THE HUMAN ELEMENT OF WILDLIFE HEALTH: MENTAL MODELS, RISK
PERCEPTIONS, AND ATTITUDES TOWARD WILDLIFE DISEASE MANAGEMENT
By
Shauna Lynn Hanisch-Kirkbride

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Fisheries and Wildlife – Doctor of Philosophy
2013

ABSTRACT
THE HUMAN ELEMENT OF WILDLIFE HEALTH: MENTAL MODELS, RISK
PERCEPTIONS, AND ATTITUDES TOWARD WILDLIFE DISEASE MANAGEMENT
By
Shauna Lynn Hanisch-Kirkbride
Wildlife disease management, like other types of wildlife management, influences, and is
influenced by, biological, ecological, and social components of the management system. Human
dimensions research provides insights for the development of management objectives and
communication messages that incorporate societal values. To increase knowledge and
understanding of human dimensions aspects of wildlife disease management, my research
objectives were to: (1) reveal expert mental models of wildlife health and disease; (2) reveal lay
person mental models of wildlife health and disease and wildlife disease management; (3)
determine key variables that influence stakeholders’ wildlife disease risk perceptions; and (4)
assess factors that contribute to explaining public acceptance of wildlife disease management.
Objectives 1 and 2 were accomplished using qualitative, exploratory methods (focus groups and
the Delphi method) to promote better understanding of how experts and laypersons conceptualize
wildlife health and disease and to develop mental models representing these conceptualizations.
Objectives 3 and 4 were accomplished using a survey questionnaire mailed to a random,
nationwide sample to characterize wildlife disease risk perceptions and attitudes toward wildlife
disease management and to assess sociodemographic and social psychological factors that
influence these variables. Expert (n = 18) and layperson (n = 34) mental models of wildlife
health had several common conceptualizations, including sustainable populations, healthy
animals, habitat quality, and ecosystem health. One key difference was that experts were more
likely to view disease as being made worse by humans while laypersons were more likely to

view it as a natural phenomenon. Focus group participants viewed wildlife health as important
and most did not have serious personal health concerns related to zoonotic diseases. In the
survey, mean risk perception for three zoonotic diseases (rabies, plague, West Nile virus) using
the constructs of severity, susceptibility, and dread was moderate (2.5 on a 1 to 4 scale).
Respondents perceived zoonotic disease risk to be greater for wildlife than for humans and held
higher risk perceptions for rabies and West Nile virus than plague. The four most important
predictors of disease risk perceptions were gender, education, prior exposure to the disease, and
concern for health. With regard to attitudes toward wildlife disease management, there was
strong disagreement with allowing disease to run its course and agreement was strongest for the
options of public education and non-lethal management. Across all management options,
agreement was greatest when the goal of wildlife disease management was to protect “one
health” and least when the goal was to protect health of pets; these differences were statistically
significant but not large. The best predictor of agreement was whether respondents believed
wildlife disease management was likely to result in benefits they considered important. A
modified Health Belief Model consisting of six variables (perceived risk severity and
susceptibility, concern for health, exposure to zoonotic disease, social trust, and belief in
management being likely to result in important benefits) was found to be a good fit to the data,
thus supporting it as a useful model for understanding stakeholder evaluations of wildlife disease
management. Together, these findings provide insights to help managers better communicate
with stakeholders about wildlife health and disease and to managers to better incorporate
stakeholder perspectives into management decisions.

ACKNOWLEDGMENTS

Many individuals have supported me, both directly and indirectly, past and present, along
the road to my doctoral degree. A special thank you goes to my parents, Liz and Chuck, for
emphasizing the value of education and always supporting me in my life decisions. I’ve been
blessed with many good teachers and mentors throughout my life who deserve recognition: Bill
Dehnert and Steve Schuele at Evergreen High School, Susan Foster at Mt. Hood Community
College, Dan Pletscher at the University of Montana, and Les Alm and John Freemuth at Boise
State University. They all played much appreciated roles in educating and encouraging me.
Thanks to my friends and colleagues at the U.S. Fish and Wildlife Service, as well as those from
other natural resource agencies, with whom I worked during the cormorant management years.
Those were good years that taught me a great deal and I’m grateful I was part of such an
interesting and contentious issue. Thanks to my advisor, Shawn Riley, my committee members
(Meredith Gore, Michael Nelson, Dan O’Brien, and Jordan Burroughs), and my labmates in the
Riley-Gore human dimensions lab for encouraging and challenging me. Shawn taught me much
in my transition from “manager” to “researcher” and I’ve never regretted leaving my federal
career to work with him at Michigan State University. I’m also grateful for friends and family in
Michigan, Virginia, and elsewhere, who helped me by providing enough-but-not-too-much
distraction from school work and research over the past five years. That group includes, in
particular, Eddie Hanisch-Kirkbride and our four-legged companions, Annie, Jesse, Joey, Sam,
and Abby, all of whom provided me with much love and faithful companionship. I look forward
to the adventures to come. Finally, to the One who gave me life and guided me towards a career
I’ve been blessed to pursue: Gloria in excelsis Deo.

iv

TABLE OF CONTENTS

LIST OF TABLES………………………………………………………………………….……vii
LIST OF FIGURES………………………………………………………………………….….viii
CHAPTER 1
INTRODUCTION AND ORGANIZATION...............................…………………………….…1
CHAPTER 2
PROMOTING WILDLIFE HEALTH OR FIGHTING WILDLIFE DISEASE: INSIGHTS
FROM HISTORY, PHILOSOPHY, AND SCIENCE................…………………...………...….10
Introduction……………………………………………………………………………...…..11
Background………………………………………………………………………………….12
Methods…………………………………………………………………...….....…………..15
Results……………………………………………………………………………………….17
Discussion…………………………………………………………………………………...19
Implications ………………………………………………………………………………...21
CHAPTER 3
WHAT ARE THEY THINKING? EXPLORING LAYPERSON MENTAL MODELS OF
WILDLIFE HEALTH AND DISEASE........................……..............……………………….….30
Introduction………………………………………………………………………………….31
Methods……………………………………………………………………………………..32
Results……………………………………………………………………………………….34
Discussion………………………………………………………………………………...…43
Implications…………………………………………………………………………………46
CHAPTER 4
WILDLIFE DISEASE AND RISK PERCEPTION……………….……………….……….…...55
Introduction…………………………………………………………………………….……56
Materials and Methods…………………………………………………………………..….58
Results……………………………………………………………………………...………..60
Discussion……………………………………………………………………………...……63
CHAPTER 5
USING HEALTH BEHAVIOR THEORY AS A MODEL FOR UNDERSTANDING PUBLIC
AGREEMENT WITH WILDLIFE DISEASE MANAGEMENT……........................................75
Introduction…………………………………………………….……………………..……..75
Methods……………………………………………………………………………….…….78
Results……………………………………………………………………………………….80
Discussion……………………………………………………………………………….…..83

v

CHAPTER 6
THE UPSHOT: RECOMMENDATIONS TO WILDLIFE DISEASE MANAGERS.................97
APPENDICES……………………………………………………………………………….…108
Appendix A: Methodological considerations: The Delphi method, focus groups, and
surveys..................................................................................................................................109
Appendix B: Delphi questions..............................................................................................126
Appendix C: Focus group interview guide...........................................................................127
Appendix D: Survey instrument...........................................................................................128

vi

LIST OF TABLES

Table 2-1. Delphi expert responses to “What does wildlife health mean to you?”…….………24
Table 3-1. Themes that emerged in response to focus group questions………………….…….48
Table 4-1. Survey questions and measurement scales…………………………………….……67
Table 4-2. Perceived risk severity, susceptibility, and dread for five risk targets averaged across
three zoonotic diseases………………………………………………………………....………68
Table 4-3. Factors influencing risk perception rankings for three zoonotic diseases…….…….69
Table 5-1. Health Belief Model variables used in path analysis………………………….…….87
Table 5-2. Agreement with wildlife disease management options by health protection goal…..88

vii

LIST OF FIGURES

Figure 2-1. Mental model of expert conceptualizations of wildlife health………………………23
Figure 3-1. Layperson mental model of wildlife health…………………………………………50
Figure 5-1. Mean agreement and importance for beliefs about positive outcomes of wildlife
disease management…………………………………………………………………….………..89
Figure 5-2. Mean agreement and importance for beliefs about negative outcomes of wildlife
disease management…………………………………………………………………….………..90
Figure 5-3. Path diagram showing standardized path coefficients for the Health Belief Model as
applied to understand agreement with wildlife disease management……………………………91
.

viii

CHAPTER 1

INTRODUCTION AND ORGANIZATION
“Resource problems are not really environmental problems: They are human problems that we
have created at many times and in many places, under a variety of political, social, and
economic systems.” (Ludwig et al. 1993)
“The public has very strong opinions about their resources, how we manage them, and how they
use them... Planning for the cultural, social, and political consequences of disease incidents
should be viewed as an essential and primary outcome.” (Michigan Department of Natural
Resources Director, Rebecca Humphries, testimony to the U.S. Senate, July 8, 2009)
Wildlife diseases present a great challenge for the field of wildlife management (Decker
et al. 2006, AFWA 2007). In recent years, pathogens have emerged that threaten human and
animal health (e.g., avian influenza virus) while others, such as chronic wasting disease, have
spread geographically, threatening the health of important wildlife populations. Other diseases,
such as rabies and plague, have affected humans and animals for hundreds of years yet continue
to cause concern. Maintaining healthy wildlife populations has long been recognized as an
important goal in wildlife management, yet fisheries and wildlife agencies in the United States
continue to face multiple threats to animal and population health (AFWA 2007). Brucellosis in
bison; rabies in raccoons, skunks, and foxes; West Nile virus in migratory bird populations;
white nose syndrome in bats; chytridiomycosis in amphibians—these are just a few examples of
the many threats to the health of wildlife populations, and to human and domestic animal health
in some cases. Stakeholder reactions to wildlife disease, their ability to support or oppose its
management, and the demands they place on wildlife agencies have changed as attitudes and risk
perceptions associated with wildlife diseases, new and old, have evolved. Furthermore, the
presence of disease has the potential to alter how humans value wildlife which could result in

1

diminished public support for conservation of certain species if wildlife are viewed as vectors of
disease (Decker et al. 2011).
All of these factors help make the social component of wildlife disease management
highly complex (Peterson et al. 2006) and the need still exists to better integrate biological and
social insights in wildlife disease management (Decker et al. 2006). Given the increase in
disease risks and the uncertainty surrounding public reaction to these risks and their
management, wildlife disease management decisions are not only biological or epidemiological
in nature. Key elements of wildlife disease management include knowing the ecology or
epidemiology of the disease, creating appropriate levels of public awareness about the disease,
and implementation of sound, socially acceptable management strategies (Friend 2006, Decker et
al. 2006). The latter two strategies require knowledge of stakeholder values, beliefs, and
attitudes toward wildlife health and disease issues and stakeholder responses to disease risks and
wildlife disease management interventions. A particular challenge facing decision makers is
how to appropriately contextualize wildlife disease related risk communication in light of public
understanding of those risks. Efforts to develop successful communication efforts and gain
public support for management strategies will benefit from knowledge of stakeholder values,
beliefs, risk perceptions, and attitudes.
Research on human dimensions aspects of wildlife health and disease management is
quite limited and there are important gaps in the literature. Most publications have been
published since 2004 and many are focused on hunters and chronic wasting disease (e.g., Brown
et al. 2006, Needham and Vaske 2008, Needham et al. 2004, Vaske et al. 2004, Miller and
Shelby 2009, Evensen 2011). There has been practically no discussion of conceptual aspects of
“wildlife health” (e.g., descriptive and normative meanings of the term). Much has been written

2

about forest, river, stream, and ecosystem health (Steedman 1994, DellaSala et al. 1995,
Scrimgeour and Wicklum 1996, Meyer 1997, Boulton 1999, Lackey 2001) but efforts to develop
a common conceptual understanding of wildlife health are lacking. Since promoting wildlife
health is one of the primary objectives of many wildlife disease management interventions,
clarification of lay and expert mental models of wildlife health is important to identify key
similarities and differences and to develop a normative model of wildlife health.
Another knowledge gap entails determining key variables that influence understanding of
and concerns about wildlife disease. A key part of understanding human health concerns related
to wildlife disease is to investigate factors associated with risk perception. Risk perception
research is also essential to developing effective management strategies and risk communications
(Vaske et al. 2009, Evensen 2011). Finally, determination of key factors that influence public
acceptance of wildlife disease management is another area in need of further investigation.
Factors that have been found to play an important role in influencing acceptance of wildlife
management (in general) include: normative beliefs (Zinn et al. 1998); situational specifics
related to incident extremity and management response (Manfredo et al. 1998, Loker et al. 1999,
Reiter et al. 1999, Fulton et al. 2004, Jonker et al. 2009); risk perceptions (Needham et al. 2004,
Peterson et al. 2006); social trust (Vaske et al. 2004, Needham and Vaske 2008); wildlife value
orientations (Zinn et al. 1998); and beliefs and attitudes about management actions (Fulton et al.
2004, Dorn and Mertig 2005, Bruskotter et al. 2009). Several studies have shown that as risks to
human health and safety increase, agreement with more drastic management strategies increases,
yet little is known about the influence of risks of other types (e.g., threats to wildlife or
ecosystem health) or effects of theoretical factors on wildlife disease management specifically.

3

This dissertation, “The human element of wildlife health: mental models, risk
perceptions, and attitudes toward wildlife disease management,” aimed to increase the capacity
of natural resource agencies to address wildlife disease issues by improving knowledge and
understanding of factors that influence stakeholder beliefs and attitudes toward wildlife health
and disease and toward management interventions that promote healthy wildlife resources. This
research supports the National Fish and Wildlife Health Initiative (NFWHI), a policy framework
developed by the Association of Fish and Wildlife Agencies with the aim of helping agencies
“conserve, restore, and enhance healthy fish and wildlife resources of the United States” (AFWA
2007). Funding for this research was provided by a Multistate Conservation Grant, a Michigan
State University Distinguished Fellowship award, and a Safari Club International Michigan
Involvement Committee scholarship.
The research objectives guiding this dissertation were to: (1) reveal expert mental models
of wildlife health and disease; (2) reveal layperson mental models of wildlife health and disease
and wildlife disease management; (3) determine key variables that influence stakeholders’
wildlife disease risk perceptions; and (4) assess factors that contribute to explaining public
acceptance of wildlife disease management. I chose to accomplish these objectives using a
mixed methods approach beginning with a qualitative phase (focus groups and the Delphi
method) and followed by a quantitative phase (survey questionnaire).
This dissertation is written as four separate manuscripts, plus introductory and concluding
chapters. Chapters 2, 3, and 4 are written in the format of a scientific paper and have been or
will be submitted for publication in professional wildlife-related journals. Chapter 2, which was
published in the Wildlife Society Bulletin in September 2012, integrates the findings of an
extensive literature review on wildlife health and a Delphi exercise and relates most directly to

4

objective 1. Chapter 3 shares results from focus groups with lay stakeholders and relates most
directly to objective 2. Chapter 4, which was submitted to the Journal of Wildlife Diseases in
February 2013, highlights findings from the mail survey and relates most directly to objective 3.
Chapter 5 also presents findings from the mail survey and relates most directly to objective 4.
Finally, in chapter 6, I make recommendations by synthesizing and drawing from the research
results as a whole.

5

LITERATURE CITED

6

Literature Cited
Association of Fish and Wildlife Agencies. 2007. National Fish and Wildlife Health Initiative for
the United States.
Boulton, A.J. 1999. An overview of river health assessment: philosophies, practice, problems
and prognosis. Freshwater Biology 41: 469-479.
Brown, T.L., J.E. Shanahan, and D.J. Decker. 2006. Hunters’ and other citizens’ reactions to
discovery of chronic wasting disease in central New York. Human Dimensions of
Wildlife 11: 203-214.
Bruskotter, J.T., J.J. Vaske, and R.H. Schmidt. 2009. Social and cognitive correlates of Utah
residents’ acceptance of the lethal control of wolves. Human Dimensions of Wildlife 14:
119-132.
Decker, D.J., W.F. Siemer, M.A. Wild, K.T. Castle, D. Wong, K.M. Leong, and D.T.N. Evensen.
2011. Communicating about zoonotic disease: strategic considerations for wildlife
professionals. Wildlife Society Bulletin 35(2): 112-119.
Decker, D.J., M.A. Wild, S.J. Riley, W.F. Siemer, M.M. Miller, K.M. Leong, J.G. Powers, and
J.C. Rhyan. 2006. Wildlife disease management: a manager’s model. Human
Dimensions of Wildlife 11: 151-158.
DellaSala, D.A., D.M. Olson, S.E. Barth, S.L. Crane, and S.A. Primm. 1995. Forest health:
moving beyond rhetoric to restore healthy landscapes in the inland Northwest. Wildlife
Society Bulletin 23:346-356.
Dorn, M. L., and A. G. Mertig. 2005. Bovine tuberculosis in Michigan: stakeholder attitudes
and implications for eradication efforts. Wildlife Society Bulletin 33: 539-552.
Friend, M. 2006. Disease emergence and resurgence: the wildlife-human connection. United
States Geological Survey Circular 1285, Reston, Virginia, USA.
Evensen, D.T.N. 2011. In the eye of the beholder: Perceptions of and reactions to wildlife and
vector-borne disease risks. Thesis. Cornell University, Ithaca, New York, USA.
Fulton, D. C., K. Skerl, E. M. Shank, and D. W. Lime. 2004. Beliefs and attitudes toward lethal
management of deer in Cuyahoga Valley National Park. Wildlife Society Bulletin 32:
1166-1176.
Jonker, S.A., J.F. Organ, R.M. Muth, R.R. Zwick, and W.F. Siemer. 2009. Stakeholder norms
toward beaver management in Massachusetts. Journal of Wildlife Management 73(7):
1158-1165.
Lackey. R. T. 2001. Values, policy, and ecosystem health. BioScience 51: 437-443.
7

Loker, C.A., D.J. Decker, and S.J. Schwager. 1999. Social acceptability of wildlife management
actions in suburban areas: three cases from New York. Wildlife Society Bulletin 27(1):
152-159.
Ludwig, D., R. Hilborn, and C. Walters. 1993. Uncertainty, resource exploitation, and
conservation: lessons from history. Science 60: 547-549.
Manfredo, M.J., H.C. Zinn, L. Sikorowsksi, and J. Jones. 1998. Public acceptance of mountain
lion management: a case study of Denver, Colorado, and nearby foothills areas. Wildlife
Society Bulletin 26(4): 964-970.
Meyer, J.L. 1997. Stream health: incorporating the human dimension to advance stream ecology.
Journal of the North American Benthological Society 16: 439-447.
Miller, C.A. and L.B. Shelby. 2009. Hunters’ general disease risk sensitivity and behaviors
associated with chronic wasting disease. Human Dimensions of Wildlife 14: 133-141.
Needham, M.D. and J.J. Vaske. 2008. Hunter perceptions of similarity and trust in wildlife
agencies and personal risk associated with chronic wasting disease. Human Dimensions
of Wildlife 21: 197-214.
Needham, M.D., J.J. Vaske, and M.J. Manfredo. 2004. Hunters’ behavior and acceptance of
management actions related to chronic wasting disease in eight states. Human
Dimensions of Wildlife 9: 211-231.
Peterson, M.N., A.G. Mertig, and J. Liu. 2006. Effects of zoonotic disease attributes on public
attitudes towards wildlife management. Journal of Wildlife Management 70(6): 17461753.
Reiter, D.K., M.W. Brunson, and R.H. Schmidt. 1999. Public attitudes toward wildlife damage
management and policy. Wildlife Society Bulletin 27(3): 746-758.
Scrimgeour, G.J. and D. Wicklum. 1996. Aquatic ecosystem health and integrity: problems and
potential solutions. Journal of the North American Benthological Society 15: 254-261.
Steedman, R.J. 1994. Ecosystem health as a management goal. Journal of the North American
Benthological Society 13: 605-610.
Vaske, J.J., L.B. Shelby, and M.D. Needham. 2009. Preparing for the next disease: the humanwildlife connection. Pages 244-261 in Wildlife and society: the science of human
dimensions, M.J. Manfredo, J.J. Vaske, P.J. Brown, D.J. Decker, and E.A. Duke (eds.).
Island Press, Washington, DC, USA.

8

Vaske, J. J., N. R. Timmons, J. Beaman, and J. Petchenik. 2004. Chronic wasting disease in
Wisconsin: hunter behavior, perceived risk, and agency trust. Human Dimensions of
Wildlife 9: 193-209.
Zinn, H.C., M.J. Manfredo, J.J. Vaske, and K. Wittman. 1998. Using normative beliefs to
determine the acceptability of wildlife management actions. Society & Natural Resources
11: 649-662.

9

CHAPTER 2

PROMOTING WILDLIFE HEALTH OR FIGHTING WILDLIFE DISEASE: INSIGHTS
FROM HISTORY, PHILOSOPHY, AND SCIENCE
SHAUNA L. HANISCH, Michigan State University, Department of Fisheries and Wildlife,
East Lansing, MI 48824, USA
SHAWN J. RILEY, Michigan State University, Department of Fisheries and Wildlife, East
Lansing, MI 48824, USA
MICHAEL P. NELSON, Oregon State University, Department of Forest Ecosystems and
Society, Corvallis, OR 97331, USA
ABSTRACT Although healthy wildlife populations are often a goal of wildlife management,
ambiguity over the meaning of the term health may limit its effectiveness in guiding
management objectives. Health is a complex concept with empirical and normative qualities; if
it is to convey more than simply metaphorical value in wildlife conservation, clearer articulation
of the meaning of wildlife health is needed. We provide a brief overview of the evolution of
wildlife disease and health management, discuss important philosophical themes relevant to
developing a clarified understanding of wildlife health, and share perspectives on wildlife health
and disease from a Delphi exercise involving North American wildlife health professionals. The
Delphi group conceptualized wildlife health as a multidisciplinary concept marked
predominantly by population sustainability and resilience. Disease was considered to be a
specific abnormal condition that is a part of the broader concept of health. We suggest improved
integration of the descriptive and normative elements of wildlife health and greater inclusion of
societal values in developing wildlife health objectives as a means to broaden the scope and
effectiveness of wildlife health management. This paper has been previously published and the
10

citation is: Hanisch, Riley, and Nelson. 2012. Promoting wildlife health or fighting wildlife
disease: insights from history, philosophy, and science. Wildlife Society Bulletin 36(3): 477-482.
INTRODUCTION
The concept of wildlife health is increasingly considered a cornerstone of wildlife
management and, with greater attention being given to the role of wildlife in zoonotic disease
management, is viewed as a key element in protecting human and animal health (Friend 2006,
Wobeser 2006, Decker et al. 2011). The wildlife veterinary field has recently taken steps toward
an emphasis on health, led in part by the influence of conservation medicine (Daszak et al. 2004)
and the promotion of a One Health approach (King et al. 2008). The Association of Fish and
Wildlife Agencies recently developed a National Fish and Wildlife Health Initiative, designed to
enable state agencies to build capacity for management of healthy fish and wildlife resources
(AFWA 2007). Similarly, the National Park Service has initiated the Yellowstone Wildlife
Health Program, a multi-disciplinary program aimed at restoring healthy wildlife within the
Greater Yellowstone Ecosystem (National Park Service 2007).
This increasing orientation toward the notion of health, however, is potentially
problematic. The concept of health is not purely a scientific one in the biologicalepidemiological sense (Temkin 1977, Ehrenfeld 1993); because it is concerned with a desired
condition (e.g., healthy animals or populations) it has normative implications (Meyer 1997,
Richman and Budson 2000, Nelson 2009). Like many normative concepts (i.e., those having to
do with what ought to be), the term health can be ambiguous and vague (Lackey 2001) and has
been characterized as “slippery” (Ostfeld et al. 2002: 22), especially when applied to entities
beyond the individual organism (Kass 1981). In various contexts, health has also been described
as a “value-based ecological concept” (Lackey 2001: 438) and an “ill-defined normative

11

concept” (Callicott et al. 1999: 23). The concept of health has strong positive connotations and,
to a certain extent, people have an intuitive understanding of it (Kass 1981, Wicklum and Davies
1995). Given these qualities, some have argued that the metaphorical value of health is one of its
greatest strengths. For example, Ehrenfeld (1993: 144) suggested that trying to explain health
“with the rigor and specificity that will allow us to use it as a scientific tool may well strip it of
the intuitive, general meaning that is its chief value.” Yet health is also a scientific concept
indicative of some measurable condition; to be effective in implementing policies that promote
health, measurements of health need to be clearly defined (Lackey 2001, Salomon et al. 2003).
We review the concept of wildlife health, suggest key themes for clarifying it, and present
definitions of health from a Delphi exercise involving wildlife health professionals.
BACKGROUND
Investigation of wildlife disease is a relatively young field (Wobeser 2007) and
developed almost entirely within the germ theory era. This period began in the late 1800s and
was characterized by scientific advances in bacteriology which led to a greater understanding of,
and emphasis on, the specific causes of diseases and away from a focus on environmental factors
influencing disease (Strathern 2005, Kunitz 2007). Wildlife disease management has
traditionally emphasized infectious diseases (Wobeser 2007). Some historical examples of this
orientation include the publication of the first textbook on diseases of free-ranging wildlife,
written by a German veterinarian, in 1914 (Lowenstine and Montali 2006) and the publication of
“The Study of Epidemic Diseases Among Wild Animals” by ecologist Charles Elton in 1931. In
1933, the first American wildlife disease laboratory was established in Michigan (Thorne et al.
2005) and wildlife disease was first put into a management context in a chapter titled “Control of
Disease” in Aldo Leopold’s book Game Management. In 1951, the Wildlife Disease Association

12

was founded (Wobeser 2006) and in 1957 the Southeastern Cooperative Wildlife Disease Study
was established at the University of Georgia (Friend 2006).
Beginning in the 1970s, the vocabulary of wildlife disease management shifted to greater
use of the term health. In 1975, the U.S. Fish and Wildlife Service established the National
Wildlife Health Center (now administered by the U.S. Geological Survey) and, in 1992, the
Canadian Cooperative Wildlife Health Centre was formed (Friend 2006). Conservation
medicine, referred to as a new paradigm of health and disease (Kaufman et al. 2004), was
formally introduced in 1996 as a way of promoting integration of human, animal, and
environmental health through a multidisciplinary perspective (Tabor 2002). Even more recently,
the One Health philosophy, which is conceptually similar to conservation medicine, has emerged
and gained endorsements from the American Veterinary Medical Association and the American
Medical Association (Tabor 2002, King et al. 2008). Questions remain, however, whether these
semantic shifts are substantive and if it matters whether wildlife management focuses on health,
disease, or both. These concerns have not been well addressed in the literature, nor has a clear
vision of wildlife health been articulated.
In the field of human medicine, considerable attention has been given to the philosophy
of health and, consequently, the nature of the term health has been thoroughly discussed and
debated (Boorse 1977, Kass 1981, Nordenfelt 1995, Brülde 2000, Schramme 2007, Tengland
2007). One key issue in the human health literature is the differentiation of health and disease
(Hofmann 2005). Fundamentally, it has been argued that a health-focused perspective promotes
a broader, more integrated and multidisciplinary approach than does a reductionist, diseaseoriented strategy (Kass 1981). Indeed, increasing attention to wildlife health may be just another
example of wildlife management and other natural resource fields focusing on more integrative

13

approaches (Riley et al. 2003). Yet, unlike the debates generated on related topics such as
ecosystem health, forest health, and river or stream health (e.g., Steedman 1994, DellaSala et al.
1995, Scrimgeour and Wicklum 1996, Meyer 1997, Boulton 1999, Lackey 2001), there is a
dearth of discussion in the literature about the meaning of wildlife health.
In the field of human health philosophy, two of the most influential lines of thought in
defining and discussing health are the biostatistical and holistic theories. The biostatistical
theory defines health as essentially the absence of disease, with disease defined as any
impairment in normal functional ability (Boorse 1977). According to this perspective, health is a
matter of statistical normality of function (i.e., the ability to perform all typical physiological
functions with at least typical efficiency) at the species level. Proponents of the biostatistical
view argue that defining health relative to normality is ideal since the distribution of biological
characteristics is an objective measure (Boorse 1977, Richman and Budson 2000).
A different perspective arises from the holistic theory of health. Nordenfelt (1995), one
of the key proponents of the holistic theory, considered it preferable to take a positive approach
to defining health, rather than thinking of it as the absence of disease. The focus of this
conceptualization of health is not on disease, per se, but on health as a contributor to quality of
life. Nordenfelt (1995: 93) defined health as the ability to achieve vital goals, with a vital goal
being a “state of affairs that is…a necessary condition for…minimal happiness in the long run”
and, in the case of non-humans, Nordenfelt (2007) replaced the term happiness with welfare.
The holistic theory opposes a purely statistical perspective of health and strives to attain wellbeing rather than fight disease. Health, from this perspective, is more than the absence of disease
or abnormality.

14

Two notable definitions of wildlife health exist and help illustrate the differences between
the biostatistical and holistic theories. Reflecting tenets of the holistic theory, Deem et al. (2008)
characterized wildlife health as an ability to efficiently respond to disease and restore and sustain
a state of balance. By this definition, wildlife health is measured in terms of particular
conditions of wellness (i.e., efficient response to disease and maintenance of a state of balance).
In contrast, Mazet et al. (2006: 353) described health as a condition in which an organism is
“physically and biochemically complete, does not experience abnormal growth or atrophy of its
component parts...and does not experience drastic changes in its physical appearance or normal
functions.” This definition, with its emphasis on normality, reflects tenets of the biostatistical
theory. The holistic and biostatistical perspectives of health may not in fact be mutually
exclusive, but further evaluation of the term wildlife health in light of these viewpoints is needed
to avoid the term becoming doublespeak rather than a meaningful guideline for wildlife
management objectives.
METHODS
To assess expert perspectives on wildlife health and disease, and the relationship between
these two concepts, we conducted a Delphi exercise with a group of wildlife health professionals.
Expertise adds authoritativeness to discussions, although lay perspectives, too, are important
(Morgan et al. 2002). The Delphi exercise aided development of an expert model which pools
knowledge in a systematic manner, thereby summarizing the group’s collective knowledge rather
than that of any one expert (Morgan et al. 2002). The Delphi method is a technique for obtaining
consensus of opinion from a group of experts (Dalkey and Helmer 1963). It is an iterative,
structured communication process that is anonymous (participants’ identities are known to the
moderator but not to one another); the process allows participants to view other participants’

15

judgments and revise their own responses accordingly (Linstone and Turoff 1975). The Delphi
method was originally used for technical forecasting and developing group consensus but is also
useful for exploring underlying assumptions, revealing group values, and aiding
concept/framework development (Turoff 1970, Moore 1987, Okoli and Pawlowski 2004).
Our non-random, purposive sample consisted of 18 current and retired wildlife health
professionals from the United States and Canada. Participants were solicited by means of a
request for assistance posted on the Wildlife Disease Association (WDA) website and in the
WDA newsletter and via direct email requests to known wildlife health experts. Twenty
individuals originally agreed to participate but only 18 completed the entire Delphi process. Of
the 18 participants, four individuals were affiliated with the United States government, three
were affiliated with state wildlife agencies, one was affiliated with a Canadian provincial natural
resource agency, seven were affiliated with academia, and three were affiliated with nongovernmental organizations. Participants included 14 men and four women. Nine of 18 (50%)
participants held doctor of veterinary medicine degrees. Geographical representation included
ten states and one province, though some participants’ work can be characterized as being
national or international in scope.
The Delphi exercise was conducted by email from April to August 2010 and required
four rounds (or iterations). In the first round, participants were asked a series of open-ended
questions: 1) What does wildlife health mean to you (i.e., how do you define health)?; 2) In your
opinion, how are the concepts of wildlife health and wildlife disease different and how are they
the same?; and 3) Does a focus or emphasis on one or the other (health or disease) affect or
influence the achievement of healthy wildlife populations? Responses to these questions
obtained during the first round were broken down into 49 statements (e.g., “population

16

sustainability” or “health is more holistic than disease”). In the second round, these 49
statements were listed along with the number of Delphi participants who responded in
accordance with each statement. Participants were then asked to narrow down the list by
selecting the five to eight statements they thought best responded to the original question. It was
determined a priori that statements selected by less than 50% (i.e., fewer than nine) of the Delphi
participants would be dropped. Consequently, by the third round only 20 statements remained.
Participants were then asked to rate their agreement with each of the remaining 20
statements on a scale of 1 to 5, with 1 indicating strong disagreement and 5 indicating strong
agreement. The mean agreement ratings were calculated; it was determined a priori that items
receiving a mean agreement of ≤3.75 would be eliminated. Only two of the 20 items, however,
attained a mean agreement of ≤3.75 and, after rewording based on participant feedback, only one
item was permanently dropped. After the final iteration, 19 items related to the original three
questions remained and summary statements from these items were developed. For summary
statements to be finalized, they had to receive a mean agreement of ≥4.
RESULTS
The expert model gleaned from this Delphi exercise reveals seven key conceptualizations
associated with wildlife health (Figure 2-1). When asked to describe and define wildlife health,
the concept was characterized chiefly by the ideas of population sustainability and resiliency.
Wildlife health was also recognized as being a distinctly multidisciplinary concept and as being
influenced by multiple factors other than infectious disease, including nutrition, toxins, parasites,
and habitat quality. Wildlife health was considered by the Delphi participants to be applicable to
individuals, populations, and ecosystems yet populations were considered the most important
scale for applying health and there was greater overall disagreement about focusing health on

17

either individuals or ecosystems (Table 2-1). The final summary statement (mean agreement of
4.6 on a scale of 1 to 5, with 5 representing strong agreement) was: “Wildlife health is a
multidisciplinary concept and is concerned with multiple stressors that affect wildlife. Wildlife
health can be applied to individuals, populations, and ecosystems but its most important defining
characteristics are whether a population can respond appropriately to stresses and sustain itself.”
Only two of the 18 Delphi participants explicitly stated that human-oriented objectives were
relevant to wildlife health. For example, one participant stated that the goal of wildlife health
management is “to promote coexistence and strike a balance between the needs of people and
wildlife.” Another participant wrote that a healthy wildlife population is able to meet both
ecological and social expectations (e.g., tourism, hunting).
Concerning disease, the Delphi experts described it as a component of wildlife health
and, more specifically, as an alteration in an animal’s normal state, structure, and/or function.
Examples of Delphi participants’ definitions of disease include: a compromised state which
influences an individual animal’s ability to perform ecological roles, an infection of a host with a
pathogen that affects the hosts form or function, and an abnormal condition with recognizable
signs, symptoms and laboratory findings. The final summary statement (mean agreement of 4.3
out of 5) was, “The concept of health is more all-encompassing and holistic than is disease.
Disease is one part of the broader concept of health and, consequently, health is more than
simply the absence of disease. Health is generally considered a state of being within which
disease is a specific significant abnormal condition or deviation from health.”
Further, Delphi participants agreed that although both health and disease must be
emphasized, a broader focus on all determinants of wildlife health (beyond specific disease
pathogens) is needed to ensure healthy, sustainable wildlife populations. Additionally, a focus on

18

wildlife health was considered to be more “forward thinking” and to allow managers a better
understanding of the reasons for the disease. A focus on disease alone was generally viewed as
being inadequate to provide the conditions necessary for healthy wildlife populations. As one
Delphi participant wrote, “health connotes more a sense of preventive medicine…in contrast to
the fire-engine type approach of fighting disease.” Another wrote, “…achieving healthy wildlife
requires action to combat [environmental] factors, not reaction to treat sick individuals.” The
applicable summary statement (mean agreement of 4 out of 5) was, “Both health and disease
must be emphasized in wildlife management. A focus on disease is sometimes necessary but
alone it is insufficient. A broader focus on all aspects of wildlife health is needed to achieve
healthy, sustainable wildlife populations. A focus on health may bring about a shift from
documentation of disease occurrences to prevention and will help managers better understand
what factors are causing the disease.”
DISCUSSION
The expert model created from our Delphi exercise reveals that wildlife health
professionals believe health and disease are distinct but related concepts, both in terms of
definition and influence on management outcomes. This group of experts, diverse in
geographical location and agency of employment, held that disease is an important criterion of
health but it is not the only criterion. Moreover, health was viewed as being applicable to
individuals, populations, and ecosystems, though populations were the most acceptable scale of
reference. Indeed, populations are generally viewed as the key focus in wildlife disease
management (Wobeser 2007). Nonetheless, the individual-oriented perspective of some
veterinarians, the wildlife manager’s population perspective, and the conservation medicine

19

practitioner’s ecological orientation are all important aspects of the broader picture of wildlife
health (Ostfeld et al. 2002).
In this expert model, we observed elements of both the holistic and biostatistical notions
of health—health is understood in relation to disease but it is also viewed in terms of concepts
such as population resiliency and sustainability. In health philosophy, it has been argued that the
biostatistical and holistic perspectives are not mutually exclusive, but are simply different in their
focus or orientation (Khushf 2007, Schramme 2007). An integrative conceptualization of health
incorporates elements of both the biostatistical and holistic orientations by determining healthful
conditions while also continuing to focus on disease as an important criterion of health. Both
orientations contribute perspectives needed in wildlife management, similar to the human health
field with medical clinicians focused on diagnosis and treatment of disease in individuals and
public health practitioners emphasizing health of the broader population. To prevent these
differing scopes from hindering the achievement of wildlife health, greater cooperation across
disciplines is needed (Ostfeld et al. 2002). Although wildlife disease management occurs at the
interface between medicine and applied biology (Wobeser 2007), Leopold (1933) argued that an
overly medical, “doctoring” approach is less important in promoting health than are
environmental and population factors.
To promote comprehensive approaches to wildlife health, integration of human
dimensions with biological-epidemiological insights is needed (Decker et al. 2006). Humans are
an integral part of the wildlife health management system (Wobeser 2007) and the social
sciences provide valuable input to health disciplines such as conservation medicine (Ostfeld et al.
2002). Accordingly, we view a beneficial approach to conceptualizing health as deliberately
including both descriptive and normative elements. Descriptive elements correspond to the

20

biostatistical emphasis and include basic information about the ecology, epidemiology,
toxicology, or etiology of disease factors. This element is important to develop a baseline
measurement of health for determining what is normal for each species and to understand what
limits health and normality. Normative elements of wildlife health address two aspects: to
decide what a population needs to attain the desired condition of wellness (e.g., to survive and
sustain itself into the future), and to promote integration of societal values in the development
and achievement of wildlife health objectives (Meyer 1997, Decker et al. 2006). The types of
questions corresponding to these elements include: what is the health status of the species or
population and what are the threats to its health?; what, from a biological perspective, are
indicators of a healthy population of this species? (i.e., measures of optimal wellness in addition
to measures of current conditions); and, what are the relevant societal values that influence how
health is defined and how health should be managed for this species?
IMPLICATIONS
Wildlife management occurs through a mix of technical and value-laden judgments
(Riley et al. 2003) and management of wildlife health is no different. The hallmarks of effective
management objectives are that they are specific and measurable yet linked to values related to
the decision problem (Clemen and Reilly 2001). Inclusion of societal values is critical to making
values and value tradeoffs explicit rather than obscuring them and compromising the democratic
ideal of openness in public decisions (Lackey 2001, Meyer et al. 2005). Defining wildlife health
and setting wildlife health management objectives involve making value judgments. For
example, whether concerns for human health trump concerns for wildlife health, what outcomes
are most appropriate for wildlife populations, and which diseases are “bad” and should be
eradicated are all questions influenced by human values. Such decisions cannot be based solely

21

on biological or epidemiological expertise without the risk of ignoring important components of
the decision problem. Human activities also have a direct influence on wildlife health, including
the spread of disease and worsening of environmental impacts (Ostfeld et al. 2002, Friend 2006,
Wobeser 2007).
In the face of continued threats to the sustainability of wildlife populations, adoption of
policies that clearly articulate the meaning of wildlife health are needed. If wildlife health is to
be a meaningful, measurable concept, and one supported by stakeholders and experts alike,
additional deliberation is needed to better define and conceptualize it. A useful starting point is
to examine expert perspectives to gain understanding of existing conceptualizations of health by
those who have practical knowledge of wildlife health and disease issues. The expert model that
emerged in our Delphi exercise reveals that elements of both holistic and biostatistical notions of
health are present in professionals’ understandings of wildlife health, though greater emphasis is
placed on holistic aspects. This may indeed be a natural progression of thought as knowledge is
gained about the dynamics between wildlife disease and wildlife health. Building on this model,
we encourage consideration of both descriptive and normative aspects of health and disease in
conceptualizations of wildlife health and the integration of societal values into the normative
component. Because health is unavoidably a normative concept and management is a
prescriptive endeavor, and logically we cannot derive values and prescriptions from biology
alone, integration of human dimensions insights will increase the probability of achieving
objectives for wildlife health.

22

Figure 2-1. Mental model of expert conceptualizations of wildlife health. (For interpretation of
the references to color in this and all other figures, the reader is referred to the electronic version
of this dissertation.)

23

Table 2-1. Delphi expert responses to “What does wildlife health mean to you?”
Mean agreement (1 to
Response statements
5 scale with 5
indicating strong
agreement)
4.39
Population sustainability (e.g., healthy populations are able to
sustain themselves over the long term)
4.33
Population resiliency (e.g., wildlife populations can respond
appropriately to stresses)
4.89

Multiple types of stressors are relevant to wildlife health (e.g.,
nutrition, infectious disease, parasites, toxins)

4.78

Wildlife health is multidisciplinary in nature

4.44

Quality of the environment/habitat is part of the wildlife health
picture

4.50

Wildlife health applies to both populations and individuals

4.11

Wildlife health applies to ecosystems

24

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25

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CHAPTER 3

WHAT ARE THEY THINKING? EXPLORING LAYPERSON MENTAL MODELS OF
WILDLIFE HEALTH AND DISEASE
S.L. HANISCH-KIRKBRIDE, Michigan State University, Department of Fisheries and
Wildlife, 13 Natural Resources, East Lansing, Michigan 48824 USA
J.P. BURROUGHS, Michigan State University, Department of Fisheries and Wildlife, 13
Natural Resources, East Lansing, Michigan 48824 USA
S.J. RILEY, Michigan State University, Department of Fisheries and Wildlife, 13 Natural
Resources, East Lansing, Michigan 48824 USA
ABSTRACT Communication is an important part of wildlife disease management.
Communication efforts aim to inform the public about health and disease issues and to promote
public support for management interventions. To be most effective, communication strategies
should be based in knowledge of stakeholder understandings of key issues. We conducted focus
group interviews with diverse groups of participants in five states across the United States to
evaluate how laypersons conceptualize wildlife health and wildlife disease management. Based
on insights from the focus groups, we developed a simple mental model representing
predominant conceptualizations of wildlife health. Wildlife health was associated with absence
of disease; balanced, sustainable populations; healthy animals; habitat quality; and ecosystem
health. Wildlife disease was commonly viewed as a natural phenomenon. Factors influencing
support for wildlife disease management included the specific methods used, cost, predicted
consequences of management, level of uncertainty, and severity of the disease threat.
Knowledge attained from these focus groups provides empirical evidence of beliefs and

30

perceptions that influence public understanding and agreement with wildlife disease
management.
INTRODUCTION
Wildlife disease management presents a significant challenge to wildlife professionals
(Decker et al. 2006). For example, health and disease issues can have a range of negative
consequences including direct health impacts to humans, domestic animals, and/or wildlife;
alteration of human attitudes toward and tolerance of wildlife; reduced participation in wildliferelated recreation; and lowered perceptions of the value of wildlife (Conover 2002, Friend 2006,
Decker et al. 2011). Wobeser (2002) emphasized the importance of public education in wildlife
disease management, specifically to attain public support for management actions and to
influence human activities that contribute to disease occurrence. Increasingly, it is being
recognized that science and local knowledge (i.e., knowledge not coming from traditional
scientific expertise) are both important (Failing et al. 2007) and that communication should
emphasize a two-way information sharing process over a one-way educational approach (Gray
and Ropeik 2002). Effective communication, whether the goal is to raise public awareness about
wildlife health and disease issues, to gain public approval of wildlife disease management, or to
encourage health promoting behaviors, will benefit from both expert-based information and
knowledge of stakeholder values, beliefs, and attitudes, including basic conceptualizations of
wildlife health and disease and relevant risks.
Investigations of human dimensions aspects of wildlife health and disease are limited and
few studies provide in depth, qualitative insights (e.g., Evensen 2011, Bosch et al. 2010).
Although a few commentaries have discussed and defined wildlife health (Mazet et al. 2006,
Deem et al. 2008), and at least one has provided insights into expert conceptualizations of

31

wildlife health (Hanisch et al. 2012), we are not aware of any empirical research that has
examined layperson conceptualizations of wildlife health. Such conceptualizations, or mental
models, are fundamental to decision framing which, in turn, influences problem understanding,
decisions about how to address a problem, and attitudes toward the problem (Kahneman and
Tversky 1984, Hammond et al. 1999).
Mental models, which can provide insights relevant to wildlife management (Decker et
al. 2006, Enck et al. 2006), are psychological representations that help people describe and
explain a phenomenon (Mathieu et al. 2000, Kellermanns et al. 2008). Mental models reveal
stakeholder perceptions, assumptions, knowledge, and beliefs about a phenomenon and,
consequently, analysis of these models can inform decision-making and understanding of
problems (Kolkman et al. 2005). Expert mental model frameworks have been developed to
guide risk communication and to reveal how practitioners conceptualize the WDM system
(Atman et al. 1994, Decker et al. 2006, Hanisch et al. 2012), yet it is also important to understand
layperson mental models to reveal variability in assumptions about the management system
(Enck et al. 2006).
In this paper, we analyzed data from exploratory focus group interviews to create
preliminary mental models of conceptualizations of wildlife health and wildlife disease and to
inform the development of a survey questionnaire for testing theory-based hypotheses related to
acceptance of wildlife disease management. Our objectives were to characterize layperson
conceptualizations and understandings of wildlife health and disease and to assess factors
influencing acceptance of wildlife disease management.
METHODS

32

We used qualitative, open-ended, exploratory questions to achieve study objectives.
Qualitative data are “representations of human acts and utterances,” the analysis of which seeks
to interpret the understandings of the research subjects (Sivesind 1999: 361, Creswell and Plano
Clark 2007). Specifically, we conducted focus groups which are structured group interviews
used to collect detailed opinions and knowledge about a particular topic (Bader and Rossi 2002).
We conducted five focus groups from April to June 2010, one at each of the following locations:
East Lansing, Michigan; Albany, New York; Covington, Georgia; Flagstaff, Arizona; and
Portland, Oregon. We selected locations in different regions of the United States since regional
variations in public attitudes and opinions are common (Alm et al. 2001, Winter et al. 2002). All
sessions were moderated by a professional facilitator affiliated with Michigan State University
and lasted from 1.25 to 2 hours. All focus groups were audio recorded using a handheld digital
recorder.
Focus group participants (n = 34) were individuals interested in wildlife but who were
not employed as wildlife professionals. Participants were self-selected and were recruited by
contacting local environmental, conservation, and sportsmen’s groups (e.g., Michigan United
Conservation Clubs, Oregon Wild, Coconino Sportsmen, and Georgia Wildlife Federation) and
by posting notices online at relevant websites (e.g., Portland Hiking Club and Northern Arizona
Audubon Society). As an incentive, we provided participants with a pizza dinner and a $10 gas
or grocery gift card. Research methods were approved for use by the Michigan State University
Social Science Institutional Review Board (IRB# 09-874).
The focus group interview guide consisted of ten open-ended questions developed to
explore how people think about wildlife health and disease and wildlife disease management
interventions. The questions were pre-tested using students in an upper-level fisheries and

33

wildlife undergraduate course during spring semester 2010. In addition to the interview
questions, we administered brief exit surveys to all participants and 33 exit surveys were
collected from the 34 participants. The exit surveys collected basic demographic information,
participation in wildlife related recreation, and quantitative rankings of level of concern related
to disease risks to human health, domestic animal health, and wildlife health.
The lead author prepared verbatim written transcripts from the audio recordings for all
focus groups. (Filler words such as “um” or “like” were omitted from transcripts.) The data for
analysis came from the text of these transcripts and coding was used for analysis. Codes are
“tags or labels for assigning units of meaning to the descriptive or inferential information” that
has been collected (Miles and Huberman 1994: 56). We used theme coding to analyze data,
whereby the coder selects pieces of text relevant to a specific theme and then attaches a code to
them (Sivesind 1999). Thus, we grouped responses to each question thematically; for example,
responses to the question “What comes to mind when you hear the term wildlife health?” were
grouped into themes such as sustainable populations, balanced populations, and habitat quality
(Table 3-1). We also analyzed topics contained within the text by doing word searches for
references to specific diseases, species of animal, organizations, science, and theoretical
variables such as naturalness, situational specifics related to acceptance of wildlife disease
management, and statements related to trust in management entities. Quantitative rankings of
concern for health risks were analyzed using the non-parametrical Mann-Whitney U test (Vaske
2008).
RESULTS
About two-thirds of focus group participants were men (68% were male and 32% were
female). Distribution of those living in rural, suburban, and urban areas was roughly equivalent

34

(35%, 27%, and 38%, respectively). Nearly 80% of focus group participants regularly
participated in wildlife watching and 67% and 61% of participants fished and hunted,
respectively. A third of focus group participants were between the ages of 18-35, 24% were
between the ages of 36-55, and 42% were 56 or older. In the anonymous exit survey, we found
concern for wildlife health (𝑥̅ = 4.09) to be greater (P≤.005) than for human (𝑥̅ = 2.79) or
domestic animal (𝑥̅ = 2.97) health, a finding which is consistent with verbal comments expressed
in the focus groups.
Focus group participants associated wildlife health with disease or, more specifically, the
absence of disease; sustainable populations; balanced populations; and physically healthy
animals. Ecosystem/environmental health and habitat quality were also considered by many
stakeholders to be conceptually related to wildlife health. Deer (i.e., white-tailed or mule deer)
were the most frequently referenced species, although wildlife health was generally viewed as
including all wildlife species in multiple categories including birds, mammals, reptiles,
amphibians, and even fish. Socioeconomic factors such as harvest rates, wildlife damage levels,
and economic impacts were also discussed in relation to wildlife health. We constructed a
layperson mental model of wildlife health using the predominant themes (Figure 3-1).
Disease. In this theme related to wildlife health, participants viewed health specifically in terms
of disease, or the absence of disease.
“I think of very specific things like rabies.” (Oregon)
“Wildlife health means…the animals or reptiles can live a natural life, a full natural life,
and reproduce without problems and disease.” (Georgia)
“The first thing I thought of was the medical health of the animals with regard to whether
they have diseases or not and what their longevity is.” (Oregon)

35

Population sustainability. This theme reflects the dominant emphasis on wildlife populations
and the perspective that healthy populations are sustainable populations. More specific indicators
or measurements of wildlife health expressed by participants included population size and trends;
condition and size of animals; condition of habitat; reproduction; birth and death rates; age and
sex ratios; harvest rates; the geographic distribution of the population; and comparison of the
population to a historical baseline.
“It’s basically a sustainable, healthy population of any species be it deer, turkey, fish,
salamanders, lizards, frogs, whatever.” (Georgia)
“I think of being able to maintain a population that is neither in decline nor becoming
excessively overpopulated.” (Arizona)
“The population in terms of total habitat being able to sustain a rise in numbers over
time.” (Michigan)
Balance. In this theme, participants expressed wildlife health as a matter of populations being
below carrying capacity or in balance with their environment so as to not destroy habitat or cause
other problems associated with overabundance.
“The word that comes to my mind is the word balance. Whenever you are using health
it’s all about balance. Balancing the correct population, balancing the correct harvest…”
(Michigan)
“I think an overpopulated, I mean, a population that is too large is unhealthy.” (Arizona)
“I’m thinking of buck to doe ratios and how does the habitat look and what’s the browse
impact and things like that…more along the lines of biological carrying capacity.” (New
York)

36

“There are people who think that it’s a healthy herd because they’re seeing more deer.
And I’m thinking it’s a really unhealthy herd when you have that excess of animals.”
(New York)
Healthy animals. In this theme, participants emphasized the health of individual animals in
terms of concepts such as reproduction, appearance, and physiology.
“I usually think of it in terms of the reproductive capacity or the reproductive rates of a
particular deer or turkey.” (Georgia)
“Is it a nice healthy deer or is it kinda scrawny from the swamp up in the U.P.? Is it a
nice size Chinook or is it one out of Lake Huron where there is no food left?” (Michigan)
“When I think of wildlife health, I think of physiology of the individual animal. I would
think of going and taking samples...blood samples and hair samples and things like that to
test for disease and test for normal growth rates.” (Oregon)
Habitat quality and availability. In this theme, participants emphasized the importance of
habitat to wildlife health.
“The first thing I would think about is that they would have viable habitat because
without that there isn’t any wildlife at all.” (Arizona)
“Land quality...like [are] there habitat corridors or private property and trails or roads
disrupting what would be a natural path for plants and animals to go through.” (Oregon)
Ecosystem health. This theme reflected conceptualizations of health that included the whole
ecosystem rather than, or in addition to, a single species or animal.
“When I hear wildlife health I think of functional, sustainable ecosystems.” (Michigan)
“When I think of wildlife health I think of a holistic health and the ecosystem health.”
(Oregon)

37

“I think of healthy animals, healthy ecosystems, and healthy environment.” (Michigan)
“The relationship between different animal species with each other. So if you’re trying to
measure one’s health it would be hard to do that without knowing if other species that it’s
dependent on or that are dependent on it are also affected.” (Oregon)
Measuring wildlife health. Participants also noted that determinations of health are not
necessarily absolute but are related to context, as revealed by the following quotes.
“Sometimes when I think of health in regards to wildlife, there’s an underlying question.
Health according to who...by whose definition?” (Michigan)
“When I think of wildlife health I recognize that you can have different targets for what a
healthy population is. And so a healthy deer herd in Schenectady County is very different
from my concept of a healthy deer herd in Columbia County.” (New York)
“Health is…how does it compare to whatever I assume is a good healthy baseline which
is not necessarily based on anything real but…how [humans think] it should’ve been or
was.” (Oregon)
When asked what comes to mind when they hear the term wildlife disease, focus group
participants most frequently provided examples of specific diseases. The top four diseases
identified, comprising 72% of all references to specific diseases, were rabies, chronic wasting
disease, bovine tuberculosis, and Lyme disease. Some participants also conceptualized disease as
including phenomena such as invasive species and environmental contaminants. Chief concerns
about wildlife disease expressed by participants were, in descending order of frequency: threats
to wildlife health, overreaction and fear responses from the public leading to negative attitudes
toward wildlife, threats to human health (though most people indicated they did not have
personal concerns about health risks from wildlife disease), and impacts on domestic animals

38

including pets and agricultural livestock. In addition, many participants viewed wildlife as being
more vulnerable to disease than are humans and domestic animals. Participants considered
humans to have important, chiefly negative, influences on the occurrence of wildlife disease
through, for example, environmental contamination, habitat encroachment, or introduction of
invasive species.
Examples of disease. When thinking about disease, participants tended to name a specific
malady such as rabies, but also included more general concepts such as invasive species.
“The first thing I think of is [chronic wasting disease]…and then the obvious things,
rabies, [epizootic hemorrhagic disease]...and then to go down on the list it would be the
white nose syndrome with the bats and colony collapse disorder with honeybees.” (New
York)
“[I think] of invasive species or how we’re domesticating elk and buffalo and then it’s
spreading disease to the wild elk and buffalo.” (Oregon)
“To me a wildlife disease is something that seems to be extremely detrimental to a
specific animal like [chronic wasting disease] or the bighorn sheep here [that] get a
disease from a fly.” (Arizona)
Threats associated with disease. Participants expressed concerns mainly related to health risks
from disease, including human, domestic animal, and wildlife health. Concerns about economic
impacts were also raised.
“I think of two things: the interplay of wildlife and domestic animals. One giving disease
to the other and back and forth. And then, secondly, the possibility of bringing in a
disease that nobody’s ever seen before.” (Michigan)

39

“If I had to list ten things I’m worried about with health regards, [zoonotic disease]
would not fall in the top ten for my personal health. No way. Personally, no concerns.”
(Oregon)
“I would be concerned about the devaluation, if that’s the correct word, of wildlife in
general because of diseases. Where people don’t want deer around. Where people don’t
want raccoons around. You know, eliminate these populations because of potential for
disease.” (Michigan)
“Maybe I care more about wildlife than I do my friends or my neighbors or my animals.
To me it’s more important because if you look at it economically, hunters, naturalists,
people who get outside and work, a lot of our livelihood in this country depends on
wildlife.” (Georgia)
Vulnerability of wildlife. When asked whether they were most concerned about the impacts of
zoonotic disease on human, domestic animal, or wildlife health, participants often stated that
they believed wildlife to be more vulnerable to disease.
“I think wildlife are more, I guess, vulnerable because they don’t have vets or doctors
they go to if they’re sick whereas humans of course we got doctors. And domesticated
animals if there’s a problem there you can call your vet.” (Georgia)
“All too often wildlife becomes infected with one thing or another and people aren’t
aware of it. Even the Game and Fish isn’t always aware of it right away and so it can
have a greater impact. Where with people it usually gets noticed very quickly so then
you can do something about it.” (Arizona)
“We’re smart enough, we have enough technology that we’re able to deal with the effects
on humans of some disease. And even with our pets and domesticated animals [we have]

40

veterinarians and all that…but it’s very hard to control or manage the effect it has on the
wildlife.” (New York)
Naturalness of disease. Another theme that emerged from the focus groups was naturalness or,
specifically, ways in which participants talked about wildlife disease issues as being natural or
unnatural. Beliefs in the naturalness of wildlife disease ranged from the perspective that it is part
of the “cycle of life” or “balance of nature” to the view that humans have made disease problems
worse.
“The diseases and illnesses within all types of wildlife, that’s part of creation. X amount
of skunks are gonna have rabies. Not to have rabies in skunks would be an unnatural
situation. If a certain amount of deer didn’t die from natural causes, the coyotes wouldn’t
have anything to eat.” (Michigan)
“I think that most diseases occur naturally in the wild. It’s all part of the cycle. You
have outbreaks just like you do with human diseases. I don’t think we have a great many
man-caused diseases at this point in time.” (Arizona)
“I guess we could argue that [wildlife disease is] natural but we’ve affected everything
on the planet so nothing is natural anymore except maybe very few remote areas.”
(Oregon)
Misunderstandings of wildlife disease. Although we found the focus group participants on the
whole to be quite thoughtful and informed with respect to the issues discussed, we heard some
comments indicating oversimplifications or misperceptions of wildlife disease problems.
“Rabies is a disease that will in the long run manage itself.” (New York)
“If we quit trying to manage then maybe it would all kinda fix itself given enough time.”
(Oregon)

41

“[White nose syndrome] will improve the overall health of our bat population.”
(Michigan)
Focus group participants were asked when they thought wildlife disease managers
should, and should not, intervene. The key factors discussed as influencing agreement with
wildlife disease management were the specific management methods being used, cost in dollars
of management, the predicted outcome or consequences of management, effectiveness of
methods, level of uncertainty, severity of the disease threat (e.g., human health threat, decimation
of wildlife populations), risk of the disease spreading or becoming an epidemic, and the
particular species involved.
“Depends on the severity of whatever it is. If it’s severe and threatening the entire area or
perhaps even the entire species within a certain region, then you really should intervene
definitely because it’s going to affect everything.” (Arizona)
“If it’s considered a nuisance animal, you’ve got too many of them and it’s a nuisance
animal and the disease doesn’t affect other animal species, [then you don’t intervene].”
(Georgia)
“When it impacts human beings, they have to [manage]. It’s a human health issue.”
(Michigan)
“Depends on the intervention method. What does intervening consist of? Does
intervening consist of using some kind of chemical toxin? Really depends on the method.
Extermination of the species?” (Oregon)
Another important factor influencing agreement with wildlife disease management was
the desire for uncertainty to be minimized and to know that managers understand the outcomes
of management activities.

42

“If there’s not a plan...that anticipates consequences...and intervention is going to involve
spraying something or killing wolves [it] seems like it shouldn’t happen.” (Oregon)
“If the wildlife managers said we’re gonna go exterminate the javelina herd in Tucson
because there’s a disease there, I think you’d have to know what the possible outcomes
are of exterminating or not... It just can’t be thrown out there, we’re gonna do this
because we want to.” (Arizona)
“[I would want to know about] the situation, the management action they would propose,
and the effect it would have if they didn’t do it.” (Michigan)
“Don’t we do things to control one thing and it causes another problem?” (Georgia)
DISCUSSION
Knowledge of layperson mental models related to wildlife health and disease is a
frequently overlooked yet useful component of communication strategies related to wildlife
health issues. We found support for wildlife health to be high among this diverse group of
stakeholders and, overall, their conceptualizations of wildlife health were holistic. For example,
health was generally viewed as more than the absence of disease and as applicable to a wide
variety of species and multiple organizational levels. Several findings point toward a high
degree of concern for the health of wildlife among the focus group participants. In the exit
survey, participants rated concern for effects of disease on wildlife health as greater than concern
for human or domestic animal health. Similarly, many focus group participants expressed the
perspective that wildlife is particularly vulnerable to disease, more so than are humans or
domestic animals.
Our findings challenge the notion that wildlife disease management must be framed in
terms of protecting human health or benefitting economic interests to gain public support, at least

43

for some groups of stakeholders. Protection of human health is typically given highest priority
when justifying wildlife disease management (Wobeser 2007). We do not claim that
anthropocentric factors are unimportant but it appears that communication messages
emphasizing health effects on valued wildlife species could garner greater agreement with
wildlife disease management among some stakeholder groups. In particular, the idea of wildlife
being more vulnerable to disease may prove persuasive in creating awareness about the
seriousness of disease risks. Perceived severity and susceptibility associated with a health risk
influence assessment of the risk which, in turn, influences judgments about risk management
(Sharma and Romas 2008). If people do not perceive themselves as susceptible to health effects
from wildlife disease, as we found in our focus groups, another way to influence perceptions of
disease could be to emphasize the severity of disease to valued wildlife species.
Our findings suggest that many stakeholders view wildlife health at least partly in terms
of balance, making it a particular communication challenge if wildlife professionals seek to
dispel the concept of the balance of nature in relation to disease. Focus group participants viewed
health as applicable to different organizational levels (i.e., individual, population, ecosystem),
but the emphasis was on populations, and population balance was discussed particularly in
reference to being below carrying capacity or not so abundant as to damage habitat or vegetation.
The idea of a “balance of nature” has been described as a myth that oversimplifies the dynamic
workings of the natural world (Kricher 2009). It was argued by ecologist Charles Elton to be
fictitious, yet the idea of balanced nature was central to Aldo Leopold’s view of the natural
world (Christensen 1999) and it has proven to be a persistent heuristic in wildlife management.
With regard to wildlife disease, the communication need lies in discouraging complacency about

44

the effects of disease because it is viewed as something that maintains balanced wildlife
populations.
Some focus group participants expressed beliefs that wildlife disease is natural, which
supports Wobeser’s (2007: 27) assertion that disease in wild animals is still commonly viewed as
“a natural phenomenon and that nature will take care of itself.” For any given risk, the belief that
the risk is natural as opposed to anthropogenic can lead to lower risk perceptions (Sjöberg 2000)
which can lead to less support for human intervention in mitigating the risk. If wildlife disease is
construed as simply being part of nature, stakeholders may become complacent about the need
for, and less likely to support, wildlife disease management. Consequently, an important area of
communication pertinent to wildlife disease management may be to emphasize human influences
on disease prevalence. For example, movement of animals and disease agents is a particularly
deleterious cause of anthropogenic disease problems (Wobeser 2007).
With respect to factors expressed by participants as influencing their support for, or
opposition to, potential wildlife disease management interventions, we discovered concerns that
haven’t previously been highlighted in human dimensions of wildlife disease management
research. Uncertainty related to management interventions and a desire for assurance that
managers understand the potential outcomes of wildlife disease management were expressed as
concerns. Similarly, a focus group study that examined acceptability of forest fuel management
strategies found an important consideration among stakeholders to be agency competence which
was related to the belief that wildfire professionals knew what they were doing (Winter et al.
2002). Individuals make judgments about the appropriateness of actions based on their
understanding of the costs and benefits and it is important in communication campaigns to
emphasize both benefits gained and costs avoided. In wildlife disease management, a

45

particularly relevant cost stems from management uncertainty and the need to ensure the public,
as much as is practical, that actions aren’t being taken that could result in significant unintended
consequences. Just as there is concern for vulnerability of wildlife to disease, there also appears
to be concern for vulnerability of wildlife, or the ecosystem more broadly, to management
actions.
These focus group participants provided a good representation of the diversity of people
who are likely to be key stakeholders with respect to wildlife issues (e.g., hunters, outdoor
enthusiasts, environmentalists) and included representation from several states across the United
States. The individuals who participated in these focus groups were not selected randomly and
results cannot be generalized to the public at large. Focus group results, however, can provide
insights beyond the specific group of participants so long as extrapolations are kept more general
and tentative than would be the case with a random survey sample (Minnis et al. 1997).
IMPLICATIONS
Insights from these focus groups contribute to the knowledge base of wildlife disease
management and encourage consideration of how laypersons, as opposed to experts, think about
the complex notion of wildlife health. Because communication is a two-way process requiring
mutual understanding, non-expert views are a critical piece of information in the development of
risk communication related to wildlife disease management. While not representative of all
stakeholders, the support and concern for wildlife health expressed by these participants
indicated that a broad suite of concerns, much broader than human health impacts alone, are
relevant to communicating about wildlife health and disease issues. Beliefs in the vulnerability of
wildlife and the naturalness of disease have potential to influence attitudes toward wildlife

46

disease management and offer opportunities to improve communication with the public about
wildlife health and disease issues.

47

Table 3-1. Themes that emerged in response to focus group questions.
QUESTION TOPIC
RESPONSE THEMES
Characteristics of wildlife health

 Healthy, sustainable populations
 Balanced populations (e.g., populations
that are within carrying capacity)
 Absence of disease
 Physical and physiological condition
(e.g., normal lifespan, normal
reproduction)
 Habitat quality
 Functional ecosystems
 Includes a broad array of species,
including fish, although some species
considered more important than others

Indicators or measurements of wildlife health












Hindrances to wildlife health








Characteristics of wildlife disease





48

Abundant harvest
Condition of habitat
Population size and trends
Condition, size, weight of animal
Reproduction, recruitment, birth rates
Age and sex ratios (e.g., buck to doe
ratios)
Mortality
Normal geographic distribution
Comparison of population to historical
numbers or some baseline
Human encroachment (development,
urban sprawl, etc.)
Invasive species
Environmental contaminants
Overpopulation (of the species)
Climatic factors (e.g., drought or hard
winters and climate change)
Captive wildlife facilities (including
fish farms)
Exists in all species and is “natural” to
a certain extent
Lack of personal or public awareness
Emphasis on infectious diseases but
includes more than that
Refers to a specific health problem

Table 3-1 (cont’d)
Concerns about wildlife disease

 Impacts on wildlife (most frequently
expressed concern)
 Impacts on humans (health, food
supply, recreation)
 Cost and economic impact
 Impacts on pets or livestock
 Fear responses from people leading to
devaluation of wildlife
 Upsetting of ecological balance
 Specific methods (e.g., how invasive?)
 Cost in dollars
 Predicted outcome or consequences
 Effectiveness
 Level of uncertainty
 Severity of disease threat (e.g., human
health threat, decimation of wildlife
populations)
 Risk of disease spreading or becoming
an epidemic
 Species involved (e.g., is it a nuisance
or overabundant species?)

Factors influencing agreement with wildlife
disease management

49

Figure 3-1. Layperson mental model of wildlife health.

Sustainable
populations
Habitat quality
& availability

Healthy
ecosystem

Socioeconomic
factors

Balanced
population

Absence of
disease

Healthy animals

50

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51

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CHAPTER 4

EVALUATION OF WILDLIFE DISEASE RISK PERCEPTIONS
S.L. HANISCH-KIRKBRIDE, Michigan State University, Department of Fisheries and
Wildlife, 13 Natural Resources, East Lansing, Michigan 48824 USA
S.J. RILEY, Michigan State University, Department of Fisheries and Wildlife, 13 Natural
Resources, East Lansing, Michigan 48824 USA
M.L. GORE, Michigan State University, School of Criminal Justice, 560 Baker Hall, East
Lansing, Michigan 48824 USA
ABSTRACT Risk perception has an important influence on wildlife management and is
particularly relevant to issues that present health risks, such as those associated with wildlife
disease management. Knowledge of risk perceptions is useful to wildlife health professionals in
developing communication messages that enhance public understanding of wildlife disease risks
and that aim to increase public support for disease management. To promote knowledge of
public understanding of disease risks in the context of wildlife disease management, we used a
self-administered questionnaire mailed to a stratified random sample (n = 901) across the
continental United States to accomplish three objectives: 1) assess zoonotic disease risk
perceptions, 2) identify sociodemographic and social psychological factors underlying these risk
perceptions, and 3) examine the relationship between risk perception and agreement with
wildlife disease management practices. Diseases we assessed in the surveys were rabies, plague,
and West Nile virus. Risk perception as measured by an index consisting of severity,
susceptibility, and dread was greatest for rabies and West Nile virus (𝑥̅ = 2.62 and 2.59,
respectively, on a scale of 1 to 4) and least for plague (𝑥̅ = 2.39). The four most important

55

variables associated with disease risk perception were gender, education, prior exposure to the
disease, and concern for health effects. We found that stronger risk perception was associated
with greater agreement with wildlife disease management. We found particular concern for the
vulnerability of wildlife to zoonotic disease and for protection of wildlife health, indicating that
stakeholders may be receptive to messages emphasizing the potential harm to wildlife from
disease and to messages promoting One Health (i.e., those that emphasize the interdependence of
human, domestic animal, wildlife, and ecosystem health).
INTRODUCTION
Many wildlife diseases pose risks to human, domestic animal, and wildlife health and
have negative economic and ecological effects (Wobeser 2007), making wildlife disease
management amenable to risk management approaches. Technical risk assessments, such as
epidemiological models, contribute to predictions about the likely occurrence of a specific
disease (Miller 2007), however, wildlife disease management is influenced by public opinion in
addition to biological and epidemiological factors (Heberlein 2004, O’Brien et al. 2006). Risk
management decisions are inevitably social decisions (Glicken 2000), in part because a
fundamental concern is “what is acceptable risk?”, and answers to this question are influenced by
deeply-held values and beliefs (Fischhoff et al. 1981, Wildavsky and Dake 1990). Consequently,
different stakeholder groups may assign and assess risks and benefits differently, resulting in
divergent risk perceptions and beliefs about the best management approach for a given risk
(Maule 2004). Particularly relevant to wildlife disease management is that experts and
laypersons commonly perceive risks differently (Sjöberg 1999, Loewenstein et al. 2001). In
general, risk from an expert’s perspective tends to be based on probabilities and a narrow range
of concerns while the public tends to be more focused on uncertainty and a broader range of

56

potential concerns (Slovic 2000, Hampel 2006). For example, in the case of chronic wasting
disease in Wisconsin, experts emphasized the health benefits of disease eradication while local
landowners focused on potential negative outcomes associated with wildlife disease
management, including threats to property rights and changes to the population density of whitetailed deer (Heberlein 2004). Such disparities can lead to differing judgments about acceptable
risks and appropriate management responses and can make risk communication particularly
challenging (Hampel 2006).
Through their role in managing and communicating risks, agencies can help strengthen or
weaken public risk perceptions, and therefore have an influence on societal debates about risk
(Kasperson and Kasperson 1996). The ability to purposefully influence relevant beliefs can be
enhanced when disease risks and wildlife disease management goals are communicated in ways
that sync with stakeholder values and concerns (Decker et al. 2006). Research on risk perception
helps managers understand how the public thinks about and responds to risks (Slovic 1987,
Vaske 2010), aids in alignment of management objectives and stakeholder values (Morgan et al.
2002, Decker et al. 2006), sheds light on how publics are likely to respond to wildlife disease
management efforts (Vaske 2010), and informs development, implementation, and evaluation of
risk communication strategies (Gray and Ropeik 2002, Gore et al. 2009, Decker et al. 2010).
Aside from studies emphasizing particular stakeholder groups and diseases (e.g., hunters and
CWD), research on wildlife disease management risk perception has been limited. This gap is
unfortunate given that there are many other diseases of interest to wildlife health professionals
and there are diverse stakeholder groups that affect, and are affected by, wildlife disease
management.

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The science of estimating and understanding risk typically focuses on two aspects of risk.
One is technical risk assessment to quantify risk probabilities and the other is risk perception
which is influenced by cognitive (related to the perceived probabilities of an adverse outcome)
and affective (a feeling state related to the adverse outcome) components (Slovic 1987, Renn
1992). Risk perception is commonly measured in terms of the concepts of severity, or how
serious the risk is perceived to be; susceptibility, or the perceived likelihood of the risk
occurring; and dread, or the degree of worry or fear associated with the risk (Sjöberg 1998, Gore
et al. 2009). Risk perception research has identified several characteristics that influence risk
judgments, including an individual’s level of knowledge and sense of control related to the risk
and characteristics of the risk itself such as how novel it is and the degree of uncertainty
surrounding it (Fischhoff et al. 1978, Slovic 1987, Siegrist et al. 2000). Factors such as gender,
level of education, worldview, and trust in experts or management authorities also influence risk
perceptions (Palmer 1996, Finucane et al. 2000, Siegrist and Cvetkovich 2000).
To provide greater empirical knowledge of disease risk perceptions relevant to wildlife
disease management, our objectives were to assess risk perceptions of three different zoonotic
diseases (i.e., rabies, plague, West Nile virus), identify relevant sociodemographic and social
psychological factors that influence these risk perceptions, and examine the relationship between
risk perception and agreement with wildlife disease management practices.
MATERIALS AND METHODS
We collected data from a self-administered questionnaire mailed to a random sample of
adults (n = 901) residing in the continental United States. We obtained an address sample from
Survey Sampling International (Shelton, Connecticut). Addresses on the mailing list were
stratified by rural, suburban, and urban classification and by region (northwest, southwest,

58

midwest, northeast, southeast). We designed and implemented the questionnaire according to
Dillman’s (2007) standard four wave procedure. An initial survey was mailed on April 1, 2011;
a postcard reminder was mailed on April 15, 2011; a second survey was mailed on May 11,
2011; and a third and final survey was mailed on June 1, 2011. Research methods were
approved for use by the Michigan State University Social Science Institutional Review Board
(IRB# 09-874).
The key dependent variable, risk perception, was measured using three conceptual
elements widely used in the risk literature: severity (Table 4-1, A), susceptibility (Table 4-1, B),
and dread (Table 4-1, C). For each respondent, we calculated a risk perception index score
(severity + susceptibility + dread / 3). We used one of three different zoonotic disease examples
in each geographic region to increase salience to respondents and to maximize response rate.
Plague was the disease used in the northwest and southwest, rabies in the northeast, and West
Nile virus in the midwest and southeast. We also measured risk perception across five different
risk targets (i.e., entities affected by a risk): self, others, pets, domestic livestock, and wildlife.
We examined several independent variables based on practical and theoretical
considerations: age; gender; education level; whether the respondent hunted; whether the
respondent had children under the age of 18; whether the respondent lived in a rural area,
suburban area, or city; respondent’s knowledge of preventive measures (Table 4-1, D);
respondent’s exposure to the disease (Table 4-1, E); respondent’s concern about health (Table 41, F and G); and respondent’s beliefs about the influence of humans on nature (Table 4-1, H).
We also examined relationships between risk perception and agreement with six wildlife disease
management practices of varying intensity: letting the disease run its course, monitoring and

59

surveillance, public education, non-lethal management, selective killing (i.e., killing of relatively
few individuals without the goal of reducing the population), and population reduction.
We examined relationships between variables using linear regression and assessed
differences in means using paired samples and independent samples t-tests (Vaske 2008). To
check for non-response bias in our sample, we conducted phone interviews with 100 randomly
selected non-respondents who were asked a subset of 18 questions from the original
questionnaire. We compared respondents and non-respondents using independent sample t-tests
(Vaske 2008). We chose not to weight variables. Although weighting the data by demographic
characteristics makes them more representative of the American population as a whole, doing so
introduces additional biases and may compromise internal validity (Teel et al. 2002, Gore et al.
2005). Moreover, in addressing risk perceptions, it is important to consider the values of active
stakeholders (e.g., those most likely to respond to the questionnaire) rather than treat the public
as a single, homogeneous entity (Bennett et al. 2010).
RESULTS
The number of deliverable surveys was 5,073 from which 901 (17.8%) surveys were
completed. Despite the overall low response rate, the sample size was adequate for generalizing
to a large population at a 95% confidence level with a ±5% margin of error (Dillman 2007,
Vaske 2008). Based on a post hoc telephone survey, we did not find evidence of significant nonresponse bias. Nearly two-thirds (65.1%) of our survey respondents were male. The median age
of respondents was 58 years and 48.3% had at least a four-year college education. All
geographic regions of the United States were adequately represented, ranging from 14 to 20% of
total respondents. Respondents reported that they lived in rural areas (27.7%), suburban areas
(20.1%), small towns (23.1%), mid-sized cities (16.5%), and large cities (12.7%). When asked

60

about participation in wildlife-related activities, 85.9% of respondents stated that they watch or
observe wildlife often or from time to time and 28.7% hunted often or from time to time.
Risk perception index scores were greatest for rabies (2.59; SD = .52) and West Nile
virus (2.62; SD = .62), which were not statistically different from each other, and least for plague
(2.39; SD = .53). Respondents’ risk perceptions were greatest when evaluating effects of disease
on wildlife (2.72; SD = .68) and were lowest for effects of disease on humans, including others
(2.44; SD = .61) and self (2.39; SD = .63). For the individual risk constructs, risk severity was
judged to be higher for humans (self and others), but risk susceptibility and dread were judged to
be higher for non-human targets such as wildlife and domestic animals. The consequences of
contracting zoonotic disease were reported as most serious for humans (self and others) and least
serious for pets and wildlife (Table 4-2). Overall mean risk severity (across diseases and risk
targets) was 3.32 (SD = .66). A gradient of perceived severity occurred for the three diseases
with mean severity greater for rabies than plague (t = 2.01, P = .045) and greater for plague than
WNV (t = 2.95, P = .003).
Wildlife was reported as being most susceptible to zoonotic disease, followed by pets and
livestock, with humans rated as least susceptible. Overall mean risk susceptibility (across
diseases and risk targets) was 2.17 (SD = .67). Mean susceptibility was evaluated as being
greatest for WNV (t = 3.82, P ≤ .001), then rabies, then plague (t = 3.09, P = .002). For the dread
construct, respondents were most worried or fearful of zoonotic disease affecting wildlife,
followed by domestic livestock and pets, and were least worried or fearful about zoonotic disease
affecting humans (Table 4-2). Overall mean dread (across diseases and risk targets) was 2.13
(SD = .83). Mean dread was greatest for WNV (t = 2.22, P = .027) and least for plague (t = 4.58,
P ≤ .001).

61

The four strongest predictors of zoonotic disease risk perception were gender, level of
education, previous exposure to the disease, and concern for health effects of zoonotic disease
(adjusted R2 = .45). Women judged disease risks to be greater than did men (t = 3.40; P =
.001)(Table 4-3) and respondents with at least a four-year college degree expressed lower risk
perceptions than those without a four-year college degree (t = 6.90; P ≤ .001). Respondents who
had been, or knew someone who had been, affected by rabies, plague, or WNV reported greater
risk perceptions than those who did not (t = 3.30; P ≤ .001). Concern for health was associated
with greater risk perception, whether the concern was general (i.e., the survey question asked
about concern for health without specifying a disease; t = 6.94, P ≤ .001) or specific (i.e., the
survey question asked about concern for health effects of rabies, plague, or West Nile virus; t =
25.18, P ≤ .001).
Disease risk perception increased with age of respondents (t = 3.10; P = .002) and was
also greater for those who agreed that environmental problems are caused by humans interfering
with nature and that disease has been made worse by humans (t = 2.37; P = .02). Risk perception
was lower (t = 2.57; P = .01) for respondents who had children under the age of 18. We did not
detect a difference between hunters and non-hunters in their risk perception index scores.
However, when the three risk constructs were examined separately, hunters reported being more
worried about zoonotic disease than non-hunters (t = 2.89; P = .004). Significant differences in
disease risk perception were not detected based on whether a person lived in a rural area,
suburban area, or city.
Respondents with greater disease risk perceptions were more likely to agree with wildlife
disease management in general (t = 3.04; P = .003). With respect to specific wildlife disease
management practices, individuals with greater disease risk perceptions were less likely to agree

62

with letting disease run its course (t = -5.11; P ≤ .001) and more likely to agree that public
education (t = 3.60; P ≤ .001), non-lethal management (t = 3.64; P = .009), and selective killing
(t = 2.49; P = .013) were acceptable management interventions. For wildlife population
reductions, only the construct of risk severity had a positive influence on agreement (t = 2.30; P
= .02). The positive association between agreement with wildlife disease management and risk
perception occurred for all risk targets except “you personally” (t = 1.64; P = .102). The
association was strongest for risks to domestic livestock (t = 3.34; P = .001) and wildlife (t =
3.19; P = .002).
DISCUSSION
When stakeholders evaluate wildlife disease risks they are not concerned solely with
effects on humans (Cooney and Holsman 2010). In our study, risk perception was judged to be
greatest in terms of effects on wildlife which indicates a belief in and concern for the
vulnerability of wildlife to disease. Understanding key health concerns and the extent to which
stakeholders emphasize one risk target over another (e.g., wildlife or humans) is important to
promote public support for wildlife disease management actions and to help frame
communication messages in ways that garner such support (Cooney and Holsman 2010).
Different health priorities among stakeholder groups can be a source of significant conflict in
wildlife disease management. For instance, in the case of brucellosis management in and around
Yellowstone National Park, considerable conflict has developed from the perception of some
stakeholder groups that agencies emphasize the health of domestic livestock at the expense of
native wildlife (Bidwell 2010). Concern for the vulnerability of wildlife to zoonotic disease,
together with the positive association between risk perception and concern for health protection,
suggests that many stakeholders will be amenable to messages specifically emphasizing the

63

threat disease presents to wildlife health and ways in which specific management actions are
anticipated to benefit wildlife health.
Health protection is a common frame used by agencies to explain and justify wildlife
disease management activities, with threats to human health typically receiving the most
attention (Wobeser 2007). Our findings demonstrated broad health concerns related to zoonotic
disease and wildlife disease management and raise the possibility that the public will be
responsive to One Health messages (i.e., those that emphasize the interdependence of human,
domestic animal, wildlife, and ecosystem health). Although the One Health paradigm is gaining
prominence in the medical, veterinary, and conservation professions (King et al. 2008) its public
appeal has not been closely examined, yet many of the most significant disease threats are
zoonotic and therefore have potential for application of One Health-oriented messages.
Our data also indicated that disease risk perceptions are greatest among those who
believe more strongly that disease and environmental problems are largely caused by humans.
This result is consistent with other risk research that when a particular risk is viewed as involving
negative and immoral human interference with nature, people tend to view the risk as being
worse (Sjöberg 2000). Similarly, risks believed to be caused by human actions or failures, rather
than by nature, are viewed with greater concern (Hampel 2006). It is helpful to understand this
association between risk and naturalness because the perception that disease in wild animals is
largely a natural phenomenon can lead to complacency, the belief that wildlife disease
management is not important, and possible opposition to wildlife disease management practices
(Wobeser 2007). Communication efforts that emphasize the influence of anthropogenic changes
on zoonotic disease transmission and occurrence may be effective at influencing beliefs, thereby
gaining greater public support for wildlife disease management programs. For example, diseases

64

such as Hanta virus, plague, West Nile virus, and Lyme disease can be linked to human
influences on the environment such as changing weather patterns, flooding, and landscape
alteration (AVMA 2008).
Our findings that gender and education level are strongly related to risk perception are
consistent with other risk research. Gender consistently influences risk perception, with males
expressing the lowest risk judgments across a variety of risks (Flynn et al. 1994, Finucane et al.
2000). The association between lower education level and higher risk perception related to
wildlife has been observed by others (Sjöberg 1998, Riley and Decker 2000), although it remains
unclear whether the difference is due to amount of knowledge, opposing value orientations, or
other factors such as worldview or ideology (Wildavsky and Dake 1990, Sjöberg 1999). In light
of evidence that risk denial (i.e., judging oneself to be less vulnerable to risks), gender, and
education level influence risk perceptions, managers of disease risks can tailor communication
messages to specifically vulnerable social and cultural groups to improve awareness of risks
(Renn 2008). For example, one study specifically measured West Nile virus perceptions and
prevention behaviors of women in the southeastern United States with the intent of being used to
help develop West Nile virus prevention campaigns aimed at women in that region (Yerby
2007).
Our results suggest that level of risk perception influences stakeholder agreement with
specific wildlife disease management practices and that the effect of risk perception on
agreement with wildlife disease management exists regardless of whether the risk targets are
human or non-human. This finding further supports the notion that stakeholders are concerned
with health risks to animals (domestic or wild) as well as humans. Previous research suggests
perceived risks to human health and safety can be strong influencers of attitudes about wildlife

65

and appropriate wildlife management practices (Fulton et al. 2004, Dorn and Mertig 2005,
Jonker et al. 2009). Future research in this area that includes categories beyond human health
and safety, such as One Health and wildlife health, will provide greater detail about the breadth
of stakeholder health concerns related to wildlife disease management.
Our results have implications for researchers interested in the nature of risk perceptions
related to wildlife health and management of wildlife disease and for managers who seek to
improve communication with the public about wildlife health and disease issues. In an era of
decreasing public trust in the expertise of health professionals (Jacobs 2005), effective risk
communication is a critical component of wildlife disease management, particularly as it relates
to prevention and mitigation of zoonotic diseases. Our research indicates that communication
messages that emphasize the negative effects of disease on wildlife health specifically and that
focus on the impacts of humans on zoonotic disease occurrence may encourage more supportive
attitudes toward wildlife disease management practices. It seems evident that much of the public
views wildlife as particularly vulnerable to disease and that public response to wildlife disease
management is likely to be more supportive when the anticipated benefits to wildlife are made
clear.

66

Table 4-1. Survey questions and measurement scales.
A. If the following were to contract [plague, rabies, West
Nile virus], how serious do you think the consequences
would be…?
B. In your opinion, how likely to contract (or catch) [plague,
rabies, West Nile virus] are…?
C. Do you worry about or feel fearful of [plague, rabies,
West Nile virus] affecting…?
D. Are you aware of practical, effective measures you can
take to prevent [plague, rabies, West Nile virus]?
E. Have you, or do you know someone, who has contracted
or otherwise been affected by [plague, rabies, West Nile
virus]?
F. How concerned are you that [plague, rabies, West Nile
virus] could affect the health of…[humans, pets, domestic
livestock, wildlife, the overall ecosystem]?

1 – 4 (not serious, somewhat
serious, serious, very serious)
1 – 4 (very unlikely, unlikely,
likely, very unlikely)
1 – 4 (not at all, rarely, from time
to time, a great deal)
Yes or no
Yes or no

1 – 5 (1 = not at all concerned, 3
= somewhat concerned, 5 =
extremely concerned)

G. In general, how concerned are you about protecting the
health of [humans, domestic animals, wildlife, the overall
ecosystem]?

1 – 5 (1 = not at all concerned, 3
= somewhat concerned, 5 =
extremely concerned)

H. Do you agree or disagree that: most environmental
problems are caused by humans interfering with nature, the
occurrence of disease (in general) has been made worse by
humans and their activities?

1 – 5 (strongly disagree,
disagree, neither agree nor
disagree, agree, strongly agree)

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Table 4-2. Perceived risk severity, susceptibility, and dread for five risk targets averaged across
three zoonotic diseases.
Target
Mean severity
Mean
Mean dread (SD)
(SD)
susceptibility
(SD)
Self
3.44 (.77)
1.81 (.78)
1.95 (.94)
Others

3.39 (.75)

1.95 (.76)

2.00 (.90)

Pets

3.22 (.83)

2.15 (.78)

2.15 (.93)

Livestock

3.34 (.74)

2.33 (.80)

2.25 (.95)

Wildlife

3.24 (.79)

2.61 (.84)

2.34 (.97)

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Table 4-3. Factors influencing risk perception rankings for three zoonotic diseases.
Variables and direction
t and P values
Gender: women > men

t = 3.40 (P = .001)

Age: older > younger

t = 3.10 (P = .002)

Education: 4-year college degree < no degree

t = 6.90 (P ≤ .001)

Children under age 18 < no children

t = 2.57 (P = .01)

Exposure to disease > no exposure

t = 3.30 (P ≤ .001)

General concern for health: high concern > low

t = 6.94 (P ≤ .001)

Specific concern for health: high concern > low

t = 25.18 (P ≤ .001)

Tampering with nature: strong agreement > weak
agreement

t = 2.37 (P = .02)

69

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70

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CHAPTER 5

USING HEALTH BEHAVIOR THEORY AS A MODEL FOR UNDERSTANDING
PUBLIC AGREEMENT WITH WILDLIFE DISEASE MANAGEMENT
ABSTRACT In support of the Association of Fish and Wildlife Agencies National Fish and
Wildlife Health Initiative, the objectives of this research were to evaluate public agreement with
different wildlife disease management strategies, assess factors influencing agreement, and test a
health behavior theory, the Health Belief Model, as a theoretical framework for understanding
agreement with wildlife disease management. We obtained data from a mail survey of United
States residents (n = 901). Of six wildlife disease management strategies examined, there was
strong disagreement with allowing disease to run its course and strongest agreement with public
education and non-lethal management. Mean agreement with wildlife disease management was
highest when the goal was to protect “one health” and lowest when the goal was to protect health
of pets. The best predictor of agreement with wildlife disease management was whether
respondents believed that it was likely to result in benefits they considered important. Based on
path analysis, the Health Belief Model, consisting of the variables of perceived risk severity and
susceptibility, concern for health, exposure to zoonotic disease, social trust, and belief in wildlife
disease management having important benefits, was found to be a useful theoretical model for
understanding factors that influence stakeholder agreement with wildlife disease management.
INTRODUCTION
Emerging and persistent zoonotic disease threats affect and are affected by wildlife,
emphasizing the importance of effective wildlife management decisions that promote wildlife
health. Good management decisions depend on accurate information about biological facts and
about stakeholder values, preferences, and priorities (Gregory et al. 2006). The challenges faced
75

in wildlife disease management demand interdisciplinary collaboration by all relevant disciplines
(Deem et al. 2001). This includes human dimensions expertise since wildlife disease
management entails managing people in addition to organisms and habitat (Henke et al. 2007,
Wobeser 2007). Social science insights shed light on potential drivers of human behavior and
inform managers of beliefs and attitudes that influence health concerns (Ostfeld et al. 2002).
Prior experiences have shown that disease management driven chiefly by the perspectives of
experts can lead to problems with public acceptance (Heberlein 2004, Beck et al. 2005, O’Brien
et al. 2006). Understanding public attitudes toward management interventions helps agencies
gauge public support for or opposition to future management policies and guides development of
communications to gain public support for potentially controversial strategies (Carroll and Bright
2009).
Human dimensions research has revealed several factors that influence stakeholder
agreement with wildlife management. Individual cognitive factors such as normative beliefs
(Zinn et al. 1998); risk perceptions (Needham et al. 2004, Peterson et al. 2006); social trust
(Vaske et al. 2004, Needham and Vaske 2008); wildlife value orientations (Zinn et al. 1998); and
beliefs and attitudes about management actions (Fulton et al. 2004, Dorn and Mertig 2005,
Bruskotter et al. 2009) affect agreement with management. Additionally, how a particular
situation is perceived by stakeholders, including incident extremity and the specific management
response, also affects agreement with management methods (Zinn et al. 1998, Manfredo et al.
1998, Loker et al. 1999, Reiter et al. 1999, Fulton et al. 2004, Decker et al. 2006, Jonker et al.
2009). A large body of research supports the conclusion that as threats to human health and
safety increase, acceptance of more intensive (e.g., lethal) management strategies tends to
increase (Dorn and Mertig 2005, Zinn et al. 1998, Peterson et al. 2006, Needham et al. 2004,

76

Loker et al. 1999, Koval and Mertig 2004, Fulton et al. 2004, Jonker et al. 2009). However,
human dimensions research focused specifically on wildlife disease management has been
limited in scope with several studies exclusively examining chronic wasting disease (CWD) and
hunters.
Research on the human dimensions of wildlife disease management can benefit from a
theoretical focus. Theory helps identify the beliefs that need to be targeted to change people’s
attitudes and behavioral intentions and it is particularly useful for informing communication
campaigns (Fishbein and Yzer 2003). Undergirding stakeholder judgments about appropriate
management responses is a decision making process that leads to an expressed preference.
Multiple theories exist about how humans decide on such preferences and one important group is
that of the expected utility theories. Theories in this category hold that people “make decisions
that maximize some subjective measure of value or welfare” (Slovic 1995: 364). The Theory of
Reasoned Action (TRA) is an example of an expected utility theory based on evidence that
beliefs about the likelihood of important outcomes inform attitudes toward a behavior which in
turn influence intentions to engage in the behavior (Ajzen and Fishbein 1980). The TRA
framework has been used to understand beliefs and attitudes related to wildlife management. For
example, one study found that people were more likely to agree with white-tailed deer
management strategies they believed would result in important, positive outcomes (Fulton et al.
2004).
No previous human dimensions research has quantitatively assessed agreement with
wildlife disease management using health behavior theory as the theoretical framework. Since
management of wildlife disease is commonly aimed at promoting health (e.g., wildlife, domestic
animal, and/or human health), insights from theories of human health behavior have the potential

77

to add to our understanding of public acceptance of wildlife disease management. The Health
Belief Model (HBM) is one such theoretical approach. The HBM, which is a value expectancy
theory and is closely related to the TRA (Kretzer and Larson 1998), was developed by social
psychologists to describe how specific beliefs influence health behavior and behavioral decisionmaking under conditions of uncertainty (Sharma and Romas 2008). Key elements of the HBM
include beliefs about: susceptibility to a health risk; seriousness or severity of the health risk;
perceived benefits (i.e., the extent to which a management strategy is believed to actually reduce
risk susceptibility and/or severity); perceived barriers (i.e., the extent to which significant
barriers are associated with a management strategy); and self-efficacy, or one’s ability to
successfully engage in a health behavior (Janz and Becker 1984, Rosenstock et al. 1988, Koch et
al. 2005). Additional HBM constructs include general health motivation and cues to action,
which refer to events that influence people to change their behavior (Abraham et al. 1999).
This research supports the National Fish and Wildlife Health Initiative, a policy
framework developed to strengthen the ability of fish and wildlife agencies to address health
issues and to minimize the negative impacts of fish and wildlife health issues (AFWA 2007).
The research objectives were to evaluate agreement with wildlife disease management strategies,
to assess factors influencing agreement with wildlife disease management, and to test the HBM
as a theoretical framework for helping to understand and evaluate agreement with wildlife
disease management.
METHODS
Research data were obtained from self-administered mail surveys. Addresses in the
sample (N = 5,393) were obtained from Survey Sampling International (Shelton, Connecticut)
and were randomly selected from within the continental United States. The sample was stratified

78

by rural, suburban, and urban classification and into five regions: northwest, southwest, midwest,
northeast, and southeast. Data collection involved a four-wave mailing strategy (Dillman 2000)
beginning with a survey mailed on April 1, 2011; a postcard reminder mailed on April 15, 2011;
a second survey mailed on May 11, 2011; and a third and final survey mailed on June 1, 2011.
To encourage recipients to complete the survey, a lottery incentive (a chance to win one of 25
$50 gift cards) was used for the final mailing. To check for potential non-response bias, phone
interviews were conducted with 100 randomly selected non-respondents who were asked a
subset of 18 questions from the original questionnaire. This research was approved by the
Michigan State University Social Science Institutional Review Board (IRB# 09-874).
The key dependent variables were mean agreement with each of six specific wildlife
disease management strategies: the disease should be left alone and allowed to run its course,
monitoring and surveillance, public education, non-lethal management such as vaccination,
selective killing of diseased animals, and population reduction of the wildlife species to reduce
disease spread. Another dependent variable was overall mean agreement with wildlife disease
management which was calculated as average agreement across all six specific management
strategies. Respondent agreement with wildlife disease management strategies was assessed
across five different health protection goals: human health; health of pets; health of domestic
livestock; wildlife health; and one health, which refers to protecting health of humans, domestic
animals, and wildlife simultaneously. Respondents rated their agreement on a scale with
response options of “strongly disagree,” “disagree,” “neither agree nor disagree,” “agree,” and
“strongly agree.” In conducting analyses, responses of “neither agree nor disagree” were
dropped since such responses indicate a lack of opinion.

79

The key independent variables (Table 5-1) used to test the HBM were perceived risk
severity [RISKSEV], perceived risk susceptibility [RISKSUSC], benefit index [BENEFIT], cost
index [COST], health concern [HEALTH], experience with disease [EXPERIENCE], and trust in
management authorities [TRUST]. The two risk perception elements, RISKSUSC and
RISKSEV, were assessed in relation to one of three specific zoonotic diseases: rabies, plague, or
West Nile virus. To maximize salience of the disease used in the survey, a different disease was
selected for each geographical region: rabies in the northeast, plague in the northwest and
southwest, and West Nile virus in the midwest and southeast. TRUST, an indicator of
willingness to rely on those who have formal management and decision making responsibilities
(Cvetkovich and Winter 2003), was used in lieu of the HBM variable of self-efficacy since the
research question focused on actions carried out by wildlife management entities rather than the
respondent personally. Data were analyzed using IBM SPSS Statistics 19 (2011). Differences in
mean agreement levels were examined using a paired samples t-test or analysis of variance,
associations between variables were tested using linear regression, and the HBM was analyzed
using path analysis.
RESULTS
The total number of deliverable surveys was 5,073 and 901 surveys were completed,
giving a final response rate of 17.8%. Upon comparing mail and telephone respondents, one
estimate was found to differ (P > .05) between the two groups of respondents. Essentially,
respondents were more likely to fish than non-respondents. Because this was not a variable
important to the research question, non-response error was not considered to be problematic for
the study.

80

Mean agreement with the individual wildlife disease management strategies ranged from
1.69 to 3.31 (Table 5-2). Agreement was greatest for non-lethal management and public
education (t ≥ 3.82, P ≤ .001). Agreement with letting the disease run its course was
significantly less than for all other management strategies (t ≥ −25.78, P ≤ .001). Mean
agreement with wildlife disease management, excluding the let the disease run its course option,
for the different health protection goals ranged from 3.10 for protection of pet health (SD = .43)
to 3.17 for protection of one health (SD = .47). Mean agreement for one health was statistically
higher than human health (t = 2.24, P = .025); pet health (t = 5.23, P ≤ .001); livestock health (t =
2.43, P = .015); and wildlife health (t = 3.12, P = .002). Mean agreement for pet health was
statistically lower than human health (t = −3.13, P = .002); livestock health (t = −3.76, P ≤ .001);
wildlife health (t = −2.40, P = .016); and one health.
Nearly two-thirds (65.1%) of survey respondents were male and the median age was 58
years. Nearly half of respondents (48.3%) had at least a four-year education. Geographically, all
regions (northwest, southwest, midwest, northeast, southeast) were well represented in the
sample, ranging from 14 to 20% of respondents. More respondents characterized their place of
residence as rural (27.7%) than as suburban (20.1%), small town (23.1%), mid-sized city
(16.5%), or large city (12.7%). When asked about participation in wildlife-related recreation,
85.9% of the sample responded that they watch or observe wildlife from time to time or often
and 28.7% responded that they hunt from time to time or often.
Women had greater agreement than men with public education (t = 3.70, P ≤ .001) and
non-lethal management (t = 2.47, P = .014) and were less likely than men to agree with selective
killing (t = −2.06, P = .039) and population reduction (t = −2.77, P = .006). Hunters were more
likely than non-hunters to agree with letting disease run its course (t = 2.93, P = .003), selective

81

killing (t = 2.93, P = .004), and population reduction (t = 2.75, P = .006). Non-hunters were
more likely to agree with monitoring and surveillance (t = 2.69, P = .007), public education (t =
3.28, p=.001), and non-lethal management (t = 3.10, P = .002). We observed no significant
effects on agreement with wildlife disease management based on geographic region, age,
education level, or urban/rural living areas.
Mean RISKSEV was 3.32 (SD = .66) and mean RISKSUSC was 2.17 (SD = .66). The
mean for HEALTH was 4.30 (SD = .70). Concern was greatest, and did not statistically differ,
for protecting health of humans and the ecosystem. The mean for TRUST was 1.86 (SD = 1.45).
For EXPERIENCE, 10.8% of the sample responded affirmatively. Figures 5-1 and 5-2 show
assessed importance of and agreement with the statements used to measure BENEFIT and
COST. For the variable BENEFIT, the three most important outcomes were protecting human
health, preventing the disease from spreading, and protecting ecosystem health, with 84.9%,
81.1%, and 79.8%, respectively, rating these outcomes important or extremely important. For
the variable COST, the two most important outcomes were reducing uncertainty before managers
act and being able to continue activities that one enjoys, with 86.9% and 69.3%, respectively,
rating these outcomes important or extremely important.
Path analysis revealed four observed, endogenous variables: mean agreement with
wildlife disease management, BENEFIT, RISKSEV, and RISKSUSC (Figure 5-3). The three
observed, exogenous variables were HEALTH, TRUST, and EXPERIENCE. The chi-square
goodness-of-fit test (Χ2 = 56.42, P ≤ .001) and Comparative Fit Index (CFI = .927) values
indicated adequate fit of the model to the data. The model is considered a good fit when the ratio
between Χ2 and degrees of freedom is < 2 (Ullman 1996) and when CFI is > .90 (Hu and Bentler

82

1999). The variable BENEFIT had the greatest direct effect on wildlife disease management
agreement and acted as a mediating variable for RISKSEV, HEALTH, and TRUST.
DISCUSSION
Agreement with management methods depends partly on stakeholder perceptions of the
problem and its proposed solution and can be influenced by how the situation is framed (Decker
et al. 2006). One key way that agencies frame wildlife disease management is in terms of
protecting health, whether human, wildlife, or domestic animal health (Wobeser 2007). The
finding that mean agreement changed only slightly for different health goals supports the idea
that framing wildlife disease management in “one health” terms (e.g., emphasizing the
interconnectedness of all health categories), when applicable, may be an effective way of
communicating with the public to explain and justify wildlife disease management. In contrast
with the commonly held belief that the public will only support wildlife disease management
when framed in terms of human health benefits, this research suggests there is likely to be a solid
foundation of public support for the One Health concept. One Health has been endorsed by
health professionals in many fields as a promising approach for more effective management of
zoonotic diseases (Decker et al. 2011). However, success of the One Health message in
promoting wildlife health may be contingent upon wildlife being framed as a victim of disease
rather than a perpetrator (Muter et al. 2009) to avoid the development of negative attitudes
toward wildlife if they become perceived as disease vectors.
Communication programs aimed at influencing stakeholder perceptions of wildlife
disease will be more effective if grounded in behavioral theory (Decker et al. 2011). The TRA
was supported by the finding that beliefs about positive and negative outcomes influenced
agreement with particular wildlife disease management strategies. Similar results have been

83

reported for white-tailed deer management in a national park in Ohio, where it was observed that
differences in the acceptability of no action and lethal control management alternatives were
strongly related to beliefs about, and evaluation of, outcomes associated with those management
alternatives (Fulton et al. 2004). However, this study is unique in finding support for the TRA
with respect to attitudes toward wildlife disease management at a national scale. These findings
confirm that acceptance of management actions is influenced by preferences for particular
outcomes of management as well as beliefs that the action will result in those desirable outcomes
and not cause other, negative outcomes (Enck et al. 2006). In other words, management
preferences are driven by anticipated consequences, both positive and negative, of actions.
Further investigation of the extent to which management attitudes are influenced by these
expected outcomes (ends) versus specifics of management actions (means) would provide
valuable insights for understanding public attitudes related to wildlife disease management and
other wildlife management actions.
Factors having the greatest influence on agreement with wildlife disease management
strategies were risk perception and beliefs about the likelihood and importance of positive and
negative outcomes related to wildlife disease management. These are key components of the
HBM which hypothesizes that health related decisions and behaviors are influenced by saliency
of health issues to the individual, belief that the health issue is serious and that one is susceptible
to it, and belief that particular behaviors will be beneficial in reducing the health risk
(Rosenstock et al. 1988, Sharma and Romas 2008). The low response rate obtained for this
survey may be indicative of a general low saliency of zoonotic disease issues to much of the
public. Yet, path analysis using HBM variables indicated that it is a useful model for
understanding attitudes toward wildlife disease management. In addition to the direct effects of

84

beliefs about the likelihood and importance of positive outcomes, perceived risk susceptibility
and severity, general health concern, agreement that wildlife management agencies make good
decisions (an indicator of trust), and prior experience with zoonotic disease had significant
indirect effects on agreement with wildlife disease management.
The HBM has been used as a framework with respect to zoonotic diseases in other
studies. For example, it has been used as a theoretical framework to guide focus group
discussions about wildlife biologists’ use of personal protection equipment to reduce zoonotic
disease risks (Bosch et al. 2010) and to understand personal protective behaviors related to West
Nile virus (Aquino et al. 2004, Yerby 2007). All of these studies identified important barriers to
health behaviors which can be used to develop communication messages. Potentially important
barriers to agreement with wildlife disease management that emerged from my research include
high uncertainty surrounding management and negative effects of management on activities one
enjoys. Key positive outcomes, or benefits, were protection of human and ecosystem health and
preventing disease from spreading. Other important explanatory variables are not included in the
model but, based on the good fit of the HBM to survey data, agreement with wildlife disease
management may be improved by the use of communication messages that highlight the various
components of the HBM, including the nature of disease risks, benefits and barriers associated
with wildlife disease management, and health impacts of wildlife disease.
All wildlife management efforts should ensure that the outcomes of management are in
harmony with socially relevant impacts (Riley et al. 2003). An important implication of this
research is that to influence stakeholder agreement with wildlife disease management, wildlife
professionals need to address specific beliefs about the outcomes of management. This research
provided a suite of beliefs relevant to wildlife disease management generally, but it is also

85

important that managers and researchers identify outcome beliefs salient to immediate
stakeholders to ensure well-designed communication campaigns (Fishbein and Yzer 2003). This
research indicates that agreement with wildlife disease management is influenced by multiple
health concerns and not all stakeholders are most concerned with protecting human health; in
fact, ecosystem health was considered equally important which indicates that many stakeholders
have a holistic perspective of health. Messages about risk severity and risk susceptibility are also
important, although efforts to amplify or attenuate risk perceptions to promote wildlife disease
management can backfire by negatively affecting public trust. Consequently, risk
communication should be developed carefully (WHO 2002, Beck et al. 2005) and with the
recognition that messages provided by wildlife professionals are just one of many potential
sources of information about wildlife diseases available to the public (Heberlein and Stedman
2009). Finally, stakeholders consider both potential benefits and costs in evaluating wildlife
disease management strategies and this implies that in conveying to the public the how and why
of management, it will help to emphasize desirable, anticipated benefits and how feared negative
outcomes will be minimized or mitigated.

86

Table 5-1. Health Belief Model variables used in path analysis.
RISKSEV
Respondents were asked one question with five parts: “If [X] were to
(1 – 4 scale)
contract [Y], how serious do you think the consequences would be?” X
= you personally, people in your community, pets, domestic livestock,
and wildlife. Y = West Nile virus, rabies, or plague, depending on the
respondent’s geographic region. This variable reflects each respondent’s
mean response across the five categories.
RISKSUSC
Respondents were asked one question with five parts: “In your opinion,
(1 – 4 scale)
how likely to contract [X], are [Y]?” X = West Nile virus, rabies, or
plague, depending on the respondent’s geographic region. Y = you
personally, people in your community, pets, domestic livestock, and
wildlife. This variable reflects each respondent’s mean response across
the five categories.
BENEFIT
Respondents were asked to rate their agreement with seven potential
positive outcomes, or benefits, of wildlife disease management and the
importance of each of these benefits to them personally. This variable is
an index of agreement x importance. Individuals with higher scores on
this variable believe positive outcomes that are important are more
likely to occur.
COST
Respondents were asked to rate their agreement with seven possible
negative outcomes, or costs, of wildlife disease management and the
importance of each of these costs to them personally. This variable is an
index of agreement x importance. Individuals with higher scores on this
variable believe negative outcomes that are important are more likely to
occur.
HEALTH
Respondents were asked “In general, how concerned are you about
(1 – 5 scale)
protecting the health of [X]?” X = humans, domestic animals, wildlife,
the overall ecosystem. This variable reflects each respondent’s mean
response across the 4 categories.
EXPERIENCE
Respondents were asked “Have you, or do you know someone who has,
(Yes or no)
contracted or otherwise been affected by [Y]?” Y = West Nile virus,
rabies, or plague, depending on the respondent’s geographic region.
TRUST
Respondents were asked “Do you agree or disagree that wildlife
(1 – 4 scale)
management agencies make good wildlife management decisions?”

87

Table 5-2. Agreement with wildlife disease management options by health protection goal.1
Health of
Human Health of
Health of
One
Mean
domestic
health
pets
wildlife
health
agreement
livestock
The disease should be
left alone and allowed
to run its course

1.73 (.70) 1.83 (.69) 1.72 (.67) 1.90 (.75)

1.70
(.70)

1.69 (.60)

Monitoring and
surveillance of the
disease

3.07 (.65) 3.05 (.63) 3.09 (.66) 3.12 (.60)

3.14
(.69)

3.13 (.57)

Public education

3.26 (.60) 3.24 (.54) 3.25 (.58) 3.25 (.55)

3.28
(.59)

3.29 (.50)

Management of the
disease with non-lethal
methods

3.28 (.57) 3.27 (.55) 3.25 (.58) 3.22 (.56)

3.28
(.57)

3.31 (.48)

Selective killing of
diseased animals

3.14 (.63) 3.05 (.63) 3.12 (.64) 3.10 (.61)

3.13
(.62)

3.16 (.54)

Population reduction
of the wildlife species
to reduce disease
spread

2.97 (.75) 2.91 (.73) 2.97 (.75) 2.97 (.72)

3.01
(.73)

3.01 (.67)

1

Agreement measured on a four point scale: 1 = strongly disagree, 2 = disagree, 3 = agree, 4 =
strongly agree.

88

Figure 5-1. Mean agreement and importance for beliefs about positive outcomes of wildlife
disease management.1
8

Importance
Agreement

7
6
5

4.3

4.4
4.1

4

4.1

4.1

4.2

4.0

2.4

2.4

2.4

3
2

2.4

2.6

Prevents
disease from
becoming a
widespread
problem in
the area
where you
live

1

Protects
human
health

2.4

2.2

0

1

Protects the Reduces Protects the Protects Protects the
health of pets negative
health of
ecosystem
health of
health game species
impacts on threatened or
your state’s endangered
wildlife
agricultural
economy

Importance measured on a five point scale. Agreement measured on a four point scale.

89

Figure 5-2. Mean agreement and importance for beliefs about negative outcomes of wildlife
disease management.1
7

Importance
Agreement

6

4.4
5

3.6

3.9

3.6
3.5

3.6

3.3

4

3

2

1.5
1

1.7

1.6
1.3
1.1

1.3

1.2

0

Managers act Results in the Financial
Interferes
even when killing of
cost is too
with
they are
wildlife
high
activities you
uncertain
enjoy
about
effectiveness
1

Benefits
nuisance
species

Benefits
special
interest
groups

Is
unnecessary
because most
disease will
go away on
their own

Importance measured on a five point scale. Agreement measured on a four point scale.

90

Figure 5-3. Path diagram showing standardized path coefficients for the Health Belief Model as
applied to understand agreement with wildlife disease management.
Wildlife disease
management
agreement

e2

.30
Benefits
e3

.28

.29
Trust

Health concern

.31
Risk severity
e4

.26
.27

Risk susceptibility

.15
Past experience
w/zoonotic
disease

91

e1

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92

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Reiter, D.K., M.W. Brunson, and R.H. Schmidt. 1999. Public attitudes toward wildlife damage
management and policy. Wildlife Society Bulletin 27(3): 746-758.
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Belief Model. Health Education Quarterly 15(2): 175-183.
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Vaske, J. J., N. R. Timmons, J. Beaman, and J. Petchenik. 2004. Chronic wasting disease in
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Dissertation. University of Alabama, Tuscaloosa, USA.

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CHAPTER 6

THE UPSHOT: RECOMMENDATIONS TO WILDLIFE DISEASE MANAGERS
“Public education is a valuable component of many management programs; both to gain public
support through demonstrating the need for management and to change human activities that
influence the disease.” (Wobeser 2002)
“[D]ecision-making…is a social and political process and…decisions are neither objective nor
defensible if based on clinicians’ own interests and value systems.” (Sorensen and Iedema 2007)
My research has aimed to shed light on expert and lay stakeholder understandings of
wildife health, zoonotic disease, and related management interventions. This goal is based in the
belief that wildlife management will have reduced success if it consistently emphasizes wildlife
professionals’ values, worldview, and associated beliefs at the expense of understanding the
perspectives of the public. In the face of complex natural resource problems such as wildlife
disease management, it seems important to recognize that relevant knowledge is not limited to
scientific experts (McDaniels et al. 1999), and that management requires an understanding of,
and respect for, different stakeholders’ perspectives (Bosch et al. 2003). It is, after all, for the
citizens’ benefit that wildlife professionals have been given the responsibility to protect,
conserve, and manage wildlife species (Conover 2002).
Although the culture of wildlife biology has been slow to accept it, the wildlife
management system necessarily includes sociocultural elements. Human dimensions entails
improving understanding of stakeholder values, beliefs, and attitudes so that better—that is
biologically sound and socially legitimate—management decisions can be made. Greater
inclusion of human dimensions knowledge is a critical step but how wildlife professionals view
the role of human dimensions should go beyond provision of social data (Patterson and Williams
1998). Moving beyond that perspective requires explicit recognition of the integral role of both
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facts and values in management decisions. Wildlife disease management has been slower to
integrate human dimensions insights than has wildlife management generally, perhaps because
disease, health, and veterinary medicine are still viewed as highly technical issues, and it is,
consequently, still dominated by an expert-authoritative approach to stakeholder involvement
(Decker and Chase 1997). Yet, past experiences with domestic animal and wildlife diseases have
demonstrated the power of public opinion in hindering the achievement of disease management
goals. Examples include chronic wasting disease in Wisconsin, bovine tuberculosis in Michigan,
brucellosis in the Greater Yellowstone Area, and foot and mouth disease and bovine spongiform
encephalopathy in the United Kingdom; efforts to manage these diseases have revealed the great
chasm that sometimes exists between expert and lay risk perceptions related to the disease itself
and the proposed management solutions (Heberlein 2004, O’Brien et al. 2006, Shumaker et al.
2012, Wilkinson et al. 2010, Beck et al. 2005).
Why do these differences in perspective occur when, as discussed in Chapter 2, health is
essentially viewed as a universal good? My research suggests it isn’t that experts and laypersons
have starkly different perspectives on the meanings of wildlife health and disease. In comparing
expert and layperson mental models, I found a high degree of conceptual common ground. Both
groups emphasized that key elements of wildlife health include population sustainability,
physical health of animals, habitat quality, and ecosystem health. Key differences were that
experts more frequently included population resiliency as a characteristic of wildlife health
whereas laypersons more frequently applied the concept of balanced populations (i.e., within
carrying capacity) to wildlife health. Experts also viewed wildlife health as being very distinctly
a multidisciplinary concept and as influenced by multiple stressors. It also seems evident that
experts are more likely to view wildlife disease as being anthropogenic, whereas laypersons are

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more likely to view it as a natural phenomenon. As one of the Delphi panel experts commented,
“Pathogens and parasites are natural organisms within ecosystems. What is not natural is how
humans have changed which ones are present.”
Although expert and layperson conceptualizations of wildlife health and disease were
largely similar, the differences may be important. As discussed in previous chapters, the
perspective that “disease is natural” can hinder support for management. If laypersons view the
notion of population balance, especially as it relates to avoiding overabundance, as more
important than wildlife professionals, disconnect and conflict are more likely to occur when, for
example, the public desires management and experts prefer a “hands off” approach. In addition,
a common theme expressed in the focus groups as a factor influencing participants’ evaluations
of wildlife disease management was uncertainty and a desire to know that managers understand
the outcomes of what they plan to do. This emphasis on predicting consequences clearly
presents a challenge because wildlife disease management is often characterized by uncertainty,
both about the disease itself and the best management response. When a human-wildlife conflict
is characterized by high uncertainty and high stakeholder concern about uncertainty, how
wildlife disease managers address the risks and uncertainty surrounding the issue is critical. It is
also particularly important to include stakeholders in these management decisions because
failure to involve stakeholders or to effectively communicate uncertainty can backfire and
negatively affect stakeholder trust in management authorities (Renn 2008). Generally,
individuals with greater trust in the management authority responsible for a particular hazard
judge risks associated with that hazard to be lower (Siegrist and Cvetkovich 2000, Vaske et al.
2004). Given the close connection of disease management to so precious, and tenuous, a thing as

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health—be it human, wildlife, domestic animal, or ecosystem health—the importance of
maintaining trust can hardly be overstated.
My focus group research indicated that stakeholders are not only concerned about risks
from the disease itself but also risks from management. The weighting of these different risks by
experts and laypersons is a crucial aspect of conflict over wildlife disease management, a lesson
that has been learned from management of bovine spongiform encephalopathy and foot and
mouth disease in the United Kingdom where many stakeholders emphasized animal welfare
concerns while experts emphasized eradication (Wilkinson et al. 2010). Similarly, with respect
to CWD management in Wisconsin experts were focused on deer health and eradicating the
disease whereas landowners were concerned about issues such as trespassing, hunting practices,
and deer density (Heberlein 2004).
A recurring theme throughout my dissertation has been the importance of developing
communication messages that explain risk and associated management not from a technical,
expert-centered perspective, but in a way that is relevant to stakeholder values, beliefs, and
attitudes. Such an approach does not seek to encourage irrationality but rather recognizes that
experts and laypersons have different frames of reference for evaluating a given risk (Morgan et
al. 2002) and that these different perspectives will often lead to different opinions on the
acceptability of risk and associated management interventions. As noted by Blaine and Powell
(2001: 179), “the challenge is to incorporate public perceptions...without abdicating the
leadership role of [biological] science.” Wildlife disease management efforts will be more likely
to succeed if the public understands why and how management will be conducted (Wobeser
2007). Yet without the recognition that stakeholders will not necessarily agree with managers’

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assessments of the need for management, regardless of how plainly and clearly that message is
communicated, truly effective communication will be hindered.
Communication can, at best, alter old beliefs or change new ones and, therefore, effective
communication strategies will focus on appropriate beliefs that influence attitudes (Fishbein and
Yzer 2003). With this in mind, my research supports communication messages that address
expected outcomes of wildlife disease management—individuals were more likely to support
management when they believed important positive outcomes were likely to occur. At the same
time, individuals who believed more strongly that important negative outcomes were likely to
occur were more supportive of “hands off” strategies such as letting the disease run its course or
monitoring the disease, so it is important to also understand and address concerns about negative
effects of management. A model based in health theory which included the variables of
agreement with positive outcomes, perceived severity of disease risks, concern for health, and
trust in wildlife management agencies was tested and found to be a good fit for understanding
agreement with wildlife disease management. The model indicates that influences on attitudes
toward management include cognitive factors related to expected outcomes of management, the
importance of protecting health, seriousness of the disease, and trust in wildlife management
agency decisions. These beliefs can be addressed in wildlife disease communication messages by
providing evidence of the benefits of wildlife disease management, emphasizing health
implications of the disease, educating stakeholders about disease risks, and engaging in trustbuilding planning processes.
Risk perception, as measured by the constructs of severity, susceptibility, and dread
related to three zoonotic diseases (rabies, plague, West Nile virus), was found in this study to
influence attitudes toward wildlife disease management. Stronger risk perception was associated

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with less support for letting the disease run its course and greater support for other management
strategies, including lethal and non-lethal approaches. Factors that most strongly influenced risk
perception were gender and education, with women and those with less formal education having
greater risk perceptions; prior exposure to the disease, likely due to increased perception of
susceptibility to disease; and concern for the effects of zoonotic disease on health. Stronger
beliefs that environmental problems generally and disease specifically are amplified by human
actions were associated with greater risk perception. Although managers can attempt to alter
stakeholder risk perceptions through communication efforts, for example by highlighting
efficacy messages (Decker et al. 2012) and contextualizing risk probabilities, risk perception has
an affective, often fear-based, component that may prove more difficult to influence (Gray and
Ropeik 2002). Additionally, there are several factors that influence risk perception that are
outside the control of wildlife managers, including gender, age, worldview, and even media
influence (Gore et al. 2005). Of all the factors that influence stakeholder agreement with wildlife
disease management, risk perception requires particular caution. Although stronger risk
perceptions tend to encourage greater agreement with wildlife disease management, overstating
disease risks could damage stakeholder trust in agencies and could lead to public backlash
against wildlife that are viewed as vectors of disease (Cooney and Holsman 2010, Decker et al.
2011).
A factor that makes wildlife disease management unique, particularly when the relevant
disease is zoonotic, is the complex interplay of risks and benefits. Evaluation of disease
management outcomes by stakeholders is not simple when at times the main goal may be to
protect human health, the agricultural economy, or wildlife health. There is likely to be an
ambiguous effect of pro-wildlife value orientations and attitudes on agreement with wildlife

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disease management because of these varying goals, particularly in situations where management
involves lethal methods such as culling. For example, although an individual may support the
general notion of improving wildlife health, they may disagree with specific methods or
prioritize certain species over others. Consequently, situational specifics are likely to have a
stronger influence on decisions about the acceptability of wildlife disease management than on
issues that are perceived to be more black and white by many stakeholder groups (Decker et al.
2006). Because of these situational nuances, pre-existing attitudes may also be less likely to bias
information processing related to agreement with disease management, making the potential
greater for framing by others, such as via communication messages designed by wildlife
professionals, to influence judgments (Wilson and Bruskotter 2009).
The future holds promising opportunities to communicate and interact with stakeholders
about wildlife health, zoonotic disease, and disease management. The findings of my research
are, I believe, encouraging. Survey respondents were equally concerned about protecting the
health of humans and the ecosystem, thereby demonstrating a broad perspective of health, and
they also showed a high degree of concern for the effects of zoonotic disease on multiple
categories of health as well as signs of support for a one health-focused approach to wildlife
disease management. If wildlife managers continue to build off these positive indicators by
proactively communicating about disease issues and ensuring the inclusion and consideration of
stakeholder understandings of all the significant risks relevant to a management scenario, they
should find themselves in a better position to make decisions that promote the health of our
wildlife resources.

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LITERATURE CITED

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assessment: a case study of the UK bovine spongiform encephalopathy crisis. Public
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Blaine, K. and D. Powell. 2001. Communication about food-related risks. AgBioForum 4: 179185.
Bosch, O.J.H., A.H. Ross, and R.J.S. Beeton. 2003. Integrating science and management through
collaborative learning and better information management. Systems Research and
Behavioral Science 20: 107-118.
Conover, M. 2002. Resolving human-wildlife conflicts: the science of wildlife damage
management. CRC Press, Boca Raton, FL.
Cooney, E.E. and R.H. Holsman. 2010. Influences on hunter support for deer herd reduction as a
chronic wasting disease strategy. Human Dimensions of Wildlife 15: 194 – 207.
Decker, D.J. and L.C. Chase. 1997. Human dimensions of living with wildlife: a management
challenge for the 21st century. Wildlife Society Bulletin 25(4): 788-795.
Decker, D.J., W.F. Siemer, D.T.N. Evensen, R.C. Stedman, K.A. McComas, M.A. Wild, K.T.
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communication matters. Human-Wildlife Interactions 6(1): 112-122.
Decker, D.J., W.F. Siemer, M.A. Wild, K.T. Castle, D. Wong, K.M. Leong, and D.T.N. Evensen.
2011. Communicating about zoonotic disease: strategic considerations for wildlife
professionals. Wildlife Society Bulletin 35(2): 112-119.
Decker, D.J., C.A. Jacobson, and T.L. Brown. 2006. Situation-specific “impact dependency” as a
determinant of management acceptability: insights from wolf and grizzly bear
management in Alaska. Wildlife Society Bulletin 34(2): 426-432.
Fishbein, M. and M.C. Yzer. 2003. Using theory to design effective health behavior
interventions. Communication Theory 13: 164-183.
Gray, G.M. and D.P. Ropeik. 2002. Dealing with the dangers of fear: the role of risk
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Heberlein, T.A. 2004. “Fire in the Sistine Chapel”: how Wisconsin responded to chronic wasting
disease. Human Dimensions of Wildlife 9: 165-179.
Henke, S.E., A.M. Fedynich, & T.A. Campbell. 2007. Wildlife disease management: an
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public involvement in local water management decisions. Risk Analysis 19(3): 497-510.
Morgan, M. G., B. Fischhoff, A. Bostrom, and C.J. Atman. 2002. Risk communication: a mental
models approach. Cambridge University Press, Cambridge, UK.
O’Brien, D.J., S.M. Schmitt, S.D. Fitzgerald, D.E. Berry, and G.J. Hickling. 2006. Managing the
wildlife reservoir of Mycobacterium bovis: the Michigan, USA, experience. Veterinary
Microbiology 112: 313-323.
Patterson, M.E. and D.R. Williams. 1998. Paradigms and problems: the practice of social science
in natural resource management. Society and Natural Resources 11: 279-295.
Renn, O. 2008. Risk communication: insights and requirements for designing successful
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area: disease management at the wildlife-livestock interface. Human-Wildlife
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Siegcrist, M. and G. Cvetkovich. 2000. Perception of hazards: the role of social trust and
knowledge. Risk Analysis 20(5): 713-719.
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Vaske, J. J., N. R. Timmons, J. Beaman, and J. Petchenik. 2004. Chronic wasting disease in
Wisconsin: hunter behavior, perceived risk, and agency trust. Human Dimensions of
Wildlife 9: 193–209.
Wilkinson, K., P. Lowe, and A. Donaldson. 2010. Beyond policy networks: policy framing and
the politics of expertise in the 2001 foot and mouth disease crisis. Public Administration
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Wobeser, G.A. 2007. Disease in wild animals: investigation and management, 2nd edition.
Springer, New York, New York.

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APPENDICES

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APPENDIX A

METHODOLOGICAL CONSIDERATIONS: THE DELPHI METHOD, FOCUS
GROUPS, AND SURVEYS
The Delphi method
The Delphi method is a structured communication process using participants chosen for
their expertise in the subject matter (Dalkey and Helmer 1963, Linstone and Turoff 1975, Moore
1987). The Delphi method is an iterative process that entails anonymous feedback of individual
contributions, assessment and sharing of collective judgments, and opportunities for individuals
to assess differing viewpoints and revise their own views (Linstone and Turoff 1975). It can be
conducted by email and thus works well for geographically distant participants. Originally, the
Delphi method was used for developing group consensus on a topic but it can also be used to
determine alternatives, explore underlying assumptions, reveal group values, and aid in
concept/framework development (Turoff 1970, Moore 1987, Okoli and Pawlowski 2004). The
organization for this review of the Delphi method entails brief overviews of several Delphirelated papers.
The Delphi technique provides a structured process for collecting and examining group
agreement on a topic (Becker and Roberts 2009). It can be used without face-to-face contact,
thus facilitating a wider group of participants. It is a structured process in which panel members
individually respond to statements and a system of repeated rounds providing feedback of
information and iteration is used. The Delphi technique requires that due care be taken in the
choice of expert participants, development of statements, methods of analysis, and determination
of consensus to avoid bias and misrepresentation. Participants in a Delphi exercise should have

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the capacity and willingness to contribute to the exploration, time available, access by e-mail,
and ability to communicate in English (Becker and Roberts 2009).
The Delphi technique is a systematic method for reaching consensus among experts in
which absolute, quantitative answers are either unknown or unknowable (Clark et al. 2006). It is
an iterative process characterized by anonymity among the participating experts, controlled
feedback via the principal investigator, and a statistical estimator of group opinion. Dalkey
(1969) showed that the anonymous controlled-feedback process made group estimates more
accurate than estimates resulting from face-to-face discussions. Applied to complex issues, it
also has the advantage of allowing participants time to consider the questions (Clark et al. 2006).
The Delphi method operates on the principle that several heads are better than one in
making subjective conjectures about the future, and that experts will make conjectures based
upon rational judgment and shared information rather than merely guessing (Weaver 1971). It is
assumed that experts are experts because they are objective, take into account new information,
and construct logically sound deductions about the future based upon a thorough and disciplined
understanding of particular phenomena (Weaver 1971). In order to accomplish structured
communication, the Delphi method provides: feedback of individual contributions of
information and knowledge; assessment of the group judgment or view; opportunity for
individuals to revise views; opportunity to react to and assess differing viewpoints; and
anonymity for individual responses (Linstone and Turoff 1975). Delphi panelists are chosen for
their expertise in the subject matter rather than through a random process.
The objective of the Delphi method is to achieve a reliable consensus of expert opinion
(Dalkey and Helmer 1963). It seeks to accomplish this by a series of intensive questionnaires
interspersed with controlled opinion feedback. The questions, which are all centered around

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some central problem, are designed to bring out the respondent’s reasoning that went into his
reply to the primary question and the factors he considers relevant to the problem. The Delphi
method is considered to be very conducive to development of preliminary insights on the subject
even though the opinion consensus may not have high reliability (Dalkey and Helmer 1963).
One of the Delphi method’s principal uses has been to make future projections and
forecasts. However, it can also be used to identify goals and objectives, array possible
alternatives, establish priorities, reveal group values, and gather information. A Delphi exercise
is useful when: a problem does not lend itself to precise analytical techniques but can benefit
from subjective judgments on a collective basis; more individuals are needed than can effectively
interact in a face-to-face exchange; time and cost make frequent group meetings infeasible
(Linstone and Turoff 1975).
In a Delphi exercise, an expert panel of ten is probably ideal, but more than ten may be
used (Crance 1987). Overrepresentation by stakeholders or individuals from a single agency,
interest group, or geographical area should be avoided where possible. The number of rounds
required to complete an exercise can be kept to a minimum by providing the panelists clear
objectives, definitions, guidelines, and instructions at the beginning of round 1. The quality of
Delphi responses is very much influenced by the interest and commitment of the participants
(Crance 1987). The questionnaire is the essential tool of the Delphi method. The assumptions of
the Delphi method are that respondents are selected because they are experts on the subject and
the purpose is to pool judgments and/or give advice, not to generalize about a population.
Nominal groups and ideawriting (and presumably focus groups) are useful techniques for frontending the Delphi (Moore 1987).

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A Policy Delphi is a variant of the Delphi method that can be used to analyze lines of
divergence within the policy community (Buck 1993). The Policy Delphi was introduced in
1970 as a means of defining and differentiating the viewpoints of advocate groups rather than
establishing the consensus of unbiased experts; it is used as a means of analysis instead of a tool
for decision making. The Policy Delphi is a reiterative statistical technique used to discern
differences in perspective among and within groups responsible for formulating policy. Each
group in the policy community has a different operational perspective and hence a potentially
different perception of the overall goals of the policy process. The Delphi can answer questions
such as where and to what extent do differences exist on the relative importance of policy goals?
Possible objectives of a Policy Delphi include: to explore or expose underlying
assumptions or information leading to differing judgments; to correlate informed judgments on a
topic spanning a wide range of disciplines; to educate the respondent group as to the diverse and
interrelated aspects of the topic. Turoff (1970: 151) stated that, “A policy question is one for
which there are no experts, only advocates and referees. An expert or analyst may contribute a
quantifiable or analytical estimation of some effect resulting from a particular resolution of a
policy issue, but it is unlikely that a clear-cut (to all concerned) resolution of a policy issue will
result from such an analysis; the issue then ceases to be one of policy.” A Policy Delphi deals
largely with statements, arguments, comments, and discussion. To establish some means of
evaluating the expressions of the respondents group, rating scales must be established for such
items as the relative importance, desirability, confidence, and feasibility of various policies and
issues (Turoff 1970).
The Delphi method has traditionally been used to generate a group opinion or statistical
consensus from a panel of experts on technical or prognostic issues. An underlying assumption

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of the Delphi technique is that the statistical information provided to each panelist in successive
rounds brings about a reconsideration of previous estimates and thereby generates a higher
degree of consensus. For example, the Policy Delphi method has been used to test for
differences and consistency in the perception of legislative goals and objectives among and
within the various participant groups who create and implement policy (Buck 1993). The
following Delphi features were used in that particular study: anonymity, controlled feedback, and
statistical measurement of agreement and dissent. The criteria used to decide when to terminate
the Delphi process were stability and convergence. Two panels of participants were established,
the first group had 20 members and approximated an ‘expert panel’ in the classical Delphi sense.
The second panel, a ‘policy panel,’ consisted of position advocates in the implementation
process. This policy panel was given the task of assigning relative weights to each of the goals
and objectives developed by the expert panel (Buck 1993).
The two main applications of the Delphi method have been forecasting and issue
identification/prioritization and concept/framework development (Okoli and Pawlowski 2004).
The literature recommends ten to 18 experts on a Delphi panel. The Delphi method can be used
by researchers in a number of ways related directly to theory building. It can help them to
identify the variables of interest and generate proposition and it can help with definition of
constructs and creation of a common language for discourse (Okoli and Pawlowski 2004). One
study in particular selected the Delphi method for the following reasons: the topic was complex
and the authors thought a Delphi study would answer their questions more appropriately; a panel
study does not require experts to meet physically; Delphi panel size requirements are modest;
and the Delphi study is flexible in its design and amenable to follow up interviews which allows
collection of richer data leading to deeper understanding. The Delphi study administration

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process includes three main phases, each of which involves one or more questionnaires (Okoli
and Pawlowski 2004):
Phase 1: Brainstorming
Within this phase, Questionnaire 1 entails open-ended solicitation of ideas and Questionnaire 2
entails validation of categorized list of factors, asks experts to verify that their responses have
been correctly interpreted, and refines the categorizations of the factors.
Phase 2: Narrowing down factors
Within this phase, Questionnaire 3 entails sending the lists to each expert and asking them to
rank factors according to their importance. Factors selected by over 50% of experts are retained
in the next list iteration.
Phase 3: Ranking relevant factors
In this phase, Questionnaire 4 strives to reach a consensus in the ranking of the relevant factors
which entails each panelist ranking factors on the pared-down list (they are also asked to explain
or justify their rankings). The mean rank for each item is calculated and consensus is assessed
using the statistic Kendall’s W.
Focus groups
A focus group is a special type of group interview that is structured to gather detailed
opinions and knowledge about a particular topic from selected participants (Bader and Rossi
2002). The focus group process provides a structured, organized method to collect input from
groups (Bader and Rossi 2002). “The purpose of a focus group is to listen and gather
information…a focus group study is a carefully planned series of discussions designed to obtain
perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger

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and Casey 2000). The intent of the focus group is to promote self-disclosure among participants,
to find out what they really think and feel (Krueger and Casey 2000).
Focus groups are not random discussions among a group of individuals who are brought
together haphazardly. Rather, they are group discussions among carefully selected individuals
guided by a skilled moderator who follows a well-constructed interview guide (Stewart and
Shamdasani 1990). Five particular features of focus groups are: (1) people who (2) possess
certain characteristics and (3) provide qualitative data (4) in a focused discussion (5) to help
understand the topic of interest (Krueger and Casey 2000).
Focus groups can be used when: you are looking for the range of ideas or feelings that
people have about an issue/topic; you are trying to understand differences in perspectives
between groups or categories of people; the purpose is to uncover factors that influence opinions,
behavior, or motivation; and/or you want ideas to emerge from the group (Krueger and Casey
2000). Advantages of using focus groups include: focus group data reveals more information
than do surveys, they provide detailed qualitative data that allows for greater depth of
understanding, and they allow participants to express their opinions and concerns without much
preparation or effort (Bader and Rossi 2002).
One of the key disadvantages of using focus groups is transcription management, though
Scott et al. (2009) noted that the use of professional court reporters for conducting “real-time
transcription” is a reliable and valid way of transcribing focus group interviews. Advantages of
using court reporters are increased accuracy, timely receipt of transcripts, less distraction for
focus group facilitators, time saved reviewing transcripts, and convenience.
The most important step in planning a focus group is agreeing on the purpose for the
session. Six questions guide development of the purpose statement: 1) What is the reason behind

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the decision to use focus groups?; 2) What is the general issue?; 3) What are the specific issues?;
4) What goals do you hope to meet using focus groups?; 5) What purpose will the information
serve?; and 6) From whom do you want to collect information? (Bader and Rossi 2002, Krueger
and Casey 2000).
The interview guide sets the agenda for a focus group discussion. When designing the
interview guide it is important to remember that its purpose is to provide direction for the group
discussion rather than acting as “a verbal version of a survey questionnaire” (Stewart and
Shamdasani 1990). A focus group should not last more than 2 hours, although some topics
create “cognitive fatigue” more quickly than others. Different groups will spend very different
amounts of time on individual topics but, generally, the more complex a topic, the more
emotionally involving the topic, or the greater the difference in views about the topic, the fewer
the topics and specific questions that can be covered. In practice, most interview guides consist
of less than a dozen questions (Stewart and Shamdasani 1990).
It isn’t always the case that less structure is better in focus group interviews. Some
people need help in articulating a response; providing a key word or cue may help respondents to
formulate answers. On the other hand, the interviewer should not “lead” the respondent to
answer a certain way. Because the objective of a focus group is to stimulate discussion,
questions that call for a direct, one- or two-word response should be avoided. Questions that
include words such as how, why, under what conditions and similar probes are useful. Questions
should be phrased simply and in language that respondents understand. Pretesting of the
interview provides an opportunity to determine whether the wording of questions is appropriate,
to determine whether the questions elicit discussion, and to identify questions that are not
understood easily (Stewart and Shamdasani 1990).

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Two critical elements in successful focus group research are the recruitment of
participants and the design of the interview guide. Only by careful definition of the research
question can the type of group required for a focus group be identified. Once the research
question has been articulated clearly, it is possible to move on to the recruitment of participants
and the design of an interview guide (Stewart and Shamdasani 1990). The intent of virtually all
focus groups is to draw some conclusions about a population of interest, so the group must
consist of representative members of the larger population. When pre-existing lists are not
available, the only alternative is to contact individuals by telephone, by mail, or by intercepting
them in public places. The first step in recruiting a focus group is an initial contact (by mail,
telephone, or in person) to invite participation (Stewart and Shamdasani 1990).
The purpose drives the study (Krueger and Casey 2000). When deciding who to invite to
participate in a focus group, think about the purpose of the study and what kind of people can
give you the information you’re looking for. Is the target audience distinctive, identifiable, and
reasonable to locate? The goal with focus groups is to have a homogeneous group. A focus
group should have homogeneity but with sufficient variation among participants to allow for
contrasting opinions (Krueger and Casey 2000).
The optimal size for a focus group is ten to 12 participants (Bader and Rossi 2002).
However, Krueger and Casey (2000) recommend that for non-marketing research involving
complex topics, the ideal size is 6-8 participants (they advise never to have more than ten people
in a focus group). Mini-focus groups with four to six people are even becoming popular because
they are easier to recruit and host and participants feel more comfortable (Krueger and Casey
2000).

117

The rule of thumb is to plan three to four focus group sessions with any one participant
group. Once these have been conducted, determine if you’ve reached “theoretical saturation”
(the point at which you’ve heard the range of ideas and aren’t getting new information). Single
category design focus groups only interview one participant group. Multiple category design
focus groups interview multiple groups, either sequentially or simultaneously; the researcher can
make comparisons from one group to another within a category and from one category to another
category. A double layer design focus group involves multiple “layers”; one layer could be
geographic region and the second layer is the different audiences (e.g., experts and the
public)(Krueger and Casey 2000).
An effective facilitator should be energetic, personable, organized, a good listener,
experienced, and knowledgeable of the issue or topic. It is best to use the same facilitator for all
focus group sessions (Bader and Rossi 2002). Participants are the central players in the focus
group session because their input about needs, values, and perceptions delivers the end results.
General rules for participant selection are: select only participants who are affected by the
issue/topic; do not include volunteers; for each session, participants should be from the same
organizational level (i.e., no mixing of staffers and bureau chiefs); select a representative sample,
though it need not be random (Bader and Rossi 2002).
Surveys
A survey involves the gathering of data by use of some form of questionnaire
administered to a sample of individuals (Vaske 2008). Survey research enables generalization
from a sample to a population to allow inference about certain characteristics, attitudes, and/or
behaviors of the population (Babbie 1990, Creswell 2009). The goal of conducting a survey is
commonly “explanatory” in nature, i.e., to identify possible causal relationships and improve

118

understanding of those relationships (Vaske 2008). Advantages of survey research include
usefulness for describing characteristics of larger populations, ability to compare among groups
because of standardization of questions, ability to gather data from large samples in a short
amount of time, and ability to ask numerous questions in one survey (Vaske 2008).
Disadvantages of survey research are that it requires all questions to be understandable to all
potential respondents, questionnaires are not very flexible, survey questions can come across as
artificial to respondents, and survey questions cannot capture the depth and complexity that
qualitative data do (Vaske 2008).
The survey process involves more or less 6 steps: determining the research objectives,
developing the questionnaire, designing the sampling approach, developing plans for data
collection and processing, collecting and processing the data, and analyzing data (Biemer and
Lyberg 2003). Vaske (2008) reduced the survey process to 3 major tasks: specification of
questions or hypotheses, determination of survey methodology, and data analysis. The ideal
survey question allows each potential respondent to interpret it in the same way, to respond
accurately, and to be willing to answer (Dillman 2000).
Self-administered mail surveys require an address list of a sample from a population
(Vaske 2008) so that the survey can be mailed to and potentially completed by each sample
member. Data quality is closely related to the quality of the questionnaire design (Biemer and
Lyberg 2003). Mail surveys are quicker and cheaper than interviews, and less prone to social
desirability bias, but they are prone to low response rates (Vaske 2008). The Dillman total
design method (Dillman 2000) provides a strategy for collecting mail survey data using a step by
step process and social exchange theory (a theory of human behavior which holds that
individuals’ actions are motivated by the return their actions are expected to bring from others)

119

to increase response rates. Its goal is to reduce overall survey error (Dillman 2000). The 5
elements for achieving high response rates, according to Dillman (2000), are: a respondentfriendly questionnaire, multiple contacts (mailings) by first class mail, return envelopes with real
first-class stamps, personalization of correspondence, and financial incentives.
The use of random sampling to conduct surveys has several advantages: it produces
samples that are representative of the entire population; it provides a theoretical basis for
statistical inference; it is fairly easy to apply; it entails reproducible methods; and it provides
measures of precision of estimates that can be predetermined accurately by the researcher based
on the sample size (Biemer and Lyberg 2003). In a simple random sample, every unit within the
sampling frame has an equal probability of selection. When conducting statistical inference from
a sample, although the closeness of an estimate to the actual population parameter is unknown, a
confidence interval can be computed to give an idea of how close the estimate is to the
parameter. A confidence interval is associated with a particular parameter (Biemer and Lyberg
2003). Stratification is the partitioning of the frame population into mutually exclusive groups
which are internally homogeneous with respect to characteristics measured in the survey.
Stratified sampling can produce estimates of the entire population that are more precise than
unstratified sampling (Biemer and Lyberg 2003).
Sources of error in sample survey research include coverage error, measurement error,
non-response error, and sampling error. Coverage error occurs when the list or frame from
which the sample was drawn fails to contain all of the subjects in the population of interest.
Measurement error relates to the instrument used to collect data and is reduced by using items
that are valid, reliable, and unambiguous to the research subjects. Non-response error occurs
when respondents and non-respondents differ in key characteristics such that the sample does not

120

accurately reflect the study population. Sampling error results from measuring a characteristic in
some but not all of the people in the population of interest and can be reduced through use of
larger samples but cannot be eliminated short of using a census (Dillman 2000, Lindner et al.
2001).
Addressing low survey response rates
Important properties of empirical measurement include external validity, internal validity,
measurement reliability and validity, and research reliability (Vaske 2008). External validity is
related to generalizability and addresses the question to what can the observed effects be
generalized? Internal validity relates to the extent to which conclusions are causal or
correlational and depends on the strength of the research design (e.g., experimental designs have
higher internal validity). Measurement reliability refers to the degree to which respondents report
the same or very similar answers on the survey. Measurement validity deals with the accuracy of
generalizations and is concerned with whether the variables in the survey actually measure the
concepts they were supposed to measure (includes content, criterion, and construct validity).
Research reliability refers to the extent to which a survey yields the same results on repeated
trials (i.e., repeatability of findings).
Although some sources denote 50% as an acceptable response rate, there really is no
solid threshold response rate above which one doesn’t have to worry about non-response bias.
Babbie (2003) advised that a demonstrated lack of response bias is more important than a high
response rate. To ensure generalizability of research findings, “the researcher must satisfactorily
answer the question of whether the results of the survey would have been the same if a 100%
response rate had been achieved” (Lindner et al. 2001: 50). Concerns associated with low
response rate include chiefly non-response bias or error and low statistical power which is related

121

to inaccurate effect size estimation. Non-response bias occurs when those who responded to the
questionnaire are different in some significant/systematic way from those who did not respond,
thus limiting the extent to which survey results are representative. This kind of bias increases the
risk of error in generalizing from the sample to the study population and threatens the external
validity of survey findings.
Low statistical power and inaccurate effect size estimation make it difficult to detect
effects, increase the risk of accepting a false null hypothesis, and threaten statistical conclusion
validity by reducing the “power to detect relationships that exist and determine with precision the
magnitude of these relationships” (Sivo et al. 2006: 355). Effect size is defined as “the strength
of the relationship between the independent variable and the dependent variable” (Vaske 2008:
106). It provides an indicator of the strength of a statistically significant relationship and helps
differentiate between statistical and practical significance; for example, when statistical
significance is produced from very large sample sizes (Vaske 2008).
Methods for estimating non-response bias include comparison with known values for the
population, subjective estimates, and extrapolation (Armstrong and Overton 1977). Post hoc
methods to examine non-response bias can entail: comparison of demographic and
socioeconomic characteristics of respondents to census data and then weighting accordingly
(Vaske 2008); comparison of early and late responders on key variables since studies have
shown that non-respondents tend to be similar to late respondents (Radhakrishna and Doamekpor
2008); and follow up telephone surveys. It is important that the subsample is also random and
has a relatively large number of responses to avoid non-response error in the non-response
survey itself (Sivo et al. 2006).

122

LITERATURE CITED

123

Literature Cited
Armstrong, J.S. and T.S. Overton. 1977. Estimating nonresponse bias in mail surveys. Journal of
Marketing Research 14: 396-402.
Bader, G.E. and C.A. Rossi. 2002. Focus groups: a step-by-step guide, 3rd edition. The Bader
Group.
Becker, G.E. and T. Roberts. 2009. Do we agree? Using a Delphi technique to develop
consensus on skills of hand expression. Journal of Human Lactation 25(2): 220-225.
Bickman, L. and D.J. Rog, eds. 1998. Handbook of applied social science research methods.
Sage Press, Thousand Oaks, California, USA.
Biemer, P.P. and L.E. Lyberg. 2003. Introduction to survey quality. Wiley & Sons, Inc.
Hoboken, New Jersey, USA.
Buck, A.J., M. Gross, S. Hakim, and J. Weinblatt. 1993. Using the Delphi process to analyze
social policy implementation: a post hoc case from vocational rehabilitation. Policy
Sciences 26: 271-288.
Clark, K.E., J.E. Applegate, L.J. Niles, and D.S. Dobkin. 2006. An objective means of species
status assessment: adapting the Delphi technique. Wildlife Society Bulletin 34(2): 419425.
Crance, J.H. 1987. Guidelines for using the Delphi technique to develop habitat suitability index
curves. United States Fish and Wildlfie Service Biological Report 82(10.134).
Dalkey, N. and O. Helmer. 1963. An experimental application of the Delphi method to the use of
experts. Management Science 9(3): 458-467.
Dillman, D.A. 2000. Mail and internet surveys: the tailored design method, 2nd edition. John
Wiley & Sons, Inc. New York, New York, USA.
Krueger, R.A. and M.A. Casey. 2000. Focus groups: a practical guide for applied research. 3rd
edition. Sage Publications, Inc. Thousand Oaks, California, USA.
Lindner, J.R., T.H. Murphy, and G.E. Briers. 2001. Handling nonresponse in social science
research. Journal of Agricultural Education 42(4): 43-53.
Moore, C.M. 1987. Group techniques for idea building. Applied Social Research Methods
Series, Volume 9. Sage Publications, Newbury Park, California, USA.
Morgan, D.L. (ed.). 1993. Successful focus groups: advancing the state of the art. Sage
Publications, Newbury Park, California, USA.

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Okoli, C. and S.D. Pawlowski. 2004. The Delphi method as a research tool: an example, design
considerations and applications. Information & Management 42: 15-29.
Radhakrishna, R. and P. Doamekpor. 2008. Strategies for generalizing findings in survey
research. Journal of Extension 46(2): 2TOT1.
Scott, S.D., H. Sharpe, K. O’Leary, U. Dehaeck, K. Hindmarsh, J.G. Moore, and M.H. Osmond.
2009. Court reporters: a viable solution for the challenges of focus group data collection?
Qualitative Health Research 19(1): 140-146.
Sivo, S.A., C. Saunders, Q. Chang, and J.J. Jiang. 2006. How low should you go? Low response
rates and the validity of inference in IS questionnaire research. Journal of the Association
for Information Systems 7(6): 351-414.
Stewart, D.W. and P.N. Shamdasani. 1998. Focus groups: theory and practice. Applied Social
Research Methods Series, Volume 20. Sage Publications, Newbury Park, California,
USA.
Turoff, M. 1970. The design of a policy Delphi. Technological Forecasting and Social Change
2: 149-171.
Weaver, W.T. 1971. The Delphi forecasting method. Phi Delta Kappan.
Vaske, J.J. 2008. Survey research and analysis: applications in parks, recreation and human
dimensions. Venture Publishing, Inc., State College, Pennsylvania, USA.

125

APPENDIX B

DELPHI QUESTIONS
1. Please describe what wildlife health means to you; i.e., how do you define it?
2A. In your opinion, how are the concepts of wildlife health and wildlife disease different? How
are they the same?
2B. Does a focus or emphasis on one or the other (health or disease) affect or influence the
achievement of healthy wildlife populations?
3. In your opinion, what are the most important threats to the health of wildlife populations in
North America? (Please list at least 5 specific threats.) Why do you think they’re important?
4A. What do you think are the most significant risks posed by wildlife disease?
4B. Do you think there are cases where it’s appropriate not to manage wildlife disease?

126

APPENDIX C

FOCUS GROUP INTERVIEW GUIDE
1. What comes to mind when you hear the term “wildlife health”?
2. What are some ways of measuring wildlife health?
3. What comes to mind when you hear the term “wildlife disease”?
4. What can you tell me about wildlife disease issues in your state?
5. What concerns do you have about zoonotic diseases?
6. Overall, are you most concerned about zoonotic diseases having impacts to humans, domestic
animals, or wildlife populations?
7. Do you consider wildlife disease issues today to be largely natural or are they a “man made”
problem?
8. When do you think humans (wildlife managers) should intervene in wildlife disease
situations?
9. Are there situations where managers should not intervene?
10. What kind of information would be important to you in making a decision to support or
oppose wildlife disease management actions?

127

APPENDIX D

Human, Animal, and Wildlife Health
A Survey of Your Opinions
A study conducted by Michigan State University
Department of Fisheries & Wildlife
13 Natural Resources Building
East Lansing, MI 48824

Hello, my name is Dr. Shawn Riley and I am asking for your participation in an important research
project focusing on public views related to health and the management of disease. The attached
questionnaire is part of a national study to help wildlife managers better understand your views
and opinions about health topics and the management of disease in wildlife.

Your participation in this survey is desired even if you have no experience with or special
knowledge about wildlife or wildlife disease issues. Although not something most of us think
about every day, wildlife diseases (such as rabies, West Nile virus, avian influenza, plague,
brucellosis, and chronic wasting disease) are important issues to agencies that manage America’s
natural resources. Your responses will help answer important questions about how the American
public thinks about health issues and management of diseases that occur in wildlife.

Your participation in this study is completely voluntary. If you are willing to participate,
please complete this brief questionnaire at your earliest convenience and return it in the
postage-paid envelope that has been provided. The survey will only take you about
15 minutes to complete. If there are multiple adults (age 18 or older) living in your
household, please have the adult who will next celebrate a birthday complete the
questionnaire.
By completing and returning the questionnaire, you indicate your voluntary agreement to
participate in the study. Your responses are confidential and your privacy will be protected
to the maximum extent allowable by law. The questionnaire has an identification number
only so that I may remove your name from the mailing list when your questionnaire is
returned.
If you have any questions or concerns, you are welcome to contact me at MSU Dept. of
Fisheries & Wildlife, 13 Natural Resources Bldg, East Lansing, MI 48824; by email at
rileysh2@msu.edu; or by phone at (517) 432-4943. If you have any questions or concerns
regarding your rights as a study participant, you may contact (anonymously, if you wish)
the chair of the University Committee on Research Involving Human Subjects by phone at
(517) 432-4503; by email at ucrihs@msu.edu; or by regular mail at 202 Olds Hall, East
Lansing, MI 48824.
Thank you in advance for your time and consideration!

128

Please completely and carefully fill in each chosen circle with a #2 pencil or blue/black
pen.
1. Listed below are some different ways that you might interact with or enjoy wildlife. (Wildlife includes
wild birds, mammals, reptiles, and amphibians.) Fill in one circle per line.
How frequently do you participate in the following wildliferelated activities?

Never

Rarely

1
1
1
1
1
1

2
2
2
2
2
2

a. Reading about wildlife (2.87)
b. Hunting (1.88)
c. Fishing (2.39)
d. Watching/observing wildlife (3.28)
e. Feeding wildlife (2.52)
f. Watching movies or TV shows about wildlife (3.15)

From
time to
time
3
3
3
3
3
3

Often

4
4
4
4
4
4

2. Listed below are some beliefs you might have about the relationship between humans and wildlife. Fill
in one circle per line. SD = strongly disagree, D = agree, N = neither agree nor disagree, A = agree,
SA = strongly agree.
Do you agree or disagree that…?
a. Humans should manage wildlife to benefit people. (3.16)
b. Humans do more harm than good to wildlife. (3.27)
c. Humans should learn to better coexist with wildlife. (4.15)
d. Hunting wildlife is an activity that I support. (3.45)
e. It is important for humans to manage wildlife populations. (3.81)
f. The right of wildlife to exist is more important than the right of
humans to use wildlife for their own purposes. (3.29)
g. Human needs should take priority over the needs of wildlife. (2.97)
h. All living things are interconnected. (4.32)

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

3. Listed below are some beliefs about the causes of environmental problems and disease. Fill in one
circle per line. SD = strongly disagree, D = agree, N = neither agree nor disagree, A = agree, SA =
strongly agree.
Do you agree or disagree that…?
a. Most environmental problems are caused by humans interfering
with nature. (1.53)
b. The occurrence of disease (in general) has been made worse by
humans and their activities. (1.26)
c. Disease (in general) is part of the balance of nature. (1.53)
d. The occurrence of disease (in general) will get worse with time.
(.91)

129

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1
1

2
2

3
3

4
4

5
5

4. Listed below are some beliefs concerning wildlife management agencies (e.g., governmental
organizations, such as the Department of Natural Resources, that manage and conserve wildlife
resources). Fill in one circle per line. SD = strongly disagree, D = agree, N = neither agree nor
disagree, A = agree, SA = strongly agree.
Do you agree or disagree that wildlife management agencies…?
a. Make good wildlife management decisions. (3.43)
b. Provide reliable scientific information. (3.63)
c. Make decisions that reflect your values concerning wildlife. (3.27)
d. Are staffed by competent wildlife professionals. (3.48)

SD

D

N

A

SA

1
1
1
1

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

5. Listed below are 4 different categories of health. Fill in one circle per line. 1 = not at all concerned, 3
= somewhat concerned, 5 = extremely concerned.
In general, how concerned are you about protecting the health of…?
1
a. Humans (4.47)
1
b. Domestic animals (4.13)
1
c. Wildlife (4.16)
1
d. The overall ecosystem (4.42)

2
2
2
2

3
3
3
3

4
4
4
4

5
5
5
5

The next set of questions (#6 - 11) relates to a specific disease, RABIES [or] PLAGUE [or] WEST
NILE VIRUS, which can cause illness or death in humans, domestic animals, and wildlife.
[Northwest/southwest = plague; Northeast/national = rabies; Midwest/southeast = West Nile
virus.]
6. Listed below are 5 groups that could potentially be affected by rabies/plague/WNV. Fill in one circle
per line. 1 = not at all concerned, 3 = somewhat concerned, 5 = extremely concerned.
How concerned are you that [rabies/plague/WNV] could affect the health of…?
1
2
a. Humans (3.85)*
1
2
b. Pets (3.74)
1
2
c. Domestic livestock (3.81)
1
2
d. Wildlife (3.81)
1
2
e. The overall ecosystem (3.82)
*Values given reflect average across all 3 diseases.

3
3
3
3
3

4
4
4
4
4

5
5
5
5
5

7. Are you aware of practical, effective measures you can take
to prevent [rabies/plague/WNV]? (.56)

Yes

No

1

0

8. Have you, or do you know someone, who has contracted or
otherwise been affected by [rabies/plague/WNV]? (.11)

Yes

No

1

0

130

9. If the following were to contract [rabies/plague/WNV],
how serious do you think the consequences would be…?
a. You personally (3.44)*
b. People in your community (3.39)
c. Pets (3.22)
d. Domestic livestock (3.34)
e. Wildlife (3.34)
f. The overall ecosystem (3.24)
*Values given reflect average across all 3 diseases.

10. In your opinion, how likely to contract (or catch)
[rabies/plague/WNV] are…?
a. You personally (1.81)*
b. People in your community (1.95)
c. Pets (2.15)
d. Domestic livestock (2.33)
e. Wildlife (2.61)
*Values given reflect average across all 3 diseases.

11. Do you worry about or feel fearful of
[rabies/plague/WNV] affecting…?
a. You personally (1.95)*
b. People in your community (2.00)
c. Pets (2.15)
d. Domestic livestock (2.25)
e. Wildlife (2.34)
f. The overall ecosystem (2.32)
*Values given reflect average across all 3 diseases.

1

Somewhat
serious
2

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

Unlikely

Likely

2

3

Very
likely
4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

No, not
at all

Rarely

From
time to
time

A great
deal

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

Not
serious

Very
unlikely
1

Serious

Very
serious

3

4

The next section (#12-15) relates to your attitudes toward wildlife disease management.
Wildlife disease management includes a variety of actions that natural resource agencies may take
to prevent or control diseases that affect human, domestic animal, and/or wildlife health. Examples
of such diseases include rabies, plague, bovine tuberculosis, brucellosis, some types of influenza,
and West Nile virus.

131

12. Listed below are some benefits that people may associate with wildlife disease management. Fill in
one circle per line. SD = strongly disagree, D = agree, N = neither agree nor disagree, A = agree, SA
= strongly agree.
In general, do you agree or disagree that wildlife disease
management…?
a. Prevents disease from becoming a widespread problem in the area
where you live. (3.80)
b. Protects human health. (3.89)
c. Protects the health of pets. (3.80)
d. Reduces negative impacts on your state’s agricultural
economy.(3.71)
e. Protects the health of threatened or endangered wildlife. (3.82)
f. Protects ecosystem health. (3.76)
g. Protects the health of game (hunted) species. (3.79)

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

13. Fill in one circle per line. 1 = not at all important, 3 = somewhat important, 5 = extremely
important.
How important is each of these management outcomes to you personally…?
1
2
a. Preventing disease from becoming a widespread problem in the
area where you live. (4.32)
1
2
b. Protecting human health. (4.41)
1
2
c. Protecting the health of pets. (4.10)
1
2
d. Reducing negative impacts on your state’s agricultural
economy.(4.13)
1
2
e. Protecting the health of threatened or endangered wildlife. (4.13)
1
2
f. Protecting ecosystem health. (4.22)
1
2
g. Protecting the health of game (hunted) species. (4.02)

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

3

4

5

14. Listed below are some “costs” that people may associate with wildlife disease management. Fill in
one circle per line. SD = strongly disagree, D = agree, N = neither agree nor disagree, A = agree, SA
= strongly agree.
In general, do you agree or disagree that…?
a. Managers take wildlife disease management actions even when
they are not certain about their effectiveness. (3.36)
b. Wildlife disease management results in the killing of wildlife.
(3.25)
c. The financial cost of wildlife disease management is too high.
(2.87)
d. Wildlife disease management interferes with activities you
enjoy.(2.37)
e. Wildlife disease management benefits species that are a
nuisance.(2.88)
f. Wildlife disease management benefits special interest groups.
(3.16)
g. Wildlife disease management is unnecessary because most
diseases will go away on their own. (2.15)

132

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

15. Fill in one circle per line. 1 = not at all important, 3 = somewhat important, 5 = extremely
important.
How important is each of these considerations to you personally…?
a. Ensuring that managers have a good understanding of the
effectiveness of wildlife disease management before they act.
(4.38)
b. Avoiding the killing of wildlife. (3.59)
c. Keeping the financial cost of wildlife disease management low.
(3.55)
d. Being able to continue activities you enjoy. (3.93)
e. Managing disease to avoid benefitting nuisance species. (3.54)
f. Managing disease to avoid benefitting special interest groups.
(3.58)
g. Managing only the most dangerous diseases. (3.32)

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

The next set of questions (#16-20) asks about your agreement/disagreement with various
management options for different goals of wildlife disease management. (The management options
listed are not mutually exclusive.) Fill in one circle per line.
SD = strongly disagree, D = agree, N = neither agree nor disagree, A = agree, SA = strongly agree.
16. When the goal of wildlife disease management is to
protect human health, do you agree or disagree that…?
a. The disease should be left alone and allowed to run its course
(no management should occur). (2.01)
b. Monitoring and surveillance of the disease are acceptable.
(3.83)
c. Public education is acceptable. (4.11)
d. Management of the disease agent with methods that are nonlethal to the animal is acceptable (e.g., vaccination). (4.07)
e. Selective killing of diseased animals is acceptable. (3.91)
f. Population reduction of the wildlife species to reduce disease
spread is acceptable. (3.63)

17. When the goal of wildlife disease management is to
protect the health of pets, do you agree or disagree that…?
a. The disease should be left alone and allowed to run its course
(no management should occur). (2.14)
b. Monitoring and surveillance of the disease are acceptable.
(3.82)
c. Public education is acceptable. (4.09)
d. Management of the disease agent with methods that are nonlethal to the animal is acceptable (e.g., vaccination). (4.07)
e. Selective killing of diseased animals is acceptable. (3.77)
f. Population reduction of the wildlife species to reduce disease
spread is acceptable. (3.55)

133

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

18. When the goal of wildlife disease management is to
protect the health of domestic livestock, do you agree or
disagree that…?
a. The disease should be left alone and allowed to run its course
(no management should occur). (1.97)
b. Monitoring and surveillance of the disease are acceptable.
(3.85)
c. Public education is acceptable. (4.10)
d. Management of the disease agent with methods that are nonlethal to the animal is acceptable (e.g., vaccination). (4.09)
e. Selective killing of diseased animals is acceptable. (3.88)
f. Population reduction of the wildlife species to reduce disease
spread is acceptable. (3.64)

19. When the goal of wildlife disease management is to
protect the health of wildlife, do you agree or disagree that…?
a. The disease should be left alone and allowed to run its course
(no management should occur). (2.27)
b. Monitoring and surveillance of the disease are acceptable.
(3.91)
c. Public education is acceptable. (4.09)
d. Management of the disease agent with methods that are nonlethal to the animal is acceptable (e.g., vaccination). (4.01)
e. Selective killing of diseased animals is acceptable. (3.87)
f. Population reduction of the wildlife species to reduce disease
spread is acceptable. (3.66)

20. When the goal of wildlife disease management is to
protect the health of humans, domestic animals, AND wildlife,
do you agree or disagree that…?
a. The disease should be left alone and allowed to run its course
(no management should occur). (1.97)
b. Monitoring and surveillance of the disease are acceptable.
(3.92)
c. Public education is acceptable. (4.13)
d. Management of the disease agent with methods that are nonlethal to the animal is acceptable (e.g., vaccination). (4.11)
e. Selective killing of diseased animals is acceptable. (3.90)
f. Population reduction of the wildlife species to reduce disease
spread is acceptable. (3.70)

134

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

SD

D

N

A

SA

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

This final set of questions will help us summarize demographic characteristics of survey
participants.
21. Are you male or female?
(Male = 65.1%)

Male

Female

0

1

22. Do you have children
under the age of 18? (Yes =
21.7%)

Yes

No

1

0

23. How old are you?
Please write the number
under “Years” and fill in the
appropriate circles. (58.1
years)

Years
0
1
2
3
4
5
6
7
8
9

0
1
2
3
4
5
6
7
8
9

24. What is your highest level of education? Select only one.
High school or
Trade or
Some college
GED
vocational school
1 (20.9%)

2 (6.5%)

3 (24.3%)

4-year college
degree
4 (25.7%)

Graduate or
professional
degree
5 (22.7%)

25. How would you describe the area where you currently live? Select only one.
Rural

Suburban area on
edge of city

1 (27.7)

2 (20.1)

Small town/city
(population
<50,000)
3 (23.1)

Mid-size city
(population of
50,000 to 250,000)
4 (16.5)

Large city
(population
>250,000)
5 (12.7)

THANK YOU FOR YOUR TIME and PARTICIPATION! Please return the survey in the
postage-paid envelope provided. If you are interested in learning more about wildlife
health issues, please visit: http://www.nwhc.usgs.gov/.

135