Macroecological laws in experimental microbial communities

Abstract

AbstractEcology has historically benefited by characterizing statistical patterns of biodiversity within and across communities. This approach, encompassed by the discipline of macroecology, has achieved considerable success in microbial ecology in recent years. Macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models that do not include explicit interactions between community members. Experimentation has simultaneously played a crucial role in the development of our understanding of the ecology of microbes, as the advent of highly replicated time-series has allowed researchers to investigate how ecological forces govern community dynamics. However, there remains a gap between microbial experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know if and how experimental manipulations produce macroecological effects. Here, we work to bridge the gap between the experimental manipulation of communities and their macroecological consequences. 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 Stochastic Logistic Model of growth (SLM). We found that demographic manipulations and their effect on community-level variation can alter empirical patterns in a manner that diverges from predictions obtained from the SLM. By incorporating experimental details (e.g., number of migrants), we were able to restore the predictive capacity of the SLM by linking demographic manipulations with macroecological effects. Finally, we demonstrate the extent that experimental manipulations are capable of altering macroecological patterns under the SLM, establishing a demarcation between macroecological outcomes we can and cannot observe in a laboratory setting.