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Entomology

EFFECT OF WEATHER VARIABLES ON DECOMPOSITION AND TIME OF DEATH

ESTIMATION IN TWO HABITATS IN CENTRAL PENNSYLVANIA

A Thesis in

Entomology

Patricia L. Hunt

2010 Patricia L. Hunt

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

August 2010

The thesis of Patricia L. Hunt was reviewed and approved* by the following:

Ke Chung Kim Professor of Entomology Thesis Advisor

Dennis Calvin Professor of Entomology

James Marden Professor of Biology

Gary Felton Professor of Entomology Head of the Department of Entomology

*Signatures are on file in the Graduate School

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ABSTRACT

The goal of this thesis was to look at the application of forensic entomology in Central

Pennsylvania by looking at the meteorological factors involved in post-mortem interval estimation (PMI) during both the summer and winter months. Additionally, the applicability of site-specific weather modeling was explored to see how accuracy in PMI estimation can be improved by technology. It was found that site-specific weather modeling does improve the accuracy of PMI estimations over temperatures recorded at the nearest government weather station. Along with this finding, it became evident that ambient temperature is not the only consideration in insect development associated with decomposition. Soil and maggot mass temperature are also important factors in obtaining greater accuracy in PMI estimations as well as wintertime survival of larvae. During the winter months, it was found that there are differences in decomposition pattern and insect colonization patterns when comparing both wintertime environment (open field vs. deciduous forest) and time of year (winter vs. summer).

TABLE OF CONTENTS

LIST OF FIGURES ...... vi

LIST OF TABLES ...... ix

ACKNOWLEDGEMENTS ...... x

Chapter 1 Introduction ...... 1

Chapter 2 Literature Review: Microclimatology in Forensic Entomology ...... 4

Chapter 3 Materials and Methods ...... 8

3.1 Materials and Field Study Design ...... 8 3.2 Study Sites...... 11

Chapter 4 Exploratory Experiment on Meteorological Factors Associated with Environmental Locations ...... 15

4.1 Ambient Temperature ...... 15 4.2 Maggot Mass Temperature ...... 20 4.3 Soil/Ground Temperature...... 20 4.4 Base Model and Data Analysis: Site-Specific Weather Modeling ...... 21 4.4.1 Data Analysis ...... 22 4.5 Conclusion ...... 25

Chapter 5 Effects of Weather Variables on Decomposition and Insect Development ...... 27

5.1 Impacts of Temperature ...... 28 5.2 Impacts of Light ...... 31 5.3 Soil Temperature and Moisture Dynamics ...... 35 5.4 Conclusion ...... 37

Chapter 6 Comparative Ecology of Arthropod Diversity and Decomposition between Open Grassland and Woodland ...... 39

6.1 Materials and Methods ...... 40 6.1.1 Experimental Layout ...... 40 6.1.2 Experimental Design ...... 40 6.1.3 Insect Collect and Decomposition Observations ...... 41 6.1.4 Weather Data Collection Methods ...... 42 6.2 Decomposition Observations at Each Experimental Site ...... 45 6.3 Colonization Timing, Population Stage Structure, Species Composition and Biodiversity ...... 48 6.3.1 Stage Structure ...... 48

6.3.2 Species Composition and Biodiversity...... 50 6.4 Relationships Between Temperatures In and Around the Decaying Carcass, Insect Life Stage, and Time After Death ...... 52 6.4.1 Ground and Maggot Mass Temperature Related to Decomposition Stage and Insect Population ...... 52 6.4.2 Ambient Air Temperature Related to Decomposition Stage and Insect Population ...... 58 6.5 Conclusion ...... 60

Chapter 7 Meteorological Factors Influencing Winter Larval Survival ...... 62

7.1.Methods and Materials ...... 63 7.1.1 Experimental Sites ...... 63 7.1.2 Experimental Materials and Methodology ...... 65 7.3 Results and Discussion ...... 67

Chapter 8 Summary and Conclusion ...... 81

Literature Cited ...... 83

Appendix General Methodology for Determining PMI ...... 91

A.1 Development Equation and Degree Hour Derivation ...... 91 A.2 Estimating the Window of Uncertainty ...... 95

LIST OF FIGURES

Figure 2-1: Graphical representation of ASOS station locations for the United States (Courtesy of NOAA) ...... 6

Figure 3-1: Example of field set-up for experiments...... 9

Figure 3-2: Diagram of logger set-up around the carcasses...... 10

Figure 3-3: Map of the Penn State campus and surrounding area. Red dot represents Location 1 while the yellow dot represents Location 2. The green box represents the Walker Building, and the blue box represents the location of the University Park Airport (KUNV). (map from campusmaps.psu.edu)...... 12

Figure 3-4: On the right, a photograph of the location taken on May 23, 2005. On the left, a close-up map of the Lot 18 location. The locations of carcasses 1.1 (A), 1.2(B), and 1.3 (C) are also indicated (map from campusmaps.psu.edu)...... 13

Figure 3-5: On the left, a close-up map of the Penn State Living Filter indicating the relative position of carcasses 2.1 (D) and 2.2 (E). On the right, a photograph of the location taken May 23, 2005. Carcass 2.is location on the right side of the pipe, while carcass 2.2 is to the left (map from campusmaps.psu.edu)...... 14

Figure 4-1: Time series of ambient temperature from May 30, 2005 through June 7, 2005 at the exposed experimental site (Site 1.3), a semi-shaded site (Site 1.2), and a shaded site (Site 2.1)...... 17

Figure 4-2: Time series of average hourly ambient temperature from May 30, 2005 through June 7, 2005 at the exposed site (Site 1.3), shaded site (Site 2.1), and the control site of KUNV...... 18

Figure 4-3: Comparison of the ambient temperatures at the control sites and the exposed site (above) and the shaded site (below)...... 19

Figure 4-4: Difference between TA and Tm (TA-Tm) over the course of decomposition at Carcass 1.1 in May/June 2005...... 20

Figure 4-5: Time series of Tm and Tg over the entire decomposition process...... 21

Figure 4-6: Map of Pennsylvania showing relative locations of temperature information. The yellow star represents the experimental location, while the blue stars represent the airport locations...... 23

Figure 5-1: Sources of radiation affecting the decomposition process...... 27

Figure 5-2: Accumulated degree hours calculated at site A using the base development threshold for Phormia regina (Meigen) and the various temperature sources. The

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black bars represent when each life stage was found on the carcass (1 - eggs, 2 - 1st Instar Larvae, 3 - 2nd Instar Larvae, 4 - 3rd Instar Larvae, 5 - Pupa)...... 31

Figure 5-3: Difference in light exposure between exposed and shaded carcasses during Spring 2005...... 32

Figure 5-4: Example of the sun’s path throughout the day...... 34

Figure 5-5: Soil Texture Pyramid developed by the USDA. (http://soils.usda.gov/technical/aids/investigations/texture/) ...... 36

Figure 6-1: (A) Aerial photograph of Cattle Research Facility. The yellow marker indicates the location of the woodland environment while the red marker indicates the location of the open grassland environment. (B) Photograph of the experimental set-up in the open grassland. (C) Photograph of the experimental set-up in the woodland environment...... 44

Figure 6-2: Photographic time series of decomposition for carcasses in the open grassland (top) and the woodland (bottom) environments from September 12-30, 2007...... 47

Figure 6-3: Chart of days in each stage of decomposition for carcasses in the open grassland (top) and woodland (bottom) environment. PMI is displayed as house after death...... 48

Figure 6-4: Population structure of maggots found at the open grassland (top) and woodland (bottom) experimental sites...... 49

Figure 6-5: Time series of soils temperatures with indicated insect larval stage of P. regina in the woodland (top) and grassland (bottom) environments. Stages of decomposition for each carcass is also denoted...... 54

Figure 6-6: Time series of maggot mass temperatures with indicated insect larval stage of P. regina in the woodland (top) and grassland (bottom) environments. Stages of decomposition for each carcass is also denoted...... 55

Figure 6-7: Time series of ambient temperatures with indicated insect larval stage for P. regina in the woodland (top) and grassland (bottom) environments. Stages of decomposition are also noted...... 59

Figure 7-1: Map of Cattle Ranch Facility. The pink dot represents the woodland environment, while the green dot represents the open grassland.(Map from www.campusmaps.psu.edu) ...... 63

Figure 7-2: Photographs of experimental locations: woodland (top) and open grassland (bottom). Photographs were taken October 27, 2005. Replace this with figure caption below figure...... 64

Figure 7-3: Illustration of carcass set-up for experimental sites during Winter 2005-2006. .. 66

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Figure 7-4: Time series of average daily ambient temperature between the woodland and open grassland experimental sites...... 69

Figure 7-5: Scatterplots of the comparison between ambient temperatures recorded at the experimental sites woodland (top) and open grassland (bottom) with the control site, KUNV...... 71

Figure 7-6: Time series of 2” soil temperature, maggot mass temperature, and ambient temperature...... 72

Figure 7-7: Snowcovered carcass in open grassland. Photo taken December 7, 2005. Snowfall on the ground measured 1.4 inches...... 72

Figure 7-8: Comparison of Tg (top) and Tm (bottom) and snow cover in the open grassland...... 73

Figure 7-9: Time series of Tg and Tm while carcass was snow covered from December 8, 2005 through December 28, 2005...... 74

Figure 7-10: Comparison of thermal gradients between maggot masses and the soil or air respectively in the open grassland (top) and wooded (bottom) experiments. Positive vales indicate times were Tm was greater than either Tg or Ta; therefore, the thermal gradient was moving outward from the carcass...... 75

Figure 7-11: Pattern of “normal” insect feeding during the decomposition process (Photo taken during Spring 2005 experiment)...... 76

Figure 7-12: Photo of head of carcass taken one week after field placement in winter (top) and spring (bottom)...... 77

Figure 7-13: Photograph taken after three weeks in the field. Much of the interior of the mouth has been consumed; however, all of the skin on the outside of the snout remains...... 78

Figure 7-14: Photographs of the top of carcass (top) and under carcass (bottom) in the open grassland taken in early spring after winter decomposition. Photograph was taken on April 4, 2006 (159 days after death)...... 79

Figure A-1: Example of how insect development information is presented in literature. (From Byrd & Allen, 2001) ...... 91

Figure A-2. Example of linear regression for the 1st instar of Phormia regina (Meigen)...... 92

LIST OF TABLES

Table 4-1: Average ambient temperatures (°C) and standard deviations (σ) for the week of May 30-June 7, 2005...... 16

Table 4-2: Population structure of entomological “evidence” collected on 2005 May 26...... 23

Table 4-3: Regression models for temperatures recorded by the nearest weather station and site-specific weather model compared to on-site temperature logger...... 24

Table 4-4: PMI estimations and Time of Death using P. regina and the temperature information from the weather stations...... 24

Table 5-1: PMI estimations of L. sericata at Site A (Spring 2005). Calculations were based upon development data from Anderson (2000). No actual specimens were used in this calculation...... 28

Table 5-2: Correction values to ground radiation to account for slope...... 34

Table 6-1: The effect of carcass location and days after death on species richness, species evenness and species biodiversity...... 52

Table 6-2: Maximum maggot mass and ground temperatures for each experimental location...... 56

Table 7-1: Monthly mean temperatures for experimental sites and control site...... 68

Table A-2.Predicted developmental duration calculated using derived equations. Data presented in hours...... 93

Table A-3.Minimum development temperatures for each immature life stage calculated from derived development equations...... 94

Table A-4.Accumulated degree hours for the life stages of Phormia regina (Meigen)...... 95

Table A-5.ADH values using the mean and standard deviation as inputs for the derivation for each life stage based upon data presented in Byrd and Allen (2001) for Phormia regina (Meigen)...... 96

ACKNOWLEDGEMENTS

I would like to thank the members of my committee: Dr. Ke Chung Kim, Dr. Dennis Calvin, Dr.

James Marden, for their patience and encouragement throughout this whole process. I came into this with little knowledge of entomology and biology as a whole, and they were willing to teach me so that I could better understand the context of my graduate work. I would like to thank Dr.

Joseph Russo for the original idea and encouragement for me to return to graduate school. I would like to thank Mary Shaw and Erica Smyers for their help in the field and lab. It was nice to have company in the field on those days when it was only 4°F outside and the wind was howling.

I would also like to that Dr. Jason Byrd and Dr. Robert Shaler for their interest in my work and for providing me the opportunity to show people the importance of good weather data at the

AAFS Conference and Crime Scene University respectively. Finally, I would like to thank my family for listening to me talk about my research during holiday dinners and for their support throughout my life.

Chapter 1

Introduction

Insects, the largest group of animals in species richness and abundance, are closely associated with humans and their activities. Insects occupy diverse habitats, including many closely attached to human lives and enterprises. Because of this close association with humans, their presence often provides critical evidence in litigations. Forensic Entomology is a sub- discipline of the entomological sciences that focuses on the role insects play in the decomposition process and method to help provide scientifically sound evidence.

Establishing a timeline of events is essential to every criminal investigation. Forensic experts from diverse disciplines such as anthropology, pathology, and entomology are commonly consulted by law enforcement personnel to help construct a timeline for a case under investigation. In homicide cases, one of the key elements of this timeline is the time of death, commonly referred to as the “postmortem interval (PMI).” The PMI is the estimated time interval from the time of death to when a corpse was discovered (Byers, 2002, Aranaldos et al 2005). The

PMI is important to homicide cases because it helps law enforcement narrow down a suspect list, establish the identity and whereabout of a victim, and determine if a body has been moved from the original crime scene.

Estimation of a PMI can be derived from diverse scientific disciplines, including medical pathology, taphonony, anthropology, chemistry, and even botany, with varying degrees of success. Entomology, however, has proven to be a reliable indicator of PMI in both early and late stages of decomposition (Kashyap and Pillay 1989, Catts and Haskell 1990, Wells and

LaMotte 2001). An entomologically-derived PMI uses the pattern and sequence of insect colonization and development that begins when a deceased body is first exposed in an

2 environment (Benecke, 2004). This colonization is dominated by a few key dipteran species that depend on temperature and humidity regimes associated with the decomposing corpse.

The use of insect succession for PMI estimation that is synchronized with the process of decomposition is highly dependent upon the environmental conditions at a location (Smith 1986).

Varying geographic features and habitat characteristics can drastically affect the decomposition process and the associated succession of insects (Smith 1986). In the tropic and sub-tropic region the pattern of larval development associated with normal decomposition is relatively simple and predictable without much temporal or seasonal variation. In temperate regions, however, the pattern of decomposition and insect succession associated the decomposing body is more variable making the application of insect succession data in criminal cases more complicated.

Furthermore, the magnitude and synchrony of the succession process related to local temporal insect fauna vary from region to region (Smith 1986, Anderson 2001).

For these reasons, especially in temperate regions, the use of insect succession for PMI estimation is augmented with information on insect development rates. Because insect developmental rates and progression through life stages is temperature dependant, it can be captured mathematically and used to predict the PMI. Warmer temperatures allow insect development rates to be faster while cooler temperatures slow development. These mathematical models of insect development require accurate weather data to reliably predict the PMI. Thus, it is critical for PMI estimators to not only have good insect development models, but the most accurate weather data.

The goal of my research was to study meteorological factors involved in post-mortem interval estimation (PMI) during both the summer and winter months and the applicability of site- specific weather modeling to improve accuracy in PMI estimation. Our studies demonstrated that site-specific weather and microclimatology differs from commonly used official government weather report based on the data collection at the nearest governmental weather stations. Thus,

3 our study focused on developing site-specific weather modeling and thermal dynamics of cadaver decomposition and associated insect development including temperature gradients of soil and maggot mass temperatures in relation to accuracy of PMI estimation. Also, we studied wintertime survival of larvae with focus on decomposition and insect colonization patterns in wintertime environment (open field vs. deciduous forest) to be compared with those of warmer time of year.

In this thesis, I present the gaps in temperature data between controlled laboratory conditions and variable outside environments by modern temperature interpolation method applied to derive site-specific temperature data which help improve PMI estimation. The following objectives were identified:

Objective 1: Study the thermal pattern with focus on temperature measurements

in decomposition process and related insect colonization and development

Objective 2: Study the application of site-specific meteorological modeling to refine the

meteorological measurements to provide greater accuracy and confidence in PMI

estimations.

Objective 3: Describe and discuss the decomposition process and faunal

succession of cadavers in Central Pennsylvania, particularly in the winter season.

This research, primarily conducted in the field, was designed to show what and how seasonal climates in central Pennsylvania affect decomposition of corpse and its insect colonization and development. As a result, we conducted field experiments during the time between seasons and winter months for which our knowledge base is quite scarce. The results of this research should help advance the challenge of estimating PMI based on insect development and succession in variable and complicated temporal environments.

Chapter 2

Literature Review: Microclimatology in Forensic Entomology

Forensic entomology is the application of entomological science in judicial proceedings.

Applications can range from termite-caused structural failure (Kim 2005), insect food contamination (Disney 1994), to the determination of time of death of an animal or person

(Bergeret 1855). The latter application plays a significant role as evidence for a homicide investigation and prosecution. This history of forensic entomology has been long explained in many resources (Smith 1985, Byrd and Castner 2001, Gennard 2007). However, the application of meteorological sciences to the forensic sciences has not clearly been established. This literature review attempts to highlight some of the research that has been completed while highlighting the lack of research in this area.

The McGraw-Hill Dictionary of Earth Science defines microclimate as “the local, rather uniform climate of a plane or habitat, compared with the climate of the entire area of which it is a part.” The five primary meteorological variables that define a microclimate are sun exposure, wind (magnitude and direction), precipitation, temperature (both of air and soil) and humidity

(Davies-Colley et al. 2004). These variables, especially, temperature and sun exposure, can greatly affect the rate of corpse decomposition and the rate of insect colonization and development (Payne 1965; Whittaker 1975; Goddard and Lago 1985; Introna et al. 1991;

Grassberger and Frank 2004).

Shean et al. (1993) performed a study in Washington State to show differences in the rates of pig decomposition and insect colonization between a shaded and a sun-exposed environment. They noted that the exposed pig decomposed at a faster rate, and was colonized with insects sooner than the shaded pig, even though the pig subjects were only three hundred meters apart. Centeno et al. (2002) also explored seasonal differences in arthropod succession

5 patterns between exposed and sheltered corpses. In addition to sun exposure, the time of year a corpse is discovered can have a profound effect on the entomofauna present at a crime scene

(Mann et al. 1990, Dadour et al. 2001, Archer 2004a).

While researchers have investigated how specific thermal environments effect the developmental rates of insects and subsequently how these development rates affect the estimation of the postmortem interval (PMI) (Faucherre et al.1999; Anderson 2000; Grassberger and Reiter 2001, Grassberger and Reiter 2002a, Grassberger and Reiter 2002b, and Ames and

Turner 2003), few have actually studied how site-to-site, and season-to-season variations among local thermal environments impact the derivation of the PMI (Archer 2003).

Current methodology for estimating the PMI utilizes weather data, primarily temperature, taken from the nearest official weather station to estimate temperatures at the crime scene. In the

United States, these weather stations are maintained by the National Oceanic and Atmospheric

Administration (NOAA) and Federal Aviation Administration (FAA). However, there are great distances between weather stations, especially in the western United States (Figure 2.1). While valuable, there is still a great deal of error between weather station temperature observations and temperature experienced at the crime scene due to differences in microclimate between the weather station and the crime (Stefanick 1981).

Figure 2-1: Graphical representation of ASOS station locations for the United States (Courtesy of NOAA)

The effect of the difference in microclimates between weather station locations and crime scenes was brought home by Archer (2004b) when she compared in situ body temperatures with distant weather stations. She advocated a “correction” to the weather station data using a linear regression. This correction, while accounting for local thermal conditions at the body, which can differ significantly from the official weather station setting, was still a crude attempt at estimating temperatures that are site-specific to the crime scene.

As Kuuseoks et al. (1997) indicate, the nearest weather station is not necessarily reflective of the local climate. They illustrated that other factors such as topography, wind direction, and distance from bodies of water play a more significant role in reflecting on-site weather patterns than geographic distance. In their study, which took place in Northern Michigan, temperature predictions made for random locations from regression models derived from weather stations and local temperature measurements were more accurate than using the nearest weather station alone. The use of site-specific weather modeling has further been illustrated by Felland et

7 al. (1997) where they validated the use of site-specific weather modeling for insect development and disease modeling in apple orchards.

Differences in microclimate can be seen over short distances, such as tens of meters.

Young and Mitchell (1994), while looking at edge effects on forests, found that temperature and humidity effects from an open field can be seen up to fifty meters into the forest. Therefore, the microclimatic conditions felt by a decomposing body on the edge of the forest may actually be more reflective of the environment in the open field as opposed to the wooded environment even if the body is discovered within the forest. Breshears et al. (1998) showed that the type of vegetation in the forest can affect the microclimate of the ground beneath. They showed that woody plants influence the soil temperature and soil moisture available to other plants in the forest, especially during the winter months. During this time, the soil temperature is warmer within the canopy due to lower solar angles and insulation by leaf litter. This could have potential impacts for insect development as warmer soil temperatures can become a source of energy when air temperatures are not conducive to larval development.

All of these microclimatic features, as well as others, introduce uncertainty into the PMI estimations made by forensic entomologists. Site-specific weather modeling can aid in reducing uncertainty in PMI estimations by providing better on-site temperature prediction by taking into account differences in topography, vegetation, and soil properties. The importance of site-specific weather modeling has already been validated for agricultural settings (Seem et al. 2000, Russo

2000, Magarey et al. 2001).

Chapter 3

Materials and Methods

3.1 Materials and Field Study Design

Five pigs (Sus scrofa), weighing approximately fifty pounds were obtained from the

Swine Research Center at The Pennsylvania State University for this exploratory experiment. (In subsequent experiments, two pigs were used in each experimental location.) The animals were euthanized by a licensed veterinarian with lethal injection of Euthansol (sodium pentolbarbital).

The drug was injected into either the ear or neck of each swine in an amount appropriate to weight. For purposes of this study, the effect of Euthansol was presumed minimal on insect colonization and development; however, this should be studied further as other narcotic drugs have shown an effect on insect development (Musvasva et.al. 2001). After death, all five carcasses were transported by truck from the Swine Research Center to the Chemical Ecology

Lab (CEL) on campus, a driving time of approximately five minutes. Once there, four of the carcasses were placed into heavy duty black garbage bags and placed into a walk-in freezer located in the laboratory. The condition of the freezer was set to mimic 24-hour darkness at a temperature of 20°C. These four pigs were placed into the freezer so that the placement of the pigs into the field could be staggered for the Forensic Entomology Workshop, which took place

May 25-27, 2005. Subsequently, one week and two weeks after death, pigs were removed from the freezer and taken to the field and placed within thirty minutes of removal from the freezer.

The process of freezing is known to have an effect on insect colonization in that the frozen carcasses are more susceptible to insect colonization (Micozzi, 1986). However, this effect was not taken into account for the purposes of these initial experiments,. The fifth pig (fresh kill) was placed in the field within one hour of death with no contact to the freezer.

All carcasses were placed under cages made from 2” x 1” wood planks measuring 4’x 2’ x 2’ and covered with chicken coop wire (Figure 3.1). The cages allowed for excellent airflow and access for insects, while excluding larger carnivores from the carcass.

Figure 3-1: Example of field set-up for experiments.

The cages were tethered to the ground using rope that was attached to the four top corners of each cage using eye screws and a plastic stake on the other end. The ropes were pulled taunt and the stakes were pounded into the ground using a hammer until secure.

Four Watchdog™ Series 100 Button Data Loggers were placed around the carcass. Data loggers were placed at the site at the same time as the fresh killed pig, while the data loggers associated with the frozen carcasses were placed forty-eight (48) hours after carcass placement.

Since these carcasses were frozen, the delay allowed for the carcasses to thaw before temperature readings were made. At each carcass, one logger was placed under the middle of body approximately two inches below the ground surface (Tg), one logger was placed above the middle of the body approximately six inches above the body surface (T6), a third logger was placed inside the body within a maggot mass (Tm), and the final logger was placed proximal to the body at an approximate height of 1.5 meters to record ambient temperature at the experimental sites

(Ta) (Figure 3.2). Additional data loggers were placed in maggot masses as necessary when additional masses formed.

Figure 3-2: Diagram of logger set-up around the carcasses.

The data loggers were set to record temperature every hour. As controls, ambient temperature conditions were collected from the University Park Airport (TUNV) and the

Department of Meteorology weather station located at the Walker Building (TPSU) (Figure 3.3).

Site-specific weather data was obtained from ZedX, Inc. which uses a model that spatially interpolates temperature to the exact GPS location of each carcass.

Insect specimens were collected periodically throughout the experiments, up until the larvae reach the post-feeding third instar, to obtain a sample of the dipteran population during initial and active decay. Methodology used to collect insects included using a sweep net to collect flies hovering around the carcass. Approximately ten sweeps were made over the body successively. Adult flies caught in the net were immediately transferred to killing jars containing a few milliliters of ethyl acetate (C4H8O2). Then flies were transferred to vials containing 75% ethyl alcohol (EtOH). Maggot specimens were collected using tweezers to pull larvae from each maggot mass on the body. Approximately twenty-five to thirty maggots from each mass were collected during each collection. Maggots were initially placed in small mason jars with small samples of beef kidney until they could be processed in the laboratory. Within thirty minutes of

11 collection, maggots were fixed in boiling water and then placed in vials containing 75% ethyl alcohol (EtOH). Identification of both maggot and adult fly specimens was made using morphological characteristics and insect identification keys provided by Hall (1948), Arnett

(1985), Smith (1986) and Whitworth (2006).

3.2 Study Sites

There were two field locations used in this experiment (Figure 3.3). The red location

(Location 1 GPS: 40.821N, 77.859W) was located adjacent to Football Lot 18 on campus near

Wylie Lane. This area was characterized as grassland bordered by woodland, a manure barn, and pastureland (Figure 3.4). Three carcasses were place at this location. The fresh killed pig was placed at site 1.1 (carcass 1.1), which was located in a slightly wooded area behind a brush pile.

This site was considered to be semi-exposed with sunlight at times being obscured by the overhead trees which consisted of mainly black walnut (Juglans nigra) and red maple (Acer rubrum),. The pig at site 1.2 (carcass 1.2) was placed into the field one week after it was killed on 11 May 2005. Site 1.2 was located at the base of a large black walnut tree (Juglans nigra) located half way between a manure barn and the woodland. This site was considered to be semi- exposed, with some light penetration in the afternoon, but limited sun in the morning due to the position of the carcass relative to the tree. A third pig was placed at site 1.3 (carcass 1.3) on 18

May 2005. This experimental site was located behind the manure barn, beside a wire animal fence which separated the grassland from the pasture land. This site was classified as exposed, with excellent sun exposure at all times.

The yellow location (Location 2 GPS: 40.829N, 77.866W) was part of the Penn State Living

Filter near Standing Stone Lane (Figure 3.3). The Penn State Living Filter is an area of agricultural and woodland utilized for water filtration. The experimental sites were located in the southeast corner of the Living Filter and were characterized as old field scrub. However, this area contained a great deal of vegetation due to the presence of irrigation sprayers located around the area which enhanced vegetative plant growth (Figure 3.5). One pig was placed at Site 2.1

(carcass 2.1) one week after death on 11 May 2005. Sun exposure at this site was limited by the dense woodland, which was characterized by deciduous trees, primarily oak and maple varieties.

A second pig was placed at Site 2.2 (carcass 2.2) was placed two weeks after death, on 18 May

2005 at the base of an American elm (Ulmus americana). Site 2.2 was located across the irrigation pipeline from site 2.1 approximately fifty feet away.

Although both sites 2.1 and 2.2 were characterized as old field scrub, they consisted of very difference ecological habitats. Site 2.1 consisted of red maple (Acer rubrum), northern red oak (Quercus rubra), and several honeysuckle varieties (genus: Lonicera). Site 2.2, in addition to the American elm, consisted of black cherry (Prunus serotina), red maple (Acer rubrum), and honeysuckle (genus: Lonicera). However, site 2.2 also contained dense, tall grass and vines covering many of the trees and bushes. The vines were identified as poison ivy (Toxicodendron radicans), virginia creeper (Parthenocissus quinquefolia), and wild grape (genus: Vitis). This additional vegetation was a result of the water irrigation system employed at the Living Filter.

Site 2.2 was under direct water spray from the irrigation system and therefore was more vegetative than site 2.1.

Chapter 4

Exploratory Experiment on Meteorological Factors Associated with Environmental Locations

Many studies in decomposition commonly focus on biological or chemical aspect of the decomposition process related the chemical breakdowns and insect activities associated with various locations around the world. There were only few studies that looked into the environmental factors that influence decomposition and associated insect colonization beyond the comparative effects of shaded versus open non-shaded environments on the rate of decomposition

(Shean et al. 1993, Joy et al. 2003, Joy et al. 2006). This study was designed to identify specific meteorological parameters that occur within or between shaded and open (non-shaded) environments and to determine the effect of these specific factors on the development of insects in relation to the decomposition process. Several studies have shown that aside from insects, the rate of decomposition is highly influenced by temperature in the form of accumulated degree days

(Megysei et al. 2005, Adlam and Simmons 2007, Simmons et al. 2010). Therefore, it is increasingly important that the best possible temperature is used when calculating the postmortem interval. The purpose of this experiment is to realize the accuracy of PMI estimation by taking these meteorological parameters into account.

4.1 Ambient Temperature

Ambient temperatures varied between the five experimental sites and the two control sites (Table 4.1) with a range of ambient temperatures from 30.0°C to 38.5°C. It is interesting to note that both extreme temperatures (maximum high temperature and minimum low temperature) were reached at the same experimental site, as in the Site 1.3 which is the most exposed of all experimental sites. A time series comparison was also made to compare ambient temperatures at

16 the exposed, semi-shaded, and shaded sites (Figure 4.1). There was daily fluctuation in temperatures between days and the time at which maximum and/or minimum temperature was reached varied by location.

Table 4-1: Average ambient temperatures (°C) and standard deviations (σ) for the week of May 30-June 7, 2005.

Temperature Sensor Location Walker Site KUNV Site 1.2 Site 1.3 Site 2.1 Site 2.2 Bldg. 1.1 Average Temperature 25.5 18.9 24.3 36.8 22.3 20.5 25.0 °C (σ) (9.2) (3.8) (8.8) (8.7) (5.3) (3.0) (8.2)

This difference was likely attributable to differences in vegetation that limited the amount of sunlight reaching the carcass and influencing the ambient temperature. Another interesting feature was the difference in the amplitude of the temperatures between sites. Site 1.3 (the exposed site) experienced a greater diurnal fluctuation in temperature than both the semi-shaded and the shaded sites. Carcass 1.3, which was placed into the field seven days after carcasses 1.2 and 2.1, decayed at a much faster rate and was at the same stage in the decomposition process at the time of the Forensic Entomology Workshop.

Ambient Temperature of Exposed, Shaded, and Semi-Shaded Sites 40.0 35.0

) 30.0 °C ( 25.0 20.0 15.0

Temperature 10.0 5.0

0.0

8:01 AM 8:01 AM 1:01 PM 9:01 AM 7:01 PM 5:01 AM 3:01 PM 1:01 AM 9:01 PM 7:01 AM 5:01 PM 3:01 AM 1:01 PM 9:01 AM 7:01 PM 5:01 AM 3:01 PM 1:01

12:01PM 10:01PM 11:01AM 11:01PM 11:01AM Time Semi-Shaded Exposed Shaded

Figure 4-1: Time series of ambient temperature from May 30, 2005 through June 7, 2005 at the exposed experimental site (Site 1.3), a semi-shaded site (Site 1.2), and a shaded site (Site 2.1).

To get a better feel for the daily fluctuations in ambient temperature between the sites, the average hourly temperature for the time period from 30 May through 7 June 2005 was calculated for the shaded, exposed, and control site, TUNV (Figure 4.2). The control site (TUNV) was much warmer than both experimental sites during the overnight hours by approximately 6.28°C (σ =

0.90°C); however, during the day, TUNV was cooler than the experimental sites by approximately

1.86°C (σ = 2.72°C). It is interesting to note that even though the exposed and shaded sites trend similarly, the shaded site was warmer than the exposed site during the overnight hours, while being cooler than the exposed site during the daylight and evening hours. Also, as shown by the standard deviation, there was more variability between the differences in temperature between

TUNV and the experimental sites during the day than at night. The peak in temperature for the day was reached earlier in the day in the shaded sites compared with the exposed site, a difference of

18 one hour, while the overnight low was reached at approximately the same time. There are several reasons for this including vegetation, sun angle, and location of the carcass.

Average Hourly Ambient Temperature for Exposed and Shaded Sites

) 30 °C ( 25

Temperature 10

2:00 AM 4:00 AM 6:00 AM 8:00 AM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 12:00 AM 10:00 AM12:00 PM 10:00 PM Time Exposed Shaded Control (KUNV)

Figure 4-2: Time series of average hourly ambient temperature from May 30, 2005 through June 7, 2005 at the exposed site (Site 1.3), shaded site (Site 2.1), and the control site of KUNV.

Next, comparisons in ambient temperature between the experimental sites and the control weather stations were made. The relationship between the ambient temperature at the exposed site (Site 1.3) and shaded site (Site 2.1) with the ambient temperatures at the University Park

Airport (TUNV) and Walker Building (TPSU) (Figure 4.3). There was a weak relationship between

2 2 the exposed sites and the control sites, (TPSU R = 0.7892 p-value = 0.000, TUNV R = 0.5932 p- value = 0.000); however, there was an even weaker relationship between the shaded site and the

2 2 control sites (TPSU R = 0.1524 p-value = 0.048, TUNV R = 0.2542 p-value = 0.009). This weak relationship between ambient temperatures at the control sites and the experimental sites introduces a significant source of error into the PMI estimations.

Comparison of Ambient Temperatures Between Exposed Site and Control Sites 45.0

40.0

35.0

30.0

25.0

(°C) 20.0

15.0

10.0

Control Site Control Temperature Ambient 5.0

0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Exposed Site (Site 1.3) Ambient Temperature (°C)

Tunv Avg (°C) Twalker Avg (°C)

Comparison of Ambient Temperature Between Shaded Experimental Site and Control Sites

45.0 40.0 35.0 30.0 25.0

(°C) 20.0 15.0 10.0 5.0

Control Site Ambient Temperature Ambient Site Control 0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Shaded Site (Site 2.1) Ambient Temperature (°C)

Tunv Avg (°C) Twalker Avg (°C)

Figure 4-3: Comparison of the ambient temperatures at the control sites and the exposed site (above) and the shaded site (below).

4.2 Maggot Mass Temperature

Since insect development is temperature driven, maggot mass temperature (Tm) is important for PMI estimation because the temperatures that the maggots are actually experiencing may be higher than the ambient temperature (TA). Heat does not stay concentrated within the maggot masses, but flows out from the mass into the immediate surroundings affecting the temperature. Therefore, the temperature that the corpse and maggots are exposed to was not the same as the ambient temperature measured outside the corpse (Figure 4.4).

Figure 4-4: Difference between TA and Tm (TA-Tm) over the course of decomposition at Carcass 1.1 in May/June 2005.

4.3 Soil/Ground Temperature

As shown in figure 4.5, there is similarity between the maggot mass temperature and the soil temperature. Over the entire decomposition process, the two temperatures averaged less than

1°C different, which was not statistically significant (p-value = 0.000). Because this difference

21 was relatively small, it can be assumed that the two areas were very close to being energy balanced.

Time series of Tm and Tg Over Entire Decomposition Process 50

Temperature(°C) 10

0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850

-10 Hours After Death

Tm (°C) Tg (°C)

Figure 4-5: Time series of Tm and Tg over the entire decomposition process.

The energy balance between the maggot mass and the soil temperature was an interesting finding because it indicates that there was a strong relationship between the maggot mass and its local surroundings. It appears that this relationship could be a driving factor in the survivability of larvae during periods of the year when ambient temperatures are not conducive to their survival and development.

4.4 Base Model and Data Analysis: Site-Specific Weather Modeling

Site-specific weather modeling has been used for years in agriculture to help growers better defend against pests and better manage their crops (Magarey et al., 2001). In this section, a regression analysis was performed using the on-site ambient temperature measurements for both

22 the Lot 18 and the Living Filter experimental sites and simple site-specific weather modeling provided by ZedX, Inc. The model is considered simple because there are no site characteristics, such as ground cover, accounted for in the model and is based solely upon the latitude and longitude of the experimental sites.

But what effect would site-specific weather modeling have on PMI estimations? In order to investigate this, PMI estimation was made using the fresh killed pig placed at Lot 18 on 04

May 2005 and the insect specimens collected on 26 May 2005.

4.4.1 Data Analysis

One pig carcass (fresh killed) was utilized to create a mock PMI estimation using the entomological specimens collected and weather information from various sources in the surrounding area on May 26, 2005. The methodology for this experiment was outlined in section

3.1. The date for the mock PMI estimation was chosen arbitrarily.

The sources of ambient temperature information came from the following weather stations: the University Park Airport (TUNV), the Altoona-Blair County Airport (TAOO), and the site-specific weather model (TSSWM) (Figure 4.6). The University Park Airport and the Altoona-

Blair County Airport were chosen because they are the two closest FAA weather stations to the experimental site. Daily weather information was obtained from the National Weather Service –

State College Office.

Figure 4-6: Map of Pennsylvania showing relative locations of temperature information. The yellow star represents the experimental location, while the blue stars represent the airport locations.

The larval specimens collected were primarily in the second and third instar. There were four larval species found within the sample: Calliphora vomitoria (Linnaeus), Phaenicia sericata

(Meigen), Phormia regina (Meigen), and Calliphora vicina (Robineau-Desvoidy) (Table 4-2).

The predominant species found associated with the carcass was Phormia regina (Meigen) in both the larval and the adult populations.

Table 4-2: Population structure of entomological “evidence” collected on 2005 May 26.

Insect Order Insect Species % larvae % adult C. vomitoria 28.6 11 P. sericata 9.5 9 Diptera P. regina 42.9 73.3 C. vicina 19 6.7 C. maxilliosus 0 60 Coleoptera O. noveboracense 0 40

A linear regression analysis was performed to determine the correlation between the weather stations and the experimental site (Table 4-3). The regression model between the on-site temperature logger and the modeled site-specific temperature shows a good correlation, which leads to a question of how this effects the PMI estimation based upon the model. To investigate this, the degree hours were calculated for each station using the minimum development temperature threshold calculated using development information for P. regina (Meigen) provided by Byrd and Allen (2001). For a better explanation of how to calculate the PMI from development data, refer to the Appendix.

Table 4-3: Regression models for temperatures recorded by the nearest weather station and site-specific weather model compared to on-site temperature logger.

Weather Station Regression Model R2 value p-value

TUNV TA = 1.37447*TUNV - 7.4055 80.8% < 0.001

TAOO TA = 1.37663*TAOO - 5.2813 81.9% < 0.001 TSSWM TA = 1.3221*TSSWM - 6.1962 84.1% < 0.001

The fresh pig was laid in the field on May 4, 2005 at approximately 10:30 AM. Since the modeled site-specific temperatures (TSSWM) yielded a PMI of May 4-5, the site-specific weather model yielded the most accurate PMI estimation (Table 4-4).

Table 4-4: PMI estimations and Time of Death using P. regina and the temperature information from the weather stations.

Weather Station PMI Estimation Time of Death

TUNV 370-394 Hours May 10-11

TAOO 394-418 Hours May 9-10 TSSWM 538-562 Hours May 4-5

4.5 Conclusion

This experiment identified important meteorological and microclimatic variables that will effect PMI estimation using insect development. Simmons et al (2010) stated that aside from insect presence the most influential factor in the rate of decomposition is accumulated degree days. Therefore, temperature is one of the most important variables, and ambient temperature is of vital importance. The accuracy of the ambient temperature data greatly affects the accuracy of

PMI estimations whether made from insect evidence or the state of decomposition. This experiment determined the validity of using site-specific weather modeling to better estimate the temperature at the scene. Although the model did a good job capturing the temperature at the location over the closest weather station, the model showed problems predicting accurate temperatures where temperatures were warmer (i.e. there was more variability around the regression model when on-site temperatures were warmer). However, the developing maggots are not experiencing temperatures matching that of the ambient, but they are experiencing extreme heat as they develop and revolve within maggot masses. This extreme heat can make temperatures within the maggot masses upwards of 50°C, roughly 15°C above record high temperatures experienced in Central Pennsylvania. Since maggots are experiencing these extreme temperatures, PMI estimations must account for this heat in order to improve accuracy.

Another interesting finding through this research was the interaction between the maggot mass temperature and the soil temperatures under the carcass. This relationship has led to questioning in the ability to make accurate PMI estimations when air temperatures are not conducive to larval development and growth. Since heat energy from the maggot mass is being transferred into the ground, is there the possibility that the ground can become a source of heat for the maggots during the winter months, when maggots are thought not to survive and develop.

Therefore, winter experiments were planned to continue to look at the role that soil temperature may play in allowing larva to survive these harsh cold temperatures in the winter and perhaps

26 begin to show that entomological evidence can be used when temperatures are colder and insects are not as prevalent.

27 Chapter 5

Effects of Weather Variables on Decomposition and Insect Development

There are several microclimatological factors that influence the decomposition process. A better methodology for characterizing these factors can reduce the error in PMI estimation. Aside from ambient air temperature, the decomposition of the carcass is affected by sunlight (radiation),

Figure 5-1: Sources of radiation affecting the decomposition process. soil dynamics, terrain, and vertebrate disturbance within the physical characteristics of its locations. It can be noted from the figure that the radiation effects experienced by the carcass alone are quite complex (Figure 5.1). This chapter discusses some of these factors (temperature, both ambient and within maggot masses, radiation, and soil dynamics) further and discusses ways to better characterize these external factors so that, with further research, they can be used to improve PMI estimations made using entomological evidence.

28 5.1 Impacts of Temperature

Oviposition and larval development are highly dependent upon temperature associated with carcass. Therefore, the importance of accurate, reliable temperature information is essential to accurate PMI estimation made from entomological evidence. As shown in the spring experiments, there is a poor relationship between ambient temperatures at the scenes and the closest weather station (Figure 3.10). The difference between temperatures can cause differences in PMI estimation (Table 5.1).

Table 5-1: PMI estimations of L. sericata at Site A (Spring 2005). Calculations were based upon development data from Anderson (2000). No actual specimens were used in this calculation.

Lucilia sericata -- 15.8 C (Anderson, 2000)

KUNV Ambient Difference 1st 5/8/2005 @ 15:00 5/6/2005 @ 14:15 48 Instar 5/8/2005 @ 16:00 5/6/2005 @ 15:00 hours

2nd 5/9/2005 @ 17:00 5/8/2005 @ 15:00 22 Instar 5/9/2005 @ 19:00 5/8/2005 @ 16:45 hours 3rd 5/10/2005 @ 18:45 5/9/2005 @ 16:30 22.5 Instar 5/11/2005 @ 11:15 5/10/2005 @ 13:30 hours Pre- 5/14/2005 @10:00 5/11/2005 @ 13:45 70 pupal 5/15/2005 @ 14:15 5/12/2005 @14:00 hours 5/26/2005 @ 16:00 5/18/2005 @ 14:00 155. Pupal 5/27/2005 @ 13:00 5/19/2005 @ 15:45 5 hours

6/5/2005 @ 17:30 6/4/2005 @ 15:30 11.1 Adult 6/7/2005 @ 10:45 6/7/2005 @ 13:30 5 hours

Total Development 32.3 days - 33.9 days 31.2 days - 34.1 days Time

29 This study (Table 5.1) exemplifies how the location of the temperature information affects PMI estimations. Ambient temperatures were collected at the site and the closest weather station was the University Park Airport. PMI estimations were calculated using the development data for Lucilia sericata (Meigen) based upon work by Anderson (2000).

PMI estimations are calculated using experimental development data found rearing insects under constant temperatures in the laboratory. At each temperature there is a set period of time that it takes for the insect to grow from one life stage to another. These are usually presented in the literature as the number of hours the insect remains in a particular life stage. Degree hours

(DH) are calculated by taking the number of hours (t) in the life stage and multiplying by difference in temperature between the experimental temperature (Texp) and the minimum developmental temperature (Tmin) found using linear regression. This becomes the optimum number of degree hours that the insect remains in that life stage (equation 1).

DH = t*(Texp-Tmin) (1)

To utilize this information for field observations, the experimental temperature closest to the average temperature in the field is used. The observed temperatures are converted to degree hours using by subtracting the ambient temperature from the experimental temperature for the data being used. The calculated degree hours are then added together backwards in the time from the point of specimen collection until the optimum number is attained. This optimum number is the one that was found experimentally for each life stage.

Although in some instances, the difference in PMI estimations were relatively close, i.e.

22 hours, as the larvae develop, the difference in PMI estimation based upon the control weather station versus the on-site weather station becomes apparent. Because of this error, as decomposition, and resulting insect colonization, proceeds the accuracy in PMI estimation began to decline.

30 In addition to the ambient temperature at the crime scene, as shown by the experiments of chapter 4, there are other temperature readings that have a bearing on larval development such as ground temperature and maggot mass temperature (Figure 5.2). Degree hours were calculated by taking the hourly temperature and subtracting the base threshold temperature. All negative values are considered to be equal to zero. The degree hour values are added together to create the accumulated degree hour (ADH) value.

As shown in figure 5.2, all temperature sources are in good agreement until right after 2nd instar larvae are found on the carcass (line 3). At this time, the maggot mass began heating due to the metabolic heat generation from the accumulation of maggots. This was to be expected.

However, what is more interesting is that the by the time the 3rd instar maggots begin to leave the carcasses to pupate (between lines 4 and 5), the ground temperature increases, bringing its ADH value in line with the ADH value with the mass. This indicated that there was a relationship between the increased temperature of the maggot mass and the increased temperature of the ground below the carcass. Figure 5.2 also illustrate the large difference in accumulated degree hours between what the maggots are experiencing (ADH Mass) and the ambient temperature

(ADH KUNV and ADH Ambient).

Currently, there is no accounting for the maggot mass heat in development rate calculations. Since development and growth of the maggots is strongly influenced by temperature, i.e. they grow faster in warmer environments and slower in cooler environments; the increased temperature experienced by the maggots as they revolve within masses will directly influence their rate of development. Since these maggot mass temperatures average much higher than the ambient temperature, using the ambient temperature to calculate the rate of development can drastically underestimate how quickly the maggots are developing.

Accumulated Degree Hours for Site A, Spring 2005 P. regina (Meigen) Development 1 2 3 4 5 9000 8000 7000 6000 5000 4000

3000 ADHbase 8.5C 2000 1000 0 1 47 93 139 185 231 277 323 369 415 461 507 553 599 645 691 737 783 829 PMI (Hours After Death) ADH KUNV ADH Ambient ADH Ground ADH Mass

Figure 5-2: Accumulated degree hours calculated at site A using the base development threshold for Phormia regina (Meigen) and the various temperature sources. The black bars represent when each life stage was found on the carcass (1 - eggs, 2 - 1st Instar Larvae, 3 - 2nd Instar Larvae, 4 - 3rd Instar Larvae, 5 - Pupa).

5.2 Impacts of Light

Sunlight provides a great deal of energy to the Earth’s surface. However, the amount of energy available on the Earth’s surface is not constant around the world. There are several factors that affect the amount of available energy on the ground from the Sun including latitude, time of year, cloud cover, and vegetation. The amount of energy from the Sun affects practically all ecological processes from plant growth to decomposition. Sean et al (1993) showed differences in decomposition rates and patterns between exposed and shaded sites. They showed that decomposition occurred more rapidly in exposed areas as opposed to shaded areas. They noted that maggot development was a major determinant in decomposition rate between the two areas in comparison to shaded areas, and that maggot development was slower in the shaded

32 areas. Their work was further expanded by Joy et al. (2002, 2006) by illustrating that there are significant correlations between maggot mass temperatures and ambient temperatures in exposed areas whereas no significant correlation between maggot mass and ambient temperature exists in shaded areas.

In the current experiments, a light meter was used to look into the different light intensity that the carcasses were exposed to (Figure 5.3). Figure 5.3 shows how light intensity and exposure varied throughout the daylight period. The values represent average hourly values calculated from site 1.3 (exposed) and site 2.1 (shaded) during the spring 2005 experiments.

There were distinct differences between the two experimental sites when the maximum light occurred throughout the day. In the exposed site, light intensity peaked during the mid-morning hours while in the shaded site light intensity peaked during the mid-afternoon hours. However,

Light Intensity Between Exposed and Shaded Sites 5000

4500

4000

3500

3000

2500

2000

1500

LightIntensity (lumens/sqf) 1000

500

0 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 Time (EDT) Exposed Site Shaded Site

Figure 5-3: Difference in light exposure between exposed and shaded carcasses during Spring 2005.

33 the sites were located within a couple of miles from each other, so the actual incident radiation coming from the sun was relatively equal, but yet there is a difference. One difference was with the vegetation, however, the vegetation itself did not change throughout the day, so another possibility for the difference in intensity is due to the exposure that each carcass received.

To better characterize the amount of sunlight exposure in an area, the amount of available energy must first be quantified. This can be accomplished through the use of several radiative transfer equations. Formula 2 can be used to calculate the amount of energy horizontal to a plane at the top of the atmosphere at any latitude and time of year can be calculated for a particular day of the year (Stot).

86400 Sc Stot (Ha sin φ sin δ + cos φ cos δ sin Ha) (2) r 2

2 Where, SC is the solar constant, 1367 W/m , φ is the latitude of the crime scene, δ is the solar declination (formula 3), and Ha is the daily hour angle (formula 4), where d is the Julian date.

2 δ = 23.5 cos [ ( ) (d +10)] (3) 365

Ha = -tan φ tan δ (4)

Using information about cloud cover either from ASOS observations from the nearest weather station or the local National Weather Service office, the radiation reaching the ground can be calculated using Angstrom’s formula (formula 5).

Sg = [0.25 +0.50 (% sun)] Stot (5)

The above can also be calculated on a hourly basis by calculating Stot using formula 6 and formula 7.

Sc Stot = cos z0 (6) r 2

cos z0 = sin φ sin δ + cos φ cos δ sin ha (7)

Where z0 is the zenith angle and ha is the hour angle and is equal to 15°/hr. The hour angle (ha) is positive in the afternoon, and negative in the morning. It ranges from -90° to +90°.

The sunlight calculations found using the above formulas are for a horizontal, flat plane perpendicular to the sun beam. The sun rises in the east and sets in the west. The path of sunlight is depicted in Figure 5.4. Therefore, a south-facing slope would allow for the greatest amount of sun exposure (Table 5.2). Since the amount of exposure that a corpse has will directly influence the maggot development, introducing error into the PMI estimation. By better characterizing the level of exposure, one can reduce this error resulting in better PMI estimations for law enforcement.

Figure 5-4: Example of the sun’s path throughout the day.

Table 5-2: Correction values to ground radiation to account for slope. Slope Face Correction Factor Slope Face Correction Factor

South Sg 1 North Sg 0.50 South-East Sg 0.88 North-West Sg 0.63 East Sg 0.75 West Sg 0.75 North-East Sg 0.63 South-West Sg 0.88

35 5.3 Soil Temperature and Moisture Dynamics

Soil temperature also plays a significant role in microclimatology. The primary way heat travels through the soil is through conduction (Singer and Munns, 1996). The amount of conduction within the soil is determined by the temperature gradient as well as the conductivity of the soil (vanLoon et al. 1998). The conductivity of the soil is related to both the texture and the moisture content of the soil. Heat flows through the soil in a gradient from warmer to cooler. Heat flow stops when temperature equilibrium is reached within the soil. However, temperature equilibrium is rarely achieved in the soil (Singer and Munns, 1996), and therefore there is constant flow of heat energy throughout the soil.

In chapter 4, it was shown there was a heat exchange between the maggot mass and the ground due to differences in temperature between the mass and ground. The ability of the ground to conduct heat both into the soil and from the soil back into the body might become important for insect development, especially during the cooler, winter months. Therefore it is important for

PMI prediction that the soil moisture, type, and texture be known for better accuracy.

Two properties about the soil are necessary to determine the thermal conductivity of the soil: texture and moisture content. Texture is the property of the soil that illustrates the particle size of the soil sample. The particles can be of three types, clay, silt, and sand. The proportion of these particle types determines the texture of the soil. The USDA has developed a pyramid to help determine the texture of the soil (Figure 5.5). It used the percentage of sand, silt, and clay within the soil to determine the texture of the soil. Once texture is known, the amount of moisture in the soil must also be determined. A moist soil will conduct better than a dry soil. This is because the space between individual particles within the soil is filled with water, which is a better conductor of heat than air (Singer and Munns, 1996). The most common way to measure

Figure 5-5: Soil Texture Pyramid developed by the USDA. (http://soils.usda.gov/technical/aids/investigations/texture/)

soil moisture is to determine the ratio of mass between a wet and dry sample of the same volume of the soil, or the gravimetric soil moisture. It is found by taking a sample of soil of a known volume and determining its mass. The sample is the dried either in the open air or within an oven, and then the mass is taken once the sample has been dried. This ratio between the dry mass and the wet mass determines the soil moisture. However, as the decomposition process progresses, the soil moisture will change as the soil becomes saturated with the “juices” of decomposition. The soil may change from being dry to saturated, to even super-saturated as the soil becomes laden with chemicals from the decomposition process. So as the moisture property of the soil changes, so will the thermal conductivity.

Thermal conductivity of the soil is calculated using Fourier’s Law of Conductivity

(equation 8), where λ is the thermal conductivity of the soil. This allows for the calculation of the

37 dT (8) dx heat flux (φ) between the body and the soil. In this case, the medium is the soil. However, this is a simplified case. Since maggot masses rarely lay directly upon the soil, there is also conductivity of heat through the body as it lies upon the ground. Vendrick and Vos (1957) calculated the thermal conductivity of human skin as 1.4 ± 0.2 x 10–3 cal2/cm4sec °C2; however, they noted that because skin is not the same all over the body, there are vast differences in this value across the entire body.

An additional complication comes in the form of the decomposition process itself. As the body decomposes, the thermal conductivity within the body will change as the composition of the body changes. Since there are differences in the decomposition pattern between the spring/summer (head to hind decomposition) and the fall/winter (bottom to top decomposition), the thermal conductivity pattern found in one season will not hold true for the other.

5.4 Conclusion

Maggot mass temperature must be accounted for as maggots are experiencing temperature vastly higher than ambient air temperature. However, the period of maggot mass heating is not consistent and is dependent on radiation, the types species of insects within the mass, and scavenging. Additionally, maggots are not experiencing a uniform temperature within the mass and that there can be a difference of upwards of 20°C possible between the center of the mass and the surface (Higley and Haskell, 2001). Therefore, more research is necessary to better model the temperatures experienced by the maggots in order to improve PMI estimations.

Solar radiation accounts for a great deal of energy absorbed by the carcass. It has been shown that carcasses decay faster in exposed environments as opposed to shaded (Joy et al.

2006). The amount of solar radiation reaching the carcass is influenced by vegetation, solar angle,

38 and slope. However, other types of radiation also influence the carcass. This includes long wave radiation reflected from the carcasses surroundings. Long wave radiation comes from solar energy being reflected off of nearby vegetation, the ground, and even the body itself. This long wave radiation will increase the temperature immediately surrounding the carcass greatly depending on the surface, much the same way temperature readings from bank thermometers are really much higher than the actual ambient temperature. This phenomenon is caused by long wave radiation from the blacktop roads and parking lots below bombarding the thermal sensor with excess radiation. By accounting for radiation bombarding the carcass, PMI estimations can be improved by accounting for this increase in temperature as a result of the radiation, both short wave and long wave.

Soil moisture will influence the ability of the soil to conduct and store heat. Saturated soils are better conductors of heat. Soils become saturated during the decomposition process by the fluids created by aerobic and anaerobic organism and become better conductors of the heat produced by the maggot masses. While the importance of this conduction may be minimal in the spring and summer, this relationship between the soil and maggot masses may be very important for larval survival during the cooler, winter months.

This chapter serves to illustrate some of the complex microclimatic variables experienced by the decaying carcass. In order for better models for PMI estimation to be developed, more research is necessary about how these variables affect the microclimate surrounding the carcass.

Chapter 6

Comparative Ecology of Arthropod Diversity and Decomposition between Open Grassland and Woodland

Populations of living organisms are affected by the natural environment in which they live. The natural environment influences an organism’s development, life history, food supply, shelter, and survivability. The natural environment not only has an effect on organisms while they are living, but also upon death. After death, organisms proceed through a sequential process of decomposition. The decomposition process varies depending on the natural environment where decomposition takes place. For example, if a carcass is submerged under water, the decomposition process occurs slowly and is primarily the result of feeding by fungi, algae, and bacteria, whereas decomposition of a carcass exposed to air (on land) occurs more rapidly and insects are the primary decomposers (Catts and Goff, 1992); although, bacteria, fungi and other organisms play an important role.

All decomposition that occurs on land, where the carcass is exposed to air, is affected by the ambient environment. In hot, arid locations decomposition occurs very quickly, while in cooler, wetter environments, decomposition generally occurs more slowly. Even between very similar environments the composition of decomposer species and the decomposition process can be quite different. The following experiment was conducted to examine differences in decomposition pattern, arthropod species diversity, and, arthropod colonization between a woodland and open grassland environment. To investigate these questions, two hypotheses were tested. The first hypothesis was species richness, evenness and diversity (composition) are similar between the two environments. A second hypothesis was the rate of decomposition is similar between sites.

40 6.1 Materials and Methods

6.1.1 Experimental Layout

Two habitats were selected for the experiment, an open grassland mixed with other herbaceous species and a woodland (Figure 6.1). One freshly euthanized pig carcass was placed in a cage measuring 4’ X 2’ X 2’ to prevent access from animals. The cage was made from 2” x

1” pine boards covered on five sides with chicken coop wiring. The bottom of each cage was left open for easy placement over the carcass. The carcasses were placed in the experimental areas on

12 September 2007 and allowed to decompose through September 30, 2007.

Both pigs were euthanized using EuthanasolTM injected into the neck at the Swine

Research Center on The Pennsylvania State University- University Park Campus. After injection they were immediately transported to the study locations (the Cattle Research Facility) and placed in a cage.

6.1.2 Experimental Design

A time series analysis was conducted using an ANOVA approach to compare the species richness, evenness and diversity between the open grassland and woodland pig carcass locations.

The response or independent variables were species richness, species evenness, and species diversity, expressed as the Shannon-Weiner Index (citation). The dependent variables in the study were days after carcass placement and location of carcass placement. The values for the variable, days after death, used in the analysis were 5, 7, 9, 11, 13, 15, and 17 which corresponded to 16,

18, 20, 22, 24, 26, and 28 September 2009. On each date, species richness (S) was measured as the total number of species sampled on and around each carcass. The Shannon-Wiener Index, which is one of several diversity indices used in ecological studies, was calculated using the formula:

(1) H’ = - Pi log P1; Where Pi = ni/N

N is the number of insects in total and ni is the number of the ith species. The species evenness is the relative abundance or proportion of individuals among the species and is calculated using the following equation:

(2) E = H’ / H max; where H max = log S.

Using these values, differences in biodiversity between the pig carcasses in two environments were tested. The ANOVA procedure tested whether time after death and location influenced the biodiversity at the corpse. Both location and days after death were treated as fixed effects in the statistical model. The analysis was considered a repeated measure because each subject was measured sequentially over time.

The assumption of random errors being normally distributed with a mean of zero and constant variance was also tested. Based on this analysis, the assumptions appeared to be satisfied. The Bonferroni adjusted significance level was applied to make sure that observe differences were not due to applying the test simultaneously to species richness, species evenness, and the Shannon-Weiner Index.

6.1.3 Insect Collect and Decomposition Observations

The pig carcasses were checked at the sites for state of decomposition and to collect entomological specimens every two days. During each visit, photographs were taken of the general area around the carcasses and to capture the level of carcass decomposition. Any insects and maggot masses found were recorded and sample specimens collected. Occasional videos of maggot mass activity were also recorded.

Flying insects associated with the carcass were collected using a sweep net. The sweep net was waved twice for six seconds, over each carcass. Insects collected in the net were transferred to a killing jar containing a few drops of ethyl acetate (C4H8O2). The specimens were then transferred either to a vial containing 70% ethyl alcohol (C2H5OH) or left in the kill jar and later pinned and placed in a collection box.

42 For maggots and associated other insects, approximately twenty to thirty specimens were collected from each maggot mass using forceps. They were transferred to the laboratory and preserved using a boiling water bath within an hour of collection. They were then placed into vials containing 70% ethyl alcohol (C2H5OH). Beetles were also collected using forceps and placed either into a kill jar at the site to be pinned later or placed directly into 70% ethyl alcohol

(C2H5OH).

During the final three visits to the carcasses, soil samples were also taken to check for insects, particularly pupae. Two samples were collected on each observation date, one approximately six inches away from the carcass and the other approximately three feet from the carcass. Both samples were collected by following maggot trails away from the body. Soil samples were collected using a garden spade and the soil placed into plastic bags. The soil samples were returned to the laboratory, screened and any insects found were identified using a microscope. One soil sample from each site was placed under the fume hood to allow pupae to emerge as adults easier species identification.

All adult specimens were identified to species by using taxonomic keys (Smith, 1986) and larvae were aged using morphological analysis (Smith, 1986). The data was input and archived in Microsoft Excel for later analysis using Minitab v. 14.0, and SAS v.9.1.

6.1.4 Weather Data Collection Methods

Ten HOBO® Data Logger Pendants were used at each study site to record hourly temperature data. Two loggers were used as controls, one placed inside a Stevenson screen, and the second placed at a two inch soil depth at the base of the Stevenson screen. The Stevenson screen shielded the loggers from precipitation and direct solar radiation, which can cause erroneous temperature readings, while allowing air to move freely around the logger. Three data loggers were placed at a two inch soil depth below each carcass - one near the head, one near the hip, and the remaining sensor near the midsection of each carcass. Two data loggers were placed

43 one foot above each carcass - one above the head, and the other above the hip. The seventh data logger was placed two-inches above the center of each carcass. Additional data loggers were placed in maggot masses upon their formation to record mass temperature (maximum = 2 loggers/carcass). The final data logger was attached to the top of each cage and was set to record ambient light on an hourly basis.

(A)

(B)

(C)

45 6.2 Decomposition Observations at Each Experimental Site

In situ observation of carcass decomposition patterns suggested differences between woodland and open grassland experimental sites (Shean, 1993). In the experiment Shean recognized five stages of decomposition: 1) fresh decay, 2) bloat decay, 3) active decay, 4) dry decay, and 5) skeletonization. The results of this experiment were classified using Shean’s classification system. The progression of decomposition for the carcass in the open grassland and woodland sites is shown in Figure 5.2.

Fresh Decay refers to the initial stage of decomposition. The carcass begins to turn from a pink color to a more pale appearance (Figure 6.2[A] and 6.2[J]). The body begins its cooling process in a steady temperature decline until the body is the same temperature as its surrounding environmental. Insects are initially attracted to the corpse and begin to lay eggs in the open orifices with body fluids during this stage. There is little to no odor associated with the carcass during the “Fresh decay” stage. The fresh decay stage ends with the onset of bloat.

The Bloat Stage begins with ballooning of the body with gas and it takes on a greenish- grey appearance, especially in the abdominal area (Figure 6.2[B/C] and Figure 6.2[M]). This stage was reached between 54 and 102 hours (2.25 – 4.25 days) in the open grassland, but required approximately 150 hours (6.25 days) in the woodland. Ballooning of the body is caused by the accumulation of gases within the body cavity from the chemical breakdown of its organs.

The bloat stage ends when the body “deflates”, sometimes breaking open the skin of the distended areas. At this point the body enters the Active Decay Period (Figure 6.2[D/E] and

Figure 6.2[N/O]). Active decay began around 150 hours (6.25 days) at the open grassland location, but required 198-246 hours (8.25-10.25 days) in the woodland location. This is a period of strong odor associated with decay and is highly attractive to insects. The odor resembles that of rotten meat and ammonia. There is usually strong maggot feeding over much of the body during this stage.

46 The fourth period of decay is referred to as Dry Decay (Figure 6.2[G] and Figure

6.2[Q/R]). During this period, much of the flesh has been removed from the carcass. There is still some maggot feeding on the remaining skin layers, but most maggots have migrated away from the carcass to pupate. Dry decay was seen at about 246 (10.25 days) hours at the open grassland location and approximately 342 hours (14.25 days) at the woodland location.

The final stage of decomposition is the Skeletonization Process (Figure 6.2[I]). At this stage, all that is left of the carcass is bone and mummified (or hardened) skin. Pigs at both locations reached this stage at about 390 hours (16.25 days) after placement in the field.

The differences in rate of decomposition in the study were similar to Shean (1993). These differences were likely the result of cooler temperatures at the woodland location and possible difference in timing of initial fly colonization.

(A) PMI = 0 hours (B) PMI = 54 hours (C) PMI = 102 hours

(D) PMI = 150 hours (E) PMI = 198 hours (F) PMI = 246 hours

(G) PMI = 294 hours (H) PMI = 342 hours (I) PMI = 390 hours

(J) PMI = 6 hours (K) PMI = 54 hours (L) PMI = 102 hours

(M) PMI = 150 hours (N) PMI = 198 hours (O) PMI = 246 hours

(P) PMI = 294 hours (Q) PMI = 342 hours (R) PMI = 390 hours

Figure 6-2: Photographic time series of decomposition for carcasses in the open grassland (top) and the woodland (bottom) environments from September 12-30, 2007.

Figure 6-3: Chart of days in each stage of decomposition for carcasses in the open grassland (top) and woodland (bottom) environment. PMI is displayed as house after death.

6.3 Colonization Timing, Population Stage Structure, Species Composition and Biodiversity

6.3.1 Stage Structure

In addition to the differences in decomposition, there were differences in insect population structures (Figure 6.4). Insects were observed landing on the open grassland carcass within minutes of initial placement in the field, and eggs were deposited within one hour. Insects were slower to find the carcass at the woodland site and were not observed during the initial placement of the carcass in the field. Upon returning to the field four hours later, insects were observed landing on the carcass and egg lying had occurred in the mouth.

Figure 6-4: Population structure of maggots found at the open grassland (top) and woodland (bottom) experimental sites.

50 Eggs were noted on the snout of the woodland carcass for the entire period of the experiment, whereas no new eggs were found on the open grassland carcass by the fourth day of the experiment. It was assumed that some of these eggs on the woodland carcass were not viable due to colder temperature experienced during the course of the experiment and were therefore not going to develop further. Therefore, collections of egg masses were halted mid-way through the experiment on the woodland carcass because counts of these collections would skew the population structure information gathered from the rest of the samples.

To maintain consistency in the larval numbers, twenty larvae were chosen at random to be used in the analysis from each collection sample. Figure 6.4 also shows the population distribution of maggot stadia in each collection sample as percentages of the whole. These are estimations for the entire population based upon the collected samples. However, the structure of the entire maggot population associated with the carcass is unknown. This is due to the fact that these are simply samples made from the entire population. Precise density information is difficult to obtain because the mere collection of samples reduces the population density on the carcass.

Careful techniques were used to minimize the impact sample collections made upon the population as a whole since any significant drop in population effects the resulting temperature gradients. There were distinct differences in the population structure between the two environments. Early stages were observed associated with the woodland carcass much longer than they were associated with the open grassland carcass (early stages are defined as Egg, 1st instar, and 2nd instar). By the last week of the experiment, there was little to no larval activity associated with the open grassland carcass as many of the maggots had migrated away from the carcass for pupation.

6.3.2 Species Composition and Biodiversity

In both sites, the predominant species found feeding, or laying eggs, on the carcasses was

Phormia regina (Meigen) (62.4%). However, P. regina comprised a slightly different proportion

51 of the population associated with the woodland and open grassland. On the carcass in the woodland site, P. regina comprised 62.6% of the population while at the open grassland carcass, the proportion was 62.0%. Other species found feeding, or laying eggs on, the carcasses included

Calliphora vicina (Robineau-Desvoidy) (5.6% - open grassland, 24.2% - woodland), Lucilia sericata (Meigen) (32.4% - open grassland, 11.1 % - woodland), and Lucilia illustris (Meigen)

(0% - open grassland, 2.1% - woodland). Other dipterans found associated with the carcasses without detecting the presence of larva were Hydrotaea leucostoma (Wiedemann), Piophila casei

(Linneaus), Phoridae sp., and Sepsidae sp.

As decomposition progressed saprophagouse adult beetles were found associated with the carcass: Necrobia rufipes (DeGeer), Creophilus maxillosus (Linneaus), Nicrophorus orbicollis

(Say), and Hister sp. These specimens were all found during the later stages of decomposition, i.e. late active decay and beyond. Coleoptera often visit a decaying corpse in order to feed on eggs and maggots. More coleopteran species were found associated with the woodland carcasses

(five) as opposed to the open grassland (two). This was expected as there is more decaying material associated with the woodland environment as opposed to the open grassland because of leaf litter in the woods.

In terms of location, species richness, species evenness, and species biodiversity were all significantly different between the woodland and open grassland locations (Table 6-1).

However, the effect of days after death was not significantly different for these indices.

This result suggests that there are differences in species biodiversity between sites, but that these differences in richness, evenness, and biodiversity tracked similarly over time.

Table 6-1: The effect of carcass location and days after death on species richness, species evenness and species biodiversity.

Fall Type 3 Tests of Fixed Effects - Evenness Effect Num DF Den DF F Value P-value Location 1 1 ∞ <.001 Days After Death 5 1 0.11 0.9704

Fall Type 3 Tests of Fixed Effects - Shannon Index (Biodiversity)

Effect Num DF Den DF F Value P-value

Location 1 1 ∞ <.001

Days After Death 6 3.86 0.19 0.9625

Fall Type 3 Tests of Fixed Effects - Species Richness

Effect Num DF Den DF F Value P-value

Location 1 1 ∞ <.001

Days After Death 6 3.74 0.35 0.8804

6.4 Relationships Between Temperatures In and Around the Decaying Carcass, Insect Life Stage, and Time After Death

6.4.1 Ground and Maggot Mass Temperature Related to Decomposition Stage and Insect Population

Maggot mass temperatures increase as the maggot mass develops and detectable metabolic heat generation begins in the transition from second instar to third instar (Turner and

Howard, 1992). In this experiment, the period of greatest metabolic heat generation was observed as an increase of the ground temperature (Tg) and maggot mass temperature (Tm) (Figure 6.5 and

53 Figure 6.6). Third instar larvae were first found associated with the open grassland carcass ninety- six (96) hours after death and in the woodland environment one hundred seventy-five (175) hours after death. The increase in both the Tg and Tm occurred a shortly following the presence of third instar larvae.

The time of maximum heating detected in the ground and in the maggot mass occurred at within twenty-four (24) hours of each other at both locations (Table 6-2). Additionally, at both locations, open grassland and woodland, Tg(max) was reached only one hour later than Tm(max)

(Hours After Death = 267 vs. 268 in the woodland, and Hours After Death = 245 vs. 246 in the open grassland). The maximum temperature reached by the maggot mass in the open grassland was Tm(max) = 46.5°C and in the woodland location the maximum maggot mass temperature recorded was Tm(max) = 43.6°C. The maximum ground temperature reached in the open grassland was Tg(max) = 43.9°C and in the woodland location the maximum ground temperature was Tg(max) =

43.5°C.

Table 6-2: Maximum maggot mass and ground temperatures for each experimental location.

When Location T When Reached? T m(max) g(max) Reached?

267 Hours After 268 Hours After Woodland 43.6°C 43.5°C Death Death 245 246 Hours After Grassland 45.6°C Hours After 43.9°C Death Death

Also, the diurnal fluctuation in Tg in the woodland location was smaller compared with the open grassland (Figure 6.5), which may be due to greater heat buffering capacity in the woodland location. As the woodland location was within deciduous woodland, thick leaf litter may have added to the soil’s ability to hold the heat generated by the maggot masses. This also holds true for the decline in ground temperature after maximum maggot mass heating. The

“cooling off” of the ground occurs at a much faster rate in the open grassland than in the woodland (α(grassland) = -0.6016, α(woodland) = -0.385). However, this result is not statistically significant (T = -3.16, p = 0.002). There was also great diurnal fluctuation in the maggot mass temperatures in the grassland environment compared with the woodland (Figure 6.6). This greater fluctuation in temperature was not a direct result of the maggot mass itself, but rather an effect of solar radiation the carcass was exposed to in the open grassland. The illuminance, a measure of the intensity of the incident light, during the daylight hours in the open grassland was 8790.4 lux compared with only 592.8 lux in the woodland. This means that the open grassland carcass was exposed to a higher level of radiation (both solar and reflective) allowing for temperatures to be warmer when compared with those in the woodland.

There was a distinct increase in ground temperature beginning approximately 200 hours after death in the wooded location. At the beginning of the increase in Tg, the maggot mass

57 consisted of primarily first, second, and third instar maggots (19%, 37%, and 17% respectively) with Tg(max) being reached around 267 hours after death when the maggot population consisted of primarily third instar maggots (approximately 60% of the maggot population). On the other hand, the increase in ground temperature begins more gradually, approximately 125 hours after death in the open grassland location. At this time the maggot mass population consisted of first, second, and third instar maggots (approximately 15%, 45%, and 40% respectively), with Tg(max) being reached around 246 hours after death when the maggot population consisted primarily of post- feeding maggots (approximately 63%).

The fact that Tg(max) was found when maggots were in the post-feeding stage in interesting. As in the woodland environment, Tg(max) was reached at a time when the maggots who produce the most metabolic heat were present; however, this was not the case in the open grassland. There are a couple of plausible explanations for this. First, there may have been predominantly more third instar maggots than estimated by the collections because they were surrounding the temperature logger which was located deep within the carcass and therefore not collected. Secondly, the sky conditions at this time were clear and sunny. This would increase the amount of solar radiation reaching the carcass. In figure 6.2(f), much of the abdomen of the carcass had been consumed by the maggots, yet there are still maggots in this section of the body only beginning to migrate away from the carcass. Soil temperature loggers were placed under the abdomen of each carcass, and with much of the abdomen consumed; there was less of a buffer to keep solar radiation from warming the ground directly. In combination with the wetness of the soil due to the decomposition process, the ground could have been artificially warmed by this increase in solar radiation.

58 6.4.2 Ambient Air Temperature Related to Decomposition Stage and Insect Population

Ambient temperature (Ta) was distinctly different between the woodland and open grassland sites (Figure 6.7). The average ambient temperature in the woodland environment was

16.1°C (σ = 5.4°) and in the open grassland the average ambient temperature was 17.4 (σ = 9.7°).

As shown by the standard deviations, there was greater variation around the mean in the open grassland versus the woodland environments.

The effect that ambient temperature has on the decomposition process is seen in the duration of fresh stage of decomposition (Figures 6.3 and 6.7). The fresh decomposition stage lasted only 48 hours in the open grassland compared with 144 hours in the woodland. This difference caused a delay in the onset of the bloat decomposition stage in the woodland environment compared with the open grassland. It is interesting to note that the rest of the decomposition stages (bloat, active, skeletonization, and dry) all took approximately the same amount of time to complete regardless of environment. Therefore, the ambient temperature must play a role in the initialization of the decomposition process i.e. cooler environments slow the onset of decomposition while warmer environments encourage the onset of the decomposition process.

Figure 6-7: Time series of ambient temperatures with indicated insect larval stage for P. regina in the woodland (top) and grassland (bottom) environments. Stages of decomposition are also noted.

60 Additionally, insects were observed visiting each carcass within minutes of placement at the experimental locations regardless of environment. However, when carcasses were visited six

(6) hours after placement in the field, eggs were observed in large masses within the mouth and nose of the open grassland carcass and there was a great deal of insects activity around the carcass. But at the woodland carcass, eggs masses were smaller and were only observed within the nose. Additionally, there was less insect activity around the carcass.

6.5 Conclusion

Differences in environment play a significant role in the decomposition process and insect colonization of deceased bodies. The fresh stage of decomposition is effected by ambient temperature in that the duration of this stage can be longer in cooler environments compared with warmer environments. Additionally, initial insect colonization and egg lying may be influenced by ambient temperature as insects tend to lay eggs sooner on exposed carcasses than on those in shaded areas.

Vegetative differences in the environment play a role in both rate of decomposition and insect colonization. The rate of decomposition in the fresh decomposition stage was slower in the woodland environment than in the open grassland environment. The fresh decay stage of decomposition lasted five days longer in the woodland environment as compared with the open grassland. Additionally, the population structure of maggot masses colonizing the carcasses differs significantly between the woodland and open grassland locations. This difference has potentially significant implications in a criminal investigation. By knowing the colonization pattern of dipterans on a carcass, the investigator is provided with information about where a crime took place, or if a body has been moved from another crime scene.

Ambient temperature is thought to be the driver of insect colonization, development, and the decomposition process. However, this research shows that ambient temperature, while

61 important for the initial stages of decomposition and insect colonization, may play a secondary role to other microclimatic elements such as solar radiation and soil temperature. If soil temperature increases as maggot mass temperatures increase, there is the potential for “heat storage” in the soil from the heating maggot mass. This may have implications in forensic investigations occurring during cooler periods of the year ambient temperatures may not be conducive to insect development. With further study, these effects of both maggot mass temperature and soil temperature/properties can be better understood providing greater accuracy to forensic entomological investigations.

62 Chapter 7

Meteorological Factors Influencing Winter Larval Survival

In chapter 5, it was shown that soil temperature has a significant influence on the thermal environment of insects involved in the decomposition process. It has been assumed by many in the forensic community that dipteran larvae which infest a body during the late fall or early winter has limited survivability during the winter. Thus, an accurate PMI estimation is very difficult or impossible when the body has been exposed over the winter. Results from the spring experiments indicated that energy generated as metabolic heat in the maggot masses is transferred into the ground which acted as a heat buffer. Based on this finding, it is hypothized that dipteran larvae that were hatched from the eggs laid on a body in the fall may have the capacity to overwinter successfully and that understanding the thermal dynamics of heat exchange between the soil and the maggot mass might provide information that can be used to estimate the PMI of an overwintering corpse. To test this hypothesis, a fall and winter time study of insect colonization and development on decaying carcasses was conducted. The objectives of this experiment were:

Objective 1. To investigate how larvae survive the winter months under sub-

optimal atmospheric temperature conditions.

Objective 2. To investigate what role, if any, environment (either shaded or

exposed) plays in larval survivability.

Objective 3. To examine the thermal dynamics of the evolving maggot masses

and the role that soil temperature plays in survivability of the larvae.

63 7.1.Methods and Materials

This study was conducted between 27 October 2005 and 23 March 2006 which covers the late fall, winter, and early spring in Central Pennsylvania. The fall time period was chosen to allow insect colonization prior to temperatures becoming too cold for insect activity. The experiment was terminated in the spring once the majority of dipteran colonization was completed and the main fauna of the corpse became coleopteran species.

7.1.1 Experimental Sites

The research location (location 3) used for this experiment was located within Penn

State’s Cattle Research Facility on campus (Figure 7.1 and 7.2). There were two experimental sites used within the facility, a wooded/shaded site and an open field/exposed site. Both experimental sites were located towards the rear of the facility behind a storage barn. The wooded/shaded area was characterized as a mixed hardwood

Figure 7-1: Map of Cattle Ranch Facility. The pink dot represents the woodland environment, while the green dot represents the open grassland.(Map from www.campusmaps.psu.edu)

Figure 7-2: Photographs of experimental locations: woodland (top) and open grassland (bottom). Photographs were taken October 27, 2005. Replace this with figure caption below figure.

65 Forest, with an under story was comprised of young sugar maple (Acer saccharum) that provided a great deal of shade when leaves were present (leaves fell from the trees soon after the experiment began). The remainder of the canopy was comprised of pignut hickory (Carya glabra), eastern white pine (Pinus strobus), black cherry (Prunus serotina), and white oak

(Quercus alba). The open field site was located approximately fifty meters to the south of the wooded site. The open field was bordered by the woods on the north, and a storage barn to the south. This stie was characterized as a grassland.

7.1.2 Experimental Materials and Methodology

Four pigs (Sus scrofa) were obtained from the Swine Research Center on the

Pennsylvania State University campus on October 27, 2005. The methodology used for this experiment was similar to the methodology explained in Chapter 3. However, for this study, two carcasses were placed in each environment (woodland and grassland) for replication purposes.

Carcasses 3.1 and 3.2 were placed in the woodland site while carcasses 3.3 and 3.4 were placed in the open grassland site. The carcasses were placed in the center of each site twenty-four feet apart.

Temperature was collected at eight locations around the carcass. Ambient temperature was recorded using a Stevenson screen was placed twelve feet from the center of each carcass to record ambient temperature information (Figure 7.3). A Stevenson screen is a hollow box located approximately five feet above the ground. Slits were cut into all four sides to allow for good airflow. The top and bottom of the box are solid. The control soil temperature data was collected at the base of the Stevenson screen, using a data logger with its temperature sensor placed at a two inch depth. Under each carcass, three data loggers were also placed at a two inch depth to record soil temperature. Each carcass had a logger placed below the head, below the center of the body, and below the hip. In addition, three data loggers were placed two inches above each body

66 in the same locations as the soil temperature loggers to measure air temperature near the corpse.

Additional loggers were placed inside each carcass, as maggot masses appeared to collect maggot mass temperature information. A cage measuring 4’ X 2’ X 2’ made from 2” x 1” pine boards was placed over each carcass to prevent larger vertebrates from feeding on the body. This set-up was used for both sites and was similar in construction and setup as was performed for the spring experiments.

Figure 7-3: Illustration of carcass set-up for experimental sites during Winter 2005- 2006.

The data loggers used in this experiment were a combination of HOBO® Data Logger

Pendants and Watchdog® I-buttons. Even with the logging failures from the Watchdog® buttons during the spring experiment, many of those failures resulted from those loggers which were placed inside the maggot masses. In this experiment the Watchdog® I-buttons were not utilized to record maggot mass temperature, but they were used to record soil and air temperatures. For

67 maggot mass temperatures, HOBO® Pendants were used both within the bodies as well as above the bodies. All data loggers were set to record temperature hourly.

Insect collections were made monthly using the methodology explained in Chapter 3.

However, visits to the experimental sites were made weekly to ensure set-up was maintained.

7.3 Results and Discussion

Developmental studies conducted under controlled laboratory conditions suggest that larvae of most calliphorid species do not survive at temperatures lower than approximately 10°C

(Byrd and Allen, 2001). These types of studies have lead to the assumption that most calliphorids will not have the capacity to successfully overwinter when conditions commonly drop below

10°C at a geographic location. Therefore, if a homicide victim was discovered during periods of the year where temperatures are regularly below 10°C (i.e. winter), entomological evidence found associated with the body was assumed to have limited value for PMI estimation.

The spring experiments indicated that there was an association between soil temperatures and maggot mass temperatures. Thus, it was hypothesized that the soil could act as a temperature buffer and plays a significant role in larval survival during the winter months and that it was possible for larva to endure the cold temperatures. If proven correct, this hypothesis would have a significant influence on how PMI estimations are made during the winter months in areas where temperatures and other environmental factors (i.e. snow and ice) are not normally thought of as conducive to larval survival.

Overall, the winter of 2005-2006 was slightly above normal averaging 3.28°C above the normal temperature for the period from December – February, the traditional climatological winter (NCDC). Additionally, the winter ranked as the 51st wettest winter and the 75th snowiest

1895 (PSU Meteorology Records). A comparison of monthly average temperature was made

68 between the exposed experimental site, the shaded experimental site and the control site,

University Park Airport (Table 7.1)

Table 7-1: Monthly mean temperatures for experimental sites and control site.

Nov Dec Jan Feb Mar Mean Temp (ºC) 6.59 -2.85 1.95 -1.20 2.55 UNV TA σ 7.21 4.48 4.65 5.36 6.30 Mean Temp (ºC) 5.36 -3.58 1.34 -1.61 2.31 Grass TA σ 7.45 5.06 5.06 5.76 6.89 Mean Temp (ºC) 6.16 -2.81 1.98 -1.02 2.99 Woods TA σ 7.06 4.69 4.76 5.47 6.46 Mean Temp (ºC) 6.47 2.13 1.94 0.33 2.40 Grass TG σ 3.16 0.78 1.58 1.48 2.80 Mean Temp (ºC) 8.19 2.90 2.83 1.76 3.55 Woods TG σ 3.00 0.78 1.55 1.41 2.96

The coldest month during the experiment was December using ambient air temperature measurements on-site and at the control weather stations (KUNV Airport and PSU Meteorology).

The 30-year normal mean temperature for the month of December is -0.79°C (PSU Meteorology

Records). This coincidently was also the month that saw significant snowfall; the official snowfall total for State College in December 2005 was 15.4 inches with a maximum snow depth of 8 inches. However, the coldest month for the ground temperatures was February.

In contrast to the springtime (Table 4.1), there were not distinct differences in the ambient temperature between the two experimental sites, exposed vs. shaded (Figure 7.4) Even though the temperatures in figure 7.4 are average daily temperatures instead of hourly temperatures as presented in Table 4.1, the similarity in the ambient temperatures between the

69 exposed and shaded sites was easily seen. This similarity in ambient temperatures was due to the increased stability in the air, which is very common during the winter months.

Figure 7-4: Time series of average daily ambient temperature between the woodland and open grassland experimental sites.

Similarity in temperatures was not only apparent in the ambient temperature recorded at the experimental sites, but between the ambient temperatures recorded at the experimental sites and at the control site, KUNV Airport (Figure 7.5). The correspondence between the two sites is expressed by the regression equation (shaded: R2= 0.9288, p-value = 0.001 exposed: R2=0.9003, p-value = 0.000). This close relationship between the ambient temperatures at the experimental sites and the control was also seen using an alternative control station, the Penn State

Meteorology Weather Station.

One of the goals of the winter experiments was to look into the relationship between the soil temperature and the maggot mass temperature to see whether the energy exchange alluded to

70 during the spring experiments was present and possibly strong enough to keep maggots alive and developing throughout the winter. To examine this, average daily temperatures for the maggot mass and the ground temperatures were calculated and plotted over time (Figure 7.6). This graph shows how the maggot mass temperatures and the 2” soil temperatures were nearly identical over the majority of the experimental period. More specifically, the temperatures align around the same time as heavy snowfall blanketed the corpse on December 9, 2005. This effect was indicative of an energy balance that occurred between the maggot mass and the ground, acting as a repository for heat for the other. Additionally, the ambient temperature, the blue line, was cooler than both the maggot mass and the soil temperature during much of the time period.

As mentioned earlier, there were a couple of periods during the experimental period when snowfall covered the carcasses (Figure 7.7). The first was from November 23, 2005 through

November 25, 2005 and a second period was from December 3, 2005 through December 28,

2005. There were also several periods where trace amounts of snowfall on the ground. However due to the body temperature of the carcasses, there was little to no snow lying directly on the body surface of the carcasses. During the period of heaviest snow cover, from December 8, 2005 through December 28, 2005, ground temperatures remained relatively stable varying within 2°C

(Figure 7.8). Figure 7.8 also shows that maggot mass temperatures also remained relatively consistent, ranging within 2°C, except on December 24, 2005. It is hypothesized that the increase in maggot mass temperature during the afternoon of the 24th was a response to increased solar radiation from clear skies above.

Ambient Temperature Comparison Between Control and Wooded Environments 30.0 y = 0.9613x + 0.0974 25.0 2

) R = 0.9288

ºC 20.0 ( 15.0 10.0 5.0 0.0 -5.0 -10.0

-15.0 Exposed Temp Ambient Site -20.0 -25.0 -25.0 -20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 KUNV Ambient Temp (ºC)

Comparison of Ambient Temperatures Between the Control and Exposed Environments 30.0 y = 0.9739x - 0.8464 25.0 R2 = 0.9003

20.0

) ºC ( 15.0 10.0 5.0 0.0 -5.0 -10.0

Shaded Ambient Temp Temp Ambient Shaded -15.0 -20.0 -25.0 -25.0 -20.0 -15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 KUNV Ambient Temp (ºC)

Figure 7-5: Scatterplots of the comparison between ambient temperatures recorded at the experimental sites woodland (top) and open grassland (bottom) with the control site, KUNV.

Figure 7-6: Time series of 2” soil temperature, maggot mass temperature, and ambient temperature.

Figure 7-7: Snowcovered carcass in open grassland. Photo taken December 7, 2005. Snowfall on the ground measured 1.4 inches.

Ground Temperature Fluctuations During Period of Snowcover on Exposed Carcass 24 11.0 22 9.0 20 18 7.0 16 5.0 14 12 3.0 10 1.0 8

Snow Cover Snow (inch) 6 -1.0 4 -3.0 Ground Temperature (°C) 2 0 -5.0 12/8 12/10 12/12 12/14 12/16 12/18 12/20 12/22 12/24 12/26 Date Snow Cover Ground Temperature

Maggot Mass Temperature Fluctuations During Period of Snowcover on Exposed Carcass 24 11.0 22 9.0 20 18 7.0 16 5.0 14 12 3.0

10 Maggot 1.0 8

Snow Cover Snow (inch) 6 -1.0

4 MassTemperature (°C) -3.0 2 0 -5.0 12/8 12/10 12/12 12/14 12/16 12/18 12/20 12/22 12/24 12/26 Date Snow Cover Maggot Mass Temperature

Figure 7-8: Comparison of Tg (top) and Tm (bottom) and snow cover in the open grassland.

74 To further investigate the consistencies between the ground and maggot mass temperatures during this period of heavy snow cover, a time series comparison was made over the same period of time as used in figure 7.8 (Figure 7.9). There were periods of time were the maggot mass temperature and the ground temperatures were similar. A Pearson correlation for the entire time period was also calculated to be ρ=0.742, p-value=0.000 indicating that there is a strong positive correlation between ground temperature and maggot mass temperature.

Ground and Maggot Mass Temperature Fluctuations During Period of Heavy Snow Cover 10.0

8.0

6.0

4.0

2.0 Temperature(°C)

0.0

-2.0 12/8 12/10 12/11 12/13 12/14 12/16 12/17 12/19 12/20 12/22 12/23 12/25 12/26 12/28 Date Ground Temperature Maggot Mass Temperature

Figure 7-9: Time series of Tg and Tm while carcass was snow covered from December 8, 2005 through December 28, 2005.

From this, it was hypothesized that there was an energy exchange occurring between the soil and the maggot mass that was creating the stabilization of the temperatures. To look into this further, the temperature gradient (dT/dz) was calculated between the soil and the maggot mass

(Figure 7.10). The distance variable (dz) was estimated to be ten centimeters between the soil temperature sensor and the maggot mass temperature sensor. Figure 7.10 shows an energy

75 balance occurring between the ground and the maggot mass as indicated by the thermal gradient between the maggot mass and the soil being zero in the exposed environment, and nearly zero in the shaded. This energy balance specifically between the soil and maggot mass temperatures was maintained throughout the winter months.

Thermal Gradient Comparison of Heat Away from the Carcass in an Exposed Environment During December 14, 2005 through December 20, 2005 1.2

0.9

0.6

cm / 0.3

0 1:01 12:01 23:01 10:01 21:01 8:01 19:01 6:01 17:01 4:01 15:01 2:01 13:01 0:01 -0.3

-0.6

Thermal Gradient ºC Gradient Thermal -0.9

-1.2

-1.5 Time dT/dz Soil dT/dz Air

Observationally, the behavior of the maggot mass during the winter months was quite interesting. During the spring/summer, insect consumption of the carcass occurred from head to tail on all five carcasses. Initially, eggs were laid in the head area within the nostrils, eyes, ears, and mouth. Oviposition preference was noted in areas where the body would offer some protection to the eggs similar to results found for Calliphoridae by Archer and Elgar (2006).

Once hatched, the larvae tended to congregate, consume all soft flesh, and move together in a mass in one direction towards the hind end of the carcass (Figure 7.11). While chemical

76 decomposition of the carcass was occurring, as indicated by the odors associated with the carcass, primary decomposition of the carcass was accomplished through the consumption of the carcass by the maggots. However, during the winter experiments, all four carcasses did not follow this pattern of oviposition preference, maggot mass movement, and decomposition.

Figure 7-11: Pattern of “normal” insect feeding during the decomposition process (Photo taken during Spring 2005 experiment).

Instead, all four pigs decomposed from the ground up instead of head to toe. Initial insect colonization occurred deep within the mouth within days of placement in the field; however, there was limited oviposition occurring on the eyes, nose, or the ears as compared with the spring carcasses (Figure 7.12). Once hatched, the maggots consumed much of the soft flesh within the mouth, and nasal cavity but the soft tissue and skin remained on the snout and rest of the head

(Figure 7.13). Temperatures soon turned colder and the pattern of feeding by maggots changed.

Instead of feeding from head to toe, as they do in the spring/summer, the maggots moved into the

77 brain and neck cavities and remained close to the ground until the end of the experimental period

(Figure 7.14). Figure 7.14 shows that overall consumption of the carcass occurred primarily within the body cavity, leaving the outer skin for protection.

Figure 7-12: Photo of head of carcass taken one week after field placement in winter (top) and spring (bottom).

Figure 7-13: Photograph taken after three weeks in the field. Much of the interior of the mouth has been consumed; however, all of the skin on the outside of the snout remains.

As shown in the photograph on the right of figure 7.14, showing the underside of the carcass, much of the muscles and tissues were eaten, some layers of fat and skin remained on the underside closer to the spine. Much of the tissue was removed from the abdominal area. Much of the internal organs were either consumed by maggots or liquefied by enzymes associated with the chemical breakdown of the organs.

80 7.4 Conclusions

These experiments demonstrated that larval survival is possible throughout the winter.

Therefore, entomological evidence collected during the winter months cannot be dismissed. More study is needed to determine how PMI estimations from entomological evidence should be handled in these wintertime situations. This experiment was designed to explore the possibility of larval survival during sub-optimal conditions for survival. It should be noted that some of the larvae were already into the 3rd instar before the cooler temperatures arrived. It is not the intention of this work to imply that all larvae would be able to survive the winter. This work is simply the beginning showing that wintertime larval survival is possible and should not be discounted during homicide investigations.

81 Chapter 8

Summary and Conclusion

Forensic entomologists estimate the PMI by applying the knowledge about the arrival time of each species and the extent of corpse decomposition. While the inclusion of forensic entomological evidence is widely accepted in courtrooms (Lord 1998, Campobasso and Introna

2001), the validity of such data has been called into question. In a recent, highly publicized case in 2002, the Danielle Van Dam Murder (Court TV 2005), seven forensic entomologists were divided as to the length of PMI, which ranged from 1 to 23 days. During the court proceedings, two entomologists wavered on their derivations and acknowledged the possibility of different values, which were based on weather station data that in reality do not represent the microclimate regime in and around the victim’s body at the scene. This disparity in the PMI determination made it difficult for the jury to assess the entomological data in light of other evidence.

Because of this conflicting testimony by forensic entomologists, lawyers now often question the validity of entomological evidence and are taking time to learn about the science and the limitations inherent in the forensic analysis, such as the PMI calculation. These limitations, coupled with disagreement among experts as to the correct derivation of PMI, have exposed technical difficulties associated with this forensic procedure.

From the experimental data presented in this thesis, it is easy to see that microclimate plays a significant role in decomposition patterns, insect colonization, and larval survival. First, the temperature at the closest official weather station was not necessarily a good predictor of temperature at the experimental sites. Methods of site-specific weather modeling could greatly improve estimations of ambient temperatures allowing for greater accuracy in PMI estimations. Secondly, vegetation surrounding a carcass can impact the temperature in the area surrounding the carcass. Vegetation plays a role in the thermal gradients within an environment.

82 In areas with lush vegetation (i.e. woodlands), diurnal variations in temperature are greatly reduced compared with open environments with little no canopy vegetation. Thirdly, maggot mass temperature greatly impacts the immediate environmental temperature. Studies on the temperature distribution within maggot masses need to be further studied to better understand the effect the maggot mass as a whole has on the immediate temperature environment surrounding the carcass.

Finally, the winter experiment showed that insect larvae can and do survive temperatures much lower than published minimum threshold temperatures. Larval survival may in fact be linked to the role soil plays in maintaining temperatures higher than ambient air temperatures.

More study may be necessary to fully characterize the role the soil plays in maintaining temperatures conducive to larval survival both in ideal and substandard climates and seasons.

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91 Appendix

General Methodology for Determining PMI

It has long been established that insect developmental rates are primarily temperature dependent and can be mathematically represented in developmental equations. These developmental equations are commonly used to predict the timing of key life stages of pests in pest management systems. They are also used to help estimate the post-mortem interval (PMI) for key dipteran species of importance in forensic investigation. The objective of this paper is to provide a step-wise illustration of how a complete set of development rate data can be used to estimate degree hours for each life stage and how a measurement of individual variation in developmental duration (i.e. standard deviation) can be used to estimate a simple statistical interval for the PMI.

A.1 Development Equation and Degree Hour Derivation

Data concerning the development rates of insects important in forensic entomology are rarely reported in the form of development equations. In most cases, the information is presented as a table containing the number of hours the insect spent in each life stage at different fixed

(constant) temperatures (Figure A.1).

Figure A-1: Example of how insect development information is presented in literature. (From Byrd & Allen, 2001)

92 To apply this information to a wider range of temperatures, developmental equations can be derived by fitting either linear or nonlinear regression lines to the developmental duration estimates for each temperature and insect life stage. The first step in fitting the developmental equation is to calculate the rate of development for each life stage at each temperature. This is done by taking the reciprocal of the mean life stage duration (in hours) at each temperature. For example, at 40°C, the mean stage duration of first instar maggots was 12 hours; therefore, the rate of development for the maggots at 40°C is 1/12 or 0.083. Once the reciprocal of stage duration is calculated for each temperature, the development equation can be estimated using linear or nonlinear regression (Figure A.2).

Development rate for the 1st instar life stage for P. regina

0.09

0.08 y = 0.0026x - 0.0232 R2 = 0.9785 0.07

0.06

0.05

1/hrs. 0.04

0.03

0.02

0.01

0 0 5 10 15 20 25 30 35 40 45 Temp (°C)

Figure A-2. Example of linear regression for the 1st instar of Phormia regina (Meigen).

Using the fitted developmental equations (Table A-1), the duration of each life stage can be predicted at any temperature (Table A-2). For example, the estimated hours spent in the 1st instar based upon the derived equation (Table A-)1 is shown in Equation 1.

93 Hours = 1/((0.0026*X) - 0.0232) , where X = temperature (1)

Table A-1.Development equations for all immature life stages derived from Byrd and Allen (2001 for Phormia regina (Meigen). The value of Y is the reciprocal of the hours spendt in each life stage (1/h), while X is the temperature in degrees Celsius. Life Stage Development Equation R2 P-value Egg Y= 0.00273X – 0.0188 86.5% 0.007 1st Instar Y= 0.00259X – 0.0232 97.9% 0.000 2nd Instar Y= 0.00129X – 0.0105 96.1% 0.001 3rd Instar Y= 0.000395X + 0.00072 75.6% 0.024 Post-feeding Y= 0.000181X + 0.00119 69.4% 0.039 3rd Instar Pupa Y= 0.000207X – 0.000606 94.3% 0.001

Table A-2.Predicted developmental duration calculated using derived equations. Data presented in hours. Temp. Post-feeding egg 1st Instar 2nd Instar 3rd Instar Pupa (°C) 3rd Instar 40 11.06 12.44 24.33 60.53 118.62 130.31

35 13.03 14.83 28.86 68.75 132.89 150.63

30 15.85 18.35 35.46 79.55 151.057 178.44

25 20.22 24.07 45.98 94.38 174.98 218.87

20 27.93 34.97 65.36 116.01 207.90 22.97

15 45.15 63.90 113.00 150.49 256.08 400.16

The base threshold for development (BTD) can also be calculated once the developmental equations are fitted (Table A-3). The BTD is calculated by setting the Y-value of the development equation to zero (i.e. the point where the developmental rate is zero) and then solving the development equation for its X (i.e. the temperature).

Table A-3.Minimum development temperatures for each immature life stage calculated from derived development equations. Minimum Development Life Stage Temperature (°C) Egg 6.89 1st Instar 8.96 2nd Instar 8.14 3rd Instar -1.82 Post-feeding 3rd -6.57 Instar Pupa 2.93 Average Minimum Development Temperature Egg through 2nd 7.99 Instar 3rd Instar through -1.82 Pupa Egg through Pupa 3.09

Using the developmental equations and the base threshold for development for each life stage, the estimated degree hours that the maggot spends in each life stage (stage duration) can be calculated. The estimated accumulated number of degree hours spent in a stage is calculated by subtracting the BTD from the temperature and then multiplying that value by the calculated development hours for that temperature (Equation 2). For example the estimated accumulative degree hours (ADH) spent in the egg stage is:

ADH(egg) = (temperature – BTD)*(1/Y) where, Y = 0.00273X – 0.0188 (2)

If calculated at each temperature it becomes clear that the accumulated degree hours is relatively constant across temperatures (Table A-4).

Table A-4.Accumulated degree hours for the life stages of Phormia regina (Meigen). Post- Temperature Egg 1st Instar 2nd Instar 3rd Instar feeding 3rd Pupa (°C) Instar 40 366.0 752.2 1528.3 4058.6 9584.4 14417.3 35 366.0 752.2 1528.3 4058.6 9584.4 14416.0 30 366.0 752.2 1528.3 4058.6 9584.4 14414.1 25 366.0 752.2 1528.3 4058.6 9584.4 14411.4 20 366.0 752.2 1528.3 4058.6 9584.4 14407.1 15 366.0 752.2 1528.3 4058.6 9584.4 14399.2

Once the ADH for each life stage has been calculated, it can be used to estimate the PMI.

Degree hours per hour are calculated by taking the measured hourly temperatures and subtracting the BTD. The degree hours per hour can then be summed until the value equals the ADH required to complete the stage. By calculating degree hours from the time of corpse discovery back in time using historic temperature data, the PMI can be estimated. For example, if the most advanced life stage collected on the corpse is 3rd instar, then 1528 degree hours back in time would be the minimum needed to estimate the time of death or corpse exposure to Phormia regina.

A.2 Estimating the Window of Uncertainty

Biology, however, is not as cut and dry as the development equations might suggest.

There is always variation in individual insect development within a population. The data presented by Byrd and Allen (2001) showed that there were differences in the number of hours each maggot remained in a life stage at each temperature. For example, at 40°C, maggots stayed in the 1st instar stage from between eight and sixteen hours. However, as development progressed, the range widened to the point that by the time the maggots achieved the pupa stage, the time each maggot individual remained in the pupal stage varied by up to a day and a half.

Another source of uncertainty is in the equations themselves. First of all, the equations were derived using the mean value for the hours spent in each life stage at each temperature. Even

96 though the standard deviation was presented in the literature, the derivation of the development equations does not take it into account. A way around this is to derive a set of developmental equations using the values for one standard deviation above and below the mean duration for each life stage and then following the process outlined in Table A.1 to arrive at ADH values. This would create a range of ADH values for each life stage (Table A-5).

Table A-5.ADH values using the mean and standard deviation as inputs for the derivation for each life stage based upon data presented in Byrd and Allen (2001) for Phormia regina (Meigen). ADH from Mean ADH from ADH Mean plus Life Stage minus Standard Mean Standard Deviation Deviation Egg 333 366 378 1st Instar 674 752 817 2nd Instar 1190 1527 1866 3rd Instar 2935 4059 5279 Post-feeding 3rd Instar 7245 9584 12082 Pupa 11650 14398 17893

Using the upper and lower ADH values, a range of possible PMI times can be calculated.