The activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four-species interactions. Within this community, interactions between any pair of microbial species were positive, while higher-order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.
Many wild microbial ecosystems contain hundreds to thousands of species, suggesting that interactions between species likely play an important role in regulating the behavior of such complex cellular networks. Predicting how these interactions impact the overall activity of microbial communities remains a challenge. Here we quantify the contribution of interactions between more than two species to the overall metabolic rate of a mixture of four freshwater bacteria. We systematically measure interactions between these species and use theoretical models to examine the influence cell-cell interactions on spatially non-uniform microbial populations. Our results demonstrate that although interactions between species are key regulators of system behavior, only considering interactions between pairs of species is sufficient to predict ecosystem activity. Simulations demonstrate that activity at both the single-cell and population level would be strongly influenced by how microbes are distributed in space. These findings improve our understanding of how best to examine groups of microbes that coexist in environments such as soil, water, and the human body.