Quantifying human-environment interactions through Bayesian modeling of species-resolved microbial transfer...

Area of Science:

• Microbiology
• Forensic Science
• Bioinformatics

Background:

• Current forensic methods lack quantitative frameworks for microbial trace evidence.
• 2bRAD-M sequencing offers species-level resolution for low-biomass samples.
• Integrating sequencing with statistical models for forensics is unexplored.

Purpose of the Study:

• Develop a quantitative framework for microbial transfer analysis.
• Integrate 2bRAD-M sequencing with Bayesian modeling.
• Reconstruct human-environment interactions using microbial data.

Main Methods:

• Developed an integrated framework combining 2bRAD-M sequencing and a Bayesian hierarchical model.
• Incorporated geospatial, persistence, and temporal decay parameters into the model.
• Generated 2bRAD-M data from host-associated and environmental samples, integrating public 16S rRNA data.

Main Results:

• The Bayesian model showed preliminary accuracy in attributing microbial traces to source environments (~100m).
• Initial estimates for microbial deposition time were provided.
• Observed persistent microbial transfer from personal devices, with taxa like *Staphylococcus hominis* remaining for over 72 hours.

Conclusions:

• Integrating 2bRAD-M sequencing with Bayesian modeling offers a framework for quantitative microbial transfer reconstruction.
• The approach shows potential for forensic applications.
• Extensive validation is required for reliability and admissibility in casework.