Learning beyond- pairwise interactions enables the bottom-up prediction of microbial community structure

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher - order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher - order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven- member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three- member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher - order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher - order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond- pairwise combinations.