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

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

Figure 1. Decomposition timeline illustrating the post-mortem changes observed and the forensic subdisciplines used to aid in detecting remains and estimating the post-mortem and post-burial intervals. Adapted from Ralebitso-Senior and Olakanye (2018) and Metcalf (2019).

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).

Table 2

Table 2. Summary of the methodological confounders and control measures in several studies applying microbial data for PMI estimation.

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

Figure 2. Intersection of the post-mortem microbiome, including the epinecrobiome, thanatomicrobiome and soil microbiome of mammalian remains. After Javan et al. (2016a, 2016b) and Wu et al. (2024). The artwork used in this figure was adapted from Servier Medical Art (https://smart.servier.com/). Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License.

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

Figure 3. Schematic showing the ordering and overlap of the post-mortem timelines at a primary deposition or burial site. The PMI is inclusive of the PBI and the PTI.

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

Figure 4. Theatre of decomposition activity based on the microbial “theatre of activity” modelled after Whipps et al. (1988) and Berg et al. (2020).

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).

Table 1

Table 1. Study matrix summarising experimental models, environmental conditions, sequencing approaches, and key findings in reviewed studies estimating the PMI from microbial communities.

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

Figure 5. Conceptual framework for using gravesoil microbial communities to estimate the PBI and the PTI. This framework is modelled after the Pechal et al. (2014) framework to estimate PMI using microbial communities from the body.

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.

Table 3

Table 3. Study matrix summarizing experimental models, environmental conditions, sequencing approaches, and key findings in reviewed studies from gravesoil microbial communities.

Table 4

Table 4. Summary of the methodological confounders and control measures in several studies applying microbial data from gravesoil.

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.

Table 5

Table 5. Recommended structured reporting template outlining the essential information and methodological elements for reproducible, standardized and transparent forensic ecogenomics workflows.

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.

Author contributions

CdB: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing. KS: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. HP: Supervision, Writing – original draft, Writing – review & editing. FB: Supervision, Writing – original draft, Writing – review & editing. KR-S: Conceptualization, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. The authors declare that this research was funded through the Forensic Research Institute (FORRI) Thematic Doctoral Programme at Liverpool John Moores University, UK.

Acknowledgments

We thank the Forensic Research Institute (FORRI) of Liverpool John Moores University for funding this research. The authors also acknowledge the reviewers for their incisive appraisals and constructive comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The authors declare that no Gen AI was used in the creation of this manuscript.

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