Untangling the Effects of Plant Genotype and Soil Conditions on the Assembly of Bacterial and Fungal Communities in the Rhizosphere of the Wild Andean Blueberry (Vaccinium floribundum Kunth)

The study investigates how abiotic (soil region and edaphic factors) and biotic (plant genotype) drivers shape the rhizosphere microbiome of a wild plant, Vaccinium floribundum, in its native Andean páramo ecosystem. Prior microbiome assembly research has focused largely on crops and Arabidopsis, showing strong influences of soil properties (nutrients, pH) and plant genotype via root exudates and hormones. However, comparable studies on wild species in highland ecosystems are scarce. Leveraging four previously defined plant genetic clusters across two distinct Ecuadorian Highland soil regions (nutrient-richer Andisols in the north vs nutrient-poorer Paleosols in the south), the authors test whether soil region explains bacterial and fungal diversity and composition, and whether plant genotype contributes, particularly for fungi, to rhizosphere community assembly in situ.

Previous work indicates that soil abiotic factors, especially nutrient content and pH, directly and indirectly influence microbiome composition, while plant genotypes modulate microbiota via exudates and developmental traits. Studies with Arabidopsis and crops have established genotype effects and responses to phosphorus gradients, including roles for the phosphate starvation response in shaping microbiota. Wild plant studies, especially in high-elevation Andes ecosystems, are limited. The oligotrophic–copiotrophic framework has been used to interpret bacterial phylum distributions relative to nutrient availability, though its applicability to fungi is more limited. The authors position their work to fill gaps by assessing both soil-driven and genotype-driven effects on bacteria and fungi in a wild, undomesticated plant across natural environmental gradients.

Design and sampling: 39 rhizosphere soil samples were collected from Vaccinium floribundum individuals spanning four plant genetic clusters (defined previously using SSRs) across two Ecuadorian Highland soil regions (north vs south), which differ in morphology, mineralogy, and physicochemical properties (north: Andisols, higher nutrient content; south: Paleosols, poorer due to erosion). Soil adhering to roots (~5 g) was collected per plant, transported at 4°C, and stored at −80°C. DNA extraction and sequencing: DNA was extracted from 0.25 g soil using the PowerSoil DNA Isolation Kit. Amplicon libraries targeted bacteria (16S rRNA V3–V4) and fungi (ITS1). Libraries were sequenced on Illumina MiSeq v3 (2×300 bp) at UNC HTSF. Edaphic measurements: From bulk soil (100 g near plants), the following were measured: organic carbon (%), pH, conductivity (µS/cm), total nitrogen (%), Ba, Cd, Co, Cu, Cr, K, Mn, Na, Ni, P, and Pb (mg/kg) following standard methods (ASTM D2974; EPA 6010C). Sequence processing: 16S reads were processed with MT-Toolbox to identify usable reads, quality-filtered with Sickle (min Q20, sliding window), and samples with <120,000 reads were discarded. ASVs were inferred with DADA2 (v1.8.1). Taxonomy was assigned using DADA2's naive Bayes k-mer classifier against SILVA 132. ITS forward reads were processed with DADA2 (default parameters), taxonomy assigned using MOTHUR's naive Bayes classifier with UNITE as training reference. Diversity analyses: Bacterial and fungal count tables were processed with ohchibi. Both were rarefied to 120,000 reads/sample. Alpha diversity was computed as Shannon index (vegan::diversity), with ANOVA and Tukey HSD (emmeans). Beta diversity used Bray–Curtis dissimilarity with PCoA visualization. PERMANOVA (vegan::adonis) quantified variance explained by factors; R2 of significant terms was visualized with ohchibi. Modeling alpha diversity drivers: For bacteria and fungi, ANOVA models included Soil_region, Plant_genotype, Altitude_range, sequencing depth (LDepth) and their interactions: Shannon ~ Soil_region*Plant_genotype*Altitude_range + LDepth. For bacterial alpha diversity, to identify specific soil drivers, a stepwise regression (stepAIC) started from a model with all measured edaphic factors (pH, log10-transformed OC, conductivity, N, Co, Cu, Cr, K, Mn, Ni, P, Pb, Ba, Cd, Na) plus LDepth. The optimized model retained log10(P) and log10(Pb). Robustness was checked by adding principal components (PC1–PC3) of the remaining edaphic variables (covering ~80% variance): Shannon ~ LDepth + log10(P) + log10(Pb) + Edsha_PC1 + Edsha_PC2 + Edsha_PC3.

- Community composition: Proteobacteria and Acidobacteria were the most abundant bacterial phyla in all samples. Fungal communities lacked dominance by specific taxa across regions. - Soil region effects: Bacterial alpha diversity (Shannon) was lower in the southern region; fungal alpha diversity showed the opposite pattern. Beta diversity separated northern and southern samples for both bacteria and fungi on PCoA. PERMANOVA variance explained by soil region was higher for bacteria (R2 = 0.0703, p < 0.0001) than fungi (R2 = 0.0502, p < 1×10^-5). - Plant genotype effects: Bacterial communities showed little variation across plant genetic clusters, whereas fungal communities in clusters 3 and 4 differed (reduced Archaeorhizomycetes, increased Mortierellomycetes) and had higher Shannon diversity. PERMANOVA variance explained by plant genotype was similar for bacteria (R2 = 0.1106, p = 0.0290) and fungi (R2 = 0.1121, p = 0.0099), but clear separations were prominent in fungi (clusters 3 and 4). - Alpha diversity models: For bacteria, soil region was the main predictor (Table 1: Region F = 7.857, p = 0.0088; model R2 = 0.3623, p = 0.0058). For fungi, the interaction Plant genotype × Altitude range was the strongest predictor (F = 9.652, p = 0.0036; model R2 = 0.4694, p = 0.0155); soil region had only a marginal effect (F = 3.581, p = 0.0681). - Edaphic drivers of bacterial diversity: Stepwise selection identified soil phosphorus (P) and lead (Pb) as sufficient predictors of bacterial Shannon diversity, jointly explaining 40.65% of variance (model R2 = 0.4137, p = 0.0005). Individually: P R2 = 0.2627 (p < 1×10^-5); Pb R2 = 0.1806 (p = 0.0041). Adding PCs of remaining edaphic variables increased variance explained by <1%. - Gradient patterns: Northern soils had higher P concentrations and higher bacterial alpha diversity, aligning with regression trends; southern soils had lower P and lower bacterial alpha diversity.

The study demonstrates differential assembly rules for rhizosphere bacteria and fungi in a wild Andean shrub across natural environmental gradients. Bacterial communities are more strongly structured by abiotic soil factors, with soil region and specific edaphic variables (P and Pb) explaining substantial variation in bacterial alpha diversity. The link between P and bacterial diversity aligns with mechanistic evidence that plant phosphate starvation response shapes root microbiome composition. Pb also correlated with bacterial diversity, consistent with reports of metal impacts on microbial communities. In contrast, fungal communities were less tied to soil nutrient differences and more responsive to biotic and spatial factors, particularly the interaction of plant genotype and altitude. Fungal assemblages showed clear genotype-related separations and higher diversity in certain genetic clusters, suggesting host-specific relationships possibly mediated by elevation-driven plant population structure and reduced gene flow. These findings indicate that in natural highland ecosystems, bacteria respond predominantly to edaphic filters, while fungi are influenced by host genetic context and environmental gradients like altitude, addressing the central question of how abiotic and biotic factors differentially govern rhizosphere microbiome assembly.

This work elucidates that in the rhizosphere of the wild Andean blueberry, bacterial community diversity is chiefly determined by soil region and specific edaphic factors—particularly phosphorus and lead—whereas fungal diversity is driven by the interaction between plant genotype and altitude. The study advances understanding of microbiome assembly in an underexplored highland ecosystem and underscores the need to consider bacteria and fungi separately when assessing drivers in the wild. Future research should extend to additional undomesticated species and regions, incorporate functional profiling and metabolomics to link root exudates with microbiome shifts, and conduct manipulative experiments to test causality of key edaphic factors and host genetic traits across elevation gradients.

- Scope limited to a single host species (Vaccinium floribundum) in the Ecuadorian Highlands, potentially constraining generalizability. - Observational, natural experiment design limits causal inference; unmeasured environmental covariates may confound associations. - Sample size (39 rhizosphere samples) and rarefaction thresholds may influence diversity estimates. - 16S/ITS amplicon sequencing provides taxonomic resolution but limited functional insight; forward-only ITS reads may reduce fungal resolution for some taxa. - No direct measurements of root exudates or plant physiological traits; mechanisms linking plant genotype to fungal shifts are inferred rather than demonstrated.