WILDLIFE DISEASE AND RISK PERCEPTION

Many wildlife diseases pose risks to human, domestic animal, and wildlife health and have negative economic and ecologic effects (Wobeser, 2007), making wildlife disease management (WDM) amenable to risk management approaches. Technical risk assessments, such as epidemiologic models, contribute to predictions about the likely occurrence of a specific disease (Miller, 2007); however, WDM is influenced by public opinion in addition to biologic and epidemiologic 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 WDM 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 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 WDM, including threats to property rights and changes to the population density of white-tailed deer, Odocoileus virginianus (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 influence relevant beliefs purposefully can be enhanced when disease risks and WDM 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 the public is likely to respond to WDM 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 chronic wasting disease [CWD]), research on WDM-related 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, WDM.

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 an 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 WDM, our objectives were to assess risk perceptions of three zoonotic diseases (rabies, plague, West Nile virus [WNV]), identify relevant sociodemographic and social psychologic factors that influence these risk perceptions, and examine the relationship between risk perception and agreement with WDM practices.

We collected data from a self-administered questionnaire mailed to a random sample of adults residing in the continental United States. We obtained an address sample from Survey Sampling International (Shelton, Connecticut, USA). Addresses on the mailing list were stratified by rural, suburban, and urban classification and by region (Northwest, Southwest, Midwest, Northeast, Southeast). We designed and implemented the questionnaire according to Dillman's (2007) standard four-wave procedure. An initial survey was mailed on 1 April 2011; a postcard reminder was mailed on 15 April 2011; a second survey was mailed on 11 May 2011; and a third and final survey was mailed on 1 June 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 1, A), susceptibility (Table 1, B), and dread (Table 1, C). For each respondent we calculated a risk perception index score (severity+susceptibility+dread/3). We used one of three 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 WNV in the Midwest and Southeast. We also measured risk perception across five 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 exposure to the disease (Table 1, D); respondent's concern about health (Table 1, E, F); and respondent's beliefs about the influence of humans on nature (Table 1, G). We also examined relationships between risk perception and agreement with six WDM practices of varying intensity: letting the disease run its course, monitoring and surveillance, public education, nonlethal 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). Statistical significance was determined a priori to be attained when P<0.05. To check for nonresponse bias in our sample, we conducted phone interviews with 100 randomly selected nonrespondents who were asked a subset of 18 questions from the original questionnaire. We compared respondents and nonrespondents 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).

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 mean age of respondents was 58 yr and 48.3% had at least a 4-yr 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 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 = 0.52) and WNV (2.62; SD = 0.62), which were not statistically different from each other, and least for plague (2.39; SD = 0.53). Respondents' risk perceptions were greatest when evaluating effects of disease on wildlife (2.72; SD = 0.68) and were lowest for effects of disease on humans, including others (2.44; SD = 0.61) and self (2.39; SD = 0.63). 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 nonhuman 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 2). Overall mean risk severity (across diseases and risk targets) was 3.32 (SD = 0.66). A gradient of perceived severity occurred for the three diseases with mean severity greater for rabies than plague (t = 2.01, P = 0.045) and greater for plague than WNV (t = 2.95, P = 0.003).

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 = 0.67). Mean susceptibility was evaluated as being greatest for WNV (t = 3.82, P≤0.001), then rabies, then plague (t = 3.09, P = 0.002). 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 2). Overall mean dread (across diseases and risk targets) was 2.13 (SD = 0.83). Mean dread was greatest for WNV (t = 2.22, P = 0.027) and least for plague (t = 4.58, P≤0.001).

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 = 0.45). Women judged disease risks to be greater than did men (t = 3.40; P = 0.001; Table 3) and respondents with at least a 4-yr college degree expressed lower risk perceptions than those without a 4-yr college degree (t = 6.90; P≤0.001). Respondents who had, 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≤0.001). 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≤0.001) or specific (i.e., the survey question asked about concern for health effects of rabies, plague, or West Nile virus; t = 25.18, P≤0.001).

Disease risk perception increased with age of respondents (t = 3.10; P = 0.002) 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 = 0.02). Risk perception was lower (t = 2.57; P = 0.01) for respondents who had children under the age of 18. We did not detect a difference between hunters and nonhunters in their risk perception index scores. However, when the three risk constructs were examined separately, hunters reported being more worried about zoonotic disease than were nonhunters (t = 2.89; P = 0.004). 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 WDM in general (t = 3.04; P = 0.003). With respect to specific WDM practices, individuals with greater disease risk perceptions were less likely to agree with letting disease run its course (t = −5.11; P≤0.001) and more likely to agree that public education (t = 3.60; P≤0.001), nonlethal management (t = 3.64; P = 0.009), and selective killing (t = 2.49; P = 0.013) were acceptable management interventions. For wildlife population reductions only the construct of risk severity had a positive influence on agreement (t = 2.30; P = 0.02). The positive association between agreement with WDM and risk perception occurred for all risk targets except “you personally” (t = 1.64; P = 0.102). The association was strongest for risks to domestic livestock (t = 3.34; P = 0.001) and wildlife (t = 3.19; P = 0.002).

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 WDM 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 WDM. 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 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 WDM activities, with threats to human health typically receiving the most attention (Wobeser, 2007). Our findings demonstrated broad health concerns related to zoonotic disease and WDM 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 greater 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 WDM is not important, and possible opposition to WDM 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 WDM programs. For example, diseases such as Hantavirus pulmonary syndrome, plague, WNV disease, and Lyme disease can be linked to human influences on the environment such as changing weather patterns, flooding, and landscape alteration (American Veterinary Medical Association, 2008).

The finding that many respondents worried more about the effects of disease on wildlife than on themselves personally may be influenced by the perception of wild animals as being particularly vulnerable to disease or risk denial (i.e., judging oneself to be less vulnerable to risks). We expected to find those having children under age 18 to have greater risk perceptions but instead found that age was positively correlated with risk perception. The mean age of survey respondents was 58 years and <22% of respondents had children under age 18; given a younger sample with a higher proportion having children, we would expect a positive relationship between risk perception and child rearing to be observed. The finding that hunters worry more about the effects of disease may be associated with greater awareness of wildlife disease and the possibility of disease affecting valued wildlife populations and hunting opportunities.

The 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, 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 WNV perceptions and prevention behaviors of women in the southeastern United States with the intent to help develop WNV disease-prevention campaigns aimed at women in that region (Yerby, 2007). Our results suggest that level of risk perception influences stakeholder agreement with specific WDM practices, and that the effect of risk perception on agreement with WDM exists regardless of whether the risk targets are human or nonhuman. This finding further supports the notion that stakeholders are concerned with health risks to animals (domestic or wild) as well as to humans. Previous research suggests perceived risks to human health and safety can be strong influencers of attitudes about wildlife 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 WDM.

Our low survey response rate, even in the absence of any demonstrated nonresponse bias, may reflect the low saliency of wildlife disease to most Americans not directly affected by these diseases. Human dimensions insights can be augmented by focusing on individuals who are particularly interested in the topic and avoiding those who are indifferent (McCleery et al., 2006). Nonetheless, such an approach limits generalizability of survey results. A related issue possibly indicated by the low response rate is apathy toward wildlife disease and its management. The low saliency of wildlife disease in general may be beneficial if it limits negative attitudes toward wildlife caused by wildlife being viewed as reservoirs of disease agents. Conversely, low response may suggest a lack of awareness of disease factors that influence wildlife, animal, and human health. Because of the risk of unintended consequences to public perception, caution when communicating about wildlife disease in a One Health context has been advised in the absence of research to illuminate the relationship between risk perception and wildlife disease (Decker et al., 2010). This study is a first step toward promoting understanding of that relationship.

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 WDM, 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 effects of humans on zoonotic disease occurrence, may encourage more-supportive attitudes toward WDM practices. It seems evident that much of the public views wildlife as particularly vulnerable to disease and that public response to WDM is likely to be more supportive when the anticipated benefits to wildlife are made clear.

We are grateful to the hundreds of people who took the time to respond to our survey. We thank D. J. Case and Associates for assisting with data collection. Funding for this research was provided by a Multistate Conservation Grant (with money from the Sportfish and Wildlife Restoration Program), Michigan State University, and the Safari Club International Michigan Involvement Committee.