Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

几十年来，定向进化一直被用来设计处于或低于有机体水平的生物系统。在生物体水平之上，少数研究尝试人为选择微生物生态系统，但取得的成功参差不齐且普遍有限。我们对人工生态系统选择的理论理解是有限的，特别是对于无性生物的大型组合，并且我们对设计有效的方法来指导其进化知之甚少。在这里，我们开发了一个灵活的建模框架，使我们能够系统地探索任意一组社区和所选功能的任意选择策略。通过在相同条件下人工选择数百个计算机微生物元群落，我们首先表明，迄今为止使用的主要育种方法不一定能让群落达到生态平衡，但其性能优于对足够成熟群落的简单筛选。然后，我们确定了一系列替代的定向进化策略，特别是在组合应用时，非常适合大型、多样化和稳定的微生物群落的自上而下的工程。我们的结果强调，定向进化允许引导生态结构-功能景观，以寻找具有所需数量属性的动态稳定和生态弹性的群落。