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Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2008-02-29.

Permanent Link: http://ufdc.ufl.edu/UFE0021309/00001

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Title: Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2008-02-29.
Physical Description: Book
Language: english
Creator: Bucaram, Santiago
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Statement of Responsibility: by Santiago Bucaram.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Lee, Donna J.
Electronic Access: INACCESSIBLE UNTIL 2008-02-29

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021309:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021309/00001

Material Information

Title: Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2008-02-29.
Physical Description: Book
Language: english
Creator: Bucaram, Santiago
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Statement of Responsibility: by Santiago Bucaram.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Lee, Donna J.
Electronic Access: INACCESSIBLE UNTIL 2008-02-29

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021309:00001


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1 THE IMPACT OF INVASIVE PLANTS ON THE RECREATIONAL VALUE OF FLORIDAS AQUATIC AREAS By SANTIAGO BUCARAM A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007

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2 2007 Santiago Bucaram

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3 In memory of Blanca Luzuriaga, my dear grandma

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4 ACKNOWLEDGMENTS I express my deep appreciation to my advi sory committee: Dr. Donna Lee, Dr. Alan Hodges and Dr. Ronald Ward. I thank Dr. Lee for giving me the opportunity to be part of this interesting project which taught me the dos and do nots of a research wor k. This thesis is the re sult of her trust and support. I thank Dr. Hodges for his time and patience, as well as his financial support to this project, which was essential in order to finish it. I thank Dr. Ward for his guidance and teachings in the statistical field, which will be always appreciated. I thank Frida Bwenge and Dr. Damian Adams who collaborated in this project providing me with their invaluable assistance a nd unique perspective during this work. I thank my parents Emma and Santiago, for th eir continuous care, love, and moral support despite the distance. I am also very thankful to Dr. Steve Sargent who was the first person in UF to offer me the opportunity to participate in a re search project (Florida Tomato Committee Project). Without his support it would have been difficult to acco mplish this important step in my career. I also thank my friends Luis M., Luis C., Andres, Mayra, Juan Manuel and Liliana for giving me the strength to never give up. Finally the most special thanks goes to my be st friend and real love, my wife Maria Jose, who gave me her unconditional support and love through this long process.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES................................................................................................................ .......12 ABSTRACT....................................................................................................................... ............14 CHAPTER 1 INTRODUCTION..................................................................................................................16 Problem Setting................................................................................................................ ......17 Objectives..................................................................................................................... ..........18 Hypotheses..................................................................................................................... .........19 Literature Review.............................................................................................................. .....19 Economic Assessment of Natural Amenities..................................................................19 General Economic Impact of Invasive Species...............................................................21 Specific Economic Impact of Invasive Species on Recr eational Activities....................23 Methods and Procedures.........................................................................................................24 Revealed Preference Methods.........................................................................................25 Stated Preferences Methods............................................................................................25 Contingent Valuation Techniques (CV)...................................................................26 Multi-Attribute Techniques (MA)............................................................................27 2 PUBLIC AWARENESS OF INVASIVE PLANTS IN FLORIDA.......................................33 Introduction................................................................................................................... ..........33 Description of the Survey Process..........................................................................................33 Respondent Demographic Description...................................................................................35 Extent of Respondents Knowledge and Th eir Attitudes Toward Invasive Plants................35 Knowledge Model: Determinants of the Real (Observed) Knowledge of Respondents........40 Conclusions.................................................................................................................... .........44 3 DETERMINATION OF RELEVANT ATTR IBUTES OF AQUATIC PARKS IN FLORIDA........................................................................................................................ .......53 Introduction................................................................................................................... ..........53 Preliminary Relevant Attributes.............................................................................................54 General Description of the Survey.........................................................................................56 Respondent Demographic Description...................................................................................57 Model for the Degree of Importance of Park s Attributes on Floridians Decision to Participate in Outdoor Recreationa l Activities in Aquatic Parks........................................59 Ranking of the Relevant Attrib utes and Weighted Models................................................62

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6 Conclusions.................................................................................................................... .........66 4 MULTIATTRIBUTE ANALYSIS FOR AQUATIC AREAS IN FLORIDA.......................84 Introduction................................................................................................................... ..........84 Attributes Levels and Pair-wise Choice Design....................................................................84 Survey Design and Respondents Profile.................................................................................89 Survey Design.................................................................................................................89 Respondents Profiles......................................................................................................91 Statistical Analysis........................................................................................................... .......92 Econometric Modeling of Pair-wise Choices..................................................................92 Statistical Results for th e Multi-Attribute Model.....................................................94 Ranking, Net Willingness to Pay and Likeli hood of Preference Measures for Multi-Attribute Models..............................................................................................105 Effects of Socioeconomic and Experienti al Characteristics on Preferences for Aquatic Parks Alternatives........................................................................................108 Estimation of Annual Marginal Willingness to Pay (MWTP).............................................116 Conclusions.................................................................................................................... .......121 5 CONCLUSIONS AND RECOMMENDATIONS...............................................................149 APPENDIX A SURVEY OF AWARENESS OF INVA SIVE SPECIES (COVER LETTER)...................153 B SURVEY OF AWARENESS OF INVASI VE SPECIES (QUESTIONNAIRE)................154 C PARKS MANAGERS SURVEY........................................................................................161 D SURVEY OF NATURE RELATED OUTDO OR ACTIVITIES (COVER LETTER).......162 E SURVEY OF NATURE RELATED OUTDOOR ACTIVITIES (QUESTIONNAIRE)....163 F MULTIATTRIBUTE UTILITY SURVEY RIVER AND LAKE/PLANT SPECIES COMBINATION (RLPS) EXAMPLE (COVER LETTER)................................................171 G MULTIATTRIBUTE UTILITY SURVEY RIVER AND LAKE/PLANT SPECIES COMBINATION (RLPS) E XAMPLE (QUESTIONNAIRE).............................................172 LIST OF REFERENCES.............................................................................................................180 BIOGRAPHICAL SKETCH.......................................................................................................191

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7 LIST OF TABLES Table page 2-1 Socioeconomic characteristics for the surv ey sample and comparisons to the Florida population..................................................................................................................... .....46 2-2 Classification and sample proportion of the level of stated knowle dge vs. the level of observed knowledge of survey respondents......................................................................46 2-3 Paired-samples test for stated and observed knowledge of survey respondents................47 2-4 Contingency table relating peoples level of stated know ledge about invasive species and the impact of these species on respondents satisfaction............................................47 2-5 Independence test for the stated knowledge about invasive species and the impact of these species on respondents satisfaction.........................................................................47 2-6 Contingency table relating the peoples level of observe d knowledge about invasive species and the impact of these species on respondents satisfaction................................47 2-7 Independence test for the observed knowle dge about invasive species and the impact of these species on respondents satisfaction.....................................................................48 2-8 Ordinal predictors of th e observed knowledge model.......................................................48 2-9 Coefficient estimates for the knowledge model.................................................................48 2-10 Global test of significance for the knowledge models estimators....................................49 2-11 Goodness of fit test for the knowledge model...................................................................49 2-12 Parameters of the simulated distribution f unction of the percentage of Floridians who know about invasive species..............................................................................................49 2-13 Odds ratio and likelihoo d of awareness for location categories in rows against location categories in columns...........................................................................................49 2-14 Odds ratio and likelihood of awareness for environmental consciousness categories in rows against environmental c onsciousness categories in columns................................50 2-15 Odds ratio and likelihood of awareness for income categories in rows against income categories in columns.........................................................................................................50 2-16 Odds ratio and likelihoo d of awareness for age categories in rows against age categories in columns.........................................................................................................51 3-1 Socioeconomic characteristics for the surv ey sample and comparisons to the Florida population..................................................................................................................... .....68

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8 3-2 Frequency of travel to state a quatic parks by surveys respondents..................................68 3-3 Closest distance traveled to a stat e aquatic park by survey respondents...........................69 3-4 Farthest distance traveled to a st ate aquatic park by survey respondents..........................69 3-5 Time spent in a state aquatic park by survey respondents.................................................69 3-6 Money spent in a state aqua tic park by survey respondents..............................................69 3-7 Explanatory predictors for ordered probit models.............................................................70 3-8 Likelihood Ratio statistics and pseudo-R2 for the attribute models for ocean/beach location....................................................................................................................... ........70 3-9 Likelihood Ratio statistics and pseudo-R2 for the attribute models for the rive/lake location....................................................................................................................... ........70 3-10 Likelihood Ratio statistics and pseudo R2 for the attribute models for ocean/beach location weighted sample................................................................................................71 3-11 Likelihood Ratio statistics and pseudo R2 for the attribute models for ocean/beach location weighted sample................................................................................................71 3-12 Tax preferences of survey respondents fo r improvement investments in ocean/beach and river/lake parks........................................................................................................... .71 4-1 Summary of attributes and attributes levels.....................................................................124 4-2 Pair-wise choice sets for the plant species combination survey......................................124 4-3 Pair-wise choice sets for the animal species combination survey...................................125 4-4 Total valid response rates for each survey.......................................................................125 4-5 Socioeconomic characteristics for the survey sample and comparisons with the Florida population............................................................................................................126 4-6 Predictors for multi-attr ibute utility model (MAUM).....................................................126 4-7 Coefficient estimates for the river a nd lake/animal species combination (RLAS)..........127 4-8 Coefficient estimates for the ocean a nd beach/animal species combination (OBAS).....127 4-9 Coefficient estimates for the river a nd lake/plant species combination (RLPS).............127 4-10 Coefficient estimates for the ocean a nd beach/animal species combination (OBPS).....127 4-11 Global test of significance of estimators..........................................................................127

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9 4-12 Standardized estimators for each location/attributes combination model.......................127 4-13 Wald Test for facilities and plan t/animal species attributes for each location/attributes combination model.............................................................................128 4-14 Wald Test for weight equivalence of fac ilities and invasive plant species attributes for each location/attributes combination model...............................................................128 4-15 Wald Test for weight equivalence of anim al/plant species and invasive plant species attributes for each location/at tributes combination model...............................................128 4-16 Wald Test for weight equivalence of fees and invasive plant species attributes for each location/attributes combination model....................................................................128 4-17 Ranking on the relative importance of each attribute on the respondents utility for each location/attribut es combination...............................................................................128 4-18 Marginal willingness to pay (MWTP) fo r each attribute in models with animal species attribute combinations.......................................................................................129 4-19 Marginal willingness to pay (MWTP) fo r each attribute in models with plant species attribute combinations.......................................................................................129 4-20 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of facilities c ondition for the combination RLAS..................................129 4-21 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of facilities c ondition for the combination OBAS..................................129 4-22 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination RLPS...................................130 4-23 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination OBPS...................................130 4-24 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of diversity of anim al species for the combination RLAS......................130 4-25 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of diversity of anim al species for the combination OBAS.....................130 4-26 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of diversity of pl ant species for the combination RLPS.........................131 4-27 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of diversity of plan t species for the combination OBPS.........................131 4-28 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination RLAS..........131

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10 4-29 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination OBAS..........131 4-30 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination RLPS...........132 4-31 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination OBPS..........132 4-32 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination RLAS..........................................................132 4-33 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination OBAS.........................................................132 4-34 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination RLPS...........................................................133 4-35 Odds ratio and likelihood of preference of Park A (row) over Park B (columns) given different levels of fees speci es for the combination OBPS..............................................133 4-36 Socioeconomic variables for multi-attribute utility model (MAUM) with interactions..133 4-37 Experiential variables for multi-attribut e utility model (MAUM) with interactions.......134 4-38 Eigenvalues for the socioeconomics variab le for the animal species/river and lake combination (RLAS)........................................................................................................134 4-39 Eigenvalues for the socioeconomics variab le for the animal species/ocean and beach combination (OBAS).......................................................................................................134 4-40 Eigenvalues for the socioeconomics variab le for the plant species/river and lake combination (RLPS)........................................................................................................134 4-41 Eigenvalues for the socioeconomics variab le for the plant species/ocean and beach combination (OBPS)........................................................................................................135 4-42 Factor loadings for the socioeconomics vari able for the animal sp ecies/river and lake combination (RLAS)........................................................................................................135 4-43 Factor loadings for the socioeconomics variable for the animal species/ocean and beach combination (OBAS).............................................................................................135 4-44 Factor loadings for the socioeconomics va riable for the plant sp ecies/river and lake combination (RLPS)........................................................................................................136 4-45 Factor loadings for the socioeconomics variable for the plan t species/ocean and beach combination (OBPS)..............................................................................................136

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11 4-46 Rotated factor loadings for the socioec onomics variable for the animal species/river and lake combination (RLAS).........................................................................................136 4-47 Rotated factor loadings for the socioeconomics variable for the animal species/ocean and beach combination (OBAS)......................................................................................137 4-48 Rotated factor loadings for the socioec onomics variable for th e plant species/river and lake combination (RLPS)..........................................................................................137 4-49 Rotated factor loadings for the socioec onomics variable for th e plant species/ocean and beach combination (OBPS).......................................................................................137 4-50 Coefficient estimates for the interaction model for the river and lake/animal species combination (RLAS)........................................................................................................138 4-51 Coefficient estimates for the interact ion model for the ocean and beach/animal species combination (OBAS)...........................................................................................139 4-52 Coefficient estimates for the interaction model for the river and lake/plant species combination (RLPS)........................................................................................................140 4-53 Coefficient estimates for the interaction model for the ocean and beach/plant species combination (OBPS)........................................................................................................141 4-54 Global test of significance of estimators of interaction models.......................................141 4-55 Total valid response rates for each survey.......................................................................141 4-56 Coefficient estimates for the frequency models for river/lake and ocean/beach locations...................................................................................................................... .....142 4-57 Global test of significance of estimators of frequency models........................................142 4-58 Estimated probabilities for frequency of visit to OB and RL parks................................142 4-59 Marginal willingness to pay (MWTP) per year for the invasive species (IS) attribute...143

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12 LIST OF FIGURES Figure page 2-1 Distribution function of the percentage of Floridians who know about invasive species P(X>0.4003).......................................................................................................51 2-2 Distribution function of the percentage of Floridians who know about invasive species P(X>0.40)...........................................................................................................52 2-3 Cumulative distribution function of the percentage of Floridians who know about invasive species............................................................................................................... ...52 3-1 Coefficient estimates for the attribute models for ocean/beach location...........................72 3-2 Coefficient estimates for the attri butes model for river/lake location...............................73 3-3 Rankings of ocean/beach park attribut es based on the order probit unweighted models and the survey responses.......................................................................................74 3-4 Rankings of river/lake park attributes based on the order probit unweighted models and the survey responses....................................................................................................74 3-5 Coefficient estimates for the attribute models for ocean/beach location weighted sample......................................................................................................................... .......75 3-6 Coefficient estimates for the attribute models for river/lake location weighted sample......................................................................................................................... .......76 3-7 Rankings of ocean/beach park attributes based on the order probit weighted models and the survey responses....................................................................................................77 3-8 Rankings of river/lake park attributes based on the order probit weighted models and the survey responses..........................................................................................................77 3-9 Likert ranking of animal species attri bute for ocean/beach location for unweighted and weighted models..........................................................................................................78 3-10 Likert ranking of faciliti es attribute for ocean/beach location for unweighted and weighted models................................................................................................................78 3-11 Likert ranking of plant species attribut e for ocean/beach loca tion for unweighted and weighted models................................................................................................................79 3-12 Likert ranking of number of visitors attribute for ocean/beach location for unweighted and weighted models......................................................................................79 3-13 Likert ranking of travel distance attribute for ocean/b each location for unweighted and weighted models..........................................................................................................80

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13 3-14 Likert ranking of fees attribute for ocean/beach location for unweighted and weighted models................................................................................................................80 3-15 Likert ranking of animal species attribut e for river/lake location for unweighted and weighted models................................................................................................................81 3-16 Likert ranking of facilities attribute for river/lake location for unweighted and weighted models................................................................................................................81 3-17 Likert ranking of plant sp ecies attribute for river/lake location for unweighted and weighted models................................................................................................................82 3-18 Likert ranking of number of visitors attribute for river/ lake location for un-weighted and weighted models..........................................................................................................82 3-19 Likert ranking of travel distance attribute for river/la ke location for unweighted and weighted model................................................................................................................. .83 3-20 Likert ranking of fees attribute for rive r/lake location for unweighted and weighted models......................................................................................................................... .......83 4-1 Example of a pair-wise questi on animal species combinations....................................143 4-2 Example of a pair-wise questi on plant species combinations.......................................143 4-3 Evaluation of alternatives for the animal species combination river and lake parks (RLAS)......................................................................................................................... ....144 4-4 Evaluation of alternatives for the plant species combination river and lake parks (RLPS)......................................................................................................................... ....145 4-5 Evaluation of alternatives for the animal species combination ocean and beach parks (OBAS)...................................................................................................................146 4-6 Evaluation of alternatives for the plant species combination ocean and beach parks (OBPS)......................................................................................................................... ....147 4-7 Estimated probabilities for frequency of visit to OB and RL parks................................148

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14 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE IMPACT OF INVASIVE PLANTS ON THE RECREATIONAL VALUE OF FLORIDAS AQUATIC AREAS By SANTIAGO BUCARAM August 2007 Chair: Dr. Donna Lee Major: Food and Resource Economics Invasive plants are impacting the regenera tion and management of public and private aquatic areas. These species are considered a se rious problem as they impact human uses of water resources and affect their ecological va lue through the degradati on of water quality. In Florida the situation is one of th e most severe, since invasive pl ants affect 96% of the states public lakes and rivers, comprisi ng about 1.26 million acres. This situation has had an important effect on the recreational value of Floridas aqua tic areas, given that i nvasive plants impact natural outdoor activitie s such as fishing, boating, swimmi ng among others. In turn, this restriction on recreation is havi ng a substantial effect on the stat es economy and the quality of life of its inhabitants because such activities in volve millions of people who spend billions of dollars annually. This study was intended to examine and meas ure the impact of invasive plants on recreational activities in aquatic areas1 using a multi-attribute utility model (MAUM). A survey, electronically distributed, was applied to Florida residents. In this survey participants were asked to choose from a set of pair-wise al ternatives comprising a group of attributes at 1 Two types of areas were examined; name ly, river/lake and ocean/beach parks.

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15 varying levels, including levels and coverage of invasive species as well as other four park attributes2 that are important when choosing recreati onal activities. Then, with the use of a conditional Logit model, a functional relations hip between the publics utilityderived from participation on nature-based recreational activit iesand the presence of invasive plants in aquatic areas in Florida was appraised. The results showed that people assign a negative we ight to the presence of invasive plants in aquatic parks, reflected in a negative marginal value for this attribute. In addition, the marginal value, derived from the presence of invasive plants attribute, was hi gher than that for the positive attributes (facilities, animal species and plant species). Therefore, the large negative number associated with the former indicates a st rong aversion of respondents to the presence of invasive plants. It also implies that an enhancem ent of the positive attributes of a park would not be sufficient in order to reduce the harmful impact on the publics utili ty due to the presence of invasive plants. On the contra ry, it is recommended to take dire ct actions in or der to control and attack this problem; especia lly if its potential impact for the entire Floridas population is estimated in -$1,065,516,700 for ocean/beach park s and -$992,595,790 for river/lake parks per year. Hence the implementation of programs to redu ce the presence of invasive plants in aquatic areas in Florida is justified. Nevertheless, th e results obtained from these programs would be stronger if they are accompanied by aggressive awareness campaigns since it was determined that the impact of invasive plants on peoples utility would be approximately $500 million larger in each location3 if the population were adequately informed. 2 The other four attributes were: condition of facilities, di versity of animal species, diversity of plant species, and fees. 3 In other words if the population would be well-informed, the impact due to the presence of invasive species would reach to -$1,615,092,363 for OB parks and -$1,451,2 84,807 for RL parks.

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16 CHAPTER 1 INTRODUCTION Invasive species are defined as alien species whose introduction does or is likely to cause economic or environmental harm or harm to hu man health. (Federal Register, 1999). Today there are an estimated 5,000 to 6,000 invasive sp ecies in the United States (Pimentel, 2003; Burnham, 2004) invading about 700,000 hectares of natural areas per year (Pimentel, 2000). Damages from invasive species cost government agencies and private citizens more than $138 billion per year4, excluding ecosystem impacts (Pimente l, 2002). In the case of aquatic and wetland habitats in the United States, these spec ies are considered a serious problem as they impact human uses of water resources and aff ect their ecological valu e through the degradation of water quality (Madsen, 1997). In Florida the situation is on e of the most severe, since invasive plants affect 96% of States public lakes and rivers that comprise 1.26 million acres. One specific concern about invasive species is their impact on indi viduals satisfaction when they engage in outdoor recreational activit ies in aquatic areas. For example, fishing attracts over 34 million participants to Florida who spend in excess of $35 billion annually (Adams and Lee, 2006). This recreational activit y is affected by invasi ve aquatic plants (e.g., hydrilla, water hyacinth, and water lettuce), whic h can cover the surface of aquatic areas (e.g., rivers and lakes) driving fish away. Invasive aquatic species can also affect swimming, boating, and other recreational us es. Hence the impact on recreationa l activities by invasive plants in Floridas aquatic areas can be substantial. This study proposes to examine and measure the impact of invasive plants on recreational activities in aquatic natural areas using a multi-attribute utility model (MAUM). Study participants were asked to choose from a set of pair-wise alternatives comprising a group of 4 From this $138 billion, approximately $34 billion is related exclusively to the impact of invasive plants.

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17 attributes at varying levels, in cluding levels of invasive spec ies coverage and other variables important to decisions about re creational activities. The M AU survey was electronically distributed to Florida reside nts following a prescribed methodology (Milon and Hodges, 2002; Alvarez, Sherman and VanBeselaere, 2003; Tsug e and Washida, 2003; Lee, Adams, and Rossi, 2006). Finally, with the use of a conditional Logit model (McFadden, 1974), the alternatives that visitors would prefer from a set of services we re predicted. With the payment attribute included, the model also estimated the visitors marginal willingness to pay for recreational activities in aquatic parks with fewer invasive plants and more positive attributes such as facilities and the presence of native animal and plant species. Th is study provides useful information for further analyses of public programs to control i nvasive aquatic plants in Florida. Problem Setting Florida possesses 1.5 million acres of lakes and rivers, including 7,700 lakes and ponds and 1,400 rivers and streams. The state also ha s over 1,197 miles of coastline and over 663 miles of beaches. These geographic conditions along with a favorable climate have resulted in Florida having one of the highest levels of plant divers ity in the nation. Additiona lly the conditions that foster plant diversity also make Floridas la nd and water vulnerable to newly introduced (nonnative) plants. Non-native plants that overrun natural areas are called invasive because they thrive, spread, and take over native plan t habitats aggressively. An i nvasive plant tends to survive because insects, diseases, and other environmenta l stresses that naturally keep its growth in check in its native range are not present in its new habitat (C enter for Aquatic and Invasive Plants, 2005). Aquatic areas in Florida are not free from this threat of invasive plants, which have caused damages such as 1) a decrease in the concentr ations of dissolved-oxyge n, which impacts aquatic

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18 life; 2) an increase in the amount of sediments; 3) restricti on of water flow, resulting in flooding along rivers; and 4) promotion of breeding environments for mosquitoes and other bothersome insects (Florida Department of Environmental Protection). All these ecological impacts have also an im portant effect on the recreational value of Floridas aquatic areas, since they restrict natural outdoor activities such as fishing, boating, and swimming. This restriction on recr eation activities, in turn, will have a subs tantial impact on the states economy and the quality of life of its i nhabitants, since involve millions of people who spend billions of dollars annually. All these arguments justify the importance of examining and measuring the impact invasive plants have on recreati onal activities in Floridas aquatic areas as a means to help reduce their effect on the states ec onomy and peoples quality of life. Objectives The general objective of this study is to apprai se the value that Flor idas population gives the problem of invasive plants through the determination of a f unctional relationship between the publics utilityderived from participation on nature-based recreati onal activitiesand the presence of invasive plants in aquatic areas in Florida. Along with the general objective the study has three spec ific objectives: To identify and select three relevant attribut es for valuing recreati on in aquatic areas in Florida. To determine the extent of knowledge of Flor idas residents about invasive plants in order to establish the necessary informati on that should be provided to the public to obtain an informed value of the invasive pl ants problem in aquatic areas in Florida. To design a multi-attribute utility survey base d on the selected attributes, in order to estimate the relationship between the value of recreational use and the presence of invasive plants in Fl oridas aquatic areas.

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19 Hypotheses The following hypotheses were evaluated in this study: The public assigns a negative value to the pres ence of invasive plants, reflected in less willingness to pay when residents engage in r ecreational activities in aquatic areas with a high presence of invasive plant species. The value that the public assigns to the problem of invasive plants, though important, is inferior in absolute value when compared to the assessment that th e public gives to other attributes and services that these aquatic areas provide. The value that the public assigns to the presen ce of invasive plants is contingent on the level and extent of knowledge that they ha ve about this problem and their previous experience. The publics demographic characteristics wi ll influence their expressed assessment of value to the problem of the pr esence of invasive plants in aquatic areas in Florida. Literature Review In this section relevant literature related to the measurement of the economic value of natural amenities as well as studies related to the economic impact of invasive species and their implications for recreational activities will be an alyzed. For this purpose we have divided this section in three parts. The first in which l iterature about economic assessment of natural amenities is analyzed; a second part in which re levant literature about the general economic impact of invasive species is discussed and fina lly a section in which it is summarized the most important studies about the specific economic im pact of invasive species on recreational activities. Economic Assessment of Natural Amenities The literature about valuati on of natural environments based upon various forms of outdoor recreation activities is ex tensive. Two of the most complete reviews about these works are the survey paper of Walsh et al. (1992), whic h collects most of the literature available until

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20 1989, and the Rosenberger and Loomis (2001) study, which summarizes all the relevant literature from 1967 to 1998 (covering 21 recreational activities). One of the most complete analysis of the demand and value of outdoor recreation in the United States is the study of Be rgstrom and Cordell (1991). In this study using a travel cost for activities related exclusively to aquatic parks the recreational value for these parks was estimated. The average recreati onal value was of $19.28 per person per day (ppd). Previously Bergstrom et al. (1990) had estab lished (using contingency valuation) that the recr eation value of general activities was of $15.19 ppd. In 1990 McCollum et al determined the net eco nomic value of recreation on the national forests using twelve types of primary activities across nine Forest Service regions. The estimated values ranged from $3.04 ppd for camping to $30.04 ppd for hiking. In this specific study, it was also established that the r ecreational value for fishing ranged from $8.35 ppd to $24.08 ppd. Waddington et al. (1991), using c ontingent valuation, determin ed the net economic values for bass and trout fishing, deer hunting, and wildlife watching. Th e most important conclusion of this study was that hunting has the highest valu e among all the outdoor re creational ac tivities; though it was also found that fishing has an important value for visitors of natural areas. This last finding is strengthened by other studies such as: Mc Connell (1979); Vaughan and Russell (1982); and Rowe, et al. (1985); which reports values as high as $86 ppd for this specific activity. In the specific case of Florida, Milon et al. (1986) studied the fishi ng activity in Orange and Lochloosa Lakes and found that local fish ermen spent in a range of $21 ppd to $43 ppd. Whereas the range for non-local fishers was subs tantially higher; that is, it was between $93 ppd and $192 ppd. Later on, Milon and Welsh (1989) in an other study of Lakes Harris and Griffin in

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21 Lake County, Florida, found that expenditures in fishing activities were as high as the values found in the previous study, with an average of $24.25 ppd for local fishermen, $67 ppd for other Florida fishermen and $91 ppd for non-Florida fi shermen. This study also found the willingness to pay to assure the condition of th e lakes at $41 per person per year. All these studies demonstrated the importance of natural recreation activities for the US economy as well as the fact that the economic rele vance of these activities is closely related to the natural processes and environmental conditions of landscapes. Thus any disruption in these processes and/or conditions of any natural area can produce a seri ous impact on visitors welfare through a diminishing in their utility when engagi ng in recreational activities. This provides the correct justification to analyze how a biological problem such as the invasive species presence has an extensive impact on the economy of Florida and their populations welfare. General Economic Impact of Invasive Species One of the first relevant attempts to m easure the ecological and economic impacts of invasive species was the research report, co mpiled by the Office of Technology Assessment (OTA) of the U.S. Congress in 1993 titled Harmful Non-Indi genous Species in the United States. In this report, 54 non-indigenous plant species (NIS) were analyz ed. From these plants, it was established that the most harmful are the Salt Cedar, Purple Loosestrife, Melaleuca, and Hydrilla. For example, in the specific case of Me laleuca, the OTA determined that this species has degraded the Florida Everglades wetlands system rapidly by out-competing indigenous plants and altering topography and soils. Th e OTA study estimated a cumulative loss from invasive plants damages as a minimum of approximately $600 million, with a worst-case scenario of $4.6 billion.5 One important conclusion of this st udy was that the effect of invasive 5 The total value estimated in this study for the impact of all the types of invasive species was $96 billions or $134 billions in a worst-cast scenario.

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22 species in Florida and Hawaii is unusually seriou s in each place and it requires more attention and urgent actions than in othe r states. It was determined th at in Hawaii and Florida, the problem has been particularly hard because of their distinctive geography, climate, history, and economy. Another important finding provided in th is study was that in the United States a total of $100 million is invested annually to control invasive aquatic species. In 2000, David Pimentel et al. from the Colle ge of Agriculture and Life Sciences of Cornell University published a study in which they attempted to estimate the costs of the impact of invasive species in United States. They estimat ed that the total number of invasive species in the U.S. is approximately 5,000, which was late r updated to over 6,000 sp ecies (Burnham, 2004). One of the most important findings of Pimentel et al. (2000) was that inva sive plant species are spreading and invading approxima tely 700,000 hectares of U.S. natural areas per year. They established that, in the specific case of Florida, exotic aquati c plants such as hydrilla, water hyacinth, and water lettuce are altering fish and other aquatic animal species, choking waterways, altering nutrient cycl es, and reducing recreational use of rivers and lakes. Florida spends about $14.5 million each year on hydrilla cont rol; however, there are specific cases (Lake County and Lake Harris) in which hydrilla infest ations have caused an estimated $10 million in lost recreational income annually (Center, et al. 1997). But beside s all these important facts, the most important contribution of the Pimentel et al. (2000) study was a new estimation of the total economic damages and associated control costs for the U.S. due to invasive species, which was about $138 billion annually, from which $35 billion is exclusively from the impact of invasive plants. Edward Evans (2003) outlined the importance of understanding the economic dimensions of the problem of invasive species. He establishe d that the causes of bi ological invasions are

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23 often related to economic activities. Furthermore, he also stated that the economic consequences of this problem are not only related to direct c ontrol costs and damages but also to other areas such as nutrition, prices, and market effects. Finally, he stressed th at the most important solutions to the invasive species problem must be firmly grounded in both science and economics, as economics has the capacity to va lue various market and non-market impacts and provides a means for assessing im portant tradeoffs among various management alternatives, which can improve greatly the decision-making process (Evans, 2003). Evans argument is strengthened by other authors su ch as Shogren (2000) and Perr ings, et al. (2002), who also addressed the importance of inco rporating economics into the im pact analysis of invasive species, with the idea that both the solutions and causes of the invasive species problem are economic in nature and, as such, require ec onomic solutions (Shogre n, 2000). Additionally, they also developed the idea that the exclus ion and control of inva sive species can be considered as a public good (Perrings, et al ., 2000). Consequently, for successful control and management, it is necessary to build incentives in society to change human behavior so as to enhance protection against the introduction, estab lishment, and spread of invasive species (Shogren, 2000). Specific Economic Impact of Invasive Species on Recreational Activities In the specific case of the impact of these spec ies on recreational activities, the literature is diverse (Milon, et al., 1986; Milon and Joyce, 1987; Colle, et al., 1987; Milon and Welsh, 1989; Newroth and Maxnut, 1993; Hende rson, 1995; and Bell 1998). In 1987 Colle, et al. determined that hydrilla forms dense canopies at the water surface that raise surface water temperatures, cha nges pH, excludes light, and consumes oxygen, resulting in native plant displacem ent and stunted sport fish populat ions; these biological effects

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24 can expand up the point that they affect recreati on negatively, as well as businesses that depend upon recreation to make a living. One the most important contributions in the economics literature related to the impact of invasive species on recreation activities has been the determination of B/C ratios, based on the impacts on recreation expenditures, due to the presence of these spec ies and their control costs. These analyses found different B/C values, among wh ich we have: Singh, et al. (1984), from 8:1 to 24:1 (24:1 to 91:1 adjusted); Colle, et al (1987) from 1:1 to 300:1; Newroth and Maxnut (1993) 243:1; and Milon (1986 a nd 1987) 10:1. These ratios have facilitated the task of determining the benefits derived from invasive aquatic control a nd their cost of implementation and management. This can be regarded as an im portant tool for policy de cisions about investing in control programs for these noxious species. As a result Rockwell (2003), using these B/C ratios, determined the benefits of controlling inva sive species, first for the state of Florida and later for the entire U.S. The c ontrol benefits for Florida were estimated to be $250 million. This value was obtained using an inflation-adjusted estimation of $25 million fo r the cost of treating invasive species and a Benefit/Cost (B/C) rate of 10:1. This B/C ratio came from the totalrecreation-expenditures to willingness-to pay relationship es tablished in the two studies of Walter Milons (1986 and 1987). For the U.S. case Rockwell (2003) extrapolated the results for Florida and determined that the national impact of aquatic invasive species is in the range of $500 million to $1 billion. Methods and Procedures In this part we will explain two different methodologies utilized for valuing public goods or goods that lack explicit markets. These methods differ in their assumptions as well as in their data origin and collectio n method. Revealed preference data ar e obtained from the past behavior of consumers, while stated preference data are collected through surveys.

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25 Revealed Preference Methods These methods are grounded in Paul Samuel sons revealed preference theory (1938), which states that it is possible to discern peoples preferences on the basis of their behavior. That means that peoples preferences can be rev ealed by their habits and decisions. Hence if we note that a person chose the bundle px1 over the bundle px0 (this relationship can be expressed as px1 px0), then it can be inferred that the utility of x1 must be at least as la rge as the utility of x0 ; that is, u(x1) u(x0). In this case it can be said that x1 is revealed preferred to x0. Additionally, if the condition is established as px1 > px0 and as a consequence u(x1) > u(x0), we can say that x1 is strictly revealed prefe rred to x. To sum up, a preferen ce is revealed when people confronted with two afford able consumption bundles x0 and x1, and x1 is chosen and x0 not, even though they have the same price p. Hence methods based on revealed preferences use observations of the actual choices made by people in order to measure thei r preferences. These methodologi es are also called indirect valuation methods, since in the case of non-mark et goods they rely on th e price or cost of surrogate goods or services to reveal the pub lics willingness to pay for the non-market goods. The advantage of this methodology is that it relies on actual choices, making it more reliable than those methods associated with hypothetical responses. However their disadvantage is that these methods are not viable when we want to analyze changes that may lie outside of the actual state of the world; that is outside of the current set of experience of the people. To be precise, these methods are largely limite d to observable states of the world. Stated Preferences Methods These methods have two important characteristics: first, they ask people explicitly to state their preferences instead of using actual behavi ors (which in this cas e are not observed), and

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26 second, they rely heavily on hypothe tical scenarios. In other wo rds, these methodologies build their information from peoples answers to propos itions in which they are asked to judge one or more hypothetical options and to expr ess their preferences for them. These methods are suitable when there are not proxy markets from which to estimate revealed preference values. In other words th e complementary relation between a market and non-market required for applying re vealed preference methods does not exist. Additionally, for the specific case of a non-ma rket good, non-use values can only be estimated by stated preference techniques, which are an important limitation for revealed preference methods, given the importance of this value component for non-market goods.6 The stated preferences methods can be classified in two categories: continge nt valuation techniques (CV) and multi-attribute techniques. Contingent Valuation Techniques (CV) Direct survey techniques, which obtain inform ation from people thr ough their valuation for different outcomes. This technique permits us to elicit peoples pr eferences for non-market goods by finding out what their willingness to pay is for specified improvements in these goods. The main assumption of this techni que is that the stated willingness to pay is consistently related to peoples actual preferences. The first contingent valuation study was conduc ted by Davis (1963) to estimate the value of hunting in Maine; and since that time results from CV have raised skepticism and criticism. Among the critiques one of the most known wa s the argument stated by Scott (1965) who 6 Non-use values are values expressed by humans for environmental resources that are unrelated to human use. These values include concern, sympathy, and respect for th e rights or welfare of non-h uman beings (Hajkowicz, et al., 2000).

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27 concluded that this methodology was an inaccura te short cut in whic h through hypothetical questions the only thing that the researcher may get wa s hypothetical answers. Later on, diverse studies appeared in which estimations of willingness to pay obtained from CV were similar to those obtained from other methods such as travel cost and cash transaction models; this has provided certain level of valid ity to this methodology (Carmines and Zeller, 1979). It is important to take into consideration that in the last decade this methodology has increased its academic accep tation. A proof of that is the increasing number of journals where CV studies have been published in both the economic field as well as in other academic disciplines (Hanemann, 1984 and Randall, 1998). Multi-Attribute Techniques (MA) Survey based methodologies for modeling pref erences for goods, which are described in terms of an explicit combination of their attribut es at designated levels. In other words, people express their preferences using simultaneously all the dimensions that define any non-market good. These methodologies ask people to rank, rate, or choose the best option from a bundle of similar goods, based on a set of relevant attributes at a range of specified levels. To sum up the objective of a MA study is to estimate economic values for a technically divisible set of attributes of a good. These methods offer an important advantage over CV techniques, in that they permit us to estimate the incremental benefits that consumers derive from the different relevant attributes of a non-market good; that is, provide detailed info rmation about public preferences for multiple states of the good. Additionally it allows the characterization of peoples underlying utility function for any non-market good or service. The foundation for the MA techniques is th e hedonic methods which state that the demand for goods is derived from the combination of their attributes. The basic approach of the

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28 hedonic method is that any good is really a bundle of attributes; thus even though people pay a bundled price for the good they ar e really paying for a mix of individual attributes. One of the first theoretical foundations for hedonic mode ls was articulated by Lancaster (1966) who developed a model of characterist ics, in which it was stated th at consumers care only about the intrinsic characteristics of goods. Therefore consumers purchase these goods because they deliver a desired characteristic mix, adjusting appropria tely for prices. Luce and Tukey (1964) also proposed a measurement technique in which the utility derived from a good can be estimated from deco mposing judgments regard ing a set of complex alternatives into the sum of we ights on attributes of the alte rnatives. This method known as conjoint measurement was rapidly accepted by marketing researchers. They recognized its value since it provides information about the re lative importance of goods attributes necessary when designing new products. Additionally including price as an attribute in these MA methodologies permits the estimation of economic welfare measures such as the willingness to pay. Implementation of a MA analysis should fo llow the seven steps suggested by Adamowicz, Louviere, and Swait, 1998; and L ouviere, Hensher and Swait, 2000: Identify the economic problem Identify and describe the most relevant attr ibutes that impact the utility of people. Develop an experimental design in order to construct the alternatives that will be presented to the respondents. Develop the questionnaire Collect the data Estimate the model econometrically and with that determine welfare measures and/or predictions of behavior.

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29 Interpret the results for policy analysis and decision support based on these welfare measures and/or predictions of behavior. It is important to emphasize that in any MA analysis the most important step is to determine the appropriate attributes for the de cision, since they are the core of the decision problem. These attributes must satisfy the following requirements:7 Each attribute should reflect an independent dimension of the good in order to avoid redundancy. The attributes must be measurable and easy for respondents to understand. Theoretically, the number of attributes that can be utilized in a MA analysis is unlimited, but due to the restricted cognitive skills and memory of most people, the number of attributes in practice should not be more than nine. In this study a MA technique will be applied through the appl ication of a survey in which respondents were requested to value trade-off cha nges in attribute levels against the cost of making these changes. This will be applied in a framework of random utility maximization, in which it is assumed that the utili ty is the sum of systematic ( ) and random components ( j): (1-1) Uj = (xj, pj; ) + j where Uj is the indirect utility associated with profile j, xj is a vector of attributes associated with profile j, pj is the price of profile j, is a vector of preference parameters, and j is a random error term with zero mean. Choice behavior is deterministic from the perspective of the i ndividual but from the researcher perspective this behavi or is random; thus the error term represents uncertainty about the choice. Additionally it is assumed that the utility is defined by a lineal function: (1-2) j j p jk n k k jp x U 1 7 Following from Keeney and Raiffa, 1976; Louviere, 1988; Saaty, 1980; de Palma, et al. 1994; and Miller, 1956.

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30 where k is the preference parameter associated with the attribute k; xjk is the kth attribute in profile j, and p is the cost parameter. By differentia ting (1-2) we found that each preference parameter represents marginal utilities derived from each attribute: (1-3) k kx U The parameter p is interpreted as the marginal utility of money because an increase in the price decreases individual wealth. p gathers the change in utility associated with a marginal decrease in wealth; thus it is expected that the sign of p be negative. Finally the marginal value of attribute k is computed as the ratio p k In order to estimate the preference parameters econometric techniques parallel to the theory of rational and probabilistic choice will be applied. These types of models are called discrete-choice models, partic ularly formulated for economic analysis by McFadden (1974). The conceptual foundation for McFaddens mode l lay in Thurstones (1927) idea of random utility, Luces choice axiom (1959) and th e random utility model proposed by Marschak (1960). Using this framework McFadden develo ped an econometric model that combines hedonic analysis and random utility maximiza tion. This model is known as the conditional (multinomial) Logit which allows us to determine the e ffects of explanatory variables on a subjects choice of one of a discrete set of options. In this methodology the choice problem is established in a way in which we ask respondents to choose the most pr eferred alternative from a choi ce set. Respondents focus their attention on the trade-off of attrib utes and the different levels that they can take. Then the model estimates will be based on utility differences am ong the alternatives. Thus the probability that a subject choose alternative i instead of alternative j will be expressed as:

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31 (1-4) C j P U U P C i Pj j i i j i ), ( ) ( ) ( where C contains all of the alte rnatives in the choice set, Ui and Uj is the indirect utility associated with profile i and j, i and j are the systematic compone nts of the utility, and i and j are the random components of the indirect ut ility. Equation (1-4) can be arranged in the following way: (1-5) C j P U U P C i Pi j j i j i ), ( ) ( ) ( Hence the probability that i is preferred to j depends on the probability that the differences between the systematic components ( i and j) is greater than the di fference between the random components ( i and j). An econometrical specification of co ncepts provided in equations (1-4) and (1-5) is the following (1-6) i j p jk k n k i p ik kC j p x p xe e C i P) ( ) (1) ( where xik denotes the values of the k explanator y variables (non-monetary attributes) conditional on the set Ci, which represents all th e alternatives in the choice set of response; is the vector of coefficients (weights) for the non-monetary attributes; and pi and p is the cost attribute and its respective coefficient. This choice-based methodology has proved to be useful for modeling us e values as well as non-use values, which it is valuable for differe nt analyses that range from policy/program evaluations (Viscusi et al. 1991; Hanley et al. 1998; Hanley, Wright and Adamowicz 1998) to recreational choice site studies (Boxall et al. 1996 ). In our case we will apply this model to determine how the value derived from outdoor recrea tion activities is affect ed by the presence of

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32 invasive plants, as a negative attribute in the random utility function of the average Floridian visitor. In other words this model will allow us to determine the trade-off between positive and negative attributes (invasive sp ecies) as well as their weig ht or relative importance.

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33 CHAPTER 2 PUBLIC AWARENESS OF IN VASIVE PLANTS IN FLORIDA Introduction In this chapter we will analy ze the extent of Florida residents knowledge about invasive plants; how this knowledge infl uences their enjoyment of out door activities and where they choose to pursue them. In addition, the main f actors that influence th e level of residents knowledge about invasive plants will also be determined. The conclusions obtained in this chapter wi ll facilitate the design of a multi-attribute survey about invasive plants in Florida. In par ticular, through these findings it will be possible to determine the type and degree of information that the survey needs to prov ide in order to obtain accurate and unbiased responses about the value that Floridians ascribe to this problem. Description of the Survey Process The primary information about public awaren ess of invasive plan ts in Florida was collected through a Web survey of randomly selected Florida resi dents. For this preliminary survey, Expedite Email Marketing was selected to perform the e-mail br oadcast through a cover letter (Appendix A). This le tter contained an invita tion to take the survey as well as a link to the Web address containing the survey (in this case SurveyMonkey.com). The survey (Appendix B) consisted of thirteen questions related specifi cally to the topic and nine questions about respondents socio-economic characteristics. The questions related to the topic of public awareness of invasive plants in Florida can be divided in the following four groups : 1) questions about respondents residence status in Florida, 2) questions about the extent of knowledge of invasive plants th at respondents th ink they have, 3) questions to establish the true degree of res pondents knowledge about i nvasive plants, and 4)

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34 questions to determine if respondents have been af fected by invasive plants in their enjoyment of outdoor activities and where they pursue these activities. Respondents who participated in the Web survey were told in the c over letter that the objective was to determine the le vel of knowledge that Floridians have about invasive plant species for use in a more extens ive investigation about the impact of thes e plants on visitor satisfaction with Floridas natural areas. In the first set of questions, respondents reside nce status in the state of Florida was asked. First it was confirmed whether respo ndents reside in the state of Flor ida. Then they were asked if they live in the state year-round or seasonally and how long they ha ve been living in Florida, as well as their specific location by county. Subsequently we asked how respondents charac terized the extent of their knowledge about invasive plant species in Flor idas natural area. Respondents were also presented with a statement about these plants and asked to agre e or disagree with it. These responses are contrasted with two questions in which the real (or observed) knowledge of respondents was tested. In these questions respondents were asked to classify twelve plants as either invasive or non-invasive. Names and photos of the plants were provided to facilitate re spondents analysis. Then the survey continued with a series of questions abou t the impact of invasive species on respondents welfare, including their enjoyment of outdoor activities and where they pursued them, as well as effects that they think invasive species have had on the natural environment. The survey ended up with a set of demographic questions to de termine the gender, location, environmental attitudes, age, marital status, race/ethnic background, level of edu cation, employment status, and household income of respondents.

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35 Respondent Demographic Description A total of 274 valid responses were obtained from the Web su rvey conducted with Florida residents. The main demographic characteristic s of these respondents as well as comparable figures for the state population, obtained from the U.S. Census Bureau, are summarized in Table 2-1. It can be verified from Table 2-1 that there are a group of characteristi cs (marital status and location) that matched to a high degree with th e proportions of the Flor ida population. On the other hand, there are other char acteristics in which the sample showed an important difference from the composition of Florida population. For example, the proportion of respondents who indicated that they were male was greater than that of the state population. We can also observe that the sample, compared with the state popu lation, is skewed toward employed, highly educated, and high income earning pe ople. That situation compelled us to be extremely careful in the conclusions and the applied methodology. Finally, as part of the demogr aphic part of the survey, res pondents were asked to provide their environmental views. Respondents were asked how environmentally conscious they considered themselves to be. The composition of sample respondent was: not at all 1%, a little conscious 11%, moderately conscious 48%, very conscious 30%, and extremely conscious 10%. Extent of Respondents Knowledge and Th eir Attitudes Toward Invasive Plants First, respondents were aske d about how much they agree with the following statement: At the moment non-native invasive species is an important problem for natural areas in Florida. Survey respondents answered in the fo llowing way: strongly di sagree 2%, disagree 3%, agree 52%, strongly agree 32%, and do not know 11%. Then respondents were asked about the extent of knowledge that they think they have about invasive plants in Flor ida. The answers obtained from this question had the following

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36 composition: 10% said that they have no know ledge; 30% asserted that they have little knowledge about invasive species, 47% said that they have a modest level of knowledge; 11% said that they are well versed in the topic, and 2% affirmed that they ar e experts on this topic. For compilation of the results, the categorie s no knowledge and little knowledge were merged into one category labeled limited or no knowledge. The category moderate level of knowledge was retained and the categories well vers ed and expert were merged into a single category, high level of know ledge. The result of this new classification is given in the Table 2-2 in the set of column s labeled stated knowledge. In the following two questions we tested the real level of knowledge of the respondents. We provided the names and photographs of twelve plants and respondents were asked to classify them as invasive or non-invasive species. Respond ents had four alternatives: invasive species, non-invasive species, do not know, and never heard of it. A correct answer classifying the plant counted as a point for the res pondent. A wrong answer or an answ er such as I do not know or Ive never heard of it counted as zero points. A person with a score less than six points was classified in the limited or no knowledge cate gory. A person with a score between six and eight points was categorized as having a moderate level of knowledge, and a person with a score higher than eight points was classified in the high level of knowledge category. The total result of this test for the sample is showed in the Table 2-2 in the set of columns labeled Observed Knowledge. From Table 2-2 we can observe that there is an important difference in the amount of knowledge that people say they have (stated kno wledge) compared to th e knowledge that they really have (observed knowledge), especially in the first two categ ories. In other words, we can see in Table 2-2 that the proporti on of people who asserted that th ey possess a moderate level of

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37 knowledge of invasive species is higher than the real proporti on of people who demonstrated a moderate level of knowledge. On the other hand, the proportion of people who claimed to have limited or no knowledge about invasive species is smaller than those who demonstrated this level of knowledge. The only proportion that is similar is the number of people w ho asserted that they possess a high level of knowledge. But the question is whether these differences in the proportions are stat istically significant. To answer this question, we conducted two tests comparing the samples; that is, we compared the extent of knowledge that people claimed to ha ve with the knowledge that we determined that they really have. The two tests used were a simple comparison of proportions and a paired samples test. The result of the first test, the simple difference of proportions, is given in Table 2-2, while the results of the paired sample test8 are shown in Table 2-3. In both tests, we contrasted the null hypot hesis that given a sp ecific category the proportions in both samples (stated knowledge responses and observed knowledge responses) would be the same. Using the result s of the two tests, shown in Ta bles 2-2 and 2-3, we concluded first that we cannot reject the null hypothesis of equi valence of proportions for both samples at 5% level of confidence for the third category (high level of k nowledge) in both tests. On the other hand, for the first and second category the s ituation is the opposite, si nce we reject the null hypothesis of equivalence of proportions for both samples at 5% level of confidence in both tests. Therefore we conclude that an asserti on made by an individual th at s/he has a moderate level of knowledge of invasive species is not re liable, since it is likely that the respondents demonstrated knowledge be less. In contrast, when a person asserts th at s/he is well-versed or an 8Test that we considered adequate, since for each respondent two different treatments through two different and independent questions were conducted.

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38 expert in non-native invasive species, it is not likely that we found any discrepancy between what s/he stated and wh at s/he really knows. The discrepancy between the stated knowle dge and the observed knowledge raises the question of which of these two is significant in order to model the level of knowledge and its determinants. With this in mind, we tested which of these knowledges (labeled as declared and observed) influenced respondents behavior; especially when th ey have to discern how the presence of invasive species can affect their satisfaction. For this analysis we used the information obtained from questions 9 and 10 of the web survey. In question 9 respondents we re asked if they have been a ffected by invasive species in their choice of locations for out door activities; to which respo ndents answered in the following way: yes 25%, no 70%, and not sure 5%. In que stion 10 we asked if invasive species have affected respondents enjoyment of outdoor activities, to which they answered yes 45%, no 47%, and not sure 8%. A bivariate relationship is de fined by a joint distribution of two categorical response variables X and Y. In which, Y is a dependent variable that re presents whether or not a person was affected by any invasive spec ies in either their enjoyment of outdoor activities or where they chose to pursue these activities. Th is variable has two levels; the First Level implies that the person declared that s/he was affect ed by invasive species in at leas t in one way; either in his/her choice of where s/he pursued outdo or activities or in their enj oyment of these activities. The Second Level represents all the negative or dubious responses provided for the effect of invasive species on both their choice of site and their enj oyment of outdoor activities. On the other hand X is an explanatory variable that represents whether a person possesses a level of knowledge about invasive plant species. This variable has two levels, too. The First

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39 Level implies that the person possesses eith er a high or moderate level of knowledge. The Second Level implies that th e person has either limited or no knowledge about non-native invasive plant species. In addition, in order to determine which knowledge (stated or observed) is valid for modeling the behavior of respondents, we conducted an independence test using the two types of knowledge obtained from the survey. The importance of these arrangements in a tw o-way contingency table is to examine the null hypothesis of statistical in dependence between the specific knowledge (either stated or observed) and the respondents satisfac tion impact caused by invasive species9. Then for testing independence we will use both the Pearson Chi square and Likelihood RatioDeviance statistics. From Table 2-5 we can observe that the chi-squared statistics are: 2 = 0.012 and G2 = 0.012 based on a distribution with degree of fr eedom equal to 1. Then we cannot reject the null hypothesis of statistical in dependence between the stated knowledge and the impact of invasive species on respondents sa tisfaction. We also used an ad justed residuals analysis for additional accuracy. Thus from Table 2-4 we ca n observe very small residuals (-0.11 and 0.11), which strengthen our previous conclusion to not reject the null hypothesis of statistical independence. From Table 2-7 we can determine th at the chi-squared statistics are: 2 = 19.93 and G2 = 20.17, based on a distribution with a degree of freedom equal to 1, that is, a chi-squared distribution with a mean and a standard deviatio n of 1. Therefore, values of 19.93 and 20.17 are fairly far out in the right hand tail; for that reas on, each statistic has a p-value of <0.001. Then we cannot reject the null hypothesis of statistical independence. Thus bot h test statistics suggest that the observed knowledge of a respondent and his/he r satisfaction are asso ciated. Additionally, 9 Eleven observations were eliminated in order to establis h a system that match the r eal knowledge, the observed knowledge and the peoples satisfaction impact due to the presence of invasive species.

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40 using a residual analysis we found in Table 2-6 large positive residuals for people who possess observed knowledge of invasive species and those who stated so me impact on their satisfaction by the presence of these plants, as well as fo r people who do not have observed knowledge and did not state any impact on their satisfaction by the presence of these species. This knowledge gap is more evident when we estimated the samp le odds ratio given in Ta ble 2-7 as a value of 3.19. That means that the odds that a person w ho has (observed or real) knowledge complains by the presence of invasive species is 3.19 times the odds of a pe rson who does not have (observed or real) knowledge. To sum up we found that only what we have called observed knowledge has an effect on whether respondents feel that an invasive plant has had an effect on either where they choose to pursue outdoor activities or their en joyment of those activities. For th at reason, we will use in our model the observed knowledge as a dependent va riable, since only this knowledge is important for the decision and satis faction of respondents. Knowledge Model: Determinants of the Re al (Observed) Knowledge of Respondents In this section, we will use a multiple logist ic regression model in order to estimate the determinants of respondents (observed) knowle dge. We will denote a set of eighteen variables (predictors) for a binary res ponse Y, which represents whethe r a respondent has (observed) knowledge (Y=1) or not (Y=0). In our model, we have two type s of predictors: nominal and ordinal. The ordinal predictors in the model are given in Table 28. The nominal predictor is the education variable, which takes a value of 1 for high school or less, 2 for some college courses, 3 for associates degree, 4 for a bachelors, 5 for some graduate courses, and 6 for a graduate/professional degree. Then the model is defined as follows:

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41 (2-1) 18 13 12 12 12 2 1 11 7 6 6 6 4 3 3 3) ( 1 ) ( )] ( [i i i ii i i i i i i iIncome Education Age EC Gender Location x x Log x Logit The maximum likelihood estimates for the propo sed logistic regressi on model is given in Table 2-9.In order to test the accuracy of the model proposed in Table 2-9 we first conducted a set of global tests (Table 2-10) in which we c ontrasted the null hypothesis that all the estimators are not significant ( = 0). Then we applied a second set of te sts to see if the model is correct in contrasting the null hypothesis, wh ich stated that the model speci fication fits adequately. The idea underlying these tests is to compare the fitted e xpected values to the actual values. If these differences are large, we reject the null hypothesis for an alternat ive, which indicated that the specified model did no t in fact fit. We can observe in Table 2-10 that all the test s permit us to reject the null hypothesis that all the estimators are not significant. That is w hy we stated that the model is statistically significant; that is, that all thei r estimators are different from zer o. On the other hand, the test values showed in Table 2-11 allow us to conclude that we cannot reject the null hypothesis that the specification of th e model fits adequately. In other words, there is enough evidence to conclude that the model fits, so the model is adequate for analysis and prediction. Using the estimated model, we calculate the probability that a person from the sample has knowledge about invasive species. This expected probability is equal to 40.03%, near to the observed probability of 38.69%. However, since our sample is highly skewed toward people with a high level of education and income, as well as male respondents, this probability is not adequate to use for representing the average person in Florida. For that reason, as a first step we will use the model and the characteristics of an av erage person in Florida in order to provide an approximation of the probability that s/he has knowledge about invasive species. This

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42 approximation gave us a value of 31.66%; howev er, this is not enough, since the model came from a sample with characteristics that di ffer from those of Fl oridas population. Therefore, using the model and the characteri stics of Floridas population we proceeded to conduct a Monte Carlo simulation and a bootstrap analysis, through which we determined an approximated probability distribution of the proba bility that a person knows about invasive species, and its subsequent popul ation inference. The results obtained from the probability distribution are give n in Table 2-12. Applying the expected probability estimated from the Monte Carlo we found that the probability of finding a sample of average Floridian people with more of 40% knowing something about invasive species is approximately 42% (Figure 21 right area). On the other hand, the probability of finding a sample of av erage Floridians with more than 31.66% knowing about invasive species is approximately 52.62% (F igure 2-2 right area). That allowed us to conclude that a reasonable range of the po ssible proportion of peopl e from the Floridas population who know something about invasi ve species is between 30% and 40%. For additional analysis we estimated, with a bootstrap process, th e percentiles and the cumulative distribution function of the percentage of the populat ion that knows about invasive species. The results of this anal ysis are showed in Figure 2-3. Nevertheless, up to this point we have not determined what the more important factors are that affect the probabili ty of a person having knowledge abou t invasive species. For that purpose we calculated the odds ratio for the levels of eac h predictor as compari ng relatively with their first level. From the male estimator of the knowledge mode l presented in Table 29 we concluded that male respondents are more likely to know about invasive species than female respondents. That

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43 is, the odds of a male individual for knowing a bout invasive species ar e 1.88 times, or 88.38%, higher than the odds of a female. In Table 2-14 we analyzed the impact of the respondents location on his/her knowledge of invasive species. We found that the odds of a person knowing a bout invasive species increases based on whether respondents live in less urbani zed areas. To be precise, the odds of an individual who lives in a subur ban area knowing about invasive species are 2.43 times the odds of an individual who lives in an urban area. In addition a person who lives in a rural area is 3.98 times (1.64 times) more likely to have this know ledge than a person who lives in an urban area (suburban area). When we talk about the impact of the le vel of environmental consciousness on the probability of knowing about invasive species, this is one of the traits that produce the highest level of impact on the odds of a person possessing that kind of knowledge. In Table 2-14 we can see that the maximum odds ratio between two leve ls of the variable environmental consciousness is between not conscious and very/extremely c onscious category. In other words, the odds of knowing about invasive species of any respondent who declares hi mor herself very/extremely environmental conscious is 8.76 times the odds of a person who considers him-or herself not conscious at all. In the case of education, for every one-leve l increase in the res pondents education, the odds of him or her of knowing about invasive species increases by 1.146 times, or 14.6%. Consequently, we concluded that the formal level of education does not have much impact in the odds of a person knowing about invasive specie s. Even more between the minimum and the maximum education category the odds ratio is ab out 1.97 times. A low value if we compared it

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44 with the highest odds ratio for categories such as environmental consciousness (8.76 times) and location (3.98 times). Finally, regarding to the variables income a nd age, showed in Tables 2-15 and 2-16, we can assert that the impact of these variables are not monotoni c. In the case of income the maximum impact is between the level $20, 000$39,999 and the level $80,000 $99,999 in a value of 4.5 times. To be exact, the odds of know ing about invasive species for a person who has an income in the range of $20,000$39,999 is 3 50% higher than the odd s of a person with income in a range of $80,000$99,999. In the case of age, the maximum impact is between the levels 56-65 and >65, since the odds of a pers on with an age in the range of 56 years old knowing about invasive plan ts are 1670.9% higher th an the odds of a pers on older than 65 years old. Conclusions First we determined the importance of re spondents having an adequate background of knowledge in order to fully dis cern the impact of non-native i nvasive plant species on their satisfaction with outdoor recreational activities. We also found that approximately between 30 and 40 percent of Floridas population has a mode rate knowledge about inva sive plant species. These two results allowed us to conclude that it is necessary to provide an adequate level of information about invasive species in the multiattribute survey in order to obtain accurate information about peoples valuatio ns of the impact of these speci es on natural areas in Florida. At the same time, it can be argue d that this information could pr oduce a bias in the responses of this multi-attribute survey; however, that is not true since it was proved that this information helps people to elucidate the real value that th ey assign to this problem In contrast, if we assumed that people have this knowledge and we neglect to provide it, the study can be compromised, since the odds of a person w ho does not possess a good level of knowledge to

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45 fully discern the real effect of these species on his/her welfare is 0.31 times the odds of a person who has a good level of knowledge. It was further found that there is a 44 % probability that a person who thinks s/he has knowledge of invasi ve species actually possesses little or no knowledge. Another important finding is that formal educat ion is not a constraint for people to discern the impact of invasive plant spec ies, since the effect of this va riable in the lik elihood of having this knowledge is small compared to other variables such as location, environmental consciousness, and age. That provi des a greater level of importance to the knowledge that comes from another means, such as government camp aigns, press coverage, and others. This is reinforced by the finding that formal education is not correlated with more important variables such as environmental consciousness (we f ound statistical independe nce between these two variables: 2 = 16.4368, G2 = 15.9063 with df = 20). To sum up, it is necessary to provide a good background of information in the survey in order to obtain more accurate and correct results in the peoples va luation of the invasive species problem, in contrast with the small importance of the highest level of education attained by respondents.

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46 Table 2-1 Socioeconomic characteristics for the su rvey sample and comparisons to the Florida population. Categories Sample Florida Urban 48.2%47.0% Suburban 41.6%44.0% Rural 10.2%9.0% Male 51.3%48.8% Female 48.7%51.2% 18 25 years 10.0%7.8% 26 35 years 13.7%16.9% 36 45 years 18.8%20.1% 46 55 years 20.0%16.8% 56 65 years 14.3%12.6% More than 65 years 23.2%25.9% Single 25.7%23.8% Married 54.3%54.3% Other 20.1%21.9% High school or less 9.9%48.9% Some college courses 13.3%21.8% Associate 11.6%7.0% Bachelor's 16.7%14.3% Some Graduate 14.7%2.0% Graduate 33.8%6.0% Unemployed 6.3%3.2% Employed 70.1%54.9% Not in labor force 23.6%41.9% Less than $20.000 23.9%34.0% $20,000 $39,999 13.3%15.0% $40,000 $59,999 14.9%19.0% $60,000 $79,999 10.9%14.0% $80,000 $99,999 9.3%5.0% $100,000 $120,000 11.9%4.0% More than $120,000 15.7%9.0% Source: U.S. Census Bureau 2000 Table 2-2 Classification and samp le proportion of the level of stated knowledge vs. the level of observed knowledge of survey respondents. Type of Knowledge Extent of Knowledge Stated Knowledge Observed Knowledge Z-statistic P-value Limited or No Knowledge 40.51% 61.31%4.871 0.00000 Moderate Level of Knowledge 46.72%26.28% 4.969 0.00000 High Level of Knowledge 12.77% 12.41%0.129 0.44877

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47 Table 2-3 Paired-samples test for stated and observed knowledge of survey respondents Stated vs. Observed Mean Std. Deviation Std. Error Mean t DF Sig. (2-tailed) Limited or No -0.2150 0.55350.0323 -6.6496 273 0.00000 Moderate 0.1945 0.59650.0348 5.5821 273 0.00000 High 0.0205 0.37870.0221 0.9256273 0.35547 Table 2-4 Contingency table rela ting peoples level of stated know ledge about invasive species and the impact of these speci es on respondents satisfaction Satisfaction Impact Level of Knowledge First Level Second Level 79.00* 85.00* 78.57** 85.43** First Level 0.11*** -0.11*** 47.00* 52.00* 47.43** 51.57** Second Level -0.11*** 0.11*** Note: *frequency, **expected frequency, ***residuals Table 2-5 Independence test for the stated knowledge about invasi ve species and the impact of these species on respondents satisfaction Statistic df Value Prob. Pearson Chi-Square ( 2) 1 0.012 0.91 Deviance Likelihood Ratio (G2) 1 0.012 0.91 Table 2-6 Contingency table relating the people s level of observed know ledge about invasive species and the impact of these species on respondents satisfaction Satisfaction Impact Level of Knowledge First Level Second Level 67.00* 36.00 49.35** 53.65 ** First Level 4.46*** -4.46 *** 59.00* 101.00 76.65** 83.35 ** Second Level -4.46*** 4.46 *** Note: *frequency, **expected frequency, ***residuals

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48 Table 2-7 Independence test for the observed kno wledge about invasive species and the impact of these species on respondents satisfaction Statistic df Value Prob. Odds Ratio 95% Confidence Limits Pearson Chi-Square 2) 119.930.00 Deviance Likelihood Ratio (G2) 120.170.00 3.1861.90 5.34 Table 2-8 Ordinal predictors of the observed knowledge model Variable Definition Location Dummy variable that indicates the residence of respondents according to three categories: urban, suburban, and rural. Gender Dummy variable that indi cates the respondents gender. EC Dummy variable that represents respondents answers about their degree of environmental consciousness. Age Dummy variable that represents different age ranges, which go from eighteen to more than sixty-five years old. Income Dummy variable that represents different ranges of income for respondents, which go from less than $20,000 to more than $120,000 annually. Table 2-9 Coefficient estimates for the knowledge model Variable Definition EstimatesStd Err. 2 P-value alpha Intercept -4.901.27 14.89 0.00 Location 1 Suburban 0.890.32 7.84 0.01 Location 2 Rural 1.380.50 7.58 0.01 Gender Male 0.630.30 4.44 0.04 EC 1 Little conscious 0.470.17 7.76 0.01 EC 2 Moderate conscious 1.500.58 6.77 0.01 EC 3 Very/Extremely conscious 2.170.69 9.91 0.00 Age 1 18 25 years 2.281.16 3.89 0.05 Age 2 26 35 years 1.841.15 2.59 0.11 Age 3 36 45 years 2.031.13 3.22 0.07 Age 4 46 55 years 1.781.11 2.55 0.11 Age 5 56 65 years 2.871.14 6.37 0.01 Education From high school and less to graduate 0.140.04 9.48 0.00 Income 1 $20 $39 thousands annually 0.220.50 0.20 0.66 Income 2 $40 $59 thousands annually -0.330.14 5.51 0.02 Income 3 $60 $79 thousands annually -0.480.56 0.72 0.40 Income 4 $80 $99 thousands annually -1.280.60 4.59 0.03 Income 5 $100 $120 thousands annually -0.560.57 0.94 0.33 Income 6 More than 120 thousands annually-0.250.13 4.11 0.04

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49 Table 2-10 Global test of significance for the knowledge models estimators Global Null Hypothesis (Beta = 0) TEST df Value p-value LR 1865.71610.0000 Score 1857.05190.0000 Wald 1842.46300.0010 Table 2-11 Goodness of fit test for the knowledge model TEST df Value P-value Pearson 255278.850.146 Deviance 255255.630.477 Homer Lemeshow 810.830.212 Table 2-12 Parameters of the simu lated distribution function of the percentage of Floridians who know about invasive species Statistics Value Mean 35.91% Median Effective Level 33.96% Standard Deviation 22.84% Variance 5.22% Mean Std. Error 0.02% Skewness 0.33 Kurtosis 2.09 Coeff. of Variability 0.64 Table 2-13 Odds ratio and like lihood of awareness for locati on categories in rows against location categories in columns Location Location Urban Suburban Rural Urban 1.00 0.00% 0.41 -58.88% 0.25 -74.89% Suburban 2.43 +143.17% 1.00 0.00% 0.61 -38.94% Rural 3.98 +298.25% 1.64 +63.77% 1.00 0.00%

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50 Table 2-14 Odds ratio and likeli hood of awareness for environmen tal consciousness categories in rows against environmental consciousness categories in columns Environmental Consciousness Environmental Consciousness Not conscious Little conscious Moderately conscious Very/Extremely Conscious Not conscious 1.00 0.00% 0.67 -37.22% 0.22 -77.60% 0.11 -88.58% Little conscious 0.41 -59.28% 1.00 0.00% 0.36 -64.31% 0.18 -81.81% Moderately conscious 2.46 +346.34% 2.80 +180.22% 1.00 0.00% 0.51 -49.04% Very/Extremely Conscious 8.76 +775.83% 5.50 +449.86% 1.96 +96.23% 1.00 0.00% Table 2-15 Odds ratio and likelihoo d of awareness for income categories in rows against income categories in columns Income Income <20 20-39 40-59 60-79 80-99 100-120 >120 <20 1.00 0.00% 0.80 -19.85% 1.39 +38.76% 1.61 +61.06% 3.61 +260.93% 1.74 +74.42% 1.29 +28.84% 20-39 1.25 +24.77% 1.00 0.00% 1.73 +73.13% 2.01 +100.95% 4.50 +350.33% 2.18 +117.62% 1.61 +60.75% 40-59 0.72 -27.93% 0.58 -42.24% 1.00 0.00% 1.16 +16.07% 2.60 +160.10% 1.26 +25.70% 0.93 -7.15% 60-79 0.62 -37.91% 0.50 -50.24% 0.86 -13.84% 1.00 0.00% 2.24 +124.10% 1.08 +8.30% 0.80 -20.00% 80-99 0.28 -72.29% 0.22 -77.79% 0.38 -61.55% 0.45 -55.38% 1.00 0.00% 0.48 -51.67% 0.36 -64.30% 100-120 0.57 -42.67% 0.46 -54.05% 0.80 -20.44% 0.92 -7.66% 2.07 +106.93% 1.00 0.00% 0.74 -26.13% >120 0.78 -22.38% 0.62 -37.79% 1.08 +7.70% 1.25 +25.01% 2.80 +180.13% 1.36 +35.38% 1.00 0.00%

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51 Table 2-16 Odds ratio and like lihood of awareness for age ca tegories in rows against age categories in columns Age Age 18-25 26-35 36-45 46-55 56-65 >65 18-25 1.00 0.00% 1.54 +54.34% 1.29 +28.53% 1.65 +65.12% 0.55 -44.88% 9.76 +876.20% 26-35 0.65 -35.21% 1.00 0.00% 0.83 -16.72% 1.07 +6.98% 0.36 -64.29% 6.32 +532.49% 36-45 0.78 -22.20% 0.80 +20.08% 1.00 0.00% 1.28 +28.47% 0.43 -57.11% 7.60 +659.51% 46-55 0.61 -39.44% 0.93 -6.53% 0.78 -22.16% 1.00 0.00% 0.33 -66.62% 5.91 +491.21% 56-65 1.81 +81.41% 2.80 +179.99% 2.33 +133.17% 3.00 +199.55% 1.00 0.00% 17.71 +1670.95% >65 0.10 -89.76% 0.16 -84.19% 0.13 -86.83% 0.17 -83.09% 0.06 -94.35% 1.00 0.00% Figure 2-1 Distribution f unction of the percentage of Flor idians who know about invasive species P(X>0.4003)

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52 Figure 2-2 Distribution f unction of the percentage of Flor idians who know about invasive species P(X>0.40) Cumulative Distribution Function0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.3 76% 1.0 00% 2. 500% 5.0 00% 10 .000% 20 .000% 30 .000% 40 .000% 50 .000% 60 .000% 70 .000% 80 .000% 90 .000% 95 .000% 97 .500% 99 .000% 99 .999%Expected Percentage of Florida's Population who knows of Invasive SpeciesFx Figure 2-3 Cumulative distribution function of th e percentage of Flor idians who know about invasive species

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53 CHAPTER 3 DETERMINATION OF RELEVANT ATTRIBUTES OF AQUATIC PARKS IN FLORIDA Introduction Multi-attribute utility (MAU) mo dels are mathematical tools for evaluating alternatives whose apparent desirability depend on how their attributes are viewed by a specific individual or population. In other words, the preferences of an y individual would be ba sed on the weight that s/he gives to the different attributes of the object. For example, when a person decides to buy a ca r s/he will evaluate different attributes among them: price, reliability, sa fety ratings, fuel economy, and style. Thus, the individual will make his/her decision of which car to buy based on the different at tributes each car features and the importance that s/he ascribes. According to the MAU theory, the overall eval uation V(x) of an obj ect x by an individual can be defined as a weighted average of the specific importance of the different relevant attributes of the object. To be precise, the valu e function of an object by an individual can be defined by the following lineal function: (3-1) n 1 i i i) ( v w V(x) a where V(x) is the overall evaluation of the object x; wi the specific weight (or relative importance) of the attribute i; vi(a) is the specific value of the re levant attribute i of the object in a specific level a; and n is th e number of different relevant attributes of the object x. Hence, in order to obtain an accurate valuat ion of an object x, it is important to first determine which of the relevant attributes are for a specific population. For that reason, in this chapter, we will explain the process that was used in order to determine the relevant attributes

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54 for aquatic parks in Florida. Subsequently, those attributes will be used in (Chapter 4) for an evaluation of the impact of inva sive species in those locations. Preliminary Relevant Attributes The first step was to establish a preliminary se t of relevant attributes in order to narrow them to only three in subsequent stages. We al ready have determined two attributes as fixed, given the requirements of the MA U preference elicitation method that will be applied afterward. These two attributes are presence of invasive species and fees. The first refers to the existence and extent of non-native plants know n to disrupt ecosystems. The second attribute gathers all the costs of using natu ral state parks in Florida, such as fees for admission, parking, and camping. With two attributes already established, the determination of the ot her three began with a detailed process that included an extensive literature review about peoples preferences when visiting natural areas and an opi nion survey conducted with pa rk managers regarding which attributes they think that visito rs value the most (Appendix C). The park managers survey was conducte d with the purpose of obtaining general information about the Floridas state park s. In this survey we asked for: The level of attendance per year and the maximum attendance per day; The impact of invasive speci es on the park and visitors; The satisfaction level of the parks visitors and their more frequent complaints; The average amount of visitors expenditures, including fees; The financial priorities of the park; and The parks attributes that visitors value the most. Twenty nine park managers answered th e survey and from their responses it was determined that the attributes that people value the most are the diversity of animal species, the diversity of plant species, and the condition and avai lability of facilities. At the same time, using the visitor complaints information that park managers provided, it was determined that people

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55 are also worried about the number of visitors in the park, that is, the level of congestion; the amounts of the diverse fees asked in the park; and the overall maintenance of the location. These results were contrasted with the conclusions obtained from the literature review regarding the factors that have mo re influence on park visitors satisfaction. A group of different studies in the field of recreati on, leisure, and tourism were analyzed. We concentrated on research for which the focus was the analyses of the factors that influe nce the recr eation demand (Ho et al. 2005; Mackenzie, 1992; Siderelis and Moore, 1998). Additionally, we evaluated research on how participants make choices about activities and site trips, as well as studies about participants choice behaviors when specifyi ng site demand. (Ditton et al. 1992; Williams, 1984). Consequently, it was possible from these st udies to determine that the attributes having more influence on visitors pref erences for which parks they visited and their recreation demand are: Diversity of wildlife; Diversity of flora and vegetation; Condition and availability of facilities; Congestion or number of visitors in the park; Distance from the visitors residence; and Fees asked in the park This conclusion validates park managers res ponses and strengthens th e results obtained by the two methodologies. Thus, in the following stage, these six attributes will be tested for each of the two types of aquatic parks that we ar e studying (ocean/beach parks and river/lake parks). The purpose of this is to reduce the numbe r of attributes to only three; for this purpose attributes will be ranked according to their importa nce, using Floridians opinions, obtained from a survey. The necessity of reducing our choice to only th ree attributes is based on the requirements of both the multi-attribute analysis and the survey pr ocess. This analysis provides a flexible tool

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56 to simultaneously consider a number of importa nt attributes of a d ecision; however, it is important to include a feasible and manageable nu mber of relevant attributes that take into consideration respondents limitation both in time and ability to make complex choices. Therefore reducing the number of attributes to the optimal number based on these constraints is crucial for the success of the study. General Description of the Survey The information regarding aquatic parks attributes that impact Floridians choice to participate in outdoor recreati onal activities in th ose locations was collected through a Web survey. This survey was obtained from a random ly selected sample of Florida residents; Expedite Email Marketing was selected to perf orm the e-mail broadcas t through a cover letter (Appendix D). This letter containe d an invitation to take the survey (Appendix E) as well as a link to the Web address cont aining this survey (found on www.SurveyMonkey.com). The survey consisted of nineteen questions related to the topic and ten ques tions about respondents socio-economic characteristics. Th e questions related to the topi c (Florida residents use of natural parks and particip ation in outdoor recreational activitie s) can be divided in the following groups: Type of outdoor recreational activities that re spondents have participated in the last twelve months; Reasons for participating in outdoor recreati onal activities; Frequency with which respondents particip ate in outdoor recrea tional activities; Distance driven, money spent, and time used when respondents participate in outdoor recreational activities; Importance of each of the six chosen attributes when visiting each one of the two types of aquatic parks analyzed; and Preferences for parks attributes based on their tax-use decisions.

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57 Respondents, who participated in the Web surv ey, were told in the cover letter that the objective was to determine the criteria that Florid a park visitors consider when choosing to visit a natural recreational park in order to use it in a more extensive investigation related to the impact of invasive plants on visi tor satisfaction with Floridas natural areas. It is important to emphasize that this research was focused on th e study of aquatic state parks in Florida, specifically two types: ocean/beach (OB) and river/lake (RL) parks. Respondent Demographic Description A total of 215 valid responses were obtained from the Web su rvey conducted with Florida residents. The main demographic characteristic s of these respondents, as well as comparable figures for the state population, obtained from the U.S. Census Bureau, are summarized in Table 3-1. It can be verified fr om Table 3-1 that ther e is a group of charac teristics (location, marital status, gender) that matched in a high de gree with the proportions of the general Florida population. On the other hand, it can also be pointed out that there are other characteristics where the sample showed a degree of differen ce from the composition of the Florida population such as age, education, and income. Finally, as part of the demogr aphic component of the survey respondents were also asked to provide their environmental tendencies, specifically how they considered themselves regarding their environmental consciousness. Th e composition of sample respondents was: Not at all 0%, a little conscious 5%, moderately conscious 40%, very conscious 38%, and extremely conscious 17%. In the survey respondents were also asked diffe rent questions about their behavior patterns when participating in outdoor recrea tional activities in aquatic state parks in Florida. The survey asked them specifically about the following:

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58 Type of outdoor recreational activities and reas ons for participating in the last twelve months; Frequency of participation in outdoor activi ties in aquatic state parks in Florida; Maximum and minimum distance tr aveled when visiting aquatic state parks in Florida; Time spent, on average, when visiting aquatic state parks in Florida; and Amount of money spent typically, per person, when visiting aquatic state parks in Florida. The frequency with which respondents visit aq uatic parks in Florida can be observed in Table 3-2. From those it can be determined that, on average, responde nts go to both type of parks (OB and RL) seventeen times per year (or at least once per month). This figure reflects the relevance that outdoor recreational activities (in aquatic locations) have for Floridians leisure time and as a consequence over their welfare. Th is justifies the importance of measuring the impact of invasive plant species in Floridas aquatic areas. From Table 3-3 and Table 3-4, it is possibl e to estimate the averag e range of distance traveled for respondents to aquatic state parks. For OB parks, the range is between thirty-three and 124 miles, while for RL parks the distance range is between nineteen and ninety-eight miles. We can conclude that people are willing to travel longer distances when they go to an OB park than when they go to a RL park, even though the travel frequency for both locations are similar. Furthermore as we can see in Tables 3-5 and 3-6 as well, OB parks are a more attractive location for Florida residents than RL parks. The average time spent by survey respondents in OB parks is sixteen hours, compared with the twelve hour average spent by respondents in RL parks. This difference is even more striking when we analyze the amount of money spent in these two types of parks. Respondents reported that they spend, on average, $32 when visiting RL parks compared with the $63 spent in OB parks, or almost double the first amount. To summarize, using the results obtained for time and money spent as well as distance traveled by

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59 survey respondents, it is possi ble to conclude that people give a higher value to OB than RL locations. However, given the frequency of visits to both types of parks, they presented a similar level of importance for respondents, but with a higher value for OB parks. Model for the Degree of Importance of Parks Attributes on Floridians Decision to Participate in Outdoor Recreational Activities in Aquatic Parks. In this section, we will proceed to mode l, through an ordered probit, the level of importance of six relevant attrib utes on Floridians decision to pa rticipate in outdoor recreational activities in two types of aquatic pa rks: OB and RL. The six relevant attributes used in this part were already determined by a literature review and a park managers survey. We will apply the ordered response model of A itchison and Silvey (1957). In this model, the observed denotes outcomes representing ordered or ranked categories. In our specific case, the five categories represent the level of importance that people assign specific attributes of aquatic state parks in Florida. Th e five-point Likert scale used in this analysis is the following: Extremely important; Somewhat important; Indifferent; Somewhat unimportant; and Extremely unimportant. Preferences were obtained th rough the following question in the survey: Grade the importance to you of each of the following attributes when visiting an OB (RL) park in Florida. In this question a sixth alternative (Do not know ) was also provided, and at the beginning it was planned to apply a Heckman or two-stage procedure to gather the effect of the Do not know responses, but the number of responses in this category was very small for both locations (one for OB and two for RL), so it was not necessary to apply this model. Instead, we eliminated these observations, since their effects are insignificant.

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60 In order to be a little more specific, we w ill proceed to define different aspects of the applied model. As in the binary-dependent va riable model, we can model the observed response by considering a latent (dependent) variable yi that possesses more than two levels (in our case, five levels), based on the established rank of preferences, which will depend linearly on the explanatory variables (in our case demographic variables): (3-2) i i i x y where xi and i are independent and identically distri buted random variables. The observed yi is determined from yi using the rule: (3-3) i 4 4 i 3 3 i 2 2 i 1 1 iy 5 y 4 y 3 y 2 y 1 if if if if if yi It is worth noting that the actual values c hosen to represent the categories in y are completely arbitrary. All the ordered specification requires is for ordering to be preserved so that yi* < yj implies that yi < yj. It follows that the probabilities of observing each value of y are given by: (3-4) ) ( 1 ) 5 Pr( ) ( ) ( ) 4 Pr( ) ( ) ( ) 3 Pr( ) ( ) ( ) 2 Pr( ) ( ) 1 Pr( ) (' 4 3 4 2 3 1 2 1 i i i i i i i i i i i i i i i i i i ix F x y x F x F x y x F x F x y x F x F x y x F x y y P where F is the cumulative distribution function of The threshold values are estimated along with the coefficients by maximizing the log likelihood function:

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61 (3-5) M 0 j i i i N 1 ij) (y ) x j Pr(y log )] ( ln[ L where is an indicator function that takes the valu e 1 if the argument is true and 0 if the argument is false. Additionally, the McFadden R-squared10 will be used as a measure of the improvement in fit of the model due to th e independent variables. The development of the McFadden R-squared starts with the log likelihood reported for the model. The log likelihood can be thought of as a measure of the magnitude of the error terms in the estimation. If the log likelihood is smaller (i.e., farther from zero), then the error is greater. The McFadden R-squared compares the log likelihood in two models. The first model runs a regression including only the constant term, with no other explanatory variables, which does not explain much of the variation in the dependent variable, since there are no explanatory variables. This can be thought of as the base case ( ). Then the log likelihood from the base case is compared to the log likelihood calculated from the full model ( ), which includes the explanatory variables. The following formula is used: (3-6) ) ( ) ( 1 ) ( L L McFadden Squared R where L( ) is the log likelihood from the model with the explanatory variables and L( ) is the log likelihood from the base case model with just the constant term. This measure also satisfies two desirable properties of any measure of goodness of fit: (1) lies in the interval [0, 1], and (2) increases as more explanatory variables are added. All of this makes the McFadden R-squared, a measure simple to calculate and to understand, which is adequate for our analysis. 10 This measure is also known as pseudo R-squared since it mimics the R-squared analysis, but is not an R-squared itself. Also, as was previously stated, the interpretation is not the same, but can be interpreted as an approximate variance (deviance ) in the outcome, accounting for the infl uence of the explanatory variables.

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62 Thus after defining the characteristics of th e model and the goodness of fit measure, the explanatory variables are explai ned in Table 3-7. The maximum likelihood estimates for the ordered probit models for each of the six attributes in each location are presented in Figures 3-1 and 3-2. In both the OB and RL models, the McFadden va lues are in the range above 0.09 and less than 0.32, indicating a moderate gain associated with the demographic and related variables, but this is not substantial. It can also be observed in Tables 3-8 and 3-9 that all the models have large LR statistics (and small p-values), thus th e null hypothesis that all slope coefficients except the constant are zero can be reject ed in all the models. As a conse quence, it can be stated that all the models are statistically significant; that is, that all their estimators are different from zero. Ranking of the Relevant Attributes and Weighted Models Using the ordered probit models from Figures 3-1 and 3-2, the probabi lity of each score for the average person (Likert scale) can be estimate d for each park attribute, recalling that a score of one indicates the most favorab le indicator. Combining the pr obabilities for scores one and two provides insight into the favorability of each attribute relative to neutrality or non-favorable opinions. It is necessary to remind that the five attributes analyzed in this study were: 1) Diversity of wildlife, 2) Diversity of flor a and vegetation, 2) Condition and availability of facilities, 3) Congestion or num ber of visitors in the park, 4) Distance from the visitors residence; and 5) Fees. In Figur es 3-3 and 3-4, the probabiliti es of scoring one and two are shown, with the probabilities rank ed using the importance criteria. For both types of parks, the three more important attributes that infl uence visitors decision to participate in outdoor r ecreational activities in those locations are: (1) plant species, (2) animal species, and (3) facilities. In the case of OB parks, the pr eferences are: 88.60%, 81.90%, and 79.00% for plant species, animal species, and faciliti es, respectively. In the case of RL parks, the

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63 preferences are: 93.41%, 90. 92%, and 71.84%, respectively. From the analysis it can additionally be verified that the im portance of the other three attri butes is fairly high, because of the rate of acceptance of more than 50%, which validates th e determination of the preliminary relevant attributes from both the literatur e review and the park managers survey. However, one problem exists that can raise ques tions about the applied procedure. This is the fact that three socio-demographics features of the sample, age, education, and income, are different from the proportions showed in the Florida population. That can produce a certain degree of bias in the results that must be correc ted. For that reason, it was decided to apply a weighting procedure. It can be said that a weighting procedure is meant to transform a realized sample into estimates of the reference populatio n, improving their precision. In our specific case, we will apply a weighting strategy because of the under-representa tion of people with low income, low education, aged 65 years and older. At the end, the objective would be to increase the representation in the adjusted sample of the strata that are unde rrepresented in order to obtain a sample that mirrors the population. The procedure applied to correct the samp le by weighting was the following: using the sample proportions and the population proportions of each category in each level, we determined a set of possible combinations of feasible we ights. In other words, with seven sociodemographic categories, we construc ted the weights using the formula: (3-7) r q m l k j i r q m l k j iemployment income education marital age location gender employment income education marital age location genderp p p p p p p w where xap is the population proportion of the cat egory a in the level x (e.g., the proportion of the population aged 26-35), and xais the sample proportion of the same category a in the same level x. The level x of these two proportions is determined by the specific

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64 characteristic of each observation, with the levels being mutually independent in each category; this means that one observation cannot be in more than one level (in each category) at the same time. In the end, applying this ratio comparis on for each observation and its specific characteristics permitted us to obtain one wei ght for each observation. These weights were applied to all the variables a nd with that new maximum likeli hood coefficients were estimated for the six attributes models in each location. These estimates were obtained and showed in Figures 3-5 and 3-6. In addition from Tables 310 and 3-11 it can be concluded that all the weighted models are statistically significant; that is, that all their estimators are different from zero. Using the ordered probit models from Figures 3-5 and 3-6, the probabi lity of each score for the respondents in the weighted sample can be es timated for each park attribute, recalling that a score of one indicate s the most favorable indicator. As in the first estimation, the probabilities for scores one and two were combined in order to provide insight into the favorability of each attribute relative to neutrality or non-favorable opinions. In Figures 3-7 and 3-8, the probabilities of scoring one and two are shown with the probabilities ranked using the importa nce criteria. In this case this new estimation is a corrected estimation of respondents preferences, wh ich correspond more clos ely to the population behavior. It can be verified from Figures 3-7 and 3-8 th at, like in the un-weighted models, the three more important attributes that influence visitors decisions to participate in outdoor recreational activities in both types of parks are: (1) plant specie s, (2) animal species, and (3) facilities. In the case of OB parks, the preferences are: 95.38% 83.03%, and 82.25% for plant species, animal

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65 species, and facilities, respectivel y. In the case of RL parks, the preferences are: 94.06%, 89.56%, and 79.02%, respectively. From this analys is it can also be verified that the importance of the other three attributes is also fairly high. This is consistent with the results obtained from the preliminary literature revi ew and park managers survey. It is important to emphasize that similar results were obtained with the application of the original sample model. The only difference was th e magnitude or intensity of the preferences for the three more relevant attributes, which were higher than those obtained in the original models. In order to attain a more explicit analysis of the corre spondence and robustness of both procedures, the results obtained for each model in the five scores of each attribute (for each location) were compared. From Figure 3-9 to Figure 3-20 we can observe an important correspondence among the results obtained from th e unweighted and the we ighted models. The only difference that can be noted is the discrepa ncy between the percentages of acceptance in the three less important attributes models of each location. The weighted models provide a higher intensity of preference to the inferior categorie s scores than the superior ones when compared with the unweighted models. In the three more important attr ibute models of each location, the situation is the inverse, where the weighted models provide a higher inte nsity of preference to the superior categories scores rather than the inferior ones when comp ared with the unweighted models. However, the three primordial attributes that are selected for visitors are the same as in the original model, which strengthens the conclusion that animal sp ecies, plant species, and facilities are the three most important attributes for people when they visit aquatic parks in Florida.

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66 As an additional contrast, we can use the re sults obtained from a sp ecific question in the survey: If the state government wants to invest tax revenues in natural areas (either OB or RL), where would you want to see improvements?. The results obtained from this survey question can be examined in Table 3-12, from which it can be inferred that the thre e most preferred improvements ar e related to the diversity and preservation of animal and plant species, as well as the maintenance and availability of facilities. In the case of OB parks, the investment that people most preferred was the maintenance and availability of facilities, with 33.47% of th e responses, followed by investments in the preservation of animal species, with 28.29%, and plant species, with 18.73%. In RL parks, the investment most preferred was the conservati on of animal species with 37.85% followed by maintenance and availability of facilities, with 28.29%, and preservation of plant species, with 20.72%. In both locations, the pr eference for these three types of governmental investments are over 80% of all the responses, whic h validates our previous conclusion of the relevance of these three attributes when members of the public visit either OB parks or RL parks. Conclusions This analysis helped to determine which attributes are the most relevant to peoples decision to participate in outdoor recreational activities in both OB and RL parks in Florida. These attributes will be subsequently used in a multi-attribute survey, through which the importance that people give to the problem of invasive species in these natural locations in Florida, and their willingne ss to pay to address this problem, will be estimated. It was stated that it was necessary to narrow th e number of attributes in order to obtain a manageable and feasible MAU survey that at the same time provides accura te results. Three was considered an adequate number of attribut es, given the cognitive and time limitations of respondents. For that purpose, a preliminary survey was conducte d, in which respondents were

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67 asked to provide a valuation about the attribut es they considered important when making the decision to participate in outdoor recreational activities in OB a nd RL parks in Florida. The responses to this survey were used for a poste rior analysis in whic h an ordered probit was applied. Initially, three attributes were obtained: pl ant species, animal species, and facilities; however, since a group of categories from the samp le demographics did not match with Floridas population proportions, a refinement was applied to reduce the margin of error. Therefore, the sample was weighted using the probability of occurrence of each observation in the sample and the general population. That provided us with one weight for each observation of the sample. Using this weighted sample, new estimators were obtained and a new simu lation was applied. The results obtained were the same as those in the original model; that is, the three most important attributes of an aquatic park fo r people wee plant species, animal species, and facilities. This result was also contrasted with another survey question in which it was asked in which areas people considered gove rnmental investment in those parks to be appropriate. The result was the same with a clear preponderance of the three attributes al ready selected. All of these allow us to reasonably conclude that for Fl oridians the three most important attributes for both types of aquatic parks (OB and RL) are plant species, animal species, and facilities. These attributes will be applied in the core analysis of this research, where we will estimate the value that people give to the probl em of invasive species in na tural locations in Florida.

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68 Table 3-1 Socioeconomic characteristics for the su rvey sample and comparisons to the Florida population. Category Sample Florida Urban 46.51%47.00% Suburban 45.58%44.00% Rural 7.91%9.00% Male 48.84%48.80% Female 51.16%51.20% 18 25 years 8.37%7.80% 26 35 years 18.14%16.88% 36 45 years 20.47%20.12% 46 55 years 20.00%16.75% 56 65 years 15.35%12.59% More than 65 years 17.67%25.85% Single 20.93%23.80% Married 59.07%54.30% Other 20.00%21.90% High school or less 22.33%48.90% Some college courses 13.02%21.80% Associate 5.12%7.00% Bachelor's 20.47%14.30% Some Graduate 13.02%2.00% Graduate 26.04%6.00% Unemployed 2.44%3.20% Employed 56.28%54.90% Not in labor force 41.28%41.90% Less than $20.000 26.51%34.00% $20,000 $39,999 10.70%15.00% $40,000 $59,999 9.30%19.00% $60,000 $79,999 8.37%14.00% $80,000 $99,999 12.09%5.00% $100,000 $120,000 10.23%4.00% More than $120,000 22.80%9.00% Source: U.S. Census Bureau 2000 Table 3-2 Frequency of travel to stat e aquatic parks by surveys respondents. Type of park Daily WeeklyMonthly 2 3 months 4 6 months 7 12 months Not at all Ocean / Beach 0.93% 16.28%26.05%28.84%13.49%8.84% 5.58% River / Lake 2.10% 11.39%16.28%26.98%15.35%13.95% 13.95%

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69 Table 3-3 Closest distance traveled to a state aquatic park by survey respondents Type of Park 0 to 15 miles 16 to 30 miles 31 to 45 miles 46 to 60 miles 61 to 75 miles More than 75 miles Ocean / Beach 41.04% 14.62%6.60%15.09%10.85% 11.79% River / Lake 59.90% 21.32%8.63%4.57%1.02% 4.57% Table 3-4 Farthest distance traveled to a state aquatic park by survey respondents Type of Park 0 to 40 miles 41 to 80 miles 81 to 120 miles 121 to 160 miles 161 to 200 miles More than 200 miles Ocean / Beach 20.50% 16.50%14.00%11.00%10.00% 28.00% River / Lake 32.72% 20.99%9.88%9.26%10.49% 16.67% Table 3-5 Time spent in a state aquatic park by survey respondents Type of Park Less than 1 hour 1 2 hours 3 6 hours 7 hours one day 2 3 days 3 7 days More than 7 days Ocean / Beach 5.66% 18.40%44.81%18.87%8.02%3.30% 0.94% River / Lake 10.81% 22.70%39.46%17.84%7.57%1.08% 0.54% Table 3-6 Money spent in a state aq uatic park by survey respondents Type of Park Less than $10 $10 $50 $51 $100 $101 $150 $151 $250 $251 $350 $351 $500 More than $500 Ocean / Beach 38.05% 39.51%8.78%3.90%2.44%2.93%2.93% 1.46% River / Lake 43.65% 44.75%6.63%2.76%1.10%1.10%0.00% 0.00%

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70 Table 3-7 Explanatory predicto rs for ordered probit models Variable Definition Residence Dummy variable that represents the respondents type of residence in Florida. Freq Dummy variable that represents th e frequency with which respondent participates in outdoor recreational activities in the specific natural location. Time Dummy variable that represents th e time spent for each visit to the specific location. Location Dummy variable that indicates the residence of respondents, according to three categories: urban, suburban and rural. Gender Dummy variable that indi cates the respondents gender. Age Dummy variable that represents di fferent ranges of age, which go from 18 to more than 65 years old. Marital Dummy variable that represents the marital status of the respondent. Education Dummy variable that represents th e degree of education level for the respondent, which varies from some college courses to graduate studies. Employment Dummy variable that represents diffe rent types of employment status for respondents. Income Dummy variable that represents different ranges of income for respondents, which go from less th an $20,000 to more than $120,000 annually. EC Dummy variable that represents th e respondents answers about their degree of environmental consciousness. Table 3-8 Likelihood Ratio statistics and pseudoR2 for the attribute models for ocean/beach location Animal Species Facilities Plant Species Number of Visitors Traveled Distance Fees LR Statistics 79.667 87.97480.96367.31273.307 70.922 P-value (LR) 0.000 0.0000.0000.0060.001 0.003 Pseudo R2 0.121 0.1530.1710.0990.135 0.126 Table 3-9 Likelihood Ratio statistics and pseudo-R2 for the attribute models for the rive/lake location Animal Species Facilities Plant Species Number of Visitors Traveled Distance Fees LR Statistics 142.360 74.652 129.79563.28275.796 95.442 P-value (LR) 0.000 0.001 0.0000.0140.001 0.000 Pseudo R2 0.311 0.142 0.3070.1180.135 0.181

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71 Table 3-10 Likelihood Ratio st atistics and pseudo R2 for the attribute models for ocean/beach location weighted sample Animal Species Facilities Plant Species Number of Visitors Traveled Distance Fees LR Statistics 82.344 95.745 83.79274.75774.499 78.182 P-value (LR) 0.000 0.000 0.0000.0010.001 0.000 Pseudo R2 0.160 0.354 0.1920.1020.140 0.188 Table 3-11 Likelihood Ratio st atistics and pseudo R2 for the attribute models for ocean/beach location weighted sample Animal Species Facilities Plant Species Number of Visitors Traveled Distance Fees LR Statistics 117.18 79.795134.11461.71282.060 110.000 P-value (LR) 0.000 0.0000.0000.0200.000 0.000 Pseudo R2 0.384 0.1580.3460.1050.159 0.203 Table 3-12 Tax preferences of survey respondent s for improvement investments in ocean/beach and river/lake parks Improvement of Parks by Attributes Type of Park Animal Species Facilities Plant Species Number of Visitors Fees Ocean/Beach 28.29% 33.47% 18.73% 15.14% 4.38% River/Lake 37.85% 28.29% 20.72% 6.77% 3.19%

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72 Figure 3-1 Coefficient estimates for the at tribute models for ocean/beach location

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73 Figure 3-2 Coefficient estimates for the at tributes model for river/lake location

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74 Figure 3-3 Rankings of ocean/beach park attribut es based on the order probit unweighted models and the survey responses Figure 3-4 Rankings of river/lake park attribut es based on the order pr obit unweighted models and the survey responses

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75 Figure 3-5 Coefficient estimates for the attribute m odels for ocean/beach location weighted sample

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76 Figure 3-6 Coefficient estimates for the attribute models for river/lake location weighted sample

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77 Figure 3-7 Rankings of ocean/beach park attribut es based on the order probit weighted models and the survey responses Figure 3-8 Rankings of river/lake park attributes based on the order probit weighted models and the survey responses

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78 Figure 3-9 Likert ranking of animal species at tribute for ocean/beach location for unweighted and weighted models Figure 3-10 Likert ranking of f acilities attribute for ocean/beach location for unweighted and weighted models

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79 Figure 3-11 Likert ranking of plan t species attribute for ocean/b each location for unweighted and weighted models Figure 3-12 Likert ranking of nu mber of visitors attribute for ocean/beach location for unweighted and weighted models

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80 Figure 3-13 Likert ranking of trav el distance attribute for ocean/ beach location for unweighted and weighted models Figure 3-14 Likert ranking of fees attribute for ocean/beach location for unweighted and weighted models

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81 Figure 3-15 Likert ranking of anim al species attribute for river/ lake location for unweighted and weighted models Figure 3-16 Likert ranking of facilities attribut e for river/lake location for unweighted and weighted models

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82 Figure 3-17 Likert ranking of plan t species attribute for river/lake location for unweighted and weighted models Figure 3-18 Likert ranking of number of visitors attribute for river/lake location for un-weighted and weighted models

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83 Figure 3-19 Likert ranking of trav el distance attribute for river/ lake location for unweighted and weighted model Figure 3-20 Likert ranking of fees attribute for river/lake locati on for unweighted and weighted models

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84 CHAPTER 4 MULTIATTRIBUTE ANALYSIS FOR AQUATIC AREAS IN FLORIDA Introduction In chapters two and three we determined: 1) th e attributes that people value the most when they visit aquatic areas in Florida and 2) the exte nt of Florida residents knowledge of invasive plants. Therefore the three most important at tributes of aquatic parks (ocean/beach and river/lake) for Floridians are: plant species, animal species, and facilities. In addition, approximately between 30 and 40 percent of Floridas population have at least moderate knowledge about invasive plants. These two conclusions are the foundations of the following Multi-attr ibute Analysis (MA), in which the main objective is to appraise th e value that Floridas population assign to the problem of invasive plants. This was done throug h the determination of a functional relationship between the publics utilityderiv ed from participation on nature-based recreational activities and the presence of invasive plants in aquatic areas in Florida. This chapter is divided into five parts: 1) De termination of attributes levels and pair-wise choice design; 2) Survey design a nd respondents profile; 3) Statistic al Analysis; 4) Estimation of annual marginal willingness to pay (MWTP); and 5) Conclusions. Attributes Levels and Pair-wise Choice Design In chapter three we determined that the most important attributes of aquatic parks for Floridas residents are: animal species, plant species and faci lities. Along with these three attributes it was already determined two additiona l attributes as fixed. These two attributes are

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85 presence of invasive plant species and fees A more detailed definition of these five attributes, which were used in the MA, is given next11: Park Facilities Condition: State of the fac ilities available in the park which include parking lots, boat docks, boat ramps, picnic tables, restrooms, showers, among others. Diversity of Native Plant Species: Variety of the native or indigenous plants present in the park. Diversity of Animal Species: Variety of all those animals living in the wild, as well as wild birds and all fish found in inland and coastal areas. Presence of Invasive plant sp ecies: Incidence of non-native plants (known to disrupt ecosystem processes) in the park. Fees: Include fees for admission, parking, camping among others The next stage was to determine the levels in which each attribute w ould vary. These levels must satisfy the following requirements: Levels within each attribute should be mutually exclusive. Attribute levels should cover the full range of possibilities for exis ting goods as well as goods that may not yet exist, but that it is required to investigate. The number of levels among attributes must be balanced in a fixed number, since all else equal, attributes defined with more levels tend to get more importance. This is called the Number-of-Levels Effect. The number of levels on each attribute must be limited. It is suggested to not include more than five levels to describe attributes. In our specific case it was determined to limit the number of levels to three. This number was the same for each attribute in order to avoi d the Number-of-Levels effect. Additionally it was decided to define each level as simple as possible; in order to avoid fatigue, reduce the decision time and increase response rates. 11 This succinct definition of the five attributes was also provided to respondents in the survey so that they fully understand their meaning.

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86 For the process of determining the level of each attribute we used the results obtained from the preliminary survey conducted to park manage rs (referred in chapter 3). This survey was conducted to determine the attributes that park managers thought that visitors value the most; however it also helped us to determine the le vel of each of the five chosen attributes. In addition a group of experts12 were consulted in order to depurate the preliminary set of attributes levels and comply with the requireme nts of conciseness and explicitness. Thus the levels range for each attribute was established in the way that is summarized in Table 4-1. One of the most discussed attributes was the fee attribute which gathers all the costs of using aquatic State Parks in Florid a, such as fees for admission, parking, and camping. Initially its levels were established in a range from $5 to $35. However it was necessary to establish a range closer to the current situat ion of State Parks in Florida. Then after several analyses (using the park managers survey as a main source) it was decided to employ free as a minimum and a va lue of $20, somewhat above from the current average of fees in aquatic parks ($8.5 according to the park managers opinion) as a maximum. Hence this new range (from free to $20) is both closer to the exis ting situation a nd satisfies the requirement of being beyond the average situatio n. This would permit us to obtain a better measure of respondents sensitivity to the fees vari ations as well as the level changes of the other attributes. The five attributes previously defined as well as their levels were used in a set of pair-wise choices for each of the two aquatic areas an alyzed (ocean/beach and river/lake parks). Nevertheless since two of the five attributes (plant species and animal species) by definition have a high level of correlation between th em, we decide to divide the initial set of attributes in two 12 The experts that collaborated with important opinion and suggestion for the cleansing of these levels were: Dr. Donna Lee, Dr. Alan Hodges, Dr. Janaki Alavalapat y, Dr. Sherry Larkin and Dr. Randall Stocker.

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87 different combinations of four attributes. The first combination included the animal species attribute and the other three attr ibutes: facilities, invasive pl ant species and fees. The second combination included all else equal to the first combination except for the inclusion of the plant species attribute as a substitution of the animal species attribute. Each combination (Plant Species Combination and Animal Species Combin ation) was conducted in a different survey. Therefore four different surveys (one for each co mbination and for each location) were designed. An example of a pair-wise choice question with the animal species combination is presented in Figure 4 -1. The same process of de scribing the attribute le vels for each pair of possible parks was followed in the surveys using the plant species combination (Figure 4-2). Thus as it can be observed in Fi gure 4-1 and Figure 4-2 respondent s were asked to express their preference for two fictitious parks in each pair-wise question but under the following assumptions: These two parks are the only al ternatives available, The two parks are on the same distan ce from the respondents home, and Both parks offer the same number and type of facilities and r ecreational activities. These facilities only differ in thei r quality level or condition. It is important to emphasize that is optimal to present no more than ten set of pair-wise choices to respondents due to their limitations in time, resources and attentiveness. In our case a full factorial experiment was not feasible since four attributes with three levels each resulted in 81 (34) possible attribute-leve l combinations, and 34 x 3 4, possible combinations in a paired choice design. For that reason a fractional factorial design was em ployed to reduce the number of pair-wise combinations and to create a balanced sa mple of possible attribut e alternatives, in order to satisfy both the cognitive and time limitations but maintaining the consistency of the responses.

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88 SAS FACTEX (SAS Institute) procedure wa s applied obtaining a set of 20 optimal combinations paired in a block of ten. Then we ruled out alternatives that were obviously dominated by others13. After that, we established seven de finitive pair-wise choices (fourteen attribute-level combinations) used in the survey This design provides significant savings in time and research costs but still allows a robust estimation of all main effects. The seven choices sets used in both attributes combina tions (plant species and animal species) for both aquatic parks (river/lake and ocean/beach) are sh own in Table 4-2 and Table 4-3. Another characteristic of our pa ir-wise choice design is the non inclusion of a status quo or do-nothing alternative. Even though in most cases a status quo option is included, there are some situations where it cannot be considered as a feasible choice. One of those situations is when it is impossible to standardize a state of re ality. In our specific case, Florida has over 1.5 million acres of lakes and rivers, including 7,700 lakes and ponds and 1,400 rivers and streams. Florida also has over 1,197 miles of coastline and over 663 miles of beaches. For that reason it would be impossible to generalize all the river/lake and/or ocean/beach parks of Florida in a homogenous set of characteristics; hence a stat us quo option is not reasonable (Morey, 2001). Additionally, it is necessary to note that this study sought to measure preferences for park features and not specific policy options. Then a study of how to allocate efficiently resources from programs of control and management of invasive plants is more adequate 14(Longo, 2007). Consequently an experimental design without a status quo option is more appropriate since such method allows us to determine the trade-off among the relevant parks attributes impacting in peoples preferences and not necessarily in their actual choices. In addition with this approach 13 For example, if park A and B were compared, and the le vels of all attributes were identical, but B was more expensive, A would be, clearly, a dominating choice and it is necessary to rule it out. 14 This is even more reasonable when public agencies have already determ ined a budget to allocate to these programs.

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89 we will be able to measure not only peoples use value but also thei r non-use value of the nonmarket good, in this case aquatic parks. It is also necessary to reca ll that for respondents the pro cess of answering a number of choice questions can produce fatigue and indifference. This effect is increased when the analysis is broad and outside of a particular spectrum or st ate. Respondents can become frustrated if they dislike all of the available alternatives, a nd they may have little incentive for sufficient introspection to determine their preferred altern ative. Hence if a status quo option is included there can be a bias towards it, and the res pondent might ignore his constraints behaving strategically. Then since our analys is is not about a park or region in particular but about all the aquatic parks in Florida to include a status quo option in the survey would have been extremely prejudicial since it had increased signifi cantly the exposition to a status quo bias. To summarize, given the objectives and charac teristics of our study (generic per se), a status quo or do-nothing option is not appropriate by any means. Survey Design and Respondents Profile Survey Design The information required for this MA an alysis was collected through Web surveys15. For this purpose four different surveys were de signed. One for each specific aquatic location (rive/lake or ocean/beach parks) and for each spec ific attribute combination (animal species or plant species combination), which were labeled as following: Ocean and Beach Parks Animal Species Combinations (OBAS) Ocean and Beach Parks Plant Species Combinations (OBPS) River and Lake Parks Animal Species Combinations (RLAS) 15 The MAU survey draft underwent several revisions and was extensively pre-tested using experts (Dr. Donna Lee, Dr. Alan Hodges, Dr. Janaki Alavalapati, Dr. Sherry Larkin) as well as a group of undergraduate students (242) of the Food and Resource Economics Department in the University of Florida from the classes of Dr Donna Lee, Dr. Damian Adams and Allison Lutz.

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90 River and Lake Parks Plant Species Combinations (RLPS) These surveys were obtained from a randomly selected sample of Florida residents. Zoomerang Email Marketing was selected to pe rform the e-mail broadcast by early May, 2007 through a cover letter (Appe ndix F). This letter contained an invitation to take the survey (Appendix G) as well as a link to the Web address containing the survey (found on www.SurveyMonkey.com ). The survey was also streamlined so it could be completed in about six minutes. It is important to emphasize that for these surveys respondents who successfully completed the surveys were provided with 50 Zoom points. Zoom erang survey panel participants collect points that can be redeem ed for merchandise. The approximate value of 50 Zoom points is $0.65. In order to obtain a higher ra te of response and to avoid conf usion and tiredness, the surveys were designed following the methodology specif ied by Dillman, Tortura and Bowker (1998) who recommended: Introduce the questionnaire with a welcome screen that is motivational, emphasizes the ease of responding, and instructs respondents on the action needed for proceeding to the next page. Choose an initial question that is likely to be interesting to most respondents, easy to answer, and fully visible on the screen. Present each question in a format sim ilar to that found in paper surveys. Avoid differences in graphical appearance between questions. Provide specific instructions. Provide to respondents a sense of their nearness to completing the survey. Avoid questions known to have measurement problems, such as open-ended questions or check all that apply options. Hence respondents, who participated in the We b survey, initially were told in the cover letter that the objective of the study was to determine the impact of invasive plants on recreation

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91 activities in Floridas aquatic parks. Each survey was divided in three parts. In the first part respondents were asked to provide different valuations about a specific natural area and a second one of their choice, which was optional. In the second part respondents were asked to give their opinion about what effects invasive plant species have had in their decision of which location to attend and enjoyment when engaging in outdoor recr eational activities in a quatic parks. Finally, in the third part respondents were asked to give some soci o-economic information for the analysis. In chapter two it was determined the importance of respondents having an adequate background of knowledge in order to fully discern the impact of invasive plant species on their satisfaction with outdoor recreational activities. It was also found that between 30 and 40 percent of Floridas population approximately has a mode rate knowledge about invasive plant species. These results indicated the necessity of providi ng an adequate level of information about invasive plants in the multi-attribute survey. Th is information would allow us to obtain more accurate valuation about the impact of these spec ies on peoples utility and at the same time it would reduce the bias due to the unawareness effect. Consequently along with the full questionnaire we included in the survey a brie f description of the study, background information about invasive plants and photos depicti ng invasive plants in natural areas. Respondents Profiles The total numbers of valid responses obtai ned from each of the four Web surveys conducted to Florida residents ar e shown in Table 4-4. The main demographic characteristics of these respondents as well as comparable figures for the state population, obtained from the U.S. Census Bureau, are summarized in Table 4-5. From Table 4-5 it can be verified that ther e are a group of characteristics (income and education) that matched to a high degree with the proportions of the Florida population. On the

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92 other hand, there are other categories such as ge nder and age in which the sample showed an important difference from the composition of Flor ida population. That situation compelled us to be extremely careful in the conclusions and th e applied methodology. A process of weighting the sample was employed in order to correct the problem of imbalance of the sample, in special for those set of categories that present a highe r level of discrepancy with respect to the population. At the end, with this weighting process it was possibl e to increase th e representation in the adjusted sample of the st rata that are underrepresented and in turn to obtain a sample that mirrors the population. Statistical Analysis Econometric Modeling of Pair-wise Choices It was already stated that the MA analysis su rvey consisted of a set of choice questions in which respondents express their preference among two hypothetical parks with a limited set of attributes that vary. With each choice, responde nts face a tradeoff between attribute levels, and select the park whose attributes mix maximize th eir utility. As they make their choices between the two parks, the utility associ ated with changes in the levels of specific attributes can be specified. The MA analysis will permit to determine the respondents tradeoff for the parks attributes as long as each attr ibute: 1) reflect an independent dimension of the good, 2) be measurable and 3) be easy to understand. Assume a set of alternatives of a good (in our specific case aquatic parks) in which the utility derived from their consumption can be ex pressed by a linear combination of four relevant attributes as follow:

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93 (4-1) ) (4 4 3 3 2 2 1 1 i i i i i i i i i iX U X U x x x x U where Xi is a set of four attributes for alternative i and i is a random error term. Then assume that in a MA setting responde nts compare alternatives A and B by their attributes-mix and select the alternative that provi des a higher level of utility (e.g. alternative A): (4-2) B AX U X U where U(.) represents a res pondents utility function and XA and XB represent the set of attributes for alternatives A and B. Utility can be also expressed as the sum of a systematic component (.), determined by the attri butes, and a random component ( i) : (4-3) Ui = (Xi) + i The error term represents researches uncertainty about the choice since the choice behavior is deterministic from the perspectiv e of the individual but from the researcher perspective this behavior is random. Furthermore the probability that the respondent will choose alternative A over B depends on the probability that the diffe rence between the systematic co mponent of A and B be greater than the difference between the random components such that the probabili ty of choosing A is: (4-4) ] P[ P(A) )] ( ) ( ) ( P[ P(A)A B U X XB A Thus for this analysis, econometric techni ques parallel to the theory of rational and probabilistic choice will be applie d. These types of models ar e called discrete -choice models and the most representative is the McFaddens conditional Logit (1974), which allows us to

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94 determine the effects of explanat ory variables on a subj ects choice of one of a discrete set of options. It is important to specify that the main charac teristic of these models is that for a given subject an explanatory variable x takes di fferent values. For instance for a subject and response choice i, x i = (x i1, x ik) denote the values of the k explanatory variables conditional on the set C which represents all of the altern atives in the choi ce set of response choices for subject An econometrical specification of thes e concepts is given in equation 4-5. (4-5) C h x x ih ie e) ( ) ( where is the vector of coefficients (weights) fo r the attributes used in the analysis. In addition for each pair of alternatives a and b the model (4-5) has the following Logit form (Domencich and McFadden, 1975): (4-6) ) ( ) ( ) ( logb a b b a ax x x x Thus conditional on a subjects choice between a or b, the influen ce of an explanatory variable depends on the distance between the pe rsons valuation of that variable for those alternatives. If the values ar e the same, we can say that the variable has no influence on the choice between a or b. In our specific case, the explanatory variable s will be the relevant park attributes which vary around a specific range of levels. Statistical Results for the Multi-Attribute Model In this section, following the prescr ibed methodology of McFadden (1974); and Domencich and McFadden (1975), a logistic regression will be us ed in order to estimate the relative importance of each attribute on the responde nts utility. We will establish a set of four

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95 explanatory variables that repres ent each park attribute and a bi nary variable Y representing the dependent variable, which takes the value of 1 if a respondent chose alternative A, and 0 otherwise. In our model, the explanatory vari ables (attributes) have been considered as quantitative; for that reason a set of scores have been assigned to them16. The reason for this is that in our experimental desi gn the status quo is not present and therefore it would not be possible to estimate the welfare change from a ba seline (natural of a model with qualitative variables as dummies where the omitted dummy is the status quo/baseline) On the contrary, an experimental design like ours (with out status quo) requires estimati ng the trade-off and weight of the attributes in average, as a form to describe the relative importance of the components in the moment of the decision (Longo, 2007). The pr edictors of the model and their scores17 are given in Table 4-6. It is necessary to specified that in order to correct the problem of imbalance of the sample a weighting procedure was applied. The procedur e used the sample proportions and the population proportions and with those a set of possi ble combinations of feasible weights18 were determined. These weights were estimated for each obser vation and then applied to the sample. The models for each attribute combination are defined as follows: Animal Species Combination: (4-7) ) ( ) ( ) ( ) ( ) ( ) ( ] [B A fe B A is B A as B A fa B A ABFe Fe Is Is As As Fa Fa X X Log P Logit 16 This is also possible given the natural or der of intensity of each attribute level. 17 According to Agresti (1996), the ch oice of scores has little effect on the results; then any concern about misspecification due to the score selection is not justified as long as we have a large sample, which is our case. 18 See chapter 3, equation 3-6.

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96 Plant Species Combination: (4-8) ) ( ) ( ) ( ) ( ) ( ) ( ] [B A fe B A is B A ps B A fa B A ABFe Fe Is Is Ps Ps Fa Fa X X Log P Logit The discrete choice models proposed in 4-7 and 4-8 follows the structure of McFadden (1974) which in turn follows Brad ley and Terry (1952). Therefore PAB denotes the probability that respondents prefer A over B (given their attributes-mix) since a lternative A provides a higher utility. Tables 4-7 to 4-10 present the estimation results for the multi-attribute models for different location/attributes combination. In all these models the coefficients of the four chosen attributes are significant. Hence it can be concluded that on each of these models all attributes (given the specific location/attributes combination) are releva nt for respondents uti lity. Consequently they form their preferences based on the utilities deri ved from the mix of a ll the four relevant attributes of each alternative. In the specific case of the facilities (FA) and animal/plant species (AS/PS) estimators, they were positive and significant in all the mode ls. That means that respondents preferred parks with both facilities of higher quali ty and higher diversity of anim al/plant species. In contrast the fees19 (FE) and invasive plant species (IS) estimators were negative and statistically significant indicating that respond ents preferred parks with low er fees and less presence of invasive plant species. These statistical resu lts indicate that higher level of animal/plant diversity, higher quality on the faci lities, lower fees and less presence of invasive plant species increased the likelihood that respondents would prefer a park over another. 19 FE U and in consequence fee gathers the change in utility associated with a marginal decrease in wealth; thus it is expected that the sign of fee be negative

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97 In Table 4-11 a global test for each mode l (RLAS, OBAS, RLPS, OBPS) was conducted. In this test the null hypothesis that all the estimators of these mode ls are not significant ( = 0) was contrasted. Thus it can be verified from Ta ble 4-11, that the null hypothesis that all the estimators are not significant (or equal to zero) is rejected for all the mode ls. Then all the four models can be considered statistically si gnificant at 5% and 1% confidence level. The multi-attribute utility (MAU) parameters for each location/attributes combination can be compared through the use of standardized estimators20. From Table 4-12 it can be observed that respondents gave a positive weight to the FA and AS/PS attributes. These results reinforce the conclusion that respondents preferred aquatic pa rks with facilities in excellent conditions and with a high diversity of either pl ant or animal species. On the other hand the other two attributes: IS and FE, were negatively weighted. That means that parks with high fees and large presence of invasive plant species are less preferred by respondents. An important thing to note from Table 412 is that in all the location/attributes combinations the two attributes that have mo re weight on the utility and in turn on the respondents preferences are FE a nd IS, in that order. Thus gi ven their negativ e weight these attributes in high levels not only would reduce but also would null the positive impact of the other two attributes namely FA and AS/PS. This imposes the necessity of controlling and keeping in acceptable levels both the fees and the presence of invasive plant species in all the aquatic parks in Florida to avoid a ny impact on the populations welfare. 20 This is obtained by the following formula y x where is the standardized estimator, is the original estimator, x is the standard deviation of the explanatory variable and y is the standard deviation of the dependent variable.

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98 In third place of importance appears, in so me combinations FA and in others AS/PS. However from Table 4-13 we can verify that the re lative importance of these two attributes is not statistically different. We applied a Wald test in Table 4-13 and in this test the null hypotheses that, Ho: FA = AS or Ho: FA = PS (given the specific location/a ttributes combination) were contrasted. In other words we tested that the es timators for FA and AS/PS were equal. Then it can be concluded, from Table 4-13, that for a ll the location/attributes combinations the null hypothesis can not be rejected at 5% and 1% confidence level. Therefore the effect of the condition of facilities and diversity of plant/ animal species attributes on the respondents utility are statistically the same. In Tables 4-14 to 4-16 it was also contrasted other three hypotheses; to be precise: Ho: IS = FA; Ho: IS = AS or Ho: IS = PS; and Ho: IS = FE. In all the three cases we reject, at the 5% and 1% confidence level, the null hypothesis of identical effect of these attribut es on respondents utility. Thus the ranking of the attributes according to their effect on the respondents utility and their specific location/attributes combination is established in the way that is showed in Table 4-17. Furthermore if we compare the relevance of the presence of invasive species in each location we find the following. First, with the combination animal species; the importance of the IS attribute for the respondents utility is sim ilar for both locations (river/lake and ocean/beach). In other words when people imply that the invasi ve plant species will affect animal species in aquatic areas, the importance of this effect on the respondents utility will be the same either this location is a river/lake (RL) pa rk or an ocean/beach (OB) park. On the contrary with the combination plant species; the importance of the IS attribute is different for both locations. In

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99 this specific case the weight or relative importance of this attri bute to the responde nts utility is larger for the RL parks than for the OB parks. This effect could be due to the small importance that visitors give to the presen ce of plant species in OB parks21. In other words people do not hold a high value for the presence of native plan ts in OB parks and as a consequence when people imply that the invasive plant species will a ffect native plants in this type of park, the importance of this effect will be small but still negative; (even more it is the lowest from all the location/attributes combinations). In contra st when we analyze the RLPS combination the importance or absolute weight of the IS attribute on the respondents utility is the highest one (-1.311). This is because people va lue in a high degree the environm ental and aesthetical benefits provided by native plants in these locations. Hence if it is implied that the invasive plant species will affect native plant species in a RL park, the importance of this effect will be high, at the point of being the highest one from all the combinations (Table 4-12). A further approach to measure the rela tive importance of each attribute on the respondents utility is through a monetary measure called marginal willingness to pay (MWTP)22. This measure gathers the monetary imp act (on respondents) for changes in a nonmonetary attribute of a good (in this case an aqua tic park). However, it is necessary to emphasize that in this specific case the estimated willingnes s to pay not only will gather the use value that respondents provide to a specific attribute and/or alternative bu t also the non use value or 21 A proof of that is that the standardized estimator of PS for OB locations (PS=0.67) is the smallest one from all the other estimators in all the location/attributes combinations. 22 The willingness to pay can also be defined as the quantity of money needed to equate the original level of utility with the level associated with an improvement of a non-market good. In other words is the amount of money that an individual or group could pay, along with a change in a non-market good, without being made worse off. It is therefore a monetary measure of the benefit to them of the improvement. If negative, it measures its cost.

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100 existence value23 of the good. For that reason it can be st ated that in this study the estimated willingness to pay represent the average valu ation of the non-market good based on peoples preference system instead on their actual choices like in the case of an experimental design with the status quo option. Thus once models 4-7 and 4-8 are estimated the rate of tradeoff between any two attributes is the ratio of their respective coefficients. The marginal value of each attribute is computed as the negative of the coefficient on that attribute (i), divided by the coefficient on the price or cost variable (FE). Then the Willingness to pay for an attribute is computed as: (4-9) FE U i U i MWTP ) ( where MWTP (i) is the marginal willi ngness to pay for changes in attribute i, i U is the marginal utility derived form changes in attribute i, and FE U is the marginal utility coefficient of the monetary attribute of the model. The full effects of respondents preferences fo r each park attribute across the sample are reflected in the MWTP values presented in Tables 4-18 and 4-19. In the animal species combinations the AS attribute had the highest positive WTP for both locations; indicating that respondents derived the most satisfa ction from an increase in this attribute. However the IS attribute shows the highest WTP in absolute in al l the location of this first combination (animal species). That means that the IS attribute presen ts the highest level of impact on the respondents utility compared to the others. Ev en more since the sign for this at tribute is negative; this large 23 Non-use value is the value that people derive from goods (especially natural resources) independent of any use (present or future) that people might make of those goods. The simple availability of the good provides some value to the individual in the sense that s/he may eventually be in a position to use the good. In other words the utility is not only derived from the direct use of the resource but also from knowing that the resource exists (Chen, 2003).

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101 negative number associated to th is attribute indicates a strong aversion of respondents to the presence of invasive plants. The fact that this negative effect is higher (in absolute value) than the positive effects of the other attributes (facilities and animal species) implies that is not sufficient an enhancement in the other attribut es (pure tradeoff effect ) to reduce the harmful impact (on the peoples utility) fr om the presence of invasive species. On the contrary it is necessary to take direct actions to control and att ack this problem. The situation is similar for the plant species combinations where the attribute th at presents the highest willingness to pay in absolute value in all the locations is the IS at tribute. This value, like in the animal species combinations, is negative and larger than the others, which reflect the serious impact of the presence of invasive species on the peoples utility when participating in recreational activities in aquatic areas. One thing that is important to highlight is the fact that in all the location/attributes combinations the MWTP for the IS attribute are similar, (with an aver age value of -$6.21), except for the case of the OBPS combination which presents the lowest value (-$5.41). This is because people give a small importance to the pres ence of plant species in OB parks; and as a consequence when people relate the main impact of invasive species over the native plants in this location a small MWTP (lower than those in the other three combinations: RLAS, OBAS and RLPS) is obtained. At this point it is necessary to analyze how these attributes influence the respondents decision toward one park over another. For that purpose the odds ratios and the likelihood of preferences of a hypothetical pa rk A over another park B (for each location/attributes combination) were estimated. The results of these analyses are provided from Table 4-20 to Table 4-35.

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102 From Tables 4-20 and 4-22 it can be examined that the probability that a person prefers a RL park with facilities in adequate conditi on is between 32.65% and 40.76% higher than the probability that this person prefers another RL pa rk but with facilities in minimal condition. Moreover the probability that a person prefers a RL park wi th facilities in excellent condition is between 75.96% and 98.13% higher than the probab ility that s/he prefers a RL park with facilities in minimal condition. The situation follows the same pattern for OB parks (Tables 4-21 and 4-23); that is, the probability that a person prefers an OB park w ith facilities in adequate condition is between 23.00% and 33.76% higher than the probability that this person prefers another OB park but with facilities in minimal condition. Furthermore the pr obability that a person prefers an OB park with facilities in excellent conditions is between 51. 29% and 78.93% higher than the probability that s/he prefers an OB park with f acilities in minimal conditions. It can also be concluded that the relative importance of th e facilities condition for the respond ents decision and preferences is higher for RL parks than for OB parks. In the specific case of the AS attribute the impact for the RL parks is given in Table 4-24. Thus it can be examined that the likelihood that a person prefers a RL park with moderate diversity of animal species is 39.10% higher than the likelihood th at this person prefers another RL park but with low diversity of animal sp ecies. In addition the probability that a person prefers a RL park with high divers ity of animal species is 93.50% higher than the pr obability that s/he prefers a RL park with low diversity of anim al species. On the other hand in the case of OB parks (Table 4-25) the probability that a person prefers an OB pa rk with moderate diversity of animal species is 37.78% higher than the probabilit y that this person prefers another OB park but with low diversity of animal species. Additiona lly the likelihood that a person prefers an OB

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103 park with high diversity of anim al species is 89.83% higher than the likelihood that s/he prefers an OB park with low diversity of animal species It can be observed that the impact of the diversity of animal species in both aquatic locatio ns is almost the same. Then we can conclude that the AS attribute has the same effect on the peoples utility derived form recreational activities in both RL and OB parks. The impact of the PS attribute on the peoples utility derived from engaging in recreational activities in aquatic areas is pr ovided in Tables 4-26 and 4-27. Hence the likelihood that a person prefers a RL park with moderate diversity of plant species is 33.92% higher than the likelihood that this person prefers another RL park but with low diversit y of plant species. In addition the likelihood that a person prefers a RL park with high diversity of plant species is 79.35% higher than the likelihood that s/he prefers a RL park with low diversity of plant species. In contrast in the case of OB parks the likelihood that a person prefers an OB pa rk with moderate diversity of plant species is 24.68% higher than the likelihood that this pers on prefers another OB park but with low diversity of plant species. In addition the probability that a pers on prefers an OB park with high diversity of plant specie s is 55.44% higher than the probabi lity that s/he prefers an OB park with low diversity of plant species. It can be observed that the impact of PS diversity in OB parks is less than the registered on RL parks. Even more the impact of PS in OB parks is significantly small compared with the other attrib utes in both locations, which reflects the low importance of this attribute (PS) for people when visiting any OB location. As it was previously determined the IS attribut e is the second most important attribute that impact on peoples utility when e ngaging in recreational activities in aquatic parks in Florida. For that reason it was found (Tables 4-28 and 4-30 ) that the probability that a person prefers a RL park with a presence of few and dispersed invasive spec ies is between 49.57% and 59.59%

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104 higher than the probability than s/he prefers a pa rk with a numerous presence of invasive species. Furthermore the probability that a person prefer s a RL park with none invasive species is between 123.73% and 154.70% highe r than the probability than s/he prefers a park with a numerous presence of invasive species. On the other hand in the case of OB parks th e preferences are expressed in the following way (Tables 4-29 and 4-31): the prob ability that a person prefers an OB park with a presence of few and dispersed invasive species is between 43.31% and 52.47% higher than the probability than s/he prefers an OB park with a numer ous presence of invasive species. Moreover the probability that a person prefers an OB park with none invasive species is between 105.37% and 132.48% higher than the probability than s/he prefers an OB park with a numerous presence of invasive species. Again the sensitivity of res pondents to the presence of invasive species is stronger for the case of the RL location. However the individual impact of this attribute (IS) on respondents preferences for both lo cations is higher than the im pact of all the positive park attributes (FA, AS and PS), which indicates an impossibility of a full tr adeoff from enhancement on these attributes. Finally the impact of the fees over the respondents utility is the most important among all the other attributes. As a consequence in RL pa rks (Tables 4-32 and 4-34) the probability that a person prefers a RL park with fees in a level of $10 is betw een 93.44% and 112.50% higher than the probability that s/he prefers a RL park with fees in $20. Additionally the probability that a person prefers a free RL park is between 274.20% and 351.57% higher than the probability that s/he prefers a park with fees in $20. These di fferences on probability are impressive compared with the values obtained for the other attributes which indicates the great importance of this attribute (FE) on peoples pref erence (as high as the FA a nd AS/PS effects combined).

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105 Furthermore in the case of OB parks (Table s 4-33 and 4-35) the pr obability that a person prefers a park with fees in a level of $10 is between 94. 40% and 94.82% higher than the probability that s/he prefers an OB park with fees in $20. Additionally the probability that a person prefers a free OB park is between 277.93% and 279.56% higher than the probability that s/he prefers an OB park with fees in $20. Also in this type of a quatic park, the FE attribute is the most important of all producing th e highest level of impact on pe oples preferences. Thus it can be concluded that peoples pref erence on RL and OB locations are driven mainly by the level of fees and second by the presence of invasive species. Ranking, Net Willingness to Pay and Likelihoo d of Preference Measures for MultiAttribute Models. In this section we proceed to evaluate how respondents w ould answer to hypothetical combinations of the attributes using the utility function estimations showed from Table 4-7 to 4-10. We will apply three different procedures that can be summarized as follows: 1) ranking based on the overall utility of each alternative; 2) estimation of the net willingness to pay of each alternative, and 3) assessme nt of the likelihood of pref erence of each alternative. The ranking evaluation of alternatives will be obtained from equation 4-7 and 4-8. The net score (S) for the jth alternative would be computed as: Animal Species Combination (4-10) A fe A is A as A faFe Is As Fa S Plant Species Combination (4-11) A fe A is A ps A faFe Is Ps Fa S where the s are the estimated coefficients obtai ned from Tables 4-7 to 4-10; and FaA, AsA, IsA and FeA are the attributes at the level of the al ternative A. This net score will be applied to the 81 feasible alternatives that correspond ing to the full fractional experiment of each

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106 location/attributes combination. Once all the 81 ne t scores have been calc ulated, the alternatives will be ordered so as to obtain an ordinal ra nking of all the feasible alternatives. For the willingness to pay approach, first it would be necessa ry to determine the alte rnative that provides a zero utility in order to use it as a baseline. Th is alternative will be determined using the net score procedure previous ly explained. Then the net wil lingness to pay can be defined as: Animal Species combination (4-12) ) ( ) ( ) ( ) (Base A Base A fe is Base A fe as Base A fe fa AFe Fe Is Is As As Fa Fa NetWTP Plant Species Combination: (4-13) ) ( ) ( ) ( ) (Base A Base A fe is Base A fe ps Base A fe fa AFe Fe Is Is Ps Ps Fa Fa NetWTP where the s are the estimated coefficients obt ained from Tables 4-7 to 4-10; FaA, AsA, IsA and FeA are the attributes at the leve l of the alternative A; and FaBase, AsBase, IsBase and FeBase are the attributes at the level of the baseline alte rnative (defined as the alternative that generates zero utility). This procedure will also be applied to the 81 feasible alternatives providing us some indication of the intensity of preferences fo r each alternative.Finally the probability that respondents prefer alternative A instead of the baseline alterna tive will be also estimated. For this purpose we will apply the following equations: Animal Species Combination (4-14) ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( .1Base A fe Base A is Base A as Base A fa Base A fe Base A is Base A as Base A faFe Fe Is Is As As Fa Fa Fe Fe Is Is As As Fa Fa Base vs Ae e P Plant Species Combination (4-15) ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( .1Base A fe Base A is Base A ps Base A fa Base A fe Base A is Base A ps Base A faFe Fe Is Is Ps Ps Fa Fa Fe Fe Is Is Ps Ps Fa Fa Base vs Ae e P

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107 where the s are the estimated coefficients obt ained from Tables 4-7 to 4-10; FaA, AsA, IsA and FeA are the attributes at the leve l of the alternative A; and FaBase, AsBase, IsBase and FeBase are the attributes at the level of the baseline alternative. The evaluations of the 81 possibl e alternatives for each locati on/attributes combination are presented from Figures 4-3 to 4-6. First, it is necessary to specify that in all the location/attributes combinations the park that wa s specified as the baselin e (BL) was that which had the following attribute mix: FA= minimal, AS/PS= low, IS= none and FE= free. This BL park offers the minimal level in their entire positive attributes (FA and AS/PS), but in turn it offers the minimal levels for their negative attrib utes (FE and IS). This produces an absolute compensation that generates an expected util ity of zero for all the locations/attributes combinations. Figures 4-3 and 4-4 show the evaluation for alte rnatives in the RL location. Then the park alternative which ranked first for the two attribut es combination (AS and PS) of the RL location was that which had the following attributes mix: FA= excellent, AS/PS= high, IS= none and FE= free. This plan can be considered as the full benefits park (FBP). Therefore among 77% and 78% of respondents would prefer this FBP to the baseline park with a WTP that is between $16.82 and $18.57. It is important to note th at between 26% and 28% of the 81 possible alternatives can be considered feasible24. From this group of feasible parks, approximately 80% of them are free, and the other 20% has a fee leve l of $10. This reflects the high impact of the fees on the utility of re spondents who provide a larg e weight to this attribute in comparison to the others. 24 An alternative is considered feasible if generates a utility greater or equal than zero.

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108 On the other hand in the case of OB parks, Fi gures 4-5 and 4.-6 show the evaluation for all the possible alternatives. Again th e highest ranked park is the FBP for both OB combinations. In this specific case among 70% and 77 % of respondents would prefer this FBP to the baseline park with a WTP that ranges between $12.86 and $18.34. The minimum of this range is lower than that in the RL case since as we previously stated people provide a small value to the presence of native plants in OB parks. Thus the valuati on for the combination OBPS is undervalued due to the effect on the peoples perception of the PS attri bute. It is important to emphasize (like in the RL case) that between 21% and 27% of the 81 possible OB alternatives can be considered feasible. However the proportion of feasible OB parks with fees e qual to zero is higher than that of RL parks, and ranges from 82% to 94%. The ot her 6% and 18% of feas ible parks have a fee level of $10. This reflects again the high importan ce that people provide to the fee attribute and how the utility and preferences of respondents are sk ewed to alternatives th at offer an adequate or maximum (minimum) level of positive (negative) attributes but at no cost. Effects of Socioeconomic and Experiential Characteristics on Preferences for Aquatic Parks Alternatives In equations 4-5 and 4-6 the econometrical sp ecification of the multi-attribute model was determined. It is important to identify respondent s weighting of different parks attributes but it is also useful to evaluate whether socioeconomic characteristics (gender, region, age, education and income), as well as experi ential features (extent of decl ared knowledge, real knowledge and benefits perceived from invasi ve species) have any effect over the attributes importance on respondents utility. For that purpose, explanatory variables that are characteri stics of the chooser can be incorporated to the conditional Logit models (e quations 4-7 and 4-8) as interaction with the attribute variables (Swallow et al. 1994). In this analysis, we have two types of dummies

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109 variables, those that represents socioeconomic charac teristics of the resp ondents (Table 4-36) and those that represent a set of variables (Table 4-37) related with the extent of experience and knowledge that respondents have regarding to in vasive species. These two types of variables come into the model interacting with the park s attributes; which can be expressed as. (4-16) 4 11 4 1) ( ) ( ) ( ) ( ] [i n j B i A i j ij i B i A i i B A ABX X X X X X Log P Logit where Xi denotes the ith attribute, i are the coefficients for the main effects of each attribute, ij are the coefficients for the interactive variables of the attributes with the socioeconomic and experien tial dummies represented by j. It is important to note that there is a probabi lity that the socioeconomic interactions create multicolinearity in the model. Therefore it was dete rmined to apply a procedure that allows us to overcome this potential problem. This method is th e principal component an alysis (PCA), which can be defined as a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables calle d as principal components. The objectives of a PCA are usua lly interrelationship modeling, score replacement, or both. In our specific case we are interest ed in a score replacement; that is, the substitution of an original set of variables (socio economic variables) with new vari ables or scores that summarize the data parsimoniously. In other words, the objec tive of this analysis is to take our nine socioeconomic variables X1, X2, X9 and find combinations of these to produce a new set of variables (indices) SC1, SC2, SCq that are uncorrelated. For this purpose we started finding for each location/attributes combination (RLA S, OBAS, RLPS and OBPS) the eigenvalues 1, 2, 9 (Tables 4-38 to 4-41) of thei r socioeconomic variables. Then the number of factors to retain is determined according to the Kaiser criterion which reco mmends to keep only the factors

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110 with eigenvalues greater than 1. Hence the co rresponding principal compone nts selected for each location/attributes combination are presented fr om Tables 4-42 to 4-45. It is important to emphasize that it is possible to determine as ma ny principal components as there are original variables. However it is advisable to choose few of them, which will be mutually uncorrelated. Thus the objective of parsimony and independence can be attained. A rotation process (Varimax) wa s also applied (Tables 4-46 to 4-49). This is a method of altering the initial factors in or der to achieve more interpretabi lity through a simpler structure. A factor structure is considered to be simple if each of the original variables relates highly to only one factor and each factor can be identified as represented what is common to a relatively small number of variables. Therefore a structure is co nsidered simple if for each factor the weights for most variables are near to zero a nd the remaining ones are relatively large. Then the factor can be considered as depicting the variation shared in common by the subset of va riables highly related (large weights) and not to the others. Finally, factor scores for each set of variable s of every attribute/lo cation combination were estimated. A factor score is a specific value of a factor calculated for a particular sampling unit (observation) and is formed as a weighted sum of the variables for that sampling unit. These factor scores are particularly us eful when one wants to perform fu rther analyses like in our case a regression analysis. Thus the factors associated with the components obtained via rotation and used for the estimation of the score variables (SCi) of each location/attribute combination are given as following:

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111(4-17) 2 1 2 1 2 1 2 1 1 5 2 1 2 1 2 1 2 1 1 4 2 1 2 1 2 1 2 1 1 3 2 1 2 1 2 1 2 1 1 2 2 1 2 1 2 1 2 1 1 1REG 0.075 REG 0.075 IN 0.009 IN 0.017 AG 0.287 AG 0.226 ED 0.013 ED 0.076 G 0.950 SC REG 0.029 REG 0.053 IN 0.009 IN 0.040 AG 0.786 AG 0.829 ED 0.010 ED 0.022 G 0.001 SC REG 0.896 REG 0.893 IN 0.004 IN 0.015 AG 0.047 AG 0.012 ED 0.009 ED 0.035 G 0.002 SC REG 0.022 REG 0.002 IN 0.935 IN 0.925 AG 0.057 AG 0.012 ED 0.009 ED 0.069 G 0.022 SC REG 5 0.04 REG 0.003 IN 0.057 IN 0.139 AG 0.089 AG 0.092 ED 0.927 ED 0.927 G 0.076 SC Factors RLAS (4-18) 2 1 2 1 2 1 1 1 4 2 1 2 1 2 1 1 1 3 2 1 2 1 2 1 1 1 2 2 1 2 1 2 1 1 1 1REG 0.006 REG 0.008 IN 0.083 IN 0.028 AG 0.570 AG 0.594 2 ED 0.080 ED 0.038 G 0.236 SC REG 0.553 REG 0.558 IN 0.028 IN 0.005 AG 0.015 AG 0.028 2 ED 0.002 ED 0.012 G 0.050 SC REG 0.017 REG 0.013 IN 0.538 IN 0.533 AG 0.011 AG 0.075 2 ED 0.007 ED 0.009 G 0.050 SC REG 0.003 REG 0.021 IN 0.059 IN 0.072 AG 0.015 AG 0.119 2 ED 0.486 ED 0.503 G 0.196 SC tors Fac OBAS (4-19) 2 1 2 1 2 1 1 1 4 2 1 2 1 2 1 1 1 3 2 1 2 1 2 1 1 1 2 2 1 2 1 2 1 1 1 1REG 0.002 REG 0.010 IN 0.011 IN 0.000 AG 0.617 AG 0.584 2 ED 0.052 ED 0.006 G 0.154 SC REG 0.012 REG 0.028 IN 0.077 IN 0.009 AG 0.017 AG 0.076 2 ED 0.514 ED 0.525 G 0.197 SC REG 0.549 REG 0.555 IN 0.012 IN 0.012 AG 0.020 AG 0.007 2 ED 0.002 ED 0.007 G 0.072 SC REG 0.000 REG 0.023 IN 0.546 IN 0.527 AG 0.022 AG 0.040 2 ED 0.041 ED 0.015 G 0.043 SC Factors RLPS (4-20) 2 1 2 1 2 1 1 1 4 2 1 2 1 2 1 1 1 3 2 1 2 1 2 1 1 1 2 2 1 2 1 2 1 1 1 1REG 4 0.00 REG 04 0.0 IN 83 0.0 IN 50 0.0 AG 547 0. AG 654 0. 2 ED 121 0. ED 43 0.0 G 4 8 0.1 SC REG 546 0. REG 548 0. IN 28 0.0 IN 16 0.0 AG 0.017 AG 20 0.0 2 ED 008 0. ED 010 0. G 027 0. SC REG 020 0. REG 004 0. IN 38 0.0 IN 68 0.0 AG 11 0.0 AG 134 0. 2 ED 516 0. ED 518 0. G 157 0. SC REG 8 0.00 REG 35 0.0 IN 526 0. IN 523 0. AG 490.0 AG 129 0. 2 ED 013 0 ED 018 0 G 045 0 SC Factors OBPS

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112 To sum up, starting from the in teraction model proposed in e quation 4-16 we replaced the socioeconomic variables by five scores variables (SCi) for the RLAS case and by four scores variables for the other cases (RLPS, OBAS, OBPS). This will allow us to obtain more parsimonious models that are not affected by multicolinearity. This will reduce the likelihood of wrong interpretations and conclusions since th e multicolinearity problem could influence erroneously on the significance and va lue of the estimated parameters. On the other hand in the case of experiential variables it is important to specify that the variable K represents the declared knowledge or the knowledge that people state that they have. As we found in chapter two there is a discre pancy between this decl ared knowledge and the real knowledge that people have. Both are not equal and only the real knowledge has a real effect in peoples satisfaction when confronti ng to the presence of invasive plants. In this specific case we have a proxy variable that mimics the effect of the rea l knowledge this is the AF variable which represents a ny declaration of respondents about if s/he has been affected (in any form) by invasive plant species when visiting aquatic parks. In Tables 2-6 and 2-7 we found a strong association among the real (or observed) knowledge of a respondent and his/her satisfact ion expressed by their complaints of being affected somehow for these species. Then as a proxy of the real level of knowledge of respondents we will use the variable AF for the interaction models. The maximum likelihood estimates for the pr oposed logistic regr ession model for each location/attribute combinations ar e given from Tables 4-50 to 453. We also presented a global test for each model. In this test the null hypothesi s that all the estimators of these models are not significant ( = 0) was contrasted. Thus from Table 454 it can be concluded that all the four

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113 interactions models are statistical ly significant at 5% and 1% level of confidence; that is, the null hypothesis is rejected for all the models. In the case of RL parks (Tables 4-50 and 4-52) the interaction effect s of the socioeconomic variables (repres ented by the SCi factors) with the IS attribute are not statistically significant. As a consequence the socioeconomic ch aracteristics do not have any st atistical signif icant effect on the negative weight that people perc eived from IS attribute when at tending RL parks. In the case of the experiential variables it was found that AF interactions are sign ificant at 0.05 and 0.01 level of confidence; and BE at 0.05 level of confidence only. C onsequently it can be concluded that the extent of peoples real knowledge (AF) and the benefits (BE) that they perceived from these species do have an important effect in thei r preferences and utility for RL parks. However the effect of BE is weaker for the PS combina tion, which indicates a less importance on the perception of perceived benefits fr om invasive species when their impact is related to the natural scenario derived from the presence of native plants in RL parks. It was also concluded that a person who has at least a mode rate level of knowledge about invasive species is between 27.71% and 51.56% more likely to prefer parks with less presence of invasive species than those who have no know ledge about these species. Furthermore people who perceived a benefit from invasive plants are between 13.40% and 35.34% less likely to prefer parks with less presence of these species than those who does not perceived any benefit. In addition the effect of the st ated or hypothetical knowledge (K) wa s not statistically significant. This reinforces our previous conclusion that the hypothetical knowledge (or the knowledge that people state that they have) does not have a ny statistical signifi cant impact on peoples discernment of the effect of inva sive plants on their utility. To sum up in this location (RL) we found the following results: all the socioeconomic characteristics of the respondents have not any

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114 statistical significant impact on th e negative weights that they provi de to the effect of the IS on their utility. Although both the r eal knowledge (AF) and benefits (BE) perceived by the people have an effect on the perception about the negative effect of IS. In addition the K variable does not have any statistical significant effect on respo ndents utility when we talked of RL parks. This is consistent with the resu lts obtained in chapter two, in which it was found that the stated or hypothetical knowledge does not have a statistical significan t impact on peoples perception of the invasive species problem. On the other hand from the results presented in Tables 4-51 and 4-53 it can be concluded that the interaction of the socioeconomic va riables (represented by the score variables SCi) with the IS attribute are not statistical ly significant for any OB parks m odels. As a consequence it can be concluded that peoples soci oeconomic characteristics do not have any statistical significant impact on the negative weight (p erception) that people provide to the IS attribute when attending OB parks. On the contrary if the impact of the experientia l variables are examined, it can be found that both AF and BE variables are significant at 0.05 and 0.01 level, but the K variable is not. Therefore the extent of peoples real knowledge (AF) and the benef its (BE) that they perceived from these species do have an important effect in their preferences and utility. Thus a person who has at least a moderate level of knowledge about invasive species is between 25.62% and 34.74% more likely to prefer parks with less pres ence of invasive specie s than those who have no knowledge about these species. Moreover people who perceived a benefit from these plants are between 12.97% and 31.57% less likely to prefer parks with less presence of these species than those who does not perceived any benefit. In addition the st ated or hypothetical knowledge (K) does not have any influence on respondents va luation of the impact of invasive species on

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115 their utility, which confirms th e findings obtained in chapter tw o about the irrelevance of the stated knowledge. Another effect derived from the inclusion of socioeconomic variables in the utility model was a discrepancy in the MWTP related to the IS attribute for different gr oup of people. In other words, people who have at least a moderate le vel of knowledge about in vasive plant species show larger negative MWTP for the IS attribut e than people who have little or no knowledge. This is because the former are more affected on th eir utility by the presence of these plants due to their enhanced discernment. In other words in Tables 4-18 and 4-19 it wa s determined that the average MWTP for the IS attribute in RL parks range from -$6.10 to $6.20. However if we consider a difference in the level of knowledge (about these plants) among respondents, we found profound divergences among their MWTP as well as over their perception of th e impact of invasive plants in their utility. Thus for people who have at least a moderate level of knowledge about invasive species their MWTP derived from the presence of these pl ants in RL parks range from -$7.08 to -$10.90. This impact in absolute value is higher than th e average MWTP (derived from the IS attribute), which was estimated for all the responde nts without categorizing them by knowledge background. Moreover for people who lack this type of knowledge, their MWTP for the IS attribute range from -$3.89 to -$5.06; less than the average obtained from the initial utility functions. This reinforces our conclusion about the influence of the knowledge on the perception of the impact of invasive plants on the people utility, which it is translated in a larger negative MWTP and as a consequence a higher disposition to avoid this problem. The situation is similar for the OB location in which the average MWTP derived from the presence of invasive plants was estimated in Tables 4-18 and 4-19 and ranged from -$5.41 to

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116 -$6.32. These values were obtained without considering the differences in the level of knowledge about invasive species. Thus if we ta ke into account the difference of knowledge among respondents we found that th e MWTP derived from the IS attribute for people who have at least a moderate leve l of knowledge range from -$7.94 to -$9.95. This range is larger than the average range obtained from the or iginal utility function (without the inclusion of socioeconomic and experiential variables). On the contrary an alyzing the group of people with limited or no knowledge about invasive species, it was found that their MWTP for the IS attribute range from $4.72 to -$5.71. These results confirm our conclusi on that asserts that people who have at least a moderate level of knowledge about invasive species are more able to discern the impact of these plants on their utility and as a consequence they present a larger (negative) willingness to pay than people who do not have this extent of knowledge. In ot her words this group of well-informed people is more sensitive to the presence of invasive plan ts than the rest; therefore any action tending to eradicate these plants it is going to be more valued by this group of knowledgeable people. Estimation of Annual Marginal Willingness to Pay (MWTP) In this section we will estimate the annual willingness to pay or benefits derived from a reduction in the presence of invasi ve species in Floridas aquatic areas. For that purpose, firstly, we will proceed to model -through an ordered prob itthe frequency of vi sit to two types of aquatic parks (OB and RL) in Florida. We w ill apply the ordered response model of Aitchison and Silvey (1957). In this model, the dependent variable represents orde red or ranked categories that characterize peoples frequency of visit to aquatic parks in Fl orida. The seven-point Likert scale (in descendent order) used in this analysis is the following: Daily; Weekly;

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117 Monthly; Once every 2 to 3 months; Once every 4 to 6 months; Once every 7 to 12 months Not at all The data for this analysis were obtained through the following question in the survey: How frequently have you partic ipated in nature related outdoor activities at each of the following locations during the past 12 months?. The applied model is defined as follow: the dependent variable model yi that possesses more than two levels (in our case, seven levels) represents the frequency of visit of respondents to aquatic parks and will depe nd linearly on the explan atory variables (in our case demographic variables): (4-21) i i i x y where xi and i are independent and identically distri buted random variables. The observed yi is determined from yi using the rule: (4-22) i 6 6 i 5 5 i 4 4 i 3 3 i 2 2 i 1 1 iy 7 y 6 y 5 y 4 y 3 y 2 y 1 if if if if if if if yi It is worth noting that the actual values c hosen to represent the categories in y are completely arbitrary. All the ordered specificati on required is for ordering to be preserved so that yi < yj implies that yi < yj. It follows that the probabilit ies of observing each value of y are given by system 4-22.

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118 (4-23) ) ( 1 ) 7 Pr( ) ( ) ( ) 6 Pr( ) ( ) ( ) 5 Pr( ) ( ) ( ) 4 Pr( ) ( ) ( ) 3 Pr( ) ( ) ( ) 2 Pr( ) ( ), 1 Pr( ) (' 6 5 6 4 5 3 4 2 3 1 2 1 i i i i i i i i i i i i i i i i i i i i i i i i i i ix F x y x F x F x y x F x F x y x F x F x y x F x F x y x F x F x y x F x y y P where F is the cumulative distribution function of The threshold values are estimated along with the coefficients by maximizing the log likelihood function: (4-24) M 0 j i i i N 1 ij) (y ) x j Pr(y log )] ( ln[ L Where is an indicator function that takes the valu e 1 if the argument is true and 0 if the argument is false. The explanatory variables that will be used in the model are presented in Table 4-36 with the only difference that the e ducation category has b een divided in three dummies (high school or less, associate or some college courses, and bachelors degree) instead of the two that originally was separated. The samp le sizes of each of the two locations are shown in Table 4-55. The maximum likelihood estimates for the ordered probit models proposed for the two aquatic parks are given in Table 4-56. It is n ecessary to emphasize that for this estimation a weighting procedure was also applied. It can be observed in Table 457 that the models showed larg e chi-square statistics (and small p-values), thus the null hypot hesis that all slope coefficients (except the c onstant) are zero can be rejected in both models. As a consequenc e, it can be stated that the OB and RL models are statistical significant; that is, that all their estimator s are different from zero.

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119 Using the ordered probit estimators from Tabl e 4-56, we were able to calculate the probability of each score for the re spondents (Likert scale). If we r ecall that the scores represent different frequencies of visit we can build a probability distribu tion of this variable for each location. The results are showed in Table 4-58 and Figure 4-7. From Table 4-58 it can be determined that, on av erage, respondents vis it OB parks thirteen times per year (12.97 times); and for the case of RL parks respondents go twelve times (11.59 times) per year. That means a frequency of vis it of at least once per month for both types of locations. These high frequencies of visit reflect the importance of these locations for the welfare of the Floridas population since they seem to be e ssential components of the leisure time of Floridians and in turn of their quality of life. At this point the results obtained on Tabl e 4-58 can be used to estimate the annual willingness to pay for respondents. This measure will allow us to determine a more accurate measure of the MWTP value derived from the invasive species problem. The reason is because this value uses both the average MWTP per visit and the peoples frequency of visit as a weight measure of the importance of each location (OB a nd RL parks) for respondents. For example we can see in Figure 4-7 that the freque ncy of visit is larger for OB parks than for RL parks; this is a proxy of the importance that people provide to the former location compared to the latter. In Table 4-59 we can note that the average MWTP for the IS attribute is higher in absolute value for the OB location than for the RL parks. In other words a signi ficant reduction in the presence of invasive species in OB parks ge nerates an annual increment of the populations surplus in $76.47 and in the case of RL parks the impact is lower; that is, $71.26 approximately. These measures gather both the effect of the impact of invasive plants in respondents utility and the preferences for the type of park reflected in their freque ncy of visits. Hence it can be

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120 observed that the effect of the preferences for th e type of park which is larger for OB parks results in this location presenti ng the highest average MWTP for th e IS attribute per year. This indicates us that welfare of th e Floridas population derived from leisure activities are skewed to this type of location. In addition if we take into account the leve l of knowledge of res pondents about invasive plants the results differ from the average case. He nce it can be verified that the MWTP derived from a significant reduction in the presence of invasive plants for people who have a least a moderate level of knowledge presents an increm ent of approximately $36 per year comparing to the average. In other words, for OB parks a significant reduction of the presence of invasive plants generates an increment of the surplus fo r the well-informed people in $115.95 per year and in the case of RL parks the impact for th e same group of people is about $104.19 per year. In contrast in the case of the people who have limited or no knowledge about invasive species the impact of the presence of these plan ts on their utility is smaller in $14.12 per year comparing to the average case. In other word s for OB parks a significant reduction of the presence of invasive plants gene rates an increment of the surplu s of the people (with little or no knowledge) in only $67.63 per year a nd in the case of RL parks th e impact for the same group of people is $51.86 per year. Extrapolating these results for the Floridas adult population it can be inferred that the potential average impact on the people utility of th e presence of invasive plants in aquatic areas is substantial. This impact is estimated in an amount25 (per year) of -$1,065,516,700 for OB parks and -$992,595,790 for RL parks approximately However this potential impact would be 25 For this estimation the 2006 projection of the Floridas adult (older than 18) population provided in the 2000 Census is applied.

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121 even higher if the population will be well-informed. Therefore the impact would be in an amount (per year) of -$1,615,092,363 for OB parks and -$1,451,284,807 for RL parks approximately. Then any action to reduce the pr esence of invasive plants in these natural areas in Florida is justified because of the sizeab le potential impact of these speci es on the welfare and quality of life of Floridians. Moreover it is necessary to emphasize that educational campaigns about these problems would produce better results since will increase the WTP of the population due to an enhancement of their awareness and discernm ent about the benefits of any control and management program of these plants. Therefore this study has demonstrated that an important and necessary action is not only to develop control and management programs but al so to design and to implement educational programs that facilitate the publics partic ipation and their willingness to collaborate26 through an enhancement of their awareness of the problem and the benefits of these programs. This can be proved through the higher level of aversion to the presence of invasive plants (reflected in larger negative WTP) for informed respondents co mpared to those who do not have an adequate level of knowledge. Conclusions In this chapter we determined the utility function of respondents when participating in nature-based recreational activities in OB and RL parks. We found in these functions that the presence of invasive plants has a negative impact on the peoples utility in a magnitude superior to the effect of the positive attributes such as: facilities, animal specie s and plant species. This large negative number associated to the IS a ttribute indicates; first, a strong aversion of respondents to the presence of invasive plants and second, that an enhancement of the other 26 That can be raised through indirect taxes as a comp ensation measure for providing aquatic areas free of these noxious plants.

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122 positive attributes is not sufficien t in order to compensate for a high presence of these plants. On the contrary, it is necessary to attack directly this problem through effective management and control programs that must be accompanied by educational progr ams as well. In addition the most important attribute that im pacts peoples utility when visiting these aquatic parks is the level of fees. In other words, people are very sensitive to the fee levels hen ce they not only demand parks with a small presence of i nvasive species but also with low fees. Furthermore, using rankings we corroborated the conclusion that people provide a high importance to the fee attribute. Hence, the utility and preferences of res pondents are skewed to alternatives that offer an adequate or maxi mum (minimum) level of positive (negative) attributes but at no or low cost. An analysis with interaction terms was al so conducted, using both demographics and experiential variables. For OB and RL parks no so cioeconomic variable (gender, age, education, income and region) were statisti cally significant. Hence, people s socioeconomic characteristics do not have any impact on the ne gative weight that people provi de to the IS attribute when attending OB and RL parks. In the case of the experiential variables the findings were also the same for both locations. That is, both the extent of peoples real know ledge (AF) and the bene fits (BE) that they perceived from invasive plants do have an important effect in peoples preferences and utility but the stated knowledge (K) does not. These results support two ideas: firs t, the necessity of educational programs as an effective approach to attack the invasive plant species problem in aquatic areas in Florida; and second, that the stated knowledge (or the knowledge that people state that they have) does not have any statis tical significant impact on peoples discernment of the effect of invasive pl ants on his/her utility.

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123 Finally the importance of control and manage ment program along Florida is justified by the potential impact of the presence of these sp ecies on the populations welfare, which reaches to -$1,065,516,700 for OB parks and -$992,595,790 for RL parks. However it is recommended that this type of programs should be implemen ted along with educationa l projects that would increase the publics awareness a nd knowledge of invasive species. This can be verified in the larger impact of this problem if the population w ould be adequate informed. This impact reaches to -$1,615,092,363 for OB parks and -$1,451,284,807 for RL parks; that is, approximately $500 millions additionally in each location. Therefore any type of strategy of control and management implemented along with educational programs w ould increase peoples willingness to pay to eradicate the invasive plant species problem27 and in turn will increase the publics collaboration facilitating the eradication of these plants in Florida. 27 Since a person with at least a moderate level of knowledg e is more able to fully discern what the real impact of invasive plants is in his/her utility

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124 Table 4-1 Summary of attribut es and attributes levels Attributes Levels a) Park facilities condition 1. Minimal 2. Adequate 3. Excellent b) Diversity of Plant Species 1. Low 2. Moderate 3. High c) Diversity of Animal Species 1. Low 2. Moderate 3. High d) Presence of Invasive plant species 1. None 2. Few and dispersed 3. Numerous and dense e) Fees 1. Free 2. $10 3. $20 Table 4-2 Pair-wise choice sets for th e plant species combination survey Attributes Pair-wise Choice Park Facilities Condition Native Plant Diversity Presence of Invasive plant species Fees A Minimal Moderate Few and dispersed $10 1 B Adequate High Numerous and dense $20 A Minimal Low None Free 2 B Excellent High Few and dispersed $20 A Excellent High None $20 3 B Adequate Low Numerous and dense Free A Minimal High Few and dispersed $10 4 B Excellent Moderate None $20 A Adequate Moderate None $10 5 B Excellent High Numerous and dense Free A Excellent Moderate Few and dispersed $10 6 B Minimal High Numerous and dense Free A Excellent High Numerous and dense $20 7 B Minimal Low None $10

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125 Table 4-3 Pair-wise choice sets for th e animal species combination survey Attributes Pair-wise Choice Park Facilities Condition Native Plant Diversity Presence of Invasive plant species Fees A Minimal Moderate Few and dispersed $10 1 B Adequate High Numerous and dense $20 A Minimal Low None Free 2 B Excellent High Few and dispersed $20 A Excellent High None $20 3 B Adequate Low Numerous and dense Free A Minimal High Few and dispersed $10 4 B Excellent Moderate None $20 A Adequate Moderate None $10 5 B Excellent High Numerous and dense Free A Excellent Moderate Few and dispersed $10 6 B Minimal High Numerous and dense Free A Excellent High Numerous and dense $20 7 B Minimal Low None $10 Table 4-4 Total valid res ponse rates for each survey RLAS OBAS RLPS OBPS Total Valid Responses 681890618 911 Total Observations for the Models* 476762304326 6377 *This is equal to the total of valid responses multiplied by seven that is the number of pair-wise choices in each survey

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126 Table 4-5 Socioeconomic characteristics for the su rvey sample and comparisons with the Florida population. Categories RLAS RLPS OBAS OBPS Florida Urban 25.8%30.2%27.1%31.3% 47.0% Suburban 58.0%53.7%57.5%54.4% 44.0% Rural 16.3%16.2%15.4%14.2% 9.0% Male 36.2%36.7%34.6%36.5% 48.8% Female 63.8%63.3%65.4%63.5% 51.2% 18 25 years 1.9%1.5%2.4%1.1% 7.8% 26 35 years 8.7%9.3%8.5%9.9% 16.9% 36 45 years 20.5%22.3%21.6%19.4% 20.1% 46 55 years 24.6%23.8%23.7%25.5% 16.8% 56 65 years 28.8%25.4%27.1%26.5% 12.6% More than 65 years 15.5%17.6%16.8%17.7% 25.9% High School or less 36.6%40.3%33.8%36.6% 48.9% Associate or some college 25.9%25.1%26.3%25.7% 28.8% Bachelor's degree 24.6%19.1%24.7%21.5% 14.3% Advanced degree beyond bachelor's 12.9%15.5%15.2%16.2% 8.0% Less than $14,999 4.8%5.1%3.9%5.0% 16.3% $15,000 $34,999 20.9%23.0%21.3%21.3% 28.7% $35,000 $59,999 29.1%28.5%28.1%32.7% 24.8% $60,000 $74,999 16.7%15.7%17.3%14.5% 11.1% $75,000 $99,999 15.0%14.5%15.0%13.5% 8.7% $100,000 $149,999 9.7%10.4%10.8%9.0% 6.3% More than $150,000 3.7%2.8%3.6%4.0% 4.1% Source: U.S. Census Bureau 2000 Table 4-6 Predictors for multi-attribute utility model (MAUM) Variable Description Facilities (FA) Variable that indicates the condition of facilities in a park. This variable takes value of: 0 when minimal, 1 when adequate and 2 when excellent. Animal Species (AS) Variable that denotes the level of diversity of wildlife in a park. This variable takes value of: 0 when low, 1 when moderate and 2 when high. Plant Species (PS) Variable that denotes the level of diversity of native plant species in a park. This variable takes value of: 0 when low, 1 when moderate and 2 when high. Invasive plant species (IS) Variable that denotes the degree of pr esence of invasive plant species in a park. This variable takes value of: 0 when none, 1 when few and dispersed and 2 when numerous and dense. Fees (FE) Variable that denotes the degree of pr esence of invasive plant species in a park. This variable takes value of: 0 when none, 1 when few and dispersed and 2 when numerous and dense.

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127 Table 4-7 Coefficient estimates for the river and lake/animal species combination (RLAS) Var. Estimates Std. Err. z P>|z| 95% C.I FA 0.283 0.03897.2600.0000.206 0.359 AS 0.330 0.03878.5400.0000.254 0.406 IS -0.403 0.0394-10.2200.000-0.480 -0.325 FE -0.066 0.0064-10.2500.000-0.079 -0.053 Table 4-8 Coefficient estimates for the ocean and beach/animal species combination (OBAS) Var. Estimates Std. Err. z P>|z| 95% C.I FA 0.291 0.03388.6100.0000.225 0.357 AS 0.320 0.03419.3800.0000.254 0.387 IS -0.422 0.0345-12.2400.000-0.489 -0.354 FE -0.067 0.0056-12.0000.000-0.078 -0.056 Table 4-9 Coefficient estimates for the river and lake/plant species combination (RLPS) Var. Estimates Std. Err. z P>|z| 95% C.I FA 0.342 0.04108.3300.0000.261 0.422 PS 0.292 0.04067.1900.0000.212 0.372 IS -0.467 0.0412-11.3600.000-0.548 -0.387 FE -0.075 0.0068-11.1500.000-0.089 -0.062 Table 4-10 Coefficient estimates for the ocean and beach/animal species combination (OBPS) Var. Estimates Std. Err. z P>|z| 95% C.I FA 0.207 0.03316.2600.0000.142 0.272 PS 0.221 0.03276.7400.0000.156 0.285 IS -0.360 0.0335-10.7400.000-0.425 -0.294 FE -0.066 0.0055-12.0600.000-0.077 -0.056 Table 4-11 Global test of significance of estimators Global Null Hypothesis Beta = 0 MODEL df 2 p-value RLAS 4122.610.0000 OBAS 4176.890.0000 RLPS 4166.120.0000 OBPS 4255.720.0000 Table 4-12 Standardized estimators for each location/attributes combination model Location/Attributes Combinations Attribute RLAS OBAS RLPS OBPS FA 0.929 0.954 1.124 0.680 AS 0.999 0.972 NA NA PS NA NA 0.885 0.669 IS -1.129 -1.178 -1.311 -1.007 FE -1.762 -1.819 -2.046 -1.808

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128 Table 4-13 Wald Test for facilities and plant/anim al species attributes for each location/attributes combination model RLAS OBAS RLPS OBPS Wald (FA vs. AS/PS) 1.390.72 1.400.16 P-value 0.240.40 0.240.69 Table 4-14 Wald Test for weight eq uivalence of facilities and invasive plant species attributes for each location/attributes combination model RLAS OBAS RLPS OBPS Wald (FA vs. IS) 92.56 131.62116.8387.15 P-value 0.00 0.000.000.00 Table 4-15 Wald Test for weight equivalence of animal/plant species and invasive plant species attributes for each location/attributes combination model RLAS OBAS RLPS OBPS Wald (AS/PS vs. IS) 99.82131.92 97.7687.07 P-value 0.000.00 0.000.00 Table 4-16 Wald Test for weight equivalence of fees and invasive plant species attributes for each location/attributes combination model RLAS OBAS RLPS OBPS Wald (FE vs. IS) 95.51138.33 118.95100.69 P-value 0.000.00 0.000.00 Table 4-17 Ranking on the relative importance of each attribute on the respondents utility for each location/attributes combination Ranking RLAS OBAS RLPS OBPS 1st Place FE FE FE FE 2nd Place IS IS IS IS 3rd Place FA and AS* FA and AS* FA and PS* FA and PS* It was proved in table 4.13 that the importance of these two attributes was statistically the same

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129 Table 4-18 Marginal willingness to pay (MWTP) for each attribute in models with animal species attribute combinations. River and Lake Ocean and Beach Attributes MWTP Lower Limit Upper Limit MWTP Lower Limit Upper Limit FA $ 4.282 $ 2.625 $ 6.723 $ 4.362 $ 2.896 $ 6.400 AS $ 5.002 $ 3.235 $ 7.604 $ 4.805 $ 3.268 $ 6.943 IS $ (6.102) $ (6.105) $ (6.098) $ (6.325) $ (6.350) $ (6.307) Table 4-19 Marginal willingness to pay (MWTP) for each attribute in models with plant species attribute combinations. River and Lake Ocean and Beach Attributes MWTP Lower Limit Upper Limit MWTP Lower Limit Upper Limit FA $ 4.535 $ 2.950 $ 6.797 $ 3.114 $ 1.840 $ 4.884 PS $ 3.874 $ 2.397 $ 5.983 $ 3.318 $ 2.024 $ 5.114 IS $ (6.201) $ (6.226) $ (6.185) $ (5.413) $ (5.506) $ (5.283) Table 4-20 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination RLAS. Park A MinimalAdequate Excellent Minimal 1.0000 0.00% 1.3265 +32.65% 1.7596 +75.96% Adequate 0.7539 -24.61% 1.0000 0.00% 1.3265 +32.65%Park B Excellent 0.5683 -43.17% 0.7539 -24.61% 1.0000 0.00% Table 4-21 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination OBAS. Park A MinimalAdequate Excellent Minimal 1.0000 0.00% 1.3376 +33.76% 1.7893 +78.93% Adequate 0.7476 -25.24% 1.0000 0.00% 1.3376 +33.76%Park B Excellent 0.5589 -44.11% 0.7476 -25.24% 1.0000 0.00%

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130 Table 4-22 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination RLPS. Park A MinimalAdequate Excellent Minimal 1.0000 0.00% 1.4076 +40.76% 1.9813 +98.13% Adequate 0.7104 -28.96% 1.0000 0.00% 1.4076 +40.76%Park B Excellent 0.5047 -49.53% 0.7104 -28.96% 1.0000 0.00% Table 4-23 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of facilities condition for the combination OBPS. Park A MinimalAdequate Excellent Minimal 1.0000 0.00% 1.2300 +23.00% 1.5129 +51.29% Adequate 0.8130 -18.70% 1.0000 0.00% 1.2300 +23.00%Park B Excellent 0.6610 -33.90% 0.8130 -18.70% 1.0000 0.00% Table 4-24 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of diversity of anim al species for the combination RLAS. Park A Low Moderate High Low 1.0000 0.00% 1.3910 +39.10% 1.9350 +93.50% Moderate 0.7189 -28.11% 1.0000 0.00% 1.3910 +39.10%Park B High 0.5168 -48.32% 0.7189 -28.11% 1.0000 0.00% Table 4-25 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of diversity of anim al species for the combination OBAS. Park A Low Moderate High Low 1.0000 0.00% 1.3778 +37.78% 1.8983 +89.83% Moderate 0.7258 -27.42% 1.0000 0.00% 1.3778 +37.78%Park B High 0.5268 -47.32% 0.7258 -27.42% 1.0000 0.00%

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131 Table 4-26 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of diversity of pl ant species for the combination RLPS. Park A Low Moderate High Low 1.0000 0.00% 1.3392 +33.92% 1.7935 +79.35% Moderate 0.7467 -25.33% 1.0000 0.00% 1.3392 +33.92%Park B High 0.5576 -44.24% 0.7467 -25.33% 1.0000 0.00% Table 4-27 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of diversity of pl ant species for the combination OBPS. Park A Low Moderate High Low 1.0000 0.00% 1.2468 +24.68% 1.5544 +55.44% Moderate 0.8021 -19.79% 1.0000 0.00% 1.2468 +24.68%Park B High 0.6433 -35.67% 0.8021 -19.79% 1.0000 0.00% Table 4-28 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination RLAS. Park A None Few Numerous None 1.0000 0.00% 0.6686 -33.14% 0.4470 -55.30% Few 1.4957 +49.57% 1.0000 0.00% 0.6686 -33.14%Park B Numerous 2.2373 +123.73% 1.4957 +49.57% 1.0000 0.00% Table 4-29 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination OBAS. Park A None Few Numerous None 1.0000 0.00% 0.6558 -34.42% 0.4301 -56.99% Few 1.5247 +52.47% 1.0000 0.00% 0.6558 -34.42%Park B Numerous 2.3248 +132.48% 1.5247 +52.47% 1.0000 0.00%

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132 Table 4-30 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination RLPS. Park A None Few Numerous None 1.0000 0.00% 0.6266 -37.34% 0.3926 -60.74% Few 1.5959 +59.59% 1.0000 0.00% 0.6266 -37.34%Park B Numerous 2.5470 +154.70% 1.5959 +59.59% 1.0000 0.00% Table 4-31 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of presence of invasive plant species for the combination OBPS. Park A None Few Numerous None 1.0000 0.00% 0.6978 -30.22% 0.4869 -51.31% Few 1.4331 +43.31% 1.0000 0.00% 0.6978 -30.22%Park B Numerous 2.0537 +105.37% 1.4331 +43.31% 1.0000 0.00% Table 4-32 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination RLAS. Park A Free $10 $20 Free 1.0000 0.00% 0.5169 -48.31% 0.2672 -73.28% $10 1.9344 +93.44% 1.0000 0.00% 0.5169 -48.31%Park B $20 3.7420 +274.20% 1.9344 +93.44% 1.0000 0.00% Table 4-33 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination OBAS. Park A Free $10 $20 Free 1.0000 0.00% 0.5133 -48.67% 0.2635 -73.65% $10 1.9482 +94.82% 1.0000 0.00% 0.5133 -48.67%Park B $20 3.7956 +279.56% 1.9482 +94.82% 1.0000 0.00%

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133 Table 4-34 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of fees for the combination RLPS. Park A Free $10 $20 Free 1.0000 0.00% 0.4706 -52.94% 0.2214 -77.86% $10 2.1250 +112.50% 1.0000 0.00% 0.4706 -52.94%Park B $20 4.5157 +351.57% 2.1250 +112.50% 1.0000 0.00% Table 4-35 Odds ratio and likeli hood of preference of Park A (row) over Park B (columns) given different levels of fees species for the combination OBPS. Park A Free $10 $20 Free 1.0000 0.00% 0.5144 -48.56% 0.2646 -73.54% $10 1.9440 +94.40% 1.0000 0.00% 0.5144 -48.56%Park B $20 3.7793 +277.93% 1.9440 +94.40% 1.0000 0.00% Table 4-36 Socioeconomic variables for multi-attr ibute utility model (MAUM) with interactions Variable Definition REG Dummy variables that indicate the region in which the respondents residence is located. This category is divided in two dummies: reg1 = people who live in central Florida and reg2 = people who live in south Florida. G Dummy variable that indicates the respondents gender. The variable g1 represents male respondents. ED Dummy variables that represent respondents level of education. This category is divided in three dummies: ed1 = people with a level of education between high school (or less) and either an associate degree or some college courses and ed2= people who have a bachelors degree. AG Dummy variables that represent different age ranges for respondents. It is divided in two dummies: ag1 = people from 18 to 34 years old and ag2 = people from 35 to 54 years old. IN Dummy variables that represent different ranges of income for respondents. It is divided in two dum mies in1 = people with income that ranges from less than $14,999 to $34,999 (low); and in2= people with income that ranges from $35,000 to $99,999 (intermediate)

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134 Table 4-37 Experiential variables for multi-attri bute utility model (MAUM ) with interactions Variable Definition K Dummy variable that represents if respondents have at least a moderate level of knowledge about invasive species (K=1). AF Dummy variable that represents if respondents have been affected by invasive plants on either their enjoyment of outdoor recreation activities or their frequency of visit to aquatic parks (AF = 1). BE Dummy variable that represents if respondents perceived any benefit from the presence of invasive plants in aquatic parks in Florida (B=1). Table 4-38 Eigenvalues for the socioeconomics vari able for the animal sp ecies/river and lake combination (RLAS) Factor Eigenvalue Proportion Cumulative 1 1.94400.21600.2160 2 1.65170.18350.3995 3 1.56480.17390.5734 4 1.28230.14250.7159 5 1.03020.11450.8303 6 0.64960.07220.9025 7 0.38850.04320.9457 8 0.30120.03350.9791 9 0.18780.02091.0000 Table 4-39 Eigenvalues for the socioeconomics va riable for the animal species/ocean and beach combination (OBAS) Factor Eigenvalue Proportion Cumulative 1 2.03870.22650.2265 2 1.83800.20420.4307 3 1.54020.17110.6019 4 1.21900.13540.7373 5 0.93620.10400.8413 6 0.57330.06370.9050 7 0.37120.04120.9463 8 0.30770.03420.9805 9 0.17570.01951.0000 Table 4-40 Eigenvalues for the socioeconomics va riable for the plant sp ecies/river and lake combination (RLPS) Factor Eigenvalue Proportion Cumulative 1 2.00780.22310.2231 2 1.59890.17770.4007 3 1.52870.16990.5706 4 1.31500.14610.7167 5 0.99090.11010.8268 6 0.62680.06960.8965 7 0.37690.04190.9383 8 0.35450.03940.9777 9 0.20050.02231.0000

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135 Table 4-41 Eigenvalues for the socioeconomics va riable for the plant species/ocean and beach combination (OBPS) Factor Eigenvalue Proportion Cumulative 1 2.12990.23670.2367 2 1.84810.20530.4420 3 1.55140.17240.6144 4 1.12360.12480.7392 5 0.96830.10760.8468 6 0.54890.06100.9078 7 0.32800.03640.9442 8 0.30860.03430.9785 9 0.19320.02151.0000 Table 4-42 Factor loadings for the socioeconomics variable for the animal species/river and lake combination (RLAS) Variables 1 2 3 4 5 Uniqueness g1 0.1843 -0.08220.0960 0.11080.91960.0921 ed1 -0.7306 0.3751-0.4356 0.05250.06210.1291 ed2 0.6817 -0.38740.4709 -0.1015-0.11770.1393 ag1 -0.0216 -0.26550.0450 0.7477-0.33940.2527 ag2 0.1016 0.27410.0483 -0.7666-0.19680.2858 in1 -0.6945 -0.56070.2241 -0.16970.03540.1230 in2 0.5537 0.6468-0.3203 0.2220-0.01940.1227 reg1 -0.1995 0.49270.7132 0.09390.07750.1940 reg2 0.2684 -0.4827-0.6916 -0.15270.07300.1879 Table 4-43 Factor loadings for the socioeconomic s variable for the animal species/ocean and beach combination (OBAS) Variables 1 2 3 4 Uniqueness g1 -0.4189-0.18190.1743 0.22620.7099 ed1 0.63270.5551-0.3312 0.17780.1502 ed2 -0.5907-0.53480.3068 -0.22640.2197 ag1 -0.29720.1220-0.1620 0.70680.3709 ag2 0.5592-0.10950.0639 -0.60330.3072 in1 -0.52550.6623-0.3162 -0.27630.1089 in2 0.6596-0.49450.2604 0.37660.1107 reg1 0.02080.49380.7537 0.02710.1870 reg2 -0.0358-0.5069-0.7347 -0.04930.1995

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136 Table 4-44 Factor loadings for the socioeconomics variable for the plant species/river and lake combination (RLPS) Variables 1 2 3 4 Uniqueness g1 0.18240.16070.3247 0.10390.8247 ed1 -0.6788-0.1432-0.5496 0.20490.1747 ed2 0.62600.11890.5426 -0.26370.2300 ag1 0.01620.05700.1714 0.76030.3891 ag2 -0.0743-0.0208-0.3453 -0.75440.3057 in1 -0.7330-0.21020.5293 -0.11750.1246 in2 0.64360.1949-0.6306 0.16720.1223 reg1 -0.24300.86630.0196 -0.05200.1875 reg2 0.3251-0.8380-0.0057 0.03250.1910 Table 4-45 Factor loadings for the socioeconomics variable for the plant species/ocean and beach combination (OBPS) Variables 1 2 3 4 Uniqueness g1 -0.45930.09760.1027 0.09650.7597 ed1 0.6099-0.5167-0.3736 0.25760.1551 ed2 -0.53200.50500.3662 -0.33490.2157 ag1 -0.33910.0832-0.0144 0.74150.3281 ag2 0.65420.00370.0079 -0.51250.3094 in1 -0.5427-0.5695-0.4596 -0.20490.1279 in2 0.64980.45590.4040 0.28440.1258 reg1 -0.0151-0.61910.6742 0.02260.1614 reg2 0.06330.6278-0.6617 0.00090.1640 Table 4-46 Rotated factor loadings for the socioe conomics variable for th e animal species/river and lake combination (RLAS) Variables 1 2 3 4 5 Uniqueness g1 0.0761 0.02200.0022 0.00090.94950.0921 ed1 -0.9266 -0.06870.0352 -0.0225-0.07630.1291 ed2 0.9275 0.0091-0.0094 -0.01030.01320.1393 ag1 0.0918 -0.01180.0122 0.8291-0.22600.2527 ag2 0.0888 0.05650.0468 -0.7865-0.28690.2858 in1 -0.1385 -0.92500.0149 0.0397-0.01660.1230 in2 -0.0571 0.9348-0.0041 -0.00860.00920.1227 reg1 -0.0030 0.00160.8930 -0.05330.07500.1940 reg2 0.0445 0.0220-0.8962 -0.02860.07480.1879

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137 Table 4-47 Rotated factor loadings for the soci oeconomics variable for the animal species/ocean and beach combination (OBAS) Variables 1 2 3 4 Uniqueness g1 -0.4015-0.01000.0563 0.35440.7099 ed1 0.9203-0.00540.0297 -0.04440.1502 ed2 -0.88200.0205-0.0414 -0.01310.2197 ag1 0.10350.0154-0.0448 0.78490.3709 ag2 0.1398-0.1744-0.0211 -0.80150.3072 in1 0.10670.93250.0483 0.08880.1089 in2 0.1269-0.9336-0.0004 -0.03970.1107 reg1 0.01660.02820.9010 -0.01370.1870 reg2 -0.0469-0.0243-0.8931 -0.00220.1995 Table 4-48 Rotated factor loadings for the soci oeconomics variable for the plant species/river and lake combination (RLPS) Variables 1 2 3 4 Uniqueness g1 -0.03730.10560.3364 0.22260.8247 ed1 -0.09530.0299-0.9024 -0.03180.1747 ed2 0.0480-0.03580.8749 -0.03020.2300 ag1 0.04260.0180-0.0783 0.77630.3891 ag2 0.04290.0287-0.0725 -0.82850.3057 in1 -0.92520.0208-0.1374 0.00750.1246 in2 0.9367-0.0152-0.0065 0.00050.1223 reg1 0.00550.90130.0080 -0.00850.1875 reg2 0.0446-0.89590.0663 0.00080.1910 Table 4-49 Rotated factor loadings for the soci oeconomics variable for the plant species/ocean and beach combination (OBPS) Variables 1 2 3 4 Uniqueness g1 -0.15040.34200.0360 0.31540.7597 ed1 -0.0006-0.91270.0291 -0.10490.1551 ed2 0.01430.8849-0.0316 -0.00130.2157 ag1 0.0623-0.0365-0.0397 0.81560.3281 ag2 0.2472-0.2002-0.0323 -0.76710.3094 in1 -0.9263-0.09480.0420 0.05790.1279 in2 0.9297-0.0866-0.0140 -0.04780.1258 reg1 -0.0047-0.04100.9148 -0.00530.1614 reg2 0.05250.0126-0.9127 0.00240.1640

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138 Table 4-50 Coefficient estimates for the interact ion model for the river and lake/animal species combination (RLAS) Variable Coef. Std. Err. Z P>|z| 95% C.I. FA 0.3245 0.03878.3860.00000.2487 0.4004 AS 0.3565 0.03909.1390.00000.2800 0.4329 IS -0.3609 0.0541-6.6740.0000-0.4668 -0.2549 FE -0.0712 0.0064-11.1010.0000-0.0838 -0.0587 K*IS -0.0317 0.0475-0.6680.5042-0.1249 0.0614 AF*IS -0.4158 0.0466-8.9230.0000-0.5071 -0.3245 BE*IS 0.4360 0.05767.5740.00000.3232 0.5488 SC1*FA 0.0486 0.03981.2220.2215-0.0293 0.1265 SC1*AS 0.0098 0.03930.2500.8026-0.0673 0.0869 SC1*IS -0.0032 0.0403-0.0800.9366-0.0822 0.0758 SC1*FE -0.0065 0.0066-0.9800.3273-0.0194 0.0065 SC2*FA 0.1007 0.03822.6360.00840.0258 0.1756 SC2*AS 0.0245 0.03810.6430.5201-0.0502 0.0993 SC2*IS -0.0232 0.0391-0.5920.5537-0.0998 0.0535 SC2*FE -0.0008 0.0064-0.1250.9004-0.0133 0.0117 SC3*FA -0.0240 0.0394-0.6080.5433-0.1012 0.0533 SC3*AS 0.0191 0.04050.4720.6368-0.0603 0.0986 SC3*IS 0.0089 0.04140.2140.8304-0.0723 0.0900 SC3*FE -0.0024 0.0065-0.3750.7075-0.0152 0.0103 SC4*FA -0.0462 0.0376-1.2280.2196-0.1199 0.0276 SC4*AS -0.0094 0.0380-0.2480.8041-0.0838 0.0650 SC4*IS 0.0371 0.03880.9570.3388-0.0389 0.1131 SC4*FE 0.0056 0.00620.8960.3701-0.0066 0.0178 SC5*FA -0.0155 0.0384-0.4030.6866-0.0907 0.0597 SC5*AS 0.0051 0.03860.1310.8958-0.0706 0.0807 SC5*IS 0.0238 0.03950.6020.5471-0.0536 0.1012 SC5*FE 0.0028 0.00640.4380.6611-0.0097 0.0153

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139 Table 4-51 Coefficient estimates for the inte raction model for the ocean and beach/animal species combination (OBAS) Variable Coef. Std. Err. Z P>|z| 95% C.I. FA 0.3125 0.03518.900.00000.2437 0.3813 AS 0.3723 0.036610.170.00000.3005 0.4440 IS -0.4013 0.0490-8.190.0000-0.4973 -0.3053 FE -0.0703 0.0057-12.250.0000-0.0815 -0.0590 K*IS -0.0673 0.0504-1.330.1822-0.1661 0.0316 AF*IS -0.2982 0.0428-6.970.0000-0.3820 -0.2144 BE*IS 0.3794 0.04887.780.00000.2838 0.4749 SC1*FA 0.0263 0.03780.700.4869-0.0478 0.1003 SC1*AS 0.0375 0.03591.040.2962-0.0328 0.1078 SC1*IS -0.0475 0.0339-1.400.1609-0.1140 0.0189 SC1*FE -0.0078 0.0059-1.310.1898-0.0194 0.0039 SC2*FA -0.0543 0.0359-1.510.1304-0.1246 0.0160 SC2*AS -0.0399 0.0342-1.170.2435-0.1068 0.0271 SC2*IS 0.0063 0.03300.190.8489-0.0584 0.0710 SC2*FE 0.0013 0.00570.230.8194-0.0099 0.0125 SC3*FA -0.0831 0.0344-2.420.0157-0.1505 -0.0157 SC3*AS -0.0096 0.0353-0.270.7859-0.0789 0.0597 SC3*IS 0.0399 0.03571.120.2643-0.0301 0.1099 SC3*FE 0.0058 0.00571.030.3024-0.0053 0.0170 SC4*FA -0.0586 0.0357-1.640.1005-0.1285 0.0113 SC4*AS -0.0433 0.0341-1.270.2041-0.1102 0.0236 SC4*IS 0.0494 0.03301.500.1342-0.0153 0.1142 SC4*FE 0.0054 0.00570.960.3374-0.0057 0.0166

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140 Table 4-52 Coefficient estimates for the interaction model for the river and lake/plant species combination (RLPS) Variable Coef. Std. Err. Z P>|z| 95% C.I. FA 0.3578 0.04068.8070.00000.2782 0.4374 AS 0.3002 0.04047.4300.00000.2210 0.3795 IS -0.2983 0.0509-5.8650.0000-0.3980 -0.1986 K*IS -0.0767 0.0067-11.4450.0000-0.0898 -0.0635 AF*IS -0.0829 0.0539-1.5380.1240-0.1886 0.0227 BE*IS -0.2446 0.0563-4.3420.0000-0.3550 -0.1342 BENIS 0.1439 0.06862.0970.03600.0094 0.2784 SC1*FA 0.0098 0.04010.2430.8081-0.0689 0.0884 SC1*AS 0.0070 0.03990.1760.8605-0.0712 0.0853 SC1*IS 0.0571 0.04071.4030.1605-0.0226 0.1368 SC1*FE -0.0002 0.0066-0.0360.9713-0.0132 0.0128 SC2*FA -0.0469 0.0410-1.1440.2525-0.1273 0.0334 SC2*AS -0.0224 0.0409-0.5480.5837-0.1025 0.0577 SC2*IS 0.0277 0.04170.6630.5071-0.0541 0.1094 SC2*FE 0.0014 0.00680.2130.8316-0.0119 0.0147 SC3*FA 0.0179 0.04160.4300.6673-0.0637 0.0995 SC3*AS -0.0215 0.0410-0.5250.5996-0.1018 0.0588 SC3*IS 0.0146 0.04140.3520.7247-0.0666 0.0958 SC3*FE 0.0034 0.00690.4950.6206-0.0100 0.0168 SC4*FA -0.0282 0.0404-0.6990.4849-0.1074 0.0510 SC4*AS 0.0100 0.04020.2490.8033-0.0687 0.0887 SC4*IS 0.0538 0.04101.3110.1898-0.0266 0.1342 SC4*FE -0.0026 0.0067-0.3820.7027-0.0156 0.0105

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141 Table 4-53 Coefficient estimates for the inte raction model for the ocean and beach/plant species combination (OBPS) Variable Coef. Std. Err. Z P>|z| 95% C.I. FA 0.2348 0.03476.7700.00000.1668 0.3028 AS 0.2481 0.03477.1450.00000.1800 0.3161 IS -0.3350 0.0460-7.2880.0000-0.4251 -0.2449 FE -0.0709 0.0057-12.3540.0000-0.0822 -0.0597 K*IS -0.0119 0.0485-0.2460.8059-0.1070 0.0831 AF*IS -0.2281 0.0427-5.3360.0000-0.3118 -0.1443 BE*IS 0.1390 0.05222.6610.00780.0366 0.2413 SC1*FA 0.0174 0.03540.4900.6238-0.0520 0.0868 SC1*AS -0.0038 0.0331-0.1150.9084-0.0686 0.0610 SC1*IS -0.0016 0.0319-0.0500.9601-0.0641 0.0609 SC1*FE 0.0023 0.00570.4030.6867-0.0088 0.0134 SC2*FA 0.0412 0.03721.1050.2692-0.0318 0.1142 SC2*AS -0.0033 0.0343-0.0970.9230-0.0706 0.0640 SC2*IS 0.0198 0.03260.6060.5446-0.0442 0.0838 SC2*FE -0.0006 0.0059-0.0960.9232-0.0120 0.0109 SC3*FA -0.0140 0.0331-0.4220.6728-0.0789 0.0509 SC3*AS -0.0274 0.0330-0.8300.4067-0.0920 0.0373 SC3*IS -0.0094 0.0339-0.2790.7804-0.0758 0.0569 SC3*FE 0.0037 0.00550.6680.5043-0.0072 0.0146 SC4*FA 0.0330 0.03730.8840.3765-0.0401 0.1061 SC4*AS 0.0072 0.03380.2140.8307-0.0591 0.0736 SC4*IS 0.0323 0.03151.0280.3042-0.0293 0.0940 SC4*FE -0.0025 0.0058-0.4390.6610-0.0139 0.0088 Table 4-54 Global test of significance of estimators of interaction models Global Null Hypothesis Beta = 0 MODEL df 2 p-value RLAS 27301.560.0000 OBAS 23250.460.0000 RLPS 23318.800.0000 OBPS 23310.710.0000 Table 4-55 Total valid res ponse rates for each survey River and Lake Ocean and Beach Total Valid Responses 1,299 1,801

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142 Table 4-56 Coefficient estimates for the fre quency models for river/lake and ocean/beach locations RIVER AND LAKE OCEAN AND BEACH Variable Description Coef. z P-value Coef. z P-value g1 Male -0.165 -2.36 0.018 -0.076 -1.29 0.197 ag1 18 34 years -0.414 -4.02 0.000 -0.429 -4.84 0.000 ag2 35 54 years -0.225 -3.14 0.002 -0.284 -4.77 0.000 ed1 High school and less 0.385 3.21 0.001 0.398 4.56 0.000 ed2 Associate or some college courses 0.196 1.61 0.107 0.234 2.64 0.008 ed3 Bachelor's degree 0.171 1.40 0.163 0.236 2.73 0.006 in1 Less than $14,999 $34,999 0.079 0.66 0.511 0.341 3.66 0.000 in2 $35,000 $99,999 -0.038 -0.35 0.725 -0.008 -0.11 0.915 reg1 Central Region 0.081 1.00 0.319 0.038 0.54 0.588 reg2 South Region 0.271 2.69 0.007 -0.020 -0.25 0.799 -1.781 -10.59 0.000 -1.867 -14.11 0.000 -1.141 -7.22 0.000 -0.881 -7.28 0.000 Threshold -0.640 -4.14 0.000 -0.315 -2.65 0.008 Values -0.283 -1.83 0.067 0.180 1.52 0.128 0.020 0.13 0.897 0.517 4.36 0.000 0.430 2.77 0.006 0.949 8.00 0.000 Table 4-57 Global test of significance of estimators of frequency models Global Null Hypothesis Beta = 0 MODEL df 2 p-value River and Lake 10 53.92 0.0000 Ocean and Beach10 106.11 0.0000 Table 4-58 Estimated probabilities for fr equency of visit to OB and RL parks Frequency River and Lake Ocean and Beach Daily 3.22%2.40% Weekly 7.88%13.19% Monthly 11.99%16.71% Once every 2 to 3 months 11.73%18.47% Once every 4 to 6 months 11.37%12.62% Once every 7 to 12 months 15.73%14.01% Not at all 38.08%22.62%

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143 Table 4-59 Marginal willingness to pay (MWTP) per year for the invasive species (IS) attribute Knowledge Background Type of Park Average MWTP Yes No River and Lake $ (71.26) $ (104.19) $ (51.86) Ocean and Beach $ (76.47) $ (115.95) $ (67.63) Which of the two parks do you prefer? Park A Park B Figure 4-1 Example of a pair-wise ques tion animal species combinations Which of the two parks do you prefer? Park A Park B Figure 4-2 Example of a pair-wise qu estion plant species combinations

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144 Figure 4-3 Evaluation of alternatives for the anim al species combination river and lake parks (RLAS)

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145 Figure 4-4 Evaluation of alternatives for the pl ant species combination river and lake parks (RLPS)

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146 Figure 4-5 Evaluation of alternatives for the anim al species combination ocean and beach parks (OBAS)

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147 Figure 4-6 Evaluation of alternatives for the pl ant species combination ocean and beach parks (OBPS)

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148 Figure 4-7 Estimated probabilities for frequency of visit to OB and RL parks

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149 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS In this study we were able to appraise th e value that Floridas population give to the problem of invasive plants thr ough the determination of the publics utility function derived from their participation on re creational activitie s in two types of aquatic parks: ocean/beach and river/lake parks. It was determined that people assigns a negative value to the presence of invasive plants reflected in a negative willingness to pay for this attribute in each location. To be precise it was found that the average MWTP (per visit) for the IS attribute derived from peoples utility function were -$6.15 for RL parks and -$5.95 fo r OB parks. This was the highest MWTP in absolute value among all the attributes in both lo cations. Therefore the IS attribute presented the highest level of impact (MWTP) on the respondent s utility compared to the other attributes. Even more since the sign for this attribute wa s negative; this large ne gative number indicates a strong aversion of respondents to th e presence of invasive plants in aquatic areas. Furthermore the fact that this negative effect (MWTP) is higher in absolute value than the positive effects of the other attributes (faci lities, animal species and plant species ) implies that is not sufficient an enhancement in these other attr ibutes (pure tradeoff effect) to reduce the harmful impact of invasive plants on the publics utility. On the cont rary it is recommended to take direct actions to control and attack this problem. Conversely when we analyzed the standardized estimators of the pe oples utility function including the cost attributes (tha t in our case represents all the fees asked in a park), we found that the FE attribute is the mo st important of all in both locations followed in a second place by the IS attribute. This result induce us to think th at peoples utility derived from participation in

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150 recreational activities on RL and OB parks are driven mainly by the level of fees and second by the presence of invasive plants. This was more evident when we built a ranking for all the 81 possible park alternatives for each location. It was determined that among 80% and 95% of parks that generates a utility greater or equal than zero for re spondents were free. This reflect s again the high importance that people provide to the fee attribute and how the ut ility and preferences of respondents are skewed to alternatives that offer an adequate or maximum (minimum) level of positive (negative) attributes but at no cost. This analysis was extended to evaluate whethe r socioeconomic characteristics, as well as experiential features of people ha ve any effect over the attributes importance on their utility. It was found that peoples socioeconomic characteris tics do not have any st atistical significant impact on the negative weight th at people provide to the IS attr ibute when attending OB and RL parks. On the other hand when the experiential factors are an alyzed both the real knowledge (AF) and benefits (BE) perceive d from invasive plants have a statistical significant effect on peoples perception about the effect of these spec ies. In contrast the stated knowledge (K) does not have any influence on respondents valuation of the impact of invasive plants on their utility. This permits us to conclude th at there is an important discre pancy between the effects of the stated (K) and real (AF) knowledge on peoples utility. This is congruent with the findings obtained from chapter two. In that part it was determined the importance for people of having an adequate background of knowledge in order to fully discern the impact of invasive plants on their satisfaction when engaging in outdoor recreational activities in aquatic parks. It was also established that the odds that a person who does not possess a good level of knowledge to rec ognize the real effect of these species on

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151 his/her welfare is 0.31 times the odds of a person who has a good level of knowledge. Furthermore there is a 44% probability that a pe rson who thinks s/he has knowledge of invasive species actually possesses little or no knowledge. Th erefore if we assume that the knowledge that people say to have is their real level of knowledge we will ove rrating them and this would be prejudicial for the success of a ny control and management policy since both type of knowledge have different impacts on peoples utility. This result is more relevant when we analy ze the effect of the r eal knowledge on peoples utility and preferences. It was found that when people have a good level of knowledge they will require stricter measures of cont rolling invasive plants in aquati c areas. This is reflected in a higher WTP for people well-informed about the i nvasive species problem than those who lack this type of knowledge. This higher WTP can be tr anslated in the fact that informed people are more eager to collaborate in invasive plants eradication. This is beca use a higher information background increases the perceived impact of th ese plants on peoples utilities due to an enhancement in their discernment. In addition it was determined that the pot ential impact on the populat ions welfare due to the presence of invasive plants in Floridas aquatic areas could be substantial. This impact is estimated in an amount (per year) of -$1,065,150,630 for OB parks and -$992,580,540 for RL parks approximately. For this reason any action to reduce the presence of invasive plants in these natural areas in Florida is justified because of their sizeable potential impact on the welfare and quality of life of Floridians. Although the results obt ained from these programs would be even stronger if they are accompanied by an aggressive educational program. Since it was determined that the impact of invasive pl ants on peoples utility would be larger if the population were adequate informed. This impact would reach to -$1,615,092,363 for OB parks and

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152 -$1,451,284,807 for RL parks; that is, approximate ly $500 million additionally in each location. Therefore any type of strategy of control and management impl emented along with educational programs would increase peoples awareness on this issue and in turn thei r WTP to eradicate the invasive plant species problem. Hence we recomm end that if any governmental agency wants to be successful in this war against invasive plants, it is essential to educate the population first, so as to reach the necessary level of social effo rt in order to accomplish such an important objective. Finally further research is required on this topi c to determine the impact of the presence of invasive plants in specific aquatic parks. In ot her words, in order to test unambiguously what the effects of invasive plants are on the visitors utility and to m easure peoples WTP as consumers, analyses for particular cases in specific aqua tic parks along Florida woul d be necessary. With those more detailed inferences of this problem on aquatic areas would be obtained due to the application of narrower experimental designs. Additionally, in this study the importance of the real knowledge was determined however further research about the source of this knowledge is necessary. To be preci se it is relevant to determine if this knowledge comes from formal education, experience, or awareness campaigns from the government. This would permit to estimat e the level of effectiveness of governmental educational programs about this issue and how these programs can be successfully applied to enhance the eradication task of these species through the involvement of the population.

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153 APPENDIX A SURVEY OF AWARENESS OF INVA SIVE SPECIES (COVER LETTER) Dear Florida Resident, We are asking for your collaboration in a study about Peoples Awareness of Invasive Plants, conducted by the Institute of F ood & Agricultural Scienc es of the University of Florida, and funded by The Florida Department of Environmental Protection. This survey is the first step in helping us analyze how invasive or non-native plants affect pe oples satisfaction when visiting Natural Areas in Florida. You have been selected as a part of a small sa mple of Florida resident s who are being asked to complete an online questionnaire (the link to the su rvey webpage is located at the bottom of this letter). When answering to the survey, please keep in mind that there are no right or wrong answers. We truly value your honest opinion. Y our participation is completely voluntary and you do not have to answer any que stion you do not wish to answer You are free to discontinue participation in the questionn aire at any time without c onsequence. Completion of the questionnaire should take no longer than 10 minute s, and you must be 18 years old or older in order to complete the questionnaire. You are assured complete confidentiality. Results will only be reported as summarized data. There is no co mpensation or anticipated risks for participating in this survey. Thank you very much for your participation in th is study. If you have any questions regarding this research study or the questionnaire, you ma y contact the investigators Santiago Bucaram (santibu@ufl.edu ) or Frida Bwenge (fbwenge@ufl.edu ). For questions about your rights as a research participant, contact the University of Florida Institutional Review Board (PO Box 112250, Gainesville, Fl 32611, telephone 352-392-0433) Sincerely, SURVEY LINK: http://www.surveymonkey.com/s.aspx?sm=UuCoQDb3rRiSM2rQNFgErg_3d_3d

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154 APPENDIX B SURVEY OF AWARENESS OF INVA SIVE SPECIES (QUESTIONNAIRE)

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161 APPENDIX C PARKS MANAGERS SURVEY Dear Park Managers, Thank you very much for agreeing to help us with this FDEP study on the effect of invasive plants on recreational use of Floridas na tural areas. Your respon ses to these questions are vital to our creation of a good survey that will allow us to measure how recreational users of natural areas are impacted by various levels of invasive plants. Please fill out the questions in the attachment and send it back to us as an attachment as well. If you have any questions regarding this st udy, please feel free to contact us. Once again thank you very much for your help. 1. What is the name of th e park that you manage? 2. What do you think that are the characteristics of this park that people value the most? 3. How has the trend of visits been in your park (increasing, decreasing, static). 4. Approximately how many visitors on average do you get per year? 5. What is the most number of visitors on a single day th at have visited your park? 6. Have you had problems related with non native i nvasive plant species? Please explain what types of species? 7. If so, do you think that this s ituation (non native invasive pl ant species) has affected the visitors satisfaction? If yes, explain why and how? 8. What are the most frequent complaints (other than invasive species) that you have received from visitors (i.e. parking lots, fees, facilities, congestion, etc.)? 9. If you had $200,000 to improve your park to attr act more visitors, how would you spend it? 10. Roughly speaking, how much does the average visi tor to your park spend per visit (park fees and other expenses)? 11. Roughly what percentages of your visitors come from: Distance Percentage Within 10 miles Within 50 miles More than 50 miles 12. As a park manager, do you have any othe r comments regarding invasive species?

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162 APPENDIX D SURVEY OF NATURE RELATED OUTDO OR ACTIVITIES (COVER LETTER) Dear Florida Resident, We are asking for your collaboration in a study on Florida Residents Use of Natural Aquatic Parks and Participation in Ou tdoor Recreational Activities, conducted by the Institute of Food & Agricultural Sciences of the University of Florida and funded by The Florida Department of Environmental Protection. Through this survey we will try to determine th e criteria that Florida park visitors consider when choos ing to visit a natural recreational park. The results of the survey will be used in a more extensive investigation rela ted to the impact of non-native plant species on visitor satisfaction of Floridas natural areas. You have been selected as a part of a small sa mple of Florida resident s who are being asked to complete an online questionnaire (the link to the su rvey webpage is located at the bottom of this letter). Your participation is completely voluntary. You do not have to answer any question you do not wish to answer. You are fr ee to discontinue participation in the questionnaire at any time without consequence. Completion of the questi onnaire should take no longer than 10 minutes, and you must be 18 years old or older in order to complete the questi onnaire. You are assured complete confidentiality. Results will only be reported as summarized data. There is no compensation or anticipated risks fo r participating in this survey. Thank you very much for your participation in th is study. If you have any questions regarding this research study or the questionnaire, you ma y contact the investigators Santiago Bucaram (santibu@ufl.edu) or Frida Bwenge (fbwenge@ufl.edu). For questions about your rights as a research participant, contact the University of Florida Institutional Review Board (PO Box 112250, Gainesville, Fl 32611, telephone 352-392-0433) Sincerely, SURVEY LINK: http://www.surveymonkey.com/s.aspx?sm=XerOGJ1z1YtZ7W5rX_2fxvKQ_3d_3d

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163 APPENDIX E SURVEY OF NATURE RELATED OUTDOOR ACTIVITIES (QUESTIONNAIRE)

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171 APPENDIX F MULTIATTRIBUTE UTILITY SURVEY RIVER AND LAKE/PLANT SPECIES COMBINATION (RLPS) EXAMPLE (COVER LETTER) Dear Florida Resident, We are requesting your participation in a University of Florida survey on Recreation and Invasive Plants in Floridas State Parks (the link to the survey webpage is located at the bottom of this letter). You have been selected as a part of a small sample of Florida residents who are being asked to complete this online questionnaire Please take a few minutes to complete the survey. This survey is divided in three parts. In the first part you will be asked to provide different valuations about a specific natural area and a second one of your choice, which is optional. In the second part you will be asked to give your opinion about what effects invasive species have had in your decision of which location to attend and enjoyment when engaging in outdoor recreational activities. Finally, we will ask you to give us some socio-economic information for our analysis. Remember that to participate in this survey you must be 18 years or older. Participation is voluntary. You do not have to answer any questions you do not wish to answer. You are free to stop the questionnaire at any time. There are no anticipated risks, compensation, or other direct benefits to you as a participant in this study. You may be assured of complete confiden tiality. You will not be identified or connected with the questionnaire in any way and participation is totally anonymous. Results will only be reported as summarized data. The information gathered in this study may be published in professional journals or presented at scientific meetings, but will not be accessible as individual data. The survey is funded by the Florida Department of Environmental Protection and is administered by the University of Florida and the Institute of Food and Agricultural Sciences. For questions about this study, please feel free to contact graduate student investigators Santiago Bucaram (santibu@ufl.edu) or Frida Bwenge (fbwenge@ufl.edu). For questions about your ri ghts as a research participant, please contact the University of Florida Institutional Review Boar d (PO Box 112250, Gainesville Fl 32611, telephone 352392-0433). Please remember that your answers to this survey ar e extremely important and may impact your future enjoyment of Floridas state parks. Thank you for your cooperation. SURVEY LINK: http://www.surveymonkey.com/s.aspx?sm=4hO2JNiwP2qIMFj38zT92w_3d_3d

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172 APPENDIX G MULTIATTRIBUTE UTILITY SURVEY RIVER AND LAKE/PLANT SPECIES COMBINATION (RLPS) EXAMPLE (QUESTIONNAIRE)

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176 In this part of the survey respondents were asked to answer another set of questions regarding to another type of park It was provided three options; two of them about two different parks and one of them in which respondents were able to express that they are not willing to answer more pair-wise choice questions but instead agree to proceed to the rest of the survey. If they agreed to answer an additional set of pair wise questions this would be similar to the previously presented but with images and explanations re lated to the specific park that was chosen.

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180 LIST OF REFERENCES Adamowicz, W., P. Boxall, M. W illiams, and J. Louviere 1998. Stated Preference Approaches for Measuring Passive Use Values: Choice Ex periments and Contingent Valuation. American Journal of Agricultural Economics 80:64-75. Adamowicz, W., J. Louviere, and J. Swait. 1998. Introduction to Attri bute-Based Stated Choice Methods. Final Report to Resource Valuatio n Branch, Damage Assessment Center, NOAA, U.S. Department of Commerce. Adamowicz, W., J. Swait, P. Boxa ll, J. Louviere, and M. Williams. 1997. Perceptions versus Objective Measures of Environmental Quality in Combin ed Revealed and Stated Preference Models of Environmental Valuation. Journal of Environmental Economics and M anagement 32:65-84. Adamowicz, W., J. Louviere, and M. Williams. 1994. Combining Stated and Revealed Preference Methods for Valuing Envi ronmental Amenities. Journal of Environmental Economics and Management 11:271 -292. Adams, D.C. and D.J. Lee. 2006. Statewide bioe conomic model of the invasive aquatic plants hydrilla, water hyacinth, and water lettuce. Dr aft report to FDEP. U npublished, University of Florida. Addelman, S. 1962. "Ort hogonal Main-Effect Plans for Asymmetrical Factorial Experiments." Technometrics 4: 21-46. Aitchison, J. and S.D. Silvey. 1957. "The Gene ralization of Probit Analysis to the Case of Multiple Responses," Biometrika 44:131-140. Agresti, A. 1990. Categorical Data Analysis New York: John Wiley & Sons. Agresti, A. 1996. An Introduction to Categorical Data Analysis New York: John Wiley & Sons, Inc. Alvarez, R.M. and C. VanBeselaere. 2003. Web-Based Surveys California Institute of Technology. Retrieved from http://survey.caltech.edu/encyclopedia.pdf Alvarez, R. M., R. P. Sherman, and C. E. VanBeselaere. 2003. Subj ect Acquisition for WebBased Surveys. Political Analysis 11: 23-43. Bell, F.W. 1998. The Economic Value of Lake Tarpon, Fl orida, and the Impacts of Aquatic Weeds. A.L. Burruss Institute of Public Service, Kennesaw State University. Bergstrom, J.C., and H.K. Cordell. 1991. An analysis of the demand for and value of outdoor recreation in the United States. Journal of Leisure Research 23:67-86. Bergstrom, J.C., J.R. Stoll, J.P. Titre, and V. L. Wright. 1990. Economic value of wetlands-based recreation. Ecological Economics 2:129-147.

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191 BIOGRAPHICAL SKETCH Santiago Bucaram was born in Guayaquil, Ecuador. He earned his B.A. in economics from the Escuela Superior Politecni ca del Litoral (ESPOL) in 2003. Th en in 2005 he earned his MBA from the University of Sabana, Colombia. He came to UF for his masters degree in food and resource economics in spring 2006.