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VOLUME: 1 | ARTICLE NUMBER: 0150
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Divergent plant-soil feedbacks could alter future elevation ranges and ecosystem dynamics

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Michael E. Van Nuland1*, Joseph K. Bailey1, and Jennifer A. Schweitzer1

1Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville,
Tennessee, 37996, United States of America.

*Corresponding author

Author emails:
[email protected]
[email protected]
[email protected]

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Soil conditioning effects
Measures of total soil C, total soil N, and pH had significant soil location effects (conditioned
versus unconditioned), but little or no effects of range location (interior versus edge) or soil
location × range location interactions (Supplementary Table 2). Conditioned soils that were
collected beneath trees had higher amounts of C (F = 9.0; p < 0.01), N (F = 4.8; p = 0.03),
1,50 1,50
and more basic pH (F = 6.4; p = 0.02) compared to unconditioned soils (Supplementary Fig.
1,50
2a-c). However, post-hoc analyses reveal that interior trees are responsible for creating these
effects. Within range interiors, conditioned soil C was 52% higher (mean = 5.7 ± 0.7 standard
error [SE]; z = 3.5, p < 0.01), conditioned soil N was 40% higher (mean = 0.33 ± 0.04 SE; z =
2.8, p = 0.03), and conditioned soil pH was 5% higher (mean = 5.6 ± 0.04 SE; z = 4.5, p < 0.001)
relative to paired unconditioned soils. By comparison, edge trees had no conditioning effect on
soil C, N, or pH, suggesting there may be less plant-soil conditioning at range limits. In
surveying the diversity of dominant rhizosphere bacteria (using next-generation 16S amplicon
sequencing) associated with a representative subset of P. angustifolia trees in the field, only the
relative abundance of Betaproteobacteria (7.5% overall) showed a difference in conditioned
soils between interior and edge range locations (Supplementary Fig. 2d). Betaproteobacteria
abundance was 63% higher in edge (mean = 0.09 ± 0.01 SE) versus interior tree-conditioned
soils (F = 6.4, p = 0.05). This observation could be due to differences in plant-soil
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conditioning or to site differences between range interiors and edges. No other dominant
bacterial class (including Actinobacteria [9%], Alphaproteobacteria [19%],
Gammaproteobacteria [18%], or Synergistia [4%]) differed by range location (see
Supplementary Table 3). Moreover, differences in conditioning between range locations could be
related to tree age since there is a marginal effect of interior trees being 20.7% larger (and
presumably older) than edge trees (F = 3.7, p = 0.06), and this pattern is consistent across
1,210
populations (population × range location: F = 1.4, p = 0.2).
6,210

Allometric equation for aboveground biomass of Populus angustifolia cuttings
Allometric equations use plant traits to predict biomass when destructive sampling is not
possible. Recent work has constructed allometric equations for Populus spp. that are close
relatives of P. angustifolia (Chojnacky et al. 2014), as well as for P. angustifolia saplings
(Lojewski et al. 2009). However, both approaches use mature trees or saplings that relate DBH to
plant biomass; therefore, these equations are not intended to accurately scale down to the size of
trees in our greenhouse experiment. As a result, we established an allometric equation using six
different P. angustifolia genotypes that were collected at three time periods (June 2012, 2013,
and 2014) and grown under equal greenhouse conditions at the University of Tennessee. We
measured height and basal stem diameter from these six plants in September 2014 before the
aboveground portion was dried at 72° C for 48 h and dry biomass was measured. We calculated
plant cross sectional areas from basal stem diameter measurements (π(0.5*diameter)2), and
multiplied area by plant height to quantify total stem volume (mm3). We then used a linear
regression to test the relationship between stem volume and aboveground biomass. Stem volume
predicted more than 98% of the variation in aboveground biomass (Supplementary Fig. 1); as a
result, we created the following allometric equation: Aboveground biomass (g) = (stem volume
(mm3) * 0.41899) - 2.40137. Measurements of height and basal stem diameter from trees in the
feedback experiment were recorded in May 2015 to calculate final aboveground biomass using
this allometric equation; measures of total growth responses were calculated as final - initial
biomass. Calculations of biomass growth that were negative (due to calculation errors) were
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removed from analyses, resulting in a total of 609 trees that were incorporated in experimental
analyses.

Analysis of maternal effects on tree growth traits
Maternal effects transferred from trees in the field to tree cuttings used in the greenhouse
experiments may influence growth traits measured in this study. We tested whether “maternal”
tree size (DBH) corresponded to measures of initial aboveground biomass, final aboveground
biomass, or aboveground biomass growth in our experiment. First we calculated genotype-means
for each growth traits across all treatment types to compare to DBH of the corresponding
genotype in the field (N = 209). Next, we created a model with the three growth traits as the
response variable, DBH as the fixed effect, and population as a random effect. Any relationship
between DBH and growth traits in the greenhouse would suggest that maternal tree size could be
influencing growth responses in the feedback experiments and might be confounding effects
from the various treatments.
We find no patterns of DBH relating to any of the growth traits measured in the
greenhouse. Specifically, there was no relationship between DBH and initial aboveground
biomass (r2 = 0.0, p = 0.4), final aboveground biomass (r2 = 0.01, p = 0.9), or aboveground
biomass growth (r2 = 0.03, p = 0.3). These results suggest that maternal effects likely play a
minimal role affecting variation in P. angustifolia growth in our study.

Amplicon sequencing and bioinformatic processing
Soil samples were sent to the Environmental Genetics and Genomics Laboratory at Northern
Arizona University for sequencing of marker gene amplicon pools. Samples were amplified in
triplicate PCR reactions for the 16S v4 region using the universal prokaryotic primers 515F and
806R. First round reactions were performed in triplicate in 384 well plates. The 8 µL volumes
contained the following: 1 µM each primer (Eurofins MWG Operon, LLC, Louisville, KY), 200
µM each dNTP (Phenix Research, Candler, NC), 0.01 U/µL Phusion Hot Start II DNA
Polymerase (Life Technologies, Carlsbad, CA), 1X HF Phusion Buffer (Life Technologies), 3
mM MgCl , 6% glycerol, and 1 µL normalized template DNA. Cycling conditions were: 2
2
minutes at 95°C followed by 20 cycles of 30 seconds at 95°C, 30 seconds at 55°C, 4 minutes at
60°C. Triplicate reactions for each sample were pooled by combining 4 µL from each, and 2 µL
was used to check for results on a 1% agarose gel. The remainder was diluted 10-fold and used
as template in a second PCR reaction in which 12 base Golay indexed sequencing tails were
added. Second round reaction conditions were identical to the first round except only one
reaction was conducted per sample and only 15 total cycles were performed. Indexed PCR
products were purified using a 1:1 ratio of 18% PEG and carboxylated magnetic beads,
quantified by PicoGreen fluorescence, and an equal mass of each sample was combined into a
final sample pool. The pool was purified and concentrated, and subsequently quantified by
quantitative PCR against Illumina DNA Standards (Kapa Biosystems, Wilmington, MA).
Sequencing was carried out on a MiSeq Desktop Sequencer (Illumina Inc, San Diego, CA)
running in paired end 2x250 mode.
We processed 16S amplicon data using akutils (https://github.com/alk224/akutils-v1.2),
which includes modifications to a QIIME 1.9.1 workflow (Caporaso et al. 2010) since it has been
shown that the default settings can significantly overestimate microbial diversity (Krohn et al.
2016a). Prior to sample processing, primers were trimmed and PhiX Control was identified and
removed using the “akutils phix_filtering” command (Krohn et al. 2016b). Paired end reads were
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joined with the “akutils join_paired_reads” command (mean length = 253 bp), which uses fastq-
join from ea-utils (Aronesty et al. 2011). The raw joined data was demultiplexed with the
“split_libraries_fastq.py” script in QIIME and underwent stricter minimum quality thresholds of
q20 (q = 19), 0-3 low quality base calls allowed (r = 1-3), and each read required to be at least
95% high quality (p = 0.95) (Krohn et al. 2016b). We used vsearch 1.1.1 (Rognes et al. 2015) to
remove chimeras against the Gold database (http://drive5.com/uchime/gold.fa). Demultiplexing
and quality filtering resulted in a total of 203,259 high-quality reads (average per sample =
16,938 ± 468 SE). We used the “akutils pick_otus 16S” command for OTU picking and
taxonomy assignment (Krohn et al. 2016b). Specifically, the prefix_suffix OTU picker in QIIME
was used to dereplicate sequences on the first 100 bases. OTUs were picked at 97% similarity
using Swarm (d1) and taxonomy assignment was performed with BLAST (Altschul et al. 1990).
To assess microbial taxonomic diversity across the samples, we used the “akutils core_diversity”
command that incorporated an OTU table having been filtered at the Kircher threshold (0.3% per
sample; Kirchner et al. 2012).

References in Supplementary Information
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic Local alignment
search tool. J. Mol. Biol., 215, 403-410 (1990).
Aronesty, E. ea-utils: command-line tools for processing biological sequencing data. Expression
Analysis, Durham, NC (2011).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data.
Nature Methods, 7, 335-336 (2010).
Chojnacky, D. C., Heath, L. S., & Jenkins, J. C. (2014). Updated generalized biomass equations
for North American tree species. Forestry, 87, 129-151.
Kirchner, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex
sequeuncing on the Illumina platform. Nucleic Acids Res., 40, e3, (2012).
Krohn, A. akutils-v1.2: facilitating analyses of microbial communities through QIIME. Zenodo,
10.5281/zenodo.56764 (2016b).
Krohn, A., Stevens, B., Robbins-Pianka, A., Belus, M., Allan, G. J. & Gehring, C. Optimization
of 16S amplicon analysis using mock communities: implications for estimating
community diversity. Peer J, 10.7287/peerj.preprints.2196v2 (2016a).
Lojewski, N. R., Fischer, D. G., Bailey, J. K., Schweitzer, J. A., Whitham, T. G., & Hart, S. C.
Genetic basis of aboveground productivity in two native Populus species and their
hybrids. Tree Physiology, 29, 1133-1142 (2009).
Rognes, T., Mahé, F., Flouri, T., Quince, C. & Nichols, B. vsearch: VSEARCH version 1.0.16.
Zenodo. 10.5281/zenodo.15524 (2015).
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Supplementary Table 1. Summary of soil nutrients, pH, and relative abundance of dominant bacteria measured in this study
Conditioned/Unconditioned Proteobacteria
Site a Carbon (% total) Nitrogen (% total) pH Actinobacteria a b g Synergistia
BL
Interior 2.2/1.0 0.16/0.10 5.6/5.3 0.09 0.19 0.08 0.09 0.04
Edge 1.4/2.4 0.10/0.17 5.4/5.3 0.18 0.12 0.15 0.03 0.0
Beyond 1.2 0.11 5.0
OC
Interior 2.7/1.9 0.17/0.14 5.2/5.3
Edge 2.5/2.1 0.16/0.16 5.2/5.3
Beyond 4.0 0.27 5.3
SJ
Interior 5.5/4.0 0.40/0.40 5.4/5.1 0.07 0.11 0.04 0.10 0.04
Edge 3.8/1.5 0.26/0.20 5.4/5.5 0.20 0.10 0.06 0.05 0.03
Beyond 1.2 0.14 5.5
SMIG
Interior 8.1/4.7 0.44/0.30 5.6/5.4
Edge 4.1/3.2 0.26/0.18 5.6/5.1
Beyond 24.6 1.4 5.0
OGC
Interior 6.3/4.5 0.34/0.25 5.7/5.0
Edge 4.1/1.6 0.25/0.13 5.4/5.5
Beyond 4.9 0.40 5.7
WR
Interior 6.3/4.1 0.40/0.26 5.7/5.2
Edge 8.4/3.0 0.40/0.22 5.5/5.4
Beyond 5.5 0.32 5.11
SNR
Interior 8.1/3.4 0.41/0.16 5.6/5.5 0.17 0.23 0.02 0.08 0.02
Edge 4.8/1.3 0.26/0.14 5.6/5.8 0.24 0.11 0.05 0.23 0.06
Beyond 4.04 0.23 5.3
a Sites are from the following Populus angustifolia populations: BL = Blue River, AZ; OC = Oak Creek, AZ; SJ = San Juan River,
CO; SMIG = San Miguel River, CO; OGC = Ogden Canyon, UT; WR = Weber River, UT; and Snake River, WY.
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Supplementary Table 2. Summary of soil nutrient and pH conditioning results by P. angustifolia trees in the field
Carbon Nitrogen pH
Model df F df F df F
Soil location 50 9.0 ** 50 4.8 * 50 6.4 *
Range location 50 3.7 † 50 3.0 † 50 1.0
Soil x range 50 0.1 50 0.2 50 3.3 †
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Supplementary Table 3. Summary of rhizopshere soil microbial variation across P. angustifolia elevation ranges in the field
Actinobacteria a-proteobacteria b-proteobacteria g-proteobacteria Synergistia
Model df F df F df F df F df F
Range Location 11 1.8 11 1.7 11 6.4 * 11 0.1 11 0.0
Population 10 0.5 10 0.8 10 8.9 ** 10 1.3 10 0.4
Range x population 10 0.1 10 0.4 10 0.7 10 1.8 10 1.2
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Supplementary Table 4. Results of plant-soil feedbacks generated by P. angustifolia conditioning of soil communities
Interior & edge
Model df F
Soil location 359 0.6
Soil treatment 391 0.4
Location x treatment 368 4.0 *
Interior Edge
Model df F df F
Soil location 270 0.6 88 0.1
Soil treatment 288 0.0 95 0.7
Location x treatment 284 2.3 84 1.6
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
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Supplementary Table 5. Summary of the relationships between soil microbial relative abundance and P. angustifolia growth
Actinobacteria a-proteobacteria b-proteobacteria g-proteobacteria Synergistia
Model df F df F df F df F df F
Rel. abundance 11 0.7 11 1.2 11 11.1 ** 11 0.1 11 0.8
Range Location 11 0.7 11 1.2 11 1.7 11 0 11 0
Abundance x Range 11 0.7 11 1.3 11 0 11 0.7 11 1.1
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Supplementary Table 6. Results of how range shift categories and soil treatments influence range shift PSF
Home-away Local-foreign
Model df F df F
Range shift 213 13.1 *** 141 2.5
Soil treatment 211 2.5 144 0.0
Shift x treatment 211 2.3 † 144 0.1
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Supplementary Table 7. Results of how soil conditioning (difference in soil traits between soil locations) predicts PSF
Carbon Nitrogen pH
Model df F df F df F
Soil conditioning 104 0.53 76 0.2 73 0.05
Range location 111 0.17 114 1.4 114 0.04
Conditioning x location 76 5.2 * 73 4.7 * 95 0.7
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

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Supplementary Table 8. Results of how residual conditioned soil differencesa between elevation sites predict range shift PSF
Model Carbon Nitrogen pH
Interior-Interior (I->I) df F df F df F
Residual conditioned soil differences 113 0.2 113 1.7 113 15.7***
Range shift 112 10.2*** 112 12.6*** 112 3.0
Resid. soil difference x range shift 112 0.8 112 1.8 112 3.6*
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
a This metric refers to conditioned soil differences across elevation sites after background soil variation was removed.

Supplementary Table 9. Results of field soil chemical variation related to aboveground biomass growth in the experiment.
Modela df F p
Growth ~ Soil Carbon 608 0.3 0.6
Growth ~ Soil Nitrogen 608 1.6 0.2
Growth ~ Soil pH 608 2.2 0.2
a All models include population, genotype, soil treatment (live or sterile inoculum), and location (conditioned/unconditioned or range
shift category) as random effects.

Supplementary Table 10. Results of soil chemical variation across populations, elevation, and multiple sampling years.
Carbon Nitrogen pH
Model df F df F df F
Population 315 5.3 ** 315 5.2 ** 218 3.3 *
Year 316 4.3 * 316 8.1 *** 220 493.9 ***
Elevation 317 1.4 317 0.3 220 7.7 **
Population X elevation 315 3.8 * 315 0.6 218 0.5
Year X elevation 316 0.7 316 1.3 220 0.9
Population X Year 312 0.9 312 1.1 218 3.0 *
Population X Elevation X Year 312 0.5 312 0.7 218 1.0
† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Results from 2012, 2013, and 2014 sampling of the following sites: Blue River, AZ, San Juan, CO, San Miguel, CO, and Snake River,
WY. Note, pH data were compared between 2012 and 2014 collection dates since a different protocol was used for 2013 samples.
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Supplementary Figure 1. Plant-soil conditioning in the field varies by range location. Soils
conditioned beneath interior P. angustifolia trees have higher soil carbon (a), soil nitrogen (b),
and soil pH (c) compared to unconditioned interspace locations away from trees. In contrast, no
conditioning effects were found at the range edge. The relative abundance of Betaproteobacteria
(d) in soils collected beneath P. angustifolia trees was found to be lower within the range interior
compared to the range edge (unconditioned soils outside of the influence of trees were not
sequenced). It should be noted that although these soils were collected directly from the
rhizosphere and range location differences may be caused by variation in conditioning, it is also
possible that Betaproteobacteria are more abundant at higher elevations regardless of P.
angustifolia conditioning effects. Bars depict least square means ± 1 standard error.
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Supplementary Figure 2. Stem volume of Populus angustifolia cuttings is an excellent
predictor of aboveground biomass. Using a combination of basal diameter and plant height
traits, total stem volume predicts more than 98% variation in aboveground biomass of trees that
were collected over three years (2012-2014) and grown under equivalent greenhouse conditions.
We used this relationship to establish the following allometric equation: Aboveground biomass
(g) = (stem volume (mm3) * 0.41899) - 2.40137. Grey area represents 95% confidence interval.

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