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Vegetation Ecology of an Impounded Wetland

Permanent Link: http://ufdc.ufl.edu/UFE0022810/00001

Material Information

Title: Vegetation Ecology of an Impounded Wetland Information for Landscape-Level Restoration
Physical Description: 1 online resource (128 p.)
Language: english
Creator: Zweig, Christa
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The Florida Everglades, an area of global significance, is an example of an ecosystem whose original pattern and process have been irrevocably altered and is currently the focus of a landscape level restoration effort. Projects of this scope, if they are to achieve the intended restoration, require substantial information regarding ecological mechanisms that are poorly understood. We provide critical information for the restoration of the Everglades and a methodological approach that affords the opportunity to expand knowledge of wetland vegetation pattern beyond the scope of our study area. To address the issue of the non-linear success of wetlands, we develop a general, non-spatial S & T succession conceptual model, and apply the general framework by creating annotated succession/management models as hypotheses for use in impact analysis on a portion of an imperiled wetland. We consider the application of these theories, our S & T succession models, as a fraction of the framework for the Everglades and our understanding will only build with time. They are hypotheses for use in adaptive management as the restoration of the Everglades continues. These models represent the community response to hydrology and illustrate which hydrologic values (temporal or seasonal) are important to community structure. We also synthesize recent literature on ridge and slough landscape maintenance and suggest additional mechanisms. We also demonstrate how these maintenance mechanisms have been disturbed, their effects on the landscape pattern, and how this creates alternate feedback loops that affect persistence of the multiple stable states of ridge and slough. We use techniques from previous analyses to assess the quality of habitat for the endangered Florida Snail Kite by tracking the dynamics of foraging communities within its reduced breeding range in the Everglades by use of multivariate analyses, and identify environmental and demographic correlates of vegetation community composition for habitat restoration purposes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christa Zweig.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Kitchens, Wiley M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022810:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022810/00001

Material Information

Title: Vegetation Ecology of an Impounded Wetland Information for Landscape-Level Restoration
Physical Description: 1 online resource (128 p.)
Language: english
Creator: Zweig, Christa
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The Florida Everglades, an area of global significance, is an example of an ecosystem whose original pattern and process have been irrevocably altered and is currently the focus of a landscape level restoration effort. Projects of this scope, if they are to achieve the intended restoration, require substantial information regarding ecological mechanisms that are poorly understood. We provide critical information for the restoration of the Everglades and a methodological approach that affords the opportunity to expand knowledge of wetland vegetation pattern beyond the scope of our study area. To address the issue of the non-linear success of wetlands, we develop a general, non-spatial S & T succession conceptual model, and apply the general framework by creating annotated succession/management models as hypotheses for use in impact analysis on a portion of an imperiled wetland. We consider the application of these theories, our S & T succession models, as a fraction of the framework for the Everglades and our understanding will only build with time. They are hypotheses for use in adaptive management as the restoration of the Everglades continues. These models represent the community response to hydrology and illustrate which hydrologic values (temporal or seasonal) are important to community structure. We also synthesize recent literature on ridge and slough landscape maintenance and suggest additional mechanisms. We also demonstrate how these maintenance mechanisms have been disturbed, their effects on the landscape pattern, and how this creates alternate feedback loops that affect persistence of the multiple stable states of ridge and slough. We use techniques from previous analyses to assess the quality of habitat for the endangered Florida Snail Kite by tracking the dynamics of foraging communities within its reduced breeding range in the Everglades by use of multivariate analyses, and identify environmental and demographic correlates of vegetation community composition for habitat restoration purposes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christa Zweig.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Kitchens, Wiley M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022810:00001


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VEGETATION ECOLOGY OF AN IMPOUND ED WETLAND: INFORMATION FOR LANDSCALE-LEVEL RESTORATION By CHRISTA ZWEIG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008 1

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2008 Christa Zweig 2

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To My Family 3

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ACKNOWLEDGMENTS I would like to thank my husband, Zach We lch, for his support through this arduous process, even though he didnt like to talk shop at home he did it anyway. I would also like to thank Wiley Kitchens for putting up with me. The list of people w ho contributed to my dissertation, be it data co llection, reviewing papers, or just mo ral support, is long: Paul Wetzel, Paul Conrads, Erik Powers, Peter Frederick, R ob Fletcher, Mike Binford, Emilio Bruna, Becky Hylton-Keller, and an army of students and technici ans who collected and sorted all of the plants necessary for these analyses. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF TABLES ...........................................................................................................................8 LIST OF FIGURES .......................................................................................................................10 ABSTRACT ...................................................................................................................................12 CHAPTER 1 BRIEF OVERVIEW OF THE EVERGLADES.....................................................................14 Pre-drainage Vegetation .........................................................................................................14 Drainage and Compartmentalization ......................................................................................14 Hydrology ........................................................................................................................15 Vegetation ........................................................................................................................15 Restoration ..............................................................................................................................16 2 EFFECTS OF LANDSCAPE GRADIENTS ON WETLAND VEGETATION COMMUNITIES: INFORMATION FO R LARGE-SCALE RESTORATION...................18 Methods ..................................................................................................................................20 Data Collection ................................................................................................................21 Combined Data Analysis .................................................................................................22 A Priori Physiognomic Type Analysis ............................................................................23 Results .....................................................................................................................................23 Combined Data Analysis .................................................................................................23 A Priori Physiognomic Type Analysis ............................................................................25 Prairie Analysis.........................................................................................................25 Slough Analysis ........................................................................................................26 Sawgrass Analysis ....................................................................................................27 Discussion ...............................................................................................................................27 Hydrologic Correlations ..................................................................................................28 Vegetation Communities: Past, Present, and Future .......................................................29 3 MULTI-STATE SUCCESSION IN WETLA NDS: A NOVEL USE OF STATE AND TRANSITION MODELS.......................................................................................................43 Methods ..................................................................................................................................45 Study Area .......................................................................................................................45 General Framework .........................................................................................................46 5

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Everglades Model ............................................................................................................46 Delineating Communities .........................................................................................46 Classification and Re gression Tree (CART) ............................................................48 Vegetation Dynamics Developm ent Tool (VDDT) analysis ...................................49 Results .....................................................................................................................................50 Delineating Communities and Transition Probability .....................................................50 CART ..............................................................................................................................51 VDDT Analysis ...............................................................................................................51 Discussion ...............................................................................................................................52 4 SYNTHESIS OF PATTERN AND PROCESS FEEDBACK CYCLES AND THEIR EFFECT ON A STEPPED WETLAND LANDSCAPE........................................................65 Methods ..................................................................................................................................67 Study Site .........................................................................................................................67 Classification and FRAGSTATS Analysis .....................................................................68 Results .....................................................................................................................................69 Classification and FRAGSTATS Analysis .....................................................................69 Discussion ...............................................................................................................................70 Synthesis ..........................................................................................................................70 Pattern/Process Feedback Loops in the Everglades ........................................................73 5 HABITAT, HYDROLOGY, AND REPRO DUCTION RELATIONSHIPS FOR AN ENDANGERED SPECIESTHE FLORIDA SNAIL KITE...............................................85 Methods ..................................................................................................................................87 Multivariate Analysis ......................................................................................................88 Univariate Analysis .........................................................................................................89 Demographic analyses .....................................................................................................90 Results .....................................................................................................................................90 Multivariate analysis .......................................................................................................90 Univariate Analysis .........................................................................................................91 Demographic analysis .....................................................................................................92 Discussion ...............................................................................................................................92 6 DISCUSSION................................................................................................................... ....104 Wetland Restoration .............................................................................................................104 Habitat Restoration ...............................................................................................................106 Information for Large-Scale Restoration ..............................................................................106 APPENDIX DETAILED VEGETATION SAMPLING PROTOCOL AND SITE INFORMATION..................................................................................................................107 LIST OF REFERENCES .............................................................................................................119 BIOGRAPHICAL SKETCH .......................................................................................................128 6

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7

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LIST OF TABLES Table page 2-1 Hydrologic environmental variables used in NMS correlations for Water Conservation Area 3AS.. ...................................................................................................33 2-2 Percent Importance Value of 7 main species for landscape level communities in Water Conservation Area 3A South. ...............................................................................34 2-3 Community summary statistics for al l physiognomic types in Water Conservation Area 3A South. Water and peat depths are in cm. ............................................................35 2-4 Biomass and density characteristics of sawgrass sub-communities in Water Conservation Area 3AS. ....................................................................................................35 2-5 Summary of temporal and seasonal correla tions for the community compositions in 3 physiognomic groups within Water Conservation Area 3AS. ...........................................36 3-1 Transitions of community states in Water Conservati on Area 3A South, FL, from 2002-2005.. ........................................................................................................................56 3-2 Percent change in total area (ha) of comm unity states for four management scenarios in Water Conservation Area 3A South, FL, run with Vegetation Dynamics Development Tool software. ............................................................................................57 3-3 Percent Importance Value (average of re lative biomass and relative density) of species within community states within Water Conservation Area 3A South. ..............58 4-1 Producer and users accuracy for non-pa rametric, supervised classification of LANDSAT TM and ETM+ satellite im ages from 1988 and 2002 of Water Conservation Area 3A South, FL, USA. ............................................................................76 4-2 FRAGSTATS indices calculated for slough and sawgrass communities in Water Conservation Area 3A South, FL, USA. Data is from classified LANDSAT TM and ETM+ satellite images from 1988 and 2002. ....................................................................77 5-1 Number of nests and nest success fr om 2002 within Water Conservation Area 3A South, FL, USA. ...........................................................................................................96 5-2 Increasing maximum water levels and hydr ologic range in cm within the core breeding area (Plots 7, 8, and 9) of Water Area 3A South, FL, USA. 96 A-1 GPS coordinates for study plot corn ers in Water Conservation Area 3AS. Coordinates are UTM, NAD83. N = nor th, S = south, E = east, W = west. ...................109 8

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A-2 Characteristics of transects and GPS coordinates of transect start (STA), ecotone (BND), and end poles (END) in Water C onservation Area 3AS. Coordinates are UTM, NAD83. .................................................................................................................111 9

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LIST OF FIGURES Figure page 2-1 Hydrograph for Water Conserva tion Area 3A South from 1978. ..........................37 2-2 Satellite view of south Florida, USA. The white line indicates general boundaries of the Everglades and Water Conservation Area 3AS, the study site. The locations of study plots are inset. ...........................................................................................................38 2-3 Axis 1 and 3 of the 3-dimensional NMS solution for all physiognomic types and spatial distribution of vegetation types in Water Conservation Area 3A South. Some similar communities were combined for ease of interpretation in the spatial element. .....39 2-4 NMS graphs. ......................................................................................................................40 3-1 Satellite view of the Everglades in so uthern Florida, USA. The study site, Water Conservation Area 3A South, is outlined in white. ...........................................................59 3-2 General state and transition model for wetlands ................................................................60 3-3 Landscape level state and transition model for Water Conservation Area 3A South in the Everglades, Florida, USA. ...........................................................................................61 3-4 State and transition model 62 3-5 Ecological complexity graphs for three communities in Water Conservation Area 3A South.. ................................................................................................................................64 4-1 The study area, Water Conservation Area 3A South in the Everglades, FL, USA, is outlined in white. ...............................................................................................................78 4-2 LANDSAT TM and ETM+ satellite images from 1988 and 2002 of Water Conservation Area 3A South, FL USA. Bands shown are 4,3,2. ....................................79 4-3 Non-parametric, supervised classifi cation of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA. ............................................................................80 4-4 Non-parametric, supervised classifi cation of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA fr om the 2 meter elevation contour and higher. ................................................................................................................................81 4-5 Non-parametric, supervised classifi cation of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA fr om the 2 meter elevation contour and lower. .................................................................................................................................82 4-6 Conceptual model of ridge and slough ma intenance mechanisms in the Everglades, FL, USA. indicates this paper. Numb ers indicate cites labeled in references. .............83 10

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4-7 Example of fragmented sawgrass ridges in Water Conservation Area 3A South, FL, USA. ...................................................................................................................................84 5-1 Expanded study sites in Water C onservation Area 3A South, FL, USA. ..........................97 5-2 Movement of nesting concentration in Water Conservation Area 3A, FL, USA. White circle indicates cri tical breeding area and focus of study. Adapted from Bennetts et al 1998. ............................................................................................................98 5-3 Densities of key emergent slough species by community in Water Conservation Area 3A. PDG = P. geminatum, PAH = P. hemitomon, NYO = N. odorata, ELsp = E. cellulosa, and ELG = E. elongata. .....................................................................................99 5-4 Non-metric multidimensional scaling ordina tion of Snail Kite habitat communities in Water Conservation Area 3A. Vectors repr esent key environmental correlates with r2 0.15 (p < 0.009). ....................................................................................................100 5-5 Temporal movement of Snail Kite hab itat communities in Water Conservation Area 3A from 2002-2006.. ........................................................................................................101 5-6 Nest success and densities of Snail Kite foraging habitat species in Water Conservation Area 3A.. ....................................................................................................102 5-7 Snail kite habitat network in FL, USA. ............................................................................103 A-1 Belt transect diagram for vegetation sampling in Water Conservation Area 3AS ..........118 11

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Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy VEGETATION ECOLOGY OF AN IMPOUND ED WETLAND: INFORMATION FOR LANDSCALE-LEVEL RESTORATION By Christa L. Zweig December 2008 Chair: Wiley Kitchens Major: Wildlife Ecology and Conservation The Florida Everglades, an area of global sign ificance, is an example of an ecosystem whose original pattern and proce ss have been irrevocably altered and is currently the focus of a landscape level restoration effort. Projects of this scope, if they are to achieve the intended restoration, require substantial information re garding ecological mechanisms that are poorly understood. We provide critical information fo r the restoration of the Everglades and a methodological approach that affords the oppor tunity to expand knowledge of wetland vegetation pattern beyond the scope of our study area. To addr ess the issue of the non-linear success of wetlands, we develop a general, non -spatial S&T succession conceptual model, and apply the general framework by creating anno tated succession/management models as hypotheses for use in impact analysis on a portion of an imperiled wetland. We consider the application of these theori es, our S&T succession models, as a fraction of the framework for the Everglades and our unders tanding will only build w ith time. They are hypotheses for use in adaptive management as the restoration of the Ever glades continues. These models represent the community respons e to hydrology and illustrate which hydrologic valuestemporal or seasonalare im portant to community structure. We also synthesize recent literature on ridge and slough landscape maintena nce and suggest additi onal mechanisms. We 12

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13 also demonstrate how these maintenance mechanis ms have been disturbed, their effects on the landscape pattern, and how this creates alternate f eedback loops that affect persistence of the multiple stable states of ridge and slough. We us e techniques from previous analyses to assess the quality of habitat for the e ndangered Florida Snail Kite by tracking the dynamics of foraging communities within its reduced breeding range in the Everglades by use of multivariate analyses, and identify environmental and demographic co rrelates of vegetation community composition for habitat restoration purposes.

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CHAPTER 1 A BRIEF OVERVIEW OF THE EVERGLADES The Everglades is a relatively young ecosy stem (Gleason et al 1984), which formed approximately 5000 YBP. The shallow basin that ga ve rise to the Everglades was shaped by the fluctuations of sea level over 500,000 years, depositi ng reef ridges as late as the Pleistocene that created a pseudo-atoll (Petuch 1987 ). The combination of geologic and climactic factors led to the seasonal, flooding shallow peatland that are observed today. Pre-drainage Vegetation The Everglades watershed extends from the Kissimmee chain of lakes (KCOL) near Orlando, FL, to Florida Bay. Water flows from the KCOL through the Kissimmee River into Lake Okeechobee, a large, extremely shallow (4.0 to 6.1 m) sub-tropical lake. Lake Okeechobee would overflow its southern bank seasonally, prov iding sheetflow for the Everglades (Parker 1974). Fringing the south shore of Okeechobee was a swamp dominated by Annona glabra. This area transitioned into a large sawgrass plain that thinned into a sawgrass mosaic further south (Davis et al 1994). This region wa s flanked by two large areas of sawgrass/wet prairie/slough/tree islands in H illsborough Lake and Shark River Slough. Marl marsh formed the southern end of the Everglades, extending to mangrove communities in Florida Bay. Drainage and Compartmentalization Although limited draining of the Everglades oc curred as early as the late 1800s, it wasnt until the Central and Southern Florida Flood Control Project for Flood Control and Other Purposes was passed by Congress in 1948 that landscape-wide drainage and compartmentalization took place. The Everglades Agriculture Area (EAA) was established in the mid-1950s (Snyder and Davidson 1994) and it encompassed the A. glabra swamp and the entire sawgrass plain south of Lake Okeechobee (Dav is et al 1994). The Everglades peat formed 14

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by sawgrass was particularly suited for farm ing, unlike Loxahatchee peat whose primary component was Nymphaea odorata (Gleason and Stone 1994). The unsuitability of Loxahatchee peat for farming (high shrinkage when drained, highly volatile, and low mineral content) might have saved large parts of the Everglades from being incorporated into the EAA. The Water Conservation Areas (WCA) were established in the 1960s to provide water storage and flood control (Figur e 2-2). WCA1 was included in the U.S. Fish and Wildlife Refuge system in 1986 and is now known as A.R. M. Loxahatchee Wildlife Refuge. WCA 3 was bisected by Alligator Alley, later known as I-75, which created two very distinct hydrologies and vegetation community compositions. WCA 3A South is the primary source of water for Everglades National Park, which extends from Tamiami Trail to Florida Bay. Hydrology Seasonal flooding and sheetflow are two defi ning characteristics of Everglades hydrology, and have shaped the landscape ove r the last 5000 years. Sheetflow, the broad expanse of slowly flowing, shallow water (4 cm/s; Larsen et al 2007) has been interrupted by compartmentalization, and seasonal water fluctu ations are altered by water management and pooling effects of levees. Vegetation The most recent comprehensive descripti on of central Everglades vegetation on a landscape level was by Loveless in 1959, and is often cited to de scribe the present vegetation communities. Impoundment and water control over the intervening half century have created altered landscapes where hydrologic change has had important effects at the community level. Because each WCA is managed for different hydr ologic regimes, the disturbance varies by compartment. WCA3A North ha s been kept relatively dry and sawgrass has encroached on sloughs, degrading the distinctive ridge and sloug h pattern. WCA3A Sout h has been relatively 15

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wet, with deeper water depths and the sawgrass strands are be coming stressed and fragmenting into sloughs (Chapter 4). Over all and individual area of tree islands has decreased since the earliest photographic records of the Everglades in 1940 both from dry conditions increasing vulnerability to fire and wet conditions drowning out less tolerant tree species (Willard et al 2006). Most of the uplands and hardwood hammocks a ssociated with the Everglades have been converted to the megalopolis of West Palm Beach/Ft. Lauderdale/Miami (Kranzer 2004). Approximately 10% of the rockland pine forest s have been conserved, most in Everglades National Park (Gunderson 1994). Restoration Along with the Save our Evergl ades program from the Florid a legislature, Congress passed the Water Resources Development Act of 2000, appropriating money to restore timing, amount, and quality of water to the Everglades. The scope is ambitious and is one of the largest restoration projects in the world. The political and scientific pr ocess of Everglades restoration will serve as a model (either positive or negative) for future large-scale restoration efforts (Sklar et al 2005). Projects of th is scope require substantial information regarding ecological mechanisms that are often poorly understood. In this dissertation, I examine the vegetation ecology of WCA 3A South to prov ide critical information for th e restoration of a large wetland landscape, and present a process that afford s the opportunity to expand and contribute beyond the scope of the study area. Specifically, identifying the current communities and the specific hydrologic variables that affect them, and modeling the community /hydrologic rela tionships are initial steps to provide the cap ability of documenting and predic ting the effect of restoration hydrologic alternatives on the Everglades ecosy stem. I also examine the effect changing 16

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17 vegetation communities have as habitat to an endemic, endangered species and the consequences for its conservation

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CHAPTER 2 EFFECTS OF LANDSCAPE GRADIENTS ON WETLAND VEGETATION COMMUNITIES: INFORMATION FOR LARG E-SCALE RESTORATION1 The Florida Everglades, an area of global sign ificance, is an example of an ecosystem whose original pattern and proce ss have been irrevocably altered and is currently the focus of a landscape scale restoration effort. Projects of th is scope, if they are to achieve the intended restoration, require substantial information re garding ecological mechanisms that are poorly understood. Available information is often outdated, anecdotal, or insufficient to address issues at the multiple scales required. We provide critical information for the restoration of the Everglades and a methodological approach that affords the oppor tunity to expand knowledge of wetland vegetation pattern beyond the scope of our study area. The Everglades was once an area characterized by its large spatial extent (1.2 million ha), habitat heterogeneity, sheetflow, and seasonall y varying hydrology (Kitchens et al. 2002). Draining, compartmentalization, a nd agriculture have reduced the spatial extent of the Everglades by 50% (Light and Dineen 1994). Key drivers such as hydroperiod, fire frequency and intensity, water flow, seasonality, peat accretion, and nutrient inputs were altered, eliminating the wetlands origin al structure and function. The present hydrology of the area is highly managed and largely disconnected from th e natural wet and dry seasons. Natural wet season rainfall initiates in June and extends thro ugh September with the driest months in April and May (MacPherson and Halley 1996). However, urban water needs in south Florida require maxima to extend into November and December, and minima from May to July. Conflicting water demands necessitate separate water sche dules for the Water Conservation Areas (WCA) within the Everglades, causing some compartm ents to be overdrained while others are 1 Reprinted with permission from Wetlands 18

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consistently flooded. This diffe rential hydrology fragments the Ever glades into a collection of wholly different landscapes that change temporally as well as spatially. The goal of Everglades restoration is to return the area to a more natural state by reestablishing approximate historic water quan tity, quality, and timing, while still providing flood control and water storage for south Fl orida (Science Subgroup 1994). An important indicator of restoration success wi ll be the response of vegeta tion communities to the proposed hydrologic alternatives. However, this is a challenging concept because vegetation pattern is dynamic and the exact effects of hydrology a nd timing on Everglades vegetation community composition are poorly understood. On the short-term temporal scale (< 10 years), Everglades vegetation patterning is a f unction of hydrology (Davis 1943, Gunderson 1989, Armentano et al. 2006, Bazante et al. 2006) and disturbances such as fire, hurricanes, and nutrient input (Gunderson 1994). These drivers ha ve created a highly heteroge neous mosaic of vegetation types, where the importance fine-scale gradie nts in community composition can supersede the control of landscape level elevat ion and hydrologic gradients. Small changes in elevation, and thus hydrology, at the local level create abrupt changes in vegetation communities (David 1996). Researchers have documented the response of Everglades vegetation communities to disturbance for decadesfrom system-wide (Davis et al. 1994) and local levels (Gunderson 1994, Busch et al. 1998) to the response of vegetati on to specific disturbances (Craft et al. 1995, Davis 1994, Childers et al. 2003). The most r ecent comprehensive description of central Everglades vegetation on a landscape scale was by Loveless in 1959, and is often cited to describe the present vegetation communities Impoundment and water control over the intervening half century have created an altere d paradigm (Figure 2-1) where hydrologic change has had subtle, yet important, eff ects at the community state level. We believe the vegetation in 19

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this region has shifted from the communities described by Loveless to deeper water communities formed by the present wet hydrology, and that id entifying the current communitiesand specific hydrologic variables that affect th emis the initial step in docume nting the effect of restoration hydrologic alternatives on the Everglades ecosy stem. We characterize the existing vegetation communities of a central, impounded Everglades remnant, describe how both present and historic hydrology affect wetland vegetation co mmunity composition, and document the change from communities described in previous studi es, all to provide baseline knowledge for Everglades restoration science. Methods Our study area was a portion of the Everglades in the peninsular region of Florida, USA. Water Conservation Area 3A (WCA 3A) is the largest remnant of the original Everglades, approximately 200,000 ha (Figure 2-2). Our study ar ea, the southern half of 3A (3AS), is a matrix of tree islands, sawgrass strands ( Cladium jamaicense Crantz.), and sloughs, and is designated critical habitat for endangered species such as the Florida snail kite ( Rostrhamus sociabilis Vieillot) (Kitchens et al. 2 002). Several landscape gradients affect the ecology of 3AS, particularly the vegetation community states (herein referred to as communities). There is an east-west peat depth gradient with peat sh allowest on the west side and deepest on the east, and a north-south elevation gradient with slightly highe r elevations in the north, which used to maintain a natural hydrologic gradient. Due to impoundment, there is also an artificial northsouth water depth gradient, with deeper depths at the south fro m pooling, that is currently the main driving factor of plant community struct ure. Water Conservation Area 3AS is the main focus of Everglades restoration for the next 30 years. The Decompartmentalization and Sheetflow Enhancement Project (DECOMP) will e liminate much of the levee and canal system that now restricts sheetflow in these areas. Approximately 70% of the eastern levees and canals 20

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in 3AS will be removed, and the highway which forms the southern barrier will be raised to restore natural flow. This is an area that will see radical hydrologi c changes in the future and is a critical region for restoration monitoring. Data Collection Data for this analysis are taken from a ve getation monitoring proj ect in 3AS that was initiated in 2002. Twenty 1-km2 plots (Figure 2-2) were placed in a stratified random manner across the landscape gradients in 3AS. Plots were stratified by the landscape level gradients of peat depth and water depth. Five a priori physiognomic types were id entified: slough, sawgrass, tree/shrub island, catta il, and wet prairie. Two or three transects in each plot were placed perpendicular to ecotones, beginning in one a priori type and terminating in another, e.g., slough to sawgrass. We collected 0.25 m2 samples of all standing bi omass along a belt transect, clipping the vegetation at peat level at 3 m interv als, and included any submerged aquatic plants within the sample. Shrubs were sampled in the same manner as the herbaceous vegetation; there were no trees in transects. Samples were collect ed from every transect in every plot during the dry (May/June) and wet season (November/Decem ber) of each year. These were sorted by species, counted, dried to a c onstant weight, and weighed to the nearest 0.1 g. The 0.25 m2 samples represent pseudorepeated measures, as destructive samples were taken and we could not resample the exact location. Approximately 9,500 samples were collected and processed between 2002 and 2005. Our analysis focused on wet season data from the study period, as there were fewer issues of sampling error due to sma ll, new growth and matted prairie vegetation than in the dry season. Hydrologic data were provided by 17 wells installed in December 2002. On each sample date, water depths were measured with a meter stick at every quadrat a nd linked to water depth measurements at the nearest well (within a radi us < 1 km) by subtracting the quadrat water depth 21

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from the reading at the well for that day. Hi storic hydrologic data for all 17 wellsfrom 1991 to 2002were hindcast using an artificial neural network model (see C onrads et al. 2006). To account for high densities of some low bi omass species and high biomass of some low density species, data were re lativized using an index, importa nce value (IV), calculated by: IV for species i = ((Rdi + Rbi)/2)*100, where Rdi is the relative dens ity of species i and Rbi is the relative biomass of species i. Relative measures are the sum of biomass or dens ity of species i divided by the sum of biomass or density of all sp ecies within the 1 km2 plot. The importance values for all species in a plot sum to 100. Species that were in < 5% of the community samples were considered rare and not included in the analysis. Combined Data Analysis Our data were designed to be analyzed at several spatial levelsfrom the physiognomic community using each 0.25 m2 sample to the landscape level by grouping samples. For this analysis, we pooled al l data within a 1 km2 plot for each a priori physiognomic type for each year and referred to them as community sa mples (n = 234). Using PC-ORD (McCune and Mefford 1999), we performed a hierarchical, agglomerative cluster anal ysis on the community samples from every plot and year using a relativ e Sorenson distance measure with a flexible beta of -0.25. We chose the optimal number of cluste rs with an indicator species analysis (ISA) (Dufrne and Legendre 1997) and attempted to iden tify the associate species for each cluster. Communities were named according to the indicato r species from the ISA and their position on the peat and water depth gradient in a non-me tric multidimensional scaling ordination (NMS) (Kruskal 1964, Mather 1976). A Multi-Res ponse Permutation Procedure (MRPP) was performed with a Sorenson distance measure to determine the separa bility of the clusters. MRPP 22

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(Mielke and Berry 2001) is a non-parametric test that confirms or rejects the hypothesis of no differences between groups. We then performed a NMS to determine the e nvironmental factors that affect community composition in 3AS. The NMS was performed using a Sorensen distance measure, 40 runs with real data, and 50 Monte Carlo runs. Environmental variable s that represented the major landscape gradients and had the gr eatest influence on community structure were overlain on the NMS. They included peat depth and a suite of both recent and histor ic hydrologic variables (Table 2-1), as they both coul d affect establishment of plant species (Seabloom et al. 2001). Recent, for this analysis, is defined as hydrology affecting the area in the past year and historic is hydrology 2+ years pr evious to the sample event. A Priori Physiognomic Type Analysis Community sample data for 3 of the physiognomic types were analyzed separately (prairie (n = 47), slough (n = 72), and sawgrass (n = 80)) using the same procedures described above. These communities were the most abundant and also should exhibit rapid responses to hydrologic alteration due to their herbaceous growth structures. This afforded us the opportunity to further refine our community types and the anal ysis of the landscape gradients that affect them without the variation associated with data from combined physiognomic types. Stronger gradients for one physiognomic type might overwhelm the more subtle gradients for another, so we separated the data to more fully capture the variation within each physiognomic type. A MRPP analysis corroborated the separation of communities in our a priori groups. Results Combined Data Analysis For the combined data, there were 10 plant communities evident from the cluster and indicator species analysis (ISA) shallow peat wet prairie, sha llow peat prairie, slough, longer 23

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hydroperiod slough, wet prairie, sh rub island, cattail, sawgrass, st rand/slough transition, and deteriorated island (Table 2-2). Results from the MRPP analysis support the separation of these clusters (T = -87.65, A = 0.526, p < 0.0001). The T-st atistic describes the amount of separation among groups, with the more negative the T-stat istic, the more the separation. The A-statistic describes how similar the samples are within each group (0 = no agreement, 1 = perfect agreement), and our data exhibit a relatively hi gh within-group agreement. Values for A are often below 0.1 in community ecology (McCune and Grace 2002). Thus, we reject the null hypothesis of no differences among groups. Plant species richness of the cl usters was independent of bot h the hydrologic variables and peat depth, with a range of 14 species in a co mmunity cluster (Table 2-3). Average richness was 20 ( 2.6) species. Slough/ strand transition and the sawg rass communities exhibited the highest richness, and the longer hydroperiod slough the lowest. Spatially, 7 of the 10 commun ities were found across the entir e landscape (Figure 2-3). The shallow peat wet and shallow peat prairies were found only in the western portion of the study area, while the longer hydroperiod slough community occurred only in the south and western section of our study area. The most common communities in ou r sites were the slough and sawgrass strands, reflecting the dominance of these communities on the landscape. The NMS analysis yielded a 3-dimensional solution with a fina l stress of 10.26 and a Monte Carlo p-value of 0.0196. The three axes ex plained 93.4% of the variance in the data. Axis 1 explained a majority of the variation, with axes 2 and 3 havi ng similar values. The ordination was rotated 10 degrees for ease of interpreta tion (Figure 2-3). Axis 1 corresponded to hydrologic gradients and axis 3 to a peat depth gradient, togeth er explaining 73.6% of variation in the data. The variables fo r axis 1 with an r-squared > 0.25 were Mean1W (r = 0.603), Min1W 24

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(0.589), MeanPD (0.567), Min3W (0.551), Min4 W (0.537), Max1W (0.537), Mean4D (0.532), Min2W (0.529), Mean3D (0.523), and Mean1D (0.508) (see Table 2-1 to d ecipher codes). All but MaxPD, Max3W, Max4W, and Max5W fell above an r-squared 0.15 and were positively correlated to axis 1. Peat depth correlated to axis 3 with an r-squared of 0.391. No environmental factors from our analys is were correlated to axis 2. A Priori Physiognomic Type Analysis The MRPP rejected the hypothesis that community compositions of our a priori groups were identical, confirming their utility for further analyses (T = -76.65, A = 0.314, p < 0.0001). Within-group agreement was high, and between-group agreement was low. We can reject the null hypothesis of no differences among groups. Prairie analysis The cluster and indicator species analysis s uggested 5 prairie sub-t ypes in our study area: mixed transition prairie; wet prairie; Eleocharis cellulosa Torr. prairie; sparse sawgrass prairie; and Eleocharis elongata Chapman prairie. Spatially, the E. elongata prairie community was located across the whole landscap e, while the wet prairie community was found only in the central and west, the E. cellulosa and sparse sawgrass prairie onl y in the west, and the mixed transition prairie only in the southeast. The NMS suggested a 2 dimensional ordinati on, with a final stress of 9.57 and Monte Carlo p-value of 0.0196; 95.4% of the variance in the data was explained by the 2 axes. The ordination was rotated 140 degrees for ease of interpretation (F igure 2-4a). Axis 1, which correlated to the hydrologic variables, explained 85.3% of the variation. The vectors in Figure 24a represent environmental variables with r-squared 0.15. Mean2 (r = -0.412) was correlated to axis 1. Mean8 (r = 0.430), Min8 (0.401), and Max8 (0.420) were correlated to axis 2, which explains 10.1% of the variance in the data. In summary, the mean water depth of the previous 25

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wet season and the mean, minimum, and maximum of the wet season 4 year s previous correlated with an axis that explained a larg e portion of community composition. Slough analysis The cluster and indicator species analysis yielded 6 slough sub-type s in our study area: shallow slough invaded by sawgrass, lily slough, slough, mixed emergent slough, Eleocharis slough, and hurricane effects. The hurricane effect cluster only occurred at one time period, after hurricane Wilma, and the main difference in community composition was its lack of Utricularia spp. The high winds from Wilma deposited Utricularia into the strand areas, almost completely removing Utricularia from the sloughs (C. Zweig, pers. obs.). The shallow slough invaded by sawgrass only occurred in the northeast, wh ile the slough and lily slough occurred across the entire study area. The mixed emergent slough comm unity was confined to the western side, and the Eleocharis slough was found only in the southwest. The hurricane effects sub-type was established in all areas, but again only in 2005. The NMS for slough community sub-types genera ted a 2-dimensional solution with a final stress of 11.93, a Monte Carlo p-value of 0.196, a nd 94.4% of the variance being explained by the 2 axes. The ordination was rotated 50 degr ees for interpretation purposes (Figure 2-4b). Axis 1 correlated to both hydrologic and peat depth variables and explained 46.6% of the variation. Variables with an r-squared 0.25 include Mean4D (0.557), Peat Depth (-0.542), Min4D (0.535), Max4D (0.519), Min1W (r = 0.509) Min5W (0.503), and Mean5W (0.464). No other variable had an r-square d greater than 0.15 for axis 1. Axis 2 explained 47.8% of the variation, and was correlate d to Min2W (-0.507), Min3W (0.448), Mean1W (-0.450), Max1W (0.435), MeanPD (-0.429), and Min1W (-0.389). In su mmary, a very broad temporal hydrologic range affects community composition of a slough in this area. Peat depth had a large influence on species on a landscape scale. The placement of E. cellulosa in the water depth gradient seems 26

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counterintuitive, however, and it highlights the importanc e of peat depth for species presence and density. For example, E. cellulosa communities were shallow peat communities, but not necessarily shallow water communities as we prev iously suspected, and can occur in areas of deeper water. Sawgrass analysis The cluster and indicator species analysis s uggested 5 sawgrass sub-types: deteriorated sawgrass strand; shallow peat, short sawgrass stra nd; shallow peat, tall sawgrass strand; sawgrass with Peltandra ; and sawgrass with E. cellulosa and Justicia The labels short and tall were calculated from the average biomass (g) per stem within the community (Table 2-4). Spatially, the deteriorated strand was found only in the east while the shallow peat short strand was found only in the southwest. The othe r three sub-types were establishe d across the entire landscape. The NMS of a priori sawgrass community data yielded a 3-dimensional solution (Figure 24c), with a final stress of 11.55 and a Monte Carl o p-value of 0.0196. The ordination was rotated 40 degrees for ease of interpretation. The 3 axes explained 91.5% of the variation in the data. Peat depth was correlated to both axis 1 (r = 0.507) and 3 (0.733). These two axes explained 26.8% and 47.6% of the variati on, respectively. Axis 2 was co rrelated to Max4D and MeanPD and explained 17.2% of the variation in the data. The environmental variables with an r-squared > 0.15 were MeanPD (r = 0.396) and Max4D (0.416) In summary, peat depth has a strong correlation with sawgrass community composition, as do water depths in the recent and historic (up to 4 years previous) dry seasons. Discussion An objective of this study was to characterize the vegetation communities in WCA 3AS as baseline data for Everglades restoration m onitoring. We believe the communities described previously in studies of 3AS are no longer repr esentative due to the change in overall hydrology 27

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(Figure 2-1). A trend toward Nymphaea-dominated, deep sloughs due to impoundment in the southern end of 3AS was documented by Wood and Tanner as early as 1990. In approximately 1991, the hydrology of 3AS shifted to the deeper water and extended hydroperiods of the new, wet hydrologic era, and now vegetation commun ities north of the impoundment effects have changed accordingly. Hydrologic Correlations The hydrologic correlations of each physiognomic group are quite different in regards to season (Table 2-5). The dominant species in each physiognomic type are most sensitive to hydrology during their preferred gr owing conditions (Edwards et al. 2003, Childers et al. 2006). Species can tolerate harsher c onditions in their dormant season, but are more vulnerable to abnormal highs and lows within their growing season. Eleocharis cellulosas growth improves in moderately flooded, but not high, water conditi ons (Macek et al. 2006). A wet season with too much or too little water would have an impact on Eleocharis communities, but hydrologic alterations in the dry, dormant season would not. Temporally, the sub-types within the separa te physiognomic types (slough, sawgrass, wet prairie) had correlations that a ll occurred within 1 to 4 years previous to the sample, which indicates a relatively short time lag between hydrologic alteration a nd vegetation change. Armentano et al. (2006) suggest that Everglades vegetation co mmunity response to hydrologic change is normally no more than 4 years, a nd our results agree that, for these physiognomic types, the communities respond within 4 years. We are not proposing that our environmen tal variables are the only influences on community composition, but they are representa tive of the complex hydrology that affects vegetation in 3AS and provide a basis for experimentation and management. These environmental correlates do not capture all of the variability in the data, and there are probably 28

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additional hydrologic characteristics that control the compos ition of communities, including changes in hydrologic era (dry vs. wet periods) and other long-term hydrologic variables such as duration. Large scale changes due to restoratio n might alter the determinants of community composition, and thus monitoring should be continuous in order to understand the mechanisms of vegetation change. Vegetation Communities: Past, Present, and Future The Everglades communities we encountered were dynamic and will continue to respond to recent hydrologic alterations Loveless (1959) described comm unity states of Everglades vegetation that existed in a drier hydrologic era, but his observations are still frequently cited as a benchmark for vegetation restoration in the Ev erglades. While all of the common species identified by Loveless are still prevalent toda y, they have rearranged into communities that reflect the present we tter hydrologic era. Rhynchospora flats no longer exist in our study area, nor do extensive Panicum hemitomon Schult. flats (although remnants of the P. hemitomon flats were observed outside of our sample locations). The concept of an Everglades wet prairi e in 3AS now needs to include additional definitions. In 1959, there were 3 prairie sub-types dominated by Rhynchospora spp., Panicum spp., and E. cellulosa In 1990, Wood and Tanner questioned th e classification of their sites as wet prairies because they did not contain Rhynchospora spp. We also identified 3 prairie subtypes in our landscape analysis but they were dominated by E. elongata Paspalidium geminatum (Forssk.) Stapf, and E. cellulosa These do not conform to the original definition of prairie, nor would they be c onsidered sloughs as defined prev iously (Loveless 1959, Gunderson 1994, Busch et al 1998). Panicum geminatum and E. elongata were located deeper on the hydrologic gradient than P. hemitomon in our ordination, so we infer that the community subtypes from our analysis are deeper forms of prairie than those in Loveless (1959). The 29

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community sub-types delineated in the separate physiognomic analysis did not have a dominant Panicum or Rhynchospora element. Rhynchospora was rarely encountered, even in dry season samples. Eleocharis has long been considered a slough sp ecies in the Everglades (Davis 1943, Loveless 1959, Gunderson 1989, Wood and Tanner 1990) but more recently as conditions became wetter (1991-present), it has become accep ted as a wet prairie species (Gunderson 1994, Daoust and Childers 1999, Armentano et al. 2006). This suggests that the perception of the Everglades wet prairiea shor t-stature graminoid community interspersed among sawgrass has changed considerably, and the extent of vegetation community transformations within 3AS is more significant than pr eviously recognized. The only deep water slough described from 3AS prior to our study was a Nymphaea odorata Ait./Utricularia spp. slough (Loveless 1959, Gunders on 1994). Our combined data analysis suggested two types of slough: Utricularia spp. slough, and a N. odorata slough with a longer hydroperiod. The separate physiognomic an alysis indicated 6 sub-types of slough with varying amounts and species of emergents. Species of Eleocharis were abundant in these sloughs, underscoring their role as both slough and prairie vegetation. The three sawgrass sub-communities that Loveless observed ( C. jamaicense/Sagittaria lancifolia L. /P. hemitomon Myrica cerifera L. /Ilex cassine L., and C. jamaicense/P. hemitomon ) are not as evident in 3AS in the present water era, and M. cerifera and I. cassine were completely absent from sawgrass sub-communities in our study sites. Cephalanthus occidentalis L. and Salix caroliniana Michx. were observed within the dete riorated sawgrass strand sub-type, the only sawgrass community that contained woody species. The 5 sawgrass sub-communities indicated by the separate physi ognomic analyses conform to previous designations of tall and short sawgrass communities, but no t necessarily as a function of peat depth, which was thought 30

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to be the cause of difference in sawgrass heights (Gunderson 1994). Even though sawgrass is still a dominant plant after decades of impounded, stressful conditions, the sawgrass subcommunities of Loveless time no longer exist in 3AS. Vegetation community response depends on th e nature and magnitude of the hydrologic alteration, but ecology and life histor y traits make some species bett er indicators of either shortterm or long-term shifts. Nymphaea odorata and sawgrass are probably slower to respond to hydrologic fluctuations due to their growth st ructures. Sawgrass is sympodial (Snyder and Richards 2005) and can form tussocks in deeper water, climbing dead roots and culms to reach drier, more hospitable conditions. Sawgrass can ma intain its canopy for some time, even while it fragments at the substrate level. Once gone it le aves areas of open water with little other vegetation due to past canopy shading (C. Zweig, pe rs obs). Long-term fl ooding will continue to degrade sawgrass strands, but will benefit N. odorata. David (1996) states that N. odorata is sensitive to dry downs and need s near optimum conditions to pe rsist, making it an excellent indicator for sloughs. However, N. odorata is also a rhizomatous perennial that forms dormant root stalks and can survive extended droughts (Zar emba and Lamont 1993), so it is an indicator of both short-term and long-term slough conversion. Eleocharis spp. have less physical structure and respond quickly to hydrologic ch ange, although they have specific responses to alterations in water depth. Eleocharis cellulosa grown in shallow water conditions (~10 cm) responds to rising water by elongating, but when grown in de eper water (~50 cm), its response to a rapid drying event is a collapse of the long, thin shoo ts (Macek et al. 2006), senescence, and complete regrowth (Edwards et al. 2003). It can completely recover within 9 weeks of hydrologic alteration, but recovery by plants in deep water from a precipitous drawdown is slower than that of plants in shallow water (Edwar ds et al. 2003). Considering sp ecies life history characteristics, 31

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32 wet prairie/slough species such as Eleocharis spp. and N. odorata are short-term sentinel species of community change, while sawgrass and N. odorata should be monitored for long-term change. We conclude that the wetland vegetation of 3A S is influenced by both recent and historic hydrology (up to 4 years earlier), and communities of the mid-1900s no longer exist in our study area. Through a combination of time, anth ropogenic activities, and past/current water management actions, the vegetation has changed to communities suited to deeper flooding, with some being eliminated completely. The vegetation communities and correlating hydrologic gradients described in this paper should be considered in future management decisions for 3AS.

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Table 2-1: Hydrologic envi ronmental variables used in NMS correlations for Water Conservation Area 3AS. Hydrologic Characteristics Dry Season Wet Season MaxPD/MinPD/MeanPD Previous Max1W/Min1W /Mean1W One year previous Max1D/Min1D/Mean1D One year previous Max2W/Min2W/Mean2W Two years previous Max2D/Min2D/Mean2D Two years previous Max3W/Min3W/Mean3W Three years previous Max3D/Min3D/Mean3D Three years previous Max4W/Min4W/Mean4W Four years previous Max4D/Min4D/Mean4D Four years previous Max5W/Min5W/Mean5W Five years previous The number refers to the season and timing previous to the sample for which the characteristics were calculated. Max = maximum water depth, Min = minimum water depth, Mean = mean water depth. 33

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Table 2-2: Percent Importance Value of 7 main species for landscape level communities in Water Conservation Area 3A South. Community CEO CLA ELG ELC NYO PNC UTsp Deteriorated Island 26.7% 16.7% 0.4% 0.0% 1.5% 32.9% 2.3% Shrub Island 53.9% 3.0% 4.9% 0.1% 3.3% 53.0% 2.9% Sawgrass 4.2% 23.9% 15.6% 1.5% 1.5% 3.4% 1.5% Cattail 1.0% 20.0% 0.1% 17.6% 0.9% 3.4% 1.7% Wet Prairie 2.3% 2.8% 31.0% 2.7% 20.2% 2.6% 7.0% Strand/Slough Transition 11.7% 18.8% 7.6% 2.8% 2.5% 3.8% 3.9% Shallow Peat Wet Prairie 0.0% 1.4% 6.2% 24.0% 7.0% 0.1% 22.9% Shallow Peat Prairie 0.0% 10.9% 1.0% 40.6% 2.2% 0.0% 3.4% Slough 0.3% 0.8% 27.4% 2.1% 18.2% 0.1% 30.2% Longer Hydroperiod Slough 0.0% 1.7% 5.9% 8.6% 42.7% 0.7% 24.2% CEO = Cephalanthus occidentalis CLA = Cladium jamaicense ELG = Eleocharis elongata ELC = Eleocharis cellulosa NYO = Nymphaea odorata PNC = Pontideria cordata UTsp = Utricularia sp. For some communities, indicator species were not among the main species. 34

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Table 2-3: Community summary st atistics for all physiognomic types in Water Cons ervation Area 3A South. Water and peat depths are in cm. Previous wet season Previous dry season Community Type Mean Max Min Mean Max Min Species Richness Peat Depth Deteriorated Island 36.0 58.2 11.6 10.4 43.0 7.6 21 99.2 Shrub Island 46.0 70.4 19.8 13.4 52.7 6.7 19 94.7 Sawgrass 51.8 75.0 26.5 22.6 60.4 18.3 23 108.4 Cattail 57.0 78.6 32.3 27.4 64.9 25.3 21 55.3 Wet Prairie 61.6 89.9 32.3 34.4 84.4 24.4 21 90.2 Strand/Slough Transition 62.8 81.7 39.3 36.3 68.3 -3.7 23 101.0 Shallow Peat Wet Prairie 67.7 84.1 46.6 43.9 69.2 25.3 18 55.0 Shallow Peat Prairie 70.1 91.1 46.6 41.5 78.6 23.8 19 38.0 Slough 74.1 93.0 51.5 44.8 73.8 31.1 20 95.4 Longer Hydroperiod Slough 76.5 99.7 52.1 46.6 82.6 -2.7 14 83.8 35Table 2-4: Biomass and density char acteristics of sawgrass sub-communities in Water Conservation Area 3AS. Community Biomass(g)/quadrat St ems/quadrat Biomass(g)/stem Deteriorated Sawgrass Strand 53.4 23.51 3.1 0.52 17.1 5.98 Shallow Peat, Short Sawgrass Strand 69.1 20.34 4.1 0.80 16.5 7.55 Shallow Peat, Tall Sawgrass Strand 78.9 23.57 3.7 1.11 22.5 3.32 Sawgrass with Peltandra 85.4 22.23 4.0 0.88 22.3 7.55 Sawgrass with Justicia and Eleocharis 87.7 26.34 4.3 1.3 20.7 3.32

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Table 2-5: Summary of temporal and s easonal correlations for the community compos itions in 3 physiognomic groups within Water Conservation Area 3AS. Slough Sawgrass Prairie A. Community Characteristics Dominant Species Nymphaea odorata Cladium jamaicense Eleocharis spp Conditions for Optimum Growth Flooded (Wiersema 1988) Requires dry season (Herndon et al 1991) Moderately flooded (Macek et al 2006) Response to Sub-optimal conditions Rhizomatous tuber (Zaremba and Lamont 1993) Vertical sympodial growth (Snyder and Richards 2005) Elongation of stem (Edwards et al 2003) Consequences of Sub-optimal conditions Suspend reproduction, tuber formation (Zaremba and Lamont 1993) Fragmentation, reduced reproduction (Wu et al 1997, Snyder and Richards 2005) Reduced biomass, suspend reproduction (Macek et al 2006) B. General Hydrologic Factors 36Previous Dry Season Mean Mean Wet 1 Year Previous Max, Min, Mean Mean Dry 1 Year Previous Wet 2 Years Previous Min Dry 2 Years Previous Wet 3 Years Previous Min Dry 3 Years Previous Wet 4 Years Previous Max, Min, Mean Dry 4 Years Previous Max, Min, Mean Max Wet 5 Years Previous Mean

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Figure 2-1: A hydrograph for Water Conservation Area 3A South from 1978. The solid horizontal line indicates gene ral ground elevation for the ar ea and the dashed vertical lines represent the average stage in cm for th e wet ( ) and dry (-) eras. The vertical dotted line indi cates a transition in water eras in approximately 1991. 37

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Figure 2-2: A satellite view of south Florida, USA. The white line indicates general boundaries of the Everglades and Water Conservation Area 3AS, the study site. The locations of study plots are inset. 38

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Figure 2-3: Axis 1 and 3 of the 3-dimensi onal NMS solution for all physiognomic types and spatial distribution of vegetation types in Water Conservation Area 3A South. Some similar communities were combined for ease of interpretation in the spatial element. 39

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A Figure 2-4: NMS graphs for th e a) prairie b) slough and c) sawgrass a priori community in Water Conservation Area 3A South. 40

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B Figure 2-4. continued 41

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42 C Figure 2-4. continued

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CHAPTER 3 MULTI-STATE SUCCESSION IN WETLANDS: A NOVEL USE OF STATE AND TRANSITION MODELS It is well known that the concept and app lication of succession theory is extremely complex (Platt and Connell 2003). Yet the complexity of ecosystems and mechanisms of succession are often simplified into linear math ematical models (Ryan et al 2007) used to understand and predict system behavior. These li near models can not incorporate multivariate, non-linear feedbacks in pattern and process that include multiple sc ales of organization inherent within real-world systems (Proulx 2007). It is th is complexity that creates the possibility of restoration actions producing une xpected results due to relia nce on traditional succession patterns that are no longer valid in a degraded system (Suding et al 2004). As wetlands are a major ecosystem type currently impacted and bei ng restored by humans, my goal is to provide a non-linear, easily interpretable, community-based wetland vegetation change/succession model for use in restoration monitoring and management. Wetlands have a unique pattern of succession (any vegetation change over time (Peet 1992)) due to the regular, but often inconstant, presence of water on the landscape. The vegetation exhibits both terrestr ial and aquatic vegetative characteristics and is frequently considered transitional between the two (Whited et al 2007), increasing the complexity of the system. Accordingly, wetland succession has multip le trajectories and endpoints, created by hydrology, competition, edaphic factors, and other external and internal controls. Typical succession is initiated by a disturbance, partial or total (Platt and Connell 2003), in which the communities are reset. Succession progresses in a relatively directional manner (Tilman 1990, Sousa and Connell 1992) to one of many endpoi nts (Law and Morton 1996). Wetland reset points have two possible trajectories that ar e opposite each othermore aquatic or more terrestrial communities. The position of the wetlands reset point, in the middle of a bi43

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directional succession, is unique a nd is a key factor in the diversity of wetlands such as the Pantanal (Alho 2005, Junk et al 2006) and the Ok avango Delta (Ellery et al 2003). The initial direction of succession, and whether the ecosyst em response is continuous or discontinuous, depends on the intensity of the reset, current conditions, and vegetative and hydrologic legacies of the site. The intensity of reset determines wh ich species are present to recolonize the affected area. The variable position of the reset point and its mu ltiple trajectories create the possibility of multiple stable states (Beisner et al 2003) within dynamic regimes (Mayer and Rietkerk 2004) in vegetation community succession. Tr ansitions between stable states are typically characterized by dramatic changes, e.g. from oligotrophic to eutrophic lakes (Scheffer and Carpenter 2003), but fine-scale changes within communities are al so functionally important (Arscott et al 2002), especially in areas with subtle environmental gr adients (Givnish et al 2008). Here my model accounts for non-linear succession at multiple scal es, including fine-scale changes from transitions within communities, defined here as state shifts, and changes between communities defined as community shifts. State and transition (S&T) models were devel oped as conceptual models to address the need for flexibility (e.g. open-ended, multidir ectional, and adaptive) and non-linearity in succession models for management (Westoby et al 1989). They have been widely applied in rangeland, arid, and semi-arid grasslands (Alle n-Diaz and Bartolome 1998, Bestelmeyer et al 2006, Qutier et al 2007), but have had limited use in other ecosystems. They provide a simple, flexible framework for both scientists and mana gers and apply dynamic vegetation change theory to management models. S&T models may capture the complexity of wetland succession that is unattainable with other m odels and these approaches offer an excellent opportunity to build an 44

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adaptive framework for restoration/management use. This adaptability is especially useful in a time of accelerated human impacts, globa l climate change, and sea level rise. An excellent system to test the S&T m odels ability to capture complex, non-linear interactions for management use is one of the largest restoration pr ojects in the world, the Florida Everglades. The Everglades is a seasonally floo ded wetland in sub-tropical south Florida, USA, which is subject to extremely subtle environmenta l gradients (north-south el evation gradient of 2 cm/km and 1.15 cm/second flow rate (Larsen et al 2007)). Spatial and temporal variance in natural and altered hydrologic re gimes maintain a highly heter ogeneous landscape. Vegetation dynamics of the system have been modified al ong with its hydrology a nd represent a disturbed regime whose successional pathways are unknown. I develop a general, non-spatial S&T succession conceptual model for wetlands, and apply the general framework by creating annotated succession/management models as hypotheses for use in impact analysis on a portion of an imperiled wetland. Methods Study Area The study area is Water Conservation 3A South (WCA3), one of the larg est intact areas of the Everglades ridge and slough landscape in so uthern Florida (Figur e 1). It comprises approximately 200,000 ha and the vegetation communities are subject to several key environmental gradientsan east-west peat dept h gradient, north-south elevation gradient, and an artificial north-south wate r depth gradient due to impoundment. Hydrologic regimes in WCA3 were altered for restoration purposes beginning in 2002, an action that increased hydroperiods and water depths. Climate cycles and water control have resulted in higher maximum water depths and an increased hydrolog ic range from wet to dry seasons (C Zweig unpublished data ). This is disturbance to an area alre ady under stress from decades of sustained 45

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ponding. I monitor WCA3 to track changes in vegetation communities during this altered regime. General Framework The general wetlands S&T succession framework loosely follows definitions in Stringham et al (2003). I constructed my framework with multiple community states within a community and, at this four year time scal e, the transitions between states tend to be dominated by hydrology (Figure 2). Each community ha s a finite number of states po ssible, but the number varies between communities. Transitions between states are considered reversible and have less distinct thresholds, defined as moving thresholds, but community shifts may require relatively more extreme disturbances to transition. State fo rcing functions influence state shifts and outside forcing functions influence community shifts. State and outside forcing functions share most factors: hydrologic timing, eda phic factors, autogenic effects, topography, intensity of rest, disturbance, intensity of distur bance, exotic invasion, flow, hy droperiod, nutrient cycles, seed bank, and vegetative and hydrologic legacy. Forc ing functions that are considered state-only include competition and microtopography. This framework can accommodate multiple communities and states as the landscape responds to autogenic or allogenic change. I applied this general framework to the study area (Figure 3), restricted to a temporal scale of < 50 years. In my study systems, the main forcing functions consist of disturbanc e and long-term hydrologic variation. Everglades Model Delineating communities Community state analyses were initially conducted for a previous study (Zweig and Kitchens in press ) but are provided here in less detail, as they are input for the S&T models. Data for the Everglades analysis are taken from a vegetation monitoring project in WCA3 from 46

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2002-2005. Five a priori physiognomic types were identified: slough, sawgrass, tree/shrub island, cattail, and wet prairie. Two to three transects were placed in each of 20 study plots perpendicular to ecotones, beginning in one a priori type and terminating in another (e.g. slough to sawgrass). I collected 0.25 m2 quadrat samples of all above-ground standing biomass at three meter intervals along a belt tran sect and included any submerged aquatic plants within the sample. Samples were collected on every transect in each plot in the dry (May/June) and wet season (November/December) of each year. These were sorted by species, counted, dried to a constant weight, and weighed to the nearest 0.1 g. Approximately 9,500 samples were collected and processed between 2002 and 2005. Seventeen water wells were installed in December 2002 and historic hydrologic dataf rom 1991 to 2002were hindcast us ing an artificial neural network model (Conrads et al 2006). To account for high densities of low biomass species and high biomass of low density species, the data were relativized in an inde x called importance valu e (IV), calculated by: IV for species i = ((Rdi + Rbi)/2)*100, where Rdi is the relative dens ity of species i and Rbi is the relative biomass of species i. Relative measures are the sum of biomass or dens ity of species i divided by the sum of biomass or density of all sp ecies within the 1 km2 plot. The importance values for all species in a plot sum to 100. Species that were in less than 5% of the community samples were considered rare and not included in the analysis. The IV data for each plot were analyzed using PC-ORD (McCune and Mefford 1999), a multivariate statistics software, as I was intere sted in changes of comm unity structure and not focused on one species at a time. My data were designed to be analyz ed at several spatial levelsfrom the community state using each 0.25 m2 sample to the landscape level by grouping 47

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samples. For this analysis, I pooled all data within a 1 km2 plot for each a priori physiognomic type for each year and referred to th em as community samples (n = 234). I performed a hierarchical, agglomerative clus ter analysis on the community samples from every plot and year for three of the a priori vegetation types (wet prai rie (n=47), slough (n=72), and sawgrass (n=80)) using a relative Sorenson di stance measure with a flex ible beta of -0.25 in order to delineate community states present in my study area. I chose the optimal number of clusters/states with an indicator species analysis (ISA) and identified the associate species for each cluster (Dufrne and Lege ndre 1997). Community states were named according to the indicator species from the ISA. I performed a non-metric multidimensional scaling (Kruskal 1964, Mather 1976) ordination (NMS) on the vegetati on community data with Sorensen distance measure, 40 runs with real data, and 50 Monte Ca rlo. I then constructed a secondary matrix of environmental factors to determine which correl ate to community state composition in WCA3. PC-ORD overlaid the secondary matrix and ca lculated correlation coefficients for each environmental variable, which included peat de pth and a suite of both recent and historic hydrologic variables (maximum, minimum, and mean of every dry and wet season up to five years previous to the sample). Recent, for this analysis, is defined as hydrology affecting the area in the past year and histo ric is hydrology 2 or more year s prior to the sample event. Classification and Regression Tree (CART) I performed a CART analysis (Breiman et al 1984) on the three physiognomic types of interest (slough, sawgrass, and we t prairie) to provide quantitat ive measures of environmental variables to annotate the transiti ons in the S&T models, but the sample size of wet prairie was too small to provide results with acceptable error. The CART (S-plus 1993) analyses classified my community states for slough and sawgrass communities by the environmental variables used in the NMS and provided environmental thresholds that delineated community states. The 48

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CART results were interpreted with the NMS results to supply annotated (quantitative) transitions in the S&T models, which are normally conceptual, qualitative models. Vegetation Dynamics Develo pment Tool (VDDT) analysis VDDT (Beukema 2003) is an freeware program th at simulates succession and disturbance, using S&T models, based on two types of user-defined transitions: probabilistic and deterministic. Probabilistic transitions are cont rolled by management actions or disturbance; and deterministic transitions are based on succession due to time with no disturbance or change in management. There were no probabilistic transi tions defined for my simulations, as I do not consider a typical hydrarch or linear successional pathway valid here, thus the reason for my study. I identified qualitative management acti ons/disturbances (high and low dry season water depths, high and low wet season water depths high winds, fire, and peat deposition and subsidence) and assigned them transition probab ilities calculated from observed transitions within my S&T models, i.e. a high water wet season will have a 4% chance to changed a mixed transition prairie state to an Eleocharis elongata Chapman prairie state. I simulated 100 year time intervals with 50 Monte Carlo runs over 500 cells, with vegetation community configuration for 2002 as the in itial conditions, for four mana gement actions: equal, wet conditions, dry conditions, and increased hydrologic range. The equal category was added as a control to predict vegetation communities if the probability of all disturbances or management actions were equal and they occurred randoml y. Wet conditions had high probability of high water depths in the wet and dry season and low probability of all other disturbances, while dry conditions had high probabilities for low water in the wet and dry season and low for all others. Increased hydrologic range included high wet seasons and low dry seasons. 49

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Results Delineating communities and transition probability State transitions in the S&T model for each a priori group were based on my data, but transitions between communities represent extrem e changes that were not present during my study period and are hypotheses only (Figure 3). The cluster/ISA suggested five prairie states, five sawgrass states, and six slough states from 2002-2005 (see Zweig and Kitchens in press ): sparse sawgrass prairie, E. elongata prairie, wet prairie, mixed transition prairie, Eleocharis cellulosa Torr. prairie; shallow pe at/short sawgrass strand, shallo w peat/tall sawgrass strand, sawgrass with Peltandra virginica (L.) Schott & Endl, sawgrass with Justicia alata Vahl and Eleocharis spp; slough, mixed emergent slough, lily slough, shallow slough invaded by sawgrass, Eleocharis spp. slough, and hurricane effects. General hydrologic tr ansitions (Figure 4) were supplied by the environmental correlat es within the NMS analysis (See Zweig and Kitchens in press ). Transitions were affected by hydrologic alteration that occurred within 4 years of the sample (Armentano 2006, Zweig and Kitchens in press ). Community composition of prairie states were controlled by water depths in the wet season, but sawgrass and slough states were affected by water depths in both the wet and dry seasons. Overall, transitions probabilities were low within each community (Tab le 1), barring the hurricane effects state, but were highest in the communities not on the extreme ends of the peat or hydrologic gradients the less extreme states are more likely to change There was no spatial pattern in the likelihood or number of transitions and no community wa s more likely to change between states. A majority of the transitions that occurred in sloughs were from the slough state to hurricane effects state in 2005. Winds from hurricane Wilma displaced the floating aquatic Utricularia spp. from the sloughs into the sawgrass strands. As Utricularia spp is the indicator species for the slough state, its absence is consider ed the hurricane effect state. Lesser effects 50

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were found in the mixed emergent and lily slough st ates. Transitions probabilities for any other a priori community states (sawgrass, prairie) to the hurricane effects stat e were small. CART The CART analysis augmented the number of transitions in my S&T models and supplied quantitative information to annotate existing trans itions. Peat depth was a factor for both slough and sawgrass models which corresponds to NMS environmental correlations (Zweig and Kitchens in press ). The maximum water depths of wet seasons one and two years prior to the sample were relevant to the sawgrass analysis, while the minimum of dry seasons three and four years previous to the sample were important for slough communities. The slough model classified only four of six states (CV erro r = 0.588, misclassificati on rate = 0.208), but the sawgrass model classified all 5 states (CV e rror = 0.647, misclassificatio n rate = 0.238). CV error was high for both models. VDDT Analysis I used the VDDT program as an exploratory application to compare different management actions for the study area using S&T models constr ucted from 4 years of data collection and to examine the results of the models over time. For the wet conditions and increased range scenarios, there is nearly a complete disappearan ce of the wet prairie stat e and proliferation of the mixed emergent prairie, a deeper state (Table 2). The most dramatic changes occur in the slough and sawgrass communities. The slough state is greatly decreased, replaced by the deeper lily slough state in all but the dry conditions. The sawgrass with Justicia state decreases with the increased hydrologic range and wet conditions scenarios and is repl aced by deteriorated strand. Sawgrass with Peltandra increases in the equal and dry c onditions, replacing the sawgrass with Justicia state Increased range is almost identical to the wet conditions management action and, as is expected, the wet conditi ons action is quite different th an the dry conditions scenario. 51

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Discussion Hydrology is the primary mechanism for multistate transitions within the study period. Water depth is a strong control of community state composition and pattern in the Everglades (Larsen et al 2007), and I show both an immedi ate and lagged temporal and seasonal effect on vegetation, depending on community state. More than two years of sustained depths over 61 cm in the wet season can initiate fragmentation of sawgrass communities. Drying sloughs below surface level (-2cm) for three or more years couple d with low wet season water depths allows for the encroachment of sawgrass. I do not propose that the environmental variables here are the only influences of community composition, but th ey are representative of the complex hydrology that affects vegetation in WCA3. The reality is these hydrologic co rrelates do not capture all of the variability in the data and there are additiona l characteristics that control the composition and transition of community states, which are likely a combination of factors that incorporate duration. The VDDT analysis is interes ting in that consistent high water conditions and increased hydrologic range (high wet season, lo w dry season) are very similar in their final configuration and are very different than th e dry conditions management acti on, particularly for the sawgrass and slough communities. Drying WCA3 completely during the dry season does not seem to offset the effect of high water in the wet season to community states. This is of interest as there has been a trend of increasing wet season maxi mums and range within the study area since the new hydrologic schedule began in 2002 (C. Zweig unpublished data). According to the model, transitioning of all three commun ity types (slough, sawgrass, wet pr airie) to deeper states will continue if this trend is maintained. Predicting 1 00 years into the future from four years of data strains the limits of th e model, but data collec tion is ongoing and I will continue to update the models with current data and new management scenarios. 52

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The relationship between structure, func tion, and complexity is important when considering restoration alternat ives and can be incorporated into S&T models to identify priorities in restoration and ev aluate restoration ac tions (Cortina et al 2006). As hydrology is such a strong control of community composition in the Everglades, I would infer that hydrology would have a significant effect on the ecological complexity of a state, represented by structure and function (biomass and species richness (Cortina et al 2006, Ryan et al 2007)); however water depth is not a key mechanism. The deepest a nd shallowest states do not exhibit the highest complexity (Figure 5a-c), but they are not, as a rule, the least complex. Outside and state forcing functions, as discussed in the general wetland model, likely c ontribute to the structure and complexity of each state, particularly edaphi c factors, vegetative legacy, and competition. Sloughs generally exhibit lower complexity than wet prairies and sawg rass states contain significantly higher biomass, which increases their complexity along the x-axis of biomass/quadrat but not the y-axis of species diversity/quadrat (Figure 5d). The most common states have the highest comp lexity, corresponding to Odums (1969) ideas of ecosystem developmentmaximum protection and maximum production. Complexity is of interest as it can indicate the stability of a community state (Pimm 1984, Jansen and Kokkoris 2003). Whether maximum state complexity in the Everglad es is a desirable restoration goal remains to be seen and should be further investigated. It would provide a rare applied link between restoration ecology and dive rsity-stability relation ships (Seabloom 2007). Although specific to the Everglades, my approach to creating S&T models is useful in other landscapes, especially those with subtle environmental gradients such as the Okavango Delta, boreal fens, and some fl oodplain riparian wetlands (Larse n et al 2007) and allows scientists to address and resolve the complex ity of these ecosystems. The NMS and cluster 53

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analyses can characterize stat es from communities that are continuous and are adaptable enough to define moving thresholds. For example, th e community states in the Everglades are not characterized so much by the introduction or exclusion of a species like other systems (Connell and Slayter 1977, Platt and C onnell 2003, Seabloom 2007), but by the importance (biomass and density) of that species w ithin the state (Table 3). With this method I have defined successional community states which can be categorized, as with single species, with early or late successional stages. The community states are distinct in situ and not ephemeralthey are temporally persistent within the landscape but change spatially, supporting a shifting mosaic steady state model (Arscott et al 2002). These st ates can be seen as variance within a largerscale system, but that does not diminish thei r functional importance. While time and dataintensive, the ability to describe states at such a fine scale a ffords the opportunity to define a dynamic regime and create more realistic models than conventional linear relationships. As systems do not always respond in a predictable manner (Suding et al 2004), awareness of the mechanisms of vegetation change minimizes the possi bility of less desirable states (Briske et al 2006). It also provides additional, critical info rmation for restoration management decisions (Mayer and Rietkerk 2004) particularly as these relate to the ha bitat attributes for critical fauna. These models provide a link between succession al theory and the practice of ecosystem management. They represent the application of ecological models such as the shifting mosaic steady state model (Whited et al 2007), alternative stab le states (Beisener et al 2003), dynamic regime (Mayer and Rietkerk 2004), and the nonequilibrium persistent model of vegetation dynamics (Suding et al 2004). The existence of multiple stable states has been debated (Schrder et al 2005), but I provide field evidence and the ab ility to define states that are spatially and temporally stable within a dynami c regime. Identifying the possible states and 54

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55 pathways of vegetation change can be used to predict restoration success or the possibility of hysteresissystems following a different path for recovery than the initial trajectory of change (Suding et al 2004). I observed ev idence of multiple pathways from one state to another within out study area, indicating potential hysteresis. Managers could al so explore the possibility of transitory communities that would be necessary intermediates for a final, restored system (Connell and Slayter 1977). The concepts of multiple steady states and shifting mosaics are key theories for understanding the dynamic nature of wetlands, including the Everglades. I consider the application of these theories, th e S&T succession models, as a fraction of the framework for the Everglades and my understanding will only build with time. They are hypotheses for use in adaptive management as the restoration of the Ever glades continues. Thes e models represent the community response to hydrology and illustrate which hydrologic valuestemporal or seasonalare important to community structure. I intend for them to act as a foundation for further restoration management and experimentation. Future data will refine my current understanding of the impacts of altered hydrology on vegetation succession in the Everglades and increases the ability to a pply succession theory to resolve restoration issues (Odum 1969).

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Table 3-1: Transitions of co mmunity states in Water Conser vation Area 3A South, FL, from 2002-2005. Community states Times transitioned Percent transitioned SG w/ Peltandra 1 1.7% Shallow Peat, Tall 3 5.0% Shallow Peat, Short 0 0.0% SG w/ Justicia and Eleocharis 9 15.0% Deteriorated sawgrass 2 3.3% Sparse Sawgrass Prairie 1 3.0% E. elongata Prairie 0 0.0% E. cellulosa Prairie 1 3.0% Mixed Transition Wet Prairie 1 3.0% Wet Prairie 5 15.2% Hurricane Effects 0* 0.0% Lily Slough 1 1.9% Eleocharis Slough 0 0.0% Shallow slough invaded by Sawgrass 0 0.0% Mixed Emergent Slough 2 3.7% Slough 11** 20.4% *States did not transition from the hurricane e ffect state to another because 2005 was the last sample date. **A majority of slough state transformations were to hurricane effects in 2005. 56

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Table 3-2: Percent change in total area (h a) of community states for four manageme nt scenarios in Water Conservation Area 3A South, FL, run with Vegetation Dynamics Development Tool soft ware. Parameters were set from data collected in 20022005. Initial conditions are e qual to conditions in 2002. Vegetation Type Equal Deeper Conditions Increased Range Dry Conditions Sparse Sawgrass Prairie 50% 9% 44% 48% E. elongata Prairie 22% 2% 6% 37% Wet Prairie -92% -100% -100% -69% Mixed Transition Prairie 36300% 48900% 56100% 11600% E. cellulosa Prairie -34% -8% -32% -32% Slough invaded by Sawgrass 1% 0% 1% 2% Slough -81% -100% -100% -28% Mixed Emergent Slough 20% 122% 5% 3% Eleocharis Slough 0% 0% 0% 0% Lilly Slough 1105% 1204% 1389% 378% 57 Deteriorated Sawgrass Strand 449% 1185% 1153% 19% Sawgrass with Justicia -95% -100% -99% -49% Sawgrass with Peltandra 872% -12% 217% 798% Shallow Peat, Tall Sawgrass -55% -15% -55% -56% Shallow Peat, Short Sawgrass 0% 0% 0% 0% Equal = all management actions/disturbance pr obabilities were set equal as a control. Deeper conditions = deep water depths in wet and dry season. Increased range = deep water depths in wet seas on and very low water depths in dry season. Dry conditions = l ow water depths in wet and dry season.

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Table 3-3: Percent Importance Value (avera ge of relative biomass and relative densit y) of species within community states with in Water Conservation Area 3A South. Community Community State BAC CLA ELG Elsp NYO UTsp Slough Slough 2.6% 1.6% 49.2% 3.0% 15.5% 28.0% Hurricane Effects 1.5% 7.7% 46.2% 5.0% 34.3% 5.2% Shallow Slough Invaded by Sawg rass 3.9% 27.1% 63.4% 1.1% 3.4% 1.1% Mixed Emergent Slough 9.8% 3.6% 5.8% 29.7% 22.5% 28.5% Lily Slough 0.2% 4.2% 15.0% 5.7% 48.7% 26.2% Eleocharis Slough 6.3% 2.7% 23.3% 46.3% 0.0% 21.5% Prairie E. elongata Prairi e 8.6% 2.1% 64.4% 5.0% 7.0% 12.9% Wet Prairie 5.3% 1.8% 19.7% 44.0% 5.2% 24.0% Sparse Sawgrass Prairie 11.7% 18.3% 2.8% 55.7% 4.8% 6.7% E. cellulosa Prairie 1.5% 16.4% 1.6% 75.6% 1.0% 3.7% Mixed Transition Prairie 33.9% 0.9% 3.3% 23.6% 18.5% 19.7% 58Sawgrass Sawgrass with Justicia and Eleocharis 9.4% 73.7% 13.6% 1.0% n/a 2.4% Sawgrass with Peltandra 6.6% 53.7% 35.0% 3.0% n/a 1.6% Deteriorated Sawgrass Strand 19.6% 38.0% 31.6% 3.9% n/a 6.9% Shallow Peat, Tall Sawgrass Strand 10.8% 59.7% 0.6% 26.4% n/a 2.5% Shallow Peat, Short Sawgrass Strand 0.0% 43.0% 0.0% 56.5% n/a 0.4% BAC = Bacopa caroliniana CLA = Cladium jamaicense ELG = Eleocharis elongata Elsp = Eleocharis cellulosa NYO = Nymphaea odorata UTsp = Utricularia sp.

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Figure 3-1: Satellite view of the Everglades in southern Flor ida, USA. The study site, Water Conservation Area 3A South, is outlined in white. 59

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Figure 3-2: General state a nd transition model for wetlands 60

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Figure 3-3: Landscape level state and transition model for Water Conservation Area 3A South in the Everglades, Florida, USA. 61

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A Figure 3-4: State and trans ition model for the a) wet prai rie b) sawgrass and c) slough communities in Water Conservation Area 3A South. Community states are arranged on a general hydrologic gradient with dr ier communities at the top and deepest communities at the bottom. Percentages (times transitioned/total number of possible transitions) represent the how often tran sitions occurred during the study period (2002-2005). B Figure 3-4. continued 62

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C Figure 3-4. continued 63

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Figure 3-5: Ecological complexity graphs for three communities in Water Conservation Area 3A South(a) wet prairie, (b) slough, (c) sawgra ss, (d) all communities. Complexity is represented by species richness and biomass per 0.25 m2 quadrat 64

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CHAPTER 4 A SYNTHESIS OF PATTERN AND PROCESS FEEDBACK CYCLES AND THEIR EFFECT ON A STEPPED WETLAND LANDSCAPE Large wetlands all over the wo rld face the possibility of de gradation, not only by complete conversion, but by subtle changes in their struct ure and function (Dobson et al 1997, Arscott et al 2002). Human activities such as drainage, impoundment, and increased nutrients have fragmented wetlands, leaving few completely int act. Wetlands are often considered landscape components, but rarely an entire landscape themselves. Fragmentation of wetlands within a landscape is important, but the disruption of spatial patterns and fragme ntation particularly within large, patterned wetlands is just as significant. How fragmentation within wetland landscapes affects and is affected by the resulting alteration of maintenance processes, such as peat deposition and natural disturbance regimes, is of great interest (Turner 1989, Brgi et al 2004), particularly in regard to two pertinent topics in ecology today: restoration and global climate change (Zedler 2002, Opdam and Wascher 2004). Degraded wetland systems do not always respond to landscape changes in a linear predictable manner (Zedler 2000, Suding et al 2004). A greater understanding of pattern/p rocess relationships (e.g. how flow/peat deposition/disturbance/nutr ient cycles affect patterning in we tlands and vice versa) and what occurs when they are altered could help avoid undesirable consequences of restoration actions (Briske et al 2006, Alvarez-Cobelas et al 2008). This is critical as wetland complexes such as the Pantanal and Okavango Delta are curren tly being impacted by human activities on a landscape level (Ellery et al 2003, Junk et al 2006), and one such wetland, the Everglades, is the focus of the worlds largest restoration effort. These large wetlands are subject to subtle grad ients that, in concert with fluvial dynamics, create extremely heterogeneous systems with di stinct patterning (Ward et al 1999). Subtle should not be confused with continuous, especial ly regarding elevation. Disturbances will have 65

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differential effects across the landsca pe in response to abrupt gradient changes, such as stepped elevation (Williams et al 1999). There is a very tight feedback loop between pattern and processes for the creation and maintenance of the patterned landscape (L arsen et al 2007). Natural or anthropogenic changes to either may m odify the feedback loop in a way that is not readily apparent but ecologically important (Sklar et al 2005). The Everglades is an example of how human disturbance can alter the patterns and processes within a wetland ecosystem, creating a feedback cycle that ch anges both the pattern and processes of the landscape, while it continues to function superficially as a wetland (Sklar et al 2005). The Everglades was once characterized by its large spatial exte nt, sheetflow, habitat heterogeneity, and ridge and slough landscape (Davis et al 1994). Due to human activities, the Everglades ecosystem has been developed and co mpartmentalized, losing over 50% of its spatial extent (Leonard et al 2006) a nd over 80% of its upland habitats. Sheetflow has been disrupted by agriculture and compartmenta lization, and hydrology has become highly regulated. Nutrient inputs from surrounding agriculture have caused fr agmentation of the landscape by invasion of Typha sp. (Wu et al 1997) and by alternating loss of slough and sa wgrass (Cladium jamaicense Crantz.) strands with different hydrologic regimes (Ogden 2005). This fragmentation is particularly importa nt in the characteristic ridge and slough landscape (RSL) within the Everglades. The RSL was a dominant landscape type of the southern portion of the Everglades and has been highly affected by compartmentalization and reduced flows (Ogden 2005). The RSL consisted of long, linear strands of sawgrass interspersed with deeper hydroperiod sloughs and occasional tree islands oriented paralle l with the slow-moving flow of water from north to south. The RSL provided refugia for aquatic organisms during the dry season, provided a large amount of water st orage potential, and was the primary area for 66

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primary and secondary production (Ogden 2005). This area is now of particular interest in the restoration (Science Coordination Team 2003) an d understanding the maintenance processes for the RSL is a focal target of the Everglades rest oration (Sklar et al 2005 ). There have been numerous hypotheses, especially re garding the role of flow, on the creation and maintenance of the RSL and I synthesize recent literature and suggest additional mechanisms. I also demonstrate using remote sensing and landscap e indices how these maintenance mechanisms have been disturbed, their effects on the lands cape pattern, and how th is creates alternate feedback loops that affect pe rsistence of the multiple stable states of ridge and slough. Methods Study Site The study site, Water Conservation Area 3A South (WCA3AS), is the largest intact remnant of the RSL in the Everglades, FL (F igure 4-1). It is subject to several key environmental gradientsan east-west peat dept h gradient, north-south elevation gradient, and an artificial north-south wate r depth gradient created by im poundment. When examining the hydrology of the sites, I realized that the elevation gradient is not continuous as previously thought, and divided the study site into north and south sections along an elevation break. The break, visible on satellite imagery, was delineat ed using the 2.0 m contour line on a digital elevation map provided by the Everglades Depth Estimation Network (http://sofia.usgs.gov/eden/models/groundelevmod.php) This contour corresponded closely to the visual break and follows the general boundary between the underlying Miami and Fort Thompson formations (Gleason and Stone 1994). The northern section of the study area is considered over-drained and cut off from sheetflow by Alligator Alley (Interstate 75), which is the north boundary of WCA3AS. Construction on Alligator Alley to create culverts for wildlife crossings began in the late 1980s 67

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and was completed by 1992. This produced improved flow for the norther n section of WCA3AS (Gunderson et al 1995). An era of increased water depths and hydroperiods also began circa 1991, changing conditions within WCA3AS (Zweig and Kitchens in press ). This had the greatest impact on the southern section due to the influence of the levee and the resultant increased ponding. WCA3AS was already managed as the wettest compartment within the water conservation areas (Childers et al 2003) and higher water levels exacerbated the increase of sloughs and disappearance of ridges within the system. Classification and FRAGSTATS Analysis Two cloud-free satellite images (Figure 43a) were obtained of the study area from approximately the same season (dry season). The images obtained were from 1988 and 2002 2002 approximating present conditions and 1988 repr esenting the drier hy drologic era. The imagesMarch 5, 1988 (Landsat TM) and February 5, 2002 (Landsat ETM+)were geometrically and radiometrically corrected. Both images were classified, using non-parametric, supervised classification in ERDAS ImagineTM, into three vegetation classes: slough, sawgrass, and tree island (Figure 4-3b). The 1988 and 2002 classified images were split in to north and south se ctions (Figure 4-4) along the elevation break and were input into FRAGSTATS. FRAGSTATS (McGarigal et al 2002) is public domain software that computes landscape metrics on spatial categorical data. There are dozens of metrics on three different scal eslandscape, class, or patch. These metrics have been widely used in ecology, especially landscape ecology. I chose the following class metrics for the main types of vegetation communities in WCA3AS (slough, sawgrass, tree island) to quantify the total area of communities and degree of fragmentation over time (McGarigal and Marks 1995): 68

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Total Class Area: total class ar ea in hectares in the landscape Percentage of Landscape: total area of a class divided by the total area of the landscape Number of Patches: sum of patc hes that belong to a certain class Patch Density: the number of class patches per 100 hectares Largest Patch Index: a percentage calculated by the area of the largest patch divided by the total landscape, multiplied by 100 Contiguity: contiguity or spatial connectedne ss of cells. 0 = one cel l patch and increases to a limit of 1. Related circumscribing circle: describes linearity of patches. 0 = circular patch and 1 = linear patch one cell wide. Results Classification and FRAGSTATS Analysis The 1988 and 2002 images were classified with an accuracy of 88.76% and 95.51% respectively (Table 4-1), using field test points obtained in January 2007. The decrease in accuracy could be explained by the time lag be tween the image and the field data and the obvious vegetation community changes that ha ve occurred. Visually, WCA3AS still has remnants of the characteristic linear landscape pa ttern that was formed by historic sheetflow which generated tear-drop shaped tree islands an d the RSL pattern orient ed north/south. Much of the sawgrass loss occurs along the south and eas t levees. The tree islands in the south also shrink considerably. There are ar eas of higher loss in the northern and eastern sides. In the southern end, there is a pattern of sawgrass disappearing from the fringes of tree islands. The landscape metrics are reported only for sa wgrass and slough and illu strate a decline in the area of sawgrass and increased fragmentation (Table 4-2). Total sawgrass area in 3AS declined by 21% from 1988 to 2002, and the dens ity and number of patches increaseda 33% increase in the number of patches. Conversel y, slough communities increased in total area and 69

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decreased in number and density of patches. The contiguity and circle metrics indicate that sawgrass and slough in the north section were sligh tly more linear in 2002 but decreased slightly in contiguity. Sawgrass in the southern s ection was much less contiguous in 2002 and more linear, likely reflecting the fragmenta tion and shrinking of sawgrass strands. Discussion Synthesis The Everglades distinctive ridge and slough pattern originated approximately 2700 years BP (Larsen et al 2007), and is believed to be ma intained by both autogenic and allogenic factors (Figure 6) (Leonard et al 2006, Wu et al 2006). This RSL is a product of long-term (geology, soils, and climate) and short-term maintenan ce processes (hydrology, flow, nutrient cycles, microtopography, variations in rainfall, and di sturbance regimes) (Gunderson 1994). The shortterm processes, particularly hydrology and fire, are extremely variable in their temporal and spatial extent, and maintain the patterns of the heterogeneous landsca pe. Flow and hydrology have become highly regulated by an extensive canal/levee system and the RSL pattern is degrading (Science Coordination Team 2003, Sklar et al 2005). I found relatively little published literature on the maintena nce of the RSL considering the Everglades is a target of a $10 billion restoration effort. I present two types of RSL maintenance: cons tant and pulse maintenance processes. One hypothesis for the constant maintenance of the RS L is differential sediment accumulation due to flow velocities in vegetated and open areas, or ridge and slough (Leonard et al 2006). This phenomenon is well documented in other wetlan ds (Christiansen et al 2000, Neumeier and Ciavola 2004), and there are several pub lished mechanism hypotheses for sediment accumulations within the Everglades. The litera ture suggests that flow velocityhigher in sloughs which scours floc (unconsolidated peat ) and reduced in ridges by stem density and 70

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biomassallows for greater settling of susp ended solids (Leonard 2006) in ridges, stemgenerated turbulence needed to settle particulates out of the water column does not occur at current flow rates (1.15 cm/sec, Larsen et al 2007). Stem-generated turbulence in Eleocharis stands could be high enough to cause settling if flow rates were 3-5 cm/sec (Leonard 2006). Predrainage flow rates for the Everglades were approximately 4 cm/sec (Larsen et al 2007) and could have historically contributed to th e processes that maintained the RSL. Another hypothesized process is the diffe rential decay of vegetation in slough and sawgrass (Foster and Fritz 19897. Peat forms in the sloughs primarily from rhizomes and roots of Nymphaea odorata Ait., the white water lily (Gleason and Stone 1994). Above-ground slough biomass is extremely labile, as opposed to the refractory characteristics of sawgrass (Godshalk and Wetzel 1978, Davis 1991), increasing the microt opological differences in peat accretion. Sawgrass-dominated Everglades peat is most common within the Everglades basin, indicating persistence, and lily-dominated Loxahatchee peat is second in abundance (Jones and Bennett 1948). The differential topography might be offset by the oxidation of peat when the higher sawgrass strands are exposed during the dry season (Fleming et al 1994), except for the significantly greater amount of vegetation bioma ss available for decomposition in the sawgrass strands (Lockwood et al 2003) as opposed to th e sloughs (modeled estimates from data provided by Zweig and Kitchens in press : sawgrass biomass per 0.25m2 quadrat = 150.2g (SE = 3.0849) and slough biomass per 0.25m2 quadrat = 48.0g (SE = 0.867), p < 0.0001). This difference could very well compensate for the sawgrass strands pe riodic exposure to ai r and subsequent peat oxidation. Loxahatchee sl ough peat demonstrates a high rate of shrinkage when dried (Gleason and Stone 1994), a mechanism to further reduce th e elevation of sloughs when they are exposed during dry downs. Sloughs are rarely dry, so the increased exposure and oxidation of ridges 71

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could be the reason that, in the presence of differe ntial peat accretion in the ridges, the RSL still retains a very low microtopographic gradient. The maintenance of the RSL could also be a ttributed to processes that occur as pulses instead of constant influences. Data indicates that organic partic ulates in the water column are attracted to periphyton growing on emergent stems and submerged aquatic vegetation (Lee et al 2004, Harvey et al 2005). Organic matter is added to the sediment layer when the plant dies or settles to the ground during low water events (Leonard 2006). However, sediment-laden submerged aquatics such as Utricularia sp. are deposited in sawgrass strands during high wind events, transporting sediment that would have or iginally settled in slough s onto strands. This was witnessed in 2005 with Hurricane Wilma (Larsen et al 2007, Zweig and Kitchens in press ) and to a lesser degree in 2006 with Hurricane Er nesto (C. Zweig pers. comm.). Virtually all Utricularia sp. was removed from the sloughs, displa cing a large amount of organic matter to ridges. Wind events also distribute peat isla nds that are dislodged from sloughs onto ridges, greatly increasing topography and heterogeneity of the pattern (Gleason and Stone 1994). Fire is a pulsed phenomenon that maintains th e heterogeneity of the RSL (Gunderson and Snyder 1994). In wet conditions it burns through the sawgrass, removing wrack and releasing nutrients back into the strand for regrowth, wh ile sloughs are relatively resistant to burning (Gunderson and Snyder 1994). Ridges can recover quick ly from moderate fires, returning to prefire conditions after two years (L oveless 1959). An intense fire during drier conditions will burn peat, lower the elevation of the ridge, and kill the vege tation (Herndon et al 1991), and subsequent flooding will convert the burned area into slough (C raighead 1971, Herndon et al 1991). 72

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It is likely that differential sediment transport in all its variations was responsible for a large part of maintaining the RSL, as well as the sheer biomass available for the strands to convert to substrate. Reduced flows and alte red hydroperiods have had varied effects on the landscape patternover-drained sec tions lose sloughs due to sawg rass encroachment (Craft and Richardson 1993) and under-drained sections sl owly convert to slough (Wood and Tanner 1990). When landscapes change, the processes change with the landscape (Brgi et al 2004), creating a feedback loop that alters the syst ems response to structuring va riables, especially disturbance (Nowacki and Abrams 2008). Pattern/process feedback loops in the Everglades Draining of the Everglades began as early as 1881, but the Central and Southern Florida Project for Flood Control in 1948 created large-sc ale fragmentation with a canal/levee system and water conservation areas (Light and Din een 1994). Restoring the RSL pattern is of particular interest for the entire ecosystem restoration (Science Subcommittee 2003) and I quantify the current amount of fragmentation and loss of sawgrass ridges as baseline information. Most literature concerned with fragmentation of the RSL include Typha sp. invasion (Wu et al 1997, Childers et al 2003) or tree island disa ppearance (Willard et al 2006), but do not address the loss of patte rn by the effects of deep water. The data show the general replacement of sawgrass strands by more aquatic sloughs, presumably due to higher water depths in the wet era circa 1991 or improvements to Alligator Alley around the same time period. An important distinction is the differen tial effect altered hydrology has on WCA3AS, particularly the north and south sections due to th e elevation break. I can state that the study area is losing sawgrass strands, but the manner in wh ich they are being lost and fragmented is important to the understanding of the pattern/process fee dback loops. The northern area of the study site was over-drained and dry, allowing sa wgrass to encroach into the sloughs. The 1988 73

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image shows solid areas of sawgrass that beco me fragmented in 2002. This fragmentation appears to be a restoration, not degradation, of the RSL pattern and is supported by the increase of linearity of both slough and sawgrass in 2002. Th e southern section has lost a considerable amount of sawgrass to slough, but the fragmenta tion appears to be degradation of the RSL pattern and drowning of the sawgrass strands from high water and levee effects. Linearity of sawgrass increased slightly and contiguity de creased. Slough area increased, decreased in number of patches, and became less linear and le ss contiguous. This suggests the thinning and fragmentation of ridges and conso lidation of sloughs, but not a tota l disappearance of sawgrass. The sympodial growth form of sawgrass allows it to persist in less than ideal conditions for an extended period (Snyder and Richards 2005) by growing up instead of out, forming tussocksor fragmentinginstead of growing in continuous strands (Figure 7). The disruption of one process or pattern, here the pattern of the RSL and a process that maintains it, will affect others in a system (T urner 1989), reinforcing degraded feedback loops. If water depths were deep enough and of long enough duration to stress enough sawgrass to form tussocks, stem density and biomass of the ridge could be greatly reduced, further decreasing peat accretion in the ridges by reducing the stem-turbu lence from flow and the subsequent deposition of suspended sediments, and by the simple reduction of biomass from the ridges available to create peat. Increased flow velocities from reduced stem densities within the strands will increase scouring of unconsolidated material, also slowing peat accretion. Sawgrass fragmented by tussock growth will also affect the ability of fire to travel through the RSL by reduced biomass and increased fuel moisture cont ent (Lockwood et al 2003), reducing nutrient availability and reducing its role as a maintenance process. Disruption of sheetflow by impoundment has already reduced flow velociti es to a level that does not support the pre74

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drainage maintenance processes (< 3 cm/sec), which would also reduce the microtopographic differences in the RSL. It is likely, based on the synthesis of avai lable literature, that suspended sediment deposition due to differential flow velocities was once a RSL maintenance process in the predrainage Everglades, and that natural water de pths and hydrologic duration also contributed to microtopographic differences in the landscap e (Craft and Richar dson 1993). WCA3AS, especially the southern secti on, is now experiencing degraded forms of these processes (high water and slow/no flow) and the landscape is responding. I demonstrated how the RSL pattern has fragmentedthat the characteristic linea r sawgrass strands are being drowned out and replaced by sloughsreinforcing a degraded feedback loop of altered maintenance processes. I hypothesize that the mechanisms of this fragmentation are prolonged ponding and the reduction of flow by compartmentalization an d deeper water. There is an attempt to restore flow and natural hydrologic regimes to WCA3AS within Ever glades restoration, but more direct evidence of the pattern/process linkages, consideration of the stepped nature of WCA3AS, and monitoring of the RSL pattern are critical to the su ccess of the complete Everglades ecosystem. 75

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76 Table 4-1: Producer and users accuracy for non-parametric, supervised classification of LANDSAT TM and ETM+ satellite im ages from 1988 and 2002 of Water Conservation Area 3A South, FL, USA. The Kappa statistic is a measure of agreement and is used as a measure of accuracy ( 0 < Kappa > 1). Class Producer's Accuracy User's Accuracy 1988 Slough 96.77% 88.24% TreeIsland 80.00% 100.00% Sawgrass 89.29% 80.65% Overall Classification A ccuracy = 88.76% Overall Kappa Statistics = 0.8314 2002 Slough 100.00% 93.94% TreeIsland 93.33% 100.00% Sawgrass 96.43% 96.43% Overall Classification A ccuracy = 96.63% Overall Kappa Statistics = 0.9494

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Table 4-2: FRAGSTATS indices calculated for slough and sawgrass communities in Wate r Conservation Area 3A South, FL, USA. Data is from classified LANDSAT TM and ETM+ satellite images from 1988 and 2002. Total Area in Landscape Percent of Area in Landscape Number of Patches Patch Density Related Circumscribing Circle Contiguity North Section Slough 1988 19692.72 37.5387 3066 5.8445 0.7678 0.7745 Slough 2002 22197.87 42.3141 3086 5.8826 0.7873 0.754 Difference 2505.15 4.7754 20 0.0381 0.0195 -0.0205 Sawgrass 1988 31147.47 59.374 3099 5.9074 0.6363 0.849 Sawgrass 2002 26811.63 51.109 4361 8.313 0.6776 0.7642 Difference -4335.84 -8.265 1262 2.4056 0.0413 -0.0848 South Section Slough 1988 34883.91 72.0399 1174 2.4245 0.7476 0.9014 Slough 2002 37353.51 77.1399 911 1.8813 0.7332 0.8922 Difference 2469.6 5.1 -263 -0.5432 -0.0144 -0.0092 Sawgrass 1988 11387.88 23.5175 6522 13.469 0.7254 0.7023 77Sawgrass 2002 9703.89 20.0398 8627 17.816 0.7504 0.602 Difference -1683.99 -3.4777 2105 4.347 0.025 -0.1003

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Figure 4-1: The study area, Water Conservation Area 3A South in the Everglades, FL, USA, is outlined in white. 78

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Figure 4-2: LANDSAT TM a nd ETM+ satellite images from 1988 and 2002 of Water Conservation Area 3A South, FL USA. Bands shown are 4,3,2. 79

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Figure 4-3: Non-parametric, supervised classification of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA. 80

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Figure 4-4: Non-parametric, supervised classification of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA fr om the 2 meter elevation contour and higher. 81

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Figure 4-5: Non-parametric, supervised classification of 1988 and 2002 images of Water Conservation Area 3A South, FL, USA from the 2 meter elevation contour and lower. 82

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83 Figure 4-6: Conceptual model of ridge and slough maintenance me chanisms in the Everglades, FL, USA. indicates this paper. Numb ers indicate cites labeled in references.

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Figure 4-7: Example of fragmented sawgrass ridges in Water Conservation Area 3A South, FL, USA. 84

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CHAPTER 5 HABITAT, HYDROLOGY, AND REPRO DUCTION RELATIONSHIPS FOR AN ENDANGERED SPECIESTHE FLORIDA SNAIL KITE The Florida Snail Kite ( Rostrhamus sociabilis ) is a wetland-dependa nt endangered species adapted to a unique and extremely dynamic system the Everglades. The Snail Kites range encompasses the entire Everglades watershed, a mo saic of wetland habitat types that are highly impacted by anthropogenic activities (Davis et al 1994). Alterations in water depths, hydroperiods and habitat degradation have s hort and long-term impacts on Snail Kite demography, principally nest success (Beissinger and S nyder 2002, Bennetts et al 2002, Kitchens et al 2002). Especially in this time of Everglades restoration, understanding the effect that environmental processes can have on habitat, what changes will occur with alteration of those processes, and how it aff ects Snail Kite reproduction pote ntial is essential to a sound conservation strategy (Bennetts et al 1998). This is especially important as the declining Snail Kite population has halved in the last two years and is reaching critical lows (W.M. Kitchens, pers. comm.). The Snail Kite is a dietary specialist and its primary prey is the apple snail ( Pomacea paludosa), whose population levels and availability as prey are also controlled by hydrology and habitat (Darby et al. 2002). Apple snail availabi lity has decreased (P. Darby, pers. comm.) and is a suspected contributor to Snail Kite decline. However, even with sufficient prey available, habitat structure is crit ical in enabling Snail Kites to find f ood resources (Bennets et al 2006). I believe that not only is the rate at which Snail K ites encounter apple snails important, but just as critical is the rate at which Snail Kites en counter apple snails on emergent vegetation, particularly during the breedi ng season. Simply studying constr aints on the apple snail would not explain changes in Snail Kite demogra phy (Bennetts et al. 2006), but incorporating 85

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constraints on availability of foraging habitat, especially in breeding re gions, would contribute significantly to the entire conservation perspective. Water Conservation Area 3A (Figure 5-1) wa s the largest and most consistently used component of the habitat designat ed critical to the Snail Kite (Kitchens et al. 2002, Mooij et al. 2002). Its historic contribution to kite reproduction is significan t (Kitchens et al. 2002). The current negative population trends of the Snail Kite may reflect the degradation of foraging and nesting habitat quality in Wa ter Conservation Area 3A S outh (WCA3A) alone (Martin 2007, Martin et al. 2007), particularly the decline of the two habitats in proximity to each other. Shifts of Snail Kite nesting density up the slight, but significant elevation gr adient in WCA3A have been documented over the past tw o decades (Bennetts and Kitchens 1997). This is presumably in response to degradation of nes ting or foraging habitat as a resu lt of sustained high water levels from impoundment and water management (Kitchens et al 2002). Nesting activity has shifted up the elevation gradient to the west, and has al so moved south in response to recent increased drying rates, restricting current nesting to the southwest corn er of WCA3A (Figure 5-2). Reproduction in this critical breeding area has waned significantly (Table 5-1). No birds were produced in WCA3A in 2005, and only 9 of 81 ne sts were successful in 2006. In 2007 there were no nesting attempts. In WCA3A, kites forage mainly in wet prai ries and emergent sloughs where their primary prey, the apple snail, are most visible and abundant (Bennetts et al. 2006, Karunarante et al. 2006). Although apple snails are found in varied wetland habitats, abundan ces tend to be higher in sparse prairies and emergent sloughs and very low in Nymphaea odorata Ait. dominated sloughs (Karunarante et al. 2006) Previous studies in th is region (Wood and Tanner 1990, David 1996) indirectly documented the conversio n of wet prairies to aquatic sloughs, which 86

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constitutes a loss of quality Snail Kite foraging habitat (Kitchens et al. 2002). None of these studies were designed to provide inference beyond the isolated sites in which they were conducted, and unfortunately occurred largely outsid e kite foraging and nesting areas. There is concern that conversion of wet prairie/emergent slough habitats to deeper, less desirable sloughs will lower kite reproduction, primarily through lower prey base availability in those communities (Karunarante et al 2006). To address the issue of habitat degradation with in breeding areas and its effect on snail kite reproduction success, a vegetation study was initia ted in 2002 to monitor critical kite foraging habitat in WCA3A. It is now particularly vital to monitor kite habitat gi ven their critical state and a continuing trend towards higher maximum water levels and a more extreme hydrologic range (Table 5-2) within WCA3 AS. In this study, we hypothesi ze that there is a link between vegetation community composition and environmental and demographic factors for the snail kite. Methods To monitor foraging habitat, I used data in the breeding area descri bed in Figure 5-2 from a large scale vegetation study in WCA3A (Zweig a nd Kitchens 2008), plots 7, 8, and 9. Twenty 1km2 plots (Figure 5-1) were placed in a stratifie d random manner across the landscape gradients in WCA3A South. Plots were st ratified by the landscape level gr adients of peat depth, water depth and snail kite ne sting activity. Five a priori physiognomic types were identified: slough, sawgrass, tree/shrub island, cattail, and wet prairie. Two or three transects in each plot were placed perpendicular to ecotones, beginning in one a priori type and terminating in another, e.g., slough to sawgrass. I collected 0.25 m2 samples of all standing biomass along a belt transect, clipping the vegetation at peat level at 3 m interv als, and included any submerged aquatic plants within the sample. Shrubs were sampled in the same manner as the herbaceous vegetation; there 87

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were no trees in transects. Samples were collect ed from every transect in every plot during the dry (May/June) and wet season (November/Decem ber) of each year. These were sorted by species, counted, dried to a constant weight, and wei ghed to the nearest 0.1 g. Approximately 10,000 samples were collected and processed between 2002 and 2006. Our analysis focused on the slough samples in th e core Snail Kite ne sting area, plots 7,8 and 9, and only the wet season data and as there were fewer issues of sampling error due to small, new growth and matted prairie vegetation than in the dry season. I used only the a priori slough samples within this ar ea as the area contained no a priori wet prairie communities. The a priori labels were general and sloughs also contained emergent vegetation. Hydrologic data were provided by 17 wells in stalled in December 2002 within plots that did not already have permanent water wells. On each sample date, water depths were measured with a meter stick at every quadr at and linked to water depth meas urements at the nearest well (within a radius < 1 km) by subtracting the quadrat water depth from the reading at the well for that day. Historic hydrologic data for all 17 wellsfrom 1991 to 2002were hindcast using an artificial neural network mode l (see Conrads et al. 2006). These wells are currently in place and six have become permanent, real-time wells for the Everglades Depth Estimation Network (EDEN). Multivariate Analysis To account for high densities of low biomass species and high biomass of low density species, the data were relativized in an index, importance value (IV), calculated by: IV for species i = ((Rdi + Rbi)/2)*100, where Rdi is the relative dens ity of species i and Rbi is the relative biomass of species i. Relative measures are the sum of biomass or dens ity of species i divided by the sum of biomass or density of all species within each sample. 88

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I combined the a priori slough 0.25 m2 samples on each transect that contained sloughs (Transects 7-1, 7-3, 8-1, 8-2, 9-1, an d 9-3) into one point (n = 35) and performed a hierarchical cluster analysis on the IV data with a Sorenson distance measure and flexible beta of -0.25 in PC-ORD (McCune and Mefford 1999). To choose how many clusters were present during the study period, I ran an indicator sp ecies analysis (ISA) (Dufrne and Legendre 1997) to prune the cluster dendrogram. I used a non-metric multidimensional scaling (NMS) (Mather 1976, Kruskal 1964) ordination on the same IV data wi th a Sorenson distance measure to identify the strongest environmental and demographic correla tes with species composition. Environmental variables in the analysis included maximum, mean, and minimum water depths for each wet and dry season up to five years previous to the samp le; the mode (referred to as frequency) of water depths for each season up to two years previ ous; annual water depth ranges (max min); and average stem density/0.25 m2 of four slough/prairie species for each year. The hydrologic variables were calculat ed with the modeled well data (C onrads et al 2006) by a custom Excel application and tailored to each transect by water depths collected at the samples. Univariate Analysis I ran an analysis of variance (ANOVA) (PRO C GLM, SAS Institute 1989) on density data of dominant wet prai rie and slough species ( Eleocharis cellulosa Torr. Eleocharis elongata Chapman Panicum hemitomon Schult. Paspalidium geminatum (Forssk.) Stapf, Bacopa caroliniana Walt., Nymphaea odorata Ait., and Utricularia spp.) to determine significant changes over time. I did not combine samples as in the multivariate analysis, but used the 0.25 m2 samples separately for the ANOVA to provide a more precise estimate of local density. 89

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Demographic analyses Adult survival tends to be constant for Snail Kites in the recent past (Dreitz et al. 2002), excepting drought events, and ferti lity has currently emerged as mo re important than survival (Martin 2007), so I only included repr oductive variables in the analyses. Our nest success data was provided by a data base maintained by W.M. Kitchens at the Florida Cooperative Fish and Wildlife Research Un it. Snail Kite nesting and survival data has been collected at the Cooperative Unit, range-wid e, since the late 1990s. Nests are located by quasi-systematic searches (Dreitz et al 2001) during the breeding season in all known active and recently reported areas. Crews revisit nests during the season to assess survival and to band chicks. I used the nest survival model in Program MARK (White and Burnham 1999) on nests located within WCA3AS to estimate nest success by year from 2002. Only nests with complete data were used in the analysis (n = 79), and sample size per year was 21, 14, 6, 2, and 36, respectively. I provided the day each nest was found, the last day the active nest was checked, the last day the nest was checked, the fa te of the nest, and used nest success estimates in the NMS secondary matrix to explore correlat ions between success, environmental variables, and habitat community composition. I ran MARK with two pre-defined models: dot, which assumes no difference between years; and group which assumes a difference between years. Results Multivariate analysis The cluster and ISA suggested five clusters or vegetation sub-co mmunities within the sloughs, which were named according to their indi cator species and placement on the ordination axes: transitional to sawgrass, emergent slough, emergent/lily slough, slough, and longer hydroperiod slough. Spatially, the transitional to sawgrass commun ity only occurred in plot 8 90

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and temporally only in 2006, but no other communiti es exhibited spatial or temporal trends. Average densities of species of interest were cal culated for each cluster to further describe the sub-community (Figure 5-3). The NMS analysis yielded a two-dimensional solution, with 40 runs of real data and 50 Monte Carlo runs (stress = 11.6, p = 0.02). The ax es explained 92.6% of the variation in the model, 55.7% and 36.9% respectively. Environmen tal vectors were overlain on the ordination, with the angle and length of the line indicating the direction and streng th of the correlation (Figure 4). Hydrologically, axis 1 was positively correlated (r2 = 0.15.257, r = |0.401.507|, p < 0.001) with the minimum of the previous dr y season, the minimum of the dry season four years previous, and the frequency for the wet season two years previous. Axis 2 was positively correlated (r2 = 0.193.470, r = |0.439.686|, p < 0.001) with the maximum water depth of entire previous water year; the dry seasons two, three, and four years previous; and all variables in the wet season two years previous. Densities of E. elongata P. geminatum, and P. hemitomon were negatively correlated with axis 2, as was nest success (r = -0.529, p < 0.001). N. odorata was positively correlated with axis 2 (r=0.606, p < 0.001). I also traced the progressi on of the sloughs through time in the NMS (Figure 5-5). Temporally, transects tend to move up both axes towards deeper communities (Figure 5-5). The ordination points for 2006 were spread in a wi der pattern across both ax es. A majority of transects transitioned from emergent slough to slough over time, indicating a loss of emergent species. Univariate Analysis The density data exhibited significant (p < 0.05) decreases of the emergents E. elongata, P. geminatum, B. caroliniana, and P. hemitomon and a significant in crease in the longer hydroperiod species N. odorata (Figure 5-6). This trend of decreasing emergent species 91

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corresponded to the species re sponse across the whole WCA3A la ndscape. Density over time was also analyzed for all 20 plots in the expa nded study area, and there was a significant (p < 0.05) decrease in density of all the major wet prairie species: E. elongata P. geminatum, E. cellulosa, and P. hemitomon. There was no significant change across the landscape in N. odorata This decline in emergent species de nsity supports the movements of slough subcommunities in the study area to deeper co mmunities in the multivariate analysis. Demographic analysis Program MARK modeled nest success w ithin WCA3AS and the most parsimonious model was the group model (AICc = 102.3251, AICc = 0.00, AICc weight = 1.0, number of parameters = 5). Yearly nest succes s estimates for 2002 were 53.6% (SE = 0.00613), 100% (SE = 0.000), 100% (SE = 0.000), 0.941% (SE = 0.0601), and 8.67% (SE = 0.00820), respectively. The dot model was for comparison only and AICc = 129.7933, AICc = 27.4682, AICc weight = 0, and number of parameters = 1. Discussion Snail Kites were once thought to be highly nomad ic and resistant to localized disturbances (Bennetts and Kitchens 2002), but a recent study (M artin et al. 2006) suggests they exhibit more site fidelity than previously considered, especia lly juveniles. From the kites perspective, the Everglades watershed can be cons idered a network of discrete ha bitats or regions (Kitchens and Bennetts 2002). Theoretically, this network continues to function pr operly (i.e. net gain in kite population) even if regions are off line (Figure 5-7), but there is a threshold at which the viability of the network is compromised (Bennetts and Kitchens 1997). The Snail Kites network seems to have exceeded this threshold and the popul ation is responding ne gatively with reduced reproductive success (Kitchens et al 2006). Offline regions could have more of an effect on the Snail Kite population than simply forcing migrat ion to an online region as previously believed 92

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they could trap birds with high natal philopatry and decrea se juvenile survival and recruitment. This underscores the importance of maintaining multiple online regions of quality habitat, especially those considered cr itical to the Snail Kites. WCA3A has been the most critical habitat uni t within the Snail Kites range, providing both the largest extent of quali ty nesting and foraging habitats and the highest juvenile production (Kitchens et al 2006). Given the im portance of WCA3A within the Snail Kites habitat network (Kitchens et al. 2002, Martin 2007), the vegetation community transformations documented in this study are particularly per tinent and may help expl ain why WCA3A appears to be offline for reproduction and recruitment. Four out of seven transects in the study transitioned or remained in a deeper, less desi rable Snail Kite foraging habitat, while two transitioned to light sawgrass. Many transects made abrupt changes in community composition in 2005 due to hurricane Wilma, but returned to more normal community compositions in 2006. I demonstrated that even in a re latively short period of four ye ars, wet prairie/emergent sloughs are converting to deeper, less desirable Snail Kite habitats in response to hydrologic factors, with a strong temporal trend (F igure 5-5). Important em ergent species, such as E. elongata P. geminatum, and P. hemitomon declined significantly in a relatively short amount of time. Emergents were replaced by N. odorata, a species that has less valu e as foraging habitat to the Snail Kite and its prey base (Bennett s et al. 2006, Karunarante et al. 2006). Both Eleocharis species used in the study are perenni als that grow best in shallowly flooded conditions (Macek et al. 2006). Accordi ng to the results, if the minimum and maximum water levels of the recent and historic (>1 y ear previous) dry seasons are too low, the area transitions to light sawgrass. However, if dr y seasons are too wet, emergent vegetation is reduced and results in a N. odorata-dominated community. Once N. odorata and sawgrass are 93

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established, their life history characteristics allo w them to persist in non-ideal conditions. Once established, they could shade out Eleocharis spp which are less tolerant to harsh conditions (Edwards et al. 2003), slowing the return to an emergent slough community when favorable hydrologic conditions return, resulting in a long-term loss of foraging habitat. Connecting foraging and nesting habitat availa bility and breeding performance is the key to a sound conservation strategy for the Florida Sna il Kite. Variation in habitat quality and water levels influence breeding success of birds (Johnson 2007), particular ly the Snail Kite that is adapted to dynamic wetlands (B eissinger and Snyder 2002, Bennetts et al 2006). While there is not a solid link between nest success and vege tation community composition, the correlations demonstrated here suggest that nest success co uld be associated to the structure of slough communities in WCA3A and the hydrology that shapes those communities. Nest success is also associated with the density of P. geminatum and negatively associated with the density of N. odorata similar to the abundance of th e Snail Kites main prey, the apple snail (Karunarante et al. 2006). This suggests that the decline of emergent species and increase in N. odorata could have an effect on nest success. I demonstrate that foraging habitats respond relatively quickly to altered hydrology, especially maximum and minimu m water depths in the dry seasons, which encompass a large part of the Snail Kites bree ding season. Restoring and maintaining quality foraging habitat for reproduction of Snail Ki tes in WCA3A, using the provided hydrologic variables as input, might be completed quickly and should be a primary consideration in water management decisions for the future. One point of note was that only nest success, not number of nests, was associated with habitat community composition. Ne st success was used as the primary demographic variable because it is a sensitive indicat or of Snail Kite population stability (Donovan and Thompson 94

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2001) that responds quickly to subtle environmental perturbations, and reproductive success, not the previously considered adult survival, is vital to this critic ally endangered population (Martin 2007). Our conclusions are supported by the fact th at hydrology has previously been associated with Snail Kite reproductive success. Highe r maximum water depths prior to the breeding season are linked to poor Snail Kite nest succe ss, and increased drying event frequencies are linked to lower juvenile survival, population growth, and nest success (Beissinger and Snyder 2002, Martin et al. 2006). The time period in which community and species density changes occurred is similar to previous studies of Everglades vegetation (A rmentano et al. 2006, Childer s et al. 2006), but I provide specific hydrologic factors that can be used as inputs in adaptive management decisions to improve Snail Kite habitat in WCA3A. This is not to say that hydrol ogic variables computed for this study are the only environmental factor s correlated with community composition in Snail Kite foraging habitat of WCA3A. They ar e, however, a starting pointa foundation for adaptive management decisions to increase re productive success by re storing a critically endangered species habitat. 95

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Table 5-1: Number of nest s and nest success from 2002 within Water Conservation Area 3A South, FL, USA. Year Total Nests Percent Nest Success 2002 22 0.5366 2003 22 1 2004 8 1 2005 2 0.941 2006 76 8.67 Table 5-2: Increasing maximum water levels and hydrologic range in cm within the core breeding area (Plots 7, 8, and 9) of Water Area 3A South, FL, USA. Linear regression and R2 values of depth and range are shown below each section of the table. Maximum Water Depths Year 2000 2001 2002 2003 2004 2005 Well 7 55.9 73.5 76.2 89.8 93.5 95.6 Well 8 70.0 86.7 89.4 103.1 107.1 111.2 Well 9 60.9 77.3 79.9 94.5 98.8 89.8 y = 7.7683x + 53.575 R2= 0.9155 Hydrologic Range Year 2000 2001 2002 2003 2004 2005 Well 7 38.2 34.9 62.9 61.1 76.4 74.2 Well 8 47.5 33.3 76.4 61.1 87.3 80.3 Well 9 47.0 34.6 71.3 66.7 86.0 80.1 y = 8.6574x + 27.631 R2 = 0.8395 96

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Figure 5-1: Expanded study s ites in Water Conservation Area 3A South, FL, USA 97

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Figure 5-2: Movement of nesting concentrati on in Water Conservation Area 3A, FL, USA. White circle indicates cri tical breeding area and focus of study. Adapted from Bennetts et al 1998. 98

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Figure 5-3: Densities of key emergent sl ough species by community in Water Conservation Area 3A. PDG = P. geminatum, PAH = P. hemitomon, NYO = N. odorata, ELsp = E. cellulosa, and ELG = E. elongata. 99

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Figure 5-4: Non-metric multidimensional scaling or dination of Snail Kite habitat communities in Water Conservation Area 3A. Vectors repres ent key environmental correlates with r2 0.15 (p < 0.009). NYO = N. odorata, PDG = P. geminatum, Success = nest success. 100

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Figure 5-5: Temporal movement of Snail Kite habitat communities in Water Conservation Area 3A from 2002-2006. Dotted and solid lines repr esent different transects within a plot moving through time. Bubbles on All graph en compass a majority of points from that year and show trends of communities over time. 101

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102 Figure 5-6: Nest success and densities of Snail Kite foraging habitat species in Water Conservation Area 3A. Values have been relativized for display purposes (value/max in group).

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Figure 5-7: Snail kite habitat network in FL, USA. Black squares = region is online for reproduction, diagonal lines = area is questio nable, and white squares = offline. ? for a region indicates that I am unsure if it is a source or a sink due to widespread presence of an exotic apple snail. Off line = production index <= 5%. Questionable = production index <=15%. Production index = # young produced in area in year/ Total # young produced in year production poten tial. Production potential = Total # young produced in year/ maximum total of young produced per year. 103

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CHAPTER 6 DISCUSSION An essential function of scienc e within restoration is to provide the knowledge to predict outcomes of management actions (Zedler 2000). Lockwood et al (2000) identified a lack of knowledge concerning Everglades processes that are needed to complete, or even begin, restoration. This dissertation explores the complex interactions between wildlife, hydrology, and vegetation ecology in the Everglades, and provi des links between conceptual and applied information and baseline da ta for its restoration. Wetland Restoration Zedler (2000) provided 10 ecol ogical principles that are of ten neglected in many wetland restoration efforts: 1. Landscape context and position are crucial to wetland restoration 2. Natural habitat types are the appropriate reference system 3. The specific hydrological regime is crucial to restoring biodiv ersity and function 4. Ecosystem attributes deve lop at different paces 5. Nutrient supply rates affect biodiversity recovery 6. Specific disturbance regimes can increase species richness 7. Seed banks and dispersal can limit recovery of plant species richness 8. Environmental conditions and life history tr aits must be considered when restoring biodiversity 9. Predicting wetland restoration be gins with succession theory 10. Genotypes influence ecosystem structure and function This dissertation addresses 5 of 10 of thes e principles in reference to Everglades restoration: numbers 2, 3, 4, 8, and 9. 104

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Point 2 and 3: Choosing reference habitat t ypes is difficult in Ev erglades restoration, because there are only general de scriptions of the plant communiti es and their arrangement at the multiple scales required. Drainage began in the 1880s (Sklar et al 2005), so most accounts describe an altered system. While the comm unities described in Chapter 2 would not be reference communities, they are the most detailed descriptions available of vegetation in Water Conservation Area 3A South and should be re ferences for water management decisions, particularly considering the hydrologic correlat es I provide for community composition. These correlates in both Chapters 2 and 3 relate specific hydrologic characteristics to vegetation communities, which provide initial data for restoration water regime alternatives, especially when restoring a specific sub-community type. Point 4: Ecosystem attributes of the Everglades develop at three differe nt temporal scales: long-term, periodic, and discrete (DeAngleis and White 1994). Ch apter 4 explores the complex pattern/process relationships of two scales (periodic and discre te) in the Everglades and how degraded feedback loops are created and mainta ined. I also quantify the degradation of the characteristic ridge and slough landscape from the disturbance of the pattern and process on both periodic and discrete levels. Point 8: The range of possible environmenta l conditions should be taken into account when considering targets for Ever glades restoration. A complete restoration is not possible as the human population of south Fl orida still requires wa ter storage and flood control from the remnants of the Everglades (Sklar et al 2005) I need to be conscious of the feasible environmental conditions and the vegetation commun ities that are suited to those conditions, and analyses within this dissertation contri bute to the required knowledge base of 105

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106 vegetative/hydrologic interactions I also suggest species that are possible indicators of restoration based on life-history traits and how they interact with the environmental conditions. Point 9: Chapter 3 provides a general wetland and applied succession m odel in the form of state and transition models. I explore the non-linearity of wetland su ccession (Zedler 2000), potential hysteresis, and transitory communities that are possible within restoration. Awareness of these possibilities allows managers to av oid unwanted states that could occur from uninformed water management decisions. Habitat Restoration I provide evidence for the alteration and possible degradation of an endangered species habitat within the Everglades, and how nest su ccess of the Snail Kite is linked to community composition and hydrologic factors. By iden tifying the hydrology correlated to vegetation community composition, I also provide possible pathways for habitat restoration. Foraging habitats respond relatively qui ckly to altered hydrology, espe cially maximum and minimum water depths in the dry seasons, which encompas s a large part of the Snail Kites breeding season. Restoring and maintaini ng quality foraging habitat for re production of Snail Kites in WCA3A, using the provided hydrologic variables as input, might be completed quickly and should be a primary consideration in wate r management decisions for the future. Information for Large-Scale Restoration I provide a detailed look at th e vegetation ecology of an Everglades remnant and explore how vegetation communities intera ct with environmental character istics. The most important use of this information is to information for the restoration of the Everglades. The political and scientific process of Everglades restoration will serve as a model (either positive or negative) for future large-scale restoration efforts (Sklar et al 2005), and I attempt to provide current vegetation information and contribute as scientists to the process.

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APPENDIX DETAILED VEGETATION SAMPLING PROTOCOL AND SITE INFORMATION Sampling began in November 2002 and concl uded in July 2005. We selected 20 1-km2 plots by randomly choosing 20 points on an 1 km x 1 km grid over the study area to represent the northwest corner of each plot (Table A-1). Two or three belt transects we re located in each plot (Table A-2) by dividing each plot into a 100 m x 100 m grid. Two random numbers were generated between 1 and 10 (a and b). Using an ai rboat and starting in the northwest corner of a plot, we drove 100*a meters due east and 100*b meters due south. At that point, a transect was placed perpendicular to an ecotone between two representative a priori communities (ex: slough to sawgrass). PVC poles were plac ed at the start and end of a tr ansect, and at the transition(s) between a priori communities. The length, orientation, a nd number of samples on the transect were recorded (Table A-3). Belt transects were used to allow the removal of biomass at every sample site for multiple sample events. Each belt transect (Figure A-1) consists of three pairs of lettered transects spaced 4 m apart. Each lettered transe ct has a 1 m walkway to keep trampling of the sample area to a minimum, and two alternating sample events on each side of the walkway. For example, sample event B would start at the bottom lettered transect and a 0.25m2 sample would be taken at the transect pole and every 3 m after that. The first 0.25m2 sample of C would start 1.5m from the bottom transect pole and continue every 3 m from that point. Quarter-meter squared vegetation samples were taken every 3 meters at an arms length away from the walkway. A wooden dowel was placed at the sample point as a refere nce for the sampling hoop, and we collected any floating vegetation and clipped ot her vegetation at substrate leve l. Approximately 1200 samples per sample event were cut. Plants were sort ed by species, dried to a constant weight, and weighed to the nearest 0.01 g. 107

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We scheduled two sample events each yearone in the wet and one in the dry season. The dry season sample event typically occurred in J une or early July. Crit erion for the start date of the dry season sample event was accessibility of the area by airboat after the lowest water depths of the season had occurred. The wet season sample event occurred in November or early December. Criterion for the start of the wet seas on sample was to sample as near peak water depths as possible without sacrificing accurate sa mpling protocols due to deep water depths and poor water visibility. 108

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Table A-1: GPS coordinates fo r study plot corners in Water Conservation Area 3AS. Coordinates are UTM, NAD83. N = nor th, S = south, E = east, W = west. Plot corner UTM X UTM Y 0NE 525847 2852281 0NW 524847 2852281 0SE 525847 2851281 0SW 524847 2851281 1NE 520847 2857281 1NW 519847 2857281 1SE 520847 2856281 1SW 519847 2856281 2NE 519847 2854281 2NW 518847 2854281 2SE 519847 2853281 2SW 518847 2853281 3NE 521847 2852281 3NW 520847 2852281 3SE 521847 2851281 3SW 520847 2851281 4NE 527847 2855281 4NW 526847 2855281 4SE 527847 2854281 4SW 526847 2854281 5NE 530847 2853281 5NW 529847 2853281 5SE 530847 2852281 5SW 529847 2852281 6NE 529847 2858281 6NW 528847 2858281 6SE 529847 2857281 6SW 528847 2857281 7NE 522847 2860281 7NW 521847 2860281 7SE 522847 2859281 7SW 521847 2859281 8NE 522847 2858281 8NW 521847 2858281 8SE 522847 2857281 8SW 521847 2857281 9NE 520847 2860281 9NW 519847 2860281 9SE 520847 2859281 9SW 519847 2859281 10NE 524847 2869281 10NW 523847 2869281 109

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110 Table A-1. Continued Plot corner UTM X UTM Y 10SW 523847 2868281 11NE 525847 2870281 11NW 524847 2870281 11SE 525847 2869281 11SW 524847 2869281 12NE 531847 2868281 12NW 530847 2868281 12SE 531847 2867281 12SW 530847 2867281 13NE 532847 2877281 13NW 531847 2877281 13SE 532847 2876281 13SW 531847 2876281 14NE 533847 2869281 14NW 532847 2869281 14SE 533847 2868281 14SW 532847 2868281 15NE 532847 2878281 15NW 531847 2878281 15SE 532847 2877281 15SW 531847 2877281 16NE 529847 2877281 16NW 528847 2877281 16SE 529847 2876281 16SW 528847 2876281 17NE 521847 2869281 17NW 520847 2869281 17SE 521847 2868281 17SW 520847 2868281 18NE 522847 2876281 18NW 521847 2876281 18SE 522847 2875281 18SW 521847 2875281 19NE 522847 2872281 19NW 521847 2872281 19SE 522847 2871281 19SW 521847 2871281

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Table A-2: Characteristics of transects a nd GPS coordinates of transect start (STA), ecotone (BND), and end poles (END) in Wate r Conservation Area 3AS. Coordinates are UTM, NAD83. Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 0-1 slough/sawgrass 262 57.8 21 BND 525655 2852133 0-1 BND2 525614 2851796 0-1 END 525628 2852121 0-1 STA 525683 2852142 0-2 prairie/sawgrass/slough/ghos t island 91 98.4 33 BND2 525588 2851797 0-2 BND3 525643 2851803 0-2 END 525662 2851801 0-2 STA 525566 2851796 0-3 slough/sawgrass 91 45.6 16 BND 525161 2851697 0-3 END 525186 2851703 0-3 STA 525130 2851694 1-1 prairie/sawgrass 100 75 26 BND 519939 2856715 1-1 END 519966 2856715 1-1 STA 519888 2856720 1-2 slough/sawgrass 120 36 13 BND 520781 2856743 1-2 END 520789 2856741 1-2 STA 520751 2856742 1-3 prairie/sawgrass 120 28.8 10 BND1 520817 2857044 1-3 BND2 520830 2857036 1-3 END 520852 2857026 1-3 STA 520814 2857051 2-1 prairie/typha 270 60 21 BND 519013 2853940 2-1 END 518979 2853938 2-1 STA 519064 2853944 2-2 prairie/typha 279 63 22 BND 519234 2854116 2-2 END 519204 2854118 2-2 STA 519267 2854115 1112-3 sawgrass/prairie/sawgrass 110 66 23 BND1 519343 2853608 2-3 BND2 519371 2853593

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Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 2-3 END 519385 2853587 2-3 STA 519325 2853615 3-1 slough/wet prairie 122 76 26 BND1 521502 2851785 3-1 BND2 521539 2851770 3-1 END 521555 2851762 3-1 STA 521488 2851794 3-2 slough/ sawgrass 180 51.5 17 BND 521088 2851700 3-2 END 521055 2851701 3-2 STA 521102 2851697 4-1 slough/sawgrass/slough 74 76.3 27 BND1 527351 2854845 4-1 BND2 527367 2854850 4-1 END 527383 2854859 4-1 STA 527285 2854830 4-2 slough/shrub island 108 37.6 12 BND 527272 2854538 4-2 END 527310 2854533 4-2 STA 527236 2854545 4-3 slough/sawgrass 81 48 16 BND 527791 2855072 4-3 END 527820 2855068 4-3 STA 527773 2855061 5-1 slough/sawgrass 283 60 21 BND 530083 2852784 5-1 END 530043 2852793 5-1 STA 530101 2852782 5-2 slough/sawgrass 210 50.4 18 BND 530594 2852337 5-2 END 530582 2852306 5-2 STA 530598 2852352 5-3 slough/sawgrass 58 27 10 BND 530556 2852717 5-3 END 530570 2852728 5-3 STA 530547 2852711 1126-1 slough/sawgrass/slough 63 66 24 BND1 529102 2858010 6-1 BND2 529116 2858021

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Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 6-1 END 529141 2858035 6-1 STA 529086 2858003 6-2 slough/sawgrass 78 40.8 14 BND 529262 2857430 6-2 END 529290 2857434 6-2 STA 529248 2857426 6-3 slough/sawgrass 270 39.3 14 BND 529485 2857747 6-3 END 529470 2857739 6-3 STA 529507 2857743 7-1 slough/sawgrass 60 42 15 BND 522569 2859938 7-1 END 522586 2859947 7-1 STA 522550 2859923 7-2 prairie/sawgrass/shrub island 290 41.3 14 BND 521914 2859473 7-2 END 521905 2859473 7-2 STA 521943 2859475 7-3 slough/light sawgrass/slough 124 83 29 BND1 522317 2859722 7-3 BND2 522306 2859728 7-3 END 522350 2859704 7-3 STA 522277 2859742 8-1 slough/sawgrass 105 45 16 BND 522533 2858025 8-1 END 522559 2858030 8-1 STA 522515 2858034 8-2 slough/sawgrass 213 30 11 BND 522644 2857588 8-2 END 522633 2857576 8-2 STA 522663 2857594 8-3 slough/sawgrass 270 30 11 BND 522585 2857319 8-3 END 522576 2857318 8-3 STA 522605 2857319 9-1 slough/sawgrass 282 39 14 BND 520170 2860114 1139-1 END 520155 2860118 9-1 STA 520189 2860113

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Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 9-2 slough/sawgrass/shrub island/sawgrass/slough 92 94.3 32 BND1 520686 2859967 9-2 BND2 520731 2859969 9-2 END 520762 2859971 9-2 STA 520669 2859963 10-1 slough/sawgrass/slough 114 87 30 BND2 524325 2868818 10-1 BND1 524473 2869064 10-1 END 524379 2868801 10-1 STA 524298 2868829 10-2 slough/sawgrass/slough/sawgrass 88 99 34 BND2 524493 2869070 10-2 END 524549 2869075 10-2 STA 524450 2869062 10-3 slough/sawgrass 240 45 16 BND 524614 2868518 10-3 BND1 524624 2868520 10-3 END 524603 2868511 10-3 STA 524643 2868530 11-1 slough/sawgrass/slough 76 82 28 BND 525528 2870138 11-1 END 525578 2870158 11-1 STA 525501 2870129 11-2 prairie/sawgrass 58 72 25 BND 525520 2869521 11-2 END 525542 2869535 11-2 STA 525485 2869492 11-3 slough/sawgrass 43 29 10 BND 525704 2869725 11-3 END 525718 2869735 11-3 STA 525681 2869716 12-1 slough/light sawgrass/slough 80 51.2 18 BND 531164 2868038 12-1 END 531195 2868044 12-1 STA 531122 2868014 11412-2 slough/sawgrass 80 66.2 23 BND 531740 2867605 12-2 END 531762 2867603

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Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 12-2 STA 531702 2867596 13-1 prairie/sawgrass 60 69.2 24 BND 532651 2876890 13-1 END 532667 2876889 13-1 STA 532619 2876868 13-2 slough/sawgrass,typha 259 60.2 21 BND 532544 2876668 13-2 BND2 532486 2876668 13-2 END 532478 2876669 13-2 STA 532601 2876666 13-3 slough/prairie/sawgrass 244 97 33 BND1 532319 2876426 13-3 BND2 532301 2876434 13-3 END 532264 2876411 13-3 STA 532344 2876455 14-1 slough/sawgrass 80 58 20 BND 533148 2869079 14-1 END 533171 2869085 14-1 STA 533116 2869067 14-2 slough/light sawgrass/slough 80 63 22 BND 533853 2868895 14-2 END 533890 2868896 14-2 STA 533833 2868890 14-3 slough/sawgrass 100 42 15 BND1 533214 2868748 14-3 BND2 533231 2868752 14-3 END 533236 2868744 14-3 STA 533193 2868752 15-1 slough/sawgrass 310 92 31 BND 532503 2877856 15-1 END 532429 2877888 15-1 STA 532511 2877855 15-2 slough/ghost island 235 78 27 BND1 532529 2877522 15-2 END 532466 2877490 15-2 STA 532606 2877572 11516-1 sawgrass/prairie/sawgrass 105 99.2 34 BND1 529598 2877096 16-1 BND2 529638 2877092

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Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 16-1 END 529681 2877084 16-1 STA 529584 2877100 16-2 prairie/sawgrass 240 44.2 16 BND 529576 2876445 16-2 END 529555 2876431 16-2 STA 529595 2876453 16-3 ghost island/sawgrass, shrub 95 93.4 32 BND 528904 2876819 16-3 END 528951 2876816 16-3 STA 528859 2876805 17-1 prairie/sawgrass/shrub island 94 17.8 25 BND1 521188 2868735 17-1 BND2 521213 2868756 17-1 END 521228 2868738 17-1 STA 521162 2868734 17-2 slough/sawgrass/slough 90 87.3 30 BND1 521851 2868722 17-2 BND2 521887 2868724 17-2 END 521896 2868722 17-2 STA 521822 2868722 17-3 prairie/sawgrass/prairie 100 67.3 23 BND 521573 2868907 17-3 BND2 521591 2868903 17-3 END 521621 2868895 17-3 STA 521553 2868909 18-1 sawgrass/prairie/sawgrass,shrub 110 58.7 21 BND1 522207 2876156 18-1 BND2 522171 2876167 18-1 END 522161 2876168 18-1 STA 522219 2876153 18-2 sawgrass/prairie/slough/sawgrass 105 70.3 24 BND1 522165 2875526 18-2 BND2 522178 2875524 18-2 BND3 522211 2875522 18-2 END 522223 2875523 11618-2 STA 522151 2875528 18-3 slough/sawgrass/slough 270 73 25 BND1 522607 2875941

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117Table A-3. continued Plot-Transect Community types Orientation (degrees) Length (m) # of samples Point Type UTM X UTM Y 18-3 BND2 522563 2875939 18-3 END 522553 2875941 18-3 STA 522623 2875938 19-1 slough/prairie/sawgrass 261 48 17 BND 522135 2871678 19-1 END 522155 2871684 19-1 STA 522108 2871673 19-2 prairie/light sawgrass/prairie 274 72 25 BND1 522566 2871978 19-2 BND2 522547 2871979 19-2 END 522521 2871979 19-2 STA 522593 2871978 19-3 slough/sawgrass 90 42.2 15 BND 522530 2871411 19-3 END 522552 2871413 19-3 STA 522512 2871411

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118 Figure A-1: Belt transect di agram for vegetation sampling in Water Conservation Area 3AS

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BIOGRAPHICAL SKETCH Christa Zweig grew up in Springfield, MO and spent a significant portion of her summer breaks on the beach in coastal GA. She earned her B.S. in Biology at the University of Richmond, VA, and tried out life as a biologist at her first field techni cian job in the Great Smoky Mountains during her summers in Richmond. This led to technician jobs across the country, from Florida to Texas a nd California. She found herself back in Florida in 1999 starting a M.S. degree at the University of Florida. She studied the body condition of alligators as an indicator of restoration success a nd transitioned into her PhD at th e University of Florida, still working in the Everglades. 128