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Microbial decomposition in experimental single and mass graves: New insights on post-burial interval estimation

Nengi Ogbanga, Hayley L. Mickleburgh, Andrew Nelson, Darren Smith, Timothy P. Gocha, Daniel J. Wescott, Noemi Procopio*

*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

Soil microbiomes are increasingly recognized as valuable indicators in forensic investigations, but microbial dynamics in mass graves remain poorly understood. This study investigates differences in microbial succession between individual graves (IG) and mass graves (MG) with human body donors and evaluates the potential of soil microbiome data to predict post-burial interval (PBI). Using ASV-level assessment, we analysed soil samples collected over time from both grave types in a controlled decomposition experiment. At the final timepoint (M18), MG and IG soils exhibited significantly different microbial compositions, with specific taxa, some associated with specific decomposition stages, enriched in each context. A regression model trained on IG samples predicted PBI with a mean error of 2.68 months when adjusted for seasonal variation but performed poorly on MG samples (RMSE = 7.12 months), highlighting ecological complexity and reduced generalisability. These findings underscore the importance of studying MG-specific microbial processes and caution against applying models developed from single-body burials to mass grave contexts. As mass graves are encountered in humanitarian and criminal investigations and establishing the duration of burial can be an important component of forensic reconstruction, our findings highlight the value of further research into context-specific microbiome models and their integration alongside existing methods for detection and time estimation in complex burial environments.

Original languageEnglish
Article number103517
Pages (from-to)1-14
Number of pages14
JournalForensic Science International: Genetics
Volume84
Early online date29 Apr 2026
DOIs
Publication statusPublished - 1 Jun 2026

Keywords

  • Decomposition
  • Machine Learning
  • Mass graves
  • Microbiome
  • PBI
  • Soil

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