Demonstrating microbial co-occurrence pattern analyses within and between ecosystems.

Area of Science:

• Ecology and Microbial Ecology
• Bioinformatics and Computational Biology

Background:

• Co-occurrence patterns are crucial for understanding ecological interactions and coexistence in biological communities.
• Previous analyses of microbial co-occurrence have often been limited to pairwise comparisons or single-scale investigations.
• Studying co-occurrence across multiple ecosystems and organizational scales is essential for a comprehensive understanding of microbial community structure.

Purpose of the Study:

• To introduce a novel, multi-scale approach for analyzing microbial co-occurrence patterns.
• To compare microbial co-occurrence across different ecosystems and taxonomic levels using 16S rRNA amplicon data.
• To provide statistical methods and code for reproducible co-occurrence analysis in microbial ecology.

Main Methods:

• Utilized publicly available 16S rRNA amplicon datasets from diverse ecosystems.
• Implemented a nested co-occurrence analysis framework examining community, module, and pairwise scales.
• Applied network statistics to assess microbial relationships at various taxonomic levels.

Main Results:

• Identified community-scale co-occurrence patterns influenced by environmental filtering across ecosystems.
• Discovered consistent pairwise co-occurrences that can inform ecological trait inference for uncharacterized microbes.
• Observed that network statistics and conclusions vary significantly based on the chosen taxonomic level and network construction methods.

Conclusions:

• A multi-scale approach provides a more robust understanding of microbial co-occurrence than single-scale analyses.
• Environmental filtering shapes community-level co-occurrence, while specific pairwise interactions offer insights into microbial traits.
• Caution is advised when interpreting microbial network statistics due to sensitivity to taxonomic resolution and network building criteria.