Macroecological patterns in experimental microbial communities

Abstract

Historically, ecology has benefited by characterizing statistical patterns of biodiversity within and across communities. This approach, known as macroecology, has achieved considerable success in microbial ecology in recent years, having identified universal patterns of diversity and abundance that can be captured by effective models that do not require interactions between community members. 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 microbial experiments performed in the laboratory and macroecological patterns documented in natural systems. Here, we work to bridge the gap between the experimental manipulation of communities and their 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 Stochastic Logistic Model of growth (SLM). We find that demographic manipulations and their effect on community-level variation can alter empirical patterns in a manner that diverges from our predictions, though the predictive capacity of the SLM can be restored by incorporating explicit experimental details. Finally, we demonstrate the extent that experimental manipulations are capable of altering macroecological patterns under the SLM, establishing a demarcation between macroecological effects we can and cannot observe in a laboratory setting.

Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth

“…converging to a delta distribution as the number of sites increases), whereas empirical data tends to follow a broad lognormal distribution ( Grilli, 2020 ). Contrastingly, recent efforts have determined that the predictions of a model of self-limiting growth with environmental noise, the SLM, is capable of quantitatively capturing multiple empirical macroecological patterns in observational and experimental microbial communities ( Grilli, 2020 ; Zaoli and Grilli, 2021 ; Zaoli et al, 2022 ; Descheemaeker et al, 2021 ; Descheemaeker and de Buyl, 2020 ; Shoemaker et al, 2023c ; Lim et al, 2023 ). The stationary solution of this model predicts that the abundance of a given community member across sites follows a gamma distribution ( Grilli, 2020 ), a result that provides the foundation necessary to predict macroecological patterns among and between different taxonomic and phylogenetic scales.…”