Towards an Evolutionary Model of Animal-Associated Microbiomes.

Area of Science:

• Microbial Ecology
• Evolutionary Biology
• Computational Biology

Background:

• Second-generation sequencing provides deep insights into host-associated microbial communities.
• Existing research on microbiome diversity and trends is fragmented in its evolutionary application.
• A disconnect exists between biological observations and computational models of microbiomes.

Purpose of the Study:

• To bridge the gap between biological, ecological, and computational approaches to microbiome research.
• To develop a unified framework for understanding microbiome evolution.
• To re-evaluate ecological theories in the context of host-microbe interactions.

Main Methods:

• Comprehensive literature review across biological, ecological, and computational fields.
• Analysis of top-down selection pressures on microbiomes.
• Evaluation of existing ecological theories for microbiome applicability.

Main Results:

• Ecological theories developed for macro-organisms may not directly apply to microbial communities.
• Multi-level selection theory warrants reconsideration for microbiome dynamics.
• Modularity is a crucial factor in understanding microbiome complexity.

Conclusions:

• A transdisciplinary framework is needed to advance microbiome evolutionary studies.
• Revisiting multi-level selection and emphasizing modularity can guide future microbiome research.
• Integrating diverse methodologies will enhance our understanding of host-microbiome co-evolution.