Introduction
Competition between humans and carnivores over limited resources, such as space and food, often leads to human–carnivore conflicts. These conflicts occur worldwide, and are increasing in regions where human land-uses, driven by population growth, are expanding and intensifying (Treves & Karanth, Reference Treves and Karanth2003; Inskip & Zimmermann, Reference Inskip and Zimmermann2009). Human–carnivore conflicts are defined here as direct threats to the economic (e.g. livestock depredation), health and safety (e.g. transmission of disease, attacks on people), and psychological (e.g. fear of attack) well-being of people (Woodroffe et al., Reference Woodroffe, Thirgood and Rabinowitz2005). A common reaction to real or perceived conflicts is to reduce carnivore populations (Woodroffe & Ginsberg, Reference Woodroffe and Ginsberg1998). Responses range from individual people killing specific animals to governments sanctioning large-scale carnivore eradications. For example, in the 19th and 20th centuries eradication programmes nearly extirpated wolves Canis lupus and cougar Puma concolor from the contiguous United States to relieve negative effects of these species on livestock (Woodroffe, Reference Woodroffe2000). Similarly, beginning in the 1950s, tigers were subjected to uncontrolled killing in China after the government declared tigers a pest (Seidensticker et al., Reference Seidensticker, Jackson and Christie1999). More recently, several conflict-prone carnivore species, including African wild dog Lycaon pictus, spotted hyaena Crocuta crocuta and grizzly bear Ursus arctos horribilis have declined because of human persecution (Woodroffe, Reference Woodroffe, Gittleman, Funk, Macdonald and Wayne2001).
The long-term viability of threatened carnivores is significantly jeopardized when local people take action to eliminate so-called problem animals (e.g. by poisoning them). Human-caused mortality affects carnivore population dynamics by creating population sinks and decreasing the probability of population persistence (Woodroffe & Ginsberg, Reference Woodroffe and Ginsberg1998). The extent and degree of these impacts on carnivore populations are substantial as much of the remaining range of threatened carnivores is on human-dominated land (Dickman et al., Reference Dickman, Macdonald and Macdonald2011).
Sustaining threatened carnivore species therefore depends on the capacity of local people to tolerate carnivore-related risks and to desire increasing or expanding carnivore populations or, at the very least, policy favourable to their conservation (Riley & Decker, Reference Riley and Decker2000b). The capacity of local people to cohabit with wildlife is strongly influenced by subjective psychological factors, including beliefs and perceptions (Decker & Purdy, Reference Decker and Purdy1988; Riley & Decker, Reference Riley and Decker2000b; Zinn et al., Reference Zinn, Manfredo and Vaske2000; Bruskotter et al., Reference Bruskotter, Vaske and Schmidt2009). Several studies have assessed these factors independently with respect to threatened carnivores in various regions (Saberwal et al., Reference Saberwal, Gibbs, Chellam and Johnsingh1994; Marker et al., Reference Marker, Mills and Macdonald2003; Romañach et al., Reference Romañach, Lindsey and Woodroffe2007) but none of these studies integrated psychological concepts into a comprehensive framework.
We developed a novel psychological framework for conservation in regions of the world where there are human–carnivore conflicts, and used the tiger Panthera tigris to explore the utility of this framework. We evaluated the effects of past interactions with tigers as well as beliefs and perceptions towards tigers on preferred future tiger population size among people living near Chitwan National Park, Nepal, where there is an increasing frequency of human–tiger conflicts (Gurung et al., Reference Gurung, Smith, McDougal, Karki and Barlow2008).
Our research is the first quantitative evaluation of the relationships between past interactions with carnivores, psychological concepts and preferred future carnivore population size. Our psychological framework integrates a comprehensive and generalized set of concepts, allowing researchers systematically to test hypotheses and develop theories regarding the ways people relate to carnivores under a wide range of conditions. The framework also facilitates the identification of interventions with the greatest effect on preferences for carnivores.
Psychological framework
In our framework, an individual's personal experience or interactions with carnivores affects his or her beliefs and perceptions associated with these species, which, in turn, affects his or her preferences for population sizes (Fig. 1).
Fig. 1 Psychological framework illustrating relationships between interactions with carnivores, psychological concepts, and preferred future carnivore population size. The dashed lines illustrate potential management actions influencing preferences for carnivores. Management actions include those that affect the physical environment in which carnivores and humans interact and those that affect the human dimensions. The three hypotheses tested are indicated. H1, the effect of past interactions with carnivores on preference for future carnivore population size is mediated by beliefs and perceptions about carnivores; H2, perceptions of carnivore-related risks are strongly influenced by beliefs about carnivores and carnivore-related risks; H3, preferred future carnivore population size is strongly influenced by beliefs about carnivores, carnivore-related risk, and perceptions of those risks.
Past interactions with carnivores
Personal interactions with carnivores may occur in places where humans and carnivores live in close proximity. We defined these interactions as direct, including predation of livestock and encounters between people and carnivores in the wild (e.g. carnivore scat or pugmark seen, carnivore seen or heard, person attacked; Saberwal et al., Reference Saberwal, Gibbs, Chellam and Johnsingh1994; Wang et al., Reference Wang, Lassoie and Curtis2006). In addition to direct interactions, theory about the social amplification of risk posits that interpersonal connections and media outlets amplify risk signals (i.e. intensify information about risk) regarding a particular event or interaction (Kasperson et al., Reference Kasperson, Renn, Slavic, Brown, Emel and Goble1988). We defined these interactions as indirect, including hearing or reading news about human–carnivore encounters from the media or personal contacts (Gore & Knuth, Reference Gore and Knuth2009). Several studies demonstrate that direct and indirect interactions with carnivores affect cognitions (e.g. beliefs) and emotions (e.g. worries) towards the carnivore species (Saberwal et al., Reference Saberwal, Gibbs, Chellam and Johnsingh1994; Wang et al., Reference Wang, Lassoie and Curtis2006; Gore & Knuth, Reference Gore and Knuth2009).
Beliefs about carnivores and carnivore-related risks
Beliefs are broadly defined as ‘associations or linkages that people establish between the attitude object and various attributes’ (Eagly & Chaiken, Reference Eagly and Chaiken1993). Beliefs mediate the relationships between broad, abstract values (e.g. honesty) and behaviour (Fulton et al., Reference Fulton, Manfredo and Lipscomb1996). Beliefs about carnivores are based on beneficial and undesirable attributes associated with the species. Benefits associated with carnivores include cultural, religious, economic (e.g. tourism), ecological and existential attributes (Kellert, Reference Kellert1985), whereas nuisance behaviour and resource competition are undesirable attributes (Sharma, Reference Sharma1990; Kissui, Reference Kissui2008). Beliefs about carnivore-related risks are associated with the perceived duration that the individual has been exposed to risk, rate of change in exposure to risks, ability of the individual to adapt to and avoid risks, ability of authorities to understand and address risks, and balance of benefits compared to risks (Slovic, Reference Slovic1987; Sjöberg, Reference Sjöberg1998; Riley & Decker, Reference Riley and Decker2000b; Gore et al., Reference Gore, Knuth, Curtis and Shanahan2006).
Perceptions of carnivore-related risks
Riley & Decker (Reference Riley and Decker2000a) suggested that risk perceptions are a product of an underlying belief system rather than a cause of these beliefs. Perceptions of risk towards encounters with carnivores that threaten livelihoods (e.g. carnivore attacks livestock) and safety (e.g. carnivore attacks someone) can be affective or cognitive. Whereas affective risk measures the dread or worry an individual feels towards a specific negative human–carnivore encounter occurring (Sjöberg, Reference Sjöberg1998; Gore & Knuth, Reference Gore and Knuth2009), cognitive risk estimates the perceived likelihood of the encounter occurring (Riley & Decker, Reference Riley and Decker2000a). Importantly, perceptions and beliefs are subject to manipulation through information and education (Zinn et al., Reference Zinn, Manfredo and Vaske2000).
Preferences for future carnivore population size
According to theories of human cognition (Ajzen, Reference Ajzen1991; Fulton et al., Reference Fulton, Manfredo and Lipscomb1996) and empirical studies (Fulton et al., Reference Fulton, Manfredo and Lipscomb1996; Riley & Decker, Reference Riley and Decker2000a; Bruskotter et al., Reference Bruskotter, Vaske and Schmidt2009; Gore & Knuth, Reference Gore and Knuth2009) beliefs about wildlife, wildlife-related risks, and perceptions of those risks strongly influence an individual's ‘acceptance capacity’ for wildlife. Wildlife acceptance capacity, first introduced by Decker & Purdy (Reference Decker and Purdy1988), was initially defined as the ‘maximum wildlife population level in an area acceptable to people’, and acknowledged that people have a limited capacity to cohabit with wildlife (Carpenter et al., Reference Carpenter, Decker and Lipscomb2000). We used preference for future carnivore population size to measure an individual's acceptance capacity for carnivores. As human behaviours limit carnivore distribution and population sizes (Woodroffe & Ginsberg, Reference Woodroffe and Ginsberg1998) the ability to support a carnivore population within any habitat may be as much a function of wildlife acceptance capacity as biological carrying capacity (Riley & Decker, Reference Riley and Decker2000a). Unlike previous research on wildlife acceptance capacity our study focused on conservation of a threatened carnivore rather than management of abundant wildlife species (Zinn et al., Reference Zinn, Manfredo and Vaske2000; Whittaker et al., Reference Whittaker, Vaske and Manfredo2006; Lischka et al., Reference Lischka, Riley and Rudolph2008).
Hypotheses
Based on our psychological framework we formulated three hypotheses concerning the relationships between tigers and people living near Chitwan National Park: (1) The effect of past interactions with tigers on preference for future tiger population size is mediated by beliefs and perceptions about tigers. (2) Perceptions of tiger-related risks are strongly influenced by beliefs about tigers and tiger-related risks. (3) Preferred future tiger population size is strongly influenced by beliefs about tigers, tiger-related risk, and perceptions of those risks.
Study area
Our study site was western Chitwan district to the north of Chitwan National Park (Fig. 2). Chitwan district is located in a river valley basin along the flood plains of the Rapti, Reu and Narayani rivers at altitudes of 150–815 m. The area is subtropical, with a summer monsoon from mid June to late September, and a cool dry winter (Laurie, Reference Laurie1982). Chitwan district has a complex mixture of ethnicities (Axinn & Ghimire, Reference Axinn and Ghimire2007). Many people there depend on crops and livestock for their livelihood, and rely on nearby forests for thatch, reeds, fodder, fuelwood, timber and other products to support their agricultural lifestyles (Sharma, Reference Sharma1990). Chitwan National Park, established in 1973, is a globally important protected area for conservation of tigers (Walston et al., Reference Walston, Robinson, Bennett, Breitenmoser, da Fonseca and Goodrich2010). At the time of our research the study area comprised a mosaic of land uses, including National Park, National Forest, community forests, agriculture and urban development. In 2001 the human population in the study area was c. 200,000, and the total number of households c. 41,300 (CBS, 2001).
Fig. 2 Location of the study site in Chitwan. The shaded area on the inset indicates the location of Chitwan district in Nepal.
Methods
Survey design
In June 2008 we conducted a focus group with 10 people from Chitwan to discuss their beliefs and perceptions about tigers. We used insights from this group to understand the vernacular and modify previously tested survey items, particularly Riley (Reference Riley1998) and Peyton et al. (Reference Peyton, Bull, Reis and Visser2001). Local Nepali experienced in social survey research design worked with us to ensure internal validity of our survey measures. We designed a structured survey to record interactions with tigers, beliefs about tigers, beliefs about tiger-related risks, perceptions of tiger-related risks, and preferred future tiger population size, as well as respondent age, ethnicity, gender, education level and occupation. In December 2009 we pre-tested the survey (n=17) in a site adjacent to our study area, to improve survey effectiveness. Preferred number of tigers living nearby in the next 10 years was based on a 5-point bipolar scale (i.e. much less, less, same, more and much more). We chose 10 years because it is a round number and a conceivable time-frame in which tiger population size can change significantly. All belief questions were binary (i.e. no, yes) or on a 3-point bipolar scale (i.e. less, same, more). All risk perception questions were on a 3-level nominal scale (i.e. none, somewhat, very). ‘Don't know’ options were provided on all questions.
Sample selection
Wards (the smallest administrative unit in the district) that had at least 50% of their area within 1 km of Chitwan National Park or the multiple-use forest adjacent to the Park, where the majority of human–tiger conflicts occur (Department of National Parks and Wildlife Conservation, unpubl. data), were selected. Ward boundary data were extracted from 1996 digital topographic data obtained from the Nepal Survey Department (NSD, 1996); these are the most recent data and little change in ward boundaries has occurred since. We randomly selected 500 residences within the wards based on residence locations in 1996 (n=5,400). The number of residences has increased since 1996. In February 2010 the name and age of all persons living in each of the 500 residences (inclusion criterion was that they must have been residing in the house during the week prior to the time when the survey would be administered) were recorded and compiled. From this list a single individual (age 15–59) was randomly selected for survey from each of the 500 residences, resulting in a total of 500 possible respondents. From March to April 2010 trained Nepali interviewers contacted each possible respondent to administer the survey face-to-face.
Data analyses
We used structural equation modelling (SEM) to assess interrelationships among interactions with tigers, beliefs about tigers and tiger-related risks, perceptions of tiger-related risks, and preferred future tiger population size as hypothesized in our psychological framework. Unweighted data were used to develop the models. SEM is ideal for evaluating our multi-level framework because it simultaneously measures associations among several independent and dependent variables.
Confirmatory factor analysis, an inherent procedure in SEM, was conducted to assess the degree to which 20 survey items (i.e. observed variables) loaded on four a priori defined latent (unobserved) variables: (1) beliefs about tigers, (2) beliefs about tiger-related risks, (3) perceptions of affective risk, and (4) perceptions of cognitive risk. We constrained each item to load on only one latent factor. The factor analysis could not support missing data (i.e. survey items with ‘don't know’ responses). We therefore omitted 11 cases (2.2%) with missing data from our survey sample using listwise deletion, which is acceptable if the number of cases omitted is < 5% of the total sample (Schafer, Reference Schafer1999).
Structural equation models measure the direct and indirect effects of variables on one another according to a path diagram. We developed a path diagram that linked a variable we created to describe interactions with tigers to the belief and perception latent variables, and linked these latent variables to preferences for future tiger population size. The variable we created to describe interactions with tigers ranged from 1 (very low) to 5 (very high). Category 1 (very low) included respondents who indicated no direct or indirect interactions with tigers, category 2 (low) included respondents who had read or heard about tiger–human conflicts as well as those who had seen evidence of tigers nearby (e.g. pugmark, scat), category 3 (moderate) included respondents who had seen a tiger in the wild, knew somebody whose livestock had been attacked by a tiger, and/or had a friend, neighbour, or relative that had been threatened or attacked by a tiger, category 4 (high) included respondents whose livestock were killed by tigers, and category 5 (very high) included respondents who had been threatened directly by a tiger. If a respondent reported multiple interactions with tigers (e.g. read about tiger–human conflict and livestock killed by tiger) we used the category (1–5) corresponding to the greater degree of interaction with a tiger.
We used maximum likelihood estimation to calculate model parameters. Parameter significance was estimated from bias-corrected bootstrap 95% confidence intervals based on 2,000 bootstrap samples (Preacher & Hayes, Reference Preacher and Hayes2008). The standardized root mean square residual and comparative fit index were used to assess the goodness-of-fit of our model to the data. Structural equation modelling was performed using Amos v. 18 (SPSS Inc., Chicago, USA). We used PASW v. 18 (SPSS Inc., Chicago, USA) to compute Cronbach's alpha reliability coefficients for each set of items composing the belief and perception variables in the final model.
Results
Characteristics of respondents
The survey was completed by 499 of 500 individuals contacted; one individual opted not to participate. Average age of respondents was 33.7 (15–59) years and nearly 37% were male. Respondents were predominantly female as many men leave Chitwan to work in urban centres in Nepal or elsewhere (Bohra & Massey, Reference Bohra and Massey2009). Respondents ranged in ethnic background, including high caste Hindu, hill Tibetoburmise, lower caste Hindu, Newar, Terai Tibetoburmise, and other Indian castes. Respondents were crop farmers (51%), livestock producers/keepers (0.5%), mixed farmers (i.e. crop and livestock farmers, 18%), business owners (3%), business employees (8%), daily wage labourers (2%), students (15%), and various other occupations (2.5%). They had a range of education levels ranging from none (31%) to post graduate degrees (1%).
Past interactions with tigers
Interactions between respondents and tigers varied but a large majority of respondents experienced low to moderate levels of interaction with tigers (72%). Approximately 11% reported no direct or indirect interactions with tigers and 17% of respondents reported that a tiger had attacked their livestock (high) or threatened them directly (very high). Nearly 82% of respondents interacted with tigers on multiple occasions.
Beliefs and perceptions about tigers
A majority of respondents (59–91%) associated tigers with cultural, religious, economic (i.e. tourism), ecological and existential benefits. However, nearly 40% of respondents believed tigers were a nuisance and that there was not enough room for both tigers and people in the nearby forests. A majority of respondents believed that negative encounters with tigers have occurred for a long time (73%) but agreed that the risks from tigers were avoidable (71%) and something that local people could adapt to over time (65%). Nearly two-thirds of respondents agreed that risks from tigers were understood by government officials (63%) but only 45% believed officials were satisfactorily addressing the risks. Proportions were approximately the same for those who believed the number of conflicts with tigers had decreased (36%), stayed the same (31%), or increased (33%) over the previous 10 years. The proportion of respondents who thought that the risks of living near tigers outweighed the benefits (39%) was much larger than the proportion of those who thought the benefits outweighed the risks (12%).
In general, respondents perceived greater cognitive risk (i.e. likelihood) than affective risk (i.e. worry) of attacks by tigers on pets and livestock. Some respondents reported high levels of cognitive risk despite low levels of affective risk perception. Yet, in nearly all cases high levels of affective risk were accompanied by high levels of cognitive risk perception. This relationship was strongest in situations where human safety was at risk. The proportions of respondents who perceived cognitive and affective risk of tiger attacks on someone in their village and tiger attacks on them or someone in their family were comparatively equal (Fig. 3a–d).
Fig. 3 The percentage of respondents who expressed varying degrees of affective (1, not worried; 2, somewhat worried; 3, very worried) and cognitive risk (1, not likely; 2, somewhat likely; 3, very likely) towards tiger attacks on (a) their pets, (b) their livestock, (c) someone in their village, and (d) themselves or someone in their family.
Preferred future tiger population size
The distribution of respondents’ preferences for future tiger population size was nearly uniform. An equal proportion of respondents (40%) preferred fewer tigers and more tigers in nearby forests over the next 10 years compared to 2010.
Structural equation model
The initial model (χ2=1,102.83, df=261) fitted the empirical data poorly as the standardized root mean square residual was > 0.8 and the comparative fit index was < 0.9. Thus, we re-specified the model post hoc based on modification indices and standardized factor loadings. Modification indices > 100 indicated that beliefs about tigers were better explained using one latent variable measuring beneficial attributes and one latent variable measuring undesirable attributes. Beliefs about tiger-related risks were also better explained using two latent variables: government poorly manages tiger-related risks and people are vulnerable to these risks (Table 1). Belief about the balance of benefits compared to the risks of having tigers nearby loaded adequately (standardized factor loading > 0.5) as a beneficial attribute (Table 1) rather than a belief about tiger-related risks. Beliefs about the religious importance of tigers, the duration that the individual has been exposed to risks from tigers, and the rate of change in exposure to risks from tigers were removed from the model as they did not load adequately on any of the latent variables. The reliability of each set of items composing the belief and perception variables was acceptable (i.e. > 0.65, Vaske, Reference Vaske2008) in the re-specified model (Table 1). Results from the re-specified model (χ2=622.13, df=188) adequately fit the data as the standardized root mean square residual = 0.07 and comparative fit index=0.91.
Table 1 Reliability and confirmatory factor analysis of latent variables used in the final structural equation model.
1 Factor loadings were standardized and were all significant at P<0.05
2 Items were reverse coded
Determinants of preferred future tiger population size
There was a significant direct effect in 10 of 20 paths between model variables (Fig. 4). Past interactions with tigers had a significantly positive relationship with affective risk perception but was not related to any other variable. Additionally, the indirect effect (i.e. mediated effect) of interactions with tigers on preferences for future tiger population size was insignificant (Table 2), leading us to reject Hypothesis 1. Beliefs about tigers and tiger-related risks strongly influenced affective and cognitive risk perceptions (Fig. 4), which supports Hypothesis 2. However, risk perceptions did not have a significant direct effect on preferences for future tiger population size, leading us to reject Hypothesis 3. Beliefs about benefits of having tigers nearby had the greatest effect on preferences (Fig. 4). Respondents who preferred fewer tigers in the future were less likely to associate tigers with beneficial attributes, and more likely to associate tigers with undesirable attributes. These respondents were also less likely to believe that risks from tigers are avoidable and that people can adapt to risks from tigers over time. Additionally, they were less likely to believe that risks from tigers in Chitwan are understood and satisfactorily addressed by government officials.
Fig. 4 Path diagram used in final structural equation model. See text and Table 1 for definitions of variables. Solid black lines between variables indicate significant direct effects (P<0.05) with standardized regression coefficients shown to the left of line. Dashed grey lines indicate no significant direct effect. Correlation between beneficial attributes associated with tigers and undesirable attributes associated with tigers is −0.527 (P<0.05). Correlation between government poorly manages tiger-related risks and people are vulnerable to tiger-related risks is −0.054 (P>0.05). Correlation between affective risk of tiger attack and cognitive risk of tiger attack is 0.576 (P<0.05).
Table 2 Indirect and total effects of independent variables on preferred future tiger population size (the dependent variable) determined from the structural equation model.
1 Standardized regression coefficients
2 Significant at P<0.05
Discussion
Our model results affirm the idea that human cognitions and emotions towards carnivores are complex and inter-related (Fulton et al., Reference Fulton, Manfredo and Lipscomb1996). The hierarchical relationships among variables would have been missed if a multiple linear regression was used to evaluate the data, as in some other studies on local attitudes towards wildlife (Kaczensky et al., Reference Kaczensky, Blazic and Gossow2004; Lischka et al., Reference Lischka, Riley and Rudolph2008). In this study, we hypothesized that interactions with tigers (Hypothesis 1) and perceptions of tiger-related risks (Hypothesis 3) would affect preferences for future tiger population size. This was not the case, however, suggesting that decreasing the frequency of negative encounters with tigers (e.g. translocating a conflict-prone tiger) and perceptions of tiger-related risks (e.g. campaigns to communicate the relatively low probability of tiger attacks) may only have a small effect on preferences for tigers. In contrast, we found that preferences for future tiger population size were primarily driven by beliefs about tigers and tiger-related risks, not unlike Bruskotter et al. (Reference Bruskotter, Vaske and Schmidt2009) who found that similar cognitive factors were related to the acceptability of specific management actions towards wolves. Beliefs about tigers and tiger-related risks also shaped perceptions of tiger-related risks (Hypothesis 2), suggesting that these beliefs are higher-order cognitions that strongly determine how people relate to tigers.
Beliefs about tigers
Beliefs about tigers in Chitwan reflect changes in social-economic conditions. For example, tourism has grown to become a powerful industry in Chitwan, with 836 entry permits to the Park sold in 1974 increasing to 113,788 in 2009 (Curry et al., Reference Curry, Moore, Bauer, Cosgriff and Lipscombe2001; GoN, 2009). This may explain why 50% more respondents agreed that tigers should stay in nearby forests because they attract tourists than the number who agreed that tigers should stay because they keep the forests healthy. However, tigers regulate ungulate populations (Terborgh et al., Reference Terborgh, Estes, Paquet, Ralls, Boyd-Heger, Miller, Noss, Soule and Terborgh1999) that, if unchecked, may eat crops in nearby agricultural fields more frequently and to a greater extent. Crop raiding by protected fauna has already been identified as a major source of resentment among local people towards the Park (UNEP/WCMC, 2008). Education programmes that target specific beliefs may positively condition (Zinn et al., Reference Zinn, Manfredo and Decker2008) local people to view larger tiger populations in terms of their beneficial contributions to people (e.g. ecotourism revenue), society (e.g. cultural significance) and the environment (e.g. ecological value) than merely as threats to livelihood and safety.
Assessing the effect of beliefs about carnivores on preferred future carnivore population size is important, as conservation actions that fail to account for differences in local beliefs with respect to region and species may not effectively increase the capacity of local people to support those carnivores. For instance, Kissui (Reference Kissui2008) found that cultural sentiments of local people towards African lions Panthera leo, leopards Panthera pardus and spotted hyaenas influenced the vulnerability of those species to retaliatory killing. Therefore carnivore persecution, influenced by subjective beliefs, may continue despite reductions in livestock predation.
Beliefs about tiger-related risks
Local people living near and among carnivores incur the greatest costs from these species (Wang & Macdonald, Reference Wang and Macdonald2006; Gurung et al., Reference Gurung, Smith, McDougal, Karki and Barlow2008). Yet management responsibility of threatened carnivores typically rests with government or conservation agencies rather than local communities (Treves et al., Reference Treves, Wallace, Naughton-Treves and Morales2006). Our results from Chitwan underscore a need for government and conservation agencies to supplement mitigation of human–tiger conflicts through direct intervention (e.g. translocating or killing a conflict-prone tiger) by building local trust and satisfaction in agency programmes (Slovic, Reference Slovic1993). This is particularly important in developing rural areas where local people often have limited options with which to respond to human–wildlife conflicts (Ogada et al., Reference Ogada, Woodroffe, Oguge and Frank2003).
Respondent belief that people are vulnerable to tiger-related risks reflects a perceived inability to control one's environment (Ajzen, Reference Ajzen2002). This sense of vulnerability combined with perceived inefficacy of government and conservation agencies may compel local people to resolve human–carnivore conflicts by illegal means (Treves et al., Reference Treves, Jurewicz, Naughton-Treves, Rose, Willging and Wydeven2002). Employing a number of conservation tools, including proactive education and awareness programmes, effective compensation programmes (Dickman et al., Reference Dickman, Macdonald and Macdonald2011) and carnivore-response teams with a contingent of local people (Gurung et al., Reference Gurung, Smith, McDougal, Karki and Barlow2008), may increase satisfaction in government/conservation agencies and reduce the sense of vulnerability among local people to carnivore-related risks. In addition, incorporating local communities as partners in conservation planning and implementation may increase the capacity of those communities to cohabit with carnivores (Treves et al., Reference Treves, Wallace, Naughton-Treves and Morales2006).
Interactions with tigers and risk perceptions
Previous wildlife acceptance capacity studies have alluded to the strong effect that past experiences with carnivores, particularly negative interactions (e.g. livestock attack), have on preferences for carnivore population size (Riley & Decker, Reference Riley and Decker2000b; Bruskotter et al., Reference Bruskotter, Vaske and Schmidt2009). However, these studies focused on perceived impacts rather than an explicit measure of past experience with carnivores because actual human–carnivore interactions at the study sites (Montana and Utah, USA) were so infrequent. By explicitly measuring past experiences we found that the proportion of people in Chitwan who had direct interactions with tigers (i.e. high impact) was surprisingly high, probably similar to many other places where human–carnivore conflicts are severe.
People in Chitwan have lived in close proximity to tigers for hundreds of years (McLean, Reference McLean1999) and frequently enter the forests to collect natural resources for subsistence or for sale in nearby markets (Straede & Treue, Reference Straede and Treue2006). Thus, tiger–human interactions in Chitwan are generally more consistent and direct than those between carnivores and people in developed countries (Manfredo et al., Reference Manfredo, Vaske, Brown, Decker and Duke2009). After such long and persistent exposure to threats posed by tigers, people in Chitwan may have internalized the risks into their day-to-day lives (Slovic, Reference Slovic1987), which may explain why past interactions with tigers and risk perceptions did not influence preferences for future tiger population size. Whereas, in Montana, USA, where one fatality from cougar attack in the 20th century has been documented, perceptions of cougar-related risks were significantly related to preferences for future cougar population size (Riley & Decker, Reference Riley and Decker2000a). Similar research conducted in other areas facing similar human–carnivore conservation issues would be useful in refining model variables and testing the external validity of our model.
Conclusions
Human–carnivore conflicts are predicted to increase in developing regions of the world (Baillie et al., Reference Baillie, Hilton-Taylor and Stuart2004), which will probably lead to increased rates of retaliatory killings and additional burdens on limited conservation resources that already suffer from monetary and personnel shortages (Treves et al., Reference Treves, Wallace, Naughton-Treves and Morales2006). Our novel psychological framework can help address current and future conservation challenges because it (1) integrates an expansive and generalized set of concepts, (2) enables the identification of conservation interventions that foster coexistence between people and conflict-prone carnivores, and (3) is applicable to species in other parts of the world. For instance, in places with human–lion conflicts using our framework could inform policies that address the beliefs and perceptions germane to local preferences for future lion population size, such as implementing education programmes in Tanzania, where human–lion conflict is severe (Chardonnet et al., Reference Chardonnet, Soto, Fritz, Crosmary, Drouet-Hoguet and Mésochina2010), to reduce the belief that attacks on people are unavoidable. Moreover, focusing conservation efforts on people who prefer smaller future populations of threatened carnivores may be an effective means of distributing limited resources, reducing human–carnivore conflicts and engaging local communities in conservation.
Acknowledgements
We acknowledge our colleagues in the Center for Systems Integration and Sustainability at Michigan State University and the Institute for Social and Environmental Research—Nepal in Chitwan for their contributions to this article. Research was supported by a grant from the U.S. Fish and Wildlife Service Rhinoceros and Tiger Conservation Fund, National Science Foundation (Partnerships in International Research and Education, Dynamics of Coupled Natural and Human Systems Program), and fellowships from Michigan State University and NASA's Earth and Space Science programme.
Biographical sketches
Neil Carter studies the complex interrelationships between humans and carnivores, with a particular focus on improving conservation planning. He is also interested in human dimensions of wildlife management, wildlife behaviour and habitat use, and protected area management. Shawn Riley studies human–wildlife interactions, most recently focusing on tolerance of wildlife in various settings, wildlife health management, and how better to integrate insights from ecological and social science in decisions about wildlife management. Jianguo Liu's research interests include household–environment interactions, complexity of coupled human and natural systems, wildlife ecology, landscape ecology, systems modelling and the science of sustainability.
