Decomposition in an extreme cold environment and associated microbiome-prediction model implications for the...

Area of Science:

• Forensic Science
• Microbiology
• Environmental Science

Background:

• Accurate postmortem interval (PMI) estimation is critical in forensic investigations.
• Estimating PMI in extreme cold presents significant challenges due to slowed decomposition and potential for error.
• Existing methods are often unreliable in prolonged cold conditions.

Purpose of the Study:

• To address the lack of data on decomposition in extreme cold environments.
• To develop and validate the first postmortem microbiome prediction model for PMI estimation in cold climates.
• To provide a reliable method for PMI determination when visual decomposition is arrested.

Main Methods:

• Conducted a 23-week experiment on animal models in Grand Forks, North Dakota (extreme cold, down to -39°C).
• Collected 393 samples, utilizing 16S rRNA gene microbial data from nasal swabs.
• Developed Random Forest analysis models incorporating microbial data and environmental parameters (snow depth, temperature).

Main Results:

• The best model, using both internal and external swabs, achieved a Mean Absolute Error (MAE) of 1.36 weeks and R2 of 0.91.
• A model relying solely on external swabs had a higher MAE of 2.89 weeks and R2 of 0.73.
• Key microbial predictors identified across models included genera: *Psychrobacter*, *Carnobacterium*, and *Pseudomonas*.

Conclusions:

• The study presents the first microbial model for predicting PMI in extreme winter conditions over six months.
• The model demonstrates an accuracy of 9.52 days for postmortem interval estimation.
• This research offers a novel forensic tool for cold environments, overcoming limitations of traditional methods.