UNIVERSITY OF CINCINNATI

Date:______November 19, 2008

Trevor I. Stamper I, ______, hereby submit this work as part of the requirements for the degree of:

Doctor of Philosophy in: Biological Science

It is entitled:

Improving the Accuracy of Postmortem Interval Estimations

Using Carrion Flies

(Diptera: Sarcophagidae, Calliphoridae and Muscidae)

This work and its defense approved by:

Chair: ______Ronald W. DeBry

______Theresa Culley

______George Uetz

______Gregory Dahlem

______Anthony Perzigian Improving the Accuracy of Postmortem Interval Estimations Using Carrion Flies (Diptera: Sarcophagidae, Calliphoridae and Muscidae)

A dissertation submitted to the Graduate School Of the University of Cincinnati In partial fulfillment of the requirements for the degree of Doctor of Philosophy In the Department of Biological Sciences Of the McMicken College of Arts and Sciences

By

Trevor I. Stamper

M.A., Anthropology, New Mexico State University at Las Cruces, January 2002 B.A., Anthropology, New Mexico State University at Las Cruces, May 1997

Committee chair: Ronald W. DeBry Abstract The use of flies in forensic entomology in postmortem interval estimations is hindered by lack of information. For accurate postmortem interval estimations using flies, the single most important information is the species identity of the immature flies found upon a corpse. One of the three major groups that associate with carrion, the fleshflies (Diptera: Sarcophagidae), are difficult to identify in almost all life stages, especially in the larval stages commonly found upon carrion sources. Additionally, behavioral information on nocturnal oviposition activity amongst carrion flies in general is needed to resolve a time window of up to twelve hours in postmortem interval estimations. The two major goals of this study are: 1) to resolve the phylogenetic relationships of several major genera within the sub-family

Sarcophaginae and 2) to investigate the behavioral patterns of nocturnal oviposition for carrion flies. Relationships of one of the major lineages of Sarcophagidae, the Sarcophaginae, remain unresolved. Most forensically important fleshfly species fall within the Sarcophaginae and so resolving this group can help identify unknown forensically important specimens. I analyzed the complete cytochrome oxidase I (COI) and cytochrome oxidase II (COII) mtDNA genes, along with portions of the dehydrogenase subunit four (ND4) mtDNA gene and elongation factor one-alpha nDNA gene from 21 species of fleshfly representing 11 genera, and four representatives from other closely related dipteran families. I confirm the monophyly of the family Sarcophagidae and the generic-level organization within the Sarcophaginae of those genera supported by multiple exemplars included in this study (Helicobia, Sarcophaga, Ravinia, Peckia and Boettcheria). The behavioral patterns of nocturnal oviposition represent a window of time that potentially has a large impact on postmortem interval estimations. I investigated the behavioral patterns of carrions flies at night by exposing euthanized rats between sunset and sunrise to see if carrion flies oviposited upon the carrion over three subsequent summers. I encountered significant methodological problems that I corrected. I also investigated urban and rural locations, in both lit and unlit conditions. In the end, I found that nocturnal ovipositing did not occur in the Cincinnati metropolitan area.

ii iii Acknowledgements

The molecular portion of this work was supported by Grant No. 2005-DA-BX-K102 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice to Ronald W. DeBry. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the Department of Justice. The behavioral portion of this work was funded by multiple Wiemenn/Wendel/Benedict Summer Fellowships to Trevor Stamper and from the kind donations of previously euthanized rodents from Dr. Rebecca German and the Genome Research Institute.

I have been blessed to have a wonderful committee to work with while developing my ideas and testing them. I thank my advisor, Ronald W. DeBry, for his support and trust that eventually, I would produce something to justify his time. Ron allowed an errant Anthropologist to come into his lab with precious little Biology experience, but a whole lot of interest in dead people and molecules. Without Ron’s unfailing support, my life would be very different than it is today. Much to his credit, he didn’t even complain when I started breeding flies in odd corners of the lab, kept asking for equipment that no self-respecting molecular biologist should need (who needs dress zippers?), or decided that hand sewing an outrageous number of collapsible traps was not that bad of an idea, especially since we couldn’t find a trap to do what I wanted mine to do. Ron has tried his best to keep me focused and it worked sometimes. I am also deeply indebted to Dr. Gregory Dahlem, who spent an awful lot of time teaching me about flies and how they shouldn’t all be ground up for DNA. We don’t see eye-to-eye that ALL flies should be preserved carefully, but I grind up far fewer than I used to, and have even dedicated a portion of my (as of yet unfinished) house to their proper storage and preservation! Greg went out of his way to help me learn about teaching in academics, mentoring me through the PFF program and providing me with opportunities to teach when our interests aligned. I owe special thanks to Dr. Theresa Culley, who listened to and talked me through organizing large portions of the projects detailed herein and many that are not. Theresa spent countless hours editing manuscripts for me, often with little notice and was always available to listen to my latest crazy idea on how to use biology in postmortem interval estimations. She is a dedicated educator who is always willing to listen to a student and has really good insights. I am a better researcher and teacher for knowing and learning from her. Dr. George Uetz was willing to step

iv outside the life of spiders for a while to help me investigate fly behavior (or lack thereof) on carcasses at night, and his insight into insect behavior was often dead-on despite my protests. Dr. Anthony Perzigian kindly agreed to come over from Anthropology and lend his insight into Jurisprudence and Forensic Anthropology to my work. While our time discussing these topics was necessarily limited by his busy schedule, his understanding of how science and the justice system work hand-in-hand was invaluable. I would also like to thank the other members of the department who have proved so helpful. Specifically, Dr. Carl Huether, who directed the Preparing Future Faculty program until recently, helped me navigate that program’s requirements in addition to my normal lab commitments. Finally, to the office staff: without you, this department wouldn’t function. Thanks a lot for helping me navigate the esoteric byways of the University of Cincinnati graduate system.

I am indebted to my colleagues in the DeBry lab. First, there is Dr. Hector Miranda, who helped me get going on molecular biology and patiently explained many of the common mistakes I routinely made. Second, to Mr. David Hauber, who retired from the crime lab system in Kentucky to escape forensics only to find himself in a lab with someone who wanted to talk about nothing but forensics (and dead people). David has been a nearly constant companion my many years in the DeBry lab, and his advice and expertise in many things, professional and personal, has always been helpful. Finally, to Dr. Alicia Timm, who came into the lab late in the game, but nevertheless helped me with the final push to finish sequencing a seemingly endless number of species and who saved my bacon with a few problematic specimens.

Several undergraduate students came into the DeBry lab during my tenure who have helped with these projects. Colin White studied under me while learning molecular lab techniques. Colin helped characterize some of the PCR settings used in the molecular portion of my work, and helped me cut out net trap templates during 2004. Paula Davis came into the lab under the auspices of the WISE program and helped with both some of the last of the molecular work and spent a summer driving around Cincinnati dropping off and picking up dead rodents. Without Paula’s help, running four sites at the same time for nocturnal ovipositing would have never happened!

Outside of the obvious influences from work, others have helped me with this research. I would like to thank Mathew Klooster, Brian and Laura Gilkison, Christine and Brian Moskalik, Colin White and

v my wife, Christina, all of whom let me place dead rats in their yards just after sunset for several years, in the name of science. This is a sacrifice not common to most people, who get a bit squimish around dead rats. Thank-you all, and I hope I didn’t wake anyone up when I came to collect the poor little dead rats before dawn!

I extend special thanks to me friends and family, almost all of whom have had to deal with my little obsession for collecting “forensically important” flies using decomposing chicken in mason jars for the past half decade, and many of whom have had to mail pupae back to me because my stay was too short to accommodate the fly maturation process and those poor fly maggots just needed “a few more days” eating the bacteria rotting chicken to grow up big and strong. Special attention needs to be paid to the following debts in preparing this manuscript. Michael P. Cosentino spent many hours editing drafts of various portions of this work (and others). There is passive voice left in these chapters, and it is my fault, not his. John Olszewski and Joel Phillips, who both spent time helping me catch flies along power lines in Tennessee in the heat of summer. Honestly, many of those flies DID end up in my studies (just see table 1 of chapter 2).

Finally, to my wife, Christina, and my two daughters, Caitlin and Stephanie. It is hard to imagine how much my work has impacted their lives and I appreciate the sense of humor they have shown in accommodating my research. This work has taken me away form my family several times, and I always regret the lost time with them. However, on a few trips I have been able to bring my family along, and their help on those occasions has been invaluable. Special thanks goes to my daughter Caitlin, who helped me catch all of the flies from Hocking county one sunny afternoon. That collecting trip was the most fun of all them! Of course, it has not been without constant support from my wife Christina (who sewed all those net traps for me when my sewing skills proved non-existent) that I have gotten as far as I have—thank-you for all the love and encouragement!

vi Table of Contents Abstract...... ii Acknowledgements...... iv List of Tables and Figures...... x Chapter 1: Issues in forensic science: problems with interring the postmortem interval...... 1 Introduction...... 1 Dissertation organization...... 2 Understanding death: the process is not an event...... 3 The chemical events of death: Autolysis and Putrefaction...... 4 Mechanical modifications of the carcass...... 5 Invasive microbes cause decay...... 5 Arthropods most often speed decomposition...... 6 Vertebrate predation...... 8 Climatological influences upon the death process...... 8 Temperature influences upon decomposition...... 9 Humidity influences upon decomposition...... 9 How to estimate death intervals: an overview...... 10 Flies could be key to PMI estimates...... 11 Assumptions in PMI estimates based on flies...... 11 Species: the first key to the fly PMI estimation puzzle...... 12 Behavior and physiology: the rest of the fly PMI estimation puzzle...... 12 How to model death intervals: PMI using flies...... 13 A brief description of the “nuts and bolts” of PMI estimates using flies...... 15 Conclusion: application of the results...... 17 Chapter 2: The phylogeny of Sarcophaginae using mtDNA and nDNA loci...... 20 Introduction...... 20 Chapter organization...... 20 Background: an introduction to the Sarcophaginae...... 21 Sarcophagid systematics to date...... 22

vii Lopes’ taxonomy...... 23 Roback’s taxonomy...... 24 Pape’s taxonomy...... 24 Species missing from regional catalogues...... 24 Taxonomy & phylogenetics of Sarcophagidae...... 24 Morphological techniques in Sarcophaginae taxonomy...... 25 Phylogenetics of Sarcophagidae using DNA...... 26 The uses of molecular biology by the forensic community...... 27 Uniting the goals of forensics and biology...... 28 BLAST methods for species identification...... 29 Distance-based methods for species identification...... 30 Tree-based methods for species identification...... 31 Which method is best for species identification?...... 33 Reliability in tree-based methods...... 33 Materials and Methods...... 34 Specimens...... 34 DNA extraction...... 34 PCR and nucleotide sequencing...... 34 Data alignment...... 35 DNA sequence analysis...... 35 Results...... 36 Sequence characteristics...... 36 Variation between loci...... 38 Comparison of the different genetic loci...... 38 Combined dataset analyses results...... 39 Discussion...... 40 Agreement with morphological organization schemes...... 40 Roback’s topology...... 41 Lopes’ topology...... 43

viii Pape’s topology...... 45 Agreement with previous molecular work...... 46 Conclusions...... 46 Chapter 3: Nocturnal ovipositing in the Cincinnati metropolitan area...... 63 Introduction...... 63 Chapter organization...... 64 Background: A history of nocturnal oviposition field studies...... 64 Methods...... 65 Sites...... 65 Rat exposure protocol...... 66 2004 season protocol...... 67 2005 season protocol...... 67 2006 season protocol...... 69 2007 season protocol...... 70 Results and Protocol Discussion...... 71 Methodological problems encountered...... 71 2005 season...... 71 2006 season...... 72 2007 season...... 73 Combining 2006-2007 field season results...... 73 Broader forensic implications...... 74 Are there environmental differences between studies?...... 74 Could bait type/condition be a factor in behavioral differences?...... 74 Locality: a possible urban vs. rural dichotomy?...... 75 Conclusions...... 76 Placing Sarcophaginae relationships and nocturnal oviposition behavior in the forensic context...... 84 Works Cited...... 87

ix List of Tables and Figures

Figure 1.1 Diagram of the generalized fly-based PMI estimate model...... 19 Table 2.1 List of species involved in this study...... 48 Table 2.2 Primers used for amplification and sequencing in this study...... 49 Table 2.3 Raw distance values for COI and COII...... 50 Table 2.4 Raw distance values for ND4 and EF1α...... 51 Table 2.5 Raw distance values for all taxa with all data...... 52 Figure 2.1 Cladogram from Roback (1954)...... 53 Figure 2.2 Cladogram from Lopes (1969 and 1982)...... 54 Figure 2.3 Cladogram from Pape (1996)...... 55 Figure 2.4 Examples of Sarcophagid larvae and pupae...... 56 Figure 2.5 Comparison of Neighbor Joining trees for each locus...... 57 Figure 2.6 Neighbor Joining tree of AD dataset...... 58 Figure 2.7 Neighbor Joining tree of mtDNA dataset...... 59 Figure 2.8 Maximum Parsimony tree of +nDNA dataset...... 60 Figure 2.9 Zehner et al. Sarcophagid phylogeny...... 61 Figure 2.10 Wells et al. Sarcophagid phylogeny...... 62 Table 3.1 Abridgement of data from previous nocturnal oviposition studies...... 77 Figure 3.1 The impact of nocturnal ovipositing upon PMI estimations...... 78 Figure 3.2 Site locations for the nocturnal oviposition study...... 79 Figure 3.3 Carrion net trap...... 80 Figure 3.4 Delnet bag use on bait container...... 81 Figure 3.5 Carrion bait shed...... 82 Figure 3.6 Empty fly egg casings on mesh screen lid...... 83

x “Time of Death. Throughout the long annals of true crime lore, countless murder convictions, and acquittals have come down to this: when did the killer strike? When did the victims breathe their last? In the absence of credible witnesses, the lack of an easy answer has bedeviled our criminal justice system since its inception.” —J. Sachs, 2001

Chapter One: Issues in forensic science: problems with inferring the postmortem interval INTRODUCTION People want to know the events surrounding other people’s deaths. This quest for understanding the specifics of a given demise—the who, the what, the where, the why and the when of it—has a long history (Greenberg & Kunich 2002). Of these basic necessary facts, this study is concerned with the “when.” My goal is to improve accuracy in postmortem intervals (PMI) by increasing the accuracy of species identity for flesh flies (Diptera: Sarcophagidae) and to assess the frequency of nocturnal ovipositing behavior amongst carrion flies. The time frame in which death took place, the “when,” is an essential question for the modern legal system. An accurate understanding of this singular point in time can include or exclude individuals as suspects when that death is the result of possible foul play. Understandably, the exact time of death is one of the primary pieces of information that death-scene investigators seek to determine in the United States (Hall 1990). When the death in question has no credible witnesses, an assessment of the length of time between death and discovery is necessary. Death investigators call this evaluation of the time passed between death and discovery a postmortem interval estimation. Determining a postmortem interval can be complicated and investigators must bring together a plethora of facts to create an understanding of the events that transpired at a death scene, all aimed at answering those most basic five questions (who, what, where, why and when). The modern legal system borrows heavily from scientific disciplines when piecing together the puzzle of a death scene. Many of these techniques focus on the material evidence surrounding a corpse to answer those basic questions in a “life history” approach and are informed by archaeological practices (Scott & Connor 1996) such as the theoretical concepts of taphonomy (the transition of animal remains from the biosphere to the lithosphere; Efremov 1940, cited in Shipman 1981) and the applied concepts of modern archaeology (e.g. site

1 stratification and excavation techniques). While each individual death is a unique event, certain shared elements certainly exist—particularly in the biological realm. An understanding of these “common” elements of decomposition is vital to one’s understanding when unique (possibly, but not necessarily, human-effected) events have modified how death would otherwise play out.

Dissertation organization In this chapter I will first review decomposition, highlighting the ways in which one can infer the postmortem interval from this process. This review orients the reader to the problem at hand. Specifically, it answers the question: how do the two very different lines of investigation (phylogenetic assessment of Sarcophaginae and carrion-fly nocturnal ovipositing activity) fit together to improve accuracy in postmortem interval inferences? Chapter Two discusses the problem of fly species identification. Taxonomists have been studying relationships between these fly species for over a century. The result of their research is a fairly clear organization at higher taxonomic levels (phylum, class and order), but a lack of clear resolution at lower taxonomic levels (family, genus and species levels). I review the current state of morphological and molecular species identification for one of the two major groups of carrion flies, the Sarcophagidae or “fleshflies”, and then focus my attention on one of the major groupings within the Sarcophagidae, the Sarcophaginae. Most carrion species known as fleshflies fall under the Sarcophaginae. My research provides the first in-depth molecular phylogenetic assessment of this morphologically-derived organization for the sub-family Sarcophaginae to date, with outgroups drawn from sister clades (mostly Calliphoridae, or “blowflies”) that are also of forensic significance. Chapter Three discusses the problem of fly nocturnal ovipositing behavior, and how this behavior has an impact upon PMI estimation. This examination of nocturnal ovipositing tackles the long-held view that these carrion flies do not oviposit at night. However, a mistake in understanding this behavior could alter PMI estimates by as much as 12 hours. Thus, nocturnal oviposition also represents a crucial piece of the puzzle in pinpointing PMI, and deserves continued examination to make sure our comprehension of this behavior is well founded. My research examines nocturnal ovipositing behavior in the greater Cincinnati metropolitan area over multiple years and then discusses methodological issues with nocturnal ovipositing studies and the forensic significance of these findings. Finally, I bring these two studies together, summarizing them and discussing how these projects

2 can impact our ability to produce PMI estimations as well as discussing possible future avenues or investigation.

Understanding death: the process is not an event Western society generally views death as an event. While a society may see death in this manner, from a scientific viewpoint death is better understood not as an event, but a process. Perhaps chemist W.E.D. Evans said it best: Death is not an unaltering state, and far from being an inert mass, the dead body is, under normal circumstances, subject to many complex, and often enough, only partly investigated changes arising from intrinsic as well as extrinsic causes which bring about quite substantial chemical and morphological alterations to the tissues…(ix, 1963a).

Thus, investigators need an understanding of the complexities in the death process to allow for insight on how to derive PMI from them. This section outlines the death process while the next section highlights those portions of it that provide useful clues to puzzling out PMI estimations with the modern techniques I will use in chapters two and three. Taphonomists divide decomposition into three types of phenomena: chemical processes, mechanical modifications and environmental influences. At first glance, these generally appear to occur in a specific order, but they actually overlap and impact each other (Micozzi 1997). There are two main chemical processes: autolysis and putrefaction. As for mechanical modifications, there are several types with three predominating: arthropod predation, vertebrate predation and microbial colonization. If something interrupts or prevents either the chemical or mechanical modifications, the other process (mechanical or chemical) is fully capable of moving a cadaver through the progression of death to the end result: complete obliteration of the flesh and skeletal material and reintegration with the surrounding environment (Clark et al. 1997). Environmental influences impact both chemical and mechanical phenomena. There are five primary environmental influences at play in most situations: temperature, humidity, altitude, wind speed and solar radiation. Of these, temperature and humidity are the largest players and so are a primary focus of this review. Investigators often find it difficult if not impossible to parse apart the influence of environmental conditions such as temperature and humidity, and thus it is important for them to understand how these processes work, and how they impact one another.

3 The Chemical Events of Death: Autolysis and Putrefaction Stedman’s Medical Dictionary (1990) defines autolysis as “the enzymatic digestion of cells (especially dead or degenerate) by enzymes present within them.” In autolysis, cells destroy themselves via an internal process. This internal self-digestion is actually the end result of a series of developments, discussed briefly below. To maintain itself, a living animal cell normally liberates potential energy locked in covalent bonds of carbohydrates. The cell does this via a series of chemical reactions referred to as the central metabolic pathway (CMP). The energy released in this pathway drives the biosynthesis of the lipids, carbohydrates, proteins and nucleic acids necessary for the replacement or repair of organelles, membranes, cell products and cell division. The cell accomplishes this harnessing of the bond energy by enzyme-mediated reactions within fairly narrow limits of temperature, pH, and concentration of substrates. Most importantly, the process requires the presence of oxygen as the final electron acceptor in the pathway. Any event which removes oxygen as a final electron acceptor disrupts the freeing of energy (production of adenosine-5’-triphosphate [ATP]) and consequently disrupts crucial biosynthesis (Kapit et al. 1987) Autolysis begins with the deprivation of oxygen to cells due to failure of the organism’s circulatory system. Once the circulatory system can no longer deliver oxygen to a given cell, autolysis of that cell is inevitable (Gill-King 1997). Plasma and intercellular tissue pH decline quickly as the buffer systems inside the cell fail. The cell shifts from the CMP to a fermentative pathway, utilizing pyruvic acid, which is then converted to lactic acid. Although the fermentative pathway does produce small amounts of ATP, it is not enough to maintain all cell functions. Without the assistance of the CMP, the cell is unable to move ions against concentration gradients and facilitated transport collapses. Hydrolytic enzymes that were previously compartmentalized then disgorge into the cytoplasm where they begin to quickly destroy molecules and the remaining membrane (Gill-King 1997). Autolysis occurs at different rates within different regions of the body (Gaines 1999). Since different cell types have different metabolic rates, certain areas become unviable before others, and thus autolysis and putrefaction (see below) usually exist simultaneously on the same carcass but in different regions of the body (Gill-King 1997). Autolysis ends for a given region when the cells are no longer viable (Gill-King 1997).

4 Putrefaction begins once cells reach end-stage autolysis and is the breakdown of organic molecules into constituent elements. Stedman’s Medical Dictionary (1990) defines putrefaction as:

“decomposing or rotting, the breakdown of organic matter usually by bacterial action, resulting in the formation of other substances of less complex constitution with the evolution of ammonia or its derivatives and hydrogen sulfide; characterized usually by the presence of toxic or malodorous products.”

Putrefaction is the second part in the death process; a milestone reached with the development of a nearly complete anaerobic environment (Gill-King 1997) within the body. This promotes the rapid growth of the bacteria of the large bowel, and to lesser extent, colonization by external bacteria (Gill-King 1997).

Above 0ºC, putrefaction proceeds through several highly complex chemical chains of events (depending on tissue type), best outlined by Clark et al. (1997). The term “putrefaction” deals with the breakdown of tissues and subsequent metabolic wastes (both from microbial activity, and the results of autolysis) and other chemical actions. It is putrefaction that promotes the rapid growth of bacteria, thus providing an abundant food source for the carrion-loving insects (mainly fly larvae) discussed later in this chapter. While putrefaction is generally the second process of death, occurring directly after autolysis, freezing followed by decay can occur instead. Decomposition and decay are defined as separate events (as discussed below), and decay is considered a mechanical modification of the carcass since it involves microbial invasion.

Mechanical Modifications of the Carcass Beyond the destruction of the carcass by internal microorganisms (putrefaction), external organisms predate upon the carcass. These necrophages are of three primary types: microbes, arthropods, and vertebrates. In many cases, these life forms are critical in consuming the ephemeral resource of the carcass, reducing it much faster than autolysis and putrefaction alone could do. Invasive microbes cause decay

When the environment is sufficiently cold, the corpse freezes, halting autolysis and putrefaction. After the corpse thaws, “decay”—the destruction of the corpse by external microbial activity—assumes a role of greater importance than putrefaction because the invasive microbes simply out-reproduce the reduced internal microbe load (Micozzi 1986, 1991 and 1997). Decay is different from putrefaction because invading external aerobic bacteria degrade the corpse instead of leaving it to internally-based

5 anaerobic bacteria (Micozzi 1986, 1991 and 1997). Specifically, decay occurs in an opposite manner from putrefaction: from outside to inside instead of inside to outside. Furthermore, decay denotes specific events that the corpse has undergone (usually freezing[s]) among which investigators must distinguish. For example, freezing is the most common event that halts putrefaction. The fact that freezing has occurred is usually obvious during early decay from tissue ultrastructural analysis, since frozen tissue displays characteristic damage summarized by Micozzi (1986). During the remainder of this discussion, wherever I discuss putrefaction, decay is a possible alternative event that could also happen. I will only make note of decay if there is a significant difference between decay and putrefaction. Arthropods most often speed decomposition

The chemical processes of death (autolysis and putrefaction) can move a cadaver through the progression of death to complete obliteration and reintegration with the surrounding environment by themselves. However, in most cases these chemical processes are greatly facilitated by the actions of arthropods (Payne & Crossley 1966). Arthropods, especially fly larvae (maggots), can consume almost all of the soft tissue of a corpse (Lord 1990, Payne 1965). Brothwell (1981) comments that insect activity may well be responsible for the destruction of even bone. All of this is easily proven, since when researchers use physical barriers to exclude arthropods from the death process, the measured decomposition rates are considerably slower (Payne 1965, Payne & Crossley 1966). Arthropods that interact with a corpse fall into four categories. These are: 1) necrophagous species, that directly scavenge the carcass itself; 2) omnivorous species that consume carrion as part of their diet (but not all of it) and also prey upon the other arthropods which are associated with carrion; 3) predaceous and parasitic species, which feed on arthropods only but do not consume the corpse directly; and 4) accidental species, that seek shelter and thus are only chance inhabitants of the carrion. Two insect orders, flies (Diptera) and beetles (Coleoptera), are recognized as the groups of preeminent importance in reducing carrion and are present at most death scenes (Lord 1990). Diptera are generally the most aggressive actors in the early stages of decomposition, and are the focus of this project because the larva of flies are literally layed upon the carcass, while the immature forms of coleoptera encounter the carcass at some point after being layed. This means that fly larvae are potentially a useful “clock” for the carrion they consume, whereas coleoptera larvae are not useful in this manner. Diptera larvae can exist in a semi-liquid medium, and adult flies tend to be the first insects attracted to and colonizing decomposing remains. Maggots actively accelerate putrefaction and the

6 disintegration of the corpse (Payne 1965). These larvae, after hatching from eggs left by the adults, initially begin consuming the liquid between the muscles, then move on to the muscle fibers themselves (Hobson 1932). Most maggot species consume not only the bacteria found amongst the tissue (their primary food source), but utilize scraping mouthparts and enzymatic secretions that attack the tissue itself, thus speeding breakdown of the corpse (Gunn 2006). Several phenomena constrain the succession and colonization of flies: geographic location (general environment and available species), temperature, humidity, light, shade, seasonal and daily periodicity, and the manner of the organism’s death (Smith 1986). These environmental conditions directly impact decomposition because flies mature at different rates under different conditions (Nourteva

1977). Some of these phenomena bear mentioning here. Geography influences a number of different factors such as temperature, humidity, sun versus shade, and seasonality to create a unique set of conditions for a specific region. In general, with warmer climate comes greater carrion fly taxonomic diversity (Smith 1986). Under polar conditions, as few as one blow fly species (Boreellus atriceps) associate with a corpse. Temperate regions see an increase in the fauna associated with carrion with reports ranging from 36 to 308 different species depending upon the study (Smith 1986). Tropical regions tend to produce higher-order diversity rather than more species diversity as groups not normally associated with carrion become involved in the reduction of the corpse and seasonal variation is less marked (Smith 1986). The two controlling factors for development rate in insect species are temperature and humidity (Smith 1986). Thermal death points for insects usually lie between -15°C to -30°C (Knipling & Sullivan 1957) and above 60°C (Knipling 1958), although there appears to be wide variation among species (Knipling & Sullivan 1957). Constant high temperatures have been found to aid in the initial development of fly larvae (Nagasawa & Kishino 1965). However, too high a temperature can have a negative impact; often leading to the death of maggot masses when the heat generated by the maggots themselves, in combination with the high ambient temperature, are lethal for the larvae (Kasson 1999). In general, low ambient temperatures lead to the slowing of arthropod activity (Bass 1997). However, if the maggot mass is large enough, even low temperatures will not inhibit its growth, since it will generate enough heat to maintain itself (Catts 1990). Bass (1997) reports steam rising from maggot masses in winter conditions in Tennessee, providing an observation that if these masses get large enough, they can maintain themselves despite otherwise harsh conditions. Some reports indicate that maggot masses can

7 raise the immediate temperature by up to 20°C (Gunn 2006). Cold weather and rain are reported to inhibit carrion fly activity in general (Smith 1986) although some researchers indicate that flesh flies are less susceptible to inclement weather as their blowfly relatives (Gunn 2006). Seasonal effects certainly occur in arthropod colonization upon a corpse. In general, corpse deposition during the spring and summer will yield far more active and numerous arthropod colonizers, while the converse is true of fall and winter (Smith 1986). However, the corpse may be deposited during winter, freeze and then begin to decay during the spring thaw. When insects detect and arrive at a newly thawed corpse, they will begin (or resume) their consumption of the resource (Bass 1997). Furthermore, carrion flies may be active during the deposition and early stages of decomposition, but simply are not ovipositing at that time (Smith 1986). Insects have daily periodicity, which impacts when they interact with a corpse (Haskell 1993). The frequency of individual flies visiting carrion throughout the day has been examined for many fly species (Baumgartner & Greenberg 1984, 1985; Byrd 1998; Haskell 1993; Nuorteva 1959) with the general conclusion that ovipositing activity begins in the late morning, peaks in late afternoon and declines sharply before sunset. All of these factors combined mean that the investigator’s careful attention to detail at the discovery site is vital to deciphering the many confounding problems that can exist. Vertebrate predation

Scientists recognize that many vertebrates scavenge corpses but rarely include them in decomposition studies. Vertebrates move and disperse corpses with regularity (Smith 1986). Although a reality, this movement confounds studies of the invertebrates utilizing the corpse, and thus researchers often exclude vertebrates for the simple fact that the animals tend to remove the carcasses being studied. Payne & Crossely (1966) described how pig carcasses had to be caged to prevent dogs from carrying them off or consuming them on the spot. This work began a trend that continues to this day: researchers often handle invertebrate succession issues with caged carrion. These protective enclosures prevent access by vertebrates, but allow open access by invertebrates. However, the reality of the situation is this: vertebrate scavengers exist, and they commonly have a significant impact on the decomposing corpse.

Climatological influences upon the death process A corpse inevitably has a geographic location with climatological factors. The chemical, mechanical, and biological processes drive the reduction of that carcass given enough time. These

8 processes are themselves highly dependent upon the environments in which they act. The existence, pace, and interaction between the various decomposition processes are ultimately regulated by the context of the specific abiotic conditions of that location which the corpse “inherits.” A study could consist of only just looking at these conditions as they are numerous and varied. However, of all the climatological influences, scientists best understand and consider temperature and humidity as the most critically important. Temperature influences upon decomposition

Temperature impacts a corpse in several ways. After deposition and once the carcass loses the ability to self-generate heat (as it would have when alive), it still generally receives heat in a number of ways. First, for the purposes of heat transfer, the carcass is effectively a body of water. Thus, it is heated via conduction from the ground, via convection from the surrounding atmosphere, or it may absorb radiation from other sources such as the sun. These heat sources all work from the outside to the inside. In addition to this, internal sources impact the carcass, as described earlier. Hewadikaram & Goff (1991) state that two different circumstances regulate internal carcass temperatures. In early stages of death byproducts of autolysis and putrefaction account for the heat generated internally, but in later stages (essentially once maggots have started feeding) arthropods and putrefaction are responsible for internal heat generation. Both autolysis and putrefaction are limited by temperature. Early on in the decomposition process the internal carcass temperature is in effect a balance between the original internal body temperature and the possibly rapidly-changing but also usually lower external temperature. The difference between the environmental temperature and the carcass temperature determines the rate at which the carcass cools initially. As putrefaction and arthropod activity come into play, they generate their own heat and become independent of the external temperature—provided that temperature is not prohibitive to the processes, as in the case of overheating maggot masses. Temperature plays a wider role in decomposition as well. It influences the behavior of both invertebrates and vertebrates, as discussed above. In general, the warmer the environment, the higher the insect and vertebrate scavenger load is upon the carcass in the same time frame. Humidity influences upon decomposition

Researchers often describe temperature as the single most important factor in decomposition because it has such an impact on the growth and development rates of the organisms that ultimately

9 consume the bulk of the energy in the carcass. This is apparent by the great deal of weight given to temperature in models of insect growth (discussed briefly before and more in-depth later in this chapter). However, just as decisive in altering how those organisms develop is humidity, and for this reason, investigators should view both temperature and humidity as equally important in decomposition. After all, both limit the process of decomposition, often in complimentary ways. High humidity speeds the decomposition process by providing plenty of water for bacterial growth and favorable conditions for maggot development. Low humidity desiccates the corpse, hardening the skin, which then presents a barrier to the sunlight (Galloway 1997). Underneath this barrier, tissues are frequently moist, continue to produce a strong odor associated with putrefaction, and also provide an excellent feeding ground for maggots during the early stages of this process. Eventually, the tissue dries, turning it into a dark, brown-black, viscous, adhesive paste (Di Maio 1989) that is not conducive to further maggot infestation. Humidity is especially important once temperatures reach a point where they are no longer limiting. Such conditions are seen every summer in much of the United States, where once temperatures reach above 21°C at night, growth of the species consuming the corpse is virtually unlimited. In these conditions, destruction of the body can still be halted by extremely dry conditions, or can be greatly accelerated by extremely wet conditions. As such, environmental situations that promote humid conditions are seen to accelerate decomposition levels.

How to estimate death intervals: an overview Decomposition leaves evidence that informs the investigator about the length of time since death. As time moves forward from the beginning of autolysis, one needs more and more data peripheral to the corpse in order to make an accurate PMI estimate. With the “life history” approach, investigators gather credible facts from the scene and analyze them to recreate the history of the corpse in as intimate detail as possible. In general, the longer a corpse has to decompose, the wider investigators must cast their “net” to produce a reliable PMI estimate. In the very short term (minutes to hours), forensic pathology techniques that reflect immediate chemical and biological processes within the body (rigor mortis, liver mortis, lividity, body temperature, etc.) are useful, and can provide PMI estimates (Di Maio 1989). In the very long term (weeks to years), a varied complex of techniques such as (but not limited to) pollen samples (Stanley 1991), plant growth (Hall 1997), and secondary modification of the corpse by animals (Haglund 1991) can all come into play to produce the best possible picture of time since death.

10 It is in between these two extremes that the majority of corpses are found (Galloway 1997) and also when PMI estimation techniques turn from primarily pathological methods to ecological ones. The carcass presents itself as an ephemeral food source for a wide variety of fauna (Fuller 1934, Chapman & Sankey 1955, Bornemissza 1957, Denno & Cothran 1975, Beaver 1984, Braack 1987, Bourel et al. 1999).

Flies could be key to PMI estimates Flies have a long history in helping to determine the perpetrators of violent crimes, stretching back as far as the era of Sun Tzu (Greenberg & Kunich 2002) in the written record. This association is common sense—carrion flies are attracted to the volatile fatty acids given off by the corpse and they use the corpse either as a protein meal for egg and sperm production (Wall et al. 2002) or they deposit their offspring upon the corpse so the next generation can take advantage of this ephemeral source of food (Gunn 2006). Carrion fly PMI estimates combine species identity, environmental data, and species- specific physiological and behavioral models to produce the final inference of time since death (Wells & Lamotte 2000). Assumptions in PMI estimates based on flies

Entomologists infer PMI based on maggot age, usually reported in accumulated degree hours (or days). Because of this, such estimation is only an inference to time since death. What is really being estimated is time since infestation, since infestation begins with the ovipositing of the larvae (if Sarcophagidae) or eggs (if any other diptera of note) upon the corpse (Tomberlin et al. 2006, but see Haskell et al. 2007). Generally, time since infestation tracks well with time since death. However, the estimations made by entomologists are based on several assumptions, two of which have direct bearing here: the first that “…most homicides occur at night, under the cover of darkness when flies are presumably inactive”, and the second is that “flies will begin ovipositing as soon as they discover the body.” (Catts 1990, p.126). It is widely recognized in the forensic entomological literature that these assumptions generally hold true: 1) most murders do occur at night, 2) carrion flies are not active at night and 3) flies generally do oviposit immediately (within minutes). However, there are potentially an unlimited number of scenarios that could be constructed where these assumptions do not hold true, several of which have actually occurred and have subsequently been reported in the literature (Greenberg 1990), that significantly impact the PMI estimate. For example, it is possible that maggots were present upon

11 the individual before he/she died. Some of the flies that might associate with the corpse are known to be primarily involved in myiasis, or the infestation of living tissue with fly larvae. Because of this, investigators pay attention to first the species of fly involved, and then know how that fly’s behavior and maturation physiology. Species: the first key to the fly PMI estimation puzzle

It is well established that fly species have different maturation rates under similar conditions (Wells & Lamotte 2000) and also have species-specific behaviors (Haskell 1993). Thus, species identity is the first step in producing an accurate PMI estimate. There are three primary groups of flies whose larvae utilize the carcass: Calliphoridae, Sarcophgidae and Muscidae. Species from these three groups of carrion flies are active at different times of the year, favoring some conditions but not others (Haskell 1993). Some flies live only in certain regions only while others have a worldwide distribution. Even within a species, there are hints that there are regional differences in seasonal activity and diapause times (Kurahashi & Ohtaki 1989) and even hourly activity levels (G. Dahlem, personal communication), especially along a north-south cline. Thus, an important step for the investigator is that of definitively establishing the species of fly being used in the PMI model. The key problem is this: investigators find that they cannot easily discern all maggots as a specific or known species of fly (Wells & Stevens 2008). It takes a taxonomic expert to identify even adults of the majority of these species from each other (Gunn 2006) and even entomologists widely recognized that, “it is generally impossible to morphologically distinguish the species of [forensically important] immature diptera at egg to pupal stage (Saigusa et al. 2005)”. Behavior and physiology: the rest of the fly PMI estimation puzzle

Once the species is known, the next hurdles are behavioral and physiological. Important behaviors for adults include ovipositing behavior (Greenberg 1990, Singh & Bharti 2001, Browne 1960), resource location (Gomes et al. 2007), diurnal/nocturnal activity (Nuorteva 1959, Baumgartner & Greenberg 1984, Haskell 1993 and Byrd 1998), and seasonal activity (Bass 1997). There are important larval behaviors as well, including feeding preferences (Browne & Dudzinski 1968), aggregation preferences (Ireland & Turner 2006), and response to environmental conditions such as temperature and humidity (Browne 1962). Much of the basic research in these areas has not been done on most forensically important species.

12 In terms of physiology, majority of relevant fleshfly species have no maturation data for them, although recently data have become available for Sarcophaga africa (Byrd & Butler 1998) and S. argyrostoma (Grassberger & Reiter 2002). Blowflies have been studied in more detail, with data presented for numerous species of forensic relevance, including: Calliphora alpina (Davies & Ratcliffe 1994); C. vicina (Kamal 1958, Reiter 1984, Greenberg 1991, Davies & Ratcliffe 1994 and Donovan et al. 2006); C. vomitoria (Kamal 1958, Greenberg & Tantawi 1993, Davies & Ratcliffe 1994); Chrysomya megacephala (Wells & Kurahashi 1994), C. albiceps (Grassberger & Frank 2003); C. rufifacies (Greenberg 1991, Byrd & Butler 1997); Cochliomyia macellaria (Greenberg 1991, Wells & LaMotte 1995, Byrd & Butler 1996); Lucilia sericata (Kamal 1958, Ash & Greenberg 1975, Greenberg 1991, Wall et al. 1992, Davies

& Ratcliffe 1994, Grassberger & Reiter 2001, Clark et al. 2006); Phormia regina (Kamal 1958, Ash & Greenberg 1975, Greenberg 1991, Byrd & Allen 2001); and Protophormia terraenovae (Kamal 1958, Greenberg 1991, Greenberg & Tantawi 1993, Davies & Ratcliffe 1994, Grassberger & Reiter 2002). As useful as diptera are in PMI estimations, holes in our current understanding of which flies do what, limits the accuracy of PMI models. In order to know how impacted models are by these knowledge gaps, one needs to know how the models are constructed.

How to model death intervals: PMI using flies There are two ways in which flies are useful in PMI estimation: as individual specimens considered mostly out of context or as a group of specimens considered in a successional context. First, individual maggots can be used to directly infer PMI. In order to use flies in this way, the scientist needs to know the species, behavior and physiology of the fly as well as environmental data from the crime scene. This approach provides a minimalist view of PMI, since the investigator uses only a small number of samples to calculate the estimate. He or she does not know how many other fly species were present on the body, or even if other life stages of the species someone provided were also present on the carrion source. Since it is still rare for the forensic entomologist to visit a death scene, this is often the manner in which a case is presented to the expert. It is not an uncommon experience amongst the forensic entomological community for the police to contact them about a case and provide one or two desiccated husks as the material evidence (Tomberlin 2007). When someone actually collects enough samples, the assemblage of flies can be considered as a succession “time-slice.” This method uses the full assemblage of insects in light of published succession

13 data from numerous studies. In this way, all the species found on the corpse are being compared to known succession ranges and the “best fitting” range is the most likely candidate range for the PMI estimate. Regardless of the method employed, certain basic information is needed for each specimen: the species identity, species-specific behavior towards ovipositing on carrion, and species-specific maturation rates at different environmental parameters. This modeling of insect development has some history. In 1855, Candolle proposed the degree-day summation method that was later incorporated into the nonlinear method of Sharpe & DeMichele (1977). The other mathematical model relevant to insect development is the nonlinear temperature inhibition model (Johnson & Lewin 1946). These two models discuss the relationship between temperature and development rate in insects, a central issue because insects are poikilothermic (cold-blooded). The fundamental development question the expert must tackle is this: how long did it take for the maggot to get from egg to the present instar (stage of development)? While modeling the development of insects has a long history, the modeling of the behavior of forensically important species does not. The first real attempt to quantify such fly behavior (Schoenly 1992) took a quantitative community ecology approach to analyzing 23 previously published succession studies. This study revealed that the majority of taxa are generally non-reoccurring at a carcass, meaning that these taxa arrive and depart over a single time interval. However, some of the most critical taxa (Calliphoridae and Sarcophagidae) are reoccurring taxa, meaning they are seen to arrive, depart and then return multiple times upon the same carcass. The fact that the most important genera are reoccurring taxa causes a significant problem because it is harder to use succession models to pinpoint a time since infestation since multiple infestation waves are present on the corpse at the same time. Entomologists tackle this problem by choosing the largest larvae present, on the presumption that these larger larvae have been present upon the corpse longest. Schoenly’s (1992) “big picture” view of arthropod behavior around a carcass provides statistical baseline data on the behavioral and developmental patterns of carrion fauna. Numerous studies further supplement it with investigations into the behavior of dipteran adults (mainly Calliphoridae and to some extent Sarcophagidae) on and around carrion (e.g. Nuorteva 1959; Baumgartner & Greenberg 1984, 1985; Haskell 1993; Byrd 1998). All of this work helps answer the fundamental behavioral question for the forensic entomologist: how long did it take for an adult female to encounter the body and deposit eggs? The investigations into fly development and behavior have lead to the first modeling of carrion-fly attraction to a body (Byrd 1998, Byrd & Allen 2001). This model is the first to not only rely exclusively

14 upon developmental data, but to also include behavioral and environmental information concerning the state of the carcass (a generalized version of the model is presented in Figure 1.1). Such intricacies provide a quantum jump forward in modeling capabilities of the PMI process, and highlight that the three critical pieces of information (fly species, behavior and physiology) are necessary to model fly behavior around a carcass when trying to determine PMI. A brief description of the “nuts and bolts” of PMI estimates using flies

Actually using maggots in PMI estimation follows four basic steps (although see Catts 1990 for views on the possibility of more steps). First, a crude estimation of decomposition staging is established. Several authors have proposed various staging models (Galloway 1997, Bass 1997, Payne 1965), and this is an area that researchers will likely continue to be refine in the future. With any one of these, the investigator determines the rough state of the carcass. This is a useful general indicator of age, and knowing how far along a body is “generally” provides reminders when working with the entomological data. For example, if the body has advanced to the “skeletal” stage of decomposition but the fauna presented to the investigator indicate only “fresh” remains, this is cause for further investigation into the context of the site, body and how the maggots were collected. Second, determine species identity of the specimens presented as evidence. This species needs to be checked against locality data to make sure it is known to occur in the region the body was found. If it does not, this is a possible indication the body was moved or might indicate other issues, such as species movement into new regions. Third, once the species is known, the “age” of individual specimens must be determined. All major age determination uses the accumulated degree-day (ADD) method. Due to their poikilothermic nature, fly maturation rates track in a relationship between development rate and temperature (Allen 1976). In the linear model, the amount of development (measured in length or weight usually) is equal to the rate of development which in turn is an integral of temperature along a time axis with units of temperature-time (the degree-day) (Byrd & Allen 2001). Thus, by measuring the length of the maggot, one can estimate how many degree-days it has lived. Fourth, the environmental data must be factored into the degree-day summary. Here the entomologist takes the real environmental data and applies it to known maturation rates for the maggot species. Thus, the rate at which a given species of maggot grows is informed by the thermal units introduced (usually by the sun) into the environment for the timeframe in which the body was present

15 at a given site. Maggots have species-specific upper and lower lethal thresholds of temperatures and the degree-day method recognizes these thresholds for individual species of flies. If the environmental evidence indicates periods of time where these thresholds are met, the entomologist will have to investigate why those temperatures might not have impacted the specimens on hand (or perhaps did) (Catts 1990). Entomologists do not all agree upon these species-specific thresholds, and this highlights not only the need for species-specific research, but even population-level research. For example, for Calliphora vicina some researchers published lower thresholds ranging from 2°C (Vinogradova & Marchenko 1984, Greenberg 1991) to 6°C (Haskell et al. 2000). However, Ames & Turner (2003) claimed to have successfully reared this species as low as 1°C and indicated there was no clear-cut minimum development threshold, suggesting that lower temperatures might be possible. There is increasing evidence that latitude may play a significant role in diapause responses for C. vicina and that this response might be genetic (McWatters & Saunders 1996, 1998). Unfortunately, application of environmental thermal information to maggot development is not universally supported. Catts (1990) asserts that unless corpse temperatures are precisely known, ADD estimates should be avoided because they lend a false perception of scientific accuracy where none exists. In early stages of maggot colonization, this may well be true. However, since maggots aggregate into masses at a certain point, they become self-regulating and there is a strong relationship between aggregate size and internal temperature of the maggot mass that appears to be independent of ambient ambient temperature beyond volumes roughly equivalent to a tennis ball (Slone & Gruner 2007). Thus maggot masses of certain sizes overcome the problem of environmental or corpse thermal input, although this is not always the case (Kasson 1997). Catts (1990) proposes that instead of using ADD for maggot age investigation, a much more methodical route of investigating morphological and physiological changes that occur as maggots pass through instars would be much more fruitful and produce finer age estimates. However, this is confounded by the problem that most investigators cite for species identification—namely that most immature instars are not thoroughly documented enough to allow for species identification, let alone grading specimen age. Beyond these four steps, additional information is always useful when considering time since death. For example, many environmental factors beyond temperature can (and often should) be considered, but there are no hard rules on how most conditions such as foliage, sun exposure, slope drainage, etc. impact the corpse and the fauna that feed upon it. The same is true for other potential

16 influences, such as anthropogenic factors (those relating to the influence of human beings on nature). Clearly, the impact of such factors could potentially be important, and so this is an open area for ongoing research. What the entomologist is attempting to accomplish is model reality with as little data as possible. The four areas outlined allow for such a PMI model. At this time, the best an investigator can do is keep an open mind concerning the context of any fact of a specific case, and how the fact might impact the PMI estimate. Ultimately, the forensic entomologist walks a fine line between gaining too much or not getting enough information about a case, and this is where ethics and self-imposed distancing of the investigator become important. Not enough information, and the estimate can be missing important data that expands or contracts the estimation outside of the actual, but unknown, interval. Too much information about a case, especially knowledge that falls outside the direct realm of the investigator’s expertise, can equally prejudice the investigator and lead to flawed estimations as well (Bass 1997).

Conclusion: application of the results In the end, the entomologist is left with a list of facts (date/time of corpse discovery, environmental data, toxicology reports, etc.) and a list of fly species found at the scene and their inferred ages. These ages produce a time window that is the PMI estimate and has minimum and maximum boundaries based upon the oldest and youngest ages of the maggots, respectively. If this were all that was needed, then this dissertation wouldn’t exist in the form it does now. Given the importance that society places on the accuracy of myscientific work when applied to practical matters like law enforcement, we continue to refine our methods in response to issues that more recent research (cited above) raised about the assumptions investigators often use in PMI work. In this case, I argue that there is also a pressing need for both the comprehensive investigation of the species identities of the flies involved in carrion visitation, and then determination of those species behaviors and maturation rates in ways that are applicable to a broad spectrum of locales. Beyond this basic framework are a host of contextual factors, often unique to a given scene (but not always), that confound the accuracy of the estimates. These additional factors are the realm of future research. With this in mind, this study aims to refine the methods currently used worldwide by both researchers and crime scene investigators. The establishment of the phylogenetic relationships between Sarcophaginae flies opens up the use of a group that investigators have long collected, but not used to

17 their full extent. This in turn leads to more robust PMI estimates, and even the possibility of linking the species identification to faster, more accurate, molecular-based methods of larval aging currently being investigated (e.g. Tarone et al. 2007, Hoopengardner & Helfand 2002, Helfand 1995). Besides establishing phylogenetic relationships within Sarcophaginae, this study tackles a continuing controversy over carrion fly nocturnal behavior (or lack thereof). This research has direct application to the modeling of PMI and investigates a fundamental behavioral question. Thus, by conducting a scientific inquiry into carrion fly identification and behavior, this study seeks to bring more modern tools and techniques to bear on problems that have been with humanity for a long time. Given the importance of crime and punishment in the modern society, this research is directly relevant to practices at the core of our justice system.

18 Environment

Development Development Development Development Development Rate Rate Rate Rate Rate (Species (Species (Species (Species (Species Specific) Specific) Specific) Specific) Specific)

Ovipositing Larva Larva Larva Adult Behavior Egg Pupae (Species (Instar 1) (Instar 2) (Instar 3) Fly Specific)

Mort. Mort. Mort. Mort. Mort. Mort.

Figure 1.1: Diagram of the generalized fly-based PMI estimate model. This model is a generalization of the of the model proposed by Byrd (1998). This diagram highlights the four pieces of information necessary fora fly-based PMI estimate: 1) environmental data, 2) species identitity, 3) behavioral data and 4) developmental data. The model recognizes that at each lifestage, a certain proportion of flies will die (MORT), and thus a subset of the previous lifestage passes on to the next lifestage (e.g. from egg to first instar).

19 “Owing to their abundance and diversity of lifestyles, invertebrates can provide a wide variety of forensic evidence…the quality of the evidence depends upon accurate identification and a thorough understanding of invertebrate biology” —A. Dunn, 2006 “[A]ll valid uses of forensic entomology are based on accurate identification of species.” —B. Greenberg, 2002

Chapter Two: The phylogeny of Sarcophaginae using mtDNA and nDNA loci. INTRODUCTION As I stated in chapter one, species identity is the first key to using flies in solving the PMI problem. Without species identity, neither behavioral nor physiological decisions are possible and then there is no possible PMI estimate except in the broadest of possible terms. Realizing this issue, the question is: how does one determine a fly species? The long-held working species definition by entomologists is based on morphological variation. Morphological species keys are available for many Diptera, including Calliphoridae and Sarcophagidae (e.g. Whitworth 2006, Dahlem & Downes 1996). In forensics, these keys are the operative guide from which species are identified (Greenberg & Kunich 2002) and are built off of the original species descriptions and drawings that comprise the baseline knowledge for the group. The problem with relying on morphology as a species-concept is that it is not always possible for the taxonomist to develop accurate baseline data for all life phases of the organism they are describing. This is especially true for flies, where adults are frequently caught with no knowledge of the larval forms that came before or even how to unite males and females as a single species. Even though identification relies upon morphology, the use of molecular technology, specifically DNA analysis, can provide an independent verification of this morphology and possibly even discover the presence of cryptic species when this would otherwise not be possible.

Chapter organization In this chapter I use molecular techniques to assess phylogenetic relationships amongst the Sarcophaginae, one of the three sub-families of the Sarcophagidae. First I introduce the Sarcophagidae and review the currently held systematic and phylogenetic understanding of this group. Then I discuss

20 the different possible forms of species-identification and explain why I ultimately settled on tree-based methods for my analysis. I then cover the methods used for data acquisition and analysis before I discuss results. Finally, I place these results in the context of the three major cladistic viewpoints presented in the literature to date and also discuss how my findings relate to the two other molecular studies conducted on this group thus far.

BACKGROUND An introduction to the Sarcophagidae Sarcophagid flies (flesh flies) are a worldwide family of Diptera that are most famous because the life history of a few species includes larval development on decomposing corpses. Of the 2500+ species in the family, most have larval stages that either are coprophagous, facultative or obligate parasites (Pape 1996). Flesh fly species that are carrion flies contribute to nutrient cycling in ecosystems and are of substantial ecological, medical and forensic importance. Species such as Helicobia rapax, Sarcophaga utilis, and Sarcophaga bullata are often collected at carrion (Tabor et al. 2005), and are thus of forensic as well as ecological importance. Sarcophaga bullata has been the subject of behavioral and physiological studies, including such research as body water homeostasis (Yoder et al. 2006), eclosion (Rivers et al. 2004), and circadian rhythms (Goto & Numata 2005). Ravinia querula and R. stimulans are important dung decomposers (Coffey 1966; Merritt & Anderson 1977). Several species of the genus Wohlfahrtia are known to cause myiasis in humans, as are four species of Sarcophaga (S. africa, S. bullata, S. crassipalpis and S. peregrina), three of which (sans S. peregrina) are included in this study. While the Wohlfahrtia species are considered pests of significant economic importance (James 1947), the three Sarcophaga species have been considered for use in maggot therapy (Sherman et al. 2000). Phylogenetic consideration of Sarcophagidae has a long but sparse history. Roback (1954) presented a detailed scheme for the evolutionary relationships of the Sarcophaginae based on male genitalia. In the intervening decades, some morphological phylogenies of various subgroups have been published (Kurahashi & Kano 1984, Lopes 1984, Verves 1989 and Pape 1994). None provide the broad perspective found in Roback (1954) for the Sarcophaginae and, unfortunately, none have much bearing on the taxa considered here. Most recently strictly molecular techniques have been applied to the group (Wells et al. 2001, Zehner et al. 2004). These studies support the monophyly of the family Sarcophagidae and its organization into two of the three subfamilies: Paramachronychiinae and Sarcophaginae (Wells

21 et al. 2001, Zehner et al. 2004). Specimens of the subfamily Miltogramminae have yet to be analyzed using molecular techniques. Molecular work also supports monophyly of the genus Sarcophaga, with the majority of all specimens worked on being from this group. However, the molecular work brings little clarity to proposed subgeneric structure within Sarcophaga outlined in the current world catalogue (Pape 1996). Molecular research on sarcophagid species has focused on identification for forensic uses (Wells et al. 2001, Zehner et al. 2004). Molecular identification of these and similar species is useful to overcome problems with identifying the immature larvae by morphology alone (Higley & Haskell 2001). In addition, several sarcophagid species have appeared in molecular phylogenetic studies, mostly as outgroups for Calliphoridae (e.g., Wallman & Donnellan 2001) currently believed to be the sister taxon to the Sarcophagidae. The present study seeks to uncover the phylogenetic relationships amongst the Sarcophagidae focusing on the subfamily Sarcophaginae. I examine twenty-five species from eleven genera, including several representatives of putative subgenera of the large genus Sarcophaga (Neobellieria, Bercaeopsis, Wohlfahrtiopsis, Bercaea, Sarcophaga and Liopygia) proposed by Pape (1996). I utilize four different loci, three mitochondrial in origin and one nuclear locus.

Sarcophagid systematics to date Sarcophagidae is one of six recognized families in the superfamily Oestroidea, which is generally regarded as the sister to the superfamily Muscoidea (McAlpine 1989, Yeates et al. 2007). The latest full revision of the family is Aldrich (1916), although more restricted revisionary works have been published recently focusing on particular subgroups and/or genera (e.g., Dahlem & Downes 1996, Pape 1994). A recent, authoritative list of names and synonymies is available (Pape 1996). Monophyly of the family Sarcophagidae appears to have a solid morphological basis, as all sarcophagids possess a unique bilobed uterus (Pape 1996). Sarcophagidae are commonly divided into three generally accepted subfamilies: the Miltogramminae, the Paramachronychiinae and the Sarcophaginae. The subfamily Sarcophaginae is presumed to be monophyletic, based on the following synapomorphies: 1st instar larva with reduced labrum, notopleuron with two primary and two subprimary bristles, prosternum and metasternum setose, coxopleural streak absent (Pape, 1996). Existing molecular work supports this monophyly (Wells et al. 2001, Zehner et al. 2004).

22 Relationships within the Sarcophaginae are unclear, as evidenced in the various regional and world catalogues (Downes 1983, Nandi 2002; Povolny & Verves 1997; Lopes 1969; and Pape 1996) which are not always in agreement with each other. While each of the regional catalogues favors a unique organization strategy for Sarcophaginae, the specimens I examined are largely New World species, and so the works of Roback (1954), Lopes (1969 and 1982), and Pape (1996) are the most relevant to our present purpose. The relationships proposed by Roback (1954, Figure 2.1), Lopes (1969 and 1982, Figure 2.2) and Pape (1996, Figure 2.3) are illustrated in Figures 2.1-2.3, respectively. These cladograms are pruned to show only the species included in the present study. All genus and species names used in the following text conform to the nomenclature currently upheld by the world catalogue (Pape 1996). Alternative nomenclature, from various original works, and the synonymy list provided by Pape (1996) can be found in the respective figures. Lopes’ taxonomy

Lopes (1969) originally organized the Sarcophaginae into six tribes (Microcerellini, Notochaetini, Tephromyiini, Raviniini, Sarcophagulina, and Sarcophagini) and arranged genera phylogenetically within each tribe. Lopes later discarded the tribal name Sarcophagulani in favor of the tribal name Sarothromyiini (Lopes 1975) and provided phylogenetic relationships for the genera under this tribe (Lopes 1990). Lopes (1982) later expanded the tribes under the Sarcophaginae to ten, adding the Sarcodexiini, Impariini, Sarothromyiopsini and Cuculomyiini to the original six tribes listed above. Originally, Lopes considered the tribe Sarcophagini to include the genera Argoravinia, Helicobia and Cucullomyia (Lopes 1969). These genera were moved by Lopes (1982) and the Helicobia and Argoravinia were placed under the tribe Sarcodexiini along with a new genus, Pattonella. The genus Pattonella included the previously unplaced species Sarcophaga intermutans (Lopes 1969), which Lopes renamed Pattonella intermutans (Lopes 1974) and is now known as Peckia intermutans (Pape 1996). These genera were placed under new subtribes Pattonellina (which included the genus Pattonella) and the Argoraviniina (which included the genus Argoravinia). The genus Cucullomyia was placed under the new tribe Cuculomyiini by Lopes (1982). Lopes included the subtribe Helicobiina (which included the genus Helicobia) into the new tribe of Sarcodexiini, first introduced in Rohdendorf (1967). The species I include in my study are exemplars for five of the ten tribes recognized by Lopes (Figure 2.2).

23 Roback’s taxonomy

Roback (1954) organizes the Sarcophaginae into two main tribes (Agriini and Sarcophagini) each with several sub-tribes. The Agriini contain the subtribes Wohlfahrtiina, Sarcofahrtiina and the Agriina. Roback’s Sarcophagini consists of the subtribes Impariina, Hypopeltina, Sarcophagulina, Sarcophagina, Ravinia, Hystricocnemina, Boettcherina, Sarcodexiina, and Servaisiina (Figure 2.1). Roback supported his relationship claims with morphological features of the male genitalia. In the present study, I include exemplars for seven of the nine subtribes that Roback recognized for the Sarcophagini (Figure 2.1). Pape’s taxonomy

In contrast to Lopes and Roback, Pape (1996) adopted a stance of strict alphabetical organization with no taxa of tribal rank and no phylogenetic implications below the family level (Figure 2.3). For example, Pape shed the tribal, subtribal and group organization schemes presented by both Lopes and Roback (as well as other authors’ work not discussed here as they have little direct bearing on the taxa in my study). Many of these names persist as subgenera within some of the genera (e.g., the genus Sarcophaga). Thus, Pape’s work presents numerous species-level polytomys that point to some possible organization by including subgeneric organizations, but that are currently organized alphabetically at all levels (subfamily, genera, subgenera and species). Species missing from regional catalogues

Several species included in our study are not discussed in the works of Lopes or Roback. For Roback, these species are Helicobia resinata, Peckia uncinata, Peckia intermutans and Sarcophaga mimoris. For Lopes, these species are Boettcheria bisetosa, Fletcherimyia fletcheri, Sarcophaga polistensis, Sarcophaga aldrichi, Sarcophaga mimoris and Sarcophaga bullata. In large part, these species are missing from the catalogues because they range outside the geographic areas considered by either Roback or Lopes.

Taxonomy & phylogenetics of Sarcophagidae The genus Sarcophaga was established in 1826 by Meigen (Aldrich 1916). Following this classification several authors, including Westwood (1840), Zetterstedt (1845), and Rondani (1856) worked on expanding the genus and adding clarifications, mainly to European taxa. However, it was not until Pandelle (1897) provided descriptions (but not illustrations) of the male genitalia that even a hope of organizing the group was possible. Even then, without clear illustrations of Pandelle’s points,

24 little work was possible: taxonomists had a hard time understanding species within the genus. Bottcher’s (1912, 1913) illustrations of several species’ male genitalia were a leap forward for the group, and opened the field of study. Aldrich’s 1916 monograph was the first and only full revision of the North American Sarcophagidae and by this time the North American taxa list had grown to number one hundred and forty-five species belonging to sixteen genera. Aldrich spent considerable time in his (1916) monograph detailing how to identify the adult males of this group using male genital characters and this technique has long since dominated adult determinations within Sarcophagidae. Morphological techniques in Sarcophaginae taxonomy

Roback picked up the idea of male genitalia as an organization scheme and used it as the basis for his landmark work, The Evolution and Taxonomy of the Sarcophaginae (1954). In this work Roback provided nearly 150 illustrations of male genitalia, and even goes so far as to propose hypothetical evolutionary transitional morphologies from an ancestral (muscoid) type and within lineages of Sarcophaginae itself. With the primary means of adult identification relying on male genitalia, females have been hard to work on for the group. Some authors have made progress with using internal female genitalia characters (e.g., Shewell 1987, Dahlem & Naczi 2006) but many species remain without identified females to this day. Several features of the ovipositor morphology have also been suggested as useful characters for identifying females (Kulikova 1982). Female specimens are problems for forensic workers, who often encounter and catch adult females at carrion sites. There are many carrion articles in which Sarcophagidae specimens are listed, but not identified beyond “Sarcophaga sp. (e.g., Hall & Doisy 1993, Dillon 1997).” Larval morphological characters have not had as much success as that of the adult male genitalia, even though, according to Lopes (1982), they contained important characters for classification. Because flesh fly larvae are generally very similar in appearance, they are difficult to identify (Smith 1986). Banks provided some early work that allowed larval structures to be used to separate several families, including Sarcophagidae (1912) but there was no in-depth coverage of the group. Aldrich commented that, “Almost no specific characters are known in the larvae, so the identification can go no further than “Sarcophaga sp.” unless the adult is reared (1916, pp.16).” Since this early work larval morphology has continued to evolve as a method. Modern identification of larvae tends to focus on the mouth-hooks and terminal organs and utilizes very

25 specialized equipment such as scanning electron microscopes (e.g., Lopes & Leite 1986 and Figure 2.4), while identification of puparia deals mainly with the morphology of the posterior cavity and anterior spiracle (Greene 1925). Some more detailed descriptions of puparia were made by Greene (1925), but little work has been done since. Several species have at least some larval information documented (Ebejer 2000, Ishijima 1967, Lopes 1982, Leite & Lopes 1987, Lopes & Leite 1986, Sukontason et al. 2003, Mendez & Pape 2002, Greene 1925, Kirk-Spriggs 1999, Kirk-Spriggs 2000, Kirk-Spriggs 2003, Perez- Moreno et al. 2006 and Dahlem 1991) and this is clearly an ongoing front for current research. Most of these larval morphologies characterize the third-instar larval form and so information is often patchy or nonexistent across the first and second instars (except for Lopes 1982).

Considering there are 2,510 species within Sarcophagidae recognized in Pape’s (1996) world catalogue, larvae, pupae, or female adult morphology is a vast open frontier that has been little explored. And this is the crux of the problem with a group this large—a faster method needs to be employed that provides more data to bring to the investigation of several facets of these species, be they basic taxonomic questions (are these valid species?), phylogenetic questions (how do these species relate to each other?), or species identification questions (what species is this?). Phylogenetics of Sarcophagidae using DNA

Two previous papers infer sarcophagid phylogenies from DNA sequence data (Wells et al. 2001, Zehner et al. 2004). Together these papers present sequence data from three mtDNA loci (COI, COII and subunit five of the dehydrogenase complex [ND5]) for a total of twenty species, most of them representatives of the genus Sarcophaga. Both articles largely support the current organization presented in Pape (1996). However, these works do not have overlapping datasets or species lists and this prevents any synthesis of these investigations as a whole. Wells et al. (2001) analyzed a portion of COI for thirteen Sarcophagidae species. Their analysis included two genera from the proposed subfamily Paramachronychiinae with the other nine species coming from the subfamily Sarcophaginae. Amongst the species of the Sarcophaginae were members of four genera (Ravinia, Blaesoxipha, Peckia and Sarcophaga), with the majority of species coming from Sarcophaga (seven species). This work found some support for the splitting of the two subfamilies (Paramachronychiinae and Sarcophaginae) and monophyly for the genera of the Sarcophaginae. There was some support for relationships within the genus Sarcophaga, and this supported two of Pape’s (1996) proposed subgenera, Neobellieria and Liopygia.

26 Zehner et al. (2004) analyzed COI and ND5 for twelve species of the genus Sarcophaga across six subgenera (Sarcophaga, Parasarcophaga, Bercaea, Liopygia, Pandelleana, Thyrsocnema and Helicophagella) under Pape’s world catalogue (1996). Both loci supported the existence of a monophyletic group of six (Sarcophaga, Bercaea, Liopygia, Pandelleana, Thyrsocnema and Helicophagella) of the seven subgenera presented, excluding Parasarcophaga from this clade. Both loci supported the close relationship of species within the subgenus Sarcophaga but provided no resolution for the other five subgenera. ND5 did provide some support for inter-sub-generic relationships within Liopygia, but did not unite all the Liopygia species into a monophyletic group. This present study includes 25 of the approximately 600 recognized species within

Sarcophaginae. I sequenced subunits one and two of the cytochrome oxidase gene complex (COI & COII respectively) and subunit four of the dehydrogenase gene complex (ND4). COI, COII and ND4 are all mitochondrial genes. COI was sequenced in its entirety, resulting in 1539 bp fragment. COII is represented with a 683 bp fragment. Most of ND4 was sequenced, resulting in a 702 bp fragment. ND4 has also seen increasing use in phylogenetic inferences, and is possibly more reliable than COI for inferring the correct ancestral tree (Steinbachs et al. 2000). I also sequenced a 593 bp fragment of the nuclear gene Elongation Factor One-Alpha (EF1α). EF1α has also been used extensively in molecular phylogenetics within arthropods (Caterino et al. 2000).

The uses of molecular biology by the forensic community Moving species identification from morphology to molecular techniques is a growing trend in biology, and is especially prevalent in the forensic context. The use of DNA technology to identify species in this context does not ignore or seek to contest prevailing morphological identification, but rather acts as an independent confirmation of morphological species concepts. DNA technology has come to dominate amongst the new techniques in criminal science and brings with it a pedigree of already being vetted by the scientific community before it was adopted by the justice system (Saks & Koehler 2005). There are two broad categories of specimen interest when DNA is used: 1) individual person identification of humans and 2) species determination of non-humans. I will not discuss individual human identification here, but a comprehensive overview of the topic can be found in Li (2008). For non-humans, phylogenetic (tree-based) analysis is the accepted forensic method for flies

27 (e.g. Wells et al. 2001, Zehner et al. 2004), endangered land mammals (e.g. Hsieh 2001) and endangered marine mammals (e.g. Dizon et al. 2000, Baker et al. 1996, Baker & Palumbi 1994, Ross & Murugan 2006).

Uniting the goals of forensics and biology I have two goals with our phylogenetic investigation of Sarcophaginae. First, I wish to be able to develop a tool that can be used in species identification for forensic purposes. Second, I wish to understand the historical relationships between extant species and genera, and how well the classification schemes put forth by taxonomists hold up under independent molecular data. I believe, as other researchers who have developed similar tools for cetaceans (Dizon 2001, Baker et al. 1996, Baker &

Palumbi 1994, Ross & Murugan 2006) and blowflies (Wallman & Adams 1997, Wells & Sperling 2001, Wells et al. 2001, Wallman & Donnellan 2001, Stevens & Wall 2001, Wallman et al. 2005 and Wells & Stevens 2008) that the primary way to develop a molecular species identification tool is to investigate the phylogenetic relationships for these flies using molecular techniques. Only with an understanding of the validity of the morphological-based species and genera can molecular data then be applied to species identification in a forensic context. Such basic research is the first step towards forensic utilization, but it should not be considered a pilot study. This research has valid uses within the biological community, putting forth support (or lack thereof) for the tireless work of the taxonomic community. By itself, this work is of utmost importance because the species is the basic taxonomic unit through which other areas of biology (behavior, physiology, pest management, etc.) are viewed (Weins 2007). With these two goals in mind (forensic species ID and phylogenetics), I must employ an analysis strategy that is useful for both goals. Tree-based analysis is not the only molecular method for species identification using molecules. Other methods include BLAST (Basic Local Alignment Search Tool) and genetic distance. Each of the three major techniques used for species identification, have strengths and weaknesses discussed in turn below. These approaches differ in how they estimate a relationship and what they use to make that estimate, but they are all legitimate statistical methods with different focus. For our purposes, what matters is how well they stand up to scrutiny in a forensic setting at this time. Most importantly, how well do each of these methods handle incomplete sampling in the reference database when unknown samples are queried against them?

28 BLAST methods for species identification

BLAST (Altschul et al. 1990) is a commonly used method for identification of a given sequence by searching (called BLASTing) an unaligned reference database (Little & Stevenson 2007). A similarity score is computed that looks at the portion of the query that aligns to a given reference sequence. The search algorithm compares the query sequence to the database in a manner that emphasizes speed over sensitivity. This type of search program is designed to query large databases, often those that contain hundreds of thousands of sequences, even entire genomes. In fact, I used BLAST as a screening method to check for contamination of the raw sequences just prior to forward and reverse alignment (see methods below). Since BLAST was first introduced, several improvements have been made, including the identification of protein coding regions from DNA sequences (Gish & States 1993); the first networking of BLAST for use over the internet (Madden et al. 1996); the development of gapped BLAST which allows the generation of gapped alignments and Position-Specific Iterative-BLAST (PSI-BLAST) which is more sensitive to distant evolutionary relationships than other BLAST algorithms (Altschul et al. 1997) and even the development of interactive or automated sequence analysis and annotation (known as PowerBLAST; Zang & Madden 1997). BLAST has a lot of promise, and is good first-stage exploratory research such as identifying previously unknown gene homologs (e.g. Ruepp et al. 2000). However, BLAST algorithms are perhaps not the best choice for species identification. Little & Stevenson (2007) found that unambiguous species-level identification accuracy was much lower (68%) than unambiguous genus-level identification accuracy (100%) in gymnosperms using matK sequences. Koski & Golding (2001) have pointed out that it is very possible that the closest BLAST hit is often not the nearest neighbor phylogenetically because the query is limited by the completeness of the database, and often the database does not contain enough species to be considered “complete”. Koski & Golding (2001, p. 542) are quite straightforward in what this means, “comparisons that rely on the closest BLAST hit alone should be interpreted with great caution as it does not imply phylogenetic proximity.” Wells & Stevens went even further in their assessment of BLAST for forensic purposes (2008, p. 110), “the most common amateur mistake is species identification by an uncritical BLAST search of the huge and easily queried GenBank/EMBL/DDJB sequence database.” Ross et al. (2008) show that BLAST is less reliable than either distance or tree-based methods for identification when a database does not contain representatives from every desired specimen. Other parties interested in species identification, such as the Consortium for the Barcode of Life (CBOL) tried BLAST as the pre-eminent sorting criteria for their

29 Barcode of Life Data Storage (BOLD) system but ultimately decided to go with distance-based search algorithms for reasons pointed out here and because, “users had difficulty interpreting results because scores are influenced by sequence length as well as by sequence similarity (Ratnasingham & Herbert 2007).” Clearly, at this time BLAST is a useful tool for initial analysis but it is limited by the database it queries and at this time is unsuitable for forensic purposes, which demand a high degree of confidence in the results. Distance-based methods for species identification

The distance-based method is based upon the number of differences between any two sequences studied. The user establishes a similarity of distance threshold for species identity for the search. Fixed distance thresholds for species identification are often put forth by supporters of distance-based methods, with Ratnasingham & Herbert (2007) recently reporting that BOLD now uses a 1% threshold. Previously, a 3% threshold was common (Herbert et al. 2003) and is sometimes still used (Abdo & Golding 2007). The search then compares the query sequence against other sequences, choosing those sequences that are most similar to the query and below the threshold as being the same species. When multiple taxa fit within these parameters, then multiple possible species-identifications are presented to the user. This approach has wide support by proponents of DNA Barcoding (e.g. Herbert et al. 2003, Ratnasignham & Herbert 2007, Dawnay et al. 2007) and as such distance-based methods are embroiled in the DNA barcoding debate (Will & Rubinoff 2004). There are several criticisms of the distance-based method, especially when it is paired with the ideology of DNA barcoding. From a biological standpoint, how effective is a distance-based method at identifying species? Sperling (2003) noted that distance-based methods work fine in, “ all except the kinds of identifications that matter most, which is the level of closely related sister species that cannot readily be distinguished by traditional morphological characters.” More specifically, low-levels of molecular variation (as displayed by recently diverged species) place recently diverged groups outside the ability of strict distance methods to recognize (Little & Stevenson 2007). This is a major problem of the distance-method, because while it is possible to identify a species to family and even genera, species-level identification is another hurdle altogether. Like BLAST, distance-based methods are effectively limited by the completeness of the reference database, no matter what level of identification is desired, be it species, genus, family or even order in level (Ekrem et al. 2007).

30 DNA barcoders have placed added burden to the distance-method by an insistence that a set species threshold be applied across all taxa and that a single-gene can carry the weight of barcoding all life on earth. It is unlikely that distance between species will be uniform for the locus chosen (COI) by most DNAbarcoders, causing problems with set species thresholds (Little & Stevenson 2007, Balakrishnan 2005). The single-gene criteria that DNA barcoders have burdened the distance-based method with has recently been called into question because the gene chosen (COI) clearly varies within species and is thus insufficient for single-gene barcoding (Meier et al. 2006). Meier et al. (2006) suggest the creation of consensus barcodes that are the sum total of the variability within a species as the best way to address within-species differences. This creates further problems because Meier et al. (2006) found that 21% of those species that had consensus barcodes shared the same barcode. So, the more species sampling that occurs, the less specific the consensus barcode becomes. Also, as more species are sampled, barcodes would necessarily change. So a species may have a barcode at one point but this barcode could change as more specimens are added to the database because these new species reduce the uniquely variant sites that made-up a species old barcode. Thus, barcodes change over time as datasets increase and these new barcodes could become potentially less specific than before. Clearly, consensus barcodes are not very useful for species identification. Tree-based methods for species identification

Phylogenetic analysis is the methodology with the longest-running track record for the molecular identification of non-human species in forensic cases (Dizon et al. 2000). These hierarchical clustering methods align an unknown specimen (the “target”) to an alignment of well-established known samples of possible suspect taxa. Once an alignment is made, the algorithms search for an optimal topology or set of topologies (if no single optimal topology is found). Species identification using tree-based methods is possible when the target nests within a branch of the tree that contains a monophyletic species grouping (Baker et al. 1996). It is not always possible to place a target within a monophyletic grouping and failure to place a target into confirmed taxa could indicate a hybrid organism, a previously undetected polymorphism from one of the groups immediately surrounding the target, or an unsampled taxa (Baker et al. 1996). These issues are resolved by dense taxon sampling and sequencing of multiple specimens from each taxa, which are conditions required for both BLAST and distance-based methods as well. Perhaps more importantly, tree-based methods can place

31 targets within higher taxonomic ranking (family, genus, etc.) even when those targets cannot be identified to species. This higher-level exclusion is important still, because the limiting of unknown samples to just a few taxa still allows for the possibility of running those samples through PMI estimations for each of the possible species, or through a “composite” species—a pseudo-species that is created from the compilation of several possible species into one dataset for the purpose of getting some idea of the PMI from the unknown samples. Such “compilation” studies are beginning to be attempted by forensic entomologists (Cervenka 2006) when no better option is possible. It is common to build trees using multiple methods and then compare results (Felsenstein 2004). There are three main tree-based methods to be considered: Maximum Parsimony, Maximum Likelihood and Neighbor-Joining. Maximum Parsimony (MP, or just Parsimony hereafter; Edwards & Cavalli- Sforza 1963) is a non-parametric statistical method that states that the preferred tree is one with the least number of evolutionary changes (Felsenstein 2004). Maximum Likelihood (ML; Fisher 1912, Edwards & Cavalli-Sforza 1964, Felsenstein 1981) is a parametric statistical method that employs a specific model of character evolution to evaluate the maximum likelihood of any given change amongst a dataset. Both MP and ML use optimality criterion and commonly (but not always) use stepwise-addition to build trees (Felsenstein 2004). Neighbor-Joining (NJ; Saitou & Nei 1987) is an improvement on the distance-based tree building methods first introduced by Cavalli-Sforza & Edwards (1967) and by Fitch & Margoliash (1967). The basic premise of a distance matrix method for building trees is to calculate a distance measure between each species pair and then find a best-fitting tree that predicts the observed distances (Felsenstein 2004). NJ is an improvement over older forms of this method such as minimum evolution (Kidd &

Sgaramella-Zonta 1971) and UPGMA (Unweighted Pair Group Method with Arithmetic mean; Sokal & Michener 1958) in that it does not assume a molecular clock and is practical up to several hundred species (Felsenstein 2004). Neighbor-Joining uses a Star Decomposition method for building a tree. In this method, all species are present with a totally unresolved tree. The tree is then achieved by grouping two lineages at a time using a nearest-neighbor interchange (Felsenstein 2004). Like BLAST and distance-based methods, confidence in phylogenetic comparison relies heavily upon the adequacy of the query database. However, it is here that phylogenetic comparison for the purpose of species-identification stands out from the other methods. First, phylogenetic tree-building relies upon bootstrap-style confidence measures which are largely absent in the other two methods (see

32 reliability in tree-based methods below for details on these confidence measures). Second, phylogenetic comparison is able to handle previously unsampled genotypes, something neither BLAST nor distance- based methods address. Which method is best for species identification?

I agree with Ross et al. (2008) that all three of the major identification tools discussed above (BLAST, distance and tree-based methods) are generally equivalent when the molecular dataset contains representatives from all relevant taxa. What this means is that given perfect information, all of the methods handle species identification well. However, data is rarely perfect, so the important question is this: given imperfect data, which method handles the situation best? In this case, Ross et al. (2008) make it clear that tree-based methods are far superior to either BLAST or distance-based methods when data are less than perfect. Because of this, I have chosen to use tree-based analyses (MP, ML and NJ) in this chapter. Reliability in tree-based methods

Reliability in the phylogenetic methods outlined above is estimated by re-sampling techniques such as “Bootstrapping” (Efron 1979, Felsenstein 1985) or “Jacknifing” (Mueller & Ayala 1982). Both of these are statistical techniques for empirically estimating the variability in the phylogenetic test being carried out. In the Jacknife, one observation is dropped from the sample set at a time, and a new estimate is generated. In the Bootstrap, re-sampling with replacement from the original dataset occurs to produce a new, fictional datase and then a new estimate is performed. These re-sampling techniques are carried out for a number of permutations, often 1,000, and then the number of times a given branch occurred in the estimated trees is counted. This results in a P value for each branch that provides an estimate of how much support a given branch has. The P value ranges from 0-100 with the higher the result indicating greater support. If anything, such re-sampling techniques have a problem in that they are conservative, in that they underestimate higher P values when they are “large” (Felsenstein 2004). Re-sampling can be time-consuming, especially when computationally intensive techniques such as Maximum Likelihood are being re-sampled. Because of this, I have chosen to re-sample faster methods (Parsimony and Neighbor- Joining) 1,000 times each and Maximum Likelihood 300 times each (see below).

33 MATERIALS AND METHODS Specimens Thoracic, head or leg muscle samples were collected from adult individuals of the species listed on Table 2.1. Specimens were stored in 95% ETOH at -80°C until time of whole genomic DNA extraction. Diagnostic morphological characters (usually male genitalia) were preserved wherever possible as voucher specimens.

DNA extraction Tissue samples were subjected to DNA extraction using Qiagen’s DNeasy kit (Qiagen Group, catalogue number 69506) with the following modifications: (1) body portions subject to DNA extraction were cut manually apart by scalpel and suspended in 180 ul of PBS buffer and 20 ul of Proteinase K then incubated at 55°C overnight; (2) After overnight digestion, major exoskeletal remnants were pelleted via centrifugation and the supernatant was removed. This supernatant was carried over to step three of the “Purification of Total DNA from Animal Tissues” standard protocol provided by the manufacturer. All subsequent steps were as described under this protocol, except for the addition of a one-minute spin in between steps seven and eight to remove additional ethanol.

PCR and nucleotide sequencing Amplification of desired mitochondrial loci was achieved via PCR using Rapid Cyclers (Idaho Technologies) in “l0μL” capillary tubes (capillary tubes are 10 ul at 5.1 cm capacity and are 10 cm long) using 0.083 units of Platinum Pfx (Life Technologies), 1.09 pM primer, 3.3 nM dNTPs and ~5 ng of genomic DNA. Test reactions were carried out at 11μL, with final PCR reactions of 100 μL prepared according to the above reagent ratios. Cycle parameters were as follows for COI and COII fragments: 95°C for three minutes, followed by 35 cycles of: 94°C for zero seconds, 45°C for zero seconds and 68°C for thirty seconds. Reaction parameters were as follows for ND4 fragments: 95°C for three minutes, followed by 35 cycles of: 94°C for zero seconds, 50°C for zero seconds and 68°C for thirty seconds. Reaction parameters were as follows for EF1α fragments: 95°C for three minutes, followed by 35 cycles of: 94°C for zero seconds, 48-56°C for zero seconds and 68°C for thirty seconds. Primer sequences are listed in Table 2.2. PCR products were purified using the QIAquick PCR Purification Kit (Qiagen Group, catalogue number 28106) following manufacturer specifications. Purified products were sequenced by either Davis Sequencing, Inc. (1490 Drew Avenue, Suite 170, Davis, CA 95616) or the High-Throughput

34 Genomics Unit (WTC East, Suite 600�2211 Elliott Avenue �Seattle, WA 98121).

Data alignment Raw sequences were compared to archived sequences in the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/) using BLAST. All sequences returned very close matches to other diptera species, usually either a calliphorid or sarcophagid. The sequences obtained in this study were aligned with two reference sequences: Chrysomya putoria (Genbank accession number NC002697), and Cochliomyia hominivorax (Genbank accession number NC002660). These two species were chosen for alignment because they have their entire mtDNA genome available. The programs FinchTV v.1.4.0 (Geospiza, Inc. www.geospiza.com) and MacClade v.4.03 (Maddison & Maddison 2001) were used for sequence editing and alignment. FinchTV was used to view the raw electropherograms and to remove poor quality ends. These modified sequences were exported in FASTA format and imported into MacClade. Reverse and forward reads were checked for consistency; and discrepancies were resolved by referring to the raw electropherograms. Each final sequence was aligned to our reference sequences using the automated most-parsimonious pairwise alignment tool in MacClade. Each taxon was aligned with the automated aligner tool as nucleotides and alignments were double-checked manually first as nucleotides and then as translated amino acids.

DNA sequence analysis Phylogenetic analysis was carried out using PAUP* v.4.0b10 (Swofford 2001). Each locus was individually analyzed using two methods, NJ and MP. MP used a heuristic search with stepwise addition and tree-bisection-reconnect (TBR; Felsenstein 2004) branch-swapping algorithm. NJ used a heuristic search with star decomposition. Reliability of the branches was assessed using bootstrap re-sampling with 1000 pseudo-replicates (Figure 2.5). The combined mtDNA dataset was also analyzed via ML consisting of a heuristic search with stepwise addition and the TBR algorithm, in addition to NJ and MP as described above. The model for ML was estimated using Modeltest software (Posada & Crandall 1998) with a successive-approximations approach (Sullivan et al. 2005). The best-fitting model was the general time reversible+ invariable sites+ gamma distribution (GTR+I+Γ). ML reliability was assessed using bootstrap re-sampling with 300 pseudo-replicates (Figure 2.7). Finally, the combined dataset (COI, COII, ND4 and EF1α) with only taxa for which EF1α was recovered was analyzed with NJ, MP and ML. For ML, The best-fitting model was again the general

35 time reversible+ invariable sites+ gamma distribution (GTR+I+Γ). All three methods were assessed for reliability using bootstrap re-sampling with 1000 (NJ/MP) and 300 (ML) pseudo-replicates (Figure 2.8).

RESULTS Sequence characteristics COI dataset

COI data was obtained for almost all specimens, resulting in a nucleotide matrix consisting of 1539 aligned sites, of which 1001 (64.0%) were invariant, 102 (6.4%) were parsimony uninformative and 436 (9.6%) were parsimony informative. 353 (81%) of the parsimony informative sites occurred at third positions. For both Sarcophaga crassipalpis and S. cimbicis, the 3’ ends of COI were not obtained.

Average nucleotide frequencies among all taxa were A (30.531%), T (37.850%), C (16.635%) and G (14.984%). No significant variation in nucleotide frequencies was observed among taxa (X2= 35.310039, d.f. = 87, P=0.99999985). Two sequences are of particular note: (1) the sequence of Ravinia querula differs from the sequence published for Ravinia lherminieri (Genbank AF259513) by only two basepairs for the 5’ end of COI and (2) my sequence for Phormia regina differs from a sequence published on Genbank (AF295550) for the same species in that my sequence has a four codon deletion in COI not seen in the GenBank sample. Four taxa had portions of the COI locus missing over 100 bp: Boettcheria cimbicis (~100 bp portion from the middle of the locus), Sarcophaga crassipalpis (~200 bp portion from the middle of the locus), Argoravinia rufiventris (~200 bp portion from the middle of the locus) and Tricharaea femoralis (~150 bp portion from the middle of the locus). COII dataset

COII data was obtained for almost all specimens, resulting in a nucleotide matrix consisting of 683 aligned sites, of which 428 (63.0%) were invariant, 54 (8.0%) were parsimony uninformative and 201 (29.0%) were parsimony informative. 137 (68%) of the parsimony informative sites occurred at third positions. Average nucleotide frequency among all taxa was A (34.279%), T (38.827%), C (14.514%) and G (12.380%). No significant variation in nucleotide frequencies was observed among taxa (X2= 42.304628, d.f. = 87, P=0.99998625. Sarcophaga aldrichi and Tricharaea femoralis were both missing the 5’ end of the locus. One taxon, Boettcheria bisetosa, did not amplify at all for COII. ND4 dataset

ND4 data was obtained for all specimens, resulting in a nucleotide matrix consisting of 702 aligned sites, of which 439 (62.5%) were invariant, 67 (9.5%) were parsimony uninformative and 196 36 (28.0%) were parsimony informative. 156 (79.6%) of the parsimony informative sites occurred at third positions. Average nucleotide frequency among all taxa was A (28.443%), T (45.254%), C (9.422%) and G (16.881%). No significant variation in nucleotide frequencies was observed among taxa (Χ2= 23.472038, d.f. = 87, P=1.0). Combined mtDNA dataset

Combining COI/COII and ND4 data produced a nucleotide matrix consisting of 2994 aligned sites, of which 1925 (64.0%) were invariant, 227 (7.5%) were parsimony uninformative and 842 (28.5%) were parsimony informative. 673 (79.9%) of the parsimony informative sites occurred at third positions. Average nucleotide frequency among all taxa was A (30.981%), T (39.836%), C (14.339%) and G

(14.844%). No significant variation in nucleotide frequencies was observed among taxa (X2= 49.330735, d.f. = 87, P= 0.99961941). EF1α dataset

EF1α data was obtained for 20 of the 29 specimens, resulting in a combined nucleotide matrix consisting of 593 aligned sites, of which 497 (83.8%) were invariant, 41 (6.9%) were parsimony uninformative and 55 (9.3%) were parsimony informative. 35 (63.4%) of the parsimony informative sites occurred at third positions. Average nucleotide frequency among all taxa was A (24.924%), T (25.614%), C (25.899%) and G (23.563%). No significant variation in nucleotide frequencies was observed among taxa (X2= 5.377679, d.f. = 87, P=1.0). One taxa, Boettcheria cimbicis, failed to amply the 3’ end for this locus. Seven sarcophagid taxa failed to amplify at all for this locus: Sarcophaga triplasia, Helicobia rapax, Boettcheria latisterna, Sarcophaga mimoris, Musca autumnalis, Phormia regina and Sarcophaga bullata. Full dataset

Combining COI/COII, ND4 and EF1α data produced a nucleotide matrix consisting of 3587 aligned sites, of which 2422 (67.5%) were invariant, 268 (7.4%) were parsimony uninformative and 897 (25.1%) were parsimony informative. 708 (78%) of the parsimony informative sites occurred at third positions. Average nucleotide frequency among all taxa was A (30.120%), T (37.814%), C (15.982%) and G (16.084%). No significant variation in nucleotide frequencies was observed among taxa (X2= 37.303254, d.f. = 66, P= 0.99832149) for which there is EF1α data.

37 Variation between loci Interspecific variation has to be great enough to allow the unambiguous association of unknown specimens (maggots) to a certain species using sequence data (Zehner et al. 2004). Pairwise sequence differences for individual loci and for the combined dataset are presented in Tables 2.3, 2.4 and 2.5. Within-group variation for all genera ranged from 7.6-9.1% with a mean of 8.3% for the combined dataset. Between-group variation for genera ranged from 10.5-14.2% with a mean of 12.2% within the Sarcophaginae genera. Variation between the Calliphoridae outgroups and Sarcophaginae genera ranged from 13.3-16.5% with a mean of 14.3%. These ranges are consistent with the distance data presented by both Zehner et al. (2004), Wells et al. (2001) and data for the similar loci for diptera from outside these groups (e.g., Bernasconi et al. 2000).

Comparison of the different genetic loci Each of the four loci used in this study were analyzed separately using NJ and MP methods with bootstrap resampling (n=1000; Figure 2.5). Two of the three mtDNA loci (COI and ND4) support monophyly for Sarcophagidae with bootstrap support (Figure 2.5), while both COII and EF1α fail to support monophyly. In terms of a single locus, COI offers the most branch support of any of the loci sampled. COII does not offer much branch support by itself. In contrast, ND4, which is roughly the same size fragment as COII, provides quite a bit more support than COII. Finally, at first glance EF1α seems to provide little branch support. However, lack of bootstrap values appears at branches where taxa failed to amplify for this locus and so this may be a result of spotty data coverage across species rather than of the inherent poor showing of this locus. Each locus is generally congruent with the results from other loci and where differences appear, these happen when taxa were missing from a given analysis. For example, the two species of Helicobia in our study, were always monophyletic based on COI, COII and ND4 (Figure 2.5, trees A, B and C respectively), but do not appear to be closely related to any other genera. Helicobia rapax is not present in the EF1α analysis and in this locus I see an inter-generic relationship between Helicobia and Oxysarcodexia not otherwise supported by the other loci. However, Oxysarcodexia attaches itself to various taxa in the different single-locus trees and this throws doubt upon the relationship between it and Helicobia in the EF1α-only analysis (Figure 2.5, tree D). The monophyly of Peckia is not always supported by the individual gene trees (Figure 2.5, tree C vs. trees A, B and D) nor in the combined data analyses (Figures 2.6-2.8). In addition, the relationship

38 between Sarcophaga triplasia and S. utilis is inconsistent between the gene trees. Both COI and COII support a close relationship between these taxa (Figure 2.5, trees A and B), but this relationship does not hold for ND4 (Figure 2.5, tree C). S. triplasia did not amplify for EF1α.

Combined dataset analyses results Because of the large number of species lacking data for EF1α, I performed three separate analyses of our data. First, I considered the full dataset (mtDNA + nDNA = 3587 bp) for all taxa, which I will refer to hereafter as the all data (AD) analysis. In Figure 2.6 I present phylogenetic relationships between 25 sarcophagid species and the three calliphorid and one muscid species used as outgroups using the AD dataset. The topology of this tree was obtained by NJ analysis, and is generally congruent with trees obtained by MP (not presented). Second, I investigated the mtDNA dataset (mtDNA = 2994 bp) for all taxa (25 ingroup fleshflies with 4 outgroup species), which I will refer to hereafter as the mtDNA analysis (Figure 2.7). Third, I considered all loci (mtDNA + nDNA = 3587 bp), but only for the taxa for which EF1α was recovered (Figure 2.8). The taxa in this analysis included nineteen sarcophagid species and one calliphorid species. Hereafter I will refer to this as the +nDNA analysis. The topology of the tree obtained from the AD analysis (Figure 2.6) is also generally congruent with trees obtained by analysis the mtDNA analysis (Figure 2.7) and the +nDNA analysis (Figure 2.8). These different analyses do put forth different nodal support (presented in their respective figures), but generally recover the same topologies, especially within genera represented by multiple species (Ravinia, Sarcophaga, Peckia, Helicobia, Boettcheria). Our data recover a monophyletic Sarcophagidae. All analyses consistently demonstrate monophyly for the five genera that are represented by multiple species. Our data also provide modest support for some inter-generic relationships. I see some indication that a sister-group relationship between Oxysarcodexia and Ravinia (Figure 2.7) exists, although support is not universal (Figures 2.6 and 2.8). Likewise, I see weak indication for a relationship between Fletcherimyia and Blaesoxipha (Figures 2.6 and 2.7) but support for this relationship is not found in every analysis (see ML in Figure 2.6 and all analyses in Figure 2.8). The monophyly of the genus Sarcophaga is fully supported (Figures 2.6-2.8). Within Sarcophaga, none of the sub-generic classification are fully supported when multiple species of the same subgenus are present but our analysis of subgeneric organization is limited past Neobellieria due to a

39 lack of availability of multiple specimens for the proposed subgenera. Two of three Neobellieria species (S. polistensis and S. bullata) appear related in all analyses (Figures 2.6-2.8). However, S. triplasia, also classified under Neobellieria, is never seen to associate with the other two representatives of this group (Figures 2.6-2.8). There is support from all analyses for a S. crassipalpis and S. africa clade (Figures 2.6-2.8). There is also MP support for a relationship between S. mimoris and S. carnaria in the AD and mtDNA analysis (Figures 2.6 and 2.7). Monophyly of the genera Peckia and Boettcheria are fully supported (Figures 2.6-2.8). The relationships I find within the genus Peckia are consistent with the sub-generic classification proposed by Pape (1996) in that Peckia uncinata and P. chrysostoma share a closer relationship with each other than with P. intermutans (Figures 2.6-2.8). Pape (1996) placed P. uncinata and P. chrysostoma together in the subgenus Peckia. For Boettcheria, I find that Boettcheria cimbicis and B. latisterna share a closer relationship with each other than with B. bisetosa (Figures 2.6-2.8). Pape (1996) placed all three of these species as equally related to each other. The genera Ravinia and Helicobia are also monophyletic in all analyses (Figures 2.6-2.8). The rest of the genera included in this study (Titanogrypa, Argoravinia, and Tricharaea), are each represented by a single species, so no determination can be made regarding generic monophyly. On a final note, Peckia and Sarcophaga are always closely related to each other in all topologies (Figures 2.6-2.8) although this association never receives any bootstrap support.

DISCUSSION Phylogenetic analyses of COI, COII, ND4 and EF1α loci confirms monophyly of the family Sarcophagidae, consistent within the family organization of Roback (1954), Lopes (1969, 1982) and Pape (1996). Because my analyses did not include exemplars from Paramachronychiinae and Miltogramminae, I cannot comment on the organizations of those groups. Roback, Lopes and Pape all generally agree on the generic-level organization within Sarcophaginae (Figures 2.1-2.3) and my analyses support the monophyly of those genera with multiple exemplars included in this study (Helicobia, Sarcophaga, Ravinia, Peckia, Boettcheria; Figures 2.6-2.8).

Agreement with morphological organization schemes The differences between the classification schemes of Roback (1954), Lopes (1969, 1982) and Pape (1996) primarily occur at the subfamily and tribe levels for the species present in our study. All three authors agree about the monophyly of Sarcophaginae and the species that should be contained

40 therein. Likewise, the organization of species within genera (except where missing or unplaced) by the three authors is also generally the same. It is the organization of those genera under Sarcophaginae where the three authors differ. Roback (1954) held a view of the Sarcophaginae which included several sub- tribes and even groups (Figure 2.1). Using male genital characters, Roback presents a detailed historical perspective of the possible relationships between the present-day genera, providing possible intermediate genital forms. Roback’s synthesis does not involve anything resembling modern cladistic analysis, but is the most detailed organization scheme to date. In contrast to Roback, Lopes (1982) uses five main tribal divisions for organizing the genera of the taxa I consider here (Figure 2.2). Lopes originally did not consider the use of sub-tribes in his classification scheme, but later (1982) adopted several sub- tribes, especially those proposed by Rohdendorf (1967). Lopes does provide characters for why he splits groups the way he does (e.g., Lopes 1982 which focuses on first instar characters), but he never creates a phylogenetic synthesis the way Roback does. Finally, Pape (1996) presents a view of extreme lumping— everything exists under the Sarcophaginae label as distinct genera with no organization scheme between sub-family and genera (Figure 2.3). Pape specifically presents the genera (and species) in alphabetical order—implying that phylogenetic relationships between all these groups is uncertain. Pape has united many of the genera presented by both Roback (1954) and Lopes (1969). However, many of the older generic names provided by Lopes and Roback live on as sub-genera for Pape. Roback’s Topology

Roback presents phylogenetic relationships that divide the taxa from this study into six subtribes (Hystricocnemina, Sarcophagulina, Sarcophagina, Raviniina, Sarcodexiina, Boetcheriina and Servaisiina) under one tribe (Sarcophagini; Figure 2.1). I consider support for each of these sub-tribes in turn. Hystricocnemina. The subtribe Hystricocnemina is represented in my study by Blaesoxipha plinthopyga. When present in my analyses (it did not amplify for EF1α and so is missing from the +nDNA analysis), Blaesoxipha is closely related to the genus Fletcherimyia which is itself a member of the subtribe Servaisina. NJ returned bootstrap support for this relationship in two analyses (Figures 2.6 and 2.7). I can neither support nor contest the subtribe Hystricocnemina with my data. Sarcophagulina. My analyses neither support nor dispute the organization of the Sarcophagulina since the exemplar, Tricharaea femoralis, shows no supported relationship to any other group inside the Sarcophaginae (Figures 2.6-2.8). More data are needed to resolve this relationship.

41 Sarcophagina. The exemplars in my study representing the subtribe Sarcophagulina are divided into two groups, Johnsonia and Sarcophaga, according to Roback (1954). Johnsonia is comprised of the genera Argoravinia and Helicobia. The group Sarcophaga is comprised of four genera in my study: Arachnidomyia (comprised of Sarcophaga aldrichi), Wolhfahrtiopsis (comprised of Sarcophaga utilis), Sapromyia (comprised of Sarcophaga bullata and S. polistensis) and Sarcophaga (comprised of S. crassipalpis, S. triplasia, S. africa and S. carnaria) all of which were condensed into the genus Sarcophaga by Pape (1996). There is strong bootstrap support in all analyses (Figures 2.6-2.8) that the four genera Arachnidomyia, Wolhfahrtiopsis, Sapromyia and Sarcophaga (as defined by Roback 1954) are actually one single genus (as defined by Pape 1996). I do not believe these are four valid genera and agree with Pape (1996) that they should be lumped into one genus, which I refer to under this discussion of Roback’s topology as Sarcophaga, following Pape (1996). My analyses lend no support to the idea that the three genera Argoravinia, Helicobia and Sarcophaga form a monophyletic group. Rather, there is weak support that Argoravinia is related to Titanogrypa which is an exemplar of Roback’s Boettcheriina (Figure 2.6). There is no bootstrap support for the association of Helicobia with either Argoravinia nor Sarcophaga. For example, all three analyses show Helicobia as existing just outside a Sarcophaga + Peckia clade, but there is no bootstrap support for the organization (Helicobia (Sarcophaga+Peckia)). Confounding both of Roback’s sub-tribal definitions of Sarcophagina and Sarcodexiina, my data support a relationship between the genera Sarcophaga and Peckia. This relationship is again weak, only having bootstrap support in one analysis (Figure 2.8), but the clade was returned in every analysis (Figures 2.6 and 2.7). It is possible that further support for this relationship will be found with the addition of more loci. Raviniina. The Raviniina is comprised of the genera Ravinia and Oxysarcodexia in my study. Both Roback (1954) and Lopes (1969, 1982) agree that the genera Ravinia and Oxysarcodexia were closely related (Figures 2.1 and 2.2). My analyses provide weak support for this close relationship in two of the three analyses I performed (Figures 2.6 and 2.7 but not 2.8). Sarcodexiina. My exemplar for the Sarcodexiina is Peckia chrysostoma. As per our discussion under Roback’s Sarcophagina sub-tribe, I do not believe that Sarcodexiina is a valid subtribe at this time. Boetcheriina. The Boetcheriina is comprised of the genera Boettcheria and Titanogrypa (named Cucullomyia by Roback [1954]). In my analyses, Titanogrypa does not seem to have a relationship with

42 the genus Boettcheria, the other exemplar for Roback’s Boettcheriina (Figures 2.6 and 2.7) although there is no bootstrap support for a split between the two, so this might also be due to lack of data. I believe the addition of more data will resolve this relationship. Servaisiina. My exemplar for the Servaisiina is Fletcherimyia fletcheri. Owing to the fact that I only have one exemplar for the group in my study, I cannot speak to the validity of this subtribe. However, Roback closely allied the Servaisiina to the Boettcherina. My analyses show that Fletcherimyia more closely associates with members of Roback’s subtribe Hystricocnemina than Boettcherina. In two of my analyses, there is bootstrap support for a close relationship between Fletcherimyia and Blaesoxipha (Figures 2.6 and 2.7), although this is not the case in the +nDNA analysis (Figure 2.8). I feel more data will further resolve this relationship, but that at this time, I do not support Roback’s (1954) concept of Servaisiina. Lopes’ Topology

Lopes (1982) presents phylogenetic relationships that divide the taxa from this study into five tribes (Sarothromyiini, Sarcophagini, Raviniini, Sarcodexiini and Cuculomyiini) under the subfamily Sarcophaginae (Figure 2.2). I consider each of these tribes in turn. Sarothromyiini. My exemplar for this tribe is Tricharaea femoralis, and while my analyses frequently placed this taxon near to both Titanogrypa luculenta and Argoravinia rufiventris (both placed in the tribe Sarcodexiini under Lopes) there was no bootstrap support for these relationships. I feel more data is required to resolve these relationships and to determine whether or not Sarothromyiini is a valid tribe. Sarcophagini. The Sarcophagini is represented by the following genera in my study: Blaesoxipha, Boettcheria, Peckia (Peckia) and several genera distinguished by Lopes but lumped together under the genus Sarcophaga by Pape (1996): Parasarcophaga, Bercaea, Neobellieria and Wohlfahrtiopsis. There is strong bootstrap support in all analyses (Figures 2.6-2.8) that these four genera (as defined by Lopes 1982) are actually one genus (as defined by Pape 1996). I do not believe these are four valid genera and agree with Pape (1996) that they should be lumped into one genus. Two of the genera Lopes placed in Sarcophagini, Sarcophaga (as defined by Pape 1996) and Peckia, share a relationship in our analyses (Figures 2.6-2.8) with some bootstrap support (Figure 2.8). These two genera also appear as a clade in all AD analyses, but without bootstrap support (Figure 2.6).

43 Boettcheria is clearly a valid genus with bootstrap support in our analyses where multiple species are present (Figures 2.6 and 2.7). What is not clear is whether Boettcheria is related to the other genera as Lopes suggested. There is no real support for or against Boettcheria being related to the other genera of Lopes’ Sarcophagini. I feel that more data are needed to resolve this relationship. One taxon which Lopes placed into the tribe Sarcophagini shows a closer relationship to the Raviniini subtribe than to the rest of the taxa from Lopes’ tribe Sarcophagini. My analyses show that both Fletcherimyia fletcheri (not discussed by Lopes) and Blaesoxipha plinthopyga (placed in Sarcophagini) are closely related to each other (Figures 2.6 and 2.7) and possibly share closer relationships with Lopes’ Raviniini tribe than with the Sarcophagini tribe (Figures 2.6 and 2.7). Because of the lack of supportable relationships discussed above, I feel that Lopes’ Sarcophagini is probably not a valid tribe. Raviniini. My data support Lopes’ tribe Raviniini with multiple exemplars from the genera Ravinia and one from the genus Oxysarcodexia showing closer relationships to each other than to the rest of the taxa presented here (Figures 2.6 and 2.7). Because these relationships have some bootstrap support, my data support the tribe Raviniini. Sarcodexiini. I include exemplars from Helicobiina, Argoraviniina and Pattonellina subtribes in our study, represented by the genera Helicobia, Argoravinia and Peckia (Pattonella), respectively. Helicobia does not seem to share a closer relationship to Argoravinia as Lopes’ organization would indicate. Rather, Helicobia consistently clusters just outside of Sarcophaga and Peckia (Figures 2.6 and 2.7) while Argoravinia clusters with Titanogrypa. However, there is scant bootstrap support for a Helicobia + Titanogrypa relationship (Figure 2.6). I feel more data are needed to resolve this relationship issue. One thing is clear from this analyses: the species Peckia intermutans, unplaced in Lopes catalogue (1969) and later placed in the tribe Sarcodexiini under the genus Pattonella (Lopes 1982), clearly has close relationships with the other Peckia species in this study (Figures 2.6 and 2.7) and so has been correctly placed into the Peckia genus by Pape (1996). Because of this, I find little support for the Sarcodexiini at this time. Cuculomyiini. My sole exemplar for the Cuculomyiini is Titanogrypa luculenta. Lopes placed this genus within it’s own tribe and so I would expect no close relationships with any other taxa in our study. However, I find that Titanogrypa consistently appears near Argoravinia (Figures 2.6 and 2.7, but not 2.8) and sometimes near Tricharaea (Figures 2.6 and 2.8, but not 2.7). There is little bootstrap support

44 for these relationships. I feel more data are required to resolve these relationships and to determine whether or not Cuculomyiini is a valid tribe. Pape’s Topology

Pape (1996) presents no phylogenetic relationships for the species in my investigation beyond consigning them to one large polytomy under the subfamily Sarcophaginae. However, Pape does not completely cast off most of the high-level classification presented by either Roback (1954) or Lopes (1969, 1982). Rather he reanimates many of these names as sub-generic organizers within the genera (Figure 2.3). For example, the Pattonella intermutans of Lopes (1982) becomes the Peckia (Pattonella) intermutans of Pape (1996). My work supports the monophyly of all the genera presented in Pape’s world catalogue that I have discussed here for which there are multiple exemplars (Table 2.1, Figures 2.6-2.8). Sub-generic organization support. Pape (1996) uses sub-generic organization. I cannot speak to the validity of most of the sub-genera I included in this analyses, since only one group within the genus Sarcophaga, Sarcophaga (Neobellieria), and one group within the genus Peckia, Peckia (Peckia), have multiple specimens. Specifically, the data supports the sub-generic classification of Peckia (Peckia) as being distinct from Peckia (Pattonella), but I do not support Sarcophaga (Neobellieria) as being a valid clade (Figures 2.6-2.8). For Peckia, there is clear bootstrap support along Pape’s sub-generic organization scheme in all analyses (Figures 2.6-2.8). My data support a united Peckia genus (as does Pape 1996) whereas Lopes (1982) considered the three Peckia species I included in my study to belong to two separate genera (Peckia and Pattonella). Roback (1954) only considered one Peckia species (P. chrysostoma) and did not comment on the other two species (P. intermutans and P. uncinata). For Sarcophaga, only the sub-genera Neobellieria has multiple species present. These three species do not cluster together in any of my analyses. Two of the species, Sarcophaga polistensis and Sarcophaga bullata, do show a strong relationship with 100% bootstrap support in two of the analyses (Figures 2.6 and 2.7). The third Neobellieria species, Sarcophaga triplasia, tends to show closer relationships with Sarcophaga utilis (Figures 2.6 and 2.7), which belongs to the sub-genera Wohlfahrtiopsis in Pape (1996). When S. triplasia and S. bullata are not present in the analysis (Figure 2.8), S. polistensis seems most closely related to S. utilis. This might indicate a relationship between the sub-genera Neobellieria and Wohlfahrtiopsis that may be resolved with increased taxon sampling. Two other sub-generic relationships within the genus Sarcophaga are noteworthy. Sarcophaga aldrichi, which is unplaced by Pape (1996), seems to be closely related to the S. (Bercaeopsis) mimoris

45 + S. (Sarcophaga) carnaria clade that is present in both Figures 2.6 and 2.7. Bootstrap support for the S. aldrichi (Bercaeopsis+Sarcophaga) relationship is not always present, but the branch does appear in all topologies from the AD analysis. The relationship between Bercaeopsis and Sarcophaga sub-genera is itself noteworthy and has bootstrap support in all but the NJ analysis sans EF1α data (Figure 2.6). Sarcophaga (Bercaeopsis) mimoris did not amplify for EF1α and so is not present in the +nDNA analysis (Figure 2.8), but here S. aldrichi and S. carnaria appear most closely related to each other. Sarcophaga (Liopygia) and Sarcophaga (Bercaea) appear to be closely related as well. This relationship appears with bootstrap support in all analyses (Figures 2.6-2.8).

Agreement with Previous molecular work Zehner et al. (2004) examined sarcophagid phylogeny using 296 bp of COI and 386 bp of Dehydrogenase sub-unit five (ND5; Figure 2.9). They found bootstrap support for the Sarcophaga sub-genus with both COI and ND5 using MP. I cannot comment on the monophyly of Sarcophaga (Sarcophaga) they present because I only provide one specimen for the Sarcophaga sub-genus (S. carnaria). Wells et al. (2001) examined sarcophagid phylogeny using 783 bp of COI (Figure 2.10). Due to differing choices of taxa in our studies, I do not find many areas where I can comment on the Wells et al. topology except for two areas. Both Wells et al. (2001) and my own studies support monophyly for the genera Ravinia, Peckia and Sarcophaga. Both studies also agree on a Peckia + Sarcophaga relationship with Ravinia being more distantly related to the other two genera (Ravinia (Peckia + Sarcophaga).

CONCLUSIONS All genera (defined by Pape 1996) with multiple specimens (Boettcheria, Helicobia, Peckia, Ravinia, and Sarcophaga) were monophyletic in the two analyses that included all taxa (Figures 2.6 and 2.7) and were supported with reasonable bootstrap values (67-100%) by various tree-building methodologies (MP, ML and NJ). The sub-generic organization for the three Peckia species was returned in all analyses with 100% bootstrap support. I consistently saw relationships between the Peckia + Sarcophaga, Ravinia + Oxysarcodexia groups across all analyses, but they did not garner much (if any) bootstrap support. My analysis support the monophyly of Sarcophaginae and the generic organization put forth by Pape (1996). Most of the time, the data supported the generic organizations of Roback (1954) and Lopes

46 (1969, 1982) with one notable exception: the condensation of multiple genera into Pape’s modern idea of the genus Sarcophaga. I support some of the organization suggested by both Lopes (Sarcophagini and Raviniini; 1969) and Roback (Raviniina; 1954), especially where these authors agreed upon higher-level organization (Raviniini of both Lopes and Roback). However, this support is almost never complete, and so the higher-order relationships between these species are currently unresolved. Unfortunately, the data provide few well-supported deep branches upon which to reorganize the relationships between sub- families and genera. Likewise, I see little enlightenment below the generic level. Specifically, my analyses provide little support for sub-generic classifications within the genus Sarcophaga in the current World Catalogue (Pape 1996).

Despite the work of Zehner et al. (2004) and myself, the sub-generic structure of the genus Sarcophaga remains unclear. More work needs to be done in this area, with the inclusion of many more sub-genera and multiple exemplars of each of the identified sub-genera. Wells et al. (2001) conclusion that there is a relationship between the genera Peckia and Sarcophaga and Ravinia being monophyletic is improved upon by my analyses of many more genera. The data provide some clarifications, but more data are needed to resolve these relationships further. Specifically, exploration of additional mtDNA loci may prove beneficial as might the utilization of additional nuclear loci. In order to further differentiate between the hard to seperate Sarcophaga species, a fast-changing locus such as the mtDNA origin of replication might prove useful. Further, strategic inclusion of additional taxa so that there are at least two members of a given genus would prove very helpful. I hope that the findings presented here will stimulate both molecular and morphological investigation of the relationships within the Sarcophaginae (especially expanding the number of genera represented) and also expand analyses to include the Paramachronychiinae and Miltogramminae subfamilies of the Sarcophagidae.

47 Species # Origin Argoravinia rufiventris Wiedemann*, 7 E79 Grand Tacaribe, Trinidad Blaesoxipha (Gigantotheca) plinthopyga Wiedemann*, 5 F01 Heredia, Santo Domingo, Costa Rica Boettcheria cimbicis Townsend E35 Clermont Co., Ohio, USA Boettcheria cimbicis Townsend E36 Clermont Co., Ohio, USA Boettcheria latisterna Parker E22 Hocking Co., Ohio, USA Boettcheria bisetosa Parker A37 Hocking Co., Ohio, USA Fletcherimyia fletcheri Aldrich* E76 Ottawa, Ontario, Canada Helicobia resinata Hall* E66 Amacayacu, Columbia Helicobia rapax Walker E04 Hamilton Co., Ohio, USA Oxysarcodexia ventricosa Wulp E08 Hamilton Co., Ohio, USA Peckia (Pattonella) intermutans Walker* E67 Amacayacu, Columbia Peckia (Peckia) chrysostoma Wiedemann*, 5 E65 Amacayacu, Columbia Peckia (Peckia) uncinata Hall* E64 Amacayacu, Columbia Ravinia stimulans Walker E07 Hamilton Co., Ohio, USA Ravinia querula Walker A22 Hocking Co., Ohio, USA Sarcophaga (Bercaeopsis) mimoris Reinhard E39 Adams Co., Ohio, USA Sarcophaga (Bercaea) africa Wiedemann1,2,4,5,8 F18 Durham Co., North Carolina, USA Sarcophaga (Bercaea) africa Wiedemann1,2,4,5,8 F17 Durham Co., North Carolina, USA Sarcophaga (Bercaea) africa Wiedemann1,2,4,5,8 AJ12 Coos Co., Oregon, USA Sarcophaga (Neobellieria) polistensis Hall E43 Adams Co., Ohio, USA Sarcophaga (Neobellieria) triplasia Wulp8 A23 Hocking Co., Ohio, USA Sarcophaga (Neobellieria) bullata Parker2,3,5,8 AF37 Yolo Co., California, USA Sarcophaga (Liopygia) crassipalpis Macquart*, 1,5,8 E74 Riverside Co., California, USA Sarcophaga (Sarcophaga) carnaria Linnaeus*, 1,4,6 F05 Lejre, Denmark Sarcophaga (Wohlfahrtiopsis) utilis Aldrich8 E42 Adams Co., Ohio, USA Sarcophaga aldrichi Parker E23 Hocking Co., Ohio, USA Titanogrypa (Cucullomyia) luculenta Lopes* E63 Amacayacu, Columbia Trichareaea (Sarothromyia) femoralis Schiner* E80 Grand Tacaribe, Trinidad Cynomya cadaverina Robinaeu-Desvoidy E40 Adams Co., Ohio, USA Calliphora vomitora (Linnaeus) * E68 Skelleftea, Sweden Phormia regina (Meigen) E51 Washington Co., Tennessee, USA Musca autumnalis De Geer E46 Washington Co., Tennessee, USA Table 2.1: List of species involved in this study. Taxa that are reported in the forensic literature as collected at or on carrion are noted witht he following superscripts: 1= Smith 1986, 2=Byrd & Castner 2001, 3=Tessmer et al. 1995, 4=Gregor 1971 (as reprinted in Greenberg & Kunich 2002), 5= Wells et al. 2001, 6= Zehner et al. 2004, 7=Watson & Carlton 2003 and 8= Payne & Crossley 1966. Where multiple specimens are present, it was not possible to gain all loci from one specimen, so the final data is chimeric across all specimens listed. #= accession number. Taxa noted with the “*” superscript were kindly donated by Dr. Thomas Pape.

48 Locus Primer Sequence COI TY-J-1460 5’-tacaatttatcgcctaaacttcagcc-3’ COI C1-N-2191 5’-cccggtaaaattaaaatataaacttc-3’ COII TK-N-3775m 5’-gagaccattacttgctttcagtcat-3’ COI TL2-N-3014 5’-tccaatgcactaatctgccatatta-3’ COII C1-J-2792 5’-atacctcgacgttattcaga-3’ ND4 N4-J-8502 5’-gttggaggagctgctatattag-3’ ND4 N4-N-9194 5’-attttttgaaagaagtttaattcc-3’ EF1α EF1α 55f 5’-ggtatcaccattgatattgctttgtgg-3’ EF1α EF1α 836r 5’-cagcagcacctttaggtgggctagcctt-3’ EF1α EF2Rev 5’-atgtgagcagtgtggcaatcaa-3’ EF1α EF1α175fwd 5’-ggaaatgggaaaaggctccttcaagtagctggg-3’

Table 2.2: Primers used for amplification and sequencing in this study. Nomenclature fol- lows Simon et al. (1994).

49 0.115 0.118 0.112 0.110 0.118 0.118 0.110 0.111 0.079 0.030 0.136 0.120 0.096 0.096 0.088 0.109 0.075 0.109 0.104 0.071 0.086 30 0.106 0.067 0.121 0.108 0.102 0.120 0.078 0.075 0.117 0.111 0.110 0.112 0.111 0.114 0.116 0.083 0.086 0.137 0.109 0.101 0.103 0.089 0.105 0.073 0.105 0.125 0.108 0.081 0.068 29 0.104 0.069 0.121 0.108 0.107 0.107 0.078 0.075 0.111 0.119 0.119 0.114 0.114 0.110 0.110 0.113 0.114 0.117 0.112 0.081 0.075 0.137 0.098 0.106 0.096 0.086 0.108 0.122 0.079 0.078 28 0.073 0.127 0.102 0.124 0.072 0.067 0.122 0.115 0.113 0.114 0.114 0.104 0.104 0.139 0.124 0.106 0.124 0.099 0.122 0.101 0.102 0.104 0.078 0.079 0.072 27 0.103 0.095 0.124 0.103 0.104 0.109 0.127 0.100 0.124 0.101 0.131 0.117 0.110 0.117 0.115 0.116 0.112 0.117 0.113 0.144 0.127 0.127 0.120 0.129 0.125 0.124 0.123 0.104 0.133 0.129 0.102 0.124 0.120 26 0.104 0.134 0.126 0.105 0.134 0.107 0.137 0.113 0.114 0.118 0.118 0.112 0.115 0.125 0.159 0.145 0.122 0.121 0.131 0.134 0.134 0.126 0.139 0.175 0.142 0.167 0.147 0.153 25 0.130 0.138 0.120 0.136 0.129 0.141 0.143 0.141 0.119 0.119 0.110 0.112 0.111 0.119 0.113 0.118 0.117 0.115 0.116 0.107 0.142 0.133 0.123 0.103 0.155 0.073 0.098 0.085 0.090 24 0.109 0.101 0.125 0.123 0.121 0.098 0.104 0.124 0.114 0.111 0.113 0.113 0.110 0.116 0.107 0.137 0.126 0.121 0.104 0.132 0.091 0.120 0.103 0.121 0.167 0.124 0.133 0.103 0.103 23 0.107 0.093 0.123 0.121 0.104 0.128 0.102 0.134 0.117 0.113 0.119 0.085 0.074 0.146 0.124 0.123 0.093 0.091 0.088 0.091 0.096 0.088 0.138 0.127 0.079 0.095 0.068 0.073 22 0.106 0.072 0.122 0.105 0.105 0.120 0.073 0.076 0.121 0.118 0.110 0.110 0.111 0.119 0.116 0.116 0.112 0.110 0.127 0.089 0.123 0.124 0.108 0.136 0.125 0.149 0.148 0.106 0.135 0.098 0.107 21 0.104 0.105 0.134 0.124 0.108 0.123 0.057 0.110 0.112 0.119 0.115 0.119 0.112 0.118 0.113 0.096 0.089 0.134 0.089 0.085 0.122 0.094 0.094 0.151 0.076 0.091 0.085 0.091 20 0.109 0.075 0.126 0.107 0.109 0.084 0.091 0.115 0.117 0.110 0.119 0.116 0.116 0.115 0.113 0.118 0.111 0.119 0.156 0.126 0.122 19 0.126 0.108 0.134 0.095 0.122 0.133 0.107 0.133 0.105 0.134 0.156 0.133 0.121 0.115 0.102 0.127 ” denotes no data. * 0.115 0.114 0.112 0.119 0.149 0.132 0.010 0.104 0.103 18 0.124 0.094 0.133 0.101 0.134 0.102 0.124 0.096 0.133 0.099 0.101 0.147 0.128 0.123 0.051 0.135 0.097 0.121 0.097 0.123 0.118 0.117 0.115 0.111 0.118 0.116 0.146 0.134 0.097 0.010 0.092 17 0.088 0.128 0.109 0.102 0.131 0.095 0.123 0.098 0.121 0.094 0.098 0.135 0.122 0.046 0.126 0.095 0.097 0.108 0.118 0.110 0.111 0.117 0.115 0.115 0.113 0.114 0.141 0.098 0.097 0.127 0.099 16 0.103 0.142 0.123 0.129 0.107 0.127 0.123 0.135 0.120 0.120 0.129 0.122 0.105 0.097 0.131 0.091 The symbol “ 0.111 0.116 0.115 0.114 0.119 0.116 0.117 0.118 0.158 0.070 0.122 0.120 15 0.148 0.127 0.127 0.126 0.138 0.141 0.121 0.101 0.105 0.132 0.135 0.122 0.122 0.078 0.122 0.122 0.118 0.119 0.165 0.150 0.122 0.140 0.126 0.128 14 0.143 0.132 0.152 0.146 0.139 0.139 0.151 0.138 0.145 0.136 0.129 0.140 0.141 0.134 0.131 0.141 0.141 0.164 0.134 0.129 0.122 0.170 0.118 0.110 0.117 0.110 0.110 0.110 0.151 0.126 0.075 0.082 0.070 0.023 13 0.109 0.068 0.128 0.105 0.124 0.076 0.121 0.075 0.124 0.080 0.126 0.122 0.102 0.120 0.091 0.079 0.092 0.114 0.115 0.118 0.114 0.136 0.105 0.075 0.085 0.068 0.060 12 0.056 0.121 0.109 0.101 0.109 0.078 0.108 0.076 0.072 0.101 0.095 0.089 0.092 0.107 0.078 0.122 0.070 0.106 0.076 0.112 11 0.115 0.114 0.112 0.119 0.116 0.116 0.118 0.150 0.138 0.137 0.121 0.124 0.139 0.121 0.124 0.128 0.120 0.131 0.122 0.133 0.096 0.128 0.141 0.145 0.131 0.033 0.130 0.121 0.117 0.117 0.115 0.118 0.153 0.128 0.089 0.072 0.087 0.077 10 0.010 0.052 0.127 0.106 0.095 0.107 0.064 0.134 0.075 0.080 0.151 0.125 0.123 0.094 0.136 0.095 0.133 0.094 0.119 0.112 0.117 0.115 0.110 0.114 0.112 0.117 0.117 0.116 0.157 0.107 9 0.086 0.132 0.125 0.130 0.052 0.120 0.107 0.143 0.089 0.128 0.108 0.102 0.120 0.102 0.144 0.107 0.135 0.108 0.118 0.114 0.119 0.119 0.145 0.080 0.056 0.067 0.044 0.032 8 0.098 0.060 0.105 0.103 0.106 0.092 0.065 0.125 0.045 0.050 0.121 0.096 0.070 0.075 0.096 0.082 0.121 0.056 0.062 0.114 0.113 0.117 0.115 0.116 0.112 0.112 0.126 0.107 0.126 0.104 0.132 0.010 0.153 0.097 0.120 0.125 0.086 0.038 0.135 0.088 0.120 0.134 0.010 0.122 7 0.105 0.083 0.126 0.130 0.114 0.114 0.110 0.109 0.075 0.106 0.087 0.123 0.080 0.149 0.125 0.071 0.090 0.080 0.088 0.104 0.081 0.106 0.010 0.120 0.072 0.086 0.129 0.103 0.107 6 0.104 0.102 0.125 0.094 0.116 0.119 0.115 0.113 0.119 0.123 0.095 0.126 0.086 0.077 0.148 0.074 0.089 0.091 0.083 0.080 0.106 0.083 0.010 0.094 0.125 0.065 0.081 0.125 0.091 5 0.094 0.085 0.133 0.104 0.112 0.112 0.115 0.114 0.111 0.114 0.096 0.080 0.120 0.090 0.105 0.140 0.128 0.095 0.104 0.079 0.089 0.081 0.089 0.106 0.123 0.081 0.088 0.135 0.108 0.109 4 0.103 0.127 0.010 * * * * * * * * * * * * * * * * * * * * * * * * * * * 3 0.130 0.121 * 0.114 0.112 0.118 0.109 0.092 0.073 0.079 0.155 0.122 0.080 0.089 0.076 0.070 0.071 0.108 0.050 0.109 0.093 0.121 0.055 0.080 0.124 0.120 0.106 0.103 2 0.099 0.087 0.074 * 0.102 0.106 0.096 0.134 0.094 0.141 0.091 0.146 0.134 0.088 0.135 0.126 0.127 0.131 0.111 0.135 0.127 0.091 0.097 0.155 0.118 0.100 0.083 0.093 0.132 0.082 0.111 1 0.090 25 Argoravinia rufiventris Argoravinia 25 26 Tricharaea femoralis Tricharaea 26 29 Sarcophaga africa 30 Sarcphaga bullata 27 Blaesoxipha plinthopyga 28 Sarcophaga carnaria 23 Sarcodexia lambens 20 Peckia intermutans 21 Calliphora vomitora 22 Sarcophaga crassipalpis 17 Peckia uncinata 18 Peckia chrysostoma 19 Helicobia resinata 14 Musca atumnalis 15 Phormia regina 12 Sarcophaga utilis 13 Sarcophaga polistensis 8 Sarcophaga aldrichi 9 Boettcheria cimbicis 10 Sarcophaga mimoris 4 Helicobia rapax 5 Ravinia stimulans 6 Oxysarcodexia ventricosa 7 Boettcheria latisterna 1 Ravinia querula 2 Sarcophaga triplasia 3 Boettcheria bisetosa 16 Titanogrypa luculenta Titanogrypa 16 24 Fletcherimyia fletcheri 11 Cynomya cadaverina 11 Raw distance values for COI (upper triangle) and COII (lower triangle). 2.3. Raw distance values for COI (upper triangle) and COII (lower triangle). Table 50 0.110 0.116 0.069 0.033 0.108 0.101 0.091 0.010 0.106 0.105 0.095 0.080 0.084 0.130 0.123 0.101 0.083 0.084 0.102 30 0.088 0.083 0.106 0.093 0.086 0.090 0.098 0.074 0.123 0.078 0.110 0.116 0.112 0.115 0.111 0.111 0.113 0.092 0.120 0.130 0.120 0.106 0.130 0.120 0.082 0.147 0.134 0.107 0.102 0.092 * 29 0.107 0.102 0.101 0.124 0.090 0.127 0.089 0.134 0.117 0.115 0.112 0.114 0.112 0.110 0.110 0.089 0.091 0.129 0.127 0.101 0.124 0.089 0.079 0.085 0.135 0.132 0.093 0.105 0.025 * 28 0.104 0.089 0.129 0.086 0.123 0.062 0.131 0.110 0.113 0.111 0.078 0.088 0.103 0.104 0.095 0.105 0.107 0.088 0.104 0.087 0.120 0.123 0.101 0.032 0.024 * 27 0.090 0.101 0.098 0.085 0.074 0.109 0.104 0.125 0.095 0.120 0.113 0.115 0.101 0.095 0.125 0.125 0.091 0.090 0.094 0.093 0.086 0.099 0.125 0.128 0.029 0.049 0.042 * 26 0.103 0.108 0.109 0.091 0.100 0.104 0.101 0.107 0.127 0.097 0.122 0.118 0.115 0.112 0.109 0.127 0.138 0.132 0.123 0.107 0.137 0.135 0.135 0.129 0.127 0.134 0.040 0.030 0.044 0.037 * 25 0.121 0.130 0.140 0.135 0.124 0.128 0.129 0.127 0.142 0.119 0.135 0.135 0.143 0.140 0.127 0.127 0.138 0.137 0.136 0.128 0.121 0.042 0.040 0.032 0.046 0.042 * 24 0.133 0.135 0.140 0.131 0.130 0.122 0.130 0.133 0.162 0.125 0.155 0.115 0.115 0.119 0.101 0.092 0.098 0.087 0.080 0.097 0.107 0.077 0.100 0.087 0.032 0.030 0.036 0.015 0.025 0.025 * 23 0.097 0.107 0.102 0.084 0.084 0.082 0.096 0.079 0.107 0.112 0.113 0.083 0.087 0.128 0.122 0.105 0.087 0.103 0.109 0.101 0.109 0.022 0.035 0.034 0.037 0.017 0.025 0.010 * 22 0.098 0.091 0.097 0.107 0.109 0.077 0.129 0.067 0.120 0.111 0.111 0.111 0.113 0.119 0.116 0.114 0.099 0.106 0.094 0.076 0.126 0.065 0.055 0.061 0.065 0.072 0.055 0.068 0.063 * 21 0.103 0.106 0.126 0.109 0.095 0.151 0.053 0.118 0.114 0.116 0.115 0.106 0.106 0.135 0.106 0.090 0.103 0.057 0.024 0.014 0.032 0.030 0.036 0.017 0.035 0.030 * 20 0.097 0.122 0.120 0.094 0.102 0.108 0.091 0.104 0.103 0.117 0.118 0.118 0.112 0.116 0.118 0.110 0.039 0.035 0.024 0.032 0.030 * 19 0.134 0.088 0.107 0.102 0.135 0.145 0.136 0.106 0.125 0.124 0.096 0.106 0.024 0.065 0.029 0.015 0.037 0.118 0.110 0.113 0.032 0.039 0.015 0.030 0.025 * 18 0.101 0.096 0.093 0.105 0.096 0.105 0.124 0.101 0.127 0.103 0.106 0.133 0.125 0.055 0.020 0.015 0.059 0.019 0.010 0.034 0.113 0.110 0.029 0.035 0.015 0.029 0.025 * 17 0.087 0.087 0.108 0.094 0.082 0.097 0.089 0.102 0.109 0.094 0.120 0.087 0.089 0.126 0.003 0.017 0.015 0.057 0.019 0.007 0.030 0.116 0.112 0.118 0.114 0.112 0.118 0.020 0.030 0.017 0.030 0.024 * 16 0.139 0.121 0.109 0.120 0.142 0.126 0.109 0.123 0.121 0.015 0.019 0.025 0.017 0.057 0.020 0.017 0.029 The Symbol “*” dentes no data. 0.113 0.111 0.115 * * * * * * 15 0.108 0.128 0.138 0.120 0.102 0.135 0.126 0.153 0.120 0.080 0.103 * * * * * * * * * 0.113 0.119 0.112 0.115 * * * * * * 14 0.127 0.128 0.131 0.107 0.123 0.132 0.157 0.131 0.114 * * * * * * * * * * 0.114 0.030 0.037 0.020 0.029 0.020 * 13 0.088 0.086 0.088 0.096 0.098 0.101 0.088 0.121 0.085 0.123 0.071 * * 0.017 0.019 0.019 0.032 0.019 0.057 0.014 0.020 0.035 0.114 0.116 0.027 0.031 0.017 0.033 0.026 * 12 0.090 0.095 0.103 0.086 0.093 0.087 0.129 0.080 0.104 0.016 * * 0.014 0.013 0.017 0.029 0.020 0.059 0.018 0.016 0.035 11 0.111 0.064 0.067 0.051 0.057 0.052 * 0.131 0.137 0.123 0.106 0.120 0.125 0.136 0.146 0.128 0.056 0.052 * * 0.056 0.051 0.052 0.054 0.051 0.015 0.056 0.049 0.057 * * * * * * 10 0.108 0.088 0.109 0.101 0.105 0.105 0.105 0.076 0.120 * * * * * * * * * * * * * * 0.055 0.059 0.045 0.062 0.043 * 9 0.121 0.135 0.087 0.145 0.135 0.129 0.074 0.135 * 0.068 0.043 0.043 * * 0.043 0.043 0.047 0.052 0.047 0.068 0.045 0.041 0.055 0.040 0.047 0.027 0.022 0.024 * 8 0.105 0.098 0.125 0.104 0.097 0.096 0.121 0.059 * 0.062 0.025 0.024 * * 0.027 0.025 0.025 0.032 0.030 0.072 0.020 0.022 0.042 0.113 0.112 * * * * * * * * * * * * * 0.108 * * * * * * * * * * 7 0.120 0.075 0.120 0.026 0.019 0.022 0.058 0.028 0.019 0.031 0.031 0.033 0.026 0.031 0.028 * * 0.031 0.053 * 0.053 0.026 0.028 * * 0.021 0.022 6 0.091 0.109 0.122 0.099 0.089 0.026 0.026 0.024 0.051 0.028 0.021 0.030 0.031 0.028 0.021 0.038 0.031 * 0.028 * 0.035 0.054 * 0.049 0.020 0.028 * * 0.019 0.023 5 0.073 0.096 0.124 0.010 0.111 * * * * * * * * * * * * * * * * * * * * * * * * * 4 0.103 0.120 * 0.011 0.040 0.046 0.042 0.067 0.037 0.036 0.047 0.046 0.054 0.039 0.044 0.037 * 0.045 * 0.044 * 0.061 0.043 0.037 * * 0.035 0.037 3 0.128 0.129 * 0.045 * * * * * * * * * * * * * * * * * * * * * * * * * 2 0.107 * * * 0.047 0.039 * 0.037 0.034 0.042 0.044 0.037 0.030 * 0.030 0.029 0.032 0.037 0.030 0.057 0.057 * 0.054 0.031 0.034 * 0.052 * 0.017 0.038 * 0.044 1 * 16 Titanogrypa luculenta Titanogrypa 16 29 Sarcophaga africa 30 Sarcphaga bullata 27 Blaesoxipha plinthopyga 28 Sarcophaga carnaria 23 Sarcodexia lambens 20 Peckia intermutans 21 Calliphora vomitora 22 Sarcophaga crassipalpis 17 Peckia uncinata 18 Peckia chrysostoma 19 Helicobia resinata 14 Musca atumnalis 15 Phormia regina 12 Sarcophaga utilis 13 Sarcophaga polistensis 8 Sarcophaga aldrichi 9 Boettcheria cimbicis 10 Sarcophaga mimoris 5 Ravinia stimulans 6 Oxysarcodexia ventricosa 7 Boettcheria latisterna 1 Ravinia querula 2 Sarcophaga triplasia 3 Boettcheria bisetosa 4 Helicobia rapax 24 Fletcherimyia fletcheri rufiventris Argoravinia 25 femoralis Tricharaea 26 11 Cynomya cadaverina 11 Raw distance values for ND4 (upper triangle) and EF1α (lower triangle). 2.4. Raw distance values for ND4 (upper triangle) and EF1α (lower Table 51 0.086 29 0.073 0.077 28 0.090 0.086 0.090 27 0.111 0.091 0.099 0.097 26 0.116 0.118 0.113 0.105 0.127 25 0.120 0.108 0.086 0.101 0.100 0.107 24 0.113 0.099 0.097 0.086 0.093 0.090 0.103 23 0.111 0.083 0.094 0.088 0.079 0.074 0.061 0.077 22 0.112 0.113 0.095 0.106 0.121 0.105 0.109 0.101 0.104 21 0.102 0.079 0.078 0.093 0.106 0.096 0.077 0.083 0.081 0.091 20 0.117 0.111 0.112 0.110 0.095 0.086 0.102 0.103 0.099 0.102 0.101 19 0.111 0.103 0.091 0.095 0.090 0.099 0.010 0.077 0.109 0.079 0.092 0.105 18 0.101 0.098 0.085 0.090 0.088 0.093 0.042 0.097 0.077 0.106 0.076 0.082 0.099 17 0.112 0.104 0.094 0.090 0.101 0.098 0.100 0.104 0.103 0.096 0.101 0.090 0.098 0.098 16 0.112 0.116 0.116 0.116 0.115 0.119 0.112 0.119 0.145 0.121 0.128 0.128 0.081 0.121 0.129 15 0.116 0.116 0.153 0.138 0.123 0.135 0.129 0.126 0.129 0.132 0.141 0.149 0.131 0.131 0.141 0.135 14 0.117 0.108 0.097 0.080 0.071 0.077 0.030 0.130 0.098 0.083 0.089 0.098 0.080 0.100 0.066 0.090 0.096 13 0.110 0.105 0.092 0.079 0.076 0.072 0.071 0.066 0.126 0.093 0.079 0.085 0.098 0.080 0.010 0.069 0.090 0.095 12 0.112 0.119 0.119 0.110 0.115 0.118 0.111 0.119 0.113 0.115 11 0.126 0.109 0.104 0.109 0.124 0.094 0.104 0.046 0.105 0.114 0.112 0.112 0.132 0.109 0.096 0.069 0.082 0.076 0.125 0.077 0.080 0.136 0.120 0.103 0.104 0.093 0.080 0.106 0.106 10 0.113 0.116 0.116 0.110 0.110 0.118 0.114 0.111 0.126 0.107 0.109 0.104 0.125 0.097 0.108 0.141 0.132 0.105 0.099 0.120 0.104 9 0.112 0.119 0.101 0.088 0.082 0.064 0.066 0.071 0.108 0.068 0.066 0.065 0.131 0.091 0.080 0.085 0.092 0.073 0.104 0.059 0.086 0.090 8 0.111 0.119 0.110 0.116 0.116 0.116 0.113 0.119 0.116 0.113 0.113 0.111 0.117 0.118 0.119 0.136 0.053 0.127 0.104 0.138 0.129 0.125 0.128 7 0.111 0.115 0.117 0.112 0.097 0.085 0.098 0.089 0.093 0.096 0.096 0.079 0.094 0.087 0.010 0.086 0.105 0.104 0.088 0.091 0.127 0.099 0.091 0.094 6 0.111 0.118 0.119 0.097 0.087 0.104 0.085 0.094 0.092 0.094 0.079 0.090 0.091 0.092 0.085 0.109 0.096 0.107 0.078 0.085 0.128 0.093 0.087 0.092 5 0.078 0.111 0.119 0.110 0.114 0.094 0.103 0.097 0.109 0.132 0.109 0.098 0.103 0.099 0.120 0.101 0.128 0.102 0.121 0.010 0.099 0.138 0.121 0.105 0.107 4 0.100 0.010 0.115 0.114 0.112 0.113 0.110 0.118 0.113 0.106 0.120 0.097 0.101 0.108 0.103 0.102 0.082 0.105 0.072 0.124 0.122 0.105 0.105 0.146 0.146 0.104 0.107 3 0.126 0.108 0.117 0.113 0.116 0.115 0.089 0.105 0.079 0.103 0.104 0.134 0.108 0.093 0.082 0.080 0.071 0.106 0.070 0.069 0.120 0.066 0.077 0.127 0.093 0.100 2 0.126 0.098 0.085 0.097 0.113 0.116 0.114 0.117 0.107 0.092 0.098 0.086 0.098 0.097 0.010 0.084 0.096 0.093 0.098 0.087 0.109 0.104 0.105 0.091 0.089 0.136 0.097 0.094 0.103 1 0.099 0.107 0.073 0.087 Raw distance values for all taxa with all data (AD). 16 Titanogrypa luculenta Titanogrypa 16 11 Cynomya cadaverina 11 26 Tricharaea femoralis Tricharaea 26 24 Fletcherimyia fletcheri rufiventris Argoravinia 25 30 Sarcphaga bullata 27 Blaesoxipha plinthopyga 28 Sarcophaga carnaria 29 Sarcophaga africa 21 Calliphora vomitora 22 Sarcophaga crassipalpis 23 Sarcodexia lambens 18 Peckia chrysostoma 19 Helicobia resinata 20 Peckia intermutans 14 Musca atumnalis 15 Phormia regina 17 Peckia uncinata 12 Sarcophaga utilis 13 Sarcophaga polistensis 8 Sarcophaga aldrichi 9 Boettcheria cimbicis 10 Sarcophaga mimoris 5 Ravinia stimulans 6 Oxysarcodexia ventricosa 7 Boettcheria latisterna 2 Sarcophaga triplasia 3 Boettcheria bisetosa 4 Helicobia rapax 1 Ravinia querula Table 2.5. 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Figure 2.1. Cladogram from Roback (1954), pruned of the taxa not discussed in this study. Roback’s original designations are listed under the author’s name (left column), and any modifi cations to those listings are noted under the Pape (right column). Thus, for those interested in investigating Roback’s literature and comparing it to Pape’s world catalogue for these taxa original and modern designations are listed. For ease of use, several higher taxonomic units have been color-coded (tribe, sub-tribe and group). 53 ����� ���� ������������������ ���������� ����������������

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Figure 2.2. Cladogram from Lopes (1969 and 1982), pruned of the taxa not discussed in this study. Lopes’ (1982) designations are listed under the author’s name (left column), and any modifi cations to those listings are noted under the Pape (right) column. Thus, for those interested in investigating Lopes’ literature and comparing it to Pape’s world catalogue for these taxa original and modern designations are listed. For ease of use, several higher taxonomic units have been color-coded (sub-family, tribe and sub-tribe). 54 �����������������������

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Figure 2.3. Cladogram from Pape’s world catalogue (1996), pruned of taxa not discussed in this study.

55 Figure 2.4: Examples of Sarcophagid larvae and pupae illustrations, from Greene (1925).

56 R. querula R. querula R. stimulans R. stimulans 66/61 O. ventricosa F. fletcheri 65/80 S. triplasia B. plinthopyga S. utilis B O. ventricosa A S. aldrichi –/56 S. triplasia S. mimoris S. utilis 94/67 S. carnaria 80/77 S. mimoris 100/100 S. polistensis S. carnaria S. bullata 80/58 S. polistensis S. africa 100/100 S. bullata 100/100 P. uncinata S. crassipalpis 76/91 P. chrysostoma S. africa P. intermutans 90/94 100/100 P. uncinata 95/98 H. rapax P. chrysostoma H. resinata 100/100 B. bisetosa P. intermutans H. rapax 78/64 B. latisterna F. fletcheri H. resinata –/51 B. plinthopyga 100/100 B. latisterna T. luculenta B. cimbicis 99/100 C. cadaverina T. luculenta C. vomitora A. rufiventris M. atumnalis 65/89 C. cadaverina P. regina P. regina M. atumnalis 63/91 R. querula R. stimulans O. ventricosa 69/82 R. querula –/61 B. plinthopyga 51/– R. stimulans S. triplasia T. femoralis S. aldrichi 73/81 S. mimoris F. fletcheri C 87/91 S. carnaria D 60/55 O. ventricosa S. crassipalpis H. resinata S. africa –/53 S. utilis S. utilis S. polistensis –/71 T. luculenta 99/100 S. bullata B. bisetosa A. rufiventris –/67 B. latisterna 83/92 P. uncinata B. cimbicis P. chrysostoma P. intermutans F. fletcheri P. intermutans A. rufiventris B. plinthopyga –/52 T. luculenta T. femoralis 77/86 S. aldrichi 83/89 H. rapax S. carnaria H. resinata S. crassipalpis P. uncinata 63/76 90/99 P. chrysostoma S. africa 70/98 C. cadaverina S. polistensis 84/90 C. vomitora B. bisetosa P. regina M. atumnalis C. cadaverina

Figure 2.5. Comparison of the Neighbor Joining trees found by a heuristic search. MP used a heuristic search with stepwise addition and tree-bisection-reconnect (TBR; Felsenstein 2004) branch-swapping algorithm. NJ used a heuristic search with star decomposition for the following loci: A) COI, B) COII, C) ND4 and D) EF1α. Data above branches indicate bootstrap support (n=1000) values higher than 50% for MP/NJ, respectively. 57 83/96 Ravinia querula

—/62 Ravinia stimulans

Oxysarcodexia ventricosa

Fletcherimyia fletcheri —/51 Blaesoxipha plinthopyga

80/— Sarcophaga (Neobellaria) triplasia

Sarcophaga (Wohlfahrtiopsis) utilis

—/59 Sarcophaga aldrichi

Sarcophaga (Bercaeopsis) mimoris 97/— Sarcophaga (Sarcophaga) carnaria 92/99 100/100 Sarcophaga (Neobellieria) polistensis

Sarcphaga (Neobellieria) bullata

Sarcophaga (Liopygia) crassipalpis 87/83 Sarcophaga (Bercaea) africa

100/100 Peckia (Peckia) uncinata 94/100 Peckia (Peckia) chrysostoma 77/91 Peckia (Pattonella) intermutans

Helicobia rapax 98/99 Helicobia resinata

Boettcheria bisetosa 100/100 95/100 Boettcheria latisterna

Boettcheria cimbicis

—/70 Titanogrypa (Cucullomyia) luculenta

Argoravinia rufiventris

Tricharaea femoralis

100/100 Cynomya cadaverina

85/50 Calliphora vomitora

Phormia regina

Musca atumnalis 0.01

Figure 2.6. Neighbor Joining tree of the AD dataset found by heuristic search. MP used a heuristic search with stepwise addition and tree-bisection-reconnect (TBR; Felsenstein 2004) branch-swapping algorithm. NJ used a heuristic search with star decomposition. Data above branches indicate bootstrap support values higher than 50% for MP/NJ respectively. Circles at nodes of the tree indicate clades identified in both analyses, with boot- strap support higher (black) or lower (clear) than 50%. 58 69/88/65 Ravinia querula –/67/– Ravinia stimulans Oxysarcodexia ventricosa

–/58/– Fletcherimyia fletcheri Blaesoxipha plinthopyga 77/84/– Sarcophaga (Neobellieria) triplasia Sarcophaga (Wohlfahrtiopsis) utilis Sarcophaga aldrichi –/66/62

95/96/99 Sarcophaga (Bercaeopsis) mimoris Sarcophaga (Sarcophaga) carnaria 86/99/98 100/100/100 Sarcophaga (Neobellieria) polistensis Sarcphaga (Neobellieria) bullata

75/69/55 Sarcophaga (Liopygia) crassipalpis Sarcophaga (Bercaea) africa 100/100/100 Peckia (Peckia) uncinata 71/90/78 Peckia (Peckia) chrysostoma Peckia (Pattonella) intermutans 99/100/100 Helicobia rapax Helicobia resinata 92/99/100 100/100/100 Boettcheria bisetosa 94/100/78 Boettcheria latisterna Boettcheria cimbicis Tricharaea femoralis Titanogrypa luculenta Argoravinia rufiventris 100/100/100 Cynomya cadaverina 84/96/− Calliphora vomitora Phormia regina Musca atumnalis 0.01

Figure 2.7. Neighbor joining tree of the mtDNA dataset found by heuristic search. MP and ML used a heuris- tic search with stepwise addition and tree-bisection-reconnect branch-swapping algorithm. NJ used a heuristic search with star decomposition. Data above branches indicate bootstrap support values higher than 50% for MP/NJ/ML respectively. Both MP and NJ have1000 bootstrap replicates while ML has 300 bootstrap replicates.

59 71/94/84 Ravinia querula

Ravinia stimulans

Boettcheria bisetosa

–/61/– Argoravinia rufiventris

Helicobia resinata

60/65/87 Sarcophaga aldrichi

66/–/55 Sarcophaga (Sarcophaga) carnaria

83/75/56 Sarcophaga (Liopygia) crassipalpis

88/99/76 Sarcophaga (Beraea) africa

Sarcophaga (Wohlfahrtiopsis) utilis 67/75/79 –/70/– Sarcophaga (Neobellieria) polistensis

100/100/100 Peckia (Peckia) uncinata

63/89/68 Peckia (Peckia) chrysostoma

Peckia (Patonella) intermutans

Blaesoxipha plinthopyga

Titanogrypa luculenta

Tricharaea femoralis

Fletcherimyia fletcheri

Oxysarcodexia ventricosa

Cynomya cadaverina 10

Figure 2.8. Maximum parsimony tree of +nDNA dataset found by heuristic search. MP and ML used a heuris- tic search with stepwise addition and tree-bisection-reconnect branch-swapping algorithm. NJ used a heuristic search with star decomposition. Data above branches indicate bootstrap support values higher than 50% for MP/NJ/ML respectively. Both MP and NJ have1000 bootstrap replicates while ML has 300 bootstrap replicates.

60 90 Sarcophaga carnaria 90 Sarcophaga variegata Sarcophaga subvicina Sarcophaga (Liopygia) argyrostoma Sarcophaga (Liopygia) crassipalpis A Sarcophaga (Liopygia) teretirostris 52 Sarcophaga (Liopygia) tibialis Sarcophaga (Pandelleana) protuberans Sarcophaga (Thyrsocnema) incisilobata Sarcophaga (Helicophagella) melanura Sarcophaga (Bercaea) africa Sarcophaga (Parasarcophaga) albiceps Drosophila yakuba

85 Sarcophaga carnaria 99 Sarcophaga variegata B Sarcophaga subvicina 59 Sarcophaga (Liopygia) argyrostoma Sarcophaga (Liopygia) crassipalpis 77 Sarcophaga (Liopygia) teretirostris 83 Sarcophaga (Liopygia) tibialis Sarcophaga (Pandelleana) protuberans Sarcophaga (Thyrsocnema) incisilobata Sarcophaga (Helicophagella) melanura Sarcophaga (Bercaea) africa Sarcophaga (Parasarcophaga) albiceps Drosophila yakuba

Figure 2.9. Zehner et al. (2004) sarcophagid phylogeny using A) 296 bp of COI and B) 386 bp of dehydroge- nase sub-unit five (ND5). Based upon a maximum parsimony tree, numbers at the branches indicate bootstrap support above 50% (pseudo-replicates = 1000).

61 Musca domestica Calliphora albiceps �� Brachicoma devia Wohlfahrtia vigil �� Ravinia lherminieri Blaesoxipha plinthopyga �� Peckia chrysostoma �� Sarcophaga bullata �� Sarcophaga cooleyi Sarcophaga africa Sarcophaga peregrina �� Sarcophaga crassipalpis �� Sarcophaga ruficornis Sarcophaga argyrostoma

Figure 2.10. Adaptation of Wells et al. (2001) sarcophagid phylogeny using 783 bp of COI. Based on a single most parsimonious cladogram (heuristic search with step-wise additions), numbers at the branches indicate bootstrap support if greater than 50% (pseudo-replicates = 5000). See Wells et al. (2001) for a full description of species. This figure differs from the Wells et al. (2001) figure 3 from which it is derived in that we have col- lapsed all non-supported branches into polytomies. 62 “Are flies on the job when murders occur in the dark of night? It has been generally accepted that blowflies are not active at night and do not lay eggs then. If true, this eliminates a block of about 10 to 12 hours from calculations of the time of death. But what if egg laying can occur? Inclusion or exclusion of 10 or 12 hours can make or break an alibi”

—B. Greenberg, 2002

Chapter Three: Nocturnal ovipositing in the Cincinnati metropolitan area1 INTRODUCTION Greenberg (1990) first pointed out that the occurrence of nocturnal ovipositing could impact Postmortem Interval (PMI) estimations in a significant manner. The reason for this is simple: if nocturnal ovipositing does occur, then estimates that result in nocturnal timeframes for oviposition are believable and the best PMI estimate is achieved with the collected data (Figure 3.1, example B). However, if nocturnal ovipositing does not occur, this presents a definite time block that can be excluded from an analysis (Figure 3.1, example C). Thus, night becomes a potential barrier for carrion flies that is important to understand because it could throw off PMI estimates up to 12 hours. It is important to discover the true prevalence of nocturnal oviposition. If this behavior does occur, then it needs to be further explored to determine how frequently it takes place, which species are involved in this behavior (and which are not), whether location influences rates of oviposition and a whole host of additional questions that will allow entomologists to better model the behavior. Even if this behavior does not occur, this information still is vital to modeling carrion fly behavior, for the reasons provided above. While limited research has been conducted on fly ovipositing behavior in lab conditions (Byrd 1998, Woolridge et al. 2007) and whether carrion fly eye morphology supports nocturnal activity or not (Sukontason et al. 2004, Sukantason et al. 2008), these have not provided any substantial support for or against nocturnal ovipositing behavior in the wild. Several field studies have been conducted on carrion fly nocturnal ovipositing behavior (Table 3.1), but these provide contradictory results.

1 This article is an expansion of the previously published article, Stamper, T., & DeBry, R. W. (2007). Nocturnal Oviposition Behavior of Carrion Flies in Rural and Urban Environments: Methodological Problems and Forensic Implications. Canadian Society of Forensic Science Journal, 40(4), 173-182.

63 Chapter organization In this chapter I first discuss the previous work conducted on carrion fly nocturnal ovipositing. This summary sets the stage for presenting the research I conducted on ovipositing behavior over four field seasons (2004-2007). Crucial to this fieldwork were the methodological problems I encountered and how I overcame these issues. Because of this, I first discuss these problems and how changes in protocol were made to address them. Finally, I discuss the implications of this research in a broader context.

BACKGROUND A history of nocturnal oviposition field studies Necrophagus insects are increasingly used in death scene investigations as a component in estimating postmortem interval (PMI), one of the primary pieces of information sought by death scene investigators (Hall 1990). Of these sarcosaprophagic visitors, carrion flies (primarily members of the families Calliphoridae, Muscidae, and Sarcophagidae) are the most useful for determining short-term intervals (Smith 1986). Carrion fly PMI estimates require knowledge of several parameters (species identity, environmental conditions, fly behavior, etc.) as input into physiological/developmental models (Wells & Lamotte 2001). Of these, a critical component in estimating PMI is the activity pattern of egg laying, or ovipositing (and including larviposition) behavior. The frequency of individual flies visiting carrion throughout the day has been examined for many species (Nuorteva 1959; Baumgartner & Greenberg 1984, 1985; Haskell 1993; Byrd 1998) with the general conclusion being that ovipositing activity begins in the late morning, peaks in late afternoon and declines sharply before sunset. Because of this general activity schedule, carrion flies are presumed (Nourteva 1977, Erzinclioglu 1996) and modeled (Byrd & Allen 2001) to only oviposit during the day (see Figure 1.1). There are, however, published field experiments that suggest that flies may oviposit at night, at least under some conditions (Table 3.1). Among studies that specifically looked for evidence of nocturnal oviposition, Greenberg (1990), Singh & Bharti (2001), and Baldridge et al. (2006) all reported positive results. In the case of Baldridge et al. (2006), there was only a single incidence of nocturnal oviposition, although the authors reported multiple species laying eggs during this one event. Greenburg (1990) and Sing & Bharti (2001) report much higher rates of nocturnal oviposition activity – up to 33% of nocturnal trials in both studies were positive. In contrast, Tessmer et al. (1995) reported that nocturnal ovipositing did not occur in southern Louisiana. Haskell et al. (1997) noted that Haskell’s unpublished two-year study

64 on nocturnal ovipositing failed to detect the behavior in Indiana, but the details of this work have yet to be presented. Byrd (1998) investigated nocturnal ovipositing behavior in the laboratory and the field, but characterized it as “rare” and concluded that flies would need to be within one meter of a carcass in order to be tempted to crawl to it and oviposit. Spencer (2002) investigated nocturnal ovipositing in England and concluded that it did not occur. Both Byrd (1998) and Tessmer et al. (1995) reported the same taxa visiting carrion during daylight hours as those found by Greenberg (1990), Baldridge et al. (2006) and Singh & Bharti (2001; see Table 3.1 for species lists). This indicates that at least some of the species reported to have oviposited at night in some studies did not oviposit at night in other investigations, although they were reported as being active during the day at those other sites. This raises the question of why those species might be active at night some times but not during other times. Are there environmental or behavioral factors that were not controlled for during the contradictory experiments, or did the experimental design itself influence the outcome of those earlier studies? This study investigates nocturnal oviposition activity, in both rural and urban environs, in the summers of 2004, 2005, 2006 and 2007 in southwest Ohio. The 2004 field season was a pilot season that tested several parameters in the methods established for later seasons, and is discussed below for that reason. Data from the 2005 field season appeared to indicate a high frequency of nocturnal ovipositing activity, but I discovered considerable post-exposure contamination of my nocturnal samples. Several potential sources of contamination were then eliminated in the 2006 and 2007 field seasons. The results and methodological problems encountered in the 2005 season are discussed below, along with how these problems impacted subsequent seasons, and the broader implications for forensic entomology.

METHODS This project was carried out over four field seasons. During this time, several problems were identified and required modification to overcome. Because of this, methods were refined over the seasons, and as interest in utilizing more and more sites grew, new sites were added to the investigation.

Sites I carried out nocturnal ovipositing studies from late July through September of 2004, 2005, 2006 and 2007 at five sites in and around the metropolitan area of Cincinnati, Ohio (Figure 3.2). Site one was an urban location, utilizing the front and back yard of a house in the densely populated Green township

65 in the western portion of Cincinnati. Lot size in this area is about 0.15-acres, with single or multi-family buildings on most lots in the surrounding area. The front yard contained a streetlight that operated constantly throughout the study, while the back yard contained a motion-activated light that was otherwise unlit. Site one was used in 2005, but was not used in either 2006 or 2007. In 2006, this site was replaced by site two due to a desire to visit multiple sites in the same night. By 2007, site one had changed ownership and so another site within the vicinity of this location (site four) was used instead. Site two was another urban location, situated in Mount Washington township on the east side of the metropolitan area of Cincinnati. This location followed the same site general layout as site one, except that average lot size was just slightly larger at 0.2 acres. Site two was used in the 2006 and 2007 seasons.

Site three was a more rural location, in the less populated portion of Batavia township, east of the Cincinnati metropolitan area. The house sat on a 7-acre lot that adjoined the ~6900-acre East Fork State Park. Lots in this area averaged several (>3) acres in size, each containing only a single-family home, and were largely zoned agricultural for the farming of hay and keeping of horses. The front yard of this property was within ten meters of two streetlights while the back yard was unlit. Site three was used in 2004 for the pilot study and in 2005, 2006 and 2007. Site four was located in the west side of the City of Cincinnati, another urban location. Lot size in this area is about 0.08 acres, with single-family buildings in the surrounding area. The front yard of this property was directly under a streetlight while the back yard was lit only by a motion-activated light. Site four was used in the 2007 season only. Site five was a final urban location, located in the north central side of the City of Cincinnati. Lot size in this area is about 0.06 acres, with single-family buildings in the surrounding area. The front and east side yard of this property was within ten meters of a streetlight. The west side yard was in darkness due to the close proximity of an adjacent house (<5 meters between the two houses) that shaded the west yard from streetlights further down the street. Rats were placed in the west yard (unlit) and the east yard (lit). Site five was used in the 2007 season only.

Rat exposure protocol In 2004 I tested several components of the overall final exposure protocol. These are discussed in detail below. In 2005 I upscaled the project and tried out the full protocol described later. The initial 2005 protocol exposed several flaws in the project design (discussed below). Because of this, several protocol changes were implemented between the 2005 and 2006 field seasons.

66 2004 season protocol

The 2004 pilot season explored the use of a net bait trap to investigate adult fly visitation/ oviposition differences at baited sites. The idea behind this line of reasoning was that comparisons between adults caught in the nets versus maggots raised from the bait would possibly reveal if there were differences between visiting and ovipositing fauna. To date, no studies have been performed that explicitly investigate this issue, but it is central to several “assumed” fly behaviors that forensic entomologists employ (Catts1990). The original plan was to measure both visitation/oviposition and nocturnal/diurnal activity research at the same time. The majority the 2004 season was devoted to the design and production of the net bait traps.

These net bait traps perform very well at catching adult flies during the day, sometimes capturing upwards of 500 flies at a time in a three to five hour period of time during the day (Figure 3.3). In the 2005 season no flies were caught at all in the net traps at night, despite the appearance that ovipositing was occurring (for specifics, see discussion below). Due to this, the use of net bait traps was abandoned in the 2006 and 2007 season since we were concerned that the traps might be obfuscating the bait from the flies. It is likely that the lack of visitation data in 2004 was a direct result of the lack of attraction to oviposit in the nocturnal trials. Whether or not the same assemblage of carrion flies visit a carcass as oviposit upon that same carcass still warrants further investigation. A diurnal study of this activity should be conducted in the future, since it is still a relevant issue that informs a very basic assumption of PMI estimation: that flies are attracted to and oviposit on a corpse within minutes of death (Catts 1990). At the time, I felt the study fell outside the parameters for this project as it was reorganized in the 2006 season and thus investigations of visiting/ovipositing were abandoned. Results from this season will not be discussed further. 2005 season protocol

Adult, frozen lab rats (Rattus norvegicus), sealed in batches in Ziploc® plastic bags (S.C. Johnson and Sons, Inc., Racine, WI), were obtained from the University of Cincinnati Genome Research Institute subsequent to their euthanization. The rats were thawed long enough to be separated, then they were refrozen, individually, in ZipLoc® bags. To prepare each rat for use in the experiment, rats were double bagged in plastic garbage bags for 48 hours, allowing them to achieve ambient temperatures. Rats were visually inspected for maggots before being used as bait. Twelve rats (15%) were discarded in 2005 due to pre-exposure contamination (discussed below).

67 For the 2005 season, total sample size was 65 rats. On the first night of observations, 13 rats were placed out at the rural site (Site Three), at evenly spaced intervals in a one-acre field. Thereafter, four rats were placed at a site each night it was visited. Carcasses were placed at five-meter intervals along a line. The rural site (Site Three) was visited six more times and the urban site (Site One) was visited seven times using the four-rat, five-meter plan, for a total of 14 sampling nights. On a given night, rats would be placed at only the rural site or only the urban site, but rats were never placed at both sites on the same evening. All carcasses were placed in unlit conditions in the 2005 field season. Bait was placed in round Gladware® plastic containers (approximately twenty centimeters in diameter and ten centimeters high) with a sand substrate one hour after sunset and collected one hour prior to sunrise. Bait was covered with a one-mesh/cm metal exclosure screen that was attached to the ground with common garden staples to prevent large animal predation. Environmental conditions recorded for each site at the time of deposition and collection included temperature, wind speed, light index (LUX), humidity and general weather observations (rain, cloud cover, etc.). Temperature, wind speed, light index and humidity were all collected with a Lutron LM-8000 environmental anemometer (Lutron Electronic Enterprise CO., LTD., 4F, 106, Min Chuan West Road, 103 Taipei, Taiwan). Upon collection, the bait containers were fitted with a ventilated lid. This allowed for ventilation during the maggot maturation phase, but prevented direct access by adults to the bait. Bait containers were then taken to a storage tent for an incubation period of up to one month to allow for observations of any maggot activity. The bait storage tent was intended to give maggots an isolated environment in which to mature, while allowing maximum ventilation of the bait containers and tracking of ambient temperature. This tent was modified in several ways to make it less accessible by insects. First, the netting was sewn to the support fabric using heavy thread. A tarp flooring was sewn to the netting and tent hems to create a barrier to ground access by insects. Second, plastic shelving was placed inside the tent to allow for placement of several dozen baited containers. The tent was placed on a rural site (Site Two), over 1000 feet from the locations used for the nocturnal exposure. Following exposure, rats were allowed to mature in the bait storage tent for about a month before being checked for maggot load. This period of time was chosen because when nocturnal ovipositing has been observed, total maggot load has been usually very low: approximately 120 total maggots from three different species in one ovipositing exposure (Baldridge et al. 2006), compared to the daytime typical

68 ~180 eggs per individual ovipositing female (Erzinclioglu 1996). The maturation time of one month largely followed Greenberg’s (1990) protocol. The 2005 protocol also included diurnal sampling. This was to allow us to determine if all, or only some, of the species active during the day were ovipositing at night. Diurnal rats were laid out at the same sitesaccording to the same protocols as the nocturnal samples, except they were only exposed to the environment for a four-hour period of time. This exposure generally occurred in the morning hours, but not always. Time of exposure was ultimately not relevant, because within a four-hour window of oviposition, the rat carcasses were always over-blown, producing far more larval flies than they could possibly sustain. These positive controls caused several subsequent problems, due to the exceptionally large maggot masses the rats encumbered. The problems caused by the positive control samples would have been discovered sooner had we included negative controls in the 2005 experimental design. In this context a negative control would be a rat that was thawed according to the protocol, but placed directly into the maturation container in the sample-storage tent, without being exposed outside. Details on these problems are discussed below. 2006 season protocol

For 2006, the problem of pre-exposure contamination (see below) was addressed by thawing the rats inside sealed plastic containers (18-liter cat litter buckets) in addition to using garbage bags. No pre-bait infestation was observed in 2006 (or in 2007). The sampling protocol was also modified as follows: two rats were placed at each location (Site Two and Site Three) all on the same night, with one rat in lit and one rat in unlit conditions at each location. Lit conditions always consisted of continual ambient light from streetlights. For the 2006 season, total sample size was 48 rats (12 nights, two sites, two rats per site), with both sites being visited on the same night. Bait was exposed in the same way and environmental conditions recorded for each site at the time of deposition and collection in the same manner as that employed in the 2005 season. Upon collection, the bait containers were fitted with a ventilated lid in a manner consistent with the previous season. To solve problems involving post-exposure contamination (described below), these containers were placed inside bags made of Delnet PQ218 material, described below (DelStar Technologies Inc. www.delstarinc.com; Figure 3.4). To further minimize any possible contamination, the bait tent used in 2005 was replaced with a bait storage shed in the 2006 season. This bait storage shed (WR Engineering, http://www.wrengineering. com.au/sheds_durabuilt.php, Figure 3.5) was built of wood framing and plywood materials and was

69 equipped with a 0.6 x 0.9 meter window on each side of the building and a full-sealing standard metal external house door. Windows were covered with both the standard full insect screens that came as part of the window unit and an additional layer of “no-thrip” screening (Bioquip, www.bioquip.com, product number 7261B, 81 mesh/cm). These additional screens completely covered the windows (attaching directly to the exterior molding) on the outside of the shed, preventing access to the windows by insects. Windows were left open for ventilation during the experiment period. Any small openings found in the shed’s exterior were sealed with expanding foam sealer. Exposed rats were allowed to mature in the bait storage shed for a minimum of one week before being checked for maggot load. This was a reduction in the one-month maturation time in the 2005 experiment that followed Greenberg’s (1990) protocol. However, my observations of maggot maturation rates among the contaminated nocturnal samples in 2005 indicated that growth of maggots was nearly exponential with ambient temperatures so high and several maggot behaviors (denuding of the carcass and extensive wandering trails left on container walls) meant that, if present, maggots were clearly visible within one week’s time. Positive and negative control experiments were conducted prior to the beginning of the field season, to check for contamination between samples and from outside sources. Negative controls tested if the new maturation shed prevented entry of adult carrion flies and if the extra bagging was sufficient to prevent maggots from migrating from the heavily infested positive controls. Positive controls (rats exposed to the environment for four hours during the day) were added to the shed later the same day as werethe negative controls. During the positive control test, the existing negative control rats were monitored for any sign that maggots had successfully migrated from one carcass to another within the shed. Negative controls were continued throughout the 2006 field season, with one negative control rat being placed amongst the baited samples each night that sampling took place. These negative controls were then removed at the end of the one-week maturation period, along with all baited samples that showed no signs of maggot activity. 2007 season protocol

The 2006 field protocol was continued into 2007 but expanded to include two additional sites, bringing the site total to four. Sites two and three were visited by one researcher, while an assistant visited sites four and five at the same time. Once rats were collected, both researchers met at a predetermined location and then all rats were taken to the same storage shed as used in 2006. Rats from each site were

70 stored together during transport back to the storage shed, but kept separate from rats from other sites. Negative and positive controls were carried out as in 2006.

RESULTS AND PROTOCOL DISCUSSION I divide my discussion into two categories: methodological problems encountered and broader forensic implications. I provide the raw results by season in the methodological problems section, then summarize the final results and launch into a discussion of the broader forensic implications of our own results in light of previous studies.

Methodological problems encountered 2005 Season

The overall sample size in 2005 was 65 rats with a range of temperature (13-25°C) and relative humidity (59-93%). Light level ranges for unlit conditions were 0-30 LUX. I encountered several problems during the 2005 field season that lead me to ultimately discard all 2005 data. The first of these problems was pre-exposure contamination of bait. The use of frozen carrion sources is an established method, being used in over half the nocturnal ovipositing literature (see Table 3.1), but it involves some risk that flies will gain access to the bait during thawing, before the experiment begins. The double-bag method used in 2005 was largely based on descriptions of previous workers (Singh & Bharti 2001), but in our case resulted in the contamination of twelve rats. This did not affect conclusions regarding nocturnal ovipositing behavior, but it did reduce the available sample size from 77 to 65 rats. Using a similar approach, Baldridge et al. (2006) also noted contamination via pinpricks made in ZipLoc bags. In 2006, individually frozen rats were placed in hard plastic cat litter containers with tight sealing lids, then allowed to thaw for 48 hours. This modification prevented any contamination of rats pre-placement in the 2006 season. The second and more serious problem encountered in 2005 was post-exposure contamination. My initial assessment was that 12% (8/65) of the nocturnal samples were positive for flies when they were checked after one month of maturation. However, I directly observed two sources of contamination that call all of those putatively positive results into question. In what I term primary contamination, some adult flies gained entry into the bait maturation tent and laid eggs directly on the surface of the mesh covering the nocturnal sample rats. Cases of likely primary contamination could be identified by the presence of remnant eggs on the mesh (Figure 3.6). Presumably, the eggs hatched on the mesh and the first instar larvae were able to pass through the mesh and drop onto the rat below. 71 I also experienced secondary post-exposure contamination. This was caused by migration of larvae from the heavily infested positive control (diurnal) rats to some of the nocturnal samples. In some cases, large numbers of maggots were able to escape the containers housing the diurnal control rats and migrate across the shelving. Some of the escaped maggots were, apparently, able to enter the storage container of a nocturnal rat through the ventilation holes. Cases of likely secondary contamination could be identified by trails of maggots leading from a diurnal control rat’s container to an infested nocturnal rat’s container. Of the eight of the putatively positive nocturnal rats in 2005, every one showed evidence of one of those two forms of contamination. A final problem with the 2005 field season was caused by the remnants of Hurricane Katrina.

Several days of wind and heavy rains collapsed the bait maturation tent, causing all remaining nocturnal bait to become potentially contaminated towards the end of the one month maturation time. Thus, I decided to abandon all 2005 data and run the experiment again in 2006, in modified form. 2006 season

Total sample size for 2006 was 48 rats (12 nights x two rats per site x two sites per night). No maggots were observed in 2006 on any nocturnal bait, even though the experiments were conducted through a period of temperate conditions conducive to diurnal carrion fly activity. Temperature ranged from 13-25°C, relative humidity varied from 67-97% with some trace rain on a few nights, and light levels were 52-100 LUX for lit areas and 0-34 LUX for unlit areas. There were no observed differences between urban and rural sites for any of the above conditions. I did not run side-by-side diurnal experiments in 2006 as it was deemed a contamination risk. Several techniques have been employed for post-exposure storage in previous studies. Some authors have provided no “aging” time to their experiments, simply examining the carcass immediately post-exposure (Amendt et al. 2008). However, the majority of researchers do choose to age their bait and review it for maggot infestation at a later time. Spencer (2002) simply stored exposed carrion in sealed plastic bags pricked with ventilation holes in her garden shed. Greenberg (1990) placed his bait in ZipLoc bags, exposed it for several hours, then sealed the bags and made ventilation pinpricks. These plastic bags were then placed inside empty fly-rearing cages and allowed to age for up to several months. Aging bait certainly has advantages: if maggots are present, they are likely to appear in reduced loads due to the reduced number of females thought to be ovipositing at night (Greenberg & Kunich 2002). Thus, by aging the bait, allowances are made for the chance a researcher might not notice a reduced number of

72 eggs (or, in the case of Sarcophagidae, first instar larvae). In contrast, immediate review has the advantage of greatly decreasing the possibility of post-exposure contamination. The majority of the contamination encountered in the 2005 field season was a result of an attempt to allow bait to age post-exposure; the longer a bait ages, the more opportunity it has to become contaminated. In 2006, I desired the advantages of aging bait (increased chance of finding small eggs/larvae in a complicated carcass) without the associated problems (increased probability of contamination). My solution was to replace the tent with the much more secure shed and to put into place bagging of the ventilated storage containers. 2007 season

Total sample size for 2007 was 80 rats (ten nights x two rats per site x four sites per night). To check for post-exposure contamination, a negative control rat was placed inside the maturation shed each night of sampling, then removed when that night’s samples were removed from the shed at the end of their maturation period. On two occasions, negative controls turned up with contamination but no flies were reared from the baited samples from those nights, so I concluded that the baited samples had not themselves been contaminated. On three occasions, baited samples showed maggot activity that was attributed to contamination as well. These contaminated data points were removed from the analysis, reducing the 2007 field season sample size down to 77. No maggots were observed in 2007 on any nocturnal bait, even though the experiments were conducted through a period of temperate conditions conducive to diurnal carrion fly activity. Temperature ranged from 17.2-31.6°C with the relative humidity ranging from 20-90%. Light level ranges for unlit conditions were 0-10 LUX and lit conditions were 12-25 LUX. Combining 2006-2007 field season results

I see no real differences between seasons for ranges of temperature, relative humidity or light levels between any of the years in which such data were recorded (2005-2007). With the exclusion of the 2005 data due to contamination, I combined the 2006-07 field seasons into one sample set, resulting in a total sample size for this experiment of 125, with 48 samples taken in the 2006 season and 77 samples taken in the 2007 season. Pooling environmental observations from the 2006 and 2007 seasons provides us with environmental ranges of temperature (13-31.6°C) and relative humidity (20-97%). Light level ranges for unlit conditions were 0-34 LUX and lit conditions were 12-100 LUX. In all of these samplings, no credible nocturnal ovipositing activity was observed.

73 BROADER FORENSIC IMPLICATIONS My findings contradict previous studies (Greenberg 1990, Singh & Bharti 2001) that reported nocturnal ovipositing activity. Instead, my data corroborate the findings of Tessmer et al. (1995) and Byrd (1996) who reported no nocturnal ovipositing behavior. This leads me to the question: Why would flies apparently oviposit at night some times, and not at other times? The spotty pattern of positive results has led to the common belief that nocturnal ovipositing activity is likely a rare event. Yet both Greenberg (1990) and Singh & Bharti (2001) report nocturnal ovipositing in 33% of their trails, which does not seem “rare”. Several studies reporting no evidence of nocturnal activity have small sample sizes (see Table 3.1), so it is possible that they simply missed activity that would have been apparent in a larger study. On the other hand, Amendt et al. (2008) also reported no nocturnal activity, despite a sample size above 50, and I see no evidence for nocturnal activity in the present study despite a sample size of 125. If the true rate of nocturnal ovipositing activity is 33%, then Amendt et al. (2008) should have observed about 15 positive results while the current study should have seen about 40 positive results. This did not happen. Are there environmental differences between studies?

The various experimenters report roughly the same temperature ranges (see Table 3.1) and these ranges are in-line with our results as well. When relative humidity and light index are reported, they fall within approximately the same levels and are also consistent with our observations. In particular, the studies that do report nocturnal ovipositing do not stand out in any way from those studies that fail to find nocturnal ovipositing, in regards to recorded environmental conditions. I see no reason why the proximal conditions (temperature, humidity, etc.) measured in these experiments provide any indication of possible environmental differences between the studies that might influence fly behavior. Could bait type/ condition be a factor in behavioral differences?

A large variety of bait types in various states have been used by the various researchers of this problem over the years (see Table 3.1). Some researchers used ground animal parts such as ground beef (Greenberg 1990, Baldridge et al. 2006) while others preferred whole organisms (Byrd 1998, Tessmer et al. 1995, Baldridge et al. 2006). Some researchers chose fresh bait (Baldridge et al. 2006, Tessmer et al. 1995, Greenberg 1990, Amendt et al. 2008) while some selected frozen bait (Singh & Bharti 2001and 2008, Baldridge et al. 2006, Spencer 2002; this study). There is no apparent difference in nocturnal

74 ovipositing behavior when considering the condition, the size, or the piece versus whole animal status of the bait. For my study, it was most convenient to use rats euthanized by a local research center as this presented a steady supply of carrion. It is possible, even likely, that carrion flies are differentially attracted to different types of carrion bait, whether never or previously frozen (then thawed), or whole organism versus processed parts. This is a possible area of future study, but fell outside the realm of my experiments. Locality: a possible urban vs. rural dichotomy?

Locality, as defined by either “urban” or “rural” may be important in nocturnal ovipositing. The two most credible reports of nocturnal ovipositing, Greenberg (1990) and Singh & Bharti (2001), recorded the behavior in “urban” locales—Chicago, Illinois and Patiala City, India. These positive reports support Greenberg & Kunich’s (2002) supposition that nocturnal activity is more likely in urban than rural environments. The sole other positive nocturnal ovipositing report, Baldridge et al. (2006), reports one incidence of nocturnal ovipositing in a “rural” site. However, Baldridge et al. concluded that this positive result might have extenuating circumstances: the pig used was not fresh, but was in the “bloat” stage of decomposition, and the strike occurred within a half to one hour of sunset (although neither the exact time or light level was reported). A bloated carcass will produce a different profile of chemical odors (Vass 1991). These odor chemicals are thought to be the key chemicals flies use to locate carrion (Erzinclioglu 1996), so a “bloated” carcass might be more attractive than a fresh one. In my 2006 season, a few nocturnal placements were delayed by several days due to poor weather. This resulted in bloated rats (usually 72-96 hours aged) being placed outside. However, none of the bloated rats showed any sign of nocturnal fly activity. Because the research with positive nocturnal ovipositing hits both occurred in urban environments, I decided to make sure that in all seasons we used sites that were urban (sites 1, 2, 4 and 5) and a reference site that was rural (site 3) in order to test whether there was an urban/rural dichotomy at play for this behavior. While several previous nocturnal ovipositing studies have used the terms “urban” and “rural”, none have actually defined what those terms might mean in any way, referring to them in a more general sense. Furthermore, there is no consistency to the definition of the terms “rural” and “urban” in any current field of study (Walker & Willig 1999, McIntyre & Hobbs 1999, McIntyre et al. 2000, McDonnell & Pickett 1990) and so we chose a definition based upon lot size. Thus, my “urban” locations all featured dwellings on small lots (usually less than a quarter acre) and were located in neighborhoods of

75 similar lot size structure while our “rural” location featured a large lot size (several acres) with similar lot sizes surrounding it. These definitions allowed us to easily find sites across the metropolitan area and (had we seen differences to explore further) would have allowed us to look for sites within the metropolitan area that were decidedly less “urban” than the row-house areas we did settle on for sites. Perhaps more importantly, I felt that smaller lots sizes per dwelling were a good indication of garbage density in a given region, and thus might be an indicator of background fly density. This line of reasoning follows that of Greenberg (1990) and the history of fly densities in cities in relation to garbage density (Melosi 2005). However, I found no evidence that flies preferentially oviposit in urban over rural locations at night.

CONCLUSIONS Nocturnal carrion fly ovipositing behavior appears to be unlikely in the Cincinnati area of Ohio. Potentially positive results in the 2005 pilot run were clearly due to methodological problems. Several major factors can contribute to contamination of nocturnal ovipositing experiments before, during and after exposure and these issues need to be carefully examined and constantly watched for by future researchers. Important questions remain regarding the possibility and prevalence of nocturnal ovipositing behavior, and further research is needed. Future work should report certain minimum information: time of bait deposition, temperature, humidity, light levels, and bait choices and the decomposition stage of bait. Increased sample sizes are critical to elucidate if this behavior does occur. The nature of sites needs to be more completely discussed, and sites should be defined as urban or rural (although these terms need definition themselves to be useful). Finally, concurrent diurnal studies are helpful for identifying which species are generally present at the time of the nocturnal studies, but should only be carried out if they can be prevented from contaminating nocturnal samples.

76 Noct. Species Diur. Species Temp Citation Oviposit Oviposit (C) RH% Lux U/R Bait n O Singh & Bharti Calliphora vicina Not reported (NR) 16-27 75-85 0.6-0.8 U Mutton* 14 5 2001 Chrysomya megacephala Chyrsomya rufifacies Greenberg 1990 Calliphora vicina Not in study design 17-24 40-100 0.2-0.7 U Rat & 21 6 Phormia regina beef Phaenicia sericata Byrd 1998 None Chrysomya rufifacies 16-21 NR NR R Pig 4 0 Chrysomya megacephala Calliphora vicina Phormia regina Phaenicia sericata Sarcophaga africa Phormia cuprina Phormia coeruliviridis Cochliomya macellaria Tessmer None Cochliomya macellaria 23-36 NR varied B chicken 2 0 et al. 1995 Phaenicia sericata Sarcophaga bullata Baldridge et al. Phaenicia coeruleiviridis Phaenicia coeruliviridis 19-36 33-94 NR B fresh Rat, Varied 1 2006 Musca domestica Cochliomyia macellaria Beef, Cochliomyia macellaria Sarcophaga bullata Pig* Phaenicia cuprina Cynomyopsis cadaverina Musca domestica Amendt et al. 2008 None Lucilia sericata 13-24 NR NR U Hedgehog, Varied 0 Lucilia ceaser Beef liver Calliphora vicina Calliphora vicina Protophormia terraenovae Spencer 2002 None NR 20-22 NR NR R Pig* 9 0 Singh & Bharti Sarcophaga albiceps NR 21-28 70-78 0.7-0.8 Mutton 10 2 2008 Sarcophaga hirtipes

Table 3.1:Abridgement of data from previous nocturnal oviposition studies. Abridgement of data from the previous nocturnal oviposition studies. Noct. Species Oviposit = species reported to oviposit on bait during nocturnal periods. Diur. Species Oviposit = species reported to oviposit during diurnal periods. Temp = tem- perature range encountered on site during the observations. RH(%) = relative humidity observed on site during the observations. Lux = amount of light recorded on site during the observations. U/R = whether the site was consid- ered urban or rural. U = urban, R = rural and B = both urban and rural sites existed in the study. This designation is extracted from the site descriptions in most instances. Bait = type of carrion bait used. Those bait with an “*” next to them were reported as frozen, then thawed for the experiment. “N” = number of replicate observations made. O = number of instances for which nocturnal ovipositing was reported in a natural setting.

77 � � � � � � �� �� �� X X X � � � ����� ����� ����� �� �� �� Z � � � � � � � � � � � � Y �� �� �� � � � ����� ����� ����� �� �� �� � � � � � �

night-time estimate is not legitimate and the time either needs to be moved to a time before that night (Y), or after that night (Z). night-time estimate is not legitimate and the time either needs to

The impact of nocturnal ovipositing upon PMI estimations. Where: M= midnight, T= twilight, DA= dawn, N= noon, and DU=dusk. In T= twilight, DA= dawn, N= noon, and DU=dusk. Where: M= midnight, PMI estimations. The impact of nocturnal ovipositing upon 3.1. Figure infestation during that daytime. In examples B and C, a body (X) A, a body (X) is found and the larval estimation indicates a time of first example between examples B and C is that in The difference during the previous night. infestation is found and the larval estimation indicates a time of first so the in example C, nocturnal ovipositing does not occur, so the night-time estimate is legitimate, but example B, nocturnal ovipositing does occur, C

B A 78 1 4 5

2 3

Figure 3.2: Site locations for the nocturnal oviposition study. Numbers next to red dots indi- cate the approximate locations of the studysites 1-5.

79 Figure 3.3: Carrion net traps were effective at catching carrion flies if present. In many cases (as seen above) upwards of 500 flies cold be caught in a few hours. The example shown is located at the base of Rabun Bald in Georgia during a 2006 collecting trip.

80 Figure 3.4: This picture illustrates the placement of bait into a bait container fitted with a ven- tilated lid and then placed inside a delnet bag. This provided protection from contamination between sites while allowing for ventilation of the bait itself.

81 Figure 3.5: The carrion bait shed used for 2006 and 2007 seasons. Note the double screening visible on the windows and the expanded foam sealing cracks under the roof eves. This design features four windows for constant cross-ventilation to keep the structure cooled and as open to the outside air as possible. However, this design also kept flies out of the structure and did not allow rain to penetrate the windows.

82 Figure 3.6: SEM photomicrograph of empty egg casings left on the underside of the screen lid to a rat cage. This occurred when a fly made it intot he shed enclosure and oviposited through the mesh screen, laying it’s eggs on the underside of the screen where they could then easily drop onto the rat carcass below.

83 “Species determination is the only genotyping application now routinely used by forensic entomologists. Accurate identification of an insect specimen is usually a crucial first step in a forensic entomological analysis. Closely related carrion species can substantially differ in growth rate, diapause response, or ecological habits. Species-diagnostic anatomical characters are not known for the immature stages of many forensically important insects, and an existing key may be incomplete or difficult for nonspecialists to use.” —Wells & Stevens, 2008

Conclusion: Placing Sarcophaginae relationships and nocturnal oviposition behavior in the forensic context I began this inquiry with an overview of a fundamental problem in forensic science: when did a victim die? In order to understand how I might use biological evidence to answer this question, I went over the death process, and highlighted the major insects that play a part in that process: Diptera (flies). Flies represent a tool that can be used to infer the postmortem interval (PMI) and help answer the “when” of death, because of how rapidly adult flies respond to carrion and lay their larvae directly on the corpse for their maggots to consume. Thus, these flies represent little clocks that can be used to investigate the important “when” question, if researchers know how to read the “clock”. In chapters two and three, I explored the relationships of the genera and species within the Sarcophaginae (fleshflies) as well as whether carrion flies are likely to oviposit on carrion at night. The reason for these investigations has to do with the current model (chapter one, figure 1) developed by entomologist Jason Byrd (1998) to use carrion fly larvae to determine the PMI in humans. This model includes behavioral and species inputs to infer how long it took a fly larva to reach a certain age in days usually based on weight or length of the larva at time of collection. Both species identity and nocturnal ovipositing behavior are crucial inputs into this model, but scientists do not understand either fleshfly species identity or carrion fly nocturnal behavior well enough at this time to make accurate estimations using fleshflies. So, without more work, investigators cannot correctly read the larval “clocks” to determine that all-essential PMI.

As presented in chapter two, the work described here on Sarcophaginae phylogenetics takes the understanding of the relationships in this group a step forward. Many of the general relationships

84 presented by the morphological literature hold up under this molecular scrutiny and this is a tribute to the hard work of the many taxonomists who have spent countless hours of work describing the critical structures of these flies, most notably the male genitalia. Specifically, the genera appear valid as does the monophyly of the group as a whole. Few sub-generic relationships held much support and in order to increase the understanding of these relationships, more work must be done. Several of the genera included here only have a few more species counterparts in North America (e.g. Boettcheria) and it would be useful to add these taxa so that there is a comprehensive representation of the specimens for a given genus in North America. The species in this research represent nine sarcophagid species that are reported as forensically important (as reported in Table 2.1: Argoravinia rufiventris, Blaesoxipha plinthopyga, Peckia chrysostoma, Sarcophaga africa, Sarcophaga triplasia, Sarcophaga bullata, Sarcophaga crassipalpis,

Sarcophaga carnaria and Sarcophaga utilis) and sixteen that are not. This provides the structure for quick identification of unknown, forensically-relevant, specimens if needed, much like the work of other molecular biologists who use phylogenetic tools to infer relationships in non-human species (Wells et al. 2001, Zehner et al. 2004, Hsieh 2001, Dizon 2001, Baker et al. 1996, Baker & Palumbi 1994, Ross & Murugan 2006). However, more work is still needed. More taxa and additional sites will yield further insights into not only Sarcophaginae systematics and history, but how to develop this group as a tool for forensic use. To this end, multiple specimens (especially of the nine species listed above) would be a nice addition to the work completed thus far. In many instances this was not possible for this project, especially since many specimens used in this investigation were donated from parts of the world I did not have the time or money to collect in personally (note the specimens in Table 2.1, marked with an asterisk, all donated by Dr. Thomas Pape). As presented in chapter three, species behavior is perhaps the second most important suite of facts necessary for a PMI estimate, following species identity. Because of this, I focused part of my research on nocturnal ovipositing. The work presented here on nocturnal ovipositing goes a long way towards understanding this behavior. As the largest single study to date, with the most sites in a single region, it now seems unlikely that nocturnal ovipositing occurs in any appreciable manner in the greater Cincinnati region. This work has implications for the PMI model suggested by Byrd (1998), allowing the “window” of possible ovipositing time to pass over darkness instead of having to consider it as a likely time for when the fly larvae were layed on the body. The problems with contamination, especially the propensity

85 for females to oviposit through small pinpricks or netting to allow their offspring access to a corpse the adults themselves cannot reach, possibly call into question the methods of those researchers who have relied upon adult exclusion systems to keep their exposed carrion contamination free. While this work supports the view that nocturnal ovipositing does not occur, more work is needed to verify this in other locations, especially in areas further north and south of Cincinnati. Finally, it is clear that to have any grasp on the rarity of his activity, large sample sizes are critical to this type of research. The work presented in this dissertation does not fill all of the information gaps necessary to maximize the potential of Byrd’s (1998) PMI model. More work is needed in the maturation rate for many species, since growth curves are the other major component to using the model. However, my research does provide support for the model in the following two ways. First, unknown Sarcophagidae larvae can now be identified if not to species, then likely to genera for most of the forensically important sarcophagid groups in North America. Second, it seems very likely that the act of nocturnal ovipositing does not need to be included in the model, thus allowing an entomologist to jump this window of time to either before dusk or after dawn if fly maturation rates indicate a night-time ovipositing event.

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