Advancing time-since-interval estimation for clandestine graves: a forensic ecogenomics perspective into burial and translocation timelines using massively parallel sequencing

Abstract

Forensic taphonomy and entomology has focused on estimating the post-mortem interval (PMI), particularly for surface depositions, using human cadavers and other mammalian models by considering morphological changes of the body and insect activity during decomposition. The PMI is crucial in forensic investigations as it provides key information regarding the victim’s identity, the circumstances of their death and can confirm or refute a suspect’s alibi. Gravesoil microbial communities are a potential tool that can complement traditional approaches to detect and confirm the presence of human remains in clandestine burials, aiding forensic investigations. The estimation of the time-since-burial (post-burial interval; PBI), and the time-since-translocation (post-translocation interval; PTI), a new concept, have potential to aid clandestine grave location but have received relatively little attention in forensic ecology research. Advances in massively parallel sequencing (MPS) provide a high-throughput means to estimate PBI and PTI by characterising soil microbial communities in graves with remains, from early to skeletal stages of decomposition, or where remains have been intentionally removed from crime scenes and relocated. This review presents a perspective on the use of the soil microbiome as an indicator for post-mortem time-since-interval estimations, with specific focus on the PBI and PTI. In addition, it provides a framework, supported within forensic ecogenomics, on how the PBI and PTI can be used as a forensic tool complemented by MPS. The review highlights the need for further research to validate microbial community analysis across diverse biogeographical regions to enhance its precision and reliability as a forensic investigative tool. Such validation could potentially enhance the accuracy of post-burial and post-translocation interval estimations, ultimately improving methods for clandestine grave identification.

1 Introduction

The timeline of events prior to and after the death of an individual can provide crucial information to forensic investigators. The information can include, but is not limited to: victim identification, time of death estimation, crime scene reconstruction, and confirming or refuting a suspect’s alibi (Cockle and Bell, 2015). For this reason, extensive research using a range of approaches has been conducted focusing on predicting and reliably estimating the time-since-death or the post-mortem interval (PMI). The PMI is typically defined as the period from the death of an individual until the body is discovered (Wilson-Taylor and Dautartas, 2017). The estimation of the PMI has advanced in parallel with an understanding of the decomposition process (Payne, 1965; Payne et al., 1968). Temperature has traditionally been viewed as the primary catalyst for body decomposition (Mann et al., 1990; Vass et al., 1992; Bass, 1996; Megyesi et al., 2005). However, subsequent investigations involving mammalian cadavers revealed that while temperature is crucial for decay, it is not necessarily the primary factor driving the decomposition process. Instead, a wide range of biotic and abiotic factors have emerged as significant contributors to the mammalian decomposition process, both individually and in various combinations. Several living components (biotic) [such as insects (Rodriguez, 1982; Ames and Turner, 2003; Matuszewski and Mądra-Bielewicz, 2022), arthropods (Goff et al., 1988; Singh et al., 2018; Bonacci et al., 2021), vertebrate scavengers (DeVault et al., 2004; Young, 2017; Spies, 2022; Adams et al., 2024), fungi (Sagara, 1975; Carter and Tibbett, 2003; Gemmellaro et al., 2023), and microbial communities (Carter et al., 2007; Metcalf et al., 2013; Pechal et al., 2014; Hauther et al., 2015)] and non-living variables (abiotic factors) [such as soil pH (Haslam and Tibbett, 2009), temperature (Archer, 2004; Laplace et al., 2021), soil conditions (Haslam and Tibbett, 2009; Carter et al., 2010; Quaggiotto et al., 2019), burial conditions (Ahmad et al., 2011; Bhadra et al., 2014; Matuszewski et al., 2014; Martín-Vega et al., 2017; Pawlett et al., 2018; Cogswell and Cross, 2021; Bisker et al., 2024), weather/climate (Voss et al., 2009; Englmeier et al., 2023; Maisonhaute and Forbes, 2023) and individual characteristics of the victim (Mason et al., 2022)] can affect the body after death, impeding the forensic investigations.

Several approaches (Figure 1) have been developed to assess the influence of biotaphonomic agents (such as environmental and climatic conditions, biotic factors and individual characteristics of the deceased like their body mass and height) and to monitor geotaphonomic changes (associated with the cadaveric processes and ground disturbances, including soil colour changes, changes in soil chemistry, changes in vegetation) resulting from the burial activity and decomposition process (Hochrein, 2002; Nawrocki, 2016; Prada-Tiedemann et al., 2024). These approaches have been used complementary to aid PMI estimation for remains across the decomposition timeline. Beyond the scope of estimating the PMI, forensic entomology (focusing on insect behaviour and succession during the decomposition process) (Rodriguez, 1982; O’Flynn, 1983; Micozzi, 1986; Galloway et al., 1989; Mann et al., 1990; Campobasso et al., 2001; Rai et al., 2022) vegetation growth patterns and forensic botany (use of vegetation in forensic investigations) (Watson and Forbes, 2008; Caccianiga et al., 2012), forensic mycology (use of fungi in forensic investigations) (Sagara, 1995; Carter and Tibbett, 2003; Bellini et al., 2016) and geophysical approaches (Pringle et al., 2020) have been used to locate human remains, clandestine burials and mass graves and additionally to estimate the time that has passed since a body was buried or deposited until it is discovered [the post-burial interval (PBI)] (Finley et al., 2015; Pringle et al., 2015; Singh et al., 2016).

Figure 1

While traditional approaches used to estimate the PMI and PBI are useful, they are nonetheless limited and largely applicable for estimating reliable time-since-intervals for remains in early decomposition stages (Henßge and Madea, 2004; Pittner et al., 2020; Laplace et al., 2021; Heinrich et al., 2024). For extended post-mortem intervals (remains in advanced stages of decay), these methods provide estimates with different margins of error because decomposition proceeds at a highly variable rate (Giles et al., 2022; Madea, 2023). In the case of forensic entomology, the PBI for advanced stages of decomposition is based on the succession of insects rather than oviposition and larval development (Campobasso et al., 2001). Succession patterns for remains in advanced decomposition or in prolonged desiccation are less precise because insect and arthropod diversity and abundance decrease over time as the remains become skeletonised and dry, which also leads to a reduction in nutrients and resource availability (Sharanowski et al., 2008; Rai et al., 2022). The ability of insects to colonise remains as well as vegetation to benefit from the release of nutrients from the body is further dependent on the treatment of the body and the burial depth (Rodriguez and Bass, 1985; VanLaerhoven and Anderson, 1999; Congram, 2008; Caccianiga et al., 2012; Pastula and Merritt, 2013; Bonacci et al., 2021). Additionally, due to the variability in burial conditions, environmental conditions and regional variation in species, further empirically validated studies to test the reliability of forensic entomology, forensic botany, and forensic mycology approaches in PBI estimation post-skeletonisation or in prolonged decomposition are needed (Coyle et al., 2005; Menezes et al., 2008; Sidrim et al., 2010; Bellini et al., 2016; Ventura et al., 2016; Watson et al., 2021). Even when biotic and abiotic factors are considered, the identification of victims of homicide and mass conflicts, and the estimation of the PMI and PBI by extension, become increasingly more challenging without their remains. Perpetrators can attempt to hide the victim’s remains by recovering them from their primary deposition site and intentionally reburying and concealing them at secondary locales (Karčić, 2017; Shapiro, 2020; Anstett, 2023).

The characterisation of the mammalian post-mortem microbiome, including the thanatomicrobiome (consisting of microbial communities found internally in blood, organs and fluids) (Javan et al., 2016b), the epinecrotic microbiome (consisting of microbial communities found externally on the surfaces of the body, with their roles elucidated in the decomposition of mammals) (Pechal et al., 2014), and soil microbiome (Olakanye et al., 2014; Procopio et al., 2019) have provided forensic scientists with a tool to aid in time-since-interval estimation (Figure 2). The use of microbial communities to aid in PMI estimation has become more prominent since proposing their application as a post-mortem microbial clock (Metcalf et al., 2013). Changes in the form of shifts in the abundance (how many) and diversity (variety and type) of microbial communities coincide with the physiochemical changes of the body as decomposition progresses. Understanding the shifts (when microbial communities appear, proliferate, and decline) over time, for different burial and environmental conditions, provides a microbial timeline that can act as a clock, allowing forensic scientist to estimate the PMI of a body (Metcalf et al., 2013; Finley et al., 2015).

Figure 2

Several reviews have been undertaken to elucidate the role of microbial communities within forensic investigations, specifically to present an overview of how microbial communities can be adopted for PMI estimation (Metcalf, 2019; Jangid et al., 2023; Moitas et al., 2023), the succession of the thanatomicrobiome (Javan et al., 2019; Zapico and Adserias-Garriga, 2022) and the epinecrotic microbiome (Dash and Das, 2020), and their use the PMI estimation of advanced decomposition (Franceschetti et al., 2023), as well as to present a comparison of microbial fingerprinting techniques in forensic science in estimating the PMI (Finley et al., 2015). Yet, none of the reviews consider the role of gravesoil microbial communities for utilisation beyond PMI estimation to grave location. Consequently, within the reviews, and the broader body of knowledge, there is a lack of clarity, which has led to confusion, especially when describing PMI and PBI for buried remains (Forbes, 2008), where these two time-since-intervals are sometimes used interchangeably (Procopio et al., 2019; Zhang et al., 2021). For application in criminal investigations and legal proceedings it is imperative that a clear distinction on the use, meaning and purpose of these post-mortem intervals are made. This will ensure that appropriate methods are developed to provide crucial and precise information related to the victims remains. Solving crime and aiding in victim identification is at the centre of forensic research, but it requires targeted approaches that are reliable and robust.

Forensic ecogenomics (Ralebitso-Senior, 2018), a sub-discipline of forensic microbiology (Carter et al., 2017), can aid clandestine grave identification through the use of molecular microbial ecology approaches to analyse gravesoils. Since less than 1% of bacterial communities can be cultured (Amann et al., 1995), microecophysiology approaches such as denaturing gradient gel electrophoresis (DGGE) (Muyzer et al., 1993; Zhang and Fang, 2000; Olakanye et al., 2014, 2015; Iancu et al., 2015), polymerase chain reaction (PCR), and terminal restriction fragment length polymorphisms (T-RFLP) (Parkinson et al., 2009; Handke et al., 2017), are useful tools to measure and characterise shifts in microbial communities from gravesoil during decomposition (Ralebitso-Senior et al., 2016). The ability of DGGE and T-RFLP methods to characterise microbial communities from soils is useful due to their low cost and easy data analysis, which makes them favourable for quick analysis and in cases where forensic laboratories do not have access to massively parallel sequencing (MPS) (Lerner et al., 2006; Thies, 2007; Jousset et al., 2010; Lenz and Foran, 2010; Bergmann et al., 2014; Jurkevitch and Pasternak, 2021; Olakanye and Ralebitso-Senior, 2022). However, these techniques are limited by the resolution and depth of taxonomic data (Lerner et al., 2006; Handke et al., 2017). Forensic ecogenomic approaches paired with MPS can be used to locate clandestine burials through the analysis of shifts within gravesoil microbial communities. Considering the use of microbial communities in PMI estimation, we are positing their use in PBI as well as in a new concept called the time-since-translocation [post-translocation interval (PTI)]. It is argued that integrating forensic ecogenomic approaches, which uses molecular microbial ecology techniques to analyse changes to ecosystems as a result of the decomposition process, could enhance the accuracy of PBI and PTI estimations (Ralebitso-Senior, 2018). The conceptual framework within this review addresses this by discussing the PBI and PTI using gravesoil microbial communities, and showing, for the first time, their relationship with the PMI. The framework and synthesis of knowledge is based on a broad review of literature focused on the application of the post-mortem microbiome in forensic science. The literature search was conducted between October 2024 and January 2025 using the Web of Science and Scopus databases and the Google Scholar academic search engine. Searches were conducted using the keywords “microbiome,” “post-mortem interval,” “time since death,” “post-burial interval,” “time since burial,” “soil microbiome,” “relocation,” and “exhumation.” Additionally, reference lists from reviewed articles were also scanned for additional citations. This search strategy was employed because it affords flexibility in the exploration of concepts and the mapping of emerging research themes and approaches within the literature. Given the growing research activity that emphasises the role of microbial communities in decomposition, this review provides more details for the use of forensic ecogenomics. The focus will be on time-since-interval estimation for terrestrial environments and will not include a discussion on the estimation of the post-mortem submersion interval (PMSI) for aquatic environments. In-depth discussions regarding the PMSI have been conducted previously (Dickson et al., 2011; Humphreys et al., 2013; Benbow et al., 2015; Cartozzo et al., 2021).

2 The three time-since-intervals

The three time-since-intervals (PMI, PBI and PTI) offer insight regarding the circumstances surrounding the disappearance and death of an individual. Their purpose also extends to narrowing the investigation window, revealing information regarding the manner and time of death (whether it was accidental or intentional), assisting in estimating the time gap between death, body movement and disposal (or the perimortem and post-mortem behaviour of perpetrator) and potentially connecting suspects to crime scenes at specific points in time (Turner and Wiltshire, 1999; Introna et al., 2011; Sharma et al., 2015). The main function of the PMI estimation within forensics is used to aid in victim identification. Its estimation is based on either the observable physiological changes of the body, or from the thanatomicrobiome and the epinecrotic microbiome of the body (Can et al., 2014; Pechal et al., 2014, 2018; Damann et al., 2015; Hauther et al., 2015; Iancu et al., 2015, 2016, 2023, 2024; Javan et al., 2016b, 2017; Johnson et al., 2016; DeBruyn and Hauther, 2017; Liu et al., 2020; Lutz et al., 2020; Ashe et al., 2021; Deel et al., 2021; Zhang et al., 2021; Zhao et al., 2022; Burcham et al., 2024), to provide a timeframe for the period that has passed since death. This information is useful because it can help to identify individuals, generate investigative leads by following up on their behaviour prior to the crime (from CCTV footage, for example), and narrow a suspect pool (Simmons, 2017).

The time-since-burial (PBI) and the time-since-translocation (PTI), while distinct in their function, form two parts that can be used to infer information about the broader time-since-death (PMI) (Figure 3). The PBI and PTI, while contributing to victim identification, are primarily focussed on providing a contextual interpretation and temporal estimation of the treatment of the body in relation to the grave and deposition site. Here, the burial environment and specifically the grave soil becomes the focus. There are some caveats that need to be considered regarding the PBI and PTI. In cases concerning buried remains, the PBI and PMI can refer to the same time-since-interval or to two separate events. The first instance occurs when a body is placed in a surface deposition environment or a grave within the first couple of hours to a day after death (Burcham et al., 2021). In this scenario, the PMI and the PBI, within a margin of error, can be used to calculate the time-since-death (Forbes, 2008; Singh et al., 2016). However, this information is rarely available at the time a body is discovered in forensic cases. Perpetrators might try to conceal and destroy the evidence of the crime and prevent victim identification to avoid being caught (Kamaluddin et al., 2018). To avoid detection by police, perpetrators can delay depositing the victim’s remains in a burial environment immediately after death (Byard, 2024; Ploeg et al., 2024). Reasons for delaying depositing or burying the remains could include that the offender wanted to conceal the remains to avoid suspicion or confuse the investigation, which delays the likelihood of an arrest or conviction (Tumer et al., 2013; De Matteis et al., 2021; Byard, 2024). Since deposition or burial might have taken place sometime after death, the estimation of the PBI should be considered distinct from the PMI. In which case the PBI will be shorter than the PMI (Forbes, 2008; Watson et al., 2021).

Figure 3

The movement of the body from its original deposition or burial site can complicate the interpretation of the crime scene and the PMI estimation. Post-mortem translocation of a body from its original burial or deposition site can occur due to several reasons, such as the post-mortem movement or contractions after death, shifting a body from its original location (Wilson et al., 2020), religious or cultural exhumation or reburial, as has been observed in the archaeological record (Weiss-Krejci, 2005; Carroll, 2009), natural disasters (Magni and Guareschi, 2021) or through scavenger activity (Haglund et al., 1989; Berryman, 2002; Young, 2017). Bodies are also moved as part of legal case work (Morovic-Budak, 1965), or as part of ongoing humanitarian work related to conflict, as exemplified by Ferrándiz (2013) for mass graves in Spain. Post-mortem translocation can also occur within forensic contexts. After a victim has been murdered, perpetrators, depending on their relationship with the victim, the context and the location of the crime, might use a range of body disposal methods as their modus operandi (Beauregard and Field, 2008; Chai et al., 2021; Terribile et al., 2024; Whitehead Apm et al., 2024). One such approach could be to hide the body or move it from the primary crime scene or hiding place to a shallow grave (Davenport, 2001; Hawksworth and Wiltshire, 2011; Berezowski et al., 2022). To distinguish this post-mortem movement of the body from the time-since-death (PMI) and time-since-burial (PBI), we define the period since remains were intentionally removed and relocated by perpetrators as the post-translocation interval (PTI). The estimation of the PTI as a post-mortem clock can be used in cases involving the intentional post-mortem movement of buried remains to a secondary locale away from the original crime scene in forensic investigations. Within the broader time-since-death interval, the PTI occurs after body deposition or burial at a primary locale. Once the remains are moved to a secondary (or sometimes a tertiary) location the secondary PBI (or tertiary PBI) commences. While PMI and PBI, depending on the context, can refer to one or two separate post-mortem events, the PTI will be the period in the post-mortem timeline, after the PBI, when a body is excavated and translocated to a secondary locale. There have been limited studies investigating the post-mortem movement of remains by perpetrators. The limited number of reported or published cases in which perpetrators have removed remains from the primary locales and translocated them to secondary locales could potentially explain why relatively little attention has been directed to PTI estimations. Notwithstanding this, cases of translocated remains have been reported, as exemplified by incidents where the remains of victims were moved by perpetrators in Bosnia and Serbia (Skinner et al., 2001; Jessee and Skinner, 2005; Congram, 2008; Tuller and Hofmeister, 2014; Klinkner, 2016), and more recently in the United States (Shapiro, 2020). The estimation of the PBI and PTI contributes to investigations as the intervals may be useful to link a suspect to a crime scene, especially in cases where there is no reliable witness testimony or other circumstantial evidence. Collectively, this information is needed by police for crime reconstructions to address key questions of who, what, why, when and how; and crucially by prosecutors to make sound judgments in court proceedings, avoiding wrongful prosecutions (Introna et al., 2011).

3 The intersection between microbial communities, forensic ecogenomics and post-mortem time-since-interval estimations

3.1 Microbial activity during decomposition: the post-mortem interval

The microbiome of individual human beings is unique and consists of a diversity and abundance of bacteria, archaea, fungi, and algae across different regions of the body (Huttenhower et al., 2012), where they vary depending on their “theatre of activity” (Whipps et al., 1988; Berg et al., 2020). The “theatre of activity” encompasses the collective genetic material of the microbes, the products of their metabolic activities, and molecules produced in the environment in which they function and interact (Whipps et al., 1988; Berg et al., 2020). Microbial communities like bacteria, that live on skin and within the digestive system, genital tract, and oral cavity of mammals, play a crucial role in maintaining immune systems, protecting against pathogens and breaking down and metabolising complex molecules (Jordán et al., 2015; Cundell, 2018; Rowland et al., 2018; Lambring et al., 2019; Moeller and Sanders, 2020). In death, these microbial communities play an equally central role during decomposition, taking centre stage in what we designate as the ‘theatre of decomposition activity’ (Figure 4). During decomposition of the body, they are crucial in the biochemical breakdown of structural elements (Janaway, 1995; Gill-King, 1996; Hopkins et al., 2000; Parkinson et al., 2009) as complex molecules like proteins, lipids, carbohydrates, and nucleic acids are broken down into simple molecules (Mackie et al., 1991; Vass et al., 2002; Hyde et al., 2013; Fiedler et al., 2015; Forbes et al., 2017; Lutz et al., 2020; Nolan et al., 2020; Burcham et al., 2024). During the biochemical breakdown, microbial metabolites, or the byproducts of the decomposition process, are released (Agapiou et al., 2015; Irish et al., 2019; Furuta et al., 2024). A combination of biotic and abiotic factors can alter, impede or accelerate the decomposition process and the biochemical breakdown of the structural elements (Carter, 2005; Schotsmans et al., 2017, 2022; Young, 2017; Spies et al., 2020, 2024). Within terrestrial ecosystems decomposer communities such as bacteria and fungi have evolved to take advantage of decaying organic matter (DeBruyn et al., 2024). The sensitivity and transiency of microbial communities in response to the pulse of nutrients that are released into the soil, creating a Cadaver Decomposition Island (CDI) (Carter, 2005), make them a valuable indicator of internment and exhumation of bodies (Humphreys et al., 2013; Pechal et al., 2013; Benbow et al., 2015; Cobaugh et al., 2015; Hyde et al., 2017). This is especially the case considering the impact of decomposition and decay on the soil biogeochemistry and microbial community composition (Hopkins et al., 2000).

Figure 4

Temporal and compositional shifts in the post-mortem mammalian microbiome (thanatomicrobiome, and the epinecrotic), have facilitated the estimation of the PMI (Metcalf et al., 2013; Bergmann et al., 2014; Can et al., 2014; Olakanye et al., 2014; Pechal et al., 2014; Javan et al., 2016b, 2016a, 2017). The analysis of microbial community composition through MPS has revolutionised forensic researchers’ ability to rapidly characterise diverse microbial communities from the soil microbiome, the thanatomicrobiome, and the epinecrotic microbiome, thereby enhancing forensic analyses (Table 1). MPS is becoming the preferred method to sequence the hypervariable V3-4 region of the 16S rRNA (Gao et al., 2021) because of its improved capacity to characterise diverse microbial communities rapidly from the body (gut, oral, anal cavities and skin and organs) (Hyde et al., 2013; Pechal et al., 2013, 2014; Iancu et al., 2016, 2024; Javan et al., 2016a; Burcham et al., 2024) and gravesoil (Finley et al., 2016; Cui et al., 2022; Olakanye and Ralebitso-Senior, 2022). Complex computational microbial community analysis is achieved through a bioinformatics pipeline [QIIME (Caporaso et al., 2010), QIIME2 (Bolyen et al., 2019), and Mothur (Schloss et al., 2009)] (Table 2). These pipelines allow for the characterisation of raw sequence data into Operational Taxonomic Units (OTU), with a cutoff at 97%, or more recently into Amplicon Sequence Variants (ASV), with a cutoff at 99%, due to the need for higher taxonomic resolution (Gao et al., 2021; Fasolo et al., 2024). Taxonomic assignment is achieved through reference databases, such as the Ribosomal Database Project (RDP) (Wang et al., 2007) which is useful for genus-level assignments or with SILVA (Quast et al., 2013) and Greengenes (DeSantis et al., 2006) which are databases preferred for species-level classification, and visualisation options (phylogenetic tree generation) (Liu et al., 2024).

To evaluate the reliability of the post-mortem microbiome, a diverse range of models [human donors (Can et al., 2014; Damann et al., 2015; Hauther et al., 2015; Johnson et al., 2016; DeBruyn and Hauther, 2017; Javan et al., 2017; Ashe et al., 2021; Deel et al., 2021; Iancu et al., 2023; Burcham et al., 2024), corpses from casework (Javan et al., 2016a; Pechal et al., 2018; Lutz et al., 2020; Zhang et al., 2021), rodent (Liu et al., 2020; Zhao et al., 2022) and pig (Pechal et al., 2014; Iancu et al., 2015, 2016, 2024)] have been tested in indoor and outdoor scenarios. Sample collection to characterise the thanatomicrobiome, and the epinecrotic microbiome generally consisted of vigorously swabbing anatomical sites, such as the oral cavity, nose, hands, the torso and rectum (Pechal et al., 2014, 2018; Iancu et al., 2015, 2016, 2023, 2024; Johnson et al., 2016; Ashe et al., 2021; Zhang et al., 2021; Zhao et al., 2022; Burcham et al., 2024), as well as collecting tissue samples from skeletal elements (ribs) (Damann et al., 2015; Deel et al., 2021) and internal organs (blood, heart, brain, liver, spleen and cecum) (Can et al., 2014; Hauther et al., 2015; Javan et al., 2016a; DeBruyn and Hauther, 2017; Liu et al., 2020; Lutz et al., 2020). Different anatomical regions of the body harbour a unique microbiome in life. In the early post-mortem period after death, these regions exhibit distinct successional shifts in microbial community diversity and abundance between body regions, tissues and organs (Can et al., 2014; Pechal et al., 2014, 2018; Javan et al., 2016a; Lutz et al., 2020). While the early post-mortem preserves the individuality of anatomical-site microbial signature, as decomposition progresses microbial communities from the gut, oral cavity and rectum migrate and colonise the body (Javan et al., 2016a; Moitas et al., 2023).

Identification of core bacterial taxa within numerous empirically validated studies lends additional support towards forming a universal network of decomposers with a finer taxonomic resolution that would ensure the estimation of more consistent time-since-intervals and the development of a reliable “microbial clock” (Metcalf et al., 2013; Lauber et al., 2014; Pechal et al., 2014; Burcham et al., 2024). For PMI estimation shifts at the phylum level (Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria) (Metcalf et al., 2013; Pechal et al., 2014; Iancu et al., 2015) have been observed providing a board overview of microbial changes during death (Table 1). Specific shifts during decomposition of taxa at genus, family and species level within these phyla require that a finer taxonomic resolution is developed for reliable post-mortem clocks using microbial communities. Specific families of bacterial taxa have been identified from the phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria found in the gut/abdominal cavity (Clostridiaceae, Lactobacillaceae, Bacteroidaceae, Xanthomonadaceae, Enterococcaceae) (Metcalf et al., 2013; Hauther et al., 2015; DeBruyn and Hauther, 2017), on the skin [Campylobacteraceae, Pseudomonadaceae, Sphingobacteriaceae, Streptococcaceae, Clostridiaceae, Lactobacillaceae and Xanthomonadaceae] (Metcalf et al., 2013; Iancu et al., 2023), and in the oral cavity [Prevotellaceae (Prevotella), Streptococcaceae (Streptococcus), Veillonellaceae (Veillonella), Micrococcaceae (Rothia), Pseudomonadaceae (Pseudomonas), and Moraxellaceae (Psychrobacter)] (Hyde et al., 2013; Iancu et al., 2015; Javan et al., 2016b; Ashe et al., 2021; Wang et al., 2024). These taxa exhibit changes in diversity and richness during decomposition, which can be used as a microbial clock for PMI estimation (Metcalf et al., 2013, 2016). Shifts in the abundance and diversity of microbial communities have been correlated with internal biochemical changes of the body (Pechal et al., 2018; Deel et al., 2021). For instance, the decline of anaerobic bacteria, Bacteroides and Lactobacilus, has been found to coincided with the shift in conditions of the body cavity as oxygen is reintroduced post-rupture (Hauther et al., 2015), while Pseudomonas and Clostridium have been cited to release collagenases to break down bone (Deel et al., 2021). The “Post-mortem Clostridium Effect” (PCE), a concept introduced by Javan et al. (2017) refers to the ubiquitous nature of Clostridium spp. found throughout decomposition, making it a key microbial marker in the PMI. The effect is characterised by the rapid colonisation of the body by this species as conditions become more anaerobic (Can et al., 2014; Iancu et al., 2016; DeBruyn and Hauther, 2017; Liu et al., 2020) and due to their proteolytic function for breaking down collagen (Javan et al., 2017). A diverse range of Clostridium spp. have been characterised in the early post-mortem period from the thanatomicrobiome at 4 h (112), 12 h (Iancu et al., 2023), as well as at 24h and 58 h (Can et al., 2014) (Table 2), making it an essential biomarker for PMI estimation.

Complex algorithms leveraging machine learning (Table 2) (Johnson et al., 2016; Metcalf, 2019; Liu et al., 2020; Cui et al., 2022; Yang et al., 2023), allows for interpretation of the microbiome composition data through diversity and richness calculations, as well as the development of predictive modelling through machine learning algorithms (random forest regression, xgboost method and neural networks) (Pechal et al., 2014, 2018; Johnson et al., 2016; Liu et al., 2020; Zhao et al., 2022; Burcham et al., 2024; Iancu et al., 2024). These models have demonstrated robust performance, with accuracy assessed by metrics such as mean absolute error (MAE), which quantifies the average deviation between predicted and actual PMI values. Studies have shown that random forest models built on microbiome data from skin, organ and in some cases gravesoil samples, particularly using 16S rRNA gene markers, provide reliable PMI estimates, often within a small error margin over decomposition periods of up to several weeks. This has been demonstrated using mouse models where the PMI was estimated within approximately 3 days over a period of 48 days (Metcalf et al., 2013, 2016). Models leveraging random forest regression algorithms appear to be the preferred machine learning method used for PMI estimation (DeBruyn and Hauther, 2017; Liu et al., 2020; Lutz et al., 2020; Deel et al., 2021; Zhang et al., 2021; Zhao et al., 2022; Burcham et al., 2024; Iancu et al., 2024). Random forest algorithms are based on supervised learning and use multiple decision trees to make predictions (Berk, 2008). Random forest have effectively been used for PMI estimation because they can work with and process large datasets (Pechal et al., 2018; Zhang et al., 2021; Burcham et al., 2024), reduce errors, increase reliability for PMI estimation (Pechal et al., 2018; Namkung, 2020; Li et al., 2023; Wu et al., 2024) and allow for the integration of various multi-omics datasets (Burcham et al., 2024; Li et al., 2024). In principle, each decision tree is constructed from different subsets of microbiome sequencing data, capturing patterns in the microbial taxa present in the samples (Namkung, 2020; Schonlau and Zou, 2020). The algorithm combines the outputs into a final prediction. Despite growing interest, the predictive performance of random forest models remains variable across studies, species, and sampling strategies. Using a mouse model Liu et al. (2020) predicted PMI from microbial communities in the internal organs with a high accuracy of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. Yet in human cadavers Deel et al. (2021) reported a MAE of 724 to 853 ADD ± 39 days over a total of 5,201 ADD, and a MAE of 793.33 ± 34 days over two seasons using multiple ribs. Yang et al. (2023) further highlighted seasonal affects from swab samples collected from the rectum and gravesoil of pig carcasses. The winter trail rectal samples yielded a MAE of 2.478 days, while gravesoil performed slightly better with a MAE of 2.001 days. In summer, rectal samples had a MAE of 1.375 days and the gravesoil sample had a MAE of 1.567 days (Yang et al., 2023). In a severe cold environment Iancu et al. (2024) found the best model for PMI prediction, combined internal and external swabs of pig carcasses for a MAE of 1.36 weeks. It is worth noting that the cross-validation of predictive models improve the accuracy and objectivity of PMI estimates by minimising human bias, while training and test datasets add to the robustness of the findings (Johnson et al., 2016; Pechal et al., 2018; Liu et al., 2020; Deel et al., 2021; Hu et al., 2021; Zhang et al., 2021; Burcham et al., 2024; Iancu et al., 2024). Leveraging similar machine learning approaches for gravesoil microbiome signatures could be applied to the estimation of the PBI and PTI.

3.2 Microbial succession for post-burial and post-translocation interval estimation

Anthropogenic activities, such as the act of burying and translocating a body, can disturb the natural stratigraphy of soil, impacting ecosystems and microbial communities (Jansson et al., 2023) to accommodate the needs of humans. Studies of Vindolanda, a Roman auxiliary fort in the UK, revealed the significance of the interplay between human intervention in the environment, local ecological conditions and soil microbial communities, and the lasting impact it can have (Driel-Murray, 2001; Birley, 2009; Orr et al., 2021). For example, microbial analysis by Orr et al. (2021) revealed that soils dating to the earliest occupation of the Vindolanda site were dominated by the phyla Bacteroidetes, Firmicutes and Proteobacteria, and contained better preserved artefacts when compared to control soils, which were characterised by increased abundances of Acidobacteria, Actinobacteria and Planctomycetes. This study highlights the interconnectedness of microbial community shifts and human activity in combination with the unique environmental conditions, which collectively led to better preservation at Vindolanda. Similarly, a study from Western Kazakhstan showed the long-term impact of human intervention on soil microbial communities, specifically relating to palaeosoils below a burial mound, dating to 2,500 years ago (Kichko et al., 2023). In contrast to surface control soils, the specific burial conditions, including reduction of air, water and organic material in buried soils, led to decreases in the abundances of Actinobacteria clades of Gaiella, Solirubrobacteriales, and Frankiales. Conversely, there were increases in the diversities of Actinobacteria (Acidimicrobiia, Propionibacteriales, Micromonosporales, Euzebyales), Firmicutes (Bacilli), Chloroflexi (Thermomicrobiales), Acidobacteria (Subgroup 6), and Proteobacteria (Tistrellales) (Kichko et al., 2023). These studies are examples of how microbial communities in soils impacted by human intervention have distinctive compositions that differ from the microbial communities found within natural soils in that specific environment, with no human impact. Moreover, these studies highlight that the occurrence, distribution and abundance of these distinctive microbial communities are influenced by specific environmental and burial conditions.

The decomposition of a body has a similar effect on soil microbial communities. Shifts in soil microbial composition for surface depositions of mammalian or human donor cadaver remains have been reported in studies conducted in China (Guo et al., 2016; Yang et al., 2023), and the USA (Lauber et al., 2014; Cobaugh et al., 2015; Weiss et al., 2016; Burcham et al., 2024). Studies considering soil microbial shifts for buried human, pig or rodent (mice or rats) carcasses have been conducted in China (Zhang et al., 2021; Cui et al., 2022; Yang et al., 2023; Wang et al., 2024), the USA (Keenan et al., 2018), and the UK (Olakanye et al., 2017; Olakanye and Ralebitso-Senior, 2018; Olakanye and Ralebitso-Senior, 2022; Procopio et al., 2019; Bisker et al., 2021, 2024). Underpinning these studies is the focus to develop more reliable methods for PMI estimation by using gravesoil, sometimes complemented by the analysis of the post-mortem human microbiome (Lauber et al., 2014; Yang et al., 2023; Burcham et al., 2024). Similar to predicting PMI from the thanatomicrobiome and epinectrotic microbiome, PMI estimation from gravesoil is also sensitive to species (rodent, pig and human) and seasonal context. Zhang et al. (2021) used gravesoil microbial community from buried rat models to achieve a MAE of 2.04 ± 0.35 days, which improved to a MAE of 1.82 ± 0.33 days when the biomarker set was considered during 60-day decomposition. Cui et al. (2022) refined this approach by focusing on the 18 dominant genera from buried mice to obtain an MAE of 1.27 ± 0.18 day within 36 days. Seasonality affected the generalizability of the models. Yang et al. (2023) found that gravesoil from buried pigs produced a MAE of 1.567 days for summer, but the accuracy decreased for winter with a MAE of 2.001 days. Wang et al. (2024) investigated a different effect by introducing fresh and buried pig femurs and reported a MAE of 55.65 ADD. Placed in the correct interpretive frame, the results of studies analysing gravesoil yield meaningful information about the dynamics of microbial shifts in clandestine graves, and the PBI. The analysis of gravesoil in these studies, alters the parameter of interest (specifically the biological process being captured and the timeframe being estimated). Rather than providing an estimate of the PMI, as is commonly assumed, this experimental design directs researchers toward estimating PBI instead. Previous studies have undoubtedly laid an important foundation for PMI estimation, and their methodological contributions and statistical analysis remain valid. However, the concern arises from how their findings have been interpreted. Since soil microbial communities shift after the inclusion (deposition or burial) of mammalian remains (and not at the start of death unless death occurs at the exact same time and place), the presented evidence about PMI is, in fact, evidence for PBI estimation. An example of the difference between the PMI and PBI has been highlighted by Damann et al. (2015) who reported the two intervals, the first is the interval between the time of death and sample collection (the PMI), and second is the interval between placement and sampling (the PBI). As illustrated in the study, the PBI is shorter than the PMI as it begins once a body is deposited (or buried), with microbial change in the burial environment shifting at the moment of placement and not at death.

Estimating the PBI and PTI relies on characterising non-native microbial taxa in soil, which serve as markers to distinguish natural soils from gravesoils (Tables 3, 4). During decomposition, microbial communities will migrate into the soil, exploiting the resources that are available and forming a microbial community that is unique to that CDI (Weiss et al., 2016). Changes in bacterial community structure over time and season for buried pig tissue and plant litter samples have been observed by Olakanye and Ralebitso-Senior (2018). Changes were recorded for the phyla Actinobacteria (Micromonosporaceae), Bacteroidetes (Sphingobacteriaceae), Firmicutes (Planococcaceae) and Proteobacteria (Rhizobiaceae, Hyphomicrobiaceae and Xanthomonadaceae) with unique microbial shifts persisting up to day 365 after burial. Control soils were characterised by Actinobacteria (Nocardioidaceae), Firmicutes (Alicyclobacillaceae), and Proteobacteria (Comamonadaceae and Bradyrhizobiaceae). Microcosms containing pig tissue were characterised by Actinobacteria (Nocardiaceae and Micrococcaceae), Proteobacteria (Alcaligenaceae and Hyphomicrobiaceae) (Olakanye and Ralebitso-Senior, 2018). These findings are also consistent with Procopio et al. (2019), who identified that mammal-derived Bacteroides (Bacteroidacea) could be identified in grave soils collected directly next to the superior part of the carcass, and distinguished from control soils 6 months post-burial. Human-derived Bacteroides have also been detected in soils collected from underneath the body, 198 days after cadaver surface placement (Cobaugh et al., 2015). Other studies have reported the existence of decomposition-related microbial taxa at post-burial intervals of 120 days (Wang et al., 2024) for soils collected from pig femurs burials and 720 days from homogenised soil samples collected at 4 sides of the grave (Bisker et al., 2021). These findings indicated further that non-native taxa do persist in gravesoils and that they might serve as a universal microbial marker for buried remains, demonstrating the value of using microbial succession. Studies have attempted to map shifts in microbial composition over several years to determine whether gravesoils return to basal levels after decomposition. Singh et al. (2018) showed that decomposition-impacted soils from 0 to 10 cm below human cadavers did not recover to basal levels even after 732 days, reflecting similar findings by Cobaugh et al. (2015). A second study by Keenan et al. (2018) reported on the impact of human cadaver decomposition on the soil microbial communities and soil composition, which still measurable after 4 years. Additionally, in the same study human-associated Bacteroides was still detectable at the bottom of the grave (Keenan et al., 2018), reflecting similar findings by Cobaugh et al. (2015). The Burcham et al. (2021) study highlighted that faint microbial signatures from soils collected directly underneath cadaveric remains could be used to differentiate gravesoils from natural soils after 10 years. The strongest distinction between gravesoils and natural soils was up until 12 months after deposition, after which the soil microbial communities began to return to basal levels (Burcham et al., 2021).

The decomposition of mammalian remains has a lasting spatiotemporal effect on soil microbial communities (DeBruyn et al., 2024; Taylor et al., 2024), which can potentially be used as markers for PBI estimations. The persistence of specific bacterial phyla, such as Acidobacteria, Firmicutes, and Proteobacteria for extended periods post-burial in gravesoil, underscores their potential as indicators for mammalian decomposition and for PBI estimation. Given these studies leveraging gravesoil two things are clear: first the decomposition of mammalian remains has a lasting spatiotemporal effect on soil microbial communities (DeBruyn et al., 2024; Taylor et al., 2024), which can be used as markers for PBI estimations; and secondly currently for the extended burial period, gravesoil identification relies primarily on the detection, inclusion, or persistence of specific microbial taxa that differ from the background, undisturbed soil community. For more reliable PBI estimations, research should prioritise the development of models that incorporate finer taxonomic classifications, beyond phylum and genus. Additionally, further research and results need to be tested and evaluated across different biogeographic locations and burial conditions. For more reliable PBI estimations, research should prioritise the development of predictive regression models.

Although few cases involving the translocation of single clandestine graves are published, the PTI is an important time-since-interval and can provide valuable information to forensic investigators regarding the context of the crime, body disposal patterns and treatment of a victim after death. The need to investigate the translocation of remains, and hence for PTI estimations, has been highlighted in previous publications. In their paper discussing the use of ninhydrin reactive nitrogen in soil to detect graves, Carter et al. (2008) stated that “Bodies can be moved from the original site of death (and subsequent scenes).” As such, depending on when the body was moved and translocated from the original burial or deposition site, it is possible that the “removed human may leave a persistent effect in former gravesoil” (Carter et al., 2008). The persistence and uniqueness of microbial communities from the human microbiome that are found in soil have also been posited by Cobaugh et al. (2015) as a forensic tool which could “prove useful in cases where body remains have been moved from the original location of decomposition.” Considering this, the potential of shifts and the persistence of soil microbial communities during the decomposition process and after translocation, can offer further insight as a “microbial clock” beyond PMI estimations (Metcalf et al., 2013) to estimate the PBI and PTI. Ralebitso-Senior et al. (2016) proposed that the microbial communities found within gravesoils could be used as a means to link a victim to a crime scene, which could be especially useful in instances where “remains have been moved and/or decomposed.” Building on this concept, it is also possible that the PTI could provide evidence linking suspects to both primary and secondary locales as crime scenes, and at specific temporal intervals such as time of deposition or burial. Fu et al. (2019) also stated that the “dissimilarity in soil communities may help experts to identify the original location from which a cadaver has been moved.” Although Gemmellaro et al. (2023) referred specifically to fungal communities, their recommendation highlights the need for further research to investigate how the translocation of buried mammalian carcasses and human donor cadavers affects the decomposition process and the microorganisms that drive it. Therefore, there is potential for soil microbial communities to not only provide a post-mortem time-since-interval for when remains were translocated but also to aid investigators in narrowing down the original location the remains were moved from if discovered at the secondary locale. The use of multidisciplinary approaches such as forensic ecogenomics, forensic archaeology, and forensic geology to determine when remains were intentionally recovered and reburied by perpetrators, would offer insight into creating a potential timeline of events, which can aid investigators in linking suspects to specific sites and crime scenes, aiding the investigation and prosecution process (Dirkmaat and Cabo, 2016; Ralebitso-Senior et al., 2016).

The utility of microbial communities in forensic investigations also lies in their ability to provide valuable information about post-mortem treatment of the body and a timeline of events after death, and becomes useful in cases where bodies have been moved (Demanèche et al., 2017; Karadayı, 2021). In their study using surface deposition of human donor cadavers, Cobaugh et al. (2015) reported increases in the abundance of Proteobacteria and Firmicutes, while Acidobacteria abundance decreased during active decay. The researchers argued that microbial communities from the human microbiome, including Actinobacteria (Eggerthella), Firmicutes (Phascolarctobacterium and Tissierella) and Proteobacteria (Paenalcaligenes), that were introduced into the surrounding soil during decomposition, would not persist for long outside of their natural environment. This study found that once the dry remains were removed from the site, there was a decrease in microbial community abundance. It was also found that members of the genus Bacteroides (human-associated) persisted within the CDI 198 days after cadaver deployment on site. Once the dry remains were removed from the deposition site, there was a decrease in their abundance by day 126 and taxa was not detectable in the grave by day 204 (Cobaugh et al., 2015). Subsequently, a 180-day study by Olakanye and Ralebitso-Senior (2022) characterised microbial shifts in gravesoil mesocosms before and after the exhumation of whole piglets and demonstrated that microbial community structure and composition can be used in PTI estimation. Their study showed changes at 150 days post-burial at which point Proteobacteria (Xanthomonadales and Xanthomonadaceae) and Verrucomicrobiota (Verrucomicrobiaceae) were abundant. On the other hand, Bacteroidetes (Bacteroidales), Firmicutes (Clostridiales and Clostridiaceae_1) and Proteobacteria (Hydrogenophilales and Hydrogenophilaceae) were abundant in homogenised soils samples collected from random mesocosm positions 120 days after exhumation, indicating a potential decomposer network for translocated remains and PTI estimation (Olakanye and Ralebitso-Senior, 2022). Considering the impact of a decomposition event, which alters the biochemical signature of soils (Benninger et al., 2008; Macdonald et al., 2014; DeBruyn et al., 2021), the burial and exhumation of mammalian remains will induce specific shifts in the composition of the soil microbial community throughout the post-mortem period, driven by the decomposition process (Olakanye et al., 2014, 2015). This allows a unique gravesoil microbial community to develop which will be distinct from microbial communities in the natural background soil. Similar to how environmental conditions aided in the uniqueness of microbial communities in Vindolanda and Western Kazakhstan, surface depositions and subsurface burials can preserve microbial signatures, allowing them to be used as evidence in forensic investigations, for the estimation of PBI and PTI, and as markers to distinguish natural control and gravesoils.

4 Framework for PBI and PTI

The dynamics of, and shift in, gravesoil microbial communities can provide a substantial contribution to the estimation of the PBI and PTI. Specifically, the distinct microbial communities resulting from decomposition provide a reliable means of identifying grave sites. The temporal persistence of these microbial communities in terrestrial burial environments indicates that they could be a potential tool in the estimation of post-mortem time-since-intervals for forensic investigations and aid in clandestine grave location. Just as these communities can be used to estimate the PMI, they could also serve as a post-mortem “microbial clock” to estimate the time-since-burial as well as the time since a body was removed from its burial or depositional environment. This approach leverages the same principles used for time-since-death estimation using the microbiome, extending their application to scenarios involving intentional exhumation and translocation of remains in forensic cases. We propose a framework (Figure 5) showcasing how microbial communities from gravesoil can be incorporated into case work to estimate the PBI and PTI.

Figure 5

Once a clandestine grave site is located, investigators would collect soil from the grave pits. For graves that contain a body or skeletonised remains, in situ soil samples can be collected from around and underneath the remains using sterilized soil corers or metal spatulas. In the case of empty grave pits soil samples can be collected from the bottom of the grave. To avoid contamination of the crime scene and grave environment, any tools used in the excavation and recovery of the remains must be sterilised before and after use according to the preferred protocol of the research laboratory or crime investigation unit. Soil should be collected from 4 cardinal points in and at the centre of the grave, to ensure a representative sample of the entire microbial community at the time the grave is discovered. Sterile sample tubes that are DNA/RNA-free and DNase/RNase-free need to be labelled clearly with the case number, location and sample date. Triplicate control soil samples can be collected from around the grave site at 2 m, 5 m, and 10 m intervals to serve as reference samples for the site from undisturbed natural areas. On-site and during transport, all the samples must be packed individually and kept in an icebox. Once in the laboratory, the samples can be stored in a − 20 °C freezer until analysis. Control soils need to be sieved through 2 mm mesh to remove any twigs, small stones, or debris, while ensuring no contamination from the laboratory environment. After microbial DNA extraction, the samples can be prepared for 16S rRNA sequencing through MPS. During the bioinformatic pipeline, sequences should be classified, and the microbial composition and relative abundance determined.

At this stage, the sequence data can be used to determine based on the presence or absence of microbial biomarkers whether a sample comes from a human-derived gravesoil , with reference to previously published data for the specific environment and conditions (Tables 3, 4). The sequence data and microbial relative abundances in the samples can also be used to estimate the PBI and PTI. This can be done by comparing the community abundance of the sample to a trained regression/classification model, such as a machine learning random forest model. While the regression and classification models central to this approach are still under development, the framework is grounded in established principles of machine learning, forensic ecology and forensic ecogenomics from previous studies (Johnson et al., 2016; Liu et al., 2020; Burcham et al., 2024). As a conceptual tool, it highlights a path for future empirical research. By outlining the process from sampling to predictive modelling, the proposed framework aims to bridge the gap in current post-mortem time-since-interval estimation, specifically for the use of gravesoil to contribute to the PBI and the PTI.

In order to establish the PBI and PTI as a reliable framework for time-since-interval estimation, it needs to pass through the three categories of validation, which are development, internal validation and external validation to prove reliability (Budowle et al., 2008). This includes setting up empirically sound experimental proof-of-concept or pilot study designs to develop and optimise the PBI and PTI protocol for gravesoil, from collection to sequencing, as well as developing a regression-based model for predictive estimation. The field and laboratory protocol based on the framework in Figure 5, and predictive model can be validated internally by assessing its performance, sensitivity and reliability against control samples as well as additional empirical studies. Finally, the robustness of the entire framework can be validated externally through collaboration between independent laboratories. The validation process can involve two stages. The first stage can entail future research that contributes to building, testing and validating of such a predictive succession model using data from diverse geographic regions and burial conditions. The second stage can align with inter-laboratory proficiency where the framework’ and model performances are assessed independently by multiple laboratories. In so doing, the developed knowledge would contribute to the growing discourse of using microbial communities as a high-resolution and reliable tool in forensic investigations, particularly for use as a temporal indicator not only for time-since-death, but also the time-since-burial and time-since-translocation of a victim’s remains.

Lastly, complementing the validation process is a protocol that delineates essential information required for reporting (Table 5), thereby promoting reproducibility and standardization across studies. This template addresses the variability in methodological approaches and reporting of findings across current microbiome studies and aims to foster more inclusive and detailed reporting of key elements that form part of experimental designs from sampling to sequencing. Ultimately, the inclusion of the information highlighted in the table will allow for the advancement of forensic ecogenomics and use of soil microbial communities to aid PBI and PTI estimations, contributing to the admissibility and reproducibility within forensic science.

The framework proposed in this review is modelled after established forensic microbial workflows to estimate physiological time (Pechal et al., 2014). The framework proposed by Pechal et al. (2014) is specifically designed for human-associated microbial succession. Although similar to the Pechal et al. (2014) framework, the analytical flow for the current proposed framework is adapted specifically for gravesoil-based samples and the PBI and PTI time-since-intervals being estimated. However, due to the shared focus on 16S rRNA gene profiling, there is a methodological overlap. By maintaining methodological continuity, the current framework allows for easier integration into existing or recommended forensic and post-mortem microbial clock workflows from sample collection to analysis, thereby contributing to a streamlined overall forensic workflow.

To aid in crime scene reconstruction, multidisciplinary empirical research are crucial for developing and refining novel and sensitive forensic methods for post-mortem time-since-interval estimation and clandestine grave location (Mansegosa et al., 2021; Berezowski et al., 2022). As a complementary approach, microbial data can also be integrated into multidisciplinary forensic workflows alongside forensic entomology (Iancu et al., 2015, 2016, 2018), forensic botany (Coyle et al., 2005; Wiltshire, 2009), drone-based remote sensing (Bodnar et al., 2019; de Bruyn et al., 2025) and geophysical approaches (Molina et al., 2015; Berezowski et al., 2022). The value-added outcome will be enhanced strength of the generated and collected data, and subsequent interpretation related to the temporality and treatment of the victims remains.

5 Laboratory and data analysis: biases and limitations

16S rRNA-based techniques are useful for characterising the microbiome of terrestrial ecosystems (Gkarmiri et al., 2017; DiLegge et al., 2022), aquatic ecosystems (Méndez-Pérez et al., 2020; Burtseva et al., 2021), the human body (Huttenhower et al., 2012; Kho and Lal, 2018) and for forensic analyses (Akutsu et al., 2012; Jesmok et al., 2016; Yang et al., 2024). Several challenges can, however, influence sequencing data, resulting in misrepresentation of the results, downstream interpretation and overall reliability of the derived PMI, PBI and PTI estimations. For instance, a considerable issue can be primer bias, where the primers do not align with the target DNA template to be amplified (Green et al., 2015). This can lead to distortions in the data as communities are either under- or over-represented in a sample (Lee et al., 2012; Poretsky et al., 2014; Silverman et al., 2021). During the sequencing run, it is also possible that cross-sample contamination can occur when indexes are misassigned to the wrong samples (sequences) due to barcode mismatching or index hoping (Guenay-Greunke et al., 2021). Additionally, a common problem in molecular laboratories is contamination of samples with low biomass input from shared reagents, equipment or workflows (Salter et al., 2014; Minich et al., 2019). Many of these challenges can lead to skewed results.

16S rRNA-based techniques are also limited by their reliance on the relative abundance of microbial communities (Poretsky et al., 2014). For microbial studies, raw sequence data are reported as proportions, as data are normalised by dividing counts for microbial features (OTUs or ASVs) by the total number of reads resulting in relative abundances (Zemb et al., 2020; Xia, 2023). However, the challenge of transforming counts to proportions to normalise data is that the observed microbial shifts are not necessarily reflective of the actual change in the total microbial community of the sample (Tsilimigras and Fodor, 2016). Instead, they could indicate compositional artefacts related to the expression of microbiome data as proportions normalised to a constant sum (Weiss et al., 2017; Alteio et al., 2021). While normalisation such as through rarefying (McMurdie and Holmes, 2014; Hong et al., 2022) is an important step in the bioinformatics workflow to correct for technical read depth or amplification biases, and to allow for the cross-sample comparisons, it can affect the reported microbial community composition (Weiss et al., 2017; Kumar et al., 2018; Swift et al., 2023). Apparent shifts in the relative abundance of one microbial community might be due to a decrease in the relative abundance of another microbial community, rather than reflecting biological change within the sample (Poretsky et al., 2014; Weiss et al., 2017; Swift et al., 2023). This can confound the results by obscuring increases, or overemphasising declines, as a portion of the data is removed (Tsilimigras and Fodor, 2016; Xia, 2023). Understanding the compositional bias within samples is important, especially when the total microbial load matters, such as in developing post-mortem microbial clocks for forensic investigations (Kaszubinski et al., 2020; Tozzo et al., 2022).

Transforming 16S data from relative to absolute abundances can be achieved through quantitative PCR (qPCR) (Zemb et al., 2020) or by for instance, cell counts through flow cytometry (Frossard et al., 2016; Vandeputte et al., 2017). When paired with appropriate internal standards, techniques such as qPCR (Dreier et al., 2022) and shotgun metagenomics (Poretsky et al., 2014) can complement 16S rRNA sequencing. By basing sequencing output on known quantities or absolute abundances, these approaches enable researchers to detect actual changes in microbial community abundance within samples (Durazzi et al., 2021). 16S rRNA, qPCR, and shotgun metagenomics are limited by the inherent variability of the experimental design and the preferred protocol for DNA extraction (Sui et al., 2020; Shaffer et al., 2022) and molecular microbial analysis (Schloss et al., 2011; Zhao et al., 2023). This inherent variability underscores the need for reliable absolute standards for reproducibility and comparison of sample data across biogeographic regions and time periods. Also, incorporating internal standards can aid in overcoming compositionality issues (Poretsky et al., 2014; Harrison et al., 2021). This is especially the case for interpreting shifts in microbial community abundance and diversity across different samples, time periods and environmental conditions. Spike-in control via the inclusion of a known amount of synthetic DNA to samples can help track the loss of DNA from the initial extraction, purification and amplification process (Poretsky et al., 2014; Tourlousse et al., 2016; Camacho-Sanchez, 2024). As part of good scientific practice and for quality control purposes, the inclusion of several controls in sample collection, processing and analysis is essential for veracity in forensic research. The inclusion of negatives and positive controls (Edmonds and Williams, 2018) and field blanks (Hornung et al., 2019) is useful to monitor contamination at different stages of the molecular analysis workflow, particularly in cases of outdoor field sampling. The inclusion of blanks during the extraction process can aid in further detecting any contaminated reagents in extraction kits (“kitome”) (Salter et al., 2014; Olomu et al., 2020). Ultimately, the controls incorporated into the workflow from sampling to analysis, including the potential contamination identified, should be reported transparently (Hornung et al., 2019).

Equally important is the appropriate use of machine learning approaches in microbiome research for forensic application to ensure they are scientifically sound and practical for real-world forensic cases. Currently, the limitations of machine learning for post-mortem time-since-interval estimation are that for datasets to be comparable, models are generated based on data from overlapping periods of decomposition, i.e., sample data from different studies are cut to the same decomposition timeline or post-mortem days. This means data from longer PMI and PBI periods are excluded from the datasets (Belk et al., 2019). Additionally, models are based on biases inherent in the dataset and experimental design, such as sampling site, project study period (weeks, months), environmental conditions, and molecular microbial analysis protocols (Metcalf, 2019; Namkung, 2020). As such, the same abiotic and biotic factors impacting the decomposition will also limit the application of machine learning models as universal predictive models (Chourasia et al., 2025). Because machine learning does not perform well at extreme ends of PMI (Belk et al., 2018), it is recommended that datasets need to be expanded to include microbiome data for extended post-mortem periods, applied further to prolonged post-burial and post-translocation intervals. The integration of machine learning into the development of PMI, PBI and PTI microbial clocks necessitates the standardisation of analytical protocols. Key methodological considerations include: cross-validation of data to prevent overfitting (Namkung, 2020) through holdouts, where machine learning models are trained on datasets, e.g., from specific sites, while withholding a single dataset such as a single site to validate the algorithm’s performance (Sharma et al., 2020; Papoutsoglou et al., 2023); and reporting of the variance of the model performance, i.e., sensitivity of the model’s predictions to changes in the training set (Ma et al., 2025; Romano et al., 2025).

To avoid inadvertently boosting model performance due to data leakage (Papoutsoglou et al., 2023), research and practitioner teams must ensure that, for example, temporally distinct samples from the same source (cadaver or gravesoil) are not unintentionally mixed into the training dataset. While the use of AI allows for the inclusion of large and complex datasets, their predictive models raise questions regarding generalizability and applicability to real-world forensic cases (Metcalf, 2019; Chourasia et al., 2025). Thus, further model testing is needed to capture more nuanced shifts in microbial communities after death for more reliable time-since-interval estimations across regions and seasons. Models need to be tested and cross-validated on diverse datasets including different burial conditions, different host models, different environmental conditions, and unknown training data to assess the generalizability of the machine learning models (Kubinski et al., 2022; Papoutsoglou et al., 2023), and to develop better predictive outcomes for post-mortem time-since-intervals using microbial data. However, there is currently a lack of complete and available datasets for PBI and PTI estimations, limiting their incorporation in machine learning algorithms to develop more reliable time-since-interval estimations. Finally, to make PMI, PBI and PTI results comparable and transferable between species and biogeographic regions, standardised protocols are needed to ensure the scientific rigour and robustness of data and the reproducibility and validity of results (Poussin et al., 2018; Schloss, 2018; Singh and Agarwal, 2024; Swayambhu et al., 2025), ultimately contributing to admissibility in forensic investigations.

6 Conclusion: challenges and future directions

Traditional methods, such as forensic entomology, forensic botany and forensic taphonomy, for estimating time-since-intervals in forensic investigations exhibit significant limitations due to the variability introduced by biotic and abiotic factors influencing the decomposition process. Additionally, these approaches rely largely on the experience and knowledge of the practitioner and the availability of regional databases for specimen identification (insects and plants), both of which are often lacking. Moreover, current experimental research designs fall short, often lacking replication and control burials, and failing to reflect current forensic casework. The characterization of the soil microbiome is a useful tool for clandestine grave identification. This review aimed to enhance the discussion related to post-mortem microbial clocks with an overview and introduction to the time-since-burial (PBI) and newly introduced concept of time-since-translocation (PTI).

This review recommends that microbial molecular ecology analysis through forensic ecogenomics offers a promising avenue for achieving accurate post-mortem time-since-interval estimations, encompassing PMI, PBI and PTI. Leveraging molecular approaches from ecology, we argue that forensic ecogenomics provides a viable tool to investigate clandestine burials through the analysis of shifts within gravesoil microbial communities for more precise post-mortem time-since-interval estimations. MPS and other molecular techniques, such as proteomics and transcriptomics have shown potential in characterising microbial communities, offering an innovative approach for reliable time-since estimations. Advancements in MPS have significantly enhanced our understanding of post-mortem microbial communities (thanatomicrobiome, epinecrotic microbiome, and soil microbiome) involved in decomposition. These microbial communities demonstrate considerable potential to be used as a universal microbial network for forensic applications.

The several examples presented in this perspective indicate that shifts in soil microbial communities for buried remains cannot only be used as a “microbial clock” to estimate the PMI. Instead, depending on the burial and environmental conditions, they can distinguish gravesoils months to years after deposition and burial. Additionally, the persistence of microbial communities in gravesoils is useful because it allows for the differentiation of gravesoils from equivalent undisturbed natural soils due to the presence of non-native bacterial taxa. This is useful not only for PBI estimation but also for locating clandestine graves. Emerging evidence from the reviewed studies indicates that the soil microbiome offers a useful tool that can contribute to post-mortem time-since intervals. The decomposition of a body leaves a lasting impression on the soil composition and microbial communities, which can persist from weeks to years depending on the burial conditions and the treatment of the body. For PBI and PTI estimations, this review identified bacterial phyla, Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, and Proteobacteria, as consistent and informative biomarkers in burial contexts. At genus level, studies have reported that the presence or absence of specific microbial communities can be used to distinguish experimental (decomposition) soils from control soils without decomposing remains. However, a finer resolution, most likely species level characterisation is needed to distinguish plant litter from mammalian decomposition soils, and potentially animal from human derived decomposition. Additionally, considering that soil microbial communities undergo further shifts once remains are translocated, they could be useful in establishing a “microbial clock” for translocated remains. However finer taxonomic classification of microbial communities is needed for a more robust approach. By leveraging the changes in microbial community structure over time, forensic scientists can develop models to estimate both PBI and PTI.

This review highlights the need for further research to validate microbial community analysis across diverse biogeographical regions to enhance its precision and reliability as a tool for forensic investigations. Such validation could potentially improve the accuracy of post-burial interval (PBI) and post-translocation interval (PTI) estimations, ultimately enhancing methods for clandestine grave identification. To address the variability in reporting and methodological approaches across current microbiome studies, there is a need for standardisation and validation of experimental designs across diverse biogeographic regions and seasonal conditions to ensure broader applicability and reliability. Parallel with scenarios of surface depositions, future research should also focus on remains that have been buried in the sub-surface or relocated to refine and validate these models. To support standardization, transparency and reproducibility, it is recommended that methodological details and metadata related to the experimental designs, bioinformatics pipeline and machine learning protocol be included in future studies following community standards such as the Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al., 2011). This review introduces a novel conceptual framework for PBI and PTI estimation alongside a reporting template. The reporting template outlines key information and methodological elements that must be systematically recorded and reported, including site metadata, sampling methodology, sample handling, the inclusion of controls and standards, microbial analysis and sequencing pipelines, and data availability. Along with standardised, reproducible and transparent outcomes, the recommended approaches will also allow for the cross-study comparisons and the inclusive integration of forensic ecogenomics into other multidisciplinary workflows. Integration of PBI and PTI estimation into the broader post-mortem time-since-interval estimations provides a more comprehensive approach, contributing to forensic investigations. The proposed conceptual framework, while still in the developmental stages, can contribute to and enhance ongoing efforts toward stringent practices and external validation for forensic acceptance. Ultimately, continued research and validation across diverse biogeographic regions are essential to establish forensic ecogenomics approaches as a standard practice, thereby enhancing the precision and reliability of forensic investigations, contributing to the resolution of crimes.

References

• 1

  AdamsK. S.FinaughtyD. A.GibbonV. E. (2024). Forensic taphonomic experimental design matters: a study assessing clothing and carrion biomass load on scavenging in Cape Town, South Africa. Int. J. Legal Med.138, 1669–1684. doi: 10.1007/s00414-024-03171-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  AgapiouA.ZorbaE.MikediK.McGregorL.SpiliopoulouC.StatheropoulosM. (2015). Analysis of volatile organic compounds released from the decay of surrogate human models simulating victims of collapsed buildings by thermal desorption–comprehensive two-dimensional gas chromatography–time of flight mass spectrometry. Anal. Chim. Acta883, 99–108. doi: 10.1016/j.aca.2015.04.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  AhmadA.AhmadA. H.DiengH.SathoT.AhmadH.AzizA. T.et al. (2011). Cadaver wrapping and arrival performance of adult flies in an oil palm plantation in northern peninsular Malaysia. J. Med. Entomol.48, 1236–1246. doi: 10.1603/ME10247

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  AkutsuT.MotaniH.WatanabeK.IwaseH.SakuradaK. (2012). Detection of bacterial 16S ribosomal RNA genes for forensic identification of vaginal fluid. Legal Med.14, 160–162. doi: 10.1016/j.legalmed.2012.01.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  AlteioL. V.SénecaJ.CanariniA.AngelR.JansaJ.GusevaK.et al. (2021). A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem.160:108357. doi: 10.1016/j.soilbio.2021.108357

  • CrossRef
  • Google Scholar

• 6

  AmannR. I.LudwigW.SchleiferK. H. (1995). Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev.59, 143–169. doi: 10.1128/mr.59.1.143-169.1995

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  AmesC.TurnerB. (2003). Low temperature episodes in development of blowflies: implications for postmortem interval estimation. Med. Vet. Entomol.17, 178–186. doi: 10.1046/j.1365-2915.2003.00421.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  AnstettÉ. (2023). Never-ending funerals. Annual burials and reburials of victims of mass violence in present-day Bosnia and Herzegovina. Death Stud.47, 666–678. doi: 10.1080/07481187.2022.2131046

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  ArcherM. S. (2004). Rainfall and temperature effects on the decomposition rate of exposed neonatal remains. Sci. Justice44, 35–41. doi: 10.1016/S1355-0306(04)71683-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  AsheE. C.ComeauA. M.ZejdlikK.O’ConnellS. P. (2021). Characterization of bacterial community dynamics of the human mouth throughout decomposition via metagenomic, metatranscriptomic, and culturing techniques. Front. Microbiol.12:689493. doi: 10.3389/fmicb.2021.689493

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  BassW. (1996). “Outdoor decomposition rates in Tennessee” in Forensic Taphonomy. eds. HaglundW.SorgM. (Boca Raton: CRC Press), 181–186.

  • Google Scholar

• 12

  BeauregardE.FieldJ. (2008). Body disposal patterns of sexual murderers: implications for offender profiling. J. Police Crim. Psych.23, 81–89. doi: 10.1007/s11896-008-9027-6

  • CrossRef
  • Google Scholar

• 13

  BelkA. D.DeelH. L.BurchamZ. M.KnightR.CarterD. O.MetcalfJ. L. (2019). Animal models for understanding microbial decomposition of human remains. Drug Discov. Today Dis. Model.28, 117–125. doi: 10.1016/j.ddmod.2019.08.013

  • CrossRef
  • Google Scholar

• 14

  BelkA.XuZ. Z.CarterD. O.LynneA.BucheliS.KnightR.et al. (2018). Microbiome data accurately predicts the postmortem interval using random forest regression models. Genes9:104. doi: 10.3390/genes9020104

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  BelliniE.AmbrosioE.ZottiM.NucciG.GabbrielliM.VanezisP. (2016). The usefulness of cadaveric Fungi as an investigation tool. Am J Forensic Med Pathol37:23. doi: 10.1097/PAF.0000000000000210

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  BenbowM. E.PechalJ. L.LangJ. M.ErbR.WallaceJ. R. (2015). The potential of high-throughput metagenomic sequencing of aquatic bacterial communities to estimate the postmortem submersion interval. J. Forensic Sci.60, 1500–1510. doi: 10.1111/1556-4029.12859

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  BenningerL. A.CarterD. O.ForbesS. L. (2008). The biochemical alteration of soil beneath a decomposing carcass. Forensic Sci. Int.180, 70–75. doi: 10.1016/j.forsciint.2008.07.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  BerezowskiV.MoffatI.ShendrykY.MacGregorD.EllisJ.MallettX. (2022). A multidisciplinary approach to locating clandestine gravesites in cold cases: combining geographic profiling, LiDAR, and near surface geophysics. For. Sci. Int. Synergy5:100281. doi: 10.1016/j.fsisyn.2022.100281

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  BergG.RybakovaD.FischerD.CernavaT.VergèsM.-C. C.CharlesT.et al. (2020). Microbiome definition re-visited: old concepts and new challenges. Microbiome8:103. doi: 10.1186/s40168-020-00875-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  BergmannR. C.Ralebitso-SeniorT. K.ThompsonT. J. U. (2014). An RNA-based analysis of changes in biodiversity indices in response to Sus scrofa domesticus decomposition. Forensic Sci. Int.241, 190–194. doi: 10.1016/j.forsciint.2014.06.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  BerkR. A. (2008). Statistical learning from a regression perspective. New York, NY: Springer New York.

  • Google Scholar

• 22

  BerrymanH. E. (2002). “Disarticulation pattern and tooth mark artifacts associated with pig scavenging of human remains: A case study” in Advances in forensic Taphonomy. eds. HaglundW. D.SorgM. H. (Boca Raton: CRC Press), 487–495.

  • Google Scholar

• 23

  BhadraP.HartA. J.HallM. J. R. (2014). Factors affecting accessibility to blowflies of bodies disposed in suitcases. Forensic Sci. Int.239, 62–72. doi: 10.1016/j.forsciint.2014.03.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  BirleyR. (2009). Vindolanda: Everyday life on Rome’s northern frontier. Stroud: Amberley Publishing Limited.

  • Google Scholar

• 25

  BiskerC.TaylorG.CarneyH.OrrC. H.JavanG. T.Ralebitso-SeniorT. K. (2024). Comparative soil bacterial metabarcoding after aboveground vs. subsurface decomposition of Mus musculus. Sci. Rep.14:31179. doi: 10.1038/s41598-024-82437-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  BiskerC.TaylorG.CarneyH.Ralebitso-SeniorT. K. (2021). A combined application of molecular microbial ecology and elemental analyses can advance the understanding of decomposition dynamics. Front. Ecol. Evol.9:605817. doi: 10.3389/fevo.2021.605817

  • CrossRef
  • Google Scholar

• 27

  BodnarS. R.CiottiJ.SorokaJ.LarsenR. A.SpragueJ. E. (2019). Drone-assisted thermal imaging to determine the location of unmarked graves. J. Forensic Identif.69:378.

  • Google Scholar

• 28

  BolyenE.RideoutJ. R.DillonM. R.BokulichN. A.AbnetC. C.Al-GhalithG. A.et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol.37, 852–857. doi: 10.1038/s41587-019-0209-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  BonacciT.MendicinoF.BonelliD.CarlomagnoF.CuriaG.ScapoliC.et al. (2021). Investigations on arthropods associated with decay stages of buried animals in Italy. Insects12:311. doi: 10.3390/insects12040311

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  BuchanA.HaddenM.SuzukiM. T. (2009). Development and application of quantitative-PCR tools for subgroups of the Roseobacter clade. Appl. Environ. Microbiol.75, 7542–7547. doi: 10.1128/AEM.00814-09

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  BudowleB.SchutzerS. E.MorseS. A.MartinezK. F.ChakrabortyR.MarroneB. L.et al. (2008). Criteria for validation of methods in microbial forensics. Appl. Environ. Microbiol.74, 5599–5607. doi: 10.1128/AEM.00966-08

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  BurchamZ. M.BelkA. D.McGivernB. B.BouslimaniA.GhadermaziP.MartinoC.et al. (2024). A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables. Nat. Microbiol.9, 595–613. doi: 10.1038/s41564-023-01580-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  BurchamZ. M.WeitzelM. A.HodgesL. D.DeelH. L.MetcalfJ. L. (2021). A pilot study characterizing gravesoil bacterial communities a decade after swine decomposition. Forensic Sci. Int.323:110782. doi: 10.1016/j.forsciint.2021.110782

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  BurtsevaO.KublanovskayaA.FedorenkoT.LobakovaE.ChekanovK. (2021). Gut microbiome of the White Sea fish revealed by 16S rRNA metabarcoding. Aquaculture533:736175. doi: 10.1016/j.aquaculture.2020.736175

  • CrossRef
  • Google Scholar

• 35

  ByardR. W. (2024). Concealed homicides—A postmortem study and review. Am J Forensic Med Pathol45, 20–25. doi: 10.1097/PAF.0000000000000868

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  CaccianigaM.BottacinS.CattaneoC. (2012). Vegetation dynamics as a tool for detecting clandestine graves. J. Forensic Sci.57, 983–988. doi: 10.1111/j.1556-4029.2012.02071.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  Camacho-SanchezM. (2024). A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria. Int. Microbiol.27, 719–730. doi: 10.1007/s10123-023-00422-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  CampobassoC. P.Di VellaG.IntronaF. (2001). Factors affecting decomposition and Diptera colonization. Forensic Sci. Int.120, 18–27. doi: 10.1016/S0379-0738(01)00411-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  CanI.JavanG. T.PozhitkovA. E.NobleP. A. (2014). Distinctive thanatomicrobiome signatures found in the blood and internal organs of humans. J. Microbiol. Methods106, 1–7. doi: 10.1016/j.mimet.2014.07.026

  • CrossRef
  • Google Scholar

• 40

  CaporasoJ. G.KuczynskiJ.StombaughJ.BittingerK.BushmanF. D.CostelloE. K.et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nat. Methods7, 335–336. doi: 10.1038/nmeth.f.303

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  CarrollM. (2009). Dead soldiers on the move: transporting bodies and commemorating men at home and abroad. In: A Morillo, N Hanel & E Martin (eds), Ediciones Polifemo, Madrid.Limes XX. Actas del XX Congreso. Int. Estud. Sobre Front. Romana13, 823–832.

  • Google Scholar

• 42

  CarterD. (2005). Forensic taphonomy: processes associated with cadaver decomposition in soil (Doctoral dissertation). Australia: James Cook University.

  • Google Scholar

• 43

  CarterD. O.TibbettM. (2003). Taphonomic mycota: Fungi with forensic potential. J. Forensic Sci.48, 1–4. doi: 10.1520/JFS2002169

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  CarterD. O.TomberlinJ. K.BenbowM. E.MetcalfJ. L. (2017). Forensic microbiology. 1st Edn. West Sussex: Wiley.

  • Google Scholar

• 45

  CarterD. O.YellowleesD.TibbettM. (2007). Cadaver decomposition in terrestrial ecosystems. Naturwissenschaften94, 12–24. doi: 10.1007/s00114-006-0159-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  CarterD. O.YellowleesD.TibbettM. (2008). Using ninhydrin to detect gravesoil. J. Forensic Sci.53, 397–400. doi: 10.1111/j.1556-4029.2008.00681.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  CarterD. O.YellowleesD.TibbettM. (2010). Moisture can be the dominant environmental parameter governing cadaver decomposition in soil. Forensic Sci. Int.200, 60–66. doi: 10.1016/j.forsciint.2010.03.031

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48

  CartozzoC.SinghB.SwallJ.SimmonsT. (2021). Postmortem submersion interval (PMSI) estimation from the microbiome of Sus scrofa bone in a freshwater lake. J. Forensic Sci.66, 1334–1347. doi: 10.1111/1556-4029.14692

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  ChaiA. M. M.BeauregardE.ChopinJ. (2021). “Drop the body”: body disposal patterns in sexual homicide. Int. J. Offender Ther. Comp. Criminol.65, 692–714. doi: 10.1177/0306624X20931436

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  ChourasiaA.MalikP.HarsolaA.KaushalR.SinghR. (2025). “Applying machine learning techniques to estimate post-mortem interval from decomposition stages” in Proceedings of the international conference on recent advancement and modernization in sustainable intelligent technologies & applications (RAMSITA-2025). eds. BhaleraoS.GuptaP.KateV. (Dordrecht: Atlantis Press International BV), 250–261.

  • Google Scholar

• 51

  CobaughK. L.SchaefferS. M.DeBruynJ. M. (2015). Functional and structural succession of soil microbial communities below decomposing human cadavers. PLoS One10:e0130201. doi: 10.1371/journal.pone.0130201

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  CockleD. L.BellL. S. (2015). Human decomposition and the reliability of a ‘universal’ model for post mortem interval estimations. Forensic Sci. Int.253, 136.e1–136.e9. doi: 10.1016/j.forsciint.2015.05.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  CogswellG. C.CrossP. A. (2021). The effects of surface variation on the decomposition of pig carcasses. J. Forensic Leg. Med.79:102108. doi: 10.1016/j.jflm.2020.102108

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  CongramD. R. (2008). A clandestine burial in Costa Rica: prospection and excavation. J. Forensic Sci.53, 793–796. doi: 10.1111/j.1556-4029.2008.00765.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  CoyleH. M.LeeC.-L.LinW.-Y.LeeH. C.PalmbachT. M. (2005). Forensic botany: using plant evidence to aid in forensic death investigation. Croat. Med. J.46, 606–612.

  • Google Scholar

• 56

  CuiC.SongY.MaoD.CaoY.QiuB.GuiP.et al. (2022). Predicting the postmortem interval based on gravesoil microbiome data and a random forest model. Microorganisms11:56. doi: 10.3390/microorganisms11010056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57

  CundellA. M. (2018). Microbial ecology of the human skin. Microb. Ecol.76, 113–120. doi: 10.1007/s00248-016-0789-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  DamannF. E.WilliamsD. E.LaytonA. C. (2015). Potential use of bacterial community succession in decaying human bone for estimating postmortem interval. J. Forensic Sci.60, 844–850. doi: 10.1111/1556-4029.12744

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  DashH.DasS. (2020). Thanatomicrobiome and epinecrotic community signatures for estimation of post-mortem time interval in human cadaver. Appl. Microbiol. Biotechnol.104, 9497–9512. doi: 10.1007/s00253-020-10922-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60

  DavenportG. C. (2001). Remote sensing applications in forensic investigations. Hist. Archaeol.35, 87–100. doi: 10.1007/BF03374530

  • CrossRef
  • Google Scholar

• 61

  de BruynC.Ralebitso-SeniorK.ScottK.PanterH.BezombesF. (2025). Search, detect, recover: a systematic review of UAV-based remote sensing approaches for the location of human remains and clandestine graves. Drones9:674. doi: 10.3390/drones9100674

  • CrossRef
  • Google Scholar

• 62

  De MatteisM.GiorgettiA.VielG.GiraudoC.TerranovaC.LupiA.et al. (2021). Homicide and concealment of the corpse. Autopsy case series and review of the literature. Int. J. Legal Med.135, 193–205. doi: 10.1007/s00414-020-02313-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  DeBruynJ. M.HautherK. A. (2017). Postmortem succession of gut microbial communities in deceased human subjects. PeerJ5:e3437. doi: 10.7717/peerj.3437

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64

  DeBruynJ. M.HoelandK. M.TaylorL. S.StevensJ. D.MoatsM. A.BandopadhyayS.et al. (2021). Comparative decomposition of humans and pigs: soil biogeochemistry, microbial activity and metabolomic profiles. Front. Microbiol.11:608856. doi: 10.3389/fmicb.2020.608856

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65

  DeBruynJ. M.KeenanS. W.TaylorL. S. (2024). From carrion to soil: microbial recycling of animal carcasses. Trends Microbiol.33, 194–207. doi: 10.1016/j.tim.2024.09.003

  • CrossRef
  • Google Scholar

• 66

  DeelH.EmmonsA. L.KielyJ.DamannF. E.CarterD. O.LynneA.et al. (2021). A pilot study of microbial succession in human rib skeletal remains during terrestrial decomposition. mSphere6:e00455-21. doi: 10.1128/mSphere.00455-21

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  DemanècheS.SchauserL.DawsonL.FranquevilleL.SimonetP. (2017). Microbial soil community analyses for forensic science: application to a blind test. Forensic Sci. Int.270, 153–158. doi: 10.1016/j.forsciint.2016.12.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  DeSantisT. Z.HugenholtzP.LarsenN.RojasM.BrodieE. L.KellerK.et al. (2006). Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol.72, 5069–5072. doi: 10.1128/AEM.03006-05

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  DeVaultT. L.BrisbinI. L.RhodesO. E. (2004). Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposer. Can. J. Zool.82, 502–509. doi: 10.1139/z04-022

  • CrossRef
  • Google Scholar

• 70

  DicksonG. C.PoulterR. T. M.MaasE. W.ProbertP. K.KieserJ. A. (2011). Marine bacterial succession as a potential indicator of postmortem submersion interval. Forensic Sci. Int.209, 1–10. doi: 10.1016/j.forsciint.2010.10.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  DiLeggeM. J.ManterD. K.VivancoJ. M. (2022). Soil microbiome disruption reveals specific and general plant-bacterial relationships in three agroecosystem soils. PLoS One17:e0277529. doi: 10.1371/journal.pone.0277529

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  DirkmaatD. C.CaboL. L. (2016). Forensic archaeology and forensic taphonomy: basic considerations on how to properly process and interpret the outdoor forensic scene. Acad. Forensic Pathol.6, 439–454. doi: 10.23907/2016.045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73

  DreierM.MeolaM.BerthoudH.ShaniN.WechslerD.JunierP. (2022). High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota. BMC Microbiol.22:48. doi: 10.1186/s12866-022-02451-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  Driel-MurrayC. V. (2001). Vindolanda and the dating of Roman footwear. Britannia32, 185–197. doi: 10.2307/526955

  • CrossRef
  • Google Scholar

• 75

  DurazziF.SalaC.CastellaniG.ManfredaG.RemondiniD.De CesareA. (2021). Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci. Rep.11:3030. doi: 10.1038/s41598-021-82726-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  EdmondsK.WilliamsL. (2018). The role of the negative control in microbiome analyses. FASEB J.31, 940–943. doi: 10.1096/fasebj.31.1_supplement.940.3

  • CrossRef
  • Google Scholar

• 77

  EnglmeierJ.MitesserO.BenbowM. E.HothornT.von HoermannC.BenjaminC.et al. (2023). Diverse effects of climate, land use, and insects on dung and carrion decomposition. Ecosystems26, 397–411. doi: 10.1007/s10021-022-00764-7

  • CrossRef
  • Google Scholar

• 78

  FasoloA.DebS.StevanatoP.ConcheriG.SquartiniA. (2024). ASV vs OTUs clustering: effects on alpha, beta, and gamma diversities in microbiome metabarcoding studies. PLoS One19:e0309065. doi: 10.1371/journal.pone.0309065

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  FerrándizF. (2013). Exhuming the defeated: civil war mass graves in 21st-century Spain. Am. Ethnol.40, 38–54. doi: 10.1111/amet.12004

  • CrossRef
  • Google Scholar

• 80

  FiedlerS.BernsA. E.SchwarkL.WoelkA. T.GrawM. (2015). The chemistry of death – Adipocere degradation in modern graveyards. Forensic Sci. Int.257, 320–328. doi: 10.1016/j.forsciint.2015.09.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  FinleyS. J.BenbowM. E.JavanG. T. (2015). Microbial communities associated with human decomposition and their potential use as postmortem clocks. Int. J. Legal Med.129, 623–632. doi: 10.1007/s00414-014-1059-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82

  FinleyS. J.PechalJ. L.BenbowM. E.RobertsonB. K.JavanG. T. (2016). Microbial signatures of cadaver Gravesoil during decomposition. Microb. Ecol.71, 524–529. doi: 10.1007/s00248-015-0725-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  ForbesS. L. (2008). “Potential determinants of postmortem and Postburial interval of buried remains” in Soil analysis in forensic Taphonomy. eds. TibbettM.CarterD. O. (Boca Raton: CRC Press), 2225–2246.

  • Google Scholar

• 84

  ForbesS. L.PerraultK. A.ComstockJ. L. (2017). “Microscopic post-mortem changes the chemistry of decomposition” in Taphonomy of human remains: Forensic analysis of the dead and the depositional environment. eds. SchotsmansE.Marques-GrantN.ForbesS. L. (West Sussex: John Wiley & Sons, Ltd), 26–38.

  • Google Scholar

• 85

  FranceschettiL.AmadasiA.BugelliV.BolsiG.TsokosM. (2023). Estimation of late postmortem interval: where do we stand? A literature review. Biology12:783. doi: 10.3390/biology12060783

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  FrossardA.HammesF.GessnerM. O. (2016). Flow cytometric assessment of bacterial abundance in soils, sediments and sludge. Front. Microbiol.7:903. doi: 10.3389/fmicb.2016.00903

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  FuX.GuoJ.FinkelbergsD.HeJ.ZhaL.GuoY.et al. (2019). Fungal succession during mammalian cadaver decomposition and potential forensic implications. Sci. Rep.9:12907. doi: 10.1038/s41598-019-49361-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  FurutaK.ByrneJ.LuatK.CheungC.CarterD. O.TiptonL.et al. (2024). Volatile organic compounds produced during postmortem processes can be linked via chromatographic profiles to individual postmortem bacterial species. J. Chromatogr. A1728:465017. doi: 10.1016/j.chroma.2024.465017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  GallowayA.BirkbyW.JonesA.HenryT.ParksB. (1989). Decay rates of human remains in an arid environment. J. Forensic Sci.34, 607–616. doi: 10.1520/JFS12680J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  GaoB.ChiL.ZhuY.ShiX.TuP.LiB.et al. (2021). An introduction to next generation sequencing Bioinformatic analysis in gut microbiome studies. Biomolecules11:530. doi: 10.3390/biom11040530

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  GemmellaroM. D.LorussoN. S.DomkeR.KovalskaK. M.HashimA.Arevalo MojicaM.et al. (2023). Assessment of fungal succession in decomposing swine carcasses (Sus scrofa L.) using DNA metabarcoding. J. Fungi9:866. doi: 10.3390/jof9090866

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  GilesS. B.ErricksonD.Márquez-GrantN. (2022). Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations. J. Forensic Leg. Med.85:102292. doi: 10.1016/j.jflm.2021.102292

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  Gill-KingH. (1996). “Chemical and ultrastructural aspects of decomposition” in Forensic Taphonomy. eds. HaglundW.SorgM. (Boca Raton: CRC Press), 93–108.

  • Google Scholar

• 94

  GkarmiriK.MahmoodS.EkbladA.AlströmS.HögbergN.FinlayR. (2017). Identifying the active microbiome associated with roots and rhizosphere soil of oilseed rape. Appl. Environ. Microbiol.83:e01938-17. doi: 10.1128/AEM.01938-17

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  GoffM.OmoriA.GunatilakeK. (1988). Estimation of postmortem interval by arthropod succession. Am J Forensic Med Pathol9, 220–225. doi: 10.1097/00000433-198809000-00009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  GreenS. J.VenkatramananR.NaqibA. (2015). Deconstructing the polymerase Chain reaction: understanding and correcting Bias associated with primer degeneracies and primer-template mismatches. PLoS One10:e0128122. doi: 10.1371/journal.pone.0128122

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  Guenay-GreunkeY.BohanD. A.TraugottM.WallingerC. (2021). Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology. Sci. Rep.11:19510. doi: 10.1038/s41598-021-98018-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  GuoJ.FuX.LiaoH.HuZ.LongL.YanW.et al. (2016). Potential use of bacterial community succession for estimating post-mortem interval as revealed by high-throughput sequencing. Sci. Rep.6:24197. doi: 10.1038/srep24197

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  HaglundW.ReayD.SwindlerD. (1989). Canid scavenging/disarticulation sequence of human remains in the Pacific northwest. J. Forensic Sci.34, 587–606. doi: 10.1520/JFS12679J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  HandkeJ.ProcopioN.BuckleyM.Van Der MeerD.WilliamsG.CarrM.et al. (2017). Successive bacterial colonisation of pork and its implications for forensic investigations. Forensic Sci. Intl281, 1–8. doi: 10.1016/j.forsciint.2017.10.025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  HarrisonJ. G.CalderW. J.ShumanB.BuerkleC. A. (2021). The quest for absolute abundance: the use of internal standards for DNA-based community ecology. Mol. Ecol. Resour.21, 30–42. doi: 10.1111/1755-0998.13247

  • CrossRef
  • Google Scholar

• 102

  HaslamT. C. F.TibbettM. (2009). Soils of contrasting pH affect the decomposition of buried mammalian (Ovis aries) skeletal muscle tissue. J. Forensic Sci.54, 900–904. doi: 10.1111/j.1556-4029.2009.01070.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  HautherK. A.CobaughK. L.JantzL. M.SparerT. E.DeBruynJ. M. (2015). Estimating time since death from postmortem human gut microbial communities. J. Forensic Sci.60, 1234–1240. doi: 10.1111/1556-4029.12828

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 104

  HawksworthD. L.WiltshireP. E. J. (2011). Forensic mycology: the use of fungi in criminal investigations. Forensic Sci. Int.206, 1–11. doi: 10.1016/j.forsciint.2010.06.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  HeinrichF.Rimkus-EbelingF.DietzE.RaupachT.OndruschkaB.Anders-LohnerS. (2024). An assessment of the Henssge method for forensic death time estimation in the early post-mortem interval. Int. J. Legal Med.139, 105–117. doi: 10.1007/s00414-024-03338-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106

  HenßgeC.MadeaB. (2004). Estimation of the time since death in the early post-mortem period. Forensic Sci. Int.144, 167–175. doi: 10.1016/j.forsciint.2004.04.051

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  HochreinM. (2002) Introducing geotaphonomy to the archaeologist and crime scene reconstructionist, The Midwest Bioarcheology and Forensic Anthropology Association 9th Annual Meeting, October 19, 2002 Indianapolis, Indiana, USA

  • Google Scholar

• 108

  HongJ.KaraozU.De ValpineP.FithianW. (2022). To rarefy or not to rarefy: robustness and efficiency trade-offs of rarefying microbiome data. Bioinformatics38, 2389–2396. doi: 10.1093/bioinformatics/btac127

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109

  HopkinsD.WiltshireP.TurnerB. D. (2000). Microbial characteristics of soils from graves: an investigation at the interface of soil microbiology and forensic science. Appl. Soil Ecol.14, 283–288. doi: 10.1016/S0929-1393(00)00063-9

  • CrossRef
  • Google Scholar

• 110

  HornungB. V. H.ZwittinkR. D.KuijperE. J. (2019). Issues and current standards of controls in microbiome research. FEMS Microbiol. Ecol.95:fiz045. doi: 10.1093/femsec/fiz045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  HuL.XingY.JiangP.GanL.ZhaoF.PengW.et al. (2021). Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm. Sci. Justice61, 516–527. doi: 10.1016/j.scijus.2021.06.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  HumphreysM. K.PanacekE.GreenW.AlbersE. (2013). Comparison of protocols for measuring and calculating postmortem submersion intervals for human analogs in fresh water. J. Forensic Sci.58, 513–517. doi: 10.1111/1556-4029.12033

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  HuttenhowerC.GeversD.KnightR.AbubuckerS.BadgerJ. H.ChinwallaA. T.et al. (2012). Structure, function and diversity of the healthy human microbiome. Nature486, 207–214. doi: 10.1038/nature11234

  • CrossRef
  • Google Scholar

• 114

  HydeE. R.HaarmannD. P.LynneA. M.BucheliS. R.PetrosinoJ. F. (2013). The living dead: bacterial community structure of a cadaver at the onset and end of the bloat stage of decomposition. PLoS One8:e77733. doi: 10.1371/journal.pone.0077733

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  HydeE. R.MetcalfJ. L.BucheliS. R.LynneA. M.KnightR. (2017). “Microbial communities associated with decomposing corpses” in Forensic microbiology. eds. CarterD. O.TomberlinJ. K.BenbowM. E.MetcalfJ. L. (West Sussex: Wiley), 245–273.

  • Google Scholar

• 116

  IancuL.BonicelliA.ProcopioN. (2024). Decomposition in an extreme cold environment and associated microbiome—prediction model implications for the postmortem interval estimation. Front. Microbiol.15:1392716. doi: 10.3389/fmicb.2024.1392716

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  IancuL.CarterD. O.JunkinsE. N.PurcareaC. (2015). Using bacterial and necrophagous insect dynamics for post-mortem interval estimation during cold season: novel case study in Romania. Forensic Sci. Int.254, 106–117. doi: 10.1016/j.forsciint.2015.07.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  IancuL.JunkinsE. N.Necula-PetrareanuG.PurcareaC. (2018). Characterizing forensically important insect and microbial community colonization patterns in buried remains. Sci. Rep.8:15513. doi: 10.1038/s41598-018-33794-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  IancuL.MuslimA.AazmiS.JitaruV. (2023). Postmortem skin microbiome signatures associated with human cadavers within the first 12 h at the morgue. Front. Microbiol.14:1234254. doi: 10.3389/fmicb.2023.1234254

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  IancuL.SahleanT.PurcareaC. (2016). Dynamics of Necrophagous insect and tissue Bacteria for postmortem interval estimation during the warm season in Romania. J. Med. Entomol.53, 54–66. doi: 10.1093/jme/tjv156

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  IntronaF.De DonnoA.SantoroV.CorradoS.RomanoV.PorcelliF.et al. (2011). The bodies of two missing children in an enclosed underground environment. Forensic Sci. Int.207, e40–e47. doi: 10.1016/j.forsciint.2010.12.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122

  IrishL.RennieS. R.ParkesG. M. B.WilliamsA. (2019). Identification of decomposition volatile organic compounds from surface-deposited and submerged porcine remains. Sci. Justice59, 503–515. doi: 10.1016/j.scijus.2019.03.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  JanawayR. C. (1995). “The decay of buried human remains and their associated materials” in Studies in crime. eds. HunterJ.RobertsC.MartinA. (London: Routledge), 58–85.

  • Google Scholar

• 124

  JangidC.DalalJ.KumariK. (2023). Deciphering the microbial signature of death: advances in post-mortem microbial analysis. Probl. Forensic Sci.2023, 95–115. doi: 10.4467/12307483PFS.23.006.19055

  • CrossRef
  • Google Scholar

• 125

  JanssonJ. K.McClureR.EgbertR. G. (2023). Soil microbiome engineering for sustainability in a changing environment. Nat. Biotechnol.41, 1716–1728. doi: 10.1038/s41587-023-01932-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  JavanG. T.FinleyS. J.AbidinZ.MulleJ. G. (2016a). The thanatomicrobiome: A missing piece of the microbial puzzle of death. Front. Microbiol.7:225. doi: 10.3389/fmicb.2016.00225

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127

  JavanG. T.FinleyS. J.CanI.WilkinsonJ. E.HansonJ. D.TaroneA. M. (2016b). Human thanatomicrobiome succession and time since death. Sci. Rep.6:29598. doi: 10.1038/srep29598

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  JavanG. T.FinleyS. J.SmithT.MillerJ.WilkinsonJ. E. (2017). Cadaver thanatomicrobiome signatures: the ubiquitous nature of clostridium species in human decomposition. Front. Microbiol.8:2096. doi: 10.3389/fmicb.2017.02096

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  JavanG. T.FinleyS. J.TuomistoS.HallA.BenbowM. E.MillsD. (2019). An interdisciplinary review of the thanatomicrobiome in human decomposition. Forensic Sci. Med. Pathol.15, 75–83. doi: 10.1007/s12024-018-0061-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  JesmokE. M.HopkinsJ. M.ForanD. R. (2016). Next-generation sequencing of the bacterial 16S rRNA gene for forensic soil comparison: a feasibility study. J. Forensic Sci.61, 607–617. doi: 10.1111/1556-4029.13049

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131

  JesseeE.SkinnerM. (2005). A typology of mass grave and mass grave-related sites. Forensic Sci. Int.152, 55–59. doi: 10.1016/j.forsciint.2005.02.031

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  JohnsonH. R.TrinidadD. D.GuzmanS.KhanZ.ParzialeJ. V.DeBruynJ. M.et al. (2016). A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS One11:e0167370. doi: 10.1371/journal.pone.0167370

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  JordánF.LauriaM.ScottiM.NguyenT.-P.PraveenP.MorineM.et al. (2015). Diversity of key players in the microbial ecosystems of the human body. Sci. Rep.5:15920. doi: 10.1038/srep15920

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  JoussetA.LaraE.NikolauszM.HarmsH.ChatzinotasA. (2010). Application of the denaturing gradient gel electrophoresis (DGGE) technique as an efficient diagnostic tool for ciliate communities in soil. Sci. Total Environ.408, 1221–1225. doi: 10.1016/j.scitotenv.2009.09.056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  JurkevitchE.PasternakZ. (2021). A walk on the dirt: soil microbial forensics from ecological theory to the crime lab. FEMS Microbiol. Rev.45:fuaa053. doi: 10.1093/femsre/fuaa053

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  KamaluddinM. R.ShariffN.MatsaatG. (2018). Mechanical profiles of murder and murderers: an extensive review. Malays. J. Pathol.40, 1–10.

  • Google Scholar

• 137

  KaradayıS. (2021). Assessment of the link between evidence and crime scene through soil bacterial and fungal microbiome: A mock case in forensic study. Forensic Sci.Int.329:111060. doi: 10.1016/j.forsciint.2021.111060

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  KarčićH. (2017). Uncovering the truth: the Lake Perućac exhumations in eastern Bosnia. J. Muslim Minor. Aff.37, 114–128. doi: 10.1080/13602004.2017.1294374

  • CrossRef
  • Google Scholar

• 139

  KaszubinskiS. F.PechalJ. L.SchmidtC. J.JordanH. R.BenbowM. E.MeekM. H. (2020). Evaluating Bioinformatic pipeline performance for forensic microbiome analysis. J. Forensic Sci.65, 513–525. doi: 10.1111/1556-4029.14213

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  KeenanS. W.EmmonsA. L.TaylorL. S.PhillipsG.MasonA. R.MundorffA. Z.et al. (2018). Spatial impacts of a multi-individual grave on microbial and microfaunal communities and soil biogeochemistry. PLoS One13:e0208845. doi: 10.1371/journal.pone.0208845

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 141

  KhoZ. Y.LalS. K. (2018). The human gut microbiome – A potential controller of wellness and disease. Front. Microbiol.9:1835. doi: 10.3389/fmicb.2018.01835

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142

  KichkoA. A.SergalievN. K.IvanovaE. A.ChernovT. I.KimeklisA. K.OrlovaO. V.et al. (2023). The microbiome of buried soils demonstrates significant shifts in taxonomic structure and a general trend towards mineral horizons. Heliyon9:e17208. doi: 10.1016/j.heliyon.2023.e17208

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143

  KlinknerM. (2016). Karadžić’s guilty verdict and forensic evidence from Bosnia’s mass graves. Sci. Justice56, 498–504. doi: 10.1016/j.scijus.2016.07.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144

  KozichJ. J.WestcottS. L.BaxterN. T.HighlanderS. K.SchlossP. D. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol.79, 5112–5120. doi: 10.1128/AEM.01043-13

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145

  KubinskiR.Djamen-KepaouJ.-Y.ZhanabaevT.Hernandez-GarciaA.BauerS.HildebrandF.et al. (2022). Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease. Front. Genet.13:784397. doi: 10.3389/fgene.2022.784397

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146

  KumarM. S.SludE. V.OkrahK.HicksS. C.HannenhalliS.Corrada BravoH. (2018). Analysis and correction of compositional bias in sparse sequencing count data. BMC Genomics19:799. doi: 10.1186/s12864-018-5160-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 147

  LambringC. B.SirajS.PatelK.SankpalU. T.MathewS.BashaR. (2019). Impact of the microbiome on the immune system. Crit. Rev. Immunol.39, 313–328. doi: 10.1615/CritRevImmunol.2019033233

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148

  LaplaceK.BaccinoE.PeyronP.-A. (2021). Estimation of the time since death based on body cooling: a comparative study of four temperature-based methods. Int. J. Legal Med.135, 2479–2487. doi: 10.1007/s00414-021-02635-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 149

  LauberC. L.MetcalfJ. L.KeepersK.AckermannG.CarterD. O.KnightR. (2014). Vertebrate decomposition is accelerated by soil microbes. Appl. Environ. Microbiol.80, 4920–4929. doi: 10.1128/AEM.00957-14

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 150

  LeeC. K.HerboldC. W.PolsonS. W.WommackK. E.WilliamsonS. J.McDonaldI. R.et al. (2012). Groundtruthing next-gen sequencing for microbial ecology–biases and errors in community structure estimates from PCR amplicon pyrosequencing. PLoS One7:e44224. doi: 10.1371/journal.pone.0044224

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 151

  LenzE. J.ForanD. R. (2010). Bacterial profiling of soil using genus-specific markers and multidimensional scaling. J. Forensic Sci.55, 1437–1442. doi: 10.1111/j.1556-4029.2010.01464.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152

  LernerA.ShorY.VinokurovA.OkonY.JurkevitchE. (2006). Can denaturing gradient gel electrophoresis (DGGE) analysis of amplified 16s rDNA of soil bacterial populations be used in forensic investigations?Soil Biol. Biochem.38, 1188–1192. doi: 10.1016/j.soilbio.2005.10.006

  • CrossRef
  • Google Scholar

• 153

  LiN.LiangX.ZhouS.DangL.LiJ.AnG.et al. (2023). Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans. Forensic Sci. Int. Genet.66:102904. doi: 10.1016/j.fsigen.2023.102904

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154

  LiJ.WuY.LiuM.LiN.DangL.AnG.et al. (2024). Multi-omics integration strategy in the post-mortem interval of forensic science. Talanta268:125249. doi: 10.1016/j.talanta.2023.125249

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 155

  LiuR.GuY.ShenM.LiH.ZhangK.WangQ.et al. (2020). Predicting postmortem interval based on microbial community sequences and machine learning algorithms. Environ. Microbiol.22, 2273–2291. doi: 10.1111/1462-2920.15000

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 156

  LiuR.QiaoX.ShiY.PetersonC. B.BushW. S.CominelliF.et al. (2024). Constructing phylogenetic trees for microbiome data analysis: A mini-review. Comput. Struct. Biotechnol. J.23, 3859–3868. doi: 10.1016/j.csbj.2024.10.032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157

  LoreilleO. M.DiegoliT. M.IrwinJ. A.CobleM. D.ParsonsT. J. (2007). High efficiency DNA extraction from bone by total demineralization. Forensic Sci. Int.: Genet.1, 191–195. doi: 10.1016/j.fsigen.2007.02.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 158

  LutzH.VangelatosA.GottelN.OsculatiA.VisonaS.FinleyS. J.et al. (2020). Effects of extended postmortem interval on microbial communities in organs of the human cadaver. Front. Microbiol.11:569630. doi: 10.3389/fmicb.2020.569630

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 159

  MaJ.FangY.LiS.ZengL.ChenS.LiZ.et al. (2025). Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis. Front. Immunol.16:1528046. doi: 10.3389/fimmu.2025.1528046

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 160

  MacdonaldB. C. T.FarrellM.TuomiS.BartonP. S.CunninghamS. A.ManningA. D. (2014). Carrion decomposition causes large and lasting effects on soil amino acid and peptide flux. Soil Biol. Biochem.69, 132–140. doi: 10.1016/j.soilbio.2013.10.042

  • CrossRef
  • Google Scholar

• 161

  MackieR. I.WhiteB. A.BryantM. P. (1991). Lipid metabolism in anaerobic ecosystems. Crit. Rev. Microbiol.17, 449–479. doi: 10.3109/10408419109115208

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 162

  MadeaB. (2023). Estimation of the time since death. Boca Raton: CRC Press.

  • Google Scholar

• 163

  MagniP. A.GuareschiE. E. (2021). After the flood: a multidisciplinary investigation of human remains found in a floodplain and first record of raft spiders colonizing a corpse. J. Clin. Health Sci.6, 68–75. doi: 10.24191/jchs.v6i1(Special).13166

  • CrossRef
  • Google Scholar

• 164

  MaisonhauteJ.-É.ForbesS. L. (2023). Decomposition and insect succession on human cadavers in a humid, continental (Dfb) climate (Quebec, Canada). Int. J. Legal Med.137, 493–509. doi: 10.1007/s00414-022-02903-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 165

  MannR. W.BassW. M.MeadowsL. (1990). Time since death and decomposition of the human body: variables and observations in case and experimental field studies. J. Forensic Sci.35, 103–111. doi: 10.1520/JFS12806J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 166

  MansegosaD. A.GiannottiP. S.MarchioriJ. I.JofréF. N.AballayF. H.Fernandez AisaC. (2021). The story of a homicide: the location, exhumation, and multidisciplinary analysis of a clandestine burial. Forensic Sci. Int. Rep.3:100165. doi: 10.1016/j.fsir.2020.100165

  • CrossRef
  • Google Scholar

• 167

  Martín-VegaD.Martín NietoC.CifriánB.BazA.Díaz-ArandaL. M. (2017). Early colonisation of urban indoor carcasses by blow flies (Diptera: Calliphoridae): An experimental study from Central Spain. Forensic. Sci. Int.278, 87–94. doi: 10.1016/j.forsciint.2017.06.036

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 168

  MasonA. R.McKee-ZechH. S.HoelandK. M.DavisM. C.CampagnaS. R.SteadmanD. W.et al. (2022). Body mass index (BMI) impacts soil chemical and microbial response to human decomposition. mSphere7:e00325-22. doi: 10.1128/msphere.00325-22

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 169

  MatuszewskiS.KonwerskiS.FrątczakK.SzafałowiczM. (2014). Effect of body mass and clothing on decomposition of pig carcasses. Int. J. Legal Med.128, 1039–1048. doi: 10.1007/s00414-014-0965-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170

  MatuszewskiS.Mądra-BielewiczA. (2022). Competition of insect decomposers over large vertebrate carrion: Necrodes beetles (Silphidae) vs. blow flies (Calliphoridae). Curr. Zool.68, 645–656. doi: 10.1093/cz/zoab100

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 171

  McMurdieP. J.HolmesS. (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol.10:e1003531. doi: 10.1371/journal.pcbi.1003531

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172

  MegyesiM.NawrockiS.HaskellN. (2005). Using accumulated degree-days to estimate the postmortem interval from decomposed human remains. J. Forensic Sci.50, 1–9. doi: 10.1520/JFS2004017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173

  Méndez-PérezR.García-LópezR.Bautista-LópezJ. S.Vázquez-CastellanosJ.Alvarez-GonzálezC.Peña-MarínE.et al. (2020). High-throughput sequencing of the 16S rRNA gene to analyze the gut microbiome in juvenile and adult tropical gar (Atractosteus tropicus). Lat. Am. J. Aquat. Res.48, 456–479. doi: 10.3856/vol48-issue3-fulltext-2419

  • CrossRef
  • Google Scholar

• 174

  MenezesR. G.KanchanT.LoboS. W.JainA.BhatN. B.RaoN. G. (2008). Cadaveric fungi: not yet an established forensic tool. J. Forensic Leg. Med.15, 124–125. doi: 10.1016/j.jflm.2007.02.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 175

  MetcalfJ. L. (2019). Estimating the postmortem interval using microbes: knowledge gaps and a path to technology adoption. Forensic Sci. Int.: Genet.38, 211–218. doi: 10.1016/j.fsigen.2018.11.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 176

  MetcalfJ. L.Wegener ParfreyL.GonzalezA.LauberC. L.KnightsD.AckermannG.et al. (2013). A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife2:e01104. doi: 10.7554/eLife.01104

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 177

  MetcalfJ. L.XuZ. Z.WeissS.LaxS.Van TreurenW.HydeE. R.et al. (2016). Microbial community assembly and metabolic function during mammalian corpse decomposition. Science351, 158–162. doi: 10.1126/science.aad2646

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 178

  MicozziM. S. (1986). Experimental study of postmortem change under field conditions: effects of freezing, thawing, and mechanical injury. J. Forensic Sci.31, 953–961. doi: 10.1520/JFS11103J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 179

  MinichJ. J.SandersJ. G.AmirA.HumphreyG.GilbertJ. A.KnightR. (2019). Quantifying and understanding well-to-well contamination in microbiome research. mSystems4:10.1128/msystems.00186-19:19. doi: 10.1128/msystems.00186-19

  • CrossRef
  • Google Scholar

• 180

  MoellerA. H.SandersJ. G. (2020). Roles of the gut microbiota in the adaptive evolution of mammalian species. Philos. Trans. R. Soc. B375:20190597. doi: 10.1098/rstb.2019.0597

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 181

  MoitasB.CaldasI. M.Sampaio-MaiaB. (2023). Microbiology and postmortem interval: a systematic review. Forensic Sci. Med. Pathol.20, 696–715. doi: 10.1007/s12024-023-00733-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 182

  MolinaC. M.PringleJ. K.SaumettM.HernándezO. (2015). Preliminary results of sequential monitoring of simulated clandestine graves in Colombia, South America, using ground penetrating radar and botany. Forensic Sci. Int.248, 61–70. doi: 10.1016/j.forsciint.2014.12.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 183

  Morovic-BudakA. (1965). Experiences in the process of putrefaction in corpses buried in earth. Med. Sci. Law5, 40–43. doi: 10.1177/002580246500500110

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 184

  MuyzerG.De WaalE. C.UitterlindenA. G. (1993). Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol.59, 695–700. doi: 10.1128/aem.59.3.695-700.1993

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 185

  NamkungJ. (2020). Machine learning methods for microbiome studies. J. Microbiol.58, 206–216. doi: 10.1007/s12275-020-0066-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 186

  NawrockiS. P. (2016). “Forensic Taphonomy” in Handbook of forensic anthropology and archaeology. eds. BlauS.UbelakerD. (New York: Routledge), 373–391.

  • Google Scholar

• 187

  NolanA.-N.MeadR. J.MakerG.SpeersS. J. (2020). A review of the biochemical products produced during mammalian decomposition with the purpose of determining the post-mortem interval. Aust. J. Forensic Sci.52, 477–488. doi: 10.1080/00450618.2019.1589571

  • CrossRef
  • Google Scholar

• 188

  O’FlynnM. A. (1983). The succession and rate of development of blowflies in carrion in southern Queensland and the application of these data to forensic entomology. Aust. J. Entomol.22, 137–148. doi: 10.1111/j.1440-6055.1983.tb01860.x

  • CrossRef
  • Google Scholar

• 189

  OlakanyeA. O.NelsonA.Ralebitso-SeniorT. K. (2017). A comparative in situ decomposition study using still born piglets and leaf litter from a deciduous forest. Forensic Sci. Int.276, 85–92. doi: 10.1016/j.forsciint.2017.04.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 190

  OlakanyeA. O.Ralebitso-SeniorT. K. (2018). Soil metabarcoding identifies season indicators and differentiators of pig and Agrostis/Festuca spp. decomposition. Forensic Sci. Int.288, 53–58. doi: 10.1016/j.forsciint.2018.04.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 191

  OlakanyeA. O.Ralebitso-SeniorT. K. (2022). Profiling of successional microbial community structure and composition to identify exhumed gravesoil—a preliminary study. Forensic Sci.2, 130–143. doi: 10.3390/forensicsci2010010

  • CrossRef
  • Google Scholar

• 192

  OlakanyeA. O.ThompsonT.Komang Ralebitso-SeniorT. (2014). Changes to soil bacterial profiles as a result of Sus scrofa domesticus decomposition. Forensic Sci.Int245, 101–106. doi: 10.1016/j.forsciint.2014.10.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 193

  OlakanyeA. O.ThompsonT.Ralebitso-SeniorT. K. (2015). Shifts in soil biodiversity—a forensic comparison between Sus scrofa domesticus and vegetation decomposition. Sci. Justice55, 402–407. doi: 10.1016/j.scijus.2015.07.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 194

  OlomuI. N.Pena-CortesL. C.LongR. A.VyasA.KrichevskiyO.LuellwitzR.et al. (2020). Elimination of “kitome” and “splashome” contamination results in lack of detection of a unique placental microbiome. BMC Microbiol.20:157. doi: 10.1186/s12866-020-01839-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 195

  OrrC. H.WilliamsR.HalldórsdóttirH. H.BirleyA.GreeneE.NelsonA.et al. (2021). Unique chemical parameters and microbial activity lead to increased archaeological preservation at the Roman frontier site of Vindolanda, UK. Sci. Rep.11:15837. doi: 10.1038/s41598-021-94853-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 196

  PapoutsoglouG.TarazonaS.LopesM. B.KlammsteinerT.IbrahimiE.EckenbergerJ.et al. (2023). Machine learning approaches in microbiome research: challenges and best practices. Front. Microbiol.14:1261889. doi: 10.3389/fmicb.2023.1261889

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 197

  ParkinsonR. A.DiasK.-R.HorswellJ.GreenwoodP.BanningN.TibbettM.et al. (2009). “Microbial community analysis of human decomposition on soil” in Criminal and environmental soil forensics. eds. RitzK.DawsonL.MillerD. (Dordrecht: Springer Netherlands), 379–394.

  • Google Scholar

• 198

  PastulaE. C.MerrittR. W. (2013). Insect arrival pattern and succession on buried carrion in Michigan. J. Med. Entomol.50, 432–439. doi: 10.1603/ME12138

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 199

  PawlettM.RicksonJ.NiziolomskiJ.ChurchillS.KešnerM. (2018). Human cadaver burial depth affects soil microbial and nutrient status. AEFS1, 119–125. doi: 10.1558/aefs.33662

  • CrossRef
  • Google Scholar

• 200

  PayneJ. A. (1965). A summer carrion study of the baby pig Sus Scrofa Linnaeus. Ecology46, 592–602. doi: 10.2307/1934999

  • CrossRef
  • Google Scholar

• 201

  PayneJ. A.KingE. W.BeinhartG. (1968). Arthropod succession and decomposition of buried pigs. Nature219, 1180–1181. doi: 10.1038/2191180a0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 202

  PechalJ. L.CrippenT. L.BenbowM. E.TaroneA. M.DowdS.TomberlinJ. K. (2014). The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int. J. Legal Med.128, 193–205. doi: 10.1007/s00414-013-0872-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 203

  PechalJ. L.CrippenT. L.TaroneA. M.LewisA. J.TomberlinJ. K.BenbowM. E. (2013). Microbial community functional change during vertebrate carrion decomposition. PLoS One8:e79035. doi: 10.1371/journal.pone.0079035

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 204

  PechalJ. L.SchmidtC. J.JordanH. R.BenbowM. E. (2018). A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. Sci. Rep.8:5724. doi: 10.1038/s41598-018-23989-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 205

  PittnerS.BugelliV.BenbowM. E.EhrenfellnerB.ZisslerA.CampobassoC. P.et al. (2020). The applicability of forensic time since death estimation methods for buried bodies in advanced decomposition stages. PLoS One15:e0243395. doi: 10.1371/journal.pone.0243395

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 206

  PloegO. H. J.GraceR. C.CordingJ. R. (2024). An examination of body disposal patterns in sexual and non-sexual homicides. Police Pract. Res.26, 251–275. doi: 10.1080/15614263.2024.2415448

  • CrossRef
  • Google Scholar

• 207

  PoretskyR.Rodriguez-RL. M.LuoC.TsementziD.KonstantinidisK. T. (2014). Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics. PLoS One9:e93827. doi: 10.1371/journal.pone.0093827

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 208

  PoussinC.SierroN.BouéS.BatteyJ.ScottiE.BelcastroV.et al. (2018). Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov. Today23, 1644–1657. doi: 10.1016/j.drudis.2018.06.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 209

  Prada-TiedemannP. A.GuentherK. M.KeenanJ. P. (2024). “Understanding death using animal models in forensic taphonomy” In When animals die: Examining justifications and envisioning justice, Katja M. Guenther and Julian Paul Keenan (eds). (New York: NYU Press), 173–194.

  • Google Scholar

• 210

  PringleJ. K.CassellaJ. P.JervisJ. R.WilliamsA.CrossP.CassidyN. J. (2015). Soilwater conductivity analysis to date and locate clandestine graves of homicide victims. J. Forensic Sci.60, 1052–1060. doi: 10.1111/1556-4029.12802

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 211

  PringleJ. K.StimpsonI. G.WisniewskiK. D.HeatonV.DavenwardB.MiroschN.et al. (2020). Geophysical monitoring of simulated homicide burials for forensic investigations. Sci. Rep.10:7544. doi: 10.1038/s41598-020-64262-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 212

  ProcopioN.GhignoneS.WilliamsA.ChamberlainA.MelloA.BuckleyM. (2019). Metabarcoding to investigate changes in soil microbial communities within forensic burial contexts. Forensic Sci. Int.: Genet.39, 73–85. doi: 10.1016/j.fsigen.2018.12.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 213

  QuaggiottoM.-M.EvansM. J.HigginsA.StrongC.BartonP. S. (2019). Dynamic soil nutrient and moisture changes under decomposing vertebrate carcasses. Biogeochemistry146, 71–82. doi: 10.1007/s10533-019-00611-3

  • CrossRef
  • Google Scholar

• 214

  QuastC.PruesseE.YilmazP.GerkenJ.SchweerT.YarzaP.et al. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res.41, D590–D596. doi: 10.1093/nar/gks1219

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 215

  RaiJ. K.PicklesB. J.PerottiM. A. (2022). The impact of the decomposition process of shallow graves on soil mite abundance. J. Forensic Sci.67, 605–618. doi: 10.1111/1556-4029.14906

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 216

  Ralebitso-SeniorT. K.OlakanyeA. Q. (2018). “Chapter 1 - General Introduction: Method Applications at the Interface of Microbial Ecology and Forensic Investigation,” in Forensic Ecogenomics, ed. T. K. Ralebitso-Senior. London: Academic Press, 1–35. doi: 10.1016/B978-0-12-809360-3.00001-1

  • CrossRef
  • Google Scholar

• 217

  Ralebitso-SeniorT. K.ThompsonT. J. U.CarneyH. E. (2016). Microbial ecogenomics and forensic archaeology: new methods for investigating clandestine gravesites. Hum. Remains Viol.2, 41–57. doi: 10.7227/HRV.2.1.4

  • CrossRef
  • Google Scholar

• 218

  RodriguezW. (1982). Insect activity and its relationship to decay rates of human cadavers in east Tennessee. Knoxville: University of Tennessee.

  • Google Scholar

• 219

  RodriguezW.BassW. (1985). Decomposition of buried bodies and methods that may aid in their location. J. Forensic Sci.30, 836–852. doi: 10.1520/JFS11017J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 220

  RomanoS.WirbelJ.AnsorgeR.SchudomaC.DucarmonQ. R.NarbadA.et al. (2025). Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson’s disease. Nat. Commun.16:4227. doi: 10.1038/s41467-025-56829-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 221

  RowlandI.GibsonG.HeinkenA.ScottK.SwannJ.ThieleI.et al. (2018). Gut microbiota functions: metabolism of nutrients and other food components. Eur. J. Nutr.57, 1–24. doi: 10.1007/s00394-017-1445-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 222

  SagaraN. (1975). Ammonia fungi: a chemoecological grouping of terrestrial fungi. Contrib. Biol. Lab. Kyoto Univ.24, 206–273.

  • Google Scholar

• 223

  SagaraN. (1995). Association of ectomycorrhizal fungi with decomposed animal wastes in forest habitats: a cleaning symbiosis?Can. J. Bot.73, 1423–1433. doi: 10.1139/b95-406

  • CrossRef
  • Google Scholar

• 224

  SalterS. J.CoxM. J.TurekE. M.CalusS. T.CooksonW. O.MoffattM. F.et al. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol.12:87. doi: 10.1186/s12915-014-0087-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 225

  SchlossP. D. (2018). Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. MBio9:18. doi: 10.1128/mbio.00525-18

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 226

  SchlossP. D.GeversD.WestcottS. L. (2011). Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One6:e27310. doi: 10.1371/journal.pone.0027310

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 227

  SchlossP. D.WestcottS. L.RyabinT.HallJ. R.HartmannM.HollisterE. B.et al. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol.75, 7537–7541. doi: 10.1128/AEM.01541-09

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 228

  SchonlauM.ZouR. Y. (2020). The random forest algorithm for statistical learning. Stata J.20, 3–29. doi: 10.1177/1536867X20909688

  • CrossRef
  • Google Scholar

• 229

  SchotsmansE. M. J.Georges-ZimmermannP.UelandM.DentB. B. (2022). “From flesh to bone” In The Routledge handbook of Archaeothanatology, Christopher J. Knüsel and Eline M. J. Schotsmans (eds). (London: Routledge), 500–541.

  • Google Scholar

• 230

  SchotsmansE. M. J.Márquez-GrantN.ForbesS. L. (2017). Taphonomy of human remains: Forensic analysis of the dead and the depositional environment: forensic analysis of the dead and the depositional environment. 1st Edn. West Sussex: Wiley.

  • Google Scholar

• 231

  ShafferJ. P.CarpenterC. S.MartinoC.SalidoR. A.MinichJ. J.BryantM.et al. (2022). A comparison of six DNA extraction protocols for 16S, ITS and shotgun metagenomic sequencing of microbial communities. BioTechniques73, 34–46. doi: 10.2144/btn-2022-0032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 232

  ShapiroE. (2020). Man apologizes as he’s sentenced to life in prison for Mackenzie Lueck slaying. ABC News. Available online at: https://abcnews.go.com/US/man-apologizes-sentenced-life-prison-mackenzie-lueck-slaying/story?id=73792792 (Accessed March 7, 2025).

  • Google Scholar

• 233

  SharanowskiB. J.WalkerE. G.AndersonG. S. (2008). Insect succession and decomposition patterns on shaded and sunlit carrion in Saskatchewan in three different seasons. Forensic Sci. Int.179, 219–240. doi: 10.1016/j.forsciint.2008.05.019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 234

  SharmaR.Kumar GargR.GaurJ. R. (2015). Various methods for the estimation of the post mortem interval from Calliphoridae: a review. Egypt. J. Forensic Sci.5, 1–12. doi: 10.1016/j.ejfs.2013.04.002

  • CrossRef
  • Google Scholar

• 235

  SharmaD.PatersonA. D.XuW. (2020). TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction. Bioinformatics36, 4544–4550. doi: 10.1093/bioinformatics/btaa542

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 236

  SidrimJ. J. C.Moreira FilhoR. E.CordeiroR. A.RochaM. F. G.CaetanoE. P.MonteiroA. J.et al. (2010). Fungal microbiota dynamics as a postmortem investigation tool: focus on aspergillus, Penicillium and Candida species. J. Appl. Microbiol.108, 1751–1756. doi: 10.1111/j.1365-2672.2009.04573.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 237

  SilvermanJ. D.BloomR. J.JiangS.DurandH. K.DallowE.MukherjeeS.et al. (2021). Measuring and mitigating PCR bias in microbiota datasets. PLoS Comput. Biol.17:e1009113. doi: 10.1371/journal.pcbi.1009113

  • CrossRef
  • Google Scholar

• 238

  SimmonsT. (2017). “Post-mortem interval estimation: an overview of techniques” in Taphonomy of human remains: Forensic analysis of the dead and the depositional environment. eds. SchotsmansE.Marques-GrantN.ForbesS. L. (West Sussex: John Wiley & Sons, Ltd), 134–142.

  • Google Scholar

• 239

  SinghS.AgarwalJ. (2024). Review on forensic analysis of microbiota in human. J. Forensic Sci. Res.8, 024–027. doi: 10.29328/journal.jfsr.1001060

  • CrossRef
  • Google Scholar

• 240

  SinghB.MinickK. J.StricklandM. S.WickingsK. G.CrippenT. L.TaroneA. M.et al. (2018). Temporal and spatial impact of human cadaver decomposition on soil bacterial and arthropod community structure and function. Front. Microbiol.8:2616. doi: 10.3389/fmicb.2017.02616

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 241

  SinghR.SharmaS.SharmaA. (2016). Determination of post-burial interval using entomology: A review. J. Forensic Leg. Med.42, 37–40. doi: 10.1016/j.jflm.2016.05.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 242

  SkinnerM. F.YorkH. P.ConnorM. A. (2001). “Postburial disturbance of graves in Bosnia-Herzegovina,” in Advances in Forensic Taphonomy: Method, Theory, and Archaeological Perspectives, eds. Haglund, W.D and Sorg, M. H. Boca Raton: CRC Press. 294–309.

  • Google Scholar

• 243

  SpiesJ. (2022). The effect of clothing and carrion biomass load on decomposition and scavenging in a forensically significant thicketed habitat in Cape Town, South Africa. Cape Town: University of Cape Town.

  • Google Scholar

• 244

  SpiesM. J.FinaughtyD. A.FriedlingL. J.GibbonV. E. (2020). The effect of clothing on decomposition and vertebrate scavengers in cooler months of the temperate southwestern cape, South Africa. Forensic Sci. Int.309:110197. doi: 10.1016/j.forsciint.2020.110197

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 245

  SpiesM. J.FinaughtyD. A.GibbonV. E. (2024). Portion size matters: carrion ecology lessons for medicolegal death investigations—a study in Cape Town, South Africa. J. Forensic Sci.69, 28–39. doi: 10.1111/1556-4029.15396

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 246

  SuiH.WeilA. A.NuwagiraE.QadriF.RyanE. T.MezzariM. P.et al. (2020). Impact of DNA extraction method on variation in human and built environment microbial community and functional profiles assessed by shotgun metagenomics sequencing. Front. Microbiol.11:953. doi: 10.3389/fmicb.2020.00953

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 247

  SwayambhuM.GysiM.HaasC.SchuhL.WalserL.JavanmardF.et al. (2025). Standardizing a microbiome pipeline for body fluid identification from complex crime scene stains. Appl. Environ. Microbiol.91:e0187124. doi: 10.1128/aem.01871-24

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 248

  SwiftD.CresswellK.JohnsonR.StilianoudakisS.WeiX. (2023). A review of normalization and differential abundance methods for microbiome counts data. WIREs Comput. Stats.15:e1586. doi: 10.1002/wics.1586

  • CrossRef
  • Google Scholar

• 249

  TaylorL.MasonA.NoelH.EssingtonM.DavisM.BrownV.et al (2024). Soil microbial communities and biogeochemistry during human decomposition differs between seasons: evidence from year-long trials (Preprint). Research Square. doi: 10.21203/rs.3.rs-3931135/v1

  • CrossRef
  • Google Scholar

• 250

  TerribileF.IamarinoM.LangellaG.MeleG.GargiuloL.MiletiF. A.et al. (2024). Integrated, multiscale forensic soil science applied to an unsolved murder case in Italy. Eur. J. Soil Sci.75:e70019. doi: 10.1111/ejss.70019

  • CrossRef
  • Google Scholar

• 251

  ThiesJ. E. (2007). Soil microbial community analysis using terminal restriction fragment length polymorphisms. Soil Sci. Soc. Am. J.71, 579–591. doi: 10.2136/sssaj2006.0318

  • CrossRef
  • Google Scholar

• 252

  TourlousseD. M.YoshiikeS.OhashiA.MatsukuraS.NodaN.SekiguchiY. (2016). Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res.45:gkw984. doi: 10.1093/nar/gkw984

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 253

  TozzoP.AmicoI.DelicatiA.ToselliF.CaenazzoL. (2022). Post-mortem interval and microbiome analysis through 16S rRNA analysis: A systematic review. Diagnostics12:2641. doi: 10.3390/diagnostics12112641

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 254

  TsilimigrasM. C. B.FodorA. A. (2016). Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol.26, 330–335. doi: 10.1016/j.annepidem.2016.03.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 255

  TullerH.HofmeisterU. (2014). “Spatial analysis of mass grave mapping data to assist in the reassociation of disarticulated and commingled human remains” in Commingled human remains. eds. AdamsB. J.ByrdJ. E. (San Diego: Academic Press), 7–32.

  • Google Scholar

• 256

  TumerA. R.KaracaogluE.NamliA.KetenA.FarasatS.AkcanR.et al. (2013). Effects of different types of soil on decomposition: An experimental study. Legal Med.15, 149–156. doi: 10.1016/j.legalmed.2012.11.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 257

  TurnerB.WiltshireP. (1999). Experimental validation of forensic evidence: a study of the decomposition of buried pigs in a heavy clay soil. Forensic Sci. Int.101, 113–122. doi: 10.1016/S0379-0738(99)00018-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 258

  UrakawaH.Martens-HabbenaW.StahlD. A. (2010). High abundance of Ammonia-oxidizing Archaea in coastal waters, determined using a modified DNA extraction method. Appl. Environ. Microbiol.76, 2129–2135. doi: 10.1128/AEM.02692-09

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 259

  VandeputteD.KathagenG.D’hoeK.Vieira-SilvaS.Valles-ColomerM.SabinoJ.et al. (2017). Quantitative microbiome profiling links gut community variation to microbial load. Nature551, 507–511. doi: 10.1038/nature24460

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 260

  VanLaerhovenS.AndersonG. (1999). Insect succession on buried carrion in two biogeoclimatic zones of British Columbia. J. Forensic Sci.44, 32–43. doi: 10.1520/JFS14409J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 261

  VassA.BarshickS.SegaG.CatonJ.SkeenJ.LoveJ.et al. (2002). Decomposition chemistry of human remains: a new methodology for determining the postmortem interval. J. Forensic Sci.47, 542–553. doi: 10.1520/JFS15294J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 262

  VassA.BassW.WoltJ.FossJ.AmmonsJ. (1992). Time since death determinations of human cadavers using soil solution. J. Forensic Sci.37, 1236–1253. doi: 10.1520/JFS13311J

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 263

  VenturaF.Di PiazzaS.CecchiG.GrecoG.BelliniE.ZottiM. (2016) A microfungal colonisation of a corpse oral cavity, in IALM Intersocietal Symposium P5 Medicine & Justice, 21st-24th June, Venice, Italy.

  • Google Scholar

• 264

  VossS. C.SpaffordH.DadourI. R. (2009). Annual and seasonal patterns of insect succession on decomposing remains at two locations in Western Australia. Forensic Sci. Int.193, 26–36. doi: 10.1016/j.forsciint.2009.08.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 265

  WangQ.GarrityG. M.TiedjeJ. M.ColeJ. R. (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol.73, 5261–5267. doi: 10.1128/AEM.00062-07

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 266

  WangX.LeC.JinX.FengY.ChenL.HuangX.et al. (2024). Estimating postmortem interval based on oral microbial community succession in rat cadavers. Heliyon10:e31897. doi: 10.1016/j.heliyon.2024.e31897

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 267

  WatsonC. J.ForbesS. L. (2008). An investigation of the vegetation associated with grave sites in southern Ontario. J. Can. Soc. Forensic Sci.41, 199–207. doi: 10.1080/00085030.2008.10757177

  • CrossRef
  • Google Scholar

• 268

  WatsonC. J.UelandM.SchotsmansE. M. J.SterenbergJ.ForbesS. L.BlauS. (2021). Detecting grave sites from surface anomalies: A longitudinal study in an Australian woodland. J. Forensic Sci.66, 479–490. doi: 10.1111/1556-4029.14626

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 269

  WeissS.CarterD. O.MetcalfJ. L.KnightR. (2016). Carcass mass has little influence on the structure of gravesoil microbial communities. Int. J. Legal Med.130, 253–263. doi: 10.1007/s00414-015-1206-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 270

  WeissS.XuZ. Z.PeddadaS.AmirA.BittingerK.GonzalezA.et al. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome5:27. doi: 10.1186/s40168-017-0237-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 271

  Weiss-KrejciE. (2005). “Excarnation, evisceration, and exhumation in medieval and post-medieval Europe” in Interacting with the dead: perspectives on mortuary archaeology for the new millennium. eds. RakitaG.FM.BuikstraJ. E.BeckL. A.WilliamsS. R. (Gainesville: University Press of Florida), 155–172.

  • Google Scholar

• 272

  WhippsJ. M.LewisK.CookeR. C. (1988). “Mycoparasitism and plant disease control” in Fungi in biological control systems. ed. BurgeN. M. (Manchester: Manchester University Press), 161–187.

  • Google Scholar

• 273

  Whitehead ApmJ.FranklinR.MahonyT. (2024). Where are homicide victims disposed? A study of disposed homicide victims in Queensland. Forensic Sci. Int.8:100451. doi: 10.1016/j.fsisyn.2023.100451

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 274

  WilsonA.NeilsenP.BerryR.SeckinerD.MallettX. (2020). Quantifying human post-mortem movement resultant from decomposition processes. Forensic Sci. Int. Synergy2, 248–261. doi: 10.1016/j.fsisyn.2020.07.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 275

  Wilson-TaylorR. J.DautartasA. M. (2017). “Time since death estimation and bone weathering: The postmortem interval,” In: N. R. Langley and M. A. (eds.). Forensic Anthropology. A Comprehensive Introduction, Tersigni-Tarrant. Boca Raton: CRC Press, 291–330.

  • Google Scholar

• 276

  WiltshireP. E. J. (2009). “Forensic ecology, botany, and palynology: some aspects of their role in criminal investigation” in Criminal and environmental soil forensics. eds. RitzK.DawsonL.MillerD. (Dordrecht: Springer Netherlands), 129–149.

  • Google Scholar

• 277

  WuZ.GuoY.HayakawaM.YangW.LuY.MaJ.et al. (2024). Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location. Front. Microbiol.15:1334703. doi: 10.3389/fmicb.2024.1334703

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 278

  XiaY. (2023). Statistical normalization methods in microbiome data with application to microbiome cancer research. Gut Microbes15:2244139. doi: 10.1080/19490976.2023.2244139

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 279

  YangM.-Q.WangZ.-J.ZhaiC.-B.ChenL.-Q. (2024). Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification. Front. Microbiol.15:1360457. doi: 10.3389/fmicb.2024.1360457

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 280

  YangF.ZhangX.HuS.NieH.GuiP.ZhongZ.et al. (2023). Changes in microbial communities using pigs as a model for postmortem interval estimation. Microorganisms11:2811. doi: 10.3390/microorganisms11112811

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 281

  YilmazP.KottmannR.FieldD.KnightR.ColeJ. R.Amaral-ZettlerL.et al. (2011). Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol.29, 415–420. doi: 10.1038/nbt.1823

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 282

  YoungA. (2017). “The effects of terrestrial mammalian scavenging and avian scavenging on the body” in E. Schotsmans, N. Marques-Grant, and S. L. Forbes. (eds). Taphonomy of human remains: Forensic analysis of the dead and the depositional environment (John Wiley & Sons, Ltd), 212–234. doi: 10.1002/9781118953358.ch16

  • CrossRef
  • Google Scholar

• 283

  ZapicoS. C.Adserias-GarrigaJ. (2022). Postmortem interval estimation: new approaches by the analysis of human tissues and microbial communities’ changes. Forensic Sci.2, 163–174. doi: 10.3390/forensicsci2010013

  • CrossRef
  • Google Scholar

• 284

  ZembO.AchardC. S.HamelinJ.De AlmeidaM.GabinaudB.CauquilL.et al. (2020). Absolute quantitation of microbes using 16S rRNA gene metabarcoding: a rapid normalization of relative abundances by quantitative PCR targeting a 16S rRNA gene spike-in standard. Microbiology9:e977. doi: 10.1002/mbo3.977

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 285

  ZhangT.FangH. H. P. (2000). Digitization of DGGE (denaturing gradient gel electrophoresis) profile and cluster analysis of microbial communities. Biotechnol. Lett.22, 399–405. doi: 10.1023/A:1005680803442

  • CrossRef
  • Google Scholar

• 286

  ZhangJ.WangM.QiX.ShiL.ZhangJ.ZhangX.et al. (2021). Predicting the postmortem interval of burial cadavers based on microbial community succession. Forensic Sci. Int.: Genet.52:102488. doi: 10.1016/j.fsigen.2021.102488

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 287

  ZhaoJ.RodriguezJ.Martens-HabbenaW. (2023). Fine-scale evaluation of two standard 16S rRNA gene amplicon primer pairs for analysis of total prokaryotes and archaeal nitrifiers in differently managed soils. Front. Microbiol.14:1140487. doi: 10.3389/fmicb.2023.1140487

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 288

  ZhaoX.ZhongZ.HuaZ. (2022). Estimation of the post-mortem interval by modelling the changes in oral bacterial diversity during decomposition. J. Appl. Microbiol.133, 3451–3464. doi: 10.1111/jam.15771

  • Pubmed Abstract
  • CrossRef
  • Google Scholar