Modeling the Health of Free-Living Illinois Herptiles: an Integrated Approach Incorporating Environmental, Physiologic, Spatiotemporal, and Pathogen Factors

Home , Silvery salamander, Tutin (toxin)

MODELING THE HEALTH OF FREE-LIVING ILLINOIS HERPTILES: AN INTEGRATED APPROACH INCORPORATING ENVIRONMENTAL, PHYSIOLOGIC, SPATIOTEMPORAL, AND PATHOGEN FACTORS

LAURA ADAMOVICZ

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in VMS-Comparative Biosciences in the Graduate College of the University of Illinois at Urbana-Champaign, 2019

Urbana, Illinois

Doctoral Committee:

Assistant Professor Matthew Allender, Chair and Research Director Professor CheMyong Jay Ko Associate Professor Megan Mahoney Assistant Professor Rebecca Smith Professor Mark Mitchell, Louisiana State University Sharon Deem, Saint Louis Zoo Institute for Conservation Medicine

ABSTRACT

Human expansion has contributed to an unprecedented global environmental crisis and significantly impacted the stability of many wildlife populations. The health of wildlife populations influences their ability to recover from a complex array of anthropogenic and natural stressors. Promotion of positive health status may improve conservation outcomes in wild animals. However, wildlife health status is dynamic and determined by a variety of factors.

Studying such a complex system requires a comprehensive approach with careful consideration of multiple determinants simultaneously. The purpose of this dissertation is to holistically characterize health in three herptile species of conservation concern in Illinois by combining traditional veterinary health assessments with environmental and spatio-temporal data within a modeling framework. The main objective is to identify the best means of assessing wellness in wild herptiles to inform management strategies which support robust population health.

Health was investigated in three species of conservation concern in Illinois: the eastern box turtle (Terrapene carolina carolina), the ornate box turtle (Terrapene ornata ornata), and the silvery salamander (Ambystoma platineum) over the course of three years using a combination of physical examination, qPCR pathogen screening, and clinical pathology (box turtles). Mortality events were characterized as they occurred and spatiotemporal data were used to screen for locations and times associated with pathogen presence or poor health outcomes.

Diagnostic tests including hematology, plasma biochemistry, protein electrophoresis, venous blood gas, hemoglobin-binding protein, and erythrocyte sedimentation rate were investigated for clinical utility in box turtles, and a novel qPCR assay for detection of Mycoplasma sp. in box turtles was developed and validated. Health models were generated for each study species and used to identify drivers of poor health, determine the most clinically useful diagnostic tests for

ii each species, and generate management recommendations supporting good individual and population health.

Health assessment in silvery salamanders (N = 770) identified a parasite

(Amphibiocystidium sp.) that has never been previously detected in the Midwest and characterized significant larval mortality due to ranavirus (FV3). Poor health was best predicted by the additive effects of year, location, FV3 detection, and the presence of fresh traumatic injuries. FV3 is considered to represent a threat to silvery salamanders in Illinois, and recommendations for future research include characterization of silvery salamander biology and ecology, continued health monitoring to identify emerging threats, and focused study on ranavirus to identify potential management interventions. Ornate box turtle (N = 168) health assessment revealed a high prevalence of shell lesions associated with predator trauma (51-59% annually). There was no association between pathogen detection (Terrapene herpesvirus 1,

Terrapene adenovirus), clinical signs of illness, and clinical pathology alterations. Poor health was best predicted by the presence of active or inactive shell lesions and deviations from population median values for total leukocyte count, eosinophils, basophils, and heterophil:lymphocyte (H:L) ratios. Population viability analysis revealed that annual removal of two adult female turtles above background mortality rates dramatically increased the probability of population extinction within the next century (54-97%). Continued health assessment

(including hematology), mesopredator control, and disease screening of newly introduced turtles to prevent novel pathogen ingress was recommended to support individual and population wellness for ornate box turtles. Eastern box turtle (N = 507) health assessment uncovered mortality events due to FV3 and a suspected necrotizing soft tissue infection. Detection of other pathogens (Terrapene adenovirus, Terrapene herpesvirus 1, Mycoplasma sp., Salmonella

iii typhimurium) was not related to health status. Poor health was best predicted by the presence of active shell lesions, clinical signs of upper respiratory disease (URD), and deviations from population median values for total leukocyte count and H:L. PVA revealed that annual removal of 1 - 5 adult female turtles above background mortality rates dramatically increased the probability of population extinction within the next century for all populations, but the effects were especially pronounced in small populations from fragmented and degraded habitats. FV3 is considered a conservation threat for eastern box turtles, especially in smaller populations like the one at Kickapoo State Park which experience recurrent ranavirus outbreaks in both amphibians and turtles. Forest management to promote habitat quality, consideration of box turtle activity patterns when planning burn schedules, road signage to decrease vehicular strikes, additional research into ranavirus dynamics in natural systems, and continued health assessment to monitor trends and identify new threats were recommended to support individual and population wellness.

This dissertation demonstrates the feasibility of modeling health in wildlife and illustrates its use for determining drivers of poor health, identifying clinically useful diagnostic tests, and generating management recommendations which support overall individual and population wellness, improve conservation outcomes, and promote herptile and ecosystem health.

ACKNOWLEDGEMENTS

This dissertation has been made possible by many people, and I feel that I should start at the beginning in acknowledging them. First, I would like to thank my parents for encouraging my love of animals – especially creepy-crawly insects, reptiles, and amphibians, which are not the typical favorite taxa of young girls. I also thank Maryanne and Richard Hahn for taking me under their wing as a teenager at the Catoctin Zoo and instilling my passion for conservation, especially of herptile species. I would also not be here today without some excellent veterinary mentors that have helped shape my professional trajectory, including (in no particular order)

Sharon Deem, Greg Lewbart, Ellen Bronson, Vanessa Grunkemeyer, and many others.

The adage “it takes a village” doesn’t even come close to covering the number of people it took to make this dissertation happen. I am incredibly grateful to John Rucker and the turtle dogs, all the student members of the Turtle Team that helped collect my data, and especially to the student leaders of this project including Kayla Boers, Megan Britton, Marta Kelly, Samantha

Johnson, and John Winter. The passion and dedication of this group is inspiring, and their good attitudes are infectious. My population-level data would also be woefully incomplete without the contributions of several ecologists, post-docs, and grad students affiliated with the Illinois

Natural History Survey including Chris Phillips, Michael Dreslik, Sarah Baker, Ethan Kessler, and Kelsey Low. The hard work, knowledge, and good humor of this group made all the long, hot (and cold!) fieldwork days much more enjoyable. I have also received a huge amount of help from my lab mates Jeremy Rayl and Ellen Haynes and our laboratory technician Emilie Ospina.

Thanks for being a sounding board for ideas and sharing food, laughs, and caffeine these last few years!

I thank my committee members, Sharon Deem, Mark Mitchell, Rebecca Smith, Megan

Mahoney, and Jay Ko for their advice and guidance during this project. I also thank my major advisor, Matt Allender, for his continual support throughout this process, and the Comparative

Biosciences Department for their generous scholarship funding. Additional funding for this project was provided by the Friends of Nachusa and a State Wildlife Grant (T-104-R-1). Last, but not least, I thank my husband Nick for his help, flexibility, and love through all the moving and stress of vet school, internships, and grad school. This has been an amazing learning experience, and I am proud to present my dissertation and contribute to existing scientific knowledge about health and disease in free-living herptiles.

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ...... 1

CHAPTER 2: LITERATURE REVIEW ...... 6

PART I: DIAGNOSTIC TECHNIQUES ...... 41

CHAPTER 3: TISSUE ENZYME ACTIVITIES IN FREE-LIVING EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA) ...... 42

CHAPTER 4: VENOUS BLOOD GAS IN FREE-LIVING EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA) AND EFFECTS OF PHYSIOLOGIC, DEMOGRAPHIC, AND ENVIRONMENTAL FACTORS ...... 60

CHAPTER 5: ERYTHROCYTE SEDIMENTATION RATE IN FREE-LIVING EASTERN (TERRAPENE CAROLINA CAROLINA) AND ORNATE (TERRAPENE ORNATA ORNATA) BOX TURTLES AND HEMOGLOBIN BINDING PROTEIN IN ORNATE BOX TURTLES: REFERENCE INTERVALS AND DEMOGRAPHIC EFFECTS ...... 92

PART II: SILVERY SALAMANDER HEALTH AND DISEASE ...... 117

CHAPTER 6: INITIAL CHARACTERIZATION OF AMPHIBIOCYSTIDIUM SP. INFECTION IN A MIDWESTERN STATE-ENDANGERED SALAMANDER (AMBYSTOMA PLATINEUM) ...... 118

CHAPTER 7: HEALTH ASSESSMENT OF THE STATE-ENDANGERED SILVERY SALAMANDER (AMBYSTOMA PLATINEUM) IN VERMILION COUNTY, ILLINOIS ...... 136

PART III: ORNATE BOX TURTLE HEALTH AND DISEASE ...... 166

CHAPTER 8: IDENTIFYING DETERMINANTS OF INDIVIDUAL & POPULATION HEALTH IN FREE-LIVING ORNATE BOX TURTLES (TERRAPENE ORNATA ORNATA): RECOMMENDATIONS FOR MANAGEMENT AND MONITORING ...... 167

PART IV: EASTERN BOX TURTLE HEALTH AND DISEASE ...... 211

CHAPTER 9: INVESTIGATION OF MULTIPLE MORTALITY EVENTS IN EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA) ...... 212

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CHAPTER 10: IDENTIFYING DETERMINANTS OF INDIVIDUAL & POPULATION HEALTH IN FREE-LIVING EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA): RECOMMENDATIONS FOR MANAGEMENT AND MONITORING ...... 248

CHAPTER 11: CONCLUSIONS & FUTURE DIRECTIONS ...... 299

CHAPTER 12: REFERENCES ...... 302

APPENDIX A: DEVELOPMENT OF A QUANTITATIVE POLYMERASE CHAIN REACTION FOR DETECTION OF EMYDID MYCOPLASMA SP...... 386

APPENDIX B: INITIAL ASSESSMENT OF THE CLOACAL MICROBIOME IN EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA): CHARACTERIZING DEMOGRAPHIC AND ENVIRONMENTAL ASSOCIATIONS ...... 394

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CHAPTER 1: INTRODUCTION

Human expansion has created an unprecedented global environmental crisis characterized by habitat loss and fragmentation, modification of ecological processes, unsustainable harvesting practices, environmental toxification, climate change, and introduction of non-native plants, animals, and pathogens (Pimm 1995, Wilcove 1998, Tabor 2001, Clavero 2005, Aguirre 2002).

Wildlife are increasingly threatened by this complex array of interacting stressors, and as a result current extinction rates are 100-1,000 times higher than historical baselines (Pimm 1995).

Understanding and promoting wildlife health is important for public health, food safety, and proper ecosystem functioning. Approximately 60-75% of human emerging infectious diseases are zoonotic, and the majority of these, such as West Nile virus, Ebola, and Nipah virus, originate in wildlife (Anderson 1999, Monath 1999, Chua 2000a, Chua 2000b, Taylor 2000,

Jones 2008, Steele 2000). Wildlife can similarly serve as sources or reservoirs for economically important diseases of livestock, including avian influenza, brucellosis, foot and mouth disease, bovine tuberculosis, and others. Disease emergence from wildlife is promoted and sustained by progressive encroachment of humans and domestic species into wild spaces; harvesting of wildlife for bushmeat, traditional medicines, and the pet trade; progressive loss of biodiversity and degradation of ecosystem services (which can release pathogens from biological control); climate change affecting host and vector distribution; and rapid global transport of infected people, animals, plants, and associated materials (Cunningham 1996, Vitousek 1997, Daszak

2000, Ostfeld 2000, Ross 2000, Daszak 2001, Karesh 2005). Continued human development and unsustainable land use practices are likely to perpetuate these problems, highlighting the need to

1 study wildlife health in order to protect people, livestock, and ecosystems (Dobson 1986, Mahy

2000, Daszak 2001, Aguirre 2002, Aguirre 2012).

Understanding wildlife health is also important for conservation, as the underlying health status of a population can affect its resiliency in the face of multifactorial natural and anthropogenic stressors (Aguirre 2002). The effects of infectious diseases on wildlife conservation have garnered much attention in recent years (Lafferty 2002, Lafferty 2003a, De

Castro 2005, Smith 2006, Pedersen 2007, Smith 2009a, Thompson 2010). Disease-induced extinction has been documented in the sharp-snouted day frog (Taudactylus acutirostris) and a species of Polynesian land snail (Partula turgida), and it has been recognized as a contributing factor for extinction in several other species (McCallum 1995, Cunningham 1998, Daszak 1999,

Woodroffe 1999, Van Riper 2001, Van Riper 2002, Lips 2006, Schloegel 2006, Smith 2006,

Wyatt 2008). Disease-related extirpations and ecological assemblage alterations have also been described, clearly illustrating the importance of health for species and ecosystem stability

(Thorne 1988, Cleaveland 1995, Berger 1998, Cunningham 1998, Daszak 1999, Harvell 1999,

Harvell 2002, Smith 2009a, Smith 2009b). Beyond direct mortality, non-lethal health problems such as endocrine disruption, immunosuppression, physical deformity, and altered fecundity secondary to disease, toxins, and other stressors can be insidious, pervasive, and equally damaging to the sustainability of wildlife populations, underscoring the need for holistic health assessment practices to support wildlife conservation (Dobson 1986, McCallum 1995, Daszak

1999, Ross 2000, Lafferty 2003b, Walsh 2003).

While there are several compelling reasons to study wildlife health, it can be a daunting task due to challenges associated with experimental design, data collection, and interpretation of findings (Mörner 2002, Stallknecht 2007, Wobeser 2006). These impediments are exacerbated

2 by a poor understanding of “normal” health parameters and their age, sex, and seasonal variation, incompletely characterized disease epidemiology, lack of validated diagnostic tests, and absence of baseline data documenting expected levels of disease prevalence, toxin exposure, and mortality rates (Stallknecht 2007, Artois 2009, Rhyan 2010, Sleeman 2012, Ryser-Degiorgis

2013, Jenkins 2015). These hurdles, combined with scarce funding for such studies, have historically contributed to a very reactionary response to wildlife health issues and a narrow focus on individual threats, with limited consideration for the possibility of effect modification from concurrent stressors (Stephen 2015a, Stephen 2018). This approach is problematic because it can provide limited or misleading information on wildlife health threats, and by the time a problem is detected and investigated, intervention may be expensive, technically difficult, and have limited efficacy (Stephen 2018). In recognition of these issues, leaders in the field of wildlife health and conservation medicine have called for a more comprehensive and proactive approach to better understand and support wildlife health, positing that cultivating health, instead of just combatting specific threats, may improve population resiliency in the face of stressors, promoting better ecosystem functioning and benefitting the wellbeing of all species, including humans (Rhyan 2010, Aguirre 2012, Hanisch 2012, Stephen 2015a, Stephen 2015b, Stephen

2018).

Epidemiologic modeling is utilized to explore complex systems, control for confounding factors, quantify uncertainty, and identify predictors (risk factors) associated with disease outcomes. In the field of wildlife health, models are commonly employed to understand disease dynamics (Craft 2011, McCallum 2015, White 2018), assess the potential efficacy of management interventions (McCallum 2015, Drawert 2017), and quantify risk (e.g. Yap 2015,

Richgels 2016). As with many wildlife health studies, these models tend to focus on individual

3 disease outcomes, and their utility for promoting overall wellness may be limited. This dissertation seeks to determine whether this powerful modeling framework can be applied to identify factors supporting positive health status in wildlife using data from a large-scale, longitudinal, multi-species health assessment program for herptiles (reptiles and amphibians) in

Illinois. The overall objective is to identify the best means of assessing wellness in wild herptiles to inform management strategies which support robust population health. This objective will be achieved through the following three specific aims, with associated testable hypotheses:

MAIN HYPOTHESIS: Modeling approaches which incorporate comprehensive veterinary health assessments with environmental, spatial, and temporal factors will improve the characterization of health status in wild populations of Illinois herptiles, identify drivers of wellness, prompt the development of practical field health assessment protocols, and support the design and implementation of effective conservation management strategies.

Specific Aim 1. Assess the utility and validity of physical examination, clinical pathology,

and pathogen detection for health assessment in three species of Illinois herptiles.

• Hypothesis 1. Plasma biochemical enzymes will be associated with specific tissue

distributions in eastern box turtles (Chapter 3).

• Hypothesis 2. qPCR will be sensitive and specific for detection of Mycoplasma sp. in

box turtles (Appendix A).

• Hypothesis 3. Clinical pathology profiles for eastern box turtle (Terrapene carolina

carolina) and ornate box turtle (Terrapene ornata) populations in Illinois will differ

based on body condition, location, season, sex, age class, and quantitative PCR (qPCR)

pathogen detection status (Chapters 4, 5, 8, 9, 10).

• Hypothesis 4. Physical examination abnormalities, body condition, and weight will differ

based on location and qPCR pathogen detection status in silvery salamanders

(Ambystoma platineum) (Chapter 7).

• Hypothesis 5. qPCR pathogen detection will differ based on sex, age class, season, and

location for all species investigated (Chapters 7, 8, 9, 10).

• Hypothesis 6. Eastern box turtle cloacal microbiome diversity and richness will vary by

location, sex, age class, and season (Appendix B).

Specific Aim 2. Investigate novel disease presentations (Chapter 6) and the causes of historic and ongoing mortality events.

• Hypothesis 7. Mortality events will have predictable pathogen detection patterns, spatial

influences, and temporal associations (Chapters 7 & 9).

Specific Aim 3. Generate models to evaluate individual and population-level health.

• Hypothesis 8. Potential drivers of individual and population health will be identified

using deterministic and stochastic models. These models will identify the most cost-

effective and clinically useful diagnostic tests for health assessment, and will enable the

design of evidence-based conservation strategies supporting individual and population

health (Chapters 7, 8, 10).

CHAPTER 2: LITERATURE REVIEW

Defining Health

Achieving and maintaining good health is a fundamental goal for promoting wellness in individuals, populations, communities, ecosystems, and the biosphere which encompasses each of these smaller units. Despite the importance of health, it is notoriously difficult to define.

Debates on the definition of health have taken place in human medicine for centuries, and consensus has still not been fully achieved (Breslow 1972, Callahan 1973, Boorse 1977, Saracci

1997, Larson 1999, Brulde 2000, Richman 2000, Khushf 2007, Schramme 2007, Jadad 2008,

Huber 2011, Bircher 2014, Lerner 2014, Svalastog 2017).

Historically, health was defined negatively (i.e., what health is not) as the absence of illness. More recent definitions have shifted towards positive (i.e., what health is) approaches.

Perhaps the most famous of these belongs to the World Health Organization, which defined health in 1946 as “A state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity”. In 1998, this definition was amended, including that health should be considered as a dynamic state of physical, mental, social, and spiritual wellbeing. This positive definition ensures a more holistic consideration of health, accounting for biological, mental, and social needs (Niebrój 2006). Some medical philosophers have taken this one step further by proposing “harmonistic” definitions of health, reviving a concept that has been around since ancient Greece (Svalastog 2017). This view emphasizes homeostasis and defines health as

“A unity and harmony within the mind, body, and spirit which is unique to each person and is as defined by that person. The level of wellness or health is, in part, determined by the ability to deal with and defend against stress. Health is on a continuum with movements between a state of

6 optimum well-being and illness which is defined as degrees of disharmony. It is determined by physiological, psychological, sociocultural, spiritual, and developmental factors” (Niebrój 2006).

The concept of health as a continuum not only acknowledges fluctuations over time, something experienced by all living organisms, but also allows for “normal” to be slightly different for each person and implies that “normality” can shift over time due to natural processes like aging

(Niebrój 2006). An interesting extension of the concept of health has been submitted recently due to the advance of value-based care for our aging human population (Card 2017), which defines health fairly simply as “The experience of physical and psychological well‐being”. Good health and poor health do not occur as a dichotomy, but as a continuum. The absence of disease or disability is neither sufficient nor necessary to produce a state of good health.” This definition has substantial utility for value-based care because it does not necessarily aim for the

(potentially) unachievable goal of “perfect” health, instead prioritizing improving and maintaining a level of health which is acceptable to the patient (Card 2017). These positive, holistic notions of health can be extended to domestic and wild animals, though this has not been attempted as aggressively for veterinary species as for humans (Gunnarsson 2006).

Animal health is typically considered within the context of homeostasis and population stability. Some authors define wildlife health as:

1. “The capacity to deal with life’s demands, changes, and challenges.” (Stephen 2014)

2. “[The] ability to efﬁciently respond to challenges (diseases) and effectively restore

and sustain a ‘state of balance’.” (Deem 2008)

3. “A condition in which an organism is physically and biochemically complete, does

not experience abnormal growth or atrophy of its component parts [...] and does not

experience drastic changes in its physical appearance or normal functions.” (Mazet

2006)

4. “The outcome of non-linear, dynamic interactions between individuals and their

social, biotic and abiotic environments.” (Stephen 2015a)

5. “The cumulative effect of a complex system of interacting threats, hazards, and

determinants of health that vary in time, space and populations, making research

results subject to effect modiﬁcation in different populations at different times. Being

a product of a complex system, surprises, unanticipated outcomes, and emerging

phenomena are a normal part of health.” (Stephen 2017)

6. “‘A multidisciplinary concept […] concerned with multiple stressors that affect

wildlife. Wildlife health can be applied to individuals, populations, and ecosystems,

but its most important deﬁning characteristics are whether a population can respond

appropriately to stresses and sustain itself. […] The concept of health is more all-

encompassing and holistic than is disease. Disease is one part of the broader concept

of health and, consequently, health is more than simply the absence of disease. Health

is generally considered a state of being within which disease is a speciﬁc signiﬁcant

abnormal condition or deviation from health. […] Both health and disease must be

emphasized in wildlife management. A focus on disease is sometimes necessary but

alone it is insufﬁcient. A broader focus on all aspects of wildlife health is needed to

achieve healthy, sustainable wildlife populations. A focus on health may bring about

a shift from documentation of disease occurrences to prevention and will help

managers better understand what factors are causing the disease.’’ (Hanisch 2012)

In contrast, wildlife disease has been defined as:

1. “Any compromised (unhealthy) state in which an individual is unable to perform its

ecological roles in an ecosystem, due to either an infectious (e.g., viral, bacterial,

fungal, parasitic) or noninfectious (e.g., toxins, trauma) etiology.” (Deem 2008)

2. “An infection of a host with a pathogen that affects the host’s form or function.”

(Hanisch 2012)

3. “An abnormal condition with recognizable signs, symptoms, and laboratory ﬁndings.”

(Hanisch 2012)

4. “A non-balanced perturbation of one or more body function(s), including responses to

infectious and non-infectious agents. Disease can affect individual hosts by reducing

growth rates or fecundity, increasing metabolic requirements, or changing patterns of

behavior. It may ultimately cause death.” (Rsyer-Degiorgis 2013)

As in humans, it is important to note that health is more than just the absence of disease.

This is especially true for wildlife, because disease is a natural part of ecosystems. Some level of pathogen infection is expected and typically well-tolerated in wild animals, especially if the organism co-evolved with its host (Thompson 2010). However, the degree of tolerable disease depends upon host and pathogen factors, environmental quality, temporality, and the presence of concurrent stressors, illustrating that the relationship between health and disease in wildlife is complicated and likely exists on a continuum (Pirofski 2012, Méthot 2014). Furthermore, the presence of significant disease at an individual level does not necessarily imply poor health at the population level, indicating that assessment of health at multiple scales is necessary to inform appropriate management strategies in wildlife (Stephen 2014).

The above definitions of wildlife health recognize the potential for many interacting determinants from biological, ecological, and external sources which all have the capacity to

9 affect individual and population wellness (Stephen 2014). This demonstrates the need for health assessment strategies that go beyond the effects of individual hazards, and instead consider health within the context of a range of potential drivers at different spatial and temporal scales.

Wildlife health was not always viewed in such a holistic context and our current understanding represents a natural progression of thought based on several decades worth of research effort.

Scientific study of wildlife health is fairly new, with the publication of the first textbook on wildlife disease occurring just over a century ago in 1914 and the establishment of the first

American wildlife disease laboratory in Michigan in 1933 (Lowenstine 2006, Hanisch 2012).

Consideration of wildlife diseases from a management standpoint was first described in detail in

Aldo Leopold’s book “Game Management” in 1937, and the Wildlife Disease Association, an international scientific organization dedicated to the study and understanding of wildlife health, was officially established in 1952. The American Association of Wildlife Veterinarians was founded in 1979, and the discipline of conservation medicine, which links human, animal, and ecosystem health within the framework of unprecedented global environmental change, was created in the late 1990s (Kock 1996). Important development and advancement of wildlife health research has therefore occurred within the last few generations of scientists.

Historical studies of wildlife health typically occurred in response to obvious morbidity and mortality events and focused on determining the specific etiologies of these events (Aguirre

2002, Aguirre 2012, Stephen 2015a, Stephen 2018). This became especially true with the advent of One Health which (perhaps unintentionally) refocused research efforts onto zoonotic diseases, frequently considering wildlife only as the source of pathogens, as opposed to potential victims

(e.g. Ebola virus in gorillas) or components within a complex system promoting disease emergence (Bermejo 2006, Hanisch 2012, Grogan 2014, Stephen 2015a, Stephen 2015b, Buttke

2015, Manlove 2016). In fact, one recent literature review determined that over 90% of wildlife health publications in the last 10 years focused on determinants of poor health by way of pathogen surveillance, morbidity and mortality investigations, transmission cycles, and host- parasite interactions (Stephen 2018). These studies in disease ecology, epidemiology, toxicology, and pathology are essential for establishing “normal” baselines, characterizing certain health hazards, and highlighting the complexity of health and disease in natural systems, especially when viewed against the backdrop of progressive anthropogenic change. However, while knowledge gained from these studies is incredibly useful, focusing on determinants of poor health fosters a very reactionary, and frequently incomplete, approach to managing wellness in wildlife. Furthermore, attempting to manage established health threats can be expensive, logistically difficult, controversial, and can have variable efficacy, prompting a call to move towards more proactive, preventative approaches to wildlife health management (Sleeman 2012,

Joseph 2013, Langwig 2015, Stephen 2017, Stephen 2018).

Proactive “harm-reduction” approaches have been recently adopted in Canada. These approaches recognize that many wildlife health threats cannot be fully eliminated (climate change, persistent organic pollutant exposure, etc.), and instead focus on achievable changes that support health and enable animals to persist in the face of existing challenges (Stephen 2018).

This pathway supports a holistic view of health, recognizing the potential for multiple complex health determinants, the various cultural, ethical, and socioeconomic factors that drive decision- making about conservation issues, and the need to intervene in the face of uncertainty.

Importantly, this approach does not supplant efforts to mitigate the effects of specific hazards, but instead supplements them by promoting positive outcomes while allowing more time for development of effective hazard reduction strategies (Stephen 2018). This framework represents

11 a forward-thinking way to achieve the goal of improving wellness in wildlife, though it has yet to be applied and critically assessed. The practical utility of approaches that proactively support health therefore represents an important avenue for future conservation and wildlife health research.

Wildlife Health Assessment: Tools & Challenges

Methods for health assessment in wildlife are generally similar to those utilized in domestic animals (Grogan 2014). Complete physical examination, including weight and morphometric measurements, age and sex classification, assessment of reproductive status and body condition, and identification of acute and chronic abnormalities are often the first steps in a hands-on evaluation. Beyond this, we rely upon health indices provided by a variety of diagnostic tests. Routine clinical pathology tests such as hematology, biochemistry panels, protein electrophoresis, and urinalysis (among others) can be utilized to identify evidence of inflammation, infection, stress, neoplasia, organ dysfunction, and electrolyte disturbance associated with disease or toxicant exposure. Samples can be collected for serology, toxicology, cytology, molecular disease detection, bacterial and fungal cultures, virus isolation, and parasitologic evaluation depending on the methods and goals of the study. Basic and advanced medical imaging such as radiographs, computed tomography, and ultrasound are occasionally possible, but extremely limited by cost and the need to transport equipment or animals.

Destructive sampling enables necropsy evaluation and histopathology, which is frequently the most efficient method for obtaining information, but requires permanent and rapid collection of wild individuals.

Adjunctive tests which reduce animal capture events are also increasing in popularity.

Examples include hair, feather, salivary, or fecal monitoring for corticosteroid concentrations or pathogen burden (e.g. Sheriff 2011, Palme 2018, Bárbara 2017), detection of inflammation or febrile states using thermography cameras (e.g. Cilulko 2013), trail cameras for visually- identifiable diseases such as mange (Ryser-Degiorgis 2013), sample collection via drone (e.g.

Geoghegan 2018), and environmental DNA testing (e.g. Hall 2016), among others. While these non-invasive health assessment tools have utility to address specific research questions and monitor trends, they provide very limited health data with a high potential for sampling bias. As such, they should be considered adjuncts to the more traditional veterinary health assessment tests listed above.

While methods for health assessment in wildlife are similar to those used in routine veterinary practice, the nature of wild animals and diagnostic test limitations must be accounted for when planning and interpreting wildlife health assessment studies. There is frequently a lack of baseline knowledge about populations of interest in terms of size, demographic breakdown, behavior, and distribution; especially for species that are cryptic or that have no commercial or recreational value. The degree of expected natural variability in health indices and some or all aspects of disease epidemiology may also be uncharacterized (Mörner 2002, Wobeser 2006,

Stallknecht 2007, Sleeman 2012, Artois 2013, Ryser-Degiorgis 2013, Grogan 2014). As a result, wildlife health assessment studies must typically collect a variety of basic biological, climactic, and environmental data in addition to specific health information to facilitate diagnostic test interpretation and better understand the system of study (Stallknecht 2007, Ryser-Degiorgis

2013).

Logistical difficulties abound when it comes to sampling free-living wildlife in their natural habitats. Locating adequate numbers of animals to meet study objectives can be technically difficult, expensive, and time consuming, especially for small or cryptic species and those occupying remote locations (Stallknecht 2007, Artois 2009, Ryser-Degiorgis 2013, Grogan

2014). Field conditions can limit the use of specialized equipment due to transportation requirements, space, and the potential for damage. Sampling limitations can also be dictated by the nature of the study site (e.g., lack of refrigerator/freezer access for sample storage). Ideally, wildlife health studies should examine multiple populations longitudinally to characterize changes in health indices and disease prevalence over time, in different demographic groups, and in different locations (Mörner 2002, Stallknecht 2007, Artois 2009, Sleeman 2012, Ryser-

Degiorgis 2013). However, logistic and monetary hurdles can limit the extent and completeness of these investigations, leading to patchy understanding of the determinants of health in wildlife.

In light of these challenges, there are two main strategies for assessing health in wildlife: passive and active surveillance (Mörner 2002, Stallknecht 2007, Artois 2009, Sleeman 2012,

Ryser-Degiorgis 2013). Passive surveillance relies upon the identification and submission of animal remains in adequate post-mortem condition to an appropriate diagnostic laboratory by researchers, land managers, and the public. Passive surveillance is technically easy to perform, however, there is a high degree of inherent bias, as animals that are larger and more likely to occupy human traffic areas are much more likely to be detected and reported. This method is also poorly sensitive, as human detection of natural wildlife mortality events is extremely limited, even for large and abundant species (Stallknecht 2007, Ryser-Degiorgis 2013).

Furthermore, decomposition is an important ecosystem service, and dead animals are typically removed from the landscape by scavengers within 1-2 days of death, further decreasing detection

14 probability (Wobeser 1992). Remains recovered in advanced states of decomposition have significantly diminished diagnostic utility, as autolysis can interfere with histopathologic interpretation and pathogen detection methods (OIE 2018b). Finally, passive surveillance strategies are less likely to detect subtle problems affecting fitness, which may ultimately have significant impacts on population health (Grogan 2014). Despite these limitations, passive surveillance has been credited with initial detection of several important diseases in the United

States, including West Nile Virus (Steele 2000), chronic wasting disease (Williams 1980), avian vacuolar myelinopathy (Thomas 1998), and others (Brand 2013). Passive surveillance efforts can also be supplemented by wildlife rehabilitation centers and farmed wildlife, though these sample populations are still biased (Hartup 1996, Tangredi 1997, Deem 1998; Morishita 1998; Gulland

1999, Brown 2002, Kelly 2003, Duncan 2008, Sleeman 2008, Allender 2011, Randall 2012,

Ryser-Degiorgis 2013, Brand 2013, Sack 2017).

Active surveillance methods involve targeted screening for specific diseases or health states. This can be performed on convenience samples (e.g. screening of hunter-killed cervids for chronic wasting disease), banked biological specimens (e.g. surveying museum specimens for chytridiomycosis), or using prospective field studies, depending on research or management objectives. Active surveillance requires more time and resource investment, but can glean more information and potentially limit some of the bias associated with passive surveillance, depending on study design. Cross-sectional surveys are the most common experimental format for active health surveillance in wildlife because they require a single capture event, minimizing the time and financial resources required to complete a study (Stallknecht 2007, Ryser-Degiorgis

2013). While valuable health information can be gleaned from these studies, concentration on a single population at a single time point can fail to capture important spatial and temporal

15 variability in health status. Alternatives to enhance the utility of cross-sectional studies include serial evaluations of populations or individuals through the use of radiotelemetry. These studies are still subject to sampling bias depending on capture methodology, and the information obtained can be limited by the number and type of diagnostic tests pursued.

Frequently, active health surveys arise from unusual morbidity or mortality events detected by passive surveillance. Combinations of active and passive surveillance strategies can therefore be quite useful to study specific disease processes while simultaneously screening for changes in health status that may require further investigation. Recent examples of such efforts include high-profile mortality events such as the deaths of 200,000 saiga antelope (Saiga tatarica tatarica) in Kazakhstan over a three-week period due to Pasteurella multocida type B and catastrophic declines in bat populations due to white nose syndrome (Pseudogymnoascus destructans) (Blehert 2009, Brand 2013, Kock 2018). Incorporating knowledge from both surveillance strategies, especially within the context of longitudinal data, can support a more holistic understanding of wildlife health determinants (Brand 2013, Stephen 2015a).

Active health assessment approaches frequently rely on the use of antemortem diagnostic tests to characterize health status and identify pathogens and toxicants of concern. However, use of diagnostic tests in wildlife can be challenging for two main reasons, 1) lack of knowledge about test performance in the study species and 2) lack of baseline information on “normal” populations and disease epidemiology to inform interpretation of results. Many wildlife health assessment studies rely on diagnostic tests that were developed and validated for use in humans or domestic animals. Application of these tests to new species can change metrics of test performance such as sensitivity and specificity and can ultimately lead to erroneous conclusions.

Validation of diagnostic tests determines the fitness of an optimized and standardized assay in a particular species sampled in a certain way. It requires assessment of analytical characteristics followed by determination of diagnostic performance (OIE 2018a, 2018b).

Analytical characteristics include analytical sensitivity (i.e., operational range and limits of detection), analytical specificity (i.e. failure to detect closely or distantly related non-target organisms), and repeatability (level of agreement for the same sample within and between runs)

(OIE 2018a, 2018b). Diagnostic performance metrics include diagnostic sensitivity (the proportion of known positive animals that test positive) and diagnostic specificity (the proportion of known negative animals that test negative) based on comparison to a “gold-standard” test (or using latent-class models if a gold-standard is unavailable) (OIE 2018a, 2018b). The final stage of validation involves extensive application of the test in different laboratories to determine between-lab repeatability (e.g. ring testing). The entire process of assay validation typically requires hundreds of known positive and negative samples of appropriate quality from the species of interest, which can be impossible to obtain in wildlife (OIE 2018b). In recognition of this, the World Organization for Animal Health (OIE) has developed pared-down guidelines for diagnostic test validation in wildlife, though full validation based on these reduced guidelines can still be difficult to achieve (OIE 2018b). While validation is an important goal for applying diagnostic tests to wildlife, interpretation of test results must also account for the nature of the test and the pathogenesis of disease within the organism.

Diagnostic tests for infectious diseases can be split into two main categories: those targeting the causative agent and those detecting the host response to infection. Tests targeting the causative agent can include direct visualization (e.g. macroparasites), culture, molecular detection (e.g. polymerase chain reaction, PCR), and antigen detection (e.g.

17 immunohistochemistry). Cultivation of the causative agent followed by secondary confirmation

(sequencing or matrix-assisted laser desorption ionization time-of-flight mass spectrometry

[MALDI-TOF MS]) is the most specific means of pathogen detection and confirms that the agent is alive and potentially infectious. Isolation of the agent in culture also enables infection trials, which can be utilized to fulfill Koch’s postulates for novel agents, determine transmission routes, and study within-host pathogenesis (Evans 1976). However, pathogen isolation methods may have decreased diagnostic sensitivity compared to other approaches. For example, overgrowth by competing organisms can mask the presence of a bacterium or fungus of interest, poor sampling technique or handling can degrade the viability of the organism, some agents (such as Brucella spp.) must be cultured in specialized biosecure facilities, and some agents are not able to be isolated in culture (Rappe 2003). In contrast, molecular detection, especially quantitative PCR

(qPCR) using TaqMan primer-probes, is incredibly sensitive and specific. This advantage combined with a high degree of affordability has contributed to the widespread adoption of molecular diagnostics in wildlife studies in recent years (Black 2017). The main limitations to

PCR are the potential for decreased specificity if primers are not designed carefully (though this

“flaw” becomes quite advantageous for the development of consensus assays and facilitates deep sequencing for microbiome analyses and other applications), the need for post-hoc testing to confirm positives in some cases (sequencing for conventional PCR, melting curve analysis for

SYBR green qPCR), and the inability to differentiate between the presence of an infectious organism and non-infectious genetic material. Interpretation of any test targeting a pathogen must consider the location and timing of infection. Collecting an inappropriate sample or testing an appropriate sample at the incorrect time can result in false negative classification of an infected animal.

Prior to the advent of molecular diagnostics, serologic surveys were typically relied upon to identify exposure to pathogens of interest in wildlife. These tests rely upon the detection of antibodies, which typically persist beyond the presence of the infectious agent and lengthen the window in which pathogen exposure can be detected. However, lack of validated tests or species-specific reagents, cross-reactivity with non-target antigens, and variability in cutoff values are major challenges for interpretation of serologic testing in wildlife (Weyer 2008;

Horton 2010; Mansﬁeld 2011). This is further complicated by a lack of immunology knowledge in most wildlife species. For example, some animals may take multiple exposures to a pathogen prior to initiating an antibody response (e.g. ranavirus in Xenopus), the duration of detectable antibody following infection (and parturition, for maternal antibodies) is unknown in most species, and it is impossible to differentiate previously exposed but currently seronegative animals from those with a lack of exposure history based on serology alone (Gilbert 2013).

Serologic results can be especially challenging to interpret in groups such as reptiles, where research into immunological processes is in its infancy.

Routine veterinary diagnostic tests such as hematology, biochemistry panels, and protein electrophoresis are universally applicable, with some taxon-specific approaches. For example, automated hematology analyzers can be used for mammalian species, but manual hematology methods must be used for birds, reptiles, amphibians, and fish due to the presence of nucleated red blood cells in these groups. These manual methods require more time and technical experience to complete, and introduce the potential for observer bias (Campbell 2015).

Urinalysis is straightforward in mammals, but birds and reptiles can perform post-renal urine modification within their colon and/or bladder and their urine samples have the potential to mix with fecal products prior to excretion, complicating the interpretation of urine testing in these

19 animals (Goldstein 1988). Other important species-specific considerations regarding test selection concern the choice of anticoagulants, which prevent clotting but can also contribute to hemolysis in some species, the choice of sampling sites, which can influence clinical pathology values, and the volume of blood which can safely be collected for analysis (Gottdenker 1995,

Crawshaw 1996, Muro 1998, Perpiñán 2010, López-Olvera 2003). Challenges to the use of routine veterinary testing in wildlife can include a prolonged sampling-to-analysis window, which can affect certain analytes such as glucose and potassium and degrade the appearance of leukocytes (Harr 2005, Sheldon 2016). Additional limitations include lack of knowledge of tissues of origin for biochemistry enzymes, unknown serum/plasma half-lives of these enzymes, and uncharacterized patterns of enzyme activity in response to organ insult in certain taxa (Boyd

1983, Boyd 1988). Finally, reference intervals are not available for most tests in most wildlife species. Values are typically available for comparison from zoo-maintained animals, but several studies have demonstrated variability between captive and free-living populations (Brenner

2002, Moen 2010, Schmidt 2011, Ruykys 2012, Pitorri 2015, Broughton 2017). Other studies have shown variability in these values between populations; and age, sex, and seasonal changes are well-documented (Friedrichs 2012, Gunn-Christie 2012).

Toxicological and nutritional testing are also generally applicable across species, though interpretation may be hindered by a lack of “normal” information in free-living wildlife.

Furthermore, experimental attempts to evaluate relationships among tissue residue data, environmental concentrations, and biological effects of toxicants and/or nutrient excess/deficiency are uncommon in groups like reptiles, limiting the probative value of surveys in wild populations (Hopkins 2000, Rouse Campbell 2002). Similarly, necropsy and histopathology can be applied to any species, but interpretation can be greatly improved by

20 having these diagnostics performed at specialized laboratories with specific expertise in non- traditional species (Stallknecht 2007, Ryser-Degiorgis 2013). Furthermore, destructive sampling is not appropriate for every study, and there is social and ethical pressure to move towards non- invasive, antemortem diagnostics for wildlife health assessment purposes (Lindsjö 2016).

The choice of study design and diagnostic tests for wildlife health assessment ultimately depends on research objectives, sample size, cost, species, and test characteristics. Collection of basic biological, ecological, and population data are frequently necessary during health assessment studies, and this can be greatly facilitated by collaborating with ecologists and/or site managers familiar with the population(s) of interest. Hands-on evaluation and routine veterinary diagnostic testing are necessary for comprehensive health assessment, though inclusion of ancillary tests such as genetic and hormone analyses may supplement the information obtained and provide a more holistic view of population wellness. Diagnostic tests validated in the species of interest should be used whenever possible. If these are not available, validation procedures should be employed and/or test interpretation should be performed very cautiously. Longitudinal analyses across seasons and populations provide the most comprehensive information about health determinants, and are necessary to distinguish normal fluctuations from unusual changes associated with deteriorating health status. Similarly, incorporating elements of both passive and active health surveillance can help identify changes in morbidity and mortality beyond baseline levels. While the obstacles to wildlife health assessment are not insignificant, they can be tackled with large-scale, comprehensive studies planned and interpreted in the context of the known limitations described above. The current outlook for biodiversity and species conservation is dire, underscoring the need to rapidly improve our ability to assess and improve the wellness of our remaining wild animal populations.

Herptile Conservation & Illinois Species in Greatest Conservation Need

Many life forms are currently facing conservation challenges due to natural and anthropogenic stressors, but reptilian and amphibian taxa (“herptiles”) are especially vulnerable compared to other vertebrates (Hoffmann 2010). Approximately 41% of the world’s 6,600 amphibian species are threatened, endangered, or extinct and 22% have insufficient information to classify (IUCN 2018). The Caudata (salamanders and newts) are the most at risk, with approximately 50% of the 560 species listed as threatened or worse. Known general threats to amphibians include habitat loss, pollution, fire, disease, invasive species, human disturbance, overcollection, and natural disasters (Vié 2009). Perhaps more concerning is the fact that over

1,000 amphibian species are declining due to unknown reasons (Vié 2009).

Thirty five percent of the world’s 10,000 reptile species are threatened with extinction, and another 15% are data deficient (IUCN 2018). Known general threats to reptiles include habitat loss, human disturbance, overcollection, invasive species, pollution, and disease (Gibbons

2000, Böhm 2013). Chelonians (turtles and tortoises) are the most imperiled vertebrates, with an estimated 60% of the world’s 356 species either threatened, endangered, or extinct (Turtle

Taxonomy Working Group 2017). Chelonians have significant cultural value and are useful indictors of ecosystem health due to their longevity and slow recovery from exploitation, making them ideal candidates for conservation action (Mittermeier 2015).

The United States contains the highest number of chelonian species in the world and the

Southeastern US is considered one of the top two global chelonian biodiversity hotspots

(Mittermeier 2015). Similarly, 48% of the world’s salamander species are native to North

America, providing an excellent opportunity to develop and employ chelonian and amphibian conservation strategies at local levels (Mittermeier 2015, Yap 2015). The 2015 Illinois Wildlife

Action Plan (IWAP) consisting of the 2005 Illinois Comprehensive Wildlife Conservation Plan

& Strategy and the 2015 Implementation Guide to the Wildlife Action Plan identifies 30 herptile species in greatest conservation need (SGCN), of which eight are chelonians and eight are caudates (Table 2.1).

Table 2.1. Chelonian and caudate species in greatest conservation need (SGCN) according to the

2015 Illinois Wildlife Action Plan.

Chelonians Common Name Scientific Name Smooth softshell turtle Apalone mutica Spotted turtle Clemmys guttata Blanding’s turtle Emydoidea blandingii Illinois mud turtle Kinosternon flavescens Alligator snapping turtle Macrochelys temminckii River cooter Pseudemys concinna Eastern box turtle Terrapene carolina carolina Ornate box turtle Terrapene ornate Caudata Common Name Scientific Name Jefferson salamander Ambystoma jeffersonianum Spotted salamander Ambystoma maculatum Silvery salamander Ambystoma platineum Hellbender Cryptobranchus alleganiensis Spotted dusky salamander Desmognathus conanti Four-toed salamander Hemidactylium scutatum Mudpuppy Necturus maculosus Eastern newt Notopthalmus viridescens

Eastern box turtles (Terrapene carolina carolina) have been the subject of an ongoing health survey in Vermilion County Illinois that was initiated in 2011 in response to a mortality event at Forest Glen Nature Preserve (Chapter 9). This species is considered vulnerable by the

International Union for Conservation of Nature (IUCN) and is listed under Appendix II by the

Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES).

Eastern box turtles are experiencing range-wide, ongoing, gradual declines exceeding 30% of their total numbers over the last three generations (van Dijk 2011a). Threats to eastern box turtle conservation include habitat destruction and fragmentation, road mortality, decreased recruitment due to subsidized predators (e.g. raccoons, coyotes), removal for human use (pets, turtle races), and disease (Dodd 2001, van Dijk 2011a). In Illinois, historical eastern box turtle distribution was 63 counties, but in 2015 that dropped to 49 counties (IWAP 2015). Specific threats considered important for eastern box turtle conservation in Illinois include habitat fragmentation, direct mortality from road collisions and lawn mowers, and disease (IWAP 2015,

Shepard 2008a, Shepard 2008b).

The IWAP places eastern box turtles within its Forest and Woodland campaign and specifically identifies open woodlands as preferable habitat for this species. Open woodlands have 30-80% canopy cover composed primarily of fire-tolerant oak and hickory trees and poorly developed understories, which promotes a diverse herbaceous layer of forbs, grasses, and sedges covering 50-100% of the ground (IWAP 2015). Unfortunately, the quality of forest habitats in

Illinois can be poor due to alteration of natural disturbance (e.g. fire), inappropriate timber harvest, and altered flooding, all of which can shift community composition by increasing the abundance of maple trees and changing open woodlands to closed woodlands (IWAP 2015).

Invasive species such as gypsy moths (Lymantria dispar), Asian long-horned beetles

(Anoplophora glabripennis), emerald ash borers (Agrilus planipennis), autumn olive (Elaeagnus umbellate), honeysuckle (Lonicera spp.), buckthorn (Rhamnus spp.), kudzu (Pueraria spp.), and garlic mustard (Alliaria petiolate) (among others) degrade forest biodiversity, reduce the recruitment of desirable species, and replace native species, ultimately reducing wildlife

24 diversity due to alteration of ecosystem services (IWAP 2015). Increasing fragmentation of forest habitats facilitates the ingress of invasive species and promotes generalist predators, further impacting the success of native forest species. Active management to promote natural disturbance, remove invasive species, and increase acreage and connectivity between remaining forest patches is planned to promote recovery of SGCN within this campaign.

Silvery salamanders (Ambystoma platineum) are an Illinois SGNC within the Forest and

Woodland campaign that shares habitat with eastern box turtles. Silvery salamanders are an all- female, triploid species which originated thousands of years ago through hybridization of

Ambystoma laterale and Ambystoma jeffersonianum (Phillips 1999). This species is not currently listed by the IUCN, but it is considered endangered in Illinois and is only found in Vermilion and

Crawford Counties. Knowledge of silvery salamander ecology and health is extremely limited.

Most scientific study of this species focuses on its unusual reproductive strategy, termed kleptogenesis, and on the survival of offspring with different chromosome counts (Phillips 1997,

Teltser 2015). Kleptogenic salamanders require spermatophores for the initiation of embryonic development, though typically male genetic material is not incorporated into the offspring.

Occasionally, A. platineum eggs do become fertilized resulting in tetraploid or pentaploid offspring. Generally, triploid salamander species have wider habitat tolerances, hatch earlier, grow larger, and feed more aggressively than the diploid species they originated from (Teltser

2015). However, they also have a high embryonic mortality rate, up to 80% in A. platineum

(Phillips 1999, Teltser 2015). Tetraploid and pentaploid individuals are less common due to decreased overall viability, but male salamanders of other species prefer tetraploid and pentaploid A. platineum over triploid females, conferring a selective advantage upon salamanders with higher ploidy levels (Phillips 1997).

In Illinois, silvery salamanders occupy upland forests with ephemeral ponds which are necessary for survival of their fully-aquatic larvae (Phillips 1999, IWAP 2015). While there is a high degree of uncertainty about threats to the persistence of this species, the IWAP identifies the following as possibilities: extent and fragmentation of habitats, hydrology, disease, genetics, availability of sympatric Ambystoma spp. for reproduction, and recruitment. The isolated nature of silvery salamanders within Illinois and the already poor survival of their offspring renders this species especially vulnerable to extirpation due to stochastic events like disease introduction, making them an ideal candidate for health assessment (De Castro 2005). Furthermore, the joint study of sympatric eastern box turtles and silvery salamanders may provide more information about disease threats shared by both species and can help infer the overall health of their surrounding ecosystem.

Ornate box turtles (Terrapene ornata) are a SGCN that shares habitat with eastern box turtles. This species is considered near-threatened by the IUCN, is listed under CITES Appendix

II, and is state-threatened in Illinois (van Dijk 2011b). General threats to ornate box turtles are similar to those of eastern box turtles, though the health of ornates has not been thoroughly investigated (van Dijk 2011). Recognized threats to ornate box turtles in Illinois can be split into four main categories: habitat, community, population, and anthropogenic stressors. Of these, habitat fragmentation, disease, and direct mortality are ranked as top challenges for this species

(IWAP 2015). Prior to 1980, ornate box turtles were found in 49 counties in Illinois. By 2015 this number had dropped to 21 counties, confirming an ongoing steep decline in local populations. This decline was considered so severe that the US Fish and Wildlife Service

(USFWS) began a long-term study of Illinois ornate box turtle population dynamics, home ranges, and habitat use in 2008 to inform effective management strategies. In conjunction with

26 this project, head-start programs for the ornate box turtle have been adopted by the Niabi Zoo in

Coal Valley, IL and the Lincoln Park Zoo in Chicago (Delaski 2014). Significant effort has been put forth by the USFWS, the Illinois Department of Natural Resources, and many partner institutions to understand and support ornate box turtle populations occupying the sand prairies of the Upper Mississippi River National Wildlife and Fish Refuge (Bowen 2004, Kuo 2004,

Refsnider 2011, Refsnider 2012; Tucker 2014, Tucker 2015, Mitchell 2016, Tucker 2017).

However, other prairie habitats, such as the Nachusa Grasslands, a “highest priority” focus area of the IWAP, also support understudied populations of the imperiled ornate box turtle.

Prairie and grassland habitats in Illinois have undergone historic declines due to agricultural development. At present, over half of the land area in the state (approximately 22 million acres) is comprised of corn and soybean fields (USDA 2015). From 2008 – 2014 a spike in corn and soybean prices drove conversion of an additional 400,000 acres of grassland, reducing habitat options even further for prairie-dwelling species like ornate box turtles (IWAP

2015). Remaining natural and restored prairies can suffer from poor management, allowing establishment of invasive species, preventing natural disturbance, or enhancing direct mortality via mowing or inappropriate burning schedules. Furthermore, prairie remnants can be too small to provide protection from subsidized edge predators, affecting overall survival and recruitment.

Disease Threats to Eastern Box Turtles, Ornate Box Turtles, & Silvery Salamanders

While habitat destruction, degradation, and fragmentation with resulting direct mortality are important considerations for herptile wellness in Illinois, infectious disease can also pose a significant threat. Of the three species discussed above, disease has been best studied in eastern box turtles.

Historic reports of disease in free-living eastern box turtles are sporadic and include isolated cases of granulomatous pneumonia, myiasis, uncharacterized respiratory disease, aural abscessation, yolk coelomitis with intracoelomic eggs, and poorly characterized mortality events, which may have been attributable to extreme weather conditions and/or disease (Evans 1983,

Gould 1991, Dodd 2001). The first organized reports of disease in free-living eastern box turtles were produced as the result of passive surveillance conducted at wildlife rehabilitation centers, and the first disease condition of wild box turtles to be thoroughly investigated was aural abscessation (Tangredi 1997, Brown 2002).

Reported prevalence of aural abscessation at rehabilitation centers is 21% in New York,

11% in Virginia, 10% in Tennessee, approximately 9% (aural abscess cases combined with other diseases into an “infectious” category) in Illinois, and 9.7% in North Carolina (Tangredi 1997,

Brown 2002, Brown 2004b, Schrader 2010, Rivas 2014, Sack 2017). Reported prevalence in free-living populations includes 0.6% in Tennessee, 5.2% in Maryland, and 8.5% in New York

(Calle 1998, Rose 2011, Adamovicz 2015). Aural abscesses are caused by accumulation of caseous debris within the tympanic cavity, leading to deformity and sometimes rupture of the tympanic membrane. A variety of bacteria have been cultured from the aural abscesses of wild turtles, including opportunists like Proteus vulgaris, Escherichia coli, Aeromonas hydrophila, and Morganella morganii, but no single etiologic agent has been clearly identified (Tangredi

1997, Wilier 2003, Joyner 2006a). Positive bacterial cultures have also been obtained from the tympanic cavities of apparently healthy box turtles, suggesting that aural abscesses are not the result of primary bacterial infection (Joyner 2006a). Other suspected underlying causes include immunosuppression and/or vitamin A deficiency secondary to organochlorine exposure, however, evidence for these theories in box turtles is mixed and they are not supported by

28 prospective studies in red-eared sliders (Trachemys scripta elegans) (Tangredi 1997, Holladay

2001, Sleeman 2008). An epidemiologic study failed to identify human, demographic, or temporal predictors of aural abscess development in eastern box turtles presented to a wildlife rehabilitation center in Virginia, however cases did cluster spatially and tended to occur in larger

(i.e. older) turtles (Brown 2004b). Other studies have identified increased precipitation as a risk factor for aural abscess development in eastern and Florida (Terrapene carolina bauri) box turtles, although it is difficult to make a direct connection between rain and aural abscess formation (Dodd 2004, Schrader 2010). Larger-scale, longer-term studies in wild populations are needed to clarify the epidemiology of this condition in eastern box turtles.

Upper respiratory disease (URD) is a disease syndrome of free-living eastern box turtles that is commonly identified singly or in conjunction with aural abscesses (Tangredi 1997, Wilier

2003, Feldman 2006, Rivas 2014, Sack 2017). URD is characterized by one or more of the following clinical signs: depression, ocular discharge, conjunctivitis, blepharitis, blepharoedema, and nasal discharge (Feldman 2006). Reported prevalence in eastern box turtle rehabilitation populations is 3% in Virginia, 6.3% in North Carolina, approximately 8% (all infectious diseases lumped into one category) in Tennessee, and 21% in New York (Tangredi 1997, Brown 2002,

Schrader 2010, Sack 2017). Mortality rates reported in association with URD in rehabilitating box turtles range from 9 – 63% (Brown 2002, Schrader 2010). Mycoplasmosis due to M. agassizii and M. testudineum is a recognized cause of URD in desert (Gopherus agassizii) and gopher (Gopherus polyphemus) tortoises, as evidenced by challenge studies (Brown 1994,

Brown 1999b). An un-named Mycoplasma sp. is associated with URD in free-living eastern box turtles, though it has also been detected in asymptomatic animals at a prevalence of 2-100% depending on the study (Feldman 2006, Ossiboff 2015c, Archer 2017, Goodman 2018). This is

29 not necessarily unusual, as Mycoplasma sp. has also been detected by PCR in other free-living chelonians both with (three-toed box turtles [Terrapene carolina triunguis], marginated tortoises

[Testudo marginata], Hermann’s tortoises [Testudo hermanni]) and without (bog turtles

[Glyptemys muhlenbergii], spotted turtles [Clemmys guttata], common snapping turtles

[Chelydra serpentina], red-eared sliders [Trachemys scripta elegans], Southern River terrapins

[Batagur affinis], western pond turtles [Emys marmorata], spur-thighed tortoises [Testudo graeca]) clinical signs of URD (Lecis 2011, Salinas 2011, Silbernagel 2013, Ossiboff 2015c,

Palmer 2016, Kolesnik 2017, Seimon 2017, Aplasca 2018). Positive antibody titers for

Mycoplasma sp. have also been identified in sick and healthy box turtles, but as this assay is not validated for this species, results are difficult to interpret (Calle 1998, Adamovicz 2015, Palmer

2016). It has been proposed that Mycoplasma spp. may be commensal organisms in emydid turtles, and that onset of clinical disease requires some perturbation to underlying health, as demonstrated in tortoises with environmental and anthropogenic stressors (Sandmeier 2009,

Ossiboff 2015c). Several aspects of M. agassizii epidemiology in tortoises have been elucidated by over 30 years of intensive study (Jacobson 2014). However, the box turtle Mycoplasma sp. has not yet been isolated in culture, and as a result current understanding of epidemiology is limited.

Herpesviruses have also been detected in free-living eastern box turtles. Herpesviruses are enveloped, double-stranded DNA viruses characterized by life-long infections and latency, with the possibility of recurring virus replication and shedding during times of stress

(Maclachlan 2017b). Many herpesviruses co-evolved with their hosts and cause relatively little pathology, except in young or immunosuppressed individuals (Marschang 2011, Maclachlan

2017b). However, the consequences of host-switching in herpesviruses can be severe, a classic

30 example is the fatal encephalitis resulting from human infection with herpes B virus from macaques (Ostrowski 1998).

All currently recognized reptilian herpesviruses belong to the subfamily

Alphaherpesvirinae, which tend to cause minor localized lesions of the skin, respiratory, or genital tracts – though severe generalized infection is also occasionally reported in young or immunosuppressed animals (Marschang 2011, Maclachlan 2017b). Herpesviruses have been reported in multiple captive and free-living chelonians with and without clinical signs of illness.

They are associated with epidermal necrosis in green sea turtles (Chelonia mydas, “grey patch disease”), tracheal and genital ulceration in loggerheads (Caretta caretta, “loggerhead genital- respiratory herpesvirus”), cutaneous plaques and oral ulcers in loggerheads (“loggerhead orocutaneous herpesvirus”), pneumonia and necrotizing oral and upper respiratory lesions in green sea turtles (“lung, eye, and trachea virus”), and fibropapillomatosis in all species of sea turtles (Jacobson 1986, Lackovich 1999, Williams 2006, Stacy 2008). Fatal systemic herpesvirus infections associated with necrosis and intranuclear inclusions in the liver and other organs were suspected in Pacific pond turtles (Actinemys marmorata), painted turtles (Chrysemys picta), and map turtles before the advent of affordable molecular diagnostics (Frye 1977, Cox 1980,

Jacobson 1982). Sudden mortality was also reported in a group of captive eastern river cooters

(Pseudemys concinna concinna) with histologic, TEM, and molecular confirmation of emydid herpesvirus 1 in the liver (Jungwirth 2014). A captive juvenile northern map turtle (Graptemys geographica) died after a brief period of lethargy and necropsy revealed necrosis of the spleen, liver, and lung with an accompanying pneumonia. Herpesviral intranuclear inclusions were observed in association with these lesions, and Emydid herpesvirus 1 was amplified from the affected tissues (Ossiboff 2015a).

Testudinid herpesviruses 1-4 infect multiple species of tortoises including Russian tortoises (Testudo horsfieldii), pancake tortoises (Malacochersus tornieri), desert tortoises, bowsprit tortoises (Chersina angulata), spur-thighed tortoises, marginated tortoises, Hermann’s tortoises, Egyptian tortoises (Testudo kleinmanni), and leopard tortoises (Stigmochelys pardalis), among others (Pettan-Brewer 1996, Une 2000, Origgi 2004, Johnson 2005, Marschang 2009,

Bicknese 2010, Salinas 2011, Stöhr 2011, Kolesnik 2016, Kolesnik 2017). Testudinid herpesviruses are typically associated with ulcerative to necrotizing stomatitis, conjunctivitis, rhinitis, glossitis, and pharyngitis, but some infected animals also develop neurologic sequelae, and severity of disease can vary between species and virus (Marenzoni 2018). Proliferative cutaneous lesions have been associated with herpesviruses in freshwater aquatic turtles, including Williams’ mud turtle (Pelusios williamsi), common snapping turtles (Chelydra serpentina), matamata (Chelus fimbriatus), and Krefftʼs river turtle (Emydura macquarii krefftii)

(Frye 1991, Cowan 2015, Široký 2018). Finally, herpesviruses have been detected in apparently healthy captive and free-living chelonians including Blanding’s turtles (Emydoidea blandingii,

Emydoidea herpesvirus 1), bog turtles (Glyptemys muhlenbergii, Glyptemys herpesvirus 1 & 2), wood turtles (Glyptemys insculpta, Glyptemys herpesvirus 2), painted turtles (Chrysemys picta,

Emydid herpesvirus 1), northern map turtles (Emydid herpesvirus 1), spotted turtles (Clemmys guttata, Emydid herpesvirus 2), and West African mud turtles (Pelusios castaneus, Pelomedusid herpesvirus 1) (Marschang 2015, Ossiboff 2015a, Ossiboff 2015b, Lindemann 2018).

Terrapene herpesvirus 1 (TerHV1) was initially detected in a hatchling eastern box turtle with severe necrotizing and ulcerative rhinitis, stomatitis, esophagitis, tracheobronchitis, and pneumonia (Sim 2015). Histopathology revealed characteristic eosinophilic to amphophilic herpesviral intranuclear inclusions, and herpesviral DNA was amplified via PCR (Sim 2015).

Subsequent screening of zoo-maintained and free-living populations with a species-validated qPCR assay has revealed a relatively high prevalence of TerHV1 (12 – 97%) with significant seasonal variability, and no association with clinical signs of illness (Sim 2015, Archer 2017,

Kane 2016, Kane 2017). Terrapene herpesvirus 2 (TerHV2) and a novel Spirorchis sp. were detected in fibropapillomas from a single free-living eastern box turtle, but additional information is not yet available for this pathogen (Yonkers 2015). Detection of TerHV1 in a large proportion of eastern box turtles with no clinical signs of disease suggests that this is a host-adapted herpesvirus. Monitoring this virus in box turtles may therefore provide information on population health, as recrudescence and viral shedding may be associated with periods of stress and immunosuppression. Like Mycoplasma sp., isolation of TerHV1 and TerHV2 in culture has not yet been accomplished, so information on disease epidemiology is limited.

Ranaviruses, important pathogens of eastern box turtles, are members of the Iridoviridae family of large, double-stranded DNA viruses. The Iridoviridae is divided into five genera which infect invertebrate and ectothermic vertebrate species. Members of the Iridovirus and

Chloriridovirus genera infect invertebrates, the Lymphocystivirus and Megalocytivirus genera infect only fish, and viruses belonging to the genus Ranavirus can infect fish, amphibians, and reptiles. There are eight currently recognized species in the genus Ranavirus including

Ambystoma tigrinum virus (ATV), Bohle iridovirus (BIV), common midwife toad virus

(CMTV), epizootic haematopoietic necrosis virus (EHNV), European catfish virus (ECV), frog virus 3 (FV3), and Singapore grouper iridovirus (SGIV) (ICTV 2017). The type species of the genus Ranavirus is frog virus 3 (FV3), which was initially isolated from cell culture of a leopard frog (Lithobates pipiens) adenocarcinoma in 1965 (Duffus 2015).

Frog virus 3 infection has been documented in over 100 species of free-living herptiles from every continent except Antarctica (Duffus 2015). The consequences of infection range from inapparent to mortality, and the severity of infection varies depending on species, life stage, temperature, level of environmental contamination, and other individual, pathogen, and environmental factors (Gray 2007, Hoverman 2010, Hoverman 2011, Haislip 2011, Brenes

2014a, Brenes 2014b). In natural systems, FV3 can infect multiple species within a single site and can be transmitted between different classes of ectothermic vertebrates (Duffus 2010, Brenes

2014b, Brunner 2015). Ranavirus infections in free-living herptiles are considered to be emerging because their host range and distribution is expanding (Daszak 1999). FV3 has the potential to cause population declines and extirpations, and highly susceptible hosts present at low abundance are considered especially vulnerable (Teacher 2010, Earl 2014, Heard 2013,

Price 2014).

Frog virus 3 has been associated with hematopoietic tissue necrosis, fibrinoid vasculitis, and death in multiple species of chelonians, including captive and/or free-living eastern box turtles, Florida box turtles, ornate box turtles (experimentally), gopher tortoises, Russian tortoises, Burmese star tortoises (Geochelone platynota), Hermann’s tortoises, leopard tortoises,

Egyptian tortoises, marginated tortoises, red-eared sliders, eastern mud turtles (Kinosternon subrubrum), and soft-shelled turtles (Pelodiscus sinensis) (Chen 1999, Marschang 1999, De Voe

2004, Allender 2006, Johnson 2006, Johnson 2007, Johnson 2008, Benetka 2007, Blahak 2010,

Ruder 2010, Allender 2011, Belzer 2011, Allender 2012, Allender 2013c, Farnsworth 2013,

Brenes 2014a, Currylow 2014, Hausman 2015, Winzeler 2015, Perpiñán 2016, Sim 2016, Agha

2017, Archer 2017, Butkus 2017, Kimble 2017). Koch’s postulates have been fulfilled in red- eared sliders, and further laboratory studies have illustrated the effects of temperature, species,

34 and age on FV3 infection dynamics and clinical pathology parameters (Johnson 2007, Allender

2013b, Moore 2014, Moore 2015, Allender 2018b). Clinical signs of FV3 infection include blepharoedema, ocular, oral, and/or nasal discharge, oral plaques, respiratory distress, depression, cutaneous abscessation or erosions, cloacal plaques, edema, and death, though several of these signs are variably reported (De Voe 2004, Allender 2006, Johnson 2007,

Johnson 2008, Ruder 2010, Farnsworth 2013, Sim 2016, Kimble 2017). FV3 has also been detected in eastern painted turtles (Chrysemys picta picta) with no clinical signs of illness, which could represent false positive test results due to sampling methodology (the skin was not disinfected prior to tissue collection) or diagnostic test application (conventional PCR was used but positive samples were not sequenced for confirmation) (Goodman 2013). Other possibilities include detection of pre-clinical infections, recovered carriers, or differences in clinical manifestations of infection across species (Goodman 2013).

In eastern box turtles, ranavirus mortality events in wild populations have been identified across the species’ range in Kentucky, Indiana, Tennessee, Pennsylvania, New York, Georgia,

West Virginia, and Maryland, and diagnosis is greatly facilitated by the availability of a species- validated qPCR assay (Allender 2006, Johnson 2008, Ruder 2010, Belzer 2011, Allender 2013a,

Farnsworth 2013, Currylow 2014, Perpiñán 2016, Agha 2017, Kimble 2017). Some of the larger events were associated with translocations due to road development and resulted in the loss of hundreds of animals (Farnsworth 2013, Kimble 2017). Survival of FV3 infection in eastern box turtles is uncommon without aggressive intervention, and even then the best documented survival rate is only 58% (Sim 2015). Individuals that do survive appear clinically normal but can become persistently infected with intermittent detection of viral DNA in oral-cloacal swabs and tissues (Sim 2016, Kimble 2017). Reinfection of these recovered individuals with ranavirus

35 results in much milder clinical signs consistent with URD and shedding of high numbers (e.g. millions) of viral copies from the oral cavity and cloaca (Hausman 2015). Box turtle ranavirus is suspected to occur as a spillover event from infected amphibians, but arthropod vectors may also play a role (Belzer 2011, Brenes 2014a, Currylow 2014, Brunner 2015, Kimble 2015, Agha

2017). Field epidemiology studies have demonstrated a very low prevalence (< 5%) of ranavirus in free-living box turtles, due likely to the rapid onset and high case fatality associated with this disease (Johnson 2010, Allender 2011, Vannatta 2015, Archer 2017). Because identification and thorough investigation of ranavirus outbreaks in wild eastern box turtles is challenging, ranaviral epidemiology in this species is currently poorly characterized.

Adenoviruses, an infectious disease recently detected in eastern box turtles, are non- enveloped double-stranded DNA viruses currently organized into five families: the

Mastadenoviruses infects mammals, Aviadenoviruses infect birds, Ichtadenoviruses infect fish,

Siadenoviruses infect amphibians, chelonians, and birds, and the Atadenovirus family infects reptiles, birds, and mammals (Doszpoly 2013). Within the Reptilia, adenoviruses are best described from squamates, where they are commonly associated with hepatic and intestinal necrosis (Marschang 2011). Affected animals often present with anorexia and wasting, though neurologic symptoms can also occur. Disease is most common in young and immunosuppressed lizards, and adenoviruses can be detected in many adults with no clinical signs of illness

(Doneley 2014). In chelonians, siadenoviruses have been linked to fatal systemic infections with associated necrosis of the liver, bone marrow, and intestine in Sulawesi tortoises (Indotestudo forsteni), impressed tortoises (Manouria impressa) and a Burmese star tortoise (Geochelone platynota) (Rivera 2009, Schumacher 2012). A novel atadenovirus was recently isolated from a captive spur-thighed tortoise with severe hyperplastic esophagitis and stomatitis (Garcia-Morante

2016). Adenoviruses have also been detected in captive pancake tortoises, red-eared sliders, and yellow-bellied sliders (Trachemys scripta scripta) with no clinical signs of illness (Doszpoly

2013). A new adenovirus was detected in a deceased captive ornate box turtle with inclusion- body hepatitis (Farkas 2009). The same virus was identified in eastern box turtles in quarantine at a zoological facility, but no clinical signs of illness were reported (Farkas 2009, Doszpoly

2013). A species-validated qPCR assay is available for adenovirus in box turtles (Blum 2014).

The individual and population level effects of adenovirus in box turtles are currently unknown, but deserving of further study due to their association with significant disease in other chelonians.

Additional description of disease conditions in free-living eastern box turtles is largely limited to case reports, including concurrent ranavirus and phaeohyphomycosis resulting in mortality in a turtle from GA, phaeohyphomycosis resulting in mortality in a turtle from VA, identification of hemotropic Mycoplasma sp. in a deceased turtle from North Carolina, and management of multi-drug resistant E. coli associated with URD (Joyner 2006b, Perpiñán 2016,

Cerreta 2018, Jarred 2018).

Several studies report the use of clinical pathology tests in eastern box turtles, including hematology, plasma biochemistry, protein electrophoresis, and hemoglobin-binding protein, providing information on age, sex, and seasonal differences in some cases (Rose 2011, Kimble

2012, Flower 2014, Adamovicz 2015, Lloyd 2016). This information facilitates comparison between populations and identification of values outside of expected ranges for this species, potentially indicating differences in relative health status. It also provides a useful starting point for future health investigations, however, few of these studies incorporated aspects of box turtle ecology, which is also important for classifying population health.

Early research findings relating to the evolution, behavior, ecology, and conservation of eastern box turtles are compiled through the year 2000 (Dodd 2001). Since then, eastern box turtle research efforts outside the veterinary sphere have focused on detectability and population monitoring (Currylow 2011, Kapfer 2012, Cross 2014, Erb 2015, Boers 2017, Melvin 2018), demographics (Henry 2003, West 2016), general ecology (Forsythe 2004, Wilson 2005, Flitz

2006, Wilson 2008, Rossell 2006, Felix 2008, Burke 2011a, Burke 2011b, Willey 2012, Kapfer

2013, Greenspan 2015, Burke 2016, DeGregorio 2017, Hughes 2017, Boucher 2017, Laarman

2018), thermal biology (Savva 2010, do Amaral 2002a, Currylow 2012, Currylow 2013a,

Fredericksen 2014, Roe 2017, Parlin 2017, Parlin 2018), behavior (Kashon 2018), genetics

(Marsack 2009, Kimble 2011, Kimble 2014a, Kimble 2014b), effects of habitat fragmentation

(Budischak 2006, Iglay 2007, Nazdrowicz 2008), effects of management interventions including prescribed fire (Gibson 2009, Howey 2013, Cross 2016), mowing (Erb 2011), silviculture (Agha

2018), translocation (Hester 2008, Henriquez 2017), and effects of roads and train tracks

(Kornilev 2006, Shepard 2008a, Shepard 2008b, Hartzell 2015). Additional studies covering physiology relevant to health assessment have also been completed (do Amaral 2002b, Currylow

2013b, Watson 2017, West 2018). This provides a wealth of knowledge to contextualize health assessment findings and enables a more holistic view of factors that may be important for promoting population sustainability.

In contrast to eastern box turtles, no health information is available for silvery salamanders and ecological knowledge is limited. Potential disease threats to the conservation of this species may include ranavirus (previously summarized) and chytridiomycosis.

Chytridiomycosis due to Batrachochytrium dendrobatidis (Bd) is a fungal skin disease of amphibians. It has been dubbed the most devastating disease impacting vertebrate biodiversity

38 and has been associated with global amphibian declines and extinctions since the 1970s (Lips

2016). Amphibian skin plays a major role in osmoregulation, electrolyte balance, and defense against pathogen invasion (Woodhams 2007, Rollins-Smith 2009). Disruption of these vital functions in animals with heavy Bd burdens results in death due to fatal electrolyte disturbances and/or through the development of opportunistic secondary infections (Campbell 2012).

Bd infection has been documented in over 700 amphibian species, and has contributed to the threatened status of approximately 400 species (Olson 2013, Bellard 2016, Lips 2016). While

Bd has been associated with extinctions, extirpations, mortality events, and precipitous and persistent population declines in multiple species, others are relatively tolerant of infection

(Laurance 1996, Lips 2003, Lips 2006, Crawford 2010, Vredenburg 2010, Berger 2016).

Thorough investigation in laboratory and natural populations has revealed that the outcome of

Bd infection depends on a complex array of host, pathogen, and environmental factors (Lips

2003, Harris 2009, Farrer 2011, Rosenblum 2013, Rowley 2013a, Ellison 2014, Woodhams

2014, Han 2015, Bataille 2015, Berger 2016). Bd has been present in the United States since at least 1888, with the earliest detections occurring in southern leopard frogs (Lithobates sphenocephala) from Illinois (Talley 2015). The prevalence of Bd in Illinois is fairly high, but does not appear to be associated with mortality events or population declines in native species, potentially making it less of a concern for silvery salamanders (Talley 2015).

A second cause of chytridiomycosis, Batrachochytrium salamandrivorans (Bsal), may pose a more distinct threat. Bsal was discovered in 2010 in association with steep population declines in ﬁre salamanders (Salamandra salamandra) from the Netherlands (Martel 2013). This pathogen originated in Asia, but is now present in wild salamander populations in Belgium and

Germany, and in multiple captive caudate species (Martel 2014, Cunningham 2015, Sabino-Pinto

2015, Spitzen-van der Sluijs 2016, Laking 2017, Fitzpatrick 2018, Sabino-Pinto 2018). Bsal has not yet been detected in North America, but its introduction could have devastating impacts due to the amount of suitable habitat, high biodiversity of salamanders, and demonstrated susceptibility of North American salamander species to Bsal in laboratory experiments (Martel

2014, Bales 2015; Yap 2015; Richgels 2016, Parrott 2017; Iwanovicz 2017, Klocke 2017, Yap

2017). Screening for ranaviruses, Bd, and Bsal in addition to collection of routine health assessment and ecological data should provide a good baseline for understanding silvery salamander health.

Like silvery salamanders, information on ornate box turtle health is scarce. Ornates are susceptible to challenge with ranavirus (Johnson 2007). An adenovirus was isolated from a deceased pet ornate box turtle in Hungary that died during brumation (Farkas 2009).

Histopathology revealed hepatocellular degeneration with intranuclear inclusions, potentially consistent with clinical adenovirus infection. A Mycoplasma sp. was also detected in the intestine of this animal, which likely represents pass-through DNA from the oral cavity (Farkas

2009). Baseline data are available for biochemistry panels in a small number of serially evaluated wild turtles (Harden 2018). Fibrinogen levels have also been reported from turtles maintained in captivity (Parkinson 2016). Ornate box turtles are likely susceptible to similar disease processes as eastern box turtles, especially where habitat is shared. Health screening with similar diagnostic tests is also likely appropriate. As discussed earlier, several recent publications provide perspective on ornate box turtle ecology in Illinois, facilitating contextualization of health findings.

PART I: DIAGNOSTIC TECHNIQUES

CHAPTER 3: TISSUE ENZYME ACTIVITIES IN FREE-LIVING EASTERN BOX

TURTLES (TERRAPENE CAROLINA CAROLINA)

Abstract: Plasma biochemical enzymes are commonly assayed as part of a diagnostic evaluation for zoological species, but their interpretation is complicated by a lack of knowledge about tissue of origin in many reptiles. This study evaluated tissue specificity of six biochemical enzymes

(alanine aminotransferase, ALT; aspartate aminotransferase, AST; alkaline phosphatase, ALP; creatine kinase, CK; gamma-glutamyl transferase, GGT; and glutamate dehydrogenase, GLDH) in 10 tissues (skeletal muscle, cardiac muscle, lung, liver, gallbladder, pancreas, gastrointestinal tract, kidney, spleen, and reproductive tract) from 10 free-living eastern box turtles (Terrapene carolina carolina). CK activity was highest in skeletal muscle, cardiac muscle, and gastrointestinal tract; GLDH and ALT activities were highest in liver, kidney, and gallbladder;

ALP and GGT activities were elevated in kidney and gastrointestinal tract; and AST was relatively non-specific, with significantly higher activity in the cardiac muscle, liver, kidney, skeletal muscle, and gallbladder compared to other tissues (P < 0.05). These results serve as a first step towards improving clinical interpretation of plasma biochemistry panels in box turtles.

** This chapter is adapted from a manuscript accepted for publication in the Journal of Zoo and Wildlife Medicine:

Adamovicz L, Griffioen J, Cerreta A, Lewbart GA, Allender MC. Tissue enzyme activities in free-living eastern box turtles (Terrapene carolina carolina).

INTRODUCTION

The biochemistry panel is an important diagnostic test for assessing organ integrity and function in veterinary species (Boyd 1983). Evaluation of serum or plasma enzyme activity is one component of the biochemistry panel used to identify targets for further diagnostic testing or therapeutic intervention. Serum or plasma enzyme activity depends largely on the amount of enzyme present within different cells, the intracellular location of the enzyme, the degree of cellular leakage, the rate of excretion from the body, and the type, duration, and severity of tissue insult (Boyd 1983). Elevation of enzyme activities in plasma or serum can indicate increased cellular leakage or enzymatic induction secondary to disease processes, while decreased activity can be consistent with diminished functional tissue mass (Boyd 1988, Lumeij 2008).

Clinical interpretation of biochemistry panels and subsequent case management depends largely on knowledge about tissues of origin for each enzyme, which can be highly species- specific. The tissue specificity of serum and/or plasma biochemical enzymes has been experimentally determined in a number of mammals and birds, as well as a few fish and a single amphibian species (Boyd 1962, Zimmerman 1986, St Aubin 1977, Clampitt 1978, Geraci 1979,

Keller 1981, Casillas 1982, Boyd 1983, Keller 1985, Sulakhe 1985, Yora 1986, Lumeij 1988,

Battison 1996, Fauquier 2008, Lumeij 2008, Anderson 2010, Boyd 2017). However, the sources of plasma biochemistry enzymes have only been determined in five reptile species to date including loggerhead sea turtles (Caretta caretta), Kemp’s Ridley sea turtles (Lepidochelys kempii), green iguanas (Iguana iguana), yellow rat snakes (Pantherophis [Elaphe] obsoleta quadrivitatta), and American alligators (Alligator mississippiensis) (Ramsay 1995, Wagner

1999, Anderson 2013, Bogan 2014, Petrosky 2015). With over 10,000 species in the class

Reptilia, there is a clear need for additional research to improve diagnostic interpretation and clinical management of reptiles.

Eastern box turtles (Terrapene carolina carolina) are common within the pet trade and in zoological collections, while free-living populations are experiencing range-wide declines.

Primary causes of these declines include anthropogenic factors such as habitat destruction/fragmentation, vehicle collisions, overharvesting for the pet trade, and subsidization of mesopredators (Dodd 2001). Diseases, such as ranavirus, have also been recognized for their potential to impact population stability (De Voe 2004, Allender 2011, Allender 2012, Farnsworth

2013, Sim 2016, Agha 2017, Kimble 2017). Recent veterinary research efforts have focused on characterizing health and disease epidemiology of wild and zoo-maintained individuals in order to clarify the role of disease in eastern box turtle conservation (Hausman 2015, Lloyd 2016, Sim

2016, Archer 2017, Butkus 2017, Kane 2016, Kane 2017). As a result, hematology, plasma biochemistry, protein electrophoresis, and hemoglobin-binding protein values have been reported for this species (Rose 2011, Kimble 2012, Flower 2014, Adamovicz 2015). However, tissues of origin for plasma biochemistry enzymes have not been determined for box turtles, complicating the clinical interpretation and diagnostic utility of these values.

The objective of this study was to determine tissues of origin for six common biochemical enzymes in free-living eastern box turtles. The hypotheses were 1) Activity of creatine kinase (CK) and aspartate aminotransferase (AST) will be highest in skeletal and cardiac muscle, 2) Activity of glutamate dehydrogenase (GLDH) will be highest in the liver, 3) Activity of alanine aminotransferase (ALT) and alkaline phosphatase (ALP) will be detected in multiple tissues and will be relatively non-tissue specific.

MATERIALS & METHODS

Study Design

A sample size calculation (www.openepi.com) determined that eight individuals were necessary to detect a 100 IU/g difference in enzyme activity between organs (standard deviation

= 50 IU/g, power () = 0.8,  = 0.05). This difference was targeted based on previous research

(Bogan 2014). A total of 10 animals were included in the study to maintain adequate sample size while allowing for some data loss due to machine error during chemistry analysis.

To reduce the use of live animals in research, this study collected tissues from free-living eastern box turtles that were euthanized due to irreparable skull fractures associated with vehicular trauma. Turtles were selected for inclusion in the study if immediate euthanasia was deemed necessary due to grave prognosis, injuries were confined to the skull, forelimbs, and marginal scutes with no accompanying coelomic breaches, and if they appeared otherwise healthy on physical examination and gross necropsy. Exclusion criteria included small size (<

250 g), administration of medications prior to euthanasia, and a > 24hr interval between euthanasia and tissue collection.

Sample Collection

All procedures involving live animals were approved by the North Carolina State

University College of Veterinary Medicine (NCSU-CVM) Institutional Animal Care and Use

Committee (protocol #16-049-O). Free-living eastern box turtles were opportunistically presented to the NCSU-CVM Turtle Rescue Team at various time intervals following vehicle collisions. Complete physical examinations were performed by two individuals (JG & AC).

Turtles meeting the selection criteria of the study were euthanized using pentobarbital

(Beuthanasia®-D Special 390mg IV, Merck & Co, Inc., Madison, NJ 07940, USA) injected

45 intravenously via the subcarapacial sinus, brachial vein, or jugular vein. Euthanized turtles were placed on a heating pad until a heartbeat was no longer detectable using Doppler ultrasonography

(Parks Medical Electronics Inc., Aloha, OR 97078, USA). Turtles were then shipped overnight on ice packs to the University of Illinois College of Veterinary Medicine (UI-CVM).

At UI-CVM, a full gross necropsy was performed on each turtle and samples of the following organs were collected: skeletal muscle (pectoralis), cardiac muscle (combined atria and ventricle), lung, liver, gallbladder (emptied of bile), pancreas, gastrointestinal tract (equal portions of stomach, small intestine, and colon), spleen, kidney, and reproductive tract (equal portions of ovary and oviduct for females, testis for males). Connective tissue and blood vessels were removed, and tissue samples were trimmed to 0.5 g, if possible. Tissues were rinsed with sterile water and frozen at -20oC for 1-3 months.

Sample Processing

Thawed tissues were immersed in 4oC sterile water at a ratio of 10mL/g (5mL per 0.5g tissue) and homogenized using a Polytron homogenizer (Type P10/35 with power control unit

(PCU-2-110), Brinkmann Instruments, Westbury, NY 11590, USA) with a sterilized saw-toothed rotor/stator at 6,000rpm for 20 seconds on ice. Homogenized samples were further disrupted using a sonicator (Model W-225R, Heat Systems – Ultrasonic Inc. Haverhill, MA 01835, USA) with a microtip probe at 45% amplitude for 30 seconds on ice. Disrupted samples were centrifuged at 2,000 x g for 15 minutes at 4°C. Supernatants were poured off and stored at 4oC.

Pellets were resuspended in 10mL/g 4oC sterile water, and the homogenization, sonication, and centrifugation steps were repeated. The second supernatant was added to the first, and resuspension, homogenization, sonication, and centrifugation was repeated a third time. The final volume of all three combined supernatants was recorded.

Chemistries were performed within 3 hours of sample processing using a commercial chemistry analyzer (AU680 Chemistry System, Beckman Coulter, Brea, CA 92821, USA).

Enzyme activities (IU/L) were determined for ALT, AST, ALP, CK, GGT, GLDH. These activities were multiplied by the total volume of supernatant (in L) and divided by the original weight of the tissue (in g) to determine enzyme activity per gram of tissue (IU/g).

Statistical Analyses

Distribution for each biochemical analyte was evaluated for normality using histograms, q-q plots, skewness, kurtosis, and the Shapiro-Wilk test. Summary statistics (median, range, 25th-

75th percentiles) were determined for each analyte and grouped by tissue. The Friedman rank sum test was applied to determine whether enzyme activity was significantly different between tissues. Post-hoc testing using the Nemenyi multiple comparison test from the “PMCMR” package was utilized to evaluate pairwise differences (Pohlert 2014). The Nemenyi post-hoc test adjusts critical values based on the number of comparisons being performed, thus controlling for family-wise error and eliminating the need for p-value adjustments (Nemenyi 1963). The activities of CK and AST in each tissue were compared between turtles with limb/shell fractures and those without using a series of Mann-Whitney U tests with a post-hoc Bonferroni correction to control for family-wise error. All statistical analyses were conducted in R version 3.4.3 at an alpha level of 0.05 (R Core Team 2013).

RESULTS

Six adult male turtles and four adult females were included in this study. Admission dates to the NCSU-CVM Turtle Rescue Team ranged from 5/14/16 to 8/9/17. All animals presented with significant head trauma including skull fractures, mandibular fractures, and bilateral proptosis, and prognosis for recovery and release was considered grave. Four turtles presented

47 with concurrent forelimb fractures or minor carapacial fractures, but no coelomic breaches or damage to internal organs were detected. Euthanasia was performed for humane purposes in each case. Gross necropsy abnormalities were not identified, though one female was gravid.

The chemistry analyzer failed to provide results in some cases, potentially due to the presence of interfering substances within the tissue homogenates. Results were not provided for eight GLDH tests (one kidney, one liver, one gallbladder, one GI tract, two pancreas, and two female reproductive tract samples), two ALP tests (female reproductive tracts), one ALT test

(liver), and four CK tests (one kidney sample, three pancreas samples).

All data were non-normally distributed (Shapiro-Wilk, P < 0.05), and each Friedman test was significant (P < 0.05), indicating statistically significant differences in enzyme activity between tissues. Post-hoc testing was conducted to test differences between each tissue combination.

CK activity was detectable in every tissue and was significantly higher in skeletal muscle compared to liver (P < 0.0001), gallbladder (P = 0.03), pancreas (P = 0.02), spleen (P < 0.0001), and reproductive tract (P = 0.002) (Figure 3.1, Table 3.1). It was significantly higher in cardiac muscle compared to liver (P < 0.0001), spleen (P <0.0001), and reproductive tract (P = 0.03), and higher in the GI tract compared to liver (P = 0.0005) and spleen (P = 0.0005). CK activity was most concentrated in the skeletal muscle, with a median value 1,961 IU/g higher than the cardiac muscle. The median value in the cardiac muscle was 569 IU/g higher than that of the GI tract, which in turn was 377-463 IU/g higher than all other tissues. CK activity was not significantly different between turtles with forelimb/shell fractures and those without in any tissue (P > 0.05).

AST activity was detectable in every tissue and was significantly higher in cardiac muscle compared to lung (P = 0.0005), pancreas (P = 0.02), spleen (P < 0.0001), and reproductive tract (P = 0.0005). It was higher in liver and kidney compared to lung (P = 0.01, P =

0.01), spleen (P = 0.0005, P = 0.0007), and reproductive tract (P = 0.01, P = 0.01); and higher in the skeletal muscle and gallbladder compared to the spleen (P = 0.004, P = 0.02). The difference in median enzyme activity between tissues ranged from 8.9-31.7 IU/g, indicating that AST has similar activity profiles in many tissues. AST activity was not significantly different between turtles with forelimb/shell fractures and those without in any tissue (P > 0.05).

ALT activity was detectable at low levels in every tissue and was significantly higher in liver compared to lung (P < 0.0001), pancreas (P = 0.0004), spleen (P = 0.0006), reproductive tract (P = 0.0003), and gastrointestinal tract (P = 0.02). It was higher in kidney and gallbladder compared to lung (P < 0.0001, P < 0.0001), pancreas (P = 0.02, P = 0.02), spleen (P = 0.03, P =

0.02), and reproductive tract (P = 0.02, P = 0.017). The maximum difference in median enzyme activity between tissues was 6.3 IU/g, indicating that ALT is not highly concentrated in any one tissue.

GLDH activity was detectable in every tissue and was higher in liver compared to skeletal muscle (P < 0.0001), cardiac muscle (P = 0.0008), lung (P = 0.008), and reproductive tract (P = 0.002). Activity was higher in the kidney compared to skeletal muscle (P = 0.0005), cardiac muscle (P = 0.01), and reproductive tract (P = 0.02). Finally, activity was higher in the gallbladder and pancreas compared to the skeletal muscle (P = 0.008, P = 0.02). GLDH activity was most concentrated in the liver, with a median value 106 IU/g higher than the kidney. The median activity in the kidney was 42 IU/g higher than the gallbladder, and the gallbladder median was within 33 IU/g of the remaining tissues.

ALP activity was detectable in all tissues and was higher in kidney compared to skeletal muscle (P < 0.0001), cardiac muscle (P <0.0001), lung (P = 0.02), liver (P <0.0001), and pancreas (P = 0.01). Activity was also higher in the GI tract compared to skeletal muscle (P =

0.0001), cardiac muscle (P = 0.001), and liver (P = 0.001), and higher in the reproductive tract than the skeletal muscle (P = 0.03). ALP activity was most concentrated in the kidney, with a median value 22 IU/g higher than that of the GI tract. The median value of the GI tract was within 7.7 IU/g of all other tissues.

GGT activity was detected in at least one sample from the skeletal muscle, liver, kidney, lung, GI tract, and reproductive tract. Consistent detection in > 90% of samples was only achieved for the kidney, GI tract, and reproductive tract. GGT activity was not detected in cardiac muscle, gallbladder, pancreas, and spleen. Statistical analyses were restricted to tissues where GGT activity was present. Activity was higher in the kidney than the skeletal muscle (P =

0.0008), lung (P = 0.01), and liver (P = 0.0008) and higher in the GI tract than the skeletal muscle (P = 0.04) and liver (P = 0.04). GGT activity was primarily detected in the kidney, with a median value 2.28 IU/g greater than that of the GI tract and 2.38 IU/g greater than the reproductive tract, respectively.

DISCUSSION

This study documents tissues of origin for six common plasma biochemical enzymes in eastern box turtles. Results indicate that CK activity is greatest in skeletal muscle, cardiac muscle, and GI tissue; ALT and GLDH activities are highest in the liver, kidney, and gallbladder; ALP and GGT activites are highest in the kidney and GI tract; and AST activity is higher in cardiac muscle, liver, kidney, skeletal muscle, and gallbladder compared to other tissues. Within box turtle tissues, enzyme activity was high (> 1000 IU/g) for CK, moderate (100

- 1000 IU/g) for GLDH, lower (10 - 100 IU/g) for AST and ALP, and lowest (< 10 IU/g) for

GGT and ALT. No enzymes were fully specific to a single tissue. Differences in methodology, enzymes considered, and tissues included prohibit direct comparison of enzyme activities between studies, but when overall patterns of activity are compared between species, a few clear patterns emerge.

CK activity is consistently present at high levels in skeletal and cardiac muscle in every species evaluated, and is also detectable in the smooth muscle from organs such as the GI tract

(Boyd 1962, St Aubin 1977, Clampitt 1978, Geraci 1979, Keller 1981, Keller 1985, Lumeij

1988, Ramsay 1995, Wagner 1999, Fauquier 2008, Lumeij 2008, Anderson 2010, Anderson

2013, Bogan 2014, Petrosky 2015, Bogan 2017). Tissue distributions for GGT and GLDH are also highly conserved across taxa, with greatest activity levels in the kidney and liver, respectively (Boyd 1962, Clampitt 1978, Geraci 1979, Keller 1981, Boyd 1983, Keller 1985,

Lumeij 1988, Ramsay 1995, Fauquier 2008, Lumeij 2008, Anderson 2010, Anderson 2013,

Bogan 2014, Petrosky 2015, Bogan 2017). Eastern box turtle tissue specificity is consistent with other vertebrates for these three enzymes.

AST activity is concentrated in hepatic and muscle tissue in most animals, though the specific site with the highest activity is species-dependent and low-moderate activity is commonly present in all tissues (Boyd 1962, Zimmerman 1968, St Aubin 1977, Clampitt 1978,

Geraci 1979, Keller 1981, Casillas 1982, Boyd 1983, Keller 1985, Lumeij 1988, Fauquier 2008,

Lumeij 2008, Anderson 2010, Bogan 2017). In box turtles and other reptiles, renal AST activity can be comparable to that of the liver, cardiac muscle, and skeletal muscle (Ramsay 1995,

Wagner 1999, Anderson 2013, Petrosky 2015, Bogan 2017). Low overall tissue specificity for

AST appears somewhat universal, though it still maintains clinical utility in many species (St

Aubin 1977, Bogan 2014).

ALT activity tends to divide mammals into one of two groups, those where the highest activity is present in the liver (canids, felids, primates, etc.), and those where the highest activity is present in skeletal and cardiac muscle (ungulates) (Clampitt 1978, Boyd 1983, Keller 1985,

Fauquier 2008). In reptiles, ALT activity is either significantly higher in the kidney than any other tissue (American alligators, yellow rat snakes), or, like most avian species, has a low overall activity which is concentrated in the liver and kidney (sea turtles, eastern box turtles, and green iguanas) (Lumeij 1988, Ramsay 1995, Wagner 1999, Lumeij 2008, Anderson 2013, Bogan

2014, Petrosky 2015). The diagnostic sensitivity and specificity for ALT is typically greatest for mammals with high hepatic concentrations of this enzyme, though its clinical utility for box turtles and other reptiles cannot be immediately ruled out without further studies (Clampitt 1978,

Boyd 1983, Keller 1985, Boyd 1988).

ALP activity is present at low-moderate levels in the tissues of mammals and birds, and it is typically most concentrated in the kidney and GI tract (Clampitt 1978, Boyd 1983, Keller

1985, Lumeij 1988, Lumeij 2008, Yu 2009). Eastern box turtles and yellow rat snakes follow this pattern, but most other ectothermic vertebrates have a somewhat different tissue distribution

(Ramsay 1995). In green iguanas ALP activity is concentrated in the kidney, while in American alligators it is highest in the intestine followed by the cardiac muscle, liver, and kidney (Wagner

1999, Bogan 2017). ALP activity is highest in the spleen of loggerheads and the lung of Kemp’s

Ridley sea turtles (Anderson 2013, Petrosky 2015). Theoretically, ALP activity is also elevated in the bone of all species due to the presence of a bone isozyme, however few studies have

52 confirmed this due to difficulty associated with bone homogenization protocols (Anderson 2013,

Boyd 1983, Boyd 1988).

Eastern box turtle tissue enzyme distribution is generally similar to other species. While these findings provide an important first step towards understanding plasma biochemical analysis in box turtles, tissue enzyme distribution alone does not fully explain patterns of enzyme elevation in plasma (Boyd 1983, Boyd 1988, Lumeij 1988). The location of the enzyme within the cell, rate of clearance from the plasma, nature of the enzyme (leakage vs. induction), type, duration, and severity of cellular insult, relative mass of affected tissue, and characteristics of the source tissue can also affect plasma enzyme activity, and contribute significantly to the clinical value of a particular enzyme (Boyd 1983, Boyd 1988).

Diagnostically relevant enzymes are typically found in the cytosol (CK, ALT, AST), mitochondria (GLDH, AST), and/or cellular membranes (GGT, ALP) within their tissues of origin (Boyd 1983, Boyd 1988). This intracellular organization dramatically influences the degree of leakage in response to cellular insult. Cytosolic enzymes can elevate in the plasma due to mild forms of cell injury, such as reversible increases in membrane permeability associated with inflammation. In contrast, enzymes anchored to cellular components, such as GLDH, are typically not released from cells unless necrosis and subsequent cellular lysis is present (Boyd

1983, Boyd 1988). The type and degree of cellular damage can therefore significantly impact the pattern of plasma enzyme activities, and affect the diagnostic sensitivity of certain enzymes. In box turtles, cytosolic enzymes including CK, ALT, and AST may be expected to rise in plasma in response to injury in their tissues of origin. Mitochondrial enzymes such as GLDH may rise with hepatocyte necrosis, though changes in liver-associated cytosolic enzymes (ALT, AST) may be more sensitive for liver damage.

Diagnostic sensitivity is also highly dependent upon the half-life of an enzyme in serum or plasma. Enzymes which are rapidly eliminated from the body have limited clinical utility because of their narrow window of elevation (Boyd 1983, Boyd 1988, Lumeij 1988). There is currently no information on plasma half-lives of diagnostic enzymes in reptiles, which is a significant limitation for clinical interpretation of biochemistry panels in this taxon.

Biologic behavior and function of biochemistry enzymes is another important consideration for determining clinical relevance. Most enzymes evaluated in the present study

(CK, AST, ALT, GLDH) are leakage enzymes with relatively straight-forward associations between plasma activity and tissue activity. However, the activities of enzymes such as GGT and

ALP can be significantly up-regulated during disease states due to enzyme induction, despite fairly low levels in normal tissues (Boyd 1983, Boyd 1988, Lumeij 1988). For example, GGT activity is typically highest in the kidney, but plasma levels of GGT elevate most reliably in response to biliary disease in mammals (Boyd 1983). It is therefore much more difficult to infer potential clinical utility for these enzymes from their relative tissue specificity. Future studies pairing histologic diagnoses with plasma biochemistry changes will be necessary to assess the diagnostic capabilities of induction enzymes in reptiles.

Finally, tissue characteristics are important to consider when interpreting the results of tissue enzyme specificity studies. Enzymes produced by the excretory organs (kidney and GI tract) tend to be expelled in waste instead of circulated in plasma (Boyd 1983, Boyd 1988). For example, GGT activity is highly concentrated in the kidney in most species, but plasma GGT levels do not elevate in response to renal disease. Instead, GGT activity rises in the urine (Boyd

1983, Boyd 1988). Urinary GGT can be used as a diagnostic test to evaluate renal health in mammals, but interpretation of urinary GGT in reptiles and birds may be complicated by the

54 semi-solid consistency of the urine, mixing of urine and feces within the cloaca, and the presence of post-renal urine modification within the urinary bladder or colon (Goldstein 1988). Similar to the excretory organs, the activity of enzymes within the brain can be masked by the blood-brain barrier, despite this organ having high activity for CK in multiple species (Boyd 1983, Boyd

1988, Anderson 2013).

In box turtles, activities of GGT, ALT, ALP, AST, and GLDH are high in the kidney while CK and ALP are high in the GI tract, however, damage to the kidney or GI tract may result in loss of enzymes into the feces and urine instead of elevation in the plasma. ALT and GLDH may therefore primarily be markers for liver disease/damage, though the overall concentration of

ALT in box turtle tissues is low; AST may be a good marker for muscle and liver insult, and CK may be best associated with muscle damage. GGT and ALP are inducible enzymes, and it is difficult to determine their diagnostic utility without further testing.

Future studies may advance reptile clinical pathology and improve the interpretation of plasma biochemistry panels. Next steps include determining tissue enzyme specificity in more species, quantifying enzyme clearance rates from the plasma, assessing association between clinical pathology changes and histologic evidence of disease in retrospective case-control studies, experimentally inducing organ damage and monitoring plasma enzyme activity changes, and potentially using proteomic approaches to identify novel enzyme targets in order to improve detection and diagnosis of organ disease in reptiles.

Limitations of the present study are largely related to methodology. This study utilized tissues from free-living eastern box turtles with irreparable skull damage due to vehicular trauma in order to avoid euthanasia of otherwise healthy animals. Comparison of muscle damage enzyme activity (CK and AST) between turtles with concurrent limb/shell fractures and those

55 with skull fractures alone demonstrated no difference between groups, supporting the validity of this approach. Pentobarbital was utilized for euthanasia of the turtles in this study, and while alteration of tissue enzyme activities secondary to administration of this drug is unexpected, the possibility cannot be fully ruled out. Several other tissue specificity studies have also used tissues collected through wildlife rehabilitation centers, and while there are some drawbacks associated with potential drug administration and incompletely characterized health status, this appears to be a good way to obtain scientifically useful data while reducing animal use in science

(Fauquier 2008, Anderson 2013, Petrosky 2015, Lindsjö 2016). While all turtles included in the study appeared outwardly healthy, the presence of infectious disease cannot be fully ruled out.

Box turtles exercise behavioral fever in response to pathogen challenge, and open areas such as roads are popular basking sites (do Amaral 2002b). It is possible that at least some of the animals included in this study were struck on roads while basking due to illness, instead of crossing the road for other behavioral reasons such as foraging or nesting (Gibbs 2002). The results of this study may have been slightly different if completely healthy, uninjured animals were utilized, though such a study design would have been hard to ethically justify.

This study may have been improved by considering paired enzyme activities in antemortem plasma samples and tissues to determine relative concentrations in each compartment. This was not pursued because the presence of traumatic injuries in the study animals would have likely altered the plasma distribution of muscle leakage enzymes and decreased the generalizability of results to healthy individuals.

Additional limitations include the short delay between death and tissue collection, which was necessary due to shipment of animal remains. All turtles were maintained on ice after death, and it is unclear if or how this < 24 hour delay may have impacted tissue enzyme activity.

Similarly, tissue samples were frozen at -20oC for 1-3 months until sample processing and chemistry analysis. Previous research in humans and rats indicates that the plasma biochemical enzymes evaluated in this study remain stable in serum at -20oC for up to three months (Cray

2009, Cuhadar 2013). Freezing samples for variable periods of time, sometimes even longer than three months, is commonly performed in tissue specificity studies, and results obtained in the present study are comparable to those of previous research efforts (St Aubin 1977, Geraci 1979,

Battison 1996, Wagner 1999, Fauquier 2008, Yu 2009, Anderson 2010, Anderson 2013,

Petrosky 2015). Lastly, though the targeted sample size for each organ in this study was eight, only six male and four female reproductive tracts were available for testing. Due to small sample size, effects of sex on enzyme activity in other organs could not be rigorously assessed.

This study determined the activity levels of six common biochemical enzymes in 10 eastern box turtle tissues. The findings are generally comparable to other species, with some potentially important exceptions. This research lays the groundwork for future studies which will improve clinical interpretation of reptile biochemistry panels, and ultimately advance reptilian veterinary practice.

Table 3.1. Enzyme activity data in 10 tissues from eastern box turtles (Terrapene carolina carolina). SM = skeletal muscle, CM = cardiac muscle, GB = gallbladder, Repro = reproductive organs, GI = gastrointestinal tract.

Creatine Kinase (IU/g) Aspartate Aminotransferase (IU/g) Alanine Aminotransferase (IU/g) Median Quartiles Range Median Quartiles Range Median Quartiles Range SM 3000A,D 2892.45, 3000 1620.9 - 3470.4 19.77A,D 10.4, 32.28 6.96 - 42.15 0.66 0.26, 0.92 0.15 - 1.11 CM 1038.68A,C,D 869.03, 1611.41 584.25 - 2277.6 35.415A 15.39, 71.12 5.25 - 85.41 0.5 0.34, 0.86 0.21 - 1.2 Liver 5.73B 4.41, 9.87 2.07 - 27.12 28.68A,C 18.5, 54.26 6.45 - 117.66 6.48A 3.81, 11.04 2.79 - 25.98 Kidney 99.6 84.84, 107.46 84.27 - 123.69 33.585A,C,D 16.65, 37.94 5.73 - 79.5 2.27A,C 0.85, 3.91 0.54 - 9 GB 79.71B,C,D 53.49, 93.26 30.42 - 96.12 16.17A 11.85, 20.45 9.9 - 33.78 2.21A,C 1.12, 3.08 0.51 - 5.58 Pancreas 47.28B,C,D 32.43, 81.68 9.96 - 180.24 6.15B,C,D 2.95, 12.25 1.05 - 29.55 0.24B 0.22, 0.34 0.09 - 0.63 Lung 92.03 76.39, 108.61 55.38 - 163.38 3.78B,D 2.75, 4.96 2.31 - 7.02 0.14B 0.09, 0.15 0.09 - 0.24 Spleen 7.7B 7.22, 8.5 3.39 - 22.65 1.845B 1.14, 3.31 0.33 - 4.26 0.24B 0.18, 0.39 0.09 - 0.63 Repro 25.82B,D 12.76, 58.78 7.59 - 154.56 4.35B,D 1.25, 5.69 0.18 - 10.41 0.18B 0.15, 0.3 0.09 - 0.93 GI 469.2C,D 363.11, 638.55 129.36 - 938.4 8.4 3.8, 9.32 1.68 - 23.85 0.3B,C 0.27, 0.3 0.18 - 0.87 Glutamate Dehydrogenase (IU/g) Alkaline Phosphatase (IU/g) Gamma Glutamyl Transferase (IU/g) Median Quartiles Range Median Quartiles Range Median Quartiles Range SM 1.80B 1.38, 2.69 1.17 - 4.38 0.78B 0.78, 1.01 0.45 - 1.35 0B 0, 0 0-0.03 CM 4.97B,D 3.92, 5.47 1.47 - 6.29 1.425B,D 0.83, 1.79 0.21 - 2.46 0 0, 0 0-0 Liver 183.96A 133.17, 259.8 55.66 - 398.91 0.81B,D 0.74, 1.2 0.36 - 1.77 0B 0, 0 0-0.03 Kidney 77.25A,C 68.28, 94.71 42.89 - 126.06 31.14A 26.22, 37.46 9.27 - 50.82 2.46A 1.67, 3.44 0.96-4.68 GB 34.95A,D 13.11, 50.6 7.16 - 78.89 2.985 2.55, 3.92 2.13 - 11.7 0 0, 0 0-0 Pancreas 21.25A,D 15.78, 25.05 13.02 - 33.17 1.935B,C,D 1.13, 2.54 0.72 - 5.73 0 0, 0 0-0 Lung 5.38B,C,D 4.46, 5.84 3.94 - 6.71 2.325B,C,D 1.9, 2.64 1.59 - 2.73 0B,C 0, 0.02 0-0.33 Spleen 7.32 6.83, 8.21 5.28 - 9.77 5.28 3.12, 9.43 1.2 - 25.11 0 0, 0 0-0 Repro 3.28B,D 1.95, 5 1.63 - 7.35 4.305A,D 3.58, 5.01 2.07 - 6.96 0.075 0.038, 0.15 0-0.33 GI 18.12 12.79, 21.66 10.58 - 27.58 8.505A,C 6.7, 11.13 5.82 - 14.37 0.18A,C 0.13, 0.28 0.03-0.36 A, B, C, D Median enzyme activities with different superscript letters are significantly different between tissues (p < 0.05)

Figure 3.1. Enzyme activity in 10 eastern box turtle (Terrapene carolina carolina) tissues. CK = Creatine Kinase, AST = Aspartate

Aminotransferase, ALT = Alanine Aminotransferase, GLDH = Glutamate Dehydrogenase, ALP = Alkaline Phosphatase, GGT =

Gamma Glutamyl Transferase. SM = skeletal muscle, CM = cardiac muscle, GB = gallbladder, Repro = reproductive organs, GI = gastrointestinal tract.

CHAPTER 4: VENOUS BLOOD GAS IN FREE-LIVING EASTERN BOX TURTLES

(TERRAPENE CAROLINA CAROLINA) AND EFFECTS OF PHYSIOLOGIC,

DEMOGRAPHIC, AND ENVIRONMENTAL FACTORS

Abstract: Sustainable wildlife populations depend on healthy individuals, and the approach to determine wellness of individuals is multifaceted. Blood gas analysis serves as a useful adjunctive diagnostic test for health assessment, but it is uncommonly applied to terrestrial reptiles. This study established reference intervals for venous blood gas panels in free-living eastern box turtles (Terrapene carolina carolina, N=102) from Illinois and Tennessee, and modeled the effects of environmental and physiologic parameters on each blood gas analyte.

Blood gas panels included pH, partial pressure of oxygen (pO2), partial pressure of carbon

- dioxide (pCO2), total carbon dioxide (TCO2), bicarbonate (HCO3 ), base excess (BE), and lactate. Candidate sets of general linear models were constructed for each blood gas analyte and ranked using an information-theoretic approach (AIC). Season, packed cell volume (PCV), and activity level were the most important predictors for all blood gas analytes (p<0.05). Elevations

- in PCV were associated with increases in pCO2 and lactate, and decreases in pH, pO2, HCO3 ,

TCO2, and BE. Turtles with quiet activity levels had lower pH and pO2 and higher pCO2 than

- bright individuals. pH, HCO3 , TCO2, and BE were lowest in the summer, while pCO2 and lactate were highest. Overall, blood pH was most acidic in quiet turtles with elevated PCVs during summer. Trends in the respiratory and metabolic components of the blood gas panel tended to be synergistic rather than antagonistic, demonstrating that either 1) mixed acid-base disturbances are common or 2) chelonian blood pH can reach extreme values prior to activation of compensatory mechanisms. This study shows that box turtle blood gas analytes depend on

60 several physiologic and environmental parameters, and the results serve as a baseline for future evaluation.

** This chapter is adapted from a manuscript published in Conservation Physiology:

Adamovicz L, Leister K, Byrd J, Phillips CA, Allender MC. Venous blood gas in free-living eastern box turtles

(Terrapene carolina carolina) and effects of physiologic, demographic and environmental factors. Conserv Physiol.

2018;6(1):coy041.

INTRODUCTION

Blood gas analysis facilitates the clinical assessment of acid-base status, oxygenation, and ventilation. It serves as a useful adjunctive diagnostic test for the assessment of individual animal health (Keller 2012, Stacy 2013, Stacy 2017). In wildlife, blood gas panels are employed primarily to evaluate the physiologic effects of capture/restraint techniques (Harms 2016, Innis

2014) and sedative/anesthetic protocols (Spriggs 2017, Buss 2015); with relatively infrequent application for population health assessment (Ratliff 2017, Lewbart 2014, Lewbart 2015,

Muñoz-Pérez 2017, Páez-Rosas 2016). However, increasing availability and affordability of point-of-care analyzers has boosted reporting of blood gas analytes from free-living wildlife populations in recent years (Stoot 2014). The establishment of baseline physiologic parameters aids in complete characterization of population wellness, and may enhance conservation efforts

(Cooke 2013, Madliger 2016).

Species which experience frequent respiratory and acid-base challenges are especially likely to benefit from blood gas analysis (Camacho 2013). For example, critically-ill sea turtles commonly experience significant acid-base abnormalities and metabolic compromise (Anderson

2011, Camacho 2013, Camacho 2015, Innis 2007, Innis 2009, Keller 2012). Blood gas panels are

61 used to guide veterinary management of these cases (Camacho 2015, Innis 2007, Innis 2009), and form a component of mortality prediction indices for cold-stunned Kemp’s ridley sea turtles

(Lepidochelys kempii) (Keller 2012, Stacy 2013, Stacy 2017). They have also been utilized to investigate the sublethal effects of bycatch (Innis 2010, Harms 2003), and the coupling of blood gas abnormalities with direct mortality data has contributed to trawling regulation protecting free-living sea turtles (Henwood 1987, Stabenau 1991). Similarly, blood gas panels paired with behavioral analyses have been used to justify the use of turtle exclusion devices and/or altered fyke netting protocols for freshwater fisheries (Larocque 2012, Stoot 2013, LeDain 2013). While blood gas panels have contributed to direct conservation action for aquatic chelonians, they are infrequently applied to free-living terrestrial species.

Eastern box turtles (Terrapene carolina carolina) are in decline due to a combination of factors including habitat loss, road mortality, subsidized predation, and disease (Dodd 2001).

Several disease presentations in box turtles have respiratory manifestations including pneumonia in a hatchling turtle infected with Terrapene herpesvirus 1 (Sim 2015), rhinitis with serous to mucopurulent nasal discharge in turtles infected with Mycoplasma sp. (Feldman 2006), and a constellation of signs from nasal discharge to respiratory distress associated with ranavirus infection (Johnson 2008). Non-specific upper respiratory disease is also common in box turtles presented to wildlife rehabilitators (Sack 2017). With the exception of ranavirus, which causes high morbidity and mortality in box turtles, the effects of the other diseases on population health are currently uncharacterized (Johnson 2008, De Voe 2004, Sim 2016). Evaluating infection status in conjunction with complete health assessments, including blood gas analysis, may elucidate the effects of these respiratory pathogens at the population level. However, the dramatic physiologic changes associated with growth, reproduction, and brumation in reptiles

62 translate to extreme variability in bloodwork parameters based on season, sex, age class, and reproductive status (Anderson 1997, Flower 2014, Rose 2011, Tamukai 2011). It is therefore important to define the range of expected normal values in free-living populations prior to applying blood gas panels for clinical health assessment.

The objectives of this study were to 1) assess the impacts of physiologic, demographic, and environmental factors on venous blood gas parameters in eastern box turtles and 2) to generate venous blood gas reference intervals appropriately partitioned over a range of physiologically relevant environmental conditions. The specific biological hypotheses were that blood gas parameters would be non-directionally influenced by environmental factors including season and temperature, and by physiologic factors including activity level and packed cell volume.

MATERIALS & METHODS

Animal Populations

Eastern box turtles were captured using human and canine search teams in Oak Ridge,

Tennessee (36.008°N, -84.22392°W), and Vermilion County, Illinois including the Middle Fork

State Fish and Wildlife Area (40.2595°N, -87.7939°W), Kickapoo State Park (40.01167°N, -

87.7359°W), Forest Glen Nature Preserve (40.0118°N, -87.5653°W), and Kennekuk Cove

County Park (40.2085°N, -87.7232°W) during the 2014 active season. Turtles were sampled during the months of May (“Spring”), June/July (“Summer”), and September (“Fall”). Capture locations were recorded using global positioning software (GPS) via eTrek Vista HCx hand held devices (Garmin International Inc., Olathe, KS, USA). Turtles were placed in individual cloth bags and transported in backpacks to a field sampling station for physical examination and blood collection. Following sampling, each turtle was released at its exact site of capture. Procedures

63 were approved by the University of Illinois Institutional Animal Use and Care Committee

(Protocols #13061 and #15017).

Sample Collection and Processing

All sample collection occurred within 2 hours of capture. Each animal was assigned a permanent ID via shell notching, and the date, weight, size (straight carapace length, width, and height; anterior and posterior plastron length), approximate age (annuli estimate), sex, and age class were recorded. Sex was determined based on iris color, plastron concavity, and positioning of the cloacal opening (Dodd 2001). Turtles with a carapace length less than 9 cm (3.5 in) and an annuli count less than or equal to seven were characterized as juveniles, all others were classified as adults. Physical examinations were performed, noting visual appearance of the eyes, nose, tympanic membranes, oral cavity, appendages, shell, cloaca, and integument. Each of these systems was coded as “normal” or “abnormal”. Activity level was assessed as “bright” (moving or active) or “quiet” (boxed up or minimal movement).

Whole blood (<0.8% body weight) was collected from the subcarapacial sinus using a 22 gauge, 1.5 inch needle and 3 ml syringe (adults) or a 25 gauge, ¾ inch needle and 1 ml syringe

(juveniles). Following collection, blood was immediately loaded into a CG4+ blood gas cartridge

(iSTAT, Abbott, North Chicago, IL) and analyzed (iSTAT 2, Abbott) for the following parameters: pH, partial pressure of oxygen (pO2), partial pressure of carbon dioxide (pCO2), total

- carbon dioxide (TCO2), bicarbonate (HCO3 ), base excess (BE), and lactate. The iSTAT system

o heats blood samples to 37 C and measures pH and pCO2 using direct potentiometry, while pO2 is

- measured amperometrically. HCO3 , TCO2, and BE are calculated using human-derived algorithms (CLSI, 2001). Heparinized, clotted, and lymph-diluted samples were excluded from analysis.

Values for pH, pO2, and pCO2 were corrected based on average air temperature (TA), and

- HCO3 and TCO2 were re-calculated using the following formulae (Stabenau 1993, CLSI 2001,

Anderson 2011):

pH(TA) = pHI – 0.0147 x (TA – 37) + 0.0065 x (7.4 – pHI) x ( TA – 37)

-0.0058*(TA – 37) pO2(TA) = pO2 I x 10

0.0019*(TA – 37) pCO2(TA) = pCO2 I x 10

- (pH(TA) – pKa) HCO3 ( TA) = CO2 x pCO2(TA) x 10

- TCO2(TA) = HCO3 (TA) + (CO2 x pCO2(TA))

-2 -3 -5 2 -7 3 CO2 = 9.174 x 10 – 3.269 x 10 x TA + 6.364 x 10 x TA – 5.378 x 10 x TA

-2 -4 2 -6 3 pKa = 6.398 – 1.341 x 10 x TA + 2.282 x 10 x TA – 1.516 x 10 x TA pH(TA) + 0.011 x TA – 10.241 pH(TA) + 0.001 x TA – 8.889 – log(1.011 + 10 + 10 )

Packed cell volume was determined using heparinized microhematocrit tubes (Jorgensen

Laboratories, Inc., Loveland, CO 80538) centrifuged at 14,500 rpm for 5 minutes. Environmental temperature data (daily minimum, maximum, and mean air temperature) were obtained from

NOAA weather stations nearest to the study site for the dates corresponding to each search.

Statistical Methods

All statistical assessments were performed in R at an alpha value of 0.05 (R Core Team

2013). Descriptive statistics (mean, median, range, standard deviation, and 10% and 90% percentiles) were tabulated for all continuous variables. Distribution was assessed visually using box plots and statistically using the Shapiro-Wilk test. Data transformation was pursued if needed to support statistical assumptions during modeling. Differences in categorical variables

(sex, age class) between study sites and states were evaluated using Fisher’s exact tests. Sex

65 ratios were evaluated using binomial tests (expected ratio 0.5). Differences in continuous variables (weight, PCV) between study sites, states, and seasons were assessed using general linear models. For each model, the assumption of normally distributed error in the dependent variable was evaluated using boxplots, histograms, and Q-Q plots of residual values. The assumption of homoscedasticity was assessed using plots of actual vs. fitted residuals and

Levene’s tests. The impact of influential values was assessed using Cook’s Distance plots. Post- hoc between group differences were evaluated using the contrasts function in the lsmeans package (Lenth 2016).

A directed acyclic graph (DAG) was generated to demonstrate the expected relationships among measured predictors and their effect on blood gas parameters. This diagram was used to identify potential confounding variables (variables which influence both the predictor of interest and the response variable) and structure statistical analyses (Joffé 2012). The effects of confounding variables on parameter estimates were controlled using multivariable linear regression. Continuous predictor variables were assessed for multicollinearity using Pearson’s correlation coefficient, with r > 0.5 considered “strongly” correlated. Strongly correlated predictor variables were not included together in future models. Following this, sets of univariate and multivariate candidate models were constructed for each blood gas parameter and ranked using information-theoretic approaches with the AICcmodavg package (Mazerolle 2015).

Reference intervals for each blood gas parameter were partitioned based on the results of general linear modeling and constructed according to American Society for Veterinary Clinical

Pathology guidelines (Friedrichs 2012). Turtles with evidence of active disease processes

(ocular/nasal discharge, oral plaques, open-mouth breathing, etc.) or non-healed traumatic injuries were excluded from the reference interval dataset. Outliers were visually identified using

66 box plots and excluded using Horn’s method (Horn 2001). The mean, standard deviation, median, and range were determined for each parameter. Data were evaluated for normality using the Shapiro-Wilk and Kolmorgorov-Smirnov tests. The nonparametric method was used to generate 95% reference intervals for each blood gas parameter based on the observations of Le

Boedec et al. (2016). Ninety percent confidence intervals were generated around the upper and lower bounds of each reference interval using nonparametric bootstrapping with 5000 replicates.

The width of the confidence intervals (WCI) was compared to the total width of the reference interval (WRI) to infer the need for a larger sample size (improved n is recommended when

WCI/WRI > 0.2). All reference interval generation was performed using the referenceIntervals package.

RESULTS

Sample Population

A total of 102 eastern box turtles were included in this study. The sampled populations are described by season, state, sex, age class, and habitat in Table 4.1. There were no significant differences in sex or age class distribution between sites or states.

Physical Exam

Most turtles had a quiet activity level (n=66; 67.3%), but some were bright (n=32;

32.7%). Carapacial lesions were the most common physical exam abnormality (n=9; 9%), followed by asymmetrical nares (n=3; 3%), plastron lesions (n=2; 2%), a nodule on the hard palate (n=1; 1%), unilateral pthisis bulbi (n=1; 1%), necrotic bridge fracture (n=1; 1%), open- mouth breathing (n=1; 1%), and a combination of ocular swelling, nasal discharge, and diarrhea

(n=1;1%). The last three aforementioned turtles with active lesions were excluded from the dataset used for reference interval generation.

Physiologic and Environmental Parameters

Continuous physiologic parameters are summarized in Table 4.2. Illinois turtles had a greater mass than Tennessee turtles (effect size 137g, p<0.0001). Within Illinois, EBT at

Collison were significantly heavier than those at Forest Glen Nature Preserve (effect size = 83g, p=0.04). PCV in EBT from TN was significantly higher than in IL turtles (effect size 4.1%, p=0.004).

Relationships between environmental and physiologic variables were explored

o statistically. Average daily air temperature (TA, C) was significantly associated with season

(p<0.0001). Spring temperatures were lower than summer temperatures (effect size = 5.47oC, p<0.0001), summer temperatures were higher than fall temperatures (effect size = 7.82oC, p<0.0001), and fall temperatures were lower than spring temperatures (effect size = 2.34oC, p=0.0001). PCV was positively associated with TA (r= 0.3, p=0.0034) and dependent upon season (p<0.0001), with higher PCV values in the summer compared to both spring (effect size =

7%, p<0.0001) and fall (effect size = 4.7%, p=0.012). PCV tended to be higher in quiet turtles compared to bright individuals, though this relationship was not statistically significant (effect size = 2.9%, p=0.059).

Blood Gas Data Assessment

iSTAT errors resulted in missing values for BE (n = 2), and lactate (n = 1). During initial assessment of blood gas parameter distribution, three turtles were identified with outlier values for two or more blood gas parameters. These animals were fully assessed and determined to have significant pathologic acid-base disturbances, as such they were excluded from modeling and reference interval generation. Blood gas values for these excluded animals are provided in

Supplementary Table 4.1.

Blood Gas Modeling

Modeling of blood gas analytes was pursued to fulfill two main goals: 1) Determine the most precise effect estimates for each significant predictor variable, and 2) Identify the most parsimonious models to predict blood gas values. To address goal 1, models of each blood gas analyte were structured and assessed inclusive of confounding variables and without intervening variables (variables on the causal pathway between the predictor of interest and the response variable) based on a directed acyclic graph (Figure 4.1). General relationships are displayed in

Figures 4.2 and 4.3; final effect estimates are shown in Supplementary Table 4.2.

pH was negatively associated with TA (p<0.001) and PCV (p<0.0001). Lower pH was also observed in quiet turtles (effect size = 0.19, p<0.0001) and the lowest pH values were observed in the summer compared to both spring (effect size = 0.13, p=0.003) and fall (effect size = 0.22, p=0.001). pO2 was negatively associated with PCV (p=0.001) and lower in quiet turtles (effect size = 9.3mm Hg, p=0.018). pCO2 was positively associated with TA (p<0.0001) and PCV (p<0.0001). It was higher in quiet turtles (effect size = 15mm Hg, p<0.001) and the highest values were observed in the summer compared to both spring (effect size = 10, p=0.002)

- and fall (effect size = 17.5, p<0.001)). HCO3 was negatively associated with TA (p<0.0001) and

PCV (p<0.0001). It was lower in summer than fall (effect size = 4.37mmol/L, p=0.009). TCO2 was negatively associated with TA (p=0.0005) and PCV (p<0.0001) and was dependent upon season, with lower values in the summer compared to the fall (effect size = 3.89mmol/L, p=0.02). Lactate was positively associated with TA (p=0.017) and PCV (p= 0.009), and was highest in summer compared to spring (effect size = 2.36mmol/L, p=0.0003) and fall (effect size

=2.72mmol/L, p=0.0008). BE was negatively associated with TA (p=0.002) and PCV

(p<0.0001). It was lowest in the summer compared to spring (effect size = 3.07mmol/L, p=0.006) and fall (effect size = 4.71mmol/L, p=0.0009).

To address goal 2, sets of univariate and multivariate candidate models constructed from a common dataset were ranked using information-theoretic model selection procedures. Case- wise deletion was used to address missing data. One record was removed due to a missing PCV, three records were removed due to missing activity levels, and one record was removed due to missing both PCV and activity level entries. Records with missing BE and lactate values due to iSTAT errors were also removed. Season was included in place of TA in all models due to the highly correlated nature of these two variables and the antecedent position of Season relative to

TA in the DAG. All biologically important predictor variables were included for model selection regardless of statistical significance. The results of predictive model construction and selection are displayed in Table 4.3 and Figures 2 and 3.

For pH and pCO2, the models containing the additive effects of all predictors identified in the original DAG garnered the most support with Akaike weights of 1. For lactate and TCO2,

PCV + Season was the most parsimonious model with Akaike weights of 0.69-0.71. There was high model selection uncertainty for the remaining blood gas parameters, but they all shared the same top two models which accounted for ≥ 0.95 of the Akaike weights: PCV + Season and the full additive model (PCV + Season + Activity Level). Objectively, top models had adjusted R2 values from 0.12-0.62 and p-values from 0.002 - <0.0001. Subjectively, models explained a fair- moderate degree of variability in the data, with decreased explanatory power at extreme values

(Figure 4.2, Figure 4.3).

Venous Blood Gas Reference Intervals

Reference intervals for blood gas parameters were partitioned based on season. Statistical differences were not identified based on sex or age class, so data from all apparently healthy animals was combined for reference interval generation. Excluded data included values missing

- due to the iSTAT errors mentioned above and outliers: HCO3 (17.7mm Hg in summer) and lactate (14.17mmol/L in summer). Summary data, reference intervals, and 90% CI of the reference interval bounds are reported in Table 4.4. The WCI/WRI ratios indicate that the

- reference interval bounds for HCO3 in the spring and TCO2 in the spring may gain precision with a larger sample size. Reference intervals were not calculated for the fall sampling period due to sample size limitations, per ASVCP recommendations. Histograms showing the distribution of all data used to calculate reference intervals are available as supplemental figures

(Supplementary Figures 4.1-4.3).

DISCUSSION

This study determined seasonal reference intervals for venous blood gas parameters in eastern box turtles across the active season, assessed environmental and physiologic predictors of blood gas status, and built predictive models for blood gas analytes. The results represent an important first step towards understanding blood gas analysis in free-living chelonians, and generate several important research questions for further study.

Expected Associations Between Predictors and Blood Gas Parameters: The Directed

Acyclic Diagram

The directed acyclic diagram which guided modeling in this study was based on previous research into blood gas parameters in reptiles. Factors such as temperature and exercise level

71 have direct effects on chelonian blood gas parameters, while season and packed cell volume can indirectly influence blood gases (Gatten 1974, Toledo 2008, da Silva 2013, Malte 2014).

In closed systems, increasing temperature decreases the solubility of CO2 and O2 in plasma (CO2, O2 respectively), decreases (typically) hemoglobin’s affinity for O2, and decreases the pK of buffering systems (pKPr, pKHCO3-), leading to a decrease in pH and an increase in pO2 and pCO2. These changes are tightly coupled by the Bohr/Haldane effect (Malte

2014). In ectothermic vertebrates, temperature directly affects pH, pO2, and pCO2 due to its effect on metabolic rate (da Silva 2013, Malte 2014). Increasing temperature increases oxygen consumption, oxygen uptake (VO2), and pulmonary ventilation (VE), but decreases the air convection requirement (VE/VO2) resulting in relative hypoventilation. This elevates the concentration of alveolar CO2, which in turn increases arterial CO2 and decreases pH (da Silva

2013). Temperature was placed antecedent to all blood gas parameters in the DAG to represent these relationships.

Season can affect reptile metabolic rates independently of temperature, potentially due to differences in light cycle, resource availability, and/or reproductive status (Toledo 2008). This may lead to seasonal changes in blood gas parameters (Christopher 1999). In the DAG, season was placed antecedent to temperature and all blood gas parameters.

Exercise directly affects oxygen consumption and lactate production in chelonians, and can be expected to influence blood gas parameters (Gatten 1974, Bagatto 1999). We were unable to directly assess exercise level in the turtles, so activity level (whether or not the turtle was moving upon examination) was used as a proxy variable in the DAG and was placed antecedent to all blood gas parameters.

Packed cell volume, an indicator of red blood cell concentration, is highly correlated to the concentration of hemoglobin, which serves as the oxygen carrying metalloprotein in vertebrates. PCV has been demonstrated to affect pO2 due to its role in oxygen carrying capacity, though its degree of importance depends on temperature, oxygen solubility, and the level of affinity of hemoglobin for O2 (Malte 2014). PCV was placed antecedent to all blood gas parameters in the DAG.

Relationships between predictor variables must also be considered for modeling purposes. Temperature is clearly influenced by season. This was demonstrated in the DAG by placing temperature on a causal pathway between season and all blood gas parameters. PCV is positively associated with temperature in loggerhead sea turtles (Caretta caretta), potentially related to temperature-dependent rates of erythropoiesis (Kelly 2015). Season also affects PCV in chelonians, with higher values typically observed during summer months (Christopher 1999,

Chung 2009, Yang 2014). These relationships were illustrated in the DAG by creating causal pathways from season and temperature to PCV.

Predictor Variables Not Included in the DAG

Additional important causal factors in blood gas analysis are related to cardiorespiratory function. Turtles have a three-chambered heart with an incompletely-separated ventricle which permits admixture of oxygenated and unoxygenated blood. The net direction of blood flow within the heart can be right-to-left (away from the pulmonary circulation) or left-to-right

(towards the pulmonary circulation). The direction of the shunt is vagally mediated, with the L-R direction predominating during periods of ventilation (with a corresponding increase in heart rate) and the R-L direction dominating during periods of apnea (with a corresponding decrease in heart rate), likely representing an evolutionary mechanism to cope with diving (Shelton 1976,

White 1989, Wang 1996, Hopkins 1996). Intracardiac shunting has been demonstrated to affect blood gas parameters, with R-L shunts associated with lower arterial pO2 (Hicks 1999, Platzack

2001). However, there is no non-invasive way to evaluate intracardiac shunting in chelonians.

The closest measurable proxy variable is heart rate, which was not evaluated in this study, but should be considered in future research efforts.

Turtle respiratory patterns are characterized by alternating periods of ventilation and apnea (Burggren 1979). In box turtles, lung ventilation is primarily due to the function of the oblique abdominis and transverse abdominis muscles, with potential contribution from limb- pumping motions at rest (Landberg 2003). Respiratory rate was not recorded during field work due to the difficulty associated with accurate measurement of this variable in box turtles.

Digestion also elevates metabolic rate and can influence blood gas parameters in reptiles

(Arvedsen 2005). However, as our study subjects were wild, their ingestion history was unknown. As neither cardiorespiratory rates nor digestive status were measured in this study, they were not included in the DAG, despite their potential as biologically important predictor variables.

Box Turtle Blood Gas Modeling

Most of the relationships depicted in the final DAG were statistically supported. As expected, PCV was positively associated with temperature and dependent upon season, with the highest values in summer. This finding is in agreement with previous research on the hematology of Asian yellow pond turtles (Ocadia sinensis), yellow-marginated box turtles (Cuora flavomarginata), loggerhead sea turtles, and desert tortoises (Gopherus agassizii) (Christopher

1999, Chung 2009, Yang 2014, Kelly 2015). The relationship between PCV and activity level was borderline significant, with quieter turtles having higher PCV values. This may be consistent

74 with a stress response. When faced with a threatening stimulus, most animals have the option of

“fight or flight”, but box turtles have a unique third option: retract the head and limbs within the shell and close the hinge to hide. This is the most frequent response to a perceived threat in box turtles (Smith 1985). A transient elevation in PCV is another component of the reptilian stress response (Sturbaum 1981, Sturbaum 1981, Franklin 2003). Taken together, an elevated PCV in boxed or partially-boxed turtles may be indicative of stress.

pH decreased and pCO2 increased with increasing air temperatures, and these changes were most extreme during the summer months. This follows the trend of other ectothermic

- vertebrates, and is likely driven by an increased metabolic rate (da Silva 2013). HCO3 , TCO2, and BE, all indicators of the metabolic component of acid-base status, had a negative association with temperature and were lowest in summer. The opposite relationship was identified for

- lactate. While anion gap was not determined in this study, the combination of lower pH, HCO3 ,

TCO2, and BE with elevated lactate suggests a relative titrational metabolic acidemia in eastern box turtles during the summer compared to spring and fall. Elevated lactic acid production in the summer is likely secondary to increased activity levels associated with foraging, mating, and nesting behaviors (Gatten 1974, Bagatto 1999). Concurrent elevation in pCO2 may indicate a mixed acidemia driven by the decreased air convection requirement associated with elevated temperature (da Silva 2013).

This is an interesting finding because changes in the respiratory and metabolic components of pH balance typically oppose each other in order to maintain normal physiologic functions. However, in apparently healthy free-living box turtles, it appears that these components are working in an additive fashion to drive pH down in summer months.

Furthermore, this acid-base change is not large enough to activate compensatory mechanisms,

75 indicating that box turtle acid-base balance may be naturally regulated within wide limits. This information has direct implications for clinical assessment of chelonian blood gas panels, and clearly demonstrates the importance of seasonally-based reference intervals for box turtles, and possibly other ectotherms.

While the pH of blood decreased in the summer months, we did not evaluate the pH of other body compartments in this study. Many aquatic chelonians have elevated levels of bicarbonate in peritoneal/pericardial fluids and high buffering capacity in their shells which allow some species to survive incredible levels of lactic acidosis during prolonged submergence

(Jackson 2000). The degree and speed at which these extravascular buffering sources contribute to changes in arterial and venous pH appears to be species-dependent (Bagatto 1999). It is unclear how the buffering capacity of these additional systems may play a role in venous acid- base balance in box turtles, but this could be an avenue for future study.

Blood gas changes associated with PCV mirrored those of temperature, i.e. turtles with higher PCV values had an assumed relative titrational metabolic acidemia. The biological mechanism explaining this finding might be related to hydration status, because elevations in

PCV are commonly associated with dehydration during summer months in chelonians

(Christopher 1999). Dehydration with a mild decrease in circulatory volume may promote a shift towards anaerobic respiration in the peripheral tissues, driving lactate up and pH down.

Quiet turtles had lower pH, higher pCO2, and lower pO2 values than bright turtles. This pattern of changes is indicative of a relative respiratory acidemia potentially associated with impaired ventilation in boxed turtles. There were no statistically significant changes in the metabolic component of the blood gas panels, suggesting that the effect of activity level on blood gas parameters is fairly transient compared to some of the other predictor variables evaluated.

Taken together, the findings of this study indicate that the clinical interpretation of venous blood gas panels in eastern box turtles should account for changes associated with season, activity level, and PCV.

The models constructed for each blood gas parameter contained all of the predictor variables proposed in the DAG, though the inclusion of the activity level variable resulted in a

- high degree of model selection uncertainty for pO2, HCO3 , TCO2, and BE. The models explained a fair-moderate degree of variability in the data, indicating that some important predictor variables may be missing. Heart rate, respiratory rate and plasma electrolyte levels (to calculate anion gap) should be explored as non-invasive measures which may improve the predictive capability of these models. Improvement of proxy variables (such as substituting a more comprehensive metric for activity level) and obtaining dietary history should also be considered in future blood gas modeling efforts.

Blood Gas Reference Intervals

Reference intervals were constructed for temperature-corrected venous blood gas values.

Temperature correction is important for blood gas analyses in ectothermic animals because many portable blood gas analyzers (including the iSTAT) heat blood samples to 37oC prior to analysis,

- resulting in closed-system changes to the pH, pO2, and pCO2, as described above. HCO3 , TCO2, and BE are calculated in part using pH and pCO2, so these values are also affected by machine methodology. A variety of temperature correction formulae are used to calculate “true” blood gas values from the output of these analyzers for ectotherms (e.g. Stabenau 1993, Anderson 2011,

Harter 2014, Malte 2014). Some of these formulae are derived from human medicine, while others rely on experimental determination of factors such as CO2, pKaCO2, sodium and protein concentrations, and others to generate species-specific correction formulae. Temperature

77 correction has not been specifically researched for box turtles, and species-specific formulae are not available.

Previous research has demonstrated that human-derived temperature correction formulae provide adequate estimates for pH and pCO2 in slider turtles (Trachemys scripta sp.) and several species of snakes, including ball pythons (Python regius), water pythons (Liasis fuscus), yellow anacondas (Eunectes notaeus) and boas (Boa constrictor) (Malte 2014). The same study illustrated that human-derived temperature correction formulae for pO2 produced substantially biased estimates for reptiles. Based on these results, the present study utilized the iSTAT’s built- in temperature correction formulae for pH and pCO2, but relied upon a pO2 correction formula from the sea turtle literature (Anderson 2011). Following temperature correction of pH, pO2, and pCO2, values for CO2 and pKaCO2 were determined using equations derived for Kemp’s ridley

- sea turtles (Lepidochelys kempi), and HCO3 was calculated using an adaptation of the

Henderson-Hasselbalch equation (Stabbenau 1993). TCO2 was calculated using temperature- corrected pCO2 and calculated CO2 values. Base excess is calculated using bicarbonate, pH, and hemoglobin levels. As hemoglobin was not directly measured in this study, BE could not be recalculated using temperature corrected values, and raw iSTAT output is reported.

The use of different temperature correction formulae by different researchers can make direct comparisons between chelonian blood gas studies difficult, however, general observations can still be made. Reference intervals for eastern box turtle blood venous gas parameters were fairly broad and generally consistent with those of apparently healthy, sedated, and/or pre-release green sea turtles (Chelonia mydas) (Anderson 2011, Lewbart 2014), loggerheads (Chittick 2002,

Harms 2003, Camacho 2013, Phillips 2017), Kemp’s ridley sea turtles (Innis 2007, Keller 2012), leatherback sea turtles (Dermochelys coriacea) (Innis 2010, Innis 2014), hawksbill sea turtles

(Eretmochelys imbricata) (Muñoz-Pérez 2017), desert tortoises (Christopher 1999, Sleeman

2000, Dennis 2002), Negev Desert tortoises (Testudo werneri) (Eshar 2014), and diamondback terrapins (Malaclemys terrapin) (Christiansen 2013). Reference intervals were partitioned based on season, and parameter estimates are provided in Supplementary Table 4.2 to assist with clinical assessment of blood gas panels based on the contributions of other statistically significant predictor variables.

Limitations and Future Directions

Limitations of this study are largely related to methodology. The iSTAT is not validated for use in box turtles, and validation of this equipment was beyond the scope of this study.

However, previous studies in other ectotherms have identified significant discrepancies in the blood gas values reported by the iSTAT compared to benchtop methods (Stoot 2014, Harter

2014, Harter 2015). One study involving several reptile species demonstrated that iSTAT results for biochemistry parameters were correlated to the results from other analyzers, confirming the clinical utility of this machine with iSTAT-specific reference intervals (McCain 2010). It is considered likely that the results reported from the present study are not truly representative of gold-standard blood gas values, but that they could be related to these values using mathematical transformations once the iSTAT is validated for box turtles (Harter 2014, Harter 2015, Malte

2014).

Lymph dilution is possible from any sampling site in chelonians, but the risk is considered to be higher from the subcarapacial sinus which was used in this study (Hernandez-

Divers 2002, Heatley 2010). Blood gases were not measured on visually lymph-contaminated samples, however, the possibility of inapparent lymph dilution cannot be ruled out. The timing of blood gas sampling relative to capture can also affect blood gas parameters (Harms 2003, Innis

2010, Innis 2014, Harms 2016). Time of venipuncture relative to time of capture was not recorded in this study, but could be considered as another covariate in future studies involving free-living wildlife.

Directions for future research should include validation of the iSTAT for box turtles and determination of coefficients for temperature correction formulae. The effects of heart rate and respiratory rate on box turtle blood gas parameters should be evaluated, and anion gap should be calculated to better characterize changes in acid-base status.

Conclusions

This study is a good first step towards understanding blood gas analysis in free-living box turtles. The models established in this study help predict changes in blood gas parameters associated with different physiologic and environmental states, enhancing our ability to distinguish between normal and pathologic variation. The reference intervals can be used for comparison within and between sites in future studies. The ability to comprehensively establish the general wellness of box turtles as sentinel species may also give the clinician a broader understanding of the health of the surrounding ecosystem (Lloyd 2016). Finally, the clinical application of blood gas analysis in the study of disease threats to box turtles may improve conservation strategies for these declining animals.

Table 4.1. Venous blood gas sample size by state, sex, age class, and season in eastern box turtles (Terrapene carolina carolina).

Spring Summer Fall State Illinois 20 19 16 Tennessee 20 23 4 Sex Male 23 19 12 Female 15 21 5 Unknown 2 1 3 Age Class Adult 37 40 17 Juvenile 3 2 3

Table 4.2. Descriptive statistics for continuous physiologic variables from eastern box turtles

(Terrapene carolina carolina) sampled for venous blood gas analysis. TA = average air temperature, PCV = packed cell volume.

o Weight (g) TA ( C) PCV (%) Mean 429 20.97 25 Median 411 20.83 25 SD 143 3.82 8 Range 41-761 13.89-26.67 6-45.5

Table 4.3. Model selection criteria for venous blood gas parameters in eastern box turtles

(Terrapene carolina carolina). AICc = AIC corrected for sample size, AICc = difference compared to the smallest AICc value, wi = Akaike weight.

Parameter Model N K AICc AICc wi pH Activity + PCV + Season 96 6 -104.56 0 1 PCV + Season 96 5 -77.98 26.57 0 Activity 96 3 -49.63 54.93 0 Season 96 4 -45.56 59.00 0 Null 96 2 -29.32 75.24 0

pO2 (mm Hg) PCV + Season 96 5 761.99 0 0.53 Activity + PCV +Season 96 6 762.45 0.46 0.42 Activity 96 3 767.21 5.22 0.04 Null 96 2 770.52 8.53 0.01 Season 96 4 774.37 12.38 0

pCO2 (mm Hg) Activity + PCV + Season 96 6 655.72 0 1 PCV + Season 96 5 688.59 32.87 0 Activity 96 3 713.08 57.35 0 Season 96 4 717.21 61.49 0 Null 96 2 736.06 80.34 0 - HCO3 (mmol/L) PCV + Season 96 5 509.08 0 0.55 Activity + PCV + Season 96 6 509.48 0.40 0.45 Season 96 4 530.62 21.54 0 Activity 96 3 533.91 24.82 0 Null 96 2 536.32 27.24 0

TCO2 (mmol/L) PCV + Season 96 5 506.33 0 0.69 Activity + PCV + Season 96 6 507.95 1.62 0.31 Season 96 4 523.50 17.17 0 Activity 96 3 526.83 20.50 0 Null 96 2 527.43 21.10 0 Lactate (mmol/L) PCV + Season 96 5 430.56 0 0.71 Activity + PCV + Season 96 6 432.49 1.93 0.27 Season 96 4 437.64 7.09 0.02 Null 96 2 450.64 20.09 0 Activity 96 3 452.12 21.56 0 BE (mmol/L) PCV + Season 88 5 509.24 0 0.57 Activity + PCV + Season 88 6 509.77 0.53 0.43 Season 88 4 534.25 25 0 Activity 88 3 544.18 34.94 0 Null 88 2 545.01 35.76 0

Table 4.4. iSTAT venous blood gas reference intervals for eastern box turtles (Terrapene carolina carolina) in the spring, summer, and fall. SD = standard deviation, NG = non-Gaussian distribution, G = Gaussian distribution, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval.

Parameter Season N Mean SD Median Min Max Distribution Reference Range 90% CI LB 90% CI UB pH Spring 38 7.49 0.18 7.49 7.18 7.84 G 7.18-7.84 7.16-7.18 7.84-7.88 Summer 41 7.36 0.2 7.33 7.00 7.78 G 7.00-7.78 6.92-7.01 7.78-7.88 Fall 17 7.59 0.14 7.60 7.29 7.80 G NA NA NA pO2 (mm Hg) Spring 38 57 19 60 18 87 G 18-87 10-18 87-90 Summer 41 58 21 59 8 98 G 8-97 0-9 97-103 Fall 17 58 11 59 34 77 G NA NA NA pCO2 (mm Hg) Spring 38 32.8 12.1 31.2 15.1 64.9 G 15.1-64.9 13.0-15.1 64.9-74.6 Summer 41 43.9 16.8 42.8 16.5 75.1 G 16.5-74.7 12.4-16.6 74.4-82.4 Fall 17 25.3 7.0 24.7 16.4 40.2 G NA NA NA a HCO3 (mmol/L) Spring 38 29.4 4.8 28.8 22.6 41.8 G 22.6-41.8 22.3-22.6 41.8-47.1 Summer 40 28.2 5.1 27.7 17.7 40.6 G 20.4-40.6 18.4-20.4 40.5-42.9 Fall 17 32.0 4.6 33.5 23.0 41.5 G NA NA NA a TCO2 (mmol/L) Spring 38 31 4.7 30 24 43 G 24-43 23-24 43-48 Summer 41 30 5.0 29 20 43 G 20-43 17-20 43-46 Fall 17 33 4.5 35 25 43 G NA NA NA Lactate (mmol/L) Spring 38 4.97 2.84 4.25 1.23 11.78 NG 1.23-11.78 1.08-1.23 11.78-13.06 Summer 40 7.40 2.72 7.63 2.43 14.17 G 2.45-12.16 1.77-2.46 12.15-13.38 Fall 16 4.61 3.21 4.08 1.04 11.36 NG NA NA NA BE (mmol/L) Spring 37 1.9 4.9 2 -7 11 G -7-11 -10, -7 11-13 Summer 40 -0.5 5.6 -1 -13 14 G -13-14 -17, -13 14-19 Fall 17 3.5 4.2 4 -5 10 G NA NA NA a WCI/WRI > 0.2

Figure 4.1. Directed acyclic graph depicting the relationships between venous blood gas parameters and their predictors in eastern box turtles (Terrapene carolina carolina).

Figure 4.2. Measured and predicted venous blood gas values in free-living eastern box turtles (Terrapene carolina carolina). Left panels: measured values. Right panels: values predicted from general linear models containing the additive effects of activity level, season, and packed cell volume (PCV, %), +/- 95% confidence intervals of the estimates.

Figure 4.3. Measured and predicted venous blood gas values in free-living eastern box turtles (Terrapene carolina carolina). Left panels: measured values. Right panels: values predicted from general linear models containing the additive effects of activity level, season, and packed cell volume (PCV, %), +/- 95% confidence intervals of the estimates.

Supplementary Table 4.1. Eastern box turtle (Terrapene carolina carolina) venous blood gas values excluded from data analysis due to the presence of multiple outlier values from the same individual.

- pO2 pCO2 HCO3 TCO2 BE Lactate ID Season pH (mm Hg) (mm Hg) (mmol/L) (mmol/L) (mmol/L) (mmol/L) 14-1130 Spring 7.65 73 40 48.5a 50.1a 22a 2.59 14-1144 Spring 7.79 30 28 62.5a 64.2a 23a 3.58 14-1604 Fall 7.71 53 41 76.9a 79.3a 31a 3.44 a Outlier values

Supplementary Table 4.2. Parameter estimates with 95% confidence intervals for the effects of environmental and physiologic predictor variables on blood gas parameters in eastern box turtles (Terrapene carolina carolina) while controlling for the effects of confounding variables. NS = no statistically significant relationship.

pO2 pCO2 BE - pH (mm Hg) (mm Hg) HCO3 (mmol/L) TCO2 (mmol/L) Lactate (mmol/L) (mmol/L) Tavg (oC) -0.027 NS 2.36 -0.496 -0.441 0.058 -0.128 (-0.036, -0.018) (1.69, 3.02) (-0.747, -0.245) (-0.687, -0.194) (0.032, 0.084) (-0.209, -0.047) PCV (%) -0.014 -0.951 0.968 -0.343 -0.308 0.17 -0.342 (-0.02, -0.009) (-1.52, -0.386) (0.579, 1.36) (-0.475, -0.212) (-0.437, -0.178) (0.087, 0.252) (-0.48, -0.204) Activity (Quiet) -0.194 -9.26 15 NS NS NS NS (-0.115, -0.273) (-16.97, -1.55) (9.28, 20.73) Spring* vs. Summer -0.128 NS 10.01 NS NS 2.36 -3.07 (-0.211, -0.045) (3.86, 16.16) (3.67, 1.05) (-0.89, -5.26) Summer* vs. Fall 0.224 NS -17.49 4.37 3.89 -2.72 4.71 (0.117, 0.330) (9.66, 25.33) (1.68, 7.06) (1.29, 6.48) (-1.01, -4.43) (7.47, 1.96) Fall* vs. Spring NS NS NS NS NS NS NS

Supplementary Figure 4.1. Histograms of venous blood gas values used to determine reference intervals in eastern box turtles

(Terrapene carolina carolina) during the spring.

Supplementary Figure 4.2. Histograms of venous blood gas values used to determine reference intervals in eastern box turtles

(Terrapene carolina carolina) during the summer.

Supplementary Figure 4.3. Histograms of venous blood gas values from eastern box turtles (Terrapene carolina carolina) during the fall.

CHAPTER 5: ERYTHROCYTE SEDIMENTATION RATE IN FREE-LIVING

EASTERN (TERRAPENE CAROLINA CAROLINA) AND ORNATE (TERRAPENE

ORNATA ORNATA) BOX TURTLES AND HEMOGLOBIN-BINDING PROTEIN IN

ORNATE BOX TURTLES: REFERENCE INTERVALS AND DEMOGRAPHIC

EFFECTS

Abstract: The acute phase response (APR) in vertebrates is a highly conserved reaction to infection, inflammation, trauma, stress, and neoplasia which can be used to monitor individual and population health. However, APR components are infrequently assayed in reptiles. This study established reference intervals and determined demographic associations in apparently healthy, free-living box turtles for two diagnostic tests, 1) erythrocyte sedimentation rate (ESR) in eastern (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata) and 2) hemoglobin-binding protein (HBP) in OBT. Level of agreement was assessed between three different methods of ESR measurement: 1) commercially available ESR tubes

(Winpette, W), 2) commercially available ESR tubes measured with a digital caliper (Winpette caliper, WC), and 3) microhematocrit tubes (MHT). ESR was negatively associated with packed cell volume for all measurement methods in both species (p < 0.05). Female OBT had larger

ESR values than males using all measurement methods (p < 0.05). ESR was higher for EBT in the spring compared to the summer using the W and WC methods (p < 0.05). ESR using the

MHT method was moderately to highly correlated with the W and WC methods (Kendall’s tau p

< 0.0001), but MHT tended to proportionally overestimate ESR compared to the WC method in both species. HBP was significantly higher in adult turtles than juveniles (p < 0.05), but was not affected by sex, year, or population (p > 0.05). ESR values for EBT and OBT were generally

92 comparable to other reptiles, while HBP values in OBT were slightly higher. This study provides useful baseline information to aid the clinical application and interpretation of ESR and HBP in box turtles.

INTRODUCTION

The acute phase response (APR) consists of a series of highly conserved transcriptomic, proteomic, and metabolomic alterations in response to infection, trauma, neoplasia, inflammation, and/or stress (Cray 2009, Cray 2012). Acute phase proteins (APP) and inflammatory markers are measurable components of the APR which can be clinically useful for evaluating severity, duration, and prognosis of injuries and disease states in humans and animals undergoing medical care (Cray 2009, Eckersall 2010, Cray 2012). Furthermore, many APR analytes have increased sensitivity for detecting inflammatory states in domestic species compared to health assessment techniques such as hematology (Solter 1991, Skinner 1994,

Horadagoda 1999, Ohno 2006, Nakamura 2008, Cray 2009). Components of the APR have been used to evaluate population health in livestock, and they have been proposed as a means of monitoring health in wildlife (Duffy 1993, Zenteno-Savin 1997, Murata 2004, Petersen 2004,

Lee 2015). Health assessment in wildlife species is frequently complicated by the presence of seasonal, sex, age, and location-based variability in traditional health measures, poor recognition of subclinical disease processes, limited understanding of disease epidemiology, and a lack of species-specific reagents for diagnostic test validation (Stallknecht 2007, Ryser-Degiorgis 2013).

The addition of APR testing that non-specifically screens for evidence of disease, inflammation, and stress utilizing highly conserved biomarkers may significantly augment health assessment

93 efforts in wildlife and identify populations in need of additional study or intervention (Lee 2015,

Glidden 2018).

Eastern (EBT) and ornate (OBT) box turtles (Terrapene carolina carolina, Terrapene ornata ornata) are small, terrestrial, omnivorous chelonians native to North America. These species are in decline due primarily to anthropogenic factors such as habitat destruction, road mortality, overcollection for the pet trade, and disease (Dodd 2001, van Dijk 2011a, van Dijk

2011b). A variety of baseline health data have been published for free-living EBT including hematology, plasma biochemistry, venous blood gas, protein electrophoresis, and hemoglobin- binding protein (Rose 2011, Kimble 2012, Flower 2014, Adamovicz 2015, Lloyd 2016,

Adamovicz 2018). Free-living OBT have not been studied as intensively, but baseline data are available for biochemistry panels in wild turtles and fibrinogen from turtles maintained in captivity (Parkinson 2016, Harden 2018). While some components of the APR have been characterized in both species, additional analytes may supplement existing data and improve our understanding of chelonian health.

Haptoglobin is a positive acute phase protein that scavenges free hemoglobin to prevent oxidative damage and inhibit bacterial growth via sequestration of iron (Cray 2009, Cray 2012).

Haptoglobin has been investigated in multiple wildlife species including Pyrenean chamois

(Rupicapra pyrenaica pyrenaica), European mouflon (Ovis musimon), ringed seals (Pusa hispida), and eastern massasauga rattlesnakes (Sistrurus catenatus) (Krafft 2006, Allender 2015,

Smitka 2015, Tvarijonaviciute 2017) and has been confirmed as clinically useful for distinguishing health from disease or inflammatory states in African buffalo (Syncerus caffer), dromedary camels (Camelus dromedaries), Alpine ibex (Capra ibex), capybara (Hydrochoerus hydrochaeris), Grant’s zebra (Equus burchelli), Steller sea lions (Eumetopias jubatus), harbor

94 seals (Phoca vitulina), red-tailed hawks (Buteo jamaicensis), common guillemots (Uria aalge), and loggerhead sea turtles (Caretta caretta) (Zenteno-Savin 1997, Mellish 2007, Thomton 2007,

Troisi 2007, Rahman 2010, Bernal 2011, Cray 2013, Dickey 2014, Lee 2015, Glidden 2018,

Greunz 2018). Proteins with hemoglobin-binding capacity are highly conserved across species, however, genetic origin may vary (Wicher 2006). As a result, avian and reptile haptoglobin analogs are referred to as “hemoglobin-binding protein” (HBP). HBP quantitation uses commercially available kits that detect the binding of HBP to hemoglobin, and the results appear reliable across species (Tecles 2007, Cray 2009).

Erythrocyte sedimentation rate (ESR) measures the sedimentation rate of erythrocytes in plasma and is accelerated by the presence of acute phase proteins (especially fibrinogen) (Jou

2011, Kratz 2017). Larger ESR values are generally indicative of increased levels of inflammation, though elevated ESR is also associated with multiple non-inflammatory disease states in people such as prostate cancer, coronary artery disease, and stroke (Saadeh 1998). ESR has been adopted and widely utilized as an adjunctive health assessment method in cetaceans

(Fair 2006, Venn-Watson 2011, Wells 2013, Venn-Watson 2014, Manire 2018), and it has recently been evaluated in other wildlife species including African lions (Panthera leo), mute swans (Cygnus olor), brown bears (Ursus arctos), gopher tortoises (Gopherus polyphemus), grass snakes (Natrix natrix natrix), Cuban crocodiles (Crocodylus rhombrjer), Bengal monitor lizards (Varanus bengalensis), Indian roofed turtles (Kachuga tecta), Indian softshell turtles

(Trionyx gangeticus), and oriental garden lizards (Calotes versicolor) (Castellanos 1979,

Banerjee 1981, Verma 1981, Mishra 1988, Wojtaszek 1991, Kusak 2005, Babu 2008, Dolka

2014, Broughton 2017, Rosenberg 2018). ESR measurement does not require species-specific reagents and availability and cost of equipment facilitate its use in wildlife.

Incorporating ESR and HBP into box turtle health assessments may enhance detection of inflammatory conditions, however, free-living reptiles can exhibit extreme variability in bloodwork parameters based on season, sex, age class and reproductive status (e.g. Anderson

1997, Rose 2011, Tamukai 2011, Flower 2014). Demographic variability in HBP and ESR has been documented in other species, for example, HBP can vary by age class (Krafft 2006, Smitka

2015), sex (Krafft 2006, Flower 2014) and year (Allender 2015). ESR typically elevates in older animals (Saadeh 1998, Venn-Watson 2011, Dolka 2014) and sex differences have been observed in some species (Banerjee 1981, Mishra 1988, Kusak 2005). ESR is also elevated by altered physiologic states such as anemia (Jou 2011). It is therefore important to define the range of expected normal values and to identify factors influencing those values in free-living populations prior to applying ESR and HBP for clinical health assessment.

The objectives of this study were to 1) validate, generate reference intervals, and assess agreement between three different methods for ESR measurement in apparently healthy free- living EBT and OBT, 2) validate a commercially available diagnostic test for HBP in apparently healthy free-living OBT and determine reference intervals, and 3) characterize the effects of demographics (sex, age class) and study site on these measures. The hypotheses were 1) there will be good agreement between three ESR measurement methods, 2) ESR will be negatively associated with packed cell volume and will be greater in adults and females, and 3) HBP will be higher in adults and females.

MATERIALS & METHODS

Fieldwork

Ornate box turtles (OBT) were evaluated at the Nachusa Grasslands in Lee County,

Illinois (41.92o, -89.35o), during May 2016, 2017, and 2018 and Ayers Sand Prairie State Nature

Preserve (42.05o, -90.10o) in May 2018. Eastern box turtles (EBT) were evaluated at four different capture sites in Vermilion County, Illinois in mid-May and early August 2018.

Vermilion County study sites included Forest Glen Nature Preserve (40.06o, -87.56o), Kennekuk

County Park (40.19o, -87.69o), Collison (40.28o, -87.78o), and Kickapoo State Park (40.14, -

87.73).

Turtles were located using a combination of human and canine searches (Boers 2017), and GPS coordinates and habitat type were recorded at each capture site. Turtles were weighed to the nearest gram, assigned to an age class (> 200 g was considered an adult, < 200 g was considered a juvenile), and sexed using a combination of plastron shape, eye color, and tail length (Dodd 2001).

Complete physical examinations were performed by a single observer (LA). Turtles with clinical signs of illness (ocular/nasal discharge, oral plaques, open-mouth breathing, etc.) or active physical examination abnormalities (non-healed traumatic injuries, etc.) were excluded from the study. Blood samples (< 0.8% body weight) were collected from the subcarapacial sinus using a 1.5” 22 gauge needle and a 3cc syringe. If obvious lymph contamination of the sample was observed, it was discarded and a new sample was collected. Blood samples were immediately placed into lithium heparin microtainers (Becton Dickinson Co., Franklin Lakes, NJ

07417) and lithium heparin plasma separator tubes (Becton Dickinson Co., Franklin Lakes, NJ

07417), and stored on wet ice until processing (1-3 hours). The marginal scutes of each individual were notched for permanent identification, and turtles were released at their original sites of capture (Cagle 1939). Protocols for animal handling and sampling were approved by the

University of Illinois Institutional Animal Care and Use Committee (#13061).

Clinical Pathology

Packed cell volume (PCV) was determined using sodium heparinized microhematocrit tubes (Jorgensen Laboratories, Inc., Loveland, CO 80538) centrifuged at 14,500 rpm for five minutes.

ESR was performed in EBT and OBT from all sampling sites in 2018 with a commercially available kit using the Wintrobe method (Winpette, Guest Scientific AG,

Switzerland). The Winpette kit was used according to manufacturer instructions; briefly, heparinized blood tubes were inverted at least 8 times to ensure even mixing, the provided reservoir tube was filled with 0.6mL of heparinized blood, the Winpette was inserted, and the entire unit was placed into a leveled ESR stand. The result was read at 60 minutes using 1) the millimeter markings on the Winpette (W) and 2) digital calipers (WC). For comparison, ESR was also performed using a microhematocrit tube (MHT). This method requires < 0.1mL of blood, making it attractive for application to small wildlife species. The MHT was filled approximately ¾ full, plugged with clay at one end, and positioned upright in a leveled stand.

The result was similarly read at 60 minutes using digital calipers. The Winpette and MHT assays were initiated within 2 minutes of each other for each blood sample to prevent bias due to differences in sample storage time. Inter-assay coefficients of variation were determined for approximately 10% of the study population (11 turtles). Samples with air bubbles within the columns were excluded from analysis for both methods.

Plasma was separated from red blood cells within 3 hours of collection via centrifugation at 6,000 rpm for 10 minutes (Clinical 200 Centrifuge, VWR, Radnor, PA 19087, USA). Plasma aliquots were frozen at -80oC from 2 months to 2 years to allow batching of HBP tests. Plasma

HBP was quantified in OBT from the Nachusa Grasslands in 2016, 2017, and 2018 using a

98 commercially available kit (Haptoglobin Phase Colorimetric Assay, Tridelta Development Ltd.,

Maynooth, Ireland) according to the manufacturer instructions. Briefly, plasma samples and standards were assayed in duplicate and results were read at 630nm (Synergy 2 Multi-Mode

Microplate Reader, BioTek Instruments, Inc, Winooski, VT 05404). Absorbance values were averaged for each sample, and HBP was quantified based on a five-point standard curve from 0-

2.5mg/mL. To assess linearity of HBP quantitation, pooled plasma was diluted 0%, 20%, 40%,

60%, 80%, and 100% with sterile saline and a linear regression model was applied to the results.

Passing-Bablok agreement analysis was performed between observed HBP values and those predicted by the linear regression model. A runs test was applied to determine whether the observed data differed significantly from the linear model. Intra-assay and inter-assay coefficients of variation (CV) were determined, and the limit of detection was set as the mean concentration of blank samples, as determined by the standard curve.

Statistical Methods

All statistical assessments were performed using R version 3.5.1 at an alpha value of 0.05

(R Core Team, 2013). Data distributions were assessed for normality using histograms, skewness, kurtosis, and the Shapiro-Wilk statistic. Summary data including means, standard deviations, and ranges (normally distributed data) or medians, 10th and 90th percentiles, and ranges (non-normally distributed data) were tabulated.

Differences in categorical variables (sex, age class) between study sites were evaluated using Fisher’s exact tests. Sex ratios were evaluated using binomial tests (expected ratio 0.5).

Differences in continuous variables (PCV) between study sites, season, and sex were assessed using general linear models. For each model, the assumption of normally distributed error in the dependent variable was evaluated using boxplots, histograms and Q–Q plots of residual values.

The assumption of homoscedasticity was assessed using plots of actual versus fitted residuals and Levene’s tests. The impact of influential values was assessed using Cook’s Distance plots.

Data transformation was pursued if needed to support statistical assumptions. Sets of univariate and multivariable candidate models predicting ESR and HBP were constructed and ranked using information-theoretic approaches with the AICcmodavg package (Mazerolle 2017).

Reference intervals were constructed according to American Society for Veterinary

Clinical Pathology guidelines (Friedrichs 2012). Outliers were visually identified using box plots and excluded using Horn’s method (Horn 2001). The nonparametric method was used to generate 95% reference intervals for each blood gas parameter based on the observations of Le

Boedec et al. (2016). Ninety percent confidence intervals were generated around the upper and lower bounds of each reference interval using nonparametric bootstrapping with 5000 replicates.

The width of the confidence intervals (WCI) was compared to the total width of the reference interval (WRI) to infer the need for a larger sample size (improved n is recommended when

WCI/WRI > 0.2). All reference interval generation was performed using the referenceIntervals package.

Agreement in ESR values from the different measurement methods was evaluated using

Passing-Bablok regression and Bland-Altman plots (packages mcr, BlandAltmanLeh). Passing-

Bablok builds a linear regression model relating the results of two different diagnostic methods performed on the same set of samples. While a p-value is not generated, 95% confidence intervals (CI) are produced for both the slope and the y-intercept and these are used to assess agreement in the following way: if the CI for the slope contains one and the CI for the y-intercept contains zero, the diagnostic methods agree. If the CI for the slope does not contain one, proportional error is present and if the CI for the y-intercept does not contain zero, constant error

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(systemic bias) is present. Passing-Bablok allows for measurement errors, makes no distributional assumptions, and is robust to outliers, however, it does assume that the results of the diagnostic methods are highly positively correlated (Passing 1983, Bablok 1988). To test this assumption, Kendall’s tau was calculated for each set of test results. Bland-Altman figures plot the differences between diagnostic methods against the mean, along with lines representing the limits of agreement (LOA), defined as the mean difference +/- 1.96 times the standard deviation of the differences. Values outside the LOA are considered to demonstrate poor agreement, and patterns in the data can indicate proportional and systematic error.

RESULTS

Erythrocyte Sedimentation Rate

ESR was performed in 85 EBTs and 105 OBTs in 2018. Study population demographics are summarized in Table 5.1. Differences in ESR between study sites were not statistically significant (EBT p = 0.46, OBT p = 0.98), so data from different sites were combined for statistical analysis. A significant male bias was observed during the spring sampling period for

EBTs (p = 0.002). Male turtles of both species had higher PCV values than females (EBT effect size = 3.75%, p = 0.02; OBT effect size = 2.1%, p = 0.001). PCV was higher in the summer sampling period compared to the spring for EBTs (effect size = 3.75%, p = 0.02). Due to potential confounding, both season (EBT only) and sex were included in models evaluating the effects of PCV on ESR.

ESR was measured three different ways in this study, and the effects of predictor variables on each different measurement in each species (using multivariable modeling to control for the effects of confounders) are summarized in Table 5.2. PCV was consistently negatively associated with ESR for all measurement methodologies in both species. Female OBTs had

101 higher ESRs than males for all measurement methodologies, while this relationship in EBT was only statistically significant for the MHT method. EBT in spring had higher ESR than those in the summer for the W and WC methods, while this effect was not statistically significant for

MHT ESR.

The most parsimonious models for all EBT ESR measurement methodologies contained the additive effects of season, sex, and PCV (Table 5.3). Top models had adjusted R2 values of

0.21-0.28 and p-values of < 0.0001. The most parsimonious models for OBT ESR measurements contained the additive effects of sex and PCV (Table 5.4). Top-performing OBT ESR models had adjusted R2 values of 0.11 - 0.15 and p-values of 0.003 - < 0.0001.

Bland-Altman plots for the EBT ESR data illustrate that the WC method tends to overestimate ESR compared to W (Figure 5.1), and the MHT method tends to overestimate both the W and WC methods, with a more noticeable difference at larger ESR values (Table 5.5,

Figure 5.2). In OBT, the MHT method tended to overestimate the WC method (Figure 5.1), with greater differences at larger ESR values (Table 5.5, Figure 5.2).

Reference intervals were constructed for each ESR measurement methodology in each species. While ESR was significantly associated with sex and season, the effects of these variables (approximately 0.5mm/hr) were small enough to be biologically insignificant for the purposes of reference interval generation, and partitioning was not pursued. Partitioning based on age class was not possible because only two turtles with ESR data were classified as juveniles.

Outliers were identified and removed for the MHT method in EBTs (1.1mm/hr, 14mm/hr) and

OBTs (2mm/hr), OBT WC method (6.6mm/hr), and OBT W method (2mm/hr, 7mm/hr).

Summary data, reference intervals and 90% CI of the reference interval bounds are reported in

Table 5.6. The WCI/WRI ratios indicate that the upper reference interval bounds may gain

102 precision with a larger sample size for the W and WC methods in EBTs and the WC and MHT method in OBTs.

Hemoglobin-Binding Protein

HBP was performed in 116 OBT from the Nachusa Grasslands over the course of three years. Study population demographics are summarized in Table 5.7. A male bias was present in the study sample in 2016 and 2017 (p = 0.04 each year). HBP levels were not significantly different between years (p = 0.91) or sexes (p = 0.12), but HBP was significantly higher in adult turtles than juveniles (effect size = 0.12mg/mL, p = 0.01). Multivariable modeling was not pursued due to the low number of significant predictor variables.

Reference intervals were constructed for adult OBTs, while summary data are reported for juveniles in accordance with ASVCP guidelines (Table 5.8) (Friedrichs 2012). The following outliers were identified and removed: 0.092, 0.157, 0.187, 0.892, 1.25mg/mL. The WCI/WRI indicates that the upper bound of the adult reference interval is predicted to gain precision with an increased sample size.

Assay Validation

The inter-assay CV for the HBP assay was 4.23%, (95% CI: 1.68 – 6.79%) while the intra-assay CV was 3.82%, (95% CI: 2.23 – 5.41%). Linearity of HBP detection under dilution was supported by Passing-Bablok regression because the 95% confidence interval of the slope contained 1 (0.77 - 1.18) and the 95% confidence interval for the y-intercept contained 0 (-0.04 -

0.05). The runs test also supported linearity with a non-significant p-value (p = 0.648). The limit of detection was 0.01mg/mL.

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The inter-assay CV for Winpette ESR was 7.9% (95% CI: 1 - 15%), while for the

Winpette caliper method it was 7.2% (95% CI; 2 - 12%). The inter-assay CV for MHT ESR was lower at 3.2% (95% CI: 2 - 4.5%).

DISCUSSION

This study evaluated assays for erythrocyte sedimentation rate in two species of box turtles and hemoglobin-binding protein in ornate box turtles, identified associations with temporal and demographic variables, and generated reference intervals for use in future health assessment studies. These results help characterize normal variation in the acute phase reactants of box turtles and supplement existing knowledge about inflammatory markers to characterize apparently healthy individuals and populations.

ESR was determined using three different techniques in this study. The Winpette, a commercially available kit, was initially selected as the ESR reference method due to the small blood volume requirement (0.6mL). Microhematocrit tubes were tested for comparison due to their cost-effectiveness, availability, and the added advantage of a minimal blood volume requirement (<0.1mL). Furthermore, various adaptations of microhematocrit tube measurements have proven to correlate well with gold standard ESR methodologies in people (Shah 1982,

Varela 2009, Preet 2018), and application of MHT ESR to small wildlife species is logistically attractive. Measurement of ESR using the MHT method was performed using digital calipers with a sub-millimeter precision, while the W results were reported at the millimeter level. To enable a better comparison with the MHT results, the Winpette ESR was also measured with calipers to the sub-millimeter (WC). Agreement analysis revealed moderate to high correlation between the methods, with improved correlation between the MHT and WC methods compared

104 to MHT and W. The MHT method tended to overestimate ESR compared to either of the

Winpette based methods, especially at larger ESR values. This demonstrates that results from the

W and MHT methods are not directly comparable to one another, however, both methods may have clinical utility with their own reference intervals.

Values for ESR in reptiles are infrequently reported, and differences in methodology make direct comparisons challenging, especially in studies that do not describe whether individuals are deemed apparently healthy. Existing studies in gopher tortoises (Gopherus polyphemus), grass snakes (Natrix natrix natrix), Cuban crocodiles (Crocodylus rhombrjer),

Bengal monitor lizards (Varanus bengalensis), Indian roofed turtles (Kachuga tecta), Indian softshell turtles (Trionyx gangeticus), and oriental garden lizards (Calotes versicolor) all report

ESR values of < 8mm/hr, which is consistent with the values for EBT and OBT in the present study (Castellanos 1979, Banerjee 1981, Verma 1981, Mishra 1988, Wojtaszek 1991, Babu

2008, Rosenberg 2018). Sex-based ESR differences have also been reported in Bengal monitors

(males > females) and oriental garden lizards (females > males) and are presumably associated with physiologic differences related to reproductive status (Banerjee 1981, Mishra 1988).

Sampling season was weakly associated with ESR for EBT in the present study, but seasonality was not associated with ESR in oriental garden lizards or gopher tortoises (Banerjee 1981,

Rosenberg 2018). Future reptile ESR studies should strive to sample over multiple seasons to determine whether temporal effects are expected (similar to hematological and plasma biochemical changes) and important for clinical interpretation (Anderson 1997, Rose 2011,

Tamukai 2011, Flower 2014). ESR was negatively associated with PCV in the present study.

Similar findings have been reported in Indian softshell turtles and people and are due largely to changes in frictional forces associated with altered red blood cell numbers (Verma 1981, Saadeh

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1998, Jou 2011). Increasing age is associated with ESR elevation in multiple species, though too few juveniles were evaluated in the present study to vigorously assess the effects of age on ESR in box turtles (Saadeh 1998, Venn-Watson 2011, Dolka 2014).

In mammals, ESR is typically performed using blood anticoagulated with EDTA or sodium citrate (Jou 2011, Katz 2017). The present study performed ESR using heparinized whole blood samples because, with few exceptions, lithium heparin is the preferred anticoagulant in reptiles (Hattingh 1976). While other reptilian ESR studies have also utilized heparinized samples, it is important to recognize that results may vary with different anticoagulants

(Wojtaszek 1991, Rosenberg 2008). Winpettes rely on the Wintrobe method for ESR determination, which utilizes undiluted, anticoagulated blood samples in a 100mm tube. While

Wintrobe ESR has been performed for decades, the International Council for Standardization in

Haematology recommends that the Westergren method be used as the gold standard for ESR determination in people due to improved sensitivity and high reproducibility (Katz 2017). The

Westergren method was not utilized in this study due to the larger blood volume requirement

(2mL), which would make measurement of ESR in juvenile box turtles impossible and would severely limit the number of additional blood tests that could be performed for each adult. The results of Wintrobe and Westergren ESR in people do not always reliably correlate, therefore the results in the present study may not reflect ESR values determined using different methods.

Hemoglobin-binding protein values in reptiles are reported extremely infrequently. HBP from OBT in the present study (median = 0.359) were slightly higher than those previously reported for EBT (median = 0.25) (Flower 2014). HBP was higher in adults than juveniles in both species, but female EBTs had significantly higher HBP than males (Flower 2014), while no sex differences were noted for OBTs. This may be due to the evaluation of all OBTs during the

106 spring, while EBT were sampled throughout the active season, allowing time for reproductive differences to emerge. HBP values in OBTs were also higher than those reported for eastern massasaugas (Sistrurus catenatus) (median = 0.19) and loggerhead sea turtles (Caretta caretta) both pre (median = 0.04) and post (median = 0.27) rehabilitation (Dickey 2014, Allender 2015).

Inter-annual variation in HBP was not observed for OBT in the present study, though it has been previously documented in massasaugas (Allender 2015).

HBP was determined in batches and some plasma samples were frozen for up to two years before analysis. Previous studies have shown that HBP remains stable in stored serum samples for up to 4 years, and it is suspected that sample storage methods utilized in the present study were adequate to preserve HBP activity (Beechler 2017). An additional potential limitation for both analytes in this study is the use of the subcarapacial sinus for venipuncture. The risk of lymphatic contamination is considered to be higher at this site than from an isolated vessel such as the jugular vein (Hernandez-Divers 2002, Heatley 2010). While obviously lymph- contaminated samples were not included in this study, inapparent lymph contamination can never be fully ruled out, and increased variability in ESR and possibly in HBP may have resulted.

This study demonstrated that ESR from apparently healthy EBT and OBT did not vary between populations. Similarly, HBP did not vary between years or populations in apparently healthy OBT. The apparent inter-annual and inter-population stability of these analytes is incredibly attractive from a diagnostic standpoint, because it may lead to a clearer differentiation of healthy and unhealthy individuals. Many reptile clinical pathology analytes vary significantly between populations and years, and this variability can result in uncertainty about the necessity of medical or management interventions. (Christopher 1999, Flower 2014, Allender 2015).

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Future research efforts should continue to assess the stability of ESR and HBP in apparently healthy turtles from widespread populations and over more sampling seasons. Next steps include assaying unhealthy box turtles for ESR and HBP to determine the clinical utility of these analytes for individual and population health assessment. If these values appear useful, experimental trials could be undertaken to determine the duration and degree of alteration for

ESR and HBP in the face of a known inflammatory stimulus.

This study illustrated that ESR values in apparently healthy free-living EBT and OBT are comparable to those in other reptile species; and demonstrated that performing ESR in a microhematocrit tube may be a viable alternative to commercial kits if test-specific reference intervals are utilized. Sex and PCV were found to influence ESR in both EBT and OBT, and seasonal effects were also documented for EBT. HBP was strongly affected by age class and was higher in OBT compared to other reptile species, including EBT (Flower 2014). ESR values were not significantly different between populations, and HBP values were not different between years or populations, indicating that these analytes may be more stable than traditional health metrics in reptiles. This baseline information and the reference intervals generated in this study represent the first steps towards clinical application of ESR and HBP in free-living box turtles.

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Table 5.1. Population demographics of free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata) sampled for erythrocyte sedimentation rate during 2018.

EBT OBT Spring Summer Spring Sex Female 17 13 46 Male 36 18 59 Unknown 1 0 0 Age Class Adult 52 31 101 Juvenile 2 0 4

Table 5.2. Relationships between potential predictor variables and erythrocyte sedimentation rate (ESR) in free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata) determined using multivariable general linear models. W

= Winpette ESR, WC = Winpette ESR measured with digital calipers, MHT = Microhematocrit tube ESR. Reference level for sex is female, reference level for season is spring.

EBT OBT Effect Size P-value Effect Size P-value Sex (Male) W -0.5mm/hr 0.06 -0.6mm/hr 0.001 WC -0.48mm/hr 0.07 -0.4mm/hr 0.01 MHT -0.91mm/hr 0.03 -0.62mm/hr 0.003 Season (Summer) W -0.5mm/hr 0.04 NA NA WC -0.64mm/hr 0.02 NA NA MHT -0.37mm/hr 0.36 NA NA PCV (%) W -0.07mm/hr 0.0001 -0.075mm/hr 0.008 WC -0.08mm/hr <0.0001 -0.073mm/hr 0.01 MHT -0.12mm/hr <0.0001 -0.1mm/hr 0.0009

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Table 5.3. Model selection parameters for erythrocyte sedimentation rate in free-living eastern box turtles (EBT, Terrapene carolina carolina).

Model N K AICc AICc wi Winpette Sex + Month + PCV 84 5 255.23 0 1 Sex + Month 84 4 268.81 13.58 0 Null 84 2 271.37 16.14 0 Winpette Calipers Sex + Month + PCV 84 5 247.9 0 1 Sex + Month 84 4 266.82 18.92 0 Null 84 2 270.92 23.02 0 Microhematocrit Tube Sex + Month + PCV 84 5 327.09 0 1 Sex + Month 84 4 341.89 14.8 0 Null 84 2 342.96 15.87 0

Table 5.4. Model selection parameters for erythrocyte sedimentation rate in free-living ornate box turtles (OBT, Terrapene ornata ornata).

Model N K AICc AICc wi Winpette Sex + PCV 105 4 281.19 0 1 Sex 105 3 291.58 10.39 0 Null 105 2 299.83 18.64 0 Winpette Calipers Sex + PCV 105 4 199.23 0 0.99 Sex 105 3 208.55 9.32 1 Null 105 2 213.08 13.85 0 Microhematocrit Tube Sex + PCV 105 4 295.97 0 1 Sex 105 3 309.32 13.35 0 Null 105 2 316.18 20.2 0

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Table 5.5. Passing-Bablok agreement analysis parameters comparing three different measurement methods for erythrocyte sedimentation rate (ESR) in free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT,

Terrapene ornata ornata). W = Winpette ESR, WC = Winpette ESR measured with digital calipers, MHT = Microhematocrit tube

ESR, P = proportional error.

Test Method Reference Method Kendall's Tau (P-value) Slope (95% CI) Y-intercept (95% CI) Error Present EBT MHT WC 0.71 (< 0.0001) 1.44 (1.28, 1.63) 0.12 (-0.55, 0.62) P MHT W 0.7 (<0.0001) 1.6 (1.4, 1.8) 0 (-0.6, 0.6) P WC W 0.78 (< 0.0001) 1.1 (1, 1.2) 0.05 (-0.3, 0.4) None OBT MHT WC 0.55 (<0.0001) 1.38 (1.17, 1.63) -0.89 (-1.87, 0) P MHT W 0.43 (<0.0001) 1.3 (1, 1.8) -0.9 (-2.9, 0.4) None WC W 0.6 (< 0.0001) 1 (0.8, 1.3) -0.3 (-1.6, 0.6) None

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Table 5.6. Summary data including data distribution, measure of central tendency (mean for normally distributed variables, median for non-normally distributed variables), measure of dispersion (standard deviation for normally distributed variables, 10th – 90th percentiles for non-normally distributed variables), and reference intervals for erythrocyte sedimentation rate (ESR) determined using three different methods in free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata). W = Winpette ESR, WC = Winpette ESR measured with digital calipers, MHT = Microhematocrit tube ESR, Dist = distribution, NG = Non-Gaussian, G = Gaussian, CT = measure of central tendency, CI = confidence interval, LB = lower bound of reference interval, UB = upper bound of reference interval, WCI = width of the confidence interval, WRI = width of the reference interval.

N Dist CT Dispersion Min Max Reference 90% CI LB 90% CI UB (mm/hr) (mm/hr) (mm/hr) (mm/hr) Interval EBT W 85 NG 3 2 - 4.6 1 8 1 - 6 0 - 1 4 - 7a WC 85 NG 3.3 2.1 - 4.9 1.2 8.4 1.7 - 6.2 1.6 - 2.2 4 - 7a MHT 85 NG 4.7 3.1 - 7.3 1.1 14 2.6 - 8.4 2.3 - 2.6 7.7 - 8.6 OBT W 105 NG 4 3 - 5 2 7 3 - 6 2.4 - 2.9 5.5 - 6 WC 105 G 3.9 0.82 2 6.6 2.2 - 5.4 1.9 - 2.5 5.1 - 5.8a MHT 105 G 4.4 1.1 2 7.3 2.6 - 6.8 2.2 - 2.7 6.4 - 7.3a a WCI/WRI > 0.2

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Table 5.7. Population demographics of free-living ornate box turtles (Terrapene ornata ornata) sampled for hemoglobin-binding protein analysis in May 2016, 2017, and 2018.

2016 2017 2018 Sex Female 9 15 21 Male 17 24 22 Unknown 1 7 0 Age Class Adult 26 39 42 Juvenile 1 7 1

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Table 5.8. Summary data including data distribution, measure of central tendency (mean for normally distributed variables, median for non-normally distributed variables), measure of dispersion (standard deviation for normally distributed variables, 10th – 90th percentiles for non-normally distributed variables), and reference intervals for hemoglobin-binding protein (HBP) free-living ornate box turtles (Terrapene ornata ornata). Dist = distribution, NG = Non-Gaussian, G = Gaussian, CT = measure of central tendency, CI

= confidence interval, LB = lower bound of reference interval, UB = upper bound of reference interval, WCI = width of the confidence interval, WRI = width of the reference interval.

N Dist CT Dispersion Min Max Reference 90% CI LB 90% CI UB (mm/hr) (mm/hr) (mm/hr) (mm/hr) Interval Adults 102 NG 0.359 0.272 - 0.49 0.092 1.25 0.228 - 0.532 0.201 - 0.24 0.397 - 0.559a Juveniles 9 G 0.26 0.097 0.169 0.472 NA NA NA a WCI/WRI > 0.2

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Figure 5.1. Bland-Altman plots comparing three different measurement methodologies for erythrocyte sedimentation rate (ESR) in free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata). Top row:

EBT, Bottom row: OBT. Central dashed line = Mean difference between measurement methodologies, Top and bottom dashed lines = limits of agreement, defined as the mean difference +/- 1.96 times the standard deviation of the differences.

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Figure 5.2. Passing-Bablok regression plots comparing three different measurement methodologies for erythrocyte sedimentation rate

(ESR) in free-living eastern box turtles (EBT, Terrapene carolina carolina) and ornate box turtles (OBT, Terrapene ornata ornata).

Top row: EBT, Bottom row: OBT. Dashed line = line of perfect agreement with slope = 1 and y-intercept = 0. Solid line: Passing-

Bablok regression line. Shaded region: 95% confidence interval of Passing-Bablok regression line.

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PART II: SILVERY SALAMANDER HEALTH AND DISEASE

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CHAPTER 6: INITIAL CHARACTERIZATION OF AMPHIBIOCYSTIDIUM SP.

INFECTION IN A MIDWESTERN STATE-ENDANGERED SALAMANDER

(AMBYSTOMA PLATINEUM)

Abstract: Global amphibian declines have been attributed to anthropogenic factors and diseases such as ranavirus and chytridiomycosis. Mesomycetozoean parasites can also cause mortality and affect amphibian population stability, but they remain understudied. A mesomycetozoean

(Amphibiocystidium sp.) was identified in free-living, state-endangered silvery salamanders

(Ambystoma platineum) from Illinois in 2017 and 2018. Affected salamanders (N = 24) had single to multiple 1-3mm raised, white, round to dumbbell-shaped skin nodules concentrated on the trunk. Annual prevalence of skin nodules ranged from 0 – 11.1% in six ponds distributed between three sites. Histologic evaluation of skin nodules (N = 3) was consistent with mesomycetozoean sporangia, and 18S phylogeny revealed a close relationship to

Amphibiocystidium viridescens, the only other described Amphibiocystidium species in North

America. This is the first report of Amphibiocystidium sp. in the Midwestern United States, and the first detection of this organism in North American ambystomatid salamanders. Future research is needed to determine the effects of this pathogen on individual and population health in silvery salamanders, and to assess the conservation implications of this organism.

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INTRODUCTION

Global amphibian declines have been attributed to multiple factors including habitat loss and fragmentation, invasive species, pollution, climate change, and disease (Kiesecker 2001,

Daszak 2003, Blaustein 2012). Pathogens such as ranavirus and chytridiomycosis, which have a wide spatial distribution and contribute to mortality events that can affect population stability, are obvious targets for research benefitting amphibian conservation (Skerratt 2007, Duffus 2010,

Price 2014). However, many other pathogens affect free-living amphibians and may have population-level effects which are poorly understood or masked by co-infection with more virulent agents (Pessier 2008). One example is the Mesomycetozoea, a class of protists in the

Opisthokonta. The two orders within the Mesomycetozoea are the Ichthyophonida, which includes fish parasites and saprotrophic organisms, and the Dermocystida, which consists entirely of pathogens affecting mammals, birds, fish, and amphibians (Mendoza 2002).

Dermocystid parasites infecting amphibians are currently classified into one of two genera: Amphibiocystidium and Amphibiothecum (Pascolini 2003, Feldman 2005, Pereira 2005).

Representatives of these genera have been documented in several European anurans and urodeles including common frogs (Rana temporaria), water frogs (Pelophylax esculentus complex),

Italian stream frogs (Rana italica), common midwife toads (Alytes obstetricans), palmate newts

(Lissotriton helveticus), common newts (Lissotriton vulgaris), marbled newts (Triturus marmoratus), and crested newts (Triturus cristatus) from Italy, France, Switzerland,

Czechoslovakia, and Scotland (Pérez 1907, Pérez 1913, Granata 1919, Guyénot 1922, Remy

1931, Poisson 1937, Broz 1952, Pascolini 2003, Pereira 2005, Duffus 2010, González-

Hernández 2010, Courtois 2013, Federici 2015, Fiegna 2016). North American dermocystids include Amphibiothecum penneri, which affects American toads (Bufo americanus), Fowler’s

119 toads (Bufo fowleri), and Yosemite toads (Bufo canorus) (Jay 1981, Green 2001, Green 2005), and Amphibiocystidium viridescens, which was identified in eastern red-spotted newts

(Notophthalmus viridescens) from Pennsylvania, Massachusetts, and West Virginia (Raffel

2008).

Amphibians infected with dermocystids develop single or multiple cutaneous lesions, typically appearing as either 1-5mm round, c-shaped, or dumbbell-shaped white nodules or 1-

7mm vesicular structures which may coalesce and ulcerate. Lesions have also been observed within the oral mucosa, liver, skeletal muscle, gastrointestinal lumen, and cloaca (Raffel 2008,

Fiegna 2016). Histologically, nodular foci consist of sporangia containing microscopic endospores (Mendoza 2002). Host response to the sporangia is typically minimal until they rupture, at which point a localized immune response can cause lesions to appear inflamed (Pérez,

1913, Broz 1944, Pascolini 2003, Raffel 2008, Fiegna 2016). Information on recovery period is limited, though anecdotal evidence suggests that complete resolution of these lesions in mildly- affected individuals is possible within a few weeks (Raffel 2008).

Infection with dermocystid pathogens was initially believed to be fairly benign, however, heavily-infected newts from France, Rum Island, and the United States have demonstrated more severe clinical signs and pathologic changes, including regional edema, hemorrhages, extensive cutaneous ulceration, development of secondary bacterial infections, and death (Moral 1913,

Gambier 1924, Gonzalez-Hernandez 2010, Raffel 2008, Fiegna 2016). Amphibian skin plays a major role in osmoregulation, electrolyte balance, and defense against pathogen invasion

(Woodhams 2007, Rollins-Smith 2009). Disruption of these vital functions in animals with heavy dermocystid burdens may result in death due to fatal electrolyte disturbances, similar to chytridiomycosis (Campbell 2012). Alternatively, development of opportunistic secondary

120 infections may contribute to morbidity and mortality. Indeed, an increased mortality rate was reported for captive N. viridescens infected with A. viridescens, compared to newts without visible lesions (Raffel 2008). Further study of dermocystid parasites is indicated to understand the threat that these organisms may pose to vulnerable amphibian populations.

The triploid, unisexual silvery salamander (Ambystoma platineum) occurs in the northeastern and midwestern United States and originated through hybridization of A. laterale and A. jeffersonianum, resulting in retention of two sets of A. jeffersonianum chromosomes and one set of A. laterale chromosomes (JJL, Uzzell 1964, Uzzell 1967). Silvery salamanders typically coexist in habitats with one of their parent species and act as sexual predators on the males of that species, however some populations, including those in Illinois, do not occur with either parent species. In these populations, silvery salamander utilize sperm from small-mouth salamanders (A. texanum) (Uzzell 1967, Morris 1984, Spolsky 1992, Phillips 1997). Silvery salamanders are found in only two counties in Illinois, and are considered state-endangered

(Phillips 1999). Threats to persistence of silvery salamanders in Illinois are currently uncharacterized, but an ongoing demographic and health assessment study has been initiated to document conservation challenges for this species. The purpose of this manuscript is to provide an initial morphologic, histologic, and molecular description of Amphibiocystidium sp. infection in free-living silvery salamanders from Vermilion County, Illinois.

MATERIALS & METHODS

Demographic Survey

Silvery salamanders were evaluated at six ephemeral ponds distributed among three study sites including Collison (40.28o, -87.78o), Middle Fork (MF, 40.20o, -87.75o), and Kickapoo

State Park (KSP, 40.14°, -87.74o) in Vermilion County, Illinois. In 2016, salamanders were

121 captured using minnow traps and dip-nets with a standardized number of traps and dips used based on pond size. In 2017 and 2018, study ponds were permanently fenced and bucket traps were installed to monitor salamander ingress during spring breeding. Traps were checked daily from February 1st to April 15th 2017 and 2018 and salamanders were toe clipped using a pond specific numbering system. All animal handling and procedures were approved by the

University of Illinois Institutional Animal Care and Use Committee (protocol #18186)

Health Assessments

Salamander health assessments were performed for 3-4 days each year during breeding migrations. Animals were weighed and a complete physical examination was performed by a veterinarian (LA) noting appearance of the eyes, nose, oral cavity, integument, musculoskeletal system, and cloaca. During health assessments in 2017, white cutaneous nodules consistent with

Amphibiocystidium sp. infection were observed on multiple animals. Two digits with nodules from two different salamanders were opportunistically excised and immediately placed in 100% ethanol. In 2018, one heavily affected silvery salamander (21 nodules) and one heavily affected small-mouthed salamander (> 50 nodules) were euthanized using an overdose of buffered MS-

222 (3-Aminobenzoic Acid Ethyl Ester, Sigma Chemical Co. St. Louis, MO 61378). Two skin nodules from each animal were collected using a sterile scalpel blade and dry-frozen at -20oC.

The bodies were then preserved in 10% buffered formalin and submitted for histopathology.

Pathologic Evaluation

Fixed tissues collected during health assessments were processed routinely for histopathology and stained with hematoxylin and eosin. Slides were evaluated via light microscopy by a single anatomic pathologist (DBW). Images were taken using an Olympus

DP71 digital microscope camera.

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DNA Extraction, PCR, and Sequencing

DNA was extracted from frozen and ethanol-preserved tissue samples using a commercial kit (DNeasy Blood & Tissue Kit, Qiagen, Valencia, CA, USA). Quality and purity of resulting DNA samples was evaluated spectrophotometrically (NanoDrop 1000, Thermo

Fisher Scientific, 260 Waltham, MA, USA). Two sets of existing consensus PCR primers targeting the 18S rRNA gene were used to screen tissue samples for the presence of dermocystids (González-Hernández 2010, Fiegna 2016). Each reaction contained 1x PCR buffer,

2.5mM magnesium chloride, 0.2mM dNTPs, 2.5U Platinum Taq polymerase, 0.2μM of each primer, 1L template DNA (<1g), and sterile deionized water to bring the final volume to 50L

(Platinum Taq DNA Polymerase, Invitrogen, Carlsbad, CA 92008). Thermocycler settings consisted of a five minute initial denaturation at 95oC followed by 40 cycles of 95oC for 30 seconds, 57.7oC for 30 seconds, and 72oC for 120 seconds, then a final elongation step of 72oC for seven minutes. The optimal annealing temperature of 57.7oC was determined using a thermal gradient testing 10 temperatures from 53oC – 65oC. A positive control consisting of sequence- confirmed Amphibiothecum penneri from a Fowler’s toad and a non-template control (sterile water) were included in each run. Products were resolved on a 1% agarose gel, samples with appropriately-sized bands (1400bp) were treated with ExoSAP-IT (USB Corporation, 26111

Miles Road, Cleveland, OH, USA), and commercially sequenced in both directions. Sequences were trimmed of primers and low-quality base-pair calls in Geneious v.11.1.3 (Kearse 2012) and compared to known sequences in the NCBI GenBank database using BLASTN (Benson 2008).

Phylogenetic Analysis

18S rRNA sequences that were 98-92% similar to that of the silvery salamander dermocystid were recovered from GenBank using BLASTN. Sequences were aligned using

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PASTA v.1.8.2 with default parameters (Mirarab 2015). The ends of the resulting alignment file were manually adjusted and trimmed in Geneious. In order to estimate the best substitution model for phylogenetic analysis, PartitionFinder v.2.1.1 (Lanfear 2017) was used to search through all 195 nucleotide models using the corrected Akaike Information Criterion (AICc;

Sugiura, 1978). Maximum likelihood phylogenetic reconstruction was performed in Garli v.2.0

(Zwickl 2006) using three searches of 500 bootstrap replicates with a GTR + I + G substitution model. The resulting bootstrap trees were summarized with a 50% majority-rule consensus tree using Sumtrees v.4.3.0 (Sukumaran 2010). Bayesian phylogenetic reconstruction was performed using MrBayes v.3.2.6 (Ronquist 2012) with a GTR + I + G substitution model and 20 million generations of Markov Chain Monte Carlo (MCMC) models for three runs of four chains each, sampling every 1000 trees. Trace files were viewed in Tracer v.1.6 (Rambaut 2007) to assess parameter convergence. To assess topological convergence, .t files were assessed using the R package RWTY v.1.0.1 (Warren 2017). Based on these assessments, the first 10% (2000 trees) were discarded as burn-in.

Museum Specimen Survey

Historic A. platineum specimens collected from Vermilion county, Illinois and maintained in the Illinois Natural History Survey Biological Collection were visually examined for the presence of white skin nodules consistent with Amphibiocystidium sp. infection.

Statistical Analysis

All statistical evaluations were performed in R v3.4.3. Descriptive statistics for skin nodule prevalence were tabulated by year and pond. 95% confidence intervals for binomial probabilities were constructed around prevalence estimates using the binconf function in the

Hmisc package. The effect of pond and study site on the probability of dermocystid detection

124 was assessed in each year using generalized linear models, package brglm (Kosmidis 2017).

Post-hoc between group differences were evaluated using the contrasts function in the lsmeans package (Lenth 2016).

RESULTS

Sample Population

Thirty-six silvery salamanders were evaluated on March 3-4th 2016, 390 individuals were assessed on February 24th, February 28th, and March 7th, 2017, and 220 were examined on

February 19-21st and 25th, 2018. Total sample size and the prevalence of cutaneous nodules consistent with Amphibiocystidium sp. infection are summarized by pond in Table 6.1.

White cutaneous nodules consistent with Amphibiocystidium sp. infection were not observed in 2016. In 2017, prevalence of nodules was higher at KSP than MF (OR = 6.65, 95%

CI = 3.59 – 36.18, p = 0.028) and Collison (OR = 20, 95% CI = 1.2 – 340, p = 0.039).

Prevalence was also higher at pond 283 than ponds 76 (OR = 6.95, 95% CI = 1.23 – 39.19, p =

0.028) and 73 (OR = 25.8, 95% CI = 1.45 – 447.8, p = 0.026). In 2018, prevalence of nodules was not significantly different between sites or ponds. Skin nodules consistent with

Amphibiocystidium sp. were not detected in Collison at any point during the study.

Gross and Histologic Lesion Description

Skin nodules consistent with Amphibiocystidium sp. infection consisted of 1-3mm white, raised, slightly firm subcutaneous nodules concentrated on the trunk (Figures 6.1 & 6.2).

Individual nodules were round to dumbbell-shaped and most salamanders had fewer than 10 lesions. Visible nodules were not identified in the internal organs at gross necropsy.

On histologic examination, grossly apparent skin nodules were consistent with

Mesomycetozoean sporangia (Figure 6.3). Sporangia were located within the superficial dermis

125 and mildly compressed surrounding tissue. Each sporangium had a thin (5-10 µm) eosinophilic wall and contained numerous 6-10 µm round endospores. Only minimal tissue reaction was observed in association with these sporangia and consisted primarily of mild hyperplasia in the overlying epidermis. Rare sporangia were segmentally ruptured with minimal heterophilic inflammation in the immediately adjacent dermis, though most observed sporangia were intact with no associated inflammation. Sporangia were confined to the skin and not identified in any other organs or tissues.

Molecular & Phylogenetic Analysis

An approximately 1350bp product was amplified from the two digits collected in 2017 and the euthanized silvery salamander and small-mouth salamander collected in 2018 using both sets of primers. All sequences were identical, and a representative sequence has been deposited in GenBank (Accession number MK330881). Sequences from the present study were 99% similar to A. viridescens and an Amphibiocystidium sp. infecting Italian stream frogs in Europe

(Accession number EU650666).

After the 10% burn-in all parameters and topologies from the Bayesian phylogenetic analysis had ESS values >>200 or average standard deviation of split frequencies <0.01, indicating the MCMC runs had converged to stationarity. Bootstrap support (BS) values in the maximum likelihood analysis averaged 87%, and posterior probability (PP) support averaged

95% in the Bayesian analysis. The maximum likelihood and Bayesian phylogenetic analyses produced similar trees, recovering Sphaerothecum as sister to Rhinosporidium, Dermocystida,

Dermocystidium, and Amphibiocystidium, with >74% BS and >97% PP (Figures 6.4 & 6.5). The silvery and small-mouthed salamander sequence was recovered in a monophyletic group with

Amphibiocystidium spp. (67 BS/0.91 PP).

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Historic Occurrence

One hundred and twenty-three A. platineum specimens collected in Vermilion county,

Illinois from 1973-2018 were visually screened for the presence of white nodules consistent with dermocystid infection. Sampling effort during this time period was uneven, with the most specimens collected in the early 1980’s (18 salamanders in 1980, 32 in 1981). Following this peak, sample sizes only exceeded 10 individuals per year in 2000 and 2018. Consistent lesions were not identified in any specimen. Tissue sampling for molecular screening was not pursued due to initial fixation of specimens in formalin prior to long-term storage in ethanol.

DISCUSSION

This study provides an initial morphologic, histologic, molecular, and phylogenetic description of an Amphibiocystidium sp. infecting a state-endangered salamander. Affected salamanders had single to multiple cutaneous nodules which were grossly and histologically consistent with mesomycetozoan parasites infecting amphibians (Raffel 2008, Fiegna 2016).

Phylogenetic analysis revealed a close relationship with A. viridescens, the only other

Amphibiocystidium sp. infecting salamanders in North America, and with another

Amphibiocystidum sp. from Europe. This is the first report of Amphibiocystidium sp. in the

Midwestern United States, and the first detection of this organism in North American ambystomatid salamanders.

Dermocystid infections are associated with very distinctive skin nodules in amphibians.

Gross appearance of these nodules has been previously reported as highly sensitive (93 – 97%) and specific (100%) for the presence of dermocystid parasites, and identification of 1-3mm raised, white, round to dumbbell-shaped skin nodules in silvery salamanders is likely very sensitive and specific for Amphibiocystidium sp. infection (Raffel 2008, González-Hernández

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2010, Fiegna 2010). Nodular skin lesions consistent with Amphibiocystidium sp. infection were infrequently identified at five different ponds and two different study sites over the course of two years, but were not observed at a third site. It is currently unclear whether salamanders at

Collison are truly free from Amphibiocystidium sp. infection, or whether it is present at low levels and has yet to be detected. Occurrence of skin nodules at the same pond between years was not always consistent, which may be due to resolution of infection, mortality of infected individuals, or failure to examine infected individuals. Most affected animals had small numbers of skin nodules and appeared to be in good physical condition, but some animals had over 20 nodules. The fate of affected salamanders is unknown as all animals were released immediately after physical examination. Longitudinal evaluation of infected salamanders in laboratory or mesocosm settings will be necessary to determine the impacts of Amphibiocystidium sp. on individual survival and overall wellness, and to assess the conservation implications of this pathogen.

Amphibiocystidium sp. infections appeared to suddenly emerge in silvery salamander populations in 2017, however, it is possible that this pathogen had been historically present, but not detected. Sampling effort was considerably higher than historical levels in 2017 and 2018, and while consistent skin nodules were not identified in museum specimens dating back to 1973, it is difficult to confidently determine disease freedom for low-prevalence parasites. Statistical models are available to estimate the probability of disease freedom given estimated population size, sample size, number of positives, test sensitivity and specificity, design prevalence, type I error rate (), and type II error rate () (Cameron 1998). Unfortunately, population size estimates are unavailable for historical silvery salamander samples and one additional year of mark- recapture data is needed to estimate population sizes for 2017 and 2018. As such, freedom from

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Amphibiocystidium sp. infection prior to 2017 cannot be assumed for ambystomatid salamanders in Illinois.

Taxonomy of dermocystid organisms traditionally relied upon morphology, but more recent classifications have utilized molecular phylogenetics (Feldman 2005, Pereira 2005,

Borteiro 2018). While this approach has improved taxonomic resolution compared to morphology alone, limitations persist due to the restricted number and size of available sequences, and the frequent reliance upon one gene target (18S) for phylogenetic assessments

(Feldman 2005). The dermocystid parasite identified in this study is an Amphibiocystidium sp., but it is difficult to determine whether it is truly distinct from A. viridescens with currently available sequence data. A. viridescens was originally detected in eastern red-spotted newts

(ERSN) from Pennsylvania, Massachusetts, and West Virginia (Raffel 2008). However, ERSN also occur in Illinois. Transmission of Amphibiocystidium sp. between species and/or human translocation of infected individuals between sites and ponds may have contributed to the development of infection in silvery and small-mouthed salamanders in Vermilion county. Future studies should survey multiple salamander species from several ponds across the state to determine the number of species affected and the prevalence of Amphibiocystidium sp. infection at different sites. Genetic studies of Amphibiocystidium sp. from multiple species and locations can then be pursued to explore how the infection may have historically spread.

While dermocystid infections can cause morbidity and mortality in individual hosts, more research is needed to clarify the ecology, life cycle, and impacts of these parasites at the population level (Gonzalez-Hernandez 2010, Raffel 2008, Fiegna 2016). Long-term studies of the Pelophylax esculentus complex in central Italy have demonstrated steep, recurrent population declines in Pelophylax lessonae in association with severe A. ranae infections (Pascolini 2003,

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Pereira 2005). However, extreme weather events, elevated tissue organochlorine levels, developmental abnormalities, and non-lethal chytridiomycosis were also documented during these declines, and it is difficult to disentangle the complex interaction between disease and other anthropogenic and climactic stressors in this case (Fagotti 2005, Di Rosa 2007). Unfortunately, longitudinal studies involving dermocystid parasites are uncommon and evidence for population- level impacts is limited. Despite this, dermocystids share several characteristics of parasites capable of causing host extinction including low host specificity (confirmed for

Amphibiocystidium sp. in the present study), the potential for high virulence, presence of a free- living infectious stage for pathogen dispersal (endospores), and the ability to survive prolonged adverse environmental conditions (Mendoza 2002, Fisher 2012, Rowley 2013b). Longitudinal assessment of the affected silvery salamander populations in upcoming years will help determine the effects of Amphibiocystidium sp. infections in this state-endangered species, and potentially identify targets for management intervention, if necessary.

Emerging infectious diseases can pose a significant threat to amphibians, especially species of conservation concern. Dermocystid parasites can affect individual survival, and have the potential to impact population stability alone or in conjunction with other stressors. The identification of Amphibiocystidium sp. in a state-endangered salamander is concerning and further study is indicated to determine its effects on individual and population health. The present study provides useful baseline information on morphology, pathology, phylogeny, and prevalence that can be used to direct future research efforts and to monitor changes in the affected populations over time. Understanding the health of free-living amphibians may promote more effective management strategies and improve conservation outcomes.

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Table 6.1. Sample size and dermocystid prevalence in adult silvery salamanders (Ambystoma platineum) surveyed in Vermilion County, Illinois.

# Salamanders with Dermocystid Prevalence (%) Pond Site Year N Cutaneous Nodules (95% CI)

282 KSP 2016 1 0 0 2017 30 1 3.3 (0.17 - 16.7) 2018 19 0 0

283 KSP 2016 21 0 0 2017 108 12 11.1 (6.5 - 18.4) 2018 70 5 7.1 (3.1 - 15.7)

67 KSP 2016 1 0 0 2017 53 4 7.5 (3 - 17.9) 2018 15 0 0

71 Collison 2016 0 0 NA 2017 99 0 0 2018 57 0 0

76 MF 2016 12 0 0 2017 81 1 1.3 (0.06 - 6.7) 2018 39 0 0

285 MF 2016 2 0 0 2017 19 0 0 2018 20 1 5.0 (0.26 - 23.6)

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Figure 6.1. Gross appearance of skin lesions associated with Amphibiocystidium sp. infection in free-living adult silvery salamanders (Ambystoma platineum) from Vermilion County, IL 2017-

2018.

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A B

< 5% 10 - 20% > 60%

Figure 6.2. Intensity (A) and distribution (B) of Amphibiocystidium sp. lesions in 24 affected adult silvery salamanders (Ambystoma platineum) from Vermilion County, IL 2017-2018.

A B

Figure 6.3. Skin from Ambystoma platineum. Within the superficial dermis is a single

Mesomycetozoean sporangium that elevates the epidermis and slightly compresses surrounding tissues (A, hematoxylin and eosin stain, 100X). The sporangium has a thin eosinophilic hyalinized wall and contains numerous 6-10 µm round endospores (B, hematoxylin and eosin stain, 600X).

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Figure 6.4. Phylogenetic tree for Mesomycetozoean organisms produced by maximum- likelihood analytic methods.

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Figure 6.5. Phylogenetic tree for Mesomycetozoean organisms produced by Bayesian analytic methods.

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CHAPTER 7: HEALTH ASSESSMENT OF THE STATE-ENDANGERED SILVERY

SALAMANDER (AMBYSTOMA PLATINEUM) IN VERMILION COUNTY, ILLINOIS

Abstract: Global amphibian declines are largely driven by anthropogenic factors and disease.

The health status of threatened populations can influence responses to various stressors, and understanding factors associated with good health can potentially lead to the development of more effective conservation strategies. Silvery salamanders (Ambystoma platineum) are state- endangered in Illinois. The objective of this study was to characterize silvery salamander health status at three study sites using physical examination, qPCR pathogen screening, and modeling approaches. A total of 770 salamanders were evaluated at 13 ponds in 2016 and six ponds in

2017-2018. Common physical examination abnormalities included encysted parasites, fresh and healed injuries, developmental abnormalities, and hemorrhages. Frog virus 3 (FV3) was detected in association with hemorrhages (p < 0.05) and was associated with mortality of over 80% of the larval population from four ponds in 2016. Individual health was best predicted by the additive effects of year, study site, body condition score, FV3 status, and by the presence of fresh injuries.

FV3 is considered to represent a threat to silvery salamanders in Illinois, and recommendations for future research include characterization of silvery salamander biology and ecology, continued health monitoring to identify emerging threats, and focused study on ranavirus to identify potential management interventions. Continued health assessment in conjunction with ecological research should supplement our understanding of threats and enable development of effective conservation strategies for this imperiled species.

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INTRODUCTION

Amphibians are facing a global conservation crisis, with approximately 41% of species threatened, endangered, or extinct and an additional 22% having unknown conservation status

(IUCN 2018). The Caudata (salamanders and newts) are the most at risk, with approximately

50% of species considered threatened or worse. Development of targeted management strategies for imperiled species may be improved by intensive study of populations in situ, as threats facing amphibians are complex and the relative importance of different stressors can be species, population, and location dependent (Vié 2009, Blaustein 2012). Health assessment is one important component of in situ conservation projects, however, many existing studies evaluate individual health determinants (e.g. pathogens, toxins) in isolation and tend to focus on drivers of mortality, as opposed to characterizing sublethal effects that can also affect population stability

(Stephen 2015a, Hopkins 2016, Stephen 2018). While studies in disease ecology, epidemiology, toxicology, and pathology are essential for establishing “normal” baselines, characterizing hazards, and highlighting the complexity of disease in natural systems, a broader focus on all aspects of health may inform conservation strategies that better support sustainable wildlife populations (Hanisch 2012, Sleeman 2012, Joseph 2013, Langwig 2015, Stephen 2017, Stephen

2018). Previous studies in amphibians demonstrate the importance of health status for mounting an appropriate stress response and for surviving disease challenge, illustrating the value of characterizing health in free-living populations (DuRant 2015, Hopkins 2016, Kirschman 2018).

The Illinois Wildlife Action Plan (IWAP) identifies eight caudates in greatest conservation need including the silvery salamander (Ambystoma platineum), which is present in two counties in Illinois and is considered state-endangered. Silvery salamanders are a unisexual triploid species resulting from hybridization of Ambystoma laterale and Ambystoma

137 jeffersonianum (Uzzell 1964, Uzzell 1967). This species typically co-occurs with one of its parent species in the northeastern and midwestern United States and acts as sexual predators on the males. However, some populations, including those in Illinois, co-occur with closely-related small-mouthed salamanders (Ambystoma texanum) instead (Uzzell 1967, Morris 1984, Spolsky

1992, Phillips 1997). Previous study of silvery salamanders has focused heavily on their reproductive strategy and knowledge of basic biology and ecology is limited (Phillips 1997,

Teltser 2015). Threats to the health of silvery salamanders are currently uncharacterized.

The purpose of this study is to fill this research gap by characterizing health status and identifying conservation threats in Illinois silvery salamanders using physical examination, pathogen screening, mortality investigation, and modeling approaches. The hypotheses are 1) chytridiomycosis due to Batrachochytrium dendrobatidis (Bd) will be detected, but will not be associated with poor health outcomes, 2) chytridiomycosis due to Batrachochytrium salamandrivorans (Bsal) will not be detected, 3) Ranavirus, specifically frog virus 3 (FV3), will be detected in association with larval mortality at Kickapoo State Park, and 4) poor health status will be best predicted by the presence of physical examination abnormalities and ranavirus status.

MATERIALS & METHODS

Demographic Survey

Silvery salamanders were evaluated at thirteen ephemeral ponds distributed among three study sites including Collison (40.28o, -87.78o), Middle Fork (MF, 40.20o, -87.75o), and

Kickapoo State Park (KSP, 40.14o, -87.74o) in Vermilion county, Illinois during 2016. Adult salamanders were captured using minnow traps and dip-nets with a standardized number of traps and dips used based on pond size. Larval salamanders were captured using dip-nets, then

138 transferred into clean individual Ziploc bags containing a small amount of pond water for examination. In 2017 and 2018, six of the original study ponds were permanently fenced and bucket traps were installed to monitor salamander ingress during spring breeding. Bucket traps were checked daily from February 1st to April 15th, 2017 and 2018. Adult salamanders were toe clipped using a pond specific numbering system for mark-recapture purposes, then released on the opposite side of the fence. Larval salamander capture and handling was performed in the same manner as 2016. All animal handling and procedures were approved by the University of

Illinois Institutional Animal Care and Use Committee (protocol #18186)

Health Assessments

Salamander health assessments were performed for 2-4 days each year. Moistened nitrile gloves were used to handle salamanders and changed between individuals. Boots and scales were disinfected between ponds using 1% Virkon S with a 1 – 3 minute contact time (DuPont Animal

Health Solutions, Wilmington, DE 19880). Adults were evaluated in late February – early March and larvae were evaluated in late May – early June. All salamanders were weighed and a complete physical examination was performed by a veterinarian (LA) noting appearance of the eyes, nose, oral cavity (adults), integument, musculoskeletal system, cloaca, and gills (larvae).

Body condition score (BCS) was assessed on a 1 – 5 scale based on body fill. A combined swab

(Cotton-tipped, plastic handled applicators, Fisher Scientific, Pittsburgh, PA 15275) of the ventral skin and oral cavity was collected for adults, using five passes over the ventral limbs and abdomen as recommended for detection of Bd (Hyatt 2007). For larvae, the ventral skin and external mouthparts were swabbed with the animal temporarily restrained within the bag, but outside of the water (Retallick 2006). Swabs were placed into sterile 2mL Eppendorf tubes and stored on wet ice in the field, then at -20oC in the laboratory.

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Mortality Investigation

Targeted dip-netting was used to retrieve dead larvae daily during mortality events. The total number of dead larvae and the presence of clinical signs (hemorrhage, edema) were recorded. Deceased larvae were stored on wet ice during transport back to the laboratory, then frozen at -20oC. Samples of liver and spleen were obtained for qPCR pathogen screening and/or virus isolation within three months of collection using sterile scalpel blades.

Molecular Diagnostics

DNA was isolated from skin-oral swabs and tissues using a commercially available kit

(QIAmp DNA Blood Mini Kit and DNAeasy kit, Qiagen, Valencia, CA, USA) according to the manufacturer instructions. DNA concentration and purity was assessed spectrophotometrically

(NanoDrop 1000, Thermo Fisher Scientific, Waltham, MA, USA) and DNA samples were stored at -80oC.

DNA samples were assayed for FV3, Bd, Bsal, and the Chlamydiaceae family simultaneously using existing TaqMan quantitative polymerase chain reaction (qPCR) assays on a Fluidigm platform as previously described (Everett 1999, Boyle 2004, Allender 2013a, Blooi

2013, Archer 2017). Quantification was performed for frog virus 3 (FV3), Bd, and Bsal using

Fluidigm Real Time PCR analysis software by comparing each sample’s cycle threshold (Ct) value to a standard curve consisting of six tenfold dilutions from 107 –101 copies. Standard curves were not available for the Chlamydiaceae family assay, and results for this pathogen were considered positive at a Ct value of 24 or less. Non-template controls were included in each

Fluidigm run, and all samples and controls were evaluated in triplicate. Positive samples were rechecked in single-plex assays. Briefly, samples, standard curves, and negative controls were run in triplicate using a real-time PCR thermocycler (7500 ABI realtime PCR System, Applied

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Biosystems, Carlsbad, CA). Data analysis and quantification was performed using commercially available software (Sequence Detection Software v2.05, Applied Biosystems, Carlsbad, CA) and standard curves as described above. Positive pathogen quantities (copies/L) were normalized based on DNA concentration to produce final quantities in copies/ng DNA.

Conventional PCR for ranavirus was performed using existing consensus assays targeting the major capsid protein (MCP, primers 1+2, 3+4, 5+6), the viral homolog of the alpha subunit of eukaryotic initiation factor 2 (vIF-2), and the DNA polymerase gene (DNApol) (Hyatt 2000,

Holopainen 2009, Stöhr 2015). A positive control consisting of sequence-confirmed FV3 from an eastern box turtle and a non-template control (sterile water) were included in each run.

Products were resolved on a 1% agarose gel and samples with appropriately-sized bands

(DNApol = 560bp, vIF-2 = 250 or 1050bp, and MCP = approximately 500bp) were treated with ExoSAP-IT (USB Corporation, 26111 Miles Road, Cleveland, OH, USA), and commercially sequenced in both directions. Sequences were trimmed of primers and low-quality base-pair calls in Geneious v.11.1.3 (Kearse 2012), the full MCP sequence was constructed from the three different MCP primer-pairs, and all products were compared to known sequences in the

NCBI GenBank database using BLASTN (Benson 2008).

Virus Isolation

Samples of liver, spleen, and kidney from frozen larvae were pooled by pond, finely diced with a scalpel blade, immersed in 10mL Minimum Essential Medium (MEM, Thermo

Fisher Scientific, Waltham, MA, USA) with 5g/mL amphotericin B, 200U/mL penicillin,

200g/mL streptomycin, and 100g/mL gentamycin (Sigma Chemical Co. St. Louis, MO

61378), and homogenized using a Polytron homogenizer (Type P10/35 with power control unit

(PCU-2-110), Brinkmann Instruments, Westbury, NY 11590, USA) with a sterilized saw-toothed

141 rotor/stator at 6,000rpm for 60 seconds on ice. Each homogenized sample was centrifuged at

2000rpm for 20 minutes at 4oC. The supernatant was passed through a 0.45M filter and inoculated onto Terrapene heart cells (TH-1) and fathead minnow (FHM) epithelial cells grown to 80% confluence in 75 cm3 flasks. Following a 10-minute incubation at 27oC, 20mL of

Dulbecco’s modified eagle medium (DMEM, Thermo Fisher Scientific, Waltham, MA, USA) with 10% fetal bovine serum (FBS, Thermo Fisher Scientific, Waltham, MA, USA), 100U/mL penicillin, 100g/mL streptomycin, and 2.5g/mL amphotericin B was added to each flask, and

o cells were maintained at 27 C with 5% CO2. Blind passaging was performed after inoculated cells reached 100% confluency up to three times. Flasks were frozen at -80oC, thawed, and vortexed three times. One milliliter of the flask contents was used to inoculate a new flask containing TH-1 or FHM cells grown to 80% confluency. Infected flasks were monitored daily for signs of cytopathic effects (CPE).

Phylogenetic Analysis

Phylogenetic analysis was pursued for a ranavirus recovered from tissues of deceased silvery salamander larvae at KSP in 2016 (SS-RV) and a historic ranavirus isolate associated with mortality in eastern box turtles at KSP in 2014. BLASTN was used to recover ranavirus sequences from the NCBI GenBank database that were 96-100% similar to SS-RV and EBT-RV

(Benson 2008). Sequence alignment was completed using the MAFFT aligner and default parameter settings in Geneious v.11.1.3 (Kearse 2012). PartitionFinder v.2.1.1 (Lanfear 2017) was used to search through all 195 nucleotide models under the corrected Akaike Information

Criterion (AICc; Sugiura 1978) in order to estimate the best substitution model for phylogenetic analysis. Maximum likelihood phylogenetic reconstruction was performed using RAxML v.8.1.3

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(Stamatakis 2014) and a GTR GAMMA model with 100 rapid bootstrap replicates. Bootstrap support values were summarized on the best tree.

Statistical Assessment

Statistical analyses were performed to meet two specific goals: 1) Describe the sample population, and 2) Identify predictors of health status and internally validate models for predicting individual health. All statistical assessments were performed in R at an alpha value of

0.05.

The distribution of each continuous variable (weight, BCS) was assessed visually using box plots and histograms and statistically using the Shapiro-Wilk test. Descriptive statistics

(mean, standard deviation, range for normally distributed variables, median, 10% and 90% percentiles, and range for non-normally distributed variables) and counts were tabulated for continuous and categorical variables, respectively.

A directed acyclic graph (DAG) was generated to demonstrate expected relationships among measured predictors (Figure 7.1). This diagram was used to identify potential confounding variables and structure statistical analyses, using multivariable linear regression when necessary to control for confounders (Joffé 2012). Continuous predictor variables were assessed for multicollinearity using Pearson’s correlation coefficient, with r > 0.5 considered

“strongly” correlated. Strongly correlated predictor variables were not included together in statistical models. Data transformation was pursued if needed to support statistical assumptions during modeling.

A liberal p-value of 0.15 was utilized during univariate analyses to include all variables which may explain biologically important variation in health parameters. Predictors of continuous outcomes were evaluated using general linear models. For each model, the

143 assumption of normally distributed error in the dependent variable was evaluated using boxplots, histograms, and Q-Q plots of residual values. The assumption of homoscedasticity was assessed using plots of actual vs. fitted residuals. The impact of influential values was assessed using

Cook’s Distance plots. Post-hoc between group differences were evaluated using the contrasts function in the lsmeans package (Lenth 2016). Predictors of categorical outcomes (pathogen status, presence/absence of physical exam abnormalities) were assessed using generalized linear models using package brglm (Kosmidis 2017). The assumption of a linear relationship between continuous predictors and the log-odds of the output variable was visually evaluated using scatterplots.

All salamanders were then assigned to a health category (“apparently healthy” vs.

“unhealthy”). Apparently healthy salamanders met the following criteria:

1. No clinically significant physical examination abnormalities. A clinically

significant abnormality is one expected to affect the animal’s ability to navigate,

prehend food, mate, and protect itself from predation. Examples include open

wounds, active fractures, or poor body condition. Physical examination abnormalities

that are not considered clinically significant include healed injuries and congenital

abnormalities such as fused digits or mandibular deformity.

2. No clinically apparent qPCR detection of infectious disease. The presence of a

pathogen does not necessarily equate to disease (Thompson 2010, Pirofski 2012,

Méthot 2014). Furthermore, some pathogens are host-adapted and have limited

associated clinical signs in an otherwise healthy animal (Ryser-Degiorgis 2013). The

present study only considers pathogen-positive animals “unhealthy” if they have

concurrent clinical signs of illness.

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Individual health status was evaluated using sets of candidate generalized linear models ranked using an information-theoretic approach with the MuMIn package. Internal model validation was performed via bootstrapping using 500 replicates with the rms package.

RESULTS

Sample Populations, Pathogen Screening, & Physical Examination

Seven hundred and seventy live salamanders were evaluated on March 3 – 4 2016, June

16 -17 2016, February 24th, February 28th, March 7th, and May 26th 2017, and February 19 – 21 and June 6th 2018. While twice the ponds were assessed in 2016, the change in methodology to bucket traps at fewer ponds more than doubled the sample size in 2017 and 2018. Pathogen screening was performed for all 98 salamanders in 2016, but only for a subset in 2017 and 2018

(N = 88 each year). For these two years, salamanders were selected via random number generation (https://www.random.org/) targeting an equal number of animals from each pond when possible. In 2017, qPCR was performed for 70 adult salamanders with the following pond distribution: 12 animals from ponds 67, 73, and 76, 13 animals from ponds 282 and 283, and 8 animals from pond 285. Larval qPCR pathogen testing was pursued for a random subset of 18 larvae in 2017 with the following breakdown: 1 animal from ponds 282 and 285, 4 animals from pond 73, and 6 animals from ponds 67 and 283. In 2018, qPCR was performed for 72 adults: 12 each from ponds 67, 73, 76, and 285, 10 from pond 282, and 14 from pond 283. Larval testing included 4 each from ponds 73, 76, 282, and 283. In 2016, FV3 was detected in seven adults from ponds 75 and 284 and five larvae from ponds 67, 69, and 73. In 2017, FV3 was detected in a single larva from pond 285, and FV3 was not detected in any salamander during 2018. Bd,

Bsal, and the Chlamydiaceae family were not detected during the study period.

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Common PE abnormalities included skin-colored cutaneous nodules suspected to represent encysted parasites, yellow skin nodules consistent with Clinostomum sp. (a.k.a.

“yellow grub”) infection, white skin nodules consistent with Amphibiocystidium sp. infection

(Chapter 6), developmental abnormalities of the jaw and/or limbs, healed and active injuries, and hemorrhage (Figure 7.2). Annual sample sizes, physical examination (PE) findings, and qPCR results by pond are presented in Table 7.1 for adults and Table 7.2 for larval salamanders. Data from ponds with fewer than 10 sampled animals (ponds 69, 70, 284, 289, and 290) were omitted prior to univariate statistical analyses.

Predictors of PE Abnormalities & Pathogen Detection

In 2016, adult salamanders at KSP were heavier and in slightly higher body condition than those at MF (weight effect size = 1.33g, p = 0.04; BCS effect size = 0.31, p=0.007), though statistically significant differences in weight and body condition were not noted between ponds.

In 2017 adult weight and BCS were not significantly different between sites, but weight was higher at pond 285 than pond 67 (effect size = 1.7g, p = 0.015) and pond 282 (effect size =

1.74g, p = 0.02) and BCS was higher at pond 285 than pond 67 (effect size = 0.4, p = 0.04) and pond 73 (effect size = 0.4, p = 0.04).

In 2018, adult salamanders at KSP were heavier than those at MF (effect size = 1.8g, p <

0.0001) and COL (effect size = 0.85g, p = 0.02), and COL salamanders were heavier than MF salamanders (effect size = 0.99g, p = 0.02). Adults at KSP also had higher BCS than MF (effect size = 0.46, p < 0.0001) and COL (effect size = 0.26, p = 0.007). Adult salamanders at pond 282 had higher weight and BCS than pond 76 (weight effect size = 1.37g, p = 0.03; BCS effect size =

0.49, p = 0.003), and 285 (weight effect size = 2g, p = 0.005; BCS effect size = 0.56, p = 0.002) and higher BCS than pond 73 (effect size = 0.32, p = 0.04). Salamanders at pond 283 had higher

146 weight and BCS than pond 76 (weight effect size = 1.9g, p = 0.0001; BCS effect size = 0.47, p =

0.0001), 285 (weight effect size = 2.5g, p < 0.0001; BCS effect size = 0.55, p = 0.0003), and pond 73 (weight effect size = 1g, p = 0.007; BCS effect size = 0.3, p = 0.004). Salamanders at pond 285 were heavier than those at pond 73 (effect size = 1.4g, p = 0.01).

Adult BCS was higher in 2018 compared to both 2017 (effect size = 0.24, p < 0.0001) and 2016 (effect size = 0.4, p < 0.0001). Controlling for the effects of year, adult BCS was higher at KSP than Collison (effect size = 0.15, p = 0.02) and MF (effect size = 0.14, p = 0.02), and adult weight was higher at KSP than MF (effect size = 0.47g, p = 0.04). Larval weight was higher in 2016 compared to 2017 (effect size = 1.15g, p = 0.002) and 2018 (effect size = 1.08g, p

= 0.002). BCS was lower in 2018 compared to 2016 (effect size = 0.57, p = 0.01) and 2017

(effect size = 0.35, p = 0.04).

Hemorrhages were significantly more common in larvae than adults (OR = 13.21, 95%

CI = 7.4 – 23.59, p < 0.0001). Occurrence of skin-colored nodules was more common at MF compared to KSP (OR = 16.89, 95% CI = 3.08 – 92.67, p = 0.001), and was more likely in 2016 compared to 2017 (OR = 41.6, 95% CI = 7.44 – 232.68, p < 0.0001) and 2018 (OR = 14.8, 95%

CI = 3.67 – 59.76, p = 0.0001). The likelihood of observing healed injuries was higher at COL compared to KSP (OR = 2.07, 95% CI = 1.21 – 3.53, p = 0.008) and higher in 2017 than 2016

(OR = 5.78, 95% CI = 1.59 – 21.07, p = 0.008) and 2018 (OR = 2.71, 95% CI = 1.54 – 4.76, p =

0.0005). The odds of FV3 detection were higher in 2016 compared to 2017 (OR = 8.81, 95% CI

= 1.28 – 60.6, p = 0.02) and 2018 (OR = 35.65, 95% CI = 7.91 – 160.61, p = 0.02). FV3 detection was also more likely in salamanders with fresh injuries (OR = 34.04, 95% CI = 2.31 –

502, p = 0.01) and hemorrhages (OR = 44.08, 95% CI = 3.17 – 613, p = 0.005).

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Modeling Individual Health

Fifteen salamanders fit the criteria to be classified as “unhealthy” during the three-year study period, including seven adults and eight larvae. Adults were classified as unhealthy due to poor BCS (N = 2) and active injuries (N = 5), while larvae were classified as unhealthy due to poor BCS (N = 4), extensive hemorrhaging (N = 3), and active injuries (N = 1). The following variables were significantly associated with health at a liberal alpha value of 0.15: BCS, FV3, presence of active traumatic injuries (“Trauma”), and hemorrhages. Inclusion of the FV3 variable necessitated case-wise deletion of all salamanders which were not tested for pathogens via qPCR, resulting in a final sample size of 274 for model ranking. Year and location were included in all models to control for confounding associated with the BCS, FV3, and Trauma variables.

The most parsimonious model included the additive effects of year, location, BCS, FV3, and Trauma (Tables 7.3 & 7.4). Performance metrics produced by internal validation via bootstrapping with 500 replicates are reported in Table 7.5.

Mortality Investigation and Virus Isolation

Mortality events resulting in the deaths of over 300 silvery salamander larvae were identified in four ponds in 2016 (Figure 7.3). Approximately 80% of the estimated silvery salamander larval population was killed during these outbreaks (Kelsey Low, personal communication). Two general epidemic patterns were present: a point source pattern starting at the beginning of May in ponds 66 and 283 and rapidly resolving within 10 days, and a more spread out distribution lasting several weeks beginning in May in pond 282 and June in pond 73.

This pattern may be consistent with either a common-source, multiple event epidemic pattern or a propagating pattern slowed by a decreased transmission rate between hosts – perhaps

148 secondary to decreased larval density, impaired viral replication due to alterations in temperature or pond conditions, or changes in host immunity due to developmental stage. The largest outbreak occurred in pond 66 and resulted in 237 documented deaths within eight days.

Deceased larvae at each pond frequently displayed hemorrhaging and edema, and moribund larvae were often uncoordinated with inconsistent righting reflexes. Liver and spleen samples from fifty larvae were tested for FV3 during each outbreak using qPCR, and all were positive. Virus isolation was attempted from multiple pools of organs at each pond, but cytopathic effects were not observed after up to three blind passages. Ranavirus conventional

PCR was pursued on DNA extracted from KSP liver and spleen samples to enable comparison between the virus affecting silvery salamanders (SS-RV) and a historic ranavirus isolate (EBT-

RV) associated with mortality in eastern box turtles at KSP in 2014 (Chapter 9).

Mortality of small numbers of silvery salamanders was observed in 2017 at ponds 76 and

285. Twelve deceased silvery salamanders, eleven of which displayed hemorrhages and edema, were recovered from pond 76 from April 26th – May 11th. Pooled tissues tested qPCR positive for FV3. A single clinical silvery salamander was observed in pond 285 on June 2nd following a ranavirus mortality event in wood frog (Lithobates sylvaticus) larvae. This individual was FV3 positive via qPCR. Larval mortality was not observed in 2018 for any amphibian species, including silvery salamanders.

Conventional PCR and Phylogenetics

PCR of the MCP produced a 1509bp fragment for EBT-RV and a 1485bp fragment for

SS-RV, DNApol PCR produced a 519bp fragment for EBT-RV and a 504bp fragment for SS-

RV, and vIF-2 PCR produced a 211bp fragment for EBT-RV and a 928bp fragment for SS-RV.

Aligned portions of the EBT-RV and SS-RV MCP were identical except for two base pairs. The

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EBT-RV sequence was 100% identical to multiple FV3 sequences in Genbank, while the SS-RV isolate was 99% identical to the same sequences (accession numbers MG953520.1,

MG953519.1, MG953518.1, KJ175144.1). Aligned portions of the EBT-RV and SS-RV

DNApol sequences were identical except for three base pairs, and both the EBT-RV and SS-RV sequences were 99% homologous to multiple FV3 sequences (accession numbers MG953520.1,

MG953519.1, MG953518.1, KJ175144.1). There was a 23 base pair difference between the aligned portions of EBT-RV and SS-RV vIF-2, making this the most divergent gene sequenced

(Figure 7.4). The EBT-RV vIF-2 was 100% identical to multiple FV3 sequences, while the SS-

RV sequence was 99% similar to multiple amphibian ranaviruses (e.g. KM516750.1,

KF512820.1, KF033124.1, KX397571.1), indicating that the ranavirus affecting silvery salamanders may be distinct from that historically affecting sympatric box turtles at KSP.

Bootstrap support [BS] values in the maximum likelihood phylogenetic analysis averaged

70.3%. The EBT-RV MCP, EBT-RV vIF-2, and SS-RV MCP were recovered in the same clade as ranavirus isolates from the Maryland Zoo in Baltimore including sequences from

Meller’s chameleons (Trioceros melleri) and eastern box turtles (Figure 7.5). This clade is recovered as sister to frog virus 3 with >50% BS. Both the SS-RV and EBT-RV DNApol sequences were recovered in a monophyletic group with SS-RV vIF-2 (100BS/65BS). This clade is sister to the larger clade where the rest of the ranaviruses of interest are located, but that relationship is recovered with low support (15BS).

DISCUSSION

This is the first reported health assessment of free-living silvery salamanders. Findings indicate that encysted parasites, developmental abnormalities, and trauma are common, however, of these only fresh injuries are negatively associated with overall health status. FV3 was

150 associated with larval mortality events and poor health, but Bd, Bsal, and members of the

Chlamydiaceae family were not detected. This information serves as a useful baseline for future health assessments and identifies avenues of research which may improve our understanding of conservation threats to the state-endangered silvery salamander.

Weight and body condition of adult and larval salamanders were influenced by year, site, and pond. This may reflect local differences in resource availability and utilization, which are expected to vary spatially and temporally. The finding of generally higher weight and BCS at

KSP may be due to differences in ploidy level, as silvery salamander populations at KSP have historically included higher numbers of tetraploid and pentaploid individuals compared to populations in Indiana (Phillips 1997). Tetraploid and pentaploid individuals are larger than triploid salamanders. They tend to have decreased overall viability, but male salamanders of other species prefer tetraploid and pentaploid A. platineum over triploid females, conferring a selective advantage upon salamanders with higher ploidy levels (Phillips 1997). Persistent high levels of tetraploidy and pentaploidy at KSP may explain larger body size compared to salamanders at COL and MF, though investigation of ploidy level using PCR or measurement of red blood cell nuclear size would be necessary to confirm this hypothesis. Larger size at KSP may also be explained by differences in physiologic stressors, for example, salamanders at COL were more likely to have healed injuries and salamanders at MF were more likely to have skin- colored cutaneous nodules consistent with encysted parasites (though other differentials including cysts, neoplasia, and granulomas are also possible). This may imply increased investment in immune function and decreased investment in growth in the COL and MF populations compared to KSP.

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Individual health was not associated with cutaneous nodules consistent with Clinostomum sp. and Amphibiocystidium sp. infection. Clinostomum sp. are digenean trematodes with a three- host life cycle typically involving a snail (1st host), amphibian or fish (2nd host), and heron or egret (definitive host) (McAllister 2007). In amphibians, Clinostomum sp. infection produces distinctive yellow cutaneous nodules (Converse 2005). C. marginatum has previously been diagnosed in multiple North American amphibian species including A. jeffersonianum and A. texanum in Illinois (McAllister 2007, McAllister 2010). Infection with this parasite appears benign in most cases unless there is a heavy parasite burden, though physical deformity and death have been documented in zoo-maintained salamanders with Clinostomum sp. infection

(Perpiñán 2010). While tissue sampling was not performed in the present study to confirm

Clinostomum sp. infection, gross appearance is typically diagnostic for this parasite (Converse

2005). The lack of clinical consequence associated with yellow skin nodules is consistent with many previous reports of Clinostomum sp. infection in free-living salamanders.

Amphibiocystidium sp. and other mesomycetozoean parasites have been documented in

Europe for over 100 years, and have occasionally been associated with population declines

(Pascolini 2003, Pereira 2005, Di Rosa 2007). Recently, Amphibiocystidium sp. was described for the first time in Eastern red-spotted newts (Notophthalmus viridescens) from the Northeastern

United States. Visibly-infected newts from a population observed in captivity experienced higher mortality rates than uninfected conspecifics (Raffel 2008). The current impacts of

Amphibiocystidium sp. infections on free-living amphibian populations are unknown, but their recent emergence in the US is concerning and information on natural infections is important to determine their potential impact on species of conservation concern. Follow-up assessment of the

152 affected silvery salamander populations will be required to characterize the long-term effects of this emerging pathogen.

Developmental abnormalities were common in all silvery salamander populations and were not associated with poor health status. These abnormalities are not uncommon in triploid, tetraploid, and pentaploid salamanders potentially due to the presence of extra genetic material

(Phillips 1997). Amphibian deformity has also been associated with a variety of other causes including UV light exposure, chemical exposure, and parasitic infection, especially with

Ribeiroia sp. and microsporidia (Blaustein 2003, Johnson 2003, Gamble 2006). Testing for these other possible causes would likely require lethal sampling, which was outside the scope of the present study.

Pathogens of international concern, including Bd and Bsal, were not identified in silvery salamanders. Chytridiomycosis due to Bd has been associated with global amphibian population declines, extirpations, and even extinctions since the 1970s (Schloegel 2006, Olson 2013, Bellard

2016, Lips 2016). However, Bd has been present in Illinois since 1888 and is infrequently associated with clinical disease in native species (Lannoo 2011, Muelleman 2013, Phillips 2014,

Talley 2015). A new chytrid species, Bsal, has recently emerged in association with precipitous fire salamander (Salamandra salamandra) population declines in Europe (Martel 2013). While this pathogen is not yet present in North America, it has been detected in salamanders in the pet trade and challenge studies have demonstrated susceptibility in many North American species

(Martel 2014, Bales 2015, Cunningham 2015, Sabino-Pinto 2015, Spitzen-van der Sluijs 2016,

Parrott 2017, Iwanovicz 2017, Klocke 2017, Laking 2017, Fitzpatrick 2018, Sabino-Pinto 2018).

Introduction of this disease into the United States could be devastating for native salamander populations.

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Lack of Bd detection in silvery salamander populations could be due to absence of infection or detection failure. Swabbing was performed in the recommended manner for Bd detection, but very early or low-level infections may have been missed (Retallick 2006, Hyatt

2007). Similarly, Bd loads can vary seasonally, and Bd levels in February and March may be lower than those at different points in the year (Kriger 2007). Bd is present in sympatric amphibians at ponds occupied by silvery salamanders (Kelsey Low, personal communication), and the possibility of infection in silvery salamanders cannot be ruled out. Continued monitoring for Bd presence in silvery salamanders is recommended to determine changes in prevalence over time and determine association with clinical illness. While Illinois is not considered one of the most likely points of entry for Bsal into the United States, continued screening for this pathogen is recommended to ensure early detection if it becomes introduced (Yap 2015, Richgels 2016).

Individual health was best predicted by the additive effects of year, location, BCS, FV3, and active trauma. FV3 was also associated with significant larval mortality in 2016 and limited mortality in 2017, indicating that this pathogen is a cause of both morbidity and mortality in larval silvery salamanders. Ranaviruses, including FV3, have been associated with mortality events in over 100 species of ectothermic vertebrates on every continent except Antarctica

(Duffus 2015). Recurrent outbreaks can contribute to population declines and extirpations, which is especially concerning for a state-endangered species with limited geographical distribution

(Teacher 2010, Earl 2013, Heard 2013, Price 2014). Clinical consequences of infection depend on several host, viral, and environmental factors, and transmission between different classes of ectothermic vertebrates is possible (Duffus 2008, Brenes 2014b, Brunner 2015). FV3 has been previously identified in association with eastern box turtle mortality at KSP, though comparison of three gene targets from the EBT-RV and SS-RV indicates that the 2014 isolate is genetically

154 distinct from the strain associated with silvery salamander larval mortality in 2016. This may indicate recent introduction of a novel virus, though the SS-RV and EBT-RV are more closely related to each other than to other ranaviruses based on phylogenetic analysis. Another possible explanation is the circulation of multiple strains of ranavirus at KSP, underscoring the need for additional research to understand ranavirus dynamics at this site. The mortality events in 2016 and 2017 also involved amphibian species other than silvery salamanders, and thorough characterization of these outbreaks including environmental factors (pond area, depth, water quality, temperature, etc.), individual factors (Gosner stage, clinical presentation), and community ecological factors will be described in another series of manuscripts. Ranavirus can cause significant larval mortality in silvery salamanders and should be considered a conservation threat for this species.

The study of silvery salamander ecology and health is inherently complicated due to the cryptic nature of this species. Adult salamanders spend much of their time in burrows and only emerge in large numbers for breeding in February and March each year. Assessing health during this window is the best way to obtain large sample sizes, however, this population is likely biased towards animals which are healthy enough to attempt breeding. This may explain why so many of the adults in this study were categorized as “apparently healthy”. Salamanders also have very limited defense options against predators, and while both active and healed injuries were commonly identified in adults during this study, the number of salamanders which do not survive depredation attempts is unknown. Assessing adult salamander health at more time points during the year may improve our understanding of health determinants in this species, but would be logistically difficult due to their frequent habitation of underground burrows. Assessing population health is also difficult, as basic biological knowledge such as lifespan, dispersal

155 probability and distance from natal ponds, degree of philopatry, and baseline survival probabilities of different age classes is uncharacterized in this species. This lack of background knowledge impedes assessment of population viability and prevents modeling the impacts of specific threats such as recurrent ranavirus outbreaks. Future studies on the biology and ecology of silvery salamanders are needed to contextualize health assessment findings and determine when management intervention is needed in response to different threats.

This study provides initial characterization of health determinants in silvery salamanders and identifies ranavirus as a potential conservation threat. Recommendations for future research benefitting silvery salamanders include assessment of the basic biology and ecology of this species in Illinois, continued health assessment to monitor for changes in health status, and research on the dynamics of ranavirus at each site to identify targets for management intervention. Characterization of silvery salamander health has filled an important knowledge gap for this species and helped generate targeted research questions for future exploration.

Continued health assessment in conjunction with ecological research will enhance our understanding of threats and enable development of effective conservation strategies for this species.

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Table 7.1. Demographic data for adult silvery salamanders (Ambystoma platineum). Median values are presented for weight and BCS.

BCS = body condition score, SC = skin-colored, Hem = hemorrhages, FV3 = frog virus 3 qPCR status.

Weight (g) BCS SC White Yellow Develop Fresh Healed Site Pond Year N (Range) (Range) Nodules Nodules Nodules Abnorm Injury Injury Hem FV3 KSP 66 2016 23 14 (9-21) 3.25 (3-4) 0 0 0 0 0 0 0 0 2016 1 14 3 0 0 0 0 0 0 0 0 KSP 67 2017 53 12.1 (9.3-18.6) 3 (1.5-5) 0 4 (8%) 0 3 (6%) 2 (4%) 6 (11%) 0 0 2018 15 13 (9-15) 3.5 (3-4) 1 (7%) 0 2 (13%) 0 3 (20%) 0 0 2016 1 13 3 0 0 0 0 0 0 0 0 KSP 282 2017 30 12.4 (8.3-19.1) 3.25 (2-5) 0 1 (3%) 0 2 (7%) 2 (7%) 2 (7%) 0 0 2018 19 13.5 (10-17) 3.8 (3-5) 0 0 1 (5%) 2 (10%) 0 1 (5%) 0 0 2016 21 14.2 (9-21) 3.3 (3-5) 0 0 0 0 0 0 0 0 KSP 283 2017 108 13.1 (8.2-22.2) 3 (2-5) 0 12 (11%) 3 (3%) 3 (3%) 4 (4%) 12 (11%) 0 0 2018 70 14 (10-19) 3.8 (2-5) 0 5 (7%) 0 8 (11%) 0 3 (4%) 0 0 2016 0 ------COL 73 2017 99 13 (8.9-18.4) 3 (2-5) 0 0 4 (4%) 8 (8%) 3 (3%) 23 (23%) 2 (2%) 0 2018 57 13 (7-18) 3.5 (2-5) 1 (2%) 0 3 (5%) 11 (19%) 2 (4%) 6 (11%) 1 (2%) 0 MF 69 2016 2 13 (11-15) 3 (3-3) 0 0 0 0 0 0 0 0 MF 75 2016 17 13 (8-18) 3 (2-4) 10 (59%) 0 0 1 (6%) 0 2 (12%) 0 3 (18%) 4 MF 284 2016 4 14.8 (11-19) 3.25 (3-4) 0 0 0 0 0 0 0 (100%) MF 290 2016 1 10 3 0 0 0 0 0 0 0 0 2016 12 12 (7-17) 2.83 (2-4) 1 (8%) 0 0 0 0 0 0 0 MF 76 2017 81 13.9 (7.5-20.7) 3 (2-5) 1 (1%) 1 (1%) 1 (1%) 13 (16%) 1 (1%) 12 (15%) 2 (3%) 0 2018 39 12 (6-19) 3.3 (3-4) 0 0 0 1 (3%) 3 (8%) 1 (3%) 0 0 2016 2 12.5 (12-13) 3 (3-3) 0 0 0 0 0 0 0 0 MF 285 2017 19 14.7 (9.1-19.5) 3.5 (3-5) 1 (5%) 0 0 1 (5%) 1 (5%) 5 (25%) 0 0 2018 20 11.5 (9-16) 3.2 (2-4) 0 1 (5%) 0 1 (5%) 0 2 (10%) 0 0

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Table 7.2. Demographic data for larval silvery salamanders (Ambystoma platineum). Median values are presented for weight and

BCS. BCS = body condition score, FV3 = frog virus 3 qPCR status.

Weight (g) Develop Fresh Healed Site Pond Year N (Range) BCS (Range) Abnormality Injury Injury Hemorrhage FV3 2016 4 2.5 (2-3) 3.13 (2-4) 0 1 (25%) 0 0 3 (75%) KSP 67 2017 7 2 (1-2) 3 (2.5-3.5) 0 0 2 (29%) 1 (14%) 0 2018 0 ------2016 0 ------KSP 282 2017 1 4 3 0 0 0 0 2018 5 2.8 (2-4) 3.1 (2-4) 0 0 0 0 0 2016 0 ------KSP 283 2017 8 2 (1-4) 3 (1.5-4) 0 1 (13%) 2 (25%) 0 0 2018 7 1.7 (1-3) 1.5 (1-2) 0 0 0 2 (29%) 0 2016 5 3.8 (2-5) 2.8 (2-3) 0 1 (20%) 0 2 (40%) 1 (20%) COL 73 2017 14 1.36 (1-2) 2.5 (2-3) 1 (7%) 0 2 (14%) 1 (7%) 0 2018 9 1.9 (1-3) 2.7 (1.5-3.5) 0 1 (11%) 0 0 0 MF 69 2016 1 3 3 0 1 (100%) 0 0 1 (100%) MF 75 2016 2 3 (3-3) 3 (3-3) 0 1 (50%) 0 0 0 2016 0 ------MF 76 2017 0 ------2018 10 2.4 (1-4) 2.4 (2-3) 0 1 (10%) 0 0 0 2016 0 ------MF 285 2017 1 4 3 0 0 0 1 (100%) 1 (100%) 2018 0 ------MF 289 2016 1 3 3.5 0 0 0 0 0 MF 70 2016 1 3 4 0 0 0 0 0

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Table 7.3. Model selection parameters for generalized linear models predicting health status in free-living silvery salamanders

(Ambystoma platineum). BCS = body condition score, FV3 = Frog virus 3 qPCR status, Trauma = presence of fresh traumatic injuries.

Model N K AICc AICc wi Year + Location + BCS + FV3 + Trauma 274 8 44.4 0 0.67 Year + Location + BCS + FV3 + Trauma + Hemorrhage 274 9 46.4 1.95 0.25 Year + Location + BCS + Fresh + Hemorrhage 274 8 48.9 4.5 0.07 Year + Location + FV3 + Trauma + Hemorrhage 274 8 53.9 9.48 0.006 Year + Location + BCS + FV3 274 7 56.6 12.17 0.002 Year + Location + BCS + FV3 + Hemorrhage 274 8 59 14.6 0 Year + Location + BCS 274 6 61.8 17.37 0 Year + Location + FV3 274 6 77.2 32.81 0 Null 274 1 80.5 36.04 0 Year * Location * BCS * FV3 * Trauma * Hemorrhage 274 38 121 76.51 0

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Table 7.4. Parameter estimates for the most parsimonious model predicting health status in free-living silvery salamanders

(Ambystoma platineum). KSP = Kickapoo State Park, MF = Middle Fork Fish and Wildlife Area, FV3 = Frog virus 3 qPCR status,

Trauma = presence of fresh traumatic injuries, BCS = body condition score.

Healthy = Year + Location + BCS + FV3 + Trauma  SE Z value p-value Intercept 5.58 4.1 1.36 0.17 Year: 2017 -6.61 3.24 -2.04 0.04 Year: 2018 -8.25 3.58 -2.3 0.02 Location: KSP -1.21 1.32 -0.92 0.36 Location: MF -0.31 1.38 -0.23 0.82 FV3: Pos -6.87 2.78 -2.48 0.01 Trauma: Yes -3.55 1.07 -3.31 0.0009 BCS 2.37 0.72 3.29 0.001

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Table 7.5. Model fit metrics from internal validation of the most parsimonious model predicting health status in free-living silvery salamanders (Ambystoma platineum).

Metric Internal Scale Ideal Brier Score 0.027a 0 - 1 Close to 0 AUC 0.97 0.5 - 1 Close to 1 Accuracy (%) 0.98 0 - 1 Close to 1 Sensitivity 0.67 0 - 1 Close to 1 Specificity 0.99 0 - 1 Close to 1 Somer's Delta 0.8a -1 - 1 Close to -1 or 1 a Optimism-corrected values based on bootstrapping

Figure 7.1. Directed acyclic diagram depicting the relationships between health status and its predictors in silvery salamanders

(Ambystoma platineum).

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A B

C D

Figure 7.2. Physical examination abnormalities in adult silvery salamanders (Ambystoma platineum) from Vermilion County, IL during spring emergence. A) Fresh traumatic injury, B)

Developmental abnormality of the mandible, C) Yellow subcutaneous nodules consistent with

Clinostomum sp. infection, D) Skin-colored cutaneous nodule.

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Pond 66 Pond 283

Pond 282 Pond 73

Figure 7.3. Mortality event timeline for larval silvery salamanders (Ambystoma platineum) at four ponds during 2016.

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Figure 7.4. Partial alignment of the viral homolog of the alpha subunit of eukaryotic initiation factor 2 (vIF-2) from a ranavirus isolated in association with silvery salamander (Ambystoma platineum) larval mortality (SS-RV) and eastern box turtle (Terrapene carolina carolina) mortality (EBT-RV). Asterisks beneath aligned sequences indicate agreement between nucleotides.

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Figure 7.5. Phylogenetic tree for ranaviruses sequenced in association with mortality in sympatric silvery salamanders

(Ambystoma platineum) and eastern box turtles (Terrapene carolina carolina) produced by maximum-likelihood analytic methods. Sequences produced in the present manuscript are bolded.

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PART III: ORNATE BOX TURTLE HEALTH AND DISEASE

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CHAPTER 8: IDENTIFYING DETERMINANTS OF INDIVIDUAL & POPULATION

HEALTH IN FREE-LIVING ORNATE BOX TURTLES (TERRAPENE ORNATA

ORNATA): RECOMMENDATIONS FOR MANAGEMENT AND MONITORING

Abstract: Ornate box turtles (Terrapene ornata) in eastern North America are declining due to anthropogenic stressors, but little is known about health and disease risks in free-living populations. The objectives of this study were to model predictors of health in wild ornate box turtles, to identify clinically useful diagnostic tests for health assessment, to relate individual health to population stability, and to generate evidence-based management recommendations supporting box turtle health. We hypothesized that physical examination (PE) abnormalities and protein electrophoresis (EPH) values would best predict poor health, and that population stability would be most sensitive to adult turtle removal. Turtles (N=168) from an Illinois prairie restoration site were evaluated using PE, hematology, plasma biochemistry, EPH, and hemoglobin-binding protein in May 2016-2018. DNA from oral/cloacal swabs and blood was assayed for four ranaviruses, three Mycoplasma spp., two herpesviruses, two Salmonella spp.,

Terrapene adenovirus, intranuclear coccidiosis, Borrelia burgdorferi, and Anaplasma phagocytophilum using qPCR. Health was modeled as a categorical outcome using generalized linear models on the 2016 and 2017 data, followed by information-theoretic model ranking.

External validation was performed using the 2018 data. “Healthy” turtles had 1) no significant

PE abnormalities, 2) no pathogens affecting clinical condition, and 3) fewer than three abnormal bloodwork values. Turtles violating these criteria were classified as “Unhealthy”. Population viability analysis (PVA) was performed to determine the stability of this population and its sensitivity to removal of individual turtles. Many turtles examined (51-59%) had shell damage

167 from suspected predator trauma. Pathogen detection (adenovirus, Terrapene herpesvirus 1) was not related to health status. Predictors of “Unhealthy” classification from the most parsimonious model included the presence of shell abnormalities and deviations from population median values for total leukocyte count, eosinophils, basophils, and heterophil:lymphocyte ratios (p <

0.05). Modeling demonstrated that PE and hematology sufficiently predict health within this population (AUC = 0.87, predictive accuracy = 77%), and that commonly-identified pathogens do not significantly impact individual health. PVA revealed that annual removal of two adult female turtles above background mortality rates dramatically increased the probability of population extinction within the next century. Continued health assessment (including hematology), mesopredator control, and disease screening of newly introduced turtles to prevent novel pathogen ingress was recommended to support individual and population wellness. This study reveals the feasibility of modeling health in wildlife and illustrates its use for identifying clinically useful diagnostic tests and informing practical conservation interventions to support health in understudied populations.

INTRODUCTION

Ornate box turtles (Terrapene ornata) are small, omnivorous, terrestrial chelonians native to prairie and grassland habitats of North America. They are classified as near-threatened by the

IUCN, are listed in CITES Appendix II, and are state threatened and considered a species in greatest conservation need in Illinois (van Dijk 2011b). Threats to ornate box turtle conservation include habitat loss, mortality from roads, mowers, and farm equipment, historical collection for the pet trade, and predation (van Dijk 2011b). Diseases associated with mass mortality events

(ranavirus) have been proposed as a conservation threat for a closely-related sympatric

168 chelonian, the eastern box turtle (Terrapene carolina carolina), and have spurred multiple investigations into the health of this species (e.g. Allender 2006, Johnson 2008, Rose 2011,

Kimble 2012, Allender 2013c, Adamovicz 2015, Lloyd 2016, Agha 2017, Kimble 2017).

However, the health of free-living ornate box turtles is understudied and the potential for disease threats to this species is currently uncharacterized (Christiansen 2004, Rainey 1953, Loomis

1956, Legler 1960, Harden 2018).

Wildlife health status is affected by a variety of environmental, host, and pathogen factors, and understanding the multifactorial determinants of health requires a holistic approach

(Hanisch 2012, Stephen 2014, Stephen 2017). Comprehensive health assessments that simultaneously consider aspects of habitat quality, demographics, and spatiotemporal variation when interpreting diagnostic test results are uncommon in terrestrial reptiles. Furthermore, many studies focus on the effects of individual pathogens in isolation, missing the opportunity to detect potentially important interactions between multiple pathogens and other stressors (e.g. Allender

2011, Allender 2013c, Adamovicz 2015, Kane 2016, Archer 2017). Considering the results of veterinary diagnostic tests and pathogen screening together with demographic, environmental, and spatiotemporal factors using epidemiologic modeling may provide a more comprehensive approach to assessing individual health in understudied species, including ornate box turtles.

Population health is defined in terms of resiliency in the face of stressors; with healthy populations characterized by stability and persistence despite continual challenge (Deem 2008,

Hanisch 2012, Stephen 2014, Stephen 2017). This can be assessed using existing frameworks like population viability analysis (PVA). Studying threatened species at both the individual and population health levels may better inform effective conservation strategies.

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The objectives of this study are to 1) model predictors of individual health in ornate box turtles and identify factors associated with positive health status, 2) identify the most clinically useful diagnostic tests for individual health monitoring in free-living turtles, 3) relate individual- level health to population stability using PVA, and 4) generate evidence-based management recommendations supporting individual and population health. The main hypothesis is that a combination of physical examination (PE) abnormalities and protein electrophoresis (EPH) values will best identify poor health states, and that population health will be most sensitive to loss of adult turtles.

MATERIALS & METHODS

Fieldwork

Ornate box turtles were evaluated at the Nachusa Grasslands in Lee County, Illinois

(41.92o, -89.35o), in May 2016, 2017, and 2018. Air and substrate temperature were collected at the beginning and end of each search period (1-3 hours), and the results were averaged and recorded for each animal encountered (Kestrel 3000 Weather Meter, Nielsen-Kellerman,

Boothwyn, PA 19061; Taylor 9878 Digital Pocket Thermometer, Taylor Precision Products, Oak

Brook, IL 60523). Turtles were located using a combination of human and canine searches and

GPS coordinates, habitat (field, forest, edge) and microhabitat (grass, brambles, leaves, soil) data were recorded at each site of capture. Microhabitat classification was assigned based on the primary ground cover beneath and within a 12” radius of the turtle. Turtles were weighed to the nearest gram, assigned to an age class (> 200 g: adult, < 200 g: juvenile), and sexed using a combination of plastron shape, eye color, and tail length (Dodd, 2001). Straight carapace length

(SCL), straight carapace width (SCW), and carapace height (CH) were recorded to the nearest millimeter and width of the left pectoral scute (Lpect) was measured to the nearest 0.01mm.

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Body condition score (BCS) was determined using a formula derived from eastern box turtles that predicts body fat content (determined by computed tomography) from body weight and

SCW (unpublished data).

Complete physical examinations were performed by a single observer (LA). Turtle mentation was categorized as bright (moving) or quiet (in shell). The following systems were evaluated and coded as within normal limits (WNL) or abnormal: eyes, nares, tympanic membranes, oral cavity, integument, appendages, and cloaca. Condition of the shell was classified into one of three categories: WNL, Active Lesion (AL), or Inactive Lesion (IL). Active lesions were characterized by open fractures or wounds, exposure of necrotic bone, and/or the identification of soft areas or bleeding upon manipulation of the shell. Inactive lesions were firm on palpation and covered by an adherent layer of keratin, indicating appropriate healing. The following clinical signs were coded as present or absent: blepharoedema, ocular discharge, nasal discharge, oral discharge, oral plaques, open-mouth breathing, diarrhea, and ectoparasites.

Blood samples (< 0.8% body weight) were collected from the subcarapacial sinus. If obvious lymph contamination or clotting of the sample was observed, it was discarded and a new sample was collected. Blood samples were immediately placed into lithium heparin microtainers

(Becton Dickinson Co., Franklin Lakes, NJ 07417) and lithium heparin plasma separator tubes

(Becton Dickinson Co., Franklin Lakes, NJ 07417), and stored on wet ice until processing (1-3 hours). Combined swabs of the oral cavity and cloaca were collected using cotton-tipped plastic handled applicators (Fisher Scientific, Pittsburgh, PA 15275) and stored at -20oC. The marginal scutes of each turtle were notched to enable identification of recaptured individuals (Cagle

1939). Following evaluation, sample collection, and notching, turtles were released at their

171 original sites of capture. Protocols for animal handling and sampling were approved by the

University of Illinois Institutional Animal Care and Use Committee (#13061, 15017, 18000).

GPS coordinates and habitat data were collected from the remains of deceased turtles as described above. Remains were stored in individual sealable bags and frozen at -20oC. A bone marrow sample was collected from the right bridge of each shell using aseptic technique in a biosafety cabinet as previously described (Butkus 2017).

Clinical Pathology

Packed cell volume (PCV) was determined using sodium heparized capillary tubes

(Jorgensen Laboratories, Inc., Loveland, CO 80538) centrifuged for 5 minutes at 14,500 rpm.

Total solids (TS) was measured from the plasma in each capillary tube using a hand-held refractometer (Amscope RHC-200ATC refractometer, National Industry Supply, Torrance, CA,

USA). Total leukocyte counts (WBC) were determined following the Avian Leukopet (Vet lab

Supply, Palmetto Bay, FL, USA) manufacturer protocol using Bright-line hemacytometers

(Hausser Scientific, Horsham, PA, USA). Blood smears from heparinized whole blood samples were stained using a modified Wright-Giemsa stain (Hema 3) and 100-cell leukocyte differential counts were performed by a single observer (LA). Leukocytes were classified as heterophils, eosinophils, lymphocytes, monocytes, and basophils as previously described in box turtles

(Heatley 2010).

Plasma was separated from red blood cells within 3 hours of collection via centrifugation at 6,000 rpm for 10 minutes (Clinical 200 Centrifuge, VWR, Radnor, PA 19087, USA). Samples with obvious hemolysis or lipemia were not included in this study. Plasma samples were frozen at -20oC for up to two months (plasma biochemistries, protein electrophoresis) or up to two years

(hemoglobin-binding protein) until diagnostic testing.

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Plasma biochemistry parameters were measured using a commercial chemistry analyzer

(AU680 Chemistry System, Beckman Coulter, Brea, CA 92821, USA). Analytes included calcium (Ca), phosphorous (P), aspartate aminotransferase (AST), creatine kinase (CK), bile acids (BA), and uric acid (UA). Protein electrophoresis (EPH) was performed using the procedure provided by the Helena SPIFE 3000 system with Split Beta gels (Helena Laboratories,

Inc., Beaumont, Texas 77707, USA) at the University of Miami Miller School of Medicine, as previously described (Flower 2014).

Plasma hemoglobin-binding protein (HBP) was quantified using a commercially available kit (Haptoglobin Phase Colorimetric Assay, Tridelta Development Ltd., Maynooth,

Ireland) according to the manufacturer instructions. Briefly, plasma samples and standards were assayed in duplicate and results were read at 630nm (Synergy 2 Multi-Mode Microplate Reader,

BioTek Instruments, Inc, Winooski, VT 05404). Absorbance values were averaged for each sample, and HBP was quantified based on a five-point standard curve from 0-2.5mg/mL.

Pathogen Screening

DNA was isolated from oral-cloacal swabs, whole blood, and bone marrow using a commercially available kit (QIAmp DNA Blood Mini Kit and DNAeasy kit, Qiagen, Valencia,

CA, USA). Manufacturer instructions were followed with some alterations for bone marrow samples, as previously described (Butkus 2017). DNA concentration and purity was assessed spectrophotometrically (NanoDrop 1000, Thermo Fisher Scientific, Waltham, MA, USA) and

DNA samples were stored at -80oC.

173 herpesvirus 1 (TerHV1), emydid Mycoplasma sp., Terrapene adenovirus, Mycoplasma agassizii, and Mycoplasma testudineum using Fluidigm Real Time PCR analysis software by comparing each sample’s cycle threshold (Ct) value to a standard curve consisting of six tenfold dilutions from 107 –101 copies. Standard curves were not available for the remainder of the assays, and results for these pathogens were considered positive at Ct value of 24 or less. Non-template controls were included in each Fluidigm run, and all samples and controls were evaluated in triplicate.

Positive samples were rechecked in single-plex assays. Briefly, samples, standard curves, and negative controls were run in triplicate using a real-time PCR thermocycler (7500 ABI realtime PCR System or QuantStudio3, Applied Biosystems, Carlsbad, CA). Each reaction contained 12.5 l of 20x TaqMan Platinum PCR Supermix-UDG with ROX (TaqMan Platinum

PCR Supermix-UDG with ROX, Invitrogen, Carlsbad, CA), 1.25 l 20x TaqMan primer-probe,

2.5 l sample, NTC, or plasmid dilution, and water to a final volume of 25 l. Thermocycler settings were as follows: 1 cycle at 50oC for 2 min followed by 95oC for 10 min, then 40 cycles at 95oC for 15 seconds and 60oC for 60 seconds, and a final cycle of 72oC for 10 min. Data analysis and quantification was performed using commercially available software (Sequence

Detection Software v2.05, Applied Biosystems, Carlsbad, CA) and standard curves as described above. Positive pathogen quantities (copies/L) were normalized based on total DNA concentration to produce final quantities in copies/ng DNA.

Statistical Analyses

174 clustering of qPCR pathogen detection, physical examination abnormalities, and extreme clinical pathology values; 5) Assess longitudinal trends in health status for free-living ornate box turtles.

All statistical assessments were performed in R at an alpha value of 0.05.

The distribution of each continuous variable was assessed visually using box plots and histograms and statistically using the Shapiro-Wilk test. Descriptive statistics (mean, standard deviation, range for normally distributed variables, median, 10% and 90% percentiles, and range for non-normally distributed variables) and counts were tabulated for continuous and categorical variables, respectively. Descriptive statistics were used to document longitudinal changes in individual health status and pathogen detection for serially sampled turtles.

A directed acyclic graph (DAG) was generated to demonstrate expected relationships among measured predictors (Figure 8.1). This diagram was used to identify potential confounding variables and structure statistical analyses, using multivariable linear regression when necessary to control for confounders (Joffé 2012). Continuous predictor variables were assessed for multicollinearity using Pearson’s correlation coefficient, with r > 0.5 considered

Univariate analyses were conducted using data from 2016 and 2017 to select predictors for inclusion in the final health models. A liberal p-value of 0.15 was utilized to include all variables which may explain biologically important variation in health parameters. Sex ratios were evaluated using binomial tests (expected ratio 0.5). Predictors of continuous outcomes

(BCS, clinical pathology parameters) were evaluated using general linear mixed models with turtle ID as a random effect in package nlme (Pinheiro 2018). For each model, the assumption of

175 normally distributed error in the dependent variable was evaluated using boxplots, histograms, and Q-Q plots of residual values. The assumption of homoscedasticity was assessed using plots of actual vs. fitted residuals. The impact of influential values was assessed using Cook’s

Distance plots. Post-hoc between group differences were evaluated using the contrasts function in the lsmeans package (Lenth 2016). Predictors of categorical outcomes (pathogen status, presence/absence of physical exam abnormalities) were assessed using generalized linear mixed models with turtle ID as a random effect in package lme4 (Bates 2015). The assumption of a linear relationship between continuous predictors and the log-odds of the output variable was visually evaluated using scatterplots.

Reference intervals were constructed for each clinical pathology parameter in each year using the nonparametric method in the referenceIntervals package according to American

(Horn 2001). Ninety percent confidence intervals were generated around the upper and lower bounds of each reference interval using nonparametric bootstrapping with 5000 replicates. The width of the confidence intervals (WCI) was compared to the width of the reference interval

(WRI) to infer the need for a larger sample size, which is generally recommended when

WCI/WRI > 0.2 (Friedrichs 2012).

All turtles were then assigned to a health category (“apparently healthy” vs. “unhealthy”).

Apparently healthy turtles met the following three criteria:

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3. No clinically significant physical examination abnormalities. A clinically

significant abnormality is one expected to affect the animal’s ability to navigate,

prehend food, mate, and protect itself from predation. Examples include open

wounds, active fractures, or respiratory distress. Physical examination abnormalities

that are not considered clinically significant include healed injuries and congenital

abnormalities such as fused digits or supranumerary scutes.

4. No more than three clinical pathology parameters outside of reference intervals.

By definition, reference intervals comprise 95% of the values of a “healthy”

population, so 5% of “healthy” animals can be expected to have a value outside of the

reference interval (Freidrichs 2012). Increasing the number of abnormal values

required to classify an animal as “abnormal” decreases the risk of misclassifying an

otherwise “healthy” animal.

5. No clinically apparent qPCR detection of infectious disease. The presence of a

pathogen does not necessarily equate to disease (Thompson 2010, Pirofski 2012,

Méthot 2014). Furthermore, some pathogens are host-adapted and have limited

associated clinical signs in an otherwise healthy animal (Ryser-Degiorgis 2013). The

present study only considers pathogen-positive animals “unhealthy” if they have

concurrent clinical signs of illness or if they have greater than three abnormal clinical

pathology values, indicating a negative physiologic effect of the pathogen.

Individual health status was evaluated using sets of candidate generalized linear models ranked using an information-theoretic approach with the AICcmodavg package (Mazerolle

2017). To generate predictive health models with a minimum number of variables, each significant clinical pathology parameter was centered based on age, sex, and year-specific

177 median values and health models were reconstructed. Internal model validation was performed via bootstrapping with 500 replicates using the 2016 and 2017 datasets and the rms package.

External model validation was performed with the 2018 data using the rms and pROC packages

(Robin 2011).

Spatial Epidemiology

Turtle GPS locations were mapped using ArcGIS on the World Imagery background layer and visually examined for demographic clustering (ArcMap 10.4.1, Esri, Redlands, CA

92373). Spatial clustering of poor BCS and extreme clinical pathology values were evaluated using Gettis G Ord hotspot analysis. The Bernoulli model was used to test for statistically significant spatial clusters of physical examination abnormalities and pathogens using SaTScan v9.4.4 (Kulldorff 1997). Output files from SaTScan were uploaded into ArcGIS to visualize the extent of significant clusters.

Estimating Population Size & Population Viability Analysis

Population size estimates with 95% confidence intervals were produced using the

Schumacher-Eschmeyer method in the fishmethods package (Schumacher 1943). Individual- based population viability analyses were performed using VORTEX v10 (Lacy 2018). This program utilizes deterministic and stochastic modeling to produce estimates of population growth rate, extinction rate, and extinction time, enabling the user to examine factors that affect population stability (health). Basic model inputs include demographic information, reproductive parameters, age class-specific mortality rates, and carrying capacity, along with estimates of uncertainty. Catastrophic events, harvesting, supplementation, and inbreeding depression can also be modeled depending on the purpose of the analysis. VORTEX utilizes Markov Chain

Monte Carlo simulations to model population dynamics as discrete, sequential events that

178 probabilistically occur based on random variables following user-specified distributions (Lacy

2000). Stochasticity is introduced into these analyses via demographic and environmental variability, modeled using binomial distributions.

PVA scenarios were simulated over 100 years with 1000 iterations for three different starting population sizes: 1) expected, 2) optimistic, and 3) pessimistic based on the Schumacher-

Eschmeyer estimate and its confidence intervals. Parameter and rate estimates were obtained from the ornate box turtle literature, however, age class-specific mortality rates have not yet been produced for this species, so eastern box turtle estimates were utilized instead (Table 8.1).

Sensitivity testing was performed to examine the effect of removing turtles of different age classes and sexes on each PVA scenario, representing increased losses due to predation, disease, or human collection. The specific scenarios tested included annual removal of 1-5 adult females, annual removal of 1-5 adult males, and annual removal of 1-5 female hatchlings.

RESULTS

Environmental Data, Sample Populations, & Physical Examination

Turtle sampling was conducted on May 6-9, 2016, May 15-18, 2017, and May 7-10,

2018. Monthly temperatures in May from NOAA’s 1988-2010 Climate Normals database for

Dixon, Illinois are 9.22oC (minimum), 15.33oC (average), and 21.39oC (maximum). Air temperatures during the 2017 (20 – 26.05oC) and 2018 (17.75 – 26.4oC) sampling periods were generally warmer than the NOAA values. They were also warmer than the turtle sampling days in 2016 (13.5 – 18.8oC), excepting the very first search day on May 6th 2016 (23.9oC). Substrate temperatures were also generally warmer in 2017 (15.75 – 19.65oC) and 2018 (15.4 – 19.6oC) than 2016 (11.9 – 15.8oC).

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One hundred sixty-eight individual turtles were evaluated over the course of three years and forty-five turtles were captured more than once, for a total of 226 health assessments. Of these, 25 turtles sampled in 2016 were resampled in 2017 (15% of samples), 21 turtles sampled in 2017 were resampled in 2018 (13.7% of samples), and 21 turtles sampled in 2016 were resampled in 2018 (15% samples). Seven deceased turtles were located, two in 2016 and five in

2018. The physical condition of deceased turtles ranged from complete shells to partial boney remains; none had remaining soft tissue and most were lacking keratin reflecting a significant time interval between death and discovery.

All turtles were found within fields and microhabitat use was predominantly grass (N =

159) followed by brambles (N = 55) and soil (N = 12). Demographic data for live turtles is summarized in Table 8.2. A male bias was observed in 2016 and 2017 (32% female in 2016, p =

0.001; 33% female in 2017, p = 0.003) and juvenile turtles were found each year, reflecting active recruitment.

Physical examination findings were generally similar between years and sites (Table 8.2).

Shell lesions were the most frequent and striking physical exam abnormality with a prevalence from 51 – 59% each year (Figure 8.2). These lesions consisted of mild to significant predator damage, erosions, flaking, burns, and developmental abnormalities, and many turtles had multiple lesion classifications. Reclassification of lesions into WNL, AL, and IL groups promoted consideration of the overall condition of the shell and reduced the number of categories to include in statistical models.

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Predictors of PE Abnormalities, Clinical Pathology Values & Pathogen Detection: 2016 &

2017

Preliminary statistical assessment was performed using linear mixed models for 2016 and

2017 datasets, guided by the DAG (Figure 8.1). Microhabitat use varied by age class and sex; specifically, juvenile turtles were found in soil more frequently than adult males (p = 0.005) and adult males were found more frequently in brambles than adult females (p = 0.006).

qPCR pathogen testing was performed on 116 turtles and two pathogens were detected: adenovirus and TerHV1. Pathogen prevalence differed significantly by year (Table 8.2). The odds of adenovirus (p = 0.04) and TerHV1 (p = 0.007) detection were 12 and 19 times higher in

2016 compared to 2017, respectively. The odds of TerHV1 detection were 4 times higher in turtles occupying brambles compared to other microhabitats (p = 0.006), however, TerHV1 was only detected in 2016 and this microhabitat association may not persist in subsequent years.

Pathogen status was not associated with clinical signs of illness, clinical pathology changes, or poor BCS (p > 0.15).

Condition of the shell was significantly associated with age class; the odds of any shell abnormality were 8 times higher for adult turtles than juveniles (p < 0.0001). Sex, year, microhabitat, weight, and BCS were not significantly associated with condition of the shell or other physical exam abnormalities (p > 0.15).

Hematology and HBP results were available for 155 turtles, plasma biochemistries were performed for 111 turtles, and EPH was performed in 113 turtles. Chemistries and EPH were only available for six and four juvenile turtles, respectively, and statistical comparisons based on age class were not performed for these diagnostics.

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Clinical pathology values were frequently influenced by year, sex, age class, and condition of the shell. Association of HBP with environmental and demographic data is reported separately (Chapter 5). All absolute EPH values were higher in 2017 than 2016, due mostly to elevated total solids levels in 2017 (effect size = 1.34g/dL, p < 0.0001).

Relative beta globulins (effect size = 3.27%, p = 0.0065), calcium (effect size = 4.93, p <

0.0001), and phosphorous (effect size = 1, p = 0.0001) were higher in females than males, while relative albumin concentration (effect size = 1.76%, p = 0.02) was lower. Packed cell volume

(PCV) was significantly higher in male turtles (effect size = 2%, p = 0.03). The following parameters were higher in juveniles than adults: absolute monocytes (effect size = 403, p = 0.04), relative monocytes (effect size = 1.84, p = 0.049), and relative lymphocytes (effect size = 11%, p

= 0.009). The following parameters were lower in juveniles than adults: relative heterophil count

(effect size = -8.61%, p = 0.015), relative eosinophil count (effect size = -5.5%, p = 0.01), and

HBP (effect size = -0.145, p = 0.001).

Absolute heterophil count was elevated in turtles with AL compared to both IL (effect size = 1440, p = 0.03) and WNL turtles (effect size = 1358, p = 0.04). Turtles with AL also had lower relative lymphocyte counts (effect size = -7.8%, p = 0.02) and higher heterophil : lymphocyte ratios (H:L; effect size = 0.26, p = 0.02) than turtles with normal shells. AL turtles had higher creatine kinase (CK) values than IL (effect size = 561, p = 0.02) and WNL (effect size = 726, p = 0.005) turtles, and higher aspartate aminotransferase (AST) values than WNL turtles (effect size = 34, p = 0.03). Turtles with normal shells had higher relative albumin concentrations than IL (effect size = 2.3%, p = 0.0001) and AL (effect size = 2.1%, p = 0.01) individuals. They also had higher albumin : globulin ratios (A:G) than IL (effect size = 0.04, p =

0.001) and AL (effect size = 0.04, p = 0.01).

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Clinical pathology reference intervals were generated using combined data from 2016 and 2017 (Tables 8.3-8.7). Reference intervals for values which differed by year, age class, or sex were appropriately partitioned. For serially sampled turtles, clinical pathology values were only included in the reference interval dataset from the animal’s initial capture event. Evaluation of the WCI/WRI revealed that reference intervals for several parameters would be improved with a larger sample size (Tables 8.3-8.7).

Predictors of Health

Prior to generating the health model, clinical pathology parameters for each turtle were converted to the absolute value of the distance from year, age class, and sex-specific median values. This was done for two reasons: 1) to capture the relationship between poor health and extreme clinical pathology values at both the low and high end of the reference interval, without introducing non-linear terms, and 2) to control for the effects of potential confounders and reduce the total number of variables to include in the final health model. Resulting median-based values were highly right-skewed, and were box-cox transformed to better support modeling assumptions. Health modeling was conducted using only fixed effects to facilitate prediction from the final model.

The following predictors were associated with health status at a liberal alpha value of

0.15: WBC, absolute heterophil count, relative heterophil count, absolute lymphocyte count, relative lymphocyte count, H:L, absolute basophil count (Baso), absolute eosinophil count (Eo), and shell classification (Shell; AL, IL, WNL). The H:L is calculated from both heterophil and lymphocyte counts, so H:L was used to represent information for all heterophil and lymphocyte measures in the final health model.

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The most parsimonious model contained the additive effects of Shell, WBC, Eo, Baso, and H:L, though inclusion of the Eo variable resulted in a high degree of model selection uncertainty (Table 8.8). Internal and external validation were performed for the top two models separately and averaged (data not shown), and predictive capability of the model including Eo was superior, therefore this model was selected for full description. The top model including each of its parameter estimates and p-values is reported in Table 8.9, and performance metrics produced by internal and external validation are reported in Table 8.10. External validation revealed a tendency to under-identify “unhealthy” animals, represented by a slight decrease in sensitivity, however, overall model performance remained acceptable with an accuracy of 77% and an AUC of 0.87.

Spatial Epidemiology

Significant spatial clusters were not identified for any parameter tested, indicating that pathogens and poor health indices were distributed homogeneously within the population evaluated. Similarly, turtles did not cluster based on age class or sex, despite differences in microhabitat use. This may indicate that the Nachusa grasslands contains a patchy mosaic of microhabitats enabling turtles to occupy preferred areas without significant travel.

Longitudinal Analyses

Of the 45 turtles evaluated multiple times, 14 were classified as healthy each time, 10 were classified as unhealthy each time, five turtles changed status from healthy to unhealthy, 10 turtles changed status from unhealthy to healthy, and six turtles vacillated between healthy and unhealthy more than once during the study period. Of the turtles classified as unhealthy at each capture, eight had both clinically significant physical exam abnormalities and > 3 clinical pathology values outside reference intervals, one had an abnormal PE alone, and one had

184 abnormal bloodwork alone. Of turtles that converted from healthy to unhealthy, two did so because of PE changes alone and three changed due to a combination of PE and bloodwork changes. Almost all turtles that changed status from unhealthy to healthy (N = 14) had only been classified as unhealthy due to clinical pathology abnormalities, while two animals suffered mild shell injuries (with > 3 accompanying clinical pathology changes) and were considered healed the following year. Ten of the serially sampled turtles tested qPCR positive for Terrapene adenovirus at their initial capture, and all were negative at subsequent evaluations. Similarly, eight turtles tested qPCR positive for TerHV1 at initial capture, and all were negative on follow up samples.

Estimating Population Size & Population Viability Analysis

The Schumacher-Eschmeyer population size estimate was 268 turtles, with a 95% confidence interval from 184 – 493 individuals. Starting population sizes for the three baseline scenarios were therefore 268 (estimated), 493 (optimistic), and 184 (pessimistic). Populations tended to decline slightly over the simulated 100-year period as evidenced by negative growth rates, but extinction probability was zero for all three baseline scenarios (Figure 8.3). Sensitivity testing revealed that annual removal of 2 adult females beyond background mortality rates elevated the probability of population extinction to over 50% in every scenario (Table 8.11,

Figure 8.4). In the pessimistic scenario, removal of 1 adult female per year was enough to increase the probability of population extinction to over 80%. Removal of up to five adult males per year was also impactful, but much less so than adult female removal (Table 8.12, Figure 8.4).

In contrast, removal of hatchling females was much less destabilizing compared to adult turtle removal (Table 8.13, Figure 8.4). Population health of Nachusa ornate box turtles appears

185 extremely sensitive to the loss of relatively small numbers of adult females. Strategies to protect this demographic may improve population stability over time.

DISCUSSION

This study illustrates the practicality of combining comprehensive health assessment methods with modeling approaches to better understand the drivers of health in a population of state-threatened reptiles. Results indicate that shell lesions associated with predator trauma are the most common threat to individual health, detection of adenovirus and TerHV1is not significantly associated with health status, and that individual health is best predicted using a combination of physical examination and hematology values. PVA demonstrated that loss of a small number of adult females from any cause (including succumbing to predator injuries) could potentially lead to population extirpation within the next century. These findings can be used to inform management actions which support box turtle health and to plan future health monitoring strategies for this population.

Individual Health Assessment

The main objectives of assessing individual box turtle health were to identify drivers of poor health and determine which diagnostic tests were most clinically useful for health assessment. These objectives were pursued through the application of physical examination, pathogen screening, spatial analyses, and multiple diagnostic tests. Modeling was used to synthesize these results, account for demographic and annual variability, and ultimately to determine the relative importance of each predictor for individual box turtle health.

The final model demonstrated that individual health status is best predicted by the condition of the shell and four normalized hematology values. Active and inactive shell lesions were highly prevalent in this box turtle population (51-59%/year), and approximately half of

186 these lesions were considered to negatively impact health status based on visual examination alone. Turtles with active shell lesions also had multiple clinical pathology abnormalities supporting negative health impacts, including elevations in absolute heterophil count, H:L, CK, and AST, and decreases in relative lymphocyte count, relative albumin, and A:G. These changes are consistent with tissue damage and inflammation (Campbell 2015). Interestingly, turtles with inactive shell lesions also had decreased relative albumin and A:G ratios compared to turtles with normal shells, reflecting prolonged physiologic changes that persist after clinical resolution of shell injury. This likely represents a diversion of resources away from growth and reproduction and towards wound healing, a shift that may temporarily or permanently affect fitness, fecundity, and survival.

The number of turtles that die from these shell injuries is unknown, however, a previous demographic study at Nachusa documented seven predation-associated deaths out of 153 uniquely identified individuals over the course of three years (Kim Schmidt, personal communication). This is likely an underestimate of the true predation-specific mortality rate due to the poor detectability of ornate box turtles using visual encounter surveys (Refsnider 2011).

Wildlife rehabilitation centers report mortality rates of 10-43% for multiple species of reptiles presented due to animal attacks (Rivas 2014, Sack 2017). The effects of attempted predation on fecundity are more difficult to determine, though previous studies have illustrated trade-offs in maternal health and reproductive success in reptiles (Uller 2006, Perrault 2012, Rafferty 2014).

Ultimately, determining the long-term impacts of shell damage on individual health will require longitudinal evaluation of affected and non-affected turtles, likely via monitoring of radiotelemetered animals.

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Terrapene adenovirus and TerHV1 were detected within the Nachusa box turtle population, but these pathogens were not associated with clinical signs of illness or significant changes in clinical pathology. Multiple turtles that tested positive at initial capture were negative in subsequent samples. This could be due to qPCR detection of non-infectious DNA, clearance of infection, or viral latency (Maclachlan 2017a, Maclachlan 2017b). Latency is a feature of both herpesviral and adenoviral infections in other species, and affected animals may recrudesce, develop clinical signs, and shed virus in response to stress (Maclachlan 2017a, Maclachlan

2017b). TerHV1 was originally detected in association with pneumonia in a hatchling eastern box turtle, but subsequent screening of wild turtles has revealed high prevalence and no associated clinical signs of illness (Sim 2015, Kane 2016, Kane 2017). It has therefore been proposed that TerHV1 is a host-adapted pathogen in box turtles, and that clinical signs are rare except in young and immunosuppressed animals (Kane 2017). Adenovirus was identified in a captive ornate box turtle that died during brumation and had inclusion body hepatitis at necropsy

(Farkas 2009). It has also been detected in captive eastern box turtles of unknown health status, but free-living populations have not yet been screened for this pathogen (Doszpoly 2013). The results of the present study support a limited role for TerHV1 and adenovirus in individual health outcomes, but further epidemiologic study is necessary to define the host and population-level effects of these pathogens in ornate box turtles.

Modeling identified hematology as the most important diagnostic test for individual health assessment in this population. Turtles that were classified as “unhealthy” had WBC, H:L, absolute basophil, and absolute eosinophil counts further from their age, sex, and year-specific median values compared to apparently healthy turtles. Changes in WBC can be attributed to infection, inflammation, and neoplasia while changes in H:L are indicative of stress (Case 2005).

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The function of eosinophils and basophils is less well understood in reptiles, though changes are thought to be related to parasitic infection and allergy, similar to mammals (Campbell 2015). The association between these values and “unhealthy” status highlights the need for additional research to determine the roles that these inflammatory cells play in the reptile immune response.

Application and interpretation of hematology in free-living reptiles has historically been hampered by poor understanding of significant season, sex, and age-related variability, but the use of models to identify and control for these confounding factors has uncovered the diagnostic utility of this test for ornate box turtle health (Campbell 2015).

Several other clinical pathology tests (plasma biochemistries, HBP, EPH) were assessed for diagnostic utility in individual box turtles during this study. Chemistry parameters (CK, AST) and EPH values (relative albumin, A:G) were significantly different for turtles with active shell lesions compared to turtles with no lesions, however, these values were not ultimately related to health status in the most parsimonious model. This apparent lack of predictive power may be related to the underlying causes of poor health in the ornate box turtle population at Nachusa. A previous study comparing healthy and unhealthy eastern box turtles found decreases in albumin, globulin, total protein, calcium, phosphorous, sodium, and potassium and increases in H:L in unhealthy individuals (Adamovicz 2015). Most unhealthy turtles in that study presented with aural abscesses, though signs of infectious disease such as blepharoedema and oral plaques were also common. Shell fractures were only present in two turtles, thus the common presenting complaints in that study differed from those in the ornate box turtles at Nachusa. That study also failed to account for age, sex, and seasonal differences in hematology parameters when assessing statistical differences between healthy and unhealthy groups, ignoring important confounding variables and further complicating direct comparison with the present study. However, the

189 common increase in H:L as a predictor of poor health in box turtles supports the inclusion of this parameter in future box turtle health assessment studies. A variety of clinicopathologic alterations have been documented in sea turtles presenting for stranding, cold-stunning, toxin exposure, and trauma, and parameters from hematology, blood gas, and biochemistry panels can differ between sick and healthy individuals and survivors and non-survivors in rehabilitation settings (Flint 2010, Keller 2012, Stacy 2013, Flint 2015, Stacy 2017, March 2018). The relative importance of different parameters for differentiating healthy and unhealthy sea turtles appears to be presentation, species, and population-specific (Flint 2010, Flint 2015, March 2018). This likely applies to box turtles as well, therefore the findings of the present study may not be generalizable to other populations and diagnostic tests may have varying levels of practical utility for ornate box turtles in different settings. The model developed in the present study should be applied to other populations in the future to evaluate its external validity.

The reference intervals for clinical pathology tests generated in this study are useful for future application to this population, and for comparison between populations. Currently, clinical pathology parameters in free-living ornate box turtles are only reported in a single study with a limited sample size (Harden 2018). The hematocrit reported by that study (mean 17.9%) is lower than the packed cell volume identified in the present study (mean = 23% for females and 25% for males). This could represent differences in health status between the two study populations, lymph dilution of the blood samples in the aforementioned study, or could be due to methodological differences (iSTAT vs. centrifugation of microhematocrit tubes) (Harden 2018).

Limitations of the individual health assessment portion of the study are largely related to methodology. Blood samples were collected from the subcarapacial sinus in all turtles. This is one of the most common venipuncture sites in box turtles, though lymph contamination is

190 possible due to the proximity of multiple lymphatic vessels (Hernandez-Divers 2002, Heatley

2010). Visibly lymph-contaminated samples were not included in this study, but inapparent lymph contamination can never be fully ruled out. If it occurred, decreases in PCV, WBC, uric acid, albumin, globulin, calcium, phosphorous, and AST, and increases in relative and absolute lymphocyte counts may be expected (Wilkinson 2004). Plasma samples were frozen at -20oC for up to two months prior to biochemistry and EPH analysis and up to two years prior to HBP analysis. Previous research in humans and rats indicates that the biochemistry analytes evaluated in the present study remain stable at -20oC for up to three months (Cuhadar, 2013; Cray, 2009).

A study in horned vipers (Vipera ammodytes ammodytes) demonstrated stability of total protein and all EPH fractions except for relative alpha 1 globulins following storage at -20oC for 70 days

(Proverbio, 2012). HBP has been documented to remain stable at -20oC for up to four years

(Beechler 2018). While it is impossible to know whether this period of storage affected the stability of ornate box turtle analytes, research in other species suggests that changes to the measured analytes would have been minimal. This is further supported by the similarity of clinical pathology values in the present study compared to other box turtle species. Finally, turtle sampling was only performed at one time point during each year, and as a result knowledge about box turtle health for the rest of the active season is lacking. Future studies should include health assessment data from more time points throughout the summer and fall to promote an understanding of temporal trends in health status.

Population Health Assessment

Population viability analysis revealed that the ornate box turtle population at Nachusa is highly sensitive to the removal of adult females. This is similar to other chelonian studies which consistently demonstrate the importance of adult survivorship for maintaining population

191 stability (Crouse 1987, Brooks 1991, Congdon 1993, Congdon 1994, Heppel 1998, Dodd 2016).

A recent simulation study in Florida box turtles (Terrapene carolina bauri) showed that annual removal of 3.8% of the population would drive stable and declining populations extinct within

50 years (Dodd 2016). It therefore appears that the loss of small numbers of adult turtles is extremely destabilizing for multiple species of box turtles. This is likely due to a suite of chelonian biological characteristics including delayed reproduction, low fecundity, low recruitment, and high adult survivorship – all of which make the survival of reproductively active adults (especially females) crucial for population stability (Heppel 1998). This is concerning for the wellness of the Nachusa ornate box turtle population, as loss of a small number of turtles each year due to depredation/disease/poor health seems very plausible given the results of the individual health model. Furthermore, the overall estimated size of the Nachusa population is small, increasing the potential for stochastic events such as climatological phenomena or disease to negatively impact population stability.

There are several limitations to the PVA model constructed in this study. PVA modeling was performed based on estimates of population size generated using the Schumacher-

Eschmeyer method, which assumes that the population size is constant with no recruitment or losses, that sampling of individuals is random, and that all individuals have an equal probability of being sampled. The first assumption is clearly violated in any wild animal population, while the remaining assumptions are frequently violated to varying degrees based on experimental design. The present study searched a pre-defined area each day using trained dogs to find turtles.

Dogs dramatically increase detection of box turtles compared to visual encounter surveys, and population estimates based on detector dog data are likely more accurate than those produced using human searches alone (Boers 2017).

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To reflect uncertainty in population size estimates, PVA was conducted using three different starting population estimates based on the Schumacher-Eschmeyer confidence intervals.

Additional sources of uncertainty in this analysis stem from unknown population-specific vital parameters and rates including carrying capacity, reproductive parameters, age class-specific mortality rates, and the degree of expected demographic and environmental variability.

Obtaining population-specific values for these parameters typically takes several years of intensive mark-recapture efforts, and availability of such complete datasets is uncommon in many wildlife populations. This study instead relied on reasonable estimates from other eastern and ornate box turtle populations. The level of uncertainty associated with using this data has been included in the analysis by way of large user-provided standard deviation estimates. Despite all the potential sources of uncertainty in this analysis, results made biological sense and were consistent with previous studies in other chelonians. Improvement of this model using population-specific mortality rates and vital parameters will increase confidence in its predictions and enable modeling of management strategies such as population supplementation via head-starting or translocation. This may facilitate the use of this model for decision-making.

Management Recommendations

Management recommendations for this site include strategies that support both individual and population health. Predator injuries are the most commonly identified threat to individual health, and mortality secondary to predator trauma may impact population stability. Therefore, identification and control of box turtle predators, specifically human-subsidized mesopredators present in unnaturally high abundances at the site, may rapidly improve individual health and population viability. Infectious diseases which negatively impact eastern box turtles, such as ranavirus, were fortunately not detected in the ornate box turtles at Nachusa. However,

193 introduction of infectious diseases could negatively impact both individuals and the population.

Therefore, if population supplementation methods such as head-starting or translocation are adopted, disease screening must be implemented to reduce the risk of novel pathogen entry.

Continued health monitoring of this population, including hematology, will also be important for identifying emerging or changing threats. Expanding the health assessment program to include additional sampling periods later in the active season would provide more information on expected temporal fluctuations and improve longitudinal health assessment for individual turtles.

Future research efforts should focus on clarifying the effects of predator damage on fecundity and survival, determining genetic structure of the population to help identify suitable source animals for translocation, and deriving population-specific vital and mortality rates to improve the predictive accuracy of PVA. This will allow further refinement of management recommendations and enable appropriate support of box turtle and ecosystem health.

Conclusions

This study demonstrates the feasibility of modeling health in wildlife and illustrates its use for identifying clinically useful diagnostic tests and informing practical conservation interventions. Considering wellness at both the individual and population levels provided more information than examining either component in isolation, ultimately resulting in a more complete understanding of how individual health may impact population stability.

Comprehensive approaches to identifying and managing conservation threats are becoming more attractive as wildlife face an increasingly complex array of natural and anthropogenic stressors.

The modeling framework developed in this manuscript identifies ways to simultaneously support health and combat threats, and may represent one pathway to improving conservation outcomes in box turtles and other species.

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Table 8.1. Parameter estimates and sources for population viability analysis in free-living ornate box turtles (Terrapene ornata ornata). Parameter Value Source Breeding System Polygamy Dodd 2001 Age of 1st Offspring: Female 10 Legler 1960 Age of 1st Offspring: Male 8 Legler 1960 Maximum Lifespan 37 Christiansen 2004 Maximum # Broods/Year 2 Legler 1960, Vogt 1981, Temple 1987, Doroff 1990, Blair 1976 Maximum # Progeny/Brood 5 Vogt 1981, Temple 1987, Doroff 1990, Blair 1976, Caldwell 1981 Sex Ratio at Birth 0.5 Maximum Age of Reproduction 37 Henry 2003 % Adult Females Breeding 0.57 Doroff 1990, Redder 2006 Mean # Offspring/Female/Brood (SD) 2 (2) Doroff 1990 Mortality Rates (SD): 0-1 42% (10) Burke 2011b, Congello 1978, Dodge 1978, Ewing 1933, Flitz 2006, 1-2 33% (10) Willey 2012, Wilson 2005, Forsythe 2004, Heppell 1998, 2-3 28% (10) Currylow 2011, Nazdrowicz 2008 3-4 24% (10) 4-5 19% (3) 5-6 15% (3) 6-7 6% (3) 7-8 6% (3) 8-9 6% (3) 9-10 3% (2) 10 and up 3% (2) Bowen 2004 Initial Population Size 184, 268, 493 Present study Carrying Capacity 1000

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Table 8.2. Demographic distribution, physical examination findings, and qPCR pathogen detection of free-living ornate box turtles

(Terrapene ornata ornata) sampled in Lee County, Illinois during May 2016, 2017, and 2018.

2016 2017 2018 Captures Total 72 88 65 Recaptures 0 25 32 Sex Male 44 41 27 Female 21 29 37 Unknown 7 18 1 Age Class Adult 65 63 63 Juvenile 7 25 2 Clinical Signs Blepharoedema 0 1 (1%) 0 Nasal Discharge 0 0 1 (1.5%) Asymmetrical Nares 2 (3%) 1 (1%) 1 (1.5%) Missing Extremities 8 (11%) 5 (6%) 1 (1.5%) Inactive Shell Lesion 34 (47%) 29 (33%) 20 (31%) Active Shell Lesion 8 (11%) 19 (22%) 19 (29%) Pathogens Adenovirus 16 (22%) 1 (1%) 1 (1.5%) TerHV1 21 (30%) 0 0

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Table 8.3. Hematology parameters (absolute counts) from free-living ornate box turtles (Terrapene ornata ornata) sampled in Lee

County, Illinois during May 2016 and 2017. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB

= lower bound of the reference interval, UB = upper bound of the reference interval. PCV = packed cell volume, WBC = total leukocyte count.

90% CI Parameter Partition N Dist CT Dispersion Min Max Reference 90% CI UB Interval LB PCV (%) Male 34 Gb 25 4.4 14.5 36 17 - 32 14 - 17 32 - 32.5 c Female 18 G 23 4.5 15 34 - - - Total Solids d a (g/dL) 66 G 6.55 1.05 4 9.6 4.87 - 8.12 4.34 - 5.14 8.07 - 8.37 10904 - 5847 - 41114 - a WBC (/L) 72 NG 18053 26675 7323 41507 8048 - 41310 8774 51508 Heterophils (/L) 72 G 4767 1691 1491 8615 1548 - 8464 861 - 1606 8314 - 9393 Lymphocytes 2387 - 23895 - (/L) 72 NG 9605 4867 - 18717 2709 24349 3316 - 24122 3923 27478 Monocytes (/L) Adult 52 NGe 621 207 - 1420 0 2490 171 - 1956 134 - 187 1943 - 2359a Juvenile 16 G 1225 815 0 2889 - - - f a Eosinophils (/L) 70 NG 1320 468 - 4237 73 7471 169 - 5505 0 - 185 4009 - 6585 a Basophils (/L) 72 NG 483 84 - 1286 0 2781 0 - 2189 0 - 1 1597 - 2677 a WCI/WRI > 0.2

Outliers: b 14.5, 36; c 15, 34; d 4, 4.2, 4.4, 8.85, 8.85, 9.6; e 0, 0, 0, 2490; f 73, 7471

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Table 8.4. Hematology parameters (relative counts) from free-living ornate box turtles (Terrapene ornata ornata) sampled in Lee

County, Illinois during May 2016 and 2017. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB

= lower bound of the reference interval, UB = upper bound of the reference interval.

90% CI Parameter Partition N Dist CT Dispersion Min Max Reference 90% CI UB Interval LB Heterophils (%) Adult 53 Gb 28 9 7 55 12 - 45 8 - 12 44 - 49 Juvenile 16 G 25 9 11 45 - - - Lymphocytes (%) Adult 51 Gc 54 13 22 88 37 - 77 32 - 37 77 - 81 Juvenile 16 G 59 10 42 77 - - - Monocytes (%) Adult 56 G 4 2 0 12 0 - 11 0.6 - 1 11 - 15a Juvenile 16 G 6 2 0 11 - - - Eosinophils (%) Adult 56 NG 10 3 - 21 1 29 0 - 26 0.15 - 1 24 - 31a Juvenile 16 G 6 4 1 15 - - - a Basophils (%) 72 NG 4 2 - 8 1 12 1 - 11 1 - 1 10 - 13 Heterophil : Lymphocyte 69 NGd 0.5 0.2 - 0.83 0.08 1.87 0.13 - 1.28 0.09 - 0.17 1.16 - 1.67a a WCI/WRI > 0.2

Outliers: b 7, 7, 55; c 22, 23, 80, 80, 88; d 0.08, 1.49, 1.87

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Table 8.5. Plasma biochemistry parameters from free-living ornate box turtles (Terrapene ornata ornata) sampled in Lee County,

Illinois during May 2016 and 2017. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval. Ca:P = calcium/phosphorous ratio.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval Calcium (mg/dL) Male 36 G 8.74 1.17 6.8 12.1 6.8 - 12.1 6.2 - 6.8 12.1 - 13.7a a Female 20 NG 13.9 9.07 - 22.52 9 28.4 9 - 28.4 8.9 - 9 28.4 - 35.3 Phosphorous (mg/dL) Male 36 NG 2.35 1.95 - 3.1 1.9 4.2 1.9 - 4.2 1.9 - 1.9 4.2 - 5.3a b Female 18 G 4.06 1.3 2.2 7.6 - - - Ca:P 48 G 3.6 0.64 2.52 4.84 2.53 - 4.82 2.46 - 2.55 4.79 - 5.07 a Bile Acids (mol/L) 48 NG 4.5 2.7 - 8.6 2 12.7 2 - 12.6 1.4 - 2 12.5 - 15.6 c a Uric Acid (mg/dL) 38 NG 1.4 0.9 - 2 0.8 5.6 1 - 2.2 1 - 1 2.2 - 2.6 a Creatine Kinase (U/L) 48 NG 188 46 - 697 10 3002 11 - 2698 0 - 12 2394 - 4472 Aspartate Aminotransferase (U/L) 48 NG 77 48 - 159 33 222 33 - 222 26 - 33 222 - 258

a WCI/WRI > 0.2

Outliers: b 2.2, 7.6; c 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 2.6, 3.3, 4.4, 5.6

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Table 8.6. Protein electrophoresis parameters (absolute values) from free-living ornate box turtles (Terrapene ornata ornata) sampled in Lee County, Illinois during May 2016 and 2017. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval. TP = total protein.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval

TP (g/dL) 2016 25 G 3.6 0.92 1 5 1 - 5 0 - 1a 5 - 5.2

2017 22 Gb 5.2 1.3 2.4 8.2 3.8 - 8.2 3.6 - 3.8 8.2 - 9.4a Prealbumin (g/dL) 2016 22 NGc 0.05 0.014 - 0.12 0 0.2 0 - 0.15 0 - 0.02 0.15 - 0.18

2017 23 G 0.09 0.04 0.01 0.2 0 - 0.2 0 - 0.01 0.2 - 0.26a Albumin (g/dL) 2016 25 G 1 0.27 0.32 1.4 0.32 - 1.4 0 - 0.32a 1.4 - 1.47

2017 23 G 1.34 0.37 0.74 2.3 0.74 - 2.3 0.53 - 0.74 2.3 - 2.8a Alpha 1 Globulins (g/dL) 2016 23 NGd 0.31 0.26 - 0.4 0.11 0.57 0.25 - 0.42 0.24 - 0.25 0.42 - 0.45

2017 23 G 0.46 0.16 0.13 0.82 0.13 - 0.82 0 - 0.13 0.82 - 0.96 Alpha 2 Globulins (g/dL) 2016 25 G 0.91 0.25 0.26 1.4 0.26 - 1.4 0 - 0.26a 1.4 - 1.57

2017 23 G 1.4 0.31 0.71 1.9 0.71 - 1.9 0.33 - 0.71a 1.9 - 2.1 Beta Globulins (g/dL) 2016 24 Ge 1 0.32 0.21 1.76 0.59 - 1.76 0.44 - 0.59 1.76 - 2.09a

2017 22 NGf 1.42 0.85 - 2.11 0.55 4.15 0.55 - 2.17 0.28 - 0.55 2.17 - 2.28 Gamma Globulins (g/dL) 2016 25 G 0.29 0.09 0.08 0.45 0.08 - 0.45 0 - 0.08a 0.45 - 0.5

2017 23 G 0.4 0.13 0.19 0.67 0.19 - 0.67 0.13 - 0.19 0.67 - 0.79a

a WCI/WRI > 0.2

Outliers: b 2.4; c 0, 0, 0.2; d 0.11, 0.57; e 0.21; f 4.15

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Table 8.7. Protein electrophoresis parameters (relative values) from free-living ornate box turtles (Terrapene ornata ornata) sampled in Lee County, Illinois during May 2016 and 2017. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval. TP = total protein.

Reference 90% CI 90% CI Parameter Partition N Dist CT Dispersion Min Max Interval LB UB

Prealbumin (%) 48 NG 1 0.7 - 3 0 5 0 - 5 0 - 0.01 4.51 - 5.55a Albumin (%) Male 27 G 28 2.7 23 33 23 - 33 22 - 23 33 - 33 Female 19 G 25 4.5 17 32 - - - Alpha 1 Globulins (%) 48 G 9 2 5 13 5 - 13 4 -5 13 - 14 Alpha 2 Globulins (%) 46 Gb 26 3.3 18 36 21 - 34 19 - 21 33 - 37a Beta Globulins (%) Male 27 G 27 4 18 37 18 - 37 15 - 18 37 - 43a Female 17 Gc 31 7.5 19 51 - - - Gamma Globulins (%) 48 G 8 2 5 11 5 - 11 4 - 5 11 - 11 Albumin:Globulin 48 G 0.41 0.08 0.22 0.6 0.22 - 0.59 0.19 - 0.22 0.57 - 0.65a a WCI/WRI > 0.2

Outliers: b 18, 36; c 19, 51

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Table 8.8. Model selection parameters for generalized linear models predicting health status in free-living ornate box turtles

(Terrapene ornata ornata). H:L = heterophil : lymphocyte, WBC = total leukocyte count, Baso = absolute basophil count, Eo = absolute eosinophil count.

Model N K AICc AICc wi Shell + H:L + WBC + Baso + Eo 155 7 151.46 0 0.49 Shell + H:L + WBC + Baso 155 6 151.62 0.16 0.45 Shell + H:L + WBC 155 5 155.91 4.45 0.06 Shell + H:L 155 4 161.62 10.16 0 Shell * H:L * WBC * Baso * Eo 155 18 173.61 22.15 0 Null 155 1 216.84 65.39 0

Table 8.9. Parameter estimates for the most parsimonious model predicting health status in free-living ornate box turtles (Terrapene ornata ornata). AL = active lesion, IL = inactive lesion, H:L = heterophil : lymphocyte, WBC = total leukocyte count, Baso = absolute basophil count, Eo = absolute eosinophil count. Model: Healthy = Shell + WBC + H:L + Baso + Eo

 SE Z value p-value Intercept -3.263 1.3 -2.514 0.01 Shell: AL 3.664 0.77 4.762 0.000002 Shell: IL 1.602 0.464 3.452 0.0006 WBC 0.024 0.009 2.681 0.007 H:L 1.603 0.364 4.391 0.00001 Baso 0.106 0.041 1.49 0.009 Eo 0.084 0.056 2.592 0.136

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Table 8.10. Model fit metrics from internal and external validation of the most parsimonious model predicting health status in free- living ornate box turtles (Terrapene ornata ornata).

Metric Internal External Scale Ideal Brier Score 0.156a 0.177 0 - 1 Close to 0 AUC 0.865a 0.872 0.5 - 1 Close to 1 Accuracy (%) 78 77 0 - 1 Close to 1 Sensitivity 0.81 0.7 0 - 1 Close to 1 Specificity 0.75 0.9 0 - 1 Close to 1 Somer's Delta 0.716a 0.745 -1 - 1 Close to -1 or 1 a Optimism-corrected values based on internal validation via bootstrapping

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Table 8.11. Population viability analysis projections and sensitivity to adult female removal in a population of free-living ornate box turtles (Terrapene ornata ornata) at three different starting population sizes.

Final Extant Final Population Probability of Median Years to Mean Years to Growth Rate Population Size Size (All) Extinction Extinction Extinction Scenario Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Baseline (Estimated) -0.003 0.099 240 155 240 155 0 0 0 0 0 0 Loss of 1 adult female/year -0.025 0.103 107 106 57 93 0.48 0.01 3 17.41 81 0.55 Loss of 2 adult females/year -0.046 0.100 93 98 3 22 0.97 0.01 57 0.57 58 0.40 Loss of 3 adult females/year -0.060 0.096 16 2 1 1 1 0 40 0.29 41 0.30 Loss of 4 adult females/year -0.069 0.091 0 0 0 0 1 0 32 0.47 32 0.16 Loss of 5 adult females/year -0.077 0.088 0 0 0 0 1 0 27 0.29 27 0.13 Baseline (Pessimistic) -0.003 0.103 167 115 167 115 0 0 0 0 0 0 Loss of 1 adult female/year -0.035 0.108 69 70 13 39 0.83 0.01 74 0.82 70 0.38 Loss of 2 adult females/year -0.057 0.101 20 4 1 1 1 0 41 0.52 42 0.32 Loss of 3 adult females/year -0.071 0.095 0 0 0 0 1 0 29 0.00 30 0.16 Loss of 4 adult females/year -0.082 0.091 0 0 0 0 1 0 23 0.37 24 0.09 Loss of 5 adult females/year -0.092 0.088 0 0 0 0 1 0 20 0.00 20 0.06 Baseline (Optimistic) -0.003 0.096 422 218 422 218 0 0 0 0 0 0 Loss of 1 adult female/year -0.012 0.096 253 196 239 199 0.06 0.01 0 0 90 1.51 Loss of 2 adult females/year -0.028 0.097 170 162 81 138 0.54 0.02 98 1.45 81 0.54 Loss of 3 adult females/year -0.041 0.096 141 138 14 57 0.91 0.01 69 0.65 68 0.57 Loss of 4 adult females/year -0.050 0.093 146 137 2 21 0.99 0 54 0.50 56 0.37 Loss of 5 adult females/year -0.057 0.091 63 13 1 3 1 0 44 0.48 46 0.31

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Table 8.12. Population viability analysis projections and sensitivity to adult male removal in a population of free-living ornate box turtles (Terrapene ornata ornata) at three different starting population sizes.

Final Extant Final Population Probability of Median Years to Mean Years to Growth Rate Population Size Size (All) Extinction Extinction Extinction Scenario Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Baseline (Estimated) -0.003 0.099 240 155 240 155 0 0 0 0 0 0 Loss of 1 adult male/year -0.004 0.104 232 154 226 156 0.02 0.004 0 0 86 2.15 Loss of 2 adult males/year -0.007 0.110 242 155 198 168 0.19 0.01 0 0 76 1.12 Loss of 3 adult males/year -0.013 0.115 276 166 145 181 0.48 0.02 30 47.56 65 0.72 Loss of 4 adult males/year -0.020 0.119 328 186 85 170 0.75 0.02 58 1.39 52 0.72 Loss of 5 adult males/year -0.029 0.122 384 200 40 131 0.90 0.01 39 0.61 41 0.44 Baseline (Pessimistic) -0.003 0.103 165 114 165 114 0 0 0 0 36 48 Loss of 1 adult male/year -0.006 0.111 164 111 148 116 0.10 0.01 0 0 80 1.04 Loss of 2 adult males/year -0.013 0.118 194 124 102 131 0.49 0.01 21 42.16 65 0.82 Loss of 3 adult males/year -0.024 0.123 247 148 45 113 0.82 0.01 49 0.94 47 0.50 Loss of 4 adult males/year -0.039 0.125 306 166 11 64 0.97 0.004 31 0.52 34 0.41 Loss of 5 adult males/year -0.053 0.122 363 180 2 29 0.99 0.003 22 0.48 25 0.32 Baseline (Optimistic) -0.003 0.096 416 217 416 217 0 0 0 0 0 0 Loss of 1 adult male/year -0.003 0.098 408 218 408 218 0 0 0 0 31 45.5 Loss of 2 adult males/year -0.004 0.101 400 219 393 223 0.02 0.004 0 0 87 2.74 Loss of 3 adult males/year -0.005 0.104 402 217 372 233 0.08 0.01 0 0 82 1.55 Loss of 4 adult males/year -0.007 0.107 418 216 339 252 0.19 0.01 0 0 76 1.27 Loss of 5 adult males/year -0.010 0.110 447 224 288 276 0.37 0.01 0 0 69 0.89

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Table 8.13. Population viability analysis projections and sensitivity to hatchling female removal in a population of free-living ornate box turtles (Terrapene ornata ornata) at three different starting population sizes.

Final Extant Final Population Probability of Median Years Mean Years Growth Rate Population Size Size (All) Extinction to Extinction to Extinction Scenario Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Baseline (Estimated) -0.003 0.099 242 158 242 158 5E-05 0.0002 0 0 5 21.5 Loss of 1 hatchling female/year -0.007 0.100 185 140 184 140 0.003 0.0018 0 0 92 5.51 Loss of 2 hatchling females/year -0.012 0.102 134 122 130 122 0.03 0.0053 0 0 91 1.46 Loss of 3 hatchling females/year -0.019 0.103 93 103 81 101 0.12 0.01 0 0 90 0.65 Loss of 4 hatchling females/year -0.026 0.103 67 83 46 75 0.32 0.02 0 0 87 0.81 Loss of 5 hatchling females/year -0.032 0.102 51 69 24 51 0.56 0.02 97 0.94 84 0.44 Baseline (Pessimistic) -0.003 0.103 164 113 164 113 0.0003 0.0005 0 0 20 39.1 Loss of 1 hatchling female/year -0.009 0.106 111 94 109 95 0.02 0.005 0 0 90 1.57 Loss of 2 hatchling females/year -0.018 0.108 68 75 59 73 0.14 0.01 0 0 88 0.85 Loss of 3 hatchling females/year -0.027 0.109 46 61 27 51 0.43 0.02 0 0 85 0.42 Loss of 4 hatchling females/year -0.034 0.108 32 44 10 27 0.72 0.01 87 0.72 80 0.34 Loss of 5 hatchling females/year -0.039 0.105 25 33 3 13 0.90 0.01 76 0.67 74 0.36 Baseline (Optimistic) -0.003 0.096 419 217 419 217 0 0 0 0 0 0 Loss of 1 hatchling female/year -0.005 0.096 368 212 368 212 0 0 0 0 0 0 Loss of 2 hatchling females/year -0.007 0.096 315 206 315 206 0.0004 0.0006 0 0 37 47.4 Loss of 3 hatchling females/year -0.010 0.096 264 199 263 199 0.004 0.002 0 0 94 2.05 Loss of 4 hatchling females/year -0.013 0.097 216 185 212 186 0.02 0.005 0 0 93 1.17 Loss of 5 hatchling females/year -0.017 0.096 176 170 165 170 0.06 0.008 0 0 92 0.87

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Figure 8.1. Directed acyclic diagram depicting the relationships between health status and its predictors in ornate box turtles

(Terrapene ornata ornata).

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A B C

Figure 8.2. Shell categorization in free-living ornate box turtles (Terrapene ornata ornata). A. Within normal limits. B. Active lesion: plastron fracture (blue arrow). C. Inactive lesions (white arrows).

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Figure 8.3. Population size estimates +/- standard deviation simulated over 100 years for free-living ornate box turtles (Terrapene ornata ornata) at three different starting population sizes: Estimated = 268 turtles, Pessimistic = 184, Optimistic = 493 turtles.

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Figure 8.4. Simulated mean survival probability over time in a population of ornate box turtles (Terrapene ornata ornata) experiencing annual loss of 0 – 5 female hatchlings (top row), adult males (middle row), or adult females (bottom row). Starting population sizes: left column = 184 turtles, middle column = 268 turtles, right column = 493 turtles.

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PART IV: EASTERN BOX TURTLE HEALTH AND DISEASE

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CHAPTER 9: INVESTIGATION OF MULTIPLE MORTALITY EVENTS IN EASTERN

BOX TURTLES (TERRAPENE CAROLINA CAROLINA)

Abstract: Wildlife mortality investigations are important for conservation, food safety, and public health; but they are infrequently reported for cryptic chelonian species. Eastern box turtles

(Terrapene carolina carolina) are declining due to anthropogenic factors and disease, and while mortality investigations have been reported for captive and translocated individuals, few descriptions exist for free-living populations. We report the results of four natural mortality event investigations conducted during routine health surveillance of three Illinois box turtle populations in 2011, 2013, 2014, and 2015. In April 2011, over 50 box turtles were found dead and a polymicrobial necrotizing bacterial infection was diagnosed in five survivors using histopathology and aerobic/anaerobic culture. This represents the first reported occurrence of necrotizing bacterial infection in box turtles. In August 2013, paired histopathology and qPCR ranavirus detection in nine turtles was significantly associated with occupation of moist microhabitats, identification of oral plaques and nasal discharge on physical exam, and increases in the heterophil count and heterophil to lymphocyte ratio (p < 0.05). In July 2014 and 2015, ranavirus outbreaks reoccurred within a 0.2km radius of highly-disturbed habitat containing ephemeral ponds used by amphibians for breeding. qPCR ranavirus detection in five individuals each year was significantly associated with use of moist microhabitats (p < 0.05). Detection of single and co-pathogens (Terrapene herpesvirus 1, adenovirus, and Mycoplasma sp.) was common before, during, and after mortality events, but improved sample size would be necessary to determine the impacts of these pathogens on the occurrence and outcome of mortality events.

This study provides novel information about the causes and predictors of natural box turtle

212 mortality events. Continued investigation of health, disease, and death in free-living box turtles will improve baseline knowledge of morbidity and mortality, identify threats to survival, and promote the formation of effective conservation strategies.

** This chapter is adapted from a manuscript published in PLoS One:

Adamovicz L, Allender MC, Archer G, Rzadkowska M, Boers K, Phillips C, Driskell E, Kinsel MJ, Chu C.

Investigation of multiple mortality events in eastern box turtles (Terrapene carolina carolina). PLoS One.

2018Apr5;13(4):e0195617.

INTRODUCTION

Wildlife diseases have caused mass mortality events in all vertebrate taxa, including mammals (white-nose syndrome), birds (West Nile virus), fish (infectious salmon anemia), amphibians (chytridiomycosis), and reptiles (snake fungal disease) (Berger 1998, Blehert 2009,

LaDeau 2007, Clark 2011, Crane 2011, Martel 2013). These mortality events can threaten species survival, and in some cases, lead to extinction (Schloegel 2006). Furthermore, emerging zoonotic diseases have been uncovered during wildlife mortality events (i.e. West Nile virus), highlighting the importance of these outbreaks from a public health perspective (Daszak 2001,

Taylor 2001). Investigation of wildlife disease outbreaks can identify etiologic agents, determine risk factors and population impacts, and lead to management recommendations to avoid or mediate future episodes – potentially impacting wildlife conservation, food security, and human health (Daszak 2001, Wobeser 2006, Sleeman 2012).

The dynamics of health, disease, and death in wildlife populations are inherently complex, and mortality investigations are most useful when conducted in conjunction with routine health surveillance programs which document “expected” causes and rates of morbidity

213 and mortality (Mörner 2002, Stallknecht 2007, Artois 2009, Ryser-Degiorgis 2013). Historical data incorporating spatiotemporal factors can help identify patterns of disease and mortality associated with specific demographic groups, stocking densities, and landscape features, all of which can suggest mechanisms for pathogen transmission and maintenance (Stallknecht 2007,

Artois 2009, Ryser-Degiorgis 2013). Longitudinal data provide a context for identifying mortality events which are larger than expected, attributed to an unusual etiology, or which impact population size and viability. Health datasets which incorporate baseline health surveillance, spatiotemporal factors, and specific mortality investigations from longitudinally sampled populations therefore promote a greater understanding of the proximate causes and drivers of mortality events affecting species survival. Combined mortality investigation and health surveillance programs have been used to identify and address disease-related threats to several endangered species in the United States, including bubonic plague in black-footed ferrets

(Mustela nigripes), avian vacuolar myelinopathy in bald eagles (Haliaeetus leucocephalus), and parasitism in Laysan ducks (Anas laysanensis) (Brand 2013).

While mortality investigations yield valuable information for conservation, they are subject to some important biases. Identification of mortality events is most likely in areas which are highly populated or traveled, and in large, charismatic, or highly visible species (Wobeser

2006). Failure to detect mortality events is more common in remote areas, cryptic animals, and species in which remains are quickly scavenged (Mörner 2002, Wobeser 2006, Sleeman 2012).

Chelonians represent one of the most imperiled vertebrate groups, with over 60% of species considered threatened or worse (Turtle Taxonomy Working Group 2017). Turtle population stability is highly impacted by removal of mature adults, especially females, due to a combination of delayed sexual maturity and low juvenile survivorship (Heppell 1998). Mortality

214 events involving multiple individuals may therefore represent an important threat to chelonian population viability. However, due to the small size and cryptic nature of turtles in the family

Emydidae, mortality events are infrequently reported and the opportunity to formulate targeted conservation strategies is lost (Stacy 2014).

Eastern box turtles (Terrapene carolina carolina) are experiencing range-wide declines due to a combination of anthropogenic factors and disease (Dodd 2001). Disease-related mortality events, primarily associated with frog virus 3-like ranavirus (FV3), are reported for captive (De Voe 2004, Johnson 2008, Belzer 2011, Sim 2016, Agha 2017) and translocated

(Farnsworth 2013, Kimble 2015) box turtles, but few reports involve free-living populations.

While FV3 represents a known cause of box turtle mortality, several novel pathogens including

Terrapene herpesvirus 1 (TerHV1), box turtle adenovirus (BTADV), and an un-named

Mycoplasma sp. (BTMyco) have been recently identified (Feldman 2006, Farkas 2009, Doszpoly

2013, Ossiboff 2015c, Sim 2015, Kane 2017). The impact of these pathogens on free-living populations and their relationship to mortality events is unclear. Characterization of natural mortality events is an important step towards improving baseline knowledge of box turtle health and disease, identifying potential threats, and forming effective conservation strategies for this species.

We have performed mortality investigations, health assessments, and multi-pathogen surveillance in several populations of Eastern box turtles since 2011. The objectives of this manuscript are to describe four natural mortality events in T. carolina carolina, to characterize the causes of death in each outbreak, and to identify predictors of mortality.

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MATERIALS & METHODS

Ethics Statement

Our study involved the use of wild box turtles living on public lands. Permission to work at each public field site was granted by the Illinois Department of Natural Resources, and all animal procedures were approved by the Institutional Animal Care and Use Committee at the University of Illinois (Protocols: 10051 and 13061). Euthanasia of some wild turtles encountered during this study was deemed necessary by a veterinarian (author MC Allender) due to the presence of severe clinical signs and overall grave prognosis for recovery. Euthanasia was carried out by a veterinarian (author MC Allender) using the following American Veterinary Medical

Association and IACUC approved protocol: turtles were first heavily sedated using ketamine

(100mg/kg IM, MWI Animal Health, Boise, ID 83705) and then euthanized with an overdose of pentobarbital (390mg IV, Vortech Pharmaceutical Ltd, Dearborn, MI 48126).

Study Sites

Box turtle populations were surveyed at Forest Glen Nature Preserve (FGNP [40.01°, -

87.57°]), Kennekuk Cove County Park (KCCP [40.19°, -87.72]), and Kickapoo State Park (KSP

[40.14°, -87.74]) in east-central Illinois from April-October beginning in 2011. FGNP is an 1800 acre park composed primarily of beech-maple and oak-hickory forest bounded by the Vermillion

River. KCCP contains 3000 acres of oak-hickory forest interspersed with small prairies bounded by the Middle Fork National Scenic River. KSP is a 2800 acre park situated on a reclaimed surface mine. It includes relatively young successional forests and the Middle Fork National

Scenic River. Invasive plant species are actively removed at FGNP, but they are not removed and have become pervasive at KCCP and KSP. Natural and man-made wetlands are present at all sites.

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Animal Populations and Sample Collection

Non-outbreak sampling

Routine turtle health assessment occurred using canine searches one to three times each active season (May, June/July, and September) as previously described (Boers 2017). Briefly, teams of 3–4 dogs were used to locate eastern box turtles during a 2-4 hour search period. The search path was equivalent each time a site was visited, but due to the differences in topography, size, and hydrology, sampling efforts were not directly comparable between sites. Capture locations were recorded using global positioning software (GPS) via handheld devices (Garmin

International Inc., Olathe, KS, USA). Date, time, and categorical habitat (field, forest, edge) and microhabitat (leaves, grass, brambles, soil, road, moist area) data were also recorded at each turtle location. Microhabitat classification was assigned based on the primary ground cover beneath and within a one foot radius of the turtle. Moist areas consisted of ephemeral bodies of water. Air and substrate temperature were collected at the start and stop of each turtle search, and the results were averaged and recorded for each animal encountered (Kestrel 3000 Weather

Meter, Nielsen-Kellerman, Boothwyn, PA 19061; Taylor 9878 Digital Pocket Thermometer,

Taylor Precision Products, Oak Brook, IL 60523).

Turtles were weighed to the nearest gram, categorized by sex and age class, and measured (straight carapace length (SCL), carapace height (CH), and straight carapace width

(SCW)) to the nearest millimeter. Age class (adult vs. juvenile) was assigned based on carapace length and annuli count. Turtles with a carapace length less than 9 cm (3.5 in) and an annuli count less than or equal to seven were characterized as juveniles (Dodd 2001). Sex was classified as male, female, or unknown based on plastron concavity and tail length. Blood samples (less than 0.8% body weight) were collected from the subcarapacial sinus into lithium heparin coated

217 microtainers (Becton Dickinson Co., Franklin Lakes, NJ 07417) for hematology and pathogen detection. Swabs of the oral cavity and choana were collected using cotton-tipped plastic handled applicators (Fisher Scientific, Pittsburgh, PA 15275) and stored at -20oC. A single veterinarian evaluated clinical signs (MCA). Physical examination abnormalities were recorded as present (1) or absent (0) for the carapace, plastron, appendages, tympanic membranes, integument, nares, and cloaca. The presence of diarrhea, ectoparasites, and clinical signs of upper respiratory disease (ocular discharge, blepharoedema, nasal discharge, oral discharge, oral plaques, and open mouth breathing) were coded as present (1) or absent (0). The marginal scutes were notched with a unique pattern for permanent identification (Cagle 1939). Each turtle was released at its original capture site within six hours of initial contact.

Outbreak sampling

Outbreaks were identified by park or lab staff and targeted human searches were conducted every 1-3 days by lab staff. Searches were discontinued when 3 consecutive visits to the site failed to identify any new cases. Sampling and evaluation of live turtles was performed as described for non-outbreak turtles. In injured turtles, wound beds were flushed with sterile saline. Aerobic and anaerobic cultures were collected with sterile cotton-tipped applicators, placed into transport media (Remel Stuart Transport Medium, Remel A.C.T. II, Thermo Fisher

Scientific, Waltham, MA, USA), and stored at 4oC for 2-4 hours until submission to the

University of Illinois College of Veterinary Medicine Veterinary Diagnostic Laboratory. Turtles with severe clinical signs of illness (stuporous mentation, respiratory distress) were hospitalized for supportive care and monitoring. Turtles in moribund condition, or those that declined during hospitalization, were euthanized as described above. Euthanized turtles were either submitted for full necropsy with histopathology, or frozen at -20oC for future testing.

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Shells or freshly dead turtles found in the field were collected, double bagged, and transported to the lab on wet ice. GPS locations of the remains and categorical habitat and microhabitat data were recorded as previously described, and each animal was given a unique identification number. Samples of remaining soft tissue were collected, if present, and banked at

-80oC for future testing. Shells were stored at -20oC. In 2015, a bone marrow sample was collected from the right bridge of each shell using a hand-held drill with sterilized bits (Butkus

2017). Bone marrow samples were stored at -20oC until DNA extraction.

Live and dead amphibians were opportunistically collected from moist areas at each study site during visual encounter surveys as part of an ongoing demographic study. Up to five individuals of each species were humanely euthanized using an overdose of buffered tricaine methylsufate (3-Aminobenzoic Acid Ethyl Ester, Sigma Chemical Co. St. Louis, MO 61378) for vouchering with the Illinois Natural History Survey and either frozen at -20oC or preserved in

100% ethanol (Decon Laboratories Inc., King of Prussia, PA 19406). In 2013, samples of liver and kidney were harvested from amphibians collected within the same stream as five deceased box turtles and stored at -20oC for future qPCR pathogen testing.

Sample Analysis

Hematology

Packed cell volume (PCV) was determined using sodium heparinized microhematocrit tubes (Jorgensen Laboratories,Inc., Loveland, CO 80538) centrifuged at 14,500 rpm for five minutes. Total solids was determined with a hand-held refractometer (Amscope RHC-200ATC refractometer, National Industry Supply, Torrance, CA, USA) using plasma from the microhematocrit tube. Total white blood cell (WBC) counts were determined using an Avian

Leukopet (Vet lab Supply, Palmetto Bay, FL, USA) on a Bright-line hemacytometer (Hausser

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Scientific, Horsham, PA, USA) following the manufacturer’s protocol. Blood smears made from lithium heparinized microtainers were stained with a modified Wright’s Giemsa stain (Diff-quik) and used for differential leukocyte counts.

Microbiology

Aerobic culture swabs were plated on Columbia blood agar, Colistin, Naladixic Blood

Agar (CNA), and MaConkey Agar (Remel, Thermo Fisher Scientific, Waltham, MA, USA) both

o o at 37 C with 5% CO2, and at 25 C in room air. Anaerobic culture swabs were plated on Brucella

Agar, Laked blood w/ kanamycin, vancomycin (LKV Agar), and phenol ethyl alcohol agar

(PEA) (Remel, Thermo Fisher Scientific, Waltham, MA, USA) at 37oC in anaerobic gas mixture.

Organisms were identified using gram negative identification plate (GNID) and/or gram positive identification plate (GPID) panels (VersaTREK and Sensititre, Thermo Fisher Scientific,

Waltham, MA, USA). Fastidious and/or non-fermenting gram negative organisms were identified using GN2 microplate panels (Biolog, Hayward CA, USA). Anaerobes were identified by the Wadsworth Disk method (Jousimies-Somer 2003) or anaerobic identification test panel microplates (Biolog, Hayward CA, USA).

Virus Isolation

Frozen whole blood (1mL) was added to 5mL erythrocyte lysis buffer (Buffer EL,

Qiagen, Valencia, CA, USA), vortexed for 15 seconds, and incubated on ice for 30 minutes with additional vortexing every 15 minutes. The lysed sample was centrifuged at 1700rpm for 10 minutes at 4oC and the supernatant was discarded. The pellet was resuspended in 2mL Minimum

Essential Medium (MEM, Thermo Fisher Scientific, Waltham, MA, USA) with 5g/mL amphotericin B, 200U/mL penicillin, 200g/mL streptomycin, and 100g/mL gentamycin

(Sigma Chemical Co. St. Louis, MO 61378). This was inoculated onto Terrapene heart cells

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(TH-1) grown to 80% confluence in 75 cm3 flasks. Following a 10 minute incubation at 27oC,

20mL of Dulbecco’s modified eagle medium (DMEM, Thermo Fisher Scientific, Waltham, MA,

USA) with 10% fetal bovine serum (FBS, Thermo Fisher Scientific, Waltham, MA, USA),

100U/mL penicillin, 100g/mL streptomycin, and 2.5g/mL amphotericin B was added to each

o flask, and cells were maintained at 27 C with 5% CO2. If necessary, blind passaging was performed after inoculated cells reached 100% confluency. Flasks were frozen at -80oC, thawed, and vortexed three times. One milliliter of the flask contents was used to inoculate a new flask containing TH-1 grown to 80% confluency. Infected flasks were monitored daily for signs of cytopathic effects (CPE).

Molecular Diagnostics

DNA was isolated from oral swabs, whole blood, virus isolation flasks, bone marrow, and amphibian tissue samples using a commercially available kit (QIAmp DNA Blood Mini Kit and DNAeasy kit, Qiagen, Valencia, CA, USA). Manufacturer instructions were followed with some alterations for bone marrow samples, as previously described (Butkus 2017). DNA concentration and purity was assessed spectrophotometrically (NanoDrop 1000, Thermo Fisher

Scientific, Waltham, MA, USA) and DNA samples were stored at -80oC. Turtle oral swab DNA samples were assayed for FV3, BTADV, BTMyco, and TerHV1. Turtle whole blood, bone marrow, and amphibian tissues were tested for FV3. Testing for FV3, TerHV1, and BTADV was performed using existing TaqMan qPCR assays (Allender 2013a, Blum 2014, Kane 2016).

Briefly, reactions were run in triplicate using a real-time PCR thermocycler (7500 ABI realtime

PCR System, Applied Biosystems, Carlsbad, CA). Real-time qPCR data analysis and quantification was performed using commercially available software (Sequence Detection

Software v2.05, Applied Biosystems, Carlsbad, CA). Quantification was performed by

221 comparing each sample’s cycle threshold (Ct) value to a standard curve consisting of purified,

PCR generated target segments of each pathogen’s genome (seven tenfold dilutions from 108 –

101 copy numbers).

A novel conventional PCR assay targeting the 16S rRNA gene was developed and used to screen for BTMyco using the forward 5’ GGAGAATCTCGCTAACGCAG 3’ and reverse 5’

AGCCTTCAATCCGAACTGAG 3’ primer set. Each 50μL reaction included 36.5μL sterile deionized water, 5μL 10x buffer, 4μL 50mM magnesium chloride, 1μL 10mM dNTPs, 0.5μL

Taq polymerase (5U/μL), and 1μL of each 10μM primer (Taq DNA Polymerase 500U,

Invitrogen, Carlsbad, CA 92008). Thermocycler settings consisted of a five minute initial denaturation at 95oC followed by 35 cycles of 95oC for 45 seconds, 59oC for 60 seconds, and

72oC for 120 seconds, then a final elongation step of 72oC for seven minutes.

A 470 base pair product obtained from an eastern box turtle oral swab was sequenced in both directions (W.M. Keck Center for Comparative and Functional Genomics, University of

Illinois at Urbana-Champaign, Urbana, IL) and compared to known sequences in GenBank using

BLASTN to confirm consistency with BTMyco. This PCR product was cloned into Escherichia coli (TOPO TA Cloning Kit, Invitrogen, Carlsbad, CA), and plasmids were purified using a commercially available kit (QIAfilter plasmid Maxi Kit, Qiagen, Valencia, CA). Cloning products were verified via sequencing and plasmids were linearized using EcoRI (Clontech

Laboratories, Mountain View, CA). Linearized plasmids were phenol-chloroform precipitated and DNA concentration and purity was assessed spectrophotometrically. Bacterial copy number was calculated using the following formula:

(plasmid + insert ng⁄µl) ∗ (6.022 x 1023 copies⁄mol) #copies/µl = (base pair length)(1 x 109 ng⁄g)(650 g⁄mol of base pair)

222

Assay sensitivity and limit of detection was evaluated using the BTMyco plasmid serially diluted from 109-100 copies. Specificity was assessed using sequence-confirmed positive samples for M. agassizii, M. testudineum, M. canis, M. hyorrhinus, M. hyopneumoniae, M. bovis. Oral swab DNA collected during routine health assessments and outbreaks was assayed. PCR products of the appropriate size were sequenced and blasted to confirm accurate detection. All

Mycoplasma sp. assays included a positive control (plasmid) and a negative control (water).

Conventional PCR assays targeting the major capsid protein (MCP) gene were utilized to confirm FV3 growth in virus isolation, as previously described (Hyatt 2000). Each assay included a sequence-confirmed FV3 positive sample as a positive control, and water as a negative control. Bands of the appropriate size (approximately 500bp) were sequenced in both directions and compared to existing sequences in Genbank using BLASTN.

Mapping and Spatio-Temporal Analyses

GPS locations of turtles and amphibians were mapped using ArcGIS on the World

Imagery background layer (ArcMap 10.4.1, Esri, Redlands, CA 92373). Hydrology data were obtained from the Illinois Geospatial Data Clearinghouse (https://clearinghouse.isgs.illinois.edu/, accessed March 2017) and a 2015 Illinois highway shapefile was obtained from the Illinois

Department of Transportation (http://www.idot.illinois.gov/transportation-system/local- transportation-partners/gis-data-share, accessed 3/10/17). Both were uploaded into ArcMap, and the “Near” tool was used to calculate the distance from each turtle’s capture site to the nearest road, and permanent body of water. The Bernoulli model was used to test for statistically significant purely spatial or spatio-temporal clusters of disease using SaTScan v9.4.4 (Kulldorff

1997). Output files from SaTScan were uploaded into ArcGIS and overlayed onto turtle and amphibian maps to visualize the extent of significant clusters.

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Statistical Analysis

All turtles evaluated within the same study site and year were assigned to the following three categories: pre-outbreak (PreOB), outbreak (OB), and post-outbreak (PostOB). An outbreak was defined as the time period encompassing the first and last death due to a common etiology. Turtles evaluated before the first death were classified as PreOB, while turtles assessed after the last death were assigned to the PostOB group. The effects of outbreak grouping on pathogen detection status and hematology parameters were evaluated using logistic and linear regression, respectively. The mean, standard deviation, median, range, and distribution (normal vs. non-normal as assessed using skewness, kurtosis, and the Shapiro-Wilk statistic) were determined for each hematology parameter and tabulated based on outbreak grouping.

Turtles within the same site and year were then re-grouped based on detection of the pathogen responsible for the mortality event – i.e. the cause of death (considered categorically – case or non-case). Logistic regression was used to evaluate the effects of categorical (habitat, microhabitat, sex, age class) and continuous predictors (hematology parameters, weight, distance metrics) on detection of the cause of death. Odds ratios were determined by exponentiating the coefficient estimates of statistically significant logistic regression models. An alpha value of 0.05 was used to establish statistical significance. All statistical evaluations were performed in R v

3.2.3 (R Core Team 2013).

RESULTS

Population Demographics, Habitat Use, Physical Exam, and Timeline

In April 2011, fifty-three box turtle shells were encountered by FGNP park personnel.

Subsequently, we conducted three searches from April 26th – May 2nd, and 12 live turtles were evaluated. Demographics, habitat use, physical examination findings, and timeline are

224 summarized in Tables 9.1 and 9.2 and Figure 9.1. Five of the live turtles were missing one or more appendages with necrosis of the proximal limb tissues (Figure 9.2). Two animals were humanely euthanized and submitted for necropsy. Aerobic and anaerobic cultures were collected from the wound beds of four affected turtles, including the necropsied animals.

In late July 2013, six dead box turtles were identified by park personnel within an ephemeral stream at KCCP. Subsequently, we conducted five searches from August 2nd –

August 29th and nine turtles were encountered (Table 9.1; Figures 9.1 and 9.3). On August 16th, five apparently healthy cricket frogs (Acris crepitans), three green frogs (Rana clamitans), and five two-lined salamanders (Eurycea bislineata) from the same ephemeral stream were euthanized and liver and kidney samples were collected for FV3 testing. The PostOB group included one turtle opportunistically sampled on September 5th and 18 turtles (13 live, five shells) evaluated during routine health surveillance on September 30th.

From July 16-17th 2014, four deceased turtles were discovered at KSP. Six turtles were located during daily searches from July 16-25th (Table 9.2, Figures 9.1 & 9.4). Four depressed individuals were hospitalized for supportive care and monitoring consisting of a thermal gradient and daily warm water soaks. Two of the hospitalized turtles died and two were euthanized after

15-57 days due to progression of clinical signs and moribund condition. Pre-mortality health surveillance was conducted for 20 live box turtles on May 21st, one turtle opportunistically sampled on May 30th and a shell found on June 19th. Post-outbreak surveillance was conducted from July 29th – September 17th and five turtles were evaluated (Table 9.2).

On July 8th 2015, two freshly deceased box turtles were found during routine health surveillance at KSP within 0.2km of the 2014 mortality event. Mortality investigation was conducted from July 8-28th. Three additional shells were recovered, but no living turtles were

225 located (Figures 1 & 4). Pre-mortality investigation was conducted on May 21st and June 30th.

Four live adult turtles and one freshly dead shell were evaluated (Table 9.2). Post-mortality investigation was conducted at the end of July and 13 adult turtles were sampled (Table 9.2).

The novel BTMyco conventional PCR successfully amplified 101-109 bacterial copies, resulting in a lower limit of detection of 10 copies. The assay produced appropriately-sized bands for BTMyco, M. agassizii, and M. testudineum. Bands larger and smaller than the 470bp target were produced for all other Mycoplasma species tested. Sequencing of products producing appropriately-sized bands is necessary to distinguish between BTMyco, M. agassizii, and M. testudineum. All BTMyco-positive box turtle sequences were 99-100% identical to Mycoplasma sp. sequences in Genbank (accession numbers FJ159564, KJ623622-KJ623625).

During the 2011 outbreak at FGNP, BTMyco was detected in two of the 12 live turtles, one with carapacial trauma and one with no clinical signs (Table 9.3). BTADV was detected in one turtle that was euthanized and necropsied, but intranuclear inclusion bodies were not identified histologically (Farkas 2009). Gross necropsy findings for both submitted turtles included missing limbs with discoloration and necrosis of the remaining soft tissues. Regionally extensive subacute necrotizing heterophilic osteomyelitis, myositis, cellulitis, and dermatitis with intralesional mixed bacteria, hemorrhage and edema were identified on histopathology.

Intravascular, extracellular bacteria (sepsis) was also present. Primary necrotizing bacterial infection was considered the most likely cause of the lesions. The presence of a morphologically and microbiologically (alpha Streptococcus sp., Aeromonas hydrophila, Corynebacterium sp., and Bacteroides sp.) similar bacterial population was suggestive of a common etiology.

In 2013, all outbreak turtles in which FV3 was detected as a sole pathogen (N=4) presented with oral plaques (Table 9.1). One of these animals also had nasal discharge and another had diarrhea,

226 blepharoedema, and depressed mentation. Turtles with FV3 and BTMyco co-detection (N=2) presented with oculonasal discharge and blepharoedema or oral plaques and inactivity. A single turtle co-infected with FV3, TerHV1, and BTMyco had oral plaques and diarrhea. Turtles in which BTMyco was the sole pathogen detected (N=2) displayed oculonasal discharge or oral plaques, extreme pallor, and depressed mentation. Five turtles evaluated with necropsy and histopathology exhibited one or more lesions consistent with ranavirus infection including: fibrinoid necrosis in the splenic vessels with or without fibrinoid necrosis in various other organs

(3 turtles) and ulcerative stomatitis, esophagitis, and/or gastritis (3 turtles). The cause of the mortality event was determined to be FV3. FV3 was also detected from liver and kidney samples of apparently healthy amphibians, including two sympatric A. crepitans (40%), one R. clamitans

(33.3%), and two E. bislineata (40%).

The odds of FV3 and BTMyco detection were 20 (p = 0.008) and 15 (p = 0.02) times higher during the 2013 outbreak compared to PreOB and PosOB, and the odds of testing negative for all pathogens were 33 (p = 0.004) times higher PostOB. The odds of FV3 detection were 18.6 times higher (p = 0.02) in turtles occupying a moist microhabitat, 35 times higher in turtles with oral plaques (p = 0.007), 15 times higher in turtles with nasal discharge (p = 0.03), and 12 times higher in turtles with any physical exam abnormality (p = 0.03). The odds of FV3 detection also rose 1.5 times with every 1 unit increase in the heterophil/lymphocyte ratio (HL), a change primarily driven by a disproportionate increase in relative heterophil count (p = 0.03).

FV3 was detected as a single or co-infection in four live OB turtles and two shells from

KSP in 2014 (Table 9.2). Live FV3 was isolated from the blood of one 2014 turtle. CPE consisting of cellular rounding and detachment was apparent after a single blind passage, and sequencing of the MCP gene was 100% identical to an FV3 sequence in Genbank (accession

227 number KJ175144.1). Hospitalized FV3 positive turtles survived 15-37 days while one individual with TerHV1 and BTMyco co-detection lived for 57 days. Several single and co- pathogens were detected in the PreOB group, but only TerHV1 was detected in the PostOB group (Figure 9.3, Table 9.3). The cause of the mortality event was determined to be FV3. The odds of FV3 detection were 22 times higher in the OB group compared to the PreOB group (p =

0.009) and 23.8 times higher in turtles occupying a moist microhabitat (p=0.007).

At KSP in 2015, FV3 was detected in the bone marrow from all OB turtle shells, and the cause of death was determined to be FV3 (Table 9.3). The odds of FV3 detection were 25 times

(p = 0.03) and 65 times (p = 0.005) higher in the OB group compared to the PreOB and PostOB groups, respectively. The odds of FV3 detection were 45 times higher (p = 0.004) in turtles occupying a moist microhabitat.

Hematology

In 2011, significant hematological differences were not detected between turtles with necrotizing infections and apparently healthy turtles (Table 9.4). The adenovirus positive turtle had the highest total leukocyte count and monocyte count of all OB turtles.

CBCs were performed for four outbreak turtles and thirteen post-outbreak turtles in 2013

(Table 9.5). Heterophils (p < 0.001) and the HL ratio (p = 0.004) were significantly higher in the outbreak turtles compared to the post-outbreak turtles, while the difference in total leukocyte count and monocyte count approached significance (p = 0.08). One FV3 positive OB turtle with oral plaques had the highest values for all leukocytes, while the FV3 positive turtle with diarrhea had the lowest TS. The turtle with FV3, BTMyco, and TerHV1 co-detection had the lowest values for all leukocytes and the highest HL ratio compared to other OB turtles. A single PostOB

228 turtle with TerHV1, asymmetrical nares, and a missing rear foot had the highest HL ratio of any

PostOB turtle.

CBC data were available for eleven of the pre-outbreak turtles and four of the post- outbreak turtles in 2014 (Table 9.6). Heterophils were significantly higher in the PreOB group than the PostOB group (p = 0.03). The PreOB turtle with TerHV1, BTMyco, and BTADV co- detection had the lowest WBC count and lymphocyte count of its group, but had no clinical signs of illness. In 2015, complete CBCs were performed for three PreOB turtles and twelve PostOB turtles (Table 9.5). Eosinophils (p = 0.001) and WBC (p = 0.01) were significantly lower in the

PreOB group compared to the PostOB group. The PreOB turtle with BTMyco detection and diarrhea had the highest values for WBC, basophils, eosinophils, and lymphocytes and the lowest heterophil and HL ratio values of its group. WBC (p = 0.01) and heterophils (p = 0.002) were significantly higher in the 2014 PreOB group compared to the 2015 PreOB group, but no other differences were noted at KSP between years.

Spatial Epidemiology

Statistically significant clusters of necrotizing bacterial infection cases were not identified at FGNP in 2011. However, all FV3 outbreaks had significant spatio-temporal associations including a 0.16km cluster of FV3 cases from August 2nd – August 16th 2013 at KCCP (p =

0.0005, Figure 9.3), a 0.03km cluster from July 15th – July 25th 2014 at KSP (p = 0.002, Figure

9.4), and a 0.16km cluster from July 8th – July 23th 2015 at KSP (p = 0.01, Figure 9.4).

DISCUSSION

The present report details four mortality events in Illinois eastern box turtles. We found that box turtle mortality occurs commonly and has the potential for reoccurrence at a single site.

Pathogens were frequently detected before, during, and after mortality events; and FV3 was the

229 most common cause of death. One mortality event was investigated in isolation, but led to the development of health assessment projects that characterized the other three outbreaks. The important findings and limitations of each event will now be reviewed.

2011 – Necrotizing Bacterial Infection at Forest Glen Nature Preserve

In April, 2011 over 50 box turtles were found dead at a single location. Remains were completely skeletonized, precluding a definitive diagnosis. However, FV3 was not detected in banked shells, lowering the likelihood of one known cause of mass mortality (Butkus 2017).

Almost half of the live turtles were diagnosed with a necrotizing bacterial infection (NBI) based on histopathology (N=2), consistent culture isolates (N=4), and compatible clinical signs (N=5).

NBI are characterized by rapidly progressive soft tissue necrosis due to direct cellular damage from bacterial toxins, thrombosis, and the development of compartment syndrome (Sarani 2009,

Sartelli 2014). Sepsis and toxemia can occur rapidly, contributing to a high mortality rate. NBI can develop secondary to relatively minor trauma such as needle sticks and insect bites, but in up to 50% of human cases no underlying cause is identified (Sarani 2009, Edlich 2010, Phan 2010).

In human medicine, NBI are divided into three categories: Type I is due to polymicrobial infections and is most common, while Types II and III are due to monomicrobial infections with group A Streptococcus sp. or methicillin-resistant Staphylococcus aureus and Clostridium sp.,

Vibrio sp., Aeromonas sp., and others, respectively. In Type I infections, common aerobic isolates include Streptococcus sp., Staphylococcus spp., Enterococcus spp. and

Enterobacteriaceae. Bacteroides sp. are the most common anaerobic isolates (Sarani 2009,

Edlich 2010, Phan 2010, Sartelli 2014).

The syndrome identified in box turtles was clinically, histologically, and microbiologically consistent with a polymicrobial NBI. This diagnosis is rare in veterinary

230 medicine, and even more unusual in reptiles. To the authors’ knowledge, the only report of NBI in reptiles involves a series of monomicrobial Streptococcus agalactiae infections in farmed saltwater crocodiles (Crocodylus porosus), suspected to be associated with an environmental bacterial source (Bishop 2007). An environmental source of bacteria transmitted by inoculation of wounds or through the bite of insect vectors was also suspected in the present report.

However, this cannot be confirmed because environmental samples were not collected for culture. Furthermore, interpretation of wound culture results should be approached with caution, as the causative agent may have either been outcompeted in culture, or may have been an organism which does not routinely grow in standard culture conditions (Rappe 2003). The end of the outbreak was also poorly defined due to a lack of follow-up sampling at FGNP within the same year. While a novel disease syndrome was discovered during this mortality investigation, late recognition and lack of baseline health assessments reduced the quantity and quality of diagnostic information obtained. A routine health surveillance program was established at FGNP in 2012 and no additional mass mortalities have been identified, though an additional case of

NBI was diagnosed and successfully treated in 2017.

FV3 at Kennekuk Cove County Park (2013) and Kickapoo State Park (2014, 2015)

Mortality events attributed to FV3 were identified at two sites in Illinois over the course of three years. FV3 has previously been associated with free-living eastern box turtle mortality events in Kentucky, Indiana, Tennessee, Pennsylvania, New York, Georgia, West Virginia, and

Maryland, encompassing approximately the entire range of the species (Allender 2006, Johnson

2008, Ruder 2010, Belzer 2011, Farnsworth 2013, Currylow 2014, Agha 2017, Kimble 2017).

FV3 is typically diagnosed in box turtles during their active season (approximately April –

October), but expected outbreak durations are poorly defined. In Kentucky, a suspected FV3

231 mortality event began in June and lasted for approximately 43 days (Agha 2017). In

Pennsylvania, recurrent mortality events began in late July or August and lasted approximately

60 days (Belzer 2011). These reports used radio-telemetry to closely monitor turtles, facilitating early recognition and response to mortality events.

The FV3 outbreak at KCCP occurred in August and lasted 30 days, while both events at

KSP were identified in July and ranged from 9-20 days. Outbreak identification in the present study was opportunistic, and the true duration of FV3 mortality events may be underestimated.

However, all observed FV3 cases clustered both spatially and temporally, which is consistent with previous reports (Belzer 2011, Agha 2017). Detection of FV3 during outbreaks, but not during pre and post outbreak surveillance, supports classification of FV3 as an epidemic disease in our study populations.

It is hypothesized that FV3 epidemics in box turtles are due to spillover from infected amphibians. This theory is supported by transmission studies documenting inter-class spread, a characteristic 3-4 week time lag between natural amphibian and chelonian infections, and field observations documenting concurrent FV3 infection in sympatric amphibians and box turtles

(Brenes 2014a, Currylow 2014, Brunner 2015). Ranavirus-infected box turtles have been reported to congregate in wetlands, suggesting a potential route for contact and disease transmission between infected amphibians and turtles (Belzer 2011, Agha 2017). The present report documented significantly higher odds of FV3 detection in box turtles occupying moist microhabitats used by FV3 positive amphibians. This lends statistical credence to anecdotal observations, supports microhabitat use as a predictor of FV3 infection in box turtles, and identifies ephemeral wetlands as a possible target for disease management interventions.

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While FV3 was the ultimate cause of death in the outbreaks from 2013-2015, co- pathogen infections can modulate ranavirus mortality rates in amphibians and box turtles (Sim

2016, Wuerthner 2017) and may have impacted the timing of infection or susceptibility of the animals in this report. In one study, captive eastern box turtles naturally co-infected with FV3,

TerHV1, and BTMyco had a higher survival rate (63%) than turtles infected with FV3 alone

(50%) (Sim 2016). Due to the cross-sectional nature of the present study, mortality outcomes were unknown for most FV3 cases and the impact of co-pathogen infections on mortality rates could not be assessed directly. Anecdotally, blepharoedema and ocular discharge were identified more commonly in turtles with co-pathogen detection compared to those with single pathogens; though an improvement in sample size would be needed for rigorous statistical assessment of these findings. There were no consistent patterns of infection during any of the outbreak groupings, and neither single nor co-pathogen status were significant predictors of FV3 detection. Co-pathogen detection appears common preceding, during, and after box turtle mortality events, and the influence of co-pathogen infections on mortality outcomes warrants further study in both captive and natural settings.

Clinical signs of FV3 infection include blepharoedema, ocular, oral, and/or nasal discharge, oral plaques, respiratory distress, depression, cutaneous abscessation or erosions, cloacal plaques, edema, and death, though several of these signs are variably reported (Allender

2006, De Voe 2004, Johnson 2007, Johnson 2008, Ruder 2010, Farnsworth 2013, Sim 2016,

Kimble 2017). The only physical exam abnormalities significantly associated with FV3 detection in the present study were oral plaques and nasal discharge, but these signs were not consistently present between sites and years. Importantly, these clinical signs were indistinguishable from those associated with detection of BTMyco during the 2013 outbreak, and with previous reports

233 of BTMyco infection in box turtles (Feldman 2006, Sim 2016). Clinical signs alone are not a reliable method to diagnose FV3, and confirmatory testing must be pursued when investigating upper respiratory disease outbreaks in wild box turtles.

Clinical pathology changes in FV3 positive box turtles were limited to elevations in the heterophil count and the HL ratio (driven primarily by increased heterophils). Intracytoplasmic inclusion bodies consistent with FV3 infection are sometimes visualized in box turtle blood smears, but they were not observed in affected turtles in the present report (Allender 2006). Red- eared sliders also show minimal hematologic changes during FV3 infection, with transient elevations in WBC and a persistent decrease in TS over time despite overwhelming systemic inflammation (Allender 2013b). While hematology findings for FV3 positive chelonians are infrequently reported, available information indicates that complete blood counts have limited diagnostic utility for FV3 infection unless they are performed serially or at the population level to detect subtle changes (Allender 2013b). It is possible that clinically useful information may be gained from evaluation of additional CBCs in FV3-positive box turtles, but an increase in sample size is needed.

Ranavirus outbreaks can recur in herptile populations over the course of multiple years, though the mechanisms for persistence are unknown (Teacher 2010, Belzer 2011, Kimble 2017).

In the present report, recurrent FV3 infection was observed within the same 0.2km area in KSP during 2014 and 2015. This location encompasses several ephemeral ponds used for amphibian breeding and multiple small fields heavily invaded by autumn olive (Elaeagnus umbellata) shrubs. These invasive plants are hypothesized to degrade box turtle habitat quality by eliminating access to fields, a favored site for foraging, thermoregulating, and nesting (Dodd

2001). It is unclear whether the recurrent ranavirus outbreaks may be related to poor habitat

234 quality, habitat fragmentation, frequent contact with ephemeral ponds containing infectious amphibians, increased exposure to human disturbance in public parks, or other factors. However, one of the most useful outcomes of mortality investigation is identifying populations for targeted study, and the 2014 and 2015 mortality investigations at KSP accomplished this. We have since launched both a box turtle ratio-telemetry study and a sympatric amphibian health surveillance program focusing on the recurrent outbreak site. These projects will facilitate the collection of baseline health data and the characterization of future natural mortality events incorporating individual, environmental, and community ecological factors.

Potential limitations in the present study include small sample size, incomplete hematology data, and reliance upon molecular diagnostics for pathogen detection. Sample size is commonly problematic in wildlife mortality studies, especially for cryptic species (Wobeser

2006, Sleeman 2012). The use of trained dogs has been proposed as a means to improve carcass recovery, and while we rely upon a canine search team to locate live turtles, their recovery of deceased turtles is limited (Homan 2001, Boers 2017). Increasing the number of people involved in human searches, the search frequency, or investing in carcass recovery dogs may improve the number of turtles recovered during future mortality events, though imperfect detection will likely remain a significant hurdle.

The use of molecular diagnostics for detecting wildlife diseases has rapidly expanded in recent years due to ease of use and affordability, though the use of PCR as a sole diagnostic test for amphibian ranavirus infections has recently been questioned (Black 2017). PCR-based tests are highly sensitive, and validated TaqMan primer-probes are quite specific. All qPCR assays used in this manuscript have been validated for use in box turtles, and the risk of non-specific target amplification is considered incredibly low (Allender 2013a, Blum 2014, Kane 2016).

235

Inability to distinguish between infectious and non-infectious pathogen presence is the main limitation of a TaqMan qPCR assay, and the possibility of detecting non-infectious pathogen

DNA cannot be fully excluded in the present study.

Confirmatory non-invasive diagnostic tests including virus isolation for the detection of

TerHV1, FV3, and BTADV, and culture to confirm the presence of live Mycoplasma sp. were either not used or not consistently applied in this study. TerHV1 and BTADV have never been successfully isolated, and Mycoplasma organisms are notoriously difficult to culture (Brown

1994). An ELISA is available for Mycoplasma agassizii, but this test has not been validated for use in box turtles (Schumacher 1993). Another ELISA is available for FV3, but its sensitivity and specificity have not been objectively assessed during active infections in box turtles

(Johnson 2010). Other means of confirming pathogen presence (histopathology, immunohistochemistry, electron microscopy, etc.) require invasive tissue sampling, which was outside the scope of this study. Molecular diagnostics were considered adequate to achieve the study goals given the planned sampling methods, the unpredictable nature of mortality events, and the limited availability of validated methods for minimally-invasive diagnosis of box turtle infectious diseases.

The present study provides valuable descriptions of four natural mortality events in

Eastern box turtles, including the first report of necrotizing bacterial infection in this species.

Several predictors for FV3 detection were confirmed over the course of three outbreaks, including use of moist microhabitats, presence of oral plaques and nasal discharge, heterophil count, and the HL ratio. Single and co-pathogen detection was common before, during, and after mortality events. Finally, mortality investigation initiated longitudinal box turtle health surveillance projects at each study site. Successful mortality investigations in free-living

236 chelonians require significant personnel time and financial investment, but the information gained is vital for planning future studies and management interventions. Continued investigation of health, disease, and death in free-living box turtles and their communities will improve baseline knowledge of morbidity and mortality, identify threats to survival, and promote the formation of effective conservation strategies.

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Table 9.1. Demographics, habitat use, and physical examination abnormalities in eastern box turtles (Terrapene carolina carolina) captured during (OB), and after (PostOB) mortality events in Vermilion County, IL from 2011-2013. NR = not recorded.

Year Group # Dead # Live Adult Female Adult Male Adult Unknown Juvenile Microhabitat Clinical Signs 2011 OB 53 12 5 7 49 4 NR Limb Necrosis (N=5) Depression (N=6) Oral Plaques (N=6) Moist Area (N=14) Nasal Discharge (N=3) OB 6 9 3 0 9 3 Brambles (N=1) Blepharoedema (N=2) Ocular Discharge (N=2) 2013 Diarrhea (N=2) Leaves (N=7) Mild Scute Trauma (N=3) Moist Area (N=5) Missing Tail/Foot (N=2) PostOB 5 14 2 6 8 3 Brambles (N=1) Asymmetrical Nares (N=1) NR (N=4) Oral Plaques (N=1)

238

Table 9.2. Demographics, habitat use, and physical examination abnormalities in eastern box turtles (Terrapene carolina carolina) captured before (PreOB), during (OB), and after (PostOB) mortality events in Vermilion County, IL from 2014-2015. NR = not recorded.

Year Group # Dead # Live Adult Female Adult Male Adult Unknown Juvenile Microhabitat Clinical Signs Grass (N=16) PreOB 1 21 12 7 3 0 Moist Area (N=4) Mild Scute Trauma (N=5) Leaves (N=2) 2014 Moist Area (N=8) OB 4 6 2 1 6 1 Depression (N=4) Grass (N=2) Leaves (N=3) PostOB 0 5 3 2 0 0 Asymmetrical Nares (N=1) Grass (N=2) Plastron Erosions (N=2) Leaves (N=3) Diarrhea (N=1) PreOB 1 4 1 3 1 0 Grass (N=2) Asymmetrical Nares (N=1) Missing Foot/Digits (N=2) Moist Area (N=3) 2015 OB 5 0 0 0 5 0 Grass (N=1) NA Leaves (N=1) Brambles (N=5) Mild Scute Trauma (N=2) PostOB 0 13 4 8 1 0 Leaves (N=5) Missing Digits (N=1) Grass (N=3) Missing Tail (N=1)

239

Table 9.3. Number of turtles with pathogens detected and cause of death in free living Terrapene carolina carolina encountered before (PreOB), during (OB), and after (PostOB) mortality events in east-central Illinois from 2011 – 2015. Turtles with single pathogen detections are counted in the individual pathogen columns. Turtles with co-pathogens are counted in the co-infections column. FV3 = Frog virus 3-like ranavirus, BTADV = box turtle adenovirus, TerHV1 = Terrapene herpesvirus 1, BTMyco =

Mycoplasma sp., NBI = necrotizing bacterial infection.

Year Group # Tested FV3 TerHV1 BTMyco BTADV Co-Infections Cause of Death 2011 OB 61 0 0 2 1 0 NBI 2 (FV3 + BTMyco) OB 13 6a 0 2 0 1 (FV3+BTMyco+TerHV1) 2013 FV3 1 (TerHV1 + BTADV) PostOB 16 0 1 0 0 1 (TerHV1 + BTMyco) 3 (TerHV1 + BTMyco) PreOB 13 0 2 1 0 1 (TerHV1+BTADV+BTMyco) 1 (FV3 + TerHV1) 2014 FV3 OB 8 3b 0 0 0 1 (FV3 + BTMyco) 1(TerHV1 + BTMyco) PostOB 5 0 5 0 0 0 PreOB 5 0 0 1 0 0 2015 OB 5 5 0 0 0 0 FV3 PostOB 13 0 0 0 1 0

a 4 live turtles, 2 shells b 1 live turtle, 2 shells

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Table 9.4. Complete blood counts for free-living Terrapene carolina carolina encountered during a mortality event in Forest Glen

Nature Preserve, Vermilion County, Illinois in 2011.

Parameter N Mean SD Median Minimum Maximum Distribution PCV (%) 10 18.85 7.56 19.5 9 34.5 Normal TS (g/dL) 10 2.96 1.46 2.87 1 5.4 Normal WBC (x103/μL) 10 4.939 2.654 5.028 0.605 9.166 Normal Heterophils (x103/μL) 10 2.179 1.402 2.137 0.157 4.626 Normal Lymphocytes (x103/μL) 10 1.152 1.014 0.881 0.109 2.836 Normal Monocytes (x103/μL) 10 1.049 1.158 0.796 0.136 4.125 Non-Normal Eosinophils (x103/μL) 10 0.192 0.129 0.212 0.036 0.39 Normal Basophils (x103/μL) 10 0.22 0.242 0.143 0 0.845 Non-Normal Heterophil/Lymphocyte 10 4.275 6 2.19 0.3 20.5 Non-Normal

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Table 9.5. Complete blood counts for free-living Terrapene carolina carolina encountered during and after a frog virus 3-like ranavirus mortality event in Kennekuk Cove County Park, Vermilion County, Illinois in 2013. Dist = Data distribution, G = Gaussian,

NG = Non-Gaussian.

Outbreak Post-Outbreak Parameter N Mean SD Median Min Max Dist N Mean SD Median Min Max Dist

PCV (%) 5 20.2 9.52 23 4 27 G 13 19.9 5.3 20 7 27 G TS (g/dL) 8 4.05 1.07 4.2 2 5.2 G 13 3.66 1.03 3.6 2.2 6.4 G WBC (x103/mL) 4 18.094 14.777 13.807 5.346 39.418 G 13 9.755 4.838 7.98 2.337 17.132 NG Heterophils (x103/mL)a 4 9.176 5.844 8.032 3.688 16.95 G 13 2.308 1.46 2.107 0.631 6.419 NG Lymphocytes (x103/mL) 4 1.405 0.882 1.567 0.267 2.219 G 13 1.15 0.821 0.872 0.397 2.845 NG Monocytes (x103/mL) 4 2.211 3.522 0.544 0.267 7.489 NG 13 0.494 0.475 0.335 0 1.422 NG Eosinophils (x103/mL) 4 3.711 4.165 2.201 0.588 9.854 G 13 4.226 2.657 3.102 0.794 8.663 G Basophils (x103/mL) 4 1.589 1.267 1.335 0.534 3.153 G 13 1.575 1.301 1.041 0.187 3.7 NG Heterophil/Lymphocytea 4 8.45 4.56 8.67 2.65 13.8 G 13 2.75 2.433 1.93 0.8 10 NG

a Statistically significant difference between outbreak and post-outbreak turtles

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Table 9.6. Complete blood counts for free-living T. carolina carolina encountered before and after frog virus 3-like ranavirus mortality events at Kickapoo State Park, Vermilion County, Illinois in 2014 and 2015. CT = measure of central tendency (mean or median), Dispersion = standard deviation or 10-90th percentiles. N = normally distributed data, NN = non-normally distributed data.

2014 PreOB 2014 PostOB 2015 PreOB 2015 PostOB CT; Dispersion CT; Dispersion CT; Dispersion CT; Dispersion Parameter N (Range) N (Range) N (Range) N (Range) 24.25; 4.18N 25.63; 5.96N 22.8; 2.93N 22.7; 4.3N PCV (%) 12 4 3 13 (17.5-30) (19-33.5) (19.5-25) (13.5-30.5) 5.34; 1.21N 6.85; 1.11N 6.1; 1.83N 5.49; 1.5N TS (g/dL) 12 4 3 13 (3.85-7.75) (5.65-7.85) (4.2-7.85) (3.1-8.9) 12.46; 3.92N 10.43; 4.32N 5.82; 5.8-6.4NN 13.13; 4.43N WBC (x103/μL) 11 4 3 12 (6.26-19.8) (6.29-15.65) (5.8-6.53) (7.36-19.06) 5.35; 1.6N 3.25; 1.04N 1.56; 1.18N 4.11; 2.08N Heterophils (x103μL) 11 4 3 12 (2.82-7.65) (1.72-4.03) (0.72-2.9) (1.78-8.07) 1.2; 0.27-1.7NN 0.88; 0.37N 1.22; 0.66-1.24NN 1.4; 0.73-3.15NN Lymphocytes (x103/μL) 11 4 3 12 (0.25-4.94) (0.53-1.4) (0.52-1.24) (0.66-7.43) 0.74; 0.65N 0.16; 0.15N 0.48; 0.24N 0.71; 0.5N Monocytes (x103/μL) 11 4 3 12 (0-2.18) (0-0.37) (0.23-0.7) (0.09-1.63) 2.75; 1.52N 4.22; 2.62N 2.26; 0.25N 5.22; 1.25N Eosinophils (x103/μL) 11 4 3 12 (1.13-6.02) (1.57-6.89) (1.97-2.41) (2.65-7.17) 2.26; 1.51N 1.91; 2.43N 0.76; 0.77N 1.12; 0.91N Basophils (x103/μL) 11 4 3 12 (0.6-5.15) (0.25-5.48) (0.17-1.63) (0.08-3.28) 4.85; 2.74-16.9NN 4.45; 2.52N 2.33; 2.79N 2.83; 1.6N Heterophil/Lymphocyte 11 4 3 12 (0.57-16.67) (1.22-2.44) (0.58-5.56) (0.64-6.4)

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Figure 9.1. Number and timing of Eastern box turtle (Terrapene carolina carolina) captures before (PreOB), during (OB), and after

(PostOB) mortality events in Vermilion County, Illinois from 2011-2015. The size of the marker reflects the number of animals captured.

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Figure 9.2. Gross appearance of necrotizing bacterial infection in Terrapene carolina carolina from Forest Glen Nature Preserve,

Vermilion County, Illinois during a mortality event in 2011. Left forelimb (A) and right forelimb (B).

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Figure 9.3. Locations of sympatric Eastern box turtles (Terrapene carolina carolina) and amphibians (Acris crepitans, Rana clamitans, Eurycea bislineata) at Kennekuk Cove County Park in Vermilion County, Illinois during and after a natural ranavirus

(FV3) outbreak in 2013. A 0.16km spatio-temporal cluster of FV3 cases was identified from August 2nd – August 16th in association with an ephemeral stream.

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Figure 9.4. Locations of Eastern box turtles (Terrapene carolina carolina) and ephemeral ponds used for amphibian breeding at

Kickapoo State Park in Vermilion County, Illinois in 2014 and 2015 before, during, and after ranavirus (FV3) outbreaks. In 2014 a

0.03km spatio-temporal cluster of FV3 cases was identified from July 15th – July 23rd. In 2015 a 0.16km spatio-temporal cluster of

FV3 cases was identified from July 8th – July 23th.

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CHAPTER 10: IDENTIFYING DETERMINANTS OF INDIVIDUAL & POPULATION

HEALTH IN FREE-LIVING EASTERN BOX TURTLES (TERRAPENE CAROLINA

CAROLINA): RECOMMENDATIONS FOR MANAGEMENT AND MONITORING

Abstract: Eastern box turtles (Terrapene carolina carolina) are declining due to anthropogenic factors and disease. Identifying drivers of individual and population health in this species may supplement existing management strategies and promote better conservation outcomes. The objectives of this study were to model predictors of health in multiple populations of free-living eastern box turtles, to identify clinically useful diagnostic tests for health assessment, to relate individual health to population stability, and to generate evidence-based management recommendations supporting box turtle health. We hypothesized that physical examination (PE) abnormalities and protein electrophoresis (EPH) values would best predict poor health, and that population stability would be sensitive to removal of adult female turtles. Turtles (n=507) from five populations were evaluated using PE, hematology, plasma biochemistry, EPH, and pathogen detection from 2016-2018. DNA from oral/cloacal swabs and blood was assayed for four ranaviruses, three Mycoplasma spp., two herpesviruses, two Salmonella spp., Terrapene adenovirus, intranuclear coccidiosis, Borrelia burgdorferi, and Anaplasma phagocytophilum using qPCR. Health was modeled as a categorical outcome using generalized linear models on the 2016 and 2017 data, followed by information-theoretic model ranking. External validation was performed using the 2018 data. “Healthy” turtles had 1) no significant PE abnormalities, 2) no pathogens affecting clinical condition, and 3) fewer than three abnormal bloodwork values.

Turtles violating these criteria were classified as “Unhealthy”. Population viability analysis

(PVA) was performed to determine the stability of this population and its sensitivity to removal

248 of individual turtles. PE abnormalities commonly involved the musculoskeletal system, respiratory system, and tympanic membranes. Pathogen detection (adenovirus, Terrapene herpesvirus 1, Mycoplasma sp., Salmonella typhimurium) was not related to health status.

Predictors of “Unhealthy” classification from the most parsimonious model included the presence of shell abnormalities, clinical signs of upper respiratory disease (URD), and deviations from population median values for total leukocyte count and heterophil:lymphocyte ratios (p <

0.05). PE and hematology sufficiently predict health within this population (AUC = 0.84, predictive accuracy = 91.5%), and commonly-identified pathogens do not significantly impact individual health. PVA revealed that annual removal of 1 - 5 adult female turtles above background mortality rates dramatically increased the probability of population extinction within the next century for all populations, but the effects were especially pronounced in small populations from fragmented and degraded habitats. Forest management to promote habitat quality, consideration of box turtle activity patterns when planning burn schedules, road signage to decrease vehicular strikes, and continued health assessment to monitor trends and identify new threats were recommended to support individual and population wellness. This comprehensive approach illustrates how models can be used to explore the complex factors driving box turtle health, and demonstrates how modeling can be used to prioritize health assessment efforts and inform evidence-based management interventions for a cryptic reptile.

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INTRODUCTION

Eastern box turtles (Terrapene carolina carolina) are small, omnivorous, terrestrial chelonians native to the eastern United States. They are classified as vulnerable by the IUCN, are listed in CITES Appendix II, and are considered a species in greatest conservation need in

Illinois (van Dijk 2011a). Threats to eastern box turtle conservation include habitat loss and fragmentation, road mortality, decreased recruitment due to subsidized predators (e.g. raccoons, coyotes), removal for human use (pets, turtle races), and disease (Dodd 2001, van Dijk 2011a).

Mortality events due to ranavirus have prompted multiple investigations into the health of free- living eastern box turtles (e.g. De Voe 2004, Allender 2006, Johnson 2008, Ruder 2010, Rose

2011, Kimble 2012, Allender 2013c, Farnsworth 2013, Currylow 2014, Adamovicz 2015, Lloyd

2016, Perpiñán 2016, Agha 2017, Kimble 2017). As a result, baseline information is available on eastern box turtle hematology, plasma biochemistry, protein electrophoresis, and hemoglobin- binding protein, and in some cases information on age, sex, and seasonal differences is also available (Rose 2011, Kimble 2012, Flower 2014, Adamovicz 2015, Lloyd 2016). Species- validated diagnostic tests have also been produced for qPCR detection of ranavirus, Terrapene herpesvirus 1, Terrapene adenovirus, and Mycoplasma sp., facilitating epidemiologic investigation of infectious disease in captive and free-living populations (Allender 2013a, Blum

2014, Kane 2016, Kane 2017, Appendix 1). While these studies provide a strong foundation to expand knowledge about eastern box turtle health and disease processes, they have all focused on individual health components such as diagnostic tests or pathogens. No study has yet combined multiple health assessment modalities to holistically evaluate individual and population health in this species.

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In this study, comprehensive individual health assessments were performed in multiple populations of free-living eastern box turtles using a combination of clinical pathology, qPCR pathogen screening, and spatial analyses. The stability of each population and its resiliency to perturbance was evaluated as a proxy for population health using population viability analysis

(PVA). The objectives of this study are to 1) model predictors of individual health in eastern box turtles and identify factors associated with positive health status, 2) identify the most clinically useful diagnostic tests for individual health monitoring in free-living turtles, 3) relate individual- level health to population stability using population viability analysis and 4) generate evidence- based management recommendations supporting individual and population health. The main hypothesis is that a combination of physical examination (PE) abnormalities and protein electrophoresis (EPH) values will best identify poor health states, and that population health will be most sensitive to loss of adult turtles.

MATERIALS & METHODS

Fieldwork

Box turtles were evaluated at four study sites within the Vermilion County Conservation

Opportunity Area in east-central Illinois: Forest Glen Nature Preserve (FGNP [40.01°, -87.57°]),

Kennekuk Cove County Park (KCCP [40.19°, -87.72]), Kickapoo State Park (KSP [40.14°, -

87.74]), and Collison (COL [40.28o, -87.78o]). Turtles were also evaluated at Stephen A. Forbes

State Park (38.73o, -87.78o). Sampling was performed at the Vermilion county sites in the spring

(May) and summer (June -August) of 2016 - 2018, and the fall (September) of 2016. Sampling was conducted at Forbes in the spring of 2016 - 2018 and the fall of 2016.

Air and substrate temperature were collected at the start and stop of each turtle search, and the results were averaged and recorded for each animal encountered (Kestrel 3000 Weather

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Meter, Nielsen-Kellerman, Boothwyn, PA 19061; Taylor 9878 Digital Pocket Thermometer,

Taylor Precision Products, Oak Brook, IL 60523). Turtles were located using a combination of human and canine searches and GPS coordinates, habitat (field, forest, edge) and microhabitat

(grass, brambles, leaves, soil) data were recorded at each site of capture. Microhabitat classification was assigned based on the primary ground cover beneath and within a 12” radius of the turtle. Turtles were weighed to the nearest gram, assigned to an age class (> 200 g: adult, <

200 g: juvenile), and sexed using a combination of plastron shape, eye color, and tail length

(Dodd 2001). Straight carapace length (SCL), straight carapace width (SCW), and carapace height (CH) were recorded to the nearest millimeter and width of the left pectoral scute (Lpect) was measured to the nearest 0.01mm. Body condition score (BCS) was determined using a formula that predicts body fat content (determined by computed tomography) from body weight and SCW (unpublished data).

Complete physical examinations were performed by a single observer (LA). Turtle mentation was categorized as bright (moving) or quiet (in shell). The following systems were evaluated and coded as within normal limits (WNL) or abnormal: eyes, nares, tympanic membranes, oral cavity, integument, appendages, and cloaca. Condition of the shell was classified into one of three categories: WNL, Active Lesion (AL), or Inactive Lesion (IL). Active lesions were characterized by open fractures or wounds, exposure of necrotic bone, and/or the identification of keratin discoloration or peeling, soft areas, or bleeding upon manipulation of the shell. Inactive lesions were firm on palpation and covered by an adherent layer of keratin, indicating appropriate healing. The following clinical signs were coded as present or absent: blepharoedema, ocular discharge, nasal discharge, oral discharge, oral plaques, open-mouth breathing, diarrhea, and ectoparasites. An upper respiratory disease (URD) variable was also

252 recorded as present or absent. Turtles with URD had one or more of the following: nasal discharge, blepharoedema, and/or ocular discharge.

(Becton Dickinson Co., Franklin Lakes, NJ 07417) and lithium heparin plasma separator tubes

1939). Following evaluation, sample collection, and notching, turtles were released at their original sites of capture. Protocols for animal handling and sampling were approved by the

University of Illinois Institutional Animal Care and Use Committee (#13061, 15017, 18000).

GPS coordinates and habitat data were collected from the remains of deceased turtles as described above. Remains were stored in individual sealable bags and frozen at -20oC until processed. A bone marrow sample was collected from the right bridge of each shell using aseptic technique in a biosafety cabinet as previously described (Butkus 2017).

Clinical Pathology

Packed cell volume (PCV) was determined using sodium heparized capillary tubes

(Jorgensen Laboratories, Inc., Loveland, CO 80538) centrifuged for 5 minutes at 14,500 rpm.

Total solids (TS) was measured from the plasma in each capillary tube using a hand-held refractometer (Amscope RHC-200ATC refractometer, National Industry Supply, Torrance, CA,

USA). Total leukocyte counts (WBC) were determined following the Avian Leukopet (Vet lab

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Supply, Palmetto Bay, FL, USA) manufacturer protocol using Bright-line hemacytometers

(Hausser Scientific, Horsham, PA, USA). Blood smears from heparinized whole blood samples were stained using a modified Wright-Giemsa stain (Hema 3) and 100-cell leukocyte differential counts were performed. Leukocytes were classified as heterophils, eosinophils, lymphocytes, monocytes, and basophils as previously described in box turtles (Heatley 2010).

(hemoglobin-binding protein) until diagnostic testing.

Plasma biochemistry parameters were measured using a commercial chemistry analyzer

Inc., Beaumont, Texas 77707, USA) at the University of Miami Miller School of Medicine, as previously described (Flower 2014).

Pathogen Screening

DNA was isolated from oral-cloacal swabs, whole blood, and bone marrow using a commercially available kit (QIAmp DNA Blood Mini Kit and DNAeasy kit, Qiagen, Valencia,

CA, USA). Manufacturer instructions were followed with some alterations for bone marrow samples, as previously described (Butkus 2017). DNA concentration and purity was assessed

254 spectrophotometrically (NanoDrop 1000, Thermo Fisher Scientific, Waltham, MA, USA) and

DNA samples were stored at -80oC.

DNA samples were assayed for 15 pathogens simultaneously using existing TaqMan quantitative polymerase chain reaction (qPCR) assays on a Fluidigm platform as previously described (Archer 2017). Quantification was performed for frog virus 3 (FV3), Terrapene herpesvirus 1 (TerHV1), emydid Mycoplasma sp., Terrapene adenovirus, Mycoplasma agassizii, and Mycoplasma testudineum using Fluidigm Real Time PCR analysis software by comparing each sample’s cycle threshold (Ct) value to a standard curve consisting of six tenfold dilutions from 107 –101 copies. Standard curves were not available for the remainder of the assays, and results for these pathogens were considered positive at Ct value of 24 or less. Non-template controls were included in each Fluidigm run, and all samples and controls were evaluated in triplicate.

PCR Supermix-UDG with ROX, Invitrogen, Carlsbad, CA), 1.25 l 20x TaqMan primer-probe,

Detection Software v2.05, Applied Biosystems, Carlsbad, CA) and standard curves as described

255 above. Positive pathogen quantities (copies/L) were normalized based on total DNA concentration to produce final quantities in copies/ng DNA.

Statistical Analyses

Statistical analyses were performed to meet five specific goals: 1) Describe the sample population; 2) Identify predictors of clinical pathology values and pathogen detection; 3) Identify predictors of health status and validate models for predicting individual health; 4) Identify spatial clustering of qPCR pathogen detection, physical examination abnormalities, and extreme clinical pathology values; 5) Assess longitudinal trends in health status for free-living ornate box turtles.

All statistical assessments were performed in R at an alpha value of 0.05.

A directed acyclic graph (DAG) was generated to demonstrate expected relationships among measured predictors (Figure 10.1). This diagram was used to identify potential confounding variables and structure statistical analyses, using multivariable linear regression when necessary to control for confounders (Joffé 2012). Continuous predictor variables were assessed for multicollinearity using Pearson’s correlation coefficient, with r > 0.5 considered

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(clinical pathology parameters) were evaluated using general linear models. For each model, the assumption of normally distributed error in the dependent variable was evaluated using boxplots, histograms, and Q-Q plots of residual values. The assumption of homoscedasticity was assessed using plots of actual vs. fitted residuals. The impact of influential values was assessed using

Cook’s Distance plots. Post-hoc between group differences were evaluated using the contrasts function in the lsmeans package (Lenth 2016). Predictors of categorical outcomes (pathogen status, presence/absence of physical exam abnormalities) were assessed using generalized linear models with package brglm (Kosmidis 2017). The assumption of a linear relationship between continuous predictors and the log-odds of the output variable was visually evaluated using scatterplots.

Reference intervals were constructed for each clinical pathology parameter in each year using the nonparametric method in the referenceIntervals package according to American

Society for Veterinary Clinical Pathology guidelines (Friedrichs 2012). Turtles with abnormal physical examination findings were excluded from the reference interval dataset, and intervals were further partitioned based on year, season, age class, and/or sex as appropriate based on univariate analyses. Outliers were visually identified using box plots and excluded using Horn’s method (Horn 2001). Ninety percent confidence intervals were generated around the upper and lower bounds of each reference interval using nonparametric bootstrapping with 5000 replicates.

The width of the confidence intervals (WCI) was compared to the width of the reference interval

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(WRI) to infer the need for a larger sample size, which is generally recommended when

WCI/WRI > 0.2 (Friedrichs 2012).

All turtles were then assigned to a health category (“apparently healthy” vs. “unhealthy”).

Apparently healthy turtles met the following three criteria:

6. No clinically significant physical examination abnormalities. A clinically

significant abnormality is one expected to affect the animal’s ability to navigate,

prehend food, mate, and protect itself from predation. Examples include open

wounds, active fractures, or respiratory distress. Physical examination abnormalities

that are not considered clinically significant include healed injuries and congenital

abnormalities such as fused digits or supranumerary scutes.

7. No more than three clinical pathology parameters outside of reference intervals.

By definition, reference intervals comprise 95% of the values of a “healthy”

population, so 5% of “healthy” animals can be expected to have a value outside of the

reference interval (Freidrichs 2012). Increasing the number of abnormal values

required to classify an animal as “abnormal” decreases the risk of misclassifying an

otherwise “healthy” animal.

8. No clinically apparent qPCR detection of infectious disease. The presence of a

pathogen does not necessarily equate to disease (Thompson 2010, Pirofski 2012,

Méthot 2014). Furthermore, some pathogens are host-adapted and have limited

associated clinical signs in an otherwise healthy animal (Ryser-Degiorgis 2013). The

present study only considers pathogen-positive animals “unhealthy” if they have

concurrent clinical signs of illness or if they have greater than three abnormal clinical

pathology values, indicating a negative physiologic effect of the pathogen.

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Individual health status was evaluated using sets of candidate generalized linear models, each testing a specific biologic hypothesis, and ranked using an information-theoretic approach with the MuMIn package. To generate predictive health models with a minimum number of variables, each significant clinical pathology parameter was centered based on age, sex, and year-specific median values and health models were reconstructed. Internal model validation was performed via bootstrapping with the 2016 and 2017 datasets using the rms package. External model validation was performed with the 2018 data using the rms and pROC packages (Robin

2011).

Spatial Epidemiology

Turtle GPS locations were mapped using ArcGIS on the World Imagery background layer and visually examined for demographic clustering (ArcMap 10.4.1, Esri, Redlands, CA

92373). Spatial clustering of poor body condition and extreme clinical pathology values was evaluated using Gettis G Ord hotspot analysis. The Bernoulli model was used to test for statistically significant spatial clusters of physical examination abnormalities and pathogens using SaTScan v9.4.4 (Kulldorff 1997). Output files from SaTScan were uploaded into ArcGIS to visualize the extent of significant clusters.

Estimating Population Size & Population Viability Analysis

Population size estimates with 95% confidence intervals were produced using the

Schumacher-Eschmeyer method in the fishmethods package (Schumacher 1943). Mark-recapture data for these estimates was available for Collison from 2009, Forest Glen from 2011, Kennekuk and Kickapoo from 2013, and Forbes from 2015.

Individual-based population viability analyses were performed using VORTEX v10

(Lacy 2018). PVA scenarios were simulated over 100 years with 1000 iterations using estimated

259 started population sizes. Parameter and rate estimates were obtained from the eastern box turtle literature (Table 10.1). Sensitivity testing was performed to examine the effect of removing 1-5 adult female turtles on each population, representing increased losses due to predation, disease, or human collection.

RESULTS

Environmental Data, Sample Populations, & Physical Examination

Five hundred and seven individual turtles were evaluated over the course of three years.

Thirty-eight turtles were captured more than once, for a total of 589 health assessments. Of these, two turtles were recaptured within 2016, 1 was recaptured within 2017, seven were recaptured within 2018, 7 turtles sampled in 2016 were resampled in 2017, seven turtles sampled in 2017 were resampled in 2018, and 13 turtles sampled in 2016 were resampled in 2018. One individual was sampled in 2016, 2017, and 2018. Resampled individuals always represented < 5% of evaluations, and resampling was not accounted for statistically. Demographic information for live turtles is summarized by season and location in Table 10.2.

Twenty-five deceased turtles were recovered in 2016 (Forbes n=8; KSP, n=4; and

Collison, n=1), 2017 (Forbes; n=5; Kennekuk, n=1; and Forest Glen, n=1), and 2018 (Forbes, n=4; and Forest Glen, n=1). Eleven deceased turtles were female, six were male, and seven were of unknown sex. Thirteen turtles were new, nine were notched, and two had unknown encounter history due to shell damage. Of the notched turtles, the time interval between most recent health assessment and recovery of the remains ranged from four months to six years. One individual had missing and discolored keratin on the caudal half of its carapace prior to being found dead, but the other eight turtles had no obvious signs of poor health and no pathogens detected via qPCR. The condition of the remains ranged from freshly killed to completely skeletonized with

260 no remaining keratin. Cause of death was known for two turtles: one female that was hit by a car at Forbes, and one female from Forest Glen that died of sepsis secondary to a bite wound on its leg. All bone marrow samples were qPCR negative for FV3.

Habitat use differed by study site, but not year, season, sex, or age class. Turtles at Forbes were found in forests more often than fields and edges compared to Collison (p = 0.0005, p =

0.004), Kennekuk (p = 0.0001, p = 0.0001), and Forest Glen (p = 0.0064, p = 0.0003). Turtles at

Kennekuk were found in fields and edges more often than forests compared to Collison (p =

0.008, p = 0.003), Forest Glen (p = 0.0004, p = 0.0017), and Kickapoo (p = 0.0001, p = 0.0003).

Microhabitat use also differed by study site, but not the other antecedent predictor variables. Turtles at Forbes were found in leaves more frequently than grass and brambles compared to Kennekuk (p = 0.0001, p = 0.004) and Forest Glen (p = 0.04, p = 0.02). Turtles at

Kickapoo were found in leaves more often than grass (p = 0.0001) and brambles (p = 0.03) compared to Kennekuk.

Physical examination abnormalities typically involved the musculoskeletal system, respiratory system or tympanic membranes (Table 10.3). Most shell lesions were related to predator trauma, though the majority of these were mild, healed, and confined to the marginal scutes. Two turtles from Forbes had active or healed fractures which appeared related to vehicular trauma. Less common findings included dyspnea (N = 2, Forest Glen and Kennekuk

2017), myiasis (N = 1, Kennekuk 2016), oral plaques (N = 1, Kickapoo 2016), and oral discharge

(N = 1, Forbes 2016).

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Predictors of PE Abnormalities, Clinical Pathology Values & Pathogen Detection: 2016 &

2017

Physical examination abnormalities were associated with site and age class. The odds of asymmetrical nares were 3.36 times higher for turtles from Collison compared to turtles from

Forbes (95% CI = 1.32 – 8.54, p = 0.01). The odds of having a completely normal shell were 2.6 times higher in juvenile turtles compared to adults (95% CI = 1.33 – 5.09, p = 0.003).

Statistically significant associations between physical exam abnormalities and clinical pathology values were not identified.

Three common box turtle pathogens were detected during the present study: TerHV1,

Mycoplasma sp., and Terrapene adenovirus (Table 10.4). Salmonella typhimurium was detected in three turtles in 2017 including one from Kennekuk, one from Forest Glen, and one from

Kickapoo. Co-detection of multiple pathogens was less common than single pathogen detection.

The most common co-detected pathogens were TerHV1 and adenovirus (N = 13) closely followed by Mycoplasma sp. and adenovirus (N = 12), then S. typhimurium and adenovirus (N =

1) and S. typhimurium and Mycoplasma sp. (N = 1).

TerHV1 was detected more frequently at Forbes than Collison (OR = 10.9, 95% CI =

1.32 – 89.49, p = 0.02), Kennekuk (OR = 10.7, 95% CI = 1.78 – 64.32, p = 0.009), and Kickapoo

(OR = 26, 95% CI = 1.17 – 583, p = 0.03). TerHV1 detection was also more likely in the summer compared to both spring (OR = 7.8, 95% CI = 1.57 – 39.05, p = 0.01) and fall (OR =

14.6, 95% CI = 2.11 – 101.21, p = 0.006). Adenovirus detection was more likely in the summer compared to spring (OR = 2.7, 95% CI = 1.19 – 6.05, p = 0.01) and fall (OR = 9, 95% CI = 2.65

– 31.54, p = 0.0005), and detection was more likely in the spring compared to the fall (OR = 3.4,

95% CI = 1.15 – 10.07, p = 0.03). Detection of Mycoplasma sp. was associated with the presence

262 of aural abscesses (OR = 8.87, 95% CI = 1.66 – 47.5, p = 0.01), but clinical signs of illness were not significantly associated with other pathogens. Relative lymphocyte count was higher in adenovirus positive turtles (effect size = 4%, p = 0.04) and H:L was higher in TerHV1 positive turtles (effect size = 0.5, p = 0.001).

Several clinical pathology parameters differed by year, age class, sex, season, and study site. In 2016 turtles had higher TS (effect size = 0.6g/dL, p < 0.0001), relative basophil count

(effect size = 1.5%, p = 0.02), and absolute basophil count (effect size = 436/L, p = 0.04). In

2017, turtles had higher PCV (effect size = 2%, p = 0.008), relative monocyte count (effect size

= 1%, p = 0.0001), absolute monocyte count (effect size = 211/L, p = 0.002), calcium (effect size = 1.3mg/dL, p = 0.01), and phosphorous (effect size = 0.46mg/dL, p = 0.001).

Adult turtles had higher PCV (effect size = 3.5%, p = 0.01), calcium (effect size =

4mg/dL, p < 0.0001), Ca:P (effect size = 0.58, p = 0.004), and relative beta globulins (effect size

= 7.4%, p = 0.01) than juveniles. Juveniles had higher absolute monocytes (effect size = 305/L, p = 0.01), relative albumin (effect size = 6%, p = 0.007), absolute albumin (effect size = 0.3g/dL, p = 0.05), relative alpha 1 globulins (effect size = 1.4%, p = 0.01), and A:G (effect size = 0.09, p

= 0.008).

Male turtles had higher PCV (effect size = 3%, p < 0.0001), relative albumin (effect size

= 3.8%, p = 0.001), relative alpha 1 globulins (effect size = 0.65%, p = 0.04), and A:G (effect size = 0.06, p = 0.002) than females while calcium (effect size = 4.1mg/dL, p < 0.0001), phosphorous (effect size = 1mg/dL, p < 0.0001), relative beta globulins (effect size = 5.3%, p =

0.001), and absolute beta globulins (effect size = 0.27g/dL, p = 0.03) were higher in females.

Relationships between clinical pathology values and study site are summarized in tables

10.5 – 10.8. TS was highest at Kickapoo, calcium was highest at Collison, and total protein,

263 albumin, alpha 2 globulins, and gamma globulins were highest at Forbes compared to all other sites. Forbes also had the lowest heterophil count compared to all other sites. Bile acids were highest and relative lymphocyte counts were lowest at Kickapoo compared to all sites except

Collison. Relative basophil count and alpha 1 globulins were higher at Forbes compared to all other sites except Kickapoo and Forest Glen, respectively. Clinical pathology values from

Vermilion county study sites were more similar to each other than they were to values from

Forbes, based on the number of significant differences between sites. Within Vermilion county, values from Collison, Kennekuk, and Forest Glen were generally more similar to each other than they were to Kickapoo. Kickapoo also had the most significant differences compared to Forbes

(N = 16) vs. all other Vermilion county sites (N = 11 – 14). Relationships between clinical pathology parameters and season are summarized in tables 10.9 and 10.10.

Clinical pathology reference intervals were generated using combined data from 2016 and 2017, where appropriate (Supplementary Tables 10.1 – 10.6). Reference intervals for values which differed by year, season, age class, or sex were appropriately partitioned. Evaluation of the

WCI/WRI revealed that reference intervals for several parameters would be improved with a larger sample size.

Predictors of Health

Prior to generating the health model, clinical pathology parameters for each turtle were converted to the absolute value of the distance from year, age class, season, site, and sex-specific median values. This was done for two reasons: 1) to capture the relationship between poor health and extreme clinical pathology values at both the low and high end of the reference interval, without introducing non-linear terms, and 2) to control for the effects of potential confounders and reduce the total number of variables to include in the final health model. Resulting median-

264 based values were highly right-skewed, and were box-cox transformed to better support modeling assumptions.

The following predictors were associated with health status at a liberal alpha value of

0.15: WBC, relative heterophil count, absolute heterophil count, relative lymphocyte count, absolute lymphocyte count, absolute basophil count, H:L, relative alpha 2 globulins, absolute beta globulins, shell classification (Shell; AL, IL, WNL), tympanum classification (WNL vs.

Aural Abscess), nares classification (WNL vs. Asymmetrical), presence of URD signs (Yes vs.

No), and classification of appendages (WNL vs. Missing Digits vs. Missing Feet/Limbs). The

H:L is calculated from both heterophil and lymphocyte counts, so H:L was used to represent information for all heterophil and lymphocyte measures in the final health model.

Relative alpha 2 globulins and beta globulins were available for significantly fewer turtles (N = 75) than the rest of the predictor variables (N = 339). Case-wise deletion was performed to evaluate the contribution of these variables and a set of candidate models was ranked using AIC. The top-performing model in the reduced dataset did not contain relative alpha 2 globulins or beta globulins (data not shown), so further model ranking was performed using the full dataset without these variables.

The most parsimonious model contained the additive effects of WBC, H:L, Shell, and

URD, though there was a high degree of model selection uncertainty associated with omission of several other physical examination variables (Table 10.11). Internal and external validation were performed for the top three models separately and averaged (data not shown), and predictive capability of the model omitting Tympanum, Nares, and Appendages was superior, therefore this model was selected for full description. The top model including each of its parameter estimates and p-values is reported in Table 10.12, and performance metrics produced by internal and

265 external validation are reported in Table 10.13. External validation revealed a tendency to under- identify “unhealthy” animals, represented by a slight decrease in specificity, however, overall model performance remained acceptable with an accuracy of 91% and an AUC of 0.84.

Spatial Epidemiology

Significant spatial clusters were not identified for any parameter tested, indicating that pathogens and poor health indices were distributed homogeneously within the population.

Turtles did not cluster by age class or sex, at least at the spatiotemporal scale evaluated in this study.

Estimating Population Size & Population Viability Analysis

The Schumacher-Eschmeyer population size estimates from smallest to largest were

Kickapoo: 161 (120 – 246), Forest Glen: 257 (220 – 308), Collison: 353 (242 – 647), Kennekuk:

521 (347 – 926), and Forbes: 1037 (758 – 1555). Using vital parameter estimates from the literature all sampled populations tended to be stable over the simulated 100-year period.

Baseline extinction probability was zero for all sites except Kickapoo, where the extinction probability was 0.2% (Figure 10.2). Sensitivity testing revealed that annual removal of adult females impacted every population, but the effect was dependent upon population starting size

(Tables 10.14 & 10.15, Figures 10.3 & 10.4). Kickapoo was the most affected, with removal of one female per year leading to a high probability of population extinction within the next century. Forest Glen and Collison could tolerate loss of two females per year and Kennekuk could lose three before the probability of extinction rose above 90%. Forbes could lose up to 5 females per year with an 83% probability of extinction, revealing an overall superior resiliency to disturbance in this population compared to the Vermilion County sites.

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DISCUSSION

The individual and population health assessment of 500 turtles from five different populations revealed associations between individual health and hematology, EPH, condition of the shell, presence of URD signs, aural abscesses, asymmetrical nares, and missing appendages.

The most parsimonious model predicting individual health contained the additive effects of total leukocyte count, H:L, condition of the shell, and presence of URD signs. Specifically, turtles with clinical signs of URD (nasal discharge, blepharoedema, and/or ocular discharge), active shell lesions, and deviations from population median values for total leukocyte count and H:L were more likely to be classified as “unhealthy”. Detection of TerHV1, adenovirus, Mycoplasma sp., and S. typhimurium was not associated with overall health status, though the odds of aural abscessation were higher in Mycoplasma sp. positive turtles and TerHV1 positive turtles had higher H:L. PVA demonstrated that annual loss of small numbers of adult female turtles led to declines in all populations, and a high probability of extinction in all populations except the largest one (Forbes). These findings can be used to inform management actions which support box turtle health and to plan future health monitoring strategies for this population.

Relationships between clinical pathology parameters and season, demographics, and location were highly complex. Interpreting this complicated system was greatly facilitated by the use of statistical models to control for confounding and isolate the effects of each individual predictor variable. Age, sex, and seasonal differences in clinical pathology parameters are common in chelonians, and many findings in the present study have been previously identified in box turtles or other species (Anderson 1997, Christopher 1999, Chung 2009, Rose 2011, Kimble

2012, Flower 2014, Yang 2014, Adamovicz 2015, Lloyd 2016).

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Higher TerHV1 prevalence during the summer and lack of age, sex, and clinical sign associations is consistent with previous studies, and supports the classification of TerHV1 as a host-adapted pathogen in eastern box turtles (Kane 2017). Elevated H:L in TerHV1 positive turtles may be consistent with recrudescence due to stress (Maclachlan 2017b). Monitoring

TerHV1 prevalence may therefore serve as a means of identifying stressed populations in future box turtle health studies. Detection of Mycoplasma sp. in free-living turtles without clinical signs of illness is not uncommon (Ossiboff 2015c). Turtles that are clinically ill due to mycoplasmosis typically have signs of URD (Feldman 2006). While aural abscesses have been anecdotally identified in Mycoplasma sp. positive box turtles, this is the first study to illustrate a statistically significant association between this pathogen and this specific clinical sign (Feldman 2006). This relationship may not be causal, as URD and aural abscesses are commonly present simultaneously in debilitated box turtles (Tangredi 1997, Wilier 2003, Feldman 2006, Rivas

2014, Sack 2017). However, previous studies of aural abscessation have failed to identify a consistent cause that fulfills Koch’s postulates (Tangredi 1997, Wilier 2003, Joyner 2006a,

Holladay 2001, Sleeman 2008). Future transmission studies will help determine the causal association between Mycoplasma sp. and aural abscess formation in this species. Adenovirus screening has not been previously reported in free-living populations of box turtles. The present study identified an apparent seasonal association, but no age or sex predilection and no association with clinical signs of illness. Future studies in additional wild populations may elucidate more aspects of adenovirus epidemiology in box turtles, and ultimately help identify the individual and population level effects of this virus.

While study site did not significantly predict individual health, it did influence population size and resiliency to adult female removal. Forbes had the largest and most robust box turtle

268 population compared to the Vermilion county sites. This is not necessarily surprising, as Forbes provides larger and more contiguous habitat options than the highly fragmented and degraded

Vermilion county sites. Furthermore, the forest at Forbes is managed more intensively to promote open woodland habitat (summer and winter burns) and combat ingress of invasive species. Management of the Vermilion county sites is less aggressive, and invasive plant species are present in varying degrees at each site. They are most problematic at Kickapoo, where they have reduced the number, size, and quality of field habitats available for thermoregulation and nesting. Reductions in field size by over 50% in the last five years have been proposed. Fields at

Kennekuk have also been focally heavily reduced over the last 10 years by ingrowth of invasive autumn olive shrubs (Elaeagnus umbellata), however several large fields remain open and are heavily utilized by box turtles.

Habitat and microhabitat use were most similar for turtles at Forbes and Kickapoo compared to other study sites, however, these two sites had the most clinical pathology differences. Specifically, turtles at Kickapoo had higher TS, absolute and relative heterophil counts, relative eosinophil counts, and bile acids and lower relative lymphocyte counts compared to turtles from Forbes. Many of these changes may indicate poorer overall health status in the

Kickapoo population, however, the difference in health status between these sites (with more

“unhealthy” turtles at Kickapoo than Forbes) only approached significance (p = 0.06). In addition to poorer habitat quality, Kickapoo turtles have also experienced recurrent outbreaks of ranavirus starting in 2014 (Chapter 9). Ranavirus is a high morbidity, high mortality pathogen that has already killed at least ten adult turtles at Kickapoo in 2014 and 2015. A radiotelemetry study at Kickapoo documented mortality in 22 turtles between 2016 and 2018 due to a variety of causes, the most common of which was overwintering mortality secondary to an unusually warm

269 winter (Rayl 2018). The PVA model for Kickapoo indicates that removal of a single adult female each year above the expected annual mortality rate will destabilize this population and likely lead to extinction within the next 40 years. The current rate of adult loss from Kickapoo is not sustainable, indicating the need for additional study and management interventions to promote box turtle health at this site. While box turtle health trended towards being better at Forbes, this was the only site where road mortality was documented for box turtles, potentially indicating a need for signage to alert motorists to turtle crossings. Similarly, Forbes and Kennekuk were the only sites where box turtles were observed with burn injuries secondary to land management practices. A more judicious burning schedule which considers the activity patterns of box turtles may reduce the individual health impacts of this management strategy (Gibson 2009, Howey

2013, Cross 2016).

Individual Health Model

Modeling identified hematology as the most important diagnostic test for individual health assessment in eastern box turtles. Turtles that were classified as “unhealthy” had WBC and H:L further from their age, sex, and year-specific median values compared to apparently healthy turtles. Changes in WBC can be attributed to infection, inflammation, and neoplasia while changes in H:L are indicative of physiologic stress (Case 2005). The combination of these values appears to be a sensitive indicator of stress and illness in both eastern and ornate box turtles (Chapter 8). Interpretation of hematology in free-living reptiles can be difficult due to significant seasonal, sex, and age-related differences (Campbell 2015), however, using modeling to control for these differences helped to reveal the clinical utility of this diagnostic test.

While multiple physical examination abnormalities were significantly associated with health status, the most parsimonious (and best predictive) model contained only the condition of

270 the shell and the presence of URD signs. Turtles with active shell lesions due to trauma, burns, or infection were significantly more likely to be classified as unhealthy than turtles with inactive lesions or normal shells. The mortality rate associated with these active shell lesions is unknown in the evaluated populations, however wildlife rehabilitation centers report mortality rates of 10 –

43% for reptiles presented due to animal attacks and 22 – 61% for all trauma cases (Schrader

2010, Rivas 2014, Sack 2017). Turtles with clinical signs of URD were also more likely to be classified as unhealthy. Unfortunately, estimates on natural mortality from URD in free-living box turtles are not available, but mortality rates for turtles with URD at rehabilitation centers range from 3 – 63% (Brown 2002, Schrader 2010, Sack 2017). While mortality rates from wildlife rehabilitation centers are frequently gleaned from multiple species and come from biased sample populations, they do confirm that some chelonians do not survive shell damage and

URD. Non-lethal effects of physical examination abnormalities are more difficult to quantify but can also be important for population stability. An individuals’ investment in immune function and wound healing can decrease resource allocation to growth and reproduction, potentially leading to decreases in fecundity (Uller 2006, Perrault 2012, Rafferty 2014). Ultimately, determining the long-term impacts of physical exam abnormalities on individual health will require longitudinal evaluation of affected and non-affected turtles, likely via monitoring of radiotelemetered animals.

Detection of TerHV1, Terrapene adenovirus, Mycoplasma sp., and S. typhimurium was not associated with health status in this study. Ranavirus was not detected in box turtles during this study period, which is consistent with previous cross-sectional studies and is likely due to the acute onset, rapid progression, and high mortality nature of this pathogen (Allender 2011,

Allender 2013c). However, ranavirus has been previously associated with mortality events at

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Kennekuk and Kickapoo, and this pathogen should definitely be considered to negatively impact health in eastern box turtles (Chapter 9).

The individual health model has high specificity for identifying unhealthy turtles, but fairly low sensitivity. This may indicate that some important predictor variables are missing.

Testing for RNA viruses, using a broader range of clinical pathology tests, including toxicologic screening, and/or incorporating genetic or immunological data into future health evaluation studies may improve the sensitivity of this model.

Limitations of the individual health assessment portion of the study are largely related to methodology. Blood samples were collected from the subcarapacial sinus, which may increase the risk of lymph contamination (Hernandez-Divers 2002, Heatley 2010). Visibly lymph- contaminated samples were not included in this study, but inapparent lymph contamination can never be fully ruled out. If it occurred, decreases in PCV, WBC, uric acid, albumin, globulin, calcium, phosphorous, and AST, and increases in relative and absolute lymphocyte counts may be expected (Wilkinson 2004). Plasma samples were frozen at -20oC for up to two months prior to biochemistry and EPH analysis. The biochemistry analytes evaluated in the present study remain stable at -20oC for up to three months in humans and rats (Cuhadar, 2013; Cray, 2009). A study in horned vipers (Vipera ammodytes ammodytes) demonstrated stability of total protein and all EPH fractions except for relative alpha 1 globulins following storage at -20oC for 70 days

(Proverbio, 2012). While it is impossible to know whether this period of storage affected the stability of eastern box turtle analytes, research in other species suggests that changes to the measured analytes would have been minimal.

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Population Health Assessment

Population viability analysis revealed that eastern box turtle populations are highly sensitive to the removal of adult females. This is consistent with other chelonian studies, and is likely due to a combination of several biological characteristics including delayed reproduction, low fecundity, low recruitment, and high adult survivorship – all of which make the survival of reproductively active adults (especially females) crucial for population stability (Crouse 1987,

Brooks 1991, Congdon 1993, Congdon 1994, Heppel 1998, Dodd 2016). While the large population size of Forbes made it relatively resistant to extinction, the smaller populations in

Vermilion county were much more likely to be rapidly driven extinct by the loss of adult females. This was especially obvious for Kickapoo, which was significantly affected by annual removal of a single adult female turtle. The Vermilion county populations are therefore more likely to be negatively impacted by stochastic events such as adverse weather events or disease introduction.

Limitations to the PVA models constructed in this study are largely linked to the use of vital parameters and rates derived from the literature. Obtaining population-specific values for these parameters typically takes several years of intensive mark-recapture efforts, and availability of such complete datasets is uncommon in many wildlife populations (Hernandez

2015). The level of uncertainty associated with using this data has been included in the analysis by way of large user-provided standard deviation estimates. However, projections of population stability over time may change significantly when population-specific information on reproductive rates and age class-specific mortality rates is included (Hernández-Camacho 2015).

Interpretation of baseline models must therefore be approached cautiously. However, the effects of adult female removal are unlikely to change significantly. A recent simulation study in Florida

273 box turtles (Terrapene carolina bauri) showed that annual removal of 3.8% of the population would drive stable and declining populations extinct within 50 years, and annual loss of 4% of the population would drive an increasing population extinct (Dodd 2016). Chelonian population sensitivity to adult removal is a conserved trait of this taxon, and chronic loss of adult female eastern box turtle for any reason (human removal, road mortality, disease, predator encounter, etc.) will likely retain a destabilizing effect on turtle populations at all study sites.

Management Recommendations and Future Directions

Individual eastern box turtle health was not predicted by a single disease process or a specific threat such as predation or road mortality. Recommendations to improve individual and population health therefore focus on practical actions which may reduce individual mortality and contribute to population stability. Restoration of habitat quality via appropriate forest management practices is recommended to reduce the occurrence of invasive species and promote natural box turtle behaviors in Vermilion county. This may involve burning, however, burning schedules should be designed in consideration of box turtle activity patterns to prevent injury.

Placement of road signs encouraging drivers to watch for turtles should be considered at Forbes to reduce vehicular trauma. Health assessments (including hematology) should be continued at each study site to monitor existing trends and identify new threats as they occur.

Future research efforts should focus on clarifying the effects of physical examination abnormalities and environmental factors on fecundity and survival through longitudinal assessment of individual turtles. This may enhance understanding of health determinants in eastern box turtles and facilitate the linkage of individual and population health. Genetic, toxicologic, and infectious disease testing should be pursued to identify other factors potentially associated with health status. Additional epidemiologic research on ranavirus in free-living

274 amphibians and chelonians is also needed to identify potential targets for management intervention. Research is also needed to derive population-specific vital and mortality rates to improve the predictive accuracy of PVA. This will allow further refinement of management recommendations and enable appropriate support of box turtle and ecosystem health.

Conclusions

This large-scale study combined comprehensive veterinary health assessments with modeling and population viability analysis to investigate determinants of health in free-living eastern box turtles. Findings indicate that poor health is not driven by a single obvious cause, and that many box turtle populations occupying fragmented and degraded forest habitat in east- central Illinois are small and susceptible to extinction secondary to stochastic events. Detection of single and co-pathogens via qPCR was common, but not significantly related to health outcomes (with the notable exception of ranavirus). This holistic approach to health assessment generated several specific management recommendations to improve individual and population wellness in eastern box turtles and identified research questions for future investigation. Wildlife health is inherently complex and driven by a wide range of natural and anthropogenic phenomena. The application of traditional veterinary health assessment modalities in conjunction with modeling approaches may be a useful framework to explore this complicated system and extract actionable information to promote wellness and ensure positive conservation outcomes.

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Table 10.1. Parameter estimates and sources for population viability analysis in free-living eastern box turtles (Terrapene carolina carolina).

Parameter Value Source Breeding System Polygamy Dodd 2001 Age of 1st Offspring: Female 8 Dodd 1997 Age of 1st Offspring: Male 6 Dodd 1997 Maximum Lifespan 50 Stickel 1978 Maximum # Broods/Year 2 Burke 2011b, Willey 2012, Wilson 2005, Wilson 2008 Maximum # Progeny/Brood 6 Burke 2011b, Willey 2012, Wilson 2005 Sex Ratio at Birth 0.5 Maximum Age of Reproduction 50 Stickel 1978 % Adult Females Breeding 0.33 Willey 2012, Wilson 2005 Mean # Offspring/Female/Brood (SD) 2 (2) Burke 2011b, Willey 2012, Wilson 2005 Mortality Rates (SD): 0-1 42% (10) Burke 2011b, Congello 1978, Dodge 1978, Ewing 1933, Flitz 2006, 1-2 33% (10) Willey 2012, Wilson 2005, Forsythe 2004, Heppell 1998, 2-3 28% (10) Currylow 2011, Nazdrowicz 2008 3-4 24% (10) 4-5 19% (3) 5-6 15% (3) 6-7 6% (3) 7-8 6% (3) 8 and up 6% (3) Initial Population Size Pop-based Present study Carrying Capacity 1000 - 5000 Present study

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Table 10.2. Demographic distribution of free-living eastern box turtles (Terrapene carolina carolina) sampled at five study sites in

Illinois during 2016, 2017, and 2018.

Age Class Sex Season Adult Juvenile Male Female Unknown Spring Summer Fall Total 2016 Collison 27 2 9 18 2 9 10 10 29 Forest Glen 22 6 8 15 5 12 8 8 28 Kennekuk 17 8 7 11 7 9 11 5 25 Kickapoo 15 5 8 8 4 8 5 7 20 Forbes 118 19 70 52 15 82 NA 55 137 2017 Collison 9 2 3 7 1 9 2 NA 11 Forest Glen 19 5 9 13 2 17 7 NA 24 Kennekuk 32 3 10 23 2 21 14 NA 35 Kickapoo 6 1 4 2 1 3 4 NA 7 Forbes 51 10 23 29 9 61 NA NA 61 2018 Collison 26 5 10 15 6 13 18 NA 31 Forest Glen 51 7 37 16 5 30 28 NA 58 Kennekuk 33 13 20 15 11 24 22 NA 46 Kickapoo 19 4 12 7 4 12 11 NA 23 Forbes 49 5 26 24 4 54 NA NA 54 Total 494 95 256 255 78 364 140 85 589

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Table 10.3. Physical examination findings in free-living eastern box turtles (Terrapene carolina carolina) sampled at five study sites in Illinois during 2016, 2017, and 2018. Each column contains the number of turtles with each clinical sign and the (population prevalence).

Collison Forest Glen Kennekuk Kickapoo Forbes 2016 2017 2018 2016 2017 2018 2016 2017 2018 2016 2017 2018 2016 2017 2018 Upper Respiratory 1 4 1 2 3 2 2 1 2 2 1 0 0 0 0 Disease (URD) (3.5%) (17%) (2%) (8%) (8.6%) (4%) (10%) (14%) (1.5%) (3.3%) (2%) Asymmetrical 6 2 6 2 2 2 2 6 5 2 3 8 6 8 0 Nares (21%) (18%) (20%) (7%) (8%) (4%) (8%) (17%) (11%) (10%) (13%) (6%) (10%) (15%)

3 1 2 1 2 1 5 2 Aural Abscesses 0 0 0 0 0 0 0 (10%) (2%) (8%) (3%) (4%) (5%) (8%) (4%) 1 1 1 5 3 Soft Tissue Injury 0 0 0 0 0 0 0 0 0 0 (4%) (2%) (5%) (8%) (6%)

4 6 1 1 3 4 1 8 3 3 Active Shell Lesion 0 0 0 0 0 (14%) (20%) (2%) (4%) (8.6%) (8%) (4%) (6%) (5%) (6%) Inactive Shell 6 5 14 12 9 20 7 14 15 5 1 9 39 18 17 Lesion (21%) (45%) (27%) (43%) (38%) (34%) (28%) (40%) (33%) (25%) (14%) (39%) (28%) (30%) (31%)

Developmental 1 2 2 2 3 4 4 2 3 6 5 2 0 0 0 Shell Problem (3%) (18%) (6%) (7%) (13%) (7%) (11%) (4%) (13%) (4%) (8%) (4%) 1 1 3 3 3 2 Burn 0 0 0 0 0 0 0 0 0 (4%) (3%) (6%) (2%) (5%) (4%)

2 2 1 3 1 3 2 4 1 3 2 1 Missing Digits 0 0 0 (7%) (6%) (3.5%) (13%) (4%) (8.6%) (4%) (20%) (14%) (2%) (3.3%) (2%)

2 2 1 4 3 Missing Limbs 0 0 0 0 0 0 0 0 0 0 (7%) (8%) (3%) (3%) (6%)

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Table 10.4. qPCR pathogen detection in free-living eastern box turtles (Terrapene carolina carolina) sampled at five study sites in

Illinois during 2016, 2017, and 2018. Each column contains the number of turtles with each pathogen and the (population prevalence).

TerHV1 = Terrapene herpesvirus 1.

TerHV1 Mycoplasma sp. Adenovirus Spring Summer Fall Spring Summer Fall Spring Summer Fall 2016 Collison 0 1 (12.5%) 0 0 0 0 0 1 (10%) 0 Forest Glen 0 1 (20%) 0 1 (8%) 0 0 2 (17%) 2 (25%) 1 (12.5%) Kennekuk 0 0 0 0 5 (45%) 0 0 2 (18%) 0 Kickapoo 0 0 0 1 (12.5%) 0 0 1 (12.5%) 3 (60%) 1 (14%) Forbes 4 (8%) NA 4 (7%) 3 (6%) NA 1 (2%) 8 (16%) NA 2 (4%) 2017 Collison 0 0 NA 2 (25%) 0 NA 1 (12.5%) 1 (50%) NA Forest Glen 0 2 (28.5%) NA 0 0 NA 5 (30%) 2 (29%) NA Kennekuk 1 (5%) 2 (15%) NA 1 (5%) 0 NA 4 (21%) 1 (8%) NA Kickapoo 0 0 NA 1 (50%) 0 NA 0 1 (30%) NA Forbes 7 (16%) NA NA 2 (4.5%) NA NA 13 (30%) NA 2018 Collison 0 4 (24%) NA 0 0 NA 0 2 (12%) NA Forest Glen 1 (8%) 6 (24%) NA 0 1 (4%) NA 3 (25%) 2 (8%) NA Kennekuk 0 0 NA 2 (15%) 3 (17%) NA 3 (23%) 3 (17%) NA Kickapoo 0 0 NA 0 0 NA 1 (9%) 0 NA Forbes 11 (25%) NA NA 2 (4.5%) NA NA 13 (30%) NA NA

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Table 10.5. Hematology values which differ by study site in free-living Illinois eastern box turtles (Terrapene carolina carolina). ES

= effect size, or the effect of each location on each outcome variable compared to the reference level.

Total Solids PCV (%) Heterophils (%) Heterophils (/L) Eosinophils (%) Eosinophils (/L) (g/dL) Location Reference ES P - value ES P - value ES P - value ES P - value ES P - value ES P - value Collison Forbes - - - - 4 0.01 1113 0.0007 - - - - Forest Glen ------Kennekuk ------Kickapoo - - 0.88 0.006 ------Forbes Forest Glen ------709 0.01 - - - - Kennekuk ------825 0.006 - - - - - 121 Kickapoo -3.3 0.01 -1 0.0001 -5 0.005 -890 0.02 -4 0.01 7 0.002 - Forest Glen Kennekuk ------788 0.02 - - 148 Kickapoo - - 0.88 0.004 - - - - -5.5 0.003 8 0.0006 Kennekuk Kickapoo -3.5 0.01 -1.3 < 0.0001 ------

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Table 10.6. Hematology and plasma biochemistry values which differ by study site in free-living Illinois eastern box turtles

(Terrapene carolina carolina). ES = effect size, or the effect of each location on each outcome variable compared to the reference level.

Lymphocytes Basophils Bile Acids Calcium Phosphorous Calcium Basophils (%) (%) (/L) (mol/L) (mg/dL) (mg/dL) Phosphorous P - P - P - P - P - P - P - Location Reference ES value ES value ES value ES value ES value ES value ES value Collison Forbes - - -4 0.0001 - - - - 3 <0.0001 - - 0.42 0.006 Forest Glen ------2.4 0.004 0.47 0.03 - - Kennekuk ------3.3 0.0001 - - 0.64 0.0002 Kickapoo ------2.6 0.01 - - - - Forbes Forest Glen - - 3 0.002 637 0.02 ------0.32 0.02 Kennekuk - - 3.6 0.0001 550 0.04 ------Kickapoo 7.5 0.01 - - - - -2.6 0.03 ------Forest Glen Kennekuk ------0.54 0.0006 Kickapoo 10 0.002 - - - - -2.8 0.03 ------Kennekuk Kickapoo 9 0.005 - - - - 2.7 0.04 ------

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Table 10.7. Absolute protein electrophoresis values which differ by study site in free-living Illinois eastern box turtles (Terrapene carolina carolina). ES = effect size, or the effect of each location on each outcome variable compared to the reference level.

Alpha 1 Alpha 2 Beta Gamma Total Protein PreAlbumin Albumin Globulins Globulins Globulins Globulins (g/dL) (g/dL) (g/dL) (g/dL) (g/dL) (g/dL) (g/dL) P - P - P - P - P - P - P - Location Reference ES value ES value ES value ES value ES value ES value ES value Collison Forbes -1.6 0.002 - - -0.5 0.0003 -0.14 0.01 -0.55 0.0003 - - -0.14 0.01 Forest Glen - - -0.07 0.0001 ------Kennekuk - - -0.02 0.02 ------Kickapoo - - -0.05 0.0001 ------Forbes Forest Glen 1 0.02 -0.07 0.0001 0.35 0.002 - - 0.36 0.003 - - 0.16 0.0007 Kennekuk 2.3 0.0001 -0.02 0.005 0.65 0.0001 0.23 0.0002 0.66 0.0001 3.2 0.002 0.17 0.001 Kickapoo 1.3 0.005 -0.05 0.0001 0.5 0.0003 0.13 0.01 0.35 0.02 - - 0.19 0.0005 Forest Glen Kennekuk 1.3 0.01 0.05 0.0001 - - 0.17 0.006 - - 0.5 0.02 - - Kickapoo ------Kennekuk Kickapoo - - -0.03 0.0001 ------

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Table 10.8. Relative protein electrophoresis values which differ by study site in free-living Illinois eastern box turtles (Terrapene carolina carolina). ES = effect size, or the effect of each location on each outcome variable compared to the reference level.

Albumin Alpha 2 Beta Globulins Gamma Globulin PreAlbumin (%) Albumin (%) Globulins (%) (%) Globulins (%) Location Reference ES P - value ES P - value ES P - value ES P - value ES P - value ES P - value Collison Forbes -0.06 0.04 - - -3.8 0.03 -3.9 0.003 7 0.005 - - Forest Glen - - -1.5 < 0.0001 ------1.8 0.04 Kennekuk - - -0.75 0.008 ------Kickapoo - - -1.3 < 0.0001 - - -4.3 0.007 - - 1.9 0.04 Forbes Forest Glen - - -1.5 < 0.0001 - - 2.6 0.02 -4.6 0.02 1.3 0.03 Kennekuk - - -0.77 0.0005 ------Kickapoo - - -1.3 < 0.0001 4.7 0.007 ------Forest Glen Kennekuk - - 0.75 0.003 ------2.7 0.002 Kickapoo ------3.1 0.03 - - - - Kennekuk Kickapoo - - -0.58 0.04 ------2.8 0.003

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Table 10.9. Hematology values which differ by season in free-living Illinois eastern box turtles (Terrapene carolina carolina). ES = effect size, or the effect of each season on each outcome variable compared to the reference level.

Heterophils Heterophils Eosinophils Eosinophils Lymphocytes Basophils PCV (%) (%) (/L) (%) (/L) (%) (%) P - P - P - P - P - P - P - Season Reference ES value ES value ES value ES value ES value ES value ES value Spring Summer - - - - 1100 0.0002 -3.5 0.006 - - - - -3 0.002 Fall -2 0.04 - - - - -8 0.0001 1425 <0.0001 10 0.0001 - - Summer Fall - - -4 0.02 -1180 0.0007 -4 0.007 1171 0.001 6.5 0.01 2.5 0.02

Table 10.10. Plasma biochemistry values which differ by season in free-living Illinois eastern box turtles (Terrapene carolina carolina). ES = effect size, or the effect of each season on each outcome variable compared to the reference level.

Bile Acids Uric Acid Creatine Kinase Phosphorous Calcium Calcium (mg/dL) (mol/L) (mg/dL) (U/L) (mg/dL) Phosphorous Season Reference ES P - value ES P - value ES P - value ES P - value ES P - value ES P - value Spring Summer - - -0.2 0.01 -190 0.03 - - - - 0.38 0.006 Fall -3.4 < 0.0001 - - - - -1.6 0.005 -0.53 0.001 - - Summer Fall ------2.4 0.002 - - -0.37 0.02

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Table 10.11. Model selection parameters for generalized linear models predicting health status in free-living eastern box turtles

(Terrapene carolina carolina). H:L = heterophil : lymphocyte, WBC = total leukocyte count, URD = presence of upper respiratory disease clinical signs.

Model N K AICc AICc wi WBC + H:L + Shell + URD 339 6 165.9 0 37 WBC + H:L + Shell + URD + Tympanum + Nares + Appendages 339 10 166.4 0.43 29.8 WBC + H:L + Shell + URD + Nares 339 7 167 1.11 21.3 WBC + H:L + Shell + URD + Tympanum + Nares 339 8 168.3 2.42 11.1 WBC + H:L + Shell + URD + Tympanum + Nares + Appendages + Location 339 14 174.1 8.2 0.6 H:L + Shell + URD 339 5 175.8 9.87 0.3 Shell + URD + Tympanum + Nares + Appendages + Location 339 8 186.7 20.76 0 WBC + H:L + Shell 339 5 232.8 66.86 0 WBC + H:L + URD 339 4 251.4 85.47 0 WBC + H:L 339 3 312.2 146.31 0 Null 339 1 321.6 155.63 0

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Table 10.12. Parameter estimates for the most parsimonious model predicting health status in free-living eastern box turtles

(Terrapene carolina carolina). WNL = within normal limits, IL = inactive lesion, H:L = heterophil : lymphocyte, WBC = total leukocyte count, URD = presence of upper respiratory disease clinical signs.

Healthy = Shell + URD + WBC + H:L  SE Z value p-value Intercept 3.96 1.93 2.05 0.04 Shell: AL 7.63 1.74 4.38 < 0.0001 Shell: IL 0.69 1.74 1.63 0.104 URD: Yes 6.6 1.65 4.01 < 0.0001 WBC 0.105 0.032 3.26 0.001 H:L 0.914 0.258 3.54 0.0004

Table 10.13. Model fit metrics from internal and external validation of the most parsimonious model predicting health status in free- living eastern box turtles (Terrapene carolina carolina).

Metric Internal External Scale Ideal Brier Score 0.062a 0.075 0 - 1 Close to 0 AUC 0.889 0.841 0.5 - 1 Close to 1 Accuracy (%) 93.2 91.5 0 - 1 Close to 1 Sensitivity 0.62 0.53 0 - 1 Close to 1 Specificity 1 1 0 - 1 Close to 1 Somer's Delta 0.754a 0.682 -1 - 1 Close to -1 or 1 a Optimism-corrected values based on internal validation via bootstrapping

286

Table 10.14. Population viability analysis projections and sensitivity to adult female removal in a population of free-living eastern box turtles (Terrapene carolina carolina) at three different study sites.

Final Extant Final Population Probability of Median Years to Mean Years to Growth Rate Population Size Size (All) Extinction Extinction Extinction Scenario Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Kickapoo Baseline -0.002 0.113 159 104 159 104 0.002 0.004 0 0 16 35.75 Loss of 1 female/year -0.040 0.116 64 25 2 11 0.97 0.01 54 2.16 56 1.49 Loss of 2 females/year -0.062 0.107 0 0 0 0 1 0 30 0.88 30 0.76 Loss of 3 females/year -0.079 0.100 0 0 0 0 1 0 21 0.44 22 0.21 Loss of 4 females/year -0.092 0.096 0 0 0 0 1 0 17 0.49 17 0.24 Loss of 5 females/year -0.104 0.094 0 0 0 0 1 0 15 0.37 15 0.20 Forest Glen Baseline -0.001 0.104 265 158 265 158 0 0 0 0 0 0 Loss of 1 female/year -0.026 0.108 110 105 44 82 0.62 0.06 90 4.21 77 1.34 Loss of 2 females/year -0.045 0.101 55 0 1 6 0.99 0.005 48 1.75 50 1.30 Loss of 3 females/year -0.058 0.096 0 0 0 0.02 1 0 34 0.69 35 0.40 Loss of 4 females/year -0.068 0.092 0 0 0 0 1 0 27 0.51 27 0.38 Loss of 5 females/year -0.077 0.090 0 0 0 0 1 0 22 0.50 23 0.36 Collison Baseline -0.001 0.100 368 195 368 195 0 0 0 0 0 0 Loss of 1 female/year -0.018 0.103 165 147 122 144 0.27 0.04 0 0 84 2.29 Loss of 2 females/year -0.037 0.099 131 107 12 43 0.93 0.03 65 1.73 65 1.06 Loss of 3 females/year -0.049 0.095 63 0 1 7 1 0.005 45 1.38 46 1.05 Loss of 4 females/year -0.057 0.092 0 0 0 0 1 0 35 0.81 36 0.76 Loss of 5 females/year -0.065 0.089 0 0 0 0.02 1 0 29 0.70 30 0.55

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Table 10.15. Population viability analysis projections and sensitivity to adult female removal in a population of free-living eastern box turtles (Terrapene carolina carolina) at two different study sites.

288

Figure 10.1. Directed acyclic diagram depicting the relationships between health status and its predictors in eastern box turtles

(Terrapene carolina carolina).

289

Figure 10.2. Mean population size estimates simulated over 100 years for free-living eastern box turtles (Terrapene carolina carolina) at five different study sites.

290

Figure 10.3. Simulated mean population size over time in a population of eastern box turtles

(Terrapene carolina carolina) experiencing different levels of adult female removal each year.

291

Figure 10.4. Simulated mean survival probability over time in a population of eastern box turtles

(Terrapene carolina carolina) experiencing different levels of adult female removal each year.

292

Supplementary Table 10.1. Hematology parameters from free-living eastern box turtles (Terrapene carolina carolina) sampled from

2016 - 2018. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval. PCV = packed cell volume, WBC = total leukocyte count.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval

PCV (%) Female 162 G 23 6.3 7.5 45.5 9 - 33 6 - 10 32 - 34

Male 146 G 26 6.2 10.5 45 13 - 36 11 - 14 34 - 38 Juvenile 61 G 20 6.3 5 30 6 - 30 2 - 7 30 - 30.5 Total Solids (g/dL) 2016 231 NG 7.4 5.98 - 9.02 3.8 12 5.23 - 10.19 4.88 - 5.33 9.88 - 10.68 2017 133 G 6.7 1.3 2.5 9.86 4.67 - 9.17 4.44 - 5.27 8.97 - 9.68 WBC (/L) 365 NG 19766 10258 - 35687 3259 168080 7215 - 50034 6022 - 7597 47424 - 55255 Heterophils (/L) Spring 226 NG 2594 921 - 5067 296 12539 581 - 7039 477 - 712 6071 - 8144a

Summer 58 NG 1896 872 - 3612 261 7691 450 - 4842 240 - 457 4533 - 5985a Fall 85 NG 2543 1232 - 5190 585 9433 743 - 8405 444 - 901 7376 - 10977a Lymphocytes (/L) 369 NG 11432 4554 - 25807 259 157995 3365 - 40688 3047 - 3520 37410 - 45717a Monocytes (/L) Adult 313 NG 364 0 - 1124 0 2728 0 - 1763 1 - 1 1763 - 1763

Juvenile 61 NG 417 0 - 1647 0 2953 0 - 2860 1 - 1 2767 - 3617a Eosinophils (/L) Spring 220 NG 2041 836 - 3904 0 11812 641 - 5361 483 - 970 5157 - 5926

Summer 61 NG 2429 1180 - 5126 619 7109 622 - 6735 397 - 625 6361 - 7835 Fall 84 NG 2653 1459 - 6769 380 12635 1153 - 12201 978 - 1606 11768 - 16823a Basophils (/L) 2016 232 NG 1606 492 - 4416 0 14467 317 - 6334 246 - 356 5925 - 7485 2017 138 NG 1367 219 - 3063 0 6336 0 - 5667 0 - 1 5134 - 7093a a WCI/WRI > 0.2

293

Supplementary Table 10.2. Hematology parameters from free-living eastern box turtles (Terrapene carolina carolina) sampled from

2016 - 2018. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound of the reference interval.

Parameter Partition N Dist CT Dispersion Min Max Reference 90% CI LB 90% CI UB Interval Heterophils (%) Spring 231 NG 13 5 - 26 2 49 2 - 36 1 - 2 24 - 39a

Summer 56 NG 11 5 - 22 1 50 2 - 27 0 - 2 25 - 31a Fall 85 NG 13 6 - 29 4 41 4 - 40 3 - 4 38 - 35 Lymphocytes (%) Spring 231 G 63 13 29 94 36 - 86 34 - 39 82 - 90

Summer 61 NG 63 40 - 79 4 88 12 - 87 0 - 19a 86 - 91 Fall 85 G 53 13.5 21 86 24 - 82 16 - 27 77 - 87 Monocytes (%) 2016 237 NG 2 0 - 5 0 10 0 - 6 1 - 1 6 - 6 2017 127 NG 3 0 - 6 0 10 0 - 8 1 - 1 8 - 8 Eosinophils (%) Spring 225 NG 10 4 - 22 0 43 3 - 29 2 - 4 28 - 32 Summer 60 NG 14 7 - 30 1 37 3 - 37 0 - 4 36 - 40 Fall 85 G 19 8.5 3 40 3 - 38 1 - 3 36 - 41 Basophils (%) Female 161 NG 6 2 - 16 0 32 1 - 23 0 - 1 22 - 26

Male 148 NG 8 3 - 16 0 25 0 - 23 0 - 0 20 - 27a Juvenile 58 NG 10 4 - 18 1 33 1 - 22 0 - 2 20 - 23 0.029 - Heterophil : Lymphocyte 367 NG 0.2 0.07 - 0.58 0.01 12 0.036 - 0.99 0.044 0.737 - 1.15a a WCI/WRI > 0.2

294

Supplementary Table 10.3. Plasma biochemistry parameters from eastern box turtles (Terrapene carolina carolina). Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval, LB = lower bound of the reference interval, UB = upper bound.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval

Calcium (mg/dL) Female 165 NG 13.55 9.9 - 21.53 7.1 37.3 8.25 - 28.79 7.4 - 8.88 24.36 - 33.17a

Male 124 NG 10.45 7.49 - 12.43 4.8 24.1 7.1 - 14.11 6.7 - 7.4 13.52 - 15.4a Juvenile 37 G 10.4 2.26 5.1 15.2 5.1 - 15.2 2.7 - 5.1a 15.2 - 16.5 Phosphorous (mg/dL) Female 165 NG 4.2 2.9 - 5.61 2 8.8 2.3 - 7.75 2 - 2.55 7 - 8.65a Male 124 NG 3.1 2.3 - 4.51 1.5 6.6 1.8 - 5.29 1.4 - 1.9 5 - 5.58 Ca:P Adult 314 NG 3.39 2.34 - 4.39 1.65 6.4 2 - 4.87 1.88 - 2.06 4.58 - 5.07

Juvenile 61 G 2.93 0.78 1.2 4.38 1.2 - 4.38 0.53 - 1.2a 4.38 - 4.47 Bile Acids (mol/L) Spring 229 NG 4.9 2.6 - 12 1.1 42.3 2.1 - 18.3 1.8 - 2.2 17 - 21.7a

Summer 61 NG 7 2.7 - 15.6 2 28.9 2 - 27 1.6 - 2.1 25 - 36a Fall 85 NG 8.2 4.2 - 15.3 2.6 27.8 2.7 - 26 1.81 - 2.8 24 - 29.8a Uric Acid (mg/dL) Spring 228 NG 1.4 0.9 - 1.5 0.8 3 0.8 - 1.8 0.8 - 0.8 1.6 - 2

Summer 61 NG 1.4 0.8 - 2.6 0.8 3.1 0.8 - 3.04 0.8 - 0.8 2.98 - 3.31 Fall 84 NG 1.5 0.9 - 2.3 0.8 4.4 0.8 - 2.7 0.8 - 0.8 2.2 - 2.9a Creatine Kinase (U/L) Spring 230 NG 307 125 - 777 31 2521 85 - 1424 68 - 107 939 - 1758a

Summer 56 NG 373 156 - 699 42 4268 118 - 1058 77 - 124 1010 - 1354a Fall 83 NG 304 121 - 661 17 5163 61 - 1165 31 - 63 433 - 1555a Aspartate Aminotransferase (U/L) 371 NG 65 38 - 123 19 1230 32 - 199 29 - 34 166 - 232a

295

Supplementary Table 10.4. Protein electrophoresis parameters (absolute values) from free-living eastern box turtles (Terrapene carolina carolina) sampled from 2016 - 2018. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval,

LB = lower bound of the reference interval, UB = upper bound of the reference interval. TP = total protein.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval

TP (g/dL) 75 NG 5 2.88 - 6.32 2 7.2 2 - 7.2 1.2 - 2 7.2 - 7.4 Prealbumin (g/dL) 75 NG 0 0 - 0.07 0 0.11 0 - 0.09 0 - 0 0.09 - 0.09 Albumin (g/dL) Adult 63 G 1 0.43 0.3 1.94 0.31 - 1.85 0.23 - 0.31 1.76 - 2.05 Juvenile 12 G 1.2 0.4 0.66 1.74 - - - Alpha 1 Globulins (g/dL) 75 NG 0.48 0.25 - 0.65 0.18 0.82 0.2 - 0.72 0.16 - 0.22 0.62 - 0.78a Alpha 2 Globulins (g/dL) 75 NG 1.25 0.66 - 1.75 0.46 2.09 0.56 - 2.05 0.49 - 0.66 2 - 2.21 Beta Globulins (g/dL) Female 32 G 1.76 0.53 1.04 2.99 1.04 - 2.99 1.01 - 1.04 2.99 - 3.48a Male 35 G 1.5 0.5 0.63 2.63 0.63 - 2.63 0.52 - 0.63 2.63 - 3.03 Juvenile 12 G 1.3 0.32 0.77 1.85 - - - Gamma Globulins (g/dL) 75 G 0.43 0.15 0.14 0.9 0.16 - 0.72 0.09 - 0.18 0.71 - 0.78 a WCI/WRI > 0.2

296

Supplementary Table 10.5. Protein electrophoresis parameters (relative values) from free-living eastern box turtles (Terrapene carolina carolina) sampled from 2016 - 2018. Dist = data distribution, G = Gaussian, NG = Non-Gaussian, CI = confidence interval,

LB = lower bound of the reference interval, UB = upper bound of the reference interval. TP = total protein.

Reference Parameter Partition N Dist CT Dispersion Min Max 90% CI LB 90% CI UB Interval Prealbumin (%) 75 NG 0 0 - 1.7 0 4 0 - 2 0 - 0 2.38 - 2.38

Albumin (%) Adult 63 NG 22 13 - 27 11 30 11 - 30 9.9 - 11.6 29.5 - 31.3 Juvenile 12 G 25 3.2 21 30 - - - Alpha 1 Globulins (%) Female 32 G 9.1 1 6 11 6 - 11 4.5 - 6.3a 11 - 11.7

Male 35 G 9.6 1.16 7 13 7 - 13 6.8 - 7.3 12.5 - 13.8a Juvenile 12 G 10.4 1.5 8 13 - - - Alpha 2 Globulins (%) 75 G 25 3.4 17 32 18 - 31 18 - 21a 30 - 32 Beta Globulins (%) Female 32 G 39 7.24 27 51 27 - 51 26 - 27 51 - 53.7 Male 35 G 31 5.51 20 44 20 - 44 15 - 20 44 - 47

Juvenile 12 G 29 5.21 21 36 - - - Gamma Globulins (%) 75 NG 9 7 - 12 6 15 5.98 - 14.4 5.52 - 6.2 14.2 - 16.5a Albumin:Globulin Female 32 NG 0.22 0.15 - 0.36 0.13 0.4 0.13 - 0.4 0.11 - 0.13 0.4 - 0.44 Male 33 G 0.31 0.06 0.16 0.44 0.21 - 0.42 0.18 - 0.21 0.42 - 0.46 Juvenile 12 G 0.35 0.06 0.26 0.42 - - - WCI/WRI > 0.2

297

Supplementary Table 10.6. Outliers from hematology, plasma biochemistry, and protein electrophoreses panel reference intervals generated for free-living eastern box turtles (Terrapene carolina carolina).

Parameter Partition Outliers Packed Cell Volume (%) Female 7.5, 40, 45.5 Packed Cell Volume (%) Male 42, 45 Total Solids (g/dL) 2016 12, 11.7, 12, 4.4, 4.4, 11.4, 4.6, 3.8 Total Solids (g/dL) 2017 9.85, 2.5, 2.8, 3.4, 9.6

Total Leukocytes (/L) NA 120560, 3259.26, 4169.85, 73822.22, 63946.67, 168080, 5540.74, 4928, 69181, 3300, 90933 Heterophils (/L) Spring 12438, 9704, 316, 296, 11019 Lymphocytes (/L) NA 102476, 1825, 792, 56273, 157995, 259, 1584, 74565 Eosinophils (/L) Spring 0, 0, 0, 0, 0, 11744, 9499, 7153, 11813, 138 Eosinophils (/L) Fall 380 Basophils (/L) 2016 0, 0, 0, 14468, 10945, 8068, 7314 Heterophil : Lymphocyte NA 0.0117, 0.0114, 2.09, 2.63, 0.024, 0.023, 0.021, 1.9, 12, 0.023 Heterophils (%) Summer 1, 1, 46, 50, 48 Heterophils (/L) Summer 7314, 7691, 260 Monocytes (%) NA 13 Eosinophils (%) Spring 0, 0, 0, 0, 0, 43 Eosinophils (%) Summer 1 Basophils (%) Spring 32, 33 Calcium (mg/dL) Male 24.1, 4.9, 5.1, 6.1, 5.6, 4.8, 4.9, 15.9, 16.1, 22.8, 17.4, 6.7 Phosphorous (mg/dL) Male 1.5, 1.5, 1.5, 1.5, 5.8, 6.6 Ca:P Adult 6.24, 6.4 Uric Acid (mg/dL) Spring 2.8, 3, 2.9 Uric Acid (mg/dL) Fall 4.4 Creatine Kinase (U/L) Spring 31 Creatine Kinase (U/L) Summer 4268, 42, 59, 44 Creatine Kinase (U/L) Fall 17, 5163 Bile Acids (mol/L) Spring 1.1, 42.3 Aspartate Aminotransferase (U/L) NA 22, 1230, 19, 20, 22, 737 Alpha 1 Globulins (g/dL) NA 6.296, 13.23 Gamma Globulins (g/dL) NA 0.9 Albumin:Globulin Male 0.16, 0.44

298

CHAPTER 11: CONCLUSIONS & FUTURE DIRECTIONS

This dissertation sought to holistically characterize health in three herptile species of conservation concern in Illinois by combining traditional veterinary health assessments with epidemiologic modeling and spatio-temporal considerations. This approach assessed the relative utility of common veterinary diagnostic tests such as hematology, plasma biochemistry, and protein electrophoresis in two species of box turtles (Chapter 3, Chapter 8, Chapter 10), investigated the use of adjunctive diagnostic tests in box turtles (Chapter 4, Chapter 5, Appendix

B), generated novel molecular assays for pathogen detection and assessed their diagnostic performance (Chapter 9, Appendix A), characterized mortality events in box turtles and salamanders (Chapter 7, Chapter 9), and provided the first description of a mesomycetozoean parasite in the midwestern United States (Chapter 6). Following this, models were constructed to identify factors associated with positive health status (Chapter 7, Chapter 8, Chapter 10). These models were used to identify the most useful diagnostic tests for health assessment in eastern and ornate box turtles (hematology), characterize potential threats to the conservation of each species

(predation in ornate box turtles, ranavirus in silvery salamanders), and generate management strategies to support individual and population wellness.

Final recommendations consisted of additional health monitoring in all species, land management to restore habitat quality (eastern box turtle) and reduce subsidized mesopredator abundance (ornate box turtle), and additional research into basic biology and ranavirus dynamics to inform silvery salamander conservation. This dissertation demonstrates how a modeling framework can be utilized to understand the complex determinants of herptile health and extract actionable information to support conservation goals. It also illustrates how consideration of

299 health at multiple scales, from individual to population, provides a more nuanced understanding of the threats facing wild herptiles in Illinois. Finally, it supports the idea that health is more than just the absence of disease, and confirms that strategies targeting disease management may not always be necessary to support positive health status in wildlife. While this dissertation represents an important proof of concept, additional research is needed to advance the incorporation of modeling into traditional health assessment approaches.

Many health determinants were considered and assessed during this dissertation, but other factors including toxicants, genetics, and immune function may also affect individual and population health. Future studies should build upon the existing modeling framework by incorporating data on toxin exposure, genetic variability, and markers of innate and acquired immunity to identify additional health threats and improve predictive capabilities of the established models. Additional research into the epidemiology of infectious diseases in free- living populations, including parasites and RNA viruses, may also improve health modeling in wildlife. The current framework is also limited by reliance upon a cross-sectional study design.

Longitudinal monitoring of individual health at more frequent time intervals may clarify the degree and duration of detrimental effects imposed by pathogens and injuries. Pairing this data with concurrent estimates of population stability could better contextualize the broader impacts of poor individual health. Longitudinal assessment may also improve our ability to detect spatiotemporal trends associated with poor health states, and potentially help target management interventions to specific locations and times when they are most needed. Demographic surveys generating population-specific reproductive and mortality parameters are needed to increase confidence in PVA models and enable more precise predictions about the effects of threats and management interventions on long-term population stability.

300

Application of the modeling framework developed in this dissertation to new populations and species will provide the ultimate test of external validity, and demonstrate the generalizability of this method. Additional application and refinement of wildlife health modeling approaches may ultimately enhance our understanding of wildlife health, improve management recommendations and conservation outcomes, and enable appropriate support of herptile and ecosystem health.

301

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APPENDIX A: DEVELOPMENT OF A QUANTITATIVE POLYMERASE CHAIN

REACTION FOR DETECTION OF EMYDID MYCOPLASMA SP.

INTRODUCTION

Upper respiratory tract disease (URD) is the best-characterized disease processes in tortoises (Jacobson 2014). Mycoplasma agassizii has been determined to fulfill Koch’s postulates and produce URD in desert tortoises (Gopherus agassizii) and gopher tortoises (Gopherus polyphemus), while M. testudineum fulfills Koch’s postulates in gopher tortoises (Brown 1994,

Brown 1999b, Brown 2004a). Diagnostic testing options for mycoplasmosis in tortoises include culture, serology, and conventional and quantitative polymerase chain reaction (PCR) (Brown

1994, Brown 1995, Brown 1999a, Brown 1999b, Brown 2004a, Wendland 2007, DuPre 2011).

Culture of mycoplasmas is typically performed on nasal flushes, and false negative results are common due to the fastidious nature of Mycoplasma spp. and a tendency to be outcompeted in culture (Brown 1995, Brown 2004a, Jacobson 2014). Serologic assays for detection of M. agassizii and M. testudineum have been validated for use in gopher and desert tortoises, however, interpretation can be difficult due to the presence of natural antibodies, unknown duration of maternal antibodies in many species, and bacterial strain variation resulting in inconsistent test results (Wendland 2007, Wendland 2010, Jacobson 2014). Molecular diagnostics, including conventional and quantitative PCR, are attractive testing options for

Mycoplasma spp. due to their high specificity, though they cannot determine the viability of detected organisms.

An un-named Mycoplasma sp. has been detected in association with URD in eastern box turtles (Terrapene carolina carolina) and in free-living bog turtles (Glyptemys muhlenbergii),

386 spotted turtles (Clemmys guttata), western pond turtles (Emys marmorata), and red-eared sliders

(Trachemys scripta elegans) with no clinical signs of illness (Feldman 2006, Silbernagel 2013,

Ossiboff 2015c). Sequencing of the 16S ribosomal RNA gene and the 16S-23S intergenic spacer from the Mycoplasma sp. detected in each of these different turtle species has revealed 99% homology, likely representing a new species (Ossiboff 2015c). This organism has not yet been isolated in culture. Detection of this organism therefore currently relies upon molecular diagnostics (Feldman 2006, Ossiboff 2015c). A validated qPCR assay for use in box turtles would facilitate diagnosis and enable monitoring of bacterial copy numbers in infected animals over time to assess response to treatment and recovery. This assay would also be useful for epidemiologic surveillance in free-living box turtles. The purpose of this appendix is to present the development and validation of a novel Mycoplasma sp. qPCR assay targeting the RNA polymerase beta subunit gene in eastern box turtles.

MATERIALS & METHODS

Conventional PCR

Four DNA samples extracted from eastern box turtles with clinical signs of URD were screened for Mycoplasma sp. using three existing conventional PCR assays targeting the 16S ribosomal RNA (16S rRNA) gene, the rRNA 16S-23S intergenic spacer (ITS), and the RNA polymerase beta subunit gene (RNApol) (Chapter 9, Volokhov 2006, Volokhov 2012). Products were electrophoresed on a 1% agarose gel and samples producing bands of the appropriate size

(16S = 500bp, ITS = 1000bp, RNApol = 1800bp) were commercially sequenced in both directions.

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Target Selection

Box turtle sequences for all potential target genes were compared to known sequences in

Genbank using BLASTN. Alignment of each eastern box turtle sequence was performed against the top 10 most homologous Genbank sequences using clustalw (https://www.genome.jp/tools- bin/clustalw) and areas of high divergence between sequences were identified and targeted for qPCR primer-probe design. Both the 16S and ITS sequences were highly similar between box turtle and Genbank sequences (97-98% identical), and primer-probes specific for box turtle

Mycoplasma sp. in silico could not be designed. The RNApol gene appeared more promising, with only 86% homology to M. agassizii and 81% homology to M. testudineum. Assay design was therefore pursued using the RNApol target.

Cloning, Plasmid Preparation, and Primer-Probe Design

A representative RNApol PCR product was cloned into Escherichia coli (TOPO TA

Cloning Kit, Invitrogen, Carlsbad, CA), and plasmids were purified using a commercially available kit (QIAfilter plasmid Maxi Kit, Qiagen, Valencia, CA). Cloning products were verified via sequencing and plasmids were linearized using EcoRI (Clontech Laboratories,

Mountain View, CA). Linearized plasmids were phenol-chloroform precipitated and DNA concentration and purity was assessed spectrophotometrically. Bacterial copy number was calculated using the following formula:

(plasmid + insert ng⁄µl) ∗ (6.022 x 1023 copies⁄mol) #copies/µl = (base pair length)(1 x 109 ng⁄g)(650 g⁄mol of base pair)

TaqMan qPCR primer probes were designed in areas of high sequence divergence using commercially available software (Primer Express, Applied Biosystems, Carlsbad, CA). Two primer-probes (BTMycoRNApol, and BTMycoRNApol1) were assayed in quadruplicate with

388 the following master mix components: 12.5 l of 20x TaqMan Platinum PCR Supermix-UDG with ROX (TaqMan Platinum PCR Supermix-UDG with ROX, Invitrogen, Carlsbad, CA), 1.25

l 20x TaqMan primer-probe, 2.5 l sample, NTC, or plasmid dilution, and water to a final volume of 25 l. Thermocycler settings were as follows: 1 cycle at 50oC for 2 min followed by

95oC for 10 min, then 40 cycles at 95oC for 15 seconds and 60oC for 60 seconds, and a final cycle of 72oC for 10 min. Data analysis was performed using commercially available software

(Sequence Detection Software v2.05, Applied Biosystems, Carlsbad, CA).

Assay Performance, Variability, Specificity, and Efficiency

Initial assay performance including slope, efficiency, R2, and limits of detection was assessed by testing each primer-probe in quadruplicate using a non-template control (NTC, sterile water) and a plasmid-derived standard curve consisting of 10-fold dilutions from 100 – 108 bacterial copies. Intra-assay and inter-assay variability were assessed by calculating the coefficient of variation for cycle threshold values within a single plate run and between plate runs, respectively. Specificity was assessed using sequence-confirmed positive samples for M. agassizii, M. testudineum, M. canis, M. hyorrhinus, M. hyopneumoniae, and M. bovis.

Efficiency curves were performed using uninfected cell culture lysates and box turtle oral swab

DNA samples (negative for Mycoplasma sp.) spiked with plasmid dilutions.

Diagnostic Sensitivity and Specificity

Latent class modeling was used to determine values for diagnostic sensitivity and specificity with the randomLCA package in R version 3.5.1 (Beath 2017, R Core Team 2013).

There is currently no gold-standard test for Mycoplasma sp. in box turtles, so performance of the qPCR assay was compared to two existing conventional PCR assays targeting the ITS and

RNApol genes (Kim 2003, Ossiboff 2015c) in 26 box turtle oral-cloacal swab DNA samples.

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RESULTS

The BTMycoRNApol1 primer-probe set targeting a 63bp region of the RNApol gene

(Forward primer = 5’ GCGGAAGCGCCAATAGTG 3’, Reverse primer = 5’

ATTGGCTGCGGAATATTTAGCT 3, Probe = 5’ CTACAGGTATTGAAGCTGAC 3’) had superior reaction efficiency compared to the BTMycoRNApol primer-probe on initial testing, so full validation was pursued for this primer-probe. The linear range of this assay ranged from 101

– 108 bacterial copies and the limit of detection was 100 copies. Assay performance was subjectively and objectively good, with R2 = 1, efficiency = 99.013%, slope = -3.346, and y- intercept = 38.561 (Figure A1). The assay is precise and repeatable, with inter and intra-assay coefficients of variation < 2% for all dilutions (Table A1). It is also specific, as DNA from other

Mycoplasma spp. failed to amplify. Efficiency of plasmid amplification in cell lysate, low concentrations of box turtle cloacal-oral DNA (2.4ng/L), and high concentrations of box turtle cloacal-oral DNA (77ng/L) was within 3% of the standard curve, indicating no significant inhibition of the assay by box turtle DNA.

Conventional PCR for both the ITS and RNApol gene targets in 27 box turtle DNA samples produced appropriately-sized bands for 18 samples. The BTMycoRNApol1 assay detected Mycoplasma sp. in 17 of these samples. Conventional PCR products from the sample with discordant results were sequenced and confirmed to contain Mycoplasma sp. DNA, indicating a false negative call by the qPCR assay. Latent class modeling estimated 100% specificity and 94% sensitivity for the qPCR assay.

DISCUSSION

This appendix describes creation and validation of a qPCR assay for detection of

Mycoplasma sp. in eastern box turtles. The assay is efficient, precise, specific, repeatable, and

390 has a good linear range of detection for diagnostic purposes. Designing specific PCR assays for differentiating Mycoplasma spp. is somewhat challenging, as genome size for these bacteria is relatively small and there is high homology in traditional genetic targets such as the 16S rRNA gene (Volokhov 2006, Volokhov 2012). While the assay described in the present study is very specific for box turtle Mycoplasma sp., it is less sensitive than some conventional PCR assays.

This likely indicates more genetic variability in the RNApol gene than was originally appreciated in the four samples used to design the BTMycoRNApol1 primer-probe. This variability could be due to differences in bacterial strain, which have been identified in two other highly conserved genes (16S rRNA and ITS) in Mycoplasma sp. from box turtles and other emydid chelonians

(Ossiboff 2015c). Strain variation in multiple genes of Mycoplasma sp. will likely continue to complicate the design of molecular assays that are both sensitive and specific for detection of this pathogen. Future studies should characterize the genetic variability of box turtle

Mycoplasma sp. in order to better understand disease epidemiology and enable design of more sensitive diagnostic tests. Until then, the assay developed in the present study can be employed, but results must be considered within the context of imperfect sensitivity.

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Table A1. Intra-assay and inter-assay cycle threshold coefficients of variation for a quantitative polymerase chain reaction detecting Mycoplasma sp. in eastern box turtles (Terrapene carolina carolina).

Intra-Assay Inter-Assay CT CT CV CT CT CV Copy Number Mean SD (%) Mean SD (%) 100,200,000 12.05 0.05 0.42 11.89 0.17 1.47 10,020,000 15.03 0.06 0.39 15.02 0.06 0.41 1,002,000 18.38 0.05 0.27 18.41 0.05 0.29 100,200 21.72 0.02 0.08 21.68 0.05 0.21 10,020 25.07 0.04 0.15 25.08 0.03 0.12 1,002 28.45 0.08 0.27 28.47 0.10 0.35 100.0 31.89 0.01 0.04 31.92 0.10 0.30 10.0 35.37 0.07 0.20 35.23 0.29 0.83 1* 36.91 0.32 0.86 36.92 0.28 0.75 NTC ND ND ND ND ND ND * Outside of the linear range of detection

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Figure A1. qPCR assay performance for detection of Mycoplasma sp. in eastern box turtles

(Terrapene carolina carolina). A) Amplification plot for 10-fold serial dilutions from 101 – 108 bacterial copies. B) Standard curve, R2 = 1, efficiency = 99.013%, slope = -3.346, y-intercept =

38.561

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APPENDIX B: INITIAL ASSESSMENT OF THE CLOACAL MICROBIOME IN

EASTERN BOX TURTLES (TERRAPENE CAROLINA CAROLINA):

CHARACTERIZING DEMOGRAPHIC AND ENVIRONMENTAL ASSOCIATIONS

INTRODUCTION

The gastrointestinal (GI) microbiome has received considerable research attention due to its association with medical conditions in humans and animals (Findley 2016, Barko 2017).

Mammals are overrepresented in these studies, but consideration of microbial communities in reptiles is increasing (Keenan 2013, Keenan 2015, McLaughlin 2015, Abdelrhman 2016, Kohl

2016, Rawski 2016, Ahasan 2017, Jiang 2017, Price 2017, Ahasan 2018, Allender 2018a,

Campos 2018). Factors demonstrated to affect reptile gut microbial communities include diet

(Jiang 2017), administration of probiotics (Rawski 2016), origin (Jiang 2017), location (Price

2017, Campos 2018), and health status (Ahasan 2017, Ahasan 2018). Most existing studies involve small numbers of animals, and determination of demographic and environmental associations with microbiome data is limited. Reptiles exhibit significant variability in physiologic parameters in association with season, sex, and age-related factors (e.g. Anderson

1997, Rose 2011, Tamukai 2011, Flower 2014). Understanding how these factors may influence

GI microbiome data is important for contextualizing microbiome studies and identifying confounding variables to inform experimental design and analytical control. The objective of this study is to characterize environmental and demographic associations with the cloacal microbiome of free-living eastern box turtles (Terrapene carolina carolina). The hypothesis is that cloacal microbiome community metrics will vary by sex, age class, season, and site.

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MATERIALS & METHODS

Fieldwork

Box turtles were evaluated at four study sites within the Vermilion County Conservation

Opportunity Area in east-central Illinois from 2013-2015: Forest Glen Nature Preserve (FGNP

[40.01°, -87.57°]), Kennekuk Cove County Park (KCCP [40.19°, -87.72o]), Kickapoo State Park

(KSP [40.14°, -87.74o]), and Collison (COL [40.28o, -87.78o]). Turtles were also evaluated in

Oak Ridge, Tennessee (36oN, 84o13’12’’W) from 2013-2014 and at the Stephen A. Forbes State

Park (38.73o, -87.78o) in Marion County, Illinois in 2015.

Turtles were located using a combination of human and canine searches and GPS coordinates, habitat (field, forest, edge) and microhabitat (grass, brambles, leaves, soil) data were recorded at each site of capture. Microhabitat classification was assigned based on the primary ground cover beneath and within a 12” radius of the turtle. Turtles were assigned to an age class

(> 200 g: adult, < 200 g: juvenile), and sexed using a combination of plastron shape, eye color, and tail length (Dodd 2001).

Complete physical examinations were performed by a single observer (MCA). The following systems were evaluated and coded as within normal limits (WNL) or abnormal: eyes, nares, tympanic membranes, oral cavity, integument, appendages, shell, musculoskeletal, and cloaca. Cloacal swabs were collected using cotton-tipped plastic handled applicators (Fisher

Scientific, Pittsburgh, PA 15275) and stored on wet ice in the field. Samples were frozen at -

20oC within 3 hours of collection and stored at -80oC long-term. The marginal scutes of each turtle were notched to enable identification of recaptured individuals (Cagle 1939). Following evaluation, sample collection, and notching, turtles were released at their original sites of

395 capture. Protocols for animal handling and sampling were approved by the University of Illinois

Institutional Animal Care and Use Committee (#13061).

DNA Extraction, Sequencing, Data Processing, & Statistical Analysis

DNA was isolated from cloacal swabs using a commercially available kit (QIAmp DNA

Blood Mini Kit and DNAeasy kit, Qiagen, Valencia, CA, USA) following manufacturer instructions. DNA concentration and purity was assessed spectrophotometrically (NanoDrop

1000, Thermo Fisher Scientific, Waltham, MA, USA) and DNA samples were stored at -80oC.

Bacterial and fungal amplicons were PCR amplified from cloacal swab DNA using universal primers targeting the V4 region of the bacterial 16S rRNA gene and the fungal ITS2 region. For the bacterial 16S rRNA gene, amplicons were generated with primers 515F and 926R while the fungal ITS2 region was amplified with primers ITS3 and ITS4 (White 1990, Apprill

2015, Parada 2014). Barcoded amplicons were sequenced at the W. M. Keck Center for

Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign

(Urbana, IL, USA) on an Illumina MiSeq platform with a 2 x 250 bp read configuration

(Illumina, San Diego, CA, USA).

The dada2 pipeline was used to demultiplex sequence data, trim primers, and filter the reads (Callahan 2016). Silva (version 132) was used as the reference for V4 data and USEARCH

(release 01.12.2017) was used at the reference for ITS data for quality filtering, removal of chimeric reads and duplicate sequences, and merging of paired reads (Edgar 2010). Trimming and filtering parameters were kept at default values except minimum sequence length following primer trimming, which was set to 50 nucleotides. Resulting amplicon sequence variant (ASV) tables were uploaded into QIIME2 and filtered. In order to be retained, each sample had to have at least 1000 sequences and each feature (ASV) had to occur in at least 2 samples and contain at

396 least 5 sequences. QIIME2 was used to calculate Shannon scores and OTU abundance (alpha diversity) and Jaccard scores (beta diversity) and to run statistical analyses including

PERMANOVA to evaluate differences between categorical predictors (state, site, season, sex, age class, adenovirus status, TerHV1 status) and beta diversity. Tests for homogeneity of dispersion (PERMDISP) were run to assess the assumption of homogeneous dispersion in

PERMANOVA (Anderson 2006). Principle component analysis (PCoA) plots were generated to visualize differences in beta diversity. Shannon scores and OTU abundance were exported from

QIIME2 and further evaluated in R version 3.5.1 (R Core Team 2013) using general linear models to enable statistical analyses including covariates, and to control for confounding. An alpha value of 0.05 was used for all statistical analyses.

RESULTS

DNA samples from 528 box turtle cloacal swabs were submitted for microbiome analysis. Sample size and demographic information by study site and year is presented in Table

B1. Very few turtles presented with clinical signs of illness, preventing rigorous statistical assessment of the relationship between cloacal microbiome and physical examination abnormalities. One turtle was excluded from bacterial analysis and 20 turtles were excluded from fungal analysis due to low numbers of sequencing reads. The final number of bacterial sequences included in the analysis was 11,859,886 and the final number of fungal sequences was

2,040,651. Proteobacteria, Bacteroides, Firmicutes, and Actinobacteria were the most common bacterial taxa associated with the eastern box turtle cloaca, while Ascomycota and

Basidiomycota were the most common fungal taxa.

Bacterial alpha diversity varied with year, state, site, and season, but not with age class or sex. Both OTU abundance and Shannon indices were higher in 2015 compared to 2013 (OTU

397 effect size = 27.78, p = 0.002; Shannon effect size = 0.389, p = 0.0005) and 2014 (OTU effect size = 20.05, p = 0.03; effect size = 452, p = 0.0001). OTU abundance was higher in Illinois compared to Tennessee (effect size = 14.6, p = 0.01), while this difference approached significance for Shannon index (p = 0.09). Abundance was higher at Kennekuk compared to

Forbes (effect size = 38.52, p = 0.007) and Oak Ridge (effect size = 28, p = 0.009); and lower at

Forbes compared to Kickapoo (effect size = 27.37, p = 0.04). Shannon index was lower at Forbes compared to Kickapoo (effect size = 0.358, p = 0.03) and Oak Ridge (effect size = 0.327, p =

0.04); and lower at Forest Glen compared to Kickapoo (effect size = 0.3, p = 0.02) and Oak

Ridge (effect size = 0.27m p = 0.008). Abundance was lower in spring compared to summer

(effect size = 23.5, p = 0.0001) and fall (effect size = 30.75, p = 0.0001), while Shannon index was highest in fall compared to summer (effect size = 0.217, p = 0.03) and spring (effect size =

0.414, p < 0.0001) and higher in summer than spring (effect size = 0.198, p = 0.009).

Fungal alpha diversity varied by year, state, site, season, sex, and age class. Both OTU abundance and Shannon indices were lower in 2013 compared to 2014 (OTU effect size = 21.45, p < 0.0001; Shannon effect size = 0.867, p < 0.0001) and 2015 (OTU effect size = 20.31, p <

0.0001; effect size = 0.715, p = 0.0001). Shannon index was higher in Illinois compared to

Tennessee (effect size = 0.43, p = 0.03), while this difference was not significant for OTU abundance (p = 0.5). OTU abundance was higher at Kickapoo compared to Collison (effect size

= 9.82, p = 0.04). Shannon index was lower in Oak Ridge compared to Forest Glen (effect size =

0.371, p = 0.02), Kennekuk (effect size = 0.595, p = 0.005), and Kickapoo (effect size = 0.629, p

= 0.0004). Both OTU abundance and Shannon index were lower in the summer compared to both spring (OTU effect size = 6.86, p = 0.02; Shannon effect size = 0.366, p = 0.002) and fall

(OTU effect size = 11.42, p = 0.002; effect size = 0.625, p = 0.0001). Higher OTU abundance

398 and Shannon index values were present in males compared to females (OTU effect size = 12.14, p < 0.0001; Shannon effect size = 0.373, p = 0.0003). Shannon index was higher in adults than juveniles (effect size = 0.361, p = 0.04), while this difference approached significance for OTU abundance (p = 0.15).

Bacterial and fungal beta diversity varied by year, site, sex, age class, and season (p =

0.0001) (Figures B1 & B2). The PERMDISP test was significant for bacterial season and site and fungal season and sex analyses, indicating heterogeneity of dispersion, which could affect the results of these analyses.

DISCUSSION

This study provides initial characterization of the cloacal microbiome in free-living eastern box turtles. Differences in cloacal community abundance and diversity were observed between years, study sites, seasons, ages, and sexes, indicating that each of these variables must be considered for planning, analyzing, and interpreting chelonian cloacal and GI microbiome studies.

Direct comparison of cloacal microbiome data to GI microbiome data is somewhat challenging, as the cloaca is in contact with excretions from the urinary tract and reproductive system in addition to the GI. Furthermore, community differences are common even within GI segments, for example, differences can be detected between the GI lumen and the GI wall

(Goodrich 2014, Barko 2017). Despite this, eastern box turtle cloacal flora was generally similar to the cloacal and GI flora reported in other reptiles. For example, Proteobacteria, Bacteroides, and Firmicutes are also common distal gut inhabitants of green sea turtles (Chelonia mydas) and crocodile lizards (Shinisaurus crocodilurus) (Abdelrhman 2016, Ahasan 2017, Jiang 2017,

Ahasan 2018, Campos 2018). Proteobacteria and Bacteroides are common in the American

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Alligator (Alligator mississippiensis) GI tract, though Fusobacterium dominates in this species

(Keenan 2013, Keenan 2015). Proteobacteria is the most abundant phylum in the timber rattlesnake GI microbiome (McLaughlin 2015). The four most abundant bacterial taxa identified in eastern box turtles are also similar to those reported in mammals (Balko 2017).

The quality of microbiome data can be affected by sample collection methods, DNA extraction methodology, and sample handling – specifically the number of freeze-thaw cycles prior to analysis (Goodrich 2014). It is impossible to rule out the possibility that some of these factors may have influenced the data generated in this study, though sample collection, processing, and handling was performed in a consistent manner each year to reduce this potential source of variability. Several PERMDISP tests produced significant p-values, indicating that some of the variation identified in the PERMANOVA tests may have been due to heterogeneity of dispersion. PERMANOVAs tend to be fairly robust to these violations, however, results for bacterial season and site and fungal season and sex analyses should be interpreted with caution

(Anderson 2006).

The baseline information produced in the present study is useful for future comparison to sick and injured turtles. Shifts in the cloacal microbiome have been identified in stranded and rehabilitating green sea turtles, indicating that cloacal microbiome analysis may provide insight into the health status of chelonians. As commonly-used veterinary diagnostic tests are not always sensitive for detecting poor health in reptiles, exploration of adjunctive tests such as microbiome analysis may enhance veterinary understanding of health and disease in reptiles.

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Table B1. Sample sizes by year, location, season, sex, and age class for free-living eastern box turtles (Terrapene carolina carolina) included in cloacal microbiome analysis.

Year Location N Spring Summer Fall Female Male Unknown Adult Juvenile 2013 Collison 45 18 25 2 28 14 3 38 7 Forest Glen 36 16 8 12 16 18 2 32 4 Kennekuk 13 0 0 13 2 7 4 10 3 Kickapoo 27 6 9 12 12 13 2 26 1 Oak Ridge 112 49 30 33 32 61 18 99 12 2014 Collison 21 12 9 0 8 12 1 20 1 Forest Glen 22 12 10 0 7 13 2 18 3 Kennekuk 11 6 5 0 5 4 2 9 2 Kickapoo 14 12 2 0 9 5 0 14 0 Oak Ridge 106 44 40 22 45 57 4 102 4 2015 Collison 24 10 14 0 9 14 1 22 2 Forest Glen 19 7 12 0 7 11 1 18 1 Kennekuk 14 9 5 0 12 2 0 12 2 Kickapoo 16 3 13 0 5 10 1 16 0 Forbes 47 0 47 0 13 29 5 43 4

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Figure B1. Principle component analysis plot of Jaccard distance scores representing fungal beta diversity in cloacal DNA samples from free-living eastern box turtles at six different study sites.

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Figure B2. Principle component analysis plot of Jaccard distance scores representing bacterial beta diversity in cloacal DNA samples from free-living eastern box turtles. Colors represent age class.

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