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ABSTRACT Due to climate change, drought frequencies and severities are predicted to increase across the United States. Plant responses and adaptation to stresses depend on plant genetic and environmental factors. Understanding the effect of those factors on plant performance is required to predict species’ responses to environmental change. We used reciprocal gardens planted with distinct regional ecotypes of the perennial grassAndropogon gerardiiadapted to dry, mesic, and wet environments to characterize their rhizosphere communities using 16S rRNA metabarcode sequencing. Even though the local microbial pool was the main driver of these rhizosphere communities, the significant plant ecotypic effect highlighted active microbial recruitment in the rhizosphere, driven by ecotype or plant genetic background. Our data also suggest that ecotypes planted at their homesites were more successful in recruiting rhizosphere community members that were unique to the location. The link between the plants’ homesite and the specific local microbes supported the “home field advantage” hypothesis. The unique homesite microbes may represent microbial specialists that are linked to plant stress responses. Furthermore, our data support ecotypic variation in the recruitment of congeneric but distinct bacterial variants, highlighting the nuanced plant ecotype effects on rhizosphere microbiome recruitment. These results improve our understanding of the complex plant host–soil microbe interactions and should facilitate further studies focused on exploring the functional potential of recruited microbes. Our study has the potential to aid in predicting grassland ecosystem responses to climate change and impact restoration management practices to promote grassland sustainability. IMPORTANCEIn this study, we used reciprocal gardens located across a steep precipitation gradient to characterize rhizosphere communities of distinct dry, mesic, and wet regional ecotypes of the perennial grassAndropogon gerardii. We used 16S rRNA amplicon sequencing and focused oligotyping analysis and showed that even though location was the main driver of the microbial communities, ecotypes could potentially recruit distinct bacterial populations. We showed that differentA. gerardiiecotypes were more successful in overall community recruitment and recruitment of microbes unique to the “home” environment, when growing at their “home site.” We found evidence for “home-field advantage” interactions between the host and host–root-associated bacterial communities, and the capability of ecotypes to recruit specialized microbes that were potentially linked to plant stress responses. Our study aids in a better understanding of the factors that affect plant adaptation, improve management strategies, and predict grassland function under the changing climate. 

Microbial interactions in natural environments are intricately complex. High numbers and rich diversity of microorganisms, along with compositional heterogeneities complicate the cause. It is essential to simplify these complex communities to understand the microbial interactions. We proposed a concept of “ simple state community ,” which represents a subset of microbes and/or microbial functions of the original population that is necessary to build a stable community. By combining microbial culturing and high-throughput sequencing, we can better understand microbe-microbe and microbe-host interactions. To support our proposed model, we used carbon-based and nitrogen-based media to capture the simple state communities. We used 16S rRNA amplicon sequencing and assigned taxonomic identity to the bacterial populations before and after simple state communities. We showed that simple state communities were a subset of the original microbial communities at both phyla and genera level. We further used shotgun metagenomics to gain insights into the functional potential of the assembled simple state communities. Our proposed model supported the goal of simplifying the complex communities across diverse systems to provide opportunity to facilitate comprehension of both the structure and function of the subset communities. Further applications of the concept include the high-throughput screening of simple state communities using the BIOLOG ® system and continuous culturing (Chemostat). This concept has the potential to test diverse experimental hypotheses in simplified microbial communities, and further extend that knowledge to answer the overarching questions at a more holistic level.