Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities

Area of Science:

• Ecology
• Evolutionary Biology
• Microbiology

Background:

• Microbial communities perform vital functions reliant on inter-species interactions.
• Artificial selection is a method to improve community function by selecting high-performing communities.
• Current experimental selection strategies often yield unimpressive improvements.

Purpose of the Study:

• To investigate and design effective in silico selection strategies for improving microbial community function.
• To understand how ecological interactions influence evolutionary trajectories and selection efficiency.
• To develop experimentally-implementable manipulations to enhance community function improvement.

Main Methods:

• Simulated community selection to optimize a two-species community function.
• Utilized a "community function landscape" to visualize function dependency on species and genotype composition.
• Analyzed evolutionary trajectories and community function heritability under ecological interactions.

Main Results:

• Ecological interactions can restrict evolutionary paths on the community function landscape, leading to counter-intuitive dynamics.
• These restrictions can prevent maximal function attainment and trap communities in low heritability states.
• Devised manipulations to shift evolutionary paths towards higher heritability, accelerating function improvement.

Conclusions:

• Community function heritability is a critical factor for effective artificial selection.
• Ecological interactions pose significant challenges to optimizing microbial community function through selection.
• Experimentally-guided manipulations can overcome these challenges, improving selection efficiency for complex microbial communities.