IMPACTS OF LAND USE/COVER ON ECOSYSTEM  
 
 
CARBON STORAGE IN APALACHICOLA, FL 
 
 
 
 
 
 
Except where reference is made to the work of others, the work described in this thesis is 
my own or was done in collaboration with my advisory committee. This thesis does not 
include proprietary or classified information. 
 
 
 
 
 
_____________________________ 
Rachel Chelsea Nagy 
 
 
Certificate of Approval: 
 
 
 
 
 
________________________                                             ________________________ 
Luke J. Marzen B. Graeme Lockaby, Chair 
Professor Professor 
Geography  Forestry  
 
 
 
 
________________________ _________________________ 
Wayne C. Zipperer  George T. Flowers 
Research Forester  Dean 
USDA Forest Service  Graduate School 
 
 
 
 
 
 
 
 
IMPACTS OF LAND USE/COVER ON ECOSYTEM  
 
 
CARBON STORAGE IN APALACHICOLA, FL 
 
 
Rachel Chelsea Nagy 
 
 
 
 
 
A Thesis 
 
 
Submitted to 
 
 
the Graduate Faculty of 
 
 
Auburn University 
 
 
in Partial Fulfillment of the 
 
 
Requirements for the 
 
 
Degree of 
 
 
Master of Science 
 
 
 
 
 
Auburn, Alabama 
May 9, 2009 
 
 
iii 
 
 
 
 
 
 
IMPACTS OF LAND USE/COVER ON ECOSYSTEM 
 
 
CARBON STORAGE IN APALACHICOLA, FL 
 
 
 
 
 
Rachel Chelsea Nagy 
 
 
Permission is granted to Auburn University to make copies of this thesis at its discretion, 
upon request of individuals or institutions and at their expense. The author reserves all 
publication rights. 
 
 
 
 
 
______________________________ 
 Signature of Author 
 
 
 ______________________________ 
 Date of Graduation 
 
 
 
 
 
 
 
 
 
iv 
 
 
 
 
THESIS ABSTRACT 
IMPACTS OF LAND USE/COVER ON ECOSYSTEM  
CARBON STORAGE IN APALACHICOLA, FL 
 
Rachel Chelsea Nagy 
 
Master of Science, May 9, 2008 
(B.P., Miami University, 2005) 
 
145 Typed Pages 
 
Directed by B. Graeme Lockaby 
Rapid coastal development in response to a growing population raises concerns 
about human degradation of ecosystems.  The importance of the carbon cycle and its role 
in climate regulation warrant the study of the effects of land use/cover on ecosystem 
carbon storage in an area of hastening anthropogenic development on the Florida Gulf 
Coast.  Samples were collected to determine the carbon storage of vegetation and soils in 
natural pine forests, pine plantations, urban forests, urban lawns, and forested wetlands.   
An analysis of all land use/cover types revealed that forested wetlands have the 
greatest capacity to store soil and total ecosystem (soil + vegetation) carbon.  Urban 
forests contained the highest vegetation carbon content and had the greatest productivity 
of the five land use/cover classes.  No significant differences in the total vegetation or 
soil carbon content existed between natural forests and plantations or between urban 
forests and urban lawns.  An urbanization analysis on better drained soils illustrated that 
v 
 
urban forests had greater soil carbon content than natural pine forests and greater total 
vegetation carbon storage than plantations.  The high carbon content of urban forests may 
reflect long-term protection from fire which plays an important role in reducing carbon 
pools in pine forests and plantations.  
The total ecosystem carbon storage of forested wetlands was notably higher than 
all other land use/cover types.  Thus, protection of these ecosystems is of the utmost 
importance in order to maintain stability within the carbon cycle.  A unique result of this 
study was greater carbon storage in urban ecosystems than in natural forests and 
plantations.  Pine plantations, which tended to have the youngest, smallest trees, had the 
lowest carbon storage of all land uses/covers.  Low productivity of these pine plantations 
is partially due to understocking and younger stands, but even if these systems were at 
rotation age, the carbon storage of plantations would still be lower than other land 
uses/covers.  For example, a 25-year old plantation could store up to 80 Mg C/ha in the 
standing crop of vegetation while these urban forests store 93 Mg C/ha.  Thus, plantations 
should not be promoted as a method of carbon sequestration for this particular location.  
County-level land use change predictions suggest that declines in ecosystem carbon 
storage are possible but can be lessened by protecting forested wetlands and 
incorporating patches of remnant forests within urban areas.  A shift from timber 
production to community development by the largest private land owner in Florida will 
shape the future of this region.  Conscientious development is essential to ensure stability 
in these coastal ecosystems.          
 
 
vi 
 
 
 
 
ACKNOWLEDGEMENTS 
  
First, I would like to thank Dr. Graeme Lockaby, for his outstanding support and 
guidance throughout my graduate career. I would also like to thank my committee 
members Dr. Luke Marzen and Dr. Wayne Zipperer for their critical review and 
suggestions for my thesis.  Funding for this research was provided by the Center for 
Forest Sustainability.  I am very thankful for Robin Governo?s assistance in the lab and 
with field preparation.  Jennifer Trusty also deserves special thanks for all of her help 
with project development and implementation.  I would like to recognize Dr. Tom Doyle 
of the USGS National Wetlands Research Center for the analysis of all tree cores for this 
study.  I am also grateful for the help of Andrew Williams of the USDA Natural 
Resources Conservation Service in the characterization of soil profiles in the field.  
Thanks to all who helped with lab and fieldwork, project insight, and other assistance: 
Herbert (Tug) Kesler, Rich Pouyat, Tanka Acharya, Mark MacKenzie, Patti 
Staudenmaier, Tim Bottenfield, Danielle Haak, Nathan Click, Jennifer Mitchell, Eve 
Brantley, Sherry Broderick, Jody Thompson, John Dow, and Nick Bradley.  Last, but not 
least, thanks to my family and James Diewald for all your support.   
 
 
vii 
 
 
 
Style manual or journal used: Journal of Environmental Quality (JEQ) 
 
Computer software used: SAS V.9.1, Sigma Plot V.10.0, ArcGIS V.9.2, Erdas  
Imagine V.9.1, Microsoft Word 2007  
 
 
 
 
 
 
 
 
 
 
 
viii 
 
 
 
 
TABLE OF CONTENTS 
 
LIST OF TABLES???????????????????????????..ix 
LIST OF FIGURES??????????????????????????...xii 
I.         INTRODUCTION????..????????????????????..1 
II.        EFFECTS OF LAND USE/COVER ON SOIL CARBON AND  
            NITROGEN POOLS?????????????????????.........17 
 
III.       LAND USE/COVER EFFECTS ON VEGETATION AND ECOSYSTEM 
            CARBON STORAGE???????????????..?.........................71 
 
IV.      CONCLUSIONS???????????????????????.?.120 
 
REFERENCES???????????????????.????????...125 
 
 
 
 
 
 
 
 
 
ix 
 
 
 
LIST OF TABLES 
II.  Table 1: Mean total carbon concentration (mg/kg) by land use/cover and 
depth?????????????????????????????...61 
 
Table 2: Mean total nitrogen concentration (mg/kg) by land use/cover and 
depth???...........................................................................................................61 
 
Table 3: Mean mineral soil bulk density (g/cm
3
) by land use/cover and depth.....61  
 
Table 4: Mean mineral soil bulk density (g/cm
3
) by land use/cover
 
?????.62 
 
 Table 5: Correlations between mineral soil content, concentration, and bulk 
            density??..??????????????????????????..62 
 
Table 6. Mean mineral soil carbon content (kg/m
2
) by land use/cover and 
depth?????????????????????????????...62 
 
Table 7: Mean mineral soil nitrogen content (kg/m
2
) by land use/cover and 
depth????????????????????????????.......63 
 
Table 8: Mean C:N  by land use/cover in different soil depths and total soil 
profile.....................................................................................................................63 
 
Table 9: Mean forest floor mass, carbon content and nitrogen content (kg/m
2
) by 
land use/cover??.......?..??????????????????.??..
 
63 
 
Table 10: Mean carbon and nitrogen content (kg/m
2
) of total soil profile including 
forest floor???????................????????????????..64 
 
Table 11: Visual evidence of fire...........................................................................64 
 
Table 12: Mean covariate statistics??????????????????65   
 
Table 13: Regression relationships between explanatory variables (X1-X12) vs. 
response variables (Y1-Y15)??????????..???????..??.65 
 
x 
 
Table 14. Paired t-test p-values for difference of mean carbon content in soil 
depths, total soil profile, forest floor, and total soil profile + forest floor: 
Plantation vs. Natural pine forest??????????????????...70   
 
Table 15. Paired t-test p-values for difference of mean nitrogen content in soil 
depths, total soil profile, forest floor, and total soil profile + forest floor: Urban 
vs. Urban forest?????????????????????????..70   
 
III.  Table 1: Overstory equations to estimate dry weight???????...??...110 
Table 2: Midstory equations for dry weight???????????.??...112 
Table 3: Regression results of explanatory variables with biomass (overstory, 
midstory, understory, and total) and ANPP?????????????.?114  
  
Table 4: ANOVA results for average number of trees per plot, average number of 
overstory hardwood trees per plot, average overstory tree size (dbh in inches), 
overstory species richness, percent cover in understory (0-6 ft), and basal area 
(m
2
/ha)???????????????..??????????..?..?115  
 
Table 5: Mean overstory biomass (g/m
2
), carbon content (g/m
2
), and ANPP 
(gC/m
2
/yr) by land use/cover type?????????????????...115 
 
Table 6: Mean ANPP (kg/m
2
/yr) of all land uses by year???????.......116 
Table 7: Mean midstory biomass (g/m
2
) and carbon content (g/m
2
)????...116 
Table 8: Mean understory biomass (g/m
2
) and carbon content (g/m
2
) by land 
use/cover?????...???????????????????.??..116 
 
Table 9: Mean understory percent cover of Serenoa repens??????...?116  
Table 10: Mean total vegetation carbon content (g/m
2
) by land 
use/cover??????...?????????????????.???..117 
 
Table 11: Paired t-test results for difference in mean carbon content of  
vegetation pools????????????????????????...117  
 
Table 12: Paired t-test results for difference in ANPP of overstory?????117 
Table 13: Mean difference (g/m
2
: biomass, carbon content, and nitrogen content; 
g/m
2
/yr: ANPP) in vegetation pools: Urbanization analysis??????...?.118  
 
 
xi 
 
Table 14: Vegetation, soil, and vegetation + soil mean carbon content (kg/m
2
) by 
pool and land use/cover???????????????..??????.119 
 
Table 15: Remote sensing analysis: Land use/cover area estimates and resulting 
carbon storage of each??????????????????????.119  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
xii 
 
 
 
 
 
 
LIST OF FIGURES 
 
II. Figure 1: Location of Study Site: Apalachicola, Florida.......................................48 
 
Figure 2: Distribution of plots along coast............................................................48 
 
Figure 3: Mean carbon concentration (mg/kg) by land use/cover type and 
depth .......................??????????...........................??....................49 
 
Figure 4: Mean nitrogen concentration (mg/kg) by land use/cover type and  
depth?.???????????????????????????......49 
 
Figure 5: Mean bulk density (g/cm
3
) by depth and land use/cover type??...?
 
50 
 
Figure 6: Mean bulk density (g/cm
3
) of all depths by land use/cover type??...
 
50   
 
Figure 7: Mean soil carbon content (kg/m
2
) by depth and land use?????..
 
51 
 
Figure 8: Mean soil carbon content (kg/m
2
) for total profile (0-90 cm)???....51 
 
Figure 9: Means soil nitrogen content (kg/m
2
) by depth and land use????..
 
52 
 
Figure 10: Mean soil nitrogen content (kg/m
2
) for total profile (0-90 cm)??...
 
52 
 
Figure 11: Mean soil C:N ratio by land use/cover type and depth?????....
 
53 
 
Figure 12: Mean soil C:N ratio of total soil profile by land use/cover?...??...53 
 
Figure 13: Mean forest floor mass (kg/m
2
)??????????????...
 
54 
 
Figure 14: Forest floor mean carbon content (kg/m
2
)??????????...
 
54 
 
Figure 15: Forest floor mean nitrogen content (kg/m
2
)??????????.55 
 
Figure 16: Mean carbon content (kg/m
2
) for total soil profile + forest floor?....55 
 
Figure 17: Mean nitrogen content (kg/m
2
) of total soil profile including forest 
floor?????????????????????????????....56 
 
xiii 
 
Figure 18:  Mean soil carbon content (kg/m
2
) by depth: Urbanization effects?..56 
 
Figure 19: Mean soil carbon content (kg/m
2
) of total soil profile (0-90 cm): 
Urbanization effects???????????????????????...57 
 
Figure 20: Mean forest floor carbon content (kg/m
2
): Urbanization effects??.
 
57 
 
Figure 21: Mean soil + forest floor carbon content (kg/m
2
): Urbanization 
effects????????????????????????????.....58 
 
Figure 22: Mean soil nitrogen content (kg/m
2
) by depth: Urbanization effects?
 
58 
 
Figure 23: Mean soil nitrogen content (kg/m
2
) of total soil profile (0-90 cm): 
Urbanization effects??????????????????????...?59 
 
Figure 24: Mean forest floor nitrogen content (kg/m
2
): Urbanization effects?...59 
 
Figure 25: Mean soil + forest floor nitrogen content (kg/m
2
): Urbanization  
effects????????????????????????????.....60 
 
III.       Figure 1: Location of study site: Apalachicola, Florida?????????..
 
101 
 
Figure 2: Distribution of plots along coast?????????????......
 
101 
 
Figure 3: Example plots: (a) natural forest, (b) plantation, (c) urban, (d)  
urban forest, and (e) forested wetland........???????......???...........
 
102 
 
Figure 4: Mean overstory biomass (g/m
2
)????????.?????......
 
103  
 
Figure 5: Mean overstory carbon content (g/m
2
)???????????.....103  
 
Figure 6: Mean overstory ANPP (g/m
2
/yr)?????????????......104 
 
Figure 7: Mean midstory biomass (g/m
2
)?????????????...?.
 
104 
 
Figure 8: Mean midstory carbon content (g/m
2
)????????????..105 
  
            Figure 9: Mean understory biomass (g/m
2
)??????????????.
 
105  
 
Figure 10: Mean understory carbon content (g/m
2
)??????????....
 
106  
 
Figure 11: Mean understory nitrogen content (g/m
2
).....???????..??106 
 
Figure 12: Pine plantation with extensive cover of Serenoa repens?........?....107 
 
xiv 
 
Figure 13: Mean total vegetation carbon content (g/m
2
)????.................?
 
107 
 
Figure 14: Relative carbon content by pool (kg/m
2
)?......??????......?108 
 
Figure 15: Spatial display of plot carbon storage totals...?...?..?????...108 
 
Figure 16: Classified image??.....?????................???????.?109 
 
1 
 
 
 
 
I. INTRODUCTION 
 Land Use/Cover Change and Ecosystem Structure and Function 
Biogeochemical processes such as the carbon cycle are important indicators of 
ecosystem function and are subject to both anthropogenic and natural forces.  Land 
use/cover change is a major driver of carbon storage and fluxes and may induce 
ecosystem vulnerability.  Some of the major patterns of land conversion occurring 
worldwide include deforestation and conversely afforestation or reforestation, cropland 
abandonment or alternatively cropland expansion, and urbanization.  Conversion and 
modification of coastal habitats exacerbates pressures such as increased population and 
pollution in these ecosystems.  According to the World Resources Institute, ?Globally, 
the number of people living within 100 km of the coast increased from roughly 2 billion 
in 1990 to 2.2 billion in 1995?39 percent of the world?s population? (Burke et al., 
2000).  Rivers transport pollutants to estuaries and coastal waters, thus enhancing the 
pressure on coastal ecosystems (Burke et al., 2000).  Half of the U.S. population lives in 
coastal counties with an additional 1500 new homes built on coastlines each day (Bourne, 
2006).  This study aims to quantify differences in terrestrial ecosystem carbon storage in 
natural pine forests, pine plantations, urban forests, urban lawns, and forested wetlands 
along a stretch of the Florida Gulf Coast. 
 
2 
 
Land Conversion and Modification Impacts on Carbon Storage 
Deforestation occurs to accommodate the growing human population through 
agricultural expansion and urbanization and also for the purpose of producing forest 
products such as timber. Concerns about deforestation include increased climatic 
fluctuations, increased variability in water flow and balance, and changes in carbon pools 
(Foley et al, 2007).  Specifically, deforestation lowers net primary productivity and 
decreases the standing stock of vegetation carbon while simultaneously releasing carbon 
dioxide to the atmosphere (Houghton & Hackler, 1999). Soil carbon may display an 
initial increase due to litter input from trees left onsite, but then decline thereafter for 
about 20 years (Levy et al., 2004).  Deforestation for cropland or plantations has been 
shown to result in an average 42% and 13% decline in soil organic carbon respectively 
(Guo & Gifford, 2002) although individuals have observed both increases and decreases 
in soil carbon following harvesting (Johnson, 1992).  The transition of forests to pasture 
may not significantly alter soil carbon (Schwendenmann & Pendall, 2006, Guo & 
Gifford, 2002, Murty et al. 2002). 
Conversion to agricultural land and urbanization often coincide with 
deforestation. Some suggest that decreases in both soil and vegetation carbon pools can 
be expected from these practices (Tian et al., 2003).  However, other studies show that 
urbanization can actually lead to increased soil carbon (Pouyat et al., 2006), depending on 
the climate.  Pouyat et al., 2006 showed that urbanized areas had declines in soil carbon 
in the northeast part of the U.S., but in warmer and/or drier parts of the U.S., increases in 
soil carbon were observed.  Alternatively, pasture may be converted to agriculture or 
3 
 
urban areas; the transition of pasture to cropland resulted in an average 59% decrease in 
soil organic carbon (Guo & Gifford, 2002).  
Afforestation (land returning to forest after a long period of non-forest) or 
reforestation (land returning to forest after a short time of non-forest) may lead to carbon 
sequestration (Zaehle et al., 2007, Huang et al., 2007). If a harvested area is abandoned, 
vegetation regrowth can accumulate carbon to approximately undisturbed levels in about 
30-35 years and may depend on climate (Levy et al., 2004). Conversion of cropland to 
natural forest results in an average 53% increase in soil organic carbon while conversion 
of pasture to plantation results in an average 10% decrease in soil organic carbon (Guo & 
Gifford, 2002).   
Cropland abandonment may coincide with natural afforestation and therefore may 
lead to carbon sequestration or net carbon uptake due to increased carbon storage in 
vegetation and soils (Zaehle et al., 2007, Post et al, 2007, Houghton & Hackler, 2003). A 
more detailed look at the soil carbon pool following cropland abandonment indicates that 
there is an initial increase in soil carbon due to herb-dominated inputs with fast turnover 
rates, then a decrease as trees take over with lower litter inputs, and finally a recovery 
(Levy et al., 2004). Alternatively, cropland may also be converted for urban land use (Liu 
et al., 2005, Xu, 2004) which may result in only small changes in the carbon storage in 
both vegetation and soils (Houghton, 2002). 
Effects of Land Use/Cover on Carbon and Nitrogen 
Carbon and nitrogen cycles are intricately linked and thus many studies have 
examined both cycles simultaneously in response to land use/cover change.  For 
4 
 
urbanization studies, there have been two main approaches: 1) comparing a native 
ecosystem type to a different developed type and 2) comparing a single ecosystem along 
a rural-to-urban gradient.  An example of the first type of study was in Arizona when 
conversion of desert to urban areas was examined.  Following conversion, soil organic 
matter (SOM) and total soil nitrogen (TSN) increased 44% and 48% respectively 
(Jenerett et al., 2006).  It is important to note, however, that in this study, only the top 0-
10cm of soil was sampled and that their category of ?urban? included agricultural areas.  
Thus, results may be confounded as agricultural areas can react much differently than 
urban areas.   
The second type of urbanization study has become fairly common because it 
provides continuous data regarding the processes of the same land cover type across 
differing distances to/from an urban core.  For example, Groffman et al., 2002 found that 
urban riparian zones had more incised streams, lower water tables, higher NO
3-
 pools, 
higher nitrification rates, and decreased consumption rates of NO
3-
 than reference riparian 
zones.  This result was important because riparian areas are assumed to be sinks for NO
3-
; 
however, these urban riparian zones proved to be less efficient than their reference 
counterparts.  McDonnell et al., 1997, detected that urban forests had poorer litter quality, 
faster decomposition, and faster nitrification than the rural forests.  The increased 
decomposition and nitrification were associated with increased temperature in the urban 
areas (urban heat island effect) and increased prevalence of earthworms.  In general, rural 
forests had faster nitrogen mineralization (McDonnell et al., 1997).  However, regarding 
the effects of urbanization vs. natural controls, Groffman et al., 2006 found that 
5 
 
productivity and nitrogen cycling in forests were more closely related to natural soil 
conditions (in this case soil fertility) than proximity to an urban core.   
Land used for plantations may also display marked changes in carbon-nitrogen 
relationships.  After conversion of natural forest to banana plantations, carbon, and to a 
lesser extent nitrogen, decreased in terms of both concentration and content (Powers, 
2004).  Thus, a lower C:N ratio in the plantation than the natural forest could be 
expected.  Similarly, soil total carbon and nitrogen were significantly higher in the 
natural forest than the plantation (Burton et al., 2007).  Conversely, the C:N ratio in the 
soils, litter, and roots was higher in the plantation than the natural forest (Burton et al., 
2007).  The plantation displayed decreased rates of gross nitrification (Burton et al., 
2007).  Another example which demonstrated decreased concentration and pools of both 
carbon and nitrogen is the study of Yang et al., 2004 which showed that soil organic 
carbon (SOC) and TSN, especially in 0-20 cm and 20-40 cm zones, were lower in the 
plantation than in the natural forest (Yang et al., 2004).  Also noted were increases in pH 
and bulk density and decreased soil moisture content.   
The indirect effects of forest management practices on ecosystem processes have 
also been examined.  A study by Sanchez et al., 2006 found that soils were surprisingly 
resilient to forest management practices.  With OM removal, soil compaction, and 
competition control, soil carbon and nitrogen increased but not significantly (Sanchez et 
al., 2006).  After five years, there were no significant effects on soil carbon and nitrogen 
(Sanchez et al., 2006).  The authors caution, however, there could be noticeable declines 
in OM in the surface horizon and rooting zone of fine-textured soils due to the tendency 
6 
 
of fine particles to bind tightly with OM; thus fine-textured soils are more likely to 
accumulate dissolved organic matter (DOM) in lower soil horizons (Sanchez et al., 
2006).  Others have seen much greater declines in soil carbon in coarse-textured soils 
because of the inability of coarse particles to protect the OM in soil aggregates (Vance, 
2000).   
Conversion of forest to pasture has been examined in a few studies including 
assessment of changes in total ecosystem pools of carbon and nitrogen, as well as 
separate above- and belowground estimates.  Overall, the transition from forest to pasture 
led to decreased ecosystem carbon (25%) and decreased ecosystem nitrogen (1-24%) 
(Jaramillo et al., 2003).  Lower aboveground and root biomass were observed (Jaramillo 
et al., 2003).  Aboveground nitrogen was lost to a greater extent than carbon, resulting in 
increased C:N ratios, while in the roots, more carbon was lost in proportion to nitrogen 
and thus decreased C:N ratios were observed (Jaramillo et al., 2003).  In this same study, 
the SOC decreased after conversion to pasture (Jaramillo et al., 2003).     
Nitrogen and carbon have reportedly declined following conversion of forest to 
agriculture (Murty et al., 2002, Yang et al., 2004).  Decreased C:N ratios were presented 
in Murty et al., 2002 (24% and 15% declines for carbon and nitrogen respectively).  
Concentrations and pools of SOC and TSN decreased, especially at the 0-20 cm and 20-
40 cm depths (Yang et al., 2004).  Additionally, changes in soil physical and chemical 
properties, such as pH, bulk density, and soil moisture content were observed (Yang et 
al., 2004).   
7 
 
 The transition of pasture to cropland has been shown to lead to declines in both 
soil carbon and nitrogen concentration and content (Powers, 2004).  The author attributes 
the declines in carbon to decreased root biomass in cropland vs. pastures and losses due 
to cultivation (Powers, 2004).  However, increased quantity of aboveground litter was 
observed and may lead to long-term carbon accumulation (Powers, 2004).  Decreased N 
pools are counterintuitive considering that the croplands were fertilized, which suggests 
that excess nitrogen was exported from the system.  The soil C:N ratio increased at most 
depths examined (Powers,2004).   
Land Use/ Cover Change Patterns in the Southeastern U.S. 
Understanding the social forces and policy that drive land use change can help to 
explain the patterns of change that have occurred.  Historically, the southeastern United 
States has undergone periods of agricultural and timber exploitation, followed by a period 
of recovery (Wear, 2002).  More recently, the southeastern United States has transitioned 
into a period of rapid population growth and consequently, fast urbanization (Rain et al., 
2007, Wear, 2002, Clouser & Cothran, 2005).  In particular, both forests and cropland 
have been lost to urban areas (Wear, 2002).  Coastal development is widespread globally 
and in particular in the southeastern United States, is expected to continue in the coming 
years (Yang & Liu, 2005, Rain et al., 2007, O?Hara et al., 2003).       
Florida has development laws to limit urban sprawl, but these laws have not been 
thoroughly implemented.  For example, the 1985 Growth Management Act was amended 
with three new policies during the 1990s (Ben-Zadok, 2006).  These amendments aimed 
to limit urban and suburban development occurring in agricultural and natural systems 
8 
 
and alternatively encourage compact development in areas already in urban use (Ben-
Zadok, 2006).  The policies were sound, but the implementation tools were too flexible 
and vague (Ben-Zadok, 2006).  Policies were not made mandatory by the state and 
enforcement was left up to the local government?s discretion.  The majority of land in the 
southeastern U.S. is privately owned, and thus independent decisions in conjunction with 
policy change the land use over time (Evans et al., 2002, Ziewitz & Wiaz, 2004).   
Another prominent land use in the southern U.S. is pine plantations, making it the 
dominant timber producing region in the United States (Wear, 2002, Ziewitz & Wiaz, 
2004).  From 1953-1999, planted pines increased from 2 million to 30 million acres 
(Wear & Greis, 2002).  The area of planted pines is expected to grow to 54 million acres 
by 2040 (Wear & Greis, 2002).  It is expected that primarily agricultural land will be 
converted to planted pines and that natural forests will be converted to urban land or 
make up the rest of the planted pines increase (Wear & Greis, 2002).  In particular, a 58% 
decrease in areas of natural forest is predicted for the state of Florida by 2040 (Wear & 
Greis, 2002).  Plantation expansion and establishment may lead to increases in carbon 
emissions as compared to leaving hardwoods on site (Sohngen and Brown, 2006). 
Apalachicola, FL 
Apalachicola, FL (29?43'31.87"N, 84?59'13.20"W), located in Franklin County 
(Figure 1), was established in 1831 
(http://www.apalachicolabay.org/apalachicolahome.php).   
 
Figure 1: Location of Study Site: Apalachicola, Florida 
With the advancement of railroads, Apalachicola was a major port for shipping cotton 
and later timber before it became an important source of oysters 
(http://www.apalachicolabay.org/apalachicolahome.php).  Today Franklin County 
supplies 90% of Florida?s oysters 
(http://www.apalachicolabay.org/apalachicolahome.php).  Consequently, the livelihood 
of many people in Apalachicola revolves around the fishing industry.  Apalachicola has 
to a large extent avoided the development trends impacting much of coastal Florida and is 
known with a few other neighboring towns as ?Florida?s Forgotten Coast?.  Recently, 
however, development along the coast has increased markedly.  Apalachicola?s 
population in 2000 was 2,334 (http://www.apalachicolabay.org/apalachicolahome.php) 
and that of Franklin County in 2006 was 10,264 (U.S. Census Bureau).  The population 
density in Franklin County, and almost all other Florida counties, increased by more than 
50% from 1950-1999 (Wear, 2002). 
 
 
9 
 
10 
 
Changes in Apalachicola 
Population increases and changes in land ownership in Apalachicola and 
surrounding areas may lead to great land use/cover changes.  Much of the land in 
Franklin County is state owned, but the population is predicted to rise by 20.2% from 
2004-2010, suggesting coastal development as a likely outcome (Clouser & Cothran, 
2005).  The St. Joe Company (formerly St. Joe Paper Company) is the largest private land 
owner in Florida, with around 1.1 million acres (Bennett, 1997, Ziewitz & Wiaz, 2004).  
St. Joe Company is now planning development of 4,000 acres of their land in the town of 
Port St. Joe, northwest of Apalachicola (Jehl, 2002 and Broadfoot, 2005).  They propose 
to develop 5% of their land in the Florida Panhandle, which is roughly 50,000 acres 
(Ziewitz & Wiaz, 2004).  The current land transformation along the Gulf Coast is 
considered the biggest construction growth in Florida since the development of Disney 
World (Jehl, 2002).  Rapid population increase, changes in land ownership, and resulting 
landscape alterations will likely affect ecosystem biogeochemical cycles.  
Development influences are manifested in threats to the fishing industry in 
Apalachicola including increased salinity stemming from water shortages in the rivers 
leading into the bay (Bragg & Yoder, 2002).  Water shortages are caused by the 
increasing population pressure in Apalachicola and surrounding areas.  Currently, 
development is lingering in the background as Apalachicola remains one of the few 
coastal areas devoid of massive hotels and condos; meanwhile there has been significant 
expansion of residential areas nearby (Ziewitz & Wiaz, 2004).  Oyster beds may not 
replenish themselves if this area is developed due to problems including fertilizer runoff 
11 
 
and storm water runoff from parking lots.  Oyster farmers continuing their family?s 
traditional work may be forced to cede to development and the concomitant negative 
environmental impacts (Bragg & Yoder, 2002).  In short, they will have to find a new 
way of life.      
Hypotheses 
The effects of land use changes and specific management practices on carbon 
storage were examined in this study.  Hypotheses for specific land use changes examined 
were the following:  
1) Development along the Gulf Coast including conversion of forest to urban 
areas will have caused declines in vegetation carbon.  Typically urban areas are expected 
to have reduced soil carbon, but cases of increased soil carbon in urban areas have been 
reported in hot and/or dry climates.  Therefore, in this subtropical climate, higher soil 
carbon is expected in urban lawns and urban forests than in natural forests.  Additionally, 
urban lawns and urban forests will be compared in a paired approach to see distinctions 
within urban ecosystems.   
2) Plantation establishment and expansion near Apalachicola will cause decreased 
carbon storage in the system as compared to pre-disturbance (natural forest) pools.  The 
initial deforestation will have caused decreases in soil and vegetation carbon.  
Afforestation of principally slash (Pinus elliottii) or sand pine (Pinus clausa) will have 
ameliorated this to some extent, but the overall carbon decline should be measurable.  
Site preparation will influence changes in carbon storage; with increased disturbance pre- 
and post-harvesting, soil carbon losses should be greater.  Additionally, other 
12 
 
management practices such as prescribed fire will influence the amount of carbon lost.  
With evidence of recent fire, smaller pools of carbon, particularly in the forest floor and 
vegetation are anticipated.  C:N ratios may also be reduced in plantations because carbon 
is often lost more rapidly than nitrogen in fires.  
Objectives 
The main objectives of this study are to: 1) identify patterns of land use/cover 
along the Florida Gulf Coast, 2) calculate the carbon storage of each land use/cover type, 
3) determine the effects of urbanization on carbon storage, 4) quantify the effects of 
plantation establishment on carbon storage, and 5) create an overall carbon estimate for 
these coastal ecosystems.    
Development in the Gulf Coast will cause dramatic environmental changes to the 
landscape in the coming years as it has elsewhere (Yang & Liu, 2005).  It is important to 
have a clear understanding of existing patterns of land use/cover along the Gulf Coast in 
order to understand the resulting patterns of carbon storage.  Measurements of soil and 
vegetation carbon will be made for each ecosystem type.  Finally, the specific land 
conversions from natural forest to plantation and from forest to urban will be examined to 
determine the change in carbon storage associated with each practice.     
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17 
 
 
 
 
II. EFFECTS OF LAND USE/COVER ON SOIL  
CARBON AND NITROGEN POOLS 
Abstract 
 Soil carbon and nitrogen storage are influenced by land use changes and 
management practices, as well as natural disturbances and climatic conditions.  In a 
stretch along the Gulf Coast near Apalachicola, Florida, forested wetlands had 
exceedingly greater soil carbon and nitrogen storage than natural pine forests, pine 
plantations, urban lawns, or urban forests. Paired plots revealed that plantations and 
natural pine forests did not exhibit differences in carbon and nitrogen storage in the 
mineral soil and forest floor.  Within urban ecosystems, no significant difference in 
carbon storage of the total soil profile was noted between urban forests and urban lawns, 
although urban lawns had significantly higher mineral soil nitrogen content compared to 
urban forests.  In a comparison among land uses with better drained soils, urban forests 
had higher mineral soil carbon storage than natural pine forests.  This ecosystem response 
of increased carbon storage in urban soils has been observed in hot and/or dry climatic 
regions, which have small native carbon pools.  Urban lawns had greater soil nitrogen 
storage compared to natural pine forests or plantations and urban forests also had greater 
nitrogen storage than natural forests.  This study suggests that coastal forested wetlands 
should be closely monitored and a high priority should be placed on their preservation 
18 
 
due to their contributions to ecosystem function.  Additionally, these urban ecosystems 
which do not experience regular burning are able to store larger quantities of carbon in 
soils than pre-urban ecosystems of natural pine forests and plantations.    
Introduction 
Land use/cover change and its potential to influence ecosystem functions, such as 
biogeochemical cycles, have become topics of great interest.  Land conversion practices 
alter vegetation patterns and soil physical properties, which thereby alter the movement 
and storage of carbon and nitrogen in soils.   For example, changes in soil moisture 
influence rates of respiration and decomposition, thus enriching or depleting carbon 
pools.  There is also a considerable degree of natural variation in soil organic matter and 
carbon storage of different ecosystems (Schlesinger, 1991, Sabine et al., 2004).  Wetlands 
for example, store more soil carbon than other systems due to a unique balance between 
decomposition and primary productivity (Schlesinger, 1991).   However, the extent of 
land use/cover change globally warrants the investigation of impacts on ecosystem 
function in an effort to secure a sustainable future.  Worldwide, coastal development is 
encroaching on unique ecosystems and causing drastic changes to terrestrial landscapes 
(Burke et al., 2000).    
The global importance of the carbon cycle and its influence on climate has been 
demonstrated in a number of studies (Fung et al., 2005, Guo & Gifford, 2002, Falloon et 
al., 2007, Levy et al., 2004).  Releases of carbon in the form of atmospheric CO
2
, a 
greenhouse gas, can lead to alterations in climate.  Climate, in return, influences 
physiological processes such as photosynthesis thereby influencing the magnitude of 
19 
 
terrestrial carbon pools in vegetation and soils (Malhi et al., 2002).  Therefore, human 
modification of the carbon cycle, through increased atmospheric carbon dioxide from 
land use change and fossil fuel combustion, results in a positive feedback with climatic 
controls on ecosystem carbon storage (Field et al., 2004).   
Additionally, carbon-nitrogen interactions have the potential to control ecosystem 
processes such as productivity.  Nitrogen limitation is common in terrestrial ecosystems 
and aboveground net primary productivity usually increases with increased nitrogen 
availability (Fisher & Binkley, 2000).  Therefore, changes caused by alterations of land 
use/cover in soil carbon, nitrogen, and their relative proportions, can influence ecosystem 
function.   
Two land conversions prominent throughout the Southeastern U.S., urbanization 
and plantation establishment, were examined in this study.  The study site was located 
near Apalachicola, Florida, an area which has a long history of both urban and plantation 
land uses.  A portion of the land in Franklin County, Florida and a greater extent of the 
surrounding counties in the Florida Panhandle are owned by the St. Joe Company.  
Formerly the St. Joe Paper Company, this entity is the largest private land owner in the 
state.  In an effort to develop this region of Florida, the St. Joe Company has shifted their 
priorities from timber production to ?place making?, or themed community development 
(Ziewitz & Wiaz, 2004).  Thus, the St. Joe Company holds an important position in 
shaping the future of Florida?s Gulf Coast (Ziewitz & Wiaz, 2004).  The resulting land 
use/cover changes will undoubtedly influence biogeochemical cycles in this region.    
 
20 
 
Urbanization Effects on Soil Carbon and Nitrogen 
Soil carbon changes following urbanization are related to climate, type of 
construction activity, type of urban area (residential, commercial, etc.), whether the urban 
area is analyzed as a homogenous unit or as an assemblage of urban patches, and the pre-
urban land use (Pouyat et al., 2006, Pouyat et al., 2007).  Soil variation, for example, may 
be more influential to soil carbon and nitrogen cycles than land use/cover as found in 
Groffman et al., 2006.  Also, study results must be interpreted in light of the soil depths 
sampled as well as the scale at which the study took place.  In regard to the impacts of 
urbanization on soil carbon, some suggest that declines will follow (Tian et al., 2003), 
while others have seen mixed results.  When cropland is converted to urban land use, 
small changes in soil carbon have been observed (Houghton, 2002).  When converted 
from ?natural? land use types such as forest, a study found that urban areas in the 
northeastern U.S. exhibited declines in soil carbon, while in warmer and/or drier climates, 
increases in soil carbon were noted (Pouyat et al., 2006).   
Effects of Plantation Establishment on Soil Carbon and Nitrogen 
Conversion of natural forests to plantations and the site preparation and 
management practices employed can impact soil carbon and nitrogen.  Most have noted 
declines in soil carbon following conversion of natural forest to plantation in individual 
studies such as Chen et al., 2004, but others have seen varied results.  An average 13% 
decline in soil organic carbon was calculated for the conversion of natural forests to 
plantations in Guo & Gifford, 2002, a meta-analysis of land use change impacts.  Initial 
clearing of vegetation reduces inputs to the soil.  Widely used in the Florida Panhandle, 
21 
 
the practice known as ?bedding? moves soils into raised rows 30-60 cm high and alters 
soil physical properties including bulk density and soil moisture.  Additionally, 
management regimes such as prescribed burning to reduce understory competition may 
decrease soil carbon in plantations and natural pine forests by removal or redistribution of 
organic matter (OM) to greater soil depths (Pritchett & Fisher, 1987).   
Study Objectives 
The primary objective of this study was to determine and compare the soil carbon 
and nitrogen storage in different land use/cover types for a section of the Florida Gulf 
Coast (Figure 1).  Land use/cover was determined in the field and categories sampled 
included natural pine forest, pine plantation, urban lawn, urban forest, and forested 
wetland.  Forested wetlands were expected to have the highest carbon and nitrogen 
content.   Plantations were expected to have smaller carbon and nitrogen pools than 
natural forests due to losses through harvesting and management practices such as 
prescribed fire.  According to Pouyat et al. 2006 in which urban land use types in warm 
and/or dry climates stored more carbon than the natural ecosystems they replaced, urban 
plots were expected to have greater carbon and nitrogen storage than natural forests.     
Study Area 
 A total of 61 plots were established around Apalachicola (29?43?31?N, 
84?59?33?W), Eastpoint (29?44?30?N, 84?52?37?W) and Carrabelle (29?51?14?N, 
84?39?57?W), in Franklin County, Florida.  The climate is humid subtropical with an 
average annual rainfall of about 1450 mm (NCDC, 2008).  Apalachicola is about 4 m 
above sea level (NCDC, 2008).   
22 
 
 The five land uses/covers included in this study were natural pine forest, pine 
plantation, urban lawn, urban forest, and forested wetlands.  Important differences 
between land use types include fire frequency, variation in vegetation structure and 
composition, and variation in soil characteristics.  Natural pine forests and plantations 
were typically on moderately drained sandy soils (ultisols, inceptisols, and some 
spodosols).  Both natural forests and plantations experienced frequent fires.  They had 
fairly similar vegetation structures (understory, midstory, and overstory) although they 
differed in species composition and richness.  Prominent overstory species of natural 
forests and plantations included slash pine (Pinus elliottii) and sand pine (Pinus clausa).    
Urban forests and urban lawns likely have not been burned in the last 25 years, as 
estimated from visual inspection of plots.  Urban lawns had a different structure than all 
other land uses/covers; they generally lacked a midstory component of vegetation but 
rather maintained an understory of grass with a few overstory trees.  Urban lawns and 
urban forests were on soils similar to the natural forests and plantations (ultisols, 
inceptisols, and some spodosols).  Live oaks (Quercus virginiana), sand live oaks 
(Quercus geminata), and slash pines (Pinus elliottii) were common in urban lawns and 
urban forests.  
In contrast to the other land uses, forested wetlands were on poorly drained soils 
such as histosols which are characterized by high levels of organic matter (Lal et al, 
1995).  Similar to urban lawns and urban forests, no visual evidence of recent fire existed 
in forested wetlands.  Water tupelo (Nyssa aquatica), titi (Cyrilla racemiflora), green ash 
23 
 
(Fraxinus pennsylvanica), and bald cypress (Taxodium distichum) were widespread in the 
overstory of forested wetlands.   
Methods 
Plots are circular with a 7.32 m radius, in accordance with the Forest Inventory 
and Analysis (FIA) Phase 3 plot standards.  The distribution of plots in this section of 
Florida?s Gulf Coast is shown in Figure 2.  Plots were established and samples were 
collected between October 2007 and July 2008.  Characteristics such as dominant 
overstory species (for the natural pine forests and plantations), soil properties (series, 
moisture), and topography (depressional plots were excluded) were used in plot selection.  
For each plot, basal area, a visual estimate of the most recent burn, percent cover of 
understory, midstory, and overstory by species, and any additional site notes were 
recorded.  Some of these variables (such as percent cover of understory) were used as 
covariates because they were expected to explain some of the variation in the parameters 
of interest.   
An important component of this study involved direct comparisons of natural pine 
forests and plantations and of urban lawns and urban forests to examine the effects of 
plantation establishment and the variability within urban ecosystems, respectively.  Plots 
were paired based on some important physical site properties.  The main priority was to 
establish pairs on similar soil series in attempt to limit variability in carbon and nitrogen 
pools due to natural soil variation.  We used soil morphological characteristics such as 
color and texture to judge profile similarity.  Additionally, Andrew Williams, a soil 
scientist of the USDA National Resources Conservation Service (NRCS) provided field 
24 
 
assistance in characterizing soils.  Other important considerations included dominant 
overstory species (this was more important for the plantation-natural forest pairs), 
topography, and proximity to one another.     
To determine soil carbon and nitrogen, three cores were taken per plot at each of 
four depths: 0-7.5 cm, 7.5-30 cm, 30-60 cm, and 60-90 cm.  Roots were removed from 
the soil sample and the soil was mixed until homogenous.   Each sample was sent to the 
University of Georgia Soil, Plant, and Water Analysis Laboratory for chemical analysis.  
Percent total carbon and nitrogen were determined using the dry combustion method 
(LECO CNS 2000; LECO Corporation, 3000 Lakeview Ave., St. Joseph, MI).  An 
additional test was conducted by the University of Georgia to determine the soil pH (0.01 
M CaCl2 method).      
A separate soil core of a known volume was taken at corresponding depth 
intervals (0-7.5, 7.5-30, 30-60, 60-90 cm) to determine bulk density.   Each sample was 
dried at 105?C for a minimum of 72 hours (Blake & Hartge, 1986).  Bulk density was 
then used with the concentration of carbon or nitrogen to calculate the carbon or nitrogen 
content per square meter to a particular depth.   
Three forest floor samples (0.10m
2
) were collected at random to determine the 
forest floor carbon or nitrogen content of each plot.  Forest floor samples were dried at 
70?C for a minimum of 72 hours and then weighed to measure the total mass of the 
sample, which was extrapolated to the mass per square meter.  A subsample was ground 
for chemical analyses (C, N, and P).  Plant tissue carbon and nitrogen were determined 
using thermal combustion (Perkin-Elmer 2400 series II CHNS/O analyzer; Perkin-Elmer 
25 
 
Corp., Norwalk, CT.) as outlined in Nelson and Sommers, 1996.  Plant tissue 
phosphorous was determined following Jackson, 1958.  
Statistical Analyses 
All statistical analyses were done in SAS version 9.1 (SAS Institute 2002-2003).  
Analysis of variance (ANOVA) (proc glm with Tukey?s HSD) was used to determine any 
significant differences among the carbon and nitrogen storage of the five major land 
use/cover types (natural forest, plantation, urban lawn, urban forest, and forested 
wetland).  Bulk density, as well as carbon and nitrogen concentration and content, are 
presented for each land use/cover type in the Results section.  Linear regression was used 
to determine the relationship between explanatory variables (soil characteristics, 
vegetation species richness, and land use/cover within a 1km-radius buffer of the plot) 
and the soil carbon or nitrogen storage.  Land use/cover data for the 1km buffer are from 
the following chapter, Land Use/Cover Effects on Vegetation and Ecosystem Carbon 
Storage, determined with remote sensing. Comparisons of carbon and nitrogen content in 
paired plots (plantation vs. natural pine forest and urban lawn vs. urban forest) were 
evaluated with paired t-tests (proc ttest).  These comparisons aimed to identify the effects 
of conversion to plantation and to examine differences within urban areas, respectively.  
Finally, another set of ANOVAs (proc glm with Tukey?s HSD) was run to compare 
natural forests, plantations, urban lawns, and urban forests to quantify the effects on 
carbon and nitrogen content due to urbanization.  Forested wetlands were excluded from 
this analysis because they are on poorly drained soils and thus are less likely to be 
developed than natural pine forests and plantations.  Relationships were considered 
26 
 
significant at p < 0.05 unless otherwise stated, but results p < 0.10 are also presented for 
informational purposes.       
Results 
Carbon and Nitrogen Concentration for all Land Use/Cover Types 
 Mean concentrations of carbon and nitrogen by depth are presented in Tables 1 
and 2 and Figures 3 and 4.  Forested wetlands had significantly (all p-values <0.0001) 
higher mean carbon and nitrogen concentrations than natural pine forests, pine 
plantations, urban lawns, or urban forests for depths 0-7.5, 7.5-30, 30-60, and 60-90 cm 
(Tables 1 and 2 and Figures 3 and 4).   
Bulk Density for All Land Use/Cover Types 
 Table 3 presents the mean bulk density at each depth for each land use/cover type.  
Figure 5 displays these means with significant differences between groups.  In the surface 
soil (0-7.5 cm), urban lawns had significantly higher (p<0.0001) mean bulk density than 
urban forests, natural pine forests, and forested wetlands (Table 3 and Figure 5).  Also in 
the surface soil, pine plantations, natural pine forests, and urban forests had a higher 
mean bulk density in comparison to forested wetlands.  At the 7.5-30 cm depth, 
plantations and natural pine forests had significantly higher (p=0.01) bulk density than 
forested wetlands (Table 3 and Figure 5).  Compared to forested wetlands, all land 
use/cover categories had significantly higher (p=0.0006) mean bulk densities in the 30-60 
cm range (Table 3 and Figure 5).  Finally, at the depth of 60-90 cm, the mean bulk 
density of plantation plots was higher (p=0.08) than forested wetlands (Table 3 and 
Figure 5). 
27 
 
 Bulk densities of all depths in the mineral soil profile were averaged (Table 4 and 
Figure 6).  Forested wetlands had significantly lower (p<0.0001) mean bulk density than 
natural pine forests, plantations, urban lawns, and urban forests (Table 4 and Figure 6).   
Concentration vs. Bulk Density  
 In order to ascertain whether soil density or element concentration drove soil 
carbon and nitrogen content to the greatest extent, correlations (proc corr) between 
content and concentration and bulk density were analyzed.  For carbon content, the 
correlation with carbon concentration was 0.71 and the correlation with bulk density was 
0.33 (Table 5).  The correlation of nitrogen content with nitrogen concentration was 0.64 
and the correlation with bulk density was 0.30 (Table 5).  Therefore, while the content is 
calculated as a factor of both bulk density and concentration, concentration is 
predominantly the driving force behind the content values.     
Soil Carbon and Nitrogen Content for all Land Use/Cover Types 
Mineral Soil 
All land use/cover types were considered together and the mean mineral soil 
carbon content, mineral soil nitrogen content, and C:N ratio were calculated at each depth 
(0-7.5cm, 7.5-30cm, 30-60cm, and 60-90cm) and for the total soil profile (0-90cm).   
At each depth (0-7.5, 7.5-30, 30-60, and 60-90) the mean mineral soil carbon 
content was significantly higher in forested wetlands than in natural pine forest, pine 
plantation, urban lawn, and urban forest (all p-values of <0.0001) (Table 6 and Figure 7).  
Additionally, compared to either natural pine forests or plantations, urban forests had 
significantly higher (p<0.0001) mineral soil carbon content in the surface soil.  For the 
28 
 
total mineral soil profile (0-90cm), the mean carbon content of forested wetlands is 
significantly higher (6-12 times that of the other land uses) than natural pine forests, 
plantations, urban lawns, and urban forests (p-value of <0.0001) (Table 6 and Figure 8).  
Although not significantly different, the total soil profile carbon content displayed the 
following pattern: urban forest > urban lawn > pine plantation > natural pine forest 
(Table 6 and Figure 8).  
Table 7 and Figure 9 display the results for nitrogen content by depth.   The 
general trend for mean soil nitrogen content was forested wetland was higher than urban 
forest and urban lawn which were higher than plantation and natural pine forest.  At each 
depth (0-7.5 cm, 7.5-30 cm, 30-60 cm, and 60-90 cm) the mean soil nitrogen content is 
significantly higher (at least 5 times greater) in forested wetlands as compared to natural 
forest, plantation, urban lawn, and urban forest (all p-values of <0.0001) (Table 7 and 
Figure 9).  Generally, the nitrogen content increased then decreased with depth except in 
the case of plantations which continually increased with depth (Table 7 and Figure 9).   
 Compared  to urban forests, urban lawns, natural pine forests, or pine plantations, 
forested wetlands had significantly higher mean nitrogen content for the total mineral soil 
profile (0-90 cm) (p<0.0001) (Table 7 and Figure 10).  Numerically, urban lawn had the 
next highest mean nitrogen content followed by urban forest, plantation, and natural 
forest respectively (Table 7 and Figure 10).     
The mean mineral soil C:N ratio increased with increasing depth for forested 
wetlands, urban lawns, and urban forests, but not for natural pine forests or plantations 
(Table 8 and Figure 11).  Specifically, at the depth of 0-7.5 cm, the C:N ratio of natural 
29 
 
pine forest plots was significantly higher (p<0.0001) than urban lawn, urban forest, and 
forested wetlands (Table 8 and Figure 11).  Also, compared to urban lawns and forested 
wetlands, plantations had a significantly greater (p<0.0001) C:N ratio.  Lastly, the C:N 
ratio of urban forests was significantly higher (p<0.0001) than urban lawns.  For 7.5-30 
cm, natural forest, urban forest, and plantation C:N ratios were all significantly higher 
(p<0.0001) as compared to urban lawn C:N ratios (Table 8 and Figure 11).  Also, the 
natural forest C:N ratio was significantly greater (p<0.0001) than forested wetland.  For 
30-60 cm, urban forests had a significantly higher (p=0.04) C:N ratio compared to urban 
lawns (Table 8 and Figure 11).  Finally, there were no significant differences among the 
C:N ratio of different land use/cover classes for 60-90 cm (Table 8 and Figure 11).   
Numerically, the mean soil C:N ratio of the total mineral soil profile followed the 
following pattern: natural pine forest > plantation > urban forest > forested wetland > 
urban lawn (Table 8 and Figure 12).  For the total mineral soil profile (0-90 cm), the C:N 
ratios of natural pine forests, pine plantations, and urban forests were all significantly 
higher (p=0.0006) than urban lawns (Table 8 and Figure 12).   
Forest Floor 
Compared to forested wetlands, urban forests had significantly greater (p=0.0029) 
forest floor mass and carbon content (p=0.01) (Table 9 and Figures 13 and 14).  The 
forest floor nitrogen content of urban forests was significantly greater (p=0.0008) than 
forested wetlands, natural forests, or plantations (Table 9 and Figure 15). 
 
 
30 
 
Total Mineral Soil Profile + Forest Floor 
 Table 10 presents the mean carbon and nitrogen content for the total soil profile 
including forest floor.  The total carbon and nitrogen content closely resembled the 
results from the summed mineral soil (0-90 cm).  Compared to natural pine forests, pine 
plantations, urban lawns, or urban forests, forested wetlands had significantly higher 
(p<0.0001) mean carbon content (Table 10 and Figure 16) and mean nitrogen content 
(p<0.0001) (Table 10 and Figure 17).   
Covariates 
 Certain factors were anticipated to be related to the measured response variables.  
Specifically, fire frequency, land use/cover surrounding the plot, vegetation species 
richness, soil characteristics, and the prevalence of wax myrtle were expected to be 
influential.  Visual evidence of fire presence/absence in the last 10 years is displayed in 
Table 11.  Mean statistics of all explanatory variables except surrounding land use/cover 
are presented in Table 12.  The surrounding land use/cover variable, derived from a land 
use classification (see next chapter), is appropriate for individual plots but comparing the 
means between land uses is not particularly helpful.  Natural forests and plantations have 
had more recent fire than urban lawns, urban forests, and forested wetlands (Table 11).  
The overstory species richness is least in plantations and midstory species richness as 
well as midstory + overstory species richness are lowest in urban lawns (Table 12).  Bulk 
density results were already presented in Table 3 and Table 4.  Urban lawns and urban 
forests have the highest mean pH (Table 12).  Lastly, the percent cover of wax myrtle, a 
nitrogen-fixing species, was highest in forested wetlands followed by plantations (Table 
31 
 
12).  Results of regression between the explanatory variables and 15 dependent variables 
(forest floor mass and mineral soil carbon and nitrogen at different depths) including the 
r-squared and p-values are displayed in Table 13.  Significant relationships between 
explanatory and response variables are described in the Discussion.   
Paired Approach: Plantation vs. Natural Forest and Urban vs. Urban Forest 
Soil Carbon Content: Paired 
The mineral soil carbon content showed no significant difference between natural 
forest and plantations at individual depths throughout the soil profile (Table 14).  Also, 
the carbon content in the total mineral soil profile (0-90cm), forest floor, or mineral soil + 
forest floor were not significantly different between plantations and natural forests (Table 
14).       
No significant differences in carbon content were observed at depths throughout 
the mineral soil profile between paired urban lawn and urban forest plots.  Additionally, 
the carbon content of the total mineral soil profile (0-90cm) was not significantly 
different between urban lawns and urban forests (p=0.42).     
Because there is no ?forest floor? mass in urban lawn plots (grass was included in 
a separate vegetation analysis), the carbon and nitrogen content in the forest floor of 
urban lawns was calculated as zero.  Consequently, the forest floor carbon and nitrogen 
content of urban lawn and urban forest plots were not compared.  However, when the 
forest floor component (urban forests only) was added to the total of the mineral soil 
profile (0-90cm) there was no significant difference between the carbon content of urban 
lawn and urban forest plots (p=0.60) (Table 15). 
32 
 
Soil Nitrogen Content: Paired 
 Similar to the comparison of carbon content in natural pine forests and 
plantations, there were no significant differences observed for soil nitrogen content  at 
any individual depth in the mineral soil profile, the total mineral soil profile (0-90cm), the 
forest floor, or the total mineral soil + forest floor (Table 14).   
 Urban lawns exhibited significantly greater soil nitrogen content than urban 
forests in the 7.5-30 cm depth as well as in the total mineral soil profile (0-90cm) (Table 
15).  No other significant differences were observed for nitrogen content in the other 
three depths or the total soil profile including the forest floor (Table 15).   
Urbanization Effects: Natural Forest or Plantation to Urban or Urban Forest 
 Typical land conversions include the change of either natural forests or 
plantations to urban land uses because they are typically on more well-drained soils than 
forested wetlands.  Therefore, to understand the effects of urbanization on carbon and 
nitrogen storage, ANOVAs were used to compare natural forests, plantations, urban 
lawns, and urban forests.  While the mean soil carbon content by depth, as well as total 
profile, and forest floor are all the same values from earlier analyses (see Tables 6-7 and 
9-10), relationships changed when forested wetlands were removed from the analysis.  
Figures 18-25 present the urbanization effects for mean mineral soil carbon content by 
depth, total mineral soil carbon content (0-90 cm), forest floor carbon content,  mineral 
soil + forest floor carbon content, mineral soil nitrogen content by depth, total mineral 
soil nitrogen content (0-90 cm), forest floor nitrogen content, and mineral soil + forest 
floor nitrogen content, respectively.    
33 
 
 Urban forests had significantly higher mean soil carbon content in comparison to 
natural forests and plantations at 0-7.5 cm and 7.5-30 cm (p=0.0004 and 0.0083 
respectively) (Figure 18).  No significant differences in mean soil carbon content were 
found between land uses for 30-60 or 60-90 cm (Figure 18).  For the total mineral soil 
profile (0-90 cm), urban forests had significantly higher mean carbon content than natural 
forests (Figure 19).  There were no significant differences in forest floor carbon content 
or the total mineral soil + forest floor carbon content between land use types (Figures 20 
and 21).     
Mean nitrogen content in the surface soil was significantly (p<0.0001) higher in 
urban lawn and urban forest plots than in natural forests or plantations (Figure 22).  
Compared to natural forests or plantations, urban plots had significantly (p=0.01) higher 
mean soil nitrogen content in the 7.5-30 cm range.  Similar to carbon, mean soil nitrogen 
content showed no significant differences between land use types for 30-60 and 60-90 
cm.   Urban lawns had significantly (p=0.0010) higher mean nitrogen content than natural 
forests or plantations for the total soil mineral profile of 0-90 cm (Figure 23).  Urban 
forests also had significantly (p=0.0010) higher mean nitrogen content in the total 
mineral soil profile in comparison to natural forests (Figure 23).  The mean forest floor 
nitrogen content of urban forests was significantly (p= 0.0029) greater than natural pine 
forests and plantations (Figure 24).  Finally, there were no significant differences 
between land use types for the total mineral soil + forest floor nitrogen content (Figure 
25).    
 
34 
 
Discussion 
All Land Use/Cover Types 
 Estimates of carbon and nitrogen storage in different land use/cover types were a 
primary goal of this study.  ANOVA tests for all land use/cover categories revealed that 
compared to natural pine forests, pine plantations, urban lawns, and urban forests, the 
mean carbon and nitrogen content of forested wetlands was significantly higher at each 
depth in the soil profile, for the sum of the mineral soil profile, and for the mineral soil + 
forest floor (Tables 6, 7, and 10 and Figures 7-10 and 16-17).  These results were not 
surprising given that other studies have found wetlands to store great pools of carbon 
compared to other ecosystem types (Schlesinger, 1991, Cui et al. 2005).  Decomposition 
may be slowed in wetland soils by saturation thus allowing for accumulation of carbon 
and nitrogen in the mineral soil.  The total mineral soil profile including forest floor (total 
soil profile + ff) nitrogen and depths 1-4 nitrogen were significantly related to water table 
depth (Table 13).  Also, the total mineral soil profile + ff carbon and depth 1 carbon were 
significantly related to water table depth (Table 13).  These relationships suggest that the 
soil properties such as the high water table of forested wetland soils may reduce rates of 
decomposition and thereby lead to increased carbon and nitrogen storage.    
Forested wetlands generally contain large pools of carbon and nitrogen and 
therefore, the coverage of forested wetlands within a given area may be a good predictor 
of carbon and nitrogen pools.  Carbon and nitrogen pools at each depth in the mineral soil 
except depth 1 carbon (p=0.07) had a significant relationship with the percent forested 
wetland in a 1km buffer around each plot (Table 13).  Placing too much emphasis on this 
35 
 
explanatory variable is cautioned, however, because of the difference in scale between 
the plot and the buffer size.  Rather this variable is useful to explain some of the variation 
between individual plots (why one natural forest plot surrounded by urban land uses may 
display different patterns than a natural forest surrounded by forested wetlands).        
Nitrogen-fixing species such as wax myrtle (Myrica cerifera) can be influential to 
both soil carbon and nitrogen pools.  Aboveground, net primary productivity and biomass 
may be increased in the presence of nitrogen-fixing species due to increased nitrogen 
availability (Fisher & Binkley, 2000).  Soil organic matter may increase due to greater 
inputs of vegetation and reduced decomposition of old soil carbon (Fisher & Binkley, 
2000).  All mineral soil carbon and nitrogen pools, as well as the mineral soil + forest 
floor, had significant regression relationships with the understory percent cover of wax 
myrtle (Table 13) which suggests that wax myrtle does play a role in soil carbon and 
nitrogen pools in these Gulf Coast ecosystems.  Percent cover of wax myrtle was the 
highest in forested wetlands followed by plantations; the other land uses had fairly low 
wax myrtle cover (Table 12).           
 Mean carbon and nitrogen content of the forest floor was higher in urban forests 
than in urban lawns, natural pine forests, pine plantations, or forested wetlands (Table 9).  
In general, urban forest plots were undisturbed patches of remnant forest within the urban 
core.  With less recent fire (Table 11), the forest floor of urban forests was able to 
accumulate to greater quantities and therefore accumulate more carbon.  Additionally, 
there was a significant relationship of forest floor mass with soil pH (Table 13).  Forest 
36 
 
floor accrual in systems dominated by pines may lower the soil pH.  Therefore, a suite of 
complex interactions is likely driving forest floor accumulation.    
With regard to the C:N ratios of the mineral soil, forested wetlands did not stand 
out from other land uses/covers as was the case for carbon or nitrogen individually.  
Urban lawns had lower C:N ratios than other land use/cover classes at all depths except 
60-90 cm (Table 8 and Figure 11).  Relatively high nitrogen in urban plots produced low 
C:N ratios at different depths and for the total mineral soil profile (Table 8 and Figures 11 
and 12) and may be a result of management practices such as fertilization to maintain 
grass.  Low C:N ratios of urban soils may prove to be beneficial for vegetation 
productivity because sufficient nitrogen is essential for plant growth.  
This study suggests that forested wetlands play a major role in carbon storage, but 
it should be noted they perform many other ecosystem functions as well.  Wetlands serve 
as ?kidneys? for surrounding ecosystems by filtering incoming nutrients and pollutants 
(Cavalcanti & Lockaby, 2006).  Franklin County has a large portion of land set aside in 
protected areas which include some valuable forested wetlands in Tate?s Hell State Park 
and the Apalachicola National Forest.  However, increased infrastructural needs and 
development account for some losses of wetlands along the Florida Gulf Coast (Ziewitz 
& Wiaz, 2004).  Protection of forested wetlands may be imperative to maintaining 
stability in these coastal environments.     
Fire plays a large role in the accumulation and storage of carbon and nitrogen in 
these ecosystems.  In the absence of fire, urban forests, urban lawns, and forested 
wetlands, are able to accumulate carbon and nitrogen in soils.  Conversely, soil carbon 
37 
 
and nitrogen pools in natural pine forests and plantations which have experienced more 
recent fires are reduced. This result is consistent with Johnson & Curtis 2001 who found 
an average decline in soil carbon following prescribed fire when the time since the last 
fire was less than ten years.  Conversion of natural pine forests and plantations to urban 
land uses may increase soil carbon and nitrogen pools in these systems of low native soil 
nutrients.    
Paired Plots: Natural Pine Forest vs. Pine Plantation 
 Paired plots allowed for examination of the transition from natural forests to 
plantations and distinctions between urban lawns and urban forests.  There was no 
significant difference in carbon or nitrogen content of the total mineral soil profile (0-90 
cm), forest floor, total mineral soil + forest floor, or any of the individual depths in 
natural forest vs. plantation comparisons (Table 14).  Smaller pools of soil carbon and 
nitrogen were expected in plantations in accordance with Chen et al., 2004 and Guo & 
Gifford, 2002.  However, values presented in the Guo & Gifford article represent the 
average change from many individual studies.  Although not significantly different, the 
high carbon content in plantations at 60-90 cm suggests a redistribution of carbon and 
organic matter to lower depths which may be caused by prescribed burning and/or 
disking (Pritchett & Fisher, 1987).  Loss of organic matter due to burning is generally 
greater in sandy soils such as those found on these plots.  This result is also consistent 
with Yang et al. 2004 who observed that carbon and nitrogen losses due to plantation 
establishment were mainly in the surface soil.   
38 
 
Plantation establishment is recommended as a method of reducing carbon 
emissions from ecosystems.  This practice is particularly successful when cropland is 
converted to plantations, for example, because trees store greater vegetation (and also 
enhance storage in soils) carbon than crops.  However, as shown by Sohngen and Brown 
(2006), the conversion of natural hardwood forests to pine plantations can have opposite 
effects on carbon sequestration and actually emit more carbon from the system.  Results 
of plantation establishment on carbon storage vary due to underlying climatic, soil, and 
vegetation characteristics and thus need to be evaluated on a case-by-case basis.  Here 
along the Gulf Coast of Florida, natural forests are often dominated by slash or sand pine 
trees.  In these ecosystems, as was shown above, there was no significant difference in 
soil carbon content for the total mineral soil profile (0-90 cm) between natural forests and 
plantations.  This may again be related to the fact that both natural pine forests and 
plantations have frequent fires.   
Paired Plots: Urban Lawn vs. Urban Forest 
 For the total mineral soil profile, urban lawns had higher (p=0.05) nitrogen 
content than urban forests, but there was no significant difference in carbon content 
(Table 15).  Higher nitrogen content in the mineral soil in urban lawns than the urban 
forests may be a result of maintenance practices.   Maintenance of lawns in residential 
areas and public parks involves periodic fertilizer application, which typically includes 
nitrogen, phosphorous, and potassium (Cheng et al., 2008).  In contrast, urban forests 
receive little, if any, maintenance; they are typically patches of forest remnants with a 
well developed understory and midstory.  Other studies have found that fertilizer 
39 
 
application and irrigation of turfgrass can increase soil carbon storage to levels 
approximating grasslands or forests (Pouyat et al., 2008, Golubiewski, 2006).  
There was no significant difference in carbon or nitrogen content for total mineral 
soil profile + forest floor between urban forest plots and urban lawn plots (Table 15).  
This suggests that maintenance practices in urban lawns are able to maintain soil carbon 
and nitrogen pools that approximate the pools found in urban forests despite different 
disturbance regimes and vegetation structure.  While urban lawns had higher nitrogen 
content in the mineral soil, the forest floor of urban forests causes the latter to be 
statistically indistinguishable from urban lawns with regard to the total soil nitrogen 
(which includes the forest floor).  The soil carbon content of urban forests may be higher 
(Table 6 and 10) due to additional vegetation inputs, but the difference is not statistically 
significant.   
Analysis of Urbanization 
Comparison of natural pine forests, pine plantations, urban lawns, and urban 
forests gives an indication of the effects of urbanization on soil carbon and nitrogen 
storage.  Compared to natural forests, urban forests had significantly higher soil carbon 
content in the total mineral soil profile (0-90 cm) (Table 6 and Figure 19).  Urban forests 
also had significantly higher soil carbon content in the top two soil depths (0-7.5 and 7.5-
30 cm) than natural forests and plantations (Figure 18).  Urban forests are typically not 
disturbed by fires as is the case with natural forests and plantations.  This allows a greater 
forest floor mass and carbon pool to accumulate (Table 9).  Based on observation alone, 
it is speculated that urban forest plots had greater canopy closure and thus less light 
40 
 
reached the forest floor than in the other forests.  This may have produced lower surface 
temperatures in urban forest soils and thus slowed decomposition.   
The higher mean carbon content of urban lawns and urban forests than plantations 
and natural forests is consistent with the results of Pouyat et al., 2006.  Urban forests and 
residential plots had some of the highest carbon densities of urban land use/covers 
(Pouyat et al., 2006).  Pouyat et al. 2006 found elevated carbon storage in urban land 
uses/covers for warm areas such as the Southern U.S.  In contrast, the greatest declines in 
soil carbon content following urbanization were found in the northeastern U.S. which has 
naturally high soil carbon content.  These Gulf Coast ecosystems have sandy soils with 
innately low native soil carbon content.        
 Urbanization effects on nitrogen storage include elevated nitrogen in urban lawns 
for the total mineral soil profile (0-90 cm) (Table 7 and Figure 23).  Both urban lawns 
and urban forests had significantly higher mean nitrogen content in the surface soil than 
natural forests or plantations (Figure 22).  Higher soil nitrogen in urban lawns and urban 
forests may be related to overstory species.  Soil nitrogen in plots with hardwoods can be 
higher than plots with conifers (Garten & Ashwood, 2004).  The presence of wax myrtle, 
a nitrogen-fixing species, was expected to play a role in soil nitrogen; however, the 
abundance of wax myrtle in plantations (Table 12) should have elevated soil nitrogen on 
these plots, which is not the case.  Perhaps the fire component was stronger than the 
effect of wax myrtle in plantations.  The lack of a forest floor in urban lawn plots (as 
previously noted, grass was included in the understory vegetation analysis) may be the 
reason that there are no significant differences when the forest floor nitrogen content is 
41 
 
added the mineral soil nitrogen content (Figure 25), but differences were observed in the 
mineral soil alone (Figure 23).   
Excess nitrogen can be problematic in coastal ecosystems at the terrestrial-aquatic 
interface.  Nitrogen is commonly a limiting nutrient in terrestrial ecosystems; however, 
with high nitrogen inputs, ecosystems can reach a state of nitrogen saturation at which 
point vegetation growth may be inhibited (Aber et al., 1989).  Elevated nitrogen in water 
can lead to eutrophication, a condition of surplus nutrients, which can cause algal blooms 
and interfere with other ecosystem functions (Paerl, 1997).  Urban areas are susceptible 
to increased pools and fluxes of nitrogen because of fertilizer application to lawns 
coupled with increased runoff due to reduced infiltration of impervious surfaces (Tong & 
Chen, 2002, Erickson, 1999).     
Future Work 
It would be interesting in a future study to pair natural forest plots with urban 
forest plots as was done in this study for natural forests and plantations.  This would 
create a more direct comparison of forests along an urban-rural gradient while ensuring 
that plots are in similar soil series and have similar vegetation characteristics.  While 
most natural forest, plantation, urban lawn, and urban forest plots appeared to be in the 
soil orders of inceptisols, spodosols, and ultisols, our results here for urbanization 
impacts could be compounded by differences in soil series characteristics.  A detailed 
characterization of the soils on each plot is needed.  It may also be useful in the future to 
sample carbon and nitrogen in ?developing? areas to quantify changes during the 
conversion from natural ecosystems to urban land uses.       
42 
 
Land use/cover change presents a more serious threat to carbon storage when 
coupled with a changing climate.  Alterations in land use/cover can alter the climate 
through increases or reductions of carbon dioxide.  Conversely, alterations in climate can 
lead to changes in land cover (Falloon et al., 2007) as certain plants and animals are 
excluded or introduced to a different climatic regime.  The interactive effects of land 
use/cover change and climate change are not fully understood.  One study suggests that 
with the combined influence of land use change and climate change, the net effect on 
carbon storage will be approximately zero on the global scale (Levy et al., 2004).   
Changes in temperature and precipitation can greatly alter soil carbon storage.  
Climate is one of the major factors affecting processes such as decomposition and 
respiration, which in turn have a large role in the carbon storage of soils (Zheng et al., 
2006).  Decomposition rates are generally highest in warm, wet conditions.  Ecosystems 
with slow decomposition (cold and/or wet conditions) tend to have the largest pools of 
soil organic matter (Schlesinger, 1991).  At the regional scale, soil carbon may be 
regulated by precipitation, with wetter conditions having higher carbon storage because 
the increase in NPP is greater than the increase in soil respiration (Falloon et al., 2007).  
At the global scale, however, temperature may have more of an effect on carbon storage 
than precipitation, and in general decreased carbon stocks are predicted due to climate-
induced land cover changes (Falloon et al., 2007).  Therefore, future studies to examine 
interactions between human practices such as land use change, carbon, and climate would 
help provide a more comprehensive understanding of ecosystem function in this region.   
 
43 
 
Conclusions 
Forested wetlands had overwhelmingly higher soil carbon and nitrogen storage 
than all other land use/cover types (natural pine forest, plantation, urban lawn, urban 
forest) and should be a high priority for ecosystem conservation.  According to the paired 
plots, there were no significant differences in carbon and nitrogen storage between 
plantations and natural pine forests for the total mineral soil profile, at specific depths 
throughout the profile, or in the forest floor.  Similarly, there were no significant 
differences in carbon storage between urban forests and urban lawns in any of the pools 
examined.  However, the total mineral soil (0-90cm) nitrogen content of urban lawns was 
higher than urban forests.  In the urbanization analysis, urban forests had higher carbon 
and nitrogen storage than natural forests for the total mineral soil profile which is likely 
related to the presence of fire in natural forests.  Also for the total mineral soil profile, 
urban lawns had greater nitrogen storage than natural forests or plantations, which again 
may be a factor of the absence of fire in urban ecosystems.  Carbon and nitrogen storage 
in these Gulf Coast ecosystems are influenced by land use/cover, but are also subject to 
management practices, disturbances, and environmental conditions as well.  Thoughtful 
policy provisions are necessary to balance the growing coastal population and the current 
trends of land use/cover change with maintained ecosystem function (Chapin et al., 2006, 
Foley et al., 2007, Rousseaux, 2005, Zhao et al., 2006).    
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Figure 1: Location of Study Site: Apalachicola, Florida 
 
 
 
 
 
Figure 2: Distribution of plots along coast 
 
48 
 
 
 
Figure 3: Mean carbon concentration (mg/kg) by land use/cover type and depth; 
significant differences at each depth are indicated by different letters 
 
 
 
Figure 4: Mean nitrogen concentration (mg/kg) by land use/cover type and depth; 
significant differences at each depth are indicated by different letters 
49 
 
 
 
Figure 5: Mean bulk density (g/cm
3
) by depth and land use/cover type; significant 
differences at each depth are indicated by different letters 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean
 B
ul
k
 D
ens
i
t
y
 (
g
/
c
m
3
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
b
a
a
a
a
 
Figure 6: Mean bulk density (g/cm
3
) of all depths by land use/cover type; significant 
differences are indicated by different letters   
 
50 
 
 
 
Figure 7: Mean soil carbon content (kg/m
2
) by depth and land use; significant differences 
at each depth are indicated by different letters 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean
 C
a
r
bon
 C
o
nt
ent
 
(
k
g/
m
2
)
0
20
40
60
80
a
b
b
b
b
 
Figure 8: Mean soil carbon content (kg/m
2
) for total profile (0-90 cm); significant 
differences are indicated by different letters 
 
51 
 
 
Figure 9: Mean soil nitrogen content (kg/m
2
) by depth and land use; significant 
differences at each depth are indicated by different letters 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean N
i
t
r
og
en C
ont
ent
 
(
k
g/
m
2
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
a
b
b
b
b
 
Figure 10: Mean soil nitrogen content (kg/m
2
) for total profile (0-90 cm); significant 
differences are indicated by different letters 
 
52 
 
 
Figure 11: Mean mineral soil C:N ratio by land use/cover type and depth; significant 
differences at each depth are indicated by different letters 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
a
n
 C
:
N
 R
a
ti
o
 fo
r
 
T
o
ta
l
 M
i
n
e
r
a
l
 S
o
i
l
 
P
r
o
f
i
l
e
0
10
20
30
40
a
a
a
b
ab
 
Figure 12: Mean mineral soil C:N ratio of total soil profile by land use/cover; significant 
differences are indicated by different letters 
 
53 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean F
or
es
t
 F
l
oor
 M
as
s
 (
k
g/
m
2
)
0
1
2
3
4
5
6
7
a
b
ab
ab
 
Figure 13: Mean forest floor mass (kg/m2) by land use/cover; significant differences are 
indicated by different letters 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean 
F
or
es
t
 F
l
oo
r
 C
a
r
bon
 C
on
t
en
t
 
(
k
g/
m
2
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
b
a
ab
ab
 
Figure 14: Mean forest floor carbon content (kg/m
2
) by land use/cover; significant 
differences are indicated by different letters 
 
54 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean F
or
e
s
t
 
F
l
oo
r
 N
i
t
r
ogen C
o
nt
e
nt
 (
k
g/
m
2
)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
a
b
b
b
 
Figure 15: Mean forest floor nitrogen content (kg/m
2
) by land use/cover; significant 
differences are indicated by different letters 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ean C
ar
bon C
ont
ent
 (
k
g
/
m
2
) 
:
 S
o
i
l
 
+
 F
o
re
s
t
 F
l
o
o
r
0
20
40
60
80
100
a
b
b
b
b
 
Figure 16: Mean carbon content (kg/m
2
) for total mineral soil profile + forest floor; 
significant differences are indicated by different letters 
 
55 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
an 
N
i
t
r
o
gen
 C
on
t
ent
 (
k
g
/
m
2
)
:
 S
o
i
l
 +
 
Fo
r
e
s
t
 FL
o
o
r
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
a
b
b
b
b
 
Figure 17: Mean nitrogen content (kg/m
2
) of total mineral soil profile + forest floor; 
significant differences are indicated by different letters 
 
Land Use/Cover Type
natural forest plantation urban urban forest
M
ean M
i
ner
al
 S
oi
l
 C
ar
bon C
ont
ent
 
(
k
g/
m
2
)
0
1
2
3
4
5
6
7
0-7.5 cm
7.5-30 cm
30-60 cm
60-90 cm
a
b
b
ab
a
b
b
ab
a
a
a
a
a
a
a
a
 
Figure 18:  Mean mineral soil carbon content (kg/m
2
) by depth: Urbanization effects; 
significant differences at each depth are indicated by different letters 
 
56 
 
Land Use/Cover
natural forest plantation urban urban forest
M
e
an 
M
i
ner
al
 S
oi
l
 C
ar
b
on 
C
ont
ent
 (
k
g
/
m
2
)
0
2
4
6
8
10
12
14
16
18
a
b
ab
ab
 
Figure 19: Mean mineral soil carbon content (kg/m
2
) of total soil profile (0-90 cm): 
Urbanization effects; significant differences are indicated by different letters 
 
Land Use/Cover
natural forest plantation urban urban forest
M
ea
n F
or
es
t
 F
l
oor
 C
ar
bo
n C
ont
ent
 (
k
g/
m
2
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
a
a
a
 
Figure 20: Mean forest floor carbon content (kg/m
2
): Urbanization effects; significant 
differences are indicated by different letters 
 
57 
 
Land Use/Cover
natural forest plantation urban urban forest
M
e
an C
a
r
bon
 
C
ont
e
nt
 (
k
g
/
m
2
):
 S
o
i
l
 +
 
F
o
re
s
t
 F
l
o
o
r
0
5
10
15
20
25
a
a
a
a
 
Figure 21: Mean mineral soil + forest floor carbon content (kg/m
2
): Urbanization effects; 
significant differences are indicated by different letters 
 
 
 
Land Use/Cover Type
natural forest plantation urban urban forest
M
e
a
n
 M
i
n
e
r
a
l
 
S
o
i
l
 N
i
tr
o
g
e
n
 
C
o
n
te
n
t (k
g
/
m
2
)
0.00
0.05
0.10
0.15
0.20
0.25
0-7.5 cm
7.5-30 cm
30-60 cm
60-90 cm
a
a
b
b
b
b
a
ab
a
a
a
a
a
a
a
a
 
Figure 22: Mean mineral soil nitrogen content (kg/m
2
) by depth: Urbanization effects; 
significant differences at each depth are indicated by different letters 
 
58 
 
Land Use/Cover
natural forest plantation urban urban forest
M
ean M
i
ne
r
a
l
 S
oi
l
 N
i
t
r
oge
n C
o
nt
ent
 (
k
g/
m
2
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
a
ab
c
bc
 
Figure 23: Mean mineral soil nitrogen content (kg/m
2
) of total soil profile (0-90 cm): 
Urbanization effects; significant differences are indicated by different letters 
 
Land Use/Cover
natural forest plantation urban urban forest
M
e
an
 F
or
es
t
 F
l
o
or
 
N
i
t
r
og
en
 C
o
nt
en
t
 
(
k
g
/
m
2
)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
a
b
b
 
Figure 24: Mean forest floor nitrogen content (kg/m
2
): Urbanization effects; significant 
differences are indicated by different letters 
 
59 
 
Land Use/Cover
natural forest plantation urban urban forest
M
e
an
 N
i
t
r
og
en
 C
on
t
e
n
t
 
(
k
g
/
m
2
)
:
 S
o
i
l
 +
 
Fo
r
e
s
t
 
Fl
o
o
r
0.0
0.1
0.2
0.3
0.4
0.5
0.6
a
a
a
a
 
Figure 25: Mean mineral soil + forest floor nitrogen content (kg/m
2
): Urbanization 
effects; significant differences are indicated by different letters 
60 
 
61
61 
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a
b
l
e
 
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:
 
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a
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i
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(
g
/
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m
3
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b
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d
 
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62
62 
 T
a
b
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4
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(
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7
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3
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r
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c
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(
k
g
/
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2
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63
63 
 T
a
b
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7
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(
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m
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c
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(
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(
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m
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4
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  T
a
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n
 
(
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f
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t
 
f
l
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m
a
s
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(
k
g
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m
2
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c
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(
k
g
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m
2
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d
 
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t
ro
g
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n
 
c
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t
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(
k
g
/
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2
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y
 
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a
n
d
 
u
s
e
/
c
o
v
e
r
 
 
 
 
 
 
 
 
 
 
 
 
Note: 
urban 
FF 
mass 
i
s
 
assumed 
to 
be 
zer
o
;
 
g
r
a
s
s
 
i
n
c
l
u
d
e
d
 
i
n
 
u
n
d
e
r
s
t
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y
 
v
e
g
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t
a
t
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n
.
 
L
a
n
d
 
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s
e
/
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o
v
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r
 
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F
 
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a
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s
 
(
k
g
/
m
2
)
 
F
F
 
C
 
C
o
n
t
e
n
t
 
(
k
g
/
m
2
) 
F
F
 
N
 
C
o
n
t
e
n
t
 
(
k
g
/
m
2
) 
n
 
N
a
t
u
r
a
l
 
f
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s
t
 
3
.
9
1
 
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0
.
2
8
 
1
.
7
3
 
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0
.
1
3
 
0
.
0
3
 
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0
.
0
0
3
 
3
6
 
P
l
a
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t
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4
.
0
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5
3
 
1
.
8
3
 
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0
.
2
4
 
0
.
0
3
 
?
 
0
.
0
0
4
 
3
3
 
Urban 
-------
---------
--- 
-------
---------
---- 
----------------
--- 
42 
U
r
b
a
n
 
f
o
r
e
s
t
 
5
.
4
5
 
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0
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5
8
 
2
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8
 
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w
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2
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6
5
 
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6
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1
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2
9
 
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3
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0
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64
64 
  T
a
b
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(
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E
)
 
c
a
r
b
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d
 
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t
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g
e
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c
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t
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n
t
 
(
k
g
/
m
2
)
 
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f
 
t
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l
 
p
r
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n
c
l
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d
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g
 
f
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s
t
 
f
l
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r
 
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a
n
d
 
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s
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/
C
o
v
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r
 
C
a
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b
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n
 
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(
k
g
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2
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t
r
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g
e
n
 
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n
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n
t
 
(
k
g
/
m
2
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n
 
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f
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9
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3
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l
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3
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r
b
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6
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5
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f
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4
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0
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1
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4
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F
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w
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3
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6
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3
0
 
  T
a
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e
 
1
1
:
 
V
i
s
u
a
l
 
e
v
i
d
e
n
c
e
 
o
f
 
f
i
r
e
 
Land 
Use/Cover 
Evidence 
of 
fire 
in 
the 
last 
10 
y
ears?
 
Natural 
forest
 
Yes 
P
l
a
n
t
a
t
i
o
n
 
Y
e
s
 
U
r
b
a
n
 
N
o
 
U
r
b
a
n
 
f
o
r
e
s
t
 
N
o
 
F
o
r
e
s
t
e
d
 
w
e
t
l
a
n
d
 
N
o
 
         
65
65 
 Table 
12: 
Mean 
covariate 
s
t
atistics 
Covariate 
Land 
Use/Cover 
 
N
a
t
u
r
a
l
 
F
o
r
e
s
t
 
P
l
a
n
t
a
t
i
o
n
 
U
r
b
a
n
 
U
r
b
a
n
 
F
o
r
e
s
t
 
F
o
r
e
s
t
e
d
 
W
e
t
l
a
n
d
 
X
5
:
 
O
v
e
r
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
1
.
3
3
 
1
.
0
9
 
1
.
3
6
 
2
.
0
7
 
2
.
2
0
 
X
6
:
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
1
.
6
7
 
1
.
0
9
 
0
.
4
3
 
2
.
1
4
 
3
.
1
0
 
X
7
:
 
O
v
e
r
s
t
o
r
y
 
+
 
m
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
3
.
0
0
 
2
.
1
8
 
1
.
7
9
 
4
.
2
1
 
5
.
3
0
 
X
8
:
 
W
a
t
e
r
 
t
a
b
l
e
 
d
e
p
t
h
 
(
f
t
)
 
1
.
8
5
 
1
.
6
3
 
1
.
8
9
 
1
.
6
6
 
0
.
5
0
 
X
9
:
 
B
u
l
k
 
d
e
n
s
i
t
y
 
(
g
/
c
m
3
)
 
1
.
1
4
 
1
.
1
8
 
1
.
2
0
 
1
.
1
0
 
0
.
7
7
 
X
1
0
:
 
P
e
r
m
e
a
b
i
l
i
t
y
 
(
i
n
/
h
r
)
 
1
2
.
0
8
 
1
3
.
0
7
 
1
0
.
9
1
 
1
3
.
0
9
 
7
.
9
5
 
X
1
1
:
 
p
H
 
3
.
8
7
 
3
.
6
7
 
5
.
9
8
 
4
.
7
1
 
3
.
8
7
 
X
1
2
:
 
P
e
r
c
e
n
t
 
c
o
v
e
r
 
w
a
x
 
m
y
r
t
l
e
 
0
.
0
0
 
3
.
5
5
 
0
.
1
4
 
0
.
6
4
 
5
.
0
5
 
   T
a
b
l
e
 
1
3
:
 
R
e
g
r
e
s
s
i
o
n
 
r
e
l
a
t
i
o
n
s
h
i
p
s
 
f
o
r
 
e
x
p
l
a
n
a
t
o
r
y
 
v
a
riables 
(X1-X
12) 
vs. 
response 
variables 
(Y1-Y15) 
 
Y1: 
Summed 
soil 
+ 
ff 
c
a
r
b
o
n
 
Y2: 
Summed 
soil 
+ 
ff 
n
i
t
r
o
g
e
n
 
Y
3
:
 
f
f
 
c
a
r
b
o
n
 
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
X
1
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
6
*
 
0
.
0
5
 
0
.
0
4
 
0
.
1
3
 
X
2
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
+
 
u
r
b
a
n
 
f
o
r
e
s
t
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
2
 
0
.
2
3
 
X
3
:
 
P
e
r
c
e
n
t
 
t
o
t
a
l
 
f
o
r
e
s
t
 
0
.
1
1
*
*
 
0
.
0
1
 
0
.
1
0
*
 
0
.
0
1
 
0
.
0
6
 
0
.
0
6
 
X
4
:
 
P
e
r
c
e
n
t
 
f
o
r
e
s
t
e
d
 
w
e
t
l
a
n
d
 
0
.
0
9
*
 
0
.
0
2
 
0
.
1
6
*
*
 
0
.
0
0
 
0
.
0
0
 
0
.
7
0
 
X
5
:
 
O
v
e
r
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
0
 
0
.
6
9
 
0
.
0
3
 
0
.
1
5
 
0
.
0
0
 
0
.
9
1
 
X
6
:
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
1
 
0
.
5
7
 
0
.
0
3
 
0
.
1
6
 
0
.
0
0
 
0
.
9
7
 
X
7
:
 
O
v
e
r
s
t
o
r
y
 
+
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
1
 
0
.
5
6
 
0
.
0
5
 
0
.
1
0
 
0
.
0
0
 
0
.
9
8
 
X
8
:
 
W
a
t
e
r
 
t
a
b
l
e
 
d
e
p
t
h
 
(
f
t
)
 
0
.
0
9
*
 
0
.
0
2
 
0
.
0
9
*
 
0
.
0
2
 
0
.
0
0
 
0
.
8
3
 
X
9
:
 
B
u
l
k
 
d
e
n
s
i
t
y
 
0
.
4
6
*
*
 
0
.
0
0
 
0
.
3
2
*
*
 
0
.
0
0
 
0
.
0
1
 
0
.
4
7
 
X
1
0
:
 
P
e
r
m
e
a
b
i
l
i
t
y
 
(
i
n
/
h
r
)
 
0
.
0
7
*
 
0
.
0
4
 
0
.
1
7
*
*
 
0
.
0
0
 
0
.
1
3
*
*
 
0
.
0
0
 
X
1
1
:
 
p
H
 
0
.
0
7
*
 
0
.
0
5
 
0
.
0
1
 
0
.
3
7
 
0
.
2
0
*
*
 
0
.
0
0
 
X
1
2
:
 
P
e
r
c
e
n
t
 
c
o
v
e
r
 
w
a
x
 
m
y
r
t
l
e
 
0
.
1
2
*
*
 
0
.
0
1
 
0
.
2
8
*
*
 
0
.
0
0
 
0
.
0
2
 
0
.
3
2
 
 
66
66 
 T
a
b
l
e
 
1
3
 
c
o
n
t
.
 
 
Y
4
:
 
d
e
p
t
h
 
1
 
c
a
r
b
o
n
 
Y
5
:
 
f
f
 
n
i
t
r
o
g
e
n
 
Y
6
:
 
d
e
p
t
h
 
1
 
n
i
t
r
o
g
e
n
 
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
X
1
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
0
.
0
1
 
0
.
4
7
 
0
.
0
1
 
0
.
4
6
 
0
.
0
1
 
0
.
5
3
 
X
2
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
+
 
u
r
b
a
n
 
f
o
r
e
s
t
 
0
.
0
1
 
0
.
4
1
 
0
.
0
1
 
0
.
5
3
 
0
.
0
1
 
0
.
3
7
 
X
3
:
 
P
e
r
c
e
n
t
 
t
o
t
a
l
 
f
o
r
e
s
t
 
0
.
0
3
 
0
.
2
0
 
0
.
0
3
 
0
.
1
6
 
0
.
0
2
 
0
.
2
8
 
X
4
:
 
P
e
r
c
e
n
t
 
f
o
r
e
s
t
e
d
 
w
e
t
l
a
n
d
 
0
.
0
5
 
0
.
0
7
 
0
.
0
0
 
0
.
9
7
 
0
.
1
0
*
 
0
.
0
1
 
X
5
:
 
O
v
e
r
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
3
 
0
.
2
2
 
0
.
0
0
 
0
.
6
7
 
0
.
0
9
*
 
0
.
0
2
 
X
6
:
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
3
 
0
.
1
9
 
0
.
0
0
 
0
.
7
0
 
0
.
1
0
*
 
0
.
0
1
 
X
7
:
 
O
v
e
r
s
t
o
r
y
 
+
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
4
 
0
.
1
4
 
0
.
0
0
 
0
.
6
4
 
0
.
1
3
*
*
 
0
.
0
1
 
X
8
:
 
W
a
t
e
r
 
t
a
b
l
e
 
d
e
p
t
h
 
(
f
t
)
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
0
 
0
.
8
8
 
0
.
0
9
*
 
0
.
0
2
 
X
9
:
 
B
u
l
k
 
d
e
n
s
i
t
y
 
0
.
5
7
*
*
 
0
.
0
0
 
0
.
0
1
 
0
.
4
7
 
0
.
5
8
*
*
 
0
.
0
0
 
X
1
0
:
 
P
e
r
m
e
a
b
i
l
i
t
y
 
(
i
n
/
h
r
)
 
0
.
0
3
 
0
.
2
1
 
0
.
1
5
*
*
 
0
.
0
0
 
0
.
0
8
*
 
0
.
0
2
 
X
1
1
:
 
p
H
 
0
.
0
5
 
0
.
0
9
 
0
.
1
9
*
*
 
0
.
0
0
 
0
.
0
3
 
0
.
2
2
 
X
1
2
:
 
P
e
r
c
e
n
t
 
c
o
v
e
r
 
w
a
x
 
m
y
r
t
l
e
 
0
.
2
5
*
*
 
0
.
0
0
 
0
.
0
2
 
0
.
3
0
 
0
.
3
3
*
*
 
0
.
0
0
 
                
67
67 
 T
a
b
l
e
 
1
3
 
c
o
n
t
.
 
 
Y
7
:
 
d
e
p
t
h
 
1
 
+
 
d
e
p
t
h
 
2
 
c
a
r
b
o
n
 
Y
8
:
 
d
e
p
t
h
 
1
 
+
 
d
e
p
t
h
 
2
 
n
i
t
r
o
g
e
n
 
Y
9
:
 
d
e
p
t
h
 
2
 
c
a
r
b
o
n
 
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
X
1
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
0
.
0
3
 
0
.
2
0
 
0
.
0
3
 
0
.
1
8
 
0
.
0
3
 
0
.
1
6
 
X
2
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
+
 
u
r
b
a
n
 
f
o
r
e
s
t
 
0
.
0
3
 
0
.
1
8
 
0
.
0
4
 
0
.
1
3
 
0
.
0
3
 
0
.
1
5
 
X
3
:
 
P
e
r
c
e
n
t
 
t
o
t
a
l
 
f
o
r
e
s
t
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
7
*
 
0
.
0
5
 
0
.
0
8
*
 
0
.
0
3
 
X
4
:
 
P
e
r
c
e
n
t
 
f
o
r
e
s
t
e
d
 
w
e
t
l
a
n
d
 
0
.
0
7
*
 
0
.
0
4
 
0
.
1
3
*
*
 
0
.
0
0
 
0
.
0
7
*
 
0
.
0
4
 
X
5
:
 
O
v
e
r
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
0
 
0
.
6
1
 
0
.
0
4
 
0
.
1
2
 
0
.
0
0
 
0
.
7
7
 
X
6
:
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
1
 
0
.
5
4
 
0
.
0
4
 
0
.
1
4
 
0
.
0
0
 
0
.
7
0
 
X
7
:
 
O
v
e
r
s
t
o
r
y
 
+
 
M
i
d
s
t
o
r
y
 
s
p
e
c
i
e
s
 
r
i
c
h
n
e
s
s
 
0
.
0
1
 
0
.
5
0
 
0
.
0
5
 
0
.
0
8
 
0
.
0
0
 
0
.
6
8
 
X
8
:
 
W
a
t
e
r
 
t
a
b
l
e
 
d
e
p
t
h
 
(
f
t
)
 
0
.
0
6
 
0
.
0
7
 
0
.
0
8
*
 
0
.
0
3
 
0
.
0
5
 
0
.
0
8
 
X
9
:
 
B
u
l
k
 
d
e
n
s
i
t
y
 
0
.
5
4
*
*
 
0
.
0
0
 
0
.
6
0
*
*
 
0
.
0
0
 
0
.
5
3
*
*
 
0
.
0
0
 
X
1
0
:
 
P
e
r
m
e
a
b
i
l
i
t
y
 
(
i
n
/
h
r
)
 
0
.
0
2
 
0
.
2
9
 
0
.
0
7
*
 
0
.
0
4
 
0
.
0
2
 
0
.
3
3
 
X
1
1
:
 
p
H
 
0
.
0
5
 
0
.
0
8
 
0
.
0
4
 
0
.
1
5
 
0
.
0
5
 
0
.
0
7
 
X12: 
Percent 
cover 
wax 
my
rtle 
0
.
1
3
*
*
 
0
.
0
0
 
0
.
2
5
*
*
 
0
.
0
0
 
0
.
1
0
*
 
0
.
0
2
 
               
68
68 
 T
a
b
l
e
 
1
3
 
c
o
n
t
.
 
 
Y
1
0
:
 
d
e
p
t
h
 
2
 
n
i
t
r
o
g
e
n
 
Y
1
1
:
 
d
e
p
t
h
 
3
 
c
a
r
b
o
n
 
Y
1
2
:
 
d
e
p
t
h
 
4
 
c
a
r
b
o
n
 
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
r
-
s
q
u
a
r
e
d
 
p
-
v
a
l
u
e
 
X
1
:
 
P
e
r
c
e
n
t
 
u
r
b
a
n
 
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Table 14. Paired t-test mean difference of carbon and nitrogen content in soil depths, total soil 
profile, forest floor, and total soil profile + forest floor: Plantation vs. natural pine forest.  
Significant values at ? =0.05 are indicated by *.  A positive difference indicates that plantation > 
natural forest and a negative difference indicates that natural forest > plantation. 
Pool Depth Difference in Carbon: 
Plantation- Natural Forest 
Difference in Nitrogen: 
Plantation- Natural Forest 
0-7.5 cm -0.16 -0.00 
7.5-30 cm -0.16 -0.01 
30-60 cm -0.35 -0.02 
60-90 cm 1.85 0.06 
Soil 
Total Profile 1.17 0.03 
Forest 
Floor 
 0.13 -0.01 
Soil + FF  1.31 0.03 
 
 
Table 15. Paired t-test mean difference of carbon and nitrogen content in soil depths, total soil 
profile, forest floor, and total soil profile + forest floor: Urban vs. urban forest. Significant values 
at ? =0.05 are indicated by *.  A positive difference indicates that urban > urban forest and a 
negative difference indicates that urban forest > urban. 
Pool Depth Difference in Carbon: 
 Urban- Urban Forest 
Difference in Nitrogen: 
 Urban- Urban Forest 
0-7.5 cm -0.18 0.01 
7.5-30 cm 0.28 0.06* 
30-60 cm 0.91 0.05 
60-90 cm 0.34 0.02 
Soil 
Total Profile 1.36 0.14* (p=0.0511) 
Forest Floor  -------- -------- 
Soil + FF  -1.00 0.09 
 
 
 
 
 
 
 
 
 
 
 
71 
 
 
 
 
III. LAND USE/COVER EFFECTS ON VEGETATION  
AND ECOSYSTEM CARBON STORAGE  
Abstract 
Concern about human modification of ecosystems including extensive and rapid 
coastal development has led to a significant body of research investigating the impacts on 
ecosystem structure and function.  Changes in the carbon cycle are of particular 
importance due to the carbon-climate interactions.  Carbon storage in vegetation and soils 
along the Florida Gulf Coast were investigated in this study.  Each vegetation pool 
(overstory, midstory, and understory) displayed unique patterns of carbon storage in 
different land use/cover conditions.  Urban forests had significantly greater overstory and 
total vegetation carbon storage than plantations.  Midstory carbon content was 
significantly higher in natural pine forests and plantations than urban lawns.  Urban 
lawns, however, had significantly higher understory carbon storage than urban forests or 
forested wetlands.   Total ecosystem carbon (vegetation + soil) was significantly higher in 
forested wetlands than all other land use/cover classes due to the organic nature of 
wetland soils.  Predictions of land use change in Franklin County, Florida through the 
year 2020 suggest that declines in carbon storage are possible due to the loss of forests 
(including forested wetlands) and continued urbanization.  Because forested wetlands 
 
 
72 
 
store substantial pools of carbon, their protection is imperative to maintain ecosystem 
function in these dynamic coastal environments.   
Introduction 
Alteration of ecosystem function through practices such as deforestation for 
agriculture and urbanization has become a global concern.  Additionally, a rapidly 
growing population is placing disproportionate pressure on coastal ecosystems.  
According to a recent estimate, roughly half of the U.S. population lives in coastal 
counties (Bourne, 2006).  Consequently, diminished and/or degraded resources and 
pollution may be concentrated in coastal ecosystems.  Studies to quantify the effects of 
land use/cover change on ecosystem function are of great importance to minimize 
environmental degradation and inform management and policy initiatives in coastal 
areas.    
Many studies have demonstrated the global importance of the carbon cycle and its 
influence on climate (Guo & Gifford, 2002, Falloon et al., 2007, Levy et al., 2004).  
Emissions of the greenhouse gas carbon dioxide through land use change and fossil fuel 
combustion can lead to alterations in climate.  Processes such as photosynthesis and 
decomposition which influence the magnitude of vegetation and soil carbon pools are 
influenced by climatic factors (Malhi et al., 2002).  Therefore, a combination of natural 
processes and human modification of the carbon cycle results in positive feedback of 
climatic controls on ecosystem carbon storage (Field et al., 2004).   
Both soil and vegetation carbon pools and fluxes can be extensively altered by 
land use changes.  Initial land clearing and vegetation removal has obvious and 
 
 
73 
 
immediate impacts on the standing crop.  In addition, through a series of feedbacks 
including altered inputs of organic material to soil and thus altered nutrients available for 
future plant growth, the productivity of forests is also influenced by land use/cover 
change.  Quantification of baseline vegetation, soil, and total ecosystem carbon storage of 
different land use/cover conditions allows for projections of future impacts of land 
use/cover change on ecosystem function.          
Urbanization effects on vegetation pools 
The effects of urbanization on vegetation carbon depend largely on the pre-urban 
land use and initial carbon storage.  For example deforestation reduces NPP and 
decreases the standing stock of vegetation carbon in urban land conversions (Houghton & 
Hackler, 1999, Milesi et al., 2003, Tian et al., 2003).  Alternatively, when cropland is 
converted to urban land uses, there will likely be only small changes in the vegetation 
carbon pool (Houghton, 2002).  Grass in residential lawns and public parks can maintain 
a substantial pool of carbon, although when compared to soil carbon or carbon stored in 
trees and shrubs, it seems quite small (Jo & McPherson, 1995).  Roots are the dominant 
component of grass carbon storage (Jo & McPherson, 1995).    
Plantation/agriculture effects on vegetation pools 
It has been well documented that deforestation lowers productivity and decreases 
the standing stock of vegetation carbon in conversion to agricultural land use (Houghton 
& Hackler, 1999, Milesi et al., 2003, Tian et al., 2003).  Initial site preparation, including 
activities such as disking and bedding, reduces the soil carbon pool and may restrain 
vegetation pools in the future due to alterations in soil physical properties (Turner et al., 
 
 
74 
 
2005).  Additionally, management practices such as prescribed burning promote reduced 
vegetation pools.  Plantation harvesting can have mixed results and may not lead to 
noticeable changes in vegetation carbon (Johnson, 1992).     
Florida?s Gulf Coast 
Similar to much of the Southeastern U. S., the Gulf Coast of Florida has been 
widely influenced by forest utilization.  The Florida Panhandle has been involved in the 
forest products industry since colonial times (Ziewitz & Wiaz, 2004).  In the 1920s and 
1930s, the St. Joe Company, the largest private landowner in the Florida Panhandle, 
purchased the majority of their land to be used in silviculture (Ziewitz & Wiaz, 2004).  
However, in the last decade, following changes in management and company objectives, 
the St. Joe Company has transitioned into a major developer of the region (Ziewitz & 
Wiaz, 2004).  The future of the Florida Panhandle will undoubtedly be shaped both 
economically and ecologically by the decisions of the St. Joe Company.   
Florida?s Gulf Coast presents a fitting study site for examining vegetation and soil 
carbon storage in natural pine forests, pine plantations, urban lawns, urban forests, and 
forested wetlands (Figure 1).  By summing the pools of soil and vegetation, the total 
carbon storage of each land use/cover type was calculated.  Coupled with a remote 
sensing analysis of land use/cover, the mean total carbon content of each land use/cover 
type was applied on an area basis to create an overall estimate of carbon storage for the 
coastal section.  This can be utilized as a baseline analysis to predict future changes in 
carbon storage in response to changes in land use/cover.   
 
 
 
75 
 
Methods 
Field Sampling 
 The study site is a section of the Florida Gulf Coast including the towns of 
Apalachicola (29?43?31?N, 84?59?33?W), Eastpoint (29?44?30?N, 84?52?37?W), and 
Carrabelle (29?51?14?N, 84?39?57?W) chosen for its potential for development and 
encompassment of desired land use/cover classes. The climate of this region is humid 
subtropical and has an average annual rainfall of about 1450 mm (NCDC, 2008).  
Apalachicola is about 4 m above sea level (NCDC, 2008).  In general, soils are sandy 
with adequate drainage (NCDC, 2008).  Slash pine (Pinus elliottii), sand pine (Pinus 
clausa), live oak (Quercus virginiana), water tupelo (Nyssa aquatica), and titi (Cyrilla 
racemiflora) are among the prominent overstory species. 
Vegetation and soil samples were collected for each land use/cover type on a total 
of 61 circular plots (7.32m radius according to Forest Inventory & Analysis (FIA) Phase 
3 plot standards) (Figure 2).  All plots were established and samples collected between 
October 2007 and July 2008.    Selected plots were in the local land use/cover categories 
of natural pine forest, pine plantation, urban lawn, urban forest, or forested wetland as 
determined in the field (Figure 3).  Carbon storage in barren lands such as sand dunes is 
negligible so this land cover type was excluded from sampling.   
Several approaches were used to meet the study objectives.  First, a baseline 
analysis of natural pine forests, pine plantations, urban lawns, urban forests, and forested 
wetlands was used to create an estimate of total carbon storage for this section of the 
coast.  Additionally, a paired approach was used to compare natural pine forests to pine 
 
 
76 
 
plantations and urban lawns to urban forests.  Paired plots were selected based on factors 
such as dominant overstory species (for natural pine forest and plantation), soil 
morphological characteristics, and topography.  These paired comparisons aimed to 
quantify the effects on carbon storage of plantation establishment and examine variation 
within urban land uses.  Lastly, a separate analysis excluding forested wetlands designed 
to determine the effects of urbanization in land uses with better drained soils. 
Vegetation carbon was estimated for over-, mid-, and understory.  Overstory was 
categorized as anything taller than 4.88 m, midstory was 1.83-4.88 m, and understory was 
0-1.83 m.  These thresholds were chosen based on the Forest Inventory and Analysis 
Phase 3 protocol for vegetation sampling.  For the overstory, every tree ? 12.7 cm 
diameter at breast height (dbh) was cored.  In cases where more than six trees of the same 
species (and approximately same size) existed within the plot boundary, a minimum of 5 
trees was cored and the average growth from the 5 trees was used as an estimate of any 
additional trees of the same species in the plot.  Increment growth for the last five years 
was measured by Dr. Tom Doyle of the USGS National Wetlands Research Center in 
Lafayette, LA.  Species-specific dry weight equations were used to estimate the standing 
crop and productivity from the tree core data.  When species-specific equations were not 
available, general equations for plant groups were used.  A list of the equations used and 
their sources is provided in Table 1.  Carbon content was calculated as 50% of the 
biomass (Fried & Zhou, 2008).  Midstory trees were tallied and dbh was recorded to 
estimate the standing crop using allometric equations, similar to calculations of the 
overstory.  Table 2 lists the equations used to estimate midstory biomass.  In some cases, 
 
 
77 
 
these vary from the overstory equations because of differences in applicable dbh ranges 
associated with the equations.  In the same manner as the overstory, carbon content was 
calculated as 50% of the biomass.     
One 1m
2
 subplot per plot was used to estimate understory carbon pools on a 
subsample of a minimum of 35% of plots within each land use/cover category.  Urban 
lawn plots instead used three 0.1m
2
 subplots and 100% of all plots were sampled.  All 
living vegetation less than 1.83 m tall was clipped to the ground, dried at 70?C for at least 
72 hours, and weighed.  A subsample was ground for C and N analysis using thermal 
combustion (Perkin-Elmer 2400 series II CHNS/O analyzer; Perkin-Elmer Corp., 
Norwalk, CT.) as outlined in Nelson and Sommers, 1996 to measure the concentration of 
carbon (mg/kg).  Carbon concentration was used to calculate carbon content of the 
sample.  Percent cover was recorded for every plot and categorized into groups based on 
the distribution of percent cover.  Each un-sampled plot was assigned the group average 
understory carbon content based on the plot?s percent cover.    
Remote Sensing 
Digital orthoimagery quarter quadrangles (DOQQs) with 1m spatial resolution 
were used to produce a classification of land use/cover for a band along the coast (and 
~10 km inland) from roughly 85?18?18? W to 84?31?28? W (Figure 1).  April 2004 color 
infrared (CIR) images were obtained from the Land Boundary Information System at 
http://data.labins.org/2003/index.cfm (last accessed May 2008).  The classification 
scheme used for the land use/cover was selected to capture all dominant classes for the 
study area.  Classes included water, natural forest, bare ground, urban built-up, urban 
 
 
78 
 
vegetation (including both lawns and forest), non-forested wetland, forested wetland, and 
plantation.   
To produce the land cover dataset multiple geographic information system (GIS) 
methods were utilized.  An initial unsupervised classification using an ISODATA 
algorithm was performed with 50 classes to distinguish water, forest, and barren land.  
The boundaries of urban areas were modified from data delimiting the city boundaries 
obtained from the Florida Geographic Data Library (http://www.fgdl.org/) for 
Apalachicola, Carrabelle, Eastpoint, and Port St. Joe.  These areas were subset from the 
original images and a separate unsupervised classification with 50 classes was performed 
on these urban areas.  This separate classification distinguished urban built up areas from 
urban vegetation (including urban forest and urban lawns) and barren land.  GIS layers 
for roads were obtained from the FGDL and were given a 4 m buffer, converted to raster, 
and then overlaid with the classified images as built-up areas.  Heads-up digitizing was 
used to separate natural forests from pine plantations in the initial forest class done with 
ISODATA algorithm. Plantations were digitized from the DOQQs based on the 
considerations of color, texture, pattern, and parcel shape.  Only areas that had harvest 
potential were considered plantations; areas that had already been harvested were 
considered barren land.  A GIS layer for wetlands was obtained from the Wetlands 
Mapper of the U.S. Fish and Wildlife Service at 
http://wetlandsfws.er.usgs.gov/wtlnds/launch.html.  The freshwater emergent and marine 
estuarine wetlands categories were extracted and classified as non-forested wetlands.  
Forested wetlands were distinguished by extracting the pixels of forest from the initial 
 
 
79 
 
50-class unsupervised classification and then masking these with the wetlands of the 
forested/shrub category from the Fish and Wildlife data.  Vector data of plantations and 
non-forested wetlands were converted to raster and were overlaid with the two 
unsupervised classifications and forested wetlands in a GIS to create the combined 
classification. Finally, a low-pass spatial filter was run on the classified image to 
diminish noise and ideally improve accuracy 
Statistical Analyses of Vegetation Data 
All statistical analyses were done in SAS version 9.1 (SAS Institute 2002-2003).  
Analysis of variance (ANOVA) (proc glm with Tukey?s HSD) was used to determine 
significant differences in carbon storage among the land use/cover types.  Linear 
regression was used to determine the relationship between the covariates (soil 
characteristics, vegetation species richness, and land use/cover within a 1 km buffer 
around the plot) and the biomass or productivity.  Comparisons of paired plots (urban 
lawn vs. urban forest and natural pine forest vs. pine plantation) were done with paired t-
tests (proc ttest).  Finally, an analysis was done to quantify the effects of urbanization on 
carbon storage.  For this, another ANOVA (proc glm with Tukey?s HSD) compared 
natural forests and plantations (representing pre-urban states) to urban lawns and urban 
forests (representing post-urban states).  Forested wetlands were not included in this 
analysis because they tend to be on poorly drained soils and thus are not ideal for 
conversion to urban land uses.  Relationships were considered significant at p < 0.05 and 
results of p < 0.10 are also presented for informational purposes.       
 
 
 
80 
 
Results and Discussion 
Regression was used to examine relationships between hypothesized explanatory 
variables and response variables (biomass of different vegetation pools and productivity).  
Regression results are displayed in Table 3 and will be discussed in reference to each 
relative vegetation pool.  Additionally, some general vegetation statistics were calculated 
by plot and analyzed by land use/cover.  Results from these ANOVA tests are presented 
in Table 4 and significant results will be discussed below.     
Overstory Biomass and Carbon Content 
Table 5 presents the mean overstory biomass, carbon content, and productivity by 
land use/cover type.  Mean overstory biomass and carbon content were significantly 
higher (p=0.03 for both biomass and content) in urban forests than in plantations (Table 5 
and Figures 4 and 5).  Overstory biomass values are comparable to Gholz & Fisher, 1982 
for natural and planted slash-pine dominated forests.  Forested wetlands had a 
significantly higher average number of trees per plot than urban lawns, as well as a 
significantly greater number of hardwood trees per plot than all other land use/cover 
types, but this did not translate into a significant difference in biomass or carbon content 
(Table 4).  Hardwoods can store more carbon than pines (Sohngen & Brown, 2006).  
However, the average tree size of forested wetland plots was significantly smaller than 
that of the urban lawns (Table 4).  Urban lawns and urban forests tended to have the 
largest trees and presumably oldest stands, although tree size is also a factor of the 
growth rate of the species (Table 4).      
 
 
81 
 
Overstory biomass had a significant regression relationship with overstory species 
richness and soil permeability (Table 3).  The relationship between species richness and 
biomass/carbon will be explained further in the productivity section below.  Soil 
permeability may influence biomass by allowing more nutrients in solution to make their 
way to the plant roots in more permeable soils.  Fire, common in both natural forests and 
plantations (see previous chapter: Effects of Land Use/Cover on Soil Carbon and 
Nitrogen Pools), reduces biomass in the understory and may also alter nutrient allocation 
to plants of other vegetation strata.  With less recent fire, overstory biomass increased to 
greater levels in urban forests (Table 4).  It is likely that a combination of younger aged 
stands and frequent burning contributed to reduced biomass in pine plantations and to a 
lesser extent natural forests.   
Overstory Productivity 
Overstory productivity generally matched the patterns of biomass and carbon 
content (Table 5).  Productivity was highest in urban forests, but there were no significant 
differences (p=0.11) between land use/cover groups (Figure 6).  The productivity values 
are lower than those found in Gholz and Fisher 1982 for slash pine plantations and 
Messina and Conner 1998 for forested wetlands.  However, in order to make a direct 
comparison to this study, the age of the trees must be determined because productivity is 
related to tree age.  Productivity and species richness had the strongest relationship of any 
of the variables tested (Table 3).  Forested wetlands and urban forests had significantly 
higher mean species richness than pine plantations (Table 4).   
 
 
82 
 
Species richness and productivity are two common indices of biodiversity and 
ecosystem function.  A plethora of studies have examined this relationship with both 
species richness and productivity acting as the explanatory variable (Hooper et al., 2005, 
Waide 1999).  In general, there is a positive correlation between species richness and 
productivity (Hooper et al., 2005, Loreau et al., 2001, Fargione et al., 2007, Lavers & 
Field, 2006, Waide, 1999), although this can show somewhat of a bell-shaped curve, 
experiencing saturation at higher levels (Catovsky et al., 2002, Kadmon & Benjamini, 
2006).  Lavers and Field 2006 caution that this may not be a causal relationship. 
Some of the variation in diversity-ecosystem function studies stems from the fact 
that the measure of species richness does not account for characteristics such as the 
identity and functional role of the species present.  Alternatively, the number of 
functional types, or groups of species with similar impacts on ecosystem processes, can 
be used as an indicator instead of species richness.  However, this measure still does not 
capture important identity effects of species diversity (Schwartz et al., 2000) while 
assuming that all species within a group function equivalently (Lyons& Schwartz, 2001).  
As an example, total aboveground productivity may not be impacted by decreased 
biodiversity in the short-term because dominant species may be able to compensate for 
the loss of subordinate species (Smith & Knapp, 2003).  However, the long-term effects 
of the loss of subordinate species might have important impacts on ecosystem resilience 
(Smith & Knapp, 2003).     
  As a facet of species identity, the role of nitrogen-fixing species may also be 
important for this study.  Nitrogen-fixing plants may be used in silviculture to increase 
 
 
83 
 
available nitrogen to crop trees with the intention of enhancing growth (Fisher & Binkley, 
2000).  Productivity and biomass may be increased in the presence of nitrogen-fixing 
species due to increased nitrogen availability (Fisher & Binkley, 2000).  Despite the high 
abundance of wax myrtle in plantations, productivity and biomass of pine plantations in 
these plots were low (Tables 4 and 5).  
Basal area, a function of both the number of trees and their relative sizes, is used 
to determine the percent stocking of a plot.  Forested wetlands had the highest basal area 
of all land uses/covers, followed by urban forests (Table 4).  On average, plantations, 
natural forests, and urban lawns were less well stocked.  While productivity is affected by 
many other factors such as tree species and age, the less stocked plantations would be 
expected to have lower productivity than well stocked land uses such as urban forests and 
forested wetlands (Tables 4 and 5). 
Average productivity of all land use/cover types for the last five years is presented 
in Table 6 with year 1 representing the most recent year of growth.  Productivity 
decreased over the last 5 years for all land use/covers, but not significantly (Table 6).  
Productivity varies with age; in general, it increases up to a certain point and then 
declines after passing a threshold of maximum growth (Gholz & Fisher, 1982).  In 
general, trees in urban land uses were older than trees in the other land uses/covers so it is 
difficult to discern the relationship between productivity and age.     
Changes in productivity over time may also be a factor of annual climatic 
variability.  Warm and wet climate types generally have the highest productivity. High 
temperatures stimulate nutrient mineralization which stimulates productivity.  High 
 
 
84 
 
precipitation ensures sufficient water for plant growth and respiration.  According to data 
from the Florida Climate Center (http://www.coaps.fsu.edu/climate_center/), 
Apalachicola did have higher precipitation in 2003 (year 5) than more recent years, but 
whether or not this is responsible for the higher productivity is unclear.    
Midstory Biomass and Carbon Content 
Midstory biomass and carbon content displayed the following pattern: natural 
pine forest > plantation > urban forest > forested wetland > urban lawn (Table 7).  Mean 
midstory biomass and carbon content were significantly higher (both p=0.01) in natural 
forests and plantations than in urban lawns (Table 7 and Figures 7 and 8).  The low 
values for urban lawn midstory biomass and carbon content are not surprising.  Urban 
lawns did not have a developed midstory stratum; generally plots consisted of a grass 
understory with a few large trees (Table 4).  The fire regime (whether unmanaged or 
managed) in natural and planted pine forests may have helped facilitate the development 
of the midstory stratum in these land use types.  Similar to the overstory, the midstory 
biomass had a significant relationship with the midstory species richness and may be a 
factor of complementarity among species and individuals of this vegetation pool (Table 
3).  A diverse assemblage of midstory species could increase nutrient use efficiency 
between species.  Whether or not this is a causal relationship remains to be determined.       
Understory Biomass and Carbon Content 
Mean understory biomass (g/m
2
), carbon content (g/m
2
), and nitrogen content 
(g/m
2
), are presented in Table 8 and Figures 9-11 respectively. Urban lawns had 
significantly higher understory biomass (p<0.0001) and nitrogen content (p<0.0001) than 
 
 
85 
 
plantations, urban forests, or forested wetlands (Table 8 and Figures 9 and 11). Urban 
lawns also had higher carbon content (p<0.0001) than urban forests or forested wetlands 
(Table 8 and Figure 10).  Additionally, natural forests had significantly higher understory 
carbon content than forested wetlands (Table 8 and Figure 10).  Understory biomass had 
a significant but negative relationship with overstory species richness, midstory species 
richness, and overstory + midstory species richness (Table 3).   
In urban lawns, understory growth may be facilitated by increased access to 
sunlight, which is a result of typical manicuring processes that favor an open canopy.  
The understory percent cover of urban lawns is not the highest (Table 4) because this 
measure is a function of height and grass only occupies a small portion of the 1.83 m 
included in the understory.  However, the consistent, dense coverage of grass in urban 
understories produces a considerable biomass pool.  Grass is also known to substantially 
contribute to carbon storage (Jo & McPherson, 1995), so it is not surprising that urban 
plots have the highest understory carbon content (Table 8 and Figure 10) of all land 
use/cover types.  Higher nitrogen content in understory vegetation of urban plots (Table 8 
and Figure 11) may be due to fertilizer application (which typically includes three 
macronutrients: nitrogen, phosphorous, and potassium) to enhance residential and public 
lawns (Cheng et al., 2008).  
Natural pine forests, pine plantations, and a few urban forests had a unique 
understory composition with abundant Serenoa repens, or saw palmetto (Figure 12).  
Table 9 presents the understory (0-1.83 m) percent cover of saw palmetto by land 
use/cover type.  Natural pine forests had significantly greater (p=0.01) percent cover of 
 
 
86 
 
saw palmetto than urban lawns or forested wetlands (Table 9).  The presence of saw 
palmetto in the understory is a contributing factor to the relatively high biomass and 
carbon content of natural pine forests and plantations (Table 8).  Similar to urban lawns, 
the open canopy of plantations (as compared to the mostly closed canopies of urban 
forests and forested wetlands) allows more light to reach the forest floor and thus 
promotes understory growth. Serenoa repens is very flammable because of its stand dead 
biomass (Behm et al., 2004), but it may also be quick to establish post-fire, which helps 
explain its presence in plantations and natural pine forests.  The understory biomass 
estimates presented here are higher than those in Gholz & Fisher 1982 for similar slash 
pine/saw palmetto sites.  Some of the other species that comprised a substantial part of 
the understory percent cover estimate and may be contributing to the higher biomass and 
carbon content in this study include: Ilex glabra, Quercus chapmanii, Lyonia ferruginea, 
Quercus geminata, Cliftonia monophylla, Cyrilla racemiflora, Lyonia lucida, and 
Quercus myrtifolia.   
Total Carbon Content of All Vegetation Pools 
 Overstory, midstory, and understory carbon content of each plot was summed and 
then averaged within each land use/cover class.  Table 10 presents the mean total 
vegetation carbon content by land use/cover.  Urban forests had significantly higher 
(p=0.06) total vegetation carbon content than plantations (Table 10 and Figure 13).  No 
other significant differences in total vegetation carbon were found between land 
use/cover groups.  Similar to overstory biomass, total vegetation biomass was 
significantly related to overstory species richness (Table 3).  Plantations had 
 
 
87 
 
predominately either slash or sand pine overstories.  Also, hardwoods generally store 
more carbon than pines (Songhen & Brown, 2006) and the lowest average number of 
hardwoods per plot was found in plantations (Table 4).  No evidence of fire was observed 
in urban areas in the recent past.  As a result, urban forests had a diverse assemblage of 
older, large overstory trees (Table 4), which greatly contributed to the total vegetation 
biomass and carbon content.  Therefore, plantations which can be limited in species to a 
single dominant pine and are subjected to prescribed burning are at a disadvantage in 
terms of total vegetation carbon storage.  Young slash and sand pine trees in plantations 
store less carbon than the vegetation of the other land uses/covers.      
Paired Approach 
 Paired plots were used to further examine the impacts of land use/cover by 
reducing site differences such as soil variation.  Plots on similar soil series were 
compared for plantations vs. natural pine forests and urban (lawns) vs. urban forests.  
This approach examined the effects of plantation establishment and estimated the 
variation within urban ecosystems.   
Plantation vs. natural forest paired t-tests present some interesting results.  There 
was no significant difference between carbon content of natural forests and plantations in 
the overstory (p=0.15), midstory (p=0.78), or understory (p=0.30) (Table 11).  However, 
the overstory ANPP of natural forests was significantly higher (p=0.05) than the ANPP 
of plantations on paired plots (Table 12).  The mean total vegetation carbon content was 
also numerically higher (p=0.09) in natural forests than plantations (Table 11). 
 
 
88 
 
Plantations were expected to have smaller pools of carbon than natural forests due 
to initial site disturbance including vegetation removal, bedding, and management 
regimes such as prescribed burning in accordance with Houghton & Hackler, 1999, 
Milesi et al., 2003, Tian et al., 2003, and Turner et al., 2005.  Numerically, these patterns 
held true for all vegetation pools, but a significant difference was found only for 
productivity (Tables 11 and 12).  As previously mentioned, the higher carbon in natural 
forests than plantations could be related to greater species richness including some 
hardwoods in the former (Tables 3 and 4).           
 Urban lawn vs. urban forest paired comparisons also showed differences.  There 
was no significant difference (p=0.31) in overstory carbon content in urban forest and 
urban plots (Table 11).   The midstory of urban forests had significantly (p=0.0007) 
higher mean carbon content than urban lawns (Table 11).  Urban forests had a more 
diverse vertical structure than their paired urban lawn plots which included a developed 
midstory stratum in the former.  However, urban lawns had significantly higher (p=0.04) 
understory carbon content than urban forests (Table 11) and as previously noted, this is 
likely a function of a high percent cover of grass (Table 4).  Although small in 
comparison to the magnitude of the total vegetation pool, the ability of grass to store 
carbon is particularly noteworthy when examining the understory pool (Jo & McPherson, 
1995).  No significant differences (p=0.39) in overstory ANPP were found for urban vs. 
urban forest paired plots (Table 12).  Both urban lawn and urban forest plots tended 
towards a small number of large trees per plot which included some hardwoods (Table 4).  
Urban lawn vs. urban forest paired plots showed no significant difference (p=0.24) in 
 
 
89 
 
mean total vegetation carbon content (Table 11).  The absence of fire in urban areas 
promotes similar vegetation carbon pools in both lawns and forests despite structural 
differences.   
Urbanization Effects on Vegetation Carbon 
 A separate ANOVA which included natural pine forest, pine plantation, urban 
lawn, and urban forest was used to quantify the impacts of urbanization on vegetation 
carbon storage.  As previously noted, forested wetlands were excluded from this analysis 
because development typically occurs on better drained sites.  Results were similar to 
those of the ANOVA with all land use/cover categories.  Overstory biomass and carbon 
content were significantly higher (both p= 0.01) in urban forests than in plantations 
(Table 13).  Trees in urban forests were older and larger than trees in pine plantations and 
thus the biomass and carbon storage were higher (7.33 cm mean dbh for plantations 
compared to 12.48 cm mean dbh for urban forests).  Mean midstory biomass and carbon 
content were significantly higher (both p=0.01) in natural forests and plantations than in 
urban lawns (Table 13).  Urban lawns generally did not have a midstory stratum.  
Conversely natural pine forests and plantations have a developed midstory stratum and 
thus maintain a substantial carbon pool here.  Mean understory biomass, carbon content, 
and nitrogen content were significantly higher (p=0.01, 0.03, and 0.0013 respectively) in 
urban lawns than urban forests (Table 13).  The presence of grass and its associated 
fertilization in urban lawns may be the principle factors in maintaining large understory 
biomass, carbon, and nitrogen pools (Jo & McPherson, 1995, Cheng et al., 2008).  Urban 
lawns also had significantly higher understory biomass and nitrogen content than 
 
 
90 
 
plantations (p=0.0015 and p=0.0013 respectively) (Table 13).  Prescribed fires in 
plantations may be responsible for reduced biomass and depleted nutrient pools in the 
understory and overstory.   
Total vegetation biomass and carbon content were significantly higher (p=0.02 
and 0.03 respectively) in urban forests than in plantations (Table 13).  Reduced 
vegetation pools were predicted in urban areas on account of initial vegetation clearing.  
However, the trees that remain in the patches of urban forest contain large stocks of 
biomass and carbon that now exceed those in plantations and natural pine forests.  
Similarly, urban lawns are able to maintain large pools of vegetation carbon in the trees 
left onsite or regenerated after the initial clearing along with a substantial pool in the 
grass.  Additionally, species identity and community composition can play a large role in 
nutrient cycling and productivity.  Lower species richness and frequent fires may be 
responsible for reduced vegetation carbon pools in plantations and natural forests.    
Ecosystem Carbon Storage: Soils + Vegetation 
 Results for total carbon storage (soils + vegetation) by land use/cover type can be 
found in Table 14 and Figure 14.  Soils data are from the previous chapter: Effects of 
Land Use/Cover on Soil Carbon and Nitrogen Pools.  Forested wetlands had significantly 
higher (p<0.0001) carbon storage than all other land uses due to organic soils (Table 14 
and Figure 14).  The numerical ranking for total ecosystem carbon storage is as follows: 
forested wetlands > urban forests > urban lawns > natural pine forests > plantations 
(Table 14).  Natural forests and plantations were very close in terms of their total 
ecosystem carbon storage.  No obvious spatial patterns of carbon storage related to 
 
 
91 
 
distance from the coast or gradients of carbon density (for example, high carbon storage 
in the east and low carbon storage in the west) were observed (Figure 15).  Rather, the 
carbon storage of each plot appears to be more influenced by land use and other 
environmental differences among plots.     
Plantation establishment is promoted as a method of reducing carbon emissions.  
For example, the conversion of cropland to plantations sequesters carbon because trees 
store greater vegetation carbon (and resultantly soil carbon) than crop systems.  However, 
conversion of natural forests, especially hardwood forests, to plantations can lead to 
increased carbon emissions from the system (Sohngen & Brown 2006).  Climatic, soil, 
and vegetation characteristics influence the results of plantation establishment and should 
be evaluated on a case-by-case basis.  Along the Florida Gulf Coast, plantations stored 
less carbon than all other land uses and should not be recommended as a mechanism for 
enhanced carbon sequestration.  In this particular location, natural forests as well as urban 
land uses were more effective at storing carbon.   
The importance of protecting forested wetlands cannot be overstated.  They 
perform critical ecosystem functions including climate regulation by storing carbon 
(Schlesinger, 1991) and filtration of nutrients and pollutants (Cavalcanti & Lockaby, 
2006).  Increased infrastructural needs and development of the Florida Gulf Coast 
account for some losses of wetlands (Ziewitz & Wiaz, 2004).  Enforcement and 
continuation of wetland protection should be a priority for all levels of government. 
 
 
 
 
92 
 
Remote Sensing Results 
 An important component of this study was the production of a land use/cover map 
of the region of interest in the Florida Gulf Coast.  The final classified image is presented 
in Figure 16.  An accuracy assessment involving a combination of ground truthing and 
visual inspection of the raw image, calculated 81.97% accuracy for the classified image.  
Statistics on the area that each land use/cover type occupies are presented in Table 15.  
Plantations and forested wetlands are the most widespread land use/covers, representing 
about 32 and 31% respectively.  Urban areas (all urban built-up areas plus all vegetated 
areas (lawns, forests)) total < 7% of the land area.  This is roughly equal to the percent of 
land covered by natural pine forests.                  
Carbon Storage Estimate for Gulf Coast 
 Also included in Table 15 is the total carbon storage for the section of the Gulf 
Coast, broken down by land use/cover type.  This value was derived by multiplying the 
mean carbon content (on an area basis) for each land use/cover by the area that the land 
uses/cover represents in the region of interest.  The total carbon storage 0.03 PgC is an 
impressive figure for such a small area and is likely underestimated due to fact that the 
carbon content of non-forested wetlands and built-up areas is not represented in this 
value.  To put this in perspective, all forests of the U.S. are estimated to store a total of 
41.52 PgC (this estimate includes aboveground and belowground biomass, deadwood, 
litter, and soil organic carbon) (U.S. EPA, 2007).    
 
 
 
 
93 
 
Projections of Land Use Change and Estimates for Future Carbon Storage 
Wear and Greis (2002) predict a decrease of 3.69 % of forest cover from 1992-
2020 for the Florida Coastal Lowlands (Western) region.  For Franklin County 
specifically, they predict between -0.5 and 0.5 percent change in forested and urban areas 
from 1992 to 2020.  These are coarse estimates of land use change, but can be useful for 
estimating the corresponding changes in carbon storage.  It should be noted that although 
these estimates of land use change may seem trivial, due to the fact that much of Franklin 
County is public land (including Tate?s Hell State Forest and Apalachicola National 
Forest), the remaining areas (primarily the privately-owned land along the coast) will 
undergo much greater changes in land use in coming years.   
 A 0.5% decrease of forested land (including forested wetlands, natural pine forest, 
and plantation) in the section of interest would correspond to a loss of 133,858 Mg C.  A 
0.5% increase of urban land (including built-up, urban lawn, and urban forest) would lead 
to an increase of 2018 Mg C.  Urban built-up areas are currently assumed to store zero 
carbon for this estimate; for example the carbon stored in the soils beneath buildings and 
roads is not accounted for.  Therefore, this increase in carbon due to the growth of urban 
areas may be underestimated.  However, this may be negated because soil carbon lost in 
excavation for construction was not accounted for in this study.  The net change in carbon 
corresponding to a 0.5% decrease of forests and a 0.5% increase of urban areas would be 
a loss of 131,839 Mg C.  This calculated loss of carbon may not noticeably impact 
ecosystem function at the local scale; however, the estimates for all of the Southern U.S. 
are a bit more daunting.  For example, the estimated area of forest loss (corresponding to 
 
 
94 
 
a 0.5% decrease in forest area) in this study area represents only 0.0028% of the 
predicted forest losses for all of the Southern U.S.by the year 2040 in Wear 2002.   
If natural pine forests and plantations are converted to urban land uses to a greater 
extent than forested wetlands, the carbon losses would be considerably smaller.  For 
example, if the 0.5% loss of forests came solely from natural pine forests and plantations, 
the loss of carbon would be 12,153 Mg C.  With the same 0.5% increase in urban areas as 
above (leading to an increase of 2018 Mg C) the net change in carbon would be a loss of 
10,138 Mg C.  Therefore, more detailed land use change projections, especially those that 
address the fate of forested wetlands, would clarify the estimates of future carbon storage 
changes.   
Future Work 
 This study could be enhanced by distinguishing urban lawns from urban forests in 
the land use/cover classification.  To this effect, GIS zoning data could be used to 
examine individual parcels and determine whether they are primarily lawn or forest.  This 
would allow for a more direct expansion from the plot-level averages to the whole section 
of interest, or even further to the regional scale.  
 As previously stated, more fine-scale estimates of future land use change would 
be useful to accurately estimate corresponding changes in carbon storage.  The current 
county-level projections do not capture the spatial variation of land use/cover changes 
occurring within Franklin County.  Use of fine-scale historical and current land use/cover 
data could be used to predict future changes in both land use/cover and carbon storage.  
Incorporating ecosystem models with validation data from a field-based study can be 
 
 
95 
 
particularly powerful.  Although field studies are necessary for model calibration, rarely 
are the two conducted in the same experiment.    
Ecosystem models can also examine the interactions between climate change, 
land use change, and carbon storage.  Although general conclusions have been drawn 
regarding climate and carbon processes, the interactive effects of climate change and land 
use change on these processes remain uncertain.  In general, there is somewhat of an 
inverse relationship between NPP and soil carbon storage.  NPP decreases with 
increasing latitude, whereas soil carbon increases with increasing latitude.  NPP and rates 
of decomposition are greatest in warm, wet conditions, but carbon storage is greatest in 
cold, wet conditions.  Maximum carbon storage can be achieved under conditions of 
moderate to high productivity and slow to moderate decomposition.  The effects of land 
use change on climate and the resulting impacts on ecosystem processes such as 
productivity and decomposition should be explored.  Data including temperature, 
precipitation, and decomposition rates would be a useful addition to this study to explore 
the interactions between ecosystem processes, land use, climate, and carbon storage. 
Conclusions 
 Vegetation carbon storage is unique to each land use/cover class and varies by 
pool (overstory, midstory, understory).  Urban forests had significantly higher overstory 
biomass and carbon content than plantations.  Urban forests also had the highest 
productivity rates, although these were not significantly different from other land use 
classes.  Midstory biomass and carbon content were significantly higher in natural pine 
forests and plantations than urban lawns.  Urban lawns, however, had significantly higher 
 
 
96 
 
understory biomass, carbon content, and nitrogen content than urban forests and forested 
wetlands.  Because the overstory represents the largest pool, urban forests had the highest 
total vegetation carbon storage of the land use/cover types.  The total vegetation carbon 
of urban forests was significantly higher than plantations.  However, the total ecosystem 
carbon (vegetation + soil) of forested wetlands is significantly higher than all other land 
use/cover classes due to the high organic content of wetland soils.  Land use change 
predictions through the year 2020 for Franklin County suggest that declines in carbon 
storage are possible due to the loss of forests (especially if these losses include forested 
wetlands) and the growth of urban land uses.   
 The effects of land use/cover change on ecosystem function should be a top 
priority for research.  With a rapidly growing population, land use/cover change is 
inevitable; however, conscientious development decisions can reduce or eliminate human 
impacts on ecosystems.  Site assessments on potential impacts of land use/cover change 
can help to ensure protection of ecological integrity.  Land use policy must integrate both 
socio-economic and environmental perspectives to maintain ecosystem structure, 
function, and services (Chapin et al., 2006, Foley et al., 2007, Rousseaux, 2005, Zhao et 
al., 2006).    
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Figure 1: Location of Study Site: Apalachicola, Florida 
 
 
 
Figure 2: Distribution of plots along coast 
 
101 
 
 
 
    
(a) natural pine forest (b) pine plantation 
   
(c) urban lawn (d) urban forest 
 
(e) forested wetland 
Figure 3: Example plots: (a) natural forest, (b) plantation, (c) urban, (d) urban forest, (e) 
forested wetland. 
102 
 
 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
an O
v
er
s
t
or
y
 
B
i
om
as
s
 (
g/
m
2
)
0
5000
10000
15000
20000
25000
a
b
ab
ab
ab
 
Figure 4: Mean overstory biomass (g/m
2
)  
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ea
n O
v
er
s
t
o
r
y
 C
ar
bo
n 
C
ont
en
t
 
(
g
/
m
2
)
0
2000
4000
6000
8000
10000
12000
a
b
ab
ab
ab
 
Figure 5: Mean overstory carbon content (g/m
2
)  
 
103 
 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
a
n
 O
v
e
r
s
t
o
r
y
 AN
PP 
(
g
/
m
2
/y
r
)
0
100
200
300
400
500
a
a
a
a
a
 
Figure 6: Mean overstory ANPP (g/m
2
/yr) 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
Me
a
n
 Mi
d
s
t
o
r
y
 B
i
o
ma
s
s
 (
g
/
m
2
)
0
500
1000
1500
2000
2500
3000
ab
a
a
ab
b
 
Figure 7: Mean midstory biomass (g/m
2
) 
 
 
104 
 
 
 
Land Use/ Cover
forested wetland natural forest plantation urban urban forest
M
ean
 M
i
ds
t
or
y
 C
a
r
bon
 C
o
nt
en
t
 (
g/
m
2
)
0
200
400
600
800
1000
1200
1400
a
a
b
ab
ab
 
Figure 8: Mean midstory carbon content (g/m
2
) 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
ea
n 
U
n
der
s
t
or
y
 B
i
om
a
s
s
 (
g/
m
2
)
0
500
1000
1500
2000
2500
a
ab
b
b
b
 
Figure 9: Mean understory biomass (g/m
2
)  
105 
 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
a
n
 U
n
d
e
rs
t
o
ry
 C
a
rb
o
n
 C
o
n
t
e
n
t (g
/
m
2
)
0
200
400
600
800
1000
b
ac
abc
a
bc
 
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2
)  
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
M
e
an
 U
nde
r
s
t
or
y
 N
i
t
r
og
en C
o
nt
en
t
 (
g/
m
)
0
5
10
15
20
25
b
ab
b
a
b
 
Figure 11: Mean understory nitrogen content (g/m
2
)  
106 
 
 
 
 
Figure 12: Pine plantation with extensive cover of Serenoa repens 
 
 
Land Use/Cover
forested wetland natural forest plantation urban urban forest
T
ot
al
 V
eget
at
i
on C
ar
bon C
ont
ent
 (
g/
m
2
)
0
2000
4000
6000
8000
10000
12000
14000
a
b
ab
ab
ab
 
Figure 13: Mean total vegetation carbon content (g/m
2
) 
 
107 
 
 
 
 
Figure 14: Relative carbon content by pool (kg/m
2
); the size of the circle is indicative of 
the carbon content of the plot with larger circles representing greater carbon storage 
 
 
 
Figure 15: Spatial display of plot carbon storage totals (soils + veg); land uses are 
represented with different colors; the size of the circle is indicative of the carbon content 
of the plot with larger circles representing greater carbon storage 
 
108 
 
 
 
 
Figure 16: Classified Image 
 
 
109 
 
110
 
 
110 
  Table 1: Overstory equations
 
to 
estim
ate 
dry 
weight 
S
p
e
c
i
e
s
 
S
p
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c
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t
 
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q
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n
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t
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f
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r
 
d
r
y
 
w
e
i
g
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t
,
 
d
b
h
,
 
h
e
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g
h
t
 
(
i
f
 
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) 
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l
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h
 
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n
e
 
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b
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y
 
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h
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t
r
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(
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u
m
p
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an
 
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e
a
r
 
e
t
 
a
l
.
 
1
9
8
4
 
l
o
g
1
0
(
b
m
)
=
 
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1
.
1
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7
5
+
2
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5
6
4
1
*
l
o
g
1
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(
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h
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k
g
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c
m
 
Sand 
Pi
ne 
Sand 
Pi
ne 
Total 
aboveground 
b
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m
a
s
s
 
T
a
r
a
s
 
1
9
8
0
;
 
o
u
r
 
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e
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s
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l
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g
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m
=
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1
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3
 
+
 
0
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(
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b
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(
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b
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lb
, 
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ft 
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f
 
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l
 
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b
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l
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m
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+
 
2
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4
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4
9
 
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m
 
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b
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(
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1
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et 
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1999, 
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112 
 Table 
2: 
Midstory 
equations 
for 
dry 
weight 
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c
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riacea 
Liqui
dam
b
ar 
 
s
t
yraciflua 
 
 
 
 
 
 
Cl
ark 
et 
al. 
1985 
bm
=1.82108(
(
d
b
h
)
^
2
)
1
.
2
6
350 
lb, 
in 
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m
 
Melia 
 
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ch 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Mixed 
hardwood-g
ene
r
al 
Jenkins 
et 
al. 
2004 
bm
=Exp(
-2.48 
+ 
2.4835 
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n
 
dbh) 
kg, 
cm
 
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spp.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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114
 
 
114 
 Table 
3: 
Regression 
results 
of 
explanatory variables with biom
ass (overstory, m
i
ds
tory, understory, and total) and ANPP.  
* denotes significance at 

.
=0.05 
and 
** 
denotes 
significance 
at 

.
=0.01. 
 
O
v
e
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y
 
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n
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t
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A
N
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P
 
 
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p
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3
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p
eci
es 
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0
0
 
0
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9
 
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0
8
*
 
0
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3
 
0
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2
 
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2
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O
v
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t
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y
 
+
 
m
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d
s
t
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s
p
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c
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s
 
richness 
0
.
0
4
 
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3
 
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0
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0
8
 
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1
4
*
*
 
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4
 
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1
3
 
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2
 
W
a
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B
u
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si
t
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m
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9
9
 
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2
 
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7
2
 
P
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r
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a
b
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l
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t
y
 
(
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n
/
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r
)
 
0
.
0
6
*
 
0
.
0
5
 
0
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0
5
 
0
.
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7
 
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0
0
 
0
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9
9
 
0
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0
5
 
0
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8
 
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0
4
 
0
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1
5
 
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H
 
0
.
0
1
 
0
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4
9
 
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0
4
 
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1
1
 
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7
2
 
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4
4
 
     
115
 
 
115 
 Table 
4: 
ANOVA 
results 
for 
average 
nu
m
ber 
of
 
trees 
per 
plot, 
average 
number 
of 
ove
rstory 
hardw
ood 
trees 
per 
plot, 
average 
overstory 
tree 
size 
(dbh 
in 
inches), 
over
story species richness, percent cover in 
understory 
(0-6 
ft), 
and 
basal 
area 
(m
2
/
h
a
)
.
 
S
i
g
n
i
f
i
c
a
n
t
 
d
i
f
f
e
r
e
n
c
e
s
 
a
t
 

.
=0.05 
are 
indicated 
with
 different letters. 
Land Use/Cover 
# of trees per plot  
#
 o
f
 
hardwoods p
e
r
 p
l
o
t
 
Average tree d
b
h
 (
i
n
)
 
P
l
o
t
 
s
p
e
c
i
e
s
 
richness 
%
 
understory c
o
v
e
r
 
Plot basal area 
(
m
2
/
h
a
)
 
Natural forest 
4.00 (ab) 
0.92 (b) 
9.94 (bc) 
1.33 (ab) 
121.21 (ab) 
13.77 (bc) 
P
l
a
n
t
a
t
i
o
n
 
5
.
3
6
 (
a
b
)
 
0
.
0
0
 (
b
)
 
7
.
3
3
 (
c
)
 
1
.
0
9
 (
b
)
 
1
2
9
.
8
6
 (
a
)
 
1
6
.
7
0
 (
b
c
)
 
U
r
b
a
n
 
2
.
0
7
 (
b
)
 
1
.
0
7
 (
b
)
 
1
5
.
7
5
 (
a
)
 
1
.
3
6
 (
a
b
)
 
8
5
.
1
1
 (
a
b
)
 
9
.
1
8
 (
c
)
 
Urban forest 
4.86 (ab) 
2.71 (b) 
12.48 (ab) 
2.07 (a) 
115.25 (ab) 
22.30 (b) 
F
o
r
e
s
t
e
d
 w
e
t
l
a
n
d
 
7
.
7
0
 (
a
)
 
7
.
4
0
 (
a
)
 
9
.
7
0
 (
b
c
)
 
2
.
2
0
 (
a
)
 
6
8
.
1
0
 (
b
)
 
4
7
.
9
8
 (
a
)
 
   Table 5: Mean (?SE) overstory biom
ass (g/m
2
), carbon content (g/m
2
), an
d ANPP (g/m
2
/yr) by land use/cover type 
L
a
n
d
 U
s
e
/
C
o
v
e
r
 
B
i
o
m
a
s
s
 (
g
/
m
2
)
 
C
a
r
b
o
n
 C
o
n
t
e
n
t
 
(
g
/
m
2
) 
ANPP (g/
m
2
/
y
r
)
 
n
 
Natural forest 
8582.01 ? 2801.59 
4291.01 ? 1400.79 
260.77 ? 72.68 
12 
Plantation 
4775.22 ? 1233.66 
2387.61 ? 616.83 
107.28 ? 19.31 
11 
Urban 
14,192.68 ? 3439.53 
7096.34 ? 1719.77 
258.29 ? 58.09 
14 
Urban forest 
18,527.69 ? 3358.84 
9263.85 ? 1679.42 
349.32 ? 69.71 
14 
Forested wetland 
14,319.65 ? 4128.71 
7159.82 ? 2064.36 
237.28 ? 63.69 
10 
   
 
 
116 
 
 
Table 6: Mean ANPP (g/m
2
/yr) of all land uses/covers by year; statistical significance (at 
?=0.05) between groups is indicated by different letters. 
Year Mean ANPP (g/m
2
/yr) 
1 249.00  (a)    
2 264.40  (a) 
3 333.83  (a)    
4 336.25  (a)    
5 371.99  (a)   
 
 
Table 7: Mean (?SE) midstory biomass (g/m
2
) and carbon content (g/m
2
) 
Land Use/Cover Biomass (g/m
2
) Carbon Content (g/m
2
) n 
Natural forest 1826.46 ? 605.28 913.23 ? 302.64 12 
Plantation 1815.88 ? 572.60 907.94 ? 286.30 11 
Urban 57.81 ? 55.43 28.91 ? 27.71 14 
Urban forest 1486.25 ? 339.19 743.12 ? 169.59 14 
Forested wetland 1261.03 ? 283.17 630.51 ? 141.58 10 
 
 
 
Table 8: Mean (?SE) understory biomass (g/m
2
), carbon content (g/m
2
), and nitrogen 
content (g/m
2
) by land use/cover 
Land Use/Cover Biomass (g/m
2
) Carbon Content 
(g/m
2
) 
Nitrogen Content 
(g/m
2
) 
n 
Natural forest 1235.72 ? 126.29      589.22 ? 62.64 10.42 ? 1.43 12 
Plantation 1126.61 ? 122.17     519.50 ? 60.44      9.38 ? 0.95 11 
Urban 2000.92 ? 341.78      755.81 ? 141.34      16.84 ? 2.83 14 
Urban forest 742.66 ? 167.88      345.25 ? 79.14    6.17 ? 1.32 14 
Forested wetland 423.76 ? 176.60       183.25 ? 73.35      3.54 ? 1.44 10 
  
 
Table 9: Mean understory percent cover of Serenoa repens; statistical significance (at 
?=0.05) between groups is indicated by different letters. 
Land Use/Cover Percent Cover 
(0-2 ft) 
Natural forest 41.67 (a) 
Plantation 21.81 (ab) 
Urban 0.43 (b) 
Urban forest 16.43 (ab) 
Forested wetland 0.00 (b) 
 
 
 
117 
 
Table 10: Mean (?SE) total vegetation carbon content (g/m
2
) by land use/cover type 
Land Use/Cover Total Carbon Content (g/m
2
) n 
Natural forest 5793.46 ? 1270.10 12 
Plantation 3815.05 ? 581.14 11 
Urban 7881.06 ? 1762.33 14 
Urban forest 10,352.22 ? 1737.43 14 
Forested wetland 7973.58 ? 1994.83 10 
 
 
 
Table 11: Paired t-test results for difference in mean carbon content of vegetation pools 
Comparison Mean difference P-value 
Plantation vs. natural forest overstory -2.11 0.15 
Plantation vs. natural forest midstory -0.12 0.78 
Plantation vs. natural forest understory -0.11 0.30 
Plantation vs. natural forest total veg -2.34 0.09 
Urban vs. urban forest overstory -2.35 0.31 
Urban vs. urban forest midstory -0.82 0.00 
Urban vs. urban forest understory   0.41 0.04 
Urban vs. urban forest total veg -2.76 0.24 
 
 
Table 12: Paired t-test results for difference in ANPP of overstory 
Comparison Mean difference P-value 
Plantation vs. natural forest -0.17 0.05 
Urban vs. urban forest -0.07 0.39 
118
 
 
118 
  Table 13: Mean difference (g/m
2
:
 b
i
o
m
a
s
s
,
 c
a
r
b
o
n
 c
o
n
t
e
n
t
,
 
and nitrogen content; g/m
2
/yr: ANPP) in vegetation pools: 
Urbanization analysis. S
i
g
n
i
f
i
c
a
n
t
 r
e
s
u
l
t
s
 a
t
 

.
=
0
.
0
5
 
a
r
e
 
i
n
d
i
c
a
t
e
d
 
w
i
t
h
 
a
n
 
a
s
t
e
r
i
s
k
.
 
 
O
v
e
r
s
t
o
r
y
 
M
i
d
s
t
o
r
y
 
U
n
d
e
r
s
t
o
r
y
 
T
o
t
a
l
 
 
Biomass 
Carbon 
ANPP 
Biomass 
Carb
o
n
 
B
i
o
m
a
s
s
 
C
a
r
b
o
n
 
N
i
t
r
o
g
e
n
 
C
a
r
b
o
n
 
Natural 
forest
- 
p
l
a
n
t
a
t
i
o
n
 
3
.
8
1
 
 
 
 
 
 
 
1
.
9
0
 
 
 
 
 
 
0
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1
5
 
 
0
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0
1
 
 
 
 
 
0
.
0
1
 
 
 
 
 
0
.
1
3
 
 
 
 
0
.
0
7
 
 
 
 
 
0
.
0
0
 
 
 
 
 
1
.
9
8
 
 
 
 
 
 
Natural 
forest
- 
u
r
b
a
n
 
-
5
.
6
1
 
 
 
 
 
 
-
2
.
8
1
 
 
 
 
 
 
0
.
0
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7
7
*
 
 
 
 
 
 
 
0
.
8
8
*
 
 
 
 
 
 
-
0
.
7
5
 
 
 
 
 
 
-
0
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1
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-
0
.
0
1
 
 
 
 
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2
.
0
9
 
 
 
 
 
 
Natural 
forest
- 
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r
b
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n
 
f
o
r
e
s
t
 
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.
9
5
 
 
 
 
 
 
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4
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9
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0
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3
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5
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2
4
 
 
 
 
0
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u
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n
 
forest 
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119
 
 
119 
  Table 14: Mean (?SE) vegetation, soil, 
and 
vegetation 
+ 
soil 
carbon 
content 
(kg/m
2
) 
by 
pool 
and 
land 
use/cover 
type 
L
a
n
d
 
U
s
e
/
C
o
v
e
r
 
V
e
g
e
t
a
t
i
o
n
 
T
o
t
a
l
 
S
o
i
l
 
T
o
t
a
l
 
V
e
g
e
t
a
t
i
o
n
 
+
 
S
o
i
l
 
T
o
t
a
l
 
Natural forest 
5.79 ? 1.27 
7.29 ? 0.93 
13.08 ? 1.52 
Plantation 
3.81 ? 0.58 
8.82 ? 1.64 
12.63 ? 1.94 
Urban 
7.88 ? 1.76 
10.66 ? 2.56 
18.54 ? 3.27 
Urban forest 
10.35 ? 1.74 
15.91 ? 4.43 
26.26 ? 4.86 
Forested wetland 
7.97 ? 1.99 
63.33 ? 18.15 
71.30 ? 17.88 
  T
a
b
l
e
 
1
5
:
 
R
e
m
ot
e
 
s
e
n
s
i
n
g
 
a
n
a
l
y
s
i
s
:
 
L
a
n
d
 
u
s
e
/c
o
v
e
r
 
a
r
e
a
 
e
s
t
i
m
a
t
e
s
 
a
n
d
 
r
e
s
u
l
t
i
n
g
 
c
a
r
b
o
n
 
s
t
o
r
a
g
e
 
o
f
 
e
a
c
h
.
 
N
o
t
e
 
t
h
a
t
 
i
n
 
t
h
e
 
r
e
m
o
t
e
 
sensing 
approach 
urban 
f
orest 
includes 
al
l 
urban 
vegetation 
(ie. 
lawns). 
 
T
his 
cont
radicts 
the 
field 
sam
pling 
that 
counted 
urba
n 
lawns as ?u
rban?.  Urban
 built-up con
sists of build
ings and im
pe
rvious 
su
rfaces 
such 
as 
roads 
and 
parking 
lots.
 
 
Carbon 
sto
r
age
 o
f
 
urban 
built-up 
areas 
is 
assum
e
d 
to 
be 
zero. 
Land 
Use/Cover 
Area 
(% 
of 
Total) 
Area 
(m
2
) 
Carbon Storage (kg C) 
Natural forest 
6.32 
63,145,662 
825,945,259 
Plantation 
31.96 
319,491,069 
4,035,172,201 
Urban (built-up) 
2.45 
24,533,217 
0 
Urban 
vegetation 
(forest 
+ 
lawns) 
4.26 
18,021,258 
403,676,179 
Forested wetland 
30.74 
307,299,272 
21,910,438,094 
Total 
 
 
27,175,231,733 
 
120 
 
 
 
 
IV. CONCLUSIONS 
Carbon and nitrogen storage of Gulf Coast ecosystems are a function of land 
use/cover, management practices, climatic conditions, and natural variation.  Interactions 
between these factors lead to unique soil and vegetation storage within the land use/cover 
types.  Carbon storage in soils is generally greater than storage in vegetation.  
Consequently, due to the organic nature of wetland soils, the total ecosystem carbon 
(vegetation + soil) of forested wetlands is significantly higher than all other land 
use/cover classes.  After forested wetlands, the numerical rank of total ecosystem carbon 
is as follows: urban forests, urban lawns, natural pine forests, pine plantations. 
Forested Wetlands 
Forested wetlands had higher carbon and nitrogen storage compared to other land 
use/cover types (natural pine forest, pine plantation, urban lawn, urban forest).  Forested 
wetlands have a unique balance of productivity and slow decomposition due to anaerobic 
conditions, enabling large quantities of carbon to be stored. The ecosystem services that 
forested wetlands perform, such as climate regulation through carbon storage as well as 
filtration of nutrients and pollutants from water, make these areas a top priority for 
ecosystem conservation and restoration.         
 
 
121 
 
Urban Ecosystems 
Urban forests had dense vegetation and a thick forest floor due to high 
productivity and the absence of fire.  Trees were much older and larger in urban forests 
than in either natural forests or plantations.  Additionally, overstory biomass had a 
significant relationship with overstory species richness.  High species richness can initiate 
complementary resource use between species and enhance productivity.  All of these 
factors leading to greater organic inputs in urban forests likely contributed to higher 
levels of soil carbon than in natural pine forests and pine plantations.   
Urban lawns including residential yards and public parks require intensive 
management for aesthetic purposes.  Grass maintenance including watering and 
fertilization in urban lawns can lead to large pools of carbon. Urban lawns had higher 
understory biomass and carbon content than urban forests and forested wetlands.  In 
addition to a large understory pool, the absence of fire in urban lawns has allowed for 
large overstory trees to persist thus increasing the organic inputs to the soil.   
An important finding of this study was that urban ecosystems are able to store 
greater quantities of carbon than natural pine forests and pine plantations largely due to 
the influence of fire in the two latter systems.  Fires directly affect the structure of 
vegetation and indirectly affect soils by altering inputs of organic matter.  Increased 
carbon storage in urban ecosystems has been observed in other studies with warm 
climates.    
Accumulation of soil nitrogen in urban ecosystems, both urban lawns and urban 
forests, can be attributed to increased nutrient inputs through fertilizers and increased 
122 
 
runoff as a result of reduced infiltration due to impervious surfaces.  Urban lawn and 
urban forest soils had greater nitrogen storage than natural pine forests or pine plantations 
near the surface and in the total mineral soil profile.   
Natural Pine Forests and Pine Plantations 
Although the soil carbon of pine plantations and natural pine forests was 
statistically indistinguishable, natural forests had higher total vegetation carbon content 
than plantations.  Complementary resource use due to higher species richness may 
support greater biomass production in natural pine forests.  Additionally, more frequent 
burning and young even-aged stands (7.33 cm mean dbh for plantations vs. 12.48 cm 
mean dbh for urban forests) contributed to low overstory biomass in plantations.  These 
plantations have lower productivity rates than other plantations in the literature (1.1 
Mg/ha/yr vs. 5 Mg/ha/yr in other studies) due to low stocking.  Consequently, even if 
these plantations were at rotation age, the carbon storage would be less than in urban 
forests (plantations would likely be less than 80 Mg/ha in the standing crop of vegetation 
while urban forests have 93 Mg/ha).  Both natural forests and plantations were subject to 
burning which likely plays a large role in reduced soil carbon pools as compared to urban 
ecosystems.   
Land Use Change 
 Land use change predictions through the year 2020 for Franklin County suggest 
that declines in carbon storage are possible with a 0.5% loss of forests (especially if these 
losses include forested wetlands) and a 0.5% growth of urban land uses.  If only natural 
forests and plantations are lost, the decrease in carbon would be considerably smaller.  
123 
 
Fine-scale predictions of land use change would help to project a more accurate estimate 
of change in ecosystem carbon in future years.     
The results from this study suggest that intelligent land management and urban 
planning may offer solutions towards maintaining stability in the carbon cycle.  In 
particular, the carbon sequestration capacities of urban forests offer a means towards 
more sustainable development.  Practices such as leaving patches of forest interspersed 
within the urban core are already supported for aesthetic and ecological purposes such as 
increased infiltration.  This study shows that urban forests in the Florida Gulf Coast may 
also increase carbon storage in soils and vegetation compared to natural pine forests.  The 
net effect on the carbon cycle by urban development may be minimized through the 
adoption of planting requirements and land preservation criteria.  Further studies may 
allow for the creation of comprehensive development guidelines outlining actions 
necessary to increase carbon sequestration in systems with low native carbon storage. 
 This is not to say that widespread urban growth in the Panhandle should be promoted, 
however, smart growth with conscientious decisions can meet both economic and 
environmental concerns.  Lastly, the importance of forested wetland areas in the Florida 
Panhandle must not be overlooked in the midst of future development activities.     
A rapidly growing coastal population, coupled with changing objectives of the 
largest private land owner in the Florida Panhandle from timber production to community 
development, is likely to cause dramatic changes in the region, both economically and 
ecologically.  Intensive site assessments to predict development impacts on ecosystem 
function are an important step towards understanding human degradation of ecosystems.  
124 
 
In the case of carbon storage, urbanization can increase carbon pools in this section of the 
Florida Gulf Coast, so site-specific assessments are essential. It is necessary to unite 
ecosystem protection with development and growth to ensure a sustainable future for the 
Gulf Coast of Florida. 
 
 
 125
 
 
 
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