Microbial communities often perform important functions that depend on inter-species interactions. To improve community function via artificial selection, one can repeatedly grow many communities to allow mutations to arise, and “reproduce” the highest-functioning communities by partitioning each into multiple offspring communities for the next cycle. Since improvement is often unimpressive in experiments, we study how to design effective selection strategies in silico. In this video, we try to walk readers through our recent theoretical/simulation paper on how to improve the efficiency of artificial community selection. We will discussed how intracommunity ecological and evolutionary processes affect the heritability of community function, which in turn determines the dynamics and outcome of community selection.  We then devise experimentally-implementable manipulations to enhance community function heritability, which speeds up community function improvement under various scenarios.