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

vii

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

x

3-14 Distribution of sampling locations within the USEPA Nutrient Ecoregions. ..........73

xi

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).

4

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

7

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

9

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)

10

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

11

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

12

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,

13

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

14

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

15

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.