Quantifying patterns of microbial community assembly processes in bioreactors using different approaches leads to...

Area of Science:

• Microbiology
• Environmental Engineering
• Ecological Modeling

Background:

• Engineered bioreactors are essential for waste-to-resource conversion processes like wastewater treatment and bioremediation.
• Microbial communities drive these bioconversions, making their composition and dynamics critical for bioreactor design and operation.
• A fundamental gap exists in understanding microbial community assembly (MCA) within engineered bioreactor environments.

Purpose of the Study:

• To propose and apply a toolset for assessing MCA in diverse engineered bioreactor systems.
• To connect MCA patterns with specific microbial groups across various bioreactor types.
• To evaluate the influence of different MCA assessment methods and inform best practices.

Main Methods:

• Utilized multiple MCA assessment tools, including null and neutral modeling approaches.
• Integrated a trait-based analysis to link MCA patterns to microbial groups.
• Applied these methods across seven experimental bioreactor systems with varying processes.
• Performed statistical modeling to determine optimal sample sizes for different modeling approaches.

Main Results:

• The relative contributions of MCA processes varied significantly depending on the assessment method employed.
• Null modeling approaches indicated a greater influence of stochastic processes compared to neutral modeling.
• Generalist anaerobic microbes exhibited more deterministic assembly than specialist anaerobic microbes.
• Statistical modeling suggested minimum sample sizes of 30-40 for neutral modeling and 50-60 for null modeling.

Conclusions:

• Caution is advised when interpreting results from a single MCA assessment method due to inter-method variability.
• Understanding microbial community assembly is key to optimizing engineered bioreactor performance.
• The proposed toolset and findings contribute to advancing the field of microbial ecology in engineered systems.