Evaluating Community Design for the Conservation of Fire Dependent Ecosystems and Regional Processes

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Title:
Evaluating Community Design for the Conservation of Fire Dependent Ecosystems and Regional Processes
Physical Description:
1 online resource (174 p.)
Language:
english
Creator:
Rothberg, Scott J
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Master's ( M.A.U.R.P.)
Degree Grantor:
University of Florida
Degree Disciplines:
Urban and Regional Planning
Committee Chair:
STEINER,RUTH LORRAINE
Committee Co-Chair:
KOBZIAR,LEDA NIKOLA
Committee Members:
HOCTOR,THOMAS SCOTT

Subjects

Subjects / Keywords:
corridor -- development-design -- fire-dependent -- fireshed -- firewise -- fuels -- planning -- pyrophillic -- resiliency -- wildfire -- wildlife
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre:
Urban and Regional Planning thesis, M.A.U.R.P.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
The current approach to development design in the Southeast does not fully envision the ramifications of development at the regional level. In Florida considerable progress has been made in how we plan for development around water and wetlands; however, less attention has been given to planning at the regional level for the movement of wildfires and the movement of wide-ranging species that depend on fire-dependent ecosystems. Planners use geospatial modeling tools to assess what aspects of development have negative impacts on regional processes. This research is an approach to understanding how spatial modeling of landscape level processes can influence investment-backed decisions in development. By employing these methods -before the concrete is poured- developers can understand how to reduce the impact of development on wide-ranging wildlife movement and reduce the risk of property to wildfire. One conclusion of this research suggests how development design of land use components at the site scale influences functional movement of wildlife at the regional scale. The second outcome of this research concludes that development components are least vulnerable to wildfire when located in fire-dependent ecoystems which are already managed with prescribed fire. The outcomes of this work offer planners an expanded toolset to understand how to work with developers to maintain large scale processes in fire dependent, ecologically significant areas.
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In the series University of Florida Digital Collections.
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Includes vita.
Bibliography:
Includes bibliographical references.
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Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Scott J Rothberg.
Thesis:
Thesis (M.A.U.R.P.)--University of Florida, 2014.
Local:
Adviser: STEINER,RUTH LORRAINE.
Local:
Co-adviser: KOBZIAR,LEDA NIKOLA.

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Applicable rights reserved.
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lcc - LD1780 2014
System ID:
UFE0046792:00001


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1 EVALUATING COMMUNITY DESIGN FOR THE CONSERVATION OF FIRE DEPENDENT ECOSYSTEM S AND REGIONAL PROCESSES By SCOTT JACOB ROTHBERG A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 201 4

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2 2014 Scott Jacob Rothberg

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3 A Quote t o Shara Himmel pass o n

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4 ACKNOWLEDGMENTS There are many individuals who made this thesis possible. I thank G d for guidance through difficult times, and for leasing to us time on this earth to fill our li ves with the majesty and diversity of the ecosystems that surround us. I thank my parents, David and Debbie Rothberg, for expanding my horizons and instilling in my mind and heart a heightened emotional quotient that continues to bring me success. Our time spent traveling together gives depth and foresight to my life. I thank my siblings, Jackie and Andy, for without their joy and criticisms I would not be who I am today. I also thank my grandparents, Tom and Amy Pruski, and Alan and Ruth Rothberg. Your personalities resonate throu gh my extended family and allow me to see the world in a way in which few have earned the privilege. I thank Elle Grayson from Brooker Creek Preserve for showing me how to plan for the future and act in the now. You inspire d me to stay true to my course. I thank my instructor, Dr. Mark Hostetler, for bringing me to New Zealand and illustrating so graphically how landscape level processes are shaped by efforts on the small scale and are steered by people who are willing to make change possible. I further th ank my mentors, Drs. Leda Kobziar and Tom Hoctor. They entertained my interest in and gave me the tools to merge two schools of thought to address a topic that is contentious, critical, and has real world consequences. I thank my advisor, Dr. Ruth Steiner. Your ethic and collective approach to research design gave strength to my work. Your guidance produced a dedicated track for me to maintain over a two year project. Lastly, I thank Dick Brubaker and the Suwannee River Plantation for allowing me to access and stud y your lands. Your stewardship of Florida lands is a demonstration of forethought and how planning can bring regional suc cess to more than shareholders.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ........................... 14 ABSTRACT ................................ ................................ ................................ ................... 15 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 17 Intent ................................ ................................ ................................ ....................... 17 Justification ................................ ................................ ................................ ............. 19 Terminology ................................ ................................ ................................ ...... 19 Summary ................................ ................................ ................................ .......... 26 2 LITERATURE REVIEW ................................ ................................ .......................... 34 Introduction ................................ ................................ ................................ ............. 34 Scenario Planning ................................ ................................ ............................ 34 Resiliency Planning ................................ ................................ .......................... 37 The Pattern of Florida Policy ................................ ................................ ................... 39 Contemporary Methods and Development Designs in Florida ................................ 44 Attributes of Firewise Developme nt Designs ................................ .................... 44 Attributes of Conservation Development Designs ................................ ............ 46 Wildlife Corridor Design Methods ................................ ................................ ..... 47 Wildfire Planning Methods ................................ ................................ ................ 49 3 METHODOLOGY ................................ ................................ ................................ ... 55 Overview of Methodology ................................ ................................ ....................... 55 Site Description ................................ ................................ ................................ ....... 56 Study Area ................................ ................................ ................................ ........ 56 Development Design ................................ ................................ ........................ 57 Research Design ................................ ................................ ................................ .... 58 Part 1: The Production of Before and After Wildlife Corridor Scenarios ........... 58 Determining the effect of the application of wildlife corridor modeling on development design during the pre development, site synthesis phase. ................................ ................................ ................................ ..... 59

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6 Determining the impact of development desig n on the facilitation of wildlife movement in regionally significant ecological corridors. .............. 59 Summary ................................ ................................ ................................ .... 61 Part II: The Production of Surface Wildfire Scenarios ................................ ...... 61 4 RESULTS ................................ ................................ ................................ ............... 81 Before and After Least Cost Corridor Modeling ................................ ...................... 81 Regional Scale Analysis ................................ ................................ ................... 81 Site Scale Analysis ................................ ................................ ........................... 83 Comparative Analysis of a Surface Wildfire ................................ ............................ 84 Data Processing ................................ ................................ ............................... 84 Comparison of Standard Fuel Models to Custom Fuel Model. ......................... 87 Simulations of Surface Wildfire ................................ ................................ ......... 91 Wildfire Flame Lengths and Development Scenarios ................................ ....... 93 5 DISCUSSION ................................ ................................ ................................ ....... 147 Resiliency of Development, of Wildlife Movement, and of Fire dependent Systems ................................ ................................ ................................ ............. 147 Reiteration of Target Objectives ................................ ................................ ..... 147 Development Design and Regional Wildlife Movement ................................ .. 148 Development Design and Landscape Level Wildfire Movement ..................... 149 Limitations ................................ ................................ ................................ ...... 152 Conclusion ................................ ................................ ................................ ............ 155 Outcome 1 ................................ ................................ ................................ ...... 155 Outcome 2 ................................ ................................ ................................ ...... 155 Recommendations for Planning Improvement in Interior Counties ................. 156 APPENDIX A LEAST COST CORRIDOR M ETHODOLOGY ................................ ...................... 158 B SURFACE WILDFIRE MODELING METHODOLOGY ................................ ......... 162 LIST OF REFERENCES ................................ ................................ ............................. 167 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 174

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7 LIST OF TABLES Table page 3 1 Weather Import from FireFamilyPlus for Low, Medium, and High Fire Intensity Scenarios. ................................ ................................ ............................ 80 4 1 A Corridor Analysis quantifies the change in corridor size between groups. .... 102 4 2 Program Screen Shots of Cu stom Fuel Models as entered into Program FARSITE. ................................ ................................ ................................ ......... 113 4 3 Program Screen Shots of Custom Fuel Models and their associated Standard Fuel Models that were used as a Base for processing and input into Program FARSITE. ................................ ................................ ........................... 114

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8 LIST OF FIGURES Figure page 1 1 An illustration of community development and depth to water table. Community profile fr om Mike Roess Gold Head Branch State Park, Keystone Heights, Florida. (Myers and Ewel 1990; Adapted from IBPF 2012). ................. 28 1 2 Map of La Floride (Florida) circa 1657 (Sanson 1657). ................................ ..... 29 1 3 Florida Population Growth Rate. The Study Extent for this research contains projected increases ranging from 4 to greater than 10 %. (Zwick 2006). ............ 29 1 4 Florida 2060. An urbanized area expansion model created in partnership with 1000 Friends of Florida. (Zwick 2006). ................................ ............................... 30 1 5 Vulnerability of Coastal Florida to changes in mea n sea level, By Robert A. Rohdes (FWC 2008). ................................ ................................ .......................... 30 1 6 The Florida Ecological Greenway Network. ................................ ........................ 31 1 7 The Florida Ecological Green way Network: December 2013 Update. ................ 31 1 8 Metapopulations are connected populations of varying size and spatial configuration. Stochastic disturbances create a pattern of local populations tha t are connected through the migration of abiotic and biotic materials, i.e. water, pollen, air, nutrients, plants, and wildlife (Levins 1969). ........................... 32 1 9 Varieties of fragmentation of a landscape. ................................ ......................... 32 1 10 Edge effects, as the outcome of fragmentation (Laurance et al. 2008). ............. 32 1 11 Six Florida ecotypes and their associated fire return intervals (FRI). It is important to note that though the frequency of the FRI may operate on significantly different time frames, the effect of the FRI is to perpetuate the species that depend on fire as a stochastic disturbance process. ...................... 33 1 12 Research Design Conceptual Framework. ................................ ......................... 33 2 1 .................. 52 2 2 A decision making tool for selection between multiple management options. The chart is categorized according to the level of control and the level of uncertainty (Randolph 2012). ................................ ................................ ............. 52 2 3 Diagram of defensible space. Each of the four Zones has progressively stricter landscaping principles that reduce the availability of hazardous fuels near the structure. ................................ ................................ .............................. 53

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9 2 4 The Development of Regional Impact Review Process (FS 380.06). ................. 53 2 5 Intercept Method. ................................ ................................ ................................ 54 3 1 Research Design. Two embedded units of Analysis will be used to assess development designs in the SLP property. ................................ ......................... 67 3 2 Res earch Design Conceptual Framework. ................................ ......................... 67 3 3 Study area (Right) and site location (Left). The study area is outlined in Red and the site location is highlighted in yellow. ................................ ...................... 68 3 4 Outline of the Suwannee Lake Plantation. The Florida Ecological Greenways Network is visible around the exterior and interior of Gilchrist County. ............... 69 3 5 Soil Groups in Gilchrist County represented by the Florida Geological Survey (Nolan 1997). ................................ ................................ ................................ ...... 70 3 6 Example of the Site Synthesis Process. This example is the Group 3 Plan Synthesis Di agram. The iterations progressively lead to the final area of the site that is considered developable and outline the future potential land uses within the property. ................................ ................................ ............................. 71 3 7 Development designs created in Fall 2012 Studio. These will be the basis for comparison for both embedded units of analysis. ................................ ............... 72 3 8 Site location is illustrated in black outline. The entire property is located w ithin a Priority 5 link of the FEGN (Right). The Base model of a Least Cost Path (Left). The surface of the roster is a representation of the weighted average. A greater or more difficult cost is represented in purple, and less resistant costs to traversal are indicated in green. This base model was provided to the Fall 2012 Planning Studio as an initial representation of regional wildlife movement through the SLP property. ................................ ....... 73 3 9 A side by sid e of the cost rasters. The rudimentary base model used in LCP analysis (right) and the technical model used in the before and after LCC analysis (left). ................................ ................................ ................................ ..... 74 3 10 Portion of Modelbuilder designe d to create the random points as part of the WildfireVulnerability Toolbox. ................................ ................................ ............. 74 3 11 Average Nearest Neighbor for Upland Points Dataset ................................ ....... 75 3 12 Average Nearest Neighbor for Wetland Points Dataset ................................ ...... 76 3 13 Average Nearest Neighbor results for combined SLP Points. ............................ 77

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10 3 14 Data Collection Tool used to collect fuel load estimates at SLP. ........................ 78 3 15 Example of Browns Planar Intercept transect collection. The 60 foot transect is identified with yellow fla gging extending across the pine flatwoods. Courtesy of Scott Rothberg. Fuel Data Collection Documentation 03755 06/26/2013. Gilchrist County, Fl, Personal Collection. ................................ ........ 79 4 1 A C. Development designs created in Fall 2012 Studio. These have been reproduced from Figure 3 7 in this section for ease of access. .......................... 96 4 2 Legend for All Least Cost Corridor Maps (both Regional and Site Scal e). ......... 96 4 3 The Least Cost Corridor for the before development scenario. .......................... 97 4 4 The Least Cost Corridor for the Group 1 after dev elopment scenario. ............... 98 4 5 The Least Cost Corridor for the Group 2 after development scenario. ............... 99 4 6 The Least Cost Corridor for the Group 3 after development scenario. ............. 100 4 7 Site level comparison of the change in size of a least cost wildlife corridor as a result of land use changes from each development design. In this representation the Before Corridor is indicated in light yellow, and the After corridor is indicated in orange. ................................ ................................ .......... 101 4 8 Histogram with general statistics for upland fuel load estimates. ...................... 103 4 9 Histogram with general statistics for wetland fuel load estimates. .................... 103 4 10 Upland fuel load IDW cross validation of base model with power of 2 compared to power of 2 with sectors adjusted from 1 to 4 sectors. .................. 104 4 11 Upland fuel load IDW cross validation of 4 sectors to 4 sectors with a minor axis change to 15 000. ................................ ................................ ...................... 105 4 12 Wetlands IDW of Fuel load. A power level of 1 cross validated with a power level of 2. ................................ ................................ ................................ .......... 106 4 13 Wetland IDW of pow er level of 2 compared to 4 sectors, than to 8 sectors ...... 107 4 14 Wetland IDW cross validation with Point 45 removed. ................................ ..... 108 4 15 A. Upland IDW Final Model (Left). B. Wetland IDW Final Model (Right). ......... 109 4 16 A. Upland IDW Final Model Clipped (Left). B. Wetland IDW Final Model Clipped (Right) ................................ ................................ ................................ .. 110 4 17 Suwannee Lake Plantation Combined Final Custom Fuel Model. .................... 111

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11 4 18 Land use classes for the Suwanee Lake Plantation. ................................ ........ 112 4 19 Group 1, Low Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 115 4 20 Group 1, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 116 4 21 Group 2, Low Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 117 4 22 Group 2, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 118 4 23 Group 3, Low Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 119 4 24 Group 3, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 1 20 4 25 G roup 1, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ .............. 121 4 26 Group 1, Medium Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ .............. 122 4 27 Group 2, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ .............. 123 4 28. Group 2, Med ium Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 124 4 29 Group 3, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ .............. 125

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12 4 30 Group 3, Medium Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ .............. 126 4 31 Group 1, High Fire Inten sity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 127 4 32 Group 1, High Fire Intensity 2 Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 128 4 33 Group 2, High Fire Intensity 1. Prog ram FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 129 4 34 Group 2, High Fire Intensity 2. Program FA RSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 130 4 35 Group 3, High Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 131 4 36 Group 3, High Fire Intensity 2. Program FARSITE displa ys each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model. ................................ ................................ ................. 132 4 37 Group 1, Low Fire Intensity. Flame lengths are represented f rom low to high as blue to red. ................................ ................................ ................................ ... 133 4 38 Group 1, Medium Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ........................... 134 4 39 Group 1, High Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ................................ ... 135 4 40 Group 2, Low Fire Intensity. Flame lengths are represented from low to hi gh as blue to red. ................................ ................................ ................................ ... 136 4 41 Group 2, Medium Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ........................... 137 4 42 Group 2, High Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ................................ ... 138 4 43 Group 3, Low Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ................................ ... 139

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13 4 44 Group 3, Medium Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ........................... 140 4 45 Group 3, High Fire Intensity. Flame lengths are represented from low to high as blue to red. ................................ ................................ ................................ ... 141 4 46 Spread Rate Comparison, Low Fire Intensity. Group 1, Group 2, Group 3 (Left to Right). ................................ ................................ ................................ ... 142 4 47 Spread Rate Comparison, Medium Fire Intensity. Group 1, Group 2, Group 3 (Left to Right). ................................ ................................ ................................ ... 143 4 48 Spread Rate Comparison, High Fir e Intensity. Group 1, Group 2, Group 3 (Left to Right). ................................ ................................ ................................ ... 144 4 49 Bar graphs representing the average flame lengths of a surface wildfire. The flame lengths are presented per the land use class ifications of the scenario development designs. A. The Low Fire Intensity scenario. B. The Medium Fire Intensity scenario. C. The High Fire Intensity scenario. ............................ 145 4 50 ArcScene screenshot of flame length displayed using a variable base height for the flame length surface. A. The Group 1 design and roadway network is visible in areas with the greatest flame lengths. B. The group 2 design and roadway network. ................................ ................................ ............................. 146

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14 LIST OF ABBREVIATIONS DRI Development of Regional Impact FDEM Florida Division of Emergency Management FEGN Florid a Ecological Greenways Network FEMA Federal Emergency Management Agency FGDL Florida Geographic Data Library FLMA Florida Managed Lands FM Fuel Model IBPF Interagency Basic Prescribed Fire Training Program MFSL Missoula Fire Science Laboratory NEPA National Environmental Policy Act NFPA National Fire Protection Agency PI Principal Investigator SLP Suwannee Lake Plantation SWOT Strengths Weak ness Opportunity and Threat (Analysis) WRCC Western Regional Climate Center WUI Wildland Urban Interface

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15 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the De gree of Master of Arts in Urban and Regional Planning EVALUATING COMMUNITY DESIGN FOR THE CONSERVATION OF FIRE DEPENDENT ECOSYSTEMS AND REGIONAL PROCESSES By Scott Jacob Rothberg May 2014 Chair: Ruth L. Steiner Major: Urban and Regional Plannin g The current approach to development design in the Southeast does not fully envision the ramifications of development at the regional level. In Florida considerable progress has been made in how we plan for development around water and wetlands; however, less attention has been given to planning at the regional level for the movement of wildfires and the movement of wide ranging species that depend on fire dependent ecosystems. Planners use geospatial modeling tools to assess what aspects of development h ave negative impacts on regional processes. This research is an approach to understand ing how spatial modeling of landscape level processes can influence investment backed deci sions in development By employing these methods developers can understand how to reduce the impact of development on wide ranging wildlife movement and reduce the risk of property to wildfire. One conclusion of this research suggests how development design of land use components at the site scale influ ences functional movement of wildlife at the regional scale. The second outcome of this research concludes that development components are least vulnerable to wildfire when located in fire dependent ecoystems which are

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16 already managed with prescribed fire. The outcomes of this work offer planners an expanded toolset to understand how to work with developers to maintain large scale proces ses in fire dependent, ecologically significant areas

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17 CHAPTER 1 INTRODUCTION Intent When people think of Florida their f irst thoughts are often of beaches, hurricanes, oranges, swamps, and mosquitos. Planning for wetlands and watershed planning has been adopted into practice by developers to minimize vulnerability to flooding from major weather events As a result, green co nstruction improvements and best management practices for development in wetland areas have been adopted to support the ecologically sensitive wetlands in Florida. During the development design phase this accepted practice has shifted the focus to develop on land s that are within ecologically sensitive, fire dependent upland ecosystems. The r esiliency of development designs in the interior part of Florida will be measured in the ability to plan for watersheds, and more importantly in the ability to plan fo r firesheds. It i s undeniable that development interacts with the connectivity of landscape and regional level processes. Before development began in Florida water and open land had a continuous connection throughout evolutionary time. The balance of wetla nd and upland areas creates the diversity of fire dependent ecosystems that compose the landscape (Figure 1 1) Ecosystem type in Florida can be determined by the depth to the water table and the fire return interval (Interagency Basic Prescribed Fire Trai ning Program (IBPF) 2012) The structure and composition of pyrophillic ecosystems are determined by ecological functionality; something that is itself dependent on landscape level disturbance processes. Pyrophillic ecosystems do not harbor fire, they adap ted to coexist and make the disturbance possessable. Developments can also be designed to accommodate disturbance from fire in ecologically significant corridors of open space in

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18 the Southeast. remaining connected open space and wide ranging wildlife cannot afford to repeat the past and learn lessons from the results of poor planning. Though many ecosystems in Florida depend on fire, Florida has seen an increase in destructive wildfires in recent decades. The loss of property would not occur i f fires happened at greater distances to uninhabited areas ( NFPA 2014 ). The wildland urban interface (WUI) is the general term for areas where people, structures, and natural areas intermix As populations continue to grow in Florida so will the size of th e WUI. Development has fragmented the landscape and the ability of these landscape level processes to continue. The Florida Ecological Greenways Network is a system of open space that can be applied by planners to focus conservation priorities. Ecological ly significant corridors are defined by the variety and availability of functional open space that support landscape level processes The network of open space historically supported the distribution of wide ranging species throughout the state of Florida. Development has ranging wildlife. Planners can play a major role in addressing safety concerns in WUI near fire dependent ecotypes. Historically, the creation of hazard mitigation plans has been a responsive action by planners after a disaster occurs. A retrospective, or in fill, approach to wildfire planning could be avoided if planners assessed landscape level ecological processes before development occurs on lands previously in agricultural sil vicultur al, or conservation uses. This research use s scenario planning for resiliency to assess and reduce the risk of developments to catastrophic wildfire in ecologically significant corridors. The research approach is to determine the scenario planning

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19 outcomes o f a development project in Florida by first modeling ecologic ally significant ecosystem corridors then designing developments a nd then modeling wildland fire before physical constr uction occurs. The purpose of this research is to simulate land use conflicts with landscape level processes before they occur. My research is about Justification The following justification will look at small segments of general background details. First, relevant terminolo gy will be defined to establish transparency and convey background information important to this thesis. Then, a summary of objectives will be provided. Terminology Human Population Growth in Florida. It is common knowledge that when people populate a giv en space they interact with the environment. Across the United States human population growth has had a two fold consequence. Human population growth has accelerated the exposure of developed property to natural hazards and has disrupted the natural landsc ape. The economic disruption that has occurred from poor anticipatory planning has created a need for proactive land development and land management practices. The amount of visible information about the landscape available in the 21 st Century is far more accurate and detailed than when the first European settlers arrived in the United States. At the time the Spanish settled in Florida the landscape level comprehension of the land was far different than it is today (Figure 1 2). Juan Ponce De Le n document ed that in the 16 th century the Timucuan race settled in regional tribes across Florida. Native American populations reached approximately 100,000 900,000

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20 before Europeans arrived (Dobyns 1966). The Pre DeLeon Floridian population was not reached aga in unt il the 1920 s (Dobyns 1983 ). In present time, The University of Florida GeoPlan Center uses data from the Bureau of Economic and Business Research (BEBR) to project population growth in Florida to the year 2060. In 2005 17.9 mi llion people inhabited Florida Using moderate population growth models BEBR predicts that the population of Florida will increase to 35.8 million people by 2060, as shown in Figure s 1 3 and 1 4 ( Zwick 200 6 ). Projections made before the 2008 financial crisis have since changed. A stro nger focus on conserved, less speculative real estate development spending and informed investment into equities has affected the geographic distribution of population growth forecasts. Additionally, climate change scenarios have led policy makers to reeva luate the resiliency of land at low elevations susceptible to flooding along the coast (Figure 1 5) Pending updates to the 2060 population growth model are expected to hold current population growth forecasts constant, and factor in the loss of coastal de velopment to moderate climate change scenarios (Zwick, pending ). This means that Florida will still expect 35.8 million people by 2060, except it will be concentrated away from coastal counties. The result is hypothesized to show a greater dependence on de velopable land inland, and will disproportionately impact pyrophillic, upland ecosystems. Development Design For this research development designs are classified as the geospatial creation of housing and built infrastructure that integrates environmental policies and development strategies (Roufechaei et al. 2013). Development designs inherently satisfy human needs and aim to provide a quality of life (Manoliadis et al. 2007). The history of development ranges extensively After World War II the American

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21 d ream of a traditional rural home on a large estate rapidly evolved amidst social, The Bulldozer in the Countryside reflects that at this point in United States history, troops were returning to the mainl and techniques greatly intensified the environmental impact of homeb uilding (Rome 1994). This type of cookie cutter, perfectly manicured, development pattern lead to spraw ling networks of roads, unstable demands on water, and unsustainable wastewater treatment. Today, development design seeks balance between environmental, social, and economic factors through planning, implementation and decision making to create developmen t designs that are able to serve current and future generations (Szekely and Knirsch 2005; Goebel 2007; Roufechaei et al. 2013). Site Synthesis One of the key initial steps in a development design is the site synthesis phase. In practice, the site synthe sis process begins with a site inventory and a data synthesis (Conservation Design and Planning Studio 2012) Site specific development suitability analyses inform the development design process before planning and construction occur. A site synthesis crea tes information on potential development conflicts and constraints, as well as potential opportunities for the appropriate distribution of development elements Florida Ecological Greenway Network. Comprehensive Wildlife C onservation Strategy, is a report for the Florida Fish and Wildlife Conservation Commission that was drafted by The Nature Conservancy. The report uses a problem and conservation action identification process to review threats and create attainable goals f or terrestrial, freshwater, and marine ecosystems. In this

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22 document, and in the Five Year review completed in 2010, it was explicitly claimed that conversion to housing and other forms of urban development is the most pervasive habitats. In this report dry prairie, natural pineland, sandhill, and scrub are all listed as threatened by the development process. A significant statement from section 2.7, Conversion to Housing and Urban Development, is that stress to the conversion of natural wildlife habitat in many cases includes converting habitat in adjacent results in substantial loss of function of adjoining natural habitat. Conversion to housing/urban development is implicated as the source of many ecological stresses, including habitat destruction, habitat fragmentation, altered hydrologic regime, altered fire regime, altered from which cooperative research and g rant funding was commissioned by the State Wildlife Grant from the Florida Fish and Wildlife Conservation Commission to the and the Critical Land and Waters Identificat ion Project (CLIP). The CLIP 2.0 project is System. The first installation to the Statewide Greenways System was t he Florida Ecological Greenways Network (FEGN) planned by Dr Tom Hoctor and Dr. Peggy Carr at the Unive rsity of Florida GeoPlan Center (Figure 1 6 ). The participatory process used to design the FEGN [ accomplish ed] the goal of identifying a functionally connected statewide ecological network of public and private conservation lands. [The FEGN is intended to] maximize the protection of high priority natural communities and

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23 species identified in the Florida State Wildlife Action Plan, and maximize the potential of f unctionally respond to climate and other changes Regional landscape connectivity is the cornerstone of the statewide ecological network. Seen in Figure 1 7, t he updated FEGN is divided into two classes of critical linkages and six classes of priority linkages to achieve re silient statewide ecological connectivity (Florida Greenways and Trails System Plan 2013) Connectivity and Scales Connectivity is the strength of interactions across and between ecotones ( Tewksbury et al. 2002 ). Urban planners have previously used the te (May 2006). This type of place based thinking has been repurposed from the original definition of c onnectivity. The original definition and the new connotation overl ap in their environmental context. Connectivity is an ecological term that describes the natural habitat continuum wit hin the landscape (Harrison 199 ). Large ranging wildlife have first, second, and third orders of selection that lead them in space through the continuum of their habitat (Johnson 1980). The first, second, and third orders directly correspond to local, landscape, and regional scales. At the local scale specific resources such as water, protein sources, and cover influence the character of hab itat. At the landscape scale vegetation classes and available space become factors that begin to outline connectivity for a given specie. At the regional scale landscape processes and available landscape connectivity inhibit or assist the connectivity of m ultiple populations of species or ecosystems Lan d scape Level Processes Upland and wetland ecosystems are pa ttern based ecosystems. Figure 1 8 reflects the concept of disturbance Natural s tochastic

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24 disturbances disrupt upland and wetland systems and supp ort a greater diversity of flora and fauna across the landscape. M andates and ecological requisites exist that recognize the importance of maintaining uplands and wetland s to provide ecosystem services to people such as recreational space, water recharge, and carbon sequestration. That policy also recognizes wetlands for construction and planning purposes because of the vulnerability of human infrastructure to hurricanes in flood prone areas. Yet, there is little recognition of the upland s ide of the syner gistic system when risk assessments for resiliency are performed in t he pre construc tion, development design phase. Wildlife Continuity When ecosystems become fragmented the proximity of the remaining patches has measureable influences on genetic connecti vity for species of both plants and animals (Levins 1969) The strength and weakness of these connected linkages (or corridors) can assist or inhibit the ability of flora and fauna adapted to fire dependent ecosystems to persist. h has disrupted natural landscape scale disruption of extensive habitats into isolated and 1997 ). Figure 1 9 clarifies the types of fragmentation that can occur. Furthermore, Figure 1 10 serves to illustrate faunal relaxation; how after fragmentation occurs, the change in conditions on the edge adver sely changes the interior compo sition of the fragment ( Laurance et al. 2 008). When patch fragments are ini tially formed there is an increase in population size of species within an isolated patch. Sometimes referred to

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25 of past actions has already occurred. The remaining l inkages (corridors) affect pl ants, animals, and their landscape and regional scale interactions Fire Dependent Ecosystems. Longleaf pine ( Pinus ellioti ) and the associated bunch grass, scrub, and prairie ecosystems are among the most distinct upland ecoty pes in the Southeastern Coastal Plain ( Figure 1 11) U pland ecotypes are characterized by the frequency of fire return, flat topography, a pine dominated overstory, little to no midstory, and an understory dominated by grasses and forbs. Historically, trop ical cyclones and periodic lightning strikes provided stochastic fire as a natural disturbance process that promotes species biodiversity and maintain s the understory in these systems During the summer growing season, the natural range of variation in the se fires stimulates connectivity, growth and reproduction in native, pyrophillic species (fire liking) (Hilmon 19 65: Willcox 2010). Fuels Continuity The biomass of connected, pyrophillic vegetation communities are also called fuels. Natural hazards like wildfire flip the focus from concern for the environment and the disruption of ecological processes to concern for the community and for the social disruption caused by natural processes (Prior and Eriksen 2013). After a series of wildfires hit Florida in a federally endangered species, increased from an estimate of 200 individuals to over 2000 individuals (Grace 2000). The largest barrier to successful comprehensive wildland fire mitigation is that wildfires are viewed as a threat to development, and development is not viewed as a threat to naturally occurring wildfires in the landscape. Because landscape and regional scale wildfires are combatted to prevent the loss of households and human structures, t he abu

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26 prairies now does not exist naturally without human fire management. Prescribed burning is a necessary tool utilized by land managers to reduce fuel loads and to perpetuate the pyrophillic species who inhabit fir e dependent, upland areas. Prescribed f ire management decreases the relative risk of development to wildfires by removing large buildup of fuels In Firewise development design fuels are measured to determine the appropriate action need ed for reducing wild fire hazard and to create separated space for infrustructure. A method is needed to determine if development designs are disrupting fuel continuity and if developments are fragmenting the landscape level processes of firesheds in the interior counties of F lorida Summary T here has been no direct Firewise design investigation that attempts to merge the two concepts of wildlife continuity and fuels continuity in regional analysis. Both corridor movement of wide ranging species and wildfire defense planning fo r developments have dependencies on the existence of an open space network. Yet upon review of comprehensive federal hazard plans state comprehensive natural resource regulation and local community development strategies it appears that the two p lanning methods remain separate. The two approaches that this research will be concerned with, conservation planning and development planning, both rely on the geospatial pattern of development but have separate policy objec tives and standards. This research will apply t w o embedded units of scenario analysis wildlife corridor modeling and wildfire modeling to determine if t hese two influences on development can be mutually beneficial rather than mutually exclusive (Figure 1 12)

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27 The main objectives of this sce nario planning research are as follow: 1. To compare proposed development designs by their impact on the facilitation of wildlife movement. 2. T o rank proposed development designs from most to least resilient to wildfire.

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28 Figure 1 1 An illustrati on of community development and depth to water table. Community profile from Mike Roess Gold Head Branch State Park, Keystone Heights, Florida. (Myers and Ewel 1990; Adapted from IBPF 2012).

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29 Figure 1 2 Map of La Floride (Florida) circa 1657 (Sanson 16 57). Figure 1 3 Florida Population Growth Rate. The Study Extent for this resea rch contains projected increases ranging from 4 to greater than 10 %. ( Zwick 2006 ).

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30 Figure 1 4 Florida 2060. An urbanized area expansion model created in partnership wit h 1000 Friends of Florida. ( Zwick 2006 ). Figure 1 5. Vulnerability of Coastal Florida to changes in mean sea level, By Robert A. Rohdes (FWC 2008).

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31 Figure 1 6. The Florida Ecological Greenway Network. Figure 1 7. The Florida Ecological Greenway Net work: December 2013 Update.

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32 Figure 1 8. Metapopulations are connected populations of varying size and spatial configuration. Stochastic disturbances create a pattern of local populations that are connected through the migration of abiotic and biotic ma terials, i.e. water, pollen, air, nutrients, plants, and wildlife (Levins 1969). Figure 1 9 : Varieties of fragmentation of a landscape. Figure 1 1 0 : Edge effects as the outcome of fragmentatio n (Laurance et al. 2008)

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33 Figure 1 11. Six Florida eco types and their associated fire return intervals (FRI). It is important to note that though the frequency of the FRI may operate on significantly different time frames, the effect of the FRI is to perpetuate the species that depend on fire as a stochastic disturbance process. Figure 1 12 Research Design Conceptual Framework.

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34 CHAPTER 2 LITERATURE REVIEW Introduction The Justification section in Chapter 1 provided a brief summary of the subject area of this research. There are three purposes for this l iterature review. First, the review will establish a conceptual foundation for research in scenario planning and resiliency planning. Second, the review will look at Federal and State policy in order to concentrate the socio ecological focus to Florida Th is section of the review will present a nd follow a policy improvement model that summarizes Community Resiliency policy pattern as cataloged by the Florida Department of Economic Opportunity (DEO) ( Figure 2 1 ). Following this discu ssion will be an outline of the methods of previous community and development planning attempts to correct the policy pattern and examples of the development designs that were created from those attempts. Scenario Planning Scenario planning is a comparati ve method of analysis that investigates alternative futures to inform decisions in the present Rather than focusing on the accurate prediction of a single outcome, scenario planning evaluates multiple possible futures (Peterson et al. 2003). The evolution of scenario planning has its roots in forecasting and in adaptive management. Adaptive management seeks to improve passive or active ada ptive management depending on financial or resource limitations (Randolph 2012) Passive management concentrated on constructing one hypothesis based on historical or comparative

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35 analysis to create one action. The real outcomes of that action were used to alter management if the desired results were not produced (U.S. DOI 2009). In its infancy active management was implemented to overcome the shortcomings of a passive single loop approach to planning and management The passive approach can be useful when there is a high level of scientific confidence in ecological responses to management decisions or where regulatory constraints crea te limitations (Randolph 2012 ). However even today it is difficult to predict the ecological outcome of action in the rapidly changing global environment. The operating policy of federal, state, and municipal agencies have shown heightened recognition of the current biodiversity crisis (U.S DOI 2009; Dearing et al. 2012). Valuable time and resources are wasted through the passiv e single loop trial and error approach In active management multiple competing adaptive management strategies are hypothesized and operationalized. In practice the str ategy that most effectively meets the targeted objective transpires over time through a process of monitoring and analysis. Then, the optimal strategy is reevaluated and applied over a greater area (U.S. DOI 2009; Randolph 2012) ced from work by Herbert Kahn in response to the difficulty of creating accurate forecasts (Kahn and Weiner 1967; May minor variations to future potential demographic trends, socio technical development s and strategy assumptions (K ahn and Weiner 1967). The difficulty in creating accurate forecasts occurs because small variations in trends introduce large levels of uncertainty into projection s

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36 Figure 2 2 is a chart synthesized by John Randolph based on Scenario Planning: A tool for Conservation Planning in an Uncertain World and the Department of the Interior s Technical Guide to Adaptive Management The chart is intended to be a deterministic tool for decision making between the management options that have previously be en discussed. The appropriate choice of approach to management is dependent on both the level of controllability and the level of uncertainty (Randolph 2012). In the field of spatial planning perhaps the most evident key shift from forecasting, to adaptive management, to scenario planning is that the purpose of urban and regional planning is to go beyond preparation for a range of potential futures and use modeling to identify what features of strategies create more desirable futures (Myers and Kitsuse 2000 ; Larsen et al. 2011). From Figure 2 2 it can be inferred that scenario planning is most applicable in situations of low controllability and high uncertainty. For this research the scenario planning process model outlined by Garry Peterson and colleagues i s the most appropriate because it is a linear process with the opportunity for iterative review. There are six interactive stages: 1. Identification of a Focal Issue 2. Assessment 3. Identification of Alternatives 4. Building Scenarios 5. Testing Scenarios 6. Policy Screeni ng ( Exempt from this research ) Scenario alternatives are created in stage 3 as a result of the central issue becoming a focusing device for assessment in stage 2 (Peterson et al. 2003). In the assessment stage the framework for identifying alternatives ca n be thought of as a framed circle inscribed within a square of reality. Peterson et al. views uncertainty recognition as a valuable result of the process model because early into the process the

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37 focal issue is confronted with the complexity of the world. When the uncertainty figuratively takes the form of the remaining space in the square, a set of dynamic alternative scenarios can be built and tested within the framework. In stages 4 Peterson et al. caution that the appropriate number of scenarios ranges between three and four (For further discussion see Wack 1985; Schwartz 1991; van der Heijden 1996). The multiplicity of scenarios are used in stage 5 to spot the benefits and shortcomings between future scenarios (Gunnarsson stling and Hjer 2011; Tapinos 2012). One of the primary goals of this research is to inform development design decisions, and consequently this will exclude the testing and analysis portion of policy screening in stage 6. Instead, scenario planning in this research is a technique for analyzing resiliency in future alternative development designs. Resiliency Planning The theory of resiliency binds the dynamic relationship of conservation planning and development planning at the regional scale. The resilience conce pt is generally unders the capacity of a system to absorb disturbances and still retain its basic applicability of resiliency to planning emerged with the evolution of sustainability in the beg inning of the 21 st century. Early facets of sustainability conceptualized sustainability as taking equal account of the social political, economic, and ecological components nd the three legged stool all are first generation sustainability diagrams that envision these components as additives ; it was assumed that a balance of the components, informed by the public interest, would lead to system integrity (Campbell 1996; Elkingt on 1997; Holden 2012). Second generation sustain [recognized] that human and natural

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38 systems are dynamic and constantly evolving [processes, and that] uncertainty is The growth of resiliency in practice paralleled the inter pretation of sustainability in landscape and urban planning. Jac k Ahern describes smart growth and new urbanism approaches to design in the context of the rudimentary first generation approach to sustainability. The argument is that if each o f the three sustainability components is accounted for, a static development design will endure from generation to generation. In light of the evolution of the second, non equilibrium generation of sustainability Ahern poses ndition be sustainable in a to (uncertainty) is anticipated, contained, and minimized beforeha nd (Steiner 2006; Ahern 2011) Presently, sustainability is emerging into a third generation at which point integration of environmental concern is dependent on interdisciplinary collaboration. The incorporation of environmental objectives into non environ mental arenas is often recognized as a need yet it is seldom successfully characterized in policy T o fully integrate the capacity for resilience into a plan the built evidence needs to be counterbalanced by visible, dynamic information about the landscape resilience capacity through landscape and urban planning requires that planners and designers identify the stochastic processes and disturbances that a particular landscape or city is likely to face, the frequency and intensity of these events, and how cities can build the adaptive capacity to respond to these disturbances while remaining in a

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39 functional state of resilience (Vale et al. 2005; Ahern 2011). This research will be limited to the physical resilience capacity of development designs an d to the functional resilience of landscape processes and will not test social infrastructure or stakeholder participation in planning and policy decision making Multiple landscape processes are integrated into this study to establish a level of redundan cy when comparing the resiliency of a design to its regional landscape Redundancy spreads anticipatory site scale risk and can be useful in identifying cross scale interactors. Rather than placing e planning for when a system, such as development design, fails (Ahern 2011). The Pattern of Florida Policy support a diverse economy, vibrant communities, and provide econom ic incentives to businesses that create new job is the intent of the Community Planning and Development (CPD) Programs of the Florida DEO. The CPD Programs include Comprehensive Planning, Areas of Critical State Concern Technical Assistance (Community Resiliency), and Developments of Regional Impact and Florida Quality Development. The CPD policy p rograms evolved out of the 1990 s to maintain to help mitigate the impact of developm ent on to development). A simplification of the evolution of CPD is provided in Figure 2 1 F rst this cycle, before CPD bec omes involved in the late 20 th century. Wetlands. The history of development in wetlands and the recognition of coastal vulnerability to water hazards outline the beginning of the cycle before policy

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40 intervention occurred in Florida. Figure 2 1 depicts two linked cycles in which shifts in development and conservation polic y lead to conseq uences. In the first revolution of the cycle from Point 1 to Point 5 Florida policy clearly prioritizes water and watershed planning. Wetlands have played a unique role i characterized by flat topography, a high water table, and high rainfall. After statehood was granted to Florida in 1845, 15 million acres of wetlands, or 40% of the state, was transferred from the federal level to the state le them Resources Atlas of Florida catalogs satellite imagery of the state. Depictions fr om the early 1970 wetlands and their associated op en water areas accounted for 1998, Rubino and Starnes 2008). Still today, interspersed ephemeral wetlands are fueled by cyclic rain and drought cycles, predom inantly fro m tropical cyclones. Instrumental observations of hurricane activity only go back approximately 100 years (Liu et al. 2003). In 1926 rapid urbanization in previously drained swampland faced a category 4 hurricane. Massive flooding caused by the hurricane l ed to 392 deaths and injured 6,000 people (NOAA 2009). Only two years later a category 5 hurricane caused the dike wall surrounding Belle Glade and South Bay to collapse, drowning 2,000 people (Kleinberg 2004). In contemporary time s, [nine] of the U.S. to p 12 costliest hurricanes of all time occurred between 2001 and 2005, including four that hit Fl T he Federal Emergency Management Agency (FEMA) administers the Natural Hazard Mitigation Program and the National Flood Insuranc e Program. FEMA prepares

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41 for communities Flood Insurance Rate Maps that illustrate special flood hazard areas and insurance risk premium zones that would be applicable for assistance through the National Fl ood Insurance Program. B efore FEMA can provide any feder al funding or assistance to a locality the National Environmental Policy Act (NEPA), 42 U.S.C. 4321 4347, ensures that localities comply with environmental considerations (Florida Division of Emergency Management (FDEM) 2012). Delineated in any flood insurance rate map are base flood elevations, a 100 year, and a 500 year floodplain (FDEM 2012). Due to and the successful recognition of FEMA flood zones by developers the 100 year floodplai n is heavi ly avoided during the development design phase of planning as a method of minimizing insurance costs. Adaptation planning emerged under the Community Resiliency Initiatives to prioritize government funding for infrastructure needs and for planning in adap tation action areas. Adaptati areas below, at, or near mean high water, areas which have a hydrological connection to coastal waters, and areas 3). Florida planning policy adopted the three most referenced strategies from the literature to help coastal communities become more resilient to the impacts of rising sea levels: Protection, A ccommodation, and R etreat ( Southeast Florida Regional Climate C hange Compact Counties 2012 ; DEO 2013). Adaptation Planning In Florida a handout outlines Florida policy interpretation of the relationship between adaptation planning and hazard mitigation (DEO 2013) : The main difference is that sea level rise adaptat ion assumes a longer timeframe for impact and therefore a longer timeframe for need and implementation. Sea level rise also assumes an increase in the vulnerability of areas already subject to coastal flooding and

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42 therefore adaptation projects take into ac count the increased vulnerability Coastal High Hazard Areas (CHHA) receive individual attention as an initiative under the Community Resiliency Initiatives. This i nitiative references that the intent of the Florida legislature is for local governments to restrict development activities in CHHA (FS 163.3178(1)). Furthermore, Florida Statutes require local governments to limit public expenditures that subsidize development in CHHA, and limit or restrict residential density or the type of development permitt ed in those areas (FS 163.3177(6)6). In many ways, the underlying justification for a reevaluation of human population growth forecasts in Florida are an outcome of these initiatives. Illustrated in Point 5 in Figure 2 1 the specificity of the Community R esiliency Initiatives has discontinued the perpetuation of the first cycle, and has guided the focus of construction and development to uplands Uplands. The second cycle of Figure 2 1 is a conceptual cycle of the construction and development pattern occu rring in Florida and a forecast of the consequences To restate from the previous section federal FEMA regulation s directly i ncentivize development to occur above the 100 yr floodplain to limit rate increases to insurance premiums At the state level indi rect incentives to develop in areas with less hydrological connection to coastal waters and with less coastal hazard vulnerability leads development to occur in uplands in the interior of Florida Wildfires have historically posed a threat to the economic resources of forestlands, ecosystems, and watersheds and only recently have become a threat to urban development. In 1985, as a result of wildfire 600 homes burned down in Florida and 1,400 burned nationally (NFPA 2014). In 1998 a Florida wildfire burned f rom May

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43 25 to July 22 requiring the assistance of 7,000 firefighters from 44 states (McMillan 1998). When the wildfire was extinguished approximately 500,000 acres had burned, resulting in the loss of 270 businesses and homes and a cumulative built and nat ural resource loss of $620,000,000 USD (Mercer et al. 2000). In 2000 the same amendment to the Stafford Act that mandated local governments to prepare coastal hazard plans mandated gover n ments to create local mitigation strategies to reduce the risk and vu lnerability of developments to wildfires. The outward sprawl of urban development into wildland areas led the U.S. Congress to create the Healthy Forests and Restoration Act (HFRA) in 2003. Under this federal act the USDA Forest Service (USFS) and the Bure au of Land Man consider local communities when developing and implementing forest [management] hazardous fuels [management] and plans (Randolph 2012). To assist communities vulner able to fire the HFRA design ated the creation of a natural hazard mitigation planning assessment tool, Community Wildfire Protection Plans In 2002, the National Fire Protection Agency (NFPA) also began the Firewise Communities/USA Recognition Program These programs stress three fac tors: Collaboration of communities with local and state government representatives, Prioritized Fuel Reduction in hazardous areas, and Treatment of Structural Ignitability at the homeowner and community level (CWPP Committee et al. 2004). Community Resilie ncy Initiatives in Florida are skewed toward watershed planning. At the State level, there is a 3:1 ratio of Community Resiliency Initiatives between watershed planning and fireshed planning, and fire is only regarded in one of four sections in the Hazard Mitigation Planning Initiative. The guidebook for Hazard

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44 Mitigation contains a best practices section for wildfire risk reduction. Wildfire Risk Reduction in Florida: Home, Neighborhood and Community Best Practices describes the planning approaches for res ilient development design in Florida. In its first pages the Best Practices guide documents projected land uses to 2060 and documents the future interior counties (DACS 2010). For communitie s at risk to wildfire in the WUI p erhaps the most important principle is defensible space. Defensible space, illustrated in Figure 2 3 uses firewise construction, firewise landscaping, and fuels reduction to reduce hazard immediately around developed stru ctures. In defensible space, Zone 1 is 0 30 ft. from the structure. This area is designed with well irrigated, low growing vascular plants and built surfaces that are intended to protect the home from fire. In Zone 2 fuels reduction is extended from 30 ft. to 60 ft. in order to limit the availability of hazardous fuels to the home Zone 3, also known as the Transition Zone, is designed to progressively remove the continuity of fuels from a potential encroaching wildfire. The intention of the Best Practices Guidebook is to educate agencies and communities about the hazards of 1, this review exposes an uncertain future for development in Florida, and an opportunity to explore development designs for whether they capac itate or inhibit regional processes. Contemporary Methods and Development Designs in Florida Attributes of Firewise Development Designs Design for wildfire preparedness in practice has progressively moved away from to a social focus that emphasizes the role of huma n behavior, values, attitudes, and decisions in managing wildfires (Prior and Eriksen 2013). Under the Firewise index on the Florida Forest Service website it could not be

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45 uth is that few fire departments have adequate Firewise Communities/USA is a recog nition program that standardizes and certifies communities based on an assessment of risk to catastrophic wi ldfire. Many Florida communities have gained recognition under the Firewise Communities program ( NFPA 2014 ). The premier review of community design in Florida for risk to wildfire is Planning Strategies for Community Wildfire Defense Design Brzuszek et al pro vide a descriptive analysis of the 1998 catastrophic wildfire season and then provide a comparative analysis between three Firewise subdivisions in Florida. The findings of the study suggest that communities which balanced green infrastructure, blue i nfrastructure, and gray infrastructure create the greatest defenses for communities at risk to wildland fires. G reen infrastructure is naturally vegetated, undeveloped, open lands. Green infrastructure is a collective name describing riparian areas, open f ields, upland pine, and other non wetland features in the landscape. B lue infrastructure includes the streams, lakes, swamps and other wetland feature types. Lastly, gray infrastructure i ncludes urbanized land uses including buildings, infrastructure, road ways and their associated rights of way. Brzuszek conclude s and claims that for Firewise developmen t natural and constructed fire barriers, including wetlands, roads, and maintained utility corridors. The report supports the Fl orida Hazard Best Practices guide by stating that s tructures should be concentrated and located proximally to fire barriers separating them from wildland fuels and primary fire travel directions. Land use planning should identify areas at risk to wildfire and use this information to inform regulations that mitigate the relative risk (Brzuszek et al. 2010). T he Brzuszek

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46 article describes that green belts are useful for supporting wildlife conservation and plant species conservation, but conservation design principles have received separate individual attention in the literature. Attributes of Conservation Development Designs Today, building new infrastructure in the WUI means dealing with spatial process pattern relationships within landscapes (Forman 1995). Many of the design principles from Planning Strategies for Community Wildfire Defense Design and from Wildfire Risk Reduction in Florida: Home, Neighborhood and Community Best Practices in Conservat ion Design for Subdivisions in which Arendt describes densification of structures as a method to promote efficient resource use (Arendt 1996). Conservation subdivisions are one of many site scale design approaches that were created to correct the issues o f traditional development. In the design phase, conservation subdivisions employ a technique of clustering built land uses into a consolidated space to effectively conserve natural areas, productive agriculture, and forested lands from development (Arendt 1997). To conserve biodiversity in developments the common focus of planning is to est ablish green infrastructure, referred in this case as protected natural open space and corridors (Hostetler et al. 2011). Target objectives for conservation subdivisions are reached when the pattern of development and the densities of that pattern can operate in the least resource intensive manner achievable. Conservation subdivisions have grown in popularity in the 21 st century. Yet, many studies have shown that homeowne rs often are unaware or do not understand the importance of individual actions toward the functionality of conserved natural areas and

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47 waterways (Hostetler et al. 2011). As an alternative, developments of regional impact or sector plans have gained favor f or their thorough participation processes, government agency involvement, and growth management focus at the regional scale. D evelopments of Regional I mpact (DRIs) are any development which, because of its character, magnitude, or location, would have a substantial effect upon the health, To illustrate the participation component, the DRI process is diagramed in Figure 2 4. Sector plans are DRIs that have a minimum size area of 15,000 acres. Sector plans require a two stage approval process involving two separate, but linked, components. The first is a long term master plan, and the second is a detailed specific area plan. The long term master plan is required to identify regi onally significant natural resources as part of the framework map for the development (FS 163.3245(3)(a)). The detailed specific area plan must also identify the areas that will be placed under permanent preservation, including documentation for all conser vation easements (FS 163.3245(3)(b)). There are many examples of sector plans and there are many examples of modeling tools that have been used to demonstrate the ability of sector plans to promote or inhibit the functionality of areas under conservation e asements. Wildlife Corridor Design Methods As technology has improved modeling approaches have been developed to conceptualize the functional need for linkages across the landscape. Many of these modeling techniques have been used to assess the health of c urrently conserved lands and have been used to demonstrate the impact of DRIs at the regional scale. Tewksbury and his colleagues investigated the indirect effects of corridors to facilitate movement using a suite of focal species. The suite of species inc luded fauna, seed, and

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48 pollen so that the experimental design would cover taxa with a diverse range of life history characteristics. This type of experimental landscape design allow ed the authors effects due to corridor facilitated movement from effects to changes in was that movement for the entire suite of species from connected patches was greater than movement from unconnected patches. Their research cla that corridors can have substantial effects on [colonization, pollination, and dispersal], and thereby help overcome the depressed reproduction frequently reported for [isolated popul (Tewks bury et al. 2002). A sub claim made by Tewksbury et a l. is that effects of corridors can have notable consequences fo r population dynamics at larger, regional scales. The authors caution and advocate that future large scale research should include studi es of unmanipulated landscapes. Identifying Florida land s before physical development occurs will enable landscape connectivity analysis to be included in this research into the site synthesis portion of the development design phase Due to the rapid pace of urbanization a focal species approach can be used to assess landscape connectivity. This app species based management is accepted and supported by managers, decision makers, and public opinion (Lambeck 1997; Miller et al. 1999; Carroll et al. 2001; Bani et al. 2002; Noss and Dal y Chapter 23; Beier 2006). Least cost corridor analysis is a geospatial method for determining the optimal location of a landscape linkage (Singleton et al. 2002; Beier 2006). Geographic Information Systems (GIS) are digitized visualizations that can be sp atially analyzed to display geographically ref erenced information (ESRI 2011). This research will u s e geostatistical analysis, r ather than investing in r esearch of

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49 inter and int ra corridor movement, to measure the degree to which facilitation and impedim ents of movement of a wide ranging specie the Florida black bear ( Ursus americanus floridanus ), is provided by land use changes Habitat factors customarily used in corridor analysis in the study of black bear are: Vegetation/Land Use Elevation metric Top ographic features Road Density These habitat factors are weighted based on surveys of biologist that work closely wi th the focal specie. The first two habitat factors, Vegetation/Land U ses and Elevation are ranked on their relative importance and relativ e deterrence to the specie. Topographic features that influence movement typically include ridges, canyon bottoms, flats, or slope. Last, r oad densities are usually paved road densities per km 2 Road densities can also be substituted for distance to roadwa ys when contiguous parcel size is more indicative of movement for the focal specie. (Beier 2006: Beier 2008). Wildfire Planning Methods Many of the same inputs required for least cost corridor analyse s are also required as inputs for wildfire spread predic tion. ArcGIS can be used to transform spatial data into inputs for wildfire simulators. Program FARSITE 4 is a fire behavior and movement simulator that is used by fire behavior analysts, including the United States Forest Service and the Florida Forest Se rvice (Fire.org 2008). FARSITE 4 incorporates spatial and temporal information on topography, fuels, and weather to display fire growth (MFSL 2010). In ArcGIS, topographic information is transformed into elevation, slope, and aspect data in the form of tex t based ASCII inputs. When combined with fuel

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50 models and weather and wind files FARSITE can process hourly rates of spread for a wildfire while recording output feedba ck on flame length (MFSL 2010). Program FamilyFirePlus can be used to retrieve and transf orm weather and wind data into processable information for Program FARSITE 4. The Western Regional Climate Center (WRCC) is one such climate information service that monitors and records high quality historic al climate data. The WRCC feeds from weather st ations across the United States and is partnered with the National Climate Data Center, DOI Climate Science Centers, and DOI Landscape Conservation Cooperatives among others (WRCC 2013). Weather and wind information is collected in hourly intervals in most stations, but erroneous records often blight datasets. Therefore quality control is necessary when working with historic al weather information. After correcting for erroneous records, FireFamilyPlus has the ability to categorize and divid e historic al weat her patterns into three categories of fire weather; low, medium, and high fire intensity (Weise et al. 2010) The 90 th 95 th and 97 th percentiles are normally attributed to low, medium, and high fire intensities although in Florida the 80 th 90 th and 95 th percentiles are sufficient ( Helfmen et al. 1980; Weise et al. 2010). An hourly data analysis is performed to generate an hourly list which is a requirement to incorporate weather data into program FARSITE for wildfire simulation s In wildfire simulation modeling, a fuel model is a numeric attribute for a cell in a raster surface. Together, a raster coverage of fuel models provides the fuel inputs for Fuel models are derived from the following sources: The 53 standard behavior models (National Fire Danger Rating System 1972) The 13 original models (Anderson 1982)

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51 The 40 defined models (Scott and Burgan 2005) Custom Fuel Models Each of these models use s average fuel load esti mates for the different fuel types and size classes. When simulating a wildfire, one option is to use a fuel model that resembles the ecosystem to be modeled. To reduce generalization and increase the t he Cust om Fuel M odel is an option that can be used in the place of the standard fire behavior fuel models (FARSITE 2009). This option uses precise field measurements of fuel loading. Field measurements include Shrub and Herbaceous Loads, Downed Woody Debris loads and Duff and Litter Planar Intercept method (Brown 1974). This method uses three random azimuth transects at specific stratified point s to collect sample fuel volume and surfa ce area characteristics to quantify fuel load in tons/acre (Brown 1971). The surface area characteristics are referred to as Timelag Size Cla sses and are based on diameter c lasses taken in inches (Figure 2 5 ). a single plane transect, th e intercept methodology is highly susceptible to changes in surface slope

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52 Figure 2 1 Figure 2 2. A decision making tool for s election between multiple management options. The chart is categorized according to the level of control and the level of uncertainty (Randolph 2012).

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53 Figure 2 3 Diagram of defensible space. Each of the four Zones has progressively stricter landscaping principles that reduce the availability of hazardous fuels near the structure. Figure 2 4. The Development of Regional Impact Review Process (FS 380.06).

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54 Diameter C lass (inches) : 0 0.25 0.25 1 1 3 Timelag Size Class: 1 hour 10 hour 10 0 hour 1000 hour Figure 2 5 Intercept Method.

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55 CHAPTER 3 METHODOLOGY Overview of Methodology Two embedded units of regional analysis were used to assess alternative development designs for one property in Florida. The first is a before development and after development analysis of a least cost wildlife corridor (LCC). The second analysis is a comparative analysis of a surface wildfire across the development site (Figure 3 1). First, the meth odology will overview the Site Description. Second, a site synthesis is briefly described in the Site Description to outline the participation process that led to the preparation of alternative development designs. Next, the methodology is divided into two parts to discuss the technical procedure for each embedded unit of analysis of the research design Part I will be The Production of Before and After Wildlife Corridor Scenarios Part II will be The Production of Surface Wildfire Scenarios The main obje ctives and sub objectives of this research follow : 1. To compare proposed development designs by their impact on the facilitation of regional wildlife movement. i. What is the effect of the application of wildlife corridor s modeling on development design during the pre development, site synthesis phase? ii. What is the impact of development design on the facilitation of wildlife movement in regionally significant ecological corridors? 2. T o rank proposed development designs from most to least susceptible to surface wi ldfire. i. What is the effect of development design on fire behavior (flame length, area burned, burn duration, and spread rate) during a wildfire? ii. What characteristics of development design increase vulnerability to wildland fire in an ecologically signific ant corridor? iii. What lessons can be learned from fuel load estimates in ecologically significant areas adjacent to future development in the Wildland Urban Interface?

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56 Site Description Study Area To study the effects of conservation planning before developmen t physically occurs, a study location was chosen with in the context of a regio nal area in Florida. The r egional area north of Crystal River National Wildlife Refuge, West of Interstate 75 and South of Ecofina River State Park was chosen for this study b ec ause it contains all six priority linkages and both classes of critical linkages as described in the FEGN (Figure 3 3 ) Within the regional study area the Principal Investigator (PI) Scott Rothberg, contact ed private landowners interested in development. For this study the PI enter ed into a land use contract with Suwannee Lake Plantation Inc. (SLP) in Gilchrist County in the Spring of 2012 SLP is a ~ 28,000 acre site located entirely within a Priority 5 Link of the FEGN (Figure 3 4 ). The property is loca ted in an area called the Waccasassa Flats. The Waccasassa Flats is a general term for a landscape originally described as 65,070 acres, or 102 square miles, composed of remnant swamp and sandhill barrier islands to the Brooksville Ridge that formed during the Pleistocene (Vernon 1951, Nolan 1997, Yon and Puri 2004). The geomorphology of the area contains Miocene and Pleistocene sediments that create a relatively high water table and characteristically a high level of water retention The soils in the Wacc asassa Flats are mainly Group C and Group D, as seen in Figure 3 5 (USDA 1992, Nolan 1997). Soils in Group C and D generally lie shallow to impervious layers in the soil horizon and tend to be clayey, impeding the downward movement of water (Nolan 1997). Relative to the context of Gilchrist County, the Waccasassa Flats have higher elevations and steeper gradients (Col et al. 1997). Sandhill ridges in this area reach an average of 90 to 100 feet above sea level and the ridges lie within a

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57 mosaic of gum slo ughs, cypress ponds, and wetlands averaging approximately 60 feet in elevation (Nolan 1997). Inferences by R. Vernon attribute the Wacassasa Flats as ancient sand dunes during a time when the Gulf Coastal Lowlands were previously submerged below sea level, and later on as a stream course (Vernon 1951). The vegetation and land use history of the Waccasassa Flats are a reflection of the divergent conditions in the Flats compared to Gilchrist County. The land surrounding the Waccasassa Flats historically has b een used for productive agriculture and human settlement. In recent decades the Waccasassa Flats have been zoned for silvicultural use with some intermittent settlement. FNAI classification of this area identifies primary ecosystem types as pine mesic oak forests, pine plantation, oak sandhill scrub, wetland mixed forest, wet flatwoods, and cypress swamp (FGDL 2013). Development Design The development design scenarios were gener ated by a graduate level Design, Construction, and Planning Landscape Studio in the Fall of 2012 at the University of Florida The studio was divided into three different groups of both graduate level planners and landscape architects Each gro up create d a development design for the SLP property. Preliminary site synthesis was a coll ective effort involving all three groups. The site synthesis star ted with information gathering. Geographic information was collected from the Florida Geographic Database Library (FGDL) and was shared between groups. Additional information was gathered fro m the Gilchrist County Property Appraisers, the North Central Florida Economic Development Plan, and both Florida and local municipality Compr ehensive Plans. The process of a site synthesis is outlined in Figure 3 6

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58 To create the des igns each group applie d value based decision making First, a maximum build out design was created by each group. The maximum build out design is intended to determine the maximum amount of developable land space available above the 100 year floodplain and to design and examine the road network that would be needed to connect that space. For an example of a maximum build out design, see Figure 3 6 Strengths, Weaknesses, Opportunities, and Threats (SWOT) of the site were analyzed to inform the group s individual decisions on the proportion of developed land uses to include in the final development designs (GTZ 2002) A pplying this value based decision approach create d three unique development designs with independent proportions and geospatial distributions of high, medium, and l ow density residential development, commercial areas, civic buildings, utility infrastructure, educational areas, recreation and open space areas, and roadway networks (Figure 3 7 ) Re search Design An expansion of Figure 3 1, Figure 3 2 illustrates the co nceptual framework of the methodology Part 1: The Production of Before and After Wildlife Corridor Scenarios For this modeling approach ArcGIS 10.1 was used to analyze habitat factors for the focal specie, the Florida black bear ( U. floridanus ). Due to fu nding limitations, habitat factors were adopted from previous literature (Garshelis and Pelton 1981; Powell et al. 1997; Beier 2006; Rose 2013). Habitat factors customarily used in corridor analysis in the study of black bear s are: Vegetation/Land Use Topo graphic features Elevation metric Road Density

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59 The habitat factors that were used in this corridor analysis of the Florida black bear differ from typical factors because the Florida landscape is less influenced by topographic relief. Additionally, rather than using road densities E uclidean distance to roads was used in the analysis. Euclidean distance is a more accurate account of road proximity as well as road density for wildlife because it considers the compounded effects of multiple roads. Because dev elopment patterns are dependent on road network and development design, this research used the opportunity to compare an initial ecological corridor to a future scenario with encroached development. Determining the effect of the application of wildlife co rridor modeling on development design during the pre development, site synthesis phase. During the site synthesis phase a base model of regional wildlife movement was provided to all groups (Figure 3 8 ) The base model was a simple Least cost Path Corrido r (LCP) model produced in ArcGIS that illustrated the concept of a before development scenario to the three design groups The LCP and LCC model uses existing land cover information, topographic position, elevation, and distance to paved roads as inputs to create a surface raster of resistance. The cost surface is on a virtual value scale of 1 to 10, with 1 being the lowest cost to traversal and 10 being the highest resistance cost. This base model was provided to the Fall 2012 Planning Studio as an initi al representation of regional wildlife movement through the SLP property. Determining the impact of development design on the facilitation of wildlife movement in regionally significant ecological corridors For the before and after analysis, the LCP mod el was replaced with a more technical LCC model (Figure 3 9 ). A comprehensive technical discussio n of the LCC

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60 model methodology is available in Appendix A A summary of the required inputs is outlined here for clarity. The LCC map algebra expression use s m ean factor weights (%) to synthesize and attribute a n output value for each cell in the new cost raster ( ESRI 20 11 ). The expression uses the following python sintax ("%flm a_raster %" 0.02) + ("% euc dis_rds_raster %" 0.15) + ("%dist_water_raster_ %" 0 .02) + ("%rec lassify_slope %" 0. 13) + ("%Internal_parcel_raster %" 0.68) where Distance to FLMA is weighted 2%, Distance to Water Bodies is 2%, Euclidean Distance to roads is weighted 15%, Topography is we ighted 13%, and Land Cover per p arcel is weight ed 68%. To create the before scenario this research used the current Land C over per parcel information available through the FGDL. T he lowest accumulative cos t resistance was analyzed from the top of the FEGN network entering the Northern end of the Waccas assa Flats and from the Southern end of the FEGN network entering the Waccasassa Flats This final before corridor was analyzed and saved for com parison between the before development scenario and the after development scenarios When the three developmen t designs were finalized, adjustments were made to the Land Cover per parcel and Euclidean Distance to roads inputs to reflect changes in land use. After this proces s was completed for each group the new datasets were recalculated with t he other weighted h abitat factors that were created in the before scenario. The same python script was used to create the after development wildlife corridor scenario for each of the three groups. A qualitative and quantitative comparison

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61 of the after scenarios provide s insi ght into the effect of development design on the facilitation of regional wildlife movement. Summary The before and after analysis of a LCC through the Waccasassa Flats created quantitative metrics of regional scale movement of wildlife. The created metric s are one before development scenario and three unique after development scenarios. The scenarios are used to qualitatively compare the following objective and two sub objectives : 1. To compare proposed development designs by their impact on the facilitation of regional wildlife movement. i. What is the effect of the application of wildlife corridors modeling on development design during the pre development, site synthesis phase? ii. What is the impact of development design on the facilitation of wildlife movement in regionally significant ecological corridors? Part II : The Production of Surface Wildfire Scenarios The project inputs necessary to create an operational project file for this analysis in FARSITE 4 are a Landsc ape File, a Weather File, a Wind File and an y attached vector files that will influence surface wildfire front propagation To create the landscape file there are four input requirements; Elevation, Slope, Aspect, and Fuel Model. For FARSITE to be able t o model a surface wildfire and generate measur ements of flame length this analysis also include d an input for Duff Loading and for Course Woody Debris. First, the processing method of Elevation, Slope, and Aspect inputs are presented, followed by the processing method for the Fuel Model. Then, the me thod for creating the Weather and Wind Files is described. Last, the output information from FARSITE 4 that will be used to analyze the sub objectives is described. A

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62 comprehensive technical discussion of the Surface Wildfire modeling methodology is availa ble for review in Appendix B Elevation, Slope, and Aspect. The Spatial Analyst tool in ArcGIS 10.1 was used to create these three inputs. USGS 5 ft. contour information from the FGDL was converted in ArcGIS 10.1 into a topography raster dataset. The Spati al Analyst tool created Elevation, Slope, and Aspect raster outputs that were each converted into ASCII files for input into the Landscape Fil e in FARSITE 4. Cluster Analysis. The final required input (Fuel Model) and the optional inputs (Duff Loading and Course Woody Debris) for Program FARSITE are based on field data collected on the SLP property in 2013. T he ~28,000 acre SLP property was classified into uplands and wetlands. From the FGDL, t he FEMA 100 year flood zone was used to delineate Suwanee River Wat er Management District land use data for Gilchrist County as an uplands class or a wetlands class. 100 random GPS p oints were generated between the two classes at 50 GPS points per class Using A rcGIS 10.1, a n Average Nearest Neighbor cluster analysis w as performed on each class individually, as well as on all 100 GPS points aggregated together (Figure 3 10 ) The purpose of the cluster analysis was to determine if any portion of the property was underrepresented and likewise to avoid oversampling. Avera ge Nearest Neighbor summaries are reported in Figures 3 11 3 13 Both the upland random points and the wetland random points returned a pattern revealing no significantly different distribution deviating from random. Investigating the returned data shows that for uplands, the observed mean distance is 723.9 m., and the expected mean distance is 764.7 m. A negative z score of 0.072 further confirms that the spatial

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63 configuration of upland points is slightly clustered even though it is reported graphically as random. For wetland points the same slight deviation from random is observed. In this case the nearest neighbor ratio of 0.977 is also just less than 1. A z score of 0.311 confirms that the random spatial configuration is closer to clustered than to d ispersed and is slightly more clustered than the distribution of upland points. When the points were aggregated into a 100 point surface the level of clustering increased. Figure 3 13 illustrates the more clustered than random distribution. The ratio of ob served to expected mean distance is 0.91 and the z score has decreased to 1.72. Across the site there is significant clustering; yet it is not severely clustered. There remains a 90% likelihood that this clustered pattern could not be the result of random chance. Furthermore, the three distributions reported in Figures 3 11 3 13 were the 6 th of 10 iterations of the random points tool. This cycle of random points was selected because it out performed other observed to expected ratios in both categories. F ield Data Collection. od was used at each GPS point for data collection. This method uses three random azimuth transects at specific stratified points to collect sample fuel volume and surface area characteristics to quanti fy fuel load in tons/acre (Brown 1971). At each GPS point three transects were taken at azimuth (compass based) directions separated by a minimum of 60 cardinal degrees All data was collected on the Data Collection Tool, as shown in Figure 3 14 For each GPS point, a GPS tagged photo was taken such as in Figure 3 15 Lastly, if any GPS point was inundated with water it was marked as indeterminate and no measurements were taken Field data was then transferred into ArcGIS.

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64 Fuel Model, Duff Loading, and Coa rse Woody Debris Variables recorded in the field were used to create a spatial surface as an input for FARSITE 4. The first input to process for the surface was a Custom Fuel Model for the SLP property. The Custom Fuel Model is a raster coverage of numeri c values used to predict surface fire spread The Custom Fuel Model use s the herbaceous and shrub percentages and height estimates at each point as spatial references. T he Geostatistical Analyst tool in ArcGIS was used to separately create an Inverse Dista nce Weighted (IDW) spatial surface for uplands and wetlands. After the statistically appropriate surface model was created, the Merge tool was used to create the final Custom Fuel Model surface raster of the entire site The raster file contain s one intege r number of a fuel model for each 30 x 30m cell. The raster was then transformed using the Raster to ASCII tool in Conversion tools. The two remaining inputs required for constructing the spatial surface are Duff Loading and Coarse Woody Debris. For the Du ff Loading input, the litter depth (in.) and the duff depth (in.) measurements for each point were used in Geostatistical Analyst to process this input for the ASCII spatial surface. The same method for creating the Custom Fuel Model surface was used For Coarse Woody Debris the same process for creating the Custom Fuel Model surface was used to create this input for the ASCII spatial surface. This time, the measurements of 1, 10, and 100 hour fu els for each point were used in Geostatistical Analyst. Weathe r and Wind File s. The program, FireFamilyPlus, was used to export the Weather and Wind files into program FARSITE 4. Historic al fire weather data corresponding to the study e xtent were downloaded from the WRCC The most appropriate weather station, ID 0830 01, is located within the study extent at the Lower

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65 Suwanee National Wildlife Refuge From Station 083001 records are available from October 2003 to September 2013. Low, Medium, and High fire intensity percentiles were located using the Fire Danger project ions tab Ignition components of 15, 21, and 27 corresponded to the 79.70th, 90.70th, and 95.40th fire intensity percentiles respectively. For the low fire intensity percentile the most appropriate match was 1 10 June 2007. For the medium fire intensity pe rcentile the most appropriate match was 1 9 April 2010. For the high fire intensity percentile the most appropriate match was 1 9 April 2006 (Table 3 1) The W eather and W ind Files for these ranges were exported usin g an Hourly Data Analysis. R ank ing propo sed development designs from most to least susceptible to surface wildfire s With all of the data layers produced and in the appropriate format, all data files were imported into FARSITE 4. T he shapefiles that were added are those that can influence the pa ttern of spread during a surface fire. The shapefiles include d major water bodies and the major roadway network The road network shapefile for each development design are replaced corresponding with each wildfire simulation Three simulations of a surface wildfire were performed for each of the three development designs for a total of nine simulations. For each development design three simulations were performed using low, medium, and high fire intensity weather information respectively. To correlate with field data collection in 2013 the Point of Ignition was selected as the GPS location of a lightning strike fir e, The Horseshoe Fire, that occurred on the SLP property in the Summer of 2013 A simulated surface wildfire was then started. The simulation fini shes weather data or when the <15 day time period expires Lastly, the Output results are

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66 imported into ArcGIS 10.1 for a comparative analysis of how the application of wildfire modeling can identify the resi liency of the development designs to a surface wildfire. Summary. The three development designs and the three fire intensity scenarios led to the simulation of nine total wildfires. The wildfires produced Output information on areas consumed by fire, rates of spread, and flame lengths. The quantitative analysis of scenario wildfires investigated the following objective and three subobjectives : 1. T o rank proposed development designs from most to least susceptible to surface wildfire. i. What is the effect of deve lopment design on fire behavior (flame length, area burned, burn duration, and spread rate) during a wildfire? ii. What characteristics of development design increase vulnerability to wildland fire in an ec ologically significant corridor? iii. What lessons can be l earned from fuel load estimates in ecologically significant areas adjacent to future development in the Wildland Urban Interface?

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67 Figure 3 1 Research Design. Two embedded units of Analysis will be used to assess development designs in the SLP property. Figure 3 2 Research Design Conceptual Framework.

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68 Figure 3 3 Study area (Right) and site location (Left). The study area is outlined in Red and the site location is highlighted in yellow.

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69 Figure 3 4. Outline of the Suwannee Lake Plantation. The F lorida Ecological Greenways Network is visible around the exterior and interior of Gilchrist County.

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70 Figure 3 5. Soil Groups in Gilchrist County represented by the Florida Geological Survey (Nolan 1997).

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71 Figure 3 6. Example of the Site Synthesis Proces s. This example is the Group 3 Plan Synthesis Diagram. The iterations progressively lead to the final area of the site that is considered developable and outline the future potential land uses within the property.

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72 Figure 3 7 Development designs created in Fall 2012 Studio. These will be the basis for comparison for both embedded units of analysis.

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73 Figure 3 8 Site location is illustrated in black outline. The entire property is located within a Priority 5 link of the FEGN (Right). The Base model of a Least Cost Path (Left). The surface of the roster is a representation of the weighted average. A greater or more difficult cost is represented in purple, and less resistant costs to traversal are indicated in green. This base model was provided to the Fall 2012 Planning Studio as an initial representation of regional wildlife movement through the SLP property.

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74 Figure 3 9 A side by side of the cost rasters. The rudimentary base model used in LCP analysis (right) and the technical model used in the before and after LCC analysis (left). Figure 3 10 Portion of Modelbuilder designed to create the random points as part of the WildfireVulnerability Toolbox.

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75 Figure 3 11 Average Nearest Neighbor for Upland Points Dataset

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76 Figure 3 12 Average Nearest Neighbor for Wetland Points Dataset

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77 Figure 3 13 Average Nearest Neighbor results for combined SLP Points.

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78 Figure 3 14 Data Collection Tool used to collect fuel load estimates at SLP.

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79 Figure 3 15 Example of Browns Planar Intercept transect collection. The 60 foot transect is identified with yellow flagging extending across the pine flatwoods. Courtesy of Scott Rothberg. Fuel D ata Collection Documentatio n 03755 06/26/2013. Gilchrist County, Fl, Personal C ollection.

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80 Table 3 1 Weather I mport from FireFamilyPlus for Low, M edium, and High Fire Intensity S cenarios. Fire Intensity Scenario Month Date RN AM PM Tlo Thi Hhi Hlo ELV Low 5 31 0 300 1600 65 90 98 38 15 6 1 8 400 1100 63 81 99 57 15 6 2 518 100 1700 64 76 99 79 15 6 3 0 500 1300 68 82 92 57 15 6 4 0 100 1200 74 83 95 69 15 6 5 0 2300 1300 74 85 95 67 15 6 6 0 0 1600 73 88 99 64 15 6 7 0 600 1300 69 87 99 58 15 6 8 0 500 1500 67 90 99 55 15 6 9 0 400 1300 70 92 99 49 1 5 6 10 0 500 1200 73 92 99 54 15 6 11 0 400 1300 72 93 99 56 15 Medium 3 31 0 700 1300 48 74 99 49 15 4 1 0 500 1300 49 79 99 38 15 4 2 0 500 1400 49 85 99 39 15 4 3 0 300 1300 55 80 99 39 15 4 4 0 600 1300 53 83 99 46 15 4 5 0 5 00 1300 56 82 99 48 15 4 6 0 600 1500 56 81 99 53 15 4 7 0 600 1500 56 81 99 42 15 4 8 4 0 1300 59 82 99 60 15 4 9 1 2300 1500 57 79 97 26 15 4 10 0 500 1400 47 81 97 22 15 4 11 0 400 1500 60 83 95 36 15 High 3 31 0 300 1100 56 71 99 35 15 4 1 0 600 1600 44 63 97 34 15 4 2 0 600 1300 42 69 99 34 15 4 3 0 600 1200 47 70 73 29 15 4 4 0 600 1500 50 71 99 27 15 4 5 0 600 1200 45 71 86 17 15 4 6 0 400 1300 44 77 99 25 15 4 7 0 600 1400 47 78 99 36 15 4 8 0 100 1000 65 76 99 70 15 4 9 0 2300 1300 60 81 99 48 15 4 10 0 100 1300 58 82 99 48 15 4 11 21 600 1100 53 80 99 38 15

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81 C HAPTER 4 RESULTS Before and After Least Cost Corridor Modeling Regional Scale Analysis Before. The development de signs are reproduced in Figure 4 1 A C to allow for convenient qualitative comparison between the before and after LCC analysis. Figure 4 3 displays the regional scale LCC surface before development occurs on the SLP property. Outlined in red inside the inset map is the general study ex tent of this research. The Priority 5 portion of the FEGN that passes through the Waccasassa Flats is seen entering Gilchrist County at the Southeastern corner between Levy County and Alachua County, and exiting the Northern extent of the Waccasassa Flats in the North between Suwannee County and Columbia County. The before development cost surface is represented as a gradient of green to purple, signifying low cost to traversal and high cost respectively. Several qualitative inferences can be made from the cost surface. The Waccasassa Flats are distinctly outlined in the center of the county by a matrix of low cost and very high to moderately high costs to traversal. Urban centers are indicated as dense sections of purple with a value of 9.84 (Figure 4 2). Even though the maximum potential value of a LCC surface is 10, the raster calculator factors all weighted variables before assigning a value to each cell. For example, located 10 miles east of the Gilchrist County border and approximately 10 miles South o f the label in reality than is represented by the 9.84 value for cost to traversal in the LCC surface. Radially as one moves away from the urban center, the outer limits of the city diminishes in val ue as lower rankings for other variables take over.

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82 The extent of the before development scenario LCC is observed as a band of light yellow. The total acreage of the corridor at the regional scale is 50, 857.02 acres and is predominantly concentrated within the Waccasassa Flats. The maximum width of the before development scenario is 4.89 miles and the minimum width, also referred to as the bottleneck, is 2.08 mi (Table 4 1) A mask was used during data processing to control for sections of high cost to trav ersal, yet some small, isolated areas of high cost can still be seen within the corridor. Comparison to LandSat imagery reveals these areas as small ranchettes or one housing unit/10 acres. I n the real world i t is assumed that Florida black bears would no t be as discouraged to use the areas around these dwellings compared to urban centers. At the regional scale small perforations in the modeled corridor would not influence the landscape process of movement but it is clearly evident in the areas outside of the Flats that the synergistic effect of multiple perforations creates large swaths of unsuitable habitat for bear movement. After. This concept is distinctly recognizable in the after development scenarios. Figures 4 4 4 6 display the regional scale pe rspectives of the three after development scenarios. The a fter development LCC s are observed as a band of light orange in each of the maps. In all scenarios the geospatial distribution of development within the SLP has an effect on the total extent of the regional scale corridor. Seen in Table 4 1 the total acreage of the corridor at the regional scale decreases to 29723.13 ac., 34,553.14 ac., and 39,307.43 ac. for Group 1, 2, and 3 respectively. In the Group 1 scenario, the maximum width of the regional c orridor decreased to 2.99 mi. or 61% of the before scenario maximum width. The bottleneck in Group 1 decreases to 1.34 mi. or 64% of the before scenario. The Group 2 scenario maximum width decreased to 3.28 mi. and

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83 the bottleneck decreased to 1.38 mi. The Group 3 scenario maximum width decreased to 3.6 mi. or 73.6% of the before scenario maximum width. The bottleneck in Group 3 only decreased to 1.81 or 87% of the extent of the before d evelopment scenario bottleneck. The scenario examples illustrate the reg ional impact of development within the Waccasassa Flats. When development occurs in the SLP property the small perforations in the before scenario model are amplified. Directing attention to the northern extent of Gilchrist County, a comparison between Fig ure 4 3 and Figures 4 4 4 6 reveals that development in the South forces the after model to consider the isolated perforations in the before model as barriers to landscape level bear movement. The initial qualitative comparison at the regional scale pin points indicators of reduced facilitation of wildlife movement in a regionally significant ecological corridor. Site Scale Analysis A site scale analysis appropriately reinforces the issue of functional landscap e process connectivity. Figure 4 7 is a site scale depiction of the three after scenario LCCs. At this scale, the individual units of additional development are distinguished from t he surrounding, low cost, natural space as high cost to traversal in the LCC surface A comparison of the site scale per spective to each of the Group designs in Figure 4 1 A C shows how the raster calculator variably interprets the different geospatial arrangements of land uses. After seeing the simple LCP model during the Fall 2012 Design Studio, e ach of the groups located the majority of commercial, residential and educational areas along the edges of the SLP property. Group 1 consolidated a significant portion of their development in one urban center on the E a stern edge of the property and along the Western Edge. This gro up also permitted

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84 development at the sharp central corner of the property and conserved the remaining open space. The result of this scenario development pattern was an overall reduction in within site corridor acreage from 20,844.46 ac. down to 11,50936 a c., or 55.2% of the original corridor size. Group 2 designed larger areas of recreational space nestled within the development pattern in the Western portion of the property, effectively conserving the Eastern portion of the property from development. The scenario development pattern of Group 2 accounted for only a reduction to 34,553.14 ac., or 71.8% of the original corridor size. Group 3 was similar to Group 2 in development in the W estern portion of the property. Rather than accommodating recreational sp ace within each portion of development Group 3 outlined the recreational space as a buffer from the remaining open space in the SLP property. To reallocate space Group 3 densified the development pattern further West of County Road 47 The Group 3 scenario development reduced the before corridor to 39,307.43 ac., or 83.4% of the original corridor size. At the regional scale scenario planning identified development in the SLP property as a contributory factor to corridor constriction. At the site scale the d evelopment scenarios provide clarity to the regional wildlife movement development relationship, indicating that encroachment toward the interior of the Waccasassa Flats from County Road 47 reduces th e total area of the modeled Florida black bear corridor. Comparative Analysis of a Surface Wildfire Data Processing General Statistics. In the Geostatistical Analyst feature in ArcGIS the fuel load estimates can be obse rved using a histogram. Figure 4 8 is the upland f uel load estimate and figure 4 9 is the wet land fuel load estimate. The mean fuel load value of 16.1 tons/acre in uplands is less than the 18.3 tons/acre mean value for wetlands. This

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85 is as expected. T he data load estimates lends significan t weight to accumulated leaf and duff layers found in damp wetlands, and conversel y penalizes more xeric uplands which typically have less dense, drier accumulation of duff For the upland fuel loads the median is reported as 17 tons/acre in Figure 4 8 T his is slightly greater than our mean and is represented in the histogram by marginal negative skewness at 0.116. The histogram appears to have a large proportion of values centered about the mean, which is reflected in a leptokurtic distribution. The his togram for wetland fuel loads varies greatly from uplands. The median value of 19 tons/acre and a skewness of 0.633 describe a more clustered quantity of fuel loads that are greater than the mean. The spread of the data in Figure 4 9 depicts an underrepre sented proportion of lower fuel load estimates for wetland points. Upland Inverse Distance Weighted (IDW) Analysis A considerable amount of time was spent confirming that all age classes and ecosystems were reflected in the data collection process. A visu al, exploratory analysis of both LCP timber surveys and water management district spatial data was performed. Furthermore, a visual comparison of this information to photographs taken during data collection was performed to validate that upland and wetland data collection points varied across the site. IDW was chosen because of its ability to predict surfaces based on local variation and for its sensitivity to clustering (ESRI 2011) Though the wetland IDW was analyzed first, for the purposes of this paper the upland analysis is described first. The Geostatistical analyst tool provides sufficient information to examine statistical errors between predicted IDW models The predicted models can be ocularly examined and

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86 iteratively refined until the most predic tive surface is created The Geostatistical Analyst tool first offered a base model with an adjusted power of 2. Changes to maximum and minimum neighborhoods made no significant difference to wetland nor upland surface evaluation. When the number of secto rs was adjusted to 4 the range of maximum error in the y axis decreased from 1.65 to 1.593 as seen in Figure 4 10. When adjusted to 8 sectors or to any variation in angular offset no significant improvements in predictability of the model occurred. Change s to the major axis also provided little variation in error. When the minor axis was adjusted from ~4500 to 10,000 the model improved. Residuals were noticeably minimized and all points shifted closer to the expected line. A range of minor axis shifts were explored and 15000 provided the most effective surface based on the upland points (Figure 4 11) Figure 4 14 A d epicts the improved model and Figure 4 15 A is the same model with the inapplicable wetland areas removed Wetland IDW Analysis. Figure 4 12 is the cross validation of the wetland base model. The initial base model with a power of 1 is adjusted to a power of 2, decreasing the range of error from 1.612 to 1.494. No significant change occurred by expanding the maximum or minimum number of neighborh oods. By changing the number of sectors the model improved. 8 sectors performed far better than any 4 sector model with angular change. Figure 4 13 shows the most significant model attained, with a y axis range of error of 1.377. In Figure 4 13 there is no ticeable conditional bias. This was first highlighted in the histogram of wetland points. IDW is extremely sensitive to outliers which may have adversely effected the representation in the model of actual fuel load variation across the site. To illustrate the effect of outliers, Point 45 provides an example. Figure 4 14 is

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87 a cross validation of the same 8 sector model but with Point 45 removed. Clearly the bias has changed, and the error range reports a maximum y axis of 1.03. What does this mean in reality ? The final 8 sector model with Point 45 inclu ded is depicted in Figure 4 15 B Point 45 has an approximate measured fuel load estimate of 6 tons/acre. The expected value predicted by the model for this point is 19.77 tons/acre. The effect of this conside rable variation in the model is the central light blue circle in Figure 4 15 B The low fuel load value here is adjacent to a heavily managed restoration site. Here the canopy has been thinned, the vegetation has received frequent fire return, and vegetati on has been restored to promote forage and habitat for species such as Bobwhite quail, gopher tortoise, and fox squirrels. This point was not excluded from the final surface because significant variation in fuel load in locations such as this will have dra matic influences on fire behavior when these estimates are exported into program FARSITE. A low fuel load may be all that is necessary to slow the rate of fire spread an d bring the flame length below a hazardous flame length The final wetland model is dep icted in Figure 4 15 B, and Figure 4 16 B depicts the same model with the inapplicable upland areas removed. Figure 4 17 represents the final combination of both models into one Custom Fuel Model for the entire SLP site. As a result of the modeling process the PI hypothesizes that, by expanding the minor axis in both the wetland and upland IDW analysis, the custom model more appropriately captures the natural topographical bands that divide the landscape diagonally Northwest to Southeast into different vege tation classes (Figure 4 18) Comparison of Standard Fuel Models to Custom Fuel Model. Referring back to the literature review, the authors stated that standard fuel models as well as stereo photo series can be used as inputs for wildfir e

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88 modeling in program FARSITE. This method is useful when financial, accessibility, or time restrictions limit modelers from more precise field sampling. The conditions of this research permitted access to the site t o collect fuel load information and r edu ce generalization about the current fuels at the SLP site. The custom fuel model depicted in Figure 4 17 is more precisely described as a spatial surface of the entire property with 1 integer number assigned to each 30 x 30m cell. The 1 integer number rela tes to the 1 custom fuel model that most accurately describes the fuels of that cell. The color variation in Figure 4 17 corresponds to 18 different classes of geostatistically estimated custom fuel models at the SLP site. A qualitative comparison of the c ustom fuel model information to the Scott and Burgan standard fuel models will help demonstrate how a custom fuel model even at the regional scale i ncrease s predict fire behavior Table 4 2 displays the 18 custom fuel models t hat best describe the fuel conditions at the SLP site. The custom fuel models are named FM15 32 because FARSITE recognizes FM1 13 as the fuel models originally described by Anderson in 1982 Table 4 3 shows the relationship of the 18 custom models to the standard fuel models. The parameters in Table 4 3 are adapted from the description of standard fuel Standard Fire Behavior Fuel Models: A Six standard fue l models correlate to the 18 custom fuel models at the study site. The geostatistical estimates of fuel loading minutely varied in Fuel Models 15 19 (FM15 model This class is described as grasses or s hrubs mixed with litter from the forest canopy (Timber Understory) in a

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89 humid climate. TU3 specifically has a fuelbed with moderate litter load with grass and shrub components, a high spread rate, and moderate flame length (Scott and Burgan 2005). The cust om FM15 19 varied strongest from the standard model in a significantly lower measurement of 1hr and 100hr fuel loading. All other categories of fuel loading begin in FM15 at lower quantities in tons/ac and then increase to or marginally surpass the TU3 in FM19. The subtle variation is expected in FM15 19 because these fuel models are aggregated in a small section of the total property size located in the Southwest corner and partially in the Northeast corner. FM20 significantly changes in terms of mass of d owned woody debris and total fuel bed depth. class is described as nearly pure grass and/or forbs, with moderately course continuous grass coverage that averages about 1 foot in depth. The spre ad rate in GR2 is typically high with moderate flame lengths (Scott and Burgan 2005). GR2 regularly has no large downed woody debris and has 0.10 tons/ac of 1 hr fuels. FM20 was significantly influenced by the quail fields in the central portion of the SLP property that are managed with prescribed fire every year. FM20 also appears in the Northeastern corner and Southwestern corner that were recently prepared for pine seeding before data collection. The annual burn rotation explains why only 0.01 tons/ac o f 1hr fuels are present in FM20 and why the fuel depth was recorded at 0.30 ft instead of the standard 1.0 ft in the GR2 model. FM21 accounts for 42% of the entire custom fuel model surface. FM21 is most SH4 is des cribed as shrub coverage of at least 50%, with sparse to nonexistent grasses in a subhumid to humid climate. The

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90 loading is characterized by low to moderate shrub and litter load, with possible pine overstory, a fuel bed depth approximately 3 ft., and a hi gh spread rate with moderate flame length (Scott and Burgan 2005). FM21 differs most from SH4 by a significantly lower 1 hr fuel load, 0.03 tons/ac. in FM21 compared to 0.85 tons/ac. in SH4. This difference is compensated by a larger 10 h r fuel loading in FM21, at 1.50 tons/ac. compared to 1.15 tons/ac. The Lower values were expected in FM21 than those typical to SH4 because IDW in geostatistical analys t factors all data collection points across the regional space. FM22, which most closely relates to Scott characterized by slightly lower loading in all categories. FM22 had significantly lower live shrub loading at 1.30 tons/ac. compared to 6.20 tons/ac. SH3 has many of the same characteristics of SH4, but instead of a typically 3 f t fuel bed SH3 usually ranges from 2 3 ft. and has a larger fraction of herbaceous cover (Scott and Burgan 2005). The live herbaceous content of SH3 can classi fy most southern rough that has lower spread rates and lower flame lengths. SH3 covers 14% of the total fuel model surface and corresponds to the more xeric upland portions of the property. FM23, the last upland custom fuel model for the SLP property, relates most to SH9. SH9 is described as dense, finely branched shrubs with significant fine dead fue l about 4 to 6 feet tall, creating the potential for very high spread rate and very high flame lengths (Scott and Burgan 2005) The very high loading in FM23 and the very tall high fuel bed depth (3.42 ft.) again are slightly lower than SH9. The concluding hypothesis reached from the evaluation of the upland custom fuel models is that use of the standard models during wildfire simulation would have led to arbitrarily larger flame lengths and spread rates at the SLP property

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91 FM24 32 variably relate to TL 5. TL5 is dead and down woody fuel litter beneath a forest canopy. Specifically TL5 is characterized by high load conifer litter, with light slash, a low spread rate and a low flame length (Scott and Burgan 2005). FM24 30 appear to steadily increase in 10 hr fuel loading up to the standard TL5 loading value, and are severely under value in all other categories. FM31 and FM32 both have significantly larger 10 hr fuel loads at 2.01 and 6.00 tons/ac, and signicantly larger live shrub loading at 1.20 and 5.10 tons /ac. The variation in FM24 32 is a result of highly variable fuel measurements across the wetland portions of the SLP property, including multiple very high fuel load measurements in the center of the property. Simulations of Surface Wildfire The purpose of the wildfire simulations was to create a scenario where flame lengths can be measured and the resiliency or otherwise the vulnerability of the development design components can be interpreted. A single wildfire simulation at any of the fire intensity pe rcentiles could not cover the entire SLP site. To account for this, at each percentile of fire intensity two wildfires were simulated using two different ignition points. The result was six simulations of low, medium, and high fire intensity for Group 1, 2 and 3 for a total of 18 wildfire simulations. A few qualitative inferences highlight the success of the wildfire simulations. Screen captures of the final extents of the wildfires are displayed in Figures 4 19 4 36 beginning with low fire intensity an d progressing through high fire intensity A graph of the total fire area (acres) over time (days) is paired with each simulation (Figures 4 46 4 48) D emonstrating first with Figures 4 19 the spread of the surface wildfire is depicted as concentric whi te bands expanding outward from the ignition point. Each individual band corresponds to a two hour time step. The darker clusters that appear as

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92 bolder white lines therefore are depictions of a decrease in the rate of spread of the flaming front. Directin g attention secondly to Figures 4 23 4 29 and 4 35 the progression from Low to M edium to H igh fire intensity is portrayed as an example for one of the studio group designs (Group 3). Starting in Figures 4 23 the proximity of one band to the next illust rates the relatively low rate of spread throughout the entire simulation time frame. Compact banding is typical in a L ow fire intensity scenario where variables such as high humidity or low wind speed negatively influence fire behavior. In Figure 4 29 it is noticeable that in the M edium fire intensity scenario there are 8 or more distinct time p eriods where the rate of spread decreases the advancement of t he propagating front The L ow fire intensity scenario had only 4 or more of these abeyances which acco unts for the larger total fire area in Figure 4 23 Qualitative comparison of the graphs indicates the difference betw een Medium and L ow fire intensity Ocular estimation from the M edium fire intensity graph in Figures 4 47 shows a noticeably more exponent ial rate of spread toward the end of the simulation than the L ow fire intensity graph in Figures 4 46 During the simulation the distance between bands began to increase in the North and Northwestern portion of the SLP property as the graph of fire area be came more exponential. Observation notes marked stronger winds during that time frame as the reason for the difference between L ow and M edium fire intensity Lastly, Figures 4 48 displays the H igh fire intensity scenario for Group 3. In the High fire inten sity scenario the total surface fire area is app roximately 7,320 ac., compared to 6,300 ac. in the Low fire intensity scenario. At first glance this appears to be a discrepancy in the simulator. However there is clear justification that distinguishes the

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93 H igh fire intensity scenario. Evident in the banding pattern the High fire intensity scenario has the greatest distance between hourly time steps of all of the scenarios. Moreover, the duration of the Low fire intensity scenario is 10 days, whereas the High fire intensity scenario occurs within an 8 day interval. Fire behavior in the High fire intensity scenario is intensified due to extremely high winds from the West throughout the simulation. Banding structure and qualitative analysis confirms that suffici ent variability exists between the fire intensity percentiles. The next section will examine the resulting surface wildfire flame lengths and interactions between fuel loads, flame lengths, and geospatial locations of development design components. Wildfir e Flame Lengths and Development Scenarios The surface wildfire flame lengths are visually displayed in Figures 4 37 through 4 45 In each map the flame lengths vary from 0.01 ft, represented in dark blue, up to 4.40 ft represented in red. In the previous section the white hourly time bands were qualitative indicators of spread rate. There is evident consistency between the time bands and the flame lengths. T he flame lengths va ry clearly demonstrate the pulses in fire spread rate during the wildfire simulat ion regardless of low, medium or high fire intensity The range of fuel load classes also had a distinct effect on fire behavior. The flame lengths are uniform to the final fuel model surface in Figure 4 17 Each of the three development design scenarios had unique proportions and geospatial locations of built land uses. The only land uses that occurred in all three of the devel opment design scenarios were commercial areas, low density residential, medium density residential, and high density residential a reas. Figures 4 49 A C show the average flame lengths in each of these four land use classes, and each of the bar graphs represents a different f ire intensity scenario. The maximum and minimum

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94 flamelengths recorded in each of the categories are displayed o n the graphs. In general the graphs show that the greater the fire intensity scenario the greater the flame length. For example, in Group 1 the flame length grew from 1.84 ft., to 1.76 ft., to 2.38 ft. in low density residential for low, medium, and high f ire intensity scenarios respectively. This outcome was expected because intensification of weather and wind generally increases fire behavior. In all Groups and all fire intensity scenarios the commercial areas displayed the lowest flame length s, ranging f rom approximately 0.40 ft 2. 15 ft. The between group variation in commercial areas was also minor. Group 1 had the largest flame lengths in residential medium density areas in all fire intensity scenarios. From low to high fire intensity scenario the fla me lengths in residential low density lots ranged from 1.21 ft. to 1.56 ft. The flame lengths in medium density lots varied similarly, ranging from 3.05 ft. to 3.39 ft. Group 1 also had the largest flame lengths in the commercial areas. From low to high fi re intensity scenario the average flame lengths grew from 2.0 ft 2.17 ft Figure 4 50 A displays the justification behind why the flame heights are larger in Group 1 and 3 In Figure 4 50 A the high fire intensity scenario wildfire is represented on a va riable surface of flame height with the Group 1 design and roadway network visible beneath the surface. The simulated surface wildfire shows rapid intensification in flame height as the flaming front of the wildfire r eaches these developed land uses High fuel loading in upland areas surrounding these land uses contributes to the overall vulnerability of the Group 1 and 3 development design to wildfires. In all scenarios Group 2 had the lowest average flame lengths per land use class. The maximum average fl ame lengths for Group 2 were in the high fire intensity

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95 scenario in the residential medium density land use class, at 0.66 ft, and in the low fire intensity scenario in the residential low density land use classes, also at 0.95 ft. The principle reason for low average flame lengths in all Group 2 scenarios is the placement of developed land uses in areas of the property with currently low fuel loading. By random chance Group 2 placed a significant portion of their developed land use components in a section of the SLP property containing exceedingly low fuel loading. The low loading in this portio n is the reason why the wildfire simulations did not carry fire through the East Central part of the property. This concept is more accurately portrayed in Figure 4 50 B In this map of variable flame height, growth in flame height developed areas. Alternatively, the East Central part of the property shows no active fire during the wildfire simulation.

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96 Figure 4 1 A C Deve lopment designs created in Fall 2012 Studio. These have been reproduced from Figure 3 7 in this section for ease of access. Figure 4 2 Legend for All Least Cost Corridor Maps (both Regional and Site Scale).

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97 Figure 4 3 The Least Cost Corridor for the before development scenario.

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98 Figure 4 4. The Least Cost Corridor for the Group 1 after development scenario.

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99 Figure 4 5. The Least Cost Corridor for the Group 2 after development scenario.

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100 Figure 4 6. The Least Cost Corridor for the Group 3 after dev elopment scenario.

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101 Figure 4 7 Site level comparison of the change in size of a least cost wildlife corridor as a result of land use changes from each development design. In this representation t he Before Corrido r is indicated in light yellow, and t he After corridor is indicated in o range.

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102 Table 4 1 A Corridor Analysis quantifies the change in corridor size between groups. Metric Change Measurement Before Corridor After Corridor Group 1 Group 2 Group 3 Maximum Width (miles) 4.89 2.99 3. 28 3.6 Average Width (miles) Bottlenecks (miles) 2.08 1.34 1.38 1.81 Total Acreage (County) 50857.02 29723.13 34553.14 39307.43 Total Acreage (Site) 20844.46 11509.36 14959.84 17374.62 Percent Change Measurement Before Co rridor After Corridor Group 1 Group 2 Group 3 Maximum Width (%) 100 61.1 67.1 73.6 Average Width (%) Bottlenecks (%) 100 64.4 66.3 87.0 Total Acreage (County) 100 58.4 67.9 77.3 Total Acreage (Site) 100 55.2 71.8 83.4

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103 Figure 4 8 Histogram with general statistics for upland fuel load estimates. Figure 4 9 Histogram with general statistics for wetland fuel load estimates.

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104 Figure 4 10 Upland fuel load IDW cross validation of base model with power of 2 com pared to power of 2 with sectors adjusted from 1 to 4 sectors.

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105 Figure 4 11 Upland fuel load IDW cross validation of 4 sectors to 4 sectors with a minor axis change to 15000.

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106 Figure 4 12 Wetlands IDW of Fuel load. A power level of 1 cross validated with a power level of 2.

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107 Figure 4 13 Wetland IDW of power level of 2 compared to 4 sectors, than to 8 sectors

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108 Figure 4 14 Wetland IDW cross validation with Point 45 removed.

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109 Figure 4 15 A. Upland IDW Final Model (Left) B. Wetland IDW Fina l Model (Right).

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110 Figure 4 16 A. Upland IDW Final Model Clipped (Left) B. Wetland IDW Final Model Clipped (Right)

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111 Figure 4 17. Suwannee Lake Plantation Combined Final Custom Fuel Model.

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112 Figure 4 18 Land us e classes for the Suwan ee Lake P lanta tion.

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113 Table 4 2. Program Screen Shots of Custom Fuel Models as entered into Program FARSITE.

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114 Table 4 3. Program Screen Shots of Custom Fuel Models and their associated Standard Fuel Models that were u sed as a Base for processing and input into P rogram FARSITE.

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115 Figure 4 19 Group 1, Low Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflectin g a change in the fuel model.

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116 Figure 4 20 Group 1, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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117 Figure 4 21 Group 2, Low Fire I ntensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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118 Figure 4 22 Group 2, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. T he alternating color patterns are only color symbols reflecting a change in the fuel model.

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119 Figure 4 23 Group 3, Low Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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120 Figure 4 24 Group 3, Low Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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121 Figure 4 25 Group 1, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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122 Figure 4 26 Group 1, Medium Fire Intensity 2. Program FARSITE displays each hour as a white b and. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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123 Figure 4 27 Group 2, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols re flecting a change in the fuel model.

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124 Figure 4 28 Group 2, Medium Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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125 Figure 4 29 Group 3, Medium Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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126 Figure 4 30 Group 3, Medium Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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1 27 Figure 4 31 Group 1, High Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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128 Figure 4 32 Group 1, High Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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129 Figure 4 33 G roup 2, High Fire Intensity 1 Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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130 Figure 4 34 Group 2, High Fire Intensity 2 Program FARSITE displays each ho ur as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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131 Figure 4 35 Group 3, High Fire Intensity 1. Program FARSITE displays each hour as a white band. The alternating color patterns are only colo r symbols reflecting a change in the fuel model.

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132 Figure 4 36 Group 3, High Fire Intensity 2. Program FARSITE displays each hour as a white band. The alternating color patterns are only color symbols reflecting a change in the fuel model.

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133 Figure 4 37 Group 1, Low Fire Intensity Flame lengths are represented from low to high as blue to red.

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134 Figure 4 38 Group 1, Medium Fire Intensity Flame lengths are represented from low to high as blue to red.

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135 Figure 4 39 Group 1, High Fire Intensity Flame l engths are represented from low to high as blue to red.

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136 Figure 4 40 Group 2, Low Fire Intensity Flame lengths are represented from low to high as blue to red.

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137 Figure 4 41 Group 2, Medium Fire Intensity Flame lengths are represented from low to hig h as blue to red.

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138 Figure 4 42 Group 2, High Fire Intensity Flame lengths are represented from low to high as blue to red.

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139 Figure 4 43 Group 3, Low Fire Intensity Flame lengths are represented from low to high as blue to red.

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140 Figure 4 44 Group 3, Medium Fire Intensity Flame lengths are represented from low to high as blue to red.

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141 Figure 4 45 Group 3, High Fire Intensity Flame lengths are represented from low to high as blue to red.

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142 Figur e 4 46 Spread Rate Comparison, Low Fire I ntensity Group 1, Group 2, Group 3 (Left to Right).

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143 Figur e 4 47 Spread Rate Comparison, Medium Fire Intensity Group 1, Group 2, Group 3 (Left to Right).

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144 Figur e 4 48 Spread Rate Comparison, High Fir e Intensity Group 1, Group 2, Group 3 (Left to Right).

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145 A B C Figure 4 49 Bar graphs representing the average flame lengths of a surface wildfire. The flame lengths are p resented per the land use classifications of the scenario development designs. A. The Low Fire Intensity scenario. B. The Medium Fire Intensity scenario. C. The High Fire Intensity scenario.

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146 A B Figure 4 50 ArcScene screenshot of flame length displayed using a variable base height for the flame length surface. A. The Group 1 design and roadway network is visible in areas with the greatest flame lengths. B. The group 2 design and roadway network.

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147 CHAPTER 5 DISCUSSION Resiliency of Development, of Wildli fe Movement, and of Fire dependent Systems Reiteration of Target Objectives The initial claim of this research was that planners have little recognition of the upland side of the synergistic watershed and fireshed system when risk assessments for resilien cy are performed in the development design phase. The research approach was to determine the scenario planning outcomes o f a development project in Florida by first modeling ecologic ally significant ecosystem corridors then designing developments, a nd the n modeling wildland fire before physical constr uction occurs. The resulting outputs of the data analysis can now be evaluated for any new significance or practical application in the planning field. The two embedded units of analysis were intended to answ er the following questions: 1. To compare proposed development designs by their impact on the facilitation of wildlife movement. i. What is the effect of the application of wildlife corridors modeling on development design during the pre development, site synthe sis phase? ii. What is the impact of development design on the facilitation of wildlife movement in regionally significant ecological corridors? 1. To rank proposed development designs from most to least resilient to wildfire. i. What is the effect of development d esign on fire behavior (flame length, area burned, burn duration, and spread rate) during a wildfire? ii. What characteristics of development design increase vulnerability to wildland fire in an ecologically significant corridor? iii. What lessons can be learned f rom fuel load estimates in ecologically significant areas adjacent to future development in the Wildland Urban Interface?

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148 Development D esign and Regional W ildlife M ovement The development design studio was clearly influenced by the LCP base model. All thre e of the development designs integrated the concept of regional wildlife movement in the pre development, site synthesis phase The original conditions of the studio set a target goal at 30% development and 70% conservation of the ~28,000 ac. property. The final thre e development designs maximized at less than 20% development and had more than 80% of land in conservation uses. The scenario of resilient regional wildlife movement is evident in the placement of developed land uses along the periphery of the p roperty boundary in all designs Yet for pre development site synthesis a simple LCP model did not wholly convey the concept of regional scale processes Th e areas of blue grey in Figure 4 1 A and B are areas that were identified by Groups 1 and 2 to be c onserved space for wildlife. Groups 1 and 2 took the concept of a least cost path and determined that wildlife movement is flexible, and will adapt to land development in an ecological corridor. The value of this statement is expressed in the more technica l LCC models. The before and after least cost corridor models conveyed how design at the property scal e effectively bottleneck s functional movement of large ranging wildlife at the regional scale At the site scale roughly the same proportion of open space was provided by the Group 1 and 2 designs as was provided in Group 3 The authors believe that the practical reality of a bottleneck does not result in complete severing of regional wildlife movement. Instead the flexibility and adaptation of wildlife mov ement manifests in greater human wildlife conflict. The small percentage of commercial and housing development toward the interior section of the property in Groups 1 and 2 increases the likelihood that Florida black bears will encounter built land uses an d

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149 become earmarked as nuisance s. Often, the outcome for nuisance bears in Florida is relocation or alternatively euthanization when the initial conflict in a greenfield development could have been resolved in development design. Similarly the developed ar eas and roadway infrastructure create more susceptibility for vehicular collision with terrestrial wildlife. The outcome of modeling before and after LCC scenarios is that, yes, some open space intermixed into development designs is necessary to preserve e nvironmental and ecological assets as well as produce economic incentives that make a development human habitat; a place and space that people want to live not just reside. However landscape process modeling of large ranging wildlife indicates that a balan ce of abundant space must be provided to preserve the value of land in ecological corridors as functional wildlife habitat. Development Design and L andscape Level Wildfire Movement To preface, t his research provided little evidence that development design had any influence on flame length during a scenario wildfire. The lack of evidence can be explained by the fuel model inputs for wildfire simulation. The lake inputs distinctly carried no fire during the simulation. It was anticipated that the major road n etwork inputs would result in alteration in fire behavior and reduction in spread rate. The width of existing county roads and additional roadway networks that would provide access to the development design components in each group were less than or equal to 30 ft. The wildfire simulations showed no inhibited behavior when reaching these additional fire break simulation to classify developed land uses. However the purpose of this w ork was also to determine the flame lengths of the flaming front of a wildfire intersecting different development components, and so use of FM99 would have been counterintuitive

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150 Resiliency analysis of a development design to wildfire has multiple opportun istic outputs. Outstanding, Group 2 is the most resilient development design to all wildfire scenarios. The results section identified that resiliency was achieved in the Group 2 design because a substantial quantity of development was placed in a section of the SLP property with low fuel loading. Under a jurisprudence discipline this occurrence would be classified as a case of the tipsy coachman doctrine. The authors find it necessary to insert an excerpt the same way that the Supreme Court of Georgia qu oted (1879)): His conduct still right, with his argument wrong; Still aiming at honour, yet fearing to roam, The coachman was tipsy In the case of resiliency planning, Group 2 reached the correct result based on the wrong reasoning (Herb and Kauffman 2007) note that the 2003 LANDSAT Enhanced Thematic Mapper satellite i magery was used to identify areas of bare or clear cut soil, as well as open brushland (Conservation Design and Planning Studio 2012) The notes further state that the purpose of this method was to target areas with low cover and low ecosystem service valu e, and use these already cleared sites as an opportunity to improve the state of land through intense development (Conservation Design and Planning Studio 2012) This reasoning is flawed. The area of the property with low loading in fact coincides with 3,0 00 acres of quail fields, heavily managed by the Suwannee Lake Plantation to provide quality habitat for quail. The qua il fields are managed to resemble an open pine savanna and prairie ecotype by means of an active fire management program. The fire manage ment

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151 program includes an annual burn of all 3,000 acres. The Spring burn rotation promotes a greater diversity of animal and herbaceous species and habitat in this location than compared to the remainder of the property. A c onservative estimate for the eco system service value of this type of private forest land is $1 615.00/ac./year for pollination, habitat conservation, and aesthetic services alone or a benefit transfer value of $4,845,000 USD/yr. for this 3,000 acre area (Moore 2011). Generalization of the LANDSAT imagery classified the area of low loading as open brushland. The concept of he general classification further led Group 2 to assume these lands had a low ecosystem service value and were therefo re most suitable for development. A lesson learned from fuel load estimates in ecologically significant areas adjacent to future development in the WUI is that expansion of existing fire management programs before development occurs can lead to increased d evelopment resiliency and increased local biodiversity Redundancy and Synergies Redundancy. Taken together, a synthesis of wildlife corridor modeling and wildfire modeling convey s information that is perhaps not visibly understandable. Redundancy was incl uded in this analysis for two reasons. The first is this research was intended to explore how effective scenario planning of fire dependent ecosystems and landscape level processes can be during the development design phase. Redundancy using two embedded u nits of analysis gave the research its own resiliency, in case either unit of analysis failed to contribute to discernible differences between development design scenarios The second is redundancy allowed the researche r s to identify cross scale interactor s or even cross method interactors, in case the units of

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152 analysis collectively construct a new implication for practical application in the field of environmental planning. Synergies of W ildlife Continuity and Wildfire Continuity. The success of merging t he modeling techniques is dependent on the basic model inputs. Similarities between the least cost corridor cost raster and the custom fuel model raster allow for b oth modeling techniques to be overlapped to examine cross model interactors and reinforce th e issue of landscape level processes and development Preliminary results for Group 1 suggest l ow fuel loading is a characteristic analogous to fuel continuity and wildlife continuity, and decreases vulnerability of development to wildfire in eco logically significant greenway corridors However, this analysis cannot be completed with the information gathered in this research. The entire corridor of the Waccassassa Flats cannot be merged with the partial coverage of the wildfires. To perform this a nalysis expanded estimates on fuel loading and canopy cover are required. Limitations The first limitation of this research originated as a result of the Fall 2012 design studio. The studio allowed each group to foster innovative approaches to planning and design that could balance development values with a strong stewardship ethic (Conservation Design and Planning Studio 2012). An unintended consequence of the flexibility of the studio was each group did not necessarily need to create mixed use areas, arti st village lots, conservation subdivision lots, urban parks, eco resort areas, or institutional areas. The flexibility caused an issue with the second unit of analysis, development resiliency to a surface wildfire hazard During data processing r esiliency of a development design to wildfire was only comparable in 4 of 14 final development components. Auspiciously, compared to other infrastructure investments the

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153 development components that were able to be analyzed are those that are most frequently at the f ocal point of wildfire mitigation planning; i.e. commercial areas, low density, medium density, and high density residential areas. A second limitation to the research process was the timeframe of the thesis research design. The fuel load data collection d id not occur until Summer of 2013. For this reason there was no opportunity to directly demonstrate wildfire movement in the Waccasassa Flats to the Fall 2012 planning studio before the development design phase began In a two year graduate program small t imeline limitations are bound to befall the research design The negative side to this limitation is the studio groups had limited to no exposure to how fuel loads in the landscape can create hazards for greenfield development in the WUI The beneficial ou tcome is this limitation also acted as a control for each development design group. If the researchers disclosed the location s of low, medium, and high fuel loading the development designs could have been biased. Instead this research was able to conclude with validation that a knowledge gap exists for development in fire dependent ecosystems. Circumstantially, a recommendation for future research with development in fire dependent ecosystems is to have each design group perform scenario exercises where the groups are instructed to adjust fuel load models and in real time see the effects that different fuel loads have on the severity of wildfires. A third limitation is a compounded result of the previous timeframe issue. Data collection in Summer of 2012 wou ld have created extremely different fuel load estimates than those collected in Summer of 2013. In addition to seasonal wet and dry seasons, Florida experiences decadal drought and sequences of abundant water. Half way

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154 through data collection in the Summer of 2013 Florida had an above normal deposit of rain. Many of the wetland and upland data collection points that could have been measured successfully were instead inundated with water. A sufficient amount of data collection points were collected to meet t he needs of statistical significance (>30), but more points would have led to more accurate fuel load models and potentially a larger quantity of custom fuel load models. Additionally, the restricted time frame limited modeling to only surface wildfire mov ement. If qualitative and quantitative canopy data were collected, simulations of canopy fires could have also been modeled. Once more, this limitation is often inevitable in field data collection in a two year graduate program. The final limitation is a result of redundancy analysis. This research focused on the regional movement of U. americanus floridanus and landscape level interaction with a large scale development. The Florida black bear is known to forage in recently burned patches, but areas freque ntly burned may not always be good or ideal bear habitat. Particularly, insufficient forest cover information creates an issue with hypothesizing that low fuel loading has a relationship to low cost to traversal for a large ranging species like the Florida black bear. A suggestion for future research is to use a suite of species in addition to U. americanus floridanus to concentrate corridor analysis down to smaller scales within the landscape that are maintained by fire for habitat. Supplementary scenario planning investigation into connectivity of wildlife movement in low intensity fuel types would complement the previous conclusion of this work; which suggested that expansion of a fire management program before development begins could dually serve to inc rease wildfire resiliency and promote habit at that harbors fire adapted species. To offer an example potentially f ox squirrels ( Sciurus niger ) could be used to model site or

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155 local scale connectivity against assumptions of fuels based on various scenarios of fuel loading. Conclusion Outcome 1 This research project shows the response of regional scale wildlife movement within an ecologically significant corridor to the pattern of development design. The research also show s that as the geospatial location of land use type changes, the relative importance value of land as wildlife habitat shifts. What does this mean? In the Waccasassa Flats the shift from the before corridor scenario to the after corridor scenario concentrates the importance of lands in the int erior portion of the property. This provides evidence that planners and architects can work with developers at regional and local levels to minimize the impact of development on landscape level processes. In Florida g rowth pressure will continue to encroa ch new development into ecologically significant lands S ince planners have the resources t o identify development before the construction phase planners can use capacity building and community education to inform the operation managers of adjacen t public and private open space. In this case, i nformation transfer can shift the expenditure of resources and funds to protect potentially newly important land in the interior of the County Outcome 2 The method ology used to address this research topic is multifac eted and has a high level of complexity. To integrate the two units of analysis required the work of regional and local geospatial data sources, as well as four different analytical software programs. ArcGIS played an important role as a platform to operat e between the four programs. Modelbuilder in ArcGIS allowed the researchers to create individual Tools for

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156 each step. Albeit a considerable amount of time was expended operationalizing the Tools and creating each input, once the Tools were created and coll ected into a single Toolbox the researchers were able to replicate the method at a m uch faster rate. Now that a Toolbox exists for analyzing landscape processes in fire dependent systems the next step in the research is to explore the transferability of th e method to other developments of regional impact in the wildland urban interface. Recommendations for P lanning I mprove ment in Interior Counties The first recommendation for planners in interior counties is to rely on both regional wildlife movement model ing and wildfire modeling before developing designs. If planners and designers cannot rationalize the impact of development on long term functional connectivity than their ability to see greenfield development in the context of regional processes will be i nhibited. Both modeling techniques require a particular level of scientific knowledge to successfully illustrate the accurate details of landscape process data. But as mapping technologies improve this simple type of mapping interface will become a reliabl e method to transfer knowledge. Development has become a joint venture requiring public and private partnership. This research justifies that interior counties and developers literally cannot afford to subject development to the damages of catastrophic wil dfire. Equally, community risk assessment and mitigation plans in interior counties cannot afford to be responsive after greenfield development occurs. Budgetary constraints and insufficient partnership between regional, state, and local planners and priva te developers continue to be cited as reasons for why wildfire assessments are not implemented. Indisputably community Wildfire Protection Plans and Firewise Communities Recognition Programs are invaluable options for communities. The impact

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157 of pro active scenario planning for wildfires fits the informative gap in wildfire protection and identifies the regional impact that development in the WUI has on fire dependent ecological systems, not just the impact the fire has on development. The last recommendatio n to improve planning in interior counties is to correct the maldistribution of development design above and below the 100 year floodplain. To reiterate from Chapter 2, at the State level in Florida there is a 3:1 ratio of Community Resiliency Initiatives between watershed planning and fireshed planning, and fire is only regarded in one of four section in the Hazard Mitigation Planning Initiative. Interior counties which host environmentally sensitive, fire adapted ecosystems face the challenge of balancing watersheds and firesheds with human population growth in the wildland urban interface Given the cost efficiencies of computer modeling, it is becoming more and more opportunistic to scenario plan the resiliency of development designs to wildfire at a fra ction of the cost that the impact of wildfires would have on developments and human security.

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158 APPENDIX A LEAST COST CORRIDOR METHODOLOGY In ArcGIS 10.1, the Geoprocessing environment is first set. A Study Extent shapefile will be created using the Selec t feature to select the counties surrounding Gilchrist County from the county boundary shapefile from FGDL. A split function will be used to constrain the area within counties that are west of I 75, and the final Study Extent is be exported as a shapefile. The Environment Geoprocessing Dialog will be accessed from the Geoprocessing tab to input the following settings: The extent is set to Study Extent. The Raster Analysis is set to a 10 x 10 m cell size. The Mask is set to Study Extent. The LCC is created using the ModelBuilder window. In ArcGIS 10.1, The LCC is created using the ModelBuilder window. To prepare all inputs for use in the Raster Calculator all features are added, transformed to match projection and resolution, and reclassified as habitat fact or raster data on a 1 10 scale. The first required input feature is land cover data. The Gilchrist County Land Use shapefile is transformed into a raster dataset using the Polygon to Raster tool. Cover typ es are reclassified Proximity to lands already man aged by the State of Florida (FLMA) is included in this input. Euclidean Distance to the shapefile, FLMA, will be used to create a raster classification. The next input is Topography. A Topography dataset for Gilchrist County is imported from the FGDL. Th e Topography dataset is the latest USGS digitization of quadrangle maps using five foot contour elevations (FGDL 2011). Topography for Gilchrist County is transformed using the Slope tool found in the Spatial Analyst toolbox

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159 under Surface. Due to the low t opographic change in the Study Extent, an exponential classification is used to reclassify this input. The next input is Elevation. Here, low variation in elevation change will create problems distinguishing permanent water bodies from seasonally inundated wetlands. Elevation within the Study extent will be substituted for water bodies. Euclidean distance to water bodies is used to convert the shapefile information into raster. An exponential classification is used again to reclassify this input. The last i nput is Road Density. Using the USGS digital line graph for the State of Florida, major roadways are clipped to the extent of Gilchrist County. Next, the major roadways shapefile is processed to remove trails and other low impact road classes. The density of remaining roadways is determined using the Line Density tool from the Density section of the Spatial Analyst toolbox. The area units will be specified to square meters. After all of the habitat factor input rasters are reclassified, the Raster Calculato r tool is added to ModelBuilder to build a single Map Algebra expression. The expression uses mean factor weights (%) to synthesize and attribute a value for each cell in the new cost raster (ESRI 2011). The expression uses the following python sintax (" %flm a_raster%" 0.02) + ("%euc dis_rds_raster %" 0.15) + ("%dist_water_raster_ %" 0.02) + ("%rec lassify_slope %" 0. 13) + ("%Internal_parcel_raster %" 0.68) where Distance to FLMA is weighted 2%, Distance to Water Bodies is 2%, Euclidean Distance to roads is weighted 15%, Topography is weighted 13%, and Land Cover per parcel is weighted 68%.

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160 Next, the Cost Distance tool calculate s the lowest accumulative cos t resistance from the top of the FEGN network entering the Northern end of the Waccasassa Flat s and from the Southern end of the FEGN network entering the Waccasassa Flats. Finally, a cost corridor is determined between the Northern and Sothern blocks of the FEGN. The final corridor is converted using the S lice tool to keep only the top 3 % of the l east resistance corridor for comparison between the before development scenario and the after development scenarios. After. When the three development designs are finalized, adjustments are made to the Land Cover and Euclidean Distance to roads inputs to reflect changes in land use. For Land Cover, the dataset for each development design contains a GX_LandUse shapefile, where the groups are numbered G1, G2, and G3. The table for GX_LandUse contains the fields FID, Shape, and LU_Type. The LU_Type field cont ains a simplified land cover classification name that requires conversion in order for it to be reclassified. column identifying the appropriate land use description from the Land Use Gilchri st A relate is added to the GX_LandUse layer identifying the new description, DESCRIPT. Next, the Update tool from the Data Management Toolbox combines the Land Use Gilchrist County shapefile with the GX_LandU se shapefile. After this process is completed for each group the new land cover datasets are reclassified using the same scal ar template as the before model. For the after development Euclidean Distance to roads input, the dataset for each development des ign contains a GX_RoadNetwork shapefile that must be reconfigured. The Clip tool is used to constrain the road network to the Study Extent.

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161 Then, the Merge tool is used to combine the new road network with the already processed roadway shapefile (the road shapefile that already has trails and other low impact road classes removed). The density of the remaining new roadways is determined for each group design using the Euclidean Distance tool from the Distance section of the Spatial Analyst toolbox. The area units are specified to square meters. For the after development Euclidean Distance to roads input, the dataset for each development design contains a GX_RoadNetwork shapefile that must be reconfigured. The Clip tool is used to constrain the road network t o the Study Extent. Then, the Merge tool is used to combine the new road network with the already processed roadway shapefile (the road shapefile that already has trails and other low impact road classes removed). The density of the remaining new roadways is determined for each group design using the Euclidean Distance tool from the Distance section of the Spatial Analyst toolbox. The area units are specified to square meters.

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162 APPENDIX B SURFACE WILDFIRE MODELING METHODOLOGY Elevation, Slope, Aspect. To be gin the analysis, a Wildfire Vulnerability toolbox is created in ArcGIS 10.1. The first tool to be created in the toolbox uses an input Topography dataset to streamline the production of each of the Elevation, Slope, and Aspect input requirements into one step. In ModelBuilder, the input file is the Topography dataset for Gilchrist County. First, the contour data (in feet) is transformed using the Polyline to Raster tool from the Conversion Toolbox. Then, the Elevation raster is transformed using the Raste r to ACII tool from the Conversion Toolbox. The output is the ASCII input of Elevation (in meters) for the .FPJ file in Program FARSITE 4. Next, the Topography for Gilchrist County dataset is transformed using the Slope tool found in the Spatial Analyst to olbox under Surface tools. Next, the Slope raster is transformed using the Raster to ASCII tool from the Conversion toolbox. The output is the ASCII input of Slope (in degrees) for the .FPJ file in Program FARSITE 4. Last, the Topography for Gilchrist Coun ty dataset is transformed again using the Slope tool found in Spatial Analyst toolbox under Surface. The slope raster is then transformed using the Aspect tool, another tool found in the Surface tab. The output is the ASCII input of Aspect (in degrees and cardinal direction) for the .FPJ file in Program FARSITE 4. This tool is then saved into the Wildfire Vulnerability toolbox for future use. Field Data Collection. The final required input (Fuel Model) and the optional inputs (Duff Loading and Course Woody Debris) for Program FARSITE is based on field data collected on the SLP property in 2013. First, a new tool in ModelBuilder was created for random point generation. The ~28,000 acre SLP property was classified into uplands and wetlands. The FEMA 100 year f lood zone is used to delineate Suwanee

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163 River Water Management District land uses for Gilchrist County as uplands or wetlands. 100 random points were stratified between the two classes at 50 points per class. Using ArcGIS 10.1, an Average Nearest Neighbor c luster analysis was performed on each class individually, as well as aggregated together. The purpose of the cluster analysis was to determine if any portion of the property was underrepresented and likewise to avoid oversampling. XY Coordinates were attri buted to each of the random points. Data frame properties were adjusted so that the X and Y fields could be entered into the Calculate Geometry tool to be converted into UTM points. At the SLP property, the PI and undergraduate volunteers from the Universi ty of data collection. T hree transects are taken at azimuth (compass based) directions separated by a minimum of 60 cardinal degrees. At each point, a center stake is dr iven into the ground. For each of the three transects, high visibility open reel fiberglass tape is used to measure 60 feet in the given cardinal direction from the center point. All data is collected on the Data Collection Tool. Up to the first 6 ft. coun ts of 1, 10, 100, and 1000 hour fuels are tallied. At 6 ft. the litter depth and the duff depth are recorded in inches. Up to 10 ft. only counts of 100 and 1000 hour fuels are tallied. At 10 ft. a meter stick are used to plot a square meter on the ground. Percentages of cover are recorded for Live Herbaceous, Dead Herbaceous, Live Shrub, and Dead Shrub within the square meter. The heights of Herbaceous and Shrub cover are recorded at 10 ft as well. Up to 15 ft. counts of 100 and 1000 hour fuels are tallied. At 15 ft a second recording of litter depth and duff depth are recorded. At 30 ft. Herbaceous and Shrub measurements are recorded again. From 15 60 ft only 1000 hour fuels are recorded. For each point, a GPS

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164 tagged photo was taken. Lastly, if any GPS poin t was inundated with water it was marked as indeterminate and no measurements were taken. Field data was lastly transferred into ArcGIS. The Relate feature in ArcGIS was used to add to each point a table of the average measurements from the three transects Fuel Model, Duff Loading, and Coarse Woody Debris Variables recorded in the field are used to create three different spatial surfaces as inputs for program FARSITE 4. The first input to process was a Custom Fuel Model for the SLP property. The Custom Fu el Model is a raster coverage of numeric values used to predict surface fire spread. The Custom Fuel Model surface uses the herbaceous and shrub percentages and height estimates at each point as spatial references. The Geostatistical Analyst tool in ArcGIS was used to separately create an Inverse Distance Weighted (IDW) spatial surface for uplands and wetlands. Inferences were made from the general statistics window, and commentary in the Results section will provide logical flow to describe the decision pr ocess for determining each surface model. After the statistically appropriate surface model is created, the Merge tool is used to create the final Custom Fuel Model surface raster of the entire site. The raster file contains one integer number of a fuel mo del for each 30 x 30m cell. The raster was then transformed using the Raster to ASCII tool in Conversion tools. The two remaining inputs are Duff Loading and Coarse Woody Debris. For the Duff Loading input, the litter depth (in.) and the duff depth (in.) m easurements for each point are used in Geostatistical Analyst to process an ASCII spatial surface. The same method for creating the Custom Fuel Model surface is used. The final ASCII file is a unit (tons/acre) for each 30 x 30m cell. For Coarse Woody Debri s the same process for

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165 creating the Custom Fuel Model surface is used to create an ASCII spatial surface. This time, the measurements of 1, 10, and 100 hour fuels for each point are used in Geostatistical Analyst. The final ASCII file is a unit (tons/acre) for each 30 x 30m cell. Weather and Wind File s. The program, FireFamilyPlus, is used to export the .WTR and .WND files into program FARSITE 4. Historic al fire weather data corresponding to the Study Extent were downloaded from the Western Regional Climat e Center (WRCC). The most appropriate weather station, ID 083001, is located within the study extent at the Lower Suwanee National Wildlife Refuge. From Station 083001 records are available from October 2003 to September 2013. After correcting for erroneou s records, FireFamilyPlus categorizes and divides historic al weather patterns into three categories of fire weather; low, medium, and high fire intensity An hourly data analysis is performed to generate an hourly list from which to incorporate into progra m FARSITE for a wildfire simulation. The FARSITE Exports Dialog box is used to generate the two FARSITE data files, .WTR (WTR for FARSITE Version 4) and .WND. Program FARSITE 4. With all of the data layers synthesized and in the appropriate format, the PI imported the data into Program FARSITE 4. First, the LCP file was generated. The Elevation ASCII, Slope ASCII, Aspect ASCII, Fuel Model ASCII, Duff Loading ASCII, and Course Woody Debris ASCII were uploaded. All other variables in the LCP Gener ation window are left unchecked. In the FARSITE Project window, the LCP file that was just created is uploaded. In this window the .WTR and button is selected to add additional surface shapefiles. For this analysis the shapefiles

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166 that will be added are those that can influence the pattern of spread during a surface fire. The shapefiles include major water bodies and the major roadway network. The road network shapefile for each developm ent design are replaced with each corresponding wildfire simulation. Three simulations of a surface wildfire were performed for each of the three development designs for a total of nine simulations. On the task bar, the Simulate tab was set to Initiated. T o correlate with field data collection in 2013 the Point of Ignition was selected as the GPS location of a lightning strike fire, The Horseshoe Fire, that occurred on the SLP property in the Summer of 2013. A simulated surface wildfire is than started. The weather data or when the <10 day time period expires. For each development design three simulations are performed using the low, medium, and high fire intensity weather information respec tively. The Output results of each simulation are than exported. In the Export and Outputs Options window, Display Units and File Output units are selected as is set t o 30 x 30m. Lastly, the Output results are imported into ArcGIS 10.1 for a comparative analysis of the resiliency of the development designs to a surface wildfire.

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167 LIST OF REFERENCES Ahern Jack. 2011 From f ail safe to safe to fail: Sustainability and resilience in the new urban world Landscape and Urban Planning. 100:341 343. Anderson, Hal. 1981. Aids to Determining Fuel Models for Estimating Fire Behavior U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. General Technical Report. Arendt Randall. 1996 Conservation Design for Subdivisions: A Practical Guide t o Creating Open Space Networks. Arendt, Randall. 1997. Basing Cluster Techniques on Development Densities Appropriate to the Area Journal of the Amer ican Planning Association. 63:1:137 144. South Coast missing linkages: restoring connectivity to wildlands in the largest metropolitan area in the United States (FW Lambeck 1997; Mille r et al. 1999; Carroll et al. 2001; Bani et al. 2002). Pp. 555 586. Beier P., Majka, D., and Spencer, W. 2008 Forks in the Road: Choices in Procedures for Designing Wildland Linkages Conservation Biology. 22:4:836 861. Brooks, H.K., 1981. Guide to the p hysiographic divisions of Florida: Gainesville, Florida Cooperative Extension Service IFAS, University of Florida Map 8 5 M 82. Brown, J.K. 1971. A Planar Intersect method for sampling fuel volume and surface area Forest Science. 17:96 102. Brown, J.K. 1 974. Handbook for inventorying downed woody material USDA Forest Service General Technical Report. 16:1 24. Brzuszek, R., Walker, J., Schauwecker, T., Campany, C., Foster, M., and Grado, S. 2010 Planning Strategies for Commmunity Wildfire Defense Design in Florida. Journal of Forestry. Pp. 250 257. Campbell, Scott. 1996. Green Cities, Growing Cities, Just Cities? Urban Planning and the Contradictions of Sustainable Development Journal of the American Planning Association 62 (3): 296 312. Carr P and Zwi ck P 2007 Smart Land Use Analysis: The LUCIS Model ESRI Press. Pp. 277. Chappells H. and Medd W. 2012. Resilience In Practice: The 2006 Drought in Southeast England Society and Natural Resources: An International Journal. 25:3:302 316.

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168 Col, Nolan, R upert, Frank, Enright, Meryl, and Horvath. 1997. Reappraisal of the geology and hydrogeology of Gilchrist County, Florida with emphasis on the Waccasassa Flats. Tallahasse, Florida. Geological Surv ey Report of Investigations 99:76. CWPP Committee et al. 20 04 Firewise Communities/USA Recognition Program Florida Department of Agriculture and Consumer Services. 2010 Wildfire Risk Reduction in Florida: Home, Neighborhood, and Community Best Practices DACS, Division of Forestry. Pp. 2 178. Florida D epartment of Economic Opportunity. 2013. Community Planning and Development. Accessed online on September 2012 at: < http://www.floridajobs.org/community planning and development > Department of Environmental Protection. 2013. Florida G reenways and Trails System Plan 2013 2017 Accessed online December 2013 at: < http://www.dep.state.fl.us/gwt/FGTS_Plan/PDF/FGTS_Plan_2013 17_publication.pdf > Dobyns H F. 1966. Estimating aboriginal American population 1: an appraisal of techniques with a new hemispheric estimate. Curr Anthropol. 7:395 449. Dobyns H F. 1983. Their number become thinned: Native American population dynamics in Eastern North America Univ ersity of Tennessee Press, Knoxville, TN. Elkington John. 1997 Cannibals with Forks: The Triple Bottom Line of 21 st Ce ntury Business New Society Publishers. Pp. 407. ESRI. 2011. ArcGIS Desktop: Release 10.1 Redlands, CA: Environmental Systems Research Institute. Fernard E. and Purdum E. 1998 Water Resources Atlas of Florida. Tallahassee, Fl. Institute of Science and Public Affairs Florida State University. Fire.org 2008. Public Domain Software for the Wildland Fire Community. Systems for Environmental Management FARSITE. Florida Division of Eme rgency Management (FDEM). 2012 Flood Mitigation Assistance Program Ac cessed online on September 2012 at: < http://floridadisaster.org/Mitigation/FMAP/ > Florida Fish and Wildlife Conservation Commission. 2008. Wildlife 2060: What s at S take for Florida? online September 20 12 at: < http://www.1000friendsofflorida.org/wp content/themes/1000freinds/formpop/form pop.php?q=wildlife 2060 >

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169 Florida Statute. 2013. 163.177 Florida Statute. 2013. 163.3177(6)6 Florida Statute. 2013. 163.3178(1). Forman R.T.T. 1995. Land Mosaics: the E cology of Landscapes and Regions Cambridge University Press, Cambridge. Dearing J., Bullock, S., Costanza, R., Dawson, T., Edwards, M., Poppy, G., and Smith, G. 2012 Navigating the Perfect Storm: Research Strategies for Socialecological Systems in a Rap idly Evolving World Environmental Management. 49:767 775. Goebel, A. 2007. Sustainable U rban Development? Low cost Housing C hallenges in South Africa Habitat Int ernational 31:291 302. Gordon, D., D. Shaw, L. Geselbracht, E. Contreras, and R. Torres. 200 5. Problem and C onservation A ction I dentification terrestrial, freshwater and marine using The Nature Conservancy's planning process Final Report. Florida Fish and Wildlife Conservation Commission. The Nature Conservancy, Gainesville, Florida, USA. G race, Susan 1999. Short term Response of Plant Species of Special Concern and Exotics to the 1998 Florida Wildfires USGS. Pp. 1 11. GTZ, 2002. Strengthening Local Urban Planning and Management: Handbook, Urban Planning and Management Project, Department of Physical and Urban Development Planning Bangkok (text by Jan Meyrick and Dr. David Meyrick) Gunnarsson stling U. and H jer, M. 2011 Scenario Planning for Sustainability in Stockholm, Sweden: Environmental Justice Considerations International Jounra l of Urban and Regional Research. 35:5:1048 1067. Hampson, P.S. 1984. Wetlands in Florida Bureau of Geology Map Series. Tallahassee, Florida. Pp. 1 110. Harrison, S. 1997. Empirical Evidence for Metapopulation Dynamics. Metapopulation Biology: Ecology, Ge netics and Evolution Academic Press, San Diego, CA. Hilmon, J.B., Hughes, R.H., 1965. Fire and forage in the wire grass type Journal of Range Management 18, 251 254. Holden, Meg. 2012 Is Integrated Planning Any More Than the Sum of Its Parts? Considera tions for Planning Sustainable Cities Journal of Planning Education and Research. 32:3:305 318. Helfman, R. Straub, R., and Deem, J. 1980. share computerized processing for fire danger rating U.S. Forest Service General Tec hnical Rep ort.

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170 Herb, J. and Kauffman J 2007. Tales of the Tipsy Coachman: Being Right for the Wrong Reason The Tipsy Coachm an is Alive and Well and Living in Florida The Florida Bar Journal. 81:11. Johnson D.J. 1980. The Comparison of Usage and Avail ability Measurements for Evaluating Resource Preference Southern Prairie Wildlife Research Center. Ecology. 61:1:65 71. Kahn H. and Weiner, A. 1967 The Next Thirty Three Years: A Framework for Speculation Macmillan. NY. Kleinberg, Eliot, 2004: Black C loud : The Great Hurricane of 1928 Carroll and Graf, New York, NY. Laurance, W.F., Laurance, S.G., and Hilbert, D.W. 2008. Long Term Dynamics of a Fragmented Rainforest Mammal Assemblage Conservation Biology 22:5:1154 1164. Manoliadis, O., Tsolas, I., Na kaou, A. 2007. Sustainable construction and drivers of change in Greece: a Delphi study 24:2:113 120. May, G. 1996. The Future Is Ours: Forecasting, Managing and Creating The Future Praeger, Westport, Connecticut. Mercer, D., Pye, E., Penstemon, J., Butr y, D., Holmes, T. 2000. Economic Effect of Catastrophic Wildfires. Unpublished final report. Forestry Sciences Laboratory. Research Triangle Park, NC. McMillan Carla. 1998. Natural Disasters: Prepare, Mitigate, Manage Accessed online September 2012 at: < http://www.csa.com/discoveryguides/archives/ndht.php > Missoula Fire Science Lab ( MFSL ). 2010 Software Downloads Accessed online on February 2012 at: < http://www.firelab.org/downloads > Myers, D. and Kitsuse, A. 2000. Constructing the future in planning: a survey of theories and tools Journal of Planning Education and Research, 19, pp. 221 231 Myers, R. L. and Ewel, J. J. 1990. Ecosystems of Florida University Presses of Florida, Gainesville. 765p. National Fire Danger Rating System 1972 In Burgan, R. 1988. 1988 revisions to the 1978 National Fire Danger Rating System Resource Papers. SE 273. U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. Asheville, NC. Pp. 39. NFPA. 2014 Firewise Communities. Accessed online on September 2012 at: < http://www.firewise.org >

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171 National Oceanic and Atmospheric Administration (NOAA). 2009 Hurricane Season Summaries and Reports National Hurricane Center. Accessed online February 2012 at: < http://www.nhc.noaa.gov/ > Nolan, C. 1997 Rea ppraisal of the Geology and H ydrogeology of Gilchrist County, Florida, with emphasis on the Waccasassa Flats Florida Geographical Survey. Report of Investigations. 99:22 23 Noss, R.F. and Daly, K.M. 2006 Inc orporating Connectivity into Broad Scale Conse rvation P lanning Pp. 587 619. Noss, R.F., LaRoe, E.T., Scott, J.M. 1995. Endangered ecosystems of the United States: a preliminary assessment of loss and degradation Biological Report United States Department of Interior, National Biological Service, Wa shington, D.C. Peterson, G., Cumming, G., and Carpenter, S. 2003. Scenario Planning: A Tool for Conservation in an Uncertain World Conservation Biology. 17:2:358 366. Prior, T., and Eriksen, C. 2013. Wildfire preparedness, community cohesion and social ec ological systems Global Environmental Change. 23:1575 1586. Puri, H. S., Yon, J. W., Jr., and Oglesby, W.R. 1967. Geology of Dixie and Gilchrist Counties, Florida Florida Geological Survey Bulletin. 49:pp. 155. Randolph John. 2012 Environmental Land Us e Planning and Management 2 nd Ed. Island Press. Roufechaei, K.M., Bakar, A.H.A., and Tabassi, A.A. 2013. Energy efficient Design for Sustainable Housing Development Journal of Cleaner Production. 65:380 388. Rubino R.G. and Starnes E.M. 2008. Lesson s Learned? The History of Planning in Florida Sentry Press. Tallahassee, Fl. Pp. 1 402. Sanson, Nicolas. 1657. La Floride Par N. Sanson d'Abbeville, Georgr. ordre. du Ro. A Paris chez l'Auteur aux Galleries du Louvre. School of Landscape Architecture an d Planning. 2012. Conservation Design and Planning Studio. The Gilchrist Club: Gilchrist County, Fl University of Florida. Pp. 3 224. Schwartz, P. 1991. The Art of The Long View: Paths To Strategic Insight For Yourself and Your Company Doubleday, New Yor k. Scott, J.H. and Burgan, R.E. 2005 Standard Fire Behavior Models: A Comprehensive USDA Forest Service. Rocky Mountain Research Station. General Technical Report.

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172 Singleton, P. H., W. L. Gaines, and J. F. Lehmkuhl. 2002. Landscape Permeability for Large Carnivores in Washington: A G eogr aphic I nf ormation System Weighted Distance and L east cost Corridor A ssessment Research Paper. Portland, OR. Steiner, F.R., 2006. Metropolitan resilience: the role of universities in facilitating a sustainable metropolitan future In: Nelson, A.C., Al len, B.L., Trauger D.L. (Eds.). Tow ard a Resilient Metropolis Metropolitan Institute Press, Alexandria, VA, Pp.1 18. Swamp Lands Act. 1850. Swamp Lands Acts of 1849, 1850, and 1860 United States Department of the Interior. Szekely, F. and Knirsch, M. 2005. Responsible Le adership and C orpora te Social Responsibility: Metrics for Sustainable P erformance European Management Journal. 23:6: 628 647. Tapinos Efstathhios. 2012 Perceived Environmental Uncertainty in Scenario Planning Futures. 44:4:338 345. Tewksbury, J., Levey D., Haddad, N., Sargent, S. Orrock, J., Weldon, A., Danielson, B., Brinkerhoff, J., Damschen, E., Townsend, P. 2002. Corridors Affect Plants, Animals, and Their Interactions in Fragmented L andscapes Proceedings of the National Academy of Science. Ecolog y. 99:20:12923 12926. Thiele, Leslie Paul. 2011 Living Sustainably in a Connected World 2011. pp. 107 US Department of Agriculture Forest Service. 2012. Sou theast Florida Regional Climate Change Compact Counties Regional Climate Action Plan. pp 1 49. US DOI 2009. Adaptive Management: The U.S. De partment of the Interior Technical G uide Accessed online August 2012 at: Vale, L., Campanella, J., Thomas, J. 2005. The Resilient City: How Modern Cities Recover from Disaster. Oxford University Press, Oxford, UK van der Heijden, K. 1996. Scenarios: The Art of Strategic Conv ersation Wiley, New York. Vernon, R. O., 1951. Geology of Citrus and Levy Counties, Florida Florida Geological Survey Bulletin 33, pp. 256 Wack, P. 1985a. Scenarios: Uncharted Waters Ahead Harvard Business Review. 63:72 89. Wack, P. 1985b. Scenarios: Shooting The Rapids Harvard Business Review. 63:139 150.

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173 Walker B. and Salt D. 2006 Resilience Practice Island Press. Weise, D., Stephens, S., Fujioka, F., Moody, T., and Benoit, J. 2010. Estimation of Fire Danger in Hawaii Using Limiited Weather Data and Simulation Accessed online August 2013 at: < http://www.treesearch.fs.fed.us/pubs/35319 > Willcox, E. V., and W.M. Giuliano. 2010. Seasonal effects of prescribed burning and roller chopping on saw palmetto in flatwoods Forest Ecology and Management 259:1580 1585. WRCC. 2008. Western Regional Climate Center: Historical Climate Information Accessed online August 2013 at: < http://www.wrcc.dri.edu/ >

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174 BIOGRAPHICAL SKETCH Scott Rothberg entered into coursework for a Master of Arts in Urban and Regional Plan ning after receiving a Bachelor of Science in Wildlife Ecology and Conservation at the University of Florida Taking the step from the College of A griculture and Life Sciences to continue into a graduate m housed in the College of Design Construction and Planning personally benefitted his workmanship as a planner Scott earned a Specialization in Environmental Planning while completing his The transition enabled Scott to practice the language of city and regional plan ning and enhance his understanding of geospatial statistical analysis. His fields of study and grant writing have focused on cities, species and ecosystems that are area dependent and fragme ntation sensitive. Specifically, Scott has drafted resear ch propo sals for upland conifer pine forests, isolated wetlands, and scrub ecotypes that support fire dependent species of scrub jays, fox squirrels, black bears, and panthers.