Direct and diffuse cross-kingdom interactions in plant microbiome assembly

Studies of plant-associated microbial communities consistently indicate a role for classic assembly mechanisms, such as environmental and host filters, but often leave substantial unexplained variation. We propose that biotic interactions within microbial communities may help to fill this gap, specifically cross-kingdom interactions between fungi and bacteria, as these are increasingly found to be important to both assembly and function. We hypothesized that direct interactions between bacterial and fungal taxa are an important driver of composition in low-diversity leaf habitats, where pairwise reciprocal interactions are more likely. In high-diversity root habitats, however, we expected diffuse, indirect interactions to be more relevant to composition. To test these hypotheses, we used 16S and ITS rRNA sequence analysis to characterize bacterial and fungal communities of switchgrass ( Panicum virgatum L.) leaves and roots within and across 14 stands spanning mountain to coastal ecoregions of North Carolina, USA. We analyzed putative direct and diffuse interactions using ecological network inference and examined the variance explained in microbial community composition using variance partitioning analysis with spatial, environmental, and biotic interactions as explanatory factors. Variance partitioning showed that cross-kingdom biotic interactions contribute to microbial community structure. The largest improvements to variance explained (5-11%) were from including direct interactions, except for root fungal communities where the variance explained by diffuse interactions (7.5%) was more than double that of direct interactions (2.8%). These contributions were comparable with those from environmental and spatial factors. The joint effects of putative biotic interactions and environmental conditions also contributed to the explained variation, highlighting the importance of the environmental tracking in microbes.

These findings provide insight into using network inference for identifying putative cross-kingdom biotic interactions in plant-associated microbiomes and their contributions to microbiome assembly, which could guide the direction of future research on biotic interactions.