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Updated: Jun 10, 2025

Simulating Temperature in a Soil Incubation Experiment

Simulating Temperature in a Soil Incubation Experiment

Published on: October 28, 2022

Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human

Allison R Mason¹, Hayden S McKee-Zech², Dawnie W Steadman²

¹Department of Microbiology, University of Tennessee-Knoxville, Knoxville, TN, United States of America.

|October 11, 2024

Summary

This summary is machine-generated.

Soil microbes can help estimate postmortem interval (PMI). This study found that while microbial data predicts PMI, environmental factors and specific markers impact accuracy, with high error rates observed.

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Last Updated: Jun 10, 2025

Simulating Temperature in a Soil Incubation Experiment

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Published on: October 28, 2022

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High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Published on: November 15, 2013

Area of Science:

Forensic Science
Microbiology
Bioinformatics

Background:

Established postmortem interval (PMI) estimation methods have limitations.
Microbial succession in soil offers a potential supplementary PMI estimation tool due to microbes' presence throughout decomposition.
Previous machine learning models for PMI estimation from soil microbiomes did not incorporate environmental factors.

Purpose of the Study:

To evaluate the impact of including environmental data on microbial-based PMI estimates from soil decomposition samples.
To compare the predictive performance of different biological markers (16S, ITS, combined) and taxonomic levels for PMI estimation.

Main Methods:

Random forest regression models were developed to predict PMI using relative microbial taxon abundances.
Data included bacterial 16S, fungal ITS, and combined 16S-ITS marker data at various taxonomic levels (phylum, class, order, OTU).
Environmental predictors (temperature, pH, conductivity, enzyme activities) were assessed for their influence on model accuracy (MAE).

Main Results:

Mean Absolute Error (MAE) ranged from 804 to 997 accumulated degree hours (ADH) across models.
Bacterial 16S marker models showed better performance than fungal ITS models (p = 0.006).
Environmental data inclusion reduced MAE for ITS models and improved 16S models at higher taxonomic levels (phylum, class).

Conclusions:

Soil microbial succession demonstrates some predictability for human decomposition stages.
Environmental factors significantly influence the accuracy of microbial-based PMI estimations.
Further research is needed to reduce error rates for reliable forensic application, especially with diverse donor populations.