1 ASSESSING THE SUSTAINABILITY OF NATUREBASED TOURISM OR RECREATION ALONG FLORIDA NATIONAL SCENIC TRAIL By BIN WAN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012
2 2012 Bin Wan
3 To my wife and son for encouragement, inspiration and support
4 ACKNOWLEDGMENTS This dissertation would not have been possible without the assistance and encouragement from several people. I thank my wife and my son for their encouragement, inspiration and support. I gratefully acknowledge my advisor, Taylor Stein, for being my mentor not only in the aspect of my academic program but also of my new American life. The other members of my committee also have my sincere thanks for all their inputs and assistance: Dr. Francisco Escobedo, Dr. Stephen Holland, D r. Martha Monroe, and Dr. Marilyn Swisher. I would like also to thank the School of Forest Resources and Conservation, and the Institute of Food and Agricultural Sciences for providing the graduate assistantship that allowed me to complete my program. My friends also deserve my sincerest apprec iation for their encouragement and help. Specially, I would like to thank Annie, Changfu, Evan, Fred, Lindsey, Namyun, Vern, Xiang, and Youchen. Lastly, but not the least, I thank my mother for her understanding and encouragement.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 8 LIST OF FIGURES ........................................................................................................ 10 LIST OF ABBREVIATIONS ........................................................................................... 12 ABSTRACT ................................................................................................................... 13 CHAPTER 1 INTRODUCTION .................................................................................................... 15 Defining the Problem .............................................................................................. 15 Statement of Problem ............................................................................................. 19 Purpose of Study .................................................................................................... 2 0 Significance of Study .............................................................................................. 22 Dissertation Format ................................................................................................ 23 2 EVALUATING NATURE BASED TOURISM OR RECREATION AREA ATTRACTIVENESS ALONG FLORIDA NATIONAL SCENIC TRAIL ..................... 26 Opening Statement ................................................................................................. 26 Literature Review .................................................................................................... 27 NatureBased Tourism, E cotourism, and Outdoor Recreation ......................... 27 Attractiveness of a NatureBased Tourism/Recreation Area ............................ 29 Methodology ........................................................................................................... 32 Study Sites ....................................................................................................... 34 Identification of Attraction Variables ................................................................. 35 Factor Analysis on Secondary Data ................................................................. 36 Primary Data Collection .................................................................................... 40 Selection and Utilization of Experts .................................................................. 40 Questionnaire Development ............................................................................. 42 Results .................................................................................................................... 44 Evaluation of Supply Attractiveness of Recreation Areas along the FNST ....... 44 Total Supply Attractiveness .............................................................................. 46 Evaluation of Demand Attractiveness of Recreation Areas along the FNST .... 47 Total Demand Attractiveness ........................................................................... 48 Overall Measure of Recreation Area Attractiveness ......................................... 50 Weight Differences and Measurement Correlations between Supply and Demand ......................................................................................................... 55 Discussion .............................................................................................................. 56 Theoretical Implications .................................................................................... 56
6 Supply and Demand Measures to Recreation Area Attractiveness .................. 57 Summary and Conclusion ................................................................................ 59 3 USING GIS TO EVALUATE ECOLOGICAL SENSITIVITY FOR NATURE BASED TOURISM OR RECREATION DEVELOPMENT ALONG FLORIDA NATIONAL SCENIC TRAIL .................................................................................... 61 Opening Statement ................................................................................................. 61 Literature Review .................................................................................................... 62 Methodology ........................................................................................................... 66 Study Sites ....................................................................................................... 66 Ecological Sensitivity Variables and Factors .................................................... 67 Vegetation .................................................................................................. 67 Soil ............................................................................................................. 69 Slope .......................................................................................................... 71 Outstanding Florida Waters ....................................................................... 72 GIS Spatial Analysis ......................................................................................... 73 GIS database ............................................................................................. 73 GIS analysis criteria (variables and factors) and procedures ..................... 73 Results .................................................................................................................... 76 Ecological Sensitivity by Habitat and Landcover .............................................. 76 Ecological Sensitivity by Soil Textures ............................................................. 77 Ecological Sensitivity by Outstanding Florida Waters ....................................... 77 Ecological Sensitivity by Slope ......................................................................... 77 Overa ll Ecological Sensitivity ............................................................................ 80 Ecological Sensitivity Scores of 16 Recreation Areas along the FNST ............ 81 Discussion .............................................................................................................. 83 Management Implications ................................................................................. 84 Model Implications ............................................................................................ 87 Limitations ........................................................................................................ 88 4 ESTIMATING THE FRAGILITY OF NATURE TOURISM BASED SOCIO ECOLOGICAL SYSTEMS ALONG FLORIDA NATIONAL SCENIC TRAIL ............ 89 Opening Statement ................................................................................................. 89 Literature Review .................................................................................................... 90 Methodology ........................................................................................................... 97 Study Sites ....................................................................................................... 97 Estimating Recreation Pressure ....................................................................... 98 Estimating Number of Visitors on the FNST ................................................... 100 A C onceptual Fragility Model .......................................................................... 102 Results .................................................................................................................. 104 FNST Visitor Counts and Recreation Pressure .............................................. 104 System Sensitivity .......................................................................................... 105 Fragility of Recreation Areas .......................................................................... 105 Phase State along Adaptive Cycle of Recreation Areas ................................. 107 Discussion ............................................................................................................ 111
7 5 CONCLUSION ...................................................................................................... 116 Attractiveness of Nature Based Tourism or Recreation ........................................ 116 Ecological Sensitivity of NatureBased Tourism or Recreation ............................. 118 Fragility of Nature Based Tourism System ........................................................... 119 Recommendations to Resource and Recreation Management ............................. 120 Future Research ................................................................................................... 122 APPENDIX A EXPERT PANEL SURVEY QUESTIONNAIRE .................................................... 124 B INSTITUTIONAL REVIEW BOARD APPROVAL .................................................. 132 C IN VITATION LETTER TO EXPERT PANEL FOR PARTICIPATING ONLINE SURVEY ............................................................................................................... 134 D REMINDER LETTER TO EXPERT PANEL FOR PARTICIPATING ONLINE SURVEY ............................................................................................................... 135 E COMPONENT SCORES COMPUTED FOR RECREATION AREAS BY DIMENSION VARIABLES ..................................................................................... 136 F DEMAND EVALUATION MEANS OF ATTRACTION DIMENSIONS BY RECREATION AREAS ......................................................................................... 139 LIST OF REFERENCES ............................................................................................. 140 BIOGRAPHICAL SKETCH .......................................................................................... 152
8 LIST OF TABLES Table page 1 1 Operational components of research questions ................................................. 22 2 1 Naturebased tourism/recreation attraction variables ......................................... 36 2 2 Factor analysis of naturebased tourism/recreation attractions .......................... 38 2 3 Supply importance of attraction dimensions ....................................................... 40 2 4 Organizational composition of resource and recreation experts ......................... 42 2 5 Number of years lived in Florida by resource and recreation experts ................. 42 2 6 Number of years of professional experience of resource and recreation experts ................................................................................................................ 42 2 7 Importance of naturebased tourism/recreation attraction dimensions ............... 43 2 8 Supply evaluation transformation of attraction dimension by recreation areas ... 45 2 9 Overall supply measure of attractiveness ........................................................... 47 2 10 Demand evaluation mean transformation of attraction dimension by recreation areas .................................................................................................. 48 2 11 Overall demand measure of attractiveness ........................................................ 49 2 12 Overall supply and demand measures of attractiveness .................................... 51 2 13 Score transformation to compare the overall measures of attractiv eness .......... 52 2 14 Overall measures of recreation area attractiveness ........................................... 54 2 15 Differences in percentages and rankings between supply and demand in attractiveness dimension weights ....................................................................... 55 3 1 Florida habitat and landcover sensitivity categories to nature tourism/recreation ............................................................................................... 69 3 2 Soil texture sensitivity categories to nature tourism/recreation ........................... 71 3 3 Slope angle sensitivity categories to nature tourism/recreation .......................... 72 3 4 The proximity to Outstanding Florida Waters sensitivity categories to nature tourism/recreation ............................................................................................... 73
9 3 5 GIS data sets of four variables ........................................................................... 73 3 6 Categorized ecological sensitivity class by accumulated sensitivity scores ........ 74 3 7 Average ecological sensitivity and relative ecological sensitivity score of 16 recreation areas .................................................................................................. 81 4 1 Correlations between FNST use level and state park attendance level in 2011 ................................................................................................................... 99 4 2 Types of visitor data on the FNST .................................................................... 101 4 3 2011 monthly FNST visitor use level over 16 recreation areas ........................ 104 4 4 Total sensitivity in 16 r ecreation areas along the FNST ................................... 106 4 5 Fragility of recreation areas .............................................................................. 107 4 6 Levels of fragility, management and activity impact and phase of Adaptive Cycle of recreation areas .................................................................................. 110 E 1 Component scores computed for recreation areas by dimensional variables I. 136 E 2 Component scores computed for recreation areas by dimensional variables II. ...................................................................................................................... 137 E 3 Component scores computed for recreation areas by dimensional variables III. ..................................................................................................................... 138 F 1 Demand evaluation means of attraction dimensions by recreation areas ......... 139
10 LIST OF FIGURES Figure page 1 1 Conceptual flow of ideas and research questions .............................................. 21 2 1 Map of study sites ............................................................................................... 34 3 1 Map of study sites ............................................................................................... 67 3 2 Flow chart of GIS analysis. ................................................................................. 74 3 3 Sensitivity reading points (recreation sites) over 16 recreation areas. ................ 76 3 4 Sensitivity score by habitat and landcover .......................................................... 78 3 5 Sensitivity score by soil texture........................................................................... 78 3 6 Sensitivity score by proximity to Outstanding Florida Waters. (Note: 0 = no sensitivity; 1 = low sensitivity.) ............................................................................ 79 3 7 Sensitivity score by slope angles ........................................................................ 79 3 8 Categorized ecological sensitivity. ...................................................................... 80 3 9 Relative ecological sensitivity value among 16 recreation areas along the FNST .................................................................................................................. 83 3 10 Categorized ecological sensitivity near Suwannee with sensitivity reading points (recreation sites) ...................................................................................... 85 3 1 1 Categorized ecological sensitivity near Ocala w ith sensitivity reading points (recreation sites) ................................................................................................. 86 4 1 The Tourism Area Life Cycle from Butler (1980). ............................................... 93 4 2 The Adaptive Cyc le proposed and illustrated by Holling (2001) ........................ 94 4 3 Map of study sites ............................................................................................... 97 4 4 2011 FNST visitor use patterns in 16 recreation areas ..................................... 105 4 5 Relationship between sensitivity (A*) and recreation pressure (U*) and fragility levels of 16 recreation areas along the FNST ...................................... 108 4 6 Spatial distribution of fragility of recreation areas with marks indicating the phase state of nature tourism/recreation system along Adaptive Cycle. .......... 111
11 4 7 Conceptual model indicating major influencing factors and variables in a decision support system for sustainable nature tourism/recreation .................. 113
12 LIST OF ABBREVIATION S DEP Florida Department of Environmental Protection FFS Florida Forest Service FNST Florida National Scenic Trail FWC Florida Fish and Wildlife Conservation Commission GIS Geographic Information System NPS National Park Service SJRWMD St. John River Water Management District SRWMD Suwannee River Water Management District USFS US Department of Agriculture, Forest Service USFWS US Fish and Wildlife Service
13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ASSESSING THE SUSTAIN ABILITY OF NATURE BASED TOURISM OR RECREATION ALONG FLORIDA NATIONAL SCENIC TRAIL By Bin Wan August 2012 Chair: Taylor Stein Major: Forest Resources and Conservation The sustainability of naturebased tourism/recreation was assessed from a socioecological approach at recreation areas along the Florida Nati onal Scenic Trail (FNST). At recreation area level, key indicators were quantified in order to d escribe naturebased tourism/ recreation areas in three key dimensio ns: 1) attractiveness, 2) ecological sensitivity, and 3) nature tourism based system risk factor s. First, for measuring naturebased tourism/recreation attractiveness, data collection included content analysis of websites from relevant public land managem ent units and an online survey administrated to an expert panel from various federal and state agencies and nongovernmental entities. Qualitative and quantitative statistical analyses were used to inventory and measure the existing attractions and their perceived importance. Second, for measuring ecological sensitivity, a GIS model was developed based on four key variables including soil, slope, vegetation, and water. Third, for creating a risk factor score of naturebased tourism/recreation, a fragility model was used to analyze the nature tourism based system, where fragility was modeled by integrating the social and ecological sensitivity with recreation pressure.
14 The findings demonstrat ed that nature tourism/recreation areas are not created equal. Diff erent areas have significantly different attractiveness related to perceptions about resource availability and desirability Areas also differed on ecological sensitivity, with six recreation areas being categorized as highly sensitive areas. Finally, the fragility model operationalized risk assessment for each recreation area showing three recreation areas at a high r isk of adverse outcomes in the system cycle. The analytical tool used here shows that areas can be better understood in their specific qual ities (i.e., attractiveness, ecological sensitivity, and fragility) to result in an overall risk factor. This risk factor provides an indication to natural resource managers and recreation and tourism planners on where their area falls on the evolution of naturebased tourism systems. For example, it can show decision makers if the natural area is in an early stage of development, midway through the development process, or on the verge of catastrophe. With this information, decisionmakers can then make appropriate management decisions; as well as, better tailor the marketing and promotion of these areas in respect to the qualities they possess and the risk they face.
15 CHAPTER 1 INTRODUCTION Defining the Problem As visitation to the worlds natural areas grows, naturebased tourism is recognized as a resourceintensive industry, which needs to be accountable in terms of sustainability at both local and global scales. Sustainable tourism is an emerging concept that requires all players in tourism (from the manager of recreation resources to the travel agent) to consider the complexity of the socioecological system in which tourism and recreation depends (But ler, 1999; Mowforth & Munt, 2009). Recent research has enhanced the understanding of the highly complex and intertwined issues of sustainable tourism, quality of life, equity and the environment (Butler, 1999; Collins, 1999; Farrell & Twining Ward, 2004; Hunter, 1997; Wall, 1997). Farrell and Twining Ward (2004) further emphasized that sustainable tourism needs to be conceptualized in a more comprehensive way in order to effectively assess its interconnectedness with the natural, social and economic elements at multiple scales and time periods. Although Hunter (1997) used the term adaptive paradigm, Farrel l and Twining Ward (2004) proposed adaptive management to construe sustainable tourism as the emerging new concept, which addresses issues of unpredictability of events, uncertainties about the outcome of events and complexities of scale and times. Nat ure based tourism, on one hand, is being promoted in rural areas based on sceneries and recreation. On the other hand, by providing access to these areas, the tourism and recreation it promotes can affect (often negatively) environmental characteristics (i .e., natural features such as clean water, fresh air and beautiful scenery). Inskeep (1991) stated that maintaining a high level of environment quality is
16 critical for the attractiveness of most types of tourism destinat ions. In their study on tourist s perceptions of environment impact, Hillery, Nancarrow, Griffin and Syme (2001) concluded that the richer biological and cultural values a site possesses, the more attractive and popular it may be, and the more likely it may be degraded because of heavy visit ation, which consequently reduces the quality of the experience for visitors. This dissertation examines natural areas using a socioecological approach, which is characterized by examining the multiple effects and interrelatedness of social and environmen tal elements in a complex system (Oetzel, Ting Toomey & Rinderle, 2006). In many areas throughout the world, and particularly in the United States, local naturebased recreation areas (e.g., state parks or greenways) have the potential to serve as tourism destinations. In fact, local decisionmakers are often looking to local naturebased recreation opportunities to simultaneously serve as tourism destinations, which could provide increased economic impacts to their local economies. The concept of sustaina bility is defined, interpreted and implemented differently by individuals, stakeholders and social groups It is often referred to as a balance or wise use of resources (Hunter, 1997). Four basic principles for the concept of sustainability have been c onsidered: (1) the idea of holistic planning and strategy making, (2) the importance of preserving essential ecological processes, (3) the need to protect both human heritage and biodiversity, and (4) the idea that productivity can be sustained over the lo ng term for future generations (WCED, 1987). Applying these concepts to sustainable tourism, the World Tourism Organization (WTO) says Sustainable tourism development meets the needs of present tourists and host regions while protecting and enhancing opportunities for the future. It is envisaged as leading to the management of all resources in such a way that economic, social and aesthetic needs can be fulfilled while maintaining
17 cultural integrity, essential ecological processes, biological diversity, and life support systems (WTO, 1996, p. 30). Ever since the publication of Bruntland Report, also known as Our Common Future, which alerted the world to the urgency of making progress toward economic development that could be sustained without depleting natur al resources (WCED, 1987), naturebased tourism or ecotourism has been considered as a type of sustainable development and promoted as a strategy to create economic impacts in rural areas or to alleviate poverty in developing countries particularly in prov iding economic returns to local residents and funding for natural resource conservation (Weaver, 2005). However, naturebased tourism/ecotourism is not a fail proof method for achieving these goals. In order to be sustainable, it must be carefully managed and monitored (Cusack & Dixon, 2006). Based on an understanding of the complexity and uncertainty of the socioecological system of which tourism is part, Farrell and Twining Ward (2005) proposed seven initial steps in understanding sustainable tourism in the context of complex system dynamics and suggested enhancing the systems resilience to disturbance to enable an effective sustainability. These steps highlight the fundamental requirements for sustainable tourism implementation involving factors such as recognition and understanding of adaptive management, learning from natural ecosystems, and integration of knowledge in different disciplines and of connected systems. Furthermore, Lu and Nepal (2009) emphasize that sustainable tourism can only be implem ented effectively if there are useful, reliable and comprehensible sustainability indicators available to evaluate visitor impacts.
18 The sustainability issue is particularly urgent in the wildlandurban interface (WUI). The WUI includes areas where urban lands meet and interact with rural lands and are thus a focal area for humanenvironment conflicts, such as the destruction of homes by wildfires, habitat fragmentation, introduction of exotic species, and biodiversity decline (Radeloff et al., 2005). Recreation, and potentially tourism, also play a major role in WUI areas. More specifically, since large populations with diverse sociodemographics often live within close proximity to WUI areas, the WUI often hosts a broad spectrum of tourism/ recreational behaviors and user expectations, which result in complicated management challenges (Ewert, 1993). As people are increasingly looking to nature to satisfy a diversity of needs, WUI areas face pressures from residential growth, landuse policy change, transport ation infrastructure, and the socioeconomics of the community (Duryea & Vince, 2005 ; Zhang, 2001). Stein (2005) points out, in order to sustain the benefits that a natural area provides, natural resource managers should work with their local stakeholders to identify a desired future condition for an area, and manage that area to achieve that condition, and to look beyond the sites they manage and identify how management affects and benefits surrounding ecosystems and communities. There are several underlyi ng tenets in this rationale. First, wildland areas in close proximity to large urban areas constitute a unique natural resource. Second, these interface areas serve important social and ecological functions for society. At the same time, however, these are as are increasingly vulnerable and impacted by high rates of human use and pressure. Third, many of the factors impacting interface areas such as population growth, environmental degradation and landuse policy will eventually influence more
19 remote wildland settings. As a result of growing pressure, the interface represents a set of challenges and management interactions that will be representative of future wildland management situations and subsequent research opportunities. Statement of Problem In Florid a, though its sunny beaches and theme parks attract the most tourists, its abundant natural assets such as springs, forests, swamp lands and wildlife are drawing a growing number of visitors; naturebase recreation and tourism are important players in the economic development of rural communities ( Shrestha, Stein & Clark, 2007; Stein, Clark & Rickards, 2003). In particular, many natural areas along the Florida National Scenic Trail (FNST), which traverses more than 1400 miles, spans most of Floridas divers e and rich ecosystems from the northwest to the south of the state and links a variety of communities across the state including rural, interface and urban areas. Although not all visitors hike on the FNST, the natural areas along the FNST attract millions of visitors annually offering vast ranges of natural beauties and entire spectrum of recreation opportunities. Many rural communities are promoting naturebased tourism as a strategy to create economic impacts However, increased visitation comes with neg ative social, economic, and environmental impacts (Gallardo & Stein, 2007; Milman & Pizam, 1988; Shrestha et al., 2007). In fact, some areas that have fragile ecosystems are experiencing high recreation pressure (Wan, Fisch, Bild & Stein, 2011); other areas possess unique attractions but lack high visitation, so both positive and negative impacts are less noticeable. Wan, Cucinella and Steins study (2009) showed that high visitation is particularly an issue for recreation areas in the interface. For exampl e, along the Florida National Scenic Trail, annual use levels of over 150,000
20 visits were observed for all sites located in the interface and urban areas while at most rural sites the annual use level s are generally below 5000 visits. Research is needed t o address sustainability issues associated with naturebased tourism as it is being promoted to achieve both economic and conservation goals and to provide operational indicators, which can help to implement sustainable tourism/recreation through planning. A ddress ing both social and eco logical constraints is a potential strategy to assist natural resource planners and managers in achiev ing these goals, but research has rarely examined tourism and recreation with such a perspective. Particularly, there is a lack of empirical research on the operational indicators to assess sustainability of naturebased tourism. Also missing are empirical analyses of how recreation attributes contribute to the attractiveness of recreation areas particularly along the FNST an d spatial analyses of how recreation characteristics such as location, pressure, timing and activity provision can be integrated into the ecological sensitivity of the resources hence supporting management to allocate resources between conflicting demands. In general, a systematic analytical tool is needed to aid public land managers in decisionmaking towards their sustainability goals. Purpose of Study The purpose of this project is to assess the sustainability of naturebased tourism/recreation along th e FNST by using a model of fragility (Z urlini, Amadio & Rossi, 1999) by incorporating evaluations of recreation areas attractiveness (Formica & Uysal, 2006) and ecological sensitivity for nature t ourism development (Olafsdottir & Runnstrom, 2009). The fol lowing research questions are developed to illustrate the objectives, to fill gaps in sustainable tourism literature, and to address the needs of decisionmakers (e.g., land managers, tourism professionals, and community planners)
21 when planning for naturebased tourism and recreation. The conceptual flow of ideas and research steps are shown in Figure 11 The operational components of three integrated research questions are shown in Table 11. Research question s 1 : What is the social sensitivity of naturebased tourism/recreation areas along the FNST as defined by their attractiveness dimensions? How does the interplay between demand and supply in determining the overall nature tourism attractiveness? Figure 11. Conceptual flow of ideas and research q uestions Research question s 2: Where can naturebased tourism/recreation be expanded and where must it be planned carefully along the FNST based on the evaluation of ecological sensitivity ? How does the sensitivity level apply to sustainable tourism planning incorporating with attractiveness and recreation pressure?
22 Research question s 3: Where do es naturebased tourism/recreation along the FNST fit in the context of the tourism area life cycle? How does the level of fragility apply to management strategies f or sustainability? Table 1 1. Operational components of research questions Research Question Variables Data Collection Outcomes 1 Tourism support facilities; Recreation facilities; Interpretive facilities; Landscape features (salt water, fresh water, land); Outdoor recreation activities (consumptive, motorized, social passive) Websites from relevant public land management agenci es; Resource and recreation management experts' evaluations Attractiveness score s from both supply and demand perspectives for re creation areas along the FNST 2 Habitat and landcover; Soil textures; Slope; Outstanding waters Public GI S database Ecological sensitivity score s for recreation areas along the FNST 3 Ecological sensitivi ty; Socia l sensitivity; Recreation pressure Results from research questions 1 & 2; Visitor use estimates on the FNST Level s of fragility of recreation areas along the FNST Significance of Study This study examined the underlying dimensions of n aturebased tourism/recreation attractions, the variables and factors that affect ecological sensitivity, and the key indicators that define the risk factor of a nature tourism based system through estimating fragility. This research is important for resource and recreation managers as it provides a quantifiable framework decision support system for sustainable nature tourism to operationalize management strategies. Results of this study demonstrate the importance of identifying and quantifying the major in dicators that determine sensitivity and attractiveness of naturebased
23 tourism/recreation, and affect the risk factors of nature tourism based systems. Findings enhance public land managers abilities to identify the current status of natural areas and provide an indication of how to manage future development based on key indicators. Along with providing an overall risk factor, this study provides several key variables that can be used in decisionmaking. First, findings provide effective analytical tools and weighting schemes to identify nature tourism attraction dimensions and a platform to simultaneously measure and compare attractiveness from both supply and demand perspectives. Second, understanding ecological sensitivity to nature tourism/recreation by incorporating temporal and spatial patterns of its location, pressure, and timing produce significant management implications in allocating resources between conflicting demands, and contributes to knowledge about the tourism/recreationnatural resourc e interaction, sustainable planning and decision support modeling Third, by providing useful indictors to estimate fragility, findings help the understanding of the temporal and spatial dynamics of risk factors associated with nature tourism based systems This research also further enhances the field implementation of sustainable nature tourism management particularly in recreation areas along the FNST. Recommendations based on this research will enhance decisionmakers abilities to design and test man agement strategies, which sustain the resources integrity and attractiveness while promoting nature tourism for long term benefits. Dissertation Format The dissertation is composed of five chapters in three thematic areas based on each research question. The three research questions build upon each other with the
24 first two research questions directly contributing to the solutions of last research questions. Chapter 2 addresses naturebased tourism/recreation areas attractiveness along the FNST as the s ocial sensitivity of the system. Specifically, a model was created to explain and measure the determinants of attractiveness of an area by measuring supply anddemand indicators. A guiding principle was used in which the overall attractiveness of an area d epends on the relationship between the availability of existing attraction features and the perceived ability of the area to provide such attraction features. Qualitative and quantitative statistical analyses were used to inventory, group, and measure exis ting naturebased tourism/recreation attraction features. Methods also included the perceptions of relevant natur e based recreation professional s o n each areas ability to provide key attraction features Results indicated if and how areas were different i n terms of resource availability and actual perception of these resources. Chapter 3 addresses ecological sensitivity of naturebased tourism/recreation areas along the FNST. A GIS model was developed based on classification of identified impact factors a nd variables. Also, classification algorithms were used to assess categories of ecological sensitivity. Resul ts categorized each area for it s sensitivity to ecological impacts based the spatial distribution of soil, slope, vegetation, and water. Chapter 4 addresses the sustainability analysis of each naturebased tourism/recreation area along the FNST as complete socioecological systems. Two models, one from Hollings conceptual sustainability model as an alternative to Butlers Tourism Area Life Cycle model, and a fragility model, were utilized to assess ecological social and risk agent factors through specifi ed indicators. Results indicated how an
25 areas current fragility level corresponded to particular phase within Hollings Adaptive Cycle. Chapter 5 provides an overall summary of how the research questions used within chapter s 2, 3, and 4 build upon each other and the resultant utility provided. This chapter included summaries of how ecological and social indicators integrate into a system analysis of sustainability of naturebased tourism/recreation along the FNST.
26 C HAPTER 2 EVALUATING NATURE BASED TOURISM OR RECREATION AREA ATTRACTIVENESS ALONG F LORIDA N ATIONAL S CENIC T RAIL Opening Statement The Florida National Scenic Trail (FNST) traverses from th e Alabama/Florida border in northwest Flori da to the Big Cypress National Pr eserve near the southern portion of the state ( Figure 21). There are about 45 designated natural sites along the FNST managed by a variety of city, county, state, and federal publ ic land agencies. These areas include most of Floridas diverse and rich ecosystems including pinehardwood forest, scrub, sandhill, tidal flat s, grassland, dry and wet prairies, cypress swamp, coastal strand, and others With abundant natural sceneries and wildlife and a full spectrum recreation opportunities, these designated natural areas along the FNST attract a growing number of naturebased tourism or recreation visitors. Nature tourism is an important activity for the economic, cultural and social well beings of many communities especially those in rural areas (Stein et al., 2003). Many communities, particularly in north central and northwest parts of the state are striving to attract more visitors to stimulate economic impacts (Shrestha et al., 2007) while some communities near interface and urban areas are concerned with negative impacts on social, environmental and economic aspects from overly heavy visitation (Gallardo & Stein, 2007). From a sustainability point of view, it is important to develop and manage nature tourism opportunities throughout Florida while simultaneously assessing the areas attractiveness Sustainable tourism is not so much about sustaining the industry of tourism, but it is more about maintaining a continuous flow of benefi ts that flow from the
27 successful implementation of nature tourism while allowing visitation (sometimes heavy visitation) to those natural areas. Planning for nature tourism is inherently complicated N ature tourism requires high ecological quality and integrity to ensure that the natural qualities of an area are sustained as well as provide for quality visitor experiences These visitor experiences are dependent upon attractive natural environment s ( Daily et al., 1997). The identification and analysis of existing patterns of nature tourism resources are critical steps in assessing the ability for an area to be able to attract visitors (Gunn, 1994). Therefore, i t i s important to develop a systemati c approach to evaluate the attractiveness of naturebased re creation areas. This chapter is a step toward developing a new systematic approach to the sustainable planning of naturebased tourism in natural areas. The concept of a ttractiveness is important in clarifying the sensitivity of an area based on the needs tourists place on an area. In other words, what aspects of those areas are potentially attractive to visitors, and how attractive are they? When this question is answered, nature tourism planners and managers can better understand the social sensitivity of their areas and better plan for how to market, plan, and manage those areas. Literature Review Nature Based Tourism, Ecotourism, and Outdoor Recreation The literature is replete with definitions of several terms used throughout this chapter Naturebase tourism, ecotourism, and outdoor recreation will frequently be used in this paper; therefore, this section will explain how these terms will be used. According to Valentine (1992), naturebased tourism is travel that primarily involves direct enjoyment of undisturbed natural environments. There is little disagreement
28 among researchers that naturebased tourism involves experiencing natural areas such as nature preserves, parks, refuges, and greenways through outdoor activities such as bird watching, hi king, fishing and so on. In contrast, ecotourism is d efined as travel to natural areas to understand the cultural and natural history of the environment, taking care not to alter the integrity of the ecosystem, while producing opportunities that make the conservation of the natural resources beneficial to local citizens (Western, 1993, p. 8). Ecotourism possesses a plethora of criteria or explanations such as education, sustainability, cultural sensitivity, conservation, and local benefits (Luo & Deng, 2 008 ; Holland, Ditton & Graefe, 1998). After nearly twenty years since the terms first appearance in the literature, the term ecotourism has received numerous other definitions; however, there is a general consensus that ecotourism should be (1) naturebas ed, (2) learning and educational, and (3) managed in a sustainable way (Weaver & Lawton, 2007). From a resource management perspective, Stein (2001) stated that naturebased tourism is a more neutral and descriptive way to illustrate the current use of nat ural areas as tourism attractions, and ecotourism refer s to an ideal situation and is possible through careful planning and management. In either naturebased tourism or ecotourism, the recreation opportunities provided in natural areas are often the focal point of decisionmaking for natural resource management (Page & Dowling, 2002; Stein, 2005). Whether the natural areas host more locals or tourists natural resource management must consider the recreation attributes of those areas, and manage those sett ings for the visitors they plan to serve (Stein, 2005). However, it is well recognized that tourists and local residents have different motivations for visiting and often expect different types of settings (Lang &
29 OLeary, 1997; M anfredo, Driver & Tarrant, 1996). Managers must consider the ecological capacities of the resource, but at the same time, recognize that local residents might attribute special m eanings to those areas, and these visitors might be extremely sensitive to any proposed changes to the area. For example, t ourists might have an entirely different perception of these same areas and expect a different set of opportunities and will likely be new to the area and expect destinations that they can easily navigate through and access educational and orientation information. As the types of visitors to areas begin to change (either by design or naturally) managers can benefit from an adaptive management framework to work within this complex socioecological system. Attractiveness of a NatureBased Tourism/Recreation Area Naturebased tourism can be a tool to help in sustainable development of loc al communities (UNEP, 2010). For a given state or local government, perhaps the most fundamental task is to reevaluate existing recreation and tourism res ources and capitalize on them to reposition themselves to attract tourist s. From an industry point of view, tourism, as a socioeconomic activity, does not occur randomly. Som e regions, destinations, or areas are more successful than others in offering tourism activities thus attract more visitors. From the perspective of resource management of a specific natural area, different area s attract tourists by providing unique recreation opportunities as a result of the management, the services desired by recreat ionists, and the natural attributes of the area. The identification and analysis of existing patterns of tourism resources are critical steps in assessing the potential for attracting visi tors to a given area (Gunn, 1994).
30 From a behavioral perspective, pe ople travel or participate in recreation activities because they are push ed by travelers motivations and pulled by destination attributes. Push factors are considered the socio psychological constructs of tourists that influence an individual to travel or to participate in recreation activities (Crompton, 1979; Dann, 1981). Pull factors, on the other hand, are believed to be the attractiveness of a destination and help people make destination choices. For a destination to respond effectively to the demand or to reinforce the effects of push factors, the attractiveness of a destination must be perceived and valued (Brayley, 1990). Previous research has shown that destination attractiveness is a function of the resource base (attraction) and of demand (those who are attracted). There is evidence to support that attractions are fundamental to tourism (Gunn, 1994) and that demand drives tourism (Dale, 1990). Research suggests that demand and supply may be independently or collectively used to measure tourism des tination attractiveness. The demanddriven approach is based on the belief that the travel destination reflects the feelings, beliefs, and opinions that an individual has about a destinations perceived ability to provide satisfaction in relation to his or her special vacation needs (Hu & Ritchie, 1993, p. 25). Similarly, Mayo and Jarvis (1981) believed that destination attractiveness depends on a combination of individual benefits and the perceived ability of the destination to deliver those benefits, while Kaur (1981) considered tourism attractiveness to be the drawing force generated by the overall attractions existing in a given place at a certain time. Therefore, the more an individual believes a tourism destination or recreation area will satisfy his or her needs, the more attractive that destination/area will be and the more likely it will be selected as a potential destination.
31 At the system level, the theoretical underpinning of assessing tourism attractiveness is partly based on the tourism system approach which implies that the functioning tourism system consists of an origin and a destination ( Gunn, 1994; Leiper, 1979; Mill & Morrison, 1985). As interpreted by Formica and Uysal (2006, p. 418), an origin represents the demand side of tourism, from which visitors originate. A destination, on the other hand, refers to the supply side of tourism that may have certain attractiveness power. The central elements of the system are the tourist and tourism attractions. The linkages, such as transportation, information, and marketing components, facilitate the tourist to reach their destinations. Demand can affect the conditions of supply, and vice versa (Formica & Uysal, 2006). The following assumptions suggested by Formica and Uysal (2006) will be used to guide the theoretical and methodological analyses of this study: (1) demandandsupply factors collectively and simultaneously influence the production and development of tourism goods and services, (2) the components of demand and supply generate the tou rist/recreationist experience, and (3) at specific destination or area level settings provide visitors with recreation opportunities to achieve desired experiences. Therefore, an analytical technique to measure attractiveness should combine the evaluation of existing resources (including physical, social, and managerial settings) and their perceived attractiveness. This chapter describes researc h (research question one) that attempts to assess the attractiveness of naturebased recreation areas along the FNST in the hope of providing a fundamental base for policy makers and land managers to begin planning for the sustainable management of these areas In other words it defines the attracti on
32 dimensions of these areas and how attractive they are. The specif ic objectives of this study are: (1) to inventory selected areas and define t he attraction dimensions (2) to calculate the supply attractiveness based on the importance and availability of attraction dimensions, (3) to determine the demand attractiveness based on perceived importance and perc eived availability of attraction dimensions, (4) to obtain the total measurement of nature tourism attractiveness in each recreation area, and (5) to identify attractiveness gaps between supply and demand. This study uses a tourism destination attractiveness model that builds on previous studies by applying new theoretical and analytical tool s to investigate the relationships between the supply and demand elements that contribute to the overall evaluation of destination attractiveness. An important outcome of this study is to define the sensitivity of study areas based on how attractive they are (i.e., social sensitivity) This information will then be integrated with ecological information and visitation pressure to ev entually develop a quantitative indicator of these areas fragility and their risk to be able sustain a healthy naturebased tourism system. Methodology The notion of attraction availability is applied from a supply and a demand perspective. In other words, supply is determined through identification and categorization of attraction attributes (e.g., availability of recreation activities, existence of unique natural attributes, presence of tourism ser vices and other attributes that potential visitors woul d find important when choosing an area as a tourism destination). Demand is measured by experts evaluations of the availability of those attracti on attributes for each area. T he ultimate measure of attractiveness for each area is
33 calculated by summing the weighted supply anddemand evaluations for each area ( Gearing Swart & Var, 1974; Lew, 1987; Nyberg, 1995). Based on a framework developed by Formica and Uysal (2006), this study ad a pts their nine step process to eight step s to measure the overall attract iveness of naturebased tourism/recreation areas along the FNST: 1. Define attraction variables that are associated with the attractiveness construct through a content analysis of websites of relevant public land management agencies ; 2. Collect data about attrac tion variables using each recreation area as a unit of measurement. Whenever a certain tourism/recreation attraction or service is discussed, it was recorded on the spreadsheet under its representative category, ( e.g. miles of hiking trail, numbers of canoe lunch and campsite under the categ ory of recreation opportunities ) ; 3. Perform factor analysis of collected data on attraction variables to identify nature tourism attraction dimensions ; 4. Obtain t he supply weights for each attraction dimension based on the sum of squared loadings (eigenvalues) of each attraction factor from factor analysis; 5. Calculate supply evaluation scores of the attraction dimensions for each recreation area by multiplying the component loadings from factor analysis with the original dat a collected on attraction variables ; 6. Determine the demand weights of attraction dimensions by surveying a panel of expert s on their perception of the importance of the nature tourism attraction dimensions ; 7. Determine demand evaluations of the attraction dim ensions by surveying the expert panel on their perception of the ability of each area to provide those attraction dimensions ; and 8. Calculate final supply and demand evaluation scores by summing t he weighted scores of attraction dimensions for each area. Th e resulting final evaluation scores indicate the overall attractiveness of recreation areas (i.e., social sensitivity) along the FNST as a function of demand and supply.
34 Study Site s Sixteen recreation areas were selected along the FNST, ranging from Blackw ater River in Northwest Florida to Big Cypress in Southern Florida (Figure 21). These recreation areas are managed by various public land agencies including US Forest Service, National Park Service, US Fish and Wildlife Service, Florida Department of Envi ronmental Protection, Florida Forest Service, Northwest Florida Water Management District, Suwannee River Water Management District, South Florida Water Management District, and Florida Fish and Wildlife Conservation Commission. Figure 21. Map of study sites For some recreation areas, the measurement unit was considered a combination of natural areas maintained by two or more agencies (e.g., Suwannee) while for other areas, a measurement unit was an individual site managed by a single agency (e.g.
35 Apalachicola). These measurement units (i.e., recreation areas) were determined based on a mix of what is defined as a FNST section by the US Forest Service and logical combinations of recreation destinations. Specifically, the following conditions were requi red for recreation areas to be included for the study sites: (1) areas that represent diverse geographic locations that the FNST traverses through, (2) areas that represent a variety of management agencies, and (3) areas where visitation data are available. Identification of Attraction Variable s Sixteen agency websites that described the setting qualities for the study areas were identified and examined. Content analysis was used to examine the c ontents of each website and revealed the frequency of characteristics that were mentioned. The overall purpose of this analysis was to place the contents of these websites i n specific attraction categories, such as, tourism support facilities, landscape features, recreation facilities, and outdoor recreation activiti es. Furthermore, each of these categories was broken down into subcategories for a comprehensive understanding of naturebased tourism/recreation along the FNST. Google Earth offered additional measurements of certain physical properties such as length of river in miles and area of lake in acres. Through the analysis of the data collected from this inventory distinctive naturebased tourism/recreation characteristics emerged For example, landscape and wildlife features, such as rivers/creeks, lakes, springs and wildlife species were consistently highlighted on most websites R ecreation activities, such as canoeing, hiking, mountain biking, horse riding, off highway vehicle ( OHV ) riding and boating as well as visitor service and facilities, such as visit or center and campground were also discussed on many websites
36 When the attraction var iables were identified, other secondary data sources were used to identify important tourism attraction items not mentioned on agency websites, but known to be important tourism attributes For example, Microsofts Streets and Trips (2011) map software provided data on lodges gas stations, and restaurants within a 10mile radius of study areas In conclusion, fifteen attraction variables were identified as measurable and available through secondary data sources (Table 21). All the inventoried attractions with their characteristics and features can be physically recognized and located along the FNST. Table 21. Naturebased tourism/recreation attraction v ariables Attractio n Variables Source Food Place (10 mile radius from entrance) Microsoft Streets and Trips Gas Station (10 mile radius from entrance) Microsoft Streets and Trips Motel/Hotel (10 mile radius from entrance) Microsoft Streets and Trips Visitor Center Agency website Campground Agency website Paddling Trail (mile) Agency website Hiking Trail (mile) Agency website Mountain Bike Trail (mile) Agency website OHV Trail (mile) Agency website Horse Trail (mile) Agency website Canoe Lunch Agency website Boat R amp Agency website Lake (acre) Agency website/Google Earth River/Creek (mile) Agency website/Google Earth Spring Agency website Note: OHV = Off Highway Vehicle. F actor A nalysis on Secondary D ata After completion of the inventor y f actor analysis was used to reduce the original data into a more manageable number of factors (Harman, 1976). Furthermore, factor analysis grouped the diversity of variables into categories that were statistically
37 independent of each other. There were three main steps of fact or analysis applied to this research. The f irst step was t o determine the minimum number of factors that explained the greatest amount of variance. P rincipal component analysis was selected as the appropriate factor model (Velicer & Jackson, 1990). The Kai sers criterion was the technique used for selecting the factor groups. Varimax orthogonal rotation was performed for better performance over Quartimax and Equimax in terms of stability and factor separation. Factor loadings less than 0.40 and eigenvalues smaller than 1.0 were dropped from analysis. The s econd step was to produce component scores that represent ed attraction scores of each are a on each attraction variables B ased on Smiths (1987) procedure, these scores were obtained by multiplying the component loading for a particular variable on a particular component by a recreation areas original score f or that variable. This process wa s repeated for every variable on a component. The products were then summed. This was repeated for every component for a study site and eventually for all study sites Finally the component scores were standardized with a mean of 0.0 and a standard deviation of 1.0. These scores were then subsequently tabulat ed to describe arealevel variations along the FNST. The init ial factor analysis of 15 attraction variables indicated a low Kaise Meyer Olkin ( KMO ) m easure of sampling adequacy (< 0.60). After dropping three variables with small communalities, including visitor center s, OHV trail s and lakes, KMO reached 0.603 and th e analysis continued.
38 The results of second round of factor analysis of the 12 attraction variables generated four factors with eigenvalue above 1.0. However, there were significant cross loading among these factors. A fter examin ation of a scree plot, the final round of factor analysis resulted in 3 factors with 12 variables, representing a subjects to variables ratio of 1.33:1 (16/12). The overall KMO was 0.603; the minimum value of all communalities was 0.676; the maximum was 0.994; and the mean value of communalities was 0.898 with a standard deviation of 0.095. There was little cross loading among the t h ree factors. There were three, four, and five loaded variables in the three factors respectively. The variable to factor ratio was 4 (12/3). Three factor s explaining 78.87% of the overall variance were identified as naturebased tourism/recreation attraction dimensions (Table 22). Each dimension was labeled based on the characteristics of the attraction variables that composed the dimension. Table 22. Fa ctor analysis of naturebased tourism/recreation attractions Variables/Factors Loadings Eigenvalues Variance Explained % Landscape Features & Facilities 4.486 37.382 Boat Ramps 0.960 Canoe Launches 0.918 Springs 0.906 Rivers/Creeks 0.795 Cam pgrounds 0.627 Tourism Support Facilities 3.268 27.232 Gas 0.967 Food 0.942 Lodge 0.939 Recreation Activities 1.710 14.252 Hiking Tails 0.545 Horse Trails 0.470 Paddling Trails 0.432 Mt Bike Trails 0.945 Total Variance Explained 78.866
39 The first dimension ide ntified was labeled landscape features & f acilities (LFF) because it consisted of five variables that stress the highest importance of naturebased tourism/recreation components, such as boat ramps, canoe launches, springs, rivers/creeks and campgrounds. This dimension explained 37.38% of total variance with an eigenvalue of 4.486. The second dim ension was labeled as tourism support f acilities (TSF) and is comprised of three variables: gas stations, food places and m otels/hotels in a 10 mile radius from the entrance. This dimension of attraction captured the second highest eigenvalue (3.268) and explained 27.23% of total variance. The third, recreation a ctivities (RA) dimension obtained an eigenvalue of 1.710, whic h explained 14.25% of total variance. This dimension is composed by four variables: hiking trails, horse trails, paddling trails and mountain bike trails. Factor analysis also generated the importance weights of the three naturebased tourism dimensions. S pecifically, the importance or weights assigned to each dimension were captured by the rotation sums of squared loadings. All of the loadings pertaining to each dimension were added. The squared loadings determine and measure the variance in a factor or di mension that is explained by each variable. Therefore, the sum of the squared loadings indicates the entire variance in naturebased tourism/recreation attraction that is explained by that attraction dimension. The three dimensions resulting from the factor analysis explained 78.87% of the variance in supply attractions. However, for the purpose of this study, the attraction dimensions were assumed to capture the entire attractiveness variance within the context of selected variable. As shown in Table 23, the l andscape f eatures & f acilities dimension o btained the highest
40 weight of 45.63%; the r ecreation a ctivities dimension obtained the lowest weight of 20.20% while the tourism s upport f acilities dimension captured 34.18% Table 23. Supply importance of at traction dimensions Factor Attraction Dimension Sum of Square d Loadings Percentage of Variance 1 Landscape Features & Facilities 3.61 45.627 2 Tourism Support F acilities 2.704 34.176 3 Recreation Activities 1.598 20.197 TOTAL VARIANCE 7.912 100 .000 Primary Data Collection Primary data were collected in the s pring of 2012 as the demand perception of attractiveness. All study participants were working and residing in Florida. The sampling frame where the group of resource and recreation experts was dr awn included the list of m embers of Florida National Scenic Trail Coalitio n maintained by US Forest Service and the list of chapter l eaders of Florida National Scenic Trail by Florida Trail Association. Initial invitation letters with web questionnaire were sent by email to the sample on February 16 and March 13, 2012. Follow up emails were sent to sample one and two weeks respectively after the invitation. By the April 13thSelection and Utilization of Experts deadline, 38 usable questionnaires were collected from a total of 58 invitations, with a response rate of 65.5%. The research instrument, the University of Florida IRB Approval invitation letter, and follow up letter can be fou nd in Appendix A, B, C, and D A group of experts i n resource and recr ea tion management along the FNST participated as r epresentatives were surveyed to determine the demand for the attracti on dimensions identified through the initial inventory and factor analysis. Panel
41 participants were asked to define the importance of the n ature tourism attraction dimensions and to evaluate the availability of attractions in each recreation area. Because they were chosen to represent visitor needs and perceptions, panel participants needed to be familiar with current and future demand for recreation areas along the FNST. Selected panel participants particularly needed to possess the following qualities: (1) thorough knowledge of the areas resource, recreation settings and its visitors and (2) ext ensive experience in the field After an init ial invitation and two follow ups, 38 experts com pleted the online questionnaire. Compared to visitors, tourism/recreation experts have an extended knowledge of visitors perceptions and preferences of the recreation areas due to their years of observations and consistent interactions with visitors. Their opinions are more reliable than the visitors who spend a limited amount time at a destination (Liu, 1988), in this case, a recreation area. Therefore, the professional involvement and consistent participat ion of experts at an area is likely to result in a solid knowledge of existing attractions. Table 2 4 identified the experts organizations. The largest group of experts was from the Florida Trail Association which is the federally designated user group t hat manages the FNST. Florida Forest Service personnel were the second highest number of participants. Two important characteristics of the expert s were considered: number of years lived in Florida and length of resource and recreation management professi onal experience. Table 2 5 and Table 2 6 suggest that a majority of experts lived in Florida and worked in the resource and recreation field more than ten years. Specifically, 74%
42 of experts have lived in Florida more than 30 years. Fifty eight percent of the experts have worked in the resource and recreation field for more than 10 years; 24% of them have been active in the field for more than 20 years. Table 2 4 Organizational c omposition of resource and recreation e xperts Organization Number of Expert F lorida Trail Association 16 Florida Forest Service 6 Florida Dept. of Environmental Protection 3 Florida Fish and Wildlife Conservation Commission 3 US Forest Service 2 US National Park Service 1 US Fish and Wildlife Service 1 National Forests in Fl orida 1 Florida Outdoor Recreation Coalition 1 Northwest Florida Water Management District 1 Suwannee River Water Management District 1 St. Johns River Water M a nagement District 1 Plum Creek Timber Company 1 TOTAL 38 Table 2 5 Number of years live d in Florida by resource and recreation experts Number of Experts (Percentage) Years Lived in Florida 3 (8%) 1 to 10 4 (10%) 11 to 20 3 (8%) 21 to 30 28 (74%) 31 and above Table 2 6 Number of years of professional experience of resource and recreati on e xperts Number of Experts (Percentage) Years Worked in Resource and Recreation 16 (42%) 1 to 10 13 (34%) 11 to 20 5 (13%) 21 to 30 4 (11%) 31 and above Questionnaire Development The questionnaire was divided into two sections. The first section wa s created to define the importance of Florida naturebased tourism/recreation attractiveness
43 attributes. The second section was created to evaluate the availability of attraction features at a recreation area level. First experts were asked to indicate t he naturebased tourism/recreation attraction importance by assigning percentages to the three attraction dimensions and their subcategories (i.e., landscape features & f acilities, tourism support facilities and r ecreation activities) Panel participants were asked to assign the percent of importance of each attraction feature ensuring that the final total resulted in 100% Second, participants were asked to rate the availability of attraction features using a 5point Likert scale The l andscape featur es and facilities dimension was considered the most important attraction dimension and captured 57.6% of Florida naturebased tourism/recreation attractiveness importance (Table 27) The r ecreation activities dimension was second in the order of importanc e with 33.1%, while the tourism support facilities dimension finished with the least importance with 9.3%. Table 2 7 Importance of natureb a sed tourism/recreation attraction dimensions Attraction Dimensions Importance Landscape Features & Facilities 57 .6 Interpretive Facilities 8.7 Outdoor Recreation Facilities 13.3 Fresh Water 12.4 Salt Water 13.0 Landscape 10.1 Recreation Activities 33.1 Consumptive Activities 6.7 Motorized Activities 5.6 Social Activities 7.1 Passive Activitie s 13.7 Tourism Support Facilities (Lodge, Gas, Food) 9.3 TOTAL 100.0
44 For the landscape features and facilities dimension, results showed that outdoor recreation facilities was the most important feature with overall variance of 13.3% while inter pretive facilities was the least important feature with overall variance of 8.7%. Noticeably, salt water and fresh water features captured the second and third most important subcategory within the dimension with 13.0% and 12.4% respectively. In the recre ation activities dimension, among all its subcategories, passive activities captured the highest importance with 13.7% while all other activities were substantially less important. Overall, among all Florida naturebased tourism/recreation attraction feat ures, passive recreation activities captured the highest importance with 13.7%; outdoor recreation facilities was second with 13.3%; while motorized and consumptive activities scored the least importance of 5.6% and 6.7% respectively. R esults Evaluation of Supply Attractiveness of Recreation A reas along the FNST The supply attractiveness evaluation score was obtained by multiplying the component loading for a particular variable on a particular dimension by a recreation areas or iginal score for that variable (Appendix E). All supply attractiveness evaluation scores were standardized (z s core) and shown in Table 28. Only three areas (i.e., Little Big E con, Greenway and Ocala) received positive z scores for the attraction dimension, tourism support facilities (TSF), which indicates that these areas have an above average amount of accessible tourism support facilities. A likely explanation for this is because all three areas are relatively close to densely populated areas A ll other recreation areas received negative z scores (i.e., below average number of tourism support facilities) Areas that were relatively remote, regardless of where they are found in the state, such as St. Mark s, Twin Rivers,
45 Osceola, Three Lakes and Big Cypress received the lowest supply evaluation scores for tourism support facilities. Table 2 8 Supply evaluation transformation of attraction dimension by recreation areas Recreation Area TSF z Score LFF z Score RA z Score Blackwater River 10.506 0.450 71.334 0.604 66.215 0.145 Econf ina 6.744 0.487 38.157 0.107 42.426 0.650 Apalachicola 17.072 0.385 110.553 1.446 96.687 0.501 St. Marks 7.611 0.478 3.135 0.859 77.090 0.085 Twin Rivers 0.967 0.544 27.903 0.327 34.440 0.820 Suwannee 14.318 0.412 59.250 0.345 104.726 0.672 Ocseola 5.721 0.497 6.348 0.790 43.665 0.624 Goldhead 19.065 0.365 5.934 0.799 7.650 1.389 Ocala 113.860 0.575 167.970 2.678 147.860 1.588 Greenway 217.850 1.606 1.881 0.886 57.405 0.332 Withlacochee 49.379 0.064 75.525 0.694 171.204 2.083 Li ttle Big Econ 369.981 3.113 17.904 0.542 25.644 1.007 Tosohatchee 39.871 0.159 25.731 0.374 95.494 0.476 Three Lakes 2.820 0.526 5.976 0.798 127.778 1.162 Kissimmee 9.545 0.459 65.169 0.472 39.170 0.720 Bi g Cypress 8.541 0.469 7.854 0.758 31. 585 0.881 Note: LFF = Landscape Features & Facilities; RA = Recreation Activities; TSF = Tourism Support Facilities; z Score = Standardized Score. O n the landscape features & f acilities (LFF) dimension, two national forests (Ocala and Apalachicola), two state forests (Blackwater River and Withlacoochee), one public use area managed jointly by Florida Fish and Wildlif e Conservation Commission and South Florida Water Management District (Kissimmee ), and one area jointly managed by state parks and the Suwannee River Water Management District (Suwannee) offer more landscape features and facilities than average (positive z scores) All other recreation areas fell below average (negative z scores) Ocala was identified as the richest of all the areas to offer l an dscape features and facilities. Similar to Ocala being a national forest, Apalachicola scor ed second. Benefit ing from the presence of hills and water systems, Blackwater River scor ed third.
46 T he recreation activities (RA) dimension seems to be associated with the presence of rivers/creeks, springs, campgrounds, canoe launches, and boat ramps. Withlacoochee and Ocala were identified as possessing the first and second most abundant recreation activities respectively while receiving scores among the highest f or their landscape features and facilities. Three Lakes scored third. Suwannee, an area composed of two state parks and Suwannee River Water Management District land, scor ed fourth Finally, Apalachicola, St. Marks, and Tosohatchee also offer above average recreation activities while the remaining areas provided a below average number of attractions Total Supply Attractiveness The tot al supply attractiveness score was obtained by multiplying the standardized evaluation scores ( i.e., z scores) by the import ance weight of each attraction dimension for each recreation area. Illustrated in Table 2 3, the importance weights of the three attractive dimensions a re the sums of squared loadings, which resulted from the factor analysis. The sums of squared loadings i ndicated the importance of the delineated dimensions that were calculated into percentages. Table 2 9 show s the total supply attractiveness scores of the sixteen recreation areas. Overall, Ocala was identified as the most attractive area among all study s ites due to its abundant attractions in all three dimensions. Three areas (i.e., Withlacoochee, Apalachicola, and Suwannee) ranked the second, third and fifth respectively with their strong supply of attraction features in both the landscape features and f acilities dimension and recreation activities dimension but weak in tourism support facilities dimension. Having below average supplies in two dimensions (LLF and RA), Little Big Econ ranked the fourth due to its high availability of tourism support facili ties.
47 Six areas (i.e., Econfina, Twin Rivers, St. Marks, Osceola, Big Cypress, and Goldhead) were identified as the least attractive areas mostly because they are unable to supply the average amount of attractions in all three dimensions, except St. Marks, which does offer above average amount of attractions in recreation activities dimension. Table 2 9 Overall supply measure of attractiveness Recreation Area TSF EW (34.18%) LFF EW (45.63%) RA EW (20.20%) Total Rank BWR 0.450 0.154 0.604 0.276 0.145 0.029 0.092 6 ECO 0.487 0.167 0.107 0.049 0.650 0.131 0.347 11 APA 0.385 0.132 1.446 0.659 0.501 0.101 0.629 3 SMK 0.478 0.164 0.859 0.392 0.085 0.017 0.538 13 TWR 0.544 0.186 0.327 0.149 0.820 0.166 0.501 12 SUW 0.412 0.141 0.345 0.157 0.672 0.136 0.152 5 OSC 0.497 0.170 0.790 0.360 0.624 0.126 0.656 14 GHD 0.365 0.125 0.799 0.364 1.389 0.281 0.770 16 OCA 0.575 0.197 2.678 1.221 1.588 0.321 1.739 1 GRW 1.606 0.549 0.886 0.404 0.332 0.067 0.078 7 WIT 0.064 0.022 0.694 0.317 2.083 0.421 0.715 2 LBE 3.113 1.065 0.542 0.247 1.007 0.203 0.614 4 TOS 0.159 0.054 0.374 0.171 0.476 0.096 0.129 9 TLK 0.526 0.180 0.798 0.364 1.162 0.235 0.309 10 KIS 0.459 0.157 0.472 0.215 0.720 0.145 0.087 8 BCY 0.469 0.160 0.758 0.345 0.881 0.178 0.684 15 Note: APA = Apalachicola; BCY = Big Cypress; BWR = Blackwater River; ECO = Econfina; EW = Evaluation Weight; GHD = Gold Head Branch; GRW = Greenway; KIS = Kissimmee; LBE = Little Big Econ ; LFF = Landscape Features & Facilities; OCA = Ocala; OSC = Osceola; RA = Recreation Activities; SMK = St. Marks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TSF = Tourism Support Facilities; TWR = Twin Rivers; WIT = Withlacoochee. The remaining five areas (i.e., Blackwater River, Greenway, Kissimmee, Tosohatchee, and Three Lakes) rank ed in the middle due to their below average availabilit y of attraction s in two different dimensions Ev aluation of Demand Attractiveness of Recreation A reas along t he FNST To determine the demand attractiveness of the recreation areas the expert panel was asked to rate their perceptions of the areas availability to offer attraction features from each dimension and its subcategories in sixteen recreation areas along the FNST
48 on a Likert type scale ranging from 1 to 5 They were provided with maps which identified a nd located each recreation area along the FNST. Appendix F illustrates the demand evaluation means of the three attraction dimensions by recreation areas along the FNST. In order to relate the demand attractiveness evaluations to the corresponding supply attractiveness evaluations, the mean scores generated from the 38 experts responses were translated into standardized scores. Table 2 10 illustrates the c hanges in recreation area scores across all three attraction dimensions. Table 2 10. Demand evaluation mean transformation of attraction dimension by recreation areas Recreation Area TSF z Score LFF z Score RA z Score Blackwater River 3.273 0.873 3.160 0.847 3.703 1.509 Econfina 2.000 1.433 2.900 0.082 3.200 0.168 Apalachicola 2.556 0.426 2.783 0.262 3.455 0.848 St. Marks 2.889 0.178 3.517 1.897 3.107 0.080 Twin Rivers 2.400 0.708 3.090 0.641 3.025 0.299 Suwannee 2.778 0.024 3.189 0.932 3.19 8 0.163 Ocseola 2.556 0.426 2.661 0.621 3.558 1.123 Goldhead 3.700 1.647 2.862 0.029 2.903 0.624 Ocala 3.600 1.466 3.201 0.968 3.826 1.837 Greenway 3.500 1.284 3.104 0.682 2.915 0.592 Withlacochee 3.375 1.058 2.929 0.168 3.356 0.584 Little Big E con 2.286 0.915 2.614 0.759 2.750 1.032 Tosohatchee 2.333 0.830 2.333 1.585 2.750 1.032 Three Lakes 2.000 1.433 2.333 1.585 2.750 1.032 Kissimmee 2.667 0.225 2.400 1.388 2.500 1.699 Big Cypress 2.750 0.074 2.875 0.009 3.188 0.136 Note: LF F = Landscape Features & Facilities; RA = Recreation Activities; TSF = Tourism Support Facilities; z Score = Standardized Score. Total Demand Attractiveness The total demand attractiveness score was obtained by multiplying the standardized evaluation scor es (i.e., z scores ) by the importance weight of each attraction dimension for each recreation area. Table 211 shows the total demand attractiveness score of the sixteen recreation areas.
49 Like its supply evaluation, Ocala received the highest attractivenes s evaluation among all study sites due to its highly perceived attraction availability in all three dimensions. Although not perceived to be able to offer above average amount s of attractions in RA dimensions, St. Marks ranked the second most attractive ar ea due to its perceived high availability of attractions in the TSF and LFF dimensions, in exactly the opposite direction of a supply evaluation. Perceived to be able to offer above average amount s of attractions in all three dimensions, Blackwater River and Withlacoochee ranked the third and fifth, respectively. Almost exactly matched with its supply evaluation, Suwannee scored the fourth and was perceived to be able to offer above average in two dimensions (LFF and RA) but below average in TSF dimension. Table 2 11. Overall demand m e asure of a ttractiveness Recreation Area TSF EW (9.3%) LFF EW (57.6%) RA EW (33.1%) Total Rank BWR 0.873 0.081 0.847 0.488 1.509 0.500 1.069 3 ECO 1.433 0.133 0.082 0.047 0.168 0.056 0.030 11 APA 0.426 0.040 0.262 0.151 0.848 0.281 0.090 8 SMK 0.178 0.017 1.897 1.093 0.080 0.026 1.083 2 TW R 0.708 0.066 0.641 0.369 0.299 0.099 0.205 7 SUW 0.024 0.002 0.932 0.537 0.163 0.054 0.589 4 OCS 0.426 0.040 0.621 0.357 1.123 0.372 0.025 10 GHB 1.647 0. 153 0.029 0.017 0.624 0.207 0.070 12 OCA 1.466 0.136 0.968 0.557 1.837 0.608 1.302 1 GRW 1.284 0.119 0.682 0.393 0.592 0.196 0.317 6 WIT 1.058 0.098 0.168 0.097 0.584 0.193 0.388 5 LBE 0.915 0.085 0.759 0.437 1.032 0.342 0.864 13 TOS 0. 830 0.077 1.585 0.913 1.032 0.342 1.332 14 TLK 1.433 0.133 1.585 0.913 1.032 0.342 1.388 16 KIS 0.225 0.021 1.388 0.800 1.699 0.562 1.383 15 BCY 0.074 0.007 0.009 0.005 0.136 0.045 0.043 9 Note: APA = Apalachicola; BCY = Big Cypre ss; BWR = Blackwater River; ECO = Econfina; EW = Evaluation Weight; GHD = Gold Head Branch; GRW = Greenway; KIS = Kissimmee; LBE = Little Big Econ; LFF = Landscape Features & Facilities; OCA = Ocala; OSC = Osceola; RA = Recreation Activities; SMK = St. Mar ks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TSF = Tourism Support Facilities; TWR = Twin Rivers; WIT = Withlacoochee.
50 Similar t o their actual abilities to offer attraction features, Osceola, Goldhead, Econfina, and Three L akes were perceived to be among the least attractive areas. Little Big Econ also was perceived to be among the least attractive areas with below average attractions in all dimensions; however, it actually does offer abundant features in the TSF dimension. The remaining areas ranked in the middle due to being perceived not able to offer above average attractions in one or two dimensions. Overall Measure of Recreation Area Attractiveness The last step to measure attractiveness involves sum ming the supply and demand measurements to create a final overall measure of recreation area at tractiveness Table 2 12 shows the overall supply and demand scores of attractiveness by the recreation areas along the FNST. Results show the range of supply scores is approximately the same range of the demand scores. The range of supply scores is 2.51; the range of demand scores is 2.69. A rank order correlation test was performed to identify if a significant direction association between the overall demand and supply measures of naturebased touris m/recreation attractiveness exists. The Spearmans rank order correlation coefficient was 0.34. This indicates a modest positive association between the supply and demand measures of recreation area attractiveness. Therefore, the greater the availability o f attraction, the higher the attractiveness perception of demand. The scores of Blackwater River, Apalachicola, Suwannee, Ocala, Greenway, and Withlacoochee were positive from both supply and demand perspectives. Noticeably, Apalachicola and Withlacoochee received substantially high supply scores and just above average scores on demand. The differences suggest that these two areas have more to offer than what is actually perceived by demand. These two areas thus have potential for further development.
51 On th e opposite side, Econfina, Osceola, Goldhead, Tosohatchee, Three Lakes and Kissimmee had all negative scores in both supply and demand perspectives. Both supply data and demand evaluations were consistent in considering those recreation areas as less attra ctive overall compared with average score These findings do not suggest that these areas are not attractive; however the results suggest that the attraction s offer ed may be limited to one or two attraction dimensions. For example, both Econfina and Osceola received above average scores on recreation activities, while Goldhead received an above average score in tourism support facilities. Table 2 12. Overall supply and demand measures of attractiveness Recreation Area Supply Demand Blackwater River 0.09 2 1.069 Econfina 0.347 0.030 Apalachicola 0.629 0.090 St. Marks 0.538 1.083 Twin Rivers 0.501 0.205 Suwannee 0.152 0.589 Ocseola 0.656 0.025 Goldhead 0.770 0.070 Ocala 1.739 1.302 Greenway 0.078 0.317 Withlacochee 0.715 0.388 Little Big Econ 0.614 0.864 Tosohatchee 0.129 1.332 Three Lakes 0.309 1.388 Kissimmee 0.087 1.383 Big Cypress 0.684 0.043 St. Marks, Twin Rivers, Little Big Econ and Big Cypress had scores with different directions. Except for Little Big Econ, the se are as received positive scores on demand and negative scores on supply. This suggests that these areas have less to offer than what is perceived by demand. I n fact, St. Marks received the highest score in landscape features and facilities by demand which caus ed the change of direction (Table 2 11 ). A
52 possible explanation is that St. Marks is located near the coastline, which seems to inc r ease the subjective evaluation. On the other hand, Little Big Econ has more attraction features to offer than what is percei ved by the demand (i.e., offering more than what is perceived by demand) Located near densely populated areas, Little Big Econ received the highest score in tourism support fac ilities by supply (Table 2 9 ) which likely explains the opposite scores. Altho ugh overall attractiveness scores for supply and demand provide meaningful information, the measurements required further data refinement in order to logically sum these two measurements into one overall attractiveness score. In order to solve this issue, two score transformations were performed. Table 2 13 shows the two step procedure utilized to create compatible of supply and demand scores. Table 2 13. Score transformation to compare the overall measures of attractiveness Recreation Area Supply Score Demand Score Supply Transformation Percent Demand Transformation Percent BWR 0.092 0.862 34.36% 1.069 2.457 91.34% ECO 0.347 0.423 16.86% 0.030 1.358 50.48% APA 0.629 1.399 55.76% 0.090 1.478 54.94% SMK 0.538 0.232 9.25% 1.083 2.471 91.86% TWR 0.501 0.269 10.72% 0.205 1.593 59.22% SUW 0.152 0.922 36.75% 0.589 1.977 73.49% OCS 0.656 0.114 4.54% 0.025 1.363 50.67% GHB 0.770 0.000 0.00% 0.070 1.318 49.00% OCA 1.739 2.509 100.00% 1.302 2.690 100.00% GRW 0.078 0.848 33.80% 0.317 1.705 63.38% WIT 0.715 1.485 59.19% 0.388 1.776 66.02% LBE 0.614 1.384 55.16% 0.864 0.524 19.48% TOS 0.129 0.641 25.55% 1.332 0.056 2.08% TLK 0.309 0.461 18.37% 1.388 0.000 0.00% KIS 0.087 0.683 27.22% 1.383 0.005 0.19% BCY 0.684 0.086 3.43% 0.043 1.431 53.20% Note: APA = Apalachicola; BCY = Big Cypress; BWR = Blackwater River; ECO = Econfina; GHB = Goldhead; GRW = Greenway; KIS = Kissimmee; LBE = Little Big Econ; OCA = Ocala; OSC = Osceola; SMK = St. Marks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TWR = Twin Rivers; WIT = Withlacoochee.
53 The first involved the transformation of the lo west supply and demand scores to zero thus making it a reference point. The rest of the scores were adjusted accordingly. For example, assigning a value of 0 to 0.770 would transform the next lowest score of 0.684 to 0.086 ( 0.684 + 0.77 = 0.086). The second step was to transform the new scale into a percentage scale. For example, the highest number in the new scales (2.509 for supply and 2.690 for demand) were assigned 100% and the lowest numbers (0.000) maintained a 0 score. The final measure of recreation area attractiveness as a result of the supply and demand attraction measures is ill ustrated in Table 2 14. Ocala ranked first apparently because of its high scores in all attraction dimensions by both supply and demand. Blackwater River, Withlacoochee and Apalachicola performed well above average in the two most important dimensions by demand, which are landscape features and facilities and recreation activities. Except receiving negative scores by supply in tourism support facilities, these three recreation areas appeared to be among the most complete in terms of the nature tourism attraction and ranked second, third and fourth respectively. Suwannee, ranked fifth, possessing well above average number of attractions in landscape features and facilities and recreation activities. St. Marks rank ed sixth due to being perceived to offer the m ost landscape features and facilities. Greenway, ranked seventh, offering above average attraction features in tourism support facilities and landscape features and facilities. Located near densely populated areas, Little Big Econ ranked eighth, which is t he richest in terms of tourism support facilities. Twin Rivers and Econfina obtained a positive (above average) landscape features and facilities score. However, because of the poor availability of other attractions the areas ranked ninth
54 and tenth respec tively. Big Cypress, Osceola and Goldhead, despite being above average in the availability of one attraction dimension, ranked eleventh, twelfth and thirteenth respectively. Because of poor availability in all attraction dimensions, Toshatchee, Kissimmee, and Three Lakes ranked at the bottom of all recreation areas along the FNST. However, they share one characteristic: they have more attractions than what is perceived by demand. Table 2 14. Overall m easures of recreation area a ttractiveness Recreation Are a Supply Demand Total Attractiveness Final Rank Blackwater River 0.344 0.913 1.257 2 Econfina 0.169 0.505 0.673 10 Apalachicola 0.558 0.549 1.107 4 St. Marks 0.092 0.919 1.011 6 Twin Rivers 0.107 0.592 0.699 9 Suwannee 0.367 0.735 1.102 5 Ocseola 0.045 0.507 0.552 12 Goldhead 0.000 0.490 0.490 13 Ocala 1.000 1.000 2.000 1 Greenway 0.338 0.634 0.972 7 Withlacochee 0.592 0.660 1.252 3 Little Big Econ 0.552 0.195 0.746 8 Tosohatchee 0.255 0.021 0.276 14 Three Lakes 0.18 4 0.000 0.184 16 Kissimmee 0.272 0.002 0.274 15 Big Cypress 0.034 0.532 0.566 11 As a clarification, the final scores shown in Table 214 were calculated to be comparable to each other. Therefore, 1.000 and 0.000 should not be interpreted as a recreation area that has a score of 1.000 is one that contains all of the possible attractions and an area with a score of 0.000 contains no attractions. In fact, they are the maximum and minimum relative scores for the evaluation.
55 Weight Differences and M easurement Correlations between Supply and Demand One of the assumptions, which this study based on, is that demandandsupply factors collectively and simultaneously influence the production and development of tourism goods and services. As a part of attr activeness evaluation, it is necessary to examine how differently the supply and demand scores measure their weights and evaluations. The supply importance of nature tourism attraction is measured by the sum of squared loadings of each attraction dimension (factor). The demand importance is measured by the weights assigned by resource and recreation management experts to attraction dimensions. From the supply perspective, the weights were generated by one observation (one time supply data collection and fac tor analysis), while on the demand side, the weights were evaluated by thirty eight experts. Table 215 shows the differences in weights and rankings from both perspectives. Table 2 15. Differences in percentages and rankings between supply and demand in a ttractiveness dimension w eights Tourism Support Facilities Landscape Features & Facilities Recreation Activities Supply 34.18 (2 nd ) 45.63 (1 st ) 20.20 (3 rd ) Demand 9.30 (3 rd ) 57.60 (1 st ) 33.10 (2 nd ) A o ne way ANOVA was performed on the demand wei ghts assigned by each expert to three attraction dimensions. The findings indicated an F value of 11.52 and significance level of < 0. 001 A correlation test was performed producing a correlation coefficient of 0.45 which indicates the importance ratings f rom demand are not significantly correlated with those measures from supply. Multiple correlation tests were performed between the supply and demand measures for each of three attraction
56 dimensions. Although no statistically significant correlations were f ound, the strongest correlation (0.36) was between the two measures in the recreation activities dimension; the medium correlation (0.19) was in the tourism support facilities dimension; and the weakest (0.09) correlation existed in the landscape features and facilities dimension. Both perspectives emphasized the most important dimension landscape features and facilities. The supply evaluation identified tourism support facilities as the second most important and recreation activities as the least important. Demand, instead, valued recreation activities as the second most important and considered tourism support facilities as the least important. D iscussion Theoretical Implications The results from this study support Formica and Uysals (2006) work that shows the objective importance of the attraction dimension is different from the subjective (perceived) importance of the same attraction dimension. The ANOVA that was performed on the demand measures showed significant differences in the distribution of attraction dimension weights. A correlation test that was performed with the attraction dimension weights from supply and demand measures revealed no significant correlation among the two sets of attraction weights. Contrary to Formica and Uysal (2006)s f indings, the strongest correlations between the measures from the two perspectives did not exist in the most important attraction dimension but correlations did exist in the weakest one. D ue to the differences between the final supply and demand measures, only the simultaneous analysis of the supply and demand measures of attractiveness will yield a useful indicator of the overall attractiveness of a recreation area.
57 This study is the first to adapt the tourism attractiveness evaluation framework from Form i ca and Uysal (2006) into the naturebased tourism or recreation attractiveness evaluation. Although there were different attraction variables and different attraction dimensions at the various context setting s, the results of this study support the necess ity of simultaneous measurements from both supply and demand aspects and further prove the interactive nature of supply and demand perspectives. Supply and Demand Measures to Recreation Area Attractiveness This study uses established analytical tools and adapts previous research on weighting schemes to identify naturebased tourism attraction dimensions, and simultaneously measures and compare s attractiveness from both supply and demand perspectives. By obtaining scores from two different perspectives, thi s approach provides a platform to investigate the interaction between supply and demand in determining the overall nature tourism attractiveness of a number of recreation areas. The ability to measure supply and demand attractiveness and analyze them simultaneously has many potential applications. For instance, from either supply or demand evaluations of recreation area attractiveness, the Ocala site performed highly in all attraction dimensions tourism support facilities, landscape features and facilities, and recreation activities. By comparison, the Blackwater River site received relatively weak evaluations in terms of tourism support facilities, and landscape features and facilities, but the area earned a relatively high recreation activities score. T he differences in evaluations and weights that existed between supply and demand (see Table 210, Table 2 12) suggest that discrepancies exist between the objective and subjective measurements of attractiveness. For example, St. Marks, Twin River, and Big Cypress sites had higher ranks by demand than by supply. Among the
58 possible reasons for this is that these three recreation areas are near the coast or within major water systems ; therefore, they might be perceived as offering a wider variety of attraction s than do other inland areas. There are significant managerial implications of this study. The St. Marks site is a good example. It ranked second in the demand attractiveness measurement and thirteenth on the supply measure. If considered separately, neit her of the two approaches would be highly usable for planning or promoti on. The two approaches went in almost opposite directions. The expert panel believed that this area had the highest availability of landscape features and facilities, and above average availability of tourism support facilities, while supply measur es were notably below average. The r ecreation activities dimension received a negative score on demand; however, the supply score was positive. It seems that St. Marks was perceived as a leadi ng nature tourism/recreation area along the FNST, but it might not delive r what it promises. Therefore, based on these results, planners could emphasize the creation and/or improvement of all three dimensions, particularly in tourism support facilities, to ensure that demand perceptions are matched by supply. Tourism support facilities are relatively adaptable components of nature tourism attractiveness, and can be adjusted based on demand trends. The components of the recreation activities dimension can al so be adaptable if there is appropriate support from landscape features and facilities dimension aspects. Although the landscape features components are hardly changeable, it is possible to emphasize the uniqueness of existing features through marketing or promotion materials
59 In contrast, areas that received higher supply scores than demand scores, such as Apalachicola, Little Big Econ, Tosohatchee, Three Lakes, and Kissimmee, have untapped potential. This is particularly true for those areas with highly a vailable attractions in landscape features and facilities that were considered particularly important For example, Apalachicola is abundant in landscape features and facilities, but the panel of experts does nt perceive this abundance. In this case, FNST nature tourism promoters should emphasize those attraction features so potential visitors will take advantage of the Apalachicola area. Similarly, the Three Lakes sites had above average scores on the recreation activities dimension but was not identi fied as such by demand. For potential nature tourism development at Three Lakes, promoters need to consult recreation planners to strengthen the tourism support facilities and landscape features and facilities dimensions. Summary and Conclusion In summary, as a naturebased tourism destination, the FNST and recreation areas along the way possess a unique mix of attractions. Noticeably, even the areas that have a lower level of general attractiveness are rich in at least one attraction component in a certain dim ension. Planners and promoters from federal and state organizations need to identify the unique attractive components of each area, and they should consider all recreation areas as a complete naturebased tourism product to offer to the public. There are some limitations of this study. Although the 15 attraction variables (12 after initial factor analysis) have been recognized in the literature as important attractiveness indicators in naturebased tourism/recreation, they might not be
60 comprehensive enough to cover as diverse an attraction as the 1400mile long FNST and will not relate to other naturebased recreation areas Another limitation has to do with the expert panel used to evaluate attractiveness. T h e expertise of 38 resource and recreation exper ts was not evenly distribute d among all the recreation areas along the FNST. Some areas had three respondents specifically associated with those areas while others had fifteen. It is possible that the uneven distribution of respondents over recreation areas might have influenced the demand data. Although resource and recreation management experts are more knowledgeable than visitors, the bias still exists. Also, t here is an unknown question of whether expert ratings of the attractiveness of an area are equi valent to perceptions of attractiveness among the general public A final limitation relates to the differing sizes of the recreation areas studied. The size variation of recreation areas could contribute some degree of evaluation bias towards larger sties, which generally offer more attraction features. One possible solution might be to use attractiveness values in proportion to recreation area size. This study was the first to attempt to adapt a tourism attractiveness framework to the naturebased touri sm/recreation field. Despite the limitations, the results of this study expand the knowledge on recreation area attractiveness measurement, particularly with the application of supply demand interactions. The primary results of this study, recreation areas attractiveness, also directly serve as a social component of sensitivity indicator of a socio eco logical system in the context of nature based tourism/recreation, and will be integrated with the ecological sensitivity study (Chapter 3) in the analysis of fragility of a socio ecosystem in Chapter 4.
61 CHAPTER 3 USING GIS TO EVALUAT E ECOLOGICAL SENSI TIVITY FOR NATURE BASED TOURISM OR RECREATION DEVELOPME NT ALONG F LORIDA N ATIONAL S CENIC T RAIL Opening Statement The Florida National Scenic Trail (FNST) traverses 1400 miles from northwest to south Florida (Sanborn, Belcher, Albritton & Stein, 2003). There are about 45 publicly managed nature areas along the FNST. As assessed in Chapter 2, various recreation areas along the trail draw millions of visitors annually to the diverse and rich natural beauties and abundant recreation opportunities. In fact, research has shown that approximately 350,000 people hike on the FNST annually (Wan et al., 2011), and that does not include the millions of other visitors who visit t hese recreation areas but do not hike on the FNST. T he richer the biological and cultural values a site possesses, the more attractive and popular it might be. Areas rich in these attractions are also likely to be highly sensitive to any type of impact including recreation visitation ( Hillery et al., 2001) Degradation of these ecological and cultural resources are a constant concern among public land management agencies, not only because these agencies missions are to protect these resources, but also because these resources are the primary reasons people choose to visit these sites (Inskeep, 1991). Naturebased tourism/recreation along the FNST is characterized by seasonality mostly due to natural factors (i.e., favorable weather conditions). There is a clear pattern that high use periods are concentrated in winter/spring seasons with peak use in March (Wan et al., 2011). This seasonality of nature tourism/recreation along the FNST might
62 contribute to negative impacts on resources because of the intense pressure concentrated in time and space on specific areas during peak periods. Nature tourism is regarded as a viable way to drive local economic development particularly in rural areas across the state. However, if this activity is not sustainably planned and managed, negative ecological impacts from high visitation might damage fragile ecosystems, which are, in turn, the resources that drive nature tourism/recreation to those areas. Therefore, in order to sustain nature tourisms long term benefits, deci sion makers need straightforward planning tools to monitor the impacts of nature tourism/recreation as they relate to ecological sustainability. For this purpose, it is critical to identify the variations of environment sensitivity and the vulnerability of natural resources in recreation areas along the FNST. The general aim of this study is to evaluate the ecological sensitivity of nature tourism/recreation resources along the FNST and develop a methodology to build a n important component of a decision sup port system to aid in the sustainable planning of nature tourism/recreation along the FNST. Although this c hapter focuses on ecological sensitivity, the sustainable planning of nature tourism must also take social factors into account. Chapter 2 addressed the social sensitivity of recreation areas (attractiveness). Chapter 4 will bring all these components together. Literature Review Natural areas that are transition zones, such as sandy beaches, lakes, riversides, and mountaintops and slopes, are character ized by species rich ecosystems. They often serve as attractive areas that draw multitudes of recreation visitors, which can result in negative ecological impact s to those resources (Wall, 1997). Typical physical
63 impacts include the degradation of important ecosystem components ( e.g., people, plants, animals, and microorganisms), their physical surroundings ( e.g., soil, water, and air), and the natural cycles (e.g., water and nutrient cycles) that sustain them. Ecological impacts are caused not only by tourism related land clearing and construction, but by continuing tourist activities. For example, trampling is the most common impact caused by tourist activities (UNEP, 2010). T ourists using the same trail over and over again trample the vegetation and soil ev entually causing damage that could lead to loss of biodiversity and other impacts. To address increasing outdoor recreation demand and the pressure it put s on natural resources the US Forest Service developed the Limits of Acceptable Change (LAC) fra mework in the mid 1980s (Stankey, Cole, Lucas, Peterson & Frissell, 1985) LAC helps planners define achievable and acceptable desired future conditions of the ecosystem and helps provide a management strategy to prevent unacceptable conditions. Similarly, in the early 1990s, the National Park Service developed the Visitor Experience and Resource Protection (VERP) framework which gave recreation planner s a process to design recreation zones and manage recreation areas within acceptable change limits (USDI/ NPS, 1997). Both LAC and VERP provide the manager with tools to develop baseline assessment s of natural resource conditions and visitors experiences. They help establish desired conditions for a range of management zones, monitor environmental and social indicators to compare affected areas with control areas, and develop management strateg ies to address indicators that are below defined standards (Stein, 2005).
64 However, from a proactive point of view, in order to sustain the benefits from naturebased tourism, decision makers need straightforward planning tools improving their ability to organize and understand large quantities of spatial data related to the sensitivity of cultural and ecological resources. Geographic Information Systems (GIS), which lets us visualize, question, analyze, interpret, and understand data to reveal rela tionships, patterns, and trends (ESRI, 2012 ), allows the user to organize data from small plot s and large areas such as regions and watersheds. GIS also can support inventories of recreation/tourism resources and impacts, provide support for allocating resources between conflicting demands, and aid decisionmakers in planning (Olafsdottir & Runnstrom, 2009). GIS might be equally useful in tourism as in other applications such as landscape management, because naturebased tourism consists of a wide variety of elements including facilities, recreation opportunities, services, and industries which combine to deliver travel experiences (Dye & Shaw, 2007). As the integration of tec hnology and decisionma king processes progress, GIS might play an important role in documenting environmental conditions and developments, assessing the suitability of resources for tourism, exposing conflicts, and revealing causeeffect relationships. Bec ause of the diverse ways GIS can aid in tourism planning, GIS analysis has been applied in tourism impact assessments (Boers & Cottrell, 2005; Boyd & Butler, 1996; Brown & Webber 2011 ; Farsari & Prastacos, 2004; McAdam, 1999). In particular, the work of O lafsdottir and Runn strom (2009) demonstrated an effective method to evaluate ecological sensitivity for impacts from tourism development. In general, the ecological sensitivity of a given habitat is defined as its proneness to environment change involving a combination of intrinsic and extrinsic factors (Nilsson
65 & Gresson, 1995; Ratcliffe, 1977). Ecological sensitivity indicators typically include habitat structural elements ( e.g., circularity ration of area and average slope), compositional elements ( e.g., presence of species at risk), abiotic risks ( e.g., landslide, fire potential and orientation in relation to main wind direction) and isolation ( e.g., distance to nearest neighborhood) (Rossi, Pecci, Amadio, Rossi, & Soliani, 2008). Chen, Wang, Ding and Ji ang (2005) specified seven ecological indictors ( i.e., slope, aspect, elevation, drainage, vegetation diversity, landscape value, and light) to gauge ecological sensitivity landscape planning for tourism. In their study in Iceland, Olafsdottir and Runnstrom (2009) estimated the ecological sensitivity for tourism/recreation pressure effectively by analyzing vegetation cover type in a given site in combination with soil type, slope angle, and the presence of certain species at risk. For example, an area consi sting of unstable sandy soil is much more sensitive to erosion or tourist trampling if the area is also situated on a steep slope. T he purpose of t his study is to assess the potential ecological risk or suitability for nature tourism development by identif ying appropriate criteria on landscape classification by the application of GIS analysis. The objective of this component of the study is to develop an operational indicator, to aid in the planning and implementing of sustainable tourism by (1) identifying major factors and variables that influence the natural environments susceptible to tourism impact, (2) categorizing factors, which correspond to degrees of impact and combining the factors to produce a spatial database describing ecological sensitivity to tourism impact, (3) mapping areas suitable for tourism development as well as areas where tourism should be restricted in a sustainable perspective, and (4) extracting values of ecological sensitivity for nature
66 tourism attractions within study areas to e xemplify a decision support system for tourism planning. The outcomes of this research will contribute to a better understanding of how spatial data can be integrated to indicate the overall sensitivity of their nature tourism areas. This knowledge can dir ectly relate to planners decisions on the amount and type of future development, and also provide important ecological knowledge in understanding nature tourism areas and how to ensure a sustainable supply of recreation and tourism benefits. Methodology Study Site s Of the 45 recreation areas defined as FNST sections by the US Forest Service along the FNST, 16 were selected as study areas for this project. Areas were s e lected based on (1) geographic location (e.g., Floridas Panhandle, north central Flor ida, southern Florida) ; (2) management agency ; and (3) availability of visitation data. These areas range from Blackwater River in northwest Florida to Big Cypress in south Florida (Figure 3 1) and are managed by various public land agencies including US F orest Service, National Park Service, US Fish and Wildlife Service, Florida Department of Environmental Protection, Florida Forest Service, Northwest Florida Water Management District, Suwannee River Water Management District, South Florida Water Management District, and Florida Fish and Wildlife Conservation Commission. The 23 Florida counties that host these recreation areas were selected for ecological sensitivity assessment, including Baker, Clay, Collier, Columbia, Hamilton, Hernando, Highlands, Jacks on, Lake, Liberty, Madison, Marion, Okaloosa, Okeechobee, Orange, Osceola, Putnam, Santa Rosa, Seminole, Sumter, Suwannee, Wakulla, and Washington.
67 Figure 31. Map of study sites Ecological Sensitivity Variables and Factors Tourism and recreation can po tentially affect a multitude of environmental factors. It is beyond the realm of his study to examine all these factors. However, past research has shown that several key environmental attributes play particularly important roles in how natural areas can w ithstand human impacts and development: vegetation, soil, slope, and water. Vegetation One of the most fundamental impact forces, where nature tourism or recreation activities directly affect vegetation and soil, is trampling. After e xamined six forest an d grassland vegetation types in the Bob Marshall Wilderness complex, Cole (1987) expanded his studies to 16 vegetation types in four Western and Eastern states (Cole 1993; Cole, 1995a; Cole, 1995b). Using standardized procedures, he compared vegetation ty pes by their various responses to trampling. T he results of these studies
68 showed the relationship between trampling intensity and vegetation damage to be curvilinear. There were different types of resistance ( ability to tolerate impact without being damaged) in reaction to trampling. Resistant vegetation types, such as sedges ( Carex spp.), were able to absorb 25 to 30 times as much trampling as the least resistant vegetation types, such as ferns ( Dryopteris spp.) (Cole, 1993) Plant morphological characteri stics were the primary factor determining plant resistance to trampling. Grasses and sedges have flexible stems growing in mats or tufts, which are more resistant. The more fragile types were woody plants and tall herbs. The resilience of plants and the ir ability to recover from trampling also varied depending on habitat and environment al conditions. In general, higher resilience was found in favorable growing environments those with higher soil fertility and water availability. For example, recovery rat es are high in riparian areas in eastern states (Cole & Marion, 1988; Marion & Cole, 1996). On the other side, trampling impacts in harsh and less resilient environments (e.g., deserts and high elevations) damaged plants required long er lengths of time to reco ver and the rates of recovery were low (Hartly, 1999; Stohlgren & Parsons, 1986). Plant characteristics, particularly the position of the perennating bud and physiological factors such as reproductive capacity and growth rate, also play a role i n res ilience (Cole, 1988; Kuss, 1986). Vegetation sensitivity factors are mainly based on the guidelines for vegetation tolerance to recreation impacts developed by Cole (1995b ). In this study, by applying these guidelines, Floridas major habitat and land cov er (based on the raster data created by Florida Fish & Wildlife Conservation Commission in 2004) were grouped to
69 four categories based on their resistance and resilience to nature tourism or recreation activities (Table 31). Table 31. Florida habitat and landcover s ensitivity categories to nature tourism/r ecreation Sensitivity Habitat/Landcover 0 no sensitivity Mixed Pine Hardwood Forest/Hardwood Hammocks and Forest/Pinelands/Cabbage Palm Live Oak Hammock/Open Water/Shrub and Brushland/Bare Soil/Clearcut/Improved Pasture/Unimproved Pasture/Sugar cane/Citrus/Row/Field Crops/Other Agriculture/Exotic Plants/Australi an Pine 1 low sensitivity Sand/Beach/Sand Pine Scrub/Sandhill/Cypress/Pine/Cabbage Palm/Scrub Mangrove/Tid al Flat/Grassland 2 moderate sensitivity Xeric Oak Scrub/Dry Prairie/Tropical Hardwood Hammock/Bay Swamp/Cypress Swamp/Mixed Wetland Forest/Hardwood Swamp/Hydric Hammock/Bottomland Hardwood Forest/Mangrove Swamp 3 high sensitivity Coastal Strand/Freshwater Marsh and Wet Prairie/Sawgrass Marsh/Cattail Marsh/Shrub Swamp/Salt Marsh Soil The major impact to soil in nature recreation areas also results from trampling or vehicle travel Manning (1979) provides a fundamental conceptualiz ation of recreational impacts on soil as a sevenstep process scuffing away of leafy litter, loss of organic material, reduction of soil macroporosity, reduction in air and water permeability, reduction in water infiltration, increase in water runoff, and increase in soil erosion. Regardless of what happened in the first two steps, the third step soil compaction, always occurs. Through compaction, soil particles are packed tightly, reducing or eliminating inner pore space. Soil structure is then altered. The result is a reduction in total porosity and macroporosity, which leads to a chain of damaging events through the steps that Manning (1979) described. As Hammitt and Cole (1998) stated, the degree of soil compaction is influenced by many soil factors, including amount of organic matter,
70 amount of soil moisture, and soil texture a nd structure. In particular, soils most prone to compaction are those with textures that have a wide variety of particle sizes (for example, loams), those with a low organic content, and those that are frequently wet when trampled. Soil compaction reduces the macropore space among soil particles, therefore reducing soil aeration and the rate which water enters and moves through the soil (Hammitt & Cole, 1987). Reduced infiltrat ion, in turn, causes increased surface water runoff. Reduction in water infiltration rates is the most important environmental consequence of compaction. Legg and Schneider (1977) observed reductions of 80% in sandy soils and 95% in sandy loam soils in a picnic area in Connecticut. On the coarser textured sandy soil, loss of macropores was less severe, and infiltration on the sandy soil was four times as fast as on the sandy loam. Compaction can occur rapidly even with light use. In a study of soil compacti on caused by recreation activities in the Boundary Waters Canoe Area, Marion and Merriam (1985) observed that in wilderness areas low use sites are usually as compacted as high use sites. Two direct, plant related, negative consequences of compaction are the hindrance of plant root elongation and the lack of suitable regeneration of sites on compacted soils. Serious root impedance occurs at much lower densities on the more finetextured silt loam soil (Hammitt & Cole, 1998). On coarser soils, it is diffic ult to compact soils to a level where pores are too small for water to penetrate. Compaction reduces germination through its effect on the smoothness of germination sites. Germination is usually greater on rough surfaces that offer heterogeneous habitats ( Zisa, Halverson & Stout 1980)
71 From these established studies, soil sensitivity to nature tourism/recreation was categorized according to soil textures in relevant counties (based on soil surveys by USDA/Natural Resource Conservation Service in 1990s) as shown in Table 32. Table 32. Soil texture s ensitivity categories to nature tourism/r ecreation Sensitivity Soil Texture 0 no sensitivity Sand/Fine Sand/Cobbly Sand/Gravel Sand/Clay/Corse Sand/Very Fine Sand 1 low sensitivity Loamy Sand/Loamy Fine Sand/Mixed/Sandy Clay/Gravel Loamy Sand/Silty Clay 2 moderate sensitivity Sandy Loam/Fine Sandy Loam/Sandy Clay Loam/Silt Loam/Gravel Sandy Loam 3 high sensitivity Muck/Muck Fine Sand/Muck Sand/Muck Corse Sand/Clay Loam/Mucky Peat/L oam/Muck Fine Sandy Loam Slope Soil erosion is the most permanent, thus serious, of soil impacts. However, most erosion is not caused by trampling but by wind and water (Hammitt & Cole, 1998). However, recreation activities provide the conditions for erosion and accelerate the erosion process. Wind erosion occurring at sand dunes is probably the best example of largescale erosion triggered by recreation activities (Carlson, 1989). Where recreation activities damage the vegetation which stabilizes the dune, the entire sand dune can be moved by wind. Also, erosion is more likely on steep slopes with shallow top layers of soils, and at locations with sparse vegetation cover, or where runoff is intense (Leung & Marion, 1999). Based on these established facts slope is utilized to analyze how sensitive land is to nature tourism or recreation in regard to impacts on soil erosion. Table 33 indicates the sensitivity categories based on slope in degree.
72 Table 33. Slope angle s ensitivity categories to nat ure tourism/r ecreation Sensitivity Slope Angles in D egree 0 no sensitivity 0 to 10 1 low sensitivity 10 to 20 2 moderate sensitivity 20 to 30 3 high sensitivity above 30 Outstanding Florida W ater s Florida Statues, section 403. 061 (27), authorizes Florida Department of Environmental Protection (DEP) to establish a special category of waterbodies within the state to be referred as Outstanding Florida Waters, which possess important natural attributes and need special protection (DEP, 2012). Outstanding Florida Waters generally include surface waters in: national parks, national wildlife refuges, national seashores, national preserves, national marine sanctuaries and estuarine research reserves, national forests, state parks & recreation areas, state preserves and reserves, state ornamental gardens and botanical sites, environmentally endangered lands program, conservation and recreational lands program, and save our coast program acquisitions, state aquatic preserves, scenic and wi ld rivers, and special waters. Currently, special waters Outstanding Florida Waters include 41 of Floridas 1700 rivers, several lakes and lake chains, several estuarine areas, and the Florida Keys. Since many Outstanding Florida Waters are part of the geographic elements of the study areas, and many recreation activities take place on and around these waters, it is important to include the proximity to Outstanding Florida Waters as one of the ecological sensitivity variables. Table 34 shows the ec ological sensitivity categories of Outstanding Florida Waters based on a 200meter buffer zone. The determination of the size of the buffer zone was based on several studies on n atural habitat such as the negative effects on
73 w aterbird specie s from outdoor recreation activities (Rodgers & Schwikert, 2002), humancaused disturbance to fish, wildlife, and water quality on watershed (Tiner, 2004), effects of human development on habitat loss and alteration in wetlands (Lathrop & Bognar, 2001), and effects of f orest best management practices on stream and riparian zones in watersheds (McClure, Kolka & White, 2004). Table 34. The proximity to Outstanding Florida Waters sensitivity categories to nature tourism/r ecreation Sensitivity Proximity to Outstanding Wat ers 0 no sensitivity Outside 200 meter buffer zone 1 low sensitivity Inside 200 meter buffer zone GIS Spatial Analysis GIS database All GIS data were acquired through the Florida Geographic Data Library (FGDL) which is a collection of geospatial data compiled by the University of Florida. Table 35 shows the formats, sources, creators and dates of GIS data used in this study. Table 3 5. GIS data sets of four v ariables Data Set Format Source Creator Date Soil Surveys Shapefile FGDL USDA/NR CS 1995 DEM Raster Data FGDL USGS 1999 Habitat and Landcover Raster Data FGDL FWC 2004 Outstanding Florida Waters Shapefile FGDL FWC 1996 Note: DEM = Digital Elevation Model; FGDL = F lorida Geographic Data Library; FWC = Florida Fish & W ildlife Conse rvation Commission; USDA/NRCS = US Department of Agriculture/Natural Resources Conservation Service. GIS analysis criteria (variables and factors) and procedures Tables 31 through 34 indicate the four ecological sensitivity variables and corresponding s ensitivity factors (categories) based on their sensitivities to impacts of nature tourism or recreation. Combinations of the four physical variables thus provide a sensitivity range that was categorized into three classes of sensitivity (Table 36).
74 Table 3 6. Categorized ecological sensitivity class by accumulated sensitivity s cores Ecological Sensitivity Class Accumulated Sensitivity Score Low 0 3.33 Medium 3.34 6.67 High 6.68 10.00 Figure 32 shows the flow chart of GIS analysis, which highlighted the flow routes, analytical operations and outputs of ecological sensitivity analysis through four variables. Specifically, for the soil variable, soil survey shapefiles of 23 Florida counties were merged to output the site soil file. This fil e was prepared for the next step of analysis through reclassification based on categories in Table 32 and raster data conversion. Figure 32. Flow chart of GIS a nalysis ( Note: Acc Eco Sens = Accumulated Ecological Sensitivity; Acc Eco Sens_V = Accumula ted Ecological Sensitivity Vector; Cat Eco Sens = Categorized Ecological Sensitivity; DEM = Digital Elevation Model; Landcr = Landcover; OFW = Outstanding Florida Waters; Pts = Points; Rast = Raster; Rast Cal = Raster Calculator; Rast Conv = Raster Convers ion; Reclas = Reclassify/Reclassified; Sites Eco Sens = Sites Ecological Sensitivity; Spat = Spatial; Vector Conv = Vector Conversion; Veg = Vegetation.)
75 For the vegetation variable, the habitat and l andcover raster file was extracted by clip feature of th e site soil survey file to output the site landcover file. After it was rec lassified based on the categories described in Table 31, the site landcover file was ready for the next step of analysis. For the slope variable, Digital Elevation Model ( DEM ) file s at 30x30 meter resolution from 23 Florida counties were merged to form the site DEM file After calculat ing for slope and reclassified based on categories in Table 3 3, the reclassified site slope file was created for future analysis. For the Outstanding Florida Waters variable, the Outstanding Florida Waters shapefile was extracted by clip feature of the site soil file to create the site Outstanding Florida Water file After buffering for 200 meters a nd converted to a raster file, this file was reclassi fied based on the categories in Table 34 to create a reclassified outstanding water file for the next of step analysis. The last output s from each of the four analyses were added through the raster calculator to create accumulated ecological sensitivity f or each site. In this process, the value of each corresponding cell from all four variable layers was added to obtain the accumulated value ( i.e., sensitivity). The cells from this resulting layer had possible values between 0 and 10. Based on the classification algorithm described in Table 36, the accumulated ecological sensitivity was reclassified into three sensitivity classes by three equal intervals. From this point, the file was further converted to vector and prepared for the next step of analysis. GPS coordinates of 111 points over 16 recreation areas along the FNST were collected using data from Google Earth and Microsofts Streets and Trips map software
76 with a GPS receiver. A layer with 111 GPS points of recreation sites was created and used to re ad sensitivity scores (Figure 33). Figure 33. Sensitivity reading points (recreation sites) over 16 recreation areas. In order to read sensitivity scores from a specific location, a spatial join operation was performed between the accumulated ecological sensitivity layer and the GPS point layer. This process allowed one layers attributes to be appended to another layers attributes based on the relative locations of the two layers thus generating spatial information indicating sensitivity value s. Res ults Ecological Sensitivity by Habitat and Landcover The reclassification of habitat and landcover over 23 counties based on their sensitivity to nature tourism or recreation activities (Table 31) revealed a diverse
77 distribution of varied sensitivities (F igure 34). Some areas of high sensitive habitats were found in the northwest of the state, while large areas of medium to high sensitivity were concentrated in central and south Florida. However, many areas of low or no sensitive habitats were also scattered in between. Ecological Sensitivity by Soil Textures The soil surveys of 23 counties from northwest Florida to south Florida showed diverse varieties of soil texture (Table 32). After reclassifying according to their sensitivity categories described in Table 32, the sensitivity variations across 23 counties were mapped (Figure 35). Results showed that the majority of soils were classified as not being sensitive. However, small areas of high sensitivity were scattered throughout the state. Ecological S ensitivity by Outstanding Florida Waters GIS analysis categorized areas as being either inside or outside the 200meter buffer zone based on proximity to Outstanding Florida Waters. The vast majority of land in the study areas were classified as outside the 200 meter buffer (not sensitive), but small areas inside the buffer were scattered throughout the state, and a large area in between Lake Okeechobee and the Gulf Coast (including Big Cypress) fell into a relatively high sensitive classification zone (Fig ure 36). Ecological Sensitivity by Slope The slope reclassification of 23 counties across Florida showed little variation of sensitivity scores (Figure 37). Not surprisingly, the dominant scores for slope were 0 indicating no sensitivity. Northwest and north central Florida were the only locations where slopes were found resulting in moderately higher sensitivity scores.
78 Figure 34. Sensitivity score by habitat and landcover. (Note: 0 = no sensitivity; 1 = low sensitivity; 2 = moderate sensitivity ; 3 = high sensitivity.) Figure 35. Sensitivity score by soil texture. (Note: 0 = no sensitivity; 1 = low sensitivity; 2 = moderate sensitivity; 3 = high sensitivity.)
79 Figure 36. Sensitivity score by proximity to Outstanding Florida Waters. (Note: 0 = no sensitivity; 1 = low sensitivity.) Figure 37. Sensitivity score by slope angles. (Note: 0 = no sensitivity; 1 = low sensitivity; 2 = moderate sensitivity; 3 = high sensitivity )
80 Overall Ecological Sensitivity The analysis through the raster calc ulator added all four variables together generating an overall ecological sensitivity score to nature tourism or recreation activities for 23 Florida counties. Eventually, a categorized overall ecological sensitivity scale was developed (Figure 38), which helped determine where nature tourism/recreation activities could be expanded and further developed. It also shows where recreation/tourism development should be restricted and planned carefully. For example, when overlai d with actual recreation sites, th e categorized ecological sensitivity map clearly show s that Big Cypress, Apalachicola, and St. Marks have recreation sites in highly sensitive zones where recreation activities need to be carefully planned. However, most recreation sites in Suwannee and Tw in Rivers are in low sensitiv ity zones and the ecological sensitivity data support potential nature tourism expansion (referring Figure 3 1 and Figure 33). Figure 38. Categorized ecological sensitivity.
81 Ecological Sensitivity Scores of 16 Re creation Areas along the FNST The GIS spatial join operation between the accumulated sensitivity layer and 111 sensitivity reading points (recreation sites) over the 16 recreation areas generated average sensitivity scores for each recreation area (Table 37). Tab le 3 7. Average ecological s ensitivit y and r elati ve ecological sensitivity score of 16 recreation areas Recreation Area Average Ecological Sensitivity Relative Value Kissimmee 3.83 1.00 Big Cypress 3.75 0.98 Econfina 3.60 0.94 Little Big Econ 3.40 0.89 St. Marks 3.20 0.84 Tosohatchee 3.00 0.78 Apalachicola 2.27 0.59 Blackerwater River 1.80 0.47 Ocala 1.73 0.45 Withlacoochee 1.63 0.42 Three Lakes 1.50 0.39 Goldhead 1.33 0.35 Osceola 0.57 0.15 Greenway 0.50 0.13 Twin Rivers 0.40 0.1 0 Suwannee 0.29 0.07 The ecologically most sensitive recreation area was the Kissimmee site with an ave rage sensitivity score of 3.83 largely due to its watershed ecosystem. It means that recreation activities, particularly high impact activities such as motorized recreation activities should be managed to avoid critical wildlife habitats and aquatic plant communities. Other very sensitive areas were found in Big Cypress, Econfina, Little Big Econ, St. Marks and Tosohatchee areas with an average sensitivity score of 3.75, 3.60, 3.40, 3.20, and 3.00 respectively. In particular, Big Cypress and St. Marks are highly sensitive due to their wetland ecosystems. Since some high impact activities (e.g., off
82 highway vehicle use ) are allowed in Big Cypress which could severel y impact sensitive ecosystems and requires intense management Similarly at St. Marks, high impact activities, such as mountain biking, horseback riding need to be planned carefully in order to avoid direct impacts on those highly sensitive areas. The lea st sensitive area to nature tourism or recreation activities was the Suwannee site with an average sensitivity score of 0.29 mainly due to its up land pine ecosystem. Although the Suwannee River winding through the area is sensitive nearly all recreation sites are outside of the butter zone; therefore, these areas can support potential nature tourism development. Figure 3 9 shows the geographic distribution of ecological sensitivity among 16 recreation areas along the FNST in relative values. Four of the m ost sensitive areas to nature tourism or recreation activities were found in central and south Florida and two in the northwest section of the state. Among all 111 recreation sites over 16 recreation areas along the FNST analyzed, a total of 4.5% of recrea tion sites were classified as highly sensitive to nature tourism/recreation, 24.3% with medium sensitivity, and 71.2% with low sensitivity. In terms of recreation sites analyzed, Kissimmee had the most sites classified as highly sensitive 2 or 33.3%. Apal achicola, Little Big Econ, and Big Cypress had one high sensitive site each or 9.1%, 20.0%, and 12.5% respectively. All recreation sites analyzed in Twin Rivers, Suwannee, Osceola Goldhead, Greenway, and Three Lakes areas were classified as low sensitivit y to nature tourism/recreation. Overall, the high sensitivity category mainly represents land that is characterized by wetland ecosystems or a combination of hi ghly sensitive habitats within i mportant water body buffer zones on hills with sensitive soil te xtures.
83 Figure 39. Relative ecological sensitivity value among 16 recreation areas along the FNST. (Note: 0 = minimum relative score of ecological sensitivity; 1 = maximum relative score of ecological sensitivity.) Discussion Similar to the work of Carv er (1995), where GIS was applied to highlight wilderness areas alongside other protected areas in the UK, this study produce d a n ecological sensitivity continuum map showing nature tourism/recreation areas along the FNST The value of this application lies in its ability to identify areas o f potential risk from nature tourism/recreation activities. The GIS analysis used here provides evaluations for landscapes related to nature tourism impact s to vegetation, soil, and water characteristics In othe r words, this
84 research enhances the ability to classify landscapes based on sensitivity to recreation and tourism impacts. It is useful in landscape management in situations that might be considered sensitive, but there is pressure to increase nature tourism opp ortunities In particular, where nature tourism is regarded as a catalyst to economic growth, this studys findings can be used to support decisions about where to limit increased tourism development for protect ing sensitive areas and where to allocate tou rism resources for potential development (Koshkariov, Krasovskaia & Tikunov, 1994). Like MacAdams work (1994), the ecological sensitivity data of specific recrea tion sites from GPS points can be further used in the formulation of a recreation resource management assessment framework. Management Implications A lthough the recreation pressures at Apalachicola and St Marks areas appear to be low (Wan et al., 2011) sustainable management is still needed due to their highly attractive attributes which could d raw large number of visitors to their highly sensitive lands. At other high ly sensitive areas, including Econfina, Little Big Econ, Kissimmee, and Big Cypress, nature tourism or recreation must be carefully planned and managed to avoid negative disturbances. For Twin Rivers, Suwannee, and Osceola, both the ecological sensitivity and recreation pressure are relatively low. In particular, at the Suwannee recreation area, almost all recreation sites including campgrounds, trailheads, and canoe l a unches are in lo w sensitivity zones (Figure 310). Therefore, nature tourism or recreation can be considered as an ecologically viable way to encourage local economic and social well being by emphasizing their unique natural attractions.
85 Figure 310. Categorized ecological sensitivity near Suwannee with sensitivity reading points (recreation s ites). (Note: Rec Site = R ecreation S ite .) The Ocala area will continue to draw a large number of visitors due to its abundant attraction features; therefore, it is critical that proper management is in place to ensure its sensitive natural resources remain intact. For example, some of the recreati on sites are located directly in high sensitivity zones (Figure 3 11); therefore, it is necessary to examine the types and intensity o f recreation activities on these sites and manage them in a way so negative impacts can be avoided or minimized A number of sites are located in the medium sensitivity zones, and recreation activities need to be monitored carefully so possible negative effects can be manag ed within acce ptable limit s For
86 those sites at low sensitivity zones, further opportunities can be developed allowing for more visitation, with appropriate management to accommodate heavy visitations Figure 311. Categorized ecologi cal sensitivity near Ocala with sensitivity reading points (recreation sites). (Note: Rec Site = Recreation Site.) Along the 1400mile FNST, natural areas provide a rich variety of ecosystems and span a diverse array of recreation opportunities from high ly primitive to urban; therefore, they attract increasing number of visitors. These situations are n o t unique to the FNST or to Florida. N atural resource and recreation management areas throughout the world are challenged to maintain the high quality and i ntegrity of natural environment while allow ing visitation s ( sometimes heavy visitation) to these areas. In fact new recreation activities might result in unexpected and high impacts in many of
87 these areas. For example, there is a high demand of off highwa y vehicles and personal water crafts ( OHV and PWC ) in some of the areas examined in this study such as those in St. Marks Kissimmee, and Big Cypress These impacts can be extreme, especially in sensitive areas; therefore, r ecreation plan n ers need effecti ve strategies to quickly identify sensitive ecosystems, and identify appropriate management strategies. This is all in a time of decreasing budgets and resources for public land management agencies. Model Implications The GIS model developed in this study efficiently categorizes physical properties of the natural landscape into ecological sensitivity variables including vegetation, soil, slope and water In themselves these variables are key indicators in the mapping of ecological sensitivity for nature t ourism or recreation impacts Although these identified variables are fewer than those identified Chen et al. s research (2005), they appear to be reasonable to establish a useful indication of ecological sensitivity for nature tourism the sites examined i n this study In particul ar, this study show s that soil texture and special water body are important ecological sensitivity indicators for nature tourism and these are likely to relate to most nature tourism areas However other areas with unique environmental qualities will require different indicator s in order to accurately reflect the ir ecological sensitivity Although endangered species was included in the GIS analysis for ecological sensitivity in the study by Olafsdottir and Runnstrom (2009), specia l water body was included in this study because of the numerous ecological characteristics, especially char acteristics related to wildlife reflected in that one indicator.
88 Much of the information is presented at the landscape level (i.e., large scale). Ho wever combined with spatial information on where specific attractions occur and where high numbers of visitors concentrat e a map of nature tourism/recreation sensitivity could be developed at a finer scale which can assist in managers in specifically lo cating and developing recreation oppor tunities For example, if ecologically sensitive areas are well defined and mapped, OHV recreation planners would be better able to route trail s around highly sensitive areas but still ensure trail s move through desired landscape features. Limitations There are some limitations of this study. Although the soil, slope, water, and vegetation variables have been recognized by the literature as important indicators of ecological sensitivity ( Chen et al., 2005; Nilsson & Gre sson, 1995; Olafsdottir & Runnstrom, 2009; Ratcliffe, 1977 ) there may be other significant ecological aspects such as endangered spec ies, critical wildlife habitat and water table level, that were not included If more indicators are included in the anal ysis, the output grid weight map will have greater variation, allowing for a finer scale examination of ecological sensitivity Consequently, spatially defined sensitivity zones will be more reliable when managers must make development decisions. Although thi s analysis could have attempted to integrate more variables, the actual goal of the research was to develop a theoretical foundation to ensure the integration of indicators would result in a useful map of sensitivity. Despite the se limitations, and the nature of adaptation of GIS technology in this study, results establish a piece of foundation for planning and managing sustainable nature tourism and future researchers could incorporate other variables using this methodology .
89 CHAPTER 4 ESTIMATING THE FRA GILITY OF NATURE TOU RISM BASED SOCIO ECOLOGICAL SYSTEMS ALONG F LORIDA NATIONAL SCEN IC TRAIL Opening Statement The Florida National Scenic Trail (FNST) traverses 1400 miles through urban and rural areas creating a footpath that stretches from Pensacola in t he extreme northwest of Florida to Big Cypress National Preserve in the southern part of the Florida Peninsula. The FNST also passes through more than 45 publicly managed natural areas, which together attract millions of visitors who use the areas wide range of natural landscapes and wide spectrum of recreational opportunities. M any rural communities particularly in central and northwest Florida, are promoting naturebased tourism as a strategy to create economic impacts and other benefits associated with recreation and tourism opportunities. However, in other areas, natural resource managers and community leaders are worried about the social, economic, and environmental costs associated with uncontrolled and unmanaged tourism development (Gallardo & Stein 2007; Milman & Pizam, 1988 ; Shrestha et al., 2007). Chapters 2 and 3 showed that the attractiveness of naturebased tourism/recreation areas can be determined along with a quantified measure of their sensitivity. Together, these two measures can be used to provide a decision support tool for planners when making decisions regarding development or attempting to increase use in these natural areas. This component of the study was designed to provide estimates of fragility of socio ecological nature tourism based systems, and to complete the framework of a decision support system for sustainable nature tourism, which Chapters 2 and 3 provided the fundamental measurements of The fragility estimates are important because when a recreation area becomes fragile, its resilience is low and the system
90 might collapse. This approach to fragility estimates is related to risk assessment (Zurlini, Rossi & Amadio, 2003), which evaluates the potential adverse effects that human activities, such as nature tourism, put upon natural resources. In other words, as a system becomes more fragile, it is at greater risk to disturbance including human development. This chapter will first review some basic concepts of the tourism area life cycle and concept of fragility for a recr eation area or nature tourism destination. This background information will provide a conceptual understanding of risk assessment through an estimation of system fragility. Next, nature tourism/recreation visitation patterns along the FNST will be discusse d. To quantify fragility within the framework of the Adaptive Cycle (Gunderson & Holling, 2002), a model of fragility (Zurlini et al., 1999) will be discussed in terms of how FNST recreation areas can be categorized on a tourism area life cycle. The final estimates of nature tourism area fragility and associated risk assessments are discussed within the context of the Adaptive Cycle (Gunderson & Holling, 2002). Literature Review Berkes and Folke (20 00) suggested that nature areas where tourism and recreation take place are part of a complex socio ecological system that is characterized by uns table, nonlinear relationships and unpredictable outcomes. Effective management of such areas needs to be adaptive in order to accommodate changing ecological and societal values and to work with unexpected events. As explained by V an Wilgen and Biggs (2011), Adaptive management implies that changes will often be necessary as understanding improves, or as environmental conditions or societal values change. It is built on the assumption that social ecological systems are
91 complex, that understanding is imperfect, and that the logical way to proceed is to learn by doing, and to adapt as new understanding emerges. It also recognizes that effective management cannot be achi eved by acting in isolation and that a partnership involving researchers, managers, administrators and society at large is required (Van Wilgen & Biggs, 2011, p. 1179). Natural areas that host recreation and tourism respond in unpredictable ways to human activities due to their complexity. In the meantime, people are learning how to adjust their behavior, which affects ecosystems in order to avoid catastrophic consequences (Scheffer, Brock & Westley, 2000). People are beginning to have a better understanding of the value ecosystems provide to society. For instance, ecosystems provide people with production goods such as seafood, timber, fuelwood, and pharmaceutical products; essential lifesupport services such as water purification and climate regulation; and life fulfilling conditions such as serenity, beauty, and cultural inspiration ( Daily et al., 1997). Daily et al. (1997) concluded that human activities that destroy habitats and impair services can severely cost humanity in the long term and exceed the benefits of economic development in the short term. Therefore, it is important to better understand the ecosystems characteristics and to inform the public and decisionmakers of the ecological tradeoffs related to human behavior. In the context of a soc io ecological system, Gunderson and Holling (2002) emphasized that instead of examining social or natural systems separately. R esearchers should integrate these systems to better describe and explain the naturebased recreation and tourism situation. Also, rather than trying to obtain and then maintain an idealized equilibrium state, adaptive management could be used to progressively accumulate knowledge about this system to better allow managers and
92 stakeholders to experiment, probe, adapt to and benefit f rom small and largescale change (Berkes & Folke, 2000). Adaptive management principles have successfully been applied to a wide range of ecosystems in North America including the Clayoquot Sound, British Columbia (Nyberg, 1999) and Columbia River Basin ( Lee, 1993) and have received a high level of interdisciplinary endorsement. However, it is rarely applied in the tourism field. One of the few examples is Rollins, Trotter and Taylor (1998) reporting on the use of adaptive management to assess the effectiv eness of three alternative strategies for dealing with recreation conflicts in British Columbia. By studying different management strategies regarding recreation conflicts in wildland urban interface (WUI) areas, they concluded that using adaptive management helped researchers design programs to generate feedback and then adjust management strategies and policies accordingly therefore to achieve the accumulation of knowledge about changes and apply the appropriate management actions. For the past 30 years, tourism researchers have relied on Butlers (1980) Tourism Area Life Cycle as the primary model of what change to a tourism area might be like (Figure 4 1). Visitors will come to an area in small numbers initially, restricted by lack of access, facilitie s, and local knowledge ( exploration stage). As facilities are provided and awareness grows, visitor numbers will increase ( involvement stage). With marketing, information dissemination, and further facility provision, the areas popularity will grow rapidl y ( development stage). Eventually, however, the rate of increase in visitor numbers will decline as level of carrying capacity are reached ( consolidation stage and stagnation stage). As the attractiveness of the area declines relative to other areas, bec ause of overuse and the impacts of visitors, the actual number of visitors may also eventually decline ( decline stage)(Butler, 1980, p. 5 6).
93 Figure 41 The Tourism Area Life Cycle from Butler (1980), as in Patterson et al. (2008). Butlers cycle makes inherent sense and accurately represents how some tourism destinations evolve from undiscovered, primitive areas to highly visited, developed destinations. However, most tourism destinations, and especially naturebased areas, are extremely complex and are likely better represented through an integration of Butlers cycle and adaptive management concepts. Hollings (2001) Adaptive Cycle (Figure 4 2) was designed to describe socioecological systems conditions as they adapt to changes and stresses. Although few researchers have integrated it with Butlers cycle, the Adaptive Cycle corresponds well to Butlers stages, but with the understanding that the system is complex and specific aspects are unpredictable. Patterson, Niccolucci and Marchettini (2008) atte mpted to integrate the two models. The early stages of the Tourism Area Life Cycle resemble the phase ( r ) in the Adaptive Cycle When the system of a tourism area becomes more connected, the tourism based system shows its high specialization, skills, networks, and mutual trust as it progresses from phase ( r ) to phase ( ). The systems connectedness increases,
94 eventually becoming over connected and increasingly rigid in its control. The system of an area becomes highly fragile and is now a crisis waiting to happen. The trigger could be an agent of disturbance such as a wind storm fire or drought from the natural environment, or stakeholders revolt in response to what they perceive as severe problems in the area. Lack of visitation and interest in the area could also trigger system collapse. The agency would have to reexamine the purpose of the area and develop an entirely new approach to its management. In terms of the Adaptive Cycle, the resources and structures of the system are suddenly released and the ti ght organization is lost during phase ( ). From phase ( ) to phase ( ) is a period of rapid reorganization and is unpredictable and highly uncertain. During this period, the previously accumulated mutations, inventions, disturbances, and capital can become reassembled into new combinations, some of which could lead to innovations and generate new opportunities. Figure 42 The Adaptive Cycle proposed and illustrated by Holling (2001), as in Patterson et al. (2008).
95 In order to define the development phas e of a tourism area life cycle with in the framework of the Adaptive Cycle, a model to quantify fragility is needed. Nilsson and Grelsson (1995) introduced the method to estimate fragility using a semi quantitative score. Based on the equation that fragilit y (Fr) of an ecosystem at time t depends on the Zurlini et al. (1999) proposed a conceptual linear model to quantify fragility: (4 1) In Eq ( 4 1), K stands for constant background fragility. It is related to specific adaptive mechanisms to expected periodic stresses (disturbances) that are embodied in the memory of the system, including human disturbances that mimic or simulate natural distu rbances system, and U stands for either disturbances (expected) or perturbations (unexpected), which systems have to cope with. The amount of stress, coupled with intrinsic factors, d etermines fragility or vulnerability. The type, magnitude, and timing of the stressor, its predictability, the responses of natural resources, and the resources inherent sensitivity have important interactive relationships, which determine the overall sys tem fragility. Fragility of tourism/recreation area s also depends on economic and social factors. Petrosillo, Zurlini, Grato and Zaccarelli (2006) suggested that a more complicated fragility model could include social and economic aspects such as impacts o f tourism on local populations, local population perceptions of tourism, and employment in the tourist sector. In a study of ten tourism sites (socioecological systems) in the Salento region of southern Italy, Petrosillo et al. (2006) demonstrated how a model of f ragility can be used
96 to depict the spatial distribution of fragility in the context of Hollings Adaptive Cycle (Holling, 2001) in examining tourism area dynamics in terms of connectedness, resilience, and ecosystem services. They concluded that a simple ecological fragility model for tourism is consistent with the Adaptive Cycle model since fragility increase s and decreases during the life of a tourism area. In other words, as fragility increases when a system is moving towards the conservation phase, and it decreases as the system enters the re organization phase (Figure 42). Petrosillo et al. (2006) effectively used a spatially explicit distribution of fragility to express the concept of an adaptive cycle, where the phase state of the tourist/ recreation area is represented by the position of a mark along the cycle. The purpose of this chapter (research question three) is t o assess the sustainability of socio ecological system s (nature based tourism areas along the FNST ) by integrating social se nsitivity (i.e., attractiveness, as defined in Chapter Two) and ecological sensitivity (Chapter Three) within the Adaptive Cycle. The specific objectives of this study are: (1) integrating the results from attractiveness evaluation and assessment of ecolog ical sensitivity into the model of fragility, (2) outputting the fragility level and corresponding level of sustainability for individual recreation areas (3) forecasting the future development of each area in the context of the tourism destination cycle, and (4) completing the decision support system for sustainable nature tourism framework by adding the las t component estimating fragility for the nature tourism based system risk factor. Two models are combined to achieve these objectives First, Hollin gs conceptual sustainability model (Holling, Gunderson & Peterson, 2002) as an alternative to Butlers life cycle model (Butler, 1980). Second, the fragility model (Zurlini
97 et al., 1999), where fragility is modeled as a resource value (ecological sensitiv ity, attractiveness, i.e., social sensitivity) combined with stress (pressure). The outcomes of this study will not only expand and enrich the knowledge of understanding of socioecological system dynamics, but also provide direct operational indicators for socio ecological system analysis and provide straightforward tools for decision making. Methodology Study Site s Sixteen recreation areas were selected along the FNST, ranging from Blackwater River in Northwest Florida to Big Cypress in Southern Florida ( Figure 43). These recreation areas are managed by various public land agencies including US Forest Service, National Park Service, US Fish and Wildlife Service, Florida Department of Environmental Protection, Florida Forest Service, Northwest Florida Water Management District, Suwannee River Water Management District, and Florida Fish and Wildlife Conservation Commission. Figure 43. Map of study sites
98 For some recreation areas, the measurement unit was considered a combination of natural areas maintained by two or more agencies (e.g., Suwannee) while for other areas, a measurement unit was an individual site managed by a single agency (e.g. Apalachicola). These measurement units (i.e., recreation areas) were determined based on a mix of what is defined as a FNST section by the US Forest Service and logical combinations of recreation destinations. Specifically, the following conditions were required for recreation areas to be included for the study sites: (1) areas that represent diverse geographic locations that the FNST traverses through; (2) areas that represent a variety of management agencies; and (3) areas where visitation data are available. Estimating Recreation Pressure Petrosillo et al. (2006) used solid urban waste records which was a rather indirect method to determine tourism pressure. This study took a more direct approach to measuring tourism/recreation pressure by using peak visitor use level for recreation areas as a way to estimate tourism/ recreation pressure (UNEP, 2010) In almost all recreation areas included in this study, there are no data available on total number of visitors. This is largely due to the lack of complete visitor counting procedures among various public land management agencies. However, to achieve this studys objec tives i t is necessary to obtain an indicator for the total number of visitors in a recreation area. The number of visitors on the FNST has been available since 2003 due to FNST Visitor Assessment Study conducted by the University of Florida ( Sanborn, Belcher, Albritton & Stein, 2003) Also state park attendance data were also available. Regression tests between the number of visitors on the FNST and the number of visitors to the state parks in the same study sites were employed. The purpose of these tests was to determine the suitability of FNST visitation numbers as an indicator for recreation
99 visitation for the entire recreation area If these tests were positive, then the peak FNST use level ( i.e., the highest monthly visitor counts) at each recreation area was used as a measure of recreation pressure. There are three state parks within the selected 16 recreation areas along the FNST. Regression tests were performed between the numbers of visitors to Gold Head Branch State Park and hikers counted on the F NST in Goldhead, and between the numbers of visitors to Steven Foster State Park and hikers counted on the FNST in the Suwannee area in 2011/2012 ( Table 41 ). Table 4 1. Correlations between FNST use level and state park attendance l evel in 2011 GBSP FNS T@GHB SFSP FNST@SUW Jan 5819 899 2579 362 Feb 4561 613 2675 344 Mar 6480 658 3830 635 Apr 7342 619 3433 297 May 5223 606 1389 277 Jun 2539 59 1781 130 Jul 3043 53 1967 141 Aug 1774 40 1276 148 Sep 2349 81 1988 99 Oct 5199 666 2777 150 Nov 5894 7 47 2680 331 Dec 3727 465 3567 309 R Square 0.77 0.85 Significance 0 .00 0 .00 Note: FNST = Florida National Scenic Trail; GBSP = Gold Head Branch State Park; GBH = Goldhead; SFSP = Steven Fos ter State Park; SUW = Suwannee. Results indicates the R2 v alue between the numbers of visitors to Gold Head Branch State Park and on the FNST in Goldhead is 0.77, and a R2 value of 0.85 between the numbers of visitors to Steven Foster State Park and on the FNST in Suwannee. This shows that there were strong correlations existing between FNST
100 visitor numbers and an extended range of recreation users, such as those in the state parks. Therefore, these results show that FNST peak use level is correlated to overall recreation use of a recreation area and can be a reas onable indicator for the recreation pressure. Estimating Number of Visitors on the FNST In 2003, Univ ersity of Florida researchers i dentified 27 sections of the FNST that were representative of all recreation areas along the trail These survey sites were geographically divided into groups, and each group was scheduled to be sampled for one year during a five year visitor assessment cycle. To obtain reliable use estimates of visitors on the FNST, two methods were used: (1) personal observations, and (2) mec hanical counters with s upplemental materials (Table 42 ). Personal observations were performed at sites where a variety of activities occur on the FNST (e.g., mountain biking and horseback riding). This allowed researcher s to differentiate between foot us e (the predominate focus of the FNST) and other uses. A stratified random sampling approach was used to assign personal observation times in conjunction with survey periods. The sampling framework consist ed of two strata: (1) day type: weekdays and weekends, (2) time of the day: morning and afternoon. During these personal observation times, surveyors kept a tally of individuals entering and exiting the FNST, as well as group size, the number of males and females, activity, and direction of travel. These observation logs were used to generate an estimate of trail use at sites where multipleuse occurred. Two types of infrared counters were used to generate visitor use estimates in most areas where FNST is designated for foot traffic only. A total of 17 count ers were installed each study year since 2004. The Diamond Traffics TCC 4420 infrared eye trail
101 counter was originally designed by the U.S. Forest Service equipment center to aid in trail monitoring in remote areas. The counter is cased within water proof aluminum, and operates on 4D batteries that usually last 12 months. The counter is installed on a tree or wooden post and is aligned with a reflector 2075 feet across the trail creating an invisible beam. When the beam is broken by a hiker, wildlife, or other user, it is recorded with no differentiation between user types. The counter has an ability to provide researchers with hourly counts for up to 420 days equating to approximately 25,000 counts. The TrailMaster 1550 active infrared eye was also instal led at several research sites over the course of the study year. This counter gathers data in the same fashion as the Diamond Traffics eye; however it records data slightly different from D iamond. The counter is cased with water proof hard plastic, and operates on 4 C batteries that usually last 5 months. The counter is installed on a tree or wooden post and is aligned with a transmitter 20 to 145 feet across. Information gathered from the counter allows researchers to evaluate trail use visits in one minut e intervals, and the counter can store a maximum of 4,000 counts. Table 42. Types of visitor data on the FNST Methods Type of Data # Years Data Collected # Access Points Measured Infrared Counters Mo n thly 8 43 Personal Observations Annual or Se asonal 7 34 Registration cards Monthly 1 3 For some areas, other informa tion regarding visitor numbers was available. This type of information ranged from formal registration cards to informal visitor logs kept in a mailbox on a nearby kiosk. The in formation found in these materials helps supplement the counters and observational counts.
102 As mentioned earlier, the infrared counters used for this study have some limitations. For one, these counters cannot distinguish between types of users, such as hi ker s and wildlife. Also when multiple visitors pass by in side by side pattern, they are not all recorded. This is particularly true in places where the trail width is wide enough to accommodate side by side pedestrians In addition, counts from these cou nters can be influenced by some environmental factors such as the angle of sun ray casting on the counter sensor, wind and vegetation as well as other factors such as forest prescribed burns. In some occasions, counts from these counters can be lost completely due to mechanic failure, vandalism or loss of battery power. To correct these limitations, several methods were used to adjust and refine the counter data, including monthly equipment calibration, and data cleaning. Equipment calibration involved cal culating the ratio of registered counts and actual counts, which was then used to formulate the average calibration factor for each counter. A d ata cleaning procedure was conducted based on the protocol that all counts obtained from one hour after sunset t o one hour before sunrise need to be eliminated to avoid nighttime wildlife counts. In addition, unusually high counts (usually site specific) that were not reported by the Florida Trail Association were also eliminated. A Conceptual Fragility Model As me ntioned in the literature review, In order to define the development phase of a tourism destination/area life in the framework of the Adaptive Cycle, a model to quantify fragility is needed. Based on the equation that fragility (Fr) of an ecosystem at time t depends on the sensitivity of disturbances and perturbations, Zurlini et al. (1999) proposed a conceptual linear model to quantify fragility:
103 (4 1) In this study, there were several improvements on the estimation of fragility over the methods in the study of Petrosillo et al. (2006). Similar to their model, recreation pressure was used as a suitable surrogate for U in Eq. ( 4 1). However, in this study, recreation pressure was estimated by the highest FNST monthly vis itor use level, which was a more direct indicator than one from solid waste records. Suggested by Petrosillo et al (2006), models of fragility can be extended to include better definitions of sensitivity. Sensitivity is not only a function of ecological attributes but also of society and economy. For ecological sensitivity, in Chapter 3, GIS analysis was utilized to obtain more accurate estimates than simply rank ing the degree of protection areas, which was the more common method in previous research. A s P etrosillo et al. (2006) stated, social sensitivity is a function of peoples perceptions of nature tourism, which could be measured and monitored through surveys of visitors, or in this case, tourism experts in the areas S ocial sensitivity in this study w as measured in Chapter 2 the assessment of attractiveness of recreation areas. Adapted from Zurlini et al (1999)s model concept to the nature tourism/recreation areas in this study, their model Eq. ( 4 1) was modified to: Fr* = A* U* (4 2) where Fr* is ranked fragility given by scores on a relative scale; A* is the system sensitivity including ecological sensitivity and social sensitivity (i.e., recreation area attractiveness). U* is the tourism/recreation pressure (i.e., the peak number of visitors the highest monthly visitor counts). Since K in Eq. ( 4 1) stands for constant background fragility, it is omitted in Eq. ( 4 2).
104 Results FNST Visitor Counts and Recreation Pressure Visitor use levels over the 16 recreation areas along the FNST in 2011 are shown in Table 43. The highest peak use measurement appeared at the Greenway site with1360 visitors in March while the Kissimmee site had the lowest peak use measurement of 73 visitors in December. Table 43. 2011 monthly FNST v isito r use lev el over 16 recreation a reas Rec Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec BWR 200 317 293 283 165 136 136 259 197 235 176 225 ECO 144 105 177 180 104 63 67 85 68 127 106 117 APA 152 192 208 146 86 39 33 41 42 103 119 126 SMK 173 178 263 147 86 64 52 37 92 153 155 126 TWR 96 121 130 116 100 32 68 100 101 103 77 108 SUW 362 344 635 297 277 130 141 148 99 150 331 309 OCS 78 121 85 65 44 35 29 26 21 44 65 36 GHB 899 613 658 619 606 59 53 40 81 666 747 465 OCA 693 841 914 521 370 200 223 152 289 404 506 531 GRW 1104 1109 1360 1147 955 662 674 329 676 913 1057 909 WIT 273 294 453 372 196 157 180 220 267 277 222 239 LBE 514 604 625 442 404 326 217 155 141 260 446 285 TOS 29 45 40 61 61 44 11 32 90 29 45 40 TLK 206 194 176 134 92 117 133 10 1 143 154 130 154 KIS 53 35 67 45 26 26 55 55 50 30 37 73 BCY 462 382 382 242 142 156 103 107 126 133 277 341 Note: APA = Apalachicola; BCY = Big Cypress; BWR = Blackwater River; ECO = Econfina; GHB = Goldhead; GRW = Greenway; KIS = Kissimmee; LBE = Lit tle Big Econ; OCA = Ocala; OSC = Osceola; SMK = St. Marks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TWR = Twin Rivers; WIT = Withlacoochee. From Figure 44, there was a clear pattern that the low use levels appeared between May and October while high use levels were found the rest of the year. Except in two recreation areas, where the peak use months appeared in September and December, for all other recreation areas, peak use months were found from January to April. Peak use months for eight r ecreation areas were found in March.
105 Figure 44. 2011 FNST visitor use patterns in 16 recreation areas. (Note: APA = Apalachicola; BCY = Big Cypress; BWR = Blackwater River; ECO = Econfina; GHB = Goldhead; GRW = Greenway; KIS = Kissimmee; LBE = Little Big Econ; OCA = Ocala; OSC = Osceola; SMK = St. Marks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TWR = Twin Rivers; WIT = Withlacoochee. ) System Sensitivity Relative values of overall attractiveness (i.e., social sensi tivity) from Chapter 2 and ecological sensitivity from Chapter 3 were summed at equal importance weights to form the system sensitivity (t otal sensitivity) (Table 44). After weighting procedures, the Ocala area had the most sensitive system with a total s ensitivity of 1.00 in relative value while the Osceola area had the least sensitive system with a total sensitivity 0.30. Fragility of Recreation Areas The application of the fragility model in Eq. (42) allowed the identification of four groups of recreat ion areas with fragility levels low, medium, high, and very high resulted
106 from cutoff fragility scores at 125, 250, and 500 respectively (Table 45, Figure 45). Three recreation areas were found to have very high fragility levels, Ocala, Greenway and Litt le Big Econ. Four recreation areas were identified to have high fragility levels included Suwannee, Goldhead, Withlacoochee, and Big Cypress. Four recreation areas, Blackwater River, Ecofina, Apalachicola and St. Marks had medium fragility levels while the remaining five recreation areas, including Twin Rivers, Osceola, Tosohatchee, Three Lakes and Kissimmee, had low fragility level scores. Tabl e 44. Total sensitivity in 16 r ecre ation areas along the FNST Recreation Area Ecological Sensitivity Evaluation W eight (50%) Social Sensitivity Evaluation Weight (50%) Total TOTAL SENSITIVITY (Relative Value) Blackwater R. 0.47 0.24 0.63 0.32 0.550 0.76 Ec onfina 0.94 0.47 0.34 0.17 0.640 0.88 Apalachicola 0.59 0.30 0.55 0.28 0.570 0.79 St. Marks 0.84 0.42 0.51 0.26 0.675 0.93 Twin Rivers 0.10 0.05 0.35 0.18 0.225 0.31 Suwannee 0.07 0.04 0.55 0.28 0.310 0.43 Osceola 0.15 0.08 0.28 0.14 0.215 0.30 Goldhead 0.35 0.18 0.24 0.12 0.295 0.41 Ocala 0.45 0.23 1.00 0.50 0.725 1.00 Greenway 0.13 0.07 0.49 0.25 0.310 0.43 Withlacoochee 0.42 0.21 0.63 0.32 0.525 0.72 Little Big Econ 0.89 0.45 0.37 0.19 0.630 0.87 Tosohatchee 0.78 0.39 0.14 0.07 0.460 0.63 Three Lakes 0.39 0.20 0.09 0.05 0.240 0.33 Kissimmee 1.00 0.50 0.14 0.07 0.570 0.79 Big Cypress 0.98 0.4 9 0.28 0.14 0.630 0.87 Since fragility is a function of sensitivity and recreation pressure, recreation areas with similar fragility levels might have had quite different sensitivities or pressures (Figure 4 5). For example, both Little Big Econ and Greenway areas were identified to be in the very high fragility group. However, the Greenway area was placed in this group because of its high visitation pressure and relatively low sensitivity while the Little
107 Big Econ area was in the same group because of it s high sensitivity with a much lower recreation pressure. Tabl e 45. Fragility of recreation areas Recreation Area Recreation Pressure Total Sensitivity Fragility Score Fragility Level Blackwater R. 317.00 0.76 240.48 Medium Econfina 180.00 0.88 15 8.90 Medium Apalachicola 208.00 0.79 163.53 Medium St. Marks 263.00 0.93 244.86 Medium Twin Rivers 130.00 0.31 40.34 Low Suwannee 635.00 0.43 271.52 High Osceola 121.00 0.30 35.88 Low Goldhead 899.00 0.41 365.80 High Ocala 914.00 1.00 914.00 Very Hi gh Greenway 1360.00 0.43 581.52 Very High Withlacoochee 453.00 0.72 328.03 High Little Big Econ 625.00 0.87 543.10 Very High Tosohatchee 90.00 0.63 57.10 Low Three Lakes 206.00 0.33 68.19 Low Kissimmee 73.00 0.79 57.39 Low Big Cypress 462.00 0.87 40 1.46 High Phase State along Adaptive Cycle of Recreation Areas As Holling (2001) stated, environmental social, economic and political components in a system interact in complex ways and influence system dynamics However, based on a simplified mathemati cal model (Petrosillo et al., 2006), t his study mostly examines the environmental perspective for nature tourism based system s. Under this condition, t he phases of the Adaptive Cycle, as discussed in the literature review, needed to be determined by both q uantitative and qualitative evaluations (Petrosillo et al., 2006). Although a mathematic fragility model was applied to analyze fragility level of recreation areas, qualitative characteristics such as levels of management and recreation activity impact were also considered. This is because appropriate
108 management of recreation in a fragile system can mitigate the recreation pressure while little, no, or incorrect management could lead to the adverse phase of the system. Figure 45. Relationship between s ensitivity (A*) and recreation pressure (U*) and fragility levels of 16 recreation areas along the FNST. (Note: APA = Apalachicola; BCY = Big Cypress; BWR = Blackwater River; ECO = Econfina; GHB = Goldhead; GRW = Greenway; KIS = Kissimmee; LBE = Little Big Econ; OCA = Ocala; OSC = Osceola; SMK = St. Marks; SUW = Suwannee; TLK = Three Lakes; TOS = Tosohatchee; TWR = Twin Rivers; WIT = Withlacoochee.) Levels of recreation management were based on the categories of lands under different management agencies, and identified as low, medium and high. Although level of management could be based on a variety of measures (e.g., amount of funding designated to recreation and number of recreationfocused employees), this study chose a more simplified approach to categ orize recreation management: agencies mission statements. It is based on the assumption that if recreation is
109 highlighted in an agencys mission statement, the agency will focus on the management of recreation on its lands Specifically lands managed by the National Park Service and Florida Park Service were categorized as high level of management because managing for recreation is an important part of these agencies mission statements and are likely to have budgets and staff designated to recreation management Lands managed by the US Forest Service and Florida Forest Service were classified as medium, because recreation is considered one of these agencies important activities; although, both agencies mission statements acknowledge that they manage for multiple uses including range and timber. Therefore, these agencies will manage for recreation, but the focus on recreation will vary for specific site. Finally, other agencies that stress another particular resource (e.g., wildlife or water) were placed in the low category because of their relative lack of emphasis on recreation management. Activity impact level was also categorized into high, medium and low following general guidelines that considered motorized and consumptive activities (e.g., hunt ing and fishing ) as high impact activities, while medium impact activities included mountain biking and horseback riding, and low impact activities included passive and social activities like hiking and camping (Buckley & Pannell, 1990; Leung & Marion, 200 0). A ll recreation areas fell into one of two phases along the Adaptive Cycle, exploitation or conservation based only on ecological and attractiveness evaluations (Table 46) Nine recreation areas were evaluated as being in the exploitation phase, includ ing Blackwater River, Econfina, Apalachicola, St. Marks, Twin Rivers, Osceola, Tosohatchee, Three Lakes, and Kissimmee based on their low or medium levels of
110 fragility. However, due to its relatively high impact activity and fragility levels, and relativel y low management levels compared to other areas in the group, Blackwater River was designated as phase exploitation plus (+), which means being on the end of the exploitation phase and moving towards the conservation phase. Seven recreation areas, including Suwannee, Goldhead, Ocala, Greenway, Withlacoochee, Little Big Econ, and Big Cypress were marked within the conservation phase due to their high or very high levels of fragility. Ocala, Greenway, and Little Big Econ had particularly high levels of fragil ity, so they were considered to be in the conservation plus (+) phase, which means being on the end of conservation phase and moving towards the release phase. Goldhead and Big Cypress were in the conservation minus ( ) phase due to their high levels of recreation planning and management, which means being on the very early stage of the conservation phase. Figure 46 shows a spatial distribution of fragility estimates in relative value among 16 recreation areas along the FNST. Table 46. Levels of fragility management and activity impact, and phase of Adaptive Cycle of recreation areas Recreation Area Fragility Level Level of Management Activity Impact Phase of Adaptive Cycle Blackwater R. Medium Medium High Exploitation+ Econfina Medium Low Medium Exploitation Apalachicola Medium Medium High Exploitation St. Marks Medium High Low Exploitation Twin Rivers Low Low Medium Exploitation Suwannee High High Medium Conservation Osceola Low Medium Low Exploitation Goldhead High High Low Conservation Ocala Very High Medium High Conservation+ Greenway Very High Low Medium Conservation+ Withlacoochee High Medium High Conservation Little Big Econ Very High High Medium Conservation+ Tosohatchee Low High Low Exploitation Three Lakes Low Medium Low Expl oitation Kissimmee Low Low Medium Exploitation Big Cypress High High High Conservation
111 Figure 46. Spatial distribution of fragility of recreation areas with marks indicating the phase state of nature tourism/recreation system along Adaptive Cycle. Discussion T his study made some key advances in the understanding of sustainable planning for nature tourism by incorporating Butlers original tourism area life cycle (Butler, 1980) with new ways to think about socioecological systems (Gunderson & Holling, 2002) through the use of GIS technologies and analysis of spatial, quantitative and qualitative data B ased on a fragility model developed by Petrosillo et al. (2006) which assessed tourism dynamics in the context of Hollings Adaptive Cycle, this research improved upon the fragility model in several ways. First, the model described here quantified system sensitivity by measuring both social and ecological components of nature tourism based system. Specifically, managers perceptions about naturebased t ourism/recreation were used to measure the social component of sensitivity and GIS
112 analysis of key ecological attributes measured ecological sensitivity. Second, actual measures of visitation (i.e., peak trail use) was used to provide a more direct measure of recreation use pressure than the methods used by Petrosillo et al. From an environmental perspective, t he process of fragility estimation integrated ecological attractiveness, and stress (pressure) indicators together to generate a road map towards sustainable nature tourism management. For instance, at the Suwannee area, when examined by ecological sensitivity alone, the data appeared to support potential nature tourism development due to relatively low ecological risk (score 0.07, Table 44). However when the social component of sensitivity, i.e., attractiveness (score 0.55) was incorporated, along with recreation pressure (score 635), the Suwannee area appears to be facing potential increase of recreation use pressure due to its highly attractive fe atures (Table 44, Table 45). When all indicators are integrated into the fragility model, it becomes evident that the Suwannee area has a relatively high overall risk towards adverse outcomes in the system dynamic s due to the interplay of sensitivity and recreation pressure (Table 46, Figure 4 6). All indicators were combined to provide a more accurate fragility estimation that could be easily incorporated into a decision support system for sustainable nature tourism. In other words, by combining components of attractive assessment (Chapter 2), ecological sensitivity assessment (Chapter 3), and the nature touri sm based system fragility (Chapter 4), relationships between compone nts can be analyzed and examined through spatial, quantitative and qualitative variables to help recreation and resource managers more accurately plan recreation areas that are potentially in threat of adverse outcomes in the system (Figure 4 7).
113 Figure 4 7 Conceptual model indicating major influencing factors and variables in a decision support system for sustainable nature tourism/recreation. (Note: LFF = Landscape Features & Facilities; RA = Recreation Activities; TS F = Tourism Support Facilities.) In contrast to the work of Patterson et al. (2008) and Petrosillo et al. (2006) which emphasized indirect tourism impact measures, t hese new and direct measures of sensitivity, recreation use pressure and fragility employed in this study allowed researchers to better clarify how managers should think about recreation areas in terms o f sustaining tourism and recreation use in these areas. For example, Suwannee, Goldhead, and the Greenway areas had similar sensitivity scores (i.e., scores ranged from 0.41 to 0.43). However, the Greenway area had a high fragility score, which means it wo uld deserve more immediate attention than these other areas; even though, they all had similar sensitivity scores. S imilar to Adams work (2010), the value of fragility estimation in this study lies in establishing the history of system dynamics of nature
114 tourism along the FNST in terms of Hollings adaptive cycle, and presenting a framework for thinking about how the adaptive cycle can integrate components from social and ecological aspects into a management plan. This framework can alert nature tourism/recreation managers to possible social trends, to changes in the local community, and to the potential negative impact s of nature tourism/recreation on the environment. As in the work of Choi and Sirakaya (2006), by specifying key social and ecological indic ators, this framework can provide decision makers with information that enables them to identify, evaluate and make appropriate decisions on critical changes being caused by nature tourism/recreation on the natural environment, communities and other resour ces in the area. In addition by considering temporal scales, i.e., seasonal peaks in visitation, this framework can incorporate an extended range of management concerns into decisionmaking in mitigating strains on infrastructure and waste processing, energy and water resources (Gossling, 2001; Kuvan, 2005). In this study, recreation management was considered as a factor to mitigate recreation pressure, and led to alter the course of phase state along the adaptive cycle. It is evident that different land m anagement agencies have different mission priorities, funding levels and staff trainings. Therefore, it is appropriate to recognize these differences. However, when management level was used to adjust the system dynamic in addition to the main factor fra gility in this study, management agencies mission statement s rather than actual measurement s were used. A better assessment could be achieved through the measurement of agencies budgets for recreation management staff training and other factors Furthermore it used a relatively simple fragility model to
115 interpret complex nature tourism dynamics. A more sophisticated model should include social and economic aspects such as impacts of nature tourism on local community and employment creation in the touri sm sector. The fragility model could be extended to include a better definition of sensitivity. Since sensitivity is not only a function of ecological but also of society and economy, future research could include social sensitivity by including perceptions of nature tourism from residents as well as from visitors. As Patterson et al. (2008) concluded, the tourism based system is complex, and resource and recreation managers should incorporate a diversity of social, economic, and ecological information, fro m the local to global scale, to better understand nature tourism associated problems in natural areas Therefore, further investigation is needed to understand how to develop indicators that reliably incorporate the social, cultural, and economic perspecti ves into understanding tourism systems. This understanding should then have clear connections to the planning and management of tourism systems across landscapes.
116 CHAPTER 5 CONCLUSION T o build a decision support system for sustainable naturebased tourism or recreation along the Florida National Scenic Trail (FNST), this study took on three large tasks: first this research examined FNST recreation areas current attractiveness from both supply and demand perspectives to determine the social component of s ystem sensitivity of these areas. It then used GIS analyses to assess the ecological sensitivity of the nature environments in these areas. Finally, this study estimated the fragility of nature tourism/recreationbased systems based on their social and ecological components of system sensi tivity and recreation use pressure to develop a reliable assessment of these areas risk to adverse outcomes of the system. In essence three key dimensions were identified in this research: attractiveness (i.e., social com ponent of sensitivity), ecological component of sensitivity, and fragility. Through the process used in this study, important indicators were quantified to describe naturebased tourism and recreation in three key dimensions: (1) attractiveness, (2) ecological sensitivity, and (3) nature tourism based system fragility. In turn, these three dimensions formed the major components of a decision support system for sustainable naturebased tourism/recreation and will be described below Attract iveness of NatureBased Tourism or Recreation The findings from evaluating attractiveness highlight the effective analytical tools and weighting schemes available to identify nature tourism attraction dimensions and simultaneously measur e and compar e attractiveness from both supply and demand perspectives. By obtaining two perspective measurements, this study provides a platform to investigate the interactions between supply and demand in determining the
117 overall nature tourism attractiveness. Three nature tourism attraction dimensions were identified through highly correlated items from a large number of variables : tourism support facilities, landscape feature & facilities, and recreation activities. The findings show that there were differences in evaluation s and weights between supply and demand, suggesting that gaps do exist between objective and subjective measurements of attractiveness This finding support s that simultaneous measurements of both supply and demand are necessary to yield a better understanding of the overall attractiveness of a recreation area. S ignificant managerial implications can be identified through findings associated with attractiveness When the demand evaluation is higher than the supply measure, it means an area is perceived to provide more than it can deliver. Therefore, tourism promoters would need to work with resource managers to develop appropriate marketing materials that accurately describe the opportunities available. Also managers might want to reduce visitation to these areas or redir ect visitation to other areas that do have ample supply. Finally, if the resource is able to accommodate increased development, managers could work with community decision makers to enhance available recreation and tourism facilities and services in the ar ea to help supply be more inline with perceived attractions. On the other hand, when an area receives higher supply evaluation than what is shown in the demand evaluation, it means the area has untapped potential because it has more than what is perceived to offer in nature tourism attractions. In this case, promotion of these areas might be appropriate to help make potential visitors aware of the opportunities available.
118 Ecological Sens itivity of Nature Based Tourism or Recreation N ature tourism may be seen as an effective catalyst for cultural, economic and social development. However, if nature tourism or recreation is not sustainably managed, natural environments can be easily disturbed and thus eventually lose their attractiveness (Wall, 1997). This will then likely reduce the quality of visitor experiences and potentially decrease uses in these areas (Boers & Cottrell, 2005; Buckley, 2000; Wall & Mathieson, 2006). The findings from assessing ecological sensitivity highlight a set of important ecolog ical indicators and their factors that influence the ecological sensitivity to naturebased tourism/recreation along the FNST. The current levels of ecological sensitivity diversification among naturebased tourism/recreation areas along the FNST were identified. The high sensitivity category mainly represents land that is characterized by wetland ecosystems or a combination of highly sensitive habitats within important waters body buffer zones on hills with sensitive soil textures. By overlaying recreation sites on the ecological sensitivity map, their sensitivity classes were identified and mapped. Results from this study can be used to not only monitor current nature tourism areas with their ecological indicators, but also to plan ecologically sustainable nature tourism along the FNST by providing appropriate criteria for the select ion of suitable land for future nature tourism development. The GIS model built through this study provides opportunity for future integrations. For instance, by combining spati al information on how an area offers the attractions and where the high concentration of visitors are, a map of nature tourism/recreation might be developed at a finer scale, which can assist in plan n ing and monitor ing management strategies for sustainable goals.
119 The GIS model developed in this study identified important variables that reflect the specific ecological importance related to the nature tourism setting. However, changing social and ecological conditions in many different types of nature tourism areas require new indicators to adequately measure potential tourism impacts. Fr agility of Nature Based Tourism System This study identified the spatially explicit distributions of fragility in the framework of the Adaptive Cycle, where the phase states of nature based tourism/recreation areas along the FNST were marked by estimating fragility A set of key indicators that influence fragility level were developed. These indictors include d recreation pressure, ecological sensitivity and social sensitivity The different phase states of naturebased tourism/recreation areas along the FNST were further described by incorporating recreation management with fragility estimation. Findings were similar to past literature with respect to helping understand the t emporal and spatial dynamics of tourism based systems risk factor s (Patterson et al., 2008; Petrosillo et al., 2006). With the identified and quantified set of indicators, a system s approach can improve managers abilities to know where a natural areas current status is in the system s cycle Therefore, incorporating indicators of attractiveness (i.e ., social component of sensitivity), ecological sensitivity, and environmental pressure from tourism provides managers the ability to respond to a variety of temporal and spatial variables that quantifying nature tourisms impact. This study examined ecological, social and nature tourism fragility components in a tourism based socioecological system. By identifying specific indicators and revealing relationshi ps and interactions among them, the underlying driving factors of system dynamics emerge. The mathematical model shows that ecological factor alone has
120 limited influences on the system dynamic change. It is the combination of ecological and social factors that determines the intensity and extent of the nature tourism, which in turn, dictates the direction and speed of system dynamic movement. However, interventions, such as learning from the changes and subsequent actions could alter or shift the system dy namics in terms of speed and direction. Results of t his study confirm that to sustain desirable outcomes and avoid catastrophic consequences in a nature tourism based system, managers must continuous ly learn how these systems respond to changes through sci entifically based monitoring of ecological and social indicators. These indicators should then relate to appropriate management actions (Van Wilgen & Biggs, 2011). From the environmental perspective, this study defined the nature tourism based system with specific i ndicators. However, from a system wid e perspective, the interactions among other components, such as political and social components influence the dynamic of nature tourism system in complex way s (Holling, 2001). For example, w hen the political component change s causing government budget reductions on public land management, the dynamic of nature tourism system might be influenced profoundly, such as, reduc ing the abilit y of the agency to protect the resource and simultaneously provide for quali ty visitor experiences. This requires future assessment of system dynamics of nature tourism based on a variety of components not just environmental Recommendatio ns to Resource and Recreation M anagement Several recommendations can be made based on the finding s of this research to improve natural resources and recreation management. Natural resource and recreation managers must understand that recreation use of and tourism development in their
121 natural areas will change the socioecological system in the area. The sustainability of this system is threatened if managers do not recognize where their areas fit in this system and where they might evolve without effective recreation and tourism management. This study developed a method to help managers and tour ism planners understand where their naturebased tourism/recreation areas fit in the system. Attractiveness, ecological sensitivity, and fragility provide largescale, but important measures of key attributes which directly relate to the sustainability of the areas. Since nature tourism attractions are unevenly distributed in time and space, planners and promoters of nature tourism from federal and state organizations need to identify the unique attractiveness components of each area and consider all recreation areas as a complete naturebased tourism product to offer to the public. Specifically associated with the ecological sensitivity for a given recreation area, managers need to give special attention to the interactions between recreation pressure, area attractiveness and ecological sensitivity because these relationship can help managers assess current impacts from nature tourism/recreation activities and foresee the potential impact directions in the future. By incorporating the entire set of quantifi able indicators included in the framework, the results of this research could be used to enhance managers abilities to define the current impacts as well to foresee possible future of courses of action. The results can provide a set of useful tools in eva luating attractiveness, assessing ecological sensitivity, and estimating risk factor s to assist decision making and test management strategies in sustainable nature tourism or recreation areas
122 Future Research Considering naturebased tourism/recreation ma nagement as a system, it is important that resource and recreation managers are assisted in considering an adaptive, rather than static management approach, which responds to a framework of natures rules, that captures the adaptive and evolutionary natur e of adaptive cycles that are nested one within the other across space and time scales (Holling, 2001. pp. 391). This study built upon past research to create such a framework; however, future research can expand and refine the variables used to measure attractiveness, sensitivity, and fragility of those complex models. For example, further research is needed to better define the dynamics of impacts, including other important variables and attributes. Specifically, in the ecolog ical sensitivity component, future research should include variables from other aspects of the environment, such as endangered species critical habitat and water table level s among other important environmental indicators. In addition, nature tourism attraction attributes and tempor al and spatial recreation pressure characteristics should be included in the next GIS modeling. In the attractiveness assessment, a different setting will require different attraction variables, and most likely additional dimensions will be identified, whi ch in turn, will generate a greater amount of attraction variance. For example, other variables that could be considered include q uantifiable landscape aesthetic and weather variables. Future research could assess demand and weighting of attractiveness dim ensions through surveys of visitors to the areas to be evaluated and then combine these evaluations with data from experts. Finally, this study used a relatively simple fragility model to interpret nature tourism dynamics in the context of system s thinking. More advance d model s should include
123 social and economic aspects, such as impacts of nature tourism to the local community, including residents perceptions towards tourism development associated economic impacts.
124 APPENDIX A EXPERT PANEL SURVEY QUESTIONNAIR E
132 APPENDIX B INSTITUTIONAL REVIEW BOARD APPROVAL Document I.
133 Document II.
134 APPENDIX C INVITATION LETTER TO EXPERT PANEL FOR PAR TICIPATING ONLINE SU RVEY Dear (first and last name): My name is Bin W an. I am a research assistant at the University of Florida, School of Forest resources and Conservation, and my advisor is Dr. Taylor Stein. We are currently conducting a study to measure naturebased tourism/recreation attractiveness along Florida National Scenic Trail (FNST). Since you are FNST chapter leader or trail coordinator, we sincerely invite you to participate in this study. I will be using the information you provide for my PhD research, but I also hope to use it to identify innovative and eff ective strategies to manage, communicate, and market the FNST. Please go to https://www.surveymonkey.com/s/D3JM6N3 and complete the online survey. It will only take 1015 minutes of your time. For your reference, attached is a Word file which contains in formed consent for your participation and the details of this study. We value your inputs because they will directly influence the outcomes of the study. We highly appreciate your prompt response to our online survey. If you have any questions about this study, please dont hesitate to contact us. Thank you very much for your cooperation! Bin Wan, Research Assistant Taylor Stein, Associate Professor School of Forest resources and Conservation University of Florida 227 Newins Ziegler Hall Gainesville, F L 32611 (353) 8460873
135 APPENDIX D REMI NDER LETTER TO EXPERT PAN EL FOR PARTICIPATING ONLINE SURVEY Dear Participants, Since last week when the invitation was sent to you, nearly 1/4 of you have already completed the online survey. I thank you very much for your quick responses. We understand that all of you have a very busy schedule each day, and we really appreciate that you can take 1015 minutes of your time to complete the survey. If you have not completed yet, please go to https://www.surveymonkey. com/s/D3JM6N3 and complete the survey. We apologize to those who already complete the survey for receiving this email due to the anonymous nature of the study. Thank you for your participation! Bin Wan, Research Assistant Taylor Stein, Associate Profess or School of Forest resources and Conservation University of Florida 227 Newins Ziegler Hall Gainesville, FL 32611 (353) 8460873
136 APPENDIX E COMPONENT SCORES COM PUTED FOR RECREATION AREAS BY DIMENSION VARIABLES Table E 1. Component scores computed for recreation areas by dimensional v ariables I. Recreation Area Food Place Gas Station Motel/Hotel Campground Loadings 0.942 CS 0.967 CS 0.939 CS 0.627 CS Blackwater River 3 2.826 6 5.802 2 1.878 14 8.778 Econfina 1 0.942 6 5.802 0 0.000 13 8.151 Ap alachicola 10 9.420 5 4.835 3 2.817 19 11.913 St. Marks 5 4.710 3 2.901 0 0.000 5 3.135 Twin Rivers 0 0.000 1 0.967 0 0.000 5 3.135 Suwannee 3 2.826 8 7.736 4 3.756 10 6.270 Ocseola 1 0.942 3 2.901 2 1.878 4 2.508 Goldhead 11 10.362 9 8.703 0 0.000 8 5.016 Ocala 76 71.592 34 32.878 10 9.390 14 8.778 Greenway 160 150.720 50 48.350 20 18.780 3 1.881 Withlacochee 25 23.550 17 16.439 10 9.390 7 4.389 L ittle Big Econ 300 282.600 69 66.723 22 20.658 3 1.881 Tosohatchee 23 21.666 13 12.571 6 5.634 3 1.88 1 Three Lakes 1 0.942 0 0.000 2 1.878 8 5.016 Kissimmee 5 4.710 5 4.835 0 0.000 3 1.881 Bi g Cypress 2 1.884 3 2.901 4 3.756 8 5.016
137 Table E 2. Component scores computed for recreation areas by dimensional v ariables II. Recreation Area Canoe launch Bo at Ramp River/Creek (mile) Spring Loadings 0.918 CS 0.960 CS 0.795 CS 0.906 CS Blackwater River 12 11.016 4 3.840 60 47.700 0 0.000 Econfina 5 4.590 4 3.840 26 20.670 1 0.906 Apalachicola 0 0.000 10 9.600 112 89.040 0 0.000 St. Marks 0 0.000 0 0.000 0 0.000 0 0.000 Twin Rivers 1 0.918 0 0.000 30 23.850 0 0.000 Suwannee 3 2.754 5 4.800 56 44.520 1 0.906 Ocseola 0 0.000 4 3.840 0 0.000 0 0.000 Goldhead 1 0.918 0 0.000 0 0.000 0 0.000 Ocala 29 26.622 34 32.640 120 95.400 5 4.530 Greenway 0 0. 000 0 0.000 0 0.000 0 0.000 Withlacochee 12 11.016 3 2.880 72 57.240 0 0.000 L ittle Big Econ 1 0.918 0 0.000 19 15.105 0 0.000 Tosohatchee 0 0.000 0 0.000 30 23.850 0 0.000 Three Lakes 0 0.000 1 0.960 0 0.000 0 0.000 Kissimmee 1 0.918 7 6.720 70 55.65 0 0 0.000 Big Cypress 1 0.918 2 1.920 0 0.000 0 0.000
138 Table E 3. Component scores computed for recreation areas by dimensional v ariables III. Recreation Area Paddling Trail Hiking Trail Mt Bike Trail Horse Trail Loadings 0.326 CS 0.545 CS 0.945 CS 0.470 CS Blackwater River 50 16.300 51 27.795 6 5.670 35 16.450 Econfina 26 8.476 14 7.630 0 0.000 56 26.320 Apalachicola 112 36.512 74 40.330 21 19.845 0 0.000 St. Marks 50 16.300 70 38.150 16 15.120 16 7.520 Twin Rivers 30 9.780 15 8.175 7 6.61 5 21 9.870 Suwannee 56 18.256 41 22.345 41 38.745 54 25.380 Ocseola 0 0.000 37 20.165 0 0.000 50 23.500 Goldhead 0 0.000 8 4.360 0 0.000 7 3.290 Ocala 45 14.670 120 65.400 22 20.790 100 47.000 Greenway 0 0.000 17 9.265 40 37.800 22 10.340 Withlacoche e 59 19.234 124 67.580 52 49.140 75 35.250 L ittle Big Econ 19 6.194 8 4.360 10 9.450 12 5.640 Tosohatchee 19 6.194 60 32.700 40 37.800 40 18.800 Three Lakes 58 18.908 31 16.895 65 61.425 65 30.550 Kissimmee 70 22.820 30 16.350 0 0.000 0 0.000 Big Cypr ess 25 8.150 43 23.435 0 0.000 0 0.000 Note: CS = Component Score
139 APPENDIX F DEMAND EVALUATION MEANS OF ATTRACTION DIMENSIONS BY RECREATION AREAS Table F 1. Demand evaluation m eans o f attraction dimensions by recreation areas Recreation Area TSF LFF R A Total Rank Blackwater River 3.273 3.160 3.703 10.136 2 Econfina 2.000 2.900 3.200 8.100 12 Apalachicola 2.556 2.783 3.455 8.794 9 St. Marks 2.889 3.517 3.107 9.513 5 Twin Rivers 2.400 3.090 3.025 8.515 11 Suwannee 2.778 3.189 3.198 9.165 7 Ocseola 2.556 2.661 3.558 8.775 10 Goldhead 3.700 2.862 2.903 9.465 6 Ocala 3.600 3.201 3.826 10.627 1 Greenway 3.500 3.104 2.915 9.519 4 Withlacochee 3.375 2.929 3.356 9.660 3 Little Big Econ 2.286 2.614 2.750 7.650 13 Tosohatchee 2.333 2.333 2.750 7.416 1 5 Three Lakes 2.000 2.333 2.750 7.083 16 Kissimmee 2.667 2.400 2.500 7.567 14 Big Cypress 2.750 2.875 3.188 8.813 8 Note: TSF = Tourism Support Facilities; LFF = Landscape Features & Facilities; RA = Recreation Activities.
140 LIST OF REFERENCES Adams, A. W. (2010). Planning for cruise ship resilience: am approach to managing cruise ship impacts in Haines, Alaska. Coastal Management, 38, 654 664. Backman, S., Uysal, M., & Backman, K. (1991). Regional a nalysis of tourism resources. Annals of Tourism Research 8 (1), 323 7. Berkes F., & Folke, C. (Eds.). (2000) Linking social and ecological systems: management practices and social mechanisms for building resilience Cambridge: Cambridge University Press Besculides A., Lee, M. & McCormick, P. ( 2002 ) Residents p erc eptions of the cultural b enefits of t ourism. Annals of Tourism Research, 29 Boers, B. & Cottrell, S. (2005) Sustainable tourism infrastructure planning: a GIS based approach. In Proceedings of the 2005 Northeastern Recreation Research Symposium (pp. 151 160). 303 319. Boyd, S. W. & Butler, R. W. (1996) Seein g the forest through the trees: Using geographical information systems to identify potential ecotourism sites in northern Ontario, Canada. In L. C. Harrison & W. Husbands (Eds.), Practicing respons ible tourism: International case studies in tourism planning, policy and development (pp. 380403). Chiches ter, UK: John Wiley S ons. Brayley, R. E. (1990). An analysis of destination a ttractiveness and the use of psychographics and demographics in s egmenta tion of the within state tourism m arket. Unpublished PhD dissertation, Texas A&M University. Brown, G., & Weber, D. (2011). Public participation GIS: a new method for national park planning. Landscape and Urban Planning, 102(1), 1 15. Brunt P., & Courtney, P. ( 1 999 ) Host p erceptions of sociocultural i mpacts. Annals of Tourism Research, 26 Buckley, R. (2000). Neat trend: current issues in nature, ecoand adventure tourism. International Journal of Tourism Research, 2(6), 437444. 493 515. Buckley, R., & Pannell J. (1990). Environmental impacts of tourism and recreation in national Parks and conservation reserves. Journal of Tourism Studies 1(1), 2432. Butler, R. W. (1980). The concept of a tourist area cy cle of evolution: implications for management of resour ces. Canadian Geogr apher, 24 Butler ( 2 ), 5 12. R. W. ( 1999 ) Sustainable t ourism: A state of the a rt r eview. Tourism Geographies 1 ( 1 ), 7 25
141 Carlson, L. H. (1989). Human impact management in a coastal recreation and natural area. Biology Conservation, 49(2), 1 41 156. Carvers, S. J. (1995). Mapping the wilderness continuum. Proceedings of the GIS research UK 1995. University of Newcastle. Casagrandi, R., & Rinaldi. (2002). A theoretical approach to tourism sustainability. Conservation Ecology, 6(1), 13. Cha, S., & Uysal M. (1994). Regional a nalysis of t ourism r esources: a case study of Korea. Journal of Hospitality & Leisure Marketing, 2 (3), 61 74. Chen, H., Wang, Y., Ding, G., & Jiang, H. (2005). Ecological sensitivity analysis on landscape Ecological planning in tourist resorts: A case of Nanhuashan National Forest Park, Hunan Province. Landscape Architecture, 2. Choi, H.C., & Sirakaya, E. (2006). Sustainability indicators for managing community tourism. Tourism Management, 27, 1274 1289. Clark W. C. ( 2002 ) Adapti ve management, h eal t hyself. Environment 44 Cole, D. N. (1987). Effects of three seasons of experimental trampling on five montane forest Communities and a grassland in western Montana, USA. Biological Conservation, 40, 219 244. ( 2 ), inside cover. Cole, D. N. (1988). Disturbance and recovery of trampled montane grassland and forests in Montana. In USDA Forest Service General Technical Report INT 389, Intermountain Forest and Range Experiment Station, Odgen, Utah. Cole, D. N. (1993). Trampling effects on mo untain vegetation in Washington, Colorado, New Hampshire, and North Carolina. In USDA Forest Service General Technical Report INT 4 6 4 Intermountain Forest and Range Experiment Station, Odgen, Utah Cole, D. N. (1995a). Disturbance of natural vegetation by camping: Experimental applications of l ow level stress. Environmental Management, 19(3), 405416. Cole, D. N. (1995b). Experimental trampling of vegetation. II. Predictors of resistance and resilience. Journal of Applied Ecology, 32, 215 224. Cole, D. N., & Marion, J. L. (1988). Recreation impacts in some riparian forests of eastern United States. Environmental Management, 12(1), 99 107. Collins, A. (1999). Tourism development and natural capital. Annals of Tourism Research, 26 (1), 98 109.
142 Cordell, H. K. (1999). Outdoor recreation in American life: A national assessment of demand and supply t rends Champaign, IL: Sagamore Publishing. Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: f our recommendations for getting t he most from your analysis. Practical Assessment, Research & Evaluation, 10 (7), 1 9. Crompton, J L. (1979). An assessment of the image of Mexico as a v acation d est in ation and the i nfluence of g eographical l oca t i on upon that image. Journal of Travel Research, 17 18 23. Cusack, D., & Dixon, L. (2006). Community based ecotourism and sustainability: cases in Bocas del Toro Province, Panama and Talamanca, Costs Rica. Journal of Sustainable Forestry, 22(1/2), 157182. Dann, M. S. D. (1981). To urism motivation : an appraisal. Annals of Tourism Research, 8 (2):187 219. Daily, G. C., Alexander, S., Ehrlich, P. R., Goulder, L., Lubchenco, J., Matson, P. A., Mooney, H. A., Postel, S., Schneider, S. H., Tilman, D., & Woodwell, G. M. (1997). Ecosystem services: benef its supplied to human societies by natural ecosystems. Issues Ecol 2 1 16. Dale, F. (1990). Consumer p erc eptions of tourist attractions. Journal of Travel Research, 28 (4), 3 10. Department of Environmental Protection, Florida (DEP). (n.d.). Retrieved May 15, 2012, from Driver, B., Brown, P., Stankey, G., & Gregoire, T. (1987). The ROS planning system: evolution, basic concepts, and research needed. Leisure Sciences, 9 (3), 201 212. http://w ww.dep.state.fl.us Duryea, M. L., & Vince, S. W. (2005). The city is moving to our frontiers doorstep. In S. W. Vince, M. L. Duryea, E. A. Macie, & L. A. Hermansen (Eds.), Forests at the wildland u rb an Interface: Conservation and m anagement (pp. 313). Boca Raton, FL: CRC Press. Dye, A. S., & Shaw, S. L. (2007) A GIS based spatial decision support system for tourists of Great Smoky Mountains National Park. Journal of Retailing and Consumer Services, 14, 269 278. ESRI. (n.d.). Retrieved June 18, 2012, from Ewert, A. (1993). The wild land urban interf ace: i ntroduction and overview. Journal of Leisure Research, 25 (1), 1 5. http://w ww.esri.com Farrell, B. H., & Twining Ward, L. (2004) Reconceptualizing tourism. Annals of Tourism Research, 31(2), 274 295.
143 Farrell, B. H., & Twining Ward, L. (2005) Seven steps towards sust ainability: tourism in the context of new knowledge. Journal of Sustainable Tourism, 13(2), 109122. Farsari, Y. & Prastacos, P. (2004) GIS a pplication in the planning and management of tourism. In A. A. Lew, C. M. Hall, & A. M. Williams (Eds.), A companion to t ourism (pp. 596607). Oxford: Blackwell Publishing. Fesenmaier, D., & Uysal M. (1990). The t ourism system: l evels of e conomic and h uman behavior. In J. B. Zeige, & L. M. Caneday (Eds.), Tourism and l eisure: Dynamics and diversity (pp. 2735). Alex andria,VA: National Recreation and Park Association Formica, S, & Uysal, M. (2006). Destination attractiveness based on supply and demand evaluations: a n analytical framework. Journal of Travel R esearch, 44(5), 418430. Gallardo, J. H., & Stein, T.V. (200 7). Participation, power and racial repr esentation: negotiating naturebased and heritage tourism development in the rural south. Society and Natural Resources 20 (7), 597 611. Gearing, C. E., Swart, W. W., & Var T. ( 1974). Establishing a measure of t our istic attractiveness Journal of Travel Research, 22 (Spring), 1 8 Gossling, S. (2001). The consequences of tourism for sustainable water use on a tropical island: Zanzibar, Tanzania. Journal of Environmental Management, 61, 179191. Gunderson, L. H., & Holling, C. S. (Ed.). (2002). Panarchy: Understanding Transformations in Human and Natural Systems Washington, DC : Island Press Gunn, C. A. (19 94). Tourism p lanning (3rdHall, M.C. (2001). Trends in o cean a nd coastal t ourism: t he e nd of t he l ast f rontier? Ocean & Coastal Management 44, 601 618. ed. ). New York, NY: Taylor & Francis. Hammitt, W. E., & Cole, D. N. (1987). Wildland recreation: Ecology and m anagement. New York, NY: John Wiley & Sons. Hammitt, W. E., & Cole, D. N. (1998). Wildland recreation: Ecology and m anagement. (2ndHarman, H. H. (1976). Modern factor analysis. Chicago: University of Chicago Press. ed.). New York, NY: John Wiley & Sons. Hartly, E. (1999). Visitor impacts on Logan Pass, Glacier National Park: a thirty year vegetation s tudy In D. Harman (Ed .), On the frontiers of c onservation: Proceedings of the 10th Conference on Research and Management in National Parks and on Public Lands, Ashville, NC. (pp. 297 305). Hancock, MI: The George White Society.
144 Hillery, M., Nancarrow, B., Gri ffin G., & Syme, G. (2001). Tourist perception of environmental impact, Annals of Tourism Research, 28 Holland, S. M., Ditton, R. B., & Fraefe, A. R. (1998). An ecotourism perspective on billfish fisheries. Journal of Sustainable Tourism, 6( 2), 97116. (4), 853 867. Holling, C. S. (2001). Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4, 390 405. Holling, C. S., Gunderson, L. H., & Peterson, G. D. (2002). Sustainability and panarchy. In L. H. Gunderson, & C. S. Holling (Eds.), Panarchy: Understanding transformations in human and natural s ystems (pp. 63102). Washington, DC : Island Press. Hu Y. Z. & Ritchie, J. R. B. ( 1993) Measuring destinatio n attractiveness: a contextual approach. Journal of Travel Research 32 Hu nter ( 2 ) 2534. C. J. ( 1997) Sustainable t ourism as an a daptive p aradigm. Annals of Tourism Research, 24, 850 867. Hunter C., & Green, H. (Ed.). (1995). Tourism and enviro nment: A s ustainable r elationship? London UK: Routledge. Inskeep, E. (1991). Tourism planning: An integrated and sustainable development approach. New York NY : Van Nostrand Reinhold. Kaur, J. (1981). Methodological a pproach to scenic r esource a ssessment. Tourism Recreation Research, 6 (1): 19 22. Koshkariov, A., Krasovskaia, T. M., & Tikunov, V. S. (1994). Towards resolving the problems of regional development in the mountains of the Commonwealth of Independence States using GIS. In M. F. Price, & D. I. Heywood (Eds.), Mountain environments and geographical information system. London, UK: Taylor & Francis. Kuss, F. R. (1986). Impact ecology knowledge is basic. In USDA Forest Service General Technical Report INT 212, Intermountain Forest and Range Experiment Station, Odgen, Utah. Kuss, F. R., Graefe A. R., & Vaske, J. J. (Ed.). (1990). Recreati on impacts and carrying c apacity Washington, DC: National Parks and Conservation Association. Kuvan, Y. (2005). The use of forests for the purpose of tourism: the case of Belek Tourism Center in Turkey. Journal of Environmental Management, 75, 263 274. Lang, C., & OLeary, J. T. (1997). Motivation, participation, and preference: a multi segmentation approach of the Australian nature travel market. Journal of Travel and Tourism Marketing, 6(3/4), 159180.
145 Lathrop, R. G., & Bognar, J. A. (2001). Habitat loss and alteration in the Barnegat Bay Region. Journal of Coastal Research, Special Issue(32), 212228. Legg, M. H., & Schneider, G. (1977). Soil deterioration on campsites: northern forest types. Soil Science Society American Journal, 41, 437 441. Leung, Y., & Marion, J. L. (1999). Assessing trail conditions in protected areas: an application of a problem assessment method in Great Smoky Mountain N ational Park, USA. Environmental Conservation, 26(4), 270279. Leung, Y., & Marion, J. L. (2000). Recreation impacts and management in wilderness: a state of knowledge review. In D. N. Cole, S. F. McCool, W. T. Borrie, & J. OLoughlin (Eds.), Wilderness Science in a Time of Change Conference. Vol. 5. Wilderness ecosystem, threats and m anagement (pp. 2348). Rocky Mountain Research Station, Ogden, Utah: USDA Forest Service. Lee, K., ( 1993) Compass and gyroscope: Integratin g science and p olitics for the e nvironment Washington DC : Island Press Leiper, N. (1979). The m odel of t ourism: t owards a d efinition of t ourism, t ouris t, and the t ourist industry. Annals of Tourism Research, 6 (4), 390 407. Lew, A. (1986). Guidebook Singapore: The spatial or ganization of u rban t ourist a ttractions. Unpublished PhD dissertation, University of Oregon. Lew, A. ( 1987). A f ramework o f tourist attraction research. Annals of Tourism Research, 14, 533 75. Liu, J. J. (1988). Touristic attractiveness of Hawaii by county. Occasional paper, Tourism Research Publications #10, University of Hawaii at Manoa. Liu J., Sheldon, P., & Var, T. ( 1987) Residents p erceptions of the e nvironmental i mpacts of t ourism. Annals of Tourism Research, 14 Lu, J., & Nepal, S. (2009). Sustainable tourism research: an analy sis of papers published in the Journal of Sustainable Tourism Journal of Sustainable Tourism, 17(1 ), 5 16. 1737. Luo, Y., & Deng, J. (2008). The new environmental par adigm and naturebased tourism mitivation. Journal of Travel R esearch, 46, 392 402. Manfredo, M. J., Driver, B. L., & Tarrant, M. A. (1996). Measuring leisure motivation: a meta analysis of the recreation experience preference scales. Journal of Leisure Research, 28, 188 213. Manning, R. E. (1979). Impacts of recreation on riparian soils and vegetation. Water Resource Bulletin, 15(1), 3043.
146 Manning, R. E., Freimund, W. A., & Lime, D. W. (2009). Crowing norms at frontcountry sites: a visual approach to setting standards of quality. Leisure Science, 18(1), 39 59. Marion, J. L., & Cole, D. N. (1996). Spatial and temporal variation in soil and vegetation impacts Campsites. Ecological A pplications, 6 ( 2), 520530. Marion, J. L., & Merriam, L. C. (1985). Recreational impacts on well established campsites in The Boundary Water Canoe Area Wilderness. In University of Minnesota Agriculture Experimental Station Bulletin AD SB2502 St. Paul, MN. Mayo E. J., & Ja rvis, L. (1981). The psychology of leisure travel Boston, MA: CBI Publishing McAdam, D. (1999) The value and scope of geographical information systems in tourism management, Journal of Sustainable Tourism, 7(1), 77 92. McClure, J. M., Kolka, R. K., & Wh ite, A. (2004). Effects of forest harvesting best management practices on coarse woody debris distribution in stream and riparian zones in three Appalachian watersheds. Earth and Environmental Science, 4(1), 245261. Mihalic T. ( 2000). Environmental management of a to urist destination: a factor of tourism competitiveness, Tourism Management 21 Mill, R. C., & Morrison A. M. (1985). The Tourism system: An i ntroductory t ext Englewood Cliffs, NJ: Prentice Hall 65 78. Milman, A. & Pizam, A. ( 1988). Social impact s of t ourism on Central Florida. Annals of Tourism Research, 15, 191 204 Mowforth, M., & Munt, I. (2009 ). Tourism and sustainability: Development, globalization and new tourism in the T hird W orld (3rdMuller, T. E. (1991). U sing personal values to define segments in an international tourism market. International Marketing Review, 8(1), 57 71. ed.) New York NY : Routledge. Nilsson C. N., & Grelsson, G. (1995). The fragility of ecosystems: a review. Journal of Applied Ecology 32 Nyberg 677 692. B. ( 1999) An i ntroductory guide to adaptive management: For project leaders and participants Victoria : British Columbia Forest Service. Nyberg, L. (1995). Determinants of the a ttractiveness of a t ourism region. In S. F. Witt, & L. Moutinho (Eds.), Tourism marketing and management handbook (pp. 2938). Hertfordshire, UK: Prentice Hall
147 Oetzel, J. G., Ting Toomey, S., & Rinderle, S. (2006). Conflict communication in contexts: a social ecological perspect ive. In J. G. Oetzel & S. Ting Toomey (Eds.), The SAGE handbook of c onflict communication. Thousand Oaks, CA: Sage. Olafsdottir, R., & Runnstrom M. (2009). A GIS approach to evaluating ecological Sensitivity for tourism devel opment in fragile environment: a case study from SE Iceland. Scandinavian Journal of Hospitality and Tourism 1 22 38. Page, S. J., & Dowling, R. K. (2002). Ecotourism. Harlow, UK: Pearson Education Ltd. Parsons E., & Clark, W. ( 1995) Sustainable development as social learning: Theoretical p erspectives and p ractical challen ges for the design of research pr ograms. In L. H. Gunderson, C. S. Holling, & S. Light (Eds.), Barriers and bridges to the renewal of ecosystems and institutions (pp. 428460). New York : Columbia University Press. Patterson, T. M., Niccolucci, V., & Marchettini, N. (2008). Ada ptive environmental management of tourism in the Province of Siena, Italy using the ecological footprint. Journal of Environmental Management, 86, 407 418. Pearce, D. G. (1987). Toward geography of tourism Annals of Tourism Research, 6 (3), 24572. Petrosillo, I., Zurlini, G., Grato, E., & Zacc arelli, N. (2006). Indicating fragility of socio ecological tourism based system. Ecological Indicators 6 104 113. Petrosillo, I. Zurlini, G., Corliano, M. E. (2007). Tourist perception of recreational environment management in a marine protected area. Landscape and Urban Planning, 79(1), 29 37. Philipp, S. F. (1993). Racial differences in the perceived attractiveness of tourism destination, Interests, and cultural resources. Journal of Leisure Research, 25(3), 290305. Pola cek, M., & Aroch, R. (1984). Analysis of cultural sites attractiveness for tourism. The Tourism Review, 39(4), 17 18. Radeloff V C Hammer R B Stewart S I Fried J. S Holcomb S S ., & McKeefry J. F (2005) The wildlandurban i nterface in the United States. Ecological Applications 15, 799 805. Ratcliffe, D. A. (1977). A nature conservation review I. UK: Cambridge University Press. Rodgers, J. A., & Schwikert, S. T. (2002). Buffer zone distances to pretect foraging and loafing waterbirds from disturbance by personal watercraft and outboardpowered boats. Conservation Biology, 16(1), 216224.
148 Rollins R., Trotter, W., & Taylor, B. ( 1998) Adaptive m anagement of r ecreation sites in the w ildland u rban i nterface. Journal of Applied Recreation Research 23 Rossi, P., Pecci, A., Amadio, V., Rossi, O., & Soliani, L. (2008). Coupling indicators of ecological value and ecological sensitivity with indicators of demographic pressure of new areas to be protected: t he case of the Oltrepo Pavese and the LiguianEmilian Apennine area (Italy). Landscape and Urban Planning, 85(1), 1226. 107 125. Sanborn, A., Belcher, A., Albritton, R.,& Stein, T. V. (2003). Florida National Scenic Trail visitor assessment. In US Forest Service Technical Report, University of Florida, Gainesville, Florida. Scheffer, M., Brock, W ., & Westley, F. (2000). Socioeconomic mechanisms preventing optimum use of ecosystem services: an interdisciplinary theoretical analysis. Ecosystems, 3 Scheyvens, R. 1999. Ecotourism and t he empowerment of local communities. TourismManage, 20 (2), 245 249. 451471. Shrestha, R. K., Stein, T. V., & Clark J. (2007). Valuing naturebased recreation in public natural areas of the Apalachicola River region, Florida. Journal of Environmental Management, 85 (4), 977 985. Smith, L. J. S. (1983). Recreation geography London, England: Longman Publishing. Smith, L. J. S. (1987). Regional analysis of tourism resources. Annals of Tourism Research, 14 (2), 254 73. Smith, L. J. S. (1995). Tourism analysis: A handbo ok ( 2nd ed. ). Harlow, England: Longman Group. Spotts, D. M. (1997). Regional analysis of tourism resources for marketing purposes. Journal of Travel Research, 35 (3), 3 15. Stankey, G. H., Cole, D. N., Lucas, R. C., Peterson, M.E., & Frissell, S. S. (1985) The l imits of a cceptable change (LAC) system for w ilderness planning. In USDA Forest Service General Technical Report INT 176 Intermountain Forest and Range Experiment Station, Odgen, Utah. Stein, T. V. (2001). Making ecotourism a reality: an integrated approach. Renewable Resources Journal 3, 6 12. Stein, T. V., Clark, J. K., & Rickards, J. L. (2003). Assessing natures role in ecotourism, development in Florida: perspectives of tourism professionals and government decisionmakers. Journal of Ecotouris m, 2 (3), 155 172.
149 Stein, T. V. (2005). Planning and managing for recreation in the wildlandurban interface. In S. Vince, M. L. Duryea, E. A. Macie, & L. A. Hermansen (Eds.), Forests at the wildland urban interface (pp. 139157). Boca Raton, FL: CRC Press Stohlgre, T. J., & Parsons, D. J. (1986). Vegetation and soil recovery in wilderness campsites closed for visitor use. Environmental Management, 10(3), 375380. Tiner, R. W. (2004). Remotely sensed indicators for monitoring the general condition of natural habitat in watershed: an application for Delawares Nanticoke River watershed. Ecological Indicator, 4(4), 227 243. Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rdUnited Nations Environment Programme. (n.d.). Retrieved February 15, 2010, from ed. ). New York: HarperCollins. U.S. Census Bureau. (n.d.) Retrieved April 12, 2009, from http://www.unep.org U.S. Department of Interior, National Park Service. (1997). The visitor experience and resourc e protection (VERP) f ramework: A handbook for planners and managers Denver, CO: Department of Interior, National Park Service, Denver Service Center. http://factfinder.census.gov U.S. Department of Agriculture, Natural Resource Conservation Service (n.d.). Retrieved February 15, 201 1, from Uysal, M. (1998). The d eterminants of t ourism d emand: a t heoretical perspective. In D. Ioannides, & K. G. Debbage (Eds.), T he economic geography of the tourist industry (pp. 79 98) London, England: Routledge. http://soils.usda.gov/survey Uysal, M. & Potts, T. (1990). An analysis of coastal tourism resources with special reference toaccommodations: a South Carolina study. In Proceedings of the1990 Congress on Coastal and Marine Tourism (pp. 4309). Honolulu, USA. V an Wilgen, B. W., & Biggs, H. C. ( 2011). A critical assessment of adaptive ecosystem management in a large savanna protected area in South Africa. Biological Conservation, 144(4), 11791187. Valentine, P S. ( 1992) Re view: naturebased tourism. In B. Weiler, & C. M. Hall, (Eds.), Special interest tourism (pp. 105127). London, England: Belhaven. Var, T., Beck R. A., & Loftus P. (1977). Determination of t ourist a reas in British Columbia. Journal of Travel Research 15 (Winter), 23 9.
150 Velicer, W. F., & Jackson, D. N. (1990). Component anal ysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25, 1 28. Wall G. ( 1997). Is Ecotourism Sustainable? Environmental Management 21, 483 491. Wall, G., & Mathieson, A. (2006). Tourism: Cha nges, impacts, and opportunities Boston, MA: PearsonPrentice Hall. Walmsley, D. J., & Jenkins J. M. (1992). Tourism cognitive m apping of unfamiliar environments. Annals of Tourism Research, 19, 26886. Wan, B., Cucinella, J., & Stein, T. V. (2009). Flor ida National Scenic Trail v isitor a ssessment. In US Forest Service Technical Report, University of Florida, Gainesville, Florida. Wan, B., Fisch, J., Bild, E., & Stein, T. V. (2011). Florida National Scenic Trail visitor a ssessment. In US Forest Service Te chnical Report, University of Florida, Gainesville, Florida Wang, Y., & Pfi ster, R. E. (2008). Residents attitudes toward tourism and perceived personal benefits in a r ural community. Journal of Travel Research, 47, 84 93. WCED. ( 1987) Our common future. World Commission on Environment and Development Oxford, England: Oxford University Press. Weaver, D B. ( 1995) Alt ernative tourism in Montserrat. Tour ism Management, 16 593604. Weaver, D. B. (2005). Comprehensive and minimalist dimensions of ecotouri sm. Annals of Tourism Research, 32(2), 439 455. Weaver, D. B., & Lawton, L. J. (2007). Twenty years on: the st ate of contemporary ecotourism research. Tourism Management, 28(5), 11681179. Western, D. (1993) Defi ning ecotourism. In K. Lindberg, & D.E. Haw kins ( E ds ) Ecotourism: A guide for planners and managers (pp. 711). North Bennington, VT: The Ecotourism Society. Wilkinson, L. (1988). SYSTAT: T he system for statistics. Evanston, IL: SYSTAT. WTO. (1996). A genda 21 for the travel and tourism industry Madrid: World Tourism Organization. Young, M. (1999). Cognitive m aps of n atureb ased tourists. Annals of Tourism Research, 26(4), 817 39.
151 Zhang, T. (2001). Community features and urban sprawl: the case of the Chicago metropolitan Region. Land Use Policy, 18, 221 232. Zisa, R. P., Halverson, H. G., & Stout, B. B. (1980). Es tablishment and early growth of conifers on compact soils in urban areas. In USDA Forest Service research Paper NE 451. Zurlini, G., Amadio V., & Rossi, O. (1999). A landscape approach to biodiversity and biological health planning: the Map of Italian Nature. Ecosystem Health, 5 Zurlini, G., Rossi O., & Amadio, V. (2003). Landscape biodivers ity and biological health risk assessment: the Map of Italian Nature. In D. J. Rapport, W L. Lasley, D. E. Rolston, N. O. Nielsen, C. O. Qualset, & A.B. Damania (Eds.), Managing for healthy ecosystems (pp. 639659). Washington DC: Lewis Publishers. 296311. Zurlini, G., Zaccarelli, N., & Petrosillo, I. (2004). Implementing an integrated framework for ecological risk assessment at the landscape scale. EcoSys, 42, 4 15.
152 BIOGRAPHICAL SKETCH Bin Wan grew up in Southwest China and received his bachelors degree i n horticulture at the Southwest Agricultural University in 1984. During his career in genetic resources for vegetable crops he had an opportunity for an advanced training in vegetable crop pathology in Asian Vegetable Research and Development Center (AVRD C) Thailand in 1988 He then pursued his graduate program in crop science at Wageningen Agricultural University, the Netherlands in 1993 After being involved in research on genetic resources, plant pathology and mt DNA analysis for more than 12 years, he decided to explore a different side of the world and went to Purdue University in 1995 and received his masters degree in visual and performing arts in 1998. During his career in photographic and digital imaging in Indiana, he asked himself often how a nd what could combine his experiences from the two different sides of the world. He found that opportunity and started his Ph. D. program a t the University of Florida in the spring of 2007. He has been involved in visitor assessment studies on the Florida National Scenic Trail and surveyed intensively numerous recreation areas across the State of Florida. His research focuses on the sustainability of naturebased tourism or recreation in Florida through assessing attractiveness, ecological sensitivity and risk factor s of nature tourism based systems.