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1 THE I N FLUENCE OF SUBDIVISION DESIGN AND CONSERVATION OF OPEN SPACE ON CARBON STORAGE AND ANNUAL SEQUESTRATION By RICHARD VAUGHN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUI REMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2013
2 2013 Richard Vaughn
3 To my parents, thank you for all the love and support throughout the years
4 ACKNOWLEDGMENTS I tha nk my parents, family, and friends for all their love and support. The start of this journey would not have been possible without the support of my friends and colleagues; David Mann, Katharine Owens, and Stu Rubenstein, whose letters of recommendation we re essential in gaining admittance to the University of Florida. I would also thank Stephen Humphrey for taking a chance on me, accepting me into the School of Natural Resources and Environment program, and finding me an assistantship position. This jour ney could not have been completed without the guidance of my advisory committee Mark Hostetler, Francisco Escobedo, and Pierce Jones. Their advice and guidance has been invaluable Finally, I would thank Greg Galpin, Allison Megrath, Mario Mighty, Al Zela ya, and the staff members at Plum Creek, Ecology and Conservation, and Program for Resource Efficient Communities for their technical support throughout this project.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 LIST OF ABBREVIATIONS ................................ ................................ ............................. 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 2 METHODS ................................ ................................ ................................ .............. 15 Study Area ................................ ................................ ................................ ........ 15 Land Cover Aggregation ................................ ................................ ................... 15 Field Sampling ................................ ................................ ................................ .. 18 Scenario and Analyses Development ................................ ............................... 20 3 RESULTS ................................ ................................ ................................ ............... 22 Forest Type, Tree Stand Age, and Baseline Carbon ................................ ........ 22 Development Scenarios and Carbon Storage and Sequestration .................... 24 Baseline ................................ ................................ ................................ ............ 24 Scenario 1: Permitted Construction ................................ ................................ .. 25 Scenario 2: Doubled Residential Density and Halved Acreage ........................ 26 Scenario 3: Conser ving Older Tree Stands ................................ ...................... 27 Scenario 4: Conserving Younger Tree Stands ................................ ................. 28 Scenario 5: Single Compact Design ................................ ................................ 29 Scenario 6: Multiple Compact Design Impacting Conservation Areas .............. 30 Scenario 7: Multiple Compact Design No Impact to Conservation Areas ........ 31 Scenario 8: Compact Design within Current Land Use Designations ............... 32 4 DISCUSSION AND CONCLUSIONS ................................ ................................ ...... 34 Discussion ................................ ................................ ................................ ........ 34 Conclusions ................................ ................................ ................................ ...... 42 ADDITIONAL DATA ................................ ................................ ................................ ...... 44 LIST OF REFERENCES ................................ ................................ ............................... 47 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 53
6 LIST OF TABLES Table page 2 1 Seventeen Land Use Land Cover (LULC) classifications were grouped into three forest types on the Gainesville 121 site. ................................ .................... 16 3 1 Tree attributes and species per forest type category. ................................ ......... 22 3 2 Carbon storage and annual sequestration for the Gainesville 121 site by percentage and average metric tons (mt) per hectare (ha) for each forest type. ................................ ................................ ................................ .................... 22 3 3 The average amount of carbon storage (metric tons per hectare) and yearly sequestration (metric tons per hectare per year) for each tree age group found at the Gainesville 121 site. Averages are based on plot level data generate d from 80, 0.04 ha plots. ................................ ................................ ....... 23 3 4 Build design scenarios for the Gainesville 121 site showing the total number of residential units, acres impacted, the amount of carbon storage and sequestrati on remaining after all vegetation is cleared in the construction area, and percent conserved in CO 2 storage and sequestration from preconstruction values. ................................ ................................ ....................... 33 A 1 Tree carbon storage and annual sequestration totals at the plot level for all 80 sample plots (0.04 hectares) in the Gainesville 121 study site. ..................... 44
7 LIST OF FIGURES Figure page 2 1 On the Gainesville 121 development site, three forest types (mesic hydric, hydric, and xeric mesic) created based on soil moisture and tree species composition. ................................ ................................ ................................ ....... 18 2 2 Stratified sample pl ot center point locations within each aggregated forest type for the Gainesville 121 study site. ................................ ............................... 19 3 1 for the Gaines ville 121 site, buildable acreage is in grey. ................................ ... 25 3 2 Scenario 2, Residential Density and Halved Acreage: Residential density has been doubled and buildable acreage, in grey, has been reduced b y half. .......... 26 3 3 Scenario 3, Conserving Older Tree Stands: Buildable area, in grey, available after conserving tree age stands greater than 18 years old. ............................... 27 3 4 Scenario 4, Conserving Younger Tree Stands: Buildable area, in grey, available after conserving tree age stands less than 19 years of age ................ 28 3 5 Scenario 5, Sing le Compact Design: Buildable area, in grey, located near existing communities with multiple major road access regardless of existing land use boundaries. ................................ ................................ .......................... 29 3 6 Scenario 6, Multiple Compact De signs: Multiple build areas, in grey, located near existing communities with multiple major road access regardless of existing land use boundaries. ................................ ................................ ............. 30 3 7 Scenario 7, Multiple Compact Designs: Multiple build areas, in grey, located near existing communities with multiple major road access without impacting land designated as conservation. ................................ ................................ ....... 31 3 8 Scenario 8, Current Land Use Designa tions: A compact design within current land use boundaries without impacting land designated as conservation. Buildable acreage, in grey, is reduced from 208 ha (in previous scenarios) to 204 ha. ................................ ................................ ................................ ............... 32
8 LIST OF ABBREVIATIONS C Carbon CO 2 Carbon dioxide DBH Diameter at Breast Height GHG Greenhouse Gas LULC Land Use Land Cover RU S Residential Units UFORE United States Department of Agriculture Urban Forest Effects VMT Vehicle Miles Traveled
9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for th e Degree of Master of Science T HE INFLUENCE OF SUBDIVISION DESIGN AND CONSERVATION OF OPEN SPACE ON CARBON STORAGE AND ANNUAL SEQUESTRATION By Richard Vaughn May 2013 Chair: Name Mark Hostetler Major: Interdisciplinary Ecology Cities are increasingly trying to offset carbon dioxide emissions and residential subdivisions are a major source of such emissions. Compact or clustered subdivision designs have the potential to improve carbon storage and sequestration through the conservation of open space and the conservation of existing trees found on built lots. However, very few empirical studies estimate how different subdivision designs and tr ee conservation strategies affect the carbon footprint of residential developments. Using a 1,743 acre (705 ha) pine plantation site that has been approved for the development of 1,835 units near Gainesville, Florida m y objectives were to determine which site designs and tree conservation strategies could maximize carbon sequestration and storage. From 8 0 stratified random plots, I measured and analyzed tree and plot characteristics a ccording to three aggregated forest type and tree stand age categories. Tree data collected from these plots were entered into the i Tree ECO model to estimate baseline predevelopment carbon storage and sequestration. ArcMap was then used to compare the impact, on baseline carbon sequestration and storage capacity, of severa l different site designs and tree conservation strategies for the proposed development. U p to 91 percent of carbon storage and up to 82 percent of carbon
10 sequestration could be maintained through a cluster development design and where older tree stands we carbon footprint can significantly improve when forest type analyses are incorporated into the design of a development.
11 CHAPTER 1 INTRODUCTION As climate change continues to become a se rious environmental and societal concern, many urban areas will come under increased pressure to balance continued population growth with greenhouse gas (GHG) reduction. Climate change is a direct result of GHG emissions and a variety of human activities consume fossil fuels and release greenhouse gases in the atmosphere ( Malhi, Meir, & Brown, 2002 ; Soloman et al., 2007 ) Of these, c arbon dioxide (CO 2 ) is of great concern, making up approximately half of all these greenhouse gas emissions ( Jo, 2002 ; David J. Nowak, 1993 ; Soloman, et al., 2007 ) Emissions from cities can vary greatly depending on industrial activities, gasoline consumption, transportation sector home heating and electricity usage. Cities like San Diego and Los Angeles emi t approximately 17.2 metric tons of CO 2 per household to Ok lahoma City and Memphis that emit approximately 29.0 metric tons ; most other cities fall in between ( Glaeser & Kahn, 2010 ) Forest s are ecosystems that store and sequester carbon (C) and their conservation and restoration could help mitigate carbon emissions worldwide ( Brown, Swingland, Hanbury Tenison, Prance, & Myers, 2002 ; Malhi, et al., 2002 ) However, g lobally, forested areas have been in decline for decades with 13 million hectares lost every year since 2000 ( UNFAO, 2010 ) Causes for deforestation can vary based on a region and can be mostly attributed to land use changes such as a gricultural development urban expansion and wood extraction ( Geist & Lambin, 2002 ) In the Southeast United States urbanization will represent a primary threat to forestland for the next 20 years ( Zhang et al., 2008 ) 2020 and 35.8 million by 2060 ( Zwick & Carr, 2006 ) and most of this growth will be in
12 urban areas ( Bar nett et al., 2007 ) Florida forests cover more than 47% of the total land area or approximately 16 million acres ( FDACS, 2006 ) At the height of land development Florida los t approximately 170 230 acres (69 93 ha) of timber per day and most of this loss is to urbanization ( FDACS, 2006 ; Long, 2005 ) New subdivision developments are usually sited on the edge of existing established urban areas. This urban rural interface region characteristically has been a large source of carbon emissions as forests have been replaced with houses and roads ( Zhang, et al., 2008 ) Development typically follows a pattern of clearing a site of all flora, recontouring the site, and then planting trees of similar size and species throughout the entire development. When new developments remove existing trees for construction and then plant new trees post construction, there is a large carbon release from th e destruction of the mature trees followed by a lengthy lag in carbon intake as the new trees mature ( Escobedo, Varela, Zhao, Wagner, & Zipperer, 2010 ; David J. Nowak & Crane, 2002 ) When tree cover is replaced with impervious surfaces or even open park spaces that require mowing, irrigation, and fertilization carbon sinks can shift to carbon emission sources ( Dobbs, Escobedo, & Zipperer, 2011 ) When land is subdivided conserv ing forests a nd large individual trees can help sequestration ( Escobedo, et al., 2010 ; Jo, 2002 ; David J. Nowak & Crane, 2002 ) If an urban site is developed and managed correct ly, urban forests can reduce CO 2 emissions through photosynthesi s and storage in biomass. In addition, trees can shad e homes and decrease ambient air temperature through e vapotranspiration ; this limits CO 2 emissions by reducing energy needs for heating and cooling homes ( Escobedo, et
13 al., 2010 ; Jo & McPherson, 2001 ; David J. Nowak & Crane, 2002 ) There is potential for urban forests to sequester more carbon than natural forests on a per unit tree basis due to the open forest structure ( McPherson Nowak, & Rowntree, 1994 ) Net carbon sequestration improve s when developers reduce impervious surfaces, allow for more greenspace, institute a multi layer, multi age vegetative habitat, and use natural shading to reduce energy consumption by homes ( Jo, 2002 ; Jo & McPherson, 2001 ; David J. Nowak & Crane, 2002 ) Not only co uld the overall design of development maximize carbon sequestration and storage, but it could maximize a number of other natural resource goals such as conserving wildlife habitat and biodiversity ( Arendt, 1996 ; Hostetler, 2012 ; Milder, 2007 ) Conservation developments, areas where homes are clustered togethe r on smaller lots conserving as much greenspace as possible, are alternative subdivision designs that integrate human needs with natural resource conservation ( Arend t, 1996 ; Hostetler & Drake, 2009 ; Milder, 2007 ) Through design and management the se developments try to conserve natural open space which c oul d reduce the overall carbon footprint. However, the placement of buil t lots, the structure of the conserved forest, and the location of the conserved forested areas are important variables in improv ing carbon storage and sequestration. For example, swamp cypress and mangrove vegetation sequester more carbon than plantation or pine rockland ( Escobedo, et al., 2010 ) A nalyzing the potential impacts of different subdivision designs on carbon sequestration and storage can help reduce resource tradeoffs and provide city planners and developers with information on the levels of carbon benefits of one design versus ano the r which can ultimately improve the overall carbon footprint of a city
14 Previous studies of urban tree carbon sequestration and storage h ave focused on city level and land use level estimates in urban areas ( Escobedo, et al., 2010 ; Jo & McPherson, 2001 ; Maco & McPherson, 2003 ; David J. Nowak & Crane, 2002 ) Little research, however, has been conducted explor ing how different potential subdivision designs impact carbon sequestration and storage before a development has been constructed In this study, I selected a forest ed area near Gainesville, Florida that is currently being managed for timber. This site has been permitted for a development and will eventually contain a mixture of resid ential and commercial land uses. M y objectives were 1) to d etermine the influence o f different forest types and tree stand age on carbon storage and sequestration within the site and 2) as sess how different subdivision designs impact carbon storage and sequestration. Results from this study will help developers and planners assess the p otential carbon benefits of various subdivision designs and how the structure of managed forests can be used to offset the carbon emissions of households.
15 CHAPTER 2 M E THODS Study Area The location of this study area is north of Gainesville, Fl orida on State Route 121 population of 125,326 inhabitants ( USCB, 2011 ) subtro pical with an average temperature of 12.5C in January and 26.2C in June. Average rainfall for January is 83.8 mm and 173.0 mm in June ( NOAA, 2011 ) The majority (56 .2 %) of s oils are a combination of Pomona and Wauchula sand and Monteocha loamy sand ( NRCS, 2 013 ) This s tudy area called hereafter as the Gainesville 121 site, was chosen because it is in the initia l stage of project development and the land owner was interested in determining how carbon storage and sequestration could be improv ed using diffe rent development designs The site wa s approved for 1,835 residential units but construction has not begun. Originally, this property consisted of individual forest lots and farms. Consolidation of these properties by National Turpentine, the predecesso r of Owens Illinois, occurred in the mid 1940s ( Galpin personal communication ) Today, the development site is owned by Plum Creek, the largest private land owner in the United States, and is comprised of 705 hectares (ha) of planted pine, mixed hardwood f orest, and wetlands. The entire site is currently managed for timber. Land Cover Aggregation ArcMap software was used to analyze land cover raster data generated by the Florida Fish and Wildlife Conservation Commission ( Commission, 2003 ) T he study
16 area is comprised of twenty one L and U se and L and C over (LULC) types. Four of these LULC classifications: bare soil/clear cut, urban residential, agriculture, and pasture/grassland/agriculture, a total of 18 ha, were exc luded because one of the goals of this study was to determine pre construction carbon storage and sequestration of the trees in the study area. The remaining seventeen LULC types were aggregated into th ree encompassing forest type classifications (hydric, mesic hydric, and xeric mesic) based on soil moisture levels and species composition in orde r to represent major community types ( Table 2 1). Table 2 1 S eventeen L and U se L and C over (LULC) classifications were grouped into three forest types on the Gain esville 121 site Forest Type Land Use Land Cover Hydric Bay/Gum/Cypress Ecological Complex Loblolly Bay Forest Swamp Forest Ecological Complex Cypress Forest Compositional Group Temperate Wet Prairie Forb Emergent Marsh Water Lily or Floatin g Leaved Vegetation Saturated Flooded Cold deciduous and Mixed Evergreen/Cold deciduous Shrubland Ecological Complex Mesic Hydric Mesic Hydric Live Oak/ Sabal Palm Ecological Complex Mesic Hydric Pine Forest Compositional Group Broad leaved Evergre en and Mixed Evergreen/Cold deciduous Shrubland Compositional Group Xeric Mesic Xeric Mesic Mixed Pine/Oak Forest Ecological Complex Live Oak Woodland Mixed Evergreen Cold deciduous Hardwood Forest Sandhill Ecological Complex Dry Prairie (Xeric Mesic) Ecological Complex Gallberry/Saw Palmetto Shrubland Compositional Group
17 Forest type cl assification was determined by comparing metadata descriptions of soil moisture profiles and vegetative species with the LULC classification scheme in Florida ( Kawula, 2009 ) and descriptions from the Florida Geographic Data Library for Florida Land Cover ( Commission, 2003 ) and in Trees, Shrubs, and Woody Vines of Northern Florida and Adjacent Georgia and Alabama ( Godfrey, 1988 ) I made these th ree forest type classifications because tree species may have different biomass ranges in soils that are wetter and nutrient rich ( Slik et al., 2010 ) W etter si te s can be nutrient rich allowing trees to have a higher biomass than areas such as upland sites or scrub that can be nutrient poor where competition for resources could result in lower biomass accumulation. I wanted to determine if carbon storage and seq uestration would be different between these classifications. Arc Map transformed 7,777 one meter raster cells (931 xeric mesic, 5393 mesic hydric, and 1,453 hydric) into three large polygons representing each new forest type class (Fig. 2 1) These polygo ns were used to randomly generate plot center points A minimum allowed distance of 11.34 meters (m) was used to prevent overlap in the generation of stratified, random plot s. These random point s were used to create 1 1 hydric, 54 mesic hydric, and 15 xer ic mesic plot sites
18 Figure 2 1. On the Gainesville 121 development site, t hree forest types (mesic hydric, hydric, and xeric mesic) created based on soil moisture and tree species composition. Field Sampling From June t hrough October 2011, 0.04 ha plots were established and tree measurements taken for each of the 8 0 sample plots ( Fig. 2 2 ) Plot center points in the field were located using a hand held Garmin GPSmap 76S unit. Each plot center point was flagged and given a unique identification number.
19 Figure 2 2 S tratified sample plot center point locations within each aggregated forest type for the Gainesville 121 study site. Tree d ata collection methodology was based on ( D. J. Nowak et al., 2008 ) All trees, living or dead with a diameter at breast height (DBH) > 2.5 cm and with greater than it s bole in the plot w ere counted. Measurements included in the data collection were species, number of stems, DBH, tota l height, crown height, crown width, percent canopy cover missing, dieback, and crown light exposure. Th e first th ree random trees from each species were used to calculate average measurements for all tree characteristics for that species within a sample plot except DBH which was recorded for all trees. All 80 plots were measured using 0.01 ha subplots (i.e. the northeast quarter of the 0.04 ha
20 plot) in order to reduce sampling effort During subsequent analyses, individual tree s on these subplots wer e multiplied by a factor of four in order to analyze data in i Tree Upon completion of data collection, field information was uploaded into the i Tree Eco software ( Crane et al., 2006 ) to calculate total tree carbon sequestration and storage for the entire study area The i Tree ECO software is adapted from the UFORE model and is an application designed to analyze field data collected from complete inventories or from randomly located plots which can be used to set priorities or make management and policy decisions Carbon s torage and sequestration calculations are based on a series of species specific, genus or family biomass allometric equations from several literature resources ( Crane et al., 2012 ) Scenario and Analyses Development Before creating scenarios mean carbon storage and sequestration data w as used to find significant differen ces between forest type classes and tree stand ages in order to target specific areas for conservation. In addition to exploring forest types, I also calculated average carbon storage and sequestration for different tree stand ages within the plots T he landowner s upplied detailed information on tree stand age across the ent ire site. Previous research has shown a moderate correlation, ( R 2 = 0 .4 0.6) between tree age and DBH ( Loewenstein, Johnson, & Garrett, 2000 ) T hus stand age is an important factor in carbon storage and sequestration because gener ally older tree stands contain larger trees which store and sequester more carbon ( Timilsina et al., 2013 ) I conducted preliminary analyses to determine whether forest type and tree stand age significantly affected carbon stor age and sequestration. Tree stand GIS shapefiles were merged in ArcMap with sample plot
21 data in order to determine the number of study plots found in each tree stand age group. Tree stand age groups were: 2 9 yrs, 10 18 yrs, 19 29 yrs, and 30 61 yrs. No older tree stand ages were recorded on the property. Each of the 8 0 plots fell into one of the four tree stand age groups, and from this I calculated average carbon and storage values for each tree stand age group. Student t tests were used to determine whether significant differences occurred between forest types and 0.05). I explored eight scenario s in ArcMap; the first is the original permitted design with a mix of residential, commercial, and conservation areas. The other seven scenarios are various alternatives that allocated build ing footprints around the study area to better explore compact and more fragmented designs. Based on the results of the above analyses, I used either tree types or tree stand age strata to targe t forested areas for conservation. For example, if carbon storage and sequestration were significantly different between certain tree stand age groups (and not so for the three forest type groups) th e n tree stand age strata were targeted for conservation instead of forest type After a scenario goal had been determined, forested areas were selected for sequestration for each of the eight subdivision development scena rios were compared to predevelopment baseline estimate s. The impact to carbon is based on the premise that buildable areas in all scenarios would be completely cleared of above ground vegetation. Details for each scenario goal are given in the Results se ction below
22 CHAPTER 3 RE S ULTS Forest Type Tree Stand Age and Baseline Carbon A total of 26 different species were identified in the sample plots. The three most common tree species: Slash Pine ( Pinus elliottii ) at 4 8.4 % Darlington/Laurel Oak ( Querc us hemisphaerica ) at 9. 8 % and Water Oak ( Quercus nigra ) at 8. 5 % comprised approximately 6 6.8 % of all species found in the sampled plots. Table 3 1 Tree attributes and species per forest type category Forest Type # of tree s trees/ha # of species Top 3 Species Top 3 as % of Total Trees Hydric 167,500 1,250 18 PIEL, QUNI, QUHE 26.7 Mesic Hydric 680,400 1,400 16 PIEL, QUHE, ACBA 72.4 Xeric Hydric 140,250 1,650 9 PIEL, QUNI, PITA 72.5 ACBA, Acer barbatum ; QUHE, Quercus hemi sphaerica ; QUNI, Quercus nigra ; PIEL, Pinus elliottii ; PITA, Pinus taeda Forest type analyses indicted no differences across forest types for carbon sequestration (all P > 0.05 ). For carbon storage, only hydric forest stored significantly more carbon then mesic hydric ( P =0.0002) but all other comparisons were not significant (all P >0.05 ); (Table 3 2 ). Plot level carbon storage and sequestration is in Appendix (A 1). Table 3 2 Carbon storage and annual sequestration for the Gainesville 121 site by percen tage and average metric tons ( mt ) per hectare (ha) for each forest type. Forest Type Hectare s Carbon Storage Gross Carbo n Sequestration mt % mt mt/ha a mt/yr % mt/yr mt/ha/yr a Hydric 134 1 0,946 3 7.3 7 8 1.7 57 7 20. 79 4.3 Mesic Hydric 486 1 4,933 50.99 3 0.9 1, 807 6 5.09 3.7 Xeric Mesic 85 3,3 49 11. 43 39. 4 39 2 14. 12 4. 6 Total 705 2 9, 288 100 2, 776 100 a Hydric: n=1 1 M esic Hydric: n= 54 Xeric Mesic: n=15 P <0.05 for hydric versus mesic hydric
23 From data provided by the landowner 68% of the entire site was managed pine plantation. For tree stand age categories 19 29 yrs and 30 6 1 years, 6 plots we re in hydric forest 16 plots we re in mesic hydric, and 8 plots we re in xeric mesic. Notably, 55 % of all hydric plots were located in the 30 61 yrs tree stan ds while only 7% of mesic hydric plots were in the same category. Of all trees measured in sample plots, Pinus spp. accounted for 2 4 % of trees measured in the hydric forest class, 5 8 % of trees in mesic hydric, and 55% of trees in xeric mesic. In the hydr ic area, large, older trees, mostly oak ( Quercus spp. ), cypress ( Taxodium spp. ), and sweetgum ( Liquidambar styraciflua ) were storing most of the carbon ( 6 6 % ) while pine was storing the greatest share of carbon ( 6 0 % ) in mesic hydric and 91% in the xeric mes ic With tree stand age analyses, two older tree stand age c ategories (19 29 and 30 61 yrs) ha d significantly greater amounts of carbon storage and sequestration than the younger tree stands of 2 9 yrs and 10 18 yrs ( P <0.05; Table 3 3 ). The one exception was carbon sequestration when comparing 10 18 yrs to 19 29 yrs tree stand age categories ( P >0.05 ) Table 3 3 The average amount of carbon storage (metric tons per hectare) and yearly sequestration (metric tons per hectare per year) for each tree age gr oup found at the Gainesville 121 site. Averages are based on plot level data generated from 8 0 0.04 ha plots. Tree Age Group (yrs)* Plots(n) Avg. C Storage mt /ha S.E. Avg. Gross C Seq. mt /ha/yr S.E. Age 2 9 2 4 9.1693 1. 9502 2. 1353 0.3 786 Age 10 18 2 6 2 9.0312 4. 5551 3.9 923 0. 5101 Age 19 29 19 4 4.8059 4.2892 4. 5113 0.3 435 Age 30 61 1 1 1 20.8465 2 2.4188 6. 9612 1.0247 P < 0.05 for all comparisons between older age groups (19 29 yrs, 30 61 yrs ) and younger age groups (2 9 yrs, 10 18 yrs); except age 1 0 18 yrs versus 19 29 yrs sequestration.
24 Development Scenarios and Carbon Storage and Sequestration G iven the above results, I considered tree stand age, instead of forest type, to explore the effect of different designs on carbon storage and sequestratio n. I explored eight development scenarios and changes in storage amounts and sequestration were calculated by multiplying the average carbon storage and sequestration to the total hectares for each tree stand age group left in the study area once buildabl e areas were designated. Overall, Scenario 1 wa s the ex isting permitted design that delineated different land uses The other seven designs focus ed on reducing buildable acreage by 50% and sometimes manipu lating the location of built areas: scenario 2 s tayed within permitted land uses; scenario 3 conserv ed older tree stand age c ategories ; scenario 4 conserv ed younger tree stand age c ategories ; scenario 5 wa s a single location compact design ; scenario 6 wa s a compact design with multiple build areas; scen ario 7 wa s a compact design with multiple buil t areas that did not impact current permitted conservation areas ; and scenario 8 wa s a final compact design that did not impact current land use d esignations The scenarios are described in detail in the foll owing section. Baseline Baseline values derived from the I Tree ECO software allometric equations are a snapshot in time because trees are constantly in a state of ecological succession. At the end of data collection the Gainesville 121 site (predev elopment) was storing approximately 2 9,938 metric tons ( mt ) and annually sequestering 2, 857 mt of carbon ( Table 3 4 )
25 Scenario 1 : P ermitted Construction The current permitted construction design calls for 1,835 units on 1,025 (415 ha) buildable acres ( Fig. 3 1 ). Figure 3 1 S cenario 1, Permitted Construction : for the Gainesville 121 s ite, buildable acreage is in grey The buildable space is comprised of residential housing and commercial spac e with a breakdown of 84% and 16% respectively. According to the Comprehensive Plan Future Land U se Element residential densities for the permitted design scenario call for a maximum Single Family unit rate of 1 residential unit (RU) per 2.5 acres, a Low Density Residential maximum unit rate of 2.75 RUs per acre, and a mixed use, Planned Use District (commercial) that requires a minimum density of 4 RUs per acre ( Radson, 2010 ) The remaining 718 acres (290 ha) of the study sit e were designated as conservation areas by a City of Gainesville approved D evelopment O rder ( Radson, 2010 ) Most of the conservation area is comprised (approximately 90%) of wetlands.
26 Results of the impact to baseline carbon values indicate that 1 6,338 mt (55%) of carbon storage and 1, 327 mt (46%) of annual carbon sequestration will be conserved with this design ( Table 3 4 ). Scenario 2 : D oubled Residential Density and Halved Acreage The goal of scenario 2 was to decrease bui ldable space in order to conserve more trees within the development site. T he number of acres from each tree stand age group that was designated as buildable space was reduced by 50% Thereby the amount of residential units (RUs) per acre was doubled (Fi g. 3 2) Figure 3 2. S cenario 2 Residential Density and Halved Acreage : R esidential density has been doubl ed and buildable acreage, in gr e y, has been reduced by half. Based on local conversations with county planners, this doubling of density was a r ealistic scenario and could be constructed under current policy. This result ed in conserving 7 7 % of the stored carbon and 73% of the sequestered carbon (Table 3 3). Instead of 1,025 (415 ha) b uildable acre s, the site now has 513 acres (207 ha) while
27 resi dential units are held constant at 1,835 RUs. All following development scena rios in this study used a 50% a creage/double density formula. Scenario 3 : C onserving Older Tree Stands For this scenario, permitted buildable areas were reduced in acreage by 50% Instead of reducing each of the tree stand age groups, I analy zed how conserving older tree stands impacted carbon storage and sequestration. Our preliminary analyses indicated that older tree stands stored and sequestered more carbon (Table 3 3). T re e stands with ages 19 29 yrs and 30 6 1 yrs were conserved in this scenario (Fig. 3 3). This resulted in conserving 89% of the stored carbon and 80% of the sequ estered carbon (Table 3 4 ). Figure 3 3. Scenario 3 Conserving Older Tree Stands: B uildable area in grey, available after conserving tree age stands greater than 18 years old.
28 Scenario 4 : Conserving Younger Tree Stands Again, permitted buildable areas were halved in acreage. Here, I explored how conserving younger trees would impact carbon s torage and sequestration. A s trees grow they sequester more carbon but sequestration drop s off and eventual ly trees begin to emit CO 2 as they reach the end of their life cycle ( Jo & McPherson, 1995 ; David J. Nowak, 1993 ) I wanted to determine how the initial conservation of young trees would affect carbon storage and sequestration since young trees will eventually mature and replace older trees. I con served trees in age groups 2 9 and 10 18 ( Fig. 3 4 ). T hi s design resulted in conserving 6 8 % of the stored carbon and 68% of the sequestered carbon (Table 3 4 ). Figure 3 4. Scenario 4 Conserving Younger Tree Stan ds : B uildable area in grey, available after conserving tree age stands less than 19 years of age
29 S cenario 5 : S ingle Compact Design This scenari subdivision design while still conserving as much storage and s equestration potential as possible. The location of this site was based on maximizing the conservation of tree stands with ages 19 29 yrs and 30 61yrs The compact build areas overlapped with the largest intact area of young trees stands (2 9 yrs and 10 1 8 yrs) regardless of areas designated as permitted conservation and buildable areas (Fig. 3 5) Figure 3 5. Scenario 5 Single Compact Design : B uildable area in grey located near existing communities with multiple major road access regardless of existing land use boundaries. I placed the development near multiple main thoroughfares for easy access and also near existing residential and commercial communities. For this and other compact design scenarios, I wanted to maximize other conservation v alues such as wildlife habitat and minimize roads built and vehicle miles traveled (VMT) Larger areas have less edge and many specialist wildlife species avoid edge dominated landscapes
30 ( Bollinger & Switzer, 2002 ) Also in compact communities less infrastructure is need which will result in less construction material used and less miles traveled per car journey. This model impacted 159 acres (64 ha) of conservation area which contain ed 149 acres (60 ha) of wetlands. This resulted in conserving 86% of the stored carbon and 77% of the sequestered carbon (Table 3 4). Scenario 6 : Multiple Compact Design I mpacting Conservation Areas In this scenario, I placed buildable space into two comp act areas and ignored permitted land use designation s Here, I targeted the conservation of older aged trees (19 29 yrs and 30 61yrs) and clustered buildable space in areas that contained younger tree stands which resulted in two development areas ( Fig. 3 6 ). Figure 3 6. Scenario 6, Multiple Compact Design s : M ultiple build areas in grey, located near existing communities with multiple major road access regardless of existing land use boundaries.
31 This scenario impacted 101 acres ( 41 h a ) of the total la nd allocated as conservation area; wetlands comprised approximately 90 acres (37 ha) of these areas This resulted in conserving 91% of the stored carbon and 82% of the sequestered carbon (Table 3 4 ). Scenario 7 : Multiple Compact Design No Impact to Cons ervation Areas According to the current D evelopment O rd er between the landowner and the City of Gainesville most of the floodplains and wetlands were in areas classified as conservation. The previou s compact design scenarios (i.e. 5 6) allow ed constructi on to take place in these protected areas and may be undesirable from a city planning or environmental regulation perspective This scenario looks at a multiple compact design solution that does not impact these conserved areas but does require changing pe rmitted land use designations because the commercial areas had to be placed in residential land uses ( Fig. 3 7 ) Figure 3 7. Scenario 7, Multiple Compact Design s : M ultiple build areas in grey, located near existing communities with multiple major roa d access without impacting land designated as conservation
32 Again, older tree stands were targeted for conservation (ages 19 29 yrs and 30 61 yrs). This resulted in conserving 80% of the stored carbon and 76% of the sequestered carbon (Table 3 4). Scenari o 8 : Compact Design within Current Land Use Designations In this scenario, I explored a compact design following both permitted building and designated conserved areas (Fig. 3 8). Figure 3 8. Scenario 8 Current Land Use Designations : A compact design within current land use boundaries without impacting land designated as conservation. Buildable acreage in grey, is reduced from 208 ha (in previous scenarios) to 204 ha. Staying within current land use designations, I targeted the largest buildable are as that have young tree stands (2 9 yrs and 10 18 yrs) to determine how compact I could get the design without changing current land use boundaries. There is a slight reduction in buildable a rea from 513 acres (207 ha) t o 505 acres (204 ha) with a corresp onding increase in residential density in order achieve a total of 1,835 residential units This
33 resulted in conserving 81% of the stored carbon and 75% of the sequestered carbon (Table 3 4 ) Table 3 4 Build design scenarios for the Gainesville 121 site showing the total number of r esidential u nits, acres impacted, the amount of carbon storage and sequestration remaining after all vegetation is cleared in the construction area, and percent conserved in CO 2 storage and sequestration from preconstruction v alues. Scenario Residential Units Acres Carbon Storage mt ( % ) Baseline Conserved Gross Carbon Seq. mt / yr ( % ) Baseline Conserved Baseline 0 1,743 29,938 100 2, 857 100 1 1,835 1,025 1 6,338 55 1, 327 46 2 1,835 513 2 3,138 7 7 2,0 92 73 3 1,835 513 2 6,6 23 89 2, 291 80 4 1,835 513 20,443 6 8 1, 932 68 5 1,835 513 2 5,776 86 2, 206 77 6 1,835 513 2 7,298 91 2, 350 82 7 1,835 513 23, 949 80 2, 175 76 8 1,835 505 2 4,312 81 2, 148 75
34 CHAPTER 4 D ISCUSSION AND CONCLUSIONS Discussion O n th e Gainesville 121 stud y site differences in carbon storage and sequestration were most prominent between tree stand age groups than between forest types. T he older tree stand age groups 19 29 yrs and 30 61 years stored more carbon than the younger tree stand age categories 2 9 yrs and 10 18 years This is similar to p revious research that has shown that stand age is an important variable in determining carbon storage ( Gough, Vogel, Schmi d, & Curtis, 2008 ; Thornton et al., 2002 ; Timilsina, et al., 2013 ; Wang et al., 2011 ) Heal thy, large trees store several times more carbon than smaller trees and even small conservation areas can have significant impacts on a ( Escobedo, et al., 2010 ; Maco & McPherson, 2003 ; David J. Nowak, 1993 ; David J. Nowak & Crane, 2002 ; David J. Nowak & Dwyer, 2007 ) Differences for carbon sequestration mirrored storage results with the exception for tree age groups 10 18 yrs when compared to 19 29 yrs where no significance was found. Lack of significance between these two age groups could be attributed to these age groups having similar growth rates and densities that encompass both age ranges ( Escobedo, et al., 2010 ; David J. Nowak & Crane, 2002 ) For the most part carbon storage and sequestration did not differ among the three forest classes with the exception that the hydric forest type stored more carbon than mesic hydric Tree species, diameter, and stand age distributions are important parameters influencing carbon storage and sequestration ( David J. Nowak, 1993 ; Timilsina, et al., 2013 ) and similar carbon values between forest type s in this study may be attributed to the fact that this is a heavily managed forest. A large portion ( 68%) of
35 the study site wa s pine plantation and the composition and abundance of tree species was probably not typical if compared to a more natural forest Both mesic hydric and xeric mesic had a large percentage of pine species ( 5 8 % ) and (55%) respectiv ely while hydric was much lower at 2 4 %. This indicates that the hydric forest type was not as heavily managed for pine. Further indication of this was almost 55 % of all hydric plots were located in 30 61 yrs tree stands while only 7% of mesic hydric plot s were in the same age category. In the hydric area large older trees, mostly oak ( Quercus spp. ), cypress ( Taxodium spp. ) and sweetgum ( Liquidambar styraciflua ) stored most of the carbon while pine stores the greatest share of carbon in mesic hydric. M any of the trees in the hydric areas may have been left as seed trees from earlier tree harvests or were left because they had no commercial value. Some of these larger trees may also not have been harvested due to the difficultly in reaching and extract trees from hydric areas. Fewer pine trees combined with older/larger tree stands most likely contributed to significantly greater carbon storage in the hydric forest category. Analysis of forest type carbon storage and sequestration on a per hectare bases is comparable with previous research conducted by ( Escobedo, et al., 2010 ) for the Gainesville and Miami Dad e regions C arbon storage for the Gainesville 121 ranged from 30.9 8 1.7 74.4 mt/ha. The wide range for the Escobedo results is due to several additional land use types that include agricultural, commercial, inst itutional, residential, and utility which was not part of the Gainesville 121 study. Carbon sequestration for the Gainesville 121 study was listed as gross sequestration per hectare but applying ( David J. Nowak & Crane, 2002 ) findings that net sequestra tion is approximately 75% of the gross sequestration value a
36 comparison with the Gainesville, Miami Dade indicates that sequestration per hectare values between the two studies are similar. Thus, in highly managed pine plantations, focus on conserving old er tree stand ages may be an appropriate strategy to maximize carbon storage and sequestration instead of concentrating on conserving areas that have different forest types. However, in a more natural setting or if the landowner does not have tree stand a ge information, it may be appropriate to conserve more hydric areas because they potentially have the largest carbon storage and sequestration due to the probability of higher nutrient levels that increase growth rates in trees ( McConnaughay, Nicotra, & Bazzaz, 1996 ) In the Gainesville 121 site, although heavily managed, hydric areas had a greater number of o lder tree stand age c ategories and the bulk of the carbon storage was made up of other tree species besides Pinus spp An added benefit of conserving hydric forest type is biodiversity conservation because the hydric areas may contain a greater diversity of large trees. This was the case in this study as the hydric areas contained a greater diversity of tree species (Table 3 1). Comparing different subdivision design s I did find that the placement of built areas could significantly impact carbon storage and sequestration. Three of the top performing scenarios had the potential to conserve over 85% of the carbon storage and over 76 % of the carbon sequestration. This is an increase of over 30% in conservation from the current permitted design scenario. Of the three top performing scenarios, two have compact designs. Below, I discuss the pros and cons for each scenario in the context of creating a sustainable development.
37 S cenario s 2 4 reduced buildable acreage by 50% doubled residential density, and st ayed within the existing land use boundaries. The 3 rd scenario targeted conservation of older tree stands and rank ed 2 nd in terms of carbon storage and sequestration whereas the scenarios focusing on younger tree stands and reducing built areas in half ra nked 6 th and 7 th in storage and sequestration All of these scenarios conserve d more carbon than the permitted design and benefit ed from residential density being doubled and buildable reduced by 50% Conserving the maximum amount of trees in stand ages 19 29 year and 30 62 year appears to be a key factor. However, all of these scenarios have fragmented buil t environments; potentially a ny carbon savings could be lost due to this fragmented site design because carbon emissions have increased through con struction activities and everyday behaviors of residents. In a fragmented subdivision design, scattered neighborhoods will account for additional tree loss due to infrastructure construction. A portion of carbon savings from these three scenarios will be lost from the additional concrete, a source of CO 2 emissions ( USEPA, 2010 ) that will be needed to connect the fra gmented lots to one another in the form of roads and sidewalks. The increase i n roadways will als o lead to an increase in vehicle miles traveled (VMT) a large contributor to CO 2 emissions ( Brownstone & Golob, 2009 ; Glaeser & Kahn, 2010 ) In Restoration, Florida, a case study on the impact of compact subdivision design on vehicle miles traveled and roads paved, significant savings were found when a conventional design was compared to a compact design. This large development has 8,500 residential units; assuming each unit has one vehicle, internal and external VMT were reduced from 593,861 miles to 349,490 miles for all the units in the subdivision (a reduction of 41%). This translated
38 into a reduction in CO 2 emission of 41% which is a reduction of over 4.5 metric tons (10,000 lbs.) CO 2e /home/year (unpublished data, Program for Resource Efficient Communities, University of Florida). T wo compact design s (scenarios 5&6) provide d the best overall conservation of c arbon storage and sequestration but they impact ed designated conservation areas. T he single compact design and the multiple compact design rank 3 rd and 1 st in c arbon conservation respectfully. These two s cenarios focus ed on conse rving o lder tree groups 19 29 yrs and 30 6 1 yrs but without sacrificing compactness. There is a broad consensus among many researchers promoting more compact city designs and reducing individual carbon footprints are needed in order to mitigate the effect s of climate change ( Breheny, 1995 ; Glaeser & Kahn, 2010 ) A key component for arguing for co mpact developme nt is that current urban development and transportation account fo r over 40 percent of CO 2 emissions ( Glaeser & Kahn, 2010 ; USEIA, 2008 ) A compact design s uch as the single location scenario and the multiple location s scenario in this study, not only maximizes storage and sequestration by conserving forest ed areas it can save a developer or municipality money and reduce additional greenhouse gas emissions Scenarios 5&6 impacted conserved areas because b uilding in conserved areas with younger tree stands allowed for additional conservation of tree age stands 19 29 yrs and 30 61 yrs Encroaching upon these areas may or may not be desirable depending on the biological integrity and functionality of these wetlands over the short and long term It is not known how pristine these wetlands were, and fu r ther anal yses is needed to determine if they should or should not be encroached upon to maximize
39 carbon storage and sequestration. Wetlands provide an array of environmental goods and services which include flood control, water/ pollutant filtration, nutrient recy cling, and aquatic habitat for thousands of species ( Keddy et al., 2009 ; Kusler & Opheim, 1996 ; Tiner, 1998 ) Caution should be taken when placing built areas near wetlands. For example scenario 3, which had the 2 nd highest carbon storage and sequestration did not impact conserved areas containing wetlands but did increase the possibility that built areas surrounded or were right next to wetlands. The impact of urbanization on local wetlands can vary greatly and is dependent on development practices. Conventional construction techniques use curbs and gutters to d irect water flow away from com munities while individual lots use fill dirt and grading to direct water away from residences. Urbanization can increase soil erosion nutrient, and fertilizer runoff ( Kusler & Opheim, 1996 ; Tiner, 1998 ) Fluctuations in water flow can affect hydroperiod and water level thereby changing the flora and fauna associated with wetland ecosystems; frequently lowering species richness ( Reinelt, Horner, & Azous, 1998 ) Built areas that are next to wetland areas can also cause higher levels of mortality for wildlife, especially herpetofauna, with roadways creating barriers to migration and dispersal ( Aresco, 2005 ) Functionality of wetlands was beyond the scope of this study but this does raise the questio n of environmental trade offs. Wetlands that are surrounded by pavement and homes may become poor habitat for wildlife and nutrient load could cause water quality issues Stormwater runoff from homes can be loaded with nutrients which can be transported rapidly across impervious surfaces to wetlands causing wide fluctuations
40 in water levels and nuisance alga e blooms that deplete oxygen levels in water bodies ( Hogan & Walbridge, 2007 ; Kusler & Opheim, 1996 ) From a n ecological perspective any development scenario will have varying degrees of fragmented and undisturbed hab itat with varying degrees of impact on local flora and fauna. Fragmented forest lan dscapes have large amounts of edge due to abundance of small forest patches remaining These edges and smaller isolated forest areas can influence populations, dis persal rates and species interactions ( Paton, 1994 ) The dynamic s of an existing forest can change dramatically when it is fragmented An increase in plants along with downed trees and snags is usually evi dent as well as an increase in species richness as shrubs, grasses, and understory tree populations proliferate filling i n gaps along newly created forest edges ( Harper et al., 2005 ) However, near urban development s, these edges are typically dominated by nonnative species and the number of native species are reduced ( Kowarik, 2008 ) Even when revegetation takes place as homeowners move into a development many time s nonnative species are typically introduced and these then spread into nearby natural areas ( McKinney, 2002 ) Increases in edge hab itat affect wildlife species in both positive and negative ways. Generalist species such as White tailed deer ( Odocoileus virginianus ), prefer edge habitat whereas interior forest bird specialists avoid edge habitats ( Blake & Karr, 1987 ; Bolger, Scott, & Rotenberry, 1997 ; McKinney, 2002 ) Predators that are generalists seem to flourish with an increase in edge habitat n atural mesopred a tors such as raccoon ( Procyon lotor ), opossum ( Didelphis virginiana ), and coyote ( Canis latrans ) numbers have increased even with the reduction of natural habitats ( Heske,
41 Robinson, & Brawn, 1999 ) Human introduced exotic predators can also affect the native wildlife. Domestic cats ( Felis catus ) and dogs ( Canis familiaris ) spread disease, prey on wildlife, and disrupt ne sting behavior ( Marks & Duncan, 2009 ) Many times edge effects are magnified where multiple edges converge, (i.e. corners) often having greater predator activit y at these locations ( Fletcher, 200 5 ) In my study, the compact designs have much less edge than the more fragmented scenarios. This allowed large, intact, forest patches that enhance connectivity and could promote the movement of wildlife ( Perault & Lomolino, 2000 ) Large tracts of land reduce the possibility of anthropogenic disturbance and promote use by specialist species overall species diversity ( Blake & Karr, 1987 ) Expansive areas allow for a more natural home range and additional space for dispersal promoting species richness ( Blake & Karr, 1987 ) In this study, I assumed that all trees (biomass), were cleared on buildable areas. This would include downed woody debris, shrubs and leaf litter. While this type of m aterial was not acco unted for in this study it should be recognized for sequestering and storing carbon. Conserving biomass on built lots can have added benefits. I f trees were conserved on built lots, they could shade homes and reduce /avoid energy consumption for individua l residences and the development as a whole. Using the best case scenario ( multiple locations impacting conservation areas ) while conserving 50% of existing forested areas could increase carbon storage by an additional 4% and carbon sequestration by an ad ditional 9%. Further, i f trees we re left in the correct location to shade homes then energy usage c ould be reduced and carbon emissions
42 avoided. Previous research in the nearby Gainesville, Florida area estimate d that urban forests offset about 3% of em issions stemming from buildings, transportation, and other human activities in a city ( Escobedo, et al., 2010 ) Additional research has shown that e nergy use in a home with the benefit of tree shading can be 20 25% lower than a home without trees ( Heisler, 1986 ) Conservation of existing biomass on build lots allows for as much of the existing forest functionality to remain in place and reduces a Conclusions The combination of future population increase and the shifting of a majority of the populous from living in rural areas to living in urban centers suggest that growth management strategies will become vital in employing energy efficient land use patterns, imp lementing GHG reduction strategies, and discourag ing urban sprawl. Through this study I have been able to show that focusing conservation of older tree stands and implementing compact designs can be a viable mitigation strategy when combined with increase s in residential density. One scenario conserve d ninety one percent of the existing carbon storage and eighty two percent of the current annual sequestration by protecting older aged tree stands. This scenario clustered built areas and conserved large, i ntact areas of open space. This cluster design has the added benefit of conserving biodiversity and minimizing carbon emissions through fewer roads built and fewer miles traveled by vehicles. Although tradeoffs occur across various sustainability objecti ves, city planners and developers can evaluate various community designs and try to balance various objectives. Thus, the appropriate cluster design can maximize carbon storage and sequestration while retain ing other sustainability objectives such as biod iversity conservation.
43 GIS and design models used in combination with vegetati on data can help developers target areas for conservation in order to maximize post construction carbon storage and sequestration. In the future this information should be incorp orated into the design and permitting process. Municipalities should also allow for flexibility in zoning so changes from standard subdivision designs can be replaced with compact designs even after the permitting process has begun. As issues of climate change and GHG reduction increase in importance so will demand for this type of information which can help cities reduce their carbon footprint
44 APPENDIX A ADDITIONAL DATA Table A 1. Tree c arbon storage and annual sequestration totals at the plot level for all 8 0 sample plots (0.04 hectares) in the Gainesville 121 study site. Age Group PLOT ID CARBON STORAGE (kg) GROSS CARBON SEQ. (kg/yr) 2 9 HY8 170.28 46.2 2 9 HY21 143.36 32.72 2 9 HY22 1408.08 141.16 2 9 HY24 91.4 23.2 2 9 HY27 135.76 30.76 30 61 HY3 634.72 47.84 30 61 HY6 9494.64 340.04 30 61 HY31 7393.8 295.16 30 61 HY33 6948.12 380.36 30 61 HY35 4019.96 239.8 30 61 HY36 5501.04 316.12 2 9 MH0 259.96 62.08 2 9 MH1 101.08 47.8 2 9 MH5 202.76 54.88 2 9 MH10 418.68 118.24 2 9 MH21 228.8 54.6 2 9 MH23 202.56 51.16 2 9 MH27 294.16 57.28 2 9 MH28 342.88 97.6 2 9 MH70 75.24 26.56 2 9 MH86 66.88 32.72 2 9 MH111 1313.44 212.6 2 9 MH122 260.32 85.96 2 9 MH128 437.24 71.28 2 9 MH132 292.32 72.32 2 9 MH133 1143.92 358.88 10 18 MH6 685. 36 98.64 10 18 MH12 874 131.96 10 18 MH13 1233.72 216.32 10 18 MH14 788.52 97.16 10 18 MH25 546.32 90 10 18 MH30 1163.68 163.4 10 18 MH37 1406.48 170.24 10 18 MH41 921.08 133.88
45 Table A 1. Continued Age Group PLOT ID CARBON STORAGE (kg) GROSS CARBO N SEQ. (kg/yr) 10 18 MH43 1116.72 186.52 10 18 MH44 962.08 136.4 10 18 MH47 635.2 153.76 10 18 MH61 599.12 69.28 10 18 MH65 1174.84 159.4 10 18 MH67 1484.88 171.6 10 18 MH84 1111.04 133.88 10 18 MH87 617.12 112.4 10 18 MH110 1300.36 180.84 10 18 MH123 844.68 127.04 10 18 MH124 1103.56 162.76 10 18 MH125 680.28 102.8 10 18 MH127 357.4 55.44 10 18 MH134 1343.36 184.2 19 29 MH11 1887.44 205.64 19 29 MH39 2041.6 198.84 19 29 MH40 2218.6 179.4 19 29 MH57 1133.64 200.92 19 29 MH74 2551.32 279.2 19 29 MH81 1249.32 123.56 19 29 MH90 1021.28 120.96 19 29 MH98 938.8 112.84 19 29 MH101 1426.88 127.16 19 29 MH107 1798.16 198.68 19 29 MH126 3400.4 237.16 30 61 MH38 3283.64 290.28 30 61 MH49 2778.2 215.28 30 61 MH55 3360.96 286.68 30 61 MH83 8 631.64 555.44 2 9 XM2 102.92 53.48 2 9 XM8 583.6 141.36 2 9 XM10 92.12 46.24 2 9 XM20 434.8 130.76 10 18 XM6 817.44 97.56 10 18 XM9 1494.84 204.36 10 18 XM13 5428.96 626.64 19 29 XM4 3129.36 251.2 19 29 XM7 2645.12 277.4 19 29 XM15 2130.44 197.16 19 29 XM18 1411.72 125.4
46 Table A 1. Continued Age Group PLOT ID CARBON STORAGE (kg) GROSS CARBON SEQ. (kg/yr) 19 29 XM19 1299.32 234.56 19 29 XM22 1864.04 157.48 19 29 XM23 1082.72 129.88 30 61 XM17 1125.76 95.92
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53 BIOGRAPHICAL SKETCH Rick wa s born in Gibson City and raised in nearby Paxton Illinois. He spent his childhood and a portion of his adult life in Illinois living, working, and sometimes attending university until moving to Charleston, South Carolina in 1998 and earning a degree in Political Science from the College of Charleston, in 2002. Starting in 1999 Rick worked in the energy industry, first as a contractor for South Carolina Electric and Gas redesigning electrical distribution systems. After completing his undergraduate degree Rick acc epted a position as an operations associate with Constellation Energy and spent the next six years working in Baltimore, Maryland; London, England; and Louisville, Kentucky. In 2010, Rick accepted a graduate assistantship position at the University of Flor ida in pursuit of a Master of Science degree in Interdisciplinary Ecology. After environmental PhD or employment opportunity that will keep him from having to move into his parent basement.