Data_Sheet_3_The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis.csv

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.

群落水平人工选择（artificial selection at the community level）以优化生态系统功能（ecosystem functions）的潜力，在应用微生物学（applied microbiology）领域已受到广泛关注。然而，目前我们尚未明确总体上具备哪些条件可保障人工群落选择（artificial community selection）取得成功。基于本领域既往研究及多级选择（multilevel selection）相关研究，我们提出6项关于决定人工微生物群落选择（artificial microbial community selection）有效性的影响因素的假说，其中重点强调了可提升群落间变异度的选择策略。随后我们对已发表的人工微生物群落选择实验开展了元分析（meta-analysis）。结果显示，不同实验中群落选择的响应差异极大，且整体效应量（effect size）与零无显著差异。当群落间不存在迁移、且参与选择的重复群落（replicated communities）数量更多时，人工群落选择的有效性更高。元分析结果还表明，人工群落选择的成功可能依赖于多项必要条件。我们认为人工群落选择是一种颇具前景的研究手段，并提出了若干可优化人工群落选择项目实施效果的策略。