Solidago species are a model system for this study because they commonly occur across North America, with 120 species native to the United States (Semple, 2016) that grow in variable habitats, with different morphologies and phenotypes. We chose to use S. caesia, S. flexicaulis, and S. gigantea in this study because they were the most abundant Solidago species found across our sampling range (northeastern TN) and vary in evolutionary history, leaf, stem, and flower morphology and habitat preference. Solidago caesia and S. flexicaulis grow in woodlands and belong to the Glomerulifloraea subgroup of Solidago (Semple, 2016). Solidago gigantea grows in meadows and fields and belongs to the Triplinerviae subgroup (Semple, 2016). Furthermore, previous work has found evidence for the influence of interspecific and genotypic diversity on above- and belowground biomass of S. altissima and S. gigantea (Genung et al., 2012, 2013), suggesting that some Solidago phenotypes may be mediated in part by modifications of soil biota from neighboring Solidago species.
Preliminary field surveys
To assess differences in plant phenotypes among the three Solidago species, we conducted field surveys of three geographically distinct populations of each species, all located throughout northeastern Tennessee, U.S.A. In May 2017, we measured stem height, stem base diameter, specific leaf area (SLA), and stomatal density of 15 randomly selected putative genotypes of each species (S. caesia, S. flexicaulis, and S. gigantea) in northeastern TN for a total of 45 individuals per species (Fig. 1a). The field survey confirmed that the three species vary in this suite of growth and physiological phenotypes (Table S1).
Soil collection and processing
To assess Hypothesis 2 that the soil microbiome sources have distinct taxonomic and/or functional microbial communities, we collected rhizosphere soil from each genotype in the field surveys by collecting soil attached to the roots of each plant (Fig. 1b). We pooled individual soil samples by field site to represent an average belowground microbiome of three soil sources (n = 3 sites per soil source). While we tried to collect soil microbes that were only associated with the rhizosphere soil of each plant species, it is likely that we also captured microbes that are representative of surrounding non-rhizosphere soil. Due to this fact and that some climatic and edaphic soil characteristics including mean annual temperature, soil organic matter content, and soil bulk density slightly varied among the three groups of Solidago species sites (Table S2), we refer to the three groups of sites as soil microbiome sources rather than soils associated with each Solidago species. Soil samples were transported to the laboratory on ice and stored at 0°C until analysis at the University of Tennessee, Knoxville, TN, U.S.A. A 2 g subsample of soil from each field site was stored at -80°C for molecular analysis. We assessed the taxonomic community composition of the soils using high-throughput amplicon sequencing of the V3-V4 region of the 16S rRNA gene (16S) and the ITS2 region of the internal transcribed spacer gene regions for bacteria and fungi, respectively. Detailed methods are described in Methods S1 of the Supplementary Materials.
We performed all amplicon sequence processing using the DADA2 platform (Callahan et al., 2016). For 16S sequences, primers were removed prior to the DADA2 pipeline using the cutadapt function in conda. Samples were normalized for sampling depth with a variance stabilizing transformation with the DESeq2 package (Love et al., 2014). We chose this method over the common practice of rarefaction because rarefaction results in loss of data by using the lowest sampling depth and it inflates variances across samples (McMurdie & Holmes, 2014). Taxonomy of ASVs was assigned using the RDP (Wang et al., 2007) and UNITE (Abarenkov et al., 2010) databases for bacteria and fungi, respectively. After processing, we had 16,245 bacterial and 2,565 fungal ASVs, respectively. Additionally, we assigned fungal ASVs to functional guilds using the FUNGuild database (Nguyen et al., 2016). For analyses, we assigned taxa to one of seven broad functional guilds: arbuscular mycorrhizal fungi, ectomycorrhizal fungi, ericoid mycorrhizal fungi, endophytic fungi, plant pathogenic fungi, saprotrophic fungi, and “other.” We considered only FUNGuild assignments with a confidence ranking of “highly probable” or “probable.” Unassigned taxa were excluded from further guild-based analyses. Of the 2,565 fungal ASVs, 1,741 were assigned to a fungal guild. Of those assigned, we used the 1,328 ASVs that had a confidence ranking of “highly probable” or “probable.”
We assessed functional community composition with shotgun metagenomic sequencing, as detailed in Methods S1 of the Supplementary Materials. Sequences retrieved from shotgun metagenomic sequencing were assigned to KEGG (Kyoto Encyclopedia of Genes and Genomes) ortholog numbers using the MG-RAST online annotation tool. KEGG orthologs assign genes to microbial complexes, functional sets, and metabolic pathways and are a common tool used to describe functional attributes of microbes (Ortiz-Álvarez et al., 2018; Sorensen et al., 2019). KEGG ortholog numbers were matched to hierarchical KEGG pathways.
To assess Hypotheses 1 and 3 that plant phenotypes are in part mediated by the taxonomic and/or functional composition of soil microbial communities and that the strength of microbial mediation varies among plant species and phenotypic traits, we conducted a glasshouse experiment and grew S. caesia, S. flexicaulis, and S. gigantea in factorial soil inoculum treatments of each microbiome source. Seeds of each Solidago species were purchased from separate nurseries to account for intraspecific variation in plant response to soil microbes (S. caesia: Ernst Conservation Seeds, Meadville, PA; NorthCreek Nurseries, Landenburg, PA; Michigan Wildflower Farm, Portland, MI; S. flexicaulis: Ernst Conservation Seeds, Prairie Moon Nurseries, Winona, MI; Minnesota Native Landscapes, Ostego, MN; S. gigantea: Prairie Moon Nurseries, Minnesota Native Landscapes). Seeds were refrigerated at 4°C prior to sowing, and then were sown by population into a commercial peat moss-based, non-mycorrhizal potting mix (Premier Promix BX, containing perlite, vermiculite, and limestone). A subset of Solidago seeds did not withstand surface sterilization trials, so we did not surface sterilize the seeds used in the experiment. While it is possible that any seed-borne microbes may have impacted plant phenotype, all plants were grown in all soil treatments and exposed to the same glasshouse conditions, such that any effect of seed-borne microbes on plant phenotype should be equally distributed across treatment categories.
After approximately three weeks of growth, 54 similar-sized seedlings of each population were individually transplanted into half-gallon circular pots into soil inoculum treatments which consisted of factorial combinations of microbiome source (Microbiome source 1 vs. Microbiome source 2 vs. Microbiome source 3) (Fig. 1c). Furthermore, since soils from each field site of each microbiome source were kept separate, seeds were planted into three sites of Microbiome source 1, three sites of Microbiome source 2, and three sites of Microbiome source 3. Each pot was inoculated with 2 teaspoons of field soil (< 1% of the total pot volume) to reduce effects of variation in soil nutrients on plant phenotypic responses (Troelstra et al., 2001). In total, 243 pots were established: 3 Solidago species x 3 seed populations x 3 microbiome sources (Microbiome source 1, Microbiome source 2, Microbiome source 3) x 3 field soil sites x 3 replicates = 243 total pots). Pots were randomly positioned in the glasshouse based on random number assignments. All plants were treated monthly for thrips and whiteflies throughout the experiment (0.5 tsp/gal Avid 0.15 EC insecticide, 0.5 tsp/gal AzaGuard insecticide). Plants were equally watered from above, as needed (approximately 4 days/week), and allowed to grow for 5 months in a glasshouse at the University of Tennessee.
A suite of plant phenotypes was measured during and post-experiment. Stem height and stem diameter were measured every two weeks for the first two months of growth, then at 13 weeks and at the termination of the experiment at 20 weeks. Relative growth rates were calculated from these data. For each individual plant, timing of flower bud formation (hereafter referred to as “flower bud break”) and flowering were monitored with daily surveys by recording the day of the appearance of the first distinguishable flower bud and first open flower, respectively. Prior to termination of the experiment, an average of four healthy and mature leaves were randomly selected per plant, scanned using WinFOLIA software (Regent Instruments Inc.), oven-dried at 70°C for 72 hours (Pérez-Harguindeguy et al., 2016), and weighed to calculate specific leaf area (cm2/g) (SLA). After five months of growth and regular watering, each individual was harvested and separated into shoot and root biomass and inflorescence biomass. Shoot and root tissue was weighed after 48 hours of oven-drying at 60°C. Prior to drying, roots were carefully rinsed over 2 and 0.5 mm sieves to remove lingering soil and collect all fine roots.
In the field survey, we analyzed differences in Solidago phenotypes using linear mixed-effects models with the lmer function in the lme4 package (Bates et al., 2014). We built separate mixed-effects models for each phenotype (stem height, stem diameter, SLA, and stomatal density) using Solidago species as the fixed effect and population as the random effect. When necessary, all data were transformed to conform to normality before analysis. To test Hypothesis 1 that phenotypes of each Solidago species differ when grown in soils inoculated with microbial communities associated with a different microbiome source, we built linear mixed effects models with the lmer function in the lme4 package. First, to identify traits most important to growth, physiology, and reproduction and to reduce Type I error, we tested for correlations between the ten phenotypes measured from the glasshouse experiment (relative growth rate in stem height, stem diameter at maturity, shoot biomass, root biomass, total biomass, root to shoot ratio, SLA, flower bud break, days to flower, inflorescence biomass) using the cor.test function. We chose to exclude stem diameter, total biomass, and root to shoot ratio from the analysis because they were all significantly correlated with two other growth phenotypes, shoot and root biomass (Table S5). We also chose to exclude days to flower and inflorescence biomass from the analysis because the experiment ended before the majority of S. gigantea individuals flowered. Relative growth rate, shoot biomass, root biomass, SLA, and flower bud break were included in the analysis.
Multiple models were used to assess Hypothesis 1. Separate models were built for the five phenotypes (relative growth rate, shoot biomass, root biomass, SLA, and timing of flower bud formation). When necessary, all data was transformed to conform to normality before analysis. First, to test that differences in soil microbial community composition have a general effect on plant phenotypes regardless of plant species, we built linear mixed effects models with microbiome source as a fixed effect and Solidago species, seed population, and field soil site as random effects. Then to identify interspecific variation in response to microbial community composition we separated the dataset by each Solidago species and built individual linear mixed effects models for each Solidago species with microbiome source as a fixed effect and seed population and field soil site as random effects. For all models, we used the Anova function to calculate ANOVA tables using Type II sums of squares, with significance assessed for each fixed effect using Wald X2 statistics. If any of the fixed effects were significant, we conducted post hoc Tukey contrasts using the TukeyHSD function.
To test Hypothesis 2 that each microbiome source is associated with distinct taxonomic and/or functional soil microbial communities, we took multiple approaches. First, we assessed microbial diversity across microbiome source by calculating hill numbers based on ASV counts and unique KEGG identities using the hill_div function in the hilldiv package (Alberdi & Gilbert, 2019). Hill numbers serve as effective numbers of diversity that provide more intuitive estimates of diversity compared to traditional diversity indices based on entropy (Chao et al., 2014). We calculated hill numbers for all orders of diversity at q = 0, q = 1, and q = 2, and tested for significant differences in hill numbers between microbiome source at each order of diversity using the div_test function in the hilldiv package. A diversity order q = 0 provides raw richness by weighting rare taxa the same as abundant taxa and thus not accounting for species’ abundances. A diversity order q = 1 weights ASVs by their abundance but without disproportionately favoring abundant taxa. A diversity order q = 2 overweighs abundant ASVs.
Second, we created Bray-Curtis distance matrices for microbial taxonomic and functional composition of the nine field soils. To assess variation in community composition of bacteria, fungi, and KEGGs across microbiome source, we conducted PERMANOVA analysis with 9,999 permutations using the adonis function in the vegan package (Oksanen et al., 2019). Prior to conducting PERMANOVA we confirmed homogeneity of dispersion across microbiome source with the betadisper function in the vegan package. We then performed a distance-based redundancy analysis (db-RDA) using the dbrda function in the vegan package to assign variation in composition of bacteria, fungi, and KEGGs to microbiome source and geographic location. We conducted three individual db-RDAs for bacteria, fungi, and KEGG composition. We used the anova.cca function in the vegan package to assess the cumulative significance of microbiome source and geographic location on community composition. We partitioned the variation in composition with respect to microbiome source and geographic location using the varpart function in the vegan package. To visualize composition of bacteria, fungi, and KEGGs among soil origin, we used principal coordinate analysis (PCoA) for ordination based on the Bray-Curtis distance matrices.
We then performed indicator species analysis with the multipatt function in the indicspecies package (Cáceres & Legendre, 2009) to identify particular bacteria, fungi, and KEGGs that are uniquely highly associated with each microbiome source. Because the FUNGuild data set contained a high amount of zero counts, we built individual zero-inflated models for each fungal guild using the glmmTMB function in the glmmTMB package (Brooks et al., 2017). We specified microbiome source as the fixed effect, count total per soil sample (i.e. site) as the random effect, zi formula as soil origin, and family as poisson. For all models, we used the Anova function in the car package (Fox et al., 2013) to calculate analysis of variance (ANOVA) tables using Type II sums of squares, with significance assessed for microbiome source using Wald X2 statistics. If the effect of microbiome source was significant, we conducted post hoc Tukey contrasts using the emmeans function in the emmeans package (Lenth et al., 2020) and the cld function in the multcomp package (Hothorn et al., 2008).
To test Hypothesis 3 that specific microbes and/or microbial functions are associated with particular Solidago phenotypes, we assessed the effect of variation in microbial indicator taxa composition on Solidago phenotypes that responded to microbiome source treatment. Since no KEGG identities were identified as indicators across the three microbiome sources, subsequent analyses were conducted only with bacterial and fungal indicator taxa. Using a db-RDA, we assigned variation in composition of bacterial and fungal indicator taxa to the three microbiome sources and geographic location. We then extracted the axes scores from the db-RDA model. For each phenotype, we built a linear model that included the two axes (CAP1, CAP2) from the db-RDA model as fixed effects. A significant relationship between db-RDA axes and plant phenotypes would indicate that differences in the community of bacterial and fungal indicator taxa associated with each microbiome source are associated with shifts in plant phenotype. To pinpoint individual bacterial and fungal indicator taxa that may be associated with particular plant phenotypes, we built linear models to test for correlations between the relative abundance of each bacterial and fungal indicator taxon and each phenotype that showed significant responses to the axes of variation from the indicator species db-RDA model.
All analyses were performed in R (R Core, 2020). Boxplot, and linear regression figures were made with the ggplot2 package (Wickham, 2016). Ordination figures were made with the phyloseq package (McMurdie & Holmes, 2013). Heatmap figures were made with the Heatplus (Ploner, 2020) and gplots (Warnes et al., 2020) packages. Individuals figures were aggregated with the patchwork package (Pedersen, 2020).