Citation
Phosphorous Storage Dynamics in Wetland Vegetation and Forage Grass Species: Facilitating Wetland Hydrologic Restoration in the Lake Okeechobee Watershed

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Title:
Phosphorous Storage Dynamics in Wetland Vegetation and Forage Grass Species: Facilitating Wetland Hydrologic Restoration in the Lake Okeechobee Watershed
Creator:
SMITH, JEFFREY D.
Copyright Date:
2008

Subjects

Subjects / Keywords:
Lake Okeechobee ( local )
Wetlands ( jstor )
Biomass ( jstor )
Highlands ( jstor )

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Source Institution:
University of Florida
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University of Florida
Rights Management:
Copyright Jeffrey D. Smith. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
8/31/2006

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Full Text












PHOSPHORUS STORAGE DYNAMICS IN WETLAND VEGETATION AND
FORAGE GRASS SPECIES: FACILITATING WETLAND HYDROLOGIC
RESTORATION IN THE LAKE OKEECHOBEE WATERSHED













By

JEFFREY D. SMITH


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


2006

































Copyright 2006

by

Jeffrey David Smith



























This thesis is dedicated to all who strive to educate themselves, and those who support
education.





"The question is, does the educated citizen know he is only a cog in an
ecological mechanism? That if he will work with that mechanism his mental
wealth and his material wealth can expand indefinitely? But that if he refuses
to work with it, it will ultimately grind him to dust? If education does not
teach us these things, then what is education for?"(Leopold, 1966).















ACKNOWLEDGMENTS

I would like to thank Dr. Mark W. Clark for his contagious enthusiasm for science,

and life in general, and for his guidance and support throughout the course of this

research. I would also like to give special thanks to Dr. Edmond J. Dunne for his

guidance and mentoring in the field, laboratory and throughout the development of this

thesis, and most of all, his friendship. Dr. Clark and Dr. Dunne are outstanding scientists,

but even better persons. My committee members, Dr. K. Ramesh Reddy and Dr. L.

Hartwell Allen, were also very influential in the development and review of this research.

Lastly, I would like to thank my parents, Sarah Anderson and many friends, from near

and far, for fostering my goals and providing unconditional support. Many other

important people are listed in Appendix D, Table D-1.











TABLE OF CONTENTS




A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TA BLE S .................. .................................. .... .. ........ ........ ....... viii

LIST OF FIGURES ......... ......................... ...... ........ ............ xi

ABSTRACT .............. .......................................... xiv

CHAPTER

1. IN TR OD U CTION ............................................... .. ......................... ..

P rob lem ............................................................ ................ .. 2
R egion al C h aracteristics ............................................................. .....................3
E v erglades .................................................... ................ .. 3
Lake Okeechobee's watershed ............. ................................ ...............5
Phosphorus loading to Lake Okeechobee ............................................... 7
Policy and Planning .................. .............................. .. ...... ................ .9
Lake Okeechobee Legislation........................................................ 10
Phosphorus Best Management Practices ........................................... .................11
Hydrologic Restoration of Isolated W wetlands ................................................12
Phosphorus in w etland soils ..................................................................... 13
A alternative forage crops .................................................... .. .... ......... 14
Thesis Objectives .............. ..... .... .... ...... ...... ............................. 15

2. PHOSPHORUS ASSIMILATION BY ISOLATED WETLAND VEGETATION ..17

In tro du ctio n ....... ......... ......................................................... 17
Factors Influencing Phosphorus Retention........................................................ 19
Research Objectives .............................. ....... ... ............ .. .........21
Research Questions and Hypotheses.............. ............................................... 22
M materials and M methods ....................................................................... ..................23
Study Sites ..........................................23.............................
Sam pling ........... ............................. ................................... 25
Sam ple processing .................... .... .... .... .. .... ........ ........ .............. 26
L laboratory A analysis ............................................ .. ........ .... ...........27
D ata and Statistics A analysis ........................................ .......................... 27
R e su lts .................. ....... .... ................... ................... ...................... 2 8
Species Composition along a Hydrologic Gradient ........................................28
Ecosystem Phosphorus Storage .................................................. ............... 31
Standing Biomass ............................ ............. .......... .................. 33
Standing Biom ass by Individual Species .................................... ............... 36


v









Phosphorus Storage in Biom ass ........................................ ....... ............... 37
Standing Biom ass by Individual Species ................................. ................ 41
D isc u ssio n ............................................................................................................. 4 2
H y d ro lo g y ...................................................................................................4 2
E cosystem storage ....................... ...... .......... ............... .... ..... .. 43
Biom ass in Pasture W wetlands ........................................ ......................... 44
D isturbance Effects on B iom ass................................... .................................... 45
Phosphorus Concentrations ........................................ ........................... 47
P hosphoru s Storage .............................. ........................ .. ........ .... ............4 8
C o n c lu sio n s........................................................................................................... 4 9

3. FACILATATING WETLAND HYDROLOGIC RESTORATION WHILE
MAINTAINING FORAGE PRODUCTION: HYDROLOGIC TOLERANCES
OF PASPALUM NOTATUM AND HEMARTHRIA AL TISSIMA ............................ 51

Introduction ............... ......... .......................51
Background ............ ......... .. ......... ..........51
Research Objectives ................................ ...... .............. .. .........53
Research Questions and Hypotheses............ ........................... ...............53
M materials and M methods ....................................................................... ..................54
E x p erim ental D esign ........................................ ............................................54
T re a tm e n ts ..................................................................................................... 5 6
Sampling .......... .. .. ......... ............... 57
S o il ......................... ..................5 8
A bove ground biom ass sam pling ........................................ ............... 58
Below ground biom ass ....... .. ...................................................... ....... 60
L laboratory analysis .............................................. ............... 60
Results ............... ..... ............ ............. ............... 60
Initial characterization ...................... ......... ...............60
F o rag e P ro du ctio n ......................................................................................... 6 1
B ahiagrass forage production............................................. 63
Limpograss forage production ......................... ........ ............... 64
Species comparison .............. ..... .......................65
Total Biomass ................. .. ...... ..................68
Root to Shoot Ratios ..... .................... ....... ........69
Phosphorus Assimilation ................................. .......................... ...... 71
Phosphorus tissue concentrations ............... ........... ........... .................71
Phosphorus storage.........................................73
Phosphorus storage root to shoot ratios.................................. ............. 76
D isc u ssio n ..............................................................................................7 7
F o rag e P ro du ctio n ......................................................................................... 7 7
Flood Tolerance ............... ......... .......... ........78
P h o sp h oru s U ptak e ........................................................................................ 80
C conclusions ................................................ 81

4. SUMMARY AND CONCLUSIONS ........................ ............... 83









Su m m ary ................... ... ............. ...................................... ................. 83
Objective I: Biomass Production and Phosphorus Storage in Wetlands............83
V vegetation Stress .................................................................... .. 84
Objective II: Facilitating Land-use and Wetland Restoration.............................84
U expected R results ......................... .. ...................... ...... ........... 85
Im plications for R restoration ............................................................ .....................85
C on clu sion s ............... .. .... .. ..... ... ....... ........... .. ............................... 8 8
Unanswered Questions and Need for Further Research...........................................89

APPENDIX

A. SUPPLEMENTAL BACKGROUND INFORMATION ..........................................90

B. SUPPLEMENTAL FIELD DATA.................................... ..................... 92

C. SUPPLEMENTAL MESOCOSM DATA............... ............................ 102

D. SUPPLEMENTAL ACKNOWLEDGEMENTS................ ...... ............... 138

L IST O F R E F E R E N C E S .................................................... ................... .................... 139

BIOGRAPHICAL SKETCH ................................................ ......... ............... 146
















LIST OF TABLES

Table page

1-1 Okeechobee watershed land use by percent of total land ................ ..................6

2-1 Mean and standard deviation of hydroperiods at each site. ................................29

2-2 Mean species hydroperiod of both sites. ...................................... ............... 29

2-3 R oot to shoot ratios by zone .................................. ............... ............... 36

2-4 Below ground biomass concentrations by zone ................................................. 38

2-5 Above ground biomass P concentrations by zone............................................40

2-6 BGB to A GB P storage ratios ............................................................................ 41

3-1 Sam pling dates and details. ...... ........................... ....................................... 58

3-2 Total biomass after 163 and 375 days. ............................... ............................... 68

3-3 R oot to shoot ratios. ......................................... ... .... ........ ..... .... 71

3-4 Total P storage species comparison. ............................................. ............... 74

3-5 Root to shoot P storage ratios with statistics................................. .................77

4-1 Estimation of P export concentrations to tributaries from various land-uses. .........87

A- Summary of Okeechobee Basins BMPs........................................ ............... 90

A-2 Total P loads to Lake Okeechobee 1991-2003.................................... ................ 91

B-l Phosphorus storage by components, site and zone. ............................................92

B-2 Biomass production by components, site and zone......................... ............... 92

B -3 Species hydroperiod. ...................... .. ...... ................ ... ...... .. ...............93

B -4 T otal biom ass production. ........................................... ........................................95

B-5 Below ground biomass production........ .................................................95









B-6 Above ground biomass production. .............................................. ............... 95

B -7 T otal biom ass P storage......................................... .............................................97

B-8 Below ground biomass P storage. ........................................ ........................ 97

B-9 Above ground biomass P storage. ........................................ ......................... 97

B-10 Phosphorus concentration in above ground biomass ..........................................100

B-11 Phosphorus storage in above ground biomass....................................................101

C-1 Nutrient concentrations on day 0. ........................................ ....... ............... 102

C-2 Species comparison of forage production per harvest ........................... ........103

C-3 Species comparison of cumulative forage production. ........................................ 103

C-4 Overall below ground biomass all treatments combined ..................................103

C-5 Forage production per harvest .............. ................... ......... .. ............... 104

C-6 Cumulative forage by treatment and day. ................................... ............... 105

C-7 Below ground biomass species comparison.......... ............ ............. 106

C-8 Residual biomass harvested on days 163 and 375. ..............................................106

C-9 Below ground biomass production time comparison...................... ..............106

C-10 Bahiagrass forage production per harvest treatment comparison........................109

C-11 Bahiagrass cumulative forage production treatment comparison ........................110

C-12 Limpograss forage production per harvest treatment comparison.....................111

C-13 Cumulative limpograss forage production treatment comparison....................112

C-14 Bahiagrass BGB production treatment comparison.......................... .........113

C-15 Limpograss BGB production treatment comparison...................... ................113

C-16 Forage P concentrations species comparison. ............ .... ................ ..............114

C-17 Bahiagrass forage P concentrations treatment comparison ..............................115

C-18 Limpograss forage P concentrations treatment comparison ............................116

C-19 Below ground biomass P concentrations species comparison. ...........................117









C-20 Bahiagrass BGB P concentrations treatment comparison ....................... 117

C-21 Limpograss BGB P concentrations treatment comparison.............................118

C-22 Phosphorus storage in forage species comparison per harvest. ...........................119

C-23 Cumulative P storage in forage species comparison.................. ............ 120

C-24 Bahiagrass forage P storage per harvest........................................... ...........121

C-25 Bahiagrass cumulative forage P storage................................................. ........... 122

C-26 Limpograss forage P storage per harvest. ................................... ............... 123

C-27 Cumulative limpograss forage P storage..................................................... 124

C-28 Below ground biomass P storage species comparison ................... .............127

C-29 Bahiagrass below ground biomass P storage ............................... ............... .127

C-30 Limpograss below ground biomass P storage .............. ................................. 127

C-31 Climatic conditions from day 1 to 375........ ................. .....................129

D -1 T hanks. ............................................................................. 138
















LIST OF FIGURES


Figure p

1-1 Phosphorus concentrations in Lake Okeechobee............. ........... ...............3

1-2 Historic, current, and future flow pattern of the Everglades..................................4

1-3 Land-use map of four priority basins of the Lake Okeechobee watershed ................8

1-4 Wetland coverage in the priority basins....................... ...................9

2-1 M echanism s driving P cycling. ........................................ .......................... 21

2-2 M ap of land use in the 4 priority basins.................................................. ........24

2-3 Isolated wetlands selected for long term monitoring ............................................24

2-4 Stratified sampling zones: center, edge and upland. ..........................................25

2 -5 L og istics fit of sp ecies ................................................................... ..................... 3 0

2-6 Logistics fit of species by site. ............................................................................ 31

2-7 Phosphorus storage components. ........................................ ......................... 32

2-8 Comparison of AGB and BGB components at Beaty and Larson. ..........................32

2-9 Total biomass at Beaty and Larson wetlands .......................................................33

2-10 Below ground biomass at Beaty and Larson wetlandss.................... ...............34

2-11 Above ground biomass at Beaty and Larson wetlands ........................................34

2-12 Biom ass partitioning AGB vs. BGB ............................................. ............... 35

2-13 Above ground biomass by species for all zones ............................................... 37

2-14 Total biom ass P storage......................................... .................. ............... 38

2-15 Phosphorus storage in BGB at Beaty and Larson wetlands.............. ................ 39

2-16 Phosphorus storage in AGB at Beaty and Larson wetlands................................40









2-17 Above and below ground biomass P storage. .................................. .................41

2-18 Phosphorus storage by species. ........................................ .......................... 42

2-19 Nutrient storage and growth in plants ........................................... ............... 47

3-1 Study site at University of Florida, Gainesville, Florida ......................................55

3-2 M esocosm diagram .......................................... ............... .... ....... 56

3-3 Inverse relationship of water depth and redox ...............................................57

3-4 H harvesting procedure ....................................................................... ..................59

3-5 Forage production per harvest for each species all treatments combined..............62

3-6 Cumulative forage production with all treatments combined ................................62

3-7 B ahiagrass treatm ent com prisons ...........................................................................63

3-8 Limpograss treatment comparisons........................................ 65

3-9 Forage production per harvest by treatment ........... .............. ........................66

3-10 Cumulative forage production by treatment....................... .................. 67

3-11 Below ground biom ass production...................................... ......................... 69

3-12 Above and below ground biomass production after 375 days ..............................70

3-13 Mean P concentrations for bahiagrass forage by harvest day and by treatments.....72

3-14 Mean P concentrations for limpograss forage by harvest day and by treatments ....73

3-15 Total P storage (AGB + BGB) after 375 days. .............................. ................74

3-16 Cum ulative P harvested in forage ........................................ ........................ 75

3-17 Relative comparison of root and shoot P storage after 375 days. ............................77

B-1 Species distribution by hydroperiod........................................ 94

B-2 Above ground biomass by species and zone. .................... .................96

B-3 Phosphorus concentrations by species. ........................................ ............... 98

B-4 Phosphorus storage by zone .............................................................................. 99

C-1 Relative root and shoot biomass after 163 days ..................................................107









C-2 Total biomass production after 163 days. ................................... ............... 107

C-3 Total biomass production after 375 days. ................................... ............... 108

C-4 Bahiagrass total biomass and P storage......... ........... ........... ............. 125

C-5 Limpograss total biom ass and P storage ..................................... .................125

C-6 Bahiagrass BGB and P storage........................................ ........................... 126

C-7 Limpograss BGB and P storage ........................................................ ............... 126

C-8 R oot to shoot P storage ratios.......................................... ........................... 128

C-9 Total P storage (AGB +BGB) at 163 days ......................................................128










Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

PHOSPHORUS STORAGE DYNAMICS IN WETLAND VEGETATION AND
FORAGE GRASS SPECIES: FACILITATING WETLAND HYDROLOGIC
RESTORATION IN THE LAKE OKEECHOBEE WATERSHED

By

Jeffrey D. Smith

August, 2006

Chair: Mark Clark
Major Department: Soil and Water Science

Nutrient export from agricultural activities in the Lake Okeechobee watershed has

contributed to eutrophication of the Lake and regulatory implementation of a phosphorus

(P) Total Maximum Daily Load (TMDL) rule. Historically, anthropogenic manipulation

of hydrology lowered water tables, creating improved conditions for upland forage grass

production. This action also increased runoff rates and P loading to the Lake. Hydrologic

restoration of historically isolated wetlands within the watershed is a proposed best

management practice (BMP) to increase P retention capacities of these wetlands.

However, longer hydroperiods could potentially decrease pasture productivity, and as a

consequence, adversely affect the economic viability of the cattle industry in the region.

Previous studies have shown that soils under longer hydroperiods in the Okeechobee

watershed have greater P storage potential than surrounding upland soils. This research

primarily focuses on the vegetative component of P storage in pasture wetlands. The

objectives were to evaluate biomass production and P storage dynamics in vegetation

under various hydroperiods and to determine the efficacy of using an alternative forage

grass species to maintain pasture productivity after wetland restoration.








Four isolated wetlands in Okeechobee County, Florida were sampled in November,

2004; March, 2005; and July, 2005. In this study, total P storage in wetlands (with a 50-

m upland buffer) included soil (10 cm depth), below ground biomass (BGB), above

ground biomass (AGB) and litter components. Soil was the primary P storage component

representing greater than 88% of the total P stored in the wetlands, while BGB, AGB and

litter represented 8%, 3%, and 1% respectively. Total biomass (AGB+BGB) production

and P storage in biomass were inversely related to hydroperiod in wetlands at the more

intensively managed pasture, while P storage in biomass was positively related to

hydroperiod in wetlands at the less intensively managed pasture. Management intensity

(i.e., cattle density and pasture maintenance) may be influencing P storage capacities of

vegetative, and affecting the relationship between hydroperiod and P storage.

In a separate mesocosm study in Gainesville, Florida, Paspalum notatum Fhi,,','

(bahiagrass) and Hemarthria altissima 'Floralta' (limpograss), a wet-tolerant forage

grass, were evaluated under five different hydrologic treatments. Water levels were

stabilized at 10, 0, -10, and -15 cm relative to the soil surface, while the control only

received rain water and was allowed to drain completely. Limpograss had greater forage

(AGB) production and P assimilation than bahiagrass in all treatments. However,

bahiagrass had greater total biomass (AGB+BGB) production in all but the 10 cm

inundated treatment. Bahiagrass total P storage was only greater than limpograss in the

-10 cm water level. This indicates that limpograss has a greater hydrologic tolerance than

bahiagrass and similar P storage potential. Therefore, to maintain pasture carrying

capacity and vegetative P storage during BMP implementation, limpograss may be a

more suitable forage in restored pastures wetlands.














CHAPTER 1
INTRODUCTION

Nutrient export from agricultural activities in the Lake Okeechobee watershed has

contributed to eutrophication of the Lake and regulatory implementation of a phosphorus

(P) Total Maximum Daily Load (TMDL) rule. Historically, anthropogenic manipulation

of hydrology drained wetlands and lowered the water table, creating improved conditions

for upland forage grass production. This action increased runoff rates and P loading to the

Lake. Four priority basins occupy 12% of the watershed's area, but export 35% of the P

load entering the Lake (FDEP, 2001). Hydrologic restoration of historically isolated

wetlands is a proposed best management practice (BMP) to increase P retention

capacities of these wetland ecosystems, thus decreasing P loads entering the Lake.

However, longer hydroperiods could potentially decrease pasture productivity, and as a

consequence, adversely affect the economic viability of the cattle industry in the region.

Previous studies have shown that soils under longer hydroperiods in the Lake

Okeechobee Basin have greater P storage potential than surrounding upland soils

(McKee, 2005). This research primarily focuses on the vegetative component of P

storage in pasture wetlands. The objectives were to evaluate biomass production and P

storage dynamics in vegetation under various hydroperiods and to determine the efficacy

of using an alternative forage grass species to maintain pasture productivity after wetland

restoration.









Problem

In 1998, Lake Okeechobee was listed as a water quality limited (WQL) water body

(FDEP, 2001). This condition was the result of over 50 years of excessive pollutant

loading. Nutrients, dissolved oxygen, unionized ammonia, chlorides, coliforms and iron

have threatened numerous societal and environmental values of the Lake, contributing to

eutrophication, resultant algal blooms, and subsequent alterations of flora and fauna

species composition (SFWMD et al., 2004).

Phosphorus is often considered the limiting nutrient in freshwater aquatic systems

(Reddy et al., 1999b). The development of agriculture (predominately animal operations)

in the mid 1900's, along with anthropogenic manipulation of hydrology in the watershed

has drastically increased P loading to the Lake (Havens et al., 2005; Reddy & Debusk,

1987). Between 1968 and 2004, the P concentration in the pelagic zone of the Lake

(Figure 1-1) increased from -40 [tg L-1 to over 120 [tg L-1 (Flaig & Reddy, 1995; Havens

et al., 2005). The measured P load entering the Lake in 2004 was 548 metric tons, while

the five year rolling average between 2000-2004 was 528 metric tons P (Havens et al.,

2005).

In 2001, 14 of the 29 drainage basins within the Lake's watershed were exceeding

their P loading targets (FDEP, 2001). Several BMPs intended to reduce P export from

agricultural lands within the watershed have been or are in the process of being

implemented (Table A-i, Appendix A). In the 1980's the implementation of BMPs

significantly reduced P concentrations in tributaries (Gunsalus et al., 1992). However, P

concentrations and loads continue to exceed established target loads (Table A-2,

Appendix A).











Lakewater Total Phosphorus
140
120
a. e*
a 100 -
c *- *
o 80 e
60 -
i 40 ..... .......................................
a,
0
S20 Restoration Goal = 40 ppb


68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98
Year


Figure 1-1. Phosphorus concentrations in Lake Okeechobee (Lake Okeechobee Issue
Team & South FLorida Ecosystem Restoration Working Group, 1999)

Regional Characteristics

Everglades

Historically, Lake Okeechobee was the central component of the Everglades

Ecosystem, interconnecting the Kissimmee River Basin in Central Florida to the

Everglades in South Florida. Central Florida's Upper Chain of Lakes were the

headwaters of the Everglades. Water migrated south through the Kissimmee River Basin

into the northern part of Lake Okeechobee. The Lake was a natural detention basin,

releasing water over its southern banks during times of high water. Overflow water

migrated south through sawgrass prairies into a vast ridge and slough system, before

eventually seeping into Florida Bay.

Currently, as a result of anthropogenic flood control and land development, Central

and South Florida hydrology is drastically different (Figure 1-2). In 1928, over 2000

people were killed when the storm surge from a major hurricane flooded hundreds of

acres surrounding Lake Okeechobee (Kleinberg, 2003). That spurred the effort to









construct the Herbert Hoover Dike around the Lake for flood control. This, in turn,

reduced the size of the Lake to 650 square miles and altered natural hydrologic

fluctuations by creating a dependence on a vast system of water control structures. In the

decades that followed, the Kissimmee River was channelized, the Caloosahatchee River

was dredged and St. Lucie canal was built to divert water from the Lake out to the

Atlantic Ocean and Gulf of Mexico. The hydrology of the Everglades also became

systematically controlled with the construction of several dikes, levies, pump stations,

roads and canals.

















Figure 1-2. Historic (left), current (middle), and future (right) flow pattern of the
Everglades (Second Louisiana--Florida Ecosystem Restoration Information
Exchange, 2001)

As a result, the Everglades ecosystem is no longer a continuous River of Grass

(Douglas, 1947), but rather, several compartmentalized aquatic systems manipulated by

anthropogenic water control structures. Agricultural and urban development throughout

South Florida drained wetlands, thus, increasing anthropogenic control of hydrology. In

the early 1900's, the sawgrass prairies south of the Lake were drained for agriculture









development, converting the area into what is currently known as the Everglades

Agricultural Area (EAA).

The paradigm that drove these vast hydrologic alterations in South Florida was

based on the need for flood prevention in populated areas and cultivatable agricultural

land. Development and wetland drainage has spawned numerous resource dilemmas.

Over fertilization in agricultural sectors has been identified as a primary source of excess

nutrient loading to Lake Okeechobee and the Everglades' Water Conservation Areas

(WCA). As a result freshwater quality and supplies have been compromised. Large

quantities of water are diverted from Lake Okeechobee to the coasts to prevent high P

loads from entering the Everglades. However, this has degraded estuarine ecosystems on

both coasts. In addition drainage has caused rapid mineralization of organic soils and

accreted nutrients, which are ultimately transported to Lake Okeechobee or the

Everglades. The Everglades is a low nutrient ecosystem, with ambient P concentrations

of -10 tg L-1. Thus, discharging water with high P concentrations from the Lake into the

Everglades will be detrimental to the ecosystem. This reinforces the importance of

reducing P loading to Lake Okeechobee.

Lake Okeechobee's watershed

Lake Okeechobee is the second largest freshwater lake within the contiguous

United States with a surface area of 1,890 km2 and contributing watershed of over 6,000

km2. It provides numerous societal and environmental values including water supply for

agriculture and urban sectors, flood protection and a multi million dollar sport and

commercial fishing industry (SFWMD, 2004).

Agriculture is the primary land use within the watershed, occupying over half of

the land area, while wetlands and terrestrial ecosystems are categorically second










(SFWMD, 1995). Improved pasture, sugarcane, upland forest, rangeland, unimproved

pasture and citrus make up the largest percentage of agricultural land area (Table 1-1).

Table 1-1. Okeechobee watershed land use by percent of total land.
Lake Okeechobee Watershed
Landuse Hectares Percentage
Improved Pasture 182,590 24.02%
Wetlands 128,884 16.96%
Water 127,339 16.75%
Sugar Cane 91,409 12.03%
Upland Forest 69,479 9.14%
Rangeland 50,283 6.62%
Unimproved Pasture 30,829 4.06%
Citrus 22,941 3.02%
Urban and built-up 22,325 2.94%
Barren 6,284 0.83%
Dairies 5,209 0.69%
Row Crops 4,926 0.65%
Transportation, Communication, and Utilities 4,619 0.61%
Field Crops 4,147 0.55%
Woodland Pasture 3,935 0.52%
Fallow Crop Land 1,756 0.23%
Sod Farms 941 0.12%
Tree Nursery 907 0.12%
Horse Farm 397 0.05%
Fruit Orchards 378 0.05%
Aquaculture 165 0.02%
Other 164 0.02%
Ornamentals 74 0.01%
Other Grove 29 0.00%
Floriculture 8 0.00%
Table created from land use data (SFWMD, 1995)

Large areas of wetlands in the watershed have been drained for agricultural

development. Riparian and non-riparian (isolated) wetlands are abundant throughout

Lake Okeechobee's watershed, covering 17% (1,290 km2) of the area (Table 1-1). The

landscape in the northern part of the watershed was historically characterized by the

presence of numerous isolated wetlands. These wetlands are referred to as 'historically'

isolated because they lacked a surface water connection to tributaries, except overland

flow in times of flooding. Extensive drainage efforts established vast networks of ditches









and canals, short circuiting the natural water retention and nutrient assimilative capacity

of the landscape. Most isolated wetlands in the northern watershed were drained in mid-

1900's to support the rapidly increasing beef cattle and dairy industry; creating improved

pasture conditions for upland forage species (Flaig & Havens, 1995). Draining wetlands

was a trend that occurred throughout the country, ultimately depleting more than half of

the wetlands within the contiguous United States (Mitsch & Gosselink, 2000).

Phosphorus loading to Lake Okeechobee

Agricultural activities are responsible for 98% of all P imported to the watershed,

the majority of which is pasture fertilizer and dairy feed (Fluck et al., 1992). There is a

high correlation between P imports to the watershed and P loading to Lake Okeechobee

(Boggess et al., 1995). Non-point source runoff from agriculture, particularly, beef cattle

and dairy operations, is recognized as a primary source of P loading to the Lake (Flaig &

Havens, 1995)

Four priority basins within the Lake's watershed, S-65D, S-65E, S-154, and S-191

(Figure 1-3), have been identified as "hot spots" based on land use intensity and high P

discharge. These four basins occupy 12% of the land, and export as much as 35% of the

total P load entering the Lake (FDEP, 2001). Within these priority basins, 61% of the

land area supports agricultural activities (47% improved pasture, 14% dairy) (Figure 1-3),

while 11% of the land is occupied by partially drained or otherwise impacted wetlands

(Figure 1-4). In fact, 45% of isolated wetlands in the priority basins have been at least

partially drained.






























dairies
I improved pastures
unirrproved pastures/ rangelands
0 5 10 20lometers
MENOMOEEEEEEEEEilomneters


Figure 1-3. Land-use map of four priority basins of the Lake Okeechobee watershed
(McKee, 2005)


















5 10 20 4. -
--











0 5 10 2lometers -


Lake Okeechobee ,,

Figure 1-4. Wetland coverage in the priority basins (McKee, 2005)

Policy and Planning

While flood control is still a priority, the water retention and contaminate

assimilative capacity of wetlands are now widely recognized. In the face of increasing

population and water demand in South Florida, major efforts are underway to carry out

some of the largest ecological restoration projects ever undertaken. The Comprehensive

Everglades Restoration Plan (CERP) (SFWMD & USACE, 2005), the Lake Okeechobee

Protection Plan (LOPP) (SFWMD et al., 2004), and the Kissimmee River Restoration

Program (KRR) are umbrella programs designed to preserve and protect water resources

in Central and South Florida.
in Central and South Florida.









CERP is an $8 billion restoration project, comprised of over 60 major projects with

the underlying objective to preserve and project the quality and supply of freshwater

resources. Currently, an average of 1.7 billion gallons of fresh water is diverted from

Lake Okeechobee out to sea annually (SFWMD, 2005). CERP will attempt to capture

and store most of that water in new reservoirs and Aquifer Storage and Recovery (ASR)

wells.

Lake Okeechobee Legislation

A water body is considered WQL or impaired when its pollutant load exceeds

water quality standards for its designated use. Lake Okeechobee is designated as a Class I

or potable water supply (FAC). In compliance with Section 303(d) of the Clean Water

Act (CWA) the establishment of TMDL is required for all impaired water bodies (FDEP,

2001). Since excessive P loading is primarily responsible for eutrophication of Lake

Okeechobee (Havens et al., 1995), a TMDL of 140 metric tons yr-1 P was developed to

achieve the target concentration of 40 ppb P within the Lake's pelagic zone by 2050.

Phosphorus is currently the only pollutant with a required TMDL requirement for Lake

Okeechobee.

Water quality problems in Lake Okeechobee have been widely recognized since the

late 1960's (Allen et al., 1975; Flaig & Havens, 1995; Fluck et al., 1992; Gunsalus et al.,

1992; Gustafson & Wang, 2002). In 1987, the Surface Water Improvement and

Management (SWIM) Act (Florida Statutes, Sections 373.451 and 373.4595) was

developed to focus on preservation and restoration of some of Florida's most significant

water bodies. Lake Okeechobee was named in that act, specifically mandating a 40%

reduction of P loads in order to achieve a P concentration of 40 ppb in the pelagic zone.

It regulated P sources from dairies by implementing farm buyout programs, BMPs and









structural retrofits to control systems (i.e. lagoon systems). As a result, P loads entering

the tributaries between the late 1980's and 1990's were reduced (Gunsalus et al., 1992).

However, by the mid-1990's loads still exceeded SWIM targets and the load reduction

trend was no longer declining (LOAP, 1999). This resulted in legislative action that

called for more aggressive action than mandated by SWIM.

The Lake Okeechobee Protection Act (LOPA) (Chapter 00-130, Laws of Florida)

was passed in 2000. LOPA mandated the implementation of a restoration and protection

program which includes a P TMDL and BMPs to reduce nutrient loading to the Lake.

The Lake Okeechobee Protection Program (LOPP) was developed to achieve and

maintain compliance with Florida water quality standards. It involves the

implementation of a P TMDL along with other research and monitoring objectives

required by LOPA. (SFWMD, 2004). In addition, the Lake Okeechobee Watershed

Project (LOWP) is a component of CERP that aims to reduce P loading to the Lake,

attenuate peak flows and restore riparian and isolated wetland habitat. LOPP and LOWP

contain similar P source control programs through the implementation of "voluntary" and

cost-share BMPs; however LOPP addresses regional projects not included in CERP.

Phosphorus Best Management Practices

Best Management Practices are conservation guidelines developed using Best

Available Technology (BAT) to reduce point and non-point source water pollution while

maintaining economically viable agricultural productivity (Bottcher et al., 1995).

However, the success of a BMP is only as effective as its level of acceptance. In fact,

overcoming social and political obstacles may be more challenging than the fundamental

science supporting the BMP, thus innovative educational approaches that facilitate an

understanding of potential costs and benefits associated with implementing the BMP is









necessary (Bottcher et al., 1995). The success and cost-effectiveness of BMPs are

dependent on regional goals and BATs. However, in all cases, mutual awareness of

potential outcomes and willingness to compromise by all parties is essential to achieve

BMP objectives and maintain the economic viability of the land-use.

Many P BMPs targeting dairy and cattle sectors have been implemented in the

Lake Okeechobee drainage basin (Table A-i, Appendix A). Required implementation of

P BMPs as part of the Rural Clean Water Program (RCWP) and the Okeechobee Dairy

Rule (FAC, 1996) have effectively reduced P discharge from dairies by 50%, thus

improving discharge water quality (Gunsalus et al., 1992). However, the discharge

reductions are relative to previous discharges, which may have been several times greater

than acceptable concentrations, and have not been enough to lower the overall P load

entering the Lake.

Hydrologic Restoration of Isolated Wetlands

Hydrologic restoration of historically isolated wetlands is a potential BMP that

could play a significant role in meeting the P TMDL of 140 metric tons yr Wetlands

are known to assimilate and immobilize nutrients and other contaminates in living and

dead (detritus) plant biomass and in soils. Phosphorus storage in wetland soils is

dependent on the P concentrations of the overlying water column and the sediment pool.

Organic matter accumulation and the abundance of iron and aluminum oxides within the

soil influences sediment P flux with the overlying water column (Reddy et al., 1999b).

Anaerobic decomposition is a slow process that facilitates the accumulation of organic

material. The accumulation of organic material immobilizes P in the process, thus acting

as a P sink as long as anaerobic conditions persist. Phosphorus assimilation in living

biomass is short term process which can re-release labile nutrients back into the









environment upon senescence and decomposition. However, P accretion in plant

biomass has been shown to account for 12-73% of total P removal from nutrient enriched

waters (Reddy & Debusk, 1985).

Phosphorus in wetland soils

Mckee (2005) conducted a survey of 118 wetlands within the four priority basins to

determine P storage in soils of isolated wetlands. Wetlands on dairy, improved and

unimproved pasture land-uses were divided into center, edge, upland and ditch zones and

sampled. Physical parameters, such as organic matter content and bulk density between

wetland center and upland were significantly different when compared between like-land

uses. Total P (TP) analysis showed significantly higher concentrations in wetland centers

compared to uplands for all three land use types. There were also significantly higher TP

concentrations in wetland centers compared with edge or ditch soils for improved and

unimproved pasture land use types.

Across land-uses, Mckee found significantly greater center and edge TP

concentrations in dairy wetland soils than in improved and unimproved pasture wetland

soils. However, no significant difference was found between improved and unimproved

pasture land-use types. Center and edge soils from different wetland types were also

significantly different. Forested swamp soils had significantly higher center TP values

than emergent marshes and open water emergent marsh soils, while edge concentrations

were significantly greater in scrub-shrub swamps compared to emergent marsh and open

water emergent marshes. There were also significantly greater P concentrations in edge

soils of forested swamps compared to emergent marshes.

McKee's results theoretically support a hydrologic restoration BMP of isolated

wetlands as a means to increase P retention in the watershed and decrease P loads to the









Lake. Hydrologic restoration would raise the water table, and increase the zone of

inundation; maximizing the potential of the wetlands to accrete P in soils. However, this

would also cause a shift in species composition, likely decreasing upland forage grass

production, and pasture carrying. Thus, a consequence of restoration may adversely

affect the economic viability of cattle operations as productive pasture area would likely

be reduced.

Alternative forage crops

BMPs with the potential to negatively impact economic viability should not be

considered BMPs, unless alternative funds are available to subsidize their implementation

(Bottcher et al., 1995). However, along with hydrologic restoration, alternative practices

could be implemented to minimize forage loss or even enhance pasture productivity. Wet

cropping systems have been suggested as potential means of reducing P imports to the

watershed and concentrations in dairy wastewater by utilizing a vegetative species with

high P assimilative capacity and sufficient forage value (Reddy et al., 2003). A forage

species could remove the majority of P in a treatment wetland, while a periphyton cell

would act as a polishing mechanism to further reduce P concentrations.

Since, P storage in vegetative biomass is short-term, wet cropping systems utilize

recycled nutrients to produce a forage, thus reducing the need for P imports to the

watershed. Wet cropping could also be a removal mechanism by harvesting and

exporting forage and assimilated P out of the watershed to be utilized in other agricultural

operations. Removal of sod from pastures is an effective way of export P because it is an

economically valuable product that can also reduce the cost of pasture renovations when

converting to more productive grasses.









Another option to maintain pasture carrying capacity after hydrologic restoration

is to utilize alternative forage species that have high productivity under wet conditions.

Hydrologic restoration will alter the water table, potentially, creating unsuitable

conditions for the existing dominant forage grass, Paspalum notatum F1i-,-,

(bahiagrass). Utilization of the wet tolerant forage grass species, Hemarthria altissima,

Tloralta', (limpograss) may be a beneficial substitute for bahiagrass, potentially reducing

the pasture area that would otherwise be lost if no alternative is implemented with

hydrologic restoration.

This has potentially positive economic implications for implementing a

hydrologic restoration BMP. If limpograss has comparable or higher forage production

and quality then this BMP may not only be ecologically beneficial, but it may also

provide an economic incentive to implement it.

Thesis Objectives

This research is part of a collaborative effort to evaluate the effectiveness of

hydrologic restoration of historically isolated wetlands as a BMP to enhance and utilize

the P storage potential within the Lake's watershed and reduce nutrient loading to the

Lake. More specifically, one objective of this research is to evaluate the role of

vegetation in wetland P storage. Since the implementation of wetland BMPs are, in part,

dependent on their level of acceptance, addressing landowner concerns for lost pasture

productivity is necessary. Another objective of this research investigates the efficacy of

using the wet tolerant forage species, Hemarthria altissima 'Floralta' (limpograss), in

upland areas, adjacent to wetlands, to alleviate the potential loss of productive pasture

due to hydrologic restoration. The thesis objectives are as follows:









I. Assess biomass production and P assimilation by wetland vegetation and
forage grasses under various hydroperiods.

II. Determine the efficacy of establishing a wet tolerant forage grass in wetland
transition zones before hydrologic restoration to minimize loss of productive
pasture

Chapter II focuses on standing biomass and P storage of various vegetative

components. Chapter III describes a mesocosm study that tested the hydrologic

tolerances of bahiagrass and limpograss. Chapter IV summarizes results from both

studies, discusses implications of wetland restoration and presents conclusions from this

study.














CHAPTER 2
PHOSPHORUS ASSIMILATION BY ISOLATED WETLAND VEGETATION

Introduction

Nutrient export, primarily phosphorus (P), from non-point source agricultural

activities in the Lake Okeechobee watershed has contributed to near hyper-eutrophic

conditions in the Lake (Reddy et al., 1999b). As a result, a Total Maximum Daily Load

(TMDL) rule for P and associated Best Management Practices (BMP) have been

implemented to reduce nutrient loading to Lake Okeechobee (Bottcher et al., 1995;

FDEP, 2001; Havens et al., 2005; SFWMD,2004). Many voluntary BMPs have

effectively lowered P exports from improved pasture and dairies in high P export basins

(Gunsalus et al., 1992) and in the Everglades Agricultural Area (EAA) (Flaig & Havens,

1995). However, in-Lake P concentrations currently average 120 [tg L-1; three times the

TMDL target concentration of 40 [ag L-1. Water column total nitrogen (TN) to TP ratios

in the Lake are 13:1, which favors cyanobacteria dominance (Havens et al., 2005)

Between 1994 and 1998, two of the Lake's northern tributaries, the Lower

Kissimmee River (LKR) and Taylor Creek/Nubin Slough (TCNS), supplied 43% of the

water, and 56% of the total P load entering the Lake. The ratio of water supplied to P

load for these two tributaries is disproportionate, LKR actually supplies 33% of the water

and 32% of the P load, while TCNS supplies 10% water and 24% of the P load (FDEP,

2001).

The high P discharge from these tributaries is primarily the result of four

"priority" drainage basins within their watersheds. These four basins (Figure 2-2) occupy









12% of the Lake's watershed, and export as much as 35% of the total P load entering the

Lake. Within the priority basins 68% of the land area supports agricultural activities

(45% improved pasture, 4% dairy), while 15% of the land is contains wetlands (SFWMD,

1995). The historical extent of wetlands is unknown; however, within the priority basins

45% of isolated wetlands have been at least partially drained (SFWMD, 2004). Ditches

that drain these wetlands act as a conduit for transporting dissolved P directly to the Lake.

Cattle ranching and agriculture have been the primary land uses in the watershed

since the mid 1800's. Beef cattle populations rapidly increased in the early to mid

1900's, spurring the drainage and transformation of native range lands into high

production improved pastures. From 1940 to 1970 the area of improved pasture

increased from 34,000 to 170,000 ha (Flaig & Havens, 1995) and by 1995 it occupied

183,000 ha of the watershed; -24 % (Table 1-1). The vast network of drainage canals

exacerbated nutrient loss from the landscape by lowering the water table and hydraulic

retention times (HRT), thus decreasing P assimilative potential of historically isolated

wetlands and perpetuating the need to import more nutrients (Flaig & Havens, 1995).

Extensive wetland drainage further intensified cattle production and increased P imports

to the watershed in the forms of cattle feed and fertilizer

Studies indicate that there is a strong correlation between P imports to watershed

and P loading to the Lake (Boggess et al., 1995; Hiscock et al., 2003). Continual net

imports of P have created an excess of bioavailable P. Soils in the northern watershed are

poorly drained and have limited P binding capacity, however, low topographic relief

limits runoff and subsequent P exports from uplands (Flaig & Havens, 1995). Boggess et

al., (1995) estimated that 90% of P imported between 1985 and 1989 was retained in the









watershed, while more recent estimates from 1997 to 2001 indicate an 83% retention of

imported P (Hiscock et al., 2003). In both studies, the majority of imported P was stored

in uplands (71% and 74%) however, of the portion that was loaded to wetlands, the

percent assimilated decreased over time from 60% to 32%. Hiscock et al., (2003)

attributes the reduction in storage to decreased assimilative potential, not decreased

wetland area. This suggests that many wetlands may already be saturated with P, and

even if imports to the watershed decrease or stop, they may become a source rather than a

sink. Therefore, reducing water flow, in addition to P imports, may be the most effective

way to reduce P loading to the Lake.

Factors Influencing Phosphorus Retention

Phosphorus is an essential nutrient for primary producers and is limiting in most

freshwater ecosystems. However, many agricultural wetlands are not limited by P, due to

its relative abundance and biogeochemical stability (Mitsch & Gosselink, 2000). In the

Okeechobee watershed, Reddy et al., (1995) found that nitrogen (N) and P concentrations

in aquatic macrophytes' tissue are generally high, indicating that neither nutrient is

limiting plant growth. Other studies have determined that wetland plants with N:P ratios

below 14 are N limited (Koerselman & Meuleman, 1996). Despite the apparent

abundance of both nutrients, the N:P ratios in tributary macrophytes were between 4 and

6 (Reddy et al., 1995), suggesting that P is more abundant than N. Although, generally

speaking, wetland plants in the Okeechobee watershed are not considered to be limited by

P or N availability.

The physical, chemical and biological mechanisms controlling P assimilation in

wetland ecosystems has been well documented (Braskerud, 2002; Flaig & Reddy, 1995;

Gilliam, 1995; Kadlec, RH, 1999; Kadlec & Knight, 1996; Mitsch & Gosselink, 2000;









Reddy et al., 1999a; Reddy et al., 1999b; Richardson, 1985; Sharpley, 1995). Unlike the

biogeochemical cycles of nitrogen, carbon, sulfur and oxygen, P does not have a naturally

occurring gaseous phase. It is accreted in wetlands by immobilization, adsorption and

precipitation processes (Figure 2-1). The relative portion of inorganic and organic forms

depends on soil, vegetation, hydrology and land use characteristics (Reddy et al., 1999a).

Adsorption and precipitation are abiotic processes that occur in the soil and are indirectly

controlled by pH. Immobilization is a temporary biotic process by which dissolved

inorganic P is assimilated in vegetative or microbial biomass as organic P. Vegetative

biomass has a high rate of turnover; often several times a year in warmer climates. After

senescence, a portion of labile P leaches back into the water column as the detrital

material breaks down. A small portion of P in recalcitrant detritus is accreted in the soil

as organic P.

Phosphorus assimilation in vegetation is dependent on species productivity and

turnover rates, nutrient availability, land-use intensity, hydrology, and biochemical and

physicochemical properties (Reddy et al., 1999a). Biomass is not be considered a

sustainable long-term P removal mechanism in wetlands because it is a short-term storage

that releases as much as 80% of assimilated P back into the water column after

senescence (Reddy et al., 1995). However, accretion of recalcitrant biomass residuals

(detritus) is the only sustainable long-term storage mechanism for P removal by

biological means (Kadlec & Knight, 1996; Richardson, 1985).












SLOW
INORGANIC



I PUARYV P I


A.
RAPID CYCUNG ORGANIC & INORGANIC

ANIMAL


SLOW
ORGANIC


Figure 2-1. Mechanisms driving P cycling. A) Mechanisms driving forms of P
(Sharpley, 1995). B). Phosphorus cycling in wetlands.

Research Objectives

The use of constructed and restored isolated wetlands in Lake Okeechobee's

watershed has been suggested as a potential means of decreasing P loads entering the

Lake (Flaig & Reddy, 1995; Havens et al., 2005; LOPA, 1999; Reddy et al., 2003; Reddy

et al., 1996; SFWMD, 2004). Since TMDLs are established based on both concentration









and flow, restoring the natural hydrology to isolated wetlands could reduce storm water

runoff while increasing wetland HRT and P accretion in residual organic material.

Previous studies in the four priority basins have shown significantly greater soil P

concentrations and organic material content in wetland centers than in adjacent uplands

(McKee, 2005). These findings suggest that hydrologic restoration could increase on-site

P storage in soils by increasing wetland area. A key component responsible for increased

P storage capacity in wetland centers is biomass production. High biomass and anoxic

conditions foster residual biomass and P accretion, stabilize soil porewater, and reduce

concentrations in surface water (Reddy et al., 1999b). Biomass production and P

assimilation are the primary focus of this research.

Research Questions and Hypotheses

1. What role does vegetative biomass play in total wetland P storage?

Hi: Biomass P storage will have a lesser role when compared to surface
soil P storage.

2. Does biomass differ along a hydrologic gradient?

H2: Biomass is higher in the center of the wetland

3. Does total P storage in standing biomass differ along a hydrologic gradient?

H3: Total biomass P storage will be higher in wetlands than uplands

4. Where is P partitioned in vegetation?

H4: More P will be stored in above ground biomass (AGB) than below
ground biomass (BGB)

While P export rates from various land-uses have been broadly established (Flaig &

Havens, 1995) the compounded influence of hydrology and grazing pressure on biomass

production and subsequent organic P storage in wetlands in the Okeechobee basin has not

been extensively studied. This chapter focuses on P storage in above and below ground









standing biomass and vegetative assemblages along hydrologic gradients in pasture

wetlands. Data presented in this chapter will be compared to vegetation data after

hydrologic restoration to evaluate the effect on total wetland P storage and vegetation

dynamics. Methods focus on above ground biomass (AGB) and below ground biomass

(BGB) sample collection, processing and laboratory analysis. In addition, soil and litter

samples were collected and pre-sampling photographs of each quadrate were taken.

Materials and Methods

Study Sites

Four wetlands from two different ranches located in the priority basins were

selected for long-term monitoring. Selection criteria were based on land use intensity and

proximity of two similarly sized, hydrologically modified wetlands. The Larson site,

located in basin S-154, is more intensely managed then the Beaty site, located in basin S-

65D (Figure 2-2). Management intensity was subjectively determined based on land-use

history, pasture maintenance regime and grazing pressure.

The two wetlands at the Larson site, Larson East (LE) (8056'28.08" W,

27o20'56.06" N) and Larson West (LW) (80056'47.49" W, 27o20'59.27" N), are roughly

2.5 ha each, while the Beaty wetlands, Beaty North (BN) (8056'54.50" W, 2724'41.41"

N) and Beaty South (BS) (8056'43.21" W, 2724'27.53" N), are roughly 1.3 and 1.4 ha

respectfully (Figure 2-3). Wetland size was calculated in ArcGIS. The perimeter was

delineated on site with GPS tracking by walking along vegetation community transitions

between upland forage grass (Paspalum notatum) and unconsolidated wetland species

(Juncus effusis).


































Figure 2-2. Map of land use in the 4 priority basins. S-191 is the Taylor Creek/Nubbin
Slough (TCNS) basin and S-65D, S-65E and S-154 part of the Lower
Kissimmee River (LKR) Basin The Larson site is located in basin S-154. The
Beaty site is located in S-65D.












a. b.

Figure 2-3. Isolated wetlands selected for long term monitoring. (a) Beaty Ranch
wetlands; top left wetland is referred to as Beaty North (BN) and the bottom
right wetland is Beaty South (BS). (b) Larson Ranch wetlands; wetland to the
left is referred to as Larson East (LE) and the wetland on right is Larson West
(LW)










Sampling

Data from three sampling events: November 19-21, 2004, March 25-26, 2005 and

July 14-16, 2005 were collected in this study. Based on results from McKee, 2005 who

found significantly greater soil P concentrations in wetland center zones than adjacent

uplands, a stratified random sampling scheme was used to sample wet marsh-(center),

transitional-(edge) and forage-(upland) zones (Figure 2-4). Respective zone data from all

sampling dates were combined and analyzed to minimize temporal variability. Five 1 m2

quadrates in each zone were located with GPS using predetermined random coordinates

from ArcGIS.


.... .. .. .. .. .. .. .. .. .. ..







.... .... ... .... .... ...

..pln


Figure 2-4. Stratified sampling zones: center, edge and upland. Five randomly placed
1 m2 quadrates (not drawn to scale) were sampled in each zone. Beaty North
shown as an example.









In the upland quadrates, AGB was cut as close to the ground as possible

(approximately 1 to 3 cm above the ground surface) using electric grass clippers, while

hand clippers were used in edge and center zones. All removable AGB from individual

quadrates was collected. After AGB was clipped, three BGB cores were extracted from

random locations within the quadrate with a 15 cm diameter aluminum cylinder, to a

depth of 20 cm. The majority of the soil was dry-shaken or wet washed in the field using

a 1 cm2 mesh sieve (depending on the whether water was present).

Since it was not possible to collect 100% of AGB in the quadrate by the initial

clipping, the remaining residual AGB (the stubble left over after clipping) was removed

from the BGB cores and placed in a separate bag. The amount of residual AGB per core-

surface area was extrapolated to estimate the total residual AGB not collected in the field

from the 1 m2 quadrate. This number was later added to the live biomass component of

AGB.

Sample processing

All samples were transported from Okeechobee County to Gainesville, Florida for

post-collection processing. Rather than homogenizing all biomass within the quadrate,

AGB of each species was sorted into living and senesced life stages. Both life stages of

individual species were sorted, weighed and analyzed separately as components of the

total biomass in the quadrate. Relative dominance of each species was determined based

on the quantity of living and senesced biomass relative to other species in the quadrate.

Above ground biomass was sorted into primary, secondary, tertiary, etc, and residual or

unidentifiable species. Due to the large quantity of AGB per quadrate, large

homogeneous samples, such as upland forage species, were sub-sampled and sorted by

life stage. Using the ratio of living to senesced biomass from sub-samples and the total









biomass of the homogeneous sample, the live and senesced portions were calculated

without processing all of the biomass.

Below ground biomass was washed and sieved to remove any remaining soil. All

sorted AGB and washed BGB samples were dried at 700C for 72 hours, and weighed.

Below ground biomass per m2 was calculated by extrapolating the biomass per core-

surface area up to 1 m2. All AGB that was sub-sampled was weighed and discarded. All

other samples were rough ground, sub-sampled and fine ground to pass through a # 40

sieve.

Laboratory Analysis

All samples were analyzed for Total Phosphorus (TP), Total Carbon (TC), and

Total Nitrogen (TN), although, since P dynamics are the primary focus in these studies,

only P data is presented in this chapter. Total P was extracted from 0.2-0.5 g of plant

tissue using the ignition method (Andersen, 1976).

Data and Statistics Analysis

Data were averaged by ranch (site), and therefore are averages from two wetlands.

Statistical comparisons were made between zones at each site. Sites were not statistically

compared. JMP Statistical Software was used to perform data analyses. For mean

comparisons of more than two parameters the Tukey-Kramer HSD (honestly significant

difference) test was used (JMP, 1989-2005). Outliers greater than four standard

deviations from the mean were excluded from data analysis. All quadrate values, except

outliers, were included in the calculation and statistical comparisons of total biomass,

total P storage, AGB and BGB. Calculations and comparisons of root-to-shoot ratios

only included quadrates that contained both BGB and AGB values.









Hydroperiods were determined for each quadrate elevation using stage data from a

pressure transducer located in the center of each wetland. To determine the length of

time the water level was above the soil surface, a bench mark elevation, which correlated

to the transducer level in the well, was established at the ground surface by the well. All

quadrate elevations were corrected relative to the benchmark elevation and the

transducer. The transducer recorded stage every half hour. The number of half hours the

water level was above the ground was counted and converted to days. A regression of

days vs. elevation (stage) was developed for each wetland. Corrected quadrate elevations

were entered into the regression equation and the corresponding hydroperiod was

returned. Zone hydroperiods were an average of all quadrates within each specified zone

at each site (i.e., Beaty center hydroperiod was the average of all quadrates within the

center zones of both wetlands).

Results

SAppendix B contains numerous tables and figures of supplemental data and
statistical comparisons.

Species Composition along a Hydrologic Gradient

Center, edge and upland zones within each wetland were determined visually using

vegetative community compositions and aerial images. Overall, center, edge and upland

zones at the Beaty site had longer hydroperiods than zones at the Larson site (Table 2-1).

The average hydroperiods of center and edge zones at the Beaty site were -122 and -96

days longer than the same zones at Larson. In fact, Beaty edge zones had similar

hydroperiods as Larson center zones.










Table 2-1. Mean and standard deviation of hydroperiods at each site.
Hydroperiod
Site Zone n (days) *Difference p value
Center 38 269 48.9 a
Beaty Edge 38 141 67.4 b < 0.01
Upland 38 31.2 64.9 c

Center 33 147 63.9 a
Larson Edge 34 44.5 28.4 b < 0.01
Upland 47 9.11 15.3 c
Mean comparisons using Tukey-Kramer HSD test.


Table 2-2. Mean species hydroperiod of both sites.
Species Hydroperiod by Site
Site Species n Indicator Days Range Difference p value
Andropogon 4 FAC 77.7 68.2 0-128 BC
Baccopa 1 OBL 150 150-150 ABC
Eleocharis 1 OBL 312 312-312 ABC
Juncus 30 OBL 155 74.3 0-302 B
Ludwigia repens 3 OBL 239 67.7 161-283 AB
Luziola + P. acuminatum 3 FACW 154 6.35 150-161 ABC
Micranthemum 1 OBL 161 161-161 ABC
Beaty Other 32 178 95.7 0-314 B < 0.01
P. notatum 39 UPL 45.3 70.9 0-304 C
Panicum 31 OBL 250 86.5 0-323 A
Polygonum 14 OBL 215 88.8 0-314 AB
Pontederia 19 OBL 269 38.7 171-315 A
Sagittaria 1 OBL 297 297-297 ABC
Utricularia 1 OBL 298 298-298 ABC

Alternanthera 10 OBL 56.9 38.5 16-121 YZ
Eleocharis 2 OBL 43.5 12 35-52 XYZ
Juncus 8 OBL 54.4 27.5 0-87 YZ
Ludwigia repens 1 OBL 67 67-67 XYZ
Luziola + P. acuminatum 25 FACW 127 72.2 16-240 X
Larson Other 30 66.1 68.9 0-282 Y <0.01

P. notatum 42 UPL 11.4 17.8 0-66 Z
Panicum 2 OBL 66 72.1 15-117 XYZ
Polygonum 16 OBL 93.6 52.6 15-195 XY
Pontederia 7 OBL 164 54.2 70-227 X
Shaded areas represent species that were present at both ranches. Indicators: (OBL)
Obligate Wetland, (FACW) Facultative Wetland, (FAC) Facultative, (UPL) Upland
Species (Tobe et al., 1998).










Average hydroperiods of species present at both sites were longer at the Beaty

wetlands than at Larson (Table 2-2). These differences are similar to the zone

hydroperiod differences between sites (Table 2-1). For instance, the average hydroperiod

of Juncus effusis, Pontederia cordata, and Polygonum hydropiperoides were 100, 105

and 121 days longer at Beaty than at Larson.


1.00X species
0.90- : > z Alternanthera
0 0 A ^X. Andropogon

.0.70 + XX Eleocharis
I 'o muncus
o 0.60- o ] + + Ludwigia
10.50- oo o ++ 1 Luzioa + P.acumintum
0O.50 o N- + + Micr+nthemm+


l .2- ,PanicMn
0.20- Polygonum
S0.0 x Pontederia
So 7* Sagittaria

0.00- Ultriculari
0 40 80 120 160 200 240 280 320
Hydroperlod (days)

Figure 2-5. Logistics fit of species. Negative log-likelihood or uncertainty relative to
hydroperiod for all wetlands. This figure illustrates the relative dominance of
species as a percentage of the total species present at a given hydroperiod
Community biodiversity is greatest between upland and center zones
(R2=0.096)

The logistics fit of species (Figures 2-5 and 2-6) account for the likelihood that a

species will be present under a given hydroperiod. It quantifies dominance of individual

species relative to other species at the same hydroperiod based on frequency of

occurrence. It does not quantify biomass. Figure 2-5 shows the species distribution

relative to hydroperiod in all wetlands. The Beaty wetlands, overall, had greater










biodiversity than Larson as measured by the number of species present. At both sites,

community biodiversity was greatest in the transitional-edge zones between upland and

center zones (Figures 2-6 A & B).


80 120 160 200
Hydroperiod (days)


1.00
0.90
.0.80
0.70
0.60
10.50
5 0.40
*|0.30
(n0.20
0.10
0.00


, z z z Z 2
0 40 80 120 160 200 240 280
Hydroperiod (days)


Species
z Alternanthera 0 Other
Andropogon a P. notatum
Baccopa t Panicum
Eleocharis Polygonum
o Juncus x Pontederia
Ludwigia Sagittaria
a Luziola + P. acuminatun Utricularia
Figure 2-6. Logistics fit of species by site (-log-likelihood). These figures illustrate the
relative dominance of species as a percentage of the total species present at a
given hydroperiod. A). Species distribution at the Beaty (R2=0.17) site is
dominated by bahiagrass in the upland and Panicum and Pontederia in the
center. B). Larson (R2=0.19) species distribution is also dominated by
bahiagrass in the upland, however, a mix of Luziolafluitans and Paspalum
acuminatum dominate the center zones.

Ecosystem Phosphorus Storage

For the purpose of this study, total P storage in wetlands (with a 50 m upland

buffer) included soil (10cm depth), AGB, BGB and litter components. At both sites, soil

was the primary P storage component, representing greater than 88% of the total P

storage in the wetlands, while BGB, AGB and litter represented 8%, 3%, and 1%

respectively (Figure 2-7). Harvested BGB was significantly greater (a = 0.05) than











standing AGB at the Beaty site (Figure 2-8 A), however, there were no significant P

storage differences between BGB and AGB at either site (Figure 2-8 B).


20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000


BGB Litter Soil AGB
Component


Figure 2-7. Phosphorus storage components. Soil within the top 10 cm stores more than
88% of the total P stored in these four components, while BGB, litter and
AGB roughly account for 8%, 1% and 3%, respectively. Table B-l in
Appendix B contains P storage totals by site, zone and component.

A. B.


2,500


2,000

'E
a 1,500


1,000


500


0


BGB AGB


2,500


2,000


1,500
E

1,000
IL


500o


0


T Beaty U Larson


T T


BGB


AGB


Component Component
Figure 2-8. Comparison of AGB and BGB components at Beaty and Larson. A). Mean
biomass harvested from all zones. B) Mean P harvested from all zones.













Standing Biomass

Total biomass (AGB+BGB) in upland zones, which mainly consisted of forage

grass, were similar at both sites, -1,900 g m-2. There were no significant differences in

total biomass between zones at the Beaty site, however Larson edge zones were

significantly greater (a = 0.05) than centers, and upland zones were significantly greater

(a = 0.05) than edges (Figure 2-9). The same relationship was true for BGB, which

accounted for 68-93% of total biomass (Figure 2-10). Upland BGB and AGB were

-2
similar at both sites; -1700 and -240 g m-2 respectively. Mean BGB was larger than

AGB in all zones at both sites (Figure 2-12). Center and edge zones at Beaty had

significantly greater (a = 0.05) AGB than upland zones, while Larson AGB was greater

in upland zones than in edge zones (Figure 2-11).

A. B.
3,000
a
3,000 a


9a 2,000

500 1,500
E 2,000 E 2 b


o 1,000 1,000
1 ,ooo 1 ,oo
500 500


Center Edge Upland enter Edge Upland
Beaty Larson
Site and Zone Site and Zone
Figure 2-9. Total biomass at Beaty (A) and Larson (B) wetlands. Different lower case
letters indicate significant differences. Note the difference in scales.















T i


Edge Uiand


2,500

2,000
E
1 1,500

S1,000

* 500


Edge -ald


Bey|| Larson
Zone and Sit Zone and Site
Figure 2-10. Below ground biomass at Beaty (A) and Larson (B) wetlands. Different
lower case letters indicate significant differences.


400


"0

I 2W





0


.- I Laman
Zoneand Sie Zneand Site
Figure 2-11. Above ground biomass at Beaty (A) and Larson (B) wetlands. Different
lower case letters indicate significant differences. Note the difference in
scales.


z600

zcxx


S1,000

S500-


0 Ie- I
Center


aoo


600

E
0 400




I a

0


-

-


Center










A. B.


500
0
;' (500)
S(1,000)
S(1,500)
S(2,000)
(2,500)
300 0fl


500
0

(500)
S(1,000)
S(1,500)
S(2,000)
(2,500)
(3.000)


.V lj ytJ i
Center Edge Upland Center Edge Upland

Beaty Larson
Zone and Site Zone and Site

Figure 2-12. Biomass partitioning AGB vs. BGB. At both sites, Beaty (A) and Larson
(B) there was more BGB than AGB in all zones

Approximately 20% of the quadrates did not have measurable biomass for both

AGB and BGB components. Therefore, since total biomass is the sum of AGB and BGB,

one component made up 100% of the total biomass for -20% of all quadrates. This

occurred when the quantity (mass) of biomass within individual quadrates was below the

harvestable threshold. In some zones standing AGB was limited by grazing, while the

quantity of harvested BGB was dependent on the types of species present within

individual quadrates. It is possible for the total biomass of a quadrate to be composted of

100% AGB and no BGB. For example, spreading ground cover species such as P.

hydropiperoides, Panicum hemitomon, Luziola fluitans and Paspalum acuminatum may

have been rooted outside of the quadrate, but AGB from plants may have grown into the

quadrate.

Since BGB root to shoot ratios (Table 2-3) were only calculated in quadrates that

contained both AGB and BGB components, they are slightly different from relative AGB

and BGB zonal averages (Figure 2-12). Ratios at both sites were greater than one in all


T
T






T-









zones, indicating that BGB was greater than AGB. Ratios at Beaty were lower than

Larson and were not significantly different by zone. Larson edge zones had significantly

greater (a = 0.05) ratios than the upland zones (Table 2-3).

Table 2-3. Root to shoot ratios by zone.
BGB:AGB
Site Zone n Ratio *Difference p value
Center 31 5.73 6.21 a
Beaty Edge 29 8.36 22.2 a 0.52
Upland 31 10.4 16.3 a

Center 25 23.4 40.5 a,b
Larson Edge 28 149 340 a 0.02
Upland 39 17.5 48.5 b

Mean comparisons using Tukey-Kramer HSD test.


Standing Biomass by Individual Species

Unidentifiable AGB species were collectively labeled as "other", and represent a

combination of multiple species. The "other" category often yielded similar biomass

values as identifiable species. Overall J. effusis had the most AGB at the Beaty site,

while Paspalum notatum (bahiagrass) was greatest at Larson (Figure 2-13). At both sites

bahiagrass had the greatest AGB in upland zones. Biomass in edge and center zones at

Beaty was dominated by J. effusis, P. hemitomon, P. hydropiperoides, and P. cordata,

while Larson edge and center zones were dominated by J. effusis, P. hydropiperoides,

and P. cordata (Figure B-2, Appendix B)





























IT



& a-u' u E E E E u
00m .c W E Z


n 2
Wc O E


500

450

400

350

300

250

200-

150-



50




0 -0
w n


Figure 2-13. Above ground biomass by species for all zones (center, edge and upland).
A) Beaty wetlands. B) Larson wetlands

Phosphorus Storage in Biomass

Phosphorus storage in total biomass (AGB + BGB) was positively related to

hydroperiod at Beaty, while Larson wetlands had an inverse relationship (Figure 2-14).

Phosphorus storage in edge and upland zones did not differ much between sites, but

-2
Beaty center zones stored -1000 mg m-2 more P in total biomass than Larson centers.

Center zones stored significantly more (a= 0.05) P than uplands at Beaty, while the

opposite trend existed at Larson (Figure 2-14).












A. B.
3,000
a 2,000 a
2,500 a

2,000 b 1,500

E 1,500 E
0) 1,000
E E
1,000 -
500 -
500


Center Edge Upland Center Edge Upland
Beaty Larson
Site andZone Site and Zone

Figure 2-14. Total biomass P storage. A). Beaty wetlands were positively related to
hydroperiod, while Larson had an inversely relationship (a= 0.05). Different
lower case letters indicate significant differences between zones at the same
ranch sites

Below ground biomass P concentrations (Table 2-4) and storage (Figure 2-15 A)

did not differ between zones at the Beaty wetlands. At Larson, P concentrations in

upland BGB were significantly lower (a= 0.05) than center and edge concentrations

(Table 2-4). However, there was still a general trend of decreasing P storage from center

to upland (Figure 2-15 B), where upland and edge BGB stored significantly more (a=

0.05) P than center zones.

Phosphorus concentrations were significantly greater in AGB than BGB in all

zones at both sites. At Beaty, P concentrations were greater in center zones than in edge

and uplands. At Larson all zones were significantly different (a = 0.05); exhibiting a

positive relationship with hydroperiod (Table 2-5). The center and edge zones at Beaty

stored more P in AGB than upland zones, while Larson center zones stored more P than










edge zones (Figure 2-16). Phosphorus storage in BGB is larger than AGB in all zones at

Beaty and in edge and upland zones at Larson. While BGB made up the largest portion

of total biomass in all zones, AGB P concentrations drastically influenced total biomass P

storage. This was exhibited in Larson centers where AGB P storage was greater than

BGB (Figure 2-17 B) despite the fact that harvested BGB was greater than harvested

AGB.

Table 2-4. Below ground biomass concentrations by zone
Below Ground TP Concentration
Site Zone n (mg/kg) *Difference p value
Center 34 765 210 a
Beaty Edge 34 719 285 a 0.23
Upland 34 674 134 a

Center 28 781 136 a
Larson Edge 29 802 168 a 0.002
Upland 40 678 145 b
Mean comparisons using Tukey-Kramer HSD test.


A. B.
a
2,000 2,000
a
1,600 1a a 1,600

1,200 IE 1,200
E E



400400

0 0
Center Edge Upland Center Edge Upland
Beaty Larson
Zone and Site Zone and Site
Figure 2-15. Phosphorus storage in BGB at Beaty (A) and Larson (B) wetlands.
Different lower case letters indicate significant differences a= 0.05.










Table 2-5. Above ground biomass P concentrations by zone.
Above Ground TP Concentration
Site Zone n (mg/kg) *Difference p value
Center 122 1830 976 a
Beaty Edge 118 1340 702 b < 0.01
Upland 68 1340 623 b

Center 69 3150 1010 a
Larson Edge 80 2690 1060 b < 0.01
Upland 89 1650 647 c

Mean comparisons using Tukey-Kramer HSD test.

A. B.
a 1,2o a
1,000
1,000
800










Beaty Larson
Soo

E E 0oo



200 200


Center Edge U0and center Edge and
Beaty Larson
Zone and Site Zne and Site

Figure 2-16. Phosphorus storage in AGB at Beaty (A) and Larson (B) wetlands.
Different lower case letters indicate significant differences (a = 0.05).

The P storage root-to-shoot ratios were calculated the same way as the harvested

biomass root-to-shoot ratios; only quadrates that contained both BGB and AGB were

used in the calculation. All ratios were greater than one and did not differ significantly

by zone, meaning P storage was greatest in BGB. These ratios contradict mean AGB and

BGB values in Larson centers. Overall, ratio data suggests that AGB stores more P than

BGB in Larson centers (Table 2-6). The difference, once again, is that the ratios (Table

2-6) are an average of individual quadrate ratios within each respective zone, which only










included quadrates that had both AGB and BGB, where as P storage values (Figure 2-17)

of each component were averages of all harvested AGB and BGB with each respective

zone.

A. B.
1,500 1,500
1,000 1,000
5001,00
500
0
(05
E (500) E


(1,500) (1 1,000)
(2,000) (1,500)
(2,500) (2,000)
Center Edge Upland Center Edge Upland

Beaty Iarson
Zone and Site Zone and Site
Figure 2-17. Above and below ground biomass P storage.

Table 2-6. BGB to AGB P storage ratios
BGB:AGB P Storage
Site Zone n Ratio *Difference p value
Center 31 3.28 3.94 a
Beaty Edge 29 6.22 14.8 a 0.45
Upland 31 6.30 9.86 a

Center 23 6.50 10.2 a
Larson Edge 28 28.4 65.6 a 0.0548
Upland 39 6.76 15.3 a
Mean comparisons using Tukey-Kramer HSD test.

Standing Biomass by Individual Species

Compared to other species, P. hydropiperoides, which was predominately present

in center and edge zones, stored the largest amount of P in AGB at both sites (Figures 2-

18). J. effusis, P. hemitomon and "other" species were secondary AGB P storage species

in center and edge zones at Beaty (Figure B-4, Appendix B). "Other" AGB was a

secondary storage in Larson center and edge zones. Bahiagrass stored the most P in












uplands at both sites. At Larson, bahiagrass and J. effusis had similar P storage in upland


and center zones.


1400

1300

1200

1100

1000

900

800
E 700
E 600
500

400

300

200

100
0-5


800


700


600


500

E 400
E

300


0O


100


0


~m""EE`E E m
0) 0 CU
0 0 0 o u E

0 W J ~
0
LU 0- 0- 0-+
< CU Io~


L EE



Lu oi
oi
W ii:+


Figure 2-18. Phosphorus storage by species. A). Beaty ranch. B). Larson ranch.


Discussion


Hydrology


Hydrology controls many physicochemical mechanisms in wetlands and is the most


important determinant of wetland type and class (Kadlec & Knight, 1996; Mitsch &


Gosselink, 2000). Hydroperiod represents the number of days a year a wetland is


inundated. The hydro-pattern or hydrologic regime is characterized by five components:


1) duration, 2) frequency, 3) depth, 4) flow, and 5) timing or season of flooding. All of


these components influence the establishment of vegetation and nutrient availability









along hydrologic gradients. The wetlands in this study were under different hydroperiods

and presumably, different hydrologic regimes as the effectiveness of the ditches varied

between wetlands and sites.

Many external factors influence hydrology making it difficult to compare wetlands

under different management intensities. For instance, ditches at the Larson site

effectively drained more wetland area than ditches at Beaty. As a result, Beaty wetlands

were inundated most of the year, while the Larson wetlands were only inundated roughly

half of a year. Anaerobic conditions create an environment that is conducive for organic

matter accumulation and P immobilization. Thus, Beaty has more potential to

accumulate organic matter.

Species biodiversity was greatest in transitional edge zones, between upland and

center zones. Relatively stable environments under short or long hydroperiods favor the

establishment of obligate species communities (i.e. bahiagrass in the upland or Panicum

and Pontederia in wetland centers). While under moderate hydroperiods in the edge

zones ecotoness), fluctuating hydrologic conditions continuously eliminate and

regenerate species, decreasing mono-dominance and increasing facultative species

richness.

Ecosystem storage

Although P storage in soil is dependent on various site physicochemical

characteristics, it is often the primary storage component in wetland and terrestrial

ecosystems (Dolan et al., 1981; Richardson, 1985). Results from this study support Hi:

the role of vegetation in total P storage is significantly less than soil P storage in

wetlands. Although vegetation plays a lesser role in terms of active P storage, its









importance can not be overstated. It indirectly increases the P storage capacity of

wetlands as residual biomass decomposes and becomes incorporated into the soil.

Biomass in Pasture Wetlands

Biomass production studies in pasture wetlands are limited, probably due to the

intrinsically high variability of disturbances to these ecosystems. Different land use

intensity, hydrologic regimes and grazing pressure create high variability, and make it

difficult to compare wetlands at different sites. Many studies have identified

relationships between environmental gradients (i.e. hydrology) and vegetation

community establishment under stable conditions (Seabloom et al., 2001; Van der Valk,

A G, 1981; Van der Valk, A G et al., 1994; Wellings et al., 1988; Whigham et al., 2002).

However, species distribution and annual biomass are generally variable over time and

reflect a balance of current environmental conditions and historical recruitment events

(Seabloom et al., 2001; Whigham et al., 2002). Our study minimizes temporal variability

over one year, but does not represent long term seasonal variability that may be present in

response to climatic variability.

Generally, environmental gradients create patterns of biomass distribution within

wetlands. A three year study of restored agricultural wetlands in Maryland found AGB

was inversely related to hydroperiod (Whigham et al., 2002). In that study, wetlands were

divided into submersed, emergent/seasonal, and temporary zones; similar to the center,

edge and upland zones in our study, however, it appears that their zones my have been

under longer hydroperiods. Biomass was significantly greater in temporary zones

[uplands] than in the emergent/seasonal zones [edge] and the emergent/seasonal zones

were significantly greater than the submersed zones [center] in each of the three years.

Even if their zones were under longer hydroperiods, our AGB results do not support any









relationship to hydroperiod, indicating that other factors, such as management intensity,

are influencing AGB.

Overall, BGB was the largest portion of total biomass, regardless of zone. This

was not surprising since emergent wetland and terrestrial macrophytes generally have

greater BGB than AGB due to extensive networks of roots and rhizomes (Reddy et al.,

1999a). The inverse relationship between BGB and hydroperiod at the Larson site may

be the result of more intensive grazing pressure. There is an observable difference in

grazing intensity between the two ranches. While cows do not graze BGB, heavily

grazed AGB directly affects BGB production and plant survivability. Total biomass at

Larson had the same inverse relationship to hydroperiod. Larson wetland zones were

more heavily grazed and dominated by low-growing, unidentified annuals, Eleocharis

spp., and L. fluitans. Intense grazing followed by prolonged inundation discourages

recruitment of perennial species. Thus during flooding events, low growing species do

not survive inundated conditions. Ultimately, when water levels recede, bare soil is

exposed and subject to mineralization and erosion. The combination of intensive grazing

and flooding primarily favors annual species in Larson wetlands.

Disturbance Effects on Biomass

Harvested biomass results do not support H2: Biomass at Larson and Beaty can not

be correlated with hydroperiod. Low AGB and large, highly variable ratios in edge zones

are likely the result of the compounding effects of the hydrologic regime, intense pasture

management and higher grazing density. Once again this suggests a significant effect of

grazing on biomass and P storage.

External disturbances occur at multiple scales and can confound relationships

between environmental gradients and wetland structure (Magee & Kentula, 2005; Van









der Valk, A G et al., 1994). Direct impacts of grazing on wetlands often include

herbivory of vegetation, nutrient inputs, and soil trampling; all of which directly or

indirectly alter species composition (Clary, 1995; Steinman et al., 2003). Grazing was

not measured in this study; however, it is evident, based on these results and visual

observations, that grazing has a dramatic affect on species composition and standing

AGB. Bohlen et al. (2004) found differences in plant species assemblages in improved

and semi-improved pastures. In their study, the less intensively grazed, semi-improved

pastures were dominated by P. hemitomon, which has high forage value, while

intensively grazed, improved pastures supported more diverse plant communities

including J. effusis. In addition, they found that cattle exclusions within improved

pastures lead to an increase in P. hemitomon coverage. This suggests that preferential

grazing ofP. hemitomon may actually foster species biodiversity including unpalatable

species such as J. effusis (Bohlen et al., 2004).

Biomass results from this study suggest that there is high variability within the

same land use classification. Although all wetlands are in improved pastures, wetland

center and edge biomass results are not even similar between ranches Thus, one

limitation of this study, was a lack of replication among sites. To minimize variability,

site data contained mean values of both wetlands at each ranch. Whigham et al., (2002)

also found high variability between sites. This suggests that different management

intensities within the same land use can be highly variable between sites and may not

represent biomass dynamics within individual wetlands. Thus, comparisons between

sites should take into account management intensity and land use type.






47


Phosphorus Concentrations

Higher AGB P concentrations in center zones at both Larson and Beaty were

similar to the trend found by Whigham et al., (2002). In two out of the three years, AGB

P concentrations in the Maryland wetlands had a positive relationship with hydroperiod;

opposite of the standing AGB trend. They concluded that nutrient cycling processes are

less variable than spatial and temporal biomass differences (Whigham et al., 2002).

Another study found that vegetation in wetlands receiving treated sewage effluent

showed increased P concentrations in AGB in response to both increased water levels and

nutrient additions (Bayley et al., 1985).


z
O
Vmax

I-L


I / Luxury consumption LU
/ I
/>/ -
I- I I
Z
UJI
I A Nutrient Nutrient Nutrient 2
$/4/ I
z deficiencyy sufficiency toxicity

NUTRIENT SUPPLY -


Figure 2-19. Nutrient storage and growth in plants. Growth is typically maximized at
lower nutrient supplies than the maximum tissue storage potential (Reddy &
Debusk, 1987)

It is hypothesized that increased P availability in wetlands centers may facilitate

"luxury uptake" of P by obligate wetland species. This occurs when plants take up P

beyond their required needs for growth (Figure 2-19). Biomass production is usually

maximized at lower nutrient supplies, while nutrient uptake by plants is maximized at









higher nutrient levels. The difference between the growth and nutrient uptake rates is the

P storage potential (Reddy & Debusk, 1987).



Phosphorus Storage

Tissue P concentrations are also temporally and spatially variable and are not a

reliable indicator of long-term P storage. They can vary with plant age, season and

nutrient availability. For instance, P concentrations are typically higher in younger plants

than in mature plants (Reddy & Debusk, 1987). Phosphorus storage potential in plants is

a function of both tissue concentrations and the maximum standing crop (Reddy et al.,

1995; Reddy & Debusk, 1987). The maximum standing crop is often the primary

determinant of P storage. For example, Sagittaria latifolia had the greatest P

concentration of any species; however, because it was not prevalent in the wetlands, the

amount of P stored was relatively small. Whigham et al. (2002) found that P storage

varied between wetlands, but exhibited similar patterns of distribution as standing AGB.

Overall, total P storage at Beaty was positively related to hydroperiod, while

Larson was inversely related. The influence ofbiomass as the primary component of

total biomass P storage is evident at the Larson site. The total biomass (Figure 2-9 B)

and P storage graphs (Figure 2-14 B) exhibit similar general trends between zones.

However, vegetation in Beaty centers stored significantly more P than upland zones, even

though biomass results (Figure 2-9 A) did not differ by zone. Therefore, differences in P

concentrations along a hydrologic gradient are also influencing total P storage.

Reddy and Debusk (1987) found greater than 50% of the nutrients in emergent

macrophytes were stored in BGB portions of plants. Results from this study suggest that

the relative roles of biomass and concentration in P storage may vary between AGB and









BGB components. Since BGB makes up the majority of the total biomass in all zones at

both sites, it was expected to store the most P. Although it was not statistically

significant, AGB in Larson centers stored more P than BGB. Thus, high P concentrations

in AGB had a greater influence on P storage than biomass in the center zones at Larson.

Phosphorus storage results do not conclusively support either H3: total biomass P

storage would be greater in wetlands than uplands, or H4: more P will be stored in AGB

than BGB. Since the Beaty wetlands stored more P in center zones, and the Larson

wetlands showed the opposite trend, there is no conclusive trend and H3 was rejected.

Since BGB stored significantly more P than AGB in all zones except Larson centers, H4

was also rejected.

Conclusions

Based on biomass results that lack specific trends, and opposite P storage trends

along a hydrologic gradient, it is hypothesized that altered hydrology, management

intensity and grazing may be influencing environmental gradients in the Okeechobee

wetlands. The positive relationship between total biomass P storage and hydroperiod at

the Beaty site may be the combined result of longer hydroperiods and lower management

intensity (including grazing pressure) relative to Larson.

Despite ongoing disturbances (grazing) to these wetlands, P concentration gradients

in vegetation, which were positively related to hydroperiod at both sites, are similar to

those found in other studies. However, P storage in vegetation is short-term, highly

variable, and represents less than 10% of total P storage in wetlands. Soil stores the

majority of the total P in these wetlands; up to 90%. Below ground biomass and P

storage is greater than AGB in all zones with the exception of P storage in Larson

centers.









Historically high net P imports to the watershed have saturated the P assimilative

capacity of some wetlands, making them P sources rather than sinks. Hydrologic

restoration would increase HRT, anaerobic conditions and organic matter accumulation.

Presumably, over a prolonged period of time, if hydrology were restored, P imports were

significantly decreased, and grazing pressure was minimized, wetland P assimilative

capacity would increase, thus reducing P exports to the Lake. In addition to reducing P

loads to the Lake, restoration also stores water in the landscape, which potentially

reduces the Lake stage and discharge of fresh water to the coasts.














CHAPTER 3
FACILATATING WETLAND HYDROLOGIC RESTORATION WHILE
MAINTAINING FORAGE PRODUCTION: HYDROLOGIC TOLERANCES OF
PASPAL UM NOTA TUM AND HEMARTHRIA AL TISSIMA

Introduction

Background

Hydrologic restoration of historically isolated wetlands in the Lake Okeechobee

watershed is considered a Best Management Practice (BMP) to decrease Phosphorus (P)

loading to the Lake. The watershed has low geographic relief and many isolated

wetlands have been drained to create improved conditions for upland forage grass

species. Restoration of drained isolated wetlands involves blocking ditches or installing

water control structures to raise the water table back to historical levels, thus retaining

water and P within these wetlands. An increase in wetland stage could greatly expand

wetland footprints and zones of inundation, thus changing hydroperiods and hydrologic

regimes of restored wetlands. Long-term flooding with decreased stage fluctuations

would likely alter existing vegetative communities along hydrologic gradients, decreasing

upland forage productivity in areas adjacent to wetlands. Since hydrologic restoration of

isolated wetlands reveres the current management objective, landowner acceptance of

this BMP may depend on the introduction of alternative forage grass species that are

tolerant of prolonged hydroperiods and less frequent stage fluctuations.

The most commonly used forage species in Florida is Paspalum notatum Fl,,i,

('Pensacola' bahiagrass). Native to Central and South America, bahiagrass is a deep

rooted, warm-season perennial grass that was originally planted for forage and soil









stabilization in the southern United States(Violi, 2000). Bahiagrass is a resilient, low

maintenance species that is tolerant of a wide range of hydrologic and soil conditions;

however, it is best adapted to moist, sandy soils. It forms tough sod mats with a vast

network of stolons and roots, often to a depth of seven feet. (Chambliss & Adjei, 2006;

Violi). Ninety percent of its forage production occurs between April and September

(Mislevy, 2002). While bahiagrass does not seem to invade established communities, it

does dominate habitats and resists invasion from other species (Violi, 2000). Once

established, it is difficult to remove. It has been estimated that bahiagrass stolons can

store enough nutrients to remain viable for two to three years (Chambliss & Adjei, 2006).

Hemarthria altissima 'Floralta' (limpograss) is a forage species that has gained

popularity since it was introduced (USDA Plant Introduction 364888) in 1984. Native to

South Africa, limpograss was originally selected for its winter hardiness, producing as

much as 35% of its total annual production between November and March (Pate, 1998).

Limpograss was specifically selected for its persistence under grazing (Quesenberry et

al., 1984). It was the fourth limpograss cultivar released in Florida and is currently the

only one recommended for pasture establishment (Pate, 1998; Sollenberger et al., 2006).

Contrary to bahiagrass, it is best adapted to poorly drained sandy soils and is not

recommended for drought sands (Pate, 1998; Sollenberger et al., 2006). In fact, it grows

well in wet areas that are often continuously flooded during the wet season (Pate, 1998).

Both bahiagrass and limpograss are exotic species as defined by the Florida

Exotic Pest Plant Council. Bahiagrass is a naturalized exotic that was once listed as a

Category I invasive exotic but has since been removed from the list. Limpograss is a

listed as a Category II invasive exotic; meaning that it shows the potential to disrupt









native plant communities but has not yet increased in abundance and frequency to be

considered a major nuisance species. (FLEPPC, 2005).

Research Objectives

Previous studies have evaluated the forage quality of and animal performance on

bahiagrass and limpograss (Holderbaum et al., 1991; Holderbaum et al., 1992;

Kalmbacher, R. S. et al., 1984; Kalmbacher, R. et al., 1998; Long et al., 1986; Newman

et al., 2002a; Newman et al., 2002b; Pate, 1998; Quesenberry et al., 1984; Sollenberger et

al., 1988; Sollenberger et al., 1989), however for the purpose of this study the primary

objectives were to evaluate survivability, productivity and P storage under different

hydrologic conditions.

Limpograss has been recommended for use in moist sites in Florida (Sollenberger

et al., 2006), however, its specific hydrologic tolerance has not been evaluated.

Bahiagrass has shown short term tolerance to flooding (David, 1999), however,

ultimately over time it gets out competed by wetland species. There are multiple

environmental factors that determine ideal habitats for species, such as grazing intensity,

hydrologic regime, competition, and soil conditions. Hydrology can influence

competition and physicochemical soil interactions. It is often considered one of the most

influential determinants of establishment and persistence of wetlands plants (Mitsch &

Gosselink, 2000). The objective of this research was to evaluate the role of hydrology on

bahiagrass and limpograss in non-competitive mesocosm studies.

Research Questions and Hypotheses


1. Which species has greater forage production?

Hi: Limpograss will have greater forage production than bahiagrass









2. What are the hydrologic tolerances of bahiagrass and limpograss?

H2: Bahiagrass will have greater total biomass production in drier
treatments and limpograss will have greater total biomass production
in the wetter treatments.

3. Which species assimilates more P?

H3: Limpograss will have higher P storage

4. Where is P partitioned within the plant?

H4: Root to shoot P storage ratios for bahiagrass will be >1, and
limpograss will be <1

This chapter compares the effect of five different water level treatments on below

ground biomass (BGB) and above ground biomass (AGB) production and P storage of

both species in non-competitive mesocosm studies.

Materials and Methods

Experimental Design

This experiment was designed to evaluate the response of two forage grass species

to a range of hydrologic conditions typical of the transitional zone between isolated

wetland and improved upland pasture. To determine how hydrology affects productivity

and nutrient uptake, limpograss and bahiagrass were evaluated in fifteen non-competitive

mesocosms (1.33 m x 0.81 m x 0.76 m polyethylene tubs). Mesocosms were located in

Gainesville, Florida (29.60 N 82.30 W).

The experiment consisted of five treatments +10, 0, -10, and -15 (water levels in

centimeters, relative to the soil surface), and a control (rain water only and well drained),

which are discussed in the next section. There were three replicate mesocosms for each

treatment. Each mesocosm contained three sub-replicates (pots) of each species. Sub-

replicate samples were combined together into one composite sample of each species.









The sub-replicates were grown in 3 gallon (25 cm diameter x 20 cm deep) poly-

ethylene pots. Soil was collected from a pasture in Okeechobee, Florida, and

homogenized before being dispensed into pots. Propagules of both limpograss and

bahiagrass were harvested from pasture plots at the Range Cattle Research Center in Ona,

Florida. Soil was washed from the propagules and seven bare-root sprigs were planted in

each pot to establish monocultures of each species. Fifty-two pots of bahiagrass and 61

pots of limpograss were established 90 days prior to treatment. During the grown-in

phase, both species were watered regularly and pruned uniformly to stimulate new

growth.

0+10 C +10 C+10 -10-15






SC 0 -10-15 -10 0 -15
a. b.

Figure 3-1. Study site at University of Florida, Gainesville, Florida: (a) mesocosms were
aligned in two rows and randomly assigned a treatment. (b) Tubs receiving
water were hooked up to the potable water line between the rows.

Mesocosms were aligned in two rows and randomly assigned a treatment. Tanks

receiving water were hooked up to a potable water supply and external overflow stand

pipes, were used to maintain water levels for each treatment (Figure 3-1). Forty-five of

the healthiest (determined visually) pots of each species were selected and three pots of

each species were randomly placed into the 15 mesocosms. Each mesocosm contained

three pots of limpograss and three pots of bahiagrass. To simulate field conditions,

regulate temperature, and prevent oxygen production by photosynthetic algae in an open









water column, mason sand was used to fill the remaining space between pots (Figure 3-

2).
















Figure 3-2. Mesocosm diagram. Each mesocosm contained three pots of each species
embedded in mason sand. Water level in four of the five treatments was
maintained by a drip irrigation system and an external overflow-standpipe.

Treatments

Four of the five hydrologic treatments were maintained at a constant stage by drip

irrigation emitters and external overflow-standpipes, while the fifth treatment, the

control, only received rain water and was allowed to drain completely. Treatments

receiving water included an inundation treatment (+10 cm), where the water level was

maintained 10 cm above the soil surface, and three saturation treatments (0 cm, -10 cm, -

15 cm), where the water level was maintained 0, 10 and 15 cm, respectively, below the

soil surface. Rainfall data is listed in Appendix C Table C-31. These treatments will

hereinafter be referred to as +10, 0, -10, -15 and control (C).

The study was initiated (day zero) on July 1st, 2004. One week prior to this date,

water levels were gradually raised to their treatment levels. The timing coincides with

the approximate beginning of the wet season in central Florida. Soil redox was measured

in randomly selected pots of each treatment at a depth of 10 cm below the soil surface.









Redox values were inversely related to water depths suggesting the effect of saturation

and inundation reduced oxygen availability and increased anaerobic conditions in the

soils (Figure 3-3).



Water Depth




+10-


-10-
-15-

Mesocosm Redox
300
250
200




50 -
0-
10 0 -10 -15 C
Treatment


Figure 3-3. Inverse relationship of water depth and redox. This diagram illustrates the
inverse relationship of treatment water depth and measured redox potential
within the five treatments. Soil redox was measured 10 cm below the soil
surface.

Sampling

Soil, BGB, and two components of AGB were sampled over the course of one year.

The components of AGB were forage, consisting of all biomass above 15 cm, and

residual biomass (RB), the biomass that remained from 0-15 cm after harvest. Soil was

sampled at the beginning of the experiment (day 1), at the end of the first growing season










(day 163) and at the end of the experiment (day 375). Forage was harvested periodically

to evaluate temporal differences in biomass production P concentration and P

assimilation. Forage samples were collected on days 27, 55, 83, 163, 305 and 375. At the

end of the first growing season (day 163), two of the three sub-replicate pots of each

species in each mesocosm were harvested to determine BGB and RB production and P

storage. Forage, RB and BGB were harvested from the remaining pots in each mesocosm

on day 375. Sampling dates and biomass components harvested are listed in Table 3-1.

Table 3-1. Sampling dates and details.
Pots per
Date Day composite Component Sampled
sample
7/1/2004 1 3 Soil, Forage (for nutrient baseline)
7/28/2004 27 3 Forage
8/25/2004 5 3 Forage
9/22/2004 83 3 Forage
12/11/2004 163 3 Soil, Forage
12/11/2004 163 2 BGB, RB
5/2/2005 305 1 Forage
7/11/2005 375 1 Soil, Forage, RB, BGB
This table summarizes the sampling events, corresponding components sampled and
number of sub-replicates in composite samples. All pots in each tank where averaged by
species and pot (i.e., for biomass g pot- tank-1 species-1 = average g pot-' of each pot in
each tank of each species).

Soil

Two soil cores (1.8 cm diameter x 20 cm depth) from pots of the same species

within the same mesocosm were combined into one composite sample. Table 3-1

contains the number of pots in each composite. Roots and litter were removed from the

soil before being dried at 70 OC for 72 hours. The soil was than machine (ball) ground,

sieved through a #40 mesh sieve and stored at room temperature.

Above ground biomass sampling

Forage sampling was designed to simulate flash grazing, by periodically

harvesting all biomass over 15 cm. This height was established 5 cm above the highest









water level treatment to enable atmospheric gas exchange with the residual biomass for

all treatments. Composite samples of each species from each mesocosm were collected

using grass shears, a 15 cm-tall grated stand and a shop vacuum to ensure accurate

collection. The grate was set over a pot to establish the clipping height and the vacuum

was used to pull the grass through the grate and gather clipped material within the

vacuum (Figure 3-4). The vacuum was emptied after each composite sample per

mesocosm. The post-harvest processing procedure involved drying vegetation in a

drying room at 70 OC for 72 hours. Dry forage was than ground in a Wiley Mill, passed

through a #40 mesh sieve and stored at room temperature.










a. b. c

Figure 3-4. Harvesting procedure. (a) Clipping grass with 15 cm stand and vacuum. (b)
Close up view of clipping processes. (c) Vacuum was emptied after each
composite sample per mesocosm.


At the end of the first growing season (day 163) the RB was harvested from two

of the three pots within each mesocosm. The post-harvest processing procedure was the

same for all vegetative components. Residual biomass from each mesocosm was added

to the respective cumulative forage production for each species and treatment to

determine total AGB production g pot- after 163 days. The same procedure was carried

out on day 375 to determine total cumulative AGB production after 375 days.









Below ground biomass

Below ground biomass included all roots, rhizomes and stolons below the crown

of the AGB shoot. Once both components of AGB were harvested (days 163 and 375)

the root ball was removed from the pot and flushed with water to remove all soil. The

same post-harvest sample processing procedure used with AGB was also used with BGB.

Since harvesting BGB was a destructive process, sampling was only preformed twice. All

BGB data is a cumulative total and presented as BGB production or P storage after 163 or

375 days.

Laboratory analysis

Soil and biomass samples were analyzed for Total Phosphorus Ash (TP) using the

Ignition Method (Andersen, 1976), Total Nitrogen (TN) and Total Carbon (TC) as

described in Chapter II methods. Composite biomass and P storage were averaged by

treatments and reported on a grams per pot basis. In addition, soil was also analyzed for

plant available P using the Mehlich I dilute concentration strong acid extraction

procedure (Kuo, 1996).

Results

SSupplemental tables and figures containing all data and statistical comparisons are
reported in Appendix C.

Initial characterization

Daily environmental conditions including air and soil temperature, rainfall and

humidity are listed in Table C-31 of Appendix C. Total P ash (TP), total nitrogen (TN)

and total carbon (TC) tissue concentrations at day zero are listed in Table C-l in

Appendix C. Soil TP, TN and TC concentrations on day 0 averaged 0.003%, 0.092%,

and 1.65 % respectively. Forage tissue concentrations ranged from 0.15-0.18%, 1.25-









1.42%, and 42-44 % for TP, TN and TC respectively. The focus of this thesis relates to P

storage in vegetation, therefore, only TP data are reported beyond the initial conditions.

All production data for forage, RB and BGB are expressed either as production per

harvest or cumulative production (sum of net harvests over time) in grams of biomass per

pot. Total AGB is the sum of cumulative forage production and residual biomass

normalized on a grams per pot basis.

Forage Production

Data in this section is presented as overall production by each species regardless of

treatment. Overall, limpograss had significantly greater (a = 0.05) forage production per

harvest than bahiagrass on all sampling days except day 83 (Figure 3-5). In the first 83

days, both species exhibited a decline in forage production. However, by the end of the

first growing season (day 163), limpograss continued to producing forage while

bahiagrass was essentially dormant until the beginning of the following growing season.

Both species increased forage productivity between early-May and mid-July of the

second growing season. On a cumulative basis, limpograss had significantly greater

forage production on all sampling days (Figure 3-6). After 375 days, bahiagrass and

limpograss had produced 9.52 2.73 and 32.4 14.7 g pot- of forage respectively.





























I 5 5I I5 10I
0 25 50 75 100


Species
* Bahia
* Floralta


125 150 175 200 225 250 275 300 325 350 375 400
Days


Figure 3-5. Forage production per harvest for each species with all treatments combined.
Day 0 is July 1st 2004. Limpograss had significantly greater forage production
per harvest on all sampling days expect day 83 (Table C-2, Appendix C).



60- Species
SBahia

50. Floralta


40-

C.
C30-


20






0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400
Days


Figure 3-6. Cumulative forage production with all treatments combined. Each
consecutive harvest was added to previous harvests. Limpograss produced more
forage than bahiagrass on a 375 day period. (Table C-3, Appendix C).


35-

30-

25-
~2-
Z 20-

15-

10-

5-

0-


I
r






I







63


Bahiagrass forage production

To determine the response of each species to various water level treatments

comparisons were made between treatments of each species using the Tukey-Kramer

HSD test. There were differences in forage production between harvests (Figure 3-7A).

Throughout the experiment, the +10 and control treatments had similar forage

production. Between the second and third harvest the +10 treatment had significantly

greater forage production than the 0, -10, and -15 cm treatments. In addition, on day 375,

the control had significantly greater forage production than the 0 cm treatment. On a

cumulative basis, forage production across all five treatments ranged between 7.88 + 1.49

to 12.1 + 3.61 g pot- after 375 days and did not differ significantly between treatments.

All treatments exhibited similar cumulative production curves over time (Figure 3-7B).

A. B.
71- 1
6- 14-
5- 12-



E E

4-

0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Elapsed time Elapsed time

A + 10 cm Ocm -10cm + -15cm Control


Figure 3-7. Bahiagrass treatment comparisons. A) Biomass production per harvest; +10
cm treatments significantly greater than the 0, -10, -15 cm treatments on days
83, while the Control was greater than the. B) Cumulative forage production;
no significant differences between treatments. (Tables C-10 & C-l1,
Appendix C).









Limpograss forage production

Limpograss exhibited significant treatment effects after day 27. Initially, on day

55, the control had significantly greater forage production than the +10, 0 and -10 cm

treatments. However, on day 163 the control was the only treatment with a lower net

harvest than its previous harvest on day 83 (Figure 3-8A, Table C-12, Appendix C).

Although the span of time between harvests (days 55-83 and 83-163) are different, by

day 163, the +10 and -10 treatments roughly doubled the amount of forage produced

between days 55 and 83. On day 305, the +10 cm treatment had greater forage

production than the 0, -10, and -15 cm, while on day 375 the control had greater

production than the 0, -10, and -15 cm treatments.

The differences in limpograss production per harvest did not cause significant

differences in cumulative forage production between treatments until days 305 and 375.

On day 305, the production per harvest treatment differences were mirrored by

cumulative forage production. The +10 treatment had greater cumulative production

(27.0 + 2.16 g pot-) than the 0, -10, and -15 cm treatments after 305 days. On day 375,

like the individual harvest differences, the control had greater production (50.2 + 16.5 g

pot-) than the 0, -10 and -15 cm treatments. While the +10 cm treatment produced

significantly more forage (44.5 + 3.98 g pot-) than the -10 cm treatment (21.1 + 3.31 g

pot-) after 375 days, it was not statistically greater than the 0, and -15 cm treatments,

despite a power value of 0.95.










A. B.
40
35- 60-
30- 50-
1'25-
-E 40
20-
151 30-
m 10 + m 20

0* _ __ 10-
I Ii ll I'I I l l
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Elapsed time Elapsed time

A + 10 cm 0cm O -10cm + -15cm Control


Figure 3-8. Limpograss treatment comparisons. A) Forage production per harvest. B)
Cumulative forage production (Tables C-12 & C-13, Appendix C).

Species comparison

This section compares biomass production of limpograss and bahiagrass within the

same hydrologic treatments. Limpograss had similar or greater forage production than

bahiagrass on all harvest days, in all treatments (Figure 3-9). By the first sampling,

limpograss had significantly greater forage production than bahiagrass in all treatments.

Both species exhibited a decline in forage production per harvest in all treatments after

day 27 (Figure 3-9), where only the control and -15 cm limpograss treatments were

significantly greater than bahiagrass. However, limpograss rebounded and produced

significantly more forage than bahiagrass in all treatments by the final harvest of the

growing season (day 163). The same trend continued in the second growing season,

where limpograss had significantly greater forage production per harvest than bahiagrass

with the exception of the -15 cm treatment on day305. As a result limpograss had

significantly greater cumulative forage production after every harvest day (Figure 3-10)

























150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Elapsed time Elapsed time


0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Elapsed time Elapsed time

40E.
35- Floralta
30- g Bahia
525-
0 -2 0
E1
o /
10-
5-
0
0 50 100 150 200 250 300 350 400
Elapsed time

Figure 3-9. Forage production per harvest by treatment. A) +10 cm treatment. B) 0 cm
treatment. C) -10 cm treatment. D) -15 cm treatment. E) Control. (Table C-5
Appendix C)
















a.25-
0)

c 20-


15



E
5-
10

5-


U I I I I I I I 1
0 50 100 150 200 250 300 350 400
Elapsed time


40
*

-
E 30-
0
m
a)
>20-
Co
E
E 10-
0-


0 50 100 150 200 250 300 350 400
Elapsed time


O
-20-


I 15-


.10-
E
E
S5-


0 50 100 150 200 250
Elapsed time


35-

630-
a

225-
o
2 20-
0
a15-


2
5-


300 350 400
300 350 400


-1-














50 100 150 200 250 300 350 400
Elapsed time


'60-
-
0)
;50-

E 40-

. 30-
C6
S20-
E
3110-


Floralta

I Bahia












50 100 150 200 250 300 350 400
Elapsed time


Figure 3-10. Cumulative forage production by treatment. A) +10 cm treatment. B) 0 cm
treatment. C) -10 cm treatment. D) -15 cm treatment. E) Control. (Table C-6,
Appendix C).


o a
0 0


0-cc


-if I


,,









Total Biomass

After 375 days, bahiagrass total biomass (AGB + BGB) was significantly greater

than limpograss in the 0 and -10 cm treatments (Table 3-2). This was not consistent with

total biomass results in the first 163 days, where bahiagrass had significantly greater total

biomass production than limpograss in all treatments. This is primarily due to

significantly greater bahiagrass BGB in all treatments.

There were no significant increases in BGB production in any treatments between

days 163 to 375 for either species. However, there were significant BGB decreases in

bahiagrass +10 and -15 treatments (a = 0.05), and in limpograss +10 cm (a = 0.08) and

control (a = 0.05) (Figure 3-11 and Table C-9, Appendix C). In addition, forage

production increased more in limpograss than bahiagrass during the same time period

(Appendix C, Table C-5). This offset the differences in total biomass production

between species on day 163 to insignificant levels in the +10, -15 cm and control

treatments by day 375.

Table 3-2. Total biomass (AGB + BGB) after 163 and 375 days.
Total Biomass (g/pot)
Days Treatment n Bahia Floralta p value
+10 3 85.0 10.8 < 57.2 1.47 0.01
0 3 109 13.0 < 63.0 6.73 0.01
163 -10 3 113 2.78 < 72.0 10.7 < 0.01
-15 3 115 3.59 < 65.2 7.88 < 0.01
C 3 105 14.3 < 77.7 4.30 0.03

+10 3 76.6 3.00 < 84.5 6.51 0.13
0 3 112 18.6 < 76.5 2.92 0.03
375 -10 3 112 16.0 < 70.6 6.62 0.01
-15 3 85.4 11.6 < 76.0 21.1 0.54
C 3 113 17.7 < 99.6 20.8 0.45

It is counter intuitive that cumulative biomass could decrease, but since BGB was

only harvested twice, these data only represent the net BGB after 163 and 375 days, not










the variability within those time periods. Therefore, the quantity of bahiagrass BGB that

died was greater than the forage produced between 163 and 375 days, resulting in

negative net total production.

A. B.
120 55
110- o 50-
100- 45


5- + 25-

60- + 20-
50I 15-I I
150 200 250 300 350 400 150 200 250 300 350 400
Elapsed time Elapsed time

A +10 cm 0cm -10cm + -15cm Control

Figure 3-11. Below ground biomass production. A) Bahiagrass BGB production B)
limpograss BGB production (Table C-9, Appendix C).

Under constant inundation, both species will survive for at least 375 days.

However, cumulative biomass production for bahiagrass actually decreased between days

163 and 375, while limpograss increased. Both species appear to have been in an

acclimation phase between days 163 and 375. The lack of significant differences in total

biomass between species after 375 days in the +10, -15 and control treatments (Table 3-

2) indicate that those treatments were influencing total biomass productivity for both

species. Although not statistically significant, bahiagrass still had more total biomass in

the -15 cm and control treatments after 375 days, while limpograss had more in the +10

treatment.

Root to Shoot Ratios

In general BGB production had an inverse relationship AG forage production for

both species. The average BGB production for all bahiagrass pots, regardless of










treatment, was 85.6 14.0 g pot- after 163 days and 79.6 20.8 g pot- after 375 days.

Limpograss BGB production was 38.0 + 9.07 g pot- after 163 days and 29.5 8.5 g pot-'

after 375 days.

Bahiagrass maintained significantly more BGB than limpograss in all treatments

after 163 and 375 days. Relative portions of total AGB (residual biomass + forage) and

BGB for each treatment and species are graphed in Figure 3-12. In all treatments,

bahiagrass had significantly greater root to shoot ratios than limpograss after 163 days.

After 375 days, all root to shoot ratios for limpograss were less than one while bahiagrass

ratios were greater than one (Table 3-3). Thus, after 375 days limpograss produced more

AGB than BGB while bahiagrass produced more BGB than AGB.


85 -

60 -

35 -


(15)
(40)

(65)
(90)

(115)
Bahia Floalta Bahia Floralta Baha Floralta Baha Floralta Bahia Floralta

+10 +10 0 0 -10 -10 -15 -15 C C

Treatment and Species


Figure 3-12. Above and below ground biomass production after 375 days. Above
ground biomass (top) is the sum of cumulative forage production and residual
biomass. Below ground biomass (bottom) is all biomass harvested below the
soil surface after 375 days.












Table 3-3. Root to shoot ratios.

Day Treatment
+10
0
163 -10
-15
C

+10
0
375 -10
-15
C


Root:Shoot Between Species
Bahia Floralta
3.55 0.56 > 0.94 0.19
5.06 2.27 > 1.17 0.22
4.25 0.85 > 1.58 0.40
5.35 0.80 > 1.42 0.34
4.22 1.19 > 1.49 0.15

2.11 0.21 > 0.31 0.07
5.48 2.59 > 0.81 0.23
5.76 0.43 > 0.90 0.39
4.26 0.76 > 0.95 0.19
3.91 1.21 > 0.36 0.15


Phosphorus Assimilation

Phosphorus tissue concentrations

Phosphorus concentrations varied by species and by treatment. On day 0, the only

significant difference in tissue concentrations between species was in the 0 cm treatment

where limpograss (1790 + 331 mg kg-1) had a significantly greater forage P concentration

than bahiagrass (1530 58.0 mg kg-1). Both species exhibited a decline in P

concentrations by the first harvest (day 27). All limpograss treatments (965 220 to

1260 152 mg kg-1) and the 0, -10 cm and control (1180 60.0 to 1220 37.0 mg/kg)

bahiagrass treatments had significantly lower forage P concentrations by the first

sampling on day 27 (Figures 3-13 and 3-14). The bahiagrass +10 treatment had

significantly greater forage P concentrations than limpograss on days 27, 55 and 375. In

addition, P concentrations of the bahiagrass forage control treatment were greater than

limpograss on day 375, although on day 163 the limpograss forage control treatment was

greater than the bahiagrass.


p value
< 0.01
0.04
0.01
< 0.01
0.02

< 0.01
0.04
< 0.01
< 0.01
0.01










On day 0, there were no significant differences in forage P concentration between

bahiagrass treatments. However, on days 27, 55, 83, and 163, the wettest bahiagrass

treatment (+10 cm) had significantly greater P concentrations than all other treatments

(Figure 3-13). By the final harvest (day 375) the bahiagrass +10 treatment had greater

forage P concentrations than the 0 and -10 treatments. Limpograss, on the other hand did

not have many differences between treatments. The only difference was on day 163

when the +10 treatment was greater than the -15 cm (Figure 3-14).


A + 10 cm

0 cm
4 -10 cm

+ 15 cm
* Control

0 50 100 150 200
Elapsed time


250 300 350


Figure 3-13. Mean P concentrations (mg/kg) for bahiagrass forage by harvest day and by
treatments (Table C-17, Appendix C).


1800-


1600-


1400-


,1200-
-
0)
E1000-
I-

800-


600-


400-










2200-
A + 10 cm
2000 -
0 cm
1800- 0- 10 cm

1600 + -15cm

1400 -,a Control
S1400-o
E
1200 -
I-







0 50 100 150 200 250 300 350 400
Elapsed time

Figure 3-14. Mean P concentrations (mg/kg) for limpograss forage by harvest day and by
treatments (Table C-18, Appendix C).

Below ground biomass cneniocentrations (303-668 mg kg) were relatively low

compared to forage. On day 163, limpograss had greater BGB P concentrations than

bahiagrass in the 0 cm and control treatments. However, on day 375, there were no

significant differences. Nor were there any significant differences for either species when

treatments were compared.

Phosphorus storage

Phosphorus assimilation (mg pot-) is a function of concentration and biomass

production. After 375 days, the only significant difference in total P storage (AGB

+BGB) between species was in the 0 cm treatment, where bahiagrass was greater than

limpograss (Figure 3-15). This was not consistent with total P storage at the end of the

first growing season (day 163) where bahiagrass was greater than limpograss in all

treatments except the control (Table 3-4). This change over time in total P storage can










be attributed to negative net BGB production in bahiagrass and greater forage production

in limpograss between days 163 and 375.

90
80
70
60
p 50
S40
30
S20
10
0-
Bahia Foralta Baia Floralta Bahia Foralta Bahia Floralta Bahia Flralta

+10 +10 0 0 -10 -10 -15 -15 C C
Treatmnt and Species

Figure 3-15. Total P storage (AGB + BGB) after 375 days.

Table 3-4. Total P storage (AGB + BGB) species comparison.
Total P Storage (mg/pot)
Day Treatment Bahia Floralta p value
+10 50.9 4.06 > 40.5 1.32 0.01
0 53.7 5.56 > 41.1 4.69 0.04
163 -10 56.9 4.48 > 39.6 4.49 0.01
-15 56.0 5.73 > 38 5.43 0.02
C 54.1 10.9 < 57.5 2.38 0.62

+10 47.8 4.84 < 50.2 2.67 0.49
0 50.5 13.1 > 37.3 2.05 0.16
375 -10 46.8 5.22 > 34.2 5.43 0.04
-15 36.0 5.13 > 36.1 5.69 0.99
C 62.8 16.4 > 55.3 9.09 0.53



Total P storage has the same general trend as biomass production. The same is true

for BGB production and BGB P storage. In addition, there is a positive relationship

between P partitioning and biomass allocation. Bahiagrass had greater P storage in BGB

than in forage, while limpograss had the greater P storage in forage.

Overall, for each species P storage in BGB was not significantly different between

treatments after 375 days. However, there was a significantly different treatment effect










on day 163. After 163 days the limpograss control stored significantly more P in BGB

than all other limpograss treatments.

Limpograss had greater P harvested in forage than bahiagrass in the all but the +10

treatment by day 27. By day 55, P harvested in limpograss forage was greater in all

treatments except the -15 cm and control treatments, while by day 83 limpograss was

only greater in the control treatment. By days 163 and 305, limpograss forage took up

more P than bahiagrass in all but the -15 cm treatment, while on day 375, limpograss had

greater P storage in all treatments except the -15 and -10 cm treatments.

On a cumulative basis, there was a strong relationship between forage P storage

(Figure 3-16) and cumulative forage production (Figure 3-7B, 3-12B). Limpograss had

greater forage P storage than bahiagrass in all treatments except the +10 cm treatment on

days 27, 55, 83 and 163. However, after day 163, limpograss had greater P storage in all

treatments.

A. B.
45
20- 40-
S 235-
015-o 30
E 25-
10o-I 2-



0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Elapsed time Elapsed time

A+ 10 cm Ocm -10cm + -15cm Control


Figure 3-16. Cumulative P harvested in forage. A) Bahiagrass P harvested B)
Limpograss P harvested (Table C-23, Appendix C)

There were no differences in harvested P between bahiagrass treatments on days

27, 55 and 305. However, on days 83 and 163, the +10 cm treatment had significantly









more P harvested than all other treatments. The +10 cm and control treatments had

greater P harvested than the 0 and -10 cm treatments on day 375. On a cumulative basis,

on all days after 55, there was significantly more P harvested in the +10 treatment than in

the 0, -10 and -15 cm treatments.

Like bahiagrass, there were no differences in harvested P between limpograss

treatments on day 27. However, by day 55, the driest treatments had assimilated the most

P. The control had significantly more P harvested than all other treatments, and the -15

cm treatment had more than the -10 cm treatment. By day 83, the influence ofbiomass

production on P storage began to emerge as P harvested in the control was greater than

the 0, -10 and -15 cm treatments. In the latter and earlier parts of the growing season

(days 163 and 305), there was more P harvested in the +10 cm treatment than all other

bahiagrass treatments, while on day 375, there was more P harvested in the +10 and

control than in the all other treatments.

On a cumulative basis, by day 83, the bahiagrass control had assimilated

significantly more P than the -10 cm treatment. By day 163, the control stored more than

the 0 and -15 cm (in addition to the -10 cm) treatments. By day 305 the +10 treatment

had significantly greater cumulative P storage than the 0, -10 and 15 cm treatments. By

day 375, the +10 cm and control had greater cumulative storage than the 0, -10, and -15

cm treatments.

Phosphorus storage root to shoot ratios

Root to shoot ratios for P storage had a positive relationship to biomass ratios.

Figure 3-17 shows relative portions of AGB and BGB P storage. After 375 days, the +10

treatment was the only bahiagrass treatment with a ratio less than one. Limpograss P

storage ratios were all less than one and positively related to biomass ratios. All










bahiagrass treatments had significantly greater P storage ratios than limpograss

treatments after 375 days (Table 3-5)


50

30 -

,. 10

b (10)

S(30)

(50) -
Bahia Floralta Bahia Fralta Bahia Florala Bahia Floralta Bahia Florata

+10 +10 0 0 -10 -10 -15 -15 C C

Treatment and Species
Figure 3-17. Relative comparison of root and shoot P storage after 375 days.

Table 3-5. Root to shoot P storage ratios with statistics.
Root:Shoot P Storage
Day Treatment Bahia Floralta p value
+10 1.27 0.27 > 0.69 0.22 0.04
0 2.45 0.23 > 1.01 0.13 < 0.01
163 -10 2.46 0.67 > 1.10 0.28 0.03
-15 2.95 0.40 > 0.95 0.05 < 0.01
C 1.95 0.10 > 1.17 0.05 < 0.01

+10 0.70 0.12 > 0.15 0.04 < 0.01
0 2.58 1.35 > 0.39 0.09 0.05
375 -10 2.29 0.14 > 0.48 0.17 < 0.01
-15 1.42 0.33 > 0.55 0.32 0.03
C 1.35 0.34 > 0.22 0.08 < 0.01


Discussion

Forage Production

Bahiagrass and limpograss are physiologically different. Bahiagrass typically has

less forage production than limpograss because it allocates -50% of its energy to root and

stolon production (Chambliss & Adjei, 2006). This was evident from the bahiagrass root









to shoot ratios, where BGB ranged from 2.11 to 5.76 times higher than AGB (Table 3-3).

In addition bahiagrass is a long day plant that is strongly influenced by photoperiod

(Marousky & Blondon, 1995). Thus its annual production is lower because it has a

shorter growing season. On the other hand, limpograss tends to allocate more energy to

AG forage production, as root to shoot biomass ratios were less than one in all

treatments.

Limpograss is known to support relatively high cattle stocking rates and for

having superior late fall and early spring production compared to bahiagrass

(Sollenberger et al., 2006). Results from this study support that statement. Overall

limpograss had greater forage production than bahiagrass in the latter and earlier parts of

the growing season in all treatments. In the first 83 days, both species exhibited a decline

in production between harvests, regardless of treatment (Figure 3-5). This was likely the

result of the combined effects of harvest stress, temperature and light effects in the latter

part of the peak growing season. Harvest stress was evident after the first harvest (day

27) as production was significantly less for both species by the second harvest. After 83

days, limpograss rebounded, while bahiagrass production continued to decrease. The

bahiagrass decline after day 83 is likely the result of decreased photoperiod during

shorter days and not harvest stress. Even during the peak of the growing season,

limpograss had greater forage production than bahiagrass. Forage production results

support H1 limpograss has greater cumulative forage production than bahiagrass in all

treatments.

Flood Tolerance

Water levels did not appear to have an effect on forage production for either species

until after the first harvest. There were no statistical differences between bahiagrass









treatments until the third harvest. This suggests that bahiagrass forage production may

not be affected by water levels as deep as 10 cm above the soil surface for up to 55 days.

The wettest bahiagrass treatment actually produced more forage than the other treatments

toward the end of the in first growing season. The same was true for the limpograss +10

treatment. In addition the limpograss +10 had the greatest forage production in the

earlier part of the following growing season. The wettest treatment seems to start forage

production earlier and extend it later in the growing season for both species, but more for

limpograss. The reasoning for this may be related to a temperature buffering effect

caused by standing water. Thus, flooding may create an artificial environment that

decreases diurnal temperature fluctuations, thus prolonging the growing season. After

375 days, forage production per harvest was similar in the wettest and driest treatments

for both species, while the intermediate treatments generally produced less forage.

Bahiagrass is resilient to many environmental conditions. However, under longer

hydroperiods bahiagrass is not as resilient and may be out competed by facultative or

obligate wetland species. Efforts to restore native wetland species in bahiagrass

dominated pastures have been challenging. While mechanically removing the sod and

applying herbicide has been the most effective way to remove bahiagrass (Violi, 2000),

prolonged flooding will also eliminate bahiagrass, and enable wetland species to establish

(David, 1999). David (1999) examined the distribution and density of bahiagrass and

other wetlands species in hydrologically restored wetlands and found that bahiagrass

persisted for 2 years after inundation, before dying off by the fourth year. In addition,

wetland species such as Panicum hemitomon and Pontederia cordata increased in

frequency of occurrence under longer hydroperiods. Clearly both species will survive









375 days in 10 cm of water in a non-competitive environment. However, even in a

competitive environment it may take up to three years for different vegetation to establish

between normal and high water after a change in hydrologic regime (Van der Valk, A G

et al., 1994).

Between days 83 and 163, both species appeared to acclimate to treatment

conditions. Initially, the controls of both species had the greatest forage production

however by day 163 there was no difference between the wettest and the driest

treatments. This suggests that in the short-term, hydrology alone in a non-competitive

environment may not cause a shift in species. Over the duration of this investigation,

bahiagrass did not have significantly greater total biomass production in drier treatments,

nor did limpograss have significantly greater total biomass in the wetter treatments.

Based on treatment effect comparisons, H2 can not be completely accepted.

Phosphorus Uptake

After 375 days, total P storage was similar for both species in all treatments

expect the -10 cm where bahiagrass stored more than limpograss. Therefore, H3 is

rejected in favor of the null hypothesis that limpograss does not store more P than

bahiagrass. Despite, similar assimilative capacities, P stored in bahiagrass is relatively

more stable than P stored in limpograss because the majority of P stored in bahiagrass is

in BGB. Above ground forage is typically more labile and is subject to grazing. As

discussed in Chapter II, vegetation is a short-term P storage mechanism. In addition, if

the vegetation is continually grazed, nutrients in digested plant tissue are more

bioavailable than senesced vegetation in wetlands. Therefore, P can be mobilized from

the soil into the water column by way of grazing.









Phosphorus partitioning in vegetation had a similar trend as biomass allocation. All

bahiagrass treatments except the +10 stored more P in BGB than in AGB. The higher P

concentrations in the bahiagrass +10 treatment caused higher P storage in AGB than in

BGB. This was consistent with all limpograss treatments more P was assimilated in

AGB than BGB. Therefore, H4 is only partially accepted. All bahiagrass ratios except

the +10 cm treatment were greater than one and all limpograss P ratios were less than

one.

Inundation actually increased P storage in both species by increasing tissue P

concentrations in the +10 treatment. The relatively high P tissue concentrations in the

+10 treatment are consistent with results found in wetland center zones as described in

Chapter II results and Whigham (2002).

Reddy and Debusk (1985) observed lower tissue concentrations in summer months

and suggested that higher productivity in the summer likely diluted concentrations. In

addition they suggested that slow growth and luxury uptake likely caused increased

concentrations in the winter (Reddy & Debusk, 1985). The same line of reasoning may

also explain the elevated P concentrations in the +10 treatments. Biomass production

decreased in the bahiagrass +10 treatment, however P concentrations were significantly

greater than the other treatments. Where nutrients are readily available, tissue

concentrations may have a direct relationship to biomass productivity. Future research

may look into the possibility of specific plant species' rates of nutrient uptake over time

vs. resultant tissue concentrations.

Conclusions

Limpograss appears to thrive in the wettest and driest mesocosm treatments. In

fact the limpograss +10 and control treatments had the greatest production of all









treatments. Although bahiagrass survived under inundated conditions for 375 days, more

than likely it would not survive competition from other plant species, trampling, grazing

and water stress in situ.

Both species had similar total P storage in all treatments except the -10 cm

treatment. The majority of P stored in bahiagrass is in BGB, while most P assimilated in

limpograss is stored in forage. Thus utilizing limpograss for P removal from wetlands

may be best optimized by harvesting and exporting hay and assimilated P away from the

wetlands.

Overall, after 375 days limpograss had greater forage production than bahiagrass in

all treatments, a greater hydrologic tolerance and similar P storage potential. Therefore,

in order to maintain pasture carrying capacity and vegetative P storage during BMP

implementation, limpograss may be a more suitable forage in restored pastures wetlands

even under higher water levels and extended hydroperiods.














CHAPTER 4
SUMMARY AND CONCLUSIONS

Summary

The overall goal of this research was to evaluate the biomass production and P

storage potential of vegetation in historically isolated pasture wetlands and determine the

efficacy of using a wet tolerant forage species to minimize the loss of improved pasture

area as a result of hydrologic restoration.

Objective I: Biomass Production and Phosphorus Storage in Wetlands

I. Assess biomass production and P assimilation by wetland vegetation and
forage grasses under various hydroperiods.

Results from Chapter II and McKee (2005) indicate that wetland soils in the

Okeechobee basin store more P per unit area than surrounding upland soils and

vegetative components. The direct role of vegetation in active total P storage is relatively

small, short-term, and highly variable compared to the physical storage capacity of soil.

However, the presence of vegetation is an important component of ecosystem P storage

because it increases the total P retention capacity of wetland soils.

Phosphorus storage in vegetation along hydrologic gradients was variable

depending on the type of species present, land-use intensity, grazing pressure and

hydrology. There were, however, similar trends in AGB P storage. In general, wetland

zones at both sites stored more P in AGB than in upland zones. In addition, total P

storage (AGB + BGB) had a positive relationship to hydroperiod at Beaty while the









opposite trend existed at Larson. This was likely related to higher AGB tissue P

concentrations in wetland centers than uplands and differences in land-use intensity.

Vegetation Stress

Hydrology is often the primary determinant of vegetation composition within

wetlands. However, in pasture wetlands, the stress of grazing likely influences

vegetation community establishment and persistence. Although grazing was not

measured in this experiment, vegetation patterns, biodiversity of species, and large

hydroperiod ranges for the same species at different sites suggests that hydrology is not

solely responsible for species distribution within pasture wetlands. In addition, grazing

may have a significant effect on wetland P storage capacity.

Objective II: Facilitating Land-use and Wetland Restoration

II. Determine the efficacy of establishing a wet tolerant forage grass in wetland
transition zones before hydrologic restoration to minimize loss of productive
pasture

Overall limpograss had greater cumulative forage production than bahiagrass in all

hydrologic treatments. This was primarily due to its ability to produce forage earlier and

later in the growing season. More importantly, limpograss production was similar in the

wettest and driest treatments, producing significantly more forage than the intermediate

water level treatments. The wettest and driest bahiagrass treatments also had the greatest

production relative to the other treatments.

Both species will survive for 375 days under non-competitive, inundated soil

conditions as long as the biomass is not completely submerged. However, it is unlikely

that bahiagrass would be competitive under wet conditions with its low productivity.









Unexpected Results

One unexpected result that was consistent between both species in the mesocosm

experiment was that the greatest biomass production and subsequent P storage occurred

in the wettest and driest treatments. It was hypothesized that biomass would have a

negative parabolic shape when treatments were aligned on the X-axis in order from

wettest to driest. Essentially, it was thought that both species would have similar curves,

expect limpograss's curve would be shifted more toward the wet treatments and

bahiagrass's more toward the dry treatments. However, as described in Chapter III, the

results were opposite. Each species had a positive parabolic shape.

Another unexpected result found in both the field and mesocosm studies was the

increased tissue P concentrations under flooded conditions. Above ground biomass tissue

P concentrations in wetland center zones and in the +10 inundated mesocosm treatment

were greater than uplands and drier mesocosm treatments. Higher concentrations of

plants in wet conditions were likely caused by slow growth and luxury uptake.

Implications for Restoration

Bottcher et al., (1995) defines BMPs as on-farm activities to reduce nutrient

exports to water bodies and tributaries to environmentally acceptable levels, while

simultaneously maintaining an economically viable farming operation. In addition,

BMPs that adversely affect the economic viability of a farming operation should be

subsidized to maintain profitability (Bottcher et al., 1995).

Many BMPs are considered voluntary; however in a watershed where a TMDL has

been mandated, water quality compliance is required. The term "voluntary" refers to the

choice of options ranchers have to comply with TMDL goals. In the Okeechobee basin,

ranchers have the option to monitor the nutrient discharge from their property to ensure




Full Text

PAGE 1

PHOSPHORUS STORAGE DYNAMICS IN WETLAND VEGETATION AND FORAGE GRASS SPECIES: FACI LITATING WETLAND HYDROLOGIC RESTORATION IN THE LAKE OKEECHOBEE WATERSHED By JEFFREY D. SMITH A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Jeffrey David Smith

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This thesis is dedicated to all who strive to educate themselves, and those who support education. “The question is, does the educated ci tizen know he is only a cog in an ecological mechanism? That if he will work with that mechanism his mental wealth and his material wealth can expand indefinitely? But that if he refuses to work with it, it will ultimately grind him to dust? If education does not teach us these things, then what is education for?”(Leopold, 1966).

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iv ACKNOWLEDGMENTS I would like to thank Dr. Mark W. Clark fo r his contagious enthusiasm for science, and life in general, and for his guidance a nd support throughout th e course of this research. I would also like to give speci al thanks to Dr. Edmond J. Dunne for his guidance and mentoring in the field, laborat ory and throughout the de velopment of this thesis, and most of all, his friendship. Dr. Clark and Dr. D unne are outstanding scientists, but even better persons. My committ ee members, Dr. K. Ramesh Reddy and Dr. L. Hartwell Allen, were also very influential in th e development and review of this research. Lastly, I would like to thank my parents, Sarah Anderson and many friends, from near and far, for fostering my goals and providing unconditional support. Many other important people are listed in Appendix D, Table D-1.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES...........................................................................................................xi ABSTRACT.....................................................................................................................xi v CHAPTER 1. INTRODUCTION........................................................................................................1 Problem........................................................................................................................ .2 Regional Characteristics........................................................................................3 Everglades......................................................................................................3 Lake OkeechobeeÂ’s watershed.......................................................................5 Phosphorus loading to Lake Okeechobee......................................................7 Policy and Planning......................................................................................................9 Lake Okeechobee Legislation.....................................................................................10 Phosphorus Best Management Practices....................................................................11 Hydrologic Restoration of Isolated Wetlands.....................................................12 Phosphorus in wetland soils.........................................................................13 Alternative forage crops...............................................................................14 Thesis Objectives........................................................................................................15 2. PHOSPHORUS ASSIMILATION BY ISOLATED WETLAND VEGETATION..17 Introduction.................................................................................................................17 Factors Influencing Phosphorus Retention..........................................................19 Research Objectives............................................................................................21 Research Questions and Hypotheses...................................................................22 Materials and Methods...............................................................................................23 Study Sites...........................................................................................................23 Sampling..............................................................................................................25 Sample processing...............................................................................................26 Laboratory Analysis............................................................................................27 Data and Statistics Analysis................................................................................27 Results........................................................................................................................ .28 Species Composition along a Hydrologic Gradient............................................28 Ecosystem Phosphorus Storage...........................................................................31 Standing Biomass................................................................................................33 Standing Biomass by Individual Species............................................................36

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vi Phosphorus Storage in Biomass..........................................................................37 Standing Biomass by Individual Species............................................................41 Discussion...................................................................................................................42 Hydrology............................................................................................................42 Ecosystem storage...............................................................................................43 Biomass in Pasture Wetlands..............................................................................44 Disturbance Effects on Biomass..........................................................................45 Phosphorus Concentrations.................................................................................47 Phosphorus Storage.............................................................................................48 Conclusions.................................................................................................................49 3. FACILATATING WETLAND HYDR OLOGIC RESTORATION WHILE MAINTAINING FORAGE PRODUCTI ON: HYDROLOGIC TOLERANCES OF PASPALUM NOTATUM AND HEMARTHRIA ALTISSIMA ..............................51 Introduction.................................................................................................................51 Background..........................................................................................................51 Research Objectives............................................................................................53 Research Questions and Hypotheses...................................................................53 Materials and Methods...............................................................................................54 Experimental Design...........................................................................................54 Treatments...........................................................................................................56 Sampling..............................................................................................................57 Soil...............................................................................................................58 Above ground biomass sampling.................................................................58 Below ground biomass.................................................................................60 Laboratory analysis......................................................................................60 Results........................................................................................................................ .60 Initial characterization.........................................................................................60 Forage Production...............................................................................................61 Bahiagrass forage production.......................................................................63 Limpograss forage production.....................................................................64 Species comparison......................................................................................65 Total Biomass......................................................................................................68 Root to Shoot Ratios............................................................................................69 Phosphorus Assimilation.....................................................................................71 Phosphorus tissue concentrations.................................................................71 Phosphorus storage.......................................................................................73 Phosphorus storage root to shoot ratios........................................................76 Discussion...................................................................................................................77 Forage Production...............................................................................................77 Flood Tolerance...................................................................................................78 Phosphorus Uptake..............................................................................................80 Conclusions.................................................................................................................81 4. SUMMARY AND CONCLUSIONS.........................................................................83

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vii Summary.....................................................................................................................83 Objective I: Biomass Production a nd Phosphorus Storage in Wetlands.............83 Vegetation Stress.................................................................................................84 Objective II: Facilitating Landuse and Wetland Restoration.............................84 Unexpected Results.............................................................................................85 Implications for Restoration.......................................................................................85 Conclusions.................................................................................................................88 Unanswered Questions and Need for Further Research.............................................89 APPENDIX A. SUPPLEMENTAL BACKGROUND INFORMATION...........................................90 B. SUPPLEMENTAL FIELD DATA.............................................................................92 C. SUPPLEMENTAL MESOCOSM DATA................................................................102 D. SUPPLEMENTAL ACKNOWLEDGEMENTS......................................................138 LIST OF REFERENCES.................................................................................................139 BIOGRAPHICAL SKETCH...........................................................................................146

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viii LIST OF TABLES Table page 1-1 Okeechobee watershed land use by percent of total land...........................................6 2-1 Mean and standard deviation of hydroperiods at each site......................................29 2-2 Mean species hydroperiod of both sites...................................................................29 2-3 Root to shoot ratios by zone.....................................................................................36 2-4 Below ground biomass concentrations by zone.......................................................38 2-5 Above ground biomass P concentrations by zone....................................................40 2-6 BGB to AGB P storage ratios..................................................................................41 3-1 Sampling dates and details.......................................................................................58 3-2 Total biomass after 163 and 375 days......................................................................68 3-3 Root to shoot ratios..................................................................................................71 3-4 Total P storage species comparison.........................................................................74 3-5 Root to shoot P storag e ratios with statistics............................................................77 4-1 Estimation of P export concentrations to tributaries from various land-uses..........87 A-1 Summary of Okeechobee Basins BMPs...................................................................90 A-2 Total P loads to Lake Okeechobee 1991-2003.........................................................91 B-1 Phosphorus storage by com ponents, site and zone..................................................92 B-2 Biomass production by com ponents, site and zone..................................................92 B-3 Species hydroperiod.................................................................................................93 B-4 Total biomass production.........................................................................................95 B-5 Below ground biomass production...........................................................................95

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ix B-6 Above ground biomass production..........................................................................95 B-7 Total biomass P storage............................................................................................97 B-8 Below ground biomass P storage.............................................................................97 B-9 Above ground biomass P storage.............................................................................97 B-10 Phosphorus concentratio n in above ground biomass.............................................100 B-11 Phosphorus storage in above ground biomass........................................................101 C-1 Nutrient concentrations on day 0...........................................................................102 C-2 Species comparison of fora ge production per harvest............................................103 C-3 Species comparison of cumulative forage production...........................................103 C-4 Overall below ground bioma ss – all treatments combined....................................103 C-5 Forage production per harvest................................................................................104 C-6 Cumulative forage by treatment and day...............................................................105 C-7 Below ground biomass species comparison...........................................................106 C-8 Residual biomass harvested on days 163 and 375.................................................106 C-9 Below ground biomass pr oduction time comparison.............................................106 C-10 Bahiagrass forage production pe r harvest treatment comparison........................109 C-11 Bahiagrass cumulative forage pr oduction treatment comparison........................110 C-12 Limpograss forage production pe r harvest treatment comparison.......................111 C-13 Cumulative limpograss forage pr oduction treatment comparison.......................112 C-14 Bahiagrass BGB production – treatment comparison............................................113 C-15 Limpograss BGB production – treatment comparison...........................................113 C-16 Forage P concentratio ns – species comparison......................................................114 C-17 Bahiagrass forage P concentrations treatment comparison..................................115 C-18 Limpograss forage P concentrations treatment comparison................................116 C-19 Below ground biomass P concentrations – species comparison............................117

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x C-20 Bahiagrass BGB P concentra tions treatment comparison...................................117 C-21 Limpograss BGB P concentra tions treatment comparison..................................118 C-22 Phosphorus storage in forage species comparison per harvest............................119 C-23 Cumulative P storage in fo rage species comparison............................................120 C-24 Bahiagrass forage P storage per harvest.................................................................121 C-25 Bahiagrass cumulativ e forage P storage.................................................................122 C-26 Limpograss forage P storage per harvest...............................................................123 C-27 Cumulative limpograss forage P storage................................................................124 C-28 Below ground biomass P st orage species comparison...........................................127 C-29 Bahiagrass below groun d biomass P storage.........................................................127 C-30 Limpograss below groun d biomass P storage........................................................127 C-31 Climatic conditions from day 1 to 375...................................................................129 D-1 Thanks....................................................................................................................1 38

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xi LIST OF FIGURES Figure page 1-1 Phosphorus concentrations in Lake Okeechobee.......................................................3 1-2 Historic, current, and future flow pattern of the Everglades......................................4 1-3 Land-use map of four priority basi ns of the Lake Okeechobee watershed................8 1-4 Wetland coverage in the priority basins.....................................................................9 2-1 Mechanisms driving P cycling.................................................................................21 2-2 Map of land use in the 4 priority basins...................................................................24 2-3 Isolated wetlands select ed for long term monitoring...............................................24 2-4 Stratified sampling zones: center, edge and upland.................................................25 2-5 Logistics fit of species..............................................................................................30 2-6 Logistics fit of species by site..................................................................................31 2-7 Phosphorus storage components..............................................................................32 2-8 Comparison of AGB and BGB co mponents at Beaty and Larson...........................32 2-9 Total biomass at Beaty and Larson wetlands...........................................................33 2-10 Below ground biomass at B eaty and Larson wetlandss...........................................34 2-11 Above ground biomass at Beaty and Larson wetlands............................................34 2-12 Biomass partitioning AGB vs. BGB........................................................................35 2-13 Above ground biomass by species for all zones......................................................37 2-14 Total biomass P storage............................................................................................38 2-15 Phosphorus storage in BGB at Beaty and Larson wetlands.....................................39 2-16 Phosphorus storage in AGB at Beaty and Larson wetlands.....................................40

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xii 2-17 Above and below ground biomass P storage...........................................................41 2-18 Phosphorus storage by species.................................................................................42 2-19 Nutrient storage and growth in plants......................................................................47 3-1 Study site at University of Florida, Gainesville, Florida..........................................55 3-2 Mesocosm diagram..................................................................................................56 3-3 Inverse relationship of water depth and redox.........................................................57 3-4 Harvesting procedure...............................................................................................59 3-5 Forage production per harvest for ea ch species all treatments combined..............62 3-6 Cumulative forage production with all treatments combined..................................62 3-7 Bahiagrass treatment comparisons...........................................................................63 3-8 Limpograss treatment comparisons..........................................................................65 3-9 Forage production per harvest by treatment.............................................................66 3-10 Cumulative forage pr oduction by treatment.............................................................67 3-11 Below ground biomass production...........................................................................69 3-12 Above and below ground biomass production after 375 days.................................70 3-13 Mean P concentrations for bahiagrass forage by harvest day and by treatments.....72 3-14 Mean P concentrations for limpograss forage by harvest day and by treatments....73 3-15 Total P storage (AGB + BGB) after 375 days.........................................................74 3-16 Cumulative P harvested in forage............................................................................75 3-17 Relative comparison of root a nd shoot P storage after 375 days.............................77 B-1 Species distribution by hydroperiod.........................................................................94 B-2 Above ground biomass by species and zone............................................................96 B-3 Phosphorus concentrations by species.....................................................................98 B-4 Phosphorus storage by zone.....................................................................................99 C-1 Relative root and shoot biomass after 163 days.....................................................107

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xiii C-2 Total biomass production after 163 days...............................................................107 C-3 Total biomass production after 375 days...............................................................108 C-4 Bahiagrass total biomass and P storage..................................................................125 C-5 Limpograss total biomass and P storage................................................................125 C-6 Bahiagrass BGB and P storage...............................................................................126 C-7 Limpograss BGB and P storage.............................................................................126 C-8 Root to shoot P storage ratios.................................................................................128 C-9 Total P storage (AGB +BGB) at 163 days.............................................................128

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xiv Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PHOSPHORUS STORAGE DYNAMICS IN WETLAND VEGETATION AND FORAGE GRASS SPECIES: FACI LITATING WETLAND HYDROLOGIC RESTORATION IN THE LAKE OKEECHOBEE WATERSHED By Jeffrey D. Smith August, 2006 Chair: Mark Clark Major Department: Soil and Water Science Nutrient export from agricultural activities in the Lake Okeechobee watershed has contributed to eutrophication of the Lake and regulatory implementation of a phosphorus (P) Total Maximum Daily Load (TMDL) rule Historically, anth ropogenic manipulation of hydrology lowered water tables, creating im proved conditions for upland forage grass production. This action also in creased runoff rates and P load ing to the Lake. Hydrologic restoration of historically is olated wetlands within the wa tershed is a proposed best management practice (BMP) to increase P retention capacities of these wetlands. However, longer hydroperiods could potentially decrease pasture pr oductivity, and as a consequence, adversely affect the economic viab ility of the cattle i ndustry in the region. Previous studies have shown that soils under longer hydroperiods in the Okeechobee watershed have greater P storage potential than surrounding upland soils. This research primarily focuses on the vegetative component of P storage in pasture wetlands. The objectives were to evaluate biomass produc tion and P storage dynamics in vegetation under various hydroperiods and to determine the efficacy of using an alternative forage grass species to maintain pasture productivity after wetland restoration.

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xv Four isolated wetlands in Okeechobee Count y, Florida were sampled in November, 2004; March, 2005; and July, 2005. In this stud y, total P storage in wetlands (with a 50m upland buffer) included soil (10 cm dept h), below ground biomass (BGB), above ground biomass (AGB) and litter components. Soil was the primary P storage component representing greater th an 88% of the total P stored in the wetlands, while BGB, AGB and litter represented 8%, 3%, and 1% respect ively. Total biomass (AGB+BGB) production and P storage in biomass were inversely re lated to hydroperiod in wetlands at the more intensively managed pasture, while P storage in biomass was positively related to hydroperiod in wetlands at the less intensively managed pastur e. Management intensity (i.e., cattle density and pasture maintenance) may be influencing P storage capacities of vegetative, and affecting the relations hip between hydroperiod and P storage. In a separate mesocosm study in Gainesville, Florida, Paspalum notatum Flgg (bahiagrass) and Hemarthria altissima ‘Floralta’ (limpograss), a wet-tolerant forage grass, were evaluated under five different hydrologic treat ments. Water levels were stabilized at 10, 0, -10, and -15 cm relative to the soil surface, wh ile the control only received rain water and was allowed to drai n completely. Limpograss had greater forage (AGB) production and P assimilation than ba hiagrass in all treatments. However, bahiagrass had greater total biomass ( AGB+BGB) production in all but the 10 cm inundated treatment. Bahiagrass to tal P storage was only greater than limpograss in the -10 cm water level. This indicates that lim pograss has a greater hydrol ogic tolera nce than bahiagrass and similar P storage potential. Therefore, to maintain pasture carrying capacity and vegetative P storage during BMP implementation, limpograss may be a more suitable forage in restored pastures wetlands.

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1 CHAPTER 1 INTRODUCTION Nutrient export from agricultural activities in the Lake Okeechobee watershed has contributed to eutrophication of the Lake and regulatory implementation of a phosphorus (P) Total Maximum Daily Load (TMDL) rule Historically, anthropogenic manipulation of hydrology drained wetlands and lowered the water table, creating improved conditions for upland forage grass production. This action increased runoff rates and P loading to the Lake. Four priority basins occupy 12% of th e watershedÂ’s area, but export 35% of the P load entering the Lake (FDEP, 2001). Hydrol ogic restoration of historically isolated wetlands is a proposed best management practice (BMP) to increase P retention capacities of these wetland ecosystems, t hus decreasing P loads entering the Lake. However, longer hydroperiods could potentially decrease pasture pr oductivity, and as a consequence, adversely affect the economic viab ility of the cattle i ndustry in the region. Previous studies have shown that soil s under longer hydroperiods in the Lake Okeechobee Basin have greater P storage potential than surrounding upland soils (McKee, 2005). This research primarily focuses on the vegetative component of P storage in pasture wetlands. The objectives were to eval uate biomass production and P storage dynamics in vegetation under various hyd roperiods and to determine the efficacy of using an alternative forage grass species to maintain pasture pr oductivity after wetland restoration.

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2 Problem In 1998, Lake Okeechobee was listed as a water quality limit ed (WQL) water body (FDEP, 2001). This condition was the result of over 50 years of excessive pollutant loading. Nutrients, dissolved oxygen, unionized ammonia, chlorides, coliforms and iron have threatened numerous societal and envir onmental values of the Lake, contributing to eutrophication, resultant algal blooms, and s ubsequent alterations of flora and fauna species composition (SFWMD et al., 2004). Phosphorus is often considered the limiting nutrient in freshwater aquatic systems (Reddy et al., 1999b). The development of agri culture (predominately animal operations) in the mid 1900Â’s, along with anthropogenic manipulation of hydrology in the watershed has drastically increased P loading to th e Lake (Havens et al., 2005; Reddy & Debusk, 1987). Between 1968 and 2004, the P concentrati on in the pelagic zone of the Lake (Figure 1-1) increa sed from ~40 g L-1 to over 120 g L-1 (Flaig & Reddy, 1995; Havens et al., 2005). The measured P load entering the Lake in 2004 was 548 metric tons, while the five year rolling average between 20002004 was 528 metric tons P (Havens et al., 2005). In 2001, 14 of the 29 drainage basins with in the LakeÂ’s watershed were exceeding their P loading targets (FDEP, 2001). Severa l BMPs intended to reduce P export from agricultural lands within the watershed have been or are in the process of being implemented (Table A-1, Appendix A). In the 1980Â’s the implem entation of BMPs significantly reduced P concentra tions in tributaries (Gunsalus et al., 1992). However, P concentrations and loads continue to ex ceed established target loads (Table A-2, Appendix A).

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3 Figure 1-1. Phosphorus concentrations in Lake Okeechobee (Lake Okeechobee Issue Team & South FLorida Ecosystem Restoration Working Group, 1999) Regional Characteristics Everglades Historically, Lake Okeechobee was the cen tral component of the Everglades Ecosystem, interconnecting the Kissimmee Ri ver Basin in Central Florida to the Everglades in South Florida. Central FloridaÂ’s Upper Chain of Lakes were the headwaters of the Everglades. Water migr ated south through the Kissimmee River Basin into the northern part of Lake Okeechobee. The Lake was a natural detention basin, releasing water over its southern banks duri ng times of high water. Overflow water migrated south through sawgrass prairies in to a vast ridge and slough system, before eventually seeping into Florida Bay. Currently, as a result of anthropogenic fl ood control and land development, Central and South Florida hydrology is drastically different (Figure 1-2). In 1928, over 2000 people were killed when the storm surge from a major hurricane flooded hundreds of acres surrounding Lake Okeechobee (Kleinbe rg, 2003). That spurred the effort to

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4 construct the Herbert Hoover Dike around the Lake for flood control. This, in turn, reduced the size of the Lake to 650 squa re miles and altered natural hydrologic fluctuations by creating a depende nce on a vast system of water control structures. In the decades that followed, the Kissimmee River was channelized, the Caloosahatchee River was dredged and St. Lucie canal was built to divert water from the Lake out to the Atlantic Ocean and Gulf of Mexico. The hydrology of the Everglades also became systematically controlled with the constructi on of several dikes, levies, pump stations, roads and canals. Figure 1-2. Historic (left), current (middle), and future (right) flow pattern of the Everglades ( Second Louisiana--Florida Ecosys tem Restoration Information Exchange 2001) As a result, the Everglades ecosystem is no longer a continuous River of Grass (Douglas, 1947), but rather, se veral compartmentalized aqua tic systems manipulated by anthropogenic water control structures. Ag ricultural and urban development throughout South Florida drained wetlands thus, increasing anthropogeni c control of hydrology. In the early 1900Â’s, the sawgrass prairies south of the Lake were drained for agriculture

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5 development, converting the area into what is currently known as the Everglades Agricultural Area (EAA). The paradigm that drove these vast hydr ologic alterations in South Florida was based on the need for flood prevention in popul ated areas and cultiv atable ag ricultural land. Development and wetland drainage has spawned numerous resource dilemmas. Over fertilization in agricultural sectors has been identified as a primary source of excess nutrient loading to Lake Okeechobee and th e EvergladesÂ’ Water Conservation Areas (WCA). As a result freshw ater quality and supplies have been compromised. Large quantities of water are diverted from Lake Okeechobee to the coasts to prevent high P loads from entering the Everglades. However, this has degraded estuarine ecosystems on both coasts. In addition drainage has cause d rapid mineralization of organic soils and accreted nutrients, which are ultimately transported to Lake Okeechobee or the Everglades. The Everglades is a low nutrien t ecosystem, with ambient P concentrations of ~10 g L-1. Thus, discharging water with high P concentrations from the Lake into the Everglades will be detrimental to the ecosy stem. This reinforces the importance of reducing P loading to Lake Okeechobee. Lake OkeechobeeÂ’s watershed Lake Okeechobee is the second largest fres hwater lake within the contiguous United States with a surface area of 1,890 km2 and contributing watershed of over 6,000 km2. It provides numerous societal and envir onmental values including water supply for agriculture and urban sectors, flood protection and a multi million dollar sport and commercial fishing industry (SFWMD, 2004). Agriculture is the primary land use with in the watershed, occupying over half of the land area, while wetlands and terres trial ecosystems are categorically second

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6 (SFWMD, 1995). Improved pasture, sugar cane, upland forest, rangeland, unimproved pasture and citrus make up the largest percentage of agricult ural land area (Table 1-1). Table 1-1. Okeechobee watershed la nd use by percent of total land. Lake Okeechobee Watershed Landuse Hectares Percentage Improved Pasture 182,590 24.02% Wetlands 128,884 16.96% Water 127,339 16.75% Sugar Cane 91,409 12.03% Upland Forest 69,479 9.14% Rangeland 50,283 6.62% Unimproved Pasture 30,829 4.06% Citrus 22,941 3.02% Urban and built-up 22,325 2.94% Barren 6,284 0.83% Dairies 5,209 0.69% Row Crops 4,926 0.65% Transportation, Communication, and Utilities 4,619 0.61% Field Crops 4,147 0.55% Woodland Pasture 3,935 0.52% Fallow Crop Land 1,756 0.23% Sod Farms 941 0.12% Tree Nursery 907 0.12% Horse Farm 397 0.05% Fruit Orchards 378 0.05% Aquaculture 165 0.02% Other 164 0.02% Ornamentals 74 0.01% Other Grove 29 0.00% Floriculture 8 0.00% Table created from land use data (SFWMD, 1995) Large areas of wetlands in the watershe d have been drained for agricultural development. Riparian and non-riparian (isolated) wetlands are abundant throughout Lake Okeechobee’s watershed, covering 17% (1,290 km2) of the area (Table 1-1). The landscape in the northern part of the watershed was historically characterized by the presence of numerous isolated wetlands. Th ese wetlands are referred to as ‘historically’ isolated because they lacked a surface wate r connection to tributar ies, except overland flow in times of flooding. Extensive drainage efforts established vast networks of ditches

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7 and canals, short circuiting the natural water re tention and nutrient as similative capacity of the landscape. Most isolated wetlands in the northern watershed were drained in mid1900’s to support the rapidly incr easing beef cattle and dairy industry; creating improved pasture conditions for upland forage species (Flaig & Havens, 1995). Draining wetlands was a trend that occurred throughout the countr y, ultimately depleting more than half of the wetlands within the contiguous Un ited States (Mitsch & Gosselink, 2000). Phosphorus loading to Lake Okeechobee Agricultural activities are responsible fo r 98% of all P imported to the watershed, the majority of which is pasture fertilizer a nd dairy feed (Fluck et al., 1992). There is a high correlation between P imports to the watershed and P loading to Lake Okeechobee (Boggess et al., 1995). Non-point so urce runoff from agriculture, particularly, beef cattle and dairy operations, is recognized as a primar y source of P loading to the Lake (Flaig & Havens, 1995) Four priority basins within the Lake ’s watershed, S-65D, S-65E, S-154, and S-191 (Figure 1-3), have been identified as “hot spots” based on land us e intensity and high P discharge. These four basins occupy 12% of the land, and export as much as 35% of the total P load entering the Lake (FDEP, 2001). Within these priority basins, 61% of the land area supports agricultural ac tivities (47% improved pasture, 14% dairy) (Figure 1-3), while 11% of the land is occupied by partia lly drained or otherwise impacted wetlands (Figure 1-4). In fact, 45% of isolated wetlands in the priority basins have been at least partially drained.

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8 Figure 1-3. Land-use map of four priority basins of the Lake Okeechobee watershed (McKee, 2005)

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9 Figure 1-4. Wetland coverage in th e priority basins (McKee, 2005) Policy and Planning While flood control is still a priorit y, the water retention and contaminate assimilative capacity of wetlands are now wide ly recognized. In the face of increasing population and water demand in South Florida, major efforts are underway to carry out some of the largest ecological restoration projects ever undertaken. The Comprehensive Everglades Restoration Plan (CERP) (SFWMD & USACE, 2005), the Lake Okeechobee Protection Plan (LOPP) (SFWMD et al., 2004), and the Kissimmee River Restoration Program (KRR) are umbrella programs designe d to preserve and protect water resources in Central and South Florida.

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10 CERP is an $8 billion restoration project comprised of over 60 major projects with the underlying objective to preserve and proj ect the quality and supply of freshwater resources. Currently, an average of 1.7 billi on gallons of fresh water is diverted from Lake Okeechobee out to sea annually (SFW MD, 2005). CERP will attempt to capture and store most of that water in new reser voirs and Aquifer Storage and Recovery (ASR) wells. Lake Okeechobee Legislation A water body is considered WQL or impair ed when its pollutant load exceeds water quality standards for its designated use. Lake Okeechobee is designated as a Class I or potable water supply (FAC). In complia nce with Section 303(d) of the Clean Water Act (CWA) the establishment of TMDL is required for all impaired water bodies (FDEP, 2001). Since excessive P loading is primarily responsible for eutrophication of Lake Okeechobee (Havens et al., 1995), a TMDL of 140 metric tons yr-1 P was developed to achieve the target concentration of 40 ppb P within the LakeÂ’s pelagic zone by 2050. Phosphorus is currently the only pollutant wi th a required TMDL requirement for Lake Okeechobee. Water quality problems in Lake Okeechobee have been widely recognized since the late 1960Â’s (Allen et al., 1975; Flaig & Havens, 1995; Fluck et al., 1992; Gunsalus et al., 1992; Gustafson & Wang, 2002). In 1987, the Surface Water Improvement and Management (SWIM) Act (Florida St atutes, Sections 373.451 and 373.4595) was developed to focus on preservati on and restoration of some of FloridaÂ’s most significant water bodies. Lake Okeechobee was named in th at act, specifically mandating a 40% reduction of P loads in order to achieve a P c oncentration of 40 ppb in the pelagic zone. It regulated P sources from dairies by im plementing farm buyout programs, BMPs and

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11 structural retrofits to control systems (i.e. lagoon systems). As a result, P loads entering the tributaries between the late 1980’s and 1990’s were reduced (Gunsalus et al., 1992). However, by the mid-1990’s loads still exceeded SWIM targets and the load reduction trend was no longer declining (LOAP, 1999). Th is resulted in legislative action that called for more aggressive action than mandated by SWIM. The Lake Okeechobee Protection Act (LOPA) (Chapter 00-130, La ws of Florida) was passed in 2000. LOPA mandated the implem entation of a restoration and protection program which includes a P TMDL and BMPs to reduce nutrient loading to the Lake. The Lake Okeechobee Protection Program (L OPP) was developed to achieve and maintain compliance with Florida water quality standards. It involves the implementation of a P TMDL along with ot her research and monitoring objectives required by LOPA. (SFWMD, 2004). In addition, the Lake Okeechobee Watershed Project (LOWP) is a component of CERP th at aims to reduce P loading to the Lake, attenuate peak flows and restore riparian and isolated wetland habitat. LOPP and LOWP contain similar P source contro l programs through the implementation of “voluntary” and cost-share BMPs; however LOPP addresses re gional projects not included in CERP. Phosphorus Best Management Practices Best Management Practices are conser vation guidelines developed using Best Available Technology (BAT) to reduce point and non-point source water pollution while maintaining economically viable agricultura l productivity (Bottcher et al., 1995). However, the success of a BMP is only as eff ective as its level of acceptance. In fact, overcoming social and political obstacles ma y be more challenging than the fundamental science supporting the BMP, t hus innovative educational appro aches that facilitate an understanding of potential cost s and benefits associated w ith implementing the BMP is

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12 necessary (Bottcher et al., 1995). The su ccess and cost-effectiveness of BMPs are dependent on regional goals and BATs. Howeve r, in all cases, mutual awareness of potential outcomes and willingness to compromi se by all parties is essential to achieve BMP objectives and maintain the economic viability of the land-use. Many P BMPs targeting dairy and cattle se ctors have been implemented in the Lake Okeechobee drainage basin (Table A1, Appendix A). Required implementation of P BMPs as part of the Rural Clean Wate r Program (RCWP) and the Okeechobee Dairy Rule (FAC, 1996) have effectively reduced P discharge from dairies by 50%, thus improving discharge water quality (Gunsalus et al., 1992). However, the discharge reductions are relative to previous discharges which may have been several times greater than acceptable concentrations and have not been enough to lower the overall P load entering the Lake. Hydrologic Restoration of Isolated Wetlands Hydrologic restoration of hi storically isolated wetlands is a potential BMP that could play a significant role in meet ing the P TMDL of 140 metric tons yr-1. Wetlands are known to assimilate and immobilize nutrien ts and other contamin ates in living and dead (detritus) plant biomass and in soils. Phosphorus storage in wetland soils is dependent on the P concentrations of the ove rlying water column and the sediment pool. Organic matter accumulation and the abundance of iron and aluminum oxides within the soil influences sediment P flux with the ove rlying water column (Reddy et al., 1999b). Anaerobic decomposition is a slow process th at facilitates the accumulation of organic material. The accumulation of organic material immobilizes P in the process, thus acting as a P sink as long as anaerobic conditions persist. Phosphorus assimilation in living biomass is short term process which can re -release labile nutrien ts back into the

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13 environment upon senescence and decomposition. However, P accretion in plant biomass has been shown to account for 12-73% of total P removal from nutrient enriched waters (Reddy & Debusk, 1985). Phosphorus in wetland soils Mckee (2005) conducted a survey of 118 wetlands within the four priority basins to determine P storage in soils of isolated wetlands. Wetlands on dairy, improved and unimproved pasture land-uses were divided in to center, edge, upland and ditch zones and sampled. Physical parameters, such as or ganic matter content and bulk density between wetland center and upland were significantly di fferent when compared between like-land uses. Total P (TP) analysis showed signifi cantly higher concentrations in wetland centers compared to uplands for all three land use t ypes. There were also significantly higher TP concentrations in wetland centers compared with edge or ditch soils for improved and unimproved pasture land use types. Across land-uses, Mckee found signifi cantly greater center and edge TP concentrations in dairy wetla nd soils than in improved a nd unimproved pasture wetland soils. However, no significant differen ce was found between improved and unimproved pasture land-use types. Cent er and edge soils from diffe rent wetland types were also significantly different. Forest ed swamp soils had significantl y higher center TP values than emergent marshes and open water emergent marsh soils, while edge concentrations were significantly greater in scrub-shrub swamps compared to emergent marsh and open water emergent marshes. There were also si gnificantly greater P c oncentrations in edge soils of forested swamps compared to emergent marshes. McKeeÂ’s results theoretica lly support a hydrologic restor ation BMP of isolated wetlands as a means to increase P retention in the watershed and decrease P loads to the

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14 Lake. Hydrologic restoration would raise the water table, and increase the zone of inundation; maximizing the potential of the wetlands to accrete P in soils. However, this would also cause a shift in species com position, likely decreasing upland forage grass production, and pasture carrying. Thus, a c onsequence of restoration may adversely affect the economic viability of cattle opera tions as productive pa sture area would likely be reduced. Alternative forage crops BMPs with the potential to negatively impact economic viability should not be considered BMPs, unless altern ative funds are available to subsidize their implementation (Bottcher et al., 1995). However, along with hydrologic restoration, alternative practices could be implemented to minimize forage lo ss or even enhance pasture productivity. Wet cropping systems have been suggested as pot ential means of reduci ng P imports to the watershed and concentrations in dairy wastew ater by utilizing a vegetative species with high P assimilative capacity and sufficient fora ge value (Reddy et al., 2003). A forage species could remove the majority of P in a treatment wetland, while a periphyton cell would act as a polishing mechanism to further reduce P concentrations. Since, P storage in vegetative biomass is short-term, wet cropping systems utilize recycled nutrients to produce a forage, t hus reducing the need for P imports to the watershed. Wet cropping could also be a removal mechanism by harvesting and exporting forage and assimilated P out of the wate rshed to be utilized in other agricultural operations. Removal of sod from pastures is an effective way of export P because it is an economically valuable product that can also re duce the cost of past ure renovations when converting to more productive grasses.

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15 Another option to maintain pasture carry ing capacity after hyd rologic restoration is to utilize alternative fora ge species that have high productivity under wet conditions. Hydrologic restoration will alter the wate r table, potentially, creating unsuitable conditions for the existing dominant forage grass, Paspalum notatum Flgg (bahiagrass). Utilization of the wet tolerant forage grass species, Hemarthria altissima, ‘ Floralta ’ (limpograss) may be a beneficial subs titute for bahiagrass, potentially reducing the pasture area that would otherwise be lo st if no alternative is implemented with hydrologic restoration. This has potentially positive economic implications for implementing a hydrologic restoration BMP. If limpograss ha s comparable or highe r forage production and quality then this BMP may not only be ecologically benefici al, but it may also provide an economic incentive to implement it. Thesis Objectives This research is part of a collaborative effort to evaluate the effectiveness of hydrologic restoration of histor ically isolated wetlands as a BMP to enhance and utilize the P storage potential within the Lake’s watershed and reduce nutrient loading to the Lake. More specifically, one objective of th is research is to evaluate the role of vegetation in wetland P storage. Since the im plementation of wetland BMPs are, in part, dependent on their level of acceptance, addressing landowner concerns for lost pasture productivity is necessary. Another objective of this research investigates the efficacy of using the wet tolerant forage species, Hemarthria altissima ‘Floralta’ (limpograss), in upland areas, adjacent to wetlands, to alleviat e the potential loss of productive pasture due to hydrologic restoration. The th esis objectives are as follows:

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16 I. Assess biomass production and P assi milation by wetland vegetation and forage grasses under various hydroperiods. II. Determine the efficacy of establishing a wet tolerant forage grass in wetland transition zones before hydrologic rest oration to minimize loss of productive pasture Chapter II focuses on standing biomass and P storage of various vegetative components. Chapter III describes a me socosm study that tested the hydrologic tolerances of bahiagrass and limpograss. Chapter IV summarizes results from both studies, discusses implications of wetland rest oration and presents conclusions from this study.

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17 CHAPTER 2 PHOSPHORUS ASSIMILATION BY ISOLATED WETLAND VEGETATION Introduction Nutrient export, primarily phosphorus (P), from non-point source agricultural activities in the Lake Okeechobee watershed has contributed to near hyper-eutrophic conditions in the Lake (Reddy et al., 1999b). As a result, a Total Maximum Daily Load (TMDL) rule for P and associated Best Management Practices (BMP) have been implemented to reduce nutri ent loading to Lake Okeec hobee (Bottcher et al., 1995; FDEP, 2001; Havens et al., 2005; SFWM D,2004). Many voluntary BMPs have effectively lowered P exports from improved pasture and dairies in high P export basins (Gunsalus et al., 1992) and in the Everglades Agricultural Area (EAA) (Flaig & Havens, 1995). However, in-Lake P concentra tions currently average 120 g L-1; three times the TMDL target concentration of 40 g L-1. Water column total nitrogen (TN) to TP ratios in the Lake are 13:1, which favors cyanob acteria dominance (Havens et al., 2005) Between 1994 and 1998, two of the Lake’s northern tributaries, the Lower Kissimmee River (LKR) and Taylor Creek/N ubin Slough (TCNS), supplied 43% of the water, and 56% of the total P load entering the Lake. The ratio of water supplied to P load for these two tributaries is disproportio nate, LKR actually supplies 33% of the water and 32% of the P load, while TCNS supplies 10% water and 24% of the P load (FDEP, 2001). The high P discharge from these tributarie s is primarily the result of four “priority” drainage basins with in their watersheds. These four basins (Figure 2-2) occupy

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18 12% of the LakeÂ’s watershed, and export as much as 35% of the total P load entering the Lake. Within the priority basins 68% of the land area supports agricultural activities (45% improved pasture, 4% da iry), while 15% of the land is contains wetlands (SFWMD, 1995). The historical extent of wetlands is unknown; however, within the priority basins 45% of isolated wetlands have been at le ast partially drained (SFWMD, 2004). Ditches that drain these wetlands act as a conduit for transporting dissolved P directly to the Lake. Cattle ranching and agriculture have been the primary land uses in the watershed since the mid 1800Â’s. Beef cattle populations rapidly increased in the early to mid 1900Â’s, spurring the drainage and transfor mation of native range lands into high production improved pastures. From 1940 to 1970 the area of improved pasture increased from 34,000 to 170,000 ha (Flaig & Havens, 1995) and by 1995 it occupied 183,000 ha of the watershed; ~24 % (Table 11). The vast network of drainage canals exacerbated nutrient loss from the landscap e by lowering the water table and hydraulic retention times (HRT), thus d ecreasing P assimilative potential of historically isolated wetlands and perpetuating the need to impor t more nutrients (Flaig & Havens, 1995). Extensive wetland drainage further intensif ied cattle production and increased P imports to the watershed in the forms of cattle feed and fertilizer Studies indicate that there is a strong co rrelation between P imports to watershed and P loading to the Lake (Boggess et al., 1995; Hiscock et al., 2003). Continual net imports of P have created an excess of bioavaila ble P. Soils in the northern watershed are poorly drained and have limited P binding capacity, however, low topographic relief limits runoff and subsequent P exports from uplands (Flaig & Havens, 1995). Boggess et al., (1995) estimated that 90% of P importe d between 1985 and 1989 was retained in the

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19 watershed, while more recent estimates from 1997 to 2001 indicate an 83% retention of imported P (Hiscock et al., 2003). In both stud ies, the majority of imported P was stored in uplands (71% and 74%) however, of the portion that was loaded to wetlands, the percent assimilated decreased over time from 60% to 32%. Hiscock et al., (2003) attributes the reduction in st orage to decreased assimilativ e potential, not decreased wetland area. This suggests that many wetlands may already be saturated with P, and even if imports to the watershed decrease or stop, they may become a source rather than a sink. Therefore, reducing water flow, in addi tion to P imports, may be the most effective way to reduce P loading to the Lake. Factors Influencing Phosphorus Retention Phosphorus is an essential nutrient for primary producers and is limiting in most freshwater ecosystems. However, many agricult ural wetlands are not limited by P, due to its relative abundance and bioge ochemical stability (Mitsch & Gosselink, 2000). In the Okeechobee watershed, Reddy et al., (1995) found that nitrogen (N) and P concentrations in aquatic macrophytesÂ’ tissue are generally hi gh, indicating that ne ither nutrient is limiting plant growth. Other studies have determ ined that wetland plants with N:P ratios below 14 are N limited (Koerselman & Meuleman, 1996). Despite the apparent abundance of both nutrients, the N:P ratios in tributary macrophytes were between 4 and 6 (Reddy et al., 1995), suggesti ng that P is more abundant than N. Although, generally speaking, wetland plants in the Okeechobee wate rshed are not considered to be limited by P or N availability. The physical, chemical and biological mech anisms controlling P assimilation in wetland ecosystems has been well document ed (Braskerud, 2002; Flaig & Reddy, 1995; Gilliam, 1995; Kadlec, R H, 1999; Kadlec & Knight, 1996; Mitsch & Gosselink, 2000;

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20 Reddy et al., 1999a; Reddy et al., 1999b; Ri chardson, 1985; Sharpley, 1995). Unlike the biogeochemical cycles of nitrogen, carbon, su lfur and oxygen, P does not have a naturally occurring gaseous phase. It is accreted in wetlands by immobiliz ation, adsorption and precipitation processes (Figure 2-1). The re lative portion of inorga nic and organic forms depends on soil, vegetation, hydrology and land use characteristics (Reddy et al., 1999a). Adsorption and precipitation are abiotic processe s that occur in the soil and are indirectly controlled by pH. Immobilization is a te mporary biotic process by which dissolved inorganic P is assimilated in vegetative or microbial biomass as organic P. Vegetative biomass has a high rate of turnover; often seve ral times a year in warmer climates. After senescence, a portion of labile P leaches back into the water column as the detrital material breaks down. A small portion of P in recalcitrant detritus is accreted in the soil as organic P. Phosphorus assimilation in vegetation is dependent on species productivity and turnover rates, nutrient availability, landuse intensity, hydrology, and biochemical and physicochemical properties (Reddy et al., 1999 a). Biomass is not be considered a sustainable long-term P removal mechanism in wetlands because it is a short-term storage that releases as much as 80% of assim ilated P back into the water column after senescence (Reddy et al., 1995). However, ac cretion of recalcitrant biomass residuals (detritus) is the only sustainable long -term storage mechanism for P removal by biological means (Kadlec & Knight, 1996; Richardson, 1985).

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21 A. B. Figure 2-1. Mechanisms driving P cycli ng. A) Mechanisms driving forms of P (Sharpley, 1995). B). Phosphor us cycling in wetlands. Research Objectives The use of constructed and restored is olated wetlands in Lake OkeechobeeÂ’s watershed has been suggested as a potentia l means of decreasing P loads entering the Lake (Flaig & Reddy, 1995; Havens et al ., 2005; LOPA, 1999; Re ddy et al., 2003; Reddy et al., 1996; SFWMD, 2004). Si nce TMDLs are established based on both concentration

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22 and flow, restoring the natural hydrology to is olated wetlands could reduce storm water runoff while increasing wetland HRT and P accr etion in residual organic material. Previous studies in the four priority basi ns have shown significantly greater soil P concentrations and organic material content in wetland centers than in adjacent uplands (McKee, 2005). These findings suggest that hydrologic restor ation could increase on-site P storage in soils by increasing wetland area. A key component responsible for increased P storage capacity in wetland centers is biomass production. High biomass and anoxic conditions foster residual biomass and P accr etion, stabilize soil porewater, and reduce concentrations in surface water (Reddy et al., 1999b) Biomass production and P assimilation are the primary focus of this research. Research Questions and Hypotheses 1. What role does vegetative bioma ss play in total wetland P storage? H1: Biomass P storage will have a lesse r role when compared to surface soil P storage. 2. Does biomass differ along a hydrologic gradient? H2: Biomass is higher in the center of the wetland 3. Does total P storage in standing bi omass differ along a hydrologic gradient? H3: Total biomass P storage will be higher in wetlands than uplands 4. Where is P partitioned in vegetation? H4: More P will be stored in above ground biomass (AGB) than below ground biomass (BGB) While P export rates from various land-uses have been broadly established (Flaig & Havens, 1995) the compounded influence of hydrology and grazing pressure on biomass production and subsequent organic P storage in wetlands in the Okeechobee basin has not been extensively studied. This chapter fo cuses on P storage in above and below ground

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23 standing biomass and vegetative assemblage s along hydrologic gr adients in pasture wetlands. Data presented in this chapter will be compared to vegetation data after hydrologic restoration to evalua te the effect on total wetl and P storage and vegetation dynamics. Methods focus on above ground biomass (AGB) and below ground biomass (BGB) sample collection, processing and labor atory analysis. In addition, soil and litter samples were collected and pre-sampling photographs of each quadrate were taken. Materials and Methods Study Sites Four wetlands from two different ranches located in the priority basins were selected for long-term monitoring. Selection criteria were based on land use intensity and proximity of two similarly sized, hydrologi cally modified wetlands. The Larson site, located in basin S-154, is more intensely managed then the Be aty site, located in basin S65D (Figure 2-2). Management intensity wa s subjectively determined based on land-use history, pasture maintenance re gime and grazing pressure. The two wetlands at the Larson site, Larson East (LE) (8056’28.08” W, 2720’56.06” N) and Larson West (LW) (80 56’47.49” W, 2720’59.27” N), are roughly 2.5 ha each, while the Beaty wetlands, B eaty North (BN) (8056’54.50” W, 2724’41.41” N) and Beaty South (BS) (8056’43.21” W, 2724’27.53” N), are roughly 1.3 and 1.4 ha respectfully (Figure 2-3). Wetland size was calculated in ArcGIS. The perimeter was delineated on site with GPS tracking by wa lking along vegetation community transitions between upland forage grass ( Paspalum notatum ) and unconsolidated wetland species ( Juncus effusis ).

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24 Figure 2-2. Map of land use in the 4 prior ity basins. S-191 is the Taylor Creek/Nubbin Slough (TCNS) basin and S-65D, S-65E and S-154 part of the Lower Kissimmee River (LKR) Basin The Larson si te is located in basin S-154. The Beaty site is located in S-65D. a. b. Figure 2-3. Isolated wetlands selected for long term monitoring. (a) Beaty Ranch wetlands; top left wetland is referred to as Beaty North (BN) and the bottom right wetland is Beaty South (BS). (b) Larson Ranch wetlands; wetland to the left is referred to as Larson East (LE) and the wetland on right is Larson West (LW)

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25 Sampling Data from three sampling events: November 19-21, 2004, March 25-26, 2005 and July 14-16, 2005 were collected in this st udy. Based on results from McKee, 2005 who found significantly greater soil P concentratio ns in wetland center zones than adjacent uplands, a stratified random sampling scheme was used to sample wet marsh-(center), transitional-(edge) and forage-(upl and) zones (Figure 2-4). Re spective zone data from all sampling dates were combined and analyzed to minimize temporal va riability. Five 1 m2 quadrates in each zone were located with GPS using predetermined random coordinates from ArcGIS. Figure 2-4. Stratified sampling z ones: center, edge and upland. Five randomly placed 1 m2 quadrates (not drawn to scale) were sampled in each zone. Beaty North shown as an example.

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26 In the upland quadrates, AGB was cu t as close to the ground as possible (approximately 1 to 3 cm above the ground su rface) using electric gr ass clippers, while hand clippers were used in edge and center zones. All removable AGB from individual quadrates was collected. After AGB was clippe d, three BGB cores were extracted from random locations within the quadrate with a 15 cm diameter aluminum cylinder, to a depth of 20 cm. The majority of the soil was dry-shaken or wet washed in the field using a 1 cm2 mesh sieve (depe nding on the whether water was present). Since it was not possible to collect 100% of AGB in the quadrate by the initial clipping, the remaining residual AGB (the stubb le left over after clipping) was removed from the BGB cores and placed in a separate bag. The amount of residual AGB per coresurface area was extrapolated to estimate the total residual AGB not collected in the field from the 1 m2 quadrate. This number was later added to the live biomass component of AGB. Sample processing All samples were transported from Okeec hobee County to Gainesville, Florida for post-collection processing. Ra ther than homogenizing all biomass within the quadrate, AGB of each species was sorted into living and senesced life stages. Both life stages of individual species were sort ed, weighed and analyzed sepa rately as components of the total biomass in the quadrate. Relative dominance of each species was determined based on the quantity of living and senesced biomass re lative to other species in the quadrate. Above ground biomass was sorted into primary, secondary, tertiary, et c, and residual or unidentifiable species. Due to the larg e quantity of AGB per quadrate, large homogeneous samples, such as upland forage species, were sub-sampled and sorted by life stage. Using the ratio of living to sene sced biomass from sub-samples and the total

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27 biomass of the homogeneous sample, the live and senesced portions were calculated without processing all of the biomass. Below ground biomass was washed and sieved to remove any remaining soil. All sorted AGB and washed BGB samples were dried at 70C for 72 hours, and weighed. Below ground biomass per m2 was calculated by extrapolating the biomass per coresurface area up to 1 m2. All AGB that was sub-sampled was weighed and discarded. All other samples were rough ground, sub-sample d and fine ground to pass through a # 40 sieve. Laboratory Analysis All samples were analyzed for Total Phosphorus (TP), Total Carbon (TC), and Total Nitrogen (TN), although, since P dynamics are the primary focus in these studies, only P data is presented in this chapter. Total P was extracted from 0.2-0.5 g of plant tissue using the ignition method (Andersen, 1976). Data and Statistics Analysis Data were averaged by ranch (site), and th erefore are averages from two wetlands. Statistical comparisons were made between zones at each site. Sites were not statistically compared. JMP Statistical Software was us ed to perform data analyses. For mean comparisons of more than two parameters the Tukey-Kramer HSD (honestly significant difference) test was used (JMP, 1989-2005). Outliers greater th an four standard deviations from the mean were excluded from data analysis. All quadrate values, except outliers, were included in the calculation a nd statistical comparisons of total biomass, total P storage, AGB and BGB. Calculations and comparisons of root-to-shoot ratios only included quadrates that contai ned both BGB and AGB values.

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28 Hydroperiods were determined for each qua drate elevation using stage data from a pressure transducer located in the center of each wetland. To determine the length of time the water level was above the soil surface, a bench mark elevation, which correlated to the transducer level in the well, was esta blished at the ground surface by the well. All quadrate elevations were corrected relati ve to the benchmark elevation and the transducer. The transducer r ecorded stage every half hour. The number of half hours the water level was above the ground was counted and converted to days. A regression of days vs. elevation (stage) was developed for each wetland. Corrected quadrate elevations were entered into the re gression equation and the co rresponding hydroperiod was returned. Zone hydroperiods were an average of all quadrates within each specified zone at each site (i.e., Beaty center hydroperiod wa s the average of all quadrates within the center zones of both wetlands). Results Appendix B contains numerous tables and figures of supplemental data and statistical comparisons. Species Composition along a Hydrologic Gradient Center, edge and upland zones within each wetland were determined visually using vegetative community compositions and aerial im ages. Overall, center, edge and upland zones at the Beaty site had l onger hydroperiods than zones at the Larson site (Table 2-1). The average hydroperiods of center and edge zo nes at the Beaty site were ~122 and ~96 days longer than the same zones at Lars on. In fact, Beaty edge zones had similar hydroperiods as Larson center zones.

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29 Table 2-1. Mean and standard devia tion of hydroperiod s at each site. Hydroperiod Site Zone n (days) *Difference p value Center 38269 48.9 a Edge 38141 67.4 b Beaty Upland 3831.2 64.9 c < 0.01 Center 33147 63.9 a Edge 3444.5 28.4 b Larson Upland 479.11 15.3 c < 0.01 Mean comparisons using Tukey-Kramer HSD test. Table 2-2. Mean species hydroperiod of both sites. Species Hydroperiod by Site Site Species n Indicator Days Range Difference p value Andropogon 4 FAC 77.7 68.2 0-128 BC Baccopa 1 OBL 150 150-150 ABC Eleocharis 1 OBL 312 312-312 ABC Juncus 30 OBL 155 74.3 0-302 B Ludwigia repens 3 OBL 239 67.7 161-283 AB Luziola + P. acuminatum 3 FACW 154 6.35 150-161 ABC Micranthemum 1 OBL 161 161-161 ABC Other 32 178 95.7 0-314 B P. notatum 39 UPL 45.3 70.9 0-304 C Panicum 31 OBL 250 86.5 0-323 A Polygonum 14 OBL 215 88.8 0-314 AB Pontederia 19 OBL 269 38.7 171-315 A Sagittaria 1 OBL 297 297-297 ABC Beaty Utricularia 1 OBL 298 298-298 ABC < 0.01 Alternanthera 10OBL 56.9 38.5 16-121 YZ Eleocharis 2 OBL 43.5 12 35-52 XYZ Juncus 8 OBL 54.4 27.5 0-87 YZ Ludwigia repens 1 OBL 67 67-67 XYZ Luziola + P. acuminatum 25 FACW 127 72.2 16-240 X Other 30 66.1 68.9 0-282 Y P. notatum 42 UPL 11.4 17.8 0-66 Z Panicum 2 OBL 66 72.1 15-117 XYZ Polygonum 16 OBL 93.6 52.6 15-195 XY Larson Pontederia 7 OBL 164 54.2 70-227 X < 0.01 Shaded areas represent species that were pr esent at both ranches. Indicators: (OBL) Obligate Wetland, (FACW) Facultative We tland, (FAC) Facultative, (UPL) Upland Species (Tobe et al., 1998).

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30 Average hydroperiods of species present at both sites were longer at the Beaty wetlands than at Larson (Tab le 2-2). These differences are similar to the zone hydroperiod differences between sites (Table 2-1). For instance, the average hydroperiod of Juncus effusis Pontederia cordata, and Polygonum hydropiperoides were 100, 105 and 121 days longer at Beaty than at Larson. Figure 2-5. Logistics fit of species. Negative log-likelihood or uncer tainty relative to hydroperiod for all wetlands. This figur e illustrates the re lative dominance of species as a percentage of the total species pres ent at a given hydroperiod Community biodiversity is greatest between upland and center zones (R2=0.096) The logistics fit of species (Figures 2-5 and 2-6) account for the likelihood that a species will be present under a given hydroperi od. It quantifies domin ance of individual species relative to other species at the same hydroperiod based on frequency of occurrence. It does not quantify biomass. Figure 2-5 shows the species distribution relative to hydroperiod in all wetlands. The Beaty we tlands, overall, had greater

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31 biodiversity than Larson as measured by the num ber of species present. At both sites, community biodiversity was greatest in the transitional-edge zones between upland and center zones (Figures 2-6 A & B). A. B. Figure 2-6. Logistics fit of species by site (-log-likelihood). These figures illustrate the relative dominance of species as a percenta ge of the total species present at a given hydroperiod. A). Species distribution at the Beaty (R2=0.17) site is dominated by bahiagrass in the upland and Panicum and Pontederia in the center. B). Larson (R2=0.19) species distributi on is also dominated by bahiagrass in the upland, however, a mix of Luziola fluitans and Paspalum acuminatum dominate the center zones. Ecosystem Phosphorus Storage For the purpose of this study, total P storage in wetlands (with a 50 m upland buffer) included soil (10cm depth), AGB, BGB a nd litter components. At both sites, soil was the primary P storage component, repres enting greater than 88% of the total P storage in the wetlands, while BGB, AGB and litter represented 8%, 3%, and 1% respectively (Figure 2-7). Harves ted BGB was significantly greater ( = 0.05) than

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32 standing AGB at the Beaty site (Figure 2-8 A), however, there were no significant P storage differences between BGB and AGB at either site (Figure 2-8 B). 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 BGBLitterSoilAGB ComponentTP ( mg m -2 ) Beaty Larson Figure 2-7. Phosphorus storage co mponents. Soil with in the top 10 cm stores more than 88% of the total P stored in these four components, while BGB, litter and AGB roughly account for 8%, 1% and 3%, respectively. Table B-1 in Appendix B contains P storage tota ls by site, zone and component. A. B. 0 500 1,000 1,500 2,000 2,500 BGBAGB Com p onentBiomass ( g m -2 ) Beaty Larson 0 500 1,000 1,500 2,000 2,50 0 BGBAGB ComponentTP ( mg m -2 ) Beaty Larson Figure 2-8. Comparison of AG B and BGB components at B eaty and Larson. A). Mean biomass harvested from all zones. B) Mean P harvested from all zones

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33 Standing Biomass Total biomass (AGB+BGB) in upland zones, which mainly consisted of forage grass, were similar at both sites, ~1,900 g m-2. There were no signifi cant differences in total biomass between zones at the Beaty site, howeve r Larson edge zones were significantly greater ( = 0.05) than centers, and upland zones were significantly greater ( = 0.05) than edges (Figure 2-9). The sa me relationship was true for BGB, which accounted for 68-93% of total biomass (Fi gure 2-10). Upland BGB and AGB were similar at both sites; ~1700 and ~240 g m-2 respectively. Mean BGB was larger than AGB in all zones at both s ites (Figure 2-12). Center a nd edge zones at Beaty had significantly greater ( = 0.05) AGB than upland zones, while Larson AGB was greater in upland zones than in edge zones (Figure 2-11). A. B. Figure 2-9. Total biomass at Beaty (A) and Larson (B) wetlands. Different lower case letters indicate significa nt differences. Note the difference in scales.

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34 A. B. Figure 2-10. Below ground biomass at Beaty (A) and Larson (B) wetlands. Different lower case letters indicate significant differences. A. B. Figure 2-11. Above ground biomass at Beaty (A) and Larson (B) wetlands. Different lower case letters indicate significant differences. Note the difference in scales.

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35 A. B. (3,000) (2,500) (2,000) (1,500) (1,000) (500) 0 500 CenterEdgeUpland Beaty Zone and SiteBiomass ( g m -2 ) (3,000) (2,500) (2,000) (1,500) (1,000) (500) 0 500 CenterEdgeUpland Larson Zone and SiteBiomass ( g m -2 ) Figure 2-12. Biomass partitioning AGB vs. BGB. At both sites, Beaty (A) and Larson (B) there was more BGB than AGB in all zones Approximately 20% of the quadrates di d not have measurable biomass for both AGB and BGB components. Therefore, since to tal biomass is the sum of AGB and BGB, one component made up 100% of the total bi omass for ~20% of all quadrates. This occurred when the quantity (mass) of bioma ss within individual quadrates was below the harvestable threshold. In some zones st anding AGB was limited by grazing, while the quantity of harvested BGB was dependent on the types of species present within individual quadrates. It is possible for the total biomass of a quadrate to be composted of 100% AGB and no BGB. For example, sp reading ground cover species such as P. hydropiperoides, Panicum hemitomon, Luziola fluitans and Paspalum acuminatum may have been rooted outside of the quadrate, but AGB from plants may have grown into the quadrate. Since BGB root to shoot rati os (Table 2-3) were only calculated in quadrates that contained both AGB and BGB co mponents, they are slightly different from relative AGB and BGB zonal averages (Figure 2-12). Ratios at both sites were greater than one in all

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36 zones, indicating that BGB was greater than AGB. Ratios at Beaty were lower than Larson and were not significan tly different by zone. Larson edge zones had significantly greater ( = 0.05) ratios than the upl and zones (Table 2-3). Table 2-3. Root to shoot ratios by zone. BGB:AGB Site Zone n Ratio *Difference p value Center 31 5.73 6.21 a Edge 29 8.36 22.2 a Beaty Upland 31 10.4 16.3 a 0.52 Center 25 23.4 40.5 a,b Edge 28 149 340 a Larson Upland 39 17.5 48.5 b 0.02 Mean comparisons using Tukey-Kramer HSD test. Standing Biomass by Individual Species Unidentifiable AGB species were collectivel y labeled as “other”, and represent a combination of multiple species. The “oth er” category often yielded similar biomass values as identifiable species. Overall J. effusis had the most AGB at the Beaty site, while Paspalum notatum (bahiagrass) was greatest at La rson (Figure 2-13). At both sites bahiagrass had the greatest AGB in upland zone s. Biomass in edge and center zones at Beaty was dominated by J. effusis, P. hemitomon, P. hydropiperoides, and P. cordata while Larson edge and cent er zones were dominated by J. effusis, P. hydropiperoides, and P. cordata (Figure B-2, Appendix B)

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37 A. B. 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700Andropogon Baccopa Eleocharis Juncus Ludwigia L uziola + P. acuminatum Micranthemum Other P. notatum Panicum Polygonum Pontedaria Sagittaria Utricularia Biomass ( g m -2 ) 0 50 100 150 200 250 300 350 400 450 500Alternanthera Eleocharis Juncus Ludwigia Luziola + P. acuminatum Other P. notatum Panicum Polygonum Pontedaria Biomass ( g m -2 ) Figure 2-13. Above ground biomass by species fo r all zones (center, edge and upland). A) Beaty wetlands. B) Larson wetlands Phosphorus Storage in Biomass Phosphorus storage in total biomass ( AGB + BGB) was positively related to hydroperiod at Beaty, while Larson wetlands had an inverse relationshi p (Figure 2-14). Phosphorus storage in edge and upland zone s did not differ much between sites, but Beaty center zones stored ~1000 mg m-2 more P in total bioma ss than Larson centers. Center zones stored significantly more ( = 0.05) P than uplands at Beaty, while the opposite trend existed at Larson (Figure 2-14).

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38 A. B. Figure 2-14. Total biomass P storage. A) Beaty wetlands were positively related to hydroperiod, while Larson had an inversely relationship ( = 0.05). Different lower case letters indicate significant differences between zones at the same ranch sites Below ground biomass P concentrations (Tab le 2-4) and storage (Figure 2-15 A) did not differ between zones at the Beaty we tlands. At Larson, P concentrations in upland BGB were significantly lower ( = 0.05) than center and edge concentrations (Table 2-4). However, there was still a gene ral trend of decreasing P storage from center to upland (Figure 2-15 B), where upland a nd edge BGB stored significantly more ( = 0.05) P than center zones. Phosphorus concentrations we re significantly greater in AGB than BGB in all zones at both sites. At Beaty, P concentrations were greater in center zones than in edge and uplands. At Larson all zones were significantly different ( = 0.05); exhibiting a positive relationship with hydroperiod (Table 25). The center and edge zones at Beaty stored more P in AGB than upland zones, wh ile Larson center zones stored more P than

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39 edge zones (Figure 2-16). P hosphorus storage in BGB is larg er than AGB in all zones at Beaty and in edge and upland zones at Lars on. While BGB made up the largest portion of total biomass in all zones, AGB P concentr ations drastically influenced total biomass P storage. This was exhibited in Larson centers where AGB P storage was greater than BGB (Figure 2-17 B) despite the fact that harvested BGB was great er than harvested AGB. Table 2-4. Below ground bioma ss concentrations by zone Below Ground TP Concentration Site Zone n (mg/kg) *Difference p value Center 34 765 210 a Edge 34 719 285 a Beaty Upland 34 674 134 a 0.23 Center 28 781 136 a Edge 29 802 168 a Larson Upland 40 678 145 b 0.002 Mean comparisons using Tukey-Kramer HSD test. A. B. Figure 2-15. Phosphorus storage in BGB at Beaty (A) and Larson (B) wetlands. Different lower case letters indicate significant differences = 0.05.

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40 Table 2-5. Above ground biomass P concentrations by zone. Above Ground TP Concentration Site Zone n (mg/kg) *Difference p value Center 122 1830 976 a Edge 118 1340 702 b Beaty Upland 68 1340 623 b < 0.01 Center 69 3150 1010 a Edge 80 2690 1060 b Larson Upland 89 1650 647 c < 0.01 Mean comparisons using Tukey-Kramer HSD test. A. B. Figure 2-16. Phosphorus storage in AGB at Beaty (A) and Larson (B) wetlands. Different lower case letters indi cate significant differences ( = 0.05). The P storage root-to-shoot ratios were ca lculated the same way as the harvested biomass root-to-shoot ratios; only quadrates that contai ned both BGB and AGB were used in the calculation. All ratios were greater than one a nd did not differ significantly by zone, meaning P storage was greatest in BG B. These ratios contradict mean AGB and BGB values in Larson centers. Overall, ratio data suggests that AGB stores more P than BGB in Larson centers (Table 2-6). The differe nce, once again, is that the ratios (Table 2-6) are an average of indivi dual quadrate ratios within ea ch respective zone, which only

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41 included quadrates that had bot h AGB and BGB, where as P st orage values (Figure 2-17) of each component were averages of all harvested AGB and BGB with each respective zone. A. B. (2,500) (2,000) (1,500) (1,000) (500) 0 500 1,000 1,500 CenterEdgeUpland Beaty Zone and SiteTP ( mg m -2 ) (2,000) (1,500) (1,000) (500) 0 500 1,000 1,500 CenterEdgeUpland Larson Zone and SiteTP ( mg m -2 ) Figure 2-17. Above and below ground biomass P storage. Table 2-6. BGB to AGB P storage ratios BGB:AGB P Storage Site Zone n Ratio *Difference p value Center 31 3.28 3.94 a Edge 29 6.22 14.8 a Beaty Upland 31 6.30 9.86 a 0.45 Center 23 6.50 10.2 a Edge 28 28.4 65.6 a Larson Upland 39 6.76 15.3 a 0.0548 Mean comparisons using Tukey-Kramer HSD test. Standing Biomass by Individual Species Compared to other species, P. hydropiperoides, which was predominately present in center and edge zones, stored the largest amount of P in AGB at both sites (Figures 218). J. effusis, P. hemitomon and “other” species were secondary AGB P storage species in center and edge zones at Beaty (Fi gure B-4, Appendix B). “Other” AGB was a secondary storage in Larson center and edge zones. Bahiagrass stored the most P in

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42 uplands at both sites. At Larson, bahiagrass and J. effusis had similar P storage in upland and center zones. A. B. 0 100 200 300 400 500 600 700 800Andropogon Baccopa Eleocharis Juncus Ludwigia Luziola + P. acuminatum Micranthemum Other P. notatum Panicum Polygonum Pontedaria Sagittaria Utricularia TP ( mg m -2 ) 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400Alternanthera Eleocharis Juncus Ludwigia Luziola + P. acuminatum Other P. notatum Panicum Polygonum Pontedaria TP ( mg m -2 ) Figure 2-18. Phosphorus storage by species A). Beaty ranch. B). Larson ranch. Discussion Hydrology Hydrology controls many physicochemical mech anisms in wetlands and is the most important determinant of wetland type a nd class (Kadlec & Knight, 1996; Mitsch & Gosselink, 2000). Hydroperiod represents th e number of days a year a wetland is inundated. The hydro-pattern or hydrologic regi me is characterized by five components: 1) duration, 2) frequency, 3) depth, 4) flow, and 5) timing or season of flooding. All of these components influence the establishmen t of vegetation and nutrient availability

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43 along hydrologic gradients. The wetlands in this study were under different hydroperiods and presumably, different hydrol ogic regimes as the effectiv eness of the ditches varied between wetlands and sites. Many external factors infl uence hydrology making it difficult to compare wetlands under different management intensities. For instance, ditches at the Larson site effectively drained more wetland area than di tches at Beaty. As a result, Beaty wetlands were inundated most of the year, while the Larson wetlands were only inundated roughly half of a year. Anaerobic conditions create an environment that is conducive for organic matter accumulation and P immobilization. Thus, Beaty has more potential to accumulate organic matter. Species biodiversity was greatest in tran sitional edge zones, between upland and center zones. Relatively stable environmen ts under short or long hydroperiods favor the establishment of obligate species communities (i.e. bahiagrass in the upland or Panicum and Pontederia in wetland centers). While under moderate hydroperiods in the edge zones (ecotones), fluctuating hydrologic conditions continuously eliminate and regenerate species, decreasing mono-domin ance and increasing facultative species richness. Ecosystem storage Although P storage in soil is dependent on various site physicochemical characteristics, it is often the primary st orage component in wetland and terrestrial ecosystems (Dolan et al., 1981; Richardson, 1985). Results from this study support H1: the role of vegetation in total P storage is significantly less than soil P storage in wetlands. Although vegetation plays a lesser ro le in terms of ac tive P storage, its

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44 importance can not be overstat ed. It indirectly increases the P storage capacity of wetlands as residual biomass decomposes and becomes incorporated into the soil. Biomass in Pasture Wetlands Biomass production studies in pasture we tlands are limited, probably due to the intrinsically high variability of disturbances to these eco systems. Different land use intensity, hydrologic regimes and grazing pressu re create high vari ability, and make it difficult to compare wetlands at different sites. Many studies have identified relationships between envi ronmental gradients (i.e hydrology) and vegetation community establishment under stable conditio ns (Seabloom et al., 2001; Van der Valk, A G, 1981; Van der Valk, A G et al., 1994; Wellings et al., 1988; Whigham et al., 2002). However, species distribution and annual biom ass are generally variable over time and reflect a balance of current environmental c onditions and historical recruitment events (Seabloom et al., 2001; Whigham et al., 2002). Our study minimizes temporal variability over one year, but does not represent long term seasonal variability that may be present in response to climatic variability. Generally, environmental gradients create pa tterns of biomass distribution within wetlands. A three year study of restored ag ricultural wetlands in Maryland found AGB was inversely related to hydroperiod (Whigham et al., 2002). In that study, wetlands were divided into submersed, emergent/seasonal, a nd temporary zones; similar to the center, edge and upland zones in our study, however, it appears that their zones my have been under longer hydroperiods. Biomass was signi ficantly greater in temporary zones [uplands] than in the emerge nt/seasonal zones [edge] and the emergent/seasonal zones were significantly greater than the submersed zo nes [center] in each of the three years. Even if their zones were under longer hydrope riods, our AGB results do not support any

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45 relationship to hydroperiod, indica ting that other factors, such as management intensity, are influencing AGB. Overall, BGB was the largest portion of to tal biomass, regardless of zone. This was not surprising since emergent wetland a nd terrestrial macrophytes generally have greater BGB than AGB due to extensive netw orks of roots and rhizomes (Reddy et al., 1999a). The inverse relationship between BGB and hydroperiod at the Larson site may be the result of more intensive grazing pressu re. There is an observable difference in grazing intensity between the two ranches. While cows do not graze BGB, heavily grazed AGB directly affects BGB production a nd plant survivability. Total biomass at Larson had the same inverse relationship to hydroperiod. Larson wetland zones were more heavily grazed and dominated by low-growing, unidentified annuals, Eleocharis spp. and L. fluitans Intense grazing followed by prolonged inundation discourages recruitment of perennial species. Thus dur ing flooding events, low growing species do not survive inundated conditions. Ultimately, when water levels recede, bare soil is exposed and subject to mineralization and er osion. The combination of intensive grazing and flooding primarily favors annua l species in Larson wetlands. Disturbance Effects on Biomass Harvested biomass results do not support H2: Biomass at Larson and Beaty can not be correlated with hydroperiod. Low AGB and la rge, highly variable ratios in edge zones are likely the result of the compounding eff ects of the hydrologic regime, intense pasture management and higher grazing density. Once ag ain this suggests a significant effect of grazing on biomass and P storage. External disturbances occur at multiple scales and can confound relationships between environmental gradients and wetla nd structure (Magee & Kentula, 2005; Van

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46 der Valk, A G et al., 1994). Direct imp acts of grazing on wetlands often include herbivory of vegetation, nutrient inputs, and so il trampling; all of which directly or indirectly alter species co mposition (Clary, 1995; Steinman et al., 2003). Grazing was not measured in this study; however, it is evident, based on these results and visual observations, that grazing has a dramatic affect on species composition and standing AGB. Bohlen et al. (2004) found differences in plant species assemblages in improved and semi-improved pastures. In their study, the less intensively grazed, semi-improved pastures were dominated by P. hemitomon which has high forage value, while intensively grazed, improved pastures s upported more diverse plant communities including J. effusis In addition, they found that ca ttle exclusions within improved pastures lead to an increase in P. hemitomon coverage. This suggests that preferential grazing of P. hemitomon may actually foster species bi odiversity incl uding unpalatable species such as J. effusis (Bohlen et al., 2004). Biomass results from this study suggest th at there is high vari ability within the same land use classification. Although all we tlands are in improve d pastures, wetland center and edge biomass results are not even similar between ranches Thus, one limitation of this study, was a lack of replic ation among sites. To minimize variability, site data contained mean values of both wetla nds at each ranch. Whigham et al., (2002) also found high variability between sites. This suggests that different management intensities within the same land use can be highly variable between sites and may not represent biomass dynamics within individu al wetlands. Thus, comparisons between sites should take into account manage ment intensity and land use type.

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47 Phosphorus Concentrations Higher AGB P concentrations in center zones at both Larson and Beaty were similar to the trend found by Whigham et al., (2002). In two out of the three years, AGB P concentrations in the Mary land wetlands had a positive re lationship with hydroperiod; opposite of the standing AGB trend. They conclu ded that nutrient cy cling processes are less variable than sp atial and temporal biomass diffe rences (Whigham et al., 2002). Another study found that vegetation in wetla nds receiving treate d sewage effluent showed increased P concentrations in AGB in response to both increased water levels and nutrient additions (Bay ley et al., 1985). Figure 2-19. Nutrient storage and growth in plants. Growth is typically maximized at lower nutrient supplies than the maximu m tissue storage po tential (Reddy & Debusk, 1987) It is hypothesized that incr eased P availability in wetla nds centers may facilitate “luxury uptake” of P by obligate wetland specie s. This occurs when plants take up P beyond their required needs for growth (Figur e 2-19). Biomass production is usually maximized at lower nutrient supplies, while nutrient uptake by plants is maximized at

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48 higher nutrient levels. The difference between the growth and nutrient uptake rates is the P storage potential (Reddy & Debusk, 1987). Phosphorus Storage Tissue P concentrations are also temporal ly and spatially variable and are not a reliable indicator of long-t erm P storage. They can vary with plant age, season and nutrient availability. For instan ce, P concentrations are typi cally higher in younger plants than in mature plants (Reddy & Debusk, 1987). Phosphorus storage potential in plants is a function of both tissue con centrations and the maximum standing crop (Reddy et al., 1995; Reddy & Debusk, 1987). The maximum st anding crop is often the primary determinant of P storage. For example, Sagittaria latifolia had the greatest P concentration of any species; however, because it was not prevalent in the wetlands, the amount of P stored was relatively small. Whigham et al. (2002) found that P storage varied between wetlands, but exhibited simila r patterns of distribu tion as standing AGB. Overall, total P storage at Beaty wa s positively related to hydroperiod, while Larson was inversely related. The influence of biomass as the primary component of total biomass P storage is evident at the La rson site. The total biomass (Figure 2-9 B) and P storage graphs (Figure 2-14 B) exhibi t similar general trends between zones. However, vegetation in Beaty cen ters stored significantly more P than upland zones, even though biomass results (Figure 29 A) did not differ by zone. Therefore, differences in P concentrations along a hydrologi c gradient are also influe ncing total P storage. Reddy and Debusk (1987) found greater than 50% of the nutrients in emergent macrophytes were stored in BGB portions of plan ts. Results from this study suggest that the relative roles of biomass and concentra tion in P storage may vary between AGB and

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49 BGB components. Since BGB makes up the majority of the total biomass in all zones at both sites, it was expected to store the most P. Although it was not statistically significant, AGB in Larson centers stored more P than BGB. Thus, high P concentrations in AGB had a greater influence on P storage th an biomass in the center zones at Larson. Phosphorus storage results do not conclusively support either H3: total biomass P storage would be greater in wetlands than uplands, or H4: more P will be stored in AGB than BGB. Since the Beaty wetlands stor ed more P in center zones, and the Larson wetlands showed the opposite trend, th ere is no conclusive trend and H3 was rejected. Since BGB stored significantly more P than AGB in all zones except Larson centers, H4 was also rejected. Conclusions Based on biomass results that lack speci fic trends, and opposite P storage trends along a hydrologic gradient, it is hypothesi zed that altered hydrology, management intensity and grazing may be influencing environmental gradients in the Okeechobee wetlands. The positive relationship between total biomass P storag e and hydroperiod at the Beaty site may be the combined result of longer hydroperiods and lower management intensity (including grazing pre ssure) relative to Larson. Despite ongoing disturbances (grazing) to these wetlands, P concentration gradients in vegetation, which were positively related to hydroperiod at both si tes, are similar to those found in other studies. However, P storage in vegetation is short-term, highly variable, and represents less th an 10% of total P storage in wetlands. Soil stores the majority of the total P in these wetlan ds; up to 90%. Below ground biomass and P storage is greater than AGB in all zones with the exception of P storage in Larson centers.

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50 Historically high net P imports to the wa tershed have saturated the P assimilative capacity of some wetlands, making them P sources rather than sinks. Hydrologic restoration would increase HRT, anaerobic conditions and organic matter accumulation. Presumably, over a prolonged period of time, if hydrology were restored, P imports were significantly decreased, and grazing pressure was minimized, wetland P assimilative capacity would increase, thus reducing P exports to the Lake. In addition to reducing P loads to the Lake, restoration also stores water in the landscape, which potentially reduces the Lake stage and discharg e of fresh water to the coasts.

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51 CHAPTER 3 FACILATATING WETLAND HYDROL OGIC RESTORATION WHILE MAINTAINING FORAGE PRODUCTION: HYDROLOGIC TOLERANCES OF PASPALUM NOTATUM AND HEMARTHRIA ALTISSIMA Introduction Background Hydrologic restoration of hi storically isolated wetla nds in the Lake Okeechobee watershed is considered a Best Management Practice (BMP) to decrease Phosphorus (P) loading to the Lake. The watershed has low geographic relief and many isolated wetlands have been drained to create im proved conditions for upland forage grass species. Restoration of draine d isolated wetlands involves blocking ditches or installing water control structures to raise the water tabl e back to historical levels, thus retaining water and P within these wetlands. An incr ease in wetland stage could greatly expand wetland footprints and zones of inundation, thus changing hydrope riods and hydrologic regimes of restored wetlands. Long-term flooding with decreased stage fluctuations would likely alter existing vegetative comm unities along hydrologic gradients, decreasing upland forage productivity in areas adjacent to wetlands. Since hydr ologic restoration of isolated wetlands reveres the current mana gement objective, landowner acceptance of this BMP may depend on the introduction of alternative forage grass species that are tolerant of prolonged hydr operiods and less frequent stage fluctuations. The most commonly used forage species in Florida is Paspalum notatum Flgg (‘Pensacola’ bahiagrass). Native to Central and South America, bahiagrass is a deep rooted, warm-season perennial grass that was originally planted for forage and soil

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52 stabilization in the southern United States(V ioli, 2000). Bahiagrass is a resilient, low maintenance species that is tolerant of a wide range of hydrologic and soil conditions; however, it is best adapted to moist, sandy so ils. It forms tough sod mats with a vast network of stolons and roots, often to a de pth of seven feet. (Chambliss & Adjei, 2006; Violi). Ninety percent of its forage pr oduction occurs between April and September (Mislevy, 2002). While bahiagrass does not se em to invade established communities, it does dominate habitats and resists invasion from other species (Violi, 2000). Once established, it is difficult to remove. It ha s been estimated that bahiagrass stolons can store enough nutrients to remain viable for tw o to three years (Chambliss & Adjei, 2006). Hemarthria altissima ‘Floralta’ (limpograss) is a fo rage species that has gained popularity since it was introdu ced (USDA Plant Introduction 364888) in 1984. Native to South Africa, limpograss was originally select ed for its winter hardiness, producing as much as 35% of its total annual production between November and March (Pate, 1998). Limpograss was specifically selected for its persistence under grazing (Quesenberry et al., 1984). It was the fourth limpograss cultivar released in Florida and is currently the only one recommended for pasture establishmen t (Pate, 1998; Sollenberg er et al., 2006). Contrary to bahiagrass, it is best adapted to poorly drained sandy soils and is not recommended for droughty sands (Pate, 1998; Solle nberger et al., 2006). In fact, it grows well in wet areas that are often continuously flooded during the wet season (Pate, 1998). Both bahiagrass and limpograss are exotic species as defined by the Florida Exotic Pest Plant Council. Ba hiagrass is a naturaliz ed exotic that was once listed as a Category I invasive exotic but has since been removed from the list. Limpograss is a listed as a Category II invasive exotic; mean ing that it shows the potential to disrupt

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53 native plant communities but has not yet incr eased in abundance and frequency to be considered a major nuisan ce species. (FLEPPC, 2005). Research Objectives Previous studies have evaluated the fora ge quality of and animal performance on bahiagrass and limpograss (Holderbaum et al., 1991; Holderbaum et al., 1992; Kalmbacher, R. S. et al., 1984; Kalmbacher R. et al., 1998; Long et al., 1986; Newman et al., 2002a; Newman et al., 2002b; Pate, 1998; Quesenberry et al., 1984; Sollenberger et al., 1988; Sollenberger et al., 1989), however for the purpose of this study the primary objectives were to evaluate survivability, productivity and P storage under different hydrologic conditions. Limpograss has been recommended for use in moist sites in Fl orida (Sollenberger et al., 2006), however, its sp ecific hydrologic tolerance has not been evaluated. Bahiagrass has shown short term tolera nce to flooding (David, 1999), however, ultimately over time it gets out competed by wetland species. There are multiple environmental factors that determine ideal ha bitats for species, such as grazing intensity, hydrologic regime, competition, and soil conditions. Hydrology can influence competition and physicochemical soil interactions. It is often considered one of the most influential determinants of establishment and persistence of wetlands plants (Mitsch & Gosselink, 2000). The objective of this research was to evaluate the role of hydrology on bahiagrass and limpograss in non-competitive mesocosm studies. Research Questions and Hypotheses 1. Which species has greater forage production? H1: Limpograss will have greater forage production than bahiagrass

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54 2. What are the hydrologic tolerances of bahiagrass and limpograss? H2: Bahiagrass will have greater total biomass production in drier treatments and limpograss will have greater total biomass production in the wetter treatments. 3. Which species assimilates more P? H3: Limpograss will have higher P storage 4. Where is P partitioned within the plant? H4: Root to shoot P storage ratio s for bahiagrass will be >1, and limpograss will be <1 This chapter compares the effect of five different water leve l treatments on below ground biomass (BGB) and above ground biom ass (AGB) production and P storage of both species in non-competitive mesocosm studies. Materials and Methods Experimental Design This experiment was designed to evaluate the response of two forage grass species to a range of hydrologic conditions typical of the transitional zone between isolated wetland and improved upland pasture. To determine how hydrology affects productivity and nutrient uptake, limpograss and bahiagra ss were evaluated in fifteen non-competitive mesocosms (1.33 m x 0.81 m x 0.76 m polyethylene tubs). Mesocosms were located in Gainesville, Florida (29.6 N 82.3 W). The experiment consisted of five treatmen ts +10, 0, -10, and -15 (water levels in centimeters, relative to the soil surface), and a control (rain water only and well drained), which are discussed in the next section. Th ere were three replicate mesocosms for each treatment. Each mesocosm contained three sub-replicates (pots) of each species. Subreplicate samples were combin ed together into one composite sample of each species.

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55 The sub-replicates were grown in 3 ga llon (25 cm diameter x 20 cm deep) polyethylene pots. Soil was collected from a pasture in Okeechobee, Florida, and homogenized before being dispensed into pots. Propagules of both limpograss and bahiagrass were harvested from pasture plots at the Range Cattle Research Center in Ona, Florida. Soil was washed from the propagules and seven bare-root sp rigs were planted in each pot to establish monocultures of each spec ies. Fifty-two pots of bahiagrass and 61 pots of limpograss were established 90 days pr ior to treatment. During the grown-in phase, both species were watered regularl y and pruned uniformly to stimulate new growth. a. b. Figure 3-1. Study site at Universi ty of Florida, Gainesville, Florida: (a) mesocosms were aligned in two rows and randomly assigne d a treatment. (b) Tubs receiving water were hooked up to the potable water line between the rows. Mesocosms were aligned in two rows and randomly assigned a treatment. Tanks receiving water were hooked up to a potable water supply and extern al overflow stand pipes, were used to maintain water levels fo r each treatment (Figure 3-1). Forty-five of the healthiest (determined visually) pots of each species were selected and three pots of each species were randomly placed into the 15 mesocosms. Each mesocosm contained three pots of limpograss and three pots of bahiagrass. To simulate field conditions, regulate temperature, and prevent oxygen pr oduction by photosynthetic algae in an open

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56 water column, mason sand was used to fill the remaining space between pots (Figure 32). Figure 3-2. Mesocosm diagram. Each meso cosm contained three pots of each species embedded in mason sand. Water level in four of the five treatments was maintained by a drip irrigation system and an external overflow-standpipe. Treatments Four of the five hydrologic treatments were maintained at a constant stage by drip irrigation emitters and external overflow-standpipes, while the fifth treatment, the control, only received rain water and wa s allowed to drain completely. Treatments receiving water included an inundation treat ment (+10 cm), where the water level was maintained 10 cm above the soil surface, and three saturation treatments (0 cm, -10 cm, 15 cm), where the water level was maintain ed 0, 10 and 15 cm, respectively, below the soil surface. Rainfall data is listed in Appendix C Table C-31. These treatments will hereinafter be referred to as +10, 0, -10, -15 and control (C). The study was initiated (day zero) on July 1st, 2004. One week prior to this date, water levels were gradually raised to their treatment levels. The timing coincides with the approximate beginning of the wet season in central Florida. Soil redox was measured in randomly selected pots of each treatment at a depth of 10 cm below the soil surface.

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57 Redox values were inversely related to water depths suggesting the e ffect of saturation and inundation reduced oxygen availability a nd increased anaerobic conditions in the soils (Figure 3-3). Figure 3-3. Inverse relationship of water de pth and redox. This diagram illustrates the inverse relationship of treatment wate r depth and measured redox potential within the five treatments. Soil redox was measured 10 cm below the soil surface. Sampling Soil, BGB, and two components of AGB were sampled over the course of one year. The components of AGB were forage, c onsisting of all biomass above 15 cm, and residual biomass (RB), the biomass that remained from 0-15 cm after harvest. Soil was sampled at the beginning of the experiment (d ay 1), at the end of the first growing season

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58 (day 163) and at the end of th e experiment (day 375). Forage was harvested periodically to evaluate temporal differences in biomass production P concentration and P assimilation. Forage samples were colle cted on days 27, 55, 83, 163, 305 and 375. At the end of the first growing season (day 163), two of the three sub-replicate pots of each species in each mesocosm were harveste d to determine BGB and RB production and P storage. Forage, RB and BGB were harvested from the remaining pots in each mesocosm on day 375. Sampling dates and biomass compon ents harvested are li sted in Table 3-1. Table 3-1. Sampling dates and details. Date Day Pots per composite sample Component Sampled 7/1/2004 1 3 Soil, Forage (for nutrient baseline) 7/28/2004 27 3 Forage 8/25/2004 5 3 Forage 9/22/2004 83 3 Forage 12/11/2004 163 3 Soil, Forage 12/11/2004 163 2 BGB, RB 5/2/2005 305 1 Forage 7/11/2005 375 1 Soil, Forage, RB, BGB This table summarizes the sampling even ts, corresponding components sampled and number of sub-replicates in composite samp les. All pots in each tank where averaged by species and pot (i.e., for biomass – g pot-1 tank-1 species-1 = average g pot-1 of each pot in each tank of each species). Soil Two soil cores (1.8 cm diameter x 20 cm depth) from pots of the same species within the same mesocosm were combined into one composite sample. Table 3-1 contains the number of pots in each compos ite. Roots and litter were removed from the soil before being dried at 70 C for 72 hours. The soil was than machine (ball) ground, sieved through a #40 mesh sieve and stored at room temperature. Above ground biomass sampling Forage sampling was designed to s imulate flash grazing, by periodically harvesting all biomass over 15 cm. This height was established 5 cm above the highest

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59 water level treatment to enable atmospheric gas exchange with the residual biomass for all treatments. Composite samples of each species from each meso cosm were collected using grass shears, a 15 cm-tall grated st and and a shop vacuum to ensure accurate collection. The grate was set over a pot to es tablish the clipping he ight and the vacuum was used to pull the grass th rough the grate and gather c lipped material within the vacuum (Figure 3-4). The vacuum was emptied after each composite sample per mesocosm. The post-harvest processing pr ocedure involved drying vegetation in a drying room at 70 C for 72 hours. Dry forage was than ground in a Wiley Mill, passed through a #40 mesh sieve and st ored at room temperature. a. b. c. Figure 3-4. Harvesting procedure. (a) Clipping grass with 15 cm stand and vacuum. (b) Close up view of clipping processes. (c) Vacuum was emptied after each composite sample per mesocosm. At the end of the first growing season (day 163) the RB was harvested from two of the three pots within each mesocosm. Th e post-harvest processing procedure was the same for all vegetative components. Resi dual biomass from each mesocosm was added to the respective cumulative forage pr oduction for each species and treatment to determine total AGB production g pot-1 after 163 days. The same procedure was carried out on day 375 to determine total cu mulative AGB production after 375 days.

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60 Below ground biomass Below ground biomass included all roots, rhizomes and stolons below the crown of the AGB shoot. Once both components of AGB were harvested (days 163 and 375) the root ball was removed from the pot and fl ushed with water to remove all soil. The same post-harvest sample processing procedure used with AGB was also used with BGB. Since harvesting BGB was a dest ructive process, sampling wa s only preformed twice. All BGB data is a cumulative total and presented as BGB production or P storage after 163 or 375 days. Laboratory analysis Soil and biomass samples were analyzed for Total Phosphorus Ash (TP) using the Ignition Method (Andersen, 1976), Total Nitr ogen (TN) and Total Carbon (TC) as described in Chapter II methods. Composite biomass and P storage were averaged by treatments and reported on a grams per pot basis. In addition, soil was also analyzed for plant available P using the Mehlich I d ilute concentration strong acid extraction procedure (Kuo, 1996). Results Supplemental tables and figures containing all data and statistical comparisons are reported in Appendix C. Initial characterization Daily environmental conditions including air and soil temperature, rainfall and humidity are listed in Table C-31 of Appendix C. Total P ash (TP), total nitrogen (TN) and total carbon (TC) tissue c oncentrations at day zero ar e listed in Table C-1 in Appendix C. Soil TP, TN and TC concentrations on day 0 averaged 0.003%, 0.092%, and 1.65 % respectively. Forage tissue concentrations ranged from 0.15-0.18%, 1.25-

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61 1.42%, and 42-44 % for TP, TN and TC respectively. The focus of this thesis relates to P storage in vegetation, therefore, only TP data are reported beyond the initial conditions. All production data for forage, RB and BGB are expressed either as production per harvest or cumulative production (sum of net ha rvests over time) in grams of biomass per pot. Total AGB is the sum of cumulativ e forage production and residual biomass normalized on a grams per pot basis. Forage Production Data in this section is presented as over all production by each species regardless of treatment. Overall, limpogra ss had significantly greater ( = 0.05) forage production per harvest than bahiagrass on all sampling days except day 83 (Figure 35). In the first 83 days, both species exhibited a decline in forage production. However, by the end of the first growing season (day 163), limpograss continued to producing forage while bahiagrass was essentially dormant until the beginning of the following growing season. Both species increased forage productivity between early-May and mid-July of the second growing season. On a cumulative ba sis, limpograss had significantly greater forage production on all sampling days (Figur e 3-6). After 375 days, bahiagrass and limpograss had produced 9.52 2.73 and 32.4 14.7 g pot-1 of forage respectively.

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62 Figure 3-5. Forage production pe r harvest for each species with all treatments combined. Day 0 is July 1st 2004. Limpograss had significan tly greater forage production per harvest on all sampling days exp ect day 83 (Table C-2, Appendix C). Figure 3-6. Cumulative forage production with all trea tments combined. Each consecutive harvest was added to previ ous harvests. Limpograss produced more forage than bahiagrass on a 375 da y period. (Table C-3, Appendix C).

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63 Bahiagrass forage production To determine the response of each speci es to various water level treatments comparisons were made between treatments of each species using the Tukey-Kramer HSD test. There were differences in forage production between harvests (Figure 3-7A). Throughout the experiment, the +10 and c ontrol treatments had similar forage production. Between the second and third ha rvest the +10 treatment had significantly greater forage production than the 0, -10, a nd -15 cm treatments. In addition, on day 375, the control had significantly greater forage production than the 0 cm treatment. On a cumulative basis, forage production across a ll five treatments ranged between 7.88 1.49 to 12.1 3.61 g pot-1 after 375 days and did not differ significantly between treatments. All treatments exhibited similar cumulative pr oduction curves over time (Figure 3-7B). A. B. Figure 3-7. Bahiagrass treatment comparisons A) Biomass production per harvest; +10 cm treatments significantly greater than the 0, -10, -15 cm treatments on days 83, while the Control was gr eater than the. B) Cumu lative forage production; no significant differences between treatments. (Tables C-10 & C-11, Appendix C).

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64 Limpograss forage production Limpograss exhibited significant treatment effects after day 27. Initially, on day 55, the control had significantly greater fo rage production than the +10, 0 and -10 cm treatments. However, on day 163 the control was the only treatment with a lower net harvest than its previous harvest on da y 83 (Figure 3-8A, Table C-12, Appendix C). Although the span of time be tween harvests (days 55-83 and 83-163) are different, by day 163, the +10 and -10 treatments roughly doubled the amount of forage produced between days 55 and 83. On day 305, the +10 cm treatment had greater forage production than the 0, -10, and -15 cm, wh ile on day 375 the control had greater production than the 0, -10, and -15 cm treatments. The differences in limpograss production per harvest did not cause significant differences in cumulative forage producti on between treatments until days 305 and 375. On day 305, the production per harvest tr eatment differences were mirrored by cumulative forage production. The +10 tr eatment had greater cumulative production (27.0 2.16 g pot-1) than the 0, -10, and -15 cm treatments after 305 days. On day 375, like the individual harvest differences, the control had greater production (50.2 16.5 g pot-1) than the 0, -10 and -15 cm treatments. While the +10 cm treatment produced significantly more forage (44.5 3.98 g pot-1) than the -10 cm treatment (21.1 3.31 g pot-1) after 375 days, it was not statistically gr eater than the 0, and -15 cm treatments, despite a power value of 0.95.

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65 A. B. Figure 3-8. Limpograss treatment comparisons A) Forage production per harvest. B) Cumulative forage production (T ables C-12 & C-13, Appendix C). Species comparison This section compares biomass production of limpograss and bahiagrass within the same hydrologic treatments. Limpograss had si milar or greater fora ge production than bahiagrass on all harvest days, in all trea tments (Figure 3-9). By the first sampling, limpograss had significantly greater forage pr oduction than bahiagrass in all treatments. Both species exhibited a decline in forage pr oduction per harvest in all treatments after day 27 (Figure 3-9), where only the control and -15 cm limpograss treatments were significantly greater than bahiagrass. However, limpograss rebounded and produced significantly more forage than bahiagrass in all treatments by the final harvest of the growing season (day 163). The same tre nd continued in the second growing season, where limpograss had significantly greater fora ge production per harv est than bahiagrass with the exception of the -15 cm treatment on day305. As a result limpograss had significantly greater cumulative forage produc tion after every harv est day (Figure 3-10)

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66 Figure 3-9. Forage production pe r harvest by treatment. A) +10 cm treatment. B) 0 cm treatment. C) -10 cm treatment. D) -15 cm treatment. E) Control. (Table C-5 Appendix C)

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67 Figure 3-10. Cumulative forage production by treatment. A) +10 cm treatment. B) 0 cm treatment. C) -10 cm treatment. D) -15 cm treatment. E) Control. (Table C-6, Appendix C).

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68 Total Biomass After 375 days, bahiagrass total biomass (AGB + BGB) was significantly greater than limpograss in the 0 and -10 cm treatments (Table 3-2). This was not consistent with total biomass results in the first 163 days, wh ere bahiagrass had signifi cantly greater total biomass production than limpograss in all tr eatments. This is primarily due to significantly greater bahiagrass BGB in all treatments. There were no significant in creases in BGB production in any treatments between days 163 to 375 for either species. However, there were significant BGB decreases in bahiagrass +10 and -15 treatments ( = 0.05), and in limpograss +10 cm ( = 0.08) and control ( = 0.05) (Figure 3-11 and Table C-9, Appendix C). In addition, forage production increased more in limpograss than bahiagrass during the same time period (Appendix C, Table C-5). This offset th e differences in total biomass production between species on day 163 to insignificant levels in the +10, -15 cm and control treatments by day 375. Table 3-2. Total biomass (AGB + BGB) after 163 and 375 days. Total Biomass (g/pot) Days Treatment nBahia Floralta p value +10 385.0 10.8 <57.2 1.47 0.01 0 3109 13.0 <63.0 6.73 0.01 -10 3113 2.78 <72.0 10.7 < 0.01 -15 3115 3.59 <65.2 7.88 < 0.01 163 C 3105 14.3 <77.7 4.30 0.03 +10 376.6 3.00 <84.5 6.51 0.13 0 3112 18.6 <76.5 2.92 0.03 -10 3112 16.0 <70.6 6.62 0.01 -15 385.4 11.6 <76.0 21.1 0.54 375 C 3113 17.7 <99.6 20.8 0.45 It is counter intuitive that cumulative biomass could decrease, but since BGB was only harvested twice, these data only repr esent the net BGB afte r 163 and 375 days, not

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69 the variability within those time periods. Therefore, the quantity of bahiagrass BGB that died was greater than the forage prod uced between 163 and 375 days, resulting in negative net total production. A. B. Figure 3-11. Below ground biomass produc tion. A) Bahiagrass BGB production B) limpograss BGB production (Table C-9, Appendix C). Under constant inundation, both species will survive for at least 375 days. However, cumulative biomass production for ba hiagrass actually decreased between days 163 and 375, while limpograss increased. Both species appear to have been in an acclimation phase between days 163 and 375. The l ack of significant differences in total biomass between species after 375 days in th e +10, -15 and control treatments (Table 32) indicate that those treatments were in fluencing total biomass productivity for both species. Although not statistically significant, bahiagrass still had more total biomass in the -15 cm and control treatments after 375 da ys, while limpograss had more in the +10 treatment. Root to Shoot Ratios In general BGB production had an invers e relationship AG forage production for both species. The average BGB production for all bahiagrass pots, regardless of

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70 treatment, was 85.6 14.0 g pot-1 after 163 days and 79.6 20.8 g pot-1 after 375 days. Limpograss BGB production was 38.0 9.07 g pot-1 after 163 days and 29.5 8.5 g pot-1 after 375 days. Bahiagrass maintained significantly more BGB than limpograss in all treatments after 163 and 375 days. Relative portions of total AGB (residual biomass + forage) and BGB for each treatment and species are gra phed in Figure 3-12. In all treatments, bahiagrass had significantly great er root to shoot ratios th an limpograss after 163 days. After 375 days, all root to s hoot ratios for limpograss were less than one while bahiagrass ratios were greater than one (Table 3-3). Thus, after 375 days limpograss produced more AGB than BGB while bahiagrass produced more BGB than AGB. Figure 3-12. Above and below ground bi omass production after 375 days. Above ground biomass (top) is the sum of cumu lative forage production and residual biomass. Below ground biomass (bottom) is all biomass harvested below the soil surface after 375 days.

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71 Table 3-3. Root to shoot ratios. Root:Shoot Between Species Day Treatment Bahia Floralta p value +10 3.55 0.56 >0.94 0.19 < 0.01 0 5.06 2.27 >1.17 0.22 0.04 -10 4.25 0.85 >1.58 0.40 0.01 -15 5.35 0.80 >1.42 0.34 < 0.01 163 C 4.22 1.19 >1.49 0.15 0.02 +10 2.11 0.21 >0.31 0.07 < 0.01 0 5.48 2.59 >0.81 0.23 0.04 -10 5.76 0.43 >0.90 0.39 < 0.01 -15 4.26 0.76 >0.95 0.19 < 0.01 375 C 3.91 1.21 >0.36 0.15 0.01 Phosphorus Assimilation Phosphorus tissue concentrations Phosphorus concentrations varied by speci es and by treatment. On day 0, the only significant difference in tissue concentrations between species was in the 0 cm treatment where limpograss (1790 331 mg kg-1) had a significantly greater forage P concentration than bahiagrass (1530 58.0 mg kg-1). Both species exhibited a decline in P concentrations by the first harvest (day 27). All limpograss treatments (965 220 to 1260 152 mg kg-1) and the 0, -10 cm and control (1180 60.0 to 1220 37.0 mg/kg) bahiagrass treatments had significantly lowe r forage P concentrations by the first sampling on day 27 (Figures 3-13 and 3-14) The bahiagrass +10 treatment had significantly greater forage P concentrations than limpograss on days 27, 55 and 375. In addition, P concentrations of the bahiagrass fo rage control treatment were greater than limpograss on day 375, although on day 163 the lim pograss forage control treatment was greater than the bahiagrass.

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72 On day 0, there were no significant differen ces in forage P concentration between bahiagrass treatments. However, on da ys 27, 55, 83, and 163, the wettest bahiagrass treatment (+10 cm) had significantly greater P concentrations than all other treatments (Figure 3-13). By the fina l harvest (day 375) the bahiag rass +10 treatment had greater forage P concentrations than the 0 and -10 treatments. Limpograss, on the other hand did not have many differences between treatme nts. The only difference was on day 163 when the +10 treatment was greater than the -15 cm (Figure 3-14). Figure 3-13. Mean P concentrations (mg/kg) for bahiagrass forage by harvest day and by treatments (Table C-17, Appendix C).

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73 Figure 3-14. Mean P concentrations (mg/kg) for limpograss forage by harvest day and by treatments (Table C-18, Appendix C). Below ground biomass concentrations (303-668 mg kg-1) were relatively low compared to forage. On day 163, limpograss had greater BGB P c oncentrations than bahiagrass in the 0 cm and control treatments. However, on day 375, there were no significant differences. Nor were there any significant differences for either species when treatments were compared. Phosphorus storage Phosphorus assimilation (mg pot-1) is a function of concentration and biomass production. After 375 days, th e only significant difference in total P storage (AGB +BGB) between species was in the 0 cm tr eatment, where bahiagrass was greater than limpograss (Figure 3-15). This was not consiste nt with total P storag e at the end of the first growing season (day 163) where bahiag rass was greater than limpograss in all treatments except the control (Table 3-4). This change over time in total P storage can

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74 be attributed to negative net BGB production in bahiagrass and greater forage production in limpograss between days 163 and 375. 0 10 20 30 40 50 60 70 80 90 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC Treatment and SpeciesTP (mg pot -1 ) Figure 3-15. Total P storage (AGB + BGB) after 375 days. Table 3-4. Total P storage ( AGB + BGB) species comparison. Total P Storage (mg/pot) Day Treatment Bahia Floralta p value +10 50.9 4.06 >40.5 1.32 0.01 0 53.7 5.56 >41.1 4.69 0.04 -10 56.9 4.48 >39.6 4.49 0.01 -15 56.0 5.73 >38 5.43 0.02 163 C 54.1 10.9 <57.5 2.38 0.62 +10 47.8 4.84 <50.2 2.67 0.49 0 50.5 13.1 >37.3 2.05 0.16 -10 46.8 5.22 >34.2 5.43 0.04 -15 36.0 5.13 >36.1 5.69 0.99 375 C 62.8 16.4 >55.3 9.09 0.53 Total P storage has the same general tre nd as biomass production. The same is true for BGB production and BGB P storage. In addition, there is a positive relationship between P partitioning and biomass allocation. Bahiagrass had greater P storage in BGB than in forage, while limpograss had the greater P storage in forage. Overall, for each species P storage in BG B was not significantly different between treatments after 375 days. However, there wa s a significantly differe nt treatment effect

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75 on day 163. After 163 days the limpograss cont rol stored significantly more P in BGB than all other limpograss treatments. Limpograss had greater P harvested in forage than bahiagrass in the all but the +10 treatment by day 27. By day 55, P harvested in limpograss forage was greater in all treatments except the -15 cm and control treatments, while by day 83 limpograss was only greater in the control treatment. By days 163 and 305, limpograss forage took up more P than bahiagrass in all but the 15 cm treatment, while on day 375, limpograss had greater P storage in all treatments ex cept the -15 and -10 cm treatments. On a cumulative basis, there was a str ong relationship between forage P storage (Figure 3-16) and cumulative forage producti on (Figure 3-7B, 3-12B). Limpograss had greater forage P storage than bahiagrass in all treatments except the +10 cm treatment on days 27, 55, 83 and 163. However, after day 163, limpograss had greater P storage in all treatments. A. B. Figure 3-16. Cumulative P harvested in forage. A) Bahiagrass P harvested B) Limpograss P harvested (Table C-23, Appendix C) There were no differences in harvested P between bahiagrass treatments on days 27, 55 and 305. However, on days 83 and 163, the +10 cm treatment had significantly

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76 more P harvested than all other treatments. The +10 cm and control treatments had greater P harvested than the 0 and -10 cm tr eatments on day 375. On a cumulative basis, on all days after 55, there was significantly more P harvested in the +10 treatment than in the 0, -10 and -15 cm treatments. Like bahiagrass, there were no differen ces in harvested P between limpograss treatments on day 27. However, by day 55, the driest treatments had assimilated the most P. The control had significantly more P harv ested than all other tr eatments, and the -15 cm treatment had more than the -10 cm treat ment. By day 83, the influence of biomass production on P storage began to emerge as P harvested in the cont rol was greater than the 0, -10 and -15 cm treatments. In the la tter and earlier parts of the growing season (days 163 and 305), there was more P harvested in the +10 cm treatment than all other bahiagrass treatments, while on day 375, ther e was more P harvested in the +10 and control than in the all other treatments. On a cumulative basis, by day 83, the bahiagrass control had assimilated significantly more P than the -10 cm treatment. By day 163, the control stored more than the 0 and -15 cm (in addition to the -10 cm ) treatments. By day 305 the +10 treatment had significantly greater cumulative P storag e than the 0, -10 and 15 cm treatments. By day 375, the +10 cm and control had greater cu mulative storage than the 0, -10, and -15 cm treatments. Phosphorus storage r oot to shoot ratios Root to shoot ratios for P storage had a positive relationship to biomass ratios. Figure 3-17 shows relative porti ons of AGB and BGB P storag e. After 375 days, the +10 treatment was the only bahiagrass treatment with a ratio less than one. Limpograss P storage ratios were all less th an one and positively relate d to biomass ratios. All

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77 bahiagrass treatments had significantly gr eater P storage ratios than limpograss treatments after 375 days (Table 3-5) Figure 3-17. Relative comparison of root and shoot P storage after 375 days. Table 3-5. Root to shoot P st orage ratios with statistics. Root:Shoot P Storage Day Treatment Bahia Floralta p value +10 1.27 0.27 >0.69 0.22 0.04 0 2.45 0.23 >1.01 0.13 < 0.01 -10 2.46 0.67 >1.10 0.28 0.03 -15 2.95 0.40 >0.95 0.05 < 0.01 163 C 1.95 0.10 >1.17 0.05 < 0.01 +10 0.70 0.12 >0.15 0.04 < 0.01 0 2.58 1.35 >0.39 0.09 0.05 -10 2.29 0.14 >0.48 0.17 < 0.01 -15 1.42 0.33 >0.55 0.32 0.03 375 C 1.35 0.34 >0.22 0.08 < 0.01 Discussion Forage Production Bahiagrass and limpograss are physiologically different. Bahiagrass typically has less forage production than limpograss because it allocates ~50% of its energy to root and stolon production (Chambliss & Adjei, 2006). Th is was evident from the bahiagrass root

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78 to shoot ratios, where BGB ranged from 2.11 to 5.76 times higher than AGB (Table 3-3). In addition bahiagrass is a long day plant that is strongly influenced by photoperiod (Marousky & Blondon, 1995). Thus its annual production is lower because it has a shorter growing season. On the other hand, lim pograss tends to allocate more energy to AG forage production, as root to shoot bi omass ratios were less than one in all treatments. Limpograss is known to support relatively high cattle stocking rates and for having superior late fall and early sp ring production compared to bahiagrass (Sollenberger et al., 2006). Results from th is study support that statement. Overall limpograss had greater forage production than ba hiagrass in the latter and earlier parts of the growing season in all treatments. In the first 83 days, both species exhibited a decline in production between harvests, regardless of tr eatment (Figure 3-5). This was likely the result of the combined effects of harvest stress, temperature a nd light effects in the latter part of the peak growing season. Harvest st ress was evident after th e first harvest (day 27) as production was significantly less for bot h species by the second harvest. After 83 days, limpograss rebounded, while bahiagrass production continued to decrease. The bahiagrass decline after day 83 is likely the result of decrea sed photoperiod during shorter days and not harvest stress. Ev en during the peak of the growing season, limpograss had greater forage production than bahiagrass. Forage production results support H1 limpograss has greater cumulative forage production than bahiagrass in all treatments. Flood Tolerance Water levels did not appear to have an e ffect on forage production for either species until after the first harvest. There were no statistical differences between bahiagrass

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79 treatments until the third harvest. This suggests that bahiagrass forage production may not be affected by water levels as deep as 10 cm above the soil surface for up to 55 days. The wettest bahiagrass treatment actually produced more forage than the othe r treatments toward the end of the in fi rst growing season. The same was true for the limpograss +10 treatment. In addition the limpograss +10 had the greatest forage production in the earlier part of the following growing season. The wettest treatment seems to start forage production earlier and extend it later in the growing season fo r both species, but more for limpograss. The reasoning for this may be related to a temperature buffering effect caused by standing water. Thus, flooding ma y create an artificial environment that decreases diurnal temperatur e fluctuations, thus prolongi ng the growing season. After 375 days, forage production per harvest was sim ilar in the wettest and driest treatments for both species, while the intermediate tr eatments generally produced less forage. Bahiagrass is resilient to many environm ental conditions. However, under longer hydroperiods bahiagrass is not as resilient and may be out competed by facultative or obligate wetland species. Efforts to re store native wetland species in bahiagrass dominated pastures have been challenging. While mechanically removing the sod and applying herbicide has been the most effectiv e way to remove bahiagrass (Violi, 2000), prolonged flooding will also eliminate bahiagrass and enable wetland species to establish (David, 1999). David (1999) examined the di stribution and density of bahiagrass and other wetlands species in hydrologically re stored wetlands and found that bahiagrass persisted for 2 years after inundation, before dying off by the fourth year. In addition, wetland species such as Panicum hemitomon and Pontederia cordata increased in frequency of occurrence under longer hydroperi ods. Clearly both species will survive

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80 375 days in 10 cm of water in a non-compe titive environment. However, even in a competitive environment it may take up to three years for different vegetation to establish between normal and high water after a change in hydrologic regime (Van der Valk, A G et al., 1994). Between days 83 and 163, both species a ppeared to acclimate to treatment conditions. Initially, the cont rols of both species had th e greatest forage production however by day 163 there was no difference between the wettest and the driest treatments. This suggests that in the short-term, hydrology alone in a non-competitive environment may not cause a sh ift in species. Over the du ration of this investigation, bahiagrass did not have significantly greater total bioma ss production in drier treatments, nor did limpograss have signi ficantly greater total biomass in the wetter treatments. Based on treatment effect comparisons, H2 can not be completely accepted. Phosphorus Uptake After 375 days, total P storage was sim ilar for both species in all treatments expect the -10 cm where bahiagrass stored more than limpograss. Therefore, H3 is rejected in favor of the null hypothesis th at limpograss does not store more P than bahiagrass. Despite, similar assimilative capaci ties, P stored in bahiagrass is relatively more stable than P stored in limpograss because the majority of P stored in bahiagrass is in BGB. Above ground forage is typically mo re labile and is subject to grazing. As discussed in Chapter II, vegeta tion is a short-term P storage mechanism. In addition, if the vegetation is continually grazed, nutrien ts in digested plant tissue are more bioavailable than senesced vegetation in wetla nds. Therefore, P can be mobilized from the soil into the water column by way of grazing.

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81 Phosphorus partitioning in vegetation had a si milar trend as biomass allocation. All bahiagrass treatments except the +10 stored mo re P in BGB than in AGB. The higher P concentrations in the bahiagrass +10 treatm ent caused higher P storage in AGB than in BGB. This was consistent with all limpogr ass treatments more P was assimilated in AGB than BGB. Therefore, H4 is only partially accepted. All bahiagrass ratios except the +10 cm treatment were greater than one and all limpograss P ratios were less than one. Inundation actually increased P storage in both species by increasing tissue P concentrations in the +10 treatment. The re latively high P tissue concentrations in the +10 treatment are consistent with results found in wetland center zones as described in Chapter II results and Whigham (2002). Reddy and Debusk (1985) observed lower tissu e concentrations in summer months and suggested that higher pr oductivity in the summe r likely diluted concentrations. In addition they suggested that slow growth and luxury uptake likely caused increased concentrations in the winter (Reddy & De busk, 1985). The same line of reasoning may also explain the elevated P concentrations in the +10 treatments. Biomass production decreased in the bahiagrass +10 treatment, how ever P concentrations were significantly greater than the other treatments. Wher e nutrients are readily available, tissue concentrations may have a direct relationshi p to biomass productivity. Future research may look into the possibility of specific plan t speciesÂ’ rates of nutrient uptake over time vs. resultant tissue concentrations. Conclusions Limpograss appears to thrive in the wettest and driest mesocosm treatments. In fact the limpograss +10 and control treat ments had the greatest production of all

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82 treatments. Although bahiagrass survived under inundated conditions for 375 days, more than likely it would not survive competiti on from other plant species, trampling, grazing and water stress in situ Both species had similar total P storage in all treatments except the -10 cm treatment. The majority of P stored in bahiagrass is in BGB, while most P assimilated in limpograss is stored in forage. Thus util izing limpograss for P removal from wetlands may be best optimized by harvesting and e xporting hay and assimilated P away from the wetlands. Overall, after 375 days limpograss had great er forage production than bahiagrass in all treatments, a greater hydrologic tolerance and similar P storage potential. Therefore, in order to maintain pasture carrying cap acity and vegetative P storage during BMP implementation, limpograss may be a more suitabl e forage in restored pastures wetlands even under higher water levels and extended hydroperiods.

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83 CHAPTER 4 SUMMARY AND CONCLUSIONS Summary The overall goal of this research was to evaluate the biomass production and P storage potential of vegetation in historically isolated past ure wetlands and determine the efficacy of using a wet tolerant forage speci es to minimize the loss of improved pasture area as a result of hydrologic restoration. Objective I: Biomass Production a nd Phosphorus Storage in Wetlands I. Assess biomass production and P assi milation by wetland vegetation and forage grasses under various hydroperiods. Results from Chapter II and McKee (2005) indicate that wetland soils in the Okeechobee basin store more P per unit area than surrounding upland soils and vegetative components. The direct role of vege tation in active total P storage is relatively small, short-term, and highly variable compared to the physical storage capacity of soil. However, the presence of vegetation is an important component of ecosystem P storage because it increases the total P rete ntion capacity of wetland soils. Phosphorus storage in vegetation alo ng hydrologic gradie nts was variable depending on the type of species present, land-use intensity, grazing pressure and hydrology. There were, however, similar trends in AGB P storage. In general, wetland zones at both sites stored more P in AGB than in upland zones. In addition, total P storage (AGB + BGB) had a positive rela tionship to hydroperiod at Beaty while the

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84 opposite trend existed at Larson. This wa s likely related to higher AGB tissue P concentrations in wetland centers than uplands and differences in land-use intensity. Vegetation Stress Hydrology is often the primary determinant of vegetation composition within wetlands. However, in pasture wetlands, the stress of grazing likely influences vegetation community establishment and persistence. Although grazing was not measured in this experiment, vegetation pa tterns, biodiversity of species, and large hydroperiod ranges for the same species at diffe rent sites suggests that hydrology is not solely responsible for species distribution w ithin pasture wetlands. In addition, grazing may have a significant effect on wetland P storage capacity. Objective II: Facilitating La nd-use and Wetland Restoration II. Determine the efficacy of establishing a wet tolerant forage grass in wetland transition zones before hydrologic rest oration to minimize loss of productive pasture Overall limpograss had greater cumulative fo rage production than bahiagrass in all hydrologic treatments. This was primarily due to its ability to produc e forage earlier and later in the growing season. More importa ntly, limpograss production was similar in the wettest and driest treatments, producing significantly more fo rage than the intermediate water level treatments. The wette st and driest bahiagrass trea tments also had the greatest production relative to th e other treatments. Both species will survive for 375 days under non-competitive, inundated soil conditions as long as the biomass is not comp letely submerged. However, it is unlikely that bahiagrass would be competitive under wet conditions with its low productivity.

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85 Unexpected Results One unexpected result that was consistent between both species in the mesocosm experiment was that the greatest biomass pr oduction and subsequent P storage occurred in the wettest and driest treatments. It was hypothesized that biomass would have a negative parabolic shape when treatments we re aligned on the X-axis in order from wettest to driest. Essentially, it was thought that both species would have similar curves, expect limpograss’s curve would be shifte d more toward the wet treatments and bahiagrass’s more toward the dry treatments. However, as described in Chapter III, the results were opposite. Each species had a positive parabolic shape. Another unexpected result found in both th e field and mesocosm studies was the increased tissue P concentrations under flooded conditions. Above ground biomass tissue P concentrations in wetland center zones a nd in the +10 inundated mesocosm treatment were greater than uplands and drier mesoco sm treatments. Higher concentrations of plants in wet conditions we re likely caused by slow grow th and luxury uptake. Implications for Restoration Bottcher et al., (1995) defines BMPs as on-farm activities to reduce nutrient exports to water bodies and tributaries to environmenta lly acceptable levels, while simultaneously maintaining an economically viable farming operation. In addition, BMPs that adversely affect the economic vi ability of a farming operation should be subsidized to maintain profita bility (Bottcher et al., 1995). Many BMPs are considered voluntary; howev er in a watershed where a TMDL has been mandated, water quality compliance is requi red. The term “voluntary” refers to the choice of options ranchers ha ve to comply with TMDL goals. In the Okeechobee basin, ranchers have the option to monitor the nutrien t discharge from their property to ensure

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86 that it is meeting water quality standard s or implement recommended BMPs for the designated land-use, which presumes that discharge water quality standards are being met. As with any industry, it is advantageous fo r ranchers to strive for economies of scale in order to maximize profitability (i.e the marginal cost per cow decreases while total production increases). However, econom ies of scale in agriculture tend to be limited compared to other industries because of finite production space and time. A hydrologic restoration BMP could potentially affect the economic viability of a cattle operation by increasing marginal costs of pr oduction. Hypothetical ly, if a rancher was stocking a 10 ha bahiagrass pastur e at a density of two cows ha-1 prior to restoration, and after restoration the usable pa sture area decreased to 9 ha, the cost per cow increases as two fewer cows support the ranchÂ’s overhead fixed costs. One study evaluating cattle productivity in pastures with high, medium and low stocking rates, representative of relative stocking rates in the Okeechobee watershed, found that a decrease in stocking rate had a one-to-one relationship with ranch revenues (Arthington et al., 2003 ). Another component of that study evaluated water quality impacts at the same stocking rates. The researchers found that cattle density did not significantly influence concentra tions or loads of nutrients in runoff waters (Bohlen et al., 2004). Despite, the fact that wetl ands in pastures with the hi ghest cattle traffic had the highest P concentration, they concluded that cattle impacts may be more related to the activity of the cattle wi thin wetlands rather than the past ure stocking density. In addition, they suggest BMPs that reduce stocking rates or remove cattle from pastures do not appear to be an effective way to reduce nut rient runoff. Further, BMPs that focus on

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87 preventing net P imports to the watershed or decreasing runoff to tributaries have the greatest potential to reduce nutrient load s to the Lake (Bohlen et al., 2004). Bohlen et al. (2004) also f ound that improved pastures e xported five to seven times more P than semi-improved pastures. They attribute the difference to a historical accumulation of P in improved pastures from fertilizer applications prior to 1987, while the semi-improved pastures had never been fe rtilized. Bottcher et al. (1995) modeled runoff concentrations using water quality da ta from various tributaries of known landuses. They also found differences in runo ff concentrations between improved and semiimproved pastures (Table 4-1). However, they attribute the difference to animal densities (Bottcher et al., 1995). Table 4-1. Estimation of P export concentratio ns to tributaries from various land-uses. Table from (Bottcher et al., 1995) It is evident that hydrolo gic restoration would reduce P exports in runoff waters, potentially meeting water quality standards. However, in bahiagrass pastures, carrying capacity would likely decrease as a result of hydrologic rest oration. Planting limpograss around pasture wetlands before restoration ma y minimize the loss of usable pasture and help facilitate BMP acceptance. Since limpogr ass has greater production than bahiagrass across a range of hydrologic conditions from + 10 cm flooded, to well-drained conditions, it has the potential to mainta in usable pasture space afte r hydrologic restoration.

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88 Conclusions It is difficult to assess the physical P storage potential of vegetation in pasture wetlands because of temporal variability, hydrolo gic stress, land-use variability and continual grazing. It is evident from this research that cattle grazing activities are influencing biomass production and P storage along hydrologic gradients. Total P storage in vegetation at the two ranches exhibited opposite trends along hydr ologic gradients. However, elevated tissue P concentrations under flooded conditions were unexpected results, consistent in both field and mesoco sm studies. Results from this research suggest that the capacity of ve getation to physically store P is highly variable in improved pasture wetlands. However, the presence of vegetation remains an important component of the total assimilative potential of wetland ecosystems. Limpograss had greater production than ba hiagrass under all hydrologic treatments in non-competitive mesocosm studies. In fact limpograss thrived in th e wettest and driest treatments. Its growth characteristics are similar to the native Panicum hemitomon Although bahiagrass survived for 375 days in 10 cm flooded conditions, it is unlikely that it would be able to survive a nd compete with other species in situ Forage quality was not evaluated in this study, however, literat ure indicates that animal performance is similar between both species (Sollenberger et al., 1988). However, proper management of limpograss pastures can carry more animals and produce greater gain ha-1 than bahiagrass pastures (Sollenberger et al., 1989). While the majority of nutrients within th e watershed are stored in uplands (Reddy et al., 1996), wetlands component s (soil, litter, AGB, BGB) store more P per unit area. Thus, hydrologic restoration would retain wa ter, increasing the z one of inundation and subsequent P assimilative capacity.

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89 The mechanism responsible for transporting P to the lake is the vast infrastructure of ditches and canals that short circuit the landscapes natural ability to assimilate nutrients and contaminates. In addition, net P imports to the watershed are highly correlated with P loading to the Lake (Hiscock et al., 2003). Even t hough the Institute of Food and Agriculture Sciences (IFAS) at the University of Florid a adopted a zero P fertilizer recommendation on bahiagrass pastures a nd a maximum of 40 kg P ha-1 for other forage grasses (Kidder et al., 2000), P imports to the wa tershed continue to exceed P exports. Thus, reducing P imports to the watershed and decreasing the quantity of runoff water to the Lake is likely the most effective short-term solution to meet TMDL goals. Unanswered Questions and Need for Further Research The higher P concentrations in wetla nd centers and in the +10 cm flooded mesocosm treatment are hypothesized to be the result of luxury uptake, induced by flooded conditions. Water level fluctuations and inundated soil conditions alter biotic and abiotic interactions, change redox potential and create concentra tion gradients. This affects bioavailability of nutrients and the ener gy required to mine for nutrients. Nutrient availability under reduced conditions likely plays an important role in plant tissue concentrations. Few studies have examined the biotic mechanisms of luxury uptake of nutrients by plants.

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90 APPENDIX A SUPPLEMENTAL BACKGROUND INFORMATION Table A-1. Summary of Okeechobee Basins BMPs Source: (Bottcher et al., 1995)

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91 Table A-2. Total P loads (M tons) to Lake Okeechobee 1991-2003 Source: (SFWMD, 2004)

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92 APPENDIX B SUPPLEMENTAL FIELD DATA Table B-1. Phosphorus storage by components, site and zone. Site Zone n BGB TP (mg/m2) Litter TP (mg/m2) Soil TP (mg/m2) AGB TP (mg/m2) Center 34 1210 869 248 185 13000 4580 632 450 Edge 34 907 576 192 150 13800 7190 476 369 Beaty Upland 34 1060 391 161 121 10100 2270 258 140 Center 30 400 402 320 305 18200 5180 509 669 Edge 29 890 449 238 238 11400 4730 250 309 Larson Upland 43 1150 589 275 177 13300 5270 344 212 Table B-2. Biomass production by components, site and zone. Site Zone n BGB (g/m2) Litter (g/m2) AGB (g/m2) Center 34 1570 1040 283 211 433 261 Edge 34 1390 912 219 167 443 284 Beaty Upland 34 1620 617 161 115 251 119 Center 30 508 523 304 271 178 233 Edge 29 1170 679 113 118 91.5 114 Larson Upland 43 1740 866 205 118 230 151

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93 Table B-3. Species hydroperiod. Species Hydroperiod Species n Indicator Days Range Difference p value Alternanthera 10OBL 56.9 38.5 16-121 BC Andropogon 4 FAC 77.7 68.2 0-128 ABC Baccopa 1 OBL 150 150-150 ABC Eleocharis 3 OBL 133 155 35-312 ABC Juncus 38 OBL 134 78.8 0-302 B Ludwigia 4 OBL 196 102 67-283 AB Luziola + P. acuminatum 28 FACW 130 68.6 16-240 B Micranthemum 1 OBL 161 161-161 ABC Other 62 124 100 0-314 B P. notatum 81 UPL 27.5 53 0-304 C Panicum 33 OBL 238 96 0-323 A Polygonum 30 OBL 148 92.8 0-314 B Pontederia 26 OBL 240 63.6 70-315 A Sagittaria 1 OBL 297 297-297 ABC Utricularia 1 OBL 298 298-298 ABC < 0.01 Indicates species present at both sites

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94 A B. Figure B-1. Species distribu tion by hydroperiod. Shaded area s represent stratified zones correlated to hydroperiod on the x-axis Bars represent the range of hydroperiods under which the corresponding species were observed. A). Species present at the Beaty site. B). Species present at the Larson site.

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95 Table B-4. Total biomass production. Total Biomass Site Zone n g/m2 *Difference p value Center 31 1950 1060 a Edge 29 1840 1160 a Beaty Upland 31 1940 598 a 0.89 Center 25 745 557 c Edge 28 1270 681 b Larson Upland 39 1870 805 a < 0.01 Mean comparisons using Tukey-Kramer HSD test. Table B-5. Below gr ound biomass production. Below Ground Biomass Site Zone n (g/m2) *Difference p value Center 331570 1040 a Edge 331390 912 a Beaty Upland 341620 617 a 0.55 Center 29508 523 c Edge 291170 679 b Larson Upland 411740 866 a < 0.01 Mean comparisons using Tukey-Kramer HSD test. Table B-6. Above gr ound biomass production. Above Ground Biomass Site Zone n (g/m2) *Difference p value Center 34433 261 a Edge 36443 284 a Beaty Upland 35251 119 b < 0.01 Center 28178 233 a,b Edge 3391.5 114 b Larson Upland 43230 151 a 0.003 Mean comparisons using Tukey-Kramer HSD test.

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96 A. B. Figure B-2. Above ground biomass by species and zone. A). Beaty AGB in upland, edge and center zones. B). Larson AGB in upland, edge and center zones.

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97 Table B-7. Total biomass P storage. Total Biomass P Storage Site Zone n (mg/m2) *Difference p value Center 31 1810 969 a † Edge 29 1440 749 a,b † Beaty Upland 31 1370 399 b † 0.05 Center 23 901 750 b Edge 28 1130 581 a,b Larson Upland 39 1400 554 a 0.01 Mean comparisons using Tukey-Kramer HSD test † Indicates = 0.10. All other comparisons = 0.05. Table B-8. Below ground biomass P storage. Below Ground P Storage Site Zone n (mg/m2) *Difference p value Center 33 1210 869 a Edge 33 907 576 a Beaty Upland 34 1060 391 a 0.16 Center 28 400 402 b Edge 29 890 449 a Larson Upland 40 1150 589 a < 0.01 Mean comparisons using Tukey-Kramer HSD test. Table B-9. Above ground biomass P storage. Above Ground Biomass P Storage Site Zone n (mg/m2) *Difference p value Center 34 632 450 a Edge 36 476 369 a Beaty Upland 35 258 140 b < 0.01 Center 27 509 669 a Edge 33 250 309 b Larson Upland 43 344 212 a,b 0.05 Mean comparisons using Tukey-Kramer HSD test.

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98 A. B. Figure B-3. Phosphorus con centrations by species.

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99 A. B. Figure B-4. Phosphorus storage by zone. A) Beaty B) Larson

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100 Table B-10. Phosphorus concentr ation in above ground biomass AGB Species TP Concentration by Site Site Species n Indicator (mg/kg) Difference p value Andropogon 10 FAC 854 549 CD Baccopa 2 OBL 3340 ABCD Eleocharis 3 OBL 1360 BCD Juncus 89 OBL 1020 477 D Ludwigia 5 OBL 1880 305 BCD Luziola + P. acuminatum 6 FACW 2260 341 BCD Micranthemum 2 OBL 3300 ABCD Other 92 1480 721 C P. notatum 119 UPL 1370 560 CD Panicum 89 OBL 1550 763 C Polygonum 39 OBL 2190 678 B Pontederia 54 OBL 2140 1090 B Sagittaria 2 OBL 5880 A Beaty Utricularia 2 OBL 4240 AB < 0.01 Alternanthera 22 OBL 4360 844 W Eleocharis 3 OBL 3040 WXYZ Juncus 24 OBL 1470 514 Z Ludwigia 2 OBL 1790 XYZ Luziola + P. acuminatum 53 FACW 3360 731 X Other 84 2510 784 Y P. notatum 123 UPL 1640 633 Z Panicum 4 OBL 3180 689 WXYZ Polygonum 43 OBL 3130 1040 X Larson Pontederia 19 OBL 2990 1100 XY < 0.01

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101 Table B-11. Phosphorus storag e in above ground biomass AGB Species P storage by Site Site Species n Indicator (mg/m2) Difference p value Andropogon 4 FAC 19.4 4.26 A Baccopa 1 OBL 22.5 A Eleocharis 1 OBL 39.2 A Juncus 30 OBL 317 389 A Ludwigia 3 OBL 223 176 A Luziola + P. acuminatum 3 FACW 125 185 A Micranthemum 1 OBL 13.3 A Other 32 256 278 A P. notatum 39 UPL 261 145 A Panicum 31 OBL 324 331 A Polygonum 14 OBL 429 388 A Pontederia 19 OBL 182 219 A Sagittaria 1 OBL 37.2 A Beaty Utricularia 1 OBL 54.0 A 0.31 Alternanthera 10OBL 12.7 10.1 Y Eleocharis 2 OBL 149 XY Juncus 8 OBL 158 210 Y Ludwigia 1 OBL 103 XY Luziola + P. acuminatum 25 FACW 72.8 70.1 Y Other 30 261 278 Y P. notatum 42 UPL 341 208 XY Panicum 2 OBL 34.8 XY Polygonum 16 OBL 611 767 X Larson Pontederia 7 OBL 170 179 XY < 0.01

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102 APPENDIX C SUPPLEMENTAL MESOCOSM DATA Table C-1. Nutrient co ncentrations on day 0. Day 0 Concentrations Type Species Treatment nTP % TN % TC % Sand 80.004 0.003 . +10 30.003 0.001 0.087 0.004 1.52 0.106 0 30.003 0.001 0.092 0.005 1.69 0.064 -10 30.003 0.001 0.098 0.004 1.76 0.105 -15 30.003 0.001 0.088 0.004 1.56 0.088 Bahia C 30.004 0.001 0.089 0.005 1.60 0.110 +10 30.003 0.001 0.090 0.006 1.57 0.173 0 30.004 0.001 0.093 0.006 1.69 0.116 -10 30.004 0.000 0.094 0.004 1.66 0.077 -15 30.003 0.001 0.094 0.005 1.71 0.101 Soil Floralta C 30.004 0.001 0.093 0.003 1.71 0.078 +10 30.151 0.008 1.32 0.110 42.6 1.200 0 30.153 0.006 1.32 0.074 42.6 0.470 -10 30.149 0.004 1.26 0.044 42.9 0.880 -15 30.150 0.007 1.28 0.066 43.1 0.347 Bahia C 30.145 0.010 1.34 0.048 44.0 1.340 +10 30.166 0.015 1.35 0.079 42.7 0.174 0 30.179 0.033 1.33 0.188 41.9 0.755 -10 30.181 0.021 1.42 0.145 42.3 0.470 -15 30.180 0.001 1.40 0.096 42.3 0.389 Forage Floralta C 30.171 0.014 1.34 0.153 40.8 1.680

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103 Table C-2. Species comparison of forage production per harvest Forage Production Per Harvest (g/pot) Days n Bahia Floralta p value 27 15 4.02 0.79 <7.52 0.99 < 0.01 55 15 1.40 0.42 <1.97 0.66 0.01 83 15 0.92 0.44 <1.13 0.46 0.22 163 15 0.32 0.15 <1.55 0.54 < 0.01 305 15 0.56 0.39 <6.96 4.63 < 0.01 375 15 2.30 1.38 <13.3 10.1 < 0.01 Mean of species forage production regardless of treatment. Table C-3. Species comparison of cumulative forage production. Cumulative Forage Production (g/pot) Days n Bahia Floralta p value 27 15 4.03 0.79 <7.52 0.99 < 0.01 55 15 5.42 0.97 <9.49 0.98 < 0.01 83 15 6.34 1.21 <10.6 1.28 < 0.01 163 15 6.66 1.31 <12.2 1.39 < 0.01 305 15 7.22 1.46 <19.1 5.49 < 0.01 375 15 9.52 2.73 <32.3 14.7 < 0.01 Mean of species forage production regardless of treatment Table C-4. Overall below ground bi omass – all treatments combined Below Ground Biomass (g/pot) Days n Bahia Floralta p value 163 15 85.6 14.0 >38.0 9.07 < 0.01 375 15 79.6 20.8 >29.5 8.52 < 0.01

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104 Table C-5. Forage production per harvest. Forage Production Per Harvest (g/pot) Days Treatment nBahia Floralta p value +10 33.93 0.90 <8.12 1.39 0.01 0 34.06 0.45 <7.56 0.9 < 0.01 -10 33.87 0.27 <8.03 0.14 < 0.01 -15 33.59 0.80 <6.86 0.42 < 0.01 27 C 34.67 1.30 <7.02 1.41 0.10 +10 31.84 0.70 >1.73 0.34 0.81 0 31.21 0.42 <1.54 0.44 0.40 -10 31.28 0.29 <1.35 0.05 0.71 -15 31.24 0.09 <2.34 0.47 0.02 55 C 31.43 0.23 <2.92 0.11 < 0.01 +10 31.61 0.30 >1.15 0.34 0.15 0 30.76 0.25 <1.04 0.37 0.35 -10 30.68 0.35 <0.81 0.27 0.63 -15 30.58 0.22 <0.81 0.33 0.37 83 C 30.96 0.04 <1.82 0.13 < 0.01 +10 30.51 0.20 <2.36 0.39 < 0.01 0 30.27 0.04 <1.24 0.29 < 0.01 -10 30.20 0.11 <1.47 0.14 < 0.01 -15 30.32 0.07 <1.27 0.65 0.07 163 C 30.30 0.13 <1.42 0.31 < 0.01 +10 30.39 0.21 <13.7 2.67 < 0.01 0 30.43 0.40 <4.02 1.32 0.01 -10 30.28 0.06 <3.95 1.76 0.02 -15 30.77 0.30 <4.59 3.55 0.14 305 C 30.93 0.57 <8.56 4.38 0.04 +10 33.44 0.55 <17.5 1.84 < 0.01 0 31.21 0.25 <7.58 2.27 0.01 -10 31.58 0.82 <5.44 2.06 0.04 -15 31.44 0.29 <7.34 3.56 0.05 375 C 33.81 1.76 <28.4 11.3 0.02

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105 Table C-6. Cumulative fora ge by treatment and day. Cumulative Forage Production (g/pot) Days Treatment nBahia Floralta p value +10 33.93 0.90 <8.12 1.39 0.01 0 34.06 0.45 <7.56 0.90 < 0.01 -10 33.87 0.27 <8.03 0.14 < 0.01 -15 33.59 0.8 <6.86 0.42 < 0.01 27 C 34.67 1.30 <7.02 1.41 0.10 +10 35.77 1.40 <9.84 1.72 0.03 0 35.26 0.50 <9.09 0.46 < 0.01 -10 35.15 0.56 <9.38 0.11 < 0.01 -15 34.83 0.83 <9.19 0.84 < 0.01 55 C 36.10 1.34 <9.94 1.38 0.03 +10 37.38 1.70 <11.0 2.06 0.08 0 36.03 0.73 <10.1 0.39 < 0.01 -10 35.84 0.67 <10.2 0.38 < 0.01 -15 35.4 0.63 <10.0 1.1 < 0.01 83 C 37.06 1.32 <11.8 1.51 0.02 +10 37.89 1.88 <13.4 2.05 0.03 0 36.30 0.77 <11.4 0.66 < 0.01 -10 36.03 0.74 <11.7 0.51 < 0.01 -15 35.72 0.69 <11.3 0.84 < 0.01 163 C 37.37 1.30 <13.2 1.33 0.01 +10 38.28 1.85 <27.0 2.16 < 0.01 0 36.73 0.86 <15.4 1.96 < 0.01 -10 36.30 0.77 <15.6 1.28 < 0.01 -15 36.49 0.98 <15.9 3.81 0.01 305 C 38.30 1.85 <21.7 5.21 0.01 +10 311.7 2.36 <44.5 3.98 < 0.01 0 37.94 1.06 <23.0 4.24 < 0.01 -10 37.88 1.49 <21.1 3.31 < 0.01 -15 37.94 1.25 <23.2 7.33 0.02 375 C 312.1 3.61 <50.2 16.5 0.02

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106 Table C-7. Below ground bi omass species comparison. Below Ground Biomass (g/pot) Days Treatment nBahia Floralta p value +10 366.0 7.37 >27.5 3.30 < 0.01 0 389.9 16.0 >34.0 6.33 < 0.01 -10 391.1 5.40 >44.1 10.6 < 0.01 -15 396.9 4.33 >38.1 7.99 < 0.01 163 C 384.2 13.3 >46.3 0.60 0.01 +10 351.9 0.98 >19.8 4.52 < 0.01 0 393.7 21.1 >33.9 6.63 0.01 -10 395.2 13.6 >32.0 3.63 < 0.01 -15 369.0 11.2 >36.6 9.76 0.02 375 C 388.3 12.8 >24.9 6.91 < 0.01 Table C-8. Residual biomass harvested on days 163 and 375. Residual Biomass (g/pot) Days Treatment nBahia Floralta p value +10 311.1 2.66 <16.3 0.31 0.03 0 312.9 4.14 <17.7 2.79 0.17 -10 315.9 4.21 <16.3 1.75 0.89 -15 312.6 3.01 <15.9 2.8 0.24 163 C 313.4 4.09 <18.2 2.71 0.17 +10 313.0 0.82 <20.2 4.17 0.04 0 310.6 4.91 <19.6 2.53 0.05 -10 38.71 1.19 <17.5 6.98 0.10 -15 38.41 1.06 <16.2 5.13 0.06 375 C 312.3 5.31 <24.5 5.92 0.06 Table C-9. Below ground biomass production time comparison. Below Ground Biomass Production (g/pot) Species Treatment Day 163 Day 375 p value +10 66.0 7.37 >51.9 0.98 0.03 0 89.9 16.0 <93.7 21.1 0.81 -10 91.1 5.40 <95.2 13.6 0.65 -15 96.9 4.33 >69.0 11.2 0.02 Bahia C 84.2 13.3 <88.3 12.8 0.72 +10 27.5 3.30 >19.8 4.52 0.08 0 34.0 6.33 >33.9 6.63 0.99 -10 44.1 10.6 >32.0 3.63 0.13 -15 38.1 7.99 >36.6 9.76 0.86 Floralta C 46.3 0.60 >24.9 6.91 0.01

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107 BGB:AGB (163 Days) (110) (95) (80) (65) (50) (35) (20) (5) 10 25 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC S p ecies and TreatmentBiomass (g pot -1 ) Figure C-1. Relative root and shoot biomass after 163 days. Total Biomass (163 Days) 0 20 40 60 80 100 120 140 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC Treatment and SpeciesBiomass ( g pot -1 ) Figure C-2. Total biomass production after 163 days.

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108 Total Biomass (375 Days) 0 20 40 60 80 100 120 140 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC Treatment and SpeciesBiomass ( g pot -1 ) Figure C-3. Total biomass production after 375 days.

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109 Table C-10. Bahiagrass forage production per harvest treatment comparison using Tukey-Kramer HSD Test Bahia Forage Production Per Ha rvest (Tukey-Kramer HSD Test) Days Treatment nBahia Difference p value +10 33.93 0.90 A 0 34.06 0.45 A -10 33.87 0.27 A -15 33.59 0.80 A 27 C 34.67 1.30 A 0.61 +10 31.84 0.70 A 0 31.21 0.42 A -10 31.28 0.29 A -15 31.24 0.09 A 55 C 31.43 0.23 A 0.34 +10 31.61 0.30 A 0 30.76 0.25 B -10 30.68 0.35 B -15 30.58 0.22 B 83 C 30.96 0.04 AB < 0.01 +10 30.51 0.20 A 0 30.27 0.04 A -10 30.20 0.11 A -15 30.32 0.07 A 163 C 30.30 0.13 A 0.10 +10 30.39 0.21 A 0 30.43 0.40 A -10 30.28 0.06 A -15 30.77 0.30 A 305 C 30.93 0.57 A 0.19 +10 33.44 0.55 AB 0 31.21 0.25 A -10 31.58 0.82 AB -15 31.44 0.29 AB 375 C 33.81 1.76 B 0.01 In this table Bahia was compared to itself not to the other species.

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110 Table C-11. Bahiagrass cumulative forage production treatment comparison using Tukey-Kramer HSD Test Cumulative Bahia Forage Production (Tukey-Kramer HSD Test) Days Treatment nBahia Difference p value +10 33.93 0.90 A 0 34.06 0.45 A -10 33.87 0.27 A -15 33.59 0.8 A 27 C 34.67 1.30 A 0.60 +10 35.77 1.40 A 0 35.26 0.50 A -10 35.15 0.56 A -15 34.83 0.83 A 55 C 36.10 1.34 A 0.57 +10 37.38 1.70 A 0 36.03 0.73 A -10 35.84 0.67 A -15 35.4 0.63 A 83 C 37.06 1.32 A 0.21 +10 37.89 1.88 A 0 36.30 0.77 A -10 36.03 0.74 A -15 35.72 0.69 A 163 C 37.37 1.30 A 0.19 +10 38.28 1.85 A 0 36.73 0.86 A -10 36.30 0.77 A -15 36.49 0.98 A 305 C 38.30 1.85 A 0.25 +10 311.7 2.36 A 0 37.94 1.06 A -10 37.88 1.49 A -15 37.94 1.25 A 375 C 312.1 3.61 A 0.07 In this table Bahia was compared to itself not to the other species.

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111 Table C-12. Limpograss forage production per harvest treatment comparison using Tukey-Kramer HSD Test Floralta Forage Production Per Harvest (Tukey-Kramer HSD Test) Days Treatment nFloralta Difference p value +10 38.12 1.39 A 0 37.56 0.9 A -10 38.03 0.14 A -15 36.86 0.42 A 27 C 37.02 1.41 A 0.46 +10 31.73 0.34 AC 0 31.54 0.44 AC -10 31.35 0.05 A -15 32.34 0.47 BC 55 C 32.92 0.11 B < 0.01 +10 31.15 0.34 AB 0 31.04 0.37 AB -10 30.81 0.27 B -15 30.81 0.33 B 83 C 31.82 0.13 A 0.01 +10 32.36 0.39 B 0 31.24 0.29 A -10 31.47 0.14 AB -15 31.27 0.65 A 163 C 31.42 0.31 AB 0.03 +10 313.7 2.67 A 0 34.02 1.32 B -10 33.95 1.76 B -15 34.59 3.55 B 305 C 38.56 4.38 AB 0.01 +10 317.5 1.84 AB 0 37.58 2.27 B -10 35.44 2.06 B -15 37.34 3.56 B 375 C 328.4 11.3 A < 0.01 In this table Floralta was compared to itself not to the other species.

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112 Table C-13. Cumulative limpograss forage production treatment comparison using Tukey-Kramer HSD Test Cumulative Floralta Forage Production (Tukey-Kramer HSD Test) Days Treatment nFloralta Difference p value +10 38.12 1.39 A 0 37.56 0.90 A -10 38.03 0.14 A -15 36.86 0.42 A 27 C 37.02 1.41 A 0.46 +10 39.84 1.72 A 0 39.09 0.46 A -10 39.38 0.11 A -15 39.19 0.84 A 55 C 39.94 1.38 A 0.82 +10 311.0 2.06 A 0 310.1 0.39 A -10 310.2 0.38 A -15 310.0 1.1 A 83 C 311.8 1.51 A 0.43 +10 313.4 2.05 A 0 311.4 0.66 A -10 311.7 0.51 A -15 311.3 0.84 A 163 C 313.2 1.33 A 0.16 +10 327.0 2.16 A 0 315.4 1.96 B -10 315.6 1.28 B -15 315.9 3.81 B 305 C 321.7 5.21 AB < 0.01 +10 344.5 3.98 BC 0 323.0 4.24 AC -10 321.1 3.31 A -15 323.2 7.33 AC 375 C 350.2 16.5 B < 0.01 In this table Floralta was compared to itself not to the other species.

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113 Table C-14. Bahiagrass BGB production – treatment comparison using Tukey-Kramer HSD Bahia B.G. Production (T ukey-Kramer HSD Test) Days Treatment nBahia Difference p value +10 366.0 7.37 A 0 389.9 16.0 AB -10 391.1 5.40 AB -15 396.9 4.33 B 163 C 384.2 13.3 AB 0.04 +10 351.9 0.98 A 0 393.7 21.1 B -10 395.2 13.6 B -15 369.0 11.2 AB 375 C 388.3 12.8 B 0.01 Table C-15. Limpograss BGB production – treatment comp arison using Tukey-Kramer HSD Floralta B.G. Production (Tukey-Kramer HSD Test) Days Treatment nFloralta Difference p value +10 327.5 3.30 A 0 334.0 6.33 AB -10 344.1 10.6 AB -15 338.1 7.99 AB 163 C 346.3 0.60 B 0.04 +10 319.8 4.52 A 0 333.9 6.63 A -10 332.0 3.63 A -15 336.6 9.76 A 375 C 324.9 6.91 A 0.06

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114 Table C-16. Forage P concentr ations – species comparison. Forage Phosphorus Concentrations (mg/kg) Day Treatment Bahia Floralta p value +10 1,510 83.0 <1,660 143 0.68 0 1,530 58.0 <1,790 331 0.04 -10 1,490 36.0 <1,810 210 0.13 -15 1,500 73.0 <1,800 13.0 0.75 0 C 1,454 98.0 <1,780 135 0.43 +10 1,600 24.0 >965 220 0.01 0 1,220 62.0 >1,010 146 0.08 -10 1,150 11.0 >967 212 0.21 -15 1,180 60.0 >1,080 72 0.12 27 C 1,210 55.0 <1,260 152 0.65 +10 1,520 117 >1,110 103 0.01 0 1,180 57.0 <1,360 110 0.06 -10 1,230 37.0 <1,280 95 0.48 -15 1,200 15.0 <1,230 88 0.56 55 C 1,280 92.0 <1,320 108 0.62 +10 1,670 34.0 >1,550 204 0.35 0 1,160 80.0 <1,360 149 0.13 -10 1,190 34.0 <1,420 189 0.10 -15 1,190 28.0 <1,440 54.0 < 0.01 83 C 1,190 66.0 <1,490 204 0.07 +10 1,400 195 <1,410 64.0 0.98 0 1,030 129 <1,030 248 0.98 -10 877 28.0 <1,010 150 0.22 -15 942 100 >789 154 0.22 163 C 817 74.0 <1,070 112 0.03 +10 1,150 105 >712 41.0 < 0.01 0 478 80.0 <708 155 0.09 -10 718 44.0 >683 75.0 0.51 -15 976 295 >580 41.0 0.08 375 C 1,130 121 >587 73.0 < 0.01

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115 Table C-17. Bahiagrass forage P concentra tions treatment comparison using TukeyKramer HSD. Bahia Forage Phosphorus Conc. (mg/kg) Tukey-Kramer Day Treatment Bahia Difference p value +10 1,510 83.0 A 0 1,530 58.0 A -10 1,490 36.0 A -15 1,500 73.0 A 0 C 1,454 98.0 A 0.73 +10 1,600 24.0 A 0 1,220 62.0 B -10 1,150 11.0 B -15 1,180 60.0 B 27 C 1,210 55.0 B < 0.01 +10 1,5120 117 A 0 1,180 57.0 B -10 1,230 37.0 B -15 1,200 15.0 B 55 C 1,280 92.0 B < 0.01 +10 1,670 34.0 A 0 1,160 80.0 B -10 1,190 34.0 B -15 1,190 28.0 B 83 C 1,190 66.0 B < 0.01 +10 1,400 195 A 0 1,030 129 B -10 877 28.0 B -15 942 100 B 163 C 817 74.0 B < 0.01 +10 1,150 105 A 0 478 80.0 B -10 718 44.0 BC -15 976 295 AC 375 C 1,130 121 AC < 0.01 In this table Bahia was compared to itself not to the other species.

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116 Table C-18. Limpograss forage P concentra tions treatment comparison using TukeyKramer HSD. Floralta Forage Phosphorus Conc. (mg/kg) Tukey-Kramer Day Treatment Floralta Difference p value +10 1,660 143 A 0 1,790 331 A -10 1,810 210 A -15 1,800 13.0 A 0 C 1,780 135 A 0.84 +10 965 220 A 0 1,010 146 A -10 967 212 A -15 1,080 72 A 27 C 1,260 152 A 0.26 +10 1,110 103 A 0 1,360 110 A -10 1,280 95 A -15 1,230 88 A 55 C 1,320 108 A 0.10 +10 1,550 204 A 0 1,360 149 A -10 1,420 189 A -15 1,440 54.0 A 83 C 1,490 204 A 0.71 +10 1,410 64.0 A 0 1,030 248 AB -10 1,010 150 AB -15 789 154 B 163 C 1,070 112 AB 0.01 +10 712 41.0 A 0 708 155 A -10 683 75.0 A -15 580 41.0 A 375 C 587 73.0 A 0.23 In this table Floralta was compared to itself not to the other species

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117 Table C-19. Below ground biomass P c oncentrations – species comparison. BGB Phosphorus Concentrations (mg/kg) Day Treatment Bahia Floralta p value +10 430 45.0 <592 115 0.08 0 432 77.0 <609 35.0 0.03 -10 441 56.0 <472 23.0 0.42 -15 433 68.0 <505 157 0.53 163 C 430 98.0 <668 11.0 0.01 +10 377 42.0 >320 48.0 0.23 0 375 60.0 >304 16.0 0.12 -10 343 26.0 >333 15.0 0.70 -15 303 17.0 <345 149 0.62 375 C 395 41.0 >383 29.0 0.67 Table C-20. Bahiagrass BGB P concentrati ons treatment comparison using TukeyKramer HSD. Bahia BGB Phosphorus Conc. (mg/kg) Tukey-Kramer Day Treatment Bahia Difference p value +10 430 45.0 A 0 432 77.0 A -10 441 56.0 A -15 433 68.0 A 163 C 430 98.0 A 0.99 +10 377 42.0 A 0 375 60.0 A -10 343 26.0 A -15 303 17.0 A 375 C 395 41.0 A 0.12

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118 Table C-21. Limpograss BGB P concentratio ns treatment comparison using TukeyKramer HSD. Floralta BGB Phosphorus Conc. (mg/kg) Tukey-Kramer Day Treatment Floralta Difference p value +10 592 115 A 0 609 35.0 A -10 472 23.0 A -15 505 157 A 163 C 668 11.0 A 0.12 +10 320 48.0 A 0 304 16.0 A -10 333 15.0 A -15 345 149 A 375 C 383 29.0 A 0.72

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119 Table C-22. Phosphorus storage in fora ge species comparison per harvest. Forage P Assimilation per Harvest (mg/pot) Day Treatment nBahia Floralta p value +10 36.28 1.50 <7.72 1.71 0.34 0 34.95 0.34 <7.60 0.95 0.01 -10 34.44 0.28 <7.77 1.70 0.03 -15 34.21 0.74 <7.39 0.84 0.01 27 C 35.62 1.28 <8.72 0.95 0.03 +10 32.81 1.16 >1.93 0.48 0.29 0 31.41 0.44 <2.06 0.45 0.15 -10 31.58 0.34 <1.72 0.15 0.53 -15 31.49 0.12 <2.86 0.35 < 0.01 55 C 31.84 0.39 <3.85 0.21 < 0.01 +10 32.69 0.55 >1.75 0.47 0.09 0 30.88 0.25 <1.37 0.38 0.14 -10 30.81 0.43 <1.15 0.38 0.36 -15 30.68 0.25 <1.16 0.47 0.20 83 C 31.15 0.07 <2.70 0.28 < 0.01 +10 30.70 0.25 <3.30 0.42 < 0.01 0 30.29 0.08 <1.26 0.36 0.01 -10 30.17 0.09 <1.47 0.10 < 0.01 -15 30.30 0.09 <0.98 0.43 0.57 163 C 30.24 0.08 <1.51 0.35 < 0.01 +10 30.44 0.21 <9.79 2.24 < 0.01 0 30.20 0.18 <2.72 0.25 < 0.01 -10 30.20 0.04 <2.71 1.22 0.02 -15 30.80 0.53 <2.69 2.14 0.21 305 C 31.03 0.63 <4.82 2.13 0.04 +10 33.98 1.03 <12.4 1.43 < 0.01 0 30.59 0.22 <5.15 0.48 < 0.01 -10 31.13 0.55 <3.77 1.66 0.06 -15 31.47 0.76 <4.24 2.11 0.10 375 C 34.24 1.90 <16.1 5.03 0.02

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120 Table C-23. Cumulative P storage in forage species comparison Cumulative Forage Tissue Phosphorus Asssimilation (mg/pot) Day Treatment nBahia Floralta p value +10 36.28 1.50 <7.72 1.71 0.34 0 34.95 0.34 <7.60 0.95 0.01 -10 34.44 0.28 <7.77 1.70 0.03 -15 34.21 0.74 <7.39 0.84 0.01 27 C 35.62 1.28 <8.72 0.95 0.03 +10 39.09 2.41 <9.65 2.12 0.78 0 36.36 0.32 <9.66 0.94 0.00 -10 36.02 0.62 <9.49 1.84 0.04 -15 35.69 0.78 <10.3 1.02 < 0.01 55 C 37.47 1.45 <12.6 0.76 0.01 +10 311.8 2.96 >11.4 2.52 0.87 0 37.24 0.57 <11.0 1.31 0.01 -10 36.83 0.76 <10.6 2.07 0.04 -15 36.38 0.57 <11.4 1.27 < 0.01 83 C 38.61 1.50 <15.3 0.64 < 0.01 +10 312.5 3.15 <14.7 2.15 0.37 0 37.52 0.64 <12.3 1.04 0.00 -10 37.00 0.81 <12.1 2.00 0.01 -15 36.68 0.65 <12.4 0.84 < 0.01 163 C 38.85 1.49 <16.8 0.63 < 0.01 +10 312.9 3.16 <24.5 3.53 0.01 0 37.72 0.63 <15.0 1.14 < 0.01 -10 37.20 0.82 <14.8 2.73 0.01 -15 37.48 1.17 <15.1 1.67 0.00 305 C 39.89 2.12 <21.6 2.66 0.00 +10 316.9 4.11 <36.9 4.94 0.01 0 38.31 0.84 <20.2 1.27 < 0.01 -10 38.32 1.25 <18.6 4.34 0.02 -15 38.95 1.93 <19.3 3.78 0.01 375 C 314.1 4.01 <37.7 7.64 0.01

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121 Table C-24. Bahiagrass fora ge P storage per harvest Forage P Assimilation per Harvest Tukey-Kramer (mg/pot) Day Treatment nBahia Difference p value +10 36.28 1.50 A 0 34.95 0.34 A -10 34.44 0.28 A -15 34.21 0.74 A 27 C 35.62 1.28 A 0.12 +10 32.81 1.16 A 0 31.41 0.44 A -10 31.58 0.34 A -15 31.49 0.12 A 55 C 31.84 0.39 A 0.09 +10 32.69 0.55 A 0 30.88 0.25 B -10 30.81 0.43 B -15 30.68 0.25 B 83 C 31.15 0.07 B < 0.01 +10 30.70 0.25 A 0 30.29 0.08 B -10 30.17 0.09 B -15 30.30 0.09 B 163 C 30.24 0.08 B 0.01 +10 30.44 0.21 A 0 30.20 0.18 A -10 30.20 0.04 A -15 30.80 0.53 A 305 C 31.03 0.63 A 0.09 +10 33.98 1.03 A 0 30.59 0.22 B -10 31.13 0.55 B -15 31.47 0.76 AB 375 C 34.24 1.90 A < 0.01 In this table Bahia was compared to itself not to the other species.

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122 Table C-25. Bahiagrass cumulative forage P storage. Cumulative Forage P Assimilation Tukey-Kramer (mg/pot) Day Treatment nBahia Difference p value +10 36.28 1.50 A 0 34.95 0.34 A -10 34.44 0.28 A -15 34.21 0.74 A 27 C 35.62 1.28 A 0.12 +10 39.09 2.41 A 0 36.36 0.32 A -10 36.02 0.62 A -15 35.69 0.78 A 55 C 37.47 1.45 A 0.06 +10 311.8 2.96 A 0 37.24 0.57 B -10 36.83 0.76 B -15 36.38 0.57 B 83 C 38.61 1.50 AB 0.01 +10 312.5 3.15 A 0 37.52 0.64 B -10 37.00 0.81 B -15 36.68 0.65 B 163 C 38.85 1.49 AB 0.01 +10 312.9 3.16 A 0 37.72 0.63 B -10 37.20 0.82 B -15 37.48 1.17 B 305 C 39.89 2.12 AB 0.02 +10 316.9 4.11 A 0 38.31 0.84 B -10 38.32 1.25 B -15 38.95 1.93 B 375 C 314.1 4.01 AB 0.01 In this table Bahia was compared to itself not to the other species.

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123 Table C-26. Limpograss forage P storage per harvest. Forage P Assimilation per Harvest Tukey-Kramer (mg/pot) Day Treatment nFloralta Difference p value +10 37.72 1.71 A 0 37.60 0.95 A -10 37.77 1.70 A -15 37.39 0.84 A 27 C 38.72 0.95 A 0.76 +10 31.93 0.48 AC 0 32.06 0.45 AC -10 31.72 0.15 A -15 32.86 0.35 C 55 C 33.85 0.21 B < 0.01 +10 31.75 0.47 AB 0 31.37 0.38 B -10 31.15 0.38 B -15 31.16 0.47 B 83 C 32.70 0.28 A < 0.01 +10 33.30 0.42 A 0 31.26 0.36 B -10 31.47 0.10 B -15 30.98 0.43 B 163 C 31.51 0.35 B < 0.01 +10 39.79 2.24 A 0 32.72 0.25 B -10 32.71 1.22 B -15 32.69 2.14 B 305 C 34.82 2.13 B < 0.01 +10 312.4 1.43 A 0 35.15 0.48 B -10 33.77 1.66 B -15 34.24 2.11 B 375 C 316.1 5.03 A < 0.01 In this table Floralta was compared to itself not to the other species.

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124 Table C-27. Cumulative limpograss forage P storage Cumulative Forage P Assimilation Tukey-Kramer (mg/pot) Day Treatment nFloralta Difference p value +10 37.72 1.71 A 0 37.60 0.95 A -10 37.77 1.70 A -15 37.39 0.84 A 27 C 38.72 0.95 A 0.76 +10 39.65 2.12 A 0 39.66 0.94 A -10 39.49 1.84 A -15 310.3 1.02 A 55 C 312.6 0.76 A 0.12 +10 311.4 2.52 AB 0 311.0 1.31 AB -10 310.6 2.07 B -15 311.4 1.27 AB 83 C 315.3 0.64 A 0.04 +10 314.7 2.15 AB 0 312.3 1.04 B -10 312.1 2.00 B -15 312.4 0.84 B 163 C 316.8 0.63 A < 0.01 +10 324.5 3.53 A 0 315.0 1.14 BC -10 314.8 2.73 B -15 315.1 1.67 BC 305 C 321.6 2.66 AC < 0.01 +10 336.9 4.94 A 0 320.2 1.27 B -10 318.6 4.34 B -15 319.3 3.78 B 375 C 337.7 7.64 A < 0.01 In this table Floralta was compared to itself not to the other species.

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125 A. B. Figure C-4. Bahiagrass total biomass and P storage. A) biomass B) P storage. A. B. Figure C-5. Limpograss total biomass and P storage. A) biomass B) P storage.

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126 A. B. Figure C-6. Bahiagrass BG B and P storage. A) biomass, B) P storage A. B. Figure C-7. Limpograss BGB and P stor age. A) biomass, B) P storage

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127 Table C-28. Below ground biomass P storage species comparison BGB Tissue Phosphorus Asssimilation (mg/pot) Day Treatment Bahia Floralta p value +10 28.2 1.57 >16.3 3.80 0.01 0 38.1 4.74 >20.7 3.60 0.01 -10 40.0 4.37 >20.7 4.12 0.01 -15 41.8 5.47 >18.5 2.89 < 0.01 163 C 35.8 7.86 >30.9 0.73 0.35 +10 19.5 1.88 >6.30 1.43 < 0.01 0 35.9 14.2 >10.4 2.34 0.04 -10 32.5 2.98 >10.6 0.78 < 0.01 -15 21.1 4.70 >12.4 5.30 0.10 375 C 35.0 6.72 >9.49 2.28 < 0.01 Table C-29. Bahiagrass belo w ground biomass P storage BGB Tissue Phosphorus Asssimilation (mg/pot) Day Treatment Bahia Difference p value +10 28.2 1.57 A 0 38.1 4.74 A -10 40.0 4.37 A -15 41.8 5.47 A 163 C 35.8 7.86 A 0.07 +10 19.5 1.88 A 0 35.9 14.2 A -10 32.5 2.98 A -15 21.1 4.70 A 375 C 35.0 6.72 A 0.06 Table C-30. Limpograss belo w ground biomass P storage BGB Tissue Phosphorus Asssimilation (mg/pot) Day Treatment Floralta Difference p value +10 16.3 3.80 A 0 20.7 3.60 A -10 20.7 4.12 A -15 18.5 2.89 A 163 C 30.9 0.73 B < 0.01 +10 6.30 1.43 A 0 10.4 2.34 A -10 10.6 0.78 A -15 12.4 5.30 A 375 C 9.49 2.28 A 0.20

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128 TP Storage (163 Days) (50) (30) (10) 10 30 50 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC Treatment and SpeciesTP ( mg pot -1 ) Figure C-8. Root to shoot P storage ratios TP Storage (163 Days) 0 10 20 30 40 50 60 70 BahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloraltaBahiaFloralta +10+1000-10-10-15-15CC Treatment and SpeciesTP ( mg pot -1 ) Figure C-9. Total P storag e (AGB +BGB) at 163 days.

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129 Table C-31. Climatic conditions from day 1 to 375. Climatic Conditions (FAWN) Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 7/1/2004 1 24.8 19.2 33.9 28. 3 27.3 30.3 1.38 84% 7/2/2004 2 26.4 19.6 34.0 28. 6 26.0 31.5 0.00 77% 7/3/2004 3 25.0 20.2 32.1 28. 7 27.1 30.3 0.11 85% 7/4/2004 4 25.2 19.9 33.2 28. 4 26.3 30.9 0.00 83% 7/5/2004 5 27.3 20.6 34.0 29. 2 26.6 31.8 0.00 77% 7/6/2004 6 28.1 21.4 35.2 30. 0 27.7 32.4 0.00 74% 7/7/2004 7 26.1 20.1 34.0 29. 8 27.9 31.6 1.51 80% 7/8/2004 8 23.9 19.7 32.1 28. 0 26.5 30.2 0.15 88% 7/9/2004 9 26.4 18.2 34.2 28. 5 25.7 31.5 0.00 77% 7/10/2004 10 27.5 20.1 35.3 29.7 27.2 32.4 0.00 74% 7/11/2004 11 25.3 20.7 33.8 29.1 27.3 31.7 1.81 83% 7/12/2004 12 24.8 20.3 34.1 28.2 26.1 30.8 0.01 87% 7/13/2004 13 25.3 20.7 33.0 28.4 26.6 30.8 0.00 85% 7/14/2004 14 27.5 21.4 32.9 29.2 26.7 31.9 0.00 76% 7/15/2004 15 25.9 21.5 31.8 28.9 27.9 29.9 0.28 84% 7/16/2004 16 25.3 21.4 33.2 28.6 26.7 31.4 0.35 82% 7/17/2004 17 24.7 21.1 31.3 28.2 26.6 30.1 0.33 87% 7/18/2004 18 23.9 22.5 26.4 27.1 26.6 27.8 0.74 92% 7/19/2004 19 25.3 22.3 31.8 27.7 26.0 30.1 0.56 87% 7/20/2004 20 25.9 22.0 33.1 28.3 26.6 30.6 0.57 86% 7/21/2004 21 26.7 21.0 33.4 28.8 26.7 31.0 0.00 76% 7/22/2004 22 26.4 18.5 34.0 28.7 26.4 30.9 0.00 74% 7/23/2004 23 27.0 20.6 34.2 29.0 27.1 30.7 0.00 77% 7/24/2004 24 26.4 22.2 35.1 29.0 27.6 30.9 0.02 82% 7/25/2004 25 26.9 21.1 33.5 28.8 27.1 30.7 0.00 76% 7/26/2004 26 27.1 19.8 34.7 29.2 27.2 31.3 0.49 74% 7/27/2004 27 26.1 21.3 34.9 29.2 27.5 31.8 0.94 83% 7/28/2004 28 25.6 21.0 32.7 28.8 27.2 30.8 0.67 87% 7/29/2004 29 26.9 21.8 33.5 28.6 27.3 30.0 0.00 79% 7/30/2004 30 27.0 21.5 33.1 28.7 27.3 30.4 0.00 76% 7/31/2004 31 25.4 20.0 31.8 28.2 26.9 29.3 0.00 85% 8/1/2004 32 26.1 21.7 33.6 28. 3 27.1 29.6 0.04 85% 8/2/2004 33 26.8 22.7 32.8 28. 7 27.7 30.6 0.84 85% 8/3/2004 34 28.0 23.3 34.1 29. 2 27.6 31.4 0.00 78% 8/4/2004 35 27.3 23.3 33.8 29. 5 27.9 31.3 0.00 81% 8/5/2004 36 28.1 22.9 34.0 29. 6 27.9 31.5 0.00 77% 8/6/2004 37 26.8 22.7 33.7 29. 6 28.4 31.1 0.39 83% 8/7/2004 38 23.8 20.4 26.9 27. 9 27.3 29.0 0.00 75% 8/8/2004 39 25.0 21.8 29.3 27. 4 26.5 28.3 0.00 79% 8/9/2004 40 25.7 22.3 30.7 27. 9 26.8 29.1 0.00 80% 8/10/2004 41 24.5 20.1 29.8 27.6 26.6 28.9 0.92 86% 8/11/2004 42 25.2 20.2 31.6 27.6 26.2 29.1 0.01 85% 8/12/2004 43 25.0 22.6 28.2 27.4 26.9 27.8 0.49 88%

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130 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 8/13/2004 44 23.1 21.2 24.7 26.4 25.9 27.2 1.48 94% 8/14/2004 45 23.2 20.2 26.6 25.8 25.2 26.5 0.03 92% 8/15/2004 46 24.8 21.3 29.9 26.4 25.6 27.5 0.00 86% 8/16/2004 47 26.3 20.0 33.4 27.2 25.6 28.8 0.00 80% 8/17/2004 48 26.8 21.7 34.4 28.1 26.9 29.7 0.44 82% 8/18/2004 49 26.0 20.0 33.5 28.0 26.6 29.4 0.31 83% 8/19/2004 50 27.3 21.2 34.5 28.4 26.9 30.0 0.00 78% 8/20/2004 51 26.9 22.1 34.9 28.7 27.5 30.3 0.00 79% 8/21/2004 52 24.2 21.2 31.7 27.7 27.2 28.5 0.51 89% 8/22/2004 53 24.8 21.1 32.8 27.4 26.4 28.2 0.13 89% 8/23/2004 54 25.1 21.3 31.8 27.6 26.5 29.0 0.13 87% 8/24/2004 55 24.9 20.2 32.6 27.5 26.4 28.9 0.00 86% 8/25/2004 56 26.2 22.5 32.4 28.0 26.9 29.3 0.00 82% 8/26/2004 57 26.1 20.9 33.4 28.1 26.9 29.3 0.13 83% 8/27/2004 58 26.5 21.9 32.8 28.2 27.0 29.5 0.00 81% 8/28/2004 59 26.3 20.4 33.3 28.1 26.8 29.2 0.00 79% 8/29/2004 60 26.8 21.4 33.2 28.3 27.0 29.5 0.00 79% 8/30/2004 61 26.6 21.9 33.2 28.3 27.1 29.8 0.00 78% 8/31/2004 62 24.8 21.1 31.5 27.9 27.1 28.7 0.00 88% 9/1/2004 63 26.2 20.3 35.1 28. 2 26.7 30.1 0.02 79% 9/2/2004 64 24.6 20.9 33.0 27. 9 27.0 29.4 0.85 87% 9/3/2004 65 25.9 22.3 30.5 27. 6 26.6 28.8 0.19 84% 9/4/2004 66 26.6 23.4 31.6 27. 7 26.8 28.7 0.02 82% 9/5/2004 67 24.8 22.2 26.7 26. 6 25.5 27.6 2.28 86% 9/6/2004 68 24.0 23.0 25.1 25. 2 24.9 25.5 2.20 90% 9/7/2004 69 25.5 23.3 29.8 25. 5 24.8 26.7 0.94 87% 9/8/2004 70 27.2 24.5 32.0 26. 8 25.7 28.2 0.00 82% 9/9/2004 71 26.4 23.3 33.8 27. 7 26.5 29.3 0.71 85% 9/10/2004 72 24.6 22.3 29.4 27.3 26.8 27.9 0.18 91% 9/11/2004 73 25.6 23.1 30.4 27.3 26.6 28.1 0.00 86% 9/12/2004 74 24.2 22.4 29.4 27.0 26.4 27.4 0.27 90% 9/13/2004 75 24.2 21.6 30.1 26.7 26.1 27.3 0.23 90% 9/14/2004 76 23.4 23.0 25.6 26.3 26.1 26.6 0.00 93% 9/20/2004 77 21.7 20.8 23.2 25.0 24.6 25.3 0.00 79% 9/21/2004 78 21.7 19.3 26.7 24.8 24.1 25.6 0.21 87% 9/22/2004 79 23.9 19.6 30.7 25.3 24.3 26.6 0.00 78% 9/23/2004 80 23.8 17.3 31.2 25.6 24.6 26.8 0.00 79% 9/24/2004 81 22.3 15.5 29.1 25.1 23.9 26.2 0.00 80% 9/25/2004 82 23.9 22.0 28.1 25.3 24.7 26.0 0.06 84% 9/26/2004 83 24.1 22.2 26.2 24.7 24.4 25.1 4.63 89% 9/27/2004 84 23.1 22.7 23.4 24.2 24.0 24.3 0.06 89% 9/28/2004 85 28.3 25.5 29.8 27.3 27.1 27.3 0.00 62% 9/29/2004 86 26.1 20.2 31.0 26.8 25.1 28.0 0.00 75% 9/30/2004 87 23.9 18.4 31.1 26.3 24.8 27.7 0.00 82% 10/1/2004 88 24.3 19.1 32.6 26.3 25.0 27.9 0.00 85% 10/2/2004 89 25.2 19.9 31.8 26.4 25.2 27.6 0.00 84%

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131 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 10/3/2004 90 25.2 20.7 32.0 26.9 25.8 28.0 0.00 82% 10/4/2004 91 24.3 18.9 30.8 26.3 25.0 27.5 0.00 82% 10/5/2004 92 23.3 18.6 30.1 26.2 25.3 27.3 0.00 80% 10/6/2004 93 22.9 18.5 29.5 25.3 24.6 26.1 0.00 78% 10/7/2004 94 21.5 17.4 27.9 24.6 23.9 25.5 0.04 76% 10/8/2004 95 21.6 17.1 28.1 24.2 23.1 25.2 0.00 80% 10/9/2004 96 23.1 18.1 28.8 24.5 23.6 25.5 0.00 80% 10/10/2004 97 23.5 19.4 28.9 24.8 24.0 25.7 0.00 80% 10/11/2004 98 21.0 17.8 22.8 24.4 23.7 24.9 0.26 92% 10/12/2004 99 22.1 17.1 28.7 24.1 23.0 25.4 0.00 86% 10/13/2004 100 21.7 15.2 28.4 24.5 23.5 26.0 0.00 80% 10/14/2004 101 19.8 11.8 27.0 23.6 22.0 25.1 0.07 75% 10/15/2004 102 16.5 8.0 20.1 23.3 21.9 24.4 0.61 79% 10/16/2004 103 16.8 7.2 26.4 21.7 19.9 23.6 0.00 75% 10/17/2004 104 20.3 12.9 29.3 22.4 20.8 24.3 0.00 79% 10/18/2004 105 20.4 13.6 27.7 22.7 21.5 23.9 0.00 82% 10/19/2004 106 23.4 16.7 30.8 23.4 22.0 25.0 0.00 83% 10/20/2004 107 24.0 19.9 29.8 24.7 23.7 26.0 0.18 86% 10/21/2004 108 21.1 17.0 26.6 24.0 23.5 24.9 0.00 91% 10/22/2004 109 19.9 15.7 26.2 23.3 22.4 24.4 0.00 83% 10/23/2004 110 18.5 12.4 26.3 22.6 21.6 23.7 0.00 78% 10/24/2004 111 18.8 10.9 27.7 22.3 20.7 24.0 0.00 82% 10/25/2004 112 20.4 15.4 28.2 23.1 21.8 24.6 0.00 82% 10/26/2004 113 19.0 13.7 26.9 22.6 21.5 23.8 0.00 82% 10/27/2004 114 18.8 12.6 27.2 22.2 20.9 23.6 0.00 81% 10/28/2004 115 20.2 13.8 27.9 22.4 21.1 23.8 0.00 86% 10/29/2004 116 23.0 19.0 29.7 23.6 22.4 25.3 0.08 85% 10/30/2004 117 22.6 18.0 30.2 24.1 23.1 25.6 0.00 87% 10/31/2004 118 22.0 16.6 29.6 23.8 22.6 25.1 0.00 86% 11/1/2004 119 22.6 16.0 29.3 23.6 22.4 24.8 0.00 82% 11/2/2004 120 23.1 18.5 30.0 24.0 22.9 25.3 0.00 81% 11/3/2004 121 22.2 16.4 30.4 23.7 22.5 25.0 0.00 81% 11/4/2004 122 22.1 15.3 29.2 23.5 22.1 24.7 0.15 84% 11/5/2004 123 15.3 9.3 21.6 22.4 21.0 23.7 0.00 76% 11/6/2004 124 13.6 7.0 23.6 20.4 19.0 21.9 0.00 78% 11/7/2004 125 13.3 5.2 24.1 19.6 18.1 21.2 0.00 73% 11/8/2004 126 15.6 6.9 26.6 19.4 17.7 21.2 0.00 76% 11/9/2004 127 16.0 11.1 22.1 19.5 18.8 20.0 0.00 74% 11/10/2004 128 17.2 11.4 26.0 19.4 18.2 20.7 0.00 76% 11/11/2004 129 17.7 10.7 25.6 19.7 18.5 20.9 0.00 82% 11/12/2004 130 19.4 14.9 26.5 20.5 19.6 21.5 0.04 85% 11/13/2004 131 19.4 15.4 24.6 20.8 20.0 21.9 0.00 87% 11/14/2004 132 15.1 13.8 16.4 19.8 19.1 20.7 0.00 86% 11/15/2004 133 15.2 9.9 21.8 19.0 18.0 20.0 0.00 79% 11/16/2004 134 13.5 6.5 23.7 18.7 17.4 20.1 0.00 77% 11/17/2004 135 12.5 4.9 22.9 18.1 16.8 19.4 0.00 74%

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132 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 11/18/2004 136 13.7 4.6 24.6 17.8 16.3 19.2 0.00 79% 11/19/2004 137 17.2 9.3 26.5 18.7 17.3 20.3 0.00 79% 11/20/2004 138 19.8 16.1 23.8 19.9 19.2 20.8 0.02 84% 11/21/2004 139 20.9 14.1 28.1 20.7 19.5 22.1 0.00 79% 11/22/2004 140 21.7 15.5 27.0 20.9 19.2 21.8 0.00 78% 11/23/2004 141 19.1 14.1 26.7 20.7 19.6 22.0 0.00 83% 11/24/2004 142 20.8 14.8 25.9 20.6 19.8 21.5 1.75 84% 11/25/2004 143 16.3 6.6 21.8 20.6 19.1 21.7 0.10 73% 11/26/2004 144 9.0 1.7 16.0 17.5 16.5 19.0 0.00 77% 11/27/2004 145 14.5 7.9 22.5 17.4 16.3 18.6 0.57 87% 11/28/2004 146 13.4 6.0 16.6 18.1 17.2 19.0 0.00 91% 11/29/2004 147 13.5 4.0 23.7 17.0 15.4 18.4 0.00 84% 11/30/2004 148 17.6 13.5 26.0 18.4 17.4 19.8 0.00 86% 12/1/2004 149 15.9 7.6 20.7 18.7 18.0 19.6 0.00 84% 12/2/2004 150 11.5 3.7 21.6 17.2 15.7 18.6 0.00 78% 12/3/2004 151 10.1 4.3 17.6 16.5 15.5 17.5 0.00 73% 12/4/2004 152 9.7 3.5 14.6 16.0 15.3 16.7 0.02 86% 12/5/2004 153 10.7 1.7 20.0 15.1 13.7 16.4 0.00 83% 12/6/2004 154 17.1 9.8 26.5 16.9 15.4 18.9 0.00 85% 12/7/2004 155 20.6 15.8 26.5 18.3 17.2 19.8 0.00 85% 12/8/2004 156 20.9 15.3 27.8 19.6 18.3 21.3 0.00 86% 12/9/2004 157 21.5 15.0 27.5 19.8 18.7 21.1 0.00 84% 12/10/2004 158 20.8 15.1 24.4 20.4 19.3 21.1 0.74 76% 12/11/2004 159 10.4 4.3 15.6 17.8 16.2 19.2 0.00 67% 12/12/2004 160 8.1 2.1 16.0 15.5 14.3 16.8 0.00 73% 12/13/2004 161 13.9 4.4 22.2 15.4 13.9 17.0 0.00 82% 12/14/2004 162 6.5 1.1 11.5 14.8 13.4 16.1 0.00 50% 12/15/2004 163 2.1 -3.1 10.2 12.4 11.4 13.4 0.00 61% 12/16/2004 164 7.8 -0.1 14.6 12.2 11.0 13.7 0.00 72% 12/17/2004 165 10.1 3.3 17.4 13.3 12.1 14.6 0.00 84% 12/18/2004 166 10.6 4.1 19.3 14.1 12.9 15.6 0.00 81% 12/19/2004 167 8.6 0.9 17.5 13.3 11.9 14.6 0.00 66% 12/20/2004 168 2.8 -2.4 9.6 12.1 11.1 13.1 0.00 50% 12/21/2004 169 7.0 -3.2 19.3 11.7 10.1 13.6 0.00 77% 12/22/2004 170 14.9 4.3 23.9 13.4 11.9 15.3 0.00 74% 12/23/2004 171 18.7 11.0 23.9 16.0 14.9 17.4 0.20 82% 12/24/2004 172 6.3 4.6 10.7 14.6 13.2 16.5 0.13 87% 12/25/2004 173 5.0 3.9 5.7 12.2 11.4 13.2 0.50 95% 12/26/2004 174 3.5 -1.6 7.9 11.0 10.2 11.6 0.23 89% 12/27/2004 175 5.1 -2.7 15.3 10.3 8.8 11.8 0.00 73% 12/28/2004 176 9.3 1.3 18.8 11.2 9.6 12.9 0.00 81% 12/29/2004 177 11.8 2.8 21.9 12.7 11.1 14.4 0.00 81% 12/30/2004 178 12.5 4.6 22.0 13.4 11.9 14.8 0.00 77% 12/31/2004 179 12.6 6.2 21.7 13.7 12.5 14.9 0.00 87% 1/1/2005 180 15.6 8.0 23.3 14. 5 13.1 16.1 0.00 82% 1/2/2005 181 16.7 10.2 24.8 15. 9 14.9 17.1 0.00 81%

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133 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 1/3/2005 182 15.2 9.0 25.0 15. 7 14.3 17.3 0.00 84% 1/4/2005 183 15.4 8.6 25.0 15. 9 14.6 17.4 0.00 83% 1/5/2005 184 16.3 9.8 25.6 16. 3 14.8 18.0 0.00 82% 1/6/2005 185 18.4 10.9 26.2 17. 1 15.9 18.7 0.00 78% 1/7/2005 186 17.9 13.2 24.3 17. 6 16.5 19.0 0.00 89% 1/8/2005 187 18.9 13.8 27.0 18. 1 16.9 19.5 0.01 84% 1/9/2005 188 18.7 12.6 26.7 18. 3 17.0 20.0 0.00 86% 1/10/2005 189 17.2 11.4 24.0 18.4 17.8 19.4 0.00 91% 1/11/2005 190 16.1 10.8 23.4 17.8 16.7 19.1 0.00 89% 1/12/2005 191 18.7 13.0 25.6 18.2 17.2 19.5 0.00 80% 1/13/2005 192 21.0 12.5 27.1 18.3 17.3 19.4 0.00 81% 1/14/2005 193 15.3 10.6 22.6 18.4 17.0 19.2 1.29 87% 1/15/2005 194 10.6 8.2 12.3 15.9 14.8 17.0 0.00 80% 1/16/2005 195 8.9 2.8 15.3 14.5 13.7 15.6 0.00 68% 1/17/2005 196 3.9 -2.0 10.6 12.8 11.8 13.8 0.00 63% 1/18/2005 197 3.3 -3.0 11.8 11.4 10.3 12.6 0.00 65% 1/19/2005 198 6.1 -2.2 15.1 11.6 9.9 13.5 0.00 63% 1/20/2005 199 10.9 6.3 17.8 12.9 11.6 14.8 0.00 70% 1/21/2005 200 12.6 3.3 22.9 13.2 11.4 15.2 0.00 81% 1/22/2005 201 16.7 14.4 21.2 15.0 14.2 16.0 0.21 90% 1/23/2005 202 5.5 -2.4 18.5 13.9 11.7 15.5 0.01 48% 1/24/2005 203 2.2 -6.4 12.8 11.0 9.4 12.5 0.00 50% 1/25/2005 204 8.3 -1.9 18.6 11.4 9.5 13.4 0.00 55% 1/26/2005 205 15.4 8.9 22.7 13.3 11.8 15.1 0.00 81% 1/27/2005 206 16.1 11.0 23.6 15.2 14.1 16.9 0.00 79% 1/28/2005 207 9.8 8.6 11.4 14.0 13.3 15.2 0.00 82% 1/29/2005 208 12.5 9.1 17.0 13.7 13.0 14.6 0.35 89% 1/30/2005 209 12.8 4.2 18.7 15.0 14.2 16.7 0.08 87% 1/31/2005 210 5.5 1.1 11.7 12.8 12.1 14.3 0.00 90% 2/1/2005 211 9.2 1.9 16.7 12. 4 11.2 13.9 0.00 82% 2/2/2005 212 10.1 8.4 14.8 12. 9 12.4 13.4 0.26 87% 2/3/2005 213 12.7 7.2 20.5 13. 9 12.8 15.4 0.08 92% 2/4/2005 214 8.1 1.2 14.8 13. 5 12.5 14.5 0.00 76% 2/5/2005 215 7.7 -1.2 17.9 12.5 10.7 14.3 0.00 72% 2/6/2005 216 12.1 3.2 21.1 13. 1 11.3 14.9 0.00 74% 2/7/2005 217 15.0 8.7 23.4 14. 5 13.4 16.1 0.00 73% 2/8/2005 218 14.3 5.3 25.2 15. 0 13.1 17.2 0.00 71% 2/9/2005 219 16.2 6.9 25.0 15. 8 13.9 17.8 0.00 75% 2/10/2005 220 15.0 6.0 19.0 16.6 14.9 17.9 0.21 60% 2/11/2005 221 6.8 -0.7 15.3 13.8 12.2 15.4 0.00 45% 2/12/2005 222 8.5 -2.0 20.1 13.2 11.1 15.5 0.00 62%

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134 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 2/13/2005 223 12.1 3.3 23.1 14.0 12.1 16.2 0.00 65% 2/14/2005 224 15.1 5.4 22.6 14.5 13.1 15.9 0.02 77% 2/15/2005 225 17.3 11.5 24.4 16.2 14.9 18.0 0.00 86% 2/16/2005 226 17.7 9.7 25.8 16.8 15.0 18.7 0.00 80% 2/17/2005 227 16.3 8.3 22.4 17.2 16.4 18.3 0.00 75% 2/18/2005 228 9.2 0.6 17.8 15.5 14.0 17.1 0.00 43% 2/19/2005 229 9.7 -1.7 21.6 14.5 12.6 16.5 0.00 61% 2/20/2005 230 13.5 4.0 24.1 15.4 13.5 17.4 0.00 70% 2/21/2005 231 17.8 10.9 23.9 16.1 14.9 17.3 0.00 77% 2/22/2005 232 20.9 18.1 26.6 18.1 16.8 20.0 0.00 82% 2/23/2005 233 21.0 18.0 26.9 19.3 18.1 20.9 0.06 78% 2/24/2005 234 19.5 16.3 23.6 19.2 18.5 19.9 0.02 83% 2/25/2005 235 13.3 10.0 16.2 17.4 16.1 18.7 0.27 92% 2/26/2005 236 12.6 9.3 17.6 16.0 15.2 16.9 0.00 84% 2/27/2005 237 15.7 13.0 21.1 16.2 15.8 16.8 1.18 93% 2/28/2005 238 15.4 10.4 21.9 17.0 15.7 18.6 0.00 76% 3/1/2005 239 11.8 4.8 18.8 15. 8 14.2 17.4 0.00 58% 3/2/2005 240 7.3 -2.1 15.6 14.8 12.8 16.8 0.00 44% 3/3/2005 241 6.1 1.2 9.0 13. 5 13.0 14.5 0.17 86% 3/4/2005 242 10.0 2.0 19.2 14. 2 12.2 16.8 0.00 71% 3/5/2005 243 11.8 1.8 22.0 14. 8 12.5 17.1 0.00 68% 3/6/2005 244 13.3 2.5 22.4 15. 5 13.3 17.9 0.00 54% 3/7/2005 245 15.8 4.4 25.1 16. 5 14.3 18.7 0.00 63% 3/8/2005 246 15.8 6.4 19.8 17. 4 16.2 18.9 0.79 59% 3/9/2005 247 7.8 3.1 9.8 14. 4 13.6 16.1 0.00 60% 3/10/2005 248 10.1 0.8 18.2 14.4 12.3 17.0 0.00 65% 3/11/2005 249 13.0 3.1 20.9 15.2 12.9 17.4 0.00 70% 3/12/2005 250 14.0 2.0 21.8 15.7 13.6 17.8 0.00 54% 3/13/2005 251 18.7 11.7 25.9 17.0 14.9 19.4 0.00 72% 3/14/2005 252 21.1 18.6 25.5 18.3 17.6 19.6 0.00 82% 3/15/2005 253 15.5 13.1 18.7 17.8 17.0 18.7 0.15 89% 3/16/2005 254 17.9 13.5 23.8 17.6 16.7 18.8 0.66 93% 3/17/2005 255 13.6 8.1 17.2 17.2 16.1 17.8 0.09 94% 3/18/2005 256 7.9 6.1 9.9 15.3 14.8 16.1 0.00 83% 3/19/2005 257 9.5 0.1 19.8 15.3 12.8 18.0 0.00 75% 3/20/2005 258 14.9 3.6 24.7 16.4 13.8 19.1 0.00 66% 3/21/2005 259 18.3 13.7 24.1 17.6 16.5 18.9 0.17 68% 3/22/2005 260 21.4 16.9 28.9 18.8 17.6 20.4 0.16 84% 3/23/2005 261 20.6 14.7 25.3 19.3 18.5 20.4 0.13 71% 3/24/2005 262 19.6 9.6 28.6 19.2 16.6 22.0 0.00 68% 3/25/2005 263 19.8 17.3 21.3 19.5 19.2 20.1 1.87 88% 3/26/2005 264 19.2 17.9 20.3 19.3 19.1 19.5 1.12 94%

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135 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 3/27/2005 265 22.7 17.6 28.9 19.9 18.5 21.4 0.00 82% 3/28/2005 266 18.3 14.2 23.2 20.5 19.3 21.7 0.08 67% 3/29/2005 267 18.4 11.7 27.0 19.8 17.5 22.4 0.00 60% 3/30/2005 268 17.8 7.8 27.9 20.2 17.6 22.8 0.00 64% 3/31/2005 269 20.2 12.5 28.6 21.0 19.0 22.9 0.17 77% 4/1/2005 270 21.4 15.3 29.3 21. 4 19.7 23.0 1.35 79% 4/2/2005 271 16.8 8.0 21.2 21. 2 20.0 23.2 0.27 61% 4/3/2005 272 13.5 3.9 22.3 19. 4 17.1 21.8 0.00 53% 4/4/2005 273 16.2 5.6 27.3 19. 5 17.0 21.9 0.00 62% 4/5/2005 274 18.7 7.3 27.9 19. 9 17.8 21.8 0.00 60% 4/6/2005 275 18.7 12.3 27.7 19. 9 18.7 22.4 0.00 76% 4/7/2005 276 19.4 16.9 23.3 19. 9 19.4 20.4 1.36 86% 4/8/2005 277 18.2 13.1 22.5 20. 0 18.5 21.5 0.00 80% 4/9/2005 278 18.8 11.8 26.6 20. 7 18.6 22.9 0.00 75% 4/10/2005 279 17.0 7.5 24.9 20.5 19.1 22.0 0.00 69% 4/11/2005 280 18.1 7.1 27.5 20.2 17.9 22.5 0.00 70% 4/12/2005 281 20.2 13.5 26.0 21.0 19.6 22.4 0.19 75% 4/13/2005 282 19.6 12.4 24.3 22.1 20.4 24.2 0.00 68% 4/14/2005 283 14.5 7.1 20.5 20.8 19.4 22.2 0.00 78% 4/15/2005 284 13.2 5.3 22.0 19.6 17.8 21.7 0.00 63% 4/16/2005 285 5.7 5.7 5.7 18.9 18.9 18.9 0.00 93% 4/17/2005 286 20.1 9.9 25.7 20.5 17.6 21.6 0.00 45% 4/18/2005 287 17.5 6.2 26.7 20.1 17.7 22.4 0.00 63% 4/19/2005 288 18.2 9.3 26.8 21.0 18.9 23.3 0.00 68% 4/20/2005 289 19.2 9.5 28.3 21.5 19.4 23.6 0.00 65% 4/21/2005 290 19.5 10.2 28.1 21.8 19.8 23.7 0.00 70% 4/23/2005 291 19.1 12.5 25.4 21.6 20.7 23.0 0.05 71% 4/24/2005 292 12.5 5.7 19.1 20.6 18.6 22.7 0.00 53% 4/25/2005 293 13.7 2.6 23.2 20.0 17.8 22.4 0.00 61% 4/26/2005 294 18.8 12.9 22.7 20.3 19.6 20.9 0.59 83% 4/27/2005 295 19.9 12.9 27.0 21.1 19.1 24.0 0.00 64% 4/28/2005 296 17.7 8.0 26.5 21.2 18.7 23.7 0.00 58% 4/29/2005 297 19.2 8.1 28.6 21.7 19.2 24.3 0.00 71% 4/30/2005 298 21.3 14.7 28.8 22.2 20.8 23.6 0.29 77% 5/1/2005 299 18.4 14.1 21.0 21. 4 20.8 22.0 0.03 88% 5/2/2005 300 19.5 12.7 27.4 22. 0 19.8 24.9 0.00 74% 5/3/2005 301 19.2 9.4 27.9 22. 4 19.9 25.0 0.00 64% 5/4/2005 302 17.3 14.0 18.8 21. 2 20.3 22.9 0.88 92% 5/5/2005 303 18.1 15.5 20.0 20. 3 20.0 20.7 1.17 93% 5/6/2005 304 16.6 11.7 22.4 20. 2 19.4 21.5 0.00 81% 5/7/2005 305 18.1 9.5 26.9 21. 1 18.4 24.0 0.00 72% 5/8/2005 306 19.5 10.9 27.7 22. 1 19.6 24.5 0.00 71% 5/9/2005 307 20.1 12.1 28.6 22. 8 20.3 25.7 0.00 71% 5/10/2005 308 21.4 12.3 29.6 23.4 20.9 25.8 0.00 68% 5/11/2005 309 21.0 16.1 28.8 23.7 22.2 25.2 0.01 81% 5/12/2005 310 22.2 13.5 30.4 23.8 21.5 26.1 0.00 70% 5/13/2005 311 21.6 13.1 29.1 24.1 22.3 25.8 0.00 71%

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136 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 5/14/2005 312 21.4 12.7 29.7 24.1 22.1 26.1 0.00 70% 5/15/2005 313 21.9 13.4 30.3 24.4 22.5 26.1 0.00 73% 5/16/2005 314 22.6 14.7 31.5 24.8 22.8 26.8 0.00 70% 5/17/2005 315 22.0 14.9 30.2 24.8 23.3 26.1 0.00 78% 5/18/2005 316 22.3 15.3 30.3 24.6 23.1 26.1 0.00 77% 5/19/2005 317 23.3 16.3 30.0 24.8 23.5 26.2 0.00 72% 5/20/2005 318 23.4 15.2 30.6 25.1 23.4 26.7 0.00 74% 5/21/2005 319 21.8 17.6 28.5 24.8 23.9 25.7 0.89 85% 5/22/2005 320 21.2 14.5 29.3 24.4 22.2 26.8 0.00 76% 5/23/2005 321 23.5 15.4 30.6 25.3 22.8 28.0 0.00 73% 5/24/2005 322 26.5 21.9 32.1 26.3 24.6 28.3 0.00 75% 5/25/2005 323 21.9 14.3 28.1 26.1 24.5 27.5 0.00 65% 5/26/2005 324 20.6 11.5 28.8 25.4 23.6 27.3 0.00 64% 5/27/2005 325 22.4 12.8 30.5 25.2 23.7 26.6 0.00 70% 5/28/2005 326 23.5 15.7 31.1 25.7 24.1 27.4 0.00 70% 5/29/2005 327 23.9 16.0 32.1 25.8 24.2 27.3 0.00 73% 5/30/2005 328 25.0 18.9 31.7 26.0 24.8 27.4 0.00 76% 5/31/2005 329 23.5 20.6 28.6 25.8 24.4 26.9 1.49 86% 6/1/2005 330 24.3 21.9 28.6 25. 6 24.7 26.7 0.01 81% 6/2/2005 331 24.4 21.4 29.2 25. 8 24.7 27.1 0.11 83% 6/3/2005 332 24.1 21.1 28.5 25. 7 24.7 26.7 1.59 84% 6/4/2005 333 24.2 21.5 28.2 25. 4 24.5 26.5 0.00 81% 6/5/2005 334 25.5 18.6 32.7 26. 3 24.3 28.5 0.00 76% 6/6/2005 335 26.0 19.9 32.8 27. 2 25.3 29.2 0.01 77% 6/7/2005 336 25.6 19.4 33.7 27. 6 25.6 29.8 0.00 79% 6/8/2005 337 25.3 20.5 33.8 28. 0 26.4 30.1 0.00 82% 6/9/2005 338 25.7 20.2 32.4 27. 8 26.2 29.6 0.02 79% 6/10/2005 339 25.1 23.8 28.0 26.8 26.2 27.6 0.08 84% 6/11/2005 340 25.7 23.9 29.3 26.3 25.6 27.3 0.69 85% 6/12/2005 341 25.6 23.6 30.3 26.8 25.9 27.8 0.02 88% 6/13/2005 342 25.2 22.0 33.0 27.1 25.8 28.7 0.18 87% 6/14/2005 343 25.9 21.4 33.5 27.5 25.9 29.7 0.81 84% 6/15/2005 344 27.6 24.2 33.5 28.3 26.8 30.2 0.00 81% 6/16/2005 345 26.5 22.0 33.9 28.7 27.1 31.0 0.10 83% 6/17/2005 346 27.2 22.1 32.8 28.8 26.9 31.0 0.00 71% 6/18/2005 347 24.1 20.5 30.8 27.9 27.1 29.4 0.43 83% 6/19/2005 348 25.3 18.9 31.0 27.7 25.8 29.8 0.00 77% 6/20/2005 349 24.1 18.3 30.7 27.7 26.0 29.6 0.00 74% 6/21/2005 350 24.1 18.1 31.3 27.6 25.8 29.6 0.00 78% 6/22/2005 351 25.0 19.1 32.6 28.0 26.3 30.0 0.00 77% 6/23/2005 352 25.1 17.8 32.8 28.0 26.2 30.0 0.00 71% 6/24/2005 353 23.8 17.7 30.2 27.4 26.3 28.6 0.06 77% 6/25/2005 354 24.4 21.6 28.7 27.4 26.3 28.6 0.00 84% 6/26/2005 355 26.4 22.7 33.4 28.1 26.7 30.0 0.00 80% 6/27/2005 356 25.5 22.0 32.9 27.9 27.1 29.4 2.00 85% 6/28/2005 357 25.0 22.5 30.5 27.2 26.5 28.1 0.13 88%

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137 Table C-31. Continued Air Temperature (C) Soil Temperature (C) Rain Humidity Date Day Avg. Min. Max. Avg. Min. Max. Inches Percent 6/29/2005 358 25.2 22.7 30.5 27.2 26.4 28.1 1.36 89% 6/30/2005 359 24.8 23.4 28.7 27.1 26.7 27.6 0.19 90% 7/1/2005 360 26.5 23.3 31.7 27. 7 26.4 29.3 0.13 83% 7/2/2005 361 26.2 23.1 31.8 28. 1 27.2 29.0 0.25 85% 7/3/2005 362 25.7 22.7 31.0 27. 9 27.0 28.6 1.21 87% 7/4/2005 363 27.6 22.5 33.1 28. 5 26.9 30.3 0.01 80% 7/5/2005 364 28.0 21.2 34.7 29. 2 27.5 31.0 0.00 74% 7/6/2005 365 27.7 22.0 34.0 29. 5 27.9 31.0 0.00 75% 7/7/2005 366 27.9 22.5 34.0 29. 5 28.0 31.2 0.00 75% 7/8/2005 367 27.6 22.0 35.2 29. 5 28.0 31.3 0.00 78% 7/9/2005 368 26.9 22.7 31.7 29. 0 28.5 29.7 0.57 82% 7/10/2005 369 25.1 22.5 28.5 27.2 26.8 28.4 1.58 86% 7/11/2005 370 26.0 23.5 32.1 27.6 26.8 28.7 1.10 86% 7/12/2005 371 26.5 21.5 33.4 28.1 26.6 29.7 0.00 81% 7/13/2005 372 26.6 21.9 34.8 28.7 27.3 30.5 0.02 81% 7/14/2005 373 26.6 21.7 33.4 28.8 27.4 30.1 0.00 81% 7/15/2005 374 25.9 22.5 32.7 28.8 27.8 30.0 0.00 82% 7/16/2005 375 27.9 21.6 34.8 28.8 27.3 30.4 0.00 73% 7/17/2005 376 27.4 21.7 34.7 29.0 27.6 30.5 0.05 79% 7/18/2005 377 27.4 21.7 34.2 29.2 27.8 30.9 0.00 76% 7/19/2005 378 27.4 22.1 34.0 29. 3 28.0 30.7 0.00 78%

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138 APPENDIX D SUPPLEMENTAL ACKNOWLEDGEMENTS Table D-1. Thanks. Many thanks to Those who have helped me along the way Brian Abrams Cecilia Kennedy Dr. Hartwell Allen Sylvia Lang-Josan Marty Anderson John Leader Sarah Anderson Chris Lewis Natlie Balcer Hui X Lu Charles Bohall Kathleen Mckee Dr. Patrick Bohlen Dr. Paul Mislevy Jeremy Bright Jeremy Paris Dr. Mark Brown Ryan Penton Charles Campbell Dan Perkins Yubao Cao Kevin Ratkus Cory Catts Dr. Ramesh Reddy Dr. Mark Clark Caroline Reis-Hamilton Dr. Matt Cohen Casey Schmidt Dr. Bree Darby Dr. Thomas Sinclair Dr. Edmond Dunne Gunner Smith Dr. Mitch Flinchum Erik Spalvins Adrienne Frisbee Ms Yu Wang Kevin Grace Ondine Wells Xiao Wei Gu Bill White Dr. Willie Harris Dr. Ann Wilkie Eric Jorczak Gavin Wilson Dr. Rob Kalmbacher This been a great experience both academica lly and personally. There have been so many people, from East Lansing to Gainesville that have directly or indirectly helped guide me along on this journey. I am grateful to have had the opportunity to cross so many paths along the way, and hope to someday cross them again. Peace.

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139 LIST OF REFERENCES Allen, L. H. Jr, E. H. Stewart, W. G. Jr. Knisel, & R. A. Slack. (1975). Seasonal Variation in Runoff and Water Quality from th e Taylor Creek Watershed, Okeechobee County, Florida. Paper presented at the Soil and Crop Science Society of Florida. Andersen, J. M. (1976). An Ignition Method for Determination of Total Phosphorus in Lake Sediments. Water Research, 10 329-331. Arthington, John, Patrick Bohl en, & Fritz Roka. (2003). Effect of Stocking Rate on Measures of Cow-Calf Productivity and Nutrient Loads in Surface Water Runoff (Document An141) Retrieved 5/16/2006, from http://edis.ifas.ufl.edu/AN141 Bayley, Suzanne E, Jr John Zoltek, Albert J Hermann, Thomas J Dolan, & Louis Tortora. (1985). Experimental Manipul ation of Nutrients and Water in a Freshwater Marsh: Effects on Biomass, Decomp osition, and Nutrient Accumulation. Limnology and Oceanography, 30 (3), 500-512. Boggess, C F, E G Flaig, & R C Fluck. ( 1995). Phosphorus Budget-Basin Relationship for Lake Okeechobee Tributary Basins. Ecological Engineering, 5 143-162. Bohlen, Patrick, Ken Campbell, John Cap ece, John Earman, J. Jeffrey Mullahey, Don Graetz, et al. (2004). Agro-Ecosystem Indi cators of Sustainability as Affected by Cattle Stocking Density in Ranch Management System. MacArthur Agro-ecology Research Center 2002-2003 Biennial Re port for the John D. and Catherine T. MacArthur Foundation Bottcher, A B, Terry K Tremwel, & Kenne th L Campbell. (1995). Best Management Practices for Water Quality Improvement in the Lake Okeechobee Watershed. Ecological Engineering, 5 341-356. Braskerud, B C. (2002). Factors Affecting P hosphorus Retention in Small Constructed Wetlands Treating Agricultural Non-Point Source Pollution. Ecological Engineering, 19 41-61. Chambliss, C. G., & M. B. Adjei. (2006). Bahiagrass (SS-AGR-36) Retrieved May, 2006, from http://edis.ifas.ufl.edu/AA184 Clary, Warren P. (1995). Vegetation and Soil Responses to Grazing Simulation on Riparian Meadows. Journal of Range Management, 48 18-25.

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140 David, Peter G. (1999). Response of Exotics to Restored Hydroperiod at Dupuis Reserve, Florida. Restoration Ecology, 7 (4), 407-410. Dolan, Thomas J, Suzanne E Bayley, Jr John Zoltek, & Albert J Hermann. (1981). Phosphorus Dynamics of a Florida Fr eshwater Marsh Receiving Treatment Wastewater. Journal of Applied Ecology, 18 205-219. Douglas, Marjory Stoneman. (1947). The Everglades; River of Grass Sarasota: Pineapple Press, Inc. FAC, Florida Administrative Code. (1996). Feedlot and Dair y Wastewater Treatment and Management Requirements. Chapter 62-670. FAC, Florida Administrative Code. (2000). Classification of Surface Waters, Usage, Reclassification, Classifi ed Waters. Chapter 62-302.400. FDEP, Florida Department of Environmenta l Protection. (2001). Total Maximum Daily Load for Total Phosphorus Lake Okeechobee, Florida. Flaig, Eric G, & K R Reddy. (1995). Fate of Phosphorus in the Lake Okeechobee Watershed, Florida, USA: Ov erview and Recommendations. Ecological Engineering, 5 127-142. Flaig, Eric G., & Karl Have ns. (1995). Historical Tre nds in the Lake Okeechobee Ecosystem. Arch. Hydrobiol./Suppl., 107 (1), 1-24. FLEPPC, Florida Exotic Pe st Plant Council. (2005). List of Florida's Invasive Species Retrieved March, 2006, from http://www.fleppc.org/list/05List.htm Fluck, R. C., C. Fonyo, & E. Flaig. (1992). Land-Use-Based Phosphorus Balances for Lake Okeechobee, Florida, Drainage Basins. American Society of Agricultural Engineers, 8 (6), 813-820. Gilliam, J W. (1995). Phosphor us Control Strategies. Ecological Engineering, 5 405414. Gunsalus, Boyd, Eric G Flaig, & Gary Ritter. (1992). Effectiveness of Agricultural Best Management Practices Implemented in the Taylor Creek/Nubbin Slough Watershed and the Lower Kissimmee River Basin. Paper presented at the National RCWP Symposium, SFWMD, West Palm Beach, FL. Gustafson, Shelley, & Deane Wang. (2002). Eff ects of Agriculture Runoff on Vegetation Composition of a Priority Conservation Wetland, Vermont, USA. Journal of Environmental Quality, 31 350-357.

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141 Havens, Karl E, Mark Brady, Erin Colbor n, Steffany Gornak, Susan Gray, R Thomas Janes, et al. (2005). Lake Okeechobee Prot ection Program State of the Lake and Watershed. In 2005 South Florida Environmental Report Havens, Karl E, Jr. Victor J. Bierman, Eric G. Flaig, Charles Hanlon, R. Thomas James, Bradley L. Jones, et al. (1995). Hist orical Trends in the Lake Okeechobee Ecosystem Vi. Synthesis. Arch. Hydrobiol./Suppl., 107 101-111. Hiscock, Jeffrey G, C Scott Thourot, & J oyce Zhang. (2003). Phosphorus Budget Land Use Relationships for the Northern Lake Okeechobee Watershed, Florida. Ecological Engineering, 21 63-74. Holderbaum, J F, L E Sollenberger, J E Moore, W E Kunkle, D B Bates, & A C Hammond. (1991). Protein Supplementati on of Steers Grazing Limpograss Pasture. Journal of Production Agriculture, 4 (3), 437-441. Holderbaum, J F, L E Sollenberger, K H Qu esenberry, J E Moore, & C S Jones, Jr. (1992). Canopy Structure and Nutritive Value of Limpograss Pastures During Mid-Summer to Early Autumn. Agronomy Journal, 84 11-16. Kadlec, R H. (1999). The Limits of Phosphorus Removal in Wetlands. Wetlands Ecology and Management, 7 165-175. Kadlec, Robert H., & Robert J. Knig ht. (1996). Chapter 14: Phosphorus. In Treatment Wetlands Boca Raton: Lewis Publishers. Kalmbacher, R S, K R Long, M K Johns on, & F G Martin. (1984). Botanical Composition of Diets of Cattle Grazing South Florida Rangeland. Journal of Range Management, 37 (4), 334-340. Kalmbacher, Rob, Jeff Mullahey, & Kevin H ill. (1998). Limpograss and Hymenachne Grown on Flatwoods Range Pond Margins. Journal of Range Management, 51 (282-287). Kidder, G., C. G. Chambliss, & R. Mylavarapu. (2000). UF/IFAS Standardized Fertilization Recommendations for Ag ronomic Crops (Fact Sheet Sl-129) Retrieved March, 2006, from http://edis.ifas.ufl.edu Kleinberg, Eliot. (2003). Black Cloud; the Great Florida Hurricane of 1928 New York: Carroll & Graf Publishers. Koerselman, Willem, & Arthur F. M. Me uleman. (1996). The Ve getation N:P Ratio: A New Tool to Detect the Natu re of Nutrient Limitation. Journal of Applied Ecology, 33 1441-1450.

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142 Kuo, S. (1996). Phosphorus Availability Indices. In J. M. Bigham (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods (pp. 893). Madison, Wisconsin: Soil Science Society of America, Inc. Lake Okeechobee Issue Team, & South Flor ida Ecosystem Restoration Working Group. (1999). Lake Okeechobee Action Plan. In Un ited States Environmental Protection & South Florida Water Management Distri ct (Ed.). West Palm Beach, Florida. Leopold, Aldo. (1966). A Sand County Almanac. New York: Ballantine. Long, K R, R S Kalmbacher, & F G Martin. (198 6). Diet Quality of Steers Grazing Three Range Sites in South Florida. Journal of Range Management, 39 (5), 389-392. Magee, Teresa K, & Mary E. Kentula. (2005). Response of Wetland Plant Species to Hydrologic Conditions. Wetlands Ecology and Management, 13 163-181. Marousky, F. J., & F. Blondon. (1995). Red and Far-Red Light Influence Carbon Partitioning, Growth and Flowering of Bahiagrass ( Paspalum Notatum ). Journal of Agricultural Science, 125 355-359. McKee, Kathleen. (2005). Predicting Soil Phosphorus Storag e in Historically Isolated Wetlands within the Lake Okeechobee Priority Basins. Unpublished Master's Thesis, University of Florida, Gainesville, FL. Mislevy, P. (2002). Forage Alternatives for Florida Cattle. Paper presented at the Annual Beef Cattle Short Course Proceedings, Gainesville, FL. Mitsch, William J., & James G. Gosselink. (2000). Wetlands (Third ed.). New York: John Wiley & Sons, Inc. Newman, Y C, L E Sollenberger, W E Kunkle, & D B Bates. (2002a). Crude Protein Fractionation and Degradation Para meters of Limpograss Herbage. Agronomy Journal, 94 1381-1386. Newman, Y C, L E Sollenberger, W E K unkle, & C G Chambliss. (2002b). Canopy Height and Nitrogen Supplementation Eff ects on Performance of Heifers Grazing Limpograss. Agronomy Journal, 94 1375-1380. Pate, Findlay. (1998). 'Floralta' Hemarthria, a Popular Grass for South Florida Retrieved September, 2004, from http://rcrec-ona.ifas.ufl.edu/PRFAR6.html Quesenberry, K H, W R Ocumpaugh, O C Ru elke, L S Dunavin, & P Mislevy. (1984). 'Floralta': A Limpgrass Selected for Yi eld and Persistence in Pastures (Circular S-312) Retrieved September, 2004, from http://rcrec-ona.ifas.ufl.edu/cirs312.html

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146 BIOGRAPHICAL SKETCH Jeffrey D. Smith grew up on a small cash-crop farm in Byron, Michigan, a small town near Flint, Michigan. He earned a B achelor of Science in horticulture/landscape design with an agribusiness specialization from Michigan State University in 2002. Before graduating, Jeff started and operated a successful landscape design/build firm in Lansing, Michigan, for about 3 years, before working with the Mich igan Department of AgricultureÂ’s Groundwater Monitoring Program---a desired transition into an environmental monitoring related field. In 2003, he moved to Florida to pursue a masterÂ’s degree in wetland ecology-a long time goal, which produced this thesis. He does not know what the future has in store pr ofessionally or academ ically, but feels the opportunities are endless.