Microbial Drivers of Plant Performance during Drought Depend upon Community Composition and the Greater Soil Environment

Abstract

The increasing occurrence of drought is a global challenge that threatens food security through direct impacts to both plants and their interacting soil microorganisms. Plant growth promoting microbes are increasingly being harnessed to improve plant performance under stress. However, the magnitude of microbiome impacts on both structural and physiological plant traits under water limited and water replete conditions are not well-characterized. Using two microbiomes sourced from a ponderosa pine forest and an agricultural field, we performed a greenhouse experiment that used a crossed design to test the individual and combined effects of the water availability and the soil microbiome composition on plant performance. Specifically, we studied the structural and leaf functional traits of maize that are relevant to drought tolerance. We further examined how microbial relationships with plant phenotypes varied under different combinations of microbial composition and water availability. We found that water availability and microbial composition affected plant structural traits. Surprisingly, they did not alter leaf function. Maize grown in the forest-soil microbiome produced larger plants under well-watered and water-limited conditions, compared to an agricultural soil community. Although leaf functional traits were not significantly different between the watering and microbiome treatments, the bacterial composition and abundance explained significant variability in both plant structure and leaf function within individual treatments, especially water-limited plants. Our results suggest that bacteria-plant interactions that promote plant performance under stress depend upon the greater community composition and the abiotic environment. IMPORTANCE Globally, drought is an increasingly common and severe stress that causes significant damage to agricultural and wild plants, thereby threatening food security. Despite growing evidence of the potential benefits of soil microorganisms on plant performance under stress, decoupling the effects of the microbiome composition versus the water availability on plant growth and performance remains a challenge. We used a highly controlled and replicated greenhouse experiment to understand the impacts of microbial community composition and water limitation on corn growth and drought-relevant functions. We found that both factors affected corn growth, and, interestingly, that individual microbial relationships with corn growth and leaf function were unique to specific watering/microbiome treatment combinations. This finding may help explain the inconsistent success of previously identified microbial inocula in improving plant performance in the face of drought, outside controlled environments.

Keywords: Plant microbiome; drought; microbial composition; plant growth; plant-microbe interactions; soil ecology.

FIG 1

Maize structural and functional measurements. Watering regimes are shown in different colors: water-limited (green) and well-watered (blue). (A) Plant height. (B) Stem diameter. (C) Dry root biomass. (D) Intrinsic water use efficiency (WUEi). (E) Leaf water content (LWC). (F) Leaf mass per area (LMA). Boxplots show the mean, the 25^th and 75^th percentiles, and the min and max values. The measurements of individual plants are shown as points. A two-way ANOVA was used to determine the statistical significance of different treatments for each of the plant metrics. Plant height and stem diameter showed significant microbiome and watering effects (shown as dendrograms) but no interaction effects. All other traits showed no significant microbiome, watering, or interaction effects at α=0.05. Detailed ANOVA results are shown in Table 1.

FIG 2

Microbial community composition and alpha diversity. (A) Nonmetric multidimensional scaled plot representing relative differences in overall community composition in the four treatments: well-watered, agricultural-derived community (violet); water-limited, agricultural-derived community (blue); well-watered, forest-derived community (dark green); and water-limited, forest-derived community (light green). An NMDS plot was generated based on the Bray-Curtis dissimilarity and was plotted using the ordinate function in Phyloseq. The NMDS stress was 0.139. A PERMANOVA analysis using the adonis function indicated significant differences in composition between the microbiome (R² = 0.121, P = 0.001) and watering treatments (R² = 0.118, P = 0.001) but not the interaction (R² = 0.021, P = 0.336). A pairwise PERMANOVA indicated significant differences between all treatment combinations (P_adj < 0.05). (B–D) Alpha diversity metrics for each microbiome source are shown by color: forest (green), agricultural (blue), and parent microbiome (gray). A two-way ANOVA indicated a significant watering effect for Shannon diversity only (F = 4.53, df =1, P = 0.039). For all other comparisons, P > 0.05.

FIG 3

Differential abundance of taxa across treatments. Each number represents a significantly differentially abundant branch. The specific taxa names corresponding to each numbered branch are listed in Table S1 for reference. (A) Differential abundance of soil taxa between pots inoculated with communities originating from forest and agricultural soils. The taxa highlighted in green were significantly more abundant in the forest-derived community, whereas those in blue were more abundant in the agricultural community. (B) Pairwise comparisons of taxa abundance between specific treatment combinations. Taxa colors matching the row or column names indicate the treatment in which the taxon was more abundant. (C) The differential abundance of taxa between watering treatments. Those highlighted in orange and blue were significantly more abundant in the water-limited and well-watered treatments, respectively. For all plots, the color indicates the log₂ ratio of the abundance proportions (differential abundance), and the node/branch size indicates the relative number of ASVs for each taxon.

FIG 4

Significant Pearson’s correlations between plant traits and individual taxa abundances at the ASV, genus, or family level. Only significant (P < 0.05) correlations are displayed. The points are sized and colored according to the magnitude and direction of Pearson’s r. Taxonomic names are provided at the finest resolution available.