a biogeochemical comparison of wetlands in the...
TRANSCRIPT
A BIOGEOCHEMICAL SURVEY OF WETLANDS IN THE SOUTHEASTERN
UNITED STATES
By
STACIE GRECO
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2004
Copyright 2004
by
Stacie Greco
This document is dedicated to my friends and family whom have allowed me the time and space for intellectual and emotional growth.
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ACKNOWLEDGMENTS
It is said that it takes a village to raise a child. Similarly, it takes a community to
write a thesis! I am thankful for the guidance and wisdom my committee provided
throughout this process. Dr. Mark Clark’s contagious enthusiasm has helped me
overcome many doubts and fears. Kevin Grace’s perpetual encouragement and patience
has greatly improved the quality of this work. The editing expertise of Dr. Tom Crisman
has been instrumental to this document. I would also like to acknowledge the hard work
of the Wetland Biogeochemistry Laboratory and the many helping hands in the field.
Finally, this research was made possible by funding from the USEPA’s Office of Water.
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TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES............................................................................................................ vii
ABSTRACT....................................................................................................................... xi
CHAPTER
1 INTRODUCTION ........................................................................................................1
Regulatory Background ................................................................................................2 Water Quality Standards........................................................................................2 Numeric Nutrient Criteria......................................................................................3
Types of Wetlands ........................................................................................................4 Defining Ecoregions .....................................................................................................7 Limiting Nutrients and Causal Variables. ....................................................................9
Biological Indicators of Nutrient Enrichment..............................................11 Biogeochemical Indicators of Nutrient Enrichment ....................................12
Reference Wetlands ....................................................................................................14 Research Objectives....................................................................................................15 Hypotheses..................................................................................................................15
2 METHODS.................................................................................................................18
Site Selection ..............................................................................................................18 Identifying Minimally Impaired Sites .................................................................18 Identifying Wetland Community Types..............................................................20
Hydrologic Classification.............................................................................21 Site Selection Criteria...................................................................................23
Sampling and Analytical Protocols ............................................................................24 Sample Locations ................................................................................................24 Sample Collection and Processing ......................................................................27
Water ............................................................................................................27 Soil ...............................................................................................................28 Leaf litter ......................................................................................................29
Data Analysis..............................................................................................................30
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3 RESULTS AND DISCUSSION.................................................................................32
Within Wetland Variability ........................................................................................35 Water Column ......................................................................................................35 Litter ....................................................................................................................37 Soil.......................................................................................................................38 Discussion............................................................................................................38
Variability among Wetland Types..............................................................................39 Vegetative Comparisons: Swamps and Marshes.................................................39
Water column ...............................................................................................40 Litter .............................................................................................................41 Soil ...............................................................................................................46 Discussion ....................................................................................................47
Hydrologic Comparisons: Riverine and Non-riverine ........................................51 Water column ..............................................................................................51 Litter .............................................................................................................53 Soil ...............................................................................................................54 Discussion ....................................................................................................54
Spatial Variation .........................................................................................................60 Water Column .....................................................................................................62 Litter ....................................................................................................................64 Soil.......................................................................................................................67 Discussion............................................................................................................70
4 CONCLUSIONS ........................................................................................................75
APPENDIX
A WETLAND CHARACTERIZATION FORM...........................................................79
B WETLAND IDENTIFICATION AND LOCAtION..................................................82
C PHYSICAL SOIL AND WATER COLUMN DATA................................................87
D SOIL, LITTER, AND WATER COLUMN CHEMICAL DATA .............................96
LIST OF REFERENCES.................................................................................................105
BIOGRAPHICAL SKETCH ...........................................................................................110
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LIST OF TABLES
Table page 1-1 Comparison of wetland characteristics reported in the literature...............................8
2-1 The NWI classification scheme................................................................................22
2-2 Summary of chemical analyses and methods...........................................................29
3-1 Various aggregations of the wetlands surveyed. ......................................................33
3-2 Results of pair-wise comparison of core and edge areas .........................................36
3-3 Results of pair-wise comparison of core and edge areas .........................................37
3-4 Results of pair-wise comparison of core and edge areas .........................................39
3-5 Water column properties ..........................................................................................40
3-6 Litter phosphorus, nitrogen, and carbon content......................................................43
3-7 Soil P, N, and C content ...........................................................................................45
3-8 Values from the current study compared to those in the literature. .........................47
3-9 Power analysis for non-significant parameters within community comparisons.....49
3-10 Water column properties. .........................................................................................52
3-11 Leaf litter properties .................................................................................................55
3-12 Soil properties ..........................................................................................................57
3-13 Number of surveyed wetlands within the three USEPA Nutrient Ecoregions.........62
3-14 Water column descriptive statistics for surveyed wetlands by ecoregion................63
3-15 Litter descriptive statistics for surveyed by Ecoregion. ...........................................66
3-16 Soil descriptive statistics for surveyed wetlands aggregated by Ecoregion.. ...........69
3-17 Summary of significant differences among USEPA Nutrient Ecoregions ..............71
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3-18 Soil total phosphorus statistics .................................................................................74
B-1 Wetland identification and location. ........................................................................83
D-1 Chemical soil, litter, and water column data for edge (E) and Core (C) sites..........97
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LIST OF FIGURES Figure page 1-1 USEPA Level IIII Nutrient Ecoregions. ....................................................................9
1-2 Two approaches for establishing reference conditions ............................................14
2-1 Sampling areas within the three USEPA Nutrient Ecoregions. ...............................20
2-2 Number of wetlands surveyed aggregated by community type. ..............................25
2-3 Sub-sample locations within the core and edge zones .............................................26
3-1 Total area of the four wetland types.........................................................................34
3-2 Percentage distribution of surveyed wetlands within ecoregions ............................34
3-3 Water column TP and TN values by vegetative type...............................................41
3-4 Litter phosphorus, nitrogen, and carbon values by community type.. .....................42
3-5 Soil %P, %N, and %C values by community type...................................................44
3-6 Water column TP and TN values by hydrologic connectivity. ................................53
3-7 Litter phosphorus, nitrogen, and carbon content comparisons.................................55
3-8 Soil TP and TN values by hydrologic connectivity .................................................56
3-9 Distribution of wetlands within the three USEPA Nutrient Ecoregions. .................61
3-10 Comparison of ecoregions aggregated by hydrology...............................................64
3-11 Comparison of ecoregions aggregated by vegetative type.......................................65
3-12 Comparison of riverine wetlands in the three ecoregions ........................................67
3-13 Comparison of litter total phosphorus among the three ecoregionss. ......................68
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3-14 Distribution of sampling locations within the USEPA Nutrient Ecoregions. ..........73
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
A BIOGEOCHEMICAL SURVEY OF WETLANDS IN THE SOUTHEASTERN UNITED STATES
By
Stacie Greco
August 2004
Chair: Thomas Crisman Cochair: Mark W. Clark Major Department: Environmental Engineering Sciences
The USEPA’s National Water Quality Inventory Reports consistently cite nutrient
enrichment as one of the leading causes of water quality impairment. To target problems
associated with nutrients, the Clean Water Action Plan of 1998 requires the USEPA to
establish numeric nutrient criteria specific to geographic region and waterbody type.
Developing nutrient criteria for wetlands is difficult due to a lack of historic data,
incompatibility of methods employed in previous studies, and inherent variability among
wetland community types.
The primary objectives of this study were to conduct a biogeochemical survey of
minimally impaired wetlands within the southeastern US and to determine the effect, if
any, of regional, hydrologic, and vegetative differences on wetland nutrient condition.
One hundred and three wetlands were sampled in three USEPA Nutrient Ecoregions
covering four states. Sampling was distributed among wetlands classified by hydrologic
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connectivity into riverine and non-riverine and by dominant vegetative cover into
swamps and marshes.
Soil and litter parameters did not differ significantly between swamps and marshes,
suggesting a distinction between vegetative types is not necessary for determining soil or
litter numeric nutrient criteria in the southeast. Water column total phosphorus
differences between swamps and marshes imply a need to set numeric nutrient criteria
specific to dominant vegetative cover.
Hydrologic connectivity appears to be important when characterizing wetland
nutrient regimes, as demonstrated by differences in water column, litter, and soil
characteristics between riverine and non-riverine wetlands. Riverine wetlands had
greater water column and litter total phosphorus content and lower soil total nitrogen
content compared to non-riverine wetlands. It is hypothesized that hydrologic
connectivity to adjacent aquatic ecosystems and larger contributing watersheds of
riverine wetlands drives these differences.
The USEPA recognized the importance of regional influences on wetland nutrient
regimes when the decision was made to determine numeric nutrient criteria specific to
ecoregions. Results demonstrate that the Southern Coastal Plain (XII) is different from
the Southern Forested Plain (IX) and the Eastern Coastal Plain (XIV), with greater water
column total nitrogen, litter total carbon, soil total nitrogen, soil total carbon, and lower
litter total phosphorus content. Variability was still large within a given ecoregion;
therefore spatial aggregation at a sub-ecoregion level may be necessary for effective
nutrient criteria development
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CHAPTER 1 INTRODUCTION
From the billions of appropriated dollars for restoration of the Florida Everglades
to the coining of wetlands as “nature’s kidneys,” it is evident that wetlands are the
ecological buzzword and ecosystem focus of the millennium. It is hard to believe that
only a few decades ago wetlands were viewed as wastelands, portrayed by the popular
image of the Swamp Thing surrounded by putrid swamp gas. Before their inherent
values were recognized, wetlands were drained and converted to human-maintained
agricultural and sylvicultural lands at an alarming rate. The conversion of wetlands to
“more productive” land uses has recently decreased to a still alarming rate of 23,674
hectares a year (United States Environmental Protection Agency 2002). However, such
losses only represent complete destruction of these ecosystems and do not account for
numerous additional hectares where wetland functions have been degraded due to
changes in hydrology, vegetation, and/or water quality. It is this change in ecosystem
function, and thereby potential loss of designated use, that led to implementation of the
Clean Water Act (CWA) in 1972 and the current directive to establish numeric nutrient
criteria for water bodies within the USA. This thesis addresses some of the issues for
establishing numeric criteria for wetlands and presents results of a wetland survey
conducted in the southeastern United States.
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Regulatory Background
Water Quality Standards
Section 304(a) of the CWA mandates the United States Protection Agency
(USEPA) to assist states, tribes, and territories in developing water quality standards.
Such standards contain three major components: 1) determination of designated uses,
2) development of numeric or narrative criteria to protect designated uses, and
3) development of antidegradation policy to avoid impacts not addressed by the developed
criteria (USEPA 1983). As of the late 1990s, 39 states lacked water quality standards
for wetlands (USEPA 2000).
States, tribes, and territories are required to determine designated uses of
waterbodies within their jurisdiction. These must meet the goals of Section 101(a) of the
CWA, which include protection and propagation of fish, shellfish, and wildlife along
with providing for recreation opportunities (USEPA 1983). Defining the designated uses
for rivers and lakes is a straightforward task since the values of swimming, fishing, and
water sports are easily recognized. This is not the case with wetlands because historically
their values have not been recognized, and they are not always obvious. Wetland values
can include flood storage, pollution and sediment control, food web support, groundwater
replenishment, and habitat for various organisms including waterfowl (Moore et al. 1999,
Morris 1979). Many states simply assign designated uses based on wetland type or
location in the landscape (USEPA 1990), since it is difficult to assign values to each
individual wetland.
Once states determine the designated uses of a waterbody, criteria must be
developed to protect those uses. The criteria of water quality standards can be narrative
or numeric. Narrative criteria are important for impacts that cannot be addressed by
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numeric criteria, such as those that do not directly affect water chemistry. For example,
discharge of dredge and fill material can be prevented using narrative criteria. Numeric
criteria are values or ranges assigned to measurable chemical, physical, and/or biological
parameters. They can be more useful than narrative criteria because they provide a clear
distinction between acceptable and unacceptable conditions, and hence, reduce ambiguity
for management and enforcement decisions (USEPA 2000b).
Under Section 305(b) of CWA, states, tribes, and territories are required biennially
to compare monitoring results with their water quality standards. To identify trends in
water quality, the USEPA compiles the data and publishes the National Water Quality
Inventory Report. These reports consistently identify nutrients as one of the leading
causes of water quality impairment and failure to sustain the designated uses of
waterbodies. Excessive nutrients are responsible for almost 50% of impaired lake area
and 60% of impaired river reaches in the US (Smith et al. 1999).
Numeric Nutrient Criteria
To target problems specifically associated with nutrient enrichment, President
Clinton introduced the Clean Water Action Plan of 1998, which requires the USEPA to
establish numeric nutrient criteria specific to ecosystem type and geographic region. The
agency responded with a document describing its approach titled the National Strategy
for the Development of Regional Nutrient Criteria. The document describes the
USEPA’s intention to publish technical guidance manuals for each of the four waterbody
types (lakes and reservoirs, rivers and streams, estuaries, and wetlands) along with
criteria recommendations for specific ecoregions.
The USEPA intended to recommend target nutrient ranges on a geographic basis
using historical nutrient data, reference conditions, and expert knowledge (USEPA 1998).
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Lakes/reservoirs, rivers/streams, and estuaries are well-monitored ecosystems with
sufficient data available to support numeric criteria development. With exception of the
Florida Everglades, wetlands lack even a skeletal survey of nutrient condition.
There is a lack of historical wetland data since their value as aquatic ecosystems is
a relatively recent phenomenon. The 2000 National Water Quality Inventory Report was
unable to make conclusions concerning wetland water quality because only 8% of total
wetlands in the US were surveyed, in contrast to 42% of US lakes (USEPA 2000a). For
those wetlands that have been monitored, numerous parameters have been measured, and
a variety of sampling techniques and methodologies have been utilized making
comparisons and regional characterization difficult. The exception is for the Florida
Everglades, which have been studied sufficiently to provide data for the USEPA to make
wetland numeric nutrient recommendations (USEPA 2000c).
Establishing numeric criteria for wetlands requires the determination of
1) designated use, 2) appropriate regional or type of wetland aggregation scheme to which
criteria are sufficiently but not overly protective, 3) limiting nutrient/casual variable to
determine which nutrients require criteria development, or in the absence of a clear cause
and effect threshold of impairment, the quantification of nutrient concentrations under
reference conditions. As discussed above, determination of designated use requires
recognition of wetland values and benefits to local communities. Determining
appropriate aggregation of wetlands for development of numeric criteria requires a
thorough investigation of potential differences among wetland types and regions.
Types of Wetlands
Definitions of wetlands include a suite of ecosystems supporting various functions.
Common wetland types of North America include freshwater marshes, peatlands,
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freshwater swamps, riparian systems, tidal salt marshes, tidal freshwater marshes, and
mangrove wetlands (Mitsch and Gosselink 2000). These terms generally define the
dominant vegetation type and hydrologic regime. Marshes are characterized by annual or
perennial herbaceous species, and swamps are dominated by perennial woody vegetation
(Brinson et al.1981). Hydrologically, wetlands are broadly categorized as riverine, tidal,
lake fringe, or isolated.
Extensive forested floodplains are common in the southeastern United States.
These riverine wetlands (also called floodplains, bottomlands, and riparian wetlands) are
connected to nearby rivers or streams, which supply water and nutrients during flood
events. Riverine systems also receive considerable inputs from runoff of the surrounding
landscape (Craft and Casey 2000).
Riparian wetlands play a critical role in maintaining water quality, as they
efficiently trap sediments and associated contaminants (Hupp 2000). Between 85 to 90%
of sediments leaving agricultural fields can be captured by wooded riparian wetlands
(Gilliam 1994). These wetlands are also important for flood control and provide valuable
forest habitat.
Although forested floodplains are more common in the southeastern United States,
herbaceous wetlands can also be found adjacent to rivers and streams. In riverine
wetlands there is a narrow opportunity for colonization between the exposure of alluvial
sediments and the return of high water levels and erosional forces (Willby et al. 2001).
Large rivers whose extensive floods deposit sediments in adjacent wetlands may have
poorly developed riparian marshes.
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Riverine marshes are included in this study because there are few studies
comparing nutrient cycling between forested and herbaceous systems. Hopkinson (1992)
concluded that the growth form of the dominant vegetation does not influence nutrient
retention, although study results showed that a forested riverine system retained slightly
more nutrients than a riparian marsh (4.3% vs. <1%). Woody vegetation serves as long-
term storage of nutrients, while herbaceous vegetation of marshes provides mainly short-
term storage (Reddy and D’Angelo 1994). These differences may lead to
biogeochemical differences between these wetland types.
Depressional wetlands (non-riverine) differ from riverine systems because they are
not directly influenced by hydrologic fluxes from rivers and streams. Non-riverine
wetlands rely on precipitation or groundwater inputs, which tend to have lower nutrient
loads than surface waters (Craft and Casey 2000). Hopkinson (1992) determined that
relatively closed marshes and swamps of Okefenokee Swamp retained 90% of inorganic
nutrient inputs, whereas small percentages were retained in riverine systems. He
concluded that the openness of a wetland determines nutrient loading, which is strongly
correlated with productivity, organic matter decomposition, and nutrient cycling.
Systems with low nutrient loading are more efficient at cycling nutrients and have lower
net primary production (Craft and Casey 2000). Therefore, riverine and non-riverine
wetlands within similar surrounding land-uses may naturally display different nutrient
concentrations, organic matter content, and biogeochemical processes.
Differences among riverine verses non-riverine systems and marshes verses
swamps hinder generalizations about wetlands (Table 1-1). One exception is that
excessive loading of nutrients can alter ecosystem dynamics. If wetland functions are to
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be protected through development of numeric nutrient criteria, individual wetland types
may need to be studied along with their regional variation as background for sound
environmental regulations.
Defining Ecoregions
The Clean Water Action Plan of 1998 includes a spatial component in its
requirement to establish nutrient criteria by geographic regions. The USEPA is
addressing spatial variability via geographic regions called ecoregions, which are areas
with relatively homogenous ecosystems that differ from adjacent regions (Omernik and
Bailey 1997) and are based on geology, physiology, vegetation, climate, soils, wildlife,
and hydrology. Omernik (1987) divided the conterminous US into ecoregions based on
regional patterns resulting from the combination of component maps including land-use,
land-surface forms, potential natural vegetation, and soils.
The USEPA adopted and adapted Omernik’s ecoregions and stratified them
hierarchically. Level I is the coarsest United States ecoregion and is composed of 15
ecological regions, Level II is represented by 52 regions, and Level III contains 84
ecoregions (Brewer 1999). Level III ecoregions with similar characteristics that
contribute to nutrient regimes were aggregated to create USEPA Nutrient Ecoregions
(Figure 1.1). The USEPA recommends that numeric nutrient criteria be established for
lakes/reservoirs, streams/rivers, estuaries, and wetlands within each of the Nutrient
Ecoregions.
The current study area includes wetlands within the Southeastern Forested Plain
(IX), Southern Coastal Plain (XII), and Eastern Coastal Plain (XIV) ecoregions.
Comparisons were made among the three ecoregions to determine if they are appropriate
aggregations for setting numeric nutrient criteria.
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Table 1-1. Comparison of wetland characteristics reported in the literature
Parameter Riverine Non-riverine
Major source of inputs (Craft and Casey 2000) Runoff Precipitation
Connectivity to other systems (Hopkinson 1992) Open Closed
Nutrient Cycling (Hopkinson 1992) Less efficient More efficient
Soil C:N ratios (Craft and Casey 2000) Similar Similar
Parameter Swamps Marshes
Nutrient retention (Wilby et al. 2001) Similar Similar
Biomass turnover rates (Hopkinson 1992) One magnitude lower One magnitude higher
Live tissure N:P ratios (Bedford et al. 1999) Greater Lower
Live tissue N:P ratios (Bedford et al. 1999)
Suggest P-limitation or co-limitation by N
and P
Less than 14, suggesting N-limitation
Litter %N (Bedford et al. 1999) Lower Greater
Litter %P (Bedford et al. 1999) Similar Similar
Soil N:P ratios (Craft and Casey 2000)
Low, suggesting P-limitation or co-
limitation by N and P
Greater, Suggesting P-limitation
Average water temperatures (Lee and Bukaveckas 2002) Cooler Warmer
Algal growth (Battle and Golladay 2001) Low Greater
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Figure 1-1.USEPA Level IIII Nutrient Ecoregions for numeric nutrient criteria
recommendations (USEPA 2003)
Limiting Nutrients and Causal Variables.
Anthropogenically derived nutrients enter aquatic ecosystems from point sources,
such as wastewater effluents, and nonpoint sources including agricultural, urban, and
construction runoff. Nonpoint sources are major contributors of nutrients to aquatic
systems and are most difficult to regulate (Smith et al. 1999). Agricultural is the primary
source of nonpoint nutrient pollution in the United States due mainly to fertilizer
application and accumulation of animal manure (Carpenter et al. 1998).
Methodologies in USEPA technical guidance manuals for establishing numeric
criteria are based on limiting nutrients, implying that primary production of plants is
limited by the nutrient that is the least available relative to the plant’s requirement for
growth. This concept is Liebig’s Law of the Minimum (Smith et al. 1999). Nitrogen (N)
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and phosphorus (P) are the nutrients most commonly cited as limiting plant growth
(Carpenter et al. 1998, Gusewell et al. 1998, Koerselman and Meuleman 1996, Smith et
al. 1999). Therefore, with increased loading of N and/or P to aquatic ecosystems,
primary production usually increases and can lead to eutrophication. The agent that
causes change in an ecosystem is referred to as the casual variable, while the factor that
reacts is called the response variable (USEPA 2000a). For example, when concentrations
of a limiting nutrient (casual variable) increase, the dominance of fast-growing species
(response variable) increases, and they replace less competitive species (Gusewell et al.
1998).
Eutrophication is the process whereby an aquatic ecosystem shifts from a low
nutrient (oligotrophic) to a highly productive, nutrient rich (eutrophic) system (Mitsch
and Gosselink 2000). If the shift is the result of human activities, the process is called
cultural eutrophication. Eutrophication is characterized by increased growth of algae
and/or macrophytes, which can hinder use of water for fishing, recreation, industry, and
domestic consumption. Decomposition of excessive algae and macrophytes reduces
oxygen supplies, which can lead to fish kills (Carpenter et al. 1998). Eutrophication can
also alter foodwebs, resulting in a loss of biodiversity (Carpenter et al. 1998, Smith et al.
1999). In fact, high species biodiversity has been correlated with low nutrient regimes
(Bedford et al. 1999).
Preventing input of nutrients from anthropogenic sources does not necessarily
result in decreased plant growth, due to internal biogeochemical cycling of nutrients
within wetlands. Decomposition of stored organic matter can provide the nutrients
required for plant growth (Reddy and D’Angelo 1994). Nutrient transformations depend
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on many factors, including hydrologic regime, influent nutrient concentrations, existing
nutrients in the system, vegetation, and sediments (Gopal 1999).
Predicting the extent of internal nutrient cycling in wetlands is difficult due to
inherent differences among wetland ecosystems. For example, nutrient cycling of
riverine and non-riverine wetlands is influenced by dissimilar hydrologic regimes.
Hopkinson (1992) found that the dominant plant growth form was the primary factor
influencing biomass turnover rates, with marshes cycling an order of magnitude greater
than swamps. Therefore, to determine nutrient effects in a wetland, it may be necessary
to examine several components of various wetland types.
If a limiting nutrient was always the factor limiting the system, it would be simple
to develop regulations. But aquatic systems are dynamic, and several factors can affect
production. For example, plant biomass changes seasonally, fluctuates with land-use,
and varies regionally (USEPA 2000a). Therefore, to establish numeric nutrient criteria, it
is necessary to develop an efficient tool for quantifying the nutrient regime of wetlands.
An effective nutrient indicator must be sensitive to varying nutrient regimes, easy to
measure and interpret, inexpensive to apply, and should have as few temporal and spatial
constraints as possible. The USEPA is exploring biological and/or chemical indicators
(or indices) to assess ecosystem integrity.
Biological indicators of nutrient enrichment
Biological assessments of wetlands often look at community-level parameters such
as abundance, biomass, density, richness, diversity, and community composition as
indicators of anthropogenic stressors (Adamus and Brandt 1990). Galatowitsch et al.
(1999) looked at possible plant, bird, invertebrate, fish, and amphibian metrics in eight
wetland types in Minnesota. Their results indicate that specific metrics would have to be
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developed for the different wetland types and ecoregions. A study in the prairie pothole
region of the US looked at the value of macrophyte abundance, species richness, and
amounts of litter and standing dead vegetation as indicators of wetland health. None of
the examined indicators successfully quantified ecosystem health (Kantrud and Newton
1996). However, Lane et al. (2004) successfully developed a wetland condition index
based on macrophytes, macroinvertebrates, and diatoms for isolated depressional marshes
of peninsular Florida.
As nutrient levels in a wetland increase, the chemical structure of the system is
altered, leading to biological changes. Microbes are normally first to respond to nutrient
pulses with algae following closely behind. There is a time lag between casual variables
and response variables, particularly in long-lived species (Fennessy et al. 2001).
Biological indicators often rely on the response of larger organisms such as plants,
invertebrates, and birds (Galatowitsch et al. 1999, Kantrud and Newton 1996, Lane et al.
2004). Once organisms respond to a change in the nutrient regime, some of the original
structure of the wetland is lost as the new community evolves. One concern with using
macrophyte structure as an indicator is that once a wetland has been dominated by stress
tolerant perennials, less aggressive species may not be capable of re-colonization after the
stress is removed (Galatowitsch et al. 1999). The community structure may be a relic of
past disturbances.
Biogeochemical indicators of nutrient enrichment
There is a well-documented correlation between nutrient additions to aquatic
ecosystems and proportional increased growth of algae and macrophytes (Carpenter et al.
1998, Morris 1991, Smith et al. 1999). Likewise, elevated P and N levels have been
associated with decreased species diversity (Bedford et al. 1999, Carpenter et al. 1998,
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Morris 1991, and Smith et al. 1999). Phosphorus and nitrogen chemistry of aquatic
ecosystems can be monitored to determine the degree of enrichment before species are
eliminated. This is important since 14% of the 130 plant species in the conterminous US
listed as endangered or threatened are found primarily in wetlands (Morris 1991).
Biogeochemical processes, such as organic matter decomposition and
denitrification, can reflect nutrient budgets before responses are evident in higher
organisms (Reddy and D’Angelo 1997). Nitrogen to phosphorus ratios (N:P) in plant
tissue (Gusewell and Koerseleman 2002, Gusewell et al. 1998, Koerseleman and
Meuleman 1996, Shaver and Melillo 1984, Wilby et al. 2001), soil (Craft and Casey
2000) and litter (Baker et al. 2001, Shaver and Melillo 1984) have been studied to assess
nutrient limitation in wetlands. Koerseleman and Meuleman (1996) concluded that when
N and P are controlling plant growth in wetlands; vegetation N:P ratios > 16 indicate P
limitation, while N:P ratios < 14 indicate N limitation.
There is disagreement in the literature regarding the limitation of wetland
productivity. Morris (1991) reviewed several wetland studies and concluded that most
wetlands are N limited. The results from numerous wetlands in Scotland, France, and
Ireland agree that most wetlands are N limited (Wilby et al. 2001). However, Craft and
Casey (2000) suggest that freshwater marshes and forested wetlands of southwestern
Georgia are P limited. Bedford et al. (1999) concluded that within temperate freshwater
wetlands of North America, marshes are N limited, while evergreen, shrub, and
deciduous wetlands are P limited. This confusion demonstrates a need for additional
information regarding nutrient regimes of wetlands. This study includes a
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biogeochemical characterization of the surveyed wetlands to determine background
levels of nutrients and biogeochemical processes in minimally impaired wetlands.
Reference Wetlands
Establishing numeric criteria for wetlands requires determination of reference
conditions as a standard for comparison. One strategy for determining reference values is
to survey wetlands representing the broad range of nutrient impairment. The lower 25th
percentile of this population would be recommended as reference conditions (Figure 1-2).
An alternative strategy explored in this study is to set reference conditions equal to the
upper 25th percentile (or 75th percentile) of wetlands identified as minimally impaired
systems (USEPA 2000a). Eventually, individual waterbodies will be sampled and
compared to reference conditions to determine appropriate management methods
(USEPA 2000b).
Figure 1-2.Two approaches for establishing reference conditions using total phosphorus
as the example variable (modified from USEPA 2000a)
Minimally Impaired Wetlands
Representative of all Wetlands
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Ideally, reference conditions should reflect conditions in the absence of
anthropogenic influences and pollution. However, human activities have impacted all
ecosystems to some degree; therefore, reference conditions realistically represent the
least impacted conditions. The USEPA Science Advisory Board endorses use of
conditions representing minimal impact as a baseline that should protect the beneficial
uses (or designated uses) of aquatic resources (USEPA 2000a). The results of this study
will help determine appropriate reference conditions for developing numeric nutrient
criteria.
Research Objectives
The survey of this thesis will assess background nutrient concentrations in wetlands
to define water quality required to maintain ecological integrity. An additional goal of
this research is to explore differences in nutrient regimes among various wetland types to
determine appropriate wetland aggregation schemes for setting criteria. Results from this
comparison may be instrumental in developing nutrient criteria that are sufficiently
protective and feasible. Furthermore, regional aggregates will be explored to gain
additional understanding of the spatial component of wetland nutrient regimes within the
southeastern United States.
Hypotheses
The “openness” of a system to hydrologic and material influxes influences its
nutrient loading and productivity (Hopkinson 1992). Riverine systems are open to such
influxes and typically act as sinks for sediment and phosphorus from the contributing
watershed (Craft and Casey 2000), whereas non-riverine systems are considerably less
open to influxes. It is hypothesized that riverine wetlands will have higher nutrient levels
within soil and water compared to non-riverine systems.
16
In open systems with high nutrient influx, plants have less efficient nutrient cycling
and reabsorb fewer nutrients from senescing leaves (Hopkinson 1992). Hence, nutrient
content of leaf litter is greater in areas with increased nutrient availability (Shaver and
Melillo 1984). It is hypothesized that riverine wetlands will have increased nutrient
levels in leaf litter compared to non-riverine wetlands.
The structure of marshes and swamps is quite different, with the former
characterized by herbaceous vegetation and the latter by woody growth forms. In marsh
ecosystems, the majority of C, N, and P is stored in the soil, whereas swamps store a
great deal of C, N, and P in plant biomass (Hopkinson 1992). Additionally, biomass
turnover rates are an order of magnitude greater in marshes than swamps (Hopkinson
1992). This continual decomposition of herbaceous organic matter releases nutrients into
the soil; therefore, it is hypothesized that marshes will have higher nutrient levels in soil
than swamps.
Battle and Golladay (2001) found that sedge marshes have higher algal growth than
cypress swamps. This difference likely reflects the absence of overstory cover in
marshes. Algal populations quickly sequester nutrients from the water column, hence
decreasing soluble nutrients available to other growth forms (Kadlec and Knight 1996).
It is hypothesized that the water column of marshes will have lower N and P content than
swamps.
There is a spatial component to the nutrient regimes of wetlands, as recognized by
the USEPA’s use of ecoregions in determining numeric nutrient criteria. The various
geologic formations of the southeastern United States affect hydrology. Hydrology is
often cited as the most important defining parameter of wetland systems (Ehrenfeld and
17
Schneider 1991, Fennessy and Mitsch 2001, Jones et al. 2000, Reinelt et al. 1998).
Therefore, it is hypothesized that there will be regional differences in the nutrient regimes
of wetlands.
18
CHAPTER 2 METHODS
To address the research goal of determining background concentrations of nutrients
in minimally impaired wetlands, it was necessary to locate and survey several types of
wetlands within areas of nominal anthropogenic disturbances. Two vegetative and two
hydrologic classes were selected, resulting in four wetland classes. The four wetland
types surveyed were riverine marshes, non-riverine marshes, riverine swamps, and non-
riverine swamps. To evaluate the spatial component of wetland nutrient regimes,
selection of wetlands was stratified within three USEPA Nutrient Ecoregions
(Southeastern Forested Plains, Southern Coastal Plains, and Eastern Coastal Plains) in the
southeastern United States.
Site Selection
The site selection process identified minimally impaired wetlands within three
ecoregions of the southeastern United States that met the criteria for wetland community
type (marsh versus swamp), accessibility (proximity to forest roads and within public
ownership), and hydrologic connectivity (riverine verses non-riverine). The large spatial
extent of the study area necessitated a Geographic Information System (GIS) for locating
sampling sites and analyzing spatial relationships. All GIS analysis was done using
ArcGIS 8.1.
Identifying Minimally Impaired Sites
Nutrient enrichment is often a result of fertilizer runoff from surrounding
agricultural and urban areas. It was assumed, as supported by Kantrud and Newton
19
(1996), that wetlands located close to agricultural areas would have greater nutrient
loading than those farther from intensive agricultural activities. Locating wetlands that
were not influenced by agriculture was problematic due to the scale of the survey. The
study area included Florida, Georgia, Alabama, and South Carolina. Collecting and
analyzing detailed land-use data for the entire study area was not logistically feasible.
Furthermore, utilizing data from various sources (such as four state agencies) can be
difficult to integrate because of different scales and varying standards for data quality and
collection.
The USEPA’s suggestion to use sites located within the boundaries of public lands
as minimally impaired wetlands was adopted (USEPA 2000a). It is likely that these sites
are less influenced by cultural nutrient enrichment than wetlands on private lands, as
indicated by a Landscape Development Intensity (LDI) Index for assessing the intensity
of various land-uses (Brown and Vivas in press). The index utilizes calculated LDI
coefficients ranging from 1.0 (natural systems) to 7.0 (high intensity agricultural).
Forestry is a common land-use on public lands. The LDI coefficient for pine plantations
is 1.58, which indicates minimal influence of wetlands near silviculture activity.
A public lands coverage was obtained and overlayed on the USEPA Nutrient
Ecoregion map (Figure 1-1). The largest public land tracts in the southeastern United
States lie within the boundaries of National Forests; therefore, efforts were concentrated
on identifying National Forests within the three USEPA Nutrient Ecoregions of the
Southeastern US (Figure 2-1). Permits were obtained to sample within Apalachicola,
Conecuh, Francis Marion, Ocala, Oconee, Osceola, Sumter, and Talladega (Oakmulgee
District) National Forests. Because Georgia had considerable aerial gaps without
20
National Forest lands, portions of Fort Benning Military Preserve, Moody Air Force
Base, and Banks Lake National Wildlife Refuge were also sampled.
Figure 2-1. Sampling areas within the three USEPA Nutrient Ecoregions. Identifying Wetland Community Types
Once an area was selected, it was necessary to identify the wetlands present,
categorize them into the four target community types, and randomly select sampling
0 240 480120 Kilometers
LegendApalachicola NF
Banks Lake NWF
Conecuh NF
Fort Benning Military
Francis Marion NF
Moody Air Force Base
Ocala NF
Oconee NF
Osceola NF
Sumter NF
Talladega NFl
Southeastern Forested Plain
Southern Coastal Plain
Eastern Coastal Plain
21
locations. To complete this task, the United States Fish and Wildlife Service (USFWS)
National Wetlands Inventory (NWI) was utilized. NWI maps were created through
photo-interpretation of aerial photography supplemented by soil surveys and field
verification. (fttp://www.nwi.fws.gov/arcdata/readme.txt, 2002). NWI data were
downloaded in 7.5 minute quadrangles from the USFWS website.
Classification of wetlands on NWI maps was based on the USFWS Wetland and
Deepwater Habitat Classification System (Cowardin et al. 1979), which groups
ecologically similar habitats together (Tiner 1999). For this study, swamps are analogous
to Cowardin’s forested wetlands, which include wetlands characterized by woody
vegetation at least six meters tall. Marsh sites correspond with Cowardin’s emergent
wetland class characterized by erect, rooted, herbaceous hydrophytes that are present for
most of the growing season. For this study, eleven NWI sub-class level communities
were aggregated into two community types (Table 2-1).
NWI data were not available for Talladega and Conecuh National Forests in
Alabama. A hydric soils shapefile was obtained from United States Forest Service
(USFS) personnel and used to identify wetlands at these sites. Community types were
determined during the Alabama site visits, since this distinction could not be made with
available GIS data.
Hydrologic Classification
Mitsch and Gosselink. (2000) defined riparian wetlands as those ecosystems
located where streams or rivers at least occasionally flood beyond their confined
channels. The littoral zone of lakes is often lumped into the riparian wetland
classification. To decrease variability among sampled wetlands, those adjacent to rivers
and streams were included in this study, while littoral wetlands of lakes were excluded.
22
Table 2-1.The NWI classification scheme aggregated into swamp and marsh wetland types
NWI Classification System Subsystem Class Sub-class
Current Study Classification
Palustrine Forested Broad-leaved Deciduous
Palustrine Forested Needle-leaved Deciduous
Palustrine Forested Broad-leaved Evergreen
Palustrine Forested Needle-leaved Evergreen
Palustrine Forested Dead
Palustrine Forested Indeterminate Deciduous
Palustrine Forested Indeterminate Evergreen
Swamp
Palustrine Emergent Persistent
Palustrine Emergent Non-persistent
Riverine Tidal Emergent Non-persistent
Riverine Lower Perennial
Emergent Non-persistent
Marsh
To identify riverine wetlands, proximity of wetlands to streams and rivers was
determined using stream data from various sources. The National Hydrography Dataset
(NHD), compiled by USGS at a scale of 1:100,000, was utilized for the three Florida
National Forests. Stream data for the remaining locations were obtained from USFS
staff. The majority of the stream data provided was also compiled by USGS. Wetlands
located at least partially within 40 meters of a river or stream were classified as riverine.
Upstream activities must be considered when classifying these wetlands as
minimally impaired. To avoid this complication, wetlands along small streams (first and
second order) were targeted because their headwaters were often within the forest
boundaries. Larger rivers not originating within the boundaries of National Forests were
23
not included in the survey due to concerns that agricultural and urban activities outside
the forest, but within the watershed, may change the desired least impaired status.
There are several definitions of isolated wetlands in use. Tiner et al. (2002) defined
a wetland as isolated if it is geographically isolated from other wetlands by uplands.
Winter and LaBaugh (2003) suggested that isolated wetlands are those not connected by
streams to other surface-water bodies. Common to both definitions is the absence of
hydrologic connectivity between the wetland in question and surrounding water bodies.
Regardless of definition, classifying isolated systems can be difficult, especially during
extremely wet years when surface water overflows connect “isolated” systems to other
aquatic ecosystems. To eliminate confusion surrounding classification of isolated
wetlands, sites were divided into riverine (as defined above) and non-riverine, as defined
by those wetlands that are at least 40 meters from rivers and streams.
Site Selection Criteria
After wetlands were categorized by vegetation type and hydrologic connectivity,
proximity to potential nutrient sources and accessibility was determined. A property
ownership shapefile was obtained from USFS personnel to identify tracts of land under
private ownership within the forest boundaries. Wetlands located on private property
were omitted from the survey. Forest Service road coverages were added to the map
projects to ensure that the wetlands were accessible. All of the forests had extensive road
systems; therefore, it was not necessary to omit sites due to accessibility concerns.
Wetland sampling sites were determined by assigning a number to each of the
individual wetland polygons that met community type and hydrologic connectivity
criteria and that were not omitted due to private ownership. A random number generator
24
was used to select those non-riverine swamps, riverine swamps, non-riverine marshes,
and riverine marshes to be sampled.
For each public land tract, approximately 30 wetlands meeting the selection criteria
were identified; although only 12 (three from each class) were sampled. The additional
sites were necessary to compensate for any sites that could not be sampled due to GIS
coverage error, misclassification, inaccessibility, or other unexpected issues.
The goal was to sample three wetlands of each community type (riverine marsh,
non-riverine marsh, riverine swamp, and non-riverine swamp) within each public land
tract. However, with the exception of the Ocala and Oconee National Forests, marsh
communities were scarce. Furthermore, as topographic relief increased in the northern
and western extents of the study area, non-riverine systems became less prevalent.
Therefore, wetland community types were sampled in proportion to their relative
abundance (Figure 2-2). More swamps were sampled than marshes, and the majority of
surveyed wetlands were riverine systems. A total of 103 minimally impaired wetlands
were surveyed.
Sampling and Analytical Protocols
Sample Locations
Selected wetlands were physically located using a GPS unit, topographic maps, and
the coordinates of the selected sites. Ground truthing least impaired status, vegetative
community type, and hydrologic connectivity was always a first step when visiting
wetlands. If GIS classification was not verified on the ground, the site was reclassified or
not sampled.
25
58
8
27
10
0
10
20
30
40
50
60
70
RiverineSwamp
RiverineMarsh
Non-riverineSwamp
Non-riverineMarsh
Num
ber o
f Sur
veye
d W
etla
nds
Figure 2-2. Number of wetlands surveyed aggregated by community type
A visual survey was conducted upon arrival, and the wetland was divided into two
general zones, referred to as the core wetland and the edge wetland. (Figure 2-3). In
riverine systems, the core (C) was adjacent to the stream, but landward of any natural
levees that have formed. The edge (E) of riverine wetlands was located parallel to the
adjacent upland, approximately 25 % of the distance between the upland and the stream.
With small non-riverine wetlands, it was possible to walk the entire edge (E) of the
wetland and sample the four cardinal points at approximately 25 % of the distance
between the upland and the center of the wetland. The center was sampled as the core
(C).
In large non-riverine systems, only one side of the wetland was sampled, as if it
was a section of a riverine wetland. The core (C) was located in the deep center of the
wetland, and the edge (E) was located parallel to the upland side of the wetland
approximately 25 % of the distance between the upland and the center of the wetland.
26
Within the edge (E) and the core (C), three sub-sample sites were located
approximately 30 paces from each other. Transects were typically orientated parallel to
the upland boundary. To prevent bias, a PVC ring was tossed into the air after 30 paces
had been traversed, and where it landed marked the sampling location. At each sub-
sample location, water (if present), soil, and leaf litter were collected. A characterization
form (Appendix A) that included a visual vegetation survey, hydrologic characteristics,
and other descriptive information was completed at each sub-sample location.
Figure2-3. Sub-sample locations. A) Within the core and edge zones of riverine wetlands. B) Small non-riverine wetlands. C) Large non-riverine wetlands.
C
C
C
E
E
E
Upland
EdgeCore
(
AE
Upland
River Core Edge
Ecotone (not sampled) Upland
Core
B
C
A
E
E
C
C
C
C C C
C
E
E
E
E
Edge
27
A handheld YSI-556 meter (Yellow Springs, CO) was used to record water column
pH, dissolved oxygen saturation, temperature, redox potential (Eh), and conductivity at
each sub-sample location with water present. Redox potential was measured as ORP
with an Ag/Ag-cl electrode. Values were converted to Eh by adding 234 mV to each
reading. Measurements were made with the probe suspended at mid-depth of the water
column, but in shallow wetlands (less than 15 cm), the probe was often placed at the
sediment-water interface.
Sample Collection and Processing
Sampling began in April 2003. The survey began with the most southern sites
(Ocala National Forest) and then proceeded to the north. Most of the sampling was
completed by August 2003. Moody Airforce Base, Fort Benning, and Banks Lake
National wildlife Refuge were sampled in September 2003.
Water
Water was collected, when present, using acid-washed 125-mL HDPE bottles. The
bottles were rinsed three times with site water prior to collecting the sample. Care was
taken to minimize non-representative particulates in the water column; however, the
water column often contained particulate matter that was included with the sample.
Samples collected at the three sub-sample locations along transect C or E were poured
into a pre-acidified (concentrated sulfuric acid) 500-mL HDPE bottle to create the zone
composite. Water samples were stored on ice for transport to the Wetland
Biogeochemistry Laboratory at the University of Florida (Gainesville, FL).
In the laboratory, a sub-sample of the water composite was filtered through 0.45
µM filter paper and analyzed for nitrate and nitrite on a rapid-flow analyzer (Table 2-2).
An additional (non-filtered) 10 ml sub-sample was digested for Total Kjendal Nitrogen
28
(TKN) analysis. Results from nitrate/nitrite and TKN analyses were added together to
determine total nitrogen concentrations. Total phosphorus (TP) was determined on a
third sub-sample (10 ml) by sulfuric acid and potassium persulfate digestion (EPA
method 365.1 1993), followed by colorimetric analysis (Technicon AA II).
Soil
Three soil samples were collected along each of the wetland transects. Prior to
sampling, litter and live vegetation were removed from the sampling area by lightly
raking the area by hand. A pre-cleaned tenite butyrate tube (7.3 cm. diameter) was driven
into the soil at least 10 cm deep. The core tube was then placed on an extruder piston,
which was used to push the top of the soil out of the core and into a 10 cm tenite butyrate
collar. Any litter remaining on the top of the core was removed and discarded. The 10 cm
core was sliced from the remainder of the core using a stainless steel bread knife and
placed in a re-sealable bag. Soils from the three sub-sample sites along transect C or E
were combined to create a composite sample. Samples were stored on ice for transport to
the laboratory.
Coring of soils in densely rooted environments was facilitated by using a coring
devise with a sharp coring head attached to make cutting through roots possible and to
avoid compacting the sample. An effort was made to avoid large roots, which complicate
bulk density calculations. Several swamps, however, contained large root mats, making
it impossible to avoid coring through large amounts of root material.
In the laboratory, wet weight of the composite sample was recorded for bulk
density calculations. Roots larger than 2 mm in diameter were removed from the sample
and discarded. The composite sample was homogenized. A sub-sample was placed in a
shallow 250 mL container, weighed, then dried at 21oC for at least 48 hours.
29
Table 2-2.Summary of chemical analyses and methods for each stratum sampled
Medium Analysis Method
TP Sulfuric acid and potassium persulfate digestion followed by colorimetric analysis
TKN Sulfuric acid digestion Water
NO2-No3 Rapid Flow Analyzer (RFA)
Organic matter content Lost on Ignition (LOI)
TN Carlos Erba NA 1500 CNS Analyzer (Haak Buchler instruments Saddlebrook, NJ)
TC Carlos Erba NA 1500 CNS Analyzer (Haak Buchler instruments Saddlebrook, NJ)
Soil
TP Ignition Method (Anderson 1976)
TP Ignition Method (Anderson 1976)
TN Carlos Erba NA 1500 CNS Analyzer (Haak Buchler instruments Saddlebrook, NJ) Litter
TC Carlos Erba NA 1500 CNS Analyzer (Haak Buchler instruments Saddlebrook, NJ)
The dry sample was re-weighed for percent moisture calculations. Dry samples were
hand-ground using a mortar and pestle, then further ground mechanically using a ball mill
grinder for at least eight minutes. The ground samples were passed through a 1 mm sieve
for quality control purposes and placed in scintillation bottles for analyses. Soil samples
were analyzed for organic matter content by loss on ignition (LOI), total nitrogen (TN),
total carbon (TC), and TP, as summarized in Table 2-2.
Leaf litter
Leaf litter samples were collected by placing a 40 cm diameter PVC ring on the soil
surface and hand-collecting all loose material within the ring. Collection was
discontinued when the soil surface was reached, as indicated by the presence of fine,
well-decomposed materials. Litter sampling was qualitative, not quantitative, since at
30
times it was necessary to collect multiple samples at a sub-sample location to ensure
adequate material for analysis. Litter samples from the three sub-sample locations were
combined to form a composite sample along the core or edge transect. All samples were
stored on ice for transport to the laboratory.
In the laboratory, litter samples were placed in a paper bag and dried at 21o C for at
least 72 hours. The dry samples were coarsely ground in a Willey mill to pass through a
1 mm screen. The samples were then further ground to pass through a 40-micron
followed by an 80-micron screen. To reduce cross-contamination, the mills were
vacuumed between each sample. The litter was analyzed for TP, TN, and TC (Table 2-2).
Data Analysis
All data were analyzed using JMP 4 (1989) software. Shapiro-Wilks normality test
was used to describe the distribution of data. When appropriate, data were log
transformed for further analysis. Mahalanobis distance was used to identify and remove
extreme outliers. Matched pairs t-tests were used to determine differences between the
core and edge sampling locations within wetlands. O’Brien’s test was used to determine
if there was equal variance between the populations. Populations with equal variance
were compared using a standard t-test. Populations with unequal variance, or non-normal
distributions, were compared using a Welch ANOVA test for unequal variance. An alpha
level of 0.05 was used as a threshold for determining when differences were significant.
When a significant difference did not exist between treatments, a power test was
applied. Power addresses Type II errors, in which there is a failure to reject a false null
hypothesis (Rotenberry and Wiens 1985). When a significant difference is not found, as
indicated by a high p value, it is often assumed that there is no difference between the
populations compared. However, there may be differences that were not expressed due
31
to the limited number of samples compared. Power can be used to determine the
probability of finding a significant difference. As the probability of significant
differences increases, so does the power. Included in the power test is the Least
Significant Number (LSN). The LSN is defined as the number of observations needed to
decrease the variance enough to achieve a significant result with the given values of
significance level, standard deviation of the error, and effect size (JMP 4 1989 Help
Files).
32
CHAPTER 3 RESULTS AND DISCUSSION
Water column, litter, and soil data from 103 minimally impaired wetlands in the
southeastern United States were analyzed. The goals were to characterize nutrient
conditions within these wetlands and determine whether differences within wetlands,
among wetland types, and between USEPA Nutrient Ecoregions were present. Results
and discussion of findings will be presented in three separate sections: within wetland
variability, variability among wetland types, and spatial variability.
There are several ways to aggregate the surveyed wetland data based on the
question of interest (Table 3-1). Aggregating by hydrologic connectivity allows for a
comparison of riverine and non-riverine systems, whereas aggregating by vegetative type
allows for a comparison between marshes and swamps. The most specific aggregation
integrates both hydrologic connectivity and vegetative type resulting in four separate
wetland community types; riverine swamps, non-riverine swamps, riverine marshes, and
non-riverine marshes.
Aerial coverage of the four wetland community types was not evenly distributed
throughout the study area (Figure 3-1). Swamps were more prevalent than marshes, and
riverine marshes were practically non-existent in the northern and western extents of the
study area. Wetland community types were sampled in proportion to their relative
abundance; therefore, there are unequal sample sizes for each wetland community type.
It is important to keep the unequal distribution in mind when comparing the various
aggregations of wetlands. For example, 83% of the surveyed wetlands are swamps.
33
Comparisons based on hydrologic connectivity are biased towards riverine and non-
riverine swamps, since only 17% of the systems compared were marshes. Similarly, 64%
of the surveyed wetlands are riverine systems, which may influence distinctions between
marshes and swamps.
Table 3-1. Various aggregations of the wetlands surveyed in this study
Grouping Criteria Aggregation Number Surveyed
None All Wetlands Combined 103
Riverine 66 Hydrologic Connectivity Non-riverine 37
Marsh 18
Vegetative Type Swamp 85
Non-riverine Swamp 27
Riverine Swamp 58 Non-riverine Marsh 10
Wetland Community Type
Riverine Marsh 8
The surveyed wetlands are not only unequal in abundance, but also in regional
distribution (Figure 3-2). The surveyed wetlands are distributed throughout four
southeastern states, which include three USEPA Nutrient Ecoregions (Figure 2-1). The
Southeastern Forested Plain contained 62% of the surveyed swamps, 50% of the marshes,
67% of the riverine, and 47% of the non-riverine wetlands. The Southern Coastal Plain
had 25% of the swamps, 44% of surveyed marshes, 21% of the riverine, and 42% of the
non-riverine systems. The least represented ecoregion was the Eastern Coastal Plain with
only 13% of the swamps, 5% of the marshes, 12% of the riverine, and 11% of the non-
riverine wetlands. Comparisons among wetland types (aggregated by hydrologic
34
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
Non-riverineSwamp
RiverineSwamp
Non-riverineMarsh
RiverineMarsh
Area
(hec
tare
s)
Oconee
Apalachicola
Ocala
Osceola
Oconee
Sumter
Francis Marion
Figure 3-1.Total area of the four wetland types within seven of the surveyed national
forests
Figure 3-2.Percentage distribution of surveyed wetlands within ecoregions, aggregated by
vegetation type (swamps and marshes) and by hydrologic connectivity (non-riverine and riverine).
Marsh RiverineNon-riverineSwamp
Southeastern Forested PlainSouthern Coastal PlainEastern Coastal Plain
Marsh RiverineNon-riverineSwampSwamp
Southeastern Forested PlainSouthern Coastal PlainEastern Coastal Plain
Southeastern Forested PlainSouthern Coastal PlainEastern Coastal Plain
35
connectivity or vegetative type) combined sites in all three ecoregions. Results may be
biased by characteristics of the Southeastern Forested Plain since the wetlands are not
equally distributed among the three ecoregions.
Within Wetland Variability
Surveyed wetlands were sampled along two transects. One transect was located in
the core area of the wetland while the other was parallel to the upland edge of the
wetland. Specific locations of sampling transects within each wetland are detailed in
Chapter 2 (Figure 2-3). Before comparisons were made among wetland types, possible
within wetland variability was investigated. Samples collected within the core area and
from the edge area within each wetland were compared using pair-wise analysis. A
comparison of physical and chemical attributes between the core and edge transects was
evaluated for water column, litter, and soil strata.
Water column
Core areas were significantly deeper (p<0.05) than edge areas for all aggregations
of wetlands compared (all wetlands combined, swamps, marshes, riverine, and non-
riverine). There were no significant differences in water column temperature, dissolved
oxygen saturation, pH, or conductivity between core and edge sites (p>0.05). Many of
the surveyed wetlands were narrow linear systems with short distances between the core
and edge areas. Therefore, similar water chemistry and physical characteristics within
the core and edge areas are not surprising since the water is probably well mixed.
Water was not always present within each zone of the wetland (core and edge), and
some wetlands had no standing water at the time of sampling. Only 52 of 103
sampled wetlands had water within both zones. Of these 52 wetlands, 34 were swamps
36
and 18 were marshes. There were 26 riverine and 26 non-riverine systems with water
present in each zone.
Table 3-2. Results of pair-wise comparison of core and edge areas for various aggregations of surveyed wetlands. “n” represents the number of wetlands compared, and “p” is the probability value from the pair-wise comparison. Significant differences (p<0.05) are denoted by bold values.
Water column TP Water column TN Grouping Criteria Aggregation n p n P
None All Wetlands Combined 50 0.017e 97 0.109
Marsh 17 0.024e 15 0.806 Vegetative Type
Swamp 33 0.188 82 0.111
Non-riverine 25 0.104 34 0.181 Hydrologic Connectivity Riverine 25 0.09 63 0.211
Non-riverine Swamp 13 0.808 26 0.023c Non-riverine Marsh 12 0.010e 8 0.903
Riverine Swamp 20 0.079 56 0.222 Wetland
Community Type
Riverine Marsh 5 0.88 7 0.783 e significantly greater values in edge areas c significantly greater values in core areas
A nutrient comparison of core and edge samples within these wetlands (Table 3-2)
indicates that the edge sites had significantly higher water column total phosphorus (TP)
concentrations (0.132 + 0.147 mg/L) than core sites (0.098 + 0.147 mg/L). Total
Nitrogen (TN) was also greater at edge than core sites, but the difference was not
significant (p=0.109). Water column TP and TN of core and edge sites were also
compared for various aggregations. Edge locations had significantly greater water
column TP for three grouping strategies: all wetlands combined, marshes, and non-
riverine marshes. Elevated water column TP values in edge samples may indicate that
37
nutrients are being introduced to wetlands from adjacent uplands or there is increased
mineralization of nutrients at the shallower edge sites.
Litter
Litter at edge sites had significantly greater total carbon (TC) content and similar
TP and TN values compared to litter at core sites. Litter of core and edge sites was
compared for the various aggregations of wetlands (Table 3-3).
Table 3-3. Results of pair-wise comparison of core and edge areas for various aggregations of surveyed wetlands. “n” represents the number of wetlands compared, and “p” is the probability value from the pair-wise comparison. Significant differences (p<0.05) are denoted by bold values.
Litter TC Litter TN Litter TP Grouping Criteria Aggregation n p n p n p
None All Wetlands Combined 90 0.023e 97 0.109 83 0.799
Marsh 15 0.58 15 0.806 14 0.486 Vegetative
Type Swamp 82 0.012e 82 0.111 69 0.783
Non-riverine 34 0.7712 34 0.181 29 0.487 Hydrologic Connectivity Riverine 63 0.017e 63 0.211 54 0.378
Non-riverine Swamp 26 0.526 26 0.023C 20 0.821 Non-riverine Marsh 8 0.575 8 0.903 8 0.753
Riverine Swamp 56 0.014e 56 0.222 48 0.852
Wetland Community
Type Riverine Marsh 7 0.883 7 0.783 5 0.038C
e significantly greater values in edge areas c significantly greater values in core areas
Edge locations had significantly greater litter TC content for all wetlands
combined, swamp vegetative type, riverine hydrologic regime, and riverine swamp
community type. One possibility for the higher carbon content at the edge of these
wetlands is that core wetland areas (within riverine systems) are adjacent to the stream
channel. Therefore, the core is more susceptible to high velocity flow, which can transfer
38
organic matter downstream while depositing inorganic sediments. Inorganic material
deposited on leaf litter was often integrated into samples from core locations. These
deposits may reduce carbon content at core sites.
Soil
When all wetlands were combined, soil carbon and nitrogen content was
significantly greater in core than edge areas, whereas phosphorus content was similar
within both areas (Table 3-4). Core areas had greater soil TC content for all aggregations
and increased TN when comparing all wetlands combined, swamp vegetative types, non-
riverine hydrologic regimes, and riverine marsh wetland communities. Soil TP content
was similar between the core and edge areas, except within non-riverine swamp
communities where core areas had significantly greater phosphorus content than edge
areas.
The core areas are significantly deeper than edge sites, which may lead to longer
hydroperiods and anaerobic conditions. Under anaerobic conditions, decomposition rates
are decreased, and levels of N, C, and P can build up in the soil. This may explain the
higher levels of these compounds in core versus edge sampling areas in some of the
aggregations of surveyed wetlands.
Discussion
The overall differences within wetlands indicate that samples collected at the edge
of a wetland will likely have greater water column TP, increased litter TC content, and
lower soil TC and TN content than samples collected within the core area of the same
wetland. These differences within wetlands suggest potential implications of inconsistent
sampling techniques on biogeochemical characterizations of wetlands. To minimize the
39
effects of within site variability on the findings of this research, only core site values
were used in comparisons for the remainder of this study.
Table 3-4. Results of pair-wise comparison of core and edge areas for various aggregations of surveyed wetlands. “n” represents the number of wetlands compared, and “p” is the probability value from the pair-wise comparison. Significant differences (p<0.05) are denoted by bold values.
Soil TC Soil TN Soil TP Grouping Criteria Aggregation n p n p n p
None All Wetlands Combined 93 0.0001c 95 0.023c 94 0.1
Marsh 13 0.0001c 14 1 15 0.6 Vegetative
Type Swamp 78 0.0001c 79 0.043c 79 0.2
Non-riverine 32 0.0001c 32 0.035c 36 0.1 Hydrologic Connectivity Riverine 60 0.0001c 61 0 58 0.7
Non-riverine Swamp 25 0.0001c 25 0 28 0.017c
Non-riverine Marsh 6 0.0001c 7 1 8 0.8
Riverine Swamp 53 0.0001c 54 0 51 1
Wetland Community
Type Riverine Marsh 7 0.0003c 7 0.006c 7 0.1
e significantly greater values in edge areas c significantly greater values in core areas
Variability among Wetland Types
Vegetative Comparisons: Swamps and Marshes
Surveyed wetlands can be aggregated by dominant vegetation into swamps and
marshes. Swamps are dominated by woody vegetation and include riverine swamps and
non-riverine swamps. Swamps were more ubiquitous in the landscape than marshes.
Therefore, 85 out of the 103 surveyed wetlands were swamps.
Marshes are characterized by herbaceous vegetation and are an aggregate of
riverine marshes and non-riverine marshes. Marshes were less common than swamps;
therefore, only 18 marshes were surveyed for this study. Differences between swamps
40
and marshes will be addressed in the following four sections: water column, litter, soil,
and discussion.
Water column
Swamps had significantly greater (p=0.0487) water column TP concentrations
compared to marshes (Table 3-5). Swamps exhibited slightly higher (p=0.5401) TN
values, but the trend was not significant (Figure 3-3). Water TP data partially support the
stated hypothesis that marshes would have lower water column nutrients compared to
swamps. This difference may be correlated with increased presence of algae in marshes
compared to swamps. Algae were present in 47% of surveyed marshes and only 10% of
surveyed swamps. Algae can quickly sequester water column P, hence lowering water
column TP in marshes (Kadlec and Knight 1996).
Table 3-5. Water column properties observed in minimally impaired wetlands aggregated by vegetative type
Swamp Marsh Parameters mean + SD median 75th n significance mean + SD median 75th n
TP (mg/L) 0.108 + 0.12 0.06 0.177 47 * 0.049 + 0.053 0.03 0.07 17
TN (mg/L) 2.24 + 1.44 1.88 2.79 48 1.82 + 0.64 1.76 2.31 18 Temp (°C) 21.9 + 3.0 21.9 24.3 36 ** 25.9 + 4.4 25.3 29.5 14
pH 4.9 + 1.1 4.9 5.9 36 5.3 + 1.1 5.2 6.4 14 DO (%) 28.2 + 21.1 24.3 42.3 36 38.1 + 24.8 39.4 55.7 14
Cond. (uS/cm) 69 + 49 68 82 36 54 + 39 47 89 12 Eh (mv) 412 + 348 397 513 26 369 + 380 318 525 11
Depth (cm) 16.5 + 16.3 14 22.6 42 ** 41.6 + 27.7 47.5 63.5 15 * Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
Water column temperatures were significantly greater in marshes than swamps.
This is most likely due to the absence of an overstory of woody vegetation in marshes,
which may also contribute to increased marsh algal growth. The core areas of marshes
41
were significantly deeper than those of swamps. No significant differences were found
between swamps and marshes with respect to water column pH, dissolved oxygen
saturation, conductivity, or oxidation reduction potential. TP
(mg/
L)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Marsh Swamp
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
Marsh Swamp
A A B A
Figure 3-3. Water column TP and TN values by vegetative type. The dashed line is the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between marshes and swamps.
Litter
Litter of swamps and marshes had similar phosphorus, nitrogen, and carbon content
(Figure 3-4), with no significant differences between vegetative types evident. The C:P
and C:N ratios also did not differ between the two vegetative types. These findings agree
with the literature review of Bedford et al. (1999) that slightly higher litter N
concentrations are found in marshes (1.22%) than in swamps (1.04%). There was a
similar trend in this study, with average litter TN concentrations of 1.43 + 0.44 % for
marshes and 1.25 + 0.29% for swamps.
42 Figure 3-4 Litter phosphorus, nitrogen, and carbon values by community type. The dashed line is the mean of each population, and
the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between marshes and swamps.
TN (%
)
0.5
1
1.5
2
TC(%
)
20
25
30
35
40
45
50
55
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
Marsh Swamp Marsh Swamp Marsh SwampAA AA AA
TN (%
)
0.5
1
1.5
2
TN (%
)
0.5
1
1.5
2
TC(%
)
20
25
30
35
40
45
50
55
TC(%
)
20
25
30
35
40
45
50
55
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
Marsh Swamp Marsh Swamp Marsh SwampAA AA AA
43
Table 3-6. Litter phosphorus, nitrogen, and carbon content observed in minimally impaired wetlands aggregated by vegetative type Swamp Marsh
Parameters mean + SD median 75th n significance mean + SD median 75th n P (%) 0.015 + 0.01 0.011 0.021 69 0.019 + 0.018 0.012 0.033 15N (%) 1.25 + 0.29 1.22 1.44 82 1.43 + 0.44 1.33 1.8 15C (%) 41.0 + 8.5 43.2 48.8 84 41.5 + 4.8 43.1 45.3 15
C/P ratio 4132 + 2958 3258 6838 67 5193 + 5021 3393 8316 14C/N ratio 32.71 + 9.87 30.78 39.06 82 28.90 + 8.50 26.09 35.59 13
* Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
44 Figure 3-5 Soil %P, %N, and %C values by community type. The dashed line is the mean of each population, and the solid line is the
overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments
TP (m
g/kg
)
0
200
400
600
800
1000
1200
Marsh SwampTN
(g/k
g)
0
5
10
15
20
25
30
35
Marsh Swamp
TC (g
/kg)
0
100
200
300
400
500
Marsh Swamp
A A A AAA
TP (m
g/kg
)
0
200
400
600
800
1000
1200
Marsh SwampTN
(g/k
g)
0
5
10
15
20
25
30
35
TN (g
/kg)
0
5
10
15
20
25
30
35
Marsh Swamp
TC (g
/kg)
0
100
200
300
400
500
Marsh Swamp
A A A AAA
45
Table 3-7.Soil P, N, and C content observed in minimally impaired wetlands aggregated by vegetative type Swamp Marsh
Parameters mean + SD median 75th n significance mean + SD median 75th n TP (mg/kg) 410 + 260 350 550 82 370 + 270 340 420 15
TP (mg/cm3) 0.19 + 0.15 0.14 0.25 82 * 0.13 + 0.12 0.093 0.17 15TN (g/kg) 5.9 + 5.5 3.7 7.4 80 8.5 + 10.4 4.6 10.5 14
TN (mg/cm3) 2.01 + 0.77 1.87 2.39 80 2.04 + 1.16 1.77 3.1 14TC (g/kg) 123 + 140 64.3 129 78 136 + 156 61.7 194 14N/P ratio 17.6 + 17.7 14.4 22.8 79 28.2 + 27.9 17.6 42.2 14C/P ratio 361 + 329 277 514 77 489 + 456 304 970 14C/N ratio 19.4 + 5.5 18.6 22 78 17.7 + 4.7 17.7 18.7 14LOI (%) 28.5 + 26.9 16.8 32.9 82 31.7 + 31.3 15.8 69.2 15
Bulk Density (g/cm3) 0.57 + 0.35 0.57 0.83 85 0.45 + 0.32 0.43 0.67 16Moisture Content (%) 53 + 21 50 73 85 63 + 23 65 86 16
* Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
46
Soil
Comparisons of soil nutrient content between vegetative community types were
conducted on a mass per unit mass basis and a mass per unit volume basis. Typically it
would not be necessary to express nutrient content using these two methods. However,
due to the wide range in bulk density found among wetland sites, normalizing for bulk
density was desirable. Results of findings using both mass and volumetric measures of P
and N are presented and distinguished based on units.
Soils of swamps and marshes were similar with respect to TP(mg/kg), TN (g/kg),
TN (mg/cm3), TC(mg/kg), N:P ratio, C:P ratio, C:N ratio(on a mass basis), loss on
ignition (LOI), bulk density, and moisture content, with no significant differences
between vegetative types evident (Figure 3-5). However, when soil total phosphorus was
normalized by bulk density, swamps had significantly higher total phosphorus (mg/cm3)
then marshes.
Craft and Casey (2000) found that forested depressions in southwestern Georgia
had higher soil nitrogen and phosphorus concentrations, as well as lower N:P ratios,
compared to depressional marshes. In the current survey, soil TP (mg/cm3) was
significantly greater in swamps than marshes, but soil TP (mg/kg), TN (g/kg), TN
(mg/cm3), or N:P ratio (Table 3-7) differences were not significant between marshes and
swamps. The mean N:P ratio was 18 for swamps and 28 for marshes. These values are
similar to the N:P ratios Craft and Casey (2000) reported for swamps that were thought to
be p-limited or co-limited by P and N. These results partially support the observations of
Hopkinson (1992) that growth form of dominant vegetation does not seem very important
in controlling nutrient regimes.
47
Discussion
Phosphorus and nitrogen data for surveyed swamps and marshes were compared to
values in the literature (Table 3-8). Phosphorus values were consistently greater in the
literature. This could be due to the least impaired status of wetlands included in this
survey. Nitrogen values were fairly consistent between the current study and the
literature.
Table 3-8. Values from the current study compared to those in the literature
Current Studya
Bedford et al. 1999b
Nicholson 1995c
Whigham and Richardson
1988d Water TP (mg/L) 0.049 + 0.053 0.248 - 0.520
Litter %P 0.019 + 0.018 0.16 Marsh Soil %P 0.037 + 0.027 0.25
Water TP (mg/L) 0.108 + 0.12 0.221 - 0.650
Litter %P 0.015 + 0.01 0.16 Swamp Soil %P 0.041 + 0.026 0.09 0.24
Water TN (mg/L) 1.819 + 0.636 2.09 - 2.67
Litter %N 1.43 + 0.44 1.22 Marsh
Soil %N 0.85 + 1.04 1.41
Water TN (mg/L) 2.243 + 1.444 2.17 - 3.01
Litter %N 1.25 + 0.29 1.04 Swamp
Soil %N 0.59 + 0.56 1.28 1.5 a = Mean values with standard deviations for 103 minimally impaired wetlands within the southeastern US b = Mean values from a literature search of North American freshwater temperate wetlands c = Range of values for wetlands within Elk Island National Park, Alberta d = Mean values from Acer rubrum swamps in Maryland, USA
The vegetative structure of marshes and swamps is quite different, with the former
characterized by herbaceous vegetation and the latter by woody growth forms. It seems
logical that wetlands with different vegetation types would have soils with varying
nutrient contents. According to Hopkinson (1992), swamps store a great deal of C, N,
48
and P in plant biomass, while marshes store the majority of C, N, and P in the soil.
Therefore, it was a hypothesis of this thesis that marshes would have greater soil nitrogen
and phosphorus content than swamps. The results of this survey do not support this
hypothesis. It was found that swamps had significantly greater TP (mg/cm3) than
marshes. These results could be influenced by the fact that 64% of the surveyed wetlands
are riverine systems which are often associated with higher nutrient concentrations.
There is a lot of variability within the data for swamps and marshes. The standard
deviation values are often as great as the mean values. This trend exists not only when
wetlands are aggregated by vegetation type, but also when all 103 wetlands are
combined. This large variability in nutrient content among wetlands likely explains why
there were minimal significant differences between swamps and marshes for the limited
number of sites surveyed.
An analysis of statistical power was used to understand the limited statistical
differences detected between vegetative community types. Power analysis (JMP 1989)
was applied to comparisons between swamps and marshes to determine if the lack of
significant differences was due to an insufficient number of wetlands compared (Table 3-
9). Water column TP differences were identified, therefore Least Significant Number
(LSN) values were fairly low for water column comparisons. This means that if
additional sample sites were included in the survey, and the data retained their current
structure, then additional significant differences between the water columns of marshes
and swamps would likely have been detected. In the case of litter and soil parameters (on
a mass per unit mass basis), LSN values were very large. Therefore, true differences in
soil (on a mass per unit mass basis) and litter parameters between swamp and marsh sites
49
are very small and unrealistic to quantify. Litter and soils from marshes and swamps in
this survey of minimally impaired wetlands of the southeastern US were similar with
respect to P (mg/kg), N (g/kg), and C (g/kg) content.
Regional differences may also be affecting marsh and soil results since wetlands
from all three USEPA Nutrient Ecoregions were combined for comparisons. To explore
this possibility, the 52 swamps and 9 marshes in the Southeastern Forested Plain were
compared. The results were fairly consistent with those including all three ecoregions.
There were no significant TC, TP, or TN differences between the vegetative communities
within litter, soil (on a mass per unit mass basis), and water column strata. The only
discrepancy was that marshes and swamps had significantly different water column TP
content when wetlands from all three ecoregions were compared. There may not be
enough samples to detect differences at the community type and ecoregion level.
However, fairly consistent results indicate that regional differences are not skewing
results.
Table 3-9.Power analysis for non-significant parameters within community comparisons
Measured Parameters Number compared in current study (n)
Least Significant Number (LSN)
Water TP 66 123 Water TN 67 143 Litter % P 82 5,439 Litter % N 97 21,645 Litter % C 97 1,936 Litter C/N 96 6,302 Litter C/P 81 22,449 Soil %P 97 409 Soil %N 94 5,276 Soil %C 92 480,226
A major difference between swamps and marshes is the presence of a canopy in
swamps. Canopy cover can limit light penetration and reduce algal populations. Battle
50
and Golladay (2001) found that sedge marshes had higher algal growth than cypress
swamps. Algal populations quickly sequester nutrients from the water column, hence
decreasing soluble nutrients available to other growth forms (Kadlec and Knight 1996).
Therefore, it was hypothesized that the water column of marshes would contain less
nitrogen and phosphorus than that of swamps. The results of this study partially support
this hypothesis. Water column TP was significantly greater in swamps, but TN was
similar regardless of dominant vegetation type. Large variation between wetlands and
the limited number of samples are likely responsible for the lack of statistical differences
detected between water column TN of swamps and marshes. Power analysis indicated
that fewer than one hundred additional samples may be sufficient to detect significantly
greater water column TN content in swamps compared to marshes.
If swamps have greater water column nutrient content, they may not be removing
nutrients from the water column as effectively as marshes. Wetlands are commonly
valued as nutrient sinks; however, it may be necessary to distinguish by vegetative type
when assigning this value to wetlands. Differences in nutrient cycling may have
implications for aquatic ecosystems downstream, in that marshes may retain more
phosphorus and nitrogen than swamps. Distinctions between swamps and marshes may
be necessary for determining water column based numeric nutrient criteria for wetlands.
Water column total nitrogen and total phosphorus and soil total phosphorus
(mg/cm3) concentrations appear to be the most sensitive parameters to differences
between marshes and swamps. Water column nutrients can be overly sensitive
indicators. For example, if water is sampled following a rain event, nutrients may be
diluted. When wetlands are sampled on different days (or even different seasons),
51
comparisons between them may be confused by parameters (such as rain events) that are
not factored into comparisons. Soil based indicators integrate conditions over a longer
period of time and are not easily influenced by sampling conditions. Soil and/or water-
based numeric nutrient criteria in the southeastern United States may necessitate a
distinction between vegetative wetland types.
Hydrologic Comparisons: Riverine and Non-riverine
The surveyed wetlands can be aggregated by hydrologic connectivity into riverine
and non-riverine systems. Riverine wetlands are adjacent to streams, and non-riverine
systems are located at least 40-meters from adjacent water bodies. Riverine wetlands
were more common; therefore, 64% of the surveyed wetlands were riverine and 36%
were non-riverine. Riverine systems include riverine marshes and riverine swamps,
while non-riverine systems include non-riverine swamps and non-riverine marshes.
Differences between riverine and non-riverine wetlands will be addressed in the
following four sections: water column, litter, soil, and discussion.
Water Column
Comparisons based on hydrologic connectivity showed that riverine systems (Table
3-10) had significantly greater water column pH and lower oxidation reduction potentials
(Eh) than non-riverine systems. Since Eh and pH are the dominant chemical factors
influencing nutrient transformations within wetlands (Reddy and D’Angelo 1994), one
would expect to see different nutrient signatures dependent on hydrologic connectivity.
However, these differences may be minimal since surveyed riverine and non-riverine
wetlands were still considered acidic (average pH<7.0) and had mean Eh values (>
300mV) indicating aerobic conditions (Reddy and D’Angelo 1994) in the water column.
52
No significant differences were found between riverine and non-riverine water
column temperature, dissolved oxygen percent saturation, conductivity, or water depth.
The presence of algae was significantly greater in non-riverine systems than riverine
systems. Algae were noted in 13% of surveyed non-riverine wetlands and only 5% of
riverine systems. It is likely that this difference is the result of the higher occurrence of
non-riverine marsh communities than riverine marsh communities and the higher
frequency of algae in marshes (47%) than that of swamps (10%).
Table 3-10.Water column properties observed in minimally impaired wetlands aggregated by hydrologic connectivity
Riverine Non-riverine Parameters mean + SD median 75th n sig mean + SD median 75th n TP (mg/L) 0.119 + 0.13 0.069 0.193 35 * 0.075 + 0.087 0.039 0.086 31 TN (mg/L) 2.20 + 1.60 1.81 2.79 36 2.18 + 1.11 1.88 2.51 31 Temp (°C) 22.6 + 3.2 23.1 25 24 23.4 + 4.39 22.2 27 26
pH 5.5 + 1.0 5.8 6.3 24 ** 4.6 + 1.1 4.3 5.4 26 DO (%) 31.7 + 18.8 32.1 44.9 24 30.3 + 25.6 18.6 52.3 26
Cond. (uS/cm) 64 + 49 59 82 23 67 + 45 58 83.2 25 Eh (mV) 349 + 356 338 430 18 * 447 + 342 247 534 19
Depth (cm) 21.1 + 19.8 14.5 38.1 27 24.9 + 24.6 15.2 42.4 30 * Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
Further comparisons between hydrologic classes (Figure 3-6) indicate that riverine
systems had significantly greater water column TP but lacked significantly different TN
values when compared to non-riverine systems. Water column TP data support the
hypothesis that riverine systems have at least some higher water column nutrient
conditions. Riverine wetlands are hydrologically connected to adjacent aquatic
ecosystems and often integrate a more extensive upstream watershed, which may be a
source of nutrients. In contrast, non-riverine wetlands often have a smaller and more
localized watershed resulting in lower nutrient loading (Craft and Casey 2000).
53
Figure 3-6.Water column TP and TN values by hydrologic connectivity. The dashed line
is the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
Litter
Litter of riverine wetlands had significantly higher phosphorus (p<0.0001) and
lower carbon content (p<0.0001) than non-riverine wetlands (Figure 3-7). Nitrogen
content of litter was similar between the two systems. Ratios of carbon to nitrogen and
carbon to phosphorus followed the carbon content trends, with non-riverine systems
having significantly higher ratios than riverine systems.
It is probable that lower litter carbon content within riverine systems is due to
hydrologic fluxes of these open systems and transport of particulate matter. Watersheds
of riverine systems often contribute inorganic materials that are deposited in wetlands
during flood events. Litter is often coated in organic and inorganic materials that were
not removed before analyses. It may be that litter in riverine and non-riverine systems
has similar organic carbon content, but that increased inorganic deposition in riverine
systems alters the percentage of total carbon relative to non-riverine systems.
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
TP (m
g/L)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
A ABA
Non-riverine RiverineNon-riverine Riverine
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
TP (m
g/L)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
A ABA
Non-riverine RiverineNon-riverine Riverine
54
Soil
Soil characteristics of riverine and non-riverine systems were also compared
(Figure 3-8). Non-riverine systems had significantly greater nitrogen (g/kg and mg/cm3),
carbon, N:P, C:N, C:P, LOI, and percent moisture content than riverine systems.
Riverine wetlands had significantly greater phosphorus (mg/cm3) and bulk density
values. Craft and Casey (2000) found that non-riverine forested wetlands had elevated
soil TP, organic C, and TN content compared to forested riverine wetlands. Soil total
phosphorus results from the current study do not coincide with Craft and Casey’s results.
Discussion
Hydrologic connectivity of riverine wetlands led to the hypothesis that the water
column of riverine wetlands would have higher nutrient concentrations compared to non-
riverine systems. Results of this survey partially support this hypothesis. Water column
phosphorus was greater in riverine wetlands, but there was no difference in nitrogen
content regardless of hydrologic connectivity. Increased phosphorus conditions in
riverine wetlands are likely due to larger contributing watersheds relative to non-riverine
systems.
Power analysis was applied to water column total nitrogen data to explore the role of
sample size in statistical conclusions. A significant difference is more likely to be
detected if approximately 800 additional wetlands were included in the survey. The large
LSN value indicates that there is not much difference between water column TN of
riverine and non-riverine wetlands. It is possible that wetlands cycle nitrogen similarly
regardless of hydrologic connectivity or that water column nitrogen is controlled by
factors not captured in this comparison.
55
Figure 3-7.Litter phosphorus, nitrogen, and carbon content comparisons between riverine and non-riverine systems. The dashed line is
the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
Table 3-11.Leaf litter properties observed in minimally impaired wetlands aggregated by hydrologic connectivity.
Riverine Non-riverine Parameters mean + SD median 75th n significance mean + SD median 75th n
%P 0.02 + 0.012 0.046 0.027 55 *** 0.008 + 0.004 0.007 0.01 29%N 1.24 + 0.28 1.22 1.45 62 1.35 + 0.38 1.31 1.68 34%C 38.21 + 7.64 39.55 44.55 64 *** 47.96 + 3.37 49.3 50.59 31
C/P ratio 2982 + 2418 1868 4450 54 *** 6984 + 3509 7134 8700 27C/N ratio 29.9 + 8.6 28.2 34.4 62 ** 36.4 + 10.37 36.9 42 34
* Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
TN (%
)
0.5
1
1.5
2
TC (%
)
20
25
30
35
40
45
50
55
A AB A A BNon-riverine Riverine Non-riverine Riverine Non-riverine Riverine
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
TN (%
)
0.5
1
1.5
2
TC (%
)
20
25
30
35
40
45
50
55
A AB A A BNon-riverine Riverine Non-riverine Riverine Non-riverine Riverine
56 Figure 3-8.Soil TP and TN values by hydrologic connectivity. The dashed line is the mean of each population, and the solid line is the
overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
TP (m
g/kg
)
0
200
400
600
800
1000
1200
Non-riverine Riverine
TN (g
/kg)
0
5
10
15
20
25
30
35
Non-riverine Riverine
TC (g
/kg)
0
100
200
300
400
500
Non-riverine Riverine
BABAAA
TP (m
g/kg
)
0
200
400
600
800
1000
1200
Non-riverine Riverine
TN (g
/kg)
0
5
10
15
20
25
30
35
Non-riverine Riverine
TC (g
/kg)
0
100
200
300
400
500
Non-riverine Riverine
TP (m
g/kg
)
0
200
400
600
800
1000
1200
TP (m
g/kg
)
0
200
400
600
800
1000
1200
Non-riverine Riverine
TN (g
/kg)
0
5
10
15
20
25
30
35
TN (g
/kg)
0
5
10
15
20
25
30
35
Non-riverine Riverine
TC (g
/kg)
0
100
200
300
400
500
Non-riverine Riverine
BABAAA
57
Table 3-12.Soil properties observed in minimally impaired wetlands aggregated by hydrologic connectivity Riverine Non-riverine
Parameters mean + SD median 75th n significance mean + SD median 75th n TP (mg/kg) 390 + 240 330 500 61 410+ 290 370 600 36
TP (mg/cm3) 0.22 + 0.16 0.16 0.29 61 *** 0.11 + 0.09 0.08 0.13 36TN (g/kg) 4.3 + 4.2 2.7 4.9 62 *** 10.0 + 8.3 5.4 16.5 32
TN (mg/cm3) 1.86 + 0.82 1.69 2.16 62 *** 2.31 + 0.78 2.35 2.83 32TC (g/kg) 75.6 + 85.2 48.7 84.4 60 *** 216.3 + 178.8 115.3 422.9 32N/P ratio 142 + 18 10.3 17.4 61 *** 28.8 + 19.3 23.2 32.7 32C/P ratio 254 + 261 174.6 325.6 59 *** 615 + 381 541 887 32C/N ratio 17.9 + 4.8 17.6 19.3 60 ** 21.3 + 5.8 21.0 24.0 32LOI (%) 19.3 + 17.0 13.4 22.5 61 *** 45.6 + 33.7 27.8 82.6 36
Bulk Density (g/cm3) 0.63 + 0.35 0.65 0.87 65 *** 0.41 + 0.30 0.39 0.63 36Moisture Content (%) 0.50 + 0.20 0.46 0.64 65 *** 0.64 + 0.22 0.61 0.86 36
* Significant difference (p<0.05) ** Significant difference (p<0.01) *** Significant difference (p<0.001)
58
Riverine systems are open to hydrologic influxes and are reported as sinks for
sediment and phosphorus from contributing watersheds (Craft and Casey 2000).
Therefore, it was hypothesized that riverine systems would have higher soil phosphorus
and nitrogen content than soils in non-riverine wetlands. This hypothesis was partially
supported by the data collected. Riverine wetlands had greater total phosphorus content
when the values were normalized by bulk density. However, non-riverine systems had
greater soil nitrogen content than riverine wetlands.
Alternating anaerobic and aerobic conditions are ideal for processing nitrogen
through wetlands, since nitrogen loss from wetland soil is limited by nitrification in
aerobic zones and ammonium diffusion from anaerobic zones to aerobic zones (Reddy
and D’Angelo 1994). It appears that surveyed riverine wetlands are storing less nitrogen
in the soil than non-riverine wetlands. Riverine wetlands are subject to pulses of flooding
when adjacent streams overflow their banks. Sudden flooding followed by recession of
floodwaters may create ideal conditions for nitrogen processing, hence lowering nitrogen
storages in riverine wetlands.
The final hypothesis based on hydrologic differences was that riverine wetlands
would have increased nutrient levels in leaf litter compared to non-riverine wetlands. It
was thought that riverine systems have high nutrient influxes that allow plants to cycle
nutrients less efficiently and reabsorb fewer nutrients from senescing leaves (Hopkinson
1992). Hence, areas with increased nutrient availability produce leaf litter with high
nutrient content (Shaver and Melillo 1984). This hypothesis was partially supported by
this study. Riverine wetlands did have higher litter TP content, but TN was similar
regardless of hydrologic connectivity.
59
Power analysis was applied to the litter TN results to determine if the lack of
significant difference was due to an insufficient number of wetlands compared. A
difference between riverine and non-riverine litter TN would likely be detected with
approximately 50 additional samples. This indicates that sample size is affecting the
results. Interestingly, additional samples would not support the hypothesis, but would
show litter in non-riverine systems to have greater nitrogen content than litter in riverine
wetlands.
Regional differences may influence hydrologic connectivity results since wetlands
from all three USEPA Nutrient Ecoregions were combined for comparisons. To explore
this possibility, the 44 riverine and 17 non-riverine wetlands in the Southeastern Forested
Plain were compared. The results were fairly consistent with those including all three
ecoregions. The only discrepancy was that riverine and non-riverine systems had
significantly different water column TP content when wetlands from all three ecoregions
were compared. This difference was not apparent within the Southeastern Forested Plain
comparisons. Additional samples may be needed to detect differences at the ecoregion
level. Comparable results indicate that regional differences are not skewing the noted
differences based on hydrologic connectivity.
As was found when the data were aggregated by vegetative type, there is a lot of
variability within the data aggregated by hydrologic connectivity. Standard deviation
values were often as great as the mean values. This variability makes it difficult to
recommend a single numeric nutrient criterion for protecting all wetlands. Aggregating
by hydrology may reduce some of the variability, since differences were identified
between riverine and non-riverine wetlands for numerous parameters (water column TP,
60
pH, Eh; litter TP, TC, C\P, C\N; soil TP TN, TC, N\P, C\P, C\N, LOI, bulk density, and
moisture content). Soil and litter strata appear to be the most sensitive to differences
between riverine and non-riverine wetlands. It may be necessary to identify wetlands as
riverine or non-riverine for recommending numeric nutrient criteria.
Spatial Variation
The surveyed wetlands were stratified within three USEPA Nutrient Ecoregions
(Figure 3-9). The vegetative type and hydrologic connectivity of wetlands surveyed were
not evenly distributed among the ecoregions (Table 3-13). The types of wetlands
sampled essentially reflected the distribution of wetland types within the National Forest
being surveyed. A greater percentage of marshes and non-riverine systems were
represented within the Southern Coastal Plain (XII). Only 12% of the surveyed wetlands
were located in the Eastern Coastal Plains (XIV).
Data were aggregated by ecoregion to explore the appropriateness of this regional
classification as an a priori grouping for establishing numeric nutrient criteria.
Comparisons among ecoregions were made for all wetlands aggregated together and
between specific vegetative and hydrologic groupings. Water column, litter, and soil
characteristics were compared. One would expect differences among ecoregions if they
represent distinct geographic regions with respect to nutrients. Differences between
ecoregions will be addressed in the following four sections: water column, litter, soil, and
discussion.
61
Figure 3-9.Distribution of wetlands within the three USEPA Nutrient Ecoregions aggregated by a) hydrologic connectivity and b)
vegetative type.
Southeastern Forested Plain (IX)
Southeastern Forested Plain (IX)
Eastern Coastal Plain (XIV
Swamp n=85
Marsh n=18
Southeastern Forested Plain (IX)
Southeastern Forested Plain (IX)
Eastern Coastal Plain (XIV)
Non-riverine n=37
Riverine n=66
62
Table 3-13. Number of surveyed wetlands within the three USEPA nutrient ecoregions
Grouping Criteria Aggregation
Southeastern Forested Plains
(IX)
Southern Coastal Plain
(XII)
Eastern Coastal Plain
(XIV) None Combined 61 29 12
Riverine 44 14 8 Hydrologic
Connectivity Non-riverine 17 15 4
Marsh 9 8 1 Vegetative Type Swamp 52 21 11
Water Column
When all wetlands were grouped together, water column total phosphorus and total
nitrogen did not differ among the three ecoregions (Table 3-14). However, when
aggregated by hydrologic connectivity (Figure 3-10), riverine wetlands in the Southern
Coastal Plain (XII) had greater water column TN than those of the Southeastern Forested
Plains (IX). There were no detectable water column TN and TP differences among non-
riverine wetlands in the three ecoregions.
When aggregated by vegetative type, swamps in the Southern Coastal Plain had
greater water column TN content than comparable sites of the Southeastern Forested
Plain (Figure 3-11). There were no detectable water column TN and TP differences
among marshes in the three ecoregions. However, fewer marshes were sampled, and
there were no water column data for the one marsh in the Eastern Coastal Plain. Water
was not present in this wetland when it was sampled.
The Southern Coastal Plain appears to have greater water column TN (statistically
significant in swamps and riverine wetlands). Water column total nitrogen differences
were not detected in vegetative type and hydrologic connectivity comparisons in the
63
Table 3-14. Water column descriptive statistics for surveyed wetlands by ecoregion. Superscript letters following standard deviations indicate significance for comparisons made across rows. Different letters indicate a significant difference (p<0.05).
Southeastern Forested Plains (IX) Southern Coastal Plain (XII) Eastern Coastal Plain (XIV) Parameters mean + SD median 75th n mean + SD median 75th n mean + SD median 75th n
TP (mg/L) 0.099 + 0.134 a 0.042 0.096 31 0.09 + 0.09a 0.045 0.140 27 0.120 + 0.087a 0.088 0.217 8 TN (mg/L) 2.02 + 1.69a 1.510 2.150 32 2.40 + 1.15a 2.330 2.860 28 2.80 + 1.82a 2.070 3.940 8
Temp (°C) 24.1 + 4.0a 13.60 26.80 21 20.8 + 3.3b 20.2 23.0 14 24.6 + 1.5ab 24.2 25.6 7 pH 5.3 + 0.9a 5.50 6.3 21 4.5 + 1.2a 4.0 5.0 14 5.5 + 1.2a 5.9 6.3 7
DO (%) 35.9 + 24.2a 33.2 53.0 21 31.1 + 21.3a 27.7 52.4 14 23.8 + 17.3a 15.7 42.3 7 Cond. (uS/cm) 61.3 + 52.0a 49.0 82.4 22 70.4 + 58.9a 70.5 126.2 13 103.3 + 59.3a 76.0 130.5 7
Eh (mV) 379 + 353a 346 489 21 481 + 347a 507 575 9 365 + 355a 327 495 6 All
Wet
land
s C
ombi
ned
Depth (cm) 11.1 + 7.6a 9.2 17.4 22 13.2 + 10.5a 9.4 21.2 14 6.2 + 8.1a 3.5 7.3 7
RiverineTP 0.118 + 0.154a 0.042 0.194 19 0.126 + 0.111a 0.074 0.197 12 0.106 + 0.060a 0.088 0.169 4
RiverineTN 1.85 + 1.74a 1.32 1.85 20 2.88 + 1.42b 2.78 3.40 12 1.93 + 0.79ab 2.07 2.61 4
Non-riverineTP 0.070 + 0.094a 0.041 0.082 12 0.063 + 0.073a 0.029 0.086 15 0.134 + 0.116a 0.132 0.239 4
Hyd
rolo
gic
(mg/
L)
Non-riverine TN 2.29 + 1.64a 1.84 2.27 12 2.05 + 0.75a 1.86 2.56 16 3.66 +2.27a 3.11 6.02 4
MarshTP 0.117 + 0.128a 0.082 0.227 7 0.033 + 0.022a 0.021 0.045 11 - - - 0
Marsh TN 2.50 + 1.99a 1.79 2.71 8 1.82 + 0.70a 1.73 2.51 11 - - - 0
Swamp TP 0.094 + 0.138a 0.040 0.093 24 0.131 + 0.106b 0.095 0.206 16 0.120 + 0.087ab 0.088 0.217 8
Veg
etat
ive
(mg/
L)
Swamp TN 1.86 + 1.59a 1.32 2.15 24 2.78 + 1.23a 2.77 3.35 17 2.80 + 1.82a 2.07 3.94 8
64
Figure 3-10.Comparison of ecoregions aggregated by hydrology. The dashed line is the
mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
sections above. Water column nitrogen concentration appears to be affected by
geographic region instead of dominant vegetation or hydrologic connectivity.
Water column TN and TP concentrations in the Eastern Coastal Plain (XIV) were
not significantly different from the other two ecoregions for any of the aggregation
schemes. This may be due to insufficient sample size, since there were only 12 surveyed
wetlands within this ecoregion.
Litter
Litter nutrient content was compared among the three ecoregions. The Southern
Coastal Plain (XII) had significantly lower total carbon and greater total phosphorus
content compared to the other ecoregions (Table 3-15). Litter total nitrogen content was
similar among the three ecoregions.
TN (m
g/L)
0
1
2
3
4
5
6
7
8
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
SE
. For
este
d
S. C
oast
al
E. C
oast
al
Non-riverine Riverine
A A A ABBA
TN (m
g/L)
0
1
2
3
4
5
6
7
8
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
SE
. For
este
d
S. C
oast
al
E. C
oast
al
Non-riverine Riverine
A A A ABBA
65
Figure 3-11.Comparison of ecoregions aggregated by vegetative type. The dashed line is the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
When aggregated by hydrologic connectivity and ecoregion, riverine wetlands in
the Southern Coastal Plain had significantly lower TC, greater TP, and similar TN
content than the other ecoregions (Figure 3-12). There were no observed TP, TN, or TC
differences among litter of non-riverine wetlands in the three ecoregions. It appears that
riverine wetlands are driving the differences found when all wetlands are combined.
Comparisons were made among the ecoregions when aggregating wetlands by
vegetative community. Marshes in the Southern Coastal Plain had significantly lower
litter TP than marshes in the Southeastern Forested Plain (Figure 3-13). Marshes within
all three ecoregions had similar litter TN and TC content. Swamps within the Southern
Coastal Plain had significantly lower litter TP (Figure 3-13) and greater TC content
TN (m
g/L)
0
1
2
3
4
5
6
7
8
SE
. For
este
d
S. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
E. C
oast
al
SE
. For
este
d
S. C
oast
al
Marsh Swamp
ABBAAA
TN (m
g/L)
0
1
2
3
4
5
6
7
8
SE
. For
este
d
S. C
oast
al
TN (m
g/L)
0
1
2
3
4
5
6
7
8
9
E. C
oast
al
SE
. For
este
d
S. C
oast
al
Marsh Swamp
ABBAAA
66
Table 3-15.Litter descriptive statistics for surveyed by Ecoregion. Superscript letters following standard deviations indicate significance for comparisons made across rows. Different letters indicate a significant difference (p<0.05).
Southeastern Forested Plains (IX) Southern Coastal Plain (XII) Eastern Coastal Plain (XIV) Parameters mean + SD median 75th n mean + SD median 75th n mean + SD median 75th n
TP (%) 0.018 + 0.012a 0.014 0.024 44 0.008 + 0.004b 0.007 0.009 25 0.024 + 0.012a 0.022 0.033 12
TN (%) 1.31 + 0.39a 1.25 1.59 56 1.24 + 0.33a 1.26 1.39 26 1.31 + 0.14a 1.33 1.40 11
TC (%) 38.70 + 8.02a 39.30 45.08 57 47.74 + 2.75b 47.97 49.86 25 39.97 + 7.92a 40.97 48.12 12
N/P ratio 136.3 + 192.2a 87.00 149.80 42 177.0 + 86.4b 159.70 202.70 26 78.5 + 53.6a 61.90 85.90 12
C/P ratio 3681 + 4000a 2225 5465 42 7056 + 3482b 6618 9374 26 2532 + 2230a 1470 2676 12
All
Wet
land
s Com
bine
d
C/N ratio 29.9 + 9.3a 27.70 34.10 56 40.0 + 10.5b 38.40 46.10 26 29.7 + 6.3a 27.40 36.80 12
RiverineTP (%) 0.021 + 0.012a 0.02 0.03 33 0.009 + 0.003b 0.008 0.010 12 0.029 + 0.009a 0.037 0.037 8
RiverineTN (%) 1.25 + 0.37a 1.19 1.45 39 1.23 + 0.29a 1.15 1.42 13 1.36 + 0.13a 1.36 1.43 7
Non-riverineTP (%) 0.008 + 0.004a 0.01 0.01 11 0.007 + 0.004a 0.006 0.008 13 0.011 + 0.005a 0.011 0.017 4
Hyd
rolo
gic
Non-riverine TN (%) 1.46 + 0.42a 1.61 1.81 17 1.25 + 0.38a 1.26 1.37 13 1.22 + 0.13a 1.22 1.34 4
MarshTP (%) 0.024 + 0.09a 0.02 0.04 8 0.005 + 0.002b 0.004 0.008 5 0.033 + NA 0.033 0.033 1
Marsh TN (%) 1.45 + 0.41a 1.46 1.81 8 1.38 + 0.55a 1.30 1.89 6 1.57 + NA 1.57 1.57 1
Swamp TP (%) 0.016 + 0.010a 0.01 0.02 36 0.008 + 0.003b 0.007 0.010 20 0.022 + 0.012a 0.021 0.033 11
Veg
etat
ive
Swamp TN (%) 1.29 + 0.39a 1.22 1.53 48 1.20 + 0.24a 1.20 1.38 20 1.28 + 0.12a 1.33 1.37 10
67
Figure 3-12.Comparison of riverine wetlands in the three ecoregions. The dashed line is
the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
compared to the other two ecoregions. Litter TN content was similar in swamps within
all three ecoregions.
The Southern Coastal Plain had lower litter phosphorus content when all wetlands
were combined and also for the following aggregations: riverine systems, swamps, and
marshes. Litter nitrogen content was also lower in the Southern Coastal Plain, but the
difference was not significant for any of the aggregations. The Eastern Coastal Plain had
the greatest litter phosphorus content (significant for all wetlands combined, riverine
systems, and swamps).
Soil
Soil characteristics were compared among the three ecoregions (Table 3-16).
When compared without sub-classification, soil TN (mg/cm3) and TP (mg/cm3) were
Riverine
TN (%
)
0.5
1
1.5
2
2.5
3
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
SE
. For
este
d
S. C
oast
al
E. C
oast
al
Riverine
A A A ABA
Riverine
TN (%
)
0.5
1
1.5
2
2.5
3
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
SE
. For
este
d
S. C
oast
al
E. C
oast
al
Riverine
A A A ABA
68
Figure 3-13.Comparison of litter total phosphorus among the three ecoregions aggregated
by vegetative type. The dashed line is the mean of each population, and the solid line is the overall mean. The bottom of the “box” is the 25th percentile, and the top is the 75th percentile. The center line within the boxplot is the median. The whiskers extend + 1.5 the interquantile range. Different letters indicate a significant difference (p<0.05) between treatments.
significantly greater in the Southern Coastal Plain than in the Southeastern Forested Plain
or Eastern Coastal Plain. However, when comparisons were conducted on a mass per
unit mass basis there were no significant differences in soil total phosphorus (mg/kg)
content among the three ecoregions.
When aggregated by hydrology there were no significant soil TP (mg/kg), TP
(mg/cm3), TN (g/kg), TN (mg/cm3) or TC (g/kg) differences among non-riverine
wetlands in the three ecoregions. However, differences among the ecoregions were
apparent with riverine wetlands. The Southern Coastal Plain had riverine wetlands with
lower TP (mg/cm3) than the other two ecoregions. The Southeastern Forested Plain had
lower TN (mg/cm3) in riverine systems than the other two ecoregions.
Marsh Swamp
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TP (%
)
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
SE
. For
este
d
S. C
oast
al
E. C
oast
al
A ABAABB
Marsh Swamp
TP (%
)
0
0.01
0.02
0.03
0.04
0.05
0.06
SE
. For
este
d
S. C
oast
al
E. C
oast
al
TP (%
)
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
SE
. For
este
d
S. C
oast
al
E. C
oast
al
A ABAABB
69
Table 3-16.Soil descriptive statistics for surveyed wetlands aggregated by Ecoregion. Superscript letters following standard deviations indicate significance for comparisons made across rows. Different letters indicate a significant difference (p<0.05).
Southeastern Forested Plains (IX) Southern Coastal Plain (XII) Eastern Coastal Plain (XIV) Parameters mean + SD median 75th n mean + SD median 75th n mean + SD median 75th n
TP (mg/cm3) 0.22 ± 0.17a 0.14 0.27 57 0.096 ± 0.067b 0.075 0.13 27 0.24 ± 0.12a 0.2 0.301 11
TN (mg/cm3) 1.75 ± 0.64a 1.62 2.11 55 2.43 ± 1.05b 2.56 3.34 26 2.43 ± 0.67a 2.34 2.44 11
TC (g/kg) 90.4 + 113.1a 48.5 93.9 54 201.2 + 172.7b 104.7 370 26 125.0 + 141.7ab 53.6 165.9 10
N/P ratio 13.3 + 11.4a 9.9 17.9 55 35.0 + 28.8b 25.3 34.6 25 12.3 + 5.4a 12.7 14.5 11
C/P ratio 266.4 + 273.7a 167.8 354.1 54 669.0 + 397.4b 513.4 936.1 25 246.7 + 206.3a 175.8 14.5 11
C/N ratio 18.2 + 3.6a 18.3 21.2 54 21.3 + 7.5a 19.2 24.3 26 17.7 + 6.0a 15.5 20.5 10
LOI (%) 23.2 + 22.8a 15.3 22.7 57 42.1 + 33.6a 27.7 86.1 27 31.3 + 25.8a 19.3 55.8 11
Bulk Density 0.60 + 0.29a 0.67 0.84 59 0.44 + 0.44b 0.43 0.55 28 0.52 + 0.31ab 0.51 0.87 12
All
Wet
land
s Com
bine
d
% Moisture 0.50 + 0.21a 0.45 0.66 59 0.65 + 0.20b 0.58 0.85 28 0.57 + 0.21ab 0.55 0.80 12
Riverine TP (mg/cm3) 0.25 ± 0.17a 0.20 0.37 41 0.095 ± 0.055b 0.083 0.13 12 0.30 ± 0.10a 0.30 0.39 7
RiverineTN (mg/cm3) 1.57 ± 0.46a 1.49 1.77 41 2.36 ± 1.25b 2.26 3.47 13 2.63 ± 0.76b 2.39 3.34 7
Non-riverineTP (mg/cm3) 0.12 ± 0.11a 0.084 0.13 16 0.097 ± 0.077a 0.061 0.13 15 0.13 ± 0.038a 0.13 0.15 4 H
ydro
logi
c
Non-riverine TN (mg/cm3) 2.27 ± 0.82a 2.32 2.65 14 2.50 ± 0.85a 2.71 3.19 13 2.07 ± 0.28a 2.12 2.32 4
MarshTP (mg/cm3) 0.19 ± 0.13a 0.16 0.28 8 0.061 ± 0.034b 0.05 0.096 7 - - - 0 Marsh TN (mg/cm3) 1.65 ± 1.13a 1.30 1.95 7 2.43 ± 1.12a 2.92 3.31 7 - - - 0
Swamp TP (mg/cm3) 0.22 ± 0.17a 0.14 0.27 49 0.11 ± 0.072b 0.099 0.14 20 0.24 ± 0.12a 0.20 0.30 11 V
eget
ativ
e
Swamp TN (mg/cm3) 1.77 ± 0.55a 1.69 2.14 48 2.43 ± 1.06b 2.36 3.43 19 2.43 ± 0.67b 2.34 2.44 11
70
Soil data were also aggregated by vegetation type and compared among ecoregions.
Marsh soil phosphorus and nitrogen concentrations did not show significant differences
among the ecoregions when compared on a mass per unit mass basis. When nutrient
concentrations were normalized by bulk density, marshes in the Southern Coastal Plain
had greater total TN (mg/cm3) and lower total phosphorus (mg/cm3) compared to the
Southeastern Forested Plain. There were no soil data for the one marsh within the
Eastern Coastal Plain.
Swamps within the Southern Coastal Plain had significantly greater soil TN
(mg/cm3) and TN (g/kg) content compared to swamps of the Southeastern Forested Plain.
There were no significant differences among ecoregions for TP (mg/kg) content in
swamps. However, the Southern Coastal Plain had significantly lower soil TP (mg/cm3)
than the other two ecoregions.
Discussion
It was hypothesized that there would be regional differences in the nutrient regimes
of wetlands. Differences among the three ecoregions support this hypothesis. The
Southern Coastal Plain (XII) is different from the other two ecoregions (Table 3-17), with
greater water column TN, litter TC, soil TN, soil TC, and lower litter TP content. These
differences suggest that it is a distinct region with its own nutrient characteristics.
However, standard deviations were still high even when wetlands were aggregated by
ecoregion and hydrologic connectivity or vegetative type. There is still considerable
variability among the aggregated wetlands, indicating that the ecoregions may be too
large to aggregate regional differences among wetland nutrient conditions appropriately.
71
Table 3-17.Summary of significant differences (p<0.05) among the three USEPA Nutrient Ecoregions for the various aggregations of surveyed wetlands.
All Wetlands Combined Riverine Non-
riverine Marsh Swamp
Water column TP = = = = =
Water column TN = XII > IX = = XII > IX Litter TN = = = = = Litter TC XII >IX and XIV XII >IX and XIV = = XII >IX and XIV Litter TP XII <IX and XIV XII <IX and XIV = XII < IX XII <IX and XIV Soil TN (g/kg) XII > IX XIV > IX = = XII > IX
Soil TC (g/kg) XII > IX = = XII > IX XII > IX
Soil TP (mg/kg) = XIV > XII and IX = = =
There were no soil TP (mg/cm3) differences between the Southeastern Forested
Plain and the Eastern Coastal Plain. However, these ecoregions had significantly greater
soil TP (mg/cm3) content than the Southern Coastal Plain. The Southeastern Forested
Plain was the largest ecoregion surveyed and included a few northern Florida sites, all
Georgia and Alabama sites, and half of the surveyed wetlands in South Carolina. Several
of these areas are known for their clay mineral soils. Mineral soils retain phosphorus
better than organic soils, due to higher iron and aluminum content (Richardson 1985).
Therefore, it is not surprising that the Southeastern Forested Plain had greater soil TP
content than the Southern Coastal Plain.
The Eastern Coastal Plain included wetlands within coastal South Carolina. High
sedimentation rates in alluvial floodplains are common in this ecoregion. Upland inputs
to streams may have resulted in higher phosphorus and nitrogen accumulations in the
wetland soils of this ecoregion compared to wetlands in the Southern Coastal Plain.
72
When non-riverine water column, litter, and soils were compared among the three
ecoregions, there were no significant differences. Non-riverine wetlands may be less
affected by regional differences because they have smaller contributing watersheds than
riverine wetlands. Watershed properties, such as soil types and topography may be
driving some of the differences noted between riverine and non-riverine wetlands in the
section above.
Variability within ecoregions was explored by examining regional differences in
wetland nutrient regimes at a scale finer than the USEPA Nutrient Ecoregions. The
Southeastern Forested Plain was subdivided into smaller regions by aggregating the
surveyed wetlands by National Forest (or military base). This ecoregion was chosen
because it has the largest area and contained most of the surveyed wetlands (60%). Fort
Benning Military Base, Moody Air Force Base, Banks Lake National Wildlife Refuge,
Conecuh, Oconee, Sumter, Talladega, along with portions of Apalachicola National
Forest are located in the Southeastern Forested Plain (Figure 3-14). Moody Air Force
Base and Banks Lake National Wildlife Refuge are adjacent to each other; therefore the
two surveyed wetlands from Banks Lake National Wildlife Refuge were combined with
the three surveyed wetlands from Moody Airforce Base. These five wetlands are referred
to as Moody Air Force Base.
Soil total phosphorus (mg/cm3) and nitrogen (mg/cm3) content were compared
among these National Forests and military bases within the Southeastern Forested Plain.
There were no significant differences regarding soil total nitrogen content. There were,
however, significant differences among some of the regions with regards to soil total
phosphorus content (Table 3-18). Apalachicola National Forest had the lowest TP
73
Figure 3-14. Distribution of sampling locations within the USEPA Nutrient Ecoregions.
0 240 480120 Kilometers
LegendApalachicola NF
Banks Lake NWF
Conecuh NF
Fort Benning Military
Francis Marion NF
Moody Air Force Base
Ocala NF
Oconee NF
Osceola NF
Sumter NF
Talladega NFlN
Southeastern Forested Plain
Southern Coastal Plain
Eastern Coastal Plain
74
content while Oconee National Forest had the greatest TP content. There is almost an
order of magnitude difference between the means of these regions.
The USEPA has discussed setting numeric nutrient criteria at the 75th percentile
value of least impaired wetlands within an ecoregion. Results from this study suggest
that the 75th percentile of soil TP in the Southeastern Forested Plain is 0.27 mg/cm3. It is
unlikely that the USEPA would adopt this recommendation without additional research.
However, if this value was adopted as the numeric nutrient criteria for this ecoregion,
wetlands in the Apalachicola area would not be sufficiently protected from nutrient
enrichment. The mean soil TP content of these wetlands would have to increase by a
factor of five before exceeding the numeric nutrient criteria. Likewise, wetlands in
Sumter and Oconee National Forests already exceed the hypothetical nutrient threshold.
It is clear that there are significant regional differences in wetland nutrient regimes at a
scale finer than the USEPA Nutrient Ecoregions.
Table 3-18.Soil total phosphorus statistics for surveyed wetlands in the Southeastern
Forested Plain aggregated by National Forest (or military base). Vertical lines connect means that are not significantly different (p<0.05).
Soil TP (mg/cm3) mean ± SD median 75th n Apalachicola, FL 0.056 ± 0.021 0.048 0.077 5 Moody AB, GA 0.11 ± 0.042 0.09 0.15 5 Conecuh, GA 0.13 ± 0.08 0.089 0.14 7
Oakmulgie, AL 0.15 ± 0.10 0.14 0.14 11 Fort Benning, GA 0.15 ± 0.05 0.14 0.19 8
Sumter, SC 0.38 ± 0.20 0.27 0.51 11 Oconee, GA 0.39 ± 0.12 0.44 0.5 9
75
CHAPTER 4 CONCLUSIONS
Establishing nutrient criteria for wetland ecosystems requires understanding
variability in nutrient regimes among wetlands. The primary goal of this study was to use
consistent sampling methods to understand background nutrient conditions in some of the
least impaired watersheds of the southeastern US. An additional objective was to
contrast results based on vegetative community, hydrologic connectivity, and geographic
region. It is hoped that these findings will aid the USEPA in developing numeric nutrient
criteria for wetlands in this region.
One finding from this study stresses the importance of consistent sampling
locations within wetlands for surveying and/or monitoring programs. Water column,
litter, and soil characteristics between the core areas and the edge areas of wetlands
demonstrated significant differences in some parameters. Samples collected at the edge
of a wetland had greater water column total phosphorus and litter total carbon content and
lower soil total carbon and total nitrogen content than samples from the core area of the
same wetland. These differences within wetlands suggest potential implications of
inconsistent sampling techniques on biogeochemical characterizations of wetlands.
Response of wetlands to nutrient change will likely be partially influenced by
vegetative characteristics of the wetland. It was hypothesized that marshes would have
higher soil nutrient concentrations than swamps. Findings from this survey do not
support this hypothesis. It was found that swamps had significantly greater TP (mg/cm3)
than marshes. These results could be influenced by the fact that 64% of the surveyed
76
wetlands are riverine systems which are often associated with higher nutrient
concentrations.
An additional hypothesis was that marshes would have lower water column
nutrients than swamps. The results of this survey partially support this hypothesis. Total
nitrogen was similar regardless of dominant vegetation type, and total phosphorus
concentrations were significantly greater in swamps. This difference may be correlated
with increased presence of algae in marshes compared to swamps. Algae can quickly
sequester water column P, hence lowering water column TP in marshes (Kadlec and
Knight 1996).
Litter parameters were similar between swamps and marshes, suggesting
distinguishing between these two ecosystem types is not necessary for determining
numeric nutrient criteria. In contrast, water column and soil (mg/cm3) total phosphorus
differences between swamps and marshes demonstrate the need to set numeric nutrient
criteria specific to dominant vegetative cover. A water column based numeric nutrient
criteria may not be the best indicator of wetland nutrient regime. Only 52 of 103 sampled
wetlands had water present within the core and edge area of the wetland at the same time.
Furthermore, water column nutrients can be overly sensitive indicators, since they are
influenced by drought, wind, rain events, and other factors.
It is likely that hydrologic connectivity will also affect the response of wetlands to
nutrient changes. It was hypothesized that riverine wetlands would have higher soil,
litter, and water column nutrient levels than non-riverine systems. The results support
some of the hypotheses. Riverine wetlands had greater water column total phosphorus
than non-riverine systems, but total nitrogen content was similar. Litter total phosphorus
77
content was also greater in riverine systems, but again nitrogen content was similar. The
results partially support the hypothesis that riverine wetlands would have greater soil
nutrient levels than non-riverine wetlands. Riverine wetlands had greater soil total
phosphorus (mg/cm3), but lower soil total nitrogen (g/kg and mg/cm3) content than non-
riverine wetlands.
Results indicate that it may be necessary to identify wetlands as riverine or non-
riverine in order to assign appropriate numeric nutrient criteria. For example, when
riverine and non-riverine wetlands are combined, the soil total nitrogen 75th percentile
value is 7.39 g/kg. When aggregated by hydrologic connectivity, the value is 4.9 g/kg for
riverine systems and 16.5 g/kg for non-riverine systems. If 7.39 g/kg was the numeric
nutrient criterion for soil total nitrogen, then the non-riverine systems would be identified
as threatened by nutrient enrichment. However, non-riverine systems appear to have
approximately three times the soil total nitrogen content of riverine systems. Numeric
nutrient criteria specific to hydrologic connectivity will serve as a more effective
threshold for indicating the nutrient status of wetlands than a single criterion for all
wetlands combined.
The USEPA recognized the importance of regional influences on wetland nutrient
regimes when the decision was made to determine numeric nutrient criteria specific to
ecoregions. Results demonstrate that the Southern Coastal Plain (XII) is different from
the Southern Forested Plain (IX) and the Eastern Coastal Plain (XIV), with greater water
column total nitrogen, litter total carbon, soil total nitrogen, soil total carbon, and lower
litter total phosphorus content. These differences suggest that it is a distinct region with
78
its own nutrient characteristics, although variation was great enough to warrant further
investigation.
The Southeastern Forested Plain was subdivided into smaller regions by
aggregating the surveyed wetlands by National Forest (or military base). There were no
significant soil total nitrogen (mg/cm3) differences among the sub-regions. However,
there were significant differences among some of the regions with regards to soil total
phosphorus (mg/cm3) content. There was almost an order of magnitude difference
between the extreme regions for soil total phosphorus. It is clear that there are significant
regional differences in wetland nutrient regimes at a scale finer than the USEPA Nutrient
Ecoregions. If the ecoregions are sub-divided for determination of numeric nutrient
criteria, the assigned values will more accurately reflect background nutrient
concentrations.
Surveying additional wetlands will assist in determining appropriate water quality
criteria. If similar methods are employed, results can be combined to increase statistical
robustness and decrease variability. Additional studies should concentrate on regional,
vegetative, and hydrologic influences on wetland nutrient regimes.
79
APPENDIX A WETLAND CHARACTERIZATION FORM
Wetland ID: Date: Observer Name: Picture ID: Weather Condition: Is the wetland adjacent to a body of water? Circle the appropriate choice:
River Stream Lake Estuary Ocean None Characterization for the Entire Wetland (Please circle one of the vegetation classes)
1) Is the vegetation composed predominantly non-vascular (mosses and lichens) ...…Moss-Lichen 2) Is the vegetation herbaceous?
i) Is the vegetation dominated by rooted emergent vegetation?.....................Emergent Wetland ii) Is the vegetation predominately submergent, floating-leaved, or free-floating?....Aquatic Bed
3) Is the vegetation mostly trees and/or shrubs? i) Is it dominated by vegetation less than 6 meters tall? ………………Scrub-Shrub Wetland ii) Are the dominants 6 meters or greater? …………………………………. Forested Wetland
Land-Use Characterization 1) Circle the following land-uses that best characterizes the adjacent upland and estimate the
percentage of the area that is represented by the circled land uses: a) Commercial ______ g) Rural (scattered homes) ______ b) Industrial ______ h) Unimproved pasture______ c) Golf course ______ i) Forested or wetland ______ d)High density residential (>20 units/acre) ______ j) Pine plantations ______ e) Low density residential ______ k) Row crops ______ f) Feed lots or Dairy operations ______ l) Other ______
2) Please circle the following fire indicators present within the vegetation zone: a) Charred ground surface e)Burnt dead trees b) Burnt trees with new shoots f) Burnt crowns of trees c) Burn marks on trees and shrubs g) Burned ground with no understory d) No evidence of fire
3) Is trash present in the wetland?: Yes or No (describe) 4) Is there green algae present in the wetland?: Yes or No (describe) 5) Is there evidence of sedimentation in the wetland? Yes or No (describe) 6) Is there floating vegetation?: Yes or No (describe) 7) Circle any visible indicators of hydrologic disturbances:
a) Ditch e) Dam b) Nearby road impeding flow f) Dyke c) Canals g)Piped inflows d) None noticed h) Other (describe) 8) Circle any visible indicators of vegetative disturbances:
a) Large stand of vines e) Cutting or grazing in wetland b) Cutting or grazing in adjacent upland f) Insect damage c) Large stand of exotic species g) Large % of dead trees d) None noticed h) Other (describe) 9) Circle any direct indicators of nutrient loading to the wetland a) Presence of cattle in wetland d) Yard waste dumping in/near wetland b) Fertilizer or manure application in watershed e) None noticed
c) Other (describe) 10) What is the approximate size of the wetland: ________________ Shape: ___________________
80
Vegetation Community Characterization Form Sub-sample C (Deep Center) Wetland ID: Date: Start Time: Finish Time: Photo ID:
Sub-sample C1 Sub-sample C2 Sub-sample C3 Comments Temp 0C
pH
DO %
Conductivity
ORP
Water Depth (inches)
Depth of Organic layer (inches)
Distance from ground to lichen lines (inches)
Algal mats (circle one)
Present Not present
Present Not present
Present Not present
Aquatic plants (circle one)
Present Not present
Present Not present
Present Not present
Morphological adaptations (circle any that apply)
Buttressed roots Adventitious roots Hummocks None Present
Buttressed roots Adventitious roots Hummocks None Present
Buttressed roots Adventitious roots Hummocks None Present
Circle the ONE Characterization that best describes the zone being sampled
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
% cover of overstory
List the dominant overstory vegetation within a 10-ft radius of sampling and the % cover they represent
% cover of understory
List the dominant understory story vegetation within a 10-ft radius of sampling and the % cover they represent
81
Vegetation Community Characterization Form Sub-sample E (Edge) Wetland ID: Date: Start Time: Finish Time: Photo ID:
Sub-sample E1 Sub-sample E2 Sub-sample E3 Comments Temp 0C
pH
DO %
Conductivity
ORP
Water Depth (inches)
Depth of Organic layer (inches)
Distance from ground to lichen lines (inches)
Algal mats (circle one)
Present Not present
Present Not present
Present Not present
Aquatic plants (circle one)
Present Not present
Present Not present
Present Not present
Morphological adaptations (circle any that apply)
Buttressed roots Adventitious roots Hummocks None Present
Buttressed roots Adventitious roots Hummocks None Present
Buttressed roots Adventitious roots Hummocks None Present
Circle the ONE Characterization that best describes the zone being sampled
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
Emerg. Macrophytes Grasses/sedges Floating aquatics Forested Scrub-Shrub Other:
% cover of overstory
List the dominant overstory vegetation within a 10-ft radius of sampling and the % cover they represent
% cover of understory
List the dominant understory story vegetation within a 10-ft radius of sampling and the % cover they represent
APPENDIX B WETLAND IDENTIFICATION AND LOCATION
83
Table B-1. Wetland identification and location ID Hydrology Community Ecoregion Location Longitude Latitude AL1 Riverine Swamp SE Forested Plain (IX) Conecuh NF -86.52833 31.27944 AL10 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.38222 33.02167 AL11 Riverine Marsh SE Forested Plain (IX) Talladaga NF -87.48722 32.87222 AL12 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.69389 33.09444 AL13 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.37389 33.17611 AL14 Riverine Marsh SE Forested Plain (IX) Talladaga NF -87.34556 33.18083 AL15 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.39139 33.10583 AL16 Riverine Marsh SE Forested Plain (IX) Talladaga NF -87.55278 33.03639 AL17 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.56583 33.05917 AL18 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.68639 33.08500 AL19 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.46028 33.05611 AL2 Riverine Swamp SE Forested Plain (IX) Conecuh NF -86.68444 31.22083 AL20 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.55833 33.08306 AL3 Riverine Swamp SE Forested Plain (IX) Conecuh NF -86.73472 31.24194 AL4 Non-riverine Marsh SE Forested Plain (IX) Conecuh NF -86.57417 31.21528 AL5 Non-riverine Swamp SE Forested Plain (IX) Conecuh NF -86.84861 31.14194 AL6 Non-riverine Swamp SE Forested Plain (IX) Conecuh NF -86.65667 31.22278 AL7 Non-riverine Marsh SE Forested Plain (IX) Conecuh NF -86.85611 31.13000 AL8 Riverine Swamp SE Forested Plain (IX) Conecuh NF -86.75611 31.33972 AL9 Riverine Swamp SE Forested Plain (IX) Talladaga NF -87.32667 32.80056 FL10 Non-riverine Swamp S. Costal Plain (XII) Ocala NF -82.14167 29.44056
Table B-1.Continued ID Hydrology Community Ecoregion Location Longitude Latitude FL11 Non-riverine Swamp S. Costal Plain (XII) Ocala NF -81.98306 29.35611 FL12 Non-riverine Marsh S. Costal Plain (XII) Ocala NF -81.80000 29.23361 FL13 Non-riverine Marsh S. Costal Plain (XII) Ocala NF -81.84778 29.33861 FL14 Riverine Swamp S. Costal Plain (XII) Ocala NF -81.99472 29.34278 FL15 Riverine Swamp S. Costal Plain (XII) Ocala NF -81.99333 29.34528 FL16 Riverine Marsh S. Costal Plain (XII) Ocala NF -81.81750 29.53917
84
FL17 Non-riverine Swamp S. Costal Plain (XII) Ocala NF -81.80528 29.42778 FL18 Riverine Swamp S. Costal Plain (XII) Ocala NF -81.67722 29.09278 FL19 Riverine Swamp S. Costal Plain (XII) Ocala NF -81.68139 29.08500 FL20 Non-riverine Marsh S. Costal Plain (XII) Ocala NF -81.79250 29.25861 FL21 Riverine Swamp S. Costal Plain (XII) Osceola NF -82.66861 30.34944 FL22 Non-riverine Swamp SE Forested Plain (IX) Osceola NF -82.75167 30.31722 FL23 Riverine Marsh S. Costal Plain (XII) Ocala NF -81.73083 29.43250 FL24 Non-riverine Marsh S. Costal Plain (XII) Apalachicola NF -84.48167 30.40944 FL25 Non-riverine Marsh SE Forested Plain (IX) Apalachicola NF -84.68611 30.48611 FL26 Non-riverine Swamp S. Costal Plain (XII) Apalachicola NF -84.58944 30.36417 FL27 Riverine Swamp S. Costal Plain (XII) Apalachicola NF -84.59472 30.36333 FL28 Non-riverine Swamp SE Forested Plain (IX) Apalachicola NF -84.63694 30.53778 FL29 Riverine Swamp Apalachicola NF FL30 Non-riverine Swamp Apalachicola NF FL31 Riverine Swamp SE Forested Plain (IX) Apalachicola NF -84.84000 30.35444 FL32 Non-riverine Marsh S. Costal Plain (XII) Apalachicola NF -84.89139 30.23194 FL33 Riverine Swamp S. Costal Plain (XII) Apalachicola NF -85.15889 30.18500 FL34 Non-riverine Swamp SE Forested Plain (IX) Apalachicola NF -84.73111 30.35556 FL35 Non-riverine Swamp SE Forested Plain (IX) Apalachicola NF -85.01222 30.36778 FL36 Non-riverine Swamp S. Costal Plain (XII) Osceola NF -82.48417 30.35389 FL37 Riverine Swamp S. Costal Plain (XII) Osceola NF -82.62333 30.52083 FL38 Non-riverine Marsh S. Costal Plain (XII) Osceola NF -82.64639 30.62028 FL39 Non-riverine Swamp S. Costal Plain (XII) Osceola NF
Table B-1.Continued ID Hydrology Community Ecoregion Location Longitude Latitude FL40 Riverine Swamp S. Costal Plain (XII) Osceola NF -82.66806 30.47111 FL41 Non-riverine Swamp S. Costal Plain (XII) Osceola NF -82.59639 30.46472 FL42 Riverine Swamp S. Costal Plain (XII) Osceola NF -82.57417 30.40667 FL43 Non-riverine Swamp S. Costal Plain (XII) Osceola NF -82.60306 30.48167 FL44 Riverine Swamp S. Costal Plain (XII) Osceola NF FL45 Non-riverine Swamp S. Costal Plain (XII) Osceola NF -82.58417 30.31583
85
GA1 Riverine Swamp SE Forested Plain (IX) Oconee NF -83.54250 33.37333 GA10 Riverine Marsh SE Forested Plain (IX) Oconee NF -83.54639 33.80583 GA16 Non-riverine Swamp SE Forested Plain (IX) Grand Bay NWF -83.42917 30.99750 GA17 Non-riverine Swamp SE Forested Plain (IX) Grand Bay NWF -83.29778 31.09556 GA19 Non-riverine Swamp SE Forested Plain (IX) Moody AFB -83.36667 31.08250 GA2 Riverine Swamp SE Forested Plain (IX) Oconee NF -83.77556 33.39028 GA20 Non-riverine Swamp SE Forested Plain (IX) Moody AFB -83.27889 30.97083 GA21 Non-riverine Swamp SE Forested Plain (IX) Moody AFB -83.28861 31.03861 GA25 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.91056 32.43806 GA26 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.90056 32.44694 GA27 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.88167 32.59917 GA28 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.92556 32.50000 GA29 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.90500 32.41500 GA3 Non-riverine Swamp SE Forested Plain (IX) Oconee NF -83.53111 33.38556 GA30 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.77333 32.40972 GA31 Non-riverine Swamp SE Forested Plain (IX) Fort Benning MB -84.95111 32.72000 GA32 Riverine Swamp SE Forested Plain (IX) Fort Benning MB -85.06278 32.60111 GA4 Riverine Swamp SE Forested Plain (IX) Oconee NF -83.82722 33.32361 GA5 Non-riverine Marsh SE Forested Plain (IX) Oconee NF -83.86167 33.24444 GA6 Riverine Swamp SE Forested Plain (IX) Oconee NF -84.05972 33.35528 GA7 Riverine Swamp SE Forested Plain (IX) Oconee NF -83.87694 33.48306 GA8 Riverine Swamp SE Forested Plain (IX) Oconee NF -84.06056 33.47056 GA9 Riverine Swamp SE Forested Plain (IX) Oconee NF -83.89250 33.23167
Table B-1.Continued ID Hydrology Community Ecoregion Location Longitude Latitude SC1 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.85333 33.35917 SC10 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -80.11722 33.25917 SC11 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.73611 33.39028 SC12 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.46889 33.35361 SC13 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.70639 34.45361 SC14 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.53806 34.56056
86
SC15 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.81222 34.63000 SC16 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.67611 34.75056 SC17 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.88556 34.77583 SC18 Riverine Marsh SE Forested Plain (IX) Sumter NF -81.97722 34.66667 SC19 Riverine Swamp SE Forested Plain (IX) Sumter NF SC2 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.99917 33.55389 SC20 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.78639 34.56167 SC21 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.73833 34.47556 SC22 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.39694 34.47028 SC23 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.58944 34.43167 SC24 Riverine Swamp SE Forested Plain (IX) Sumter NF -81.68750 34.59500 SC3 Non-riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.99083 33.43083 SC4 Non-riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.95528 33.44806 SC5 Non-riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.79750 33.26639 SC6 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.87194 33.17861 SC7 Riverine Marsh E. Coastal Plain (XIV) Francis Marion NF -79.91556 33.27750 SC8 Riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.97083 33.28278 SC9 Non-riverine Swamp E. Coastal Plain (XIV) Francis Marion NF -79.86306 33.21389
APPENDIX C PHYSICAL SOIL AND WATER COLUMN DATA
88
Table C-1.Physical soil and water column data. Water column data is an average of sub-sample locations within the core (C) or edge (E) transect.
ID Area Soil moisture
content (%)
Soil bulk density (g /cm3)
Soil LOI (%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity (uS/cm)
Water Eh
(mv)
Water Depth(cm)
AL1 C 39% 0.73 9.7 AL1 E 30% 0.95 6.3 AL10 C 66% 0.39 20.4 22.4 5.31 52.40 18.0 414.0 10.0 AL10 E 80% 0.21 37.9 19.4 5.21 13.70 22.0 342.7 2.3 AL11 C 50% 0.67 10.0 23.6 5.80 14.30 57.7 267.3 15.3 AL11 E 64% 0.41 15.6 23.4 5.57 49.53 46.7 298.5 5.2 AL12 C 59% 0.51 12.9 24.8 5.69 33.17 53.7 5.3 AL12 E 35% 0.99 7.3 22.3 5.54 18.40 38.0 -3.0 AL13 C 46% 0.70 10.3 AL13 E 38% 0.92 7.2 AL14 C 67% 0.41 15.6 23.0 6.45 4.13 133.7 151.7 26.0 AL14 E 67% 0.42 18.2 23.1 6.30 8.00 125.3 175.1 17.3 AL15 C 36% 0.97 23.2 AL15 E 72% 0.31 24.9 AL16 C 73% 0.30 28.4 26.2 5.46 4.77 47.3 276.1 19.0 AL16 E 56% 0.61 10.4 27.7 5.38 33.53 32.7 291.4 10.0 AL17 C 31% 0.72 8.6 AL17 E 33% 0.76 8.5
89
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
AL18 C 82% 0.18 34.8 22.6 5.22 45.77 15.0 346.4 5.2 AL18 E 64% 0.40 17.9 -1.0 AL19 E 66% 0.36 19.0 AL2 C 35% 0.97 25.7 5.80 38.07 44.7 8.0 AL2 E 57% 0.52 15.8 25.6 6.38 13.60 197.5 150.8 6.0 AL20 C 24% 0.88 5.8 AL20 E 20% 0.93 5.4 AL3 C 30% 0.72 8.0 AL3 E 40% 0.61 9.8 AL4 C 86% 0.13 77.7 29.2 4.90 29.67 47.0 25.0 AL4 E 77% 0.24 49.3 -3.0 AL5 C 87% 0.12 86.0 21.7 3.68 13.00 46.3 500.7 3.7 AL5 E 61% 0.38 24.4 28.7 4.87 52.00 55.0 6.3 AL6 C 86% 0.13 82.8 27.3 3.70 70.87 83.7 534.2 5.7 AL6 E 61% 0.44 23.0 -3.0 AL7 C 48% 0.74 19.2 30.1 4.64 53.50 17.0 438.0 15.5 AL7 E 45% 0.87 13.1 24.7 5.44 6.70 85.0 257.0 -0.7 AL8 C 71% 0.31 33.6 23.1 4.51 69.40 28.5 476.3 5.8 AL8 E 72% 0.29 40.6 35.0 4.86 97.03 16.0 435.2 3.5 AL9 C 79% 0.20 24.3 25.1 5.70 42.13 28.7 282.8 20.3 AL9 E 77% 0.26 24.0 -2.7 FL10 C 50% 0.63 15.8 16.2 6.49 9.37 139.0 380.7 5.0 FL10 E 42% 0.75 12.5 25.0 5.87 32.37 60.7 237.9 15.0 FL11 C 83% 0.10 89.1 18.5 3.49 12.97 184.0 506.6 4.7 FL11 E 75% 0.21 64.1 16.8 6.27 12.23 133.8 365.9 5.3 FL12 C 88% 0.10 86.1 18.7 4.57 61.40 22.7 461.1 20.5 FL12 E 86% 0.12 79.1 18.8 3.49 12.60 188.0 499.6 0.4
90
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI (%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
FL13 C 85% 0.13 69.2 19.8 4.57 51.87 27.7 525.0 23.5 FL13 E 76% 0.22 53.4 18.0 4.37 46.60 28.0 578.4 14.6 FL14 C 87% 0.11 87.5 17.8 3.76 6.50 73.0 478.3 8.8 FL14 E 86% 0.11 96.0 19.4 4.74 90.87 22.3 573.2 8.0 FL15 C 55% 0.47 31.4 FL15 E 62% 0.39 33.3 FL16 C 86% 0.12 89.2 FL16 E 80% 0.18 82.4 FL17 C 89% 0.09 98.2 20.3 3.36 27.40 134.5 5.7 FL17 E 83% 0.16 90.8 -1.3 FL18 C 49% 0.46 16.8 FL18 E 70% 0.27 51.8 FL19 C 83% 0.15 63.3 FL19 E 78% 0.21 64.3 FL20 C 44% 0.62 12.5 22.5 4.06 37.37 27500.0 555.6 25.3 FL20 E 32% 0.62 7.2 -2.0 FL21 C 69% 0.17 23.2 17.1 3.82 30.10 593.5 5.0 FL21 E 63% 0.20 11.2 22.8 4.08 50.35 19960.0 529.3 18.3 FL22 C 77% 0.23 35.6 18.8 3.75 79.73 8.3 FL22 E 43% 0.62 16.2 18.6 3.74 38.93 4.2 FL23 C 80% 0.17 15.8 24.5 6.90 53.87 1146.7 4.7 FL23 E 45% 0.43 6.1 26.6 7.22 57.57 1182.7 3.3 FL24 C 54% 0.66 13.4 28.2 3.84 70.83 12.3 237.6 38.0 FL24 E 42% 0.87 9.9 28.0 3.69 63.13 10.3 573.7 20.3 FL25 C 24% 1.10 2.5 32.4 3.96 80.13 19.3 573.0 10.0 FL25 E 68% 0.13 6.8 30.9 3.93 105.20 23.3 559.3 13.5 FL26 C 51% 0.50 27.7 -5.5
91
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
FL26 C 58% 0.41 27.9 -5.5 FL27 C 58% 0.46 28.3 FL27 E 63% 0.50 20.9 FL28 C 38% 0.62 18.6 FL28 E 27% 0.81 10.4 FL29 C 18% 0.88 7.1 FL29 E 30% 0.80 10.4 FL30 C 43% 0.75 9.3 24.3 5.64 10.15 30.0 280.6 1.3 FL30 E 55% 0.43 17.1 FL31 C 25% 1.14 5.9 FL31 E 36% 0.85 7.1 FL32 C 11% 0.92 5.3 FL32 E 24% 1.09 4.5 FL33 C 37% 0.81 6.3 FL33 E 23% 1.04 4.1 FL34 C 55% 0.39 22.1 -5.0 FL34 E 16% 0.68 3.6 FL35 C 59% 0.51 16.2 -1.3 FL35 E 48% 0.59 13.4 FL36 C 88% 0.11 94.1 0.7 FL36 E 78% 0.24 FL37 C 84% 2.35 5.0 FL37 E 87% 0.17 7.1 FL38 E 22% 1.32 3.6 20.3 3.58 9.55 119.0 3.8 FL39 C 88% 0.10 91.1 20.1 3.57 27.95 118.0 10.0 FL39 E 77% 0.23 55.9 22.0 3.95 35.70 53.7 9.0 FL40 C 59% 0.41 21.2 3.87 47.40 59.0 9.0
92
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
FL40 E 76% 0.22 16.9 -8.0 FL41 C 55% 0.53 22.1 24.4 4.63 5.22 55.0 3.2 FL41 E 37% 0.64 14.4 -4.0 FL42 C 50% 0.55 14.5 -5.0 FL42 E 34% 0.84 8.6 FL43 C 54% 0.47 25.2 FL43 E 62% 0.21 61.3 -5.5 FL44 C 53% 0.55 13.1 21.4 3.89 17.20 70.5 10.5 FL44 E 48% 0.50 22.5 22.1 5.97 12.10 57.0 12.3 FL45 C 82% 0.17 63.6 21.9 6.21 21.45 78.5 20.3 FL45 E 69% 0.31 30.5 21.1 5.94 13.63 69.3 234.9 20.0 GA1 C 45% 0.76 11.6 21.4 6.38 19.27 68.7 236.0 15.0 GA1 E 46% 0.68 12.2 24.8 6.36 48.83 56.0 6.7 GA10 C 63% 0.46 18.3 23.7 6.35 41.35 99.5 2.0 GA10 E 63% 0.46 15.8 21.0 4.66 9.20 32.0 419.6 10.3 GA16 C 84% 0.16 60.1 21.8 5.32 5.10 50.7 323.2 23.0 GA16 E 81% 0.17 65.8 5.3 GA17 C 62% 0.38 14.7 11.3 GA17 E 57% 0.50 12.1 20.7 3.67 5.65 85.5 545.7 3.0 GA19 C 90% 0.11 81.8 20.8 3.67 7.53 82.7 531.5 8.3 GA19 E 83% 0.16 60.5 GA2 C 36% 0.90 11.8 GA2 E 26% 0.98 8.1 GA20 C 87% 0.11 90.7 20.1 3.66 6.90 79.7 575.2 5.7 GA20 E 78% 0.19 52.7 GA21 C 85% 0.15 78.3 22.0 4.67 5.50 50.7 420.3 6.3 GA21 E 85% 0.15 77.5
93
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
GA25 C 54% 0.58 9.5 GA25 E 42% 0.78 10.9 GA26 C 34% 0.59 16.7 GA26 E 28% 0.80 10.0 GA27 C 35% 0.67 17.1 GA27 E 16% 1.07 4.5 GA28 C 65% 0.31 21.8 GA28 E 49% 0.57 16.4 GA29 C 63% 0.41 16.3 GA29 E 38% 0.80 7.7 GA3 C 34% 0.77 15.3 GA3 E 34% 0.79 16.1 GA30 C 77% 0.24 33.6 14.6 5.46 65.60 17.7 343.2 -0.3 GA30 E 53% 0.55 16.3 GA31 C 44% 0.57 19.0 GA31 E 43% 0.57 25.5 -6.0 GA32 C 43% 0.69 12.7 GA32 E 24% 1.13 7.2 GA4 C 52% 0.54 20.0 GA4 E 33% 0.84 10.0 GA5 E 29% 1.07 10.0 22.0 5.75 18.60 52.0 GA6 C 33% 0.94 9.7 GA6 E 34% 0.83 12.4 GA7 C 45% 0.76 13.5 21.4 6.32 27.10 79.0 363.5 -0.3 GA7 E 45% 0.70 16.1 GA8 C 28% 0.89 9.0 GA8 E 40% 0.82 12.9
94
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
GA9 C 37% 0.89 14.2 22.8 6.25 17.55 82.0 341.2 6.0 GA9 E 36% 0.84 23.4 6.07 9.70 238.0 262.0 -1.0 SC1 C 48% 0.65 18.2 25.6 6.60 31.97 76.0 334.3 23.7 SC1 E 36% 0.59 19.3 0.3 SC10 C 33% 0.88 9.5 SC10 E 25% 1.01 6.7 SC11 C 62% 0.43 32.6 23.5 6.12 7.00 228.0 213.8 SC11 E 55% 0.52 22.3 SC12 C 81% 0.18 55.8 25.5 5.92 42.30 82.3 319.7 2.3 SC12 E 79% 0.23 48.4 SC13 C 43% 0.81 16.6 SC13 E 37% 0.93 12.0 SC14 C 38% 0.64 SC14 E 42% 0.75 13.0 SC15 C 23% 0.95 6.3 SC15 E 36% 0.97 7.5 SC16 C 32% 0.88 12.3 SC16 E 34% 0.95 12.2 SC17 C 32% 0.79 11.8 SC17 E 38% 0.77 14.5 SC18 C 59% 0.51 12.6 27.8 6.28 16.70 101.0 256.6 18.7 SC18 E 55% 0.58 -5.0 SC19 C 26% 0.92 6.3 SC19 E 33% 0.75 15.8 SC2 C 44% 0.58 19.3 SC2 E 42% 0.53 19.4 SC20 C 26% 0.98 9.0
95
Table C.1.Continued
ID Area Soil moisture content (%)
Soil bulk density (g /cm3)
Soil LOI(%)
Water temp (°C)
Water pH
Water DO (%)
Water conductivity
(uS/cm)
Water Eh (mv)
Water depth (cm)
SC20 E 41% 0.76 SC21 C 31% 0.83 11.4 SC21 E 44% 0.71 16.2 SC22 C 26% 0.84 8.5 SC22 E 34% 0.78 9.1 SC23 C 33% 0.84 9.5 SC23 E 39% 0.78 10.0 SC24 C 26% 1.03 8.3 SC24 E 30% 0.83 9.5 SC3 C 39% 0.88 12.2 26.9 6.25 49.33 76.0 302.9 7.3 SC3 E 40% 0.85 14.1 25.4 4.19 11.60 29.0 -0.5 SC4 C 37% 0.87 9.9 24.1 5.79 8.25 130.5 0.0 2.3 SC4 E 28% 0.83 9.0 28.2 5.96 44.00 68.0 336.8 0.3 SC5 C 87% 0.11 88.6 22.5 3.65 12.20 62.5 537.0 5.3 SC5 E 86% 0.12 90.0 24.0 5.93 7.00 101.0 266.9 SC6 C 63% 0.45 28.2 SC6 E 60% 0.51 25.6 SC7 C 88% 0.12 SC7 E 89% 0.10 63.1 SC8 C 30% 0.86 11.3 SC8 E 27% 0.94 7.2 SC9 C 75% 0.22 58.9 24.2 3.80 15.70 68.0 481.4 0.7
APPENDIX D SOIL, LITTER, AND WATER COLUMN CHEMICAL DATA
97
Table D-1. Chemical soil, litter, and water column data for edge (E) and Core (C) sites
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP
(mg/L)
Water Column TN
(mg/L) AL1 C 121.6 1.76 39.01 0.009 0.89 21.63 0.012 1.53 AL1 E 107.2 1.05 20.60 0.011 1.08 37.31 AL10 C 363.7 4.55 80.63 0.032 1.11 39.81 0.021 1.24 AL10 E 405.1 9.19 198.60 0.009 1.36 47.18 0.115 2.56 AL11 C 259.0 1.95 33.79 0.047 1.59 30.45 0.092 1.86 AL11 E 256.9 3.49 71.33 0.035 1.91 38.03 0.465 9.13 AL12 C 157.3 2.88 62.10 0.029 1.15 31.41 0.035 1.32 AL12 E 144.4 1.29 29.53 0.024 1.06 40.08 0.022 7.37 AL13 C 204.8 2.20 40.20 0.023 0.88 27.22 0.013 0.94 AL13 E 165.0 1.44 29.65 0.022 1.33 40.55 AL14 C 339.9 3.17 59.35 0.059 1.31 36.65 0.032 1.58 AL14 E 419.0 3.76 70.33 1.48 39.74 0.150 2.50 AL15 C 438.3 3.51 65.50 0.97 27.29 AL15 E 339.2 5.30 123.50 0.008 1.53 45.18 AL16 C 307.0 6.40 141.20 0.023 1.81 37.82 0.082 1.79 AL16 E 141.9 2.36 52.91 0.022 1.35 29.72 0.120 2.21 AL17 C 156.9 1.48 36.63 0.009 1.44 44.58 0.015 0.95 AL17 E 182.2 1.40 31.00 0.009 1.37 43.46 AL18 C 485.5 9.08 158.07 0.030 1.39 35.81 0.626 8.70
98
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
AL18 E 290.3 3.36 72.01 0.010 1.22 45.10 AL19 E 320.7 3.85 96.50 0.040 1.57 39.40 0.345 4.78 AL2 C 0.024 1.13 30.16 0.042 1.31 AL2 E 221.8 3.28 66.44 0.018 1.24 40.03 0.046 1.93 AL20 C 160.8 1.17 21.92 0.009 1.35 43.34 AL20 E 171.5 1.14 22.36 0.010 1.23 39.97 AL3 C 198.2 1.34 28.42 0.006 7.87 39.30 0.018 1.33 AL3 E 177.7 1.71 38.40 0.005 0.98 47.63 AL4 C 673.8 0.015 1.97 45.76 0.353 7.25 AL4 E 437.5 0.019 1.40 46.24 0.240 5.60 AL5 C 602.4 18.39 445.20 0.006 1.80 51.07 0.019 1.38 AL5 E 266.9 6.40 107.40 0.006 1.27 45.29 AL6 C 594.2 19.10 425.20 0.006 1.61 48.66 0.039 2.29 AL6 E 461.7 3.40 67.40 0.040 1.41 42.49 0.745 13.33 AL7 C 422.2 5.36 60.67 0.012 1.80 45.06 0.024 1.79 AL7 E 145.1 3.08 41.76 0.010 1.23 44.27 0.023 1.93 AL8 C 373.2 6.70 160.20 0.008 1.39 46.37 AL8 E 434.2 9.73 219.60 0.007 1.37 48.10 AL9 C 443.6 5.15 0.00 0.038 1.48 31.51 0.028 0.96 AL9 E 471.5 4.87 104.27 0.033 1.46 29.17 0.031 0.82 FL10 C 219.0 3.74 73.79 0.018 1.33 50.09 1.63 FL10 E 218.6 4.10 62.90 0.012 1.11 48.73 0.409 1.03 FL11 C 538.0 15.57 483.10 0.005 0.87 51.35 0.256 2.38 FL11 E 319.9 9.07 353.77 0.008 0.90 51.76 0.194 1.70 FL12 C 375.5 33.39 465.40 0.009 2.24 45.26 0.019 2.24 FL12 E 596.0 30.42 426.20 0.005 1.71 45.54 0.025 1.02 FL13 C 373.7 24.67 351.77 0.004 1.33 45.40 0.016 0.83 FL13 E 640.6 17.96 248.30 0.005 0.84 43.91 0.026 0.83
99
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
FL14 C 545.8 17.58 461.50 0.006 1.34 50.45 0.351 6.64 FL14 E 431.2 11.44 498.60 0.004 0.93 51.70 0.179 3.22 FL15 C 466.6 7.56 149.21 0.016 1.37 46.60 0.149 3.62 FL15 E 501.7 8.96 190.90 0.019 1.85 46.01 FL16 C 827.7 22.68 424.92 0.039 1.77 45.04 FL16 E 891.0 19.70 399.40 0.009 1.56 47.44 FL17 C 405.5 14.15 495.95 0.005 1.30 52.66 0.026 3.17 FL17 E 363.0 13.71 486.57 0.004 1.08 53.13 0.030 3.78 FL18 C 353.3 4.91 84.25 0.010 1.39 44.47 0.213 2.78 FL18 E 935.2 14.49 257.63 0.011 1.17 47.07 FL19 C 804.5 17.87 302.50 0.011 1.66 47.30 FL19 E 696.6 15.66 288.20 0.014 1.72 48.44 FL20 C 66.0 4.88 87.75 0.021 2.01 FL20 E 45.9 1.60 35.00 0.005 1.32 46.96 0.035 2.37 FL21 C 193.2 4.92 87.44 0.009 1.15 49.36 0.024 2.86 FL21 E 98.4 2.25 55.50 0.009 1.08 49.21 0.055 2.96 FL22 C 298.5 9.30 191.20 0.008 0.00 0.00 0.018 2.14 FL22 E 82.7 4.14 0.00 0.007 1.02 50.86 0.024 2.07 FL23 C 333.1 4.76 87.33 0.007 0.99 43.12 0.079 0.91 FL23 E 51.9 0.85 25.15 0.007 1.04 29.70 0.049 1.76 FL24 C 180.8 4.41 62.70 0.003 1.26 46.71 0.014 1.64 FL24 E 109.7 2.35 48.69 0.005 1.44 44.06 0.028 1.80 FL25 C 44.0 0.25 4.55 0.006 0.95 44.40 0.007 1.22 FL25 E 56.0 2.27 31.15 0.007 1.77 39.47 0.007 1.15 FL26 C 239.4 5.46 126.77 0.006 1.09 49.49 FL26 C 261.1 0.006 1.09 49.49 FL27 C 291.4 7.38 137.70 0.005 0.84 47.97 0.030 1.83 FL27 E 133.3 3.55 137.80 0.004 0.72 49.06
100
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
FL28 C 130.2 4.28 97.51 0.002 1.03 52.04 FL28 E 50.2 2.25 58.45 0.005 1.06 50.21 FL29 C 87.2 2.19 48.02 0.007 1.03 45.88 FL29 E 176.4 2.23 44.12 0.008 1.10 38.60 FL30 C 91.3 2.13 48.69 0.011 1.05 37.36 FL30 E 230.4 3.55 81.12 0.004 0.80 47.17 FL31 C 64.7 1.18 22.76 0.004 0.84 43.98 FL31 E 39.5 1.28 31.10 0.008 0.84 41.96 FL32 C 28.8 0.99 30.32 0.004 0.70 42.78 FL32 E 5.5 0.73 25.12 0.004 0.71 39.03 FL33 C 42.3 5.90 32.42 0.008 0.88 26.51 FL33 E 46.9 0.92 16.80 0.002 0.54 44.49 0.004 0.85 FL34 C 75.9 4.77 117.45 0.007 0.88 50.83 FL34 E 21.9 0.57 16.89 0.003 0.62 51.02 FL35 C 95.1 3.80 81.10 0.007 0.92 38.70 0.086 2.92 FL35 E 71.8 34.70 54.50 0.008 0.75 48.16 FL36 C 540.7 16.12 503.39 0.006 0.91 50.59 0.034 2.86 FL36 E 0.011 1.16 50.10 FL37 C 38.8 1.59 64.31 0.008 0.93 46.43 0.069 2.50 FL37 E 42.0 1.01 31.00 0.008 0.97 49.97 FL38 E 33.5 0.30 6.20 0.014 1.24 34.25 FL39 C 506.5 0.007 1.57 51.08 0.141 3.53 FL39 E 272.0 8.60 290.50 0.006 0.96 50.35 0.471 5.51 FL40 C 2.37 53.67 0.009 1.16 45.07 FL40 E 270.0 3.79 85.30 0.009 1.08 47.93 0.059 1.65 FL41 C 630.0 6.63 96.24 0.005 1.03 49.63 0.184 1.57 FL41 E 240.0 3.49 61.03 0.010 1.15 45.57 FL42 C 137.9 3.40 74.00 0.010 1.46 44.78
101
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
FL42 E 95.2 1.65 43.66 0.009 1.40 48.27 FL43 C 278.5 5.27 113.20 0.007 1.25 49.30 FL43 E 385.6 10.45 338.34 0.004 0.81 50.67 0.063 1.34 FL44 C 120.3 2.53 59.90 0.006 1.13 49.03 0.042 2.29 FL44 E 82.5 4.19 118.47 0.008 1.19 49.00 FL45 C 812.0 17.71 321.40 0.009 1.41 49.55 0.086 1.41 FL45 E 214.1 6.88 154.75 0.010 1.36 49.87 GA1 C 700.0 1.73 19.05 0.025 1.64 34.62 0.194 1.25 GA1 E 851.1 2.01 21.16 0.019 1.37 36.44 0.214 1.82 GA10 C 954.5 3.44 49.30 3.00 GA10 E 845.4 2.90 39.50 0.151 1.44 GA16 C 546.9 16.69 310.27 1.87 42.46 0.049 1.31 GA16 E 323.0 12.06 339.60 0.045 1.54 GA17 C 214.0 3.74 77.16 1.82 47.64 0.026 1.32 GA17 E 133.8 2.12 63.04 1.34 45.01 0.086 1.69 GA19 C 870.2 2.02 47.58 0.044 1.89 GA19 E 716.0 14.73 314.78 1.86 49.22 0.133 3.09 GA2 C 314.9 1.80 30.87 0.000 0.98 38.44 GA2 E 230.1 1.27 23.00 0.009 1.25 44.48 GA20 C 1088.2 23.16 467.53 1.76 51.04 0.070 2.24 GA20 E 867.9 14.96 278.02 2.09 50.77 GA21 C 1215.2 19.05 416.06 1.66 48.81 0.096 1.77 GA21 E 1331.4 18.17 406.95 1.72 48.89 0.090 1.62 GA25 C 180.2 3.05 56.67 1.37 45.14 GA25 E 251.3 2.41 41.92 1.02 44.48 GA26 C 399.6 3.70 64.36 0.98 43.03 GA26 E 230.4 2.13 38.29 1.22 46.14 GA27 C 297.8 3.22 59.75 1.21 41.62
102
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
GA27 E 74.6 0.66 19.48 1.10 43.88 GA28 C 334.7 4.81 92.75 1.22 34.85 GA28 E 476.8 1.27 45.31 GA29 C 289.6 4.74 84.39 0.92 37.32 GA29 E 147.5 1.67 30.37 1.01 40.24 GA3 C 604.1 2.69 32.08 0.014 1.32 32.66 GA3 E 638.5 2.80 39.50 0.013 1.33 35.77 GA30 C 619.7 9.09 146.51 93.75 43.19 0.017 0.63 GA30 E 442.2 3.90 65.88 1.44 47.72 GA31 C 236.6 4.57 97.65 1.23 45.10 GA31 E 285.8 4.81 112.51 GA32 C 200.3 2.53 47.80 1.13 45.17 GA32 E 102.9 1.05 26.19 1.12 41.00 GA4 C 467.4 3.62 66.44 0.018 1.45 36.38 GA4 E 380.0 120.82 30.96 0.014 1.74 41.80 GA5 E 203.4 1.20 16.70 0.057 1.36 GA6 C 470.9 1.86 27.66 0.013 1.01 27.14 GA6 E 539.8 2.47 43.88 0.012 1.12 32.76 GA7 C 456.4 2.32 33.68 0.015 1.70 36.09 0.177 3.08 GA7 E 583.3 2.75 36.73 0.016 1.71 36.56 0.204 2.72 GA8 C 263.3 1.60 22.90 0.007 1.01 43.58 GA8 E 538.1 1.80 21.70 0.013 1.64 41.01 0.355 5.60 GA9 C 623.5 2.25 37.07 0.019 1.12 27.71 0.243 2.16 GA9 E 0.015 0.00 0.00 0.184 2.29 SC1 C 732.5 3.69 45.39 0.031 1.40 36.68 0.057 0.86 SC1 E 702.3 4.47 53.18 0.022 1.68 38.39 SC10 C 232.3 2.00 28.70 0.023 1.36 33.02 0.080 2.26 SC10 E 148.8 1.38 25.33 0.014 1.26 30.82
103
Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
SC11 C 894.2 9.17 120.89 0.041 1.96 40.95 0.193 2.73 SC11 E 488.5 4.84 74.65 0.032 1.77 43.04 0.142 2.62 SC12 C 972.5 12.99 0.00 0.033 1.33 42.18 0.096 1.89 SC12 E 691.9 10.60 204.30 0.041 1.53 42.53 SC13 C 848.3 2.03 29.20 0.041 1.07 24.05 0.304 1.14 SC13 E 531.9 1.90 27.00 0.020 1.01 34.78 SC14 C 0.016 1.54 41.42 SC14 E 674.1 2.16 30.16 0.021 1.25 38.58 SC15 C 262.4 0.88 22.40 0.013 0.89 21.91 SC15 E 378.0 1.57 19.73 0.014 0.93 19.81 SC16 C 889.8 1.40 22.80 0.027 1.01 27.05 0.060 0.78 SC16 E 645.1 1.68 24.81 0.035 0.77 22.57 SC17 C 314.8 1.96 25.74 0.021 1.45 35.54 SC17 E 328.6 2.60 32.42 0.023 1.34 31.56 SC18 C 407.0 2.30 39.30 0.023 1.33 37.96 0.227 1.49 SC18 E 0.029 1.72 38.68 0.112 1.39 SC19 C 418.0 1.19 20.02 0.012 2.77 44.41 SC19 E 561.1 0.033 1.64 29.80 SC2 C 522.8 4.24 60.04 0.038 1.24 33.09 SC2 E 314.2 3.80 69.19 0.026 1.18 37.37 SC20 C 152.1 1.50 20.90 0.022 1.19 32.40 SC20 E 2.12 27.06 0.025 1.20 32.85 SC21 C 476.2 1.80 21.70 0.024 0.68 16.99 SC21 E 493.0 2.50 32.80 0.031 1.08 29.30 0.321 5.09 SC22 C 609.5 1.55 23.13 0.014 0.81 24.95 SC22 E 856.2 1.70 25.70 0.019 1.43 35.67 SC23 C 317.6 1.60 23.10 0.020 38.67 11.85 SC23 E 424.3 1.85 23.07 0.030 1.40 28.23
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Table D.1.Continued
ID Area Soil TP (mg/kg)
Soil TN (g/kg)
Soil TC (g/kg)
Litter TP (%)
Litter TN (%)
Litter TC (%)
Water Column TP (mg/L)
Water Column TN (mg/L)
SC24 C 258.3 1.34 19.23 0.013 1.58 42.62 SC24 E 411.4 0.015 1.53 41.88 SC3 C 177.7 2.58 47.22 0.016 1.10 43.23 0.040 1.87 SC3 E 234.8 2.84 53.15 0.021 1.16 43.93 0.058 2.48 SC4 C 178.3 2.27 39.62 0.017 1.12 26.11 0.225 6.58 SC4 E 98.2 1.81 41.63 0.021 1.29 37.78 SC5 C 674.3 15.54 458.63 0.007 1.35 50.52 0.028 1.86 SC5 E 638.8 18.26 463.42 0.006 1.38 51.50 0.019 1.37 SC6 C 572.1 7.43 123.80 0.021 1.44 49.76 SC6 E 510.2 6.38 106.76 0.021 1.36 38.95 0.061 1.93 SC7 C 0.033 1.57 41.00 SC7 E 1453.3 18.46 25.93 0.014 1.31 41.17 SC8 C 346.8 2.48 33.76 0.013 1.16 32.59 SC8 E 248.3 1.54 18.79 0.016 1.64 36.42 0.213 2.86 SC9 C 531.5 10.68 292.21 0.007 1.32 50.58 0.243 4.35 SC9 E 529.9 10.42 282.99 0.008 1.47 48.76 0.086 3.29
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BIOGRAPHICAL SKETCH
Stacie Greco’s environmental career began when she visited the coastal North
Carolina forest of her childhood, to find a parking lot and mall where the pines and oaks
once flourished. Stacie received her undergraduate degree in environmental studies at
Warren Wilson College in Asheville, NC, in 1999. Shortly after graduation Stacie moved
to the Virgin Islands to work in the ecotourism industry. Upon returning to Asheville in
2000 she worked as a project manager at an environmental consulting firm. Stacie began
pursuing her master’s degree in the fall of 2001in the Department of Environmental
Engineering Sciences at the University of Florida. In the future Stacie hopes to build
partnerships with government, industry, and citizens to improve the environmental and
social well being of communities.