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

ABSTRACT: Microbial succession has been suggested to supplement established postmortem interval (PMI) estimation methods for human remains. Due to limitations of entomological and morphological PMI methods, microbes are an intriguing target for forensic applications as they are present at all stages of decomposition. Previous machine learning models from soil necrobiome data have produced PMI error rates from two and a half to six days; however, these models are built solely on amplicon sequencing of biomarkers (e.g., 16S, 18S rRNA genes) and do not consider environmental factors that influence the presence and abundance of microbial decomposers. This study builds upon current research by evaluating the inclusion of environmental data on microbial-based PMI estimates from decomposition soil samples.

SUBMITTER: Mason AR 

PROVIDER: S-EPMC11469530 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature