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

Abstract

The structure and diversity of microbial communities is intrinsically hierarchical due to the shared evolutionary history of community members. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how diversity and dynamics depend on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using predictions derived from a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g., genus vs. family), an empirical pattern predicted by the Diversity Begets Diversity (DBD) hypothesis. We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members. Contrastingly, only by including correlations between species abundances (e.g., as consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.

Interactions-based classification of a single microbial sample

“…Since the IDOA can be applied to any taxonomic level, an open question for further research is the optimal taxonomic level to which the IDOA should be applied and whether it can be generalized to capture also interactions across different taxonomic levels. 30 …”