Abstract
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 validate our approach using long-term (10+ year) phytoplankton datasets, where absolute abundance counts are available. As a case study of our metrics’ utility, we show that community cohesion is a strong predictor of Bray-Curtis dissimilarity (R2 = 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 five lakes analyzed here. Our cohesion metrics can be used as a predictor for many community-level properties, such as phylogenetic diversity, nutrient fluxes, or ecosystem services. We explain here the calculation of our cohesion metrics and their potential uses in microbial ecology.
Conflict of Interest The authors declare no conflict of interest.
