Health Surveillance of Thinhorn Sheep (Ovis dalli) Herds in ...

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2019-09-18

Health Surveillance of Thinhorn Sheep (Ovis dalli)

Herds in British Columbia and Alaska

Thacker, Caerleon

Thacker, C. (2020). Health Surveillance of Thinhorn Sheep (Ovis dalli) Herds in British Columbia

and Alaska (Unpublished master's thesis). University of Calgary, Calgary, AB.

http://hdl.handle.net/1880/112563

master thesis

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UNIVERSITY OF CALGARY

Health Surveillance of Thinhorn Sheep (Ovis dalli) Herds in British Columbia and Alaska

by

Caerleon Thacker

A THESIS

SUMBITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN VETERINARY MEDICIAL SCIENCE

CALGARY, ALBERTA

SEPTEMBER, 2020

© Caerleon Thacker 2020

ii

Abstract

The health of wildlife populations influences their sustainability in the face of ecological

challenges. There is a paucity of information about the health status of free ranging thinhorn

sheep populations (Ovis dalli), despite their economic, ecological, and cultural significance.

Identification of health concerns in related species, bighorn sheep (Ovis canadensis), as well as

concern from local communities, First Nations, hunters, and conservationists that thinhorn

subpopulations may be declining in some areas, prompted the call for comprehensive thinhorn

sheep herd health assessments. We used a standardized approach based on similar work on

bighorn herd health and conducted herd health assessments of thinhorn sheep in five study herds

across their range that included both subspecies, Dall’s sheep (O. dalli dalli) and Stone’s sheep

(O. dalli stonei).

We used a broad definition of health and surveyed exposure to multiple pathogens

common to domestic small ruminants and other wildlife species, and evaluated other

comprehensive health measures including nutritional status, parasite burden, contaminant

exposure, stress, pregnancy, and indices of body condition. From 2017 to 2020 we collected

tissue and blood samples from 46 Stone’s sheep ewes and immature rams live-captured in the

Skeena and Peace regions of British Columbia (BC), and 67 Dall’s sheep in the Talkeetna and

Chugach mountains of Alaska (AK). We also analyzed samples from 63 hunter-harvested Stone’s

sheep rams from the Skeena region of BC from 2016 to 2019.

We found evidence of Mycoplasma ovipneumoniae exposure in Dall’s sheep in Alaska and

inconclusive results in Stone’s sheep in BC. There was minimal evidence of exposure to other

bacterial and viral respiratory pathogens in all subspecies and herds. A high seroprevalence to

ovine herpesvirus (P = 89.5%) was detected in all Stone’s sheep. Parasite burdens were similar to

previously reported results, including winter tick (Dermacentor albipictus) infestations of Stone’s

sheep sampled at low elevation along the Peace Arm of Williston reservoir. A high seroprevalence

to Toxoplasma gondii was detected in sheep in Alaska (P = 100% in 2019, and 73.9% in 2020).

Fecal glucocorticoid metabolite concentrations determined from hunter-harvested and live-

captured sheep increased annually. Serum and tissue copper levels in some herds were in the

range considered deficient for domestic sheep. Other trace minerals, including zinc and selenium,

iii

were deficient only in some study areas. Body condition of hunter-harvested rams decreased

annually from 2016 to 2018.

Our findings confirm that thinhorn sheep, in general, are relatively naïve, and in some

populations, very naïve, to diseases carried by domestic ruminants and other wildlife species.

This information provides a baseline for thinhorn sheep herd health monitoring. If continued, it

will allow for early detection of disease introductions and other population-limiting health

factors. The results inform conservation and One Health decision making and can be incorporated

into science-based management of thinhorn sheep in BC and Alaska.

iv

Preface

This thesis is original, unpublished, independent work by the author, C. Thacker. The capture and

handling of wildlife was covered by Certificate of Animal Use Protocol Approvals AC19-0005 and

AC19-0054, issued by the University of Calgary for the project “Assessment of thinhorn sheep

herd health status: An investigation to address conservation and management concerns through

development of a comprehensive health baseline for thinhorn sheep” on March 4, 2019 and May

27, 2019 respectively and by British Columbia Wildlife Permits SM16-244528 and FJ19-485655,

issued January 12, 2017 and June 12, 2019.

v

Acknowledgements

I would first and foremost like to thank Dr. Helen Schwantje for inspiring this project and

being an exceptional mentor and friend, past, present, and future. Thank you to my supervisor,

Dr. Doug Whiteside, for his trust and guidance over the past couple of years. Thank you to Dr.

Kathreen Ruckstuhl for her thoughtful contribution to my research. Thank you to Dr. Tom Lohuis

for your generosity and the opportunity to collaborate and work in Alaska. Thank you to: Bill Jex

(BC Ministry of FLNRO) for his expert wild sheep knowledge, planning and organization, and care

of the field crew in BC; Billy Oestreich for keeping us safe in the field, coordinating hunter-harvest

sample collection, and bringing communities together for wildlife; Fraser MacDonald for his

expert calm animal handling; Shari Willmott and Maeve Winchester (BC Wildlife Health Program)

for helping manage samples and data over the years; Dr. Naima Jutha for the moral support and

statistics wisdom; Krista Sittler and Robin Routledge for planning and coordinating the Williston

project; Dr. Erin Zabek, Dr. Tomy Joseph, and the others at AHC for answering my many questions

and providing endless support for this project; Dr. Hank Edwards and Jessica Jennings-Gaines for

the training in sampling techniques in 2017; Janelle De la Pena for organizing logistics in Alaska,

her hospitality and all the laughs; Kyle Smith for his expertise in the field and kind hospitality;

Mike Meekins and Troy Cambier for finding us sheep, and keeping us safe and entertained. I also

thank my friends and family for their support throughout this process.

I would also like to thank the many organizations and individuals who recognize the

importance of wild sheep health and have supported this project through funding, both

monetary and in-kind. This includes the Tahltan Guide and Outfitters Association (TGOA), Wild

Sheep Foundation, Wild Sheep Society of BC, Rinke family, and Habitat and Conservation Trust

Fund.

This project has evolved since we began in 2017. We have adapted as new information

and technologies have become available. Despite setbacks along the way, such as the global

pandemic of 2020, this project has been a success due to the wonderful individuals and

organizations who have contributed.

Thank you to the sheep and wild places!

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Table of Contents

Abstract ...................................................................................................................................... ii

Preface ...................................................................................................................................... iv

Acknowledgements .................................................................................................................... v

List of Tables .............................................................................................................................. ix

List of Figures ........................................................................................................................... xii

CHAPTER 1 - INTRODUCTION .................................................................................................. 1

1.1 Context ................................................................................................................................. 1

1.2 Purpose ................................................................................................................................ 2

1.3 Research Significance ........................................................................................................... 3

CHAPTER 2 - LITERATURE REVIEW .......................................................................................... 9

2.0 Wildlife Health ...................................................................................................................... 9

2.1 Viruses .................................................................................................................................. 9

2.2 Bacteria .............................................................................................................................. 12

2.2.1 Mycoplasma ovipneumoniae ...................................................................................... 12

2.2.2 Pasteurellaceae ........................................................................................................... 17

2.2.3 Other Bacteria ............................................................................................................. 21

2.3 Parasites ............................................................................................................................. 21

2.3.1 Gastrointestinal Parasites ........................................................................................... 22

2.3.2 Lung Worms ................................................................................................................ 23

2.3.3 Trematodes ................................................................................................................. 26

2.3.4 Tissue-dwelling Protozoans ......................................................................................... 26

2.3.5 External Parasites ........................................................................................................ 27

2.3.6 Diagnostic Testing ....................................................................................................... 28

2.4 Non-Infectious Indicators of Health ................................................................................... 31

2.4.1 Trace Minerals ............................................................................................................. 31

2.4.2 Contaminants .............................................................................................................. 33

2.4.3 Stress ........................................................................................................................... 34

2.4.4 Stress and Immunity .................................................................................................... 37

2.4.5 Allostatic Load ............................................................................................................. 38

2.4.6 Genomics ..................................................................................................................... 39

2.4.7 Body Condition - Marrow Fat ...................................................................................... 41

2.4.8 Reproduction ............................................................................................................... 42

2.4.9 Horn growth ................................................................................................................ 43

2.5 Status and Management of Thinhorn Sheep Populations ................................................. 43

CHAPTER 3 - METHODS .......................................................................................................... 46

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3.1 Study Areas and Populations ............................................................................................. 46

3.2.1 Live-capture Sample Collection ................................................................................... 49

3.2.2 Hunter-harvest Sample Collection .............................................................................. 52

3.3 Laboratory Methods .......................................................................................................... 54

3.3.1 Serology ....................................................................................................................... 54

3.3.2 Mycoplasma ovipneumoniae Detection ...................................................................... 55

3.3.3 Tonsil Swab Bacteriology ............................................................................................. 56

3.3.4 Fecal Parasitology ........................................................................................................ 56

3.3.5 Hair Cortisol Concentration ......................................................................................... 57

3.3.6 Fecal Glucocorticoid Metabolites ................................................................................ 57

3.3.7 Serum Trace Minerals ................................................................................................. 58

3.3.8 Tissue Mineral and Heavy Metals ............................................................................... 58

3.3.9 Marrow Fat .................................................................................................................. 59

3.3.10 Pregnancy .................................................................................................................. 59

3.3.11 Morphometric Measurements .................................................................................. 60

3.3.12 Mortality Investigations ............................................................................................ 60

3.4 Data Analysis .................................................................................................................. 61

CHAPTER 4 - RESULTS ............................................................................................................ 62

4.1 Serology .............................................................................................................................. 62

4.2 Mycoplasma ovipneumoniae ............................................................................................. 63

4.3 Tonsil Bacteriology ............................................................................................................. 64

4.4 Parasites ............................................................................................................................. 65


4.4.2 Lungworm ................................................................................................................... 66

4.4.3 External Parasites ........................................................................................................ 66

4.5 Hair Cortisol Concentration ................................................................................................ 67

4.6 Fecal Glucocoid Metabolites .............................................................................................. 67

4.7 Serum Trace Mineral Levels ............................................................................................... 68

4.8 Tissue Mineral and Heavy Metal Levels ............................................................................. 69

4.9 Condition ............................................................................................................................ 71

4.9.1 Body Condition Score .................................................................................................. 71

4.9.2 Rump Fat Depth .......................................................................................................... 73

4.9.3 Back Fat Depth ............................................................................................................ 73

4.9.4 Marrow Fat .................................................................................................................. 73

4.11 Morphometrics ................................................................................................................ 75

4.12 Relationships .................................................................................................................... 75

4.13 Mortality Investigation ..................................................................................................... 76

CHAPTER 5 - DISCUSSION ...................................................................................................... 78

viii

5.1 Viruses ................................................................................................................................ 79

5.2 Bacteria .............................................................................................................................. 81

5.2.1 Mycoplasma ovipneumoniae ...................................................................................... 81

5.2.2 Tonsil Bacteriology ...................................................................................................... 86

5.3 Toxoplasma gondii ............................................................................................................. 87

5.4 Parasites ............................................................................................................................. 89


5.4.2 Lungworm ................................................................................................................... 92

5.4.3 Ecotoparasites ............................................................................................................. 94

5.5 Stress .................................................................................................................................. 94

5.5.1 Hair Cortisol ................................................................................................................. 96

5.5.2 Fecal Glucocorticoid Metabolites ................................................................................ 97

5.7 Serum Trace Mineral Levels ............................................................................................... 99

5.8 Tissue Mineral and Heavy Metal Levels ........................................................................... 102

5.9 Body Condition ................................................................................................................. 105

5.9.1 Body Condition Score ................................................................................................ 106

5.9.2 Rump Fat Depth ........................................................................................................ 108

5.9.3 Marrow Fat ................................................................................................................ 108

5.10 Pregnancy ....................................................................................................................... 110

5.11 Mortalities ...................................................................................................................... 114

CHAPTER 6 - LIMITATIONS, CONCLUSIONS, FUTURE DIRECTIONS OF STUDY ........................ 115

6.1 Limitations ........................................................................................................................ 115

6.2 Conclusions ...................................................................................................................... 116

6.3 Recommendations and Future Areas of Study ................................................................. 117

REFERENCES ........................................................................................................................ 118

APPENDICES ........................................................................................................................ 134

ix

List of Tables

Table 1. Thinhorn sheep population size estimates and number of individuals health sampled in

the areas included in this study. .................................................................................................. 48

Table 2. Seroprevalence (Serop.) and 95% confidence interval (95% CI) expressed as

percentages for selected respiratory and systemic pathogens: bovine viral respiratory virus

(BRSV), ovine progressive pneumonia (OPP), parainfluenza virus-3 (PI3), infectious bovine

rhinotracheitis (IBR), malignant catarrhal fever (MCF) and Mycoplasma ovipneumoniae (M. ovi),

in live-captured adult female and immature male Stone’s sheep in the Skeena and Peace

Regions of British Columbia, 2017 – 2020. The Dome herd was sampled in 2017, Cassiar in 2018

and 2019, and Williston in 2020. .................................................................................................. 62

Table 3. Seroprevalence (Serop.) and 95% confidence interval (95% CI) expressed as a

percentage for selected respiratory and systemic pathogens: bovine viral respiratory virus

(BRSV), ovine progressive pneumonia (OPP), parainfluenza virus-3 (PI3), infectious bovine

rhinotracheitis (IBR), malignant catarrhal fever (MCF), Mycoplasma ovipneumoniae (M. ovi),

Mycobacterium avium spp. paratuberculosis (MAP, Johne’s), Brucella ovis (B. ovis), and

Toxoplasma gondii (T. gondii) in live-captured adult female and male Dall’s sheep in the

Talkeetna and Chugach mountains of southcentral Alaska.. ....................................................... 63

Table 4. Mycoplasma ovipneumoniae detection numbers by polymerase chain reaction (PCR)

and enyme-linked immunoassay (ELISA) in free-ranging thinhorn sheep from BC and Alaska. .. 64

Table 5. Pasteurella spp. detections in cultured samples from thinhorn sheep tonsil swabs

collected in winters 2017-2020 expressed as the number and prevalence of positive sheep

within a herd. ............................................................................................................................... 65

Table 6. Fecal egg and larvae detections in Stone’s sheep, expressed as the number and

prevalence of sheep within each herd, for the following nematode groups: Strongyles (Str.),

Nematodirinae (Nem.), Marshallagia spp. (Mar.), Moniezia spp. (Mon.), Eimeria spp. (Eim.), and

Trichuris spp. (Tri.), and lungworms including dorsal spined larvae (DSL) and Protostrongylus spp.. .............................................................................................................................................. 65

Table 7. Hair cortisol concentration (pg/mg) in guard hairs collected from the shoulder region

on live-captured (ewes and immature rams) and hunter-harvested (mature rams) Stone’s sheep

from 2016 to 2020. ...................................................................................................................... 67

Table 8. Fecal glucocorticoid metabolite concentration (ng/g) in feces collected from live-

captured Stone’s and Dall’s ewes and hunter-harvested Stone’s sheep rams from 2016 – 2020.

..................................................................................................................................................... 68

x

Table 9. Serum trace mineral levels of live-captured Stone’s sheep. Samples collected in late

winter 2017. Data were normally distributed; mean, median (med.), and range of the values are

reported. ...................................................................................................................................... 69

Table 10. Trace mineral and heavy metal concentrations (mg/kg dry weight) in hunter-

harvested Stone’s sheep ram livers and kidneys submitted in 2016-2018. ................................. 70

Table 11. Stone’s sheep ram body condition metrics. Rams were harvested between August 1st

and October 15th, 2016 – 2019 in the Skeena Region of BC. Body condition score (BCS) is a

subjective measurement (0 = very skinny, 1 = skinny, 2 = good, 3 = fat, 4 = very fat) reported by

hunters. Marrow fat proportion was determined using metatarsal bones collected by hunters.

Back fat depth was measured and reported by hunters. ............................................................. 73

Table 12. Metatarsal data: percent marrow fat, bone mass and morphometric measurements

for metatarsal bones collected by hunters from Stone’s sheep rams in the Skeena region of BC

from 2016 to 2019. The data for each parameter was not distributed normally. No significant

difference was found between years for any of the following measurements (ANOVA). ........... 74

Table 13. Pregnancy rates (expressed as a percentage) in thinhorn sheep in BC and Alaska from

2017 through 2020. Pregnancy was determined by serum PSPB serum by ELISA. ...................... 74

Table 14. Stone’s sheep metatarsal morphometric measurements. Metatarsal bones were

collected by hunters from Stone’s sheep rams in the Skeena region of B.C. from 2016 to 2019.

The data for each parameter was not distributed normally. ....................................................... 75

Table 15. Thinhorn sheep health sampling methods and references employed in our

surveillance study from 2016 – 2020. ........................................................................................ 136

Table 16. Liver tissue concentrations of trace minerals and heavy metals by wet weight (wwt;

unless otherwise specified) for Stone’s rams harvested in BC from 2016 – 2018 (n = 42). A

reference range for domestic sheep and previous findings in California bighorn sheep (BHS),

Rocky Mountain BHS, and Dall’s sheep are included for comparison. ...................................... 138

Table 17. Kidney tissue concentration of trace minerals and heavy metals by wet weight (wwt;

unless otherwise specified) for Stone’s rams harvested in BC from 2016 – 2018 (n = 51). A

reference range for domestic sheep and previous findings in California bighorn sheep (BHS),

Rocky Mountain BHS, and Dall’s sheep are included for comparison as a mean or mean and

standard deviation (SD). ............................................................................................................. 140

Table 18. Serum trace mineral levels for Stone’s ewes and immature rams sampled in BC from

2016 – 2018 (n = 26). A reference range for domestic sheep and previous findings in California

bighorn sheep (BHS), Rocky Mountain BHS, and Dall’s sheep are included for comparison as a

mean or mean and standard deviation (SD). ............................................................................. 142

Table 19. Detections and exposure to selected pathogens in live-captured Stone’s sheep in BC

from 2017 to 2020; including Mycoplasma ovipneumoniae (M. ovi), malignant catarrhal fever

xi

(MCF) virus, ovine progressive pneumonia (OPP), parainfluenza 3 (PI3), bovine respiratory

syncytial virus (BRSV), and infectious bovine rhinotracheitis (IBR) using enzyme-linked

immunosorbent assays (ELISA) or virus neutralization (VN). ..................................................... 143

Table 20. Pathogen detection and exposure of live-captured Dall’s sheep in Alaska from 2019 to

2020; including Mycoplasma ovipneumoniae (M. ovi), malignant catarrhal fever (MCF) virus,

ovine progressive pneumonia (OPP), parainfluenza 3 (PI3), bovine respiratory syncytial virus

(BRSV), bovine viral diarrhoea virus (BVD), Mycobacterium avium ssp. paratuberculosis (MAP),

Brucella ovis (B. ovis), Toxoplasma gondii (T. gondii) and infectious bovine rhinotracheitis (IBR)

using enzyme-linked immunosorbent assays (ELISA) or virus neutralization (VN). ................... 145

Table 21. Anaerobic culture grown from tonsil swabs collected from live free-ranging thinhorn

sheep in BC and Alaska from 2017 to 2020. Samples were cultured on Columbia blood agar

plates at 36 Celsius at 2 percent oxygen for 48 hours. Detections (Y) of bacterial species

previously implicated in polymicrobial pneumonia in wild sheep are recorded (Bibersteinia

trehalosi, Mannheimia haemolytica, Mannheimia spp. and Neisseria spp.). ............................ 148

Table 22. Capture details and non-infectious determinants of health in free-ranging thinhorn

sheep captured for health sampling in winters 2017 to 2020. Stone’s sheep were captured in

British Columbia and Dall’s sheep in Alaska. .............................................................................. 151

Table 23. Details and health findings of hunter-harvested Stone’s rams from 2016 – 2019. Blank

cells indicate the sample/data was not collected or the quality was not adequate for testing. 156

xii

List of Figures

Figure 1. Body condition of hunter-harvested Stone’s sheep rams in the Skeena Region of BC in

2016 – 2019. Body condition is a subjective 5-point scale (0 = very skinny, 1 = skinny, 2 = good, 3

= fat, 4 = very fat). ........................................................................................................................ 72

Figure 2. Body condition of live-capture Stone’s sheep ewes in the Skeena (Dome and Cassiar)

and Peace (Williston) Regions of BC in 2017 – 2020. Body condition is a subjective 5-point scale

(0 = emaciated, 1 = poor, 2 = fair, 3 = good, 4 = excellent; note that the scale for live-captured

sheep is slightly different than for hunter-harvest sheep). ......................................................... 72

Figure 3. Body condition of live-capture Dall’s sheep ewes in the Talkeetna and Chugach

mountains in 2019. Body condition is a subjective 5-point scale (0 = emaciated, 1 = poor, 2 =

fair, 3 = good, 4 = excellent; note that the scale for live-captured sheep is slightly different ). .. 73

Figure 4. Management Units in the Skeena (Region 6) and Peace (Region 7) Regions of British

Columbia where hunter-harvested ram samples were collected. ............................................. 134

Figure 5. Locations of study areas for thinhorn sheep live-capture. Dall’s sheep (Ovis dalli dalli)

were captured and sampled in the Chugach and Talkeetna Study areas (Alaska), and Stone’s

sheep (O. dalli stonei) were captured and sampled in the Cassiar, Dome, and Williston study

areas (British Columbia). ............................................................................................................ 135

1

CHAPTER 1 – INTRODUCTION

1.1 Context

Wildlife health is influenced by a combination of factors related to complicated disease

ecology, climate change, and anthropogenic disturbances, thus health status should be

considered as the cumulative effect of multiple stressors (Acevedo-Whitehouse et al. 2009;

Stephen 2014; Wittrock et al. 2019). Historically, the definition of health included only the

absence of disease. Current thinking expands this definition to include the resiliency and

adaptability of a population to its environment and the ability to sustain itself (Stephen 2014).

Important metrics that establish a more complete picture of wildlife health include measures of

body condition, contaminant load or exposure, reproductive success, survival, stress response,

population trends, morphometrics, ecological factors, immune function, nutritional status, and

distribution and habitat use (Patyk et al. 2015). Threats to the health of bighorn sheep (Ovis

canadensis) populations has been identified as the most significant challenge for conservation

and, while far less studied, should also be assessed for thinhorn sheep (Jex et al. 2016).

Thinhorn sheep are mountain ungulates endemic to North America. Both subspecies, Dall’s

sheep (Ovis dalli dalli) and Stone’s sheep (Ovis dalli stonei), occupy rugged mountainous terrain

in Alaska and northwestern Canada (Jex et al. 2016; Bunnell, 2005). Extensive genetic variation

exists in thinhorn sheep across their range with clusters separated by mountain ranges and other

landscape features (Worley et al. 2004). Sim et al. (2016, 2018) examined the genetic structure

of this species and redefined the geographical range of both subspecies based on glacial refugia,

finding Stone’s sheep to occur primarily in British Columbia (BC).

2

Both subspecies are listed as ‘Special Concern’ in BC, meaning they are “deemed particularly

sensitive and vulnerable to anthropogenic disturbance and natural events” (Sharpe & Ramsay

2017). Dall’s sheep are listed as ‘Secure’ in the Yukon and Northwest Territories (WGGSN 2016).

The estimated total populations of thinhorn sheep are 40,000-50,000 Dall’s in Alaska (AK), 20,000

thinhorn sheep in Yukon and 28,000 Dall’s in the Northwest Territories (NWT), and 400-600 Dall’s

and 9,500-11,500 Stone’s sheep in BC (Thinhorn Summit 2017). Thinhorn sheep populations may

be experiencing localized declines in some areas in BC, however, no direct causal agent is

currently identified (Bill Jex pers comm.). Thinhorn sheep have significant economic, ecological,

and cultural significance to communities in all jurisdictions in which they occur.

Thinhorn sheep population trends are acknowledged to be affected by availability of

suitable habitat and food, intra- and interspecific competition, predation, human harvest, and

health. The extent to which these factors contribute to population declines or prevention of

recovery is unknown and likely varies across the range (Koizumi & Derocher 2019). Dall’s sheep

populations in the northern Richardson Mountains have been declining over the past couple of

decades, with grizzly bear (Ursus arctos) and wolf (Canis lupis) predation suspected to be the

main cause (Koizumi & Derocher 2019). Harsh winter conditions, such as deep snow, firm icy

crusts, and late season snow, are thought to cause large fluctuations in the northernmost

populations (Burles & Hoefs 1984; Earner 2014; Thinhorn Summit 2017).

1.2 Purpose

The purpose of this study is to establish a baseline herd health dataset to support ongoing

monitoring and to identify areas of concern regarding thinhorn sheep population health. This is

3

the first comprehensive, multijurisdictional, thinhorn sheep herd health assessment. Our results

will provide information to inform science-based management for conservation of thinhorn

sheep across their range.

1.3 Research Significance

The resilience of sheep populations and therefore their ability to withstand the effects of

environmental change associated with climate-driven habitat loss, industrial and backcountry

recreational activity, and invasion by other wildlife and domestic animal species due to climate

change or human introduction is directly affected by health. The historic and current health

challenges devastating bighorn sheep herds, impairing population recovery and restoration are

well recognized and include respiratory disease, often as a result of contact with domestic small

ruminants. Little is published on the health status of thinhorn sheep populations. Thinhorn sheep

were presumed to be unexposed and immunologically naïve to many pathogens normally carried

by livestock and other wildlife species, due to their remote habitat and relative lack of potential

exposures. The risk of disease to individuals, herds and populations is considered high should

contact and disease transmission occur.

Where inventory data exists, thinhorn sheep populations are known or suspected to be

declining. Thinhorn sheep herds are generally small and often isolated, and therefore are at

significant risk of extirpation if adverse conditions, whether health-related or not, lead to

increased mortality or reduced recruitment. Thinhorn sheep populations fluctuate periodically

with climatic conditions driving forage and habitat availability and predator population dynamics

(Hoefs & Bayer 1983). Increased focus on wildlife health by wildlife agencies and organizations,

4

coupled with concern from local communities, First Nations, hunters, biologists, and

conservationists that thinhorn herds may be declining in some areas, has prompted the need for

comprehensive herd health assessments. The role of health in population dynamics, recruitment,

and survival is currently unknown.

A literature review (Chapter 2) summarizes extensive publications on primary research

and thorough investigation of various aspects of bighorn sheep health, however, we found a

significant information gap regarding thinhorn sheep health. The Western Association of Fish and

Wildlife Agencies (WAFWA) Wild Sheep Working Group and the Wild Sheep Foundation (WSF)

Thinhorn Initiative have identified this gap and encouraged jurisdictions with thinhorn sheep to

summarize concerns. Two meetings have been summarized in the Thinhorn Sheep Conservation

Challenges and Management Strategies for the 21st Century (Jex et. al. 2016). The meetings and

document emphasized the lack of baseline information on thinhorn health and its value for

informed science-based management decision making and emphasized the need for a

comprehensive and holistic thinhorn sheep population health assessment at a herd level.

Historical Stone’s sheep health assessments and research in BC includes basic health sampling

and laboratory testing from several herds. Remote locations, and the time-sensitive nature of

accurate pathogen detection in biological samples prevented application of some tests now key

to developing health profiles of wild sheep. In the past several years, sampling techniques,

processing methods, new methods of analyses and standard operating procedures for bighorn

sheep herd health testing have been developed and adapted to remote field settings. We applied

these methods to identify the health risks faced by free-ranging thinhorn sheep.

5

Respiratory disease is the primary health concern for wild sheep populations (Jex et al. 2016).

Exposure of bighorn sheep to respiratory pathogens that may be shared with domestic small

ruminants has created substantial impacts on bighorn populations. These impacts are generally

seen as all-age die-offs where 10-90% of a herd may be affected by acute to subacute

bronchopneumonia caused by a number of bacterial pathogens (Cassirer et al. 2017; Lula et al.

2020). Die-offs may be followed by years to decades of poor lamb recruitment and subsequent

population declines (Jex et al. 2016) but this pattern can vary. Mycoplasma ovipneumoniae (M.

ovi) is identified as a consistent primary causative agent of this polymicrobial pneumonia in

bighorn sheep. M. ovi has recently been detected in Dall’s sheep in Alaska; while the significance

of this finding is unknown, it appears to be a unique strain and has not been recognized in

thinhorn populations in other jurisdictions (Highland et al. 2018). Our lack of knowledge

regarding the presence of M. ovi as well as other potentially harmful pathogens in thinhorn sheep

populations, such as contagious ecthyma, highlights the need for baseline health information.

The concept of ‘One Health’, the co-dependence of human, animal, and environmental health

outcomes, is central to conservation of thinhorn sheep. The health of wildlife species is directly

influenced by their environment; for example, depressed immune function is a demonstrable

response to stress from environmental change, pollutant exposure, and altered distributions of

predator and prey species (Acevedo-Whitehouse 2009). The socio-economic influences from and

on conservation must be considered, such as human perceptions of the value of biodiversity,

cultural significance, and economic value of wildlife and the environment (Buttke et al. 2015).

Wild ungulates are an important source of protein for northern and Indigenous communities and

are a valued part of northern economies. The aspects of One Health addressed in this project

6

include the values of these animals hold to local human populations, the potential for pathogen

spillover from reservoir host species (wild or domestic) to thinhorn sheep, and an assessment of

determinants of population health and resiliency. Northern mountain ungulates are increasingly

subjected to human-induced environmental stress from habitat changes, industrial, recreational,

potential agricultural activities, all associated with climate change. These stressors can

exacerbate existing natural factors such as intra- and interspecific competition, population

density, weather and climate patterns, parasites, and predation (Bertram et al. 2018).

1.4 Thesis Overview

In this cross-sectional observational study, we determined the presence and prevalence of

pathogens, disease, and other metrics of health such as glucocorticoid metabolite (“stress

hormone”) concentrations, pregnancy status, contaminant exposure, and nutritional condition

of selected thinhorn sheep herds in BC and Alaska with the overarching goal of providing

information to local communities and wildlife managers to aid in conservation of thinhorn sheep.

Health indices included in this study were selected based on previous findings in thinhorn and

bighorn sheep, as well as those with One Health implications. The role of health status in the

persistence of thinhorn sheep on the landscape is then discussed. Our research objectives are:

1) Determine the exposure and infection status of thinhorn sheep to transmissible

pathogens, including respiratory bacteria, specifically Mycoplasma ovipneumoniae and

Pasteurellaceae, selected viruses, and internal and external parasites.

Expected Outcomes:

7

• Observation of herd-level differences in pathogen exposure, related to regional

human activities, habitat differences and contact with domestic livestock.

• In M. ovi-positive declining herds of bighorn sheep, prevalence in excess of 10% is

expected within the herd. Previous research on bighorn sheep found a prevalence of

5.1 to 82.8% M. ovi PCR-positive individuals in infected herds with poor lamb

recruitment (Butler et al. 2016; median 22%, Cassirer et al. 2017). In M. ovi positive

herds, pneumonia-induced lamb mortality is expected to be in excess of 20%, which

significantly impacts herd productivity and stability.

• Exposure to other viral and bacterial pathogens is expected based on previous

research.

• A higher prevalence of pathogens commonly found in domestic animals is expected

where there is potential contact with thinhorn sheep. This is likely in the southern

thinhorn range, with use of domestic ruminants for backcountry recreation or

vegetation management or near communities where domestic livestock are near

thinhorn habitat. Disease prevalence variation between sampled populations will be

assessed. Lower exposure and disease prevalence is expected in sheep within

provincial parks and in more remote areas.

2) Examine specific health parameters as potential indicators of vulnerability or resilience in

wild sheep.

Expected Outcomes

8

• Baselines of non-infectious determinants of health, such as serum and tissue trace

mineral levels, heavy metal contaminants, hair and fecal cortisol, pregnancy, body

condition including rump fat depth.

• Variation in non-infectious determinants of health among populations, with relatively

better health expected in more remote herds. Declining herds are expected to have

higher levels of hair and fecal glucocorticoid metabolites. Previous research has

shown that chronic elevation of glucocorticoid levels depresses fitness and

reproduction (Acevedo-Whitehouse & Duffus 2009), and lower neonate birth weight

and juvenile survival (Downs et al. 2018). Body condition (rump fat depth, body

condition score, or marrow fat percentage) and hair glucocorticoid levels are expected

to be positively correlated. The pregnancy rate of a herd is expected to be associated

with body condition and glucocorticoid levels as survival and reproduction are two

key pathways for allocation of resources in wildlife species (Downs et al. 2018).

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CHAPTER 2 - LITERATURE REVIEW

2.0 Wildlife Health

The health of wildlife populations must be considered as a ‘cumulative effect’ of multiple

stressors (Wittrock et al. 2019). The definition of wildlife health introduced by Craig Stephen

(2014) identifies that health is a dynamic social construct which prompts examination of factors

associated with ‘vulnerability, resilience, and sustainability’ rather than the mere absence of

diseases and parasites to determine if wildlife populations are healthy. Other stressors faced by

wildlife species must be considered, such as habitat loss or change, human activities, and invasion

by other wildlife and domestic animal species due to climate change or human introduction

(Stephen 2014; Acevedo-Whitehouse et al. 2009). Wittrock et al. (2019) present a definition of

‘determinants of health’ for wildlife based on a human health model and identify six themes:

“needs for daily living, abiotic environment, social environment, biological endowment, direct

mortality pressures, and human expectations”.

In this literature review, we explore parameters that may be used to determine the

relative health of populations, herds, and individuals of thinhorn sheep. Little previous work has

focused on thinhorn sheep specifically, so we include references regarding related species and

other mountain ungulates which may share habitat range or other aspects of their biology.

2.1 Viruses

Respiratory syncytial virus (RSV) and parainfluenza-3 virus (PI3) contribute to respiratory

disease in domestic ruminants (Dassanayake et al. 2013; Spraker & Collins, 1986), and antibodies

10

to these viruses have been detected in wild bighorn sheep herds (Dassanayake et al. 2013;

Dunbar et al. 1985). Dassanayake et al. (2013) experimentally examined the role of BRSV and PI3

in bighorn sheep epizootic pneumonia, finding that the viruses induced lethargy and non-fatal

signs of pneumonia when administered in combination. Fatal bronchopneumonia resulted when

Mannheimia haemolytica was added to the mix or administered alone.

In 2002, two bighorn sheep in Arizona had post-mortem lesions consistent with, and

tested positive for, epizootic hemorrhagic disease (EHD) virus (Noon et al. 2002). There have been

a few previously reported cases of EHD, and antigenically similar bluetongue virus (BT), in bighorn

sheep in Arizona, BC, and Texas. EHD and BT viruses are transmitted by mosquitos in the genus

Culicoides and typically infect domestic ruminants. In 1999, four bighorn sheep and eleven white-

tailed deer died in southern BC with lesions consistent with EHD virus infection (Pasick et al.

2001).

Malignant catarrhal fever (MCF) is a clinical syndrome caused by at least nine different

gammaherpesviruses, named for their reservoir host (Li et al. 2005). MCF viruses are usually

asymptomatic in their in their natural hosts, but cause sporadic disease characterized by systemic

infection, lymphoproliferation and infiltration, and death in ungulate species that are non-

adapted to the virus (Himsworth et al. 2008; Zarnke et al. 2002, Pesavento et al. 2018).

Susceptibility to the virus differs between individuals in a herd (Himsworth et al. 2008). Evidence

of MCF was found in two Stone’s sheep from a zoological park in BC. Perirenal hemorrhages were

observed on post-mortem examination and both sheep were found to be positive using

polymerase chain reaction (PCR) molecular testing methods for Ovine herpesvirus 2 (OvHV-2).

An OvHV-2 serosurvey of Alaskan wildlife found high MCF antibody titers in Dall’s sheep (95%;

11

Zarnke et al. 2002). A high seroprevalence (37%) for OvHV-2 has been found in bighorn sheep in

the USA (Li et al. 1996). Bighorn sheep are considered a natural asymptomatic host of OvHV-2;

however, in 2015, a bighorn sheep in southeast Alberta was identified as having clinical MCF with

a cutaneous presentation (Slater et al. 2017). Recent evidence suggests that the herpesviruses

carried by domestic sheep and bighorn sheep are related, but genetically distinct, based on an

85.88% nucleotide similarity in a conserved region of the DNA polymerase gene (Cunha et al.

2019). OvHV-2 infections have previously been identified in bighorn sheep as the primers used

are able to amplify virus from both domestic and bighorn sheep (Cunha et al. 2019).

Bovine viral diarrhea virus (BVDV) can cause clinical disease, including

immunosuppression, gastrointestinal and respiratory disease, and reproductive loss in bighorn

sheep and mountain goats. Persistently infected young are produced when pregnant females

become infected during the first trimester of pregnancy, this contributes to maintenance of the

virus in a population (Wolff et al. 2016). High titers to BVDV were reported in bighorn sheep and

mountain goats in Nevada (Wolf et al. 2016). Seasonal migration, reproductive timing, and

exposure to domestic ruminants and other sympatric wildlife species, may play a role in

transmission and maintenance of BVDV in the population studied. This, and previous studies,

suggest that BVDV may be endemic in mountain ungulate populations in North America. The

impact of BVDV infection on bighorn sheep, thinhorn sheep, and mountain goat populations is

still unknown (Wolf et al. 2016).

Orf virus is a species of Parapoxvirus that occurs worldwide in a variety of mammalian

hosts causing the disease ‘contagious ecythema’ (CE). A surveillance study of Alaskan wildlife

found orf virus in free-ranging Dall’s sheep, muskoxen, caribou, mountain goats, and Sika black-

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tailed deer. Sequencing the virus revealed that the strain in Dall’s sheep is more closely related

to the orf virus found in domestic sheep than the other wildlife species (Tryland et al. 2018).

Zarnke et al (1983) conducted a serosurvey for orf virus and found a 37% prevalence in free-

ranging Dall’s sheep in the Brooks Range and Kenai Peninsula using serum antibody neutralization

tests.

2.2 Bacteria

2.2.1 Mycoplasma ovipneumoniae

Pneumonia was recognized as a significant contributing factor to bighorn sheep

population declines in the early 1900’s. Since then, epizootic pneumonia has continued to cause

all-age die-offs and subsequent declines on individual herd and population levels. However, it

was not until relatively recently that all associated pathogens were identified (Besser et al. 2008;

Besser et al. 2013; Dassanayake et al. 2013; Safaee et al. 2006). Besser et al. (2013) examined

potential primary pathogens with regard to criteria of causation. Mycoplasma ovipneumoniae

(M. ovi) and Pasteurellaceae: Bibersteinia trehalosi (B. trehalosi), Mannheimia haemolytica (M.

haemolytica), and Pasteurella multocida (P. multocida) are commonly associated with and

identified in pneumonia in bighorn sheep (Besser et al. 2013; Wood et al. 2016; Dassanayake et

al. 2013; Drew et al. 2014); weak association with pneumonia was found for lungworm and

Pasteurellaceae, and strong association for M. ovi. These findings also fit the temporality of

disease, with declines in naïve bighorn populations corresponding with the introduction of

domestic sheep (Ovis aries) and goats (Capra aegagrus hircus) to North America.

13

M. ovi generally has high prevalence in domestic sheep and goat flocks, and while it

typically does not cause significant clinical signs in these species, there can be subclinical losses

in production or clinical disease noted (Besser et al. 2013; Butler et al. 2018; Manlove et al. 2019).

Besser et al (2013) reported experimental evidence of M. ovi transmission from domestic hosts

to wild sheep through co-mingling experiments. Three bighorn sheep penned with M. ovi-free

domestic sheep did not develop pneumonia after 100 days (Besser et al. 2012a), whereas almost

all bighorn sheep comingled with M. ovi positive domestic sheep or goats developed or died of

pneumonia in previous studies (Besser et al. 2013, 2014, 2017). The lesions observed with goat

strains of M. ovi were significantly milder than those observed with domestic sheep strains;

however, all lambs born to bighorn ewes previously commingled with domestic goats died at less

than 7 days of age with signs of ocular, systemic, and/or gastrointestinal tract disease, not

pneumonia. (Besser et al. 2017). In another experiment, a captive bighorn sheep was inoculated

with M. ovi and introduced into a pen of naïve bighorn sheep. All naïve sheep developed

bronchopneumonia after co-mingling (Besser et al. 2014).

To address the question of plausibility, Besser et al. (2012b) used culture and culture-

independent methods to identify pneumonia-related pathogens in lung tissue from 44 bighorn

sheep from eight free-ranging herds experiencing pneumonia outbreaks. M. ovi was found to be

the only pathogen detected at a significantly higher prevalence in outbreak herds when

compared to samples from non-outbreak herds, suggesting that M. ovi is a primary pathogen in

epizootic pneumonia (Besser et al. 2012b; Besser et al. 2008, Manlove et al. 2019). M. ovi initiates

polymicrobial pneumonia; it appears to predispose or potentiate the pathology associated with

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leukotoxigenic Pasteurellaceae infection (Besser et al. 2008, Besser et al. 2012b, 2013; Raghavan

et al. 2016).

There are several mechanisms by which M. ovi is pathogenic. The bacterium colonizes

and interferes with the mucocilliary defense mechanism by binding to cilia in the trachea,

reducing the clearance of inhaled debris and bacteria, allowing for invasion of secondary

pathogens (Besser et al. 2008, 2012a, 2013; Heinse et al. 2016; Rifatbegovic et al. 2006). M. ovi

reduces the cytolytic activity of macrophages, suppresses lymphocyte activity, and induces

production of autoantibodies to the ciliary antigen (Niang et al. 1997; Rifatbegovic et al. 2006).

Humoral immunity is also important for host defense against M. ovi and the relative contribution

or which likely accounts for some of the variation of serological response among individuals and

herds during epizootic events (Cassirer et al. 2017).

Infection of bighorn sheep herds with M. ovi results in one of three outcomes:

development of fatal bronchopneumonia, seroconversion and clearance of infection, or

asymptomatic persistent carriage leading to chronically infected groups or herds. Post-mortem

findings of experimentally infected lambs showed subclinical disease exists for several days to

weeks before clinical disease becomes apparent (Besser et al. 2008; Cassirer et al. 2018). Usually,

all-age die-offs are followed by epizootic pneumonia persistence in a herd (Raghavan et al. 2016).

Morbidity is typically high. Mortality rates associated with M. ovi introduction into a herd vary

widely (Cassirer et al. 2018), with 10-90% loss of a herd observed with new introductions of M.

ovi (Butler et al. 2018). Introduction of a new strain of M. ovi into a chronically infected herd in

Washington and Oregon resulted in adult morbidity and mortality rates expected with a novel

introduction. M. ovi genotypes (strains) appear to differ in virulence, with only some persisting

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and spreading through populations of bighorn sheep. Immunity to M. ovi is strain-specific

(Cassirer et al. 2017). Twenty-eight unique strains have been identified from studies bighorn

sheep studies (Cassirer et al. 2017). Infection with a novel strain of M. ovi resulted in 57.9%

mortality of bighorn sheep translocated to the Black Hills of North Dakota, despite previous

exposure through vaccination with a strain known to be resident (Werdel et al. 2020). While

much has been learned on why different bighorn herds have differing patterns of respiratory

disease, how those are associated with the pathogens endemic and introduced, there is still much

that is difficult to explain.

Bighorn herds that have persistent M. ovi infections, or “carrier herds”, tend to decline,

or fail to recover, due to poor lamb survival and recruitment (Besser et al. 2013; Cassirer et al.

2017). Butler et al. (2018) found higher lamb recruitment in M. ovi-negative herds than those

where M. ovi was detected (ewe:lamb ratios 0.37 and 0.25, respectively). Pneumonia in lambs

generally occurs prior to weaning (< 4 months) despite passive transfer of maternal antibodies

(Cassirer et al. 2017). Lambs may also die earlier of gastrointestinal complications and/or

systemic disease associated with M. ovi (Besser et al. 2017). Wood et al. (2017) studied lamb

recruitment in a poorly doing remnant herd of bighorn sheep from Colorado (Gribble herd). All

the surviving ewes were live-captured and relocated to a research centre in Wyoming where six

lambs of the seven born displayed signs of bronchopneumonia and were euthanized or died

naturally and necropsied. M. ovi, leukotoxigenic M. haemolytica, leukotoxigenic B. trehalosi, and

P. multocida were isolated from the lamb mortality samples, with variation in pathogens and

disease processes between lambs indicating that the relative contribution of each pathogen

depends on time. The ewes were euthanized due to their disease, and when sampled, had a

16

consistent combination of all above pathogens. Gross lesions of bronchopneumonia included

“consolidation of the cranioventral lung lobes, scattered necrotic foci within affected lung tissue,

and fibrinous pleuritis” (Wood et al. 2017).

M. ovi strains were present in 37.5% of domestic sheep and goat flocks sampled within

bighorn ranges in Washington State (n = 9/24). The likelihood of a positive result increased with

flock size (Heinse et al. 2016). Eighty-eight percent of flocks tested across the United States tested

positive for M. ovi (Cassirer et al. 2017; Manlove et al. 2019). This highlights the high potential

risk for spillover of this significant pathogen from domestic sources and the need for tools to

prevent transmission. Traditionally efforts to mitigate transmission relied on physical and

temporal separation of wild and domestic Caprinae, but more recently, attempts to remove M.

ovi from domestic animals using management and treatment methods have been trialed (T.

Besser, pers. comm.). Repeated transmission from host sources can and does devastate

populations of wild sheep. This also poses a management concern between promoting

connectivity in habitats and risk of pathogen spill over with dispersal (Cassirer et al. 2017).

Domestic goats can be asymptomatic carriers of M. ovi, but there is ample evidence that

infection in mountain goats (Oreamnos americanus) can be as severe as with bighorn sheep

(Wolff et al. 2019). Mountain goat populations are declining in parts of their range, despite

conservation efforts that include translocation. Mountain goats and wild sheep share range and

habitat, therefore pose a risk for intraspecific disease transmission (Lowrey et al. 2018).

Blanchong et al. (2018) observed respiratory disease negatively affecting survival of mountain

goat kids following an outbreak of M. ovi and die-off of sympatric bighorn sheep. During a bighorn

sheep pneumonia outbreak, one adult mountain goat mortality also was found to have gross

17

evidence of bronchopneumonia (Blanchong et al. 2018). Lowrey et al. (2018) surveyed the

respiratory pathogen community of 98 wild mountain goats in the Greater Yellowstone Area

(GYA) and southeast Alaska, finding M. ovi in two GYA herds and Pasteurellaceae in all herds.

Wolff et al (2019) found bronchopneumonia in seven mountain goat kid mortalities during a

bighorn sheep epizootic in Nevada. During the bighorn sheep die-off in 2009-2010 in the US, 10%

and 13% declines were documented in sympatric mountain goat populations. The same strain of

M. ovi was detected in both species of mountain ungulate suggesting a shared pathogen.

Highland et al. (2018) detected M. ovi in moose (2.6%), caribou (2.1%) from Alaska using

molecular testing methods (PCR). M. ovi had not previously been detected in non-Caprinae

species, but possibly due to under-testing and poor detection methods. It was assumed to infect

and be carried by Caprinae only. Genetic testing of the strain found in non-Caprinae in Alaska

showed divergence from a strain isolated from M. ovi-positive wild sheep (Highland et al. 2018).

Previously, no evidence of M. ovi in wild Dall’s or Stone’s sheep has been documented (Zarnke &

Rosendal 1989). In 2019, M. ovi was identified in a yearling Barren ground caribou (Rangifer

tarandus granti) from Alaska with a polymicrobial pneumonia; however, the role of M. ovi

infection in this species is not clear as poor nutritional condition, co-infections, and high parasite

burdens were also evident (Rovani et al. 2019).

2.2.2 Pasteurellaceae

Bacteria in the family Pasteurellaceae are often identified in bighorn sheep pneumonia

epizootics; the species isolated include leukotoxigenic Mannheimia spp. (M. haemolytica, M.

glucosida, M. verigena, M. ruminalis), leukotoxigenic B. trehalosi, and leukotoxigenic P.

18

multocida. Non-leukotoxigenic Pasteurellaceae can be normal inhabitants of the upper

respiratory tract of bighorn sheep (Raghavan et al. 2016; Safaee et al. 2006). Virulence of these

bacterial species is associated with hemolysis, which is correlated with leukotoxin A (lktA)

production (Drew & Weiser 2017; Butler et al. 2018; Herndon et al. 2017). All the above species

have been isolated from domestic goats (Drew & Weiser 2017) and domestic sheep (Heinse et

al. 2016), making spillover from domestic flocks to wild sheep an ongoing risk. While M. ovi is

considered to be a primary initiating pathogen in bighorn pneumonia, direct inoculation of

immunologically naïve bighorn sheep with leukotoxigenic M. haemolytica or B. trehalosi has

produced fatal pneumonia (Butler et al. 2018) suggesting that in some situations, M. ovi is not

required for pneumonia mortality events.

Dassanayake et al. (2013) experimentally examined the role of B. trehalosi in the

pathogenesis of pneumonia in bighorn sheep, comparing leukotoxigenic and non-leukotoxigenic

strains. All animals inoculated with leukotoxigenic B. trehalosi developed bronchopneumonia,

whereas none of those inoculated with the non-leukotoxigenic strain did, indicating that

virulence of B. trehalosi depends on lktA. Besser et al. (2012b) and Shanthalingam et al. (2014)

found 100% of bighorn sampled sheep to be lktA-negative.

M. haemolytica is a normal species-specific commensal organism in the upper respiratory

tract of ruminants (Garcia-Alvarez 2018) and many bighorn sheep carry non-leukotoxigenic B.

trehalosi asymptomatically (Raghavan et al. 2016). M. haemolytica lktA is a key virulence factor

in bighorn sheep pneumonia (Herndon et al. 2017), with neutrophils being the most susceptible

leukocyte (Dassanayake et al. 2009; Dassanayake et al. 2017). Bighorn sheep neutrophils are

more susceptible than domestic sheep neutrophils to lktA in vitro. The leukocyte lktA receptor,

19

CD18, was hypothesized to factor into this observed difference, however, Dassanayake et al.

(2017) found that CD18 expression was actually higher on domestic sheep monoclonal

neutrophils compared with those in bighorn sheep and suggest that a difference in activation of

signaling pathways within the neutrophils likely contributes to the observed difference.

Maternally derived immunity to M. ovi and leukotoxigenic Pasteurellaceae does not

appear to be protective to bighorn lambs. In an experiment where pregnant naïve bighorn sheep

ewes were co-mingled with M. ovi exposed rams, the result was high lamb mortality for 2 years

following and the production of carrier ewes (Raghavan et al. 2016). Domestic sheep ewes

produce higher levels of antibodies to commensal Pasteurellaceae and lktA compared to bighorn

ewes, therefore it is postulated that bighorn lambs acquire lower levels of protective antibodies

from their dams and are far more susceptible to these organisms. This could be similar for M. ovi,

as most lambs succumb to pneumonia at around 6-8 weeks of age, coinciding with waning of

maternal antibodies. Lambs born to carrier ewes and administered antimicrobial treatment

against Pasteurellaceae still succumbed to pneumonia, indicating inadequate levels of protective

antibodies. The pathogenesis of lamb pneumonia is still unclear; plausible factors include low

passive immunity, carrier dams and rapid transmission of pathogens, waning of maternally

derived antibodies, or a combination of factors (Raghavan et al. 2016). Herndon et al. (2017)

discovered that bighorn lambs had approximately 18 times lower titers of leukotoxin-neutralizing

antibodies to M. haemolytica than domestic lambs when the dams were submitted to the same

infection challenge during pregnancy, and the colostrum of bighorn ewes contained lower titers

than the colostrum of domestic ewes. These results have important implications for lamb survival

in pneumonia-infected herds (Herndon et al. 2017).

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Bacterial pneumonia as a cause of death in Dall’s sheep was first described in two ewes

from the Mackenzie Mountains, Northwest Territories (NWT; Jenkins et al. 2000). Findings

included chronic, fibrinopurulent bronchopneumonia associated with Trueperella pyogenes

(Arcanobacteria pyogenes) and Mannheimia spp. M. ovi was not detected in lung tissue, but this

species is notoriously difficult to culture and the genetic tests currently used to detect M. ovi

were not yet available. It is unclear whether there was any histological evidence of the typical

changes associated with a mycoplasma infection. T. pyogenes has also been isolated from the

lungs of healthy Dall’s sheep in the Mackenzie Mountains, suggesting that these pathogens may

be opportunistic invaders in the presence of stressors or a primary pathogen (Jenkins et al. 2000).

A study in southcentral Alaska from 2009 to 2014, diagnosed pneumonia in four of 22 adult Dall’s

sheep mortalities. Pneumonia accounted for 2.6% of lamb mortalities between 35 and 45 days

of age in the same area from 2009 to 2012. However, in an adjacent study area in 2012, no

pneumonia was detected in 26 lamb mortality investigations (Lohuis 2013). The prevalence and

impact of Pasteurellaceae on lambs and has not been investigated in thinhorn sheep.

Nasal sinus tumors in bighorn sheep were identified in many herds of bighorn sheep in

the past decade. The specific etiology of these tumors is unknown. Grossly and histologically,

nasal sinus tumors can be recognized by thickening of the sinus mucosa with a thick mucoid

exudate and as they advance, invasion of the underlying bone. Alone, they do not appear to cause

morbidity or mortality, but cause narrowing of the sinuses and prevent clearing of foreign

material and potentially pathogenic bacteria (Fox et al. 2015). This association with chronic

bacterial infection suggests that sinus tumors may play a role in maintenance of Pasteurellaceae

or other upper respiratory pathogens in bighorn sheep herds. Sinus tumors have not been

21

reported in thinhorn sheep, but the sampling has been minimal, if at all, due to the need for

sectioning of the nasal sinuses to detect this condition.

2.2.3 Other Bacteria

Mandibular osteomyelitis (“Lumpy jaw”) is a syndrome caused by bacterial invasion and

boney proliferation of the mandibles. In domestic ruminants, lumpy jaw is primarily associated

with Actinomyces bovis invading mucosal or dental deficits caused by trauma to the oral cavity;

however, Trueperella (formerly Arcanobacterium) pyogenes, Fusobacterium necrophorum,

Staphylococcus aureus, and Streptococcus spp. are more commonly isolated from lesions in wild

sheep when cultured (Hoefs & Bunch 2001). Lumpy jaw prevalence in thinhorn sheep (25.7% in

Alaska and 37% in Yukon Territory), were previously reported, with regional variation (Hoefs &

Bunch 2001). A study by Hoefs and Bunch (2001) examining 3,359 jaws from North American wild

sheep found prevalences of 29.3% in Stone’s sheep, 23.3% in Dall’s sheep, and 12.1% in Rocky

Mountain bighorn sheep (Ovis canadensis canadensis). The relatively higher prevalence in

thinhorn sheep was postulated to be associated with increased dental wear and irritation from

high levels of fine particulates on forage at high elevation from glacial deposits. However, the life

expectancy of affected wild sheep does not appear to be reduced (Hoefs & Bunch 2001).

2.3 Parasites

Increases in environmental temperatures, reduced climatic stability, anthropogenic

landscape and habitat changes may alter parasite communities and host immunity through co-

infections, timing and quality of nutrient availability, and host stress (Jolles et al. 2015). Climate

22

change patterns are more impactful at higher latitudes and therefore will likely have a larger

effect on arctic and subarctic wildlife species (Aleuy et al. 2018). A link between stress and

parasite burden has been studied in Soay sheep; adult sheep experiencing stress due to harsh

winters (high snowfall, ice crust formation, and/or long periods of extreme cold) have higher

parasite burdens indicating that stress plays an important role in immune regulation. Sheep that

survive harsh winters and maintain body condition may have physiologically invested more in

parasite resistance, but at the cost of reproductive performance (Jolles et al. 2015).

2.3.1 Gastrointestinal Parasites

High intestinal burdens of nematode parasites can have a detrimental effect on their

host’s fitness, including decreased pregnancy rates, body condition, and survival of young, and

increased mortality, presumably due to competition between the host and parasite for resources

within the intestine (Aleuy et al. 2018). Aleuy et al. (2018) studied the gastrointestinal parasite

diversity in 108 Dall’s sheep from the Mackenzie Mountains, Northwest Territories. Marshallagia

marshalli was found to be the most abundant nematode, both in prevalence and intensity of

infection, of the eight species identified. This is consistent with other studies on wild sheep and

mountain goats (Hoberg et al. 2012). The alternate morphotype, M. occidentalis was not

observed in this study. The association with body condition and pregnancy was examined for four

nematodes, M. marshalli, Trichuris spp. Ostertagia spp. and Nematodirus spp.. M. marshalli is a

trichostrongylid nematode with a direct life cycle that is able to persist in extreme environmental

conditions. M. marshalli precipitates gastroenteritis and an increase in abomasal pH due to loss

of parietal cells leading to appetite suppression, weight-loss, constipation, or diarrhea, all which

23

can alter the uptake of nutrients and weaken adult sheep. Another large intestinal parasite found

in abundance in Dall’s sheep, but not in sympatric wild ungulates, is Trichuris spp. Trichuris spp.

had a higher intensity in young sheep while M. marshalli had a higher infection intensity in

mature adult sheep. Body condition was negatively associated with gastrointestinal nematode

infection intensity, as was pregnancy, with a significant effect noted for M. marshalli (Aleuy et al.

2018). The most prevalent Nematodirus species varies among Dall’s sheep populations. Cestodes

are rare in Dall’s sheep, with only Moniezia bendedeni reported. Ostertagia gruehneri infections

are common in other wild ungulates, and may increase in Dall’s sheep in the summer, when there

is more range overlap with muskoxen. (Aleuy et al. 2018). O. gruehneri can negatively impact

host fitness in a dose-dependent manner. High burdens are associated with reduced nutritional

condition and fecundity in reindeer (Carlsson et al. 2016).

2.3.2 Lung Worms

The lungworm species typically found in bighorn sheep include Protostrongylus stilesi and P. rushi

and rarely Muellerius capillaris. Nematode larvae in the feces of thinhorn sheep are generally

assumed to be P. stilesi, the only lung-dwelling nematode in Dall’s sheep in the Mackenzie

Mountains (Kutz et al. 2001). Both species have an indirect lifecycle involving gastropod

intermediate hosts. The first larval stage (L1) is shed in the feces of the host. Peak larval shedding

occurs in the spring coinciding with emergence of gastropod intermediate hosts, however a

proportion (20-60%) of L1s survive in the environment over the winter and remain infective.

Development from L1 to the infective stage, L3, usually occurs by late July, depending on the

summer climatic conditions. The majority of lungworm infection occurs on winter habitat when

24

sheep return in the fall (Jenkins et al. 2006). Infection rates of definitive hosts may correlate with

precipitation and population densities of the intermediate snail hosts; however, lungworms live

up to seven years in their hosts so variation in larval output may not be observed between years

(Goldstein et al. 2005). Fourth stage larvae and adult lungworms develop in the ungulate

definitive host. Transplacental infection of lambs with L3 P. stilesi occurs (Jenkins et al. 2006).

Plane of host nutrition and immune suppression of parasite reproduction are thought to relate

to seasonal variation of fecal larval shedding (Goldstein et al. 2005; Jenkins et al. 2006). Climate

warming has shortened the development period of P. stilesi larvae in the environment, allowing

L3s to be infective earlier in the spring (Jenkins et al. 2006).

Goldstein et al. (2005) experimentally examined the relationship between ewe lung worm

burden and lamb survival in Rocky Mountain bighorn sheep by repeated administration of an

anthelmintic to one group and comparing to an untreated control group. Fecal cortisol levels

were analyzed as a measure of the hypothalamic-pituitary-adrenal axis (HPA) response to the

stress of parasite burden. They discovered that fecal cortisol levels did not significantly differ

between the treatment and control group, and even though parasite burden was significantly

lower in the treatment group, lungworm burden did not cause clinical respiratory disease or

measurable signs of chronic stress in adult bighorns in the control group, and lamb survival was

not correlated with parasite burden (Goldstein et al. 2005). Infection of lambs has been

associated with ‘summer lamb mortality’, likely through transplacental movement of L3s

(Goldstein et al. 2005).

Protostrongylus stilesi and the muscleworm Parelaphostrongylus odocoilei are ubiquitous

in thinhorn sheep in NWT, with almost 100% prevalence described by Jenkins et al. (2006). P.

25

odocoilei infection was associated with generalized granulomatous interstitial pneumonia with

pulmonary edema and hemorrhage in experimentally infected Stone’s sheep. Adult P. odocoilei

in the muscle tissue of Stone’s sheep was associated with inflammatory infiltrates and chronic

hemorrhage in fascial planes (Jenkins et al. 2007). Migration of P. odocoilei via the brain and

muscle to the lungs induces neurological, muscular, and pulmonary pathology in thinhorn sheep.

Eggs are distributed throughout the lung parenchyma (Jenkins et al. 2007). P. stilesi and P.

odocoilei were also observed in ‘healthy’ adult thinhorn sheep, suggesting that parasites may

cause ‘additive or synergistic pulmonary damage’ (Kutz et al. 2001). Verminous pneumonia

associated with these lungworms may predispose individuals to bacterial pneumonia (Jenkins et

al. 2007) and a ‘stress-lungworm complex’ has been reported as a factor of mortality in bighorn

sheep (Kutz et al. 2001).

A fecal survey by Jenkins and Schwantje (2002; Muskwa-Kechika) of Stone’s sheep

parasites from three herds in the Muskwa-Kechika area or northeast BC from 2000 to 2002

demonstrated parasitism consistent with other wild sheep populations and recommended

‘expanded population health monitoring’. Marshallagia sp. Nematodirus spp. Trichuris sp.

trichostrongyles, Skrjabinema sp. Moniezia sp. Eimeria spp. eggs, and P. odocoilei and

Protostrongylus spp. larvae, were identified in the 408 samples. Seasonal differences were found

in prevalence and intensity of infection between the parasite species. Larval shedding of

Protostrongylus spp. was consistently higher than P. odocoilei. The role of parasitism was not

found to be significantly impacting the Stone’s sheep population studied at that time (Jenkins &

Schwantje 2002). Wood et al. (2010) also documented Eimeria spp. Trichuris spp. Nematodirus

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spp. Marshallagia spp. and Moniezia spp. in a population of Stone’s sheep live-captured in the

Peace Region of BC.

2.3.3 Trematodes

Foreyt (2009) demonstrated patent infection of bighorn sheep with the liver fluke Fasciola

hepatica. Nine bighorn sheep were experimentally inoculated with metacercaria and developed

patent infections. A prepatent period of 10 weeks was determined, similar to what is found in

domestic ruminants (8-10 weeks). Anemia, which is a characteristic sign of fluke infection in

domestic ruminants, occurred in the bighorn sheep. F. hepatica infection has not been reported

in bighorn or thinhorn sheep previously, likely because their ranges rarely overlap with the

wetland habitat required by the intermediate gastropod host (Foreyt 2009).

2.3.4 Tissue-dwelling Protozoans

Toxoplasma gondii and Neospora caninum are tissue-dwelling protozoans that can be

transmitted between wild and domestic species through fecal environmental contamination.

Canids are definitive hosts for N. caninum and felids are definitive hosts for T. gondii.

Intermediate hosts including birds, rodents, mustelids, ungulates, and humans become infected

through ingestion of contaminated food or water (Sharma et al. 2019). T. gondii and N. caninum

can cause neurologic disease and abortions in their intermediate hosts. Serological evidence of

T. gondii exposure and latent infection has been documented in a range of large terrestrial

mammals in Alaska, including Dall’s sheep (22 out of 319 infected; Zarnke et al. 2000). The source

of infection in Alaska is unknown, but lynx as felids are the most likely definitive host for T. gondii.

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Dubey and Foreyt (2000) found serological evidence of exposure in 25 of 697 bighorn sheep

tested in Washington, Idaho, Oregon, Nevada, Wyoming, Montana, and Alberta. This was

considered a low seroprevalence, likely due to lack of exposure in of thinhorn sheep in the remote

areas they inhabit (Dubey and Foreyt 2000). Encephalitis associated with toxoplasmosis was

reported in bighorn sheep lamb found dead near Hells Canyon, Washington (Baszler et al. 2000).

N. caninum exposure was documented in a variety of in Alaska wildlife species, including its

definitive canid hosts such as dogs and coyotes (Stieve et al. 2010). Young caribou had higher

titers than adult caribou, indicating that vertical transmission may occur.

2.3.5 External Parasites

Wood et al. (2010) studied a population of Stone’s sheep exhibiting hair loss in the

Dunlevy/Schooler study area of Williston Lake in northeast British Columbia. The study confirmed

that the hair loss was due to winter tick (Dermacentor albipictus) infestations. This one host tick

is most commonly associated with moose (Alces alces) but other large ungulates can serve as

hosts. Hair loss is caused by overgrooming or skin irritation and may be associated with poor

body condition and exposure as well as blood loos anemia if tick burdens are high. This

population of Stone’s sheep is unique as it overwinters on low elevation benches at the edge of

a reservoir established by flooding the upper Peace River valley and resulting in sharing of winter

habitat with other large ungulates, in this situation overlap with a high density population of

Rocky Mountain elk (Cervus canadensis). Animals from affected and nonaffected sheep

populations were live-captured, samples, tick burdens were assessed and each radiocollared for

analysis. Stone’s sheep herds that did not winter at low elevation did not show evidence of winter

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tick associated hair loss and for this study there was no apparent difference in survival between

affected and nonaffected sheep. This was the first record of winter tick infestations of thinhorn

sheep.

2.3.6 Diagnostic Testing

Uncertainty in disease prevalence estimations may arise due to the agent, the ability to

identify the agent, the disease process, the study design, or the knowledge of the disease leading

to bias (Lachish & Murray 2018). Diagnostic testing of wildlife is very nonspecific. This is important

for assessing disease dynamics in wild populations (Miller et al. 2012b) and making

health/disease-based management decisions. Infection status can be defined as population

prevalence or individual infection intensity or burden (Miller et al. 2012b). Miller et al. (2012b)

and Lachish and Murray (2018) introduce models to account for uncertainty in disease

prevalence measurements and increase the reliability of the results.

Butler et al. (2017) analyzed data from nasal and oropharyngeal swab samples from 476

bighorn sheep in Montana and Wyoming to estimate detection probabilities of pathogens

associated with epizootic pneumonia, assess the precision of estimation, and evaluate power to

detect respiratory pathogens, including M. ovi, M. haemolytica, Mannheimia spp. B. trehalosi,

and P. multocida. The detection probability of was above 0.6 for all three protocols tested, with

the Wyoming protocol for M. ovi 1 and for Pasteurellaceae 2 having the highest detection

1 placing the swab in Port-A-Cul transportTM or Amies media, within 72 hours transferring it to tryptone soy broth, incubating at 37oC in 5% Co2 for 48 hours, then testing for the presence of M. ovi using polymerase chain reaction 2 two tonsil swabs collected , one used to inoculate a Columbia Blood Agar immediately, and the other placed in Port-A-Cul transport media before inoculating a CBA plate, incubated at 37oC in 5% Co2 for 48 hours, then testing for the presence of Pasteurellaceae by PCR

29

probability of detection. The detection probability of Pasteurellaceae using culture only was less

than 0.5, making detection of Pasteurella spp. unreliable at the population level if prevalence is

low and the host population is not intensively sampled. The precision of prevalence estimates

was low for all protocols, therefore, the authors advise against using prevalence of pathogens

detected to infer causation in sheep respiratory disease. Increasing the precision requires

intensive repeat sampling of individuals. The power to detect the different pathogens varied

significantly between protocols.

Reliability of diagnostic testing is crucial for interpreting results of individual infection and

herd prevalence, which affect management decision making (Lachish et al. 2011). Reliability of

testing is based on the sensitivity and specificity of tests and can be affected by the population

prevalence of disease (Miller et al. 2012b). Differences in detection probabilities between

“diseased” or carrier animals and healthy animals can bias population prevalence estimates

(Lachish et al. 2011). Concordance in laboratory testing of certain bighorn respiratory pathogens

was evaluated by Walsh et al. (2016) across six different laboratories and three reference

laboratories. The laboratories used different polymerase chain reaction (PCR) testing protocols

for lktA from Pasteurellaceae detection. ‘Substantial agreement’ was found for within

(repeatability) and between (consistency) laboratory comparisons using Mycoplasma broth and

lung-homogenate-supernatant. Inconsistency in PCR results arose with weakly positive or weakly

negative results. For the Pasteurellaceae, agreement between and within laboratories was high

for B. trehalosi and M. haemolytica using PCR and culture methods, but inconsistent for P.

multocida.

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Culture-based testing significantly underestimates bacterial diversity and the prevalence

of and Pasteurellaceae in samples. Mycoplasma species are notoriously difficult to culture.

Culture-independent diagnostic techniques in the past two decades have revolutionized the

understanding of epizootic pneumonia in bighorn sheep (Safaee et al. 2006). Culture-

independent methods were found to be repeatable, however caution must be taken when

sampling dead animals as post-mortem invaders may be confused for ante-mortem pathogenic

organisms (Safaee et al. (2006). Shanthalingam et al. (2014) found that culture methods were not

adequate to identity M. haemolytica but PCR was useful in detecting the virulence factor lktA;

77% of the lung tissue samples that were culture-negative for M. haemolytica were PCR positive.

When present, B. trehalosi and P. multocida can inhibit the growth of M. haemolytica

(Shanthalingam et al. 2014). Cassirer et al. (2017) reported that PCR testing requires a minimum

of 10 animals to determine presence in a herd, while serological methods require at least 15

samples to confirm exposure in a herd.

Walsh et al. (2012) examined detection probability (the probability of detecting the

organism of interest) and occupancy probability (the probability an individual sheep in a herd is

infected) for Pasteurella spp. using culture methods. They confirmed that oropharyngeal swabs

had a higher detection probability for these bacteria than lung swabs and could be performed on

live animals. The time from sample collection to plating also had an effect; the mean detection

probability for M. haemolytica, B. trehalosi, and P. multocida was 0.32 for samples submitted

immediately and 0.24 for samples submitted with a 2-day delay. The number of samples required

to detect Pasteurella spp. in a herd with 95% confidence ranged from sampling two animals twice

to 40 animals once (for M. haemolytica: 16 animals once to 40 animals once, for B. trehalosi: 2

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animals twice to 40 animals once, for P. multocida: 9 animals once to 40 animals once depending

on strain type) depending on the biovariant and strain.

Determining freedom from infection is challenging given the detection probabilities for

various pathogens and the typically small number of animals sampled. Butler et al. (2018) used a

modeling approach to predict true absence of infection when a negative test result is obtained

and found this to only be possible at very low probabilities of pathogen presence (<0.10).

2.4 Non-Infectious Indicators of Health

2.4.1 Trace Minerals

Trace minerals are an essential part of nutrition for all living organisms, but little is known

about the requirements and appropriate levels in free-ranging wildlife. Trace mineral

concentrations of forage species are influenced by climate, substrate type and mineral

composition, and rainfall. Forage quality and availability, as well access to mineral licks, drive

movements of wild ungulate populations (Bleich et al. 2017).

Selenium is an essential trace element required for bone metabolism, immune function,

reproduction, muscle function, and growth. As a component of enzymes, selenium is important

for removal of reactive oxygen species, which degrade cell membranes. Subclinical chronic

selenium deficiency may lead to decreased reproductive performance (Flueck et al. 2012; Jolles

et al. 2015). Selenium levels in soil are often low which translates into deficient concentrations

in the forages consumed by ungulates and increased environmental levels of heavy metals can

sequester selenium, decreasing its bioavailability (Flueck et al. 2008). Nutritional requirements

for wild sheep are unknown but may be lower due to adaptation to their natural environments

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(Mincher et al. 2008). Flueck et al. (2008) suggest hepatic selenium levels less than 0.15mg/kg as

deficient, 0.15-0.22mg/kg as marginal, and greater than 0.22mg/kg as adequate.

The use of mineral licks, or geophagia, has been shown to be an important modality for

free-ranging ungulates to increase their intake of essential trace minerals (Mincher et al. 2008).

Often driven by sodium content in the soil, geophagia is most prevalent in lactating females with

young due to increased nutritional demands. Mincher et al. (2008) analyzed soil and forage

mineral composition from several sites around North America and determined that available

trace mineral levels were higher in lick soils than summer range soils for calcium, magnesium,

potassium, copper, selenium, and sodium. Extrapolating from known trace mineral levels in feed

required by domestic sheep (Se: 0.1-0.2mg/kg, Na: 3,600-4,700mg/kg), most sites were found to

be selenium deficient.

Dietary requirements for trace minerals and adequate serum and tissue levels have not

been established for thinhorn sheep. Serum reference ranges for selenium, calcium, copper, iron,

magnesium, phosphorus, potassium, sodium, and zinc were defined for bighorn sheep by

Poppenga et al (2012) based on a survey of 388 sheep from five metapopulations in California.

Trace mineral levels in bighorn sheep largely fell within ranges reference ranges for adequate

serum trace mineral levels in domestic sheep. Serum and tissue trace mineral levels can differ by

sex due to habitat choices at least partially based on social structure. For example, Bleich et al.

(2017) found significant differences in the trace mineral composition of forages between two

study sites where the vegetation species were similar.

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2.4.2 Contaminants

Elevated levels of heavy metals in tissue pose a health risk for the wildlife species, as well

as for First Nation communities and hunters in the north. ‘Country meats’ make up approximately

50% of the dietary protein for residents of northern communities (Larter et al. 2016); filtering

organ (e.g. liver and kidney) consumption is popular as well (Gamberg et al. 2016). Contaminants

measured include cadmium, lead, arsenic, copper, aluminum, mercury, selenium, and zinc

(Gamberg et al. 2016; Larter et al. 2016). Elevated levels of cadmium have been documented in

moose and caribou in Northern Canada due to local natural sources, soil types, and vegetation

species (Larter et al. 2016). Cadmium concentration in tissues tends to increase with age in

moose, with levels in moose kidneys up to ten times higher than other sympatric ungulates in

the area. Thus, moose kidneys can be a significant source of cadmium for those who consume

them (Larter et al. 2016). Elevated heavy metal levels in the kidney lead to cellular damage and

impaired function, however, the level at which this occurs has not been established. No

significant histopathological renal damage was observed in this study (Larter et al. 2016).

Differences in measured contaminant levels between moose, caribou, mountain goats, and Dall’s

sheep can be due to variations in their organs’ storage ability or their diet. Other sources of

contaminants include residue from nuclear reactor accidents (e.g. cesium radioisotopes) or

industrial environmental contamination. Gamberg et al. (2016) attributed different levels of

heavy metals in two geographically near caribou populations in Greenland to atmospheric

deposition in lichens. Gamberg (2000) reports trace mineral and heavy metal (arsenic, cadmium,

copper, lead, mercury, and zinc) level findings in wildlife species from Yukon territory, including

Dall’s sheep. They found that age was positively correlated with tissue cadmium concentration

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and differences between species were due to diet. Arsenic toxicity was identified as the cause of

death of two Stone’s ewes in a mine site in the Skeena Region of BC (Golden Bear Mine; H.

Schwantje, pers. comm.)

2.4.3 Stress

Hair and fecal glucocorticoid (cortisol/corticosterone) measurements are useful non-

invasive tools for evaluating the hypothalamic-pituitary-adrenal (HPA) axis response to stressors

in wildlife (Ashley et al. 2011; Sheriff et al. 2011, Koren et al. 2019; Dulude-de Broin et al. 2019).

Glucocorticoid levels in blood or saliva are influenced by circadian rhythm and acute stress

events, making these substrates less useful to detect chronic physiological changes.

Glucocorticoids are produced upon activation of the HPA axis when gluconeogenesis is triggered

to supply energy during periods of acute stress (Ewacha et al. 2017). High baseline glucocorticoid

concentrations are indicative of reduced fitness of an individual or population, as explained by

the Cort-fitness Hypothesis, the assumption that baseline cortisol levels are negatively correlated

with fitness (Bonier et al. 2009). Glucocorticoid production and concentration in tissue varies

with age, sex, body mass, season, resource availability, and population size, often making

interpretation challenging (Dulude-de Broin et al. 2020).

Glucocorticoids in biological samples are analyzed by gas chromatography and mass

spectrometry, high-pressure liquid chromatography, or immunoassays, all which have specific

advantages and disadvantages (Cook 2012). Immunoassay is the most common commercially

used test protocol but requires tissue-specific validation. Schoenemann and Bonier (2018) found

that repeatability of glucocorticoid baseline measurements in vertebrates were repeatable for

35

individuals (repeatability of 0.230 – 0.386), and that repeatability within life stages, rather than

between, was higher.

Hair can be used to proportionately measure glucocorticoid levels in circulation during

the hair growth cycle. Glucocorticoid levels in hair have been correlated with reproductive and

social status, health and body condition, and exposure to toxins. Increased hair cortisol

concentrations result from chronic stress. Shifts in hair cortisol concentrations (HCC) can be

associated with disease and pregnancy (Acker et al. 2018). Males often have higher baseline HCC

than females, as demonstrated in moose (Alces alces; Madslien et al. 2020) and muskoxen

(Ovibos moschatus; Di Francesco et a. 2017). Sex did not significantly influence HCC in Alpine ibex

(Capra ibex ibex; Prandi et al. 2018). Using hair as a sample substrate allows for assessment of

glucocorticoid levels associated with physiological changes over the duration of growth of the

hair. Cortisol is absorbed into hair in a bio-active (unbound) form from the circulatory system and

from local production in hair follicles and sebaceous glands (Koren et al. 2019). Hair cortisol

concentration varies with the location on the body hair was collected from; whenever possible

hair collection sites should be standardized, and caution is advised when interpreting the results

of samples where the collection site is unknown (Acker et al. 2018; Macbeth et al. 2010). Seasonal

variation in body location differences also were observed. Repeatability of glucocorticoid

measurements in hair was found to be 0.32 (95% confidence interval of 0.24-0.41; Schoenemann

and Bonier 2018). Cattet et al. (2017) examined the difference in stress-associated and sex

hormone concentrations in hair that was plucked or shaved; they found that plucked hair had

higher concentrations of hormones than shaved hair, likely due to blood contamination of the

follicle. Dulude-de Broin et al (2019) demonstrated that HCC varied with age class and sex of

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mountain goats, and hair type sampled. Cortisol levels were much lower in undercoat hairs

compared to guard hairs.

Fecal glucocorticoid metabolite (FGM) analysis is a useful biomarker for the HPA axis

response to stressful events during the passage time of feces through the intestinal tract (Cook

2012). The concentration of glucocorticoid metabolites in feces is a useful biomarker as long as it is

representative of adrenocortical activity (Cook 2012). With exogenous ACTH administration, Ashley

et al. (2011), demonstrated a peak in fecal glucocorticoid levels 8 hours after in adult reindeer. A

lower response was observed in subadult reindeer. Hair cortisol was not affected by the single

ACTH dose (Ashley et al. 2011). Seasonal variability in FGM concentrations was observed in Rocky

Mountain Bighorn sheep, with a peak in fall and winter during the rut for males (Goldstein et al.

2005). Contrary to expectation, Carlsson et al. (2016) did not find evidence of a glucocorticoid

response to abomasal parasitism in hair or fecal samples from reindeer. Coburn et al. (2010)

validated FGM testing for assessment of acute stress episodes by subjecting captive- and wild-

raised bighorn sheep to a drop-net capture situation. FGM concentrations in mountain goats

varied with date of collection and duration between sample collection and freezing (Dulude-de

Broin et al. 2019); highest FGM levels occurred mid-summer (Dulude-de Broin et al. 2019;

Goldstein et al. 2005). Fecal cortisol degradation also was assessed with the conclusion that

samples should be kept at low temperature and frozen as quickly as possible.

Hair glucocorticoid deposition and baseline levels may vary among species, season, sex,

and reproductive status (Dulude-de Broin et al. 2019; Sheriff et al. 2011). Dulude-de Broin et al.

(2019) used exogenous ACTH to validate the use of hair and FGM concentrations as biomarkers

for HPA axis activity in mountain goats. Peak FGM concentrations were detected 20 to 32 hours

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post-ACTH injection, which is slightly longer than has been reported for other ungulates. HCC also

increased after a series of ACTH administration for 5 doses, but significant peak variability was

observed between individuals.

Cumulative effects from increased climatic temperatures and industrial activity have been

proposed as contributing factors limiting caribou herd productivity (Ashely et al. 2011). In some

areas, thinhorn sheep face these same population stressors. Ewacha et al. (2017) found that

higher HCC in boreal woodland caribou was associated with recent logging and smaller home

ranges. Logging is associated with environmental disturbances and degradation during road

building activity, and a change of forage composition. The physiologically-mediated results of

disturbance of free-ranging wildlife may contribute to population declines (Ewacha et al. 2017).

2.4.4 Stress and Immunity

Physiological responses to stress negatively affect the immune system which may

predispose wildlife to poor health outcomes (Edes et al. 2018) including mortality (Acevedo-

Whitehouse & Duffus 2009; Coburn et al. 2010). Acevedo-Whitehouse et al. (2009) examined

immune function as a component of wildlife health and described stress-related

immunosuppression due to environmental change, pollutant exposure, and altered distribution

of predator and prey species as an important implication for individual and population health.

Survival and reproduction are two key pathways for allocation of resources in wildlife species.

Immune function affects survival through reallocation of resources and increased vulnerability to

infection and is impacted by nutrition and previous life stage evens, such as stress (Downs et al.

2018). Acute increases in circulating glucocorticoids are crucial for physiological responses that

38

affect survival (Acevedo-Whitehouse & Duffus 2009) and facilitate reproduction and immunity.

Chronic elevations of glucocorticoid levels depress fitness by suppressing immune defenses and

reproduction (Acevedo-Whitehouse & Duffus 2009), resulting in lower neonate birth weight and

juvenile survival (Downs et al. 2018).

As hair growth occurs in autumn in Dall’s sheep during the reproductive period or rut, hair

can be used to determine an animal’s stress response status at this critical time when resource

allocations are being made (Downs et al. 2018). Using ‘bactericidal capability and haptoglobin’,

Downs et al. (2018) examined resource allocation between immunity and pregnancy and HCC in

Dall’s sheep in southeast Alaska. No relationship was observed between maternal HCC and

pregnancy. A weak negative relationship between HCC and lamb birth weight, and between

bactericidal capability and pregnancy, was elucidated. Population differences in

immunocompetence were observed, therefore population/geographic location may exert a

larger effect than body condition or pregnancy status (Downs et al. 2018).

2.4.5 Allostatic Load

Allostatic load (stress-induced physiological damage), used in human medicine and

increasingly in veterinary medicine, is a comprehensive way to evaluate stress in wildlife using

neuroendocrine, cardiovascular, metabolic, and immune biomarkers (Edes et al. 2018).

Glucocorticoid activity is an example of one useful biomarker used in an allostatic load index;

other biomarkers may include inflammatory markers, physioactive proteins, and

neurotransmitter levels measured in a variety of tissues (e.g. serum, urine, saliva, hair, or feces)

collected at the same time point. In an allostatic load index, each biomarker is commonly

39

assessed on a quartile scale and ideally should include biomarkers from different body systems

to reflect both acute and chronic stress exposure (Edes et al. 2018). To date, no studies of

allostatic load have been carried out on wild sheep.

2.4.6 Genomics

Genomics, the measurement expression of specific segments of the genome, can be used

to detect pathogens and understand pathogen transmission, host susceptibility, and impacts on

wildlife populations as well as host relatedness and fitness-related characteristics (Blanchong et

al. 2016; Bowen et al. 2019; Sim & Coltman 2019). Detection of pathogen gene sequences using

polymerase chain reaction (PCR) techniques is widely employed in domestic and wild animal

disease surveillance, and quantification of pathogen load is often possible. This, however,

requires knowing which pathogens are likely present. Detection of genetic structure of pathogens

can be used to determine transmission pathways, such as when an insect vector is required.

Genomics will allow for screening individuals and populations for pathogen presence and

exposure as well as immunocompetence and disease risk (Blanchong et al. 2016).

Gene transcription analysis is increasingly being used in wildlife health investigations,

changing the focus of wildlife herd health assessment from disease presence or absence to host

population susceptibility and function. The number of RNA replications of a specific segment of

DNA, or gene can be measured using molecular techniques, such as PCR. Gene transcription

patterns are physiologically changed in response to environmental conditions and an organism’s

pathophysiological state, and therefore, can be examined as the first indicator of health

disturbance (Bowen et al. 2020). By examining gene transcription patterns, the role of extrinsic

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and intrinsic factors as they contribute to animal health and disease susceptibility can be

examined (Bowen et al. 2020). A bighorn sheep-specific gene transcription assay has been

developed by Bowen et al. (2020) to examine the link between transcription and antibody levels

to current and historical infections in four populations of free-ranging desert bighorn sheep in

Nevada. The fourteen genes examined were categorized as reference, general immune function,

immune system transcription factors, detoxification, muscle metabolism, apoptosis, general

stress, and anti-viral. To date, no other gene transcription studies are published on wild sheep.

Metagenomic sequencing, a relatively new approach, allows for detection of all microbes

in a sample of tissue or environmental substrate using amplification of known gene targets and

comparison to a reference library; for example, all known bacterial species in a sample of rumen

content may be identified using metagenomic sequencing. Metagenomic evaluation of biological

samples generate a huge amount of data (Blanchong et al. 2016). There exists a gap between

generation of genomic data, analysis of results, and application of findings into management

actions (Fitak et al. 2019). It is recommended to develop standardized ‘best practices’ for

genomic research in order to facilitate comparison between studies, reproducibility, and easier

adoption of techniques for new researchers (Fitak et al. 2019).

Genomic research can aid in establishing a baseline of pathogen presence in a population

and determine if pathogens are endemic in those populations or if detection represents a novel

introduction (Forde et al. 2016). During a disease outbreak event in muskox in the Canadian

Arctic, Forde et al. (2016) used genomics to quantify molecular diversity and evolutionary

relationships of a bacterial pathogen, Erysipelothrix rhusiopathiae. Pathogen genetic diversity

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analysis explained heterogeneous sources of infection and suggested that E. rhusiopathiae may

be endemic in certain arctic wildlife populations.

Detection of genetic structure within a population to inform the preservation of species

genetic diversity in wildlife species is another important application of genomic technology. This

has application for active population management, for example with the translocation or

breeding of individuals and herds, and preservation of genetic loci and host fitness. One risk from

the introduction of individuals and their new genes to a population is the creation of ‘outbreeding

depression’ and subsequent reduction of fitness. A study by Miller et al. (2012a) related genomic

data from a herd of bighorn sheep with decades of observational data to identity, age, lifespan,

and lifetime productivity of individuals. This data was used to inform a successful ‘genetic rescue’

management intervention to reduce inbreeding depression in a small herd in Montana (Miller et

al. 2012a). Sim and Coltman (2019) used genomic technology to investigate relatedness and the

heritability of fitness-related traits in Dall’s sheep, using horn growth and size as examples. They

found that horn dimensions are moderately heritable (0.33 to 0.36) and detected two genes

related to horn morphology in the thinhorn sheep genome.

2.4.7 Body Condition - Marrow Fat

Mech and Giudice (1985) caution the use of marrow fat as an indicator of body condition,

other than emaciation, as marrow fat is the last fat depot used for energy metabolism, after

subcutaneous, omental, pericardial and perirenal stores; therefore, an animal may have lost a

considerable percentage of its total body fat and still have substantial marrow deposits. However,

42

animals that have serous atrophy of fat in their bone marrow would be expected to be in very

poor, if not emaciated body condition.

2.4.8 Reproduction

Breeding in wild sheep generally occurs in late November to early December with lambing

in late April to early May, although some variation can occur with latitude. Thinhorn sheep are

capital breeders and produce only one lamb per year (Downs et al. 2018). Females in good body

condition are more likely to conceive and maintain pregnancy (Downs et al. 2018). Age of

reproductive maturity is thought to be 2½ years of age for thinhorn ewes, however, records of

pregnant yearling ewes exist from both captive and free-ranging Dall’s sheep ewes. Lambing at 2

years of age has been reported in both increasing and declining populations (Simmons et al.

1984). Lambs from younger ewes tend to be born later in the season (Hoefs & Nowlan, 1993).

There is a case report of a captive 8-month-old ewe lamb becoming pregnant and giving birth to

a viable lamb at just over a year of age Hoefs and Nowlan (1993). Simmons et al. (1984) reported

a pregnancy rate of 77.7% for Dall’s ewes in the Mackenzie Mountains, Northwest Territories.

Juvenile survival is the best demographic indication of health in bighorn populations

(Cassirer et al. 2017) under stable environmental conditions and challenge from predators.

Suppression of reproduction is a non-consumptive effect of predation pressure (Dulude-de Broin

et al. 2020). High exposure to predators leads to prolonged activation of the HPA axis, which in

turn leads to energy diverted from physiological functions such as reproduction, growth, and

immunity. Dulude-de Broin et al. (2020) found a 30% decrease in the proportion of reproductive

female mountain goats in years with higher than average FGM concentrations in the population.

43

In female mountain goats, this strategy maximizes lifetime reproductive output through favoring

their own fitness and increasing longevity, despite the trade-off on annual reproduction (Dulude-

de Broin et al. 2020). Whether the same holds true in thinhorn sheep populations remains to be

determined.

2.4.9 Horn growth

Rapid horn growth in Dall’s sheep from Yukon is associated with reduced longevity. This

pattern occurs in both hunter-killed and natural-mortality samples (Loehr et al. 2006).

Understandably, males that have more rapidly growing horns will be hunted at a younger age as

hunting regulations are based on horn sizes over a specific threshold; however, this pattern in

natural mortalities suggests a physiological cost associated with horn growth rate. Growth rate

is associated with forage availability and quality, but a trade-off exists between forage efficiency

and predation risk (Loehr et al. 2006).

2.5 Status and Management of Thinhorn Sheep Populations

The jurisdictions where thinhorn sheep are found (Alaska, British Columbia, and Yukon

and Northwest Territories) are developing specific management plans (Ryder 2017). Thinhorn

sheep management goals outlined by the Wild Sheep Foundation (Hurley et al. 2018) and Wild

Sheep Working Group (Jex et al. 2016) include: development of regional management plans that

include habitat protection; separation of wild sheep and domestic small ruminants, control of

industrial and agricultural activity; application of standardized health sampling to expand

knowledge of the current status of populations; and public outreach.

44

Conservation challenges facing wild sheep are associated with habitat, disease, predation,

population management, organizational challenges, and climate change (Jex et al. 2016). Suzuki

& Parker (2016) modeled the available preferred habitat within different management zones in

the Muskwa-Kechika and found that commercial mineral exploration and exploitation has the

potential to significantly impact Stone’s sheep populations in that area. Habitat degradation

associated with climate change may decrease nutritional status, limit gene flow, and increase the

opportunity for pathogen transfer between wild and domestic species (Acevedo-Whitehouse &

Duffus 2009). Climate change has been identified as an important factor affecting Dall’s sheep

populations in Alaska (Thinhorn Summit, 2017). Northwestern North America experiences a high

degree of seasonal, year-to-year, and decadal climatic variability (Pojal 2009). Winters with

higher snowfall and greater snow depths are followed by reduced lamb productivity the following

spring, due to delayed green-up and poor ewe body condition (Burles & Hoefs 1984). Large

population declines have been attributed to winter icing events (B. Jex pers. comm.).

A thorough risk assessment of disease transmission from domestic small ruminants and

camelids to wild sheep identified a low risk of exposure but high-risk impacts of transmission in

the Yukon Territory. Similar risk has been identified for Alaska and Northwest Territories due to

a low degree of range overlap (Ryder 2017; Garde et al 2005). Potential reservoirs of pneumonia-

causing disease are other wild ungulate species (including muskox, although there is not currently

range overlap with wild sheep) and domestic cattle, sheep, goats, and camelids. A search of the

Canadian Wildlife Health Cooperative (CWHC) database revealed 12 cases of pneumonia in

submitted thinhorn sheep from Yukon and Northwest Territories (NWT) since 1996 (CWHC 2016).

45

Thinhorn sheep in NWT are found in the Richardson and Mackenzie Mountain ranges. As

of July 1st, 2019, NWT has introduced ‘Phase 2 Regulations’ under the NWT Wildlife Act, which

will eliminate llamas, alpacas, domestic sheep, and domestic goats from mountain areas west of

the Mackenzie River. While there is little agriculture in the area, this action removes the risk to

thinhorns. Yukon implemented the Sheep and Goat Control Order in January 2020

(https://yukon.ca/sites/yukon.ca/files/emr-sheep-goat-control-order-fact-sheet.pdf) to reduce

the risk of contact with domestic small ruminants to wild sheep and mountain goats.

The thinhorn population in BC has been relatively stable in BC since the mid-1980s (Kuzyk

et al. 2012), however, localized decline has been observed in some populations including the

Muskwa-Kechika (Demarchi & Hartwig 2004). Wild Sheep Herd Health Monitoring

Recommendations have been developed by the Western Association of Fish & Wildlife Agencies

Wildlife Health Committee (WAFWA 2009). The recommendations state, “thinhorn sheep (O.

dalli) are susceptible to respiratory and other pathogens that cause epidemics in bighorns, and

their populations also likely would be harmed by disease introductions”; therefore, assessment

and monitoring of herd health is essential (WAFWA 2009). Monitoring should include population

and herd demographics including survival of all age classes and evidence of clinical and subclinical

disease.

46

CHAPTER 3 – METHODS

3.1 Study Areas and Populations

We conducted live-capture and sampling of thinhorn sheep in five study areas (Dome,

Cassiar, Talkeetna, Chugach, Williston; Appendix A). All populations of thinhorn sheep included

in this study are naturally occurring. The study areas are geographically separated from each

other, with no known connectivity between herds, based on rut and winter ranges. The closest

capture sites are separated by less than 200 kilometres. Study areas were selected by capitalizing

on concurrent capture for radiocollaring projects or where a health concern had been raised.

Study areas are characterized by steep rugged alpine terrain, with windswept plateaus and lower

elevation areas with substantial snow depth in winter, sparse vegetation in the alpine and lower

elevation coniferous forests.

Dome Mountain (Dome) is near Dease Lake in Management Unit (MU) 6-23, Skeena

region of northwest BC. The Dome herd is a unit of the larger Cry Lake population. This unit was

selected for collaring to assess habitat use near a mine site in light of an increase in industrial

development and seasonal movement across a decades old mining access road. An increase in

road activity was postulated to cause an obstacle to sheep movement, increasing the potential

for predation by slowing sheep travel across the valley, and risk of vehicle collision. Capture,

sampling and radiocollaring were initially conducted in February 2017. Health samples were

collected as a standard permit requirement. In 2018, 153 Stone’s sheep were counted in the

Dome Mountain population (Table 1).

47

Reduced recruitment and subsequent population decline (B. Jex, pers. comm.) in the

Cassiar Mountains (Cassiar) near the townships of Jade City and Fort Good Hope in MU 6-24,

Skeena Region, prompted a research project with captures occurring in 2018 and 2019. This

Stone’s sheep population is in close proximity to a defunct asbestos mine and abandoned town

site. Anecdotes of domestic small ruminants (goats) free-ranging from one community in this

area during the period of decline highlighted the need for a health assessment. GPS collars were

applied for a habitat use component of the research. During the last partial inventory in 2018,

114 sheep were observed, including only 19 lambs, which is consistent with our understanding

of the declining trend of this population (B. Jex pers. comm.).

Capture and sampling of Dall’s sheep in Alaska (Talkeetna and Chugach) was prompted

by the recent discovery of M. ovi in wild ungulate populations (Highland et al. 2018) in the state

and a need for comprehensive research to determine the effects, extent and severity of infection.

The Talkeetna Mountains (Game Management Units (GMU) 13A and 14A) and Chugach

Mountains (GMU 14C) in southcentral Alaska were selected due to detection of M. ovi in rams

harvested in the 2017 season. Collars were deployed to monitor movement and to facilitate

recapture annually for 5 years to examine the transmission of M. ovi between animals and its

effects on the population (Alaska Department of Fish and Game).

Stone’s sheep were captured from the Dunlevy and Schooler herds on the north side of

the Peace Arm of the Williston Reservoir, MU 7-36, Peace Region, BC in 2020 (Williston). This

area is the most southern extent of Stone’s sheep range. This subpopulation was extensively

studied from 1999 through 2005, but recent concerns around health impacts of potential contact

with domestic livestock, implementation of a browse-reduction program using domestic small

48

ruminants south of the reservoir, and an expanding range of Rocky Mountain elk prompted the

current investigation. These herds were inventoried in 2000 and 2002, the minimum population

size was estimated to be 117 and 92 respectively (Wood et al. 2010). In 2019, an inventory of the

Dunlevy herd estimated a minimum of 50 individuals.

Thinhorn sheep typically use high-elevation, steep, rugged, windswept alpine habitats in

the winter. The Williston herd is separated into two subgroups, one which occupies the higher

elevation habitat, and the other which overwinters around low-elevation bedrock outcrops along

the north shore of the Williston Lake.

Table 1. Thinhorn sheep population size estimates and number of individuals health sampled in the areas included in this study.

Study Area Management Area Estimated population size (inventory year)

Number sampled (% of population)

Domea MU 6-23 153 (2018) 13 (8.5%) Cassiarb MU 6-24 165 (2018) 25 (15.2%) Williston MU 7-36 150 (2005) 8 (5.3%) Talkeetna GMU 13A/14A 1048 (2019) 45 (4.3%) Chugach GMU 14C 1208 (2019) 26 (2.2%)

a The Dome population is part of the larger Cry lake population and is contiguous with the Three Sisters population. Inventory conducted by BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development (FLNRORD) b The Cassiar population was historically estimated at 250 individuals. A survey of approximately 70% of the range in 2018 led to the estimate given in the table. Inventory conducted by BC Ministry of FLNRORD.

Hunter-harvest samples were obtained from Stone’s rams harvested in the Skeena (MU

6-23 and 6-24) and Peace (MU 7-52) regions of BC (Appendix A). These areas account for

approximately 20 - 25% of the total Stone’s harvest in BC annually. The mean harvest from 1982

through 2017 was 12.7 rams in MU 6-23, 11.9 in MU 6-24, and 52.8 in MU 7-52; in 2017, 11, 13,

and 49 rams were harvested in those respective MUs. Only rams harvested in Tahltan First Nation

traditional territory are subject to inclusion in our hunter-harvest health sampling program

49

through collaboration with the Tahltan Guide and Outfitters Association. The territory

encompasses parts of all three MUs. Additionally, there is a small indigenous ewe harvest from

the Cassiar herd.

3.2.1 Live-capture Sample Collection

We followed protocols recommended by the Western Association of Fish and Wildlife

Agencies (WAFWA) Wild Sheep Working Group (WAFWA 2015) for capture, handling, and

sampling of thinhorn sheep. Methods were approved by University of Calgary Animal Care

Committee (ACC Certification AC19-0054) and the Ministry of Forests, Lands, Natural Resource

Operations, and Rural Development (Wildlife Act Permits; SM16-244528 and FJ19-485655). Free-

ranging thinhorn sheep were captured using aerial netgunning from rotary aircraft in late winter

in open alpine terrain. In BC, only mature Stone’s ewes were targeted for live-capture, however

immature rams captured incidentally were sampled as well. A mixture of mature ewes and rams

were captured in Alaska.

Sheep were restrained with leg hobbles and a blindfold without sedatives or

tranquillizers. Helicopters hazed animals into appropriate terrain once sighted and close chases

were limited to two minutes or less. Sheep were driven uphill or into deep snow in order to

minimize stress and the risk of capture-related injury. Captures were all performed during winter

months and occurred at temperatures from -10 to -25 C. Once nets were removed and restraints

applied, sheep were restrained for up to 30 minutes for sample and data collection and

application of collars and other transmitting devices. Rectal body temperature was always

50

recorded at the beginning of handling and during handling to monitor for hyperthermia. Sheep

were released by removing hobbles and blindfold.

We used standardized sampling kits from the BC Wildlife Health Program for collection of

biological samples in the field. The kits were adapted slightly between years (e.g. additional nasal

swabs or blood collection tubes added) based on the findings of other researchers and novel

diagnostic techniques when available. We collected a minimum of 30 millilitres of blood from the

jugular vein. Blood was immediately divided amongst vacuum-sealed blood tubes to extract

serum (BD Vacutainer® SST; BD Vacutainer® Trace Element Serum), plasma, buffy coat (BD

Vacutainer® EDTA, New Jersey, USA), and ribonucleic acid (RNA; Pangea Laboratory, DNA/RNA

ShieldTM). We collected nasal swab samples by gently inserting a dry polyester swab (BBL

CultureSwab EZ) approximately 6-10 centimetres into the nasal cavity and rotating it gently to

contact as much as the nasal mucosa as possible. Nasal swabs were stored dry (BC and Alaska)

and in various transport/growth media (Alaska; Hardy Diagnostic Mycoplasma Broth, Hardy

Diagnostics, Santa Maria, CA, USA and Universal Transport Media, BD, Franklin Lakes, NJ, USA )

depending on the requirements of the laboratory performing the testing. Oropharyngeal swab

samples were collected using a sterile polyester swab and were stored chilled in Amies transport

media (Copan Diagnostics, CA, USA). A sliding mouth gag (Panhandle Fab Inc. Nebraska, USA) was

used to elevate the mandible and immobilize the tongue to allow visualization and insertion of

the swab into the tonsillar crypts. Ear swab samples were collected by inserting a dry polyester

swab (Puritan® 25-806 1PD Guilford, ME, USA) into the external ear canal and storing in a small

vial. We used 6 millimetre biopsy punches (AcuPunch, Acuderm Inc. FL, USA) to collect a full-

thickness pinna tissue sample. Hair (minimum of 100 guard hairs) was collected by plucking from

51

the dorsal midline at the level of the shoulders and put into a non-manila envelope. Feces (20 –

30 pellets if possible) were collected carefully per rectum, placed in a whirlpak and frozen.

A physical exam was carried out on every animal with notation of any injuries or

abnormalities. We categorized body condition of live-captured animals based on subcutaneous

fat by palpation of key boney prominences (lumbar and caudal spinal processes, pelvis, scapular

spine, and ribs) on a 5-point scale (0 = emaciated to 4 = excellent). This method was developed

for domestic sheep, bighorn sheep, and caribou (Beef and Lamb NZ 2019; CARMA 2008; WAFWA

2009). We recorded lactation status, dental condition (wear of incisors), and haircoat condition.

Morphometric measurements, including total body length, neck circumference, chest

circumference, and tarsus length were recorded. We determined age by counting horn annuli

and examination of dentition for incisor eruption. For concurrent projects, a GPS radio collar

(various brands) was fitted and the animal was identified with a permanent uniquely numbered

ear tag, specific for the jurisdiction. For Stone’s sheep in the Cassiar Range, we used

transabdominal ultrasound (Ibex Pro, E.I. Medical Imaging, Loveland, California, USA) to

determine pregnancy status and a vaginal implant transmitter (VIT) was inserted to the level of

the cervix of pregnant ewes. In Alaska, we measured rump fat thickness between the head of the

tail and the greater trochanter using ultrasound.

We processed biological samples to a state in which they could be stored in the field lab

daily. Vacutainers were centrifuged at 2500 rpm for 12 minutes. Serum, plasma, and buffy coat

were transferred to labelled 2 mL cryovials (VWR®, Pennsylvania, USA) using disposable pipettes

and frozen immediately at -20 oC. Tissue biopsies and hair samples were air dried. We stored one

pharyngeal swab chilled at 4oC and the other we plated on a Columbia Blood Agar plate by rolling

52

the swab over one third of the plate. Disposable inoculation loops (Copan Diagnostics Inc, CA,

USA) were used to spread the sample over the remaining two thirds. Plates were incubated for

24 hours 37o C in an anoxic chamber (Sigma-Aldrich, 28029, MO, USA; AnaeroPack-CO2,

Mitsubishi Gas Chemical Co. Inc. Japan) in a mobile incubator (Appendix D). After 24 hours, a

sterile polyester swab was used to collect a sample of the primary growth region on the culture

plate and the plate was returned to the incubator for a further 24 hours. Both swabs in Amies

media and the culture plate were submitted to the laboratory. Feces were separated into two

Whirl-Paks (Nasco, WI, USA) and stored at -200C.

Repeat sampling was conducted in the Cassiar (two consecutive years sampling different

animals from the same herd), and in the Chugach and Talkeetna (repeat sampling of the same

individuals over multiple years) but not the Dome or Williston study areas.

3.2.2 Hunter-harvest Sample Collection

Health samples were collected from Stone’s rams harvested in the Skeena (MU 6-23 and

6-24) and Peace (MU 7-52) regions between August 1st and October 15th, 2016 through 2019. The

Tahltan Guide and Outfitters Association (TGOA), in collaboration with the BC Wildlife Health

Program initiated a local guide/outfitting industry-based health sampling program in 2016 in

response to concerns from local outfitters, indigenous communities, and wildlife managers that

wildlife populations were declining. All ungulate species hunted within Tahltan traditional

territory are included in the program. This provided the opportunity to collect a standardized set

of biological samples from hunter-harvested Stone’s sheep rams. Workshops to discuss the

53

concerns and to train outfitters and guides in sampling methods were held and standardized

sample-collection kits were distributed to outfitters and hunters through the TGOA.

Kit return rate was variable between years. Approximately 334-338 Stone’s rams are

harvested in BC each year, with between 60 and 81 harvested annually between 2007 and 2017

in this study area (Jex pers. comm.). In 2016, 23 kits were returned, 20 in 2017, 9 in 2018, and 8

in 2019. Sample and data collection require additional effort from the guide outfitter or hunter;

therefore, kit return is dependent on the value the individual places on the information. The

completeness of sample collection and the quality of samples were also variable. Data sheets

were rarely completely filled out. The exact location of the ram at harvest was requested, but

often not provided by the hunter. Wildlife Management Units provide a general herd location.

Despite the low numbers of kit returns, this sampling program was considered highly successful

compared to similar efforts in BC.

Hunter-collected samples include blood soaked Nobuto strips, hair collected from the

dorsal midline at the level of the shoulders, feces from the distal colon, whole left hind

metatarsus, mandible, ear tip, liver, kidney, muscle, and tissue from any abnormal findings. Hair

and ear tips were air dried at ambient temperature, all other samples were frozen within a few

days of collection and stored at -20oC. A data-sheet was also included in the kits to record details

about the animal, location, and hunt. Hunter-collected samples from sheep are all from mature

rams that meet the legal size and age requirement in the fall (August 1 – October 15), in line with

the non-Indigenous legal hunt regulations.

Compulsory inspection of hunted thinhorn sheep skulls with horns is a requirement under

the BC Wildlife Act (BC Government Hunting & Trapping Regulations 2018). This affords an

54

opportunity for an inspector to collect nasal swab samples, genetic samples, and in some cases

hair samples from hunter-harvested Stone’s sheep rams throughout their range.

3.3 Laboratory Methods

Biological samples were tested by commercial and academic laboratories based on the

laboratory’s established record of health and disease testing in wildlife (Appendix B). Samples

from hunter-harvested thinhorn sheep rams submitted for many of the same health tests as for

live-captured sheep, making them valuable for increasing the power to make population-level

inferences. Additional validation testing was possible using the tissues collected by hunters,

including liver and kidney trace mineral and heavy metal levels, bone marrow analysis, and jaw

and dentition assessment.

3.3.1 Serology

We used serum and plasma from live-capture sheep, and blood from hunter-harvested

sheep dried on Nobuto strips to test for antibodies to specific viral and bacterial pathogens using

capture enzyme-linked immunoassay (cELISA), virus neutralization (VN), and indirect

immunofluorescence assay (IFA) techniques (Appendix B). Eluates were made from dried blood

by soaking each Nobuto strip in 400 microlitres of Dulbecco’s Phosphate Buffered Saline with

penicillin and streptomycin for 24 hours. Approximately 4mL of dilute serum (eluate) was

extracted from each animal. The eluate was stored at -20oC. Serum ELISA has not been validated

for thinhorn eluate samples, however, the concurrent live-capture portion of this study allowed

for collection of paired fresh serum and eluate for comparison (n=5).

55

Exposure to the paramyxoviruses parainfluenza-3 (PI3; VN) and bovine respiratory

syncytial virus (BRSV; VN), the pestivirus bovine viral diarrhoea virus (BVDV; VN),

alphaherpesvirus infectious bovine rhinotracheitis (IBR; VN), lentivirus ovine progressive

pneumonia (OPP; cELISA), gammaherpesvirus malignant catarrhal fever (MCF; cELISA) was

assessed for thinhorn sheep in BC and Alaska. Additional serological testing of Alaska’s Dall’s

sheep titres included Brucella ovis (B. ovis; ELISA), Toxoplasma gondii (T. gondii: IFA),

Mycobacterium avium spp. Paratuberculosis (MAP; ELISA).

Antibodies to M. ovi were tested for in serum and blood eluates using cELISA (SOP-SERO-

45) at Washington Animal Disease Diagnostic Lab (WADDL; Pullman, WA, USA). This test has been

validated for bighorn and domestic sheep serum only. The cut-off (50%) used is consistent with

previous exposure or current infection with M. ovi.

3.3.2 Mycoplasma ovipneumoniae Detection

Nasal swab samples were collected from all live-captured and hunter-harvested thinhorn

sheep in this study and submitted to diagnostic and research laboratories. Polymerase chain

reaction (PCR) was used to determine the presence of specific segments of the M. ovi genome.

The PCR primers and assays were not the same across laboratories. All BC samples were tested

at the Animal Health Centre (AHC), the provincial diagnostic laboratory (Abbotsford, BC, Canada).

We collected multiple nasal swab samples from live-captured Dall’s sheep in Alaska.

Samples from the same individual were tested at the AHC, Wyoming Veterinary Laboratory (WVL;

Laramie, WY, USA), Washington Animal Disease Diagnostic Laboratory (WADDL; Pullman, WA,

USA), and US Department of Agriculture Animal Disease Research Unit (USDA-ADRU; Pullman,

56

WA, USA). PCR techniques for Mycoplasma spp. are used to isolate sequences associated with

the 16S variable region of the rRNA gene. This is a highly conserved region and is used to

differentiate between species of Mycoplasma. WADDL uses a Universal Mycoplasma assay which

uses 717 base pairs. An LM40 assay is used at WVL and USDA-ADRU (Appendix A).

3.3.3 Tonsil Swab Bacteriology

Each live-captured animal had two swab samples collected from the oropharynx. Swabs

were inserted into the tonsillar crypts or wiped over the mucosa in the area of the tonsils. We

used polyester swabs (Copan Transystem, Murrieta, California, USA) and stored them in Amies

media for the up to 8 hours. Aerobic culture of each swab was used to identify if the species of

Pasteurellaceae were present in the upper respiratory tract. Columbia blood agar plates (CBA)

with 5% sheep blood (Hardy Diagnostics #A16, Santa Maria, CA, USA) were inoculated and

incubated at 37oC in 5% CO2 for 48 hours. A subsample was collected from the primary growth

region at 24 hours, placed in Amies transport media, and chilled. Bacterial colonies were visually

identified to species where possible at the Animal Health Centre (AHC; Abbotsford, BC, Canada).

3.3.4 Fecal Parasitology

Feces were collected from both live and hunter harvested sheep when possible but not

for every animal. Internal parasite burden was determined using fecal float (Wisconsin double

fecal floatation test; MacMaster fecal floatation test), fecal sedimentation (modified Baermann

beaker test) in-house and at Canadian Wildlife Health Cooperative laboratories (CWHC; Calgary,

AB; Saskatoon, SK, Canada). Helminth eggs were identified to the genus. Protostrongylid

57

nematode dorsal-spined larvae (DSLs) were identified to species by PCR and DNA sequencing for

the samples analyzed at CWHC. Genetic identification of lungworm species was performed in

2017 only.

3.3.5 Hair Cortisol Concentration

A minimum of 50 milligrams of hair, primarily guard hairs, was used for hair cortisol

concentration (HCC) testing at the Toxicology Centre (University of Saskatchewan, SK, Canada).

After collection, hair was placed in paper envelopes, dried and stored at room temperature. At

the lab, surface contamination was removed by washing five times with 0.04mL methanol/mg

hair for 3 min/wash. After air-drying for 3 days, hair was ground to a fine powder and steroids

were extracted using 0.5mL methanol rotated for 24 hours. Steroid supernatant was

reconstituted with phosphate buffer. Aliquots were tested using a commercially available

enzyme-linked immunoassay kit (methodology previously described by Macbeth et al. 2010).

Cortisol concentration was reported in picogram per milligram (pg/mg). In 2019 and 2020, hair

shaft colour and hair type also were reported. However, as this data was not available for all

years of the present study, the colour and hair type were excluded from analyses.

3.3.6 Fecal Glucocorticoid Metabolites

The two fecal glucocorticoid metabolites (FGM) commonly examined are cortisol and

corticosterone. We measured cortisol levels because it is the dominant glucocorticoid in this

species (Koren et al. 2012). Samples were stored at -20oC and submitted frozen to the

Endocrinology Laboratory of the Reproductive Physiology Unit at the Toronto Zoo (Scarborough,

58

Ontario, Canada). FGM concentration was determined with enzyme immunoassay (EIA)

techniques using cortisol antiserum (methods described in Kummrow et al. 2011). FGM levels are

reported as mass/gram of dry feces.

3.3.7 Serum Trace Minerals

We collected blood into royal blue-topped trace mineral tubes with clot activator (BD

Vacutainer, NJ, USA), and sent serum frozen for trace mineral analysis (Guelph Animal Health

Laboratory, Guelph, ON, Canada). Trace mineral levels in serum were quantified using inductively

couple mass spectrometry (ICP-MS, CHEM-162). Serum is diluted with a combination of 1% nitric

acid, 1% isopropanol, 0.01% TrixonX-100, and 0.01% EDTA solution at a ratio of 1:20 (methods

adapted by Ross Wenzel, Senior Hospital Scientist, Trace Elements Laboratory, Pacific Laboratory

Medicine Services, Royal North Shore Hospital, St. Leonards, NSW, Australia).

3.3.8 Tissue Mineral and Heavy Metals

Sample processing involved removing all external surfaces of the tissue with a new sterile

scalpel blade in order to reduce the likelihood of metal contamination. Samples were weighed

and refrozen for shipment to ALS Global Laboratory (Vancouver, BC). Samples were analyzed

following methods described in BC Environmental Laboratory Manual Metals in Animal Tissue

and Vegetation (Biota) – Prescriptive; homogenization and subsampling are followed with

hotblock digestion with nitric and hydrochloric acids and hydrogen peroxide. Instrumental

analysis of minerals and metals is by collision cell ICP-MS (method modified from EPA Method

6020B). Quality control measures include running samples in duplicates with less than 20%

59

deviation accepted, using laboratory control samples and blanks as positive and negative controls

respectively, and calibration with Certified Reference Materials (CRMs; National Research

Council DORM-4 “Fish Protein Certified Reference Material for Trace Metals”). This method

provides a conservative estimate of bio-available metals. The water content of the sample is

determined by drying the sample at 105oC for a minimum of six hours.

3.3.9 Marrow Fat

Marrow was extracted from long bones collected from live capture mortalities or hunter

collected bone by physically fracturing the metatarsal bone. Marrow was weighed to the nearest

0.01g and placed in heat-resistant vessels. The sample was either airdried or placed in an oven

at 85oC. Samples were weighed every 3 days until the mass did not change. Marrow fat

percentage was calculated as the mass of sample remaining after dehydrating relative to the

initial mass. This protocol adapted from one developed for caribou (Circumarctic Rangifer

Monitoring and Assessment Network (CARMA) 2008) and was performed in house (BC Wildlife

Health Program, Nanaimo, BC, Canada).

3.3.10 Pregnancy

Pregnancy was determined by using the commercially available BioPRYN Flex assay

(analyzed by Herd Health Diagnostics (Pullman, WA, USA) which measures pregnancy-specific

protein B (PSPB) concentrations through ELISA and measurement of optical density. It is validated

in domestic sheep and goats (positive predictive value = 93-95%, negative predictive value =

60

99.9%; BioPRYN 2019) as well as mountain goats (Houston et al. 1986) and other non-domestic

ungulates (Love et al. 2017).

3.3.11 Morphometric Measurements

We collected morphometric measurements while handling live sheep in a manner

consistent within but not between jurisdictions, following methods employed in previous

thinhorn research (Lohuis pers. comm.). For Alaskan Dall’s sheep we measured total body length,

metatarsus length, and jaw length to the nearest 0.5cm and body mass to the nearest 0.1kg. In

BC, we measured total body length, neck circumference and chest circumference. Metatarsal

measurements are used for evaluating body size relative to age as metric for growth. Body mass

was estimated for Stone’s sheep using chest circumference (mass in kg = -37.5 + 0.88(chest

circumference); Bunnell & Seip 1984).

Metatarsal mass, length, diameter, and circumference were measured for hunter-

harvested Stone’s rams. This data will be used to evaluate patterns of body size across years.

3.3.12 Mortality Investigations

All live-sheep sampled in this study were fitted with a GPS-collar (ATS G2110E2 Iridium,

Advanced Technology Systems, Isanti, Minnesota, USA) capable of detecting movement and

transmitting an alert when mortality was suspected (no movement within a 6 hour period).

Whenever possible, carcasses were recovered for post-mortem examination, and the site of the

mortality investigated thoroughly, for identification of a cause of death or predator species. A

thorough carcass examination was performed by a veterinarian following a standardized

61

mortality investigation protocol. Tissue samples were collected as available and submitted to

AHC for histopathology and further diagnostic work up as indicated.

3.4 Data Analysis

Statistical analysis of data is primarily descriptive in this study. Normality was assessed using

Shapiro-Wilks tests for continuous data (P < 0.05 = non-normal distribution). Q-q plots were

visually assessed for a linear relationship to confirm normality. Means were reported for normal

data, otherwise medians and interquartile ranges are reported. If distributions were not normal,

they were log-transformed to normalize the variance, where possible, prior to determining

relationships with other variables. T-tests and analysis of variance (ANOVA) were used for single

and multiple comparisons of means, respectively, for normally distributed data with post-hoc

Tukey tests. Mann-Whitney U or Kruskall-Wallis tests for data without a normal distribution. All

analyses were performed using the statistical program R version 3.2.0 (R Core Team 2015).

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CHAPTER 4 – RESULTS

4.1 Serology

Serum antibodies to ovine herpesvirus-2 (OvHV-2, MCF) were found in 89.5% of Stone’s

sheep tested (n = 34/38). Antibodies to the paramyxovirus, BRSV, were found in two Stone’s ewes

from the Dome study area. All Dall’s sheep were seropositive for T. gondii in 2019 (n = 67/67).

Seroprevalence decreased to 73.9% in the Talkeetna study area in 2020 (Table 3). A similar

prevalence of BRSV seropositive sheep was found in herds in BC and Alaska (Tables 2 and 3). No

clinical signs were observed in affected sheep. Antibodies to M. ovi were detected in two Dall’s

sheep from the Talkeetna study area and two samples returned indeterminant results. No

evidence of exposure was found in Stone’s sheep.

The overall seroprevalence of OPP is 5.7% (95% CI = 0.81, 10.18), PI3 is 9.9% (95% CI = 3.8,

16), BRSV is 15.4% (95% CI = 0, 86.1), and T. gondii is 93.41% (95% CI = 44.8, 100).

Table 2. Seroprevalence (Serop.) and 95% confidence interval (95% CI) expressed as percentages for selected respiratory and systemic pathogens: bovine viral respiratory virus (BRSV), ovine progressive pneumonia (OPP), parainfluenza virus-3 (PI3), infectious bovine rhinotracheitis (IBR), malignant catarrhal fever (MCF) and Mycoplasma ovipneumoniae (M. ovi), in live-captured adult female and immature male Stone’s sheep in the Skeena and Peace Regions of British Columbia, 2017 – 2020. The Dome herd was sampled in 2017, Cassiar in 2018 and 2019, and Williston in 2020.

Pathogen Dome (n = 13) Cassiar (n =25) Williston (n = 8) Serop. 95% CI Serop. 95% CI Serop. 95% CI

BRSV 15.4 (0, 35) 0 - 0 - OPP 0 - 0 - 0 - PI3 0 - 0 - 0 - IBR 0 - 0 - 0 - MCF 84.6 (65, 100) 92 (81.4, 100) - - M. ovi 0 - - 0 -

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Table 3. Seroprevalence (Serop.) and 95% confidence interval (95% CI) expressed as a percentage for selected respiratory and systemic pathogens: bovine viral respiratory virus (BRSV), ovine progressive pneumonia (OPP), parainfluenza virus-3 (PI3), infectious bovine rhinotracheitis (IBR), malignant catarrhal fever (MCF), Mycoplasma ovipneumoniae (M. ovi), Mycobacterium avium spp. paratuberculosis (MAP, Johne’s), Brucella ovis (B. ovis), and Toxoplasma gondii (T. gondii) in live-captured adult female and male Dall’s sheep in the Talkeetna and Chugach mountains of southcentral Alaska.

Pathogen Talkeetna (n = 53) Chugach (n = 23)

Serop. (%) 95% CI Serop. (%) 95% CI BRSV 16.8 (0.7, 24.9) 13 (0, 26.8) OPP 0 0 - PI3 13.2 (5.2, 21.3) 0 - IBR 0 0 - M. ovi 7.4 (0.1, 13.6) 0 - MAP 0 - 0 - BVD 0 - 0 - B. ovis 0 - 0 - T. gondii 91.2 (84.4, 98) 100 (100, 100)

4.2 Mycoplasma ovipneumoniae

Mycoplasma ovipneumoniae was detected in 3 (6.4%) Dall’s sheep in Alaska from the

Talkeetna study area in 2019 using PCR of nasal swab samples at WADDL (Table 4). The two

‘indeterminant’ results from the Talkeetna study area are not included in our analysis. No positive

results were found in the Chugach study area of Alaska or any of the live-captured sheep in BC.

Compulsory inspection nasal swabs of hunter-harvested Stone’s sheep returned ‘suspect’

positive results in 2019; 1 from the Skeena region and 1 from the Peace region (Table 4). These

rams were not associated with a hunter-harvest sample kit or other biological samples. A suspect

positive or indeterminant result is one in which rRNA is amplified, but the quantitation cycle (cq)

value is greater than a positive cut-off threshold.

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Table 4. Mycoplasma ovipneumoniae detection numbers by polymerase chain reaction (PCR) and enyme-linked immunoassay (ELISA) in free-ranging thinhorn sheep from BC and Alaska.

Study Area/Region Year n

PCR ELISA AHC WADDL WSVL ADRU WADDL

Live-capture

Dome 2017 13 0 - - - 0 Cassiar 2018 12 0 - - - 0

2019 13 0 - - - 0 Talkeetna 2019 45 0 3 0 2 2020 23 0 0 0 - 0 Chugach 2019 23 0 0 0 0 0

Hunter-harvest

Skeena 2017 8 0 - - - - 2018 9 0 - - - - 2019 16 1 (suspect) - - - - Peace 2019 7 1 (suspect) - - - -

4.3 Tonsil Bacteriology

Twenty six species of bacteria were isolated from thinhorn sheep tonsil swab samples. Of

the species previous shown to be implicated in wild sheep polymicrobial pneumoniae, incidence

differed by study area. B. trehalosi was not isolated from any sheep in the Skeena Region of B.C.

but was found in 62.5% of Williston sheep and in 58.7% of sheep in Alaska. Mannheimia spp.

were isolated from 61.5% of sheep in the Dome Mountain study area, 8.0% in the Cassiar, 25.0%

in Williston, 30.0% in the Talkeetnas, and 17.4% in the Chugach. One M. haemolytica isolate was

found in each of the Cassiar, Talkeetnas, and Chugach (Table 5).

Beta-hemolysis was reported for one Mannheimia ruminalis isolate from a Dall’s sheep

from the Talkeetna study area in 2019.

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Table 5. Pasteurella spp. detections in cultured samples from thinhorn sheep tonsil swabs collected in winters 2017-2020 expressed as the number and prevalence of positive sheep within a herd.

Location Year n Pasteurella spp. detections (n (%))

B. trehalosi M. haemolytica Mannheimia spp. Dome 2017 13 0 0 8 (62) Cassiar 2018 12 0 0 0 2019 13 0 1 (8) 2 (15) Williston 2020 8 5 (63) 0 2 (25) Talkeetna 2019 40 17 (43) 1 (3) 12 (30) Chugach 2019 23 20 (87) 1 (4) 4 (17)

4.4 Parasites

Fecal analysis for internal parasite eggs and larvae was conducted for Stone’s sheep in BC

(Table 6). The method of analysis varied between 2017 and the other years for live-captured

sheep, which may account for some of the observed differences.

Table 6. Fecal egg and larvae detections in Stone’s sheep, expressed as the number and prevalence of sheep within each herd, for the following nematode groups: Strongyles (Str.), Nematodirinae (Nem.), Marshallagia spp. (Mar.), Moniezia spp. (Mon.), Eimeria spp. (Eim.), and Trichuris spp. (Tri.), and lungworms including dorsal spined larvae (DSL) and Protostrongylus spp..

Study Area Year

Fecal egg detectionsa (n (%)) Fecal larvae

detections (n (%)) n Str Nem Mar Mon Eim Tri nb DSL Pro

Live-capture

Dome 2017 13 2 (15)

12 (92)

11 (85)

0 13 (100)

13 (100)

0 13 (100)

Cassiar 2018 7 0 2 (29) 2 (29)

0 0 0 8 0 8 (100)

2019 13 0 1 (8) 3 (23)

0 0 0 8 0 8 (100)

Hunter-harvest

Skeena 2016 13 0 0 1 0 0 0 21 0 18 (86)

2017 5 1 (20)

0 1 (20)

0 0 0 7 0 6 (86)

a In 2017 and 2020, floatation was used to determine gastrointestinal worm burden of live-capture samples. For 2018 and 2019 live-capture samples, and for all hunter-harvest samples, McMaster technique was used instead. b Testing for lungworm larvae was prioritized, therefore the number of samples tested for nematode eggs by floatation is limited by the available mass of the fecal sample in 2018.

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In fecal samples from 32 live-captured and 19 hunter-harvested Stone’s sheep, we found

eggs of the following parasite species: Trichuris sp. Eimeria sp. Marshallagia sp. Nematodirinae,

strongyles, and mites. Gastrointestinal nematode eggs were detected in a higher proportion of

live-captured Stone’s ewes than hunter-harvested rams (Table 6).

4.4.2 Lungworm

All live-captured Stone’s ewes (n = 29/29) and 85.7% (n = 26/28) of hunter-harveted rams

had patent Protostrongylus sp. lungworm infections (Table 6). The burden of infection ranged

from 16 to 6587 larvae per gram of feces. No dorsal-spined larvae were detected in 2017.

4.4.3 External Parasites

In the Williston study area, all four sheep (three mature ewes and one immature ram)

captured from the lower-elevation Dunlevy herd had winter ticks (Dermacentor albipictus) and

evidence of irritation, including barbering of hair along the dorsum and ventral and lateral

abodmen. Ticks were collected for identification. The four sheep captured and sampled at the

higher-elevation area along Butler Ridge showed no evidence of external parasitism.

No external parasites were observed on live-captured Stone’s sheep in the Skeena Region

of BC or from Dall’s sheep in Alaska. There was no evidence of hairloss or skin irritation.

One hunter recorded abnormal skin and a ‘worn’ haircoat on a ram harvested in the

Skeena region in 2019, but no parasites were noted.

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4.5 Hair Cortisol Concentration

Hair cortisl concentration (HCC) was measured in 25 hair samples from hunter-harvestd

Stone’s rams and 38 live-captured Stone’s ewes and immature rams. HCC was normally

distributed for live-capture and hunter-harvest samples.

For live-captured Stone’s sheep, HCC varied by year (P = 0.004; table 7) and by herd only

when capture year was accounted for in the model. For hunter-harvest samples, HCC did not vary

by year (p = 0.83; Table 7).

For interest, in this study live-captured Stone’s sheep had a significantly higher HCC than

hunter-harvested (p < 0.01). HCC was not compared between the two sample collection

methods, live-capture and hunter-harvest, as season has shown to effect HCC.

Table 7. Hair cortisol concentration (pg/mg) in guard hairs collected from the shoulder region on live-captured (ewes and immature rams) and hunter-harvested (mature rams) Stone’s sheep from 2016 to 2020.

Hunter-harvest Live-capture Year n Mean SD n Mean SD 2016 2 9.47 3.25 - - - 2017 - - - 12 12.65 2.53 2018 15 7.99 5.24 13 13.94 2.42 2019 8 9.54 3.78 13 10.04 2.42

4.6 Fecal Glucocoid Metabolites

Fecal glucocorticoid metabolites (FGM) were normally distributed in the live-capture and

hunter-harvest samples. We found an increasing trend FGM levels for hunter-harvested Stone’s

rams and live-capture ewes in the Cassiar study area (Table 8). A significantly higher level of FGM

was found in 2019 relative to 2016 and 2017 (P2016/2019 = 0.006, P2017/2019 = 0.036). FGM increased

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from 2017 to 2018 for live-captured sheep in the Cassiar study areas (p = 0.049). Immature rams

were not included for comparison in these figures. Significantly higher fecal glucocorticoid

metabolites concentrations were found in hunter harvested rams (P < 0.01) by an order of

magnitude (meanhunt = 314.4 ng/g, meancapture = 42.5 ng/g).

We examined trends in FGM relative to body condition and found a significant

relationship for live-captured Stone’s sheep. FGM significantly increased with decreasing body

condition score when we take year of capture into account (p = 0.038). This trend was not

observed for hunter-harvested samples (p = 0.40).

Fecal glucocorticoid metabolites were measured in Dall’s sheep in 2019 only. Dall’s ewes

had a higher mean FGM concentration than Stone’s ewes in 2019 and Dall’s rams (Table 8).

Table 8. Fecal glucocorticoid metabolite concentration (ng/g) in feces collected from live-captured Stone’s and Dall’s ewes and hunter-harvested Stone’s sheep rams from 2016 – 2020.

Hunter-harvest (rams) Live-capture – Stones (ewes) Live-capture – Dall’s (ewes) Year n Mean SD n Mean SD n Mean SD 2016 14 235.30 145.73 - - - - - 2017 14 288.64 124.92 - - - - - 2018 - - - 10 33.60 12.33 - - - 2019 5 586.63 456.95 11 47.70 17.70 22 54.07 16.24

4.7 Serum Trace Mineral Levels

Physiologically essential serum trace mineral levels in live-captured Stone’s sheep from

the Dome and Cassiar study areas are displayed in Table 9. Mean serum copper was significantly

lower than the reported normal levels for domestic sheep (Ovis spp.) (1.17 – 2.56 µg/mL; Puls

1994) in both study areas. Mean iron levels were higher than reported normals (0.90 – 2.56

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µg/mL). Mean zinc levels were below normal (0.90 – 1.84 µg/mL). Selenium was within normal

range for the Dome study area, but considered deficient in the Cassiar study area (0.13 – 0.20

µg/mL). Cobalt was below normal in the Dome study area and within normal range in the Cassiar

(0.9 – 15 ng/mL). Magnesium, manganese, and molybdenum were within normal ranges (10 – 33

ppm, > 0.006 µg/mL, and 001 – 0.1 µg/mL respectivey). Serum magnesium concentration was

only measured in the Dome study area. See Appendix C for individual trace mineral results.

Table 9. Serum trace mineral levels of live-captured Stone’s sheep. Samples collected in late winter 2017. Data were normally distributed; mean, median (med.), and range of the values are reported.

2017 Dome (n=13) 2019 Cassiar (n=13) Minerala mean med. range mean med. range Co (ng/mL) 0.57* 0.43 0.39 – 1.30 2.18 1.40 0.67 – 7.40 Cu (µg/mL) 0.44* 0.43 0.35 – 0.54 0.68* 0.66 0.50 – 1.10 Fe (µg/mL)) 6.28† 3.26 2.06 – 24.40 3.9† 3.6 1.9 – 8.2 Mg (µg/mL) 26.0 26.0 23.4 – 27.9 - - - Mn (ng/mL) 2.6 3.0 1.0 – 4.0 3.0 2.8 1.6 – 4.6 Mo (ng/mL) <10.0 <10.0 - 6.3 2.5 1.1 – 27.0 Se (µg/mL) 0.22 0.16 0.11 – 0.89 0.11* 0.09 0.05 – 0.28 Zn (µg/mL) 0.42* 0.43 0.35 – 0.54 0.68* 0.86 0.33 – 0.90

a2017 samples were analyzed at Prairie Diagnostic Services (Saskatoon, SK, Canada), 2018, 2019, and 2020 samples were analyzed at University of Guelph Animal Health Laboratory (Guelph, ON, Canada). * mean serum level is below reference range for domestic sheep (Puls 1994) † mean serum level is above reference range for domestic sheep (Puls 1994) 4.8 Tissue Mineral and Heavy Metal Levels

Trace mineral and heavy metal concentrations in kidney and liver tissue from hunter-

harvested Stone’s rams from the Skeena region of BC are reported in Table 11 (see Appendix C

for individual values and reference ranges). The concentration per dry weight of sample, rather

than wet weight, is reported as it allows for comparison with most other studies.

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As with the live-captured serum trace mineral results, copper levels are low in the hunter-

harvested rams. Of the 42 liver samples, 0.95% (n = 4/42) had copper levels considered deficient

for domestic sheep (Puls 1994). Mean zinc levels in kidney and liver tissue were within normal

range for domestic sheep (Puls 1994), however 16.7% (n = 7=42) of the samples had

concentrations higher than the reference range, and 7.01% (n = 3/42) had concentrations almost

double the upper limit of the reference range.

Very high cadmium levels were found in kidney (178 mg/kg) and liver (16.2 mg/kg) tissue

from one ram.

Table 10. Trace mineral and heavy metal concentrations (mg/kg dry weight) in hunter-harvested Stone’s sheep ram livers and kidneys submitted in 2016-2018.

Liver (n=43) Kidney (n=41) Mineral Mean Median Range Mean Median Range Arsenica 0.023 0.010 0.010 – 0.327 0.044 0.01 0.010 – 0.730 Cadmium 1.178 0.397 0.111 – 16.200 14.296 6.180 1.92- – 178.0 Cobalt 0.264 0.252 0.083 – 0.671 0.248 0.223 0.151 – 0.720 Copper 287.8 260.0 4.6 – 827.0 16.9 15.9 12.1 – 32.9 Iron 333.0 303.0 134.0 – 960.0 287.3 223.0 116.0 – 1580.0 Lead 0.266 0.025 0.010 – 9.760 0.071 0.047 0.010 – 0.500 Manganese 10.5 10.8 1.0 – 13.5 7.27 7.60 3.07 – 10.30 Mercury 0.034 0.025 0.007 – 0.176 0.535 0.341 0.023 – 2.61 Molybdenum 2.91 2.75 0.15 – 6.05 1.76 1.51 0.61 – 3.63 Selenium 0.992 0.640 0.194 – 6.570 5.39 5.34 3.12 – 8.12 Zinc 123.5 103.0 87.6 – 666.0 114.0 108.0 81.3 – 315.0

a lowest detectable limits (LDL): arsenic 0.020, cadmium 0.0050, cobalt 0.20, copper 0.10, iron 3.0, lead 0.020, manganese 0.050, molybdenum 0.020, selenium 0.050, zinc 0.50 mg/kg. If the reported value was less than the LDL, it is reported as half the LDL.

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4.9 Condition

4.9.1 Body Condition Score

A decreasing trend in body condition score by year was observed between years for

hunter-harvested rams (P = 0.002; Figure 1). The mean score in 2016 was 3.87, in 2017 was 3.52,

and in 2019 was 2.17. The relative proportion of rams scored as ‘very fat’ was highest in 2016.

For the live-captured Stone’s sheep, there was no significant difference in BCS between

years or study areas. There appears to be a difference in BCS between the Talkeetna and Chugach

study for ewes and rams captured in the winter of 2019 when sex is accounted for in the model,

however it is not significant (P = 0.06). Dall’s sheep in the Talkeetna study area have a higher

mean body condition (mean = 2.1) than those in the Chugach study area (mean = 1.9).

For live-captured sheep in BC, FGM and BCS are significantly negatively correlated when

capture year is account for (p = 0.04). This pattern was not observed for hunter-harvested rams

(p = 0.46). Body condition and HCC are not correlated in our study (p = 0.49).

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Figure 1. Body condition of hunter-harvested Stone’s sheep rams in the Skeena Region of BC in 2016 – 2019. Body condition is a subjective 5-point scale (0 = very skinny, 1 = skinny, 2 = good, 3 = fat, 4 = very fat).

Figure 2. Body condition of live-capture Stone’s sheep ewes in the Skeena (Dome and Cassiar) and Peace (Williston) Regions of BC in 2017 – 2020. Body condition is a subjective 5-point scale (0 = emaciated, 1 = poor, 2 = fair, 3 = good, 4 = excellent; note that the scale for live-captured sheep is slightly different than for hunter-harvest sheep).

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Figure 3. Body condition of live-capture Dall’s sheep ewes in the Talkeetna and Chugach mountains in 2019. Body condition is a subjective 5-point scale (0 = emaciated, 1 = poor, 2 = fair, 3 = good, 4 = excellent; note that the scale for live-captured sheep is slightly different ).

4.9.2 Rump Fat Depth

Subcutaneous rump fat depth was measured by ultrasound for 61 Dall’s sheep in the

Talkeetna and Chugach study areas. No visible fat was recorded for 70.5 %, the remainder had 2

milimetres or less. No relationship between rump fat depth and BCS was found (p > 0.05).

4.9.3 Back Fat Depth

Back fat depth in hunter-harvested Stone’s rams was normally distributed. No difference

was found between years. The median depth of back fat was 18.0 milimetres (Table 11).

Table 11. Stone’s sheep ram body condition metrics. Rams were harvested between August 1st and October 15th, 2016 – 2019 in the Skeena Region of BC. Body condition score (BCS) is a subjective measurement (0 = very skinny, 1 = skinny, 2 = good, 3 = fat, 4 = very fat) reported by hunters. Marrow fat proportion was determined using metatarsal bones collected by hunters. Back fat depth was measured and reported by hunters.

Condition Metric Median Interquartile Range Range Marrow fat (proportion) 0.7 0.6 – 0.9 0.3 – 1.0 Back fat depth (mm) 18.0 10.0 – 22.0 3.0 – 27.0

4.9.4 Marrow Fat

Metatarsal marrow fat content from hunter-harvested Stone’s rams was not normally

distributed. The median marrow fat content was 70% (Table 12).

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Table 12. Metatarsal data: percent marrow fat, bone mass and morphometric measurements for metatarsal bones collected by hunters from Stone’s sheep rams in the Skeena region of BC from 2016 to 2019. The data for each parameter was not distributed normally. No significant difference was found between years for any of the following measurements (ANOVA).

Metatarsal Parameter Median Interquartile Range Range Marrow fat (percentage) 70 60-90 30-100 Mass (g) 107.3 101.5 – 112.7 64.0 – 132.8 Length (cm) 19.2 18.3 – 20.1 17.4 – 20.5 Circumference (cm) 6.1 6.0 – 6.3 4.7 – 6.7 Diameter (cm) 1.9 1.8 – 2.0 1.4 – 2.1

4.10 Pregnancy

Pregnancy rate is reported as the proportion of thinhorn ewes determined to be pregnant

at the time of capture (Table 13). All captured Dome ewes (N = 10) were pregnant in 2017. In

2018, 75% of the Cassiar ewes (N = 12) were pregnant and 91% were pregnant in 2019 (N = 11).

All Williston ewes (N = 6) were pregnant in 2020. Pregnancy rate did not differ between years or

study areas for Stone’s ewes.

The pregnancy rate was 86% in the Talkeetna study area (N = 21) and 95% in the Chugach

study area in 2019. Pregnancy rate did not differ between study areas in Alaska (p = 0.45).

Table 13. Pregnancy rates (expressed as a percentage) in thinhorn sheep in BC and Alaska from 2017 through 2020. Pregnancy was determined by serum PSPB serum by ELISA.

Location Year n Pregnancy rate Dome 2017 10 100 Cassiar 2018 12 75 2019 11 91 Williston 2020 6 100 Talkeetna 2019 18 86 Chugach 2019 19 95

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We also assessed pregnancy as it relates to hair cortisol, fecal cortisol, year, and study

area using logisitc regression. For Stone’s ewes, pregnancy rates did not differ by year, even when

study area was accounted for. We found pregnancy rate to increase with increasing FGM levels

in Stone’s ewes. This relationship was not significant in Dall’s ewes. No relationship between hair

cortisol and pregnancy was detected.

4.11 Morphometrics

The metatarsal measurements from hunter-harvested rams had a non-normal

distribution. Median, interquartile range, and range of bone mass, length, circumference, and

diameter are presented in Table 14. This data serves as baseline information only.

Table 14. Stone’s sheep metatarsal morphometric measurements. Metatarsal bones were collected by hunters from Stone’s sheep rams in the Skeena region of B.C. from 2016 to 2019. The data for each parameter was not distributed normally.

Metatarsal Parameter Median Interquartile Range Range Mass (g) 107.3 101.5 – 112.7 64.0 – 132.8 Length (cm) 19.2 18.3 – 20.1 17.4 – 20.5 Circumference (cm) 6.1 6.0 – 6.3 4.7 – 6.7 Diameter (cm) 1.9 1.8 – 2.0 1.4 – 2.1

4.12 Relationships

For live-captured sheep in BC, FGM and BCS are significantly negatively correlated when

capture year is account for (p = 0.04). This pattern was not observed for hunter-harvested rams

(p = 0.46). Body condition and HCC were not correlated in our study (p = 0.49).

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4.13 Mortality Investigation

Of the 67 Dall’s sheep fitted with GPS-collars, and excluding one capture-related

mortality, 17 sheep died between March 2019 and June 2020.

Seven of the 46 Stone’s sheep with GPS-collars in BC have been confirmed dead and the

carcasses recovered. A ewe from the Dome herd died in the fall of 2017. The carcass was

recovered within one day of receipt of a mortality signal. Necropsy revealed evidence of acute

trauma with survival, estimated for several days. The liver capsule was ruptured and bilateral

ocular hemorrhage was present. Lungworm were present in the caudal lung lobes. The carcass

was in poor nutritional condition. There were no abnormal histological findings suggestive of

underlying disease processes and the cause of death was presumed to be trauma from a fall. A

second ewe from the Dome herd died in the winter of 2020. Only the thorax was present with

the collar; it has not yet been examined. Collars from seven sheep in the Cassiar study area have

stopped transmitting. This is likely due to collar battery depletion, but we cannot rule out

mortality.

Following the Williston captures in March 2020, one adult female Stone’s sheep from

Butler Ridge died. Her collar initiated a mortality signal on March 17th, the day after she was

captured, and the carcass was recovered the following day. The carcass was stored frozen and a

necropsy was performed a month later. On post-mortem examination she was found to be in

excellent body condition with some subcutanous and omental fat, and excellent renal and

pericardial fat stores. The cause of death was determined to be trauma associated with capture

as this ewe required the deployment of two nets that caught on vegetation and she struggled

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extensively. There was marked hemorrhage around the cervical spine muscles. While no

abnormal histological findings were reported, Mannheimia haemolytica and Streptococcus sp

were cultured from lung tissue. The ewe appeared healthy at the time of capture and had

excellent fat stores on necropsy exam. The signficance of this finding is unknown.

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CHAPTER 5 - DISCUSSION

This study describes the current status of thinhorn sheep health across their range and

how it may relate to population persistance. Our key findings were to identify the presence of

respiratory pathogens, including M. ovi, in some herds, significant exposure to T. gondii in

Alaskan Dall’s sheep, increasing concentrations of fecal and hair glucocorticoids in some

populations, and a lack of antibody response to many other viral respiratory pathogens common

to domestic livestock and other wildlife species. We did not test for all health parameters in all

study areas so are limited in the comparisons we can make between populations. This study is

primariy observational.

The identification of knowledge gaps regarding thinhorn sheep health outlined in

“Thinhorn Sheep Conservation Challenges and Management Strategies for the 21st Century” (Jex

et al. 2016) provided the impetus for this study. While our study began in 2016, recent

identification of M. ovi in Dall’s sheep and other wildlife species in Alaska (Highland et al. 2018)

highlighted the need for baseline health information in order to understand the risks of disease

introduction and ecological change on thinhorn sheep populations.

Current and historical anthropogenic occupation of the landscape varies between study

areas and may contribute to the potential for pathogen presence and relative health of the

thinhorn sheep occupants. Similarly, the composition of megafauna, density of thinhorn sheep,

and landscapes differ among study areas. We did not examine the effects of intra- and

interspecific competition, or predation, on thinhorn health beyond noting that in some areas

predation may have a significant impact on thinhorn sheep populations. For example, in the

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northern Richardson Mountains, Northwest Territories, mountain mammals (including Dall’s

sheep, caribou, and Arctic ground squirrels) comprised important food sources for grizzly bears

and wolves (Koizumi & Derocher 2019). Natural fluctations of thinhorn populations occur with

predator-prey cycles or environmental conditions (Thinhorn Summit 2017). The range of

thinhorn sheep in North America experiences climatic variability through influences of the El Nino

Southern Oscillation, Pacific Decadal Oscillation, Arctic Oscillation, and Aleutian Low (Pojal 2009).

We do not completely understand demographic or population patterns for thinhorn sheep

populations across their range, however, there is evidence of fluctutations associated with

climatic conditions (B. Jex pers. comm.) Climatic temperature changes may be exacerbating

habitat changes resulting in suitable habitat losses and differences in interspecific dynamics in

some areas.

5.1 Viruses

We demonstrated low seroprevalences for most viral respiratory pathogens carried by

domestic livestock or other wild ungulate species. This was not totally unexpected as in the

remote areas that thinhorn sheep typically inhabit there is minimal potential contact with

domestic sheep and goats. Our findings are consistent with results from a health survey of Stone’s

sheep in the Dunlevy and Schooler herds along the Williston Reservoir (Wood et al. 2010). The

low seropositivity to pathogens of domestic species indicates that the associated diseases are

not circulating in thinhorn populations and therefore are not having population-limiting effects.

However, as thinhorn sheep are immunologically naïve to many diseases, if introduction does

occur, there is a risk of high morbidity and potentially, mortality. Respiratory syncytial virus and

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parainfluenza-3 have been implicated in multifactorial pneumonia in bighorn sheep (Spraker &

Collins 1986; Besser et al. 2012, Dassanayake et al. 2013) but are not always identified with

pneumonia events. Coinfections may dampen the immune response to other infections and

potentiate the effects of pathogens, such as M. ovi (Besser et al. 2008).

A high seroprevalence of malignant catarrhal fever virus (MCFV) was present in Stone’s

sheep in the Skeena region of BC. This result is consistent with previous findings by Zarnke et al

(2002), who reported a 95% seroprevalence in Dall’s sheep (n = 212/222) in Alaska. MCFV was

detected in our study using an ELISA specific for ovine herpesvirus-2.

There are numerous viruses in the MCFV group, genus Macavirus; ovine herpesvirus-2 is

an MCFV carried by domestic sheep (Cunha et al. 2019). The effects of MCFV infection in free-

ranging ungulates are largely unknown (Zarnke et al. 2002). There is a single report of a suspected

MCFV-associated disease in a bighorn sheep from Banff National Park, Alberta, Canada (Slater et

al. 2017). MCFVs are host-adapted, existing as subclinical enzootic infections in ruminants and

causing disease in susceptible hosts (O’Toole & Li 2014). The MCFV carried by bighorn sheep,

ovine herpesvirus-3 (OvHV-3) is closely related to but genetically distinct from OvHV-2. The MCF-

cELISA used to detect anti-OvHV-2 antibodies used in our study, also cross reacts to anti-OvHV-3

antibodies (Cunha et al. 2019). Genotyping of the MCFV detected in thinhorn sheep has not been

conducted but based on recent findings in bighorn sheep it likely either is or is closely related to

OvHV-3 or less likely is a genetically distinct MCFV specific to thinhorn sheep. There does not

appear to be a health significance to this infection, instead is an enzootic strain variety carried

asymptomatically by thinhorn sheep.

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Contagious ecthyma (CE), caused by a parapoxvirus is highly contagious and has been

identified as pathogen of concern for transmission from domestic small ruminants to free-

ranging thinhorn sheep (Garde et al. 2005). Clinical signs have been documented in Dall’s sheep

in Alaska, bighorn sheep, and mountain goats (Tryland et al. 2018). The clinical signs associated

with CE infection in wild sheep include blisters at mucocutaneous junctions of the lips, nostrils,

and coronary bands, progressing to proliferative masses covered in thick crusts due to secondary

bacterial infection. Disease is most often observed in young animals and may reduce growth rates

and survival with fatal results often observed in adult mountain goats. We observed no clinical

signs of CE in live-captured Dall’s or Stone’s sheep and no documentation of blisters or scabs was

noted on hunter-harvested Stone’s rams. Of human health concern, CE also is a zoonotic disease,

causing painful fluid-filled blisters on the skin; hunters could be exposed when handling the

animals.

5.2 Bacteria

5.2.1 Mycoplasma ovipneumoniae

Mycoplasma ovipneumoniae is a primary causative agent in polymicrobial pneumonia

epizootics in bighorn sheep (Cassirer et al. 2017), believed to be play an initiating role in outbreak

events. It is often carried in the upper respiratory tract of domestic small ruminants usually

asymptomatically, but can cause subclinical disease and reduce weight gains with occasional

clinical respiratory disease in young or compromised animals (Manlove et al. 2019). In bighorn

sheep, the bacteria initiates respiratory disease, while subsequent infection or proliferation of

bacterial pathogens, such as Pasteurella spp. results in clinical outcomes.

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Disease events in bighorn sheep are characterized by acute bronchopneumonia with high

mixed age morbidity and mortality with chronic lamb mortality in subsequent years (Besser et al.

2013; Heinse et al. 2016; Highland et al. 2018; Blanchong et al. 2018). Outcomes of disease events

may differ by age class of sheep. Chronic impacts on herds following acute epizootics is from

lamb mortality, typically observed when lambs are over one month of age and maternally derived

immunity has waned (Highland et al. 2017). These lambs will die from pure M. ovi infections of

the inner ear and lungs. Epizootic events seen as all-age die-offs can be followed by up to 100%

mortality of bighorn lambs for years. This low lamb recruitment often persists for years after an

outbreak event. Because a carrier-state can exist for wild and domestic ungulates, transmission

of M. ovi between conspecifics can lead to rapid spread within a herd, particularly when ewes

and lambs are together in the late spring and early summer (Cassirer et al. 2017; Highland et al.

2017; Butler et al. 2018).

Twenty-eight strains of M. ovi have been identified in bighorn sheep, with varying degrees

of pathogenicity patterns in herd pneumonia events. Strain-specific immunity does not prevent

subsequent outbreak events in bighorn sheep herds when separate spillover events from

different reservoir hosts occur (Cassirer et al. 2017). The persistence of a herd previously infected

with M. ovi is depended on ecological and demographic factors as well as the strain type (Butler

et al. 2018) Preliminary sequencing demonstrates that the strain found in Alaskan non-Caprinae

wildlife species is phylogenetically divergent from other M. ovi isolated from bighorn sheep, by

comparison to a known M. ovi sequence in genbank, Y98 (Highland et al. 2018). The same strain

of M. ovi has been present in Alaska since at least 2004 in Dall’s sheep and 2007 in caribou

without notable associated mass mortality, so is likely enzootic in at least one of these species.

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M. ovi was detected in Dall’s sheep in the Talkeetna and Chugach mountains and recently,

sporadic deaths with M. ovi isolated from tissue samples have been noted (Beckman and Lieske

unpublished data). Previous work by Zarnke and Rosendal (1989) found no evidence of exposure

to M. ovi in Dall’s sheep in Alaska from 1979 to 1987 using indirect hemagglutination tests,

unvalidated for Dall’s sheep. However, due to the history of gold mining and colonization in

Alaska, there was contact between wild and domestic sheep since at least the 1880’s in published

photographs and observations(Beckman & Lieske 2020). In BC, a few isolated cases of potential

contact between Stone’s sheep and domestic goats have been noted, including near the Fort

Good Hope town site adjacent to the Cassiar study area (Bill Jex, pers. comm.).

Two areas in BC were identified for further investigation of M. ovi exposure in free-

ranging Stone’s herds based on ‘indeterminant’ M. ovi PCR results from nasal swabs collected

from rams harvested in the fall of 2019. The Williston study area is approximately 150 km from

the harvest location of one of the rams, and no M. ovi PCR positive ewes or immature rams were

detected in this area in 2020. In addition, there has been no reports of population declines or

changes in ewe:lamb ratios as would be expected with herd exposure to M. ovi, however, further

sampling in closer proximity to the ram locations is recommended. Rams carrying M. ovi may

transmit infection to ewes and other rams during the mating period in the fall. If ewes become

chronic carriers, transmission from ewes to lambs in the spring poses considerable risk to the

population. As seen for bighorn sheep, introduction of a bacterial species that acts as an initiating

agent for a pneumonia epizootic in naïve herds can have devastating effects (Jenkins et al. 2007).

M. ovi PCR results were not consistent between all four laboratories employed to test

serial nasal swab samples from Dall’s sheep in Alaska. Sampling protocols differed by the media

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used to store swab samples and preserve bacterial genetic material prior to testing as specified

by each laboratory (see Methods 3.2.2). PCR protocols differed by their target sequence for

amplification, using primers targeting varying fragments of either the 16S region or the

associated intergenic spacer (IGS) region of the M. ovi rRNA genome. The 16S region is highly

conserved and used to differentiate between different Mycoplasma spp. A recent concordance

study by Beckmen and Lieske (2020; unpublished data) showed fair agreement in test results

between LM40 and UM PCR tests, with the most positive detections made using the LM40 primer

at USDA-ADRU. In contrast, we found more positive results using the universal mycoplasma (UM)

assay at WADDL. We collected serial nasal swabs rather than splitting a single nasal swab as was

done by Beckmand and Lieske (2020), which could at least partially explain the difference in

detections.

Indeterminant PCR results are those that show some amplification of genetic material but

are below a threshold level of quantification to be considered positive. Results in the

indeterminant range could be due errors from “noise”, such as amplification of a fragment of the

genome, the presence of a different Mycoplasma spp. with minor sequence variation, or

insufficient bacterial organisms collected in the sample; indeterminant results do not rule in or

rule out the presence of the organism and positive detection does not mean there is infection in

the animal (Walsh et al. 2018; WADDL 2019).

We found discrepancy in M. ovi PCR and ELISA results. M. ovi was not detected on nasal

swab samples from the two serologically positive and two indeterminant individuals from the

Talkeetna study area. The presence of the organism in the nasal passages and detection by PCR,

does not necesasrily equate with infection of the animal, and infection does not always lead to

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an immune reponse. Antibodies at a detectable level are transient, as demonstrated in bighorn

and domestic sheep (K. Beckmen, pers. comm.) This is could be due to transient infection with

subsequent clearance, or a subclinical infection where the possible immune reponse is B-cell

mediated only. Immune function resulting in seroconversion and subsequent return to

seronegativity after clearance of M. ovi is influenced by many factors and varies between

individuals, making it difficult to define a timeline of infection versus serostatus. While the

sensitivity of the ELISA test is very good, we cannot rule out false negative results (Sp = 99.3%, Se

= 88%; WADDL 2019). Experimental infection of captive bighorn sheep and observations from

free-ranging M. ovi-postive herds where test and remove strategies were applied demonstrate

declining antibody levels occurred two years after PCR negative status (Manlove et al. 2017; T.

Besser & F. Cassirer, pers. comm.). Of further interest, the PCR-positive Dall’s sheep in our study

were not serologically positive. This may indicate that an M. ovi infection occurred very recently,

the animal was not infected, or no immune response was elicited. Anti-M. ovi antibodies were

expected when M. ovi infected individuals are immunocompetent, however, antibodies are not

produced by the innate immune system functioning at the mucosa. Other possible explanations

for the lack of evidence of seroconversion is a strain-type mismatch between what is detected

using PCR and ELISA methods if a non-pathogenic strain is detected, a low sensitivity of the ELISA

test, or poor sample quality. A finding of less than 40 percent inhibition using the M. ovi capture

ELISA is considered a negative result as defined by Washington Animal Disease Diagnostic

Laboratory (WADDL). Serological testing is useful for determining the exposure status of

populations rather than individuals; populations where M. ovi exposure has occurred typically

have over 30 percent of individuals with detectable antibodies (over 50% inhibition; WADDL

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2019). Indeterminant results represent 40 to 50 percent inhibition, a range with lower specificity

but higher sensitivity. In an unexposed herd, typically less than one percent of individuals have

‘detectable’ test result. Thus, since approximately ten percent of the Dall’s sheep sampled in the

Talkeetna study area in 2019 were serologically positive, this does not meet the 30% threshold

and we cannot interpret the timeline of introduction or current exposure.

5.2.2 Tonsil Bacteriology

Respiratory disease in bighorn sheep is usually polymicrobial but single pathogens can be

involved. M. ovi and the Pasteurellacae family are typically recognized as causal organisms. In

the absence of specific primary pathogens, such as M. ovi, the combination of lungworms,

Pasteurellaeceae bacteria,and/or viruses, and stress may contribute to pneumonia in free-

ranging thinhorn sheep populations (Cassirer et al. 2018; Jenkins et al. 2000; Weiser et al. 2003).

The Pasteurellaceae spp. Mannheimia, Pasteurella, Pasteurella multocida, and

Bibersteinia genera isolated from pharyngeal swabs are considered normal commensal

organisms and have been detected in apparently healthy bighorn sheep populations (Schwantje

1988; Wolffe et al. 2019). These bacterial species have the capacity to carry lktA, the gene

encoding leukotoxin A which is considered the key virulence factor of Pasteurellaeceae (Walsh et

al. 2018). Leukotoxigenic strains can play a role in polymicrobial bacterial pneumonia and are

generally thought to be the cause of cellular damage once they reach pulmonary tissue [cilia]

(Rifatbegovic et al. 2011). Peak mortality of bighorn sheep lambs between 6 and 11 weeks of age

is associated with waning of maternal leukotoxin-neutralizing antibodies (Cassirer et al. 2001).

We did not determine the presence of lktA directly in Pasteurella spp. isolates in our study,

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however, leukotoxin production is well correlated with beta-haemolysis observed on a CBA plate,

which we assessed through culture and colony identification at diagnostic laboratories. Butler et

al. (2017) found the detection probability of the Pasteurellaceae as poor (less than 0.5 for all

Pasteurella spp.) using different testing protocols. Given the extreme cold environment in which

we were collecting samples, our detection of respiratory bacteria by culture methods is likely

lower than reported for bighorn sheep. We implemented protocols to increase the success of

detection in our remote study areas, including field culture using a portable incubator and

maintenance of swab samples at body temperature in transport media prior to culture within 8

hours of collection.

We isolated Pasteurella spp. from few thinhorn sheep in this study, and report a single

haemolyic Mannheimia ruminalis result. Our findings support those of a survey study of mortality

investigations of collared Dall’s adults and lambs in southcentral Alaska from 2009 to 2014 that

concluded that respiratory disease was not having population-level effects (Lohuis 2013).

5.3 Toxoplasma gondii

Our most significant serological finding is very high levels of exposure of Dall’s sheep to

T. gondii in southcentral Alaska (100% in 2019 and 73% in 2020). T. gondii is an apicomplexan

parasite that can induce neurologic disease or abortion in the intermediate host (wild or domestic

ungulates or small mammals) and rarely in the definitive host (wild or domestic felids). Thinhorn

sheep act as an intermediate host, becoming infected through environmental contamination

with T. gondii oocysts shed by infected wild felids. In Alaska, the only likely wild felid species are

Canada lynx (Lynx canadensis). T. gondii antibodies in the sera of Dall’s sheep (7%; Zarnke et al.

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2000) and other wildlife species (Zarnke et al. 2001; Stieve et al. 2010) in Alaska were previously

described, but at lower seroprevalences than we demonstrated. Seroprevalence typically

increases with age due to increasing risk of lifetime exposure, however T. gondii has been shown

to transmit vertically in other species (Stieve et al. 2010).

Several reasons may explain the difference in the prevalence we detected between herds

and comparted to those previously reported including methodology used and the threshold of

positive titres at different laboratories. For example, Zarnke et al. 2000 used a modified

agglutination test (MAT) with a positive titre threshold of ³ 1:2 while we used an indirect

fluorescent antibody test (IFT) with a positive titre threshold of ³ 1:64. Alternatively, the

prevalence of antibodies may vary with region, or exposure may be increasing over time.

Lynx-snowshoe hare cycles are well documented and may provide an explanation for

change in prevalence of T. gondii antibodies over time. Snowshoe hare (Lepus americanus) and

lynx populations increase and decrease in synchrony on an approximate ten-year cycle (Krebs et

al. 2013). T. gondii infection of intermediate host species is dependent on the density of lynx and

their fecal contamination of the shared landscape (Stieve et al. 2010). The lynx population either

peaked in the Talkeetna Mountains area in 2019 or is going to peak in 2020. Population trends in

the Chugach area around Anchorage are not so clear, but lynx are currently abundant there (Kyle

Smith, ADGF pers. comm.).

These findings present potential causes of poor recruitment in Dall’s herds. T. gondii can

cause late-term abortions in ewes that become infected during pregnancy. Lambs that are

congenitally infected are often weak (Dubey 2013) and may be abandoned or succumb to

predation, exposure, or starvation. In addition, lynx at high density could easily predate neonatal

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lambs in lambing habitats. Toxoplasma contamination of wild foods also poses a risk to human

health as humans can serve as intermediate hosts and experience ocular, neurological and

reproductive disease (Dubey 2013). Further investigation is required to assess if infection by T.

gondii (toxoplasmosis) is causing population-limiting effects on thinhorn sheep; this will require

assessing if ewes determined to be pregnant at the time of capture produce a viable lamb

through collar movement data or direct observation of a lamb at heel. Lamb carcasses that are

discovered should be tested for the presence of T. gondii tachyzoites or antibodies.

5.4 Parasites

Parasite burden can have detrimental effects on individuals and populations of wild

ungulates (Aleuy et al. 2018). Our findings compliment recent literature documenting the

species, prevalence, and intensity of parasite infections in thinhorn sheep throughout their range

(Kutz et al. 2001; Jenkins & Schwantje 2002; Jenkins et al. 2006; Aleuy et al. 2018). Fecal parasite

analysis allows detection and relative quantification of burden (infection intensity) in live

animals, however, this method has its limitation. It is only possible to identify many parasite

species by their eggs or larvae morphology to genus or family level. Parasite shedding, or patency,

is influenced by season, host sex and age, and demographics of the herd (Jenkins & Schwantje

2002). Thinhorn sheep habitat selection varies with season and sex. In our study, samples were

collected from different sexes at different times of year, and method of analysis differed between

years.

Only samples from live-captured sheep in BC in 2017 and 2019 were analyzed at a

commercial laboratory, other samples were analyzed in house. The method of analysis and level

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of expertise may have differed. Therefore, comparisons of parasite prevalence and intensity

cannot be made between study areas.

Differences in shedding of parasite eggs and larvae between populations are difficult to

interpret and rarely correlate with other indicators of health (Jenkins & Schwantje 2002). A study

in bighorn sheep found no relationship between lungoworm burden and FGMs (Goldstein et al.

2005). In our study, fecal parasite burdens were not determined in a consistent way across all

years, so comparisons cannot be made to other indicators of health.


The gastrointestinal parasite burdens we detected were within established intervals of

previous studies. In live-captured ewes in the winter of 2017, the most prevalent species were

Eimeria sp. and Trichuris sp. followed by Nematodirinae and Marshallagia sp. (Table 6). Jenkins

and Schwantje (2002) found seasonal differences in parasite species prevalence and intensity of

infection in Stone’s sheep in the Muskwa-Kechicka area of BC in spring, which would be most

similar to the time period we collected fecal sample from live-captured ewes. Marshallagia sp. is

the most prevalent macroparasite species documented in Dall’s sheep throughout their range

(Jenkins & Schwantje 2002, Aleuy et al. 2018). Owing to their remote range, thinhorn sheep are

unlikely to share many parasite species with domestic sheep and cattle, in contrast to bighorn

sheep that are infected with domestic hoofstock parasites in parts of their range (Jenkins &

Schwantje 2002; Goldstein et al. 2005).

Gastrointestinal nematode infections have been associated with reduced fitness in

thinhorn sheep populations (Aleuy et al. 2018). Reduced lamb survival, and even adult mortality,

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may result from high-intensity infections due to chronic appetite suppression, reduced digestive

capability, and alteration of protein metabolism. This is particularly evident in harsh northern

environments where food is scarce throught the winter months (Aleuy et al. 2018). The intensity

of M. marshalli infection of Dall’s sheep from the Mackenzie Mountains was negatively

associated with body condition and pregnancy. Aleuy et al. (2018) found the M. marshalli

prevalence increased with host age. Similarly, our results from live-captured ewes in 2017

demonstrate a relationship between M. marshalli prevalence and host age. There also is a

relationship between infection prevalance and HCC. In our study, all Stone’s sheep with M.

marshalli eggs detected in feces were pregnant in 2017 (n = 6/10; Table 6).

Despite the high prevalence of Eimeria sp. found in our study, and by Jenkins and

Schwantje (2002), there was no evidence of clinical coccidiosis in wild sheep. There does not

appear to be large burdens of gastrointestinal parasites in Stone’s sheep. However, there is no

estalished threshold the level of parasitism that is associated with negative outcomes in wild

sheep and there are likely other stressors involved in reduced overall host fitness.

The gastrointestinal nematode burden results in our study must be interpreted with

consideration of our methods. In 2017, fecal samples were analyzed at an academic laboratory

using Wisconsin double centrifugation fecal floatation. The hunter-harvest and 2018 and 2019

live-capture fecal samples were analyzed in-house using a modified MacMaster egg enumeration

technique. The Wisconsin method is better for identifying positive samples (higher sensitivity),

but may not recover all of the eggs present (Egwang & Slocombe 1981). We report prevalence in

our study, therefore, the results in 2017 may be higher partly due to the detection method used.

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5.4.2 Lungworm

Protostrongylus spp. infection can cause severe verminous pneumonia in thinhorn sheep,

however pathology is typically linked to coinfections with P. odocoilei or secondary bacterial

pathogens. Persistence of this parasite at high latitudes is due to overwinter survival of L1 larvae

in gastropods and transplacental transmission (Jenkins et al. 2006). The Protostrongylus spp. in

this study are most likely P. stilesi or P. rushi based on previous findings in Stone’s sheep in BC

(Jenkins & Schwantje 2002). The larvae of the two species are morphologically indistinguishable.

Bertram et al (2018) identified protostrongylid lungworm larvae in 97% of live-captured Dall’s

sheep from the White Mountains of Alaska. Jenkins and Schwantje (2002) demonstrated

lungworm larvae in a higher proportion of animals in spring versus summer and studies looking

at fecal larval burden in bighorn sheep have detected peaks in the winter (Goldstein et al. 2005),

albeit in far different environments than thinhorn sheep. Our samples were collected mainly from

ewes in the late winter and from rams in the fall, so seasonal and sex differences make direct

comparison unsound. However, anecdotelly, we also observed a higher prevalence of lungworm

larvae from late-winter versus fall sample collection. This finding may be due to reduced

immunity to internal parasites with a lower plane of nutrition in the winter and the timing of

development of 3rd stage larve (L3) to patent adult nematodes within their host (Kutz et al. 2012).

Previous findings of dorsal-spined larvae, Paraelaphostrongylus odocoilei, in samples

from the Muska-Kechika and Spatzisi Plateau suggest that the Dome and Cassiar sheep may also

carry this muscle worm (Jenkins & Schwantje 2002). P. odocoilei is a muscle worm with life stages

that migrate through the lung parenchyma and other tissues of the body. Central nervous system

signs and bronchopneumonia have been reported (Jenkins et al. 2000, 2007). P. odocoilei has

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been implicated in diffuse interstitial lung disease and respiratory failure in experimentally

infected thinhorn sheep (Jenkins et al. 2005, 2007). We did not find dorsal-spined larve in live-

captured or hunter-harvested Stone’s sheep in BC; however, testing was done in-house for the

hunter-harvested samples and live-captured samples from 2018 and 2019, which likely limited

the probability of detection. We opted for in-house testing as consistent laboratory testing

became unavailable; however, we feel the expertise of the laboratory personnel and the use of

genetic confirmation allowed for more robust larval identification in 2017. Further genetic

investigation is required of thinhorn parasites. Interestingly, sheep near the Williston Reservoir

were previously found to be negative for P. odocoilei, indicating that isolated herds may not be

infected (Jenkins 2004).

An increase in parasite intensity in the spring is seen in other wild and domestic species

and is thought to occur due to reduced host immunity associated with stress from a lower plane

of nutrition and from pregnancy (Jenkins & Schwantje 2002). Jenkins et al. (2006) discovered a

peak in shedding of 1st stage larve from P. stilesi and P. odocoilei in sheep on their winter range

from March to May in the Northwest Territories. Larval shedding in feces is not always a reliable

indicator of infection intenstiy of adult worms, however, larval count of P. odocoilei in feces

correlated with eggs in the lung parenchyma. The density of eggs is directly correlated with

degree of lung damage (Jenkins et al. 2007).

Trauma associated with lungworm larval migration may predispose sheep, especially

juveniles, to bacterial invasion and development of pneumonia. P. odocoilei is particularly a risk

factor for development of bacterial pneumonia owing to their diffuse pattern of damage

migrating larvae cause to pulmonary tissue. (Jenkins et al. 2007). No overt signs of verminous or

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bacterial pneumonia were noted on post-mortem examination of collared Stone’s sheep

mortalities during our study.

5.4.3 Ecotoparasites

The Stone’s sheep inhabiting the higher-elevation sites along Butler Ridge, in the Williston

study area, did not show evidence of external parasitism, whereas all four sheep captured from

the lower-elevation part of the study area at 20-Mile Point had varying degrees of tick-associated

hairloss. Winter ticks (Dermacentor albipictus) were found on all sheep with hairloss. This pattern

was first described by Wood et al. (2010). Winter ticks are generally associated with moose (Alces

alces), but are not host-specific (Welch et al. 1991). Range overlap with Rocky Mountain elk

(Cervus canadensis nelsoni) likely contributed to winter tick infestation of Stone’s sheep

overwintering at low elevation in this area. S heep use specific escape terrain in this area, and

due to the mild climatic conditions that allow ticks to persist overwinter, infection may now be

self-perpetuating in this herd (Wood et al. 2010).

5.5 Stress

The physiological responses associated with environmental disturbance is increasingly

recognized as having population-limiting effects (Macbeth et al. 2010; Bondo et al. 2018). Stress

is a non-specific response to changes in internal and external stimuli. Glucocorticoids are

produced by the adrenal glands to meet metabolic demands associated with physiological stress

via stimulation of the hypothalamic-pituitary-adrenal (HPA) axis. Circulating levels increase

beyond the normal predictable patterns associated with season and life-history events in

response to stressors and with chronicity can have deleterious impacts (Downs et al. 2018).

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Plasma, saliva, urine, feces, and hair have been used to assess cortisol levels on different time

scales (Macbeth et al. 2010; Sheriff et al. 2011). Hair and fecal cortisol levels have been validated

by exogenous ACTH administration as a measure of stress in bighorn sheep and mountain goats

and were found to be useful biomarkers of physiologic stress (Miller et al 1991; Coburn 2010;

Dulude-de Broin et al. 2019). While previous research examined cortisol levels relative to other

indicators of health in bighorn sheep (Acker et al. 2018) and thinhorn sheep (Downs et al. 2018),

the role of these health determinants in thinhorn sheep and other wildlife species population

dynamics is unknown.

Hair and feces are useful media for measuring glucocorticoid levels because they can be

collected passively for population monitoring over time, collection does not require specialized

equipment or skills, and results are not influenced by the collector (i.e. handling of free-ranging

wildlife does not cause an acute increase of HCC or FGM as it does in plasma) (Sheriff et al. 2011).

Chronic elevation of glucocorticoids is associated with reduced immune function,

reproduction, and growth (Acevedo-Whitehouse & Duffus 2009; Macbeth et al. 2010). The

impact of chronic stress may be increased susceptibility to pathogens, altered behaviour,

decreased ability to withstand or adapt to changing ecological conditions, and reduced

recruitment (Coburn et al. 2008; Downs et al. 2018). Chronic elevation of cortisol, associated with

diminishing home range size and increased predation pressure, has been demonstrated in other

wild ungulate species (Ewacha et al. 2017; Dulude-de Broin et al. 2019b). Long-term monitoring

of herd-level stress response as it relates to extrinsic factors such as climatic conditions or

environmental changes could provide managers with a biologically useful metric to identify

populations at risk of decline.

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5.5.1 Hair Cortisol

Hair cortisol concentration (HCC) gives an indication of relative levels of stress in

individuals or populations over the period of hair growth as circulating glucocorticoids are

incorporated into the hair shaft. It can provide a useful metric for monitoring stress over time

and potentially predicting population outcomes associated with changing environmental

conditions. Here we provide a baseline from which continued monitoring can build in order to

detect patterns in long-term physiological stress.

We examined HCC in live-captured and hunter-harvested Stone’s sheep. We found that

Stone’s ewes captured in the winter had significantly higher HCC levels than rams harvested in

the summer and fall. We cannot rule out that observed differences in HCC levels are due to sex

and season alone as HCC has been found to vary with age and sex in other wildlife species

(Madslien et al. 2020).

Annual haircoat molting occurs in the spring and early summer. Due to the timing of

sample collection in the late winter, HCC levels in live-captured thinhorn sheep in this study are

indicative of almost a full growth cycle whereas HCC levels collected from rams in the late

summer/fall are representative of a much shorter time period. Some of the observed difference

between Stone’s rams and ewes in our study may be explained by the fact that the ewes had

experienced winter conditions, pregnancy, and a longer duration of nutritional stress than the

rams at the time of hair collection. While rams during the rut period are considered stressed from

breeding behaviour and low feeding activity, this is unlikely to be represented in HCC due to the

timing of sample collection in the hair growth cycle.

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We used hair plucked from a consistent body location that consisted of guard hairs. We

requested that hunters collect hair samples from the dorsal midline at the level of the shoulders,

but we cannot confirm that this protocol was adhered to. Guard hairs have significantly higher

HCC than undercoat hairs (Macbeth et al. 2010; Dulude-de Broin et al. 2019). Macbeth et al.

(2010) did not find hair colour to significantly influence HCC results; we did not differentiate

between hair colour in our analysis.

We did not detect a difference in mean HCC levels between the Dome and Cassiar Stone’s

sheep herds. A higher proportion of ewes in both herds were assessed in poorer body condition

in 2019 compared to 2018 and mean HCC levels in 2018 were higher than 2019 for the Cassiar

herd; however, this relationship was not found to be significant and the sample size is small. We

did not find a significant relationship between HCC and pregnancy for Stone’s ewes.

Unfortunately, there is insufficient data to compare between locations in the same year,

therefore we cannot infer relationships between HCC and climatic factors.

Previous studies looking at HCC and parasite burden have found varying results (Madslien

et al. 2020). We did not find a relationship between individual fecal egg or larvae counts and HCC.

5.5.2 Fecal Glucocorticoid Metabolites

Circulating glucocorticoids are metabolized in the liver and excreted into the feces via the

bile ducts (Sheriff et al. 2011). We examined trends in FGM relative to other indicators of health.

For live-captured Stone’s sheep FGM increases with decreasing body condition score when we

take year of capture into account. This pattern was not apparent in Dall’s sheep in Alaska or for

hunter-harvested samples, however, this may be due to a small sample size as only one ram was

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scored as ‘skinny’. Delayed time until freezing decreases the concentration of detectable FGM in

a sample, which may account for some of the variation observed among the hunter-harvested

samples (Dulude-de Broin et al. 2019).

An almost ten-fold higher mean FGM level in hunter-harvested Stone’s rams relative to

live-captured Stone’s ewes was present. There was no difference in processing or testing

methods between the two groups. While the magnitude of this difference is unexpected, a

cortisol peak in the late summer and early fall may be expected due to the approaching mating

season. Increased agonistic interactions with other rams and rapid changes in food consumption

and body condition may acutely elevate circulating cortisol levels. Seasonal variation in FGM

levels has been observed in bighorn sheep and other free-ranging northern ungulates; however,

the timing of FGM peaks is inconsistent among species. Higher FGM levels were found in the

summer months in captive bighorn sheep and mountain goats (Goldstein et al. 2005; Dulude-de

Broin et al. 2019). A peak in FGM during the mating season in the fall was found in free-ranging

red deer stags (Pavitt et al. 2015). Dulude-de Broin et al (2019) did not find a sex difference in

FGM in mountain goats when fecal samples were collected at the same time of year for both

sexes. Ewes in the late winter have additional metabolic demands associated with pregnancy,

increased parasite burdens, and nutritional stress. As we did not collect fecal samples from ewes

at other times of the year, we are unable to assess the importance of these factors in determining

FGM levels.

Fecal glucocorticoid metabolite monitoring is most useful in the context of determining

relationships with environmental factors and herd response when carried out over time.

Collection of feces from one point in time does not allow us to determine if cortisol levels

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represent baseline or if they are elevated due to a recent stressful event for an individual (Coburn

et al. 2008). Comparison of FGM and HCC levels gives some indication of the chronicity of stress.

We found a consistent trend in FGM and HCC levels for hunter-harvested Stone’s sheep; both

matrices had higher levels in 2019 relative to previous years. For live-captured Stone’s sheep, an

inverse pattern was observed; 2019 samples had a higher FGM level than 2018 samples, but a

lower HCC was found in 2018. The cause of the difference in the two matrices is unknown but

highlights the fact that they are important on different time scales.

Opportunistic fecal collection through hunter-based sampling provides a means for long-

term population monitoring. Unless fecal samples are collected throughout the year from a

particular herd and a baseline value is established, then HCC may be a better measure of chronic

stress levels in free-ranging wildlife when we do not have knowledge of recent stress events. It is

generally chronic stress and the production of cortisol, rather than acute stress and the

production of epinephrine, that correlates with detrimental effects in a population (Coburn et al.

2008).

5.7 Serum Trace Mineral Levels

Trace mineral levels of forage are influenced by climate, substrate type and mineral

composition, and rainfall (Bleich et al. 2017). Within a population, trace mineral levels can differ

by sex due to habitat choices and social structure. The use of mineral licks, or geophagia, has

been shown to be important for free-ranging ungulates to increase intake of essential trace

minerals. Often driven by sodium and phosphorus content in the soil, geophagia is most

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prevalent in lactating females with young as they have increased nutritional demands (Mincher

et al. 2008; Slabach et al. 2015).

Serum trace mineral reference ranges have not been determined for free-ranging

thinhorn sheep. Using reference intervals from Ovis spp. and previous findings in bighorn sheep

(Puls 1994; Lemke & Schwantje 2005), we see are potential deficiencies in BC Stone’s sheep in

the Cassiar and Dome study areas, if thinhorn requirements are similar to those of bighorn and

domestic sheep. However, our findings are similar to those from free-ranging Dall’s sheep in the

White Mountains of Alaska for iron, selenium, and zinc, but lower for copper (Bertram et al.

2018).

Copper is important for enzyme function, formation of red blood cells, and connective

tissue. Copper deficient sheep may be unthrifty and weak, anaemic, and have a discoloured coat,

and reduced fertility (Lemke & Schwantje 2005). Deficiency may be primary due to low copper

levels in soil and forage plants, or secondary due to interfering substances. Serum copper

concentrations do not appear to be related to pregnancy rates in either study area. However,

serum copper is not a reliable way to evaluate the available copper as it can remain relatively

stable as liver stores are depleted (Herdt & Hoff 2011). The Dome herd had a 100% pregnancy

rate in 2017 and a lower mean copper level compared to pregnant ewes in the Cassiar herd. Iron

rich soils may contribute to the low observed copper levels in these areas (Herdt & Hoff 2011).

Zinc plays a role in immune function, stress hormone regulation, and reproduction. Zinc

levels do not appear to be affecting fertility in the Dome and Cassiar herds. There was no

significant difference in HCC concentration between herds, despite a difference in serum zinc

concentration (Table 9). However, serum zinc concentrations are an unreliable indicator of body

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stores as they vary significantly with stress and limited feed intake on a daily basis (Lemke &

Schwantje 2005).

Selenium deficiency is associated with several clinical conditions in sheep related to

immunity, reproduction, metabolism, and enzyme function. Deficiency may be chronic and lead

to poor recruitment in a popuation without obvious clinical signs (Hnilicka et al. 2002; Flueck et

al. 2012). Selenium also has a role in sequestering heavy metal contaminants in the body,

therefore if heavy metal exposure in the environment is high, selenium requirements also are

higher. Ungulates that live at high elevations in North America may be prone to selenium

deficiency due to the low availability in forage plants (Flueck et al. 2012). However, as selenium

levels in forage species are positively correlated with elevation in some areas of BC, further

investigation is warrented in the specific habitats of thinhorn herds (H. Schwantje pers. comm.).

The Cassiar herd had a significantly lower mean serum selenium concentration than the Dome

herd (0.11 µg/mL and 0.22 µg/mL respectively). The Cassiar study area had concentrations

considered deficient for domestic sheep (0.13 – 0.2 µg/mL; Puls 1994), and while the mean serum

selenium concentration in the Dome study area was in the adequate range, two individuals were

deficient (Appendix C).

Other essential minerals, such as calcium, chromium, cobalt, fluorine, iodine, and

vanadium are not included in the study as they are not part of routine serum mineral analysis

(Poppenga et al. 2012).

We only have serum trace mineral data from 2 study areas in BC, making comparisons

across thinhorn sheep range unfeasible. Serum levels of copper, zinc,cobalt, and selenium

demonstrated in the Dome and Cassiar study areas are deficient or low relative to reference

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ranges corresponding to optimal performance in other species. These potentially deficient

mineral levels may contribute to reduced population productivity and ability to withstand

environmental challenges or they may represent a normal state for this species.

5.8 Tissue Mineral and Heavy Metal Levels

The term ‘heavy metal’ refers to minerals which are toxic at low levels in the body (Singh

et al. 2009). They are not required for physiological function but may be acquired through

ingestion from environmental deposition in plant forage material, mineral licks, or heavily

contaminated water sources. Contaminants enter the northern terrestrial food web through

atmospheric delivery and rivers as well as local sources, including from industrial development

and naturally-occurring deposits (Gamberg et al. 2005). Even trace minerals may be considered

as heavy metals when circulating in the body at high levels or found in forage plant species at

levels above safe thresholds (Inchem, World Health Organization). In this study, heavy metals

tested included cadmium (Cd), lead (Pb), and arsenic (Ar), as well as copper (Cu) when found in

toxic concentrations. Understanding the trace mineral status and heavy metal contamination of

free-ranging wildlife is of particular importance from a One Health point of view as northern

communities have relied on wild ungulates traditionally and currently as a valuable source of

protein (Gamberg et al. 2016). Tissue samples examined in this study are all from non-resident

hunter-harvested Stone’s rams between 7 to 14 years of age as hunts are restricted to males with

at least full curl horns. Age has shown to be significantly related to increased tissue concentration

of some minerals due to accumulations in body organs over time (Gamberg 2005).

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We found mean heavy metal concentrations in kidney tissue below reported values for

four Dall’s sheep from NWT (Gamberg 2000). However, compared to another study by Larter et

al. (2016), mean renal iron and copper levels were higher in our study than in their ten Dall’s

sheep from the Northwest Territories; other minerals were found at similar concentrations.

Hepatic copper and zinc levels in our study were below reported levels for Dall’s sheep in NWT

(Gamberg 2000).

A very wide range of hepatic copper levels was determined (Table 11), with

concentrations in two samples deemed deficient and 16 samples above adequate levels for

domestic sheep (Puls 1994). Copper is stored in the liver, however when levels reach a threshold,

it is directed to the kidney for excretion. Renal copper concentrations were found to be higher in

fall than the spring due to accumulation from the diet over the summer (Gamberg et al. 2005).

Gamberg (2000) did not find copper to vary significantly with age or location of the animal.

Excessive zinc ingestion can interfere with copper uptake (Gamberg 2000).

High normal zinc levels were found in kidney and liver tissue from Stone’s rams. This is

contrary to the findings from Stone’s ewe serum zinc levels, collected in the late winter. There

are no body stores of zinc, therefore this difference is either artificial due to the reference ranges

used for different tissue types, or is due to a difference in forage quality between late summer

and late winter. A wide range of hepatic zinc levels were present (Table 10). Two samples had

concentrations above what is considered adequate for domestic sheep (Puls 1994). Excess zinc is

excreted and toxicity is rare in wildlife species (Gamberg 2000).

Cadmium accumulates in kidney, and to a lesser extent liver, and other tissues over time

with chronic exposure. The toxic effects include renal tubular damage, anaemia, enteropathy,

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and osteoporosis (Gamberg 2000). The toxic level in wildife species is unknown, but in some

domestic animal species, renal levels exceeding approximately 30mg/kg wet weight (wwt) have

been associated with histologic changes in the proximal renal tubules (Gamberg et al. 2005;

Larter et al. 2016). The Stone’s ram renal samples had a mean Cd of 2.72 mg/kg wwt, with one

sample having a concentration of 33.5 mg/kg wwt. An age was not provided for this ram. While

this may be a true result, environmental or handler contamination (e.g. cigarette smoke) of the

tissue sample during processing could cause an outlying result like this and we have no way of

confirming that potential error.

Mean arsenic levels in tissues were slightly above the minimum detectable limit for the

method used for testing. Similar levels were found in Dall’s sheep in the Northwest Territories

(Gamberg 2000). Arsensic contamination of the environment is primarily from (gold) mine

tailings, which the Cassiar herd has access to. The mean liver arsenic level was within a baseline

range reported in cows (0.032 – 0.048 ppm dry weight; Marr et al. 2004).

Lead is stored in liver and kidney tissue for short periods, but does not bioaccumulate

(Marr et al. 2004). Most tissue samples had lead concentrations below the minimum detectable

limit for the test used and lower than reported in Dall’s sheep (Gamberg 2000; Larter et al. 2016).

Sheep may be exposed to lead through byproducts of mining and industrial activity. The most

recognizable signs of toxicity are anaemia and blindness due to central nervous system damage

(Gamberg 2000). We did not find concerning concentrations of lead in Stone’s sheep tissues.

Mercury also is an important heavy metal that bioaccumulates, particularly in renal tissue,

and should be monitored for human and animal health purposes in areas where potential for

exposure is high, such as near mining activity and pulp and paper mills (Marr et al. 2004; Gamberg

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2000). We found a mean mercury concentration (0.109 mg/kg wwt), well below that reported by

Gamberg (2000; 0.43 mg/kg wwt), and similar to those of Larter et al. (2016; 0.08 mg/kg wwt).

Mercury concentration at this level is unlikely to be a concern for human consumption.

The mean liver selenium concentration was considered adequate at 0.3 mg/kg, however

16% of the tested samples are in the range considered deficient in domestic sheep (0.15 mg/kg;

Puls 1994; Table 16). Wildlife species, particularly those that live at high elevation, may have

lower physiological requirements as there are many reports of populations with blood and liver

concentrations considered deficient in domesticated species that do now show clinical signs

(Flueck et al. 2012). Hebert (1973) found that bighorn sheep that wintered at high elevation used

forage plants with higher levels of selenium than those that wintered at low elevation so it is

possible that this pattern also applies to forages that Stone’s sheep use in northern BC…..

As location data was not provided for many of the hunter-harvested Stone’s rams, we

were unable to look at spatial relationships between mineral levels and landscape features. There

is no evidence of large scale heavy metal/mineral contamination in hunter-harvested Stone’s

rams in the areas studied.

5.9 Body Condition

Fat stores are critical for winter survival for northern ungulates as forage availability can

be limited during the winter months, particularly if environmental conditions cause the formation

of a dense surface crust of snow and ice (Sivy et al. 2018). Energy from body stores is used to

maintain physiologic functions and growth in young animals. We used three different parameters

as proximate estimates of body condition. Hunter-harvested and live-captured animal body

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condition estimates are not comparable as body condition varies with season, sex, and pregnancy

status (Giudice 1985). Cook et al (2001) reviewed indices to assess nutritional condition of wild

ungulates and found that most were poorly correlated with actual body fat content or were

useful for only a narrow range of body condition. Calculated indices and predictive models using

body mass and morphometric measurements have been developed for Dall’s sheep and other

ungulate species (Stephenson et al. 1998; Cook et al. 2012; Aleuy 2019). Body mass is used in the

calculation of scale mass index (SMI) in Dall’s sheep; we did not weigh Stone’s sheep in this study,

so are unable to use this index without the introduction of considerable error from first

calculating mass using morphometric measurements.

5.9.1 Body Condition Score

Body condition score is a subjective measure of subcutaneous fat stores and muscle mass

(Aleuy et al. 2018). Body condition observations were recorded using different categorical scales

for live-captured Dall’s sheep in Alaska, live-captured Stone’s sheep in BC, and hunter-harvested

rams in BC, preventing direct comparison between projects. As BCS is a subjective measurement

of condition, there is possible error due to measurement bias, despite several opinions from

experienced animal evaluators on each capture. All live-captured sheep in BC were scored by one

person, and live-captured sheep in Alaska were scored by the same few people. The data form

distributed to hunters for sample collection has a sketch and description of BCS categories in an

attempt to remove some subjectivity in estimating BCS in rams. We attempted to increase

objectivity by requesting a measurement of subcutaneous fat along the dorsal rump in a standard

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manner and for collection of a whole kidney plus renal fat. Unfortunately, these measures and

samples were rarely provided.

Visual evaluation of BCS data (Figures 1-3) revealed a greater proportion of ewes were in

higher BCS categories in Alaska compared to BC live-captured sheep. A slightly higher BCS was

found in the Talkeetna study area versus the Chugach study area. A similar lack of subcutaneous

fat was reported from captures in GMU 14C (Chugach study area) in 2012 (Lohuis pers. comm.).

Ewes that are barren or that lose neonatal lambs lose less mass in the spring than ewes nursing

a lamb due to the energetic costs of reproduction, therefore, have less mass to gain back in the

summer (Douhard et al. 2018). We found a higher BCS in Alaska where the pregnancy rate was

lower than BC. Potentially related to body condition, our small number of ewe recaptures in the

Cassiar study area show that ewes may not produce a lamb every year, and first pregnancy may

be delayed to three years of age in some cases.

For hunter-harvested rams, a comparison between years shows a decreasing trend in BCS

from 2016 to 2019. We did not find another health indicator to be an explanatory variable in this

pattern. Examination of external factors, such as climatic conditions, timing of forage green-up,

and demographics of other wildife populations may offer some insight into the observed pattern

but are difficult to collect in these remote locations. Douhard et al. (2018) found that spring

temperatures had the greatest influence on seasonal mass changes; we do not have the climatic

data to evaluate this relationship in our study.

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5.9.2 Rump Fat Depth

Rump fat depth, when recorded from a consistent body location, is a relatively sensitive

measure of condition when subcutaneous fat is present. At lower body conditions, internal fat

stores are a better measure of body condition, for example a kidney fat index or marrow fat

content, because of the order of fat deposition and utilization (Nieminen & Laitinen 1986). In

other wild ungulates indices of rump fat have strong correlations with ingesta-free body fat

percentage (Stephenson et al. 1998, Cook et al. 2010). More species-specific research is needed

to determine the relationship between rump fat depth and body fat percentage in thinhorn

sheep.

Very few live or dead sheep in our study had detectable rump fat. Therefore, we were

unable to compare rump fat with other indicators of health and did not find rump fat depth to

be a useful indicator of condition in our study. At the time of capture and sampling in late-winter,

free-ranging thinhorn sheep are close to their condition nadir and have catabolized the majority

of subcutaneous fat for energy.

5.9.3 Marrow Fat

Marrow fat percentage does not reflect general body fat content (Mech & Giudice 1985).

Marrow fat is a useful index of body condition when an animal is in a declining plane of nutrition

as it is the last fat storage location to be depleted following subcutaneous, omental, renal, and

pericardial fat stores (Mech & Delgiudice 1985; Neiminen & Laitinen 1986). A study of fatness

indices in mule deer found that marrow fat percentage varied less with age and season than

other fat deposits (Neiminen & Laitinen 1986). Unfortunately, we did not receive enough hunter-

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harvested kidney samples with intact renal fat to make useful comparisons with marrow fat or

to use renal fat as a measure of population condition.

Depletion of marrow fat is indicative of poor body condition. Cook et al (2001b) examined

marrow fat and body condition in Rocky Mountain elk and found that a femur marrow fat

percentage <90% indicated poor condition (<6% body fat). We found a large range in marrow fat

proportion of hunter-harvested rams (30 – 100% marrow fat content; Table 12). We used the

metatarsus bone instead of the femur for consistency with hunter-harvest health sampling in

caribou (CARMA 2008). Depletion of fat stores in long bones may differ between species of wild

ungulate, but to our knowledge this has not been assessed for bighorn or thinhorn sheep.

Marrow fat ranged from 90% in early winter to 30% in April in Dall’s sheep in the Kenai Mountains

(Nichols 1971). Based on these findings, we would expect hunter-harvested rams to have marrow

fat content approaching 90% in late summer and fall. A limitation to the use of marrow fat in our

study is that we do not know how bones were handled by hunters prior to us receiving them. We

cannot account for desiccation that may have occurred through exposure or freezing and the

difference between bones stored wrapped in plastic or unwrapped (Murden et al. 2017).

We used metatarsal marrow instead of femur marrow as they are well correlated

(Neiminen & Laitinen 1986) and these animals are harvested for consumption, so we would likely

obtain fewer femurs than metatarsals. However, only femur marrow fat has been validated

against total body fat percentage (Cook, pers. comm.).

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5.10 Pregnancy

Mating or the rut of thinhorn sheep occurs mid-November to early December with

parturition 170 days later in late May to early June; therefore, at the time of sample collection in

late-February to early April, serum PSPB is a reliable method of pregnancy detection (Houston et

al. 1986). Radioimmunoassay for PSPB has a sensitivity of 98% in domestic sheep (Willard et al.

1995), 100% in mountain goats (Houston et al. 1986), and 100% in bison (Love et al. 2017). It has

been/not been validated for thinhorn sheep.

Wild sheep in North America are thought to typically reach sexual maturity at 2.5 years

of age and deliver their first lamb at 3 year. There are, however, reports of pregnant yearling

Dall’s sheep from the Kenai area of Alaska and the Mackenzie Mountains, NWT (Hoefs & Nowlan

1993). Previous research in southcentral Alaska found that ewes first breed in the fall of their

third year and lamb as four year-olds (n = 9; Lohuis 2013). In 2019 in the Talkeetna study areas,

we captured and sampled seven ewes in their second or third year, four of which were pregnant

(n2 year-olds = 2/3, n3 year-olds = 2/4). All of the three year-olds captured in the Chugach range (n =

4/4) were pregnant; no two year-olds were captured. Variance in age of primiparity allows for

allocation of resources towards growth or reproduction as needed (Festa-Blancet et al. 2000).

Mass of lambs during early development is positively correlated with mass as an adult,

and lifetime reproductive success in bighorn sheep, when accounting for other herd demographic

factors. Douhard et al (2018) found that greater maternal mass changes between summer and

winter were linked with lower lamb mass, likely due to reduced lactation. Adult mass changes

can be strongly related to climatic conditions. This explains why lambs born the year after a harsh

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winter may be less likely to survive and will likely have a lower lifetime productivity (Douhard et

al 2018).

The pregnancy rates we determined in Dall’s sheep in 2019 are near the maximal rates of

what has previously been reported in GMU 14C, Alaska (Table 13). Pregnancy rates in BC

appeared to be consistent with other studies, however, it should be noted that data collected

during the Cassiar study is not considered an unbiased sample. One of the goals of capturing and

collaring ewes in this population was to examine ewe health, causes of lamb mortality, and

through the installation of collars and vaginal implant transmitters (VITs) over two years of

capture effort, identify habitat selection by ewe associated with their lambing. Therefore,

selection of ewes for capture was adapted based on observations and lessons learned, with ewes

that were more likely to be pregnant being preferentially targeted in the second year of capture

(Year Two). Our Year One observations suggested that ewes with a lamb-at-heel were less likely

to be pregnant in the current year, so for this project, ewes without a lamb were preferentially

selected and sampled. As such, the capture group was expected to have a higher pregnancy rate

than a truly unbiased capture effort that did not target specific unaccompanied ewes. Wood et

al. (2010) reported a 95.3% pregnancy rate in Stone’s sheep in the Dunlevy and Schooler

populations, determined by serum progesterone concentrations of greater than 2 ng/ml.

Pregnancy rates below 100% in ewes over four years of age indicate that not all mature

ewes reproduce annually. A previous study of Dall’s sheep in the Chugach range in Alaska found

pregnancy rates of 43% in 2012 and 94% in 2013. There was a record snowfall in 2011 which likely

contributed to the low rate in 2012 (Lohuis et al. 2012). Failure to reproduce may be due to

resource allocation for maintenance of body condition in the face of nutritional stress, or stress

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due to climatic conditions, herd structure dynamics, anthropenic stressors, or infection.

Pregnancy rates are not independent from year to year; low pregnancy may be followed by a

higher rate as females are likely in better body condition going into the mating season and more

likely to conceive if they did not rear a lamb the previous year (Downs et al. 2018). The high

seroprevalence of T. gondii in Dall’s sheep is concerning, but there was no ability to assess

lambing in the Dall’s ewes so we currently do not have direct evidence of infection having

detrimental effects.

Reproduction, immune function, and glucocorticoid levels are linked and exhibit trade-

offs in response to metabolic demands. Reduced reproductive output may come at the cost of

immune function when resources are limited; thus priortizing future over current reproduction

(Downs et al. 2018). Chronic elevation of endogenous glucocorticoids can suppress reproduction,

conversely, acute increases of endogenous glucocorticoids facilitate reproduction (Downs et al.

2018; Dulude de Broin et al. 2020). Downs et al (2018) specifically examined these relationships

in Dall’s sheep in southcentral Alaska in more detail than our study allowed for. Downs et al

(2018) found no relationship between cortisol levels at mating and pregnancy success. We did

not evaluate cortisol levels in the fall in ewes, so are not able to examine this relationship directly.

However, HCC provides an indication of mean glucocorticoid production over the duration of hair

growth that overlaps the gestation period. We found no relationship between HCC and

pregnancy in Stone’s sheep and did not assess HCC in Dall’s sheep.

A concurrent study of the Cassiar herd used GPS-collars and VITs to assess lambing habitat

selection and timing. All VITs were expelled, indicating parturition. In 2018, three of nine

pregnant ewes expelled their VITs earlier than expected, which may be due to placement of the

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device, grooming (they have an external antenna). Abortion may also cause early expulsion of at

VIT, however, collar movement patterns and aerial surveys of this herd may be used to determine

if a lamb was born at term. In 2019, nine of ten Stone’s ewes that were pregnant at the time of

capture and sample collection in late winter appeared to lamb based on their collar movement

patterns (Grace Enns, pers. comm.). Causes of abortion in thinhorn sheep include exposure to

infectious agents, nutritional stress/poor body condition, and environmental or anthropogenic

stress.

The reproductive success required to sustain a population is influenced by many other

factors, including lamb survival. We did not measure the proportion or causes of lamb mortalities

in this study. Previous work in Alaska concluded that the vast majority of mortalities in the first

year of life are due to predation. Scotton (1997) collared lambs within their first 3 days and found

59% of lambs born in 1995 and 1996 survived to one year of age (mortality rate was 41%) in the

Central Alaska Range. In the Chugach Range in 2009 – 2012, lamb survival to one year of age

ranged from 9% to 63% (Lohuis et al. 2012).

We found a significant positive correlation between pregnancy rate and mean FGM for

Stone’s sheep herds. This relationship may be explained by circulating cortisol levels typically

being higher during pregnancy than in other species (Cook 2012). With a higher proportion of

pregnant ewes in a herd, the mean FGM level will be higher due to the increased metabolic stress

associated with pregnancy. We did not find a relationship between pregnancy and HCC.

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5.11 Mortalities

We did not find any health-related causes of death on investigation of recovered thinhorn

sheep carcasses. Incidentally, respiratory bacterial species were cultured from pulmonary tissue,

which may represent normal respiratory flora. Mannheimia haemolytica was identified in the

lungs of one Stone’s ewe that died of capture related trauma without histological evdience of

pneumonia. On tonsil swab culture, collected the day prior to death, Mannheimia spp. was

isolated.

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CHAPTER 6 - LIMITATIONS, CONCLUSIONS, FUTURE DIRECTIONS OF STUDY

6.1 Limitations

The health data presented in this study is observational and represents the status of

thinhorn herds at a single point in time each year and must be interpreted accordingly. Although

we can make assumptions about health at other times of the year based on the development

period for certain health conditions, we cannot rule out acute changes due to recent events,

determine the influence of season, or make direct comparisons between populations in other

seasons.

Thinhorn herds included in this study were selected based on known or suspected

population declines. This may lead to detection of higher disease exposure prevalences and

alterations in targeted health metrics relative to what we would have found if we had examined

stable or increasing populations and possibly result in overestimation of health concerns if

applied to all thinhorn sheep populations. Selection of individual sheep was often non-random.

Provincial hunting regulations require that thinhorn rams be over 8 years of age or ‘full curl’ at

harvest. This introduces sex and age-bias into the dataset as some health parameters are known

to be influenced by age. Younger rams were targeted for live-capture in Alaska for GPS-collaring

so that several years of data could potentially be collected before they are eligible for harvest.

Immature rams were captured incidentally and sampled in BC. While we cannot compare

between ram groups, we can continue to monitor rams in these herds longitudinally. Apparently

healthy ewes without lambs were selected for capture and collaring in BC in order to maximise

the potential for collaring lambs in the spring as part of a concurrent lamb survival study. As we

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cannot make direct comparisons between all individuals included in this study, the effective

sample size on which to draw conclusions is small.

6.2 Conclusions

Here we report baseline health information as a recommended framework for continued

thinhorn sheep herd health monitoring. Our study is the first comprehensive assessment of

thinhorn sheep health across jurisdictions. The health data presented in this study was collected

over several years with consistency in sample collection methods and timing of sampling. We

documented high exposure to T. gondii and MCF, and naivity to other pathogens common to

domestic livestock and other wildlife species. We confirmed findings of previous researchers,

including winter tick infestation of Stone’s sheep in the Williston study area (Wood et al. 2010),

M. ovi carriage and exposure in free-ranging Dall’s sheep in Alaska (Highland et al. 2018), and

nematode species abundance and diversity (Jenkins & Schwantje 2002). Although our sample size

for each herd was relatively small, we were able to show associations between stress and other

indicators of health.

Thinhorn sheep populations across their range are not exhibiting significant differences

in non-infectious indicators of health. Year has a greater effect than location on most health

parameters in our study, highlighting the importantance of longitudinal health surveillance.

Pathogen prevalences, notably M. ovi and T. gondii, vary between thinhorn populations in Alaska

and BC.

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6.3 Recommendations and Future Areas of Study

Capture and sampling of live animals affords the opportunity to validate less invasive

sampling techniques. We used paired serum and filter paper samples from five live-captured

Stone’s sheep to assess test performance for filter paper eluates. Future research may include

validation of other test methods such as hair trace mineral and heavy metal analyses.

We examined some indicators of health that relate to population resiliency. Further

investigation into the predictive value of indicators of health on population persistance and

inclusion of other specific indices would provide useful information to wildlife managers. Future

herd health assessment could include quantification of compounds related to inflammation and

immune function, such as the acute phase proteins haptoglobin and serum amyloid A (Downs et

al. 2018, Bondo et al. 2019), however, useful and accurate interpretation of these parameters is

difficult. Archived blood samples collected during this study are being analyzed for transcription

levels of genes associated with physiological responses to stressors for comparison with bighorn

sheep populations (Bowen et al. 2020). Examination of other steroids in thinhorn hair (e.g.

thyroxine, testosterone, and estrogen) may provide insight into reproduction, social structure,

metabolism, and growth (Koren et al. 2019). Future research should continue to archive samples

as available and consider determining the relationships between various indicators of health,

including respiratory pathogen prevalence, with demographic data including ewe:lamb ratios and

age-specific survival rates in order to inform management decisions on conservation of thinhorn

sheep.

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APPENDICES

Appendix A – Study Areas

Figure 4. Management Units in the Skeena (Region 6) and Peace (Region 7) Regions of British Columbia where hunter-harvested ram samples were collected.

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Figure 5. Locations of study areas for thinhorn sheep live-capture. Dall’s sheep (Ovis dalli dalli) were captured and sampled in the Chugach and Talkeetna Study areas (Alaska), and Stone’s sheep (O. dalli stonei) were captured and sampled in the Cassiar, Dome, and Williston study areas (British Columbia).

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Appendix B – Health Testing and Diagnostics Methods Table 15. Thinhorn sheep health sampling methods and references employed in our surveillance study from 2016 – 2020.

Health Determinant Sample Type Methoda Laboratory

Paramyxovirus [Parainfluenzavirus-3 (PI3)]

serum VN Animal Health Centre, Abbotsford, British Columbia, Canada VN Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA

Paramyxovirus [Bovine Respiratory Syncytial Virus (BRSV)]


Pestivirus [Bovine Viral Diarrhea Virus (BVDV)]


Lentivirus [Ovine Progressive Pneumonia Virus (OPP)]

serum cELISA Animal Health Centre, Abbotsford, British Columbia, Canada

Gammaherpesvirus [Malignant Catarrhal Fever (MCF)]

serum/plasma ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA

Orbivirusb

[Epizootic Hemorrhagic Disease (EHD)] serum ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA

Alphaherpesvirus [Bovine herpesvirus-1, Infectious Bovine Rhinotracheitis (IBR)]


Mycoplasma ovipneumoniae nasal swab PCR (UM assay) Animal Health Centre, Abbotsford, British Columbia, Canada

PCR (LM40 assay) U.S Department of Agriculture Animal Disease Research Unit, Pullman, Washington, USA Real-time PCR (UM assay) Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA Real-time PCR (LM40 assay) Wyoming State Veterinary Laboratory, Laramie, Wyoming, USA

serum cELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA nasal swab aerobic culture Wyoming State Veterinary Laboratory, Laramie, Wyoming, USA

Pasteurellaceae tonsil swab Culture Animal Health Centre, Abbotsford, British Columbia, Canada Leptospira spp.b serum ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA Coxiella burnetiib (Q Fever) serum ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA Mycobacteria avium ssp. paratuberculosisb

serum ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA

Brucella ovisb serum ELISA Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA Gastrointestinal Nematodes Cestodes (Tapeworms) Coccidia (Enteric Protozoans)

feces Wisconsin double centrifugation fecal floatation

Canadian Wildlife Health Cooperative, Saskatoon, Saskatchewan, Canada

Modified McMaster

In-house at BC Wildlife Health Program Laboratory, Nanaimo, British Columbia, Canada

Lungworms (Protostrongylids and DSL)

feces Modified Baermann beaker test Canadian Wildlife Health Cooperative, Saskatoon, Saskatchewan, Canada and In-house at BC Wildlife Health Program Laboratory, Nanaimo, British Columbia, Canada

Trematodes (Flukes) feces Modified fecal sedimentation Canadian Wildlife Health Cooperative, Saskatoon, Saskatchewan, Canada Toxoplasma gondiib serum IFA, multispecies kit (Innovative

Veterinary Diagnostics, Grabels, France)

Washington Animal Disease Diagnostic Laboratory, Pullman, Washington, USA

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Hair Cortisol Concentration (HCC) hair (200 guard hairs with bulbs removed)

ELISA Oxford EA-65 Cortisol Competitive EIA kit (Oxford Biomedical, Lansing, Michigan, USA)

University of Saskatchewan, Toxicology Laboratory, Saskatoon, Saskatchewan, Canada

Fecal Glucocorticoid Metabolites (FGC) feces (3-5 pellets) EIA Toronto Zoo, Reproductive Biology Laboratory, Toronto, Ontario, Canada Serum Trace Nutrient Levels (Mn, Fe, Co, Cu, Zn, Se, Mo)

serum ICP-MA (Bruker 820 S; Bruker Ltd. Milton, Ontario, Canada)

University of Guelph, Animal Health Laboratory, Guelph, Ontario, Canada

Tissue Mineral Levels kidney liver

ICP-MS ALS, Burnaby, British Columbia, Canada

Pregnancy serum ELISA (BioPRYN Flex) Herd Health Diagnostics, Pullman, Washington, USA

Body Condition live animal Body condition score (BCS) - manual palpation

field

live animal

rump fat depth - ultrasound

carcass direct measurement of back fat depth at the last rib

metatarsal bone marrow fat drying in-house at BC Wildlife Health Program Laboratory, Nanaimo, British Columbia, Canada a Laboratory testing methods included virus neutralization (VN), capture enzyme-linked immunosorbent assay (cELISA), polymerase chain reaction (PCR), Indirect immunofluorescence assay (IFA), enzyme immunoassay (EIA), and inductively couple mass spectrometry (ICP-MS).

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Appendix C – Health Testing Results Table 16. Liver tissue concentrations of trace minerals and heavy metals by wet weight (wwt; unless otherwise specified) for Stone’s rams harvested in BC from 2016 – 2018 (n = 42). A reference range for domestic sheep and previous findings in California bighorn sheep (BHS), Rocky Mountain BHS, and Dall’s sheep are included for comparison.

Reference and reported ranges and means / Sample ID

Cd (mg/kg wwt)

Co (mg/kg wwt)

Cu (mg/kg wwt)

Fe (mg/kg wwt)

Pb (mg/kg wwt)

Mg (mg/kg wwt)

Mn (mg/kg wwt)

Mo (mg/kg wwt)

Se (mg/kg wwt)

Zn (mg/kg wwt)

Domestic sheep (Puls 1994)

- - 25 - 100 40 - 100 - 130 - 190 2.0 – 5.0 - 0.25 – 0.80 25 - 50

California BHS (Lemke & Schwantje 2005)

- - 67.85 111.17 - 169.33 4.09 - 0.34 44.18

Rocky Mountain BHS (Lemke & Schwantje 2005)

- - 38.20 160.98 - 158.36 3.04 - 0.36 67.43

Dall’s sheep (Gamberg 2000; mean ± SD; dry weight)

1.26 ± 1.08 - 130.2 ± 122.4

- <0.77 ±0.40 - - - - 157.3 ± 132.0

Stone’s sheep - Project Mean ± SD

0.39 ± 0.99 0.08 ± 0.03 91.03 ± 56.75

103.2 ± 60.67

0.08 ± 0.45 188.24 ± 22.57

3.30 ± 0.81 0.92 ± 0.32 0.31 ± 0.33 38.19 ± 23.75

214 0.144 0.107 16.9 71.1 0.0067 187 2.42 0.67 0.106 40.7 254 0.127 0.0788 130 65 0.0065 214 4.12 0.744 0.178 35 17-9506 0.106 0.055 62.1 256 0.0247 253 2.73 0.821 0.152 178 18-11577 0.199 0.215 49.4 69.2 0.005 197 4.01 0.695 0.278 46.6 18-11578 0.127 0.0767 153 98.7 0.0053 185 2.82 0.695 0.126 30.2 18-11580 0.153 0.0707 167 65.3 0.0056 205 3.63 1.06 0.239 37.7 18-11581 0.0931 0.0758 5.7 88.7 0.002 144 2.14 1.14 0.154 27.6 18-11582 0.551 0.0766 65.5 47.8 0.0203 194 3.92 1.77 1.93 33.3 18-11584 6.41 0.0841 131 214 0.002 173 2.8 0.935 1.12 80 18-11585 0.07 0.0828 65.8 52.5 2.91 169 3.4 0.505 0.191 30.6 18-11586 0.141 0.0952 95.2 104 0.0592 192 4.52 0.815 0.337 31.2 18-11587 0.514 0.11 73.8 109 0.013 245 4.35 0.968 0.354 44.4 18-11588 0.0494 0.0715 83.7 160 0.0173 197 2.12 1.54 0.704 32.4 18-11589 0.605 0.0801 70 58.1 0.0403 174 3.36 1.26 0.255 33.6 18-11590 0.266 0.0761 160 53.6 0.0051 177 3.8 0.997 0.165 33.2 18-11591 0.0772 0.0989 112 258 0.0103 196 4.08 0.625 0.18 31.3 18-11594 0.21 0.0943 141 58 0.009 195 3.23 0.681 0.302 34.1 18-11595 0.0821 0.0538 121 49.1 0.004 187 4.11 0.812 0.173 33.9 18-11596 0.13 0.0766 275 51.9 0.002 147 4.48 0.876 0.186 35.4 18-11597 0.3 0.0718 36.6 88.7 0.0266 178 3.16 1.35 0.747 27.9 18-11599 0.101 0.0732 119 66.6 0.002 185 3.66 0.667 0.428 30.9 18-11600 0.152 0.0839 78.4 65.3 0.0109 192 3.12 0.741 0.213 34 18-11601 0.0695 0.0663 28.5 99.5 0.004 197 3.37 0.878 0.157 29.3 18-11603 0.0878 0.0759 47.5 76.3 0.002 210 3.62 1.08 0.134 29.4 18-11605 0.0624 0.0793 189 111 0.0074 175 3.21 0.842 0.4 32.8 18-11606 0.583 0.096 142 46.8 0.0064 214 4.55 1.59 0.249 40 18-11610 0.0355 0.0784 103 66.4 0.002 206 3.31 1.17 0.14 37.5

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18-11611 0.0965 0.0664 191 70.1 0.004 167 2.79 0.739 0.154 29 18-11613 0.148 0.0793 126 287 0.009 174 3.3 0.719 0.142 32.5 18-11614 1.09 0.0871 61.8 135 0.0043 199 3.58 1.39 0.582 42.4 18-11618 0.0696 0.0829 72.4 135 0.0114 194 3.7 0.415 0.203 34.1 18-11619 0.0953 0.0772 75.5 82 0.005 196 2.89 1.37 0.311 36 18-11620 1.53 0.0191 1.07 177 0.005 172 0.228 0.0334 0.227 44.7 18-11621 0.654 0.115 24.2 87.6 0.0053 160 2.46 0.761 0.175 37.6 19-0359 0.26 0.0807 8.73 111 0.0116 174 2.77 1.14 0.338 28 19-0360 0.215 0.0585 55 106 0.002 138 2.45 0.972 0.273 23.3 19-0361 0.0685 0.0781 34.2 78 0.0082 172 3.24 0.879 0.103 29.3 19-0362 0.112 0.0741 101 42.4 0.002 195 4.21 0.636 0.172 31.1 19-0363 0.095 0.0895 101 124 0.0375 189 3.48 0.818 0.092 30 19-0364 0.101 0.083 98.6 58.7 0.0163 202 3.53 0.869 0.123 33.6 NW-? 0.279 0.118 67.3 104 0.0085 210 3.5 0.994 0.494 33.2 NW-197 0.202 0.0735 83.2 186 0.0976 176 2.39 1.1 0.062 28.3

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Table 17. Kidney tissue concentration of trace minerals and heavy metals by wet weight (wwt; unless otherwise specified) for Stone’s rams harvested in BC from 2016 – 2018 (n = 51). A reference range for domestic sheep and previous findings in California bighorn sheep (BHS), Rocky Mountain BHS, and Dall’s sheep are included for comparison as a mean or mean and standard deviation (SD).


Cd (mg/kg wwt)

Co (mg/kg wwt)

Cu (mg/kg wwt)

Fe (mg/kg wwt)

Pb (mg/kg wwt)

Mg (mg/kg wwt)

Mn (mg/kg wwt)

Mo (mg/kg wwt)

Se (mg/kg wwt)

Zn (mg/kg wwt)


- - 3.0 – 8.0 40 - 100 - 120 - 180 1.5 – 3.5 - 0.45 13 - 30

Dall’s sheep (Gamberg 2000; mean ± SD; dry weight)

12.07 ± 11.95

- 23.84 ± 7.99 - <1.24 ± 0.58 - - - - 123.0 ± 37.2


- - 6.39 88.33 - 154.25 - - 1.19 27.93

Rocky Mountain BHS (Lemke & Schwantje (2005)

- - 5.39 98.47 - 140.0 1.70 - - 32.24

Dall’s sheep (Larter et al. 2016; mean ± SD)

2.53 ± 2.88 0.04 ± 0.01 3.94 ± 1.24 42.3 ± 23.2 0.02 ± 0.02 155 ± 16.8 1.13 ± 0.21 0.24 ± 0.18 1.27 ± 0.41 25.7 ± 6.09

Stone’s sheep – project mean and SD

2.72 ± 2.79 0.05 ± 0.02 6.21 ± 18.07 59.83 ± 48.26

0.01 ± 0.01 169.33 ± 17.55

1.53 ± 0.42 0.36 ± 0.16 1.07 ± 0.25 23.47 ± 8.99

214 0.676 0.0441 3.13 62.3 0.002 168 1.13 0.305 0.921 22.1 254 0.975 0.041 3.72 26.3 0.0125 182 1.85 0.247 1.08 23.1 17-9506 0.879 0.0463 7.53 158 0.0287 163 1.32 0.504 1.01 72.1 18-11577 0.53 0.103 2.94 28.7 0.0051 169 1.05 0.225 0.957 20 18-11578 1.3 0.0436 4.17 38.2 0.0202 187 1.71 0.318 1.26 22 18-11579 0.442 0.0333 2.46 31.6 0.009 127 1.08 0.11 0.762 15.7 18-11579 0.11 0.07 119 99.5 0.0068 172 3.28 0.287 0.309 30.5 18-11580 1.76 0.0431 2.85 52.4 0.0086 177 1.55 0.388 1.12 21.9 18-11584 33.5 0.0786 3.66 60.2 0.002 165 1.36 0.246 0.839 40.2 18-11585 0.987 0.0593 3.24 56.3 0.0285 180 1.62 0.201 1.22 22.3 18-11588 0.557 0.04 2.54 28.6 0.0081 144 0.969 0.541 1.18 17.3 18-11590 1.98 0.0459 3.76 58.9 0.0084 177 1.67 0.378 1.01 24.8 18-11591 0.564 0.0531 3.23 38.7 0.0237 163 1.62 0.218 1.06 16.4 18-11594 1.77 0.0555 3.65 43 0.0089 183 1.53 0.19 1.21 22.8 18-11595 1.43 0.0423 3.29 25.4 0.0159 157 1.92 0.346 0.984 20.5 18-11596 1.77 0.0481 3.96 51.5 0.0059 159 1.84 0.249 1.09 22.8 18-11597 6.24 0.0517 3.79 31.4 0.0125 159 1.56 0.481 1.3 22 18-11599 1.39 0.0521 3.48 78.6 0.0054 189 1.38 0.223 1.21 20.3 18-11600 1.38 0.0464 2.52 28 0.0118 171 1.37 0.264 1.09 19.4 18-11601 0.611 0.0397 2.56 26 0.0074 167 1.38 0.283 1.16 16 18-11605 1.33 0.0709 4.91 51 0.0193 193 1.84 0.366 1.91 32.3 18-11606 3.2 0.048 2.94 52.1 0.0072 171 1.83 0.647 1.42 24.5 18-11607 2.13 0.0408 4.49 75.8 0.0055 176 1.84 0.884 0.76 24.6 18-11610 0.39 0.0452 3.09 23.5 0.002 184 1.6 0.56 1.08 19 18-11611 1.74 0.0514 4.83 88.7 0.0071 177 1.86 0.434 1.17 27.4 18-11612 2.81 0.0452 3.07 41.7 0.0065 135 1.43 0.669 1.34 25.1 18-11613 1.97 0.0411 3.18 27.9 0.011 154 1.39 0.278 0.931 23.3 18-11614 4 0.0292 3.49 41.8 0.002 170 1.01 0.424 1.16 22.1

141

18-11618 0.692 0.0501 3.78 46.4 0.0128 187 1.64 0.162 1.09 24.1 18-11619 0.444 0.0477 3.24 43.4 0.0097 206 1.59 0.486 1.39 20.3 18-11620 19.6 0.132 3.88 32.5 0.0084 160 0.563 0.15 0.74 25.4 18-11621 3.95 0.0308 2.91 99.8 0.002 130 0.808 0.283 1.09 21.9 18-11624 0.396 0.0434 2.8 40.9 0.0248 136 1.15 0.344 1.1 17.2 19-0359 1.95 0.0513 3.46 46.4 0.0082 178 1.67 0.396 1.22 21.9 19-0360 2.7 0.0403 2.59 149 0.0041 153 1.24 0.544 1.26 19.7 19-0361 0.901 0.0507 3.56 56.8 0.0175 193 1.67 0.403 1.31 24.1 19-0362 0.95 0.035 2.52 297 0.0265 166 1.53 0.194 0.733 17.7 19-0363 0.781 0.0512 4.11 65.1 0.0112 168 1.77 0.303 0.908 19.9 19-0364 1.17 0.0423 2.74 53.3 0.0215 177 1.56 0.246 1.06 20.4 NW-154 1.23 0.049 3.71 61.7 0.0557 177 1.74 0.291 1.12 23.2 NW-190 0.999 0.0391 3.33 39.8 0.005 189 1.62 0.351 0.913 19.1 NW-197 2.14 0.0458 2.63 54.7 0.0913 173 1.59 0.583 0.747 20.5 19-0359 1.95 0.0513 3.46 46.4 0.0082 178 1.67 0.396 1.22 22.1 19-0360 2.7 0.0403 2.59 149 0.0041 153 1.24 0.544 1.26 23.1 19-0361 0.901 0.0507 3.56 56.8 0.0175 193 1.67 0.403 1.31 72.1 19-0362 0.95 0.035 2.52 297 0.0265 166 1.53 0.194 0.733 20 19-0363 0.781 0.0512 4.11 65.1 0.0112 168 1.77 0.303 0.908 22 19-0364 1.17 0.0423 2.74 53.3 0.0215 177 1.56 0.246 1.06 15.7 NW-154 1.23 0.049 3.71 61.7 0.0557 177 1.74 0.291 1.12 30.5 NW-190 0.999 0.0391 3.33 39.8 0.005 189 1.62 0.351 0.913 21.9 NW-197 2.14 0.0458 2.63 54.7 0.0913 173 1.59 0.583 0.747 40.2

142

Table 18. Serum trace mineral levels for Stone’s ewes and immature rams sampled in BC from 2016 – 2018 (n = 26). A reference range for domestic sheep and previous findings in California bighorn sheep (BHS), Rocky Mountain BHS, and Dall’s sheep are included for comparison as a mean or mean and standard deviation (SD).


Co (ng/mL)

Cu (µg/mL)

Fe (µg/mL)

Mg (µg/mL)

Mn (µg/mL)

Mo (µg/mL)

Se (µg/mL)

Zn (µg/mL)


0.9 - 15 1.17 – 2.56 1.6 – 2.2 10 – 33 > 0.006 0.01 – 0.1 0.13 – 0.2 0.9 – 1.84

California BHS (Poppenga et al. 2012)

- 0.90 – 1.30 p 1.66-2.22 20 – 35 - - 0.08 – 0.50 (whole blood)

0.8-0.12


- 0.86 - 2.30 - - 0.09 1.61

Rocky Mountain BHS (Lemke & Schwantje 2005)

- 0.71 2.16 2.31 - - 0.16 1.93

Stone’s sheep Dome study area

0.57 0.44 6.28 26.0 2.6 <10.0 0.22 0.42

Stone’s sheep Cassiar study area

2.18 0.68 3.9 - 3.0 6.3 0.11 0.68

17-9500 0.427 0.427 4.69 24.7 2.0 <10.0 0.161 0.4 17-9501 1.3 0.42 2.99 26.0 1.0 <10.0 0.143 0.359 17-9502 0.405 0.405 2.19 27.5 4.0 <10.0 0.89 0.434 17-9503 0.837 0.434 4.89 26.8 4.0 <10.0 0.155 0.418 17-9504 0.595 0.471 14 23.4 4.0 <10.0 0.149 0.456 17-9505 0.465 0.417 2.69 25.6 3.0 <10.0 0.11 0.386 17-9506 0.397 0.502 9.74 25.7 3.0 <10.0 0.131 0.435 17-9507 0.694 0.534 3.26 27.9 1.0 <10.0 0.243 0.438 17-9508 0.422 0.451 2.54 26.3 2.0 <10.0 0.189 0.349 17-9509 0.415 0.383 5.2 25.8 2.0 <10.0 0.183 0.362 17-9545 0.386 0.412 24.4 25.4 4.0 <10.0 0.199 0.442 17-9546 0.625 0.437 2.06 26.4 3.0 <10.0 0.249 0.541 17-9548 0.405 0.478 3.01 26.6 1.0 <10.0 0.118 0.427 18-13323 0.86 0.58 3.0 . 2.6 1.1 0.150 0.64 18-13324 2.10 0.66 3.0 . 4.6 3.1 0.140 0.90 18-13325 1.80 0.83 3.0 . 3.3 3.5 0.140 0.77 18-13326 1.40 0.71 3.6 . 2.6 27.0 0.180 0.88 18-13327 2.60 0.55 8.2 . 2.8 1.9 0.053 0.74 18-13328 4.20 0.74 3.4 . 2.4 . 0.069 0.64 18-13329 0.98 0.68 3.8 . 2.5 . 0.067 0.73 18-13330 0.90 0.57 4.5 . 3.0 . 0.063 0.56 18-13331 3.30 0.67 4.2 . 2.9 . 0.085 0.77 18-13332 0.88 1.10 1.9 . 1.6 . 0.280 0.33 18-13333 1.20 0.65 3.0 . 2.1 1.4 0.100 0.60 18-13334 0.67 0.50 4.2 . 4.2 . 0.061 0.56 18-13335 7.40 0.54 4.9 . 4.2 . 0.063 0.68

143

Table 19. Detections and exposure to selected pathogens in live-captured Stone’s sheep in BC from 2017 to 2020; including Mycoplasma ovipneumoniae (M. ovi), malignant catarrhal fever (MCF) virus, ovine progressive pneumonia (OPP), parainfluenza 3 (PI3), bovine respiratory syncytial virus (BRSV), and infectious bovine rhinotracheitis (IBR) using enzyme-linked immunosorbent assays (ELISA) or virus neutralization (VN).

ID

PCR Serology

M. ovi M. ovi MCF OPP PI3 BRSV IBR

17-9500 negative negative positive negative negative negative negative



17-9503 negative negative positive negative negative positive negative

17-9504 negative negative negative negative negative negative negative

17-9505 negative negative positive negative negative positive negative



17-9508 negative negative negative negative negative negative negative





17-10248 negative . positive negative negative negative negative











17-10259 negative . negative negative negative negative negative



144



18-13327 negative . negative negative negative negative negative









19-2748 negative . . negative negative negative negative








145

Table 20. Pathogen detection and exposure of live-captured Dall’s sheep in Alaska from 2019 to 2020; including Mycoplasma ovipneumoniae (M. ovi), malignant catarrhal fever (MCF) virus, ovine progressive pneumonia (OPP), parainfluenza 3 (PI3), bovine respiratory syncytial virus (BRSV), bovine viral diarrhoea virus (BVD), Mycobacterium avium ssp. paratuberculosis (MAP), Brucella ovis (B. ovis), Toxoplasma gondii (T. gondii) and infectious bovine rhinotracheitis (IBR) using enzyme-linked immunosorbent assays (ELISA) or virus neutralization (VN).

PCR (laba) Culture Serology

ID M. ovi (WADDL)

M. ovi (AHC)

M. ovi (WSVL)

M. ovi (ADRU) M. ovi M. ovi T. gondii PI3 IBR B. ovis BVD BRSV MAP OPP

2019-1 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-2 detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-3 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-4 indeterminate . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-5 indeterminate . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-6 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-7 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-8 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-9 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-10 detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-11 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-12 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-13 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-14 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-15 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-16 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-17 not detected . not detected not detected no growth not detected positive positive negative not detected negative negative negative negative 2019-18 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-19 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-20 not detected . not detected not detected no growth detected positive negative negative not detected negative negative negative negative 2019-21 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-22 not detected . not detected not detected no growth indeterminate positive negative negative not detected negative positive negative negative 2019-23 not detected . not detected not detected no growth indeterminate positive negative negative not detected negative negative negative negative 2019-24 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-25 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-26 not detected . not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-27 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-28 not detected . not detected not detected no growth detected positive negative negative not detected negative negative negative negative 2019-29 detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-30 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-31 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-32 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-33 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-34 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-35 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative

146

2019-36 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-37 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-38 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-39 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-40 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-41 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-42 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-43 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-44 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-45 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-46 not detected . not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-47 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-48 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-49 not detected negative not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-50 not detected negative not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-51 not detected negative not detected not detected no growth not detected positive negative negative not detected negative positive negative negative 2019-52 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-53 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-54 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-55 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-56 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-57 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-58 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-59 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-60 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-61 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-62 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-63 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-64 not detected negative not detected not detected no growth not detected positive positive negative not detected negative negative negative negative 2019-65 not detected negative not detected not detected no growth not detected positive positive negative not detected negative negative negative negative 2019-66 not detected negative not detected not detected no growth detected positive negative negative not detected negative negative negative negative 2019-67 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative negative 2019-68 not detected negative not detected not detected no growth not detected positive positive negative not detected negative negative negative negative 2020-1 not detected negative not detected not detected no growth not detected positive positive negative not detected negative negative negative . 2020-2 not detected negative not detected not detected no growth not detected negative positive negative not detected negative negative negative . 2020-3 not detected negative not detected not detected no growth not detected positive positive negative not detected negative negative negative . 2020-4 not detected negative not detected not detected no growth not detected negative negative negative not detected negative negative negative . 2020-5 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-6 not detected negative not detected not detected no growth not detected negative negative negative not detected negative negative negative . 2020-7 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-8 not detected negative not detected not detected no growth not detected negative negative negative not detected negative negative negative . 2020-9 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-10 not detected negative not detected not detected no growth not detected negative negative negative not detected negative negative negative .

147

2020-11 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-12 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-13 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-14 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-15 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-16 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-17 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-18 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-19 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-20 not detected negative not detected not detected no growth not detected negative negative negative not detected negative negative negative . 2020-21 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-22 not detected negative not detected not detected no growth not detected positive negative negative not detected negative negative negative . 2020-23 not detected negative not detected not detected no growth not detected positive negative not detected negative negative negative .

a Serial testing was conducted at four different laboratories: Washington Animal Disease Diagnostic Laboratory (WADDL; Pullman, Washington, USA), Animal Health Centre (AHC; Abbotsford, British Columbia, Canada), U.S Department of Agriculture Animal Disease Research (USDA-ADRU; Pullman, Washington, USA), Wyoming State Veterinary Laboratory (WSVL; Laramie, Wyoming, USA) ;

148

Table 21. Anaerobic culture grown from tonsil swabs collected from live free-ranging thinhorn sheep in BC and Alaska from 2017 to 2020. Samples were cultured on Columbia blood agar plates at 36 Celsius at 2 percent oxygen for 48 hours. Detections (Y) of bacterial species previously implicated in polymicrobial pneumonia in wild sheep are recorded (Bibersteinia trehalosi, Mannheimia haemolytica, Mannheimia spp. and Neisseria spp.).

ID Year Location B. trehalosi M. haemolytica Mannheimia spp. Neisseria spp.

17-9500 2017 Dome . . . .

17-9501 2017 Dome . . Y Y

17-9502 2017 Dome . . Y .

17-9503 2017 Dome . . Y .

17-9504 2017 Dome . . . Y

17-9505 2017 Dome . . Y .

17-9506 2017 Dome . . . .

17-9507 2017 Dome . . Y .

17-9508 2017 Dome . . . .

17-9509 2017 Dome . . Y .

17-9545 2017 Dome . . Y .

17-9546 2017 Dome . . Y Y

17-9548 2017 Dome . . . .

17-10248 2018 Cassiar . . . .

17-10249 2018 Cassiar . . . .

17-10250 2018 Cassiar . . . .

17-10251 2018 Cassiar . . . .

17-10252 2018 Cassiar . . . .

17-10253 2018 Cassiar . . . .

17-10254 2018 Cassiar . . . .

17-10255 2018 Cassiar . . . .

17-10256 2018 Cassiar . . . .

17-10257 2018 Cassiar . . . .

17-10258 2018 Cassiar . . . .

17-10259 2018 Cassiar . . . .

18-13323 2019 Cassiar . . . .

149

18-13324 2019 Cassiar . . Y .

18-13325 2019 Cassiar . Y Y .

18-13326 2019 Cassiar . . . .

18-13327 2019 Cassiar . . . .

18-13328 2019 Cassiar . . . .

18-13329 2019 Cassiar . . . .

18-13330 2019 Cassiar . . . .

18-13331 2019 Cassiar . . . .

18-13332 2019 Cassiar . . . .

18-13333 2019 Cassiar . . . .

18-13334 2019 Cassiar . . . .

18-13335 2019 Cassiar . . . .

D1 2019 Talkeetna . . . .

D10 2019 Talkeetna . . . .

D11 2019 Talkeetna . . Y .


D13 2019 Talkeetna Y . . .













D25 2019 Talkeetna . . . .

D26 2019 Talkeetna . . . .

D27 2019 Talkeetna . . . .


D29 2019 Talkeetna . . . .

150



D31 2019 Talkeetna . . . .

D32 2019 Talkeetna Y 1 . .



D35 2019 Talkeetna . . .



D38 2019 Chugach Y Y . .

D39 2019 Chugach Y . Y .


D40 2019 Chugach Y . . .

D41 2019 Chugach Y . . .

D42 2019 Chugach Y . . .

D43 2019 Chugach Y . . .

D44 2019 Chugach Y . . .

D45 2019 Chugach Y . . .

D46 2019 Chugach Y . . .

D47 2019 Chugach . . Y .

D48 2019 Chugach Y . . .

D49 2019 Chugach Y . . .


D50 2019 Chugach Y . . .

D51 2019 Chugach Y . . .

D52 2019 Chugach Y . . .

D53 2019 Chugach Y . . .

D54 2019 Chugach Y . . .

D55 2019 Chugach Y . . .

D56 2019 Chugach Y . . .

D57 2019 Chugach . . Y .

D58 2019 Chugach . . Y .

D59 2019 Chugach Y . . .


D60 2019 Chugach Y . . .

151







19-2748 2020 Williston . . . .

19-2749 2020 Williston Y . . .

19-2750 2020 Williston Y . . .

19-2751 2020 Williston Y . . .

19-2752 2020 Williston Y . . Y

19-2753 2020 Williston Y . . .

19-2754 2020 Williston . . Y .

19-2755 2020 Williston . . Y Y

Table 22. Capture details and non-infectious determinants of health in free-ranging thinhorn sheep captured for health sampling in winters 2017 to 2020. Stone’s sheep were captured in British Columbia and Dall’s sheep in Alaska.

Species Location Capture Year Unique Identification Agea Sex BCSb

Pregnancy statusc HCC (pg/mg)d FGM (ng/g)e

Stone's sheep Dome 2017 17-9500 7 F 2.5 pregnant 10.53 .









Stone's sheep Dome 2017 17-9509 5 F 2.5 pregnant . .

Stone's sheep Dome 2017 17-9545 2 M 2.5 . 12.55 .



Stone's sheep Cassiar 2018 17-10248 4 F 2 pregnant 17.7 34.85

152



Stone's sheep Cassiar 2018 17-10251 5 F 2.5 pregnant 11.1 .

Stone's sheep Cassiar 2018 17-10252 4 F 2.5 pregnant 12.3 36.75


Stone's sheep Cassiar 2018 17-10254 8 F 1.5 open 10.2 45.06

Stone's sheep Cassiar 2018 17-10255 4 F 3 open 16.5 24.56


Stone's sheep Cassiar 2018 17-10257 4 F 2 pregnant 13.7 .


Stone's sheep Cassiar 2018 17-10259 2 F 2 open 14.2 3.49




Stone's sheep Cassiar 2019 18-13326 1 M 2 NA 13.7 .



Stone's sheep Cassiar 2019 18-13329 1 M 2.5 NA 9.40 44.64





Stone's sheep Cassiar 2019 18-13334 6 F 2.5 open 10.0 29.34

Stone's sheep Cassiar 2019 18-13335 6 F 3 pregnant 7.70 . Stone's sheep Williston 2020 19-2748 6 F 2 pregnant . 39.21 Stone's sheep Williston 2020 19-2749 2 M 2.5 . . 42.43 Stone's sheep Williston 2020 19-2750 7 F 2.5 pregnant . 38.55 Stone's sheep Williston 2020 19-2751 7-8 F 2 pregnant . 57.58 Stone's sheep Williston 2020 19-2752 7-8 F 2.5 pregnant . 71.28 Stone's sheep Williston 2020 19-2753 3 M 2 . . 106.24 Stone's sheep Williston 2020 19-2754 6+ F 3 pregnant . 72.82 Stone's sheep Williston 2020 19-2755 6 F 2.5 pregnant . 31.43 Dall's sheep Talkeetna 2019 2019-1 2 M 2 . . 46.61 Dall's sheep Talkeetna 2019 2019-2 5 M 2+ . . 48.71 Dall's sheep Talkeetna 2019 2019-3 4 M 2+ . . 49.39

153

Dall's sheep Talkeetna 2019 2019-4 1 M 1.5 . . 52.29 Dall's sheep Talkeetna 2019 2019-5 7 M 2 . . 71.13 Dall's sheep Talkeetna 2019 2019-6 3 F 2 open . 61.24 Dall's sheep Talkeetna 2019 2019-7 4 M 2+ . . 38.64 Dall's sheep Talkeetna 2019 2019-8 2 F 2- pregnant . 57.95 Dall's sheep Talkeetna 2019 2019-9 5 F 2+ pregnant . 77.21 Dall's sheep Talkeetna 2019 2019-10 2 F 2 open . 66.21 Dall's sheep Talkeetna 2019 2019-11 8 F 2 pregnant . 102.46 Dall's sheep Talkeetna 2019 2019-12 5 M 1.5 . . 35.49 Dall's sheep Talkeetna 2019 2019-13 10 F 2+ pregnant . 48.11 Dall's sheep Talkeetna 2019 2019-14 4 M 2 . . 53.19 Dall's sheep Talkeetna 2019 2019-15 10 F 2 pregnant . 34.89 Dall's sheep Talkeetna 2019 2019-16 3 F 2 pregnant . 45.38 Dall's sheep Talkeetna 2019 2019-17 4 M 2 . . 63.55 Dall's sheep Talkeetna 2019 2019-18 4 M 1.5 . . 30.33 Dall's sheep Talkeetna 2019 2019-19 2 F 2 pregnant . 40.51 Dall's sheep Talkeetna 2019 2019-20 9 F 2+ pregnant . 44.85 Dall's sheep Talkeetna 2019 2019-21 6 M 2 . . 61.19 Dall's sheep Talkeetna 2019 2019-22 6 F 3 pregnant . 47.64 Dall's sheep Talkeetna 2019 2019-23 3 M 2 . . 80.20 Dall's sheep Talkeetna 2019 2019-24 5 F 2- pregnant . 28.58 Dall's sheep Talkeetna 2019 2019-25 5 M 2+ . . 61.98 Dall's sheep Talkeetna 2019 2019-26 5 M 2+ . . 49.69 Dall's sheep Talkeetna 2019 2019-27 3 F 1+ open . 52.68 Dall's sheep Talkeetna 2019 2019-28 7 F 2 pregnant . 44.06 Dall's sheep Talkeetna 2019 2019-29 4 M 2 . . . Dall's sheep Talkeetna 2019 2019-30 7 F 3 pregnant . 65.38 Dall's sheep Talkeetna 2019 2019-31 3 M 2- . . 48.19 Dall's sheep Talkeetna 2019 2019-32 3 M 2- . . 34.39 Dall's sheep Talkeetna 2019 2019-33 4 M 1.5 . . 41.22 Dall's sheep Talkeetna 2019 2019-34 4 M 2+ . . 35.30 Dall's sheep Talkeetna 2019 2019-35 8 F 2+ pregnant . 68.49 Dall's sheep Talkeetna 2019 2019-36 3 F 2 pregnant . 62.47 Dall's sheep Talkeetna 2019 2019-37 4 F 2- pregnant . 42.83 Dall's sheep Chugach 2019 2019-38 4 M 1.5 . . 58.67 Dall's sheep Chugach 2019 2019-39 7 F 2 Pregnant . 54.41 Dall's sheep Chugach 2019 2019-40 1 M 1.5 . . 39.34

154

Dall's sheep Chugach 2019 2019-41 8ish F 2+ Pregnant . 51.78 Dall's sheep Chugach 2019 2019-42 8+ F 2+ Pregnant . 45.34 Dall's sheep Chugach 2019 2019-43 4 F 2 Pregnant . 57.31 Dall's sheep Chugach 2019 2019-44 4 M 1.5 . . 43.17 Dall's sheep Chugach 2019 2019-45 1 M 2 . . 45.73 Dall's sheep Chugach 2019 2019-46 3 M 2 . . 34.19 Dall's sheep Chugach 2019 2019-47 3 F 2 Pregnant . . Dall's sheep Chugach 2019 2019-48 3 F 2 Pregnant . . Dall's sheep Chugach 2019 2019-49 9 F 1.5 Pregnant . . Dall's sheep Chugach 2019 2019-50 4 F 1.5 Pregnant . . Dall's sheep Chugach 2019 2019-51 4 F 2 Pregnant . . Dall's sheep Chugach 2019 2019-52 5 F 1.5 Pregnant . . Dall's sheep Chugach 2019 2019-53 3 F 2- Pregnant . . Dall's sheep Chugach 2019 2019-54 4 F 2- open . . Dall's sheep Chugach 2019 2019-55 7 F 2+ Pregnant . . Dall's sheep Chugach 2019 2019-56 5 M 2 . . . Dall's sheep Chugach 2019 2019-57 3 F 2+ Pregnant . . Dall's sheep Chugach 2019 2019-58 3 M 2 --- . . Dall's sheep Chugach 2019 2019-59 5 F 2 Pregnant . . Dall's sheep Chugach 2019 2019-60 4 F --- Pregnant . . Dall's sheep Talkeetna 2019 2019-61 4 M 2 . . . Dall's sheep Talkeetna 2019 2019-62 5 M 2- . . . Dall's sheep Talkeetna 2019 2019-63 5 F 2+ pregnant . . Dall's sheep Talkeetna 2019 2019-64 3 M 2- . . . Dall's sheep Talkeetna 2019 2019-65 7 F 2+ pregnant . . Dall's sheep Talkeetna 2019 2019-66 7 F 2- pregnant . . Dall's sheep Talkeetna 2019 2019-67 2 M 2- . . . Dall's sheep Talkeetna 2019 2019-68 4 M 2+ . . . Dall's sheep Chugach 2019 2019-69 7 F --- . . . Dall's sheep Chugach 2019 2019-70 4 F 2 . . . Dall's sheep Chugach 2019 2019-71 3 F 2- . . . Dall's sheep Talkeetna 2020 2020-1 7 F 2 . . . Dall's sheep Talkeetna 2020 2020-2 4 M 2 . . . Dall's sheep Talkeetna 2020 2020-3 5 M 2+ . . . Dall's sheep Talkeetna 2020 2020-4 7 F 2+ . . . Dall's sheep Talkeetna 2020 2020-5 9 F 2+ . . . Dall's sheep Talkeetna 2020 2020-6 5 F 2 . . .

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Dall's sheep Talkeetna 2020 2020-7 5 F 2 . . . Dall's sheep Talkeetna 2020 2020-8 5 M 2 . . . Dall's sheep Talkeetna 2020 2020-9 5 M 2+ . . . Dall's sheep Talkeetna 2020 2020-10 6 M 2+ . . . Dall's sheep Talkeetna 2020 2020-11 6 M 2+ . . . Dall's sheep Talkeetna 2020 2020-12 3 M 2+ . . . Dall's sheep Talkeetna 2020 2020-13 6 F 2+ . . . Dall's sheep Talkeetna 2020 2020-14 4 F 2+ . . . Dall's sheep Talkeetna 2020 2020-15 6 M 2 . . . Dall's sheep Talkeetna 2020 2020-16 5 M 2 . . . Dall's sheep Talkeetna 2020 2020-17 6 M 2 . . . Dall's sheep Talkeetna 2020 2020-18 6 M 2+ . . . Dall's sheep Talkeetna 2020 2020-19 3 F 2- . . . Dall's sheep Talkeetna 2020 2020-20 5 F 2+ . . . Dall's sheep Talkeetna 2020 2020-21 6 F 2- . . . Dall's sheep Talkeetna 2020 2020-22 4 F 2+ . . . Dall's sheep Talkeetna 2020 2020-23 5 M 2+ .

a Age is determined by counting horn annuli and confirming with tooth eruption and wear patterns b Body condition score (BCS) is recorded on a five-point scale (0 = emaciated, 1 = poor, 2 = fair, 3 = good, 4 = excellent) for Stone’s sheep and a five-point scale with more intermediate divisions in Alaska (0 – 4 with +/-). c Pregnancy status was determined by measuring pregnancy-specific protein B (PSPB) in serum. d Hair cortisol concentration (HCC) measured in hair shafts with hair bulbs removed. Hair collected from between the shoulders. HCC was not measured in Dall’s sheep. e Fecal glucocorticoid metabolite (FGM) concentration. FGM was not measured in all years in Dall’s sheep.

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Table 23. Details and health findings of hunter-harvested Stone’s rams from 2016 – 2019. Blank cells indicate the sample/data was not collected or the quality was not adequate for testing.

ID Year Age Back fat (cm) BCS Marrow Fat FGM HCC (pg/mg) M. ovi PCR M. ovi ELISA 18-11577 2017 . . . 0.6 . . . . 18-11578 2017 9 . 4 0.3 . . . . 18-11579 2016 10 10 . 0.9 485.75 . . . 18-11580 2016 10 24 4 0.7 127.77 6.73 . . 18-11581 2016 . . . 0.6 412.69 . . . 18-11583 2017 . . . . . . . . 18-11584 2016 . . . . 51.49 2.79 . . 18-11585 2016 adult 24 4 0.4 352.19 4.67 . . 18-11586 2017 13 18 . . . . . . 18-11587 2017 13 10 2 . . . . . 18-11588 2017 9 23 4 0.9 226.77 5.74 negative . 18-11589 2017 8 20 4 . 455.35 7.59 . . 18-11590 2017 9 10 4 . 528.07 11.14 . . 18-11591 2017 . . . 0.7 202.31 . . . 18-11592 2017 . . . . . . . . 18-11593 2017 8 25 3 0.6 . . . . 18-11594 2017 9 8 3 0.5 308.16 . . . 18-11595 2017 8.5 . 4 0.3 117.42 . . . 18-11596 2017 7.5 . 4 1.0 . . . . 18-11597 2017 14 10 2 0.8 141.57 . negative . 18-11598 2017 adult . 4 0.5 . . . . 18-11599 2017 11.5 21 4 0.8 347.79 . negative . 18-11600 2017 8.5 . 4 0.5 191.16 25.36 . . 18-11601 2017 10 10 3 0.8 237.36 . negative . 18-11602 2017 10 20 4 . . . . . 18-11603 2017 9 . 3 0.9 427.17 . negative . 18-11604 2017 . . . 1.0 . . . . 18-11605 2016 . . . 0.9 382.67 . . . 18-11606 2016 9 5 4 0.7 . . . . 18-11607 2017 8 25 4 0.6 341.85 . . . 18-11608 2016 adult . . . . 9.56 . . 18-11609 2016 . . . 0.6 275.21 . . . 18-11610 2016 8 . 3 0.9 205.25 . . . 18-11611 2016 8.5 11 4 0.9 . 5.78 . . 18-11612 2016 8-9 25 4 . . 5.69 . . 18-11613 2016 8 15 4 0.9 278.77 7.58 . . 18-11614 2016 . . . . . . . . 18-11616 2017 . . . . 145.75 . . .

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18-11617 2017 . . . . 444.08 . . . 18-11618 2016 . . . 0.5 289.97 7.43 . . 18-11619 2017 . . . 1.0 278.34 . . . 18-11620 2016 . . . . 78.93 . . . 18-11621 2017 . . . . 225.15 . . . 18-11622 2016 . . . . 78.67 7.39 . . 18-11623 2016 . . . . 1345.85 . . . 18-11624 2016 . . . 0.6 344.22 . . . 18-11626 2017 . . . . . . negative . 18-11627 2017 . . . . . . negative . 18-11628 2017 . . . . . . negative . 18-11629 2017 . . . . . . negative . 18-11630 2017 . . . . . . negative . 18-11631 2017 . . . . . . negative . 18-11632 2017 . . . . . . negative . 18-11633 2017 . . . . . . negative . 18-11634 2017 . . . . . . negative . 18-11635 2017 . . . . . . negative . 18-11636 2017 . . . . . . negative . 18-11637 2017 . . . . . . negative . 19-0358 2018 . . . . . . . . 19-0359 2018 . . . 0.4 . . . negative 19-0360 2018 . . . 0.7 . 9.92 . negative 19-0361 2018 . . . 0.9 . 3.64 . negative 19-0362 2018 . . . 0.9 . . . negative 19-0363 2018 . . . 0.9 . . . negative 19-0364 2018 . . . 0.7 . . . negative 19-0365 2018 . . . . . . . negative 19-2721 2019 9 20 3 . . 13.27 . . 19-2722 2019 NR NR . 0.9 . . . . 19-2723 2019 7 27 4 . . . . . 19-2724 2019 9 18 2 . . 8.12 . . 19-2725 2019 8 15 3 . 677.45 9.61 . . 19-2726 2019 10 3 2 0.9 . 5.12 . . 19-2727 2019 9 9 2 0.9 251.99 14.02 . . 19-2728 2019 9 20 3 . . 12.59 . .

Health Surveillance of Thinhorn Sheep (Ovis dalli) Herds in ...

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