Cohesion: a Method for Quantifying the Connectivity of Microbial Communities

The ability to predict microbial community dynamics lags behind the quantity of data available in these systems. Most predictive models use only environmental parameters, although a long history of ecological literature suggests that community complexity should also be an informative parameter. Thus, we hypothesize that incorporating information about a community's complexity might improve predictive power in microbial models. Here, we present a new metric, called community 'cohesion,' that quantifies the degree of connectivity of a microbial community. We analyze six long-term (10+ years) microbial data sets using the cohesion metrics and validate our approach using data sets where absolute abundances of taxa are available. As a case study of our metrics' utility, we show that community cohesion is a strong predictor of Bray-Curtis dissimilarity (R=0.47) between phytoplankton communities in Lake Mendota, WI, USA. Our cohesion metrics outperform a model built using all available environmental data collected during a long-term sampling program. The result that cohesion corresponds strongly to Bray-Curtis dissimilarity is consistent across the six long-term time series, including five phytoplankton data sets and one bacterial 16S rRNA gene sequencing data set. We explain here the calculation of our cohesion metrics and their potential uses in microbial ecology.