responsible for soil biotic effects on phenology due to their proximity for signaling in the rhizosphere and their ability to manipulate plant phenotypes through hormonal, soil nutritional, or other unknown mecha-nisms (Friesen et al., 2011; Lau & Lennon, 2011, 2012; Turner et al., 2013; Wagner et al., 2014; Panke-Buisse et al., 2015, 2017). We cannot be sure of the direct or indirect mechanisms by which the soil inocula treatments affected bud-break, but it is possible that microbial communities reliably altered small-scale environmental cues (e.g. local soil N conditions that affect plant growth hormones; Lu et al., 2018) and contributed to the sys-temic signaling of spring leaf-out through influencing the differ-ential expression of bud-break-related genes in plant roots (Bouch�e et al., 2016). To facilitate experimental controls, our experiments took place in a controlled glasshouse setting, and a clear next step is to compare the relative importance of soil com-munities with other signals that initiate P. angustifolia foliar bud-break using a mix of field-based observations and experimental transplants across populations, elevation, and other environmen-tal gradients. This would be particularly relevant given the weaker statistical support for elevational differences in soil microbiome effects on foliar phenology than among populations.

Plant–microbiome interactions delay phenology and reduce productivity with range shifts

Our results indicate that the soil microbiome may have impor-tant but underappreciated effects on signaling spring phenology as plant ranges shift towards higher elevation. Field soil inoculum generally had reproducible effects on foliar phenology – higher elevation soil communities associated with ‘late’ genotypes delayed the bud-break of lower elevation ‘early’ genotypes – using a glasshouse experiment designed to quantify the outcomes of novel plant–soil microbiome interactions with upward range shifts. On average, spring bud-break was delayed by slightly over 3 d when plants were grown with live soil communities from higher elevations compared with their original elevation. This phenology delay was stronger with larger elevational differences between soil communities, though we note that this effect also had marginal statistical support. Nonetheless, the entirety of our findings related to soil microbes and elevation have a common explanation: soil communities have undergone a selection process that reproduces ‘early’ vs ‘late’ bud-break phenotypes along eleva-tion gradients. Although there were few ‘significant’ (i.e. P < 0.05) soil microbiome 9 elevation effects on phenology in the Home–Away and Range Shift group tests, they all showed qualitatively consistent trends (with marginally significant results), which is arguably stronger evidence than a single signifi-cant result. Soil microbial selection across elevation that drives

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Research 11

earlier and later bud-break phenotypes would match recent multigenerational selection experiments resulting in different microbial consortia that reproduced early and late-flowering Ara-bidopsis phenotypes (Panke-Buisse et al., 2015, 2017) and stud-ies showing that phenology can be phenotypically plastic to soil microbial variation (Batten et al., 2008; Wagner et al., 2014; Ware et al., 2021). Because the experiment focused on upwards range shifts, we do not know if lower elevation soils microbial communities act to advance bud-break in higher elevation plants – although some results point in that direction: soil community conditioning effects were strongest at lower elevations promoting earlier phenology in the Home–Away group; and see Ware et al., (2021). This downslope range shift comparison could be espe-cially useful to explore in future experiments to identify key microbial taxa that have disproportionate effects on primary pro-ductivity in warmer sites by promoting earlier leaf emergence.

Higher elevation soil communities signaling later foliar bud-break in lower elevation plants shortened the growing season and reduced aboveground biomass production. The timing of spring leaf emergence is a critical point for plants to maximize ecosystem productivity and soil resource uptake while avoiding frost damage from freezing temperatures (Nord & Lynch, 2009; Polgar & Pri-mack, 2011). Most research to date has focused on increased temperature effects that advance spring bud-break and increases in productivity from the extended growing season (particularly at higher elevations; Morin et al., 2009; Lebourgeois et al., 2010). However, some evidence indicates that global warming effects on leaf emergence are decreasing in magnitude over time (Fu et al., 2015), suggesting that spring foliar phenology may become more sensitive to changes in other abiotic or biotic cues. Our results show that soil microbial communities (in their current form) affect the timing of leaf emergence across elevation gradients such that phenological changes affect plant productivity. Specifically, P. angustifolia trees associate with soil communities that promote earlier bud-break at low elevations and later bud-break with increasing elevation (Fig. 3). Higher elevation genotypes with later leaf emergence have less biomass growth (Fig. 4). Soil micro-bial effects from simulated upward range shifts cause spring phe-nology delays in lower elevation plants that are linked to decreased productivity (Fig. 5). As higher elevation soil commu-nities also respond to warming independently of, or interactively with, plant influences, such changes could conflict with their abil-ity to influence leaf emergence and ecosystem function as plant distributions change. Though it remains challenging to predict exactly how communities and ecosystems will respond to eleva-tional range shifts that disrupt established plant–soil relation-ships, examining plant–microbiome interactions in this climate change context provides important ecological and evolutionary information on these dynamic relationships and offers clues as to how we might manage such interactions for target phenotypes and sustainable ecosystem processes in the future.

Conclusions

Soil microbial communities can have diverse effects on plant phe-notypes critical to the persistence of populations and

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sustainability of ecosystem function. Here, we show that plant population and elevation influenced the soil microbiome associ-ated with trees and impacted plant biomass growth by altering the timing of leaf emergence. These effects were predictable based on how soils were conditioned and where they were collected from the plant species’ range at broad (i.e. population) and fine (i.e. elevation) scales, which has important implications for our ability to strategically guide plant–soil interactions towards restoration and management goals. Taken together, these data suggest that ecosystem responses to climate change may depend in part on how plant differentiation relates to soil microbial com-munity variation on the landscape, a perspective that is rarely accounted for in understanding, managing, or modelling forest responses in a changing world.

Acknowledgements

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship to MVN under grant no. DGE-0929298, and the Department of Energy Joint Genome Institute Community Sequencing Project (ID no. 1112832). Addi-tional funding for the project was provided from the University of Tennessee. Special thanks to Phil Patterson (Northern Arizona University glasshouses), Ken McFarland, Jeff Martin, Peter Meidl, Caroline Daws, and Erica Johnson for field sampling and glasshouse support. ORNL is managed by UT-Battelle LLC, for the US Department of Energy, under contract DEAC05-00OR22725. The authors have no conflicts of interests to declare.

Author contributions

MEVN, JKB, and JAS designed the study. MEVN and IMW performed the field sampling and soil DNA extraction. CWS and ZY performed the library preparation for sequencing, and MEVN processed and analyzed the amplicon data. MEVN set up the glasshouse experiment, collected and analyzed the exper-imental data, and wrote the initial manuscript draft. All authors discussed the results and contributed significant manuscript edits.

ORCID

Chris W. Schadt https://orcid.org/0000-0001-8759-2448 Jennifer A. Schweitzer https://orcid.org/0000-0003-4890-7632 Michael E. Van Nuland https://orcid.org/0000-0002-3333-0212 Ian M. Ware https://orcid.org/0000-0002-2101-5653

Data availability

Microbial rRNA amplicon sequences are archived in the National Center for Biotechnology Information SRA database (BioProject accession no.: PRJNA726831). The bioinformatic workflow, ASV tables, and experimental data are archived and publicly available on Zenodo (https://doi.org/10.5281/zenodo.5047857).

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Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

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Fig. S1 Visual depiction of experimental design with plant geno-types and soil treatments.

Fig. S2 Soil community changes across elevation levels and con-tributions of top 20 genera.

Fig. S3 Soil bacterial and fungal community turnover across ele-vation gradients.

Fig. S4 Variation in the relative abundance of the top 25 soil fun-gal families.

Fig. S5 Population-level phenology and growth responses by soil position and sterilization.

Fig. S6 Plant growth responses across Home-Away treatments and elevation.

Fig. S7 Plant growth differences compared to soil inoculum ele-vational differences.

Fig. S8 Phenology and growth responses to beyond range limit soil inoculum.

Table S1 Site characteristics of the elevation transect within each population.

Table S2 Analysis of bacterial phyla and fungal class composition and relative abundance.

Table S3 Elevation I-spline coefficients from generalized dissimi-larity models.

Table S4 Analysis of the top 25 most dominant fungal families.

Table S5 Elevation effects on phenology and growth by soil posi-tion (Home-Away group).

Table S6 Results from phenology and growth log response ratio analysis (log(Home/Away)).

Table S7 Results of plant biomass growth predicted by foliar phenology across treatments.

Table S8 Foliar phenology and plant growth variation across the Range Shift group.

Table S9 Relationship between phenology and growth differ-ences (Range shift group).

Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

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