Microbiome assembly statistics toward ecosystem-scale insights, forecasting, and management

Abstract Microbiomes are increasingly recognized as key to addressing global challenges in health and sustainability, as they can provide emergent biological functions unattainable with single microbial species. However, microbial communities occasionally undergo abrupt shifts in species composition despite their intrinsic steadiness, making it difficult to maintain highly functional microbiome states. Here, we outline emerging statistical frameworks that integrate ecological stability theory with empirical analyses of microbiome structure and function. Approaches inspired by the concept of “stability landscapes” now enable inference of how the relationship between community structure and assembly potential changes along environmental gradients. Such empirical analyses offer bird’s-eye perspectives for maintaining or restoring community states with desirable microbiome functions. Moreover, identifying the attractors of microbiome dynamics facilitates forecasting of abrupt transitions into dysfunctional states (i.e., dysbiosis). Bridging classic ecological theory and empirical microbiome analyses will deepen our understanding of the principles governing species-rich community assembly, expanding the scope of microbiome-based solutions across medical, industrial, agricultural, and environmental sciences.