Microbial interactions hold vast potential for improving sustainability, including waste upcycling, greenhouse gas mitigation, contaminant bioremediation, and host performance. However, attempts to manage interactions in complex, open microbial communities often fall short of expectations, in part because interactions are highly context-dependent, with net effects arising from multiple component interactions, higher-order interactions, and rapid evolution-features largely revealed from studies with simplified communities. Here, we propose how these insights can be translated to complex communities by prioritizing the scales and disturbances that matter, leveraging the ecological context that constrains interactions, targeting function-critical behaviors and traits, and accounting for eco-evolutionary feedbacks. We further propose coupling top-down, trait-based, and coarse-grained approaches with bottom-up synthetic ecology approaches to diagnose failures and design robust microbial processes.
Gu et al. (Wed,) studied this question.