The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial

The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community structure start by quantifying how much communities are similar in their composition, trough beta-diversity metrics. The central role that these metrics play in microbial ecology does not parallel with a quantitative understanding of their relationships and statistical properties. In particular, we lack a framework that reproduces the empirical statistical properties of beta-diversity metrics. Here we take a macroecological approach and introduce a model to reproduce the statistical properties of community similarity. The model is based on the statistical properties of individual communities and on a single tunable parameter, the correlation of species carrying capacities across communities, which sets the difference of two communities. The model reproduces quantitatively the empirical values of several commonly-used beta-diversity metrics, as well as the relationships between them. In particular, this modeling framework naturally reproduces the negative correlation between overlap and dissimilarity, which has been observed in both empirical and experimental communities and previously related to the existence of universal features of community dynamics. In this framework, such correlation naturally emerges due to the effect of random sampling. AUTHORS SUMMARYSeveral biological and ecological forces shape the composition of microbial communities. But also contingency and stochasticity play an important role. Comparing communities, identifying which conditions are similar in communities with similar species composition, allows to identify which forces shape their structure. Here we introduce a modeling framework which reproduces the statistical features of community similarity. We identify a single relevant parameter that captures in a single number the multidimensional nature of similarity in community composition. These results set the basis for a quantitative, and predicting, theory of the statistical properties of microbial communities.