NECROBIOME SEASONAL VARIATION IN MICHIGAN AND POTENTIAL 
POSTMORTEM MICROBIAL BIOMARKERS OF INFANT DEATH INVESTIGATION 

By 

Bethany G. Mikles 

A THESIS 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements   
for the degree of 

Entomology – Master of Science 

2023 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

Death and decomposition usher in a complex community of decomposers to break down 

and recycle organic matter. Two understudied, key players in this necrobiome are postmortem 

microbiota and necrophagous insects. The objectives of this study were to characterize these 

interkingdom dynamics within two postmortem contexts: 1) active insect colonization and 

carrion decomposition in contrasting seasons; and 2) a snapshot of human infant microbiota pre-

insect colonization, to investigate their potential to inform forensic science, carrion ecology, and 

human health. It was predicted that postmortem microbial communities would change over time 

and by season. It was also hypothesized that active insect colonization of carrion by blow flies 

would facilitate the introduction of microbes, altering carrion microbiota. For microbiomes pre-

insect colonization, it was predicted that microbiome composition would be structured by body 

site microbiota and manner of death (MOD), which could indicate potential biomarkers related to 

MOD. Seasonal comparisons demonstrated significantly distinct microbiota over time and 

between carcass and fly microbes and significantly slowed decomposition in the fall, greater 

blow fly diversity in the fall, increasing alpha diversity over decomposition time in the summer, 

and evidence for the introduction of microbes by flies. In addition, manner of death and body site 

had a statistically significant structuring effect on postmortem microbiota, and significant 

interactions (age, race, sex) were identified with manner of death. A potential biomarker of 

infant death, Sneathia, associated with preterm birth and labor was also discovered. These 

studies present new data that further our understanding of complex postmortem microbiome 

dynamics and promote standardization of practical and applicable analyses within the fields of 

forensic science and carrion ecology.

 
 
 
To my parents, for their unconditional support through the good, the bad, and the weird.   
To Nate, for holding me steady.  
And to Al, for just being a part of it. Thank you. 

iii 

 
ACKNOWLEDGEMENTS 

I firstly must extend my thanks to my guidance committee. Thank you to Dr. Jen Pechal, 

for taking a chance on me and granting me this immense learning experience, and for her 

patience and honest advice. I am so grateful for the opportunity to step outside of my comfort 

zone and grow as an individual during my time here. Thank you to Dr. M. Eric Benbow, for his 

wisdom, kindness, and encouragement. And thank you to Dr. Rebecca Knickmeyer, for taking 

the time to listen and provide valuable perspective and insight. I know it’s been a journey to get 

here, and I appreciate each of your support and mentorship along the way. 

The completion of these projects would not have been possible without the help, time, 

and counsel of many others. Thank you to Dr. Anthony Cognato for helping me improve my 

skills in beetle identification. I thank my fellow students and peers that have come through the 

Benbow lab; we all learned so much from each other. Thank you to my amazingly cool 

undergrads Emily Schnettler, Kat Yoskowitz, and Alexandria Verner, whose genuine enthusiasm 

for our work never failed to encourage me. I thank Dr. Sophie Picq for her sincerity and 

guidance in and out of the lab, at all times of day, and Alexandra Bauer and Alex Rakestraw for 

sharing their knowledge with me. A huge thank you to Anthony Grigsby and Kelly Waters for 

their hard work, long days out in the field, and assistance with sample collection; I’m so excited 

to see what you will discover and accomplish. I must also give an extra, emphatic thank you to 

Kelly for lending me countless hours of her limited time, and for her invaluable friendship during 

the long nights and challenging weeks when the lab became our home away from home. 

I must lastly extend one final thank you to my friends, family, my wonderful partner 

Nate, and my cat Bella. You have supported me all the way, mostly from hundreds of miles 

away. Thank you all for believing in me. 

iv 

 
 
 
TABLE OF CONTENTS 

LIST OF ABBREVIATIONS ........................................................................................................ vi 

CHAPTER 1: NECROBIOME SEASONAL VARIATION IN MICHIGAN ................................1 

CHAPTER 2: POTENTIAL POSTMORTEM MICROBIAL BIOMARKERS OF INFANT 
DEATH INVESTIGATION…………………………………………………………………… ..53 

LITERATURE CITED ..................................................................................................................85 

APPENDIX A: RECORD OF DEPOSITION OF VOUCHER SPECIMENS .............................96 

APPENDIX B: SUPPLEMENTARY MATERIALS ....................................................................97 

v 

 
 
 
 
 
 
LIST OF ABBREVIATIONS 

ADH - Accumulated Degree Hours 

CB – Cardiac blood 

CI – Confidence Interval 

FDR – False Discovery Rate 

IF – Interhemispheric fissure 

KW – Kruskal-Wallis 

LEFSe – Linear discriminant analysis Effect Size 

MOD – Manner of death 

ND – No Data 

NIJ – National Institute of Justice 

PCoA – Principal Coordinates Analysis 

PCR – Polymerase Chain Reaction 

PERMANOVA – Permutational Multivariate Analysis of Variance 

PMI - Postmortem Interval 

SE - Standard Error 

TAE – Tris-acetate-EDTA 

TS – Trabecular space 

vi 

 
 
CHAPTER 1: NECROBIOME SEASONAL VARIATION IN MICHIGAN 

Abstract 

Decomposing remains provide ephemeral resources to a diverse host of organisms, 

supporting a dynamic framework that drives the essential recycling of nutrients back into 

ecosystems. These interactions are complex and often overlooked, particularly those among 

insects and microbes. Diptera, especially blow flies (Diptera: Calliphoridae), may play a 

significant mechanistic role in this system as primary colonizers of forensic importance. This 

study aimed to investigate these interkingdom relationships and community dynamics to improve 

our understanding of the ecological structure and functioning of the necrobiome over time. Insect 

succession, carcass epinecrotic microbial communities, and internal blow fly microbiomes were 

sampled from swine carcasses (N = 6) in East Lansing, Michigan, during decomposition in the 

summer and fall. Carcass decomposition and accumulated degree days (ADH) in the fall 

occurred nearly five times slower than summer carcass decomposition and ADH. Insect 

communities also differed, with greater blow fly species diversity observed in the fall. 

Amplicon-based 16SrRNA sequence analyses revealed statistically significant daily temporal 

effects on bacterial succession of carcasses for both seasons, as well as significant interactions 

between internal blow fly and carcass microbiota. Summer early decomposition was represented 

by high relative abundances of Moraxellaceae, while fall was represented by Enterobacteriaceae. 

Alpha diversity increased over decomposition time in the summer, and abundances of 

Vagococcaceae appeared in correlation with blow fly catches as decomposition progressed for 

the summer and fall. Blow fly internal microbiomes were characterized by high relative 

abundances of Vagococcaceae compared to carcass microbes, providing support for the potential 

introduction of exogenous microbes into carrion decomposition by blow flies. 

1 

 
 
Introduction 

Decay is a dynamic, complex process that supports critical ecosystem services, seen and 

unseen (Barton, Cunningham, Lindenmayer, et al., 2013). After death, carrion becomes a 

concentrated nutrient-rich patch for a diverse assemblage of vertebrate scavengers, insects, and 

microbes to exploit and redistribute back into the ecosystem (Barton et al., 2019). The 

culmination of these organisms and their interkingdom interactions make up the necrobiome, an 

efficient, intricate framework that can profoundly impact ecosystem structure and function over 

time (Barton, Cunningham, Lindenmayer, et al., 2013; Barton et al., 2019; Benbow et al., 2019; 

Parmenter & Macmahon, 2009). Despite the wide impacts of carrion decomposition, however, its 

scope of study has been primarily limited to individual components of the necrobiome 

framework, such as arthropod succession or microbial succession, and few studies have 

examined their potential interactions simultaneously. 

Many studies have documented the predictable assembly of necrophagous invertebrate 

taxa on carrion over time in various ecosystems, which can be used as a means of determining 

the minimum postmortem interval (PMImin) or a minimum time elapsed since death (Anderson, 

2011; Archer, 2004; Benbow et al., 2013; Early & Goff, 1986; Matuszewski et al., 2008). 

Because their development is highly temperature dependent and they are known to visit 

vertebrate carrion at differing stages of decomposition depending on their nutritional, 

reproductive, or habitat needs, insects can be used as a tool to better predict when death occurred 

(Byrd & Caster, 2009; Payne, 1965). For example, Diptera, such as blow flies (Diptera: 

Calliphoridae) and flesh flies (Diptera: Sarcophagidae), are among the first insects to detect and 

arrive at a carcass, while other insects, such as skin beetles (Coleoptera: Dermestidae), are 

typically attracted to skin and hair of drier, older carcasses (De Jong & Merritt, 2015; 

2 

 
Kulshrestha & Satpathy, 2001). Studies have also demonstrated that preventing or delaying 

insect access to decomposing remains significantly reduces biomass removal rates, slows 

decomposition processes, and can even alter insect community succession patterns (Payne, 1965; 

Pechal, Benbow, et al., 2014). These associations are critical to informing the field of forensic 

entomology, the analysis and use of arthropod evidence within the judicial system for use in 

criminal and civil litigation (Byrd & Caster, 2009).  

Other studies have examined the succession of postmortem bacterial communities, also 

demonstrating their potential as a standardized tool for estimating a PMImin range (Guo et al., 

2016; Metcalf et al., 2013). The human microbiome has proven to be a highly dynamic 

community reflective of its host’s antemortem lifestyle, environment, and physiology over time, 

with distinct variations observed in the microbiomes of individual body sites (i.e., mouth, nose, 

skin) (Costello et al., 2009; Turnbaugh et al., 2007). Recent metagenomic data have also 

indicated potential correlations between host microbiota structure and function, and health 

conditions such as inflammatory bowel disease, colorectal cancer, and obesity (Cho & Blaser, 

2012). This pattern continues after death, with antemortem host microbiomes remaining viable 

for 48-72 hours postmortem, and significant temporal changes observed in bacterial communities 

as decomposition progresses (Adserias-Garriga et al., 2017; Hyde et al., 2015; Pechal, Crippen, 

et al., 2014; Pechal et al., 2018).  

While the individual importance of microbes and insects in carrion decomposition is well 

described, the extent of their interactions has yet to be fully evaluated. Research has 

demonstrated that microbes play a role in multiple fly lifecycles from egg to adult, such as the 

black soldier fly (Diptera: Stratiomyidae), blow fly, fruit fly (Diptera: Drosophilidae), house fly 

(Diptera: Muscidae), and the primary vector of malaria, Anopheles mosquitoes (Diptera: 

3 

 
Culicidae) (Ahmad et al., 2006; Majumder et al., 2020; Wang et al., 2011; Zheng, Crippen, 

Singh, et al., 2013; Zurek et al., 2000). Flies are also well-established vectors of more than 100 

bacterial pathogens, such as Staphylococcus aureus, a leading cause of many skin infections like 

“staph”, Camylobacter spp., a leading cause of food poisoning, and Escherichia coli O157:H7, 

the primary cause of hemorrhagic colitis and hemolytic uremic syndrome in North America 

(Armstrong et al., 1996; Graczyk et al., 2001; Greenberg, 1991). It subsequently stands to reason 

that flies could be capable of introducing their own exogenous microbiota to epinecrotic 

microbial communities during colonization and oviposition, potentially altering the postmortem 

microbiome in undiscovered ways. Findings from Weatherbee et al. (2017) suggested significant 

temporal interactions between epinecrotic communities on carcasses, dipteran larval masses, and 

the internal larval blow fly microbiome, while results from Iancu et al. (2015) and Metcalf et al. 

(2016) show support for the introduction of new microbial taxa on a carcass by necrophagous 

flies. Zheng (2013) also found evidence that black soldier flies and blow flies are attracted to 

volatiles released by specific bacteria deposited on carrion by conspecifics, and may be repelled 

by bacteria from competing species, suggesting that microbes may drive competition for 

ephemeral resources and selection of oviposition sites.  

Studies documenting the extent of the role adult flies may play in the exchange of 

microbes are lacking, and there is a need for further study of this interkingdom relationship to 

better understand how these ubiquitous organisms shift necrobiome dynamics in various 

ecological settings and environmental conditions. To investigate these interactions, we conducted 

two seasonal decomposition trials to: 1) characterize insect communities and document 

decomposition processes; 2) evaluate postmortem microbial community succession across 

different body sites on carrion and the internal microbiome of necrophagous flies using high-

4 

 
throughput sequencing; and 3) compare the findings across each season to identify potential 

exchanged microbial taxa in carcass and fly microbiomes. 

 We hypothesized that progression of decomposition would be slower in the fall than the 

summer, and that microbial and insect diversity would also decrease with cooler temperatures 

with Diptera being the dominant observed insect taxa. We also hypothesized that temporal shifts 

in relative abundances of microbial taxa would occur during decomposition, on each carcass as 

well as between individual body sites, and that the timing of these shifts would correlate with the 

introduction of microbial taxa by flies, with similar community composition observed in carcass 

and fly microbiomes. 

Figure 1. Placement of swine carcasses in the field. Red markers 1-6 represent placement for the 
summer trial, and blue markers 7-12 represent the fall trial. 

5 

 
 
 
 
 
 
 
Study Site 

Materials and Methods 

The study was conducted at Box Woodland on Michigan State University’s Entomology 

Research Farm in Lansing, Michigan (42.689789 N, -84.491081 W).  Box Woodland is a 5-acre 

natural area rich in herbaceous flora and contains several maple species (Acer spp. L.), white ash 

(Fraxinus americana L.), basswood (Tilia americana L.), sycamore (Platanus occidentalis L.), 

several oak species (Quercus spp. L.), and red mulberry (Morus rubra. L.). To model human 

decomposition, six stillborn swine carcasses (Sus scrofa L.) weighing 0.7 to 2.2 kg were placed 

for two seasonal trials: summer (5 – 11 August 2022) and fall (14 October – 13 November 2022). 

Studies have shown that swine carcasses closely represent decomposition of human remains, and 

can be used to model this process in the absence of human cadavers (Catts & Goff, 1992). The 

size and age of carcasses used for this study were based on availability. 

Carcasses were obtained from the Michigan State University Swine Teaching and Research 

Center in East Lansing, Michigan on 3 June 2022 (N = 6) and 30 September 2022 (N = 6). 

Carcasses were received frozen and wrapped securely in plastic bags; they were immediately 

transported from the research center to the Pechal Laboratory at Michigan State University and 

placed in -20°C storage. Prior to placement at the field site, carcasses were allowed to thaw at 

room temperature (22-24°C) for 24 h, and then placed in 0°C storage for 12 h to prevent early 

onset of decomposition. Carcasses were placed directly on the ground on their right side at 11:50 

h (5 August 2022) and 9:30 h (14 October 2022), with each carcass approximately 10 m apart 

(Figure 1). For the summer trial, carcasses were laid in a transect running northeast-southwest 

against the west edge of the Box Woodland, with heads facing northeast, and labelled 

alphanumerically C1-C6. Carcasses for the fall trial were placed in a west-south transect along 

6 

 
the south edge of the woodland with heads facing west, and labelled C7-C12. No previous 

decomposition trials had taken place at the study site (Figure 2). 

To prevent scavenger disturbance, each carcass was covered by a wooden cage (0.6 x 

0.3m x 0.3m) constructed of poultry netting (0.3m x 1.2m x 15m, 20 gauge, 2.5cm mesh) 

(Midwest Air Technologies Inc., Long Grove, IL) and untreated pine lumber (Figure 3). For 

added security, anti-scavenger cages were tied down with nylon-polypropylene cord (0.32cm x 

15m) (Everbilt, Home Depot Inc, Atlanta, GA) and wooden stakes (2.5cm x 5cm x 46 cm) 

secured in the ground and were also weighed down at each corner with a burnt clay brick. For 

the summer trial a single NexSens DS1921G micro-T temperature logger (Fondriest 

Environmental, Inc., Alpha, OH) was affixed to the front-left of each cage with a zip tie 

(Commercial Electric, Cleveland, OH), and ambient temperature was recorded every 15 min 

until 18:30 h on 26 August 2022. For the fall trial, a single Kestrel DROP D3 wireless data 

logger (Nielsen-Kellermen Co., Boothwyn, PA) was affixed to each cage as in the summer trial, 

and ambient temperature was recorded every 10-15 min until 17:20 h on 13 November 2022.  

To observe insect succession, a butterfly trap (BugDorm, MegaView Science 

Co.,Ltd., Taiwan) hung from a steel shepherds hook (1.2 m) (Vigoro, Chicago, IL) was 

positioned approximately 1 m from each carcass (Figure 3). Written field notes were taken and 

carcasses were photographed using an iPhone XS (Apple, Cupertino, CA) to document and track 

decomposition progress. Photos were taken of each carcass in entirety, of the head, abdomen, 

and rear, and of any present larval masses or other noteworthy observations. 

7 

 
 
 
 
 
A 

B  

Figure 2. Panoramic view of field sites taken on sampling day one of each trial, (A) Summer and 
(B) Fall. 

8 

 
 
 
Figure 3. Example of the field set-up (anti-scavenger cage, data logger, and butterfly trap) for a 
swine carcass. 

Microbe and Insect Sampling 

Postmortem bacterial communities of the skin, mouth, and rectum were collected from 

each carcass twice daily for each trial, at 10:00 h and 14:00 h in the summer, and at 7:00 h and 

13:00 h in the fall. Microbial samples were taken from each body site with a 15 cm sterile DNA-

free cotton-tipped swab (Puritan Medical Products, Guilford, ME), by physically rubbing and 

rotating the swab for 30 s. Skin samples were taken by swabbing three areas of a posterior-

9 

 
 
  
 
 
anterior transect along the carcass, with care taken not to resample areas as described in 

Weatherbee et al. (2017). The cotton tip of each swab was then broken off into a 1.5 ml tube 

containing 200 µl of RNAlater stabilization solution (AM7021, Thermo Fisher Scientific, 

Waltham, MA, USA), later modified to 500 µl for the fall trial, and transported to the Pechal 

laboratory to be placed in -20°C storage until further processing.  

In the summer trial, insects were sampled every 12 h by placing butterfly traps in -20°C 

storage overnight and emptying the contents of each into a sterile 15 ml conical tube (VWR 

International, Radnor, PA). Insect samples were stored at -20°C until further processing, and 

empty traps were replaced by the next sampling event. In the fall trial, butterfly traps were 

emptied in the field due to prolonged decomposition and decreased insect activity during the 

colder weather. All insects were identified to family under a stereoscope using dichotomous keys 

from Triplehorn et al. (2004), Whitworth (2006), and Marshal et al. (2019), and stored in 100% 

molecular grade ethanol in 1.5 ml tubes at -20°C. All adult Diptera identified as Calliphoridae (N 

= 42) were further identified to species using Whitworth (2006) and Marshal et al (2011). 

DNA Isolation and Quantification 

Microbial body site samples from all time points of the summer trial were selected for 

DNA analysis due to the quick progression of decomposition in the warmer months and number 

of sampling events (N = 14), for a total of 252 summer samples. Due to the longer duration and 

number of sampling events (N = 62) in the fall trial, samples were chosen by calculating ADH 

across both seasons and choosing comparable sampling events representative of decomposition 

stages. In the fall trial, 252 samples from 14 sampling events were chosen at 0, 100, 381, 439, 

788, 901, 1166, 1250, 1470, 1526, 1690, 1723, 1912, and 2041 ADH or 0, 171, 202, 207, 250, 

10 

 
 
273, 418, 430, 477, 482, 501, 506, 564, and 608 h after trial start. All blow flies from both trials 

were also selected for DNA analysis due to their limited quantity. Temperature data were 

converted to ADH using a base temperature of 10°C (VanLaerhoven, 2008). A summary of 

sampled substrates for each day, sampling event, and ADH for both seasons can be found in 

Table 1. 

DNA was extracted from the swab samples and blow flies under a biological safety 

cabinet using a modified QIAGEN DNeasy Blood & Tissue Kit protocol (QIAGEN, Hilden, 

Germany). Swabs were first prepared for lysis on Parafilm sheets (Bemis Company, Inc, Neenah, 

WI, USA) by using sterilized general-purpose stainless steel pinning forceps (10-270, Thermo 

Fisher Scientific, Waltham, MA, USA) and scalpel handles (12-000-164, Thermo Fisher 

Scientific, Waltham, MA, USA) with sterile, single-use carbon steel blades (22-079-697, 

Surgical Design Inc. Lorton, VA, USA) to carefully cut the cotton tip off the wooden handle. 

The cotton was then placed back into its respective 1.5 ml tube, and the wooden handle and 

Parafilm were discarded. To preserve the individual microbial communities, a new piece of 

Parafilm and pair of sterile dissecting forceps and scalpel were used to prepare each sample.  

11 

 
Table 1. A summary of all substrates sampled across each seasonal trial, by sampling day, sampling event, and ADH. X indicates that 
a substrate was sampled that day and X (in bold) indicates that a particular sample was processed and sequenced for analysis. While 
all summer samples were processed due to the short study duration, * indicates the exact event fall samples were chosen from for 
processing due to their quantity.  

12 

Sampling Day​​1​​2​​3​​4​​5​​6​​7​​8​​9​​10​​11​​12​​13​​14​​15​​16​​17​​18​​19​​20​​21​​22​​23​​24​​25​​26​​27​​28​​29​​30​​31Sampling Events0*-12-34-56-78-910-1112-1314-15*16-1718*-19*20-2122*-2324*-25*26*-2728-2930-3132-3334-3536*-3738*-3940-4142*-43*44*-45*46*-4748-4950-51*52-5354*-5556-5758-5960-61SummerADH0-91367-448806-8821160-12591437-15091648-17501964-2053Insect​s​X​​X​​X​​X​​X​​X​​X​​CalliphoridaeX​​X​​X​​Microbes​​X​​X​​X​​X​​X​​X​​X​​Decomp​​osition Photos and TaphonomyX​​X​​X​​X​​X​​X​​X​​FallADH0-821-2532-4757-5757-5757-5757-6163-100198-275381-439575-647788-765901-901901-935935-963992-10171057-10491103-11231166-12051250-12831345-13931470-15261690-17231782-18111843-18751899-19121928-19692041-21262175-22132213-22132213-2215Insect​s​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​CalliphoridaeX​​X​​X​​X​​X​​X​​X​​X​​Microbes​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​Decomp​​osition Photos and TaphonomyX​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​ 
 
 
 
Table 1 (cont’d) 

13 

Sampling Day​​13​​14​​15​​16​​17​​18​​19​​20​​21​​22​​23​​Sampling Events24*-25*26*-2728-2930-3132-3334-3536*-3738*-3940-4142*-43*44*-45*SummerADHInsect​s​CalliphoridaeMicrobes​​Decomp​​osition Photos and TaphonomyFallADH901-901901-935935-963992-10171057-10491103-11231166-12051250-12831345-13931470-15261690-1723Insect​s​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​CalliphoridaeX​​X​​X​​X​​Microbes​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​Decomp​​osition Photos and TaphonomyX​​X​​X​​X​​X​​X​​X​​X​​X​​X​​X​​ 
 
 
 
Table 1 (cont’d) 

14 

Sampling Day​​24​​25​​26​​27​​28​​29​​30​​31Sampling Events46*-4748-4950-51*52-5354*-5556-5758-5960-61SummerADHInsect​s​CalliphoridaeMicrobes​​Decomp​​osition Photos and TaphonomyFallADH1782-18111843-18751899-19121928-19692041-21262175-22132213-22132213-2215Insect​s​X​​X​​X​​X​​X​​X​​X​​X​​CalliphoridaeMicrobes​​X​​X​​X​​X​​X​​X​​X​​X​​Decomp​​osition Photos and TaphonomyX​​X​​X​​X​​X​​X​​X​​X​​ 
 
 
Blow flies were prepared for DNA extraction by surface sterilizing with three, five-

minute rinses of 10% bleach, and three rinses of sterile molecular biology grade water (Pechal & 

Benbow, 2016). Flies were allowed to dry and each placed into a 2 ml Lysing Z Matrix tube 

(6961-100, MP Biomedicals, Irvine, CA, USA), then submerged in liquid nitrogen for 20-30 s 

and immediately pulverized in a FastPrep-96 high-throughput bead beating grinder and lysis 

system (MP Biomedicals, Irvine, CA, USA) for 15-45 s as needed at 1400 oscillations/min. This 

pulverizing method was adapted for use with insects from a CTAB (Cetryltrimethyl ammonium 

bromide) extraction protocol from Tann and Yiap (2009) to promote increased DNA yield.  

The manufacturer’s protocol was then followed accordingly with the addition of 10 ul of 

15 mg/ml lysozyme to promote increased microbial cell lysis. Samples were also treated with 4 

ul 100mg/ml RNase A following incubation per the MSU Genomics Core Illumina sample 

sequencing requirements, and final DNA elution volume was modified from 200 ul to 50 ul. 

DNA was then quantified using a Qubit 2.0 and a 1x dsDNA High Sensitivity Assay (Thermo 

Fisher Scientific, Waltham, MA, USA). DNA was stored at -20°C to await further processing.  

16S rRNA Confirmation  

The microbial gene amplicon V4 region was amplified using conventional PCR with 

DreamTaq Green PCR Master Mix (2x) (Thermo Fisher Scientific, Waltham, MA, USA), PCR-

grade nuclease free water, and region-specific primers 515f/806r (5′ 

GTGCCAGCMGCCGCGGTAA, 5′ GGACTACHVGGGTWTCTAAT). PCR samples were 

each prepared with 24 ul mastermix and 1 ul template DNA, loaded into 96-well plates (82006-

636 VWR International, Radnor, PA, USA), and sealed with qPCR film (6091-078, VWR 

International, Radnor, PA, USA) in a biological safety cabinet. The positive and negative 

15 

 
  
 
controls run in each plate were E. coli and PCR-grade nuclease-free water, respectively. PCR 

samples were pun down in a Sorvall ST 8 centrifuge (Thermo Fisher Scientific, Waltham, MA, 

USA), placed into a Veriti 96-well thermal cycler (Thermo Fisher Scientific, Waltham, MA, 

USA) and amplified according to previously described methods (Caporaso et al., 2011). 

Resulting PCR product was stored at -20°C.  

Visual 16S rRNA confirmation was then performed via gel electrophoresis. 3% agarose 

gels (3.6 g agarose in 120 ml 1X TAE) (N605-500G, VWR International, Radnor, PA, USA) 

were cast in a Thermo EC Midicell Primo EC-330 Horizontal Gel System (Thermo Fisher 

Scientific, Waltham, MA, USA). 5ul each of GeneRuler 1kb ladder (Thermo Fisher Scientific, 

Waltham, MA, USA), PCR product, and controls were loaded and run at 90 V for 40 minutes. 

Gels were then agitated in GelRed solution (60 ul GelRed in 200 ml DI water) (Thermo Fisher 

Scientific, Waltham, MA, USA) for 30 minutes on a 3D platform rotator (Thermo Fisher 

Scientific, Waltham, MA, USA). Gels were imaged using the Axygen Gel Documentation 

System (Corning Incorporated, Corning, NY, USA) at UV 302.  

 16S rRNA Gene Amplicon Sequencing  

DNA sequencing and library construction for microbial body site samples (total N = 400, 

summer N = 203, fall N = 197) and blow flies (N = 42) was performed at the Michigan State 

University Genomics Core facility (East Lansing, MI, USA) on the Illumina MiSeq (2 x 250 bp 

paired-end reads). The V4 region of the 16S rRNA gene was amplified using dual indexed 

515F/806R Illumina primers (5′-GTGCCAGCMGCCGCGG-3′, 5′- 

16 

 
 
TACNVGGGTATCTAATCC-3′) following the protocol described in Kozich, JJ, et al. 

(2013). PCR products were batch normalized using an Invitrogen SequalPrep DNA 

Normalization plate, and products were pooled and cleaned up using a QIAquick Spin column 

and AMPure XP magnetic beads. Library pools were quality controlled and quantified using a 

combination of Qubit dsDNA HS, Agilent 4200 TapeStation HS DNA1000, and Invitrogen 

Collibri Illumina Library Quantification qPCR assays. Amplicon pools were then loaded onto a 

MiSeq v2 standard flow cell and sequencing was carried out in a 2 x 250 bp paired-end format 

using a MiSeq v2 500 cycle reagent cartridge and custom sequencing and index primers 

complementary to the 515f/806r oligomers. Base calling was performed by Illumina Real Time 

Analysis (RTA) v1.18.54 and output of RTA was demultiplexed and converted to FastQ format 

with Illumina Bcl2fastq v2.20.0.    

Sequence Analyses  

QIIME2 (Quantitative Insights Into Microbial Ecology) (v2023.2) was used to filter and 

analyze raw 16S microbial sequencing data. Demultiplexed reads were assembled and quality 

filtered for chimeric reads and artifacts using DADA2. A Naïve Bayes classifier (V4, SILVA 

database v138-99) trained against the 16S rRNA region was used to classify filtered reads into 

taxonomic groups and assign taxonomy (Quast et al., 2013; Yilmaz et al., 2014). Sequences were 

aligned with a De novo multiple sequence alignment program in QIIME2 (Mafft v7) and 

contingency filtered to remove singletons and reads corresponding with mitochondria or 

chloroplasts (Katoh & Standley, 2013). 16SrRNA relative abundances at the phylum and family 

levels of bacterial communities were visualized over decomposition time and between sample 

types (anatomical sites on carcasses and internal blowfly microbiota) in RStudio 4.3.1 (RStudio 

17 

 
 
Inc., Boston, MA) with the phyloseq, MicrobeR, microbiome, qiime2R, microbiomeSeq, 

microbiomeutilities packages.  

Statistical Analysis 

Pairwise Wilcoxon rank-sum tests of observed and Shannon diversity (False Discovery 

Rate (FDR) correction) were performed to determine significant differences in bacterial alpha 

diversity between seasons and among sample types (Benjamini & Hochberg, 1995). 

Permutational Multivariate Analysis of Variance (PERMANOVA, 999 iterations) and pairwise 

PERMANOVA (FDR corrections) tests using weighted UniFrac distances using the vegan R 

package (Oksanen et al., 2015) were conducted to explore differences in bacterial beta diversity 

between seasons, sample types, and over time. These differences were visualized using principal 

coordinate analyses (PCoA) of weighted UniFrac distance matrices generated by sequence 

analyses in QIIME2, and shown with ellipses representing 95% CIs for the mean of each group. 

Weighted UniFrac metrics were used for these analyses because they account for phylogenetic 

distances and relative abundances among microbes (Lozupone & Knight, 2005). All statistical 

analyses were performed in RStudio, and all p-values were considered significant with alpha ≤ 

0.05.  

18 

 
 
 
Seasonal Decomposition 

Results 

Temperature data were used to calculate accumulated degree hours (ADH) to accurately 

compare rates of insect and decomposition development over time and between seasons. 

Summaries of ADH for each season are found in Figures 4 and 5, and in Tables 1 and 2. Distinct 

differences were observed in the progression of decomposition across each season , with all 

decomposition stages (fresh to dry) being observed in all carcasses in the summer within five 

days (2052 ADH), whereas each stage of decomposition was only observed in half of the fall 

carcasses during the entire trial period of 31 days (2215 ADH) (Figure 3). This difference was 

apparent from the first sampling event, as all summer carcasses reached bloat within five h of 

placement at a mean ADH of 137 (SEM = 50), compared to the beginning of bloat in the fall at a 

mean of 104 ADH (SEM = 27) at 172h, or eight days into sampling. Summer carcasses then 

progressed to active decomposition at a mean of 1176 ADH (SEM = 83) (72 h), to advanced at 

1360 ADH (SEM =48) (84 h), and to dry at 2052 ADH (SEM = 51) (148 h) (Figure 11). 

In contrast, fall carcasses progressed to active decomposition at a mean of 367 ADH 

(SEM = 112) (200 h), to advanced at 970 ADH (SEM = 115) (330 h), and dry at 1685 ADH 

(SEM =117) (501 h). Three carcasses in the fall (i.e., C8, C9, and C10) did not fully reach the 

criteria for dry stages of decomposition as defined by Payne (1965) and so are not included in 

mean fall ADH for that stage (Table 2, Figure 4). At the end of the fall study trial three carcasses 

were beginning to dry and a majority of flesh had been removed from the head and extremities, 

but some liquefying and disintegrating flesh remained in the abdominal cavities along with 

scattered fly larvae, and the underlying soil remained wet with decomposition fluid up until the 

final sampling event (n=62) at 2215 ADH (689 h) on day 31. 

19 

 
 
In addition to differences in ADH and decomposition stages, each season presented 

varying physical, taphonomic changes in carcass decomposition which can be seen in Figures 6 

and 7. Due to higher average daily temperatures in the summer, bloat manifested very quickly 

and inconspicuously before carcasses were overtaken by larval masses at the face and umbilical 

regions. In contrast, the fall bloat stage lasted a little over five days and was visually more 

pronounced, with the abdomens of carcasses very distended and bloated with the gases of 

putrefaction, and the foaming or oozing of fluids from the mouth, nose, rectum, or umbilical area 

observed at each carcass. Fall carcasses were also observed with mottled red and purple 

discoloration of the abdomen during peak bloat, which shifted into a gray-green discoloration 

accompanied by strong fetid odors and some skin slippage on the surface of the abdomen. In 

addition, fall carcasses told conflicting stories as decomposition progressed, as the mouths and 

skin of fall carcasses visible from the top developed a tough, dry, almost leathery appearance, 

while the undersides and insides of the abdomen were wet with decomposition fluid, and 

skeletonized more quickly. Summer decomposition visually progressed more uniformly across 

each carcass, with no observed signs of green-gray discoloration, skin slippage, or purge as seen 

in the fall. 

Seasonal Insect Communities 

The colonization of carcasses by fly larvae also occurred differently between the summer 

and fall (Figures 6-7). While egg masses and hatched larvae were observed initially on and in 

protected areas like the mouths, noses, ears, rectum, and between the legs of each carcass, only 

the summer carcasses developed distinct larval masses that eventually grew to overtake the entire 

carcass. In contrast, larval activity and development in the fall was not apparent externally and 

20 

 
 
 
 
took place primarily internally, within the head, esophagus, chest, and abdominal cavities. This 

made the precise observation of larval activity difficult, as they were not obvious from the top of 

each carcass compared to summer. Discrete summer larval masses formed first on the mouth and 

face, and around the umbilical area (Figure 6), while fall larval masses were looser, with the 

concentration of activity varying among body cavities. Representative larval samples were 

collected over each seasonal trial, but their analysis is not included in this study. 

A variety of adult insect taxa was also documented across each season (Figure S6). A 

total of 276 insects were collected and identified, with 206 from the summer and 70 from the 

fall.  Diptera had by far the greatest relative abundance of any taxa across both seasons, 67% in 

the summer and 74.3% in the fall. Calliphoridae relative abundances for the fall were over three 

times greater than the summer, and three more calliphorid species, Calliphora vomitoria, 

Cynomya cadaverina, and Lucilia sericata, were observed in the fall than in the summer. 

However, there were 13 more Dipteran families observed in the summer than in the fall: 

Dolichopodidae, Drosophilidae, Mycetophilidae, Phoridae, Piophilidae, Pipunculidae, 

Poleniidae, Sarcophagidae, Sepsidae, Syrphidae, Tachinidae, and Tephritidae. Interestingly, 

Chloropidae was the most abundant family in the summer of all Diptera and family taxa, 

representing 14% of insect abundance, but was present at only 2.86% (n=2) in the fall. Summer 

Chloropidae abundances were also greater than order-level abundances of Coleoptera (6.31%) 

and Hemiptera (8.25%). 

The second and third greatest relative abundant taxa after Diptera also differed by season, 

with Hymenoptera at 17.48% followed by Hemiptera at 8.25% in the summer, and Hemiptera at 

12.86% followed by Coleoptera at 10% in the fall. Negligible numbers of Psocoptera (n = 2) and 

Lepidoptera (n = 1) were also collected in the summer and fall, respectively. 

21 

 
A 

B 

C 

Figure 4. Seasonal variation in accumulated degree hours (ADH) expressed as time in hours. A) 
Summer intra-carcass variation in ADH. B) Fall intra-carcass variation in ADH. C) Mean ADH 
across both seasons. C3 is not represented in the summer due to a lack of available temperature 
data. 

22 

 
 
 
 
 
 
B 

A 

C 

Figure 5. Differences in decomposition progression expressed as accumulated degree hours 
(ADH) between seasons. A) Intra-carcass variation in the summer. B) Intra-carcass variation in 
the fall. C) Mean accumulated degree hours (ADH) for both seasons. Decomposition stages 
(Payne 1965) were identified with a combination of written field observations, insect activity, 
and photographed taphonomy from each sampling event.  

23 

 
 
 
 
 
 
Table 2. Accumulated degree hour (ADH) ranges for each stage of decomposition as defined by 
Payne (1965) for each season. Ranges were estimated based on mean ADH, written field 
observations, and photographed taphonomy from each sampling event.   

Table 3. Accumulated degree hour (ADH) ranges for each carcass during each season, according 
to decomposition stages as defined by Payne (1965). Ranges were determined using a 
combination of written field observations, insect activity, and photographed taphonomy at each 
sampling event. — indicates carcasses that did not reach that stage of decomposition. 

24 

StageSummerFallFresh0-910-61Bloat92-36762-901Active368-1160902-1782Advanced1161-15091783-2215Dry1510-20521691-2215StageC1C2C3C4C5C6Fresh0-910-910-910-910-3670-91Bloat92-36792-44792-44792-806368-80692-367Active368-1160448-1160448-1160807-1259807-1160368-1160Advanced1161-12591161-12591161-14371260-14371161-15091161-1259Dry1260-20521260-20521438-20521438-20521510-20521260-2052StageC7C8C9C10C11C12Fresh0-610-610-470-610-1980-198Bloat62-57562-90148-5762-575199-768199-901Active576-1723902-139358-1017576-1056769-1393902-1283Advanced1724-20411394-22151018-22151057-22151394-16901284-1875Dry2042-2215---1691-22151876-2215SummerFall 
 
 
 
 
 
Figure 6. Taphonomic changes from the summer of each carcass at 0, 24, 72, 96, 100, and 148 h, 
or 0, 406, 1197, 1462, 1510, and 2052 accumulated degree hours (ADH). Photos represent 
closest available visuals for mean decomposition stages as defined in table [mean table] and 
figures [stacked bars A and B]. 

25 

 
 
Figure 7. Taphonomic changes from the fall of each carcass at 0,147, 226, 460, 502, and 690 h, 
or 0, 62, 576, 1394, 1691, and 2215 accumulated degree hours (ADH). Photos represent closest 
available visuals for mean decomposition stages as defined in table [mean table] and figures 
[stacked bars A and B]. 

26 

 
 
 
A 

B 

Figure 8. Counts of insect taxa between seasons. A) Insects identified to family. B) 
Calliphoridae identified to species. Infrequent taxa are families of which 2 or fewer specimens 
were collected for that season.  

The recovery of blow flies from butterfly traps over time presented differently between 

seasons as well (Figure 8, Table 1), with 19 and 23 blow flies recovered from the summer and 

fall, respectively. Summer blow flies were collected on the first three days of decomposition, 

with the most recovered on day two (N = 12). Flies on day two were primarily Lucilia 

coeruleiviridis (N = 9), making up the majority species of all summer flies. In contrast, fall blow 

flies were recovered over eight non-consecutive days, with the most recovered on days eight and 

15 (N = 6). There were five specimens of both adult L. coeruleiviridis and Lucilia silvarum.  

Seasonal microbial community composition 

Total relative abundance of bacterial taxa across all samples showed four primary phyla 

that most represented microbial community composition for each season: Actinobacteria, 

27 

 
 
 
 
Bacteroidota, Firmicutes, and Proteobacteria (Figure 9A). Of these phyla, Firmicutes and 

Proteobacteria were most dominant overall, accounting for >90% of relative abundance, with 

Proteobacteria being most abundant (>50%) regardless of season. Firmicutes and Proteobacteria 

also maintained >90% mean relative abundance over time and among sample types regardless of 

season (Figure 10, Figure 12). These four phyla were primarily represented by 8 families shared 

between seasons: Enterobacteriaceae, Enterococcaceae, Moraxellaceae, Morganellaceae, 

Planococcaceae, Streptococcaceae, Vagococcaceae, and Wohlfahrtiimonadaceae (Figure 9B). 

Wohlfahrtiimonadaceae was the most abundant overall ( >20%) at the family level for each 

season, with the second overall being Moraxellaceae in the summer (20.6%) and Vagococcaceae 

in the fall (10.8%). Alpha diversity analyses among all samples found that season had a 

significant effect on microbial community composition (PERMANOVA,  F = 4.86, P = 0.012) 

(Table ), and measurements of Shannon and Observed alpha diversity (Wilcoxon rank-sum tests, 

FDR corrections) also indicated significant differences (P<0.001) exist in seasonal diversity, 

with greater alpha diversity in the summer for both measures (Figure 9D).  

Additional Permutational Analysis of Variance showed sample type (skin, mouth, 

rectum, or internal fly) had a significant effect in structuring communities for both summer (F = 

27.16, P = 0.001) and fall (F = 4.83, P = 0.001) (Table 4). At the phylum level, Proteobacteria 

was the most relatively abundant among summer carcass microbiota (68%, 64%, and 58%, for 

the skin, mouth, and rectum, respectively) while summer fly microbes were primarily 

represented by Firmicutes (64% relative abundance) (Figure 10A). At the family level, summer 

carcass microbe relative abundances are comprised mostly of Moraxellaceae and 

Wohlfahrtiimonadaceae, which combined made up 48% of relative abundance of rectum 

samples, and >50% of all skin and mouth samples. Summer flies however were represented by 

28 

 
primarily Enterococcaceae (31% relative abundance) and Vagococcaceae (26% relative 

abundance) (Figure 10A). 

A 

C 

B 

D 

P = 8.7E-06 

P = 2.42E-04 

Figure 9. Differences in bacterial communities between seasons. Phylum (A) and family level 
(B) bacterial mean relative abundance between seasons. Only taxa which comprised greater than 
1% of total abundance are shown. C) PCoA plot (weighted Unifrac distance) of all samples 
expressed as season. Ellipses indicate 95% confidence intervals (CI) for multivariate t-
distributions. D) Differences in observed and shannon bacterial diversity (Wilcoxon rank-sum 
test, FDR correction). 

29 

 
  
 
 
Table 4. PERMANOVA of bacterial beta diversity between seasons. PERMANOVA test was 
conducted using weighted Unifrac distance and 999 permutations. DF = Degrees of Freedom. SS 
= Sum of Squares. 

Phylum-level relative abundances among carcass microbiota in the fall were again 

dominated by Proteobacteria (>50% relative abundance), though rectal samples were split more 

evenly between Proteobacteria (48% relative abundance) and Firmicutes (50% relative 

abundance) (Figure 10B). Flies were also primarily represented by Firmicutes in the fall (52% 

relative abundance) but differed from summer flies at the family-level, with lower abundances of 

Enterococcaceae (<10%) and greater relative abundances of Enterobacteriaceae (5.4%) and 

Moraxellaceae (6.4%). Pairwise tests of Shannon and Observed diversity (Wilcoxon rank-sun 

test, FDR correction) among sample types for each season indicated that flies were less diverse 

overall and were significantly different (P<0.001) from all other sample types for each measure 

(Figure 11). Significant pairwise differences in alpha diversity were also seen between all carcass 

sites in the fall for both measures except for mouth-rectum interactions (Observed P = 0.68, 

Shannon P = 0.38), while the only significant carcass site interaction in the summer was skin-

rectum (Observed, P = 0.007).  

30 

FactorDFSSR2F.ModelPr(>F)Season10.03120.01154.86430.012Residual4172.67140.9885Total4182.70261 
 
 
 
 
 
 
A 

C 

B 

D 

Figure 10.  Bacterial communities by sample type (internal blow fly, mouth, rectum, or skin) for 
each season. A) Phylum and C) family level mean relative abundance for the summer. B) 
Phylum and D) family level relative abundance for the fall. Only taxa which comprised greater 
than 1% of total abundance are shown. 

Pairwise PERMANOVA identified further specific significant interactions among sample 

types. Due to the significant differences in alpha diversity measures between flies and carcass 

samples regardless of season (Figure 11), tests of pairwise significance were conducted with and 

without flies for each season to better understand their effects on carcass microbial communities. 

Tests of all carcass and fly microbes (Table 6) showed significant differences between flies and 

31 

 
 
 
 
all carcass sites (P = 0.002) in the summer, while flies were only significantly distinct from rectal 

samples in the fall (P = 0.023). Distinct differences were also observed between the rectum and 

skin in the summer (P = 0.023), while skin microbiota were significantly different from the 

mouth and rectum in the fall (P = 0.004). Pairwise tests of exclusively carcass microbes showed 

that fall skin microbiota were again distinct from the mouth and rectum, but with greater 

significance (P = 0.002), and no significantly distinct microbiota were observed among carcass 

sites in the summer. 

A 

B 

Figure 11. Pairwise tests of Observed and Shannon diversity (Wilcoxon rank-sum test, FDR 
correction) between sample types for the summer (A) and fall (B). Significant differences are 
denoted by brackets. * = P-adj < 0.05, ** = P-adj < 0.01. 

32 

 
 
 
 
Microbial community succession for each season was characterized by significant shifts 

in composition over time. In addition to mean relative abundance (Figure 12), total relative 

abundances for all samples over time were visualized to better identify temporal patterns among 

sample types (Figure 13). On day one of each seasonal trial, carcass bacterial communities are 

dominated by prominent abundances of Moraxellaceae, as well as Enterobacteriaceae in the fall. 

Day two of the summer was marked by a sudden increase in Vagococcaceaee and decrease in 

Moraxellaceae, which continued to decrease daily. By day three Wohlfartiimonadaceae was the 

most dominant taxa with a mean relative abundance of 37%, which increased to 55% by day four 

(Figure 12A). By day five Wohlfartiimonadaceae abundance began to decrease as 

Planococcaceae increased in prevalence, eventually becoming the most dominant taxa by day 

seven at 26% mean relative abundance.  

In the fall, similar successional patterns occurred but more gradually than in the summer 

(Figure 13). Moving from day one to day eight of decomposition, the most abundant family 

Enterobacteriaceae was replaced by Moraxellaceae, and Streptococcaceae appeared for the first 

time. By day 10 and moving into week two, Streptococcaceae and Moraxellaceae made up >40% 

of relative abundance, and microbial diversity increased as additional families including 

Lactobacilliaceae, Leuconostocaceae, Enterococcaceae, and Hafniaceae grew in abundance as 

well. Notably, Lactobacilliaceae, Leuconostocaceae, and Hafniaceae are families not observed in 

any summer carcass samples. Over days 19 – 26, Vagococcaceae grew in abundance with 

Wohlfahrtiimonadaceae, while Lactobacilliaceae, Leuconostocaceae, and Hafniaceae decreased 

but maintained steady, low abundances into late decomposition. Planococcaceae was also present 

in analyses of individual body sites, though not in as high abundances as observed in the 

33 

 
 
 
 
 
 
 
 
summer. For the remainder of the fall trial, Enterobacteriaceae maintained a relative abundance 

of <2%. 

A 

C 

B 

D 

Figure 12. Bacterial communities over time for each season. A) Phylum and family-level mean 
relative abundance for the summer. B) Phylum and family-level relative abundance for the fall. 
Only taxa which comprised greater than 1% of total abundance are shown. 

Shannon and Observed alpha diversity measures plotted daily (summer) and weekly (fall) 

suggested an increase in diversity over time, though this trend was not apparent in Observed fall 

diversity (Figure 13). Pairwise tests of Shannon and Observed diversity (Wilcoxon rank-sun test, 

34 

 
 
 
 
FDR correction) over time indicated significant differences between all days of decomposition in 

the summer except comparisons of days 1-2 and 1-3 (Observed), and 2-3, 4-5,and 6-7 (Shannon 

and Observed) (Figure 13A). Pairwise tests of fall Observed diversity indicated significant 

differences between comparisons of weeks 2-3, 2-4, and 3-4, and between all weeks except 3-4 

for Shannon diversity (Figure 13B). 

A 

B 

Figure 13. Pairwise tests of Observed and Shannon diversity (Wilcoxon rank-sum test, FDR 
correction) over time for each season expressed as sample type. A) Daily summer alpha 
diversity. B) Weekly fall alpha diversity. Significant differences are denoted by brackets. * = P-
adj < 0.05, ** = P-adj < 0.01, *** = P-adj < 0.001. 

35 

 
 
 
 
Figure 14. Total relative abundance of microbial taxa at the family level for the summer (top) and fall (bottom) grouped by sample 
type (internal fly, mouth, rectum, and skin) and sorted by day to show bacterial succession over time. 

36 

 
 
Table 5. Impact of sample type and time on seasonal bacterial beta diversity for carcass and fly 
microbes. Time in days was tested for both seasons, with an added factor of week for the fall due 
to study duration and to investigate the extent of temporal effects on fall decomposition. 
Significant values (<0.05) are indicated in bold. PERMANOVA test (weighted UniFrac distance) 
was conducted with 999 permutations. DF = Degrees of Freedom, SS = Sum of Squares.  

Permutational Analysis of Variance also confirmed the significant (P<0.001) effect of 

time on community composition regardless of season, in time measured in days, as well as time 

in weeks in the fall (Table 5). While time in days (N = 32) and weeks (N = 4) were both tested to 

investigate the extent of temporal effects on fall decomposition, a factor of week was chosen to 

best visualize fall microbial community structure in principal coordinates analysis (PCoA) 

(Figure 14). The same method used to test among sample types was applied to tests of bacterial 

communities over time. Within summer carcasses and flies, significant pairwise differences 

existed between all days of decomposition except for 1-2 (P = 1.000) and 6-7 (P = 0.205). In 

37 

SummerFactorDFSSR2F.ModelPr(>F)Sample Type30.200210.252927.16280.001Day10.045050.056918.33420.001Sample Type:Day30.035350.044654.79590.007Residual2080.511050.64554Total2150.791661FallFactorDFSSR2F.ModelPr(>F)Sample Type30.030460.063654.72780.001Week10.021150.044199.84680.001Sample Type:Week30.008170.017071.2680.241Residual1950.418760.87509Total2020.478531Sample Type30.030460.063654.83420.001Day10.025920.0541712.34340.001Sample Type:Day30.012610.026352.00120.059Residual1950.409540.85583Total2020.478531 
 
 
 
tests of only carcass microbiota, significant pairwise differences also existed between all days of 

decomposition except for days six and seven (P = 0.183) (Table 7). However, comparisons of 

subsequent days such as 2-3 (P = 0.02), 4-5 (P = 0.013), and 5-6 (P = 0.015) were less highly 

significant than comparisons of non-subsequent days like 1-4 or 2-6 (P = 0.001). In addition, 

significant differences in tests of pairwise differences in carcass microbes were overall more 

significant than those in tests of carcasses and flies. These differences in community structuring 

were visualized in PCoA (Figure 14), and ordinations excluding flies explained a greater 

percentage of variation than those including flies. 

Table 6. Comparison of seasonal Pairwise Permutational Analysis of Variance (PERMANOVA, 
FDR correction) among sample types. 

38 

SeasonComparison (Sample Type)Carcass microbesCarcass and Fly microbesFly -Mouth-0.002Fly-Rectum-0.002Fly-Skin-0.002Mouth-Rectum0.4070.465Mouth-Skin0.2160.161Rectum-Skin0.0510.023Fly-Mouth-0.065Fly-Rectum-0.004Fly-Skin-0.121Mouth-Rectum0.4920.370Mouth-Skin0.0020.004Rectum-Skin0.0020.004FallP adj.Summer 
 
 
 
 
 
 
 
Table 7. Pairwise PERMANOVA calculated from weighted UniFrac distances of summer 
bacterial communities by sampling day. Two tests were run, one including fly microbes and one 
excluding them. Values indicating significantly distinct microbiota (<0.05) are shown in bold. 

Table 8. Pairwise PERMANOVA calculated from weighted UniFrac distances of fall bacterial 
communities by week of decomposition. To examine the impact of fly microbes on communities, 
tests were run with and without flies. Values indicating significantly distinct microbiota (<0.05) 
are shown in bold. 

39 

Comparison (Day)Carcass microbesCarcass and Fly microbes1-20.0011.0001-30.0010.0021-40.0010.0021-50.0010.0021-60.0010.0021-70.0010.0022-30.020.0222-40.0010.0022-50.0010.0022-60.0010.0043-40.0010.0023-50.0010.0023-60.0010.0024-50.0130.0194-60.0010.0025-60.0150.0097-20.0010.0227-30.0010.0027-40.0010.0027-50.0010.0027-60.1830.205P adj.Comparison (Week)Carcass microbesCarcass and Fly microbes1-20.0010.0821-30.0010.0091-40.0010.0032-30.0010.0372-40.0010.0033-40.0020.009P adj. 
 
 
 
 
 
Figure 15. Seasonal comparison of principal coordinates analyses (PCoA) of weighted UniFrac 
distances with and without flies, expressed as sample type and time in days (summer) or weeks 
(fall). A) Summer fly and carcass microbes. B) Summer carcass microbes only. C) Fall fly and 
carcass microbes. D) Fall carcass microbes only. 

Fly microbial communities 

 Internal blow fly communities were predominantly composed of Enterococcaceae and 

Vagococcaceae, but in overall mean relative abundances (Figure 10) possessed more “other” 

taxa (>20%) than other sample types, suggesting some underlying diversity or other untapped 

differences in community structure may exist. Fly bacterial relative abundances were also 

visualized individually at the family level and grouped by day caught (Figure 15), which 

revealed a few families not apparent in previous analyses including carcass microbiota. For 

40 

 
 
 
 
example, Anaplasmataceae made up >20% relative abundance for 12 flies in the summer but was 

absent from flies in the fall. Dysgonomodaceae was also detected at >50% relative abundance in 

one fly identified as L. silvarum in the summer but was similarly absent from the fall. However, 

four flies from the fall, L. silvarum (N=2), L. coeruleiviridis (N=1), and L. illustris (N=2) 

contained low abundances of Leuconostocaceae not detected elsewhere. Additionally, seven fall 

flies, C. vomitoria (N=1), C. cadaverina (N=2), L. sericata (N=1), L. illustris (N=2), L. silvarum 

(N=1), contained low abundances of Pseudomonadaceae also not detected elsewhere. Principal 

coordinates analyses of flies by species and season were conducted to visualize internal blow fly 

bacterial communities (Figure 15), and due to species sample sizes 95% confidence intervals 

could only be calculated for L. coeruleiviridis, L. sericata, and L. silvarum. 

Fly-carcass interactions 

Heatmaps of family-level taxa for each season grouped by day of decomposition and 

sample type were created to visualize potential exchanges of taxa between internal fly microbiota 

and carcasses (Figures 17 and 18). Heatmaps indicate high relative abundances of Moraxellaceae 

on day one for each season and among carcass samples, with comparatively low or absent 

abundances in flies. In the summer, high Vagococcaceae and Enterobacteriaceae abundances are 

seen on days two, three, and four, in correlation with fly sampling and activity. In the fall a 

similar trend occurs, with sudden high abundances of Vagococcaceae on days six and seven. 

41 

 
 
 
 
A 

B 

Figure 16. Total relative abundance of internal microbiota for all sampled blow fly species, 
grouped by day of decomposition for the A) summer and B) fall. 

42 

 
 
 
 
 
 
Figure 17. Seasonal comparison of principal coordinates analysis (PCoA) of weighted UniFrac 
distances for blow fly species. A) Summer and fall blow fly species. B) Summer blow fly 
species. C) Fall blow fly species. Ellipses indicate 95% confidence intervals (CI) for multivariate 
t-distributions. 

43 

 
 
 
* 

* 

* 

A 

B 

Figure 18. Heatmap of family level bacterial relative abundance by day of decomposition (A) and by sample type (B) for the summer. 
(*) indicates days on which blow flies were recovered from butterfly traps. Only taxa which comprised < 1% of total abundance are 
shown.  

44 

 
 
 
 
* 

* 

* 

* 

* 

* 

*  * 

Figure 19. Heatmap of family level bacterial relative abundance by day of decomposition (A) and by sample type (B) for the fall. (*) 
indicates days on which blow flies were recovered from butterfly traps. Only taxa which comprised greater than 1% of total 
abundance are shown. 

45 

 
 
 
 
Discussion 

The objectives of this study were to observe and characterize insect-microbe interactions 

within the necrobiome by comparing insect communities, decomposition patterns, postmortem 

microbial communities on mammalian carcasses, and internal calliphorid microbiomes between 

summer and fall. The necrobiome is a complex system and decomposition is a highly variable 

process shaped by many biotic and abiotic factors, making them challenging to uniformly 

describe or quantify at times. In spite of this, studies have attempted to do so by examining 

patterns of seasonal decomposition and insect succession, and postmortem microbiomes in a 

variety of animal models such as swine (Benbow et al., 2013; Pechal, Benbow, et al., 2014), 

humans (Hyde et al., 2015; Metcalf et al., 2013; Pechal et al., 2018), mice (Metcalf et al., 2013), 

cats (Early & Goff, 1986), salmon (Pechal & Benbow, 2016), kangaroos (Barton, Cunningham, 

Macdonald, et al., 2013),white-tailed deer, bears, and even alligators (Watson & Carlton, 2005). 

However, despite demonstrating the critical role that microbes and arthropod colonizers, 

particularly blowflies, individually play in the recycling of carrion within ecosystems (Metcalf et 

al., 2013; Pechal, Benbow, et al., 2014), few studies have attempted to investigate how 

necrophagous flies influence postmortem microbial community succession. The results of this 

study represent an important addition to improving our foundational understanding of intricate 

necrobiome dynamics, and the extent of the role that microbe-insect interactions play. 

As predicted, a more diverse assemblage of insect taxa was observed in the summer 

(Figure 10) and Diptera were the most abundant insect order regardless of season. However, 

greater blow fly species diversity was observed in the fall, which could be attributed to 

prolonged decomposition providing significantly longer windows of colonization for 

necrophagous flies, or by differences in thermal tolerance of observed fall species. Additionally, 

46 

 
 
 
unlike the fall, Chloropidae were the most abundant family collected. Chloropidae, also known 

as eye gnats or frit flies, are generalist scavengers of decaying organic matter, such as feces, 

plants and fruit, and rotting wood (Merritt et al., 2009; Nartshuk, 2014). Chloropidae larvae feed 

on and develop in decomposing habitats, and have been documented previously in vertebrate 

decomposition (Early & Goff, 1986), but are not described as taxa of particular forensic interest 

or ecological significance. Additionally, it is important to note that the data presented here of 

blow fly communities cannot be assumed to be an absolute representation of blow fly diversity at 

these sites, and trapping results shared here vary from that of other studies. For example, a 

survey by Babcock et al. (2020) of adult blow fly community structure in Michigan cited catches 

of anywhere from 25 to just over 3,000 Calliphoridae per trap when left in the field for 4 h per 

day, demonstrating the abundance of blow flies in this area.  

Bacterial community succession for each season was characterized by two significant 

shifts over time: 1) a sudden decrease in the abundance of endogenous taxa associated with fresh 

carcasses in correlation with bloat stage, leading to 2) increasing bacterial community diversity 

as decomposition progressed into active and advanced stages. A third shift may have also 

occurred in the summer, where increasingly dry stages of decomposition and larval migration 

away from carcasses caused a levelling off in bacterial diversity. This event was not as 

distinguishable in the fall, which could be due to differences in patterns of larval colonization 

and development, as well as prolonged active and advanced stages due to low temperatures.  

Similar trends towards homogeneity over time have also been observed in late decomposition of 

human cadavers (Hyde et al., 2015), perhaps in correlation with the increased risk of desiccation 

and decrease in larval activity. 

47 

 
In support of our hypothesis, marked differences were observed in the progression of 

decay and microbial community succession between seasons (Tables 4-7, Figures 4-5, 13-15). 

This contrast between summer and fall was anticipated due to the highly temperature dependent 

nature of both decomposition and insect development (Wall et al., 1992), with higher 

temperatures accelerating and low temperatures slowing or halting it entirely. Fall decomposition 

lasted nearly five times as long as the summer and was characterized by taphonomic changes 

such as prolonged periods of purge, skin slippage, and gray-green abdominal discoloration. 

These results are consistent with previous observations of swine carcass decomposition in cold 

seasons, and the colorful gray-green discoloration is thought to be caused by a delay in the 

formation of propionic and butyric acid volatiles that allow for the fermentation and breakdown 

of tissues by anaerobic bacteria in cold temperatures (Iancu et al., 2015).  Inhibition of these 

anaerobic bacteria by the cold may be pivotal in delaying major shifts in microbial communities, 

as it is assumed that decreased metabolic activity would set back body cavity rupture and the 

beginnings of purge (Vass, 2001). Previous studies have proposed that abdominal purge is the 

pre-cursor to significant changes in soil microbial communities and pH as carcass nutrients are 

introduced to the surrounding habitat, which in turn likely alters endogenous carcass microbiota 

(Evans, 1963; Hyde et al., 2015; Metcalf et al., 2013).  

The results of this study show some support for the proposed impact of abdominal purge 

on microbial communities, with highly significant (P<0.001) shifts in Shannon diversity 

occurring between days one and two in the summer, corresponding with bloat and purge (Figure 

13). This significance over time was confirmed with pairwise PERMANOVA (P = 0.001) and 

visualized by PCoA (Table 6, Figure 15). Relative abundances over time in the summer also 

support the effects of purge on communities, and showed a sudden decrease in abundance of 

48 

 
Moraxellaceae, and increases in Enterococcaceae, Enterobacteriaceae, and Morganellaceae from 

day one to day two. Where Moraxellaceae is an aerobic taxon, the increasingly abundant taxa 

following purge are known to be anaerobic, suggesting that these are endogenous bacteria that 

have escaped from the abdominal cavity causing shifts in community structure. This high 

abundance in fresh stages and subsequent decrease in Moraxellaceae in swine decomposition has 

also been reported by Pechal et al (2014), but differs from observations of this taxa in late stages 

of human decomposition (Hyde et al., 2015).  

Relative to the fall, purge was not very noticeable in the summer, due to the accelerated 

rate of decomposition caused by higher seasonal temperatures. Also contributing to the swiftness 

of decomposition is presumably the smaller size of the stillborn swine carcasses used in these 

trials (mean weight of 1.87 kg in the summer and 1.32 kg in the fall). These sizes differ greatly 

from that of the adult pigs typically used in similar studies, which can range in weight from 14 

kg up to 79 kg (Benbow et al., 2013; Díaz-Aranda et al., 2018; Matuszewski et al., 2008; 

Sharanowski et al., 2008). The limited biomass provided by these smaller carcasses likely also 

shortened the window of opportunity for insect exploitation, thereby limiting our ability to 

observe the full extent of insect communities associated with the summer necrobiome here.  

Another potential explanation for the significant temporal shifts in seasonal bacterial 

communities could be the introduction of exogenous microbes via blow fly oviposition and 

larval development. The previously described decreases in abundances of endogenous taxa 

following fresh stages also correlate with observations of blow fly oviposition and larval mass 

activity (Tables 1-2, Figures 4-5). This correlation is supported by the appearance of bacterial 

taxa prevalent in internal blow fly microbiota such as Enterococcaceae, Vagococcaceae, and 

Wohlfahrtiimonadaceae in carcass microbiota on days blow flies were caught and observed 

49 

 
interacting with carcasses (Figures 10, 12, and 14). Pairwise PERMANOVA comparing daily 

and weekly bacterial communities trended towards less highly significant temporal differences 

with the inclusion of flies than without flies, and pairwise comparisons of sample types indicated 

a lack of significant differences between fly internal microbiota and mouth samples in the fall 

(Tables 5-7, Figure 15).  Taxa heatmaps support the potential introduction of Enterococaceae, 

Vagococcaceae, and Wohlfahrtiimonadaceae by flies to carcass microbial communities (Figures 

18-19) but further analyses of differentially abundant microbiota among sample types over time 

are needed to confirm and assign significance to this proposal. 

This introduction of exogenous microbiota by blowflies could occur mechanically in 

different ways: via salivary secretions when feeding on carcass tissues, via tarsal contact as flies 

land to investigate the ephemeral resource, via regurgitation or defecation, or via oviposition of 

eggs by females on carcasses (Talley et al., 2009). Due to the significant role blow fly larvae 

play in consuming and transforming carrion, the latter mechanism could be more likely. In a 

metagenomic study of bacteria associated with different life stages of L. sericata and Lucilia 

cuprina, Singh et al (2015) reported that most fly-associated microbes were retained 

intragenerationally, and saw evidence for trans-generational inheritance and horizontal 

transmission of bacteria among flies. Additionally, Weatherbee et al. (2017) found evidence to 

suggest that carcass microbiomes are influenced by larval mass microbiomes, describing a 

sudden shift in bacteria relative abundances that correlated with maggot mass presence. This 

evidence supports our findings of the significant impacts of adult blow fly internal microbiomes 

on carcass microbiomes and may implicate larval stages and maggot mass dynamics as a 

potential driving force in microbial community succession in carrion ecology. 

50 

 
This study presents the first comprehensive look at blow fly-microbe interactions in 

vertebrate decomposition in two highly contrasting seasons Michigan. This seasonal context is of 

particular importance due to the significance of temperature as a primary driver of nearly all 

biological processes (Brown et al., 2004). Because temperature is directly proportional to a 

substance’s kinetic energy, temperature governs chemical and metabolic rates for most 

organismal life. This principle is a cornerstone of the fields of forensic entomology and carrion 

ecology, as rates of insect development and decomposition are highly temperature dependent and 

are frequently measured in ADH, a convenient unit of heat and time. Microbes have also 

exhibited temperature dependence, with higher temperatures increasing microbial diversity and 

taxon richness (Zhou et al., 2016). These relationships suggest that, based on our results, 

microbe-insect interactions may have broad implications for ecosystem ecology and necrobiome 

dynamics such as rates of carrion removal and nutrient recycling. The study of decomposition 

within different temperature ranges may also become of particular importance as the effects of 

global climate change are felt, leading to potential shifts in species ranges, changes in 

community structure, and ecosystem disruption (Maxwell et al., 2019; Thuiller, 2004). These 

changes could have unforeseen consequences for decomposer communities and their ability to 

utilize and break down carrion. Additionally, understanding the effects of contrasting 

temperatures on the interdomain relationships of primary decomposers could also be a valuable 

tool for forensic investigators to estimate PMI more accurately, or positively link insect evidence 

to a set of remains. 

This study aimed to investigate and characterize potential inter-kingdom interactions 

associated with insects and necrotic microbial communities and found support for the potential 

exchange or introduction of exogenous microbiota into carcass microbiomes by blow flies. This 

51 

 
interaction appears to be the precursor to significant temporal shifts observed in carcass 

microbiota, particularly the depletion of Moraxellaceae and increase in community diversity as 

decomposition progresses. More extensive and in-depth analyses of blow fly life stages during 

carrion decomposition could be useful to more conclusively identify microbes responsible for 

driving these changes, as well as if or how these introduced bacteria vary between dipteran 

species. 

Tomberlin et al. (2011), in advocating for a unified, standardized framework for forensic 

science and decomposition research, specifically cited the need for further exploration of the role 

of microbiota in decomposition to better understand how they mediate arthropod activity and 

facilitate critical ecosystem services. One rationale for this is to improve guidelines and 

standardized practices for forensic entomological evidence in in order to meet criteria outlined 

by the Daubert standard, which states that scientific evidence admitted into a court of law must 

be testable, have known error rates, and be peer-reviewed and accepted by that scientific 

community (Solomon & Hackett, 1996). While there are still many complex facets of the 

necrobiome that need to be examined in greater detail to inform our understanding of forensic 

entomology and carrion ecology, the findings of this study help address the issues outlined by 

Tomberlin et al. (2011), and also contribute to a working foundation for future, more specialized 

forays into microbe-insect interactions that can link basic and applied sciences. Building up this 

body of knowledge and more clearly defining the mechanisms that dictate carrion decomposition 

will not only strengthen our understanding of the broad ecological impacts of nutrient cycling 

across trophic levels, but also align these fields with the Daubert standard, allowing for more 

meaningful application and utility of entomological evidence in the legal system.  

52 

 
 
 
CHAPTER 2: POTENTIAL POSTMORTEM MICROBIAL BIOMARKERS OF INFANT 

DEATH INVESTIGATION 

Abstract 

Through life and after death, the microbiome is rapidly becoming a powerful tool to 

provide insight into the human health condition. Microbial communities on the human body are 

highly dynamic, adapting to reflect their host’s environment and lifestyle over time. Studies have 

shown that the event of death is no exception, with microbiome data echoing antemortem health 

up to 48 hours following death. These predictable microbial biomarkers can critically inform 

death investigations by helping to estimate the postmortem interval and build models to identify 

cause and manner of death more accurately. However, no attempts have been made to survey and 

model potential microbial biomarkers in cases of infant (≤2 years) death. This study provided a 

cross-sectional survey of microbiota of 53 infant cases received to the Wayne County Medical 

Examiner’s Office, Detroit, Michigan, and demonstrates support for the differentiation of 

microbial communities across anatomical sites previously documented in adult cases. To build a 

robust database, microbiota were collected from black and white infants from both sexes that 

died by accident, homicide, or natural causes, by swabbing the eyes, ears, nose, mouth, 

umbilicus, brain, rectum, and cardiac blood. 16SrRNA sequence analyses showed that sex, race, 

age, body site and manner of death (MOD) had a statistically significant effect on microbiome 

composition, with a significant interaction between MOD, race, and age. Sneathia, a potential 

biomarker associated with spontaneous preterm birth and preterm prelabor rupture of membranes 

was discovered.  

53 

 
 
 
Introduction 

While speculations have been made about the importance of human microbiota as far 

back as the 1900s, the potential of the human microbiome as a tool to provide insight into the 

human health condition has become a subject of particular interest within the last decade or so 

(Blaser, 2006; Cho & Blaser, 2012; Knight et al., 2017; Metchnikoff, 1907). Due to remarkable 

advancements in DNA sequencing and bioinformatic technologies, studies have demonstrated 

that from birth microbes are a substantial presence on and in the human body, which may harbor 

an estimated 500-1,000 bacterial species and up to 20 million microbial genes at any time 

(Gilbert et al., 2018; Turnbaugh et al., 2007). This microbial diversity far exceeds the genetic 

diversity of the human genome, and also varies by anatomical site on the body, with distinct 

microbial communities observed in areas such as the eyes, ears, nose, mouth, gut, rectum, and on 

the hair and skin (Costello et al., 2009; Gilbert et al., 2018; Pechal et al., 2018). These microbial 

communities are typically specific to an individual like a fingerprint, but are also highly dynamic 

and variable through human development; adapting to their host’s environment, physiology, and 

lifestyle over time (Lax et al., 2014; Meadow et al., 2015; Relman, 2012). Studies have shown 

this pattern continues even after death, with postmortem microbiomes resembling antemortem 

microbiota in a predictable succession up to 48 hours later (Pechal et al., 2018), and in some 

cases as late as 72 hours  (Metcalf et al., 2013, 2016). In this way, human postmortem microbiota 

have a unique potential to inform our understanding of human health and death investigation. 

However, studies of the human microbiome are predominantly antemortem, have been limited 

primarily to adults, and rarely examine cases of infant death. 

The long-term impacts, importance, and community structure of the microbiome in living 

human adult hosts have been documented in many contexts. Lifestyle traits like cohabitation 

54 

 
with pets, exercise and sleep habits, diet, and even stress levels have associations with 

microbiome composition (Gilbert et al., 2018). For example, the microbiome can mediate 

glycemic responses to diet and impact blood glucose levels differently from person to person, 

while stress and sleep deprivation are associated with shifts of bacterial ratios in the gut that can 

impact nutrient absorption, metabolism, and immune responses (Benedict et al., 2016; Karl et al., 

2017; Zeevi et al., 2015). Research has also shown that human microbiomes are impacted by our 

environment, with evidence suggesting features of urban environments like abandoned buildings 

contribute to exposure to potential pathogens in sites like eyes, mouth, and nose, while green 

spaces are positively correlated with microbial biodiversity in the rectum and the nose (Pearson 

et al., 2019, 2020) 

With so much information available within an individual’s microbiome, this growing 

body of evidence has also revealed novel microbial biomarkers that can indicate the presence of 

certain immune, metabolic, and cognitive diseases, which may in turn be used to inform their 

diagnosis, treatment, and prevention strategies, as well as potential links to manner or cause of 

death (Gilbert et al., 2018; Kaszubinski et al., 2020; Pechal et al., 2018; Zhang et al., 2019). For 

example, genomic and metabolomic analyses found that Fusobacterium species are a significant 

biomarker of colorectal cancer, the third most common cancer worldwide, as well as a 

relationship between gut microbiota-dependent metabolism of phosphatidylcholine and 

cardiovascular disease, the leading cause of death globally (Balakumar et al., 2016; Ferlay et al., 

2015; Kostic et al., 2012). Multiple potential biomarkers for inflammatory bowel disease have 

also been proposed such as increased ratios of Firmicutes and Actinobacteria, as well as 

Gammoproteobacteria, Enterococcus, and Enterococcaceae (Cho & Blaser, 2012; Lo Presti et al., 

2019). While additional analyses are still needed to better understand many of these microbiome 

55 

 
configurations and their relationships with various diseases, great potential has been established 

for the microbiome as a practical tool to survey human health. However, a majority of work has 

examined the living host only, and less emphasis has been placed on the study of postmortem 

microbiota with the exception of a few studies such as Pearson et al. (2019, 2020) and Pechal et 

al (2018).  

Though comparably lacking in study, temporal and localized anatomical changes in 

bacterial community structure have been observed during mammalian decomposition. Because 

microbes are significant drivers of decomposition due to their pivotal role in putrefaction as 

cellular integrity is compromised, most studies have viewed the postmortem microbiome through 

a forensic or ecological lens as a means to develop a postmortem interval (PMI), or time elapsed 

since death, for use within the criminal justice system or to investigate complex decomposition 

dynamics (Evans, 1963; Janaway et al., 2009; Metcalf et al., 2013; Pechal, Crippen, et al., 2014). 

This is often accomplished using high-throughput DNA sequencing due to its increasing 

affordability and reliability as a forensic tool. These investigations may utilize adult deceased 

human bodies or swine carcasses (Sus scrofa L.), which have been established as appropriate, 

accessible proxies to model human decomposition when human bodies are unavailable (Catts & 

Goff, 1992).  

Like the antemortem microbiome, bacterial communities differ between body sites such 

as the skin, mouth, and gut, and change in a predictable succession over decomposition, with 

certain bacterial taxa associated with different taphonomic stages (Adserias-Garriga et al., 2017; 

Hyde et al., 2015; Metcalf et al., 2013; Pechal, Crippen, et al., 2014) and associated with manner 

of death (see citations in comments). Investigations of postmortem microbiota vary in focus 

however, with some examining only a single body site at a time or multiple at once, and nearly 

56 

 
all data are limited by the availability of human research subjects representative of real-world 

community demographics. Additionally, little to no comprehensive data is available for 

postmortem microbiota of children and infants, with the exception of a study from Pechal et al. 

(2017) examining the frozen and thawed remains of a 9-year-old and 13-year-old, and an audit 

by Pryce et al (2011) evaluating microbial cultures from routine postmortem sampling of sudden 

unexplained death in infants (SUDI) taken in the hospital to those taken at the time of autopsy. 

As evidenced by the significant variability of the antemortem microbiome from person to person, 

more robust data across different age ranges and demographics are needed of postmortem 

microbiota in order to provide a more cohesive understanding of these communities after death, 

but they are especially needed in infants.  

Table  9.  A  summary  of  infant  mortality  rates  (per  1,000  live  births)  for  the  United  States, 
Michigan, and Wayne, Co., MI. ((Centers for Disease Control and Prevention, National Center for 
Health Statistics: Linked Birth / Infant Death Records, 2007-2020 Results, n.d.). 

Infant mortality rates in the United States exceeded 19,000 deaths from 2007-2020, or 6 

per 1,000 live births. This rate in Wayne County, Michigan is just over 1.5 times that of the 

United States (Centers for Disease Control and Prevention, National Center for Health 

Statistics: Linked Birth / Infant Death Records, 2007-2020 Results, n.d.). A prevalent cause of 

57 

Geographic RegionInfant Mortality Rate (per 1,000 live births)United States6Michigan6.8Wayne Co., MI9.8 
 
 
 
 
 
death in infants is sudden unexplained infant death (SUID), also referred to as sudden infant 

death syndrome (SIDS), though the latter term is considered misleading by some pathologists 

and there remains an ongoing debate on how to appropriately define and diagnose this condition 

(Beckwith, 2003; Byard, 2018; Limerick & Bacon, 2004). Described as the “diagnosis without a 

disease”, SIDS is a term used to describe the sudden, unexpected death of an of an otherwise 

healthy infant while sleeping, and to date no tangible cause has been identified to explain these 

deaths (Byard, 2018). Another significant cause of death in infants is also co-sleeping, wherein 

an infant sleeps with a parent, sibling, or other caretaker in an adult bed, on a couch, or any area 

other than an infant crib by themselves. Studies have shown that this can create an unsafe 

sleeping situation for infants, as they are obligate nasal breathers and their airways may be easily 

blocked by the body of another person, a cushioned surface, or blankets, leading to asphyxiation 

(Byard, 2015; Mitchell, 2015). Complications thus arise when an unexplained infant death 

occurs, as SIDS and co-sleeping deaths present very similarly, and autopsy findings can rarely 

distinguish between them. This problem of determining the manner of death in suspected SUID 

cases, framed within the context of the postmortem microbiome, was the aim of this study. 

The potential of the postmortem microbiome to reveal information about antemortem 

health has already been demonstrated in adults by Pechal et al. (2018), who observed evidence 

for decreased microbial diversity in individuals with heart disease. Using machine learning 

algorithms, Zhang et al. (2019) showed that microbial samples from the eyes were the most 

informative anatomical site overall to predict manner of death, though rectal samples 

independently were highly accurate models, and that different combinations of body site 

microbes increased each algorithm’s accuracy. These results show the potential of the 

postmortem microbiome as a predictive tool to not only survey the living health condition, but to 

58 

 
inform forensic investigations. Methods described by Pechal et al. (2018) to sample the 

postmortem microbiome could be integrated into routine autopsy procedure, and analyses for 

biomarkers could be developed into analytical workflow recommendations to aid in forensic 

investigation as proposed by Kaszubiniski et al (2020). Further analyses, standardization, and 

robust datasets are needed before tools such as this can be feasibly integrated into real world 

scenarios and accepted into a court of law (Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 

U.S. 579 (1993), n.d.; Tomberlin et al., 2011).  

As the microbiome rapidly changes during the first three years of life, this could be a 

critical time to sample and investigate microbial community dynamics in infants (Knight et al., 

2017). To investigate these potential interactions, we conducted a cross-sectional survey of 53 

infant cases using 16SrRNA with the goals of 1) characterizing the composition of the infant 

bacterial postmortem microbiome for the first time; and 2) identifying potential bacterial 

biomarkers that could predict manner of death. We hypothesized that: 1) bacterial community 

composition will be structured by body site and by manner of death; and 2) that microbes from 

co-sleeping (control) deaths would be more similar in bacterial composition and structure than 

other manners of death, as these unexpected deaths are due to asphyxia of otherwise healthy 

infants. 

59 

 
 
 
Materials and Methods 

Microbiome samples were acquired from the Wayne County Medical Examiner’s Office 

(MEO) located in Detroit, MI, USA, through routine death investigations carried out since 2014. 

Only samples from cases of death in black or white individuals aged 2 years or younger were 

analyzed within this study. The following information for each decedent was compiled from 

available case reports: sex, ethnicity, date of birth, estimated age (days, months, and years), event 

location (indoors, outdoors, vehicular, or hospital), time of death, and season (winter, summer, 

etc.). In addition, the following information was collected from autopsy reports: autopsy date, 

height (crown-heel length, crown-rump length, foot length) and height percentile, weight and 

weight percentile, manner of death, cause of death, and state of decomposition at the time of 

autopsy, as well as any other significant pre-existing conditions or medical history. All cause and 

manner of death (MOD), and postmortem interval (PMI) estimates were determined by a board-

certified forensic pathologist.   

Manner of death classification 

For the purposes of this study, MOD for each case was categorized as one of the 

following based on mechanism and cause of death: natural, accidental, non-accidental, or 

control. Natural death was assigned to cases where the circumstances of death were attributed to 

natural, pre-determined causes, such as a pre-existing genetic condition. Accidental death was 

assigned to cases involving chance circumstances such as accidental drownings, or airway 

obstruction unrelated to co-sleeping with an adult. In contrast, non-accidental death was assigned 

to cases involving intentional harm caused by blunt force injury, drug toxicity, or other non-

accidental injury. Finally, control cases were those where circumstances of death were due to 

60 

 
 
 
asphyxia, airway obstruction, or over-lay specifically attributed to co-sleeping. Studies have 

shown that the act of co-sleeping with a sibling or adult caretaker can create an unsafe sleeping 

environment for infants, as their airways may be easily blocked by the body of another person, 

cushioned surfaces, or blankets, leading to asphyxiation (Byard 2015). Co-sleeping deaths were 

chosen as a control in this study since this MOD did not represent any compounding variables 

such as disease or bodily trauma.  

Sample Collection  

Sampling kits consisting of sterile, DNA-free, cotton-tipped applicators (25-806 2WC, 

FDNA, Puritan, Guilford, MA, USA), and sterile 1.5 ml microcentrifuge tubes (20170-038 VWR 

International, Radnor, PA, USA) filled with 100% molecular biology grade ethanol (BP2818-

500, Fisher Scientific, Waltham, MA, USA) were provided to trained autopsy personnel within 

the Wayne County MEO to collect individual microbial communities from each decedent. Each 

swab kit was assigned a unique kit number by our lab and returned by MEO personnel with its 

corresponding case number for processing. A total of nine anatomical locations were sampled 

and assigned L1-L9 as follows: ears, eyes, nose, mouth, umbilicus, rectum, trabecular space, 

interhemispheric fissure, and cardiac blood. Sites were sampled by manually rubbing a single 

swab over the area for at least 30 seconds and breaking off the cotton tip of each swab into an 

individual 1.5 ml tube. Bacterial samples were taken from decedents within 24-48 hours of 

admission to the Wayne County ME, and all samples were transported in cooler storage from the 

MEO and immediately placed in -20 °C storage until DNA isolation. Sampling followed 

regulations outlined by the Institutional Review Board (IRB), which states that research utilizing 

61 

 
 
human remains from deceased autopsy subjects does not require the same consideration given 

human subjects.  

DNA Isolation and Quantification  

DNA was extracted from cotton swabs in a biological safety cabinet using the QIAGEN 

DNeasy Blood & Tissue Kit (QIAGEN, Hilden, Germany). Swabs were first prepared for lysis 

on Parafilm sheets (Bemis Company, Inc, Neenah, WI, USA) by using sterilized dissecting 

forceps and scalpels (12-000-164, Thermo Fisher Scientific, Waltham, MA, USA) with sterile, 

single-use carbon steel blades (22-079-697, Surgical Design Inc. Lorton, VA, USA) to carefully 

remove cotton from the wooden handle. The cotton was then placed back into its respective 

1.5ml tube, and the wooden handle and Parafilm were discarded. To preserve the individual 

microbial communities, new Parafilm sheets and sterile instruments were used to prepare each 

sample.  The manufacturer’s protocol was then followed accordingly with the addition of 10ul of 

15 mg/ml lysozyme to promote increased microbial cell lysis. Samples were also treated with 4ul 

100mg/ml RNase A following incubation per the MSU Genomics Core Illumina sample 

sequencing requirements, and final DNA elution volume was modified from 200ul to 50ul. DNA 

was then quantified using a Qubit 2.0 and a 1x dsDNA High Sensitivity Assay (Thermo Fisher 

Scientific, Waltham, MA, USA). DNA was stored at -20°C to await further processing.   

16S rRNA Confirmation  

The microbial gene amplicon V4 region was amplified using conventional PCR in 

triplicate with Invitrogen Platinum Green Hot Start Master Mix (2x) (Thermo Fisher Scientific, 

Waltham, MA, USA), PCR-grade nuclease free water, and region-specific primers 515f/806r (5′ 

62 

 
 
 
GTGCCAGCMGCCGCGGTAA, 5′ GGACTACHVGGGTWTCTAAT). PCR samples were 

each prepared with 24ul mastermix and 1ul template DNA, loaded into 96-well plates (82006-

636 VWR International, Radnor, PA, USA), and sealed with qPCR film (6091-078, VWR 

International, Radnor, PA, USA) in a biological safety cabinet. The positive and negative 

controls run in each plate were E. coli and PCR-grade nuclease-free water, respectively. PCR 

samples were spun down in a Sorvall ST 8 centrifuge (Thermo Fisher Scientific, Waltham, MA, 

USA), placed into a Veriti 96-well thermal cycler (Thermo Fisher Scientific, Waltham, MA, 

USA) and amplified according to previously described methods (Caporaso et al., 2011). The 

resulting PCR product was stored at -20°C.  

Visual 16S rRNA confirmation was then performed via gel electrophoresis. 3% agarose 

gels (3.6 g agarose in 120 ml 1X TAE) (N605-500G, VWR International, Radnor, PA, USA) 

were cast in a Thermo EC Midicell Primo EC-330 Horizontal Gel System. 5ul each of 

GeneRuler 1kb ladder (Thermo Fisher Scientific, Waltham, MA, USA), PCR product, and 

controls were loaded and run at 90 V for 40 minutes. Gels were then agitated in GelRed solution 

(60 ul GelRed in 200 ml DI water) (Thermo Fisher Scientific, Waltham, MA, USA) for 30 

minutes on a 3D platform rotator. Gels were imaged using the Axygen Gel Documentation 

System (Corning Incorporated, Corning, NY, USA) at UV 302.  

Table 10. An overview of sample size and demographics for sequenced cases. TS = trabecular 
space, IF = interhemispheric fissure, CB = cardiac blood. 

63 

Sample Size (Cases)Median Age Age RangeEthnicitySexYears CollectedBody Sites Collected109 Days0-817 Days3.6 Months0-27 Months0.3 Years0-2.22 Years532015-2022Ears, Eyes, Nose, Mouth, Umbilicus, Rectum, TS, IF, CB26 Male, 27 Female40 Black, 13 White 
 
 
 
 
Table 11. A summary of MOD categories for sequenced cases broken down by sex and race. ND 
indicates cases with an unknown or indeterminate MOD based on available case information. 

Table 12. Manners and causes of death for sequenced cases of infant death. Cause of death was 
determined by a board-certified forensic pathologist. 

64 

Manner of DeathFemaleMaleTotalFemaleMaleTotalTotalAccidental4262139Non-accidental461020212Natural1452138Control53813412Indeterminate3360006ND2351016Total192140851353BlackWhiteManner of DeathCause of DeathAccidentalAirway obstruction, asphyxia, drug toxicity, drowningNon-accidentalAbusive head trauma, asphyxia, blunt force trauma, cranio-cerebral injuries, drug toxicity, multiple blunt force injuries, multiple blunt trauma, multiple stab wounds, premature birthNaturalAcute bronchitis, complications of edwards syndrome, congential heart disease, pneumonia, RSV bronchiolitis, prematurity complications, sepsis due to urinary tract infection, status asthmaticusControlAirway obstruction or asphyxia due to co-sleepingIndeterminateAnoxic encephelopathy, fentanyl toxicity, fentanyl toxicity with complications, sudden death, suffocation, unknown 
 
 
 
 
 
 
Table 13. Totals of each body site processed and sequenced with 16S rRNA gene amplicon high-
throughput sequencing. 

 16S rRNA Gene Amplicon Sequencing  

            DNA sequencing and library construction for 298 individual microbial body site samples 

was performed at the Michigan State University Genomics Core facility (East Lansing, MI, 

USA) on the Illumina MiSeq (2 x 250 bp paired-end reads). The V4 region of the 16S rRNA 

gene was amplified using dual indexed 515F/806R Illumina primers (5′-

GTGCCAGCMGCCGCGG-3′, 5′-TACNVGGGTATCTAATCC-3′) following the protocol 

described in Kozich, JJ, et al. (2013). PCR products were batch normalized using an Invitrogen 

SequalPrep DNA Normalization plate, and products were pooled and cleaned up using a 

QIAquick Spin column and AMPure XP magnetic beads. Library pools were quality controlled 

and quantified using a combination of Qubit dsDNA HS, Agilent 4200 TapeStation HS 

DNA1000, and Invitrogen Collibri Illumina Library Quantification qPCR assays. Amplicon 

65 

Body SiteNumber SequencedEyes16Ears38Nose49Mouth46Umbilicus3Rectum45Trabcular space (TS)40Interhemispheric fissure (IF)36Cardiac blood (CB)24Total298 
 
 
 
 
pools were then loaded onto a MiSeq v2 standard flow cell and sequencing was carried out in a 2 

x 250 bp paired-end format using a MiSeq v2 500 cycle reagent cartridge. Custom sequencing 

and index primers complementary to the 515f/806r oligomers were added to appropriate wells of 

the reagent cartridge. Base calling was performed by Illumina Real Time Analysis (RTA) 

v1.18.54 and output of RTA was demultiplexed and converted to FastQ format with Illumina 

Bcl2fastq v2.20.0.    

16S rRNA Sequence Analyses  

QIIME2 (Quantitative Insights Into Microbial Ecology) (v2023.2) was used to filter and 

analyze raw 16S microbial sequencing data. Demultiplexed reads were assembled and quality 

filtered for chimeric reads and artifacts using DADA2. A Naïve Bayes classifier (V4, SILVA 

database v138-99) trained against the 16S rRNA region was used to classify filtered reads into 

taxonomic groups and assign taxonomy (Quast et al., 2013; Yilmaz et al., 2014) . Sequences 

were aligned with a De novo multiple sequence alignment program in QIIME2 (Mafft v7) and 

contingency filtered to remove singletons and reads corresponding with mitochondria or 

chloroplasts (Katoh & Standley, 2013). 16SrRNA relative abundances of bacterial communities 

at the phylum (>1%) and family level among body sites and manners of death were analyzed 

with the phyloseq, MicrobeR, microbiome, qiime2R, microbiomeSeq, and microbiomeutilities 

packages in Rstudio (v4.3.1). 

Statistical Analyses  

Permutational Multivariate Analysis of Variance (PERMANOVA, 999 iterations) and 

pairwise PERMANOVA (FDR corrections) tests using weighted UniFrac distances using the 

66 

 
 
 
vegan R package (Oksanen et al., 2015) were conducted evaluate the effect of body site, manner 

of death, and additional covariates (age, sex, race) on overall microbiome composition. Pairwise 

Wilcoxon rank-sum tests of observed and Shannon diversity (False Discovery Rate (FDR) 

correction) were also performed to determine significant differences in bacterial alpha diversity 

among body sites and manners of death (Benjamini & Hochberg, 1995). Weighted UniFrac 

distances were used to visualize covariate relationships and microbiome structure with principal 

coordinates analysis (PCoA) and 95% confidence intervals (CI) for multivariate t-distributions. 

Weighted UniFrac metrics were used for these ordinations because they account for phylogenetic 

distances and relative abundances among microbes (Lozupone & Knight, 2005).  

To identify potential biomarkers most likely to explain differences among significantly 

distinct microbiota, covariates were tested using the linear discriminant analysis (LDA) effect 

size (LEfSe) using default settings in the microbiomeMarker package (Rstudio) (Cao et al., 

2022; Segata et al., 2011). LEfSe is an algorithm that uses Kruskal-Wallis tests to discover high-

dimensional biomarkers that can statistically significantly differentiate between biological 

groups (Fang et al., 2018; Segata et al., 2011). All statistical analyses were conducted in Rstudio 

(v4.3.1) and all p-values were considered significant with alpha ≤ 0.05.  

67 

 
 
 
Results 

16S rRNA sequences from a total of 298 body site samples (eyes, ears, nose, mouth, 

umbilicus, rectum, TS, IF, and CB) (Table 13) from 53 cases of routine infant autopsy collected 

from 2015 to 2022 (Table 10) in Wayne County, Michigan, were analyzed to characterize infant 

postmortem microbiota and identify potential microbial correlations linked to manner of death. 

Of the 53 cases there were 26 males and 27 females, 40 black and 13 white, with case ages 

ranging from 0-27 months old. 12 cases each were identified as non-accidental and controls, with 

nine accidental deaths and eight natural deaths. A more detailed breakdown of sequencing and 

demographic totals can be seen in Tables 10, 11, and 13. Manner and causes of death observed in 

this dataset are summarized in Table 12 and were categorized based on individual evaluation of 

circumstances described in autopsy and case reports. There were also six cases wherein MOD 

could not be conclusively determined through routine autopsy, referred to hereafter as 

indeterminate deaths, as well as six cases with no available data for cause or manner of death 

(ND). Due to the limited availability of samples from cases of infant death, these cases were still 

included in analyses to build a more robust database of postmortem infant microbiota, especially 

since there are no comprehensive published findings of postmortem microbiome profiles of 

humans in this age range. While microbiome composition and phylum and family-level relative 

abundances of all samples were investigated, samples from cases with an MOD classified as 

Indeterminate and ND were filtered from the dataset for the purpose of statistical analysis. 

Bacterial community structure 

Infant postmortem microbiota were represented by four primary phyla: Actinobacteria, 

Bacteroidota, Firmicutes, and Proteobacteria (Figure 20) Among all body sites the most 

68 

 
 
 
 
dominant phylum was Firmicutes, ranging from 34.3% relative abundance in umbilicus samples 

to 62% relative abundance in ear samples. The second most relatively abundant phylum was 

Proteobacteria among cardiac blood samples (31.5%), eyes (38.8%), nose (36.2%), trabecular 

space (31.7%), interhemispheric fissure (32.9%), and mouth (22.1%). Bacteroidota was second 

most abundant in rectum samples (24.2%), and 28.3% relative abundance of umbilicus samples 

was represented by “Other” phyla. Among manners of death the most dominant phylum was 

again Firmicutes (30-60% relative abundance), followed by Proteobacteria (18-42% relative 

abundance). Relative abundances for the top 13 family-level taxa were also plotted (figure []), 

and varied among body sites, with the most prevalent being Streptococcaceae (1.3% in rectum 

samples to 30% in mouth samples), Enterobacteriaceae (2.5% in umbilicus samples to 19% in 

eye samples).  

Ear samples were represented by Staphylococcaceae (44.5% relative abundance) more 

than any other site, and rectum samples had the highest abundance of Lachnospiraceae (12.7%) 

and Peptostreptococcales-Tissierellales (5.6%), but lacked Moraxellaceae, a family present at 

low relative abundance (0.6-7.2%) in all other sites. Bifidobacteriaceae was also more abundant 

in the rectum (10.7%) and umbilicus (10.1%) than other body sites (<5%). Among manners of 

death “Other” family-level taxa represented the most relative abundance (24-36%) overall. 

Enterobacteriaceae was most abundant in accidental deaths (25% relative abundance), and 

Streptococcaceae was present at 12-26% relative abundance among all MODs. Indeterminate 

deaths were represented by Prevotellaceae (18.5% relative abundance) more than other MODs. 

69 

 
 
A 

B 

Figure 20. A) Phylum and B) family level bacterial relative abundance by body site (left) and 
manner of death (right). Only phylum-level taxa which comprised greater than 1% of total 
abundance are shown. 

70 

 
 
Pairwise tests of Shannon and Observed diversity (Wilcoxon rank-sun test, FDR 

correction) among body sites indicated many significant differences in alpha diversity (Figure 

21). Shannon diversity showed distinct microbiota for comparisons of cardiac blood-mouth (P = 

0.002), cardiac blood-nose (P = 0.015), and cardiac blood-rectum (P = 0.0002), for ears-mouth 

(P = 0.030) and ears-rectum (P = 0.011), for eyes-mouth (P = 0.019) and eyes-rectum (P = 0.002, 

for interhemispheric fissure-mouth (P = 0.040) and rectum (P = 0.008), for rectum-nose (P = 

0.019) and trabecular space-rectum (P = 0.047). More distinct differences among body sites in 

pairwise tests of Observed diversity were found than Shannon, with very high significance 

(P<0.0001) between cardiac blood and the mouth, nose, and rectum, between eyes and the mouth 

and rectum, between the interhemispheric fissure and the mouth, nose, and rectum, and between 

the trabecular space and mouth, and rectum. Less highly significant differences were found for 

cardiac blood-ears (P = 0.043) and cardiac blood-umbilicus (P = 0.021), for ears- rectum (P = 

0.043), for interhemispheric fissure-eyes (P = 0.038) and interhemispheric fissure-umbilicus (P = 

0.027), and for the rectum-nose (P = 0.027). Tests were run of all sequences and filtered 

sequences (with and without ND and Indeterminate MOD cases) to investigate significant 

pairwise differences in alpha diversity for manner of death, but no significantly distinct 

microbiota for either measure were detected for either test among MODs. 

Results of permutational analysis of variance of body site and MOD showed that body 

site (F = 3.968, P = 0.001) and MOD (F = 2.626, P = 0.001) had statistically significant effects 

on microbiome composition (Table 13). However, the interaction between body site and MOD (F 

= 0.952, P = 0.632) did not have a statistically significant effects on microbiome composition. 

Further investigation of other study covariates however showed that race (black or white) had a 

significant effect on bacterial communities when tested for interaction with body site (F = 2.47, P 

71 

 
 
= 0.018) and MOD (F = 2.942, P = 0.014)), as well as a significant interaction with MOD (F = 

2.971, P = 0.001) (Table 14-15). Sex (male or female) was also shown to have a significant 

interaction with MOD (F = 1.765, P = 0.026), but alone did not have a significant effect (Table 

14). Finally, age also had a significant structuring effect on microbiomes, but had no interactions 

with body site or MOD. Principal coordinates analyses were plotted to visualize these 

interactions within microbial communities (Figure 21) 

B 

A 

C 

Figure 21. Principal coordinates analyses (PCoA) of weighted UniFrac distances expressed as 
A) body site, B) manner of death, and C) manner of death and body site. Ellipses indicate 95% 
confidence intervals. 

72 

 
 
A 

B 

Figure 22. Pairwise tests of Observed and Shannon diversity (Wilcoxon rank-sum test, FDR 
correction) between A) body sites and B) manners of death. Significant differences are denoted 
by brackets. * = P-adj < 0.05, ** = P-adj < 0.01. 

73 

 
 
Table 14. Impact of body site and MOD on bacterial beta diversity. Significant values (<0.05) 
are indicated in bold. PERMANOVA test (weighted UniFrac distance) was conducted with 999 
permutations. DF = Degrees of Freedom, SS = Sum of Squares. 

Table 15. Impact of MOD, race, sex, and age on bacterial beta diversity. Significant values 
(<0.05) are indicated in bold. PERMANOVA test (weighted UniFrac distance) was conducted 
with 999 permutations. DF = Degrees of Freedom, SS = Sum of Squares. 

74 

FactorDFSSR2FPr(>F)Manner of Death30.68560.030942.6260.001Body Site82.76290.12473.9680.001Manner of Death:Body Site221.82240.082250.9520.632Residual19416.88620.76211Total22722.15711FactorDFSSR2FPr(>F)Manner of Death30.68560.03092.4680.003Race10.27240.01232.9420.014Manner of Death:Race30.82550.03732.9710.001Residual22020.37350.9195Total22722.15711Manner of Death30.68560.03092.4050.003Sex10.06170.00280.650.746Manner of Death:Sex30.50330.02271.7650.026Residual22020.90640.9436Total22722.15711Manner of Death30.68560.03092.8970.001Age386.77010.30562.2580.001Manner of Death:Age10.1070.00481.3560.202Residual18514.59440.6587Total22722.15711 
 
 
 
 
 
 
Table 16. Impact of body site, race, sex, and age on bacterial beta diversity. Significant values 
(<0.05) are indicated in bold. PERMANOVA test (weighted UniFrac distance) was conducted 
with 999 permutations. DF = Degrees of Freedom, SS = Sum of Squares. 

Table 17. Pairwise PERMANOVA calculated from weighted UniFrac distances among manners 
of death. Values indicating significantly distinct microbiota (<0.05) are shown in bold.   

75 

FactorDFSSR2FPr(>F)Body Site82.74910.124073.91940.001Race10.21680.009782.47270.018Body Site:Race80.77910.035161.11080.26Residual21018.4120.83098Total22722.15711Body Site82.74910.124073.88280.001Sex10.08670.003910.97910.398Body Site:Sex80.73570.03321.0390.409Residual21018.58560.83881Total22722.15711Body Site82.74910.124073.83060.001Age386.82650.308092.00250.001Body Site:Age16210.8770.49090.74840.986Residual191.70450.07693Total22722.15711P adj.Non-accidentalNatural0.294Non-accidentalAccidental0.024Non-accidentalControl0.1185NaturalAccidental0.026NaturalControl0.2568AccidentalControl0.026Comparison (MOD) 
 
 
 
 
 
 
 
 
Table 18. Pairwise PERMANOVA calculated from weighted UniFrac distances among body 
sites. Values indicating significantly distinct microbiota (<0.05) are shown in bold.    

76 

P adj.EarsEyes0.058EarsNose0.029EarsUmbilicus0.207EarsRectum0.005EarsTS0.107EarsMouth0.036EarsIF0.089EarsCardiac blood0.059EyesNose0.353EyesUmbilicus0.107EyesRectum0.005EyesTS0.125EyesMouth0.036EyesIF0.107EyesCardiac blood0.058NoseUmbilicus0.107NoseRectum0.005NoseTS0.107NoseMouth0.107NoseIF0.099NoseCardiac blood0.016UmbilicusRectum0.078UmbilicusTS0.107UmbilicusMouth0.099UmbilicusIF0.133UmbilicusCardiac blood0.141RectumTS0.005RectumMouth0.005RectumIF0.005RectumCardiac blood0.005TSMouth0.059TSIF0.906TSCardiac blood0.323MouthIF0.036MouthCardiac blood0.014IFCardiac blood0.650Comparison (Body site) 
 
 
 
Pairwise PERMANOVA was used to further identify significant interactions for body site 

and manner of death (Table 16-17). Comparisons of non-accidental and accidental (P = 0.024), 

natural and accidental (P = 0.026), and accidental and control (P = 0.026) were significantly 

distinct, while ears-nose (P = 0.029), ears-rectum (P = 0.005), ears-mouth (P = 0.036), eyes-

mouth (P = 0.036), nose-rectum (P = 0.005), nose-cardiac blood (P = 0.016), all rectum 

comparisons (P = 0.005), mouth-interhemispheric fissure (P = 0.036), and mouth- cardiac blood 

(P = 0.014) were significant among body sites. 

To better understand how race, sex, and age influence microbiome structure, additional 

pairwise tests of Shannon and Observed diversity (Wilcoxon rank-sun test, FDR correction) were 

conducted (Figure 23). No significant differences were observed in alpha diversity measures 

between males and females, or black and white infants (Figure 23B). There were however 

significant distinctions observed for ranges of age in days, with microbiota originating from 

cases aged 1-30 days and 66-92 days differing from cases aged 383-820 days in Observed 

diversity (Figure 23A). Microbiota from cases aged 165-380 days old were also significantly 

different from age ranges of 1-30 days, 31-60 days, and 66-92 days, as well as between age range 

383-570 and ranges 1-30 and 31-60 for Shannon diversity measures. Additionally, infant age 

appears to be positively correlated with alpha diversity measures, which is particularly evident 

for Shannon diversity (Figure 23A). 

Identification of potential biomarkers  

To attempt to identify potential biomarkers most likely to explain differences in 

microbiome structure caused by covariates, linear discriminant analysis effect size (LEFSe) 

analyses were attempted for all covariates, and significant results were visualized as a heatmap 

77 

 
 
(Figure 24). Only analyses of sex and race were able to identify potential biomarkers. Features 

most likely to explain differences based on race were Leptotrichiaceae (P = 0.0098), Sneathia (P 

= 0.0064), and an uncultured Sneathia species (P = 0.001). These features were associated with 

microbiota from white cases and based on the heatmap (Figure 24B) also appear to correlate with 

a male and non-accidental death most closely. However much lower prevalence of Sneathia is 

also seen in alignment with black, male, and non-accidental covariates. Results of LEFSe 

analysis based on sex resulted in one identified feature, Escherichia-Shigella (P = 0.0100), which 

enriched microbiota associated with the female sex, and based on the heatmap also appear to 

correlate most distinctly with white, accidental death.  

The criteria for association with Sneathia were used to filter through sample metadata and 

identify potential candidates that may be the origin of this biomarker. One case, 2017-S077 is the 

only case that matches these criteria. This case is represented by six samples (ears, eyes, nose, 

umbilicus, rectum, and trabecular space), and is also the youngest case overall (< 1 day). The 

cause and manner of death assigned by the medical examiner were asphyxia and homicide, 

respectively. 

78 

 
A 

B 

Figure 23. Differences in alpha diversity among sex, race, and age ranges in days. Pairwise tests 
of Observed and Shannon diversity (Wilcoxon rank-sum test, FDR correction). Alpha diversity 
for A) age ranges in days and B) sex and race. Significant differences are denoted by brackets. * 
= P-adj < 0.05, ** = P-adj < 0.01.

79 

 
 
A 

B 

Figure 24. Potential biomarkers associated with manner of death in analyzed infant postmortem microbiota. A)  LEFSse analysis 
results (Wilcoxon cutoff = 0.01, kw cutoff = 0.01, LDA cutoff = 4, CPM (counts per million) pre sample normalization). B) Heatmap 
visualization at the genus-level of covariates associated with identified biomarkers and their interaction with manner of death. 

80 

ClassFeatureEnriched GroupLDA ScoreP.adj.LeptotrichiaceaeWhite4.8159350.0098SneathiaWhite4.8092750.0064Sneathia_uncultured bacteriumWhite4.8062260.0001SexEscherichia-ShigellaFemale4.5012790.0100Race 
 
 
 
 
 
Discussion 

Interest in the human microbiome as a powerful tool with great potential to improve our 

understanding of the human health condition has grown significantly in recent years. Studies of 

microbiome structure and function can reveal a wealth of information about individual health 

and lifestyle, and as this body of evidence grows it informs the way diseases may be treated, 

diagnosed, and prevented (Gilbert et al., 2018). With the advances being made in applications for 

antemortem health, the resilience and utility of human postmortem microbiota are frequently 

overlooked. Even so, studies have established that postmortem microbiota play a significant role 

in decomposition, with temporal patterns of microbial succession and distinct microbial 

communities observed at anatomical sites on human remains and other mammalian proxies 

(Hyde et al., 2015; Metcalf et al., 2016; Pechal, Crippen, et al., 2014), including manner of death 

(Pechal et al 2018; Zhang et al 2019; Kaszubiniski et al 2020). With much to be learned from the 

postmortem microbiome before it can be standardized as a practical tool for use in forensic 

investigation or public health, there is a real need for robust, cross-sectional data representative 

of human microbiota of all ages and demographics. To address this need, this study presents the 

first comprehensive survey of the infant postmortem microbiome that contributes to a larger 

effort to understand the postmortem microbiome of all age, race and ethnic groups more 

comprehensively.   

While body site and manner of death each had a significant effect on infant postmortem 

microbiome composition as predicted, there was no evidence discovered here to suggest yet that 

circumstances of death in infants are correlated with bacterial community structure in specific 

anatomical sites. Instead, of all covariates our results indicated significant interactions between 

MOD and sex and race. This was not an anticipated result, and contradicts findings by Pechal et 

81 

 
 
al (2018) which demonstrated a lack of significance (P >0.6) of race/ethnicity in structuring the 

postmortem microbiome. Fortenberry (2013) urges caution in examining racial and ethnic 

categories due to their lack of biological significance and reliability in determining human 

genomic diversity, while results from Huttenhower et al. (2012) indicate a strong relationship 

exists between race/ethnicity and microbial community composition. Other studies have 

demonstrated consistent associations with ethnicity and bacterial abundance in body sites like the 

mouth, gut, and vagina, with the aim of understanding differential disease susceptibility and 

addressing health conditions such as bacterial vaginosis that disproportionately affect minorities 

(Brooks et al., 2018; Fettweis et al., 2014; Mallott et al., 2023; Mason et al., 2013).   

Overlap of sex, race, and MOD (Figure 24) implicated a white, male, non-accidental 

(homicide) case of infant death as the origin of the Sneathia marker. Sneathia is a genus 

belonging to Leptotrichiaceae, a family of obligate or facultative anaerobes (Theis et al., 

2021).This genus has been found in the mouth, gut, and cervix and vagina, and is thought to 

correlate with spontaneous preterm birth and labor, and preterm prelabor rupture of membranes 

(PPROM) (Theis et al., 2021). Additionally, Sneathia is associated with bacterial vaginosis, may 

be capable of infecting the upper respiratory tract during pregnancy, and one species, Sneathia 

amnii is even documented invading fetal membranes and jeopardizing tissue viability (Theis et 

al., 2021). Preterm birth (PTB) has been declared the second leading cause of neonate death 

worldwide, and Sneathia species detected in early-mid pregnancy may be associated with 

spontaneous PTB (Fettweis et al., 2019; Nelson et al., 2014). Based on the information available 

about manner and cause of death (non-accidental, asphyxia) in the infant case clearly associated 

with this genus, it is unclear exactly how this biomarker became a part of this infant microbiome, 

as well as how many and which body sites it is detected in.  

82 

 
Another observation of note presented here is the significant effect of age on microbiome 

composition, more specifically the increase in alpha diversity associated with increasing age 

(Figure 23A). The development of the living infant microbiome has been studied at length, and 

this early life process is a critical window for proper immunological development, but not all 

sources and exact mechanisms of transmission are understood (Moore & Townsend, 2019). 

Infants most likely obtain microbes vertically and horizontally, receiving their first microbes 

from their mother via maternal seeding, and from the surrounding community post-birth (Moore 

& Townsend, 2019). This community or environmental effect has also been described by 

Pearson et al. (2020) who observed the relationship between neighborhood environmental 

conditions and the human microbiome. Findings from Mallott et al (2023) also demonstrate the 

diversification of infant microbes over time, with a significant shift observed at or just after three 

months of age associated with race and ethnicity. This is likely due to many factors such as 

varying diets (breastfeeding versus starting solid food), association with green spaces, and social 

exposure from other children or caretakers (Mallott et al., 2023). While we did not observe a 

similar shift at three months, samples in the age range of 165-380 days old (5.5 to 12.7 months) 

had more significantly distinct pairwise differences in Shannon diversity. 

 This study presents the first comprehensive survey of the postmortem infant microbiome 

and shows support for the significant effect of anatomical site and MOD on microbial 

community structure, as well as a potential biomarker of infant death related to PTB. These 

findings represent only one more step towards our understanding of the complex dynamics that 

make up the human microbiome, and greater sample sizes and variability are needed to develop a 

truly representative database that can proactively inform human health in the way antemortem 

microbiome studies have. Characterizing these microbiota and postmortem interactions is critical 

83 

 
 
to build our knowledge base regarding human microbiome composition across a broad range of 

ages, lifestyles, demographics, and socio-economic status. Additionally, based on the significant 

effect of MOD on postmortem microbiota presented here, additional surveys and analyses may 

also uncover more specific biomarkers that more clearly indicate correlation with circumstances 

of death. These data have already been explored as such from the human postmortem 

microbiome, and when analyzed using machine learning algorithms showed increasing 

predictive power and accuracy with the incorporation of microbiota from more anatomical sites 

(Zhang et al., 2019). Pechal et al (2014) also used a similar approach, utilizing bacterial 

communities and general additive models to estimate physiological time as a measure of carrion 

decomposition. These modelling strategies exhibit marked potential to improve accuracy rates in 

the classification of infant manner of death when traditional autopsy examination fails to 

ascertain why an infant death occurred due to a lack of bodily evidence or injury. Our 

methodology presented here already provides support for the routine collection of postmortem 

microbiota from a variety of anatomical sites as an addition to standard autopsy protocol. With 

further data collection, analysis, and systematic modelling via machine learning algorithms, 

standardized workflows and recommendations could be developed for practical applications 

during death investigation. 

84 

 
 
 
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APPENDIX A: RECORD OF DEPOSITION OF VOUCHER SPECIMENS  

The specimens listed below, or photos thereof (due to destructive sampling) have been deposited 
in the named museum as samples of those species or other taxa, which were used in this 
research.  

Voucher Number: 2023-12 

Author and Title of thesis:  
Bethany G. Mikles - Seasonal Necrobiome Variation in Michigan and Potential Postmortem 
Microbial Biomarkers of Infant Death Investigation 

Museum where deposited:  
Albert J. Cook Arthropod Research Collection, Michigan State University (MSU)  

Table 19. A list of deposited voucher specimens including their life stage, quantity deposited, 
and preservation method. Photographs of all destructively sampled blowflies were taken 
dorsally, laterally, and of the face to document morphological characteristics. Pinned specimens 
were caught supplementally using butterfly traps and are representative of sampled blowflies. 
Three pinned specimens of each sex of the most abundant species caught were provided. 

Family  

Genus-Species  

Life Stage 

Quantity  Preservation 

Calliphoridae   Lucilia coeruleiviridis  Adult 

Calliphoridae   Lucilia coeruleiviridis  Adult 

Calliphoridae   Lucilia silvarum 

Calliphoridae   Lucilia silvarum 

Calliphoridae  Lucilia sericata 

Calliphoridae  Lucilia illustris 

Adult 

Adult 

Adult 

Adult 

Calliphoridae  Calliphora vomitoria 

Adult 

Calliphoridae  Cynomya cadaverina 

Adult 

Calliphoridae  Phormia regina 

Adult 

6 

53 

6 

24 

13 

12 

7 

12 

3 

Pinned 

Photographs 

Pinned 

Photographs 

Photographs 

Photographs 

Photographs 

Photographs 

Photographs 

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APPENDIX B: SUPPLEMENTARY MATERIALS 

Figure 25. Phylum-level relative abundances grouped by sample type for the summer. 

Figure 26. Phylum-level relative abundances grouped by sample type for the fall. 

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Figure 27. Observed, Chao, and Shannon diversity expressed as season and sample type. 

Figure 28. Principal coordinates analysis (PCoA) of weighted UniFrac distances for all samples 
(skin, mouth, rectum, and blowflies) from both seasons expressed as season (left), and all 
samples and both seasons expressed as sample type (right). Ellipses indicate 95% confidence 
intervals (CI) for multivariate t-distributions. 

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Figure 29. Relative abundance boxplots of the top six family-level taxa grouped by body site. 

Figure 30. Relative abundance boxplots of the top six family-level taxa grouped by manner of 
death (MOD). 

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Figure 31. Total family-level relative abundances grouped by MOD (top) and body site (bottom). 

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Table 20. Counts and relative abundance of insects caught for the summer and fall. 

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nRelative AbundancenRelative AbundanceColeoptera133.02%74.29%20Carabidae10.23%10.61%2Coccinellidae--53.07%5Corylophidae10.23%--1Curculionidae30.70%--3Nitidulidae20.46%--2Staphylinidae61.39%10.61%7Diptera13832.02%5231.90%190Anthomyiidae112.55%31.84%14Asilidae10.23%--1Calliphoridae194.41%2314.11%42Calliphora vomitoria--21.23%2Cynomya cadaverina--42.45%4Lucilia coeruleiviridis143.25%53.07%19Lucilia illustris10.23%31.84%4Lucilia sericata--42.45%4Lucilia silvarum30.70%53.07%8Phormia regina10.23%--1Ceratopogonidae--10.61%1Chloropidae296.73%21.23%31Dolichopodidae112.55%--11Drosophilidae20.46%--2Faniidae30.70%53.07%8Heleomyzidae--21.23%2Muscidae194.41%10.61%20Mycetophilidae10.23%--1Phoridae92.09%--9Piophillidae10.23%--1Pipunculidae61.39%--6Polleniidae10.23%--1Sarcophagidae30.70%--3Scathophagidae30.70%95.52%12Sciaridae10.23%21.23%3Sepsidae40.93%--4Syrphidae102.32%--10Tachinidae10.23%--1Tephritidae10.23%--1Tipulidae20.46%21.23%4Trichoceridae--21.23%2Hemiptera173.94%95.52%26Aphididae-31.84%3Cercopidae51.16%10.61%6Cicadellidae20.46%--2Pentatomidae92.09%53.07%14Reduviidae10.23%--1Hymenoptera368.35%10.61%37Apidae40.93%--4Braconidae20.46%--2Chalcididae10.23%--1Cynipidae10.23%--1Formicidae92.09%--9Ichneumonidae61.39%10.61%7Pteromalidae51.16%--5Sphecidae81.86%--8Lepidoptera--10.61%1Noctuidae--10.61%1Psocoptera20.46%--2Psocidae20.46%--2Total431163594Total nSummerFallOrderFamilySpecies