Insects shape the cadaver decomposition microbiome and postmortem interval estimation accuracy

Clinical Snapshot

Key Findings

• P Value: Not provided

• Effect Size: Not quantified in abstract

• Primary Outcome: Delayed colonization of key decomposer microbes in indoor vs outdoor cadavers

• Nnt Or Sensitivity: PMI prediction accuracy improved with incorporation of indoor data and insect activity variables

• Confidence Interval: Not provided

Clinical Application

Requires specialized microbiome sequencing facilities and machine learning expertise, potentially limiting immediate implementation Highly relevant to Australian forensic practice where indoor deaths are common; could inform National Institute of Forensic Science protocols and assist state coroners in PMI estimation Forensic investigators, pathologists, and law enforcement dealing with indoor death scenes

Abstract

The breakdown and recycling of carrion is a crucial ecological process that largely relies on a community of necrophagous insects and microbes. Recent work has shown that a specialized microbial network, likely dispersed throughout the environment by insects, assembles during cadaver decomposition to break down flesh regardless of climate and geography. Because of their broad distribution and successional nature, decomposer microbes have been used in machine learning models to predict the postmortem interval (PMI) of human remains, an important contribution to the field of forensics. How factors such as an indoor environment, which alters insect activity, impact the composition of microbial communities decomposing human remains is unclear. Here, we investigate the effects of enclosed shelter on microbial community assembly and successional patterns during human decomposition and provide important considerations for PMI modeling. Compared to outdoor cadavers, we show that indoor cadavers experienced delayed colonization of key decomposer microbes over the course of decomposition due to restricted insect access. Consequently, machine learning models trained on outdoor cadavers frequently underestimated the PMI of cadavers decomposing indoors. Delayed colonization by blow fly maggots (Diptera: Calliphoridae) was correlated with higher PMI prediction errors, suggesting that insects are an important source of microbial decomposers that drive PMI model predictions. Incorporating microbial data from indoor cadavers and insect activity into PMI models significantly improved prediction capabilities for both indoor and outdoor decomposition environments. Ultimately, we demonstrate the important role insects play in the maintenance and distribution of microbes that help to recycle vertebrate remains.IMPORTANCEMicrobes are critical for the decomposition and recycling of organic matter. Recently, microbiome-based models have shown promising performance in estimating the postmortem interval (PMI). However, many deaths occur indoors, yet no studies have investigated the impact of enclosed shelter on the cadaver microbiome in a controlled setting. Here, cadavers were decomposed indoors, and we found that blow fly maggots serve as an important source of decomposer taxa that significantly alter the cadaver microbiome following infestation. Notably, PMI estimation models trained on outdoor data sets failed to accurately predict the PMI when insect colonization was delayed. We show that incorporating 16S rRNA amplicon data from cadavers decomposing indoors, along with environmental variables, significantly improves PMI estimates, suggesting a microbiome-based forensic tool may be feasible across decomposition environments. Importantly, this research demonstrates the critical ecological role insects play in the dispersal of specialized microbes that are involved in the breakdown and recycling of vertebrate remains.

References

1. 1.Nieciecki, V., Seitz, V. A., Burcham, Z. M., Otto, K., Cantrell, K., Kirkland, J., Ackermann, G., Knight, R., Lynne, A., Metcalf, J. L., & Bucheli, S. (2026). Insects shape the cadaver decomposition microbiome and postmortem interval estimation accuracy. mSystems. https://doi.org/10.1038/nmeth.1650