Simulations reveal insidious challenges to artificial community selection and possible strategies for success

Multi-species microbial communities often display 9community functions9 stemming from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest community functions to reproduce by randomly partitioning each into multiple 9Newborn9 communities for the next cycle. However, previous efforts in selecting complex communities have generated mixed outcomes that are difficult to interpret. To understand how to effectively enact community selection, we simulated community selection to improve a community function that requires two species and imposes a fitness cost on one or both species. Our simulations predict that improvement could be easily stalled unless various aspects of selection, including species choice, selection regimen parameters, and stochastic populating of Newborn communities, were carefully considered. When these considerations were addressed in experimentally feasible manners, community selection could overcome natural selection to improve community function, and in some cases, even force species to evolve to coexist. Our conclusions hold under various alternative model assumptions, and are thus applicable to a variety of communities.