A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.

Three-dimensional fluorescence imaging of microbial eukaryotes in environmental samples allows accurate automated taxonomic profiling and quantitative data about ultrastructures and interactions of organisms.

An open-source python package for phenotype analyses provides a versatile, modular and user-friendly solution to determine complementary fitness-related traits from large-scale assays of microbial colonies.

Microbial macroecological patterns under coarse-graining are captured by a null model of ecology, a result which was leveraged to successfully predict the relationship between fine and coarse-grained diversity.

Two optically transparent substrates enable the exploration of the ecophysiology and spatiotemporal organization and activities of bacteria and fungi within heterogeneous soil-like environments.

A multiphase hydrodynamic theory reveals that the dynamics of colony expansion in microbial swarms and biofilms are limited by the constraints of water and nutrient availability.

Interventions in feedlots and abattoirs place selective pressure on the beef cattle resistome, which differentially impacts the public health risk of antimicrobial resistance from beef production sources.

High-throughput sequencing has been used to analyze the microbiome of mouse corpses over a period of 48 days, and to provide an accurate estimate of the time since death.

With mathematical modeling being an important source of insight for microbial communities, we may need to move beyond commonly-used pairwise models that do not capture microbial interactions.

Evolution of multiple closely related strains with host-pathogen-like interactions but only one niche and no tradeoffs, can give rise to a spatiotemporally chaotic ecological state that continually diversifies even with generalist mutations that slow the evolution.