RT Journal Article
SR Electronic
T1 Simulations reveal challenges to artificial community selection and possible strategies for success
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 264689
DO 10.1101/264689
A1 Li Xie
A1 Alex E. Yuan
A1 Wenying Shou
YR 2019
UL http://biorxiv.org/content/early/2019/03/20/264689.abstract
AB Multi-species microbial communities often display “community functions” arising 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 “Newborn” 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 promoting species coexistence, suppressing non-contributors, adopting a “bet-hedging” strategy when choosing communities to reproduce, and reducing stochastic fluctuations in species biomass 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 may even force species to evolve growth restraint to achieve species coexistence. Our conclusions hold under various alternative model assumptions, and are thus applicable to a variety of communities.