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Urban Tree Growth and Mortality in Gainesville, Fl

Permanent Link: http://ufdc.ufl.edu/UFE0042226/00001

Material Information

Title: Urban Tree Growth and Mortality in Gainesville, Fl Implications for Carbon Dynamics and Green Waste
Physical Description: 1 online resource (62 p.)
Language: english
Creator: Lawrence, Alicia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: and, green, growth, mortality, tree, urban, waste
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Research on urban tree growth, mortality and in-growth is needed to project future urban forest structure more accurately. To provide more accurate information for urban forests in Gainesville, a subsample of 93 plots established in the summer of 2006 were relocated and re-measured during the growing season of 2009. These 65 plots provide a unique opportunity to study urban tree growth, mortality and in-growth. Comparative data provided rates of diameter growth, mortality, and in-growth that were analyzed based on initial tree and plot level conditions using general and generalized linear mixed statistical models. Growth in diameter at breast height (dbh) was modeled for three species groups and the four most frequent tree species: laurel oak (Quercus laurifolia; 76 trees), water oak (Quercus nigra; 62 trees), slash pine (Pinus elliottii; 62 trees) and loblolly pine (Pinus taeda; 57 trees). Mortality and in-growth models were developed for hardwood and softwood species. Results show that Gainesville trees are estimated to have an average annual mortality rate of 1.8%. In total there were 755 trees sampled in 2006 and by 2009, 128 (17%) trees were removed. Plot and tree level characteristics affected diameter growth in all species groups. Growth rates in Gainesville were higher than those reported in other studies of urban tree growth. Results provide local information that can be used for improving estimates of growth, biomass, and carbon sequestration in the Southeastern United States.
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 Alicia Lawrence.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Escobedo, Francisco Javier.
Local: Co-adviser: Staudhammer, Christina Lynn.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042226:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042226/00001

Material Information

Title: Urban Tree Growth and Mortality in Gainesville, Fl Implications for Carbon Dynamics and Green Waste
Physical Description: 1 online resource (62 p.)
Language: english
Creator: Lawrence, Alicia
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: and, green, growth, mortality, tree, urban, waste
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Research on urban tree growth, mortality and in-growth is needed to project future urban forest structure more accurately. To provide more accurate information for urban forests in Gainesville, a subsample of 93 plots established in the summer of 2006 were relocated and re-measured during the growing season of 2009. These 65 plots provide a unique opportunity to study urban tree growth, mortality and in-growth. Comparative data provided rates of diameter growth, mortality, and in-growth that were analyzed based on initial tree and plot level conditions using general and generalized linear mixed statistical models. Growth in diameter at breast height (dbh) was modeled for three species groups and the four most frequent tree species: laurel oak (Quercus laurifolia; 76 trees), water oak (Quercus nigra; 62 trees), slash pine (Pinus elliottii; 62 trees) and loblolly pine (Pinus taeda; 57 trees). Mortality and in-growth models were developed for hardwood and softwood species. Results show that Gainesville trees are estimated to have an average annual mortality rate of 1.8%. In total there were 755 trees sampled in 2006 and by 2009, 128 (17%) trees were removed. Plot and tree level characteristics affected diameter growth in all species groups. Growth rates in Gainesville were higher than those reported in other studies of urban tree growth. Results provide local information that can be used for improving estimates of growth, biomass, and carbon sequestration in the Southeastern United States.
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 Alicia Lawrence.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Escobedo, Francisco Javier.
Local: Co-adviser: Staudhammer, Christina Lynn.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042226:00001


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URBAN TREE GROWTH AND MORTALITY IN GAINESVILLE, FL: IMPLICATIONS
FOR CARBON DYNAMICS AND GREEN WASTE




















By

ALICIA LAWRENCE


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

2010

































2010 Alicia Lawrence
































To TJ









ACKNOWLEDGMENTS

I thank my parents and my sister for supporting me in every way. I also thank my

committee members for guiding me though this adventure and giving me an opportunity

to better myself. I would like to thank the United States Department of Agriculture Forest

Service for funding my graduate tuition and University of Florida School of Forest

Resources and Conservation for providing me the equipment and tools needed for my

fieldwork and analysis; resources that allowed me to take on this ambitious and

rewarding study. Finally, I thank my field crew, Dawn, Cynnamon, Ben, and Sebastian

for all their hard work.









TABLE OF CONTENTS

page

A C KNOW LEDG M ENTS .......... ..................... ....... .. ......................................... 4

LIS T O F TA B LE S ............. .. ..... ......................................................... ...... ........ 7

LIS T O F F IG U R E S .................................................................. 9

A BST RA CT ............... ... ..... ......................................................... ...... 10

CHAPTER

1 INTRODUCTION AND OBJECTIVES........................... ......... 12

U rban Forest Ecosystem Structure ................................................................ 12
Temporal Changes in Urban Forest Structure ............... .................................. 14
U rb a n T re e M o rta lity ................................................................................. ......... 15
U rban T ree G row th .......................................................... ...... ........ 17
Soil Related Stress on Urban Tree G growth ............................................................ 18
Carbon Storage and Sequestration of Urban Trees........................ ...... 20
Uses of Urban Tree Biomass........................................ ......................... 21
Application of Mortality and Growth to Urban Forest Function Studies ................. 21
O bje c tiv e s ........................................................................................... ............... 2 2

2 METHODS...................................................... 26

S tu d y A re a .......................................................................................... ......... 2 6
D ata C collection ..................................................... 27
Plot and Tree Measurem ents .................................................. .............. 27
Soil M easurem ents............................................ ............... 28
Re-measurement Method Errors .......................................... 28
D a ta O rg a n iz a tio n ..................................................................... ..... ............... 2 9
D ata A availability .. .......................................... ................ ............... 29
M watching of Individual T rees.................................................. ........... .... 29
S o il V ariable S e election ....................................................................... ............. 30
Species Groups.................................................... ......... 31
Land Use and Land Cover Descriptions ................................................... 32
C a lcu la tio n s ................ ............. ... ................................................ .....3 4
Diameter Growth and Mortality Rates ................................................... 34
B io m a ss E stim a te s ........................................................................ 3 4
S ta tistica l A n a lyse s ........................................................................ 3 5

3 RESULTS A ND D ISSC USIO N ............................................................ .......... 39

R e su lts ............................................................................................. 3 9
Change in Urban Forest Structure........................................... 39









G row th M models .............................................. ..... .................................. 39
M ortality and In-grow th M odels .................................. ................................... 41
Change in Biomass ................................. ............... 41
D discussion .................................................................................................. 42
U rban Forest Structure C hanges............................................. ... .. ............... 42
Rates of G rowth and M ortality........... ....... ........................ ............................ 43
Models of Growth, Mortality and In-growth........................... ................... 44
Green Waste Supply Potential and Carbon Sequestration.............................. 47

4 C O N C LU S IO N .......................................... ................. ............... 54

LIST OF REFERENCES .............. ............................... 57

B IO G RA P H ICA L S KETC H ............. ................. ................. .................... ............... 62





































6









LIST OF TABLES


Table page

1-1 Tree diameter growth rates by land cover used in the Urban Forest Effects
(UFORE) model to calculate urban tree growth and carbon sequestration
from Nowak and Crane (2000) ............. .. ....... .............. ... ............... 25

2-1 Status of all permanent plots in 2009 by land use and city for Gainesville,
Florida. ............ ............................... ................ 38

2-2 Gainesville, Florida's percent annual mortality and growth rates arranged by
Nowak et al.'s (2004) size classes to demonstrate differences in sample size... 38

3-1 Plot count, average annual growth (AGR) and mortality rates (AMR) for all re-
measured trees between 2006 and 2009 in Gainesville, Florida by land use
and cover categories ................................... ................................ 48

3-2 Test of fixed effects for model of annual diameter at breast height (dbh)
growth by species group in Gainesville, Florida from 2006 to 2009................ 48

3-3 Test of fixed effects for model of annual diameter at breast height (dbh)
growth of four most frequent species in Gainesville, Florida from 2006 to
2009 ....................................................................... .......... 49

3-4 Test of fixed effects for model of mortality for hardwoods and softwoods in
Gainesville, Florida from 2006 to 2009 ............... ............................................ 50

3-5 Annual mortality rates for the ten most common trees found in 2006 ranked
by total number of trees .................................... ......................... ........ 50

3-6 Test of fixed effects for model of in-growth for hardwoods and softwoods in
Gainesville, Florida from 2006 to 2009 ............... ............................................ 51

3-7 Annual carbon sequestered per hectare (CSPH) and city total (CSCT)
estimates by land use and land cover for trees in Gainesville, Florida from
2006 to 2009.................................................................. ........ 51

3-8 Top four species ranked by frequency and top six species ranked by highest
average growth rate (AGR) and standard error (SE), with corresponding
carbon sequestration per hectare (CSPH) and city total (CSCT) estimates for
trees in Gainesville Florida from 2006 to 2009 ................................................ 52

3-9 Annual removed above ground fresh weight biomass per hectare (RBPH)
and city total (RBCT) estimates by land use and land cover in Gainesville,
Florida from 2006 to 2009 ........................................ ............................. ... 52









3-10 Annual removed above ground fresh weight biomass per hectare (RBPH)
and city total (RBCT) for species comprising 90% all removed biomass in
Gainesville, Florida from 2006 to 2009 ............... ............................................ 52

3-11 Carbon stored in 2009 per hectare (CSTPH) and city total (CSTCT)
estimates by land use and land cover in Gainesville, Florida ............................. 53









LIST OF FIGURES


Figure page

3-1 Average percent change in trees per hectare and basal area per hectare by
land use and city total for Gainesville, FL from 2006 to 2009............................. 53









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

URBAN TREE GROWTH AND MORTALITY IN GAINESVILLE, FL: IMPLICATIONS
FOR CARBON DYNAMICS AND GREEN WASTE

By

Alicia Lawrence

August 2010

Chair: Francisco Escobedo
Cochair: Christina Staudhammer
Major: Forest Resources and Conservation

Research on urban tree growth, mortality and in-growth is needed to project future

urban forest structure more accurately. To provide more accurate information for urban

forests in Gainesville, a subsample of 93 plots established in the summer of 2006 were

relocated and re-measured during the growing season of 2009. These 65 plots provide

a unique opportunity to study urban tree growth, mortality and in-growth. Comparative

data provided rates of diameter growth, mortality, and in-growth that were analyzed

based on initial tree and plot level conditions using general and generalized linear mixed

statistical models. Growth in diameter at breast height (dbh) was modeled for three

species groups and the four most frequent tree species: laurel oak (Quercus laurifolia;

76 trees), water oak (Quercus nigra; 62 trees), slash pine (Pinus elliottii; 62 trees) and

loblolly pine (Pinus taeda; 57 trees). Mortality and in-growth models were developed for

hardwood and softwood species. Results show that Gainesville trees are estimated to

have an average annual mortality rate of 1.8%. In total there were 755 trees sampled in

2006 and by 2009, 128 (17%) trees were removed. Plot and tree level characteristics

affected diameter growth in all species groups. Growth rates in Gainesville were higher









than those reported in other studies of urban tree growth. Results provide local

information that can be used for improving estimates of growth, biomass, and carbon

sequestration in the Southeastern United States.









CHAPTER 1
INTRODUCTION AND OBJECTIVES

Urban Forest Ecosystem Structure

Two key aspects of a healthy ecosystem are the ability of an urban forest to

provide ecosystem services like tree shade and aesthetics, which can be valued as a

commodity that improves quality of life while still maintaining its own biophysical

integrity (Rapport 1995). The composition and structure of urban vegetation can be

modified over time due to losses from deforestation and fragmentation and gains from

reforestation and afforestation (Zipperer et al. 1997). Activities associated with

expanding human populations can also cause modifications that alter ecosystems and

impede the sustainability of resources and services provided by these ecosystems

(Zurlini & Giardin 2008). As such the products/goods generated from an urban forest

are the quantifiable results (money saved through energy avoidance) manifested

through the services (tree shade and air temperature reduction) that are the result of a

variety of functions evapotranspirationn, radiation blocked by tree canopy) as well as

contribution to overall city tree canopy. These functions in turn are made possible by the

structure of the urban forest (location and size of tree, overall city tree cover) and can

interact at multiple scales spatially and temporally (Zurlini & Giardin 2008, Pandit &

Laband 2010).

To relate the overall urban forest structure to its variety of functions in a way that

includes the spatial and temporal properties of social and ecological patterns is to view

the urban vegetation processes within "patches" or areas that are relatively

homogeneous and that differ from their surroundings which are affected by urban

influences that occur along a gradient of urbanization from the urban core to rural









(Zipperer et al. 1997). Understanding how ecosystems change over time can provide

insight into identifying potential changes that may detract from the integrity of the

ecosystem and its capacity to continue to provide resources and services into the future

(Petrosillo et al. 2007).

Growth suppression, life span reduction, increased susceptibility to insect and

disease related problems, as well as losses in aesthetics and higher replacement costs

can occur in the presence of tree stresses related to site and soil conditions (Rhoades &

Stipes 1999). Therefore, research on growth, mortality and removed biomass from

urban forests can facilitate management techniques that can increase the net benefits

of urban trees such as mitigation of atmospheric carbon dioxide (Nowak et al. 2002).

Urban forest ecosystem structural changes are influenced by tree mortality,

therefore mortality rates are needed to project future populations and benefits

associated with the urban forest while understanding the factors that affect urban tree

mortality can help urban forest managers minimize trees costs and risks while

enhancing environmental benefits (Nowak et al. 2004). Research on urban tree

diameter growth is limited and often focuses on tree populations from northern regions

of the US (Nowak 1994, Jo & McPherson 1995, deVries 1987), and if applied to tree

populations in southern US regions, they may underestimate the projected carbon

dioxide reduction functions as well as other benefits associated with tree growth.

Estimates of removed tree biomass can also reveal the potential bio waste supply in

Gainesville, Florida as well. Analyses of permanent plots and their re-measurement

should provide site-specific mortality, growth, and subsequent biomass estimates that









reflect the external factors acting upon them due to local geographical, ecological, urban

form, and socioeconomic related influences (Heyen & Lindsey 2003).

Temporal Changes in Urban Forest Structure

Tree cover, or percent tree canopy cover, is used to describe city-wide tree

structure and is often used as an indicator of health of urban forest structure by

managers, policy makers, scientists and analysts (Heyen & Lindsey 2003, Zipperer et

al. 1997). Urban tree cover has been described as the proportion of land covered by

tree crowns within a municipality or other geographic or administratively defined areas

(Heynen & Lindsey 2003). Tree cover definitions can include the measurement of all

plant material above, on and below the ground (McDonnel & Pickett 1990). There are

many drivers for changes in tree cover over time: urban development, windstorms, tree

removals, and growth (Zipperer et al. 1997, Duryea et al. 2007, Escobedo et al. 2009).

In Central Indiana, an urban forest canopy cover study showed that areas are likely to

have more canopy cover if it: has a population consisting of more individuals with

college degrees, has older housing stock, has areas with slopes greater than 15% and

a network of dense streams (Heyen & Lindsey 2003). Historical tree-cover data, field

vegetation sampling, and comparison of past aerial photographs have been used to

quantify and describe tree cover change over time (Nowak et al. 1996). In Gainesville,

tree cover has not exhibited a linear trend, decreasing over time from 66% in 1995 to

55% in 2005 and then increasing to 60% in 2007 (Szantoi et al. 2008).

Natural environment, urban morphology and human values within a city affect the

amount of tree cover (Nowak et al. 1996, Escobedo et al. 2009). For example, cities

developed in natural forested areas have higher tree cover than those developed in

grasslands (19%), or deserts (10%) (Nowak et al. 1996). Within a city, land uses









occupied by park and residential lands as well as vacant lands and forested areas

generally have the highest tree cover (Nowak et al. 1996). In Gainesville, Florida, 2006,

urban tree cover was 51%: higher tree cover was found in vacant and forested areas

and lower tree cover was found in commercial and industrial areas (Escobedo et al.

2009). Gainesville's tree cover is higher than many other cities studied in the United

States (Nowak, 1993a 1993b; Nowak 1994; McPherson 1998). Certain tree species can

contribute more to an area's overall tree cover due to their large individual tree sizes.

For example, live oaks (Quercus virginiana Mill.) contributed to 14% of the Gainesville's

total leaf area while only comprising 4% of all trees (Escobedo et al. 2009).

Urban Tree Mortality

Urban tree mortality can be minimized if the factors are better understood

(Nowak et al. 2004). However, urban tree mortality has been the subject of relatively

few scientific studies. In the limited number of studies, urban tree mortality has been

shown to be related to tree condition, size, age, land use, water and nutrient stress

socio-economic status, community participation, and management practices (Nowak

1986, Foster & Blain 1978, Nowak et al 1990, Sklar & Ames 1985, Gibertson &

Bradshaw 1985, Nowak et al. 2004).

In many studies, mortality research on urban trees has concentrated on the factors

affecting existing and newly planted street tree populations (Nowak 1986, Foster &

Blaine 1978, Nowak et al. 1990, Sklar & Ames 1985, Richards 1979, Gibertson &

Bradshaw 1985). Street tree size and condition in Syracuse, New York, were found to

influence average annual mortality rates; low mortality was found in stable and healthy

trees (1.4%) while higher mortality was found in trees larger than 77 cm dbh (5.4%) and

in trees with crown deterioration (6.4%) (Nowak 1986). Newly planted street trees in









Boston, Massachusetts had a mortality rate that averaged 9% over a ten year period,

which mostly depended on tree planting methods (Foster & Blaine 1978).

In Oakland, California, annual mortality rates for newly planted street trees

averaged 19% over a two-year period (Nowak et al. 1990) with lower tree mortality rates

occurring in areas next to single family and rapid transit stations and high mortality rates

occurred in proximity to apartments, greenspaces, and areas with low socio-economic

status and high unemployment. Another study of newly planted street trees in Oakland

California reported lower annual mortality rates for trees planted with the community's

participation: where rates as high as 50% were reported for trees planted with no

community participation versus 5.8-8.2% for trees that were planted with community

participation (Sklar & Ames 1985). Richards (1979) study in Oakland, California

involving street tree survival, suggests high mortality rates in small, un-established trees

and a positive relationship with minor accidents and vandalism. Water and nutrient

stress was the cause for mortality for 56% of newly planted trees in Northern England

while other causes of mortality included vandalism (18%), girdling by tree guards (12%),

soil compaction (9%) and improper staking and tying techniques (5%) (Gilbertson &

Bradshaw 1985).

Methods involving permanent, random plot re-measurement of urban tree

populations are limited but one study has been used to study size, condition, species

and land use effects on urban tree mortality (Nowak et al. 2004). In Baltimore,

Maryland, two-year permanent 0.04 hectare plot re-measurements yielded average

annual tree mortality and net change in number of live trees rates of 6.6% and -4.2%

respectively, with the lowest mortality rates occurring in medium to low-density









residential land uses and the highest mortality rates occurring on transportation or

commercial-industrial lands (Nowak et al. 2004). Tree size and condition also affected

mortality rates in Baltimore, where higher rates were found for trees in small diameter

and poor condition classes.

Urban Tree Growth

Natural forest and urban tree diameter growth rates used to model urban forests

are based on natural, forest-like conditions (Nowak & Crane 2000) and generally come

from a study in Indiana and Illinois where comparison of permanent forest inventory plot

measurements of dbh yielded growth rates that average 0.38 cm per year (Smith &

Shifley 1984). Average annual growth rates for a variety of hardwoods species and a

single softwood species, shortleaf pine (Pinus echinata) were 0.38 cm/yr and 0.36

cm/yr, respectively. This study compared growth rates by crown and diameter classes

as well as species groups, finding faster growth in trees from dominant and co-dominate

crown classes.

Previous urban tree growth studies have used radial growth measurements from

core samples and permanent plot re-measurement to acquire urban tree growth rates.

In a study of urban trees in two neighborhoods in Chicago, Illinois, growth rates

determined using tree ring increments from core samples were 1.09 cm per year

(N=118) for the following hardwood trees: maples (Acer negundo, A. saccharinum, A.

platanoides), elms (Ulmus americana, U. pumila), mulberry (Morus alba), crabapple

(Malus spp.), and cherry (Prunus spp.) and 0.51 cm per year (N=17) for softwood trees

including spruces (Picea pungens, P. abies, P. glauca) (Jo & McPherson 1995).

Another urban tree study in Chicago used radial growth increments to estimate tree

growth and carbon sequestration for removed, open grown trees, and found tree growth









rates ranging from 0.78 to 1.02 cm per year (Nowak 1994). In a study across five mid-

western states (Illinois, Iowa, Minnesota, Missouri and Wisconsin) radial growth rings

from the last ten years were used to compare growth rates across land uses and higher

growth rates were found on city park sites followed by residential and commercial sites

(lakovoglou et al. 2002). In Gainesville, FL an urban tree study acquired growth rates

using the five most recent annual growth rings from 12 laurel oaks (Quercus laurifolia)

and determined a growth rate of 1.3 cm per year (Templeton & Putz 2003).

In the SE, urban tree growth has been researched on a species level with

emphasis on Live Oaks, a historically important and common species and a large

component of total leaf area by species in the region. In Gainesville, for example, Live

Oaks provide 14% of the city's total leaf area (Escobedo et al. 2009). Not only are they

excellent shade trees, live oaks are well suited to urban conditions (Grabosky & Gilman

2004) and highly resistant to hurricane damage (Duryea et al. 2007). In parking lots in

Florida, one study reported that growth, described as a relationship between dbh and

canopy radius size, declined as non-paved surface area was reduced for Chinese elm

(Ulmus parvifolia Jacq.), sycamore (Platanus occidentalis L.), Shumard oak (Quercus

shumardii Britton), and laurel oak (Quercus laurifolia Michx.); but not live oak (Grabosky

& Gilman 2004). Surrounding vegetation such as other trees, shrubs and turf grass can

also limit growth as they compete when space is limited (Vrecenak et al. 1989).

Soil Related Stress on Urban Tree Growth

Soil and site conditions are often used to study factors related to tree growth and

reduction of life span. Factors that result in less than optimal growth rates in plants can

be described as "stress" (Kozlowski & Pallardy 1997). Urban soils are altered by

management regimes, disturbances, changes in surface cover, and other human









influences that can result in highly variable soil characteristics distributed across the

urban landscape (Craul 1999, Pouyat et al. 2007). Urbanization can cause alterations in

soil bulk density, microbial biomass and organic matter resulting from physical

modifications of urban soils and disturbances such as building construction, compaction

from heavy equipment, foot traffic, covering of soil with impervious surfaces, and

removal of grass clippings and yard wastes (Scharenbroch et al. 2005, Craul 1999).

These effects are reduced by the amount of time since urbanization as natural

processes improve physical, biological and chemical soil properties (Dobbs 2009,

Scharenbroch et al. 2005). Soil-nutrient concentrations can also vary across the urban

landscape depending on the time since urbanization, urban morphology (e.g. amount of

impervious surface), land use, and land cover (Pouyat et al. 2007, Grimm et al. 2008).

Water stress is also considered a major limiting factor on vegetation in all

environments (Kramer & Boyer 1995). For example urban street trees suffer from

additional water and heat stresses associated with urban site conditions such as

impervious surfaces, soil compaction, and built structures that radiate heat (pavement,

buildings, automobiles parked under trees) (Close et al. 1996). As a result, annual

diameter measurements can be negative when the tree's stem water content

decreases, causing a contraction of wood and bark in the trunk (Pastur et al. 2007).

Stresses associated with poor site conditions such as impervious surfaces

beneath the crown, soil compaction and pH have been shown to affect growth in sugar

maples (Acer saccharum) where terminal growth in trees growing in woodlots was

significantly higher than those grown on Michigan State University's campus and streets

(Close et al. 1996). Conversely, annual rates of diameter growth were higher on trees









growing on the Virginia Tech University campus than the same species growing in a

forest, implying that site conditions associated with soil properties, have effects on

urban tree growth that are lessened by open canopy conditions found in the urban

environment (e.g. less competition for light, water and nutrients) and are similar to

observed increases in branch size and survival after forest stands have been thinned

(Rhoades & Stipes 1999, Kramer & Kozlowski 1979).

Carbon Storage and Sequestration of Urban Trees

Although many components such as live biomass, litter, and soil make up an

urban forest's carbon stock, live biomass is most affected by human and natural

disturbances and can be easily tied to tree measurement data. An urban tree cover

study by Nowak & Crane (2002) estimated that urban forests across the nation

collectively store 700 million tons and sequester 22.8 million tons carbon per year.

Proper management techniques can increase the role of urban trees in

sequestering atmospheric carbon dioxide (Nowak et al. 2002). Planting low

maintenance trees that grow at moderate or fast rates, with the potential to become

large in size, usually maximize the potential quantity and duration of the carbon benefits

received by a tree. Nowak (1994) reported that carbon sequestration and carbon

storage was 90 and 1000 times greater, respectively in large versus small trees. When

selecting trees, growth rates and life spans are equally as important as considering if

the tree is appropriate for the given site conditions and maintaining the trees in a

manner that increases survival (Nowak et al. 2002).

Developing uses for wood from removed trees can delay carbon decomposition

emissions or contribute to community's fossil fuel energy needs (Nowak et al. 2002). In

addition, Nowak et al. (2002) suggest the practices such as minimizing the use of fossil









fuel burning pruning equipment and techniques, appropriate tree disposal methods,

strategic planting of deciduous shade-tree providing species that require low

maintenance near buildings, and proper maintenance and spacing for existing large

trees need to be evaluated.

Uses of Urban Tree Biomass

The amount of wood removed due to mortality caused by urbanization, pests,

hazard trees, windstorms, and other disturbances from urban forests nationally is

comparable to the total annual harvests from US National Forests and can range from

16 to 38 million green tons (Bratkovich et al. 2010, Bratkovich et al. 2008). The use of

urban tree "waste" wood to create useful products is gaining momentum as additional

resources and initiatives are organized (Bratkovich et al. 2010).

Application of Mortality and Growth to Urban Forest Function Studies

Urban tree growth and mortality studies are being used in the Urban Forest Effects

(UFORE) and the i-tree STREETS models, which are being used throughout the SE

(http://www.itreetools.org/). In the UFORE model, a street tree mortality study in

Syracuse, New York (Nowak 1986) is used to estimate emissions due to dead and

decomposing trees when calculating carbon sequestration (Nowak & Crane 2000).

Probability of mortality is determined by street tree data from Nowak (1986) where

crown dieback measurements are categorized into condition ratings (good-excellent,

fair, poor, dying and dead). The model also uses natural forest and urban tree growth

studies from northern US regions to approximate diameter growth for individual tree

carbon sequestration rates (Table 1-1). These growth rates are based on three land

cover categories (forest, urban and park) and adjusted for tree condition and regional

climate (Nowak & Crane 2000). Assuming growth rates from northern tree studies would









likely be conservative for southern regions, despite a newer version of UFORE which

uses the length of a region's growing season to determine the base growth rate

standardized to Minnesota's where there are 153 frost-free days.

Research on urban tree growth, recruitment and mortality is needed to project

future urban forest populations more accurately (Nowak et al. 2004). Better information

on urban tree growth is needed since currently there are very few growth studies for

city-wide urban tree populations from the SE US region. The Paterson's index also

called the CVP (climate, vegetation, productivity) index has been used in several

countries to estimate potential production for areas that are hard to inventory. This index

predicts maximum growth potential of trees and is based on evaop-transpiration, annual

temperature range, mean monthly temperature of the warmest month mean annual

precipitation, length of growing season and is appropriate for comparisons across

species and regions (Skovsgaard & Vanclay 2007). However, there are disadvantages

when applying the same assumptions to different regions. Natural influences that occur

on that site are specific to that geographic area; therefore, if the results are applied to

an area outside of the study's realm, the resulting predictions may not be appropriate

(Smith 1983).

Objectives

The overall goal of this study is to analyze temporal changes in urban forest

structure by improving estimates of rates of growth, in-growth and mortality and to

identify tree and plot level factors affecting these rates. Urban tree biomass removal

estimates from re-measured plots will also provide information on carbon stocks and

green waste potential in Gainesville.









Hypothesis 1 is that significantly higher mortality rates will be found on

commercial plots versus residential plots (alpha < 0.05), because low mortality rates

observed in previous studies have been found in residential areas and higher rates

have been found on commercial and transportation land uses (Nowak et al. 1990;

Nowak et al. 2004). Hypothesis 2 is that significantly higher growth rates (alpha < 0.05),

will be found on land uses with urban-park settings such as institutional and residential

that are known to have higher rates of tree and lawn maintenance as has been

observed in a previous study in similar land uses (lakovoglou et al. 2002).

Growth models are the focus of Hypothesis 3, where significant plot level factors

(alpha < 0.05) will be soil water content and bulk density measurement which can

indicate tree growth stress (Kramer & Boyer 1995, Close et al. 1996) and low

competition from other vegetation as characterized by low tree density (trees per

hectare) which also limits tree growth (Vrecenak et al. 1989). I hypothesize that tree

characteristics that will be significant (alpha < 0.05) are related to crown measurements

such as high crown light exposure and low percentages of missing crown, as these

trends have been observed to affect the growth and survival of urban live oak trees

(Templeton & Putz 2003).

Finally, hypothesis 4 is that growth rates in urban, forest, and park land cover

types in Gainesville will be significantly greater (alpha < 0.05) than the growth rates

used by Nowak & Crane (2002) for predicting tree growth and carbon sequestration in

similar land cover types. Individual species, such as Quercus virginiana may also have

higher growth rates that those used in Nowak & Crane (2002).









My analyses of permanent plot re-measurements will provide local mortality and

growth estimates that account for local natural and human influences as opposed to

assumptions based on studies from other regions. This study is unique as it describes

the rates at which urban trees in Gainesville's grow and die based on actual

measurements. Finally, results will also be used to briefly explore biomass and carbon

stock estimates in Gainesville that account for site-specific and socio-ecological

conditions.









Table 1-1. Tree diameter growth rates by land cover used in the Urban Forest Effects
(UFORE) model to calculate urban tree growth and carbon sequestration from
Nowak and Crane (2000)
Source Land Growth Tree
cover rate type
(cm/yr)
Smith WB & Shifley SR (1984) Diameter growth, survival, Forest 0.38 Forest
and volume estimates for trees in Indiana and Illinois. Res.
Pap. NC-257. St. Paul, MN: U.S. Department of
Agriculture, Forest Service, North Central Forest
Experiment Station. 10 p

Nowak, D.J. 1994. Atmospheric carbon dioxide reduction Urban 0.87 Street
by Chicago's urban forest. In: McPherson, E.G.,Nowak,
Nowak DJ (1994) Atmospheric carbon dioxide reduction by
Chicago's urban forest. In Chicago's Urban Forest
Ecosystem: Results of the Chicago Urban Forest Climate
Project (Eds McPherson EG, Nowak DJ, Rowntree RA):
83-94. USDA Forest Service General Technical Report
NE-186. Radnor, PA


deVries RE (1987) A preliminary investigation of the growth Park 0.61 Park
and longevity of trees in Central Park. New Brunswick, NJ:
Rutgers University. 95 p. M.S. thesis









CHAPTER 2
METHODS

Study Area

Gainesville, Florida is the largest city in Alachua County covering an area of 127

square kilometers and located at 29039'N and 82020'W in north central Florida. The

climate is subtropical warm and humid. The city receives an average of 1370 mm per

year; more than half is received June through September while the driest month is

November (Metcalf 2004). Although a freeze can be expected about four times a year,

the frost-free season lasts 295 days per year (Dohrenwend 1987). The elevation in

Gainesville varies around 30 meters above sea level and topography changes from

rolling hills in the northern part of the city to flat areas within the prairie-lands to the

south where seasonally high water tables are found (Metcalf 2004, Phelps 1987).

Gainesville is situated on top of two unique geological features: the northern part lies

above the Hawthorne geologic formation and Plio-Pleistocene deposits of the Ocala

Uplift lie below the southern part of the study area (Phelps 1987). Soils are

predominantly sandy siliceous, Hyperthermic Aeric Hapludods and Plinthic Paleaquults

and the texture of these soils is very sandy (95%), and the rest are composed of

different fill material (Chirenje et al. 2003). Gainesville, Florida has many remnant-

forested patches throughout the city that exhibit soil and vegetation characteristics

similar to natural areas containing non-urban natural soils and vegetation. Also most

soils in Gainesville show little signs of pollution, severe compaction or being covered by

impervious surfaces (Dobbs 2009). However urbanization effects do exist; in 2006 the

City of Gainesville's ground was covered 9 percent by buildings and 15 percent by

impervious ground cover (roads, sidewalks, etc.) (Escobedo et al 2009).









Data Collection


Plot and Tree Measurements

In 2006 Gainesville's urban forest was sampled using the UFORE sampling

protocol (Escobedo et al. 2009). Circular 0.04 ha (0.1 acre) plots were established and

land use and ground cover percentages were estimated. In 2009, the Gainesville

UFORE plots were re-measured following this same protocol. First, plot center was

located using GPS coordinate and original reference object's distance and directions.

Ground-based as well as aerial photos were also utilized while the location of individual

trees within the plot and their distance and direction measurements helped reduce re-

measurement error. Ground cover categories included plot percentages of impervious

surfaces, grass, soil, rock (e.g., pervious rock and gravel), water, duff and mulch,

herbaceous vegetation (e.g., area comprised of vegetation that is not grass or shrub

cover) and maintained and un-maintained grass (e.g., grassy area with no indication of

mowing or other maintenance activities). For comparison, original land uses were

condensed into the following: forest, institutional, commercial and residential/vacant.

Trees with diameters larger than 2.5 cm (1 inch) were measured sequentially

starting from due north and rotating clockwise around plot center; direction and distance

in feet to each tree were recorded again to reduce re-measurement errors. The

following data were collected by tree: species, diameter at breast height (dbh; cm), total

and crown base height (m), crown width in two directions (m), crown light exposure

(CLE) rating (0 to 5, where zero denotes a crown completely blocked above and on all

sides and five indicates that each side of the tree as well as the top of the tree is

completely exposed to direct light), and percent missing and percent dieback of foliage

(compared to a full crown) to determine tree condition. Since tree dbh was of particular









interest for this study, a pole marked at 1.37 m was held next to each tree where

measurements were taken to reduce measurement error.

Soil Measurements

Analyses of soils on Gainesville's UFORE plot were conducted in the summer of

2007. Soil variables such as bulk density, water content, potassium concentration and

pH were collected as described in Dobbs (2009). Bulk density measurements were

measured from three (per plot) undisturbed 5 cm diameter by 4.5 cm deep soil samples

that were then fresh weighted, oven-dried (after 48 hours) then weighed for dry weight.

Chemical properties such as potassium concentrations and pH were measured from 15

(per plot) randomly located soil cores from the top 10 cm of soil and analyzed by

University of Florida Extension Soil Testing Laboratory (Dobbs 2009).

Re-measurement Method Errors

Re-measurements using tree diameter tapes at breast height can differ from

actual tree growth for reasons associated with measurement error and changes in tree

physiology (Avery & Burkhart 1983). Even when efforts are made to reduce

measurement error by using a pole for a standard breast height measurement or

holding the tape tight and even, there are other sources of error that cannot be

corrected. For example, annual diameter measurements can be negative due to

contraction of trunk wood and bark in the presence of severe water stress (Pastur et al.

2007). In addition, changes in the height of mulch and litter below a tree can change the

breast height measurement used for subsequent measurements resulting in a

measurement taken at a different height. For these reasons, tree core increments are

often measured to determine tree growth. In urban tree studies, however, tree coring is









not appropriate since one might anticipate a high level of resistance to granting

permission for coring trees on private properties due to issues of liability.

Data Organization

Data Availability

Of the original 93 plots established in 2006, 65 plots were re-measured. A

complete re-measurement of all original plots was not possible in 28 plots, since access

was denied to 12 and even though trees on all plots were re-measured in 16 plots,

some 2006 trees could not be matched to the original plot data on these (16) plots

(Table 2-1). Also, 14 of these 16 plots were forested and plot center could not be found

so the plot was "re-established", and 2 plots were located in residential plots where new

construction eliminated the reference objects thus preventing measuring distance and

direction information necessary to accurately re-locate plot center. Soil data was

missing for 17 plots, 8 of which contained no trees. This may have been related to the

previous soil plot selection criteria that eliminated plots where pervious surfaces

covered at least 50% of the plot. To compare growth rates by land use categories used

in Nowak & Crane (2002), plots were separated into similar categories and matched

trees were found in 31, 5, and 16 plots corresponding to open, park, and forested land

cover categories, respectively. Brightly painted stakes were installed on plot center for

all forested plots to facilitate relocation for future re-measurements.

Matching of Individual Trees

Sample data from the 2006 and the 2009 sample were merged and individual

trees present in both samples were matched if they: 1. were at the same direction and

distance from plot center, 2. the same species and 3. had a larger 2009 dbh

measurement. In-growth was defined as the presence of a tree in the 2009









measurement not originally measured in 2006, indicating a new planting or natural in-

growth (that a small tree grew above the dbh threshold of 2.5 cm) as described in

Nowak et al. (2004). Mortality represented the absence of a previously measured tree

which was removed or downed since the 2006 sample. Difficulties arose while matching

trees in certain plots and were mostly related to differences in the way directional

information was interpreted (e.g. type of compass readings) and the differences in the

order in which trees were recorded in 2006. It was considered ideal when trees were

recorded starting with the tree closest to due north and plot center continuing in a

clockwise direction, and compass directions were recorded in degrees from 0 to 360

from plot center.

Soil Variable Selection

Many soil variables could have been used in this study however, some soil

variables were dropped because they were not available for many of the plots used in

this study (Table 2-1). Additionally, many of the plots missing soils data had no trees, as

the selection criteria for soil sampling required that the plot had at least 50% pervious

ground cover and access was granted. Other soil variables were removed due to their

high correlation with other parameters that might have led to multicollinearity that

causes problems in modeling, as highly correlated variables neutralize the response of

significant parameters. Multicollinearity was identified by a Principal Component

Analysis (PCA) by Dobbs (2009). In this study relating urban soils to urban morphology

and socioeconomic factors in Gainesville, Florida, an analysis of soil variables

determined that pH, soil potassium content, and bulk density were the best variables to

use for characterizing urban ecosystem structure and function in Gainesville. Soil pH

was an indicator of soil water content, fertility and quality while bulk density was an









indicator of socio-economic effects, and potassium an indicator of disturbance.

Therefore, pH, potassium, soil bulk density and water content were used for analyses in

this study based on the results from Dobbs (2009) and the documented role these soil

characteristic play on urban vegetation (Scharenbroch et al. 2005, Kramer & Boyer

1995).

Species Groups

Urban forests can often have a wide variety of tree species within very localized

areas. While reporting data for each species is informative, it is impractical for many

species due to insufficient sample sizes. For example in this study there were 19

species where only one tree was matched for growth. Therefore, summarizing the raw

data into relevant species groups is needed to make results useful for modeling. For

instance urban tree mortality rates were reported for all species by size class in

Baltimore, Maryland (Nowak et al. 2004). In Chicago, radial growth rates for removed

urban trees were grouped into major genera by size class (Nowak 1994). These species

and size grouping approaches are not suitable for this study, due to the low frequency

of trees found with some size classes. For example, growth rates for trees in the larger

size classes (greater than 61.1cm) could not be modeled separately, as sample sizes

were as few as 4 and 5 trees (Table 2-2).

In natural settings, a tree's average potential size at maturity and life span

depend on the species' individual characteristics (Nowak et al. 2002). The matched tree

dataset was used to generate growth models where all species were grouped into one

of the three following categories based on maximum height and life span characteristics

as reported in the USDA PLANTS Database (www.plants.usda.gov): large size- long life

span (LL), large size- moderate life span (LM), and medium and small size (M). Large









size trees were those that potentially could attain a height equal or greater than 18.29

meters at maturity; medium and small size trees were those that potentially could attain

a height equal or less than 18.29 meters at maturity; while a moderate life span was

considered to be less than 250 years and long life span was greater than or equal to

250 years. This methodology closely follows Nowak et al. (2002) where trees were

assessed for atmospheric carbon dioxide emissions. Whereas Nowak et al. (2002) was

able to develop fourteen categories using life span, growth rate, and size at maturity,

our data supported three categories. In addition, four growth models were created for

the four most frequent tree species in the matched tree dataset. Removed and in-

growth tree datasets were too small to support grouping species as for growth above;

therefore mortality and recruitment models were generated by assigning trees into two

classes; hardwood or softwood. Palm species were not used in any growth modeling

due to their growth form and small sample size.

Land Use and Land Cover Descriptions

Re-measured plots were grouped into two sets of categories: land use and land

cover (Table 2-1). This was done to compare mortality estimates to the Nowak et al.

(2004) mortality study based off permanent plot re-measurement in Baltimore,

Maryland, and to compare growth rate estimates to the land cover categories used to

estimate growth and carbon sequestration in Nowak & Crane (2002). These studies

used similar land use and land cover categories based on their differences in urban tree

structure and management regimes. Land cover categories separate urban areas in a

way that could be interpreted easier than when determining land use. Forest areas have

high tree cover, no management activities, and minimal disturbance; park areas are

managed, have lower tree cover than forests, no pervious surfaces, and minimal









disturbance; and urban areas have lower tree cover than forest, more pervious

surfaces, are managed, and have an increased potential for disturbances not associate

with park areas. Vacant areas due to land use designations were classified as

residential.

Land use categories used in this analysis were:

* Commercial (7 plots): These plots had the greatest variation in types of plots.
These include business-like settings that were associated with yards, parking
areas, access roads, warehouses, and also included plots in transportation routes
and airports. The combination of transportation and commercial-type plots helped
comparison because both types exhibited high mortality rates in the Baltimore
study (Nowak et al. 2004).

* Institutional (15 plots): These plots were found on lands associated with The
University of Florida, a church, a correctional facility, a community fair ground,
various medical and health facilities, and elementary, middle and high schools.

* Residential (27 plots): These plots included high, low and medium density
residential areas included apartments/condominiums, mobile homes/trailers, and
associated drive-ways and parking areas. Vacant lots in residential areas were
considered residential plots because tree cover was similar to the surrounding
residential area and it was apparent that they were sometimes maintained
(clearing of debris, small shrubs, and mowed grass).

* Forest (16 plots): These plots included mixed management forested areas, pine
plantations and abandoned areas apart from residential areas. This category also
included two plots within conserved park areas that were heavily forested, and one
natural conservation area within the University of Florida.

The Land cover categories used in this study were:

* Urban-Open (45 plots): Residential, vacant, commercial and institutional plots that
were near buildings, roads, or other structures that created open-canopy
conditions.

* Park (4 plots): These plots had park-like structures including two cemeteries, fair
grounds and a sports field in an elementary school.

* Forest (16 plots): same as forest land use (described above).









Calculations

Diameter Growth and Mortality Rates

Diameter measurements were converted into annual dbh growth (cm/yr) by

subtracting the 2009 dbh from the 2006 dbh measurements and dividing by the length

of time between measurements for each plot (an average of 2 years and 10 months).

Mortality rates were calculated as in Nowak et al (2004), using a formula for mortality

per year widely used in ecological applications (Sheil et al. 1995), where annual

mortality is m, t is the time interval, and N, and No are population counts and at the

beginning and end of measurement interval t, respectively (Eq. 2-1).

m = -(N1/N0)t (2-1)

Biomass Estimates

Biomass estimates for all live trees measured in 2006 and 2009 were obtained

using the same methods and allometric equations used in the UFORE model (Nowak

and Crane). Allometric equations use tree characteristics such as dbh, height and

species to determine above ground biomass in terms of fresh or dry weight (Jenkins et

al. 2003). This dimensional analysis approach did not involve destructive sampling

methods. The model UFORE uses a list of applicable diameter based biomass

equations from studies across North America (Nowak 1994). This approach uses the

biomass equation for an individual species if available; if not, then an average of all

equations for that specie's genera are calculated. If there are no genera-specific

equations for an individual species, then depending on its tree type an average from all

hardwood or softwood species is calculated. Finally, all equations are converted to

whole tree dry-weight biomass estimates by applying conversions for each equation's









type based on the tree portion or whether fresh or oven-dried tree weight was

estimated. Whole tree biomass totals can be achieved by converting from above ground

biomass with the root-to-shoot ration of 0.26 (Cairns et al. 1997). Fresh weight or green

weight can be achieved by using moisture content averages of 0.46 for conifers and

0.56 for hardwoods (Nowak and Crane 2002). Total tree dry weight biomass can be

converted to total stored carbon by multiplying by 0.5 (Nowak & Crane 2002). Although

sequestration estimates are for an analysis period of 2 years and 10 months, they were

annualized for comparison purposes by dividing the estimates by 2.8 years.

Statistical Analyses

Statistical modeling of growth, mortality and in-growth were conducted using plot

level factors from the original 2006 measurements and included: land use type, land

cover type, trees per hectare, basal area per hectare, percent ground covers of each

plot (described in this chapter's section on plot and tree measurements). In addition,

tree level factors (described in this chapter's section on plot and tree measurements)

from the original 2006 measurement and selected soil variables (pH, potassium, soil

bulk density and water content) collected in 2007 (as described in this chapter's section

on soil measurements) were also used. Seven growth models were created for each of

the three species groups and the four most frequently occurring species that combined

comprise 43% of matched trees: Laurel Oak (Quercus laurifolia; 76 trees), Water Oak

(Quercus nigra; 62 trees), Slash Pine (Pinus elliottii; 62 trees) and Loblolly Pine (Pinus

taeda; 57 trees). Two mortality and two recruitment models were generated by grouping

species into hardwood and softwood types.

Due to the concentration of measured growth rate values around zero, growth

rate values were transformed with the square root function for modeling by species









group and the natural logarithm plus one for modeling individual species. These

transformations stabilized the variance and allowed assumptions underlying statistical

models to be met. There were 46 trees (8% of matched trees) that were found to have

negative growth rates and were re-assigned a growth rate of zero for modeling

purposes. Measurements from these trees indicated growth rates that were less than

the actual biological tree growth and are likely due to errors associated with dbh re-

measurement and tree bole shrinkage (describe in this chapter's section on growth

measurement errors in dbh re-measurement methods). Since these errors could not be

accounted for otherwise, assigning a value of 0 to these trees approximates actual

biological growth in this sample.

Growth rates were modeled with a general linear mixed model with the SAS

procedure PROC MIXED (SAS 2006), using the above plot and tree level

characteristics as predictor variables, and a random effect to account for correlations

between trees in the same plot. A Kenward-Rogers adjustment was made to the

degrees of freedom to better reflect the effect of the autocorrelation structure in the data

(Littel et al. 2006). Mortality and recruitment models used a generalized linear mixed

model with a negative binomial distribution to characterize the response variable.

Models were fit with the SAS procedure PROC GLIMMIX (SAS 2006) using the above

plot level characteristics as predictor variables.

Examination and comparison of model results used the information criteria and p-

values associated with each independent value. Non-significant effects and their

interactions were identified by a type I error level of 0.05, and models were compared

by their corrected Akaike's information criteria (AICC), which is a small sample bias-









corrected version of the Akaike's information criteria fit statistic. To provide substantial

evidence that the data arose from these models, the final estimated models included

only those effects that were significant (alpha < 0.05) and also had the lowest AICC

values. To determine significant differences in growth and mortality rates between land

uses and land cover types, the Fischer's LSD (least significant difference) statistical

procedure was used with an (alpha < 0.05).









Table 2-1. Status of all permanent plots in 2009 by land use and city for Gainesville,
Florida.
Land Use Residential* Commercial** Institutional Forest City Total
Plots matched 23 2 11 15 51
No trees 4 5 5 0 14
Plot re-
established 2 0 0 14 16
Access denied 6 2 1 3 12
No soils data 4 (all had 9 (3 had no 3 (1 had no 1 17 (26% of
no trees) trees) trees) all plots)
* Includes plots on vacant areas; includeses plots on industrial areas



Table 2-2. Gainesville, Florida's percent annual mortality and growth rates arranged by
Nowak et al.'s (2004) size classes to demonstrate differences in sample size.
Gainesville 2006 to Nowak et al. 2004 Gainesville 2006 to
2009 2009
DBH (cm) %Annual Mortality %Annual Mortality %Annual growth rate
(number of trees (number of trees (cm/yr) (number of
removed) removed) trees matched)
0-7.6 18(70) 9 (528) 0.86 (167)
7.7-15.2 9.7 (33) 6.4 (267) 1.11 (134)
15.3-30.5 3.4 (16) 4.3 (201) 1.03 (174)
30.6-45.7 1.0(3) 0.5 (109) 0.91 (109)
45.8-61.0 5.7 (5) 3.3 (62) 2.17(33)
61.1-76.2 7.7(1) 1.8(28) 0.69 (5)
>76.2 0 (0) 3.1 (33) 1.36(4)









CHAPTER 3
RESULTS AND DISSCUSION

Results

Change in Urban Forest Structure

In 2009 the average tree height in my sample was 12.4 m, average dbh was 29.2

cm, and average crown width was 5.8 m. From 2006 to 2009 (average of 2.78 years

lapse between measurements) the overall average annual mortality was 1.8% (Table 3-

1). When comparing trees within the 65 re-measured plots, there was a net annual loss

of approximately 13 trees, and a gain of 0.64 square meters of basal. In 2006, 755 trees

were measured in Gainesville and by 2009, 128 (17%) trees were removed, which

account for 64%, 26%, 5% and 5% being removed from forest, residential, commercial

and institutional plots, respectively. Plots in 2009 contained a total of 718 trees; 627

matched (30 of which were dead and not used for growth modeling) and 91 trees were

considered in-growth. Figure 3-1 indicates that when comparing all plots that were

measured, the city's average percent change for trees per hectare and tree density

increased over time by 3% and 26%, respectively. However, forest plots had a decrease

in trees per hectare of 7%. Growth rates were highest on commercial plots (2.07 cm/yr)

followed by residential (0.90 cm/yr), forest (0.75 cm/yr) and then institutional (0.50

cm/yr) plots (Table 3-1).

Growth Models

Growth rates for LL trees were influenced by plot level factors such as ground

cover percentages of maintained and un-maintained grass as well as average tree

crown width and percent missing foliage (Table 3-2). Growth was positively influenced

by every tree level factor with the exception of percent missing foliage. Growth rates for









LM trees were influenced by the plot level factors of basal area per hectare, percent un-

maintained grass, soil bulk density and soil water content as well as the tree factors of

dbh, average crown width, percent missing foliage and crown light exposure (Table 3-

2). Growth rate was positively influenced by every factor except dbh and percent

missing foliage. Growth rates for M trees were only influenced by dbh and crown light

exposure (Table 3-2).

Models were created for the four most frequently occurring species which, when

combined, comprised 43% of matched trees. Growth rates for Pinus taeda were

influenced by plot level factors of percent rock cover and the tree level factor of crown

height (Table 3-3) where growth was negatively influenced by crown height. Growth

rates for Pinus elliottii were influenced by dbh as well as plot level factors of land use

and ground cover (percent un-maintained grass, percent maintained grass and percent

herbaceous cover) (Table 3-3). Higher growth rates (1.29 cm/yr) were found on

institutional land uses, followed by forest (0.86 cm/yr) and residential (0.62 cm/yr).

There were no Pinus elliottiis found in commercial land use plots.

Growth rates for Quercus laurifolia (0.84cm/yr) were influenced by the plot level

land use and tree level crown light exposure (Table 3-3). For Quercus laurifolia, the

most abundant tree in Gainesville (13% of all trees), growth was found to be

significantly greater in residential plots (1.34 cm/yr) compared to commercial (0.59

cm/yr) and forest (0.49 cm/yr) but not institutional (0.67 cm/yr). Quercus nigra were

influenced by the plot level percent un-maintained grass and tree level crown light

exposure (Table 3-3).









Soil variables did not result in many significant effects within the growth models

when considering the lowest AICC values. However, slightly higher AICC models were

explored to further investigate these variables. For LL trees, percent grass, percent un-

maintained grass, dbh, percent missing foliage, potassium and pH influenced diameter

growth significantly where potassium and percent missing foliage influences were

negative. In a model with a higher AICC value for Quercus nigra growth, significant

factors that negatively influenced growth were tree per hectare, bulk density, and

potassium while pH had a positive effect.

Mortality and In-growth Models

Two mortality models were developed for hardwood species. These models

indicate that land use and trees per hectare significantly influenced mortality (Table 3-

4). While the model using trees per hectare had a lower AICC (174.9 verses 191.1),

indicating more evidence that the data arose from this model, the competing model

number 2 is of interest because it does not rely on field data. There were no alternative

models to compete with the softwood mortality model, which had a positive influence of

trees per hectare on mortality (Table 3-4). Although individual species models could not

be estimated due to the paucity of data in most species, annual mortality rates for the

most common species in Gainesville were found to range from 1.1% to 6.9% (Table 3-

5). Both softwood and hardwood in-growth models show that in-growth increased with

trees per hectare (Table 3-6). No other plot level factors were found to be significant.

Change in Biomass

Considering all trees and plots in Gainesville measured in both 2006 and 2009, I

estimate that 6,082 tons of carbon was sequestered annually (Table 3-7). Carbon

sequestered was largely from Quercus virginiana, Quercus nigra, and Liquidambar









styraciflua (Table 3-8). Annual removed biomass per hectare was greatest on

institutional and forest plots, which contained approximately 6,712 and 2,202 kg/yr,

respectively (Table 3-9). Removed biomass was largely comprised of Quercus laurifolia,

Pinus taeda and Acer rubrum (Table 3-10). Annual removed biomass in Gainesville was

about 34,785 tons. Total carbon stored in urban trees in Gainesville for 2009 was about

231,950 tons (Table 3-11).

Discussion

Urban Forest Structure Changes

Despite an overall loss in number of trees, Gainesville had a net increase in basal

area and biomass. Forest plots had the greatest loss in number of trees per hectare and

had the second lowest increase in basal area per hectare (Figure 3-1). Conversely

industrial plots had the largest increase in trees per hectare, and the lowest increase of

basal area. This may imply that more trees were planted on industrial land uses than on

commercial and residential land uses; while on forest plots more trees were

removed/fallen than naturally re-generated. Therefore increases in basal area are

primarily from growth of existing trees. While measuring trees on forest plots I observed

many trees that were missing from 2006 could often be identified on the ground as

downed trees (eg. large newly fallen trees of the same species with their bases near or

on the location recorded for a large tree existing in 2006). This may be due to the loss of

individual trees associated with the thinning stage of natural succession in remnant

forest patches in urban landscapes where savanna trees enclose the canopy while

changing environmental conditions due to urbanization such as fragmentation,

construction or altered hydrologic processes (Zipperer et al. 1997, Tempeton & Putz

2003).









Rates of Growth and Mortality

Growth rates for trees in Gainesville ranked differently than those reported in five

Midwestern states by lakovoglou et al. (2002) where radial growth estimates were

higher in city park sites followed by residential and commercial sites (Table 3-5). In

Gainesville, higher diameter growth rates were found on commercial, residential, forest

and then institutional sites, which is counter to hypothesis 2.

Growth rates for trees in Gainesville when analyzed by land cover types ranked

differently than those used in UFORE (Table 1-1, 3-5), where the highest growth rates

are found on parks, followed by open then forest (Table 3-5). Although Gainesville's

urban plots did not have a higher growth rate than those in Table 1-1, growth rates in

Gainesville were higher in forest and park plots than in the studies used for UFORE,

which partially satisfies hypothesis 4. Growth rates for three of the most common trees

in Gainesville (Quercus laurifolia, Quercus nigra, and Pinus elliottii) were greater than

the two growth rates used in UFORE for trees grown in forest and park settings (Table

3-7).

The top six species by growth rate far exceed all three growth rate estimates used

in UFORE. Growth rates by land use in Table 3-1 ranged higher than those reported in

a similar study in Chicago, IL (Jo & McPherson 1995), and those used for calculating

dbh growth for carbon sequestration in the UFORE model (Table 1-1; Nowak & Crane

2000). Growth rates for Quercus laurifolia were less than the diameter growth estimate

of 1.69 cm/yr for canopy trees of this species in Templeton & Putz (2003). However, an

average growth rate of 1.34 cm/yr for Quercus laurifolia in Gainesville's residential plots

(open-canopy-like conditions) was similar to estimates found by Templeton & Putz

(2003).









Overall annual mortality rates during the analysis period were less than those

reported in a similar study in Baltimore (Nowak et al. 2004). Comparisons of mortality

rates by size classes to the Nowak et al. (2004) study show that high mortality rates for

small sized trees (0-15.2 cm dbh) and lower mortality rates for medium sized trees

(15.3-61.0 dbh) were proportionally the same in both Nowak et al.'s (2004) and my

study. Although there were no significant differences between land uses when

comparing mortality in all plots with trees, in my study mortality was highest in forest

plots, followed by commercial then residential, while institutional plots actually exhibited

in-growth (Table 3-5). These trends confirm hypothesis 1 in that mortality rates are

similar to those reported in Nowak et al. (2004) in Baltimore where mortality rates on

transportation or commercial-industrial land uses were higher than rates in medium to

low-density residential land uses.

When comparing mortality by land cover type, higher mortality rates were found on

forest, then park and urban plots. Higher mortality rates were found in both Quercus

nigra, Quercus laurifolia than in Pinus taeda or Pinus elliottii (Table 3-6). Mortality

increased as trees per hectare increased for both softwood and hardwood species

(Table 3-4).

Models of Growth, Mortality and In-growth

Hypothesis 3 correctly predicted tree characteristics that significantly affected tree

growth, but not plot characteristics. Plot factors of soil and tree density affected growth

in only one of the growth models (LM trees). However, tree factors related to crown

measurements like average crown width, percent missing foliage, and tree height were

significant factors in 6 out of 7 growth models and CLE was significant in 4 of the 7

growth models.









Results from the LL growth model suggest that once established (i.e. grown to 2.5

cm dbh) LL tree growth in the urban environment is affected by surrounding ground

covers such as maintained grass. This is possibly due to factors caused by

maintenance activities associated with maintained grass such as fertilization and

increased irrigation as well as the tree's crown size and exposure to light (Zipperer et al.

1997, Tempelton & Putz 2003). Typically, vegetation such as other trees, shrubs and

turf grass surrounding a tree can limit growth as they compete when space is limited as

in the case of urban conditions (Vrecenak et al. 1989). The LM growth model results

suggest that established LM tree growth in the urban environment is also affected by

the presence of maintained grass on plots and CLE, similar to LL trees and is positively

influenced by soil water content and soil bulk density, basal area and crown light

exposure. For LM trees, increased growth was associated with increased soil bulk

density, however this result was not expected, as deceased bulk density is known to

improve soil physical properties such as water infiltration and soil biological processes

which are conducive to plant growth (Scharenbroch et al. 2005). On the other hand,

increased bulk density was found by Dobbs (2009) on plots with high percent

maintained grass which may suggest mulitcollinearity between these variables and may

have neutralized this effect. Moreover, the bulk density values for this study are from the

top 10 cm of soil profile; therefore these measurements may not be affecting growth via

root-soil interactions in trees with deeper roots. For better tree growth analysis, deeper

soil samples might be needed.

Diameter growth was positively influenced by crown light exposure in the M growth

model, suggesting that for smaller trees, the amount of light exposure is more important









than any other factor that was analyzed and can be related to the tree's growing space

and competition with other trees, factors that have been shown to influence tree growth

(Vrecenak et al. 1989). Crown light exposure was limited in the average Pinus taeda

where only 2 sides out of a possible 5 were exposed to light. This might explain why

tree height influenced growth for this LM-categorized specie more significantly than

crown light exposure (a significant factor in the LM growth model). The taller the tree,

the more potential for increased crown light exposure, which can increase growth

(Templeton & Putz 2003). For Pinus elliottii, a positive relationship between dbh growth

and 2006 dbh is unusual, especially due to the fact that the average 2006 dbh of Pinus

elliottii (32.3 cm) describes a medium-sized tree by size class in Table 2-1. This may be

a reflection of the species' known rapid growth rate as well and large potential size and

long life span (www.plants.gov). Similar to the dbh growth model generated for LL trees

(Table 3-2), the presence of both un-maintained and maintained grass (or associated

maintenance activities) positively influenced diameter growth in Pinus elliottii. Both oak

species are categorized as LM trees and both oak growth models were positively

influenced by tree level factors such as crown light exposure.

Both in-growth models showed a positive influence for trees per hectare indicating

that plantings or tree in-growth to the one inch size class is more likely in areas that

already contain trees. There are no other models of in-growth to compare these results

to.

Plot and tree level characteristics affected diameter growth in all species groups

and the individual species model, while plot level characteristics like trees per hectare

and land use affected mortality and in-growth. Greater sampling intensity would likely









have improved growth, mortality and in-growth models. However, due to limited access

and insufficient plot re-location information, this was not possible.

Green Waste Supply Potential and Carbon Sequestration

This study's estimates account for actual biophysical and socioeconomic factors in

Gainesville as opposed to modeling these assumptions. Annually, more carbon was

sequestered (83%) on residential plots (which include vacant areas) than forested and

commercial, while institutional plots had a net emission of carbon due to tree removals.

By land cover, urban plots sequestered the most carbon followed by forest and park

plots. More carbon was sequestered by live oaks (1,727 tons city-wide) than any other

species (Table 3-8). Residential plots had higher carbon sequestration estimates due to

the fact that vacant plots were assigned a residential land use and that forested plots

decreased in trees per acre over time (Figure 3-1). Stratifying land uses differently will

result in different carbon sequestration per land use estimates.

Results from this study show that if all removed trees were collected from these

analyzed plots in Gainesville, there would be about 34,785 tons of fresh above ground

biomass removed each year and most (77%) of this biomass would come from Quercus

laurifolia, Pinus taedas, and Acer rubrum (Table 3-9 and 3-10). When analyzed by land

use, most removed biomass came from institutional plots (42%) then forest (36%),

residential (21%) and commercial (0.1%) If removed trees were only harvested from

non-forest plots, this estimate would be reduced to 28,350 tons of fresh above ground

biomass a year. In 2009, more carbon was being stored in forested plots (42%) than on

residential plots (35%) followed by institutional (18%) and commercial plots (4%; Table

3-11).









Table 3-1. Plot count, average annual growth


meas
and c


Land Use
Plots matched
Plots with trees
AGR (cm/yr)
AMR
Land Cover
AGR (cm/yr)
AMR


ured trees between
over categories
Commercial
7


2
2.07
0.86%
Urban
0.81
-0.37%


2006 and


Forest
15
15
0.75
2.70%
Park
1.49
1.56%


(AGR) and mortality rates (AMR) for all re-
2009 in Gainesville, Florida by land use


Institutional
16
11
0.50
-1.86%


Residential
27
23
0.90
0.66%


City Total
65
51
0.84
1.79%


Forest
0.75
2.70%


Table 3-2. Test of fixed effects for model of annual diameter at breast height (dbh)
growth by species group in Gainesville, Florida from 2006 to 2009
Large potential size, long life span (LL) N=126
Effect Estimate Nm DF* Den DF* F value* Pr>F*
%Maintained grass 0.0058 1 121 28.50 <0.0001
%Un-maintained grass 0.0060 1 121 7.20 0.0083
Average crown width 0.0039 1 121 6.59 0.0114
% Missing foliage -0.0024 1 121 7.35 0.0077
Large potential size, moderate life span (LM) N=284
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Basal area per hectare 0.0008 1 26 16.18 0.0004
%Un-maintained grass 0.0055 1 26 34.14 <0.0001
Dbh -0.0173 1 249 18.12 <0.0001
Average crown width 0.0078 1 249 16.98 <0.0001
% Missing foliage -0.0017 1 249 6.96 0.0089
Crown light exposure 0.0498 1 249 16.65 <0.0001
Soil bulk density 0.2673 1 26 11.19 0.0025
Soil water content 0.0096 1 26 13.35 0.0011
Medium potential size (M) N=120
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Dbh -0.0186 1 117 6.04 0.0155
Crown light exposure 0.0839 1 117 20.82 <0.0001
* Interpret as with numerator and denominator degrees of freedom (Nm DF and Den
DF, respectively) the critical value of the F distribution (F-value) means the estimate is
significant at the 5% level as determined by the p value (Pr>F).









Table 3-3. Test of fixed effects for model of annual diameter at breast height (dbh)
growth of four most frequent species in Gainesville, Florida from 2006 to 2009
Loblolly pine (Pinus taeda) N=57
Effect Estimate Nm DF* Den DF* F value* Pr>F*
%Pervious rock 0.0499 1 54 4.92 0.0307
Crown height -0.0032 1 54 5.70 0.0205
Slash pine (Pinus elliottii) N=62
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Land use- forest 0.2476 2 55 7.58 0.0012
Land use- institutional 0.2850 2 55 7.58 0.0012
Land use- residential 0 2 55 7.58 0.0012
%Maintained grass 0.0085 1 55 51.45 <0.0001
%Un-maintained grass 0.0064 1 55 18.28 <0.0001
%Herbaceous cover -0.0015 1 55 6.00 0.0175
Dbh 0.0134 1 55 15.00 0.0003
Laurel oak (Quercus laurifolia) N=76
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Land use forest -0.1471 3 71 19.07 0.0324
Land use institutional -0.0974 3 71 19.07 0.0324
Land use residential 0 3 71 19.07 0.0324
Land use commercial -0.1569 3 71 19.07 0.0324
Crown light exposure 0.0679 1 71 3.09 <0.0001
Water oak (Quercus nigra) N=62
Effect Estimate Nm DF* Den DF* F value* Pr>F*
%Un-maintained grass -0.0158 1 59 8.46 0.0051
Crown light exposure 0.0646 1 59 14.56 0.0003
* Interpret as with numerator and denominator degrees of freedom (Nm DF and Den
DF, respectively) the critical value of the F distribution (F-value) means the estimate is
significant at the 5% level as determined by the p value (Pr>F).









Table 3-4. Test of fixed effects for model of mortality for hardwoods and softwoods in
Gainesville, Florida from 2006 to 2009
Hardwood mortality #1
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Trees per hectare 0.0058 1 63 18.40 <0.0001
Hardwood mortality #2
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Land use- forest 0.9478 3 61 3.74 0.0156
Land use- institutional -1.0217 3 61 3.74 0.0156
Land use- residential 0 3 61 3.74 0.0156
Land use- commercial -1.0862 3 61 3.74 0.0156
Softwood mortality
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Trees per hectare 0.0072 1 63 5.03 0.0284
* Interpret as with numerator and denominator degrees of freedom (Nm DF and Den
DF, respectively) the critical value of the F distribution (F-value) means the estimate is
significant at the 5% level as determined by the p value (Pr>F).

Table 3-5. Annual mortality rates for the ten most common trees found in 2006 ranked
by total number of trees
Rank Species Annual mortality rate
(Trees removed)


Quercus laurifolia
Quercus nigra
Pinus taeda
Pinus elliottii
Acer rubrum
Prunus caroliniana
Liquidambar styraciflua
Nyssa biflora
Gordonia lasianthus
Celtis laevigata


4.67%
5.72%
3.55%
1.14%
3.73%
6.89%
1.01%
5.85%
6.10%
3.38%









Table 3-6. Test of fixed effects for model of in-growth for hardwoods and softwoods in
Gainesville, Florida from 2006 to 2009
Hardwood in-growth
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Trees per hectare 0.0047 1 63 14.08 0.0004
Softwood in-growth
Effect Estimate Nm DF* Den DF* F value* Pr>F*
Trees per hectare 0.0056 1 63 5.75 0.0194
* Interpret as with numerator and denominator degrees of freedom (Nm DF and Den
DF, respectively) the critical value of the F distribution (F-value) means the estimate is
significant at the 5% level as determined by the p value (Pr>F).

Table 3-7. Annual carbon sequestered per hectare (CSPH) and city total (CSCT)
estimates by land use and land cover for trees in Gainesville, Florida from
2006 to 2009
Land Use CSPH CSCT Land CSPH CSCT
(kg/ha) (tons) cover (kg/ha) (tons)
Commercial 399 557 Forest 402 1278
Forest 379 1106 Urban 434 3801
Institutional -195* -619* Park 1290 983
Residential** 955 4973
Grand Total 479 6082 479 6082
*Carbon emitted though removals exceeded carbon sequestered through growth;
includeses plots on vacant and residential areas.









Table 3-8. Top four species ranked by frequency and top six species ranked by highest
average growth rate (AGR) and standard error (SE), with corresponding
carbon sequestration per hectare (CSPH) and city total (CSCT) estimates for
trees in Gainesville Florida from 2006 to 2009
Rank Species AGR SE *CSPH *CSCT
(Number of trees) (cm/yr) (kg/ha) (tons)
By Frequency
1 Quercus laurifolia (76) 0.84 0.17 -34 -428
2 Quercus nigra (62) 0.90 0.28 91 1155
3 Pinus elliottii (62) 0.84 0.18 3 38
4 Pinus taeda (57) 0.53 0.12 -6 -76
By growth rate
1 Juniperus virginiana (4) 1.69 0.51 10 127
2 Lagerstroemia indica (12) 1.66 0.49 15 190
3 Quercus virginiana (16) 1.33 0.51 136 1727
4 Celtis laevigata (20) 1.13 0.43 2 5
5 Ostrya virginiana (5) 1.00 0.38 -1 -13
6 Acer rubrum (36) 1.00 0.38 -20 -254
10 Liquidambar styraciflua (35) 0.64 0.26 42 533
12 Cinnamomum camphora (19) 0.51 0.17 13 165
Negative values represent carbon emissions due to removals

Table 3-9. Annual removed above ground fresh weight biomass per hectare (RBPH)
and city total (RBCT) estimates by land use and land cover in Gainesville,
Florida from 2006 to 2009
Land Use RBPH RBCT Land RBPH RBCT
(kg/ha) (tons) cover (kg/ha) (tons)
Commercial 14 20 Forest 2230 7080
Forest 2203 6435 Urban 3714 32546
Institutional 6712 21311 Park 835 636
Residential 1708 8894
Grand Total 2739 34785 2739 34785
*Carbon emitted though removals exceeded carbon sequestered through growth

Table 3-10. Annual removed above ground fresh weight biomass per hectare (RBPH)
and city total (RBCT) for species comprising 90% all removed biomass in
Gainesville, Florida from 2006 to 2009
Species RBPH (kg/ha) RBCT (tons)
All trees 314 3888
Quercus laurifolia 118 1499
Pinus taeda 73 927
Acer rubrum 50 635
Platanus occidentalis 21 266
Pinus elliottii 13 165
Quercus nigra 8 102











Table 3-11. Carbon stored in 2009 per hectare (CSTPH) and city total (CSTCT)
estimates by land use and land cover in Gainesville, Florida
Land Use CSTPH CSTCT Land CSTPH CSTCT
(kg/ha) (tons) cover (kg/ha) (tons)
Commercial 7411 10353 Forest 32839 104263
Forest 33256 97141 Urban 12111 106129
Institutional 13306 42246 Park 29197 22248
Residential** 15688 81687
Grand Total 18264 231953 18264 231953


*Carbon emitted though removals exceeded carbon
**Includes plots on vacant and residential areas


sequestered through growth;


0 CQ rferdm A
RFaested
* Irstitticrd
SF1idatid
SGtyTctd


34e
T 26


987 1067 551




-9eta


Bsd are pJe hstae


Figure 3-1. Average percent change in trees per hectare and basal area per hectare by
land use and city total for Gainesville, FL from 2006 to 2009









CHAPTER 4
CONCLUSION

Growth rates presented in this study should be appropriate to apply to urban trees

in other cities with growing conditions similar to Gainesville. Using this study's local

growth rates in urban forest functional models would reduce bias and variance in the

resulting carbon estimates for Gainesville. My results indicate that mortality rates were

similar by size class and land use to a similar permanent-plot re-measurement study in

Baltimore, Maryland (Nowak et al. 2004). Based on the re-measurement of permanent

plots, Gainesville trees were estimated to have an average annual mortality rate of

1.8%. Of the 755 trees in our sample in 2006, by 2009, 128 (17%) trees had been

removed. Plot and tree level characteristics combined can be used to estimate diameter

growth as found in all growth models except the M growth model.

This study provides information on how site characteristics affect growth in trees

common to Gainesville, Florida according to their different size and potential age. In

Pinus elliottii and LL models, maintained grass was significant in enhancing growth

while in the LM model, un-maintained grass was a significant factor. In addition, crown

characteristics were often significant in my growth models. For example, characteristics

such as average crown width, percent missing foliage, and tree height were significant

factors in 6 out of 7 growth models while CLE was significant in 4 of the 7 growth

models. These results could be used to develop urban tree planting strategies, such as

selecting crown size and available light source as important factors to facilitate tree

growth. Maintained grass (as an alternative to other ground cover vegetation types)

might enhance growth because it does not compete for light and may be associated

with maintenance activities that contribute to the growth of surrounding trees such as









irrigation, fertilization, and edging of new vegetation or vines that can grow around a

tree. Although extra resources will be spent on maintaining grass, benefits associated

with enhanced tree growth might contribute to offsetting these disadvantages.

This study also provides green waste estimates for Gainesville by type and

quantity as well as where it is being generated and where it is being stored as biomass.

Higher growth rates found in Gainesville than those from previous studies used for

estimating tree growth and carbon sequestration reveal the possibility that projected

estimates in Gainesville might be underestimated. Carbon sequestration through growth

was higher in residential plots (which include vacant areas) while removed biomass

potential was greatest on institutional plots. Carbon stored in urban trees was higher in

residential areas than institutional or commercial.

Suggestions for selecting trees based on their functional ability to store and

sequester carbon can also be inferred from these results. Although only a small sample

of Quercus virginiana was analyzed, its sequestration rate was the highest and growth

rate was third highest over the other species examined in this study. This study

highlights that both Quercus virginiana and Quercus nigra provide the benefit of being a

large and sustainable potential sink for carbon in Gainesville's urban forest while

Quercus laurifolia is the largest source of carbon emissions due to tree removals.

Urban forests need to be managed in a way that enhances their secondary carbon

dioxide reduction functions of conservation and avoidance of building energy usage

though reduced ambient air temperatures and shading by trees and decreased need for

energy from fossil-fuel-based power plants (Hesiler 1986, Nowak 1993a, Simpson &

McPherson 2001, Akbari 2002, Pandit & Laband 2010). The primary carbon dioxide









emission reduction functions (carbon storage and sequestration in biomass through

growth) can surpass the emissions produced through tree maintenance activities and

emissions produced by decomposition of dead and removed trees (Nowak et al. 2002).

Regional difference in energy savings from trees near buildings can differ based on

differences in emission factors, building construction, climate, tree sizes and growth

rates (Simpson & McPherson 2001). In the summertime, large and dense shade trees in

energy-saving locations (close proximity to buildings particularly on southwest, west or

east side of a building) can significantly reduce energy consumption (Simpson &

McPherson 2001, Pandit & Laband 2010). The benefits from urban trees will continue to

improve the quality of life in cities if the factors that affect their growth and mortality are

better understood and applied urban tree management.









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BIOGRAPHICAL SKETCH

Throughout her education, Alicia Lawrence's personal goal was to contribute to

the research and conservation of the natural resources of Florida, her home state. After

high school in Venice, FL, she studied agricultural and biological engineering with a

focus in land and water resources at the University of Florida. Later her interests shifted

towards the courses she enjoyed the most in natural sciences and graduated in the fall

of 2007 with a Bachelor of Science in forest resources and conservation as a Natural

Resource Conservation Major with a focus in forest hydrology. In 2008 she began a

Graduate Research Assistantship where she collected soil field measurements in

Miami-Dade County, Florida, established permanent tree and vegetation plots in

Pensacola Florida, and relocated and re-measured permanent urban forest plots in

Gainesville, Florida. She also analyzed urban forest data from Houston, Texas to

assess the effects of Hurricane Ike. She received her Master of Science in forest

resources and conservation in the summer of 2010.





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1 URBAN TREE GROWTH AND MORTALITY IN GAINESVILLE, FL: IMPLICATIONS FOR CARBON DYNAMICS AND GREEN WASTE By ALICIA LAWRENCE 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 2010

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2 2010 Alicia Lawrence

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3 To TJ

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4 ACKNOWLEDGMENTS I thank my parents and my sister for supporting me in every way. I also thank my committee members for guiding me though this adventure and giving me an opportunity to better myself. I would like to thank the United States Department of Agriculture Forest Service for funding my graduate tuition and University of Florida School of Forest Resources and Conservation for providing me the equipment and tools needed for my fieldwork and analysis; resources that allowed me to take on this ambitious and rewarding study. Finally, I thank my field crew, Dawn, Cynnamon, Ben, and Sebastian for all their hard work.

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5 TABLE OF CONTENT S page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 9 ABSTRACT ................................................................................................................... 10 CHAPTER 1 INTRODUCTION AND OBJECTIVES ..................................................................... 12 Urban Forest Ecosystem Structure ......................................................................... 12 Temporal Changes in Urban Forest Structure ........................................................ 14 Urban Tree Mortality ............................................................................................... 15 Urban Tree Growth ................................................................................................. 17 Soil Related Stress on Urban Tree Growth ............................................................. 18 Carbon Storage and Sequestration of Urban Trees ................................................ 20 Uses of Urban Tree Biomass .................................................................................. 21 Application of Mortality and Growth to Urban Forest Function Studies ................... 21 Objectives ............................................................................................................... 22 2 METHODS .............................................................................................................. 26 Study Area .............................................................................................................. 26 Data Collection ....................................................................................................... 27 Plot and Tree Measurements ........................................................................... 27 Soil Measurements ........................................................................................... 28 Re measurement Method Errors ...................................................................... 28 Data Organization ................................................................................................... 29 Data Availability ................................................................................................ 29 Matching of Individual Trees ............................................................................. 29 Soil Variable Selection ..................................................................................... 30 Species Groups ................................................................................................ 31 Land Use and Land Cover Descriptions ........................................................... 32 Calculations ............................................................................................................ 34 Diameter Growth and Mortality Rates .............................................................. 34 Biomass Estimates ........................................................................................... 34 Statistical Analyses .......................................................................................... 35 3 RESULTS AND DISSCUSION ............................................................................... 39 Results .................................................................................................................... 39 Change in Urban Forest Structure .................................................................... 39

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6 Growth Models ................................................................................................. 39 Mortality and Ingrowth Models ........................................................................ 41 Change in Biomass .......................................................................................... 41 Discussion .............................................................................................................. 42 Urban Forest Structure Changes ...................................................................... 42 Rates of Growth and Mortality .......................................................................... 43 Models of Growth, Mortality and Ingrowth ....................................................... 44 Green Waste Supply Potential and Carbon Sequestration ............................... 47 4 CONCLUSION ........................................................................................................ 54 LIST OF REFERENCES ............................................................................................... 57 BIOGRAPHICAL SKETCH ............................................................................................ 62

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7 LIST OF TABLES Table page 1 1 Tree diameter growth rates by land cover used in the Urban Forest Effects (UFORE) model to calculate urban tree growth and carbon sequestration from Nowak and Crane (2000) ........................................................................... 25 2 1 Status of all permanent plots in 2009 by land use and city for Gainesville, Florida. ............................................................................................................... 38 2 2 Gainesville, Floridas percent annual mortality and growth rates arranged by Nowak et al.s (2004) size classes to demonstrate differences in sample size. .. 38 3 1 Plot count, average annual growth (AGR) and mortality rates (AMR) for all remeasured trees between 2006 and 2009 in Gainesville, Florida by land use and cover categories .......................................................................................... 48 3 2 Test of fixed effects for model of annual diameter at breast height (dbh) growth by species group in Gainesville, Florida from 2006 to 2009 .................... 48 3 3 Test of fixed effects for model of annual diameter at breast height (dbh) growth of four most frequent species in Gainesville, Florida from 2006 to 2009 ................................................................................................................... 49 3 4 Test of fixed effects for model of mortality for hardwoods and softwoods in Gainesville, Florida from 2006 to 2009 ............................................................... 50 3 5 Annual mortality rates for the ten most common trees found in 2006 ranked by total number of trees ...................................................................................... 50 3 6 Test of fixed effects for model of in growth for hardwoods and softwoods in Gainesville, Florida from 2006 to 2009 ............................................................... 51 3 7 Annual carb on sequestered per hectare (CSPH) and city total (CSCT) estimates by land use and land cover for trees in Gainesville, Florida from 2006 to 2009 ....................................................................................................... 51 3 8 Top four species ranked by frequency and top six species ranked by highest average growth rate (AGR) and standard error (SE), with corresponding carbon sequestration per hectare (CSPH) and city total (CSCT) estimates for trees in Gainesville Florida from 2006 to 2009 ................................................... 52 3 9 Annual removed above ground fresh weight biomass per hectare (RBPH) and ci ty total (RBCT) estimates by land use and land cover in Gainesville, Florida from 2006 to 2009 .................................................................................. 52

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8 3 10 Annual removed abov e ground fresh weight biomass per hectare (RBPH) and city total (RBCT) for species comprising 90% all removed biomass in Gainesville, Florida from 2006 to 2009 ............................................................... 52 3 11 Carbon stored in 2009 per hectare (CSTPH) and city total (CSTCT) estimates by land use and land cover in Gainesville, Florida ............................. 53

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9 LIST OF FIGURES Figure page 3 1 Average percent change in trees per hectare and basal area per hectare by land use and city total for Gainesville, FL from 2006 to 2009 ............................. 53

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10 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 URBAN TREE GROWTH AND MORTALITY IN GAINESVILLE, FL: IMPLICATIONS FOR CARBON DYNAMICS AND GREEN WASTE By Alicia Lawrence August 2010 Chair: Francisco Escobedo Cochair: Christina Staudhammer Major: Forest Resources and Conservation Research on urban tree growth, mortality and ingrowth is needed to project future urban forest structure more accurately. To provide more accurate information for urban forests in Gainesville, a subsample of 93 plots established in the summer of 2006 were relocated and remeasured during the growing season of 2009. These 65 plots provide a unique opportunity to study urban tree growth, mortality and ingrowth. Comparative data provided rates of diameter growth, mortality, and ingrowth that were analyzed based on initial tree and plot level conditions using general and generalized linear mixed statistical models. Growth in diameter at breast height (dbh) was modeled for three species groups and the four most frequent tree species: laurel oak ( Quercus laurifolia; 76 trees), water oak ( Q uercus nigra; 62 trees), slash pine ( Pinus elliottii; 62 trees) and loblolly pine ( Pinus taeda; 57 trees). Mortality and ingrowth models were developed for hardwood and softwood species. Results show that Gainesville trees are estimated to have an average annual mortality rate of 1.8%. In total there were 755 trees sampled in 2006 and by 2009, 128 (17%) trees were removed. Plot and tree level characteristics affected diameter growth in all species groups. Growth rates in Gainesville were higher

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11 than those reported in other studies of urban tree growth. Results provide local information that can be used for improving estimates of growth, biomass, and carbon sequestration in the Southeastern United States

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12 CHAPTER 1 INTRODUCTION AND OBJ ECTIVES Urban Forest Ecosystem Structure Two key aspects of a healthy ecosystem are the ability of an urban forest to provide ecosystem services like tree shade and aesthetics, which can be valued as a commodity that improves quality of life while still maintaining its own biophysical integrity (Rapport 1995). The composition and structure of urban vegetation can be modified over time due to losses from deforestation and fragmentation and gains from reforestation and afforestation (Zipperer et al. 1997). Activities associated w ith expanding human populations can also cause modifications that alter ecosystems and impede the sustainability of resources and services provided by these ecosystems (Zurlini & Giardin 2008). As such the products/goods generated from an urban forest are the quantifiable results (money saved through energy avoidance) manifested through the services (tree shade and air temperature reduction) that are the result of a variety of functions (evapotranspiration, radiation blocked by tree canopy) as well as contr ibution to overall city tree canopy These functions in turn are made possible by the structure of the urban forest (location and size of tree, overall city tree cover) and can interact at multiple scales spatially and temporally (Zurlini & Giardin 2008, P andit & Laband 2010). To relate the overall urban forest structure to its variety of functions in a way that includes the spatial and temporal properties of social and ecological patterns is to view the urban vegetation processes within patches or areas that are relatively homogeneous and that differ from their surroundings which are affected by urban influences that occur along a gradient of urbanization from the urban core to rural

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13 (Zipperer et al. 1997). Understanding how ecosystems change over time c an provide insight into identifying potential changes that may detract from the integrity of the ecosystem and its capacity to continue to provide resources and services into the future (Petrosillo et al. 2007). Growth suppression, life span reduction, increased susceptibility to insect and disease related problems, as well as losses in aesthetics and higher replacement costs can occur in the presence of tree stresses related to site and soil conditions (Rhoades & Stipes 1999). Therefore, research on growt h, mortality and removed biomass from urban forests can facilitate management techniques that can increase the net benefits of urban trees such as mitigation of atmospheric carbon dioxide (Nowak et al. 2002). Urban forest ecosystem structural changes are influenced by tree mortality, therefore mortality rates are needed to project future populations and benefits associated with the urban forest while understanding the factors that affect urban tree mortality can help urban forest managers minimize trees costs and risks while enhancing environmental benefits (Nowak et al. 2004). Research on urban tree diameter growth is limited and often focuses on tree populations from northern regions of the US (Nowak 1994, Jo & McPherson 1995, deVries 1987), and if applied to tree populations in southern US regions, they may underestimate the projected carbon dioxide reduction functions as well as other benefits associated with tree growth. Estimates of removed tree biomass can also reveal the potential bio waste supply in Gainesville, Florida as well. Analyses of permanent plots and their re measurement should provide sitespecific mortality, growth, and subsequent biomass estimates that

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14 reflect the external factors acting upon them due to local geographical, ecological, urban form, and socioeconomic related influences (Heyen & Lindsey 2003) Temporal C hanges in Urban Forest Structure Tree cover, or percent tree canopy cover is used to describe city wide tree structure and is often used as an indicator of health of urban f orest structure by managers, policy makers, scientists and analysts (Heyen & Lindsey 2003, Zipperer et al. 1997). Urban tree cover has been described as the proportion of land covered by tree crowns within a municipality or other geographic or administrati vely defined areas (Heynen & Lindsey 2003). Tree cover definitions can include the measurement of all plant material above, on and below the ground (McDonnel & Pickett 1990). There are many drivers for changes in tree cover over time: urban development, wi ndstorms, tree removals, and growth (Zipperer et al. 1997, Duryea et al. 2007, Escobedo et al. 2009). In Central Indiana, an urban forest canopy cover study showed that areas are likely to have more canopy cover if it : has a population consisting of more i ndividuals with college degrees, has older housing stock, has areas with slopes greater than 15% and a network of dense streams (Heyen & Lindsey 2003). Historical tree cover data, field vegetation sampling, and comparison of past aerial photographs have be en used to quantify and describe tree cover change over time (Nowak et al. 1996). In Gainesville, tree cover has not exhibited a linear trend, decreasing over time from 66% in 1995 to 55% in 2005 and then increasing to 60% in 2007 (Szantoi et al. 2008). N atural environment, urban morphology and human values within a city affect the amount of tree cover (Nowak et al. 1996, Escobedo et al. 2009). For example, cities developed in natural forested areas have higher tree cover than those developed in grasslands (19%), or deserts (10%) (Nowak et al. 1996). Within a city, land uses

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15 occupied by park and residential lands as well as vacant lands and forested areas generally have the highest tree cover (Nowak et al. 1996). In Gainesville, Florida, 2006, urban tree cover was 51%: higher tree cover was found in vacant and forested areas and lower tree cover was found in commercial and industrial areas (Escobedo et al. 2009). Gainesvilles tree cover is higher than many other cities studied in the United States (Nowak, 1993a 1993b; Nowak 1994; McPherson 1998). Certain tree species can contribute more to an areas overall tree cover due to their large individual tree sizes. For example, live oaks ( Quercus virginiana Mill.) contributed to 14% of the Gainesvilles total leaf area while only comprising 4% of all trees (Escobedo et al. 2009). Urban Tree M ortality Urban tree mortality can be minimized if the factors are better understood (Nowak et al. 2004). However, urban tree mortality has been the subject of relatively few scientific studies. In the limited number of studies, urban tree mortality has been shown to be related to tree condition, size, age, land use, water and nutrient stress socio economic status, community participation, and management practices (Nowak 1986, Foster & Blain 1978, Nowak et al 1990, Sklar & Ames 1985, Gibertson & Bradshaw 1985, Nowak et al. 2004). In many studies, mortality research on urban trees has concentrated on the factors affecting existing and newly planted street tree populations (Nowak 1986, Foster & Blaine 1978, Nowak et al. 1990, Sklar & Ames 1985, Richards 1979, Gibertson & Bradshaw 1985). Street tree size and condition in Syracuse, New York, were found to influence average annual mortality rates ; low mortality was found in stable and healthy trees (1.4%) while higher mortality was found in trees larger than 77 cm dbh (5.4%) and in trees with crown deterioration (6.4%) (Nowak 1986). Newly planted street trees in

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16 Boston, Massachusetts had a mortality rate that averaged 9% over a ten year period, which mostly depended on tree planting methods (Foster & Blaine 1978). In Oakland, California, annual mortality rates for newly planted street trees averaged 19% over a twoyear period (Nowak et al. 1990) with lower tree mortality rates occurri ng in areas next to single family and rapid transit stations and high mortality rates occurred in proximity to apartments, greenspaces, and areas with low socioeconomic status and high unemployment. Another study of newly planted street trees in Oakland C alifornia reported lower annual mortality rates for trees planted with the communitys participation: where rates as high as 50% were reported for trees planted with no community participation versus 5.88.2% for trees that were planted with community part icipation (Sklar & Ames 1985). Richards (1979) study in Oakland, California involving street tree survival suggests high mortality rates in small, unestablished trees and a positive relationship with minor accidents and vandalism. Water and nutrient stre ss was the cause for mortality for 56% of newly planted trees in Northern England while other causes of mortality included vandalism (18%), girdling by tree guards (12%), soil compaction (9%) and improper staking and tying techniques (5%) (Gilbertson & Bra dshaw 1985). Methods involving permanent random plot remeasurement of urban tree populations are limited but one study has been used to study size, condition, species and land use e ffects on urban tree mortality (Nowak et al. 2004). In Baltimore, Maryl and, twoyear permanent 0.04 hectare plot re measurements yielded average annual tree mortality and net change in number of live trees rates of 6.6% and 4.2% respectively, with the lowest mortality rates occurring in medium to low density

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17 residential land uses and the highest mortality rates occurring on transportation or commercial industrial lands (Nowak et al. 2004). Tree size and condition also affected mortality rates in Baltimore, where higher rates were found for trees in small diameter and poor condition classes. Urban Tree G rowth Natural forest and urban tree diameter growth rates used to model urban forests are based on natural, forest like conditions (Nowak & Crane 2000) and generally come from a study in Indiana and Illinois where comparison o f permanent forest inventory plot measurements of dbh yielded growth rates that average 0.38 cm per year (Smith & Shifley 1984). Average annual growth rates for a variety of hardwoods species and a single softwood species, shortleaf pine ( Pinus echinata) w ere 0.38 cm/yr and 0.36 cm/yr, respectively. This study compared growth rates by crown and diameter classes as well as species groups, finding faster growth in trees from dominant and codominate crown classes. Previous urban tree growth studies have used radial growth measurements from core samples and permanent plot remeasurement to acquire urban tree growth rates. In a study of urban trees in two neighborhoods in Chicago, Illinois, growth rates determined using tree ring increments from core samples were 1.09 cm per year (N=118) for the following hardwood trees: maples ( Acer negundo, A. saccharinum, A. platanoides ), elms ( Ulmus americana, U. pumila ), mulberry (Morus alba), crabapple ( Malus spp.), and cherry (Prunus spp.) and 0.51 cm per year (N=17) f or softwood trees including spruces ( Picea pungens, P. abies, P. glauca) (Jo & McPherson 1995). Another urban tree study in Chicago used radial growth increments to estimate tree growth and carbon sequestration for removed, open grown trees, and found tree growth

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18 rates ranging from 0.78 to 1.02 cm per year (Nowak 1994). In a study across five midwestern states (Illinois, Iowa, Minnesota, Missouri and Wisconsin) radial growth rings from the last ten years were used to compare growth rates across land uses a nd higher growth rates were found on city park sites followed by residential and commercial sites (Iakovoglou et al. 2002) In Gainesville, FL an urban tree study acquired growth rates using the five most recent annual growth rings from 12 laurel oaks ( Que rcus laurifolia ) and determined a growth rate of 1.3 cm per year (Templeton & Putz 2003). In the SE, urban tree growth has been researched on a species level with emphasis on Live Oaks, a historically important and common species and a large component of total leaf area by species in the region. In Gainesville, for example, Live Oaks provide 14% of the citys total leaf area (Escobedo et al. 2009). Not only are they excellent shade trees, live oaks are well suited to urban conditions (Grabosky & Gilman 200 4) and highly resistant to hurricane damage (Duryea et al. 2007). In parking lots in Florida, one study reported that growth, described as a relationship between dbh and canopy radius size, declined as nonpaved surface area was reduced for Chinese elm ( U lmus parvifolia Jacq.), sycamore ( Platanus occidentalis L.), Shumard oak ( Quercus shumardii Britton), and laurel oak ( Quercus laurifolia Michx.); but not live oak (Grabosky & Gilman 2004). Surrounding vegetation such as other trees, shrubs and turf grass c an also limit growth as they compete when space is limited (Vrecenak et al. 1989). Soil R ela ted Stress on Urban Tree Growth Soil and site conditions are often used to study factors related to tree growth and reduction of life span. Factors that result in less than optimal growth rates in plants can be described as stress (Kozlowski & Pallardy 1997). Urban soils are altered by management regimes, disturbances, changes in surface cover, and other human

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19 influences that can result in highly variable soil characteristics distributed across the urban landscape (Craul 1999, Pouyat et al. 2007). Urbanization can cause alterations in soil bulk density, microbial biomass and organic matter resulting from physical modifications of urban soils and disturbances such as building construction, compaction from heavy equipment, foot traffic, covering of soil with impervious surfaces, and removal of grass clippings and yard wastes (Scharenbroch et al. 2005, Craul 1999). These e ffects are reduced by the amount of time since urbanization as natural processes improve physical, biological and chemical soil properties (Dobbs 2009, Scharenbroch et al. 2005). Soil nutrient concentrations can also vary across the urban landscape depending on the time since urbanization, urban morpho logy (e.g. amount of impervious surface), land use, and land cover (Pouyat et al. 2007, Grimm et al. 2008). Water stress is also considered a major limit ing factor on vegetation in all environment s (Kramer & Boyer 1995). For example urban street trees su ffer from additional water and heat stresses associated with urban site conditions such as impervious surfaces, soil compaction, and built structures that radiate heat (pavement, buildings, automobiles parked under trees) (Close et al. 1996). As a result, annual diameter measurements can be negative when the trees stem water content decreases, causing a contraction of wood and bark in the trunk (Pastur et al. 2007). Stresses associated with poor site conditions such as impervious surfaces beneath the crow n, soil compaction and pH have been shown to affect growth in sugar maples ( Acer saccharum ) where terminal growth in trees growing in woodlots was significantly higher than those grown on Michigan State Universitys campus and streets (Close et al. 1996). Conversely, annual rates of diameter growth were higher on trees

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20 growing on the Virginia Tech University campus than the same species growing in a forest, implying that site conditions associated with soil properties have effects on urban tree growth that are lessened by open canopy conditions found in the urban environment (e.g. less competition for light, water and nutrients ) and are similar to observed increases in branch size and survival after forest stands have been thinned (Rhoades & Stipes 1999, Kr amer & Kozlowski 1979). Carbon Storage and Sequestration of Urban Trees Although many components such as live biomass, litter and soil make up an urban forests carbon stock, live biomass is most affected by human and natural disturbances and can be easil y tied to tree measurement data. An urban tree cover study by Nowak & Crane (2002) estimated that urban forests across the nation collectively store 700 million tons and sequester 22.8 million tons carbon per year. Proper m anagement techniques can increase the role of urban trees in sequestering atmospheric carbon dioxide (Nowak et al. 2002). Planting low maintenance trees that grow at moderate or fast rates with the potential to become large in size usually maximize the potential quantity and duration of the carbon benefits received by a tree. Nowak (1994) reported that carbon sequestration and carbon storage was 90 and 1000 times greater, respectively in large versus small trees. When selecting trees, growth rates and life spans are equally as importan t as considering if the tree is appropriate for the given site conditions and maintaining the trees in a manner that increases survival (Nowak et al. 2002). Developing uses for wood from removed trees can delay carbon decomposition emissions or contribute to communitys fossil fuel energy needs (Nowak et al. 2002). In addition, Nowak et al. (2002) suggest the practices such as minimizing the use of fossil

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21 fuel burning pruning equipment and techniques, appropriate tree disposal methods, strategic planting o f deciduous shadetree providing species that require low maintenance near buildings, and proper maintenance and spacing for existing large trees need to be evaluated. Uses of U rban T ree B iomass The amount of wood removed due to mortality caused by urbanization pests, hazard trees, windstorms, and other disturbances from urban forests nationally is comparable to the total annual harvests from US National Forests and can range from 16 to 38 million green tons (Bratkovich et al. 2010, Bratkovich et al. 2008). The use of urban tree waste wood to create useful products is gaining momentum as additional resources and initiatives are organized (Bratkovich et al. 2010). Application of Mortality and Growth to Urban Forest Function S tudies Urban tree growth and mortality studies are being used in the Urban Forest Effects (UFORE) and the i tree STREETS models, which are being used throughout the SE (http://www.itreetools.org/). In the UFORE model, a street tree mortality study in Syracuse, New York (Nowak 1986) is used to estimate emissions due to dead and decomposing trees when calculating carbon sequestration (Nowak & Crane 2000). Probability of mortality is determined by street tree data from Nowak (1986) where crown dieback measurements are categorized into con dition ratings (goodexcellent, fair, poor, dying and dead). The model also uses natural forest and urban tree growth studies from northern US regions to approximate diameter growth for individual tree carbon sequestration rates (Table 11). These growth r ates are based on three land cover categories (forest, urban and park) and adjusted for tree condition and regional climate (Nowak & Crane 2000). Assuming growth rates from northern tree studies would

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22 likely be conservative for southern regions, despite a newer version of UFORE which uses the length of a regions growing season to determine the base growth rate standardized to Minnesotas where there are 153 frost free days. Research on urban tree growth, recruitment and mortality is needed to project futu re urban forest populations more accurately (Nowak et al. 2004). Better information on urban tree growth is needed since currently there are very few growth studies for city wide urban tree populations from the SE US region. T he Patersons index also calle d the CVP (climate, vegetation, productivity) index has been used in several countries to estimate potential production for areas that are hard to inventory. This index predicts maximum growth potential of trees and is based o n evaoptranspiration, annual temperature range, mean monthly temperature of the warmest month mean annual precipitation, length of growing season and is appropriate for comparisons across species and regions (Skovsgaard & Vanclay 2007). However, t here are disadvantages when applying t he same assumptions to different regions. Natural influences that occur on that site are specific to that geographic area; therefore, if the results are applied to an area outside of the studys realm, the resulting predictions may not be appropriate (Smit h 1983). Objectives The overall goal of this study is to analyze temporal changes in urban forest structure by improving estimates of rates of growth, ingrowth and mortality and to identify tree and plot level factors affecting these rates. Urban tree biomass removal estimates from remeasured plots will also provide information on carbon stocks and green waste potential in Gainesville.

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23 Hypothesis 1 is that significantly higher mortality rates will be found on commercial plots versus residential plots (alpha < 0.05), because low mortality rates observed in previous studies have been found in residential areas and higher rates have been found on commercial and transportation land uses (Nowak et al. 1990; Nowak et al. 2004). Hypothesis 2 is that signific antly higher growth rates (alpha < 0.05), will be found on land uses with urbanpark settings such as institutional and residential that are known to have higher rates of tree and lawn maintenances as has been observed in a previous study in similar land u ses ( Iakovoglou et al. 2002) Growth models are the focus of Hypothesis 3, where significant plot level factors (alpha < 0.05) will be soil water content and bulk density measurement which can indicate tree growth stress (Kramer & Boyer 1995, Close et al 1996) and low competition from other vegetation as characterized by low tree density (trees per hectare) which also limits tree growth (Vrecenak et al. 1989). I hypothesize that t ree characteristics that will be significant (alpha < 0.05) are related to crown measurements such as high crown light exposure and low percentages of missing crown, as these trends have been observed to affect the growth and survival of urban live oak trees (Templeton & Putz 2003). Finally, hypothesis 4 is that growth rates in urban, forest, and park land cover types in Gainesville will be significantly greater (alpha < 0.05) tha n the growth rates used by Nowak & Crane (2002) for predicting tree growth and carbon sequestration in similar land cover types. Individual species, suc h as Quercus virginiana may also have higher growth rates that those used in Nowak & Crane (2002).

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24 My a nalyses of permanent plot remeasurements will provide local mortality and growth estimates that account for local natural and human influences as opposed to assumptions based on studies from other regions. This study is unique as it describes the rates at which urban trees in Gainesvilles grow and die based on actual measurements. Finally, results will also be used to briefly explore biomass and carbon stock estimates in Gainesville that account for sitespecific and socio eco logical conditions.

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25 Table 11. Tree diameter growth rates by land cover used in the Urban Forest Effects (UFORE) model to calculate urban tree growth and carbon sequestration from Nowak and Crane (2000) Source Land cover Growth rate (cm/yr) Tree type Smith WB & Shifley SR (1984) Diameter growth, survival, and volume estimates for trees in Indiana and Illinois. Res. Pap. NC 257. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station. 10 p Forest 0.38 Forest Nowak, D.J. 1994. Atmospheric carbon dioxide reduction by Chicagos urban forest. In: McPherson, E.G.,Nowak, Nowak DJ (1994) Atmospheric carbon dioxide reduction by Chicagos urban forest. In Chicagos Urban Forest Ecosystem: Results of the Chicago Urban Forest Climate Project ( Eds McPherson EG, Nowak DJ, Rowntree RA): 8394. USDA Forest Service General Technical Report NE 186. Radnor, PA Urban 0.87 Street deVries RE (1987) A pr eliminary investigation of the growth and longevity of trees in Central Park. New Brunswick, NJ: Rutgers University. 95 p. M.S. thesis Park 0.61 Park

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26 CHAPTER 2 METHODS Study Area Gainesville, Florida is the largest city in Alachua County covering an area of 127 square kilometers and located at 2939N and 8220W in north central Florida. The climate is subtropical warm and humid. The city receives an average of 1370 mm per year; more than half is received June through September while the driest month is November (Metcalf 2004). Although a freeze can be expected about four times a year, the frost free season lasts 295 days per year (Dohrenwend 1987). The elevation in Gainesville varies around 30 meters above sea level and topography changes from rolling h ills in the northern part of the city to flat areas within the prairielands to the south where seasonally high water tables are found (Metcalf 2004, Phelps 1987). Gainesville is situated on top of two unique geological features: the northern part lies above the Hawthorne geologic formation and PlioPleistocene deposits of the Ocala Uplift lie below the southern part of the study area (Phelps 1987). Soils are predominantly sandy siliceous, Hyperthermic Aeric Hapludods and Plinthic Paleaquults and the textur e of these soils is very sandy (95%), and the rest are composed of different fill material (Chirenje et al. 2003). Gainesville, Florida has many remnant forested patches throughout the city that exhibit soil and vegetation characteristics similar to natural areas containing nonurban natural soils and vegetation. Also most soils in Gainesville show little signs of pollution, severe compaction or being covered by impervious surfaces (Dobbs 2009). However urbanization effect s do exist; in 2006 the City of Gai nesvilles ground was covered 9 percent by buildings and 15 percent by impervious ground cover (roads, sidewalks, etc.) (Escobedo et al 2009).

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27 Data C ollection Plot and Tree Measurements In 2006 Gainesvilles urban forest was sampled using the UFORE sampl ing protocol (Escobedo et al. 2009). Circular 0.04 ha (0.1 acre) plots were established and land use and ground cover percentages were estimated. In 2009, the Gainesville UFORE plots were remeasured following this same protocol. First, plot center was loc ated using GPS coordinate and original reference object s distance and directions. Groundbased as well as aerial photos were also utilized while the location of individual trees within the plot and their distance and direction measurements helped reduce r e measurement error. Ground cover categories included plot percentages of impervious surfaces, grass, soil, rock (e.g., pervious rock and gravel), water, duff and mulch, herbaceous vegetation (e.g., area comprised of vegetation that is not grass or shrub cover) and maintained and unmaintained grass (e.g., grassy area with no indication of mowing or other maintenance activities). For comparison, original land uses were condensed into the following: forest, institutional, commercial and residential/vacant. Trees with diameters larger than 2.5 cm (1 inch) were measured sequentially starting from due north and rotating clockwise around plot center; direction and distance in feet to each tree were recorded again to reduce remeasurement errors. The following data were collected by tree: species, diameter at breast height (dbh; cm), total and crown base height (m), crown width in two directions (m), crown light exposure (CLE) rating (0 to 5, where zero denotes a crown completely blocked above and on all sides and five indicates that each side of the tree as well as the top of the tree is completely exposed to direct light), and percent missing and percent dieback of foliage (compared to a full crown) to determine tree condition. Since tree dbh was of particular

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28 in terest for this study, a pole marked at 1.37 m was held next to each tree where measurements were taken to reduce measurement error. Soil Measurements Analyses of soils on Gainesvilles UFORE plot were conducted in the summer of 2007. Soil variables such as bulk density, water content, potassium concentration and pH were collected as described in Dobbs (2009). Bulk density measurements were measured from three (per plot) undisturbed 5 cm diameter by 4.5 cm deep soil samples that were then fresh weighted, ovendried (after 48 hours) then weighed for dry weight. Chemical properties such as potassium concentrations and pH were measured from 15 (per plot) randomly located soil cores from the top 10 cm of soil and analyzed by University of Florida Extension Soi l Testing Laboratory (Dobbs 2009) Re measurement M ethod Errors Re measurements using tree diameter tapes at breast height can differ from actual tree growth for reasons associated with measurement error and changes in tree physiology (Avery & Burkhart 1983). Even when efforts are made to reduce measurement error by using a pole for a standard breast height measurement or holding the tape tight and even, there are other sources of error that cannot be corrected. For example, annual diameter measurements can be negative due to contraction of trunk wood and bark in the presence of severe water stress (Pastur et al. 2007). In addition, changes in the height of mulch and litter below a tree can change the breast height measurement used for subsequent measurements resulting in a measurement taken at a different height. For these reasons, tree core increments are often measured to determine tree growth. In urban tree studies, however, tree coring is

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29 not appropriate since one might anticipate a high level of resist ance to granting permission for coring trees on private properties due to issues of liability. Data Organization Data Availability Of the original 93 plots established in 2006, 65 plots were remeasured. A complete remeasurement of all original plots was not possible in 28 plots since access was denied to 12 and even though trees on all plots were remeasured in 16 plots, some 2006 trees could not be matched to the original plot data on these (16) plots (Table 21). Also, 14 of these 16 plots were forested and plot center could not be found so the plot was reestablished, and 2 plots were located in residential plots where new construction eliminated the reference objects thus preventing measuring distance and direction information necessary to accuratel y re locate plot center. Soil data was missing for 17 plots, 8 of which contained no trees. This may have been related to the previous soil plot selection criteria that eliminated plots where pervious surfaces covered at least 50% of the plot. To compare g rowth rates by land use categories used in Nowak & Crane (2002), plots were separated into similar categories and matched trees were found in 31, 5, and 16 plots corresponding to open, park, and forested land cover categories, respectively. Brightly painte d stakes were installed on plot center for all forested plots to facilitate relocation for future remeasurements. Matching of Individual Trees Sample data from the 2006 and the 2009 sample were merged and individual trees present in both samples were m atched if they: 1. were at the same direction and distance from plot center, 2. the same species and 3. had a larger 2009 dbh measurement. Ingrowth was defined as the presences of a tree in the 2009

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30 measurement not originally measured in 2006, indicating a new planting or natural ingrowth (that a small tree grew above the dbh threshold of 2.5 cm) as described in Nowak et al. (2004). Mortality represented the absence of a previously measured tree which was removed or downed since the 2006 sample. Difficult ies arose while matching trees in certain plots and were mostly related to differences in the way directional information was interpreted (e.g. type of compass readings) and the differences in the order in which trees were recorded in 2006. It was consider ed ideal when trees were recorded starting with the tree closest to due north and plot center continuing in a clockwise direction, and compass directions were recorded in degrees from 0 to 360 from plot center. Soil Variable Selection Many soil variables could have been used in this study however, some soil variables were dropped because they were not available for many of the plots used in this study (Table 21). Additionally, many of the plots missing soils data had no trees, as the selection criteria f or soil sampling required that the plot had at least 50% pervious ground cover and access was granted. Other soil variables were removed due to their high correlation with other parameters that might have led to multicollinearity that causes problems in modeling, as highly correlated variables neutralize the response of significant parameters. Multicollinearity was identified by a Principal Component Analysis (PCA) by Dobbs (2009). In this study relating urban soils to urban morphology and socioeconomic fac tors in Gainesville, Florida, an analysis of soil variables determined that pH, soil potassium content and bulk density were the best variables to use for characterizing urban ecosystem structure and function in Gainesville. S oil pH was an indicator of soil water content, fertility and quality while bulk density was an

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31 indicator of socioeconomic effects, and potassium an indicator of disturbance. Therefore, pH, potassium, soil bulk density and water content were used for analys e s in this study based on th e results from Dobbs ( 2009) and the documented role these soil characteristic play on urban vegetation (Scharenbroch et al. 2005, Kramer & Boyer 1995). Species G roups Urban forests can often have a wide variety of tree species within very localized areas While reporting data for each species is informative, it is impractical for many species due to insufficient sample sizes. For example in this study there were 19 species where only one tree was matched for growth. Therefore, summarizing the raw data int o relevant species groups is needed to make results useful for modeling. For instance urban tree mortality rates were reported for all species by size class in Baltimore, Maryland (Nowak et al. 2004). In Chicago, radial growth rates for removed urban trees were grouped into major genera by size class (Nowak 1994). These species and size grouping approaches are not suitable for this study, due to the low frequency of trees found with some size classes. For example, growth rates for trees in the larger size c lasses (greater than 61.1cm) could not be modeled separately, as sample sizes were as few as 4 and 5 trees (Table 22). In natural settings, a trees average potential size at maturity and life span depend on the species individual characteristics (Now ak et al. 2002). The matched tree dataset was used to generate growth models where all species were grouped into one of the three following categories based on maximum height and life span characteristics as reported in the USDA PLANTS Database (www.plants .usda.gov): large sizelong life span (LL), large sizemoderate life span (LM), and medium and small size (M). Large

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32 size trees were those that potentially could attain a height equal or greater than 18.29 meters at maturity ; medium and small size trees were those that potentially could attain a height equal or less than 18.29 meters at maturity; while a moderate life span was considered to be less than 250 years and long life span was greater than or equal to 250 years. This methodology closely follows Nowak et al. (2002) where trees were assessed for atmospheric carbon dioxide emissions. Whereas Nowak et al. (2002) was able to develop fourteen categories using life span, growth rate, and size at maturity, our data supported three categories. In addition, four growth models were created for the four most frequent tree species in the matched tree dataset. Removed and ingrowth tree datasets were too small to support grouping species as for growth above; therefore mortality and recruitment models were gener ated by assigning trees into two classes; hardwood or softwood. Palm species were not used in any growth modeling due to their growth form and small sample size. Land Use and Land Cover D escriptions Re measured plots were grouped into two sets of categories: land use and lan d cover (Table 21). This was done to compare mortality estimates to the Nowak et al. (2004) mortality study based off permanent plot remeasurement in Baltimore, Maryland, and to compare growth rate estimates to the land cover categories used to estimate growth and carbon sequestration in Nowak & Crane ( 2002). These studies used similar land use and land cover categories based on their differences in urban tree structure and management regimes. Land cover categories separate urban areas in a way that could be interpreted easier than when determining land use. Forest areas have high tree cover, no management activities, and minimal disturbance; park areas are managed, have lower tree cover than forests, no pervious surfaces, and minimal

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33 di sturbance; and urban areas have lower tree cover than forest, more pervious surfaces, are managed, and have an increased potential for disturbances not associate with park areas. Vacant areas due to land use designations were classified as residential. Lan d use categories used in this analysis were: Commercial (7 plots): These plots had the greatest variation in types of plots. These include business like settings that were associated with yards, parking areas, access roads, warehouses, and also included plots in transportation routes and airports. The combination of transportation and commercial type plots helped comparison because both types exhibited high mortality rates in the Baltimore study (Nowak et al. 2004). Institutional (15 plots): These plots were found on lands associated with The University of Florida, a church, a correctional facility, a community fair ground, various medical and health facilities, and elementary, middle and high schools. Residential (27 plots): These plots included high, low and medium density residential areas included apartments/condominiums, mobile homes/trailers, and associated driveways and parking areas. Vacant lots in residential areas were considered residential plots because tree cover was similar to the surrounding residential area and it was apparent that they were sometimes maintained (clearing of debris, small shrubs, and mowed grass). Forest (16 plots): These plots included mixed management forested areas, pine plantations and abandoned areas apart from resi dential areas. This category also included two plots within conserved park areas that were heavily forested, and one natural conservation area within the University of Florida. The Land cover categories used in this study were: UrbanOpen (45 plots): Res idential, vacant, commercial and institutional plots that were near buildings, roads, or other structures that created opencanopy conditions. Park (4 plots): These plots had park like structures including two cemeteries, fair grounds and a sports field in an elementary school. Forest (16 plots): same as forest land use (described above).

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34 Calculations Diameter Growth and Mortality Rates Diameter measurements were converted into annual dbh growth (cm/yr) by subtracting the 2009 dbh from the 2006 dbh measurements and dividing by the length of time between measurements for each plot (an average of 2 years and 10 months). Mortality rates were calculated as in Nowak et al (2004), using a formula for mortality per year widely used in ecological applications (Sheil et al. 1995) w here annual mortality is m t is the time interval, and N1 and N0 are population counts and at the beginning and end of measurement interval t respectively (Eq. 21). m 1 ( N1/ N0)1 t (2 1) Biomass Estimat es Biomass estimates for all live trees measured in 2006 and 2009 were obtained using the same methods and allometric equations used in the UFORE model (Nowak and Crane). Allometric equations use tree characteristics such as dbh, height and species to determine above ground biomass i n terms of fresh or dry weight (Jenkins et al. 2003). This dimensional analysis approach did not involve destructive sampling methods. The model UFORE uses a list of applicable diameter based bio mass equations from studies across North America (Nowak 1994). This approach uses the biomass equation for an individual species if available; if not, then an average of all equations for that species genera are calculated. If there are no generaspecific equations for an individual species, then depending on its tree type an average from all hardwood or softwood species is calculated. Finally, all equations are converted to whole tree dry weight biomass estimates by applying conversions for each equation s

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35 type based on the tree portion or whether fresh or ovendried tree weight was estimated. Whole tree biomass totals can be achieved by converting from above ground biomass with the root to shoot ration of 0.26 (Cairns et al. 1997). Fresh weight or green w eight can be achieved by using moisture content averages of 0.46 for conifers and 0.56 for hardwoods (Nowak and Crane 2002). Total tree dry weight biomass can be converted to total stored carbon by multiplying by 0.5 (Nowak & Crane 2002). Although sequestr ation estimates are for an analysis period of 2 years and 10 months, they were annualized for comparison purposes by dividing the estimates by 2.8 years. Statistical Analyses Statistical modeling of growth, mortality and ingrowth were conducted using pl ot level factors from the original 2006 measurement s and included: land use type, land cover type, trees per hectare, basal area per hectare, percent ground covers of each plot (described in this chapters section on plot and tree measurements). In addition, tree level factors (described in this chapters section on plot and tree measurements) from the original 2006 measurement and selected soil variables (pH, potassium, soil bulk density and water content) collected in 2007 (as described in this chapters section on soil measurements) were also used. Seven growth models were created for each of the three species groups and the four most frequently occurring species that combined comprise 43% of matched trees: Laurel Oak ( Quercus laurifolia; 76 trees), Water Oak ( Quercus nigra; 62 trees), Slash Pine ( Pinus elliottii; 62 trees) and Loblolly Pine ( Pinus taeda; 57 trees) Two mortality and two recruitment models were generated by grouping species into hardwood and softwood types. Due to the concentration of measured growth rate values around zero, growth rate values were transformed with the square root function for modeling by species

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36 group and the natural logarithm plus one for modeling individual species. These transformations stabilized the variance and allowed assumptions underlying statistical models to be met. There were 46 trees (8% of matched trees) that were found to have negative growth rates and were reassigned a growth rate of zero for modeling purposes. Measurements from these trees indicated growt h rates that were less than the actual biological tree growth and are likely due to errors associated with dbh remeasurement and tree bole shrinkage (describe in this chapters section on growth measurement errors in dbh remeasurement methods). Since these errors could not be accounted for otherwise, assigning a value of 0 to these trees approximates actual biological growth in this sample. Growth rates were modeled with a general linear mixed model with the SAS procedure PROC MIXED (SAS 2006), using the above plot and tree level characteristics as predictor variables, and a random effect to account for correlations between trees in the same plot. A KenwardRogers adjustment was made to the degrees of freedom to better reflect the effect of the autocorrel ation structure in the data (Littel et al. 2006). Mortality and recruitment models used a generalized linear mixed model with a negative binomial distribution to characterize the response variable. Models were fit with the SAS procedure PROC GLIMMIX (SAS 2006) using the above plot level characteristics as predictor variables. Examination and comparison of model results used the information criteria and pvalues associated with each independent value. Nonsignificant effects and their interactions were identified by a type I error level of 0.05, and models were compared by their corrected Akaikes information criteria (AICC), which is a small sample bias -

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37 corrected version of the Akaikes information criteria fit statistic. To provide substantial evidence that the data arose from these models, the final estimated models included only those effects that were significant (alpha < 0.05) and also had the lowest AICC values. To determine significant differences in growth and mortality rates between land uses and la nd cover types, the Fischers LSD (least significant difference) statistical procedure was used with an (alpha < 0.05).

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38 Table 21. Status of all permanent plots in 2009 by land use and city for Gainesville, Florida. Land Use Residential* Commercial** Insti tutional Forest City Total Plots matched 23 2 11 15 51 No trees 4 5 5 0 14 Plot re established 2 0 0 14 16 Access denied 6 2 1 3 12 No soils data 4 (all had no trees) 9 (3 had no trees) 3 (1 had no trees) 1 17 (26% of all plots) Includes plots on v acant areas; **Includes plots on industrial areas Table 22. Gainesville, Floridas percent annual mortality and growth rates arranged by Nowak et al.s (2004) size classes to demonstrate differences in sample size. Gainesville 2006 to 2009 Nowak et al. 2004 Gainesville 2006 to 2009 DBH (cm) %Annual Mortality (number of trees removed) %Annual Mortality (number of trees removed) %Annual growth rate (cm/yr) (number of trees matched) 0 7.6 18 (70) 9 (528) 0.86 (167) 7.7 15.2 9.7 (33) 6.4 (267) 1.11 (134) 15.3 30.5 3.4 (16) 4.3 (201) 1.03 (174) 30.6 45.7 1.0 (3) 0.5 (109) 0.91 (109) 45.8 61.0 5.7 (5) 3.3 (62) 2.17 (33) 61.1 76.2 7.7 (1) 1.8 (28) 0.69 (5) >76.2 0 (0) 3.1 (33) 1.36 (4)

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39 CHAPTER 3 RESULTS AND DISSCUSI ON Results Change in Urban Forest Structure In 2009 the average tree height in my sample was 12.4 m, average dbh was 29.2 cm, and average crown width was 5.8 m. From 2006 to 2009 (average of 2.78 years lapse between measurements) the overall average annual mortality was 1.8% (Table 31) When comparing trees within the 65 remeasured plots, there was a net annual loss of approximately 13 trees, and a gain of 0.64 square meters of basal. In 2006, 755 trees were measured in Gainesville and by 2009, 128 (17%) trees were removed, which accou nt for 64%, 26%, 5% and 5% being removed from forest, residential, commercial and institutional plots, respectively. Plots in 2009 contained a total of 718 trees; 627 matched (30 of which were dead and not used for growth modeling) and 91 trees were consid ered ingrowth. Figure 31 indicates that when comparing all plots that were measured, the citys average percent change for trees per hectare and tree density increased over time by 3% and 26%, respectively. However, forest plots had a decrease in trees per hectare of 7%. Growth rates were highest on commercial plots (2.07 cm/yr) followed by residential (0.90 cm/yr), forest (0.75 cm/yr) and then institutional (0.50 cm/yr) plots (Table 31). Growth M odels Growth rates for LL trees were influenced by plot level factors such as ground cover percentages of maintained and unmaintained grass as well as average tree crown width and percent missing foliage (Table 32). Growth was positively influenced by every tree level factor with the exception of percent mis sing foliage. Growth rates for

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40 LM trees were influenced by the plot level factors of basal area per hectare, percent unmaintained grass, soil bulk density and soil water content as well as the tree factors of dbh, average crown width, percent missing foli age and crown light exposure (Table 32). Growth rate was positively influenced by every factor except dbh and percent missing foliage. Growth rates for M trees were only influenced by dbh and crown light exposure (Table 32). Models were created for the four most frequently occurring species which, when combined comprised 43% of matched trees. Growth rates for Pinus taeda were influenced by plot level factors of percent rock cover and the tree level factor of crown height (Table 33) where growth was negatively influenced by crown height. Growth rates for Pinus elliottii were influenced by dbh as well as plot level factors of land use and ground cover ( percent unmaintained grass, percent maintained grass and percent herbaceous cover) (Table 33). Higher growth rates (1.29 cm/yr) were found on institutional land uses, followed by forest (0.86 cm/yr) and r esidential (0.62 cm/yr). There were no Pinus elliottiis found in commercial land use plots. Growth rates for Quercus laurifolia (0.84cm/yr) were influe nced by the plot level land use and tree level crown light exposure (Table 33). For Quercus laurifolia the most abundant tree in Gainesville (13% of all trees), growth was found to be significantly greater in residential plots (1.34 cm/yr) compared to co mmercial (0.59 cm/yr) and forest (0.49 cm/yr) but not institutional (0.67 cm/yr). Quercus nigra were influenced by the plot level percent unmaintained grass and tree level crown light exposure (Table 33).

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41 Soil variables did not result in many significa nt effects within the growth models when considering the lowest AICC values. However, slightly higher AICC models were explored to further investigate these variables. For LL trees, percent grass, percent unmaintained grass, dbh, percent missing foliage, potassium and pH influenced diameter growth significantly where potassium and percent missing foliage influences were negative. In a model with a higher AICC value for Quercus nigra growth significant factors that negatively influenced growth were tree per hectare, bulk density, and potassium while pH had a positive effect. Mortality and Ingrowth M odels Two mortality models were developed for hardwood species. These models indicate that land use and trees per hectare significantly influenced mortality (Table 34). While the model using trees per hectare had a lower AICC (174.9 verses 191.1), indicating more evidence that the data arose from this model, the competing model number 2 is of interest because it does not rely on field data. There were no alternative models to compete with the softwood mortality model, which had a positive influence of trees per hectare on mortality (Table 34). Although individual species models could not be estimated due to the paucity of data in most species, annual mortalit y rates for the most common species in Gainesville were found to range from 1.1% to 6.9% (Table 35). Both softwood and hardwood ingrowth models show that ingrowth increased with trees per hectare ( Table 3 6). No other plot level factors were found to be significant. Change in B iomass Considering all trees and plots in Gainesville measured in both 2006 and 2009, I estimate that 6,082 tons of carbon was sequestered annually (Table 37). Carbon sequestered was largely from Quercus virginiana, Quercus nigra and Liquidambar

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42 styraciflua (Table 38). Annual removed biomass per hectare was greatest on institutional and forest plots, which contained approximately 6,712 and 2,202 kg/yr, respectively (Table 39). Removed biomass was largely comprised of Quercus laurifolia Pinus taeda and Acer rubrum (Table 310). Annual removed biomass in Gainesville was about 34,785 tons. Total carbon stored in urban trees in Gainesville for 2009 was about 231,950 tons (Table 311). Discussion Urban Forest Structure Changes Despit e an overall loss in number of trees, Gainesville had a net increase in basal area and biomass. Forest plots had the greatest loss in number of trees per hectare and had the second lowest increase in basal area per hectare (Figure 31). Conversely industri al plots had the largest increase in trees per hectare, and the lowest increase of basal area. This may imply that more trees were planted on industrial land uses than on commercial and residential land uses; while on forest plots more trees were removed/f allen than naturally regenerated. Therefore increases in basal area are primarily from growth of existing trees. While measuring trees on forest plots I observed many trees that were missing from 2006 could often be identified on the ground as downed trees (eg. large newly fallen trees of the same species with their bases near or on the location recorded for a large tree existing in 2006). This may be due to the loss of individual trees associated with the thinning stage of natural succession in remnant fo rest patches in urban landscapes where savanna trees enclose the canopy while changing environmental conditions due to urbanization such as fragmentation, construction or altered hydrologic processes (Zipperer et al. 1997, Tempeton & Putz 2003).

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43 Rates of Growth and Mortality Growth rates for trees in Gainesville ranked differently than those reported in five Midwestern states by Iakovoglou et al. (2002) where radial growth estimates were higher in city park sites followed by residential and commercial sites (Table 35). In Gainesville, higher diameter growth rates were found on commercial, residential, forest and then institutional sites which is counter to hypothesis 2. Growth rates for trees in Gainesville when analyzed by land cover types ranked diffe rently than those used in UFORE (Table 11, 3 5), where the highest growth rates are found on parks, followed by open then forest (Table 35). Although Gainesvilles urban plots did not have a higher growth rate than those in Table 11, growth rates in Gai nesville were higher in forest and park plots than in the studies used for UFORE, which partially satisfies hypothesis 4. Growth rates for three of the most common trees in Gainesville ( Quercus laurifolia, Quercus nigra, and Pinus elliottii) were greater t han the two growth rates used in UFORE for trees grown in forest and park settings (Table 3 7). The top six species by growth rate far exceed all three growth rate estimates used in UFORE. Growth rates by land use in Table 31 ranged higher than those reported in a similar study in Chicago, IL (Jo & McPherson 1995), and those used for calculating dbh growth for carbon sequestration in the UFORE model (Table 11; Nowak & Crane 2000). Growth rates for Quercus laurifolia were less than the diameter growth est imate of 1.69 cm/yr for canopy trees of this species in Templeton & Putz (2003). However, an average growth rate of 1.34 cm/yr for Quercus laurifolia in Gainesvilles residential plots (opencanopy like conditions) was similar to estimates found by Templet on & Putz (2003).

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44 Overall annual mortality rates during the analysis period were less than those reported in a similar study in Baltimore (Nowak et al. 2004). Comparisons of mortality rates by size classes to the Nowak et al. (2004) study show that high mortality rates for small sized trees (015.2 cm dbh) and lower mortality rates for medium sized trees (15.361.0 dbh) were proportionally the same in both Nowak et al.s (2004) and my study. Although there were no significant differences between land uses w hen comparing mortality in all plots with trees, in my study mortality was highest in forest plots, followed by commercial then residential while institutional plots actually exhibited in growth (Table 35). These trends confirm hypothesis 1 in that mortality rates are similar to those reported in Nowak et al. (2004) in Baltimore where mortality rates on transportation or commercial industrial land uses were higher than rates in medium to low density residential land uses. When comparing mortality by land cover type, higher mortality rates were found on forest then park and urban plots. Higher mortality rates were found in both Quercus nigra, Quercus laurifolia than in Pinus taeda or Pinus elliottii (Table 3 6). Mortality increased as trees per hectare inc reased for both softwood and hardwood species (Table 34) Models of Growth, Mortality and Ingrowth Hypothesis 3 correctly predicted tree characteristics that significantly affected tree growth, but not plot characteristics. Plot factors of soil and tree density affected growth in only one of the growth models (LM trees). However, tree factors related to crown measurements like average crown width, percent missing foliage, and tree height were significant f actors in 6 out of 7 growth models and CLE was si gnificant in 4 of the 7 growth models.

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45 Results from the LL growth model suggest that once established (i.e. grown to 2.5 cm dbh) LL tree growth in the urban environment is affec ted by surrounding ground covers such as maintained grass. This is possibly due to factors caused by maintenance activities associated with maintained grass such as fertilization and increased irrigation as well as the trees crown size and exposure to light (Zipperer et al. 1997, Tempelton & Putz 2003). Typically, vegetation such as other trees, shrubs and turf grass surrounding a tree can limit growth as they compete when space is limited as in the case of urban conditions (Vrecenak et al. 1989). The LM growth model results suggest that established LM tree growth in the urban environment is also affected by the presence of maintained grass on plots and CLE, similar to LL trees and is positively influenced by soil water content and soil bulk density basal area and crown light exposure. For LM trees, increased growth was associated wi th increased soil bulk density, however this result was not expected, as deceased bulk density is known to improve soil physical properties such as water infiltration and soil biological processes which are conducive to plant growth (Scharenbroch et al. 20 05). On the other hand, increased bulk density was found by Dobbs (2009) on plots with high percent maintained grass which may suggest mulitcollinearity between these variables and may have neutralized this effect. Moreover, the bulk density values for thi s study are from the top 10 cm of soil profile; therefore these measurements may not be affecting growth via root soil interactions in trees with deeper roots. For better tree growth analysis, deeper soil samples might be needed. Diameter growth was positi vely influenced by crown light exposure in the M growth model, suggesting that for smaller trees, the amount of light exposure is more important

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46 than any other factor that was analyzed and can be related to the trees growing space and competition with other trees factors that have been shown to influence tree growth (Vrecenak et al. 1989). Crown light exposure was limited in the average Pinus taeda where only 2 sides out of a possible 5 were exposed to light. This might explain why tree height influenced growth for this LM categorized specie more significantly than crown light exposure (a significant factor in the LM growth model). The taller the tree, the more potential for increased crown light exposure, which can increase growth (Templeton & Putz 2003). For Pinus elliottii, a positive relationship between dbh growth and 2006 dbh is unusual, especially due to the fact that the average 2006 dbh of Pinus elliottii (32.3 cm) describes a medium sized tree by size class in Table 21. This may be a reflection o f the species known rapid growth rate as well and large potential size and long life span (www.plants.gov). Similar to the dbh growth model generated for LL trees (Table 32) the presence of both unmaintained and maintained grass (or associated maintenance activities) positively influenced diameter growth in Pinus elliottii. Both oak species are categorized as LM trees and both oak growth models were positively influenced by tree level factors such as crown light exposure. Both ingrowth models showed a positive influence for trees per hectare indicating that plantings or tree in growth to the one inch size class is more likely in areas that already contain trees There are no other models of ingrowth to compare these results to. Plot and tree level characteristics affected diameter growth in all species groups and the individual species model, while plot level characteristics like trees per hectare and land use affected mortal it y and in growth Greater sampling intensity would likely

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47 have improved grow th, mortality and in growth models. However, due to limited access and insufficient plot relocation information, this was not possible. Green Waste Supply Potential and Carbon Sequestration This studys estimates account for actual biophysical and socioeconomic factors in Gainesville as opposed to modeling these assumptions. Annually, more carbon was sequestered (83%) on residential plots (which include vacant areas) tha n forested and commercial, while institutional plots had a net emission of carbon due to tree removals. By land cover, urban plots sequestered the most carbon followed by forest and park plots. More carbon was sequestered by live oaks (1,727 tons city wide) than any other species (Table 38). Residential plots had higher carbon sequestration estimates due to the fact that vacant plots were assigned a residential land use and that forested plots decreased in trees per acre over time (Figure 31). Stratifying land uses differently will result in different carbon sequestration per land use esti mates. Results from this study show that if all removed trees were collected from these analyzed plots in Gainesville, there would be about 34,785 tons of fresh above ground biomass removed each year and most (77%) of this biomass would come from Quercus laurifolia Pinus taedas, and Acer rubrum (Table 39 and 310) When analyzed by land use, most removed biomass came from institutional plots (42%) then forest (36%), residential (21%) and commercial (0.1%) If removed trees were only harvested from nonfor est plots, this estimate would be reduced to 28,350 tons of fresh above ground biomass a year. In 2009, more carbon was being stored in forested plots (42%) than on residential plots (35%) followed by institutional (18%) and commercial plots (4%; Table 3 1 1).

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48 Table 31. Plot count, average annual growth (AGR) and mortality rates (AMR) for all re measured trees between 2006 and 2009 in Gainesville, Florida by land use and cover categories Land Use Commercial Forest Institutional Residential City Total Plot s matched 7 15 16 27 65 Plots with trees 2 15 11 23 51 AGR (cm/yr) 2.07 0.75 0.50 0.90 0.84 AMR 0.86% 2.70% 1.86% 0.66% 1.79% Land Cover Urban Park Forest AGR (cm/yr) 0.81 1.49 0.75 AMR 0.37% 1.56% 2.70% Table 32. Test of fixed effects for model of annual diameter at breast height (dbh) growth by species group in Gainesville, Florida from 2006 to 2009 Large potential size, long life span (LL) N=126 Effect Estimate Nm DF* Den DF* F value* Pr>F* %Maintained grass 0.0058 1 121 28.50 <0.0001 %U n maintained grass 0.0060 1 121 7.20 0.0083 Average crown width 0.0039 1 121 6.59 0.0114 % Missing foliage 0.0024 1 121 7.35 0.0077 Large potential size, moderate life span (LM) N=284 Effect Estimate Nm DF* Den DF* F value* Pr>F* Basal area per he ctare 0.0008 1 26 16.18 0.0004 %Un maintained grass 0.0055 1 26 34.14 <0.0001 Dbh 0.0173 1 249 18.12 <0.0001 Average crown width 0.0078 1 249 16.98 <0.0001 % Missing foliage 0.0017 1 249 6.96 0.0089 Crown light exposure 0.0498 1 249 16.65 <0.0001 Soil bulk density 0.2673 1 26 11.19 0.0025 Soil water content 0.0096 1 26 13.35 0.0011 Medium potential size (M) N=120 Effect Estimate Nm DF* Den DF* F value* Pr>F* Dbh 0.0186 1 117 6.04 0.0155 Crown light exposure 0.0839 1 117 20.82 <0.0001 Interpret as with numerator and denominator degrees of freedom (Nm DF and Den DF, respectively) the critical value of the F distribution (F value) means the estimate is significant at the 5% level as determined by the p value (Pr>F).

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49 Table 33. Test of fixed effects for model of annual diameter at breast height (dbh) growth of four most frequent species in Gainesville, Florida from 2006 to 2009 Loblolly pine ( Pinus taeda ) N=57 Effect Estimate Nm DF* Den DF* F value* Pr>F* %Pervious rock 0.0499 1 54 4.92 0.0307 Crown height 0.0032 1 54 5.70 0.0205 Slash pine ( Pinus elliottii ) N=62 Effect Estimate Nm DF* Den DF* F value* Pr>F* Land use forest 0.2476 2 55 7.58 0.0012 Land use institutional 0.2850 2 55 7.58 0.0012 Land use residential 0 2 55 7.58 0.0012 %Maintained grass 0.0085 1 55 51.45 <0.0001 %Un maintained grass 0.0064 1 55 18.28 <0.0001 %Herbaceous cover 0.0015 1 55 6.00 0.0175 Dbh 0.0134 1 55 15.00 0.0003 Laurel oak ( Quercus laurifolia ) N =76 Effect Estimate Nm DF* Den DF* F value* Pr>F* Land use forest 0.1471 3 71 19.07 0.0324 Land use institutional 0.0974 3 71 19.07 0.0324 Land use residential 0 3 71 19.07 0.0324 Land use commercial 0.1569 3 71 19.07 0.0324 Crown light exposure 0.0679 1 71 3.09 <0.000 1 Water oak ( Quercus nigra ) N=62 Effect Estimate Nm DF* Den DF* F value* Pr>F* %Un maintained grass 0.0158 1 59 8.46 0.0051 Crown light exposure 0.0646 1 59 14.56 0.0003 Interpret as with numerator and denominator degrees of freedom (Nm DF and Den DF, respectively) the critical value of the F distribution (F value) means the estimate is significant at the 5% level as determined by the p value (Pr>F).

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50 Table 34. Test of fixed effects for model of mortality for hardwoods and softwoods in G ainesville, Florida from 2006 to 2009 Hardwood mortality #1 Effect Estimate Nm DF* Den DF* F value* Pr>F* Trees per hectare 0.0058 1 63 18.40 <0.0001 Hardwood mortality #2 Effect Estimate Nm DF* Den DF* F value* Pr>F* Land use forest 0.9478 3 61 3. 74 0.0156 Land use institutional 1.0217 3 61 3.74 0.0156 Land use residential 0 3 61 3.74 0.0156 Land use commercial 1.0862 3 61 3.74 0.0156 Softwood mortality Effect Estimate Nm DF* Den DF* F value* Pr>F* Trees per hectare 0.0072 1 63 5.03 0.0 284 Interpret as with numerator and denominator degrees of freedom (Nm DF and Den DF, respectively) the critical value of the F distribution (F value) means the estimate is significant at the 5% level as determined by the p value (Pr>F). Table 35. An nual mortality rates for the ten most common trees found in 2006 ranked by total number of trees Rank Species (Trees removed) Annual mortality rate 1 Quercus laurifolia 4.67% 2 Quercus nigra 5.72% 3 Pinus taeda 3.55% 4 Pinus elliottii 1.14% 5 Acer ru brum 3.73% 6 Prunus caroliniana 6.89% 7 Liquidambar styraciflua 1.01% 8 Nyssa biflora 5.85% 9 Gordonia lasianthus 6.10% 10 Celtis laevigata 3.38%

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51 Table 36. Test of fixed effects for model of in growth for hardwoods and softwoods in Gainesville, Florida from 2006 to 2009 Hardwood in growth Effect Estimate Nm DF* Den DF* F value* Pr>F* Trees per hectare 0.0047 1 63 14.08 0.0004 Softwood in growth Effect Estimate Nm DF* Den DF* F value* Pr>F* Trees per hectare 0.0056 1 63 5.75 0.0194 In terpret as with numerator and denominator degrees of freedom (Nm DF and Den DF, respectively) the critical value of the F distribution (F value) means the estimate is significant at the 5% level as determined by the p value (Pr>F). Table 37. Annual carb on sequestered per hectare (CSPH) and city total (CSCT) estimates by land use and land cover for trees in Gainesville, Florida from 2006 to 2009 Land Use CSPH (kg/ha) CSCT (tons) Land cover CSPH (kg/ha) CSCT (tons) Commercial 399 557 Forest 402 1278 Forest 379 1106 Urban 434 3801 Institutional 195* 619* Park 1290 983 Residential** 955 4973 Grand Total 479 6082 479 6082 *Carbon emitted though removals exceeded carbon sequestered through growth; **Includes plots on vacant and residential areas.

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52 Table 38. Top four species ranked by frequency and top six species ranked by highest average growth rate (AGR) and standard error (SE), with corresponding carbon sequestration per hectare (CSPH) and city total (CSCT) estimates for trees in Gainesv ille Florida from 2006 to 2009 Rank Species (Number of trees) AGR (cm/yr) SE *CSPH (kg/ha) *CSCT (tons) By Frequency 1 Quercus laurifolia (76) 0.84 0.17 34 428 2 Quercus nigra (62) 0.90 0.28 91 1155 3 Pinus elliottii (62) 0.84 0.18 3 38 4 Pinus taeda (57) 0.53 0.12 6 76 By growth rate 1 Juniperus virginiana (4) 1.69 0.51 10 127 2 Lagerstroemia indica (12) 1.66 0.49 15 190 3 Quercus virginiana (16) 1.33 0.51 136 1727 4 Celtis laevigata (20) 1.13 0.43 2 5 5 O strya virginiana (5) 1.00 0.38 1 13 6 Acer rubrum (36) 1.00 0.38 20 254 10 Liquidambar styraciflua (35) 0.64 0.26 42 533 12 Cinnamomum camphora (19) 0.51 0.17 13 165 Negative values represent carbon emissions due to removals Table 39 Annual removed above ground fresh weight biomass per hectare (RBPH) and city total (RBCT) estimates by land use and land cover in Gainesville, Florida from 2006 to 2009 Land Use RBPH (kg/ha) RBCT (tons) Land cover RBPH (kg/ha) RBCT (tons) Commercial 14 20 Forest 2230 7080 Forest 2203 6435 Urban 3714 32546 Institutional 6712 21311 Park 835 636 Residential 1708 8894 Grand Total 2739 34785 2739 34785 *Carbon emitted though removals exceeded carbon sequestered through growth Table 3 10. Annual removed above ground fresh weight biomass per hectare (RBPH) and city total (RBCT) for species comprising 90% all removed biomass in Gainesville, Florida from 2006 to 2009 Species RBPH (kg/ha) RBCT (tons) All trees 314 3888 Quercus laurifo lia 118 1499 Pinus taeda 73 927 Acer rubrum 50 635 Platanus occidentalis 21 266 Pinus elliottii 13 165 Quercus nigra 8 102

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53 Table 311. Carbon stored in 2009 per hectare (CSTPH) and city total (CSTCT) estimates by land use and l and cover in Gainesville, Florida Land Use CSTPH (kg/ha) CSTCT (tons) Land cover CSTPH (kg/ha) CSTCT (tons) Commercial 7411 10353 Forest 32839 104263 Forest 33256 97141 Urban 12111 106129 Institutional 13306 42246 Park 29197 22248 Residential** 15688 81687 Grand Total 18264 231953 18264 231953 *Carbon emitted though removals exceeded carbon sequestered through growth; **Includes plots on vacant and residential areas 36.69 9.87 12.48 -6.94 11.76 10.67 34.63 5.51 26.53 2.79 -20 0 20 40 60 80 100 Trees per hectare Basal area per hectareAverage change (%) Commercial Forested Institutional Residential City Total Figure 31. Average percent change in trees per hec tare and basal area per hectare by land use and city total for Gainesville, FL from 2006 to 2009

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54 CHAPTER 4 CONCLUSION Growth rates presented in this study should be appropriate to apply to urban trees in other cities with growing conditions similar to Gai nesville. Using this studys local growth rates in urban forest functional models would reduce bias and variance in the resulting carbon estimates for Gainesville. My results indicate that mortality rates were similar by size class and land use to a similar permanent plot remeasurement study in Baltimore, Maryland (Nowak et al. 2004). Based on the remeasurement of permanent plots, Gainesville trees were estimated to have an average annual mortality rate of 1.8%. Of the 755 trees in our sample in 2006, by 2009, 128 (17%) trees had been removed. Plot and tree level characteristics combined can be used to estimate diameter growth as found in all growth models except the M growth model. This study provides information on how site characteristics affect growth in trees common to Gainesville, Florida according to their different size and potential age. In Pinus elliottii and LL models, maintained grass was significant in enhancing growth while in the LM model, unmaintained grass was a significant factor. In addition, crown characteristics were often significant in my growth models. For example, characteristics such as average crown width, percent missing foliage, and tree height were significant factors in 6 out of 7 growth models while CLE was significant in 4 of the 7 growth models. These results could be used to develop urban tree planting strategies, such as selecting crown size and available light source as important factors to facilitate tree growth. Maintained grass (as an alternative to other ground cover vegetation types) might enhance growth because it does not compete for light and may be associated with maintenance activities that contribute to the growth of surrounding trees such as

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55 irrigation, fertilization, and edging of new vegetation or vines that can grow around a tree. Although extra resources will be spent on maintaining grass, benefits associated with enhanced tree growth might contribute to offsetting these disadvantages. This study also provides green waste estimates for Gainesville by type and quantity as well as where it is being generated and where it is being stored as biomass. Higher growth rates found in Gainesville than those from previous studies used for estimating tree growth and carbon sequestration reveal the possibility that proj ected estimates in Gainesville might be underestimated. Carbon sequestration through growth was higher in residential plots (which include vacant areas) while removed biomass potential was greatest on institutional plots. Carbon stored in urban trees was higher in residential areas than institutional or commercial. Suggestions for selecting trees based on their functional ability to store and sequester carbon can also be inferred from these results. Although only a small sample of Quercus virginiana was analyzed, its sequestration rate was the highest and growth rate was third highest over the other species examined in this study. This study highlights that both Quercus virginiana and Quercus nigra provide the benefit of being a large and sustainable potent ial sink for carbon in Gainesvilles urban forest while Quercus laurifolia is the largest source of carbon emissions due to tree removals. Urban forests need to be managed in a way that enhances their secondary carbon dioxide reduction functions of conser vation and avoidance of building energy usage though reduced ambient air temperatures and shading by trees and decreased need for energy from fossil fuel based power plants (Hesiler 1986, Nowak 1993a, Simpson & McPherson 2001, Akbari 2002, Pandit & Laband 2010). The primary carbon dioxide

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56 emission reduction functions (carbon storage and sequestration in biomass through growth) can surpass the emissions produced through tree maintenance activities and emissions produced by decomposition of dead and removed trees (Nowak et al. 2002). Regional difference in energy savings from trees near buildings can differ based on differences in emission factors, building construction, climate, tree sizes and growth rates (Simpson & McPherson 2001). In the summertime, large and dense shade trees in energy saving locations (close proximity to buildings particularly on southwest, west or east side of a building) can significantly reduce energy consumption (Simpson & McPherson 2001, Pandit & Laband 2010). The benefits from urba n trees will continue to improve the quality of life in cities if the factors that affect their growth and mortality are better understood and applied urban tree management.

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57 LIST OF REFERENCES Akbari H (2002) Shade trees reduce building energy use and CO2 emissions from power plants. Environmental Pollution 116: 11 126 Avery TE & Burkhart HE (1983) Forest Measurements, 3rd ed. Mc Graw Hill Companies, New York, NY Bratkovich S, Bowyer J & Fernholz K (2010) Urban wood utilization and industrial cluster: a twin cities case study. Dovetail Partners Inc. Available online at http://dovetailinc.org/files/DovetailUrbanTC0510.pdf ; last accessed May 2010 Bratkovich S, Bowyer J, Fernholz K & Lindburg A (2008) Urban tree utilization and why it matters. Dovetail Partners, Inc. Available online at http://dovetailinc.org/files/DovetailUrban0108ig.pdf ; last accessed May 2010 Cairns MA, Brown S, Helmer EH & Baumgardner GA (1997) Root biomass allocation in the worlds upland forests. Oecologia 111: 1 111 Chirenje T, Ma LQ, Szulczewski M, Littell R, Portier KM & Zillioux E (2003) Aresenic distribution in Florida urban soils comparison between Gainesville and Miami. Journal of Environmental Quality 32: 109 119 Close RE, Kielbaso JJ, Nguyen PV & Schutski RE (1996) Urban vs. natural sugar maple growth: Water relations. Journal of Arboriculture 22: 187 192 Craul PJ (1999) Urban soils: applications and practices. Wiley, NY deVries RE (1987) A preliminary investigation of the growth and longevity of trees in Central Park. Rutgers University. MS thesis. New Brunswick, NJ Dobbs C (2009) An index of Gainesvilles urban forest ecosystem services and goods. School of Forest Resources and Conservation, University of Florida. MS thesis. Gainesville, FL Dohrenwend RE (1978) The climate of Alachua County, Florida. In: Institute of Food and Agricultural Sciences, Bulletin 796: 57. University of Florida, Gainesville, FL Duryea ML, Kampf E & Little RC (2007) Hurricanes and the Urban Forest: I. Effects on Southeas tern United States Coastal Plain Tree Species. Arboriculture & Urban Forestry 33(2): 83 97

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58 Escobedo FE, Seitz JA & Zipperer W (2009) Gainesvilles urban forest canopy cover. FOR 215. School of Forest Resources and Conservation, Florida Cooperative E xtension Service, Institute of Food and Agricultural Sciences, University of Florida. Available online at http://edis.ifas.ufl.edu/FR277; last accessed May 2010 Fosters RS & Blaine J (1978) Urban tree survival trees in the sidewalk. Journal of Arboricu lture 4(1): 14 17 Gilberson P & Bradshaw AD (1985) Tree survival in cities; the extent and nature of the problem. Arboricultural Journal 9: 131 142 Grabosky J & Gilman EF (2004) Measurement and prediction of tree growth reduction from tree planting sp ace design in established parking lots. Journal of Arboriculture 30(3): 154 159 Grimm NB, Faeth SH, Golubiewski NE, Redman CR, Wu J & Briggs JM (2008) Global change and the ecology of cities. Science 319: 756 760 Heynen NC & Lindsey G (2003) Correlates of urban forest canopy cover: Implications for local public works. Public Works Management Policies 8(1): 33 47 Heisler GM (1986) Energy savings with trees. Journal of Arboriculture 12(5): 113 125 Iakovoglou V, Thompson J & Burras L (2002) Characterist ics of trees according to community population level and land use in the U.S. Midwest. Journal of Arboriculture 28(2): 59 69 Jo H & McPherson EG (1995) Carbon Storage and Flux in Urban Residential Greenspace. Journal of Environmental Management 45: 109 133 Kozlowski TT & Pallardy SG (1997) Physiology of Woody Plants. Academic Press, San Diego, CA Kramer EJ & Boyer JS (1995) Water Relations of Plants and Soils. Academic Press, San Diego, CA Kramer EJ & Kozlowski TT (1979) Physiology of Plants. Aca demic Press, New York, NY Littell RC, Milliken GA, Stroup WW, Wolfinger RD & Schabenberger O (2006) SAS for Mixed Models, 2nd ed. SAS Institute Inc, Cary NC Metcalf C (2004) Regional channel characteristics for maintaining natural fluvial geomorpholog y in Florida streams for the Florida Department of Transportation. U.S Fish and Wildlife Service, Panama City Fisheries Resource Office. Panama City, FL

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59 McDonnell MJ & Pickett STA (1990) Ecosystem structure and function along urbanrural gradients: an unexploited opportunity in ecology. Ecology 71: 1232 1237 McPherson EG (1998) Atmospheric carbon dioxide reduction by Sacramentos urban forest. Journal of Arboriculture 24(4): 215 223 Nowak DJ (1986) Silvics of an urban tree species: Noway maple ( Acer plantanoides L.). State University of New York, College of Environmental Science and Forestry. Unpublished MS thesis. Syracuse, NY Nowak DJ (1993a) Atmospheric carbon reduction by urban trees, Journal of Environmental Management 37(3): 207 217 Nowak DJ (1993b) Historical vegetation change in Oakland and its implications for urban forest management. Journal Arboriculture 19(5): 313 319 Nowak DJ (1994) Atmospheric carbon dioxide reduction by Chicagos urban forest. In Chicagos Urban Forest Ecosystem : Results of the Chicago Urban Forest Climate Project ( Eds. McPherson EG, Nowak DJ & Rowntree RA): 8394. US Department of Agriculture Forest Service, Northeastern Research Station, General Technical Report NE 186. Radnor, PA Nowak DJ, McBride JR & Bea tty RA (1990) Newly planted street tree growth and mortality. Journal of Arboriculture 16(5): 124 129 Nowak DJ, Rowntree RA, McPherson GE, Sisinni SM, Kerkmann ER & Stevens JC (1996) Measuring and analyzing urban tree cover. Landscape and Urban Planni ng 36: 49 57 Nowak DJ & Crane DE (2000) The Urban Forest Effects (UFORE) Model: quantifying urban forest structure and functions. In: Integrated tools for natural resources inventories in the 21st Century ( Eds. Hansen M & Burk T): 714720. US Department of Agriculture Forest Service, North Central Research Station, General Technical Report NC 212, St. Paul, MN Nowak DJ & Crane DE (2002) Carbon storage and sequestration by urban trees in the USA. Environmental Pollution 116: 381 389 Nowak DJ, Stevens JC, Sisinni SM & Luley CJ (2002) Effects of urban tree management and species selection on atmospheric carbon dioxide. Journal of Arboriculture 28(3): 113 122 Nowak DJ, Kuroda M & Crane DE (2004) Tree mortality rates and tree population projections in Baltimore, Maryland, USA. Urban Forestry Urban Greening 2: 139 147

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60 Pandit R & Laband DN (2010) Energy savings from shade trees. Ecological Economics 69: 1324 1329 Pastur GM, Lencinas MV, Cellini JM & Mundo I (2007) Diameter growth, can live trees dec rease? Forestry 80(1): 83 88 Petrosillo I, Muller F, Jones KB, Zurlini G, Krauze K, Victorov S, Li BL & Kepner WG (2007) Use of Landscape Sciences for the Assessment of Environmental Security. Springer Science and Business Media B.V., The Netherlands Phelps GG (1987) Effects of surface runoff and treated wastewater recharge on quality of water in the Floridian aquifer system, Gainesville area, Alachua County, Florida: 62. U.S. Geological Survey, Water Resources Investigations Report 874099, Gainesv ille, FL Pouyat RV, Yesilonis ID, Russell Anelli J & Neerchal NK (2007) Soil chemical and physical properties that differentiate urban land use and cover types. Soil Science Society of America Journal 71: 1010 1019 Rapport DJ (1995) Ecosystem health: an emerging integrative science. In: Evaluating and Monitoring the Health of LargeScale Ecosystems. (Eds Rapport DJ, Gaudet C & Calow P): 5 31. Springer Verlag, Heidelberg Rhoades RW & Stipes RJ (1999) Growth of trees on the Virginia Tech campus in res ponse to various factors. Journal Arboriculture 25(4): 211 215 Richards NA (1979) Modeling survival and consequent replacement needs in a street tree population. Journal of Arboriculture 16(5): 124 129 Scharenbroch BC, Lloyd JE & JohnsonMaynard JL (200 5) Distinguishing urban soils with physical, chemical and biological properties. Pedobiologia 49: 283 296 Sheil D, Burslem D & Alder D (1995) The interpretation and misinterpretation of mortality rate measures. Journal of Ecology 85: 331 333 Simpson J & McPherson G (2001) Tree Planting to Optimize Energy and Carbon Dioxide Benefits. In: Proceeding: The 2001 National Urban Forest Conference, 57 September 2001, Washington, DC. US Department of Agriculture Forest Service, Pacific Southwest Research St ation, Center for Urban Forest Research c/o Department of Environmental Horticulture, University of California, Davis, CA Sklar F & Ames RG (1985) Staying alive: street tree survival in the inner city. Journal of Urban Affairs 7(1): 55 65

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61 Skovsgaard J P & Vanclay JK (2007) Forest site productivity, a review of the evolution of dedrometric concepts for evenaged stands. Forestry 81(1): 13 31 Smith WB (1983) Adjusting the STEMS regional forest growth model to improve local predictions. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, Research Note NC 297, St. Paul, MN Smith WB & Shifley SR (1984) Diameter growth, survival, and volume estimates for trees in Indiana and Illinois. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, Research Paper NC 257, St. Paul, MN Szantoi Z, Escobedo F, Dobbs C, & Smith S (2008) Rapid methods for estimating and monitoring tree cover change in Florida urban forests: The role of hurricanes and urbanization. In: Proceedings: The 6th Southern Forestry and Natural Resources GIS Conference (Eds Bettinger P, Merry K, Fei S, Drake J, Nibbelink N & Hepinstall J): 93104. Warnell School of Forestry and Natural Resources, University of Georgia Athens, GA Templeton M & Putz F (2003) Crown encroachment on southern live oaks in suburban settings: Tree status and homeowners concerns. Journal of Arboriculture 29(6): 337 340 US Department of Agriculture, Natural Resources Conservation Service (2003) Plants Database. Available online at http://plants.usda.gov ; last accessed May 2010 Vrecenak AJ, Vodak MC & Fleming LE (1989) The influence of site factors on the growth of urban trees. Journal of Arboriculture 15(9): 206 209 Zipperer WC, Sisinni SM, Pouyat RV & Fresman TW (1997) Urban tree cover: an ecological perspective. Urban Ecosystems 1: 229 246 Zurlini G & Giardin P (2008) Introduction to the special issue on Ecological indicators at multiple scales. Ecological Indicators 8: 781 782

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62 BIOGRAPHICAL SKETCH Throughout her education, Alicia Lawrences personal goal was to contribute to the research and conservation of the natural resources of Florida, her home state. After high school in Venice, FL, she studied agricultural and biological engineering with a focus in land and water resources at the University of Florida. Later her interests shifted towards the courses she enjoyed the most in natural sciences and graduated in the fall of 2007 with a Bachelor of Science in forest resources and conservation as a Natural Resource Conservation Major with a focus in forest hydrology. In 2008 she began a Graduate Research Assistantship where she collected soil field measurements in Miami Dade County, Florida, established permanent tree and vegetation plots in Pensacola Florida, and relocated and remeasured permanent urban forest plots in Gainesville, Florida. She also analyzed urban forest data from Houston, Texas to assess the effects of Hurricane Ike. She received her Master of Science in forest r esources and conservation in the summer of 2010.