Experimental macroecology in microbial systems

Historically the field of ecology has benefitted by characterizing statistical patterns of biodiversity within and across communities, i.e., macroecology. This approach has achieved considerable success in microbial ecology in recent years, having identified universal patterns of diversity and abundance that can be captured by a single effective model: the stochastic logistic model of growth (SLM). Experimentation has simultaneously played a crucial role in the field’s development, as the manipulation of high-replication time-series has revealed novel forces that govern community dynamics. However, there remains a gap between the experiments we perform in the laboratory and the patterns we observe in nature. Here, we bridge the gap between the experimental manipulation of communities and their resulting macroecological effects. Using high-replication time-series of experimental microbial communities, we demonstrate that macroecological laws observed in nature can be readily recapitulated in a laboratory setting and unified under the SLM. We find that demographic manipulations and their effect on variation can alter specific empirical patterns in a manner that diverges from our predictions, though the predictive capacity of the SLM can be restored by explicitly incorporating experimental details. Finally, we demonstrate the extent that experimental manipulations are capable of altering macroecological patterns under the SLM, establishing a demarcation between patterns we can and cannot observe in a laboratory setting.