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<p>Microbial communities are critical drivers of mammalian carcass decomposition in natural ecosystems. Many studies have attempted to establish a microbial clock to estimate the postmortem interval (PMI); however, several obstacles remain to be solved. This study examines how age and insect activity influence microbial dynamics and emphasizes the role of ‘rupture’ in the decay. Notably, microbial diversity exhibited more pronounced shifts in immature cadavers, while insect activity suppressed overall diversity. Conversely, older age and insect colonization promoted the dominance of the Pseudomonadota phylum. We constructed random forest models (MAE: 0.62–0.95 days, R²: 0.976–0.987) for PMI estimation. These findings provide novel insights into refining PMI estimation in forensic contexts. Future research will further investigate the mechanisms behind these changes. Additionally, it will explore how other factors influence the decay, improving the accuracy and applicability of PMI estimation in various contexts.</p>

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The impact of age and insects factors on cadaver microbial communities and application to postmortem interval Estimation

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Sile Chen,
  • Yang Xia,
  • Xiangyan Zhang,
  • Jian Zhao,
  • Sheng Hu,
  • Fan yang,
  • Zhe Deng,
  • Chengxin Ye,
  • Hai Wu,
  • Xingchun Zhao,
  • Yadong Guo

摘要

Microbial communities are critical drivers of mammalian carcass decomposition in natural ecosystems. Many studies have attempted to establish a microbial clock to estimate the postmortem interval (PMI); however, several obstacles remain to be solved. This study examines how age and insect activity influence microbial dynamics and emphasizes the role of ‘rupture’ in the decay. Notably, microbial diversity exhibited more pronounced shifts in immature cadavers, while insect activity suppressed overall diversity. Conversely, older age and insect colonization promoted the dominance of the Pseudomonadota phylum. We constructed random forest models (MAE: 0.62–0.95 days, R²: 0.976–0.987) for PMI estimation. These findings provide novel insights into refining PMI estimation in forensic contexts. Future research will further investigate the mechanisms behind these changes. Additionally, it will explore how other factors influence the decay, improving the accuracy and applicability of PMI estimation in various contexts.