Macroecological patterns in experimental microbial communities

Ecology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We found that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline.Author summary: Determining whether an empirical pattern can be manipulated is a crucial step towards building a predictive theory. Our study aimed to determine the extent that experimental manipulation could recapitulate ecological patterns observed in natural microbial communities with minimal fine-tuning. Specifically, we investigated the macroecology (i.e., statistical ecological patterns) of an experiment where a large number of microbial communities were maintained over time. We found that many of the macroecological patterns observed in nature can also be found in experiments. Such patterns can be altered by manipulating the ecological force of migration. A minimal model of ecological dynamics that incorporated experimental details (e.g., sampling) was able to capture the consequences of these ecological manipulations. The results of this work establish a demarcation between patterns that are and are not surprising given our understanding of microbial ecological dynamics and demonstrate the potential for microbial macroecology as a predictive discipline.