FACTORS AFFECTING CHRONIC WASTING DISEASE PRION TRANSMISSION AMONG
WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS) IN SOUTHERN MICHIGAN
By
Samantha Elise Courtney
A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Fisheries and Wildlife – Master of Science
2023
ABSTRACT
The potential for direct and indirect transmission of Chronic Wasting Disease (CWD)-
causing prions increases as deer gather, and understanding factors affecting deer space use and
grouping behavior can help managers identify areas where deer may congregate in winter.
Additionally, deer interactions and behaviors play an important role in direct transmission of
prions. My objectives in this study were to identify environmental and landscape features that
influence deer group size, and quantify behaviors exhibited by deer at congregation areas
including baited sites, food plots, and naturally occurring forage. I used road-based and camera-
trapping surveys from January-April 2021 and 2022, throughout a 4,262 km2 area in southern
Michigan. On road surveys, I observed 603 deer groups and group sizes ranged from 1 – 67 deer.
From trail camera footage, I conducted over 2,000 direct behavioral observations (bait sites =
1,631, food plots = 416). My results indicate that potential areas for larger deer group sizes
include larger corn and forage crop fields adjacent to woodlots that are >220m away from
buildings. For all deer observed, I detected significantly fewer direct contacts at food plots
(βFood plot = -1.45 [95% CI = -2.00 - -0.90]) and transects (βTransects = -1.12 [95% CI = -1.64
– 0.59]) compared to bait sites. I observed fewer environmental contacts at food plots (βFood
plot = -0.68 (95% CI = -0.90 - -0.47) and transects (βTransects = -0.65 (95% CI =-0.87 - -0.43)
compared to bait sites. Additionally, direct contacts varied by deer sex and age class at bait sites,
including adult males had an increased likelihood of contacts as the number of male fawns
present increased (βMale fawns = 0.45 [95% CI = 0.19 – 0.71]). My results indicate that in areas
of CWD concern, food plots and naturally occurring forage offer a less risky food source for
deer. This information can inform simulation models designed to assess CWD transmission.
ACKNOWLEDGEMENTS
This research would not have been possible without the village of people behind me who
helped make it happen. I am extremely grateful to Dr. Charlie Bahnson, Stephanie Tucker, and
Dr. Bill Jensen who helped foster and cultivate my passion for disease and deer ecology by
providing such a supportive and encouraging environment. Without them, I would not have been
put on the path to this wonderful project.
I would like to express gratitude to three individuals who have all served an advisory role
during the scientific journey I have embarked on. Thank you to Dr. David Williams and Dr. Rose
Stewart for entrusting me with this research project, getting me started, and helping me grow
professionally through the numerous leadership opportunities I was able to be a part of. Dr.
Dwayne Etter and Dr. Gary Roloff were the best combination of advisors. While they have
different areas of expertise, it was the perfect balance between deer expertise and landscape
ecology. I appreciate their sense of humor, unwavering support, and encouragement. I have
learned a tremendous amount from them both and am grateful for the patience they would
express whenever I wandered too far into the weeds.
I would like to thank my committee members: Dr. Jean Tsao, Dr. Sonja Christensen, and
Dr. Chris Cahill. They have provided immense service to me, and I am forever grateful for it.
Through their support, guidance, and constructive criticism they have helped me develop as a
scientist and professional.
Melissa Nichols will forever hold a special place in my heart for the help she has given
me since my first day on the project and the wonderful friendship that has developed between us.
She always offered reassurance and unwavering support in the most trying of times. Thank you
to Dr. Steve Gray for being so kind, patient, and positive. My data analysis was no easy feat, and
iii
he was there to lend a hand every step of the way. It is safe to say the analysis would have been
incomplete without his guidance and brilliance. This project would not have been possible
without the help of the field technicians and volunteers that helped me collect the data.
I need to thank all my lab mates including Noelle Thompson (who helped develop this
project from the start), Jack Magee, Dr. Miranda Strasburg, Nick Alioto, and Steve Gurney for
being there through the good times and bad, being a sounding board when I needed it, and all the
help given throughout my research. Two of my lab mates, Jonathan Trudeau and Nick Jaffe have
given me a tremendous amount of guidance in R and ArcMap, never backing down when I
presented them with a unique challenge and always providing professional, scientific, and
emotional support whether I asked for it or not. I would also like to thank Trey McClinton for
being a great roommate when I first got started and keeping me sane during our study groups.
Trey, Jonathan, and Nick helped me live up to my full, Frodo Baggins and Samwise Gamgee
potential. I am grateful for the great times and friends I have made with the graduate students in
the Fisheries and Wildlife Department. I feel incredibly lucky to have met such extraordinary
people.
I would like to thank my parents, Bill and Kim Courtney. They have always expressed
interest in my journey and encouraged me. In some moments of self-doubt, I would remember
my mom telling me “Just be yourself, it’s why you have gotten where you are”. I will always
appreciate her support. We lost my dad unexpectedly soon during this project, but his interest,
encouragement, and pride in me helped fuel me to finish. I cannot express enough gratitude to
everyone who reached out or helped me through that difficult time, my best friends Lee Walker
and Maddie Saylor especially. I love you and thank you all so much!
iv
Generous funding for this research was provided by the Michigan Department of Natural
Resources, the Hal and Jean Glassen Memorial Foundation, and Safari Club International
Michigan Involvement Committee. This study was conducted in cooperation with the Michigan
Department of Natural Resources and administered through Michigan State University.
v
TABLE OF CONTENTS
INTRODUCTION. ......................................................................................................................... 1
Study Area ................................................................................................................................... 8
LITERATURE CITED ............................................................................................................. 11
CHAPTER ONE: ENVIRONMENTAL FEATURES AND DEER GROUP SIZE DURING
WINTER IN SOUTHERN MICHIGAN: IMPLICATIONS FOR MANAGING CHRONIC
WASTING DISEASE ................................................................................................................... 16
Abstract. .................................................................................................................................... 16
Introduction ................................................................................................................................17
Methods ..................................................................................................................................... 20
Results ....................................................................................................................................... 25
Discussion ................................................................................................................................. 27
Chapter One Tables ................................................................................................................... 34
Chapter One Figures .................................................................................................................. 36
LITERATURE CITED ............................................................................................................. 44
APPENDIX I: FIGURES .......................................................................................................... 51
CHAPTER TWO: WHITE-TAILED DEER BEHAVIORS AT FEED SITES, FOOD PLOTS,
AND THE SURROUNDING LANDSCAPE WITH IMPLICATIONS FOR MANAGING
CHRONIC WASTING DISEASE ................................................................................................ 53
Abstract ..................................................................................................................................... 53
Introduction ............................................................................................................................... 54
Methods ..................................................................................................................................... 59
Results ....................................................................................................................................... 64
Discussion ................................................................................................................................. 67
Additional Behavioral Observations ......................................................................................... 75
Chapter Two Tables .................................................................................................................. 77
Chapter Two Figures ................................................................................................................. 80
LITERATURE CITED ............................................................................................................. 86
APPENDIX II: TABLES .......................................................................................................... 93
APPENDIX II: FIGURES ......................................................................................................... 97
CONCLUSION ........................................................................................................................... 100
vi
INTRODUCTION
Chronic wasting disease (CWD) belongs to a family of pathogens called transmissible
spongiform encephalopathies (Williams 2005). It is a unique, neurodegenerative pathogen that is
contagious and always fatal among cervids once contracted (Williams 2005). Prions, a type of
protein found in the brain that induces abnormal folding in other neural proteins, causes CWD
(Williams 2005). This abnormal folding results in spongiform changes in brain tissue, leading to
signs of disease: emaciation, drooling and excessive thirst, lack of coordination, loss of
awareness, and decreased fear of humans in cervids (Williams and Young 1980). Chronic
wasting disease is known to have a long incubation period, taking approximately 16 months
before clinical signs are exhibited (Henderson et al. 2015). Individuals may begin shedding
infectious prions as early as 3 months post-infection (Henderson et al. 2015). The long
incubation period coupled with rapid decline in health following the first noticeable signs of
CWD make it extremely difficult to identify infected cervids. Chronic wasting disease was first
documented at a research facility in Colorado during the 1960’s, since then it has expanded
nationally and globally (Williams and Miller 2002). During the late 1970’s in Wyoming, CWD
was identified in captive mule deer (Odocoileus hemionus; Williams and Young 1980). Infected
deer and elk (Cervus canadensis) were subsequently found in zoological parks in the US and
Canada (Williams and Young 1992). In 1981, CWD was documented in a free-ranging elk in
Colorado, spurring surveillance that resulted in additional positive detections in free-ranging elk
in Wyoming, and free-ranging mule deer and white-tailed deer (Odocoileus virginianus) in
Colorado and Wyoming (Williams and Miller 2002). Spread potentially occurred through
interactions between captive and free-ranging individuals and interchanging of infected
individuals between captive facilities (Williams et al. 2002; Osterholm et al. 2019). However, it
1
remains unknown whether CWD first arose in captive or free-ranging populations. Since the
1980’s, CWD has continued to expand nationally and globally. Positive cases have been
documented in 5 other countries including Canada, Finland, Norway, South Korea, and Sweden
(United States Geological Service 2023). Three Canadian provinces and South Korea first
identified CWD when an infected cervid was imported into a game farm (Osterholm et al. 2019).
Within the United States, twelve states, including Michigan, first detected CWD in captive deer
(Thompson et al. 2023).
In 2008, CWD was first documented in a captive cervid facility in the southwest Lower
Peninsula of Michigan. In 2015, CWD was documented in a free-ranging white-tailed deer in the
south-central Lower Peninsula. Surveillance of the surrounding area started, and 2 years later a
CWD outbreak was identified in the area where the captive animal was detected (CWDA 2015).
Since the initial case in a free-ranging deer, CWD has expanded in Michigan. Deer that tested
positive for CWD have been detected in the Lower and Upper Peninsulas (MDNRa 2023).
Within Michigan, CWD has been found in both suburban and rural areas. One of the many
challenges facing managers is trying to understand the factors having the greatest influence on
CWD transmission and if active management can slow spread.
Prions are difficult to inactivate, persisting in the environment for a decade or more
(Smith et al. 2011), and are highly contagious (Mathiason et al. 2009). Several methods for prion
inactivation include autoclaving at 134 ℃, alkaline detergents (Sakudo 2020), bleach solution
(Williams et al. 2019), and peroxymonosulfate solution (Chesney et al. 2016). Deer will shed
prions through mucous, blood, saliva, feces, and urine (Miller et al. 2004; Mathiason et al. 2006;
Haley et al. 2016). Prions may be transmitted through direct physical contact (Schauber et al.
2015; Mejía-Salazar et al. 2017) or contact of deer with elements of their environment, such as
2
contaminated soil or food sources (Miller et al. 2004). In one study, 3 of 12 penned deer were
indirectly infected with CWD when exposed to areas where infected, decomposing deer
carcasses were deposited, despite not coming into direct contact with carcasses (Miller et al.
2004). Two years post-decontamination, penned deer still developed clinical signs of CWD after
being introduced to a formerly infected area (Mathiason et al. 2009). As deer shed prions through
bodily fluids or tissues in localized areas (e.g., infected deer home range), the environment
becomes increasingly contaminated with infectious prions. Prions bind to soil and remain
stagnant in the soil column, allowing them to persist in soil for years (Smith et al. 2011). Hence,
naïve deer can be exposed to prions directly through animal-to-animal contact, or indirectly
through the environment. If the naïve deer is susceptible to infection, the exposed individual
becomes infected resulting in disease. A considerable knowledge gap exists in quantifying
pathways of CWD transmission (Mysterud and Edmunds 2019), and information on direct and
environmental deer contacts perceived to increase likelihood of CWD transmission is lacking
(Miller et al. 2004; Potapov et al. 2013; Mejía-Salazar et al. 2017).
Sparse literature exists that defines and describes the multitude of behaviors that deer
exhibit. Of the limited studies available, many focus on aggressive behaviors displayed in
various settings and interaction rates between different sex and age classes (Ozoga 1972; Hirth
1977; Garner 2001). Research depicting non-aggressive forms of behavior is almost non-
existent. In Georgia, a study categorized threats, displacements, and strikes as aggressive der
behaviors, with the only non-aggressive behaviors identified as grooming and suckling (Lagory
1986). Hirth (1977) described aggressive deer behaviors in the form of threats, chasing, and ear-
drop among various sex and age classes; 36 observations of grooming behavior and 18 nose-
touch behavior events were observed. In a northern Michigan winter deer yard with 77
3
deer/km2, Ozoga (1972) recorded pushing, rushing, striking, and flailing behaviors in addition to
several of the other aggressive behaviors previously mentioned. Interactions can vary among sex
and age classes of deer, and among related and unrelated individuals. Interaction rates between
adult females, and between adult females and yearlings is low, with most interactions being
aggressive because of intolerance towards each other (Hirth 1977). Most interactions of females
will occur between relatives, and there is low tolerance of non-relatives (Nixon et al. 1991).
Adult males maintain bachelor groups post-breeding season, therefore interacting with other
adult or yearling males throughout most of the year (Nixon et al. 1994).
Until now, direct physical contact rates among deer have generally been assumed based
on telemetry collar proximity data for deer located <25 m apart (Kjaer et al. 2008; Williams
2010; Tosa et al. 2016). Deer are more likely to make direct physical contact with members of
their own social group rather than members outside of their group (Schauber et al. 2007). Adult
female to adult female interactions were more common than adult female to yearling female and
varied depending on time of year in one study (Hirth 1977). Interaction rates between males is
also low during the non-breeding season and most behaviors are aggressive, consisting of
posturing and chasing (Hirth 1977). Direct physical contact within groups greatly increases
pathogen transmission within a small area (Garner 2001; Cosgrove et al. 2018), but little is
understood about how between-group contact causes CWD to spread throughout a population. It
is unknown if landscape characteristics lead unrelated groups of deer to occur closer in
proximity, enough so that it leads to between-group contacts.
Currently, managers are trying to mitigate the spread of CWD throughout North America.
The Association of Fish and Wildlife Agencies created a document to aid managers in
prevention, surveillance, and management of CWD (Gillin and Mawdsley 2018). Several
4
methods to prevent introduction of CWD include banning of baiting and feeding of deer,
preventing movement of cervid carcasses and tissues, and regulating movement of live cervids
and sale of some urine products (Gillin and Mawdsley 2018). Once CWD becomes established,
surveillance and management plans that promote testing for CWD, proper carcass disposal, and
appropriate decontamination methods become vital (Williams et al. 2002; Gillin and Mawdsley
2018; Thompson et al. 2023). Two strategies used to help slow spread of CWD in populations
involves culling and hunting, as these methods can remove infected deer and reduce local deer
density, lowering the possibility of prion transmission (Manjerovic et al. 2014; Miller et al. 2020;
Miller and Vaske 2023). Wisconsin ceased culling deer in 2007 and witnessed an annual CWD
prevalence increase of 0.63%, while Illinois maintained culling measures and noticed no change
in disease prevalence (Manjerovic et al. 2014). In Colorado, areas within the state that
experienced marked declines in hunting license sales displayed an increase in CWD prevalence
rates, while those areas where license sales increased or remained unchanged showed no change
in CWD prevalence (Miller et al. 2020). These examples provide support that once CWD is
established, culling and hunting play a role in maintaining lower prevalence rates and could help
slow pathogen spread.
Given what is known about CWD transmission and dynamics in other states, it is likely
that CWD will have long-term population level impacts on Michigan’s white-tailed deer
(Monello et al. 2014; Edmunds et al. 2016; DeVivo et al. 2017). For example, CWD can lead to
increases in deer vehicle collisions and predation, as infected individuals are more vulnerable in
their weakened state (Krumm et al. 2005, 2009). On a ranch in Wyoming, Edmunds et al. (2016)
concluded that CWD-positive deer were 4.5 times more likely to die than deer that tested
negative, suggesting that at high prevalence rates deer populations could decline (Edmunds et al.
5
2016). Adult white-tailed deer in semi-arid environments were predicted to experience CWD-
related mortalities resulting in negative rates of population growth (Foley et al. 2016). These
population-level impacts on deer could negatively affect wildlife funding, state economies and
threaten recreational hunting (Needham et al. 2004; Vaske and Lyon 2011; Price Tack et al.
2018). The Pittman-Robertson Act generates funds for wildlife conservation through an excise
tax on sporting arms and ammunition (Crafton 2019). Declines in hunter license sales and
declines in sales of sporting arms and ammunition lead to a decline in wildlife conservation
funds. In 2020, federal agencies allocated $284.1 million to CWD-related management, and state
agencies allocated $28.4 million (Chiavacci 2022). Funding needed to manage CWD will
increase as the pathogen spreads throughout the United States.
Large knowledge gaps exist in how supplemental feeding affects group dynamics and
associated contact rates, and thus, CWD transmission. Few studies have been conducted on how
deer interact with each other at different food sources (food plots, bait sites, natural forage) and
what the rates of direct contact are at these congregation sites. Storm et al. (2013) noted that
expanding foundational knowledge of how landscape features influence deer congregation and
risk of CWD transmission is necessary. One of the many challenges facing managers is trying to
understand what factors are having the greatest influence on CWD transmission and if
management action can slow pathogen spread.
Few studies have tried to quantify how deer behavior, supplemental feeding, and
landscape factors affect deer group size and CWD prion transmission. The agriculture dominated
landscape of southern Michigan provides a unique opportunity to observe contact rates among
deer and their environment during winter. Here, winter (January to April) corresponds to post-
breeding in southern Michigan, a time when deer tend to congregate around food sources (Kjaer
6
et al. 2008). Quantifying these interactions will provide vital information for epidemiological
models that aim to create efficient and effective CWD management strategies. In Chapter 1 of
this thesis, my objective was to quantify how deer group sizes in winter were affected by
landscape features. I hypothesized that landscape features influenced where deer congregated in
winter, but that these relationships may change monthly as food sources deplete and annually as
crop fields are rotated.
In Chapter 2 of this thesis, I quantified deer contact rates at bait sites, food plots, and in
the surrounding landscape. I hypothesized that bait sites and food plots increase the likelihood of
behaviors known to facilitate direct and indirect pathogen transmission among deer. The overall
goal of my research was to help wildlife managers understand the role supplemental feeding may
play in the transmission of prions and if deer are selecting for landscape characteristics in winter.
7
Study Area
The research was conducted in a 4,262 km2 study area encompassing portions of Clinton,
Eaton, Ingham, Ionia, and Shiawassee Counties in southcentral Michigan (Figure 1.1) The study
area is almost exclusively privately owned lands and consists of agriculture (68%), forest (22%),
and developed areas (9%), with the remainder in open water, emergent herbaceous wetlands,
barren land, and deciduous scrub/shrub (Figure 1.1; USDA-CDL 2020). Dominant agricultural
crops include corn (Zea mays), soybeans (Glycine max), alfalfa (Medicago sativa), and winter
wheat (Triticum aestivum) (USDA-CDL 2020, 2021). Small tracts of deciduous forest and
hedgerows are interspersed among crop fields. Classification of the regional landscape
geomorphology is described as medium-textured ground moraines with rich, loamy soils
(USDA-Forest Service 2004). Soil types are classified as Hapludalfs plus Argiaquolls (USDA-
Forest Service 2004). Depressions are poorly drained, while moraines are well drained (USDA-
Forest Service 2004). Elevation ranges from 195 to 342 m (USDA-Forest Service 2004).
In both years of my study (2021 and 2022) data collection occurred 4 January to 30 April.
In 2021, average temperature ranged from -14.0 °C to 17.2°C, while in 2022 it ranged from -13.1
°C to 16.5°C (NOAA 2023). In 2021, the study area received approximately 3.89 cm of
precipitation and 26.28 cm of snowfall, with an average snow depth of 8.55 cm (NOAA 2023).
Precipitation in the second year was 6.9 cm and 28.95 cm of snowfall with an average snow
depth of 4.74 cm (NOAA 2023). During the first field season, there was heavy snowfall in
January and February followed by warmer than average temperatures in March and April. The
second field season experienced similar amounts of snow, but temperatures remained cooler
throughout the field season with more rainfall.
8
Michigan has a rich history of deer hunting with the season in the Lower Peninsula
beginning in mid-September and continuing through January 1. Baiting and feeding of white-
tailed deer are banned in the entire Lower Peninsula on both public and private lands (MDNRb
2023). The south-central portion of the Lower Peninsula also contains a CWD management
zone, and all 5 counties from the study area fall within this zone (MDNRc 2023).
9
Figure 1.1 The five counties where road-based surveys and camera trapping data were collected
in support of deer group size and behavior studies in Michigan from 2021-2022.
10
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2014. Survival and population growth of a free-ranging elk population with a long history
of exposure to chronic wasting disease. Journal of Wildlife Management 78:214-223.
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Wildlife Management 36: 861-868.
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Possible transmission mechanisms in deer. Ecological Modelling 250:244-257.
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Schauber, E. M., D. J. Storm, and C. K. Nielsen. 2007. Effects of joint space use and group
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Schauber, E. M., C. K. Nielsen, L. J. Kjaer, C. W. Anderson, and D. J. Storm. 2015. Social
affiliation and contact patterns among white-tailed deer in disparate landscapes:
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Smith, C. B., C. J. Booth, and J. A. Pedersen. 2011. Facts of prions in soil: A review. Journal of
Environmental Quality 40:449-461.
Storm, D. J., M. D. Samuel, R. E. Rolley, P. Shelton, N. S. Keuler, B. J. Richards, and T. R. Van
Deelen. 2013. Deer density and disease prevalence influence transmission of chronic
wasting disease in white-tailed deer. Ecosphere 4:1-14.
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responses to chronic wasting disease in free‐ranging cervids. Wildlife Society Bulletin
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Tosa, M. I., E. M. Schauber, and C. K. Nielsen. 2016. Localized removal affects white-tailed
deer space use and contacts. Journal of Wildlife Management 81:26-37.
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Vaske, J. J., and K. M. Lyon. 2011. CWD prevalence, perceived human health risks, and state
influences on deer hunting participation. Risk Analysis: An International Journal 31:488–
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Williams, D. M. 2010. Scales of movement and contact structure among white-tailed deer in
central New York. Dissertation, State University of New York, Syracuse, USA.
Williams, E. S. 2005.Chronic Wasting Disease. Veterinary Pathology 42:530-549.
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scientifique et technique (International Office of Epizootics) 11:551-567.
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America. Revue Scientifique et Technique (International Office of Epizootics) 21:305-
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Williams, K., A. G. Hughson, B. Chesebro, and B. Race. 2019. Inactivation of chronic wasting
disease prions using sodium hypochlorite. PloS ONE 14:e0223659.
15
CHAPTER ONE: ENVIRONMENTAL FEATURES AND DEER GROUP SIZE DURING
WINTER IN SOUTHERN MICHIGAN: IMPLICATIONS FOR MANAGING CHRONIC
WASTING DISEASE
Abstract
Chronic wasting disease (CWD) has been steadily increasing globally since the 1960’s
and could result in long-term population declines for white-tailed deer (Odocoileus virginianus).
The potential of direct and indirect transmission of CWD-causing prions increases as deer
congregate. Thus, understanding factors affecting deer space use and grouping behavior can help
managers identify areas where deer may congregate. My objective in this study was to identify
environmental features that influence deer group size to help managers identify areas of
congregation, potentially increasing the likelihood of prion transmission. I conducted road-based
surveys along 3.5 – 4.83 km long transects from January-April 2021 and 2022, throughout a
4,262 km2 area in southern Michigan. I observed 603 deer groups and group sizes ranged from 1
– 67 deer. Total area of corn and forage crops had a positive effect on group size (β=0.12, 95%
CI = 0.02 – 0.22 [corn], β= 0.13, 95% CI = 0.04 – 0.22 [forage]) with group size increasing by
~1.5 deer across the range of corn measured, and by ~3.5 deer across the range of forage crop
measured. Deer group size was larger away from buildings (β=0.09, 95% CI = 0.006 – 0.18).
The global model identified negative associations with residential (β= -0.94, 95% CI = -1.37 - -
0.50) and forest (β= -0.34, 95% CI = -0.52 - -0.16) cover types on group size compared to
agriculture. Contagion had a negative impact on deer group size (β= -0.11, 95% CI = -0.22 - -
0.005), where small forest patches adjacent to agricultural fields corresponded with lower group
size. My results indicate that potential areas for larger deer group sizes include larger corn and
forage crop fields adjacent to woodlots that are >220m away from buildings. Wildlife agencies
can employ appropriate disease mitigation measures as they find these areas on the landscape.
16
Introduction
Biotic and abiotic factors and landscape features influence deer group size and
movements in Michigan. Hirth (1977) noted that deer group sizes and use of open areas in
Michigan likely evolved as a predator avoidance strategy (also see Hewitt 2011) or in response
to spatial patterns of vegetation growth and structure. Hirth (1977) observed that deer groups
were smaller when cover was dense, and groups were larger in open areas absent of cover.
Presently, vehicle collisions and hunting are the main causes of deer mortality in southern
Michigan (Burroughs et al. 2006; Hiller and Campa 2008), but large predators (e.g., wolves
[Canis lupus]) have been absent for >100 years, suggesting predator avoidance may no longer
influence deer grouping behavior. Little to no vegetation growth occurs during winter in
Michigan, so deer are often forced to scrape through snow for forage or browse on woody
vegetation (Beier 1987). In agricultural dominated landscapes, deer primarily feed upon
agricultural foods and waste grain that remain on the ground post-harvest (Nixon et al. 1991;
Hewitt 2011). Hence, access to forage, level of non-predator disturbance, and social bonds likely
determine deer grouping behavior during winter in southern Michigan.
Social hierarchy, group size and composition likely play roles in pathogen transmission.
A general social hierarchy exists within deer herds, but this can change depending on time of
year (Nixon et al. 1991, 1994; Hewitt 2011). Matriarchal groups are most often composed of an
adult female, a yearling female, and fawns (Hawkins and Klimstra 1970; Hirth 1977, Mathews
and Porter 1993). Group members associate throughout the year, but sexes segregate as pregnant
females establish and defend a parturition range in early summer (Hewitt 2011). Female
offspring often establish home ranges adjacent to the home range of their mother, varying
degrees of overlap depending on season and individual age (Mathews 1989; Nixon et al. 1991;
17
Porter et al. 1991; Nixon and Etter 1995). Male fawns typically disperse from their mother’s
range between 1.0 (spring)-and 1.5-years-old (fall; Nixon et al. 1991, 1994). Early dispersing
male fawns will join bachelor groups in summer (Hirth 1977; Nixon et al. 1991); however, some
yearling males will return to a portion of their mother’s range post-breeding season (Nixon et al.
2010). During the breeding season, adult males remain solitary but increase associations with
adult females and fawns in winter (Nixon et al. 1991, 1994). Low food availability, overlapping
home ranges, and fawns that have not yet parted from their mother may explain why larger
groups of deer are observed in winter (Hawkins and Klimstra 1970; LaGory 1986; Beier and
McCullough 1990). As group size increases, interaction rates among group members increase
because of increased competition for food (Ozoga 1972; Grenier et al. 1999). Adult males have
been known to exhibit more aggressive behaviors to replenish body condition post-rut (Nixon et
al. 1994; Clutton-Brock et al. 1982), this could potentially lead to an increase in direct contacts
among individuals. Annual deer group dynamics likely plays a role in CWD prevalence and
persistence. For example, highest prevalence rates of CWD are found in mature males followed
by mature females (Farnsworth et al. 2005; Miller and Conner 2005; Grear et al. 2006).
Mechanisms for this pattern in CWD prevalence are poorly understood, but because grouping
behaviors differ between the sexes (Hirth 1977), directly observing deer behaviors may provide
useful insights.
During winter in Michigan, deer shift habitat use and activity in response to snow depth,
wind speed, and temperature, opting to utilize wetland and grassland cover types (Beier and
McCullough 1990). Large agricultural fields with small tracts of forest dominate the southern
and central Michigan landscape. This fragmented landscape provides thermal and escape cover
for deer in juxtaposition with a food source (Nixon et al. 1991). Nixon et al. (1988) also found
18
that deer avoided flood prone woodlands and used wooded pastures absent of livestock. As
winter conditions force changes in habitat and food resources, deer congregation areas may shift
(Hurst and Porter 2010). My objective was to understand environmental features that determined
where deer congregated during the winter and if deer group sizes could be explained from these
environmental features. Evaluating these features is vital for creating epidemiological models
that can help managers slow the spread of pathogens during a time when deer are congregated.
19
Methods
Field Methods
To investigate relationships between deer group sizes and landscape characteristics, I
observed deer along road survey transects. Within the study area, I used ArcGIS (ArcMap
version 10.8.2, Environmental Systems Research Institute. Redlands, California, USA) to
randomly select starting and ending locations along secondary rural roads, resulting in 35
transects of 4.83 km each (Figure 1.2). Because major highways and rivers may serve as barriers
to deer movements in the region (Locher et al. 2015), I used these features to divide the survey
area into 3 areas comprised of multiple groups of transects (Fig 1.2). Prior to the field season, I
drove transects to evaluate visibility and safety and replaced transects deemed unsafe (e.g., high
traffic volume and speed). Given an estimated annual home range size of 2.25 km2 for deer in the
region (J. Trudeau, Maryland DNR, personal communication), I attempted to locate transects
>1.6 km apart, thereby minimizing the likelihood of counting the same deer on multiple transects
during the same day. Transects were placed into groups of 5-9 to maximize the number of
surveys conducted in a day. Transects that could not be located >1.6 km apart were placed into
separate groupings and surveyed on separate days. To reduce transect selection bias, I
randomized whether a morning or evening survey would be conducted, region and group to be
surveyed, order of transects to be surveyed, and direction of travel. Surveys were conducted 3-6
times per week with morning surveys occurring approximately 15 minutes before sunrise to 2
hours after sunrise and evening surveys occurring 2 hours before sunset to approximately 15
minutes after sunset.
Observers surveyed transects between January and April of 2021 and 2022,
corresponding with the end of hunting season and during post-breeding period for deer in
20
southern Michigan (Christensen 2018). I assumed population closure within each year of
sampling given high deer survival and reproduction, and limited dispersal post-breeding (Nixon
et al. 2001, J. Trudeau, Maryland DNR, personal communication). Transects were driven in a 4-
wheel drive pickup truck at approximately 24 km per hour, while two front seat passengers
observed deer from each side of the truck. Conducting surveys at this speed allowed for ideal
detection of deer and reduced the potential for double counting individuals (Zamboni et al. 2015,
Christensen 2018). Due to Michigan State University COVID-19 protocols, in 2021 only one
individual was allowed in a vehicle at a time. To account for this, two trucks were driven
separately with the front vehicle acting as the observer and the rear vehicle acting as the data
recorder. Observers in both vehicles searched for deer only out of the left side window in 2021,
reducing the area sampled compared to 2022.
When deer were detected, observers used binoculars (Leupold BX-2 Acadia 10x42) to
identify number of deer groups and number of individuals per group within 457 m of the road. I
defined a group of deer as ≥1 deer that moved and fed together with individuals separated by
<~50 m (Monteith et al. 2007). Observers used GPS (Garmin eTrex 20X, Olathe, Kansas, United
States) to record the truck location in decimal degrees and measured the radial angle to the center
of each group using a planar protractor mounted perpendicular on the vehicle window.
Subsequently, observers used a range finder (Vortex Impact 100 Laser) to measure distance (m)
from observer to the center of each deer group and spotting scope (Cabela’s CX Pro 86mm 20x-
60x) to identify sex and age class of each deer within the group. Sex and age classes included
adult male (≥1 yr old), adult female (≥1 yr old), male fawn (<1 yr old), female fawn (<1 yr old),
unknown sex adult, unknown sex fawn, and unknown sex and age (Hirth 1977; Bowyer et al.
1996).
21
Observers included graduate students, biologists from the Michigan Department of
Natural Resources (MDNR), full-time technicians, and undergraduate student volunteers. I
trained all observers prior to surveys using videos and photos accompanied by literature and
face-to-face instruction. I conducted practice surveys to train observers to accurately sex and age
deer. Volunteers were always accompanied by an experienced observer who assisted with aging
and sexing deer. All observational data were collected on a tablet (Apple iPad 6th generation)
with the Survey123 software application (ArcGIS 2010).
Quantitative Methods
I used the geosphere package (Hijmans 2022) in R (R Core Team 2023) to estimate
geographic coordinates for observed deer groups using angle and distance data collected in the
field. At group locations, I extracted crop type using the United States Department of Agriculture
Cropland Data Layer for 2020 and 2021 in ArcMap (USDA-CDL 2020, 2021). The USDA
CDLs were created using satellite imagery from Landsat 8 OLI/TIRS sensor, the ISRO
ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors that collect data annually during the
growing season (30 m resolution).
To characterize landscape covariates proximal to deer groups I created a buffer around
each deer group. Mean post-breeding home-range area for deer in the study area averaged 1.4
km2 (SE = 0.11; J. Trudeau, Maryland DNR, personal communication). To assign buffers I
applied the radius (r) of a circle representing deer post-breeding home-range +3 SE (r = 740m)
to the center of each group. This sized area helped account for deer that may have used slightly
larger home-ranges than the documented mean. Buffers for individual groups often overlapped,
but Zuckerberg et al. (2020) demonstrated minimal impact on inference in these types of studies
due to this overlap.
22
I used two raster layers (USGS-NLCD, USDA-CDL) to reclassify and evaluate dominant
cover and crop types in buffer zones. Using the NLCD layer, I combined all classifications
within the planted/cultivated category to create an agricultural cover type. I also combined
shrubland and forest classifications (n=5) to create a wooded cover type and the NLCD wetlands
classifications (n=2) were combined to form a wetland cover type. Within the CDL layer, I kept
corn and soybeans as stand-alone classifications, but reclassified winter wheat, spring wheat, rye,
oats, alfalfa, other hay, clover, speltz, and sod into a forage crop category.
Within a buffer, I measured total hectares (ha) of cover type (agriculture, residential,
wooded), and crop type (corn, soybeans, forage crop). I used the landscapesmetrics package
(Hesselbarth et al. 2019) in R to calculate crop and cover type composition, and contagion
(CONTAG) within all buffers. Contagion is a measurement of raster cell adjacencies for
different cover types; a landscape with many and smaller patch types will have lower contagion
than a landscape with larger, contiguous patch types (McGarigal and Marks 1995). I
hypothesized that during winter deer forage on leftover crop residue in agricultural fields
resulted in a positive influence on deer group size, as these areas offer food while allowing clear
sightlines to spot potential predators (Nixon et al. 1991; Hewitt 2011). I also measured distance
from each group location to buildings using US Building Footprint (Microsoft 2022) layer
without the spatial constraint of the buffer to investigate a potential relationship between group
size and anthropogenic presence.
A suite of variables (contagion (CONTAG), edge density, interspersion/juxtaposition
(IJI), length of road, nearest distance to building, total hectares of forest, agriculture, wetland,
and residential cover types, total hectares of corn, soybean, forage, and other crops) were
assessed for my original model. I first used a Spearman’s rank correlation test and identified
23
collinearity among variables. Edge density, IJI, total hectares of wetland, and total hectares of
other crop all presented moderate to strong correlations with other variables (Appendix; Figure
A.1.1). After removing these variables, I created a global model using year, total length of road,
CONTAG, nearest distance to building, total hectares of agriculture, residential, and forest cover
types, and total hectares of corn, soybean, and forage crops. After running this model, two
variables, length of road and total hectares of soybeans, were deemed unimportant (i.e., 95% CI
overlapped 0) and removed from the global model.
I used generalized linear mixed modeling (GLMM) to explore the effects of landscape
variables on deer group size. I used the “lme4” package in R (Bates et al. 2015). I specified a
truncated negative binomial distribution because 0 for the response variable (i.e., deer group
size) was excluded and to help account for overdispersion. Predictor variables included year of
observation, cover type where a deer group was observed (i.e., agricultural, residential, wooded),
nearest distance to a building, contagion (CONTAG), and amount of corn (ha), forage crop (ha),
and soybeans (ha) within a buffer (Table 1.1). Prior to running the model, nearest distance to
building, CONTAG, corn, and forage crop parameters were scaled and centered. I also included
transect ID as a random effect to account for potential observations of the same deer groups on
temporally replicated surveys on a given transect. I used a variance inflation factor (VIF) test to
assess multicollinearity in the final predictor variables and assessed model fit using residual
diagnostics (Kie et al. 2002).
24
Results
From January-April of 2021, observers conducted 346 road surveys on 26 transects and
observed 182 deer groups and 1,312 deer. Each transect was surveyed an average of 15 times
(SE = 0.52). Deer group sizes ranged from 1-47 (median = 5; Fig. 1.3). In 2022, observers
conducted 351 road surveys on 34 transects identifying 421 groups including 3,372 deer. Each
transect was surveyed an average of 12 times (SE = 0.58). Group sizes ranged from 1-67
individuals (median = 6; Fig. 1.3). Out of 603 groups observed, 1% were bachelor groups, 13%
were mixed sex and age (adult males, adult females and fawns of both sexes), and 86% consisted
of adult females and fawns of either sex (matrilineal groups). Land cover surrounding group
locations was primarily agricultural cover types (64%), followed by forest (25%) and wetlands
(11%) (Table 1.2). Within agricultural cover types, soybeans (23%) and corn (22%) were the
primary plantings followed by forage crops (12%; Table 1.2).
The VIF function for the global model indicated low correlation among variables (median
VIF = 1.13, range = 1.05 – 1.28). Diagnostics on the global model indicated that the residuals
were normally distributed (i.e., QQ plot residuals) with residuals randomly distributed around the
0.50 line with no obvious outliers (i.e., Residual vs predicted; Figure A.1.2). The global model
identified year (2022) as having the strongest positive effect on group size (β= 0.25, 95% CI =
0.07 – 0.43; Table 1.3), where group sizes were larger in 2022 than 2021 (Fig. 1.4). Total area of
corn and forage crops also had a positive effect on group size (β=0.12, 95% CI = 0.02 – 0.22
[corn], β= 0.13, 95% CI = 0.04 – 0.22 [forage]; Table 1.3) with group size increasing by ~1.5
deer across the range of corn measured (Fig. 1.5), and by ~3.5 deer across the range of forage
crop measured (Fig. 1.6). Deer group size was larger away from buildings (β=0.09, 95% CI =
0.006 – 0.18; Table 1.3), with average group size increasing by ~3 deer across the range of
25
distances measured (Fig. 1.7). On average, groups were ~221 meters from buildings (SE = 5.5).
For cover types at the group location, the global model identified negative associations with
residential (β= -0.94, 95% CI = -1.37 - - 0.50) and forest (β= -0.34, 95% CI = -0.52 - -0.16;
Table 1.3) cover types on group size compared to agriculture (Fig. 1.8). Contagion had a
negative impact on deer group size (β= -0.11, 95% CI = -0.22 - -0.005; Table 1.3), where more
interspersed cover types corresponded with lower deer group size (Fig. 1.9).
26
Discussion
I quantified the effects of environmental features on deer group sizes to identify areas on
the landscape where deer congregate in larger groups. Deer group size is relevant to disease
management because larger groups of deer can potentially increase the likelihood of CWD
transmission via direct animal to animal contact, or indirectly via prion deposition or uptake in
the environment. I concluded that area of corn and forage crop positively correlates with deer
group size. I also found a negative correlation between deer group size and distance to residential
buildings. Lastly, I observed smaller deer groups in areas where land cover composition was
more homogenous. Recognizing landscape-level patterns in deer grouping behavior can facilitate
allocation of resources by managers to more effectively control disease outbreaks. These
findings are constrained to crepuscular hours, potentially biasing inferences made regarding
group sizes. However, these times of day are when deer are most active (Kammermeyer and
Marchinton 1977) and spend more time foraging (Schmitz 1991).
Median group sizes of 5 and 6 in 2021 and 2022, respectively, likely represented
individual families of deer. Nixon et al. (1991) found that mothers, their yearling daughters, and
their fawns, were the most common groups observed in winter on a 600-ha refuge in east-central
Illinois, and white-tailed deer groups in southern Alberta, Canada showed similar patterns in
winter (Lingle 2003). In Illinois, female white-tailed deer fawns shared most of their mother’s
home range if they did not disperse in spring, and the following winter these same related
individuals shared ~50% of their mother’s range as yearlings (Nixon et al. 1991, 2010). Because
of these familial associations, deer have a higher likelihood of contracting pathogens from an
infected individual within their family group than from an infected individual outside of the
family group (Grear et al. 2010). Using proximity telemetry collars, Schauber et al. (2015) found
27
that white-tailed deer in Illinois have considerably higher direct contact rates within family
groups than between family groups, and group membership effects on direct contact rates was
strongest in winter. It is likely that pathogen transmission in the study area occurs at small,
localized spatial scales that correspond to areas used by family groups. Removal of entire family
units creates voids in an area that are not reoccupied by adjacent females for several years (Porter
et al. 1991; McNulty et al. 1997; Oyer and Porter 2004) and this may be an effective
management strategy to slow transmission of CWD prions.
During winter, 26 radio-marked female deer in Illinois used forage crops and corn fields
more often than other available crops (Nixon et al. 1991) and I observed a group size increase of
1.5 and 3 deer as area of corn and forage crop increased, respectively. Group sizes I observed in
corn (7-8 deer) and forage crops (8-9 deer) likely represented multiple family groups foraging in
the same field (Nixon et al. 1991, 2010; Porter et al. 1991; Schauber et al. 2015). Adult female
white-tailed deer have high site fidelity and tend to establish home ranges adjacent to their
mother, often creating home range overlap (Marchinton and Hirth 1984; Nixon et al. 1991,
Nixon and Etter 1995; Porter et al. 1991). In Wisconsin, winter forage that congregates deer and
potentially increases contacts among family groups can facilitate CWD persistence and
prevalence on the landscape (Samuel 2023). The probability of CWD transmission among
related female deer within 3.2 km was ≥100 fold higher than for unrelated deer in the same area
(Grear et al. 2010). Therefore, larger deer group sizes consisting of related and unrelated
individuals have a higher likelihood of infection when selecting for certain crops during winter
foraging. Working with farmers to reduce forage crops and corn fields in areas with CWD
infected deer populations could reduce potential exposures among family groups of deer.
28
Models suggest environmental transmission plays a greater role in the spread of CWD
and population level impacts than previously thought. Almberg et al. (2011) used simulation
models to predict that population decline of deer is a function of the environmental persistence
and infectiousness of the prion. Penned deer became infected with CWD when exposed to
contaminated fomites (Mathiason et al. 2009), even in areas that had been decontaminated
(Miller et al. 2004). Hamsters (Mesocricetus auratus) exposed to plants and prion-bound
materials commonly found in urban areas (i.e., wood, cement) became infected through direct
and indirect transmission routes (Pritzkow et al. 2015, 2018), and higher prevalence rates of
CWD in adult males could be explained by higher food intake from contaminated plant material
and soil (Potapov et al. 2013). Prion seeding activity is unaffected when infectious feces are
subjected to desiccation and only after 7 freeze-thaw cycles is a decrease in seeding observed
(Tennant et al. 2020). Although prion levels in deer feces are lower compared to other bodily
fluids, how prions deposited via feces or saliva react to the natural environment and bind to soil
likely has management implications (Mathiason et al. 2006; Henderson et al. 2017). As deer
excrete fluids, defecate, ingest plants, and interact with natural and anthropogenic materials risk
of infection increases. Management of CWD is thus complicated by prion deposition and decay
of infected carcasses, thereby contaminating areas and exposing individuals to the pathogen.
Although much attention has focused on direct transmission among family members as the
primary pathway for CWD prion transmission (Williams et al. 2014; Schauber et al. 2015; Tosa
et al. 2017), my observations of multiple matrilineal groups feeding in agricultural fields during
winter suggests that the potential for environmental transmission of CWD among these groups is
high. I found that certain environmental features influenced group sizes of deer. Contagion is a
measurement of patch type composition, configuration, and spatial distribution of patch types
29
(McGarigal and Marks 1995). A higher CONTAG value reflects landscapes that have fewer and
larger contiguous patches of cover types showing more even distribution and less clumping,
whereas lower CONTAG values represent landscapes with smaller, aggregated, and less
interspersed patch types. In my study, lower CONTAG scores were associated with significantly
larger group sizes, indicating that deer likely congregate in small forest patches that provide
cover with easy access to feeding areas in adjacent agricultural fields (Fig. 1.9). This likely
increases deer abundance in smaller forest patches, increasing potential contact rates and prion
deposition (Smolko et al. 2021). Additionally, Samuel (2023) identified highest CWD prevalence
growth rates in areas of Wisconsin consisting of 40% forest cover associated with small
agricultural fields, and lowest prevalence growth rates in regions composed predominately of
agriculture with only 10% forest cover. If deer group size plays an important role in transmission
of the pathogen, then I would predict that CWD would spread slowly across southern lower
Michigan because 68% of the landscape consists of agriculture with only 22% forested (see Fig.
1.1).
Roe deer (Capreolus capreolus) in southern France had smaller group sizes in areas close
to human activity, actively avoiding these areas (Hewison et al. 2001). Additionally, deer in an
suburban landscape in Illinois tended to avoid and select for areas further away from dwellings
during winter (Storm et al. 2007). However, Swihart et al. (1995) found that white-tailed deer in
Connecticut adapted to human presence, observing that 67% of houses in their study area had
been visited by deer, likely because of feeding activity and plant species richness near dwellings.
I found that deer group size was negatively associated with residential development compared to
agriculture, and group sizes increased with distance from dwellings. Most of my rural study area
was divided into roaded sections and houses were primarily built along roads. Given this
30
landscape configuration, it is possible that vehicle traffic associated with houses disturbs deer
(Sawyer et al. 2006; Meisingset et al. 2013), causing some individuals to flee and affect overall
group sizes. My results suggest that wildlife managers working at controlling CWD will find
larger groups of deer for culling further from residential buildings.
My finding that individual or multiple matrilineal family groups were most frequently
encountered during winter in the study area has implications for targeted culling. Reducing deer
abundance in CWD-positive areas with high animal density is an Association of Fish and
Wildlife Agencies recommendation to manage CWD prevalence (Gillin and Mawdsley 2018).
Removing female deer is desired for population reduction, and sharpshooting is an effective
method for selective deer harvest (DeNicola et al. 1997; Frost et al. 1997; Doerr et al. 2001;
Hygnstrom et al. 2011). Miller and Vaske (2023) surveyed all 50 U.S. states and received
responses from 38, and of the states with CWD at the time of the survey 32% used sharpshooting
to manage CWD and 41% were considering sharpshooting. Some agencies, including MDNR,
will respond to new positive CWD cases with targeted sharpshooting to slow disease spread as
soon as possible (Uehlinger et al. 2016). While this practice may be an effective tool for
maintaining low prevalence of CWD in some areas (Manjerovic et al. 2014), because deer
populations have an established social hierarchy and female groups are led by a matriarchal doe
(Nixon et al. 1991; Porter et al. 1991), removing adult females may alter herd behavior and
movements, potentially resulting in more diffuse space use by remaining deer. In one study,
larger groups of deer were allowed to dissipate prior to sharpshooting to prevent unharvested
animals from becoming educated to the tactic (Williams et al. 2008). Also in this study, 91% of
an enclosed deer herd was removed, resulting in an increase in home range size as deer sought to
restructure their social groups (Williams et al. 2008). In Virginia, orphaned male fawns were
31
more likely to stay near their natal ranges than non-orphaned males; however, orphaned males
had larger seasonal ranges overall compared to non-orphaned males (Holzenbein and Marchinton
1992). In west-central Illinois, 9 of 13 (67%) orphaned female fawns emigrated while only 35 of
94 (37%) non-orphans dispersed (Etter et al. 1995). Because groups of deer observed during my
study were likely intact family groups, targeted removal of adult female deer could have major
implications for CWD spread and prevalence. Disease spread via orphaned females could occur
over a greater spatial extent as they disperse to new areas and establish or join a new family
group, introducing the pathogen into naïve populations and increasing disease prevalence. In
these instances, removing the entire family group is likely an appropriate culling strategy.
Mature adult males have higher prevalence of CWD (Samuel and Storm 2016; Samuel 2023),
however the mechanism driving this higher rate between sexes is still unknown. One explanation
could relate to the tendency for orphaned male fawns to remain in the same area (Holzenbein and
Marchinton 1992) increasing CWD prevalence at a localized scale.
As deer congregate during the winter, and group sizes get larger as multiple family
groups come together, there is an inherent increase in prion deposition and uptake on the
landscape. Understanding these patterns and landscape features can help wildlife managers
allocate their resources in a more targeted, efficient manner to help stop or slow the spread of
disease. For example, state and federal management agencies could work with local farmers to
alter farming practices, such as crop rotations, in areas with known positive CWD cases to try
and dissipate larger groups of deer. Additionally, 2 N NaOH was an effective treatment for
deactivating prions in silt-loam soils (Sohn et al. 2019). Furthermore, culling success could be
increased for agencies by focusing efforts in larger forage crop and corn fields adjacent to
32
woodlots that are >222m away from a building. Knowing this information may save agencies
time and money when employing disease management strategies.
33
Chapter One Tables
Table 1.1 Description and source of covariates used to model winter (January – April) deer group
size relative to landscape features in south-central Michigan, USA, 2021-22.
Name
Description
Sourcea
Distance to buildings
CONTAGb
Area of corn
Area of forage crop
Agriculture cover type
Nearest distance (m) to human
buildings
Contagion: measure of the degree
to which cover types are
interspersed and spatially
distributed
Total area (ha) of corn in 172 ha
buffer
Total area (ha) of forage crop in
172 ha buffer
If a deer group was observed in an
agricultural cover type
Microsoft Building Footprints
NLCD, 2020-2021
CDL 2020,2021
CDL 2020,2021
NLCD, CDL 2020,2021
Residential cover type
If a deer group was observed in a
residential cover type
NLCD, CDL 2020,2021
Forested cover type
If a deer group was observed in a
forested cover type
NLCD, CDL 2020,2021
Year (2022)
Year of observation
a NLCD (National Land Cover Database; United States Geological Service 2019; CDL
(Cropland Data Layer; United States Department of Agriculture 2020, 2021)
b Contagion parameter analyzed referencing results from Dechen Quinn et al. 2013
34
Table 1.2 Proportion of land and crop cover within 1.7 km2 buffer around deer group centroids
during winter (January – April) in south-central Michigan, USA, 2021 and 2022.
Cover type
Agriculture
Soybean
Corn
Forage
Forest
Wetland
Proportion (SE)
.64 (0.50)
.23 (0.53)
.22 (0.56)
.12 (0.41)
.25 (1.18)
.11 (0.38)
Table 1.3 Parameter estimates from a truncated negative binomial mixed model of deer group
size relative to local and landscape features in south-central Michigan, USA, 2021 and 2022.
Reference cover type for Corn (ha) and Forage crop (ha) was Agricultural (ha). Reference for
Year (2022) was 2021. SE = standard error and CI = 95% confidence intervals.
95% CI
Parameter
Distance to buildings
Estimate (SE)
0.09(0.04)
Lower
0.006
CONTAG
-0.11(0.05)
-0.22
Area of corn (ha)
0.12(0.05)
Area of forage crop (ha)
0.13(0.04)
Residential cover type
-0.94(0.22)
Forested cover type
-0.34(0.09)
Year (2022)
0.25(0.09)
0.02
0.04
-1.37
-0.52
0.07
Upper
0.18
-0.005
0.22
0.22
-0.50
-0.16
0.43
35
Chapter One Figures
Figure 1.2 Road survey transect groupings divided into regions (NW, NE, and SW) by major
highways in south-central Michigan, USA, 2021 and 2022.
36
Figure 1.3 Frequency of mean deer group sizes observed along transects in south-central
Michigan, USA, during winter season (January – April) in 2021 and 2022.
37
Figure 1.4 Predicted deer group sizes during winter (January – April) in south-central Michigan,
USA, 2021 and 2022. Light grey circles indicate data points. Error bars are 95% confidence
intervals.
38
Figure 1.5 Predicted deer group sizes during winter (January – April) associated with corn (ha)
within 1.7 km2 surrounding deer group centroids in south-central Michigan, USA, 2021 and
2022. Light grey circles indicate data points. Gray shading represents 95% confidence intervals.
39
Figure 1.6 Predicted deer group sizes during winter (January – April) associated with forage crop
(ha) within 1.7 km2 surrounding deer group centroids in south-central Michigan, USA, 2021 and
2022. Light grey circles indicate data points. Gray shading represents 95% confidence intervals.
40
Figure 1.7 Predicted deer group sizes during winter (January – April) associated with the nearest
distance to a building in south-central Michigan, USA 2021 2022. Light grey circles indicate
data points. Gray shading represents 95% confidence intervals.
41
Figure 1.8 Predicted deer group sizes during winter (January – April) for agricultural, residential,
and wooded cover types in south-central Michigan, USA 2021 2022. Light grey circles indicate
data points. Error bars represent 95% confidence intervals.
42
Figure 1.9 Predicted deer group sizes during winter (January – April) associated with contagion
(CONTAG) within 1.7 km2 surrounding deer group centroids in south-central Michigan, USA,
2021 -2022. Light grey circles indicate data points. Gray shading represents 95% confidence
intervals.
43
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50
APPENDIX I: FIGURES
Figure A.1.1 Spearman’s rank correlation test for initial model variables (CONTAG (contagion),
IJI (interspersion-juxtaposition index), agriculture cover type, forest cover type, wetland cover
type, hectares of corn, hectares of soybeans, hectares of forage crop, hectares of other crops,
nearest distance to dwellings, and total length of road). Values 0 - -.2 and 0 - .2 indicate a weak
or non-existent correlation between variables.
51
Figure A.1.2 Quartile-quartile (QQ) plot of model residuals (left panel) showing observed values
on the y-axis and expected values on the x-axis. Residual plot (right panel) showing residual
values on the y-axis and predicted model values on the x-axis. Empirical 0.25, 0.5, and 0.75
quantiles depicted by the solid red line (left panel) are compared to theoretical 0.25, 0.5, and 0.75
quantiles depicted by black lines (right panel).
52
CHAPTER TWO: WHITE-TAILED DEER BEHAVIORS AT FEED SITES, FOOD
PLOTS, AND THE SURROUNDING LANDSCAPE WITH IMPLICATIONS FOR
MANAGING CHRONIC WASTING DISEASE
Abstract
Previous studies made assumptions of how frequently deer come into direct physical
contact based on proximity of radio-collared individuals, but this information is not precise and
does not account for potential contacts among uncollared deer. Other deer behaviors likely play a
role in transmission of prions, so I created three behavioral categories (i.e., direct contact, self-
contact, environmental contact) to portray a broader range of behaviors potentially linked to
prion transmission. My objective was to quantify behaviors exhibited by deer at congregation
areas including baited sites, food plots, and naturally occurring forage. I used camera trapping on
privately-owned lands and road-based transect surveys (surrounding landscape) during the post-
breeding period (January-April 2021 and 2022) to quantify deer behaviors among various sex
and age classes. I compiled 395 observations of known sex-age deer during road-based surveys
and conducted 2,047 observations from video surveys (bait sites = 1,631, food plots = 416). For
all deer observed, I detected significantly fewer direct contacts at food plots (βFood plot = -1.45
[95% CI = -2.00 - -0.90]) and transects (βTransects = -1.12 [95% CI = -1.64 – 0.59]) compared to
bait sites. I found a lower number of self-contacts at food plots compared to bait sites (βFood plot =
-1.14 (95% CI = -1.64 - -0.64). I observed fewer environmental contacts at food plots (βFood plot =
-0.68 (95% CI = -0.90 - -0.47)) and transects (βTransects = -0.65 (95% CI =-0.87 - -0.43))
compared to bait sites. My results indicate that the likelihood of direct and environmental
contacts at bait sites exceeds contacts at food plots and naturally occurring forage. In areas of
CWD concern, food plots and naturally occurring forage offer a less risky food source for deer.
53
Introduction
Direct contact rates among deer is a vital parameter for modeling CWD and models that
incorporate contact rates among deer are highly sensitive to this parameter (Belsare and Stewart
2020; Kjaer and Schauber 2022). Agreement on what constitutes a direct contact between
individual deer is lacking, with most studies based on proximity loggers and GPS collars to
estimate contact frequency and duration (Walrath et al. 2011; Lavelle et al. 2014; Tosa et al.
2015). Direct contacts are presumed to occur when two proximity loggers communicate
(Walrath et al. 2011), if GPS collars were <25 m apart (Kjaer et al. 2008), or proximity loggers
were ≤1 m away from each other (Tosa et al. 2015). Lavelle et al. (2014) estimated daily contacts
rates for GPS collars were 0.12, 0.66 for proximity collars, and 0.29 from video collars. Walrath
et al. (2011) found proximity loggers had a greater mean probability of detecting an encounter
between deer compared to direct observations. Additionally, location error from GPS collars and
proximity loggers may influence estimates of contact rates due to collar orientation (D’eon and
Delparte 2005), radio transmission power, distance between loggers, animal body mass and fine-
scale movements (Ossi et al. 2021). Given the importance of direct contacts for prion
transmission in CWD models, and disparity among techniques used to estimate contact rates
(Habib et al. 2011; Creech 2011; Creech et al. 2012; Williams et al. 2014), it is imperative to
have reliable estimates of direct contacts among deer. Using direct observations to evaluate
contact rates and the nature of interactions among individuals is critical for understanding prion
transmission, especially during winter and in settings where deer tend to congregate (Nixon et al.
1991).
In the Midwest United States, food habits of white-tailed deer change by season. In
winter and early spring, deer rely heavily on browse (leaves and stems of woody plants), forbs,
54
and crop residue following fall harvest (Korschgen 1962; Nixon et al. 1991). During January-
March, daily forage intake and metabolic rates decrease, then begin to increase in April as adult
does reach parturition, however, the timing can change depending on seasonal weather
conditions and green-up (Moen 1978). During winter, deer are naturally congregating and food
resources are limited, potentially increasing competition and likelihood of pathogen transmission
as deer come into more direct contact (Grenier et al. 1999). Human activities that congregate
deer unnaturally (including baiting, feeding, and the implementation of food plots) remain
popular in North America (Miller and Marchinton 2007) and pose risks for increased CWD
transmission through both direct and indirect pathways (Miller et al. 2003; Rudolph 2012).
Feeding is the act of providing food materials that might attract deer for various purposes.
Feeding can include recreational feeding and supplemental feeding. For recreational feeding,
food is provided to improve recreational wildlife viewing opportunities. Supplemental feeding
refers to producing food that will attract deer to aid in hunting, or to provide an additional food
source, usually in the form of food plots (MDNRa 2023). Baiting is the act of feeding deer to
attract them to a specific location, originally used by hunters to increase harvest success (Garner
2001). Common types of bait include corn, apples, salt, and hay (Naugle et al. 1995).
Researchers also utilize baiting to attract individuals to accomplish study objectives, such as
trapping for radio-collaring (Thompson et al. 1989; Campbell et al. 2006). A survey amongst
Michigan deer hunters in 2017 concluded that over 50% of the participants used baiting to
improve harvest success or see more deer during hunting (Frawley et al. 2018). Research broadly
shows that baiting deer has a marginal impact on overall hunter success rates; however, baiting
can increase the success rate in areas where natural food sources are limited (Langenau et al.
55
1984; Winterstein 1992; Weckerly and Foster 2010). This presents a conundrum for wildlife
managers who seek to maintain a balance of disease mitigation and appeasement of stakeholders.
Baiting and feeding provide an unnatural food source and increased nutrition for deer
outside of natural forage at certain times of the year. White-tailed deer shift core areas of activity
closer to bait sites and will frequently use bait sites that are within their home ranges, but deer
are less likely to shift or expand their home ranges to access bait sites (Kilpatrick and Stober
2002; Campbell et al. 2006; Beaver 2017). Peterson and Messmer (2011) found an increase in
deer bed sites near areas where active baiting was occurring, providing evidence that deer will
alter behaviors, movements, and space use to utilize bait. Deer presumably bed closer to food
sources to conserve energy, thus increasing energy stores. This is particularly critical in northern
climates during times when food is scarce or environmental conditions are harsh and deer must
expend energy to find sufficient food to survive winter. While there are some benefits, deer are
in closer proximity to each other for longer periods of time at bait sites, creating a potential
increase in disease spread through direct physical and environmental contacts.
Another technique for supplementally feeding deer commonly used by hunters and
wildlife managers are food plots. Two primary reasons hunters and wildlife managers use food
plots are to aid in hunting and provide a source of food during times of year when natural forage
may be scarce or lacking in nutrients (Kammermeyer and Thackston 1995; Tranel et al. 2007).
Additionally, food plots are used for recreational viewing purposes and to help mitigate crop
depredation (Tranel et al. 2007, Smith et al. 2010). Food plots can be planted in the summer to
provide a crop that will attract deer in the fall, or they can be planted in the fall to provide a food
source over the winter. Brassica species (Brassicaceae), cereal grains (Gramineae), clover
(Trifolium), and corn (Zea mays) are commonly used for food plots. Cereal grains, especially oat
56
and wheat species, are commonly planted for a fall crop. These foods provide nutrition for deer
during the rut, they regenerate quickly, and will flush again in the spring (Almy 2019). Brassica
species, such as turnips and radishes, are a highly coveted source of energy and nutrition when
food is scarce for deer in the winter (Almy 2019). McQueen (2020) found that white-tailed deer
foraged in food plots at a higher rate than natural vegetation. Comparable to bait sites, when
natural food is limited, deer will forage heavily on food plot vegetation (Sowell et al. 1985).
Sowell et al. (1985) found that 27% of mule deer (Odocoileus hemionus) winter diets consisted
of planted wheat and rye, resulting in increased diet quality. Although there has been research on
the efficacy and cost effectiveness of food plots, an extensive literature search produced no
evidence of how deer interact at food plots and how this relates to potential pathogen
transmission.
Food plots and bait sites may not pose the same level of pathogen transmission risk
because of dissimilarities in how deer use the attractant. While baiting causes deer to concentrate
in a focal area, such as around a feeder or bait pile, food plots typically extend over broader areas
(Kammermeyer and Thackston 1995; Harper 2019). Spreading food across an area may reduce
the potential for direct physical contact among deer. While baiting and food plots are commonly
used for supplemental feeding, deer behaviors and contacts among deer at each should be
assessed separately because of contrasting deer numbers at a given site and duration of foraging
activity that may occur within the feed area.
A concentrated food source that is being used by deer leads to unnatural congregation,
potentially resulting in more direct physical (i.e., among deer) and environmental interactions.
For CWD, only 300 ng of CWD-positive saliva is required to cause infection in deer (Mathiason
et al. 2006; Denkers et al. 2020). Deer also can experience indirect environmental contact with
57
CWD prions through urine and feces deposited on or near remaining feed (Plummer et al. 2017).
The level of risk associated with supplemental feed sources via direct and indirect transmission
remains poorly understood. I hypothesized that deer in winter would exhibit more direct contacts
at bait sites than food plots or the surrounding landscape presumably due to increased food
competition in a smaller area. I also hypothesized that adult males would exhibit direct contacts
more often than other sex-age groups because of increased nutritional demands following the
breeding season (Nixon et al.1994; Clutton-Brock et al. 1982).
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Methods
Field Methods
Remote cameras provide an opportunity to observe deer behavior and potential risk of
prion transmission at food plots and bait sites. In this study, comparisons to direct observations
of deer behaviors along transects in the surrounding landscape served as a control and offered a
means to evaluate contact rates among bait sites, food plots, and the surrounding landscape.
Baiting and feeding deer is illegal in the Lower Peninsula of Michigan; however, I worked in
cooperation with the Michigan Department of Natural Resources (MDNR) who granted an
exemption for this research. In collaboration with private landowners, I established 10 bait sites
and 8 food plots in 2021 and 10 bait sites and 11 food plots in 2022 (some at the same locations
for both years; Fig 2.1). I located bait sites >3.2 km away from transects and food plots to reduce
the possibility of influencing localized deer movements (Skuldt 2005; Thompson et al 2008), and
established bait sites in open agricultural fields or cleared shrubland to facilitate placement of
remote camera arrays. Several (n=8) bait sites were established prior (i.e., 2018 to 2020) to this
study for live-deer capture on another research project. Prior to my study, these sites were last
baited before 16 March 2020.
The locations of food plots and type of food planted were pre-determined by private
landowners several months before data collection. Food plots averaged 8.2 km (range = 3.8 km –
20.4 km) away from bait sites and 3.3 km (range = 0.27 km – 12.3 km) from transects. In 2021,
food plots consisted of 1 clover (Trifolium spp.), 4 brassica (Brassica rapa, Raphanus sativus),
and 1 winter rye (Secale cereale) plot. In 2022, field crews sampled 3 clover, 3 winter rye, and 3
mixed variety plots consisting of clover, rye, and brassica. Food plots varied in size from
approximately 4,046 m2 to 9,888 m2. Given difficulty finding food plots in 2021, I selected two
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larger agricultural fields planted with brassica and clover as cover crops to serve as food plots
(Fig. 2.1). In these two fields and in the two largest food plots, I set up two camera arrays (Fig.
2.2).
At bait sites and food plots, camera arrays were configured as a pentagon using t-posts at
each corner (Fig. 2.2). Each t-post was approximately 11.7 m apart and in the center of the
pentagon I positioned 4 PVC pipes in a 9.29 m2 square (Fig 2.2). Within the area of the PVC
pipes, I followed MDNR regulations for baiting deer in the Upper Peninsula of Michigan
(MDNRa) and scattered 7.5 liters of corn evenly across the ground, 2 times per week. I attached
Browning StrikeForce HD ProX cameras to t-posts 0.6 m off the ground and facing inward 10 m
from the center bait area (Fig. 2.2). I used two cameras to increase effort and detectability of
deer. A trail camera was attached to the southeast and southwest metal t-posts, facing northwest
and northeast, respectively (Fig. 2.2; Pease et al. 2016). I deployed cameras with AA lithium
batteries and configured cameras for a 2-second delay and 2-minute video upon detection of
motion. Field crews checked batteries and SD cards at bait sites twice per week and food plots
were checked once per week. Sites remained undisturbed by research staff throughout the rest of
the survey period to promote deer acclimatization.
Videos from bait sites and food plots were organized by site, date, morning and evening
periods, and camera orientation (i.e., northwest and northeast facing). The morning survey period
occurred 15 minutes before sunrise and ended 2 hours after sunrise. The evening survey period
began 2 hours before sunset and ended 15 minutes after sunset. Any videos recorded outside of
dawn and dusk were removed from the sampling pool. When choosing between northeast and
northwest facing cameras for analysis, I selected the camera that recorded more videos and then
used a random number generator to select 2-min videos from that camera to observe. My goal
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was to identify two 2-min video clips per site per day in the morning and evening to standardize
observation effort, resulting in up to 16 30-sec segments per day.
I used Survey123 (ArcGIS 2010) to record data on deer demographics, contact rates, and
behaviors from videos. I counted the maximum number of deer observed within a 2-min video
segment and the maximum number of deer observed within the center square of the camera
arrays. Every deer within the video was sexed and aged by trained technicians, if there was any
uncertainty, the deer was classified as unknown. I classified sex and age of deer as adult male
(≥1 yr old), adult female (≥1 yr old), male fawn (<1 yr old), female fawn (<1 yr old), unknown
sex adult, unknown sex fawn, and unknown sex and age (Hirth 1977; Bowyer et al. 1996). Adult
males have a higher prevalence rate of CWD, and because they are less observable and exist at
lower densities in comparison to other sex and age classes of deer (Zagata and Haugen 1974;
Nixon et al. 1991; Grear et al. 2006), I prioritized observing adult males as the focal deer if they
were present in the group. Given males shed antlers throughout the time of my research, I used
multiple morphological characteristics to sex and age individuals (Geist 1998; Mejía Salazar et
al. 2016). If deer did not have visible antlers, technicians looked for pedicels where antlers may
have recently detached (Ozoga 1972). Later in the season technicians looked for antler
protrusions, where the hair is sometimes a different color directly above the eyes. Male adults
and fawns both have blocky foreheads covered by dense hair that can be darker in color. Female
adults and fawns have a triangular, flat forehead with shorter hair. If circumstances allowed,
technicians were able to observe male genitalia. In the absence of an adult male, I used a random
number generator to select a sex-age class of deer to observe from each 2-min video segment.
Within each 2-min video segment, I recorded every unique behavior exhibited by the
focal deer within a 30-second segment until the segment ended or the deer exited the video.
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Behaviors were categorized into “interactions between deer” (direct contact), “contact with self”
(self-contact), and “interactions with environment” (environmental contact; Table A.2.1).
Technicians observed deer for 2-minutes, comprised of up to four 30-second segments, and each
categorized behavior was only recorded once per 30-sec sampling segment. For example, if the
focal deer pushed another deer in three of four 30-sec sampling segments in a 2-min video, direct
contact was recorded as three.
Deer observations along road-based surveys served as a control for how deer behaved in
the surrounding landscape. Prior to driving transects each day, field crews used a random number
generator to determine the sex-age class and order of deer to observe when adult males were not
present. Upon spotting a group of deer and identifying the focal deer for observation, technicians
used a spotting scope (Cabela’s Krotos 86 mm 20x-60x) to observe behaviors. The observer
communicated to the recorder each time a deer performed a behavior (sensu Grenier et al. 1999);
however, categorized behavior was only recorded once per 30-sec sampling segment. Each deer
was observed for 2 minutes, but on occasion the observer could break every 30 seconds for eye
relief. Deer were observed in 30-sec segments until completion of a 2-minute period or until the
individual was no longer visible. After completing observations on the first deer, a second
individual from the group was randomly selected and the process repeated. Only two deer were
observed per group. The protocols were deemed exempt by the Michigan State University
Institutional Animal Care and Use Committee as the research was non-invasive and animals
were observed undisturbed in their natural habitat.
Quantitative Methods
For each 30-sec segment on a given day, I denoted whether a behavior category (i.e.,
direct contact, self-contact, environmental contact) occurred as a “1” (else “0”). I then summed
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the number of 30-sec segments by type of contact that occurred by treatment (i.e., bait sites, food
plots, and the surrounding landscape) at a site on a given day. At most, there was potential to
tally 16 (8 in morning, 8 in evening) direct, self, and environmental contacts per day. I used the
“lme4” package (Bates et al. 2015) to run a generalized linear mixed model (GLMM) with a
zero-inflated negative binomial distribution to predict the likelihood of a behavior category. I
included date and treatment type as predictor variables. Camera array location or transect
identifier was used as a random effect in the model to help account for repeated observations of
the same deer over time. I assessed model fit by checking for overdispersion and generating
residual and prediction plots.
I was also interested in the likelihood of direct contacts at bait sites among deer sex-age
classes. For each sex-age group I determined if other deer were in the video frame (thus available
for contact with the focal deer) and when other deer were available, denoted whether a contact
occurred and the contacted sex-age group. I made this assessment for 30-sec video clips. Because
the dataset was non-normal, I used a quasibinomial model with a logit function that predicted the
likelihood of a direct contact for a focal sex-age group (e.g., adult males) based on the number of
deer within the video frame of all sex-age classes (i.e., adult males, adult females, female fawns,
and male fawns) as fixed effects. I used a GLMM and specified a penalized quasi-likelihood
(GLMM-PQL) distribution using the “MASS” package in R (Ripley et al. 2002). I also included
an array-level random effect to account for potential observations of the same deer and groups
over time, and year as a fixed effect. This model was evaluated by checking for collinearity and
generating residual and prediction plots.
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Results
Deer triggered cameras more frequently in the evenings at bait sites and food plots for
both years (Table 2.1). From these videos, I observed6,309 30-sec segments from 20 bait sites
and 19 food plots in 2021 and 2022, respectively (Table 2.2). The majority (n=5,251) of the 30-
sec segments came from bait sites, with the rest (n=1,058) at food plots (Table 2.2). This could
be attributed to the difference in size between bait sites and food plots. At bait sites, the food is
in a small square that attracts deer, but in food plots the food source is spread across a greater
geographic extent, not necessarily forcing deer in front of the video camera. Although adult
males were given preference during observations, at bait sites 30-sec observations were
relatively evenly distributed among adult males (30% of observations), adult females (26%), and
male fawns (28%), with female fawns least observed (16%; Table 2.2). The same pattern
emerged from food plots, with relatively equal observations among adult males (25%), adult
females (28%), and male fawns (28%), followed by female fawns 19%; Table 2.2).
From 26 transects, I observed deer behaviors 131 times in 2021 and 264 in 2022 resulting
in 801 30-second segments from known sex-age deer in both years combined (Table 2.2). In
2021, I observed behaviors by adult females most on transects (3% of observations), followed by
adult males (25%), female fawns (22%), and male fawns (20%; Table 2.2). In 2022 I observed
behaviors more for adult males (33%), followed by adult females (28%), male fawns (22%), and
female fawns (18%; Table 2.2).
For 5,251 30-sec video segments collected at bait sites, I observed direct contacts in 15%
of the segments and self-contacts in 7% (Table 2.3). I observed environmental contacts in 91%
of the 30-sec video segments at bait sites (Table 2.3). Of the 1,058 30-sec video segments
recorded at food plots, I observed direct contacts, self-contacts, and environmental contacts in
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9%, 3%, and 88% of segments, respectively (Table 2.3). For transects, I documented 8% direct
contacts, 11% self-contacts, and 79% environmental contacts (Table 2.3).
For all deer observed and with bait site as the reference treatment (Table A.2.2), I
detected fewer direct contacts at food plots (βFood plot = -1.45 [95% CI = -2.00 - -0.90]; Fig 2.3)
and transects (βTransects = -1.12 [95% CI = -1.64 – 0.59]). Diagnostics for this model indicated
that the residuals were normally distributed (i.e., QQ plot residuals) with residuals randomly
distributed around the 0.50 line, however, several potential outliers were noted (Residual vs
predicted; Figure A.2.9) Similarly, I found a lower number of self-contacts (Table A.2.3) at food
plots compared to bait sites (βFood plot = -1.14 (95% CI = -1.64 - -0.64; Fig 2.3), and no difference
in self-contacts between transects and bait sites (βTransects = 0.04 (95% CI = -0.39 – 0.48; Fig
2.3). The diagnostics for the self-contact model showed that there was significant deviation
within the distribution (i.e., QQ plot residuals) and several potential outliers (i.e., Residual vs
predicted; Figure A.2.10). Additionally, more direct and self-contacts occurred as Julian date
increased (β= 0.37, CI = 0.25 – 0.49; Figs 2.4, 2.5, respectively). I observed fewer
environmental contacts (A.2.4) at food plots (βFood plot = -0.68 (95% CI = -0.90 - -0.47)) and
transects (βTransects = -0.65 (95% CI =-0.87 - -0.43)) than at bait sites (Fig. 2.3). The
environmental contact model diagnostics indicated significant deviation within the distribution
(i.e., QQ plot) and significant deviation among quantiles with several potential outliers (i.e.,
Residual vs predicted; Figure A.2.11).
Sex and Age Specific Behaviors
Direct contacts between individuals facilitate deer-to-deer spread of CWD, hence I was
particularly interested in sex-age group direct contact interactions. However, low numbers of
direct contacts among some sex-age groups within treatment prohibited modeling of sex-age
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interactions for food plots and transects. Thus, I focused modeling on bait sites which had the
greatest number of direct contacts (Table 2.3).
For bait sites, models converged for adult males, adult females, and male fawns. No
multicollinearity among model variables was identified. Adult males were more likely to exhibit
a direct contact when in proximity to more male fawns (βMale fawns = 0.45 [95% CI = 0.19 – 0.71];
Fig 2.6B). I also observed more direct contacts by adult males in 2021 than 2022 (β2022 = -0.88
[95% CI = -1.37 – -0.39]; Table A.2.5). Similarly, adult females were more likely to exhibit
direct contact in 2021 than 2022 (β2022 = -1.31 [CI = -2.05 - -0.57]; Table A.2.6). Surprisingly,
for adult females, I found a decrease in the likelihood of a direct contact occurring when
numbers of adult females increased (βAdult females = -0.43 [95% CI = -0.78 – -0.09]; Fig.2.6A). For
male and female fawns, I found that direct contacts with adult females was more likely as the
number of adult females increased (Adult females =βFemale fawns = 0.65 [95% CI = 0.25 – 1.05];
Male fawns =βFemale fawns = 0.62 [CI = 0.10 – 1.13]) were present (Fig 2.6C). Like adult males,
male fawns showed a strong negative effect for year, with more direct-contacts in 2021 than
2022 (β2022 = -0.83 (CI = -1.47 – 1.13); Table A.2.7).
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Discussion
This study is the first to investigate different types of contacts (direct, self,
environmental) in a variety of deer foraging areas (bait site, food plot, surrounding landscape)
with implications for pathogen transmission. Direct contact has often been assumed as the
riskiest behavior to transmit pathogens because of the potential spread of bodily fluids among
animals (Schauber et al. 2015). This research documents that direct contact between individuals
are rare events; however, I observed more direct contacts at bait sites compared to food plots and
the surrounding landscape. Garner (2001) observed in a single winter an average of 28 face-to-
face contacts between 2 or more deer at winter bait sites during a sixty-minute period. The
following winter an average of 8.5 face-to-face contacts were observed. Cosgrove et al. (2018)
modeled the effects of winter supplemental feeding on bovine tuberculosis prevalence and found
a 2-3% increase for every 2 months individuals were fed, and a 50% increase after 5 years of
winter feeding.
Information on indirect and self-contact rates, and the nature of contacts among multiple
individuals (e.g., including unmarked deer) is generally lacking in data collected via GPS collars
and proximity loggers. Recently, accelerometers attached to GPS collars have provided
information on some indirect or self-contact behaviors (Benoit et al. 2023). Accelerometers
attached to free-ranging roe deer accurately (68-94%) portrayed running, walking, and immobile
behaviors, but grooming behaviors were only 34-38% accurate (Benoit et al. 2023). Benoit et al.
(2023) acknowledged variation in accelerometer performance as signals varied among
individuals due to collar tightness and sensitivity. Benoit et al. (2023) also found that
accelerometer data were accurate when deer were foraging with their head down, an important
environmental contact to observe for indirect pathogen transmission. Video systems attached to
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collars also can aid in portraying interactions between individuals and social group structure and
dynamics, but this technique was less accurate in estimating contact rates than other
methodologies due to battery limitations Moll et al. 2009; Lavelle et al. 2014). Additionally,
video collars can capture individuals foraging on vegetation (Lavelle et al. 2012). While several
methods may attempt to obtain direct, self, and environmental contacts, they are not without
limitations.
I conducted a comparative analysis of direct, self, and environmental contacts at bait
sites, food plots, and the surrounding landscape from January-April when deer naturally
congregate in agricultural dominated landscapes (Nixon et al. 1991). Deer in winter and spring
typically spend >95% of active time foraging (Beier and McCullough 1990), and most deer I
observed at all three treatments were actively feeding. The concentration of food varies greatly
among bait sites, food plots, and the surrounding landscape. Transects sampled the existing
landscape allowing broad spacing among deer while feeding. Food plots were 0.4 – 1 ha in size
which allowed limited spacing among deer while feeding. Bait sites were 9.06 m2, restricting
spacing of deer that wanted access to bait. Male white-tailed deer are known to reduce or shift
their core area of activity closer to bait sites, and deer of both sexes increase their use of baited
areas (Beaver 2017). In the Upper Peninsula of Michigan, white-tailed deer that were
supplementally fed had smaller home range sizes compared to those not fed, and selected for
poor quality winter habitat (Petroelje et al. Personal communication). The confined area of a bait
site forces deer to contact each other at unnatural rates (Garner 2001; Schauber et al. 2015).
An unnatural congregation of deer inherently increases risk of pathogen transmission via
increased contact and prion deposition, potentially creating infectious reservoirs. Size of a feed
site and duration the food is available might influence deer behavior and the risk of pathogen
68
transmission. While bait or supplemental feed sites can be offered year-round, food plots are
typically used in the fall for hunters to attract deer or in winter to augment forage deer
(Kammermeyer and Thackston 1995; Tranel et al. 2007). Food at bait and supplemental feed
sites can be consumed quickly, whereas food plots are available longer resulting in longer but
less condensed exposure times to deposited prions. Deer may not shift home ranges due to
presence of food plots like they might shift core use areas for a bait site, but they concentrate use
of their home ranges closest to baited sites (Vanderhoof and Jacobson 1993). Food plots are
often replanted annually (Harper 2019), influencing deer behavior and accumulation of prions as
they are available longer on the landscape than bait.
Transects were utilized as the control treatment in this study. Waste grain congregates
foraging wildlife (Nixon et al. 1991; Galle et al. 2009), and most interactions observed occurred
in agricultural fields at a time when deer were actively feeding. For all three treatments, I only
sampled deer during winter, but grouping behavior, movements and habitat use vary seasonally
(Nixon et al. 1991). Future research should include direct observations of behavior for direct and
indirect transmission throughout the year, particularly during parturition, to better understand
how contact rates vary.
During both years, I recorded deer more frequently at food plots and baits sites in evening
compared to morning. Beier and McCullough (1990) documented a similar increase in evening
deer activity in George Reserve, Michigan, but others have reported equivalent morning and
evening activity by deer (Kammermeyer and Marchinton 1977; Webb et al. 2010). However,
these studies did not record deer activity at baited sites which could influence time of activity.
Deer can become conditioned to feeding when bait is placed during specific times of day (Henke
69
1997) and because I baited deer after the peak of morning activity, it is likely that deer became
accustomed to visiting sites in evening when bait was present.
I consistently observed more direct contacts between deer in 2021 than 2022, and because
deer activity is influenced by winter weather (Beier and McCullough 1990; McCoy et al. 2011), I
explored differences in temperature, precipitation, and snowfall between years. Temperatures
ranging from 11.2-20.0 °C can increase metabolic rates and thermoregulatory costs (Moen 1985;
Jensen et al. 1999), and increased snow depth can affect white-tailed deer body condition
(Garroway and Broders 2005). Foraging becomes difficult as deer expend energy to scrape
through snow to underlying food (Ayotte et al. 2020). Average temperatures were similar
between both years of this study, but a slight decrease in precipitation was observed in 2021. An
increase in snowfall was noted in February for 2021 and 2022 (+67 and 83%) compared to the 20
-year average (NOAA 2023). Based on this information, I determined that weather did not have
an impact on differences in direct contacts between years. I also concluded that direct and self-
contacts increased as Julian date increased. This could be attributed to deer being less active
during February and March (Beier and McCullough 1990), and more active during spring green-
up as nutritional requirements in spring increase for deer (Ozoga 1972). The increase in self-
contacts with an increase in Julian date may also correlate with deer shedding their winter coats,
as I often observed large quantities of deer hair at bait sites and food plots in late March and
throughout April.
Group organization in cervids is based on strong social bonds among related females and
their offspring of the year while adult males segregate from females forming loosely associated
bachelor groups during most of the year (Hirth 1977; Weckerly 1999; Nixon et al. 1991). Within
matriarchal groups, hierarchies among related and unrelated females are developed through
70
relatively stable home-ranges and successfully producing female offspring who establish
adjacent home-ranges to their dam (Hawkins and Klimstra 1970; Hirth 1977; Mathews and
Porter 1993). Adult males dominate all other sex and age classes of deer, antlered yearling males
dominate all other sex and age classes except adult males, adult females dominate yearling
females and fawns, yearling females dominate fawns, and male fawns dominate female fawns
(Hirth 1977; Ozoga 1972). Additionally, dominance plays an important role among groups of
deer occupying similar areas; however, the probability of CWD transmission within groups of
related individuals is higher than between groups of unrelated individuals (Grear et al. 2010;
Storm et al. 2013).
Given deer social organization, as baiting congregates deer unnaturally in a confined area
(Rustand 2010; Cosgrove et al. 2018), I expected an increase in intra- and interspecific group
contacts. I also expected that deer behavior would vary among sex/age deer at different food
sources. My results indicate that a direct contact was less likely to occur between adult females
as the number of adult females present at a bait site increased. Adult females associate with their
offspring less as their offspring advance in age, but females >3 years old still associated with
their mothers 27% of the time in winter in Illinois (Nixon et al. 2010). Annual survival of adult
females was high in the study area (0.746; J. Trudeau, MDDNR, unpublished data) and as a
result, there were likely several intact, related matrilineal groups at my bait sites decreasing the
likelihood of adult females interacting aggressively. Reduced interactions among adult females at
bait sites likely lessens the risk of direct pathogen transmission. However, the likelihood of an
adult male directly contacting a male fawn at a bait site increased as the number of male fawns
increased. This increase in contact between adult and fawn males could be attributed to adult
males needing to restore body condition after the breeding season (Nixon et al. 1994) and male
71
fawns needing to increase body weight to increase lifetime breeding success (Mysterud et al.
2004; Newbolt et al. 2017). This is of particular interest as adult males have a higher prevalence
rate of CWD (Grear et al. 2006) and have an average dispersal distance of 9.55 km in Michigan
(Pusateri 2003). Thus, adult males have increased potential to transmit prions across a greater
spatial extent. As female fawn presence increased at a site, the likelihood of a direct contact with
male fawns and adult females also increased. This can most likely be attributed to adult females
grooming their female fawn offspring, and male fawns dominating female fawns over bait.
In Michigan, many hunters construct permanent deer blinds, concentrating bait sites and
food plots to attract deer at the same location annually. This could lead to bioaccumulation of
feces, urine, and saliva at the site, and thus an accumulation of prions. A common environmental
contact observed was deer scraping the ground in search of food and this behavior was almost
always followed by nose-to-ground behavior. These types of environmental contacts occurred
more frequently at bait sites compared to food plots and transects, and these combined activities
could lead to tillage of topsoil, potentially exposing buried prions and influencing indirect prion
transmission. As prions are shed into the environment and bind to soil, there is a chance of prion
uptake via soil ingestion. One study found that white-tailed deer, mule deer, elk, and moose
regularly ingested small amounts of soil (<2% of scat samples; Beyer et al. 1994); however,
despite ambiguity in the amount of exposure to infective prions needed to infect deer, this
ingestion of soil could still be an indirect risk to contacting CWD. Wildlife agencies can help
reduce or slow the spread of disease by implementing and enforcing baiting bans (Rudolph 2012;
Cosgrove et al. 2018). However, when a baiting ban was implemented in Michigan to prevent the
spread of bovine tuberculosis, there was a non-compliance rate of ~25% in the immediate area
(Rudolph 2012). Changing hunter behavior to cease the use of bait as a hunting tool can only be
72
achieved through educational awareness of associated penalties and applying penalties that
hunters consider significant, such as loss of hunting privileges (Rudolph 2012).
Scraping and nose-to-ground behavior also could have implications for self-contact
behaviors as a potential route of prion transmission. For example, as deer scrape the ground with
their hooves or put their nose to the ground, prions could adhere to their hooves or nose. Two
common behaviors I observed were deer scratching their body with their hooves and self-
grooming. If prions were present on the hooves or nose after scraping or nosing the ground, and
then they scratch their body, prions could be deposited on the body and then they could groom
the same spot. There is a risk of prion transmission in this event, however, I believe risk of
transmission is minor through this pathway because the potential for ingestion seems less likely
compared to the amount that might be ingested from direct contact with an infected individual or
indirectly through infected environmental materials. The same can be said for an individual that
scraped the ground or nosed the ground, and then grooms, nuzzles, or kicks another individual.
There are many potential routes of pathogen transmission to observe, but the level of risk with
each route may vary greatly. Risk of pathogen transmission increases over time the longer that
feed sites are maintained and deer are exposed (Thompson et al. 2008; Murray et al. 2016;
Mejia-Salazar et al. 2018).
This study is limited by not knowing familial relationships of interacting individuals.
However, I could make reasonable assumptions under certain circumstances. For example, if
only a single doe and fawn were present, they would likely be related (Nixon et al. 1991, 2010).
It would be beneficial to address relatedness by radio-collaring or marking individuals and using
DNA to establish matrilineal lines in an area. Investigating cross-species contact at supplemental
73
feed sites would also provide additional information for pathogen transmission risk across
species barriers (Bowman et al. 2015).
74
Additional Behavioral Observations
While observing deer at bait sites, food plots, and in the surrounding landscape, I noted
behaviors not analyzed for this thesis including aggressive non-contact behaviors (stomping,
rising up, posturing, and chasing). Anecdotally, it appeared adult males displayed the most
aggressive non-contact behaviors compared to other sex-age groups at bait sites. This could be
attributed to food competition to replenish body condition post-rut (Nixon et al. 1994; Clutton-
Brock et al. 1982). Alternatively, as adult males in the study area have a survival probability of
0.57 during the hunting season (J. Trudeau, MDDNR, unpublished data), aggressive behaviors
could be related to establishing dominance as dominant mature adult males are removed from the
population (Nixon et al. 1991, 1994).
Deer of all sex-age groups were often observed smelling or rubbing their noses,
sometimes licking the PVC posts at the center of the bait site or food plots. It happened most
often at bait sites, and this behavior did not seem to occur as much with t-posts that marked the
outer perimeter of the site. A marked doe from a previous study was present at one bait site from
January-April 2022; she was the matriarch of her family group and would aggressively contact
other adult females and fawns in the baited area if they did not leave. This marked adult female
had a female fawn, who over the duration of the field season became increasingly aggressive
herself. I suspect she was displaying dominance because of her mother’s dominance in the herd.
Deer of all sex-age classes were observed exhibiting “aggressive non-contact” behaviors
including stomping their front hoof, rising up on their back legs, chasing other individuals, and
posturing. These behaviors were a precursor, serving as a warning, before true aggressive contact
behaviors occurred. Hirth (1977) described many of these behaviors in detail and ordered them
relative to increasing aggression that ultimately led to direct contact.
75
Anecdotally, I observed deer checking bait sites regularly after bait was depleted for
several days between site visits. In a model utilizing GPS-collar data, researchers showed that
mule deer migrations are based on spatial memory passed on from generations and experience
rather than behavioral decisions to optimize local foraging (Merkle et al. 2019). If a site used to
feed deer is at the same location annually, prolongs the feeding period, and the frequency of use
remains high, deer may learn the location and continue to visit across generations. Within this
confined space, high concentrations of saliva, urine, and feces could be deposited. Concentration
of feces and bodily fluids could result in a buildup of prions in the area if infected animals are
present as prions are known to persist in the environment by binding to soil and plants (Pritzkow
et al. 2015; Kuznetsova et al. 2020). A localized buildup of prions could create a disease
“hotspot”.
Lastly, Garner (2001) observed deer using the heat from their breath to thaw frozen bait
for consumption which has implications for pathogen transmission. This was not something I
witnessed, but if food was frozen to the ground, I often observed deer scraping the ground with
their front hooves at bait sites, food plots, and the surrounding landscape. In some circumstances,
they were trying to access food underneath the snow and other times they were trying to unearth
corn that may have been covered due to rainfall or deer activity turning it under the ground.
Either way, it was rare to re-bait a site and observe even a few kernels of corn remaining.
76
Chapter Two Tables
Table 2.1 Number of deer-triggered videos recorded at bait sites and food plots during winter
(January through April) in Michigan, USA, 2021 and 2022. The morning survey period occurred
15 minutes before sunrise and ended 2 hours after sunrise. The evening survey period began 2
hours before sunset and ended at most 15 minutes after sunset.
Treatment
Bait site
Survey period
Morning
Evening
Food plot
Morning
Evening
Year
2021
2022
2021
2022
2021
2022
2021
2022
Total 2-min videos
813
2,815
1,783
7,287
115
354
369
1,125
77
Table 2.2 Sex-age class of deer observed in 30-second segments along transects and at bait sites
and food plots during winter (January through April) in Michigan, USA, 2021-22.
Treatment
Sex-age group
Transects
(n=35
Adult male
Adult female
Male fawn
Female fawn
Number of deer
observations
2022
2021
12
56
24
39
86
74
57
47
TOTAL
131
264
Bait Sites
(n=20)
Adult male
201
Adult female
Male fawn
Female fawn
61
94
36
298
355
361
225
30- second segments
2021
40
197
81
135
453
672
175
293
114
2022
111
102
74
61
348
966
1,129
1,193
709
TOTAL
392
1,239
1,254
3,997
Food Plots
(n=19)
Adult male
Adult female
Male fawn
Female fawn
57
42
32
11
48
74
85
67
TOTAL
142
274
158
129
82
24
393
115
181
220
149
665
78
Table 2.3 Total number of direct, self, or environmental contacts exhibited by deer sex-age class
by treatment during winter (January through April) in Michigan, USA, 2021-22. Note that
multiple behavior types could occur within one 30-second segment.
Contact Type by Year
Total deer
observations
by year
2021
2022
Direct
2021 2022
Self
2021 2022
Environmental
2022
2021
Deer Sex-Age
Group
Transects
Adult Male
Adult Female
Male Fawn
Female Fawn
Total
Bait Sites
Adult Male
Adult Female
Male Fawn
Female Fawn
Total
Food Plots
Adult Male
Adult Female
Male Fawn
Female Fawn
2
14
7
11
34
26
15
12
8
61
5
4
3
0
22
11
16
11
60
59
92
94
67
30
143
65
110
348
646
161
282
106
96
75
67
48
286
874
967
1,118
631
312
1,195 3,590
3
5
9
5
143
118
76
22
359
97
154
200
129
580
12
56
24
39
86
74
57
47
4
4
6
4
131
264
18
19
11
11
9
50
132
92
392
1,239
278
201
61
94
36
298
355
361
225
57
42
32
11
48
74
85
67
54
65
27
22
28
21
2
73
172
151
120
535
3
8
10
6
27
79
Total
142
274
12
22
Chapter Two Figures
Figure 2.1 Locations of bait site and food plot camera arrays to record deer behaviors during
winter (January through April) in Michigan, USA, 2021-22. Background is World Imagery layer
updated in 2023 by Esri, Maxar, Earthstar Geographics and the GIS User Community.
80
10.0 m
Figure 2.2 Configuration of bait site and food plot camera arrays to record deer use and
behaviors during winter (January through April) in Michigan, USA, 2021-22. T-posts
demarcated corners of the pentagon, and PVC pipe the inner square. Corn (7.5 L) was spread in
the inner square. Motion-triggered cameras were placed on the south t-posts facing towards the
bait area.
81
Figure 2.3 Average number of daily direct, environmental, and self-contacts among deer
observed at bait sites, food plots, and transects during winter (January through April) in
Michigan, USA, 2021-22. Light grey circles represent data points, and error bars represent 95%
confidence intervals.
82
Figure 2.4 Estimated number of direct contacts by day starting on January 4th thru April 25th
among deer at bait sites, food plots, and transects in Michigan, USA, 2021-22. Light grey circles
represent data points, and grey shading represents 95% confidence intervals.
83
Figure 2.5 Estimated number of self-contacts by day starting on January 4th thru April 25th
among deer at bait sites, food plots, and transects in Michigan, USA, 2021-22. Light grey circles
represent data points, and grey shading represents 95% confidence intervals.
84
Figure 2.6 Probability of direct contact occurring between conspecifics at bait sites during winter
(January through April) in Michigan, USA, 2021-22. A = graph in upper left depicting the
probability of an adult female directly contacting other adult females as the number of adult
females at a bait site increases, B = graph in the upper right showing the likelihood of an adult
male directly contacting a male fawn as the number of male fawns increases, C = graph in the
bottom left showing the probability adult females and male fawns contacting female fawns as the
number of female fawns increases at a bait site.
85
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APPENDIX II: TABLES
Table A.2.1 Categories (bold-faced) and descriptions of deer behaviors observed in Michigan,
USA, 2021-22.
Name
Direct contact
Description
Push
Bump
Flailab
Strikeab
Head-to-heada
Groom-othera
Nuzzlea
Self-contact
Self-groom
Body scratch
Head scratch
Environmental
Contact
Scrape
Pushing another deer with head or body
Contacts another deer with nose on any portion of body except the
face
Rising on hind legs and striking another deer using a paddle motion
Includes rising on hind legs and contacting another deer with front
legs or kicking another deer with front or hind foot
Two deer rub or push their heads together, includes sparring- may
or may not be aggressive
Lick another deer
Using muzzle to rub the nose or face of another deer
Deer licks itself
Hoof contacts body
Hoof contacts head
Scraping ground with hoof
Nose-to-ground
Feeding or not
Browse
Eat/chew on wood vegetation or other objects
Roll
a Hirth 1977.
b Thomas et al 1965.
Rolls on ground
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Table A.2.2 Parameter estimates from a negative binomial mixed model of direct contacts in deer
across treatments during winter (January through April) in Michigan, USA, 2021-22. Bait sites
served as the reference level in estimating treatment effects and is labeled as intercept in the
table. SE = standard error; CI = confidence intervals.
Parameter
Estimate(SE) Lower 95% CI Upper 95% CI
Intercept (Bait Site)
0.13 (0.16)
-0.19
0.46
Food Plot
-1.45 (0.27)
-2.00
Transect
Date
-1.12 (0.26)
-1.64
0.12 (0.05)
0.02
-0.90
-0.59
0.21
Table A.2.3 Parameter estimates from a negative binomial mixed model of self-contacts in deer
across treatments during winter (January through April) in Michigan, USA, 2021-22. Bait sites
served as the reference level in estimating treatment effects and is labeled as intercept in the table
below. SE = standard error; CI = confidence intervals.
Parameter
Estimate(SE) Lower 95% CI Upper 95% CI
Intercept (Bait Site)
-0.85 (0.14)
-1.14
Food Plot
-1.14 (0.25)
-1.64
Transect
Date
0.04 (0.22)
0.37 (0.06)
-0.39
0.25
-0.57
-0.64
0.48
0.49
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Table A.2.4 Parameter estimates from a negative binomial mixed model of environmental
contacts in deer across treatments during winter (January through April) in Michigan, USA,
2021-22. Bait sites served as the reference level in estimating treatment effects and is labeled as
intercept in the table below. SE = standard error; CI = confidence intervals.
Parameter
Estimate (SE) Lower 95% CI Upper 95% CI
Intercept (Bait Site)
1.93 (0.07)
1.79
Food Plot
-0.68 (0.10)
-0.90
Transect
-0.65 (0.11)
Date
0.02 (0.02)
-0.87
-0.02
2.08
-0.47
-0.43
0.06
Table A.2.5 Parameter estimates for a generalized linear mixed effects model using Penalized
Quasi-Likelihood evaluating likelihood of a direct contact exhibited by an adult male deer
relative to conspecifics, year, and Julian date at bait sites during winter (January through April)
in Michigan, USA, 2021-22. SE = standard error; CI = confidence interval.
Parameter
Intercept
-3.24 (0.49)
Estimate (SE) Lower 95% CI Upper 95% CI
-4.21
-0.22
-0.34
0.19
-0.86
-1.37
-2.27
0.27
0.53
0.71
0.81
-0.39
0.02
Adult male
0.02 (0.12)
Adult female
0.09 (0.22)
Male fawn
0.45 (0.13)
Female fawn
-0.02 (0.42)
Year (2022)
-0.88 (0.24)
Julian date
0.009 (0.005)
-0.001
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Table A.2.6 Parameter estimates for a generalized linear mixed effects model using Penalized
Quasi-Likelihood evaluating likelihood of a direct contact exhibited by an adult female deer
relative to conspecifics, year, and Julian date at bait sites during winter (January through April)
in Michigan, USA, 2021-22. SE = standard error; CI = confidence interval.
Parameter
Intercept
Estimate (SE)
Lower 95% CI Upper 95% CI
-1.84 (0.58)
-2.99
-0.68
Adult male
0.24 (0.98)
Adult female
-0.43 (0.17)
Male fawn
0.32 (0.17)
Female fawn
0.65 (0.20)
Year (2022)
-1.31 (0.37)
-1.68
-0.78
-0.02
0.25
-2.05
Julian date
0.009 (0.005)
-0.0003
2.17
-0.09
0.66
1.05
-0.57
0.01
Table A.2.7 Parameter estimates for a generalized linear mixed effects model using Penalized
Quasi-Likelihood evaluating likelihood of a direct contact exhibited by a male fawn deer relative
to conspecifics, year, and Julian date at bait sites during winter (January through April) in
Michigan, USA, 2021-22. SE = standard error; CI = confidence interval.
Parameter
Intercept
Estimate (SE) Lower 95% CI Upper 95% CI
-3.31 (0.57)
-4.44
-2.17
Adult male
0.27 (0.34)
Adult female
0.24 (0.16)
Male fawn
0.24 (0.19)
Female fawn
0.62 (0.26)
Year (2022)
-0.83 (0.32)
-0.39
-0.06
-0.14
0.10
-1.47
Julian date
0.0003 (0.004)
0.001
0.95
0.56
0.63
1.13
-0.19
0.01
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APPENDIX II: FIGURES
Figure. A.2.9 A quartile-quartile (QQ) plot of the direct contact behavior model residuals (left
panel) showing observed values on the y-axis and expected values on the x-axis. Residual plot
(right panel) showing residual values on the y-axis and predicted model values on the x-axis.
Empirical 0.25, 0.5, and 0.75 quantiles depicted by the solid red line (left panel) are compared to
theoretical 0.25, 0.5, and 0.75 quantiles depicted by black lines (right panel) Red stars in the
indicate potential outliers.
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Figure. A.2.10 A quartile-quartile (QQ) plot of the self-contact behavior model residuals (left
panel) showing observed values on the y-axis and expected values on the x-axis. Residual plot
(right panel) showing residual values on the y-axis and predicted model values on the x-axis.
Empirical 0.25, 0.5, and 0.75 quantiles depicted by the solid red line (left panel) are compared to
theoretical 0.25, 0.5, and 0.75 quantiles depicted by black lines (right panel) Red stars in the
indicate potential outliers.
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Figure. A.2.11 A quartile-quartile (QQ) plot of the environmental contact behavior model
residuals (left panel) showing observed values on the y-axis and expected values on the x-axis.
Residual plot (right panel) showing residual values on the y-axis and predicted model values on
the x-axis. Empirical 0.25, 0.5, and 0.75 quantiles depicted by the solid red line (left panel) are
compared to theoretical 0.25, 0.5, and 0.75 quantiles depicted by black lines (right panel) Red
stars in the indicate potential outlier.
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CONCLUSION
As CWD continues to spread across the United States, wild populations of cervids are
threatened. Deer, elk, and moose populations are all at risk of population decline and state and
federal agencies allocate significant funding to CWD research, prevention, surveillance, and
control once it is established in an area. The allocation of this funding coupled with the potential
decrease in Pittman-Robertson funds from reduced hunter participation could result in a
significant impact in overall conservation funding. Ongoing research may help fill in knowledge
gaps that are critical to the management of CWD.
Understanding how deer utilize landscapes can help agencies identify potential “hotspot”
areas and employ localized management practices. Few studies have tried to quantify the
landscape variables that influence where deer congregate during the winter in agriculturally
dominated areas and how this may influence group size. Additionally, epidemiological models
use a suite of variables to predict how CWD may spread across a landscape. However, some of
these variables, such as direct and indirect contact rates among deer, are estimated using GPS-
collar and proximity logger data that is not precise and does not account for contacts among
uncollared individuals. Evaluating contact rates on the dominant landscape is important for
models, but also understanding how deer interact at food plots and bait sites is important for the
hunting community, the main group funding natural resource conservation.
To address these knowledge gaps, I used road-based surveys and trail cameras across a five-
county area of southern Michigan to evaluate congregation areas and contact rates from January-
April 2021 and 2022. I ran 35 – 4.83 km long transects several times per week. I recorded
information regarding group demographics, location, and behavior of select individuals. Using
USDA-CDL in ArcMap, I evaluated dominant cover and crop types in a 740 m radii buffer
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around each recorded group location. I used a GLMM to predict the effects of landscape
variables on observed deer group sizes. I investigated deer contact rates by establishing trail
cameras at bait sites and food plots on privately-owned land. I quantified exhibited behaviors
across sex-age classes and used a GLMM to predict the likelihood of a behavior category and
included date and treatment type (bait site or food plot) as predictor variables. I also evaluated
the likelihood of direct contact occurring among conspecifics at bait sites.
I found that deer group size ranged from 1 - 67 individuals. Group sizes increased by ~1.5 –
3.0 deer as total hectares of corn and forage crop increased. As distance from the nearest building
increased, so did group size. I found that residential and forest cover types had a negative impact
on group size, and a positive impact associated with agricultural cover types. Contagion had a
lower, but significant impact on group size, with larger, homogenous land cover corresponding
to lower group sizes.
I compiled 395 observations of known sex-age deer during road-based surveys and
conducted 2,047 observations from video surveys (bait sites = 1,631, food plots = 416). Direct
contacts occurred most frequently at bait sites, followed by food plots, and lastly the surrounding
landscape. Self-contacts occurred less often at food plots compared to bait sites, but there was no
difference between bait sites and the surrounding landscape. Both direct and self-contacts
increased as Julian date increased. Environmental contacts were observed most often at bait sites.
At bait sites, adult males were more likely to exhibit a direct contact when in proximity to more
male fawns. Adult females were less likely to directly contact each other when the number of
adult females increased. The likelihood of a direct contact occurring between a male or female
fawn with an adult female increased as the number of adult females at a bait site increased.
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These findings can fill knowledge gaps to help improve epidemiological models and assist
wildlife agencies with identifying potential localized “hotspots”. As deer congregate in winter
and group sizes get larger, there is an inherent increase in prion deposition and uptake. By
understanding the features of the landscape that these larger groups select for, wildlife agencies
can allocate resources in a more targeted manner to help prevent or slow the spread of disease.
Working with local farmers to alter farming practices to address areas of congregation or
potentially in the future spread fields with prion deactivating treatments could help mitigate
disease. Culling success could increase by targeting larger corn and forage crop fields adjacent to
woodlots and >222m away from any buildings. My findings confirm that during winter direct
contacts do not happen often at bait sites, food plots, or on the surrounding landscape; however,
baiting does increase direct contact, thus increasing the probability of disease transmission. As
environmental contacts happened the most often at any setting, I believe this route of
transmission needs additional exploration. Overall, the results from my study provide models
with more accurate contact rates among sex-age groups during a critical period of congregation
and aid wildlife agencies with knowledge to employ efficacious techniques to manage disease.
While this study may have filled some knowledge gaps, it has brought forward additional
questions. By marking individuals and utilizing similar methodology, we can better understand
the interactions and contact rates among mixed family groups at bait sites and food plots. This
research was conducted only during the winter, which only sheds light upon contact rates at one
time of year. Direct observations for the remaining three seasons would greatly benefit
epidemiological models that utilize contact rates. My results indicated that environmental
contacts occurred frequently. Additional research should focus on the frequency of these
behaviors year-round and test for prions on environmental objects, such as licking branches or
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scrapes. Additional sampling for prions should occur in common congregation areas like corn
fields adjacent to woodlots. The lack of inquiry into treatment of farm fields for infected prions
warrants additional research to help combat CWD on the landscape long-term after a population
has been culled.
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