Understanding how microbiomes influence the systems they inhabit.

Area of Science:

• Microbial ecology and systems biology.

Background:

• Interpreting microbiome data to understand system-level processes is challenging due to a lack of robust conceptual frameworks.
• Correlative approaches limit the ability to generalize findings across different systems and advance microbiome science.

Purpose of the Study:

• To propose a common framework for microbiome research that links microbial characteristics to system-level processes.
• To identify key categories of microbiome characteristics and establish empirical links between them.

Main Methods:

• Identifying three distinct categories of microbiome characteristics: microbial processes, community properties, and membership.
• Proposing a framework to empirically link these categories and connect them to broader system-level processes.
• Differentiating between community-aggregated traits and emergent properties within microbial community properties.

Main Results:

• A proposed framework to link microbial processes, community properties (community-aggregated and emergent), and membership to system-level functions.
• Demonstration of how existing microbial ecology methods can be applied within this framework.
• Highlighting the importance of distinguishing predictable (community-aggregated) from unpredictable (emergent) community properties.

Conclusions:

• The proposed framework offers a promising pathway to advance discovery and understanding in microbiome science.
• Emphasizing causal mechanisms and a common framework is crucial for robust microbiome research.
• This approach can bridge the gap between microbiome data and system-level process understanding across diverse environments.