Resident members of natural bacterial communities limit the growth of non-resident bacteria primarily by increasing overall community growth.

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.

Experiments in ex-germ-free mice establish a measurable effect of colonization history on gut microbiota assembly, illuminating a potential cause for the high levels of unexplained individuality in host-associated microbial communities.

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.

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.

Predators may amplify multiple-host parasite spreading in systems with multiple transmission routes.

High-throughput measurements of simplified bacterial communities find that when multiple species jointly inhibit a focal species of interest, their individual effects do not add up, but are dominated by the strongest single-species effect.

Wide variation in cycle threshold (Ct) values from SARS-CoV-2 RT-PCR tests over calendar time offers the potential for their broader use in public testing programmes as an ‘early-warning’ system for shifts in infectious load and hence transmission.

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.

Trait-based metagenomic analysis of an ecosystem fertilization experiment shows that genomic traits that affect the costs and rates of biochemical information-processing mediate community assembly.