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

Abstract

Microbial communities are intrinsically hierarchical due to the shared evolutionary history of community members. This history is primarily 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. However, our understanding of how community diversity depends on one’s scale of observation has yet to be systematically examined. This omission is not simply a methodological detail, as the shared history of community members captured by taxonomic and phylogenetic information provides an opportunity to investigate how patterns of diversity and abundance (i.e., macroecology) are altered across scales of organization. Here, we evaluate the extent that macroecological laws endure across taxonomic and phylogenetic scales among disparate environments using data from the Earth Microbiome Project. We found that measures of biodiversity at a given scale can be consistently predicted using predictions derived from a minimal model containing zero free parameters, the Stochastic Logistic Model of growth (SLM). Extending these within-scale results, we examine the relationship between measures of biodiversity calculated at different scales (e.g., genus vs. family), an empirical prediction known as 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 (i.e., 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 can and cannot be captured by a minimal model of biodiversity.

Statistical mechanics of phenotypic eco-evolution: from adaptive dynamics to complex diversification

“…maybe simultaneously at work. In particular, environmental fluctuations seem to be the dominant force shaping the statistics of natural microbial communities [105,153,156], and little is known about their influence on evolution [157]. To formulate an eco-evolutionary theory able to predict the evolution of metabolic functions in such complex ecological scenarios is a long-term ambitious goal.…”

Macroecological laws in experimental microbial communities

“…Building on its utility, the SLM has been successfully extended to quantitatively capture additional empirical microbial macroecological patterns. Examples that explicitly use the SLM include attempts to capture measures of ecological distances between communities [32], alternative stable-states [33], patterns of richness and diversity at coarse-grained taxonomic and phylogenetic scales [34], and dynamics within and across human hosts at the sub-species level (i.e., strains) [17,35]. The results of these studies demonstrate that a minimal mathematical model of ecological dynamics can capture a broad assemblage of microbial macroecological patterns.…”