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Exploring attitudes and preferences toward species at risk in British Columbia Echeverri Ochoa, Alejandra 2015

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EXPLORING ATTITUDES AND PREFERENCES TOWARD SPECIES AT RISK IN BRITISH COLUMBIA by  Alejandra Echeverri Ochoa  BSc Universidad de los Andes, 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  September 2015  © Alejandra Echeverri Ochoa, 2015 ii  Abstract  There are 199 species at risk in British Columbia (B.C.). To elicit public support to conserve biodiversity, it is important to understand people‘s attitudes and preferences toward species at risk. Here we examine how people perceive endangered species in B.C., how message framing shapes the attitudes toward the species, and whether implicit or explicit preferences determine willingness to pay for conservation. In Study 1 reported in Chapter 2, we presented three messages about sea otters to 623 residents in B.C., and measured the change in their attitudes toward sea otters using Kellert‘s typology of basic attitudes toward wildlife. The messages were framed as either positive (as a keystone species), negative (resource conflict with First Nations‘ fishermen in the West Coast of Vancouver Island), or neutral (biological facts). We found that the negative message promoted acceptance for managing sea otters and their habitats for use values (utilitarian-consumption, utilitarian-habitat), and for exerting control over sea otters (dominionistic). This shift in attitudes occurred even though the negative message was perceived as less convincing and believable than the positive or neutral messages. The positive message, on the other hand, decreased utilitarian-consumption attitudes. In Study 2 reported in Chapter 3, we evaluated people‘s implicit and explicit preferences for four species at risk in B.C. (sea otter, American badger, caribou, and yellow-breasted chat). We found that explicit rather than implicit preference predicts willingness to pay for conservation of each species, and findings suggest that people apply the affect heuristic when judging species—species that are less liked may be perceived as riskier, and vice versa—. This finding holds for both residents in B.C. (n=55) and outside of B.C. (n=463). The results from the two studies highlight the importance of attitudes, messaging, and preference when designing conservation campaigns and efforts.  iii  Preface  A version of the study reported in Chapter 2 was submitted for publication in July 2015 with Dr. Jiaying Zhao and Dr. Kai M.A. Chan as co-authors. A. Echeverri was first author in the manuscript, responsible for data collection, data analysis, and writing the manuscript. Dr. Jiaying Zhao and Dr. Kai M.A. Chan contributed to research design and writing the manuscript. The study reported in Chapter 3 was conducted with Megan Callahan and members from the Zhao Lab in the Department of Psychology at UBC. Two research assistants from the Zhao Lab helped with data collection. A. Echeverri was primarily in charge of analyzing quantitative data and wrote the chapter presented in this thesis. Megan Callahan contributed with qualitative data analysis. Dr. Jiaying Zhao and Dr. Terre Satterfield conceptualized the study presented in Chapter 3. Dr. Jiaying Zhao and Dr. Kai M.A. Chan helped with the writing of Chapter 2 and Chapter 3.  All of the studies reported in this thesis were covered by the ethics certificate number H13-02679 obtained from The University of British Columbia Behavioral Research Ethics Board.  iv  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... iv List of Tables ............................................................................................................................... vii List of Figures ............................................................................................................................. viii List of Abbreviations .....................................................................................................................x Acknowledgements ...................................................................................................................... xi Dedication .................................................................................................................................... xii Chapter 1: Introduction ................................................................................................................1 1.1 Problem context ................................................................................................................. 1 1.2 Species at risk in British Columbia .................................................................................... 6 1.3 Objectives and research questions ..................................................................................... 9 1.3.1 Objectives ................................................................................................................... 9 1.3.2 Research questions ...................................................................................................... 9 1.4 Significance of the study .................................................................................................... 9 1.5 Thesis overview ............................................................................................................... 12 Chapter 2: How messaging shapes attitudes toward sea otters as a species at risk...............13 2.1 Introduction ...................................................................................................................... 13 2.2 Methods............................................................................................................................ 16 2.2.1 Experimental design.................................................................................................. 16 2.2.2 Data analysis ............................................................................................................. 21 v  2.3 Results .............................................................................................................................. 22 2.4 Discussion ........................................................................................................................ 26 2.5 Acknowledgements .......................................................................................................... 30 Chapter 3: Evaluating implicit and explicit preferences toward species at risk in British Columbia .......................................................................................................................................32 3.1 Introduction ...................................................................................................................... 32 3.2 Methods............................................................................................................................ 36 3.2.1 Study 1 ...................................................................................................................... 36 3.2.1.1 Participants ......................................................................................................... 36 3.2.1.2 Procedure ........................................................................................................... 37 3.2.1.2.1 MC-IAT ...................................................................................................... 37 3.2.1.2.2 Survey ......................................................................................................... 41 3.2.2 Study 2 ...................................................................................................................... 41 3.2.2.1 Participants ......................................................................................................... 41 3.2.2.2 Procedure ........................................................................................................... 42 3.2.3 Data analysis ............................................................................................................. 43 3.2.3.1 Quantitative analysis .......................................................................................... 43 3.2.3.2 Qualitative analysis ............................................................................................ 44 3.3 Results .............................................................................................................................. 45 3.3.1 Study 1 ...................................................................................................................... 45 3.3.2 Study 2 ...................................................................................................................... 50 3.4 Discussion ........................................................................................................................ 54 3.5 Conclusions ...................................................................................................................... 58 vi  Chapter 4: Conclusions ...............................................................................................................60 4.1 Novel application of research methods ............................................................................ 60 4.2 Strengths and limitations.................................................................................................. 61 4.3 Relevance to conservation and environmental management ........................................... 63 4.4 Future research directions ................................................................................................ 64 4.5 Closing ............................................................................................................................. 65 Bibliography .................................................................................................................................66 Appendices ....................................................................................................................................80 Appendix A Survey presented in chapter 2 .............................................................................. 80 Appendix B Participants‘ demographics of chapter 2 .............................................................. 93 Appendix C Instruction page for the MC-IAT for chapter 3 .................................................... 96 Appendix D Creative commons licenses for pictures used in the MC-IAT and surveys for chapter 3 .................................................................................................................................... 97 Appendix E Survey presented for chapter 3 ............................................................................. 98 Appendix F Participants‘ demographics of study 1 in chapter 3 ............................................ 113 Appendix G Participants‘ demographics of study 2 in chapter 3 ........................................... 115  vii  List of Tables  Table 1.1 Categories of risk defined in the Species at Risk Act ..................................................... 7 Table 1.2 Species at risk in British Columbia as listed on March 2015 ......................................... 8 Table 2.1 Statements presented in the questionnaire. ................................................................... 17 Table 2.2 Kellert‘s typology of basic attitudes toward animals and the natural environment to describe fundamental values and meanings attributed to species and environments. .................. 19 Table 2.3 Messages presented to participants ............................................................................... 20 Table 2.4 Measuring attitude change between pre-message and post-message responses ........... 26 Table 3.1 Labels used in the coding analysis of the word association task .................................. 45 Table 3.2 Summary of the multiple regression analyses for the WTP as dependent variable in study 1 ........................................................................................................................................... 48 Table 3.3 Summary of the multiple regression analyses for the WTP as dependent variable in study 2 ........................................................................................................................................... 52  viii  List of Figures  Figure 2.1 Beanplot showing the distributions of participants‘ attitudes toward sea otters before seeing the messages (n=324). The black solid lines represent the mean values for each attitude, and the dotted line indicates the overall mean value. ................................................................... 23 Figure 2.2 Attitude change by message and time. Pre-message is the baseline attitudes (before message), and Post-message presents attitudes after seeing the message. The three lines in each sub-plot represent the trajectories for attitude change in each messaging condition (negative, positive or neutral). ....................................................................................................................... 24 Figure 2.3 Mean values for convincingness ratings of three messages with standard errors (n=324). ......................................................................................................................................... 25 Figure 3.1 Screenshot of the MC-IAT. In this case the focal species was Caribou and participants were instructed to press the I key if they saw pictures of caribou or good words. ....................... 39 Figure 3.2 Screenshot of the MC-IAT. In this case ―wonderful‖ was a good word and participants were instructed to press the I key if they saw pictures of caribou or good words. ... 39 Figure 3.3 Four species at risk in B.C. used in the study .............................................................. 40 Figure 3.4 Beanplots showing a) implicit preferences toward species measured as D scores, and b) explicit preferences toward species among lab participants (n=55). Each polygon consists of a density trace that is mirrored to form a polygon. Black lines represent the mean for each category and the dotted line is the overall mean across the four categories in each of the plots (a and b). 46 Figure 3.5 Word associations for the four species evaluated in this study and their frequency organized by the labels. Graph shows the result from lab participants (n=55)............................. 49 ix  Figure 3.6 Positive and negative word associations for the four species presented in the study (n=55) ............................................................................................................................................ 49 Figure 3.7 Explicit preferences toward species among Mturk participants (n=463). Beanplot showing the density traces that are mirrored to form a polygon. Solid black lines represent the mean of each plot and the dotted line is the overall mean across categories. ............................... 51 Figure 3.8 Word associations for the four species evaluated in this study and their frequency organized by the labels. Graph shows the result from Mturk participants (n=463) ..................... 53 Figure 3.9 Positive and negative associations for the four species presented in the study (n=463)....................................................................................................................................................... 53  x  List of Abbreviations B.C.  British Columbia COSEWIC Committee on the Status of Endangered Wildlife in Canada HSP  Human Subject Pool IAT  Implicit Association Test IUCN  International Union for Conservation of Nature and Natural Resources MC-IAT Multicategory Implicit Association Test Mturk  Amazon Mechanical Turk  SARA  Species at Risk Act Tukey‘s HSD Tukey‘s honest significant difference WCVI  West Coast of Vancouver Island  xi  Acknowledgements  I must offer my enduring gratitude to all the people who have supported me in many different ways throughout my time as a graduate student. First, I would like to thank my thesis supervisor Dr. Jiaying Zhao for her constant support and encouragement, for being so generous with her time and knowledge, and for challenging me to think about issues from a perspective far from my starting one. My gratitude extends to my committee member Dr. Kai M.A. Chan, who introduced me to challenges and opportunities in resource management and conservation, and who has always been supportive of my research. I wish to thank my former supervisor Dr. Benjamin Richardson for believing in me in the first place, making my first year at UBC one full of learning experiences, and for introducing me to the fascinating world of environmental law.   I would like to thank Dr. Stephen Kellert, Clarisse Thornton, Dr. Terre Satterfield, Dr. Robin Naidoo, and Dr. Mark Johnson, for helping me with research design and for helping me understand the results and implications of my work. I also have to thank many people in IRES including the administrative staff and my colleagues, who have made my life in the department so enjoyable. I would like to express my deepest gratitude to my classmates and lab mates in CHANS Lab and Zhao Lab. In particular, I would like to thank Sameer Shah, Meggie Callahan, Jason Brown, Ed Gregr, Gerald Singh, Liz Williams, Yu Luo, Ru Yu, and Brandon Tomm, for helping me with my research in many different ways.   I would also like to thank my friends at Green College for their constant support, the interesting conversations that shaped my day-to-day life, and for all the days we spent together enjoying each other‘s company. Thanks to my CISV friends, especially Emily, Brynn, and Kelsey for opening their homes to me, and for being my home away from home. Thanks to my Universidad de los Andes friends for pushing me to pursue graduate school and for reminding me that we need highly educated people in Colombia so that together we can make a difference in our country someday.   Finally I want to thank my family (los Echeverris y los Ochoas) for teaching me that family comes first, and that we must always help each other no matter how far apart we are. Thank you for entertaining me every day in our family chats. Special thanks to Papá, Mamá, Tía Ofelia, Tío Edgar, Tía Marta Isabel, Tía Estela, Tío Ramiro, and Tía Beatriz O. for teaching me the value of hard work and responsibility and helping me pursue my dreams every time I come up with a ―crazy‖ idea. Thanks to Andre, Juan D, Migue, Valen, Ila, Marce, Ana, and Saris for supporting me at any time I need it. And thanks to Checho, Juanpi, Mono, and Dalia for being my role models, as we all share a love for nature and research, and we are all committed to help the environment in one way or another. xii  Dedication   To Andrea and Miguel, my sister and brother, who love animals as much as I do  1  Chapter 1: Introduction 1.1 Problem context Humans have fundamentally altered biodiversity and are generating negative, and to some extent irreversible, damages to biodiversity worldwide (Millenium Ecosystem Assessment, 2005). In fact, current extinction rates for most vertebrate taxonomic groups are as fast or faster than rates that produced the past 5 mass extinctions in the fossil record (Barnosky et al., 2011). Catastrophic faunal extinctions of the past 50,000 years have been attributed to a variety of human actions including rapid overharvesting of resources, habitat transformation, and spread of diseases or biological invasions driven by human migration patterns (Burney & Flannery, 2005; Dirzo et al., 2014; McCauley et al., 2015).   Biodiversity loss has significant ecological and social consequences. Biodiversity is fundamental to ecosystem structure and function (Millenium Ecosystem Assessment, 2005). Species composition determines the organismal traits that influence ecosystem processes (Chapin-III et al., 2000). Human-induced species loss directly affects such processes because it alters species composition. In addition, biodiversity links to ecosystem properties that have cultural, intellectual, aesthetic and spiritual values that are important to society (Chapin-III et al., 2000). Biodiversity loss threatens the future benefits that subsequent generations may derive from nature (Balvanera et al., 2014; Cardinale et al., 2012; Díaz, Fargione, Chapin-III, & Tilman, 2006). Conserving biodiversity is therefore an important and a serious ethical issue (Ehrlich, 2002). It is a matter of morality because decisions toward conservation encompass human and non-human organisms, current and future generations and the prioritization of certain species or ecosystems over others (Wilson, 1984). 2  Traditionally, conservation efforts have mostly focused on reducing the negative ecological impacts on the environment; for example, by establishing protected areas and targeting individual species. Comparatively, much less attention has been given to the moral principles that underlie biodiversity conservation (Clayton & Brook, 2014). However, conservation is subject to public opinion that differs according to social contexts (Ehrlich, 2002; Manfredo, 2008b). As Vaske & Donnelly (1999) pointed out, debates in resource management and conservation often occur because different interest groups (often called stakeholders) hold differing values. A good example that illustrates this point is the planning process for the Great Bear Rainforest on Canada‘s west coast. Stakeholders included First Nations, resource development industries, environmentalists, tourism industries, and government. Approximately ten years of negotiations were required before stakeholders reached an agreement that included everyone‘s values and interests (Low, 2011; McGee, Cullen, & Gunton, 2010). Decisions in conservation and resource management are difficult because they must take in account the human dimensions of conservation (i.e., people‘s values, value orientations, attitudes, beliefs, and preferences). The human dimensions need to be considered while planning, designing, and executing resource management and conservation actions (Manfredo, 2008b).   The field of human dimensions of conservation emerged from theories of anthropology, cultural geography, and social psychology (Manfredo, 2008b). As a field it has a lot to learn from any discipline in humanities or the social sciences. However, it is important to mention that the conceptual framework used in this thesis draws mostly from theories in psychology. The following paragraphs provide a summary of the conceptual framework and definitions of the concepts that were used in this thesis.  3  Theory suggests than an individual‘s view of the environment can be organized into a cognitive hierarchy consisting of values, value orientations, attitudes, behavioral intentions and behaviors (Ball-Rokeach & DeFleur, 1976; Rockeach, 1979). Values refer to the most abstract of the social cognitions and represent stable beliefs that individuals use as standards for evaluating attitudes and behavior (Rockeach, 1979). Values transcend objects, situations, and issues (S. H. Schwartz, 2006) and are the most central component of a person‘s belief system. They tend to be limited in numbers and very stable (i.e., difficult to change) (Vaske & Donnelly, 1999). Value orientations refer to the patterns of basic beliefs and represent the ethos that captures the personality of a cultural group or an individual (Kluckhohn & Strodtbeck, 1961). In terms of wildlife, ―value orientations are a set of basic beliefs about wildlife and wildlife management and they are revealed through the pattern of direction and intensity among these beliefs‖ (Dayer, Manfredo, Teel, & Bright, 2006, p. 5).  Attitudes are learned predispositions to respond in a favorable or unfavorable manner with respect to a given object (Fishbein & Ajzen, 1975b). A person can hold numerous attitudes and an important assumption of the attitude concept is that attitudes can change over time (Fishbein & Ajzen, 1975b; Vaske & Donnelly, 1999). People‘s attitudes toward wildlife have been studied since the 1960‘s and explicit self-reported measures (via Likert-scale questions) have been widely used to measure such attitudes (e.g., (Browne-Nuñez, Jacobson, & Vaske, 2013; Kellert, 1991; 1992; 1993; Kellert, Black, Rush, & Bath, 1996)).  An important concept in the human dimensions of conservation is preference. The concept ―preference‖ comes from economic theory and refers to individuals assigning different utilities to goods or services. A preference is stated when an individual assigns more utility to one good or 4  service over the other, when a choice is given (Sen, 1973). Social psychologists have used the term preference in different contexts (i.e., not only in behavioral economics), and have differentiated between implicit and explicit preferences. Implicit preferences refer to ―actions or judgments that are under the control of an automatically activated evaluation, without the performer‘s awareness of that causation‖ (Greenwald & Banaji, 1995). And explicit preferences refer to self-reported preferences that are consulted consciously. A person may hold different implicit and explicit preferences if there is a rationale (often moral) for stating conscious differences between choices. For example, a person may explicitly say that all human races are equal because it is the politically correct thing to say, but that person may hold internal preferences (i.e., implicit) for some human races over others (Greenwald & Banaji, 1995). The human dimensions of conservation scholarship largely features studies that evaluate people‘s explicit preferences toward wildlife, while implicit preferences have received comparatively less attention (Lassiter, 2000).  One of the main challenges for conservation and environmental management is to find ways to engage more people in caring for the environment and to understand the barriers that impede more people from engaging in pro-environmental behaviors (Kollmuss & Agyeman, 2002). While the relationship between values, attitudes, preferences, and behavior is complicated, psychology has a lot to contribute in explaining that relationship. According to the Theory of Reasoned Action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975b) and the Theory of Planned Behavior (Ajzen, 1991) the most direct predictor of a person‘s voluntary behavior is the intention to engage in that behavior. Those intentions are informed by attitudes toward that behavior, subjective norms (e.g., social pressure for engaging in a particular behavior), and perceived 5  behavioral control (i.e., self-efficacy). The relative way these factors interact to inform behavior may vary from a person to another and may change among different populations (Ajzen, 1991). Considering the specific case of biodiversity loss, little is known about how attitudes, values, value orientations, implicit, and explicit preferences inform and influence behaviors. However, there is a need to understand and monitor all of them in different contexts to inform conservation and wildlife management decisions (Vaske & Donnelly, 1999).   Interest in the human dimensions of conservation has grown significantly in the past twenty years (e.g., (Browne-Nuñez et al., 2013; Browne-Nuñez & Jonker, 2008; Browne-Nuñez, Treves, MacFarland, Voyles, & Turng, 2015; Daigle, Hrubes, & Ajzen, 2002; Manfredo, 2008a; Manfredo, Teel, & Bright, 2003a; Teel & Manfredo, 2010; Teel, Manfredo, & Stinchfield, 2007)). But there are still many more conservation studies that focus on the natural sciences. As mentioned earlier, conservation is a matter of morality that deals with humans and non-human organisms(Ehrlich, 2002). What trade-offs are evaluated when deciding on conservation of some species over others? How can conservationists gain more public support for campaigns and policies that aim to reduce biodiversity loss imposed by human threats? While these questions are fundamental to conservation, they remain −for the most part− unanswered (Manfredo, 2008a). The purpose of this thesis is to contribute to the human dimensions of conservation scholarship by helping to understand how people‘s attitudes and preferences toward endangered species might be linked to the conservation of such species. In particular, this thesis focuses on exploring people‘s attitudes and preferences toward species at risk in British Columbia (B.C.), Canada.  6  1.2 Species at risk in British Columbia B.C. is home to approximately 50,000 species (not including unicellular organisms) out of which 3,808 have been assessed in terms of their conservation status (Biodiversity BC, 2008). In B.C., 6% (233 species) of the species assessed are of global conservation concern and 43% (1,640 species) are of provincial conservation concern (Biodiversity BC, 2008). According to the B.C. Conservation Data Centre, which reviews the conservation rankings for B.C. species and subspecies annually, more species and subspecies of well-studied taxonomic groups (i.e., mammals, freshwater fish, breeding birds, and vascular plants) have experienced deterioration in conservation status since the 1990s than have experienced improvement (Biodiversity BC, 2008).   Legal measures have been introduced and proposed to safeguard endangered species in Canada, and in some instances to restore their populations. Canada introduced Bill C-5 in February 2001, which resulted in the Species at Risk Act (SARA), federal legislation to complement provincial and territorial measures aiming to protect and recover species at risk that occur on federal lands or under federal jurisdiction, including by protecting their critical habitat (SC, 2002).   The federal act established specific ways to determine a species‘ status and the categories of risk (Table 1.1). SARA recognized the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as an independent body of experts responsible for assessing and identifying species at risk. The key objectives of SARA are to: (1) prevent wildlife species from being extirpated or becoming extinct; (2) provide for the recovery of wildlife species that are extirpated, endangered, 7  or threatened as a result of human activity; and (3) manage species of special concern to prevent them from becoming endangered or threatened (SC, 2002).  Table 1.1 Categories of risk defined in the Species at Risk Act Status Meaning Extirpated If the species no longer exists in the wild in Canada, but exists elsewhere in the wild. Endangered If the species is facing imminent extirpation or extinction. Threatened If the species is likely to become endangered if nothing is done to reverse the factors leading to its extirpation or extinction Special concern If the species may become threatened or endangered because of a combination of biological characteristics and identified threats. Source: Modified from Species at Risk Act, S.C. 2002, c. 29 [SARA]  According to the public registry of species at risk in Canada, there are 763 species listed as species at risk. Only 573 are under the categories of extirpated, endangered, threatened, and of special concern. The remaining 190 have not been evaluated, which puts them under the ―No Status‖ category. Of these 573 species, 199 occur in B.C. and the majority are vertebrate species (Table 1.2). Under SARA, the relevant Ministers have the duty to prepare recovery strategies and action plans for species at risk, detailing activities that address knowledge gaps, increase population abundance and distribution, and identify threats that affect the species and their habitat (SC, 2002). Recovery strategies have been developed for ca. 90 species that were listed under SARA when the legislation came into force. These strategies represent the conservation efforts with the strongest legal support at provincial and federal levels.  According to the Species at Risk Annual Report for 2013, Environment Canada proposed final recovery documents for 40 terrestrial species, Fisheries and Oceans Canada posted recovery 8  strategies for 9 aquatic species, and Parks Canada completed and posted 11 recovery strategies in 2013. More than 200 organizations—including First Nations, provincial and federal governments, industries, and environmental organizations—conduct conservation efforts targeting species at risk in Canada annually. Approximately CAD$40 million is invested annually in the programs. The programs that provide the majority of the financial support are the Habitat Stewardship Program for Species at Risk, the Endangered Species Recovery Fund, the Aboriginal Fund for Species at Risk, and the Interdepartmental Recovery Fund (Champagne, 2007).  Table 1.2 Species at risk in British Columbia as listed on March 2015 Taxon/ Category Extirpated Endangered Threatened Special  Concern No Status Total Amphibians   4 2 4 4 14 Arthropods 1 7 2 2 9 21 Birds 1 10 10 12 7 40 Fish   16 3 7 18 44 Lichens   1 1 2 4 8 Mammals   6 5 9 11 31 Molluscs 1 3 1 4 2 11 Mosses   6 3 3 1 13 Reptiles 3 3 2 4   12 Vascular Plants 1 45 11 4 5 66 Total 7 101 40 51 61 260 Source: modified from the website of Species at Risk Public Registry. Retrieved from: http://www.registrelep-sararegistry.gc.ca/sar/index/default_e.cfm on 19/02/2015 9  1.3 Objectives and research questions 1.3.1 Objectives The overarching objective of this research is to generate insights for conservation of species at risk in B.C. by identifying how attitudes toward species at risk can be modified, and by understanding how preferences influence people‘s willingness to pay for the conservation of such species.  1.3.2 Research questions This study attempts to answer the following research questions: i. How do urban residents in B.C. perceive sea otters? ii. How does messaging shape people‘s attitudes toward sea otters? iii. What are people‘s implicit and explicit preferences toward sea otters and other species at risk in B.C.? iv. How are these preferences related to the willingness to pay for conservation?  1.4 Significance of the study Motivations for conservation of nature are diverse and are usually informed by two contested views: that nature should be protected for its own sake (intrinsic value) or that nature should be protected for people‘s sake (instrumental value) (Tallis & Lubchenco, 2014). In principle, decision-making in conservation encounters contested points of views and a wide range of values that may or may not be reconcilable. For example, some people and institutions may prioritize the protection of individual (critically endangered) species, while others may prioritize ecosystems‘ functions when making decisions regarding conservation. While there might be 10  cases where these two goals overlap, often decisions involve the evaluation of trade-offs. Choosing what is worth conserving and why, is guided in large part by people‘s values, beliefs and attitudes.   In order to design and implement effective conservation actions, human dimensions of conservation (i.e., values, beliefs, and attitudes) need to be considered. For instance, it is generally assumed that public policy is dependent upon supportive public opinion (Pierce & Lovrich, 1980). For the most part, societal concerns reflect what the members consider to be important and they will subsequently support actions regarding those issues. Thus, public support for specific conservation policies is partly achieved by aligning policy objectives with public values, and by understanding stakeholder‘s values, beliefs, attitudes, and preferences. In addition, conservation actions that address specific issues, such as the conservation of a particular species, or the mitigation of a particular human-wildlife conflict (e.g., sea otters and local communities in the West Coast of Vancouver Island), also require an understanding of the human dimensions. For example, the amount of money raised for the conservation of an individual species highly depends on what people like and think is important.   From a conservation perspective, there is a need to understand how values, beliefs, and attitudes towards nature and wildlife are formed. In addition, it is necessary to evaluate if they can be changed and if so, how they might be changed in order to promote more pro-environmental human dimensions. Increased understanding of these conceptual issues is important for wildlife management and biodiversity conservation, as it can guide visioning and planning in 11  environmental management (Manfredo, 2008a). The purpose of this research is to contribute to the advancement of knowledge of some of these issues.  This research is among the first to show how messaging can be used for shaping people‘s attitudes toward sea otters as a species at risk. This can help to clarify the significance of messaging in informing conservation campaigns. It also supports the theory that attitudes are not set and can be changed (Fishbein & Ajzen, 1975a) by providing empirical evidence. This thesis explains the relationship between implicit and explicit preferences toward species at risk as well as how they are related to the willingness to pay for conservation of such species. At the moment little is known about people‘s attitudes toward endangered species in B.C. despite the fact that public concern for environmental issues is salient in the province (Blake, Guppy, & Urmetzer, 1997). Advancing the understanding of the human dimensions of conservation in B.C. is important because (as mentioned in the previous section), B.C. holds a vast amount of biodiversity and is home to a significant number of endangered species.  This is the first study to use persuasive communication theories and methods and the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998) to explore people‘s attitudes toward endangered species. In addition, this is the first study that applies to species at risk in B.C. The novelty of this research includes the integration of methods from psychology with ecological principles by looking at conservation issues of endangered species. Results from this research provide empirical evidence about the role played by the implicit and explicit preferences toward endangered species in terms of the willingness to pay for conservation.  12  1.5 Thesis overview This thesis begins (this chapter) with a broad look at the problem of biodiversity loss and how human actions are affecting biodiversity worldwide. It also explained the role of human dimensions in the conservation of endangered species, and of species at risk in B.C. Chapter 2 reviews the case study of sea otters (Enhydra lutris) on the West Coast of Vancouver Island (WCVI) and presents the experiment conducted to answer research questions i and ii. The literature review in Chapter 2 covers the topics of behavior change, persuasive communication, and sea otter conservation. The issue of sea otter conservation is framed under the concept of wildlife poaching and how poaching negatively impacts biodiversity. Chapter 3 answers research questions iii and iv by comparing people‘s implicit and explicit preferences toward sea otters, caribou (Rangifer tarandus), American badger (Taxidea taxus), and yellow-breasted chat (Icteria virens). The literature review in Chapter 3 focuses on the factors that influence people‘s attitudes toward animals and the relationship between attitudes and preferences toward animals, with the willingness to pay for conservation. Chapter 4 discusses the overall results, their relevance in light of existing research in conservation of endangered species, and the implications for future research. 13  Chapter 2: How messaging shapes attitudes toward sea otters as a species at risk  2.1 Introduction The overexploitation of resources (Burney & Flannery, 2005) is a major cause of biodiversity loss (Chapin-III et al., 2000). Current extinction rates driven by human activities are substantially outpacing rates in the fossil record (Barnosky et al., 2011; Sala et al., 2000). In order to mitigate biodiversity loss, conservation efforts need to include addressing human behavior (St John, Edwards-Jones, & Jones, 2010), perceptions of, and attitudes toward natural resources and endangered species. Attitudes—as learned predispositions to respond in a favorable or unfavorable manner with respect to a given object (Fishbein & Ajzen, 1975b)— are important because they guide how people process information from the environment, and also have significant influences on human behavior. This suggests that attitudes can determine the way people navigate the environment in terms of how they see, hear, think, or act (Bohner & Dickel, 2011). Thus, understanding which factors determine attitudes toward biodiversity has especially important implications for conservation (Pelletier & Sharp, 2008).  Prior research has suggested that people with negative attitudes toward certain species are more likely to damage, harvest, or illegally kill these species (Browne-Nuñez et al., 2015; Don Carlos, Bright, Teel, & Vaske, 2009; Kellert, 1992). Deliberative killing of wild animals has taken place in various places and involves a wide range of species that are viewed as ‗problem species‘ because they generate human-wildlife conflicts (e.g., orangutans (Pongo pygmaeus) (Rijksen & 14  Meijaard, 1999), Asian elephants (Elephas maximus) (Choudhury & Menon, 2006), African elephants (Loxodonta africana) (Barnes, 1996; Browne-Nuñez et al., 2013)). Such actions (i.e., damaging, killing wildlife) directly contribute to species‘ population declines and reduced genetic variation (Allendorf, England, Luikart, Ritchie, & Ryman, 2008; Muth & Bowe-Jr, 1998). In contrast, people with positive attitudes tend to be more protective and supportive of conservation actions (Browne-Nuñez et al., 2013), allowing species‘ recoveries. Understanding people‘s attitudes toward wildlife and the factors that shape such attitudes is therefore essential to ensuring successful conservation (Browne-Nuñez et al., 2013; Kellert, 1985; 1992).   A prominent method for shaping people‘s attitudes is through persuasive communication and message tailoring. Messaging has been used to promote pro-environmental behavior through changing people‘s attitudes toward a specific proposition (Petty, Wegener, & Fabrigar, 1997). Most previous messaging research has focused on reducing resource consumption such as water (Goldstein, Cialdini, & Griskevicius, 2008) and energy use (Abrahamse, Steg, Vlek, & Rothengatter, 2007). Fewer studies have used messaging to shape people‘s attitudes toward species and their management. Moreover, few studies have been published that use messaging to promote the conservation and managing of endangered species that are often the targets of poaching activities, such as illegal harvest, sale and possession of wild species or their parts (Chapron et al., 2008; Milner-Gulland & Leader-Williams, 1992; Muth & Bowe-Jr, 1998). While environmental organizations use messaging in their campaigns (e.g., identifying flagship species for fundraising (Home, Keller, Nagel, Bauer, & Hunziker, 2009; Smith, Veríssimo, Isaac, & Jones, 2012)), there are few research studies evaluating the impact of different messages to different audiences. Conservation campaigns can be improved by understanding the motives 15  behind biodiversity conservation for different types of audiences (Martín-López, Montes, & Benayas, 2007).  Here we examined sea otters (Enhydra lutris) in B.C. in Canada, as a case study to understand people‘s attitudes and to test how messaging can change people‘s attitudes toward species at risk. Sea otters are listed as endangered under the International Union for Conservation of Nature (IUCN) Red List (Doroff & Burdin, 2013) and as a species of Special Concern under Canada‘s Species at Risk Act (Fisheries and Oceans Canada, 2007). They once ranged from northern Japan to central Baja California, but during the 18th and 19th centuries intensive fur trade caused the extirpation of the species in more than half of their historical range, including B.C. populations (Estes & Duggins, 1995; Kenyon, 1969). The decline of sea otters induced a trophic cascade in kelp forest ecosystems that enabled a population explosion of sea urchins and a consequent loss in kelp biomass (Estes & Duggins, 1995; Steneck et al., 2002), leaving widespread ―urchin barrens‖ (Filbee-Dexter, 2014).  In the 1970s, 89 sea otters were reintroduced in B.C. in an effort to re-establish sea otter populations (Bigg & MacAskie, 1978). By 2005 sea otters had repopulated 25-33% of their historic range in the province (Nichol, Watson, Ellis, & Ford, 2005). However, as sea otters recovered, the abundance of their prey (i.e., shellfish) declined, as also occurred in central California (Estes & VanBlaricom, 1985; Woodroffe, Thirgood, & Rabinowitz, 2005). Therefore, sea otter conservation has generated a conflict between shellfish fisheries and sea otters. Economic losses are estimated at $30-$50 million (Canadian) per year once sea otter populations are fully recovered (Fisheries and Oceans Canada, 2007). First Nations on the West Coast of 16  Vancouver Island (WCVI) are concerned with the impact of sea otters on their local fishing and economies, and have stated that they would like to exercise their right to harvest sea otters for food, social and ceremonial purposes (Fisheries and Oceans Canada, 2007).   Due to the human-wildlife conflict, sea otter conservation has polarized public opinion among the residents of B.C, particularly those on the WCVI. Although the recovery of sea otter populations reflects successful biological conservation, the social and economic impacts have induced such negative attitudes that sea otters have been found shot in WCVI (Hume, 2014). Thus, the current study was designed to understand how a subset of residents in B.C. perceives sea otters, and how messaging influences people‘s attitudes toward sea otters.   2.2 Methods 2.2.1 Experimental design We conducted an experiment with a before-after-control-impact design, to see what effect messaging (negative, neutral, or positive) had on people’s stated agreement with a suite of attitude statements. The 623 participants were recruited on the University of British Columbia campus (μage=20.67, SD=4.60, 473 female, 148 male, 2 other). A subset of participants (n=324, see Appendix B  for complete demographic information) first completed a questionnaire asking them to score their agreement with the 40 value statements aligning with Kellert’s (1985, 1993) typology (Table 2.1).     17  Table 2.1 Statements presented in the questionnaire.  Attitude Statement Aesthetic Sea otters symbolize to me the beauty and wonder of nature Sea otter sightings are special because they remind us to keep a posture of humility toward the natural world When I see a sea otter I feel amused and fascinated The slaughter of sea otters should be immediately stopped even if it means some people will be out of work Dominionistic Management plans for sea otters should enable active human use of this species People occasionally have to hunt sea otters or they will lose their fear of people and increasingly become a problem  I believe people have the right to exert mastery and control over the marine mammals of the world We should reduce the populations of sea otters if they become so abundant and cause damage to the fisheries Ecologistic It is important to maintain healthy sea otter populations to contribute to healthy ecosystems Protecting an endangered species, such as sea otters, requires the protection of the other species that interact with them and their habitat The presence of sea otters in a determined spot is a sign of a healthy environment Strictly limit the human catch of clams and crabs in order to prevent harm to sea otter populations Humanistic I enjoy watching sea otters in aquaria I think sea otter stuffed animals are great for kids I think adopting a sea otter is a great idea to protect them and their habitat Set up a rehabilitation centre for wounded or orphaned sea otters Moralistic Sea otters should have clean waters to live in The rights of people and the rights of sea otters are equally important The conservation of endangered animals should be ensured by law Do nothing. Sea otters have a right to live in the same place as fishermen Naturalistic I have great affection for sea otters Going on a camping trip or boat trip is more exciting if I see sea otters The government should provide urban residents with convenient ways to enjoy wildlife Though fishing in places where sea otters exist poses a risk, people could learn to accept the risk and co-exist with these animals 18  Attitude Statement Negativistic I would feel scared or angry to see a real sea otter in the wild I cannot imagine how some people can say they love sea otters It is foolish to impose large fines for the killing or harming of endangered or threatened species Capture and relocate sea otters, even if this is a very expensive control method Scientistic It is acceptable for humans to cause the loss of some individual animals of sea otters as long as their populations are not jeopardized I have little interest in learning about the ecology or population dynamics of whales or sea otters It is important to maintain sea otter in order to maintain the ecosystem functioning Maintain sea otter populations at levels sufficient to play their natural ecological role as predators   Utilitarian-Consumption Sea otters have to be controlled when they cause major economic losses to commercial fishermen There is nothing wrong with harvesting sea otters as long as it is properly regulated Sea otters reduce fishing opportunities and hurt the economy Compensate fishermen for their losses to sea otters Utilitarian-Habitat It is important to maintain healthy sea otter populations in order to maximize economic benefits from fisheries Sea otter watchers should help pay for the cost of marine wildlife conservation just as hunters contribute through license fees and taxes on hunting equipment Given the economic problems facing our world, it makes little sense to spend money on programs helping people observe and learn from wildlife Manage sea otters for maximum economic benefit  The scores were based on a Likert scale ranging from -5 (Strongly disagree) to 5 (Strongly agree). The 40 statements were grouped into 10 categories, each measuring one attitude toward sea otters (Table 2.2).   19  Table 2.2 Kellert’s typology of basic attitudes toward animals and the natural environment to describe fundamental values and meanings attributed to species and environments. Attitude Meaning Aesthetic Primary interest in the artistic and symbolic characteristics of animals. Dominionistic Primary satisfactions derived from the mastery and control over animals.  Ecologistic Primary concern for the environment as a system, for interrelationships between species and natural habitats. Humanistic Primary interest and strong affection for individual animals, mostly pets and species with strong anthropomorphic associations. Moralistic Primary concern for animal rights, with strong opposition to exploitation of and cruelty toward animals. Naturalistic Primary interest and affection for wildlife and the outdoors. Negativistic Primary orientation and avoidance of animals for dislike, indifference, or fear. Scientistic Primary interest in the physical attributes and biological functioning of animals. Utilitarian-habitat Primary interest in the practical human value of land associated with wildlife. Utilitarian-consumption Primary interest in the practical value of animals. Source: modified from (Kellert 1985)  Participants were then randomly assigned to view one of the three messages: positive (n=107), negative (n=106), or neutral (n=111) (Table 2.3). The positive message described the ecological importance of sea otters in maintaining the balance within kelp forest ecosystems. The negative message described the resource conflict between sea otters and local fishermen. The neutral message presented biological and physiological facts about sea otters. These messages were selected because they represent the current conflicting arguments of conservationists (i.e., ecological restoration of kelp forests) and fisheries (i.e., economic losses in the business due to resource competition) in the social-ecological system of the WCVI. After viewing the message, 20  participants rated how convincing and believable the message was in a Likert scale ranging from -5 (not at all) to 5 (a lot). Afterwards, participants completed the same questionnaire. Since every participant completed the same questionnaire twice, any change in the post-message responses could be driven by the fact that the participant completed the same questionnaire a second time. To control for this factor, we recruited a new group of 299 participants, who were randomly assigned to view either the positive (n=97), the negative (n=95), or the neutral (n=107) message, and then completed the questionnaire only once after seeing the message.  Table 2.3 Messages presented to participants Type Message content Positive  (n=107) One contribution of sea otters to ecosystems is that they maintain kelp forests. Kelps are large brown algae that live in shallow water close to shore. Kelps can grow densely in 'kelp forests', which are one of the most productive and dynamic ecosystems on Earth. Sea otters eat sea urchins, which are capable of preventing the growth of kelp forests by eating kelp 'holdfasts' (roots). A 34kg male sea otter has a daily energy requirement of 4600 kcal, the equivalent of more than 100 urchins per day. This predation on urchins enables kelp forest expansion. In the North Pacific, kelp forests are much larger and deeper in the presence of sea otters. Many animals eat kelp or kelp particles, or use kelp forests as protective habitat or feeding grounds. Accordingly, sea otters indirectly benefit many species (likely including salmon and halibut) by enhancing kelp forest growth. Negative  (n=106) Sea otters feed on invertebrates such as crabs, clams and sea urchins. A 34kg male sea otter has a daily energy requirement of 4600 kcal, the equivalent of more than 100 urchins per day. In general, sea otters eat so much that they can seriously impact shellfish aquaculture and wild populations. The 14 tribes of the Nuu-chah-nulth First Nations on the West Coast of Vancouver Island in Canada rely on shellfish harvesting for their economy and for subsistence. Since the sea otter population has exploded in their territory, they have been less able to harvest shellfish, because now the otters are eating the resources that they used to fish. Therefore, sea otters compete with fishermen for food in some places.  21  Type Message content Neutral (n=111) Unlike other mammals, sea otters do not have a layer of fat to keep them warm, but they have the densest fur of all mammals (approximately 100,000 hairs per square centimeter). Sea otters must consume the equivalent of 23% to 33% of their body weight each day to maintain their internal heat production. The metabolic rate of a sea otter is 2.4 to 3.2 times higher than that of terrestrial mammals of a similar size. Sea otters' diet consists mainly of invertebrates such as crabs, clams and sea urchins. Researchers have found that sea otters use rocks to open shellfish. They are one of the few mammals that use tools.  2.2.2 Data analysis We first examined whether any change in the post-message responses was driven by the fact that participants completed the same questionnaire twice, by comparing the post-message responses between the two groups of participants. There was no reliable difference between the post-message responses (F(1,6218)=2.55, p=.11, ηp2<.001) in the two groups, suggesting that completing the same questionnaire for the second time had little impact on people’s responses. Thus, for all subsequent analyses we only used the data of the first group of participants (n=324).   To examine how participants perceived sea otters, we first averaged the responses for each attitude and plotted the average across participants for each attitude (Figure 2.1). A one-way ANOVA and t-tests were conducted to see if certain attitudes were more prominent than others. We then applied Bonferroni corrections to all significant p values (α=.05) to correct for multiple comparisons. To examine how messages influenced people’s attitudes toward sea otters, we conducted a two-way mixed-design ANOVA (message: positive, negative, neutral, between-subjects factor × time: pre, post message, within-subjects factor) for each attitude (i.e., the change in the mean of participants’ pooled attitude values in each of Kellert‘s typology of 10 22  basic attitudes toward wildlife). This analysis allowed us to compare people’s attitudes before and after they viewed each message and across the three messaging conditions.  2.3 Results The average scores of the 10 attitudes toward sea otters in the pre-message conditions are presented in Figure 2.1. This figure is a Beanplot (Kampstra, 2008) which is an alternative for a boxplot, in which each “bean” (i.e., individual polygon for each attitude) consists of a density trace (i.e., showing the distribution of the data) that is mirrored to form a polygon shape. Solid black lines represent the mean value for each polygon, and the dotted line indicates the overall mean when all the values of all attitudes are pooled (Kampstra, 2008).   Overall, we found a significant difference among the 10 attitudes (F(9,6470)=820.80, p<.001, ηp2=.53). Specifically, participants scored the highest on moralistic (μ=2.57, SD=1.41) and ecologistic (μ=2.25, SD=1.21) attitudes toward sea otters, and they were not reliably different from each other (Tukey’s HSD (I vs. J comparison in Figure 2.1) p=.56). Out of all possible pair-wise comparisons between mean attitude values (i.e., all comparisons between A and J in Figure 2.1) most attitudes were significantly different from each other (Tukey HSD p<.05), with the exception of four comparisons that were not significant (Tukey’s HSD for I vs. J p=.56, I vs. H p=.84, G vs. H p=.09, G vs. F p=.98) (see Figure 2.1).  23   Figure 2.1 Beanplot showing the distributions of participants’ attitudes toward sea otters before seeing the messages (n=324). The black solid lines represent the mean values for each attitude, and the dotted line indicates the overall mean value.  The two-way ANOVA (message × time) revealed significant interactions for utilitarian-habitat (F(2,321)=8.86, padj=.002, ηp2=.97), utilitarian-consumption (F(2,321)=28.93, padj<.001, ηp2=.89), and dominionistic attitudes (F(2,321)=6.86, padj=.01, ηp2=.71) (Figure 2.2). Specifically, the negative message increased scores for all three attitudes (see Table 2.4). This means that participants who viewed the negative message were more likely to agree with statements that pointed out the management of sea otters when they harm local fishermen and First Nations people on the WCVI.  24   Figure 2.2 Attitude change by message and time. Pre-message is the baseline attitudes (before message), and Post-message presents attitudes after seeing the message. The three lines in each sub-plot represent the trajectories for attitude change in each messaging condition (negative, positive or neutral).  Results also showed significant differences in the convincingness and the believability of the three messages (F(2,321)=13.11, p<.001, ηp2=.08). Particularly, the negative message was perceived as less convincing (μ=1.39, SD=2.13) than the positive (μ=2.46, SD=1.50; t(211)=4.21, p<.001, d=.57) and neutral messages (μ=2.52, SD=1.75; t(215)=4.27, p<.001, d=.58) (see Figure 2.3). The negative message (μ=1.02, SD=2.09) was also rated as less believable (F(2,321)=19.17, p<.001, ηp2=.11) than the positive message (μ=2.34, SD=1.66; t(211)=5.07, p<.001, d=.69), and the neutral message (μ=2.54, SD=2.05; t(215)=5.38, p<.001, 25  d=.73). There was a positive correlation between the magnitude of change for the utilitarian-consumption attitude and how convincing (r(104)=.28, p=.004) and believable (r(104)=.20, p=.04) the negative message was perceived, but not for the change in utilitarian-habitat or dominionistic attitudes.   Figure 2.3 Mean values for convincingness ratings of three messages with standard errors (n=324).  The positive message decreased scores for the utilitarian-consumption attitude (see Table 2.4): people who viewed the positive message were less likely to agree with the active management of sea otters (e.g., culling) to reduce human-wildlife conflict. The positive message, which focused on the ecological benefit of sea otters, might be expected to increase ecologistic attitudes. However, since ecologistic was the second highest scored attitude, we examined the data from the lowest quartile of ecologistic attitudes (n=30) to analyze the impact of the positive message on ecologistic values. We found that after viewing the positive message, people were more likely to agree with ecologistic statements (t(29)=2.87, p=.007, d=.52). The neutral message decreased 26  the scores for the utilitarian-habitat attitude (Table 2.4): when presented with biological facts about sea otters, people agreed less with statements prioritizing the management of otters and their habitats primarily for human gain. Our results show that the negative message was more persuasive than the positive and the neutral messages because it had the largest effect in attitude change (see Table 2.4). Since peoples‘ preexisting attitudes toward sea otters were overwhelmingly positive, it might be possible that only the negative message could change participants‘ views because positive and neutral messages might have mainly confirm them. It might also be the case that positive and neutral messages encountered ceiling effects (i.e., no further increase in attitude is possible because they already had a high starting point).   Table 2.4 Measuring attitude change between pre-message and post-message responses Attitude Message T tests  (p values corrected for multiple comparisons using Bonferroni methods) Utilitarian-habitat Negative t(105)=2.95, p=.02, d=.29 Neutral t(110)=2.89, p=.01, d=.27 Positive t(106)=.76, p=1, d=.07 Utilitarian-consumption Negative t(105)=8.51, p<.001, d=.83 Neutral t(110)=.35, p=1, d=.03 Positive t(106)=2.45, p<.05, d=.24 Dominionistic Negative t(105)=3.90, p<.001, d=.38 Neutral t(110)=.66, p=1, d=.06 Positive t(106)=.65, p=1, d=.06  2.4 Discussion Given that the primary purposes of conservation are to mitigate the negative impacts of people on ecosystems and species, and to encourage pro-environmental attitudes and behavior (Saunders, 2003), conservationists need to understand people’s attitudes toward endangered 27  species, and how such attitudes might be formed. The current study shows that many urban B.C. residents have positive attitudes toward sea otters. Specifically, urban B.C. residents hold moralistic and ecologistic views about sea otters, showing primary concern for animal rights and the environment as a system. They care about sea otters’ rights and sea otters’ interactions with other species in their natural habitats. Previous research has shown that Canadians regularly participate in nature-related activities and have a unifying love of nature (Boyd, 2003). Our findings are consistent with past work that elucidates such biospheric values among Canadians (e.g., (Deng, Walker, & Swinnerton, 2006; Franzen, 2003)).  Using a persuasive communication and messaging framework, we showed that the negative message had the strongest impact because it caused significant changes in three attitudes, even though the negative message was perceived as the least convincing and believable (see Figure 2.3 and Table 2.4). This strongest effect of the negative message may be largely due to the overall strong positive pre-existing views of our respondents, which prevented a further increase (i.e., ceiling effect). In addition, we think that positive and neutral messages confirmed people’s beliefs while the negative message might have been the only one presenting new information and therefore might have been the only one that could generate an attitude change. In contrast, we found that the positive message reduced people’s utilitarian-consumption attitudes, and that the neutral message reduced utilitarian-habitat attitudes. A puzzling finding was that there was a positive correlation between the credibility of the negative message and the attitude change for only the utilitarian-consumption attitude, but not for the other attitudes in which attitude change was significant (i.e., utilitarian-habitat and dominionistic attitudes). This effect may be due to the fact that the negative message presented threats of sea otters to humans in terms of economic 28  losses. Similar loss-framed messages have been shown to be more persuasive in affecting attitude change than gain-framed messages in cases where threats (especially affecting humans) are amplified (Rothman, Salovey, Antone, Keough, & Martin, 1993; Tversky & Kahneman, 1991). In addition, study results for the change in the utilitarian-consumption attitude are consistent with persuasive communication research that has shown that the credibility of the message is one of the main factors influencing communication effectiveness (Johnston & Coolen, 1995; Shavitt, Swan, Lowrey, & Wanke, 1994). However, this finding was only significant for the utilitarian-consumption attitude for which a positive correlation between credibility of the message and attitude change was found.   The current study is the first to our knowledge to integrate theories of persuasive communication and messaging with conservation of endangered species. Our findings have important theoretical and practical implications. On the theoretical level, our findings add to an increasing body of research seeking the integration of psychology and conservation, by revealing new factors underlying attitude change associated with conservation actions (Goldstein et al., 2008; Saunders, 2003; St John et al., 2010). Conservation generally addresses ecological questions (e.g., identifying threatened species) but little attention has been given to people’s decision-making process that contributes to biodiversity loss. Particularly with poaching in context of human-wildlife conflicts, simple economic models have captured the financial incentives of poaching behaviors (Milner-Gulland & Leader-Williams, 1992), but the relationship between people’s attitudes toward wildlife and poaching behaviors is unclear (Browne-Nuñez et al., 2015). This study helps illustrate how messaging about the human-wildlife conflict can influence people’s attitudes toward endangered species. 29  On the practical level, our findings suggest that messages presenting information about endangered species could be used to persuade people to think differently about these species. Messaging is a useful tool that could be applied to help design conservation campaigns. However, as with many other social influence practices, the opposite effects are also possible: negative attitudes toward wildlife may also be enhanced, depending on the contents of the message. Our study suggests that such negative messages about wildlife can be effective at increasing the support for management that enhances human benefit, including through culling a highly charismatic species. Interestingly, our study suggests that such messages can have force, affecting support for intrusive management, even if the message is not generally deemed credible.  Consideration of the initial attitudes of target audiences may assist the effective design of messaging for conservation campaigns. Our findings may generalize to the relevant human-wildlife conflicts at a global scale, such as the conflict between human communities and elephants or large feline species. However, each case needs to be examined carefully because of the variation of contextual characteristics (e.g., stakeholders in the region, population’s socio-cultural characteristics). Identifying the salient attitudes toward endangered species and their management allows governments, NGOs and other entities designing conservation campaigns to target the most important concerns of a particular audience and to garner support for specific actions, by understanding the purposes of the persuasion. Given that public support for endangered species conservation in Canada has already resulted in the enactment of the Species at Risk Act and the fact that sea otters have been listed under the act, there is an urgent need to test the attitudes of people who are directly affected by the human-wildlife conflict on WCVI 30  (i.e., First Nations’ fishermen). This research will be useful in understanding the effectiveness of messaging to a broader audience. Future work could also examine species that have negative connotations (e.g., great white sharks, Carcharodon carcharias) in order to test the effectiveness of positive or negative messaging on changing people’s attitudes toward these species. Finally, more studies are needed to examine species that have broader geographic ranges and that involve diverse demographic groups that are vastly different in terms of socioeconomic and cultural factors (e.g., tigers, Panthera tigris, which are distributed throughout Southeast and Eastern Asia).   In conclusion, our findings suggest that messages presenting ecological benefits of endangered species, human-wildlife conflicts, and messages with biological facts of endangered species can substantially affect attitudes toward endangered species. Positive messages yield less agreement with actions that may impact wildlife for human gain, while negative messages may have the opposite effect. Since messaging can affect attitudes, it should be considered carefully when designing conservation campaigns. Motivating people to change their attitudes, perceptions, and behaviors that are harmful to endangered species represents a challenging task. However, we propose that by integrating and applying theory from psychology, we can help inform more effective conservation campaigns that can address issues of human-wildlife conflicts that are widespread across the globe.  2.5 Acknowledgements We are grateful for the students who participated in the study. We are also thankful for feedback on study design and research implications from S.R. Kellert, C. Thornton, and R. Naidoo. 31  Thanks to E.J. Gregr for providing information about the social-ecological context of sea otters in the WCVI and thanks to people in the Zhao Lab and CHANS Lab for providing support. We acknowledge the Canada Research Chairs program (for KC and JZ) for the Leaders Opportunity Fund from the Canadian Foundation for Innovation (F07-0010 for KC, and F14-05370 for JZ), NSERC Discovery Grant RGPIN-2014-05617 (for JZ), and the Colombian Foundation COLFUTURO (for AE) for providing funding.  32  Chapter 3: Evaluating implicit and explicit preferences toward species at risk in British Columbia  3.1 Introduction Species conservation is a function of public policy, which is partially informed by public perceptions of species (Czech, Krausman, & Borkhataria, 1998). Given that people tend to conserve what is important to them, many of the world‘s species of megafauna will survive only if humans choose to protect them (Stokes, 2007). It has long been established that humans find some species more appealing than others (Kellert, 1993; Morris, 1967; Serpell, 2004; Woods, 2000). Unsurprisingly, conservation efforts have been biased toward preferred species such as charismatic species (e.g., birds and mammals (Czech et al., 1998; Morse-Jones et al., 2012)), sometimes giving little attention to species in higher conservation needs (Brambilla, Gustin, & Celada, 2013; Metrick & Weitzman, 1998). Understanding public perceptions about species and the factors that influence those perceptions is important for designing effective conservation actions (Browne-Nuñez et al., 2015; Manfredo, 2008b; Vaske & Donnelly, 1999).   A variety of reasons have been offered to explain people‘s preferences toward other species. For the purpose of this chapter, only the factors that modify people‘s attitudes toward animals will be described. Animals are ubiquitous in people‘s lives; animals comprise an important part of people‘s diets, they are kept as pets, used for transportation and labor, and are sources of human entertainment (e.g., zoos, aquaria, circuses, legends, stories). But what determines which animals are appropriate for each of those contexts? And from a conservation perspective, why do certain 33  species and taxonomic groups tend to be valued more highly than others, and why are some species perceived as more worthy of being conserved? (Batt, 2009; H. Herzog, 2010; Ward, Mosberger, Kistler, & Fischer, 1998). The factors that shape people‘s perceptions about animals can be summarized in three categories: evolutionary factors, socio-cultural factors, and species traits (i.e., physical appearance and unique characteristics).   In his book Biophilia, E.O Wilson (1978) stated that as the human brain evolved in a biocentric world, evolutionary factors have played a major role in shaping the way humans interact with and perceive other animals. One commonly cited evolutionary factor is fear, which induces negative attitudes toward animals. Fear has been described as an adaptation to identify and avoid potential threats in the environment (e.g., spiders and snakes) (Waters, Lipp, & Randhawa, 2010). In addition, the human ability to anthropomorphize other species may have evolved as an adaptation to predict prey behavior (Mithen, 1996) and has enabled the domestication of other species (Serpell, 2003). Species that exhibit anthropomorphic characteristics tend to be preferred by humans (Batt, 2009; Serpell, 2003; 2004). Additionally, the Kindchenschema or the ―cute response‖ hypothesis refers to an evolutionary adaptation that helped ensure that human adults cared for their children, ultimately securing the survival of the species (Lorenz, 1943). The cute response hypothesis states that humans are attracted to neotenic traits in other animals such as a large head, flat face, and large eyes. These traits are more prominent in mammals and birds than in any other taxonomic groups (Gould, 1980; Lorenz, 1943; Wilson, 1984). The cute response is an important factor that shapes people‘s attitudes toward animals.  34  Factors such as culture, familiarity with species, and demographic variables are also important modifiers of human attitudes toward animals. People‘s perceptions of animals are influenced by the activation of elements in the cultural knowledge network of an individual or a community. Some species trigger different cultural procedures, values, beliefs, or attitudes (Kesebir, Uttal, & Gardner, 2010; Randall, 1986). For example, a species can be considered a pet in some cultural contexts, but represent food in others (e.g. dogs in North America vs. South Korea). In addition, the interaction between people and animals in early childhood influences individual attitudes toward animals throughout adulthood (Amiot & Bastian, 2015; Serpell, 1986). Familiarity with animals as a byproduct of early exposure is also determined by social and cultural contexts. Familiarity has been shown to have a large effect on people‘s attitudes toward animals (Amiot & Bastian, 2015) in that higher degrees of familiarity generally foster positive attitudes toward species (Martín-López et al., 2007). Demographic factors such as employment sector and gender also affect the way people think about animals. People who are exposed to consumptive and coercive activities or jobs (e.g., farmers) tend to have more utilitarian attitudes toward animals than do other people (Hills, 1993). Furthermore, women tend to express greater concern about animal rights than men, and vastly more men than women engage in recreational hunting (Herzog, 2007; Kellert & Berry, 1987).   Species‘ physical appearance, aesthetic quality, behavioral traits, and ecological characteristics are also factors that shape people‘s attitudes toward animals (Stokes, 2007). Larger animals are generally preferred over smaller animals, and mammals tend to be preferred over all other taxonomic groups (Kellert, 1993; Ward et al., 1998). In addition, bright colors are associated with human preference for animals (e.g., penguins (Stokes, 2007) and invertebrates (Kellert, 35  1993)), and the mode of locomotion and surface texture of an animal also seem to have an effect on people‘s preferences toward species. Animals with more unfamiliar locomotion types and textures, with respect to humans, are the least preferred (Woods, 2000). Lastly, with respect to ecological characteristics, people tend to attribute more intrinsic worth to endangered species than to common ones (Burghardt & Herzog, 1980). However, this has only been proved to have an effect when people are well informed about the species being considered (Martín-López et al., 2007)  Human attitudes toward animals have been studied mostly in the context of theories and methodologies of general attitude research, which are rooted in social psychology. The human-animal interactions scholarship largely features studies that evaluate people‘s explicit attitudes toward animals; implicit attitudes have received comparatively less attention (Lassiter, 2000). In addition, studies in these fields include a disproportionate number of quantitative studies using experimental approaches to answer research questions, compared to qualitative studies (Shapiro & DeMello, 2010). There is a significant lack of cross-cultural research on human-animal relations with very few studies conducted in non-Western contexts (Amiot & Bastian, 2015; Browne-Nuñez & Jonker, 2008; Teel et al., 2007). The main goals of this study were: (1) to compare people‘s implicit and explicit preferences toward four species at risk in B.C., (2) to understand how people think about the four species by evaluating people‘s associations, and (3) to evaluate whether implicit or explicit preferences toward the four species examined are related to their willingness to pay for conservation of the species.  36  3.2 Methods This chapter reports the results of two studies. In the first study, 55 undergraduate students participated in an experiment that measured their implicit preferences of four species at risk using the Multi-Category Implicit Association Test. Then, participants were surveyed on their explicit (stated) responses of four variables: preference, familiarity, perceived levels of endangerment, and willingness to pay for the conservation of the same four species. Participants did a word association task for each of the species. In the analysis, we used metrics from the implicit (i.e., D scores (Nosek, Bar-Anan, Sriram, Axt, & Greenwald, 2014)) and explicit responses (i.e., preference, familiarity, perceived endangerment) as predictors (independent variables) of the willingness to pay (the dependent variable). The second study was similar to the first study but participants only gave explicit responses for their preferences of the four species at risk (i.e., preference, familiarity, perceived endangerment) and their willingness to pay for the conservation of those species. We sampled a larger set of participants (n=463) that were recruited via Mturk (an online crowdsourcing platform). The second study was conducted to validate the findings of the first study.   3.2.1 Study 1 3.2.1.1 Participants A total of 55 undergraduate students at the University of British Columbia (37 female, 16 male, 2 other; μage=20.95, SD=3.41) participated in the study in exchange for course credit. The majority of the participants self-identified as Chinese (41.82%), followed by White or Caucasian (25.45%) (See Appendix F  for complete demographic information). Participants were selected for participation through the human subject pool (HSP).  37  3.2.1.2 Procedure The study session consisted of two components. First, participants were presented with a four-category species Multi-Category Implicit Association Test (MC-IAT). Then, participants completed a survey that evaluated their explicit preferences toward the species presented in the MC-IAT.   3.2.1.2.1 MC-IAT The MC-IAT, a variant of the Brief IAT (Axt, Ebersole, & Nosek, 2014; Sriram & Greenwald, 2009) measured the strength of association between species and positive attributes. MC-IAT is a type of Implicit Association Tests (IATs). IATs were introduced in 1998 (Greenwald et al., 1998) as tools that measure the strengths of associations between categories (e.g., Coke vs. Pepsi) with attributes representing positive versus negative valences (e.g., pleasant vs. unpleasant) (Sriram & Greenwald, 2009). These tests measure participants‘ response times when associating categories and attributes, and assumes that participants‘ response times are shorter if the association is strong and longer if the association is weak (Greenwald et al., 1998).   The test contained 13 blocks, the first of which was a practice block. Each block had 16 trials presenting 16 different stimuli. In each block, stimuli were presented one at a time and participants categorized them as quickly as possible. The 16 trials consisted of 4 pictures of species #1, 4 pictures of species #2, 4 good words (i.e., love, pleasant, great, wonderful), and 4 bad words (i.e., hate, unpleasant, awful, terrible). In each block the focal species was paired with good words, and the non-focal species was paired with bad words. Participants were asked to 38  press the ―I‖ key when they saw pictures of the focal species or good words, and the ―E‖ key when they saw pictures of the non-focal species or bad words (see Figure 3.1. and Figure 3.2).    The four species were: sea otter (Enhydra lutris), caribou (Rangifer tarandus), American badger (Taxidea taxus), and yellow-breasted chat (Icteria virens) (see Figure 3.3). Species were selected from a pool of 18 species at risk in B.C. that was presented in a pilot study (n=134, not described in this thesis). The criteria for case selection were based on results from the pilot study that indicated that these four species had similar rankings among participants in terms of familiarity and explicit preference. Species that were considered as iconic for the study population (e.g., the blue whale and the spotted owl) were avoided to evade possible triggering of emotional and cognitive responses that might bias research results relative to other species. The good and bad words used in this experiment were chosen to correspond with those in Axt, et al., (2014).  Each block was presented with a different combination of focal and non-focal species. For example, there were 3 blocks for which participants pressed the ―I‖ key for sea otter: in one block sea otters and good words were presented against caribou and bad words, in another block they were presented against American badger and bad words, and in another against yellow-breasted chat and bad words (see Figure 3.1 and Figure 3.2). Participants first did the practice block and the other 12 blocks were presented in random order. MATLAB was used to code the task and record the responses.   39   Figure 3.1 Screenshot of the MC-IAT. In this case the focal species was Caribou and participants were instructed to press the I key if they saw pictures of caribou or good words. Caribou photo: © Amanda Graham  CC BY-NC-ND 2.0    Figure 3.2 Screenshot of the MC-IAT. In this case “wonderful” was a good word and participants were instructed to press the I key if they saw pictures of caribou or good words.  MC-IAT D scores (Nosek et al., 2014) were calculated using the following formula:                                                                   )                                                ))                                )  *RT= response time *SD= standard deviation  40  Four D scores (i.e., one per species) per participant were calculated. To calculate each D score, all trials with response times larger and smaller than 3 standard deviations away from the mean of participants‘ response times were excluded, and only correct responses were used to calculate the D scores. Finally, means of D scores (across participants, i.e., one per species) were calculated in order to evaluate if participants had, on average, implicit preferences for certain species over others. All analyses were conducted in the statistical software package R (R Development Core Team, 2008).    Sea otter (Enhydra lutris) © Chuck Abbe, CC BY 2.0  Caribou (Rangifer tarandus) © National Park Service, Alaska region, CC BY 2.0  American badger (Taxidea taxus) © Jerry Oldenettel, CC BY-NC-SA 2.0  Yellow-breasted chat (Icteria virens) © Kelly Colgan Azar, CC BY-ND 2.0 Figure 3.3 Four species at risk in B.C. used in the study 41  3.2.1.2.2 Survey Participants were asked to complete a survey with 6 blocks of questions (see Appendix E  for survey sample). The first block was a word association task: participants were asked to write three words that came to mind when they thought of each of the four species (sea otter, caribou, American badger, and yellow-breasted chat). All questions were presented in random order. In the second block we assessed willingness to pay for conservation, asking participants to type in the amount of money they were willing to donate to conserve each of the species presented. In the third block, we assessed explicit preference and perceived endangerment by asking participants to rank the species (from 1=highest to 4=lowest) based on preference (as relative favorites) and based on how endangered they thought the species was. In the fourth block, we assessed participants‘ pro-environmental behavior via 7 questions (e.g., how much time do you spend in nature in a regular weekday?). In the fifth block we assessed aesthetics, having participants view the same 16 pictures of the four species that were presented in the MC-IAT and asking them to rate the beauty of the picture (from 0= not at all, to 10=extremely), how much they liked the picture, how much they liked the species, and their familiarity with each species. In the last block, participants answered 14 demographic questions (e.g., age, income, political orientation). Qualtrics was used for survey design and data collection.  3.2.2 Study 2 3.2.2.1 Participants In order to test the reliability of the findings of study 1, the Qualtrics survey was replicated with participants outside of B.C. A sample of 463 participants (208 female, 253 male, 2 other) was recruited via Amazon Mechanical Turk (Mturk), which is an online crowdsourcing system that 42  enables individuals to perform research related tasks for monetary compensation. Participants were paid US$0.50 in exchange for their participation. Participants ranged in age from 18 to 69 years old (μage=35.21, SD=11.49). The majority of participants were located in the United States (76%), and India (21%). In addition, the majority self-identified as White or Caucasian (61.99%) or South Asian (21.17%) (see Appendix G  for full demographic information).  3.2.2.2 Procedure Participants were asked to complete a survey with 12 blocks of questions. The first 6 blocks were the same questions as in the survey presented in study 1, which meant that participants had to answer questions such as their explicit preference, familiarity, and willingness to pay for conservation of the 4 species. In addition, they were presented with questions about 4 biomes: forest, ocean, tundra, and grassland. First, in a word association task, participants wrote three words that came to mind when they thought of those four biomes. Then we assessed explicit preference and perceived threat, asking participants to rank which biome was their favorite, and how threatened they thought each biome was. Next, we assessed aesthetics and familiarity, asking participants to rank 16 pictures of biomes and rate the beauty of the picture (from 0=not at all to 10=extremely); how much they liked the picture; how much they liked each biome; and their familiarity with each of the biomes. At the end, demographic information was collected. The questions about animals were always presented before the questions about biomes. Qualtrics was also used for study design and data collection. For the purpose of this thesis, only the questions regarding animal species were analyzed.   43  3.2.3 Data analysis 3.2.3.1 Quantitative analysis We ran one-way ANOVA tests to evaluate if people had implicit or explicit preferences for any of the species presented. For the implicit preferences, tests analyzed the differences between means of D scores (across participants, i.e., one per species). This analysis was only conducted with the data from study 1. For the explicit preferences, we conducted one-way ANOVAS for responses recorded in each of the variables measured (i.e., preference, familiarity, perceived endangerment, willingness to pay). We also conducted post hoc tests (Tukey‘s HSD). This was done for study 1 and study 2.  We ran correlations between all the possible pairs of variables among the five variables of the study: D scores (as indicators of implicit preference), explicit preference, familiarity, perceived endangerment, and willingness to pay. In addition, since familiarity and preference were significantly correlated for each species, we ran two partial correlations between the willingness to pay and explicit preference controlling for familiarity, and the willingness to pay and familiarity controlling for explicit preference. This analysis was done for study 1 and study 2, but in study 2 the D score variable was not included.  Lastly, for study 1 we conducted a multiple regression to analyze which variable best predicted the willingness to pay, using the following equation: Eq (1)                             *WTP= Willingness to pay *D score= implicit preference variable *EP= Explicit preference *EN= Perceived endangerment 44  And for study 2 we conducted a similar multiple regression, only we excluded the implicit preference independent variable. We used the following equation: Eq (2)                       *WTP2= Willingness to pay in study 2 *EP2= Explicit preference in study 2 *EN2= Perceived endangerment in study 2  All statistical analyses were conducted in the statistical software package R (R Development Core Team, 2008). We did not include familiarity as a predictor in the multiple regressions because familiarity was positively correlated with explicit preference in study 1 and study 2.  3.2.3.2 Qualitative analysis We used non-hierarchical axial coding (Strauss, 1987) to code the words in the word association task. First, we read the words and we created 12 labels (See Table 3.1). Then, we assigned unique labels to each word and we counted the absolute frequency of words assigned to each label. A second round of coding using only two categories (i.e., positive associations and negative associations) was conducted as an exercise to understand the results of the MC-IAT. In the second round, some of the words that were initially coded to other labels were re-coded (e.g., loud and noisy were coded as behavioral traits in round 1, but as negative associations in round 2). Chi-square tests were conducted in the statistical software package R (R Development Core Team, 2008) to evaluate the statistical differences between the amount of positive and negative associations for each species. We did a third round of coding and created an additional label called ―Anthropomorphic‖. The purpose of the third round was to identify words that resembled anthropomorphizing terminology. This third round was also conducted as an exercise to understand the results of the implicit and explicit preferences among the four species. 45  Table 3.1 Labels used in the coding analysis of the word association task Label Meaning Examples Descriptors Words used to describe the essence of the animal or its main appearance Animal, Mammal, Bird, Big, Small, Brown, White Behavioral traits Words used to describe common behaviors of the animal Active, Lazy, Calm, Cautious, Eager Environment Words used to describe the ecosystem where the animal lives Grassland, Kelp, Ocean, Water, Winter, Woods Actions Words used to describe a common activity that the animal does Swim, Sing, Chirp, Fly Part of body Words that refer to prominent body parts of the animal Antlers, Whiskers, Claws, Wings Equivalents Words that refer to a different but similar animal than the evaluated animal Moose, Raccoon, Skunk, Seal, Hummingbird Commercial/utilitarian Words that refer to a commercial product derived from the animal or that refer to branding Beer, Coin, Youtube video, sports team mascot Recreation Words that refer to a recreational activity involving the animal or its ecosystem Aquarium, Wetsuit, Surf Otherworldly Words that refer to fantasy Unicorn, Magic, Mythical, Pixies Unknown Words that indicate lack of knowledge or lack of familiarity with the animal Never seen, Don't know Positive associations Words that have positive connotations when describing the animal Beautiful, Majestic, Pretty, Cute, Adorable Negative associations Words that have negative connotations when describing the animal Annoying, Angry, Aggressive, Awful, Bad, Hate  3.3 Results 3.3.1 Study 1 Our results suggest that people‘s implicit preferences differ from their explicit preferences toward the four species presented in this study. There were no significant differences between the means of D scores (across participants, i.e., one per species) as measured by the MC-IAT (one-way ANOVA F(3,216)=1.29, p=.28, ηp2=.02) (Figure 3.4), suggesting that there were no 46  general differences in implicit preferences across species. Contrastingly, participants appeared to have differences in explicit preference for the four species: sea otter was ranked as the most liked species (μ=7.44, SD=2.35), followed by caribou (μ=6.15, SD=2.38), yellow-breasted chat (μ=5.16, SD=2.75), and American badger (μ=4.78, SD=2.69). There were significant differences in explicit preference among the four species (one-way ANOVA F(3,216)=11.9, p<.001, ηp2=.14), with Tukey post hoc comparisons showing that sea otter was the most liked (Tukey‘s HSD p<.05)1 (Figure 3.4).   a)  b)  Figure 3.4 Beanplots showing a) implicit preferences toward species measured as D scores, and b) explicit preferences toward species among lab participants (n=55). Each polygon consists of a density trace that is mirrored to form a polygon. Black lines represent the mean for each category and the dotted line is the overall mean across the four categories in each of the plots (a and b).                                                    1 Caribou is more preferred than American badger (Tukey‘s HSD p<.05), but not than yellow-breasted chat (Tukey‘s HSD p>.05), and yellow-breasted chat and American badger are not different (Tukey‘s HSD p=.86).  -2-1012American_badger Caribou Yellow_breasted_chat Sea_otterSpeciesD score47  Our survey findings also indicated positive correlations between explicit preference and familiarity across individuals: those who scored a species as familiar also tended to have higher explicit preferences (caribou (r(53)=.30, p=.02), American badger (r(53)=.35, p=.008), sea otter (r(53)=.48, p<.001), and yellow-breasted chat (r(53)=.31, p=.02)). There were significant differences in mean familiarity between species (one-way ANOVA F(3,216)=34.49, p<.001, ηp2=.32), with the sea otter as most familiar (μ=6.45, SD=2.5, Tukey‘s HSD p<.05). In addition, familiarity rankings followed the pattern of explicit preference rankings (i.e., (2nd) Caribou μ=5.46, SD=2.69; (3rd) yellow-breasted chat μ=3.54, SD=3.05; (4th) American badger μ=2.66, SD=4.93).  A strong majority (90%) of lab participants (n=52) responded that they were willing to donate for conservation between CAD$0 and $300. The additional 10% of participants‘ responses were excluded because they were higher than 3 standard deviations away from the mean and were interpreted as protest votes (e.g., one trillion dollars). We found positive correlations across individuals between the willingness to pay and explicit preference for each species: caribou (r(50)=.39, p=.004), American badger (r(51)=.41, p=.002), sea otter (r(51)=.31 p=.02), and yellow-breasted chat (r(51)=.46, p<.001). In addition, there was a significant positive partial correlation between willingness to pay and explicit preference controlling for familiarity (r(211)=.33, p<.001); but the partial correlation between the willingness to pay and familiarity, controlling for explicit preference was not significant (r(211)=.06, p=.33).   48  The multiple regression model with all three predictors (using equation 1) of the willingness to pay was significant (Adj-R2=.15, F(3, 207)=13.27, p<.001). However, only explicit preference (EP) predicted the willingness to pay for conservation (see Table 3.2).   Table 3.2 Summary of the multiple regression analyses for the WTP as dependent variable in study 1     Standard error of   t value p value Intercept (  ) -6.54 9.35 -0.69   D scores  -6.21 6.20 -1.00 0.32 Explicit preference (EP) 6.79 1.08 6.30 1.75E-09** Perceived endangerment (EN) 0.24 2.55 0.10 0.92 **p<.001  Results from the word association task suggested that descriptors (e.g., big, small) were the most frequent words given (Figure 3.5). There were significant differences between the number of negative and positive associations recorded among species (χ2(3)=35.89, p<.001), with sea otters having more positive associations than expected by chance (Figure 3.6). In addition, the American badger was the only species with more negative than positive associations (nnegative=40; npositive=33).   49   Figure 3.5 Word associations for the four species evaluated in this study and their frequency organized by the labels. Graph shows the result from lab participants (n=55)   Figure 3.6 Positive and negative word associations for the four species presented in the study (n=55)  020406080100120140160180200Frequency Labels Yellow breastedchatSea otterAmerican badgerCaribou01020304050607080American badger Sea otter Caribou Yellow breastedchatFrequency Species Negative associationsPositive associations50  3.3.2 Study 2 Mturk participants explicitly prefer some species to others among the four species evaluated (one-way ANOVA F(3,1848)=32.92, p<.001, ηp2=.05). Tukey post hoc comparisons showed that the sea otter is the most liked among all species (μ=7.84, SD=2.19, Tukey‘s HSD p<.05), followed by caribou (μ=7.12, SD=2.18) and yellow-breasted chat (μ=6.79, SD=2.41) as equally liked (Tukey‘s HSD p=.12), with the American badger least liked (μ=6.38, SD=2.48, Tukey‘s HSD p<.05) (Figure 3.7). There was a positive correlation between familiarity and explicit preference for all the species: sea otters (r(461)=.51, p<.001), caribou (r(461)=.50, p<.001), yellow-breasted chat (r(461)=.52, p<.001), and American badger (r(461)=.46, p<.001). Differences between respondents‘ familiarity with the four species studied were significant (F(3,1848)=90.97, p<.001, ηp2=.13). Post hoc comparisons indicated that respondents were most familiar with sea otter (μ=6.45, SD=2.51) and caribou (μ=5.46, SD=2.69) (Tukey‘s HSDsea otter-caribou p>.05), then American badger (μ=4.93, SD=2.66, Tukey‘s HSD p<.05). And they are least familiar with yellow-breasted chat (μ=3.55, SD=3.05 Tukey‘s HSD p<.05).  51   Figure 3.7 Explicit preferences toward species among Mturk participants (n=463). Beanplot showing the density traces that are mirrored to form a polygon. Solid black lines represent the mean of each plot and the dotted line is the overall mean across categories.  A strong majority (90%) of Mturk participants (n=438) responded that they were willing to donate for conservation between US$0-100 (n=438). As in study 1, the additional 10% of participants‘ responses were excluded because they were higher than 3 standard deviations away from the mean. We found significant positive correlations between explicit preference and willingness to pay for conservation of the four species evaluated: sea otter (r(436)=.30, p<.001), caribou (r(433)=.28, p<.001), American badger (r(440)=.26, p<.001), yellow-breasted chat (r(435)=.29, p<.001). In addition, we found significant results for the two partial correlations: willingness to pay was correlated with explicit preference controlling for familiarity (r(1752)=.22, p<.001), and willingness to pay was correlated with familiarity controlling for explicit preference (r(1752)=.06, p=.009).   52  The multiple regression model with two predictors (using equation 2) of the willingness to pay was significant (Adj-R2=.08, F(2, 1749)=78.94, p<.001). Results showed that explicit preference (EP2) predicted the willingness to pay (WTP2) (see Table 3.3).   Table 3.3 Summary of the multiple regression analyses for the WTP as dependent variable in study 2     Standard error of   t value p value Intercept (  ) -1.78 2.64    Explicit preference (EP2) 3.64 0.29 12.57 2E-16** Perceived endangerment (EN2) -0.06 0.62 -0.10 0.918 **p<.001  Results from the word association task were very similar to the results obtained in study 1. Descriptors were overall the most frequent words used, and there were significant differences between the number of positive and negative associations recorded among species (χ2(3)=442.05, p<.001), with sea otters and yellow breasted chat having more positive associations than expected by chance. In fact, sea otters were the only species for which positive associations were more common than descriptors (Figure 3.8). In addition, the American badger was the only species with more negative than positive associations (nnegative =274; npositive=155) (see Figure 3.9).  53   Figure 3.8 Word associations for the four species evaluated in this study and their frequency organized by the labels. Graph shows the result from Mturk participants (n=463)   Figure 3.9 Positive and negative associations for the four species presented in the study (n=463)  02004006008001000120014001600Frequency Labels Yellow Breasted ChatSea OtterAmerican badgerCaribou0100200300400500600American badger Sea otter Caribou Yellow-breastedchatFrequency Species Negative AssociationsPositive Associations54  3.4 Discussion The present study was designed to examine the implicit preferences for animal species using the MC-IAT, and to compare these with explicit preferences. Previous studies in social justice and race relations have used the IAT to document differences between people‘s explicit and implicit preferences toward social categories (e.g., human races). Studies have shown that even when participants appear to have equal explicit preferences toward all races, they may have implicit preferences for some groups over others (Axt et al., 2014; Ottaway, Hayden, & Oakes, 2001). Contrary to the findings of the racial studies, our study suggests that all species were equally liked implicitly, but differently liked explicitly. Specifically, both lab and Mturk participants explicitly preferred the sea otter among the four species evaluated. Findings also showed that the American badger was the least preferred (explicitly) among Mturk participants, and was ranked as the least preferred (explicitly) among lab participants. Moreover, the MC-IAT results exhibited a trend that suggests that the mean D score for the American badger is lower than the mean D scores for the other species, even if they are not significantly different (p>.05).   The results from this study are interesting for theoretical and ethical reasons. Theoretically, as indicated in the literature cited previously and summarized below, the preference for sea otters can be explained by various factors. First, of the four species, the sea otter exhibits the most anthropomorphic characteristics. In the word association task, several anthropomorphic responses were recorded (e.g., old man, moustache), indicating that some people anthropomorphized sea otters. Coding responses as anthropomorphic, Mturk participants used more anthropomorphizing terminology to describe sea otters than any other species (χ2(3)=117.38, p<.001; nsea otter=150; ncaribou=32; nAmerican badger=61; nyellow-breasted chat=46).  55  In addition, the cute response was highly prominent in participants‘ judgments of this species. The most commonly used word in relation to sea otters was ―cute‖ in both lab (n=30=54.54% of respondents) and Mturk (n=168=36.44% of respondents) participants, and while it was also a common word for other species (e.g., yellow-breasted chat and American badger), the effect was more pronounced with sea otters.   Second, in terms of socio-cultural factors, familiarity was the most important factor associated with explicit preference. Despite the fact that a demographically diverse set of participants took the survey, the sea otter was ranked as most familiar. For participants in the lab, high familiarity with this species might be due to the fact that sea otters are present on the west coast, so participants may have seen them in the wild. Also, participants might be influenced by the campaign led by the Vancouver aquarium to raise awareness about sea otters. The same could be said about the caribou, which was ranked as second most familiar. In B.C., the caribou has been emphasized in the current pipeline politics, and it is also an icon represented in commercial products such as coins, and beer brands. Figure 3.5 and Figure 3.8 show that the Caribou was the species that was most frequently associated with commercial and utilitarian goods, relative to the other species. For Mturk participants, the high familiarity with sea otters may be largely due to a near-iconic status, mainly for those who were located in the United States (76% of participants).  Third, in terms of physical characteristics, all four species exhibit some characteristics that are associated with higher degrees of preference and positive attitudes toward animals (Burghardt & Herzog, 1980; Serpell, 2004). Since the studied species were mammals or birds and these have been shown to be the most preferred taxonomic groups (Woods, 2000), little variation in physical 56  characteristics was available for analyzing the effects of physical characteristics. The overall analysis of the factors that might influence peoples‘ explicit preferences toward the species suggests that evolutionary and socio-cultural factors better explain people‘s explicit preference among the four species studied.  The case of the American badger is interesting and counter to our expectations. In theory, the American badger should have had a similar ranking to the sea otter because it is also a medium size mammal from the same family as the sea otter (Mustelidae). Therefore it has a similar physical characteristics that suggest preferences (Batt, 2009). In addition, the most frequent word used to describe them was also ―cute‖ by both lab (n=18=32.72%) and Mturk participants (n=80=17.35% of respondents), indicating that the cute response was also prominent in judgments about this species. However, the species was ranked as the least preferred of all and implicit associations showed a trend that indicated that it was the animal with the lowest value of implicit associations (i.e., D score). These results may be explained by the qualitative data on the word associations, which showed that it was the only species with more negative than positive associations. Lab participants used words such as angry (n=4), terrible (n=3), unpleasant (n=3), and mean (n=3) when they thought of American badgers, while Mturk participants used mean (n=66), aggressive (n=24), tough (n=24), and dangerous (n=19). These associations indicated that the American badger is judged in terms of risk more than any of the other species evaluated in this study. The affect heuristic proposed by Slovic, et al., 2007 suggests that relying on positive and negative feelings can predict and explain perceived risks. With regards to technology, Slovic et al. stated that ―if people like an activity, they are moved to judge the risks as low and the benefits as high; if they dislike it, they tend to judge the opposite‖ (Slovic, 57  Finucane, Peters, & MacGregor, 2007). Our findings regarding the American badger can be understood via the affect heuristic: people explicitly liked the badger the least and it was the one perceived as the most threatening among the four species evaluated.  For both lab and Mturk participants, explicit preference was positively correlated with the willingness to pay for conservation of the species at risk presented in this study. These results are consistent with previous research that has shown that the willingness to pay for the provision of public goods, such as the conservation of endangered species, is more influenced by emotional responses than it is by rational arguments such as knowledge of species‘ ecological and scientific characteristics (Kahneman & Ritov, 1994; Martín-López et al., 2007). Findings of this study are consistent with the findings of Martín-López, et al. (2007), which showed that affective factors played an important role in explaining the willingness to pay for biodiversity conservation, particularly when people had little knowledge about the species evaluated.   Results of this study are also interesting for ethical reasons. The principle of equality is widely accepted in so far as it applies to humans. For example, moral principles in many contemporary societies state that human races are equal. In the IAT studies evaluating social issues, findings are consistent with those theories (Singer, 1995) because explicitly people respond in a conscious way to abide to those moral principles and answer that all races are equal, but implicitly a difference of attitudes toward racial groups is shown. However, when evaluating preferences toward animals, this study yielded the opposite results. Species egalitarianism (i.e., the principle stating that all species have equal moral standing) (Schmidtz, 1998) does not seem to apply as a moral principle guiding conscious responses when deliberating judgments toward species. This 58  study suggests that inter-species equality does not have the same kind of social force, as does inter-racial equality. It seems plausible that the MC-IAT is best employed in order to discern preferences that people may wish to keep hidden, e.g., due to social acceptability (such as race, as it is now taboo in many contemporary societal contexts to be explicitly racist). It seems likely that the MC-IAT is less powerful, and considerably more difficult and costly to employ as a measure of preference in the absence of strong social reasons to obscure one‘s private preferences.  3.5 Conclusions The findings from this study are relevant for conservation, and may be of particular use when designing conservation campaigns and policies that address endangered species in B.C. Perhaps unsurprising given the focus of conservation marketing on likeable species, people‘s explicit preference toward a species was positively correlated with the willingness to pay for conservation of that species. In addition, familiarity and explicit preference significantly predicted the willingness to pay for conservation. Moreover, findings suggest that people apply the affect heuristic when judging species—species that are less liked may be perceived as riskier, and vice versa—which can further shape attitudes toward species and their conservation.   Our findings suggest that conservation organizations and conservation policies addressing endangered species in B.C. might benefit from explicit consideration of the human values, beliefs, and attitudes toward biodiversity. For example, if the province or a group needs to raise funds for the conservation of the American badger, campaigns might focus on portraying the badger in more positive terms trying to persuade people to have more positive affective 59  responses toward them. Further research evaluating people‘s preferences toward species from other taxonomic groups that have been demonstrated as less preferred (e.g., amphibians, reptiles, insects) would be interesting for understanding the relationship between implicit and explicit judgments toward species on a broader scale. In addition, another IAT task using different words for the association might be informative. It may be useful to design a new IAT task with the words that were recorded as common in the word association task to test if implicit preferences toward animals differ when different stimuli are used.    60  Chapter 4: Conclusions This thesis contributes to the growing effort to integrate methods and theories from psychology with principles and ideas from ecology and conservation biology (Browne-Nuñez & Jonker, 2008; Clayton & Brook, 2014; Manfredo et al., 2003a; Manfredo, Vaske, & Teel, 2003b; Saunders, 2003; Teel & Manfredo, 2010). Focusing on people‘s attitudes and preferences toward species at risk in B.C., this research provides insights on how psychology might influence the conservation of endangered species.   4.1 Novel application of research methods Persuasive communication and message framing methods have been used in the past to promote proenvironmental behaviors (e.g., see (Pelletier & Sharp, 2008). However, this research is the first to test these methods in the context of wildlife conservation with a particular focus on endangered species. Very few studies have used the IAT for evaluating implicit preferences toward things or beings that are relevant to environmental issues or nature protection. Some IAT experiments have examined people‘s preferences toward ―natural vs. non-natural‖ objects  (e.g., (Schultz, Shriver, Tabanico, & Khazian, 2004) or toward animal-related phobias (e.g., (Teachman, Gregg, & Woody, 2001). But although social psychology experiments have used the IAT with fine-scale categories (e.g., human races, religious groups), environmental psychology experiments considering fine-scale nature-related categories (e.g., individual species or landscapes) are missing. This research is the first to test people‘s implicit preferences and biases toward endangered species and evaluate its implications for conservation. In addition, while others have shown the non-economic motives behind the willingness to pay for biodiversity 61  conservation (see (Martín-López et al., 2007)), this research adds to that scholarship by comparing implicit vs. explicit preferences toward biodiversity.   4.2 Strengths and limitations One of the main strengths of this research was the thorough experimental design of both chapters that included pilot studies, control conditions and experiment replicates in order to validate study results. This research yielded a large amount of empirical evidence provided by large samples of participants in both laboratory and online contexts. The large amount of data collected in this thesis gives statistical robustness to research findings. Another important strength of this research was the multiple methods used to evaluate people‘s attitudes and preferences toward species at risk in B.C. (i.e., before-after-control-impact experimental design, implicit association test, quantitative surveys, and open-ended questions). The use of both quantitative and qualitative data provided a much better understanding of research results and implications.    Perhaps the main strength of this study was the collaboration with researchers from various disciplines from early stages, including research design. This thesis benefitted from marine biologists who understand the dynamics of kelp forest ecosystems, psychologists with computer modeling skills who helped code the task in Matlab and helped with experimental design, and conservation biologists and conservation social scientists who helped with question wording, research design, and research implications. This broad group of people with a wide variety of research skills enriched this study enormously.   62  An obvious limitation of this study was the somewhat uniform sample (demographically) for the experiments conducted in Chapter 2 and the lab experiments conducted in Chapter 3. Because convenience sampling was used, the results of the study cannot be generalized to specific geographical contexts, e.g., where people are directly affected by human-wildlife conflicts, or where people differ vastly in terms of socio-economic and cultural factors. Nonetheless, it is interesting to observe that even with a sample of young, well-educated people who exhibit high moral and ecological attitudes toward nature, differences of attitudes and preferences toward endangered species can be found. Especially as such people are likely to engage in conservation, join environmental organizations later on and/or be stakeholders in real-world contexts.  The null findings of the IAT may indicate that the analysis lacked power, perhaps because of the relatively low number of participants for that portion of the study (n=55). Another obvious limitation for the IAT is that its effects depend on the stimuli used as attributes. It may have been the case that the words used as attributes (i.e., good and bad words), do not entirely represent the ways people think about animals, and perhaps words like cute, adorable, dangerous, and/or wild could have yielded different results. It is, of course, possible that using different words will not have a dramatic change on IAT performance because nothing is known about the validity of this approach (i.e., using such words), or its implications, but more research may be informative.  Adding a larger and broader sample of participants crowd-sourced via Mturk validated the research results for chapter 3. However, as with many other research projects conducted online, there are always concerns about the validity of the sample. Limitations include whether Mturk participants are representative of the desired population, and concerns about the overall quality 63  of the data that respondents provide. While this research controlled for data quality by eliminating outliers and incomplete survey responses, these concerns are still legitimate regarding the generalizability to other populations.   More species from different taxonomic groups could have been tested to determine the extent to which research findings may apply to a wider range of species including less liked groups (e.g., insects). This would likely have yielded significant differences in the implicit preferences toward animals—because the four species chosen were all quite well liked—and research findings and conclusions may also have differed.  4.3 Relevance to conservation and environmental management Conservation organizations and environmental institutions are increasingly interested in including human dimensions of conservation-related efforts in order to improve conservation and environmental management practices. The methods and results of this thesis contribute to the growing demand for this type of research that can inform conservation practices.   Perhaps the most obvious application of this research is in conservation campaigns and species recovery strategies. Conservation campaigns—for the most part—struggle to get sufficient public attention. Relative to other issues of public concern, conservation problems seem to always rank lower. This might be because many people believe other issues are more important, but it could also be that conservation campaigns are not equally effective in marketing and communication as other campaigns. Messaging has been a useful tool in various contexts including public health campaigns (e.g., reducing smoking rates). Thus, conservationists might 64  benefit from awareness of these methods and how they might be used to garner support for specific actions. Moreover, understanding the baseline perceptions of a targeted audience could help tailor conservation campaigns so as to be more tuned to audience‘s beliefs and values at a specific point in time. This might aid the effectiveness of efforts to change attitudes.  Species recovery strategies could benefit from these findings by prioritizing resources and efforts to help species that would be less likely to be supported by the public (Ferraro, McIntosh, & Ospina, 2007). While recovery strategies depend on actions and decisions from institutions at various scales of government, they also depend on actions from individuals. For example, where sea otters have been reintroduced, they are exposed equally to environmental threats (e.g., predators, ocean acidification) and human threats (e.g., poaching). Evaluating peoples‘ perceptions and biases toward different species might provide a better understanding of certain human threats. Hence, species recovery strategies could be planned to predict and mitigate those threats. Lastly, if conservation organizations are interested in promoting the conservation of less liked species (e.g., bats or snakes), they might include messaging in order to change the social construction of such species. This might be done by promoting more positive attitudes and increasing people‘s familiarity with species of interest. Putting those species in common contexts such as films, kids‘ stories, currencies, as mascots of sports teams, etc., might generate that effect.  4.4 Future research directions Future research that evaluates human-wildlife conflicts could benefit by sampling participants who are directly affected by such conflicts. In the case of the WCVI, fishermen should be 65  sampled and the messaging experiment should be tested with them. In addition, the positive-negative-neutral messaging design should be tested with a broader set of species so as to evaluate the cases in which negative or positive messages might be more effective in inducing attitude-change.  Regarding the IAT, future research should have two directions. First, a different set of positive and negative words should be incorporated. This would allow the comparison of implicit preferences toward the same species between two treatments that have two word groups. Future research would benefit from this comparison because as the qualitative data indicated, people may not frequently use the words presented in this study when they think about animals. Second, species from a wider range of taxonomic groups should be used. Future research would benefit from understanding implicit preferences toward other taxonomic groups. People might process animals from the same taxonomic group (e.g., mammals) in similar ways that might account for the null results of the IAT. Lastly, future research would benefit by collaborating directly with conservation NGOs by testing this methods in real-world campaigns. This would validate results but would also provide insights for new research directions.  4.5 Closing In conclusion, the results from the two studies conducted in this thesis highlight the importance of attitudes, messaging, and preferences when designing conservation campaigns and efforts. 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The Journal of Tourism Studies, 11(2), 25–35.   80  Appendices Appendix A  Survey presented in chapter 2    81     82     83     84     85     86     87     88     89     90     91     92  93  Appendix B  Participants’ demographics of chapter 2 Variable n Percentage Ethnicity White or Caucasian 90 27.78% Korean 14 4.32% Chinese 136 41.98% Filipino 7 2.16% Pacific islander 2 0.62% Middle eastern 16 4.94% South Asian (from India, Bangladesh, Pakistan, etc) 15 4.63% Other 14 4.32% Multiracial 30 9.26% Total 324 100.00% Highest level of education completed High school or equivalent 241 74.38% Vocational/Technical school 2 0.62% College 35 10.80% Bachelor's degree 41 12.65% Professional degree (MD, JD, etc) 1 0.31% Master's degree 2 0.62% Doctoral degree 1 0.31% Other 1 0.31% Total 324 100.00% Employment (Select more than one) Student 316   Unemployed 40   Agriculture, forestry, fishing, hunting 2   Arts, entertainment, recreation 26   Design/publicity 2   Education College/University 24   Education Primary/Secondary 10   Business, marketing, administration 12   Government and public administration 1   Health Care, social assistance 17   Legal services 2   Scientific or technical services 10   Software 1   Transportation 2   94  Variable n Percentage Construction 5   Manufacturing 2   Other 28   Religious affiliation Muslim 14 4.32% Orthodox Church such as Greek or Russian Orthodox Church 3 0.93% Buddhist 20 6.17% Catholic 31 9.57% Protestant 32 9.88% Jewish 6 1.85% Hindu 3 0.93% Atheist 68 20.99% Agnostic 52 16.05% Other 95 29.32% Total 324 100.00% Annual household income Less than US$20.000 114 35.19% US $20.001-$40.000 30 6.17% US $40.001-$60.000 36 7.41% US $60.001-80.000 41 5.25% US $80.001-100.000 29 4.01% US $100.001-120.000 24 9.26% US $120.001-140.000 17 11.11% US $140.001-160.000 13 12.65% More than $160.000 20 8.95% Total 324 100.00% People in household 1 45 13.89% 2 47 14.51% 3 69 21.30% 4 103 31.79% 5 37 11.42% 6 15 4.63% 7 3 0.93% 8 4 1.23% 9 1 0.31% Total 324 100.00% Place of residence Large city or urban area 202 62.35% Rural area NOT on a farm or ranch 7 2.16% Rural area on a farm or ranch 1 0.31% 95  Variable n Percentage Small city or town 33 10.19% Suburban area 81 25.00% Total 324 100.00%     96  Appendix C  Instruction page for the MC-IAT for chapter 3 In this study, you will see 4 categories of animals. Please memorize these images of animals:  Caribou     Sea otter     American badger     Yellow-breasted chat      You will also see 4 good words and 4 bad words. Please memorize these words:  Good words: LOVE, PLEASANT, GREAT, WONDERFUL Bad words: HATE, UNPLEASANT, AWFUL, TERRIBLE  You will press the right ―I‖ key if you see Good words and images from one category. You will press the left "E" key for other images and words. If you respond incorrectly, you will a "WRONG" sign. We will start with some practice to help you get used to the task. Please classify words and images as quickly as you can while making as few mistakes as possible.   97  Appendix D  Creative commons licenses for pictures used in the MC-IAT and surveys for chapter 3  The pictures presented in this appendix were the pictures used in the IAT instruction page. Pictures were numbered from left to right as they appear in the instruction page for each species.  Picture Author License Caribou_1 Josh More  CC BY-NC-ND 2.0 Caribou_2 Amanda Graham  CC BY-NC-ND 2.0 Caribou_3 Ianqui Doodle CC BY-NC-ND 2.0 Caribou_4 Travis CC BY-NC 2.0 Sea_otter_1 Chuq von Rospach  With permission Sea_otter_2 Chuq von Rospach  With permission Sea_otter_3 Chuq von Rospach  With permission Sea_otter_4 Chuq von Rospach  With permission American_badger_1 Jon Nelson  CC BY 2.0  American_badger_2 Yathin CC BY-NC-ND 2.0 American_badger_3 Jerry Oldenetell CC BY-NC-SA 2.0 American_badger_4 Bethany Weeks  CC BY-NC-SA 2.0 Yellow_breasted_chat_1 HarmonyonPlanetEarth CC BY 2.0  Yellow_breasted_chat_2 HarmonyonPlanetEarth CC BY 2.0  Yellow_breasted_chat_3 Kelly Colgan Azar  CC BY-ND 2.0 Yellow_breasted_chat_4 Budgora CC BY-NC-ND 2.0    98  Appendix E  Survey presented for chapter 3    99     100     101     102     103     104     105     106     107     108     109     110     111     112    113  Appendix F  Participants’ demographics of study 1 in chapter 3 Variable n Percentage Ethnicity White or Caucasian 12 21.82% Black or African-American 1 1.82% Hispanic or Latino (includes Mexican, Central American and South American) 2 3.64% Korean 8 14.55% Chinese 23 41.82% Filipino 1 1.82% Middle eastern 1 1.82% South Asian (from India, Bangladesh, Pakistan, etc) 2 3.64% Other 1 1.82% Multiracial 4 7.27% Total 55 100.00% Highest level of education completed High school or equivalent 43 78.18% College 1 1.82% Bachelor's degree 9 16.36% Professional degree (MD, JD, etc) 1 1.82% Master's degree 1 1.82% Total 55 100.00% Employment (Select more than one) Student 55   Unemployed 18   Agriculture, forestry, fishing, hunting 1   Arts, entertainment, recreation 3   Education College/University 3   Education Primary/Secondary 1   Finance and insurance 1   Business, marketing, administration 1   Health Care, social assistance 3   Scientific or technical services 2   Transportation 1   Other 5   Religious affiliation Muslim 2 3.64% 114  Variable n Percentage Orthodox Church such as Greek or Russian Orthodox Church 1 1.82% Buddhist 3 5.45% Catholic 5 9.09% Protestant 3 5.45% Jewish 3 5.45% Atheist 13 23.64% Agnostic 9 16.36% Other 16 29.09% Total 55 100.00% Annual household income Less than US$20.000 15 27.27% US $20.001-$40.000 8 14.55% US $40.001-$60.000 3 5.45% US $60.001-80.000 9 16.36% US $80.001-100.000 10 18.18% US $100.001-120.000 2 3.64% US $120.001-140.000 1 1.82% US $140.001-160.000 3 5.45% More than $160.000 4 7.27% Total 55 100.00% People in household 1 6 10.91% 2 7 12.73% 3 12 21.82% 4 21 38.18% 5 7 12.73% 6 2 3.64% Total 55 100.00% Place of residence Large city or urban area 34 61.82% Rural area NOT on a farm or ranch 15 27.27% Rural area on a farm or ranch 6 10.91% Total 55 100.00%    115  Appendix G  Participants’ demographics of study 2 in chapter 3 Variable n Percentage Ethnicity White or Caucasian 287 61.99% Black or African-American 29 6.26% Hispanic or Latino (includes Mexican, Central American and South American) 14 3.02% Korean 5 1.08% Japanese 1 0.22% Chinese 2 0.43% Filipino 1 0.22% Pacific islander 1 0.22% Middle eastern 1 0.22% South Asian (from India, Bangladesh, Pakistan, etc) 98 21.17% Other 5 1.08% Multiracial 19 4.10% Total 463 100.00% Highest level of education completed High school or equivalent 80 17.28% Vocational/Technical school 23 4.97% College 83 17.93% Bachelor's degree 185 39.96% Professional degree (MD, JD, etc) 19 4.10% Master's degree 62 13.39% Doctoral degree 8 1.73% Other 3 0.65% Total 463 100.00% Employment (Select more than one) Student 47   Unemployed 76   Agriculture, forestry, fishing, hunting 12   Arts, entertainment, recreation 31   Design/publicity 9   Education College/University 26   Education Primary/Secondary 24   Finance and insurance 32   116  Variable n Percentage Business, marketing, administration 54   Government and public administration 16   Health Care, social assistance 34   Legal services 7   Scientific or technical services 22   Software 40   Transportation 16   Construction 16   Manufacturing 16   Other 72   Religious affiliation Mormon 4 0.86% Muslim 14 3.02% Orthodox Church such as Greek or Russian Orthodox Church 3 0.65% Buddhist 6 1.30% Catholic 75 16.20% Protestant 86 18.57% Jewish 7 1.51% Jehovah's Witness 5 1.08% Hindu 72 15.55% Atheist 57 12.31% Agnostic 83 17.93% Other 51 11.02% Total 463 100.00% Annual household income Less than US$20.000 127 27.43% US $20.001-$40.000 122 26.35% US $40.001-$60.000 86 18.57% US $60.001-80.000 51 11.02% US $80.001-100.000 37 7.99% US $100.001-120.000 16 3.46% US $120.001-140.000 11 2.38% US $140.001-160.000 4 0.86% More than $160.000 9 1.94% Total 463 100.00%  117  Variable n Percentage People in household 1 95 20.52% 2 111 23.97% 3 106 22.89% 4 73 15.77% 5 54 11.66% 6 17 3.67% 7 3 0.65% 8 3 0.65% 11 1 0.22% Total 463 100.00% Place of residence Large city or urban area 145 31.32% Rural area NOT on a farm or ranch 132 28.51% Rural area on a farm or ranch 126 27.21% Small city or town 22 4.75% Suburban area 38 8.21% Total 463 100.00%    

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