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1 AN INDEX OF GOOD S By CYNNAMON DOBBS BROWN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2009
2 2009 Cynnamon Dobbs Brown
3 To the people that join me in this path
4 ACKNOWLEDGMENTS I would like to thank Dr. Francisco Escobedo who gave me the chance to do my master in the University of Fl orida. His advice, time, discussions and comprehension were a main part for the finishing of this document. I would also like to thank the other members of my committee, Dr. Southworth, Dr. Cropper and Dr. Zipperer for all the suggestions made to improve this thesis and for their time to read it. I thanks my family and friends in Chile for all the support in completing my studies and to my family and friends in Gainesville whom without I would have lost my ability to have fun and enjoy the simple things. C arlos, Clau, Tobal, Pali, Santiago, Fede, Belen, Ana, Ana W, Bea Meaghan and Andres thanks for all the happiness and love you brought into my life, I will miss you a lot. Last but not least I would like to thank Benjamin and Alicia for taking the time to help with my English and, Ben thanks for listening to my crazy thoughts.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ ........ 11 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 2 ............... 19 Introduction ................................ ................................ ................................ ............. 19 Methods ................................ ................................ ................................ .................. 22 Study Area ................................ ................................ ................................ ........ 22 Field Sampling ................................ ................................ ................................ .. 23 Soil Analyses ................................ ................................ ................................ .... 2 4 Tree Analyses ................................ ................................ ................................ .. 24 Socioeconomics ................................ ................................ ............................... 26 Statistical Analyses ................................ ................................ .......................... 28 Results ................................ ................................ ................................ .................... 29 Socioeconomics ................................ ................................ ............................... 29 Soils ................................ ................................ ................................ ................. 32 Urban Forests ................................ ................................ ................................ ... 34 Structure and composition ................................ ................................ ......... 34 Function ................................ ................................ ................................ ..... 36 Soil Tree Relationships ................................ ................................ .................... 38 Soil Tree Socioeconomic Relationships ................................ ........................... 38 Discussion ................................ ................................ ................................ .............. 39 Socioeconomics ................................ ................................ ............................... 39 Soils ................................ ................................ ................................ ................. 40 Trees ................................ ................................ ................................ ................ 42 Conclusi on ................................ ................................ ................................ .............. 43 3 ECOSYSTEM SERVICE IN DICATORS FOR THE CIT Y OF GAINESVILLE .......... 69 Introduction ................................ ................................ ................................ ............. 69 Methods ................................ ................................ ................................ .................. 74 Indicators ................................ ................................ ................................ .......... 74 Urban Forest Ecosystem Services Analyses ................................ .................... 76 Developing Indicators for Regulation Functions ................................ ............... 77 Gas regulation (maintenance of good air quality) ................................ ....... 77
6 C limate regulation function (maintenance of favorable climate) ................. 78 Disturbance mitigation (storm protection) ................................ .................. 78 Water regulation f unction (drainage) ................................ .......................... 78 Soil productivity (maintenance of soil productivity) ................................ ..... 79 Nutrient regulation (maintenance of healthy soils) ................................ ..... 79 Waste treatment (filtering of dust particles) ................................ ................ 80 Waste treatment (noise reduction) ................................ ............................. 80 Developing Indicators for the Habitat Function ................................ ................. 81 Refugium (maintenance of biological and genetic diversity) ...................... 81 Developing Indicators for the Production Function ................................ ........... 81 Productivity (goods) ................................ ................................ ................... 81 Developing Indicators for the Information Function ................................ .......... 82 Recreation opportunities ................................ ................................ ............ 82 Aesthetics ................................ ................................ ................................ .. 83 Developin g Indicators for Disservices ................................ .............................. 83 Damage to infrastructure and human safety ................................ .............. 84 Allergenicity ................................ ................................ ................................ 84 Decrease of air quality ................................ ................................ ............... 84 Fruit fall ................................ ................................ ................................ ...... 85 Statistical Analyses ................................ ................................ .................... 86 Results ................................ ................................ ................................ .................... 86 Ecosystem Services and Goods Indicators ................................ ...................... 86 Regulation function ................................ ................................ .................... 86 Habitat function ................................ ................................ .......................... 89 Production function ................................ ................................ .................... 91 Information function ................................ ................................ ................... 92 Disservices ................................ .................... Error! Bookmark not defined. Discussion ................................ ................................ ................................ .............. 96 Comparing Functions ................................ ................................ ....................... 96 Ecosystem Services and Goods Analyses ................................ ....................... 96 Regulation function ................................ ................................ .................... 96 Habitat function ................................ ................................ .......................... 99 Production function ................................ ................................ .................... 99 Information function ................................ ................................ ................. 100 Disservices ................................ .................... Error! Bookmark not defined. Conclusion ................................ ................................ ................................ ............ 101 4 SPATIAL DISTRIBUTION N FOREST ECOSYSTEM SERVICES INDEX ................................ ................................ ................................ 116 Introduction ................................ ................................ ................................ ........... 116 System State Indices ................................ ................................ ...................... 117 Remote Sensing Indices ................................ ................................ ................. 118 Methods ................................ ................................ ................................ ................ 120 Study Area ................................ ................................ ................................ ...... 120 Ecosystem Services and Goods Index ................................ ........................... 120 Statistical analyses ................................ ................................ .................. 122
7 Spatial Analysis of ESG Indices at the City Level ................................ ........... 123 Remote Sensing An alysis of ESG Indices at the Plot Level. .......................... 124 Results ................................ ................................ ................................ .................. 125 ESG Indices ................................ ................................ ................................ ... 125 Spatial Analysis of ESG Indices at the City Level ................................ ........... 127 City quadrants analysis ................................ ................................ ............ 127 Land use analysis ................................ ................................ .................... 128 Population density analysis ................................ ................................ ...... 128 Household income analysis ................................ ................................ ..... 129 Remote Sensing An alysis of ESG Indices at the Plot Level ........................... 129 Discussion ................................ ................................ ................................ ............ 129 Conclusion ................................ ................................ ................................ ............ 133 5 CONCLUSIONS ................................ ................................ ................................ ... 149 Summary ................................ ................................ ................................ .............. 149 Limitations, Implications and Future Research ................................ ..................... 150 ................................ ................................ ............. 150 Ecosystem Services and Goods Indicators ................................ .................... 151 Urban Forest Ecosystem Serv ices and Goods Index ................................ ..... 151 Policy Management Recommendations ................................ ............................... 152 LIST OF REFERENCES ................................ ................................ ............................. 15 4 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 171
8 LIST OF TABLES Table page 2 1. Soil chemical analysis procedures ................................ ................................ ......... 52 2 2. Socioeconomic group description by quadrants for the city of Gainesville, Florida ................................ ................................ ................................ ................ 53 2 3. Descriptive statistics for socioeconomics of plots sampled in the c ity of Gainesville, FL by US Census Block (U.S Census Bureau, 2000) (n=70). ......... 55 2 Gainesville, Florida. ................................ ................................ ............................ 56 2 5. Mean and standard deviations for socioeconomics of the city of Gainesville, FL classified by land use, and city quadrants. Analysis of variance for significant differences among land use ................. 57 2 6. Descriptive statistics for soil properties in the upper 10 cm of the surface in the city of Gainesville, Florida (n=70) ................................ ................................ ....... 59 2 ................................ ................ 60 2 8. Mean and standard deviation for soil properties for the city of Gainesville, FL classified by land use, land cover, surface cover and city quadrants. Analysis of variance p values account for significant differences. ................................ .... 61 2 9. Concentrations of soil heavy metals in the upper 10 cm of soi ls in the city of Gainesville, FL (n = 9). ................................ ................................ ....................... 64 2 10. Descriptive statistics of urban forest structure and composition in the city of Gainesville FL (n=70). ................................ ................................ ........................ 64 2 11. Pearson correlation between tree structure variables using the Pearson ................................ ................................ .................. 65 2 12. Mean and standard deviations for tree structur e by land use, land cover and quadrants for the city of Gainesville and ANOVA p values. Analysis of variance p values account for significant differences. ................................ ........ 66 2 13. Descriptive statistics for u rban forest functions estimated per plot in the city of Gainesville, FL (n=70) ................................ ................................ ........................ 67 2 14. Mean and standard deviation for tree functions for land use, land cover and quadrants for the city of Gainesville and ANOVA p values. Analysis of variance p values account for significant differences. ................................ ........ 68
9 3 1. Categories of Indicators for Regulation Function ................................ ................. 105 3 2. Categories of Indicator for Habitat Function ................................ ......................... 106 3 3. Categories of Indicator for Production Function ................................ ................... 106 3 4. Categories of Indicators for Information Function ................................ ................ 107 3 5. Categories for Indicator of Disservices ................................ ................................ 108 3 6. Indicator value, statistics and normality tests for ecosystem services in the regulation function for the city of Gainesville ................................ .................... 109 3 7. Mean for ecosystem services included in the regulation fu nction and p values ................................ ................................ .............................. 110 3 8. Indicator value, statistics and normality test for ecosystem services indicators for habitat function for the city of Gainesville ................................ .................... 111 3 9. Mean for ecosystem services included in habitat function and p values ANOVA ................................ ................................ ................................ ............ 111 3 10. Indicator value, statistics and normality test for ecosystem services included in productivity function for the City of Gainesville, Florida. ................................ ... 112 3 11. Mean for ecosystem services included in production function and p values 0.05) ................................ ................................ .............................. 112 3 12. Indicator value, statistics and normality test for ecosystem services included in information function for the city of Gainesville ................................ .................. 113 3 13. Mean for ecosystem services included in information function and p values ................................ ................................ .............................. 113 3 14. Indicator value, statistics and normality test for ecosystem dis services for the city of Gainesville ................................ ................................ .............................. 114 3 15. Mean for disservices and p ................................ ......... 114 3 16. Mean values and standa rd errors for the 4 ecosystem functions ....................... 115 4 1. Indices based on remotely sensed data ................................ ............................... 146 4 2. Mean, maximum, and minimum rank for the three index methods and values for sensitivity analysis of ecosystem services and goods functions. ...................... 146 4 3. Mean, minimum, and maximum values for ecosystem services and goods indices. ................................ ................................ ................................ ............. 147
10 4 4. Mean, minimum, and maximum values for ecosystem services and goods indices by land use ................................ ................................ ........................... 147 4 5. Mean, minimum, and maxi mum values for indices by population ........................ 148 4 6. Mean, minimum, and maximum values for indices by household income ............ 148
11 LIST OF FIGURES Figure page 1 1 Flow diagram for the development of an index of ecosystem services and goods. ................................ ................................ ................................ ................. 18 2 1 Plot layout for soil sampling and vegeta tion measurements. ................................ 46 2 2 are two meter in resolution (2004) ................................ ................................ ...... 47 2 3 Diagram showing positive and negative correlations of the 12 soil variables analyzed for the city of Gainesville. BD Bulk Density, WC Water Content, TP Total Porosity, SC Soil Cover, P Extractable Phosphorus, K Extractable Potassium Ca Extractable Calcium, Mg Extractable Magnesium, Na Extractable Sodium, TKN Total Kjeldahl Nitrogen, OM % Organic Matter. Note: first (F1) and second (F2) factor derived from the Principal Component Analysis to discriminate plots by land use. Highest co rrelations are inside the blue square. ................................ ................................ ................................ ........ 48 2 4 Multiple regression model for soil pH of observed vs. predicted values. ................ 48 2 5 T en most common tree species in the city of Gainesville, FL. ............................... 49 2 6 Type of land covers (% of the area) by land use for the city of Gainesville, FL. ...... 49 2 7 Carbon storage and sequestration by mean diameter at breast height in the city of Gainesville, FL. ................................ ................................ ............................... 50 2 8 Estimated carbon sequestration by species for the city of Gainesville, FL. ............ 50 2 9 Significant correlations (R 2 adj>0.40) for soil tree relationships in the city of Gainesville, FL. The line in each graph represents the best fit. .......................... 51 3 1 Histogram for ecosystem functions and disservices in the city of Gainesville ...... 104 4 1 Plots with the best (A) and worst (B) value for the E qual Weight Index. .............. 136 4 2 Plots with the best (A) and worst (B) value for the Double Weight Information Function Index. ................................ ................................ ................................ 136 4 3 Ranking of plots according to Equal Weight Index and Double Weight Information Function Index ................................ ................................ ............... 137 4 4 Analysis of the Equal Weight Index using ordinary kriging and classification of city q uadrants. ................................ ................................ ................................ .. 138
12 4 5 Analysis of the Double Weight Information Function Index using ordinary kriging and classification of city quadrants. ................................ ...................... 138 4 6 Analysis of the Equal Weight Index using ordinary kriging and land use classification. ................................ ................................ ................................ .... 139 4 7 Analysis of the Double Weight Information Function Index using ordinary krigin g and land use classification. ................................ ................................ ... 140 4 8 Analysis of the Equal Weight Index using ordinary kriging and population classification by 2000 Census Block. ................................ ................................ 141 4 9 Analysis of the Double Weight Information Function Index using ordinary kriging and population classification by 2000 Census Block. ............................ 142 4 10 Analysis of Equal W eight Index using ordinary kriging and household income classification by 2000 Census Block. ................................ ................................ 143 4 11 Analysis of the Double Weight Information Function Index using ordinary kriging and house hold income classification by 2000 Census Block. ............... 144 4 12 Linear trend 3 Dimensional graphs for the Equal Weight Index and Modified Soil Adjusted Vegetation Index. Scales represent the range of value for each ecosystem services and goods and remote sensing index. .............................. 145 4 13 Linear trend 3 Dimensional graphs for the Double Weight Information Function Index and Normalized Difference Building Index. Scales represent the range of value for each ecosystem services and goods and remote sensing index. ................................ ................................ ................................ ... 145
13 ABSTRACT OF THESIS PRESENTED TO THE GRA DUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN P ARTIAL FULFILLMENT O F THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GOODS By Cynnamon Dobbs Brown December 2009 Chair: Francisco Escobedo Major: Forest Resources and Conservation Urb an ecosystem ha ve been shape d by time, human behavior, land use patterns and socioeconomics. Since this type of ecosystem is directly related to humans the services and good s they deliver become highly relevant. An index for the urban forest ecosystem se rvices and goods of Gainesville was developed and it s distribution across socioeconomics, morphology and landscape w e re assessed. To develop the index the description of the composition and structure of the urban forest was done. Then indicators for ser vices, goods and disservices were recognize d and calculated according to field data collection, the UFORE model and available literature Finally the indicators were aggregated using three weighting schemes. Results show that residential areas located i n older parts of the city have the highest index for the delivery of ecosystem services and goods, that the equal weight index delivered the most robust values and that the estimations of the index using ordinary kriging technique delivered reliable estima t es at the city level.
14 CHAPTER 1 INTRODUCTION Urban ecosystems include cities, suburbs, towns and peri urban areas and represent the array of possible combinations of stresses, disturbances, structures, and function occurring in ecological systems (McD onnell and Pickett, 1990). They represent localities with increas ed population density with in a dense network of non natural built environment s (Williams et al., 2009). Th ese built environment s are composed of hard surfaces such as roads, sidewalk s, roofs and buildings. U rban environments rely on vegetation and soils to provide ecosystem functions (Bolund and Hunhammar, 1999). Therefore, understa nding how urban ecosystems functions how they change from their natural condition, and how their performance may be limited can lead to a better understanding of the actual impacts of urbanization (Vitousek, 1994). The effects of urbanization can change ecosystem structure and function as previously point ed out by several authors (McDonnell and Pickett, 1990; Vitou sek et al., 1997; Grimm et al., 2008; Alberti, 2009) The concept of ecosystem services and goods has two main definitions and approaches ; one comes from the field of ecology and the other from the discipline of econom ics As defined by economist s, ecosys tem services are components of nature that are directly enjoyed, consumed or used to improve human well being, and require some interaction or at least appreciation by humans An economic value can then be assigned to them ( IPCC, 2001; Boyd and Banzhaf, 2 006 ; http://www.ecosystemvaluation.org/Indicators/economvalind.htm ). Ecosystem goods are the tangible material products that result from ecosystem processes (Brown et al., 2007) Conversely f rom an ecological point of view ecosystem services are the range of
15 conditions and processes occurring in an ecosystem that help sustain and fulfill human life. This definition is focused o n ecosystem functions, so ecosystems services are not solely valuable to humans beneficiaries, they are also valuable for maintaining natural resources. They are seen as the processes by which the ecosystem produce s resources (Daily, 1997; ESA, 1997 ). De Groot et al. (2002) identif ied 32 ecosystem services th at included biological, physical, aesthetic, recreational and cultural. Butler and Oluoch Kosura, (2006) also identified several cultural, psychological and other non mater ial benefits that come from contact with nature. The definition as applied by ec onomists will be used in this study, since the evaluation of the state of the environment in urban ecosystems i s directly related to human well being as they are the main drivers of its composition and structure. Ecosystem disservices are processes that ha ve negative impacts on human life. These are commonly found in urban and agricultural ecosystems (Abenyaga et al., 2008; Lyytimki et al., 2008; Zhang et al., 2007). They affect how urban areas are experie nced, valued, use d and developed. Ecosystem disserv ices become highly valuable when they appear in everyday life of the urban residents ( Lyytimki et al., 2008). Disservices of urban ecosystems have been rarely considered in urban studies but they can be easily found in urban areas. Trying to describe th e state of the environment is a very difficult task, since ecosystem s a re dynamic and in constant change. Other studies/efforts have used criteria and i ndicators to provide a tool that simplifies this system and allows a means for obtaining comprehensible information I ndicators could help in compiling a vast amount of information that will reflect the dynamic s occurring among humans, physical,
16 biological properties and processes Indic ators provide a way to evaluate the progress towards environmental goals and to overcome the complex information given by indices The development of an urban forest ecosystem services index m ight provide urban planners with an easy and understandable tool to evaluate the state of the urban forest. The index will deliver a picture of how the different arrangement s of urban forest structure and composition affect socio ecological function and subsequently the provision of ecosystem services and goods The in dex will show which are the variables driving the value of the index. In this study the economic definition of ecosystem services and goods as applied by De Groot et al. (2002) will be used and based on the premise that the s ervices and goods provided by t he urban forest are associated with human well being. The chapters proceed following Figure 1 1 In the 2 nd chapter the structure and Urban soils and tree structure data were sampled, urban tree fu nction and leaf biomass and area were then obtained using the Urban Forest Effects (UFORE) model. T he relationships among socioeconomic and city morphology were then explored. Second, the 3 rd chapter us e d the data obtained in the 2 nd chapter and available information from the literature to assign values to the services and goods found in urban ecosystems. The ecosystem services and goods are defined and classified by the respective ecosystem function group belong ing to De Groot et al. ( 2002) These function groups correspond to : regulation, habitat, productivity and information. Indicators for ecosystem services and goods were developed to evaluate the state of the urban forest of Gainesville, FL.
17 Indicators also explored and account for ecosystem disservice s Third ly in the 4 th chapter I illustrate the overall state of ecosystem services and goods by aggregating the indicators in to an index. Three methods for developing the index are evaluated : 1) equal weight, 2) double weight ing of the information function indicator s and 3) assign ing weight s based on a principal component s analysis. The value of the index is explored spatial ly by analyzing the distribution of the index based on urban morphology, socioeconomics and site legacy. A n index of urban forest ecosy stem services and goods provide s a way to evaluate the state of urban green area s It will present a value that can be easily understood by the community. The weight of each of the indicators that build s the index will depend on the priorities that the com munity has in relation to the services and goods they can obtain from the ir urban forest. The analysis of the index will be limited to changes in the provision ing of a certain service or good T he consequence o n human well being due to those changes in th e index, will not be analyzed or established and should be included in future studies since different humans, societies and communities will perceive the effects on their well being in different ways.
18 Output of the Chapter Intermediate output Input Analysis Calculations Figure 1 1 Flow diagram for the development of an index of ec osystem services and goods Collection of data UFORE Model Urban soils Urban forest structure Urban forest function Socioeconomic and city morphology analysis of data Socioeconomic Identify urban ecosystem services & goods (ESG) Quantify ESG Compile existing values ESG (literature) Calculate values to local area Built categories for the delivery of ESG based on literature and local values. Development of indicators classified by ecosystem function: regulation, habitat, productivity, information. Socioeconomic and city morphology analysis of ESG indicators Development of ESG index: 3 methods Equal weight inde x Double weighting of information function index Eigenvalue based index Sensitivity analysis Selection of ESG indices Biophysical data: Landsat TM 2006 NDVI, MSAVI and NDBI Spatial analysis using biophysical data at plot level Socioeconomic and morphology data Kriging index city level Spatial analysis using socioeconomic data at city level Spatial distribution/assessment of ESG index using plot and city level data
19 CHAPTER 2 STRUCTURE AND FUNCTI URBAN FOREST Introduction Florida is one of the most populous states in the U.S, and as of 2008 was forecasted to be one of five fa stest growing states during 2000 2030. The climate and landscape provided by Florida motivates peop le to move to its urban centers. T hese people provide billions of dollars statewide in economic benefits through tourism, industry recreation and sport fis hing activities (TBNEP 1996). Urban areas and their associated ecosystems often differ from natural areas i n respect to climate, soil s hydrology, vegetation dynamics, human population dynamics and flow of energy and material because of the ecological patt erns, process and disturbance effects associated with urban lands (McDonnell et al., 1993 ; Alberti, 2009 ). Understanding the role of human influences on the urban environment (Alberti, 2009) could help inform policy makers on u rban planning, development an d morphology (Campbell and Landry, 2000). Urban morphology patterns emerge from the interaction between the human and ecological processes acting at multi temporal and spatial scales. Drivers such as population growth, e conomic growth and land use polic ie s create patterns of land use, land cover, transportation, artificial watersheds and biogeochemical cycl es which in turn a ffect natural productivity, biodiversity, community dynamics and human behavior (Alberti, 2009). Urban forests offer substantial envir onmental, social and economic benefits ( Tyrvinen et al., 2005) Urban forests are defined as natural or planted, individual trees, group of trees or woodlands located where people live (urban and peri urban)
20 Urban forests are also defined as ecosystem c haracterized by the presence of trees and other vegetation and pervious soils in association with people and their infrastructure (Nowak et al., 2001). Urban forests contribut e to the physiological, economical and sociological well being of urban inhabitan ts (Carter, 1995) and improve environmental quality and thereby enhanc e human well being via the flow of several ecosystem services. Urban forests could also have costs such as pruning, pest and disease management, increase crime risks or produce volatile organic compounds (Escobedo and Seitz, 2009). Urban forests as defined in this study (i.e. the sums of trees and pervious soils) can mitigate many environmental quality problems associated with urban development by moderating climate, reducing building ene rgy use, improving air quality, reducing storm water runoff and reducing noise levels ( Aylor, 1971; Carter, 1995; Tyrvinen et al., 2005 ; Chen and Jim, 2008; Alberti, 2009 ) Trees in the urban forest are also perceived to enhance the social and e conomical environment of a city by improving aesthetics and property values ( Anderson and Cordell, 1988; Tyrvinen and Miettinen, 2000 ). Urban forest structure is influenced by socioeconomic characteristics for example, p lant diversity has been found to increase wi th income (Hope et al., 200 5 ) and land cover has a high correlation with household income and household density (Iverson and Cook, 2000; Escobedo et al., 2006).Understanding the composition, structure and the environment of urban forests can help planners maximize benefits. Urban forests are highly influenced by urban soil condition ; as they are a medium for plant growth and suppor t urban habitats (Jim, 1998). Urban soils are highly heterogeneous, having different lev els of compaction, nutrients, infiltra tion rates and
21 incorporated amount s of manmade material s (Craul, 1999) C haracteristics of urban soils c an affect inter specific plant competition, communit y composition and growth Vegetation growth is constrained in urban habitats by the presence of imp ervious surfaces such as concrete, asphalt or other subsurface material that affects the aggregation, porosity and structure of the soil (Jim, 1998). Other factors that play a significant role in shaping the biological, physical and chemical characteristic s include plant cover (Hope et al., 2005), urban morphology and time since urban development (e.g. site legacy) (Scharenbroch et al., 2005). Urban soils are also affected by the human socioeconomic conditions U nderstanding the various elements that compri se effective soils management could be us ed to help develop practices that maximize u rban forest benefits, since the soil physical and nut rient needs of vegetation is necessary for the improvement of the urban forest and its benefits (Jim, 1998). In this study we explored if the urban forest structure, composition and soils m ight be affected b y socioeconomics and urban morphology. To answer this, the following objectives were pursued: D escri be the composition and structure of the urban forest, specificall y the soils, and tree components, socioeconomics and morphology for the City of Gainesville, Florida. Establish the interrelationships between the components of the urban forest for the city of Gainesville, Florida. Statistical relationships are expected to occur between urban soil and tree variables. The distribution of the structure and composition of trees and soils in the urban area of Gainesville is expected to differ a cross land uses, land co ver and quadrants of the city.
22 Methods Study Area The City of Gainesville is the largest city in Alachua County, Florida. In 2000, Gainesville had a population of 113,942 in habitants (US Census Bureau, 2000) and is North central Florida and covers a n area of near 12 7 km 2 The economic base varies from agricultural to manufacturing however academic research, education and health care are the main activities. subtropical with a n annual average temperature of 19.4C in January and an average temperature of 33C in June with an annual of 1,228 mm of precipitation Summer is the wettest season, with 496 mm while fall is the driest sea son, with only 230 mm of precipitation (Metcalf, 2004). Northern areas of Gainesville are locat ed on the Hawthorne geologic formation and Plio Pleistocene deposits in the South ern areas (Chirenje et al., 2003) which form part of the Ocala Uplift (Phelps, 1987). The predominant soils are sandy siliceous, hyperthermic aeric hapludods and plinthic pale aquults (Chirenje et al., 2003). The major drainage basins include the Suwanee, Steinhatchee, Encofina and Aucilla which are generally below 100 feet above sea level in elevation and are characterized by seasonally high water tables (Metclaf, 2004). Gaine sville is located near the boundary of the North ern Highlands and the Alachua Lake Cross Valley physiographic provinces. I n the n orth the topography is gently rolling and transitioning to flat area s in the south characterized b y the presence of prairies c ontain ing ephemeral lakes (Phelps, 1987). Gainesville natural vegetation is temperate evergreen forest and is characterized by species such as evergreen oaks and members of the magnolia and laurel family. The understory includes ferns, small
23 palms, shrubs and herbaceous plants L are abundant (Bailey, 1980). Field Sampling A total of 98 sampling plots of 0.04 ha were established during 2005 and 2006 inside the city limits of Gainesville. Plots locations were random ly selected and captured a n array of land uses including commercial, industrial, institutional, transportation, high and medium density residential, park, open areas, forests and wetlands. Land use designations are based on the classification of the C ity o f Gainesville Planning and Development Department ( www.cityofgainesville.org ). The samples were aggregated into residential, forested, c ommercial/industrial and institutional categories Wetland land use was not included in th is analysis Following methods outlined by Nowak and Crane (2000) a circular plot was established at each random location ( Figure 1 ). General information such as pe rcentage of tree cover, plantable space, shrub cover, and bare soil was recorded. Data on the types of existing surface covers was recorded, including pervious and impervious surface D iameter at B r e ast H eight (DBH ; 1.37 m above surface ) was measured as we ll as location and condition of every tree o n the plot. Total height, crown height, crown diameter, percent canopy dieback, percent canopy missing, and crown light exposure w ere also measured Of the 98 plots, 78 wer e sampled for soils. Soil plots were selected based on criteria that : 1) more than 25% of the plot surface was pervious; 2) permission was granted to collect soil samples ; or 3) the soil was not too wet to collect the sample. Soil samples were taken from a 3.14 m 2 subplot located in the center of the 0.04 ha vegetation plot ( Figure 2 1 ). I n case of the presence of a tree, building or
24 impervious surface being located at the plot center the soil subplot center was moved 1 meter n orth Two k inds of soil sample s were obtained within the soil sub plot (3.14 m 2 ) O ne sample consisted of three undisturbed 5 cm diameter by 4.5 cm deeper soil tins, which were used to soil physical properties A second was a composite soil sample consisting of 15 soi l cores obtained from the first upper 10 cm of the soil surface using a soil sample probe. Soil Analys e s Fresh b ulk density samples were weig h ed to obtain the volumetric water content and then oven dried at 105C for 48 hours to obtain dry weight. The wei ght of the inert and organic material greater than 2 mm was removed and discounted from the vol ume of the sample to obtain total soil volume. The composite samples were air dried and sieved with a stainless steel 2 mm mesh sieve, and sent to the University of Florida Soils L aboratory for chemical analysis (Table 2 1) The a nalysis included Total Kjeldahl N itrogen (TKN), extractable phosphorus (P) potassium (K) magnesium (Mg) calcium (Ca) copper (Cu) sodium (Na) zinc (Zn) and concentrations of lead (Pb ) cadmium (Cd) nickel (Ni) as well as organic matter content and pH. Chemical analys es for heavy metals w ere done for only nine plots located in the forested, comme rcial and residential land uses. Three samples for each of these land uses were chosen si nce p revious studies indicated a lack of high concentration s of Tree A nalys e s The data was analyzed using the UFORE (Urban Forest Effect) model (Nowak and Crane, 2000) The model calculate s urban forest structure, environmental functions and values of the urban forest. The model was developed by the USDA F orest Service
25 Northe rn Research Station to help managers and researchers to quantify urban forest structure and functions. The program in corporates vegetation data, local hour ly meteorological and pollution concentration data The program is divided in to four modules. T he first one quantifies urban forest structure such as species composition, tree density, tree condition leaf area and lea f biomass, and also calculates species richness. Leaf area and leaf biomass are calculated using regression equations incorporated into the model developed by Nowak (1994). The second module estimates the volatile organic compound (VOC) emissions focusing o n the ones that contribute to ozone and carbon monoxide formation (Nowak and Crane, 2000) The estimat e s are based on tree species, leaf biomass and other meteorological factor s The module estimates the emissions for each species by land use and for t he entire city by multiplying leaf biomass with genera specific emission factors and adjusting for meteorological factors The third module deals with above ground c arbon storage and sequestration C arbon storage is calculate d usi ng allometric biomass equa tions from the literature for 84 different species (Nowak et al. 2000 ) T his value is multiplied by 0.8 to adjust for trees growing in urban areas (Lawrence et al., 2008, McHale et al., 2009) and then the total carbon store d is obtai ned by the multiplicat ion of total biomass by 0.5 to transform dry weight biomass to carbon values To estimate carbon sequestration the model calculates the diameter growth depending on the genera, the diameter clas s and adjusts for the land use by where the tree is growing. I proper growth factor of the tree adjusted by the tree condition and number of frost free
26 days T he estimated carbon sequestered is the difference between carbon storage between year o ne and year two T he fourth module estimates the hourly dry deposition of ozone (O 3 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO) and particulate matter less than 10 mic rons (PM10) to leaf surfaces T he average of hourly dry deposi tion is based on tree cover and meteorological data such as temperature, wind speed, relative humidity, and pollutant concentration monitored by the Environmental Protection Agency (EPA) data. The pollutant flux is calculated based on deposition velocity a nd the specific pollutant concentration (Nowak and Crane, 2000). The CO O 3 NO 2 and SO 2 and PM 10 removal for urban trees in the C ity of Gainesville were estimated using the UFORE model and field data collected in 2005 and 2006 weath er data from 2000 and pollution concentration data from 2004 Air pollutant removal flux of square meter of tree cover is obtained by multiplying grams of air pollutants removed per square meter by the tree cover of each plot. Socioeconomics P revious studies have used socio economic variables such as population density, household size and median income to analyze the urban ecology of a city (McIntyre et al., 2000; Hahs and McDonnell, 2006). Socioeconomic d ata for Gainesville w ere obtained from the U.S C ensus Bureau census b lock data for the year 2000 ( www.census.gov ). Census block geographical units are the smallest homogeneous subdivision derived from the U.S C ensus and group approximately 1 ,500 persons into each unit The Topologically Integra ted Geographic Encoding and Referencing (TIGER) data ( www.arcdata.esri.com/data/tiger2000 ) from the 2000 C ensus w ere used to connect each plot sample to its respective census block unit. These variab les ha ve
27 been used in other studies to see how urban forest characteristics differ across urban areas (Pickett et al., 1997; Iverson and Cook, 2000; Heynen et al., 2006; Szantoi et al., 2009). Population, race, education, household income, percent of owner ship, number of households per unit and mean age of the population were obtained for each census block exi sting inside the limits of the C ity of Gainesville. U rban morphology variables such as time since the property was deve loped and value of the propert y were obtained from the Alachua County Property Apprais Office ( http://acpafl.org ). Forested p lots on vacant land use s and forest ed land use plots with no building or urban development w ere assigned zero years since the development of the property. Land cover data was obtained from the most recent Florida Fish and Wildlife Conservation Commission land cover s derived from classification of 2004 Landsat TM satelli te imagery ( www.fgdl.org ). Classes included agriculture, bare soil, wetland, forest and urban land cover. Values for race were classified as White African American and O thers T he o ther category group s all other races ( e.g. Asian, Hawaiian, multiracial Hispanics and others). Valu es for education were calculated according to the percentage of the census block population over 25 years old that had a post high school degree, including a Bachelor M aster s, professional degree or D octorate. Land use associated w ith each plot was ass igned in the same way as described for soils. To group similar areas quadrants were subjectively delineated a priori based on analyses of the geographical distribution of Claritas social group characteristics, which is based on household and geographical l evels ( www.claritas.com ). City quadrants also include in its delineation the property ages and urban development patterns found in Gainesville ( http://www.fh wa.dot.gov/planning/megaregions4.htm Stanilov, 2007) These quadrants
28 roughly correspond to local nomenclature for the different demographic areas in Gainesville such as the zoning map of Alachua County ( http://growth management.alachua.fl.us/gis/gis_mapatlas.php ) Quadrant location and corresponding plots are shown in Figure 1 2 and corresponded to North West North East South West and South East T he quadrants are delimited b y 13th Street on the East West direction and by University Ave North South direction. P opulation characteristics in ea ch quadrant are listed in Table 2 2. The NE cover ed 43.8 km 2 and included 35 sampled plots, the NW cover an area of 56.5 km 2 and 37 plots. The SW ha d an area of 13.2 km 2 and 13 plots and the SE 13.9 km 2 with 13 plots. Statistical A nalys e s Bas ic statistics for socioeconomic soil and urban forest data were calculated. Data were checked for normality using the Kolmogorov Smirnov test (p value =0.05) (Lilliefors, 1967). To test for differences across land uses and quadrants of the city an Analysis of Variance (ANOVA) was performed including a categorical ANOVA for demographic analysis. L ocation of the plot by quadrant was included in the analysi s to explore if the location of the plots influenced their characteristics. To explore the correlation between social variables a Pearson correlation procedure was performed g the SAS Proc CORR procedure. To determine which variables expla in most of the variability of social properties a factor analysis was performed using Proc FACTOR Previous studies have used this technique to reduce the amount of demographic data and to select the most relevant variables (Lo and Faber, 1997; Vyas and Kumar anay ake, 2006). In addition, a principal component analysis (PCA) was performed using Proc PRINCOMP with a correlation matrix (SAS Institute, 2007) for soils and vegetation data
29 Thi s procedure simplifies the statistical relationships with minimal information loss (Wackernagel, 2003), since it shows the variables that explain the highest amount of variance in the data. The first principal component explain s the highest amount of variability of the data, and each following component accounts for the remaining va riability (Vyas and Kumaranayake, 2006). The explanatory variables that characterized most of the variance within each principal component or factor was chosen by an eigenvector or factor value greater than 0.7 (Garten et al., 2007). The PCA correlation ma trix account ed for differences in measurement units (Fox and Metla 2005; Grunwald et al., 2007). T his procedure is commonly use d for soil analyse s (Fox and Metla, 2005; Garten et al ., 2007; Grunwald et al., 2007) and discloses relations hips among soil vari ables. H ighly correlated variables for soils and vegetation were modeled using a stepwise selection method using PROC Reg (SAS Institute, 2007) to distinguish the variables that explained most of the behavior of the dependent variable. Results Socioeconomi cs Gainesville population density i s 897 /km 2 with a predominance of White s (67% of the total population), followed by African Americans (24%). T he remaining percentage i s composed mainly of Hispanic s and Asians. In terms of education, for the population of 25 years old and older, 88.2 % of the population ha d completed high school, of those 42 .0 % ha ve complete d a B The average family size i s composed by 2.9 people and the average household size for the city of Gainesville i s 2.16 people of which 19,738 (42.0%) were owner occupied and 27,122 (68%) were renter occupied. The amount of labor force i s 55,768 habitants, giving a per capita income of $19,122, while household income of $34,327.
30 All the variable s were normal ly distribut ed The g reat est variability exist ed in propert y values as seen by a coefficient of variation over 100% (Table 2 3). Of the total population, 20% ha d a higher educational level U rbanized proper ties in Gainesville were not old as t heir average age w as 30 years, with a few properties over 100 years of age (Table 2 3). Factor analyse s of socioeconomic s show ed that 73% of the variance of the data was explained by the first three factors, which points to a high correlation between the analyzed variables. The first factor explain ed 36% of the variance, the second factor explain ed 24% of the variance and finally, the third factor explained 13% of the variance. The contribution to the factor is dominated by four variables, the main ones were household units (0.48) and population (0.48), for the secon d factor the main contribution was by household income (0.57) and the average age of the block population (0.55), other variables show ed only a small contribution (<0.20). The third factor and remaining variabilit y was mainly determined by the property value (0.53) and by the presence of African American s ( 0.58). Significant correlations were found among the social variables T he variables that were correlated with the highest amount of social variables were hous ehold income and population while the ones that were more independent were years since the property was developed and value of the property (Table 2 4). T hese variables will be used to explore differences betw een environmental variables in this study Pop ulation density was negatively correlat ed with high er level s of education. T he same relationship ar o se between household income and population of African American s Older people (>60 years) ten ded to inhabit newer properties.
31 Results from the ANOVA ( Table 2 5) d id not show significant differences among land use categories. Income was the greatest in residential areas and was also the one with the highest variability S everal areas had incomes of $10,000 whereas other areas had value s greater than $70,000. Older areas were found in commercial and vacant land uses showing high variability resulting from different land use histor ies African American population density was higher in land uses classified as forested areas, while other ethnicities ha d higher co ncentrations in areas classified as vacant and commercial Institutional areas ha d the higher prices by acre but also ha d the highest varia bility Lower levels of high degree education were found in land uses classified as vacant and residential. City q ua drants differed significantly in property value and education levels The highest concentration of population per block (3,260 habitants) was in the SW area of Gainesville, while the lo west concentration of people/block (2,129) was in the NE. Household inc ome was greater in the western part of the city, whe reas the highest level of education was located in the SW are a of Gainesville which ha d the highest student population T he lo west level s of education w ere located on the NW side. Property value s showed d ifferences am ong quadrants T he highest values were located on the NW and SE and high value s o n average found in the SE was mainly due to the existence of three properties with very high property value s Greater numbers of o lder people (>60 years) can be f ound in the SW and NW parts of the C ity. White concentration was greater in the western part of the city, while African American s w ere concentrated in the southern part of the city; a higher amount of other races were located in the SW a region of the cit y that contain ed a high concentration of students.
32 Soils High variability was found in soil nutrient concentrations, as indicated by a coefficient of variation (CV) of over 100% showing great dispersion of the data. The P concentration showed a negative no rmal distribution. The same situation occur red with Ca where there was an accumulation of values in lower levels of concentration. Physical properties ha d low er levels of variability, especially for bulk density (CV of 29.9%) and total porosity (CV of 18.6 %). Variation of s oil chemical properties was relatively high, (Table 2 6) The PCA for soil properties showed that 69% of the total variance of the data was explained by the first three principal components T he first component explain ed 30% of the v ariability, the second 25% and the third only a t 14% (Figure 2 3). The first two components d id not show a strong contribution to the explanation of the variability of the data. T he majority of the soil properties contribut ing to the explanation of the variance had eigenvalues between 0.25 and 0.45. The higher eigenvalues in the first principal component were bulk density (0.46), water content (0.39) and organic matter (0.35). The second principal component behave d similarly but the highest contributions were made by TKN (0.45) and magnesium (0.43). Finally the th ird principal component showed a positive contribution of Ca (0.5) and a negative contribution by K concentration ( 0.47 ) in explaining the remaining variance. The correlation matrix ( Table 2 7) show s TKN as the most dependent variable I t i s positively correlated with the other nutrients in the soil, the pH and organic matter content. The variable that showed the highest influence in chemical concentrations was pH. Moreover, as expected
33 O rganic matter content was highly related to the physical variables of the soil, as higher levels of compaction were associated w ith low levels of organic matter. Organic matter was associated to Na concentration s bulk density and TKN. The concentration of Mg is correlated with P K and Ca concentrations 05) The linear regression for pH shows that bulk density, soil cover and Ca concentration were significant (p value<0.0001) with a variance inflation factor of 1 and explained 79% of the variability of the soil Also this regression equation was ass ociated with a low RMSE (0.5) (Figure 2 4 ). Results from the ANOVA show ed that 7 of the 11 soil variables were significantly different among land uses ( Table 2 8). Forested areas ha ve the lo west bulk density values and therefore have the highest value for total porosity. Bulk density was similar among other land uses, as was total porosity. Nutrient concentrations in forested and vacant areas ha d the lo west level of P. Forested areas show the lo west concentration s for K, Ca, Mg and one of the lo west for Na Levels of P were high on residential and commercial areas On the other hand, K in residential areas show s the high est concentration s of all the land uses. High levels of Ca were found in residential and vacant areas. TKN was the lo west in residential an d commerci al areas even though their differences were not significant (p value>0.05). Heavy metal concentrations were low in all land uses with the exception of residential areas where Zn concentration was higher than other l and uses. The pH values presen ted a small but significant difference between land uses, especially in forested areas which were more acidic. Land cover ha d more significant differences than the land use analysis, where 10 of the 11 analyzed variables ha d significant differences
34 The di fferences between land cover and land use ar e attribute d to agricultural and bare soil covers. Nutrients in general ha d low concentration s in bare soil areas and high concentration s in wetlands, forest s and urban areas. Values of pH are acidic in bare so ils areas while wetland s a re more alkaline. Physical soil properties show significant differences across all surface covers. High bulk density values a re found to exist in grassy surface covers while the lo west values a re observed in areas covered by duff /mulch and shrubs. Greater soil water content s are found in shrub surface cover s, while areas cover ed by grass ha d the lo west value. Total porosity behave s opposite to bulk density, finding the lo west values for shrub areas. H igher amounts of nutrients a re related with the presence of herbaceous cover, while low amounts are found in areas cover ed by shrubs. S urface cover type explain ed little of the variation in nutrient concentration with the exception of Na Significant differences can be seen among city quadrants The highest soil water content i s found in the NE quadrant which is more than twice that of other areas in the city. The concentration s of K, Ca and Mg are the greatest in the South of the city. The SW quadrant of the city show ed soil s with hi gher pH ( 6.9). T he rest of the city i s generally acidic. Overall concentrations of heavy metals a re insignificant for our limited sampling when compared to the EPA threshold for residential areas ( http://www.epa.gov/opp t ). Urban Forests S tructure and composition The es timated number of trees in Gainesville i s 3,000,000. T he mean number of trees per plot i s 19 and the city has a tree cover of 52.3%. Trees with diameters
35 between 2.5 cm and 6 cm account for 28% of all tree s T he mean diameter at br e ast height (DBH) was 17 cm with a maximum diameter of 63 cm Larger diameter s a re found in Quercus virginiana (Mill) (live oak) () and Liquidambar styraciflua (L.) (sweetgum), 99 cm and 90 cm respectively Mean t ree height i s 10 m with a maximum of 25 m T he tall est trees a re Pinus sp. and Quercus laurifolia (Michx) (laurel oak) The three most common species a re slash pine (13.3%), laurel oak (11.9%) and water o ak (6.4%) and t he 10 most common species account ed for 64% of the tre es ( Figure 2 5 ). The majority of the plots a re composed of a high percentage of native trees ( 71% ) (Table 1 10) In Gainesville around 88% of tree species a re native to the state and 10% are exotic species of trees. Leaf area and biomass present high varia bility across plots. Most of the variable s show high level s of positive correlation to each other (Table 2 11). A strong correlation exist s between leaf biomass and tree density The first three principal components of the PCA for tree structure variables explain 87% of the variance of the data. The first component explain s 59%; the second component explain s 18% and the third component explain s only 8% of the variance. The first principal component d id not identify single factors explaining the variance; ho wever the main variables a re tree height (0.40), leaf area (0.44) and leaf biomass (0.44). T he second principal component show s a positive dominance of DBH (0.67) and a negative influence of tree density ( 0.59), whereas the third component show s a value f or the eigenvector over 0.8 for native tree species density in the plot. D ominant ground cover corresponds to herbaceous type (includ ing grasses and herbs) (48.4%) followed by duff or mulch (16.3%).The l east abundant types of cover are water and buildings
36 Tree mean height i s larger in forested areas (13 m) and smaller (8 m) in institutional areas. V ariability of tree height i s higher in instit utional and residential areas. Institutional areas ha ve the highest density of exotic species, wh ereas forested ar eas are associated with the highest densit ies of native tree species. V ariability within data i s also higher for residential and institutional area s Higher tree densities a re found in vacant and forested areas, while the lo west tree densities a re in insti tutional and residential areas. The highest amount of leaf area occurs in forest areas and plantations. At the plot level significant differences in leaf area exist ed among land uses (p value=0.022). T he highest mean leaf area (1,910 m 2 /ha) i s found in fo rested areas and the lo west (642 m 2 /ha) is in institutional areas. In land cover significan t difference s appear ed for all the structure variables H ighest values are found in areas classified as agricultural and bare soil, while the lo w est values are fou nd i n areas classified as wetlands (Table 2 12). Native species abundance differed significantly among city quadrants T he NE quadrant ha s the highest percentage of native tree species (88%) wh ereas the SW quadrant show s the lowest percentage (39%) Tree cover i s higher in the NE quadrant wh ereas the other quadrants value s are similar The largest DBH for a single tree i s found in the SE quadrant while smaller diameters a re found in the SW. T allest trees a re found in the NE. The highest leaf area and l eaf biomass i s located in the NW and the lo west leaf biomass and area i s found in the SE quadrant (Table 2 12). F unction Variability in the data i s low among plots for all the forest functions explored with and carbon seques tered and stored. This is evident due to low standard error values (Table 2 13) Net carbon sequestration across Gainesville
37 is 9,885 tons a year; the total amount of stor ed carbon for the city of Gainesville was 389,843 tons. The species with greatest est imated carbon sequestration are l ive oak, l aurel oak and l oblolly pine. For the entire city, the mean net carbon sequestration (accounting for tree death, removal, and subsequent C emissions in forested land uses) is estimate d at 1. 5 tons of carbon per yea r per ha C arbon sequestration estimat e s a re based on DBH growth and condition Smaller and those over 60 cm in DBH size classes for Pinus sp. sequester less than trees in the middle range between 20 and 60 cm DBH. Smaller amount of carbon sequester ed for DBH class 72 cm i s due to the lo west amount of individuals existing in that size class in relation to the other size classes (Figure 2 7 ). Since all the pollutants removed depend on tree cover, the correlation matrix show s a direct relationship amo ng them The CO 2 sequestered and carbon stored also show a strong correlation between each other and among air pollutants. Upon performing a principal component analysis the first two components explain most of the variance of the data (92.3% and 5.7% re spectively) T he first component ha s no dominance of a factor explaining the variability of the data, all data i s positively influencing the variability of the first component, however in the second component pollutants ha d a negative influence and carbon and CO 2 are positively influencing the variability. estimated a re in forested areas follow ed by vacant areas L ow emissions occur in institutional areas which a re related to low densit ies of trees. The greatest air pollutant concentrations a re removed in vacant areas while the lo west in institutional areas. Estimated c arbon
38 storage in the different land uses show sig nificant differences due to low tree densit ies found i n institutional areas and very high value s for commercial areas. Analysis of tree functions by land cover show s significant differences in all the explored functions. T he highest amounts of removal of air pollutants a re found in agricultural cover while the lo west amount s were found in wetland classified covers. According to city quadrants i s produced in the NE quadrant wh ereas the lo west i s in the NW quadrant. Estimates of a ir pollutant removal, sequestered and stored C are highest in the NE quadrant Lo west removal of air pollutant s and lowest levels of carbon sequestration occur red in the SE quadrant Carbon storage estimat es are significantly low er in the SW quadrant Soil T ree Relation ships Carbon sequestration and carbon storage were positively correlated with bulk density and organic matter respectively. Leaf biomass and leaf area a re related with the same soil variable s. P ositive relationships occur with water content, total porosity and the content of soil organic matter and negativ e relationships a re evident in more a cidic sites which show lower amount s of biomass and leaf area. Significant relationships between soil and tree properties are shown in Figure 2 10. Soil Tree S ocioeconomic R elationships In general t he correlation matrix d id not show statistical correlations that are biologically realistic Some significant relations hips do exist The carbon sequestered and stored is related with the average age of the in habitants of the property The water content is positively related w ith the years since the property was developed, and the concentration of Na and the organic matter content is positively related with the proportion of White s
39 Discussion Socioeconomics P opulation in Gainesville has increased by 11% during the last 6 years and the city ha s expanded from 124.3 k m 2 to 140 k m 2 (US Census Bureau, 2008). The expansion of the city towards the edges has had an effect on the natural ecosystem with environmental impacts such as reduction in open space, increase of air pollution ; in creases in energy consumption rates decreased of landscape aesthetics reduction in flora and fauna diversity, i ncreased of runoff and flooding, excessive removal of native vegetation and ecosystem fragmentation (Johnson, 2001). The biggest changes in l and use in recent years have occurred o n agricultural and forested areas Chang es in forested areas occur both out side and inside the city limits leading to a change in canopy coverage. During the period from 2000 through 2004 canopy coverage ha d decrease d 9% while building cover has increased about 5% over during the same period (Szantoi et al., 2007). Socioeconomics in Gainesville is substantially influenced by the presence of the University of Florida. A high percentage of the educated population with a dvanced degrees living in Gainesville is concentrated in the more expensive areas. Given the large concentrations of student apartments around the University, h igh ly educated people ( including non U.S. citizens ) are concentrated in this area (SW) There is a large range in socioeconomic status in Gainesville, with household incomes as low as $25,000 to as high as $8 6 ,000 per year. Property values range from $100,000 to more than $1,000,000. Th e NW quadrant of the city shows the highest variabili ty for socioeconomic variables with old and new properties, suburbs and forested areas, and is currently the main area of city expansion
40 Some of the resolution of the data use d for the analysis cou ld be giving misleading results. L and use classification and census block information vary in resolution in relation to the plot size Land use classification was done using a Landsat with a 30x30 m of pixel size (0.09 ha), the census block minimum size is 0.3 ha and a maximum size of 0.37 ha and the area of the plo t is 0.04 ha. This may produc e flawed results as demonstrated by vacant areas with hou sehold income or forested areas with dense populat ions Soil s The mean value for bulk density (1.01 g cm 3 ) for Gainesville are similar to surrounding na tura l areas (1.21 g cm 3 1.34 g cm 3 ) ( http://public.ornl.gov/ameriflux/Site_Info/siteInfo.cfm?KEYID=us.slash_pine_mid.01 Gregory et al., 2006). Values for bulk de nsity are within range of other urban areas (Baltimore 0.71 g cm 3 1.74 g cm 3 ) (Pouyat et al., 2007). Soil nutrient values were compared with data available from the literature. Whitcomb (1987) recommends necessary soil nutrient concentration s for maintain ing landscape trees and w hen compar ed to Gainesville median values for almost all the nutrients were outside the recommended range; 1,663, 27, 79 and 61 mg kg 1 respectively. Values of Ca over 1,000 mg kg 1 could imply the presence of concrete around the soil sample (de Miguel et al., 1997) due to pave d roads or the presence of calcareous limestone parent material (Cooke, 1945). H igh levels of Ca were found in residential and vacant areas. The Baltimore soil study (Pouyat et al., 2007) obtained comparable Ca values and higher concentrations for K, Mg and P. In Gainesville heavy metal concentration s when compared to other urban studies is low. Areas with a longer hi story of urbanization such as Baltimore showed higher mean concentrations of 92, 17.3, 0.56, 2.8 and 100
41 mg kg 1 for Zn, Ni, Cd, Cu and Pb respectively than Gainesville (Short et al., 1986). In general, cities have been found to be sinks for nitrogen and phosphorus, besides some metals (Faerge et al., 2001; Groffman et al., 2004) I n forested are as TKN was highly variable with high values associated with fertilized plantations. Also high varia tion in TKN can be found in residential areas where the types of surface cover fertilizing regimes are variable among properties. T his existence of variabili ty was found in other studies, where more expensive areas showed higher levels of nitrogen (Hope et al., 2005) Differences also were found in residential areas with older properties with concentrations of P and K 45% higher than recently urbanized areas ( Scharenbroch et al., 2005). Analyses of s urface cover showed high variability i n water content w h ere values of duff cover varied in relation to level s of water content ( related to the depth of the duff layer ) which also leads to high differences in the am ount of organic matter and therefore TKN. Highly variable levels of Ca and TKN could be related the existence of impervious surfaces or vegetative surface s in the plots since concrete makes the soil increase its Ca concentration and vegetation increases th e concentration of N in the soil. Differences in organic matter content in relation to surface cover was related to the additions of mulch or leaf litter and herbs to the soil that help increase soil organic matter (Craul, 1999). High variability of K, Ca Mg and pH on the different quadrants of the city depend ed on the existing forest structure, which was highly variable in the NW part of the city. A griculture and bare soil areas were the ones that showed the worst condition s when compare with tree growth recommendations whereas urban areas ha d the highest concentrations of nutrients and were high ly variab le similar to forested areas.
42 Trees Th e overall tree density for the C ity of Gainesville was 475 trees/hectare, greater than that of other cities (McPh erson et al, 1994; Nowak et al, 2000; Nowak et al., 2006). Values for Tampa, Florida, were around 257 trees/hectare (Andreu et al., 2008), while cities outside the state such as Minneapolis, MN, Baltimore MD and Atlanta GA ha d densities of 64.7 trees/hec tare, 125.5 trees/hectare and 275.5 trees/hectare respectively (Nowak et al., 2006). In comparison to other cities in the U.S, Gainesville ha d a similar percentage of native species (88%) but more native species than the city of Tampa (50%) High variabil ity in the data could be found in the NW part of Gainesville, especially for leaf biomass and leaf area. L eaf biomass and leaf area variability in vacant, residential and forested areas might be related to the differen ces in urban structure such as when a land classified as vacant may actually be an abandoned parking lot or a forested area. Trees in the C ity of Gainesville sequestered a relatively high amount of carbon. Compared to a city with a similar amount of trees such as Baltimore which has 2.6 millio n trees and a ca rbon sequestration a year of 0. 98 tons/ha/yr (Nowak et al., 2006), Gainesville showed high values since with 3 million trees sequestered 1. 5 tons/ha/yr of carbon The pollution removal was slightly lower than Baltimore in that 0.021 kg /ha/yr were removed (Nowak et al., 2006) wh ereas Gainesville only removed 0.020 kg /ha/yr. The standing stock of tree carbon in Gainesville was 0.00 6 tons/m 2 of tree cover whereas in Baltimore the stock was just 0.003 tons/m 2 (Nowak et al., 2006). The com position of Gainesville by quadrants show ed differences in the percen tage of native species of trees where more exotics can be found i n the SW related to large business
43 area s This situation is also repeat ed in institutional areas such as airports, school and parks that are not necessarily incorporating native trees in their landscape. In vacant areas, h igh differences in tree density were due to different urban forest structures such as highly forested areas bare soil or impervious areas La nd cover cla ssification describes the most differences across the landscape, where all the tree variables across the different land cover had significant differences. Tree functions were less variable among land use s except for vacant areas that were either completel y forested or completely deforested where such high difference s in tree cover leads to Conclusion Gainesville has a variety of socioeconomics, urban morphology and environmental characteristics, that range from highly urb anized areas to less disturbed areas such as those on the periphery or in very old sections of the city. Given the se complexities, benefi cial functions for humans delivered by the urban forest and its surrounding environment are highly variable and are d istributed according to urban morphology and time since the urbanization Gainesville is not a highly urbanized city, even though the rates of development have been increasing during recent years. Inside the city limits, tree cover was higher than in many cities in the U.S. Remaining forested areas mak e soil and forest characteristics similar to values found in natural areas. H ighest soil quality, is found in institutional land uses. In terms of surface cover higher soil quality is found in grass y surface c overs The North quadrant shows the best soil quality, where less urbanization ha s occurred.
44 The highest calculated rates of air pollution removal a re found in the NE quadrant. T he size of the average tree in the NE quadrant was above the average for the entire city T his quadrant was also the one with the highest forested land use presence which corresponds to the highest air pollution removals by land use. Relationships within component s of the urban environment were found. Inter relationships among urba n ecosystem components (soil, trees and socioeconomics) were not evident and only soil tree relationships were found. According to these findings, l ow levels of soil compaction will allow for adequate increments of leaf biomass and leaf area which is beneficial for maximizing urban forest function s ince compacted soil s are known to decrease tree functions by rest ricting root growth (Jim, 1998). Urban forest m anagement plans should maintain or increase tree cover by the inclusion of a vegetat ed landscape in new constructions. To maintain adequate soil quality, new constructions should incorporate sustainable management practices such as adding compost as a blanket to smooth the impact of machinery and add an organic layer to the disturbed soil to avoid soil comp action and nutrient deficit (McDonald and Beatty, 2008) These practices will accelerate a process that naturally will be reduced by synergistic processes improving the soil health and quality (Scharenbroch et al., 2005). Understanding the urban forests and their surrounding environment will help in the recognition of the benefits delivered to the community. These benefits are named ecosystem services and goods, and are the products obtained from the ecosystem that affect the state of human well being. Th e information obtained in this chapter could work as a baseline to develop ecosystem functions indicators to assess the condition of the
45 forest existing in an urban area. Once the indicators for the ecosystem functions (regulation, habitat, production and information) are built they could be aggregated in an index of urban ecosystem services and goods
46 Figure 2 1 Plot layout for soil sampling and vegetation measurements. Bulk Density Cores 5 Sample s 5 Samples 5 Sample s 11.35 m 0.04 ha. 1.0 m
47 Figure 2 2 Distribution of sampled plots city quadrant s. Note: imagery are two meter in resolution (2004)
48 Figure 2 3 Diagram showing positive and negative correlations of the 12 soil variables analyzed for the city of Gainesville BD Bulk Density, WC Water Content, TP Total Porosity, SC Soi l Cover, P Extractable Phosphorus, K Extractable Potassium, Ca Extractable Calcium, Mg Extractable Magnesium, Na Extractable Sodium, TKN Total Kjeldahl Nitrogen, OM % Organic Matter. Note: first (F1) and second (F2) factor derived from the P rincipal C ompon ent A nalysis to discriminate plots by land use. Highest correlations are in side the blue square Figure 2 4 Multiple r egression model for soil pH of observed vs. predicted values.
49 Figure 2 5 Ten most common tree species in the city of Gainesville F L. Figure 2 6 T ype of land covers (% of the area) by land use for the city of Gainesville, FL.
50 Figure 2 7 Carbon stora ge and sequestration by mean diameter at breast height in the city of Gainesville FL. Figure 2 8 Estimated c arbon sequestration by species for the city of Gainesville FL
51 Figure 2 9 Significant correlation s ( R 2 adj >0.40) for soil tree relationships in the city of Gainesville, FL The l ine i n each graph represents the best fit.
52 Table 2 1 Soil chemical analysis p rocedure s 1 Test Soil variable Procedure pH Uses 20 cm 3 of soil sample with 10 ml of pure water to obtain 1:2 soil water ratio and then pH is measured with a ph meter, which has been previously calibrated with the appropriate buffer solution at pH 4.00, 7.00 and 10.00 (Robertson et al., 1999) Mehlich 1 Macro and Micronutrients This procedure uses a 4 c m 3 sample of soil and 30 ml of Mehlich 1 extraction solution providing a soil solution ratio of 1:4. The Mehlich 1 extraction is a diluted double acid mixtur e of 0.0125 M H 2 SO 4 and 0.05 M HCL. T he mixture is then filtered after shaking for 30 minutes (Robertson et al., 1999) Loss of Ignition Organic Matter The method exposes soils to a high temperature (350C) in an oxygen atmosphere for a period of at l e ast 2 hours to convert any organic carbon compounds to carbon dioxide and t hen weighed. This procedure has been reported to be consistent with sandy Florida soils (Mylavarapu and Moon, 2002). Total Kjeldahl Nitrogen Nitrogen Extracts the amount of organic ni trogen in organic materials I t mixes the soil with 2.0 g of Kjeldahl solution and start s the digestion at 250C in a digester, the 5 ml of Sulfuric Acid are added to the sample, after one hour the temperature is increased to 350C and then digested for 2. 5 to 3.0 hours (Robertson et al., 1999) 1 Soil Analysis Laboratory University of Florida http://soilslab.ifas.ufl.edu
53 Table 2 2 Soci oeconomic group description by quadrants for the city of Gainesville, Florida 2 Quadrants Household income ($) Median Age (years) Race Education NE Group 1 34,000 >65 White High school Group 2 30,000 >65 Multi ethnic Some high school NW Group 1 39,356 <35 Multi ethnic Some college education Group 2 23,545 <30 Multi ethnic College students Group 3 28,940 <55 Multi ethnic Some college education Group 4 50,000 <35 Multi ethnic Recently gradu ate from college Group 5 50,000 >65 White College grads Group 6 70,000 <55 Asian americans College grads Group 7 77,000 35 64 White Graduate education Group 8 50,000 45 64 Multi ethnic College grads Group 9 60,000 <55 White College grads 2 Claritas Socioeconomics 2009 www.claritas.com
54 Table 2 2 Continu ed Q uadrants Household income ($) Median Age (years) Race Education SE Group 1 39,356 <35 Multi ethnic Some college education Group 2 23,545 <30 Multi ethnic with high concentration of African American College students Group 3 26,000 >65 Multi ethnic with high concentration of African American Some high school Group 4 23,000 >65 Multi ethnic with high concentration of African American Some high school Group 5 28,940 <55 Multi ethnic Some college education SW Group 1 23,545 <35 Multi ethnic with high concentration of African American College students Group 2 80,000 Whites Graduate degrees Group 3 60,000 <55 Whites College grads Group 4 28,940 <55 Multi ethnic Some college education Group 5 33,100 Multi ethnic High sch ool
55 Table 2 3 Descriptive statistics for socioeconomics of plot s sampled in the city of Gainesville FL by US Census Block (U.S Census Bureau, 2000) (n=70) Socioeconomic variable Mean Min. Max. Std. Error Coefficient of variation Kolmogorov Smirnov p v alue Population (habitants) 2,475.3 403 6,287 214.9 72.1 <0.010 Household income (US$) 34,326.9 10,897 86,641 2,029.3 49.1 <0.010 Household (units) 993 200 2,590 98.4 82.3 <0.010 Ownership Household (%) 61.8 0.9 94.5 3.3 45 <0.010 Population with high er level of education (%) 19.4 0 57.3 1.9 85.5 <0.010 White 1,766.6 20 4,980 185.5 87.2 <0.010 African Americans 516.6 3 2,240 57.5 92.4 <0.010 Other 346.1 12 1,158 41.5 100.2 0.004 Population Average age (years) 33.3 20.2 48.2 0.9 23.4 0.017 Property Value ($/acre) 131,344.7 15 7,116,718 192,908 141.6 <0.010 Years since urban development (years) 28.7 0 122 2.47 71.5 <0.010
56 Table 2 4 POP HI ED R YUD PVAL PYR HU OW POP 1 HI NS 1 ED 0.24 0.26 1 White s 0.96 0.36 NS 1 African Americans 0.30 0.34 NS NS 1 Other 0.84 NS NS 0.82 NS 1 YUD NS NS NS NS NS NS PVAL NS NS NS NS NS NS PYR NS 0.74 NS NS 0.35 0 .36 1 HU 0.96 0.24 NS 0.96 NS 0.82 NS 1 OW NS 0.58 NS NS NS 0.44 0.65 NS 1 NS correlation not significant between variables. POP population per census block (habitants) ; HI household income (US$) ; HU household units ; OW ownership of the property b y block (%) ; ED population with a high education degree (%) ; R ethnicity (habitants) ; PYR median age in the property (years) ; PVAL property value (US$) ; YUD years since urban development.
57 Table 2 5 Mean and standard deviation s for socioeconomics of the city of Gainesville, FL classified by land use, and city quadrants A nalysis of variance for significant differences among land use and city quadrants POP HI HU OW ED Land Use Commercial (n= 10 ) 3 0331 733 29 88814 175 1 480900.6 7024.5 2114 Forested (n= 30 ) 2 1891 589 30 35611 698 750739 5826.6 2017.3 Institutional (n= 15 ) 1 6411 543 30 68816 436 898568 5630.7 2719 Resid ential (n= 3 5) 2 9341 954 41 81620 335 1 066905 6827.6 1616.2 Vacant (n= 8 ) 1 051916 27 0398 017 1 605104 2422 1311.8 p value NS NS NS NS NS Quads NE (n= 35) 2 1291 439 31 73416 528 818638 5426.4 2515 SE (n=13) 2 4331 382 32 05317 715 872765 6335.9 1818.7 NW (n=37) 2 5301 893 34 05316 617 1 057883 6328.3 1415.2 SW (n=10) 3 2602 415 45 95116 656 1 3481 017 784.4 2918.6 p value NS NS NS NS 0.04 NS el LU Land U se ; Com commercial ; For forested ; Ins institutional ; Res residential ; Vac vacant. POP population per census block (habitants) ; HI household income (US$) ; HU household units ; OW ownership of the property by block (%) ; ED population with a high education degree (%) ; R race (habitants) ; PYR median age in the property (years) ; PVAL property value (US$) ; YUD years since urban development
58 Table 2 5 Continued. PYR PVAL YUD R Whit e s African Americans Other LU Commercial (n=10) 357.9 99 7641 651 071 15.117.9 2 2201 577 566433.9 495313 Forested (n=30) 337.3 1 199 6311 525 119 3328.3 1 4011 372 631487.5 233328 Institutional (n=15) 339 1 773 1171 797 724 32.237.1 1 1141 223 349385.9 339398 Residential (n=35) 348 963 5531 667 258 27.215.9 2 2701 647 427498 385341 Vacant (n=8) 265 21 79017 893 6186.3 466169 551718.4 706200 p value NS NS NS NS NS NS Quads NE (n=35) 317.6 895 2751 410 569 37.232. 8 1 4821 389 473479 301302.7 SE (n=13) 348.1 1 306 9121 562 192 20.321.5 1 6241 299 622448 358331.1 NW (n=37) 318.2 1 641 0752 401 302 31.225.4 1 8361 633 494491.8 362383 SW (n=10) 377.6 322 789767 248 11.913.3 2 4351 952 615508 3 89360.3 p value NS 0.04 NS NS NS NS NS el. LU, Land U se ; Com commercial ; For forested ; Ins institutional ; Res residential ; Vac vacant. POP population per census block (habitants) ; HI household income ( US$) ; HU household units ; OW ownership of the property by block (%) ; ED population with a high education degree (%) ; R race (habitants) ; PYR median age in the property (years) ; PVAL property value (US$) ; YUD years since urban development
59 Table 2 6 Descriptive statistics for soil properties in the upper 10 cm of the surface in the city of Gainesville, Florida (n=70) Soil Property Mean Median Min imum Max imum St an d ard Error Coef ficient of variation Kolmogorov Smirnov p value Bulk Density (g/m 3 ) 1.0 1 1.12 0.13 1.46 0.04 29.9 <0.010 Water Content (%) 10.99 9.15 0.6 45.7 0.94 71.4 <0.010 Total Porosity 0.62 0.58 0.45 0.95 0.01 18.6 <0.010 Soil Cover (%) 80.37 96.5 5 100 3.19 33.3 <0.010 Extractable P (mg/kg) 61.31 28.52 0.29 538.8 12.06 164.6 <0.01 0 Extractable K (mg/kg) 27.08 21.22 0 85.36 2.72 84.1 <0.010 Extractable Ca (mg/kg) 1 663.42 652.6 2.07 6232 243.14 122.3 <0.010 Extractable Mg (mg/kg) 78.89 66.18 0 292 8.33 88.4 <0.010 Extractable Na (mg/kg) 19.87 15.63 5.16 101.48 1.76 74.3 <0.010 TKN (mg/kg) 922.10 776.39 117.7 3546.74 65.62 59.5 <0.010 pH 5.76 5.55 3.6 7.8 0.14 20.5 0.04 Organic Matter (%) 5.34 3.19 0.91 64.06 1.13 177.4 <0.010
60 Table 2 7 Pearson c orrelation among BD WC TP SC P K Ca Mg Na TKN pH OM B D 1 WC 0.58 1 TP 0.99 0.59 1 SC NS NS NS 1 P NS NS NS 0.27 1 K NS NS 0.24 0.28 0.37 1 Ca NS NS NS NS NS 0.64 1 Mg NS NS NS NS 0.44 0.66 0.62 1 Na NS 0.26 NS NS NS 0.36 0.56 0.50 1 TKN 0.39 0.57 0.38 NS NS NS 0.29 0.42 0.27 1 pH 0.49 NS NS 0.35 0.24 0.62 0.82 0.54 0.31 NS 1 OM 0.51 0.6 0.51 NS NS NS NS NS 0.39 0.72 NS 1 NS correlation not significant between variables. BD Bulk Density ; WC Water Content ; TP Total Porosity ; SC Soil Cover ; P Extractable Phosphorus ; K Extractable Potassium ; Ca Extractable Calcium ; Mg Extractable Magnesium ; Na Ext ractable Sodium ; TKN Total Kjelda hl Nitrogen ; OM % Organic Matter
61 Table 2 8 Mean and standard deviation for so il properties for t he city of Gainesville, FL classified by land use, land cover, surface cover and city quadrants A nalysis of variance p values account for significant dif ferences BD WC TP P K Ca Land Use Commercial (n=8) 1.20.1 9.34.5 0.50.05 116166.7 24.317.3 1 7322 111 Forested (n=25) 0.80.3 13.79.3 0.70.1 12.518.6 13.114.3 1 0201 872 Institutional (n=9) 1.10.2 8.68.5 0.60.08 59.773.6 49.729.4 3 2662 404 Residential (n=25) 1.20.1 9.56.2 0.60.07 95.7116 33.521 1 5551 659 Vacant (n=4) 1 .20.8 14.56.7 0.50.1 11.76.3 27.79.9 3 6193 694 p value 0.0002 NS 0.002 0.02 0.03 0.03 Land Cover Agriculture (n=1) 0.77 9.8 0.71 1.7 10.5 60.1 Bare Soil (n=2) 1.20.01 9.33.7 0.560.007 1.40.09 00.3 36.48.8 Wetland (n=3) 1.20.2 12.26. 1 0.50.09 15.88.4 25.67.8 3 0172 812 Forest (n=36) 0.90.4 12.17.5 0.60.1 66.9106.1 26.122.8 1 4861 887 Urban (n=27) 1.10.2 9.68.9 0.60.08 66.8105.4 31.623.9 1 9812 200 p value <0.0001 <0.0001 <0.0001 NS 0.004 0.02 Surface Cover Duff (n=9) 0.60.6 15.814.9 0.741.14 15.116 14.58.8 8141 518 Grass (n=17) 1.20.1 7.54.8 0.550.05 53.464 36.526.3 2 0722 300 Herbs (n=3) 0.70.5 15.49 0.60.3 29.940.3 39.819.6 4 1032 863 Shrub (n=5) 0.50.06 21.46.4 0.80.02 4.14.1 7.76. 4 215220 Tree (n=29) 10.5 10.36.5 0.60.09 66.7107.4 27.322.8 1 5741 860 Wild Grass (n=5) 1.20.01 94.8 0.530.01 178267 16.512.5 1 2341 648 p value 1.1E 06 0.018 1.26E 06 NS NS NS NS not sign .BD Bulk Densi ty (g/cm 3 ) ; WC Water Content (%) ; TP Total Porosity ; SC Soil Cover (%) ; P Concentration of Phosphorus (mg Kg 1 ) ; K Concentration of Potassium (mg Kg 1 ) ; Ca Concentration of Calcium (mg Kg 1 ) ; Mg Concentration of Magnesium (mg Kg 1 ) ; Na Concentrati on of Sodium (mg Kg 1 ) ; TKN Total Kjeldahl Nitrogen (mg Kg 1 ) ; OM Organic Matter (%).
62 Table 2 8 Continue d BD WC TP P K Ca Quads NE (n=20) 0.90.38 16.510.2 0.660.1 19.328.6 17.114.8 1 227.72 068 SE (n=8) 1.10.2 7.66.7 0.590.06 82.278.5 4 2.722.2 2 5341 780 NW (n=33) 1.10.3 8.69.9 0.610.1 85.6132.4 22.220.7 1 286.61 708.9 SW (n=9) 1.10.2 6.44.9 0.580.06 2823.7 51.329 3 603.82 700 P value NS 0.001 NS NS 0.0001 0.01 NS not sign .BD Bulk Den sity (g/cm 3 ) ; WC Water Content (%) ; TP Total Porosity ; SC Soil Cover (%) ; P Concentration of Phosphorus (mg Kg 1 ) ; K Concentration of Potassium (mg Kg 1 ) ; Ca Concentration of Calcium (mg Kg 1 ) ; Mg Concentration of Magnesium (mg Kg 1 ) ; Na Concentra tion of Sodium (mg Kg 1 ) ; TKN Total Kjeldahl Nitrogen (mg Kg 1 ) ; OM Organic Matter (%).
63 Table 2 8 Continued. Mg Na TKN pH OM Land Use Commercial (n=8) 86.493.7 17.99.9 777358 6.11.1 30.6 Forested (n=25) 52.567.2 19.819.5 953777 5.11.1 8.3 5.5 Institutional (n=9) 90.441 23.115.2 711247 6.71.2 3.31.5 Residential (n=25) 98.170.1 18.49.4 1005402 5.90.8 41.4 Vacant (n=4) 77.811.2 32.425.2 968249 6.51.5 4.20 p value NS NS NS 0.002 NS Land Cover Agriculture (n=1) 19.2 23.5 400.8 4.2 2.6 Bare Soil (n=2) 1.70.6 5.50.03 520.8407.3 5.21.1 2.11.4 Wetland (n=3) 84.814.5 28.918.8 1006187.8 6.61.1 4.30.2 Forest (n=36) 83.278 20.416.5 914.5487.3 5.51.2 5.48.5 Urban (n=27) 82.462.7 18.311.8 960.8665.1 6.11 5 .611.8 p value 0.006 <0.001 <0.0001 <0.0001 NS Surface Cover Duff (n=9) 51.142.4 19.28.3 11831 177 4.70.9 13.724.7 Grass (n=17) 77.555 17.59.7 842316 6.11.1 3.61.3 Herbs (n=3) 129.659 50.843.9 1414316 6.80.7 20.429 Shrub (n=5) 31.6 25 20.610 1033283 4.10.5 5.11 Tree (n=29) 83.977.8 17.711 894528 5.81.1 3.912.6 Wild Grass (n=5) 121.6147 18.415.2 928100 6.41.2 3.70.5 p value NS 0.027 NS 0.00063 0.044 Quads NE (n=20) 55.670.4 23.622 1082.6782.7 5.21.1 9.517 SE (n=8) 12974.5 19.48.6 1052575 6.60.5 4.83.9 NW (n=33) 7064.2 17.39.5 802359.5 5.61.1 3.61.5 SW (n=9) 118.952 23.416.5 893.9443.3 6.90.9 30.9 P=value 0.01 NS NS 0.0008 NS NS not sign .BD Bulk Density (g/cm 3 ) ; WC Water Content (%) ; TP Total Porosity ; SC Soil Cover (%) ; P Concentration of Phosphorus (mg Kg 1 ) ; K Concentration of Potassium (mg Kg 1 ) ; Ca Concentration of Calcium (mg Kg 1 ) ; Mg Concentration of Magnesium (mg Kg 1 ) ; Na Concentration of Sodium (mg Kg 1 ) ; TKN Total Kjeldahl Nitrogen (mg Kg 1 ) ; OM Organic Matter (%).
64 Table 2 9 Concentrations of soil heavy metals in the upper 10 cm of soils in the city of Gainesville FL (n = 9) Soil Property Mean Median Min. Max. Std. Error Kolmogoro v Smirnov p value M aximum concentration advise for recreational areas (Thornton, 1991) Zinc (mg/kg) 5.41 1.21 0.045 38.12 3.67 <0.010 2,300 Copper (mg/kg) 0.37 0.26 0 1.44 0.14 0.101 3.10 Cadmium (mg/kg) 0 0 0 0 0 NA 3.9 Nickel (mg/kg) 0.58 0.30 0 2.14 0.24 <0.010 160 Lead (mg/kg) 0.49 0.005 0 2.97 0.31 <0.010 400 NA not applicable. Table 2 10 Descriptive statistics of urban forest structure and composition in the city of Gainesville FL (n=70) Urban Forest Property Mean Median Min. Max. Std. Error C oefficient of variation Kolmogorov Smirnov p value Tree cover (%) 52.3 60 0 100 33.4 63.9 <0.010 Mean diameter at breast height (cm) 16.9 15.5 0 62.5 12.5 74.2 <0.010 Mean height (m) 10.1 10.1 0 25.6 6.4 63.1 >0.150* Density (trees/plot) 19.2 11.5 0 10 0 19.8 108 <0.010 Native tree species (%) 71.7 82.8 0 100 34.5 48.1 <0.010 Leaf biomass (kg/ha) 122.4 100.2 0 487 109.2 89.2 <0.010 Leaf area (m 2 /ha) 1 313.6 1057.9 0 5 805.7 1 184.8 90.2 <0.010 *Mean height fits an exponential distribution.
65 Table 2 1 1 Pearson c TC DBH TH TD NT LB LA TC 1 DBH 0.38 1 TH 0.55 0.70 1 TD 0.45 NS 0.29 1 NT 0.46 0.39 0.55 0.46 1 LB 0.55 0.42 0.59 0.61 0.56 1 LA 0.57 0.36 0.66 0.67 0.52 0.92 1 NS no significant differences. TC tree cover ; DBH mean diameter at br e ast height ; HT mean height ; NT percent of native species ; TD tree density ; LB leaf biomass ; LA leaf area.
66 Table 2 12 Mean and standard de viation s for tree structure by land use, land cover and quadrants for the city of Gainesville and ANOVA p values. A nalysis of variance p values account for si gnificant differences TC DBH TH TD NT LB LA Land Use Com (n=10) 46.932 16.314.8 85.4 0.030 .05 7733.5 88.977.8 995920.1 For (n=30) 66.432 16.36.2 134.9 0.080.06 91.89.9 169.1116.1 19101 334 Ins (n=15) 31.134.5 9.412.6 5.87.1 0.020.03 34.244.7 56110.7 641.91 223.4 Res (n=35) 47.331 20.515.4 9.86.8 0.020.02 61.633.9 110 .493.6 1 065.2826.7 Vac (n=8) 57.545.9 8.44.4 6.34.8 0.090.1 98.52.1 128.8180.9 1 380.31 938 p value NS NS 0.024 1.97E 05 2.63E 05 NS 0.022 Land Cover Ag. (n=1) 750 15.90 13.90 0.070 96.20 207.40 2 179.10 BSl (n=2) 7535.5 24.82.8 1 8.53.5 0.030.03 77.725 185.8173.9 1 9971 758.9 Wet (n=3) 4539.1 12.147.1 6.33.4 0.060.09 79.932.2 99.6137.5 1 0721 470 For (n=36) 46.335.9 17.113.4 10.36 0.050.05 76.532.5 122.8137.5 1 189.51 042 Urban (n=27) 57.729.9 16.412.5 9. 67.1 0.040.05 61.937.9 116.4111 1 4251368 p value <0.0001 <0.0001 <0.0001 0.0008 <0.0001 <0.0001 <0.0001 Quad NE (n=35) 55.831.6 17.813.1 12.56.1 0.050.04 85.715.4 110.966.8 1 236.9723.7 NW (n=37) 51.932.7 17.212.2 10.46.5 0.050.05 75. 333.9 146.9123.5 1 ,507.31,331 SE (n=13) 5038.1 19.314.9 8.33.5 0.050.05 67.634.8 58.645.4 660.3514.2 SW (n=10) 51.140.7 11.110.9 6.97.2 0.040.06 38.939.3 73.585.9 1 020.61 193 p value NS NS NS NS 0.01 NS NS NS .05 of probability level. TC total tree cover (%) ; DBH mean diameter at br e ast height (cm) ; HT mean height (m) ; NT percent of native species (%) ; TD tree density (trees/plot) ; LB leaf biomass (kg/ha) ; LA leaf area (m 2 /ha). Quads quadrants of the ci ty ; NE NorthEast; NW NorthWest; SE SouthEast; SW SouthWest ; Com commercial ; For forested ; Ins institutional ; Res residential ; Vac vacant ; Ag agriculture ; Wet wetland ; BS bare soil
67 Table 2 13 Descriptive statistics for urban forest functions estima ted per plot in the city of Gainesville FL (n=70) Forest function Mean Median Min imum Max imum St an d ard Error Coefficient of Variation Kolmogorov Smirnov p value kg yr 1 ) 27.6 189.9 0 105.1 3.2 96.3 <0.010 CO removed ( kg yr 1 ) 0.14 0.08 0 0.5 0.02 96.3 <0.010 O 3 removed ( kg yr 1 ) 1.56 0.91 0 5.9 0.18 96.3 <0.010 SO 2 removed ( kg yr 1 ) 0.31 0.18 0 1.17 0.03 96.3 <0.010 NO 2 removed ( kg yr 1 ) 0.29 0.17 0 1.11 0.08 96.3 <0.010 Pm10 removed ( kg yr 1 ) 0.73 0.08 0 2.77 0.08 96.3 <0.010 C seq uestered ( kg yr 1 ) 24 10 0 90 24 97.16 <0.010 Carbon stored ( kg ) 2,100 104 0 15,500 32 126.86 <0.010 VOC, volatile organic compound; CO, carbon monoxide; O 3 ozone; SO 2 sulfur dioxide; NO 2 nitrogen dioxide; Pm 10 particulate matter less than 10 microns ; C, carbon.
68 Table 2 14 Mean and standard deviation for tree functions for land use, land cover and quadrants for the city of Gainesville and ANOVA p values. A nalysis of variance p values account for significant differences VOCE COR OR SOR NOR PM10R COS CS Land Use Com (n=10) 22.424.2 0.110.13 1.261.4 0.250.27 0.240.3 0.60.6 23 26 3,400 5 3 00 For (n=30) 42.229.8 0.210.15 2.41.7 0.470.3 0.450.3 1.10.8 29 22 1 9 00 1 9 00 Ins (n=15) 15.227.6 0.080.1 0.861.6 0.170.3 0.160.3 0.390.7 1 3 24 1, 5 00 2, 7 00 Res (n=35) 18.915.6 0.090.08 1.10.8 0.210.2 0.20.2 0.490.41 24 25 2, 1 00 2 2 00 Vac (n=8) 3549.1 0.170.2 1.92.8 0.390.01 0.370.5 0.91.3 20 29 1, 9 00 2, 8 00 p value 0.009 0.01 0.01 0.01 0.009 0.009 NS 0.032 Land Cover Ag. (n =1) 51.40 0.250 2.890 0.570 0.540 1.350 23 0 2, 1 00 0 BSl (n=2) 37.834.8 0.190.17 2.131.9 0.420.39 0.400.3 0.990.91 22 17 2, 1 00 1 5 00 Wet (n=3) 25.838.2 0.130.19 1.452.1 0.290.42 0.270.4 0.681 18 21 1, 8 00 2 6 00 For (n=36) 25.823.8 0 .130.12 1.451.3 0.290.42 0.270.25 0.680.63 22 23 2 0 00 2 2 00 Urban (n=27) 28.830.4 0.140.15 1.621.7 0.320.34 0.310.2 0.760.8 29 26 2, 4 00 3, 4 00 p value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.001 0.008 Quad NE (n=35) 34.828.3 0.17 0.14 1.961.6 0.40.3 0.360.3 0.910.7 34 28 2, 4 00 2 9 00 NW (n=37) 27.226.7 0.140.13 1.531.5 0.30.2 0.290.2 0.710.7 27 26 2, 2 00 2, 9 00 SE (n=13) 15.416.9 0.080.08 0.870.9 0.170.2 0.160.1 0.400.4 15 17 2 3 00 2 1 00 SW (n=10) 25.429.3 0.13 0.15 1.431.6 0.280.3 0.270.3 0.670.8 25 29 9 00 9 00 p value NS NS NS NS NS NS NS <0.001 NS kg yr 1 ) ; COR carbon monoxide removed ( kg yr 1 ) ; OR ozone removed ( kg yr 1 ) ; SOR sulfu r dioxide removed ( kg yr 1 ) ; NOR nitrogen dioxide removed ( kg yr 1 ) ; PM10R particulate matter over 10 microns removed ( kg yr 1 ) ; COS carbon dioxide sequestered ( kg yr 1 ) ; CS carbon stored ( kg ). Quad quadrants of the city. Com commercial ; For foreste d ; Ins institutional ; Res residential ; Vac vacant ; Ag agriculture ; Wet wetland ; BS bare soil
69 CHAPTER 3 ECOSYSTEM SERVICE INDICATOR S FOR THE CITY OF GAIN ESVILLE Introduction An indicator is a tool for summarizing information about the environmental condition or state of an ecosystem (Segnestam, 2002). An indicator is a numerical value that provides information and describes the state of a phenomenon env ironment or area (OECD, 2001). Indicators have the advantage of reducing dimensionality of data, simplifying interpretations, and facilitating communication between experts and non experts (Segnestam, 2002). Environmental indicators condense informat ion about conditions and may show trends, and provide clues about the conditions or viability of a syst em, and its state or health (UNEP, 2006). Human well being is characterized by the array of biological, sociological, economical, environmental and political factors that humans are exposed to (Tzoulas et al., 2007). being is usually used when referring to health, from a physical, mental and social point of view (WHO, 1998). Th e term can be defined by several socio economic, psychological and psychosocial variables (Rioux, 2005) and may include feelings of connectivity towards nature (May er et al., 2004). The Millennium Ecosystem Assessment (MEA) (2005) refers to human well being as the conjunction between material security, personal freedoms, good social relations hips and physical health. According to Daily (1997), E cosystem services are the conditions and processes through which natural ecosystems, and the species that inhabit them sustain and fulfill human life. These pr ocesses produce ecosystem goods and perform the actual life Ecosystem services and goods are defi ned by the presence of
70 humans which act as drivers o n the importance and value give n to ecolo gical structure and processes (D e Groot et al., 2002; Brown et al., 2007). E cosystem functions are related to the capacity of natural processes to produce services and goods for human beings. The processes are the result of interactions between biotic and abiotic systems through flow of m atter and energy (D e Groot et al., 2002). Thus, e cosystem services are defined by their direct contributions to human well being. Ecosystem services are therefore end products of nature and ecosystem function because they are enjoyed, consumed or used by humans (Boyd and Wainger 2002). Ecosystem goods as defined by these authors are tangible material products that r esult from ecosys tem processes and, are key elements for wealth (Brown et al., 2007). De Groot et al. (2002) identifie d 32 ecosystem services that are considered biological, physical, aesthetic, recreational or cultural. Butler and Oluoch Kosura, (2006) also identified sev eral cultural, psychological and other non mater ial benefits that come from contact with nature. Conversely, e cosystem disservices are related to those processes and structure s that have nega tive consequences on human life. These disservices are characteri stic of heavily managed ecosystems such as urban and agricultural (Abenyaga et al., 2008; Lyytimki et al., 2008; Zhang et al., 2007) and affect how urban areas are experien ced, valued, used and developed. Ecosystem disservices become important when they i nfluence t he everyday life of urban residents (Lyytimki et al., 2008). They can also be considered as the negative aspects of the services or functions (Agbenyaga et al., 2008). The perceptions of these disservices will depend on personal preferences Usu ally negative services in urban ecosystems have not been taken into account when
71 assessing ecosystem services For example, increase s in pathogen density such as rats, rabbits and raccoon s c an be considered a dis service (D e Stefano et al., 2005). Personal fears related with security within parks and green areas can be viewed as a dis service (Koskela and Pain, 2000; Jorgensen and Anthropoulou, 2007) V arious health risk s such as allergies by pollen can also be classified as a dis service (Lyytimki et al., 2008). An ecosystem is defined as healthy when processes and functions are in a normal range and resil ient to stress (Costanza, 1992). The range of normal functioning for disturbed ecosystems such as ur ban areas cannot be expected to be the same as a natu ral area since the biophysical environment has been altered T he state of the urban ecosystem should be assessed in terms of how th e ecosystem affect s human well being Therefore using indicators as metrics of the cond ition of service and goods could be a means for expressing the ecosystem services and goods well being relationship Problems arise when trying to establish a non monetary relationship between ecosystem service s and human well being ( Carpent er et al., 2006). Trees can provide multiple ecosyst em functions and therefore services including reduc tion of air pollutants through the functions of dust filtering and respiration Nevertheless, air quality improvements by tree account for approximately 1% of the pollutants emitted in urban areas (McPhe r son, 1994; Nowak et al., 2004). Urban a ir pollution can pose serious risk to human health Ozone (O 3 ), for example increases asthma attacks (Bernstein et al., 2004) and carbon monoxide (CO) affects blood oxygen and fetal development (Hare, 2002). Furtherm ore, Nitrogen dioxide (NO 2 ) is involved in the creation of photochemical smog that could cause respiratory problems S ulfur
72 dioxide (SO 2 ) concentration in the air can trigger ischemic cardiac events (Sunyer et al., 2003) and asthmatic events (Bernstein et al., 2004). T rees also provide indirect benefits such as cooling of building s through shading which may reduce energy consumption (McPherson, 1998). T rees can also block solar radiation to building s and other impervious surfaces by reduci ng the heating a nd heat storage of those surfaces The energy reduction could reach savings on energy of up to 12% (Simpson and McPherson, 1996). Urban heat stress could result in increased death due to heat strokes, especially in more vulnerable population s such as child ren or the elderly. T hese high temperatures provide a sense of discomfort for people in general ( Fukuoka, 1997). Vegetation can reduce the level of noise in cities which is important because excessive noise can decrease human comfort and can lead to heari ng problems (Chen and Jim, 2008). Urbanization influences hydrologica l processes by affecting runoff. I ncreases in impervious surfaces decrease the amount of infiltration and thereby increas e flood risk (Booth, 2000; Paul and Meyer, 2001). A reduction in f orest cover (e.g. structure) could reduce water retention (e.g. function) and as a consequence produce more frequent and intense flooding events (e.g. ecosystem disservices ) (Konrad and Booth, 2002 ) Increased r unoff can decrease water quality by washing o ff fertilizers such as nitrogen and phosphorus and increasing the concentration of dust particles (Brezonik and Stadelmann, 2002). S torm water runoff implies a higher risk for flooding (Chen and Jim, 2008). Soil organic matter is important for nutrient ava ilability, air and water infiltration, decreased erosion potential and transport and mobilization of pollutants (Knoepp et al.,
73 2000). Also organic matter is highly correlated with nitrogen availability for plants as indicated by Tisdale et al. (1993) and Smethurst (2000). Biodiversity is a direct source of ecosystem goods and supplies genetic and biochemical resources, including crops and pharmaceutical products. Biodiversity in natural ecosystems may inc lude many potential new foods. The role of biodiver sity is to maintain ecosystem services and functions (MEA, 2005). Urban e cosystems provide unlimited opportunities for recreation and leisure; they are inspirational educati onal and provide opportunities for reflection and spiritual enr ichment (D e Groot et al., 2002). The existence of trees and maintained grass could improve human well being by providing the necessary environment for activities such as walking, jogging, cycling or enjoying nature (Matsuoka and Kaplan, 2007). A esthetics determine s the pref erences of people to live in pleasant environments which are reflected in economy, since more attractive environ ments have higher house prices (Costanza et al., 1997; D e Groot et al., 2002). An indicator of the state of an ecosystem can be quantitative or qualitative, thus assessing ecological condition. Ecological indicators combine m easurable characteristics of structure, such as habitat or landscape patterns with ecosystem functions and processes (Niemi and McDonald, 2004). Ecological indicators have to be sensitive to the stress of the ecosystem, have a known response to these human stress es detect changes over time and facilitate management in response to stress (Dale and Beyeler, 2001). The variables included in the indicator should be easily measure d and commonly available, such as data from urban forest inventories. Since variables with multiple scales and ranges of sensitivity are used, indicators can reflect a
74 wide range of stress levels. T he use of indicators provid es decision makers with an eval uation tool for different alternatives to improve the current situation or to decide what management activities are required to maintain the (Singh et al, 2009). In this thesis I will develop urban ecosystem services into indicat ors to assess the This study will provide a framework to show how the development of indicators could be used to evaluate ecosystem services and goods. These indicators may allow the development of management strategies to improve the health of the ecosystem for different urban forest structures Services and good s existing in urban areas will be quantified and grouped according to the specific function s of the ecosystem they are fulfilling. Disservices of the urban fore st will be recognized as the ones that decrease human well being in urban areas. I ndicators are expected to vary according to urban forest structures and composition. Tree cover is expected to be one of the most influential variables in determining the sta te of ecosystem services and goods of the urban forest. Also, t he indicator is expected to be influence d by different type s of urban socioeconomic and morphology factors Variables such as household income, population density, property values and years sin ce urban development should explain differences in the indicator s of ecosystem services and goods Methods Indicators The study site is the C ity of Gainesville, previously described in Chapter 2 The indicators for ecosystem services are developed for the 4 groups of ecosystem function defined by de Groot et al. (2002): regulation, habitat, information and production in
75 addition to ecosy stem disservices The value s of e cosystem function s are composed of the average of all the ecosystem services that are inc luded in each of the 4 function groups named above. The ecosystem services and goods selected for this study (e.g. maintenance of air quality provision of habitat and provisioning of biomass ), combine the three definitions of an ecosystem: composition, s tructure and function that directly affect human well being. The same method was followed for ecosystem disservices The categories for each indicator of ecosystem services were established according to the level of delivery of ecosystem services and goods Therefore, when there were no services or only small amount of services and good s were being deliver ed then the category was defined as low and labeled with a number 1. In case of a moderate level of services and good s deliver ed the category was named mediu m and labeled with a number 2. Finally, if a high delivery of ecosystem services and goods was recognized then the category was named high and labeled with a number 3 (Lun et al., 2006) Categories for ecosystem disservices w ere the opposite (e.g. 1 is low, and 3 is high) Indicator values and the related ecosystem services were established at the plot level and all pertinent analysis was done at this scale of analysis. The scale differences in this study were address ed by building each indicator with variab les at different scales since some of the data have a higher spatial scale. Since this analysis requires the use of multiple scales, variables chosen can be found at the scale of square m eters (e.g. leaf area) or can be found as larger geographical units, such as in the case of soil series. These types of studies have been done previously for vegetation and soil analysis, using variables of analysis that occur at different spatial scales (Famiglietti and Wood, 1994; Bellehumeui and Legendre, 1998; Katul et al., 2001; Anderson et al.,
76 2007). A Pearson matrix checked correlation among services of goods included in each ecosystem function Urban Forest Ecosystem Service s Analys e s Urban development Plots were analyzed by categories of length of time since urb an development (see Chapter 2) Forested areas were associated with zero years since urban development, since they were natural areas or plantations. The first category corresponded to plots with urban development less than 20 years, the second category be tween 21 and 40 years, the third category between 41 and 60 years, and the fourth category was greater than 61 years since urban development. Land Use see Chapter 2 Property value see Chapter 2 Values for properties w ere classified into categori es, a ccor ding to quartiles of the sample. T he first group consisted of properties with values per acre less than $12,500, the seco nd group correspond ed to properties with values per acre between $12,501 and $210,000 and the third group classifies properties wit h values per acre over $210,001. To balance the frequency by class the second category aggregates two classes existing between $12,501 and $210,000 Population density Analysis was done by classifying the values for Gainesville in four categories based o n quartiles. The first category corresponded to densities of less than 953 in habitants by census block, the second category groups densities between 954 and 1,698 in habitants; the third category corresponded to densities between 1,699 and 3,503 and the fou rth category included plots with over 3,503 in habitants per census block (U.S Census Bureau, 2000). Income Plots were separated by class of household income as determined by the quartiles of the sample. The first group corresponded to incomes of less than $21,000; the second group correspond ed to incomes between $21,001 and $32,700; thir d group from $32,701 to $44,000; and in the fourth group income was over $44,000. Categories were group ed to balance the frequency among categor ies. Analysis of the indicat or was done by comparing results according to the class of household income. Plot value was assigned according to the correspondence to census block. Household income and population density by census block were chosen for the analysis since they are highly correlated with other social varia bles, as discussed in Chapter 2 T hese social variables have been used in other studies that relate urban forests with soci oeconomic differences (Iverson and Cook, 2000; Kinzig et al., 2005;
77 Heynen et al. 2006). City mor phology effects ha d been explored by Anderson and Cordell (1988), Geoghegan et al. (1997) and Tyrvinen (1997) in relation to property value. Kinzig et al., (2005) looked at the effects of years since urban development on plant diversity. Developing Indica tors for Regulation Functions The processes and characteristics that regulate services in urban ecosystem s are listed in Table 3 1 and details concerning the categories used to classify the processes are also discussed. Values at the plot scale for polluti on removal were calculated by multiplying tons of pollutant s remov ed estimated at the city level by square meter of tree cover ( estimated by the Urban Forest Effects (UFORE) model ) using the value of tree cover in each plot (Nowak and Crane, 2000). Categor ies for air pollutant removal were assigned according to the minimum and maximum value that could be achieved by a single plot. Category 1 ( lo west ) ha d the plots with the lo west 10% of air pollutants removal. Category 3 include d the plots with the highest 10% of air pollutant removal. The same method was used to assign categories to CO 2 sequestration and temperature reduction The regulation function is composed of the following function s : gas regulation, climate regulation, disturbance mitigation water re gulation, soil quality, soils health dust particle filt ration and noise reduction. Gas regulation (maintenance of good air quality) O 3 removed (O3R) is the amount of O 3 that is removed from the a tmosphere by tree leaf surfaces. O 3 is one of the major air pollutants of modern cities (Chen and Jim, 2008), and is formed by the combinat ion of NO 2 and VOC (Nowak, 1994 ). Removal amount was related to the amount of tree cover in the plot (Table 3 1). CO 2 sequestration (CO2S) is the amount of CO 2 sequestered per year by the trees in a plot. The value was calculated by the amount of carbon sequestered per year as estimated by the UFORE model (Chapter 1); the values were transformed to CO 2 multiplying them by the factor 3.67 (Blasing et al., 2004) (Table 3 1).
78 CO Re moval (COR) refers to amount of carbon monoxide removed by tree canopy per plot as estimated by UFORE (Chapter 1) (Table 3 1). Capture of SO 2 (SO2C) amount of sulfur dioxide (SO2) that is captured by tree s. T he value was estimated using the UFORE model (Ch apter 1) (Table 3 1). Capture of NO 2 (NO2C) amount of nitrogen dioxide capture d by trees in the plot, estimated by the UFORE model (Chapter 1) (Table 3 1). Climate regulation function (maintenance of favorable climate) Temperature reductions by presence of tree (TR) T he estimation of temperature reduction account s for the effects of shad e wind and evapotranspiration from trees T emperature reduction was estimated by the UFORE model (C hapter 1). High amounts of temperature reduction by the presence of trees implied a higher delivery of services, resulting in energy use savings due to for cooling effects of trees ( Table 3 1 ). Disturbance mitigation (storm protection) Tree Density (TD) number of trees per plot. A ccordi ng to Escobedo et al. (2009) t he greater the density of urban trees the lower the amount of tree debris produced by hurricane force winds (2009) tree density by hectare to the size of the plot used i n this study (0.04 ha) (Table 3 1). A high tree density implie d a higher protection from storm s due to decreased generation of tree debris. Percent of dieback (DB) is the measure d tree condition that will result in a higher probability of breakage under a storm or hurricane. Higher percentages of dieback increase d the amount of debris that could be produced after a storm increasing the cost for the clean up (Table 3 1). Water regulation function (drainage) Storm water Runof f was calculated using the curve n umber and soil hydrologic subgroup (Engel et al., 2004). The curve number depends on : land use, hydrological soil group, hydrological condition, management treatment, and the amount of impervious surface. This method has been used in other indicators for u rban areas (Whitford et al., 2001). The soil hydrological group depends on the type of soil texture, runoff potential, infiltration and depth of each soil type in the plot ( http://websoilsurv ey.nrcs.usda.gov ). The value for hydrological group was combined with the land use and the percentage of impervious surface of the plot was assigned according to methods outlined in TR 55 th u s assigning the appropriate CN curve ( Engel et al., 2004). A hi gh value for the indicator mean s that the potential runoff in that plot is low (Table 3 1).
79 Infiltration category values were calculated according to the infiltration curve developed by Friedman et al. (2001) for urban areas. Decreased i nfiltration c an cau se flooding, pollution of water or decrease of soil health (Chen and Jim, 2008). Infiltration was calculated using soil bulk density values from Chapter 1. The curve relates permeability of the soil with the value of plot soil bulk density. This highest pro bability of runoff could cause the wash off of excess nutrients, the accumulation of heavy metals and nutrients in down slope areas such as ponds or lakes, or increase flooding potential in these same areas. Th e higher the bulk density value, the low er the rate of infiltration which leads to a higher probability of surface runoff (Table 3 1) Soil productivity (maintenance of soil productivity) Percentage of organic matter (OM) is the variable used as an indicator for soil quality, since it is related to en ergy/C storage, as well as water and nutrient retention (Schindelbeck et al., 2008). Values for organic matter were obtained from soil laboratory analysis (Chapter 2 ). The categories were established according to recommendations for planting trees in urban areas (Craul, 1999) ( Table 3 1 ). High value s imply that the owner would not invest in fertilizers to have healthy trees. pH gives information on soil toxicity and nutrient availability (Schindelbeck et al., 2008). The m easure s of pH for the plots w ere obt ained from soil lab analysis (See Chapter 2 ). Plots with a high indicator value were assigned according to neutral pH which implie s a high level of fertility ( Table 3 1 ). Recommendations for pH in urban areas were established by Craul (1999). High indicato r values for in pH mean less investment on fertilizers. Bulk Density (BD) values determine how suitable the soil is for adequate plant growth and i ndicators were calculated from plot specific field measurements of bulk density (Chapter 2 ). Thresholds were established according to reference values for ornamental trees from the literature (Mullins, 1991; Craul, 1999) ( Table 3 1 ). Low indicator values may require special management activities that may not incur cost s Nutrient regulation (maintenance of health y soils) Nutrients (NU) refers to the concentration of nutrients in the soil often referred to as soil fertility. M acro nutrients include: phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). The amount of extractable phosphorus indicates the av ailability of phosphorus Nitrogen was not included since only TKN measures were obtained Furthermore, available nitrogen is only a small fraction of TKN (Schmalzer and Hinkle, 1987, 1996) and is significantly correlated with the percentage of organic mat ter (Soil analysis Chapter 2 ; Reiss 2006). Micronutrient levels indicate soil fertility and toxicity (Schindelbeck et al., 2008). Values were obtained from laboratory results from soil sampling (Chapter 2 ). The categories were established according to the values in Roa et al. (2008) and Heckman (2006), which are used for the level of fertility for urban gardens (Table 3 1). Low
80 categories of nutrients mean low soil fertility therefore fertilizers should be use d to improve the rate of growth and the state o f the vegetation in the plot. The use of fertilizers implies a cost. Concentration of heavy metals (HM) refers to the concentrati on of: Zn Cu Ni and Pb ; Cd was not analyzed. The values for the concentration were assigned an averag e by land use, due to l ow variability in the concent ration of heavy metals for the C ity of Gainesville (Chirenje et al., 2003, 2004). Categories were established according to the highest acceptable concentration for heavy metals in soils use d for recreation established by Thorn ton (1991), since it is related to human health (Table 3 1). Low indicator values were assigned to concentrations that were no t acceptable for human health, medium val ues are lower than the limits acceptable H igh values were zero to lo w concentration muc h lower than acceptable limits. Waste treatment (filtering of dust particles) PM 10 removal (Pm 10 R) is the amount of particles greater than 10 microns that a tree can capture in its leaves as calculated by UFORE (Chapter 2 ). Values in the city were estima te d from the average removal of Pm 10 per square meter of tree cover and this rate in g/m 2 was multiplied by the tree cover of each plot to obtain the Pm 10 removal per plot. Categories were established following the same method as air pollutant removal (Table 3 1). Waste treatment (n oise reduction) Leaf area and distance (LAD) is the variable that determines absorption of sound by v egetation (Aylor, 1971). Values were weighed by the distance of the plot to the source of noise. Roads were used as the main sour ce of noise in G ainesville since noise generated by traffic i s one of the main noise nuisance in cities (Ouis, 2001; Theebe, 2004) Leaf area was estimated by UFORE model for every tree existing on each plot, depend ing on specie s and size s (Nowak and Crane 2000). Categories were established according to the range s found in Gainesville and distance to the noise source (Table 3 1). A l ow indicator was assigned to plots with leaf area less than 1000 m 2 and at any distance from the source of noise (roads). High values account for the opposite effect, where the potential abatement of noise that plot could deliver was higher by having high leaf area and proximity to the source of noise. Dominant type of foliage (FOL) determine s the continuity of noise reduction th roughout the year. Evergreen trees should capture more noise than deciduous trees, thus noise abatement is constant in all seasons (Aylor, 1971). Values for this indicator were established according to the percentage of evergreen trees in the plot. The low category was defined when less than 25% of the trees of the plot were evergreen, meaning that the amount of noise reduction is going to be less (Table 3 1).
81 Developing I ndicator s for the Habitat Function Refugium (m aintenance of biological and genetic div ersity) Shannon d iversity index (SD ) was used to characterize tree diversity in each plot using the formula where pi is the proportion of species in the i sampling unit in relation to the total amount of individuals in the sample. This index account s for richness and evenness. A high diversity indicator implie d the existence of a high amount of evenly distributed species ( Table 3 2 ). The existence of a high amount of species may be important since a diverse composition is commonly associated w ith enhanced ecosystem functions (Jensen et al., 2002; Luck et al., 2003; Elmqvist et al., 2003). Ratio of native tree (RN) The indicator was develop ed according to the percent age of native trees on the plot. W hen more than 75% of the tree species on the p lot are native the plot has high value. V alues under 25 % were classified as having low indicator for the maintenance of biological and genetics. Plots with no trees were classified as having low indicator ( Table 3 2 ). High indicator for this variable would ensure the maintenance of biologic processes and genetics of a certain environment (MEA, 2005). Developing Indicators for the Production Function The produc tion of ecosystem goods in urban ecosystem is listed below. D etails about the categories used to cl assify the processes and their effect on human well being are also menti oned. The linkage to human well being is the option of using the goods delivered by the ecosystem to meet human wants or needs. Productivity (goods) Tree Biomass (TB) was calculated fr om the carbon storage value obtained from the output of the UFORE model chapter 2 The value of carbon storage for each tree on the plot was multiplied by two to convert it to fresh weight biomass T he biomass of dead trees and green waste was subtracted f rom the plot value to obtain the live biomass for each plot. The values of the categories were established according to the amount of biomass in a plot (Table 3 3). Low indicator for this service was defined as the plots with the lo west 10% of biomass. T he highest indicator was assigned to the plots with the highest 10% of biomass. Ground litter biomass (GB) is the amount of litter present over the soil to protect against erosion (Bonan, 2002) and provide organic matter The calculation was done at the plot level, using the depth of the litter in the plot, and then through the density of the litter the weight was obtained. Values were amount of litter by square meter. The categorization was done following the same procedure as the
82 live biomass (Table 3 3). L ow values for this service indicate that the soil has very low amounts of litter Therefore, it contributes to increased investment in soil management. Dead trees biomass (DTB) is the variable that quantifies the amount of dead trees biomass in each plot. Classifications of dead tree s come from o n site observation s Results were calculated the same a s the biomass of living trees (C hapter 2 ). It is assumed that a lower amount of dead biomass increase s the quality of the plot in relation to productivity, giv en to that wood o btained is probably not in good condition and therefore the timber products to harvest are less than if the tree was in good condition. However this is considered a service and not a dis service because goods can still be obtained from the biomass of dead trees. High values of indicator were assigned to the plots that have values close to zero, when biomass of dead trees is taken into account. Low values, on the other hand were assigned to the plots that have higher amount s of dead tree bi omass (Table 3 3). Residues from de ad trees can be used for goods such as fuels for bioenergy and chips for compost. Green waste (GW) this variable includes the biomass loss through leaf fall and pruning. Leaf fall was calculated from leaf biomass obtained from the output of UFORE model and existing literature A ccording to the findings by Rowntree and Nowak (1991), 25% of conifer leaves and 90% of hardwood leaves are lost in a year. It i s assumed that all trees on plots were pruned. Values were calculated by plot a nd pruning was obtained based on tree crown area since previous studies (Ho and McPherson, 1995) estimate the biomass loss by pruning in a year to be 0.081 kg m 2 T he total amount of biomass loss by pruning comes from the multiplication of this value with the square meters that each tree covers. A low biomass los s impl ies a higher value for this service. When, less biomass is lost, less waste management is required and therefore there is less expending of money and i nvestment of hours gettin g rid of that waste (Table 3 3). Developing I ndicators for the Information Function Recreation opportunities The amount of tree cover in the urban environment can be associated with different recreational opportunities according to the level of human use Lack of tree s or maintained grass offer s an unsuitable environment for recreation, and therefore does not improve human well being A medium amount of tree cover or medium to high amount of maintained grass cover will offer a suitable environment for rec reation, leaving space f o r activities and providing sufficient trees cover to enjoy nature (Parsons,
83 1995; Kuo et al., 1998; Bjerke et al., 2006). Bjerke et al. (2006) found that densely vegetated scenes receive the highest ratings for recreational purpo ses and Kuo et al (1998) found high prefer ences in areas with densities of 22 trees/acre. T ree density value s w ere weighted by the type of land use in the plot, assuming industrial site s are unfit for recreational purposes. Aesthetic s Replacement value ( RV) includes species, condition, size and location and is related to the expense of replac ing a tree. It was calculated by the UFORE model which estimates how much the owner should received as monetary compensation for tree los s (Nowak et al., 2002). The h igher the replacement values the more the contribution to aesthetics and incre ased property value ( Table 3 4 ). Real estate value (REV) is based on the assumption that the value of the property increases with the presence of trees (des Rosiers et al., 2002 ) by 3 to 5% (Anderson and Cordell, 1985) and only when t he tree cover i s 25 to 75% because more trees eventually become unpleasant ( Table 3 4 ). Values in dollars from the Alachua County Appraisal ( http://acpafl.org/ ) were used, following the same classes of property value previously defined in Chapter 2 Developing Indicators for Disservices Because of varying human perceptions of ecosystem processes, u rban areas also have ecosystem disservices such as air pollution due to emission from tree maintenance decrease of biomass productivity and negative effects to human health such as allergies from tree pollen How different ecosystem processes result from different urban forest arrangement s and how this relationships produce different levels of these disservices is important in order to have a complete analysis of the state of the ecosystem and to understand socio ecological system processes (Zhang et al., 2007, Lyytm ki et al., 2008).
84 Damage to infrastructure and human s afe ty The existence of trees susceptible to breakage puts at risk the urban infrastructure and the well being of humans. A high index for this dis service implies that a high percent of certain species present in a plot have branches or trunks that are highl y susceptible to damage (Gilman, 2007). Besides the inherent susceptibility of breakage the value was weighted by the average tree condition for the plot ( Table 3 5 ). Allergenicity The level of allergenicity of each plot was established according to the Og ren Plant Allergy Scale ( OPALS ) (Ogren, 2000) T he value was estimated by the UFORE model, using land use and leaf biomass considering the different characteristics of the plants such as sex, disease resistance, smell (Ogren, 2000), that make them more a llergenic. The highest level of allergenicity in a plot refers to a high allergenicity ranking ( Table 3 5 ). Decrease of air quality CO emitted by trees (COE ) Amount of CO emission by trees estimated by the UFORE model (Nowak et al., 2002). Values were cal culated per plot by dividing the total kg of CO emitted by total square meter of tree cover in the city. The CO emission s in kg/m 2 were multiplied by the tree cover existing in each plot. High values for these disservices imply an increase in the concentra tion of CO in the atmosphere that could have consequences for human health ( Table 3 5 ). O 3 emitted by trees (O 3 E ) amount of contribution to ozone formation to the atmosphere by trees and was estimated using the same method as for carbon monoxide. A high v alue for these disservices implies higher emission s of ozone and increase d in concentrations that could have consequences for human health ( Table 3 5 ). CO 2 emitted by trees (CO 2 E) amount of CO 2 emitted by trees due to decomposition estimated by the UFORE m odel (Nowak et al., 2002). The emission is calculated by subtracting gross carbon sequestrated and net carbon sequestrated. This value was calculated by plot and is based on the DBH of the trees and land use. Higher values indicate high amount s of CO 2 emis sions
85 therefore increased concentration s in the atmosphere in vacant and forested land uses ( Table 3 5 ). is the amount of volatile organic compounds ( ) that are emitted by the trees. The value was estimated the same way as th e O 3 (IPCC, 2001), therefore delivering a dis service ( Table 3 5 ). CO 2 emitted by pruning (CO 2 P) Pruning activities result in CO 2 emissions To estimate the amount of CO 2 emitted one pruning per tree per year by plot was assumed with an average emission of 0.81 kg/m 2 (Jo and McPherson, 1995). This value was multiplied by the amount of square meter of trees in the plot ( Table 3 5 ). High values for this indicator imply high emissio ns of carbon dioxide. CO 2 emitted by mowing ( CO 2 M) Mowing activity increase the emission of CO 2 to the atmosphere. Jo and McPherson (1995) suggest mowing emissions values of 113.2 g m 2 were transformed to CO 2 multiplying it by 3.67, thus obtaining an ann ual emission s of CO 2 of 415.4 g m 2 of grass cover. A verage of grass cover per plot by land use was measured in the field (Chapter 2 section tree sampling ) High values for this indicator implied increased emissions ( Table 3 5 ). r (VOCEL ) t he amount of were calculated based on Shipchandler (2008) value of 11.62 ton/yr/100,000 people for emission The value for each plot was calculated according to the census bl ock level. Higher amounts of emission s implied high level for this indicator ( Table 3 5 ). NO 2 emissions from leaf blower (NO 2 EL) the value of NO 2 emissions was of NO 2 found by Sc hipchandler (2008) were 1.165 tons/yr/100,000 people. A high value for this variable signifies a high level of emission ( Table 3 5 ). Fruit fall Fallen fruit can damage infrastructure and property and need s to be cleaned which implies the expending of money and working hours on that activity. The variable was classified as high for dis service when the dominant species existing in the plot ha d fleshy fruits including acorns L ow value s for this dis service were assigned when the majority of the species f ruits in the plot were hard ( Table 3 5 ). The type of fruit was established from Gilman (2007) classification of for urban and suburban trees.
86 Statistical Analys e s A summary statistics table was developed for each ecosystem service indicator. Analyses in clude a Kolmogorov Smirnov normality test using SAS UNIVARIATE procedure (SAS Institute, 2007) for each service. A correlation matrix was calculated to check for double counting among indicators for each function. Goods and services were analyzed according to land use, time since urban development, property value, population density per census block, and mean annual household income. The mean indicator value and its standard deviation were calculated. Analyses of Variance land uses, time since urban development, property value, population density and household income using PROC ANOVA (SAS Institute, 2007). Results E cosystem S ervices and G oods Indicators R egulation function Indicat ors for maintenance of good air quality on average are medium for the C ity of Gainesville (Table 3 6). High values are found in 12% of the sampled plots and the minimum indicator value can be found in 5% of the plots. Medium levels occurred for the O 3 re moved (Table 3 6). Sequestration of CO 2 is h igh for the C ity of Gainesville. A ir pollutant removal by trees is similar among all the plots, wher e the medium indicator is the mean. Maintenance of favorable climate is also medium for the C ity of Gainesville. Of the sampled plots, 25% have the highest value reducing in a higher amount of the temperatu re. A lso 25% of the plots present the lo west value where few plots make an improvement effect on temperature.
87 Maintenance of soil productivity in Gainesville is classified as high. Values for bulk density are appropriate for almost all the plots, indicating low levels of soil compaction. Levels of organic matter in Gainesville are high S oil pH values are around neutral wi th the majority between 5 and 7. S ome of the pl ots had more acidic soils with values around 4, usually related to the presence of pine trees. At l east 50% of the plots sampled have a high value indicator and 90% have medium to high value for the indicator. Chapter 2 provides specific soil characteristi cs of the study area. Drainage for the C Values of the plots varied from medium to medium high for 43% of the sampled plots. Storm protection for Gainesville qualifies as medium indicator, and only 10% of the sample has a low indicator. Tree cover is high in Gainesville and the percentage of dieback of the sample plots is low; plots with low indicator are related with little to no presence of trees. The service of filte ring of dust particle s for the C ity of Gainesville is medium low Low indicator values can be found in 50% of the sample plots and only 20% of the plots have high value for this service. The indicat or for noise reduction for the C ity of Gainesville is medium and high indicator values a re found in 10 % of the plots and 7% has low values. For the total regulation function the plots in Gainesville have on average a rank of medium, where 10% of the plots have an overall of high indicator (>2.5), and only 10% have low indicator (<2 ). An analy sis of indicator values for the regulation function for ecosystem services across time since urban development shows few significant differences (Table 3 7). The time since urban development has an effect on the m aintenance of favorable climate Y ounger less developed areas and older areas over 60 years since development have
88 high indicator values. The filtering of dust particles also has significan t differences between classes, where low values are found in the middle classes (20 to 60 years ) and the highest values corresponded to older areas of the city. The overall trend of the regulation function show s low values for middle classes between 20 to 6 0 years since urban development. A reas with less than 20 years of urban development show medi um values, while old areas (more than 60 years) have a high indicator for all the services. When exploring indicators according to land use, more significant differences a ppeared (Table 3 7). The maintenance of a favorable climate is higher in forested an d vacant areas. Storm protection is also high in forested areas, where the presence of trees contributes to this service, but also the fact that th ose trees are in good condition. Noise reduction is also high for forested areas, even though they are far fr om the source of noise they could however eventually get closer to noise sources due to urban sprawl Commercial areas in Gainesville have a high value for this service, since they are near the source of noise and they also have enough leaf area to reduce urban noise. Services related with soil condition do not show the same trend as the previous services H igh values are attributed to institutional areas which indicate they m ight be better irrigated and fertilized F orested areas have the lo west value whic h could be related to the presence of large areas covered by pines that reduce organic matter and increase the level of acidity found in less fertile soils. The overall value for the regulation function across land uses is medium F orested areas are the hi ghest for this function and institutional areas the lo west The indicators for the regulation function follow a clear trend across property values (Table 3 7). More expensive areas have lower indicator values for all the
89 services H owever services related with soils have higher indicator values. Lower indicators could be related to lower tree densities and the existence of younge r trees, since several of these more expensive areas correspond to new urban development s High soil indicators in more expensive areas could be due to the possibil ity of using fertilizers in yards, parks or institution al areas Significant differences are found among soil services for drainage, maintenance of favorable climate and soil product ivity. T he overall trend for this f unction is that more expensive areas have highe r values than less expensive areas, but the ir values are generally classifie d as medium. Household income shows the same trends. M ore expensive areas have lower indicators than less expensive areas, due to th e presence of more trees. No significant differences arise from household income analyses. Population density show s a significant effect in the noise reduction service, where less populated areas has a higher value than more dense areas (Table 3 7). This t rend is the same for all the services and the overall function, probably since less populated areas have a lower effect on the state of the ecosystem (Nelson et al., 2009). H abitat function The Shannon index for trees in the study area is low since only 1 5% of the plots ha ve medium values and no plot ha d a high value (Table 3 8). The amount of native trees present in Gainesville increase d the indicator, since most plot s w ere classified as high, with more than 75% of the species being native. Only 15% of th e plots present low va lues. The habitat function has low values in Gainesville, and no plots present high value s. T ree diversity is low and un evenly distributed but the majority of species are native (Table 3 8).
90 No significant differences were found amo ng the different years since urbanization of the properties ( Table 3 9 ). Shannon diversity index does not have significant differences across time since urban development but increases its indicator value in older properties. On the other hand, the amoun t of native trees increases with time since urban development of the property, which shifts the indicator from a medium value to a high one after 20 years since urbanization. No significant differences were found for the overall function, however the indicator value increases with years since the property was developed. The d iversity index show s low indicator values according to land uses and significant Table 3 9 ). The ratio of native trees in the plot varies by land use ; institutional areas show the lo west whereas forested and vacant areas have the highest indicator. In general, the overall ind icator of the habitat func tion is low medium through all land uses. Vacant land use ha s the highest value, while institutional land use shows the lo west value for the function. The habitat function show s significant differences across land uses ( Table 3 9 ) probably due to differences between vacant and institutional areas. No significant differences are explored a mong different property values ( Table 3 9 ). However a general trend can be distinguished where less expensive areas have a higher indicator. Diversity index is the lo we st for areas that are more expensive, while the highest values are for the less expensive areas even though differences were not significant. The r atio of native trees follow s a similar trend. Population density does not show any particular trend across th e habitat function indicator, and no significant differences arose ( Table 3
91 9 ). A small increase of the indicator for all the variables can be seen with t he increase of household income. the ratio of natives for t he indicator is quite different moving towards high values for all the classes. P roduction function B iomass production is medium for the C ity of Gainesville. High indicator values (>2.5) are found in 10% of the plots, while 10% sho w the lo west value. Biomass of trees has a low value on average, according to t he categorization of this study where 25% of the sampled plots have the lo west value (1). Ground biomass has as mean high value, with 50% of the plots having a value of 3 and only 1% having the lo west indicator value. O nly 10% of the plots have the lo west value whereas more than 50% of the plots have all of their trees alive Green waste biomass has a low indicator implying that a big amount of waste could be produced O nly 10% of the plots present a high value, while more than 50% of the plots have a low indicator. The overall production function does not show significant differences in services or goods across time, except for ground biomass (Table 3 11). Biomass of live trees de creases with time since the property was developed but al ways maintains a low indicator, between 1.5 1.8. The other goods that can be obtained from the urban forest follow the same trend. T here is an increase o f the indicator with the older developed prope rties, and decreases for properties over 60 years since they were developed. Highest indicator values for the production function are for plots that ar e between 40 and 60 years where older trees are in good condition. In terms of goods, the highest indicat or is achieved by the soil biomass.
92 All goods across land uses in the productivity function show significant differences he live t ree biomass indicator is obviously higher in forested plots and the lo west in institutional areas. Values of the i ndicator for this good varied from medium to low. L ower indicators exist in forested areas qualifying them as medium, which is probably related to the existence of pine plantation or natural pine forests that have less litter. A low amount of dead biomass is found in residential and institutional areas, where trees were probably removed. V acant and forested areas increase in the amount of dead trees decreasing the indicator to a medium value Green waste show s the lo west value in forested areas which is pr incipally related to the existence of natural forests that produce hi gher amounts of waste. A reas where trees receive management treatments produce less er amounts of leaf waste and require less pruning therefore achieving a higher indicator. The general trend follow ed by the production function indicator is that increase s in property value inc rease the value of the indicator but differences are not significant Significant differences for the production function and dead tree biomass do exist across household incomes categories (Table 3 11) T here is a small decrease of the indicator from the lo west income to incomes less than US$44, 000. The i ndicator increases for the highest income category The same trend exists for the other goods, but no significant differences appear among indicators. I nformation function Forested areas, residential areas and institutional areas (all of which in clude parks ) have the highest value for the indicato r, while industrial/commercial areas have the smallest value s because they do not offer proper place s for recreation V acant areas have a medium value since potentially they can be transformed to a recrea tional
93 opportunity area, which in this case was assumed to be to a park (Table 3 4). R ecreational opportunities in forested areas are medium for l ess than 10% of the plots M ost of the plots show high indicator value s with a high average indicator Aesthet ics ha ve a lower average value than recreation. T he 25% of the plots ha ve the highest value and also a 25% have the lower indicator value s which includ es 1% of the plots with an indicator of 1 (Table 3 12). The higher indicator for real estate value is in more affluent areas Less affluent areas are not related to lo w replacement value s since they have several natural areas and therefore higher tree cover. Areas with medium real estate values have the lo west replacement indicator values which are related t o fewer amounts of trees The overall value for the information function is medium (Table 3 12), where 10% of the plots are concentrated in the highest value for the indicator and 25% showed the lo west values. Recreation in institutional areas has on aver age a high indicator, but 40% of the plots have medium values. R esidential areas have a high variability in recreation opportunities, 5% of the plots have a low indicator, while 50% of them have a high value showing a suitable vegetation structure for recr eation. Vacant areas that could be transformed to recreational areas have low values, since the tree cover is over 75% and no mainta in ed grass was found in the plots. Independent from their tree or grass cover commercial areas are defined as not suitable for recreational opportunities so the lo west value wa s assigned. As an overall indicator for recreation service, 25% of the plots have the highest indicator and only 10% ha ve the lo west indicator. Significant differences appear among land uses for aesthetic s (Table 3 13). Aesthetics is highest in residential areas, where v alues of real estate are high assuming the presence o f trees increas es
94 their value. F orested areas have the lo west indicator since they are no t perceived as places to live. Overall t he i nfo rmation function has a medium indicator for all the plots, with the highest value s located in residential land uses and the lo west in forested areas. Recreation indicators are not affected by the age of the property during the first 60 years since urbaniza tion and have an indicator value of medium There is a decrease in value for the recreation indicator in plots with more than 60 years of being urbanized (Table 3 13). A esthetics slight ly increase in the indicator value along the first 60 years, and decrea s e again when plots are over 60 years since urbanization D ifferences across years are not significant. (Table 3 13). Property values show a significant trend in aesthetics and t he overall information function. I ncrease s in the value of the property leads to an increase in indicator values from low to medium. No significant difference s arose with population density. T he indicator is medium for the information function and slightly decreases in densely populat ed area s The same trend can be seen in recreati onal opportunities with the increase of household income, even though no significant differences appear (Table 3 13). A esthetics and the information function had almost the same values for all the household income categories. Disservices The overall indic ator for disservices is medium. Only 3% of the plots show a high value for the overall disservices Of the sampled plots 5% have values with low disservices (Table 3 14). When performing the correlation matrix, no relationship s w ere found between the diffe rent disservices The dis service that ha s on average the highest indicator is the decrease of air quality. The CO 2 emitted by pruning are the variables increasing the average i ndicator for this dis service. This indicates that several
95 species in Gai Also CO 2 released is probably overestimated since it was assumed that all trees in the plot were pruned, which is not likely For decreases in air quality the less significant emission s corresponded to NO 2 emission s by leaf blowers. The total indicator for decrease s in air quality show s that 25% of the sampled plots have high indicator value s while only 10% of the sampled plots ha ve a low category Fruit fall is the dis service that has the l east negative effect si nce 50% of the sampled plots show the lo west value for this dis service while only 5% ha ve the highest value. This is showing that the majority of the trees in Gainesville do not have fleshy fruits that could be making inhabitants spend resources and time cleaning streets or yards. A llergenicity for the entire city has medium indicator. A ll the land uses are qualified as medium since their range spans 5 to 7 on the OPALS scale with 10 being the hig hest value of allergenicity (Table 3 14). A relation shi p of the allergenicity level to tree cover could probably improve results The damage to infrastructure indicator i s low T he majority of the plots present conditions that should keep damage s to a minim um in the case of a disturbance H owever 25% of the sa mpled plots present a high value which implies that there is a high probability for those plots to cause damage to infrastructure or humans in the case s of natur al disturbance s When years since urban development and land use were analyzed, no signific ant differences arose (Table 3 15). T he value of the overall dis service maintain s a medium value. Highest reductions of the indicator occur in vacant areas especially by the effect of the increase of air pollutants and the possibility of damage to infrast ructure and humans in the case of a windstorm.
96 Property values show that an increase in the price per acre result s in a lower indicator for aesthetics, which implies a minor decrease of ecosystem services. Significant differences occur only for damage to i nfrastructure and humans where affluent areas have low er risk s of caus ing damage. The only different trend is for fruit fall, where more expensive properties have a higher amount of trees with fleshy fruits. N either particular trends nor significant differ ences can be seen in the variation of disservices a ccording to population density Overall disservices show a medium indicator value. Household income behaves similarly to property value, in that the higher the household income the slightly lower the indic ator becomes. No significant differences appear for household income (Table 3 15). Discussion Compari ng Functions Regulation, habitat, information and productivity functions for the C ity of Gainesville have medium to medium low indicator s across all plots (Table 3 16) The habitat function presents the lo west value, while the regulation function has the highest one. P roductivity and information function s behave the same as the regulation function and t he overall average is low medium In the case of disser vices the highest amounts of values are f ound in the medium low category (Figure 3 1). Ecosystem Services and Goods Analys e s Regulation function The c ity of Gainesville is densely forest ed and temperatures may be m ilder than urbanized landscape of other climate s and larger areas of impervious surfaces. The presence of a high tree cover could be providing the service of climate amelioration. In hot areas an increase in canopy cover of 10% could reduce the temperature of a city in
97 1.4C (Konijnendijk et al ., 2005) also in Phoenix, AZ a decrease of 0.28 C in an early summer day was associated with gr e a te r vegetation cover (Jenerette et al., 2007) The calculation of this service could be improved by increasing the resolution of the estimations of temperature reduct ion and linking those reductions to tree cover instead of land use. This could cause an increas e in undesirable factors such as: summertime peak energy demand, air conditioning costs, air pollution and greenhouse gas emissions and water qualit y infringemen ts (EPA, 2008). The C ity of Gainesville has relatively low air pollution Levels of ozone and carbon monoxide removal in Gainesville are close to the ozone removal s obtained in Jacksonville, FL The amount of S O 2 removed is around the average for the cou ntry (Nowak et al., 2006) In general, areas with higher tree coverage capture more air pollutants and CO2 than areas with less tree cover; forested and vacant areas, very young or very old areas, and less affluent areas have a better indicator since they con sist on denser urban forests. Maintenance of soil quality is related to fertility, and therefore plant growth. V alues for city of Gainesville for b ulk density seem to be appropriate for plants to have suitable conditions to grow. Levels of organic matt er in Gainesville are high, probably due to the presence of several areas that are not urbanized and t he high level of tree cover Nutrients in Gainesville are inside the recommended ranges for optimal ornamental tree growth. P hosphorus levels are over th e opti mum range for fertilization regimes that could lead to wash o ff and become not available for plants. T he presence of high levels of calcium and phosphorus could be related to t he Hawthorne Formation (Gilliand, 1976). Usually areas with a longer histo ry of urbanization have better soil
98 servi ces (Scharenbroch et al., 2005) since t hey have been subject ed to fertilization over the years, compensating for the lack of nutrients existing in undisturbed areas (forested and vacant land uses). As this study onl y used soil physical and chemical properties in the indicator, more complete measurements of soil biota should be incorporated since they are the key to understanding the interaction between physical and chemical interaction s within the soils (Ritz et al., 2009). Also information on soil fertilization regimes could help in understanding the drivers of nutrient concentrations. The service of filtering dust particles had high values in forested areas This could be related to high leaf area. The lowest values for institutional areas could be related to a low tree density. This service in the C ity of Gainesville probably does not have high importan ce since this area does not have problem s with dust particles In a semi arid area P M 10 can become a problem and t he indicator for this service become important An increase in the accuracy of the valuation for this service could be obtained by weighting the indicator by the distance to the source of pollution which is also a more efficient method. If a tree is by it self or is part of a group of trees this could increase accuracy, since single trees filter less tha n tree s in groups (Nowak et al., 2006). The s torm protection service is probably more impor tant in some cities than others. A survey done in Hillsborough C ounty, FL, points out that one of the h ighest costs perceived from urban forests is the damage of trees caused by hurricanes (Escobedo et al., 2008) Alternatively, the results of a N ational survey do not show hurricane related damage to urban forests as a main cost of urban t rees (Lohr et al., 2004). Gainesville is a city that is frequently threatened by strong w ind storms or hurricanes. A ttention should therefore
99 be given to increase this service so that trees can be man a ge d to maximize storm protection and decreas e the risk of damage to human s and infrastructure. Habitat function Usually cities increase in tree species diversity with urbanization, due to the introduction of exotic species (Zipperer, 2002). Gainesville has maintain ed a high percentage of native trees. S everal of the plots were found in remnant forested land covers lo cated in the middle of the city, and therefore the ratio of native to non native tree species is high T his phenomenon is not typical of more urbanized cities. Older properties have higher diversity indices probably related to the presence of ornamental spec ies that are usually non native. T he same happens in newer areas where new constructions include non native species as ornamental trees. The increase of the number of native species through the years of urban development could depend on exist ing trends in ornament al trees. I t could be that 20 years ago when several of these properties were built the use of no native species as ornamental trees was the trend. The ratio of nativ e trees in the plot varies by land uses. L o w value s for native trees could be due to the inclusion of exotic species for ornamental use in institutional areas, parks or residential areas Production function The productio n of goods is probably not seen as important in Gainesville but the use of green waste tree material could imply revenue s for home owner s or institutions. Waste from trees can be transform ed to firewood, chip s or mulch or possibly even turned into larger wood products (Plumb et al., 1999 ). The recycling of green waste could reduce the environmental and economic costs related to landfill disposal (McPherson, 2006), especially after storm disturbances. The accuracy level of the
1 00 indicator could be improved by taking out of the analysis the p lots corresponding to natural areas or plantations since waste from those areas are used in different ways or are not used at all Accuracy could also be improved by surveying landowners to determine if management activities such as pruning are applied to trees o n the plots. Information function The valuation of recreational and aesthetics services of urban forest s need s to include the different type s of users that can be found in a c ity (Matsuoka and Kaplan, 2008). I mprovements to this indicator should be done by collecting information about the preferences of people in terms of the different structur al arrangements that urban forest can provide In one example, forested areas could be good for outdoor activities, while public parks could be used to play sp orts or for children to play. Values for pleasure that urban trees deliver also should be included. Ef fects of the presence of trees o n the emotional and psychological state s of in habitants should be explored in more detail. Disservices Each ecosystem func tion has associated disservices and the valuation of these disservices should be established according to what the in habitants of a city consider as relevant to decrease s in their well being. The priority of the se disservices will depend on the economical and sociological context s of the city, and what their environmental priorities are (e.g. polluted cities with low risk of hurricanes will probably give more importance to the decrease of air quality than to the damage to infrastructure). Disservices such as hurricane damage by trees, habitat suitability for the existence and movement of biological vectors, crime related fear s associated with tree d landscapes, and other nuisances should be considered. M ore research o n the environmental costs
101 related to the existence of urban forest s is necessary. Differ ing structure and composition of urban forests will deliver different type s and amount s of disservices M anagement plans should look towards the finding of the be tter structures of urban forest s to minimize di s service while maximizing the urban f o rest functions that increase services and goods. Conclusion The se proposed indicators can be used to depict the environmental state of a plot or site in terms of the services and goods it can deliver in the City of Gaines ville, Florida. Since the indicators for each function are an aggregate of services, the drivers of change of these services can potentially be identified T his could help in evaluating policies related to urban forest s or management regimes that have been applied in the city. Also the information obtained from the indicators could be useful to prioritize management objectives associated with certain services or goods. Clearly the inclusion of additional services and disservices is necessary to have a bette r picture of the environmental state of a city. Once urban forest data ha s been obtained the calculation of the indicator is repeatable and easy to compute and result s are simple numbers that are suitable and understandable for comparison. sults are in agreement with Whitford et al. (2001) that, the ecological state of a city depends heavily on the state of the urban trees. The establishment of indicators could help in the understanding development regimes and their impact on ecosystem servi ces and goods. An analysis of different years since the plots were urbanized could help in forecasting the future values of the indicators, but heterogeneity between plots is too great in terms of structure and composition. Shift s in land use deliver a ran domness
102 factor related with changes in urban planning policies and human behavior that could limit forecasting The indicators are useful to show the present condition of r epresentative plot s in relation to ecosystem services, and to derive from them which str uctural arrangement of urban forest s deliver the best indicator values for ecosystem services and the lo west indicator value f or disservices Forecast s of the change in the indicator by plot could only be done by assuming that human behavior and conditions of the plot will remain the same The indicator would then change if a certain management regime is applied to the different urban forest s structure to maximize ecosystem services. Future predictions of the indicator for changes in land use or major chang es in the plot would almost certainly give results low in accuracy The use of the indicator to compare cities should also be done carefully, since indicator categories were established using the distribution of Gainesville specific results, such as air po llutants emission s and removal To compare values standardization of the air pollution values should be done before assigning an indicator category (Saisana et al., 2005). Another constraint is that the indicator will depend on the type of city and w h ether it is large or small and more or less economically d eveloped. T he value that citizens g i ve to the different services will b e likely different. The use of indicators in evaluating ecosystem services is a useful tool since it provides a snapshot of the en vironmental state of a city. The indicators developed here are based on available urban forest and soils data as well as extracti on of some of the key drivers of urban ecosystem services. These indicators could be used as tool s to communicate to the public the conditions of the services in the city, given an awareness of what information was included and the significance is explained This could be u sed
103 to develop policies of urban planning in concordance with the delivery or improvement of ecosystem servic es provided by urban forest s
104 Figure 3 1 Histogram for ecosystem functions and disservices in the city of Gainesville
105 Table 3 1 Categories of I ndicators for Regulation Function Service Low Medium High Maintenance of good air quality O 3 R (tons yr 1 ) [0 0.0001] [0.00011 0.001] [>0.0011] CO 2 S (tons yr 1 ) [0 0.0 5] [ 0.05 0. 2] [> 0.2 ] COR (tons yr 1 ) [0 0.00001] [0.00001 0.0001] [>0.0001] SO 2 C (tons yr 1 ) [0 0.0001] [0.0001 0.001] [>0.001] NO 2 C (tons yr 1 ) [0 0.0001] [0.0001 0.001] [>0.001] Mainten ance of favorable climate TR (C) [0 1.6E 06 ] [1.6E 06 1E 05] [ >1.1E 05] Storm Protection TD (trees/plot) [0 2] [2 4] [>4] DB (%) [>50] [25 50] [0 25] Drainage CN [<30] [30 60] [>60] IN (in/hr) [<0.55] [0.55 7.8] [>7.8] Maintenance of soil qualit y OM (%) [<1, >15] [5.1 15] [1 5] pH [<3.5, >7.5] [3.5 4.9] [5 7.5] BD (g cm 3 ) [>1.6] [<1.1, 1.41 1.6] [1.1 1.4] Maintenance of healthy soils P (mg kg 1 ) [<3, >100] [3 15] [15 100] K (mg kg 1 ) [<40 ] [40 100] [>100] Mg (mg kg 1 ) [<8 ] [8 74] [>74 ] Ca (mg kg 1 ) [<200] [200 700] [>700] Zn (mg kg 1 ) [>300] [150 300] [0 150] Ni (mg kg 1 ) [>70] [35 70] [0 35] Cu (mg kg 1 ) [>130 ] [75 130] [0 75] Pb (mg kg 1 ) [>500] [250 300] [0 250] O3R ozone removal ; CO2S carbon dioxide sequestered ; CO carbon monoxide removal ; SO2C sulfur dioxide capture ; NO2C nitrogen dioxide capture ; TR temperature reduction ; TD tree density ; DB percent dieback ; CN drainage ; IN infiltration ; OM organic matter ; BD bulk density ; P concentration of phosphorus ; K conce ntration of potassium ; Mg concentration of magnesium ; Ca c oncentration of calcium ; Zn concentration of zinc ; Cu concentration of copper ; Ni concentration of nickel ; Pb concentration of lead ; Pm10R p articulate matter less than 10 microns removal ; LAD leaf area and distance to source of noise ; FOL type of foliage
106 Table 3 1 Continue d Service Low Medium High Filtering of dust particles Pm 10 R (tons yr 1 ) [0 0.000005] [0.000005 0.00002] [>0.00002] Noise Reduction LAD [<1,000 m 2 leaf area any dista nce to source of noise ] [1 000 2 000 m 2 leaf area ( 10 to 20 m ) distance to the source of noise, >1 000 m 2 leaf area (> 20 m ) distance to noise source] [>2 000 m 2 leaf area (< 20 m ) to source of noise, 1 000 2 000 m 2 leaf area (< 10 m ) distance to the sour ce of noise] FOL [<25% of trees evergreen] [25 50% of trees evergreen] [>50% trees evergreen] O3R ozone removal ; CO2S carbon dioxide sequestered ; CO carbon monoxide removal ; SO2C sulfur dioxide capture ; NO2C nitrogen dioxide capture ; TR temperature reduction ; TD tree density ; DB percent dieback ; CN drainage ; IN infiltration ; OM organic matter ; BD bulk density ; P concentration of phosphorus ; K concentration of potassium ; Mg concentration of magnesium ; Ca concentration of calcium ; Zn concen tration of zinc ; Cu concentration of copper ; Ni concentration of nickel ; Pb concentration of lead ; Pm10R p articulate matter less than 10 microns removal ; LAD leaf area and distance to source of noise ; FOL type of foliage Table 3 2 Categories of I ndi cator for H abitat F unction Categories Low Medium High SD [<1] [1 3.5] [>3.5] RN [<25% of trees native] [25 75% of trees native] [>75% trees native] SD, S hannon diversity Index ; RN ratio of natives. Table 3 3 Categories of I ndicator for P roductio n Function Categories Low Medium High TB (Kg.) [<1 0] [1 0 10 0] [>10 0] GB (Kg.) [<0.0005] [0.0005 0.001] [>0.001] DTB (Kg.) [> 0. 100] [ 0.00 1 0. 100] [< 0.00 1] GW (Kg.) [> 0. 100] [ 0.0 1 0. 100] [< 0.0 1] TB tree biomass ; GB litter biomass ; DTB dead tree biomass ; GW green waste biomass
107 Table 3 4 Categories of I ndicators for I nformation F unction Categories Low Medium High Recreation opportunities Forest recreation [no tree cover ] [1 25% of tree cover; 75 100% of tree cover] [25 75% tree cover] Institutional recreation [no cover of tree or maintain ed grass cover ] [1 25% of tree cover and 1 25% maintained grass cover; 75 100% tree cover and 1 25% maintain ed grass cover] [25 75% of tree cover and more than 25% of maintain ed grass cover] Resid ential recreation [no cover of tree or maintain ed grass cover ] [<50% maintain ed grass cover ; 25 75% tree cover and <50% of maintain ed grass cover [50 100% maintain ed grass; 25 75% tree cover and over 50% of maintain ed grass cover] Aesthetics RV (US$) [< 1,000] [1,000 10,000] [>10,000] REV (US$) [0 12,500] [80,000 350,000] [>350,000] RV, replacement value; REV, real estate value.
108 Table 3 5 Categories for I ndicator f or Disservices Categories Low Medium High Fruit Fall (FF) [<25% of the species have flesh y fruits] [25 75% of the species have fleshy fruits] [>75% of the species have fleshy fruits] A llergenicity OPALS (AL) [1 4] [4 7] [7 10] Damage to humans and infrastructure ( DI ) [<25% of the tree species have branches or trunks susceptible to breakage a nd excellent, good or fair average tree condition or 25 75% of the tree species have branches or trunk susceptible to breakage and in average excellent tree condition] [<25% of the tree species have branches or trunk susceptible to breakage and p oor average tree condition; 25 75% of the tree species have branches or trunk susceptible to breakage and good or fair average tree condition; >75% of the tree species have branches or trunk susceptible to breakage but on average they are in excellent condition] [>75 % of the tree species have branches or trunk susceptible to breakage and in average they have good, fair or p oor conditions or 25 75% of the tree species have branches or trunk susceptible to breakage and bad average tree condition] Decrease of air quality COE (tons yr 1 ) [0 0.0001] [0.0001 0.001] [>0.001] O 3 E (tons yr 1 ) [0 0.01] [0.01 0.05] [>0.05] CO 2 E (tons yr 1 ) [0 0.001] [0.001 0.01] [>0.01] VOCE (tons yr 1 ) [0 0.001] [0.001 0.01] [>0.01] CO 2 P (tons yr 1 ) [0 0.001] [0.001 0.01] [>0.01] CO 2 M (ton s yr 1 ) [0 0.0001] [0.0001 0.0005] [>0.0005] VOCEL (tons yr 1 ) [0 0.001] [0.001 0.3] [>0.3] NO 2 EL (tons yr 1 ) [0 0.025] [0.025 0.05] [>0.05] O 3 E ozone emission ; CO 2 E carbon dioxide emitted ; CO E carbon monoxide emission ; VOCE volatile organic compoun d emitted ; C O 2 P carbon dioxide emitted because of pruning ; C O 2 M carbon dioxide emitted because of mowing ; VOCEL volatile organic compounds emitted by leaf blowers ; NO 2 EL nitrogen dioxide emitted by leaf blowers.
109 Table 3 6 Indicator value, s tatistics a nd normality test s for ecosystem services o f the regulation function for the city of Gainesville Stats. Maint. of good air quality Maint. of favorable climate Maint. of healthy soils Maint. of soil productivity Storm protection Filtering of dust particles Noise Reduction Drainage Regulation Function Mean Indicator 2.0 2.2 2.6 2.2 2.3 1.8 2.2 2.2 2.2 Variance 0.25 0.66 0.12 0.19 0.33 0.77 0.30 0.06 0.1 Min value 1 1 1.6 1.4 1 1 1 2 1.5 Max value 2.7 3 3 2.8 3 3 3 2.5 2.8 p value <0.010 <0.010 <0.010 <0. 010 <0.010 <0.010 <0.010 <0.010 <0.010 Maint Maintenance
110 Table 3 7 Mean for ecosystem services included o f the regulation function and p AQ C HS SP STO DUST NOI DRA Mean REG St. dev.REG YUR 0 20 2.1 2.7 2.6 2.0 2.4 2.2 2.3 2.4 2.3 0.31 20 40 1.9 1.9 2.7 2.2 2.3 1.6 2.1 2.2 2.1 0.27 40 60 1.8 1.9 2.6 2.3 2.1 1.5 2.1 2.2 2.0 0.33 >60 2.3 3 2.8 3 2.5 2.5 3 2.3 2.6 0.01 p value NS 0.001 NS NS NS 0.01 NS NS 0.01 LU Forested 2.2 2.8 2.5 1.9 2.5 2.2 2.4 2.4 2.4 0.21 Reside ntial 2.0 1.6 2.8 2.3 2.3 1.5 2.1 2.0 2.1 0.29 Institutional 1.5 2.1 2.9 2.4 1.7 1.4 1.7 2.3 2.0 0.39 Commercial 1.7 2.2 2.8 2.2 2.2 1.6 2.4 2.0 2.1 0.43 Vacant 1.8 3 2.3 2.4 1.7 2.0 1.8 2.3 2.1 0.37 p value NS <0.0001 0.005 0.002 0.003 NS 0.004 <0 .001 PVAL (US$/acre) <12,500 2.1 2.5 2.6 2.0 2.5 2.1 2.4 2.3 2.3 0.27 12,501 210,000 1.9 2.5 2.5 1.9 2.2 2.1 2.2 2.3 2.2 0.34 >210,000 1.9 1.8 2.7 2.8 2.2 1.5 2.2 2.0 2.1 0.31 p value NS 0.002 NS 0.0003 NS NS NS 0.005 NS POP(hab./ce nsus block) < 953 2.0 2.5 2.7 2.2 2.4 2.0 2.4 2.3 2.3 0.32 954 1 698 2.0 2.2 2.6 2.2 2.3 1.9 2.4 2.2 2.2 0.28 1 699 3 503 2.0 2.2 2.5 2.2 2.3 1.8 2.1 2.2 2.1 0.25 >3 503 1.8 1.9 2.6 2.1 2.1 1.5 1.9 2.1 2.0 0.33 p value NS NS NS NS NS NS 0.003 NS 0.003 HI(US$) <21,000 2.1 2.3 2.6 2.1 2.5 2.1 2.3 2.2 2.3 0.26 21,001 32,700 2.0 2.3 2.7 2.1 2.3 2.0 2.4 2.2 2.2 0.26 32,701 44,000 1.9 2.2 2.6 2.2 2.4 1.5 2.2 2.2 2.1 0.38 >44,000 1.9 2.0 2.7 2.3 2.0 1.6 2.0 2.2 2.0 0.34 p value NS NS NS NS NS NS NS NS NS NS years since urban development ; LU land use ; PVAL property value ; POP population density ; HI household income. AQ maintenance of good air quality ; C maintenance of favorable climate ; HS maintenance of health y soils ; SP maintenance of soil productivity ; STO storm protection ; DUST filtering of dust particles ; NOI noise reduction ; DRA drainage ; REG r egulation function.
111 Table 3 8 Indicator value, statistics and normality test for ecosystem services indicat ors o f the habitat function for the city of Gainesville Stats. SI RN Habitat Function Mean Indicator 1.1 2.5 1.6 Variance 0.12 0.54 0.11 Min value 1 1 1 Max value 2 3 2.3 p value <0.0001 <0.0001 <0.0001 SI ; RN ratio of nativ e tree species Table 3 9 Mean for ecosystem services included in habitat function and p values SI NAT Mean HAB St. dev. HAB YUR 0 20 1.2 2.4 1.6 0.41 20 40 1.1 2.6 1.5 0.27 40 60 1.2 2.5 1.6 0.37 >60 1.5 3 1.8 0.24 p value NS NS NS LU Forested 1.3 2.6 1.8 0.25 Residential 1 2.3 1.4 0.25 Institutional 1.1 1.8 1.3 0.39 Commercial 1 2.6 1.5 0.25 Vacant 1.5 3 2 0.47 p value 0.005 <0.0001 <0.0001 PVAL (US$/acre) <12,500 1.3 2.6 1.6 0.40 12,501 210,000 1.2 2.8 1.7 0.28 >210,000 1.1 2.4 1.5 0.31 p value NS NS NS POP (hab./census block) <953 1.1 2.5 1.6 0.34 954 1 698 1.2 2.5 1.6 0.29 1 699 3 503 1.3 2.5 1.6 0.44 >3 503 1.1 2.6 1.6 0.30 p value NS NS NS HI (US$) <21,000 1.1 2.5 1.5 0.35 21,001 32,700 1.1 2.5 1.6 0.27 32,701 44,000 1.3 2.7 1.7 0.40 >44,000 1.1 2.5 1.5 0.35 p value NS NS NS NS YUR years since urban development ; LU land use ; PVAL property value ; POP population density ; HI household income SI Shannon diversity indexes ; NAT proportion of native species in the plot ; m ean HAB mean habitat function.
112 Table 3 10 Indicator value s tatistics and normality test for ecosystem services included o f the productivity function for the City of Gainesville, F lorida. Statistic TB GB DTB GW Production Function Indicator Mean 1.7 2.6 2.6 1.6 2.1 Variance 0.36 0.34 0.54 0.61 0.09 Min value 1 1 1 1 1.25 Max value 3 3 3 3 2.5 p value <0.010 <0.010 <0.010 <0.010 <0.010 TB tree biomass ; GB ground litter biomas s ; DTB dead trees biomass ; GW green waste biomass. Table 3 11 Mean for ecosystem services included in production function and p values TB GB DTB GW Mean PROD St. dev. PROD YUD 0 20 1.8 2.3 2.5 1.6 2.1 0.33 20 40 1.6 2.6 2.6 1.7 2.2 0.29 40 60 1.7 2.8 2.8 1.8 2.3 0.19 >60 1.5 2 2 1.5 1.8 0.35 p value NS 0.02 NS NS NS LU Forested 1.6 2.8 2.6 1.8 2.2 0.32 Residential 2 2.2 2 1.3 1.9 0.34 Institutional 1.3 2.6 2.8 2.4 2.3 0.17 Commercial 1.6 2.8 3 1.7 2.3 0.11 Vacant 1.5 3 2.5 2 2.3 0.35 p value 0.02 0.004 <0.0001 0.004 <0.0001 PVAL (US$/acre) <12,500 1.7 2.4 2.3 1.8 2 0.29 12,501 210,000 1.7 2.6 2.7 1.6 2.2 0.26 >210,000 1.8 2.7 2.7 1.6 2.2 0.26 p value NS NS NS NS NS Pop (hab./cens us block) <953 1.7 2.3 2.6 1 .6 2.1 0.37 954 1,698 1.8 2.5 2.6 1.5 2.1 0.26 1,699 3,503 1.5 2.6 2.5 1.9 2.1 0.27 >3,503 1.7 2.8 2.5 1.7 2.2 0.29 p value NS NS NS NS NS HI (US$) <21,000 1.4 2.7 2.9 1.9 2.2 0.16 21,001 32,700 1.7 2.6 2.8 1.6 2.2 0.25 32,701 44,000 1.9 2.3 2.1 1.4 1.9 0.40 >44,000 1.8 2.7 2.5 1.8 2.2 0.31 p value NS NS 0.04 NS 0.03 NS YUR years since urban development ; LU land use ; PVAL property value ; POP population density ; HI household income TB tree biomass ; GB ground litter biomass ; DTB dead trees biomass ; GW green waste biomass
113 T able 3 12 Indicator value, st atistics and normality test for ecosystem services included o f the information function for the city of Gainesville Statistic REC AES Information Function Mean 2.3 2 2.1 Variance 0.33 0.25 0.16 Min 1 1 1 Max 3 3 3 p value <0. 0001 <0.0001 <0.0001 REC forest recreation institutional recreation or residential recreation ; AES Aesthetics Table 3 13 Mean for ecosystem services included in information function and p values REC AES Mean INF St. dev. INF YUR 0 20 2.3 1.8 1.9 0.31 20 40 2.4 2.2 2.2 0.39 40 60 2.3 2.2 2.2 0.48 >60 1.8 2 1.9 0.12 p value NS NS NS LU Forested 2.3 1.8 2 0.39 Residential 2.5 2.3 2.4 0.31 Institutional 2.4 1.9 2.1 0.34 Commercial 1.7 2.1 1.9 0.42 Vacant 2.5 2 2.2 0 p value 0.02 0.002 0.002 PVAL (US$/acre) <12,500 2.3 1.5 1.8 0.25 12,501 210,000 2.2 1.9 2 0.27 >210,000 2.4 2.5 2.4 0.29 p value NS <0.0001 <0.0001 POP (hab./census block) <953 2.4 2 2.1 0.42 954 1 698 2.4 2.2 2.3 0.30 1 699 3 503 2.4 2 2.1 0.34 >3 503 2.1 2 2 0.46 p value NS NS NS HI (US$) <21,000 2.5 1.9 2.2 0.33 21,001 32,700 2.4 2.1 2.2 0.36 32,701 44, 000 2.3 1.8 2 0.42 >44,000 2.1 2.2 2.1 0.45 p value NS NS NS NS YUR years since urban development ; LU land use ; PVAL property value ; POP population density ; HI household income REC forest recreation insti tutional recreation or residential recreation ; AES Aesthetics ; INF, Information Function.
114 Table 3 14 Indicator value, s tatistics and normality test for ecosystem disservices for the city of Gainesville Statistic FF AL DI DAQ Disservices Mean 1.5 2 1.8 2 .2 1.9 Variance 0.4 0 0.73 0.21 0.09 Min 1 2 1 1 1 Max 3 2 3 2.9 2.6 p value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 FF Fruit Fall ; AL Allergenicity ; DI Damage to infrastructure or humans ; DAQ Decrease of air quality. Table 3 15 Mean for disservi ces and p FF AL DI DAQ Mean DIS St. dev. DIS YUR 0 20 1.3 2 1.9 2.3 1.9 0.29 20 40 1.6 2 1.6 2.2 1.8 0.29 40 60 1.6 2 1.7 2 1.8 0.30 >60 1.5 2 2.5 2.7 2.2 0.02 p value NS na NS NS NS LU Forested 1.4 2 2 2 1.8 0.33 Resi dential 1.7 2 1.6 2.3 1.9 0.27 Institutional 1.2 2 1.4 2.1 1.7 0.33 Commercial 1.5 2 2.1 2.2 1.9 0.31 Vacant 1.5 2 2.5 2.3 2.1 0.04 p value NS na NS NS NS PVAL (US$/acre) <12,500 1.3 2 2.2 2.3 1.9 0.24 12,501 210,000 1.5 2 2.2 2 1.9 0.24 >210 ,000 1.7 2 1.4 1.9 1.8 0.32 p value NS na 0.002 NS NS POP (hab./census block) <953 1.4 2 1.7 2.3 1.8 0.26 954 1 698 1.6 2 2 2.3 2 0.28 1 699 3 503 1.6 2 1.8 2.1 1.9 0.27 >3 503 1.4 2 1.7 2 1.8 0.33 p value NS na NS NS NS HI (US$) <21,000 1.5 2 1.9 2.2 1.9 0.24 21,001 32,700 1.5 2 1.9 2.3 1.9 0.25 32,701 44,000 1.7 2 1.6 2.2 1.9 0.28 >44,000 1.5 2 1.7 2.1 1.8 0.37 p value NS na NS NS NS NS t applicable. YUR years since urban development ; LU land use ; PVAL property value ; POP population density ; HI household income FF Fruit Fall ; AL Allergenicity ; DI Damage to infrastructure or humans ; DAQ Decrease of air quality ; DIS Dis service.
115 Table 3 16 Mean values and standard errors for the 4 ecosystem functions Function Indicator value Category Regulation 2.20.03 Medium Habitat 1.60.04 Medium Low Production 2.10.03 Medium Information 2.10.04 Medium Low Total 2.00.02 Medium Low Dis service 1.90.03 Medium Low Low [1 1.5], Medium Low [1.51 2.0], Medium [2.01 2.5], High [2.51 3]
116 CHAPTER 4 SPATIAL DISTRIBUTION OF FOREST ECOSYSTEM SERVICES INDEX Introduction H uman well being depends on natural resources and health of the ecosystem. The current trends associated w ith population expansion could threaten the sustainability and services provide d from ecosystems (Zurlini and Girardin, 2008). Assessing ecosystem health is determinant to provid e environmental security (Petrosillo et al., 2007) E nvironmental indices can be useful for assessing ecosystem health and quality, since they describe the socio ecological systems in a sim ple way that can be understood by scientist s managers, and policy makers (Zurlini and Girardin, 2008). Indices of an urban area s ecological conditio n can also help in evaluating the state of the urban ecosystem by comparing differences across space and the results of past policies can be evaluated (Banzhaf and Boyd, 2005). An index is a scalar form that aggregate s two or more values (MFE, 2004) and h elp s simplify a problem (Atkinson et al., 1997) by summarizing complex and multi dimensional issues (Saisana et al., 2005). If two or more indicators are combined an index is created (Segnestam, 2002). Composite indicators ( i.e. indices) may have the advan compelling way that can through a summary figure that makes the comparison across analysis units easier (Saisana et al., 2005). They can also include a we ighting scheme to even out the relationships among indicators (UNEP, 2006) and to show the importance of certain variables (Esty et al., 2005). The index should take into account variable spatial and temporal scales so standardization is accounted for t h us m ak i ng it comparable (Esty et al., 2005). Although a ggregate
117 indices are often subjective; due to choice of weights or the aggregation of the system, sensitivity analysis can help strengthen the index (Singh et al., 2007). System State Indices Several indice s had been developed to compare and evaluat e the state of a system. Indices such as Composite Leading Indicators Environmental Sustainability Index, the Human Development Index, Environmental Policy Performance Indicator, In dex of Sustainable Economy and Welfare and T he Technology Achieved Index u se an arithmetic average of normalized indicators. The Business Climate Indicator, the General Indicator of Science and Technology and the Success of Software Process Improvement use principal component analysis ( PCA) to assign weights to each indicator. T echnology performance is more related with PCA analysis (Esty et al., 2005). Other indicators are weigh ed based on user needs or weights derived from surveys such as Eco Indicator 99 and the Index of Environmenta l Friendliness. Environmental issues a re assigned equal weight s where no more importance is given to any specific variable or public opinion Anal ytical hierarchical process is occasionally used to assign weight s to the indicators This is a weighting met hod that enables decision makers to assign weights no n arbitrar il y (Saisana and Tarantola, 2002). An index of ecosystem services sh ould be able to describe the phenomena at multi scales along space and time, as long as it is constantly re evaluated and re interpreted according to the increased understanding of the socio ecological system (Zurlini and Girardin, 2008). Similar to sustainable development indices, ecosystem services indices are also related to human well being (Singh et al., 2007).
118 Remo te Sensing Indices Urban morphology ( e.g. ecosystem structure) has been mapped and mo deled in several different ways using a land use and land cover classification s or vegetation ind ices Specific ally, the normalized difference vegetation index (NDVI), mo dified soil vegetation index (MSAVI) and normalized difference building in dex (NDBI) ha ve been widely used. The NDVI has shown consistent correlation with vegetation in several ecosystems (Sellers et al., 1992) NDVI has been correlated with soil propertie s such as content of soil, clay and silt, the distribution of carbon and nitrogen, drainage and topographical variables (Lozano Garcia et al., 1991; Sumfleth and Duttman, 2008 ) NDVI also has been used to build models to estimate characteristics of urban f orests and its environment such as carbon storage or biomass (Myeong et al., 2005; Aznim and Haslim, 2007), leaf area (Jensen and Hardin, 2007) and bird speci es richness (Bino et al., 2008). M odels for estimation of housing prices based on greenness ha ve b een built (Mansfield et al., 2005). Zha et al. (2003) found that built up areas increase their reflectance for band s 4 and 5 in comparison to vegetation that slightly decrease s its reflectance, building up a relationship between the mid infrared band and t he near infrared band. The MSAVI was designed to reduce the soil radianc e from the vegetation indices, and t he refore use s the modified version for improved vegetation detection sensitivity since it increases the vegetation to soil ratio signal (Qi et al., 1994). This index has been used to map and analyze patterns of urban green space (Huapeng et al., 2007) and as a component in the delineation of built up land features (Xu, 2008). A combination of some of the se remotely sensor derived indices ha ve been us ed before for mapping
119 urban areas in the city of Nanjing, China to produce more accurate results than supervised classification method s (Zha et al., 2003). This chapter will aggregate the urban forest ecosystem services and goods (ESG) indicators into an i ndex Secondly, it will assess if the spatial distribution of the ESG ind ex is related to socioeconomic patterns at the city level Finally, it will determine if remote sensing vegetation indices (RS index) can be used to estimate the ESG index and evaluat e the effects of urbanization To accomplish this, the follow ing specific objectives are pursued: The ESG index w ill be developed using th ree common weighting schemes, equal weight, double weight and eigenvalue based weights. Sensitivity a nalysis will be u sed to select the index(ices) for subsequent analysis Spatial distribution r elationships between the ESG index and socioeconomic variables w ill be analyzed at the city level Values for the ESG index w ill be obtained by in terpolating the ESG index from p lot level to city level using an ordinary kriging technique Kriging estimat e accuracy will be assess ed by compar ing plot and the city level ESG index values To analyze the effects of urban morphology on the ESG index, a normalized difference vegetation i ndex, a modified vegetation index that accounts for soil presence and a normalized building index w ill be used Statistical r elationships between the ESG index and RS ind ices will be tested at the plot level to determine if spatially explicit remote sensin g estimation models can be developed I hypothesized that t h e ESG index will reflect the state of ecosystem services currently being provided by urban forests based on the premise that urban ecosystem composition and structure influences func tion and therefore the provision of ecosystem services and goods. The equal weight method will provide a robust method to assess ecosystem services and goods in urban areas. K rig ed s patial estimat e s of the ESG index are expected to resemble plot level res ults obtained for the index, therefore allowing for an assessment of the state of ecosystem services in Gainesville, FL. H ousehold income and population density are
120 related with the value of the ESG index so that the highe r income areas will have the high est ESG index. Conversely population density ha s an inverse relationship where the ESG index decreases with an increasing density. H ighly forested areas represented by high NDVI will have high ESG index value s, while areas dominated by impervious cover a nd a high NDBI will show low values for the ESG index. The NDVI will most effectively represent the spatial distribution of the ESG index, thus allowing the development of a n ESG index prediction model. Methods Study Area A description of the s tudy area is provided in de tail in Chapter 2 E cosystem S ervices and G oods Index The ESG index was developed based on a series of indicators of ecosystem functions representing groups of ecosystem services and goods The indicators that are aggregated in the ESG index consisted of s pecific values for each ecosystem function defined by D e Groot et al. (2002). Ecosystem functions were previously defined in Chapter 3 as regulation function (RF), habitat function (HF), production function (PF) and information function (IF), plus disservi ces (DIS) that were also included in the calculation These functions combined 3 to 26 variables derived from urban forests structure and composition values, urban soils and social values of the urban forest from the literature and available data. To make variables comparables, categories were used to standardize indicators. No weight was assigned to build the indicators, because no information of the preference s of people towards any ecosystem service w as obtained. To deal w ith different measurement units and scales, all services were standardized by assigning a unique value related to the state of each ecosystem service
121 in each plot (See Chapter 3 ). A difficulty in developing an index is assigning the appropriate weight to each indicator (Saisana and Tara ntola, 2002). Three methods for assigning weights to indicators were used in developing the ESG index. The first method ( hereafter referred to as EWI) assigned equal weight to all the individual indicators and the value of the ESG index is based on ordinal levels (1 st 2 nd 3 rd T his has the advantage of being simple and independent of outliers. However, it has the disadvantage of losing the absolute level of information since it does not inform about the real value by which the ordinal scale is built EWI works well when indicators are proper ly scal e d as described in Chapter 3 (Saisana and Tarantola, 2002). 4 n=1 Ecosystem Functions n ) DIS; interval [1 11] ( 1 ) The second w eighting method ( hereafter refer to as DWI ) subjectively assigns double weight to the information function since it s service of recre ation has been valued as one of the highest in urban ecosystems (Costanza et al., 1997). Assigning greater weights to the information function is related to how the ecosystem function directly affects human well being (Costanza et al., 1997). This method h as the advantage of representing one of the main perceptions of city inhabitants towards trees, the benefit of recreation, increase of property value and bonds with nature (Anderson and Cordell, 1988; Dwyer et al., 1991; Tyrvinen and Miettinen, 2000; Jim and Chen, 2006). 3 n=1 Ecosystem Functions n ) +2*IF DIS; interval [2 14 ] ( 2 ) Finally, t h e third method ( hereafter referred as to PCAI ) assigned weights according to the eigenvalues obtained from a PCA by the average of the first three principal components The use of a PCA justif ies the statement that weights are applied
122 in a neutral and data reliable way, based merely on the behavior of the data (Esty el al., 2005). PCAI = 0.15RF 0.65HF+0.67PF+0.13IF+0.29D IS; Interval [ 0.2 2.0] ( 3 ) The PCA is a technique that defines the weight of the indicator of an index, by showing the correlation between the indicators and not the causality. This method has been widely used to develop indices with the objective of giving more importance to the variables that explain the highest amount of variability of the data (Saisana and Tarantola, 2002). The disadvantage of using a PCA is that correlations do not necessarily represent th e real influence of the indicators in the index and do not express the priorities of decision makers nor budget constraints (Esty et al., 2005). Statistical a nalyses The ESG index analysis include d the calculation of the average, maximum, and minimum valu es. A ranking was calculated for the ESG index and for the func tions used to develop the ESG index. T he ranking shows the relative position of a certain plot in relation to the delivery of ecosystem services and goods where 1 impl ies the lowest delivery a nd 70 the highest delivery An average ESG index for the city of Gainesville was calculated and analyses of variance (A ) were performed to explore significant differences (p value<0.05) between the rankings obtained by the different ESG index method s To classify the ESG index as high, medium and low the interval of the possible values obtained was divided in to three equi distant categories. Sensitivity analyses were performed to assess the robustness of the indices by looking at the variation of t he output and measuring how the ESG index was dependent on the collected data (Singh et al., 2007), (e.g. how much the individual source of uncertainty is contributing to the variance of the output) (Saisana et al., 2005).
123 Comparison of the rankings betwee n the plots was done Pearson correlation analysis looked for associations in the components of the ESG index. Sensitivity analysis was done by systematically removing each function indicator individually for each ESG index scheme to see which influence d t he most the rank ing positions this will determine which index(ices) will be used for the rest of the analysis The ESG indices were was calculated using the spatial statistics tool in ArgGis 9.2 ( ESRI, 2008 ) look for clustering of the ESG index, showing if there is any trend in spatial distribution. and the respective attribute, where values closer to 1 indicates high dispersion of the data and values near 1 will indicate that the data is clustered (Griffith and Peres Neto, 2006). This will analyze the relationships between the ESG indices and the socioeconomic variables ( ESRI, 2008 ). Spatial Analy sis of ESG Indices at the City Level The units used to explore distribution patterns of the ESG index were land use, city quadrants and US C ensus block s See Chapters 2 and Chapter 3 for a description of the se units. For spatial analysis of the ESG index a m ong units a n ordinary kriging technique which is an interpolation method to estimate the value of unobserved locations. Estimates are obtained using the least squares method. The ordinary kriging was applied using the ArcGis 9.2 tool ( ESRI, 2008 ) to estim ate the value of the ESG index in a continuous surface to better visualize the pattern of it .To determine ESG index sensitivity to the different estimation techniques, mean and standard error were compared between the results estimated by the ordinary kri ging and those obtained using the plot level sampling. With this comparison the differences between the
124 observed and the estimated values of each ESG index method were obtained. Mean values of the ESG index by unit for all the analyzed indices, obtained fr om the plot sampling and those estimated by kriging, were tested for statistical differences using a student t test PROC TTEST for dependent samples (SAS Institute, 2007). Remote Sensing Analysis of ESG Ind ices at the Plot Level. I explored the use of a model to estimate the value of the ESG index based on easily obtainable data, such satellite images. This a nalysis of biophysical variables use d three indices derived from remote sensing data (RS Index) To explore spatial patterns variab les were plott ed i n a 3 Dimensional graph. To see similarities in trends between the ESG index and the RS indices a surface linear trend was added to each one of the indices The linear trend is calculated as a linear regression that relates the value of the ESG index with the value of the RS index by plot, r 2 adjusted is recorded Three RS indices were use d for the analysis: NDVI MSAVI and NDBI (Table 4 1) These RS indices have been proved to be highly correlated to urban features and have been used for mapping urba n areas or to show trends of the environmental behavior of urban ecosystems ( Zha et al., 2003, Myeong et al., 2006, Delm and Gulink, 2009) The remote sensing information was extracted from the Landsat Thematic Mapper (TM) image taken in April 2006. This w as the same date urban forest sampling in Gainesville occurred and when the vegetation is starting its grow ing season. Images for the summer period (June August) were not used since more than 50% of the i mage was covered by clouds, delivering an insu f ficie nt reflectance value for the pixels. The NDVI and MSAVI link ed the vegetation performance with the near infrared (NIR) and red (RED) reflectance ratio, and the NDBI link ed the mid infrared (MIR) and the near infrared reflectance wavelength. The value of t he NDVI ranges from 0 to 1,
125 where values close to 0 corr espond to absence of vegetation. V alues close to 0.5 correspond to vegetated areas with limited photosynthetic activity and values close to 1 indicate high density leaf concentration (i.e. appropriate nutrient and water availability ) (Myeong et al., 2001; Pettorelli et al., 2005, Sumfleth and Duttmann, 2008). T he NDBI was used to highlight urban morphology features such as buildings and impervious surfaces, both of importance for some of the ecosystem services that composed the index. Negative values or values close to zero show the presence of vegetation, while positive values are showing the presence of buil t up infrastructure, and the highest the NDBI the highest the building cover. The MSAVI has va lues between 0 and 1 values close to 1 show the pr esence of vegetation, and similarly, the highest index represents the highest amount of vegetative biomass (Qi et al., 1994). Results ESG Indices The calculation of the ESG indices shows differences in the ranking of each plot. The PCAI was removed from the analyses since it assigned a positive weight for disservices contrary to the effect of a dis service, which is negative showed no spatial correlation for any of the ESG indices. The E WI and DWI are 0.1 and 0.08 respectively). In the EWI, the highest significant correlation is with the information function (0.66), while the lo west is the productivity functio n. The differences between the ranking of EWI and the change in ranking when the regulation function, information function and disservices were removed from the analysis were s of the EWI for the city of Gainesville is 6.14 therefore the urban forest is delivering
126 ecosystem services and goods of a medium value M ost of the plots range between 4 and 7 (Figure 4 1). The DWI shows the highest signif icant correlation with the information function (0.66) and the regulation function (0.33), while the lo west association appears again for the productivity function. The differences between the ranking of DWI and the ranking when the information function an d disservices were extracted are significant The average for this index is 8.2, with plots ranging from 4.8 to 10.1 Analyses resulted in o ne plot with a high DWI for services and goods while most of the plots are found in a medium category T he maximum value changes between both ESG ind ices, shifting from a densely forested plot with smal l trees to a sparse tree area with bigger individuals. The maximum DWI value s are found in areas suitable for recreation (Figure 4 2) Differences between the t wo are shown i n Figure 4 3. The EWI has an average difference with the DWI of five positions, with 59% of the differences between ranking s below this average and 94% of the plots below 10 positions of difference. This indicates that the EWI or DWI forms a robust index where the major source of variation is the information function and disservices for the EWI and the productivity and information function for DWI For EWI the most influential function s are the information and disservices When the information function i s removed, the ranking of the EWI minimum value changes, while the ranking stays the same when other functions are removed. The EWI mean value has the highest increment in positions being rank as delivering more ecosystem services and goods. Eliminating d isservices from the EWI modified the medium ranking
127 positions. The DWI ranking changes when the productivity and information function s were removed and n o change occur red in maximum DWI values (Table 4 2) Both ESG indices are highly sensitive to changes i n the information function, showing the EWI to be more robust than the DWI. The EWI appears to be more sensitive and more appropriate for show ing changes in the delivery of ecosystem services and goods Spatial Analysis of ESG Ind ices at the City Level C it y quadrants analysis In EWI the highest values are located in the core of Gainesville ( Figure 4 4 ) Towards the limits of the city the EWI decreases, especially to wards the Northe ast (NE).The South e ast (SE) of the city has the highest value for the EWI wi th no areas less than 6. In t he NE almost all the quadrant has low values for the EWI especially toward s the North T o the East the EWI improve s The North w est (NW) has higher value s on the oldest part of the quadrant, while new urbanization areas show lo wer values for the EWI T owards the North of the quadrant the value of the EWI decreases. The Southw est (SW) has low EWI values. Opposite to the EWI ordinary kriging estimat e t he highest values using plot level EWI values are found in the NE, while the lo west are located in the SE. Low values for EWI contrary to the ordinary kriging estimat e are located in the SE quadrant. V alues estimated from the ordinary kriging are less accurate towards the East side of the city (Table 4 3) The DWI has similar values to EWI, where the overall trend for the city shows an increase of the DWI value s towards the core of Gainesville. The DWI d ecrease s towards the city limits and is the highest in SE. The NE has lower DWI values than the EWI for the same area and dif ferences with the o ther quadrants become sharper. The
128 NW shows a clear trend where DWI higher values are located in the quadrant corner heading to the core of the city and decreasing when moving to the North ( Figure 4 5 ) The DWI differences becom e more extreme between the ordinary krig ing estimat es and the plot values While the pl ots results exhibit the highest values in the NE, the ordinary kriging estimat e shows higher DWI values in the SE. The same happens with the lo west DWI value s which are loc ated in SW for the plots calculations and in NW for the ordinary kriging estimat e s. L and use analysis The spatial distribution of EWI shows that vacant areas have the highest value and c ommercial area s the lo w est T he greatest variability is also found in vacant areas since the krigging estimation used included bare soil and forested areas classified as vacant DWI show s slight differences, where vacant areas are still classified as the highest but the lo w est values were found in forested areas.Land use analyses show no clear patterns with the exception that forested areas tend to concentrate the lo w est ESG index values for the two indices ( Figure 4 6, Figure 4 7 ) T he overall ESG index magnitud e show that the highest and the lo w est value for EWI and DWI are the same ( Table 4 4 ). No significant di P opulation density analysis No clear spatial patterns appeared when contrasting the ESG index and population density for neither of the EWI and DWI methods ( Figure 4 8, Figure 4 9 ). Additionally no significant differences appeared between the mean ESG index obtained from the plots value and the kriging estimat es ; however differences in the rank position did occur ( Table 4 5 ).
129 H ousehold income analysis Spatial analyses for EWI indi cated that high income areas tend to have higher value s for this ESG index and the remaining household categories do not show any particular pattern (Figure 4 10 ) The DWI d o es not show a clear trend, even though high and low income areas located towards t he cente r of the city have high values (Figure 4 11) T his same situation does not occur for the rest of the city. No significant differences appeared between plot level and kriged ESG mean ranking position of the different househ old classes varie d ( Table 4 6 ). Remote Sensing Analysis of ESG Indices at the Plot Level T he 3 D trend line graphs show that in general, EWI decreases towards the SE and increase s in the opposite direction where higher values can be found in the n orth ern part of the city. DWI showed variation from East to West decreasing its value towards the East EWI show s a spatial trend similar to MSAVI (R 2 =0. 15 ) where for both the ESG ind ices decrease towards the SW and increase towards the East while increasing to the North ( Figure 4 1 2 ). The DWI shows a linear trend with the NDBI (R 2 =0.25) where highest value s NDBI correspond to the lo w est DWI value particularly in the NW quadrant ( Figure 4 1 3 ). Linear model construction do not show ESG indices and the RS indice s to be correlated ( R 2 <0.2 5 ) The NDVI did not showed similar trends with neither index. Discussion the trends observe d here are not applicable to other urban areas. Demog raphics are different, several university students are concentrated in certain areas of the city indicating high education levels and low income which are related with high tree cover
130 opposite situation s existing in other studies (Escobedo et al., 2006; Sz antoi et al., 2009). Gainesville has also large area s covered by dense fore sts in the middle of the city. These situation s could lead to different results and robustness of the ESG index if applied to other cities The application of the index in different cities different ecoregions or biogeograph ic provinces and countries with different level s of development will require changes to the index. In particular, the regulation function indicators should be rescaled since several of the categories assigned in Chapter 3 were derived from the distribution of the measured data. Rescaling c ould be done using equation (4) : ( 4 ) Where x q,c is the value of the indicator x q for the city c (Saisana and Tarantola, 2002) The ESG indices developed in this study include d many easily measured social, economical and environmental variables drivers of the urban environment (Grimm et al., 2000; Pickett and Cadenasso, 2002; Alberti et al., 2003). To improve the robustness of the ESG index a participatory approach might be warranted, such as an analytic hierarchy process (Cox et al., 1992 ; Saisana and Tarantola, 2002 ). The weighting issue is crucial to assess the robust ness of an index (Saisana and Satelli, 2008) The use of the PCA to assign weights to the composite index makes the l e ast sense since it assigned a positive value to the disservices which imply that these are increasing the delivery of ecosystem services and goods and not decreasing it as expected The EWI or DWI provides a more robust valuation for the assessment of ecosystem services and
131 goods in urban areas. Moreover the EWI is the le ast subjective, providing more robust results when no participatory information has been collected. The lo w est va lue for the provision of ecosystem services and goods for the EWI corresponded to a dense forested area, with only 5 tree species and more than 10% of this in poor condition or already dead. Its soil is sandy with bulk density values within the range recom mended for tree growth in urban areas (Craul, 1999), surface soil cover is scarce and nutrients are low (Heckman, 2006; Roa et al., 2008) and the structure of the forest does not provide a suitable area for any type of recreation. Only one plot has the hig hest value for the EWI index, which corresponded to a residential property with few large, good condition trees, where carbon storage and sequestration is high. Soil b ulk density levels are low, showing no signs of compaction, and the concentration of nutr ients is suitable for the growth of a yard or ornamental trees. The plot provides a good area for recreation and the existence of the trees increase d its value. City level analysis of spatial variables illustrate that the distribution of the ESG index by q uadrants has evident trends. I n general the center of the city has higher values than areas along the city limits which coincide with older areas and younger areas in the city, respectively This might indicate that the urban ecosystem is resilient, and t hat after years since the system was disturbed it returns to a point of equilibrium where the system can maintain ecosystem functions similar to those of natural areas (Alberti, 2009). The calculation of a time series of the ESG index could lead to a bette r understanding of which type of urbanization allows a more rapidly return to the equilibrium point of the ecosystem thereby a more sustainable development process for cities (McDonald, 2008) This information could also help in defin ing wh at is more
132 appr opriate : urban areas located in smaller but denser areas or less dense and more widely spread areas. Also the ESG index could help evaluating which type of development is more appropriate for the maximization of ecosystem services and goods. Tratalos et al (2007) observed some of these relationships in the U nited K ingdom finding that the type of development is more important in residential areas in terms of some ecosystem services delivery Land use analyses shows that forested areas have lower ESG index v alues, even though they are natural systems. High index values for low income areas could be related to the existence of older parts of the city and conse rvation areas and natural parks However Liu et al. (2009) found th is same inverse relationship betwee n environmental quality and ec onomic w ealth This trend could also be related to the existence of a reas with well educated population made up by the students of University of Florida (Margai, 1995 ; Farzin and Bond, 2009 ) R esults indicate that the us e of g eostatistics as a method to interpolate environmental variables across urban areas could deliver appropriate results using a low intensity sampling (Table 4 3, 4 4, 4 5 and 4 6) I ncreas e s in the sampling density could however raise other patterns t hat were overlooked due to the sampling size used w est ern part of the city which could be leading to less accurate results in the estimation of the ESG index obtaine d by kriging. Increases in the intensity of the sampling and the use of a systematic sampling method could also lead to more accurate results for the kriging, since the interpolation would have more information about the spatial variability existing in the urban area ( Webster and Oliver, 1990; Brus and Heuvelink, 2007)
133 The Landsat TM resulted in the calculation of the NDVI, MSAVI and NDBI at a 30x30 meter resolution; an area higher than the area covere d by the sampling unit used. T he value of the RS index could be influenced by the type of cover outside the sampled plot Previous studies found strong relationships between vegetation ind ic es derive f r o m satellite images and u rban forest plot level variables (Myeong et al., 2006; Huang et al., 2007 ) however no reliable relationship between the ESG index and the RS index could be established The use of an image with better resolution could be used to find a stronger relationship between the ESG index and RS indices derive d from a satellite image. Moreover if a h igh resolution image is used a RS index could be created using plot level spectral mixture by the combination of impervious surface, the grass surface, the tree cover and the bare soil (Rashed et al., 2007; Zoran et al., 2008) allowing the construction of a model for the rapid assessment of the ESG index in an urban area. The development of an urban forest ecosystem services and goods index therefore could have implication s on urban planning decisions if maximization of the services and goods is t he main objective. The implications on public policies are that the index could provide a comprehensive method for valuing the delivery of ecosystem services and goods from a non economical point of view. I t could provide a way to assess how public policie s and private sector activities m ight affect human well being (Banzhaf and Boyd, 2005) Conclusion Quantification of ecosystem services and goods is a difficult task, especially in urban areas, since this implies complex ecological patterns, processes and dis turbances involv i ng a lt er ed climate, hydrology, vegetation, fauna, population
134 dynamics and different flows of energy and matter (Rebele, 1994; Pickett et al., 2001). Urban areas are highly dynamic, changing at different scales in time and space. The constructio n of an ESG index in urban areas will provide a static state of the city at time t, if we moved to time t+1 the value of the ESG index will change since the urban ecosystem is constantly subjected to disturbances. Changes in socioeconomic, population, econ omic growth and policies will affect human and ecosystem dynamics (Alberti, 2009), thus changing the ecosystem services and the value of the ESG index. Variations of the ESG indices show that urbanization has an effect on the services and goods and this effect is not necessarily negative ESG Index values increase d with indicators existing in urban spaces such as areas associated with recreation, better soil quality and aesthetics provided by trees. Continuing calculation of th e index could provide inform ation for urban planning. Also the EWI could provide information about the resiliency o f urban ecosystems. Information about how the ESG index varies with the different socioeconomics could be used to identify specific areas in the city for improvements to human well being by making cities more comfortable to live in. The EWI should be under constant improvement, the addition of other services to the indicators will change the value of the index, in doing so, improving its accuracy in estimating state of th e ecosystem services in an urban area at a given certain time and urban morphology. The data required to buil d the ESG index is not difficult to compile, census data and urban forest data is available in several cities, the UFORE model can be used for free soil sampling is probably more e xpensive but the index could use just a few easily measured variables such as bulk density, pH and depth of litter. Further
135 analysis is necessary to increase the robustness of the index by its application in different cities and the comparison of its results.
136 Figure 4 1 Plots with the best (A) and worst (B) value for the E qual W eight I ndex Figure 4 2 Plots with the best (A) and worst (B) value for the D ouble W eight Information Function I ndex A B A B
137 Figure 4 3 Ra n king of plots according to E qual W eight I ndex and D ouble W eight Information Function I ndex
138 Figure 4 4 Analysis of the Equal Weight Index using ordinary k riging and classification of city quadrants Figure 4 5 Analysis of the Double Weight Informati on Function Index using ordinary k riging and classification of city quadrants
139 Figure 4 6 Analysis of the E qual W eight I ndex using ordinary k riging and land use classification
140 Figure 4 7 Analysis of the Double Weight Information Function Index usin g ordinary kriging and land use classification
141 Figure 4 8 Analysis of the Equal Weight Index using ordinary kriging and population classification by 2000 Census Block
142 Figure 4 9 Analysis of the Double Weight Information Function Index using ordina ry kriging and population classification by 2000 Census Block
143 Figure 4 10 Analysis of Equal Weight Index using ordinary kriging and household income classification by 2000 Census Block
144 Figure 4 11 Analysis of the Double Weight Information Function Index using ordinary kriging and household income classification by 2000 Census Block
145 Figure 4 1 2 Linear trend 3 D imensional graphs for the Equal Weight Index and Modified Soil Adjusted Vegetation Index Scales represe nt the range of value for eac h ecosystem services and goods and remote sensing index. Figure 4 1 3 Linear trend 3 D imensional graphs for the Double Weight Information Function Index and Normalized Difference Building Index Scales represent the range of value for each ecosystem s ervices and goods and remote sensing index
146 Table 4 1 Indices based on remotely sense d data Index Algorithm NDVI (NIR RED)/(NIR+RED) MSAVI NDBI NIR: Near infrared Band ; RED: red band ; MIR: Mid infrared band ; NDVI normalized difference vegetat ion index ; M SAVI modified soil adjusted vegetation index ; NDBI, normalized difference build up index Table 4 2 Mean maximum and minimum rank for the three index methods and values for sensitivity analysis of ecosystem services and goods functions. St ats. All included Reg. function eliminated Hab. function eliminated Prod. function eliminated Inf Function eliminated Disservices eliminated EWI Mean Plot 90 Rank #35 Plot 90 Rank #44 Plot 90 Rank #32 Plot 90 Rank #39 Plot 90 Rank #53 Plot 90 Rank #16 Min. Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #2 Plot 242 Rank #1 Max. Plot 186 Rank #69 Plot 186 Rank #68 Plot 186 Rank #67 Plot 186 Rank #63 Plot 186 Rank #64 Plot 186 Rank #68 DWI Mean Plot 314 Rank #35 Plot 3 14 Rank #33 Plot 314 Rank #34 Plot 314 Rank #46 Plot 314 Rank #12 Plot 314 Rank #39 Min. Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #1 Plot 242 Rank #2 Plot 242 Rank #1 Max. Plot 188 Rank #69 Plot 188 Rank #65 Plot 188 Rank #68 Pl ot 188 Rank #69 Plot 188 Rank #57 Plot 188 Rank #69 PCAI Mean Plot 361 Rank #35 Plot 361 Rank #34 Plot 361 Rank #41 Plot 361 Rank #41 Plot 361 Rank #44 Plot 361 Rank #29 Min. Plot 336 Rank #1 Plot 336 Rank #1 Plot 336 Rank #2 Plot 336 Rank #3 Plot 336 R ank #1 Plot 336 Rank #1 Max. Plot 114 Rank #69 Plot 114 Rank #69 Plot 114 Rank #69 Plot 114 Rank #68 Plot 114 Rank #69 Plot 114 Rank #69 Reg Regulation ; Hab Habitat ; Pro d Productivity ; Inf Information ; EWI Equal Weight Index ; DWI Double Weight Inf ormation Function Index ; PCAI Eigenvalue based Weight Index.
147 Table 4 3 Mean, minimum and maximum values for ecosystem services and goods indices. Plots Ordinary Kriging p values Quadrant Mean Std. Error Min Max Mean Std. Error Min Max EWI NE 6.3 2 0.170 5.95 6.68 6.11 0.009 6.08 6.13 NS NW 6.10 0.110 5.87 6.33 6.18 0.006 6.17 6.19 NS SE 5.95 0.190 5.49 6.41 6.36 0.009 6.34 6.38 NS SW 6.06 0.210 5.53 6.58 6.17 0.011 6.15 6.19 NS DWI NE 8.62 0.230 8.13 9.11 8.18 0.002 8.18 8.19 NS NW 8.19 0 .160 7.86 8.53 8.26 0.001 8.26 8.27 NS SE 7.97 0.290 7.29 8.65 8.72 0.002 8.72 8.73 0.03 SW 7.91 0.320 7.11 8.70 8.35 0.002 8.35 8.36 0.05 NS, no significant differences. NE NorthEast ; NW NorthWest ; SE SouthEast ; SW SouthWest ; EWI, Equal Weight In dex; DWI Double Weight Information Function Index Table 4 4 Mean, minimum and maximum values for ecosystem services and goods indices by land use Plots Ordinary Kriging p value Land Use Mean Std. Error Min Max Mean Std. Error Min Max EWI C 6.0 0 0.240 5.43 6.57 6.145 0.009 6.13 6.16 NS F 6.02 0.150 5.71 6.33 6.186 0.014 6.16 6.21 NS I 5.89 0.250 5.31 6.47 6.183 0.010 6.16 6.25 NS R 6.32 0.090 6.11 6.52 6.209 0.008 6.19 6.23 NS V 7.08 0.120 5.57 8.59 6.362 0.001 6.14 6.22 NS DWI C 7.99 0 .380 7.10 8.88 8.180 0.003 8.17 8.19 NS F 7.96 0.210 7.54 8.39 8.169 0.005 8.16 8.18 NS I 7.97 0.340 7.19 8.76 8.279 0.003 8.27 8.29 NS R 8.66 0.140 8.37 8.95 8.371 0.004 8.37 8.38 NS V 9.25 0.120 7.74 10.76 8.594 0.008 8.58 8.61 NS NS, no signifi cant differences C commercial ; F forested ; I institutional ; R residential ; V vacant ; EWI, Equal Weight Index; DWI Double Weight Information Function Index.
148 Table 4 5 Mean, minimum and maximum values for indices by population Plots Kriging P valu e Population (hab/block) Mean Std. Error Min Max Mean Std. Error Min Max EWI 0 953 6.38 0.130 6.12 6.65 6.18 0.006 6.16 6.19 NS 954 1698 5.95 0.120 5.69 6.21 6.22 0.018 6.17 6.26 NS 1699 3503 6.08 0.290 5.45 6.70 6.19 0.013 6.16 6.22 NS >3503 6.1 1 0.130 5.83 6.39 0 0 0 0 NS DWI 0 953 8.55 0.180 8.17 8.94 8.29 0.002 8.28 8.29 NS 954 1698 8.07 0.190 7.66 8.49 8.39 0.003 8.38 8.39 NS 1699 3503 8.11 0.390 7.26 8.97 8.31 0.005 8.30 8.32 NS >3503 8.24 0.210 7.79 8.69 0 0 0 0 NS NS, no significan t differences EWI, Equal Weight Index; DWI Double Weight Information Function Index. T able 4 6 Mean, minimum and maximum values for indices by household income Plots Kriging P value Household Income (US$) Mean Std. Error Min Max Mean Std. Error Min Max EWI 0 21,000 6.21 0.200 5.78 6.64 6.23 0.009 6.21 6.25 NS 21,000 32,700 6.06 0.140 5.77 6.34 6.13 0.009 6.11 6.15 NS 32,701 44,000 6.28 0.150 5.87 6.69 6.08 0.007 6.06 6.09 NS >44,000 6.16 0.130 5.89 6.42 6.23 0.008 6.21 6.24 NS DWI 0 21,000 8 .47 0.240 7.96 8.99 8.41 0.003 8.41 8.42 NS 21,000 32,700 8.09 0.210 7.67 8.52 8.24 0.003 8.23 8.24 NS 32,701 44,000 8.35 0.290 7.56 9.15 8.07 0.002 8.07 8.07 NS >44,000 8.30 0.190 7.89 8.71 8.37 0.003 8.36 8.37 NS NS, no significant differences EWI Equal Weight Index; DWI Double Weight Information Function Index.
149 CHAPTER 5 CONCLUSIONS Summary only restricted to the city limits but includes treed areas in the middle of the city. This special characteristic of the city makes that some areas found in the central core of the city have similar characteristics to natural forest existing in surroundings areas. The most influential structure variable on the calculati on of ecosystem services and goods indicators is tree cover which is used for several indicators Soils properties vary widely according to urban development but l ess urbanized areas show values similar to natural areas The most influential variable on the state of the soils is pH and organic matter. morphology and site legacy. Its state not only depends on socioeconomics dynamics and on urban planning policies but also on the priorities e stablished by city managers. I ndicators are showing the effect of urbanization on the environment through the value of the ecosystem functions that group services and goods at multiple scales. The calculation of the indicator for each service was based on field data, accepted theories, concepts, techniques and scientific standards and principles. The index provides a way to assess the state of the urban forest ecosystem services and goods at a certain moment in time. Once the data is obtained the calcul ation of the index is repeatable and easy to compute. It results in a simple number that is suitable and understandable for comparison. The index is relevant since it measures the state of the ecosystem and could have implications for human health and well being. The index could be policy
150 relevant since it could be used to aid decision makers on how a policy or management regime is affecting the ecosystem. The kriging technique used in this study can provide reliable estimat e s of the urban forest ecosystem services and goods. On the other hand, remote sensing indices do not correlate well with the ecosystem services and goods index, since it is not capturing all the urban ecosystem components B etter results m ight be achieve if the remote sensing indices were to be correlated with just the regulation function which is dependent on tree leaf are a and biomass and is more eas il y measured using remotely sensed data Limitations, Implications and Future Research The use of UFORE model for the estimation of the structure and the functions of forest. The UFORE model uses equations developed with species from the North ern US. Tree species in the South h ave different growth rates, therefore the calculations of biomass and leaf area and its respective carbon stored and sequestered and air pollution removal values are misleading. Future work should include the use of biomass models adequate to the area wher e the UFORE model is being applied. In the case of Gainesville the model should take into account algorithms that correspond to subtropical tree species, thereby increasing the accuracy of the estimates. Studies like these could be greatly benefitted by ac counting for the ecosystem functions provided by shrubs and maintained grass. With this a better system level estimation of the services and goods they are providing to urban areas could be evaluated.
151 Increas i ng sampling size could disclose relationsh ips with socioeconomics that were not evident in this study. A better distribution of the plots could also improve the results, giving a better representation of all the urban forest structures existing in the urban area. Ecosystem Services and Goods Indic ators Th is study indicators of urban forest ecosystem services and goods are dependent on local field data. The services and goods chosen for each function depend on data availability and do not include all those found in an urban area. The inclusion of additional services and disservices is necessary to give a more complete state of the urban ecosystem. Categories of the indicators were developed based on available values from the literature and the data ranges found in the city of Gainesville. Several assumption s were made to develop these indicators, however by adjusting for these local differences, this could make them applicable to different urban areas The categoriza tion of functions in this study facilitates their application to other cities by creating a s ame scale for different services. Urban Forest Ecosystem Services and Goods Index The integration of the indicators into an index aid s in determining the best structure and composition arrangements of the urban forest that could be maximizing the delivery of services and goods (e.g. best management practices) It incorporates environmental knowledge to the process of decision making. The construction of the index does not include priorities or perceptions of the people living in the study area. To improve the accuracy of the index, survey s should be done to evaluate which services or goods are seen as more valuable to the population. According to this information the
152 correct weighting could be applied to the indicators thereby facilitating the calculation of the index. The urban system is dynamic and very complex, therefore the index cannot forecast future trends. Urban ecosystems not only depend on the natural changes of the environment caused by urbanization, but also are dependent on urban planning, policies and trends in the socioeconom ics dynamics. Only rough estimat e s on how the value of the index will change if a certain policy is applied. The index should be in constant re evaluation, since new ecosystem services could be appearing. It is important that the ind ex be flexible, since ecosystem services and goods are still evolving as understanding of urban ecosystems increases. Policy Management Recommendations Recognizing that urbanization could have positive implications for human well being is determinant for m aximizing the benefits in urban areas the r eby improving quality of life. As seen in this study, urban forests are not only a source of disservices they also provide valuable service and goods to the community. Moreover with time certain urban forest ecosyst em structure variables reach an equilibrium on the delivery of ecosystem services and goods similar to natural surrounding areas. This could be showing a resilience of the ecosystems when being submitted to the process of urbanization and is directly related to s ocioeconomic characteristics and morphology of the urban areas. Variables such as the value of the property and the time since the property was urbanized are determining in most cases the delivery of ecosystem services and goods. T he use of an index could help in communicating to the population the state of their urban forest ecosystem services and goods and the management regimes that
153 could be applied to maximize this benefits The index could also be seen as a tool for comparison among urban areas, but standardization of the categories should be applied. Several management practices could be implemented to maximize and improve the benefits delivered by urban forest s. The key to the success of these management practices is letting the community know the state of their urban forest and possible improvements that they could do We should teach the community easy and effective management practices that could translate to long term improvements in the quality of the urban forest.
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171 BIOGRAPHICAL SKETCH Cynnamon Dobbs Brown was born in Sa ntiago, Chile in 1979. She obtained a B.S degree in Forest Engineer in 2003 duri ng her college years she worked on research related to urban forestry and environment in Santiago, Chile. After that she joined a n environmental consulting firm where she worked during two years developing areas in GIS, vegeta tion, soils and fauna The job req uire d field work, reports and maps and included public and private projects. She started her graduate career in 2007 with emphasis in urban ecology, specially working with trees and soils. She plans to continue her education in the Science department of Un iversity of Melbourne, Australia.