Ecological Patterns Among Bacteria and Microbial Eukaryotes Derived from Network Analyses in a Low-Salinity Lake

Area of Science:

• Microbial Ecology
• Aquatic Microbiology
• Network Analysis

Background:

• Microbial communities are complex assemblages of interacting taxa.
• Lake Texoma's microbial diversity is influenced by seasonal changes and a harmful algal bloom (Prymnesium parvum).
• Understanding microbial interactions is crucial for aquatic ecosystem health.

Purpose of the Study:

• To identify and describe microbial interactions and co-occurrence patterns between bacteria and microbial eukaryotes.
• To analyze these patterns over an annual cycle at two distinct lake locations.
• To infer ecological relationships and detect disturbance events using network analyses.

Main Methods:

• Employed network analyses on microbial eukaryotic and bacterial datasets.
• Analyzed co-occurrence patterns and connectivity at two lake sites over one year.
• Integrated environmental variables and microbial operational taxonomic units (OTUs) into network construction.

Main Results:

• Connectivity patterns reflected lake seasonality and a significant rain disturbance event.
• Localized responses to a harmful algal bloom were identified by comparing the two locations.
• Conserved associations between microbial taxa and environmental variables were detected across both sites.

Conclusions:

• Network analyses effectively detect disturbance events and characterize the ecological impact of harmful algal blooms.
• This approach reveals ecological relationships not evident from diversity statistics alone.
• Microbial interactions are dynamic and influenced by seasonality, disturbances, and localized events like algal blooms.