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Application of a land use planning decision support tool in a public participatory process for sustainable… Cavill, Jacqueline 2008

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APPLICATION OF A LAND USE PLANNING DECISION SUPPORT TOOL IN A PUBLIC PARTICIPATORY PROCESS FOR SUSTAINABLE FOREST MANAGEMENT  by JACQUELINE IRENE CAVILL B.S.F., University of British Columbia, 2003  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA December 2007 © Jacqueline Irene Cavill, 2007  ABSTRACT Persistent conflicts between stakeholders and complex tradeoffs among forest values have created a difficult decision environment for sustainable forest management. Tools developed for decision support in land use planning are essential for managing these challenges. This research study is an interactive assessment of a land use planning Decision Support Tool (DST) in the Invermere Timber Supply Area (TSA), located in the East Kootenay area of British Columbia. The aim of this study is to explore whether stakeholders' initial stated preferences change and whether trade-offs are made between various forest values upon observation of a long-term forecast of these values using a DST. Representatives from various stakeholder groups in the area were assembled for individual sessions to interact with the multi-criteria DST. Participants were required to state their preferences for six forest values using a weighting scheme. The DST developed an output for each forest value based on the participants' preferences. Upon review of the DST output, the participant had the opportunity to alter their initial preferences iteratively until a desirable output was found. The results indicate that participants' preferences changed after reviewing the DST outputs and that participants are willing to make trade-offs between various forest values using a DST to find a desirable solution. However, the preference order of the forest values changed only slightly from the participants initial to preferred scenarios; instead participants made drastic changes to the weighting of each value to find a desirable output. Participants also stated their willingness to use DSTs for land use planning decision-making, although underlying assumptions built into the model must be improved before stakeholders can trust the tool as an aid for decision-making. Studies such as this can further the development of DSTs to help find desirable decisions for sustainable resource management and to help create a productive and engaging process.  ii  TABLE OF CONTENTS ABSTRACT ^ iii  TABLE OF CONTENTS ^ LIST OF TABLES ^ LIST OF FIGURES ^ ACKNOWLEDGEMENTS ^  vi viii  DEDICATION ^  ix  1.0 GENERAL INTRODUCTION ^ 2.0 LITERATURE REVIEW ^  5  2.1 INTRODUCTION ^ 2.2 PUBLIC PARTICIPATORY PROCESSES IN SUSTAINABLE FOREST MANAGEMENT ^ 2.3 PUBLIC PREFERENCES ^ 2.4 TRADE-OFF ANALYSIS ^ 2.5 DECISION SUPPORT TOOLS ^ 2.5.1 Background^  5 7 10 12 16  16 17 21 24  2.5.2 Types of Decision Support Tools ^ 2.5.3 Application of Multi-Criteria Decision Support Tools in Forest Planning ^ 2.6 CONCLUSION ^  3.0 METHODS ^  26  3.1 INVERMERE TIMBER SUPPLY AREA ^  26  3.1.1 Dunbar/Templeton Landscape Unit ^ 31 3.2 THE MULTI-CRITERIA DECISION SUPPORT TOOL FOR SUSTAINABLE FOREST MANAGEMENT ^ 33 3.2.1 Alterations to the Multi-Criteria Decision Support Tool ^ 3.2.2 The Forest Indicators in the Decision Support Tool ^ 3.3 THE DECISION SUPPORT TOOL INTERFACE ^ 3.4 RESEARCH DESIGN ^ 3.5 QUESTIONNAIRE DESIGN ^ 3.6 SAMPLING - PARTICIPANTS ^ 3.7 SESSION METHODS ^ 3.8 DATA ANALYSIS ^  4.0 RESULTS ^  35 37 38 41 41 43 46 48  51  4.1 GENERAL OBSERVATIONS FROM SESSIONS ^ 4.2 INITIAL FOREST VALUE PREFERENCES AND OPINIONS OF DECISION SUPPORT TOOLS ^ 4.3 THE OUTPUT SUMMARY FROM THE DECISION SUPPORT TOOL EXERCISE ^ 4.4 PREFERENCE IMPACTS AND ASSESSMENT OF THE DECISION SUPPORT TOOL ^ 4.5 LINKAGES AND RELATIONSHIPS BETWEEN ALL MODES OF DATA COLLECTION ^  5.0 DISCUSSION ^ 5.1 FOREST VALUE PREFERENCES AND TRADE-OFFS ^  5.1.1 Preference Changes ^ 5.1.2 No Preference Changes ^  5.1.3 DST Preference Scenarios ^ 5.2 ASSESSMENT OF THE DECISION SUPPORT TOOL ^ 5.3 LIMITATIONS OF THIS STUDY ^ 5.4 FUTURE RESEARCH ^  6.0 CONCLUSION ^  51 52 58 67 74  78 78  78 80 82 83 87 89  91  REFERENCES ^  94  APPENDICES ^  100  APPENDIX 1. RED- AND BLUE-LISTED SPECIES WITH THE POTENTIAL TO OCCUR IN THE INVERMERE TIMBER SUPPLY AREA (TSA) ^ APPENDIX 2. QUESTIONNAIRE 1 ^ APPENDIX 3. QUESTIONNAIRE 2 ^ APPENDIX 4. INTERPOLATION SCHEME FOR EACH INDICATOR ^ APPENDIX 5. PARTICIPANTS MAIN CONNECTION TO THE DUNBAR/TEMPLETON LU ^ APPENDIX 6. PARTICIPANTS ACTIVITIES IN THE DUNBAR/TEMPLETON LU ^ APPENDIX 7. TRADE-OFFS BETWEEN INDICATORS MADE BY EACH STAKEHOLDER GROUP. ^ APPENDIX 8. UBC RESEARCH ETHICS BOARD CERTIFICATE OF APPROVAL ^  100 101 108 112 118 119 120 120  iv  LIST OF TABLES  TABLE 1. FUNCTIONS AND LIMITATIONS OF COMMON PUBLIC PARTICIPATORY PROCESSES. ^  9  TABLE 2. A LIST OF THE UNGULATE, LARGE MAMMAL, AND SMALL FURBEARER SPECIES FOUND IN THE INVERMERE TSA. ^ 31 TABLE 3. THE CRITERIA AND INDICATORS APPLIED IN THE PLANNING MODEL ^  35  TABLE 4. SUMMARY OF PARTICIPANT'S PRIORITIES TOWARDS RESOURCE VALUES IN THE INVERMERE TSA. ^  53  TABLE 5. PROBLEMS WITH DECISION SUPPORT TOOLS ^  54  TABLE  6. REQUIREMENTS TO FEEL COMFORTABLE WITH THE DST ^ 55  TABLE 7. PARTICIPANTS RELATIONSHIP WITH THE STUDY AREA ^  56  8. PARTICIPANTS CONCERNS FOR FOREST VALUES IN THE DUNBAR/TEMPLETON LU ^ 57  TABLE  TABLE  9. EDUCATION LEVEL OF PARTICIPANTS ^  58  TABLE 10. THE NUMBER OF SCENARIOS CONDUCTED BY EACH STAKEHOLDER GROUP ^  58  TABLE 11. THE PREFERRED SCENARIO CHOSEN BY EACH STAKEHOLDER GROUP ^  59  TABLE 12. CHANGE IN PREFERENCES BY STAKEHOLDER GROUPS AFTER USING THE  DST ^ TABLE  68  13. DEGREE OF PREFERENCE CHANGE AS A RESULT OF USING THE DST ^ 68  TABLE 14. FOREST GROUP RESPONSES: INDICATORS CONTRIBUTING TO CHANGES IN PARTICIPANTS' INITIAL PREFERENCES. ^ 70 TABLE 15. ASSESSMENT OF THE  DST ^  TABLE 16. IMPROVEMENTS TO THE DECISION SUPPORT TOOL ACCORDING TO THE STAKEHOLDER GROUPS ^  71  73  LIST OF FIGURES  FIGURE 1. INVERMERE TIMBER SUPPLY AREA ^  27  FIGURE 2. SUMMARY OF THE LAND BASE ^  29  FIGURE  3. THLB  FIGURE  4.  THE LOCATION OF THE DUNBAR/TEMPLETON  FIGURE  5.  THE DECISION SUPPORT TOOL INTERFACE ^  AREA BY AGE CLASS AND MAJOR SPECIES ^  30  LU IN THE INVERMERE TSA ^ 32 39  FIGURE 6. AN EXAMPLE OF THE INTERFACE OUTPUT AFTER FIVE COMPLETED SCENARIOS. ^  49  FIGURE 7A. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: PROFIT ^  59  FIGURE 7B. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: EMPLOYMENT ^  60  FIGURE 7C. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: RECREATION ^  60  FIGURE 7D. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: ECOSYSTEMS AT RISK ^  59  FIGURE 7E. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: VISUAL QUALITY ^  61  FIGURE 7F. PREFERENCE CHANGES BETWEEN THE FIRST AND PREFERRED SCENARIOS: DOMESTIC WATERSHED ^  61  FIGURE 8A. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE COLLECTIVE GROUP OF PARTICIPANTS ^  63  FIGURE 8B. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE INDUSTRY STAKEHOLDERS ^  64  FIGURE 8C. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE ENGO STAKEHOLDERS ^  64  FIGURE 8D. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE GOVERNMENT STAKEHOLDERS ^  65  FIGURE 8E. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE RECREATION STAKEHOLDERS ^  66  vi  FIGURE 8F. THE AVERAGE CHANGE IN POINTS FROM THE INITIAL SCENARIO TO THE PREFERRED SCENARIO FOR THE PRIVATE PROPERTY STAKEHOLDERS ^  67  FIGURE 9. CORRELATION BETWEEN THE NUMBER OF SCENARIOS AND THE SATISFACTION LEVEL ^  77  vi i  ACKNOWLEDGEMENTS I would like to thank my graduate supervisor, Dr. Thomas Maness for his support, guidance, and good humour throughout my graduate years. I would also like to express my appreciation to my committee members, Dr. Robert Kozak and Dr. Michael Meitner, for their contribution and advice, and my external examiner Dr. Stephen Sheppard for taking an interest in this project. Thank you also to Andre Schuetz for helping with the technical aspects of this project and the participants who offered their valuable time and input to make this study possible. I am very grateful to my parents for their unwavering support throughout my academic journey. Finally, I would also like to acknowledge the other students and staff in my research group, especially Katie Maness for her encouragement and friendship over my graduate years.  viii  DEDICATION  To my Nana, Irene Scoble  ix  1.0 GENERAL INTRODUCTION Forestry companies in British Columbia are currently implementing forest certification and ecosystem based management. Forest certification implemented by an independent third party (e.g. Forest Stewardship Council 1996, Canadian Standards Association Z809 2002) provides a sustainable forest management guarantee on wood and paper products. The purpose of ecosystem-based management is to create healthy ecosystems and healthy human communities (Coast Information Team 2001). These emerging concepts require incorporating public participation in the development of forest management plans.  Sustainable forest management requires balancing the ecological, economic, and social values over scale and time. However, this balancing act often produces conflicting management objectives. Stakeholders and the general public are routinely asked to state their preferences for various forest management alternatives; often causing conflict between interest groups. To reach resolution stakeholders may be required to make complex trade-offs between different forest values.  Complex trade-offs between forest values are very difficult for stakeholders to evaluate, and research has shown that there are a variety of problems in obtaining valid information in these processes (Gregory 2002). Shindler (2000) found that stakeholders' preferences can change, or evolve, when they are able to see the planning outcome that results from their stated preferences. Tradeoffs are often explored using simulation models to forecast potential outcomes of various forest management alternatives. Therefore, a land use planning model may be an effective aid for helping stakeholders evaluate trade-offs during the decision-making process. Scientists are using computer modeling to develop Decision Support Tools (DST) to assist with forest management decision-making (Kangas et at 2001; de Steiguer et al 2003, Mendoza and Sprouse 1989, Kangas  1  1992, Kuusipalo and Kangas 1994, Murray and von Gadow 1991). The development and use of these models for sustainable forest management has been popular in the scientific and forestry community over the last decade. However, the application of DSTs for decision-making in a public participatory environment has not been actively studied to date.  This research project involves an interactive assessment of the land use planning process in the Invermere TSA, located in the East Kootenay area of BC. The Invermere TSA has a total population of 9,000 and includes the communities of Invermere, Edgewater, Canal Flats, and Windermere. The permanent population is augmented by 40,000 part-time residents arriving primarily from Alberta with the purpose of enjoying the many recreational opportunities that this area offers (Columbia Valley Tourism 2005). There are two major forest licensees that operate in the TSA, including Canadian Forest Products located in Radium Hot Springs and Tembec Forest Products located in Canal Flats. A unique dynamic exists in the TSA due to competition for natural resources between the forest industry, a major employer of the residents in the East Kootenays, and the tourism industry, a major economic generator for the communities in the TSA. As a result, land use planning and forest management can create conflicts of interest and a complex decision environment. A Multi-Criteria Decision-making (MCDM) model has been developed for this area and will be used as a DST in this study (Maness and Farrell 2004). The DST is based on desired outcomes for specific forest values and thresholds that determine the minimum acceptable outcome for each value.  The purpose of this study is to explore the application of a multi-criteria DST in a public participatory process and to examine its affect on forest stakeholders' stated preferences. The objectives of this study are: 1) to determine how preferences change when stakeholders are directly involved in the planning process using a Decision Support Tool;  2  2) to explore whether stakeholders will make trade-offs between forest values using a Decision Support Tool; and 3) to explore the usability of the Multi-Criteria Decision Making model in a public participatory setting. Most literature focuses on eliciting stakeholders' preferences for various forest values, but the application of these preferences in forest management decisionmaking is minimal. Several studies using land use planning models to explore temporal scales have been conducted, however, none address the problem of whether stakeholders are willing to make trade-offs over time (Sheppard 2005). A widely accepted method could help to ensure that stakeholders can accurately measure trade-offs and forecast potential outcomes from trade-offs made.  Quantitative trade-off analyses can be used to forecast outcomes, but requires site-specific data and model development (Antle et al. 2002). Multi-criteria DSTs are effective in the decision-making process because alternatives are provided and outcomes are forecasted. Although multi-criteria DSTs are effective tools for public involvement in forest resource planning, these tools have also faced much criticism. Further research is required to determine whether multi-criteria DSTs are useful tools for incorporating the public's preferences for forestry-related applications and whether these tools will help stakeholders evaluate trade-offs between forest values.  This thesis is organized into six chapters, including this introduction chapter. Chapter Two provides the necessary background and a review of the literature for this area of study. The literature review provides background information and examples from other studies on four key areas contributing to decision-making in forest resources management, including: public participatory processes, stakeholder preferences, trade-off analysis, and DSTs. The methods used to conduct the study and collect the data are described in Chapter Three. The methods in this study were consistent with exploratory research and applied structured interviews with individual stakeholders. Chapter Four outlines the data  3  analyses conducted and provides the results from this study. This chapter begins with general observations, followed by data analyses related to the results gathered from the two questionnaires and the DST exercise. Chapter Five provides a discussion on the significance of the results, limitations of this study, and potential for future research opportunities. Chapter Six provides some concluding remarks.  4  2.0 LITERATURE REVIEW 2.1 Introduction The majority of land in British Columbia is publicly owned (95%). For this reason public input is an important addition to natural resource management decisions in the province. Public involvement in land use decisions has not been practiced fervidly until recent decades. The Canadian Council of Forest Ministers (1993) and the Montreal Process (1995) each initiated a separate set of criteria and indicators to measure the progress towards sustainable forest management. The Canadian Council of Forest Ministers developed the Canadian Criteria and Indicators Framework to establish directions for stewardship and to measure Canada's progress in sustainable forest management. The Montreal Process was formed in Switzerland in 1994 to create and implement a set of internationally accepted criteria and indicators for the sustainable management of temperate and boreal forests. These initiatives led to the incorporation of forest values in land use planning and elevated the importance of public and stakeholder involvement in the decision-making process.  Improvements in public participation were required to ensure the effectiveness and efficiency of decision-making in land use planning. The Commission on Resources and Environment (CORE), developed in 1996, acted as a neutral body to direct regional land use planning and resource allocation in British Columbia. Once CORE had established broad plans for four regions of the Province, some Land and Resource Management Plans (LRMP) were implemented and presently they continue to be developed across the province. The goal of the LRMP process is to foster an inclusive, co-operative process for decision-making. These initiatives provided a platform for public participation and helped develop a formalized process to implement collaborative decisionmaking.  5  Forestry companies in British Columbia are currently implementing forest certification and ecosystem based management. These emerging concepts require incorporating public participation in the development of forest management plans. Stakeholders and the general public are routinely asked to state their preferences for various types of competing forest values; often causing conflict between interest groups. To reach resolution, stakeholders may be required to make complex trade-offs between different forest values. Tradeoffs are often explored using simulation models to forecast potential outcomes of various forest management alternatives. Application of various decision support tools are currently a popular area for research examining the possibilities of various tools as an effective aid for helping stakeholders make trade-offs during the decision-making process.  This review provides background on four key areas contributing to decisionmaking in forest resource management: public participatory processes, stakeholder preferences, trade-off analyses, and decision support tools. Section 2.2 describes forms of public participation used in decision-making and explores the benefits and current limitations of involving the public. Section 2.3 provides details on the issues associated with establishing and eliciting public preferences. Section 2.4 explores trade-off analyses and difficulties with certain trade-offs. This section illustrates different methods for conducting trade-off analysis with the public, such as Contingent Valuation, structured decisionmaking, and other weighting techniques. Section 2.5 describes different Decision Support Tool (DST) methods used in forest resource planning. This section also outlines the benefits and limitations of multi-criteria Decision Support Tools in forest management decisions and explores applications used in forestry. Concluding remarks are provided in Section 2.6.  6  2.2 Public Participatory Processes in Sustainable Forest Management  The emergence of forest certification, environmental awareness, and importance of non-timber forest products has increased the need for public participation. As a result, public participation has become an important step in forest management decision-making. The public can be involved in decision-making in many ways and various types of processes can be used. However, this wide range of applications and techniques is not conducive to an efficient and well-structured public participatory process; thus, the lack of direction and boundaries cause issues to arise.  Public participatory processes can assume many functions in forest decisionmaking. For example, public involvement allows for the consideration of a wide range of values across economic, ecological, and social spectrums and allows for the incorporation of unique local knowledge. Furthermore, public participation permits decision-makers to choose a socially acceptable management direction and to set boundaries on management practices and policies. Public participation has also resulted in developing an environment for mutual learning and resolving conflicts between stakeholders (Duinker 1998). The introduction of certification schemes, criteria and indicators, and ecosystem-based management have all contributed to involving the public and stakeholders in land use decisions. To date, the public has been involved in various decision-making processes related to policy frameworks, certification schemes, legislation and regulations, land use strategies and allocation, environmental assessments, forest management plans, and community forests (Duinker 1998).  According to Konisky and Beierle (2001), different types of processes involve different participants, intended outcomes, and decision-making authorities. The structure of the process ranges from public access in open house forums to participant selection in which stakeholders must meet certain criteria to participate. The intended outcome of a participatory process may simply include  7  open communication to foster mutual education or participants may be involved in more active roles of providing recommendations or final solutions. Furthermore, the decision-making authority varies between types of processes ranging from no authority, to an advisory position, to shared decision-making power. Thus, depending on the degree of involvement and type of outcome required, there are many different ways of structuring public processes. Table 1 lists the functions and limitations of common public involvement processes.  The lack of direction and regulations surrounding public participation results in some key failings and limitations in the process. For example, temporal and budgetary constraints, uncertainty, and loss of control are all significant disadvantages related to involving the public (Blouin 1998). Firstly, time and budget constraints limit the ability of participants to engage effectively and equitably during the process (Hamersley Chambers, and Beckley 2003) and, due to arising conflicts and insufficient facilitation, consensus among stakeholders is time-consuming or is never achieved (Gregory 2002). Secondly, participants may express uncertainty in the process and in the decision-making authority. The public is cognizant of insufficient and biased information and inadequate comparisons of management alternatives, and thus, distrust is built in to the process. Thirdly, widespread dissatisfaction exists with respect to the equity of stakeholders input in land use decisions due to a lack of transparency between decision-making and implementation of land use plans (Gregory 2000). Also, decision-makers often do not conduct the public participatory process and may not even be involved in the discussion; consequently, there is a lack of control to direct participants to produce an operable, measurable, and effective management plan. Public process outcomes are usually broad and informal for forest decision-makers to translate to detailed, spatially explicit decisions (Martin  et al. 2000). Thus, experts often dominate the process to fill in the information gaps of public views and ensure that the local information is operational (Kakoyannis et al. 2001).  8  Table 1. Functions and Limitations of Common Public Participatory Processes. Source: Environmental Protection Division: Public Consultation Guide (1991 Public Process Function Limitations Public meetings - provide information to large - not suitable for consensus numbers of people and allow or discussion among for feedback on the issue participants Open house - an informal method for - must be well-advertised to disseminating information at ensure satisfactory the public's leisure attendance Workshops - a structured forum where - can result in individuals or groups are confrontations invited to discuss a common issue and build consensus -usually conducted by a facilitator and involves a small number of participants Public committees - obtain insight into different - must include stakeholders stakeholders interests representing broad concerning specific proposals interests, but consist of - build consensus between members willing to work differing views towards agreement Public discussion - there are two types: position paper paper examines a proposed policy; and options paper examines the alternatives Toll free telephone - allow the public to easily - no opportunity for line provide feedback or ask discussion amongst a questions individually group of participants Targeted briefing - closed sessions that occur - important stakeholders when the decision-making may not be included in the authority presents information discussion to a specific group Public seminars - formal events designed to - no specific targeted promote the exchange of audience; some people information on broad issues may dominate the process for their own agenda Site visits - provides the opportunity to visualize management action or issues on the ground Focus groups - used to monitor the public's - not used for decisionpotential response to a making or consensus proposed plan by selecting participants to meet and discuss the proposal  9  However, research shows that participants agree on the key principles of a successful public participatory process. Tuler and Webler (1999) used grounded theory and case study interviews to inductively identify principles of "good" processes from participants. The participants identified the following key principles for a successful public participatory process: access to the process and to information; power to influence process and outcomes; constructive interactions are promoted; adequate analysis is conducted; and future processes are possible. McCool and Guthrie (2001) conducted a similar study after interviewing forty two participants regarding the characteristics of a successful participatory process. Their results reflected similar dimensions of success, including: writing and implementing a plan; fair representation of participants interests; relationship building; impressing accountability upon participants; and gaining social and political acceptability of decisions made during the process.  Public participation can contribute to the overall planning process in forest management. According to Blouin (1998), involving the public in decisionmaking increases support and credibility of the process, potentially reduces conflict, and educates the public. Increasing the support and credibility of decisions ensures that the public is aware of the rationale behind decisionmaking and are able to provide their own opinions and local knowledge. Participation provides a forum for learning and disseminating information for all parties involved in the process. Furthermore, these processes can reduce conflict between stakeholders through inclusive decision-making. This type of decision-making process can lead to solid decisions through early communication of differing opinions and interests.  2.3 Public Preferences Conflicts among stakeholder groups arise due to differing preferences for various forest values. According to Martin et al. (2000), the fundamental basis of conflict among stakeholders is differing preferences in three areas: 1) allocation of land  10  between commodity and non-commodity; 2) allocation of land between motorized and non-motorized uses; and 3) importance of multiple use management and ecosystem management. More detailed knowledge of stakeholder preferences may help to resolve conflicts more effectively and efficiently. The study conducted by Martin et al. (2000) investigated a participatory process that incorporated the public's preferences and interests into the decision-making environment. Three different stakeholder groups were asked to rank a set of management alternatives in order to elicit preferences from each participant. The authors found that several stakeholders chose different orders for ranking the alternative management plans. Improved understanding of stakeholders' differing interests provides the opportunity for bridging gaps between these interests. This study indicates that information gathered using this method can be applied to help minimize conflict during land use planning processes.  Research incorporating stakeholder values explicitly into the decision-making process is lacking. Most literature focuses on eliciting stakeholders' preferences for various forest values, but the application of these preferences in forest management decision-making is minimal. Ananda and Herath (2003) is an exception wherein the effective incorporation of value preferences into decision making processes is investigated. Multi-Attribute Value Theory (MAVT) was applied to elicit and analyze stakeholder values in regional forest planning in Northeast Victoria, Australia from five stakeholder groups (timber, environment, farmers, recreation, and tour guides). This study elicited preferences through face-to-face detailed surveys. Single attribute value functions were developed for timber production, recreation, and old growth conservation by analyzing the survey data. The multi-attribute value functions were developed by aggregating the single attribute value functions; these functions were used to assess the forest management alternatives. At this point, the respondents applied their preferences to rank the three options. The researchers' quantified key trade-offs in the area from information collected with the MAVT approach, thus, providing an effective method for eliciting public values and evaluating management  11  options. Preferences are derived from people's interests in the land base and vary amongst stakeholders, causing conflict between competing interests. As demonstrated in the study conducted by Ananda and Herath (2003), it is possible to reach decisions through trade-offs derived from stakeholders' preferences.  2.4 Trade-off Analysis  Limits on resources and people's differing views and beliefs necessitates tradeoffs between forest values. A decision-maker can make a trade-off between two or more forest values by choosing to increase one value in exchange for a decrease in another desirable value. Trade-off analysis is defined as a method of eliciting preferences from stakeholders on alternative management actions to guide the decision-making process through socially acceptable thresholds (Brown et al. 2001, Sheppard et al. 2003). Operationally, decision-makers are provided with information on interrelationships between indicators in the system studied; decision-makers then use their own subjective valuations to decide how to balance or trade-off various competing outcomes (Antle et al. 2002). Trade-off analysis requires a set of criteria. Indicators or weights are used to operationalize the criteria. Indicators make criteria operational by providing a measure for sustainability by using thresholds and targets. Thresholds represent a minimum constraint that cannot be violated, the target is the desired indicator level; the trade-off occurs between the threshold and target of each indicator (Maness 2007). Weights can also make criteria operational. This method often requires the use of a decision model. The model generates information on alternative outcomes across the set of criteria. Stakeholders assign weights to these alternatives according to their priorities (Brown et al. 2001).  Difficult trade-offs occur during the development of forest management plans. According to Gregory (2002), value trade-offs in environmental decision-making are difficult for community stakeholders for the following reasons:  12  •  multiple value measures;  •  uncertainty of impacts;  •  unfamiliar evaluation;  •  balancing effort and accuracy;  •  incorporating feelings; and  •  learning over time;  It is difficult for stakeholders to compare different forest values, particularly when the measurement for each value is different (e.g. dollars, hectares, population). Stakeholders also have a difficult time assessing the potential impacts and outcomes for making a particular trade-off because accurate forecasts are challenging and experts tend to describe outcomes too broadly. Furthermore, participants usually have little experience with land use planning decisions, thus, the process is unfamiliar, daunting, and time consuming. Emotions such as anger from past decisions or frustration during the process can be difficult to incorporate in the process, but require consideration. Lastly, adaptive learning is important to the process because stakeholders may change their beliefs or opinions during the process (Gregory 2002).  Involving the public in trade-off analysis is important because it incorporates local knowledge and diverse priorities, and increases stakeholders' confidence in the planning process (Antle et al. 2002). Various researchers have studied contingent valuation, structured decision-making, and various weighting techniques by conducting trade-off analysis with the public and stakeholders (Niemi and Whitelaw 1999, Carson 2000, Gregory 2000, Hammond et al. 1999, and Keeney and McDaniels 1999, Sheppard et al. 2003). However, few of these studies have been applied in practice for sustainable forest management and many are regarded as controversial.  Contingent valuation measures trade-offs by attempting to establish monetary worth of non-market forest values. If the right to use the resource is not possessed, then the value an individual places on a specific use is the amount  13  the individual is willing to pay; if the right is possessed, then the value is the amount the person is willing to accept for compensation of the use (Niemi and Whitelaw 1999, Carson 2000). Contingent valuation is a controversial method because many natural resources have a passive use; in other words, the consumer does not physically use the good to receive utility, and thus, subjective valuation is applied in these circumstances. Furthermore, using monetary values to measure natural resources is of concern due to technical and ethical considerations (Carson 2000). Individuals may lie about the amount that they are willing to pay or accept in order to benefit and protect the forest value. Individuals have the ability to lie because a real transaction is not occurring; thus, people are not accountable for their answer.  A structured decision-making process allows stakeholders to work through tradeoffs by balancing competing objectives to facilitate an informed choice (Gregory 2000, Hammond et al. 1999, and Keeney and McDaniels 1999). Gregory (2000) conducted a study in Tillamook Bay, Oregon with the goal of developing a scientific, community-based management plan for the Tillamook Bay watershed. A structured decision-making approach was applied using the following fundamental principles: framing the decision, defining key objectives, establishing alternatives, identifying consequences, and clarifying trade-offs. The Tillamook Bay decision was made, firstly, by requiring participants to form objectives in the context of this decision and not on their respective interests and positions. Participants were required to establish and assess preferred alternatives by creating an 'objectives by alternatives' matrix. This matrix allows easy tracking of the potential consequences (benefits and costs) of each alternative when compared to each objective. Alternatives that are not able to satisfy the set of objectives are eliminated. The pared matrix recognizes key trade-offs amongst competing alternatives demonstrating the costs and benefits between them, further helping participants to make an informed decision. Gregory (2000) found that a structured decision-making approach can lead to a broadly acceptable agreement. Structured decision-making differs from consensus solutions  14  because the techniques used in consensus shift away from participants' divergent views to achieve common ground, whereas the structured decision process directs stakeholders to evaluate trade-offs in an effort to balance competing objectives and interests in order to assist an informed choice (Gregory 2000).  Using weightings to measure a set of criteria is another method for evaluating trade-offs. Sheppard et al. (2003) reviewed four procedures for determining stakeholder values including: choice experiments; approval rating; ranking and weighting; and contingent valuation methods. Sheppard et al. (2003) applied these methods in the Lemon Landscape Unit in the Slocan Valley of British Columbia. The purpose of this project was to test stakeholders' willingness to partake in trade-off games. Furthermore, the study evaluated the potential of these trade-off methods to inform the decision-maker regarding where and to what extent the stakeholders are willing to make trade-offs between criteria. The results demonstrated that the public is willing to participate in trade-off games with different levels of confidence. For example, participants found that tradeoffs between social (e.g. visual quality) and economic criteria (e.g. timber supply) were made with increased confidence than games requiring choices between biological criteria (e.g. Mule Deer Winter Range) and timber supply. The study also found that these methods could be adapted in further studies to aid in SFM decision support.  According to Shindler (2000), public acceptance of forest decision-making will increase when managers provide opportunities for the public to understand the rationale and potential outcomes of forest practices. Gregory (2002) believes that most public involvement initiatives do not sufficiently assist participants in evaluating interests, assessing impacts, and measuring trade-offs. Increased direction through facilitation and implementation of structured decision-making is important to explicitly deal with trade-offs. It is important that a widely accepted method is developed to ensure that stakeholders can accurately measure trade-  15  offs and forecast potential outcomes from trade-offs made. Trade-off analyses can be used to forecast outcomes by describing the behaviour and performance of a system across space and time, but requires site-specific data and model development (Antle et al. 2002).  2.5 Decision Support Tools  2.5.1 Background  The public's interest in a broad spectrum of forest values requires natural resource managers to consider a wide range of criteria at different spatial and temporal scales. Spatial differences and time lags reduce insight into the outcomes of management actions, thus creating uncertainty in the planning process. Furthermore, humans are constrained by bias and systematic errors when structuring multiple use forest resource problems. Complex decision problems and scales, uncertainty, and human constraints all contribute to the desire for applying decision support tools in the decision-making process. DST are computerized systems that amalgamate complex databases with operational research models, graphical and tabular displays, and expert input to assist decision-making and to optimize between multiple objectives (Lexer and Brooks 2005, Varma et al 2000). DSTs are intended to only provide information regarding potential forecasts and outcomes; they are not intended to make decisions or provide solutions.  The demand for DST is growing. These tools vary from general stand level growth and yield models, and landscape level wildlife habitat models to a combination of computerized models and multi-criteria decision-making (MCDM) techniques to simulate management scenarios (Lexer and Brooks 2005). DSTs are used for a broad range of purposes. For example, some methods analyze uncertainty and risk, whereas other tools manage conflict or account for poor/incomplete information. Some methods have been modified for application  16  in forest management planning (Kangas and Kangas 2005). A general classification of DST, suggested by Belton and Stewart (2002), includes: •  value measurement models;  •  goal, aspiration, or reference level models; and  •  outranking models.  Value measurement models use numerical scores to represent the degree to which one decision option may be preferred to another. An example of a value measurement model is the Analytic Hierarchy Process (AHP). Goal, aspiration, or reference level models establish desirable or satisfactory levels of achievement for each criterion. Goal Programming (GP) is an example of this type of model. Outranking models use alternative courses of action to make pairwise comparisons. The most frequently used outranking models are ELECTRE and PROMETHEE. The most widely used multi-criteria methods include AHP, GP and MCDM.  2.5.2 Types of Decision Support Tools  Analytic Hierarchy Process AHP, designed by Saaty (1980), is a mathematical approach for analyzing complex decision problems with multiple criteria. AHP involves three main steps. Firstly, the problem is structured into a hierarchical set of goals and criteria. Commonly, the hierarchy has an overarching goal with a number of alternatives which are compared to a set of criteria (Schmoldt et al. 2001). Secondly, the criteria are evaluated using pair-wise comparisons based on an appropriate measure with respect to the goal; the measure could be preference, importance, or likelihood. Thirdly, calculations are used to synthesize the pair-wise comparisons to produce a final value for each of the alternatives. The purpose of AHP is to clarify public preferences and evaluate alternative management plans related to public values. This method provides direction for areas of agreement, resulting in the potential for compromises between competing objectives, conflict resolution, and trade-off identification (Kangas 1994). Furthermore, AHP does  17  not require explicit units to describe value (Kuusipalo and Kangas 1994). However, there are problems with AHP. AHP does not allow for in-depth analyses of the comparisons, especially with regards to the uncertainty inherent in the data (Kangas and Kangas 2005). Further, increasingly complex problems involve a higher number of criteria and alternatives, substantially increasing the amount of comparisons made. An increase in the number of comparisons reduces comprehensibility and increases the cost and time of the process.  The research applications of AHP are growing but practical applications are limited. Both quantitative and qualitative decision criteria can be analyzed with AHP and it has been applied to a broad range of decision issues. However, applications involved with forest resource planning are few (eg. Mendoza and Sprouse 1989, Kangas 1992, Kuusipalo and Kangas 1994, Murray and von Gadow 1991, among others). AHP has been used to elicit public preferences when choosing a management strategy for a forest area. Kuusipalo and Kangas (1994) applied AHP to account for biodiversity in strategic land use planning for the purpose of resource allocation and priority setting. A set of management strategies were evaluated using AHP to identify the strategy that best meets the requirement of maintaining biodiversity, while producing timber income. Kuusipalo and Kangas found that AHP is a flexible tool for this purpose and provides a suitable measure for land use planning when accounting for biodiversity. Ananda and Herath (2002) examined the effectiveness of AHP when stakeholder preferences are involved with regional forest planning in the context of the Australian Regional Forest Agreement Programme. The results from this study indicate that AHP has the potential to foster a formal public participation environment for decision-making and to improve the transparency and credibility of the process (Ananda and Herath 2002).  18  Goal Programming GP is a computer modeling method based on linear programming used for multicriteria optimization. This method allows the decision-maker to either accept the compromised solution or revise the goal targets and conduct further iterations until an acceptable solution is reached. During the analysis phase, GP chooses the best solutions from those that graphically display the most realistic and attainable level to the estimated goal targets (Rustagi and Bare 1987). The following characteristics make GP an important tool: 1) the decision-maker is not required to explicitly define weights to state preferences among the objectives, instead making value judgments on the goal levels for various objectives; 2) results of each iteration is illustrated graphically; and 3) basic and non-basic solutions are explored to find the best choice (Rustagi and Bare 1987). GP allows decision-makers to easily understand conflicts and relationships between objectives due to transparency in the process, the graphical output, and the absence of a weighting framework (Rustagi and Bare 1987). However, GP is only capable of generating one solution at a time from a change in the goal targets.  van Kooten (1995) used GP in a land use planning problem to examine the economic impacts of allocating public forest land on Vancouver Island, British Columbia in a stakeholder process. van Kooten analyzed the allocation of land for multiple purposes and evaluated the impacts on employment, government revenues, and achievement of Annual Allowable Cut requirements. The goal targets were generated by experts with the assumption of matching the public's views and beliefs. The goals were ranked via two public surveys. The results of this study demonstrated that there would be losses of direct jobs, a reduction in government revenue, and an annual decline in society's welfare under current land use practices. These results occurred in spite of using high values for nontimber uses such as recreation, non-use benefits, and tourist employment.  19  Multi-Criteria Decision Making According to Yu (1997) there are four important elements in decision-making, these include: 1) a set of feasible decision alternatives; 2) a set of criteria; 3) potential outcomes of each feasible alternative; and 4) decision-makers preferences concerning the potential outcomes. Comparing and evaluating complex alternatives, such as those found in forest land use planning, can benefit from the application of MCDM models. MCDM has been created for analysis of multi-criteria decision situations wherein evaluation and comparison of alternatives is complex and planning is affected by conflicting interests (Kangas and Kangas 2005). Multi-criteria DSTs allow for thorough evaluations of multiple criteria and indicators as well as a commensurable comparisons of different criteria. This helps decision-makers explore trade-offs between various forest values and account for uncertainty (de Steiguer et al. 2003). MCDM provides decision-makers with management options, objectives, and goals to define decision problems. This type of DST generally defines a set of alternatives; thus, decision-makers contribute through preferences by providing judgments with scores, criteria weights, and alternative estimates (Mendoza 1995). On the other hand, MCDM models are limited by the ability to forecast outcomes for multiple criteria. Forecasting accurate outcomes is one of the most difficult tasks involved in model development. Nelson (2003) provides three challenges for developing credible forecasts from MCDM models: 1) advanced data management systems are needed to support DSTs (e.g. high storage capability, rapid updates, infinite queries); 2) models must be verified through sensitivity analysis, but it is difficult to understand and replicate these models due to the large number of parameters; and 3) large scale, long term forecasting ability is larger than the credibility of the data. Further research is necessary to develop credible forecasts of alternative outcomes.  MCDM models range from complex mathematical models using linear programming or spatial modeling to simple applications using Multi-Criteria  20  Analysis (MCA). MCA is a decision support method developed for complex problems involving trade-offs between multiple objectives. MCA accounts for both quantitative and qualitative data. According to Brown et al. (2001), when using the MCA process participants prioritize criteria with a weighting scheme. These weightings are translated to aggregate scores for each scenario; a matrix is created to assess the performance of each scenario. Several iterations may be required before an agreement is reached on the preferred scenario between stakeholders.  2.5.3 Application of Multi-Criteria Decision Support Tools in Forest Planning  Multiple criteria decision support has often been used in forest management applications due to its capability of integrating many forest management elements in a structured and rational manner. Also, multiple use and the presence of multiple stakeholders with individual views and beliefs make multicriteria DST useful in a public decision-making environment. Multi-criteria DST explicitly address multiple criteria, help structure the problem, focus the discussion, and provide processes that lead to rational, understandable decisions (Belton and Stewart 2002). Multi-criteria DSTs are effective in the decisionmaking process because alternatives are provided and outcomes are forecasted. Thus, transparency and consensus of the process are improved and uncertainty is decreased. The goal of multi-criteria decision-making tools is to identify possible conflicts, provide an interface to amalgamate value preferences, quantify the impact of alternatives through a defined set of criteria and indicators, and communicate potential outcomes to the public. Firstly, these tools have the potential to improve quality and transparency of decision-making due to the systematic process involved. Secondly, they can contribute to consensus by accommodating mutual understanding between stakeholders, soliciting input from stakeholders, and maintaining dialogue (Costanza and Ruth 1998). Thirdly, uncertainty is reduced when DST are involved because forecasts help decision-  21  makers understand the potential outcomes of certain alternatives. However, according to de Steiguer et al. (2003), it is not known if DST in participatory processes can improve public involvement, collaboration, and acceptance of plans.  Although multi-criteria DSTs are effective tools for public involvement in forest resource planning, they have also faced many criticisms. Firstly, DSTs can be overly technical when used for public decision-making (Kangas et al. 2001, McCool and Stankey 2001, and Mendoza and Prabhu 2005). Quantitative DSTs can be too complex for non-specialists to implement or explain to the lay public in SFM planning (Sheppard 2005), thus decreasing credibility with some stakeholders (Kangas et al. 2001, de Steiguer et al. 2003). Secondly, biases may be built into the model by the developer, thus promoting distrust (Martin et al. 2000). Thirdly, models have been referred to as a "black box" 1 (Gregory 2002, de Steiguer et al. 2003). Lastly, the generation of original alternatives is no longer a priority and the process becomes interrupted with the use of the model.  To ensure that the above criticisms do not impact decision-making, the following principles are important for developing efficient participatory decision support methods in SFM (Sheppard 2005): •  broad representation of stakeholders;  •  open access to stakeholders;  •  clearly structured decision-making process;  •  engaging process;  •  understandable and accurate information;  •  appropriate scale and detail for participants and resource managers;  •  focus on assessing sustainability over time;  •  credibility of the process;  ' The term "black box" has been used to describe models in which assumptions are only known to the programmer, therefore, when an output is generated the user has little information to validate the answer.  22  •  mutual learning and capacity building; and  •  feasibility.  Varma et al. (2000) developed a DST for sustainable forest management using GIS integrated with linear programming and data on decision rule uncertainty. This study is important because there has been minimal research into implementing sets of developed criteria and indicators. According to the authors, the two main goals of this study are to find ways to measure sustainable forest management with respect to spatial and temporal dimensions and to identify means for optimizing land use strategies. The results from the study show that a DST using criteria and indicators can facilitate the elicitation of participants' preferences in decision-making and considers trade-offs through computations made by the model. The authors conclude that this is an efficient method for decision-making; however, periodic revisions are required for continued improvement.  There have been few successful models applied in public participation in SFM planning. One example conducted in the Arrow Forest District in British Columbia by Sheppard and Meitner (2005) uses MCA and visualization in public participation. The process involved 3D landscape visualizations to illustrate alternative scenarios and experts evaluated scenarios using weightings based on priorities set by stakeholder groups. The results of this study indicated that common preferences existed among even the most polarized groups. In other words, this DST method appeared to be effective in resolving conflicts as well as promoting an open, transparent, and inclusive process (Sheppard and Meitner 2005).  Several of these studies explore temporal scales, however, research addressing the problem of whether stakeholders are willing to make trade-offs over time does not exist (Sheppard 2005). Implementation of multi-criteria DST in SFM planning is a large and complex task. An iterative, adaptive approach is required  23  to successfully develop and implement DST methods for public participation in forest decision-making.  2.6 Conclusion  The public participation literature provides detailed knowledge of various methods for involving the public and eliciting stakeholders' preferences. However, there is little information on incorporating preferences directly into the decision-making process for forestry-related applications. As the literature has shown, multi-criteria decision-making techniques have the capability of incorporating preferences directly into the decision-making process. These techniques provide structure and direction for public participatory processes, and help involve public preferences during decision-making; therefore, it is possible to include preferences using these tools. Past research has focused on building multi-criteria DST to aid decision-making for various natural resource issues. However, the literature fails to provide direct applications of multi-criteria DST for forest management in public participation. In recent years, research on trade-off analyses has become increasingly important as non-timber values become better understood and conflicts arise between competing interests in the forest land base. The linkage between trade-off analyses and multi-criteria DSTs applied in practical "real world" environments requires further development to help stakeholders find common interests and resolve conflicts.  Applying a multi-criteria DST in a public participatory setting warrants further research. Further research is required to determine whether DSTs are useful tools for incorporating public's preferences for forestry related applications and whether stakeholders will make trade-offs between forest values using these tools as an aid. It is anticipated that the results of this study, along with previous findings, will lead to the development of improved applications of multi-criteria decision-making methods in forest management public involvement. Furthermore, the application of DSTs may provide more detailed knowledge on  24  public preferences through trade-offs made and preferences elicited. DSTs have the potential to resolve conflicts more effectively and efficiently, while building awareness on stakeholders' different interests and the trade-offs stakeholders are willing to make between forest values.  25  3.0 METHODS This chapter provides the details on the research methods used in this study. Section 3.1 provides background information on the Invermere Timber Supply Area (TSA) study area and explains the reasons for limiting the study to the Dunbar/Templeton landscape unit (LU). Section 3.2 provides background on the development of the multi-criteria Decision Support Tool (DST) that was applied in this study, as well as information on the alterations made to the tool for its successful application. Section 3.3 describes the DST's user interface developed specifically for this study and the method used to connect the interface to the model. The research design and questionnaire design are explained in Section 3.4 and 3.5, respectively. Section 3.6 describes the sampling methods and Section 3.7 describes the session methods with participants. Lastly, the data analysis methods are explored in Section 3.8.  3.1 Invermere Timber Supply Area  The Invermere TSA is located in the East Kootenays of British Columbia within the Southern Interior Forest Region. The size of the TSA is 1.15 million hectares. The area is bound by the Cranbrook TSA to the south, the Golden TSA and Tree Farm License (TFL) 14 to the north, the Rocky Mountains and the Alberta border to the east, and the Purcell Mountains to the west (Figure 1). The Rocky Mountain Trench is a broad, flat valley with numerous rivers and wetlands found within the TSA between the Rocky and Purcell Mountain ranges. The Columbia River flows North through the trench forming the Columbia Wetlands, a complex and rich ecosystem.  26  Figure 1. Invermere Timber Supply Area  Source: Invermere Timber Supply Area Timber Supply Review #3 Analysis Report v. 3.0, May 12, 2004  Communities located within the Invermere TSA have a total population of 9,000. The major population centres are Invermere, Windermere, Canal Flats, and Edgewater; the smaller communities include Radium Hot Springs, Wilmer, Fairmont Hot Springs, Brisco, and Parsons. The permanent population is augmented by 40,000 part-time residents, arriving primarily from Alberta with the purpose of enjoying the many recreational opportunities offered in the area. Panorama Mountain ski hill (located 18 kilometres west of Invermere), the Hot Springs, protected parks, and many golf and resort destinations attract visitors and recreationists year round. Existing populations of approximately 400 First Nations People reside within the boundaries of the Invermere TSA. Archeological evidence shows that the Ktunaxa people have inhabited the area for 10,000 years. Two First Nations communities are located within the TSA including the Columbia Lake Band in Windermere and the Shuswap Band in Invermere. The Shuswap Band is culturally and linguistically connected to the Shuswap Nation and politically connected with the Ktunaxa Kinbasket Tribal Council. The Ktunaxa Kinbasket Tribal Council has submitted a detailed land  27  claim covering the Southeast of the Province including the Invermere TSA. However, a settlement has not been finalized.  According to the results of the 2001 Census, tourism is the largest employment sector in the TSA (33.9%), followed by the public sector (21.3%). However, the tourism industry's low wages account for a low basic sector employment income (15.7%), far below the forest industry, public sector, and those relying on pension and investment income (Brown 2004). The TSA has one of the highest diversity indices in the province; in other words, the area's economy is not reliant on only one or two sectors to maintain the quality of life.  Two major forest licensees operate in the TSA, including Canadian Forest Products (Canfor) located in Radium Hot Springs and Tembec Industries located in Canal Flats. The current Annual Allowable Cut (AAC) for the Invermere TSA, effective November 1, 2005, is 598,570 m 3 . Tembec has approximately half of the rights to the AAC and ownership of half of the wood processing capacity found in the TSA. Pulp is the main product from Tembec's mill in Skookumchuk with an estimated annual output capacity of 248,000 metric tonnes, while dimension lumber is the main product from the mill in Canal Flats with an estimated annual capacity of 166 million board feet. Canfor's main product is dimension lumber with an estimated annual output capacity of 185 million board feet (Brown 2004). Canfor and Tembec have each received forest certification from different certifying agencies. Canfor has been approved by both the Sustainable Forest Initiative (SFI) and the Canadian Standards Association (CSA); Tembec has received certification from the Forest Stewardship Council (FSC).  The land base classification of the Invermere TSA is summarized in Figure 2. The area is closely divided between the Crown Forested Land Base (CFLB) and the non-forested area with only a small fraction dedicated to non-TSA. The chart on the right illustrates the CFLB broken down into sections, the larger sections  28  including the THLB (42%), inoperable area (34%), and Parks (14%), and the remaining 10% consisting of unstable, non-merchantable, low sites, (PFT), Wildlife Tree Patches (WTP), and riparian areas (Brown 2004). 600,000  1,200.033 Non-TSA rrEr°-,  600,000  I,^0 3-  40'0,0 00  800,.00  ME  ^  Crown Forested Land Base 48%  200,00  200,000 -  Triter FLarvesting Land Base (42%)  100,000 -  ',re^e TSA  Irparable (34%) Unstable, ESA Non Merch, Low S(tes FFTs, WTPa, Rpar:an (10%)  600,000 400,000  Parks 14%)  Crown Forested Land Base  Figure 2. Summary of the Land Base Source: Invermere Timber Supply Area Timber Supply Review #3 Analysis Report v. 3.0, May 12, 2004  The forests are dominated by lodgepole pine (Pinus contorta) (40.7%), Douglasfir (Pseudotsuga menziesii) (28.7%), Engelmann spruce (Picea engelmannii) (13%), larch (7.2%), and sub-alpine fir (Abies lasiocarpa) (4.3%). There are approximately 60,000 hectares of lodgepole pine over sixty years old on the Total Harvestable Land Base (THLB) in the TSA (Figure 3); areas outside of the ESSF are susceptible to Mountain Pine Beetle infestation. According to Figure 3, half of the THLB is currently older than 80 years.  29  K C -3  -  'es  20.C:3— 15.COD — 10.000 — —-  <=20  21-40^41-6C^61-80^131-130  101-120  121-143^41-2E0  Age Class  Lodgepole Pine  . m SpnicelBalsarilOther ^rs RI Larch/Yellow Pine -  Figure 3. THLB area by age class and major species Source: Invermere Timber Supply Area Timber Supply Review #3 Analysis Report v. 3.0, May 12, 2004 One national park, Kootenay National Park, and eleven provincial parks, Mount Assiniboine Park, Height of the Rockies Wilderness Area, Purcell Wilderness Conservancy Area, Bugaboo Alpine Park, Top of the World Park, Windermere Lake, Whiteswan Lake, Premier Lake, Canal Flats, James Cabot, and Dry Gultch, are located in or directly adjacent to the TSA boundary. The six biogeoclimatic zones existing in the TSA are Ponderosa Pine (PP), Interior Douglas-fir (IDF), Montane Spruce (MS), Interior Cedar-Hemlock (ICH), Engelmann Spruce-Sub-alpine Fir (ESSF), and Alpine Tundra (AT). Abundant and diverse populations of ungulates and large predators thrive in the area due to the variety of available habitat types (Table 2). The Columbia Wetlands support 70% of the bird species known to exist in BC due to important habitat for nesting and migration (Brown 2004). There are eight red-listed (endangered and threatened) species and eighteen blue listed (species of concern) species found within the TSA (Appendix 1). The non-timber issues most significantly influencing forest management in the Invermere TSA include: biodiversity, riparian habitat, domestic and community watershed, fire maintained  30  ecosystems, ungulate winter range, grizzly bear, caribou, Identified Wildlife, viewscapes in scenic corridors, and forest recreation (Brown 2004).  Table 2. A list of the ungulate, large mammal, and small furbearer species found in the Invermere TSA. Ungulate species -  Elk Mule deer Whitetail deer Moose Rocky mountain bighorn sheep - Mountain goat - caribou  Large mammal species - mountain lions - wolves - black bear - grizzly bear  Small furbearer species - beaver^- badger - mink^- wolverine - muskrat^- bobcat - otter^- lynx - fisher^- squirrel - marten^- fox - skunk^- raccoon - weasel  The Invermere TSA has 34 landscape units (LU). Usually, a LU is managed by one or more of the companies operating in the area. The LU's range in size between 7,645.8 ha and 84,826.3 ha. The LU's differ in terms of operability, accessibility, ecology, recreational opportunity, visual quality, watersheds, and socially contentious issues. The Dunbar/Templeton LU was chosen for this study because the area represents relevant issues related to sustainable land use planning, information is available, and contention exists between high use recreational areas and timber extraction, particularly with respect to Mountain Pine Beetle salvage blocks. 3.1.1 Dunbar/Templeton Landscape Unit The area of the Dunbar/Templeton LU is 25,192 ha. It is located in the northwest corner of the Invermere TSA. The Dunbar/Templeton LU is bounded by Bugaboo Creek and Driftwood Creek from the north, the height of land between Dunbar Creek and Frances Creek from the south, Columbia River from the east, and the headwaters of the Dunbar and Templeton Creeks to the west (Figure 4). Canfor is the head licensee in the Dunbar/Templeton LU; however, British  31  Columbia Timber Sales (BCTS) manages a portion of the landscape along the Northern border of the LU. Canfor shares the land with one woodlot located in the northeast corner of the LU, many private land holdings in the eastern area of the LU, as well as a range tenure holder.  PARK tAKE CANFOR / TEMBEC BCTS  Figure 4. The location of the Dunbar/Templeton LU in the Invermere TSA  The three biogeoclimatic zones in the Dunbar/Templeton LU are the MSdk (dry cool), ESSFdk, and in the lower elevations near the Columbia River, IDFdm2. According to Canfor's Forest Development Plan (FDP), the forest health issues in the area include Mountain Pine Beetle, Douglas-fir Bark Beetle, Spruce Bark Beetle, Armillaria root disease, and lodgepole pine dwarf mistletoe. A domestic watershed exists in the area with two domestic water intakes along the Templeton River and one intake at Ramer Spring. According to Canfor's FDP,  37 blocks are assigned in the LU; 13 of these blocks are approved by Cutting Permits, and an additional 24 blocks are proposed. The Dunbar/Templeton area 32  is managed for ungulate and grizzly bear habitat; caribou habitat does not exist in the LU. Abundant recreational opportunities exist including camping, fishing, boating, hunting, hiking, and sightseeing. Many lakes have road access and camping facilities at government operated recreational sites. Recreational cottages are found along the shoreline of Dunbar, Lang, Botts, and Twin Lakes. Most of the LU is not within the visible highway corridor except for a small portion facing the Columbia River, which requires visual management. Additionally, visual assessments are conducted from the recreational sites to ensure aesthetics are not impeded. 3.2 The Multi-Criteria Decision Support Tool for Sustainable Forest Management A multiple criteria decision support tool (DST) has been developed at the University of British Columbia to forecast SFM criteria and indicators over longterm time horizons for strategic land use planning (Maness and Ristea 2004). The model uses multiple objective linear programming methodology with fuzzy sets in a "3T Approach". The 3T Approach refers to targets, thresholds, and triggers; this method is applied in the model instead of a direct weighting system such as those used in a goal programming approach. Targets are the desired outcomes for each criterion, thresholds are the minimum acceptable outcome for each criterion, and triggers are on-the-ground management activities that change the achievement levels for each criterion. The model is based on a set of criteria and indicators embedded in a hierarchical framework.  Overall, the model is solved using a hierarchical planning framework. A hierarchical planning technique was implemented using several connected optimizers, each working in different temporal realms. In this context, hierarchical planning differentiates the planning problem into temporal domains and creates a separate model for each domain. It is useful to divide the planning problem into strategic, tactical, and operational temporal realms because the objectives, as well as the required detail, are quite different in each realm. A  33  hierarchical method amalgamates existing models allowing each model to focus on the objectives that are important at a specific temporal domain. The individual models are executed iteratively.  The model uses criteria and indicators based on information from regional GIS databases and potential outputs from the model. A detailed review of the CCFM criteria and indicators, as well as criteria and indicators from a local study in the West Kootenay region of BC, were assessed. Experts conducted workshops to determine which indicators would be appropriate to include in the model. To be approved for the model an indicator was required to meet the operational standards adapted from Bunnell (1997).  A full description of the rationale for choosing indicators can be found in Maness (2003). The review concluded that four criteria and nine indicators could be applied in the planning model (Table 3). Targets and thresholds were developed for each indicator based on expert judgment 2 using the model output as a guideline. Each LU was divided into polygons, and data for each indicator was collected for each polygon. However, individual polygons were too small and too numerous to effectively execute the model if they were not amalgamated. Consequently, the individual polygons found in the TSA were aggregated into homogenous and continuous Decision Units (DUs) between 10 and 100 hectares in size. The planning horizon for the DST is 100 years, consisting of 10 periods of 10 years each.  A team of experts worked together during group meetings to develop a set of operational criteria and indicators that could be used to develop this model. Each indicator was proposed, discussed, and modified by relevant stakeholders and researchers. 2  34  Table 3. The Criteria and Indicators Applied in the Planning Model Source: Maness and Ristea (2004) Criterion Criterion I: Biological richness and its associated values are sustained within the management unit  Criterion II: Forest productivity is sustained Criterion III: The flow of economic benefits from the forest is sustained  Criterion IV: Forest management supports ongoing opportunities for quality of life benefits  Indicators 1. Ecologically distinct ecosystem types are represented in the non-harvestable land base 2. Stand and forest-level habitat elements are represented 3. Annual removal of forest products relative to the volume determined to be sustainable 4. Net profitability is sustained (proxy tax revenues) 5. Total employment in all forest sectors is sustained 6. The provincial government continues to receive a portion of benefits 7. Availability of recreation opportunities are sustained 8. Visual quality of managed landscape is acceptable to stakeholders 9. Community watersheds are sustained and protected  The development of this model is a significant advancement in DST research as it is the first to solve a complex hierarchical model for land use planning. For further details on the development of this model and subsequent studies conducted, see Maness and Ristea (2004).  3.2.1 Alterations to the Multi-Criteria Decision Support Tool  The original model has been altered in order to successfully implement the system as a DST in this study. Firstly, the model analyzes data from a single LU instead of the entire Invermere TSA. Limiting the study to a smaller area ensured that the research was feasible within the time constraints and that participants could easily focus their decision-making. The profit indicator, however, was based on a proportion of the harvest volume available in the TSA because the area of the LU was not large enough to sustain a timber volume to  35  the mill over the specified time horizon. In other words, the timber volume decreases proportionally with the rest of the TSA when the model harvests polygons in the LU. Therefore, it was assumed that the TSA emulates the same attributes (size, species composition, age and diameter classes) as the Dunbar/Templeton LU across the other LU's in the TSA.  Secondly, the manufacturing simulator was removed from the model hierarchy in order to improve the execution time of the model. The manufacturing simulator determines the Return to Log (RTL) values for each DU and optimizes the operations to determine the profit indicator. For this study, the profit indicator was solved by associating log diameter distributions with each polygon to determine the volume. A pre-assigned value in dollars ($) was attached to each diameter class.  Thirdly, the model originally produced an output with stakeholders preferred targets for each indicator. However, for the purpose of this study the model included a weighting system that interpolated the triggers for each indicator. The scores stored in the model, created by experts, are difficult to understand and to describe to non-experts. The DST used the threshold to describe the "0" on the point scale, the minimum achievement for that indicator, while the target described the maximum achievement for that indicator, or "60" on the point scale. The trigger was the allocated points assigned to each individual indicator.  Lastly, only six of the nine indicators in the model were implemented in this study. The following six criteria were applied: Ecosystems at Risk, Visual Quality, Recreation, Profitability, Employment, and Domestic Watershed. The values were limited to these six indicators in order to isolate the key conflicts in the study area and to promote participants to use critical thinking towards indicators.  36  3.2.2 The Forest Indicators in the Decision Support Tool  The six indicators were carefully chosen in this study to ensure that environmental, social, and economic values were all covered. Two indicators were chosen for each value in order to reduce any bias towards a specific value. The Ecosystems at Risk and Domestic Watershed indicators meet some of the environmental values in the Dunbar/Templeton LU. The Visual Quality and Recreation indicators cover the social values and the Profitability and Employment indicators meet the economic values in this study.  The Ecosystems at Risk indicator is based on the percentage of Old Growth Management Areas (OGMA) reserved in the Dunbar/Templeton LU. The Ecosystems at Risk indicator was previously referred to as Old Growth, however, the name was altered to account for overlap between defined biodiversity classifications and old growth. Furthermore, old growth is used to measure this indicator instead of the biodiversity classes because the model originally divided the three classes into separate indicators. Thus, there would have been a requirement to dedicate three biodiversity indicators which would bias the study towards this indicator.  The Visual Quality indicator is based on the Visual Quality Objective (VQO) as provided by prescriptions in visual management areas. The Recreation indicator is based on the recreational significance, sensitivity to development, and proximity to water. The Profitability indicator is based on the diameter distributions amongst polygons. A value is assigned to each polygon according to species and diameter size. The model determines the value of the harvested volume after the model has chosen the specific polygons for harvesting. The volume entering the mill and log sales is measured in dollars ($). It is assumed in this project that the cost of raw logs is the same as the cost of milled logs.  37  The Employment indicator is based on the harvest volume and a pre-determined cut level in which the mill can no longer operate. The minimum harvestable volume is located at the point when the mill opens and all the forest values on the interface are set to 10 points each. The indicators were set to the balanced case because it was deemed necessary to find a point where all values were given equal weighting. The minimum harvest volume was found at 189,131 m 3 , which is the volume at which the mill remains open. If the volume was set any higher, the mill would close down at certain periods or close down entirely over the 100 years. It is assumed that harvesting, log transport, road construction and maintenance, and silviculture employment all occur regardless of whether the mill is in operation or not; although, once the harvested volume reaches the minimum harvest volume, the mill opens and timber processing employment is added to the total harvesting employment. When the Employment indicator is maximized the maximum harvestable area is 210,000 m 3 . It is important to distinguish that the Profitability and Employment indicator operate differently. For example, the mill could be operating inefficiently by cutting smaller volume timber, thus producing less profit, but continuing operations at the same or higher levels of employment. In this case, profit decreases and employment increases.  The Domestic Watershed indicator is based on the number of hectares of Equivalent Clearcut Area (ECA) by watershed type; ECA is a calculated term that reflects the cumulative effect of harvesting within a watershed. An increase in the percent cover of stands greater than 6 m in height is considered to have a better ECA than a stand with an increase in stands less than 6 m in height.  3.3 The Decision Support Tool Interface The portal to the model is a user interface created uniquely for this study with the  .NET framework. The interface consists of three different sections (Figure 5).  38  ri  n act^-  New Participant^I  Points Remaining:  Employment  Profitability  .CetlatO  ocereno. UMW')  Visual Quality  Scenario 1.  Scenario 1^0  Scenario 2.  Scenario 2:  Scenario 3  Scenario  Stn,tiano );), Sc  Scenario t: Scan:402:  ^16  Scenario 3  Scenario a Somme, t  4  Scamp Scenario 6:  L  Schr.Sra,  Ssstwol  Scenario 7:  Sorsre10 7  Scenario a  Scenario a  5otnsos Se-es-41017  Employment Over Time  ,  r  Scenario  Scenano 77.. 7..  Rotitabdity Over Tme  Saxon:IS Scenario  Scenario : •^•  acenam  Visual quality Over Tone^i^Recreation Over Tone  Scenario a' Scenario 10  Scenario Ecosystems At Risk Ryer Tree  Domestic Watershed Over Tene  100 :  100  100  100  100  80  80  ,780  160  )13160  '60  e° 180  Ito  !4t)  can  1  160 1  &2o  2 20  •  020  2 1 0 °.^in lime (10 Year periods]  1  Scenario 2  Scenario  S  ml  4  60  Recreation^Ecosystems At Risk Domestic Watershed  OCel0e00  =MVP:  Ran 0: r^I  11  Told Desired Condition:  Dunbar/Templeton LU  Participant, !lest  10  Scenario Number:  Multi Criteria Decision Making Tool  0 0■- 01(1m111.01, Tone  (10 year periods)  40  4  20;  ri or-rrenamcor..tocno  0,010  Tone (10 year periods)  80  Tme (10year periods)  ° Coi—r4M•11j1,0,[00,0  Tme (10 year periods]  Trne (10 year periods)  Scenario Data Ready  Figure 5. The Decision Support Tool Interface  The first section is located at the top of the window. This section displays the title of the model, as well as four functional fields and buttons. On the top left of the screen, there is a 'New Participant' button for use only by the DST's facilitator. This button, when clicked, produces a fresh screen and a unique ID number for each new participant. Below this button is the Participant ID field which indicates the ID of the current participant. This field allows the researcher to scroll through the final screenshot of each participant.  The top right of the window includes the 'Scenario ID' field, the 'Total Desired Condition' calculator, the 'Points Remaining' calculator, and the Run #' field. The 'Scenario ID' field provides a unique ID to each scenario executed throughout the entire study. The 'Total Desired Condition' calculator refers to the points allocated by the participant for each scenario. The participant is required to allocate sixty points, hence, this field maintains a running total to help the  39  participant keep track. This field must sum to "60" before the model is executed. The 'Points Remaining' field sums the points the participant is required to allocate before the model is executed. This field must sum to "0" before the model is run. The 'Run #' field displays the number of scenarios executed by an individual. The participant applies the 'RUN' button to execute the model once the scenario is developed and all points have been allocated.  The middle of the window is divided into six columns; each column is dedicated to an indicator. Ten rows of six empty fields (one field in each column) are labeled vertically 1 through 10. These fields are used for direct input of each participants individual point allocation according to their own preferences. All fields are initially filled with a zero before each scenario is entered. The purpose of entering a zero in these fields demonstrates that the fields are numerical, and ensures that participants begin with a clean, unbiased position for their scenario. Successive rows are built into the interface to help participants identify and distinguish changes made between scenarios in order to make an informed choice for subsequent scenarios.  The bottom section of the screen provides a graph in each column summarizing the model's output for each indicator. The output is based on the participant's scenario as a whole and, therefore, the points issued for one indicator effect the other indicators. The graph illustrates the percent achieved of the total potential along the y-axis and the time horizon (ten year intervals over 100 years) along the x-axis. Each scenario, as executed by the model, is described with a continuous line on the graph in different colours for each scenario. The graph for the profitability indicator is shown differently. If shown as the others, the graph for the Profitability indicator would show large fluctuations over time because the forest company may harvest more timber in certain years than other years. Instead, the total profit discounted over the 100 years is calculated for the entire TSA by multiplying the number of hectares by the value of the cutblock per  40  hectare. This value is displayed in the graph window. The y-axis on the graph is displayed in $(millions) over time.  3.4 Research Design  This project uses an exploratory research approach. Exploratory studies have the potential to further the understanding of a topic, and to develop methods that may be used in later studies (Babbie 2004). Exploratory research is conducted when the topic examined is relatively new and unfamiliar. The intent of the study is to gain a better understanding of stakeholders' preferences and the potential of the DST through specific observations, not on the basis of general principles; thus, a hypothesis was not tested. A cross-sectional research design was implemented for this study. A cross-sectional study was used because the objectives of the study did not require data collection to occur over a period of time. Furthermore, a cross-sectional design creates a realistic decision-making environment because many individual stakeholders participate in specific forest planning issues over relatively short timeframes.  Individual face-to-face sessions were conducted with each participant. Each session ran approximately two hours and involved a pre-survey, DST exercise, and a post survey. The methods for data collection included: 1) a pre and post survey; 2) screenshots of each participant's final interface; and 3) reports generated in Microsoft Access from the database. After the pre-survey, the DST exercise was executed followed by the post-survey.  3.5 Questionnaire Design  Two written questionnaires were provided to each participant during the individual face-to-face sessions. The participants were allocated time to complete the questionnaires on-site. The questionnaires provided structure and operationalized the key concepts in this study. The study employed a pre- and  41  post-questionnaire technique. Administering a questionnaire before and after the DST exercise helped to determine and explore any changes in participant's preferences as a result of the DST output. The pre-survey (Appendix 2) was administered preceding the DST exercise and the post-survey (Appendix 3) was administered following the DST exercise.  The purpose of the pre-survey (Questionnaire 1) was to gather information on the participant's background, level of knowledge of the Dunbar/Templeton LU and its forest management, as well as preconceptions towards DSTs. Overall, the presurvey consisted of four sections, with the majority of questions using a closeended format. Some questions applied different five point scales to assess the participants' responses regarding their preferences for different forest values. The end points for the various questions ranged from not at all important to extremely important, not at all satisfied to extremely satisfied, and little/no knowledge to extremely knowledgeable. The other questions in this survey required participants to choose from an exhaustive list of multiple choices regarding their use and activities in the Dunbar/Templeton LU and their perceptions of DSTs.  The purpose of the post-survey (Questionnaire 2) was to understand how value preferences changed as a result of using the multi-criteria DST, as well as an assessment of the model from the participants' perspective. Questionnaire 2 consisted of two sections and was much shorter in length because the majority of the questions were open-ended. Four questions in this survey were open-ended to allow the participant to provide detailed, in-depth responses regarding the use of the multi-criteria DST (Babbie 2004). This survey also included a matrix question applying the Likert scale (strongly agree to strongly disagree) to evaluate a series of statements regarding the participants assessment of the DST.  42  To ensure that participants answered all of the questions in the survey, the following layout procedures were used in the questionnaires: the demographic information was located at the end of the first questionnaire, the surveys were short, there were few open-ended questions, and the survey was visually appealing. It is important to include the demographic information near the end of the survey to ensure that the participant does not believe that this is another generic form. The questionnaires are short as not to overwhelm the participant, particularly because two surveys are involved in this study (Fink and Kosecoff 1998). Also, open-ended questions can be overwhelming and tedious for participants and if there are too many the participant may not be inclined to respond thoroughly; therefore, these questions are few in number (Babbie 2004). Furthermore, a cover letter was provided to each participant before the consultation session began. The letter introduced the study, described why and how the participants were selected, and that their identities would remain anonymous and confidential.  Participants were made aware of their privacy rights from the initial recruitment letter, as well as the confirmation letter sent by e-mail. The ethical considerations in face-to-face interviews included the confidentiality of participant's individual responses and the anonymity of each participant (Babble 2004). The processes implemented to ensure confidentiality and anonymity included: employing only one researcher to complete the data collection and data entry phases; providing an ID number for each participant; instructing participants to leave their names off the questionnaires; and excluding videotaping from the sessions (Babbie 2004).  3.6 Sampling - Participants  The study does not rely on statistical descriptions of large populations, nor is it a random sample. All residents of the Invermere TSA did not have an equal chance of selection in the sample because the study required the participation of  43  actual stakeholders interested in the Dunbar/Templeton LU. The selection of "real" stakeholders abets the simulation of actual practices. In this study, the population of interest includes various stakeholders in the Invermere area. Hence, a quota sampling method was employed insofar as certain stakeholder groups were represented on the basis of pre-specified characteristics (Babbie 2004). However, the quota sampling method requires that a total sample with the same distribution of characteristics existing in the study population is accrued. This requirement was not met in the study; as a result, the sample population could include some biases. To ensure that bias towards a specific stakeholder group was reduced and various statistics could be calculated, the sample size was made the same for each stakeholder sub-group.  The DST was based on six different forest indicators; therefore, stakeholder sectors were chosen to participate in this study based on interests that coincided with the corresponding values in the study. The stakeholder groups chosen to participate included: the forest industry; Environmental Non-Government Organizations (ENGO's); the provincial government; recreation groups; and private property owners (including various licensees). Participants were residents or had a business based directly in the Invermere TSA, as well as a direct interest in the Dunbar/Templeton LU.  This study is an exploratory study using a non-probability sampling technique. A purposive, stratified, snowball sampling method was employed for selecting participants. The researcher employed purposive sampling by selecting a sample based on the knowledge of the population, its fundamentals and the purpose of the study (Babbie, 2004). A purposive technique was important for this project because the researcher was able to use judgment to sample from the membership of stakeholder organizations that have specific interests in the LU, but was not required to sample from all stakeholder groups in the Invermere TSA. Furthermore, the purposive technique permits the study of a small sector  44  of a larger population in which many members of the sector are easily identified, but the inventory of them all is not feasible (Babbie 2004).  Knowledge of the population was minimal; however, contact was made with the Radium Canfor Public Advisory Group (PAG). PAG members identified as representatives of one of the specified stakeholder sectors were chosen to initiate the snowball sampling method. Implementation of the snowball sampling technique began with participants providing further contacts in their respective interest groups; these contacts provided others, and so on. The snowball technique was employed for this study because the researcher was unfamiliar with this area and complete lists of various stakeholder groups with interests in the Dunbar/Templeton LU were not available. The purposive, snowball method used in this study allows for a realistic replication of public participatory processes because the sampling technique selects target groups (the stakeholders) instead of people from the general population with no interest in the Dunbar/Templeton LU.  The exact number of participants involved in this study was not specified before the data collection was initiated, this was determined during collection of the data. The researcher finished once the data collected became repetitive and no additional information was found. The sample size appropriate for this study was twenty participants. Twenty participants were included to involve an equal number of representatives from the five identified stakeholder groups. Four representatives with similar interests in the Dunbar/Templeton LU were enough to ensure that additional information was not required. The responses seemed consistent and no new information would have been gained by increasing the individual sample sizes.  45  3.7 Session Methods  The sessions were conducted at the Radium Hot Springs Resort. A specified meeting room was used to minimize distractions or interruptions during the course of the session. Each participant was provided a computer monitor, mouse, and keyboard. The participant was stationed in front of the computer screen during the DST exercise and the facilitator sat beside the participant during the course of the exercise to provide assistance and explanations of DST outputs.  Upon arrival, the participant was required to produce a signed consent form and was verbally briefed on the proceedings of the session by the facilitator. At this point, the pre-survey was administered. Following completion of Questionnaire 1, the researcher explained and demonstrated the use of the DST to the participant. The participant was required to allocate sixty points across all six indicators according to his/her preferences; these were referred to as the participant's initial preferences. The participant began in Row 1, and once Row 1 was filled with the allotted points, the scenario was developed and the model was ready for execution. The participant allocated the sixty points with the assumption that the points allocated to a certain indicator were the desired condition that would be fixed over the 100 year time period. Every time the participant created a new scenario, there was a model execution time of approximately 15 -20 seconds. The interface stored all of the changes in the participant's decision-making made between scenarios. This helped to explore how planning models affect public stakeholders initial preferences.  Participants only have sixty points to allocate among all six indicators; therefore, each indicator is weighted more heavily in the interpolation scheme for points between 0 to 15. The interpolation scheme refers to the translation of the sixty point scale to the potential achievement scores used in the model. Indicators were weighted more heavily for points in the low range because the addition or  46  removal of points from the participants' initial scenario creates a large difference in the lower point range. This occurs because once points are allocated across six indicators, participants are working with weighting in the lower point range. Therefore, point changes in the lower range are more meaningful than point changes to indicators with higher point allocations. The points used in the interface corresponded to a percentage ranking based on expert judgment, as well as response curves for each indicator from model executions. An interpolation scheme, detailed in Appendix 4, was used to calculate the achievement potentials for each interface point. The maximum potentials for each indicator translated into the "60" on the interface point scale. Thus, if an indicator was set at "60", it was maximized and all other indicators were set to "0". If an indicator was maximized it is the intent of the participant to protect 100% of that particular indicator or maximize profit or employment at the expense of the other indicators. Each indicator had a different interpolation scheme; however, the rationale behind each scheme was the same. For example, if the participant chose to dedicate five points to the Recreation indicator, then the interpolation scheme dictated that the participant wanted to achieve 20% of the model's achievement potential for Recreation. The interpolation between the points on the interface and the achievements in the model was necessary in order to create cohesion and interconnectedness between the interface and the model.  Each row referred to a single scenario. A scenario refers to the results of a single model execution after sixty points have been allocated across the six forest indicators. Once the row had been filled, the participant selected the RUN button to execute the model. A 20 second delay occurred while the model prepared the output. The output was displayed in the line graphs for each indicator provided directly under the scenario rows on the interface (Figure 6). The facilitator described the output displayed in the graphs. The participant was then instructed to re-weight each indicator and re-run the model until he/she was  47  satisfied with the results. After viewing the results, the participant was asked the following:  Upon review of the results, would you like to create a new scenario to get closer to the results you expected?  The DST exercise was completed once the participants chose a preferred scenario from one of the outputs of the model. The participant indicated with a checkmark on the interface which scenario output he/she preferred. Following completion of the DST exercise, the post-survey was administered as the final step in the session.  3.8 Data Analysis  The data collected from the screenshots of the DST's weighting interface and the answers to the questionnaires were used in the analysis. The screenshot analysis explored possible connections between the participants' initial preferences and the final output. The screenshots tracked the points allocated for each indicator and all executed scenarios. The data from the screenshots were entered into a Microsoft Access database. Correlations were generated from this database to make comparisons between the trade-offs applied by participants in order to reach satisfaction with the DST output. The points allocated for each forest value was calculated between scenarios. These results were averaged among stakeholder groups for each forest value. The results were used to compare differences and similarities of trade-offs between stakeholder sectors.  48  Li SFM Multi-Criteria Decision Making Tool New Participant Multi Criteria Decision Making Tool Dunbar/Templeton LU  Participant: test Profitability  Employment^Visual Quality  13.33 %t 5 )111  61.67 ^1 1 46.67% I lo 46.67  Scenario 1:  0  ^)11110  ZErEi  Es  Scenario  Scenario 4: [ 20  Scenario 4:  I  s)  1111  [a)  Scenario 9:  Profitability Over Tone  13 33 %  Employment Over Time  _80  _80  15  Eicerrairro 3 ^j  U  Scenario 7:  0 Scenario 7: ^  S c er,ario 7:^0 1  Scenario 8: ^  Scenario 8.  1 T.  I  S cerierio'9.  Scenario 9:  S carieriL) 10.  [a  _80 I \ E  Scenario 1 Oi ^ Domestic Watershed Over lime  100  100  _as  _80  Esc)  E,60 :a  60  60  i;60 :a  140  z.40  z.40  .6 40  3 2 1  20  e x,  1  &20  1 20  ' 20  Time (10 year periods)  o  N 01 sr III CO f•-• CO 0 0  Time (10 year periods)  °^N^lf) CO f•-• 03 0) 0  Time (10 year periods)  0  •-• N 'I' ID CON CO 0 0  Scenario Data Ready  Figure 6. An Example of the Interface Output after Five Completed Scenarios.  40  2  0o^ N nt Ul CON' 0) 0)0 0  Time (10 year periods)  tn t  Scenario 10: [ ^0^)  Recreation Over Tone^Ecosystems At Risk Over Tone  100 .  7'  Scenario 9.  )  E 4  0 ^ N in.1 In 0 N CO 0 0  iri  Scenario 4:  Scenario 9:^0  Visual Quality Over Tone 100  Scenario 4:  Scenario 2^—1 IN  Scenario 6:  Scenario 10  100  10  1  Scenario 6:^0  MOS %MU 10 9 8 7 11 6  Scenario 3:  Scerkyro 1  Scenario 5:  Scenario 9: )  Scenario 10:  5  Scenario 5: [ 20 )  Scenario a 0  IN  L  20  1  a 'T] 111  •Scenario ^  Run):  Ecosystems At Risk Domestic Watershed  Scenario 3: o  )  Scenario 8:  oenario  60  Scenario 2: ^ 5  Scenario 7:  cenario ^  Points Remaining:  Scenario ^  Scenario 6:^0  0  0  Scenario 2: [ 20  Scenario 5:  ,  Total Desired Condition:  Scenario 1:  5  ceriano • A.^0  c; enano . n-  15  Scenario 1: [ 10  1  13.33 X (^ 5 AMU  zceriano ^  Recreation  Scenario Number:  Time (10 year periods)^•-  oO ^ N sr^0) 0) 0 Time (10 year periods)  As stated previously, this study is exploratory. Therefore, finding statistical significance was not necessary, and hence the low participant numbers. Instead the purpose was to find relationships between the participant's preferences and the DST output. Thus, the data analysis focused on simple descriptive statistics for the close-ended question items on the surveys and the screenshots, as well as a content analysis of the open-ended question items on the surveys. The open-ended question items found in the questionnaires were analyzed using content analysis to examine the dominant themes of each stakeholder group. It was necessary to create a coding scheme for each open-ended question; this scheme defined the main themes found in the responses. The coding scheme for this analysis used manifest content because it is more reliable and specific than latent content due to the use of concrete terms (Babbie 2004). Furthermore, a quantitative content analysis was employed by recording the numerical frequency of certain words and phrases found in the responses. A more detailed description of the data analysis is found in the following Results section.  50  4.0 RESULTS This study is exploratory; therefore, the purpose is to find strong relationships between participant preferences and the Decision Support Tools (DST) output. In this chapter, general observations from the sessions are outlined in Section 4.1. Sections 4.2, 4.3, and 4.4 summarize the data from Questionnaire 1, the DST exercise, and Questionnaire 2, respectively. Section 4.5 provides linkages and relationships between all three modes of data collection. 4.1 General Observations from Sessions Overall, the participants' reaction towards the DST exercise was positive and enthusiastic; however, the majority of participants expressed some concerns and frustrations during the session. This section describes general observations by the session facilitator that were not explicitly captured in the data.  Participants least comfortable with computers were the most likely to under appreciate the value of the model as a tool for decision-making. Property owners were very keen participants 3 ; however, the participants in this sub-group had very little to no experience using a computer. This resulted in noticeable apprehension and held back these participants from using the tool to its full potential. Further, property owners conducted the least amount of scenarios amongst all five stakeholder groups.  Upon review of the DST's initial output; most participants admittedly stated that they were trying to manipulate the model to find the response they were looking for. One participant commented that he felt he was managing the model after reviewing the first output rather than managing his preferences. Participants were required to make trade-offs between the forest indicators if they were not satisfied with the DST output; therefore, feelings of manipulation may be  3  Property owners were keen participants because the study concentrated on a small area allowing them to easily visualize consequences and relate outcomes that may affect their investment.  51  connected to the discomfort associated with making a hard trade-off from their initial preferences.  Finally, many participants did not agree with the data used to create the recreation indicator. As the summary reports indicate, this discrepancy in the data greatly impacted the way participants responded to this indicator. For example, some participants stopped allocating points to the recreation indicator altogether. Most participants wanted to keep the mill running; this decision greatly affected the recreation indicator. In the model, recreation is the only indicator found in every polygon in the Total Harvestable Land Base (THLB); therefore, more areas in the THLB are cut when the mill is open. As a result, the recreation indicator experiences a larger impact than the other indicators in the model. There was a general consensus among participants that the set-up for the recreation indicator was not appropriate. Participants believe that people adapt and continue to recreate as the landscape changes (or is harvested) over time. Furthermore, many participants noted that clearcuts can enhance rather than hinder the recreational experience for certain activities. 4.2 Initial Forest Value Preferences and Opinions of Decision Support Tools  Questionnaire 1 was administered at the beginning of each session to gain an overall sense of participants' perceptions towards different forest values in the area, as a well as a general understanding of peoples attitudes and knowledge towards DSTs.  The first section of this survey implemented questions on forest value preferences and opinions on sustainability. The purpose of Question 1 was to gain a sense of each participant's preferences towards various resource values within the Invermere TSA. The participant ranked each resource value based on its overall importance to society, the level of health and well-being derived from the resource value, and the level of knowledge the participant had about the  52  value. These questions were answered on a scale from 1 (low importance /satisfaction/knowledge) to 5 (extremely important/satisfied/knowledgeable). Table 4 provides a frequency distribution of participants' highest priorities (ranking of 4 or 5) for each resource value in the Invermere TSA.  Table 4. Summary of Participant's Priorities towards Resource Values in the Invermere TSA.  Cultural/Historical Ecosystem Health and Biodiversity Jobs Recreation/Tourism Timber Supply Visual Quality Water  Importance 65% 90% 80% 90% 65% 50% 95%  Health & WellBeing 57.9% (19) 73.7% (19) 75% (19) 78.9% (19) 55.6% (18) 26.3% (19) 89.5% (19)  Knowledge 55% 75% 80% 75% 80% 90% 80%  The majority of participants found all resource values to be important or extremely important; however, variation exists between resource values. For example, 80% or more participants agreed that water, recreation, jobs, and ecosystem health and biodiversity are important indicators. Whereas fewer participants rated the cultural/historical, timber supply and visual quality resource values as important. Furthermore, the majority of participants are satisfied or extremely satisfied with the level of health and well-being derived from each resource value, except for visual quality. The data indicates 75% or more of the participants have a good knowledge of the resource values, however, the cultural/historical resource value is found to be the lowest with only 55% of participants having an extensive knowledge of this value.  The second question requires participants to provide their opinion on forest management practices; 65% of participants agreed that forest management in the Invermere TSA supports sustainable resources management. However, one quarter of the participants do not feel that forest management in the Invermere  53  TSA is sustainable. These participants cited over-harvesting as the main practice inconsistent with sustainable forest management; some participants elaborated on this point citing the mountain pine beetle salvage effort as a contributor to over-harvesting.  The final question in the forest values and sustainability section required participants to rate the six forest values found in the model from 1 (most important) to 6 (least important). Using frequency distributions for each rating unit, the collective group rated the resource values from most to least important as the following: 1) ecosystems at risk, 2) water, 3) employment, 4) recreation, 5) profit, and 6) visual quality.  The purpose of the second section in Questionnaire 1 was to gain more perspective on participants' opinions towards DSTs before conducting the exercise. The survey found that 80% of participants were either familiar with or had heard about DSTs, whereas the remaining 20% were unfamiliar with DSTs. Overall, 90% of participants agreed that DSTs should be used as a helpful aid in decision-making, but should only be used as a guide for making decisions, not as a means to determine the final solution. Participants were asked to identify the issues and problems that they felt were associated with DSTs. The responses varied amongst four key problems. The problem regarding assumptions received the most attention, with 50% of participants agreeing that too many assumptions are made by model developers (Table 5).  Table 5. Problems with Decision Support Tools DST Problem Assumptions Bias Complexity Manipulation None of the Above Don't know  Frequency Distribution (% of Participants) 50% 35% 25% 30% 15% 30%  54  Participants also provided feedback on factors that would make them feel more comfortable using DSTs. Again, the response was heterogeneous; however, 70% of participants agreed that it is necessary to understand and assess the assumptions of the model, and 80% of participants agreed that it is necessary to understand the general idea of why and how the model was built. Table 6 provides a breakdown of the frequency distribution of the responses to this question.  Table 6. Requirements to feel comfortable with the DST  Requirement Trust the output Trust development institution Understand assumptions Understand why and how the model was built Experience using the model None, I am comfortable None, I will never be comfortable  Frequency Distribution (% of Participants) 45% 45% 70% 80% 30% 0% 0%  The third section of Questionnaire 1 focuses on participants' knowledge of the area and their activities in the Dunbar/Templeton LU. The majority of participants felt that they were knowledgeable or extremely knowledgeable about the different resource values in the Dunbar/Templeton LU. This is expected as the recruitment criteria ensured participants had a vested interest in the Dunbar/Templeton LU.  Participants listed the environment, forestry, the provincial government, recreation (motorized and non-motorized), and tourism as the main connections to the forest in the Dunbar/Templeton LU (Appendix 5). Furthermore, participants listed hiking, fishing, and camping as the most frequently practised recreational activities in the Dunbar/Templeton LU (Appendix 6). Table 7 provides a breakdown of the participants' property ownership, working relationship, and license activity on the Dunbar/Templeton LU.  55  Table 7. Participants Relationship with the Study Area Activity Property Ownership Work Nearby or In the LU Licensee Water license Non-replaceable forest license Firewood permit Trapping/Hunting license  Frequency Distribution (% of Participants) 15% 60% 35% 15% 5% 5% 10%  Finally, participants were required to state any major concerns in the Dunbar/Templeton LU in an open-ended statement. Table 8 provides a quantitative content analysis, recording the numerical frequency of manifest phrases related to concerns for different resource values in the Dunbar/Templeton LU. This data shows participants are highly concerned with problems related to timber supply and forest health in the Dunbar/Templeton LU. Over-harvesting is mentioned as a concern for a declining timber supply and unbalanced age classes, whereas the key concern related to forest health is the Mountain Pine Beetle infestation. Other areas for concern include wildlife protection, recreation pressures, water protection, and profitability in the area.  The final section of Questionnaire 1 collected personal background information from each participant. In total, 15 males and 5 females participated in this study ranging between the ages of 36-85 years old. Participants live in various locations throughout the Invermere TSA including: Radium Hot Springs, Edgewater, Fairmont Hot Springs, the City of Invermere, Cranbrook, Golden, Wilmer, and Brisco. Table 9 outlines the education level of the participants; 75% of participants have a university or college degree.  56  Table 8. Participants Concerns for Forest Values in the Dunbar/Templeton LU Recreation  2 (7.4%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)  Timber supply 3 (11.1%) 1 (3.7%) 2 (7.4%) 1 (3.7%) 0 (0%)  0 (0%) 1 (3.7%) 0 (0%) 2 (7.4%) 1 (3.7%)  0 (0%) 1 (3.7%) 1 (3.7%) 1 (3.7%) 0 (0%)  0 (0%) 1 (3.7%) 2 (7.4%) 1 (3.7%) 1 (3.7%)  2 (7.4%)  7 (25.9%)  4 (14.8%)  3 (11.1%)  5 (18.5%)  Profit Industry ENGO Government Recreation Private property Total Pressure Type Response  4 5  Water  Wildlife  4  Forest Healthy 2 (7.4%) 1 (3.7%) 1 (3.7%) 2 (7.4%) 0 (0%)  Total Forest Sector Responses 7 (25.9%) 5 (18.5%) 6 (22.2%) 7 (25.9%) 2 (7.4%)  6 (22.2%)  One participants response to this question was unclear in the survey data; therefore, the sample size for this analysis is 19 participants. Forest health refers to disease and insect issues, including Mountain Pine Beetle infestations.  27 (100%)  Table 9. Education level of Participants  Education some high school high school some university/college University/college graduate degree  Frequency Distribution (% of Participants) 5% 5%  10% 75% 5%  4.3 The Output Summary from the Decision Support Tool Exercise  The number of scenarios conducted by participants ranged from 1 to 19. The average number of scenarios conducted by the collective group was 7.25 (standard deviation = 4.62), while the mode was 9 scenarios. Table 10 shows the number of scenarios conducted by each stakeholder group. The recreation group had the highest average at 9 scenarios, and the Private Property group had the lowest average at 3.25 scenarios. It was possible to compare standard deviations between stakeholder groups because the sample size is the same for each group. The standard deviation for the industry, ENGO, and Private Property group is low; thus, these groups conducted a similar number of scenarios within their stakeholder group. The government and recreation groups had larger standard deviations; thus, the number of scenarios conducted by participants in these groups varied. Table 10. The Number of Scenarios Conducted by Each Stakeholder Group Forest Stakeholder Group Industry ENGO Government Recreation Private Property  Average No. of Scenarios  Standard Deviation  7.5 8.25 8.25 9 3.25  1.73 2.363 7.805 5.888 1.708  Minimum No. of Scenarios 6 5 2 3 1  Maximum No. of Scenarios 9 10 19 17 5  58  The average preferred scenario is 5.6 for the collective group. The median and mode are both 5. The standard deviation is 4.096. The minimum chosen preferred scenario is 1 and the maximum preferred scenario is 17. On average, participants did not choose the initial scenario as the preferred scenario and instead chose a preferred scenario amongst the final scenarios conducted. The average difference between the total number of scenarios conducted and the preferred scenario is 1.65, and the median and mode is 1. The standard deviation between the difference is 1.69. Again, the Industry, ENGO, and Private Property groups have small standard deviations, whereas the Government and Recreation groups have large standard deviations. Table 11. The Preferred Scenario Chosen by Each Stakeholder Group Forest Stakeholder Group Industry ENGO Government Recreation Private Property  Preferred Scenario  Standard Deviation  6 6.25 5.5 8.25 2  0.816 1.5 5.916 6.291 1.155  Minimum Preferred Scenarios 5 5 1 2 1  Maximum Preferred Scenarios 7 8 14 17 3  The trade-offs between the first scenario and the preferred scenario for each indicator is shown in the following Figures 7a to 7f for the collective group as an increase, decrease, or no change.  40% 50%  10%  0 Decreased ■ Increased 0 Same  Figure 7a. Preference Changes Between the First and Preferred Scenarios: Profit  59  25%^25%  50%  o Decreased Increased ^ Same  Figure 7b. Preferences Changes Between the First and Preferred Scenarios: Employment  La Decreased mi Increased ^ Same Figure 7c. Preferences Changes Between the First and Preferred Scenarios: Recreation  20%^15%  65%  0 Decreased ■ Increased ^ Same  Figure 7d. Preferences Changes Between the First and Preferred Scenarios: Ecosystems At Risk  60  45%  50%  5%  o Decreased is Increased ^ Same  Figure 7e. Preferences Changes Between the First and Preferred Scenarios: Visual Quality  15%  45%  Decreased Increased ^ Same  Figure 7f. Preferences Changes Between the First and Preferred Scenarios: Domestic Watershed The charts clearly illustrate that certain indicators impacted participants initial preferences more than others. The Employment indicator and Ecosystems at Risk indicators were the only indicators in which the majority of participants increased their point allocation between their initial and preferred scenarios. The majority of participants decreased the point allocation from their initial preference scheme to the preferred scenario for the Profit and Recreation indicators. The Visual Quality indicator is the only indicator wherein the majority of participants kept their point allocation the same for both the initial scenario and the preferred scenario. The Domestic Watershed indicator did not indicate any decisive preference change, as both a decrease and increase in the point allocation between the first and preferred scenarios had a strong representation.  61  The pie charts for the individual stakeholder groups (Appendix 7) show the tradeoffs made between indicators for each group. There were general similarities amongst stakeholder groups upon review of the DST output. All stakeholder groups decreased the Profit indicator, except the Recreation group. The Recreation group was divided on whether they increased or decreased the profit indicator. Overall, the Employment indicator was increased; however, the Industry group decreased the emphasis on Employment from their initial scenarios and 50% of the Government group decreased the initial points allocated to Employment. All groups decreased the Recreation indicator. All stakeholder groups increased Ecosystems at Risk. However, the Recreation group was again divided on whether they increased or decreased the Recreation indicator. The collective data for the Domestic Watershed indicator does not illustrate a clear trade-off; interestingly, the data is very different when analyzing the sub-groups. All participants in the Industry group increased the points allocated to the Domestic Watershed indicator. The ENGO group was divided on whether to increase or decrease the emphasis on this indicator, whereas the other groups decreased the points on this indicator. The emphasis on the visual quality indicator either decreased or stayed the same; however, the Recreation group was divided between increasing and decreasing the points.  Figures 8a to 8f describe the average change in points for each indicator between the first and preferred scenario. This information will help explore the degree of preference change for each indicator. Figure 8a illustrates this change for the collective group of participants whereas, the remaining graphs show this change for each forest stakeholder group.  62  k`.^c'•`•^0^.^  e  ^,e, ^0 0q) ,o,^\10.^co^,,,co .0^ ^.4 e^c,\).(1'^_^00  ,.\\  ,  ek0  ca Initial Sc ill Preferred Sc  Figure 8a. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the Collective Group of Participants The most substantial increase between the initial and preferred scenario is shown by the Ecosystems at Risk indicator. The largest decrease from the initial to the preferred scenario is the Recreation indicator. Initially, the most points were given to the Ecosystems at Risk indicator; the Employment and Domestic Watershed indicators were also given high points. In the preferred scenario the most points were again given to the Ecosystems at Risk, Employment, and Domestic Watershed indicators. The difference in the preferred scenario is that more points were taken from the Profit, Visual Quality, and Recreation indicators and added to the Employment and Ecosystems at Risk indicators; the Domestic Watershed indicator did not change. Figures 8b to 8f illustrate the difference between stakeholder groups and the collective group.  63  30 25 20 C '5 15 0_ 10 5 0  .11 1 ^[I.  ,,,^,,^ 0^ c• 0 P \.  QC  e^.).(6.^2  r^\ .0  6\^0-^6°^,\co .),ez"^<e° ° °f  s  4  *q: '  0 Initial Scenario D Referred Scenario  Figure 8b. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the Industry Stakeholders The most substantial increase between the initial and preferred scenarios for the industry stakeholders is shown by the Ecosystems at Risk indicator; the Domestic Watershed indicator has a significant increase as well. The largest decrease from the initial to the preferred scenario is the Profit indicator. Initially, the most points were given to the Employment and Profit indicators. In the preferred scenario, the most points were given to the Ecosystems at Risk and Employment indicators; the Domestic Watershed indicator was close behind. The Industry stakeholders made negligible change to the Employment indicator.  Co a 0  eC.^'()C‘ e 6\ C)^e  e  (o  o Initial Scenario ■ Preferred Scenario  Figure 8c. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the ENGO Stakeholders  64  The most substantial increase between the initial and preferred scenarios for the ENGO stakeholders was shown by the Employment indicator; the Ecosystems at Risk indicator also had a significant increase. The largest decrease from the initial to the preferred scenario is the Recreation indicator; however, the Profit indicator is not far behind. Initially, the most points were given to the Ecosystems at Risk and the Domestic Watershed indicators, although the other indicators are closely weighted. In the preferred scenario the most points were given to the Ecosystems at Risk and Employment indicators. The ENGO stakeholders decreased the points allocated to the Profit, Visual Quality, and Recreation indicators allowing participants to add points to the Employment and Ecosystems at Risk indicators.  35 30 25 20 15 10 5 ^ 0^_  TEEL  -  117.;  111  e e^e c■"  oce  o Initial Scenario ■ Referred Scenario  Figure 8d. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the Government Stakeholders  The most substantial increase between the initial and preferred scenarios for the Government stakeholders was clearly the Ecosystems at Risk indicator. The largest decrease from the initial to the preferred scenario is the Recreation indicator. In the preferred scenario, the most points were given to the Ecosystems at Risk indicator, the Employment indicator was allocated the second highest number of points. The Government stakeholders decreased the points allocated to the Profit, Visual Quality, and Recreation indicators and added these points to the Ecosystems at Risk indicator.  65  30 25 42 20 42 15 0- 10 -5 --  0  cc"O• ,  60  ,,,Pc‘^e  R...0^cps '  (74  ■\'\Initial Scenario ■ Referred Scenario  Figure 8e. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the Recreation Stakeholders The Recreation stakeholders overall point distribution between the initial and preferred scenario did not indicate any strong trade-offs. The Ecosystems at Risk indicator has the most substantial increase between the initial and preferred scenarios for the Recreation stakeholders. The largest decrease from the initial to the preferred scenario is shown by the Domestic Watershed indicator. Initially, the most points were given to the Ecosystems at Risk and Domestic Watershed indicators. In the preferred scenario, the most points were given to the Ecosystems at Risk and Employment indicators. Interestingly, the Recreation stakeholders made only slight decreases in the Profit and Recreation indicators, whereas there are much larger decreases in the Domestic Watershed and Visual Quality indicators in order to add points to the Ecosystems at Risk and Employment indicators.  66  30 25 20 C  0 15 10 5  0 •  zse^„,›  e^ 4  ,0A1/4-^\09'^40^c.,Aco  \c-,  .q--er  400  ■ Initial Scenario  ■ Preferred Scenario'  Figure 8f. The Average Change in Points from the Initial Scenario to the Preferred Scenario for the Private Property Stakeholders The most substantial increase between the initial and preferred scenarios for the Private Property stakeholders was shown by the Ecosystems at Risk indicator and the Employment indicator. The largest decrease from the initial to the preferred scenario is the Recreation indicator. Initially, the most points were given to the Domestic Watershed indicator. In the preferred scenario, the most points were given to Domestic Watershed and the Ecosystems at Risk indicators. The Private Property stakeholders made very little change from the initial to the preferred scenarios; this is due to the participants unease with the computer and the few scenarios conducted. 4.4 Preference Impacts and Assessment of the Decision Support Tool Questionnaire 2 was administered at the end of the session to collect information on changes in participant's value preferences after completing the DST exercise. This survey also elicited an assessment of the model from the participants' perspectives. The first section addresses changes in participants' forest value preferences. The data indicates that 65% of participants believe their preferences in the Dunbar/Templeton LU changed as a result of using the DST while 35% of participants felt there was no change in their preferences. Table 12 classifies preference changes by stakeholder groups. All participants in the  67  Industry group indicated a difference in their preferences in the Dunbar/Templeton LU, whereas only 25% of the Private Property group believed their preferences were altered. The majority of participants in the ENGO and Recreation stakeholder groups believed the DST outputs helped re-evaluate their preferences. However, the Government group was divided on whether a change occurred in their preferences. Table 12. Change in Preferences by Stakeholder Groups after Using the DST  Sector Industry ENGO Recreation Government Private Property  Preference Change 100% 75% 75% 50% 25%  No Preference Change 0% 25% 25% 50% 75%  Furthermore, varying degrees of significance exist between participants' preference changes. The large majority of participants were divided between an insignificant to somewhat significant change from their initial stated preferences (Table 13). Table 13. Degree of Preference Change as a Result of Using the DST  Degree of Change Very Insignificant Insignificant Somewhat Significant Significant Very Significant  % of Participants with a Preference Change 0% 46.15% 46.15% 7.7% 0%  Table 14 provides a quantitative content analysis to summarize the reasons for preference changes provided by participants. This table records the numerical frequency of manifest phrases related to different forest values in the  68  Dunbar/Templeton LU. Participants stated which forest values contributed to a change in their initial preferences, while others cited the manipulation of points as a reason for a change in their preferences. Therefore, it was necessary to include the manipulation of points as a variable affecting stakeholders' preferences. For this analysis, all forest values mentioned by participants were included as a variable that impacted their initial preferences, regardless of whether the participant indicated whether it was a negative or positive impact. According to participants, the Employment and Ecosystem indicators had the greatest impact on participants' initial preferences.  Three key themes were listed by participants who did not experience a preference change. The three key themes are related to weak assumptions in the model, deeply entrenched personal preferences, and the requirement for additional information. Some participants mentioned that the assumptions behind the model did not capture the key trade-offs for some indicators. This weakness impacted any changes that may have occurred in their preferences. Further, some participants felt that their preferences were not altered because their deeply entrenched opinions could not be easily influenced by a DST. Also, one participant requested additional information regarding the model weighting in order to elicit changes in his/her preferences.  The second question in this section requires participants to re-rate the six forest values in order of importance if the participant felt their inherent preferences changed as a result of using the DST. Only 25% of participants indicated a change in their inherent preferences as a result of using the DST. Using frequency distributions for each rating unit, the collective group re-rated the resource values from most to least important as the following: ecosystems at risk, employment, water, recreation, visual quality, and profit.  69  Table 14. Forest Group Responses: Indicators Contributing to Changes in Participants' Initial Preferences.  Profitrelated Industry ENGO Government Recreation Private property Total Responses  Employment -related  Visual Qualityrelated  Recreation -related  Ecosystemsrelated  Domestic Watershed -related  0 1 (5.3%) 1 (5.3%) 0 0  3 (15.8%) 1 (5.3%) 1 (5.3%) 1 (5.3%) 0  1 (5.3%) 0 0 0 0  0 0 0 1 (5.3%) 0  3 (15.8%) 1 (5.3%) 0 1 (5.3%) 0  0 0 1 (5.3%) 0 0  Manipulate Points/ Evaluation of Outputs 1 (5.3%) 1 (5.3%) 0 1 (5.3%) 0  2 (10.5%)  6 (31.6%)  1 (5.3%)  1 (5.3%)  5 (26.3%)  1 (5.3%)  3 (15.8%)  Total Forest Sector Responses 8 (42.1%) 4 (21.1%) 3 (15.8%) 4 (21.1%) 0 19 (100%)  The second section in Questionnaire 2 gathered information on participants' opinions of the DST and its effectiveness in a public participatory process. This question required participants to state their level of agreement on a series of statements assessing the DST. Table 15 summarizes the participants' responses to this question and indicates the strengths and weaknesses of the model. Participants generally agreed 6 that the model provided an opportunity for stakeholders to have meaningful input into land use decisions, the model provided meaningful information to make better decisions, and would be a helpful tool when stakeholders are involved in the planning process. However, some participants did not agree that the model was useful in forecasting future outcomes for forest values, and did not agree that the model communicated the output clearly, or was efficient or timely. Table 15. Assessment of the DST Strongly Agree Agree  Provides the opportunity for stakeholders to have meaningful input into land use decisions Provides meaningful information to make better decisions Communicates the output clearly Developed a final scenario that he/she was satisfied with Was efficient/timely Was user friendly Was useful in forecasting future outcomes of forest values Would be a helpful tool when stakeholders are involved in the planning process  Neutral  Disagree  Strongly Disagree  10%  75%  15%  0%  0%  25%  65%  5%  5%  0%  10%  50%  25%  10%  5%  10% 20% 35%  60% 55% 45%  15% 15% 15%  0% 5% 0%  15% 5% 0%  10%  40%  30%  10%  10%  25%  60%  15%  0%  0%  6 80% or greater marked agree or strongly agree 71  The final questions in this survey focused on eliciting responses to improve the DST for future use. Table 16 outlines a quantitative content analysis of manifest content based on open-ended statements provided by participants to improve the DST. The most important improvement to the DST, according to participants, is improving the model's data sources and underlying assumptions. Other areas for growth in the model's development include changes related to spatial scale, the forest indicators, the interface, and the information provided to users. For example, some participants mentioned that including the entire TSA, instead of only one landscape unit would create a more accurate and holistic planning tool. Furthermore, some participants requested including more forest indicators; whereas, others wished for more clearly defined indicators. With regards to the interface, participants would like to see more detail in the output graphs such as spatial depictions, and graphical illustrations of the threshold limits. Some participants commented that a facilitator was necessary to help interpret the output graphs, and provide information on assumptions and limitations of the model. The data and assumptions variable in Table 16 is found to be the weakest aspect of the DST and requires the most improvement. This variable received the most criticism because many participants did not agree with the assumptions behind the data sources for the recreation indicator.  72  Table 16. Improvements to the Decision Support Tool According to the Stakeholder Groups  Industry ENGO Government Recreation Private property Total DST Improvement Responses  Spatial Scalerelated  Forest Valuesrelated  1 (4.2%) 0 (0%) 2 (8.3%) 1 (4.2%) 0 (0%) 4 (16.7%)  1 (4.2%) 1 (4.2%) 1 (4.2%) 1 (4.2%) 0 (0%) 4 (16.7%)  Model Data/ Assumptionsrelated 1 (4.2%) 2 (8.3%) 2 (8.3%) 2 (8.3%) 3 (12.5%) 10 (41.7%)  Interfacerelated 1 (4.2%) 0 (0%) 0 (0%) 2 (%) 0 (0%) 3 (12.5%)  Informationrelated 1 (4.2%) 0 (0%) 1 (4.2%) 1 (4.2%) 0 (0%) 3 (12.5%)  Total Forest Sector Responses 5 (20.8%) 3 (12.5%) 6 (25%) 7 (29.2%) 3 (12.5%) 24 (100%)  4.5 Linkages and Relationships between All Modes of Data Collection  The three modes of data collection, two surveys and the inputs from the DST, were applied to capture a thorough and complete review of the impacts from the DST exercise. The different modes for data analysis were linked to find relationships and make comparisons before, during, and after the DST exercise. This analysis compares initial preferences stated in the first survey to the initial preferences applied in the DST exercise, as well as compares participants preferred scenario in the DST exercise to the preferences listed in both surveys. Further comparisons were made between participants initial opinions of DSTs in the first survey and their final assessment of the DST in the second survey. Also, a correlation between the number of scenarios conducted and participants' level of satisfaction with the final output was analyzed.  The participants stated their initial opinions towards different forest resource values in a variety of formats. For example, participants were required to rate the importance of each value individually, rate the importance between the values, and allocate points to the indicators in the DST exercise. These different methods were used to ensure consistency among the responses for the collective group's initial preferences. The first questionnaire asked participants to rate the importance of the individual indicators on a scale from 1 to 5. All resource values were rated high (4 or 5) by participants as trade-offs were not required; however, some indicators were rated high by more participants than others. The following order was derived from the data: water (95%), ecosystems and biodiversity (90%), recreation (90%), jobs (80%), timber supply (65%), cultural/historical (65%), visual quality (50%). The pre-questionnaire also had participants rate the indicators from most to least important, causing participants to draw on their own preferences. The frequency distribution for the rating scheme for the collective group was the following: ecosystems at risk, water, employment, recreation, profit, and visual quality. As expected, the initial preferences provided in the DST exercise by the collective group match the  74  above rating scheme exactly. The average point allocation in the DST for the collective group shows water and employment was given very similar point allocations, as well as recreation and profit.  The collective groups preferred scenario in the DST was the following: ecosystems at risk, employment, water, profit, recreation, visual quality. The participants were required to re-rate the resource values in the second survey if they felt their inherent preferences changed. The average rating scheme for this group was the following: ecosystems at risk, employment, water, recreation, visual quality, and profit. While both methods show very similar orders, in the end, participants rated profit lower on the survey despite choosing a preferred scenario with profit rated higher. The rating scheme changed order from the preto the post-questionnaire for the water and employment indicators; this change was relayed in the DST exercise. The ratings also changed order for the visual quality and profit indicators in the questionnaires; however, this was not relayed in the DST exercise. The change from the initial scenario to the preferred scenario is not shown largely in the order, the main difference was found in the point allocation changes for each indicator.  Participants were asked to provide information on their knowledge and experience with DSTs in the pre-survey. Participants were deemed unfamiliar with DSTs if they had simply only heard of DSTs or if they knew nothing about these tools. Following the exercise, participants were required to provide feedback on their opinion of the DST. Participants unfamiliar with DSTs disagreed that the output was communicated clearly and were least satisfied with the output. The participants who were familiar with DSTs responded differently and were more likely to disagree that the DST was useful for forecasting. Different experience levels with DSTs produced different results with regard to the assessment of the DST.  75  In the pre-survey, participants were asked to list the problems associated with using DSTs for land use planning. This response was compared to the answers given in the post-survey following the DST exercise. Although, the responses varied among the list of problems in the initial survey, significant attention was paid to the assumptions in the model with 70% of participants agreeing that it is necessary to understand and assess the assumptions of the model. In the postsurvey, many participants criticized the model's underlying assumptions behind the different forest indicators. Thus, participants' initial concerns materialized.  There were seven participants who stated that their preferences did not change following the use of the DST. The majority of these participants conducted less than five scenarios in the DST exercise. Furthermore, on average these participants did not feel satisfied with the output and did not agree it was a useful forecasting tool. Interestingly, five of these participants chose a preferred scenario that was not their initial scenario.  Participants conducted a wide range of scenarios (1 to 19) before they felt able to choose a preferred scenario. Also, participants varied on their level of satisfaction with the DST output chosen for their preferred scenario. Further research would benefit from information on any correlations between the number of scenarios conducted and the participants subsequent level of satisfaction with the preferred scenario. Figure 9 displays the correlation data for the number of scenarios conducted versus the satisfaction level of participants.  76  • • • • 3^•  • •  •  • •  0 0^2^4^6^8^10^12^14^16^18^20 No. of Scenarios  Figure 9. Correlation between the Number of Scenarios and the Satisfaction Level (1 — low satisfaction; 5 — high satisfaction)  A weak correlation was found between the number of scenarios conducted by each participant and the level of satisfaction participants had with their preferred DST scenario. The Pearson correlation calculation found a positive linear relationship between these variables with a value of 0.377. This result suggests that participants' satisfaction and confidence in their preferred scenario increases as the user conducts more scenarios. For this study, the sample size was sufficient; however, to learn more about this particular relationship a larger sample size and further research is required to determine if a correlation exists.  77  5.0 DISCUSSION This chapter explores the effectiveness of a Decision Support Tool (DST) in a public participatory setting and the DSTs subsequent impact on stakeholders' initial forest value preferences.  Section 5.1 explores the key findings related to participants' forest value preferences and trade-offs made during the DST exercise. Section 5.2 provides an assessment of the DST with a discussion of the strengths and weaknesses of the model and areas for potential improvement. Limitations of the study and areas for further research are explored in Section 5.3 and Section 5.4, respectively.  5.1 Forest Value Preferences and Trade-offs  Few studies have been conducted to determine whether stakeholders involved in a public process change their forest value after assessing the outputs from a sustainable forest management DST. Much time and money is used inefficiently when stakeholders are not adequately educated and updated about interactions between forest values. A DST has the potential to provide forecasting information on forest indicators and to reduce the level of uncertainty found in forestry land use decisions (Nelson 2003; Conroy and Gordon 2003). The results indicate that 65% of participants believed a change occurred in their preferences as a result of using the DST. This is strong evidence supporting the use of DSTs to develop desirable decisions by building on the public's initial preferences.  5.1.1 Preference Changes On average, participants made few changes to the order of their forest value preferences between initial scenarios and preferred scenarios. The Domestic  78  Watershed and Employment indicators were the only indicators with a different position in the average preference order between the initial and the preferred scenarios in the DST exercise. Similarly, these small changes in the preference order are also shown in the preference ranking of forest values from the prequestionnaire to the post-questionnaire. The Domestic Watershed indicator is not heavily impacted by forest harvesting on the Dunbar/Templeton LU; therefore, this indicator was less important in the final preference order. In other words, participants found that the Domestic Watershed indicator does not require points to produce a favorable outcome over time. The Watershed indicator is not heavily impacted by harvesting because it encompasses a very small area in the LU. The importance of forecasting and educating stakeholders on harvesting impacts for certain indicators is shown in these results. In a public process, much time can be wasted on discussion around moot points. In these circumstances, a forecasting or visualization tool would help further stakeholders knowledge and allow them to spend time on indicators that require more consideration than others. The participants involved in the Arrow Forest District multi-criteria analysis pilot study conducted by Sheppard and Meitner (2005) found forecasting techniques to be helpful in sustainable forest management planning, as well. The participants agreed that techniques presenting alternative future scenarios can alleviate conflict and provide large amounts of information in a simpler format. Applying tools for decision-making has the advantage of creating a structured framework for information, amalgamating large amounts of data, and clarifying details on certain trade-offs (Hersh 1999).  The collective group did not change the order of the indicators to demonstrate a change in their preferences. Instead drastic changes in point allocations were made between indicators from the initial to the preferred scenarios. For example, the Employment and Ecosystems at Risk indicators were increased by decreasing the points on the Profit and Recreation indicator; the Visual Quality indicator stayed the same, and the Domestic Watershed indicator had either an increase or decrease in allocated points. Participants decreased points on  79  indicators that were less preferred and indicators that required fewer points to produce a desirable outcome. These points were usually added to indicators that were greatly impacted by forest harvesting, such as the Employment and Ecosystems at Risk indicators, to ensure that a favourable output was produced.  Not surprisingly, the Visual Quality indicator stayed the same for the collective group from the initial to the preferred scenarios. Participants indicated their indifference towards visual quality by rating this indicator with low importance in the pre-survey and through comments made during the sessions.  The Domestic Watershed indicator was increased by half of the participants and decreased by the other half. The sub-group data provides an explanation for this discrepancy. All representatives from the Industry group increased their points to the Domestic Watershed indicator; whereas other groups decreased their points or were divided on whether to increase or decrease their points to this indicator. A possible reason for this overall increase among Industry participants may be related to the significant overlap that occurs between Domestic Watershed areas and Ecosystems at Risk areas. If more points were given to the Domestic Watershed indicator, the output for the Ecosystems at Risk indicator would improve as well and allow more points to be allocated to the Employment indicator.  5.1.2 No Preference Changes  There were some participants (35%) who did not feel their preferences changed as a result of using the DST. Participants who felt their preferences did not change commented on three themes in their reasoning, including: weak assumptions in the model, deeply entrenched personal preferences, and the requirement for additional information on the model. The overarching theme amongst them is one of distrust in the model development. These findings indicate the importance of creating a transparent and simple model that  80  stakeholders can feel confident and comfortable using in the planning process. Distrust in the model will not aid participants' decision-making towards a desirable solution. This is a common issue among DSTs in the public planning arena. To alleviate participants' distrust associated with DSTs, Sheppard (2005) recommends involving stakeholders in the design process to provide improved transparency. Belton and Stewart (2002) suggest using more than one multicriteria DST or developing a hybrid tool to ensure that participants are interpreting the information correctly and building trust in the design and assumptions behind the applied tools.  Seven participants stated that their preferences did not change after using the DST. Incongruously, the majority of these participants chose a preferred scenario in the DST differing from their initial scenario. The majority of these participants conducted less than five scenarios and, based on their assessment of the model, were not satisfied with the model output. Potentially, if these participants had conducted more scenarios (participants satisfied with the model output conducted more than five scenarios) and been willing to make some difficult trade-offs, a more desirable output may have been forecasted.  Furthermore, many participants commented on their feelings of "manipulating" the model to produce a desirable output instead of changing their preferences. One participant commented that he felt he was managing the model after reviewing the first output rather than managing his preferences. However, participants were required to make trade-offs between the forest indicators if they were not satisfied with the DST output. Therefore, feelings of manipulation may be connected to the discomfort associated with making difficult trade-offs. Gregory (2002) has found that participants may develop negative emotions when forced to make trade-offs affecting desirable choices. Participants may be uncomfortable making trade-offs because they may not feel it is their place to make these decisions or they may be fearful of receiving criticism from others.  81  Participants respond to these negative emotions by responding with simple solutions such as the status quo, or by not responding at all (Gregory 2002).  5.1.3 DST Preference Scenarios  The average number of scenarios conducted in the DST exercise by the collective group of participants was 7.25. The average preferred scenario was 5.6. According to these findings, the majority of participants did not choose their initial or final preferences as their preferred scenario. Participants were not satisfied with the outcome their initial preferences produced after viewing the model's forecast for each forest value in the output graphs. These results may indicate that participants do not realize that their forest value preferences may not produce a desired and expected outcome. Conroy and Gordon (2004) conducted a pilot study to determine whether the use of computer-based materials enhanced public participation. The authours compared the satisfaction levels of two groups of participants at a watershed meeting. Satisfaction in one group was measured in a traditional meeting format, while the other group used a technology-based format using interactive geographic information systems (GIS) and related web materials. Participants in the meeting with the technologybased format reported a higher level of satisfaction and a stronger influence on watershed opinions than those participants in the traditional meeting.  The majority of participants seemed to require approximately five scenarios before they found a satisfactory outcome. Many factors contributed to participants requiring five scenarios to develop a satisfactory outcome. Firstly, some participants tried to trade-off by making small point allocation changes between forest values. Usually, a small trade-off between points did not produce a change in the model's output. Therefore, some of the first scenarios were used to test the model's sensitivity. Secondly, some participants used some of the middle scenarios to test the model, in an attempt to "manipulate" the output to create a satisfactory solution. These attempts sometimes produced a desirable  82  output for participants. However, participants were than forced to re-evaluate their preferences on the Dunbar/Templeton LU. Alternately, other participants actively fine-tuned their preferences to create the best outcome possible. On average, the preferred scenario was not the final scenario conducted. Participants wanted to conduct a few more scenarios to ensure that a more desirable output was not possible.  The standard deviation between the initial and preferred scenarios varied between stakeholder groups. The Industry, Environmental, and Private Property groups produced small standard deviations; whereas the Government and Recreation groups had large standard deviations. Large standard deviations occurred in these groups because one participant from each group conducted over ten scenarios. The user interface contains ten blank scenario rows on the screen, although all participants were informed of the option to open a new page to conduct more scenarios. Only two participants required this option; most participants found a satisfactory solution before conducting ten scenarios. However, the large standard deviations may also be attributed to potential preference variation between stakeholder representatives in these specific groups. The sample size satisfied the objectives of this study; however, a larger sample size from each group would help to determine if variation exists within stakeholder groups and further our understanding of polarized group dynamics.  5.2 Assessment of the Decision Support Tool  The DST was widely accepted by participants with enthusiasm and interest. However, participants were also able to clearly define major problems and weaknesses of the model and suggest some improvements to create a working DST.  Participants generally agreed (80% or more participants marked agree or strongly agree) that the model provided an opportunity for stakeholders to have  83  meaningful input into land use decisions, the model provided meaningful information to make better decisions, and would be a helpful tool when stakeholders are involved in the planning process. Harshaw et al. (2006) surveyed residents in the Radium Hot Springs and Invermere area regarding opinions and beliefs about sustainable forest management. This study indicated 64.7% of respondents strongly or mildly agreed that citizens of British Columbia require more opportunities for input into forest management. Furthermore, the study in the Lemon Landscape Unit in BC conducted by Sheppard et a/. (2003) concluded that the public is willing to participate in trade-off games with different levels of confidence. Together, these findings suggest that people are open to try new technology and willing to learn new tools to help further their understanding of forest resources and impacts from trade-offs.  Participants provided critical feedback on the weaknesses of the DST and possible improvements to further develop this tool as an aid in decision-making processes. Firstly, some participants found the model was not useful for forecasting future outcomes for forest values. Many participants were not comfortable with the assumptions built into the model for some of the indicators. These assumptions were implemented for the purposes of this study and did not emulate reality, particularly assumptions related to harvesting volumes and employment levels. To ensure the model was timely and participants could conceptualize the study area, an LU was used for the planning unit instead of the Timber Supply Area (TSA). These unrealistic assumptions may have imposed an undesirable impact on participants perception of the model's forecasting. Furthermore, many participants were not satisfied with some of the original assumptions built into the model for individual indicators, particularly the recreation indicator (discussed further below).  Secondly, some participants felt the model did not communicate the output clearly. Line graphs displayed the outputs for each indicator on the user interface. Participants may have encountered confusion with the output line  84  graphs because the dependent variable varied amongst indicators. For example, the profit indicator used a different measure ($/decade) in the output graphs than the other indicators (% achieved/decade). Another area of confusion may have been related to the different thresholds at 0% for the Ecosystems at Risk, Recreation, and Domestic Watershed indicators. Participants suggested graphical representation of these thresholds and visualization tools could improve the communication of these outputs.  Thirdly, some participants did not feel the model was efficient or timely. An efficient model output speed is very important to reduce participant fatigue (Nelson 2003). Significant effort was put into developing an efficient model with quick output speeds (approximately 20 seconds), but despite these efforts, some participants were unsatisfied.  The main problem with the DST is related to the underlying assumptions and data sources. The initial survey demonstrated participants primary concern with DSTs is related to the assumptions used to build the model. The second survey elicited responses for improvements to the DST and found that the model's data sources and underlying assumptions were the most important areas for improvement. Thus, this exercise did nothing to improve participants' confidence with the underlying assumptions used to build models. The literature describes that participants' discomfort and distrust with models is directly associated with the "black box" phenomenon. Stakeholders require more information regarding the limitations and assumptions of the model to validate the outputs (Gregory 2002, de Steiguer et al. 2003). For example, in this study, many participants did not agree with the data used to create the Recreation indicator. This discrepancy in the data greatly impacted the way in which participants responded to this indicator; some participants stopped allocating points to the Recreation indicator altogether.  85  DST's have potential to greatly reduce uncertainty by forecasting potential outcomes. However, if the underlying assumptions are incorrect, or the participant cannot clearly interpret the output, then the credibility of the model is greatly reduced. A correlation coefficient was calculated between the number of scenarios conducted and the level of participant satisfaction with the output. This weak correlation indicates that participation satisfaction decreases when fewer scenarios are run. These results may suggest that participants learn more about the underlying assumptions and potential trade-offs between indicators by conducting more scenarios, and therefore, arriving at an educated decision and increasing their satisfaction with their chosen preferred scenarios. Again, the study conducted by Conroy and Gordon (2003) supports this claim for using a technology-based format in citizen involvement to improve participants' satisfaction. However, more research is necessary to determine whether an increase in the number of scenarios or iterations of the model improves the participants' satisfaction with the output.  Other areas for growth in the model's development are related to changes in spatial scale, the forest indicators, more detail in the output graphs, and the information provided to users. Participants suggested that a larger, more realistic planning scale is important to clearly visualize the overall future impact from decisions made today. Some participants were divided between whether they would prefer a more holistic overview of all indicators; while others would like fewer indicators that are more clearly defined. Further improvements to the user interface included changes to the output graphs such as spatial depictions and graphical illustrations of threshold limits. Also, participants mentioned that a facilitator was necessary to help interpret outputs, as well as to provide information on the assumptions and limitations of the model.  86  5.3 Limitations of this Study  This study was conducted to explore the use of DSTs in trade-off analysis and decision-making with the public. While interesting results were generated, the study was limited by several factors including: the planning unit, user interface linkages, assumptions, and reduced data analysis.  This study used the Total Harvestable Land Base (THLB) in the Dunbar/Templeton LU as the basis for decision-making on land use issues. It was important to limit the model to a manageable area to ensure the feasibility and efficiency of the model. Also, it was necessary to limit the planning area to the THLB, otherwise the model would choose to meet the ecological and social indicators outside the THLB and meet the economic indicators inside the THLB; an unrealistic alternative. If the model was not limited to the THLB, participants would not be required to make trade-offs. For future research studies, a realistic planning area, such as a Timber Supply Area, is recommended for the model.  The time horizon for outputs and the regeneration of some indicators over time were two assumptions that limited the model's effectiveness in this study. Firstly, the model produced an output over time for each indicator. The time horizon was 100 years. Participants were required to provide their preferences for the forest indicators and the output displayed the result according to their present preferences. However, stakeholders and the public change their preferences over time (Shindler 2000); what an individual wants today may not be what the same individual wants in 50 years. Future studies may consider accounting for changes in preferences over time. Secondly, the original model built the Visual Quality and Domestic Watershed indicators with regeneration over time, whereas the Ecosystems at Risk and Recreation indicators did not have regeneration built into the framework. This difference created a difficult comparison of the outputs between these indicators. The outputs for Visual Quality and Domestic Watershed indicators would recover over time, whereas the Ecosystems at Risk  87  and Recreation indicators would decline slowly over time. This assumption was practical since the Ecosystems at Risk indicator was based on Old Growth Management Areas and old growth is not capable of regenerating in the 100 year timeframe. Also, modeling regeneration for recreation is a fairly new concept and difficult to forecast since recreation activities and locations can change rapidly over time. Thus, this limiting assumption must be accounted for and different methods of comparing outputs between these indicators should be studied.  The linkage between the user interface and the model requires further research. In this study, a weighting system was implemented to interpolate the points allocated to the scores in the model for each indicator. This weighting system was developed by the researcher and guided by expert advice, as well as numerous model runs. This method of interface integration with the model has the potential to introduce human error, bias, and inconsistencies in the data. It is recommended in further studies that the scores in the model be matched to the user input scores on the interface for simple and transparent linkages.  During the DST exercise, the facilitator observed many participants using the scenarios between the first and preferred scenario as "play" scenarios. Initially, trade-offs made between scenarios were set for analysis. However, many participants tried very different point schemes in the middle scenarios to test for sensitivity of the model. This limited the analysis of the study by preventing comparisons of participants' trade-offs between scenarios. In future research studies, it is strongly recommended to continue encouraging this activity as it is an important part of the learning process, as well as a trust building opportunity for the model. Some participants chose their "play" scenarios as their preferred scenario; this helped participants re-evaluate their initial preferences and furthered understanding of potential outcomes in the area.  88  5.4 Future Research  This exploratory study found that it was possible for stakeholders to change their initial preferences for a specific land use area as a result of using a DST. Therefore, this study creates the opportunity for further research. In particular, an explanatory study to determine why stakeholders make certain trade-offs between different forest values could be extremely beneficial to the field of tradeoff analysis. An explanatory study could help further our understanding of appropriate assumptions to build into decision support tools as well as the scope of these tools in a public participatory process.  This study could be expanded by comparing the results of the individual DST sessions to the results of using this DST in a group setting. A study such as this could explore the interactions between various stakeholders and the processes used to make decisions as a group. DSTs have the potential to drastically decrease conflict and improve productivity. A study using a DST to explore group dynamics would further our knowledge of the scope of DSTs in public participation. This study could be set-up as a controlled experiment using a stakeholder group that assesses various management alternatives using a DST compared to a control group that assesses the same management alternatives without a DST. This study has the potential to determine if the DST can reduce conflict, improve time efficiency, and produce a solution that participants are satisfied with when compared to the control group.  Development of the user interface is integral to helping people understand and interpret the appropriate information in the DST. A study testing different user interfaces using the same working model may be helpful to determine the impact of the interface on participants' satisfaction with the outputs, as well as reducing fatigue and maintaining the participants' interest. A selection of interfaces may include slider tools, weighting calculators, interactive changes, GIS visualizations, or different types of graphs. Furthermore, developing an interface  89  that reduces the amount of instruction by a facilitator would be very beneficial to reduce any indirect bias from the facilitator and to increase the simplicity of the DST.  A further recommendation is to involve participants directly in the creation of the DST itself. Giving participants the opportunity to provide feedback throughout the development stage may help to build trust in the tool and may increase acceptance of the assumptions. This would be a transparent, joint learning process between participants and model developers and reduce problems that may arise after the model has been completed. There are many benefits to choosing this method for model development, although the process of involving the public stakeholders could be very lengthy.  Once a significant amount of research has been conducted to improve DSTs in the development phase and studies have been conducted to determine their potential in public participation processes, there are opportunities to produce a provincially accepted system to be used in Public Advisory Groups (PAG). This system could be specific to each TSA. However, continual updates would be necessary as rules and regulations pertaining to land use issues are constantly changing in the Province.  90  6.0 CONCLUSION Stakeholders and the general public are routinely asked to state their preferences for various forest management alternatives, while attempting to balance ecological, economic, and social objectives. This often leads to complex, lengthy processes and conflict between interest groups. Complex trade-offs between forest values are required to make decisions. However, trade-offs can be very difficult for stakeholders to evaluate. Trade-offs are often explored using simulation models to forecast potential outcomes of various forest management alternatives. This study explored forest stakeholders' preference changes and whether trade-offs were made between forest values while using a Decision Support Tool (DST). This study also examined the effectiveness and efficiency of the multi-criteria DST used in this research project.  The results from this study showed that 65% of participants believed that a change occurred in their preferences as a result of using the DST. The majority of participants did not choose their initial scenario as their preferred scenario. The average number of scenarios conducted in the DST exercise by the collective group of participants was 7.25. The average preferred scenario was 5.6. These results indicate that participants were not satisfied with the output forecasted from their initial preferences. Participants who did not experience a change in their preferences cited reasons related to distrust in the DST. Distrust in the model will hinder participants' decision-making towards a desirable solution. These findings indicate the importance of creating a transparent and simple model that stakeholders can feel confident and comfortable using in the planning process. The majority of these participants conducted less than five scenarios, and based on their assessments of the model, were not satisfied with the model output. Potentially, if these participants had conducted more scenarios and had been willing to make some difficult trade-offs, a more desirable output may have been forecasted.  91  This study also found that stakeholders will make trade-offs using the DST to find a desirable solution. However, the DST outputs show that the collective group did not change the order of the forest indicators to demonstrate a change in their preferences. Instead, drastic changes in point allocations were made between indicators from the initial to the preferred scenarios. In the initial and preferred scenarios, most points were allocated to the Ecosystems at Risk, Employment, and Domestic Watershed indicators. The difference in the preferred scenarios is that more points were taken from the Profit, Visual Quality, and Recreation indicators and added to the Employment and Ecosystems at Risk indicators.  Participants were also required to assess the effectiveness and efficiency of the DST for this study. Participants generally agreed that the model provided an opportunity for stakeholders to have meaningful input into land use decisions, the model provided meaningful information to make better decisions, and would be a helpful tool when stakeholders are involved in the planning process. However, some participants felt the model was not useful for forecasting outcomes, did not communicate the output clearly, and was not timely. Furthermore, participants noted some specific areas for growth and development in the DST. Participants stated that the underlying assumptions in the model requires further research, the spatial scale must be sufficient for making land use decisions, more detail and information is necessary, and integrating visualizations with the interface would improve understanding and build trust in the model.  New research is necessary to continue developing different aspects of DSTs. This research may include exploring the effectiveness of various user interfaces or refining the underlying assumptions in the tool, possibly by including stakeholders in the model development process. Further long-term research is required to explore stakeholders' interactions with these tools. In particular, an explanatory study to determine why stakeholders make certain trade-offs between different forest values could be extremely beneficial to the field of tradeoff analysis. An explanatory study could help further our understanding of  92  appropriate assumptions to build into decision support tools, as well as the potential of these tools in a public participatory process. Studies, such as this applied in the Invermere TSA, can further the development of DSTs to help find desirable decisions for sustainable resource management and create a productive and engaging process.  93  REFERENCES  Ananda, J. and G. Herath. 2003. Incorporating stakeholder values into regional forest planning: a value function approach. Ecological Economics 45: 75-90 Antle, J.M., J.J. Stoorvogel, C.C. Crissman, and W.T. Bowen. 2002. Tradeoff analysis as a quantitative approach to agricultural/environmental policy analysis. Proceedings: The Third International Symposium on Systems Approaches for Agricultural Development. Babbie, Earl. 2004. The Practice of Social Research. 10 th Edition. Belmont, CA: Wadsworth Publishing. Belton, S., Stewart, T.S. 2002. 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Decision dynamics with an application to persuasion and negotiation. Multiple criteria decision-making. In Multiple Criteria Decision Making. TIMS Studies in the Management Sciences, 6, edited by M.K. Starr and M. Zeleny (Amsterdam: North Holland Publishing Company), pp. 159177.  99  APPENDICES Appendix 1. Red- and Blue-listed Species with the Potential to Occur in the Invermere Timber Supply Area (TSA).  RED-LISTED (Endangered or BLUE-LISTED (Species of Concern) Threatened) Scientific Name Common Name Common Name Scientific Name Argia vivida Acrocheilus Chiselmouth Vivid Dancer alutaceus Buteo swainsoni Swainson's Aeronautes White-throated Hawk saxatalis Swift Falco mexicanus Prairie Falcon Ardea herodias Great Blue Heron herodias (Herodias subspecies) Wiles pennanti Asio flammeus Fisher Short-eared Owl Rana pipiens American Bittern Northern Botaurus lentiginosus Leopard Frog Rangifer tarandus Chrysemys picta Painted Turtle Caribou (Southern population) Tamias minimus Mead's Sulphur Least Chipmunk Colias meadii selkirki (selkirki subspecies) Taxidea taxus Dolichonyx Bobolink Badger oryzivorus Sandhill Crane Grus Canadensis Gulo gulo luscus Wolverine (luscus subspecies) Melanerpes lewis Lewis's Woodpecker Northern longMyotis eared Myotis septentrionalis Long-billed Numenius Curlew americanus Cutthroat trout Oncorhynchus clarki lewisi Otus flammeolus Flammulated Owl Salvelinus Bull trout confluentus Sharp-tailed Tympanuchus grouse phasianellus columbianus Ursus arctos Grizzly Bear Source: Invermere TSA Timber Supply Review #3 Analysis Report v. 3 (Brown 2004)  100  Appendix 2. Questionnaire 1  Survey #1: Forest Values and Decision Support Systems General Instructions: The purpose of this survey is to obtain general information on your perceptions of forest management issues and land use planning models (referred hereafter as Decision Support Systems (DSS)) from residents of the Invermere Timber Supply Area. We would like to thank you for participating in this study. Please remember that your identity will remain completely confidential, and the answers you provide will remain anonymous. If you feel uncomfortable with any question(s) you need not answer it (them). Your participation is purely voluntary. Please do not write your name anywhere on this questionnaire. • You consent to participate in this research by signing the consent form. • This questionnaire is not a test of your knowledge — there are no right or wrong answers. To ensure the quality of the results, we urge you to answer the questions as completely as possible. If you want to add more information to any question please feel free to do so. • When you have completed the questionnaire please close the booklet and ring the bell to let the researcher know you have completed the questionnaire. Thank you very much!  101  Section A. Forest Values and Sustainability Q1. Resource Values/Priorities We would like an overall sense of how you value and prioritize various aspects of the environment within the Invermere Timber Supply Area (TSA). You will be asked to give your opinion on a variety of resource values or outcomes of forest resource management in the Invermere TSA. For each resource value or outcome, there are three questions addressing:  Please place a check mark in the box with the best response.  • • •  The resource values overall importance or priority to society (1 = not at all important; 5 = extremely important)  Your level of health and well-being derived from the resource value  (1 = not at all satisfied; 5 = extremely satisfied)  The level of knowledge that you already have about the resource value (1 = little/no knowledge; 5 = extremely knowledgeable)  Resource Values Cultural/Historical: special places, features or aspects of the human-modified environment that represent historic events, community values, First Nation values, or past and current uses of the area Ecosystem Heath and Biodiversity: a range of ecological values arising from the biophysical environment, including vegetation, soils, fish and wildlife  Jobs: the numbers and types of employment sustained by forest resources (including timber, valueadded wood products, non-timber products, tourism, etc) Recreation/Tourism: opportunities for recreational experiences and activities for residents and visitors, as well as for tourism activities and related facilities Timber Supply: the availability, flow, and quality of timber resources in the Invermere TSA  Importance 1  5  Health & Well Being 1  5  Knowledge 1  5  ^ ^ ^ ^ ^  ^ ^ ^ ^ ^  ^ ^ ^ ^ ^  1  1  1  5  ^ ^ ^ ^ ^  1  5  5  ^ ^ ^ ^ •  1  5  5  ^ ^ ^ ^ ^  1  5  ^ ^ ^ ^ ■  ^ ^ ^ ^ ^  1 5 ^ ^ ^ ^ ^  ^ ^ ^ ^ ^  5 1 • 0 0 0 0  0 0 0 0 0  0 0 0 0 0  5 1 0 0 0 0 0  1 5 0 0 0 0 0  1 5 0 0 0 0 0  1  1  5  5  ^ ^ ^ ^ ^  1  5  ^ ^ ^ ^ ^  1  5  Visual Quality:  the appearance and aesthetic character of the landscape in the Invermere TSA  Water: aspects of water resources including supply, quality, and associated physical, biological, aesthetic, and economic values  1  5  ^ ^ ■ ^ ^  1  5  ^ ^ ^ ^ ^  1  5  ^ ^ ^ ^ ^  102  Q2. Sustainability 2a. How would you define sustainable resources management? (Describe in the space provided)  2b. Do you think forest management practices in the Invermere TSA support sustainable resources management? ^ Yes ^ No ^ Don't know  Why do you think that the practice(s) is inconsistent with sustainable management? Explain  Q3. Forest Value Preferences Drawing on your own beliefs and opinions, please rate these forest values in order of importance from 1 (most important) to 6 (least important) in the space provided. Ecosystems at Risk^Profitability^Employment Water^ Visual Quality^Recreation  103  Section B. Decision Support Systems We would like to learn more about your opinion towards Decision Support Systems (DSS). DSS are computerized information systems that support decision-making activities. DSS support decision-making by providing outputs that describe possible outcomes if particular management activities are implemented. (Please check ONLY ONE box, unless specified, with the answer that best matches your opinions and beliefs.)  Q4. Do you know anything about DSS models? ^ yes, I am familiar with DSS models ^ yes, I have heard about DSS models ^ no, I do not know anything about DSS models Q5. The advantage for using DSS models is: ^ as a helpful aid in decision-making; the model's output should determine the final decision ^ as a helpful aid in decision-making, but should only be used as a guide for making decisions ^ no advantage; DSS models should not be used in the decision-making process at all ^ none of the above ^ I don't know Q6. The ^ ^ ^ ^ ^ ^  problem(s) with DSS models is (mark all that apply): too many assumptions are made by the developers of the model bias is inherently built into the model too complex to understand the outputs used to manipulate decisions (not trustworthy) none of the above I don't know  Q7. To feel comfortable with the DSS model, I need to (mark all that apply): ^ trust the model output ^ trust the institution behind the model development ^ understand and assess the assumptions of the model ^ understand the general idea of why and how the model was built ^ build my experience using the model ^ none of the above, I feel comfortable with DSS models ^ none of the above, I will never feel comfortable with DSS models ^ other, please specify: ^  104  Section C. Area Knowledge and Uses This study focuses on the Dunbar/Templeton Landscape Unit (LU) in the Inveremere TSA (see map provided); therefore, we would like to explore your knowledge of the area and your participation in specific uses and activities in this area.  Q8. Dunbar/Templeton LU Knowledge We would like to get an overall sense of your knowledge of various resource values in this specific LU (NOT the TSA overall, ONLY the Dunbar/Templeton LU). Please check the box that best describes your level of knowledge for each value (1 = little/no knowledge; 5 = extremely knowledgeable).  Resource Values Cultural/Historical Values:  Knowledge  special places, features or aspects of the human-modified environment that represent historic events, community values, First Nation values, or past and current uses of the area  1  Ecosystem Heath and Biodiversity: a range of ecological values arising from the biophysical environment, including vegetation, soils, fish and wildlife  1  ^  5 ^ ^ ^ ^  5  ^ ^ ^ ^ ^  Jobs:  the numbers and types of employment sustained by forest resources (including timber, value-added wood products, non-timber products, tourism, etc)  Recreation/Tourism: opportunities for recreational experiences and activities for residents and visitors, as well as for tourism activities and related facilities  5 1 0 0 0 0 0  1  5 ^ ^ ^ ^ ^  Timber supply:  the availability, flow, and quality of timber resources in the Dunbar/Templeton LU  1 5 0 0 0 0 0  Visual Quality:  the appearance and aesthetic character of the landscape in the Dunbar/Templeton LU  1  5  0 0 0 0 0  Water:  aspects of water resources including supply, water quality, and associated physical, biological, aesthetic, and economic values  Other  1 5 0 0 0 0 0  (please specify): 1  5 0 0 0 0 0  105  Q9. What is your main connection to the forests in the Dunbar/Templeton LU? (Check all that apply to you.) ^ ^ ^ ^ ^ ^ ^ ^  art^ ^ mining^^ recreation (motor) education^^ NTFP^^ recreation (non-motor) environment^^ oil and gas^^ small business First Nations^^ organized labour^^ tourism Forestry^^ photography^^ trapping Guide Outfitter^^ provincial government ^ utilities Local Government^^ ranching/agriculture^^ value-added sector other, please specify: ^  Q10. Check all the activities that you participate in while visiting/inhabiting the Dunbar/Templeton LU. ^ ^ ^ ^ ^  hiking fishing canoeing camping gather food/medicine  ^ ^ ^ ^ ^  cross-country skiing backcountry skiing rock climbing ATV or 4x4 Other, please specify:  ^ ^ ^ ^  dog-walking horseback riding snowmobiling running  Q11. I own property on the Dunbar/Templeton LU. ^ Yes ^ No Q12. I work on or nearby (within 15 km) the Dunbar/Templeton LU. ^ Yes ^ No ^ Q13. I have a license in the Dunbar/Templeton LU. ^ water ^ trapping/hunting ^ woodlot ^ Other, please specify: ^ Q14. What are your major concerns regarding the Dunbar/Templeton LU, if any?  106  Section D. Background Information Your answers to these questions will not identify you in any way. Please remember, your answers will be kept confidential.  Q15. How old are you today? ^ years old Q16. Gender: ^ Male ^ Female Q17. What community do you live in? ^ How many years have you lived here? ^ Q18. What is the highest level of education that you have completed? (Please check one.) ^ ^ ^ ^ ^ ^  some high school high school some university/college university/college degree graduate degree other (specify): ^  Q19. What is your occupation? If you are a homemaker or a student, please state this. If you are retired or unemployed, please state this and list your former occupation.  Q20. What does your company/organization do? What industry or sector do you work in (eg forest industry, mining, government, education, services, tourism, etc)?  Please use this space for any further comments you may have.  107  Appendix 3. Questionnaire 2  Survey #2: Sustainable Forest Management and Decision Support Systems General Instructions: The purpose of this survey is to understand how your value preferences changed, if at all, after completing the DSS exercise, as well as an assessment of the model from your perspective. We would like to thank you for participating in this study. Please remember that your identity will remain completely confidential, and the answers you provide will remain anonymous. If you feel uncomfortable with any question(s) you need not answer it (them). Your participation is purely voluntary. Please do not write your name anywhere on this questionnaire. • You consent to participate in this research by signing the consent form. • This questionnaire is not a test of your knowledge — there are no right or wrong answers. To ensure the quality of the results, we urge you to answer the questions as completely as possible. If you want to add more information to any question please feel free to do so. • When you have completed the questionnaire please close the booklet and ring the bell to let the researcher know you have completed the questionnaire. Thank you very much!  108  Section A. Change in Forest Value Preferences We would like a sense of your understanding of any changes that occurred in your forest value preferences as a result of using the DSS.  Q1. Do you feel that any of your preferences changed as a result of using the Decision Support System? ^ Yes I=1 No To what degree did your preferences change as a result of using the DSS? (Check the box with the best response) o Very Insignificant 0 Insignificant 0 Somewhat significant  0 Significant 0 Very Significant  Explain.  Q2a. If you feel that your response changed from Questionnaire #1 due to participating in this study, please re-rank the forest values below. Rank the values in order of importance from 1 (most important) to 6 (least important) in the space provided. Ecosystems at Risk^Profita bility^Employment Water^ Visual Quality^Recreation  Q2b. Explain why there is a change or why there is not a change in your ranking of these values.  109  Section B. Assessment of the Decision Support System  The purpose of this section is to gather information on your opinion of the DSS. Q4. I believe the Decision Support System ^ (Please put an X in the box that best matches your level of agreement with the statement) Strongly Agree  Agree  Neutral  Disagree  Strongly Disagree  ^provides the opportunity for stakeholders to have meaningful input into land use decisions ^provides meaningful information to make better decisions  ^communicates the output clearly  ^developed a final scenario that I was satisfied with  ^was efficient timely  ^was user friendly  ^was useful in forecasting future outcomes of forest values ^would be a helpful tool when stakeholders are involved in land use planning decisions  110  Q7. In your opinion, how could the Decision Support System be more effective?  Q8. Further comments regarding the DSS.  Thank you very much for your participation!  111  Appendix 4. Interpolation Scheme for Each Indicator Profitability  Table 1. Interpolation Scheme for the Profit Indicator Points Score (%)  0 3 5  0 5 15  10 24 40  68 80 95  60  100  Rationale  no harvesting, mill shutdown  maximum harvestable area of LU (7373.51 ha)  Profit: Point Conversion 100% 90% 80% 70% 60% 112 50% 40% 30% 20% 1 10% - 1 0% 0^5 10 15 20 25 30 35 40 45 50 55 60 Points  Figure 1. Graphical Depiction of the Interpolation Scheme for the Profit Indicator  112  Employment  Table 2. Interpolation Scheme for the Employment Indicator Points Score (%) 0 3 5  0 5 15  10 20 40  68 80 95  60  100  Rationale no employment, mill shutdown, no harvesting mill shutdown, logging employment  operating at minimum harvest level = 189,131 m3  maximum harvest = 210,000 m3  Employment: Point Conversion 100% -90% 80% 70% .•••■■ 60% 50% 40% cn 30% 20% 10% 0% -  0^5 10 15 20 25 30 35 40 45 50 55 60 Points  Figure 2. Graphical Depiction of the Interpolation Scheme for the Employment Indicator  113  Ecosystems at Risk  Table 3. Interpolation Scheme for the Ecosystem at Risk indicator PointsScore (%)  Rationale  0  0  no harvestable OGMA reserved  8  15  less than current OGMA reserved  12  20  current OGMA reserved  15  40  greater than current OGMA reserved  25 40  60 75  60  100  all harvestable OGMA reserved for each EG  Ecosystems: Point Conversion 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 5 10 15 20 25 30 35 40 45 50 55 60 Points  Figure 3. Graphical Depiction of the Interpolation Scheme for the Ecosystem at Risk Indicator  114  Domestic Watershed Table 4. Interpolation Scheme for the Domestic Watershed Indicator PointsScore (%)  Rationale  0 2  0 5  max 100% of trees < 6 m (min. ECA) max 80% of trees < 6 m  4  10  max 60% of trees < 6 m  5  40  max 40% of trees < 6 m (current = 30%)  7 25 60  50 80 100  max 35% of trees < 6 m (moderate ECA) max 20% < 6 m (target = 25%) max 10% < 6 m (max. ECA)  Domestic Watershed: Point Conversion 100% 90% 80% 70% : c*" 60% -I p2 50% 0 • 40% c0 30% 20% 10% 0% 0  5^10^15 20 25^30^35 40 45^50^55 60 Points  Figure 4. Graphical Depiction of the Interpolation Scheme for the Domestic Watershed Indicator  115  Visual Quality  Table 5. Interpolation Scheme for the Visual Quality Indicator PointsScore (%)  Rationale  0  0  Maximum modification (MM): alteration to majority of viewscapes  1  1  some viewscapes are M and some are MM  4  5  Modification (M): activities are dominant but have characteristics that appear natural  7  10  Partial Retention (PR) in background; Preservation (P) in foreground (current practise)  15 20  30 60  40  85  60  100  minimal background Retention (R ) and P in foreground.  no alteration to any viewscapes - complete Preservation  Visual Quality - Point Conversion 100% 90% 80% 70% 60% -8 50% 0 (f) 40% 30% 20% 10% 0% 5^10 15 20 25 30 35 40 45 50 55 60 Points  Figure 5. Graphical Depiction of the Interpolation Scheme for the Visual Quality Indicator  116  Recreation  Table 6. Interpolation Scheme for the Recreation Indicator PointsScore (%)  Rationale  0 1  0 2  rec sites altered; no access rec sites altered; access maintained  2  5  some rec sites altered; access maintained  3  15  visuals from rec sites Partial Retention  5 7 20  20 50 70  visuals from rec sites Retention visuals from rec sites (current mgmt) visuals from rec sites Preservation  60  100  Preservation of all rec sites; access maintained  Recreation - Point Conversion 100% 90% 80% 70% 60% d 50% 40% Cl) 30% 20% 10% 0%  e  0^5 10 15 20 25 30 35 40 45 50 55 60 Points  Figure 6. Graphical Depiction of the Interpolation Scheme for the Recreation Indicator  117  Appendix 5. Participants Main Connection to the Dunbar/Templeton LU  Connection  Art Education Environment FN Forestry Guide outfitter Local Govt mining NTFP Oil and Gas Organized Labour photo prov govt ranching rec (motor) rec (non-motor) small business tourism trapping utilities value-added sector  % of Participants 5%  10% 45% 0% 70% 5% 10% 5% 10% 0% 0% 10% 20% 15% 25% 75% 0% 20% 10% 0% 0%  118  Appendix 6. Participants Activities in the Dunbar/Templeton LU  Activities  hiking fishing canoeing camping gather food/med cross country skiing backcountry skiing rock climbing ATV dog walking horseback riding snowmobiling running  % of Participants 75% 70% 45% 65% 15% 5%  20% 10% 5% 10% 5% 15% 10%  119  Appendix 7. Trade-offs between indicators made by each stakeholder group. Environmental Stakeholder Group  25%  0%  75%  o Decreased Increased ^ Same  Figure 1. Preference Changes for Profit  100%  Decreased • Increased ^ Same  Figure 2. Preference Changes for Employment  0 Decreased • Increased ^ Same  120  Figure 3. Preference Changes for Visual Quality - 0%  100%  o  Decreased ■ Increased o Same  Figure 4. Preference Changes for Recreation  0%  o Decreased ■ Increased o Same  Figure 5. Preference Changes for Ecosystems at Risk  0%  50%  50%  Decreased ■ Increased o Same  Figure 6. Preference Changes for Domestic Watershed  121  Private Property Stakeholder Group  50%  0%  o Decreased ■Increased ^ Same L _^-  Figure 7. Preference Changes for Profit  0%  50%  L  50%  o Decreased ■ Increased ^ Same  Figure 8. Preference Changes for Employment  0%  o Decreased ■ Increased o Same  Figure 9. Preference Changes for Visual Quality  122  50%  50%  0%  a Decreased Increased ^ Same  Figure 10. Preference Changes for Recreation  CIO 0%  50%^ 50%  10 Decreased 0 Increased ^ Same  Figure 11. Preference Changes for Ecosystems at Risk  50%  0%  o  Decreased Increased ^ Same  Figure 12. Preference Changes for Domestic Watershed  123  Recreation Stakeholder Group  0%  50%  50%  o Decreased • Increased o Same  Figure 13. Preference Changes for Profit  50%  o Decreased ■ Increased 0 Same  Figure 14. Preference Changes for Employment  50%  50%  0%  o  Decreased • Increased 0 Same  Figure 15. Preference Changes for Visual Quality  124  0%  o Decreased is Increased ^ Same  Figure 16. Preference Changes for Recreation  0%  50%  50%  10 Decreased m Increased ^ Same]  Figure 17. Preference Changes for Ecosystems at Risk  0%  0 Decreased • Increased ^ Same  Figure 18. Preference Changes for Domestic Watershed  125  Government Stakeholder Group  o Decreased m Increased ^ Same  Figure 19. Preference Changes for Profit  50%  25  o Decreased • Increased ^ Same  Figure 20. Preference Changes for Employment  50%  50%  0%  0 Decreased • Increased ^ Same  Figure 21. Preference Changes for Visual Quality  126  Decreased ■ Increased 0 Same  Figure 22. Preference Changes for Recreation  0%  73 Decreased Increased o Same ]  Figure 23. Preference Changes for Ecosystems at Risk  50%  25%  o Decreased • Increased o Same  Figure 24. Preference Changes for Domestic Watershed  127  Industry Stakeholder Group  50%  50%  0%  o Decreased IN Increased ^ Same  Figure 25. Preference Changes for Profit  CB Decreased is Increased ^ Same  Figure 26. Preference Changes for Employment  50%  50%  0%  o Decreased ■ Increased ^ Same  Figure 27. Preference Changes for Visual Quality  128  o Decreased o Increased ^ Same  Figure 28. Preference Changes Recreation  0%  o  Decreased ■ Increased ^ Same  Figure 29. Preference Changes for Ecosystems At Risk  100%  o Decreased ■ Increased ^ Same  Figure 30. Preference Changes for Domestic Watersheds  129  Appendix 8. UBC Research Ethics Board Certificate of Approval.  'inc  The University of British Columbia Office of Research Services  Behavioural Research Ethics Board Suite 102, 6190 Agronomy Road, Vancouver, B.C. V6T 1Z3  CERTIFICATE OF APPROVAL - MINIMAL RISK PRINCIPAL INVESTIGATOR: Thomas C. Maness  INSTITUTION / DEPARTMENT:  UBC BREB NUMBER:  UBC/Forestry/Forest Resources M .t  H06-03627  INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT: I^Institution^ I^ N/A^  Other locations where the research will be conducted:  Site^  I  N/A  The data will be collected in Radium Hot Springs, BC in a meeting room at the Radium Hot Springs Resort. The data analysis will occur at UBC Vancouver.  CO - INVESTIGATOR(S): Jacqueline Cavil! SPONSORING AGENCIES: International Environmental Institute - "Stability of stakeholder values in land management planning"  PROJECT TITLE: Application of a Land Use Planning Decision Support Tool in a Public Participatory Process for Sustainable Forest Management  CERTIFICATE EXPIRY DATE: April 19, 2008 DOCUMENTS INCLUDED IN THIS APPROVAL:  DATE APPROVED: April 19, 2007  Document Name  Consent Forms: Subject Consent Form Advertisements: Participant Recruitment Email Letter of Initial Contact Questionnaire, Questionnaire Cover Letter, Tests: Survey #2 Survey #1 Letter of Initial Contact: Confirmation Letter  Version  Date  N/A^March 19, 2007 N/A^April 13, 2007 N/A^March 5, 2007 N/A^March 5, 2007 N/A^March 5, 2007 N/A^March 2, 2007  The application for ethical review and the document(s) listed above have been reviewed and the procedures were found to be acceptable on ethical grounds for research involving human subjects.  130  Approval is issued on behalf of the Behavioural Research Ethics Board and signed electronically by one of the following:  Dr. Peter Suedfeld, Chair Dr. Jim Rupert, Associate Chair Dr. Arminee Kazanjian, Associate Chair Dr. M. Judith Lynam, Associate Chair Dr. Laurie Ford, Associate Chair  131  

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