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Temporal Changes in Structure, Carbon Storage, and Vegetation Maintenance of the Urban Forest in Northeast Orlando Metro...

Permanent Link: http://ufdc.ufl.edu/UFE0044695/00001

Material Information

Title: Temporal Changes in Structure, Carbon Storage, and Vegetation Maintenance of the Urban Forest in Northeast Orlando Metropolitan Statistical Area, Florida, Usa
Physical Description: 1 online resource (115 p.)
Language: english
Creator: Horn, Joshua Lawrence
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: carbon -- footprint -- forest -- growth -- sequestration -- subtropical -- urban
Interdisciplinary Ecology -- Dissertations, Academic -- UF
Genre: Interdisciplinary Ecology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Rates of carbon sequestration in urban forests vary between and within cities, and are frequently determined using forecast models, which may be unable to accurately depict local or regional carbon storage. To determine the carbon sequestration and net carbon balance of a subtropical urban forest in NE Orlando MSA, Florida, a subsample of 69 originally measured urban plots established in 2009 were re-measured and residents surveyed during the summer of 2011. These 43 subsampled plots provided detailed information on the changes in urban forest structure, composition, tree growth and carbon storage as well as tree-related maintenance carbon (C) emissions. In total, Orlando’s urban forest stored 9.8 tC/ha in 2011 and sequestered 0.65 tC/ha annually between 2009 and 2011. Differences in C storage were observed among native, non-native and invasive tree species. The most important species both by frequency, carbon storage, and carbon sequestration were Quercus laurifolia and Quercus virginiana, accounting for 21% of the population, 56% of carbon storage and 70% of annual sequestration. Annual tree growth and C sequestration were affected by plot and tree level characteristics. Tree-related maintenance carbon emissions equaled just 0.8% of the total carbon sequestration by the urban trees. Considering the total carbon footprint of urban forest maintenance provides more accurate information on carbon dynamics in urban areas.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Joshua Lawrence Horn.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Escobedo, Francisco Javier.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044695:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044695/00001

Material Information

Title: Temporal Changes in Structure, Carbon Storage, and Vegetation Maintenance of the Urban Forest in Northeast Orlando Metropolitan Statistical Area, Florida, Usa
Physical Description: 1 online resource (115 p.)
Language: english
Creator: Horn, Joshua Lawrence
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: carbon -- footprint -- forest -- growth -- sequestration -- subtropical -- urban
Interdisciplinary Ecology -- Dissertations, Academic -- UF
Genre: Interdisciplinary Ecology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Rates of carbon sequestration in urban forests vary between and within cities, and are frequently determined using forecast models, which may be unable to accurately depict local or regional carbon storage. To determine the carbon sequestration and net carbon balance of a subtropical urban forest in NE Orlando MSA, Florida, a subsample of 69 originally measured urban plots established in 2009 were re-measured and residents surveyed during the summer of 2011. These 43 subsampled plots provided detailed information on the changes in urban forest structure, composition, tree growth and carbon storage as well as tree-related maintenance carbon (C) emissions. In total, Orlando’s urban forest stored 9.8 tC/ha in 2011 and sequestered 0.65 tC/ha annually between 2009 and 2011. Differences in C storage were observed among native, non-native and invasive tree species. The most important species both by frequency, carbon storage, and carbon sequestration were Quercus laurifolia and Quercus virginiana, accounting for 21% of the population, 56% of carbon storage and 70% of annual sequestration. Annual tree growth and C sequestration were affected by plot and tree level characteristics. Tree-related maintenance carbon emissions equaled just 0.8% of the total carbon sequestration by the urban trees. Considering the total carbon footprint of urban forest maintenance provides more accurate information on carbon dynamics in urban areas.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Joshua Lawrence Horn.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Escobedo, Francisco Javier.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044695:00001


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1 TEMPORAL CHANGES IN STRUCTURE, CARBON STORAGE, AND VEGETATION MAINTENANCE OF THE URBAN FOREST IN NORTHEAST ORLANDO METROPOLITAN STATISTICAL AREA, FLORIDA, USA By JOSH HORN 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 2012

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2 2012 Josh Horn

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3 To the world that might be

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4 ACKNOWLEDGEMENTS I would like to thank my committee, Dr. Francisco Escobedo, Dr. Ross Hinkle, and Dr. Mark Hostetler, for their guidance and support throughout this process. Additional thanks to Kimberly Hamann, Rick Vaugh n John Hoight and Ronald Cademus for sacrificing t heir weekends to assist in collecting the field data. And lastly to Dave

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5 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ................................ ................................ ............................... 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTERS 1 INTRODUCT ION ................................ ................................ ................................ .... 14 Urban Forest Structure and Its Effect on Carbon Dynamics ................................ ... 14 Objectives ................................ ................................ ................................ ............... 17 2 REVIEW OF GLOBAL URBAN CARBON STORAGE AND SEQUESTRATION .... 18 Literature Review ................................ ................................ ................................ .... 18 Objectives ................................ ................................ ................................ ............... 19 Methodology ................................ ................................ ................................ ........... 19 Results and Discussion ................................ ................................ ........................... 20 3 CHAN GES IN URBAN FOREST STRUCTURE AND COMPOSITION ................... 28 Literature Review ................................ ................................ ................................ .... 28 Urban Forest Structural Dynamics ................................ ................................ ... 29 Urban Forest Species Diversity ................................ ................................ ........ 30 Objectives ................................ ................................ ................................ ............... 32 Methodology ................................ ................................ ................................ ........... 33 Study Area ................................ ................................ ................................ ........ 33 Plot and Urban Forest Measurements ................................ .............................. 33 Individual Tree Matching ................................ ................................ .................. 36 Growth and Mortality R ates ................................ ................................ .............. 37 Species Groups ................................ ................................ ................................ 37 Statistical Analyses ................................ ................................ .......................... 38 Results ................................ ................................ ................................ .................... 38 Urban Forest Composition and Structure ................................ ......................... 38 Growth and Mortality ................................ ................................ ........................ 40 Discussion ................................ ................................ ................................ .............. 41 4 URBAN FOREST CARBON STORAGE, SEQUESTRATION, AND MAINTENANCE EMISSISONS ................................ ................................ .............. 53

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6 Literature Review ................................ ................................ ................................ .... 53 Urban Forest Carbon Dynamics ................................ ................................ ....... 53 Objectives ................................ ................................ ................................ ............... 55 Methodology ................................ ................................ ................................ ........... 57 Study Area ................................ ................................ ................................ ........ 57 Plot and Urban Forest Measurements ................................ .............................. 57 Individual Tree Matching ................................ ................................ .................. 58 Carbon Storage and Sequestration Calculations ................................ .............. 59 Species Groups ................................ ................................ ................................ 61 Residential Vegetation Maintenance Survey ................................ .................... 61 Maintenance Emission Calculations ................................ ................................ 62 Statistical analyses ................................ ................................ ........................... 63 Results ................................ ................................ ................................ .................... 64 Lawn Maintenance Pra ctices ................................ ................................ ............ 65 Maintenance Carbon Emissions ................................ ................................ ....... 66 Discussion ................................ ................................ ................................ .............. 67 5 CONCLUSION ................................ ................................ ................................ ........ 77 APPENDIX A FIELD DATA SHEET ................................ ................................ .............................. 79 B RESIDENTIAL INTERVIEW CONSENT ................................ ................................ 81 C RESIDENTIAL VEGEATION MAINTENANCE SURVEY ................................ ........ 82 D BIOMASS EQUATIONS ................................ ................................ ......................... 84 E LITERA TURE REVIEW OF GLOBAL URBAN CARBON STORAGE AND SEQUESTRATION BY LANDUSE ................................ ................................ ......... 87 LIST OF REFERENCES ................................ ................................ ............................. 107 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 115

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7 LIST OF TABLES Table page 2 1 Conversion factors used to standardize global carbon storage and sequestration values. ................................ ................................ .......................... 25 2 2 Summary of global urban forest carbon storage and sequestration with corresponding author, location, biomass estimation method, vegetation surveyed, NRCS biome, and estimated annual net primary production (NPP). .. 26 3 1 Number of plots (n), % surface and aerial coverage of select land covers by landuse in NE Orlando MSA, Florida. ................................ ................................ 50 3 2 2011 tree density and total basal area by landuse with corresponding % change between 2009 and 2011 samples in NE Orlando MSA, Florida. ............ 50 3 3 Tree and palm density (tree/ha) according to dbh size class distribution among landuse types and NE Orlando MSA, Florida study area. ....................... 50 3 4 Tree and palm density (trees/ha) distribution by landuse in NE Orlando MSA, Florida. ................................ ................................ ................................ ............... 50 3 5 Annual growth rate (AGR; cm/yr) based on landuse and tree classification in NE Orlando MSA, Florida (sample size in parentheses). ................................ ... 51 3 6 The relationship between tree and palm dbh size class annual growth rate (cm/yr) in NE Orlando MSA, Florida. ................................ ................................ .. 51 3 7 Tree average annual growth and tree mortality rates from 2009 to 2011 in NE Orlando MSA, Florida and other relevant state, regional and national studies. .. 51 3 8 Average annual growth for all trees re measured between 2009 and 2011 in NE Orlando MSA, Florida, as well as other state a nd regional values. ............... 52 3 9 Annual mortality rate for all trees re measured between 2009 and 2011 in NE Orlando MSA, Florida, as w ell as other state, regional, and national values. ..... 52 4 1 Emission factors and references for all maintenance activities. .......................... 73 4 2 2011 mean carbon storage value (kgC) and significant differences in carbon storage between 2009 and 2011 samples based on tree type and dbh size class in NE Orlando MSA, Florida. ................................ ................................ ..... 73 4 3 Correlation and significance between total impervious surface, duff an d mulch, herb and ivy, tree cover, tree and palm cover, and plantable space with carbon storage and sequestration in NE Orlando MSA, Florida. ................. 73

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8 4 4 Tree density, carbon storage and carbon sequestration by landuse and study total with corresponding % change from 2009 sample to 2011 sample in NE Orlando MSA, Florida. ................................ ................................ ........................ 74 4 5 Top eight species ranked by carbon storage, with corresponding proportion of sample size, and cumulative % of carbon storage and sequestration for NE Orlando MSA, Florida. ................................ ................................ .................. 74 4 6 Annual emission rates from lawn mowers, trimmers, leaf blowers, chainsaws, hedge trimmers, and irrigation for two resident groups in NE Orlando MSA, Florida. ................................ ................................ ................................ ............... 74 4 7 Net carbon balances across landuse gradient in NE Orlando MSA, Florida accounting for sequestration, removal from mortality, addition from in growth and tree related maintenance emissions. ................................ ........................... 75

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9 LIST OF FIGURES Figure page 3 1 Land cover imagery of east Orlando, Florida study area with original 2009 plots depicted. ................................ ................................ ................................ .... 45 3 2 Google Earth aerial imagery displaying plots within NE Orlando MSA, Florida ................................ ................................ ................................ ................ 46 3 3 An example of low density residential landuse in NE Orlando MSA, Florida (photograph courtesy of author). ................................ ................................ ........ 47 3 4 An example of medium density residential landuse in NE Orlando MSA, Florida (photograph courtesy of author). ................................ ............................ 48 3 5 An example of mixed use urban landuse in NE Orlando MSA, Florida (photograph courtesy of author). ................................ ................................ ........ 49 4 1 Relative proportion of the sample and annual sequestration from FLEPPC non invasive (0), moderately invasive (2), and highly invasive (1) palm and tree species in NE Orlando MSA, Florida. ................................ .......................... 75 4 2 Relative proportion of the sample and annual sequestration from USDA designated native (N) and exotic (E) palm a nd tree species in NE Orlando MSA, Florida. ................................ ................................ ................................ ...... 76

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10 LIST OF ABBREVIATION S ac acre AGR annual growth rate AGT Above ground tree AGV Above ground vegetation AIG annual in growth rate AMR annual mortality rate AP A erial photography BM Biomass C Commercial dbh Diameter at breast height (4.5 feet/ 1.3 7 meters) ft feet FLEPPC Florida Exotic Pest Plant Council ha hectare hr hour I Institutional lb pound km kilometer LD R Low density Residential LULC Land Use Land Cover m meter MDR Medium density Residential MFR Multi Family Residence mt m egaton (metric ton) MUU Mixed use Urban

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11 t ton (metric) T Transportation TT total (above and below ground) tree TV total (above and below ground) vegetation SFR Single Family Residence UCF University of Central Florida UF University of Florida UFORE Urban Forest Effects ( model developed by the U.S. Forest Service ) USDA United States Department of Agriculture USFS United States Forest S ervice UST United States (Imperial) ton yr year

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science TEMPORAL CHANGES IN STRUCTUR E, CARBON STORAGE AND VEGETATION MAINTENANCE OF THE URBAN FOREST IN N ORTH E AST ORLANDO M ETROPOLITAN S TATISTICAL AREA FLORIDA USA By Josh Horn December 2012 Chair: Francisco Escobedo Major: Interdisciplinary Ecology Rates of carbon sequestration in urban forests vary between and within cities, and are frequently determined using forecast models, which may be unable to accurately depict local or regional carbon storage To determine the carbon sequestration and net carbon balance of a subtropical urban forest in northeast Orlando MSA, Florida a subsample of 69 originally measured urban plots established in 2009 were re measured and residents surveyed during the summer of 2011. These 43 subsampled plots provided detailed information on the changes in urban forest structure, composition, tree growth and carbon storage as well as tree related maintenance carbon (C) emissions In total Orlando urban forest stored 9.8 tC/ha in 2011 and sequestered 0.65 tC/ha annually between 2009 and 2011. Differences in C storage were observed among native, non native and invasive tree species. The most important species both by frequency, carbon storage, and carbon sequestration were Quercus laurifolia and Quercus virginiana accounti ng for 21% of the population, 56% of carbon storage and 70 % of annual sequestration. Annual tree growth and C sequestration were affect ed b y plot and tree level characteristics. T ree related maintenance carbon emissions equaled

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13 just 0.8% of the total carbon sequestration Considering the total carbon footprint of urban forest maintenance provides more accurate information on carbon dynamics i n urban areas.

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14 CHAPTER 1 INTRODUCTION Urban Forest Structure and Its E ffect on Carbon Dynamics Over the last thirty years significant strides have been made in understanding the factors most responsible for driving urban forest structure and composition. T he three primary factors are: urban morphology (pattern of infrastructure and building within urban area), natural environment (largely determined by climate and geography), and human actions (intentional management of vegetation) (Sanders 1984). It is important to note that biophysical and human factors influence the socioeconomic and physical properties that determine urban forest structure and function, so there is interplay between ecological and social drivers (Alberti et al. 2003). C hanges in urban morphology and site specific environmental differences create unique urban forest structure s (i.e. tree cover) making comparisons between cities in different regions difficult (Sanders 1984). This is further complicated by recent changes in the global distribution of urban areas, which have historically been concentrated in Europe and North America, but population increases in Asia Africa and Latin America have changed the density and form of urban areas ( UN 2012 ) As scientists and the p ublic continue to recognize and observe the consequences of climate change understanding the carbon cycling and carbon dynamics of urban forests is gaining importance The literature on carbon sequestration and storage of urban forests is extensive ( Appen dix E ) but has not been aggregated and examined. Understanding how current urban forest structure and composition will drive long term carbon dynamics can be used by planners and land managers as a tool for managing

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15 urban forests with the goal of reducing atmospheric carbon emissions and maximizing homeowner benefits. Other studies have examined landuse and biophysical factors related to urban forest growth and structure. Zhao et al. (2010) reported structural, compositional and dominance differences betwe en residential and non residential areas of Miami Dade USA and found higher exotic tree and species richness in residential and managed, native oriented non residential. Urban soils are another key biophysical factor that is altered according to landuse a nd duration of urbanization, retaining legacy effects from previous landuse and stabilizing after initial urbanization ( Hagan et al. 2012 ). Urban forest growth and mortality rates are also important factors in predicting future structure and ecosystem service provision. These rates vary between species, landuse, tree condition, tree size, and site location (Nowak et al. 2004). Southeastern USA forests are additionally subject to strong sto chastic forces, most notably hurricanes, which can double annual mortality rates (Staudhammer et al ., 2011) Urban areas also exhibit significantly higher plant diversity than surrounding natural areas which can impact structure and carbon dynamics Unli ke most biotic groups, urban plant, and especially tree, diversity is directly controlled by human actions and management (Grimm et al. 2008). Exotic species increase short term richness, but destabilize native ecosystems, and can lead to long term simpli fication of the urban environment (Gordon 1998) Urban areas can show high native diversity, including rare and endangered species, though exotic flora may functionally replace native species unable to colonize urban areas (Pickett et al. 2008). Exotic s pecies are a threat to biodiversity globally, but they pose a specific threat to urban areas where select

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16 synanthropic species are capable of controlling resources, excluding non synanthropic species and homogenizing ecosystems ( Gordon 1998 ; Sho chat et al ., 2010 ). L ittle research has been conducted on the spatial distribution of exotic species in urban areas and their functional role in the urban ecosystem (Walker et al. 2009 ; Gulezian and Nyberg 2010 ). Urban forest structure and composition drive carbon storage and sequestration, though these properties vary between and within urban areas. In addition to direct uptake of carbon, t rees are able to reduce carbon emissions from residential buildings via shading, which decreases cooling demand, which i n turn decreases power plant emissions. Shading effect factors in the Urban Forest Effects (UFORE) model account for some variation between individuals and speci es, but fail to account for other important aspects Also, m ature species shape and height driv e the shading effects but tree crown shape is a factor of species, growing conditions and competition. Escobedo et al. (2010) found that carbon dioxide emission reductions by urban forests were moderately effective compared to other strategies in Gainesvi lle and Miami Dade, Florida (14.3 and 18.5 % of total CO 2 reduction policies in each city, respectively). In order to appropriately estimate the net benefit of urban forests, studies must account for the total net carbon inputs as well as the trees themsel ves. Tree, shrub and lawn maintenance can add carbon emissions that negate large portions of the carbon stored and sequestered by trees. Jo and McPherson (1995) estimated that tree and shrub pruning has minimal carbon emissions (6% of annual sequestration ), but mowed grass is a net source of atmospheric carbon, emittin g 144% of annual sequestration

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17 Objectives As stated above, there is a lack of information on the role of native status landuse, tree related maintenance and plot characteristics on tree gr owth, carbon storage and carbon sequestration in subtropical urban forests. Therefore, t he primary objective of this study is to determine temporal changes in urban forest carbon storage, identify tree and plot characteristics associated with those rates, and describe a net carbon balance that accounts for tree related maintenance carbon sources and sinks. This study will also analyze annual tree growth and mortality rates for comparison to other state, regional and national reports. Distribution and carbon dynamic variation between native, exotic, and exotic invasive species will also be examined. This study will directly measure the change in carbon at the plot level, accounting for demographic, climate socioeconomic and other human and natural infl uences rather than rely on model estimates and assumptions from other studies. In addition to measuring specific sequestration rates, this study will also quantify carbon emissions associated with managing the urban forest of east Orlando, permitting the calculation of a net carbon balance for each plot and corresponding landuse.

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18 C HAPTER 2 REVIEW OF GLOBAL URB AN CARBON STORAGE AN D SEQUESTRATION Literature Review As the public and scientists continue to reco gnize and observe the consequences of climate change understanding carbon dynamics of urban forests is gaining importance ( Brack, 2001; Nowak et al. 2002 ; Churkina et al., 2010; Escobedo et al., 2010 2011 ; Davies et al., 2011 ; Fissore et al., 2011 ; Hutr ya et al., 2011; Strohbach et al., 2012 ) The literature on carbon sequestration and storage of urban forests is extensive, but has not been aggregated and examined Understanding how current urban forest structure and composition will drive long term carb on dynamics can be used by planners and land managers as a tool for managing urban forests with the goal of reducing atmospheric carbon emissions and maximizing homeowner benefits ( Nowak et al., 2002, 2006 ; Escobedo et al., 2010 2011 ) Trees are able to r educe carbon emissions from residential buildings via shading, which decreases cooling demand, which in turn decreases power plant emissions ( McPherson, 1998 ) Shading effects provided in the Urban Forest Effects (UFORE) model account for some variation be tween individuals and speci es, but fail to account for other important factors ( Simpson, 1998 ) Mature species shape and height drive the shading effect but tree crown shape is a factor of species, growing conditions and competition. Escobedo et al. (2010) found that carbon dioxide emission reductions by urban forests were moderately effective compared to other reduction strategies in Gainesville and Mia mi Dade, Florida (14.3 % and 18.5 % of total CO 2 reduction in each city, respectively).

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19 Objectives The objective of this review was to compile English language literature on urban tree carbon storage and sequestration from the entire globe, determine mean va lues, and qualitatively analyze the results according to: methodology, vegetation type biome and net primary production. Give n the literature, it is expected that carbon storage and sequestration will be positively correlated with net primary production a nd biomes of increasing productivity from desert to boreal to Mediterranean to temperate to tropical. Methodology To assess similarities and differences among carbon storage and sequestration in different urban forests across the globe, a sy stematic literature search was done using JSTOR, Academic Search Premier and the University of Florida One Search that included forestry, environmental science, and urban ecology journals, such as Urban Forestry and Urban Greening, Journal of Arboricult ure, Landscape and Urban Planning, Journal of Environmental Management, BioScience, Environmental Pollution, Journal of Forestry and USDA Forest Service publications. The search utilized combinations of key words such as: urban, carbon, climate change, emi ssion, forest, life cycle analysis, sequestration, storage and trees. Studies were included if they contained carbon storage or sequestration data directly measured over a variety of urban landuses, thus excluding street tree studies These method s produced forty different publication s representing thirty eight distinct urban areas and two national estimates for the United States, as well as six relevant jurisdictions within several of the urban areas (i.e. counties or unique urban districts within the larger urban study area). Relevant information such as study area, carbon storage, carbon sequestration, carbon avoided, biomass calculation methodology, component of vegetation included

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20 and landuse were compiled. The extracted values were converted t o a single unit system, using t C /ha as the base unit, to allow for comparison between urban areas. Specific conversion factors used can be found in Table 2 1 C arbon estimation method and component of vegetation included methodologies used in the studies w ere compared Carbon estimatation methods included: diameter breast height (dbh) allometric, volume allometric, biomass estimation factors, and Urban Forest Effects (UFORE) model. Component of vegetation included: whole tree, all vegetation, above ground tree, and above ground vegetation. Each urban area was placed into one of the fourteen global biomes identified by USDA Natural Resourc e Conservation Service (NRCS 1999 ) and assigned an estimated annual net primary production value ( 0.0 1.2 kgC/m 2 in inte rvals of 0.15 kgC/m 2 ) (Foley et al. 1996; Kucharik et al. 2000) Statistical differences between methods, biomes, and estimated net primary productivity were determined test; correlations were determined using linear regression analysis and F test for significance. Results and Discussion Urban forest carbon storage ranged from 5.02 tC/ha in Jersey City, NJ, USA to 89.00 tC/ha in the Seattle, WA, USA region with a mean of 22.1 2.4 tC/ha; annual gross sequestration ranged from 0.20 tC/ha/y r in Casper, WY, USA to 2.84 tC/ha/yr in Shenyang, China with a mean of 0.75 0.08 tC/ha/yr (Table 2 2 ) There was also a great deal of variation within a single biome ; carbon storage in the temperate biome ranged from 5.0 tC/ha to 89.0 tC/ha and sequestration from 0.21 tC/ha/yr to 2.94 tC/ha/yr Twenty seven of the thirty eight urban areas included were considered temperate humid or mostly temperate humid which reflects on the location of many urban areas ( NRCS, 1999; U.N., 2012 ) as well as the sample bias; sixteen of the studies were

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21 based in the eastern United States (e ast of the Mississippi River), and only eleven were outside the United States This process was further complicated because some urban areas (n=9) sprawl into two different biomes Due to the small sample sizes, there was no significant difference (p > 0.05) between the biomes, but they roughly followed the expected pattern (ranked from highest storage to lowest): tropical (40.7 tC/ha; n=2) temperate semi humid (28.0; n=7) temperate humid ( 22.0 tC/ha; n=29 ) Mediterranean warm (18.9 tC/ha; n=3) and desert temperate (6.3; n=1) Estimated n et primary production was more strongly correlated with C storage and sequestration than biome type though not significantly (Pearson co efficient = 0.28 ; p > 0.05). This relationship is limited by the sample size and the distribution of studies; fourteen of the twenty five studies that included sequestration had an estimated NPP of 0.75 kgC/m2/yr. Although not quantitatively analyzed the cursory trend indicates that areas with higher NPP will have higher carbon storage and sequestration, which further biomass regions (i.e. forested) store more carbon tha n those in lower natural biomas s regions (i.e. desert); it also supports the stated hypothesis, though there wa s no statistical significance. The majority of studies employ ed either dbh allometric or UFORE model estimates to calculate biomass and carbon; b iomass estimation factors and volume allometric equations were used in just one (Xiao et al. 2011) and two studies ( Warren et al. 2002 ; Zhao et al., 2010 ) respectively. DBH allometric studies had a mean carbon storage of 28.7 4.3 tC/ha while UFORE studi es were 15.7 2.0 ( p < 0.10 ). Studies using dbh allometric equations also found higher sequestration than UFORE studies: 1.01 0.15

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22 tC/ha/yr and 0.61 0.07 tC/ha/yr, respectively ( p < 0.10 ). There was no significant difference in carbon storage or sequestrati on regardless of which component of vegetation (i.e. above ground tree, above ground vegetation, whole tree, above and below ground vegetation) was included (p > 0.05) though the majority of studies (twenty six) included only whole trees Carbon avoided due to shading and climate regulation was included in just six of the studies, but can play a major role in the net carbon dynamics of urban environments, accounting for 28% more carbon than is sequestered in Pittsburgh, PA, USA and equal to 5% of the carbon sequestrated in Barcelona, Spain (Davey Group 2008 ; Chapparo et al. 2009 ) Specific methods in each study can make an impact on the larger urban forest picture. Hutyra et al. ( 2010 ) included large portions of the Seattle WA surr ounding natural forest stands which may be a cause of the high storage value for Seattle, WA, USA; Horn (2012) excluded forested plots in gathering sequestration measurements, which may under report the carbon storage values for NE Orlando MSA, Florida U SA Both Strohbach et al. (2012) and Hutrya et al. (2010) excluded below ground carbon storage due to large uncertainties in the percentage of biomass stored in tree roots Analysis of studies by population, urban size and population density might illumina te additional associations, but variations in study area delineation ( i.e. extent of urban area included ) make it difficult to determine population, and in turn population density. The analyses presented in this review are limited by the accuracy of method s and component s of vegetation included descriptions p rovided in the original studies and the extension of some urban areas in to multiple biomes or estimated net primary

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23 pro duction zones due to their size (i.e. San Francisco USA was both tropical semi arid and Mediterranean warm) Although the conversions accounted for differences in units among the studies, additional variations may exist once carbon estimation tree wood fresh to dry weight conversions, species specific wood densities, and compon ent of vegetation included methodologies are standardized (i.e. convert above ground tree results to whole tree by multiplying by 1.26). As briefly discussed above these studies represent a biased sample that might not be representative of mean global urb an areas due to the almost total exclusion of Latin America n (n=0) Africa n (n=0) and Asia n (n=8) studies ; a more accurate picture of current global urban carbon storage and sequestration must include these areas and non E nglish language literature as ca rbon management in these regions will become increasingly important as populations continue to migrate toward cities There are additional studies in Australia (Brack, 2006) and Africa (Stoffberg et al., 2010) that did as well as a no n English study from Chile ( Maza et al., 2005) Recent reports in urban and regional planning have analyzed metropolitan size, degree of form distribution and degree of clustering to quantify density continuity and spatial structure of urban areas, which may describe additional variation between the carbon studies (Tsai 2005); however, this technique has not been broadly applied to global urban areas and data was unavailable for this review This re view illustrates the range of urban carbon storage and sequestration across the globe, and also highlights the need for studies of urban forests in developing countries throughout Asia Africa and Latin America. In general, biome and primary

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24 productivity are positively associated with carbon storage and sequestration though substantial differences can exist among c ities within the same region due to urban morphology and human activities. The current set of studies relies heavily on a relatively small comp ilation of equations and growth factors to estimate c arbon (i.e. root to shoot ratio ) and lacks any standardization to account for variations in carbon estimate method or vegetation components included Improving these commonalities, along with expanding t he scope of carbon studies, will provide better information urban carbon storage and sequestration characteristics

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25 Table 2 1. Conversion factors used to standardize global carbon storage and sequestration values. Measurement Type From To Multiplication Factor Area m 2 h a 10 4 Area A cre h a 4 .046x10 Area S quare mile ha 2 58 9x10 2 Area S quare km h a 10 2 Mass lb t 4 .535x10 4 Mass kg t 10 3 Mass Ton (U.S.) t 9 .071x10 1 Mass G iga g ram t 10 3 Mass Teragram t 10 6 Mass Tonne CO 2 t C 2 .729x10 Mass Tonne biomass t C 5x10 1 Energy G igajoules t C 1 .896x10 2 Figure 2 1. Mean carbon storage and sequestration (error bar = standard error) of global urban areas by net primary production (NPP). 0.00 0.20 0.40 0.60 0.80 1.00 1.20 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 0 0.15 0.3 0.45 0.6 0.75 0.9 1.05 Sequestration (tC/ha*yr) Storage (tC/ha) NPP (kgC/m2) Storage (tC/ha) Sequestration (tC/hayr)

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26 Table 2 2 Summary of global urban forest carbon storage and sequestra tion with corresponding author location biomass estimation method, vegetation survey ed NRCS biome and estimated annual net primary production ( NPP ) Author Year Location Method/ Vegetation NRCS Biome NPP (kgC/m 2 ) Storage (tC/ha) Gross Sequestration (tC/ha/yr) Churkina et al. 2010 Conterminous United States PDEN/TV na na 11.21 0.54 Nowak et al. 2002 USA DBH/TT na na 25.10 0.80 Nowak et al. 2006 Casper, WY, USA UFORE /TT D T 0.3 6.28 0.20 Yang et al. 2005 Beijing, China DBH/TT T H/ SA 0.3 7.44 0.38 Chaparro et al. 2009 Barcelona, Spain UFORE /TT T H/ SA 0.3 10.88 0.38 Xiao et al. 2011 Beijing, China BEF AP /TV T H/SA 0.3 13.33 Nowak et al. 2006 Minneapolis, MN, USA UFORE /TT T H 0.45 15.02 0.54 Nowak 1993 Oakland, CA, USA DBH/TT M W 0.6 11.00 Strohbach et al. 2012 Leipzig, Germany DBH/AGT T SA 0.6 11.81 Nowak 1994 Chicago, IL, USA DBH/AGV T H 0.6 14.10 Nowak et al. 2010 Chicago, IL, USA UFORE /TT T H 0.6 14.57 0.76 Nowak et al. 2007 San Francisco, CA, USA UFORE /TT TR SA/M W 0.6 14.80 0.38 Davey Group 2008 Milwaukee, WI, USA UFORE /TT T H 0.6 15.73 0.56 Nowak 1994 Chicago Metropolitan, IL, USA DBH/AGV T H 0.6 16.70 0.94 Smith et al. 2005 Houston, TX, USA UFORE /TT T H 0.6 18.11 Nowak et al. 2002 Syracuse, NY, USA DBH/TT T H/B H 0.6 22.82 0.73 McPherson 1998 Sacramento, CA, USA DBH/TT M W 0.6 31.00 0.92 Davies et al. 2011 Laicester, UK DBH/AGV T SA/ H 0.6 31.60 Warren et al. 2002 Pune City, India VOL/AGT TR SA 0.6 66.68 0.67 Davey Group 2008 Pittsburgh, PA, USA UFORE /TT T H 0.75 1.08 Nowak et al. 2002 Jersey City, NJ, USA UFORE /TT T H 0.75 5.02 0.21 Horn 2012 NE Orlando MSA, FL USA DBH/AGT T H 0.75 9.80 0.65

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27 Table 2 2. Continued Author Year Location Method/ Vegetation NRCS Biome NPP (kgC/m 2 ) Storage (tC/ha) Gross Sequestration (tC/ha/yr) McNeil & Vava 2005 Oakville, ON, Canada UFORE /TT T H/ SA 0.75 13.24 0.60 Nowak et al. 2009 Wilmington, DE, USA UFORE /TT T H 0.75 13.67 0.39 Nowak et al. 2007 Philadelphia, PA, USA UFORE /TT T H 0.75 14.12 0.43 Nowak et al. 2007 New York City, NY, USA UFORE /TT T H 0.75 15.24 0.47 Jo 2002 Chuncheon, Korea DBH/TV T H 0.75 16.21 1.18 Nowak et al. 2009 New Castle, DE, USA UFORE /TT T H 0.75 17.28 0.61 Jo 2002 Seoul, Korea DBH/TV T H 0.75 19.72 Nowak et al. 2002 Boston, MA, USA DBH/TT T H 0.75 20.30 0.67 Jo 2002 Kangleung, Korea DBH/TV T H 0.75 24.00 1.10 Nowak et al. 2002 Baltimore, MD, USA DBH/TT T H 0.75 25.28 0.71 Nowak et al. 2006 Washington DC, USA UFORE /TT T H 0.75 30.04 0.92 Liu et al. 2011 Shenyang, China DBH/TT T H 0.75 33.22 2.84 Nowak et al. 2002 Atlanta, GA, USA DBH/TT T H 0.75 35.74 1.23 Escobedo et al. 2010 Gainesville, FL, USA UFORE /TT T H 0.75 38.40 1.22 Hutyra et al. 2010 Seattle Region, WA, USA DBH/AGT T H/ SA 0.75 89.00 Escobedo et al. 2010 Miami Dade, FL, USA UFORE /TT T H 0.9 9.30 0.95 Zhao et al. 2010 Hangzhou, China VOL/AGT T SA/ H 0.9 23.35 Methods: BEF AP (biomass expansion factors aerial photography), DBH ( DBH allometric equations), PDEN (population density), UFORE (UFORE model), VOL (volume allometric equations) Vegetation: AGT (above ground trees) AGV (above ground vegetation) TT ( whole trees), TV (total vegetation) NRCS Biome: B (boreal), M (Mediterranean) T (temperate), TR (tropical), C (cold), H ( humid), SA (semi arid), W (warm).

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28 CHAPT E R 3 CHANGES IN URBAN FOR EST STRUCTURE AND CO MPOSITION Literature Review Over the last thirty years significant strides have been made in understanding the factors most responsible for driving urban forest structure and composition. The three primary factors are: urban morphology (pattern of infrastructure and building within urban area), natural environment (largely determined by climate and geography), and human actions (intentional management of vegetation) (Sanders 1984). It is important to note that biophysical and human factors influence the socioeconomic and physical properties that determine urban forest structure and function, so there is interplay b etween ecological and social drivers (Alberti et al. 2003). Changes in urban structure and site specific environmental differences create unique urban forest structures (i.e. tree cover) making comparisons between cities in different regions difficult (S anders 1984). This is further complicated by recent changes in the global distribution of urban areas, which have historically been concentrated in Europe and North America, but population increases in Asia Africa and Latin America have changed the densi ty and form of urban areas ( UN 2012 ). In the U.S. for example, urban tree cover a common indicator of urban forest structure, is highest in regions with natural forest s (32%), followed by grassland (18%) and then desert (10%), with an average of 26% an d a range from 1% to 55% (Nowak 1994). Heynen and Lindsey (2003) identified natural environment, geography ( i.e. amount of land), topography ( i.e. amount and degree of slopes, presence of streams), and the proportion of the population with college education as correlates of urban canopy cover. Regional urban coverage for example, is dependent on the surrounding

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29 natural environment and its biophysical limiting factors, while local and site coverage is de pendent on specific landuses due to structural and spatial limitations on vegeta tive cover (Nowak et al. 1996). Other studies have examined landuse and biophysical factors related to urban forest growth and structure. Zhao et al. (2010) reported structura l, compositional and dominance differences between residential and non residential areas of Miami Dade, with higher amounts of exotic trees and richness in residential areas compared to managed natives in non residential areas Urban soils are another key biophysical factor that is altered by landus e and duration of urbanization thus they retain legacy effects from previous landuse s and stabilize after initial urbanization ( Hagan et al. 2012) Urban Forest Structural Dynamics Urban forest growth and mortal ity rates are important factors in predicting future structure and ecosystem service provision. These rates vary between species, landuse, tree condition, tree size, and site location (Nowak et al. 2004) Lawrence et al. (2011) and Staudhammer et al. (201 1) are the only examples of subtropical urban forest studies in the USA, and they do not analyze changes along urbanization gradients nor differences in native, exotic, and invasive species distribution Southeastern forests are additionally subject to str ong stochastic forces, most notably hurricanes, which can double annual mortality rates (Staudhammer et al. 2011) Specific plot and tree characteristics such as % maintained grass, % unmaintained grass, tree height, crown width, and crown light exposure (CLE) have been observed to affect growth of urban trees (Lawrence et al. 2010) Templeton and Putz (2003) observed that these crown characteristics were correlated with growth and survival of Quercus virginiana trees in

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30 urban areas. Some literature (Love tt et al. 2000 ; Gregg et al., 2003 ) suggests that urban influences (i.e. atmospheric pollutants, carbon emissions, stormwater runoff) may increase the growth rate of urban trees by providing CO 2 and nitrogen to the urban environment. Urban Forest Species Diversity Urban areas exhibit significantly higher plant diversity than surrounding natural areas which may impact structure and carbon dynamics (Peltzer, 2009). Unlike most biotic groups, urban plant, and especially tree, diversity is directly controlled by human actions and management (Grimm et al. 2008). Exotic species increase short term richness, but destabilize native ecosystems, leading to long term simplification of the urban environment (Gordon 1998) Urban areas can show high native diversity, including rare and endangered species, though exotic flora may functionally replace native species unable to colonize urban areas (Pickett et al. 2008). Exotic species are a threat to biodiversity globally, but they pose a specific threat to urban areas w here specific synanthropic species are capable of controlling resources, excluding non synanthropic species and homogenizing ecosystems ( Gordon 1998 ; Sho chat et al., 2010 ). Williams et al. (2009) identified three sources for urban flora: native species, r egionally native species capable of colonizing novel environments, and introduced alien species. T o understand how each source for urban flora establishes specific species, four filters are used: habitat transformation, habitat fragmentation (both present in most systems), urban environmental conditions and human preference (both unique to urban systems) (Williams et al. 2009). Horticulture has also been identified as a major pathway for invasive introductions through botanical gardens, nurseries, garden

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31 a nd horticultural clubs, gardening, American Seed Trade Association (ASTA) and intentional establishment for erosion control by United States Soil Conservation Service (including Elaeagnus angustifolia Rosa multiflora and Pueraria Montana var. lobata ) (Re ichard and White 2001). However, l ittle research has been conducted on the spatial distribution of exotic species in urban areas and their functional role within urban ecosystems (Walker et al. 2009 ; Gulezian and Nyberg 2010 ). A study in Chicago found t hat an environmental gradient transect from city core to outlying suburbs found that landuse s were weakly linked to invasive presence and density; however, these results may have b e e n limited due to methodology (Gulezian and Nyberg 2010). In Arizona, Walker et al. ( 2009) observed large, regional scale landuse influences on exotic species distribution, as well as household specific patterns linked to landscaping categories (xeric, oasis, mesic). Due to regional constraints, links may not be compatible between arid, central Arizona and other landuse change and household preferences respectively) may exist. Previous research in central Arizona illustrated si milar overall diversity between urban and desert areas, but found that urban composition changed more rapidly. Past landuse played a role in determining diversity, but urban plant diversity was most strongly correlated with income, a thus was a resource availability to plant resource availability (Hope et al. 2003). In Australia homeowner landscape and gardening preferences for native species were correlated with higher education and open or permeable property boundar ies (Head and Muir 2006). Fruit type was identified as the most important factor in exotic woody plant

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32 distribution, as those species with fleshy fruits are most likely to colonize and potentially become invasive (Aronson et al. 2007). Objectives As stat ed above, there is a lack of information on the role of invasive and exotic species plot and tree characteristics and landuse on tree growth, mortality and in growth rates i n subtropical urban forests. Therefore, the primary objective of this study is to analyze annual growth and mortality rates for comparison to other state, regional and national studies Although Staudhammer et al ( 2011 ) and Lawrence et al ( 2011 ) measured gro w th and mortality in subtr o pcial urban forests, this study is different in th at it will examine the distribution of exotic and invasive sp ecies, and examine growth rate changes along a residential urbanization gradient This study hypothesizes that the urban forest structure will change along a gradient from low density residentia l (which includes pasture) to medium density residential to mixed use urban (institutional, commercial and high density residential); these changes will incl ude a decrease in tree density, change in diameter breast height (dbh) class distribution, and change in growth type distribution (alpha < 0.05). An additional hypothesis is that annual tree growth and mortality will be associated with specific tree and plot characteristics, specifically tree crown characteristics, including height, condition (i.e. % dieback), crown light exposure (CLE), crown height and crown to total height ratio (alpha < 0.05). Finally a third h ypothesis is that there will be a significant difference in the d istribution and annual growth rate between nat ive, exotic and exotic invasive tree species (alpha < 0.05). Spatially, exotic species are expected to increase in density and

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33 proportion of the sample from low density residential to medium density residential to high density residential, while total tree density declines. Methodology Study Area The NE Orlando MSA, Florida metropolitan area (Orlando Kissimmee Sanford Metropolitan Statistical Area ; MSA ) has an area of 10,400 km 2 and an estimated population of 2.2 million, spanning Orange, Seminole, Flagler, and Palm counties. It is the 26th largest and the 6th fastest growing MSA in the United States increasing by 29.8% between 2000 and 2010 (U.S. Census 2011). The University of Central Florida (UCF) is located in the northeast Orlando MSA and was the cente r of the study area. The study was located in the covariance sampling area for estimating carbon, water, and energy fluxes over an area of 200 km 2 located at W This area encompassed moderate aged t o recent urban and suburban development, as well as undeveloped areas in both Orange and Seminole counties. It has a subtropical humid climate, receives 1120 mm of rainfall annually and annually has 2.0 days with minimum temperatures below freezing (NOAA 2 012). Eastern Orange County Southern Seminole County, Florida will hereafter be identified as the NE Orlando MSA. Plot and Urban Forest Measurements To accurately determine tree growth, mortality and in growth forty three 0.04 ha plots from a 2009 sample were selected for re sampling. These plots were located on residential, agricultural, forest, shrub/prairie, and transportation landuse classification s ment District based on 2004 natural and infrared color aerial photography ( SJRWMD, 2004; Figure 3 1; Table 3 3) The

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34 original plots were selected using a stratified random sample and Tools in ArcGis 9.3. F orest plots were previously measur ed in the study area as well, but these were completed early in 2011, and it was determined that no noticeable changes in the forest would be observable. Permission to access the plot was denied at three plots, and no permission was granted (i.e. unable to contact property owner) at an additional three plots. Thus, d ue to time, funding, and accessibility limitations, only forty three of the originally measured sixty nine plots were sampled (Figure 3 2) Sampling occurred between 28 May 2011 and 19 June 2011 To improve the sample size s the selected six landuse classes were aggregated into three categories based on urban form and land cover observations: low density residential (LDR : Figure 3 3 ), medium density residential (MDR ; Figure 3 4 ), and high density residential/ mixed use urban (MUU ; Figure 3 5 ). Low density residential was expanded to included shrub/prairie and mixed use urban was created to include institutional, high density residential, and commercial. Appraised value, c onstruction and sale date data for buildings on parcels that contained the majority of a plot were also gathered from the Seminole and Orange County P roperty A ( OCPA 2012; SCPA 2012 ) The l and use categories used in this study were, low density residential (6 plots) include s all areas with between 0.5 and 2 dwellings per acre as well as open areas and forests ; m edium density residential (21 plots) include s all areas with bet ween 2 and 5 dwellings per acre, and is more distinctly committed to residences even th ough fores t and open areas may be present; high density residential (12 plots) includes all areas with more than 5 dwellings per acre, regardless of the type of dwelling, which may be single family, multi family, duplexes and mobile home parks and may also occur on another landuse, such as barracks or dormitories on institutional landuses ;

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35 i nstitutional (1 plot) is a general landuse which includes uses that would be diffi cult to individually identify, and i ncludes in this landuse are uses such as military, educational, religious, medical, governmental, and correctional; c ommercial (1 plot) includes retail, professional, cultural, and tourism among other uses and is identifiable by convenient parking structures for patrons a nd a lack of natural vegetation; p r airie/ p asture (2 plots ) includes both improved and unimproved pastures, which are both identifiable by the almost ubiquitous covering of grass; however, this can range from intensely managed, uniform pastures, to unmanaged, native grass mixtures The urba n forest sampling procedure followed methods established in the UFORE model developed by the U nited S tates F orest S ervice (Nowak et al. 2010). This methodology uses randomly located circular plots to estimate the urban forest structure, function and services of a larger study area. At each plot, data were collected on the tree, shrub, and landscape characteristics (a complete field data s heet can be found in Appendix A ). Ground cover estimates as a % of the plot area were made for building, cement, tar (roadways), other impervious (i.e. electrical box or manhole cover), pervious rock (i.e. gravel), bare soil, duff and mulch, water, herbace ous and ivy (i.e. vegetative cover that is not grass or shrub), and maintained and unmaintained grass (i.e. grass over that shows no signs of maintenance). Overstory and surface cover estimates as a % of the plot area were made for trees, palms, woody shru bs (less than 2.54 cm at diameter breast height), palm shrubs (less than 2.54 cm at dbh ), and plantable space (i.e. space not currently under tree or palm coverage that could support tree or palm establishment ). Tree data were collected for all woody peren nials with a dbh greater than 2.54 cm (1 inch), and included the following information: species (i dentified using USDA plant code; USDA 2012), number of stems, dbh of each stem (cm), total height (m), height to

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36 crown base (m), crown width (in both N orth S o uth and E ast W est directions; m), distance and direction to two story or smaller residential buildings (m) crown light exposure (CLE) rating (0 5, with zero indicating crown shaded on top and all four sides and five indicating that the top and all four si des are exposed to direct sunlight) and % dieback and % missing to determine tree condition. The distance and direction to residential buildings was measured for trees and palms greater than 6m tall within 16m of the building. Individual Tree Matching Plot s from 2009 were matched using aerial imagery to locate the general plot area, then previous measurements to two permanent structures were used to locate exact plot center. The 2009 and 2011 sample data were combined and indivi dual trees were matched if th ey were the same species and the same distance and direction from plot center as both years Mortality was the absence of a previously measured tree which was downed or removed from the plot since the 2009 sample. In growth was the presence of a tree in 20 11 that was not record ed in the 2009 sample, indicating a new planting or natural in growth (i.e. that a shrub grew above dbh threshold of 2.54 cm). There was some difficulty matching trees at particular plots, especially regarding those that were clustere d in a small area (thus having same distance and direction) and of similar size to one another. Trees with multiple stems at dbh were combined into a single dbh which was used for calculating growth. Measurements of tree dbh over time can differ from actu al tree growth due to a number of causes, including measurement error and specific physiological changes ( Avery and Burkhart, 1983 ) Despite using a tape measure set to 1.37 m and fitting the dbh tape tightly around the tree, there are potential sources of error that could not be

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37 controlled. First, the initial sampling crew may not have meticulously set dbh at 1.37 m or pulled the dbh tape tightly around the tree. Furthermore, the UFORE manual set s out specific procedures to follow in unusual situations (i.e. sloped ground surface, tree fork at dbh ) that may not have been properly followed. Any deviation from these standard procedures would limit that accuracy of repeat measurements. Aside from mea surement error, the tree may undergo physiologic changes, such as bark and trunk contraction under severe water stress, that cause dbh to decrease ( Pasture et al., 2007 ) Growth and Mortality Rates Growth rates were calculated for each species, size class, and species class as o utlined by Lawrence et al. (2012 ). An annual growth rate was calculated for each tree by subtracting the 2009 dbh from the 2011 dbh (providing a net growth) and dividing by the length of time (i.e. years and months) between measureme nts. For any tree with a negative or no change in dbh the growth rate was set to 0.0 cm/yr as done by Lawrence et al (2012 ). Mortality rates were calculated following Nowak et al. (2004), Lawrence et al. (2012 ) and Sheil et al. (1995), where m is annual mortality rate, t is the time between samples, N 1 and N 0 are the initial an d final sample sizes (Equation 3 1). m = 1 ( N 1 / N 0 ) 1/ t (3 1) Species Groups Urban forests frequently exhibit higher species diversity than surrounding natural areas ( Wal ker 2009 ), but reporting on individual species may be inadequate for a study such as this due to small sample size. This requires data be summarized into relevant species groups. Trees were grouped by species, into dbh size classes (0 7.5, 7.6 15.2, 15.3 3 0.5, 30.6 45.6, 45.7 61.0, 61.1 76.1, >76.1; cm), by tree type (palm, hardwood

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38 tree, softwood tree) and lastly by native exotic, and invasive status The USDA classifies plants as either native or exotic to each state, the conterminous United States, and the entire United States (USDA, 2012) Following the USDA, i nv asive status was defined using t he Florida Exotic Pest Plant Council (FLEPPC 2011) designation of two levels of invasive species within Florida : 1) indicates that the species is disrupting ecos ystem function and displacing native species, and 2) indicates that the species is increasing in abundance but is not yet substantially altering native ecosystems. For this 1 and 0 (i.e. those species without a FLEPPC designation) includes both native species and exotic s pecies with limited or no impact on natural systems. Statistical A nalyses All statistical analyses were performed using Mic built in functions or those available with th e Pop Tools add in. Tree growth differences in the trees test (p < 0.05); this includes analyses of species groups (i.e. native/ exotic, dbh classes) within that sample. Differences between the entire 2009 and 2011 samples as well as other un test. Correlations were established of correlations was determined using a linear regression analysis F test. Results Urban Forest Composition and Structure In 2011 the mean tree height was 7.4 m, mean dbh was 20.5 cm and mean crown width was 5.1 m. From 2009 to 2011 (an average of 1.9 years between measurements)

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39 the av erage annual mortality was 5. 9 % and average annual diameter growth was 0.82 cm/yr. There was also a net increase in the number of trees in Orlando, and corresponding increases in tree density (trees/ha) and total basal area (m 2 ) Medium density residential areas had the largest increase in trees per hectare, but experienced the largest decrease in total basa l area. Alternatively, mixed use urban areas had the greatest loss in tree density and the largest increase in total basal area ( Table 3 2 ) A total of 174 trees were measured and t he sample included forty one different tree species, of which there were tw enty four native trees, seven exotic trees, two native palms, and eight exotic palms. In total 28% of the trees and pal ms sampled were exotic species and 72% were native species T he tree sample was dominated by native species, while the palm sample was do minated by exotic species. Of the exotic species only 7% of the tree s and 5% of the palm s sample d have been designated as invasive by Florida E x ot ic Pest Control Council The prevalence of USDA designated exotic species increase s from low density residenti al to medium density residential to mixed use urban accounting for 11.6%, 30.5% and 35.4% of the total tree population of each landuse respectively ; the density of USDA exotic species also increased from 15.8 to 29.8 to 30.3 trees/ha while the total densi ty of trees and palms (Table 3 4 ) decreased, which supports the third hypothesis. The proportion of both exotic and exotic invasive species was highest on mixed use urban. The proportion of non invasive exotic species was also highest on mixed use urban, where only 35% of the exotic population was invasive; on low density residential properties all of the exotic species not only invasive, but were highly invasive.

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40 Tree density (trees/ha) increased in the study area between 2009 and 2011 by 2.4%, but decrea sed on mixed use urban by 4.1%. Tree and palm density decreased from low density residential to medium density residential to mixed use urban though there was no significant difference (p > 0.10) in the density of all dbh classes on the three landuses Pa lm density increased along the gradient (low density residential to medium density residential to mixed use urban) from 7% to 18% to 28% of the total tree and palm sample The mixed use urban 0 7.5 dbh class was significantly different than both low densit y residential (p < 0.05) and medium density residential (p < 0.10) of the same size class. There was also a difference between mixed use urban and medium density residential in the 30.5 45.6 dbh class (p < 0.10). Among softwood trees there was a significan t difference (p < 0.10 ) in density on mixed use urban compared to both low density residential and medium density residential Growth and Mortality There were a total of 150 trees and palms that were matched from the 2009 sample to obtain a mean annual growth rate of 0 .8 2 0.09 cm/yr and annual mortality rate of 5.9%. Both mortality and in growth (either natural or plantings) was concentrated in the l owest three size classes Annual growth was highest in both low density residential and mixed use urban, w ith each being significantly greater than medium density residential (Table 3 6 ) There was a significant correlation between tree dbh and annual growth rate ( Table 3 7 ; r = 0.18, p < 0.05). Plot factors significantly correlated to annual growth rate were % bare soil ( p < 0.05) % tree cover ( p < 0.01) % tree and palm cover ( p < 0.01), and % herb and ivy cover ( p < 0.10). % palm shrub cover was the only factor correlated to annual mortality rates ( p < 0.01). Annual growth was also correlated with crown width (p < 0.01), tree condition (p < 0.05), % crown missing (p <

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41 0.05), crown light exposure (p < 0.01), and crown height to total tree height ratio (p < 0.05). Mortality and in growth rates were similar in b oth low and medium density residential areas, with the mortality rate increasing in mixed use urban and the in growth rate decreasing. Annual mortality was especially high on those plots with recently constructed (less than ten years old) building structu res and those parcels that were sold in the decade prior to the re measurement sample. These two categories accounted for 53% and 53% of the total mortality respectively, and had annual mortality rates of 15.4% and 12.5%. Discussion The results indicate t hat natural in growth or plantings were more prevalent on medium density residential than either low density residential or mixed use urban, while on mixed use urban tree removal outpaced in growth and planting. The contrast between tree density and total basal area suggests that increases in total basal area were primarily attributable to the growth of existing trees rather than in growth or plantings, which is comparable to what Lawrence (2010) reported in Gainesville, Florida. Differences in tree density and composition between landuses supports previous research that urban morphology (i.e. density and type of infrastructure) and direct human actions (i.e. selection of certain species) affect the structure and composition of urban forests (Sanders 1984; Nowak 2004). These results complement previous studies on exotic and invasive species (Walker et al., 2009 ; Gulezian and Nyberg, 2010 ) by analyzing only trees and palms, and make a strong case for the importance of distinguishing between exotic and invasi ve tree species in urban areas. A study in Chicago predicted that invasive plant

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42 species density would increase along a gradient from peri urban to densely urban but no such trend was found and is similar to that observed in NE Orlando MSA, Florida (Gulez ian and Nyberg, 2010). Walker et al. (2009) reported an increase in the total plant species pool along a gradient from natural to heavy urban, primarily due to the appearance of exotic species. A key distinction between these previous studies and the curre nt one is that each of them included natural or semi natural areas as part of the gradient, while this study specifically excluded them (discussed in methods). The urban forest in the NE Orlando MSA showed growth and mortality rates similar to those record ed in other Florida, southeast U.S. and eastern U.S. cities (Lawrence 2011; Staudhammer et al. 2011, Nowak 2004). Despite being in an area experiencing high development pressures, with over 25% of buildings on sampled plots less than 10 years old, the urba n forest shows stability similar to other regional and national areas (Ta ble 3 6 ). This sample was unique in that annual growth rates increased as the dbh size classes increased (p < 0.05), which may be due to the change of species distribution between the dbh size classes and small sample size of some of the dbh classes; Staudhammer et al. (2011) reported highest growth rates in smaller size classes in Houston, Texas (7.7 15.2 and 15 .3 30.5 cm dbh classes), and Lawrence et al. (2012 ) reported sporadic growth rates between size classes, but a general trend toward an inverse relationship between dbh and annual growth (Table 3 7 ; Nowak, 1994; Nowak et al., 2004; Staudhammer et al., 2011; Lawrence et al., 2012 ). Annual growth rates were comparable between low density residential and mixed use urban, but medium density residential was significantly lower (p < 0.05). Bare soil, plot tree cover, crown width, tree condition, % of crown missing and crown light

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4 3 exposure (CLE) were all significantly (p < 0.05) correlated to annual growth. This further confirms what Lawrence et al. (2012 ) reported, who additionally reported total height as an important factor. Mortality rates for NE Orlando MSA Fl orida followed the same p attern as other studies (Table 3 8 ; Staudhammer et al., 2011; Lawrence et al., 2012 ), with mortality decreasing from the small er dbh size classes to the medium dbh size classes (Nowak 2004 ; Staudhammer et al., 2011; Lawrence et al ., 2012 ) ; however, this study reported no mortality among the largest two size classes recorded, while both Lawrence et al. (2012 ) and Nowak et al. (2004) observed a rise in mortality among the larger size classes (Table 3 9; Nowak et al., 2004; Staudhamme r et al., 2011; Lawrence et al., 2012) This may be the result of several factors: the limited sample size ( 33 total trees among the 30.6 45.7 and 45.8 61.0 size classes) and the lack of trees in the 2 largest dbh size classes used by Nowak (2004). These results are limited by the relatively small (n=43) plot sample size, incomplete sample (n=69 plots available), and exclusion of forested plots. Though a complete sample of the original plots was not obtained, based on land cover these plots are representat ive of the selected land uses in the entire study area By excluding the forested plots this study provides growth and mortality rates specific to the built human environment, which may be useful for managers concerned only with non natural urban forests, but may not be representative of the entire urban area, which includes natural areas. As discussed in the methods, there are a variety of factors capable of affecting dbh measurements; a more accurate assessment of dbh change may have been obtained with a longer period between measurements, which would allow errors to be

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44 averaged over a longer time and would diminish their impact on the calculated annual growth rate. Tree growth and mortality was related to specific plot and tree crown characteristics, sugg esting that management should gravitate toward maximizing those characteristics to promote tree growth Residential l anduse density impacted in growth and mortality, which indicates that the tree population in dense urban areas is susceptible to urban fore st loss as trees are removed without replacements Findings from this study show that t he distribution of exotic species increased in more concentrated urba n areas, but invasive species w ere most prevalent in low density areas. This may indicate a differe nce in active and passive plant selection between landuses; mixed use urban plots may select exotic species intentionally for aesthetic purposes, while low density residential plots may select for invasive s pecies by including disturbed or remnant natural areas in which invasive s pecies occur (Gulezian and Nyberg, 2009) Growth differences between species and specific plot and tree characteristics can be used by land managers to improve tree growth rates and reduce mortality Additionally, these results can help managers predict the future structure of NE Orlando growth and annual growth rates, as well as estimate potential changes in species prevalence and dominance associated with those distributions Exotic and invasive species can make a sizeable contribution to the structure and composition of the urban forest, but pose risks to natural ecosystems, and must be managed accordingly Along with othe r studies ( Staudhammer et al., 2011 ; Lawrence et al., 2012 ) these results can be

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45 employed to improve growth estimates for the southeastern USA used by the UFORE model These data on urban forest structure and composition are critical for managers seeking to understand current and a ctively partition future ecosystem services. Figure 3 1 Land cover imagery of east Orlando Florida study area with original 2009 plots depicted

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46 Figure 3 2 Google Earth aerial imagery displaying plots within NE Orlando MSA, Florida S ampled (white), permission denied (red), and permission not obt ained (orange) are depicted.

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47 Figure 3 3. An example of low density residential landuse in NE Orlando MSA Florida (photograph courtesy of author).

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48 Figure 3 4. An example of medium density residential landuse in NE Orlando MSA Florida (photograph courtesy of author)

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49 Figure 3 5. An example of mixed use urban land use in NE Orlando MSA Florida (photograph courtesy of author)

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50 Table 3 1 Number of plots (n), % surface and aerial coverage of select land covers by landuse in NE Orlando MSA Florida. Landuse n Total Impervious s Maintained Grass s Tree o Palm o Shrub o Plantable Space o LDR 8 16.3 7.4 60.5 5.9 23.5 10.4 1.4 0.9 1.9 0.9 47.0 9.3 MDR 21 45.3 4.3 43.3 3.8 15.4 3.5 2.1 0.8 2.6 0.8 33.6 3.5 MUU 14 46.5 7.2 35.6 7.5 13.4 5.5 1.7 0.7 4.8 0.8 35.3 7.3 Total 43 40.3 9.3 44.0 3.4 16.3 3.1 1.8 0.5 3.2 0.5 36.6 3.4 o Ov er story s Surface cover Table 3 2. 2011 tree density and total basal area by landuse with corresponding % change between 2009 and 2011 samples in NE Orlando MSA, Florida. Landuse Tree Density (trees/ha) % Change Total Basal Area ( m 2 /ha) % Change LDR 134.4 37.4 +4.9 7.5 3.7 +1.6 MDR 100.0 20.9 +5.1 3.9 0.9 1.9 MUU 85.7 31.6 5.9 4.6 1.6 +23.3 Total 101.2 15.9 +1.8 4.8 0.9 +6.0 Table 3 3 Tree and palm density (tree/ha) according to dbh size class distribution among landuse types and NE Orlando MSA Florida study area. Dbh Class (cm) Residential low Residential Medium Mixed use Urban Total 0 7.6 15.66.6 (5) 22.612.8 (19) 00 (0) 146.5 (24) 7.7 15.2 34.48.1 (11) 34.59.2 (28) 39.320.8 (21) 368.1 (60) 15.3 30.5 5017.7 (16) 319.3 (26) 21.48.2 (14) 31.46.2 (54) 30.6 45.6 2518.3 (8) 63.4 (5) 23.28.1 (13) 15.14.6 (26) 45.7 61.0 9.46.6 (3) 62.9 (5) 3.62.4 (2) 5.82 .0 (10) Total 134.437.4 (43) 100 .0 20.9 (83) 87.531.6 (48) 101 2 15.9 (174) Table 3 4 Tree and palm density (trees/ha) distribution by landuse in NE Orlando MSA Florida. Residential Low Residential Medium Mixed use Urban Trees All 125 .0 34.1 (40) 79.820.3 (83) 60.733.2 (34) Hardwood 90.627.9 (29) 70.219.8 (59) 60.733.2 (34) Softwood 34.420.6 (11) 9.55 .0 (8) (0) Palms All 9.46.6 (3) 17.96.6 (15) 25 .0 10.1 (14)

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51 Table 3 5 Annual growth rate (AGR ; cm/yr ) based on landuse and tree classification in NE Orlando MSA, Florida (sample size in parentheses) Tree Category LDR MDR MUU Trees and Palms 0.89 0.15 (39) 0.69 0.14 (70) 1.00 0.18 (42) Trees All 0.96 0.16 (36) 0.72 0.12 (56) 1.07 0.19 (32) Hardwood 1.22 0.18 (27) 0.74 0.14 (51) 1.07 0.19 (32) Softwood 0.23 0.12 (9) 0.49 0.15 (5) (0) Palms All (3) 0.56 0.49 (14) 0.75 0.46 (10) Table 3 6 The relationship between tree and palm dbh size class annual growth rate ( cm/yr ) in NE Orlando MSA Florida. DBH Class Trees and Palms Trees Palms All Hardwood Softwood 0 7.5 0.39 0.12 (17) 0.39 0.12 (17) 0.39 0.12 (17) (0) (0) 7.6 15.2 0.77 0.13 (50) 0.86 0.15 (42) 0.88 0.15 (41) 0.21 (1) 0.29 0.22 (8) 15.3 30.5 0.73 0.15 (51) 0.94 0.18 (39) 1.05 0.21 (32) 0.40 0.12 (7) 0.10 0.05 (12) 30.6 45.5 1.22 0.36 (23) 1.01 0.25 (16) 1.33 0.32 (11) 0.30 0.21 (5) 1.69 0.75 (7) 45.6 61.0 1.16 0.29 (10) 1.16 0.28 (10) 1.28 0.29 (9) (1) (0) Total 0.81 0.09 (151) 0.87 0.09 (124) 0.94 0.10 (110) 0.32 0.10 (14) 0.57 0.30 (27) Table 3 7 Tree average annual growth and tree mortality rates from 2009 to 2011 in NE Orlando MSA Florida and other relevant state, regional and national studies. Orlando, Florida Gainesville, Florida Houston, Texas Chicago, Illinois Baltimore, Maryland Growth Rate cm/yr 0.82 0.09 0. 9 0 0.83 0.87 0.63 Mortality Rate % 5.9 0.7 4.7 5.0 6.6 Includes non residential and non urban landuses

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52 Table 3 8 Average annual growth for all trees re measured between 2009 and 2011 in NE Orlando MSA Florida, as well as other state and regional values. DBH Class (cm) Orlando, Florida (cm/yr) Houston, Texas (cm/yr) Gainesville, Florida (cm/yr) 0.0 7.6 0.39 0.13 (17) 0.86 7.7 15.2 0.86 0.15 (50) 1.01 1.11 15.3 30.5 0.86 0.16 (51) 1.03 1.03 30.6 45.7 1.01 0.25 (23) 0.43 0.91 45.7 61.0 1.16 0.27 (10) 0.62 2.17 61.1 76.2 (0) 0.47 0.69 > 76.3 (0) 1.36 Includes non residential and non urban landuses Table 3 9 Annual mortality rate for all trees re measured between 2009 and 2011 in NE Orlando MSA Florida, as well as other state regional, and national values. DBH Class (cm) Orlando, Florida (%) Gainesville, Florida (%) Houston, Texas (%) Baltimore, Maryland (%) 0.0 7.6 11.1 18 9.0 7.7 15.2 7.4 9.7 12.0 6.4 15.3 30.5 6.2 3.4 5.1 4.3 30.6 45.7 0.0 1.0 6.8 0.5 45.7 61.0 0.0 5.7 6.7 3.3 61.1 76.2 7.7 4.8 1.8 > 76.3 3.1 Includes non residential and non urban landuses

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53 CHAPTER 4 URBAN FOREST CARBON STORAGE, SEQUES TRATION, AND MAINTEN ANCE EMISSI ONS Literature Review Urban Forest Carbon Dynamics As the public and scientists continue to recognize and observe the consequences of climate change carbon dynamics play a greater role when considering urban forests Analyses of u rban carbon storage and sequestration have been performed throughout much of the United States and in many international locations, primarily Europe and southeast Asia (Nowak et al. 1993, 1994, 2002, 2004, 2006, 2007, 2010; Jo 2002; Warren et al. 2002 ; Yang et al. 2005; Chaparro et al. 2009; Escobedo et al. 2010; Hutrya et al. 2010; Zhao et al. 2010; Davies et al. 2011; Liu et al. 2011; Xiao et a l. 2011 ) Using biomass equations, UFORE model and other methods, studies have found u rban forest c arbon storage to range from 5.0 tC/ha in Jersey City, NJ (Nowak 2006) to 89.0 tC/ha in Seattle, WA (Hutrya et al. 2010); variation among cities within a single state can also be substantial, as Florida cities rang e from 9.3 tC/ha in Miami Dade (Escobedo et al. 2010) to 63.0 tC/ha in Gainesville (Timilsina et al. In Press ). Carbon storage has been shown to decrease along a gradient from natural areas to h igh density urban areas (Hutrya et al., 2010), and change spatial distribution between landuses (Zhao et al. 2010). H owever, few of these studies directly measured changes in urban forest carbon storage or examined the carbon inputs from urban forest maintenance Forecasts examining long term carbon dynamics of urban areas suggest that tree disposal methods :

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54 decomposition eventually releases all of the stored carbon into the atmospher e, placing the tree in a landfill results in a mix of carbon release and indefinite carbon storage, and placing the tree in a permanent structure (i.e. furniture or house) may store the carbon into the foreseeable future (Nowak et al., 2002). Annual produc tion and accumulation of woody debris in urban areas can also be quite high and represents a distinct potential source of carbon emissions (Hutrya et al. 2010 ; Timilsina et al., In Press ). In addition, to appropriately estimate the net benefit of urban f orests, studies must account for the total net carbon inputs. Tree, shrub and lawn maintenance activities can add carbon emissions that diminish the overall carbon stored and sequestered by trees (Jo and McPherso n 1995; Nowak et al., 2002). Jo and McPhers on (1995) estimated that tree and shrub pruning has minimal carbon emissions (6% of annual sequestration), but maintained grass is a net source of atmospheric carbon, emittin g 144% of annual sequestration Depending on maintenance intensity and urban fores t growth urban park maintenance could produce 15.4 to 28.3 kgC/ha/yr in emissions (Strohbach et al. 2012). Landuse can also play an important role in which activities generate the majority of emissions; Jo and McPherson (1995) identified grass maintenance as the largest producer of emissions on residential properties, while Strohbach et al. (2012) estimated disposal of removed trees as the largest producer in a proposed urban park Nowak (2002) predicted that tree maintenance, removals, and decomposition could make the urban forest a net producer of carbon if energy effects are ignored Local sequestration and total local emissi ons are rarely comparable, as total urban emissions dwarf the amount of carbon sequestered by the urban forest ( Jo, 2002; Escobedo et al., 2010; Strohbach et al. 2012 ).

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55 The literature on carbon sequestration and storage of urban forests is extensive, but has rarely included the role of site specific tree maintenance related carbon emission and th e role species diversity and nativity may have on carbon dynamics Plot level measurements and information on human management systems and equipment use would all In addition u rban areas exhibit significantly higher plant diversity than surrounding natural areas primarily through the introduction of exotic species. So, t hese species are capable of functionally r eplacing native species, which may impact patterns of carbon storage and sequestration in urban areas (Pickett et al. 2008). Much of the research on exotic and invasive species in urban areas has focused on spatial distribution, with little on the functio nal role those species may play within the urban environment (Walker et al. 2009 ; Gulezian and Nyberg, 2010 ). Objectives The objective of this study is to determine temporal changes in urban forest carbon storage, identify associated tree crown and plot surface and overstory cover characteristics, and describe a net carbon balance that accounts for tree related carbon sources and sinks Spatial d istribution and carbon dynamic variation between native, exotic, and exotic invasive species will also be exami ned. This study will directly measure the change in tree carbon at the plot level, accounting for demographic, climat e socioeconomic and other human and natural influences rather than rely on estimates and assumptions from other studies. In addition to m easuring specific sequestration rates, this study will also quantify carbon emissions associated with maintaining the urban forest of NE Orlando MSA via the use of a residential survey, thus permitting the calculation of a net carbon impact for each plot and landuse.

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56 I hypothesize that urban forest structure and carbon dynamics will change along a gradient from low density residential (which includes pasture) to medium density residential to mixed use urban (institutional, commercial and high density resid ential); these changes will include a decrease in carbon storage and sequestration and an increase in carbon emissions (alpha < 0.05). A second hypothesis is that carbon storage and carbon sequestration will be associated with specific tree and plot charac teristics, specifically tree crown characteristics, including height, condition (i.e. % dieback), crown light exposure (CLE), crown height and crown to total height ratio (alpha < 0.05). Thirdly I h ypothesi ze that there will be a significant difference i n the carbon storage and sequestration between native, exotic and exotic invasive species (alpha < 0.05). Spatially, exotic species are expected to increase in density and proportion of the sample from low density residential to medium density residential to high density residential, while total tree density declines. Finally I h ypothesize that the urban forest will have a positive net carbon balance, with sequestration and in growth being greater than tree related maintenance emissions and mortality Res ults from this study can be used to u nderstand how current urban forest structure and composition will drive long term carbon dynamics can be used by planners and land managers as a tool for managing urban forests with the goal of reducing atmospheric carb on emissions and maximizing homeowner benefits.

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57 Methodology Study Area The NE Orlando MSA, Florida metropolitan area (Orlando Kissimmee Sanford Metropolitan Statistical Area ; MSA ) has an area of 10,400 km 2 and an estimated population of 2.2 million, spanning Orange, Seminole, Flagler, and Palm counties. It is the twenty sixth largest MSA in the United States and is the sixth fastest growing, increasing by 29.8% between 2000 and 2010 (U.S. Census 2011). The University of Central Florida (UCF) is located in the northeast Orlando area and was the center of the study area. The study area was 200 km 2 covariance tower located at W This area encompa ssed moderate aged to recent urban and suburban development, as well as undeveloped areas in both Orange and Seminole counties. It has a subtropical humid climate, receives 1120 mm of rainfall annually and annually has 2.0 days with minimum temperatures be low freezing (NOAA 2012). Orlando MSA Eastern Orange County Southern Seminole County, Florida will be hereafter identified as NE Orlando MSA, Florida. Plot and Urban Forest Measurements Sequestration rates were d etermined using forty three 0.04 ha plots from a 2009 sample that were selected for re sampling. These plots were located on residential, agricultural, forest, shrub/prairie, and transportation landuse classifications developed by the gement District based on 2004 natural and infrared color aerial photography ( SJRWMD, 2004 ) The original sixty nine plots were selected using a stratified random sample and forest plots was previously c onducted in the study area as well, but was completed early

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58 in 2011, and it was determined that no noticeable changes in the forest would be observable. Permission to access the plot was denied at three plots, and no permission was granted (i.e. unable to contact property owner) at an additional three plots. Sampling occurred between 28 May and 19 June 2011. Due to time, funding, and accessibility limitations, only forty three of the sixty nine available plots were sampled.. To improve the plot sample size, the selected six classes were aggregated into three categories based on urban form and land cover observations: low density residential (LDR), medium density residential (MDR), and high density residential/mixed use urban (MUU). Low density residential wa s expanded to included shrub/prairie and mixed use urban was created to include institutional, high density residential, and commercial. Construction and sale dates for buildings on parcels that contained the majority of a plot were gathered from Seminole (OCPA 2012; SCPA 2012) Individual Tree Matching Plots from 2009 were located using aerial imagery to locate the general plot area, then measurements to two permanent structures were used to locate exact pl ot center. The 2009 and 2011 sample data were combined and indivi dual trees were matched if they were the same species and the same distance and direction from plot center as previously Mortality was the absence of a previously measured tree which was dow ned or removed from the plot since the 2009 sample. In growth was the presence of a tree in 2011 that was not record ed in the 2009 sample, indicating new planting or natural in growth (i.e. that a shrub grew above diameter breast height [dbh] threshold of 2.54 cm). There was some difficulty matching trees at particular plots, especially regarding those

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59 that were clustered in a small area (thus having same distance and direction) and of similar size to one another. Measurements of tree dbh over time can differ from actual tree growth due to a number of causes, including measurement error and specific physiological changes. Despite using a tape measure set to 1.37 m and fitting the dbh tape tightly around the tree, there are potential sources of error that could not be controlled. First, the initial sampling crew may not have meticulously set dbh at 1.37 m or pulled the dbh tape tightly around the tree. Furthermore, the UFORE manual sets out specific procedures to follow in unusual situations (i.e sloped ground surface, tree fork at dbh ) that may not have been properly followed. Any deviation from these standard procedures would limit that accuracy of repeat measurements. Changes in dbh may also be caused by physiologic fluctuations, such as bark and trunk contraction under severe water stress. Carbon Storage and Sequestration Calculations Carbon storage was calculated for all trees in the 2009 and 2011 samples using biomass equations compiled from a study in Gainesville Florida (Timilsina et al. In Press ). Allometric biomass equations use a variety of tree data, such as dbh height, species and crown data, to determine biomass. In g eneral studies use eq uations obt a ined directly from the literature, but several, including some of the most common species in this study, such as Quercus virginiana Quercus laurifolia Pinus taeda and Pinus elliotii were calculated from fresh weight above ground equations specifically developed for the Gainesville Florida and north Florida area s making these equat ions region specific for north central Florida (Timilsina et al. In Press ). The Timilsina et al. ( In Press ) equations accounted for 28% of all the individuals sampled, and included three of the four most frequent species. Those species found in Orlando th at were not

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60 included in the Gainesville study were matched to the lowest classification level possible (i.e. genus, family, order, or subclass) and assigned a biomass equation or average biomass from the corresponding Appendix ( Appendix E ) Fresh weight to dry weight and dry weight biomass to carbon was converted by multiplying by a factor of 0.5 in both cases. All of the net sequestration rates represent a period of time ranging from 1.87 to 1.97 years, but sequestration was annualized individually for eac h tree. Net carbon sequestration was calculated by subtracting the 2009 carbon storage value from the re measured 2011 carbon storage value. Annual carbon sequestration was calculated by dividing the net carbon sequestration by the number of years betwe en sampling dates for each tree Carbon release through decomposition of woody debris and removal of limbs was not accounted for in this study. The commonly used UFORE model, as well as other studies, convert above ground biomass into below ground (i.e. root) biomass by multiplying by a factor of 0.26 (Nowak 2010; Escobedo et al., 2010) While this practice is commonly employed to estimate total above and belowground carbon storage, some studies have demonstrated variation from this assumption; Powell et al. (2006) reported that just over 20% of vegetation carbon storage in a scrub oak ecosystem of central Florida was from aboveground woody vegetation while belowground vegetation (i.e. roots) comprised 62% Nowak (1994) also reported that open grown urban tre es stored only 80% of the biomass of forest trees of the same species. Additionally, Jo and McPherson (1995) found that urban soils stored 78% and 88% of total carbon in residential greenspace. Due to variation among calculations and inability to directly measure it, this study excluded belowground carbon, though it is an important component of determining the net carbon footprint.

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61 Species Groups Urban forests frequently exhibit higher species diversity than surrounding natural areas, but reporting on indiv idual species may be inadequate for a study such as this due to small sample size. This requires data be summarized into relevant species groups. For this study the groups included species, dbh size classes (0 7.5, 7.6 15.2, 15.3 30.5, 30.6 45.6, 45.7 61.0 61.1 76.1, >76.1; cm), tree type (palm, hardwood tree, softwood tree) and native exotic or invasive status, though these groupings were often limited by the number of individuals within a class. The Florida Exotic Pest Plant Council (FLEPPC 2011) desig nates two levels of invasive species: 1) indicates that the species is disrupting ecosystem function and displacing native species, and 2) indicates that the species is increasing in abundance but is not yet substantially altering native ecosystems. The US DA classifies plants as either native or exotic to each state, the conterminous United States and the entire United States (USDA, 2012) For this study, FLEPPC 1 and 2 are considered exotic invasive, USDA exotic are exotic, and USDA native are native; FLE PPC 0 (i.e. those species without a FLEPPC designation) includes both native species and exotic s pecies with limited or no impact on natural systems. Residential Vegetation Maintenance Survey To determine plot level emissions generated directly by maintena nce of lawns, gardens, shrubs, and trees a 40 question, two page survey was developed to characterize maintenance practices at each residential parcel within the sampled plots. The two page questionnaire thus quantif ied vegetati on maintenance characteristics at each residential parcel on which the sampled plot was located. The questionnaire was modeled after Sewell et al. (2010) and utilized the Total Design Method (Dillman 1978).

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62 This questionnaire contained seven sections, one for each commo n vegetation maintenance activity with fuel burning equipment that contributes to carbon emissions such as : lawn mower, lawn trimmer, leaf blower, hedge trimmer, chainsaw, fertilizer, and irrigation. Residents identified a frequency (weeks between mainten ance activities ) and duration (hours) for each activity during a summer and winter season, which they designated, accordingly ( Appendix C ) Thirty participants were selected whose parcel contained the majority of one of the randomly located sample plots. F or each of the activities, participants provided the equipment type, frequency, duration, and volume/mass applied to lawn or garden (if applicable). To account for seasonal variation in maintenance practices participants through March) maintenance maintenance season (i.e. April through September), as well as changes in frequency (weeks between activity occurrences or occurrences per year) and duration (hours) of activities. A total of twenty seven su rveys were completed, accounting for a 90% response rate. The additional three residents declined to complete the survey; while unlikely, it is possible that this omission generated a non response bias. Maintenance Emission Calculations Based on the summer and winter season activity durations a total annual usage rate was calculated for each maintenance activity and emissions were calculated using emission factors der ived from Strohbach et al. (2012 ) and Nowak (2002). Using the annual us age rate (y) for each piece of equipment provided at a given property the total annual carbon emissions from that equipment was calculated using emission fac tors (z) derived from Equation 4 1. Annual estimated emissions = y z (4 1)

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63 To generate reas onable estimates of maintenance related carbon emissions, the final values were calculated using an average of the factors reported in Table 4 1 when more than one factor was reported Using the annual emissions for the entire parcel (i.e. all the vegetati on for which a single resident would be responsible), an aerial measurement was made of the total square meters of vegetation on each parcel using calculation of kgC/m 2 The kgC/m 2 emission value was multiplied by 400 ( sample plot area) times the estimated % maintained surface cover at each plot. Urban trees in each plot were assigned all plot scaled emissio ns from chainsaw activity and one third of the emissions from lea f blowers, under the assumptions that chainsaws would only be used on woody vegetation with stems greater than 2.54 cm dbh (thus excluding shrubs), and that one third of leaf blower activity was directed at leaf litter instead of grass clipping removal or other purposes. Due to limited and unclear response data, fertilizer and mulch application emissions were excluded from the total emission calculation. Statistical analyses All statistical analyses were performed using built in functions or those available with the Pop Tools add in. Carbon storage and carbon sequestration differences in the trees matched from 2009 to 2011 were assessed using a paired test (p < 0.05); this includes analyses of species groups (i.e. native/ exotic, dbh classes) within that sample. Differences between the entire 2009 and 2011 samples as well as other un matched samples were assessed using an un icient, and significance was determined using a linear regression analysis F test. Differences in survey response data were compared using an un test in Microsoft

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64 data pack. Means and standard errors were calculated using descri ptive statistics in Microsoft Excel. Results The 150 matched trees stor ed an average of 96.3 13.3 kgC in 2009 and 109.8 15.8 kgC in 2011 an d sequestered an average of 7.5 1.1 kgC annually There were significant differences in the 2009 and 2011 storage val ues amo ng all dbh size classes for trees and for all dbh size classes of trees and palms except the largest class. Softwood trees showed no significant differences between years and palm storage between years was significant only at one dbh size class (30.6 45.5cm) Softwood trees had inadequate samples for three of the five dbh classes, and palms for two of the five dbh classes (Table 3 5) An analysis of plot level factors show that % tree cover (includes only hardwood and softwood trees) % tree an d palm cover, % plantable space, and % duff and mulch we re all correlated to carbon storage and sequestration. Additionally, total impervious surface (the sum of cement, tar, building and other impervious surface) was negatively associated with carbon stor age but % herb and ivy was positively associated with carbon sequestration and weakly correlated with which carbon storage (Table 3 6) There was no significant difference among the mean carbon storage or sequestration between the three landuses. There wer e however, significant differences in mean C storage and sequestration for softwoods between mixed use urban and both other landuses (p < 0.05) ; additional difference were found for mean softwood storage between low density residential and medium density residential and mean hardwood sequestration between low density residential and mixed use urban (p < 0.05) In 2011 low density residential stored 15.4 4.6 tC/ha, medium density residential stored 8.4 3.1

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65 tC/ha and mixed use urban stored 8.6 2.7 tC/ha for a total of 9.8 2.4 tC/ha over the entire study area Annual sequestration ranged from 0.50 0.15 tC/ha/yr on medium density residential to 1.01 0.45 tC/ha/ yr on low density residential, with an average of 0.65 0.14 tC/ha/yr for the entire NE Orlando MSA, Fl orida study area (Table 4 4) There was a significant difference ( p < 0.05) in the carbon stored between exotic species and both non invasive and native species, but no difference between exotic species and inva sive and species Native trees and native pa lms stored and sequestered significantly (p < 0.05) more carbon on average than their exotic counterparts; no such trend existed between non in vasive and invasive species (p > 0.05). Invasive and exotic species had a limited contribution to total sto rage a nd C sequestration (Figure 4 1); n ative trees were responsible for over 90% of the total sequestration, while comprising just two thirds of the population (Figure 4 2). As species, Quercus laurifolia and Q. virginiana each sequestered and stored more carbon than the other thirty nine species combined, while making up just over a fifth of the total population (Table 4 5). Pinus taeda was the only other species to contribute greater than 10% of total carbon storage, and no other species contributed greater than 10% of total carbon sequestration. Approximately 80% of the carbon lost due to mortality was comprised of Quercus laurifolia ; carbon gained due to in growth was comprised of Aralia spinosa, Quercus nigra and Plata nus occidentalis Lawn Maintenance Practices Of the 27 residents surveyed, 2 6 reported mowing their lawn on a regular basis; the one exception was a renter on a property waiting to be re sodded. Residents mowed their lawns an average of 21. 7 1 .7 times each year, and spent a total of 17.0 2.2 hours annually mowing. Those lawns where residents used a lawn service or

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66 riding mower were mowed 25.6 2.8 times annually, while those where residents mowed it themselves were mowed 20.2 2.0 annually. Despite the increa se in mowing frequency, those using lawn service or riding lawn mowers were mowed for just 7.7 0.8 hours annually, which is nearly two thirds less than the 20.57.7 when residents do it themselves. Properties using lawn service or riding mower lawn trimmer s and leaf blowers were used almost as frequently as lawn mowers, 25.53.3 times/yr and 24.23.6 times/yr respectively, though their cumulative use was substantially lower, 6.52.2 hr/yr and 3.90.6 hr/yr respectively. In contrast, on homeowner mowed prope rties lawn trimmers were used 14.52.6 times/yr, and leaf blowers just 10.62.7 times/yr, resulting in 5.41.1 hr/yr and 2.70.8 hr/yr respectively. Only one respondent knew the nutrient mix of the fertilizer applied, and less than half were able to estima te their annual application rate. Maintenance Carbon Emissions The increased horsepower used by riding mowers generated an average of 14.73.7 kgC/yr, while standard gas powered push mowers emitted 4.20.6 kgC/yr. Lawn mowers were responsible for 45 % of t he carbon emissions from vegetative maintenance and irrigation contributed 44 %. The average parcel was 0.26 ha in size (range of 0.05 to 2.1 ha) and on average emitted 13.1 kgC/yr in grass maintenance 0.3 kgC/yr in shrub related maintenance and 0. 3 kgC/yr in tre e related maintenance. In total the parcels averaged 13.6 kg C/yr emissions, ranging from 0.4 kgC/yr to 27.4 kgC/yr. Pearson coefficient and linear regression showed no significant relationship between any plot level maintenance activity and t ree growth, tree mortality, carbon st orage, or carbon sequestration (p > 0.05)

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67 Discussion Counter to the first h ypothesis, mixed use urban had higher carbon storage and sequestration than medium density residential. This may be attributable to greater cro wn width (5.5 m and 4.5 m), tree height (7.2m and 6.0 m) and duff and mulch cover (11.2% and 4.5%) in mixed use urban compared to medium density residential (p < 0.05) Additional characteristics, such as total impervious cover, crown height, and tree cove r were not significantly dif ferent between the landuses (p > 0.05). Total carbon storage increased throughout the entire study area and on each of the three landuses during the analysis period despite the net loss of trees in mixed use urban and decrease in total basal area of mediu m density residential (Table 4 4 ). Carbon storage and sequestration were highest on low density residential, where values were nearly double those of medium density residential and mixed use urban. It is important to note that a ltering classification criteria for the landuses or employing a different stratification method could significantly change the carbon storage and seques tration estimates (Lawrence et al., 2012 ). Carbon storage and sequestration were inversely related to total impervious surface and plantable space (p < 0.05), both of which directly impact or are directly impacted by the amount of tree cover at a site, which carbon dynamics were strongly correlated (r = 0.88, p < 0.01). Individual tree carbon storage and s equestration was positively correlated with crown width (p < 0.01), total height (p< 0.01), and crown height (p < 0.01). This confirms hypothesis 2 that specific plot and tree characteristics are associated with tree growth and carbon dynamics. Despite acc ounting for almost a fifth of the total population, palms contributed a total of 2.6 tC of storage (3.3 kg C per individual palm ), which amounted to less than 1%

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68 of the total C storage. Palm sequestration was marginally higher than storage as a % of the tot al, but still insubstantial Continued gradual replacement of hardwood trees with The dominace of Q. laurifolia and Q. virginiana is likely due to their growth form, social valu e, and increasing proportion as dbh size classes increase ( Templeton and Putz 2003 ); Q. laurifolia and virginiana make up 50% of all trees over 30.6 cm dbh and 60% of trees over 45.6 cm dbh (Table 4 5 ) The above ground tree carbon storage in Orlando was within the range of values found globally (Table 2 2) though it was among the lowest reported at 9.8 2.4 tC/ha; carbon sequestration was also not significantly different (p > 0.05) from the global mean Timilsina et al. ( In Press ) estimated carbon storag e (63 tC/ha) and sequestration (2.4 tC/ha/yr) in Gainesville to be much higher than NE Orlando MSA tree cover has ranged from 55% to 66% since 1995 (Tim ilsina et al., In Press ) while study Although carbon storage in this area differed from nearby Gainesville, it was similar to values reported for Miami Dade, Florida (9.3 tC/ha and 14% tree cover; Escobedo et al., 2010; Zhao et al., 2010) This may indicate that the urban form of NE Orlando MSA limits the number of urban trees, but due to its subtropical location productivity is still high which results in high sequestration. Escobedo et al (2010) found that carbon avoided due to tree shading effects and climate regulation in Miami Dade and Gainesville was 0.11 tC/ha/yr and 0.41 tC/ha/yr, respectively.

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69 Carbon storage by invasive species was highest in low density residential, and lowest in mixed use urban; storage by non invasiv e exotic species was almost non existent, accounting for just 0.6% of total storage, and reaching its highest level at 1.4% on medium density residential. This is counter to hypothesis 3, and may be explained by the type of exotic and invasive species pres ent on each landuse in NE Orlando MSA, Florida ; many of the exotic s pecies found on mixed use urban plots were palms, which have a minimal impact on total carbon dynamics Although Zhao et al. (2010) reported differences in native and exotic tree distribut ion, and Gulezian and Nyberg (2009) and Walker et al. (2010) discussed exotic and invasive plant distribution, they did not examine the role that exotic and invasive trees have in ecosystem services. This study demonstrated a difference in the total carbon storage and sequestration between native and exotic species, even when controlling for tree growth form. All of the residents surveyed reported actively utilizing some vegetati on maintenance practice on a regular basis and 97% reported using a lawn mower at least once a month. Vegetati on maintenance practices vary greatly in residential areas, and most residences mow, irrigate, trim the lawn, leaf blow, fertilize, and trim hedges on a regular basis; chainsaw use and mulching were less common and reported b y less than half of the respondents. The frequency of mowing and fertilizing were similar to values reported in Nebraska according Sewell et al. (2011). The majority of vegetation maintenance carbon emissions were due to grass maintenance similar to what Jo and McPherson (1994) reported for residential areas in Chicago, Illinois Maintenance emissions of carbon related to trees was minimal, accounting for just 5.3 kgC/ha/yr, which is equivalent to 1% of the annual carbon sequestered by the urban

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70 forest, an d is lower than the 6% figure repor ted by Jo and McPherson (1994) Jo and McPherson included shrub maintenance and biowaste removal which was not analyzed in this study. The se same authors also quantified tree and shrub pruning in greater detail by gatheri ng usage rates at intervals throughout the year rather than at one time. Tree related maintenance emissions in NE Orlando MSA, Florida are so low that palm sequestration alone is enough to offset them by 166% annually Nowak et al. ( 2002) found that tree maintenance c arbon emissions were primarily concentrated in tree removal, and that without accounting for energy effects urban trees had a negative net impact on carbon dynamics Using similar methods to this study, Timilsina et al ( In Press ) reported that 2 tC /ha/yr of urban forest green waste were removed in nearby Gainesville, Florida Strohbach et al. (2012) estimated that urban park maintenance co uld produce 15.4 to 28.3 kgC/ha/yr, which includes transportation, maintenance and r emoval A full C life cycle analysis of a planted urban tree carbon footprint would additionally include the necessary carbon inputs during tree nursery production, which can reach 4.8 kgC annually per nursery grown tree (Kendall and McPherson 2011). The rate of carbon emissions decrease d from low density residential to medium density residential before reaching its peak in mixed use urban, which disproves the first hypothesis; however, the amount of sequestered carbon diminished by all maintenance emissio ns (i.e maintenance carbon emissions divided by urban tree sequestration ) increased from 22% to 36% to 57% from low density to medium density residential to mixed use urban respe ctively There was a positive net carbon balance for each landuse and the en tire NE Orlando MSA Florida study area confirming hypothesis 4 (Table 4 7 ). This study indicates that urban trees have a positive net impact on carbon

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71 dynamics of urban ecosystems; Escobedo et al. (2010) found that urban trees were moderately effective at offsetting total urban carbon emissions. To improve carbon sequestration rate estimates this study directly measured changes in growth of NE Orlando M equations compiled for Gainesville, Florida rather than rely on assumed growth rates and equations compiled primarily from the northeastern USA as the UFORE model does. This permitted both more accurate and more precise estimates of carbon storage and carbon sequestration for NE Orlando MSA Florida Further, this study determine d the net carbon impact of urban trees by calculating tree related carbon emissions based on maintenance surveys of residents. The N urban forest had a positive net carbon balance after factoring for tree related maintenance carbon emissions However analyses excluded climate and shading interactions between trees and building energy use as well as removal and eventual decomposition of woody debris So, each of these potential carbon sources, woody debris, limbs, belowground tree carbon, urban tree growth, and soil carbon, represent distinct limitations and sources of error for the carbon estimates reported by this stud y. T hus a complete carbon footprint of urban vegetation, including grass and shrubs, accounting for maintenance avoided carbon plant production, plant removal, and all decomposition would provide detailed information on the net effect urban forests have, and what capacity they may ha ve to offset additional urban carbon. The maintenance emission data could be improved by using identical spatial scales for the plot level from residential reports of parcel level maintenance. These estimates

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72 could also be improved by taking direct measurements of maintenance emissions rather than rely on emission factors reported in the literature and usage rates reported by residents. Specific t ree and plot characteristics linked to carbon storage and sequestration should be considered by managers to maximize the efficacy of urban forest s with regards to carbon. H owever, the land cover characteristics such as total impervious surface, duff and mulch, herb and ivy, and tree cover, described in this study are meaningful only for tree carbon storage and sequestration at the plot level ; a distinct set of those land cover characteristics may be more indicative of individ ual tree storage and sequestration. In conclusion vegetati on maintenance activities and carbon emission data can be used to assist in planning the urban forest structure and maximizing carbon sequestration by locating areas with high emissions and ta king actions to either reduce those emissions or offset them via vege tative establishment and growth. While carbon emissions are generally considered negative in relation to the urban forest, some maintenance activities may improve tree health and increase annual growth and carbon sequestration rates; thus, maintenance activities must be balanced to achieved desired ecosystem services, which includes a suite of additional considerations apart from carbon sequestration, such as stormwater runoff control, wil dlife habitat, pollen allergens, and aesthetics.

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73 Table 4 1. Emission factors and references for all maintenance activities. Maintenance equipment Emission factor (kgC/hr) Reference Gas push mower 0.229 Strohbach et al. 201 2 Gas riding mower 0.995 Strohbach et al. 20 12 Lawn trimmer 0.105 Strohbach et al., 2012 Leaf blower 0.105 Strohbach et al., 2012 Chain saw 0.192 Nowak et al. 2002 Chain saw 0.192 Strohbach et al., 2012 Irrigation 0.039 kgC/ha/yr Jones 2011 Table 4 2 2011 mean carbon storage value (kgC) and significant differences in carbon storage between 2009 and 2011 samples based on tree type and dbh size class in NE Orlando MSA, Florida DBH Size Class (cm) All Trees and Palms Trees (n = 124 ) Hardwood Trees (n = 110 ) Softwood Trees (n = 14 ) Palms (n = 27 ) 0 7.5 2.5*** 2.5*** 2.5*** 7.6 15.2 15.3*** 18.1*** 18.2*** 17.1 N/A 0.6 15.3 30.5 72.6*** 93.3*** 93.2*** 93.4 5.6 30.6 45.6 211.3** 302.3** 328.6** 244.4 3.3** 45.7 61.0 720.5 720.5*** 764.2*** 327.3 N/A All 109.8*** 134.0*** 130.8*** 158.6 3.5 **95% confidence; ***99% confidence; N/A insufficient sample size; no individuals Figure 4 3. Correlation and significance between total impervious surface duff and mulch, herb and ivy, tree cover, tree and palm cover, and plantable space with carbon storage and sequestration in NE Orlando MSA Florida. %TI %DM %HI %TC %TP %PS 2009 C Storage 0.35*** 0.57*** 0.39* 0.87*** 0.87*** 0.39** 2011 C Storage 0.32** 0.58*** 0.40* 0.89*** 0.89*** 0.41** C Sequestration 0.13 0.43*** 0.49*** 0.83*** 0.81*** 0.40*** 90% significance; ** 95% significance; *** 99% significance Total impervious surface (%TI), m ulch (%DM), herb and ivy (%HI), tree cover (%TC), tree and palm cover (%TP), and plantable space (%PS)

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74 Table 4 4. Tree density, carbon storage and carbon sequestration by landuse and study total with corresponding % change from 2009 sample to 2011 sample in NE Orlando MSA Florida. Land Use (# of plots) Density Trees/ha Percent Change in density Storage tC/ha Percent Change in C storage Sequestration tC/ha/yr LDR ( 8 ) 134.4 37.4 2.4% 15.44.6 5.5% 1.010.45 MDR ( 21 ) 100.0 20.9 6.5% 8.43.1 10.8% 0.500.15 MUU ( 14 ) 87.5 31.6 4. 1 % 8.62.7 15.6% 0.680.28 Total (43) 101.2 15.9 2.4% 9.82.4 10.5% 0.650.14 Table 4 5. Top eight species ranked by carbon storage, with corresponding proportion of sample size, and cumulative % of carbon storage and sequestration for NE Orlando MSA Florida. Percent of S ample Percent of Total Carbon Storage Percent of Total Carbon Sequestration Quercus virginiana 9.2 32.9 36.5 Quercus laurifolia 12.1 23.4 33.2 Pinus taeda 6.9 10.6 3.9 Magnolia grandifolia 1.7 6.7 2.3 Quercus nigra 8.0 6.4 4.4 Mealia azardich 3.4 4.1 1.4 Cinnamonum camphora 1.1 2.6 3.0 Pinus palustris 2.3 2.2 0.6 Table 4 6. Annual emission rates from lawn mowers, trimmers, leaf blowers, chainsaws, hedge trimmers, and irrigation for two resident groups in NE Orlando MSA, Florida Resident Groups ( # of respon s es ) Lawn Mower (kgC/yr) Lawn Trimmer (kgC/yr) Leaf Blower (kgC/yr) Chainsaw (kgC/yr) Hedge Trimmer (kgC/yr) Irrigation (kgC/yr) Total (kgC/yr) PM ( 18 ) 4.2 0.6 0.6 0.1 0.3 0.1 0.0 0.0 0.2 0.1 5.3 1.0 10.5 1.1 RM ( 8 ) 14.7 3.7 0.6 0.2 0.5 0.2 0.1 0.1 0.2 0.1 6.0 1.0 22.2 3.8 ** ** *** **Significant at 95% confidence; ***Significant at 99% confidence Using push lawn mowers (PM) Using riding mower or lawn service (RM)

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75 Table 4 7. Net carbon balances across landuse gradient in NE Orlando MSA Florida accounting for sequestration, removal from mortality, addition from in growth and tree related maintenance emissions. Landuse (# of plots) Gross Carbon Sequestration (tC/ha/yr) Carbon Removed due to Mortality (tC/ha/yr) Carbon Added due to In growth (tC/ha/yr) Tree Maintenance related Emissions (tC/ha/yr) Net Carbon Balance (tC/ha/yr) LDR (8) 1.01 0.38 0.03 0.01 0.65 MDR (21) 0.50 0.12 0.09 0.00 0.47 MUU (14) 0.68 0.13 0.06 0.00 0.61 Total (43) 0.65 0.17 0.07 0.01 0.54 Figure 4 1. Relative proportion of the sample and annual sequestration from FLEPPC non invasive (0), moderately invasive (2), and highly invasive (1) palm and tree species in NE Orlando MSA, Florida 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Palm-0 Palm-1 Palm-2 Tree-0 Tree-1 Tree-2 Frequency Sequestration

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76 Figure 4 2. Relative proportion of the sample and annual sequestra tion from USDA designated native (N) and exotic (E) palm and tree species in NE Orlando MSA, Florida 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Tree-N Tree-E Palm-N Palm-E Frequency Sequestration

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77 CHAPTER 5 CONCLUSION Urban forest s have global, regional and local variability, responding to changes in urban morphology, natural environment and human preferences which affect structure, composition, carbon storage, carbon sequestration, and carbon emissions Continued population growth in undeveloped regions of the world will require a subsequent geographic shift in urban forest study priorities Improved urban forest growth and carbon estimation equations, as well as standardizing methods, would permit more reliable comparisons between urban areas. Specific tree and plot traits were associated with the urban forest processes discussed in this study. Characteristics such as % tree cover, % duff and mulch, tree crown width, total tree height, and tree crown height all influence d dbh annual growth rates, and subsequently tree carb on storage and sequestration. Carbon emissions from tree related vegetative maintenance ha d a minimal effect on the overall net carbon balance of the urban forest, and may have a net positive effect by improving tree health and encouraging more effective g rowth characteristics (Nowak et al., 2004) Conversely, grass maintenance is responsible for the bulk of vegetation maintenance carbon emissions, while grass provides little carbon storage or sequestration. Efforts to improve the carbon footprint of urban forests with regards to maintenance should focus on changing lawn mowing and irrigation practices. Including analyses of carbon related inputs and outputs related to removal of woody debris and green waste and shade and climate regulation effects on buildi ng energy use could significantly change the net carbon dynamics of urban areas, but these analyses were beyond the scope of this study. It is likely that including the carbon

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78 avoided by tree building interaction would positively impact the current carbon dynamics as research and modeling have shown ( Simpson and McPherson 1999; Nowak et al., 200 2 ) Urban forests provide a diverse range of ecosystem services to the local and global population, so it is important to maximize those services by managing tree s appropriately Selecting species capable of sequestering carbon and optimally providing residential climate regulation is an important task toward increasing the services provided and minimizing the associated disservices Quercus virginiana and Quercus laurifolia both demonstrated high sequestration rates per individual and as species, though Quercus laurifolia has a more limited net effect due to its removal rate (most frequently removed species in sample) With proper management the urban forest can p lay an important role in improving the quality of life for citizens and toward combating global climate change by reducing carbon emissions. the ecosystem dynamics and processes accounting for variations in functions such as natural cycles and human values and management preferenc es. contribute towards for designing and planning urban forests with regards to tree species and location selection to maximize growth and carbon sequestration.

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79 APPENDIX A FIELD DATA SHEET

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80

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81 APPENDIX B RESIDENTIAL INTERVIE W CONSENT

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82 APPENDIX C RESIDENTIAL VEGE T ATION MAINTENANCE SU RVEY

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83

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84 APPENDIX D BIOMASS EQUATIONS Table D 1. Biomass equations f rom Timilsina et al. ( In Press ) Species Code Equation Level A_Coeff B_Coeff MSE Variable Equation Form Acer rubrum ACRU Species 1.84 2.36 0.009 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Aralia spinosa ARSP Subclass 1.84 2.36 0.035 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Arecastrum ARRO10 Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Buxus sp. BUSP Order 2.07 2.37 0.065 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Celtis CELA Species 2.24 1.71 2.036 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Chamaecyparis CHTH2 Family 3.33 1.50 0.046 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Cinnamomum camphora CICA Subclass 2.23 2.40 0.048 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Citharexylum CIFR Subclass 1.87 2.34 0.071 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Citrus sp. CISP Order 1.61 2.30 0.018 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Dypsis DYLU Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Gordonia GOLA Subclass 2.51 2.44 0.126 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Hibiscus sp. HISP Species 2.43 2.36 0.259 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Lagerstoemia indica LAIN Subclass 1.84 2.36 0.035 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2))

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85 Table D 1. Biomass equations from Timilsina et al. (In Press) Species Code Equation Level A_Coeff B_Coeff MSE Variable Equation Form Ligustrum japonica LIJA Order 1.87 2.34 0.071 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Liquidambar styraciflua LIST Species 2.62 0.87 0.205 dbh ^2*ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Livistona LICH3 Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Magnolia grandifolia MAGR Family 2.23 2.40 0.048 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Magnolia virginiana MAVI Family 2.23 2.40 0.048 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Melia MEAZ Order 1.61 2.30 0.018 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Monstera deliciosa MODE Subclass 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Morella MYCE Order 2.08 2.42 0.152 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Nyssa NYBI Family 2.03 2.38 0.052 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Phoenix PHCA Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Phoenix robellinii PHRO Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Pinus elliottii PIEL Species 4.30 2.54 0.076 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Pinus palustris PIPA Genus 0.02 0.96 0.000 dbh ^2*ht A_COEFF (VARIABLE**B_COEFF) Pinus taeda PITA Species 194.04 0.99 0.000 dbh ^2*ht A_COEFF (VARIABLE**B_COEFF) Platanus occidentalis PLOC Order 2.62 0.87 0.205 dbh ^2*ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2))

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86 Table D 1. Biomass equations from Timilsina et al. (In Press) Species Code Equation Level A_Coeff B_Coeff MSE Variable Equation Form Prestoea PRMO Species 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Quercus QULA2 Genus 2.08 2.48 0.068 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Quercus laurifolia QULA Species 1.14 2.32 0.050 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Quercus nigra QUNI Species 2.08 2.48 0.068 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Quercus virginiana QUVI Species 1.14 2.32 0.050 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Sabal palmetto SAPA Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Salix nigrum SANI Family 2.55 2.48 0.059 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Sygarus SYRO4 Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Taxodium dischtens TADI Family 3.33 1.50 0.046 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Thuja THPL Species 4.88 1.00 0.001 dbh ^2*ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Thuja occidentalis THOC Species 1.78 1.99 0.090 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Triadica SASE Subclass 1.84 2.36 0.035 dbh EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Washingtonia sp. WASHI Family 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2)) Yucca ellata YUEL Class 3.85 2.91 0 ht EXP (A_COEFF + B_COEFF LOG(VARIABLE) + (MSE/2))

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87 APPENDIX E LITERATURE REVIEW OF GLOBAL URBAN CARBON STORAGE AND SEQUESTR ATION BY LANDUSE Table E 1. Carbon storage and sequestration of global urban areas by landuse. Author Year Location Biome NPP Method Vegetation Land Use Storage ( t/ha) Gross Sequestration ( t /ha/yr) Net Sequestration ( t/ha/yr) Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Afforestation 4.02 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Agriculture 0.00 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Agriculture 0.00 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Agriculture 0.10 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Agriculture 0.20 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Agriculture 7.60 0.52 0.52 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV All 31.60 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV All 13.33 3.94 Yang et al. 2005 Beijing, China T H/T SA 0.3 DBH Allometric TT All 7.44 0.38

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88 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT All 11.00 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT All 11.81 Nowak 1994 Chicago, IL, T H 0.6 DBH AGV All 14.10 Jo 2002 Chuncheon, Korea T H 0.75 DBH Allometric TV All 16.21 1.18 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV All 16.70 0.94 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV All 17.00 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV All 17.70 Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV All 19.72 Nowak et al. 2002 Boston, MA, USA T H 0.75 DBH Allometric TT All 20.30 0.67 0.49 Nowak et al. 2002 Syracuse, NY, USA T H/B H 0.6 DBH Allometric TT All 22.82 0.73 0.54 Jo 2002 Kangleung, Korea T H 0.75 DBH Allometric TV All 24.00 1.10 Nowak et al. 2002 USA na na DBH Allometric TT All 25.10 0.80

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89 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak et al. 2002 Baltimore, MD, USA T H 0.75 DBH Allometric TT All 25.28 0.71 0.52 McPherso n 1998 Sacramento, CA, USA M W 0.6 DBH Allometric TT All 31.00 0.92 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT All 33.22 2.84 Nowak et al. 2002 Atlanta, GA, USA T H 0.75 DBH Allometric TT All 35.74 1.23 0.94 Hutyra et al. 2010 Seattle Region, T H/T 0.75 DBH AGT All 89.00 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV All 11.21 0.54 Nowak et al. 2002 Jersey City, NJ, USA T H 0.75 UFORE TT All 5.02 0.21 0.15 Nowak et al. 2006 Casper, WY, USA D T 0.3 UFORE TT All 6.28 0.20 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT All 9.30 0.95 0.88 Chaparro et al. 2009 Barcelona, Spain T H/T SA 0.3 UFORE TT All 10.88 0.38 McNeil and Vava 2005 Oakville, ON, Canada T H/T SA 0.75 UFORE TT All 13.24 0.60

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90 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak et al. 2009 Wilmington, DE, USA T H 0.75 UFORE TT All 13.67 0.39 Nowak et al. 2007 Philadelphia, PA, USA T H 0.75 UFORE TT All 14.12 0.43 Nowak et al. 2010 Chicago, IL, USA T H 0.6 UFORE TT All 14.57 0.76 Nowak et al. 2007 San Francisco, CA, USA TR SA/M W 0.6 UFORE TT All 14.80 0.38 Nowak et al. 2006 Minneapolis, MN, USA T H 0.45 UFORE TT All 15.02 0.54 Nowak et al. 2007 New York City, NY, USA T H 0.75 UFORE TT All 15.24 0.47 Davey Resource 2008 Milwaukee, WI, USA T H 0.6 UFORE TT All 15.73 0.56 Nowak et al. 2009 New Castle Metropolitan, 0 0.75 UFORE TT All 17.28 0.61 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT All 18.11 Nowak et al. 2006 Washington DC, USA T H 0.75 UFORE TT All 30.04 0.92 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT All 38.40 1.22 1.22 Davey Resource 2008 Pittsburgh, PA, USA T H 0.75 UFORE TT All 1.08

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91 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Zhao et al. 2010 Hangzhou, China T SA/T H 0.9 Volume Allometric AGT All 23.35 Zhao et al. 2010 Metropolitan County Hangzhou, China T SA/T H 0.9 Volume Allometric AGT All 24.36 Zhao et al. 2010 Country Hangzhou, China T SA/T H 0.9 Volume Allometric AGT All 24.68 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT All 30.25 6.56 Warren et al. 2002 Pune City, India TR SA 0.6 Volume Allometric AGT All 66.68 0.67 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT Attached Forest 50.17 4.78 Nowak et al. 2009 New Castle Metropolitan, DE, USA 0 0.75 UFORE TT barren/open/agricultur e 0.13 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Binjiang 34.34 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV Broad leafed forest 15.85 3.30

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92 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Broad leafed forest 68.31 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Cemetery 27.87 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Center 42.27 Zhao et al. 2010 Country Hangzhou, China T SA/T H 0.9 Volume Allometric AGT Chunnan Nowak et al. 2009 Wilmington, DE, USA T H 0.75 UFORE TT combined urban 0.30 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Commercial 0.00 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Commercial 0.30 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Commercial 0.30 #DIV/0 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Commercial 1.00 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Commercial 8.70 0.52 0.51 Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT Commercial/Industrial 0.50

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93 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Commercial/industrial 8.52 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Commercial/industrial 0.28 0.03 Nowak et al. 2009 Wilmington, DE, USA T H 0.75 UFORE TT Commercial/industrial 0.11 Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT conifer forest 182.0 0 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV Coniferous forest 27.37 6.71 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Coniferous forest 72.91 Churches et al. 2009 Arlington, TX, USA T H 0.45 UFORE TT Conterminous United States Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Domestic Gardens (Private ownership) 7.90 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT Ecological and Public Welfare Forest 29.25 2.45 Nowak et al. 2009 New Castle, DE, USA 0 0.75 UFORE TT forest 2.13 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Forest 53.90 2.26 2.26

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94 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV Forested land 19.58 3.90 Yang et al. 2005 Beijing, China T H/T 0.3 DBH TT FRRA 8.12 0.42 Zhao et al. 2010 Metropolitan County Hangzhou, China T SA/T H 0.9 Volume Allometric AGT Fuyang 22.88 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Gardens 13.48 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Gongshu 18.88 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Green urban area 29.38 Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT heavy urban 2.00 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Herbaceous Vegetation (mixed) 1.40 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Herbaceous Vegetation (Public) 1.50

PAGE 95

95 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Industrial 9.20 0.38 0.38 Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT Institutional 12.90 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Institutional 4.45 0.08 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Institutional 14.10 1.28 1.28 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Institutional 48.20 1.60 1.60 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Institutional (bldg.) 0.00 0.00 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Institutional (bldg.) 5.10 1.29 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Institutional (bldg.) 9.70 0.80 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Institutional (bldg.) 14.50 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Institutional (veg.) 33.90 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Institutional (veg.) 35.80

PAGE 96

96 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Institutional (veg.) 41.00 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Institutional (veg.) 44.20 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV intensive use without buildings 10.62 0.55 Zhao et al. 2010 Metropolitan County Hangzhou, China T SA/T H 0.9 Volume Allometric AGT Jiande 21.51 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Jinanggan 46.51 Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Jungland Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Jungland Natural 58.70 3.91 Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Jungland Urban 7.20 0.80 Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Kangnam Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Kangnam Natural 60.10 3.77

PAGE 97

97 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Jo 2002 Seoul, Korea T H 0.75 DBH Allometric TV Kangnam Urban 6.60 0.53 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV land suitable for forest 2.37 1.59 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT Landscape and Relaxation Forest 33.65 2.47 Zhao et al. 2010 Metropolitan County Hangzhou, China T SA/T H 0.9 Volume Allometric AGT Linan 26.96 Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT Low urban 38.00 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Meadow orchard 16.03 Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT Medium urban 13.00 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV Mixed forest 10.85 2.20 Strohbach et 2012 Leipzig, Ger T SA 0.6 DBH AGT Mixed forest 75.71

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98 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT Mixed forest 98.00 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Mixed urban fabric 20.00 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Multiresidential 3.20 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Multiresidential 5.70 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Multiresidential 10.80 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Multiresidential 17.30 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Multiresidential 5.73 0.35 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Multi story houses 4.20 Awal et al. 2010 Nagoya, Japan 0 0.9 Eddy Flux TV Nagoya, Japan Jo 2002 Chuncheon, Korea T H 0.75 DBH Allometric TV Natural 26.00 1.71 Jo 2002 Kangleung, Korea T H 0.75 DBH Allometric TV Natural 76.70 1.60

PAGE 99

99 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Churkina et al. 2010 C ontinental United States na na Populatio n Density TV Natural 19.83 0.99 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV newly established open forest land 6.52 3.91 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT North Agriculture/Rangeland Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT North Forest Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV nursery 5.72 4.00 Yang et al. 2005 Beijing, China T H/T SA 0.3 DBH Allometric TT Old City 12.81 0.57 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV open forest land 15.53 1.68 Hutyra et al. 2010 Seattle Region, WA, USA T H/T SA 0.75 DBH Allometric AGT other land covers Nowak et al. 2009 New Castle Metropolitan, DE, USA 0 0.75 UFORE TT Park 1.10 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Park 29.50 0.89 0.89

PAGE 100

100 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Park 30.10 3.24 3.24 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT Producation and Management Forest 13.17 1.16 Phillips et al. 2011 Corvallis, OR, USA 0 0.75 UFORE TT Public land 32.68 1.27 Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT Residential 8.80 Jo et al. 1995 Chicago, IL, USA T H 0.6 DBH Allometric TV Residential 12.77 1.58 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Residential 17.20 Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Residential 22.40 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Residential 22.50 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Residential 25.70 Jo et al. 1995 Chicago, IL, USA T H 0.6 DBH Allometric TV Residential 36.06 3.09 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Residential 23.03 1.33

PAGE 101

101 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak et al. 2009 Wilmington, DE, USA T H 0.75 UFORE TT Residential 0.76 Nowak et al. 2009 New Castle Metropolitan, DE, USA 0 0.75 UFORE TT Residential 0.85 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Residential 10.10 0.38 0.38 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Residential 35.80 1.25 1.25 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT Residential Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Riparian forest 98.26 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT Rural Davies et al. 2011 Laicester, UK T SA/T 0.6 Allometric AGV Shrub (Mixed ownership) 137.9 0 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Shrub (Public ownership) 66.60 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV shrub land 5.30 2.64 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Single story houses 13.70

PAGE 102

102 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Small woodland 80.98 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT South Agriculture/Rangeland Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT South Forest Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Tall Shrub (Mixed ownership) 123.5 0 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Tall Shrub (Public ownership) 160.3 0 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Terraced houses 5.10 Zhao et al. 2010 Country Hangzhou, China T SA/T H 0.9 Volume Allometric AGT Tonglu 22.17 Strohbach et al. 2012 Leipzig, Germany T SA 0.6 DBH Allometric AGT Transitional forest 10.12 Brack 2002 Canberra, Australia 0 0.75 0 0 Transportation Nowak 1994 Cook Co IL, T H 0.6 DBH AGV Transportation 0.00 Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT Transportation 0.70

PAGE 103

103 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Transportation 3.50 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Transportation 7.20 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Transportation 9.00 Liu et al. 2011 Shenyang, China T H 0.67 5 DBH Allometric TT Transportation 34.95 2.51 Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Transportation 7.56 0.38 Nowak et al. 2009 New Castle Metropolitan, DE, USA 0 0.75 UFORE TT transportation 0.08 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Transportation 3.90 0.23 0.23 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Transportation 22.70 0.70 0.70 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Tree (Mixed ownership) 280.6 0 Davies et al. 2011 Laicester, UK T SA/T H 0.6 Allometric AGV Tree (Public ownership) 288.6 0

PAGE 104

104 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Yang et al. 2005 Beijing, China T H/T SA 0.3 DBH Allometric TT TRRA 3.02 0.20 Xiao et al. 2011 Beijing, China T H/T SA 0.3 BEF/AP TV unstocked forest land 0.95 0.61 Jo 2002 Chuncheon, Korea T H 0.75 DBH Allometric TV Urban 4.70 0.56 Jo 2002 Kangleung, Korea T H 0.75 DBH Allometric TV Urban 6.30 0.71 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT Urban Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT Urban Built Churkina et al. 2010 Conterminou s United States na na Populatio n Density TV Urban forest 33.35 1.24 Smith et al. 2005 Houston, TX, USA T H 0.6 UFORE TT Urban Green Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Utility 1.70 0.36 0.36 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Utility 2.20 0.15 0.15 Nowak 1994 Cook County, IL, USA T H 0.6 DBH Allometric AGV Vacant 15.60

PAGE 105

105 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Nowak 1994 Chicago Metropolitan, IL, USA T H 0.6 DBH Allometric AGV Vacant 20.60 28.42 Nowak 1994 DuPage County, IL, USA T H 0.6 DBH Allometric AGV Vacant 25.00 Nowak 1994 Chicago, IL, USA T H 0.6 DBH Allometric AGV Vacant 34.20 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Vacant 11.90 2.15 2.15 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Vacant 61.40 1.62 1.62 Nowak et al. 2009 New Castle Metropolitan, DE, USA 0 0.75 UFORE TT Wetland/Water 0.00 Escobedo et al. 2010 Miami Dade, FL, USA T H 0.9 UFORE TT Wetland/Water 1.50 0.13 0.13 Escobedo et al. 2010 Gainesville, FL, USA T H 0.75 UFORE TT Wetland/Water 65.50 1.79 1.79 Nowak 1993 Oakland, CA, USA M W 0.6 DBH Allometric TT Wildland 27.90 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Xiaoshan 29.44

PAGE 106

106 Table E 1. Continued. Author Year Location Biome NPP Method Vegetation Landuse Storage (tc/ha) Gross Sequestration (tc/ha/yr) Net Sequestration (tc/ha/yr) Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Xiasha 35.96 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Xihu 33.33 Zhao et al. 2010 Downtown Hangzhou, China T H 0.9 Volume Allometric AGT Yuhang 31.63

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115 BIOGRAPHICAL SKETCH Josh Horn grew up in Louisville, Kentucky where he graduated from Trinity High School in 2006, developing an interest in biology and mathematics. Mr. Horn continued his education at Thomas More College in northern Kentuck y, where he received a Bachelor of Arts in b iology with a concentration in environmental and evolutionary b iology. In addition to pursuing his interests in ecology, Mr. Horn also showed interest in team, where he recorded the third fastest 8k in school history. After graduating summa cum laude in 2010, he started at the University of Florida to pursue a Master o f Science in interdisciplinary e cology with a focus on urban ecology He is specifically interested in human impacted environments, including endangered species protection urban design, ecosystem services restoration ecology and landscape connectivity While working on his graduate degree, Mr. Horn also coached cross country and track and field at the