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Understanding preferences for climate change adaptation for protected areas : the psychology of individual.. Tam, Jordan Yukho Sep 1, 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. Thesis Overview............................................................................................................... 7 2. 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. Protected Values (and Omission Bias) ................................................................. 15 2.6. Testable Hypotheses ..................................................................................................... 16 3. METHODOLOGY .................................................................................................................... 18 3.1. Introduction ................................................................................................................... 18 3.2. Procedure....................................................................................................................... 18 3.3. Experimental Design ..................................................................................................... 19 3.1. Manipulations ................................................................................................................ 20 3.1.1. Framing and Priming ............................................................................................. 20 3.2. Measures ....................................................................................................................... 24 3.2.1. Emotion .................................................................................................................. 24 3.2.2. Risk Perceptions and Policy Acceptability ............................................................ 25 iv  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 4. 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. 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 5. 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 pre-industrial 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, & Stoll-Kleemann, 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 decision-making 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 cost-benefit 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’ decision-making, 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 risk-aversion. 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 cognitive-dissonance 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 non-negotiable 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 Negative Images   Copyright 2009 by Lassi Kurkiljärvi. Adapted with permission from a creative commons attribution-noncommercial 2.0 generic license. Copyright 2009 by Jordan Tam. 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 affectively-loaded 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 non-interventionist 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                                                                                                                                                                    Novel Risk-averse                                                                                                                                                           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, Mann-Whitney 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 3-15. 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 H3 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 risk-tolerant (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).  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.   12345Mean AcceptabilityMean Risk37  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).    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 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 (New) Species Introduction for Ecosystem Function -.421 -.345 -.366 -.453 -.356 -.455 -.462 Notes: All correlations were found to be significant to a p < .001 level.  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 H4 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 H3 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 non-supporters (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 decision-makers 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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Deviation Std. Error Mean Migration Corridors Male 120 4.27 1.075 .098 Female 186 4.41 .835 .061 Permitting Climate Migrants Male 118 3.99 .965 .089 Female 179 3.91 .872 .065 In-situ Aid Male 117 3.68 1.065 .098 Female 177 3.94 .930 .070 (New) Species Introduction Male 116 2.66 1.022 .095 Female 177 2.65 1.045 .079 Assisted Colonization Male 116 3.31 1.075 .100 Female 172 3.29 .877 .067 Captive Breeding Male 114 3.54 1.206 .113 Female 172 3.28 1.094 .083 Conservation Triage 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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Migration Corridors Equal variances assumed 7.882 0.005 -1.344 304 0.18 -0.147 0.11 -0.363 0.068 Equal variances not assumed     -1.274 209.391 0.204 -0.147 0.116 -0.375 0.081 Permitting Climate Migrants Equal variances assumed 2.059 0.152 0.801 295 0.424 0.086 0.108 -0.126 0.299 Equal variances not assumed     0.785 232.559 0.433 0.086 0.11 -0.131 0.304 In-situ Aid Equal variances assumed 7.13 0.008 -2.235 292 0.026 -0.263 0.118 -0.494 -0.031 Equal variances not assumed     -2.174 224.769 0.031 -0.263 0.121 -0.501 -0.025 79  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper (New) Species Introduction Equal variances assumed 0.104 0.747 0.044 291 0.965 0.005 0.124 -0.238 0.249 Equal variances not assumed     0.044 249.892 0.965 0.005 0.123 -0.237 0.248 Assisted Colonization Equal variances assumed 6.115 0.014 0.17 286 0.865 0.02 0.115 -0.208 0.247 Equal variances not assumed     0.164 212.609 0.87 0.02 0.12 -0.217 0.256 Captive Breeding Equal variances assumed 4.155 0.042 1.924 284 0.055 0.265 0.138 -0.006 0.536 Equal variances not assumed     1.886 225.554 0.061 0.265 0.14 -0.012 0.541 Conservation Triage Equal variances assumed 2.324 0.128 2.913 284 0.004 0.398 0.137 0.129 0.667 Equal variances not assumed     2.822 215.502 0.005 0.398 0.141 0.12 0.676   Gender x Risk Perceptions   Group Statistics   gender N Mean Std. Deviation Std. Error Mean Migration Corridors Male 118 2.77 1.057 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 80  Group Statistics   gender N Mean Std. Deviation Std. Error Mean (New) Species Introduction Male 116 4 1.004 0.093 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   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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Migration Corridors Equal variances assumed .296 .587 .748 301 .455 .090 .121 -.147 .327 Equal variances not assumed   .739 239.178 .461 .090 .122 -.150 .330 Permitting Climate Migrants Equal variances assumed 2.505 .115 -1.039 294 .299 -.118 .113 -.340 .105 Equal variances not assumed   -1.004 218.495 .317 -.118 .117 -.349 .113 In-situ Aid Equal variances assumed 4.011 .046 .482 293 .630 .061 .126 -.187 .308 Equal variances not assumed   .464 216.290 .643 .061 .131 -.197 .318 (New) Species Introduction Equal variances assumed .312 .577 .767 290 .443 .091 .118 -.142 .324 Equal variances not assumed   .764 242.242 .446 .091 .119 -.144 .325 Assisted Colonization Equal variances assumed .449 .503 .181 286 .857 .020 .109 -.194 .234 Equal variances not assumed   .179 238.557 .858 .020 .110 -.197 .236 Captive Breeding Equal variances assumed 1.650 .200 -2.325 284 .021 -.316 .136 -.584 -.048 Equal variances not assumed   -2.293 230.739 .023 -.316 .138 -.588 -.045 Conservation Triage Equal variances assumed 3.750 .054 -2.765 284 .006 -.340 .123 -.582 -.098 Equal variances not assumed   -2.698 221.705 .008 -.340 .126 -.588 -.092  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 yes 190 4.49 .802 .058 no  91 4.24 .947 .099 Permitting Climate Migrants yes 188 3.97 .833 .061 no  91 3.88 .953 .100 In-situ Aid yes 190 3.86 .990 .072 no  90 3.83 .939 .099 (New) Species Introduction yes 190 2.62 1.031 .075 no  90 2.70 1.022 .108 Assisted Colonization yes 189 3.33 .898 .065 no  91 3.20 1.013 .106 Captive Breeding yes 189 3.38 1.131 .082 no  90 3.34 1.143 .121 Conservation Triage yes 188 2.82 1.137 .083 no  91 2.42 1.106 .116 Risk Perceptions Migration Corridors yes 188 2.62 .982 .072 no  91 2.77 1.034 .108 Permitting Climate Migrants yes 188 3.29 .903 .066 no  91 3.08 1.014 .106 In-situ Aid yes 190 2.92 1.051 .076 no  91 2.75 1.018 .107 (New) Species Introduction yes 189 3.99 .956 .070 no  90 3.96 .947 .100 Assisted Colonization yes 189 3.87 .860 .063 no  91 3.69 .927 .097 Captive Breeding yes 188 2.80 1.156 .084 no  91 2.84 1.108 .116 Conservation Triage yes 188 3.74 1.045 .076 no  91 3.69 .963 .101               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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability Migration Corridors Equal variances assumed 6.553 0.011 2.331 279 0.02 0.253 0.109 0.039 0.467 Equal variances not assumed     2.199 153.797 0.029 0.253 0.115 0.026 0.48 Permitting Climate Migrants Equal variances assumed 4.612 0.033 0.797 277 0.426 0.089 0.112 -0.131 0.309 Equal variances not assumed     0.761 158.528 0.448 0.089 0.117 -0.142 0.32 In-situ Aid Equal variances assumed 0.128 0.721 0.197 278 0.844 0.025 0.125 -0.221 0.27 Equal variances not assumed     0.201 183.385 0.841 0.025 0.122 -0.217 0.266 (New) Species Introduction Equal variances assumed 0.226 0.635 -0.64 278 0.523 -0.084 0.132 -0.343 0.175 Equal variances not assumed     -0.642 176.279 0.522 -0.084 0.131 -0.343 0.175 Assisted Colonization Equal variances assumed 0.882 0.349 1.089 278 0.277 0.13 0.12 -0.105 0.366 Equal variances not assumed     1.044 159.987 0.298 0.13 0.125 -0.116 0.377 Captive Breeding Equal variances assumed 0.048 0.826 0.251 277 0.802 0.037 0.145 -0.25 0.323 Equal variances not assumed     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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Conservation Triage Equal variances assumed 0.001 0.977 2.79 277 0.006 0.402 0.144 0.118 0.685 Equal variances not assumed     2.817 182.635 0.005 0.402 0.143 0.12 0.683 Risk Perceptions   Migration Corridors Equal variances assumed 0.011 0.918 -1.193 277 0.234 -0.152 0.128 -0.403 0.099 Equal variances not assumed     -1.172 170.227 0.243 -0.152 0.13 -0.409 0.104 Permitting Climate Migrants Equal variances assumed 0.823 0.365 1.751 277 0.081 0.21 0.12 -0.026 0.447 Equal variances not assumed     1.682 161.004 0.094 0.21 0.125 -0.037 0.457 In-situ Aid Equal variances assumed 0.003 0.953 1.271 279 0.205 0.169 0.133 -0.092 0.43 Equal variances not assumed     1.286 182.702 0.2 0.169 0.131 -0.09 0.427 (New) Species Introduction Equal variances assumed 0.004 0.953 0.277 277 0.782 0.034 0.122 -0.207 0.274 Equal variances not assumed     0.278 176.714 0.781 0.034 0.122 -0.206 0.274 Assisted Colonization Equal variances assumed 2.783 0.096 1.605 278 0.11 0.181 0.113 -0.041 0.402 Equal variances not assumed     1.563 166.28 0.12 0.181 0.116 -0.047 0.409 84  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Captive Breeding Equal variances assumed 1.165 0.281 -0.22 277 0.826 -0.032 0.146 -0.319 0.255 Equal variances not assumed     -0.223 185.007 0.824 -0.032 0.144 -0.315 0.251 Conservation Triage Equal variances assumed 1.858 0.174 0.362 277 0.718 0.047 0.13 -0.209 0.303 Equal variances not assumed     0.372 191.931 0.71 0.047 0.126 -0.202 0.296                          85  APPENDIX C – PRIMAY ANALYSES OF MANIPULATION EFFECTS  Manipulations x Affect Index  Descriptive Statistics Dependent Variable:Affect_Index Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 6.68 2.270 74 Uncertain Prompt 6.16 2.344 76 Total 6.41 2.315 150 Negative Image Certain Prompt 6.94 2.290 77 Uncertain Prompt 6.64 2.171 80 Total 6.78 2.228 157 Total Certain Prompt 6.81 2.277 151 Uncertain Prompt 6.40 2.263 156 Total 6.60 2.275 307   Tests of Between-Subjects Effects Dependent Variable:Affect_Index Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 24.034a 3 8.011 1.557 .200 Intercept 13368.579 1 13368.579 2597.448 .000 Affect_Manip 10.470 1 10.470 2.034 .155 Cert_Manip 12.746 1 12.746 2.476 .117 Affect_Manip * Cert_Manip .930 1 .930 .181 .671 Error 1559.484 303 5.147   Total 14967.000 307    Corrected Total 1583.518 306    a. R Squared = .015 (Adjusted R Squared = .005)    Manipulations x Fear Descriptive Statistics Dependent Variable:feel_fear Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 2.47 .920 75 Uncertain Prompt 2.42 1.010 76 Total 2.44 .964 151 Negative Image Certain Prompt 2.55 .953 77 Uncertain Prompt 2.59 .968 79 Total 2.57 .958 156 Total Certain Prompt 2.51 .935 152 Uncertain Prompt 2.51 .989 155 Total 2.51 .961 307   86   Tests of Between-Subjects Effects Dependent Variable:feel_fear Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 1.408a 3 .469 .505 .679 Intercept 1928.842 1 1928.842 2077.475 .000 Affect_Manip 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    Corrected Total 282.730 306    a. R Squared = .005 (Adjusted R Squared = -.005)  Manipulations x Anger  Descriptive Statistics Dependent Variable:feel_anger Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 2.28 .847 75 Uncertain Prompt 2.42 .983 76 Total 2.35 .918 151 Negative Image Certain Prompt 2.56 .877 78 Uncertain Prompt 2.55 1.090 80 Total 2.56 .987 158 Total Certain Prompt 2.42 .871 153 Uncertain Prompt 2.49 1.038 156 Total 2.46 .958 309    Tests of Between-Subjects Effects Dependent Variable:feel_anger Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 4.034a 3 1.345 1.472 .222 Intercept 1859.371 1 1859.371 2035.376 .000 Affect_Manip 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    Corrected Total 282.660 308    a. R Squared = .014 (Adjusted R Squared = .005)      87   Manipulations x Distress (Upset)  Descriptive Statistics Dependent Variable:feel_upset Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 2.23 .869 74 Uncertain Prompt 2.28 1.021 75 Total 2.26 .946 149 Negative Image Certain Prompt 2.44 .939 77 Uncertain Prompt 2.31 .908 80 Total 2.38 .923 157 Total Certain Prompt 2.34 .908 151 Uncertain Prompt 2.30 .961 155 Total 2.32 .934 306    Tests of Between-Subjects Effects Dependent Variable:feel_upset Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 1.863a 3 .621 .709 .547 Intercept 1639.814 1 1639.814 1873.087 .000 Affect_Manip 1.141 1 1.141 1.303 .255 Cert_Manip .119 1 .119 .135 .713 Affect_Manip * Cert_Manip .614 1 .614 .702 .403 Error 264.389 302 .875   Total 1909.000 306    Corrected Total 266.252 305    a. R Squared = .007 (Adjusted R Squared = -.003)    Manipulations x Certainty  Descriptive Statistics Dependent Variable:Cert_Index Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 18.18 2.461 66 Uncertain Prompt 17.91 2.986 66 Total 18.05 2.729 132 Negative Image Certain Prompt 17.65 2.681 72 Uncertain Prompt 18.40 2.662 77 Total 18.04 2.689 149 Total Certain Prompt 17.91 2.583 138 Uncertain Prompt 18.17 2.817 143 Total 18.04 2.703 281 88    Tests of Between-Subjects Effects Dependent Variable:Cert_Index Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 23.376a 3 7.792 1.067 .363 Intercept 91031.541 1 91031.541 12470.002 .000 Affect_Manip .022 1 .022 .003 .956 Cert_Manip 3.981 1 3.981 .545 .461 Affect_Manip * Cert_Manip 18.286 1 18.286 2.505 .115 Error 2022.112 277 7.300   Total 93522.000 281    Corrected Total 2045.488 280    a. R Squared = .011 (Adjusted R Squared = .001)  Manipulations x New Ecological Paradigm   Descriptive Statistics Dependent Variable:NEP_Index Affect_Manip Cert_Manip Mean Std. Deviation N Positive Image Certain Prompt 59.25 9.630 64 Uncertain Prompt 58.33 9.979 63 Total 58.80 9.776 127 Negative Image Certain Prompt 60.82 7.818 71 Uncertain Prompt 61.27 8.491 77 Total 61.05 8.151 148 Total Certain Prompt 60.07 8.726 135 Uncertain Prompt 59.95 9.273 140 Total 60.01 8.992 275   Tests of Between-Subjects Effects Dependent Variable:NEP_Index Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 383.075a 3 127.692 1.589 .192 Intercept 980768.036 1 980768.036 12206.735 .000 Affect_Manip 346.711 1 346.711 4.315 .039 Cert_Manip 3.626 1 3.626 .045 .832 Affect_Manip * Cert_Manip 32.162 1 32.162 .400 .527 Error 21773.892 271 80.346   Total 1012517.000 275    Corrected Total 22156.967 274    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 Positive Image 150 4.39 0.988 0.081 Negative Image 157 4.33 0.887 0.071 Permitting Climate Migrants Positive Image 145 3.92 0.943 0.078 Negative Image 153 3.95 0.876 0.071 In-situ Aid Positive Image 144 3.82 1.049 0.087 Negative Image 151 3.84 0.939 0.076 (New) Species Introduction Positive Image 143 2.64 1.045 0.087 Negative Image 151 2.67 1.025 0.083 Assisted Colonization Positive Image 138 3.26 1.069 0.091 Negative Image 151 3.34 0.848 0.069 Captive Breeding Positive Image 136 3.45 1.21 0.104 Negative Image 151 3.33 1.081 0.088 Conservation Triage Positive Image 137 2.74 1.201 0.103 Negative Image 150 2.65 1.093 0.089 Risk Perceptions Migration Corridors Positive Image 148 2.67 1.033 0.085 Negative Image 156 2.76 1.012 0.081 Permitting Climate Migrants Positive Image 144 3.13 0.991 0.083 Negative Image 153 3.27 0.911 0.074 In-situ Aid Positive Image 144 2.82 1.049 0.087 Negative Image 152 2.94 1.063 0.086 90  Group Statistics   Affect_Manip N Mean Std. Deviation Std. Error Mean (New) Species Introduction Positive Image 143 3.9 1.023 0.086 Negative Image 150 3.99 0.959 0.078 Assisted Colonization Positive Image 138 3.8 0.937 0.08 Negative Image 151 3.81 0.877 0.071 Captive Breeding Positive Image 136 2.72 1.159 0.099 Negative Image 151 2.87 1.109 0.09 Conservation Triage 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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability Migration Corridors Equal variances assumed 0.342 0.559 0.518 305 0.605 0.055 0.107 -0.155 0.266 Equal variances not assumed     0.517 298.009 0.606 0.055 0.107 -0.156 0.267 Permitting Climate Migrants Equal variances assumed 0.281 0.596 -0.286 296 0.775 -0.03 0.105 -0.238 0.177 Equal variances not assumed     -0.285 291.26 0.776 -0.03 0.106 -0.238 0.178 In-situ Aid Equal variances assumed 4.785 0.029 -0.187 293 0.852 -0.022 0.116 -0.249 0.206 Equal variances not assumed     -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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper (New) Species Introduction Equal variances assumed 0.076 0.783 -0.269 292 0.788 -0.033 0.121 -0.27 0.205 Equal variances not assumed     -0.269 290.389 0.788 -0.033 0.121 -0.27 0.205 Assisted Colonization Equal variances assumed 6.444 0.012 -0.68 287 0.497 -0.077 0.113 -0.299 0.146 Equal variances not assumed     -0.673 260.954 0.501 -0.077 0.114 -0.302 0.148 Captive Breeding Equal variances assumed 2.614 0.107 0.868 285 0.386 0.117 0.135 -0.149 0.384 Equal variances not assumed     0.863 272.294 0.389 0.117 0.136 -0.15 0.385 Conservation Triage Equal variances assumed 2.555 0.111 0.674 285 0.501 0.091 0.135 -0.175 0.358 Equal variances not assumed     0.671 275.64 0.503 0.091 0.136 -0.176 0.359 Risk Perceptions   Migration Corridors Equal variances assumed 0.655 0.419 -0.746 302 0.456 -0.087 0.117 -0.318 0.143 Equal variances not assumed     -0.746 300.396 0.457 -0.087 0.117 -0.318 0.143 Permitting Climate Migrants Equal variances assumed 0.015 0.904 -1.233 295 0.219 -0.136 0.11 -0.353 0.081 Equal variances not assumed     -1.229 288.902 0.22 -0.136 0.111 -0.354 0.082 92  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper In-situ Aid Equal variances assumed 0.188 0.665 -0.988 294 0.324 -0.121 0.123 -0.363 0.12 Equal variances not assumed     -0.989 293.499 0.324 -0.121 0.123 -0.363 0.12 (New) Species Introduction Equal variances assumed 1.132 0.288 -0.788 291 0.431 -0.091 0.116 -0.319 0.137 Equal variances not assumed     -0.787 287.358 0.432 -0.091 0.116 -0.319 0.137 Assisted Colonization Equal variances assumed 0.031 0.86 -0.102 287 0.919 -0.011 0.107 -0.221 0.199 Equal variances not assumed     -0.101 280.175 0.919 -0.011 0.107 -0.222 0.2 Captive Breeding Equal variances assumed 1.989 0.16 -1.146 285 0.253 -0.154 0.134 -0.417 0.11 Equal variances not assumed     -1.144 278.833 0.254 -0.154 0.134 -0.418 0.111 Conservation Triage Equal variances assumed 0.981 0.323 -0.038 285 0.97 -0.005 0.122 -0.245 0.236 Equal variances not assumed     -0.038 277.763 0.97 -0.005 0.122 -0.246 0.236         93  Certainty Manipulation x Acceptability and Risk Perceptions  Group Statistics   Cert_Manip N Mean Std. Deviation Std. Error Mean Acceptability   Migration Corridors Certain Prompt 152 4.36 0.858 0.07 Uncertain Prompt 155 4.35 1.011 0.081 Permitting Climate Migrants Certain Prompt 148 3.91 0.888 0.073 Uncertain Prompt 150 3.97 0.93 0.076 In-situ Aid Certain Prompt 147 3.78 0.91 0.075 Uncertain Prompt 148 3.88 1.068 0.088 (New) Species Introduction Certain Prompt 145 2.61 0.938 0.078 Uncertain Prompt 149 2.7 1.119 0.092 Assisted Colonization Certain Prompt 144 3.3 0.961 0.08 Uncertain Prompt 145 3.3 0.96 0.08 Captive Breeding Certain Prompt 142 3.37 1.206 0.101 Uncertain Prompt 145 3.4 1.083 0.09 Conservation Triage Certain Prompt 142 2.66 1.117 0.094 Uncertain Prompt 145 2.73 1.174 0.098 Risk Perceptions   Migration Corridors Certain Prompt 151 2.74 0.927 0.075 Uncertain Prompt 153 2.69 1.109 0.09 Permitting Climate Migrants Certain Prompt 147 3.21 0.916 0.076 Uncertain Prompt 150 3.19 0.988 0.081 In-situ Aid Certain Prompt 147 2.95 1.09 0.09 Uncertain Prompt 149 2.82 1.02 0.084 94  Group Statistics   Cert_Manip N Mean Std. Deviation Std. Error Mean (New) Species Introduction Certain Prompt 144 3.96 0.96 0.08 Uncertain Prompt 149 3.94 1.022 0.084 Assisted Colonization Certain Prompt 144 3.79 0.876 0.073 Uncertain Prompt 145 3.81 0.935 0.078 Captive Breeding Certain Prompt 143 2.7 1.12 0.094 Uncertain Prompt 144 2.9 1.142 0.095 Conservation Triage 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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 1.679 0.196 0.065 305 0.948 0.007 0.107 -0.204 0.218 Equal variances not assumed     0.065 298.822 0.948 0.007 0.107 -0.203 0.217 Permitting Climate Migrants Equal variances assumed 0.03 0.862 -0.517 296 0.605 -0.055 0.105 -0.262 0.153 Equal variances not assumed     -0.518 295.681 0.605 -0.055 0.105 -0.262 0.153 In-situ Aid Equal variances assumed 2.502 0.115 -0.831 293 0.407 -0.096 0.116 -0.324 0.131 Equal variances not assumed     -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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper (New) Species Introduction Equal variances assumed 5.8 0.017 -0.755 292 0.451 -0.091 0.121 -0.328 0.146 Equal variances not assumed     -0.757 285.711 0.45 -0.091 0.12 -0.328 0.146 Assisted Colonization Equal variances assumed 0.238 0.626 -0.043 287 0.966 -0.005 0.113 -0.227 0.218 Equal variances not assumed     -0.043 286.979 0.966 -0.005 0.113 -0.227 0.218 Captive Breeding Equal variances assumed 1.94 0.165 -0.198 285 0.843 -0.027 0.135 -0.293 0.239 Equal variances not assumed     -0.198 280.361 0.843 -0.027 0.135 -0.293 0.24 Conservation Triage Equal variances assumed 0.242 0.623 -0.51 285 0.61 -0.069 0.135 -0.335 0.197 Equal variances not assumed     -0.511 284.756 0.61 -0.069 0.135 -0.335 0.197 Risk Perceptions   Migration Corridors Equal variances assumed 7.654 0.006 0.473 302 0.637 0.055 0.117 -0.175 0.286 Equal variances not assumed     0.473 294.066 0.636 0.055 0.117 -0.175 0.286 Permitting Climate Migrants Equal variances assumed 0.028 0.868 0.159 295 0.874 0.018 0.111 -0.2 0.235 Equal variances not assumed     0.159 294.101 0.874 0.018 0.111 -0.2 0.235 96  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper In-situ Aid Equal variances assumed 0.468 0.494 1.033 294 0.302 0.127 0.123 -0.115 0.368 Equal variances not assumed     1.033 292.145 0.303 0.127 0.123 -0.115 0.368 (New) Species Introduction Equal variances assumed 2.149 0.144 0.162 291 0.872 0.019 0.116 -0.209 0.247 Equal variances not assumed     0.162 290.771 0.872 0.019 0.116 -0.209 0.247 Assisted Colonization Equal variances assumed 0.432 0.512 -0.208 287 0.836 -0.022 0.107 -0.232 0.188 Equal variances not assumed     -0.208 286.024 0.836 -0.022 0.107 -0.232 0.188 Captive Breeding Equal variances assumed 0.001 0.981 -1.524 285 0.129 -0.203 0.134 -0.466 0.059 Equal variances not assumed     -1.524 284.952 0.129 -0.203 0.134 -0.466 0.059 Conservation Triage Equal variances assumed 1.023 0.313 -0.22 285 0.826 -0.027 0.122 -0.267 0.213 Equal variances not assumed     -0.22 284.833 0.826 -0.027 0.122 -0.267 0.213        97  APPENDIX D – SECONDARY ANALYSES Overall Policy Acceptability and Rick Perceptions   Descriptives       Statistic Std. Error Acceptability Migration Corridors Mean 4.36 0.053 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 -1.419 0.139 Kurtosis 1.403 0.277 Permitting Climate Migrants Mean 3.94 0.053 95% Confidence Interval for Mean 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 -0.614 0.141 Kurtosis 0.142 0.281 98  Descriptives       Statistic Std. Error In-situ Aid Mean 3.83 0.058 95% Confidence Interval for Mean Lower Bound 3.72   Upper Bound 3.94   5% Trimmed Mean 3.89   Median 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 (New) Species Introduction Mean 2.65 0.06 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 0.155 0.142 Kurtosis -0.513 0.283 Assisted Colonization Mean 3.3 0.056 95% Confidence Interval for Mean Lower Bound 3.19   Upper Bound 3.41   5% Trimmed Mean 3.32   Median 3   Variance 0.919   Std. Deviation 0.959   Minimum 1   99  Descriptives       Statistic Std. Error Maximum 5   Range 4   Interquartile Range 1   Skewness -0.159 0.143 Kurtosis -0.109 0.286 Captive Breeding Mean 3.39 0.068 95% Confidence Interval for Mean Lower Bound 3.25   Upper Bound 3.52   5% Trimmed Mean 3.43   Median 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 Conservation Triage Mean 2.7 0.068 95% Confidence Interval for Mean Lower Bound 2.56   Upper Bound 2.83   5% Trimmed Mean 2.66   Median 3   Variance 1.31   Std. Deviation 1.145   Minimum 1   Maximum 5   Range 4   Interquartile Range 2   Skewness 0.135 0.144 Kurtosis -0.788 0.287 Risk Perceptions   Migration Corridors Mean 2.71 0.059 100  Descriptives       Statistic Std. Error 95% Confidence Interval for Mean Lower Bound 2.6   Upper Bound 2.83   5% Trimmed Mean 2.7   Median 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 Permitting Climate Migrants Mean 3.2 0.055 95% Confidence Interval for Mean Lower Bound 3.09   Upper Bound 3.31   5% Trimmed Mean 3.22   Median 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 In-situ Aid Mean 2.88 0.061 95% Confidence Interval for Mean Lower Bound 2.76   Upper Bound 3   5% Trimmed Mean 2.87   Median 3   Variance 1.115   Std. Deviation 1.056   Minimum 1   Maximum 5   101  Descriptives       Statistic Std. Error Range 4   Interquartile Range 2   Skewness 0.029 0.142 Kurtosis -0.564 0.282 (New) Species Introduction Mean 3.95 0.058 95% Confidence Interval for Mean Lower Bound 3.83   Upper Bound 4.06   5% Trimmed Mean 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 Assisted Colonization Mean 3.8 0.053 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 -0.365 0.143 Kurtosis -0.5 0.286 Captive Breeding Mean 2.8 0.067 95% Confidence Interval for Mean Lower Bound 2.67   Upper Bound 2.93   5% Trimmed Mean 2.78   102  Descriptives       Statistic Std. Error Median 3   Variance 1.286   Std. Deviation 1.134   Minimum 1   Maximum 5   Range 4   Interquartile Range 2   Skewness 0.165 0.144 Kurtosis -0.729 0.287 Conservation Triage Mean 3.72 0.061 95% Confidence Interval for Mean Lower Bound 3.6   Upper Bound 3.84   5% Trimmed Mean 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 Acceptability   Migration Corridors <= 6 175 4.43 0.9 0.068 7+ 128 4.26 0.982 0.087 Permitting Climate Migrants <= 6 171 3.94 0.852 0.065 7+ 124 3.92 0.984 0.088 103  Group Statistics   Affect_Index (Binned) N Mean Std. Deviation Std. Error Mean In-situ Aid <= 6 171 3.98 0.901 0.069 7+ 120 3.63 1.07 0.098 (New) Species Introduction <= 6 170 2.65 0.987 0.076 7+ 120 2.69 1.091 0.1 Assisted Colonization <= 6 166 3.29 0.915 0.071 7+ 120 3.32 1.029 0.094 Captive Breeding <= 6 164 3.46 1.11 0.087 7+ 119 3.31 1.191 0.109 Conservation Triage <= 6 166 2.66 1.06 0.082 7+ 118 2.75 1.254 0.115 Risk Perceptions   Migration Corridors <= 6 173 2.66 1.037 0.079 7+ 128 2.8 0.999 0.088 Permitting Climate Migrants <= 6 170 3.21 0.984 0.076 7+ 124 3.21 0.913 0.082 In-situ Aid <= 6 171 2.92 0.991 0.076 7+ 121 2.8 1.108 0.101 (New) Species Introduction <= 6 169 3.96 1.011 0.078 7+ 120 3.89 0.96 0.088 Assisted Colonization <= 6 166 3.8 0.891 0.069 7+ 120 3.81 0.929 0.085 Captive Breeding <= 6 165 2.85 1.078 0.084 7+ 119 2.71 1.21 0.111 Conservation Triage <= 6 166 3.72 1.002 0.078 7+ 118 3.69 1.074 0.099       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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 2.3 0.13 1.622 301 0.106 0.176 0.109 -0.038 0.391 Equal variances not assumed     1.6 259.514 0.111 0.176 0.11 -0.041 0.394 Permitting Climate Migrants Equal variances assumed 3.544 0.061 0.207 293 0.837 0.022 0.107 -0.189 0.233 Equal variances not assumed     0.202 241.394 0.84 0.022 0.11 -0.194 0.238 In-situ Aid Equal variances assumed 11.085 0.001 3.032 289 0.003 0.352 0.116 0.123 0.58 Equal variances not assumed     2.943 227.478 0.004 0.352 0.119 0.116 0.587 (New) Species Introduction Equal variances assumed 1.448 0.23 -0.315 288 0.753 -0.039 0.123 -0.281 0.203 Equal variances not assumed     -0.31 239.821 0.757 -0.039 0.125 -0.285 0.208 Assisted Colonization Equal variances assumed 2.244 0.135 -0.238 284 0.812 -0.028 0.116 -0.255 0.2 Equal variances not assumed     -0.234 237.879 0.815 -0.028 0.118 -0.259 0.204 Captive Breeding Equal variances assumed 0.671 0.413 1.062 281 0.289 0.146 0.138 -0.125 0.418 Equal variances not assumed     1.05 243.466 0.295 0.146 0.139 -0.128 0.421 Conservation Triage Equal variances assumed 2.551 0.111 -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. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Equal variances not assumed     -0.689 224.96 0.492 -0.098 0.142 -0.377 0.182 Risk Perceptions   Migration Corridors Equal variances assumed 0.721 0.397 -1.159 299 0.247 -0.138 0.119 -0.372 0.096 Equal variances not assumed     -1.165 279.176 0.245 -0.138 0.118 -0.371 0.095 Permitting Climate Migrants Equal variances assumed 0.378 0.539 -0.034 292 0.973 -0.004 0.113 -0.226 0.218 Equal variances not assumed     -0.034 275.737 0.973 -0.004 0.111 -0.223 0.216 In-situ Aid Equal variances assumed 4.299 0.039 0.942 290 0.347 0.116 0.124 -0.127 0.36 Equal variances not assumed     0.924 240.012 0.356 0.116 0.126 -0.132 0.365 (New) Species Introduction Equal variances assumed 0.238 0.626 0.616 287 0.538 0.073 0.118 -0.16 0.305 Equal variances not assumed     0.622 264.258 0.535 0.073 0.117 -0.158 0.304 Assisted Colonization Equal variances assumed 0.574 0.449 -0.121 284 0.904 -0.013 0.109 -0.227 0.201 Equal variances not assumed     -0.12 250.229 0.904 -0.013 0.109 -0.229 0.202 Captive Breeding Equal variances assumed 4.502 0.035 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 df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Equal variances not assumed     1.069 236.093 0.286 0.149 0.139 -0.125 0.423 Conservation Triage Equal variances assumed 0.593 0.442 0.177 282 0.86 0.022 0.124 -0.223 0.267 Equal variances not assumed     0.174 241.099 0.862 0.022 0.126 -0.226 0.27   Fear x Acceptability and Risk Perceptions  Group Statistics   Fear Categorical N Mean Std. Deviation Std. Error Mean Acceptability   Migration Corridors Afraid 157 4.4 0.919 0.073 Relaxed 42 4.26 0.964 0.149 Permitting Climate Migrants Afraid 152 3.9 0.933 0.076 Relaxed 42 4.17 0.824 0.127 In-situ Aid Afraid 150 3.97 0.93 0.076 Relaxed 40 3.58 1.059 0.168 (New) Species Introduction Afraid 149 2.6 1.039 0.085 Relaxed 40 3 1.013 0.16 Assisted Colonization Afraid 147 3.33 0.869 0.072 Relaxed 41 3.41 1.204 0.188 Captive Breeding Afraid 145 3.42 1.159 0.096 Relaxed 41 3.8 1.145 0.179 Conservation Triage Afraid 146 2.62 1.096 0.091 Relaxed 41 3.15 1.236 0.193 107  Group Statistics   Fear Categorical N Mean Std. Deviation Std. Error Mean Risk Perceptions   Migration Corridors Afraid 155 2.75 1.047 0.084 Relaxed 42 2.64 0.958 0.148 Permitting Climate Migrants Afraid 151 3.24 0.985 0.08 Relaxed 42 3.1 0.983 0.152 In-situ Aid Afraid 150 2.91 1.068 0.087 Relaxed 41 2.68 1.083 0.169 (New) Species Introduction Afraid 148 4 1.024 0.084 Relaxed 40 3.85 0.949 0.15 Assisted Colonization Afraid 147 3.9 0.882 0.073 Relaxed 41 3.63 1.019 0.159 Captive Breeding Afraid 146 2.93 1.112 0.092 Relaxed 41 2.59 1.245 0.194 Conservation Triage Afraid 146 3.86 0.99 0.082 Relaxed 41 3.44 1.026 0.16  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 0.267 0.606 0.864 197 0.389 0.139 0.161 -0.179 0.457 Equal variances not assumed     0.84 62.374 0.404 0.139 0.166 -0.192 0.471 Permitting Climate Migrants Equal variances assumed 0.031 0.86 -1.671 192 0.096 -0.265 0.159 -0.579 0.048 Equal variances not assumed     -1.793 72.741 0.077 -0.265 0.148 -0.56 0.03 108  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper In-situ Aid Equal variances assumed 4.591 0.033 2.297 188 0.023 0.392 0.171 0.055 0.728 Equal variances not assumed     2.13 56.05 0.038 0.392 0.184 0.023 0.76 (New) Species Introduction Equal variances assumed 3.027 0.084 -2.152 187 0.033 -0.396 0.184 -0.759 -0.033 Equal variances not assumed     -2.184 62.811 0.033 -0.396 0.181 -0.758 -0.034 Assisted Colonization Equal variances assumed 11.157 0.001 -0.524 186 0.601 -0.088 0.168 -0.42 0.243 Equal variances not assumed     -0.438 52.182 0.663 -0.088 0.201 -0.492 0.316 Captive Breeding Equal variances assumed 0.078 0.78 -1.879 184 0.062 -0.384 0.204 -0.788 0.019 Equal variances not assumed     -1.892 65.007 0.063 -0.384 0.203 -0.79 0.021 Conservation Triage Equal variances assumed 0.454 0.501 -2.623 185 0.009 -0.523 0.199 -0.916 -0.13 Equal variances not assumed     -2.452 58.831 0.017 -0.523 0.213 -0.95 -0.096 Risk Perceptions   Migration Corridors Equal variances assumed 0.023 0.879 0.626 195 0.532 0.112 0.179 -0.241 0.465 Equal variances not assumed     0.658 69.85 0.512 0.112 0.17 -0.227 0.451 Permitting Climate Migrants Equal variances assumed 0.108 0.743 0.834 191 0.405 0.143 0.172 -0.196 0.482 Equal variances not assumed     0.835 65.677 0.407 0.143 0.172 -0.199 0.486 109  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper In-situ Aid Equal variances assumed 0.266 0.607 1.221 189 0.224 0.23 0.189 -0.142 0.603 Equal variances not assumed     1.211 62.906 0.23 0.23 0.19 -0.15 0.611 (New) Species Introduction Equal variances assumed 0.22 0.64 0.835 186 0.405 0.15 0.18 -0.204 0.504 Equal variances not assumed     0.872 65.674 0.386 0.15 0.172 -0.193 0.493 Assisted Colonization Equal variances assumed 4.818 0.029 1.636 186 0.103 0.264 0.161 -0.054 0.582 Equal variances not assumed     1.508 57.764 0.137 0.264 0.175 -0.086 0.614 Captive Breeding Equal variances assumed 3.14 0.078 1.715 185 0.088 0.346 0.202 -0.052 0.744 Equal variances not assumed     1.61 59.129 0.113 0.346 0.215 -0.084 0.776 Conservation Triage Equal variances assumed 0.108 0.743 2.366 185 0.019 0.417 0.176 0.069 0.765 Equal variances not assumed     2.318 62.456 0.024 0.417 0.18 0.058 0.777           110  Anger x Acceptability and Risk Perceptions   Group Statistics  Anger Categorical N Mean Std. Deviation Std. Error Mean Acceptability Migration Corridors Angry 174 4.44 .915 .069 Calm 36 4.50 .845 .141 Permitting Climate Migrants Angry 168 3.85 .954 .074 Calm 36 4.14 .723 .121 In-situ Aid Angry 168 3.92 .975 .075 Calm 34 3.65 .981 .168 (New) Species Introduction Angry 168 2.54 1.020 .079 Calm 35 2.89 1.132 .191 Assisted Colonization Angry 165 3.30 .905 .070 Calm 35 3.43 1.008 .170 Captive Breeding Angry 162 3.41 1.123 .088 Calm 35 3.57 1.290 .218 Conservation Triage Angry 164 2.52 1.053 .082 Calm 35 2.77 1.215 .205 Risk Perceptions Migration Corridors Angry 172 2.75 1.015 .077 Calm 36 2.50 .910 .152 Permitting Climate Migrants Angry 167 3.23 1.000 .077 Calm 36 3.17 .910 .152 In-situ Aid Angry 168 2.92 1.052 .081 Calm 35 2.57 1.037 .175 (New) Species Introduction Angry 167 3.99 1.035 .080 Calm 35 3.91 .919 .155 Assisted Colonization Angry 165 3.84 .904 .070 Calm 35 3.57 .948 .160 Captive Breeding Angry 163 2.79 1.098 .086 Calm 35 2.60 1.193 .202 Conservation Triage Angry 164 3.82 1.070 .084 Calm 35 3.71 .987 .167           111    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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 0.414 0.521 -0.347 208 0.729 -0.057 0.165 -0.384 0.269 Equal variances not assumed     -0.366 53.41 0.716 -0.057 0.157 -0.372 0.257 Permitting Climate Migrants Equal variances assumed 2.639 0.106 -1.741 202 0.083 -0.294 0.169 -0.626 0.039 Equal variances not assumed     -2.079 64.098 0.042 -0.294 0.141 -0.576 -0.012 In-situ Aid Equal variances assumed 0.73 0.394 1.469 200 0.144 0.27 0.184 -0.092 0.632 Equal variances not assumed     1.463 47.145 0.15 0.27 0.184 -0.101 0.64 (New) Species Introduction Equal variances assumed 0.092 0.762 -1.781 201 0.076 -0.344 0.193 -0.725 0.037 Equal variances not assumed     -1.663 46.21 0.103 -0.344 0.207 -0.76 0.072 Assisted Colonization Equal variances assumed 1.294 0.257 -0.765 198 0.445 -0.132 0.172 -0.471 0.207 Equal variances not assumed     -0.713 46.343 0.479 -0.132 0.184 -0.503 0.24 Captive Breeding Equal variances assumed 3.36 0.068 -0.763 195 0.447 -0.164 0.215 -0.588 0.26 Equal variances not assumed     -0.697 45.792 0.489 -0.164 0.235 -0.638 0.31 Conservation Triage Equal variances assumed 0.24 0.625 -1.225 197 0.222 -0.247 0.202 -0.645 0.151 112               Levene's Test for Equality of Variances t-test for Equality of Means     95% Confidence Interval of the Difference F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Equal variances not assumed     -1.117 45.546 0.27 -0.247 0.221 -0.692 0.198 Risk Perceptions   Migration Corridors Equal variances assumed 0.064 0.8 1.367 206 0.173 0.25 0.183 -0.111 0.611 Equal variances not assumed     1.468 54.837 0.148 0.25 0.17 -0.091 0.591 Permitting Climate Migrants Equal variances assumed 1.217 0.271 0.37 201 0.712 0.067 0.181 -0.29 0.424 Equal variances not assumed     0.393 54.785 0.696 0.067 0.17 -0.274 0.408 In-situ Aid Equal variances assumed 0.193 0.661 1.77 201 0.078 0.345 0.195 -0.039 0.73 Equal variances not assumed     1.787 49.675 0.08 0.345 0.193 -0.043 0.733 (New) Species Introduction Equal variances assumed 0.125 0.724 0.39 200 0.697 0.074 0.189 -0.299 0.446 Equal variances not assumed     0.422 53.699 0.675 0.074 0.175 -0.277 0.424 Assisted Colonization Equal variances assumed 0.967 0.327 1.598 198 0.112 0.271 0.17 -0.063 0.605 Equal variances not assumed     1.548 47.99 0.128 0.271 0.175 -0.081 0.623 Captive Breeding Equal variances assumed 1.052 0.306 0.892 196 0.374 0.185 0.208 -0.225 0.595 Equal variances not assumed     0.845 47.176 0.402 0.185 0.219 -0.256 0.626 Conservation Triage Equal variances assumed 0.232 0.63 0.523 197 0.602 0.103 0.197 -0.285 0.491 113               Levene's Test for Equality of Variances t-test for Equality of Means     95% Confidence Interval of the Difference F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Equal variances not assumed     0.551 52.482 0.584 0.103 0.187 -0.272 0.477   Upset (Distress) x Acceptability and Risk Perceptions  Group Statistics  Upset_Categorical N Mean Std. Deviation Std. Error Mean Acceptability Migration Corridors Upset 196 4.39 .896 .064 At Ease 30 4.27 1.015 .185 Permitting Climate Migrants Upset 192 3.91 .920 .066 At Ease 30 4.00 .910 .166 In-situ Aid Upset 189 3.89 .945 .069 At Ease 29 3.66 1.010 .188 (New) Species Introduction Upset 188 2.64 1.048 .076 At Ease 29 2.76 .872 .162 Assisted Colonization Upset 186 3.38 .893 .066 At Ease 29 3.21 1.114 .207 Captive Breeding Upset 184 3.46 1.096 .081 At Ease 29 3.45 1.213 .225 Conservation Triage Upset 185 2.66 1.051 .077 At Ease 29 2.66 1.344 .250 Risk Perceptions Migration Corridors Upset 194 2.75 1.030 .074 At Ease 30 2.80 1.095 .200 Permitting Climate Migrants Upset 191 3.28 .958 .069 At Ease 30 3.13 .860 .157 In-situ Aid Upset 189 2.93 1.042 .076 At Ease 29 2.72 1.251 .232 (New) Species Introduction Upset 187 3.97 1.002 .073 At Ease 29 3.97 .823 .153 Assisted Colonization Upset 186 3.80 .905 .066 At Ease 29 3.83 .889 .165 Captive Breeding Upset 185 2.83 1.103 .081 At Ease 29 2.76 1.300 .241 Conservation Triage Upset 185 3.73 1.044 .077 At Ease 29 3.79 .861 .160 114   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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 0.614 0.434 0.677 224 0.499 0.121 0.179 -0.231 0.474 Equal variances not assumed     0.618 36.255 0.541 0.121 0.196 -0.276 0.519 Permitting Climate Migrants Equal variances assumed 0.032 0.859 -0.491 220 0.624 -0.089 0.18 -0.444 0.267 Equal variances not assumed     -0.495 38.848 0.623 -0.089 0.179 -0.45 0.273 In-situ Aid Equal variances assumed 0.921 0.338 1.257 216 0.21 0.239 0.19 -0.136 0.614 Equal variances not assumed     1.197 35.934 0.239 0.239 0.2 -0.166 0.644 (New) Species Introduction Equal variances assumed 2.075 0.151 -0.587 215 0.558 -0.12 0.205 -0.524 0.283 Equal variances not assumed     -0.672 41.546 0.505 -0.12 0.179 -0.482 0.241 Assisted Colonization Equal variances assumed 3.051 0.082 0.917 213 0.36 0.169 0.185 -0.195 0.534 Equal variances not assumed     0.781 33.844 0.44 0.169 0.217 -0.272 0.611 Captive Breeding Equal variances assumed 1.058 0.305 0.037 211 0.97 0.008 0.222 -0.43 0.446 Equal variances not assumed     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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Conservation Triage Equal variances assumed 4.763 0.03 0.044 212 0.965 0.01 0.219 -0.421 0.44 Equal variances not assumed     0.037 33.581 0.971 0.01 0.261 -0.521 0.541 Risk Perceptions   Migration Corridors Equal variances assumed 1.088 0.298 -0.258 222 0.797 -0.053 0.204 -0.454 0.349 Equal variances not assumed     -0.247 37.361 0.807 -0.053 0.213 -0.484 0.379 Permitting Climate Migrants Equal variances assumed 1.304 0.255 0.776 219 0.438 0.144 0.186 -0.222 0.51 Equal variances not assumed     0.84 41.15 0.406 0.144 0.172 -0.203 0.491 In-situ Aid Equal variances assumed 3.379 0.067 0.969 216 0.334 0.207 0.214 -0.214 0.628 Equal variances not assumed     0.848 34.224 0.403 0.207 0.244 -0.289 0.703 (New) Species Introduction Equal variances assumed 2.292 0.132 0.04 214 0.968 0.008 0.196 -0.378 0.394 Equal variances not assumed     0.046 42.028 0.964 0.008 0.169 -0.334 0.35 Assisted Colonization Equal variances assumed 0.049 0.826 -0.147 213 0.883 -0.027 0.18 -0.382 0.329 Equal variances not assumed     -0.149 37.635 0.882 -0.027 0.178 -0.387 0.334 Captive Breeding Equal variances assumed 2.415 0.122 0.327 212 0.744 0.074 0.226 -0.371 0.519 116  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Equal variances not assumed     0.29 34.609 0.774 0.074 0.255 -0.443 0.591 Conservation Triage Equal variances assumed 4.056 0.045 -0.311 212 0.756 -0.063 0.204 -0.466 0.339 Equal variances not assumed     -0.357 42.048 0.723 -0.063 0.177 -0.421 0.295   Certainty x Acceptability and Risk Perceptions  Group Statistics   Certainty in 2 groups N Mean Std. Deviation Std. Error Mean Acceptability   Migration Corridors Low Certainty 146 4.27 0.912 0.075 High Certainty 134 4.58 0.739 0.064 Permitting Climate Migrants Low Certainty 145 3.9 0.908 0.075 High Certainty 133 4.01 0.821 0.071 In-situ Aid Low Certainty 145 3.68 0.926 0.077 High Certainty 134 4.02 0.977 0.084 (New) Species Introduction Low Certainty 145 2.68 1.052 0.087 High Certainty 134 2.59 0.998 0.086 Assisted Colonization Low Certainty 146 3.24 0.978 0.081 High Certainty 133 3.35 0.898 0.078 Captive Breeding Low Certainty 145 3.43 1.129 0.094 117  Group Statistics   Certainty in 2 groups N Mean Std. Deviation Std. Error Mean High Certainty 133 3.31 1.143 0.099 Conservation Triage Low Certainty 146 2.71 1.134 0.094 High Certainty 132 2.69 1.153 0.1 Risk Perceptions   Migration Corridors Low Certainty 145 2.79 0.942 0.078 High Certainty 132 2.54 1.037 0.09 Permitting Climate Migrants Low Certainty 144 3.2 0.89 0.074 High Certainty 133 3.21 0.993 0.086 In-situ Aid Low Certainty 146 2.92 1.06 0.088 High Certainty 134 2.8 1.039 0.09 (New) Species Introduction Low Certainty 145 3.92 0.987 0.082 High Certainty 133 4.02 0.908 0.079 Assisted Colonization Low Certainty 146 3.73 0.929 0.077 High Certainty 133 3.88 0.826 0.072 Captive Breeding Low Certainty 146 2.81 1.194 0.099 High Certainty 132 2.84 1.069 0.093 Conservation Triage Low Certainty 146 3.67 0.962 0.08 High Certainty 132 3.74 1.082 0.094            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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 11.862 0.001 -3.158 278 0.002 -0.315 0.1 -0.511 -0.119 Equal variances not assumed     -3.187 273.855 0.002 -0.315 0.099 -0.51 -0.12 Permitting Climate Migrants Equal variances assumed 7.133 0.008 -0.999 276 0.319 -0.104 0.104 -0.309 0.101 Equal variances not assumed     -1.004 275.949 0.316 -0.104 0.104 -0.308 0.1 In-situ Aid Equal variances assumed 1.478 0.225 -2.981 277 0.003 -0.34 0.114 -0.564 -0.115 Equal variances not assumed     -2.975 272.199 0.003 -0.34 0.114 -0.564 -0.115 (New) Species Introduction Equal variances assumed 0.281 0.597 0.758 277 0.449 0.093 0.123 -0.149 0.335 Equal variances not assumed     0.759 276.81 0.448 0.093 0.123 -0.148 0.335 Assisted Colonization Equal variances assumed 0.059 0.808 -1.008 277 0.314 -0.114 0.113 -0.336 0.108 Equal variances not assumed     -1.012 276.981 0.312 -0.114 0.112 -0.335 0.107 Captive Breeding Equal variances assumed 0.011 0.916 0.875 276 0.382 0.119 0.136 -0.149 0.388 Equal variances not assumed     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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper Conservation Triage Equal variances assumed 0.406 0.525 0.117 276 0.907 0.016 0.137 -0.254 0.286 Equal variances not assumed     0.117 272.181 0.907 0.016 0.137 -0.254 0.287 Risk Perceptions   Migration Corridors Equal variances assumed 4.767 0.03 2.147 275 0.033 0.255 0.119 0.021 0.489 Equal variances not assumed     2.137 265.487 0.034 0.255 0.119 0.02 0.49 Permitting Climate Migrants Equal variances assumed 0.769 0.381 -0.081 275 0.936 -0.009 0.113 -0.232 0.214 Equal variances not assumed     -0.08 265.557 0.936 -0.009 0.114 -0.233 0.215 In-situ Aid Equal variances assumed 0.017 0.897 0.95 278 0.343 0.119 0.126 -0.128 0.367 Equal variances not assumed     0.95 276.803 0.343 0.119 0.126 -0.128 0.366 (New) Species Introduction Equal variances assumed 1.131 0.288 -0.863 276 0.389 -0.098 0.114 -0.323 0.126 Equal variances not assumed     -0.866 275.995 0.387 -0.098 0.114 -0.322 0.125 Assisted Colonization Equal variances assumed 4.566 0.033 -1.455 277 0.147 -0.154 0.106 -0.362 0.054 Equal variances not assumed     -1.463 276.849 0.145 -0.154 0.105 -0.36 0.053 120  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Captive Breeding Equal variances assumed 2.276 0.133 -0.24 276 0.811 -0.033 0.136 -0.301 0.236 Equal variances not assumed     -0.241 275.976 0.81 -0.033 0.136 -0.3 0.234 Conservation Triage Equal variances assumed 1.898 0.169 -0.581 276 0.562 -0.071 0.123 -0.312 0.17 Equal variances not assumed     -0.577 263.599 0.564 -0.071 0.123 -0.314 0.172  Environmental Worldviews x Acceptability and Risk Perceptions    Group Statistics   NEP_Index (Binned) N Mean Std. Deviation Std. Error Mean Acceptability Migration Corridors <= 62 149 4.22 0.965 0.079 63+ 126 4.61 0.715 0.064 Permitting Climate Migrants <= 62 149 3.89 0.839 0.069 63+ 125 4.02 0.898 0.08 In-situ Aid <= 62 148 3.8 0.974 0.08 63+ 126 3.88 1.001 0.089 (New) Species Introduction <= 62 148 2.8 1.048 0.086 63+ 126 2.47 0.969 0.086 Assisted Colonization <= 62 149 3.37 0.968 0.079 63+ 126 3.21 0.917 0.082 Captive Breeding <= 62 149 3.56 1.08 0.089 63+ 126 3.25 1.178 0.105 Conservation Triage <= 62 149 2.81 1.131 0.093 63+ 125 2.54 1.118 0.1 121    Group Statistics   NEP_Index (Binned) N Mean Std. Deviation Std. Error Mean Risk Perceptions Migration Corridors <= 62 149 2.72 0.922 0.076 63+ 125 2.62 1.106 0.099 Permitting Climate Migrants <= 62 149 3.08 0.904 0.074 63+ 125 3.36 0.971 0.087 In-situ Aid <= 62 149 2.77 1.009 0.083 63+ 126 3.01 1.07 0.095 (New) Species Introduction <= 62 148 3.8 0.967 0.079 63+ 125 4.17 0.931 0.083 Assisted Colonization <= 62 149 3.62 0.904 0.074 63+ 126 4.01 0.863 0.077 Captive Breeding <= 62 149 2.66 1.088 0.089 63+ 126 2.97 1.173 0.104 Conservation Triage <= 62 149 3.59 1.007 0.082 63+ 125 3.88 1.029 0.092   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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Acceptability   Migration Corridors Equal variances assumed 15.57 0 -3.746 273 0 -0.39 0.104 -0.594 -0.185 Equal variances not assumed     -3.838 268.588 0 -0.39 0.102 -0.589 -0.19 Permitting Climate Migrants Equal variances assumed 0.236 0.628 -1.174 272 0.241 -0.123 0.105 -0.33 0.084 Equal variances not assumed     -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     F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper In-situ Aid Equal variances assumed 0.262 0.609 -0.643 272 0.521 -0.077 0.12 -0.312 0.158 Equal variances not assumed     -0.642 262.596 0.522 -0.077 0.12 -0.313 0.159 (New) Species Introduction Equal variances assumed 0.003 0.955 2.737 272 0.007 0.336 0.123 0.094 0.577 Equal variances not assumed     2.754 270.109 0.006 0.336 0.122 0.096 0.576 Assisted Colonization Equal variances assumed 0.686 0.408 1.354 273 0.177 0.155 0.114 -0.07 0.38 Equal variances not assumed     1.36 269.45 0.175 0.155 0.114 -0.069 0.379 Captive Breeding Equal variances assumed 0.701 0.403 2.282 273 0.023 0.311 0.136 0.043 0.579 Equal variances not assumed     2.266 256.507 0.024 0.311 0.137 0.041 0.581 Conservation Triage Equal variances assumed 0.515 0.473 1.915 272 0.057 0.261 0.136 -0.007 0.53 Equal variances not assumed     1.917 264.796 0.056 0.261 0.136 -0.007 0.53 Risk Perceptions   Migration Corridors Equal variances assumed 8.604 0.004 0.889 272 0.375 0.109 0.122 -0.132 0.35 Equal variances not assumed     0.875 241.857 0.383 0.109 0.124 -0.136 0.354 123  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. (2-tailed) Mean Difference Std. Error Difference Lower Upper Permitting Climate Migrants Equal variances assumed 2.555 0.111 -2.464 272 0.014 -0.279 0.113 -0.503 -0.056 Equal variances not assumed     -2.449 256.436 0.015 -0.279 0.114 -0.504 -0.055 In-situ Aid Equal variances assumed 0 0.993 -1.934 273 0.054 -0.243 0.126 -0.49 0.004 Equal variances not assumed     -1.925 259.747 0.055 -0.243 0.126 -0.491 0.006 (New) Species Introduction Equal variances assumed 0.235 0.628 -3.152 271 0.002 -0.364 0.115 -0.591 -0.137 Equal variances not assumed     -3.162 266.338 0.002 -0.364 0.115 -0.591 -0.137 Assisted Colonization Equal variances assumed 2.36 0.126 -3.582 273 0 -0.384 0.107 -0.595 -0.173 Equal variances not assumed     -3.596 269.028 0 -0.384 0.107 -0.594 -0.174 Captive Breeding Equal variances assumed 0.098 0.755 -2.226 273 0.027 -0.304 0.136 -0.572 -0.035 Equal variances not assumed     -2.212 257.846 0.028 -0.304 0.137 -0.574 -0.033 Conservation Triage Equal variances assumed 0.108 0.743 -2.347 272 0.02 -0.289 0.123 -0.532 -0.047 Equal variances not assumed     -2.342 261.758 0.02 -0.289 0.124 -0.533 -0.046   124  Conservation Goals x Acceptability and Risk Perceptions  Descriptives     N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum     Lower Bound Upper Bound Acceptability   Migration Corridors wilderness areas 35 4.43 0.948 0.16 4.1 4.75 2 5 species 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 Total 282 4.39 0.903 0.054 4.28 4.5 1 5 Permitting Climate Migrants 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 ecosystem function 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 Total 281 3.95 0.875 0.052 3.84 4.05 1 5 In-situ Aid wilderness areas 35 3.49 1.173 0.198 3.08 3.89 1 5 125  Descriptives     N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum     Lower Bound Upper Bound species 29 3.9 0.976 0.181 3.53 4.27 1 5 ecosystem function 175 3.92 0.874 0.066 3.79 4.05 2 5 human use 19 3.74 1.368 0.314 3.08 4.4 1 5 don't know 23 3.7 1.105 0.23 3.22 4.17 2 5 Total 281 3.83 0.988 0.059 3.72 3.95 1 5 (New) Species Introduction 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 ecosystem function 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 Total 281 2.65 1.031 0.062 2.53 2.77 1 5 Assisted Colonization 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 ecosystem function 176 3.28 0.86 0.065 3.15 3.41 1 5 126  Descriptives     N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum     Lower Bound Upper Bound human use 19 3.84 0.898 0.206 3.41 4.28 1 5 don't know 23 3 1.314 0.274 2.43 3.57 1 5 Total 282 3.3 0.953 0.057 3.19 3.41 1 5 Captive Breeding 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 18 3.61 1.335 0.315 2.95 4.27 1 5 don't know 23 3.35 1.301 0.271 2.79 3.91 1 5 Total 280 3.39 1.136 0.068 3.25 3.52 1 5 Conservation Triage 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 19 3.16 1.385 0.318 2.49 3.83 1 5 don't know 23 2.52 1.123 0.234 2.04 3.01 1 5 Total 281 2.69 1.146 0.068 2.56 2.83 1 5 Risk Perceptions   Migration Corridors 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 19 2.74 1.195 0.274 2.16 3.31 1 5 don't know 23 2.91 0.848 0.177 2.55 3.28 2 5 Total 280 2.68 1.017 0.061 2.56 2.8 1 5 Permitting Climate Migrants 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 4 127  Descriptives     N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum     Lower Bound Upper Bound ecosystem function 175 3.34 0.902 0.068 3.21 3.48 1 5 human use 19 2.63 1.165 0.267 2.07 3.19 1 5 don't know 23 3.17 0.984 0.205 2.75 3.6 1 5 Total 280 3.22 0.954 0.057 3.11 3.33 1 5 In-situ Aid 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 19 2.79 1.398 0.321 2.12 3.46 1 5 don't know 23 3.22 0.998 0.208 2.79 3.65 1 5 Total 282 2.89 1.042 0.062 2.77 3.02 1 5 (New) Species Introduction 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 19 3.32 1.108 0.254 2.78 3.85 1 5 don't know 23 3.74 1.214 0.253 3.21 4.26 1 5 Total 280 3.97 0.961 0.057 3.86 4.08 1 5 Assisted Colonization 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 19 3.58 1.121 0.257 3.04 4.12 1 5 don't know 23 3.91 0.949 0.198 3.5 4.32 2 5 Total 282 3.81 0.903 0.054 3.71 3.92 1 5 Captive Breeding 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 19 2.68 1.057 0.242 2.17 3.19 1 4 don't know 23 3.3 1.295 0.27 2.74 3.86 1 5 Total 281 2.81 1.123 0.067 2.68 2.94 1 5 128  Descriptives     N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum     Lower Bound Upper Bound Conservation Triage wilderness areas 35 3.71 1.202 0.203 3.3 4.13 1 5 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 19 3.32 1.204 0.276 2.74 3.9 1 5 don't know 23 3.78 0.902 0.188 3.39 4.17 2 5 Total 281 3.71 1.038 0.062 3.59 3.83 1 5   Test of Homogeneity of Variances  Levene Statistic df1 df2 Sig. Acceptability Migration Corridors 6.908 4 277 .000 Permitting Climate Migrants .312 4 276 .870 In-situ Aid 4.917 4 276 .001 (New) Species Introduction 1.888 4 276 .113 Assisted Colonization 2.448 4 277 .047 Captive Breeding 1.270 4 275 .282 Conservation Triage 1.364 4 276 .247 Risk Perceptions 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  ANOVA     Sum of Squares df Mean Square F Sig. Acceptability Migration Corridors Between Groups 7.052 4 1.763 2.199 0.069 Within Groups 222.04 277 0.802     Total 229.092 281       129  ANOVA     Sum of Squares df Mean Square F Sig. Permitting Climate Migrants Between Groups 10.881 4 2.72 3.693 0.006 Within Groups 203.319 276 0.737     Total 214.199 280       In-situ Aid Between Groups 6.273 4 1.568 1.622 0.169 Within Groups 266.866 276 0.967     Total 273.139 280       (New) Species Introduction Between Groups 18.523 4 4.631 4.576 0.001 Within Groups 279.299 276 1.012     Total 297.822 280       Assisted Colonization Between Groups 7.758 4 1.94 2.173 0.072 Within Groups 247.22 277 0.892     Total 254.979 281       Captive Breeding Between Groups 1.401 4 0.35 0.268 0.898 Within Groups 358.942 275 1.305     Total 360.343 279       130  ANOVA     Sum of Squares df Mean Square F Sig. Conservation Triage Between Groups 11.32 4 2.83 2.192 0.07 Within Groups 356.36 276 1.291     Total 367.68 280       Risk Perceptions Migration Corridors Between Groups 2.985 4 0.746 0.718 0.58 Within Groups 285.726 275 1.039     Total 288.711 279       Permitting Climate Migrants Between Groups 11.448 4 2.862 3.249 0.013 Within Groups 242.262 275 0.881     Total 253.711 279       In-situ Aid Between Groups 8.174 4 2.044 1.908 0.109 Within Groups 296.634 277 1.071     Total 304.809 281       (New) Species Introduction Between Groups 20.117 4 5.029 5.82 0 Within Groups 237.654 275 0.864     131  ANOVA     Sum of Squares df Mean Square F Sig. Total 257.771 279       Assisted Colonization Between Groups 2.197 4 0.549 0.671 0.613 Within Groups 226.842 277 0.819     Total 229.039 281       Captive Breeding Between Groups 7.687 4 1.922 1.536 0.192 Within Groups 345.317 276 1.251     Total 353.004 280       Conservation Triage Between Groups 3.271 4 0.818 0.756 0.554 Within Groups 298.38 276 1.081     Total 301.651 280        Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Acceptability               Migration Corridors wilderness areas species 0.256 0.225 0.785 -0.36 0.87 ecosystem function -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 species wilderness areas -0.256 0.225 0.785 -0.87 0.36 ecosystem function -0.311 0.179 0.417 -0.8 0.18 132  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound human use 0.225 0.264 0.914 -0.5 0.95 don't know -0.088 0.25 0.997 -0.77 0.6 ecosystem function 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 human use 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 -0.536 0.216 0.099 -1.13 0.06 don't know -0.314 0.278 0.791 -1.08 0.45 don't know wilderness areas -0.168 0.24 0.957 -0.83 0.49 species 0.088 0.25 0.997 -0.6 0.77 ecosystem function -0.222 0.199 0.797 -0.77 0.32 human use 0.314 0.278 0.791 -0.45 1.08 133  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Permitting Climate Migrants wilderness areas species 0.499 0.216 0.144 -0.09 1.09 ecosystem function 0.389 0.159 0.107 -0.05 0.82 human use -0.217 0.245 0.902 -0.89 0.46 don't know 0.388 0.23 0.447 -0.25 1.02 species 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 ecosystem function 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 human use 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 1.17 134  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound don't know 0.604 0.266 0.158 -0.13 1.33 don't know wilderness areas -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 In-situ Aid wilderness areas 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 species 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 0.16 0.29 0.982 -0.64 0.96 don't know 0.201 0.275 0.949 -0.55 0.95 ecosystem function 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 human use 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 don't know 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 0.8 135  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound (New) Species Introduction wilderness areas species -0.405 0.253 0.497 -1.1 0.29 ecosystem function -0.074 0.186 0.995 -0.59 0.44 human use -.964* 0.287 0.008 -1.75 -0.18 don't know -0.499 0.27 0.347 -1.24 0.24 species 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 ecosystem function wilderness areas 0.074 0.186 0.995 -0.44 0.59 species -0.331 0.202 0.474 -0.88 0.22 human use -.890* 0.243 0.003 -1.56 -0.22 don't know -0.425 0.223 0.317 -1.04 0.19 human use wilderness areas .964* 0.287 0.008 0.18 1.75 species 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 don't know 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 human use -0.465 0.312 0.57 -1.32 0.39 Assisted Colonization wilderness areas species 0.038 0.237 1 -0.61 0.69 ecosystem function 0.036 0.175 1 -0.44 0.52 human use -0.528 0.269 0.288 -1.27 0.21 don't know 0.314 0.254 0.728 -0.38 1.01 species wilderness areas -0.038 0.237 1 -0.69 0.61 136  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound ecosystem function -0.003 0.189 1 -0.52 0.52 human use -0.566 0.279 0.254 -1.33 0.2 don't know 0.276 0.264 0.834 -0.45 1 ecosystem function wilderness areas -0.036 0.175 1 -0.52 0.44 species 0.003 0.189 1 -0.52 0.52 human use -0.564 0.228 0.1 -1.19 0.06 don't know 0.278 0.209 0.673 -0.3 0.85 human use 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 don't know 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 human use -.842* 0.293 0.035 -1.65 -0.04 Captive Breeding wilderness areas species -0.14 0.287 0.988 -0.93 0.65 ecosystem function -0.017 0.212 1 -0.6 0.56 human use -0.268 0.331 0.928 -1.18 0.64 don't know -0.005 0.307 1 -0.85 0.84 species 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 -0.128 0.343 0.996 -1.07 0.81 don't know 0.135 0.319 0.993 -0.74 1.01 ecosystem function wilderness areas 0.017 0.212 1 -0.56 0.6 species -0.123 0.229 0.984 -0.75 0.51 human use -0.251 0.283 0.901 -1.03 0.53 don't know 0.012 0.253 1 -0.68 0.71 137  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound human use wilderness areas 0.268 0.331 0.928 -0.64 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 don't know 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 -0.263 0.36 0.949 -1.25 0.72 Conservation Triage wilderness areas species 0.112 0.285 0.995 -0.67 0.9 ecosystem function -0.314 0.21 0.567 -0.89 0.26 human use -0.701 0.324 0.197 -1.59 0.19 don't know -0.065 0.305 1 -0.9 0.77 species 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 -0.813 0.335 0.112 -1.73 0.11 don't know -0.177 0.317 0.981 -1.05 0.69 ecosystem function 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 -0.386 0.274 0.623 -1.14 0.37 don't know 0.25 0.252 0.859 -0.44 0.94 human use 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 don't know wilderness areas 0.065 0.305 1 -0.77 0.9 species 0.177 0.317 0.981 -0.69 1.05 138  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound ecosystem function -0.25 0.252 0.859 -0.94 0.44 human use -0.636 0.352 0.372 -1.6 0.33 Risk Perceptions   Migration Corridors wilderness areas species -0.288 0.258 0.797 -1 0.42 ecosystem function -0.204 0.191 0.824 -0.73 0.32 human use -0.266 0.292 0.892 -1.07 0.54 don't know -0.442 0.275 0.494 -1.2 0.31 species 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 0.022 0.301 1 -0.8 0.85 don't know -0.154 0.285 0.983 -0.94 0.63 ecosystem function wilderness areas 0.204 0.191 0.824 -0.32 0.73 species -0.084 0.204 0.994 -0.65 0.48 human use -0.063 0.246 0.999 -0.74 0.61 don't know -0.239 0.226 0.829 -0.86 0.38 human use wilderness areas 0.266 0.292 0.892 -0.54 1.07 species -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 don't know 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 0.176 0.316 0.981 -0.69 1.04 Permitting Climate Migrants wilderness areas species -0.202 0.237 0.914 -0.85 0.45 ecosystem function -0.372 0.176 0.216 -0.86 0.11 human use 0.339 0.269 0.715 -0.4 1.08 139  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound don't know -0.203 0.253 0.93 -0.9 0.49 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 0.541 0.277 0.292 -0.22 1.3 don't know -0.001 0.262 1 -0.72 0.72 ecosystem function wilderness areas 0.372 0.176 0.216 -0.11 0.86 species 0.17 0.188 0.895 -0.35 0.69 human use .711* 0.227 0.016 0.09 1.33 don't know 0.169 0.208 0.927 -0.4 0.74 human use 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 don't know wilderness areas 0.203 0.253 0.93 -0.49 0.9 species 0.001 0.262 1 -0.72 0.72 ecosystem function -0.169 0.208 0.927 -0.74 0.4 human use 0.542 0.291 0.339 -0.26 1.34 In-situ Aid wilderness areas species 0.226 0.26 0.908 -0.49 0.94 ecosystem function -0.212 0.192 0.804 -0.74 0.31 human use -0.047 0.295 1 -0.86 0.76 don't know -0.475 0.278 0.43 -1.24 0.29 species 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 -0.272 0.305 0.9 -1.11 0.57 don't know -0.7 0.289 0.112 -1.49 0.09 ecosystem function wilderness areas 0.212 0.192 0.804 -0.31 0.74 140  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound species 0.437 0.207 0.219 -0.13 1.01 human use 0.165 0.25 0.965 -0.52 0.85 don't know -0.263 0.229 0.782 -0.89 0.37 human use 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 don't know wilderness areas 0.475 0.278 0.43 -0.29 1.24 species 0.7 0.289 0.112 -0.09 1.49 ecosystem function 0.263 0.229 0.782 -0.37 0.89 human use 0.428 0.321 0.67 -0.45 1.31 (New) Species Introduction wilderness areas species -0.233 0.233 0.855 -0.87 0.41 ecosystem function -.532* 0.172 0.019 -1.01 -0.06 human use 0.313 0.265 0.762 -0.41 1.04 don't know -0.111 0.25 0.992 -0.8 0.57 species 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 0.546 0.274 0.273 -0.21 1.3 don't know 0.123 0.26 0.99 -0.59 0.84 ecosystem function wilderness areas .532* 0.172 0.019 0.06 1.01 species 0.299 0.186 0.497 -0.21 0.81 human use .845* 0.225 0.002 0.23 1.46 don't know 0.422 0.206 0.247 -0.14 0.99 human use 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 0.37 141  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound don't know wilderness areas 0.111 0.25 0.992 -0.57 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 human use 0.423 0.288 0.584 -0.37 1.21 Assisted Colonization wilderness areas species -0.01 0.227 1 -0.63 0.61 ecosystem function -0.144 0.167 0.912 -0.6 0.32 human use 0.135 0.258 0.985 -0.57 0.84 don't know -0.199 0.243 0.925 -0.87 0.47 species 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 0.145 0.267 0.983 -0.59 0.88 don't know -0.189 0.253 0.945 -0.88 0.5 ecosystem function 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 0.279 0.219 0.706 -0.32 0.88 don't know -0.055 0.201 0.999 -0.61 0.5 human use 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 don't know 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 0.334 0.281 0.757 -0.44 1.1 Captive Breeding wilderness areas species -0.302 0.281 0.818 -1.07 0.47 ecosystem function -0.149 0.207 0.952 -0.72 0.42 142  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound human use -0.056 0.319 1 -0.93 0.82 don't know -0.676 0.3 0.164 -1.5 0.15 species wilderness areas 0.302 0.281 0.818 -0.47 1.07 ecosystem function 0.154 0.224 0.959 -0.46 0.77 human use 0.247 0.33 0.945 -0.66 1.15 don't know -0.373 0.312 0.754 -1.23 0.48 ecosystem function wilderness areas 0.149 0.207 0.952 -0.42 0.72 species -0.154 0.224 0.959 -0.77 0.46 human use 0.093 0.27 0.997 -0.65 0.83 don't know -0.527 0.248 0.212 -1.21 0.15 human use wilderness areas 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 don't know 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 human use 0.62 0.347 0.382 -0.33 1.57 Conservation Triage wilderness areas species -0.044 0.261 1 -0.76 0.67 ecosystem function -0.023 0.193 1 -0.55 0.51 human use 0.398 0.296 0.663 -0.42 1.21 don't know -0.068 0.279 0.999 -0.83 0.7 species wilderness areas 0.044 0.261 1 -0.67 0.76 ecosystem function 0.021 0.208 1 -0.55 0.59 human use 0.443 0.307 0.6 -0.4 1.29 don't know -0.024 0.29 1 -0.82 0.77 143  Multiple Comparisons Tukey HSD Dependent Variable (I) goals (J) goals Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound ecosystem function wilderness areas 0.023 0.193 1 -0.51 0.55 species -0.021 0.208 1 -0.59 0.55 human use 0.421 0.251 0.449 -0.27 1.11 don't know -0.045 0.231 1 -0.68 0.59 human use 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 don't know 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 use 0.467 0.322 0.597 -0.42 1.35 *. The mean difference is significant at the 0.05 level.                   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: Timothy L. McDaniels  UBC/College for Interdisciplinary Studies/Community & Regional Planning  H09-02174 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.    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  Version Date  Consent Forms: Study Consent Form 3 January 7, 2010 Advertisements: Study Advertisement 3 January 7, 2010 Questionnaire, Questionnaire Cover Letter, Tests: Study Questionnaire 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. Very                              Neutral                            Very                     Bad                                                                       Good                                                                                              1                    2                    3                    4                 5            2) how positive or negative you feel about it.  Very                              Neutral                            Very Negative                                                          Positive 1                    2                    3                    4                 5            3) how pleasant or unpleasant you feel about it. Very                              Neutral                            Very Unpleasant                                                    Pleasant 1                    2                    3                    4                 5            Specific Negative Emotions  4) how afraid or relaxed you feel about it. Very                              Neutral                            Very Afraid                                                               Relaxed       1                    2                    3                    4                 5            5) how angry or calm you feel about it. Very                               Neutral                           Very Angry                                                                    Calm      1                    2                    3                    4                 5            6) how upset or at ease you feel about it.  Very                               Neutral                           Very Upset                                                                At ease 1                    2                    3                    4                 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?    For example: 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.  Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5             Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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.  Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5              Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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.    Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5               Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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? Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    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.  Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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). Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5               Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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. Not  at all                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5             Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    4                5 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                                  Moderately                                        Very acceptable                                 acceptable                             acceptable         1                    2                    3                    4                5              Not  at                                        Moderately                             Extremely all risky                                             risky                                              risky 1                    2                    3                    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 Mildly disagree Unsure Mildly agree Strongly agree 1) We are approaching the limit of the number of people the earth can support 1                    2                    3                    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 1                    2                    3                    4                  5            9) Despite our special abilities humans are still subject to the laws of nature 1                    2                    3                    4                  5            150  10) The so-called “ecological crisis” facing humankind has been greatly exaggerated 1                    2                    3                    4                  5            11) The earth is like a spaceship with very limited room and resources 1                    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              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:                                                                                         Very Uncertain       Somewhat Uncertain Neutral  Somewhat   Certain      Very Certain 1) How certain are you that climate change is occurring? 1                    2                    3                    4                    5            2) How certain are you that further climate change is going to occur?  1                    2                    3                    4                    5            3) How certain are you that climate change has already had negative impacts on the environment? 1                    2                    3                    4                    5            4) How certain are you that further climate change is going to have negative impacts on the environment? 1                    2                    3                    4                    5            5) How certain are you that people can manage nature without causing negative impacts? 1                    2                    3                    4                    5             [Optional] please add any comments you may have:    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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