The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis

Abstract

The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs.