Habitat, plant height, and soil nutrients are important determinants of the Hypericum perforatum microbiome

doi:10.21203/rs.3.rs-2859556/v1

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Habitat, plant height, and soil nutrients are important determinants of the Hypericum perforatum microbiome

https://doi.org/10.21203/rs.3.rs-2859556/v1

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Aims

Saint John’s wort, Hypericum perforatum, is a medicinally and ecologically important perennial plant species that has a broad global distribution. Despite the species’ importance, little is known about the factors that structure its microbial communities and the identity of microbes that enhance plant growth and fitness. Here we aim to describe the microbial communities associated with Hypericum perforatum and elucidate factors that structure these communities.

Methods

We collected H. perforatum root samples in three adjacent habitat types: wet and dry alvars (two types of limestone barren) and fallow agricultural fields (i.e. old-fields), in Jefferson County, New York. We used high-throughput amplicon sequencing of the SSU-rRNA gene (16S, bacteria) and the internal transcribed spacer region 1 (ITS1, fungi) to characterize the root microbiome of H. perforatum. At each root sampling location, we quantified aspects of the plant phenotype and soil characteristics to evaluate habitat variables that correlate with root microbial communities.

Results

Alvars had ~ 13% higher bacterial richness compared to old-fields. In contrast, old-fields had 28% higher fungal richness than dry alvars, but similar fungal richness to wet alvars. Habitat and plant height were important predictors of microbial community composition. We identified two bacterial taxa positively associated with plant height, both belonging to the bacterial phylum Actinobacteria.

Conclusions

This work contributes to our understanding of the environmental determinants of microbial community composition. Additionally, we were able to identify bacterial taxa that are correlated with plant health and should be investigated further as indicators of soil health or plant-growth promoting rhizobacteria.

plant microbiome

16S

ITS

Hypericum perforatum

alvar

old-field

There is growing awareness that the bacteria and fungi living on and within plant roots affect adaptive phenotypes (Berendsen et al. 2012; Bakker et al. 2012; Vandenkoornhuyse et al. 2015; Haney et al. 2015; Wintermans et al. 2016). Microbes affect a range of plant functional traits (Friesen et al. 2011) that mediate a plant’s response to the environment and ultimately influence plant productivity and fitness (Rodriguez and Redman 2008; Rodriguez et al. 2008; Goh et al. 2013) Growing appreciation of the plant microbiome has reinvigorated efforts to develop microbial products that can be used in agriculture. However, advances in the field of applied microbial ecology have been hampered by a poor understanding of how biotic and abiotic factors affect microbial community composition and hence adaptive benefits for plants.

Soil macronutrients are important abiotic determinants of the soil and root microbiome. In a comprehensive examination of bacterial and fungal diversity at 25 nutrient addition sites, (Leff et al. 2015) found that nitrogen and phosphorus addition affected fungal species richness weakly but community composition strongly, with mycorrhizal fungi declining disproportionately to other fungal taxa. Similarly, bacterial species richness was only marginally impacted, but community composition shifted in response to nutrient additions.

Soil pH and depth both affect microbial communities. Bacterial richness decreases and community composition shifts in more acidic soils (Fierer and Jackson 2006; Högberg et al. 2007; Lauber et al. 2008; de Vries et al. 2012), whereas fungal diversity responds either weakly or not at all (Frostegård et al. 1993; Rousk et al. 2010; Yin et al. 2021). Soil depth is another important determinant of soil microbial community composition. Even with relatively minor changes in depth (10–20 cm) bacterial communities shift dramatically and are distinct from microbial communities sampled from soil surfaces in different biomes (Eilers et al. 2012). Likewise, fungal richness (Jumpponen et al. 2010) is strongly affected by soil depth, even within horizons. For both bacteria and fungi, diversity tends to decrease with increasing soil depths (Jumpponen et al. 2010; Ko et al. 2017).

While local soil conditions (nutrients, depth, etc.) shape soil microbial communities, further selection of microbes happens in the rhizosphere. The rhizosphere is the layer of soil directly adjacent to plant roots where plants secrete a vast array of metabolites that act as chemical attractants or repellents of microorganisms (Hartmann et al. 2008). Differences in bacterial and fungal richness and community composition near, on, or within the roots occur at the level of genotype (Schweitzer et al. 2008; Lundberg et al. 2012; Peiffer et al. 2013; Lamit et al. 2016). Genotype-specific differences in root colonization are likely mediated by plant traits that affect microbial communities, such as root architecture and exudation profiles (de Vries et al. 2012; Wehner et al. 2014). In turn, microbes that are retained in the rhizosphere and within roots are important determinants of plant fitness, stress tolerance, and nutrient acquisition (Berendsen et al. 2012). A range of important plant functional and demographic traits are affected by interactions with microbes, including germination, survival, biomass, flowering time, height, and reproductive fitness (Friesen et al. 2011; Wagner et al. 2014; Petipas et al. 2020; O’Brien et al. 2021).

Substantial work has been done to understand the root microbiome of many model/crop plants, including corn (Bouffaud et al. 2014), rice (Edwards et al. 2015), wheat (Mahoney et al. 2017), barley (Bulgarelli et al. 2015), poplar (Beckers et al. 2017), sugar cane (de Souza et al. 2016), and Arabidopsis (Bulgarelli et al. 2012; Lundberg et al. 2012). From this work we can extract some generalities about the plant root microbiome, including the importance of the soil environment, and the small but consistently reproducible effects of plant genotype on root microbiome composition.

Moving forward, microbiome sciences will benefit from exploration of the root microbiome in non-model, non-crop species under a range of environmental conditions. This area of inquiry is important for two reasons. First, expanding research efforts to new plant taxa in diverse environments will illuminate the context-dependency of the structure and function of the plant microbiome, a necessary goal given the growing interest in microbe-based technologies for agriculture and restoration (Ray et al. 2020). Second, bioprospecting for microbial taxa that enhance plant stress tolerance, productivity, and reproductive fitness in a range of plant species and habitats could lead to the discovery of plant-associated microbes that are useful for agriculture and restoration (Jack et al. 2021).

In this study, we examine how the root microbiome of St. John’s Wort (Hypericum perforatum) differs between three habitat types in northern New York. We specifically consider how soil nutrients, pH, and soil depth affect root microbial communities and how plant phenotypes are correlated with root microbial communities and specific microbial taxa. At each root sampling location, we quantified soil characteristics to evaluate habitat variables that correlate with root microbial communities. We also measured plant size and fecundity for each focal plant to identify microbes that are correlated with plant performance.

Study system

Hypericum perforatum, known commonly as Saint John’s Wort, is a medicinally important plant species that is used throughout the world to treat mild to moderate depression (Linde et al. 1996). Hypericum perforatum occurs natively in Africa, Asia, and parts of Europe and was introduced to North America in the late 1700s (Muhlenberg 1793). It is a perennial herb that occurs in a wide range of open, well-drained habitats. Saint John’s Wort has a diversity of reproductive strategies including clonal growth and outcrossing. However, pseudogamous apomixis is the most common strategy with > 90% of seeds produced apomictically (Maron et al. 2007).

We characterized the bacterial and fungal diversity present in roots of H. perforatum growing in three habitats, two types of limestone barren called alvars, and old-fields around Lake Ontario in Jefferson County, NY. Old-fields are a transitional habitat, commonly found in the northeastern U.S.A, that occurs when agricultural fields are left fallow. Alvars are a globally rare habitat that occurs throughout Northern Europe and around the Great Lakes in North America. They are characterized by limestone/dolostone bedrock that is covered by a thin and discontinuous layer of soil (Reschke et al. 1999). Three distinct alvar types occur in New York State: shrublands, grasslands, and pavement grasslands (Edinger et al. 2014). For our characterization of the H. perforatom microbiome we focus on alvar pavements and alvar grasslands. We chose alvar pavements, hereafter referred to as “dry alvar”, because they provide an extreme adaptive challenge to plants and are the most different from old-fields. We chose alvar grasslands, hereafter referred to as “wet alvar”, because they represent a mid-point between alvars and old-fields. Wet alvars have intermediate soil depths and pH (Petipas et al. 2020), and similar plant cover to old-fields (personal observation R.H. Petipas).

Site selection

We selected three geographic locations (hereafter called “sites”) where we could identify a dry alvar, a wet alvar, and an old-field habitat in close proximity for root sampling (Table S1). Alvar habitats were chosen based on the opening size (~ 0.5 hectare) and the presence of H. perforatum, and then alvar habitats were paired with a nearby old-field. Old-field sampling areas were matched to alvar sampling areas such that at each site pair we sampled roots at the same spatial scale. At two of the sampling sites (Chaumont and 3-mile), the sampling area ~ 8000 m², while in the third smaller location (Lim), the sampling area was ~ 3500 m².

Root sampling and plant phenotyping

In August of 2014, at each site, we collected rhizosphere samples from ten H. perforatum plants per habitat (N = 30 per site). The three habitat types were sampled at one site per day, and whether the old-field or alvar habitats were collected first in the day was varied. We identified focal plants by laying out transects in the four cardinal directions that spanned the natural openings (~ 50 m transects). Because H. perforatum is rhizomatous, we selected plants spaced by a minimum of two meters to avoid sampling the same individual. At each focal plant we measured the soil depth, recorded plant height, number of buds, and number of flowers. We also collected a rhizosphere sample consisting of a standardized root volume that contained surrounding soil and roots. To collect a standardized amount of soil and roots we used a 1.5 cm chisel to a depth of 10 cm, resulting in ~ 700 ml of soil, per collection point. The soil and roots were sealed in Whirlpak bags and placed on ice while in the field, and then transferred to a -20°C freezer until DNA extraction and sequencing. We removed 150–200 ml of soil from the rhizophere samples for soil nutrient analyses. These were placed in paper bags, dried at 40°C for four days, and then sieved. We measured total nitrogen by dry combustion at 1350°C. Soil pH was measured in distilled water. Organic matter was quantified by loss on ignition (LOI) at 375°C. Nutrients were extracted in ammonium acetate buffer (pH 4.8, modified Morgan). Phosphorus was determined colorimetrically by an Ion Analyzer (Lachat Instruments, Milwaukee, WI, USA). All other nutrients were measured by inductively coupled plasma-optical emission spectrometry (ICP-OES, Thermo Fisher Scientific, Waltham, MA, USA). Nutrient values are presented in parts per million (mg/kg) per soil dry weight. Soil analyses were performed at the Analytical Lab and Maine Soil Testing Service at the University of Maine.

DNA extraction and sequencing

To describe the fungal and bacterial communities we removed the roots from the soil samples that were visibly attached to the focal Hypericum plant. Root samples were washed thoroughly in deionized water to remove visible soil matter. Roots were then finely ground (< 3 mm) using liquid nitrogen and a mortar and pestle. We then weighed out ~ 0.05 g of root material for DNA extraction. Extractions were done with the MoBio PowerPlant DNA extraction kit (Qiagen, Germantown, MD, USA). We modified the extraction protocol to include a bead beating step to ensure that root tissue was disrupted sufficiently and then extractions proceeded according to manufacturer’s directions. DNA yields were quantified using Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies, Grand Island, NY, USA). Bacterial SSU-rRNA were amplified with primers 515F and 927R (V4-V5; Kozich et al. 2013) and fungal internal transcribed spacer 1 (ITS1) gene regions were amplified with primers nBITS2 and 58A2R (Koechli et al. 2019). Amplicons were prepared in triplicate reactions containing 5 ng of DNA template, 12.5 µl of Q5 Hot Start High-Fidelity 2X Mastermix (New England BioLabs, Ipswich, MA, USA), 1.25 µl bovine serum albumin, 0.6 µl of Picogreen reagent, and 2.5 µl of combined forward and reverse barcoded primers in a total volume of 25 µl. Thermocycler conditions for bacterial PCR amplification were 95°C for 2 minutes, followed by 30 cycles of 95°C for 20 seconds, 55°C for 15 seconds, 72°C for 10 seconds, with a final extension of 72°C for 5 minutes. The thermocycler conditions for fungal ITS1 amplification were 95°C for 30 sec, followed by 30 cycles of 95°C for 5 seconds, 50°C for 20 seconds, 72°C for 10 seconds, with a final extension of 72°C for 2 minutes. We then pooled triplicate reactions and standardized using the SequalPrep Normalization Plate Kit (Life Technologies, Grand Island, NY, USA). Finally, standardized reactions were pooled and then purified using the Wizard SV Gel and PCR Clean-up System (Promega, Madison, WI, USA). We submitted pooled libraries for 2 x 250 bp (bacteria) and 2 x 300 bp (fungi) paired-end sequencing using the Illumina MiSeq platform at the Cornell Biotechnology Resource Center Genomics Facility (Ithaca, NY, USA).

Bioinformatic pipeline

A total of 5,624,189 and 13,450,730 reads for bacterial 16S and fungal ITS1 amplicons, respectively, were processed using a custom bioinformatic pipeline. We merged forward and reverse reads using PEAR (v.0.9.2, minimum overlap 50 bp, assembly probability 0.001, and PHRED score cutoff of 30, Zhang et al. 2014), removed sequencing primers and adapters using cutadapt v1.14, and demultiplexed reads into individual samples with deML (Martin 2011; Renaud et al. 2015). We defined operational taxonomic units (OTUs) using a 3% dissimilarity cutoff with VSEARCH v2.5.2, an OTU clustering algorithm that simultaneously removes singletons and chimeric sequences. Taxonomic affiliations of 16S and ITS1 sequences were performed with the SINTAX algorithm within USEARCH v9.2.64 (sintax cutoff 0.8) using the GreenGenes v13.8 or UNITE v7.2 sequence databases, respectively (DeSantis et al. 2006; Edgar 2010, 2016; Kõljalg et al. 2013). Any 16S OTUs classified as chloroplast or mitochondrial in origin and any ITS1 amplicons not classified within the kingdom Fungi were removed from the data set. In this study, we were interested in the taxa closely associated with Hypericum perforatum roots and thus we chose to sequence the root microbial communities, however this led to the contamination of our data set with many non-fungal ITS sequences. The number of dereplicated ITS read counts was ~ 5 million, after removing plant ITS sequences we had 82,000 sequences remaining. Because of this loss of data and sparse remaining read counts we took a conservative approach and mostly analyzed fungi using presence absence metrics. Despite low read counts we still detect robust patterns in our data and our fungal species richness values are consistent with previous studies looking at within root diversity (Reininger et al. 2015). We rarefied the bacterial data set to 2,000 reads and fungal data set to 100 reads, this allowed us to preserve the maximum amount of data while smoothing heterogenous read counts (Table S2). Rarefying can be a highly effective normalization technique (McKnight et al. 2019), especially in studies with low sequence counts (Weiss et al. 2017). In the end, we identified 5,158 bacterial OTUs in 68 samples and 704 fungal OTUs in 58 samples. Sequences and associated metadata will be deposited into NCBI sequence database.

Data analysis

All statistical analyses were performed in R (Version 4.2.2, R Development Core Team, 2022). In the text, we report statistics associated with the main effects (F and P-values), when main effects were significant, we estimated contrasts to better understand the origin of those differences with the emmeans package (Lenth 2022).

Soil geochemistry, plant phenotype, and alpha diversity

We used analysis of variance to understand how each soil parameter, including pH, depth, and nutrients, plant phenotype, and microbial species diversity (species richness and Shannon’s diversity) varied between habitats. All models included main effect of habitat (dry alvar, wet alvar, and old-field) and a fixed effect of site to account for geographic variation not attributable to habitat. We used graphical approaches to verify homogeneity of variances and normality of residuals (Zuur et al. 2009), and as necessary we performed data transformation, so data better fit the assumptions of linear regression. Where appropriate we used Bonferroni corrections to adjust for multiple comparisons. Alpha diversity (richness and Shannon diversity) was estimated using the estimate richness function in the vegan package (Oksanen et al. 2022).

Community composition

To understand how community composition varied between the three habitats we calculated Bray-Curtis dissimilarity for bacteria and Jaccard distances for fungi to run constrained ordinations to visualize the amount of variation in microbial communities explained by habitat and aspects of plant phenotype and soil characteristics (depth and soil nutrients). Constrained ordinations were run using the capscale function from the vegan package (for comparison, unconstrained ordinations are included in Fig. S1a &b). To statistically test if habitat, plant phenotype, and environmental variables affected microbial community composition we performed a permutation analysis of variance (PERMANOVA) using the ‘adonis2’ function (McArdle and Anderson 2001).

Soil geochemical data were only collected on a subset of samples (5 samples per habitat, per site, N = 15 per habitat) so we ran two separate PERMANOVA models. The first model included the entire data set and tested how habitat, site, soil depth and plant phenotype variables were correlated with microbial community composition. The second model included a subset of the data and tested how habitat, site, and soil geochemistry were correlated with microbial community composition. To deal with multicollinearity of soil micronutrient variables we used principal component analysis to distill the micronutrient data into a small set of independent variables (principal components) for use in PERMANOVA models. Additionally, because of the number of soil geochemical variables, we opted for a model selection process, starting with a maximal model (that contained all eight soil geochemical variables plus habitat and site), and then sequentially dropped terms until we arrived at the minimal model that best explained the data. All community level analyses were run using the vegan package (Oksanen et al. 2022).

Indicator species analysis

We used indicator species analysis to determine bacterial and fungal indicator taxa. Indicator species analysis uses an organism’s relative abundance and occurrence to estimate the strength of association with a given site type or habitat (Dufrêne and Legendre 1997). Analysis was done using the multipatt function in the indicspecies package with 999 permutations (Cáceres and Legendre 2009). We only report significant habitat-specific taxa at a stringent cut-off of P = 0.001. To determine the putative identity of fungal OTUs, we queried our fungal ITS1 sequence data using the blastn algorithm (Altschul et al. 1997) against species hypotheses (SH) within the UNITE fungal database (Kõljalg et al. 2013). In each case, we calculated which SH occurred most frequently in the top ten sequences that produced significant alignments with our fungal ITS1 sequence data.

Identifying microbial taxa correlated plant phenotype

To identify taxa that correlate with aspects of plant phenotype, we used a method described in (Wagner et al. 2014) we determined which ordination axes were correlated with plant phenotype using a linear model that included sample scores from the top three axes (explaining ~ 50% of the variation) as fixed effects and plant phenotypic trait as the response variable (e.g. plant height ~ PCA1 + PCA2 + PCA3). To confirm that the correlation between the trait of interest and the principal component value was independent of habitat effects, we constructed a second linear model that tested the interactive effect of habitat and sample scores on the plant trait of interest (e.g., plant height ~ PCA2 * Habitat + Site). If we found sample scores and habitat were independently correlated with plant height (no interaction with habitat and PC sample scores) we then selected the fifty OTUs with the highest loadings on the focal axis and ran linear models to understand how OTU abundance was correlated with plant height (e.g., plant height ~ OTU5 * Habitat + Site). We only report height OTU correlations that were significant after correcting for multiple comparisons.

Soil geochemistry, plant phenotype, and alpha diversity

Overall soil nutrient conditions differed between old-fields and alvars (Table 1; MANOVA: F_{(30, 54)}= 6.56, P < 0.0001). Alvars had almost double total nitrogen (F_(2,40)= 5.41, P = 0.008), 8-9% higher pH (F₍_2,40)= 18.29, P < 0.0001), and 50-60% less magnesium than old-field soils (F_(2,40)= 13.57, P < 0.0001). The wet alvar soils had double the organic matter (LOI) (F_(2,40)= 5.70, P = 0.007), ~80 % more calcium (F_(2,40)= 5.00, P = 0.011), and more than double the amount of boron (F_(2,40)= 6.10, P = 0.005) than old-field soils. Wet alvar soils had over 2 mg/kg more of phosphorus than both dry alvar soils and old-field soils (F_(2,40)= 4.42, P = 0.018). Old-field soils had more copper (F_(2,40)= 4.04, P = 0.025) than both dry and wet alvar soils. Additionally, soil depth varied by habitat with dry alvars having the most shallow soils and old-field having the deepest soils (F_(2,81.24)= 74.30, P < 0.0001). Alvar plants were ~50% shorter than old field plants (F_(2,85)= 48.16, P < 0.0001) and produced fewer buds (F_(2,85)= 9.38, P = 0.0002) and flowers (F_(2,85)= 5.00, P = 0.009), especially in the wet alvar habitat. A summary of soil geochemistry and phenotype results can be found in Table 1. Both alvar habitats had higher observed bacterial OTUs (~13%) than old-fields (F_(2,63)= 5.88, P = 0.005; Fig. 1a). Fungal OTU richness was ~28% higher in the old-field compared to dry alvar habitats but similar to the wet alvar habitat (F_(2.52)= 3.35, P = 0.04; Fig. 1b). Old-field habitats also had lower bacterial Shannon diversity (F_{(2, 63)}= 8.77, P = 0.0004; Fig. 1c). However, fungal Shannon diversity did not significantly differ (F_(2,52)= 2.70, P = 0.08; Fig. 1d) between the habitat types.

Microbial community analysis

Habitat type drove differences in bacterial (PERMANOVA: F_(2,63)= 1.57, R2 = 0.04, P = 0.007) and fungal (PERMANOVA: F_(2,52)= 1.25, R2 = 0.05, P = 0.003) community composition (Fig. 2a &b, Table S3a&b). Plant height was also associated with bacterial (PERMANOVA: F_(1,63)= 1.80, R2 = 0.03, P = 0.01) and fungal (PERMANOVA: F_(1,52)= 1.35, R2 = 0.03, P=0.005) community composition (Fig. 2a &b, Table S3a&b). Soil depth, bud number, and flower number were not correlated with bacterial or fungal community composition (Table S3a&b). We analyzed a subset of our samples that had associated soil geochemical data to ascertain associations between principal component axes describing variation in soil attributes and microbial community composition. Using model selection, we found the minimal model for bacterial community composition included micronutrients (PERMANOVA: PC3, F_(1,34)= 1.58, R2 =0.04, P = 0.026), soil organic matter (PERMANOVA: LOI, F_(1,34)= 2.16, R2 =0.05, P = 0.001), pH (PERMANOVA: F_(1,34)= 2.76, R2 = 0.07, P = 0.001), and potassium (PERMANOVA: F_(1,34)= 1.57, R2=0.04, P = 0.031; Fig. 3a, Table S3c). The micronutrient with the heaviest loading on principal component 3 was magnesium. The minimal model for fungal community composition included micronutrients (PERMANOVA:PC1, F_(1,32)= 1.31, R2=0.04, P = 0.002 and PC2, F_(1,32)= 1.22, R2=0.04, P = 0.018) and total nitrogen (PERMANOVA: F_(1,32)= 1.48, R2=0.04, P = 0.001; Fig. 3b, Table S3d). The micronutrient with the heaviest loadings on principal component 1 was calcium and principal component 2 was iron (Table S4).

Indicator species

Thirty-six bacterial taxa were differentially abundant between the three habitats. Of these six were found on the dry alvars and six were found in the wet alvars. The remaining 24 were unique to old-fields (Table S5). Four fungal taxa were differentially abundant between the three habitats. Fungal OTU 20 was more abundant in old-fields and aligned to a potential plant pathogen (Cadophora malorum). Three taxa were more reliably found in the wet alvar (OTU 223, 477, and 613; Fig. 4). Fungal OTU 223 aligned to an ascomycete in the genus Metacordyceps chlamydosporia, an entomopathogenic fungi. OTU 613 is in the order GS05, in the phylum Rozellomycota a widely distributed soil fungus (or a fungus-like organism). OTU 477 was potentially also in the Rozellomycota but the designation was less clear (Table S6).

Identifying microbial taxa correlated with plant phenotype

We didn’t find any correlations between plant phenotypes and fungal PCA axes, independent of habitat, indicating that environmental variables are driving both plant height and fungal community composition. Similarly, we didn’t find any correlations between bacterial PCA axes and the number of buds or flowers produced by plants. We did however find a significant correlation between plant height and bacterial PCA axis 2, regardless of habitat specific effects on height (Table S7). To understand which bacterial taxa were correlated with plant height across habitats we chose fifty bacterial taxa with the highest loadings on PCA 2 and individually tested those for correlations with plant height. We found that OTU 5 (F_(1,66)= 18.16, R²=0.20, P<0.0001) and OTU 57 (F_(1,66)= 19.59, R²=0.22, P<0.0001) were positively correlated with plant height (Fig. 5a&b), after correcting for multiple comparisons. OTU 5 is in the MB-A2-108 group of the phylum Actinobacteriota and OTU 57 is in Ilumatobacteraceae in the same phylum.

It is important to understand the factors that shape the microbiome of non-model organisms so we can better understand what factors influence the structure and function of the microbiome across the plant tree of life. Additionally, expanding our understanding of the plant microbiome to include more plant species, in more places, increases the probability that we find beneficial plant-associated microbes that can eventually be used in applied settings. Here we offer evidence that habitat, soil edaphic conditions, and plant phenotype have important effects on microbial diversity and community composition of a medicinally important plant species, Hypericum perforatum.

Bacterial species richness is higher on alvars but fungal richness is higher in old-fields

We found that bacterial richness and Shannon diversity were higher on alvars compared with old- fields, whereas fungal richness was higher on old-field compared to dry alvars. Higher diversity on alvars could be driven by nutrient differences between the habitat types. In particular alvars have higher pH than old-fields, which often correlates with higher bacterial alpha diversity (Shen et al. 2019). Although unmeasured in this study, higher fungal diversity in old-fields compared with dry alvars might be driven by differences in plant diversity and/or cover. Plant diversity and cover are positively correlated with fungal diversity in some grassland ecosystems (Yang et al. 2017). Additionally, water availability can be an important determinant of fungal richness (Canini et al. 2019), which likely differed between the habitats sampled.

Plant height is an important determinant of microbial community composition

We found that bacterial and fungal community composition was significantly different between alvar compared with old-field habitats. Plant height explained additional variation beyond habitat effects for both bacteria and fungi. Larger plants often have correspondingly large root systems (Niklas and Enquist 2002; Liu et al. 2021), which could lead to more surface area for microbial colonization in turn leading to more turnover in microbial community composition (Horner-Devine et al. 2004). Conversely, microbes can affect many aspects of plant phenotype and may drive variation in plant size, for example some taxa produce plant height enhancing phytohormones or release nutrients in rhizosphere thereby increasing plant size (Ortíz-Castro et al. 2009; Jacoby et al. 2017).

Nutrient effects on bacterial and fungal community composition were inconsistent. Bacterial community composition was influenced by pH, organic matter, potassium, and micronutrients. Fungal community composition was correlated with total nitrogen and micronutrients. The effects of soil pH on bacterial community composition are well documented. It has been hypothesized that the main determinant of bacterial community composition is soil pH levels rather than factors that we typically ascribe to driving patterns of biogeography, such as temperature, plant community composition, or nutrient availability (Fierer and Jackson 2006, Lauber et al. 2008). However, it is important to note the range of pH tested in many of these studies is greater (pH 4–8) than the range experienced by our plants (pH 5.6–7.2).

Soil organic matter, measured here as loss-on-ignition, and bacterial community composition are tightly interwoven. Microbial biomass is the living component of soil organic matter, while at the same time changing levels of soil organic matter through decomposition. Given the interrelated nature of soil organic matter and microbes, shifts in community composition are often common in observational (Yavitt et al. 2021) and empirical studies (Peacock et al. 2001; Whitman et al. 2016). For example, in systems where tillage disrupts organic matter soil bacterial communities are often demonstrably different in till vs. no-till systems (Tyler 2019; Srour et al. 2020).

Potassium has been found to affect bacterial community composition across a range of studies (Yu et al. 2021; Lu et al. 2022; Rodríguez et al. 2022). Yang and coauthors (2020) found that nitrogen and potassium were the strongest predictors of bacterial community composition out of eight nutrients surveyed. Like soil organic matter, potassium levels in the soil are not only determinants of microbial community composition but microbes also solubilize potassium in soils through the release of organic acids (Olaniyan et al. 2022).

The effects of total nitrogen on fungal community composition are similarly well documented (Cline et al. 2018; Xiao et al. 2020; Moore et al. 2021; Qi et al. 2021; He et al. 2021). Total nitrogen was the major driver in fungal community composition along a desertification gradient, accounting for 43% of the variation in community composition (Qi et al. 2021). Fungal communities are also heavily involved in nitrogen cycling and thus can change nitrogen availability. For example, the Ascomycota, a dominant fungal phylum in our dataset, are often involved in decomposition. They were commonly observed in alvar samples, especially wet alvars where nitrogen levels are also significantly higher than old-fields.

Micronutrients are an underappreciated determinant of microbial community composition (Peng et al. 2022). Theoretically micronutrients can have direct effects on microbes via the crucial role they play in metabolism, or indirect effects via their effects on soil carbon (Osburn et al. 2023). A limitation to our ordination approach is an inability to identify exactly which micronutrients are driving the correlation. Principle component axis 1 explained 48% of the variation in our micronutrient data, and a suite of micronutrients had heavy loadings on this axis (the top five highest loading scores for PC1 were calcium, sulfur, boron, manganese, and zinc). Principle component 2 and 3 explained less of the variation (19 and 13% respectively), and micronutrients strongly loading on these axes included iron, copper, magnesium, aluminum, sodium, and zinc. A general caveat of our work is that many soil characteristics are confounded with each other and with habitat, and therefore it is hard to disentangle integrated habitat effects with the effects of a single soil characteristic.

In general, our predictor variables explained little of the variation in fungal community composition (17% in full ordination, 20% in subsetted ordination). Unfortunately, we had highly uneven and small library sizes for our fungal data set. To sample microbes closely associated with plants, we opted to sequence root associated microbes. This led to contamination in our data set by plant ITS sequences, resulting in suboptimal numbers of fungal reads (hence our conservative approach using presence absence data for community level analysis). Alternatively, we may not have measured variables that are relevant to fungal community composition, such as plant cover/diversity or water availability.

Old-fields are potentially enriched in plant pathogens

While we found many taxa in common in our alvar and old-field Hypericum populations we also found habitat-specific differences. For example, we found one fungal OTU that was more abundant in old-fields compared with alvars. Interestingly, the putative species assignment for this species was to a pathogenic fungal taxon (OTU 20), indicating that these former agricultural fields might be enriched in pathogens. Pathogen enrichment from former agricultural use can last many decades; when a chronosequence of former agricultural fields was surveyed in the Netherlands pathogen enrichment only declined in the fields that had been abandoned for over thirty years (Hannula et al. 2017). Although we do not have exact dates since abandonment for our fields it was likely within the last thirty years. For example, one old-field habitat (Lim) was being mowed as recently as ~ five years ago.

Actinobacterial OTUs correlate with plant height

We identified two bacterial OTUs within the phylum Actinobacteria that occur in both habitats and were positively correlated with plant height. Actinobacteria are a group of Gram-positive, often filamentous, bacteria and are one of the largest designations within the domain (Stackebrandt and Schumann 2017). There is high potential within this group for plant growth promotion. For example, Actinobacteria can solubilize phosphate, enhance iron acquisition, and produce myriad phytohormones (Sathya et al. 2017). The complexity of the bacterial and fungal community demands we develop hypotheses about taxa that could potentially positively or negatively affect plant growth. Here we have described two bacterial OTUs that correlated strongly with an important plant phenotype, height. Further work should focus on culturing and rigorously testing plant growth promoting effects of these taxa. Both of these taxa we were also recently identified as potential bioindicators of soil health (Wilhelm et al. 2022, 2023). In this case, rather than directly influencing plant height they may just be indicating soil microsite variation that is favorable to plant growth. Another caveat of this work is the correlative nature and although we are speculating that these OTUs could enhance plant growth it is also possible that these OTUs affiliate with larger plants, and only further empirical research will definitively test the growth-promoting capacity of these species.

Major advances have occurred in the field of plant microbiome research over the last decade but we are still in the very early stages of understanding the factors that affect the composition of the plant microbiome. A promising avenue of research is exploring observational data sets to identify taxa that correlate with important aspects of plant phenotype so testing putative growth-promoting taxa can occur in a targeted manner. Here we have provided important information about how soil edaphic conditions affect bacterial and fungal communities and we have identified two OTUs that could be tested in future work for plant growth-promoting effects.

Acknowledgements

We would like to thank members of the Geber lab for help in the field and many thoughtful comments on this project. We would also like to thank the research technicians and undergraduates that helped throughout the project. We thank the Nature Conservancy for granting us access to the alvar sites and old-fields in Jefferson County, NY and allowing us to collect plant and soil material. Finally, we would like to thank Drs. Jenny Kao-Kniffen and Anurag Agrawal for offering advice on this project and thoughtful comments on this manuscript. Funding from this project came from various sources including: Lewis and Clark Fund for Exploration and Field Research, American Society of Naturalists, Society for the Study of Evolution, and the Botanical Society of America.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Author contributions

Renee Petipas, Monica A. Geber, and Daniel H. Buckley conceived of and designed this study. Field work was done by Renee Petipas. Lab work was done by Renee Petipas, Steven A. Higgins, Chantal Koechli, Spencer J. Debenport. Analysis was performed by Renee H. Petipas, Steven A. Higgins and Chandra N. Jack. The first draft of the manuscript was written by Renee H. Petipas and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Altschul SF, Madden TL, Schäffer AA, et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402
Bakker MG, Manter DK, Sheflin AM, et al (2012) Harnessing the rhizosphere microbiome through plant breeding and agricultural management. Plant Soil 360:1–13. https://doi.org/10.1007/s11104-012-1361-x
Beckers B, Op De Beeck M, Weyens N, et al (2017) Structural variability and niche differentiation in the rhizosphere and endosphere bacterial microbiome of field-grown poplar trees. Microbiome 5:1-17. https://doi.org/10.1186/s40168-017-0241-2
Berendsen RL, Pieterse CMJ, Bakker PAHM (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17:478–486. https://doi.org/10.1016/j.tplants.2012.04.001
Bouffaud M-L, Poirier M-A, Muller D, Moënne-Loccoz Y (2014) Root microbiome relates to plant host evolution in maize and other Poaceae. Environ Microbiol 16:2804–2814. https://doi.org/10.1111/1462-2920.12442
Bulgarelli D, Garrido-Oter R, Münch PC, et al (2015) Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17:392–403. https://doi.org/10.1016/j.chom.2015.01.011
Bulgarelli D, Rott M, Schlaeppi K, et al (2012) Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488:91–95. https://doi.org/10.1038/nature11336
Cáceres MD, Legendre P (2009) Associations between species and groups of sites: indices and statistical inference. Ecology 90:3566–3574. https://doi.org/10.1890/08-1823.1
Canini F, Zucconi L, Pacelli C, et al (2019) Vegetation, pH, and water content as main factors for shaping fungal richness, community composition and functional guilds distribution in soils of Western Greenland. Front Microbiol 10:1-16
Cline LC, Huggins JA, Hobbie SE, Kennedy PG (2018) Organic nitrogen addition suppresses fungal richness and alters community composition in temperate forest soils. Soil Biol Biochem 125:222–230. https://doi.org/10.1016/j.soilbio.2018.07.008
de Souza RSC, Okura VK, Armanhi JSL, et al (2016) Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci Rep 6:1-15. https://doi.org/10.1038/srep28774
de Vries FT, Manning P, Tallowin JRB, et al (2012) Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol Lett 15:1230–1239. https://doi.org/10.1111/j.1461-0248.2012.01844.x
DeSantis TZ, Hugenholtz P, Larsen N, et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072. https://doi.org/10.1128/AEM.03006-05
Dufrêne M, Legendre P (1997) Species assemblages and indicator species:the need for a flexible asymmetrical approach. Ecol Monogr 67:345–366. https://doi.org/10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2
Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinforma Oxf Engl 26:2460–2461. https://doi.org/10.1093/bioinformatics/btq461
Edgar RC (2016) SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. Accessed from bioRxiv January 24, 2023. https://www.biorxiv.org/content/10.1101/074161v1
Edinger GJ, Evans, D.J., Gebauer, S., et al (2014) Ecological communities of New York state: A revised and expanded edition of Carol Reschke’s ecological communities of New York state. New York State Department of Environmental Conservation, Albany, New York, USA
Edwards J, Johnson C, Santos-Medellín C, et al (2015) Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci 112:E911–E920. https://doi.org/10.1073/pnas.1414592112
Eilers KG, Debenport S, Anderson S, Fierer N (2012) Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem 50:58–65. https://doi.org/10.1016/j.soilbio.2012.03.011
Fierer N, Jackson RB (2006) The diversity and biogeography of soil bacterial communities. Proc Natl Acad Sci 103:626–631. https://doi.org/10.1073/pnas.0507535103
Friesen ML, Porter SS, Stark SC, et al (2011) Microbially mediated plant functional traits. Annu Rev Ecol Evol Syst 42:23–46. https://doi.org/10.1146/annurev-ecolsys-102710-145039
Frostegård Å, Bååth E, Tunlio A (1993) Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biol Biochem 25:723–730. https://doi.org/10.1016/0038-0717(93)90113-P
Goh C-H, Veliz Vallejos DF, Nicotra AB, Mathesius U (2013) The impact of beneficial plant-associated microbes on plant phenotypic plasticity. J Chem Ecol 39:826–839. https://doi.org/10.1007/s10886-013-0326-8
Haney CH, Samuel BS, Bush J, Ausubel FM (2015) Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat Plants 1:1–9. https://doi.org/10.1038/nplants.2015.51
Hannula SE, Morriën E, de Hollander M, et al (2017) Shifts in rhizosphere fungal community during secondary succession following abandonment from agriculture. ISME J 11:2294–2304. https://doi.org/10.1038/ismej.2017.90
Hartmann A, Rothballer M, Schmid M (2008) Lorenz Hiltner, a pioneer in rhizosphere microbial ecology and soil bacteriology research. Plant Soil 312:7–14. https://doi.org/10.1007/s11104-007-9514-z
He J, Jiao S, Tan X, et al (2021) Adaptation of soil fungal community structure and assembly to long- versus short-term nitrogen addition in a tropical forest. Front Microbiol 12:1-14. https://doi.org/10.3389/fmicb.2021.689674
Högberg MN, Högberg P, Myrold DD (2007) Is microbial community composition in boreal forest soils determined by pH, C-to-N ratio, the trees, or all three? Oecologia 150:590–601
Horner-Devine MC, Lage M, Hughes JB, Bohannan BJM (2004) A taxa–area relationship for bacteria. Nature 432:750–753. https://doi.org/10.1038/nature03073
Jack CN, Petipas RH, Cheeke TE, et al (2021) Microbial inoculants: Silver bullet or microbial Jurassic Park? Trends Microbiol 29:299–308. https://doi.org/10.1016/j.tim.2020.11.006
Jacoby R, Peukert M, Succurro A, et al (2017) The role of soil microorganisms in plant mineral nutrition-Current knowledge and future directions. Front Plant Sci 8:1-19. https://doi.org/10.3389/fpls.2017.01617
Jumpponen A, Jones KL, Blair J (2010) Vertical distribution of fungal communities in tallgrass prairie soil. Mycologia 102:1027–1041. https://doi.org/10.3852/09-316
Ko D, Yoo G, Yun S-T, et al (2017) Bacterial and fungal community composition across the soil depth profiles in a fallow field. J Ecol Environ 41:1-10. https://doi.org/10.1186/s41610-017-0053-0
Koechli C, Campbell AN, Pepe-Ranney C, Buckley DH (2019) Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing. Soil Biol Biochem 130:150–158. https://doi.org/10.1016/j.soilbio.2018.12.013
Kõljalg U, Nilsson RH, Abarenkov K, et al (2013) Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol 22:5271–5277. https://doi.org/10.1111/mec.12481
Kozich JJ, Westcott SL, Baxter NT, et al (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 79:5112–5120. https://doi.org/10.1128/AEM.01043-13
Lamit LJ, Holeski LM, Flores-Rentería L, et al (2016) Tree genotype influences ectomycorrhizal fungal community structure: Ecological and evolutionary implications. Fungal Ecol 24:124–134. https://doi.org/10.1016/j.funeco.2016.05.013
Lauber CL, Strickland MS, Bradford MA, Fierer N (2008) The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol Biochem 40:2407–2415. https://doi.org/10.1016/j.soilbio.2008.05.021
Leff JW, Jones SE, Prober SM, et al (2015) Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci 112:10967–10972. https://doi.org/10.1073/pnas.1508382112
Lenth R (2022) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.8.1-1. https://CRAN.R-project.org/package=emmeans
Linde K, Ramirez G, Mulrow CD, et al (1996) St John’s wort for depression--an overview and meta-analysis of randomised clinical trials. BMJ 313:253–258. https://doi.org/10.1136/bmj.313.7052.253
Liu Y, Xu M, Li G, et al (2021) Changes of aboveground and belowground biomass allocation in four dominant grassland species across a precipitation gradient. Front Plant Sci 12:1-13. https://doi.org/10.3389/fpls.2021.650802
Lu Y, Cong P, Kuang S, et al (2022) Long-term excessive application of K₂SO₄ fertilizer alters bacterial community and functional pathway of tobacco-planting soil. Front Plant Sci 13:1-17. https://www.frontiersin.org/articles/10.3389/fpls.2022.1005303
Lundberg DS, Lebeis SL, Paredes SH, et al (2012) Defining the core Arabidopsis thaliana root microbiome. Nature 488:86–90. https://doi.org/10.1038/nature11237
Mahoney AK, Yin C, Hulbert SH (2017) Community structure, species variation, and potential functions of rhizosphere-associated bacteria of different winter wheat (Triticum aestivum) cultivars. Front Plant Sci 8:1-14
Maron JL, Elmendorf SC, Vilà M (2007) Contrasting plant physiological adaptation to climate in the native and introduced range of Hypericum perforatum. Evolution 61:1912–1924. https://doi.org/10.1111/j.1558-5646.2007.00153.x
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17:10–12. https://doi.org/10.14806/ej.17.1.200
McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82:290–297. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2
McKnight DT, Huerlimann R, Bower DS, et al (2019) Methods for normalizing microbiome data: An ecological perspective. Methods Ecol Evol 10:389–400. https://doi.org/10.1111/2041-210X.13115
Moore JAM, Anthony MA, Pec GJ, et al (2021) Fungal community structure and function shifts with atmospheric nitrogen deposition. Glob Change Biol 27:1349–1364. https://doi.org/10.1111/gcb.15444
Muhlenberg H (1793) Index Florae Lancastriensis, auctore Henrico Muhlenberg, D. D. Trans Am Philos Soc 3:157–184. https://doi.org/10.2307/1004866
Niklas KJ, Enquist BJ (2002) On the vegetative biomass partitioning of seed plant leaves, stems, and roots. Am Nat 159:482–497. https://doi.org/10.1086/339459
O’Brien AM, Ginnan NA, Rebolleda-Gómez M, Wagner MR (2021) Microbial effects on plant phenology and fitness. Am J Bot 108:1824–1837. https://doi.org/10.1002/ajb2.1743
Oksanen, J., Simpson, G.L., Blanchet, F.G., et al (2022) Vegan: Community Ecology Package. R package version 2.3-3. https://cran.r-project.org/web/packages/vegan/index.html
Olaniyan FT, Alori ET, Adekiya AO, et al (2022) The use of soil microbial potassium solubilizers in potassium nutrient availability in soil and its dynamics. Ann Microbiol 72:1-12. https://doi.org/10.1186/s13213-022-01701-8
Ortíz-Castro R, Contreras-Cornejo HA, Macías-Rodríguez L, López-Bucio J (2009) The role of microbial signals in plant growth and development. Plant Signal Behav 4:701–712
Osburn ED, Hoch PJ, Prather CM, Strickland MS (2023) Effects of micronutrient fertilization on soil carbon pools and microbial community functioning. Appl Soil Ecol 181:1-7. https://doi.org/10.1016/j.apsoil.2022.104664
Peacock AD, Mullen MD, Ringelberg DB, et al (2001) Soil microbial community responses to dairy manure or ammonium nitrate applications. Soil Biol Biochem 33:1011–1019. https://doi.org/10.1016/S0038-0717(01)00004-9
Peiffer JA, Spor A, Koren O, et al (2013) Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc Natl Acad Sci 110:6548–6553. https://doi.org/10.1073/pnas.1302837110
Peng Z, Liang C, Gao M, et al (2022) The neglected role of micronutrients in predicting soil microbial structure. Npj Biofilms Microbiomes 8:1–10. https://doi.org/10.1038/s41522-022-00363-3
Petipas RH, Wruck AC, Geber MA (2020) Microbe‐mediated local adaptation to limestone barrens is context dependent. Ecology 101:1-12. https://doi.org/10.1002/ecy.3092
Qi D, Wieneke X, Xue P, et al (2021) Total nitrogen is the main soil property associated with soil fungal community in karst rocky desertification regions in southwest China. Sci Rep 11:1-12. https://doi.org/10.1038/s41598-021-89448-1
R Core Team. (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/
Ray P, Lakshmanan V, Labbé JL, Craven KD (2020) Microbe to microbiome: A paradigm shift in the application of microorganisms for sustainable agriculture. Front Microbiol 11:1-15.
Reininger V, Martinez-Garcia LB, Sanderson L, Antunes PM (2015) Composition of fungal soil communities varies with plant abundance and geographic origin. AoB PLANTS 7:1-14. https://doi.org/10.1093/aobpla/plv110
Renaud G, Stenzel U, Maricic T, et al (2015) deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinforma Oxf Engl 31:770–772. https://doi.org/10.1093/bioinformatics/btu719
Reschke, C., Reid, R., Jones, J., Potter, H. (1999) Conserving Great Lakes alvars: Final technical report of the International Alvar Conservation Inititalive. The Nature Conservancy, Chicago, Illinois, USA
Rodríguez A, Ibáñez M, Bol R, et al (2022) Fairy ring-induced soil potassium depletion gradients reshape microbial community composition in a montane grassland. Eur J Soil Sci 73:1-15. https://doi.org/10.1111/ejss.13239
Rodriguez R, Redman R (2008) More than 400 million years of evolution and some plants still can’t make it on their own: plant stress tolerance via fungal symbiosis. J Exp Bot 59:1109–1114. https://doi.org/10.1093/jxb/erm342
Rodriguez RJ, Henson J, Van Volkenburgh E, et al (2008) Stress tolerance in plants via habitat-adapted symbiosis. ISME J 2:404–416. https://doi.org/10.1038/ismej.2007.106
Rousk J, Bååth E, Brookes PC, et al (2010) Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J 4:1340–1351. https://doi.org/10.1038/ismej.2010.58
Sathya A, Vijayabharathi R, Gopalakrishnan S (2017) Plant growth-promoting actinobacteria: a new strategy for enhancing sustainable production and protection of grain legumes. 3 Biotech 7:102-112. https://doi.org/10.1007/s13205-017-0736-3
Schweitzer JA, Bailey JK, Fischer DG, et al (2008) Plant-soil microorganism interactions: heritable relationship between plant genotype and associated soil microorganisms. Ecology 89:773–781. https://doi.org/10.1890/07-0337.1
Shen C, Shi Y, Fan K, et al (2019) Soil pH dominates elevational diversity pattern for bacteria in high elevation alkaline soils on the Tibetan Plateau. FEMS Microbiol Ecol 95:1-9. https://doi.org/10.1093/femsec/fiz003
Srour AY, Ammar HA, Subedi A, et al (2020) Microbial communities associated with long-term tillage and fertility treatments in a corn-soybean cropping system. Front Microbiol 11:1-18
Stackebrandt, E., Schumann, P. (2017) Introduction to the taxonomy of Actinobacteria. In: Dworkin, M., Falkow, S., Rosenberg, E., et al. (eds) The Prokaryotes: An evolving electronic resource for the microbiological community, 3rd edn. Springer, New York, New York, USA, pp 297–321
Tyler HL (2019) Bacterial community composition under long-term reduced tillage and no till management. J Appl Microbiol 126:1797–1807. https://doi.org/10.1111/jam.14267
Vandenkoornhuyse P, Quaiser A, Duhamel M, et al (2015) The importance of the microbiome of the plant holobiont. New Phytol 206:1196–1206. https://doi.org/10.1111/nph.13312
Wagner MR, Lundberg DS, Coleman-Derr D, et al (2014) Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol Lett 17:717–726. https://doi.org/10.1111/ele.12276
Wehner J, Powell JR, Muller LAH, et al (2014) Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland. J Ecol 102:425–436. https://doi.org/10.1111/1365-2745.12197
Weiss S, Xu ZZ, Peddada S, et al (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:1-18. https://doi.org/10.1186/s40168-017-0237-y
Whitman T, Pepe-Ranney C, Enders A, et al (2016) Dynamics of microbial community composition and soil organic carbon mineralization in soil following addition of pyrogenic and fresh organic matter. ISME J 10:2918–2930. https://doi.org/10.1038/ismej.2016.68
Wilhelm RC, Amsili JP, Kurtz KSM, et al (2023) Ecological insights into soil health according to the genomic traits and environment-wide associations of bacteria in agricultural soils. ISME Commun 3:1–11. https://doi.org/10.1038/s43705-022-00209-1
Wilhelm RC, van Es HM, Buckley DH (2022) Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biol Biochem 164:1-12. https://doi.org/10.1016/j.soilbio.2021.108472
Wintermans PCA, Bakker PAHM, Pieterse CMJ (2016) Natural genetic variation in Arabidopsis for responsiveness to plant growth-promoting rhizobacteria. Plant Mol Biol 90:623–634. https://doi.org/10.1007/s11103-016-0442-2
Xiao Y, Li C, Yang Y, et al (2020) Soil fungal community composition, not assembly process, was altered by nitrogen addition and precipitation changes at an alpine steppe. Front Microbiol 11:1-12
Yang P, Luo Y, Gao Y, et al (2020) Soil properties, bacterial and fungal community compositions and the key factors after 5-year continuous monocropping of three minor crops. PLOS ONE 15:e0237164. https://doi.org/10.1371/journal.pone.0237164
Yang T, Adams JM, Shi Y, et al (2017) Soil fungal diversity in natural grasslands of the Tibetan Plateau: associations with plant diversity and productivity. New Phytol 215:756–765. https://doi.org/10.1111/nph.14606
Yavitt JB, Pipes GT, Olmos EC, et al (2021) Soil organic matter, soil structure, and bacterial community structure in a post-agricultural landscape. Front Earth Sci 9:1-15
Yin C, Schlatter DC, Kroese DR, et al (2021) Responses of soil fungal communities to lime application in wheat fields in the Pacific Northwest. Front Microbiol 12:1-13. https://doi.org/10.3389/fmicb.2021.576763
Yu Z, Liang K, Huang G, et al (2021) Soil bacterial community shifts are driven by soil nutrient availability along a teak plantation chronosequence in tropical forests in China. Biology 10:1-19. https://doi.org/10.3390/biology10121329
Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinforma Oxf Engl 30:614–620. https://doi.org/10.1093/bioinformatics/btt593
Zuur AF, Ieno EN, Walker N, et al (2009) Mixed effects models and extensions in ecology with R | SpringerLink. Springer, New York, New York, USA

Table 1: Soil nutrient differences between alvar habitats (dry and wet) and old-fields. Data presented are mean, standard errors, and the number of samples reported. Organic matter is presented as % loss-on-ignition. Nitrogen, phosphorus, potassium, magnesium, calcium, aluminum, boron, copper, iron, manganese, sodium, sulfur, and zinc are measured in mg/kg. Soil depth and plant height are presented here in centimeters.

Significance is indicated by stars: * =0.05 , ** =0.001 . ***=0.0001, NS =not significant

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Habitat, plant height, and soil nutrients are important determinants of the Hypericum perforatum microbiome

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Abstract

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Introduction

Methods

Study system

Site selection

Root sampling and plant phenotyping

DNA extraction and sequencing

Bioinformatic pipeline

Data analysis

Soil geochemistry, plant phenotype, and alpha diversity

Community composition

Indicator species analysis

Identifying microbial taxa correlated plant phenotype

Results

Discussion

Bacterial species richness is higher on alvars but fungal richness is higher in old-fields

Plant height is an important determinant of microbial community composition

Old-fields are potentially enriched in plant pathogens

Conclusion

Declarations

References

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