The Pennsylvania State University

The Graduate School

USING A MULTI-FACETED APPROACH TO ASSESS ECOLOGICAL COMPONENTS

AFFECTING MUSKRAT (ONDATRA ZIBETHICUS) POPULATIONS

A Thesis in

Wildlife and Fisheries Science

by

Laken Samantha Ganoe

© 2019 Laken Samantha Ganoe

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

December 2019

The thesis of Laken Samantha Ganoe was reviewed and approved* by the following:

W. David Walter Adjunct Assistant Professor of Wildlife Ecology Thesis Advisor

Duane R. Diefenbach Adjunct Professor of Wildlife Ecology

Justin D. Brown Assistant Teaching Professor of Veterinary and Biomedical Sciences

Matthew J. Lovallo Wildlife Biologist at the Pennsylvania Game Commission Special Signatory

David Eissenstat Interim Head of the Department of Ecosystem Science and Management

*Signature are on file in the Graduate School

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ABSTRACT

The muskrat (Ondatra zibethicus) is a prominent and wide-spread furbearer in North

America. Historically valued for their waterproof pelts and abundance of capture, muskrats have been trapped for their fur in North America for centuries. However, evidence of a decline of muskrat harvest since 1970 has been observed across the United States. Theories as to why

populations declined include habitat loss, increased flooding events, predation, environmental

contamination, and disease. Two of these theories, environmental contamination and disease, can

directly affect the physical health of individual muskrat. Disease transmission between individual

muskrats and muskrat populations may be influenced by various components of muskrat ecology

(e.g. semi-colonial behavior). Therefore, it is important to not only understand the prevalence of specific pathogens, diseases, and contaminants that muskrats are exposed to, but also to

understand the role that muskrat ecology plays in population health. To address these knowledge

gaps, I employed a multifaceted approach to define pathogens, diseases and contaminants of

muskrats, including: 1) retrospective study of published data on muskrat diseases in North

America; 2) review of diagnostic data on muskrats in the Eastern United States in combinations

with active surveys of diseases and pathogens of trapper-killed muskrats throughout

Pennsylvania, and 3) a telemetry study in Pennsylvania to characterize survival and ecological

factors of muskrats that may influence disease transmission.

In Chapter 1, I conducted a literature review on muskrat parasites, pathogens, and disease

across their natural range. This review was comprised of 129 articles from 1915 to 2019. Reports

included were from 27 U.S. states and 9 Canadian provinces, and cover the following disease

etiologies: parasites, bacteria, viruses, and contaminants. Several notable diseases and pathogens

causing mortality in muskrats were cysticercosis, tularemia (Francisella tularensis), and

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Tyzzer’s disease (Clostridium piliforme). I identified gaps in the literature, specifically relating to biotoxin poisoning and impacts of both agricultural and chemical contamination on muskrat health. The retrospective study provided a basis for further investigations in this study.

In Chapter 2, I built upon previous literature based on passive surveillance which was comprised of reviewing diagnostic cases of muskrats from the Southeastern Cooperative Wildlife

Disease Study. Aside from trauma, the main causes of mortality in the diagnostic cases were

Tyzzer’s disease and cysticercosis. These commonalities, along with existing knowledge gaps identified during the retrospective study, provided me with the foundation for components to examine using active surveillance. I collected 380 muskrat carcasses from across Pennsylvania to examine exposure to bacterial (C. piliforme, F. tularensis) and parasite (Toxoplasma gondii,

Babesia spp., intestinal parasites) infections as well as heavy metal contamination. I detected

Tyzzer’s disease, sarcosystosis, and toxoplasmosis at low prevalence. I failed to detect tularemia, babesiosis, or cadmium, arsenic, and mercury contamination. Parasite burdens were typical of historic reports, however younger muskrats had larger burdens than older muskrats which is contradictory to what was observed in Pennsylvania in 1966. The Northcentral and Northwest regions had higher parasite burdens than all of the southern regions combined (P<0.03). I also documented the first positive detection of Versteria mustelae infection in muskrats using genetic sequencing. In addition, I found no relationships between heavy metal concentrations and landscape features, and only zinc concentrations varied by sex.

In Chapter 3, I investigated the dwelling structure use, movements, home range, and survival of radio-tagged muskrats (n = 17) in an urban wetland complex in central Pennsylvania.

I used locations collected from intensive radio telemetry monitoring to determine number of lodging structures used, hourly movement, and size and percent area overlap of home ranges. I

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observed muskrats sharing large amounts of space within home ranges (68% of each individual

home range) with other muskrats. I also determined that of four home range estimators (Kernel

Density Estimator (KDE) href, KDEad hoc, KDEplug-in, and Local Convex Hull estimator), KDEplug-

in provided the more appropriate home range size for muskrats in a linear-non-linear habitat matrix. I also calculated overwinter survival estimates using Known Fate models in Program

MARK®. My top model showed a positive effect of the average weekly precipitation on survival with an overwinter survival estimate of 0.59 (SE = 0.16). The main cause of muskrat mortality

was mink (n = 6). My model suggests that snowfall may be an important factor in muskrat

survival.

Using a multi-faceted approach, I was able to 1) find gaps in knowledge on muskrat

health and key pathogens and disease causing mortality, 2) define the prevalence of historic

pathogens and disease in the current muskrat populations in Pennsylvania, as well as define some

relationships between muskrat health, sex, and heavy metal contamination, and 3) document the

semi-colonial nature of muskrats which may influence disease transmission, while also

distinguishing KDEplug-in as the most appropriate estimator to use in movement studies on muskrats in a linear-non-linear habitat matrix. This project provides the groundwork for future investigations of muskrat population health.

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Table of Contents LIST OF TABLES ...... vii LIST OF FIGURES ...... viii ACKNOWLEDGEMENTS ...... x Chapter 1: A retrospective study of pathogens, diseases, and contaminants of muskrats (Ondatra zibethicus) ...... 1 Abstract ...... 2 Introduction ...... 2 Materials and methods ...... 4 Results ...... 5 Discussion ...... 25 References ...... 29 Chapter 2: Passive and active surveillance for disease of muskrat (Ondatra zibethicus) ...... 48 Abstract ...... 49 Introduction ...... 49 Materials and Methods ...... 51 Results ...... 55 Discussion ...... 57 Literature Cited ...... 64 Chapter Three: Ecology of an isolated muskrat population during regional population declines ...... 81 Abstract ...... 82 Study Area ...... 85 Methods ...... 86 Results ...... 89 Discussion ...... 91 Management Implications ...... 95 Literature Cited ...... 97 APPENDIX. Supplemental material associated with Chapter One SI Table...... 107

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LIST OF TABLES Chapter One Table 1: Number of species reported of each respective parasite category in historic literature reviews of muskrat parasites from 1947-1986…………………………….3 Table 2: Range of years, number of studies cited, and geographical representation covered in respective historical review articles on muskrat parasitology…………….4 Table 3: Mercury (Hg) concentrations found in muskrat tissue samples from four historical studies. ……………………………………………………………………20 Table 4: Cadmium (Cd) concentrations found in muskrat tissue samples from six historical studies……………………………………………………………………..21 Table 5: Lead (Pb) concentrations found in muskrat tissue samples from seven historical studies……………………………………………………………………..22 Table 6: Agricultural-related contaminants and their concentrations found in muskrat tissues…………………………………………………………………..…23 Chapter Two Table 1: Passive surveillance and diagnoses for muskrat submitted to the Southeastern Cooperative Wildlife Disease Study 1977-2018…….……………..…….68 Table 2: Active surveillance for pathogens in muskrats collected from Pennsylvania in 2018- 19………………………………………………………….……………...69 Table 3: Mean (± ) liver concentrations for heavy metals in male and female muskrats ………...………………………………………………………………….70 𝑆𝑆𝑆𝑆 Table 4: Mean (± ) liver concentrations for various heavy metals from muskrats harvested in different water body types..………………………………………………71 𝑆𝑆𝑆𝑆 Chapter Three Table 1: Total (95%) and core (50%) size of home range for individual muskrats using four different estimators………………………………………………….…...103 Table 2: Overwinter survival estimates of muskrat population in Pennsylvania for eight known- fate models………………………………………….……………………105 Table 3: Intercept and beta estimates for eight known-fate models of overwinter survival of a muskrat population in Pennsylvania…………………………..…………106

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LIST OF FIGURES Chapter One Figure 1. Historic study locations between 1936–2015 where protozoan parasites have been documented in muskrats in North America…………………………….41 Figure 2. Historic study locations between 1936–2011 where ectoparasites have been documented on muskrats in North America……………………………42 Figure 3. Historic study locations between 1915–1981 where trematode parasites have been documented in muskrats in North America…………………………….43 Figure 4. Historic study locations between 1915–2012 where cestode parasites have been documented in muskrats in North America…………………………….44 Figure 5. Historic study locations between 1915-1993 where nematode parasites have been documented in muskrats in North America…………………………….45 Figure 6. Historic study locations between 1952–2016 where bacterial infections have been documented in muskrats in North America…………………………….46 Figure 7. Historic study locations between 1966–2017 where viral infections have been documented in muskrats in North America…………………………….47 Chapter Two Figure 1. Townships in Pennsylvania where muskrat carcasses were collected in 2019 ………………………………………………………………………….72 Figure 2. Distribution of landcover type and water body type where muskrat (Ondatra zibethicus) carcasses were collected in Pennsylvania ………..……..….73 Figure 3. Muskrat carcass weight in each region from active surveillance……....74 Figure 4. Linear regression depicting the relationship between muskrat carcass weight and the surface area of muskrat tails with respect to sex………………………..75 Figure 5. Stacked prevalence of clinical signs (e.g. cysts, intestinal parasites) found in muskrats in Pennsylvania by region………………………………………………76 Figure 6. Loess regression of carcass weight (kg) to parasite burdens found in individual muskrats for both male and female muskrats..………………………….77 Figure 7. Mean liver concentrations of heavy metals in male and female muskrats …………………………………………………………………………..78 Figure 8. Relationships between muskrat carcass weight and heavy metal concentrations for each sex ……………………………………………………………………....79 Figure 9. Relationships between carcass weight and heavy metal concentrations for unhealthy and healthy muskrats…………………………………………………….80

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Chapter Three Figure 1. Location of study site in Lewisburg, Pennsylvania ……………………100

Figure 2. Comparison of all four estimators of home range for the 95% and 50% isopleths for two individual muskrats……..………………………………………….101

Figure 3. Muskrat and mink harvest estimates in Pennsylvania from 1985 to 2018 ………………………………………………………………………...... 102

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ACKNOWLEDGEMENTS

I would like to thank all parties and personnel involved in making this project possible.

To my advisor, David Walter, thank you for taking me on as a student in your lab, trusting me

with this project, and for sharing your expertise with me. I also want to thank Matt Lovallo for

everything he has done for me and for this project, from the project synthesis and funding to the

completion of the manuscripts and everything in between. In addition, I thank Justin Brown as

well for his moral support and dedication to the project, without which this project would not

have been possible. Thank you to my other committee member, Duane Diefenbach, for all of his

patience and insight along the way. Additionally, I would like to thank Michael Yabsley, Mark

Ruder, and the students and staff at the Southeastern Cooperative Wildlife Disease Study for

their support, expertise, and the processing of our samples in the lab. I am grateful for the time

and effort that regional Pennsylvania Game Commission staff contributed during muskrat

carcass collections. This project also would not have been completed without all of the hard

work and hours that my many volunteers contributed, of which I am extremely grateful.

In addition, I would like to thank everyone in the Pennsylvania Cooperative Research

Unit, the faculty and students in the Department of Ecosystem Science and Management, and my friends outside of Penn State. These people provided me with continued support, both emotionally and mentally, throughout my research. I am extremely grateful to have been surrounded by such wonderful people on the fourth-floor who kept me sane and gave me a hand whether I knew I needed one or not. Finally, I want to thank my family for their unwavering encouragement of my passions. I am truly blessed to have such a loving and supportive family standing by me.

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PLOS ONE Chapter 1

A retrospective study of pathogens, diseases, and contaminants of muskrats (Ondatra zibethicus).

Laken S. Ganoe1

Justin D. Brown2

Michael J. Yabsley3,4

Matthew J. Lovallo5

W. David Walter1,6

1 Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, Pennsylvania, United States of America. 2 Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America 3 Southeastern Cooperative Wildlife Disease Study, University of Georgia, Athens, Georgia, United States of America 4 Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, United States of America 5 Bureau of Wildlife Management, Pennsylvania Game Commission, Harrisburg, Pennsylvania, United States of America 6 U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, Pennsylvania, United States of America

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Abstract Recently, muskrat (Ondatra zibethicus) harvest declines have been observed in North America. Several theories for the decline have been proposed, including increased parasites and disease within muskrat populations. We conducted a literature review on muskrat parasites, pathogens, and disease across their natural range. This review is comprised of 129 articles spanning 1915-2019. Reports included are from 27 U.S. states and 9 Canadian provinces, and cover the following disease etiologies: parasites, bacteria, viruses, and contaminants. Several notable diseases and pathogens causing mortality in muskrats were cysticercosis, tularemia (Francisella tularensis), Tyzzer’s disease (Clostridium piliforme), and biotoxin poisoning from cyanobacteria. To understand muskrat health during the current decline, it is important to relate current research on muskrat pathogens and disease to historic reports. There are gaps in the knowledge of contaminant toxicity, bacterial prevalence, and much of the geographic range of pathogens in muskrats has yet to be documented. Continued active and passive surveillance for these, as well as new ones that may emerge or be detected using new techniques, is encouraged. Introduction The muskrat (Ondatra zibethicus) is a prominent and wide-spread furbearer in North

America (Erb and Perry 2003). Since 1970, muskrat harvest estimates have declined in the northeastern United States (Roberts and Crimmins 2010). Evidence of synchronous declines in

muskrat harvest have also been observed throughout the native range of muskrats, with decreases

exceeding 50% in some states (Ahlers and Heske 2017). Harvest estimates have historically been used in combination with other factors to estimate game species population abundance in order to adjust bag-limits on harvest during sequential years (Erickson 1982).

The observed muskrat harvest decline suggests a correlated population decline across much of North America. Several theories for the widespread muskrat declines have been proposed, including habitat loss, increased flooding events, predation, and environmental contamination (Ahlers and Heske 2017). In addition, other ancillary factors, such as pathogen and contaminant exposure, have been suggested to contribute to the observed declines

(Marcogliese and Pietrock 2011).

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Muskrats are a semiaquatic species that thrive in a variety of habitats including, marshes, ponds, streams and rivers. Consequently, muskrats are potentially exposed to a high diversity of pathogens and contaminants, including those associated with terrestrial and aquatic ecosystems.

For example, muskrats have reportedly been infested with mites commonly found on terrestrial mammals (e.g. Listrophorus and Laelaps spp.) as well as with water mites (Hydrachnidia spp.)

(Buckley and Hicks 1962). In addition, because muskrats have a wide geographic range throughout North America, regional differences in pathogen and contaminant exposures may occur.

While there is abundant literature on pathogen and contaminant exposure of North

American muskrats (Table 1 and Table 2), existing data are insufficient to evaluate whether infectious or non-infectious diseases are contributing to the observed declines. Most of the comprehensive reviews to-date have been regionally specific and/or conducted on data from

1914 to 1948, which predates the observed declines (Table 2; Knight 1951). In addition, all of the existing reviews have focused exclusively on parasites and did not include non-infectious diseases (e.g. contaminants and toxins) or bacterial, fungal, and viral infections. Consequently, an extensive review that incorporates data on all pathogens and contaminants of North American muskrats and includes historic and contemporary literature is warranted.

Table 1: Number of species reported of each respective parasite category in historic literature reviews of muskrat (Ondatra zibethicus) parasites from 1947-1986.

Musfeldt Meyer & Reilly Knight Becket & Gallicchio Kennedy 1986 1947 1950 1951 1967 Protozoa 4 0 4 0 4 Trematoda 26 29 27 17 22 Cestoda 9 8 9 8 11 Nematoda 12 12 14 7 8 Acarina 4 5 6 0 0 Pentastoma 0 1 0 0 0

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Arachnida 1 0 1 0 0 Acanthocephala 0 0 0 1 1 Insecta 0 0 1 0 0 Filaria 1 0 0 0 0 Total 57 55 62 33 46

Table 2: Range of years, number of studies cited, and geographical representation covered in respective historical review articles on muskrat (Ondatra zibethicus) parasitology

Author Years Reviewed Authors Cited Geographical Representation Musfeldt 1947 1914-1946 32 British Columbia Meyer & Reilly 1950 1909-1949 38 Maine Knight 1951 1914-1948 34 British Columbia Becket & Gallicchio 1967 1951-1966 19 Ohio Kennedy 1986 1930-1981 25 Canada

The goal of this study was to review existing peer-reviewed data and technical reports on pathogen (parasites, bacteria, virus, fungi) and contaminant exposure of North American muskrats, with an emphasis on potential population-level impacts of associated disease (if present). Unfortunately, there has been significant taxonomic changes for many of the pathogens over the time period covered in this review, and some parasite identifications are unreliable due to lack of detail or taxonomic revisions (i.e., splitting of species). These changes will be highlighted and explained throughout the manuscript, where necessary.

Materials and methods Existing literature related to exposure of muskrat in North America to pathogens or contaminants was obtained from Google Scholar and Web of Science ™ using a combination of keywords, including “muskrat,” “infection,” “disease,” “contaminant,” “parasite,” “health,” and

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“exposure,” as well as several other more specific pathogen and contaminant names. Sources

referenced within literature found using the search engines were also investigated for additional

data relevant publications. Information collected from articles (if applicable) included; year of

survey, location of survey, methodology used, number of animals surveyed, type of

microbe/parasite/contaminant, species detected, whether any detections were associated with

disease (as evidenced by reported clinical signs or lesions), prevalence, and other pertinent

information. Data were segregated into sections based on the exposure type (parasites, bacteria,

viruses, and contaminants). Along with inclusion in the table organized by exposure type, any morbidity or mortality associated with exposure were compiled into an additional reference

table.

Results Parasites

Parasites can exert negative impacts on the health of their host through a diversity of

mechanisms, including direct morbidity or mortality due to tissue damage or indirectly by

utilizing host resources, decreased growth and survival of young, or altering susceptibility to

other pathogens (Yuill 1987, Smith et al. 1993, Munger and Karasov 1994). Several taxa of parasites have been reported from North American muskrats, either sporadically or commonly, including protozoans, ectoparasites, cestodes, nematodes, trematodes, pentastomes, and acanthocephalans. For the purposes of this report, all reports have been summarized and are data relevant data presented herein; however, discussion of individual parasites will be restricted to those which have been reported in greater than 10 individuals.

Protozoa

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Protozoa are single-celled eukaryotes that vary in pathogenicity depending on the species

of protozoa and host susceptibility (Yaeger 1996; Hickman et al. 2012). Protozoan life cycles

may be complex and require different host species for development. A number of protozoa have

wide host ranges which include not only wildlife species, but also humans and domestic animals

(Thompson et al. 2009).

Since 1936, 17 articles have reported seven protozoan species in muskrat. These studies

represent samples from 17 states and Canada. Most cases in the US were from three regions;

Snake/Colorado River Drainage Areas, Mississippi River Drainage, and Northeastern states (Fig.

1). The most commonly reported protozoa included Giardia sp. (n = 10), Toxoplasma gondii (n

= 4), and Cryptosporidium sp. (n = 3). Other species of intestinal protozoan parasites documented in muskrat were Chilomastix sp. (n = 1), Eimeria spp. (n = 2), and Trichomonas sp.

(n = 2).

The protozoan species and the observed prevalence in muskrats varied among studies.

Giardia sp. was reported in nine studies representing 14 states with prevalence ranging from 36-

100%. Cryptosporidium sp. was reported in three studies representing four states and had a

prevalence ranging from 0.7-50%. While these parasites do not appear to have significant health

implications for muskrats, some may have health implications for humans and domestic animals

(e.g. Giardia and Cryptosporidium).

No clinical signs or lesions were reported with these protozoans in any of the published

studies. Smith (1938) examined 1,581 muskrats and attributed two mortalities to intestinal

coccidiosis, but the etiologic agent was not determined. Allen (1934) determined Eimeria

ondatrazibethicae (reported as E. stiedae) as the cause of liver coccidiosis in the muskrats in his study. Toxoplasma gondii is a protozoan parasite that is an important source of disease in

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humans and some domestic and wild animals (e.g. sea otters (Enhydra lutris)) (Conrad et al.

2015). Existing data indicate muskrats are commonly infected with this parasite, but disease is

not reported with these infections (Appendix).

Ectoparasites

Since 1936, ten articles have reported ten species of ectoparasites in muskrat from eight states and one Canadian province. (Fig. 2). All ectoparasite species reported are mites with the exception of one flea species, Orchopeas howardi (Bauer and Whitaker 1981). The most commonly reported species are Listrophorus spp. (n = 7), and Laelaps multispinosa (n = 7) with parasite burdens ranging from 0 to >3000 and 0-811 mites, respectively. Other ectoparasite species reported include Zibethicarus ondatrae (n = 3), Myocoptes ondatrae (n = 3), Radfordia zibethicalis (n = 2), Androlaelaps fahrenholzi (n = 1), Labidophorus hypudai (n = 1), Myobia zibethicalis (n = 1), Schizocarpus indianensis (n = 1), and an accidental finding of

Marsupialichus brasiliensis (n = 1).

There is little evidence that ectoparasites have caused significant morbidity or mortality in muskrats (Appendix). Arata (1959) collected a muskrat that had an advanced stage of a myiasis infection; however, the infection was considered incidental since the carcass was harvested by a trapper. Prendergast and Jensen (2011) compared percent body fat to the severity of ectoparasitic infestations. They found that individuals with an increasing severity of L. multispinosa infection (burden range: 0–42 mites) had a negative relationship with percent body fat. With reported L. multispinosa prevalence ranging from 25-100% and parasite burdens reaching 811 mites per muskrat, this could be cause of concern, particularly in the winter when fat reserves are crucial to a muskrat’s survival.

Class Trematoda

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Trematodes (i.e. flukes) are endoparasites transmitted to mammalian hosts by either ingesting the intermediate host (e.g. snails), or consuming water or plants that have encysted larval trematodes (Poulin and Cribb 2002). Diseases such as fascioliasis (Carrada-Bravo 2003), echinostomiasis (Graczyk and Fried 1998), and schistosomiasis (Standley et al. 2012) are caused by trematodes and occur in a variety of mammalian species.

Since 1915, 46 articles have reported 32 species of trematodes in muskrat from 19 U.S. states and six Canadian provinces (Barker 1915). The geographical distribution of these data are widespread with no obvious spatial pattern (Fig. 3). The most commonly reported species are

Echinostoma revolutum (n = 28) and Quinqueserialis quinqueserialis (n = 27). Other trematode species reported in abundance were Notocotyle urbanensis (n = 18), Plagiorchis proximus (n =

18), and Wardius zibethicus (n = 17) but several other trematode species have been repeatedly found in muskrat over the past century (Appendix). Seven cases of blood flukes (Schistosoma sp.) have been reported in muskrats since 1938. There is only a single report of a lung fluke

(Paragonimus sp.) in a muskrat (Ameel 1932).

The prevalence and burdens of individual trematode species was highly variable. For example, the prevalence of Echinoparyphium sp. in four provinces and five states is relatively low and ranges from 1.23% to 27.78% with worm burdens between 0 and 609. Nudacotyle novicia also generally occurred at a low prevalence across the seven states where it has been reported (range: 0.4–23.85%). Quinqueserialis quinqueserialis has a consistently high prevalence across all studies and has the largest worm burden of any trematode species with reports reaching up to 4,855 worms in one individual muskrat (Rigby and Threlfall 1981). High prevalence (> 80%) of the trematodes Plagiorchis nobeli and Echinostoma revolutum have also been reported (Appendix).

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The cause of death of one muskrat was attributed to severe liver infection of a trematode

from the genus Parametorchis (Smith 1938). This is the only account of any Parametochis sp.

found in muskrats. Aside from this individual case, no overt disease was reportedly associated

with any of these trematode infestations in muskrats.

Class Cestoda

Cestodes (i.e. tapeworms) parasitize a diversity of aquatic and terrestrial species. Like trematodes, cestodes have an indirect life cycle (Olsen 1974). Muskrats can serve as intermediate hosts for taeniid tapeworms (Cysticercus spp., Hydatrigera taeniaeformis, Taenia spp. and

Versteria mustelae), with cysts in extraintestinal sites, as well as definitive hosts for several

species that occur in the small intestine.

Cestodes were first identified in muskrats in the early 1900’s (Barker 1915). Since then,

40 articles spanning almost a century have reported 20 species of cestodes in muskrats from 17

states and seven Canadian provinces (Fig. 4). In 2013, Nakao et al. proposed a change to the

taxonomic classifications for several species of cestodes based on genetic data. This resulted in

the reestablishment of the Hydatigera genera (in the case of the former Taenia taeniaformis, T.

krepkogorski, and T. parva), as well as the creation of the Versteria genera (formerly Taenia

mustelae). The most commonly reported cestodes in muskrats were Hymenolepis spp. (n = 32

reports) and Hydatigera sp. (n = 20 reports). Hymenolepis evaginata and Hydatigera

taeniaformis are reported most often (n = 21 and n = 20, respectively).

The prevalence of cestodes in muskrat hosts has never exceeded 59% in any of the

historical data when the number of individuals sampled was greater than one. Prevalence for the

two most common species, Hymenolepis spp. and Hydatigera sp. ranged from 0–50% and 0–

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59%, respectively. The prevalence of Hymenolepis sp. was spatially explicit, with a higher prevalence occurring in northern North America (range: 26.19–58.82%), apart from 38.10% prevalence in Utah (Senger and Bates 1957). Taeniid species were reported with burdens ranging from 0 to 15 worms and represented 83% of cases reporting debilitating cestode infections (n =

12). Cestode burdens rarely exceeded 30, however several cases of Hymenolepis spp. and one case of Schizotaenia variabilis exceeded 100 worms in the gastro-intestinal tract of individual muskrats (range: 0–355) (Olsen 1939).

A single article documented a muskrat mortality due to severe liver infestation with

Hydatigera taeniaformis (reported as Taenia taeniaeformis, Gallati 1956). In Poland, muskrats infected with the larval form of Hydatrigera taeniaformis were observed to have lower body mass as well as smaller body measurements (e.g. neck circumference) than uninfected muskrats

(Kowal et al. 2010). Recently, some parasites in the genus Taenia, including one that infects muskrat, have been reclassified into the new genus Versteria. Lee et al. (2016) documented an introduced species of Versteria that caused a fatal infection in captive orangutan (Pongo pygmaeus). During their investigation, they found many mustelids, including mink, are definitive hosts for Versteria sp.. This may be a cause of concern for muskrats since they share the same habitat as their main predator, mink (Neovison vison). Two studies from North America document muskrat as an intermediate host with cysts containing Versteria mustelae (reported as

Taenia mustelae); however, genetic analysis was not conducted at the time and given recent taxonomic changes, molecular data are needed to confirm the true identity of the specimens.

Regardless, this particular cestode was not reported as the cause of death in either study (Todd et al. 1978, Senger and Bates 1957). Although mortality from Versteria sp. parasitism has not been reported in muskrats, further research is warranted.

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Phylum Nematoda

Phylum Nematoda (i.e. roundworms) consists of two classes, Secernentea and

Adenophorea (Blaxter et al. 1998). Depending on the nematode species, transmission can occur

through ingestion of intermediate hosts, through skin penetration or orally by consuming food

items (i.e. vegetation) contaminated by the eggs or larvae of a nematode species (Anderson

2000). Several diseases are caused by nematodes in both wildlife, domestic animals, and humans

(e.g. ascariasis and trichinosis).

There are 36 articles reporting nematodes in muskrats dating back to 1915; however, very

little contemporary data exists in peer-reviewed literature, with the latest article being from 1993

(Borucinska and Nielsen 1993). Geographically, these articles represent 17 states and four

Canadian provinces (Fig. 5). Nineteen species of nematodes have been reported from muskrats, and three of these species have been associated with disease (Appendix). The most commonly reported species are Trichuris opaca (n = 16) and Capillaria hepatica (n = 10).

Kazacos (2016) was the only report of mortality associated with nematode infection in muskrats. He reported finding four muskrats from New York and one muskrat from Ontario died from Baylisascaris larval migrans. Only one article reported a prevalence of nematode parasites in muskrat samples higher than 50% (Borucinska and Nielsen 1993). In 1942, Penn also reported a high prevalence of Capillaria hepatica (50%); however, no sample size was provided.

Capillaria sp. have the highest prevalence in the literature (Appendix). Rigby and Threlfall

(1981) reported the highest nematode burden of 692 specimens of Capillaria michiganensis in a muskrat in Newfoundland. Trichuris opaca infection rates range from 0.93–27.69% with burdens ranging between 0 and 103 (Appendix). Research completed by Rausch (1946) and Rice and

Heck (1975) both included several samples from muskrats in Ottawa County, Ohio. Differences

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in prevalence of Trichuris opaca (1.43% in 1946 vs. 25% in 1975) suggest a possible increase in

infection of this particular nematode in muskrats in Ottawa County. A contemporary study of

Trichuris opaca prevalence in muskrats from the same localities to determine if infection rates

are increasing is warranted

Phylum Acanthocephala

Acanthocephalans, also known as the “spiny-headed worms,” are parasites of the definitive host’s intestinal tract (Hickman et al. 2012). Acanthocephalans are common in bird species, where they can reportedly weaken the infected host and predispose them to predation and disease, but infection rarely results directly in mortality (Friend & Franson 1999). Six articles have reported two species of acanthocephalan in muskrats between the years 1947–1978,

Corynosoma sp. and Polymorphus spp. Most reports of acanthocephalan in muskrats have been

from Canada and Alaska, with Ohio representing the only state in the contiguous United States.

Overall, the reported prevalence in these studies was low (less than 4%) with sample sizes

exceeding 130 individuals (range: 130–326). Parasite burden ranged from 0-40 with the exception of a diagnostic report from Alberta where 138 specimens of Polymorphus paradoxus were collected from a single muskrat (Connell and Corner 1957). That individual muskrat was the only reported case of the presence of all post larval stages of Polymorphus paradoxus and no other articles mentioned clinical signs in relation to acanthocephalan infestation.

Pentastomes

Members of the subclass Pentastomida are crustaceans known as the tongue worms and

most species reside in the respiratory system of their host (Hickman et al. 2012). Pentastome

infections are especially harmful to small mammals when the infection intensity is high.

Symptoms of pentastomiasis include abscesses, inflammation and granuloma (Self 1972).

12

Only two studies, both from Louisiana in the 1940’s, report finding the Porocephalus crotali in muskrats (Penn and Martin 1941; Penn 1942). The occurrence of P. crotali infections in the muskrat as an intermediate host are limited to adults in scrub habitats, therefore limiting the geographic distribution of this parasite between muskrats (Layne 1967; Penn and Martin 1941).

P. crotali does not typically cause severe symptoms or weakness in the body and infections normally do not exceed a few individuals of the parasitic pentastome (Penn 1942; Layne 1967).

However, Penn & Martin (1941) observed overt disease associated with pentastome infection in a single muskrat with over 1,600 nymphs embedded in all organs of the body. Both studies in

Louisiana reported prevalence of 9% in sample sizes exceeding 1000 individuals. There are no other reports of pentastome parasites in muskrats exhibiting clinical signs of disease.

Bacteria

Exposure of wildlife species to both virulent and symbiotic bacteria is driven by ecological dynamics and the surrounding microbial communities. Climate and ecological dynamics are fluid, and with them is the composition of microbial communities and the bacteria residing within those communities. Rapid responses of bacteria to changing climate and ecological dynamics enables the emergence of infections and disease. Bacterial infection can occur directly (i.e. direct contact with conspecifics), vertically (i.e. in utero), or indirectly through bacteria in the environment (i.e. contaminated water) (Belden and Harris 2007). The clinical outcome of infection can vary dramatically between host species and pathogens, with even significant bacterial diseases (e.g. cholera tularemia, and plague) presenting with a spectrum of clinical signs and lesions.

In muskrats, bacterial infections were identified as the primary cause of morbidity/mortality in 16 of 22 publications. Twenty-three bacterial species have been reported

13

in muskrats dating back to 1952, of which only six are reported responsible for the

morbidity/mortality of muskrats (Appendix). These reports come from nine US states and five

provinces in Canada (Fig. 6). The most common species of bacteria reported in muskrats were

Francisella tularensis (n = 11) and Clostridium piliformis (n = 5). Of the 23 reported species,

three are not historically associated with overt disease in muskrats and presumably were

incidental findings or secondary infections (Klebsiellosis sp., endosymbiotic bacteria, and

Mycoplasma-like organisms). Six species of bacteria found in muskrat in North America are

known to be virulent species associated with morbidity/mortality in muskrats (Francisella

tularensis, Francisella philomiragia, Bacillus piliformis, Staphylococcus sp., and Anabaena flos-

aquae). In Finland, Oscillatoria agardhii also caused mortality in muskrats (Yoo et al. 1995). F.

tularensis, F. philomiragia, B. piliformis, and Staphylococcus sp. cause disease by the bacteria

invading and destroying tissues whereas Oscillatoria agardhii, and Anabaena flos-aquae species

produce exotoxins that are ingested by the host. (Celli and Zahrt 2013, Rose 1953).

Tularemia

Tularemia is caused by an infection of the bacterium F. tularensis. The two subspecies,

F. t. tularensis and F. t. holartica, which are referred to as type A and type B, respectively. Type

B occurs in the Northern Hemisphere, whereas Type A occurs only in North America. The two subpopulations of type A are type AI and type AII, and they are found generally east and west of the 100th meridian, respectively (Wobeser et al. 2009). Subpopulation type AI is more

pathogenic and mostly infects terrestrial mammals (Gabriele-Rivet et al. 2016). While hundreds

of other animal species can be infected with F. tularensis, rodents and lagomorphs are the main

terrestrial host (Mörner and Addison 2001). F. tularensis has both an aquatic and terrestrial

cycle, with muskrats and beavers (Castor canadensis) serving as host in the aquatic cycle and are

14

most likely infected with the less virulent type B, however epizootics in muskrats and muskrat

trappers with F. tularensis occur (Gabriele-Rivet et al. 2016). Outbreaks of tularemia in muskrats

have been reported in northern North America, specifically Alberta, Ontario, and Vermont.

These outbreaks are commonly associated with aquatic habitats such as streams and marshes and

are suggested to be related to increased prevalence of the bacteria in reservoirs such as voles

(Microtus spp.) since voles carry type B. (Mörner and Addison 2001). F. tularensis can be

carried by a variety of animals or insects such as rodents, ticks, fleas, and mosquitos (Sjöstedt

2007). Humans and other animals can become infected with the bacteria by ingesting

contaminated food and water, breathing contaminated air, or most commonly being bitten by a

vector (e.g. ticks) (Centers for Disease Control and Prevention 2009).

Although there are many cases of tularemia in humans and wildlife, little is known about the life

cycle and persistence of F. tularensis However, outbreaks in sheep and humans have coincided

with epizootics in rodents and lagomorphs suggesting transmission from the latter to humans and

sheep (Sjöstedt 2007). Clinical signs vary depending on the type of infecting F. tularensis,

however the most common is lesions on the liver (Friend and Franson 1999). For muskrats, six

of the 11 articles describing tularemia investigations reported mortality due to F. tularensis

infection (Appendix). The majority of these reported deaths occurred in Canada (Appendix).

Monitoring tularemia outbreaks in not only muskrats, but also other animals and humans, can aid

in the understanding of the transmission patterns of this disease.

Cyanobacteria

Two species of cyanobacteria (O. agardhii and A. flos-aquae) cause mortalities in muskrats (Appendix). Cyanobacteria (i.e. blue-green algae) can form extensive algal blooms on the surface of water and produce toxins. There are two main categories of toxin produced by 15

cyanobacteria, cytotoxins and biotoxins. Cytotoxins are not severely harmful to living organisms

that ingest them; however, biotoxins can be (Carmichael 1992). Wildlife and domesticated

animal deaths have been attributed to biotoxin poisoning from animals drinking water with

planktonic cyanobacteria floating on the surface (Codd et al. 1994; Codd et al. 1997). In Finland,

one muskrat was found dead after drinking water containing O. agardhii (Yoo et al. 1995). In the

United States, eighteen muskrats along with other wildlife species were found dead in lakes in

Iowa during the fall of 1952. These lakes were sites of A. flos-aquae blooms, and researchers

attributed the wildlife mortality to cyanobacterial toxins (Rose 1953). Clinical signs of

cyanobacterial poisoning range from anorexia and diarrhea to hypersalivation and convulsions

(Carmichael 1992). Cyanobacterial poisoning of muskrats can cause mortality directly or through

bioaccumulation in filter feeding bivalves (e.g. mussels). In lakes of Alberta, clams displayed the

ability to accumulate microcystin-LR (MC-LR), a toxin produced by cyanobacteria. Microcystis aeruginosa is the main producer of MC-LR in the study areas (Prepas et al. 1997). MC-LR’s ability to bioaccumulate and pass along the trophic level introduces a level of concern for the possible poisoning of higher trophic organisms.

Tyzzer’s Disease

Tyzzer’s disease is an acute bacterial disease caused by the bacteria Bacillus piliformes

(Tyzzer 1917). It occurs in a variety of species including, raccoon (Procyon lotor), mice (Mus spp.), coyote (Canis latrans) and cottontail rabbits (Sylvilagus spp.) (Wojcinski and Barker 1986;

Ganaway et al. 1971; Marler and Cook 1976; Ganaway et al. 1976). Outbreaks of Tyzzer’s disease are acute and commonly associated with increased stress including changing environmental conditions or secondary infections compromising immune function (Wobeser

2001). Individuals infected with B. piliformes shed more spores when stressed, therefor

16

increasing the spread of the bacteria into the environment (Ganaway et al. 1986). The spores

formed remain infectious in the environment (e.g. inside contaminated muskrat huts) for at least

five years, allowing the reinfection of muskrats re-colonizing abandoned huts and burrows

(Errington 1954). During most outbreaks, muskrats are found dead without premonitory signs,

however, clinical signs of the disease are hemorrhagic enteritis and liver lesions, (Wobeser 2001,

Tyzzer 1917). Of note, the disease was historically separated between Errington’s disease and

Tyzzer’s disease, with the causative bacteria identified as Clostridium sp. and B. piliformis,

respectively. There is much support in the scientific community that Errington’s and Tyzzer’s

are the same disease, therefor Tyzzer’s disease has become the adoptive name (Karstad et al.

1971; Wobeser et al. 1978). Tyzzer’s disease in muskrats has been reported in six studies, with

mortality reported from three states and two Canadian provinces (Appendix). Of studies

reporting sample size (n = 4), 67% mortality of muskrats was reported (Appendix).

Other bacteria and fungi

Both F. philomiragia and Staphylococcus sp. have reportedly been associated with mortality in individual muskrats; however, confirmatory diagnostic tests in many of these cases were questionable. F. philomiragia (formerly, Yersinia philomiragia) was discovered in the lungs of a dead muskrat from Utah in 1969, that reportedly had grossly hepatized lungs (Jensen et al. 1969). The mortality attributed to an infection Staphylococcus sp. was also presumably an

isolated incident. A single muskrat found dead in Illinois was screened for a variety of diseases

and parasites and the cause of death was determined to be an infection of Staphylococcus sp. that

made the muskrat susceptible to a secondary viral infection (Webb and Woods 2001). Three

species of fungi, Emmonsia crescens, Encephalitozoon cuniculi, and Trichophyton mentagrophytes, were reported in muskrats from Utah, Saskatchewan, and Iowa, respectively.

17

However, prevalence of fungal infection was low and has not been document in muskrats since

1979 (Wobeser & Schuh 1979).

Viral infections of the muskrat

Of the existing literature, exposure to or infection with viral pathogens have been reported in 14 papers representing four states and three Canadian provinces (Fig. 7). All of these reports were recent, relative to reports of other etiology and all occurred between 1966-2017.

Six different viruses have been screened for in muskrats, including canine distemper virus, rabies, orthohepevirus, Aleutian mink disease virus, adenovirus, and psittacosis- lymphogranuloma venereum (Appendix). The most commonly reported virus screened for was rabies virus (n = 9). Five of these rabies reports were from active rabies virus surveillance conducted by the CDC and the remaining four were post-mortem examinations. Thirteen cases of muskrat mortalities are associated with rabies virus (Appendix). Serologic evidence of exposure to canine distemper virus (Morbillivirus), and Hepatitis (Orthohepevirus) have been observed in muskrats during serological tests but have not been reportedly associated with morbidity or mortality (Appendix). Along with 13 individual cases of rabies virus, an outbreak of disease related to the Psittacosis-lymphogranuloma venereum group of viruses resulted in morbidity and mortality of two muskrats from Saskatchewan, Canada. Upon examination, 14% of muskrats were positive for the isolates of the virus (Spalatin et al. 1966). No further reports of this group of viruses have been documented in muskrats in North America.

Toxins and contaminants found in the muskrat

Wildlife are exposed to elements, chemicals and other contaminants both naturally (e.g. heavy metal deposits, bioaccumulation) and unnaturally (i.e. anthropogenic means).

Anthropogenic contaminants enter the environment through a variety of sources (e.g.

18

wastewater, industrial discharge, lead ammunition, etc.) and can impact the health of humans, domestic animals, and wildlife (Pal et al. 2010; Neathery and Miller 1975). Animals are exposed to environmental contamination, not only through consumption of contaminated waste and water, but also through consumption of plants and other food items that have absorbed contaminants. The ecology of the muskrat makes them particularly susceptible to exposure to environmental contamination in aquatic systems.

Heavy metals

Heavy metals are dense metallic elements. Many of these heavy metals (e.g. zinc, copper, and iron) are biologically important to the bodily function of many organisms, including humans.

Other heavy metals (e.g. mercury and lead) do not hold any biological necessity to be absorbed into the body and can become toxic depending on concentration levels (Singh et al. 2011).

Arsenic, cadmium, chromium, lead, and mercury are considered priority metals for surveillance due to their toxicity and potential effects on human and animal health (Tchounwou et al. 2012).

For most heavy metals, a variety of clinical syndromes occur at high exposure levels. The health impacts of heavy metals at lower exposures is not completely known (Pandey and Madhuri

2014).

Mercury (Hg) is not naturally found in organisms, and in its methylated forms bioaccumulates through the trophic levels. The range of lethal dose (LD50) for Hg reported for mammals is 10–40 µg/g (WHO 1976). Although Hg poisoning is seemingly rare and mostly occurs in carnivores (i.e. domestic cat (Felis catus), ferret (Mustela putorius furo), mink, otter), there is potential that more cases could be found with increased surveillance since signs of poisoning (e.g. colic, dyspnea, etc.) are usually only noticed after chronic exposure (Blakley

2019). No muskrats have been reported to have succumbed to Hg poisoning, but levels of Hg

19

have been detected in individual muskrats (less than 0.01–0.69 µg/g) (Wren 1986). Stevens et al.

(1997) observed high Hg concentrations in hair samples from muskrats in Tennessee, however these muskrats were asymptomatic (Table 3). This only occurred in adults at one out of the four sites sampled, with ranges in concentration for the combined remaining sites being low (0.03–

1.07 µg/g). Hg concentrations in hair are known to be generally much higher than Hg concentrations in other tissues (i.e. liver or muscle) (Cumbie 1975, Halbrook et al. 1994).

Table 3: Mercury (Hg) concentrations found in muskrat tissue samples from four historical studies. Tissue Sampled Concentration (µg/g) n Location Author Kidney 0.011 - 0.019 76 Virginia Halbrook et al. 1993 Liver 0.22 6 Washington Blus et al. 1987 Liver 0.029 - 0.070 63 Pennsylvania Everett & Anthony 1977 Hair 0.03 - 22.6 58 Tennessee Stevens et al. 1997

Concentration levels have been converted to µg/g. For reference, LD50 for Hg ranges from 10–40 µg/g. Cadmium (Cd) can also has detrimental effects on animals and has been studied relatively extensively. Cadmium, a micronutrient, is absorbed by plants and animals and then is usually released back into the system through the excretion of urine and fecal matter (Pandey and

Madhuri 2014). When Cd concentrations (animal LD50 = 225–890 µg/g) in the system build up, it can cause bone defects, myocardial disease, increase blood pressure, and affect DNA repair at the molecular level (USAF 1990, Beyersmann and Hechtenberg 1997). Cd toxicity was also reported as an immunosuppressant in mice as it decreased primary and secondary immune responses (Bozelka et al. 1978). Muskrats have not been reported to exhibit any detrimental effects of Cd exposure and have not had Cd concentrations higher than 0.32 µg/g in existing literature, which is much lower than the LD50 (Table 4).

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Table 4: Cadmium (Cd) concentrations found in muskrat tissue samples from six historical studies. Tissue Sampled Concentration (µg/g) n Location Author Kidney 0.0008 - 0.0018 33 Ontario Parker 2004 Kidney 0.039 - 1.071 65 Pennsylvania Everett & Anthony 1977 Kidney 0.08 - 3.08 76 Virginia Halbrook et al. 1993 Kidney 0.11 - 0.157 126 Pennsylvania Erickson & Lindzey 1983 Kidney 1.13 6 Washington Blus et al. 1987 Liver 0.00025 - 0.00044 33 Ontario Parker 2004 Liver 0.0391 - 0.3157 65 Pennsylvania Everett & Anthony 1977 Liver 0.042-0.064 126 Pennsylvania Erickson & Lindzey 1983 None* 0.163 n/a Montana Pasco et al. 1996

Concentration levels have been converted into µg/g. For reference, the LD50 of Cd is 63-1125 µg/g. *Cd concentration estimated using linear multimedia food-chain models based on ingestion rates for food items, soil, and water.

Ingestion of environmental sources of lead (Pb) can lead to toxicity. Lead poisoning in humans has been a well-studied disease for centuries (Hernberg 2000). Lead toxicity is an important disease in multiple avian groups, including waterfowl through exposure to fishing tackle and ammunition in the environment and avian scavengers through exposure to ammunition in carcasses/tissues in game species (Franson et al. 1995; Locke et al. 1982; Pain et al. 2009; Fisher et al. 2006). For mammals, lead poisoning has been reported in farm animals and can lead to a variety of syndromes (neurological, gastrointestinal, cardiovascular, etc.)

(Neathery & Miller 1975; Ma 1996). Based on the habitat use of muskrats, they can be exposed to lead through a variety of sources, including road runoff, plant roots (Smith 1976). Overt disease associated with lead toxicity has not been reported in muskrats; however, varying levels of exposure have been reported. The highest reported concentration of Pb in muskrats is 5.23

µg/g, which is just above the minimum level of toxicity in other rodent species (Table 5).

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Erickson and Lindzey (1983) did observe that muskrats with higher Pb concentrations in their tissues were adults and came from marshes with high Pb concentrations in cattail tissues suggesting that lead is accumulated in muskrats over time through their food source.

Table 5: Lead (Pb) concentrations found in muskrat tissue samples from seven historical studies. Tissue Sampled Concentration (µg/g) n Location Author Kidney 0.0009-0.2689 64 Pennsylvania Everett 1976 Kidney 0.0032 - 0.0036 33 Ontario Parker 2004 Kidney 0.71 - 1.2 76 Virginia Halbrook et al. 1993 Kidney 2.63-4.25 126 Pennsylvania Erickson & Lindzey 1983 Liver 0.0020 - 0.0021 33 Ontario Parker 2004 Liver 0.0021-0.1537 64 Pennsylvania Everett & Anthony 1977 Liver 0.27-0.96 6 Washington Blus et al. 1987 Liver 3.71-5.23 126 Pennsylvania Erickson & Lindzey 1983 Muscle 0.0-0.0048 64 Pennsylvania Everett 1976 Bone 1.117-2.226 64 Pennsylvania Everett & Anthony 1977 n/a* 0 3 BC Chalmers & MacNeill 1977 Concentration levels have been converted into µg/g. For reference, the minimum level of toxicity reported by the U.S. Environmental Protection Agency is 5µg/g. *Tissue sampled was not noted, only that screening for various heavy metals did occur during the full necropsy. Arsenic (As) poisoning is not as common in wildlife as it is in humans. Exposure to inorganic As has been shown to cause birth defects in hamsters, especially those exposed to heat stress (Hanlon and Ferm 1986). Ronald Eisler from the U.S. Fish and Wildlife Service released a synoptic review of arsenic hazards to wildlife in 1988. Many shorebirds and marine biota have arsenic concentrates, especially in tissues high in lipid content. Eisler reported that the LD50 for arsenic depends on species and ranges from 17–48 µg/g body weight and 2.5–33 µg/g body weight (bird and mammal, respectively). In aquatic systems, the LD50 varies depending on a

22

variety of water properties (e.g. pH, temperature, etc.) Negative effects on aquatic species can

occur with water concentrations anywhere between 19 and 48 µg As/l. As can be readily

absorbed in the body of an organism and can decrease both white and red blood cell formation,

and immune function, as well as cause brain damage and other physiological disturbances

(Pandey and Madhuri 2014). As toxicity has not been reported in muskrats, but low levels of As

have been detected in muskrat tissues (0.22 ppm) (Appendix).

Agricultural-related contaminants

Few studies (n = 2) have investigated exposure of muskrats to agriculture-related contaminants, such as pesticides, herbicides, and insecticides (Table 6). Together these studies screened muskrat tissue for eight contaminants including, atrazine, cyanazine, metolachlor

(herbicides), chlorpyrifos, fonofos, terbufos (insecticides), Dichlorodiphenyldichloroethylene

DDE (p.p’-DDE), and dieldrin. Only atrazine, dieldrin, and p.p’-DDE were identified at levels above the detection of these assays. No clear negative impacts were associated with the detection of these contaminants; however, muskrats in Virginia did have lower body condition associated with muskrats from one study area that was exposed to dieldrin (Halbrook et al., 1993).

Table 6: Agricultural-related contaminants and their concentrations found in muskrat tissues. Tissue Sampled Concentration Contaminant n Location Author (µg/g)

Atrazine Subcutaneous Fat 5.13-28.22 6 Illinois Juhlin & Halbrook 1997

Chlorpyrifos Subcutaneous Fat 0 6 Illinois Juhlin & Halbrook 1997

Cyanazine Subcutaneous Fat 0 6 Illinois Juhlin & Halbrook 1997

dieldrin Liver and Kidney 0.25 76 Virginia Halbrook et al. 1993

Fonofos Subcutaneous Fat 0 6 Illinois Juhlin & Halbrook 1997

Metolachlor Subcutaneous Fat 0 6 Illinois Juhlin & Halbrook 1997

23

p.p'-DDE Liver and Kidney 0.03 76 Virginia Halbrook et al. 1993

Terbufos Subcutaneous Fat 0 6 Illinois Juhlin & Halbrook 1997 Concentration levels have been converted into µg/g.

Although the influence of p.p.’DDE, dieldrin, and atrazine on muskrats is not defined, investigations in other mammals have been conducted. Studies on harbor seals (Phoca vitulina), sea lions (Ontariinae) and ringed seals (Pusa hispida) show that high levels of DDE were correlated with PCB (polychlorinated biphenyls) contamination and can negatively influence bodily function and reproductive success (Reijnders 1980; LeBoeuf and Bonnel 1971; Helle et al. 1976). p.p’-DDE is also known to affect the central nervous system and liver function (NCBI

2018). Dieldrin impairs the nervous system, is immunosuppressive and high levels of contamination in mice, birds, and other mammals is known to result in decreased lipid stores and death (ATSDR 2002; Stickel et al. 1969; Jefferies et al. 2009). Atrazine has been reported to cause increased mortality in frogs when co-contaminating an organism with other herbicides

(Relyea 2009). Since many syndromes associated with contamination from agricultural compounds are vague and non-specific, and concentrations of these compounds have been detected in muskrats, further investigation on the impacts of agricultural contaminants on muskrats are warranted.

Other contaminants

Concentration levels of two additional types of contaminants, PAHs (polycyclic aromatic hydrocarbons) and PCBs, have been reported in muskrat tissue samples. PAHs are chemicals found in a variety of products including coal tar, wood, and petroleum. Aerosol PAH contamination occurs when these products are burned, and soil and water contamination occur when the ashes are spread into the environment. Oil spills and aerial dispersal of coal dust can also result in environmental PAH contamination. PAHs are absorbed by plants and can be

24

detected in plant tissue. Animals grazing on these plants can then accumulate PAHs in their

tissues (Johnsen and Karlson 2007). Aquatic organisms are especially prone to PCB and PAH

contamination, resulting in immunological and reproductive disorders (Colborn and Smolen,

1996). Halbrook et al. (1993) observed PAH concentrations in 22 of the 35 muskrats at relatively

low tissue concentrations ranging between 0.03–0.15 ppm. The muskrats residing in sites with

high total surface sediment PAH concentration had lower carcass and spleen weight, as well as

lower fat index than the muskrat residing in low PAH concentrated areas, suggesting PAH

contamination could be impacting muskrat health.

Several of PCB isomers are highly toxic to bodily functions and can result in

immunotoxicity, weight loss, and dermal disorder, as well as other serious side-effects (Tanabe et al. 1987). Kannan et al. (2000) determined levels of NOAEL (no-observed-adverse-effect- level) and LOAEL (lowest-observed-adverse-effect-level) for PCB’s in mink livers to be 2.03

µg/g lipid weight and 44.4 µg/g lipid weight, respectively. With increased bodily PCB concentration levels, reproductive toxicity is observed resulting in reduced relative litter size and kit survival (Leonards et al. 1995). Historically, few studies have investigated PCB contamination in muskrats (n = 2). Halbrook et al. (1993) found concentration levels in 3.9% of liver and kidney samples (n = 76) between 0.45–0.66 µg/g in Virginia. Mayack and Loukmas

(2001) also detected concentrations of PCB in liver samples (n = 20) of up to 2.18 µg/g in muskrats in the Hudson River Drainage. No negative effects on muskrat health related to PCB concentration level was reported by either study.

Discussion As muskrat populations decline, it is critical we understand the possible role of disease.

An important component of this understanding involves the characterization of pathogens,

25

contaminants, and diseases that have been previously identified and monitoring that change over

time. Historical reports have identified a number of pathogens or contaminants of potential

concern for muskrat health. Notable parasitic diseases include coccidiosis and cysticercosis that

are typically caused by Eimeria spp. and Hydatigera and Versteria spp., respectively (Smith

1938, Gallati 1956). Ectoparasite infestation may have indirect impacts on muskrat health as higher infestations have been associated with a decreased in percent body fat (Prendergast and

Jensen 2011).

There is little noted about acanthocephalan parasites in muskrats; however, infection by

acanthocephalan species has been documented to cause mortality in juvenile sea otters and might

be a source of interest in future muskrat studies. Dracunculus spp. also cause severe infections in

other wildlife species and was documented in muskrats in the 1970’s but has not been noted in the literature since. Unlike other parasites of muskrats, Dracunculus spp. are found in the subcutaneous regions of the extremities and may go unobserved during traditional parasitic

surveys of the body cavity. The microsporidia Encephalitozoon cuniculi is documented in

muskrats and is another notable cause of wildlife mortality that should be investigated further in

muskrats (Hersteinsson et al. 1993).

Bacterial infections were the main cause of muskrat mortality. There were six species of

bacteria that caused mortality in muskrats; however, Francisella tularensis, Clostridium

piliformis, and cyanobacteria were the three associated with the highest mortality. With only one

report in the United States, little is understood or documented about the impacts of cyanobacteria

on muskrats. Many of the reports of bacterial infections from the species above and viral

infections were only documented after outbreaks occurred. The prevalence of these infections in

26

the general muskrat population is poorly documented, so there is an unclear understanding of the

risk of infection via these bacterial species or viruses.

On the subject of heavy metals and other contaminants, there is a great need for further

research, especially where muskrats are involved. Muskrats exist at the mid-trophic level, are

semi-abundant, and live in aquatic environments that serve as reservoirs for high concentrations

of many environmental contaminants. They are a prime study species for understanding the

impacts of environmental contamination in ecological systems because they can bioaccumulate

contaminants from their food source, and they influence the bioaccumulation of contaminants of

other organisms at higher-trophic levels. Without knowing what bodily contamination level is

toxic to muskrats for various chemicals and heavy metals, it is difficult to determine their effects

on individual muskrats, let alone population dynamics. Also, there were only two studies

conducted on contamination levels of agricultural-related contaminants in muskrat tissues, and

only a few reports on PAH and PCB levels in muskrats and their influences. However, in the few

studies conducted on PAH and PCB levels in muskrat tissues, the authors noted effects on body

condition and reproduction, which merits further research on the topic.

Muskrats serve as sentinel species for many pathogens and diseases, including Giardia

sp., Cryptosporidium sp., Guinea worm disease, and Echinostomes. They can also be used as sentinels for environmental contamination to assess aquatic ecosystem health. Continued or sustained monitoring of muskrat health parameters will help determine human health risks as many of the pathogens and contaminants that muskrats harbor have health impacts for domestic animals and humans. There is still much that is not well understood about the health of muskrat populations and the influences of disease, parasites and contaminants on survival. The intent of this paper is to be used as a reference for future investigations on ways to build upon previous

27

research. There are gaps in the knowledge of contaminant toxicity, bacterial prevalence, and much of the geographic distribution of pathogens and disease vectors of muskrats has yet to be documented. Continued active and passive surveillance for these, as well as new ones that may emerge or be detected using new techniques, is encouraged.

Acknowledgements We are extremely grateful to the U.S. Geological Survey Pennsylvania Cooperative Fish and Wildlife Research Unit, the Pennsylvania Game Commission, and the Southeastern Cooperative Wildlife Disease Study for their contributions to this paper. Funding for this study was provided by the Pennsylvania Game Commission.

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Figure 3. Historic study locations between 1936–2015 where protozoan parasites have been documented in muskrats in North America. Study locations designated by shaded regions.

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Figure 4. Historic study locations between 1936–2011 where ectoparasites have been documented on muskrats in North America. Study locations designated by shaded regions.

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Figure 3. Historic study locations between 1915–1981 where trematode parasites have been documented in muskrats in North America. Study locations designated by shaded regions.

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Figure 4. Historic study locations between 1915–2012 where cestode parasites have been documented in muskrats in North America. Study locations designated by shaded regions.

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Figure 5. Historic study locations between 1915-1993 where nematode parasites have been documented in muskrats in North America. Study locations designated by shaded regions.

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Figure 6. Historic study locations between 1952–2016 where bacterial infections have been documented in muskrats in North America. Study locations designated by shaded regions.

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Figure 7. Historic study locations between 1966–2017 where viral infections have been documented in muskrats in North America. Study locations designated by shaded regions. Rabies virus was detected in muskrats in the United States in several studies, however specific locations were not given.

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JOURNAL OF WILDIFE DISEASES

Chapter Two

Passive and active surveillance for diseases of muskrat (Ondatra zibethicus)

Laken S. Ganoe1

Justin D. Brown2

Matthew J. Lovallo3

Michael J. Yabsley4

Mark G. Ruder4

W. David Walter1,5

1 The Pennsylvania State University, Forest Resources Building, University Park, PA 16802 2 Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA 3 Bureau of Wildlife Management, Pennsylvania Game Commission, 2001 Elmerton Avenue, Harrisburg, PA 17110-9797, USA 4 Southeastern Cooperative Wildlife Disease Study, 589 D.W. Brooks Drive, University of Georgia, Athens, GA 30602, USA and the Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA 5 U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, PA 16802, USA

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ABSTRACT We conducted wildlife disease surveillance of muskrats (Ondatra zibethicus) in Pennsylvania using passive and active surveillance. We collected 380 trapper-harvested muskrat carcasses following the 2018/2019 muskrat trapping season and conducted full necropsies on each carcass. Our objectives were to define muskrat exposure to various pathogens, diseases and contaminants and how exposure may vary in comparison to previous studies. We also investigated relationships between exposure and landscape variables such as, landscape type (e.g. agricultural, forested, urban) and water body type (e.g. marsh, lake, pond, etc.) Organ and tissue samples were tested for a suite of pathogens, diseases, and contaminants and gastrointestinal tracts were examined for intestinal parasites. We detected Tyzzer’s disease, sarcosystosis, and toxoplasmosis at low prevalence. We failed to detect tularemia, babesiosis, or cadmium, arsenic, and mercury contamination. Parasite burdens were typical of historic reports, however younger muskrats had larger burdens than older muskrats which is contradictory to what was observed in Pennsylvania in 1966. The Northcentral and Northwest regions had higher parasite burdens than all of the southern regions combined (P<0.03). We also documented the first positive detection of Versteria mustelae infection in muskrats using genetic sequencing. We found no relationships between heavy metal concentrations and landscape features, and only zinc concentrations varied by sex. Keywords: disease, health, muskrat, parasite, Versteria mustelae, wildlife disease surveillance

INTRODUCTION Wildlife disease surveillance is not only important in understanding species health, but

implications of wildlife disease studies can also extend to human and domestic animal health

(Artois et al. 2009). The monitoring of wildlife for emergent and epizootic diseases has been

used to initiate preventative measures against future outbreaks in other wildlife, domestic

animals, and humans. There are two primary methods of surveillance used when studying

wildlife diseases, passive and active. Passive surveillance is comprised of investigation of

exposure of an individual to disease post-mortality and provides information on infectious or

non-infections agents that are producing disease related mortality events (Artois et al. 2009).

Active surveillance usually follows significant findings during passive surveillance and consists

of targeted investigations of pathogens and diseases through systematic collections of individual

animals (Jackson et al. 2009).

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For species, such as muskrat (Ondatra zibethicus), there are unique challenges to wildlife

disease surveillance due to the biology, habitats occupied, prey status, ability to be captured, and

size of the animal. Due to subterranean dwelling habits and semi-aquatic ecology of muskrat, as well as the rapid consumption of their carcasses by predators, it is difficult to detect muskrat mortalities through passive techniques (Errington 1961). Fortunately, the muskrat is a frequently harvested furbearer across the United State and Canada, providing a venue for possible collection of muskrat for active surveillance. Since muskrat populations are in decline across much of their range, there is an underlying necessity to understand the effects that disease and contamination have on muskrat health (Ahlers and Heske 2017).

Historically, muskrats have been documented as hosts and reservoirs for a wide range of pathogens, parasites, and contaminants (Appendix). However, the relationship between the prevalence of infection or contamination, and muskrat health is poorly understood. Many parasitological studies on muskrats only report prevalence of parasites without addressing body condition or health implications (Chapter One). Erickson and Lindzey (1983) observed relationships between lead (Pb) concentrations in muskrat tissue, age, and Pb concentrations found in the muskrat food source (i.e. cattails). Contaminants from industrial pollution have been loosely linked to disease in other freshwater semi-aquatic mammals (e.g. river otter, Lontra canadensis) and have the potential to cause disease in muskrats (Kimber and Kollias, 2000).

Overall, there are few reports on contamination in muskrats and impacts of parasitology to draw reference values from in the literature. In general, the impact of parasites on host health can be exacerbated when cooccurring with other stressors (e.g. contaminants, other pathogens, climate variability) (Marcogliese and Pietrock 2011). In response to temperature change, the distribution of aquatic parasites is predicted to shift, creating a potential for increased contact between

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parasites and hosts lacking an immunological response to infection of those geographically novel

parasites, creating concern for host (e.g. muskrat) health (Marcogliese 2001).

There are gaps in knowledge on muskrat health, and coupled with observations made of muskrat declines, disease surveillance on muskrat populations in Pennsylvania was warranted.

To address these knowledge gaps, our objectives were to 1) conduct passive surveillance to

define causes of regional historic muskrat mortality from diagnostic cases to identify key factors

for investigation, 2) conduct active surveillance for prominent pathogens, diseases, and

contaminants in muskrats, 3) identify commonalities between passive surveillance and active

surveillance observations, 4) identify prevalence of clinical signs with respect to landscape

factors (geographical location, landcover type, water body type), and 5) investigate relationships

between concentrations of heavy metals and body measurements, landscape factors, and

individual health status.

MATERIALS AND METHODS Passive Surveillance

We reviewed the diagnostic records of muskrats submitted between 1977 and 2018 to

either the Southeastern Cooperative Wildlife Disease Study (SCWDS) or one of the three

Pennsylvania Animal Diagnostics Laboratory Systems for post-mortem examination. Muskrat

carcasses were submitted to these laboratories for necropsies to determine cause of morbidity or

mortality. During necropsy, any microbes, parasites, or lesions detected grossly, microscopically,

or with ancillary diagnostic tests were noted. We compiled the following information from each

report: year of collection, collection location (i.e. state), number of animals collected, cause of

mortality, and the number of muskrat mortalities caused by each diagnosis.

Active Surveillance

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From the annual Furtaker Survey conducted by the Pennsylvania Game Commission (PGC) in

2015–2017, we collected the names and contact information of trappers in Pennsylvania that

reported harvesting at least one muskrat. In collaboration with the PGC, we mailed a survey to

the identified muskrat trappers during the summer of 2018 to find trappers willing to donate up

to ten carcasses of muskrats trapped during the 2018–19 trapping season. We mailed tags to

trappers to affix to each muskrat carcass and requested details that included name of the trapper, county and township the muskrat was harvested, date muskrat was collected, and any details on the specific location where the muskrat was trapped (e.g. beaver pond, stream, etc.). Water body type where each muskrat was harvested was determined from information provided by the trapper on each carcass tag. Carcasses were skinned then frozen by the trapper until PGC and project personnel retrieved the carcasses during the state-wide carcass collection in February and

March 2019. We used satellite imagery to determine landcover type (i.e. agricultural, forested, or urban) around the location of capture (NLCD 2016). We also assigned carcasses to their respective PGC region based on county location collected from the trapper. The six regions are as follows; Northwest (NW), Southwest (SW), Northcentral (NC), Southcentral (SC), Northeast

(NE), and Southeast (SE).

From 20 February 2019 through 12 April 2019, we conducted full necropsies on muskrat carcasses. Prior to dissection, we collected general body measurements including carcass weight and surface area of the tail to determine a possible measure of age and body condition (Dorney and Rusch 1953). Surface area of one side of the tail was calculated geometrically from measurements of the tail length, and width at the base, middle, and 10 cm from the tip. Any muskrats without tails or with tail that were partially skinned were censored. We were unable to collect data on the age of the muskrats as most aging techniques for the species are unreliable

52

with the exception of pelt primeness (Moses and Boutin 1986). We identified the sex of each

muskrat and collected uterine tracts if placental scars were visible. We collected gastrointestinal

tracts and stored them in a freezer at –20oC. We then thawed the gastrointestinal tracts for

examination for parasites between 11 April 2019 and 01 June 2019 using 1-mm sieve. We stored

all parasites in 70% ethanol and identified individual worms morphologically to their

corresponding family.

We examined each carcass grossly for any signs of lesions, parasites, or other

abnormalities. Specifically, we examined the liver from each individual for presence of gross

lesions consistent with Tyzzer’s disease, tularemia, and cysticercosis; three reportedly common

diseases of muskrats that produce gross lesions. Any liver with gross lesions was placed in 40%

formalin and was sent to SCWDS to be examined microscopically and to conduct appropriate

ancillary diagnostic tests (molecular tests, culture, or fluorescent labelling). Along with the

examination of lesions, we also submitted liver samples to SCWDS for babesia testing, feces for

detection of Clostridium piliforme, as well as heart, skeletal muscle and tongue for Toxoplasma

gondii screening. We also sent a subsample of 120 livers to the California Animal Health and

Food Safety Laboratory at the University of California Davis, School of Veterinary Medicine to screen for heavy metals and contaminants using gas chromatography-mass spectrometry

(GC/MS) and liquid chromatography-mass spectrometry (LC/MS) screening. From each of the six regions we chose 10 livers from seemingly healthy muskrats (no gross lesions or cysts) and

10 livers from unhealthy muskrats showing any clinical signs of disease to include in the subsample.

Statistical Analysis

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We used a one-way analysis of variance (ANOVA) to determine regional and landscape

differences in intestinal parasite prevalence. Intestinal parasite prevalence was defined as the

presence or absence of intestinal parasites in an induvial muskrat. A simple linear regression and

a loess regression were fit to determine relationships for each sex between surface area of tail

and carcass weight, and parasite burden (continuous variable) and carcass weight, respectively.

A hierarchical Bayesian censored ANCOVA model was used to estimate and compare chemical

concentrations among female and male muskrat, while accounting for weight (Stow et al. 2018).

The model was as follows:

~ ( ) Sex + ( ) , I(, ), for , … 2 𝑖𝑖 𝑗𝑗 𝑖𝑖 𝑖𝑖 𝑗𝑗 𝑖𝑖 𝑖𝑖 𝑦𝑦 𝑖𝑖 𝑦𝑦 𝑁𝑁�𝛼𝛼 ∙ 𝛽𝛽 ∙ 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑡𝑡 𝜎𝜎 � 𝐶𝐶 𝑖𝑖 𝑛𝑛

Where is the loge-concentration of contaminant observation i, is the intercept for

𝑖𝑖 𝑗𝑗 each sex, and 𝑦𝑦 is the slope of the loge-concentration of contaminant and𝛼𝛼 weight. We used I(, )

𝑗𝑗 𝑖𝑖 to indicate a censored𝛽𝛽 value for each contaminant with a loge-reporting level of , where I, 𝐶𝐶

𝑖𝑖 theoretically, could have its own detection limit for each observation. The reporting𝐶𝐶 levels for each heavy metal were as follows; 1 ppm for Fe, 0.4 ppm for Mo, and 0.3 ppm for Zn, Cu, and

Cd. A diffuse normal prior (N[0, 1000]) was used for the intercepts and slopes and a diffuse uniform prior (U[0,10]) was used for . We ran three parallel Markov chains beginning each

𝑦𝑦 chain with random starting values. Each𝜎𝜎 chain was run for 10,000 iterations, from which the first

5,000 were discarded. This resulted in 15,000 samples used to summarize the posterior

distribution. Convergence was assessed visually through inspection of trace plots and

quantitatively using the Brooks-Gelman-Rubin statistic (Brooks and Gelman 1998). Models were

fit in the program JAGS (Plummer 2003) using the jagsUI package (Kellner 2018) in program R

(R Core Team 2018). Differences in slopes and intercepts between male and females were

54

assessed by evaluating if the 95% credible interval of the difference between sex-specific

parameters overlapped with zero.

RESULTS

Passive Surveillance

A total of 27 muskrats were submitted as diagnostic cases to SCWDS between 1977 and

2018 and came from five states in the eastern United States (Table 1). The most commonly

reported cause of mortality in muskrats submitted for diagnostic testing was trauma (n=8). Five

other muskrats died as a result of infection of the bacteria Clostridium piliforme, the agent of

Tyzzer’s Disease. Only one muskrat died from natural causes and the cause of death for two

others was undetermined.

Active Surveillance

We sampled 380 muskrat carcasses from across Pennsylvania (Fig. 1). We sampled 214

males and 166 females with only 29% of females having placental scars. Most carcasses were

collected from the SC region (n=98) and water body types where muskrats were trapped varied

by region (Fig. 2). Streams (n=201) and ponds (n=128) were the most common water body type

muskrats were harvested followed by lakes (n=18), marshes (n=14), and rivers (n=7). Muskrats

were collected from three landcover types; agricultural (n=227), forested (n=121), and urban

(n=17). The average carcass weight was 1026.85 g (SE=10.33) with the most variation in weight

in the SC region (Fig. 3). Males had higher mean carcass weights (1053.83 g, SE=12.99) than

females (992.08 g, SE=16.33) (P=0.003). Males also had larger mean tail surface area than

females (P <0.001), and tail surface area increased uniformly as carcass weight increased (Fig.

4).

55

We found intestinal parasites in 149 muskrats (83 males, 66 females). Two of the northern regions, NC and NW had higher prevalence of intestinal parasites than any of the southern regions, SC (P<0.001, P<0.001, respectively), SE (P=0.002, P=0.023, respectively), and SW (P=0.002, P=0.018, respectively)(Fig. 5). Forested areas had higher prevalence of intestinal parasites (57%, P<0.001) than agricultural areas (41%). Intestinal parasites were found in all water body types with muskrats from lakes having a higher prevalence than muskrats from both ponds (P=0.026) and streams (P<0.001) while those in ponds had higher prevalence than streams (P=0.017). Intestinal worm burdens (i.e. total number of worms collected in each individual) ranged from 0 to 75 worms, and burdens were highest in muskrats under 1.0 kg (Fig.

6). Both cestodes (Hymenolepis sp.) and trematodes (Echinostomes) were found at low prevalence (17.37% and 24.21%, respectively) and only 10 cases of co-occurrence were detected. We found 632 individual cestodes and 959 individual trematodes, with average burdens of 9.58 worms and 10.42 worms, respectively.

We found cysts on 57 livers and the highest prevalence of cysts on the liver was in the SE region (prevalence=29%; Fig 5) and was higher than cyst prevalence in the NC region (P=0.037) which had the lowest prevalence of cysts. Of the livers with cysts, 63% had only one large cyst

(>0.1 cm). Two livers had numerous small cysts (1–2 mm) while the other remaining 19 had between two and 16 large cysts (>0.1 cm). We identified all but three of the cysts were caused by an infection of Taenia taeniaformis. The causative agent of the remaining three cysts was identified through PCR gel-electrophoresis and genetic sequencing as infections of Versteria mustelae. All three muskrats infected with V. mustelae were trapped in counties in eastern

Pennsylvania (Northumberland, Lebanon, and Lancaster). We also detected Calodium hepaticum eggs in two of the livers with cysts.

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Through laboratory testing we identified four diseases from the muskrats we collected

(Table 2). Through agglutination assay testing, 13.68% of samples returned positive antibodies

for Toxoplasma gondii. Upon further examination of T. gondii positive samples using

apicomplexan screening PCR, only one matched T. gondii genetically. Samples that initially

returned positive antibodies for T. gondii in agglutination testing but did not genetically match T.

gondii actually returned genetically positive for Sarcosystosis (prevalence=6.84%). We

identified the bacterium Clostridium piliforme, the causative agent of Tyzzer’s disease, in the

fecal matter of 11 muskrats (prevalence=2.89%). Babesia spp. were positively identified through

serology in 3.95% of liver samples.

We did not detect Pb, Hg or As in the subset of 120 livers. Only two muskrats had

detectable amounts of Cd in their liver (Table 3). The only difference in heavy metal liver

concentrations between sexes was for zinc concentrations where females had lower

concentrations than males with a mean loge-concentration difference of 0.087 (sd=0.029; Fig. 7).

There were no differences between heavy metal concentration and water body type (p > 0.16;

Table 4). Our model results indicated that while negative relationships exist between carcass weight and some heavy metal concentration, there was no difference between the slopes or intercepts for each sex. (Fig. 8). There was also no difference between the slopes or intercepts of healthy and unhealthy muskrats, however negative relationships between heavy metal concentrations and carcass weight occurred more frequently in unhealthy muskrats with the exception of zinc concentration having a negative relationship with body weight for both health classes (Fig. 9). The only chemical detected in muskrat livers during GC/MS and LC/MS testing was phenol (n=2).

DISCUSSION

57

From passive surveillance, we were able to identify cysticercosis and Clostridium piliforme infections as prominent causes of mortality in muskrats in the eastern United States.

We used these findings to inform our screening selection during the active surveillance portion of this study. During active surveillance, we were able to collect muskrat from various regions in

Pennsylvania both geographically and with respect to landcover and water body type through the assistance of trappers (Fig. 1; Fig. 2). We were unable to determine reliable methods to assess age class using ratios of body measurements (i.e. carcass weight and tail surface area). We did observe that males had higher mean tail surface areas and higher mean carcass weights than females, however, which aligns with typical sexual dimorphism previously reported in muskrats

(Fig. 4; Erb and Perry 2003).

Peak intestinal parasite burden occurred in both males and females under 1.0 kg (Fig. 6).

Since carcass weight is associated with age, those muskrats with carcass weights under 1.0 kg would be considered juveniles or subadults (Dorney and Rusch 1953). In contrast, a previous study in Pennsylvania reported higher parasite burdens in older muskrats (Anderson and

Beaudoin 1966). The prevalence and burden range we observed were similar to those reported by

Anderson and Beaudoin (1966), with only a shift of higher burdens in young muskrats.

With respect to landcover type, results from active surveillance identified intestinal parasite prevalence was moderate in agricultural (41%) and urban (32%) areas. Although we only identified intestinal parasites to family, there is potential for transmission of these parasites to humans and domestic animals as muskrats are host to several species of intestinal parasites that can be transmitted across species (Appendix). The likelihood of humans and domestic animals (e.g. livestock) coming in contact with oocysts within these areas are high, especially in ponds and streams where muskrats are trapped (Fig. 2). Our observation of higher parasite

58

prevalence in streams and ponds, matched previous reports in Pennsylvania where streams had

higher prevalence than marshes or rivers (Anderson and Beaudoin 1966). Our observation of a

shift in parasite burden between age classes while prevalence in water body types remained similar to previous reports suggests a possible shift in parasite-host interactions, possibly in

response to added stressors (e.g. contaminants, climate variability) on juveniles (Marcogliese and

Pietrock 2011).

Similar to results from passive surveillance, we detected C. piliforme in 11 of the 380

muskrats and preliminary stages of cysticercosis in 57 muskrats during active surveillance. We

also were the first study to genetically confirm Versteria mustelae in muskrats (n=3), with one

muskrat showing a severe infection with numerous small cysts covering the entire liver. This

parasite has only recently been reported in the United States (Lee et al. 2016). A singular report

documenting V. mustelae infection in humans found lesions on multiple organs along with other

clinical signs of cestode infection in a woman from Pennsylvania (Lehman et al. 2019). Although

mink and other mustelids are definitive hosts for V. mustelae, our finding that muskrats can serve

as an intermediate host and the recent report of human infection indicates that there are multiple

avenues of transmission for this species. Due to the zoonotic nature of V. mustelae, further

investigation on the transmission and impact of V. mustelae on muskrat health is warranted.

In addition to screening muskrats for C. piliforme and cysticercosis, we also investigated

a broad suite of other factors affecting muskrat health. Since muskrats have been used as sentinel

species for a wide variety of zoonotic diseases (Ahlers et al. 2015), we tested samples for two

parasites (Toxoplasma gondii, Babesia spp.) and one bacterium (Francisella tularensis) that have

broader implications for ecosystem health. We only detected T. gondii in one muskrat and did

not detect Babesia spp. in any of the muskrats sampled. T. gondii has been documented as

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causing disease in sea otter (Enhydra lutris), and there is concern for impacts of T. gondii on similar species, such as river otter (Lontra canadensis) (Conrad et al. 2015). The low prevalence of T. gondii in the muskrats in our study suggested that other aquatic animals were having limited exposure to T. gondii It should be noted, however, that only seven muskrats sampled were trapped in river systems.

While screening for toxoplasmosis, 26 muskrats tested positive for sarcosystosis.

Sarcosystosis causes human illness and losses in livestock, as well as wildlife populations (Long

1990). The only regions without sarcosystosis detections were those with the lowest sample sizes

(SW and NC), suggesting it may be present but went undetected. Tularemia (caused by the bacterium F. tularensis) is a common disease in muskrats typically resulting in outbreaks and mass mortality events (Chapter One). None of the muskrats we collected tested positive for F. tularensis, suggesting the prevalence of tularemia in the aquatic environment was minimal and that the likelihood of an outbreak of tularemia within populations where muskrats were collected was low.

Our heavy metal screening of a subset of livers resulted in no detections of lead, mercury, or arsenic. The detection limit for these heavy metals were each 1.0 ppm. While existing reports of mean Pb contamination ranged from0.27–5.23 ppm in muskrat livers, reports for Hg contamination were below the reporting limit (0.02–0.22 ppm) (Parker 2004; Erickson and

Lindzey 1983; Blus et al. 1987). There is a possibility that Pb, Hg, and As were present in muskrat tissues, but were not detected due to higher detection limits than previous studies.

Cadmium was only detected in two muskrats, although at low levels (Table 3). Cadmium concentrations higher than 0.32 ppm have not been reported in literature and no detrimental effects of Cd on muskrat health has been reported (Chapter 1).

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Copper concentrations can give insight into the health of an individual through monitoring for Cu deficiencies as Cu acts as a regulator for molybdenum. When Mo levels become too high and there is not enough Cu to mitigate extreme Mo concentrations, toxic symptoms occur (Gray and Daniel 1964). Liver concentrations of Cu in muskrats from an area exposed to heavy metal contamination ranged from 11.51 to 13.77 ppm (Parker 2004). In contrast, Blus et al. (1987) reported Cu concentrations of 1.0–2.6 ppm in muskrats in Idaho. Our results showed that Cu concentrations in muskrats ranged from 1.1 to 7.1 ppm. In addition, the

Mo concentrations we observed (range=0.4–0.98 ppm) were similar to those in muskrats from

Virginia (mean range=0.73–0.92 ppm) in an area where Mo levels in the environment were also low concluding that Mo and Cu levels in the muskrats in our study were normal (Halbrook et al.

1993).

The Zn concentrations we observed fell within ranges previously reported for healthy muskrats (range=18.4–27.4 ppm; Blus et al. 1987). In comparison, muskrats from two contaminated sites in Ontario had mean Zn concentrations in livers of 64.17 and 65.31 ppm. The

Mn and Fe concentration levels we observed were also typical of previous studies with the exception of the Fe concentration of three muskrats exceeding 800 ppm (Halbrook et al. 1993).

Although Fe deficiencies are common, Fe toxicity is uncommon but can cause phosphorus deficiency (Robbins 1983). We did not screen for phosphorus concentrations in muskrats, and therefore cannot address the possibility of Fe toxicity in the three muskrats with high Fe concentrations.

The only difference in heavy metal concentration between sexes was in Zn concentrations with females having lower concentrations than males (Fig. 7). These differences in Zn concentrations between sex is typical in avian species, however, no standards for muskrats are

61

currently known (Eisler 1993). Zinc is required for proper growth and development, therefore one would expect to see a trend with higher Zn concentrations in smaller (i.e. younger) muskrats than in larger (i.e. older) muskrats. We did detect this trend with negative relationships between carcass weight and Zn concentration for both males and females (Fig. 8). There were no differences between water body type and heavy metal concentrations, however, sample sizes for rivers, lakes, and marshes were low making statistical testing unreliable (Table 4). Although negative relationships between heavy metal concentration and body weight existed for healthy and unhealthy muskrats, there were no differences between the groups. While we did detect heavy metal concentrations in muskrat livers, there was no correlation between health, geographical location, or water body type, suggesting that we were unable to detect, if present, the impact of heavy metal contamination on health of muskrat populations.

Overall, we observed low to no prevalence of diseases such as toxoplasmosis, sarcosystis, babesiosis, and tularemia. Tyzzer’s disease was detected using both active and passive surveillance, showing that it is present on the landscape in the eastern United States. Continued monitoring for this disease is warranted to detect and prevent localized outbreaks in the future.

Intestinal parasite burdens were comparable to previous studies, however the shift to higher burdens in young muskrats is concerning. Added stressors, such as climate variability, predation, and heavy metal contamination, may further increase parasite burden on juvenile muskrats, leading to decreased survival in the future. We did observe muskrat as a novel host for V. mustelae, providing some evidence for the geographic shift of foreign parasites to immunologically naïve hosts as suggested by Marcogliese (2001). Although we did not observe any heavy metal toxicity in muskrats, chronic exposure to heavy metals in addition to various other stressors can impact survival. Our results provide baseline data on muskrat exposure to a

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suite of pathogens, diseases, and contaminants, and also provide contemporary data to track shifts in muskrat exposure within Pennsylvania.

ACKNOWLEDGMENTS We are extremely grateful to the regional biologists and staff from the Pennsylvania

Game Commission who collected muskrat carcasses from trappers. We are also thankful for the laboratory technicians at both the Southeastern Cooperative Wildlife Disease Study and the

California Animal Health and Food Safety Laboratory at the UC Davis School of Veterinary

Medicine for processing and testing our samples, as well as providing interpretations of test results. We would also like to acknowledge the dedicated trappers who donated carcasses and those volunteers who assisted in necropsy. Funding for this study was provided by the

Pennsylvania Game Commission. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Table 1: Passive surveillance and diagnoses for muskrat (Ondatra zibethicus) submitted to the

Southeastern Cooperative Wildlife Disease Study 1977–2018.

Diagnosis n State Year trauma 8 GA, VA, PA 1977, 1981, 1987, 1991, 2008, 2017 cysticercosis 6 VA, PA 2006, 2007, 2018, 2019 Tyzzer’s Disease 5 GA, VA 1981, 1984, 1992 tumor 2 MD, GA 1987, 1997 systemic bacterial infection 2 WV, MD 1984, 2012 cellulitis and dermatitis of tail 1 VA 2006 normal animal 1 GA 1981 undetermined 2 KS, VA 2006, 2008

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Table 2: Active surveillance for pathogens in muskrats (Ondatra zibethicus) collected from

Pennsylvania in 2018-2019. Disease Pathogen Tissue Test prevalence Tyzzer's Clostridium piliforme Fecal PCR 2.89% Tularemia Francisella tularensis Liver PCR 0.00% Babesiosis Babesia spp. Liver PCR 3.95% Serology: Modified Heart, Blood, Agglutination Toxoplasmosis Toxoplasma gondii Muscle, Tongue Assay (MAT) 13.68% PCR/Sequencing* 0.26% Heart, Blood, Sarcosystosis Sarcocystis spp. Muscle, Tongue PRC/Sequencing* 6.84% *Only muskrats positive for antibodies to T. gondii were PCR tested using an apicomplexan screening PCR that will amplify various genera of interest including Toxoplasma, Sarcocystis, and Hammondia. For any sample that was sequenced confirmed to have Sarcocystis or

Hammondia we also ran T. gondii-specific PCR to rule out coinfection.

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Table 3: Mean (± ) liver concentrations (ppm) for heavy metals in both male and female muskrats (Ondatra𝑆𝑆𝑆𝑆 zibethicus). Means are calculated from samples with detectable amounts of heavy metal concentrations. If no levels of a heavy metal were detected, value is reported as not detected (ND) with respective sample size (n).

Heavy Metala Total Male Female Range Pb ND ND ND ND (n=60) (n=60) (n=60) (n=60) Mn 3.37±0.27 3.49±0.49 3.27 (0.27) 0.64–26 (n=112) (n=53) (n=59) Fe 351.61±18.55 343.02±24.09 359.32 (27.94) 120–1500 (n=112) (n=53) (n=59) Hg ND ND ND ND (n=120) (n=60) (n=60) As ND ND ND ND (n=120) (n=60) (n=60) Mo 0.57±0.01 0.58±0.02 0.57 (0.02) 0.4–0.98 (n=81) (n=42) (n=39) Zn 29.43±0.46 29.45±0.58 29.41± 0.69 19–51 (n=112) (n=53) (n=59) Cu 3.77±0.08 3.94±0.12 3.62±0.10 1.1–7.1 (n=112) (n=53) (n=59) Cd 0.007±0.13 ND 0.43± 0.13 0.00–0.55 (n=2) (n=60) (n=2) a Pb = lead; Mn = manganese; Fe = iron; Hg = mercury; As = arsenic; Mo = molybdenum; Zn =

zinc; Cu = copper; Cd = cadmium.

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Table 4: Mean (± ) liver concentrations (ppm) for heavy metals from muskrats (Ondatra zibethicus) harvested𝑆𝑆𝑆𝑆 in different water body types. Means are calculated from samples with detectable amounts of heavy metal concentrations. If no levels of a heavy metal were detected, value is reported as not detected (ND) with respective sample size (n).

Heavy Stream Pond River Lake Marsh Metala (n=63) (n=40) (n=5) (n=6) (n=5) Pb ND ND ND ND ND (n=63) (n=40) (n=5) (n=6) (n=5) Mn 2.96±0.17 3.98±0.73 4.70±0.77 2.62±0.59 3.14±0.61 (n=59) (n=37) (n=5) (n=5) (n=5) Fe 343.90±18.73 386.23±46.45 284.00±19.65 266.00±16.91 364.00±58.02 (n=59) (n=37) (n=5) (n=5) (n=5) Hg ND ND ND ND ND (n=63) (n=40) (n=5) (n=6) (n=5) As ND ND ND ND ND (n=63) (n=40) (n=5) (n=6) (n=5) Mo 0.59±0.02 0.56±0.03 0.40 (0.00) 0.53±0.04 0.61±0.02 (n=46) (n=25) (n=2) (n=3) (n=4) Zn 29.98±0.65 28.84±0.73 26.20±1.96 26.80±1.07 33.00±2.75 (n=59) (n=37) (n=5) (n=5) (n=5) Cu 3.87±0.07 3.74±0.17 3.02±0.22 3.20±0.32 3.94±0.41 (n=59) (n=37) (n=5) (n=5) (n=5) Cd 0.43±0.13 ND ND ND ND (n=2) (n=40) (n=5) (n=6) (n=5) a Pb = lead; Mn = manganese; Fe = iron; Hg = mercury; As = arsenic; Mo = molybdenum; Zn =

zinc; Cu = copper; Cd = cadmium.

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Figure 1. Townships in Pennsylvania where muskrat (Ondatra zibethicus) carcasses were collected from trappers in 2019 as indicated by shaded regions. Six regions designated by the

Pennsylvania Game Commission indicated by bold outlines

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Figure 2. Distribution of landcover type and water body type where muskrat (Ondatra zibethicus) carcasses were collected in Pennsylvania in 2019. Landcover types were designated by bars within regions and included: agriculture (a), forest (b), and urban (c). Regions within

Pennsylvania are Northwest (NW), Southwest (SW), Northcentral (NC), Southcentral (SC),

Northeast (NE), and Southeast (SE).

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Figure 3. Muskrat (Ondatra zibethicus) carcass weight (kg) in each region from active surveillance in Pennsylvania (NW-Northwest, SW-Southwest, NC-Northcentral, SC-

Southcentral, NE-Northeast, SE-Southeast).

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Figure 4. Linear regression of muskrat (Ondatra zibethicus) carcass weight (kg) to surface area of tail (cm2) for males (M) and females (F) from active surveillance in Pennsylvania.

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Figure 5. Stacked prevalence of clinical signs (e.g. cysts, intestinal parasites) found in muskrats

(n=380) in Pennsylvania by region (NW-Northwest, SW-Southwest, NC-Northcentral, SC-

Southcentral, NE-Northeast, SE-Southeast).

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Figure 6. Loess regression of carcass weight (kg) to parasite burdens found in individual muskrats (Ondatra zibethicus) for males (M) and females (F) in Pennsylvania in 2019.

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Figure 7. Mean liver concentrations (ppm) of heavy metals in male and female muskrats

(Ondatra zibethicus) in Pennsylvania in 2019. *Confidence interval of the mean difference does not overlap zero.

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Figure 8. Graphical representation of a Bayesian censored ANCOVA model depicting relationships between muskrat (Ondatra zibethicus) carcass weight (kg) and heavy metal concentrations (ppm) for each sex.

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Figure 9. Graphical representation of a Bayesian censored ANCOVA model depicting relationships between muskrat (Ondatra zibethicus) carcass weight (kg) and heavy metal concentrations (ppm) for muskrats exhibiting clinical signs of disease (unhealthy) and those exhibiting no clinical signs of disease (healthy).

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THE JOURNAL OF WILDLIFE MANAGEMENT

Chapter Three

Ecology of an Isolated Muskrat Population During Regional Muskrat Population Declines

LAKEN GANOE, The Pennsylvania State University, 436 Forest Resources Building,

University Park, PA 16802

MATTHEW LOVALLO, Pennsylvania Game Commission, 2001 Elmerton Avenue, Harrisburg,

PA 17110-9797

JUSTIN BROWN, Department of Veterinary and Biomedical Sciences, The Pennsylvania State

University, University Park, PA 16802, USA

W. DAVID WALTER, U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife

Research Unit, The Pennsylvania State University, University Park, PA 16802

RH: Ganoe et al. · Muskrat Survival and Home Range

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ABSTRACT Evidence indicating a decline in muskrat populations in the United States during the past 40 years has led to speculation in regard to factors influencing muskrat survival. In order to understand population dynamics and survival, it is important to first understand the ecology of local populations. We investigated the dwelling structure use, movements, home range, and survival of radio-tagged muskrats (n = 17) in an urban wetland complex in central Pennsylvania. We used locations collected from intensive radio telemetry monitoring to determine number of lodging structures used, hourly movement, and size and percent area overlap of home ranges. Muskrats shared an average of nine lodging structures and on average 68% of a muskrat’s home range overlapped other muskrat home ranges. We used four home range estimators (Kernel Density Estimator (KDE) href, KDEad hoc, KDEplug-in, and Local Convex Hull estimator) to assess the ability of each estimator in representing muskrat home ranges. The KDEplug-in that constrained the estimate of home range to habitat boundaries provided the more appropriate home range size for muskrats in a linear-non-linear habitat matrix. We also calculated overwinter survival estimates using Known Fate models in Program MARK®. Our top model showed a positive effect of the average weekly precipitation on survival with an overwinter survival estimate of 0.59 (SE = 0.16). The main cause of muskrat mortality was mink (n = 6). Our model suggests that snowfall may be an important factor in muskrat survival. Our study provides novel data on muskrat ecology in Pennsylvania as well as preliminary evidence for future investigations of climate’s impact on muskrat survival during the winter months. KEY WORDS home range, KDE, movement, muskrat, Ondatra, Pennsylvania, precipitation,

space-use, survival

Reported muskrat harvest estimates have declined across the United States between the

1970’s and today, suggesting wide-spread muskrat population decline. Several factors may

influence muskrat survival, including predation, habitat loss and degradation, disease, or a

combination of stressors (Ahlers and Heske 2017). The ecology of muskrats as semi-aquatic

rodents presents concerns about the role of their movements and space-use in population health and persistence, especially regarding disease transmission. When disease transmission is a function of direct contact between individuals, prevalence of disease increases with higher population density and group sizes (McCallum et al. 2001). In respect to group sizes, muskrats are also semi-colonial and will share their dwelling structures (i.e. huts and bank burrows) with several related and unrelated muskrats. For instance, muskrats in China were observed sharing

75% of the area of their home range with other nonfamilial muskrats (Ching and Chih-tanc

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1962). In large marshes muskrats are also known to use an average of 1–4 huts within family

groups (Proulx and Gilbert 1983). This close spatial proximity of muskrats can result in the

direct transmission of diseases between individuals sharing the same space, in turn influencing

local population health and persistence (McCallum et al. 2001).

Along with understanding the magnitude of dwelling structure sharing, it is crucial to

define other ecological traits that may affect local population health, such as movement patterns.

In areas where populations have the ability to expand geographically, reduced disease

transmission is observed as population density decreases (Cross et al. 2009). However, movements of aquatic mammals are constrained by habitats (i.e. water boundaries) and movement corridors, such as channels. These constraints increase the chances of individual interaction and direct disease transmission within narrow habitats (Collinge and Ray 2006).

Concomitant with the diversity of habitat matrices that muskrats reside (e.g. urban wetland complexes, coastal wetlands, river systems, etc.), movement patterns and estimators used to assess size of home range vary between studies, making it difficult to compare space use among studies. For instance, in mark-recapture studies muskrats stayed within 70 m and 265 m of both huts and shoreline, respectively (Errington 1939, Errington and Errington 1937, Sather 1958). In radio telemetry studies, movements varied from 150–230 m away from huts in a marsh but were

800 m in a linear stream habitat with several bank burrows (MacArthur 1980, Ahlers et al.

2010a). A muskrat home range contains their dwelling structures, and the shape of their home range varies depending on the habitat type. Muskrats in ponds and marshes tend to have more circular summer home ranges from 7 to 85 m in diameter while those in linear habitats, such as rivers and streams, have home ranges of linear lengths ranging from 46 to 800 m (Erb and Perry

2003, Ahlers et al. 2010a). With such diversity of the size and shape of home ranges, it is

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difficult to use movements and home ranges to anticipate overlap and define contact networks of muskrats when attempting to determine if movement patterns may impact survival.

The movements of mammals living in aquatic landscapes are largely affected by the amount of flooding that occurs in the area, and movements in response to flooding have the potential to affect survival (Anderson et al. 2000, Naiman and Rogers 1997, Ahlers et al. 2010b).

Heavy rain events causing rises in water levels lead to the displacement of muskrats for up to 80 hours when their dwelling structures become flooded (Ahlers et al. 2010b). During high-intensity rain events, dispersal of muskrats due to flooding was thought to increase predation mortality, but most of the predation events observed during a small study in Illinois were during non- flooding events (Ahlers et al. 2010b). There are many predators of the muskrat, with mink

(Neovison vison), raccoon (Procyon lotor), birds of prey and other mesocarnivores responsible for most predation events (Erb and Perry 2003). In northern areas of North America, mink- muskrat interactions are stronger in areas where prey diversity is low, resulting a more mink predation pressure (Erb et al. 2001). Predation is one of the major causes of muskrat mortality

(Erb and Perry 2003). However, survival is also influenced by a combination of factors including habitat constraints, climatic factors (e.g. flooding, drought), disease, and food availability

(Ahlers et al. 2010b, Ferrigno 1968, Greenhorn et al. 2017, Ahlers and Heske 2017). Errington

(1954) concluded that in areas experiencing drought, muskrats have a severe disadvantage and become exposed to mink predation that may result in rapid local population declines. Drought exposure during the early-winter period also results in mink predation events, however they are infrequent (Errington 1954).

In addition to the influence of climatic factors and predation, muskrat survival rates vary between age class and habitat type. Juvenile survival is lowest during two periods, one between

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birth and six weeks of age and the other during the winter months (Stewart and Bider 1974).

Juvenile survival rates are extremely variable across study sites and habitats, making it difficult

to determine a reliable average survival estimate across the range of the species. Most survival

estimates are based on placental counts in harvested females which can positively bias survival

estimates. Juvenile survival estimates ranged from 10 to 87% (mean = 44%) between birth and

the first fall season, and from 4 to 58% (mean = 17%) annually (Erb and Perry 2003). Adult

survival rates reflect the same variation that juvenile survival rates do with respect to study

location. Annual adult survival estimates ranged from 4 to 17% (Clark and Kroeker 1993,

Simpson and Boutin 1993). Lower survival estimates are observed in both age classes during the

winter months, however little evidence of the causative factors of the difference in estimates of

seasonal survival exist (Erb and Perry 2003). Investigations into possible factors influencing

winter survival are warranted.

Regionally, most studies conducted on muskrat movement, home range, and survival

have been in the Great Plains region and Canada (Ahlers et al. 2010a, MacArthur 1980,

Errington 1939), and little information exists for muskrats residing in the eastern U.S. The

purpose of this investigation is to aid in understanding the ecology and space-use of individual

muskrats within a region lacking historic data. Our objectives were to 1) provide understanding of dwelling structure use, spatial distribution, and general movements of a local population of muskrats during fall and winter, 2) define possible ecological traits that may be contributing to population declines and potential disease transmission within localized muskrat populations, and

3) determine factors affecting muskrat overwinter survival.

STUDY AREA

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Survey sites were located in an urban complex of ponds straddling a stream in central

Pennsylvania (Figure 1). The stream occurring within the wetland complex has a width of about

20 m and connects to the West Branch of the Susquehanna River, 1.5 km east of the complex.

Total available pond habitat was 1.35 ha and was situated beside a recreational community park

and a shopping center with high human traffic areas. The mean annual precipitation at the site

during 2018 was 157.35 cm and mean annual temperature was 10.1oC. The number of days with

temperatures below 0oC was 29 days.

METHODS

Capture and Tagging

We captured muskrats throughout the study area June–November 2018 using double- door, collapsible Tomahawk model 203 live traps (Tomahawk Live Trap Co., Tomahawk,

Wisconsin, USA). Captured muskrats were moved from the traps into squeeze cages, weighed and marked with an ear tag imprinted with a unique ID number (Style 1005-1, National Band and Tag Co., Newport, KY, USA). Muskrats weighing less than 600 g were released immediately at the site of capture. All individuals over 600 g were transported to Metzger’s

Animal Hospital (State College, PA, USA) and surgical procedures were conducted by a licensed veterinarian.

Muskrats were anesthetized using an intra-muscular (IM) injection of Ketamine (10 mg/kg) and Medetomidine (0.1 mg/kg). All animals were provided supplemental oxygen by face mask. Level of anesthesia and vital parameters, including relative arterial oxygen saturation

(SpO2), temperature, and respiration were monitored throughout anesthesia. Each muskrat was implanted with a 13 g radio transmitter (model M1215; Advanced Telemetry Systems Inc.,

Isanti, Minnesota, USA) into the peritoneal cavity following published protocols (MacArthur

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1980; Lacki et al. 1989). We provided post-operative analgesia and antibiotic prophylaxis via

Meloxicam (1.0 mg/kg) and Penicillin G (0.1 mL), respectively. Medetomidine was reversed with Atipamezole (0.25 mg/kg) and animals were held for monitoring a minimum of two hours after surgery to ensure complete recovery prior to release at the site of capture. Young-of-the- year at time of capture were considered juveniles, and all muskrats over one year of age were considered adults (hereafter referred to as age class). Determination of age-class was based on body size and weight with respect to month of capture (Proulx and Gilbert 1988, Ahlers et al.

2010a, Dorney and Rusch 1953). All capture, handling, and surgical methods were approved by the Institutional Animal Care and Use Committee at The Pennsylvania State University (IACUC

No. PROTO201800187) and are within the guidelines of the American Society of Mammalogists

(Sikes et al. 2011).

Radio Telemetry

We monitored muskrats using a radio telemetry receiver (ATS R4000 receiver) and 3- element Yagi antennae connected by a coaxial cable from date of capture in 2018 through March

2019. Homing techniques and visual observation were used to determine the location of each individual, locations were marked on aerial photographs in the field then were digitized upon return from the field in ArcMap 10.5.1 (ESRI 2010). At least one location was recorded each week for every individual for use in survival estimates. During intensive telemetry sessions we collected 3–6 locations for each individual with at least 40 minutes between consecutive locations. We conducted a total of 22 days of intensive telemetry session spanning the months of

September–December (Ahlers et al. 2010a). We were able to home in on the muskrats using radio telemetry and record behavior as either in a dwelling structure or foraging. From this data, we calculated average number of dwelling structures used per individual by age class. Using

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intensive telemetry sessions, we calculated the average hourly movement using the mean linear

distance between consecutive locations per individual and by age class.

Size of Home Ranges

We calculated size of home range using a local convex hull estimator (LoCoH) and fixed

kernel density estimators (KDE) with three different smoothing parameters (href, plug-in, and ad hoc) in Program R (RStudio version 1.1.414) (Getz et al. 2007, Worton 1995, Bauder et al. 2015,

Walter et al. 2015). We used different estimators to determine which more appropriately represented muskrat space use and the duration between successive locations for our data. Linear home ranges were not used to determine home range estimates due to the configuration of the study site being a matrix of a linear stream and pond complex (Ahlers et al. 2010a). We only used individuals with over 50 locations in the analysis (n = 11) to ensure proper representation of space-use by the individual due to the re-use of locations such as dwelling structures. We calculated 50% and 95% isopleths to represent the core and complete home ranges of each individual, respectively. We calculated the average muskrat size of home range among all individuals (n = 11) for each isopleth, and then again by age class. Using the KDEplug-in 95% isopleths, we also calculated the percent area of home range overlap between one muskrat home range and home range of other muskrats in Program R. We then averaged the percent area overlap across all individuals as a proxy for possible disease transmission contact networks.

Survival Estimates

We monitored survival of individual muskrats via radio telemetry from capture to mortality if the individual was still available at the study location. Several telemetry-tagged

muskrats were censored in the known-fate model because they were unable to be recovered,

possibly due to dispersal, predation from birds of prey, or transmitter failure. Upon mortality, we

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determined cause of death by investigating the location of mortality for signs of predation and

the recovery of the carcass, if available. We calculated overwinter survival rates for muskrats

alive starting 8 November 2018 (n = 12) to 27 March 2019 (n = 4) with climate covariates that

included average weekly precipitation (AVP) and total degree days per week below 0oC (TDD).

We standardized the sum of the TDD indexed around 0oC to get the total weekly degree days

used in our models. This index was used instead of heating degree days because muskrat

movement during the winter months, especially in non-linear habitat, is dependent on the

presence of ice (Errington 1961). All covariates were standardized with mean of zero to be able

to directly compare beta estimates. Known-fate models were used to calculate overwinter

survival estimates from the capture histories of 14 individuals in Program MARK® (Version 6.2,

Build 9200) (White and Burnham 1999). Models were ranked using Akaike’s information

criterion adjusted for sample size (AICc) and relative variable importance was calculated by

summing model weights across models containing each covariate (Burnham and Anderson

2002).

RESULTS Dwelling Structure Use and Hourly Movement

The average number of dwelling structures used per individual regardless of age was 9.17

structures (SD = 2.86). There was no difference between the number of dwelling structures used

based on age class (P = 0.50). One burrow was used by eight individuals simultaneously during the fall months. The overall mean distance moved per hour during intensive telemetry sessions was a straight linear distance of 27.76 m (n = 13, SE = 1.77). The mean distance moved per hour by age class was 27.80 m (SE = 2.83) for juveniles (n = 7), and 27.71 m (SE = 2.24) for adults (n

= 6). There was no difference between average distance moved for each age class (P = 0.98).

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During the spring, one muskrat (m16) moved over 750 m upstream from the locations we collected during the fall.

Size of Home Range

As expected, size of home range was different among all four estimators with KDEhref resulting in the largest size of home range that overestimated the area used by muskrats in comparison to the actual locations collected. (Table 1, Figure 2). The LoCoH estimator resulted in the smallest size of home range overall and it underestimated the use of space by constraining the estimate to within the boundaries of the points while excluding several locations from the

95% isopleth (Table 1, Figure 2). The average 95% size of home range across all individuals was 32.80 km2 (SE = 11.27), 20.83 km2 (SE = 11.08), 3.58 km2 (SE = 0.61), and 2.45 km2 (SE =

0.69) for KDEhref , KDEad hoc, KDEplug-in, and LoCoH, respectively (Table 1). On average,

68.57% (SE = 11.64, range = 0–98%) of an individual muskrat home range overlapped other muskrat home ranges. The average percent area of home range overlap per age class was 56.84%

( = 0.56, SE = 0.28) and 78.33% ( = 0.78, SE = 0.27) for adults and juveniles, respectively.

𝑥𝑥̅ 𝑥𝑥̅ Survival Estimates

Of the 17 muskrats we captured, we were able to determine that the cause of death of 6 muskrats was due to predation based on sign present in and around the site of mortality of three adults and three juveniles. Three of the six predated muskrats died weeks prior to the capture of the remaining 14 individuals. Two other muskrats were unable to be relocated several weeks post-release, therefore we exclude the three predated and two missing muskrats from the survival analysis. Known-fate analysis was initiated when we had 12 telemetry equipped muskrats in

November. We had to censor 3 individuals in February and one more in March due to failure to

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locate the individuals. We had five competing models under 2.0 ΔAICc with survival estimates

ranging from 0.58 to 0.67 (Table 2). Since we were most interested in relative variable

importance, we calculated the sum of the weights of models containing each covariate. AVP had

the highest model weight ( = 0.60) and greatest support for influencing survival

𝐴𝐴𝐴𝐴𝐴𝐴 estimates, followed by age ∑class𝜔𝜔 ( = 0.41), then TDD ( = 0.28). The beta

𝑎𝑎𝑎𝑎𝑎𝑎 𝑇𝑇𝑇𝑇𝑇𝑇 estimates for AVP indicated that survival∑ 𝜔𝜔 was positively influenced∑ 𝜔𝜔 by the average weekly

precipitation (Table 3).

DISCUSSION This is the first study in the eastern U.S. to successfully monitor muskrat dwelling

structure use, movement, size of home range, and survival using implanted VHF radio-

transmitters. We observed a high number of average dwelling structures used ( = 9.17), and

both age classes used the same mean number of dwelling structures. Muskrats have𝑥𝑥̅ been

documented to exhibit more colonial behaviors in the fall months than they do in the spring

season, so it is not surprising that both juvenile and adult muskrats coinhabited dwelling

structures in our study as they prepared for winter (Marinelli and Messier 1993). For instance,

we recorded up to eight individuals utilizing the same burrow simultaneously. Proulx and Gilbert

(1984) observed 1 to 6 active houses per muskrat family group in a marsh in Ontario using mark-

recapture data. In contrast, Schooley and Branch (2006) observed round-tailed muskrats

(Neofiber alleni) using 10 to 15 dwelling structures in freshwater marshes of Florida using radio telemetry. Most of the previous studies conducted on muskrat dwelling structure use and movements used mark-recapture techniques or visual observations. We documented use of dwelling structures using radio telemetry equipment which it is more feasible to track animals both swimming and burrowing that would once have been extremely difficult using visual

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observation. Our data supports previous research that muskrats share a large amount of space, especially dwelling structures with other muskrats (Marinelli and Messier 1993, Ching and Chih- tanc 1962). Communal sharing of space supports concerns about the increased likelihood of disease transmission via direct contact in muskrats and warrants further investigation to determine the possible extent of their contact networks aiding transmission.

Along with muskrats sharing a large number of dwelling structures, we observed muskrats moving relatively short distances each hour. Overall, 78.54% of all telemetry locations were at dwelling structures. Muskrats spent a short amount of time moving across the landscape and only moved a mean distance of 28 m when moving to a new location. From a disease transmission standpoint, our isolated wetland complex identified potential implications in regard to direct transmission and spread of disease. Muskrats in isolated populations spend the majority of their time in close proximity to one another within a confined space. Therefore, if one muskrat were to contract a disease, the direct transmission to another muskrat is highly likely. Relatedly, due to the relatively short distance moved by muskrats, if an outbreak were to occur in an isolated population, the rate of transmission of the disease to a new population would likely be slow. Understanding that there is a high potential for rapid localized outbreaks while widespread disease transmission may be low is important in being able to anticipate epizootic outbreaks on a larger scale. Localized hourly movement, however, needs to be further explored in conjunction with size and shape of home range as part of a larger contact network.

Our assessment of four estimators identified considerable differences in each estimator’s ability to capture size and shape of muskrat home range (Figure 2). Since muskrat burrow along banks of waterways, they outline usable habitat from the locations of their burrows making

LoCoH a viable option for calculating size of muskrat home ranges in linear habitats. However,

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in open marshes or ponds, or combinations of linear habitats and circular habitat (e.g. bodies of

water) LoCoH underestimates the space use within non-linear habitats. LoCoH even failed to

capture 37% of locations for a muskrat within the 95% isopleth (Figure 2). Contrary to LoCoH,

KDEhref overestimates the movement of muskrats in linear habitats. We calculated distance from

foraging locations to water bodies in ArcGIS, and none of the locations were farther than 10 m

from the water’s edge. The shape of the 95% isopleth using KDEhref expanded beyond the foraging distance we observed for locations collected. The KDEad hoc estimator appeared to be a

more appropriate representation of muskrat home ranges than KDEhref, although it still appeared

to overestimate the area used. To adequately accommodate use of water sources of various

shapes and configurations by muskrat, we utilized KDEplug-in to constrain the estimate to the

boundaries of the habitat. The KDEplug-in performed well when estimating size of home range within a combined linear and non-linear matrix characteristic of the pond-stream complex at our

site. For example, one individual (m16) dispersed ~750 m upstream from the pond where all of

its locations were taken during the fall months. The KDEhref and KDEad hoc estimates for that

2 2 individual were 19 times larger than those of the KDEplug-in (137.90 km , 130.36 km , and 6.69

km2, respectively) and extended upwards of 130 m into uninhabitable land (i.e. parking lots and

shopping centers). Thus, it is important, especially for semi-aquatic and aquatic organisms, to take habitat availability into consideration when selecting an estimator of home range to adequately determine size and shape. Using KDEplug-in over the other three estimators would

appear more reliable in order to appropriately estimate the size and shape of a muskrat home

range, as well as for use in space-use analyses such as percent area overlap.

Using the home range estimator that we found most appropriate, KDEplug-in, we calculated

the percent area overlap in home ranges between individual muskrats. On average, muskrats

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shared 69% of their home range with at least one other muskrat. This is a conservative estimate

of percent area overlap because we did not capture all individuals within the population and with

more radio-tagged muskrats on the landscape, we would expect to see a much higher percent

area overlap. For instance, one muskrat was seen foraging in tandem with three other muskrats

on multiple occasions and none of the other muskrats were radio-tagged. Our findings of 69%

overlap is comparable to the 75% overlap finding in the study conducted by Ching and Chih-tanc

(1962). A high percentage of home range overlap is expected in areas with high population

density, especially in a constrained habitat. In conjunction with our finding that muskrats spend

the majority of their time in dwelling structures with other muskrats, our observation that over

half of an individual home range overlaps several other muskrat home ranges further supports

concerns about the potential for rapid disease transmission within local populations. Along with

this high potential for disease transmission, high density areas may also facilitate predation

events (Niemuth and Boyce 1995). The only source of mortality we observed in radio-tagged

muskrats was mink predation (n = 6). We did not observe any disease-related mortality in the radiomarked muskrats, however all carcasses were depredated upon recovery and we were unable to ascertain the condition of the muskrat prior to the predation event. Our anecdotal observation of a possible increase in mink populations in Pennsylvania indicated higher mink predation pressure might be affecting muskrat populations. However, estimated mink and muskrat harvests have been following a parallel decline from 1985 to 2018 (Figure 3). Vijugrein et al. (2001) also observed similar trends with no lag time between mink and muskrat harvest rates in eastern Canada suggesting weak predator-prey interactions. Since most muskrat trapping sets also capture mink, we would have expected to see an increase in mink harvest if mink populations were indeed on the rise. There are numerous predators of the muskrat, so we cannot

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directly dismiss the impact of predation on muskrat populations, however, further research is needed to determine if mink predation is acting as a mechanism for additive mortality on muskrats in Pennsylvania.

The variable of highest relative importance in our known-fate models was AVP and it positively influenced muskrat overwinter survival (Table 3). Ahlers et al. (2010b) documented that flooding events during heavy precipitation from July to November did not affect survival, however they did not monitor effects of precipitation during the winter months. In respect to the study conducted by Ahlers et al. (2010b), our findings imply that there are seasonal differences in the effect that precipitation has on survival. Most of the precipitation occurring during our study was snowfall and may correlate to low muskrat activity outside of their dwelling structures. Unlike rain, snowfall would not cause a drastic increase in water levels that would typically result in muskrat being flushed out of their burrows. It is known that trends in winter precipitation and season length have changed in the past half-century. The length of the period between the first and last days in a snow year have shortened from 1950 to 2010 in the much of the United States except for several midwestern states (Knowels 2015). Coincidentally, observations of higher rates of muskrat harvest declines are located in states where Knowels

(2015) reported snow year lengths shortening (Ahlers and Heske 2017). To accumulate snowfall and maintain snow cover, air temperatures need to be low, and therefore might also indicate ice formation on water bodies. The presence of ice may create a safety barrier for muskrats during the winter months and may be correlated with muskrat survival but requires further investigation.

MANAGEMENT IMPLICATIONS We documented numerous muskrats living in close spatial proximity in numerous burrows, burrow-sharing with up to eight other muskrats, and high overlap of home range. The

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combination of these factors would provide further insight for future investigations on causative factors of muskrat population declines, especially in regard to disease outbreak and transmission at the local level. Future research on muskrat populations would benefit from monitoring of mink-muskrat interactions and climatic variability, especially during the winter months where data is lacking. Climatic variability in the future may place an added stressor on muskrats by flooding events in summer-fall and lower snowfalls and temperatures reducing ice formation thus potentially increasing muskrat exposure to predations. Monitoring the effects of both communal denning and increased exposure to predation may provide insight into the declines in muskrat populations across North America.

ACKNOWLEDGEMENTS

This research was funded by the Pennsylvania Game Commission. We thank the numerous volunteers for their dedication, and the city of Lewisburg for their support and access to the study site. We also are grateful to Metzger Animal Hospital, particularly the doctors performing the surgery: Fred Metzger, Robert Rider, Alejandro Martinez, and Andrew VanGorder. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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LITERATURE CITED Ahlers, A.A., and E.J. Heske. 2017. Empirical evidence for declines in muskrat populations across the United States. Journal of Wildlife Management 81(8):1408-1416. Ahlers, A.A., E.J. Heske, R.L. Schooley, and M.A. Mitchell. 2010a. Home ranges and space use of muskrats Ondatra zibethicus in restricted linear habitats. Wildlife Biology 16:400-408. Ahlers, A.A., R.L. Schooley, E.J. Heske, and M.A. Mitchell. 2010b. Effects of flooding and riparian buffers on survival of muskrats (Ondatra zibethicus) across a flashiness gradient. Canadian Journal of Zoology 88:1011-1020. Anderson, D.C., K.R. Wilson, M.S. Miller, and M. Falck. 2000. Movement patterns of riparian small mammals during predictable floodplain inundation. Journal of Mammalogy 81(4):1087-1099. Bauder, J.M., D.R. Breininger, M.R. Bolt, M.L. Legare, C.L. Jenkins, and K. McGarigal. 2015. The role of the bandwidth matrix in influencing kernel home range estimates for snakes using VHF telemetry data. Wildlife Research 42:437-453. Burnham, K.P., and D.R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer, New York, New York, USA. Ching C., and Y. Chih-tanc. 1962. Burrows, lodges and home ranges of the muskrat, Ondatra Zibethica Linne. Acta Zoologica Sinica 14:474-88. Clark, W.R., and D.W. Kroeker. 1993. Population dynamics of muskrats in experimental marshes at Delta, Manitoba. Canadian Journal of Zoology 71:1620-1628. Collinge, S.K., and C. Ray, editors. 2006. Disease ecology: community structure and pathogen dynamics. Oxford University Press, Oxford, UK. Cross P.C., Drewe J., Patrek V., Pearce G., Samuel M.D., Delahay R.J. 2009. Wildlife Population Structure and Parasite Transmission: Implications for Disease Management. in Delahay R.J., Smith G.C., Hutchings M.R., editors. Management of Disease in Wild Mammals. Springer, Tokyo, Japan. Dorney, R.S., and A.J. Rusch. 1953. Muskrat growth and litter production. Wisconsin Conservation Department Technical Wildlife Bulletin Number 8, Madison, USA. Erb, J.E., M.S. Boyce, and N.C. Stenseth. 2001. Spatial variation in mink and muskrat interactions in Canada. Oikos 93:365-75. Erb, J.E., and H.R. Perry Jr. 2003. Muskrats (Ondatra zibethicus and Neofiber alleni). Pages 311-348 in G.A. Fledhamer, B.C. Thompson, and J.A. Chapman, editors. Wild mammals of North America, biology, management, and conservation, Second edition. Johns Hopkins University Press, Baltimore, Maryland, USA. Errington, P.L. 1961. Muskrats and Marsh Management. The Wildlife Management Institute, University of Nebraska Press, Lincoln, Nebraska, USA. 97

Errington 1954. The special responsiveness of minks to epizootics in muskrat populations. Ecological Monographs 24:377-93. Errington, P.L. 1939. Reaction of muskrat populations to drought. Ecology 20:168-186. Errington, P.L., and C.S. Errington. 1937. Experimental tagging of young muskrats for purposes of study. Journal of Wildlife Management 1(3):49-61. ESRI. 2010. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. Ferrigno, F. 1967. First in fur value: muskrats and their management. Part 2: Research, management, and influences. New Jersey Outdoors 17(8):13-19. Getz, W.M., S. Fortman-Roe, P.C. Cross, A.J. Lyons, S.J. Ryan, and C.C. Wilmers. 2007. LoCoH: Nonparametric kernel methods for constructing home ranges and utilization distributions. PLoS ONE 2(2): e207. doi:10.1371/journal.pone.0000207. Greenhorn, J.E., C. Sadowski., J. Holden, and J. Bowman. 2017. Coastal wetlands connected to Lake Ontario have reduced muskrat (Ondatra zibethicus) abundance. Wetlands 37:339- 349. Knowles, N. 2015. Trends in snow cover and related quantities at weather stations in the conterminous United States. Journal of Climate 28:7518-7528. Lacki, M.J., P.N. Smith, W.T. Peneston, and D.F. Vogt. 1989. Use of methoxyflurane to surgically implant transmitters in muskrats. Journal of Wildlife Management 53(2):331- 333. MacArthur, R.A. 1980. Daily and seasonal activity patterns of the muskrat (Ondratra zibethicus) as revealed by radiotelemetry. Ecography 3:1-9. Marinelli, L., and F. Messier. 1993. Space use and the social system of muskrats. Canadian Journal of Zoology 71:869-75. McCallum, H, N Barlow, and J Hone. 2001. How should pathogen transmission be modelled? Trends in Ecology and Evolution 16(6):295-300. Naiman, R.J., and K.H. Rogers. 1997. Large animals and system-level characteristics in river corridors. BioScience 47:521-529. Niemuth, N.D., and M.S. Boyce. 1995. Spatial and temporal patterns of predation of simulated sage grouse nests at high and low nest densities: an experimental study. Canadian Journal of Zoology 73(5):819-825. Proulx, G., and F.F. Gilbert. 1988. The molar fluting technique for aging muskrats: a critique. Wildlife Society Bulletin 16(1):88-89. Proulx, G., and F.F. Gilbert. 1984. Estimating muskrat population trends by house counts. Journal of Wildlife Management 48(3):917-922.

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Proulx, G., and F.F. Gilbert. 1983. The ecology of the muskrat, Ondatra zibethicus, at Luther Marsh, Ontario. Canadian Field Naturalist 97:377-90. Sather, J.H. 1958. Biology of the Great Plains Muskrat in Nebraska. Wildlife Monographs 2:1- 35. Schooley, R.L., and L.C. Branch. 2006. Space use by round-tailed muskrats in isolated wetlands. Journal of Mammalogy 87(3):495-500. Sikes, R.S., W.L. Gannon, and The Animal Care and Use Committee of American Society of Mammalogists. 2011. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. Journal of Mammalogy 92(1):235-253. Simpson, M.R., and S. Boutin. 1993. Muskrat life history: a comparison of a northern and southern population. Ecography, 16:5-10. Stewart, R.W., and J.R. Bider. 1974. Reproduction and survival of ditch-dwelling muskrats in southern Quebec. Canadian Field Naturalist 88:429-36. Viljugrein, H., O.C. Lingjaede, N.C. Stenseth, and M.S. Boyce. 2001. Spatio-temporal patterns of mink and muskrat in Canada during a quarter century. Journal of Animal Ecology 70(4):671-682. Walter, W.D., D.P. Onorato, and J.W. Fischer. 2015. Is there a single best estimator? Selection of home range estimators using area-under-the-curve. Movement Ecology 3:1-10. White, G.C., and Burnham, K.P. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46(Suppl):120-138. Worton, B.J. 1995. Using Monte Carlo simulation to evaluate kernel-based home range estimators. Journal of Wildlife Management 59:794-800.

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Figure 1. Location of study site in Lewisburg, Pennsylvania (outset) and the pond-stream matrix where muskrat (Ondatra zibethicus) trapping occurred (inset).

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Figure 2. Comparison of all four (Kernel Density Estimator [KDE]href, KDEad hoc, KDEplug-in, and

Local Convex Hull [LoCoH]) estimators of home range for the 95% and 50% isopleths for two individual muskrats. The pond-stream matrix is depicted by the gray-shaded areas and labeled as water features. 101

Figure 3. Muskrat and mink harvest estimates in Pennsylvania from 1985 to 2018. Harvest trends for each species represented by a loess regression.

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Table 1: Total (95%; km2) and core (50%; km2, * = m2 ) size of home range for individual

muskrats using four different estimators: Kernel Density Estimator (KDE) with smoothing

determined by reference bandwidth (KDEhref), ad hoc reference bandwidth (KDEad hoc), plug-in

(KDEplug-in), and Local Convex Hull Estimator (LoCoH). Number of locations (n), number of

intensive telemetry days where locations were taken at hourly intervals (B), number of days the transmitter was on the air (D) and mean size of home range ( ± ) for each isopleth.

𝑥𝑥̅ 𝑆𝑆𝑆𝑆 n B D KDEhref KDEad hoc KDEplug-in LoCoH 95% isopleth Adult m5 162 23 237 12.839 6.033 2.387 1.476 m7 115 17 104 15.318 7.436 3.554 2.334 m12 110 17 123 28.003 10.947 1.764 0.463 m16 83 13 144 137.898 130.357 6.690 2.855 m18 74 9 139 51.444 21.942 7.272 8.168 49.10 (23.23) 35.34 (23.92) 4.33 (1.22) 3.06 (1.34) Juvenile 𝑥𝑥̅ m4 155 22 237 15.097 7.527 4.381 4.716 m6 143 21 195 30.910 14.055 3.633 1.330 m8 145 23 158 24.368 11.616 3.988 2.544 m9 149 23 206 7.077 3.821 1.706 0.699 m13 113 19 108 4.475 1.862 0.749 0.241 m17 57 11 83 33.351 13.486 3.207 2.154 19.21 (4.98) 8.73 (2.10) 2.94 (0.58) 1.95 (0.66) Overall 32.80 (11.27) 20.83 (11.08) 3.58 (0.61) 2.45 (0.69) 𝑥𝑥̅ 50% isopleth 𝑥𝑥̅ Adult m5 162 23 237 2.528 0.768 0.194 0.489* m7 115 17 104 3.485 1.118 0.511 4.408* m12 110 17 123 6.203 1.946 0.125 65.006* m16 83 13 144 6.028 6.028 0.735 0.031* m18 74 9 139 8.372 1.104 1.028 222.162* 5.32 (1.04) 2.19 (0.98) 0.52 (0.17) 58.42 (42.74)* Juvenile 𝑥𝑥̅ m4 155 22 237 2.398 1.159 0.758 27.541* m6 143 21 195 6.003 1.338 0.436 2.831* m8 145 23 158 3.458 1.175 0.495 15.126*

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Table 1. Continued

n B D KDEhref KDEad hoc KDEplug-in LoCoH 50% isopleth Juvenile m9 149 23 206 1.768 0.560 0.179 0.439* m13 113 19 108 0.737 0.232 0.074 0.013* m17 57 11 83 5.226 1.423 0.320 0.155* 3.27 (0.83) 0.98 (0.19) 0.38 (0.10) 7.68 (4.62)* Overall 4.20 (0.70) 1.53 (0.47) 0.44 (0.09) 30.75 (20.05)* 𝑥𝑥̅

𝑥𝑥̅

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Table 2: Overwinter survival estimates of muskrat population (n = 14) in Pennsylvania for eight

known-fate models using an information-theoretic approach and ranked by ascending differences

in Akaike’s Information Criteria adjusted for sample size (ΔAICc). Models were calculated in

Program MARK® and summary statistics reported are number of parameters (K), survival estimate with standard error in parentheses (S), model weight (ωi), and the deviance of each

model (D). Explanatory values are average total weekly precipitation (AVP), age class of

muskrat (age), and total degree days per week indexed around 0oC and standardized (TDD).

Model K ωi D S(AVP) 2 0.59 (0.16) 0.00 0.25 34.60 S(AVP + age) 3 0.66 (0.17𝑆𝑆 ) 0.65𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥 𝛥𝛥 0.18 33.17 S(.) 1 0.60 (0.15) 0.71 0.17 37.36 S(age) 2 0.67 (0.17) 1.38 0.12 35.98 S(AVP + TDD) 3 0.58 (0.16) 1.69 0.11 34.21 S(AVP + TDD + age) 4 0.65 (0.18) 2.52 0.07 32.94 S(TDD) 2 0.60 (0.15) 2.76 0.06 37.36 S(age + TDD) 3 0.67 (0.17) 3.45 0.04 35.97

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Table 3: Intercept and beta estimates for eight known-fate models of overwinter survival of a muskrat population (n = 14) in Pennsylvania calculated in Program MARK®. Models are ranked

by ascending differences in Akaike’s Information Criteria adjusted for sample size (ΔAICc). All

covariates are standardized with mean of zero.

Model Intercept AVP age TDD S(AVP) 4.64 (1.28) 2.54 (2.25) S(AVP + age) 4.87 (1.34) 2.54 (2.27) − 0.67 (0.60) S(.) 1.25 (0.08) S(age) 3.89 (0.63) − 0.65 (0.60) S(AVP + TDD) 5.11 (1.73) 3.54 (3.17) 0.36 (0.58) S(AVP + age + TDD) 5.16 (1.64) 3.20 (2.95) − 0.64 (0.61) 0.27 (0.58) S(TDD) 3.66 (0.51) − 0.02 (0.49) S(age + TDD) 3.89 (0.63) − 0.66 (0.60) − 0.06 (0.51)

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APPENDIX. Supplemental material associated with Chapter One SI Table. KEY ***Disease caused reported as: S=sporadic C/W=common or widespread I = individual case report E=epizootic Samples used (S):

F Fecal matter Li Liver I Intestine Fn Full necropsy Bl Blood T Tissue Br Brain E Individual Ectoparasites D Digestive Tracts St Stomach Lu Lungs S Unspecified sample H Heart Su Subcutaneous tissue C Cecum Sk Skin

†Eimeria stiedae ‡Toxoplasma microti aLarvae to Taenia taeniaformis bLarvae to Taenia tenuicollis cReported as Taenia taeniaformis (Change documented - Nakao et al. 2013) dLarval stage of Versteria mustelae eReported as Taenia mustelae (Change documented - Nakao et al. 2013) fEchinostomum coalitum gHemistomum craterum hCatatropis filambriata iCatatropis filamentis jNotocotylus filamentus kNotocotylus quinqueserialis lPseudodiscus zibethicus *Accidental finding **Only 53 muskrats sampled for all parasites

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PARASITES Disease Live/ Locatio Caused Name Reported Species S Dead N Prevalence n *** Burden Author in Article Protozoans Chilomastix Frost et al. sp. F D 133 NA WA NA 1980 Cryptosporidi Zhou et al. um sp. F D 237 11.81% MD NA 2004 Ziegler et al. F D 149 0.67% NY NA 2007 PA and Bitto & Aldras F D 44 50.00% NJ NA 2009 Eimeria ondatrazibeth icae Li D NA NA Canada NA Allen 1934† †Eimeria stiedae Eimeria sp. F D NA NA MD S-I Smith 1938 Entamoeba Frost et al. muris F D 133 NA WA NA 1980 Giardia sp. F D 53 100.00% LA NA Penn 1942 F D 34 "Several" CO NA Ball 1952 Grundmann & F LD 25 100.00% UT NA Tsai 1967 Frost et al. F D 133 41.35% WA NA 1980 Pacha et al. F NI 189 82.50% WA, ID NA 1985 Kirkpatrick & F L 220 41.00% NJ NA Benson 1987 Webb & I D 1 100.00% IL NA Woods 1988 ME, NH, NY, VT, Erlandsen et F D 790 36.58% MN NA al.1990 ME, NH, NY, VT, Erlandsen et Fn D 219 95.89% MN, MA NA al.1990 PA and Bitto & Aldras F D 44 65.91% NJ NA 2009 Toxoplasma McCulloch et gondii Bl D 11 0.00% IA NA al.1966 Smith & Bl LD 42 16.67% MO NA Frenkel 1995 Ahlers et Bl L 30 60.00% IL NA al.2015 ‡Toxoplasma Br D 15 13.33% Ontario NA Karstad 1963‡ microti Trichomonas Grundmann & sp. F LD 25 100.00% UT NA Tsai 1967 F D NA NA LA NA Penn 1942 Ectoparasites Androlaelaps Bauer & fahrenholzi E D 40 2.50% IN NA 0-1 Whitaker 1981 Euschoengast Bauer & ia peromysci E D 40 2.50% IN NA 0-1 Whitaker 1981 Labidophorus "abundant "abund Grundmann & hypudai E LD 34 on many" UT NA ant" Tsai 1967 Laelaps Tetragonyssus multispinosa E D 53 25.00% LA NA "low" Penn 1942 spiniger

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Meyer & E D 56 98.21% ME NA NA Reilly 1950 Grundmann & E LD 34 100.00% UT NA NA Tsai 1967 Manitob McKenzie & E LD 60 56.67% a NA 0-811 Welch 1978 mean = Bauer & E D 40 72.50% IN NA 34.3 Whitaker 1981 E D 3 33.33% AK NA 0-3 Whitaker 1988 Prendergast & E D 20 >75% MO NA 0-42 Jensen 2011 Listrophorus sp. E D NA NA MD NA NA Smith 1938 "abundant Grundmann & E LD 34 on many" UT NA NA Tsai 1967 Manitob McKenzie & E LD 60 51.67% a NA 0-400+ Welch 1978 180- Bauer & E D 40 100.00% IN NA 3000 Whitaker 1981 0- E D 3 100.00% AK NA 17,000 Whitaker 1988 Prendergast & E D 20 100.00% MO NA 4-322 Jensen 2011 Manitob McKenzie & E LD 60 10.00% a NA NA Welch 1978 Marsupialich us Bauer & brasiliensis* E D 40 2.50% IN NA 0-1 Whitaker 1981 Myiasis Fn D 1 100% IL S-I NA Arata 1959 Myobia zibethicalis E D NA NA NA NA NA Radford 1936 Myocoptes Bauer & ondatrae E D 40 2.50% IN NA 0-1 Whitaker 1981 E D 3 66.67% AK NA 0-3 Whitaker 1988 Prendergast & E D 20 >50% MO NA 0-7 Jensen 2011 Orchopeas Bauer & howardi E D 40 2.50% IN NA 0-1 Whitaker 1981 Radfordia Bauer & zibethicalis E D 40 2.50% IN NA 0-1 Whitaker 1981 E D 3 66.67% AK NA 0-3 Whitaker 1988 Schizocarpus indianensis E D 3 33.33% AK NA 0-1 Whitaker 1988 Zibethacarus mean = Bauer & ondatrae E D 40 62.50% IN NA 187.6 Whitaker 1981 E D 3 100.00% AK NA 78-346 Whitaker 1988 Prendergast & E D 20 100.00% MO NA 3-323 Jensen 2011 Trematodes Alaria Law & mustelae I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 Sweatman I D 108 0.93% Ontario NA 0-1 1952 Lu , Li D 326 10.12% AK NA NA Dunagan 1957

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F, Bl, Manitob McKenzie & D LD 140 2.14% a NA 0-48 Welch 1978 Allassogonop orus marginalis I D 1 100.00% MI NA 45 Olivier 1938 Amphimerus pseudofelineu s Fn D 250 1.60% IL NA NA Gilford 1954 Diplostomum Newfoun Rigby & mergi D D 114 8.77% dland NA 0-38 Threlfall 1981 Echinochasm us schwartzi I D 1 100.00% MD NA NA Price 1931 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 I, "comm Li D 36 58.33% TX NA onest" Chandler 1941 D D 53 NA LA NA NA Penn 1942 D D 154 1.95% MD NA NA Abram 1951 Echinoparyph ium I, contiguum Li D 46 NA NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 I, Li D 252 5.95% MI NA NA Ameel 1942 British Columbi D D 205 14.63% a NA 0-240 Knight 1951 Sweatman I D 108 1.85% Ontario NA NA 1952 Gash & Hanna Fn D 18 27.78% KS NA NA 1972 Newfoun Rigby & D D 114 3.51% dland NA NA Threlfall 1981 Echinoparyph ium Meyer & recurvatum D D 104 18.27% ME NA NA Reilly 1950 Beckett & Gallicchio D D 130 13.85% OH NA NA 1967 Fn D 81 1.23% IL NA NA Jilek 1977 Newfoun Rigby & D D 114 5.26% dland NA NA Threlfall 1981 New Echinoparyph Brunswi MacKinnon & ium sp. Fn D 35 8.57% ck NA 0-609 Burt 1978 Echinostoma I, fEchinostomum revolutum Li D 46 100.00% NE NA 0-22 Barker 1915f coalitum Law & I D NA NA Ontario NA NA Kennedy 1932f I D NA NA Canada NA NA Allen 1934f I D 8 12.50% IL NA 0-1 Leigh 1940 I, Li D 252 63.10% MI NA NA Ameel 1942f D D 6 66.67% Ontario NA NA Rankin 1946

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D D 70 64.29% OH NA 0-81 Rausch 1946f British Columbi Fn D 202 29.70% a NA NA Musfeldt 1947f 225 Rider & Macy I D 34 35.29% OR NA total 1947 Tiner & Chin Fn D 21 9.52% IL NA 11 total 1948 D D 93 82.80% NY NA 0-10+ Edwards 1949 Meyer & D D 104 21.15% ME NA NA Reilly 1950 D D 154 27.27% MD NA NA Abram 1951 British Columbi D D 205 41.95% a NA 0-230 Knight 1951f Sweatman I D 108 67.59% Ontario NA 0-113 1952f Fn D 250 32.40% IL NA NA Gilford 1954 Newfoun Rigby & D D 114 6.14% dland NA NA Threlfall 1981 Senger & Fn D 34 35.29% OR NA 0-50 Neiland 1955 Senger & Fn D 21 47.62% UT NA 0-35 Bates 1957 I, Anderson Li D 158 29.11% PA NA 0-53 1964 Beckett & Gallicchio D D 130 46.92% OH NA NA 1967 Grundmann & Fn LD 34 52.94% UT NA NA Tsai 1967 Gash & Hanna Fn D 18 44.44% KS NA NA 1972 Rice & Heck D D 100 91.00% OH NA 0-124 1975 Fn D 81 92.59% IL NA NA Jilek 1977 New Brunswi MacKinnon & Fn D 35 51.43% ck NA 0-180 Burt 1978 F, Bl, Manitob McKenzie & D LD 140 25.00% a NA 0-264 Welch 1978 "large number S D NA NA Quebec NA " Gupta 1962b Echinostoma trivolvis I D 50 42.00% IL NA 0-67 Zabiega 1996 Echinostomu I, m armigerum Li D 46 NA NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 Echinostomu m I, callawayensis Li D 46 NA NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934

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Sweatman I D 108 0.93% Ontario NA NA 1952 Lu , Li D 326 0.61% AK NA NA Dunagan 1957 Echinostomu m sp. Fn D 34 2.94% CO NA NA Ball 1952 Detwiler et al. D D 63 84.13% VA NA 0-397 2012 S D NA NA Ontario NA NA Swales 1933 Euryhelmis Senger & pacificus Fn D 34 2.94% OR NA 0-50 Neiland 1955 Fibricola I, cratera Li D 46 2.17% NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 gHemistomum I D NA NA Canada NA NA Allen 1934g craterum Fn D 250 0.40% IL NA NA Gilford 1954 Beckett & Gallicchio D D 130 0.77% OH NA NA 1967 Grundmann & Fn LD 34 2.94% UT NA NA Tsai 1967 Beckett & Mediogonimu Gallicchio s ovilacus D D 130 0.77% OH NA NA 1967 Metorchis Sweatman conjunctus I D 108 0.93% Ontario NA NA 1952 I, Anderson Li D 158 0.63% PA NA 0-1 1964 Notocotylus sp. Fn D 250 4.40% IL NA NA Gilford 1954 Senger & Fn D 34 11.76% OR NA 0-50 Neiland 1955 Detwiler et al. D D 63 1.59% VA NA 0-6 2012 Notocotylus I, hCatatropis urbanensis Li D 46 NA NE NA NA Barker 1915h filambriata D D NA NA MD NA NA Harrah 1922 Law & iCatatropis I D NA NA Ontario NA NA Kennedy 1932i filamentis I D NA NA Canada NA NA Allen 1934i I D 8 12.50% IL NA 0-1 Leigh 1940i I, Li D 252 4.76% MI NA NA Ameel 1942i British Columbi Fn D 202 25.25% a NA NA Musfeldt 1947 Rider & Macy I D 34 35.29% OR NA NA 1947 D D 40 7.50% NY NA 0-25+ Edwards 1949 Meyer & jNotocotylus D D 104 34.62% ME NA NA Reilly 1950j filamentus British Columbi D D 205 26.34% a NA 0-128 Knight 1951 Sweatman I D 108 29.63% Ontario NA 0-60 1952i

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Lu , Li D 326 26.38% AK NA NA Dunagan 1957j Beckett & Gallicchio D D 130 48.46% OH NA NA 1967 "comm Grundmann & Fn LD 34 76.47% UT NA on" Tsai 1967 Beverley- Burton & I D NA NA Ontario NA NA Sweeny 1971 New Brunswi MacKinnon & Fn D 35 37.14% ck NA 0-318 Burt 1978 F, Bl, Manitob McKenzie & D LD 140 15.00% a NA 0-232 Welch 1978i Nudacotyle I, novicia Li D 46 2.17% NE NA NA Barker 1916 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 I, "consid Li D 36 16.67% TX NA erable" Chandler 1941 I, Li D 252 9.92% MI NA NA Ameel 1942 "most commo D D 53 NA LA NA n" Penn 1942 Meyer & D D 104 5.77% ME NA NA Reilly 1950 D D 154 17.53% MD NA NA Abram 1951 Fn D 250 0.40% IL NA NA Gilford 1954 Beckett & Gallicchio D D 130 23.85% OH NA NA 1967 New Brunswi MacKinnon & Fn D 35 22.86% ck NA 0-700+ Burt 1978 Opistorchis tonkae D D 40 2.50% NY NA 0-20 Edwards 1949 New Brunswi MacKinnon & Fn D 35 2.86% ck NA 0-24 Burt 1978 "0- heavy Paragonimus infectio sp. Lu D 79 12.66% MI NA n" Ameel 1932 Parametorchi "infeste s sp. Li D NA NA MD S-C d" Smith 1938 Paramonosto mum echinum I D NA NA CO NA NA Harrah 1922 Paramonosto mum pseudalveatu m I D 1 100.00% MD NA NA Price 1931 D D 53 1.89% LA NA "few" Penn 1942 Grundmann & Fn LD 34 5.88% UT NA NA Tsai 1967

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Paramphisto Newfoun Rigby & matid sp. D D 114 0.88% dland NA 0-4 Threlfall 1981 Phagicola I, "moder lageniformis Li D 36 2.78% TX NA ate" Chandler 1941 F, Plagiorchis Bl, Manitob McKenzie & noblei D LD 140 84.29% a NA 0-880 Welch 1978 Plagiorchis I, proximus Li D 46 NA NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 D D 6 33.33% Ontario NA NA Rankin 1946 D D 70 4.29% OH NA 0-220 Rausch 1946 British Columbi Fn D 202 6.44% a NA NA Musfeldt 1947 D D 53 26.42% NY NA 0-10+ Edwards 1949 Newfoun Rigby & D D 114 50.88% dland NA 0-4388 Threlfall 1981 Meyer & D D 104 5.77% ME NA NA Reilly 1950 British Columbi D D 205 23.41% a NA 0-233 Knight 1951 Fn D 34 76.47% CO NA NA Ball 1952 Sweatman I D 108 11.11% Ontario NA 0-114 1952 Fn D 250 1.20% IL NA NA Gilford 1954 Lu , Li D 326 69.02% AK NA NA Dunagan 1957 I, Anderson Li D 158 1.27% PA NA 0-65 1964 Beckett & Gallicchio D D 130 19.23% OH NA NA 1967 Grundmann & Fn LD 34 44.12% UT NA NA Tsai 1967 New Brunswi MacKinnon & Fn D 35 17.14% ck NA 0-145 Burt 1978 Psilostomum OR and ondatrae Li D 1 100.00% Ontario NA NA Price 1931 Law & Li D NA 1 Ontario NA NA Kennedy 1932 Li D NA NA Canada NA NA Allen 1934 Psilotrema Grundmann & sp. Fn LD 34 2.94% UT NA NA Tsai 1967 Ptyalincola ondatrae Fn D NA NA MI NA NA Wootton 1966 Quinqueserial is "most quinqueserial I, abunda is Li D 46 NA NE NA nt" Barker 1915k I D NA NA WA NA NA Harrah 1922k Law & Kennedy I D NA NA Ontario NA NA 1932k 114

I, Li D 252 79.76% MI NA NA Ameel 1942k D D 70 48.57% OH NA 0-111 Rausch 1946k British Columbi Fn D 202 49.01% a NA NA Musfeldt 1947 D D 53 88.68% NY NA 0-10+ Edwards 1949k Meyer & D D 104 77.88% ME NA NA Reilly 1950 D D 154 12.34% MD NA NA Abram 1951 British Columbi D D 205 49.27% a NA 0-850 Knight 1951 Fn D 34 76.47% CO NA NA Ball 1952k Sweatman I D 108 81.48% Ontario NA 0-989 1952k Fn D 250 6.40% IL NA NA Gilford 1954 Senger & Fn D 34 32.35% OR NA 0-50 Neiland 1955 Lu , Li D 326 92.02% AK NA NA Dunagan 1957 Senger & Fn D 21 4.76% UT NA NA Bates 1957 D D 36 36.11% IL NA NA Arata 1959 I, Anderson Li D 158 18.99% PA NA 0-150 1964 Beckett & Gallicchio D D 130 68.46% OH NA NA 1967 Grundmann & Fn LD 34 97.06% UT NA NA Tsai 1967 Beverley- Burton & I D NA NA Ontario NA NA Sweeny 1971 Rice & Heck D D 100 71.00% OH NA 0-251 1975 Fn D 81 2.47% IL NA 0-83 Jilek 1977 New Brunswi MacKinnon & Fn D 35 60.00% ck NA 0-300+ Burt 1978 F, Bl, Manitob McKenzie & D LD 140 92.86% a NA 0-1856 Welch 1978 Newfoun Rigby & D D 114 64.04% dland NA 0-4855 Threlfall 1981 I D 50 18.00% IL NA 0-5 Zabiega 1996 Detwiler et al. D D 63 26.98% VA NA 0-146 2012 Quinqueserial is zibethica S D 1 100.00% Canada NA NA Gupta 1962a Schistosomati MN and um douthitti Li D 331 9.67% MI NA NA Penner 1938 I, Li D 12 16.67% MI NA NA Ameel 1942 Li D 374 11.23% MA NA NA Penner 1942 Fn D 250 0.40% IL NA NA Gilford 1954

115

Beckett & Gallicchio D D 130 1.54% OH NA NA 1967 New Brunswi MacKinnon & Fn D 35 11.43% ck NA 0-6 Burt 1978 F, Bl, Manitob McKenzie & D LD 140 2.86% a NA 0-10 Welch 1978 Urotrema shillingeri I D 1 100.00% MD NA NA Price 1931 I, Li D 83 1.20% MD NA NA Penner 1941 Wardius I, zibethicus Li D 46 NA NE NA NA Barker 1915 Law & I D NA NA Ontario NA NA Kennedy 1932 I D NA NA Canada NA NA Allen 1934 I, lPseudodiscus Li D 252 13.10% MI NA NA Ameel 1942l zibethicus D D 70 22.86% OH NA 0-14 Rausch 1946 D D 40 5.00% NY NA 0-2 Edwards 1949l Meyer & D D 104 45.19% ME NA NA Reilly 1950l D D 154 16.88% MD NA NA Abram 1951 mean = Fn D 250 9.60% IL NA 4.8 Gilford 1954l I, Anderson Li D 158 21.52% PA NA 0-27 1964 C D 34 67.65% MI NA NA Murrell 1965 Beckett & Gallicchio D D 130 22.31% OH NA NA 1967l Gash & Hanna Fn D 18 0.00% KS NA 0 1972 Rice & Heck D D 100 26.00% OH NA 0-57 1975 New Brunswi MacKinnon & Fn D 35 45.71% ck NA 0-20 Burt 1978 F, Bl, Manitob McKenzie & D LD 140 12.14% a NA 0-29 Welch 1978 Detwiler et al. D D 63 31.75% VA NA 0-25 2012 Cestodes New Andrya Brunswi MacKinnon & macrocephala Fn D 35 2.86% ck NA 0-1 Burt 1978 Andrya ondatrae I D 70 1.43% OH NA NA Rausch 1948 Grundmann & Andrya sp. Fn LD 34 2.94% UT NA NA Tsai 1967 Anomotaenia I, telescopica Li D 46 NA NE NA NA Barker 1915 Gash & Hanna D D 18 0.00% KS NA 0 1972 Anoplocephal Newfoun Rigby & id sp. D D 114 0.88% dland NA 0-1 Threlfall 1981

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Aprostatandr Anderson ya sp. Li D 158 1.27% PA NA 0-1 1964 British Cladotaenia Columbi sp. Fn D 202 3.47% a NA NA Musfeldt 1947 British Columbi Li D 205 0.49% a S-I 0-12 Knight 1951 Cysticercus I, Law & fasciolarisa Li D NA NA Ontario NA NA Kennedy 1932 I, Meyer & Li D 104 0.96% ME NA NA Reilly 1950 Cysticercus talpaeb Li D 3 100.00% Ontario NA NA Skinker 1935 Hydatigera taeniaeformis a Li D NA NA Canada NA NA Allen 1934c I, Chandler Li D 36 0.00% TX NA 0 1941c I, Li D 252 3.17% MI NA NA Ameel 1942c D D 53 0.00% LA NA 0 Penn 1942c I, Li D 6 50.00% MA NA NA Rankin 1946c I, Li D 70 5.71% OH S-I 0-6 Rausch 1946c British Columbi Musfeldt Fn D 202 6.93% a NA NA 1947c I, Rider & Macy Li D 34 29.41% OR S-I 0-7 1947c Tiner & Chin Fn D 21 19.05% IL S-I 5 total 1948c I, Li D 53 5.66% NY S-I 0-1 Edwards 1949c British Columbi D D 205 6.83% a S-I NA Knight 1951c Fn D 54 35.19% VA NA NA Byrd 1952c I, Sweatman Li D 108 0.93% Ontario NA NA 1952c Fn D 250 2.00% IL NA NA Gilford 1954c Senger & Fn D 34 26.47% OR S-I NA Neiland 1955c Li D 1 100.00% OH S-I 3 Gallati 1956c Anderson Li D 158 21.52% PA NA 0-19 1964 Beckett & Gallicchio Fn D 130 16.15% OH NA NA 1967c I, Manitob McKenzie & Li D 171 1.17% a NA 0-27 Welch 1978c Li D 50 22.00% IL NA 0-7 Zabiega 1996c Prince Edward Li D 967 6.62% Island S-I NA Gregory 2012c Hymenolepis I, evaginata Li D 46 NA NE NA NA Barker 1915 I, Law & Li D NA NA Ontario NA NA Kennedy 1932

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I D NA NA Canada NA NA Allen 1934 I, Li D 252 26.19% MI NA NA Ameel 1942 D D 53 3.77% LA NA 0-2 Penn 1942 I, Li D 6 16.67% MA NA NA Rankin 1946 British Columbi Fn D 202 5.45% a NA NA Musfeldt 1947 I, Li D 53 11.32% NY NA 0-5 Edwards 1949 I, Meyer & Li D 104 41.35% ME NA NA Reilly 1950 Fn D 34 11.76% CO NA NA Ball 1952 I, Sweatman Li D 108 38.89% Ontario NA NA 1952 Fn D 250 0.40% IL NA NA Gilford 1954 Lu , mean = Li D 326 52.76% AK NA 4 Dunagan 1957 S D NA NA Alberta NA NA Lubinsky 1957 Senger & Fn D 21 38.10% UT NA 0-5 Bates 1957 Beckett & Gallicchio Fn D 130 3.85% OH NA NA 1967 Grundmann & Fn LD 34 23.53% UT NA NA Tsai 1967 Gash & Hanna D D 18 5.56% KS NA NA 1972 New Brunswi MacKinnon & Fn D 35 31.43% ck NA 0-84 Burt 1978 I, Manitob McKenzie & Li D 171 26.32% a NA 0-16 Welch 1978 Newfoun Rigby & D D 114 58.77% dland NA 0-110 Threlfall 1981 British Hymenolepis Columbi octocoronata Fn D 202 31.19% a NA NA Musfeldt 1947 Hymenolepis I, "consid Rider & Macy ondatrae Li D 34 35.29% OR NA erable" 1947 Macy & Biggs Fn D NA NA OR W-E NA 1953 Senger & Fn D 21 14.29% UT NA 0-10 Bates 1957 Hymenolepis Neiland & oregonensis I D 33 57.58% OR NA NA Senger 1952 Senger & Fn D 34 58.82% OR NA 0-50 Neiland 1955 Hymenolepis I, sp. Li D 70 25.71% OH NA NA Rausch 1946 I, Li D NA NA Ontario NA NA Swales 1933 Tiner & Chin Fn D 21 4.76% IL NA 0-4 1948 British Columbi D D 205 30.73% a NA 0-250 Knight 1951

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Senger & Fn D 34 11.76% OR NA 0-300 Neiland 1955 Monoecocest us sp. D D 36 2.78% IL NA 0-1 Arata 1959 Schizotaenia americana D D 4 25.00% MN NA 0-80 Olsen 1939 Schizotaenia variabilis D D 4 25.00% MN NA 0-355 Olsen 1939 Taenia crassiceps Li D 1 100.00% Ontario S-I 2 Freeman 1962 Taenia I, crassicollis Li D NA NA MD S-I 0-1 Smith 1938 Lu Taenia , Dunagan tenuicollisc Li D 326 0.61% AK NA NA 1957d New Brunswi MacKinnon & Fn D 35 11.43% ck NA 0-15 Burt 1978d Versteria Senger & mustelae Fn D 21 4.76% UT NA NA Bates 1957e "numer Todd et al. Fn D 1 100.00% IL S-I ous" 1978e Nematodes Ascaris Tiner & Chin lumbricoides Fn D 21 9.52% IL NA 0-1 1948 Fn D 250 2.00% IL NA NA Gilford 1954 Anderson D D 158 0.63% PA NA 0-1 1964 Grundmann & Ascaris sp. Fn LD 34 "present" UT NA NA Tsai 1967 Baylisascaris N 4 sp. A D NA individuals NY NA NA Kazacos 2016 N 1 A D NA individual Ontario NA NA Kazacos 2016 Capillaria Law & hepatica Li D NA NA Ontario NA NA Kennedy 1932 Li D NA NA Canada NA NA Allen 1934 Li D 252 3.17% MI NA NA Ameel 1942 D D NA 10.00% LA NA NA Penn 1942 D D NA 50.00% LA NA NA Penn 1942 D D NA 0.00% LA NA 0 Penn 1942 Meyer & Li D 104 17.31% ME NA NA Reilly 1950 Freeman & Li D NA NA Ontario NA NA Wright 1960 Manitob McKenzie & Fn D 140 1.43% a NA 0-7 Welch 1978 Borucinska & Li D 360 61.39% PA, CT E-W NA Nielsen 1993 Capillaria "numer michiganensis S D 1 100.00% Ontario NA ous" Webster 1966 Grundmann & Fn LD 34 44.12% UT NA NA Tsai 1967 Manitob McKenzie & Fn D 140 5.71% a NA 0-38 Welch 1978 Newfoun Rigby & D D 114 37.72% dland NA 0-692 Threlfall 1981 Capillaria I, ransomia Li D 46 NA NE NA NA Barker 1915

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I D NA NA Canada NA NA Allen 1934 I, Li D 70 1.43% OH NA 0-1 Rausch 1946 British Columbi Fn D 202 6.93% a NA NA Musfeldt 1947 British Columbi D D 205 17.07% a NA 0-55 Knight 1951 Beckett & Gallicchio Fn D 130 5.38% OH NA NA 1967 Dirofilaria H, immitis Lu D 12 8.33% NY S-I 0-3 Goble 1942 Dirofilaria sp. H D NA NA MD NA NA Smith 1938 Li, H D 93 0.00% NY NA 0 Edwards 1949 Dracunculus Gibson & insignis Su D 1 100.00% Ontario NA 27 McKiel 1972 Dracunculus Gash & Hanna sp. Fn D 18 5.56% KS NA NA 1972 Crichton & Beverley- Fn D 105 0.95% Ontario NA NA Burton 1974 British Hepaticola I, Columbi hepatica Li D 205 0.49% a S-I NA Knight 1951 Heligmosomu m Manitob McKenzie & carolinensis Fn D 140 0.71% a NA 0-35 Welch 1978 Litomosoides I, carinii Li D 36 11.11% TX NA NA Chandler 1941 Longistriata adunca D D 53 1.89% LA NA NA Penn 1942 Beckett & Longistriata Gallicchio dalrymplei Fn D 130 0.77% OH NA NA 1967 Longistriata I, sp. Li D 36 2.78% TX NA 0-2 Chandler 1941 Nematospiroi des longispiculatu s Fn D 250 4.40% IL NA NA Gilford 1954 "small Physaloptera number sp. D D 53 25.00% LA NA s" Penn 1942 I, Rictularia sp. Li D 36 8.33% TX NA NA Chandler 1941 "small Strongyloides I, number ondatrae Li D 36 2.78% TX NA s" Chandler 1941 1.3 larvae/g Trichinella ram Rausch et al. spiralis T D 113 0.88% AK NA tissue 1956 Beckett & Gallicchio Fn D 130 0.77% OH NA NA 1967 Trichostrongy lus calcaratus I D NA NA Canada NA NA Allen 1934

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N A D 1 100.00% PA NA 25 Chandler 1950 Beckett & Gallicchio Fn D 130 15.38% OH NA NA 1967 Newfoun Rigby & D D 114 10.53% dland NA 0-166 Threlfall 1981 Trichostrongy I, lus fiberius Li D 46 NA NE NA NA Barker 1915 Trichostrongy Rider & Macy lus sp. I D 34 8.82% OR NA NA 1947 Trichuris I, opaca Li D 46 NA NE NA NA Barker 1915 I D NA NA Canada NA NA Allen 1934 I D 252 13.89% MI NA NA Ameel 1942 I, Li D 70 1.43% OH NA 0-2 Rausch 1946 Li, H D 93 2.15% NY NA NA Edwards 1949 D D 154 16.23% MD NA NA Abram 1951 Fn D 34 2.94% CO NA NA Ball 1952 Sweatman D D 108 0.93% Ontario NA NA 1952 Fn D 250 1.20% IL NA NA Gilford 1954 Senger & Fn D 34 5.88% OR NA 0-50 Neiland 1955 Lu , Li D 326 1.84% AK NA 0-103 Dunagan 1957 Anderson D D 158 1.90% PA NA 0-4 1964 Beckett & Gallicchio Fn D 130 27.69% OH NA NA 1967 Grundmann & Fn LD 34 5.88% UT NA NA Tsai 1967 Rice & Heck Fn D 100 25.00% OH NA 0-5 1975 British Columbi I D 205 0.49% a NA "a few" Knight 1951 Frost et al. Trichuris sp. F D 133 NA WA NA NA 1980 Pentastomida e Porocephalus 103 Louisian Penn & Martin crotali Fn D 2** 9.30% a S-I 0-1600 1941 Lu , 178 Louisian Li D 0** 8.99% a NA NA Penn 1942 Acanthoceph ala Beckett & Corynosoma Gallicchio sp. D D 130 3.85% Ohio NA NA 1967 Polymorphus Connell & paradoxus I D 1 100.00% Alberta S-I 138 Corner 1957 Polymorphus sp. Fn D 202 1.98% BC NA NA Musfeldt 1947

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I D 205 1.95% BC NA 0-7 Knight 1951 Lu , Li D 326 0.31% Alaska NA 0-1 Dunagan 1957 Manitob McKenzie & D D 140 2.86% a NA 0-40 Welch 1978 BACTERIA Sa m ple Live/ Locatio Disease Species s Dead N Prevalence n Caused Author Anabaena flos-aquae Fn D 18 100% IA E-W Rose 1953 Clostridium Clostridium sp. piliforme Fn D NA NA WI E-W Mathiak 1966 (Errington's) Saskatch Karstad et al. Fn D 22 72.73% ewan E-W 1971 Bacillis piliformes Saskatch Karstad et al. Clostridium sp. Fn D 22 0.00% ewan NA 1971 (Errington's) British Columbi Chalmers & Li D 12 66.67% a E-W MacNeill 1977 Bacillis piliformes Li, I, Wobeser et al. Clostridium Lu D NA NA IA E-W 1979 piliformes Li, Lu , Sp Saskatch Wobeser et al. , I D 17 24% ewan E-C 1978 Grear et al. Fn D 18 100.00% Ohio E-C 2019 Bordetella British bronchiseptic Columbi Chalmers & a Li D 3 33.33% a NA MacNeill 1977 Campylobact Pacha et al. er jejuni F NI 189 47.50% ID NA 1985 Li, Lu , Sp Saskatch Wobeser et al. Chlamydia , I D 17 0.00% ewan NA 1978 Chlamydia Bl, Spalatin et al. psittaci Sp D 2 100.00% Canada S-I 1971 Lu British Citrobacter , Columbi Chalmers & freundii Li D 3 66.67% a NA MacNeill 1977 Endosymbioti Feely et al. c bacteria I D NA NA NA NA 1988 Escherichia Saskatch Karstad et al. coli Fn D 22 NA ewan NA 1971 Li, Lu , Sp Saskatch Wobeser et al. , I D 17 NA ewan NA 1978 Lu Francisella , Jensen et al. philomiragia Li D 1 100.00% UT S-I 1969 Francisella Jellison et al. tularensis Fn D 1 100.00% MT S-I 1942 122

Lu , Li, Labzoffsky & K D 1 100.00% Ontario S-I Sprent 1952 Lu , Li, Sp D 4 100.00% Alberta E-C Banfield 1954 Bl D 1 100.00% Alberta S-I Langford 1954 Fyvie et al. Sp D 6 100.00% Ontario E-C 1959 Sp , Ln , Bl, Ditchfield et Li D NA NA Ontario E-W al. 1960 Fn D NA NA WI E-W Mathiak 1966 Lu , Li, Young et al. Sp D 78 5.13% VT E-C 1969 Li, Lu , Sp Saskatch Wobeser et al. , I D 17 0.00% ewan NA 1978 Li, Lu , Sp Webb & , K D 1 0.00% IL NA Woods 2001 Bl, K, Li, Lu , Gabriele-Rivet Sp D 411 0.00% Quebec NA et al. 2016 Li, Lu Listeria , monocytogen Sp Webb & es , K D 1 0.00% IL NA Woods 2001 Mycoplasma- like Not Feely et al. organisms I D NA NA Reported NA 1988 Saskatch Karstad et al. Paracolon Fn D 22 NA ewan NA 1971 Pasteurella spp. Fn D NA NA WI NA Mathiak 1966 Saskatch Karstad et al. Proteus sp. Fn D 22 NA ewan NA 1971 British Providencia Columbi Chalmers & stuartii Li D 3 33.33% a NA MacNeill 1977 British Pseudomonas Columbi Chalmers & aeruginosa Li D 3 33.33% a NA MacNeill 1977 Salmonella Kirkpatrick & spp. F L 220 0.00% NJ NA Benson 1987

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Li, Lu , Staphylococc Sp Saskatch Wobeser et al. us sp. , I D 17 NA ewan NA 1978 Li, Lu , Sp Webb & , K D 1 100.00% IL S-I Woods 2001 British Streptococcus Columbi Chalmers & pyogenes Li D 3 33.33% a NA MacNeill 1977 Li, Lu , Streptococcus Sp Saskatch Wobeser et al. sp. , I D 17 NA ewan NA 1978 Yersinia Shayegani et enterocolitica F 60 0.05 NY NA al. 1986 Yersinia 0.0666666 Shayegani et intermdia F 60 67 NY NA al. 1986 Yersinia Stevenson & ruckeri I D 1 100.00% Ontario NA Daly 1982 Fungi Emmonsia Grundmann & crescens Lu D 34 2.94% Utah NA NA Tsai 1967 Encephalitozo Saskatch Wobeser & on cuniculi Br D 65 7.69% ewan S-I NA Schuh 1979 Trichophyton mentagrophyt es Sk D 1 100% Iowa S-I NA Charles 1940

VIRUSES Sa m ple Live/ Locatio Disease Species s Dead N Prevalence n Caused Author Webb & Adenovirus I D 1 100% IL S-I Woods 1988 Aleutian Mink Disease Nova Virus Sp D 59 0% Scotia NA Farid 2013 Beauregard & Rabies Virus Sa D 1 0% Canada NA Casey 1969 Hendricks Br D 52 0% IA NA 1969 14 Johnston & year Beauregard Br D s 3 cases Ontario S-I 1969 15 year United Fishbein et al. Br D s 7 cases States S-I 1986 Prins & Yates Br D 50 2% Canada S-I 1986 1 1 case United Blanton et al. Br D year reported States S-I 2011 1 No cases United Dyer et al. Br D year reported States NA 2014 1 No cases United Monroe et al. Br D year reported States NA 2016

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1 No cases United Birhane et al. Br D year reported States NA 2017 Suggest Shayegani et Morbillivirus Fn D 1 1 NY ed al. 1986 Orthohepevir us Fn D NA NA WI NA Mathiak 1966 Psittacosis- Lymphogranu loma Bl, 0.1428571 Saskatch Spalatin et al. Venereum Sp D 14 43 ewan S-I 1966 CONTAMINANT S Sa m EPA ple Live/ Observed Locatio Regula Type s Dead N Levels n tions Author Heavy Metals Chalmers & Antimony Fn D 3 0 British Columbia MacNeill 1977 British Columbi Chalmers & Arsenic Fn D 3 0 a 5.0µg/L MacNeill 1977 En Pasco et al. Arsenic vi N 0.222ppm MT 5.0µg/L 1996 70µg/L Northwe Arsenic st trioxide Territori d'Entremont (As2O3) W N es 5.0µg/L 2014 Chalmers & Bismuth Fn D 3 0 British Columbia MacNeill 1977 0.005pp Blus et al. Cadmium K D 6 1.13µg g-1 WA m 1987 0.042- 0.005pp Erickson & Cadmium Li D 126 0.064ppm PA m Lindzey 1983 0.11 - 0.005pp Erickson & Cadmium K D 126 0.157ppm PA m Lindzey 1983 0.039- 0.005pp Everett & Cadmium Li D 65 0.316ppm PA m Anthony 1977 0.168- Everett & Cadmium K D 65 1.071ppm PA Anthony 1977 0.08 - 0.005pp Halbrook et al. Cadmium K D 76 3.08ppm VA m 1993 0.00025 - 0.00044pp 0.005pp Cadmium Li D 33 m Ontario m Parker 2004 0.0008 - 0.005pp Cadmium K D 33 0.0018ppm Ontario m Parker 2004 En 0.005pp Pasco et al. Cadmium vi N 0.163ppm MT m 1996 1.0 - 2.6µg Blus et al. Copper Li D 6 g-1 WA 1987 0.83- Copper Bo D 2.96ppm PA Everett 1976 0.27- Copper M D 0.54ppm PA Everett 1976 2.46- Everett & Copper Li D 64 4.62ppm PA Anthony 1977 1.48- Everett & Copper K D 65 2.66ppm PA Anthony 1977 0.0115 - Copper Li D 33 0.0137ppm Ontario Parker 2004

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0.0096 - Copper K D 33 0.0131ppm Ontario Parker 2004 En Pasco et al. Copper vi N 0.417ppm MT 1996 0.27- <0.015 Blus et al. Lead Li D 6 0.96µg g-1 WA ppm 1987 British Columbi <0.015 Chalmers & Lead Fn D 3 0 a ppm MacNeill 1977 3.71- <0.015 Erickson & Lead Li D 126 5.23ppm PA ppm Lindzey 1983 2.63- <0.015 Erickson & Lead K D 126 4.25ppm PA ppm Lindzey 1983 0.0009- <0.015 Lead K D 64 0.2689ppm PA ppm Everett 1976 0.0- <0.015 Lead M D 64 0.0048ppm PA ppm Everett 1976 0.0021- <0.015 Everett & Lead Li D 64 0.1537ppm PA ppm Anthony 1977 1.117- <0.015 Everett & Lead Bo D 64 2.226ppm PA ppm Anthony 1977 0.71 - <0.015 Halbrook et al. Lead K D 76 1.2ppm VA ppm 1993 0.0020 - <0.015 Lead Li D 33 0.0021ppm Ontario ppm Parker 2004 0.0032 - <0.015 Lead K D 33 0.0036ppm Ontario ppm Parker 2004 0.002pp Blus et al. Mercury Li D 6 0.22µg g-1 WA m 1987 0.029 - 0.002pp Everett & Mercury Li D 63 0.070ppm PA m Anthony 1977 Li, 0.011 - 0.002pp Halbrook et al. Mercury K D 76 0.019ppm VA m 1993 Ha 44/5 0.03 - 0.002pp Stevens et al. Mercury ir L 8 22.6ppm TN m 1997 0.0013 - Nickel Li D 33 0.0044ppm Ontario Parker 2004 0.0016 - Nickel K D 33 0.0094ppm Ontario Parker 2004 Chalmers & Silver Fn D 3 0 British Columbia MacNeill 1977 Chalmers & Sulfur Fn D 3 0 British Columbia MacNeill 1977 18.4 - Blus et al. Zinc Li D 6 27.4µg g-1 WA 1987 20.2- Zinc K D 63 108.6ppm PA Everett 1976 18.8- Zinc M D 63 27.6ppm PA Everett 1976 33.4- Everett & Zinc Li D 63 81.2ppm PA Anthony 1977 97.7- Everett & Zinc Bo D 63 305.0ppm PA Anthony 1977 0.0641 - Zinc Li D 33 0.0653ppm Ontario Parker 2004 0.0435 - Zinc K D 33 0.0448ppm Ontario Parker 2004 En Pasco et al. Zinc vi N 2.415ppm MT 1996 Agricultural Contaminant s

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5,130 - Juhlin & Atrazine Su D 4/6 28,220ppb IL 300ppb Halbrook 1997 Juhlin & Chlorpyrifos Su D 6 0 IL 700ppb Halbrook 1997 2,500pp Juhlin & Cyanazine Su D 6 0 IL b Halbrook 1997 Li, 0.002pp Halbrook et al. dieldrin K D 76 0.25ppm VA m 1993 1,700pp Juhlin & Fonofos Su D 6 0 IL b Halbrook 1997 14,800p Juhlin & Metolachlor Su D 6 0 IL pb Halbrook 1997 Li, 0.05µg/ Halbrook et al. p.p'-DDE K D 76 0.03ppm VA L 1993 Juhlin & Terbufos Su D 6 0 IL 10ppb Halbrook 1997 Other Contaminant s Li, 22/3 0.03 - 0.0002p Halbrook et al. PAHs K D 5 0.15ppm VA pm 1993 Li, 0.45 - 0.0005p Halbrook et al. PCBs K D 76 0.66ppm VA pm 1993 0.0005p Mayack & PCBs Li D 20 0-2.18ppm NY pm Loukmas 2001

OTHER Sa m ple Live/ Locatio Disease Type s Dead N Prevalence n Caused Author Sk Alexander & Malocclusion ull D 1 1 Dozier 1949 Yellow Fat Disease S Debbie 1968 Pneumonia Fn D Mathiak 1966 Uremic Kidney Poisoning Fn D Failure Mathiak 1966

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