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Understanding preferences for climate change adaptation for protected areas : the psychology of individual… Tam, Jordan Yukho 2010

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UNDERSTANDING PREFERENCES FOR CLIMATE CHANGE ADAPTATION FOR PROTECTED AREAS: THE PSYCHOLOGY OF INDIVIDUAL RISK PERCEPTIONS  by  Jordan Yukho Tam B.A., The University of British Columbia, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS  in  The Faculty of Graduate Studies (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August, 2010  © Jordan Yukho Tam, 2010  ii  ABSTRACT Protected areas (PAs) are a cornerstone of conservation strategy and investment. Unfortunately, climate change and its impacts will render many PAs less effective at safeguarding the species and ecosystems they were designed to protect. To cope with climate change, a number of adaptations for conservation management have been proposed. However, adaptation responses are not without risks. One way to consider the problem of adaptation is as an issue of risk management. In this vein, identifying the factors that shape risk perceptions and the acceptability of those risks is a key question for conservation. To assess whether greater certainty of future climate change and negative feelings are significant factors in determining risk perceptions and acceptability, a 2x2 factorial experiment was conducted via online surveys. Environmental worldviews were also measured using the New Ecological Paradigm (NEP) scale. The experimental design largely failed to produce any detectable effects. In general however, a high tolerance for adaptations was observed. This study also found that adaptation policies appeared to sit along a continuum of risk and of acceptability, which were also significantly and negatively correlated. The most acceptable and least risky policies tended to be those most similar to current conservation practices. Overall, people who had more pro-environmental worldviews (as revealed by higher NEP scores) perceived all adaptations as being equally risky or more risky than people with lower NEP scores. However, despite more pessimistic risk perceptions, high NEP scorers found three policies more acceptable than the low NEP group. These findings appear to support the central tenet of Cultural Cognition theory that risk perceptions are manifestations of personal beliefs and values. Additionally, there was evidence (though non-significant) that people who were fearful and angry saw adaptations as generally more risky and less acceptable than calm individuals. Certainty of climate change did not appear to have much influence on risk perceptions and acceptability. Though more research is needed to make concrete policy recommendations, this study does suggest that risk perceptions matter in shaping people’s willingness to support adaptation and should be a focus of conservation managers.  iii  TABLE OF CONTENTS ABSTRACT ........................................................................................................................................ ii TABLE OF CONTENTS ..................................................................................................................... iii LIST OF TABLES............................................................................................................................... vi LIST OF FIGURES............................................................................................................................ vii ACKNOWLEDGEMENTS ............................................................................................................... viii 1.  INTRODUCTION ....................................................................................................................... 1 1.1.  Problem Context ............................................................................................................. 1  1.2.  Motivation and Conceptual Framework ........................................................................ 3  1.3.  Objectives, Research Questions, and Design ................................................................ 6  1.3.1.  Objectives ................................................................................................................. 6  1.3.2.  Research Questions ................................................................................................. 6  1.3.3.  Methods ................................................................................................................... 6  1.4. 2.  Thesis Overview............................................................................................................... 7  FRAMING THE ISSUE – EVIDENCE FROM THE LITERATURE.................................................. 8 2.1.  Introduction ..................................................................................................................... 8  2.2.  Rationality ........................................................................................................................ 8  2.3.  Loss Aversion, Status Quo Bias and Omission Bias ....................................................... 9  2.4.  Affect, Emotions and Risk .............................................................................................10  2.5.  Cultural Cognition..........................................................................................................13  2.5.1. 2.6. 3.  Protected Values (and Omission Bias) .................................................................15  Testable Hypotheses .....................................................................................................16  METHODOLOGY ....................................................................................................................18 3.1.  Introduction ...................................................................................................................18  3.2.  Procedure.......................................................................................................................18  3.3.  Experimental Design .....................................................................................................19  3.1.  Manipulations ................................................................................................................20  3.1.1. 3.2.  Framing and Priming .............................................................................................20  Measures .......................................................................................................................24  3.2.1.  Emotion ..................................................................................................................24  3.2.2.  Risk Perceptions and Policy Acceptability ............................................................25  iv  4.  3.2.3.  Goals of Conservation ...........................................................................................26  3.2.4.  Environmental Worldviews ...................................................................................27  3.2.5.  Climate Change Certainty .....................................................................................28  3.2.6.  Socio-demographics ..............................................................................................28  RESULTS .................................................................................................................................29 4.1.  Introduction ...................................................................................................................29  4.2.  Demographic Variables .................................................................................................29  4.3.  Experimental Manipulations ........................................................................................30  4.3.1.  Affect and Emotions ..............................................................................................30  4.3.2.  Certainty .................................................................................................................32  4.3.3.  New Ecological Paradigm ......................................................................................33  4.3.4.  Risk Perceptions and Preferences ........................................................................34  4.3.5.  Summary of the Manipulation Effects .................................................................35  4.4.  5.  Measures .......................................................................................................................35  4.4.1.  Adaptation Risk Perceptions and Preferences ....................................................35  4.4.2.  Affect, Emotions, Risk Perceptions, and Preferences .........................................37  4.4.3.  Certainty, Risk Perceptions and Preferences .......................................................41  4.4.4.  Environmental Worldviews, Risk Perceptions and Preferences ........................42  4.4.5.  Conservation Goals ................................................................................................45  4.4.6.  Socio-demographics ..............................................................................................46  DISCUSSION AND CONCLUSION ...........................................................................................50 5.1.  Discussion ......................................................................................................................50  5.2.  Strengths and Weaknesses ...........................................................................................53  5.3.  Directions for Future Research ....................................................................................54  5.4.  Policy Implications.........................................................................................................56  5.5.  Conclusion......................................................................................................................57  REFERENCES ..................................................................................................................................59 APPENDICES ..................................................................................................................................74 APPENDIX A – SOCIO-DEMOGRAPHIC DESCRIPTIVE STATISTICS ..............................................74 APPENDIX B – SOCIO-DEMOGRAPHIC INFLUENCES ON RISK AND ACCEPTABILITY .................78 APPENDIX C – PRIMAY ANALYSES OF MANIPULATION EFFECTS ...............................................85 APPENDIX D – SECONDARY ANALYSES ........................................................................................97  v  APPENDIX E – ETHICS CERTIFICATE ...........................................................................................144 APPENDIX F – QUESTIONNAIRE .................................................................................................145  vi  LIST OF TABLES Table 1  The experimental 2x2 factorial design matrix .................................................... 7  Table 2  Adaptation alternatives on a proposed continuum of risk............................... 26  Table 3  Two-tailed Pearson correlations between risk and acceptability for adaptations ......................................................................................................... 38  vii  LIST OF FIGURES Figure 1. Diagram of the survey structure ..................................................................................21 Figure 2. Affective manipulation images ....................................................................................23 Figure 3. Histograms for affect and emotions ............................................................................31 Figure 4. Histogram for the certainty index................................................................................33 Figure 5. Histograms of the NEP index by affect manipulation ................................................34 Figure 6. Mean risk and acceptability ratings ............................................................................36 Figure 7. Adaptation acceptability and riskiness by affect valence .........................................38 Figure 8. Adaptation acceptability and riskiness by fear ratings ..............................................39 Figure 9. Adaptation acceptability and riskiness by anger ratings...........................................40 Figure 10. Adaptation acceptability and riskiness by distress ratings ......................................41 Figure 11. Adaptation acceptability and riskiness by degree of certainty ...............................42 Figure 12. Adaptation acceptability and riskiness by NEP scores .............................................44 Figure 13. Adaptation acceptability and riskiness by conservation goals ................................46 Figure 14. Mean acceptability and riskiness by gender. ............................................................47 Figure 15. Mean acceptability and riskiness by support for the environmental movement ..48  viii  ACKNOWLEDGEMENTS  First and foremost, I would like to thank my advisor Dr. Timothy McDaniels for his insight, pragmatism and dedicated support. His guidance and encouragement has repeatedly rescued me from endless abstraction and many dead ends. For that I am sincerely grateful. I also extend my thanks to Dr. Joseph Arvai for his steadfast interest and constant willingness to assist at a moment’s notice. Lastly, thank you to the countless number of faculty, family, colleagues, and friends upon whom I have depended in the past two years to get me where I am today.  1  1. INTRODUCTION  1.1. Problem Context Protected areas (PAs) cover more than twelve percent of the Earth’s land surface (as parks, reserves, sanctuaries, etc.; Chape, Harrison, Spalding, & Lysenko, 2005) and 0.8 percent of the oceans (Wood, Fish, Laughren, & Pauly, 2008). They are home to many of the world’s most vulnerable species and ecosystems, and contain some of the richest concentrations of biodiversity on Earth. PAs also safeguard species and ecosystems that provision services and benefits vital to human livelihoods (Daily, 1997), and are a cornerstone of conservation strategy and investment. However, climate change and its impacts will render many PAs less effective at safeguarding the species and ecosystems they were designed to protect (Araújo, Cabeza, Thuiller, Hannah, & Williams, 2004; Hannah et al., 2002; Harris, Hobbs, Higgs, & Aronson, 2006; Hellmann, Byers, Bierwagen, & Dukes, 2008; Millar, Stephenson, & Stephens, 2007; Pyke et al., 2008). Among the impacts on species and ecosystems which have already been observed and attributed to climate change that pose significant challenges to conservation and PAs include: shifts to species’ habitable ranges to higher latitudes and altitudes (Hughes, 2000; Lavergne, Molina, & Debussche, 2006; Parmesan, 2006; Parmesan & Yohe, 2003; Root, MacMynowski, Mastrandrea, & Schneider, 2005; Sharma, Jackson, Minns, & Shute., 2007; Taniguchi, Rahel, Novinger, & Gerow, 1998; Walther, Berger, & Sykes, 2005); reduced species populations and extinctions (Foden et al., 2007; Menéndez et al., 2006; Parmesan, 2006; Parmesan & Yohe, 2003; Pauli, Gottfried, Reiter, Klettner, & Grabherr, 2007; Pounds et al., 2006; Thomas et al., 2004; Warren et al., 2001); phenological disruptions (Menzel & Fabian, 1999; Visser & Holleman, 2001; Zavaleta et al., 2003); and, the threat of new invasive species (Hellmann et al., 2008; Rahel, Bierwagen, & Taniguchi, 2008). Furthermore, evidence that climate change is happening much more rapidly than anticipated and is likely to be ongoing continues to mount (Meehl et al., 2006; Parry, Lowe, & Hanson, 2009; Ramathan & Feng, 2008; Solomon, Plattner, Knutti, & Friedlingstein, 2009;  2  Wigley, 2005). For example, even under the most aggressive climate mitigation policies proposed thus far, computer modelling by Parry et al. (2009) predicts a fifty-percent chance of warming to 2°C (the temperature often held up as the threshold for ‘dangerous climate change’) with temperatures falling to 1°C by the year 2300. Even if all emissions had been halted in 2005, a temperature increase of anywhere between 1.4 - 4.3°C above preindustrial levels could be expected (Ramathan & Feng, 2008). Another model predicts that if carbon dioxide concentrations reach 450 p.p.m. (compared to today’s 385p.p.m.), warming on the scale of a thousand of years could be expected (Solomon et al., 2009). To minimize biodiversity loss and ensure that PAs are effective and cost-efficient conservation tools in the future, adaptations in conservation management will be necessary. To cope with climate change, a number of adaptations for conservation management have been proposed. These include, among others: assisted colonization (aka the long-distance transport of threatened endemic species; Hoegh-Guldberg et al., 2008; McLachlan, Hellmann, & Schwartz, 2007); the use of non-native species to restore ecosystem services (Hershner & Havens, 2008); conservation triage (e.g., the prioritizing of protection to species that serve critical ecological functions; Bottrill et al., 2008; Marris, 2007); captive breeding and storage (e.g., frozen storage of endangered frogs, gametes, and seeds; Marris, 2008); the creation and expansion of protected areas, corridors and networks (Climate Change Science Program and the Subcommittee on Global Change Research [CCSP], 2008; Hannah et al., 2007); and, the introduction of climate resilient and desired species to meet various management objectives (CCSP, 2008). However, adaptation responses are not without their own risks, and are likely to involve trade-offs and incur unforeseeable losses (Hagerman, Dowlatabadi, Chan, & Satterfield, 2010a). Predicting outcomes for any adaptation is fraught with uncertainty. Despite the development of sophisticated climate models, many unknowns remain in predicting future climates due to unknowns in natural cycles, in future human behaviour, and in the state of future technology, all of which are important factors in climate change (Cullen & Small, 2004; Meinshausen et al., 2009). Beyond large scale climate trends, uncertainties also plague predictions about the severity and type of impacts climate change  3  will have at the level of species and ecosystems with which conservation management is concerned (e.g., Araújo, Pearson, Thuiller, & Erhard, 2005). Adding to the difficulty of adaptation planning, many of the potential responses bring into question the fundamental goals and values of conservation and conservationists (Barnosky, 2009). Given these uncertainties and what is at stake, it is not surprising that the topic of adaptation has at times been contentious. Endorsement of proposed adaptation strategies have ranged from wholehearted endorsement to marked resistance, even between and within groups of scientists, conservationists and the public (Barlow & Martin, 2004; Hagerman et al., 2010a; Pimm, 2000). While some of the controversy appears to emerge from established ideological positions (Brooke, 2008 ; Hagerman & Chan, 2009; Marris, 2007), other protests appear to stem from people’s varying tolerances for uncertainty and risk with regard to outcomes (see the following published debate as an example: Fazey & Fischer, 2009; Ricciardi & Simberloff, 2009; Sax, Smith, & Thompson, 2009; Schlaepfer, Helenbrook, Searing, & Shoemaker, 2009; Schwartz, Hellmann, & McLachlan, 2009). Under these circumstances, the preferences of interested and affected citizens, stakeholders and managers are likely to be paramount in driving policy. This research attempts to achieve a better understanding of preferences for adaptation in conservation through the study of risk perceptions.  1.2. Motivation and Conceptual Framework Different adaptation alternatives will certainly produce different consequences for biological and human systems. However, the type of action taken and the effectiveness of adaptation responses will depend on their perceived acceptability, which may limit or expand the suite of adaptation alternatives available for consideration and implementation (Adger, 2003; Tompkins & Adger, 2005). Additionally, adaptation responses may fall into two classes: adaptive and maladaptive. For instance, if the ultimate goal of protected areas is to safeguard species and ecosystems, then adaptive responses are those that contribute to said goal. Maladaptive responses (or lack thereof; Grothmann & Patt, 2005) in contrast, are those that exacerbate or fail to limit losses. As such, some perceptions may be adaptive  4  or maladaptive in generating responses. One way to consider the problem of adaptation then, is as an issue of risks (Brooke, 2008) of both climate change impacts and the possible response alternatives. Thus, identifying the factors that shape risk perceptions and policy acceptability is a key question for conservation (Hagerman & Chan, 2009). Indeed, risk perceptions have previously been identified as an important factor in motivating climate change adaptation responses (Grothmann & Patt, 2005). Risk perceptions may be especially significant in situations of great uncertainty, which is a pervasive characteristic of climate issues (Tompkins & Adger, 2005). While risk perceptions have been explored in the context of climate change concern and mitigation policy (e.g., Bord, & Fisher, 1999; Dessai et al., 2004; Etkin & Ho, 2007; Leiserowtiz, 2005; Leiserowitz, 2006; Lorenzoni & Pidgeon, 2006; Lorenzoni, Pidgeon, & O'Connor, 2005; O'Connor, Dietz, Dan, & Shwom, 2007; Oppenheimer & Todorov, 2006; Weber, 2010), little has been done with regard to climate adaptation (Adger et al., 2009; Dessai, O'Brien, & Hulme, 2007; Grothmann & Patt, 2005). What little evidence does exist suggests that risk perceptions can influence adaptation outcomes, yet the role of cognition has by and large been neglected (e.g., Grothmann & Patt, 2005; Patt & Schröter, 2008; Schliep, Bertzky, Hirschnitz, & StollKleemann, 2008; Weber, 1997). A better understanding of risk perceptions may also:   aid the design of effective and socially acceptable conservation strategies;    aid the design of risk and educational communications (e.g., Kahan, 2010; Keller, Siegrist, & Gutscher, 2006; Leiserowitz, 2005; Marx et al., 2007);    help understand or predict resistance to particular adaptation alternatives and possibly identify leverage points to help expand the set of considered options (Adger et al., 2009; Burch & Robinson, 2007); and,    provide insight into how policy adaptation is unfolding (Hagerman et al., 2010a, p. 352).  As research on risk perceptions of conservation adaptation is basically non-existent, this study turns to the broader literature for direction. Many current studies stress the centrality of affect, as well as values with a particular focus on the psychological dynamics and characteristics of individuals (Taylor-Gooby & Zinn, 2006). Taking this cue, the present  5  study focuses on the following psychological factors in an effort to build a preliminary understand of preferences in conservation adaptation:  Affect – Affect, or the sense of feeling (e.g., emotions are an affective state) is known to have complex mediating and direct influences on risk perceptions and risk judgments (e.g., Peters, Burraston, & Mertz, 2004). Affect is hypothesised to perform many functions, such as signaling what is good or bad, sensitizing attention to positive or negative qualities and activating approach or withdrawal behaviours (Peters, Västfjäll, Gärling, & Slovic, 2006). Whether and how affect and specific emotions impact perceptions and judgment in the context of adaptation in conservation is an open question.  Worldviews and Protected Values – Worldviews (or belief systems) colour how people see hazards (e.g., Slimak & Dietz, 2006) and are sometimes thought to be the chief orienting driver of risk perceptions (Kahan, Braman, Slovic, Gastil, & Cohen, 2009). An understanding of whether risk perceptions and preferences for adaptation policy consistently adhere to worldviews may, for example, be helpful in identifying the source of divisive attitudes amongst stakeholders. People may also hold certain values that are particularly powerful in shaping preferences to the degree that no trade-offs involving these ‘protected values’ are deemed acceptable (Baron & Spranca, 1997). If worldviews and protected values are rigid in the face of the emerging realities of climate change, they may prove to be formidable barriers to policy implementation.  Loss Aversion – It is well-established that individuals on the whole are highly sensitive and averse to losses (e.g., Kahneman, 2003; Kahneman, Knetsch, & Thaler, 1991; Tversky & Kahneman, 1991). How does salience of certain losses in biodiversity and ecosystem function from ongoing climate change influence adaptation preferences? Research suggests that individuals may become more willing to accept risky alternatives when faced with the prospect of significant losses. Conversely, biases stemming in part from loss aversion may instead increase resistance to adaptation (i.e., the status quo bias and omission bias).  6  1.3. Objectives, Research Questions, and Design  1.3.1. Objectives The overarching objective of this research is to generate insights for adaptation planning in conservation by identifying whether and how a set of psychological factors known to impact risk perception and judgment influence the acceptability of climate change adaptation policies for protected areas at an individual level.  1.3.2. Research Questions To this end, this study attempts to answer the following set of research questions: i.  Do underlying risk perceptions of adaptation alternatives influence the acceptability of climate change adaptation policies for protected areas? If so, how?  ii.  Does certainty of current and future climate change and its environmental impacts influence the risk perceptions and acceptability of climate change adaptation policies for protected areas? If so, how?  iii.  Do general affect and negative emotions influence the risk perceptions and acceptability of climate change adaptation policies for protected areas? If so, how?  iv.  Do environmental worldviews influence the risk perceptions and acceptability of adaptations policies for protected areas? If so, how?  1.3.3. Methods To answer the preceding questions, this research employed a 2 (certain/uncertain climate change) x 2 (negative/positive affect) factorial experiment to examine whether and how certainty of future climate change and its impacts (using informational text prompts) and affect (using affectively-loaded images) influence perceptions of risk and acceptability of proposed adaptation measures (see Table 1). As worldviews have been recognised as an important factor in risk perception, environmental worldviews were measured using the  7  New Ecological Paradigm scale developed by Dunlap, Van Liere, Mertig, and Jones (2000). Data was collected via online surveys and convenience sampling. Analyses were conducted using parametric statistics, including correlations and between-groups comparisons (i.e., Pearson correlations, ANOVAs and t-tests). Detailed descriptions of the design, measures, and analysis are covered in Chapters 3 and 4. Table 1 The experimental 2x2 factorial design matrix Climate Change: High Certainty  Climate Change: Low Certainty  Affective Images: Negative  High Certainty Negative Affect  Low Certainty Negative Affect  Affect Images: Positive  High Certainty Positive Affect  Low Certainty Positive Affect  1.4. Thesis Overview This thesis began with a broad look at the problem posed by climate change for protected areas and biodiversity conservation, and situated the issue in the context of risk perception and policy development. The second chapter frames the problem of adaptation through a review of the risk perception literature, with a focus on the prominent body of work related to loss aversion and its related biases, the function of affect and emotions, and the influence of worldviews and protected values. Due to the lack of research on risk perceptions and adaptation, where relevant, some of the literature on risk perceptions for mitigation policy and climate concern is reviewed in tandem. A set of testable hypotheses is offered at the end of the chapter. Chapter three provides a detailed account of the development of the survey instrument used in the experiment and the rationale behind each component. Chapter four summarises the results of the experiment and the analyses conducted. Chapter five discusses the results, their relevance in light of existing research, and the implications of the findings for future research and adaptation planning.  8  2. FRAMING THE ISSUE – EVIDENCE FROM THE LITERATURE  2.1. Introduction This chapter contextualises the issue of climate change adaptation in conservation through the psychological risk literature. In particular, the chapter focuses on loss aversion, affect, worldviews, and values. While many other areas of psychological research on risk, decision-making and attitude formation are likely to be relevant for conservation adaptation, this study is limited to the aforementioned areas in accordance with current trends in risk research (Taylor-Gooby & Zinn, 2006). The first section of this chapter highlights the potential relevance of loss aversion and its associated biases (i.e., status quo and omission) for individual judgments of risk and policy acceptability. Next, the chapter turns to the significance of affect and negative emotions, followed by a brief review of the theory on worldviews and protected values. Lastly, the chapter ends with a set of testable hypotheses.  2.2. Rationality Until recently, most models of human decision-making and perception assumed that individuals were consequentialist and utility maximising rational agents that perceived choices as comprised of cost-benefit analyses. Over the past two decades, a large body of research has been amassed demonstrating that this normative model of human decisionmaking is inadequate in describing actual perceptions and behaviours. For example, ‘bounded rationality’ (Simon, 1955, 1979) theory posits that people are limited in their time and cognitive capacity to solve complex issues. Rather than applying a systematic costbenefit calculus to decisions, investigations into bounded rationality have revealed that individuals use reflexive mental shortcuts or ‘heuristics’ to understand and simplify problems (see Bazerman & Moore, 2009 for a cogent summary). Although these shortcuts are valid in some circumstances, in others they lead to large and persistent biases in judgment that stray from normative expectations. Risk perceptions and judgments are also likely to be derived in part through the use of mental shortcuts and even affective cues, and  9  are thus equally vulnerable to biases. Because of this research, risk perceptions and behaviours under risk are now thought to be more accurately understood as subjective (e.g., Morgan & Henrion, 1990), emotional and value-laden rather than consequentialist and normatively rational.  2.3. Loss Aversion, Status Quo Bias and Omission Bias One type of bias, and perhaps one of the most persistent is the negativity bias, which is the propensity for people to be more sensitive and attuned to negatives (whether it is information, events, outcomes, etc.) than positives. A highly influential example of the negativity bias is described through ‘Prospect Theory’, which is a descriptive model of choice under risk (Kahneman, 2003; Kahneman et al., 1991; Tversky & Kahneman, 1992). In essence, Prospect Theory describes how people are much more sensitive to losses than they are to equivalent gains by a factor of 2-2.5; this is known as loss aversion. Research on Prospect Theory has found that due to this disproportionate aversion to losses, people are more willing to accept risks in order to avoid sure losses but less willing to accept those risks to reap equivalent gains. This pattern of loss aversion is highly significant for understanding risk choices. It is also implicated in several other biases that may plague ‘optimal’ decisionmaking, two of which will be addressed here. The first is the status quo bias (Samuelson & Zeckhauser, 1988), which is the preference to avoid changes to the current state of affairs. A major premise of Prospect Theory is that gains and losses are relative and are evaluated via comparisons to an arbitrary reference point which is often the status quo. Conforming to loss aversion, the potential disadvantage of switching away from status quo overshadows the potential gains (Kahneman et al., 1991), leading to a biased preference for the present. An important thing to note is that this phenomenon is susceptible to framing (Samuelson & Zeckhauser, 1988); whether the alternatives are cast as a gain or a loss relative to the reference point can influence preferences. Furthermore, Samuelson & Zeckhauser (1988) also note that they found instances of the bias even in the absence of gain and loss frames, and suggest that  10  other factors such as a desire to validate previous actions may also creates preferences for the status quo. The second bias that is thought to emerge from loss aversion is the omission bias. The omission bias describes individuals’ refusal to engage in or accept actions that may incur potential losses, even when inaction (i.e., omission) can result in equal or greater harms (Baron & Ritov, 1994, 2004; Ritov & Baron, 1999). Baron & Ritov (2004) observe that usually the status quo bias and omission bias work in tandem, but there may be certain cases where “all options are new, so none is the status-quo” (p. 75). In addition, these authors have linked omission bias to ‘protected values’ (Baron & Spranca, 1997). This link will be addressed later in this chapter. The potential implications of these biases for climate change adaptation in conservation are significant. While on the one hand seemingly certain losses in biodiversity from climate change might lead to more risk-seeking or risk tolerant preferences for adaptation, loss aversion may also lead to overly conservative responses (i.e., the status quo bias) or even inaction (i.e., the omission bias) that inhibits the adoption of adaptations needed to mitigate losses. Despite their likely importance, little research has been done to examine whether climate change adaptation is being influenced by these biases. At least one field study has found evidence consistent with omission bias in the perceptions of climate change adaptation policies of farmers in Mozambique (Patt & Schröter, 2008). Elsewhere, other scholars have appealed to the status quo bias to explain the lack of action on the part of governments to confront climate change (e.g., Bazerman, 2006). However, it remains to be seen whether recognition of certain losses from climate change will be sufficient to motivate adaptation actions significantly different from the status quo.  2.4. Affect, Emotions and Risk While insights from bounded rationality and the study of biases continue to enhance current understandings of risk perception, a major critique of these earlier perspectives is the neglect of affective influences. Over the past twenty years in what has become known as the Emotions Revolution (Weber & Johnson, 2009), a great deal of research has  11  conclusively demonstrated that affect and emotions are an integral and defining part of risk perception formation and decision-making (e.g., Damasio, 1994). A differentiation between kinds of feeling-states can be made between general or holistic affect and emotions. General affect normally refers to a global feeling-state that is positive or negative (or characterized by a quality of goodness or badness) that may be indiscriminate or specific to a particular stimulus (Slovic, Finucane, Peters, & MacGregor, 2007). Emotion on the other hand refers to an intense, stimulus dependent and specific feeling-states (Pham, 2007) like anger, distress, fear, love, etc. that is subsequent to general affect. Affect is also hypothesised to be a major mediating mechanism in bounded rationality (e.g., Sunstein, 2006; 2007; Anderson, 2003). The evidence to date (see Loewenstein, Weber, Hsee, & Welch, 2001; Weber & Johnson, 2009; and, Marx et al., 2007 for reviews) suggests that a critical role served by affect is ‘informational’ (e.g., the affect-as-information hypothesis; Schwarz & Clore, 1983; 1988), wherein affect is indicative of the value of different alternatives which then guides judgment; positive affect reflecting the ‘goodness’ of a stimulus while negative affect is indicative of poor qualities. In this manner, affect has been characterized as a so-called natural assessment, which is a subconscious, automatic and routine reaction (Kahneman, 2003). The power of affect’s automaticity is apparent in early studies by Zajonc (1980) and Murphy and Zajonc (1993) which demonstrated how affective reactions to stimuli can occur pre-cognition and can be activated subliminally to influence preferences (also see Winkielman, Zajonc, & Schwarz, 1997 for another example). More recently, and more directly relevant to the topic of risk, Lowenstein et al.’s (2001) ‘risk-as-feelings’ hypothesis proposes that feelings can directly influence judgment and decision-making under risk without the mediation of cognition. Similarly, in a fusion of bounded rationality and affect research, Slovic and colleagues (Slovic, Peters, Finucane, & MacGregor, 2005; Slovic et al., 2007) proposes the existence of an affect heuristic, in which rapid and automatic affective responses to a choice can shape judgments and decisions (Slovic et al., 2005; Slovic et al., 2007), particularly in time limited settings and complex problems. As applied to perceptions of risk, it is hypothesised that negative affect is  12  predictive of pessimistic risk estimates while positive affect is predictive of relatively optimistic risk estimates. Indeed, these patterns were seen in early findings from Alhakami and Slovic (1994). Experiments by Finucane, Alhakami, Slovic, and Johnson (2000) also revealed that manipulations designed to influence a person’s affective evaluation of a hazard (i.e., nuclear power) predictably altered both perceived risks and benefits in the expected directions. Their study additionally showed that the influence of affect was strongest when there was time pressure and thus a decreased opportunity for deliberation. How might affect come to serve such a function? Damasio and his somatic marker hypothesis suggests that various stimuli operate as affective triggers when bodily states and images (which can include words, pictures, symbols etc.) are coloured (or ‘marked’) with negative or positive feelings through experience and learning. When individuals are later confronted with a decision problem, the marked images and states may reoccur or reappear automatically along with the associated affect as a ‘gut’ feeling, thus channelling judgments and decisions toward the more (perceptually) positive alternative. Responses to risk, however, often involve more complex feelings than general affect to include specific emotions (Peters et al., 2004). Evidence that specific emotions can have opposite effects on risk perceptions even if they share the same valence may be one of the most compelling reasons to look beyond more global positive and negative affective responses. Experimental studies conducted by Lerner & Keltner (2001) have demonstrated a distinct influence of each fear and anger on risk perceptions. While fear was associated with pessimistic risk estimates (a finding also made by Keller et al., 2006) and risk aversion, anger was associated with optimistic risk estimates and risk-seeking (mimicking the effects of happiness and enthusiasm). Similar to Lerner and Keltner’s (2001) results, experimental research by Druckman & McDermott (2008) also found a role for anger and optimism separately leading to risk-seeking, while anxiety emotions such as distress led to riskaversion. Conversely, Peters et al. (2004) found that fear and anger had very similar influences, and that affect provided significant explanatory power for risk perceptions and stigma over and above the power provided by negative emotion (i.e., fear and anger). Notably, compared to general affect, the influence of emotion on preferences and  13  behaviour is thought to be mediated to a greater degree by contextual factors of a risky choice (e.g., ambiguity, time constraints, etc.) rather than having a uniform effect (Pfister & Böhm, 2008). Outside the realm of climate adaptation, recognition of the importance of affect and emotions in climate change perceptions continues to grow, and have variously been used to explore public concern (e.g., Weber, 2006; Subdblad, Biel, & Gärling, 2007), offered as an avenue to enhance the effectiveness of climate communication (e.g., Marx et al., 2007; Leiserowitz, 2005), and used to understand climate change risk perceptions (e.g., Leiserowitz, 2006). As a complex issue for which there is little certain information on outcomes, climate change adaptation in conservation is a topic where visceral and emotional contexts may have substantial impacts. Whether and how risk perceptions and acceptability judgments of adaptation alternatives are impacted by direct (or incidental) affective reactions and emotions may provide valuable information for policy development.  2.5. Cultural Cognition While it is now well-established that affect and emotions are influential drivers of perceptions for a wide variety of hazards, it begs the question why people react in an emotionally negative manner to some stimuli and not others? Moreover, why are perceptions not uniform across individuals? According to a theory called Cultural Cognition (Kahan et al., 2009; Kahan, Slovic, Braman, & Gastil, 2006; Kahan & Braman, 2006), the answer lies in cultural differences (loosely defined to include social groups with shared beliefs and values) and specifically differences in worldviews. Cultural Cognition is largely based on anthropological and social psychological research and is an extension to Cultural Theory (Douglas & Wildavsky, 1983). At its core, Cultural Theory hypothesises that an individual’s perceptions and expressed preferences for risks can be understood as a reflection and defence of the worldview to which a person adheres. However, no systematic account of why this would be the case is offered. It is this gap that Cultural Cognition attempts to fill. Importantly, Cultural Cognition is premised on the assumption that culture precedes  14  cognition, and thus predisposes people to think about problems in culturally specific ways (Kahan et al., 2006). Cultural Cognition theorists pinpoint a set of three psychological mechanisms that help explain how risk perceptions and preferences become aligned with worldviews: cognitive-dissonance avoidance; affect; and group-dynamics (Kahan & Braman, 2006). Notably, all three are to an extent feelings-based, especially in the case of cognitivedissonance avoidance and affect. In the former, holding beliefs and views incongruent with personal worldviews creates unpleasant psychic and emotional reactions that people seek to avoid, thus pulling beliefs in line with cultural views which are base to identity. In the latter, as we’ve seen, affect influences perceptions and preferences by indicating whether a stimulus is good or bad, but it is culture that determines the affective valence. Holding or expressing views in contradiction with your in-group may also create uncomfortable scrutiny and judgment from peers which are undesirable. In each case, affect serves as the mechanism or motivation that aligns risk perceptions and preferences with worldviews. Cultural cognition has also been proposed as a viable explanation of many of the phenomena found in bounded rationality research, primarily through the claim that accounting for cultural values explains why many heuristics and biases of bounded rationality seem to skew people’s risk perceptions and judgments in culturally predictable ways (Kahan & Slovic, 2006; Kahan, 2008; see Sunstein, 2006 for the opposing view). In other words, while “risk perceptions might or might not be accurate when evaluated from an actuarial standpoint... nevertheless, which activities individuals view as dangerous and which policies they view as effective embody coherent visions of social justice and individual virtue” (Kahan et al., 2006, p. 1088). Many studies on natural resource and conservation management appear to buttress the claims of Cultural Cognition. What may be broadly construed as ‘environmental worldviews’ and environmental values has been repeatedly found to influence risk perceptions and the acceptability of management practices (e.g., Buijs, 2009; Fisher & van der Wal, 2007; Fischer & Young, 2007; García-Llorente, Martín-López, González, Alcorlo, & Montes, 2008; Hull, Robertson, Richert, Seekamp, & Buhyoff, 2002; Hull, et al., 2001; Manfredo, Teel, & Henry, 2009; McFarlane, Stumpf-Allen, & Watson, 2006; Willis & DeKay,  15  2007; Vaske, Donnelly, & Williams, 2001). Additionally, recent studies by Dietz et al. (2007) and Leiserowitz (2006) have also incorporated and demonstrated significant effects of worldviews on risk perception and policy support in a climate change context.  2.5.1. Protected Values (and Omission Bias) If worldviews are associated with particular patterns of risk perception and preference, it is perhaps unsurprising to find that specific values may also exert a strong influence. Specific values that resist being traded-off with other values irrespective of the consequences (e.g., Tanner, 2009) are known in the literature as ‘protected values’ (Baron & Spranca, 1997), as well as taboo values or sacred values (Lichtenstein, Gregory, & Irwin, 2007; Tetlock, 2003). Protected values are largely understood to be based in deontological (rule-based) rather than consequentialist (outcome-based) ethics (Baron & Spranca, 1997; Ritov & Baron, 1999; Tanner, 2009; Tanner, Medin, & Iliev, 2008). Due to their nonnegotiable nature, protected values have also been implicated in the omission bias as an absolute opposition to actions that may harm that value (Baron, 2006; Ritov & Baron, 1999; Tanner, 2009), even if acting is relatively beneficial. Again unsurprisingly, emotions appear to have a central mediating role between protected values and preferences. For instance, Lichtenstein et al. (2007) found affect to be the dominant driver of expressed disapproval for a range of scenarios trading-off protected values (e.g., human life) with secular values (e.g., money). Hanselmann & Tanner (2008) similarly found that negative emotions were associated to a greater extent with trade-off scenarios involving protected values compared to those that involved none. What is more, Lichtenstein et al. (2007) also discovered that among a set of trade-offs, those judged the most and the least acceptable were also felt to be the least cognitively demanding, suggesting that affect facilitates decisions involving protected values by bypassing cognitive deliberation. While there is evidence to link protected values and the omission bias (e.g., Baron & Leshner, 2000; Baron & Ritov, 2004; Ritov & Baron, 1999), starkly different results have also been obtained by other researchers, finding instead that individuals endorsing moral and  16  protected values are more likely to approve of actions (e.g., Tanner & Medin, 2004; Tanner et al., 2008; Tanner, 2009); this has been called the action bias. Some scholars have reasoned that these contradictory findings can be resolved through an understanding that protected values and consequentialist concerns are not mutually exclusive (Tanner et al., 2008). Bartels (2008) for example reasons that people who hold protected values should also be more concerned about the consequences for those values which may result in a preference for actions. Instead, whether preferences for omissions or actions are expressed appears to depend on where a person’s attention is directed at the time of the judgment (i.e., the principle versus the potential outcome) (Bartels, 2008). As noted by Baron (2010), some of the most common protected values are for natural resources such as species and pristine ecosystems. In this regard, protected values for natural resources may prove to be a particular and significant challenge for practitioners and decision-makers in environmental management (e.g., in planning adaptive management; Gregory, Ohlson, & Arvai, 2006) and plausibly for climate change adaptation in conservation (Hagerman et al., 2010a). Indeed, the conservative and restrained quality of policy dialogue and development for conservation adaptation thus far has been interpreted as being a result of protected values (Hagerman, Dowlatabadi, Satterfield, & McDaniels, 2010b). Reframing the adaptation issue to a focus on the comparative consequences of action versus inaction may help shift preferences away from potentially disadvantageous resistance to tradeoffs.  2.6. Testable Hypotheses In brief summary, biases have been used to explain people’s non-normative preferences and perceptions related to risk. These include loss aversion, the status quo bias and the omission bias. While research on risk and decision-making was historically focused solely on cognition, general affect and emotions are now recognised as an integral mediating, and at times direct, factor influencing preferences and perceptions. There is evidence that worldviews are an overarching antecedent and orienting determinant of individuals’ emotional and ‘biased’ responses. Finally, specific values can also powerfully  17  alter people’s willingness to accept or support actions but may be dependent on the locus of attention. In light of this literature review, the current study will test the following six hypotheses: I.  Do underlying risk perceptions of adaptation alternatives influence the acceptability of climate change adaptation policies for protected areas? If so, how? H1 – Risk perceptions will be negatively correlated with adaptation policy support. H2 – However, as may be predicted by the status quo bias, the overall pattern of support for adaptations will be greater for adaptation strategies that are most similar to policies currently used in conservation.  II.  Does certainty of current and future climate change and its environmental impacts influence the risk perceptions and acceptability of climate change adaptation policies for protected areas? If so, how? H3 – As may be predicted from loss aversion and recent research on protected values, an emphasis on certain climate change and its consequences for biodiversity is predicted to increase the acceptability of adaptations.  III.  Do general affect and negative emotions influence the risk perceptions and acceptability of climate change adaptation policies for protected areas? If so, how? H4 – Negative affect and emotions will be associated with increased risk perceptions and decreased support for adaptation strategies; the opposite may be observed with anger.  IV.  Do environmental worldviews influence the risk perceptions and acceptability of adaptations policies for protected areas? If so, how? H5 – Risk perceptions and support for policy will differ depending on the strength of individuals’ environmental worldviews.  18  3. METHODOLOGY  3.1. Introduction This chapter details the construction and structure of the survey instrument. The first section explains the overall procedure used recruit participants and collect data. Next, an overall summary of the survey instrument is provided. The rationale for each component and how each was modified for analysis is also described beginning with the experimental manipulations and then the measures.  3.2. Procedure This study was conducted through online surveys made available on the internet to the general public from February, 2010 to June, 2010. The surveys were hosted on the Norms Evolving in Response to Dilemmas (NERD) research group website and used their survey platform. NERD is based out of the W. Maurice Young Centre for Applied Ethics at the University of British Columbia. The survey consisted of 13 sections (which are described further below), with each section displayed on a separate webpage. It was anticipated that participants would require 10-15 minutes to complete the survey. As the primary objective of this research was to test for the influence of specific psychological factors on risk perceptions and policy support rather than determining the representative attitudes of a particular population, convenience sampling was used. This study also focused on the latter because perceptions and judgments may be highly sensitive to the context of particular places, thus focusing on more ‘durable’ psychological mechanisms may have broader significance by highlighting avenues by which contextual factors may exert an influence. Participant recruitment targeted the lay public. However, participation was open to anyone over 19 years of age. A variety of means were used to recruit participants, including social networks (i.e., facebook), e-mail discussion groups, posters, online ad sites (i.e., craigslist and kijiji), and word of mouth. Ads directed potential participants to a web address where participants saw a description of the study and the consent form. To access the  19  survey, participants had to first register with the NERD website by creating a username and provide basic demographic information (e.g., age and level of education). Once registration was complete, participants were able to access a number of surveys on the site (e.g., wildlife values, robot ethics, stem cell ethics, etc.) including the current survey. A total of 370 people were recorded as accessing the survey, however only 312 people logged responses on the final page (on demographics). The 312 individuals were retained for subsequent analysis for an overall dropout rate of 15.7% (n = 58). Of this remaining sample, 124 (39.7%) were male and 187 (59.9%) were female, and one person did not indicate a gender.  3.3. Experimental Design As participants entered the survey, they were assigned sequentially to one of four surveys. The first section of each survey asked participants to read a single paragraph of text describing in non-technical terms the likelihood of future climate change, its broad impacts on species, and the need for adaptation in protected area management. This text was presented to the right of two photographs. In total there were two versions of the text, with each emphasising to different degrees the certainty of further climate change. There were also two sets of paired photographs; one pair consisted of affectively positive images while the second pair consisted of affectively negative images. The text and the photographs were used as the experimental manipulations following a 2x2 factorial design leading to four combinations of images and text; a different combination for each of four surveys. Beyond this first section, the surveys were identical in their structure and content (see Figure 1). In order of appearance, the remaining sections of the survey were: a manipulation check for affect and emotions; seven adaptation policies and strategies on independent pages with risk and acceptability ratings solicited for each; a question on what participants thought should be the primary goal of conservation; the New Ecological Paradigm Scale (Dunlap et al., 2000) of environmental worldviews; a manipulation check for people’s certainty of current and future climate change; and, a set of socio-demographic questions. In general, the survey was designed to be of minimal length to mitigate  20  participant dropout. This was especially a concern given that no incentives were provided. Thus, wherever possible, questions were omitted rather than included.  3.1. Manipulations  3.1.1. Framing and Priming It is well-known in psychology that people are vulnerable to framing (e.g., Tversky & Kahneman, 1981, 1986), which refers to the effect that non-substantive changes to how information is delivered can have on responses (Kahneman, 2003). Many experiments have shown that responses can be altered simply by altering the words used and the manner in which questions are posed. Additionally, people are known to be susceptible to affective priming (e.g., Johnson & Tversky, 1983; Murphy, Monahan, & Zajonc, 1995; Winkielman et al., 1997), in which responses to stimuli are influenced by preceding affectively-loaded stimuli and can be impactful even if completely incidental to the object or decision at hand (e.g., Au, Chan, Wang, & Vertinsky, 2003; Schwarz & Clore, 1983). For these reasons, framing and priming are time-honoured and often used techniques in psychological research, and also informed the design of the current study.  3.1.1.1.  Certainty (H3)  The survey begins by asking the participant to read a paragraph of text. The text served two purposes: 1) briefly familiarise the participant to the issue of adaptation in protected areas and provide context for the survey; and 2) test H3 by manipulating how certain people were that future climate change was going to occur with negative consequences for protected areas and biodiversity.  21  Figure 1. Diagram of the survey structure. Beginning at the top left corner, participants enter the survey and are then assigned to one of four conditions. Returning participants are automatically redirected to the concluding page. Every participant then receives the same series of questions; each oval represents a separate webpage.  22  Two versions of the text were constructed and differing only in the extent to which future climate change was emphasized to be certain or uncertain but likely. The text below is as it appears in the survey, except here the alternate words used in each version are also shown (in bold text):  “Further climate change is likely/certain. It may/will alter the pattern of life on the planet, cause species extinctions and migration, and species behaviour change. New conservation tools and techniques and more management may be/are required to help biological systems and species adapt to climate change in protected areas (e.g., park, reserves, sanctuaries).”  The paragraph was designed to be purposely short and generic to increase the likelihood that participants would read and understand the entire passage. The manipulation was also limited to only a few words and did not emphasise extreme difference (e.g., certain vs. will not occur) to preserve perceived legitimacy (as it was expected that few climate sceptics would volunteer to take the survey) and ensure that the affective ‘hit’ was as similar as possible. The content and language was also designed to mimic texts from various reputable sources found easily in the public domain, such as the David Suzuki Foundation and UNEP’s Convention on Biological Diversity websites. To minimise participant dropout due to any protests related to the information presented, a comment box accepting optional comments was available underneath the paragraph and photos to serve as a qualitative outlet.  3.1.1.2.  Affect and Emotion (H4)  Images are known to have powerful effects on emotion and risk perceptions, including in the domain of climate change (e.g., Leiserowitz, 2006; Lorenzoni, Leiserowitz, Doria, Poortinga, & Pidgeon, 2006). Furthermore, as elaborated previously, even subliminal priming through images can have lasting effects (e.g., Winkielman et al., 1997). To test H4,  23  either paired positive (i.e., sleeping grizzly and ocean scene) or negative (i.e., roaring grizzly and tornado) affective laden images appeared to the left of the text prompt described above (see Figure 2). These were intended to manipulate the affective and emotional state and sensitivity of the participants.  Positive Images  Copyright 2009 by Lassi Kurkiljärvi. Adapted with permission from a creative commons attributionnoncommercial 2.0 generic license.  Copyright 2009 by Jordan Tam.  Negative Images  Copyright by Plovercheck. Reprinted with permission per the terms of use of the website on which the image is hosted.  Copyright 2006 by Ellen. Adapted with permission from a creative commons attribution-noncommercial 2.0 generic license.  Figure 2. Affective manipulation images. Shown are the positive and negative affectivelyloaded images used as affective primes.  To avoid arousing the suspicion of participants with regard to the presence of the images, nature and weather images were used to try and match the subject matter of the text. The images used in this study were also adapted from those with known affective ratings courtesy of Lang et al.’s (2008) database of images, all of which had previously been rated by hundreds of participants for their valence (positive and negative quality) and arousal (feeling intensity). Following are the mean valence and arousal ratings for the similar images from Lang et al.’s (2008) database (with lower scores indicating more negative valence and decreased arousal): a roaring bear (valence = 4.32, arousal = 6.64); a tornado (valence = 3.49, arousal = 6.65); a sleeping adult polar bear and cub (valence = 7.97, arousal = 3.94); and, a scene of the beach (valence = 8.22, arousal = 5.71). Unfortunately, images from the database could not be used directly in the online study due to user agreement restrictions. As a result, images from the author’s personal library (i.e., the  24  beach scene), and creative commons shared photographs from online photo sharing sites Flickr (i.e., the sleeping bear; Kurkiljärvi, 2009), Photobucket (i.e., the roaring bear; Plovercheck) and Wikimedia commons (i.e., the tornado; Ellen, 2006) were employed.  3.2. Measures  3.2.1. Emotion To assess whether the image manipulation had the intended effect, a manipulation check for affect and emotion was included in the section following the informational text and image prompt. Recognising the differentiation between holistic affect and specific emotions (e.g., Lerner & Keltner, 2001), a total of three questions on each were included. To measure general affect, participants were asked to refer back to the text they had just read and to provide a rating of how good or bad, positive or negative, and pleasant or unpleasant they felt. They were also asked how relaxed or afraid, calm or angry, and at ease or upset they felt to assess three specific negative emotions. In the survey, the affect questions are mixed with the questions on emotion (see Appendix F). Five-point Likert scales were used for the ratings with lower scores associated with more negative valence. The questions on affect and emotion were adapted from previous studies on risk perception (i.e., Leiserowitz, 2006; Peters et al., 2004) as well as the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). Though the PANAS would have been appropriate, this instrument was not used due to fears of lengthening the survey instrument. The original intent was to aggregate the three affect questions into a single index; this combination yielded a high Cronbach’s alpha (α = 0.85) thus an index was created. It is also worth mentioning that the questions on specific negative emotions did not have obvious bipolar ‘opposites’ like the questions on holistic affect (e.g., good vs. bad), thus the high end was designed to tap into relatively positive feeling-states with low arousal which were not measured in the questions on affect.  25  3.2.2. Risk Perceptions and Policy Acceptability (H1 and H2) Following an examination of the conservation adaptation literature, seven different adaptation strategies were selected and presented to participants. In particular, the selection process drew heavily from the comprehensive reviews and recommendations on adaptation by Heller and Zavaleta (2009) and the CCSP (2008). In their review, Heller and Zavaleta (2009) suggest that adaptations may be viewed along a continuum of risk from the risk-averse (e.g., status quo, more of the same) to the risk-tolerant (e.g., pre-emptive interventions). Though whether such a continuum actually exists is unverified, adaptation policies were selected by roughly following this guideline to reflect the full range of proposed adaptations along the continuum (see Table 2), from the relatively noninterventionist and conventional (e.g., protected area corridors) to the more interventionist and novel (e.g., assisted migration).  Table 2 Adaptation alternatives on a proposed continuum of risk Migration Corridors  In-situ Aid  Permitting Climate Migrants  Captive Breeding  Conservation Triage  Assisted Colonization  Species Introduction for Ecosystem Function  Facilitating species migration in response to climate change through the establishment of protected passageways between PAs.  Providing aid within protected areas to native species struggling to adapt (e.g., feeding, breeding, or dispersal).  Allowing species outside of a protected area to enter and become established in the ecosystem as they migrate in response to climate change.  Ex-situ preservation of species unable to adapt to climate change. E.g., zoos, seed banks, cryogenics etc...  Explicitly prioritizing conservation objectives to maximize efficient use of limited conservation resources.  Purposely transporting threatened species to areas outside their historic range to increase their chance of survival under climate change.  The introduction of better adapted non-native species to a protected area for the purpose of replacing lost ecosystem functions due to climate change.  Conventional Risk-averse  Novel Risk-tolerant  26  Each policy was presented on its own page as a question e.g., “Is it acceptable to link protected areas using corridors that allow species to move in response to climate change?” followed by an example that was non-species or location specific e.g., “A species is expected to move from protected area A toward protected area B in response to climate change. To allow the species to move with less obstruction, a strip of land connecting the two areas is protected.” For each proposed adaptation, participants were first asked to rate how risky they saw the policy in general on a five-point Likert scale adapted from Leiserowitz (2006) and Weber, Blais, and Betz (2002), with 1 being ‘not at all risky’ and 5 being ‘very risky’. Participants were then asked to rate on another five-point scale how acceptable they found the policy, with 1 being ‘not at all acceptable’ to 5 being ‘very acceptable’. A neutral option was not included to force a response. For a more nuanced understanding of adaptation, the risk and acceptability ratings were kept as separate units of analysis. Additionally, the Cronbach’s alpha scores for the seven risk ratings (α = 0.65) and the acceptability ratings (α = 0.58) were below the accepted cut-off score of α = 0.70 to create indices (Field, 2009), thus reinforcing the decision to analyse each separately.  3.2.3. Goals of Conservation Many authors have suggested that a revision of the fundamental objectives of conservation and protected areas may be necessary to address the issue of climate change (e.g., Barnosky, 2009; Brooke, 2008; Hagerman & Chan, 2009; Harris et al., 2006; Hodgson, Thomas, Wintle, & Moilanen, 2009). Though no a priori hypotheses are made, a single question measuring people’s beliefs about the normative goals of conservation was also included for exploratory purposes. Participants were asked to choose from six options what they believed should be the primary goal of conservation: protection of wilderness areas; at risk species; at risk ecosystems and ecosystem functions; at risk landscape features; species and ecosystems with human uses; and, don’t know. Of the options, only six individuals indicated that the protection of landscape features should be the primary goal of conservation; these were omitted from further analysis.  27  3.2.4. Environmental Worldviews (H5) As discussed in Chapter 2, a literature has developed around the theory of cultural worldviews as an overarching orienting factor shaping risk perceptions and risk judgment. Though this body of research traditionally focuses on Douglas and Wildavsky’s (1983) framework of worldviews related to social organization, this study focused instead on environmental worldviews specifically, and in particular as articulated by Dunlap et al., (2000) through the New Ecological Paradigm (NEP). Originally theorised by Dunlap and Van Liere (1978), the New Environmental Paradigm as it was originally called was proposed as an emerging and direct challenge to the Dominant Social Paradigm (DSP) worldview which emphasised beliefs in economic growth and progress, limited government, and a faith in science and technology to solve problems amongst others beliefs. The NEP was operationalized by Dunlap and Van Liere (1978) with an original set of twelve questions related to human-nature relationships. It has since become the most often used psychological measure of environmental beliefs and orientation (Stern, Dietz, & Guagnano, 1995). In a review and revision of the NEP index, Dunlap et al. (2000) expanded the original set of statements to 15 with five sub-scales (of three questions each) tapping into beliefs on: the reality of limits to growth; antianthropocentrism; the fragility of nature’s balance; rejection of human exemptionalism; and, the possibility of an ecocrisis. For each statement the participant was asked to rate from 1 to 5 their level of agreement, with 1 being ‘strongly disagree’ and 5 being ‘strongly agree’. As the NEP deals specifically with human-nature relationships which have direct relevance to the issue under investigation, the choice was made to employ the 15-item NEP index. Furthermore, the beliefs measured by the NEP have been repeatedly found to be influential in resource management preferences (e.g., Buijs, 2009; Fischer & van der Wal, 2007; Fischer & Young, 2007; García-Llorente et al., 2008; Hull et al., 2002; Hull et al., 2001; Manfredo et al., 2009; McFarlane et al., 2006; Vaske, et al., 2001; Willis & DeKay, 2007). The use of the NEP index in risk research is also not without precedent (e.g., O’Connor et al., 1999; Slimak & Dietz, 2006; Willis & DeKay, 2007), and evidence has also been provided that  28  the NEP in fact overlaps, to some degree, with the worldview classes proposed by Douglas and Wildavsky (Poortinga, Steg, & Vlek, 2002).  3.2.5. Climate Change Certainty A manipulation check for the effect of the informational prompts on participants’ level of climate change certainty was included near the end of the survey. Four questions asked individuals to rate how certain they were that: climate change was currently occurring; further climate change would occur; current climate change was having negative impacts; and, further climate change would have negative impacts. A fifth question asked participants how certain they were that humans could manage nature without causing negative impacts. Participants rated their certainty using a five-point Likert scale ranging from 1 being ‘very uncertain’ to 5 being ‘very certain’. However, for the analysis, the fifth question was removed as it was felt to be redundant with several NEP questions e.g., “When humans interfere with nature it often produces disastrous consequences”. A Cronbach’s alpha of α = 0.85 was obtained for the remaining four questions. They were thus combined into a single index for analysis.  3.2.6. Socio-demographics A range of socio-demographic measures were also included in the survey. Some of these were built into the registration to the NERD website preceding the survey and included age, country of origin, country of residence, computer literacy, English literacy, level of education, and gender. A series of socio-demographic measures were also included at the end of the survey measuring political orientation, support of the environmental movement and frequency of involvement, distance of residence from a protected area, and frequency of visits to protected areas per year. Generally, these questions were included to characterise the sample. However, some factors such as age, political orientation, education, and gender are known to reliably influence climate change risk perceptions (e.g., Leiserowitz, 2009).  29  4. RESULTS  4.1. Introduction Chapter 4 reviews the findings of the current study. First, the characteristics of the sample are described, followed by the results of the experimental manipulations. It was found that the manipulations did not have the intended effects. Analyses of the manipulations checks and remaining measures were then undertaken and are subsequently described. All analyses were conducted with SPSS 17.0.  4.2. Demographic Variables By many measures, the sample (N = 312) appeared relatively homogeneous. The sample was primarily made up of young participants with over half (54.8%, n = 171) of participants between 19-29 years of age and nearly a quarter (24.4%, n = 76) between 29 and 39 years old. In terms of political orientation 49.1% (n = 153) indicated they were ‘liberal’ or ‘somewhat liberal’ while only 7.7% (n = 24) chose ‘somewhat conservative’ or ‘conservative’, however a substantial number of individuals did not respond (17.9%, n = 56). When participants were asked how often they visited protected areas in an average year, the vast majority indicated that they visited at least once a year, while only 7.1% (n = 22) said never, and nearly a quarter (23.1%, n = 72) visited ten times or more. The majority were also self-reported members or supporters of the environmental movement (61.2%, n = 191). The sample was highly educated in the extreme with 91.3% (n = 285) holding degrees at or above the university and college level. Most participants were Canadian residents (74%, n = 231) with the United States (12.8%, n = 40), the UK (2.6%, n = 8) and Australia (2.2%, n = 7) rounding out the top four, together totalling 91.6% (n = 286) of respondents. Another 16 countries were represented by the remaining participants (n = 26) and two individuals did not respond. Due to an oversight in design, the question on people’s rate of involvement in the environmental movement was dropped as a ‘not involved’ option was unavailable resulting in many non-responses (n = 110). For a full breakdown of the socio-demographic results see Appendix A.  30  4.3. Experimental Manipulations To examine whether the experimental manipulations (i.e., the affectively loaded images and informational text prompts) had the intended effects, comparisons between the treatment groups were made on each the certainty index, affect index, and the three negative emotion scales. Also of interest was the possibility of interaction effects. Of the 312 participants, 153 (49.0%) were assigned to the certain prompt condition, 159 (51.0%) were assigned to the uncertain prompt, 153 (49.0%) saw the positive images, and 159 (51.0%) saw the negative images. In all, 25.0% (n = 78) were in the positive image and uncertain condition, 24.0% (n = 75) in the positive image and certain condition, 26.0% (n = 81) were in the negative image and uncertain condition, and 25.0% (n = 78) were in the negative image and high certainty condition. Analyses showed that scores on the emotion scales and the certainty, affect and NEP indices were all non-normally distributed (see Figures 3, 4 and 5). However, because sample sizes were significantly over 30 it was felt that that parametric tests would be robust against these aberrations (Field, 2009). Furthermore, parametric statistics were also preferred due to the limitations of nonparametric statistics to assess interaction effects. As such, two-way ANOVAs were used to test for the effects of the manipulations and were preferred over the use of a single MANOVA because of the less restrictive set of assumptions and possible issues of multicollinearity between the affect and emotion scales. As a side note, MannWhitney U nonparametric tests mirrored the main effect results of the ANOVAs.  4.3.1. Affect and Emotions 4.3.1.1.  General Affect  There were a total of 307 valid cases for the affect index, which was a composite of the three individual affect questions. Recalling that higher scores indicate more positive valence, scores reached a minimum of 3 to a maximum of 13 out of a possible range of 315. The mean score was 6.60 (SD = 2.28). The Shapiro-Wilk test was significant (W = .949, p < .001) indicating a non normal distribution. Levene’s test was non-significant (F = 0.21, p > .05) indicating that the variance of affect scores across groups was equal. A two-way  31  ANOVA showed no significant main effects for the image manipulation [F(1, 303) = 2.03, p > .05] or the text manipulation [F(1, 303) = 2.48, p > .05] on affect. There was no significant interaction effect, F(1, 303) = 0.18, p > .05.  Figure 3. Histograms for affect and emotions. Distributions of responses shown for each the affect index and questions on fear, distress and anger in a clockwise direction from the top left. Note the positive skew for each indicating the greater prevalence of negative valence. 4.3.1.2.  Fear  There were 307 total responses on the question of ‘fear’. Scores ranged from 1-5 (with 1 being the most fearful). A mean of 2.51 (SD = 0.96) on the scale and a significant result on the Shapiro-Wilk test of normality (W = .89, p < .001) was obtained. Levene’s test was non-significant (F = 0.24, p > .05). Factorial ANOVA did not reveal any significant effects  32  from the image manipulation [F(1, 303) = 1.32, p > . 05] or from the certainty manipulation [F(1, 303) = 0.00, p > .05]. No interaction effect was found, F(1,303) = 0.19, p > .05.  4.3.1.3.  Anger  In response to the question on anger, 309 individuals responded. The distribution exhibited the full range of scores from 1-5 (with 1 being the most angry) and a mean of 2.46 (SD = 0.92). Again, the Shapiro-Wilk test was significant (W = 0.88, p < .001) indicating a non-normal distribution. Levene’s test was significant (F = 2.83, p < .05), as such a more conservative alpha of .01 was adopted. The two-way ANOVA found no significant effects for the affect manipulation [F(1, 305) = 3.61, p > .05] or for the certainty manipulation [F(1, 305) = 0.34, p > .05]. Once again, no interaction effect was apparent, F(1, 305) = 0.51, p > .05).  4.3.1.4.  Upset  Finally, 306 participants responded to the question on distress, or how ‘upset’ they felt. Responses ranged from 1-5 (with 1 being the most upset) with a mean was of 2.32 (SD = 0.93). Like all the measures of affect and emotion, the Shapiro-Wilk test of normality was significant (W = 0.86, p < .001). Levene’s test of equal variances was non-significant (F = 0.88, p > .05). The factorial ANOVA found no main effects for the affect manipulation [F(1, 302) = 1.30, p > .05] or the certainty manipulation [F(1, 302) = 0.14, p > .05]. Once again, no interaction effect was found, F(1, 302) = 0.70, p > .05.  4.3.2. Certainty The certainty index constituted an aggregate score of four questions which asked participants’ self-reported certainty of current and future climate change and its negative impacts on the environment. High scores indicate greater certainty. A total of 281 valid cases were obtained. The mean score was extremely high and the distribution showed little variance (M = 18.04, SD = 2.70), as seen in Figure 4. Though the scale ranged from 4-20, the minimum score obtained was eight, and a full 43.3% (n = 135) of participants registered at  33  the highest possible score of 20. Unsurprisingly, the Shapiro-Wilk statistic was significant (W = 0.74, p > .001). Levene’s test of equal variances was non-significant (F = 0.67, p > .05). The factorial ANOVA found no main effects for the affect manipulation [F(1, 277) = 0.00, p > .05] or the certainty manipulation [F(1, 277 = 0.55), p > .05]. No interaction effect was found, F(1, 277) = 2.51, p > .05.  Figure 4. Histogram for the certainty index. Displayed is the negatively skewed distribution of responses on the certainty index. Also shown are the majority of responses logged at the highest end of the scale indicating the greatest certainty. 4.3.3. New Ecological Paradigm Though the experimental manipulations were not expected to have an effect on NEP scores, observed differences in the NEP histograms between the positive and negative affect treatment groups compelled further analysis (see Figure 5). Overall, this sample scored relatively highly on the New Ecological Paradigm scale. There were a total of 275 valid cases with 37 missing; the mean score was 60.01 (SD = 8.99). While the index ranges from a low of 15 to a high of 75, participants scored a minimum of 26 and maximum of 75. The Shapiro-Wilk test of normality was significant (W = .11, p < .001) confirming that the distribution is not normal.  34  Figure 5. Histograms of the NEP index by affect manipulation. NEP index scores are shown for each negative and positive image group. Note the more peaked distribution on the right. Levene’s test of equal variances was non-significant (F = 0.87, p > .05). In line with the observed differences in the histograms, the two-way ANOVA analysis found a main effect for the affect manipulation [F(1, 271) = 4.32, p < .05, ηp2 = 0.02] with the positive image group (M = 58.50, SD = 9.78) exhibiting less endorsement of the NEP than the negative image group (M = 61.05, SD = 8.15). No main effect from the certainty manipulation [F(1, 271) = 0.05, p > .05] and no interaction [F(1, 271) = 0.40, p > .05] was found.  4.3.4. Risk Perceptions and Preferences One possible reason the manipulations did not appear to have any influence on the manipulation checks is that the checks were not appropriate measures of affect and certainty, leaving open the possibility that the manipulations had in fact impacted risk perceptions and acceptability as intended. To examine whether this was the case, t-tests for independent groups were used to assess each manipulation as the independent variable  35  and each policy’s riskiness and acceptability ratings as dependent variables. Tests showed no significant differences attributable to the manipulations. The results can be seen in Appendix C. These results do not support H 3 that an emphasis on certain biodiversity loss from climate change would decrease risk perceptions and increase acceptability. They also do not support H4 which predicted that negative affect and emotions would be associated with increased risk perceptions and decreased support for adaptation strategies.  4.3.5. Summary of the Manipulation Effects To sum up to this point, as a whole, the experiment did not produce the expected effects. Instead there was an unexpected effect of the affectively loaded images on NEP scores which will warrant future research. The results also do not provide a basis to reject the null hypotheses that affect and certainty do not have influences on risk perceptions and preferences. At this juncture we turn to the remaining measures in the survey and their relationship to risk perceptions and preferences. Additionally, an extension of the above analyses was undertaken by employing the manipulation checks for affect, negative emotions, and certainty as independent variables. This was done to assess whether the lack of experimental effects is more appropriately viewed as an inadequacy of the manipulations to produce the intended changes in participant affect and certainty.  4.4. Measures  4.4.1. Adaptation Risk Perceptions and Preferences As expected, the perceived riskiness of the various adaptation policies was not uniform and appeared to be situated along a continuum from the risk-averse to risktolerant (see Figure 6). The adaption perceived to be the least risky overall was the use of migration corridors to facilitate species movement in response to change (M = 2.69, SD = 1.15). The most risky policy was the introduction of non-native species to maintain or enhance ecosystem processes (M = 3.98, SD = 0.97).  36  Similarly, some policies were seen as more acceptable than others. The most acceptable policy was the use of migration corridors (M = 4.37, SD = 0.90) while the least acceptable was the introduction of non-native species for ecosystem function (M = 2.65, SD = 1.04). However, a very close runner-up for the least acceptable policy was the use of conservation triage (M = 2.69, SD = 1.15). It may also be noted that, broadly speaking, the most acceptable policies appear to be similar to current practices in conservation (and least interventionist) while the least acceptable are the most novel (and most interventionist). However, at least qualitatively, there appears to be a high tolerance for adaptation in this sample overall. These results support H2 that the overall pattern of support for adaptations will be greater for adaptation strategies that are most similar to the status quo. Additionally, it was found that for every adaptation policy, risk and acceptability ratings were significantly and negatively correlated to a p < .001 level, which supports H1 (see Table 3). 5  4  3  2 Mean Acceptability 1  Mean Risk  Figure 6. Mean risk and acceptability ratings. The mean ratings of acceptability and risk for each adaptation policy is shown, ordered from most acceptable to least acceptable from left to right.  37  It is also clear that the different degrees of acceptability between policies cannot be entirely attributed to differences in their riskiness. For example, conservation triage (M = 3.73, SD = 1.02) and assisted colonization (M = 3.82, SD = 0.90) do differ a little in terms of riskiness, but their acceptability ratings M = 2.69 (SD = 1.15) and M = 3.23 (SD = 0.95) respectively are disproportionately different (six times greater). Table 3 Two-tailed Pearson correlations between risk and acceptability for adaptations Migration Corridors  Permitting Climate Migrants  In-situ Aid  Captive Breeding  Assisted Colonization  Conservation Triage  -.421  -.345  -.366  -.453  -.356  -.455  (New) Species Introduction for Ecosystem Function -.462  Notes: All correlations were found to be significant to a p < .001 level.  4.4.2. Affect, Emotions, Risk Perceptions, and Preferences The following set of analyses treats the affect index and the questions on negative emotions as independent variables. Given the homogeneity of the sample and the responses, a decision was made to compare individuals who scored at the ends of each the affect index and negative emotion scales. This was done to assess whether there was any indication at all that affective factors had influences on the risk perceptions and acceptability of adaptation strategies.  4.4.2.1.  General Affect  In analysing general affect, a median split of the index was used to separate the sample at a score of 6.00 resulting in two groups. One-hundred and seventy-six individuals fell into the negative affect group and 131 in the positive affect group (i.e., those who scored 7.00 or higher). T-tests for independent groups showed only one significant difference. The negative affect group (M = 3.98, SD = 0.90) was more accepting of providing in-situ aid to struggling species than those who were more affectively positive (M = 3.63, SD  38  = 1.07), t(227) = 2.94, p < .01. Levene’s test indicated unequal variances (F = 11.09, p < .01), so the degrees of freedom were adjusted from 289 to 227. The same pattern of results was replicated using Mann-Whitney U tests.  Figure 7. Adaptation acceptability and riskiness by affect valence. Comparisons between relatively positive and negative affect groups produced from the median split of the affect index. Adaptations are organised by their overall acceptability and risk in descending order. 4.4.2.2.  Fear  As the range of the negative emotion scales was so limited, a median split was not felt to be appropriate. Thus individuals who indicated neutrality on each emotion scale (i.e., the centre point) were excluded from analysis. Remaining individuals were separated into two groups. In the case of fear, individuals who scored 1 or 2 were combined to represent those who were most ‘fearful’ while individuals who scored 4 or 5 were combined into another group and represented those who were most ‘relaxed’. Between-subjects t-tests only showed one significant difference in risk perceptions between the fearful and relaxed groups. Relaxed individuals (n = 41, M = 3.44, SD = 1.05) saw conservation triage as less risky than the fearful group (n = 146, M = 3.86, SD = 0.99), t(185) = 2.37, p < .05. Differences between the groups was near significant (i.e., p < .10) in the perceived risk of captive breeding. In comparing acceptability ratings, significant differences were found on three  39  different policies. Conservation triage was viewed as more acceptably by relaxed individuals (n = 41, M = 3.15, SD = 1.24) than fearful individuals (n = 146, M = 2.62, SD = 1.10), t(185) = 2.62, p < .01. The introduction of non-native species to maintain ecosystem function was also found to be differentially acceptable, with the relaxed group (N = 40, M = 3.00, SD = 1.01) seeing it as more acceptable than the fearful group (n = 149, M = 2.60, SD = 1.04), t(187) = -2.15, p < .05. In contrast, people who were in the relaxed group (n = 40, M = 3.58, SD = 1.06) found providing in-situ aid less acceptable than those who were fearful (n = 150, M = 3.97, SD = 0.93), t(56) = 2.30, p < .05. Since Levene’s test was significant (F = 4.60, p < .05) the degrees of freedom was reduced from 188 to 56. Differences in the acceptability of captive breeding and of allowing climate migrants to become established was also near significant (i.e., p < .10). This pattern of findings was mirrored by Mann-Whitney U tests.  Figure 8. Adaptation acceptability and riskiness by fear ratings. Shown are comparisons of adaptation acceptability and risk ratings between individuals who expressed fear versus those who were relaxed. Despite the fact that only a few significant differences were found between the two groups, the pattern of differences (as seen in Figure 8) in risk perceptions and acceptability were remarkably consistent. In general, with the exception of in-situ aid and migration corridors, people who were fearful were less accepting of all the adaptation policies. Furthermore, they saw all adaptations as riskier.  40  4.4.2.3.  Anger  As with fear, the anger scale was converted into a dichotomous independent variable. Between-subjects t-tests found no significant differences between individuals who were ‘angry’ and individuals who were ‘calm’. However, the acceptability of permitting climate migrants and of introducing non-native species to maintain ecosystem functions were near significant (i.e., p < .10), as were differences in the perceived riskiness of providing in-situ aid. Nonetheless, the pattern of responses is consistent with fear (see Figure 9). People in the angry group are by and large less willing to accept various adaptations with the exception of in-situ aid. They also judged adaptations as riskier than those who were calm. This pattern was replicated by Mann-Whitney U tests.  Figure 9. Adaptation acceptability and riskiness by anger ratings. Mean acceptability and risk ratings for each adaptation policy organised by the angry and calm groups derived from the single question on anger. 4.4.2.4.  Upset  No significant or near significant differences were found using the dichotomous distress variable. Additionally there was no clear direction or pattern of responses (see Figure 10).  41  Figure 10. Adaptation acceptability and riskiness by distress ratings. Mean acceptability and risk ratings for each adaptation policy organised by relatively upset and at ease groups derived from the single question on distress. In summary, these secondary analyses provided some weak evidence for H 4 that negative affect and emotions are associated with increased risk perceptions and decreased support for adaptation strategies. More precisely however, it appears that while there is some data to suggest that fear and anger may lead to consistent patterns of risk perception and acceptability, general affect and distress (i.e., feeling upset) did not appear to have any considerable impact.  4.4.3. Certainty, Risk Perceptions and Preferences As was seen in the histogram of responses for the certainty index, the distributions of scores is extremely skewed. A median split was used to create a ‘low’ certainty group (n = 146) consisting of individuals who scored below the mean of 19.00, and a high certainty group (n = 135). T-tests for independent groups revealed only three significant differences. The riskiness of migration corridors was significantly higher for the low certainty group (n = 145, M = 2.79, SD = 0.94) compared to the high certainty group (n = 132, M = 2.54, SD = 1.04), t(265) = 2.15, p < .05. Levene’s test was significant (F = 4.77, p < .05) so the degrees of freedom was reduced from 275 to 265. Willingness to accept the use of migration corridors was significantly higher for the high certainty group (n = 134, M = 4.58,  42  SD = 0.74) than the low certainty group (n = 146, M = 4.27, SD = 0.91), t(274) = -3.19, p < .01; Levene’s test was significant (F = 11.86, p < .01) thus the degrees of freedom was reduced from 278 to 274. Finally, the acceptability of providing in-situ aid was significantly lower in the low certainty group (n = 145, M = 3.68, SD = 0.93) than the high certainty group (n = 134, M = 4.02, SD = 0.98), t(277) = -2.98, p < .01. As such, these results indicate that the certainty of climate change and its negative impacts mostly have a null effect on risk perceptions and acceptability except for a few specific instances (see Figure 11). Though one might observe that in terms of acceptability, the only policies that appear to show sensitivity to certain climate change are those that are most similar to the status quo. As such, there is little support for H 3 that recognition of certain biodiversity loss from climate change will increase acceptability. No substantive difference between these results and the Mann-Whitney U tests was found.  Figure 11. Adaptation acceptability and riskiness by degree of certainty. Mean acceptability and risk ratings for each adaptation policy organised by the low certainty and high certainty groups created through a median split of the certainty index. 4.4.4. Environmental Worldviews, Risk Perceptions and Preferences To examine whether environmental worldviews influence risk perceptions and preferences, the New Ecological Paradigm index was employed. As noted by Dunlap et al. (2000) there has been some debate as to whether the NEP scale is more sensibly treated as  43  a single index or whether it should be broken down into sub-components for analysis. On this issue, Dunlap et al. (2000) advise that “the decision to break the NEP items into two or more dimensions should depend upon the results of the individual study…[and] should not be made beforehand” (p. 431). In comparison with Dunlap et al.’s (2000) assessment of the 15-item NEP’s dimensionality, this study found very similar results. Identical to their study, a high Cronbach’s alpha of was obtained (α = 0.83). Similarly the item-total correlations were reasonably strong ranging from 0.27 to 0.63 as compared with their correlations of 0.33 to 0.62. Principal Components Analysis conducted on the results from the current study using orthogonal (varimax) rotation was also analogous. Dunlap et al. (2000) found that all 15 items loaded heavily from 0.40 to 0.73 on the first unrotated factor, explaining 31.3% of the total variance compared to 10.0% for the second factor extracted. This study found that all the items loaded from 0.33 to 0.73 on the first unrotated factor, explaining 30.6% of the total variance compared to 9.0% for the second factor. In both cases, four factors were extracted with an eigenvalue greater than 1.0. However, Cattell’s scree plot indicated a distinct inflection at the second factor. Following the interpretation of Dunlap et al. (2000), the NEP was seen as measuring a single construct of environmental worldview and thus the NEP was preserved as a single index. However, for the analysis, a split at the median of 62.00 was used to create a relatively low (n = 149) and a relatively high (n = 126) group of NEP endorsers. Differences in acceptability were observed in three different policies. Support for migration corridors was greater amongst individuals with more pro-environmental worldviews (n = 126, M = 4.61, SD = 0.72) compared to low NEP group (n = 149, M = 4.22 SD = 0.97), t(269) = -3.84, p < .001; degrees of freedom were adjusted from 273 to 269 as Levene’s test was significant (F = 15.57, p < .001). Conversely, acceptability of captive breeding was lower amongst high NEP endorsers (n = 126, M = 3.25, SD = 1.18) than low scorers (n = 149, M = 3.56, SD = 1.08), t(273) = 2.82, p < .05. This was also observed with regard to the introduction of non-native species to maintain ecosystem function. People with more environmental worldviews (n = 126, M = 2.47, SD = 0.97) were less supportive  44  than people with lower environmental worldviews (n = 148, M = 2.80, SD = 1.05), t(272) = 2.74, p < .01. Similarly, though not quite reaching statistical significance, the acceptability of conservation triage is greater amongst low endorsers (n = 149, M = 2.81, SD = 1.13) than those who scored highly on the NEP (n = 125, M = 2.54, SD = 1.12), t(272) = 1.92, p = 0.06. These results (also see Figure 12) appear to show that greater pro-environmental worldviews are associated with a greater willingness to support passive adaptations and decreased willingness to support interventionist strategies.  Figure 12. Adaptation acceptability and riskiness by NEP scores. Mean acceptability and risk ratings for each adaptation policy organised by the low NEP and high NEP groups created through a median split of the NEP index. Turning to risk perceptions, a clear pattern emerges wherein significant differences were found between the low and high NEP groups on nearly all the policies. As can be seen in Figure 12, significant differences were found between the two groups for the following policies: permitting climate migrants, t(272) = -2.46, p < .05; introduction of non-native species for ecosystem function, t(271) = -3.15, p < .01; assisted colonization, t(273) = -3.58, p < .001; captive breeding, t(273) = -2.27, p < .05; and, conservation triage, t(272) = -2.35, p < .05. The acceptability of providing in-situ aid was also nearly significant, t(273) = -1.93, p = .054. High NEP individuals saw all policies except the establishment of migration corridors as more risky than those who scored lower on the NEP.  45  These results provide some evidence in support of H5: Risk perceptions and support for policy will differ depending on the strength of individuals’ environmental worldviews.  4.4.5. Conservation Goals A total of 288 valid responses were received with regard to people’s preferred goal of conservation. The majority (56.4%, n = 176) identifying the ‘protection and restoration of unique or at risk ecosystem processes’ as the first priority. The protection or restoration of at risk or unique wilderness areas found favour with 11.2% (n = 35) of those sampled, 9.3% (n = 29) chose at risk and unique species, 1.9% (n = 6) thought physical locations should take priority (which was removed from analysis), 6.1% (n = 19) felt conservation should be geared toward protecting species and ecosystems that have human use, and 7.4% (n = 23) didn’t know. To explore whether these normative preferences had any relation to participants’ risk perceptions and willingness to support adaptations, a one-way ANOVA was conducted. Though some previous studies suggest that an individual’s ideals related to nature and management have implications for management preferences (e.g., Buijs, 2009; Fisher & van der Wal, 2007; Fischer & Young, 2007; Vaske et al., 2001), no a priori predictions were made. As such a conservative post-hoc test was applied (i.e., Tukey’s HSD). In terms of risk perceptions, significant differences between groups were observed in two adaptations. One was for allowing climate migrants to become established [F(4, 275) = 3.25, p < .05] and the other was the introduction of new species for ecosystem function (Levene’s test was significant F = 5.11, p < .01, so the Brown-Forsythe F-ratio is reported instead), F(4, 107) = 3.56, p < .01. In the former, Tukey’s HSD test showed that individuals who chose ecosystem function as the priority (n = 175, M = 3.34, SD = 0.90) felt it was more risky than those who chose human use (n = 19, M = 2.63, SD = 1.17). In the latter, individuals who prioritised wilderness areas (n = 35, M = 3.63, SD = 1.14) found the policy less risky than those who chose ecosystem function (n = 174, M = 4.16, SD = 0.81), and those who prioritised ecosystem function found it more risky than those who chose human use (n = 19, M = 3.58, SD = 1.21).  46  The analysis revealed differences amongst the groups in the acceptability of allowing climate migrants to become established in protected areas F(4, 276) = 3.69, p < .01. Tukey’s HSD test showed that individuals who opined a preference for species (n = 29, M = 3.76, SD = 0.87) and ecosystem function (n = 175, M = 3.87, SD = 0.89) found it less acceptable than people who prioritised human use (n = 19, M = 4.47, SD = 0.70). A significant difference was also found in the acceptability of introducing non-native species to maintain and enhance ecosystem function F(4, 276) = 4.58, p < .01. Tukey’s HSD test showed that this was driven by differences between those who prioritised wilderness areas (n = 35, M = 2.46, SD = 1.15) and ecosystem function (n = 175, M = 2.53, SD = 0.90) as opposed to human use (n = 19, M = 3.42, SD = 1.17). Again, those who prioritised conservation for human interests found the policy most acceptable. There were also near significant differences in the acceptability of migration corridors, assisted colonization, and conservation triage (i.e., p < .10).  Figure 13. Adaptation acceptability and riskiness by conservation goals. Mean acceptability and risk ratings for each adaptation policy organised by expressed primary goals of conservation. 4.4.6. Socio-demographics Due to the homogeneity of the sample, only three of the socio-demographic variables were felt to have enough variance to be analysed for their influence on risk  47  perceptions and preferences: park visitation rates; membership in or support for the environmental movement; and gender.  4.4.6.1.  Gender  Overall, gender did not appear to be associated with the consistent pattern of risk perception (see Figure 14) that has been observed elsewhere, with men exhibiting more optimistic risk estimates and more risk-seeking preferences (Byrnes, Miller & Schafer, 1999; Weber et al., 2002).  Figure 14. Mean acceptability and riskiness by gender. Mean acceptability and risk ratings for each adaptation policy organised by men and women. In terms of how risky each policy appeared, only two policies showed significant differences between men and women. Men (n = 114, M = 2.61, SD = 1.17) viewed captive breeding as less risky than women (n = 172, M = 2.93, SD = 1.10), t(284) = -2.33, p < .05. Men (n = 114, M = 3.51, SD = 1.09) also saw conservation triage as less risky than women (n = 172, M = 3.85, SD = 0.97), t(284) = -2.77, p < .01. With acceptability, two policies showed significant differences between men and women. In-situ aid was seen by men (n = 117, M = 3.68, SD = 1.07) as less acceptable than women (n = 177, M = 3.94, SD = 0.93), t(292) = -2.24, p < .05, while conservation triage was  48  seen by men (n = 114, M = 2.94, SD = 1.24) as being more acceptable than women (n = 172, M = 2.54, SD = 1.06), t(284) = 2.91, p < .01. The acceptability of captive breeding was also near significant t(284) = 1.92, p = .06, with men (n = 114, M = 3.54, SD = 1.21) finding it more acceptable than women (n = 172, M = 3.28, SD = 1.09).  4.4.6.2.  Support and Membership in the Environmental Movement  Though no significant differences in risk perception were found between people who were self-identified supporters or members of the environmental movement and those who were not (see Figure 15), ‘environmentalists’ did perceive most adaptations as riskier with the exception of migration corridors and captive breeding programs.  Figure 15. Mean acceptability and riskiness by support for the environmental movement. Mean acceptability and risk ratings for each adaptation policy comparing supporters and members of the environmental movements to non-supporters. In terms of acceptability, with the exception of introducing species to maintain ecosystem function, ‘environmentalists’ were more supportive of adaptation. However, only two policies showed significant differences. The first is the establishment of migration corridors, with supporters (n = 190, M = 4.49, SD = .80) seeing it as more acceptable than nonsupporters (n = 91, M = 4.49, SD = 4.24), t(279) = 2.20, p < .05. The second was conservation triage, with supporters of the environmental movement (n = 188, M = 2.82, SD = 1.14)  49  finding it more acceptable than individuals who were not supporters (n = 91, M = 2.42, SD = 1.11), t(277) = 2.82, p < .01.  4.4.6.3.  Visit Frequency  No a priori hypotheses were formed about the influence of protected area visitation rates on risk perceptions and preferences for adaptation. Overall it does not appear to be a strong predictor of either risk perceptions or acceptability. Using one-way ANOVAs, only two policies showed significant differences between groups in their risk perceptions. The first was for the introduction of new species to maintain ecosystem function F(4, 275) = 2.45, p < .05. However, Tukey’s HSD showed no significant differences. The second was for conservation triage F(4, 275) = 2.41, p < .05. Here Tukey’s HSD test showed that individuals who visited between 7-9 times in a year (n = 22, M = 3.23, SD = 1.27) found it significantly less risky than those who visited ten times or more (n = 71, M = 3.94, SD = 1.00) in a year. The only difference in rates of acceptability between groups was found for the use of migration corridors. However, Levene’s test was significant (F = 5.28, p < .001) but the Brown-Forsythe F-ratio was nonetheless significant, F(4, 75) = 3.54, p < .05. Post-hoc comparisons using Tukey’s HSD test indicated that individuals who visited ten times or more in a year (n = 72, M = 4.67, SD = 0.67) were significantly more supportive than those who never visited (n = 21, M = 4.05, SD = 0.97) or those who visited only 1-3 times a year (n = 103, M = 4.28, SD = 0.92).  50  5. DISCUSSION AND CONCLUSION  5.1. Discussion One of the primary findings of this study is that proposed adaptation policies appear to sit along a continuum of risk and a continuum of acceptability. As expected, the most acceptable and least risky policies are generally those most similar to current conservation practices. Risk perceptions and acceptability ratings were also found to be significantly and negatively correlated, buttressing the argument that risk perceptions are an important factor in determining climate change adaptation responses (Grothmann & Patt, 2005). Evidence for a continuum supports Heller and Zavaleta’s (2009) proposed range of risk-tolerant to risk-averse adaptation measures. This is significant because, as noted by Heller and Zavaleta (2009), an application of strategies across the continuum will be necessary for effective adaptation but, to a “certain degree, [the] risk tolerance of individual actors will guide strategy selection” (pp. 28). Pinpointing and identifying those policies that are perceived as most risky provides an indicator to decision-makers for where the greatest resistance may be felt. In general however, a high tolerance for adaptations was observed, which may be attributable to participants’ high degree of environmental awareness and concern and perhaps a greater sense of urgency for action. Overall, this pattern of preferences parallels Hagerman et al.’s (2010b) findings of partiality amongst conservation experts for less interventionist adaptations, but a reserved acknowledgement that more interventionist measures are also necessary. The experiment that was the centrepiece of the study design largely failed to produce any (detectable) effects. Apart from environmental worldviews, neither the affective image manipulation nor the certainty manipulation had any measurable influence on any measure. One distinct possibility for this lack of effect may be the highly homogeneous sample which was strongly environmental in its orientation. It may be that the sample had established convictions with regard to climate change, and engrained emotional reactions which are relatively rigid. Of course, the manipulations may have themselves been too subtle to have an impact.  51  Secondary analyses treating the manipulation checks for affect and emotions as independent variables revealed no real evidence of influence from affect and distress (i.e., feeling upset) on risk perceptions or acceptability. On the other hand, fear and anger showed some evidence of influence, and both were associated with similar patterns of perceptions and preferences. In aggregate, individuals who were more fearful and angry also perceived adaptations as generally more risky and less acceptable. Of note, these emotions were incidental to the assessment of the adaptation. However, these differences were largely non-significant. Again, this may be attributable to a general lack of variance in responses. Nonetheless, this pattern does not support the notion that anger and fear have separate effects as has been shown by other researchers (e.g., Lerner & Keltner, 2001), rather they appear to exert the same influence (as demonstrated by Peters et al., 2004). It is curious that general affect failed to show the same patterns as fear and anger. One possible hypothesis, though speculative, is that arousal is the key. This was measured by the scales for fear and anger while the affect scales only differentiated between positive and negative valence. However, more research would be necessary to assess this possibility. Little evidence was found to indicate that people’s certainty of climate change and its negative impacts on the environment had an influence on risk perceptions and acceptability. The only significant differences were confined to a couple of the least interventionist policies (i.e., migration corridors and in-situ aid) where greater certainty was associated with decreased risk perceptions and increasing acceptability. All together, the lack of significant differences is not wholly surprising given that the comparison are essentially between individuals who are absolutely certain and those who are very certain that climate change is occurring. Returning to the puzzling relationship between the affective image manipulation and environmental worldviews (where the negative image group exhibiting higher NEP scores), a potential explanation may be that the negative images evoked mortality salience. In other words, thoughts and emotions related to death. Terror Management Theory (originated by Solomon, Greenberg, & Pyszczynski, 1991) suggests that salience of one’s mortality increases perceptions and behaviours that reinforce personal worldviews. If this is  52  the case, it suggests that further research on this avenue of influence on risk perceptions may be fruitful, especially given the renewed focus on worldviews as a driver (i.e., cultural cognition) and that many risks concern hazards to human life. In fact, environmental worldviews was the most consistently significant factor related to risk perceptions next to acceptability. Differences between high and low NEP groups also appear to support the central tenet of Cultural Cognition that risk perceptions are manifestations of personal beliefs and values (Kahan, 2008). Interestingly however, despite the fact that people who expressed higher NEP perceived all adaptations as nearly the same or more risky than people who scored lower, high NEP scorers rated the acceptability of three policies as greater. Notably, these three are perhaps the least interventionist of the seven policies and may imply that despite high awareness and environmental concern, a certain threshold exists with regard to certain policies in which acceptability is less determined by perceived risk. Turning to the goals of conservation, the majority of participants felt that protecting and restoring ecosystem function should be the primary objective. While the descriptive and graphical data appear to suggest that these preferences do have significant implications for adaptation risk perceptions and support, statistical analyses only revealed that to be the case for a few policies. However, Type II errors may have been committed as group sizes for goals other than ecosystem function were small. Group sizes were also unequal, reducing the power of the ANOVA test. Furthermore, a conservative post-hoc test was used. Generally, the differences that were significant were between those who held anthropocentric views as opposed to those who prioritised biological protection. Overall, the former group appeared to view adaptation as less risky and more acceptable. Finding that goals may influence management preferences is complementary to findings of other studies that highlight the existence of distinct ideals of nature which are associated with different preferences for management (e.g., Buijs, 2009; Fisher & Young’s, 2007). Curiously, this implies that resistance to adaptations is most likely to be found amongst those who hold the most biologically centred priorities. This line of inquiry is particularly relevant as a reorientation of conservation goals has been recognised as being unavoidable in discussions  53  of climate change adaptation (Hagerman et al., 2010a; Heller & Zavaleta, 2009). Finally, given the homogeneous nature of the sample, only three socio-demographic variables were analysed for their influence on risk perceptions and acceptability: gender; support or membership in the environmental movement; and frequency of protected area visits. In general, these socio-demographic factors showed scattered and seemingly random influences and were not strong or reliable predictors of risk perceptions and adaptation policy support. In the case of gender, no consistent pattern of differences between men and women was detected contrary to what many studies have shown before where men tend to have more optimistic risk perceptions and risk-seeking preferences (Byrnes et al., 1999; Weber et al., 2002). Membership or support in the environmental movement was not at all a strong predictor of risk perceptions or acceptability with the exception that they were significantly more willing to accept the implementation of migration corridors and conservation triage. The effect of protected area visitation frequency was similarly inconclusive.  5.2. Strengths and Weaknesses Perhaps the greatest weakness of this research was that the manipulations used in the experimental design were not systematically pre-tested and assessed for their effect prior to implementation. In addition, the choice of images used in the affect manipulation may have been inappropriate given the sample, and viewed not as negative but even positive or awe inspiring. As such, it was not known beforehand whether the images or the text would have any effect and is a likely explanation for the failure of the experiment. Another obvious limitation of this study was that the design of the study was mismatched to the highly homogeneous (and pro-environmental) sample which may have concealed significant effects that would have otherwise been observed in a sample with greater variance. Additionally, because convenience sampling was used, the results of this study cannot be generalised to any wider population. Nonetheless, it is interesting to observe that even with a young, liberal, well-educated, and environmentally sensitive sample, differences of perception and preferences can be found, particularly as such  54  individuals are likely to be concerned and engaged stakeholders in real-world contexts. Because this study was intentionally limited methodologically to examine the evolving conservation adaptation policy landscape from a ‘birds-eye view’, it may not correlate strongly with people’s perceptions and preferences in specific place-based contexts. However, this study may still help decision-makers by cueing them to the factors shaping the diversity of preferences, thus helping them navigate disagreements or misunderstandings in implementing adaptations. Another major weakness of the study is that it was conducted online. Specifically, the expected outcomes of the experiment depended on people attending to the priming and frame set out at the beginning of the survey. However, with an online survey there is no guarantee that participants were attentive or that the conditions under which participants completed the survey were at all similar. On the other hand, that identifiable patterns of response can be found outside a laboratory setting provides more real-world validity. This study was also weakened in several ways by the necessity of keeping the survey a manageable length to encourage participation and avoid participant dropout. More policy specific measures could have provided additional insight, for example, measures of the emotional and affective reactions to each policy may have provided a more nuanced understandings of adaptation attitudes. Furthermore, while it was assumed that perceived benefits of adaptation could be estimated via risk perceptions (as they have been shown to be linked; Finucane et al., 2000), direct measures of the perceived benefit of each policy would have been more conclusive. Lastly, some of the results (e.g., the continuum of riskiness and acceptability) may have been an artefact of ordering. Unfortunately, due to programming limitations of the online survey platform randomisation of the questions and survey components was not possible.  5.3. Directions for Future Research First and foremost, future research on attitudes toward adaptation in protected areas and conservation ought to use representative samples from targeted populations. Many factors were also shown to be significant for only one or two particular policies.  55  Though this may have been due to an inflated Type I error rate, it may also suggest that policy specific studies are required. If results from studies on adaptation and risk are to be directly applicable to decision-making and planning for conservation, they will also need to be place-based and context specific. For example, how social dynamics, socio-ecological histories, individual personality characteristics, and even the type of species of concern will almost certainly be important in how preferences are formed and what judgments are expressed. Research on the topic of risk and adaptation in conservation may also benefit from mixed method studies that include qualitative data collection. Doing so would not only provide greater richness to our understanding of attitudes, but may also help distinguish between reasoned and intuitive (e.g., heuristic) influences and their dynamics. However, this may require more controlled laboratory settings, a weakness of the present study. A revelation from this thesis research that warrants further investigation is that images can influence the strength of expressed environmental worldviews. Future investigations might examine the mechanisms of this effect and also its implications for risk communication in this and other domains of risk. As mentioned previously, a starting point would be attempting a synthesis between risk research and the study of mortality salience. The apparent insensitivity to risk of high NEP scorers in judging acceptability of certain policies may also be indicative of threshold effects related to protected values. Furthermore, overall, some policies were nearly equal in riskiness but their acceptability did not correspond (e.g., conservation triage vs. assisted colonization and in-situ aid vs. captive breeding), indicating that perceived riskiness is not the only relevant factor. However, protected values were not tested for directly. More focused efforts to determine when, whether and how protected values are having an impact in perceptions of adaptations could be enlightening for policy development. The exploratory appraisal of what people viewed as the primary goals of conservation also suggests a fruitful next step. In an era where conservation goals must shift, who holds what views, how widespread they are, and what they mean for adaptation policy are all important questions that require a deeper look.  56  5.4. Policy Implications Though much more research is needed to make concrete policy recommendations, this study does demonstrate that risk perceptions likely matter in people’s willingness to support adaptation. Managers of protected areas may also take some comfort in the signs that even the most environmentally oriented individuals (that characterise this sample) are not rabidly opposed to adaptation in general. However, it would be prudent of decisionmakers to pay particular attention to factors that may shift perceptions of policies that are most novel and interventionist and viewed as most risky. Perhaps most of all, this research indicates the need for more dialogue and suggests how risk and educational communications can be more carefully designed. Showing that even a small degree of difference in environmental worldviews has a detectable impact on preferences in this homogeneous sample is suggestive that in an actual policy context with a much more diverse set of stakeholders, a larger gulf in preferences can be expected. Understanding these differences and their origins may help avoid flashpoints of disagreement and eliminate barriers to policy implementation. Additionally, though the majority of participants felt that maintaining ecosystem function should be the first goal of conservation, it is worth remembering that 43.6% did not. Thus in communication and policy design, decision-makers should be aware that there are different audiences with different values and priorities that may need to be uniquely addressed. Recognising different worldviews and goals may help practitioners develop communications that appeal to those values, which may also facilitate collaboration and stakeholder engagement. Thus far, in both the published literature (Hagerman et al., 2010b) and in the public domain, there is a lack of open discussion about the implications and threat that climate change poses to the fundamental mission of conservation. Greater dialogue that realistically addresses the need for adaptation and the full set of alternatives available should also be an objective of conservation leaders and decision-makers. This is necessary because the consequences of any given response involve costs, and whether those costs are acceptable is not a decision that belongs solely to academics and individuals in the upper echelons of  57  conservation. Furthermore, it should also be recognised that a consensus on goals is unlikely to exist and consequently the means of adaptation are also likely to be disputable. More pragmatically, delaying such a conversation only delays the inevitable conversation that will happen, and may only serve to impede effective and acceptable response. In other words, open public dialogue is necessary to design time sensitive and socially acceptable responses to change. However, if the goal of communication is to impress upon an audience the severity and need for adaptation, the results of this study would advise against inadvertently or purposely using fear and anger inducing messages. Rather than generating concern, negative emotional content may instead create heightened risk perceptions and decreased support for adaptation, though in some cases the reverse may be true. Furthermore, emphasising inevitable biodiversity losses from climate change may be an ineffectual way to gain traction or overcome resistance for needed adaptation, at least with some groups of stakeholders (e.g., already well-informed and environmentally oriented individuals).  5.5. Conclusion In conclusion, this study fills a noted gap in the literature on risk perceptions and climate change adaptation (Adger et al., 2009; Dessai et al., 2007; Grothmann & Patt, 2005). This thesis may also be the first study to address the psychology of risk perceptions in the context of conservation adaptation for which no research was found through the literature review. The objective of this study was to understand whether and how psychological factors influence individual judgments of risk and acceptability of proposed adaptation policies in conservation. Through this research, it was demonstrated that risk perceptions and policy support are significantly and inversely correlated (supporting H1), with policies most like those currently used in conservation being judged the least risky and most acceptable (supporting H2). In identifying the factors that influence risk perceptions, environmental worldviews appeared to be the most consistent and significant predictor overall, with pro-environmental views associated with greater risk perceptions (supporting  58  H5). More inconsistently, pro-environmental worldviews also seemed to be linked to greater support for some policies but decreased support for others (weakly supporting H5). There were some faint indications that fear and anger may have a role in determining risk perceptions and acceptability (weakly supporting H4). Certainty of continued climate change and socio-demographic variables did not appear to play a noteworthy role in risk perception and attitude formation (not supporting H3). As has been concluded by many practitioners and scholars, the management of protected areas will need to adapt to minimise imminent losses in biodiversity and ecosystem function from climate change. Adapting will also require a discussion about the fundamental objectives of protected areas and conservation and the appropriate means of management. Managing the expectations and concerns of stakeholders can be aided immensely by a more nuanced understanding of human psychology in this domain. 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Ecological Monographs, 73(4), 585-604.  74  APPENDICES APPENDIX A – SOCIO-DEMOGRAPHIC DESCRIPTIVE STATISTICS Statistics Member or supporter of the environmental movement  Gender  Level of education  Computer literacy  English literacy  Goals of conservation  Involvement in the environmental movement  Protected area visiting frequency  Valid  Residence 50km from parks  Politics  Age  N  310  256  286  283  202  282  311  312  309  311  282  2  56  26  29  110  30  1  0  3  1  30  Mode  1  1  1  2  1  3  4  4  2  2  1  Range  3  6  2  2  2  5  3  3  3  1  1  Minimum  1  1  1  2  1  1  1  1  1  1  0  Maximum  4  7  3  4  3  6  4  4  4  2  1  Missing  Age  Valid  19-29  Frequency 171  Percent 54.8  Valid Percent 55.2  Cumulative Percent 55.2  30-39  76  24.4  24.5  79.7  40-49  25  8.0  8.1  87.7  50 and above  38  12.2  12.3  100.0  310  99.4  100.0  2  .6  312  100.0  Total Missing  999  Total  Member or supporter of the environmental movement  Valid  Missing Total  Frequency 91  Percent 29.2  Valid Percent 32.3  Cumulative Percent 32.3  yes  191  61.2  67.7  100.0  Total  282  90.4  100.0  30  9.6  312  100.0  no  999  75  Politics  Valid  Missing  Frequency 100  Percent 32.1  Valid Percent 39.1  Cumulative Percent 39.1  somewhat liberal  53  17.0  20.7  59.8  moderate  27  8.7  10.5  70.3  conservative and somewhat conservative  24  7.7  9.4  79.7  neutral  23  7.4  9.0  88.7  other  29  9.3  11.3  100.0  Total  256  82.1  100.0  liberal  999  Total  56  17.9  312  100.0  Residence 50km from parks  Valid  Frequency 192  Percent 61.5  Valid Percent 67.1  Cumulative Percent 67.1  no  42  13.5  14.7  81.8  don't know  52  16.7  18.2  100.0  286  91.7  100.0  26  8.3  312  100.0  yes  Total Missing  999  Total  Protected area visiting frequency  Valid  0-3  Frequency 125  Percent 40.1  Valid Percent 44.2  Cumulative Percent 44.2  4-6  64  20.5  22.6  66.8  7+  94  30.1  33.2  100.0  283  90.7  100.0  Total Missing Total  999  29  9.3  312  100.0  76  Involvement in the environmental movement  Valid  Missing  Frequency 110  Percent 35.3  Valid Percent 54.5  Cumulative Percent 54.5  medium  68  21.8  33.7  88.1  high  24  7.7  11.9  100.0  Total  202  64.7  100.0  999  110  35.3  312  100.0  low  Total  Goals of conservation  Valid  Frequency 35  Percent 11.2  Valid Percent 12.4  Cumulative Percent 12.4  29  9.3  10.3  22.7  ecosystem function  176  56.4  62.4  85.1  human use  19  6.1  6.7  91.8  don't know  23  7.4  8.2  100.0  282  90.4  100.0  30  9.6  312  100.0  wilderness areas species  Total Missing  999  Total  English literacy  Valid  Poor Average Good  Missing Total  Frequency 1  Percent .3  Valid Percent .3  Cumulative Percent .3  2  .6  .6  1.0  13  4.2  4.2  5.1  Very Good  295  94.6  94.9  100.0  Total  311  99.7  100.0  1  .3  312  100.0  999  77  Computer literacy  Valid  Frequency 2  Percent .6  Valid Percent .6  Cumulative Percent .6  Average  18  5.8  5.8  6.4  Good  71  22.8  22.8  29.2  Very Good  221  70.8  70.8  100.0  Total  312  100.0  100.0  Poor  Level of education  Valid  Frequency 24  Percent 7.7  Valid Percent 7.8  Cumulative Percent 7.8  168  53.8  54.4  62.1  Masters  84  26.9  27.2  89.3  Doctorate  33  10.6  10.7  100.0  309  99.0  100.0  Secondary and Other College/University  Total Missing  999  Total  3  1.0  312  100.0  Gender  Valid  Missing Total  Valid Percent 39.9  Cumulative Percent 39.9  59.9  60.1  100.0  99.7  100.0  Frequency 124  Percent 39.7  Female  187  Total  311 1  .3  312  100.0  Male  999  78  APPENDIX B – SOCIO-DEMOGRAPHIC INFLUENCES ON RISK AND ACCEPTABILITY Gender x Acceptability Group Statistics Gender Migration Corridors  Permitting Climate Migrants In-situ Aid (New) Species Introduction Assisted Colonization Captive Breeding Conservation Triage  N  Mean  Std. Deviation  Std. Error Mean  Male  120  4.27  1.075  .098  Female  186  4.41  .835  .061  Male  118  3.99  .965  .089  Female  179  3.91  .872  .065  Male  117  3.68  1.065  .098  Female  177  3.94  .930  .070  Male  116  2.66  1.022  .095  Female  177  2.65  1.045  .079  Male  116  3.31  1.075  .100  Female  172  3.29  .877  .067  Male  114  3.54  1.206  .113  Female  172  3.28  1.094  .083  Male  114  2.94  1.236  .116  Female  172  2.54  1.056  .081  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Migration Corridors  Equal variances assumed  F 7.882  Sig. 0.005  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  2.059  0.152  Equal variances not assumed In-situ Aid  Equal variances assumed Equal variances not assumed  7.13  0.008  t -1.344  df 304  Sig. (2tailed) 0.18  -1.274  209.391  0.204  -0.147  0.116  -0.375  0.081  0.801  295  0.424  0.086  0.108  -0.126  0.299  0.785  232.559  0.433  0.086  0.11  -0.131  0.304  -2.235  292  0.026  -0.263  0.118  -0.494  -0.031  -2.174  224.769  0.031  -0.263  0.121  -0.501  -0.025  Mean Difference -0.147  Std. Error Difference 0.11  Lower -0.363  Upper 0.068  79  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  (New) Species Introduction  Equal variances assumed  F 0.104  Sig. 0.747  Equal variances not assumed Assisted Colonization  Equal variances assumed  6.115  Equal variances assumed  4.155  0.042  Equal variances not assumed Conservation Triage  Equal variances assumed  df 291  0.044  249.892  0.965  0.005  0.123  -0.237  0.248  0.17  286  0.865  0.02  0.115  -0.208  0.247  0.164  212.609  0.87  0.02  0.12  -0.217  0.256  1.924  284  0.055  0.265  0.138  -0.006  0.536  1.886  225.554  0.061  0.265  0.14  -0.012  0.541  2.913  284  0.004  0.398  0.137  0.129  0.667  2.822  215.502  0.005  0.398  0.141  0.12  0.676  0.014  Equal variances not assumed Captive Breeding  t 0.044  Sig. (2tailed) 0.965  2.324  0.128  Equal variances not assumed  Gender x Risk Perceptions Group Statistics  118  Mean 2.77  Std. Deviation 1.057  Std. Error Mean 0.097  Female  185  2.68  1  0.074  Permitting Climate Migrants  Male  117  3.13  1.047  0.097  Female  179  3.25  0.884  0.066  In-situ Aid  Male  117  2.91  1.171  0.108  Female  178  2.85  0.975  0.073  Migration Corridors  gender Male  N  Mean Difference 0.005  Std. Error Difference 0.124  Lower -0.238  Upper 0.249  80  Group Statistics  116  4  Std. Deviation 1.004  Female  176  3.91  0.981  0.074  Assisted Colonization  Male  116  3.81  0.932  0.087  Female  172  3.79  0.887  0.068  Captive Breeding  Male  114  2.61  1.171  0.11  Female  172  2.93  1.095  0.084  Conservation Triage  Male  114  3.51  1.091  0.102  Female  172  3.85  0.968  0.074  (New) Species Introduction  gender Male  N  Mean  Std. Error Mean 0.093  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F Migration Corridors  Equal variances assumed  .296  Sig. .587  Equal variances not assumed Permitting Equal variances Climate Migrants assumed  2.505  Equal variances assumed  4.011  .046  Equal variances not assumed (New) Species Introduction  Equal variances assumed  .312  .577  Equal variances not assumed Assisted Colonization  Equal variances assumed  .449  Equal variances not assumed Captive Breeding  Equal variances assumed  1.650  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  3.750  .748  .503  df  Sig. (2Mean Std. Error tailed) Difference Difference  Lower  Upper  301  .455  .090  .121  -.147  .327  .739 239.178  .461  .090  .122  -.150  .330  294  .299  -.118  .113  -.340  .105  -1.004 218.495  .317  -.118  .117  -.349  .113  293  .630  .061  .126  -.187  .308  .464 216.290  .643  .061  .131  -.197  .318  .767  290  .443  .091  .118  -.142  .324  .764 242.242  .446  .091  .119  -.144  .325  .181  286  .857  .020  .109  -.194  .234  .179 238.557  .858  .020  .110  -.197  .236  284  .021  -.316  .136  -.584  -.048  -2.293 230.739  .023  -.316  .138  -.588  -.045  284  .006  -.340  .123  -.582  -.098  -2.698 221.705  .008  -.340  .126  -.588  -.092  .115 -1.039  Equal variances not assumed In-situ Aid  t  .482  .200 -2.325  .054 -2.765  81  Supporter or Member of Environmental Movement x Acceptability and Risk Perceptions Risk Group Statistics Enviro  N  Mean  Std. Deviation  Std. Error Mean  Acceptability Migration Corridors  Permitting Climate Migrants In-situ Aid (New) Species Introduction Assisted Colonization Captive Breeding Conservation Triage  yes  190  4.49  .802  .058  no  91  4.24  .947  .099  yes  188  3.97  .833  .061  no  91  3.88  .953  .100  yes  190  3.86  .990  .072  no  90  3.83  .939  .099  yes  190  2.62  1.031  .075  no  90  2.70  1.022  .108  yes  189  3.33  .898  .065  no  91  3.20  1.013  .106  yes  189  3.38  1.131  .082  no  90  3.34  1.143  .121  yes  188  2.82  1.137  .083  no  91  2.42  1.106  .116  yes  188  2.62  .982  .072  no  91  2.77  1.034  .108  yes  188  3.29  .903  .066  no  91  3.08  1.014  .106  yes  190  2.92  1.051  .076  no  91  2.75  1.018  .107  yes  189  3.99  .956  .070  no  90  3.96  .947  .100  yes  189  3.87  .860  .063  no  91  3.69  .927  .097  yes  188  2.80  1.156  .084  no  91  2.84  1.108  .116  yes  188  3.74  1.045  .076  no  91  3.69  .963  .101  Risk Perceptions Migration Corridors Permitting Climate Migrants In-situ Aid (New) Species Introduction Assisted Colonization Captive Breeding Conservation Triage  82  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Equal variances assumed  6.553  0.011  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  4.612  0.033  Equal variances not assumed In-situ Aid  Equal variances assumed  0.128  0.721  Equal variances not assumed (New) Species Introduction  Equal variances assumed  0.226  0.635  Equal variances not assumed Assisted Colonization  Equal variances assumed  0.882  0.349  Equal variances not assumed Captive Breeding  Equal variances assumed Equal variances not assumed  0.048  0.826  2.331  279  0.02  0.253  0.109  0.039  0.467  2.199  153.797  0.029  0.253  0.115  0.026  0.48  0.797  277  0.426  0.089  0.112  -0.131  0.309  0.761  158.528  0.448  0.089  0.117  -0.142  0.32  0.197  278  0.844  0.025  0.125  -0.221  0.27  0.201  183.385  0.841  0.025  0.122  -0.217  0.266  -0.64  278  0.523  -0.084  0.132  -0.343  0.175  -0.642  176.279  0.522  -0.084  0.131  -0.343  0.175  1.089  278  0.277  0.13  0.12  -0.105  0.366  1.044  159.987  0.298  0.13  0.125  -0.116  0.377  0.251  277  0.802  0.037  0.145  -0.25  0.323  0.25  173.482  0.803  0.037  0.146  -0.251  0.325  83  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Conservation Triage  Equal variances assumed  F 0.001  Sig. 0.977  Equal variances not assumed  t 2.79  df 277  Sig. (2tailed) 0.006  2.817  182.635  0.005  0.402  0.143  0.12  0.683  -1.193  277  0.234  -0.152  0.128  -0.403  0.099  -1.172  170.227  0.243  -0.152  0.13  -0.409  0.104  1.751  277  0.081  0.21  0.12  -0.026  0.447  1.682  161.004  0.094  0.21  0.125  -0.037  0.457  1.271  279  0.205  0.169  0.133  -0.092  0.43  1.286  182.702  0.2  0.169  0.131  -0.09  0.427  0.277  277  0.782  0.034  0.122  -0.207  0.274  0.278  176.714  0.781  0.034  0.122  -0.206  0.274  1.605  278  0.11  0.181  0.113  -0.041  0.402  1.563  166.28  0.12  0.181  0.116  -0.047  0.409  Mean Difference 0.402  Std. Error Difference 0.144  Lower 0.118  Upper 0.685  Risk Perceptions Migration Corridors  Equal variances assumed  0.011  0.918  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  0.823  0.365  Equal variances not assumed In-situ Aid  Equal variances assumed  0.003  0.953  Equal variances not assumed (New) Species Introduction  Equal variances assumed  0.004  0.953  Equal variances not assumed Assisted Colonization  Equal variances assumed Equal variances not assumed  2.783  0.096  84  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Captive Breeding  Equal variances assumed  F 1.165  Sig. 0.281  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  1.858  0.174  t -0.22  df 277  Sig. (2tailed) 0.826  -0.223  185.007  0.824  -0.032  0.144  -0.315  0.251  0.362  277  0.718  0.047  0.13  -0.209  0.303  0.372  191.931  0.71  0.047  0.126  -0.202  0.296  Mean Difference -0.032  Std. Error Difference 0.146  Lower -0.319  Upper 0.255  85  APPENDIX C – PRIMAY ANALYSES OF MANIPULATION EFFECTS Manipulations x Affect Index Descriptive Statistics Dependent Variable:Affect_Index Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  6.68  2.270  74  Uncertain Prompt  6.16  2.344  76  Total  6.41  2.315  150  Certain Prompt  6.94  2.290  77  Uncertain Prompt  6.64  2.171  80  Total  6.78  2.228  157  Certain Prompt  6.81  2.277  151  Uncertain Prompt  6.40  2.263  156  Total  6.60  2.275  307  Negative Image  Total  Mean  Std. Deviation  N  Tests of Between-Subjects Effects Dependent Variable:Affect_Index Type III Sum of Squares  Source Corrected Model  df  Intercept Affect_Manip Cert_Manip  Mean Square  F  3  8.011  1.557  .200  13368.579  1  13368.579  2597.448  .000  10.470 12.746  1 1  10.470 12.746  2.034 2.476  .155 .117  .181  .671  Affect_Manip * Cert_Manip  .930  1  .930  Error  1559.484  303  5.147  Total  14967.000  307  1583.518  306  Corrected Total  a. R Squared = .015 (Adjusted R Squared = .005)  Manipulations x Fear Descriptive Statistics Dependent Variable:feel_fear Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  2.47  .920  75  Uncertain Prompt  2.42  1.010  76  Total  2.44  .964  151  Certain Prompt  2.55  .953  77  Uncertain Prompt  2.59  .968  79  Total  2.57  .958  156  Certain Prompt  2.51  .935  152  Uncertain Prompt  2.51  .989  155  Total  2.51  .961  307  Negative Image  Total  Sig.  a  24.034  Mean  Std. Deviation  N  86  Tests of Between-Subjects Effects Dependent Variable:feel_fear Type III Sum of Squares  Source  df  Mean Square  F  Sig.  1.408a  3  .469  .505  .679  1928.842  1  1928.842  2077.475  .000  1.225  1  1.225  1.319  .252  Cert_Manip  .000  1  .000  .000  .986  Affect_Manip * Cert_Manip  .173  1  .173  .187  .666  Error  281.322  303  .928  Total  2214.000  307  282.730  306  Corrected Model Intercept Affect_Manip  Corrected Total  a. R Squared = .005 (Adjusted R Squared = -.005)  Manipulations x Anger Descriptive Statistics Dependent Variable:feel_anger Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  2.28  .847  75  Uncertain Prompt  2.42  .983  76  Total  2.35  .918  151  Certain Prompt  2.56  .877  78  Uncertain Prompt  2.55  1.090  80  Total  2.56  .987  158  Certain Prompt  2.42  .871  153  Uncertain Prompt  2.49  1.038  156  Total  2.46  .958  309  Negative Image  Total  Mean  Std. Deviation  N  Tests of Between-Subjects Effects Dependent Variable:feel_anger Source  Type III Sum of Squares  df  Mean Square  F  Sig.  a  3  1.345  1.472  .222  1859.371  1  1859.371  2035.376  .000  3.293  1  3.293  3.605  .059  Cert_Manip  .311  1  .311  .340  .560  Affect_Manip * Cert_Manip  .465  1  .465  .509  .476  Error  278.626  305  .914  Total  2147.000  309  282.660  308  Corrected Model Intercept Affect_Manip  Corrected Total  4.034  a. R Squared = .014 (Adjusted R Squared = .005)  87  Manipulations x Distress (Upset) Descriptive Statistics Dependent Variable:feel_upset Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  2.23  .869  74  Uncertain Prompt  2.28  1.021  75  Total  2.26  .946  149  Certain Prompt  2.44  .939  77  Uncertain Prompt  2.31  .908  80  Total  2.38  .923  157  Certain Prompt  2.34  .908  151  Uncertain Prompt  2.30  .961  155  Total  2.32  .934  306  Negative Image  Total  Mean  Std. Deviation  N  Tests of Between-Subjects Effects Dependent Variable:feel_upset Type III Sum of Squares  Source  df  Mean Square  F  Sig.  1.863a  3  .621  .709  .547  1639.814  1  1639.814  1873.087  .000  1.141  1  1.141  1.303  .255  Cert_Manip  .119  1  .119  .135  .713  Affect_Manip * Cert_Manip  .614  1  .614  .702  .403  264.389  302  .875  1909.000 266.252  306 305  Corrected Model Intercept Affect_Manip  Error Total Corrected Total  a. R Squared = .007 (Adjusted R Squared = -.003)  Manipulations x Certainty Descriptive Statistics Dependent Variable:Cert_Index Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  18.18  2.461  Uncertain Prompt  17.91  2.986  66  Total  18.05  2.729  132  Certain Prompt  17.65  2.681  72  Uncertain Prompt  18.40  2.662  77  Total  18.04  2.689  149  Certain Prompt  17.91  2.583  138  Uncertain Prompt  18.17  2.817  143  Total  18.04  2.703  281  Negative Image  Total  Mean  Std. Deviation  N 66  88  Tests of Between-Subjects Effects Dependent Variable:Cert_Index Type III Sum of Squares  Source  df  Mean Square  F  Sig.  a  3  7.792  1.067  .363  91031.541  1  91031.541  12470.002  .000  .022  1  .022  .003  .956  3.981 18.286  1 1  3.981 18.286  .545 2.505  .461 .115  Error  2022.112  277  7.300  Total  93522.000  281  2045.488  280  Corrected Model  23.376  Intercept Affect_Manip Cert_Manip Affect_Manip * Cert_Manip  Corrected Total  a. R Squared = .011 (Adjusted R Squared = .001)  Manipulations x New Ecological Paradigm Descriptive Statistics Dependent Variable:NEP_Index Affect_Manip  Cert_Manip  Positive Image  Certain Prompt  59.25  9.630  64  Uncertain Prompt  58.33  9.979  63  Total  58.80  9.776  127  Certain Prompt  60.82  7.818  71  Uncertain Prompt  61.27  8.491  77  Total  61.05  8.151  148  Certain Prompt  60.07  8.726  135  Uncertain Prompt  59.95  9.273  140  Total  60.01  8.992  275  Negative Image  Total  Mean  Std. Deviation  N  Tests of Between-Subjects Effects Dependent Variable:NEP_Index Source Corrected Model Intercept Affect_Manip Cert_Manip Affect_Manip * Cert_Manip  Type III Sum of Squares  df  Mean Square  Sig.  3  127.692  1.589  .192  980768.036  1  980768.036  12206.735  .000  346.711 3.626  1 1  346.711 3.626  4.315 .045  .039 .832  .400  .527  32.162  1  32.162  Error  21773.892  271  80.346  Total  1012517.000  275  22156.967  274  Corrected Total  F  383.075a  a. R Squared = .017 (Adjusted R Squared = .006)  89  Affect Manipulation x Acceptability and Risk Perceptions  Group Statistics Affect_Manip  N  Mean  Std. Deviation  Std. Error Mean  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  Positive Image  150  4.39  0.988  0.081  Negative Image  157  4.33  0.887  0.071  Positive Image  145  3.92  0.943  0.078  Negative Image  153  3.95  0.876  0.071  Positive Image  144  3.82  1.049  0.087  Negative Image  151  3.84  0.939  0.076  Positive Image  143  2.64  1.045  0.087  Negative Image  151  2.67  1.025  0.083  Positive Image  138  3.26  1.069  0.091  Negative Image  151  3.34  0.848  0.069  Positive Image  136  3.45  1.21  0.104  Negative Image  151  3.33  1.081  0.088  Positive Image  137  2.74  1.201  0.103  Negative Image  150  2.65  1.093  0.089  Positive Image  148  2.67  1.033  0.085  Negative Image  156  2.76  1.012  0.081  Positive Image  144  3.13  0.991  0.083  Negative Image  153  3.27  0.911  0.074  Positive Image  144  2.82  1.049  0.087  Negative Image  152  2.94  1.063  0.086  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  90  Group Statistics Affect_Manip Positive Image  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  N 143  Mean 3.9  Std. Deviation 1.023  Std. Error Mean 0.086  Negative Image  150  3.99  0.959  0.078  Positive Image  138  3.8  0.937  0.08  Negative Image  151  3.81  0.877  0.071  Positive Image  136  2.72  1.159  0.099  Negative Image  151  2.87  1.109  0.09  Positive Image  137  3.72  1.071  0.091  Negative Image  150  3.72  0.997  0.081  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Equal variances assumed  0.342  0.559  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  0.281  0.596  Equal variances not assumed In-situ Aid  Equal variances assumed Equal variances not assumed  4.785  0.029  0.518  305  0.605  0.055  0.107  -0.155  0.266  0.517  298.009  0.606  0.055  0.107  -0.156  0.267  -0.286  296  0.775  -0.03  0.105  -0.238  0.177  -0.285  291.26  0.776  -0.03  0.106  -0.238  0.178  -0.187  293  0.852  -0.022  0.116  -0.249  0.206  -0.186  285.886  0.852  -0.022  0.116  -0.25  0.207  91  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  (New) Species Introduction  Equal variances assumed  F 0.076  Sig. 0.783  Equal variances not assumed Assisted Colonization  Equal variances assumed  6.444  0.012  Equal variances not assumed Captive Breeding  Equal variances assumed  2.614  0.107  Equal variances not assumed Conservation Triage  Equal variances assumed  2.555  0.111  Equal variances not assumed  t -0.269  df 292  Sig. (2tailed) 0.788  -0.269  290.389  0.788  -0.033  0.121  -0.27  0.205  -0.68  287  0.497  -0.077  0.113  -0.299  0.146  -0.673  260.954  0.501  -0.077  0.114  -0.302  0.148  0.868  285  0.386  0.117  0.135  -0.149  0.384  0.863  272.294  0.389  0.117  0.136  -0.15  0.385  0.674  285  0.501  0.091  0.135  -0.175  0.358  0.671  275.64  0.503  0.091  0.136  -0.176  0.359  -0.746  302  0.456  -0.087  0.117  -0.318  0.143  -0.746  300.396  0.457  -0.087  0.117  -0.318  0.143  -1.233  295  0.219  -0.136  0.11  -0.353  0.081  -1.229  288.902  0.22  -0.136  0.111  -0.354  0.082  Mean Difference -0.033  Std. Error Difference 0.121  Lower -0.27  Upper 0.205  Risk Perceptions Migration Corridors  Equal variances assumed  0.655  0.419  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed Equal variances not assumed  0.015  0.904  92  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  In-situ Aid  Equal variances assumed  F 0.188  Sig. 0.665  Equal variances not assumed (New) Species Introduction  Equal variances assumed  1.132  0.288  Equal variances not assumed Assisted Colonization  Equal variances assumed  0.031  0.86  Equal variances not assumed Captive Breeding  Equal variances assumed  1.989  0.16  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  0.981  0.323  t -0.988  df 294  Sig. (2tailed) 0.324  -0.989  293.499  0.324  -0.121  0.123  -0.363  0.12  -0.788  291  0.431  -0.091  0.116  -0.319  0.137  -0.787  287.358  0.432  -0.091  0.116  -0.319  0.137  -0.102  287  0.919  -0.011  0.107  -0.221  0.199  -0.101  280.175  0.919  -0.011  0.107  -0.222  0.2  -1.146  285  0.253  -0.154  0.134  -0.417  0.11  -1.144  278.833  0.254  -0.154  0.134  -0.418  0.111  -0.038  285  0.97  -0.005  0.122  -0.245  0.236  -0.038  277.763  0.97  -0.005  0.122  -0.246  0.236  Mean Difference -0.121  Std. Error Difference 0.123  Lower -0.363  Upper 0.12  93  Certainty Manipulation x Acceptability and Risk Perceptions Group Statistics Cert_Manip  N  Mean  Std. Deviation  Std. Error Mean  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  Certain Prompt  152  4.36  0.858  0.07  Uncertain Prompt  155  4.35  1.011  0.081  Certain Prompt  148  3.91  0.888  0.073  Uncertain Prompt  150  3.97  0.93  0.076  Certain Prompt  147  3.78  0.91  0.075  Uncertain Prompt  148  3.88  1.068  0.088  Certain Prompt  145  2.61  0.938  0.078  Uncertain Prompt  149  2.7  1.119  0.092  Certain Prompt  144  3.3  0.961  0.08  Uncertain Prompt  145  3.3  0.96  0.08  Certain Prompt  142  3.37  1.206  0.101  Uncertain Prompt  145  3.4  1.083  0.09  Certain Prompt  142  2.66  1.117  0.094  Uncertain Prompt  145  2.73  1.174  0.098  Certain Prompt  151  2.74  0.927  0.075  Uncertain Prompt  153  2.69  1.109  0.09  Certain Prompt  147  3.21  0.916  0.076  Uncertain Prompt  150  3.19  0.988  0.081  Certain Prompt  147  2.95  1.09  0.09  Uncertain Prompt  149  2.82  1.02  0.084  Risk Perceptions  Migration Corridors  Permitting Climate Migrants  In-situ Aid  94  Group Statistics Cert_Manip Certain Prompt  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  N  Mean  Std. Error Mean 0.08  144  3.96  Std. Deviation 0.96  Uncertain Prompt  149  3.94  1.022  0.084  Certain Prompt  144  3.79  0.876  0.073  Uncertain Prompt  145  3.81  0.935  0.078  Certain Prompt  143  2.7  1.12  0.094  Uncertain Prompt  144  2.9  1.142  0.095  Certain Prompt  142  3.7  1.009  0.085  Uncertain Prompt  145  3.73  1.056  0.088  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Equal variances assumed  1.679  0.196  Equal variances not assumed Permitting Climate Migrants  In-situ Aid  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  0.03  2.502  0.862  0.115  0.065  305  0.948  0.007  0.107  -0.204  0.218  0.065  298.822  0.948  0.007  0.107  -0.203  0.217  -0.517  296  0.605  -0.055  0.105  -0.262  0.153  -0.518  295.681  0.605  -0.055  0.105  -0.262  0.153  -0.831  293  0.407  -0.096  0.116  -0.324  0.131  -0.831  286.392  0.406  -0.096  0.116  -0.324  0.131  95  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  (New) Species Introduction  Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed  F 5.8  Equal variances assumed Equal variances not assumed  0.238  Equal variances assumed  1.94  Sig. 0.017  0.626  0.165  Equal variances not assumed Conservation Triage  Equal variances assumed  0.242  0.623  Equal variances not assumed  t -0.755  df 292  Sig. (2tailed) 0.451  -0.757  285.711  0.45  -0.091  0.12  -0.328  0.146  -0.043  287  0.966  -0.005  0.113  -0.227  0.218  -0.043  286.979  0.966  -0.005  0.113  -0.227  0.218  -0.198  285  0.843  -0.027  0.135  -0.293  0.239  -0.198  280.361  0.843  -0.027  0.135  -0.293  0.24  -0.51  285  0.61  -0.069  0.135  -0.335  0.197  -0.511  284.756  0.61  -0.069  0.135  -0.335  0.197  0.473  302  0.637  0.055  0.117  -0.175  0.286  0.473  294.066  0.636  0.055  0.117  -0.175  0.286  0.159  295  0.874  0.018  0.111  -0.2  0.235  0.159  294.101  0.874  0.018  0.111  -0.2  0.235  Mean Difference -0.091  Std. Error Difference 0.121  Lower -0.328  Upper 0.146  Risk Perceptions Migration Corridors  Equal variances assumed  7.654  0.006  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed Equal variances not assumed  0.028  0.868  96  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  In-situ Aid  Equal variances assumed  F 0.468  Sig. 0.494  Equal variances not assumed (New) Species Introduction  Equal variances assumed  2.149  0.144  Equal variances not assumed Assisted Colonization  Equal variances assumed  0.432  0.512  Equal variances not assumed Captive Breeding  Equal variances assumed  0.001  0.981  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  1.023  0.313  t 1.033  df 294  Sig. (2tailed) 0.302  1.033  292.145  0.303  0.127  0.123  -0.115  0.368  0.162  291  0.872  0.019  0.116  -0.209  0.247  0.162  290.771  0.872  0.019  0.116  -0.209  0.247  -0.208  287  0.836  -0.022  0.107  -0.232  0.188  -0.208  286.024  0.836  -0.022  0.107  -0.232  0.188  -1.524  285  0.129  -0.203  0.134  -0.466  0.059  -1.524  284.952  0.129  -0.203  0.134  -0.466  0.059  -0.22  285  0.826  -0.027  0.122  -0.267  0.213  -0.22  284.833  0.826  -0.027  0.122  -0.267  0.213  Mean Difference 0.127  Std. Error Difference 0.123  Lower -0.115  Upper 0.368  97  APPENDIX D – SECONDARY ANALYSES Overall Policy Acceptability and Rick Perceptions  Descriptives Statistic  Std. Error  4.36  0.053  Acceptability Migration Corridors  Mean 95% Confidence Interval for Mean  Lower Bound  4.25  Upper Bound  4.46  5% Trimmed Mean  4.47  Median  5  Variance  0.878  Std. Deviation  0.937  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness Kurtosis Permitting Climate Migrants  Mean 95% Confidence Interval for Mean  -1.419  0.139  1.403  0.277  3.94  0.053  Lower Bound  3.84  Upper Bound  4.04  5% Trimmed Mean  4  Median  4  Variance  0.825  Std. Deviation  0.908  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness Kurtosis  -0.614  0.141  0.142  0.281  98  Descriptives  In-situ Aid  Statistic 3.83  Mean 95% Confidence Interval for Mean  Lower Bound  3.72  Upper Bound  3.94  5% Trimmed Mean  3.89  Median  (New) Species Introduction  4  Variance  0.985  Std. Deviation  0.992  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness  -0.621  0.142  Kurtosis  -0.169  0.283  2.65  0.06  Mean 95% Confidence Interval for Mean  Lower Bound  2.53  Upper Bound  2.77  5% Trimmed Mean  2.63  Median  3  Variance  1.067  Std. Deviation  1.033  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness Kurtosis Assisted Colonization  Std. Error 0.058  Mean 95% Confidence Interval for Mean  5% Trimmed Mean Median  0.155  0.142  -0.513  0.283  3.3  0.056  Lower Bound  3.19  Upper Bound  3.41 3.32 3  Variance  0.919  Std. Deviation  0.959  Minimum  1  99  Descriptives Statistic 5  Maximum  Captive Breeding  Range  4  Interquartile Range  1  Skewness  -0.159  0.143  Kurtosis  -0.109  0.286  3.39  0.068  Mean 95% Confidence Interval for Mean  Lower Bound  3.25  Upper Bound  3.52  5% Trimmed Mean  3.43  Median  Conservation Triage  Std. Error  3  Variance  1.308  Std. Deviation  1.144  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness  -0.164  0.144  Kurtosis  -0.822  0.287  2.7  0.068  Mean 95% Confidence Interval for Mean  5% Trimmed Mean Median Variance Std. Deviation  Lower Bound  2.56  Upper Bound  2.83 2.66 3 1.31 1.145  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness Kurtosis  0.135  0.144  -0.788  0.287  2.71  0.059  Risk Perceptions Migration Corridors  Mean  100  Descriptives  95% Confidence Interval for Mean  Lower Bound  Statistic 2.6  Upper Bound  2.83  5% Trimmed Mean  2.7  Median  Permitting Climate Migrants  3  Variance  1.043  Std. Deviation  1.021  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness  -0.058  0.14  Kurtosis  -0.486  0.279  3.2  0.055  Mean 95% Confidence Interval for Mean  Lower Bound  3.09  Upper Bound  3.31  5% Trimmed Mean  3.22  Median  In-situ Aid  Std. Error  3  Variance  0.905  Std. Deviation  0.951  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness  -0.201  0.141  Kurtosis  -0.088  0.282  2.88  0.061  Mean 95% Confidence Interval for Mean  5% Trimmed Mean Median  Lower Bound  2.76  Upper Bound  3 2.87 3  Variance  1.115  Std. Deviation  1.056  Minimum  1  Maximum  5  101  Descriptives Statistic 4  Range Interquartile Range  2  Skewness Kurtosis (New) Species Introduction  Mean 95% Confidence Interval for Mean  0.142  -0.564  0.282  3.95  0.058  3.83  Upper Bound  4.06 4.01  Median  4  Variance  0.98  Std. Deviation  0.99  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness  -0.707  0.142  Kurtosis  -0.143  0.284  3.8  0.053  Mean 95% Confidence Interval for Mean  Lower Bound  3.7  Upper Bound  3.91  5% Trimmed Mean  3.84  Median  4  Variance  0.819  Std. Deviation  0.905  Minimum  1  Maximum  5  Range  4  Interquartile Range  1  Skewness Kurtosis Captive Breeding  0.029  Lower Bound  5% Trimmed Mean  Assisted Colonization  Std. Error  Mean 95% Confidence Interval for Mean  5% Trimmed Mean  -0.365  0.143  -0.5  0.286  2.8  0.067  Lower Bound  2.67  Upper Bound  2.93 2.78  102  Descriptives Statistic 3  Median Variance  1.286  Std. Deviation  1.134  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness Kurtosis Conservation Triage  Mean 95% Confidence Interval for Mean  0.165  0.144  -0.729  0.287  3.72  0.061  Lower Bound  3.6  Upper Bound  3.84  5% Trimmed Mean  Std. Error  3.77  Median  4  Variance  1.063  Std. Deviation  1.031  Minimum  1  Maximum  5  Range  4  Interquartile Range  2  Skewness  -0.472  0.144  Kurtosis  -0.374  0.287  Affect x Acceptability and Risk Perceptions Group Statistics Affect_Index (Binned)  N  Mean  Std. Deviation  Std. Error Mean  <= 6  175  4.43  0.9  0.068  7+  128  4.26  0.982  0.087  <= 6  171  3.94  0.852  0.065  7+  124  3.92  0.984  0.088  Acceptability Migration Corridors  Permitting Climate Migrants  103  Group Statistics  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  Affect_Index (Binned) <= 6  N 171  Mean 3.98  Std. Deviation 0.901  Std. Error Mean 0.069  7+  120  3.63  1.07  0.098  <= 6  170  2.65  0.987  0.076  7+  120  2.69  1.091  0.1  <= 6  166  3.29  0.915  0.071  7+  120  3.32  1.029  0.094  <= 6  164  3.46  1.11  0.087  7+  119  3.31  1.191  0.109  <= 6  166  2.66  1.06  0.082  7+  118  2.75  1.254  0.115  <= 6  173  2.66  1.037  0.079  7+  128  2.8  0.999  0.088  <= 6  170  3.21  0.984  0.076  7+  124  3.21  0.913  0.082  <= 6  171  2.92  0.991  0.076  7+  121  2.8  1.108  0.101  <= 6  169  3.96  1.011  0.078  7+  120  3.89  0.96  0.088  <= 6  166  3.8  0.891  0.069  7+  120  3.81  0.929  0.085  <= 6  165  2.85  1.078  0.084  7+  119  2.71  1.21  0.111  <= 6  166  3.72  1.002  0.078  7+  118  3.69  1.074  0.099  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  104  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Equal variances assumed Equal variances not assumed  2.3  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  3.544  11.085  Equal variances assumed Equal variances not assumed  1.448  Equal variances assumed  2.244  0.13  0.061  0.001  0.23  0.135  Equal variances not assumed Captive Breeding  Conservation Triage  Equal variances assumed Equal variances not assumed Equal variances assumed  0.671  2.551  0.413  0.111  1.622  301  0.106  0.176  0.109  -0.038  0.391  1.6  259.5 14  0.111  0.176  0.11  -0.041  0.394  0.207  293  0.837  0.022  0.107  -0.189  0.233  0.202  241.3 94  0.84  0.022  0.11  -0.194  0.238  3.032  289  0.003  0.352  0.116  0.123  0.58  2.943  227.4 78  0.004  0.352  0.119  0.116  0.587  -0.315  288  0.753  -0.039  0.123  -0.281  0.203  -0.31  239.8 21  0.757  -0.039  0.125  -0.285  0.208  -0.238  284  0.812  -0.028  0.116  -0.255  0.2  -0.234  237.8 79  0.815  -0.028  0.118  -0.259  0.204  1.062  281  0.289  0.146  0.138  -0.125  0.418  1.05  243.4 66  0.295  0.146  0.139  -0.128  0.421  -0.708  282  0.479  -0.098  0.138  -0.369  0.174  105  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  Equal variances not assumed  t -0.689  df 224.9 6  Sig. (2tailed) 0.492  Mean Difference -0.098  Std. Error Difference 0.142  Lower -0.377  Upper 0.182  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  0.721  Equal variances assumed Equal variances not assumed  4.299  Equal variances assumed  0.238  0.378  0.397  0.539  0.039  0.626  Equal variances not assumed Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed Equal variances assumed  0.574  4.502  0.449  0.035  1.159  299  0.247  -0.138  0.119  -0.372  0.096  1.165  279.176  0.245  -0.138  0.118  -0.371  0.095  0.034  292  0.973  -0.004  0.113  -0.226  0.218  0.034  275.737  0.973  -0.004  0.111  -0.223  0.216  0.942  290  0.347  0.116  0.124  -0.127  0.36  0.924  240.012  0.356  0.116  0.126  -0.132  0.365  0.616  287  0.538  0.073  0.118  -0.16  0.305  0.622  264.258  0.535  0.073  0.117  -0.158  0.304  0.121  284  0.904  -0.013  0.109  -0.227  0.201  -0.12  250.229  0.904  -0.013  0.109  -0.229  0.202  1.089  282  0.277  0.149  0.136  -0.12  0.417  106  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t 1.069  df 236.093  Sig. (2tailed) 0.286  0.442  0.177  282  0.86  0.022  0.124  -0.223  0.267  0.174  241.099  0.862  0.022  0.126  -0.226  0.27  Equal variances not assumed Conservation Triage  Equal variances assumed  0.593  Equal variances not assumed  Mean Difference 0.149  Std. Error Difference 0.139  Lower -0.125  Upper 0.423  Fear x Acceptability and Risk Perceptions Group Statistics Fear Categorical  N  Mean  Std. Deviation  Std. Error Mean  157  4.4  0.919  0.073  42  4.26  0.964  0.149  152  3.9  0.933  0.076  42  4.17  0.824  0.127  150  3.97  0.93  0.076  40  3.58  1.059  0.168  149  2.6  1.039  0.085  40  3  1.013  0.16  147  3.33  0.869  0.072  41  3.41  1.204  0.188  145  3.42  1.159  0.096  41  3.8  1.145  0.179  146  2.62  1.096  0.091  41  3.15  1.236  0.193  Acceptability Migration Corridors  Afraid Relaxed  Permitting Climate Migrants  Afraid Relaxed  In-situ Aid  Afraid Relaxed  (New) Species Introduction  Afraid Relaxed  Assisted Colonization  Afraid Relaxed  Captive Breeding  Afraid Relaxed  Conservation Triage  Afraid Relaxed  107  Group Statistics Fear Categorical  N  Mean  Std. Deviation  Std. Error Mean  155  2.75  1.047  0.084  42  2.64  0.958  0.148  151  3.24  0.985  0.08  42  3.1  0.983  0.152  150  2.91  1.068  0.087  41  2.68  1.083  0.169  148  4  1.024  0.084  40  3.85  0.949  0.15  147  3.9  0.882  0.073  41  3.63  1.019  0.159  146  2.93  1.112  0.092  41  2.59  1.245  0.194  146  3.86  0.99  0.082  41  3.44  1.026  0.16  Risk Perceptions Migration Corridors  Afraid Relaxed  Permitting Climate Migrants  Afraid Relaxed  In-situ Aid  Afraid Relaxed  (New) Species Introduction  Afraid Relaxed  Assisted Colonization  Afraid Relaxed  Captive Breeding  Afraid Relaxed  Conservation Triage  Afraid Relaxed  Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference  F  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Sig.  t  0.606  0.864  197  0.389  0.139  0.161  -0.179  0.457  0.84  62.374  0.404  0.139  0.166  -0.192  0.471  1.671  192  0.096  -0.265  0.159  -0.579  0.048  1.793  72.741  0.077  -0.265  0.148  -0.56  0.03  Acceptability Migration Corridors  Permitting Climate Migrants  Equal variances assumed Equal variances not assumed  0.267  Equal variances assumed  0.031  Equal variances not assumed  0.86  108  Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed  F 4.591  3.027  11.157  0.078  0.454  Sig. 0.033  0.084  0.001  0.78  0.501  Equal variances not assumed  t 2.297  df 188  Sig. (2tailed) 0.023  2.13  56.05  0.038  0.392  0.184  0.023  0.76  2.152  187  0.033  -0.396  0.184  -0.759  -0.033  2.184  62.811  0.033  -0.396  0.181  -0.758  -0.034  0.524  186  0.601  -0.088  0.168  -0.42  0.243  0.438  52.182  0.663  -0.088  0.201  -0.492  0.316  1.879  184  0.062  -0.384  0.204  -0.788  0.019  1.892  65.007  0.063  -0.384  0.203  -0.79  0.021  2.623  185  0.009  -0.523  0.199  -0.916  -0.13  2.452  58.831  0.017  -0.523  0.213  -0.95  -0.096  0.626  195  0.532  0.112  0.179  -0.241  0.465  0.658  69.85  0.512  0.112  0.17  -0.227  0.451  0.834  191  0.405  0.143  0.172  -0.196  0.482  0.835  65.677  0.407  0.143  0.172  -0.199  0.486  Mean Difference 0.392  Std. Error Difference 0.171  Lower 0.055  Upper 0.728  Risk Perceptions Migration Corridors  Permitting Climate Migrants  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  0.023  0.108  0.879  0.743  109  Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference  In-situ Aid  (New) Species Introduction  Equal variances assumed Equal variances not assumed Equal variances assumed  F 0.266  0.22  Sig. 0.607  0.64  Equal variances not assumed Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed Equal variances assumed  4.818  3.14  0.029  0.078  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  0.108  0.743  t 1.221  df 189  Sig. (2tailed) 0.224  1.211  62.906  0.23  0.23  0.19  -0.15  0.611  0.835  186  0.405  0.15  0.18  -0.204  0.504  0.872  65.674  0.386  0.15  0.172  -0.193  0.493  1.636  186  0.103  0.264  0.161  -0.054  0.582  1.508  57.764  0.137  0.264  0.175  -0.086  0.614  1.715  185  0.088  0.346  0.202  -0.052  0.744  1.61  59.129  0.113  0.346  0.215  -0.084  0.776  2.366  185  0.019  0.417  0.176  0.069  0.765  2.318  62.456  0.024  0.417  0.18  0.058  0.777  Mean Difference 0.23  Std. Error Difference 0.189  Lower -0.142  Upper 0.603  110  Anger x Acceptability and Risk Perceptions  Group Statistics Anger Categoric al  N  Mean  Std. Deviation  Std. Error Mean  Acceptability Migration Corridors  Permitting Climate Migrants In-situ Aid (New) Species Introduction Assisted Colonization Captive Breeding Conservation Triage  Angry  174  4.44  .915  .069  Calm  36  4.50  .845  .141  Angry  168  3.85  .954  .074  Calm  36  4.14  .723  .121  Angry  168  3.92  .975  .075  Calm  34  3.65  .981  .168  Angry  168  2.54  1.020  .079  Calm  35  2.89  1.132  .191  Angry  165  3.30  .905  .070  Calm  35  3.43  1.008  .170  Angry  162  3.41  1.123  .088  Calm  35  3.57  1.290  .218  Angry  164  2.52  1.053  .082  Calm  35  2.77  1.215  .205  Angry  172  2.75  1.015  .077  Calm  36  2.50  .910  .152  Angry  167  3.23  1.000  .077  Calm  36  3.17  .910  .152  Angry  168  2.92  1.052  .081  Calm  35  2.57  1.037  .175  Angry  167  3.99  1.035  .080  Calm  35  3.91  .919  .155  Angry  165  3.84  .904  .070  Calm  35  3.57  .948  .160  Angry  163  2.79  1.098  .086  Calm  35  2.60  1.193  .202  Angry  164  3.82  1.070  .084  Calm  35  3.71  .987  .167  Risk Perceptions Migration Corridors Permitting Climate Migrants In-situ Aid (New) Species Introduction Assisted Colonization Captive Breeding Conservation Triage  111  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  F  Sig.  t  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  0.414  0.521  -0.347  208  0.729  -0.057  0.165  -0.384  0.269  -0.366  53.41  0.716  -0.057  0.157  -0.372  0.257  -1.741  202  0.083  -0.294  0.169  -0.626  0.039  -2.079  64.098  0.042  -0.294  0.141  -0.576  -0.012  Equal variances assumed  0.73  1.469  200  0.144  0.27  0.184  -0.092  0.632  1.463  47.145  0.15  0.27  0.184  -0.101  0.64  -1.781  201  0.076  -0.344  0.193  -0.725  0.037  -1.663  46.21  0.103  -0.344  0.207  -0.76  0.072  -0.765  198  0.445  -0.132  0.172  -0.471  0.207  -0.713  46.343  0.479  -0.132  0.184  -0.503  0.24  -0.763  195  0.447  -0.164  0.215  -0.588  0.26  -0.697  45.792  0.489  -0.164  0.235  -0.638  0.31  -1.225  197  0.222  -0.247  0.202  -0.645  0.151  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Equal variances not assumed Equal variances assumed  2.639  0.092  0.106  0.394  0.762  Equal variances not assumed Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed Equal variances assumed  1.294  3.36  0.257  0.068  Equal variances not assumed Conservation Triage  Equal variances assumed  0.24  0.625  112  Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  Equal variances not assumed  t -1.117  df 45.546  Sig. (2tailed) 0.27  1.367  206  0.173  0.25  0.183  -0.111  0.611  1.468  54.837  0.148  0.25  0.17  -0.091  0.591  0.37  201  0.712  0.067  0.181  -0.29  0.424  0.393  54.785  0.696  0.067  0.17  -0.274  0.408  1.77  201  0.078  0.345  0.195  -0.039  0.73  1.787  49.675  0.08  0.345  0.193  -0.043  0.733  0.39  200  0.697  0.074  0.189  -0.299  0.446  0.422  53.699  0.675  0.074  0.175  -0.277  0.424  1.598  198  0.112  0.271  0.17  -0.063  0.605  1.548  47.99  0.128  0.271  0.175  -0.081  0.623  0.892  196  0.374  0.185  0.208  -0.225  0.595  0.845  47.176  0.402  0.185  0.219  -0.256  0.626  0.523  197  0.602  0.103  0.197  -0.285  0.491  Mean Difference -0.247  Std. Error Difference 0.221  Lower -0.692  Upper 0.198  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed  0.064  1.217  0.193  0.125  0.967  1.052  0.8  0.271  0.661  0.724  0.327  0.306  Equal variances not assumed Conservation Triage  Equal variances assumed  0.232  0.63  113  Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  Equal variances not assumed  t 0.551  df 52.482  Sig. (2tailed) 0.584  Mean Difference 0.103  Std. Error Difference 0.187  Lower -0.272  Upset (Distress) x Acceptability and Risk Perceptions Group Statistics Upset_Cate gorical  N  Mean  Std. Deviation  Std. Error Mean  Acceptability Migration Corridors  Upset At Ease  Permitting Climate Migrants  Upset At Ease  In-situ Aid  Upset At Ease  (New) Species Introduction  Upset At Ease  Assisted Colonization  Upset At Ease  Captive Breeding  Upset At Ease  Conservation Triage  Upset At Ease  196  4.39  .896  .064  30  4.27  1.015  .185  192  3.91  .920  .066  30  4.00  .910  .166  189  3.89  .945  .069  29  3.66  1.010  .188  188  2.64  1.048  .076  29  2.76  .872  .162  186  3.38  .893  .066  29  3.21  1.114  .207  184  3.46  1.096  .081  29  3.45  1.213  .225  185  2.66  1.051  .077  29  2.66  1.344  .250  194  2.75  1.030  .074  30  2.80  1.095  .200  191  3.28  .958  .069  Risk Perceptions Migration Corridors  Upset At Ease  Permitting Climate Migrants  Upset At Ease  In-situ Aid  Upset At Ease  (New) Species Introduction  Upset At Ease  Assisted Colonization  Upset At Ease  Captive Breeding  Upset At Ease  Conservation Triage  Upset At Ease  30  3.13  .860  .157  189  2.93  1.042  .076  29  2.72  1.251  .232  187  3.97  1.002  .073  29  3.97  .823  .153  186  3.80  .905  .066  29  3.83  .889  .165  185  2.83  1.103  .081  29  2.76  1.300  .241  185  3.73  1.044  .077  29  3.79  .861  .160  Upper 0.477  114  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Permitting Climate Migrants  Equal variances assumed Equal variances not assumed Equal variances assumed  0.614  0.032  0.434  0.859  Equal variances not assumed In-situ Aid  (New) Species Introduction  Equal variances assumed Equal variances not assumed  0.921  Equal variances assumed  2.075  0.338  0.151  Equal variances not assumed Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed  3.051  Equal variances assumed  1.058  Equal variances not assumed  0.082  0.305  0.677  224  0.499  0.121  0.179  -0.231  0.474  0.618  36.255  0.541  0.121  0.196  -0.276  0.519  -0.491  220  0.624  -0.089  0.18  -0.444  0.267  -0.495  38.848  0.623  -0.089  0.179  -0.45  0.273  1.257  216  0.21  0.239  0.19  -0.136  0.614  1.197  35.934  0.239  0.239  0.2  -0.166  0.644  -0.587  215  0.558  -0.12  0.205  -0.524  0.283  -0.672  41.546  0.505  -0.12  0.179  -0.482  0.241  0.917  213  0.36  0.169  0.185  -0.195  0.534  0.781  33.844  0.44  0.169  0.217  -0.272  0.611  0.037  211  0.97  0.008  0.222  -0.43  0.446  0.034  35.578  0.973  0.008  0.239  -0.477  0.494  115  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Conservation Triage  Equal variances assumed Equal variances not assumed  F 4.763  Sig. 0.03  t 0.044  df 212  Sig. (2tailed) 0.965  Mean Difference 0.01  Std. Error Difference 0.219  Lower -0.421  Upper 0.44  0.037  33.581  0.971  0.01  0.261  -0.521  0.541  -0.258  222  0.797  -0.053  0.204  -0.454  0.349  -0.247  37.361  0.807  -0.053  0.213  -0.484  0.379  0.776  219  0.438  0.144  0.186  -0.222  0.51  0.84  41.15  0.406  0.144  0.172  -0.203  0.491  0.969  216  0.334  0.207  0.214  -0.214  0.628  0.848  34.224  0.403  0.207  0.244  -0.289  0.703  0.04  214  0.968  0.008  0.196  -0.378  0.394  0.046  42.028  0.964  0.008  0.169  -0.334  0.35  -0.147  213  0.883  -0.027  0.18  -0.382  0.329  -0.149  37.635  0.882  -0.027  0.178  -0.387  0.334  0.327  212  0.744  0.074  0.226  -0.371  0.519  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Equal variances assumed Equal variances not assumed  1.088  Equal variances assumed Equal variances not assumed  1.304  Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed  3.379  Equal variances assumed Equal variances not assumed  0.049  Equal variances assumed  2.415  2.292  0.298  0.255  0.067  0.132  0.826  0.122  116  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  t 0.29  df 34.609  Sig. (2tailed) 0.774  Mean Difference 0.074  Std. Error Difference 0.255  Lower -0.443  Upper 0.591  -0.311  212  0.756  -0.063  0.204  -0.466  0.339  -0.357  42.048  0.723  -0.063  0.177  -0.421  0.295  Sig.  Equal variances not assumed Conservation Triage  Equal variances assumed  4.056  0.045  Equal variances not assumed  Certainty x Acceptability and Risk Perceptions Group Statistics Certainty in 2 groups  N  Mean  Std. Deviation  Std. Error Mean  Low Certainty  146  4.27  0.912  0.075  High Certainty  134  4.58  0.739  0.064  Low Certainty  145  3.9  0.908  0.075  High Certainty  133  4.01  0.821  0.071  Low Certainty  145  3.68  0.926  0.077  High Certainty  134  4.02  0.977  0.084  Low Certainty  145  2.68  1.052  0.087  High Certainty  134  2.59  0.998  0.086  Low Certainty  146  3.24  0.978  0.081  High Certainty  133  3.35  0.898  0.078  Low Certainty  145  3.43  1.129  0.094  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  117  Group Statistics  Conservation Triage  Certainty in 2 groups High Certainty  N 133  Mean 3.31  Std. Deviation 1.143  Std. Error Mean 0.099  Low Certainty  146  2.71  1.134  0.094  High Certainty  132  2.69  1.153  0.1  Low Certainty  145  2.79  0.942  0.078  High Certainty  132  2.54  1.037  0.09  Low Certainty  144  3.2  0.89  0.074  High Certainty  133  3.21  0.993  0.086  Low Certainty  146  2.92  1.06  0.088  High Certainty  134  2.8  1.039  0.09  Low Certainty  145  3.92  0.987  0.082  High Certainty  133  4.02  0.908  0.079  Low Certainty  146  3.73  0.929  0.077  High Certainty  133  3.88  0.826  0.072  Low Certainty  146  2.81  1.194  0.099  High Certainty  132  2.84  1.069  0.093  Low Certainty  146  3.67  0.962  0.08  High Certainty  132  3.74  1.082  0.094  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  118  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Equal variances assumed  11.862  0.001  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  7.133  0.008  Equal variances not assumed In-situ Aid  Equal variances assumed  1.478  0.225  Equal variances not assumed (New) Species Introduction  Equal variances assumed  0.281  0.597  Equal variances not assumed Assisted Colonization  Equal variances assumed  0.059  0.808  Equal variances not assumed Captive Breeding  Equal variances assumed Equal variances not assumed  0.011  0.916  -3.158  278  0.002  -0.315  0.1  -0.511  -0.119  -3.187  273.855  0.002  -0.315  0.099  -0.51  -0.12  -0.999  276  0.319  -0.104  0.104  -0.309  0.101  -1.004  275.949  0.316  -0.104  0.104  -0.308  0.1  -2.981  277  0.003  -0.34  0.114  -0.564  -0.115  -2.975  272.199  0.003  -0.34  0.114  -0.564  -0.115  0.758  277  0.449  0.093  0.123  -0.149  0.335  0.759  276.81  0.448  0.093  0.123  -0.148  0.335  -1.008  277  0.314  -0.114  0.113  -0.336  0.108  -1.012  276.981  0.312  -0.114  0.112  -0.335  0.107  0.875  276  0.382  0.119  0.136  -0.149  0.388  0.875  273.325  0.382  0.119  0.136  -0.149  0.388  119  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Conservation Triage  Equal variances assumed  F 0.406  Sig. 0.525  Equal variances not assumed  t 0.117  df 276  Sig. (2tailed) 0.907  0.117  272.181  0.907  0.016  0.137  -0.254  0.287  2.147  275  0.033  0.255  0.119  0.021  0.489  2.137  265.487  0.034  0.255  0.119  0.02  0.49  -0.081  275  0.936  -0.009  0.113  -0.232  0.214  -0.08  265.557  0.936  -0.009  0.114  -0.233  0.215  0.95  278  0.343  0.119  0.126  -0.128  0.367  0.95  276.803  0.343  0.119  0.126  -0.128  0.366  -0.863  276  0.389  -0.098  0.114  -0.323  0.126  -0.866  275.995  0.387  -0.098  0.114  -0.322  0.125  -1.455  277  0.147  -0.154  0.106  -0.362  0.054  -1.463  276.849  0.145  -0.154  0.105  -0.36  0.053  Mean Difference 0.016  Std. Error Difference 0.137  Lower -0.254  Upper 0.286  Risk Perceptions Migration Corridors  Equal variances assumed  4.767  0.03  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed  0.769  0.381  Equal variances not assumed In-situ Aid  Equal variances assumed  0.017  0.897  Equal variances not assumed (New) Species Introduction  Equal variances assumed  1.131  0.288  Equal variances not assumed Assisted Colonization  Equal variances assumed Equal variances not assumed  4.566  0.033  120  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Captive Breeding  Equal variances assumed  F 2.276  Equal variances assumed  1.898  Equal variances not assumed  df 276  -0.241  275.976  0.81  -0.033  0.136  -0.3  0.234  -0.581  276  0.562  -0.071  0.123  -0.312  0.17  -0.577  263.599  0.564  -0.071  0.123  -0.314  0.172  Sig. 0.133  Equal variances not assumed Conservation Triage  t -0.24  Sig. (2tailed) 0.811  0.169  Mean Difference -0.033  Std. Error Difference 0.136  Lower -0.301  Upper 0.236  Environmental Worldviews x Acceptability and Risk Perceptions  Group Statistics NEP_Index (Binned)  N  Mean  Std. Deviation  Std. Error Mean  <= 62  149  4.22  0.965  0.079  63+  126  4.61  0.715  0.064  <= 62  149  3.89  0.839  0.069  63+  125  4.02  0.898  0.08  <= 62  148  3.8  0.974  0.08  63+  126  3.88  1.001  0.089  <= 62  148  2.8  1.048  0.086  63+  126  2.47  0.969  0.086  <= 62  149  3.37  0.968  0.079  63+  126  3.21  0.917  0.082  <= 62  149  3.56  1.08  0.089  63+  126  3.25  1.178  0.105  <= 62  149  2.81  1.131  0.093  63+  125  2.54  1.118  0.1  Acceptability Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  121  Group Statistics NEP_Index (Binned)  N  Mean  Std. Deviation  Std. Error Mean  <= 62  149  2.72  0.922  0.076  63+  125  2.62  1.106  0.099  <= 62  149  3.08  0.904  0.074  63+  125  3.36  0.971  0.087  <= 62  149  2.77  1.009  0.083  63+  126  3.01  1.07  0.095  <= 62  148  3.8  0.967  0.079  63+  125  4.17  0.931  0.083  <= 62  149  3.62  0.904  0.074  63+  126  4.01  0.863  0.077  <= 62  149  2.66  1.088  0.089  63+  126  2.97  1.173  0.104  <= 62  149  3.59  1.007  0.082  63+  125  3.88  1.029  0.092  Risk Perceptions Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Captive Breeding  Conservation Triage  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  F  Sig.  t  df  Sig. (2tailed)  Mean Difference  Std. Error Difference  Lower  Upper  Acceptability Migration Corridors  Equal variances assumed  15.57  0  Equal variances not assumed Permitting Climate Migrants  Equal variances assumed Equal variances not assumed  0.236  0.628  -3.746  273  0  -0.39  0.104  -0.594  -0.185  -3.838  268.588  0  -0.39  0.102  -0.589  -0.19  -1.174  272  0.241  -0.123  0.105  -0.33  0.084  -1.167  256.821  0.244  -0.123  0.106  -0.332  0.085  122  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  In-situ Aid  Equal variances assumed  F 0.262  Sig. 0.609  Equal variances not assumed (New) Species Introduction  Equal variances assumed  0.003  0.955  Equal variances not assumed Assisted Colonization  Equal variances assumed  0.686  0.408  Equal variances not assumed Captive Breeding  Equal variances assumed  0.701  0.403  Equal variances not assumed Conservation Triage  Equal variances assumed  0.515  0.473  Equal variances not assumed Risk Perceptions Migration Corridors  Equal variances assumed Equal variances not assumed  8.604  0.004  t -0.643  df 272  Sig. (2tailed) 0.521  -0.642  262.596  0.522  -0.077  0.12  -0.313  0.159  2.737  272  0.007  0.336  0.123  0.094  0.577  2.754  270.109  0.006  0.336  0.122  0.096  0.576  1.354  273  0.177  0.155  0.114  -0.07  0.38  1.36  269.45  0.175  0.155  0.114  -0.069  0.379  2.282  273  0.023  0.311  0.136  0.043  0.579  2.266  256.507  0.024  0.311  0.137  0.041  0.581  1.915  272  0.057  0.261  0.136  -0.007  0.53  1.917  264.796  0.056  0.261  0.136  -0.007  0.53  0.889  272  0.375  0.109  0.122  -0.132  0.35  0.875  241.857  0.383  0.109  0.124  -0.136  0.354  Mean Difference -0.077  Std. Error Difference 0.12  Lower -0.312  Upper 0.158  123  Independent Samples Test Levene's Test for Equality of Variances  t-test for Equality of Means 95% Confidence Interval of the Difference  Permitting Climate Migrants  In-situ Aid  Equal variances assumed Equal variances not assumed Equal variances assumed  F 2.555  0  Sig. 0.111  0.993  Equal variances not assumed (New) Species Introduction  Equal variances assumed  0.235  0.628  Equal variances not assumed Assisted Colonization  Equal variances assumed  2.36  0.126  Equal variances not assumed Captive Breeding  Equal variances assumed  0.098  0.755  Equal variances not assumed Conservation Triage  Equal variances assumed Equal variances not assumed  0.108  0.743  t -2.464  df 272  Sig. (2tailed) 0.014  -2.449  256.436  0.015  -0.279  0.114  -0.504  -0.055  -1.934  273  0.054  -0.243  0.126  -0.49  0.004  -1.925  259.747  0.055  -0.243  0.126  -0.491  0.006  -3.152  271  0.002  -0.364  0.115  -0.591  -0.137  -3.162  266.338  0.002  -0.364  0.115  -0.591  -0.137  -3.582  273  0  -0.384  0.107  -0.595  -0.173  -3.596  269.028  0  -0.384  0.107  -0.594  -0.174  -2.226  273  0.027  -0.304  0.136  -0.572  -0.035  -2.212  257.846  0.028  -0.304  0.137  -0.574  -0.033  -2.347  272  0.02  -0.289  0.123  -0.532  -0.047  -2.342  261.758  0.02  -0.289  0.124  -0.533  -0.046  Mean Difference -0.279  Std. Error Difference 0.113  Lower -0.503  Upper -0.056  124  Conservation Goals x Acceptability and Risk Perceptions  Descriptives 95% Confidence Interval for Mean N  Mean  Std. Deviation  Std. Error  Lower Bound  Upper Bound  Minimum  Maximum  Acceptability Migration Corridors  wilderness areas species  35  4.43  0.948  0.16  4.1  4.75  2  5  29  4.17  1.002  0.186  3.79  4.55  2  5  ecosystem function  176  4.48  0.771  0.058  4.37  4.6  2  5  human use  19  3.95  1.471  0.337  3.24  4.66  1  5  don't know  23  4.26  0.964  0.201  3.84  4.68  2  5  282  4.39  0.903  0.054  4.28  4.5  1  5  wilderness areas  35  4.26  0.78  0.132  3.99  4.53  2  5  species  29  3.76  0.872  0.162  3.43  4.09  2  5  175  3.87  0.89  0.067  3.74  4  1  5  human use  19  4.47  0.697  0.16  4.14  4.81  3  5  don't know  23  3.87  0.815  0.17  3.52  4.22  3  5  281  3.95  0.875  0.052  3.84  4.05  1  5  35  3.49  1.173  0.198  3.08  3.89  1  5  Total  Permitting Climate Migrants  ecosystem function  Total  In-situ Aid  wilderness areas  125  Descriptives 95% Confidence Interval for Mean  29  Mean 3.9  Std. Deviation 0.976  Std. Error 0.181  Lower Bound 3.53  Upper Bound 4.27  Minimum 1  Maximum 5  ecosystem function  175  3.92  0.874  0.066  3.79  4.05  2  5  human use don't know  19  3.74  1.368  0.314  3.08  4.4  1  5  23  3.7  1.105  0.23  3.22  4.17  2  5  281  3.83  0.988  0.059  3.72  3.95  1  5  wilderness areas  35  2.46  1.146  0.194  2.06  2.85  1  5  species  29  2.86  1.187  0.22  2.41  3.31  1  5  175  2.53  0.902  0.068  2.4  2.67  1  5  human use  19  3.42  1.17  0.268  2.86  3.98  1  5  don't know  23  2.96  1.147  0.239  2.46  3.45  1  5  281  2.65  1.031  0.062  2.53  2.77  1  5  wilderness areas  35  3.31  0.932  0.158  2.99  3.63  2  5  species  29  3.28  1.131  0.21  2.85  3.71  1  5  176  3.28  0.86  0.065  3.15  3.41  1  5  N species  Total  (New) Species Introduction  ecosystem function  Total  Assisted Colonization  ecosystem function  126  Descriptives 95% Confidence Interval for Mean N  Lower Bound 3.41  Upper Bound 4.28  Minimum 1  Maximum 5  19  don't know  23  3  1.314  0.274  2.43  3.57  1  5  282  3.3  0.953  0.057  3.19  3.41  1  5  wilderness areas  35  3.34  1.235  0.209  2.92  3.77  1  5  species  29  3.48  1.153  0.214  3.04  3.92  1  5  ecosystem function  175  3.36  1.078  0.082  3.2  3.52  1  5  human use don't know  18  3.61  1.335  0.315  2.95  4.27  1  5  23  3.35  1.301  0.271  2.79  3.91  1  5  280  3.39  1.136  0.068  3.25  3.52  1  5  wilderness areas  35  2.46  1.245  0.21  2.03  2.88  1  5  species  29  2.34  1.203  0.223  1.89  2.8  1  5  ecosystem function  175  2.77  1.074  0.081  2.61  2.93  1  5  human use don't know  19  3.16  1.385  0.318  2.49  3.83  1  5  23  2.52  1.123  0.234  2.04  3.01  1  5  281  2.69  1.146  0.068  2.56  2.83  1  5  wilderness areas  34  2.47  1.161  0.199  2.07  2.88  1  5  species  29  2.76  1.123  0.209  2.33  3.19  1  5  ecosystem function  175  2.67  0.972  0.073  2.53  2.82  1  5  human use don't know  19  2.74  1.195  0.274  2.16  3.31  1  5  23  2.91  0.848  0.177  2.55  3.28  2  5  280  2.68  1.017  0.061  2.56  2.8  1  5  wilderness areas  34  2.97  1.058  0.182  2.6  3.34  1  5  species  29  3.17  0.805  0.149  2.87  3.48  1  4  Total Conservation Triage  Std. Error 0.206  human use  Total  Captive Breeding  Std. Deviation 0.898  Mean 3.84  Total Risk Perceptions Migration Corridors  Total Permitting Climate Migrants  127  Descriptives 95% Confidence Interval for Mean N  Minimum 1  Maximum 5  19  2.63  1.165  0.267  2.07  3.19  1  5  23  3.17  0.984  0.205  2.75  3.6  1  5  280  3.22  0.954  0.057  3.11  3.33  1  5  wilderness areas  35  2.74  1.268  0.214  2.31  3.18  1  5  species  29  2.52  0.911  0.169  2.17  2.86  1  4  ecosystem function  176  2.95  0.961  0.072  2.81  3.1  1  5  human use don't know  19  2.79  1.398  0.321  2.12  3.46  1  5  23  3.22  0.998  0.208  2.79  3.65  1  5  282  2.89  1.042  0.062  2.77  3.02  1  5  wilderness areas  35  3.63  1.14  0.193  3.24  4.02  1  5  species  29  3.86  0.953  0.177  3.5  4.22  2  5  ecosystem function  174  4.16  0.81  0.061  4.04  4.28  2  5  human use don't know  19  3.32  1.108  0.254  2.78  3.85  1  5  23  3.74  1.214  0.253  3.21  4.26  1  5  280  3.97  0.961  0.057  3.86  4.08  1  5  wilderness areas  35  3.71  1.073  0.181  3.35  4.08  2  5  species  29  3.72  0.922  0.171  3.37  4.07  2  5  ecosystem function  176  3.86  0.833  0.063  3.73  3.98  2  5  human use don't know  19  3.58  1.121  0.257  3.04  4.12  1  5  23  3.91  0.949  0.198  3.5  4.32  2  5  282  3.81  0.903  0.054  3.71  3.92  1  5  wilderness areas  35  2.63  1.003  0.169  2.28  2.97  1  4  species  29  2.93  1.1  0.204  2.51  3.35  1  5  ecosystem function  175  2.78  1.125  0.085  2.61  2.95  1  5  human use don't know  19  2.68  1.057  0.242  2.17  3.19  1  4  23  3.3  1.295  0.27  2.74  3.86  1  5  281  2.81  1.123  0.067  2.68  2.94  1  5  Total Captive Breeding  Upper Bound 3.48  human use don't know  Total Assisted Colonization  Lower Bound 3.21  175  Total (New) Species Introduction  Std. Error 0.068  ecosystem function  Total In-situ Aid  Std. Deviation 0.902  Mean 3.34  Total  128  Descriptives 95% Confidence Interval for Mean N Conservation Triage  Std. Deviation 1.202  Std. Error 0.203  Lower Bound 3.3  Upper Bound 4.13  Minimum 1  Maximum 5  wilderness areas  35  Mean 3.71  species  29  3.76  1.023  0.19  3.37  4.15  1  5  ecosystem function  175  3.74  1.005  0.076  3.59  3.89  1  5  human use don't know  19  3.32  1.204  0.276  2.74  3.9  1  5  23  3.78  0.902  0.188  3.39  4.17  2  5  281  3.71  1.038  0.062  3.59  3.83  1  5  Total  Test of Homogeneity of Variances Levene Statistic  df1  df2  Sig.  Acceptability Migration Corridors  6.908  4  277  .000  .312  4  276  .870  In-situ Aid  4.917  4  276  .001  (New) Species Introduction Assisted Colonization  1.888 2.448  4 4  276 277  .113 .047  Captive Breeding  1.270  4  275  .282  Conservation Triage  1.364  4  276  .247  Migration Corridors  2.912  4  275  .022  Permitting Climate Migrants  1.820  4  275  .125  In-situ Aid  3.073  4  277  .017  (New) Species Introduction  5.107  4  275  .001  Assisted Colonization  3.545  4  277  .008  Captive Breeding  .829  4  276  .507  Conservation Triage  .999  4  276  .408  Permitting Climate Migrants  Risk Perceptions  ANOVA Sum of Squares  df  Mean Square  F  Sig.  2.199  0.069  Acceptability Migration Corridors  Between Groups Within Groups Total  7.052  4  1.763  222.04  277  0.802  229.092  281  129  ANOVA  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Assisted Colonization  Sum of Squares 10.881  df 4  Mean Square 2.72  Within Groups  203.319  276  0.737  Total  214.199  280  6.273  4  1.568  Within Groups  266.866  276  0.967  Total  273.139  280  18.523  4  4.631  Within Groups  279.299  276  1.012  Total  297.822  280  7.758  4  1.94  247.22  277  0.892  254.979  281  1.401  4  0.35  Within Groups  358.942  275  1.305  Total  360.343  279  Between Groups  Between Groups  Between Groups  Between Groups  Within Groups  Total  Captive Breeding  Between Groups  F 3.693  Sig. 0.006  1.622  0.169  4.576  0.001  2.173  0.072  0.268  0.898  130  ANOVA  Conservation Triage  Sum of Squares 11.32  df 4  Mean Square 2.83  Within Groups  356.36  276  1.291  Total  367.68  280  2.985  4  0.746  Within Groups  285.726  275  1.039  Total  288.711  279  11.448  4  2.862  Within Groups  242.262  275  0.881  Total  253.711  279  8.174  4  2.044  Within Groups  296.634  277  1.071  Total  304.809  281  20.117  4  5.029  237.654  275  0.864  Between Groups  F 2.192  Sig. 0.07  0.718  0.58  3.249  0.013  1.908  0.109  5.82  0  Risk Perceptions  Migration Corridors  Permitting Climate Migrants  In-situ Aid  (New) Species Introduction  Between Groups  Between Groups  Between Groups  Between Groups Within Groups  131  ANOVA Sum of Squares 257.771  df 279  Mean Square  2.197  4  0.549  Within Groups  226.842  277  0.819  Total  229.039  281  7.687  4  1.922  Within Groups  345.317  276  1.251  Total  353.004  280  3.271  4  0.818  298.38  276  1.081  301.651  280  Total Assisted Colonization  Captive Breeding  Between Groups  Between Groups  Conservation Triage  Between Groups Within Groups Total  F  Sig.  0.671  0.613  1.536  0.192  0.756  0.554  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable Acceptability  (I) goals  (J) goals  Migration Corridors  wilderness areas  species  Std. Error  Sig.  Lower Bound  Upper Bound  0.256  0.225  0.785  -0.36  0.87  -0.054  0.166  0.997  -0.51  0.4  human use  0.481  0.255  0.327  -0.22  1.18  don't know  0.168  0.24  0.957  -0.49  0.83  wilderness areas  -0.256  0.225  0.785  -0.87  0.36  ecosystem function  -0.311  0.179  0.417  -0.8  0.18  ecosystem function  species  Mean Difference (I-J)  132  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals  ecosystem function  human use  don't know  Mean Difference (I-J) 0.225  Std. Error 0.264  Sig. 0.914  Lower Bound -0.5  Upper Bound 0.95  don't know  -0.088  0.25  0.997  -0.77  0.6  wilderness areas  0.054  0.166  0.997  -0.4  0.51  species  0.311  0.179  0.417  -0.18  0.8  human use  0.536  0.216  0.099  -0.06  1.13  don't know  0.222  0.199  0.797  -0.32  0.77  wilderness areas  -0.481  0.255  0.327  -1.18  0.22  species  -0.225  0.264  0.914  -0.95  0.5  ecosystem function don't know  -0.536  0.216  0.099  -1.13  0.06  -0.314  0.278  0.791  -1.08  0.45  wilderness areas  -0.168  0.24  0.957  -0.83  0.49  0.088  0.25  0.997  -0.6  0.77  -0.222  0.199  0.797  -0.77  0.32  0.314  0.278  0.791  -0.45  1.08  (J) goals human use  species  ecosystem function human use  133  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable Permitting Climate Migrants  (I) goals wilderness areas  Mean Difference (I-J) 0.499  Std. Error 0.216  Sig. 0.144  Lower Bound -0.09  Upper Bound 1.09  0.389  0.159  0.107  -0.05  0.82  -0.217  0.245  0.902  -0.89  0.46  don't know  0.388  0.23  0.447  -0.25  1.02  wilderness areas  -0.499  0.216  0.144  -1.09  0.09  ecosystem function  -0.11  0.172  0.969  -0.58  0.36  human use  -.715  *  0.253  0.041  -1.41  -0.02  don't know  -0.111  0.24  0.991  -0.77  0.55  wilderness areas  -0.389  0.159  0.107  -0.82  0.05  species  0.11  0.172  0.969  -0.36  0.58  human use  -.605  *  0.207  0.031  -1.17  -0.04  don't know  0  0.19  1  -0.52  0.52  wilderness areas  0.217  0.245  0.902  -0.46  0.89  species  .715 *  0.253  0.041  0.02  1.41  ecosystem function  .605 *  0.207  0.031  0.04  1.17  (J) goals species  ecosystem function human use  species  ecosystem function  human use  134  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  In-situ Aid  Mean Difference (I-J) 0.604  Std. Error 0.266  Sig. 0.158  Lower Bound -0.13  Upper Bound 1.33  -0.388  0.23  0.447  -1.02  0.25  species  0.111  0.24  0.991  -0.55  0.77  ecosystem function  0.001  0.19  1  -0.52  0.52  human use  -0.604  0.266  0.158  -1.33  0.13  species  -0.411  0.247  0.458  -1.09  0.27  ecosystem function  -0.434  0.182  0.122  -0.93  0.07  human use  -0.251  0.28  0.898  -1.02  0.52  don't know  -0.21  0.264  0.932  -0.93  0.51  wilderness areas  0.411  0.247  0.458  -0.27  1.09  ecosystem function  -0.023  0.197  1  -0.56  0.52  human use don't know  0.16  0.29  0.982  -0.64  0.96  0.201  0.275  0.949  -0.55  0.95  wilderness areas  0.434  0.182  0.122  -0.07  0.93  species  0.023  0.197  1  -0.52  0.56  human use  0.183  0.238  0.939  -0.47  0.84  don't know  0.224  0.218  0.842  -0.37  0.82  wilderness areas  0.251  0.28  0.898  -0.52  1.02  species  -0.16  0.29  0.982  -0.96  0.64  ecosystem function  -0.183  0.238  0.939  -0.84  0.47  don't know  0.041  0.305  1  -0.8  0.88  wilderness areas  0.21  0.264  0.932  -0.51  0.93  species  -0.201  0.275  0.949  -0.95  0.55  ecosystem function  -0.224  0.218  0.842  -0.82  0.37  human use  -0.041  0.305  1  -0.88  0.8  (I) goals  (J) goals don't know  don't know  wilderness areas  wilderness areas  species  ecosystem function  human use  don't know  135  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable (New) Species Introduction  (I) goals wilderness areas  Mean Difference (I-J) -0.405  Std. Error 0.253  Sig. 0.497  Lower Bound -1.1  Upper Bound 0.29  -0.074  0.186  0.995  -0.59  0.44  -.964  *  0.287  0.008  -1.75  -0.18  don't know  -0.499  0.27  0.347  -1.24  0.24  wilderness areas  0.405  0.253  0.497  -0.29  1.1  ecosystem function  0.331  0.202  0.474  -0.22  0.88  human use  -0.559  0.297  0.329  -1.37  0.26  don't know  -0.094  0.281  0.997  -0.87  0.68  wilderness areas  0.074  0.186  0.995  -0.44  0.59  -0.331  0.202  0.474  -0.88  0.22  -.890  *  0.243  0.003  -1.56  -0.22  -0.425  0.223  0.317  -1.04  0.19  *  0.287  0.008  0.18  1.75  0.559  0.297  0.329  -0.26  1.37  ecosystem function  .890  *  0.243  0.003  0.22  1.56  don't know  0.465  0.312  0.57  -0.39  1.32  wilderness areas  0.499  0.27  0.347  -0.24  1.24  species  0.094  0.281  0.997  -0.68  0.87  ecosystem function  0.425  0.223  0.317  -0.19  1.04  -0.465  0.312  0.57  -1.32  0.39  0.038  0.237  1  -0.61  0.69  ecosystem function  0.036  0.175  1  -0.44  0.52  human use don't know  -0.528  0.269  0.288  -1.27  0.21  0.314  0.254  0.728  -0.38  1.01  wilderness areas  -0.038  0.237  1  -0.69  0.61  (J) goals species ecosystem function human use  species  ecosystem function  species human use don't know human use  wilderness areas species  don't know  Assisted Colonization  wilderness areas  species  human use species  .964  136  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals  ecosystem function  Mean Difference (I-J) -0.003  Std. Error 0.189  human use don't know  -0.566  wilderness areas  1  Lower Bound -0.52  Upper Bound 0.52  0.279  0.254  -1.33  0.2  0.276  0.264  0.834  -0.45  1  -0.036  0.175  1  -0.52  0.44  0.003  0.189  1  -0.52  0.52  human use don't know  -0.564  0.228  0.1  -1.19  0.06  0.278  0.209  0.673  -0.3  0.85  wilderness areas  0.528  0.269  0.288  -0.21  1.27  species  0.566  0.279  0.254  -0.2  1.33  ecosystem function  0.564  0.228  0.1  -0.06  1.19  don't know  .842  *  0.293  0.035  0.04  1.65  wilderness areas  -0.314  0.254  0.728  -1.01  0.38  species  -0.276  0.264  0.834  -1  0.45  ecosystem function  -0.278  0.209  0.673  -0.85  0.3  -.842*  0.293  0.035  -1.65  -0.04  -0.14  0.287  0.988  -0.93  0.65  ecosystem function  -0.017  0.212  1  -0.6  0.56  human use don't know  -0.268  0.331  0.928  -1.18  0.64  -0.005  0.307  1  -0.85  0.84  wilderness areas  0.14  0.287  0.988  -0.65  0.93  ecosystem function  0.123  0.229  0.984  -0.51  0.75  human use don't know  -0.128  0.343  0.996  -1.07  0.81  0.135  0.319  0.993  -0.74  1.01  wilderness areas  0.017  0.212  1  -0.56  0.6  species  -0.123  0.229  0.984  -0.75  0.51  human use don't know  -0.251  0.283  0.901  -1.03  0.53  0.012  0.253  1  -0.68  0.71  (J) goals ecosystem function  species  human use  don't know  Captive Breeding  wilderness areas  species  ecosystem function  human use species  Sig.  137  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals human use  don't know  Conservation Triage  wilderness areas  species  ecosystem function  human use  don't know  Mean Difference (I-J) 0.268  Std. Error 0.331  Sig. 0.928  Lower Bound -0.64  Upper Bound 1.18  species  0.128  0.343  0.996  -0.81  1.07  ecosystem function  0.251  0.283  0.901  -0.53  1.03  don't know  0.263  0.36  0.949  -0.72  1.25  wilderness areas  0.005  0.307  1  -0.84  0.85  species  -0.135  0.319  0.993  -1.01  0.74  ecosystem function  -0.012  0.253  1  -0.71  0.68  human use species  -0.263  0.36  0.949  -1.25  0.72  0.112  0.285  0.995  -0.67  0.9  ecosystem function  -0.314  0.21  0.567  -0.89  0.26  human use don't know  -0.701  0.324  0.197  -1.59  0.19  -0.065  0.305  1  -0.9  0.77  wilderness areas  -0.112  0.285  0.995  -0.9  0.67  ecosystem function  -0.427  0.228  0.335  -1.05  0.2  human use don't know  -0.813  0.335  0.112  -1.73  0.11  -0.177  0.317  0.981  -1.05  0.69  wilderness areas  0.314  0.21  0.567  -0.26  0.89  species  0.427  0.228  0.335  -0.2  1.05  human use don't know  -0.386  0.274  0.623  -1.14  0.37  0.25  0.252  0.859  -0.44  0.94  wilderness areas  0.701  0.324  0.197  -0.19  1.59  species  0.813  0.335  0.112  -0.11  1.73  ecosystem function  0.386  0.274  0.623  -0.37  1.14  don't know  0.636  0.352  0.372  -0.33  1.6  wilderness areas  0.065  0.305  1  -0.77  0.9  species  0.177  0.317  0.981  -0.69  1.05  (J) goals wilderness areas  138  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals  Mean Difference (I-J) -0.25  Std. Error 0.252  Sig. 0.859  Lower Bound -0.94  Upper Bound 0.44  human use  -0.636  0.352  0.372  -1.6  0.33  species  -0.288  0.258  0.797  -1  0.42  ecosystem function  -0.204  0.191  0.824  -0.73  0.32  human use don't know  -0.266  0.292  0.892  -1.07  0.54  -0.442  0.275  0.494  -1.2  0.31  wilderness areas  0.288  0.258  0.797  -0.42  1  ecosystem function  0.084  0.204  0.994  -0.48  0.65  human use don't know  0.022  0.301  1  -0.8  0.85  -0.154  0.285  0.983  -0.94  0.63  0.204  0.191  0.824  -0.32  0.73  species  -0.084  0.204  0.994  -0.65  0.48  human use don't know  -0.063  0.246  0.999  -0.74  0.61  -0.239  0.226  0.829  -0.86  0.38  wilderness areas  0.266  0.292  0.892  -0.54  1.07  -0.022  0.301  1  -0.85  0.8  ecosystem function  0.063  0.246  0.999  -0.61  0.74  don't know  -0.176  0.316  0.981  -1.04  0.69  wilderness areas  0.442  0.275  0.494  -0.31  1.2  species  0.154  0.285  0.983  -0.63  0.94  ecosystem function  0.239  0.226  0.829  -0.38  0.86  human use species  0.176  0.316  0.981  -0.69  1.04  -0.202  0.237  0.914  -0.85  0.45  -0.372  0.176  0.216  -0.86  0.11  0.339  0.269  0.715  -0.4  1.08  (J) goals ecosystem function  Risk Perceptions Migration Corridors  wilderness areas  species  ecosystem function  human use  wilderness areas  species  don't know  Permitting Climate Migrants  wilderness areas  ecosystem function human use  139  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  Std. Error 0.253  Sig. 0.93  Lower Bound -0.9  Upper Bound 0.49  (I) goals  (J) goals don't know  species  wilderness areas  0.202  0.237  0.914  -0.45  0.85  ecosystem function  -0.17  0.188  0.895  -0.69  0.35  human use don't know  0.541  0.277  0.292  -0.22  1.3  -0.001  0.262  1  -0.72  0.72  0.372  0.176  0.216  -0.11  0.86  0.17  0.188  0.895  -0.35  0.69  *  0.227  0.016  0.09  1.33  0.169  0.208  0.927  -0.4  0.74  wilderness areas  -0.339  0.269  0.715  -1.08  0.4  species  -0.541  0.277  0.292  -1.3  0.22  ecosystem function  *  -.711  0.227  0.016  -1.33  -0.09  don't know  -0.542  0.291  0.339  -1.34  0.26  wilderness areas  0.203  0.253  0.93  -0.49  0.9  species  0.001  0.262  1  -0.72  0.72  -0.169  0.208  0.927  -0.74  0.4  0.542  0.291  0.339  -0.26  1.34  0.226  0.26  0.908  -0.49  0.94  ecosystem function  -0.212  0.192  0.804  -0.74  0.31  human use don't know  -0.047  0.295  1  -0.86  0.76  -0.475  0.278  0.43  -1.24  0.29  wilderness areas  -0.226  0.26  0.908  -0.94  0.49  ecosystem function  -0.437  0.207  0.219  -1.01  0.13  human use don't know  -0.272  0.305  0.9  -1.11  0.57  -0.7  0.289  0.112  -1.49  0.09  wilderness areas  0.212  0.192  0.804  -0.31  0.74  ecosystem function  wilderness areas species human use don't know  human use  don't know  ecosystem function  In-situ Aid  Mean Difference (I-J) -0.203  wilderness areas  species  ecosystem function  human use species  .711  140  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals  Mean Difference (I-J) 0.437  Std. Error 0.207  Sig. 0.219  Lower Bound -0.13  Upper Bound 1.01  0.165  0.25  0.965  -0.52  0.85  -0.263  0.229  0.782  -0.89  0.37  wilderness areas  0.047  0.295  1  -0.76  0.86  species  0.272  0.305  0.9  -0.57  1.11  ecosystem function  -0.165  0.25  0.965  -0.85  0.52  don't know  -0.428  0.321  0.67  -1.31  0.45  wilderness areas  0.475  0.278  0.43  -0.29  1.24  0.7  0.289  0.112  -0.09  1.49  ecosystem function  0.263  0.229  0.782  -0.37  0.89  human use species  0.428  0.321  0.67  -0.45  1.31  -0.233  0.233  0.855  -0.87  0.41  ecosystem function  *  -.532  0.172  0.019  -1.01  -0.06  human use don't know  0.313  0.265  0.762  -0.41  1.04  -0.111  0.25  0.992  -0.8  0.57  wilderness areas  0.233  0.233  0.855  -0.41  0.87  ecosystem function  -0.299  0.186  0.497  -0.81  0.21  human use don't know  0.546  0.274  0.273  -0.21  1.3  0.123  0.26  0.99  -0.59  0.84  wilderness areas  *  0.172  0.019  0.06  1.01  0.299  0.186  0.497  -0.21  0.81  *  0.225  0.002  0.23  1.46  0.422  0.206  0.247  -0.14  0.99  wilderness areas  -0.313  0.265  0.762  -1.04  0.41  species  -0.546  0.274  0.273  -1.3  0.21  ecosystem function  *  -.845  0.225  0.002  -1.46  -0.23  don't know  -0.423  0.288  0.584  -1.21  0.37  (J) goals species human use don't know  human use  don't know  species  (New) Species Introduction  wilderness areas  species  ecosystem function  species human use don't know human use  .532  .845  141  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  Assisted Colonization  (I) goals don't know  wilderness areas  species  ecosystem function  human use  don't know  Captive Breeding  wilderness areas  Mean Difference (I-J) 0.111  Std. Error 0.25  Sig. 0.992  Lower Bound -0.57  Upper Bound 0.8  species  -0.123  0.26  0.99  -0.84  0.59  ecosystem function  -0.422  0.206  0.247  -0.99  0.14  0.423  0.288  0.584  -0.37  1.21  -0.01  0.227  1  -0.63  0.61  ecosystem function  -0.144  0.167  0.912  -0.6  0.32  human use don't know  0.135  0.258  0.985  -0.57  0.84  -0.199  0.243  0.925  -0.87  0.47  wilderness areas  0.01  0.227  1  -0.61  0.63  ecosystem function  -0.134  0.181  0.947  -0.63  0.36  human use don't know  0.145  0.267  0.983  -0.59  0.88  -0.189  0.253  0.945  -0.88  0.5  wilderness areas  0.144  0.167  0.912  -0.32  0.6  species  0.134  0.181  0.947  -0.36  0.63  human use don't know  0.279  0.219  0.706  -0.32  0.88  -0.055  0.201  0.999  -0.61  0.5  wilderness areas  -0.135  0.258  0.985  -0.84  0.57  species  -0.145  0.267  0.983  -0.88  0.59  ecosystem function  -0.279  0.219  0.706  -0.88  0.32  don't know  -0.334  0.281  0.757  -1.1  0.44  wilderness areas  0.199  0.243  0.925  -0.47  0.87  species  0.189  0.253  0.945  -0.5  0.88  ecosystem function  0.055  0.201  0.999  -0.5  0.61  human use species  0.334  0.281  0.757  -0.44  1.1  -0.302  0.281  0.818  -1.07  0.47  -0.149  0.207  0.952  -0.72  0.42  (J) goals wilderness areas  human use species  ecosystem function  142  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  (I) goals  species  ecosystem function  Mean Difference (I-J) -0.056  Std. Error 0.319  -0.676  wilderness areas  1  Lower Bound -0.93  Upper Bound 0.82  0.3  0.164  -1.5  0.15  0.302  0.281  0.818  -0.47  1.07  ecosystem function  0.154  0.224  0.959  -0.46  0.77  human use don't know  0.247  0.33  0.945  -0.66  1.15  -0.373  0.312  0.754  -1.23  0.48  0.149  0.207  0.952  -0.42  0.72  -0.154  0.224  0.959  -0.77  0.46  0.093  0.27  0.997  -0.65  0.83  -0.527  0.248  0.212  -1.21  0.15  0.056  0.319  1  -0.82  0.93  species  -0.247  0.33  0.945  -1.15  0.66  ecosystem function  -0.093  0.27  0.997  -0.83  0.65  don't know  -0.62  0.347  0.382  -1.57  0.33  wilderness areas  0.676  0.3  0.164  -0.15  1.5  species  0.373  0.312  0.754  -0.48  1.23  ecosystem function  0.527  0.248  0.212  -0.15  1.21  0.62  0.347  0.382  -0.33  1.57  -0.044  0.261  1  -0.76  0.67  ecosystem function  -0.023  0.193  1  -0.55  0.51  human use don't know  0.398  0.296  0.663  -0.42  1.21  -0.068  0.279  0.999  -0.83  0.7  wilderness areas  0.044  0.261  1  -0.67  0.76  ecosystem function  0.021  0.208  1  -0.55  0.59  human use don't know  0.443  0.307  0.6  -0.4  1.29  -0.024  0.29  1  -0.82  0.77  (J) goals human use don't know  wilderness areas species human use don't know  human use  don't know  Conservation Triage  wilderness areas  species  wilderness areas  human use species  Sig.  143  Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable  Mean Difference (I-J) 0.023  Std. Error 0.193  -0.021  1  Lower Bound -0.51  Upper Bound 0.55  0.208  1  -0.59  0.55  0.421  0.251  0.449  -0.27  1.11  -0.045  0.231  1  -0.68  0.59  wilderness areas  -0.398  0.296  0.663  -1.21  0.42  species  -0.443  0.307  0.6  -1.29  0.4  ecosystem function  -0.421  0.251  0.449  -1.11  0.27  don't know  -0.467  0.322  0.597  -1.35  0.42  wilderness areas  0.068  0.279  0.999  -0.7  0.83  species  0.024  0.29  1  -0.77  0.82  ecosystem function  0.045  0.231  1  -0.59  0.68  human 0.467 use *. The mean difference is significant at the 0.05 level.  0.322  0.597  -0.42  1.35  (I) goals ecosystem function  (J) goals wilderness areas species human use don't know  human use  don't know  Sig.  144  APPENDIX E – ETHICS CERTIFICATE The University of British Columbia Office of Research Services Behavioural Research Ethics Board Suite 102, 6190 Agronomy Road, Vancouver, B.C. V6T 1Z3  CERTIFICATE OF APPROVAL - MINIMAL RISK AMENDMENT PRINCIPAL INVESTIGATOR:  DEPARTMENT: UBC BREB NUMBER: UBC/College for Interdisciplinary H09-02174 Studies/Community & Regional Planning INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT: Institution Site UBC Vancouver (excludes UBC Hospital) Other locations where the research will be conducted: Research will be conducted online. Survey questionnaires will be hosted on a Canadian-based server and is anticipated to be on the University of British Columbia campus. Timothy L. McDaniels  CO-INVESTIGATOR(S): Jordan Y. Tam SPONSORING AGENCIES: N/A PROJECT TITLE: Public Preferences for the Adaptation of Park Policies to Climate Change  Expiry Date - Approval of an amendment does not change the expiry date on the current UBC BREB approval of this study. An application for renewal is required on or before: October 19, 2010 AMENDMENT(S):  AMENDMENT APPROVAL DATE: January 19, 2010  Document Name Consent Forms: Study Consent Form Advertisements: Study Advertisement Questionnaire, Questionnaire Cover Letter, Tests: Study Questionnaire  Version  Date  3  January 7, 2010  3  January 7, 2010  2  January 7, 2010  The amendment(s) and the document(s) listed above have been reviewed and the procedures were found to be acceptable on ethical grounds for research involving human subjects.  Approval is issued on behalf of the Behavioural Research Ethics Board and signed electronically by one of the following:  Dr. M. Judith Lynam, Chair Dr. Ken Craig, Chair Dr. Jim Rupert, Associate Chair Dr. Laurie Ford, Associate Chair Dr. Anita Ho, Associate Chair  145  APPENDIX F – QUESTIONNAIRE Section 1 – Protected Areas and Climate Change Please read the following passage carefully. Positive Image/Low Certainty Prompt Further climate change is likely. It may alter the pattern of life on the planet, cause species extinctions and migration, and species behaviour change. New conservation tools and techniques and more management may be required to help biological systems and species adapt to climate change in protected areas (e.g., parks, reserves, sanctuaries). Positive Image/High Certainty Prompt Further climate change is certain. It will alter the pattern of life on the planet, cause species extinctions and migration, and species behaviour change. New conservation tools and techniques and more management are required to help biological systems and species adapt to climate change in protected areas (e.g., parks, reserves, sanctuaries). Negative Image/Low Certainty Prompt Further climate change is likely. It may alter the pattern of life on the planet, cause species extinctions and migration, and species behaviour change. New conservation tools and techniques and more management may be required to help biological systems and species adapt to climate change in protected areas (e.g., parks, reserves, sanctuaries). Negative Image/High Certainty Prompt Further climate change is certain. It will alter the pattern of life on the planet, cause species extinctions and migration, and species behaviour change. New conservation tools and techniques and more management are required to help biological systems and species adapt to climate change in protected areas (e.g., parks, reserves, sanctuaries). [Optional] please add any comments you may have. If you have no comments, simply press the 'submit' button to continue:  146  Section 2 – How do you feel? While thinking about climate change and of humans managing nature as previously described, please make a quick intuitive rating of... Affect 1) how good or bad you feel about it.  2) how positive or negative you feel about it.  3) how pleasant or unpleasant you feel about it. Specific Negative Emotions 4) how afraid or relaxed you feel about it.  5) how angry or calm you feel about it.  6) how upset or at ease you feel about it.  Very Bad 1 2 Very Negative 1 2 Very Unpleasant 1 2 Very Afraid 1 Very Angry 1 Very Upset 1  Neutral 3 Neutral 3 Neutral 3  Very Good 4 5 Very Positive 4 5 Very Pleasant 4 5  Neutral 2  3 Neutral  4  2  3 Neutral  4  2  3  4  Very Relaxed 5 Very Calm 5 Very At ease 5  147  Sections 7-9 – Protected Area Policies As potential responses to climate change, a suite of management practices for protected areas have been proposed. We are interested to know whether you would accept the use of these practices and how risky they appear to you in general. 1) Is it acceptable to link protected areas using corridors that allow species to move in response to climate change?  Not at all acceptable  1  Moderately acceptable  2  Not at  For example: A species is expected to move from all risky 1 protected area A toward protected area B in response to climate change. To allow the species to move with less obstruction, a strip of land connecting the two areas is protected. 2) Is it acceptable to allow species from outside protected areas to become established within protected areas as they move in response to climate change? For example: In response to climate change, a species has moved into a protected area from outside the area’s borders. Instead of removing the species, as current policies might prescribe, the species is allowed to become established. 3) Is it acceptable to actively promote the growth and establishment of a native (i.e., local) species, within a protected area, that may otherwise struggle to adapt on its own to future climate conditions? For example: A native species within a protected area is expected to suffer from climate change. Assistance may be provided to the species in the form of extra food, through breeding programs, or by spreading members of the species to more locations in the protected area so that they are more widely established.  4) Is it acceptable to introduce non-native (i.e., non-local) species, which are better adapted to future climate conditions, into a protected area?  2  2  2  2  4  3  3  2  3  4  3  5  Very acceptable  4  5  Extremely risky  4  Moderately acceptable  2  5  Extremely risky  Moderately risky  Not at all acceptable  1  3  5  Very acceptable  Moderately acceptable  Not at all risky  1  4  Moderately risky  Not at all acceptable  1  3  5 Extremely risky  Moderately acceptable  Not at all risky  1  4  Moderately risky  Not at all acceptable  1  3  Very acceptable  5  Very acceptable  4  5  148  For example: A non-native species that is expected to be better adapted to future climate conditions than native species is brought into a protected area to help maintain, enhance, or provide an ecosystem function. 5) Is it acceptable to assist valued species threatened by climate change by moving them to areas where they have never existed but where there is more suitable habitat under future climate conditions? For example: A species is expected to go extinct from climate change in its current habitat. Members of the threatened species are transported to new habitat that is currently suitable and also expected to be suitable in the future, though the species has never existed there in the past (i.e., it is non-native). 6) Is it acceptable to breed and hold in captivity species that are unable to adapt to climate change? For example: A species is expected to go extinct from climate change in its current habitat. Members of the threatened species are bred and held in captivity indefinitely until suitable habitat becomes available once again. 7) Is it acceptable to divert resources and protection away from a species that is unlikely to survive climate change in order to protect another species that is more likely to survive? For example: Both species A and species B are expected to go extinct from climate change without human intervention. However, provided the same attention and resources, it is more likely that species B will survive. Thus, resources are reallocated from species A to ensure the protection of species B, knowing that species A will become extinct as a result.  Not at all risky  1  Moderately risky  2  Not at all acceptable  1  2  2  2  2  4  3  3  2  3  4  3  5 Extremely risky  4  5  Very acceptable  4  Moderately risky  2  5  Very acceptable  Moderately acceptable  Not at all risky  1  3  5  Extremely risky  Moderately risky  Not at all acceptable  1  4  Moderately acceptable  Not at all risky  1  3  5  Very acceptable  Moderately risky  Not at all acceptable  1  4  Moderately acceptable  Not at all risky  1  3  Extremely risky  5 Extremely risky  4  5  149  Section 10: Conservation Goals In your opinion, the PRIMARY goal of environmental conservation should be the protection or restoration of particular... : wilderness areas least impacted by human beings unique or at risk species and groups of species unique or at risk ecosystem functions and processes unique or at risk physical locations and landscape features species and ecosystem functions that have human uses don't know Section 11 – Environmental Beliefs We are interested in your beliefs about the environment. For each statement, please choose the item that best represents your views: Strongly disagree 1) We are approaching the limit of the number 1 of people the earth can support  Mildly disagree 2  Unsure 3  Mildly Strongly agree agree 4 5  2) Humans have the right to modify the natural environment to suit their needs  1  2  3  4  5  3) When humans interfere with nature it often produces disastrous consequences  1  2  3  4  5  4) Human ingenuity will insure that we do NOT make the earth unliveable  1  2  3  4  5  5) Humans are severely abusing the environment  1  2  3  4  5  6) The earth has plenty of natural resources if we just learn how to develop them  1  2  3  4  5  7) Plants and animals have as much right as humans to exist  1  2  3  4  5  8) The balance of nature is strong enough to cope with the impacts of modern industrial nations 9) Despite our special abilities humans are still subject to the laws of nature  1  2  3  4  5  1  2  3  4  5  150  1  2  3  4  5  11) The earth is like a spaceship with very limited 1 room and resources  2  3  4  5  12) Humans were meant to rule over the rest of nature  1  2  3  4  5  13) The balance of nature is very delicate and easily upset  1  2  3  4  5  14) Humans will eventually learn enough about how nature works to be able to control it  1  2  3  4  5  15) If things continue on their present course, we will soon experience a major ecological catastrophe  1  2  3  4  5  10) The so-called “ecological crisis” facing humankind has been greatly exaggerated  Section 12 – Certainty We are interested in how certain you are of the following events. For each statement, please choose the item that best represents your views:  1) 2) 3)  4)  5)  Very Somewhat Neutral Somewhat Uncertain Uncertain Certain How certain are you that climate change 1 2 3 4 is occurring? How certain are you that further climate 1 2 3 4 change is going to occur? How certain are you that climate change 1 2 3 4 has already had negative impacts on the environment? How certain are you that further climate 1 2 3 4 change is going to have negative impacts on the environment? How certain are you that people can 1 2 3 4 manage nature without causing negative impacts?  [Optional] please add any comments you may have:  Very Certain 5 5 5  5  5  151  Section 13 - Demographics Thank you for taking the survey. In order for us to understand more about you, please answer the few remaining demographic questions. As with all your responses, they will be strictly confidential. Politically, how do you consider yourself to be?: _ Liberal _Somewhat Liberal _Moderate _Somewhat Conservative _Conservative _Other Have you ever been a member of, or supported the environmental movement?: Yes No If yes, please rate your level of involvement: Low Medium High Do you live within 50km (approx. 30 miles) from a protected area?: Yes No Don't know In an average year, how often do you visit protected areas?: Never 1-3 4-6 7-9 10+  

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