University of Florida Thesis Or Dissertation Formatting

IMPACTS OF SALTWATER INTRUSION AND HYDROLOGIC CHANGE TO SALT MARSH AND COASTAL FOREST OF FLORIDA’S BIG BEND

KATIE GLODZIK

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

ACKNOWLEDGMENTS

Thank you to my advisor, Dr. David Kaplan, for your limitless enthusiasm in supporting my work and your dedication to your students’ success. I thank my committee members, Dr. Amr Abd-Ehlrahman, Dr. Todd Osborne, Dr. Peter Frederick, and Dr. Carrie Reinhardt Adams, for providing technical expertise and encouragement, and for your challenging questions that led me to continually improve my research.

Thank you to those professors who have helped me with research methods or by providing other guidance for academic success, including Dr. Bill Pine, Dr. Mark Clark,

Dr. Christine Angelini, Dr. Jon Martin, and Dr. Tom Frazer. I also sincerely thank staff at the Lower Suwannee National Wildlife Refuge for your logistical support and for sharing your extensive knowledge of refuge ecology.

I am grateful to those who helped me in the field, especially Samantha Arrowood and Rick Vaughn, for spending numerous long days boating, collecting hundreds of sediment samples, and counting thousands of black needlerush stems and fiddler crab burrows. Thank you to Garrett Simms, Jerry Beckham, and Laura Adams for your boating and field assistance and for sharing your observations of coastal changes overtime, as well as your anecdotes about clamming and oystering. Finally, thank you to my fellow Watershed Ecology Lab students for expanding my ecohydrology knowledge and for being a joy to work alongside.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 3

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 12

ABSTRACT ...... 14

CHAPTER

1 INTRODUCTION ...... 16

Coastal Ecosystems of Florida’s Big Bend ...... 16 Saltwater Intrusion and Hydrologic Changes ...... 18 Dissertation Overview ...... 20

2 UNTANGLING TRENDS AND DRIVERS OF RIVER DISCHARGE ALONG FLORIDA’S BIG BEND COASTLINE ...... 23

Global Trends in Water Supply and Scarcity ...... 23 Water Resource Challenges in Florida’s Big Bend Region ...... 24 Challenges in Explaining Big Bend River Discharge Patterns ...... 27 Surface-Groundwater Interactions ...... 27 Effects of Groundwater Extraction ...... 29 Effects of Land Cover Change ...... 30 Benefits of Dynamic Factor Analysis ...... 31 Methods ...... 32 Study Area ...... 32 Response Variables (Discharge Data) ...... 33 Explanatory Variables ...... 33 Dynamic Factor Analysis ...... 35 Modeling Approach ...... 37 Results ...... 39 Response and Explanatory Variables ...... 39 Model I Results ...... 42 Model II Results ...... 43 Model III Results ...... 44 Discussion ...... 45 Model I: Interpreting Common Trends ...... 45 Model II: Interpreting Explanatory Variables ...... 46 Model III: Model Predictive Power ...... 49 Model Summary ...... 50

3 IMPACTS OF ROADS ON TIDAL MARSH HYDROLOGY AND ECOLOGY ...... 70

Coastal Wetland Values and Threats from Human Development ...... 70 Road Impacts to Salt Marsh Tidal Flooding and Ecology...... 72 Importance of Tidal Flooding ...... 72 Potential Road Impacts ...... 73 Coastal Roads in Florida’s Big Bend Region ...... 75 Methods ...... 78 Study Sites ...... 78 Experimental Design ...... 79 Water Level and Groundwater Salinity ...... 80 Porewater ...... 80 Vegetation ...... 81 Invertebrates ...... 82 Sediment Properties and Elevation ...... 82 Statistical Analyses ...... 83 Results ...... 84 Water Level and Groundwater Salinity ...... 84 Porewater ...... 85 Vegetation ...... 86 Invertebrates ...... 89 Elevation ...... 90 Sediment ...... 91 Hydroperiod Correlations with Variables ...... 92 Discussion ...... 93

4 GEOGRAPHIC DRIVERS OF LONG-TERM COASTAL FOREST DIE-OFF AND CHANGE IN THE LOWER SUWANNEE NATIONAL WILDLIFE REFUGE ...... 122

Values of Coastal Forest Ecosystems ...... 122 Coastal Forest Die-Off along Florida’s Big Bend Coastline ...... 123 Methods ...... 128 Study Site ...... 128 Normalized Difference Vegetation Index Calculation ...... 128 Spatial Model to Analyze Geographic Drivers ...... 130 Identifying coastal forest pixels ...... 130 Distance measurements to water features ...... 131 Elevation and other land characteristics ...... 132 Statistical analysis ...... 133 Water Level and Salinity Monitoring ...... 133 Statistical analysis ...... 135 Results ...... 135 Spatial Model to Analyze Geographic Drivers ...... 135 Water Level and Salinity Monitoring ...... 138 Discussion ...... 139 Spatial Model ...... 139 Water Level and Salinity Monitoring ...... 143

5 CONCLUSIONS ...... 160

LIST OF REFERENCES ...... 165

BIOGRAPHICAL SKETCH ...... 177

LIST OF TABLES

Table page

2-1 USGS river discharge gages along Big Bend study area...... 52

2-2 Time series used in DFA (8 response variables, 55 explanatory variables) ...... 53

2-3 Size, impervious surface, and land cover types of watersheds ...... 54

2-4 Summary of models...... 54

2-5 River discharge: mean, baseflow, and intra-annual variability...... 54

2-6 Mann-Kendall two-sided test results for discharge, precipitation, PET, and discharge-to-precipitation ratio...... 55

2-7 Model II factor loadings (γm,n), canonical correlation coefficients (ρ1,n), regression coefficients (βk,n), and Nash coefficients (Ceff)...... 55

2-8 Model III multiple linear regression results...... 55

3-1 Mean salinity, tidal range, water level, and proportion of time inundated (C = coastal, I = inland) ...... 98

3-2 Vegetation species occurrence at sites (proportion of transect points with species present) ...... 98

3-3 Porewater salinity (PPT) at sites (mean ± SD) ...... 99

3-4 Invertebrate occurrence at sites (proportion of transect points with species present)...... 99

4-1 NDVI and elevation (mean and standard deviation) for wetland categories. .... 144

4-2 NDVI and elevation (mean and standard deviation) for coastal forest geographic variables ...... 144

4-3 Regression results for subsample of NDVI pixels ...... 144

4-4 Regression results for islands, where values are averaged by island ...... 145

4-5 Mean water level and salinity levels at groundwater monitoring wells...... 145

LIST OF FIGURES

Figure page

1-1 Big Bend geography, development, and managed lands...... 22

2-1 Map of eight main study river gages (black triangles) and ArcGIS-derived watersheds (green polygons) ...... 56

2-2 Annual river discharge for four northern-most rivers...... 57

2-3 Annual river discharge for four southern-most rivers...... 58

2-4 Boxplots of river discharge by month for four northern-most rivers...... 59

2-5 Boxplots of river discharge by month for four southern-most rivers...... 60

2-6 Annual precipitation of watersheds...... 61

2-7 Annual mean river discharge (m3/sec) divided by total watershed precipitation (cm)...... 62

2-8 Annual PET of watersheds...... 63

2-9 Groundwater level above NGVD 1929 at Rainbow Springs near Dunnellon...... 64

2-10 Climate indices values over time...... 64

2-11 Model I common trends and canonical correlation coefficients ...... 65

2-12 Model II explanatory variables and regression coefficients...... 66

2-13 Model II common trend and canonical correlation coefficients...... 66

2-14 Model II normalized actual river discharge (lines) and normalized modeled river discharge (points)...... 67

2-15 Model III normalized actual river discharge (lines) and normalized modeled river discharge (points)...... 68

2-16 Model I Trend 3 (black) compared to Rainbow groundwater level (blue) ...... 69

3-1 Overview map of study sites...... 100

3-2 Map of Sand Ridge Road transect points, with proximity to tidal creek connection (near/mid/far) labeled...... 101

3-3 Map of Horseshoe site transect points, with proximity to tidal creek connection (near/far) labeled...... 101

3-4 Map of Shired Island transect points, with proximity to tidal creek connection (near/mid/far) labeled...... 102

3-5 Map of Cabin Road transect points, with proximity to tidal creek connection (near/mid/far) labeled...... 102

3-6 Daily mean water depth (0 = ground level) Sept. 2015 to July 2016, for coastal vs. inland at road sites...... 103

3-7 Half-hourly groundwater salinity Sept. 2015 to July 2016, for coastal vs. inland at road sites...... 104

3-8 Half-hourly groundwater temperature Sept. 2015 to July 2016, for coastal vs. inland at road sites...... 105

3-9 Sand Ridge porewater salinity by transect from four sample events...... 106

3-10 Horseshoe porewater salinity by transect from three sample events...... 106

3-11 Shired Island porewater salinity by transect from three sample events ...... 107

3-12 Cabin Road porewater salinity by transect from three sample events...... 107

3-13 Results from temporal test of porewater salinity variability over 36 hours (left side of each graph is coastal, right side is inland) ...... 108

3-14 Results from spatial test of porewater salinity variability across 1 m2 (left side of each graph is coastal, right side is inland)...... 108

3-15 Porewater NH4 by transect from May 2016 ...... 109

3-16 Porewater NO3 by transect from May 2016 ...... 109

3-17 Percent S. alterniflora biomass by transect ...... 110

3-18 Total biomass by transect...... 110

3-19 Total J. roemerianus biomass by transect...... 111

3-20 Percent live J. roemerianus biomass by transect ...... 111

3-21 Total stem density by transect...... 112

3-22 Total J. roemerianus density by transect ...... 112

3-23 Average J. roemerianus height by transect ...... 113

3-24 L. irrorata snail densities by transect ...... 113

3-25 I. obsoleta densities by transect for Cabin Road...... 114

3-26 M. coffeus densities by transect...... 114

3-27 Uca spp. burrow densities by transect...... 115

3-28 G. demissa counts by transect ...... 115

3-29 Bulk density by transect...... 116

3-30 Percent soil organic matter by transect...... 116

3-31 Percent sand by transect...... 117

3-32 Percent silt by transect...... 117

3-33 Percent clay by transect ...... 118

3-34 Belowground biomass by transect...... 118

3-35 Belowground-to-aboveground biomass ratio by transect ...... 119

3-36 Elevation (meters > NAVD88) along Sand Ridge transects...... 119

3-37 Elevation (meters > NAVD88) along Horseshoe transects...... 120

3-38 Elevation (meters > NAVD88) along Shired Island transects ...... 120

3-39 Elevation (meters > NAVD88) along Cabin Road transects ...... 121

4-1 Ecosystem categories of the study area, based on NWI data...... 146

4-2 Example of the coastal forest grid cells and diagram of flow distance to the nearest tidal creek (yellow arrow), Gulf of Mexico (orange arrow), and Suwannee River mouth (red arrow) ...... 147

4-3 NDVI results for all pixels...... 148

4-4 Forest categories and areas excluded from analysis...... 149

4-5 NDVI (by pixel) versus elevation, distance to Gulf, and distance to tidal creek. 150

4-6 NDVI (by island) versus elevation, distance to Gulf, and perimeter-to-area ratio...... 151

4-7 Predicted NDVI versus observed NDVI for pixel model and island mean model...... 152

4-8 NDVI residuals for all pixels, applying regression results from pixel sample. ... 153

4-9 NDVI residuals in two areas showing potential road impact to forest health. ... 154

4-10 Groundwater and tidal creek monitoring sites in LSNWR...... 155

4-11 Groundwater and tidal creek monitoring, salinity (black) and water level (grey)...... 156

4-12 Daily Suwannee River discharge, rainfall, and tidal levels...... 157

4-13 Cross correlations of monitoring well water level with Suwannee River discharge, rainfall, and tidal levels at different time lags ...... 158

4-14 Cross correlations of site salinity with Suwannee River discharge, rainfall, and tidal levels at different time lags ...... 159

LIST OF ABBREVIATIONS

AIC Aikake information criterion

AMO Atlantic Multidecadal Oscillation

ANOVA Analysis of variance

CCF Cross correlation function

Ceff Nash and Sutcliffe coefficient of efficiency

CLC Cooperative Land Cover

CO2 Carbon dioxide

DEM Digital elevation model

DFA Dynamic factor analysis

DWE Dry weight equivalent

ENSO El Niño-Southern Oscillation

GEE Google Earth Engine

LiDAR Light Detection and Ranging

LOWESS Locally weighted scatterplot smoothing

LSNWR Lower Suwannee National Wildlife Refuge

MK Mann-Kendall

NAO North Atlantic Oscillation

NAVD88 North American Vertical Datum of 1988

NDVI Normalized Difference Vegetation Index

NH4 Ammonium

NHD National Hydrography Dataset

NIR Near infrared

NOAA National Oceanic and Atmospheric Administration

NWI National Wetland Inventory

NO3 Nitrate

PET Potential evapotranspiration

PPT Parts per thousand

PSD Particle size distribution r Correlation coefficient

R2 Coefficient of determination

SD Standard deviation

SWIR Short-wave infrared

TOA Top of atmosphere

TM Thematic Mapper

USGS United States Geological Survey

VIF Variable inflation factor

WMA Wildlife Management Area

WMD Water Management District

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

IMPACTS OF SALTWATER INTRUSION AND HYDROLOGIC CHANGE TO SALT MARSH AND COASTAL FOREST OF FLORIDA’S BIG BEND

Katie Glodzik

August 2018

Chair: David Kaplan Major: Interdisciplinary Ecology

Saltwater intrusion from sea level rise, climate change, and groundwater extraction threatens coastal wetlands worldwide. On Florida’s Big Bend coast, transformative ecosystem effects, including widespread coastal forest die-off, are already apparent. Studies attribute die-off to sea level rise and decreased river discharge into estuaries, though the contribution from each is unclear. The coastline’s karstic bedrock creates spatially variable, fresh groundwater discharge, which confounds typical relationships between land surface elevation and saltwater exposure.

Additionally, otherwise pristine wetlands are bisected by roads, which restrict tidal flow to one side, altering structure and ecology. This study examines climatic, geographic, and anthropogenic drivers of saltwater intrusion in the Big Bend. Chapter 1 provides an overview of physical and ecological conditions along the coastline. Chapter 2 uses

Dynamic Factor Analysis (DFA) to analyze trends in discharge of eight rivers and determine drivers of discharge, revealing a relatively high level of shared behavior between rivers. Precipitation and groundwater were the strongest discharge predictors, while evapotranspiration was less important. Chapter 3 uses field assessment to measure road impacts to salt marsh tidal flooding, physical structure, and ecology at

four sites. Tidal flooding was found to be restricted on the inland side, and the strongest impact was a dramatic reduction in invertebrate densities. Chapter 4 uses remote

sensing to examine geographic predictors of coastal forest stress, and field monitoring

to assess drivers of coastal forest groundwater signals. Characteristics associated with

greater forest stress included lower elevation, being an island rather than continuous

forest, and a higher perimeter-to-area ratio of islands. Groundwater monitoring revealed

that salinity response to Suwannee River discharge, local rainfall, and tidal levels varied

greatly across sites, even in cases where wells were located in islands close together

and at similar elevations. This dissertation provides important information on drivers of

saltwater intrusion along the Big Bend, and it advances methods for studying regional

hydrologic patterns and modeling saltwater intrusion in wetlands. By documenting road

impacts to coastal wetland ecology, it also reveals an opportunity to minimize a stressor

that constrains ecosystem resilience to sea level rise.

CHAPTER 1 INTRODUCTION

Coastal Ecosystems of Florida’s Big Bend

Extending from Apalachee Bay southeast to Anclote Key, the Big Bend of west coastal Florida is a low gradient and low wave energy coastline (Figure 1-1; Hine et al.

1998). Nearshore areas are home to seagrass beds of Thalassia testudinum (turtle grass) and Syringodium filiforme (manatee grass) and oyster reefs of Crassostrea virginica (eastern oyster). Much of the coastline has wide expanses of salt marsh, dominated by Juncus roemerianus (black needlerush), and coastal forest of primarily

Sabal palmetto (cabbage palm), Juniperus virginiana (red cedar), Quercus virginiana

(live oak), and Pinus elliottii (slash pine). These ecosystems collectively attenuate wave

action and prevent coastal erosion, reduce storm surge damage to coastal property and

inland ecosystems, improve water quality, foster economically and culturally important

fisheries, and provide foraging and nesting grounds for shorebirds (Dawes et al. 2004,

Grabowski & Peterson 2007, Scyphers et al. 2012). While much of this ecologically

unique region is in state or federal protected lands with minimal development, it has

been observed to be undergoing broad-scale ecological changes, including salt marsh

migration and coastal forest die-off and oyster reef collapse (Geselbracht et al. 2011,

Langston et al. 2017, Seavey et al. 2011). Despite these changes, this region is less

well-studied than other ecoregions throughout the coastal United States (Figure 1-1;

Mattson et al. 2007).

The distribution of wetland types along the Big Bend is greatly dependent on geological features. Underlying the coastline is the Floridan Aquifer, a broad, limestone platform with karstic features ranging from small borings along root penetrations, to tidal

creeks that form along fractures, to large, spring-discharge bays. Salt marsh and

coastal forest occur both as expanses that extend from the mainland, and as

archipelagos and individual islands. Across the region, differential dissolution of bedrock

causes variable topography, with the highest areas often supporting freshwater forests.

Archipelagos and islands formed in areas where bedrock dissolution was extensive and

irregular enough to create patches fully separated from one another by open water

(Hine et al. 1988).

Numerous river and spring systems discharge freshwater into Big Bend coastal

waters (Figure 1-1), with karst features and surface-groundwater interactions playing an

important role in discharge patterns. From the Withlacoochee River north, river

discharge is composed of spring flow and surface runoff from the drainage basin, with

spring flow dominating during low or base flow. In contrast, rivers to the south are

almost entirely spring fed (Mattson et al. 2007). Since groundwater levels change

gradually, spring-dominated rivers have higher baseflow levels and are less responsive

to individual rainfall events than surface water-dominated rivers. The high primary

porosity (space in between rock grains) of Florida karst also causes high groundwater

storage within the rock matrix, further elevating the role of groundwater in spring and

river discharge (Martin & Dean 2001, Budd & Vacher 2004). In addition to distinct

springs, diffuse seepage contributes a substantial amount of fresh groundwater into Big

Bend waters. For example, a study in St. George Sound estimated nearshore diffuse

seepage over a 7-km stretch as between 8 and 155 cubic feet per second, comparable

to a first-magnitude spring (Cable et al. 1997).

Saltwater Intrusion and Hydrologic Changes

Saltwater intrusion from sea level rise, climate change, and groundwater extraction threatens coastal marsh ecosystems of Florida’s Big Bend and globally. The

Big Bend coastline’s flat topography makes it highly susceptible to sea level rise, because small increases in sea level can inundate or salinize large areas. The Big Bend also has a low sediment supply (Hine et al. 1988), which has been shown in other ecosystems to limit salt marsh’s ability to accrete with sea level rise (Kirwan &

Megonigal 2013). Groundwater levels and river discharge in or near the Big Bend have decreased or become more variable (O’Reilly et al. 2014, Grubbs 2011, Seavey et al.

2011), likely from a combination of changes in rainfall patterns and Florida’s reliance on groundwater extraction to support its growing population (Stanton & Ackerman 2007).

Declines in aquifer level and river flows are likely to be exacerbated given increased drought frequency and higher evapotranspiration expected from climate change. Intense rain events in the southeast US have increased since the 1970s but droughts are more frequent (Groisman et al. 2008), and these changes in variability affect rainwater fate. Temperatures in the southeast US have risen 0.9°C since 1970

(Konrad II & Fuhrmann 2013), likely increasing evapotranspiration rates. Projected further increases in temperature and rainfall variability could increase reliance of agriculture and landscaping on groundwater use for irrigation, further threatening water resources. A recent study of historical drought conditions in the Suwannee River watershed has suggested that the 20th century, when many water management policies

were formulated, was unusually wet compared to the prior four centuries (Harley et al.

2017). The increase in drought frequency observed in recent years thus may not be

indicative of climate change, but of a return to previous conditions.

Major ecosystem transitions driven by sea level rise and saltwater intrusion are already occurring along the Big Bend. In their analysis of marsh change since the mid- late 19th century through 1995 using topographic surveys and satellite data, Raabe and

Stumpf (2015) found a loss of 43 km2 of salt marsh and more than twice that area of

coastal forest that had transitioned to salt marsh. Though salt marsh area grew because

of this landward migration, the loss of large areas of coastal forest reduces habitat

heterogeneity of the coastal landscape. Studies of coastal forest die-off in Waccasassa

State Preserve found more frequent tidal flooding and higher groundwater salinity in

stressed hammocks (Williams et al. 1998, DeSantis et al. 2007). Die-off rates

accelerated sharply during a 1998-2002 drought, revealing the important role of

freshwater influence in driving these changes (DeSantis et al. 2007).

Predicting consequences of sea level rise SLR and saltwater intrusion is

complicated by a number of uncertainties. In any ecosystem, historical responses

cannot be assumed for the future, because sea level rise may accelerate to rates that

exceed salt marsh’s ability to accrete or migrate (Kirwan & Megonigal 2013). For

example, wetland migration modeling in Waccasassa Bay Preserve using 1 meter of

sea level rise predicted 83 percent coastal forest loss, mostly through conversion to salt

marsh, whereas a 2-meter rise yielded 99 percent forest loss, mostly to open water

(Geselbracht et al. 2011). Coastal wetlands also face new anthropogenic stressors that

constrain salt marsh resiliency, such as nutrient pollution, reduced sediment loading

through river diking, and developed areas that block sea level rise-induced marsh

migration (Kirwan & Megonigal 2013). Although the Big Bend is a relatively low

development coastline, road networks through coastal areas appear to modify tidal

hydrology in some areas (Thom et al. 2015), potentially altering salinity, accretion, and vegetation productivity.

Geological and hydrological characteristics of the Big Bend create further challenges in predicting impacts of future chage, and several key data gaps persist.

While a limited body of research has suggested flow is decreasing in some rivers

(Grubbs 2011, Seavey et al. 2011), it is unclear how flow has changed regionally and how these changes correlate with weather and climate patterns, groundwater extraction, and land use change. Big Bend rivers have very high groundwater influence, created by high water storage and movement through karstic features and within its porous rock matrix (Budd & Vacher 2004, Florea & Vacher 2006). This creates “aquifer inertia,” meaning that long-term rainfall patterns determine flow (Florea & Vacher 2006),

complicating efforts to estimate river discharge resulting from varying rainfall scenarios.

At the coast, the heterogeneity of surface and subsurface discharge and diffuse seepage from the karstic matrix make the relative role and spatial and temporal variability of freshwater influence unclear (Hine et al. 1988, Raabe & Bialkowska-

Jelinska 2010). Within coastal forests, it is largely unknown how sea level, local rainfall,

river discharge, groundwater seepage, and island characteristics interact to drive

porewater salinity.

Dissertation Overview

In this context, the overarching objective of this dissertation is to reveal physical

and biological drivers of saltwater intrusion and ecosystem response in the Big Bend.

Chapter two uses river gage data to examine regional patterns in discharge, and it

identifies regional drivers of discharge patterns, with the goal of better understanding

future river flows under varying climate and development scenarios. Chapter three uses

field data to examine how local-scale impacts (roads through otherwise pristine salt marsh and coastal forest) alter hydrology, salinity and other ecological variables potentially affecting salt marsh resilience to sea level rise. Chapter four uses remote

sensing data, geospatial modeling, and groundwater monitoring to determine

geographic variables that contribute to coastal forest die-off, and hydrologic variables

that drive groundwater level and salinity. A final conclusions chapter summarizes the

findings and significance of the overall study, and identifies priorities for future research.

Figure 1-1. Big Bend geography, development, and managed lands. A) Big Bend extent and geography types inland of the Big Bend. B) Coastal rivers and spring systems. C) Major roads and towns/cities. D) Government managed lands.

CHAPTER 2 UNTANGLING TRENDS AND DRIVERS OF RIVER DISCHARGE ALONG FLORIDA’S BIG BEND COASTLINE

Global Trends in Water Supply and Scarcity

Water scarcity is an increasingly ubiquitous problem worldwide. It is estimated

that four billion people experience water scarce conditions for at least one month each

year (Jackson et al. 2001). It is expected that water demand will continue to increase

with growing population and increasing standards of living, which fuel water

consumption for agriculture, industrial development, and household and commercial use

(70, 19, and 10 percent of use, respectively; United Nations Water 2016). The relative

impacts of water scarcity varies across dimensions of human security, ecosystem

damage, and management response, ranging in severity from short-term droughts that

spur effective conservation mandates (e.g., the 2012-2016 record-setting drought in

California; California Natural Resources Agency 2016) to systemic overuse over decades that devastates ecosystems (e.g., the drying of the Aral Sea since the 1960s;

Micklin 2007).

Over half of fresh surface water worldwide is already tapped for human use, and most areas dependent on groundwater are pumping it faster than it is recharged

(Jackson et al. 2001, Mekonnen & Hoekstra 2016). Groundwater regulations in particular are often minimal, and most surface water policies in developed countries were written before surface-groundwater interactions were well understood (Famiglietti

2014). Projected increases in the frequency and severity of droughts and extreme rainfall events driven by climate change further complicates water supply, especially given that water-scarce periods are often driven by uneven rainfall over time coupled with storage limitations (Mann et al. 2017, Trenberth 2011). These growing challenges

emphasize the need to develop tools for discovering trends and understanding drivers of water availability across large regions. This approach can help inform policies to re- allocate and limit water use and has the potential to reveal the most crucial areas for interventions such as water use monitoring or increased water storage capacity. This study explores a method for understanding shared patterns and drivers of river discharge along Florida’s Big Bend region of the Gulf Coast.

Water Resource Challenges in Florida’s Big Bend Region

River discharge is decreasing in several areas of Florida’s Big Bend region, potentially driven by a combination of climate change, groundwater extraction, and land use change (Grubbs 2011, Marella & Berndt 2005, O’Reilly et al. 2014, Seavey et al.

2011). Although Florida has a sub-tropical climate and averages 140 cm of rainfall per year (Konrad II & Fuhrmann 2013), ensuring water supply for multiple sectors without

harming ecological resources is an ongoing challenge given limited capacity for water

storage in a flat, karstic terrain. Florida’s population of 20.5 million (in 2017) is rapidly

growing and is expected to increase to 24.4 million by 2030, and 27.4 million by 2045

(Rayer & Wang 2017). Additionally, hotter and drier conditions from global climate

change are likely to increase irrigation needs (Stanton & Ackerman 2007). Absent major

interventions in water allocation and use efficiency, water extraction in Florida may

increase dramatically, threatening ecological resources dependent on historical river

discharge regimes.

Groundwater is the principal source of Florida’s water supply, composing 65

percent of withdrawals for all uses, and 89 percent of withdrawals for public water

supply (values from 2012; Marella 2015). Given the high level of groundwater influence

in rivers along the Big Bend, and the extended residence time of groundwater before

discharge to the flow network, this use has the potential to cause long-term changes to river flow rates. Specifically, groundwater extraction increased more than five-fold between 1950 and 2000, owing both to increased water demand and a shift to relying on groundwater compared to surface water, though extraction has since leveled off

(Marella 2015, Marella & Berndt 2005). In some instances, groundwater withdrawals have been directly linked to decreased water levels and reduced spring flows in the immediate vicinity of the withdrawal point, leading to well relocation (Barlow 2003,

Marella & Berndt 2005). However, in other areas, groundwater extraction appeared to minimally influence water levels and river discharge (O’Reilly et al. 2014).

Climate change impacts in Florida create additional difficulties both for securing a

steady water supply while minimizing harm to ecological resources, and in forecasting

available water resources. Although it is unclear if climate change will alter average

annual rainfall in Florida, intense rain events in the southeastern United States are

increasing, and droughts are becoming more frequent and longer in duration (Groisman

et al. 2008). Additionally, southeastern United States temperatures have risen 0.9°C

since 1970 and in Florida are predicted to rise roughly another 2.0°C by mid-century,

with greater increases in the summer (Konrad II & Fuhrmann 2013), potentially

increasing evapotranspiration. These changes could increase reliance of agriculture on

water extraction for irrigation, especially during the dry season and during drought

periods when river flows are already low. A recent study of historical drought conditions

in the Suwannee River watershed has suggested that the 20th century, when many

water management policies were formulated, was unusually wet compared to the prior

four centuries (Harley et al. 2017). This study suggests that recent increases in drought

frequency may indicate a return to more typical conditions, rather than a symptom of anthropogenic climate change.

While altered flow regimes have the potential to harm in-stream ecological

resources, the focus of this work is on flow into estuaries, which are dependent on river

discharge for maintaining brackish salinity and are especially vulnerable to changes in

freshwater flow delivery. While Florida waters are protected by a statute that limits

“significant ecological harm” from anthropogenic water use (Florida Statute 273),

estuarine resources are seldom used as indicators for measuring harm, and therefore

they are often ignored in minimum flow requirements (Mattson 2002). Several studies in

Apalachicola Bay, the Suwannee River Estuary, and Waccasassa Bay have suggested

critical links between freshwater discharge and biological indicators, primarily through

impacts to salinity. A well-documented impact of reduced freshwater flows is higher

oyster mortality during low river flow, caused by increased access by marine predators

and parasites during high-salinity conditions, which can lead to oyster reef collapse and

inability to recover after higher flows return (Livingston et al. 2000, Bergquist et al. 2006,

Seavey et al. 2011). While coastal forest loss is often attributed to sea level rise

(Williams et al. 1999, Geselbracht et al. 2011, Langston et al. 2017), La Niña-associated

drought in 1998-2002 appeared to accelerate loss by reducing flow and elevating

groundwater salinity (DeSantis et al. 2007).

To preserve the ecological health of streams and estuaries, the growing

pressures on Florida’s water supply will require policies to carefully allocate water and

improve water use efficiency. However, while some studies have documented and

attempted to explain discharge changes in certain Big Bend rivers (mainly the

Suwannee), information on regional, large-scale trends is lacking. Identifying river flow trends and drivers across the Big Bend will allow water managers to understand current and future constraints of water availability and help to focus policy effort on watersheds

under greatest threat of water stress.

Challenges in Explaining Big Bend River Discharge Patterns

Surface-Groundwater Interactions

A major challenge in understanding river discharge drivers is the significant role

of surface-groundwater interactions throughout the Big Bend region, which makes the

rainfall-discharge relationship difficult to derive using simple statistical approaches. The region is underlain by karstic carbonate bedrock called the Floridan Aquifer System, with massive water storage that is recharged by diffuse rainfall recharge and surface water drainage (Scott et al. 2004). Close to the coastline, the aquifer is unconfined, and bedrock is only covered by a thin (< 10 meters) surficial layer of unconsolidated sediment. As a result, water can easily percolate through sediments and reach the aquifer, and surface-ground water exchange in this area is substantial and rapid (Bush and Johnston 1988). This high level of seepage creates a high density of karstic features, including caves, conduits, and sinkholes, which both store and transport water

(Miller 1990, Tihansky 1999, Budd & Vacher 2004). This unconfined area also has a high density of springs, which discharge water from the aquifer and often augment river flow (Tihansky & Knochenms 2001). Further inland, though still within the watersheds of some Big Bend rivers, the bedrock is overlain by a 10- to 30-meter thick confining clay layer called the Hawthorne Group, which has low matrix permeability. This layer limits and slows surface-groundwater exchange, reducing development of karstic features and favoring dendritic surface drainage networks (Tihansky & Knochenms 2001).

In addition to karstic features containing large quantities of water, high primary

porosity (the pore space between carbonate rock grains) of Floridan Aquifer karst

creates high water storage even within the rock matrix (Martin & Dean 2001, Budd &

Vacher 2004). Karst in this area averages 30 to 35 percent pore space, though porosity

varies depending on limestone texture (Budd & Vacher 2004). High groundwater

storage creates “aquifer inertia,” meaning groundwater levels and therefore river

discharge and spring flow depend on long-term rainfall patterns (Florea & Vacher 2006).

Because groundwater contributes to river discharge, many rivers have high base flows

even during low-rainfall periods, and discharge response to individual rainfall events is

partially muted (Florea & Vacher 2006, Martin & Dean 2001). However, some Big Bend

rivers, such as the Suwannee, have large portions of their watersheds within the

confined area, where rainwater is more likely to remain as surface water, which reaches

the river much more quickly (Mattson et al. 2007). As a result, discharge in these rivers is likely to respond more strongly to recent rainfall.

Though Big Bend rivers have the common characteristic of receiving substantial

contributions from groundwater, different aquifer dynamics throughout the study area

create spatially variable rainfall-discharge relationship. This variability, in addition to the

temporally complicating factor of aquifer inertia, makes it difficult to generalize discharge

drivers across the region. Statistical methods for comprehensively evaluating discharge

patterns and drivers would help identify river systems that respond similarly to

environmental and human drivers and improve our understanding of likely future

discharge patterns.

Effects of Groundwater Extraction

The same aquifer characteristics that confound efforts to understand the rainfall- discharge relationship also make it difficult to identify how groundwater extraction affects water levels and river discharge. That is, given the long residence time of groundwater, the vastness of the aquifer and connectivity over the region, and quick surface-groundwater interaction in some areas, the effects of groundwater extraction can be relatively muted. Studies of individual water level wells and rivers in central

Florida have found rainfall to be the most prominent signal (O’Reilly et al. 2014), suggesting the effect of groundwater extraction is either small, or difficult to identify among influence from climate variables.

An additional issue for explaining how groundwater extraction affects river discharge is that most available data on groundwater extraction over time are relatively coarse and rely heavily on estimation. The United States Geological Survey (USGS), in collaboration with Florida Department of Environmental Protection and Water

Management Districts (WMDs), generates reports on water use by sector and county every five years since 1965 (Marella 2014). Figures for some user categories (public

supply, commercial and industrial, power generation) are considered highly accurate

because of availability of metered data, while agriculture, domestic self-supply, and recreational landscaping are seldom metered. In particular, agricultural use, which composes an estimated 40 percent of freshwater withdrawals, is almost entirely estimated using crop acreage and assumed irrigation coefficients based on crop type and weather data (Marella 2014). A 2016 USGS report points out the limited accuracy of this method, citing missing data, inconsistencies in reporting methods across WMDs, and lack of information on irrigation method or whether irrigation is used, though efforts

are underway to improve reporting (Marella et al. 2016). Therefore, the five-year

frequency of these water extraction reports and reliance on estimation constrain their

utility in studies modeling drivers of annual river discharge.

Despite their limitations, these USGS reports provide insight into broad trends

across the state and shifts in water extraction within sectors. Because of improvements

in water use efficiency in agriculture and industry and increased use of reclaimed

wastewater, groundwater withdrawals largely leveled off after 2000 (Marella 2015).

Within the Suwannee River WMD, which covers a large portion of the northern Big Bend

region, withdrawals are estimated to have leveled off earlier, after 1980 (Marella et al.

2016). While groundwater extraction may play a role in causing recent reduced river

flows in some areas, it appears unlikely that it is the dominant driver.

Effects of Land Cover Change

Changes in land cover types across Big Bend watersheds may also be altering

river discharge, given that land cover influences different stages of the hydrologic cycle.

Statewide, agricultural and developed land has increased considerably since the mid-

20th century, though the increase in agricultural land has leveled off (Marella et al. 2016,

O’Reilly et al. 2014). Urbanization remains low in the Big Bend region, where the

dominant land cover types are wetland, upland forest, and agriculture (Mattson et al.

2007). Though urbanization remains low, it is still slowly increasing in the region. For

instance, in the Southwest Florida WMD, 11.3 percent of agricultural land and 10.8

percent of upland forest was replaced by urbanization between 1995 and 2006

(Hernandez et al. 2012).

Though information is available on land cover changes over time, it is not

available at a spatial and temporal scale that would enable use in models of annual river

discharge. In addition to data scarcity, how land cover affects hydrologic cycling depends on many variables and would be difficult to identify without region-specific

studies. For instance, one hydrologic study of conversion from forested land to

agriculture found increased surface water run-off and groundwater infiltration from

reduced evapotranspiration (Ranjan et al. 2006). In contrast, another study found

increased surface water run-off but reduced groundwater infiltration, because the loss of

vegetation cover led to soil compaction (Blann et al. 2009). However, hydrologic

impacts of urbanization are more consistent, typically causing an increase in surface

water run-off and reduced groundwater infiltration from increased impervious surface

(O’Reilly et al. 2014). In short, land-use conversion has the potential to affect the

watershed water budget, but analyzing these effects is beyond the scope of this work.

Benefits of Dynamic Factor Analysis

Having a comprehensive view of regional river trends and contributions from

changing weather and climate patterns would help water managers in Big Bend

watersheds with forecasting and planning. Dynamic Factor Analysis (DFA), a

multivariate analysis tool for time series, is ideal for finding broad trends and drivers

across many datasets (Molenaar 1985, Zuur et al. 2003). DFA is a data reduction tool,

meaning that it takes many response variables and reduces them into common shared

trends (fewer in number than the response variables), as well as identifying important

explanatory variables. DFA’s ability to comprehensively analyze trends across many

datasets contrasts it from other trend analysis tools, such as locally weighted scatterplot

smoothing (LOWESS) regression (Cleveland & Devlin 1988). While LOWESS and DFA

share the benefit of requiring no prior knowledge about relationships between variables,

or whether relationships are linear or nonlinear, LOWESS evaluates one response variable at a time.

Given these benefits, DFA is a useful framework for untangling trends and

variables affecting river discharge along the big Bend. Though DFA was initially

developed to analyze economics and psychology data (Geweke 1977, Molenaar 1985),

it has since been used to find trends and drivers of numerous ecological and

hydrological variables, including fisheries productivity (e.g., Lima et al. 2017), vegetation

cover as measured by remote sensing analysis (e.g., Campo-Bescós et al. 2013), and

groundwater level and salinity in a floodplain forest (Kaplan & Muñoz-Carpena 2014,

Kaplan et al. 2010). In this study, I used DFA to find common trends between annual

discharge series of eight rivers along the Big Bend. I tested the explanatory power of

watershed annual precipitation and potential evapotranspiration (PET); net recharge,

calculated as precipitation minus PET; 6-month lagged precipitation, PET, and

recharge; climate indices for El Niño Southern Oscillation (ENSO), North Atlantic

Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO); and groundwater level

data. Use of DFA in this context reveals regional grouping and divisions of river

behavior. It also determines whether changes can be explained by available time series

data, providing insight into whether variables such as land use change or groundwater

extraction are likely to be important.

Methods

Study Area

Numerous river and stream systems discharge freshwater into Big Bend coastal

waters via river mouths and spring systems (Figure 2-1). The Big Bend is divided into

two sections: the Big Bend “proper”, from Waccasassa Bay to the north, where

discharge is composed of spring flow plus surface runoff from the drainage basin, and the Springs Coast to the south, where rivers are almost entirely spring-fed (Mattson et

al. 2007). Since spring flow is composed of groundwater, spring-dominated rivers have

higher baseflow levels, and they are less variable and less responsive to individual

rainfall events than surface water-dominated river discharge (Martin & Dean 2001, Budd

& Vacher 2004). In this analysis, eight rivers were selected for the main analysis

because they had long-term (> 50 years), relatively complete data records (i.e., any

gaps were shorter than 6 months) at gages close to the river mouth.

Response Variables (Discharge Data)

Daily discharge data for 1965-2016 (USGS 2016b) was downloaded using the

waterData package (Ryberg & Vecchia 2014) in R version 3.2.2 (R Core Team 2016)

for the eight study rivers (St. Marks, Econfina, Fenholloway, Steinhatchee, Suwannee,

Rainbow, Withlacoochee, and Anclote; Tables 2-1, 2-2). I excluded Aucilla and

Waccasassa from the analysis because of long and frequent data gaps; Crystal,

Homosassa, and Weeki Wachee because of short periods of record, and

Chassahowitzka for significant tidal influence that frequently causes river flow reversal,

indicated by periods of negative discharge values. All other river gage locations are

either far enough inland to not be affected by tides, or they experience elevated river

stage during high tides when discharge is low, though this is unlikely to affect annual

discharge. Each data series was aggregated to create mean annual average discharge.

Explanatory Variables

To generate annual rainfall and PET time series for each watershed, first the

watershed for each river gage was drawn using 1/3 arc-second (roughly 10-meter

resolution) digital elevation model (DEM) data (USGS 2016a) and the the Watershed

tool in ArcGIS version 10.2 (Table 2-2). The Watershed tool uses a flow direction raster,

created from the DEM, to identify all the grid cells that flow into a pour point (in this

case, each river gage point). Watersheds varied greatly in size from 169 to 24897 km2

(mean = 4132 km2), have very low impervious surface (mean = 2.0 percent), and high

forest and wetland percent cover (mean = 72.2 percent; Table 2-3).

Monthly raster datasets for precipitation and temperature (mean [Tmean], minimum

[Tmin] and maximum [Tmax]) were downloaded from the PRISM Climate Group for 1965-

2016. Tmin and Tmax were calculated from daily minimum and maximum temperatures,

averaged over all days in the month. PRISM datasets are of 4 x 4 km spatial resolution

and they cover the continental United States. They are created by interpolating

meteorological records from weather stations across topographical data. I used the

temperature datasets and the Hargreaves-Samani equation applied to each grid cell to

calculate monthly PET as follows:

= 0.0135 × ( + 17.78) × ( ) . × (2-1) 0 5 𝑡𝑡 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚 𝑎𝑎 where K is a 𝐸𝐸constant set to𝐾𝐾 0.19𝑇𝑇 0 for coastal areas𝑇𝑇 (versus− 𝑇𝑇 0.162𝑅𝑅 for inland), and Ra

indicates monthly extraterrestrial solar radiation based on latitude (Hargreaves &

Samani 1982, Samani 2000). For Ra, values for 30 degrees latitude were used, since the study watersheds area is located between 28 and 32 degrees latitude, and using the latitude for each raster would have minimal effect (Duffie & Beckman 2013). The

Hargreaves-Samani equation includes the Tmax – Tmin term because a greater difference

between nighttime and daytime temperatures indicates lower relative humidity, which

increases PET.

Monthly precipitation rasters and the calculated monthly PET rasters were then used to calculate mean monthly values by watershed. Monthly net recharge values were also calculated by watershed by subtracting PET from precipitation. Monthly precipitation, PET, and recharge series were then aggregated to create mean annual values. Finally, to test whether discharge depends on weather from the prior few months, separate time series of 6-month lagged annual values (e.g., the 2016 value is the mean of July 2015 through June 2016) were calculated in the same way.

To test how global-scale climatic patterns affect river discharge, time series of climate indices for ENSO, NAO, and AMO were downloaded from National Oceanic and

Atmospheric Administration’s (NOAA) website. In Florida, positive ENSO index values are associated with cooler, wetter winters, positive NAO values are associated with warmer, drier weather, and positive AMO values are associated with greater precipitation (Enfield et al. 2001, Hagemeyer 2006, 2007). Finally, groundwater data for

Rainbow Springs near Dunnellon was downloaded from USGS WaterData website.

While other groundwater wells are present across the study area, they have either large data gaps or short monitoring periods relative to our 50-year timeframe.

Dynamic Factor Analysis

DFA is a multivariate data reduction technique that can be used to find common trends and explain variability in a set of time series. Data reduction techniques are used to reduce large amounts of input data and variables to several created variables that most efficiently capture data variability. DFA analyzes multiple input time series

(response variables) and generates functions that define common trends among response variables, along with factor loadings that indicate how strongly each time series correlates to each trend. Additionally, DFA uses explanatory variables (also time

series) and identifies which are important based on their weight (or “loadings”) on each response variable time series. If important explanatory variables are identified, the number of common trends required to adequately reproduce the observed response variables can typically be reduced, since the goal is to maximize the predictive power of the model while minimizing the number of trends. At this point, the remaining common trends represent “unexplained variation,” since they are caused by variables outside of the model.

In DFA, common trends are identified from a set of N response variable time

series and are modeled as linear combinations of the M trends and K explanatory variables:

( ) = 𝑀𝑀 , ( ) + + 𝐾𝐾 , ( ) + ( ) (2-2)

𝑠𝑠𝑛𝑛 𝑡𝑡 � 𝛾𝛾𝑚𝑚 𝑛𝑛𝛼𝛼𝑚𝑚 𝑡𝑡 𝜇𝜇𝑛𝑛 � 𝛽𝛽𝑘𝑘 𝑛𝑛 𝑣𝑣𝑘𝑘 𝑡𝑡 𝜀𝜀𝑛𝑛 𝑡𝑡 where sn(t) is the set of 𝑚𝑚N= 1response variable time𝑘𝑘=1 series; αm(t) is the value of the mth

common trend at time t; γm,n is the factor loading of trend m on response variable n; μn is

a constant “level” parameter; vk(t) is the value of explanatory variable k at time t; βk,n is

the regression parameter for explanatory variable k on response variable n; and εn(t) is

model error (white noise). Variables αm(t) and μn are in the same units as the response

variables, each vk(t) is in the same units as the respective explanatory variable, and γm,n and βk,n are dimensionless weighting parameters. In this study, the eight river discharge

datasets are the response variables, and datasets for watershed rainfall and PET,

climate indices, and groundwater level are explanatory variables. In many DFAs, it is

practical to normalize each time series input into the model to account for inherent

differences in their ranges and magnitudes. For instance, a shared trend may occur

between a large river and small river, but the real discharge change would be much

lower in the small river, so normalization helps the DFA identify this shared trend. All time series (response and explanatory) were normalized for this study.

DFA models common trends (αm… αM) as a random walk and predicts each time

step value using expectation maximization technique with a Kalman filter/smoothing

algorithm. The Kalman algorithm uses earlier values in a time series and relationships

between time series to estimate a probability distribution for each variable at each time

step. Expectation maximization, which is based on maximum-likelihood estimation,

iteratively refines this probability distribution based on the observed data, until the

estimates become stable. Factor loadings (γm,n… γM,N) and level parameters (μn… μN)

are also estimated using expectation maximization. When datasets are normalized,

level parameters are close to zero. DFA uses simple linear regression to estimate

regression parameters (βk,n… βK,N), and I considered parameters significant if p-value <

0.05. Canonical correlation coefficients (ρm,n) were calculated for each response

variable and common trend combination, to determine the strength of their relationship.

Correlation coefficients range from -1 to 1 and indicate the direction and strength of the

relationship with each response variable. These values were classified into minor (|ρm,n,

correlation (|ρm,n, βk,n| ≥ 0.75; Kaplan et al. 2014). DFA was performed using the

MARSS package (Holmes et al. 2012, 2013) in R version 3.2.2 (R Core Team 2016).

Modeling Approach

To characterize river discharge variation, I created three DFA models (Table 2-

4). Model I identifies common trends between discharge datasets, without considering explanatory variables. Eight candidate models were first run to find the number of trends

(from one to eight, since there are eight discharge datasets) that most efficiently

captures river discharge variability. Model I was selecting by finding the model with the

lowest Akaike’s Information Criterion (AIC; Akaike 1974), which serves to maximize

explanatory power while using the fewest trends possible (minimizing the number of

parameters). The number of trends in Model I indicates the degree of shared variability;

for instance, if the model with one trend has the lowest AIC, this would indicate

extremely similar behavior across all rivers, while eight trends would indicate absence of

shared behavior between rivers.

Model II identifies a combination of explanatory variables and common trends

that characterize river discharge. To determine which explanatory variables to include

as candidates for the final model and to help avoid multicollinearity, I first calculated the

variance inflation factor (VIF; a measure of how correlated each variable is with all other

variables) of each times series (Zuur et al. 2007), and excluded from consideration

variables with VIF > 5 (Ritter et al. 2009). Then multiple candidate models were created

using the number of trends from Model I with different combinations of explanatory

variables, and the model with lowest AIC was selected. The inclusion of explanatory

variables likely reduces model dependence on common trends, as measured by factor

loadings. To address this, I then sought to decrease the number of trends to reduce

reliance on latent variability, while maintaining similar metrics of model performance

(AIC and Nash and Sutcliffe coefficient of efficiency [Ceff]). At this point, remaining common trends were considered unexplained, or latent, variation.

Finally, Model III is a set of multiple linear regressions of the river discharge

datasets, using only the explanatory variables from Model II and no common trends.

Model III is created to evaluate how well river discharge variation can be explained from

the chosen explanatory variables.

Results

Response and Explanatory Variables

Mean discharge over the study period varied across rivers, from 1.2 m3/s in the

Fenholloway River to 268.1 m3/s in the Suwannee (Table 2-5, Figures 2-2, 2-3). The

Suwannee is by far the largest river in the region, followed by the Withlacoochee at 19.1

m3/s. As expected, mean discharge was positively correlated with watershed size

(correlation coefficient [r] = 0.99 when including Suwannee, and r = 0.64 excluding

Suwannee). Larger rivers also tended to have a higher baseflow indices (calculated with hydrostats R package; Bond 2016), lower seasonal flow variability as indicated by the mean ratio of maximum to mean annual discharge (Qmax/Qmean) and mean intra-annual

coefficient of variation (CV), and lower inter-annual variability as indicated by CV of

annual means (Figures 2-2, 2.3). The baseflow index estimation in the hydrostats R

package uses the Lyne-Hollick filter (Ladson et al. 2013, Lyne & Hollick 1979). Except

for the Suwannee, the rivers along the Big Bend Proper (St. Marks, Econfina,

Fenholloway, Steinhatchee) had two high-flow seasons, one from roughly January

through April, when rainfall is moderate but evapotranspiration is low, and another from

August through September, during the wet season (Figure 2-4). The Suwannee only

exhibited one clear high-flow season, from February through April (Figure 2-5). The

three rivers along the Springs Coast had their main high-flow season roughly August

through October, meaning peak flow lags slightly behind the wet season (Figure 2-6).

Discharge at Suwannee, Rainbow, and Withlacoochee had significant decreasing

trends, indicated by Mann-Kendall (MK) trend tests (two-sided p-value < 0.10; Table 2-

6). Although the other rivers did not have statistically significant trends, modest decreases also occured over time at Econfina, Fenholloway, and Steinhatchee. St.

Marks and Anclote showed no apparent trends.

Annual mean precipitation for most watersheds displayed no consistent trends

over time, with the exception of Anclote, which had a significant positive trend (MK two-

sided p-value < 0.10; Table 2-6, Figure 2-6). Despite the significant upward trend at

Anclote over the long-term, precipitation here and at all other watersheds decreased

through 2000, after which point it increased. All watersheds had a distinct wet season

June through September, and a secondary, less extreme wet period January through

March. Precipitation across all watersheds averaged 137.5 cm/year, ranging from 97.5

cm, in 2000, to 176.5 cm, in 2014. The four northern-most rivers (St. Marks, Econfina,

Fenholloway, and Steinhatchee) received the most precipitation, together averaging

142.7 cm/year, while the four southern-most rivers (Suwannee, Rainbow,

Withlacoochee, and Anclote) average 132.4 cm/year. Precipitation in the Suwanne

River basin was lower than the four northern rivers because its watershed extends into

Georgia, where summer rainfall is lower. Rainbow, Withlacoochee, and Anclote along

the Springs Coast had less rainfall because winters are drier than in the Big Bend

Proper.

Though not included in the DFA, the discharge rate divided by annual

precipitation provides insight into drivers of discharge by revealing trends for discharge

associated with a given amount of precipitation. Out of the six rivers with a decreasing

discharge trend, discharge-to-precipitation ratios decreased in five rivers (Fenholloway,

Steinhatchee, Suwannee, Rainbow, and Withlacoochee), while Econfina increased

through the mid-1970s and decreased thereafter (Figure 2.7). Discharge-to-precipitation ratios at Suwannee, Rainbow, and Withlacoochee had a significant downward trend

(MK two-sided p-value < 0.10; Table 2-6).

Annual mean PET for most watersheds showed no apparent trends over time, with the exception of Econfina, which had a slight but significant decreasing trend (MK two-sided p-value < 0.10; Table 2-6, Figure 2-8). This decrease was primarily driven by an increase in minimum daily temperatures that reduced the daily temperature range, which the Hargreaves-Samani equation assumes to mean higher humidity and therefore

lower PET. Compared to precipitation, PET varied less over time and across

watersheds. Average PET across all watersheds averaged 174.4 cm/year, ranging from

a low of 167.1 cm occurring in 1990 to a high of 186.1 cm in 1970. Mean PET was very

similar across watersheds, with basin averages ranging from 172 to 176 cm/year.

Groundwater level at Rainbow Springs near Dunnellon decreased significantly

over the study period (MK two-sided p-value < 0.10; Figure 2-9). Groundwater level

averaged 9.57 m above NAVD88, ranging from a low of 9.17 recorded in 2000 and a

high of 10.18 in the first year of the study period. Climate indices ENSO and NAO

showed no consistent trends over time, oscillating regularly between warm (> 0) and

cool (< 0) phases (Figure 2-10). In contrast, AMO, which has a much longer periodicity,

significantly increased over time, shifting from a cool phase through the 1990s to a

warm phase thereafter (MK two-sided p-value < 0.10; Figure 2-10).

As can be expected with rainfall and PET datasets from adjacent watersheds,

many datasets were highly correlated. It is unlikely that the most efficient model would

have highly correlated explanatory variables, since redundant variables would increase

AIC. However, to eliminate this possibility, and to reduce the number of candidate models created, a threshold of VIF = 5 was used to reduce the total number of

explanatory variables for evaluation in Model II from 55 to 20.

Model I Results

Model I, chosen by finding the model with the lowest AIC out of eight candidate

models, had four trends and a Ceff of 0.911 (Figure 2-11). Adding a fifth trend increased

Ceff slightly to 0.922, but it also increased AIC from 857.3 to 864.1, and the fifth trend’s

correlation coefficients were minimal (|ρ5,n| ≤ 0.11). Steinhatchee River had the lowest percent of its variability explained by the model (Ceff = 0.781), and the next lowest was

Withlacoochee (Ceff = 0.852).

Although Model I only explains latent variability, in addition to finding any

important shared temporal trends, it also provides information about commonalities and

differences between rivers. An important general insight is that rivers often shared

common behavior with rivers close by, though this is not always the case. For example,

Trend 1 is very highly correlated with St. Marks discharge (ρ1,stm = 0.90), and it had a

low correlation with Econfina (ρ1,eco = 0.29), which is directly adjacent, as well as with

Suwannee River, which has an adjacent watershed (ρ1,suw = 0.40). Trend 2 reveals

shared behavior between Econfina, Fenholloway, Steinhatchee, and Suwannee, which

have adjacent watersheds. Trend 2 was most strongly correlated with Fenholloway

(ρ2,fen = 0.80), moderately correlated with Econfina and Steinhatchee (ρ2,eco = 0.74, ρ2,ste

= 0.64), and has a low correlation with Suwannee (ρ2,suw = 0.46). Trend 3 was the only

trend with a clear directional change, decreasing significantly over time. Consistent with

this, it was associated with the three rivers with statistically significant decreasing trends

(MK two-sided p-value < 0.10). It was most strongly correlated with Rainbow (ρ3,rai =

0.92), moderately correlated with Withlacoochee (ρ3,wit = 0.54) directly south of

Rainbow, and had a low correlation with Suwannee (ρ3,suw = 0.43) further north. Trend 4

correlations were high with Anclote (ρ4,anc = 0.92), moderate with Withlacoochee (ρ4,wit =

0.50) directly north of Anclote, and low with Steinhatchee and Fenholloway much further

north (ρ4,ste = 0.35, ρ4,fen = 0.27). St. Marks and Anclote Rivers stood out as the most divergent from the other rivers. The other six rivers each had noteworthy associations

(ρm,n > 0.25) with two trends, whereas St. Marks and Anclote each relied very heavily on

only one trend (ρm,n > 0.90).

Model II Results

To develop Model II, 270 individual model iterations were run to test various

combinations of the 20 potential response variables with a VIF < 5. The final Model II

uses one trend and four explanatory variables: watershed precipitation at St. Marks

(Pstm), Steinhatchee (Pste), and Anclote (Panc), and groundwater (GW) level at Rainbow

(Table 2-7, Figures 2-12, 2-13). Model II explained a lower portion of variability than

Model I (Ceff = 0.80, compared to Model I Ceff = 0.91), though the AIC is improved (AIC

= 709.8, compared to Model I AIC = 857.3). Though the use of two trends would

improve the Ceff to 0.82, and three trends to 0.89, the additional trends had very low

correlation coefficients (|ρm,n| ≤ 0.16), and therefore would provide little utility for

expaining discharge patterns. The minimum Ceff for individual rivers was 0.57, occurring

at St. Marks, a large decrease from the Model II minimum Ceff of 0.78 at Steinhatchee

(Figure 2-14). Econfina and Steinhatchee were the only rivers where Ceff was higher in

Model II than in Model I, while all others were lower.

Because it was correlated with discharge at seven out of eight rivers, groundwater level at Rainbow Springs was the most important variable for explaining

discharge in Model II. It was strongly related to Rainbow and Withlacoochee discharge

(βGW,rai = 0.92, βGW,wit = 0.83), moderately related to Fennholloway and Suwannee

(βGW,fen = 0.65, βGW,suw = 0.71), and had a low relation with Econfina, Steinhatchee, and

Anclote (βGW,eco = 0.47, βGW,ste = 0.47, βGW,anc = 0.31). Each watershed precipitation

variable was positively correlated with its own river discharge, as well as with one to

three other discharge datasets typically at nearby rivers or watersheds. St. Marks

precipitation had a moderate positive loading on discharge at St. Marks and Suwannee

(β = 0.69, β = 0.52) and a low positive loading at Econfina (β = Pstm,stm Pstm,suw Pstm,eco

0.46), all of which have adjacent watersheds. Steinhatchee precipitation had a

moderate positive loading at Steinhatchee (β = 0.70) and a low loading with Pste,ste

nearby Econfina and Fennholloway (β = 0.27, β = 0.39), and Anclote much Pste,eco Pste,fen

farther south (β = 0.32). Anclote precipitation had a moderate positive loading on Pste,anc

Anclote discharge (β = 0.59) and minor loading on nearby Withlacoochee (β Panc,anc Panc,wit

= 0.22). An unexpected finding was a negative correlation between St. Marks precipitation and Anclote discharge, and between Anclote precipitation and Econfina,

Steinhatchee, and Suwannee discharge, though these were low in effect (-0.27 ≤ βk,n ≤ -

0.32). This appeared to occur because of periods of negative correlation between St.

Marks and Anclote precipitation, which had the lowest correlation of any watershed

precipitation combination owing to their distance apart (r = 0.35, while other correlations

were 0.51 ≤ r ≤ 0.95).

Model III Results

Model III is a multiple linear regression of the eight discharge datasets modeled

using the explanatory variables from Model II (St. Marks, Steinhatchee, and Anclote

precipitation, and Rainbow Springs groundwater level; Table 2-8). The purpose of

Model III is to evaluate the predictive power of these explanatory variables alone,

without contribution from common trends. The average Ceff across the eight datasets

was 0.66, compared to Ceff of 0.80 in Model II. The relatively high Ceff of Model III

suggests a relatively high potential for using known data variables to model regional

river discharge. Discharge at St. Marks had the lowest level of variability captured by

Model III (Ceff = 0.54), while Rainbow had the highest (Ceff = 0.81), despite the fact that

Rainbow groundwater level was its only significant predictor. Overall, groundwater level

measured near Rainbow River was the most important explanatory variable, indicated

both by it being a significant predictor of discharge at six out of eight rivers, and by the

magnitude of its regression coefficients.

Discussion

Model I: Interpreting Common Trends

The results of Model I indicated that shared behavior occurs between rivers, since four trends explain roughly 91 percent of variability in eight river discharge datasets. The Model I loadings and canonical correlation coefficients revealed that rivers are typically more similar to rivers close by, or with adjacent watersheds. For instance, Trend 1 was associated with St. Marks and Econfina, which are close to one another, and with Suwannee, which has its watershed close to those of St. Marks and

Econfina. One exception to this pattern occurred with Trend 4, which was strongly or moderately associated with Withlacoochee and Anclote, which are close together, yet it was also correlated with Fenholloway and Steinhatchee much farther north, though the correlations were low.

Model II: Interpreting Explanatory Variables

Model II used one trend, along with precipitation at three watersheds and groundwater level at Rainbow Springs and was able to explain 80 percent of variability across all eight rivers. Although Ceff declined, this represents a successful decrease in

reliance on latent variability. Additionally, the striking decreasing pattern in Trend 3 in

Model I was well represented by observed groundwater levels. The average Ceff in

Model III, which models discharge only using the four explanatory variables, is 0.63,

indicating that the trend in Model II still explains a fair amount of river discharge

variation.

Investigating the relative weights of explanatory variables from Model II helps to

identify the environmental factors most associated with river discharge in the region. For

example, I found precipitation to outweigh the influence of PET, a result that is

consistent with a prior study of drivers of groundwater levels and spring and river flow in

central Florida, which identified multiyear rainfall variability as the primary driver of

observed downward trends, while evapotranspiration had little effect (O’Reilly et al.

2014). Additionally, given that mean precipitation varied much more than PET from year

to year, it is expected to have a stronger role in driving discharge variability. When

adding PET variables to Model II, I found the best performing model used six-month

lagged PET at Withlacoochee; although regression coefficients with all rivers were

negative (as expected), none were significant, and AIC increased with little change in

Ceff. Additionally, while replacing the three precipitation datasets in Model II with net

recharge yields similar model performance (AIC = 714.16, Ceff = 0.79), this appears to

be driven primarily by precipitation variability. However, an important limitation is that

the DFA was performed using estimated PET, and there are some indications that the

Hargreaves-Samani equation is less reliable in coastal areas (Samani 2000).

Precipitation in the current year, as opposed to six-month lagged precipitation, was found to be more important for explaining annual discharge. For instance, replacing the three precipitation datasets with six-month lagged datasets yielded AIC = 760.0 and

Ceff = 0.76. This is a relatively unexpected result, considering the high level of aquifer

inertia for the area (Florea & Vacher 2006), which causes a lag-time of discharge

response to rainfall in some areas as rainwater percolates into groundwater. However,

the thin surficial aquifer in some areas also enables quick infiltration by rainwater. It’s

possible that discharge responds quickly to some degree to rainfall events, but the

effect of recent historical rainfall is less marked. For individual watersheds (particularly

those that are primarily unconfined and groundwater flow), six-month lagged

precipitation may be a better predictor, but this was not the case across the study rivers.

Climate indices were not found to be important predictors of discharge, despite

previous studies demonstrating their importance for Florida river discharge. For

instance, El Niño years are associated with higher stream discharge and lower salinity

in Tampa Bay (Schmidt & Luther 2002), and higher winter and spring Suwannee River

discharge (Tootle & Piechota 2004). However, the absence of these indices from Model

II is not entirely surprising, given that their impacts manifest through their forcing on precipitation and evapotranspiration, which are directly included as variables. Even so,

DFA models using the three climate indices as explanatory variables, whether current year or lagged at 6-months, performed relatively poorly. For example, a model using three common trends and the three current-year indices had Ceff = 0.82 and AIC =

878.9, while Model I, with four common trends and no explanatory variables, had Ceff =

0.91 and AIC = 857.3; in short, this means that climate indices do not generate enough

explanatory power to replace any trends. Although it is clear that these indices affect

Florida climate, studies have revealed it is extremely challenging to identify their effects,

because the amount of time it takes for weather to respond to an index shift varies and

the indices overlap (Hagemeyer 2007). For instance, while wet and stormy periods are

strongly associated with El Niño events, dry periods are only loosely associated with La

Niña. Although positive NAO is associated with warmer temperatures, this effect mainly

manifests during neutral ENSO periods (Hagemeyer 2006).

A portion of river discharge still relies on latent variability, as indicated by Trend 1

in Model II, which correlates most strongly with Suwannee, Rainbow, and

Withlacoochee discharge. Additionally, the Model II Ceff = 0.80 indicates that even

including this common trend, 20 percent of variability is not captured by the model. One

potential explanation is that rainfall extremes, either droughts or major storms, may not

be captured in annual average rainfall, but variability has large implications for rainwater

fate. For instance, La Niña, though loosely associated with drier winters, has also been connected to higher hurricane development in the Atlantic and Gulf of Mexico, which brings dramatic rainfall events (Hagemeyer 2007). The same amount of water delivered through a hurricane, compared to multiple moderate precipitation events, will lead to a different proportion of water available for evapotranspiration, flow into surface water, or percolation into groundwater. Although land cover change could be a factor, it is unlikely this would be responsible for the shape of the common trend in Model II, since it oscillates frequently instead of showing any directional trends. Groundwater extraction

is another potential driver. Although statewide estimated groundwater extraction shows a steady upward trend and a leveling off after 2000, water extraction at individual wells likely increases and decreases from year to year.

The widespread importance of groundwater level at Rainbow is a significant

finding for understanding regional discharge patterns. The significant regression

coefficients for rivers as far north as Ecofina do not suggest that Rainbow groundwater

level is directly affecting discharge at these rivers. Rather, Rainbow groundwater level is

likely correlated with groundwater levels further north in the study area. It is also

noteworthy that Trend 3 from Model I, which the DFA identified solely from the eight

discharge datasets, is highly correlated with Rainbow groundwater (r = 0.77, Figure 2-

16), though the decline over time is slightly steeper in Trend 3. Furthermore, although

Rainbow groundwater level appears partly driven by 6-month lagged rainfall in the

Rainbow watershed (r = 0.50; correlations with current year rainfall and PET datasets

have |r| < 0.25), the moderate level of correlation suggests other variables, such as

groundwater extraction or land use change, are influencing groundwater level.

Additionally, given that studies have identified increasing temperatures in Florida in

recent decades (Konrad II & Fuhrmann 2013), yet PET datasets did not show increases

over time, it is possible that these datasets did not capture actual changes in

evapotranspiration, which could be partially driving this decreasing trend.

Model III: Model Predictive Power

The results of Model III indicated similar levels of importance for the significant

explanatory variables identified in Model II. The overall Ceff of 0.66 of Model III revealed

that these four explanatory variables are relatively effective at predicting discharge at

the eight rivers. Comparing this value to the Model II Ceff of 0.80 suggests that the

Model II trend explains roughly 14 percent of variability in Model II. Between Model III

and Model II, the largest reductions in Ceff values for individual rivers occurred at

Econfina, Fenholloway, Steinhatchee, and Suwannee. These rivers also had the

strongest correlations with the remaining trend in Model II. These results suggest that a

common driver, captured by the Model II trend, is influencing discharge at these rivers.

While results from Model III can inform interpretation of results from Model II, studies

examining river discharge patterns of individual rivers would be better served by using

precipitation and evapotranspiration from within that river’s watershed.

Model Summary

The results of these models indicate that decreasing discharge patterns

occurring in some Big Bend rivers are partially caused by changes to rainfall, yet some

of this pattern is unexplained. While the use of climate data and groundwater levels

explains a relatively large portion of regional discharge variability, roughly half of

remaining unexplained variation can be captured by a single common trend. However,

the heavy reliance of Model II on Rainbow groundwater level, which decreases over

time, limits the accuracy of extrapolating these model results to estimate river discharge

with future climate change scenarios. A signal is contained in this groundwater level

dataset that is not explained by recent rainfall or PET, and it affects river discharge far

beyond the boundaries of Rainbow’s watershed. Although data on groundwater

extraction suggest a statewide leveling off since 2000, these figures rely heavily on

estimation and therefore may be flawed, and additionally, extraction varies throughout

the state. Another possible explanation for the decreasing trend could be that increases

in urbanization and impervious surface in recent years has reduced groundwater

infiltration, lowering groundwater levels. Future modeling efforts, whether seeking to

explain regional river discharge or discharge of individual watersheds, should seek to include information on groundwater extraction and land use change over time. Improved

modeling of discharge resulting from the combination of climate change, groundwater

extraction, and land use scenarios, could aid efforts to achieve more efficient water

allocation, thereby minimizing threats to ecological resources dependent on freshwater

flow.

Table 2-1. USGS river discharge gages along Big Bend study area. River Gage no. Start Used in Notes yr. analysis St. Marks 02326900 1956 Yes Years 1995, 1996, 2005 omitted for missing >25% of days; 1994, 2004 missing <25% of days annual means still calculated Aucilla 02326500 1950 No Excluded because of large multiyear data gaps Econfina 02326000 1950 Yes Fenholloway 02324400 1953 Yes Steinhatchee 02324000 1950 Yes Suwannee 02323500 1930 Yes Waccasassa 02313700 1963 No Excluded because of large multiyear data gaps and significant tidal influence (indicated by negative discharge) Rainbow 02313100 Yes Withlacoochee 02313000 1969 Yes Crystal 02310747 2002 No Excluded because of short monitoring period Homosassa 02310678 1995 No Excluded because of short monitoring period Chassahowitzka 02310650 1997 No Excluded because of significant tidal influence (indicated by negative discharge) Weeki Wachee 02310525 1993 No Excluded because of short monitoring period Anclote 02310000 1946 Yes

Table 2-2. Time series used in DFA (8 response variables, 55 explanatory variables) Series No. of Description (source and major processing series steps before annual averaging) Annual discharge (response 8 Daily discharge from USGS WaterData variable) site. Watershed precipitation 8 Monthly gridded precipitation datasets from the PRISM Climate Group, averaged by watershed. Watershed precipitation, 8 6-month lag Watershed potential 8 Monthly gridded temperature (mean, min, evapotranspiration max) datasets from the PRISM Climate Group, calculated to potential evapotranspiration with the Hargreaves equation, averaged by watershed. Watershed potential 8 evapotranspiration, 6-month lag Watershed net recharge 8 Watershed precipitation minus watershed potential evapotranspiration. Watershed net recharge, 8 6-month lag Climate indeces: AMO, ENSO, 3 Monthly climate indeces downloaded from NAO NOAA’s website. Climate indeces: AMO, ENSO, 3 NAO, 6-month lag Groundwater at Rainbow 1 Annual groundwater level at Rainbow Springs Springs well near Dunnellon, FL Note: Datasets calculated with a 6-month lag are averaged from the last 6 months of the previous year and first 6 months of the current year.

Table 2-3. Size, impervious surface, and land cover types of watersheds Watershed Size % % cover: % cover: % cover: % cover: (km2) impervious forest, wetland crops, developed* surface shrub pasture St. Marks 1366 1.4 54.7 22.2 8.3 9.6 Econfina 448 0.3 43.0 45.2 3.6 4.1 Fenholloway 173 0.5 18.1 75.2 0.1 4.8 Steinhatchee 779 0.3 37.3 54.3 0.0 3.8 Suwannee 24,897 1.0 39.5 26.5 20.6 6.4 Rainbow 169 3.4 40.0 2.7 27.2 25.6 Withlacoochee 5014 2.6 25.3 35.1 23.4 13.9 Anclote 202 6.5 23.2 34.9 17.3 21.8 * A high percentage of developed land in these watersheds (68%) is classified as “developed, open space,” where cover is > 80% lawn grasses or landscaping. Note: % covers do not add to 100% because categories grassland/herbaceous and barren (composing < 10% of watersheds) were excluded.

Table 2-4. Summary of models. Model No. Model Type Explanatory AIC Ceff trends variables Model I 4 DFA None 857.3 0.91 Model II 1 DFA Pstm, Pste, 709.8 0.80 Panc, GW Model III 0 Multiple linear Pstm, Pste, 0.66 regression Panc, GW

Table 2-5. River discharge: mean, baseflow, and intra-annual variability. Watershed Mean Baseflow Intra-annual Intra-annual 3 (m /sec) index* Qmax/Qmean CV St. Marks 19.6 0.82 2.9 0.4 Econfina 3.9 0.45 5.3 1.1 Fenholloway 1.2 0.32 9.6 1.5 Steinhatchee 8.3 0.35 8.8 1.4 Suwannee 268.1 0.72 2.4 0.5 Rainbow 19.1 0.95 1.5 0.1 Withlacoochee 22.3 0.66 2.8 0.6 Anclote 1.7 0.25 16.4 2.0 * Baseflow index is an estimate of the portion of discharge that is composed of baseflow. It was calculated with the hydrostats R package (Bond 2016), which uses the Lyne-Hollick filter (Ladson et al. 2013, Lyne & Hollick 1979).

Table 2-6. Mann-Kendall two-sided test results for discharge, precipitation, PET, and discharge-to-precipitation ratio, with direction of significant trends (two-sided MK p-value < 0.10) indicated if present. River Discharge Precip. PET Discharge/ Precip St. Marks 0.993 0.931 0.435 0.750 Econfina 0.425 0.931 0.076 (-) 0.309 Fenholloway 0.158 0.868 0.163 0.109 Steinhatchee 0.390 0.994 0.215 0.163 Suwannee 0.007 (-) 0.687 0.294 0.003 (-) Rainbow <0.001 (-) 0.301 0.795 <0.001 (-) Withlacoochee 0.006 (-) 0.416 0.746 0.002 (-) Anclote 0.630 0.078 (+) 0.746 0.969

Table 2-7. Model II factor loadings (γm,n), canonical correlation coefficients (ρ1,n), regression coefficients (βk,n), and Nash coefficients (Ceff). Significant coefficients (p-value < 0.05) indicated by asterisk (*).

River γ1,n ρ1,n βk,n βk,n βk,n βk,n Ceff (Pstm) (Pste) (Panc) (GW) St. Marks 0.17 0.13 0.69* 0.04 -0.22 0.21 0.57 Econfina 0.61 0.51* 0.46* 0.27* -0.27* 0.47* 0.97 Fenholloway 0.61 0.43* 0.15 0.39* -0.24* 0.65* 0.92 Steinhatchee 0.38 0.28* 0.03 0.70* -0.29* 0.47* 0.84 Suwannee 0.42 0.17 0.52* 0.02 -0.32* 0.71* 0.83 Rainbow 0.06 -0.30* 0.11 -0.03 -0.10 0.92* 0.82 Withlacoochee 0.20 -0.05 -0.03 0.04 0.22* 0.83* 0.73 Anclote 0.10 0.14 -0.30* 0.32* 0.59* 0.31* 0.69 Note: Intercepts, which are not included in the table, were close to zero because of data normalization.

Table 2-8. Model III multiple linear regression results. Significant coefficients (p-value < 0.05) indicated by asterisk (*). River βk,n (Pstm) βk,n (Pste) βk,n (Panc) βk,n (GW) Ceff St. Marks 0.68* 0.08 -0.20 0.13 0.54 Econfina 0.45* 0.42* -0.19 0.19 0.62 Fenholloway 0.14 0.53* -0.17 0.37* 0.58 Steinhatchee 0.03 0.79* -0.24* 0.91* 0.70 Suwannee 0.52* 0.12 -0.27* 0.51* 0.67 Rainbow 0.11 -0.01 -0.09 0.89* 0.81 Withlacoochee -0.03 0.09 0.24* 0.74* 0.69 Anclote -0.31* 0.34* 0.60* 0.26* 0.68 Note: Intercepts, which are not included in the table, were close to zero because of data normalization.

Figure 2-1. Map of eight main study river gages (black triangles) and ArcGIS-derived watersheds (green polygons)

Figure 2-2. Annual river discharge for four northern-most rivers. A) St. Marks. B) Econfina. C) Fenholloway. D) Steinhatchee.

Figure 2-3. Annual river discharge for four southern-most rivers. A) Suwannee (two- sided MK p-value = 0.007). B) Rainbow (p-value < 0.001). C) Withlacoochee (p-value = 0.006). D) Anclote.

Figure 2-4. Boxplots of river discharge by month for four northern-most rivers. A) St. Marks. B) Econfina. C) Fenholloway. D) Steinhatchee.

Figure 2-5. Boxplots of river discharge by month for four southern-most rivers. A) Suwannee. B) Rainbow. C) Withlacoochee. D) Anclote.

Figure 2-6. Annual precipitation of watersheds. A) St. Marks. B) Econfina. C) Fenholloway. D) Steinhatchee. E) Suwannee. F) Rainbow. G) Withlacoochee. H) Anclote (two-sided MK p-value = 0.078).

Figure 2-7. Annual mean river discharge (m3/sec) divided by total watershed precipitation (cm). A) St. Marks. B) Econfina. C) Fenholloway. D) Steinhatchee. E) Suwannee (two-sided MK p-value = 0.003). F) Rainbow (p- value < 0.001). G) Withlacoochee (p-value = 0.002) H) Anclote.

Figure 2-8. Annual PET of watersheds. A) St. Marks. B) Econfina (two-sided MK p-value = 0.076). C) Fenholloway. D) Steinhatchee. E) Suwannee. F) Rainbow. G) Withlacoochee. H) Anclote.

Figure 2-9. Groundwater level above NGVD 1929 at Rainbow Springs near Dunnellon (two-sided MK p-value = 0.012).

Figure 2-10. Climate indices values over time. A) ENSO. B) NAO. C) AMO (two-sided MK p-value < 0.001).

Figure 2-11. Model I common trends and canonical correlation coefficients. A) Normalized discharge common trends over time. B) Correlations for trends 1- 4 on each river.

Figure 2-12. Model II explanatory variables and regression coefficients. A). Normalized explanatory variables over time. B) Regression coefficients for each river.

Figure 2-13. Model II common trend and canonical correlation coefficients. A) Normalized discharge common trend over time. B) Correlations for trend on each river.

Figure 2-14. Model II normalized actual river discharge (lines) and normalized modeled river discharge (points). A) St. Marks (Ceff = 0.57). B) Econfina (Ceff = 0.97). C) Fenholloway (Ceff = 0.92). D) Steinhatchee (Ceff = 0.84). E) Suwannee (Ceff = 0.83). F) Rainbow (Ceff = 0.82). G) Withlacoochee (Ceff = 0.73) H) Anclote (Ceff = 0.69).

Figure 2-15. Model III normalized actual river discharge (lines) and normalized modeled river discharge (points). A) St. Marks (Ceff = 0.54). B) Econfina (Ceff = 0.62). C) Fenholloway (Ceff = 0.58). D) Steinhatchee (Ceff = 0.70). E) Suwannee (Ceff = 0.67). F) Rainbow (Ceff = 0.81). G) Withlacoochee (Ceff = 0.69) H) Anclote (Ceff = 0.68).

Figure 2-16. Model I Trend 3 (black) compared to Rainbow groundwater level (blue)

CHAPTER 3 IMPACTS OF ROADS ON TIDAL MARSH HYDROLOGY AND ECOLOGY

Coastal Wetland Values and Threats from Human Development

Coastal wetlands are uniquely valuable ecosystems, providing critical and far- reaching benefits for water quality, wildlife, and human safety. Salt marshes and mangrove forests clean nutrient pollution from river water before it reaches the ocean, migitating coastal eutrophication and harmful algal blooms that threaten aquatic ecosystems and public health (Berdalet et al. 2016, Nelson & Zavaleta 2012, Valiela &

Cole 2002). Salt marshes, mangroves and other coastal forests shield coastal human populations from storm surges, reducing fatalities and structural damage to buildings

(Barbier 2016, McIvor et al. 2012, Shepard et al. 2011). Coastal wetlands support a rich diversity of waterfowl and shorebirds, and many fisheries use salt marshes and mangroves as habitat during juvenile stages (Howe 1987, Mumby et al. 2004).

Salt marshes and mangrove forests also serve as appreciable global carbon

sinks, far exceeding carbon accumulation rates seen in terrestrial forests (Mcleod et al.

2011). These ecosystems have high primary productivity and are effective at trapping

particulate organic matter, as well as mineral sediment, from tidal waters (Chmura et al.

2003, Hutchison et al. 2013). As in freshwater wetlands, anoxic sediment then slows

decomposition rates, causing soil carbon build-up (Chmura 2013, Mudd et al. 2009). Yet

in contrast to freshwater wetlands, emissions of methane, a greenhouse gas more

potent than carbon dioxide, are low, because sulfate in seawater suppresses

methanogenesis (Chmura et al. 2003). Soil carbon accumulation enables these systems

to vertically accrete with sea level rise as long as other exogenous variables do not

constrain this ability (Mudd et al. 2009).

Coastal wetlands and their expansive array of ecosystem services face numerous anthropogenic threats. Though sea level rise is a growing pressure, coastal development has so far been responsible for the greatest degree of loss (Kirwan &

Megonigal 2013). Studies estimate that roughly 50 percent of coastal wetlands

worldwide have been dredged, drained, or filled for urbanization, agriculture,

mariculture, and other uses (Davidson 2014, Li et al. 2018). In the United States,

regulatory policies have greatly reduced this practice in recent decades, though vast

acreage had already been lost (Dahl & Stedman 2013, Davidson 2014). Some areas,

such as San Francisco Bay and the New England coastline, have endured dramatic

losses of around 80 percent, primarily for agriculture and urban development (Bertness

et al. 2002, Goals Project 2015).

Even in areas where coastal wetland removal for development has ended,

existing development creates other stressors. One widespread issue is “coastal

squeeze,” where developed land blocks wetlands from establishing further inland as sea

level rises (Doody 2004). Fertilizer run-off from developed areas, specifically lawns and

agriculture, can in some cases contribute to salt marsh collapse by reducing

belowground biomass plant allocation and causing snail over-grazing (Deegan et al.

2012, He & Silliman 2015). Coastal impoundments, dikes, and ditching in many areas

modify hydrology for mosquito control or flood protection, dramatically altering ecology

and physical conditions (Roman et al. 1984). Despite their ubiquity, a seldom-studied

issue is how coastal roads through tidal wetlands disrupt hydrology, nutrient cycling,

and ecological structure and function. This study helps fill this research gap by

examining how roads affect tidal flooding and associated variables in four salt marshes

along Florida’s Gulf Coast.

Road Impacts to Salt Marsh Tidal Flooding and Ecology

Importance of Tidal Flooding

Tidal flooding is a significant driver of physiochemical conditions, structure, and

biology in salt marshes. Inundation patterns vary along elevation gradients and with

distance from open water, from regularly flooded marsh near tidal creeks to drier high

marsh flooded only at extreme high tides (Bertness & Ellison 1987). Interactions

between flooding and vegetation determine productivity, sediment biogeochemistry,

nutrient delivery, sedimentation and accretion, and the marsh’s ability to adjust surface

elevation with sea level (Kirwan & Megonigal 2013). Because of the foundational role of

tidal hydrology, it is essential to study how coastal roads redirect tidal flow and affect

salt marsh structure and ecology.

Most importantly, tidal hydrology determines sediment hypoxia, salinity, and

hydrogen sulfide porewater concentration. These variables interact with sediment

properties to create zones of vegetation species with different tolerances to these

chemical stressors (Pennings & Bertness 2001, Pennings et al. 2005, Silvestri et al.

2005). Hydrogen sulfide is a by-product of sulfate reduction, a form of anoxic microbial

carbon mineralization that increases with higher salinity, since sulfate is mainly sourced

by seawater (Weston et al. 2006). Salt marsh vegetation has mechanisms to avoid or

tolerate salinity and hydrogen sulfide exposure, including filtering ions from roots,

reducing uptake during high salinity periods, and cellular osmotic adjustment (Parida &

Jha 2010, Touchette 2006, Touchette et al. 2009). Because use of these mechanisms

expends resources, salinities towards the upper end of a species’ tolerance can reduce productivity (Howard et al. 2016).

Tidal water movement and velocity in salt marshes influence sedimentation and organic matter accretion. More frequent tidal inundation typically increases sedimentation, with fine sediment particles imported and deposited on the marsh surface during rising tides (Craft et al. 1993, Wang et al 1993). Higher vegetation density reduces water turbulence, slowing water and encouraging particle settling

(Christiansen et al. 2000). Greater tidal flooding stimulates root growth of some salt marsh species, contributing to sediment volume (Mudd et al. 2009). Longer flood durations may reduce porewater oxygen compared to less flooded areas, slowing decomposition and promoting accretion (Howes et al. 1981). In contrast, longer flood durations contribute to hydrogen sulfide build-up in sediment, reducing productivity and organic matter contribution, curbing accretion (Roman 2016).

Potential Road Impacts

Roads through salt marsh act as physical barriers that can restrict tidal flooding through creeks and sheet flow. Tidal creeks that bisect roads typically have flow constricted through narrow culverts that were designed primarily to prevent road flooding (Eberhardt et al. 2010). The largest body of research on impacts of tidal restriction, as well as response to tidal restoration, comes from New England salt marshes. Most studies have assessed areas with deliberate tidal flow modifications in place since the mid to early 20th century, including diking for mosquito control, flood

protection, and salt hay farming (Anisfeld & Benoit 1997, Konisky et al. 2006, Portnoy

1999, Portnoy & Giblin 1997, Warren et al. 2002). Studies of road-impacted areas often

focus on tidal restriction caused jointly by tide gates or dikes (Anisfeld et al. 1999,

Burdick et al. 1997, Konisky et al. 2006, Roman et al. 1984). These studies document

extreme tidal restriction—either total removal of tidal influence, or greater than 50

percent reduction in daily tidal range or average high tides.

Tidal restriction in New England salt marshes has been shown to cause lower

water tables, large reductions in salinity, and a shift to freshwater or brackish vegetation

(Burdick et al 1997, Roman et al 1984, Warren et al 2002). The disturbance caused by

tidal restriction sometimes led to establishment of Phragmites australis (common reed),

a brackish marsh species considered invasive in North America (Roman et al. 1984,

Roman et al. 2002, Warren et al. 2002). A study by Anisfeld (1999) found a 50 to 78

percent decrease in sedimentation from an 80 percent reduction in tidal range, likely

from reduced sediment delivery and accelerated organic matter decomposition from

greater sediment aeration (Portnoy 1999, Portnoy & Giblin 1997). As a result, tidally

restricted areas were found to have lowered surface elevation relative to adjacent

unrestricted marshes (Burdick et al. 1997, Portnoy & Giblin 1997, Roman et al. 1984).

Most studies have also found reduced fish and snail populations on the restricted side

and low rates of fish passage through culverts (Burdick et al. 1997, Eberhardt et al.

2010, Roman et al. 2002, Warren et al. 2002).

While research on tidal restriction caused by diking provides insight into potential

road impacts, studies seldom assess impacts from roads alone (with some exceptions,

e.g., Eberhardt et al. 2010, Roman et al. 2002). Additionally, the existing body of

research focuses on extreme tidal restriction, whereas the effects of moderate

restriction on salt marsh ecology have not been closely examined. The New England

studies typically found reduced salinity on the restricted side, whereas tidal restriction in

warmer climates might increase salinity through evaporation and resulting salt concentration, especially if the restricted side receives low freshwater inflows. Although studies have documented accelerated decomposition from greater sediment aeration, moderate tidal restriction might have little effect, especially considering the long duration that most salt marsh sediment stays saturated (Michaels & Zieman 2013). In contrast, roads will very likely change tidal water flow paths and velocity, and therefore have strong potential to modify delivery of sediment or other materials, such as invertebrate larvae, that settle out of the water column. This study on road impacts in salt marsh of

Florida’s Big Bend Region contributes valuable new information to tidal restriction literature by examining effects of moderate restriction in a sub-tropical climate.

Coastal Roads in Florida’s Big Bend Region

Florida’s Big Bend region is a low-gradient and low-wave energy coastline extending from Apalachee Bay southeast to Anclote Key north of Tampa Bay (Hine et al. 1988). The coastline supports wide expanses of salt marsh alongside freshwater coastal forest further inland and in patches throughout the marsh. Nearshore areas are home to seagrass beds and oyster reef, which further reduce wave action and help prevent marsh erosion (Dawes et al. 2004, Scyphers et al. 2012). The Big Bend has low impervious surface cover and minimal coastal development, though pine plantations are prevalent. Nearly 75 percent of this ecologically unique coastline is held as federal,

state, or local protected lands (calculated from Florida Conservation Lands geospatial

dataset [Florida Natural Areas Inventory 2017]). Coastal waters receive freshwater

discharge from numerous rivers and spring systems (e.g., St. Marks, Suwannee, and

Withlacoochee), as well as diffuse groundwater seepage through sediments, though

estimates of groundwater contribution vary widely (Befus et al. 2017, Cable et al. 1997).

Despite this low-development setting, salt marshes in some areas of the Big

Bend are bisected by roads leading to beaches, boat ramps, campgrounds, or wildlife viewing areas. Some are unmaintained, unpaved relic roads from former pine plantations that have since been returned to coastal forest (Thom et al. 2015). Tidal restriction caused by these roads is likely moderate in degree, since they do not fully enclose salt marsh and only create partial barriers to flow, except in a few locations where roads were built to partially impound freshwater for waterfowl habitat creation

(Thom et al 2015).

Tidal restriction from roads in Big Bend salt marshes is unlikely to cause major shifts in vegetation communities, however rerouting of tidal waters or dampening of tidal variation has the potential to impact vegetation productivity. Given Florida’s sub-tropical climate, reduced tidal flooding could result in higher salinities from greater evaporation and porewater salt concentration. Juncus roemerianus (black needlerush), the dominant vegetation species in Big Bend salt marsh, uses salinity stress avoidance mechanisms, mainly reducing stomatal conductance to conserve water during high-salinity periods

(Touchette et al. 2009). However, J. roemerianus is most productive at low to moderate salinities (< 15 parts per thousand [PPT], Touchette et al. 2006), since decreased stomatal conductance reduces carbon dioxide (CO2) uptake (Eleuterius 1984,

Touchette et al. 2009). For instance, salinity of 28 PPT compared to 15 PPT reduced belowground biomass of greenhouse-grown J. roemerianus by 46.5 percent and stem heights by 15.5 percent (Howard et al. 2016).

Decreased tidal inundation frequency or reduced tidal velocities could also affect salt marsh sediment processes and properties. In particular, given the role that tidal

inundation and water velocity play in transporting sediment, the tidally restricted side could experience a decrease in fine sediment delivery, with critical implications for the marsh’s ability to keep up with sea level rise. This is especially relevant given that the

Big Bend is a low-sediment, low-gradient coastline, which predisposes it to have lower

resilience to sea level rise (Hine et al. 1988, Kirwan & Megonigal 2013). Additionally,

two separate reviews of studies on salt marsh resilience found that anthropogenic

barriers to sediment delivery is the largest impedance to accretion rates keeping pace

with sea level rise (Kirwan & Megonigal 2013, Roman 2016). Reduced tidal flooding

from roads could also lower nutrient input and impede delivery of invertebrate larvae,

and lower J. roemerianus productivity from elevated salinities could reduce organic

matter content in sediment, further lowering accretion rates. Finally, less frequent

flooding could aerate sediment and accelerate decomposition, though this effect may be

minimal under only moderate tidal restriction, given the long duration that salt marsh

sediment stays saturated during non-flood conditions (Michaels & Zieman 2013).

This study evaluates the effects of roads at four salt marsh sites along the Big

Bend. As is typical of Big Bend salt marshes, these sites are dominated by J.

roemerianus, with Spartina alterniflora (smooth cordgrass) present at low elevations

near tidal creeks. In each case, a long straight road runs parallel to coast, potentially

restricting tidal flooding on the inland-facing side. Though vegetation communities on

both sides of the road are typical of Big Bend salt marsh, I hypothesize that the roads

reduce tidal flooding frequency and variability on the inland side, and as a result,

physical and ecological variables will be significantly different between the coastal and

inland sides of each road. Additionally, I hypothesize that these differences will increase

with distance from the tidally connected end of each road, which likely correlates with intensified tidal restriction. The objective of this study is to assess the magnitude and

significance of road-induced differences in tidal flooding frequency and duration,

porewater chemistry (salinity, ammonium [NH4], nitrate [NO3]), vegetation

characteristics (stem density, aboveground biomass, stem height, percent live),

invertebrate densities (Littorina littorea [periwinkle snails], Melampus coffeus [coffee

bean snails], Ilyanassa obsoleta [mud snails], Uca spp. [fiddler crab] burrows, and

Geukensia demissa [ribbed mussels]), and sediment characteristics (percent organic

matter, bulk density, particle size distribution [PSD]).

Methods

Study Sites

Study sites are located along Florida’s Big Bend coastline, in the Big Bend

Wildlife Management Area (WMA) and the Lower Suwannee National Wildlife Refuge

(LSNWR). Tides in the region are semi-diurnal, and the mean tidal range is

approximately 1.0 m. To identify potentially road-impacted sites, aerial imagery was

used to identify four sites where salt marsh is bisected by roads running roughly parallel

to the coast. Study sites “Sand Ridge” and “Horseshoe” are located in the Big Bend

WMA Jena Unit, and “Shired Island” and “Cabin Road” are located in the LSNWR

(Figure 3-1). Sand Ridge has a maintained dirt and cobble road leading north and

ending before a large tidal creek (Figure 3-2). Horseshoe has a mixed paved and

unpaved road leading south and ending in a sand flat (Figure 3-3). Shired Island has a

paved road leading south to a beachside campground (Figure 3-4). A bridge 1.3 km

before the road terminus allows a large 90-meter wide tidal creek to pass underneath

relatively unobstructed. Cabin Road is an unmaintained relic logging road leading south

and ending before a small tidal creek (Figure 3-5). Salt marsh at all sites is dominated by J. roemerianus, with areas of S. alterniflora close to tidal creeks. Patches of Batis maritima (saltwort), Salicornia virginica (glasswort), Borrichia frutescens (sea oxeye),

and short-form S. alterniflora occur in high marsh areas.

Experimental Design

At Shired Island, Horseshoe, and Cabin Road, I designated six transects

perpendicular to the road, with three on the coastal side and three on the inland side

(Figures 3-2, 3-4, 3-5). Transect locations were arranged at varying distances from the

road terminus (or in the case of Shired Island, the bridge over the large tidal creek),

where the inland and coastal sides are assumed to again have significant tidal

exchange. A “near” set was located within 200 meters of the tidal exchange point, and

“mid” and “far” transect pairs were 200-250 meters apart up the road. Given ownership

and accessibility issues, Horseshoe had only two transects (Figure 3-3). At Sand Ridge,

the far transects start from a point on the road (29.64238,-83.39361) where a narrow

tidal creek bisects it from the coastal side and flows overtop during high tide, forming a

pond on the inland side. A similar situation occurs at the far transects of Shired Island,

where a wide, shallow tidal creek from the coastal side passes through a culvert

(29.40940, -83.20098), and a pond has formed on the inland side.

Transects were 125-250 meters long, depending on extent of contiguous

saltmarsh area and field accessibility, with sample points every 25 meters starting 50

meters from the road. To serve as an informal reference for how experimental variables

vary across undisturbed salt marsh, at Horseshoe I designated a 500-meter transect

leading from the road terminus to the coast, with points every 50 meters.

Water Level and Groundwater Salinity

Two shallow groundwater monitoring wells were installed at each site from

August 2015 through June 2016 at the 50-meter points of the middle transects (Shired

Island, Sand Ridge, Cabin Road) or far transects (Horseshoe). Wells were constructed of 10-cm diameter PVC, with a 50-cm well screen positioned at roughly 0-50 cm depth.

Conductivity, temperature, and pressure were recorded every 30 minutes using a CTD-

Diver (vanEssen Instruments, ON, Canada) at roughly 45-cm depth. Atmospheric pressure was recorded at a central location with a Baro-Diver (vanEssen Instruments,

ON, Canada) and used to calculate water levels from well pressure data.

Porewater

Porewater samples were collected at each transect point in June (Shired Island and Sand Ridge only), July, and August 2015, and May 2016. Porewater was extracted at 10-12 cm depth by inserting porous soil water samplers (Soil Moisture, Santa

Barbara, USA) into the sediment. Each road sample event was completed within a 36- hour period. Samples were analyzed for salinity using an Orion Star benchtop meter with DuraProbe conductivity sensor (Thermo Scientific, MA, USA). May 2016 samples were also analyzed for NO3 and NH4 using an Air-segmented Continuous Autoflow

Analyzer (Flow IV).

To test the reliability of our porewater sampling scheme for characterizing salinity

differences across transects, I also assessed temporal and small-scale spatial variability

at 10 points at Shired Island and 12 points at Cabin Road at their mid transects over the

course of two days. The temporal test assessed the assumption that by completing

each road’s porewater sampling event within a 36-hour period, salinity variation would

be minimized, allowing for comparison among transects for a particular sampling trip. To

do so, porewater was collected three times every five hours on the first day and twice the second day, keeping the porewater samplers inserted into the sediment. The spatial test assessed our assumption that one porewater sample collected from each transect point characterized its salinity. For this test, I used rhizon samplers to collect porewater from three locations one meter apart at each transect point to characterize spatial salinity variation over the area that vegetation surveys were conducted.

Vegetation

Vegetation surveys were conducted in June-August 2015 using three 0.4x0.4

meter quadrats haphazardly placed within two meters of the porewater collection point.

Within each quadrat, I counted live and dead stems of each species and measured

heights of 10 live stems of each species. For S. alterniflora, the height of the tallest leaf

was measured, since this has been shown to accurately predict biomass (Trilla et al

2013). To construct allometric relationships for biomass estimation, I collected 290 J.

roemerianus stems and 25-30 stems each of S. alterniflora, B. frutescens, S. virginica,

and B. maritima from Shired Island and Horseshoe. Stems were measured for height,

dried at 70°C for 48 hours and weighed. For each species, an exponential regression

was fit to estimate biomass from height.

At each transect point, total biomass, total stem density, average stem height,

percent live biomass, and species percent by live biomass were assessed. Biomass

values were estimated from the allometric relationships. To analyze species-specific characteristics and exclude effects of inter-species competition, I separately analyzed biomass, density, average height, and percent live biomass for J. roemerianus points at all sites, plus S. alterniflora points at SR, since this site had high S. alterniflora cover.

Invertebrates

Invertebrate surveys were conducted in May-June 2016 every 50 meters, using

three 0.4x0.4 meter quadrats haphazardly placed within two meters of the porewater

collection point. Surveys were completed at low tide, with 0-5 cm standing water, to

allow visibility. Live L. irrorata, M. coffeus, C. scalariformis, and I. obsoleta on stems and

the marsh surface were counted, along with Uca spp. burrows and G. demissa.

Because burrows >2 cm may be crab species other than Uca (Luk & Zajac 2013), I

counted small (≤2 cm) and large (>2 cm) burrows separately. However, because a

majority of burrows (75.1 percent) were small and both size categories had similar

patterns, I aggregated counts and assumed all were Uca.

Sediment Properties and Elevation

I collected sediment cores (30-cm depth, 10-cm diameter) in May-June 2016

from three points distributed along each transect. Cores were separated in the field into

0-5, 5-15, and 15-30 cm depth increments. In the lab, wet samples were weighed for bulk density, and then sieved through a 2-mm sieve to remote roots and sediment particles larger than sand. A 20-30 g subsample was dried at 70ºC for 72 hours to attain dry weight equivalent (DWE) through the loss-on-ignition method, which was used to adjust bulk density to dry weight. The dried sample was burned in a muffle furnace at

550ºC for 4 hours to determine ash-free dry mass and derive soil organic matter percent. PSD was measured using a modified method by Gee and Bauder (1986). First, a 25-30 g of wet sample and 25 mL sodium hexametaphosphate (for dis-aggregating

particles) were combined in a centrifuge tube and placed on shaker table for 10 hours.

Contents were added to graduated cylinders with 1 L water kept at 24ºC. After allowing

3.5 hours for larger particles to settle, a 15-mL aliquot was drawn at 5 cm depth and

dried to derive the clay portion (<0.002 mm). The graduated cylinder contents were rinsed through a 0.05 mm sieve and the sieve contents were burned in a muffle furnace to exclude particulate organic matter (consistent with Kettler et al. 2001) and derive the sand portion (>0.05 mm). Silt (0.05-2 mm) was assumed to be the remaining portion.

Sediment surface elevation was measured every 10 meters along transects and

at transect points using a laser level. At the start of each transect, I measured elevation

relative to North American Vertical Datum of 1988 (NAVD88) with a Trimble Geo 7X

handheld GPS (vertical accuracy of 2 cm). Elevation data were combined with

groundwater monitoring water level data to calculate hydroperiods (the percent of time

inundated) at each transect point, based on the simplifying assumption that water level

measured at each well is representative of a constant elevation across the transects on

the same side of the road. Since altered hydroperiod is expected to be the underlying

road impact causing changes in other variables, direct correlations between hydroperiod

and explanatory variables were also investigated, as well as how these correlations

changed between the inland and coastal sides.

Statistical Analyses

Porewater, vegetation, invertebrate, and sediment data were analyzed for each

road using 2x3 factor ANOVAs (Analysis of Variance) with road side (inland vs. coastal)

and proximity to tidal connection (far, mid, and near) as factors, plus an interaction term

between factors. Sediment characteristics and belowground biomass were analyzed

both as values aggregated across depths by core, as well as in their individual depth

layers. For each transect pair, the longer transect was shortened to match the length of

the shorter transect to ensure comparison of road impacts at equal distances from the

road. If ANOVA results showed significant differences (using p-value = 0.05), pairwise

ANOVAs were run to determine any significant differences among individual transects.

Pairwise ANOVA results for the interaction term were interpreted primarily to look for differences between the inland and coastal side for individual transect pairs, since these

are adjacent to one another and therefore differences are more likely to suggest road

impacts. In contrast, significant differences between the inland and coastal sides of

transects at different tidal connection proximities (for instance, between the inland-far

and coastal-near transects) are less likely to suggest road impacts, because they are

farther apart, and differences may result from typical spatial variation across the salt

marsh surface.

Results

Water Level and Groundwater Salinity

All sites had evidence of tidal restriction, though the magnitude of the impact

varied (Table 3-1, Figure 3-6). Marsh hydroperiods on the inland side were 23.8 to 41.7

percent lower than the coastal side. Differences in flood duration were greatest at

Horseshoe, which was flooded 47 percent of the time on the inland side compared to 79

percent on the coastal side. Shired Island had the next largest difference, with 35

percent inundation on the inland side compared to 60 percent on the coastal side. Road

effects on tidal range and mean water elevation were less consistent. The inland tidal

ranges at Sand Ridge and Horseshoe were 41 and 70 percent lower than the coastal

side, respectively, whereas inland and coastal tidal ranges at Shired Island were

roughly the same, and tidal range at Cabin Road was 30.1 percent higher on the inland

side. Overall mean water elevations were 1.9 to 8.1 cm lower on the inland side across

sites, with the greatest reduction at Sand Ridge. At each site, lower water elevations on

the inland side caused levels to consistently drop slightly below the ground surface.

Mean salinities between the inland and coastal sides were similar at all roads, though salinity variability was affected at all roads except Horseshoe (Table 3-1, Figure

3-7). Sand Ridge was the only site where salinity was more variable on the inland side.

In September and October 2015, the inland side at Sand Ridge appeared to respond

more strongly than the coastal side to rainfall events, showing decreases in salinity and

higher variability. In contrast, at Shired Island the opposite effect occurs, with coastal

salinities appearing to respond more strongly to rainfall, as well as to daily tidal

influence, during this period. At Horseshoe, inland salinities remained relatively stable,

while coastal salinities are more variable, declining roughly 10 PPT from October

through December 2015.

Porewater

I detected significant porewater salinity differences between the coastal and

inland sides for individual transect pairs at three sites, though porewater salinity was

highly variable within and across transects, and a consistent road effect was not

apparent (Figures 3-9, 3-10, 3-11, 3-12). Salinities at Sand Ridge, Horseshoe, and

Shired Island were similar, averaging 24.9 ± 8.4 (standard deviation [SD]), 26.4 ± 8.3,

and 21.6 ± 8.9 PPT across points and sample events, whereas Cabin Road salinity

averaged 10.7 ± 3.6 PPT. At Sand Ridge, the road had no significant salinity effects

during any sampling event. At Horseshoe, inland salinity was lower than coastal salinity

by 11.7 and 5.3 PPT in July 2015 and May 2016, driven primarily by decreasing salinity

moving eastward on the inland side. At Shired Island’s far transects, the inland side was

6.5 and 5.9 PPT higher than the coastal side in June 2015 and May 2016, and 10.4

PPT lower in August 2015. In contrast, no significant differences occurred at the mid or

near transect pairs. At Cabin Road, significant differences between sides occurred at

the far and near transect pairs in July 2015, though salinity was overall variable between transects. No differences were observed at Sand Ridge for any months.

In the test of temporal salinity variability, the mean salinity range of five samples taken over a 36-hour period at select transect points was 6.3 and 5.8 PPT at Shired

Island and Cabin Road (Figure 3-13). In the test of small-scale spatial variability using three samples at each point, the mean salinity range was 5.5 and 4.3 at Shired Island and Cabin Road (Figure 3-14). Salinities tended to be higher during high tide at Cabin

Road, whereas Shired Island exhibited no consistent pattern with tides.

Concentrations of NH4 were low across sites (Figure 3-15), ranging from below

detection (39.8 percent of points) to 1.11 mg/L (mean = 0.19 ± 0.22). NH4 at Shired

Island was significantly lower on the inland side, averaging 0.14 ± 0.17 mg/L versus

0.27 ± 0.26 mg/L on the coastal side. This difference was driven primarily by 13 inland

points with NH4 concentrations below detection, versus three on the coastal side. No

road effects on NH4 were detected at Sand Ridge, Horseshoe, or Cabin Road.

Concentrations of NO3 were very low across sites (Figure 3-16), ranging from below

detection (69.5 percent of points) to 0.13 mg/L (mean = 0.03 ± 0.08). No road effects

were detected. At Cabin Road, NH4 at the far-coastal transect was significantly higher than the mid-coastal and both near transects, and NO3 was significantly higher than the

inland-near transect, indicating porewater nitrogen variability unrelated to the road.

Vegetation

Across the four study sites, 77 percent of transect points had J. roemerianus present, 5 percent had S. alterniflora, 13 percent had mixed J. roemerianus and S. alterniflora, and 5 percent had J. roemerianus mixed with high marsh species (short- form S. alterniflora, S.virginica, S. patens, L. carolinianum, and B. maritima; Table 3-1).

Two-thirds of points with S. alterniflora were in Sand Ridge, where it was present at 59 percent of points. Across sites, total aboveground biomass in J. roemerianus stands

ranged from 302.8 to 2818.6 dry g/m2 (mean = 1445.3 ± 527.0) and stem density ranged

from 253 to 2448 (mean = 959.8 ± 373.9). S. alterniflora stands had lower biomass and

stem density, ranging from 150.7 to 792.7 dry g/m2 (mean = 502.2 ± 250.9) and 37 to

135 stems/m2 (mean = 93.0 ± 36.3). Because of high cell rigidity of J. roemerianus,

turnover of dead stems is slow, and live biomass averaged 46.7 percent of total

biomass in J. roemerianus stands, whereas S. alterniflora averaged 85.8 percent live.

Sand Ridge was the only study site where the road significantly affected

vegetation species dominance, with S. alterniflora composing on average 19.7 ± 33.8

percent of aboveground total biomass on the inland side versus 49.0 ± 36.9 percent to

the coastal side (Figure 3-17). At Shired Island, S. alterniflora biomass was slightly

lower on the inland side, where it was present at three points (14.7 ± 9.1 percent)

versus five points on the coastal side (50.6 ± 33.2 percent), but the difference was not

significant and overall S. alterniflora presence at the site was low. Species dominance

was not analyzed at Horseshoe or Cabin Road, where nearly all points were J.

roemerianus stands.

I detected no significant road impacts on aboveground total biomass at any site,

whether considering all vegetation species together, or separately analyzing J.

roemerianus (analyzed at all roads) and S. alterniflora (analyzed at Sand Ridge; Figures

3-18, 3-19). Aboveground biomass at Horseshoe, Shired Island, and Cabin Road were similar, ranging from 158.6 to 2818.6 g/m2 (mean = 1424.9 ± 555.6), while Sand Ridge

biomass was significantly lower (ANOVA p-value < 0.05), ranging from 301.7 to 1444.2

g/m2 (mean = 730.3 ± 335.0). Sand Ridge’s low biomass was due to high S. alterniflora

coverage and J. roemerianus stands with relatively low stem density. Percent live

biomass in J. roemerianus stands averaged 66.6 ± 23.9 percent in the far-inland

transect, higher than all other transects, but this difference was only significant relative

to inland-mid and coastal-near, which together averaged 31.5 ± 10.8 percent (Figure

20).

Despite finding no significant road impacts on aboveground biomass, total stem

density was significantly higher on the inland side at Shired Island, averaging 1087.7 ±

561.3 stems/m2 versus 794.1 ± 348.5 on the coastal side (Figures 3-21, 3-22). This difference was most pronounced at the far transects, where the inland side averaged

1414.3 ± 614.7 stems/m2 versus 649.9 ± 297.2 on the coastal side. Stem density values

were similar when considering J. roemerianus-only points, since S. alterniflora presence

was low at Shired Island. Significant differences in stem density between transects

occurred at Cabin Road, though they did not appear to indicate a road impact, because

no differences occurred between the inland and coastal side of an individual transect

pair. The road did not affect stem density at Sand Ridge or Horseshoe. Across sites,

total stem density was lowest at Sand Ridge, partly because of higher S. alterniflora

presence, though even at J. roemerianus only points, density ranged from 487 to 1139

stems/m2 (mean = 731 ± 212.8) compared to 253 to 2448 (mean = 979.6 ± 378.9) at

Horseshoe, Shired Island, and Cabin Road.

At Shired Island, the far-inland transect had significantly shorter J. roemerianus

stem heights than all other transects, averaging 89.0 ± 12.4 cm compared to 121.6 ±

16.1 cm across the other transects (Figure 3-23). There were no differences between

inland and coastal J. roemerianus stem heights at Sand Ridge, Horseshoe, or Cabin

Road, or S. alterniflora stem heights at Sand Ridge. J. roemerianus heights at all sites

ranged from 68 to 149 cm (mean = 112.7 ± 17.9), and Cabin Road J. roemerianus was

13.6 cm taller than at Sand Ridge (ANOVA p-value < 0.05).

Invertebrates

Invertebrate densities were significantly different between the inland and coastal

sides at Sand Ridge, Shired Island, and Cabin Road, but not at Horseshoe. Densities of

L. irrorata, the most abundant snail species (mean = 17.6 ± 26.8 snails/m2), were 70.8

to 100 percent lower on the inland side relative to the coastal side at these sites (Figure

3-24). Specifically, inland versus coastal densities of L. irrorata averaged 15.7 ± 20.2

versus 53.9 ± 36.4 at Sand Ridge, 6.8 ± 8.1 versus 33.2 ± 32.9 at Shired Island, and 0

versus 11.2 ± 15.5 snails/m2 at Cabin Road. At Shired Island, the difference was most

pronounced farther from the tidal exchange point, with a coastal average of 58.3

snails/m2 and an inland average of <1 snail/m2. Snail densities were 102.1 snails/m2 at

the two coastal points closest to the road, which were adjacent to an S. alterniflora

stand with extremely high snail densities.

Densities of I. obsoleta at Cabin Road were lower by 57.1 percent on the inland

side, averaging 15.9 ± 9.6 compared to 37.1 ± 29.4 snails/m2 on the coastal side. I.

obsoleta densities were low at the other three sites, present at only 4 to 25 percent of

points and in low numbers when present, except for one point at Sand Ridge with 94

snails/m2. M. coffeus snail abundance was relatively low at all sites, present at 22 to 36

percent of points and with highly variable counts where present (Figure 3-25).

Significant road impacts to Uca spp. burrow and G. demissa densities occurred

at Shired Island, but no road effects were apparent at the other three sites (Figure 3-

27). Uca burrows were lower on the inland side, averaging 42.4 ± 21.7 compared to 64

± 30.6 burrows/m2 to the coastal side. Although counts were also lower on the inland

side at Sand Ridge, which at 97.0 ± 59.8 burrows/m2 had higher burrow density than the

other sites (ANOVA p-value < 0.05), differences were not significant and the dominant

spatial trend was higher densities closer to the tidal exchange point. G. demissa

densities were lower on the inland side at the far transects at Shired Island, averaging

42.4 ± 21.7 compared to 64.0 ± 30.6 mussels/m2 to the coastal side (Figure 3-28). At

Sand Ridge, G. demissa densities at the coastal-mid transect were significantly higher

than the coastal-far and both near transects, but this did not suggest a road impact,

since no differences occurred between the inland and coastal sides of an individual

transect pair.

Elevation

Elevation transects suggest potential road impacts at all sites, though the nature

and degree of effect varies (Figures 3-36, 3-37, 3-38, and 3-39). Sand Ridge was by far

the lowest elevation site, averaging 0.156 ± 0.161 metr above NAVD88, compared to

1.639 ± 0.103 at Horseshoe, 1.512 ± 0.073 at Shired Island, and 1.725 ± 0.124 at Cabin

Road. The far transects at Sand Ridge provide the strongest evidence of road impacts

(Figure 3-36). Compared to the coastal transect, which ranges from -0.194 to 0.146

meter (mean = 0.004 ± 0.096) and has S. alterniflora present along most of its length,

the inland side is higher and has a narrow range of 0.300 to 0.415 meter (mean = 0.384

± 0.029) and it has predominantly J. roemerianus and short-form S. alterniflora.

At Horseshoe, the inland side is higher than the coastal side, averaging 1.703 ±

0.030 meter compared to 1.550 ± 0.074 (Figure 3-37). Though slightly higher elevations

further inland are consistent with natural marsh morphology, it is noteworthy that the

coastal side slopes upward from 1.406 meter elevation at the farthest transect point to

1.600 meter next to the road, whereas the inland side is relatively flat and exhibits no

spatial trend. At Shired Island, elevations were similar between the coastal and inland

side (differences between coastal and inland means of each transect pair were <6 cm;

Figure 3-38). However, the coastal-far and coastal-mid transects reveal an abrupt increase of ~15 cm from the 25-meter point to the 50-meter point, suggesting potential

entrainment of sediment close to the road.

At Cabin Road, likely road impacts occured at the mid and near transects, which had lower elevation on the inland side (Figure 3-39). Inland elevation compared to coastal elevation averaged 1.584 ± 0.042 meters versus 1.782 ± 0.062 at the mid

transects, and 1.708 ± 0.100 compared to 1.833 ± 0.106 at the near transects. In the

far-coastal transect, low elevation points from the 50-meter point out occured because

this section runs roughly parallel to a small tidal creek, 20 to 30 meters away.

Sediment

I detected no significant road impacts on any of the measured sediment characteristics or to belowground biomass across the sites (Figures 3-29, 3-30, 3-31, 3-

32, and 3-33). Bulk density varied widely across organic and mineral soils, with values ranging from 0.15 to 1.56 g/cm3. Percent organic matter was similarly variable, ranging

from 1.0 to 53.0 percent and with a strong negative correlation with bulk density (r = -

0.904. Sand Ridge and Cabin Road had similar values, together averaging 0.71 ± 0.37

g/cm3 bulk density and 16.0 ± 14.3 percent organic; Shired Island and Horseshoe were

also similar, averaging 1.05 ± 0.28 g/cm3 bulk density and 5.3 ± 5.1 percent organic.

Bulk density increased and organic matter decreased at deeper depths.

Sand composed the highest portion of sediment mass at all sites, averaging 72.6

± 25.8 percent, followed by silt (16.0 ± 16.5 percent) and clay (11.4 ± 10.4 percent;

Figures 3-31, 3-32, 3-33). PSD was very similar between Shired Island and Horseshoe,

which together had 84.5 ± 12.6 percent sand, 8.5 ± 8.6 percent silt, and 7.0 ± 4.8

percent clay. Sediment was 72.6 ± 23.6 percent sand at Sand Ridge and 56.8 ± 32.8 at

Cabin Road. Percent silt varied modestly by depth, with the 15-30 cm depth increment

8.6 percentage points lower than 0-5 cm (ANOVA p-value < 0.05), while sand and clay did not vary by depth.

Belowground biomass values ranged from 558.6 to 2804.8 g/m2 (mean = 1446.2

± 540.8) and were similar across sites (Figure 3-34). Belowground biomass was highest

in the 5-15 cm depth increment, which had on average 52.7 percent of biomass, while

the 0-5 and 15-30 depth increments had 26.8 and 20.5 percent of biomass.

Hydroperiod Correlations with Variables

Transect point hydroperiod was found to be a strong predictor of several

variables (indicated by <0.05 p-value in a simple linear regression), including some that

had no significant road impacts. Given that significant road impacts to hydroperiods

occurred at every site, this discrepancy suggests restriction may in fact be affecting

these variables. At Shired Island, longer hydroperiods were associated with greater

percent S. alterniflora biomass compared to J. roemerianus (correlation coefficient [r] =

0.522) and at J. roemerianus points, lower stem density (r = -0.642). At Cabin Road, which only has J. roemerianus, longer hydroperiods were associated with lower stem density (r = -0.644), lower biomass, though this pattern only occurred to the inland side

(inland r = -0.593), and lower belowground biomass (r = -0.803). Longer hydroperiods at

Sand Ridge and Shired Island were associated with higher densities of L. irrorata (r =

0.516, 0.420) and Uca spp. burrows (r = 0.546, 0.575), and at Cabin Road, higher

densities of I. obsoleta (r = 0.669). For both snail species, inland densities were lower

than the coastal side for a given hydroperiod, and this pattern did not occur for Uca spp.

burrows. Finally, longer hydroperiods at Shired Island were associated with lower sand

content compared to finer particles (r = -0.506) and higher soil organic matter percent (r

= 0.578). Out of 14 sampling events completed across the sites, only four showed a significant correlation between hydroperiod and salinity (r = 0.800 and 0.471 at two

Horseshoe events, and r = -0.467 and -0.474 at two Shired Island events).

Discussion

Although tidal restriction occurred to different degrees at all roads, snail density was the only strong and consistent ecological signature of road impacts across all sites, with considerably lower snail densities on inland sides. Groundwater monitoring data suggests some road effects on salinity variability, and salinities sometimes diverged for one to two months at a time at a particular road, however long-term mean salinities were largely not affected. Horseshoe was the only site with likely road impacts on porewater salinity, with lower inland salinities during two out of three sampling events.

Vegetation at two sites was likely impacted, with higher J. roemerianus presence and lower S. alterniflora presence on the inland side at Sand Ridge, and higher stem density and lower stem height on the inland side of the Shired Island far transects. No significant impacts on porewater nitrogen or sediment properties were detected.

The unexpected absence of major salinity impacts may have occurred because the tidal restriction was not severe enough to increase salinity through evaporation, or because the inland sides lacked influence from freshwater sources that would cause reduced salinity. Additionally, the spatial and temporal tests of porewater salinity

differences across a 1-meter distance and over a two-day period revealed very high salinity variation, suggesting that my methods may not have provided accurate comparison between transects. Specifically, a single porewater sample does not appear to characterize salinity throughout the area of a transect point, and porewater collected from one transect cannot accurately be compared to porewater taken from another transect hours later. In other words, a single porewater sample does not capture the salinity experienced by salt marsh vegetation roots for even several hours. This suggests that standard methods for porewater chemistry characterization in marshes

(e.g., porous soil water samplers and rhizons) must be applied with caution.

The most compelling evidence of impacts from tidal restriction was consistently lower snail densities on inland road sides (particularly of L. irrorata, the most abundant species). A likely explanation for lower L. irrorata densities is that snails produce planktonic larvae that float in open water for several weeks before being deposited in salt marsh and growing into adults (Fish & Fish 1989, Jablonski & Lutz 1983), and shorter hydroperiods caused by the road reduce larval delivery to the inland side. An

additional possibility is that lower densities are caused by reduced survival, though

given the consistency of the effect compared to the variation in environmental

conditions (elevation, inundation frequency, salinity, etc.) across sites, this explanation

is less likely. Additionally, predation is the major control of survival (since L. irrorata

forage on detritus, they are unlikely to be food-limited), and this typically decreases with

lower hydroperiods (Hughes 2012), suggesting predation would actually be lower to the

inland sides. However, there appears to be a further reduction on the inland side that is

not explained by lower hydroperiods. Although L. irrorata were positively correlated with

S. alterniflora percent biomass (Sand Ridge r = 0.493, Shired Island r = 0.593;

Horseshoe and Cabin Road lack S. alterniflora), owing to their preference for S.

alterniflora (Hamilton 1978, Hughes 2012), the additional reduction to the inland side

occurred even among J. roemerianus-only points. A likely explanation is that larvae

settle onto the marsh surface before reaching the inland side, since tidal waters have to

circumvent the road, and in addition to increasing flow distance, this would slow water

velocity and further encourage materials to settle before reaching the inland side.

It is also important to note that inland L. irrorata densities are significantly lower

even in two instances where a minor tidal exchange point connects the inland and

coastal sides of a transect pair. The far transects of at Shired Island extend from a point

on the road with a 1.22-meter diameter culvert, yet L. irrorata densities on the inland

side are 1.4 percent of densities to the coastal side. In fact, L. irrorata densities of 95.8

and 108.3 snails/m2 at the two coastal transect points closest to the road were the

highest found at Shired Island, whereas the inland transect averaged < 1 snail/m2.

Similarly, the far transects at Sand Ridge start from a point where water flows over the road daily during high tides, yet the inland side had no snail presence compared to 34.7 snails/m2 on the coastal side. Adult L. irrorata travel very short distances (one study found that over 226 days, tracked L. irrorata moved on average 4.0 meters from original positions; Hamilton 1978), so this difference likely occurred from failure of the culvert to convey invertebrate larvae. Assuming reduced larval delivery is the cause of low invertebrate densities at these far-inland transects, this suggests that these culverts may also fail to convey fine sediment particles to the inland side.

Given the foundational role of tidal flooding in vegetation productivity, an unexpected finding was the low occurrence of significant road impacts to vegetation parameters, particularly the lack of any impacts to biomass. A potential explanation is that the degree of tidal restriction was not severe enough to cause large salinity changes, and therefore vegetation productivity was not affected. In addition, the variables impacted most strongly, invertebrates, would likely not affect J. roemerianus productivity. In contrast to their destructive effects to S. alterniflora, L. irrorata have not been found to graze on J. roemerianus, instead consuming detrital particles (Hughes

2012), and Uca spp. burrows only minimally aerate sediment and therefore would hardly affect J. roemerianus productivity (Michaels & Zieman 2013). However, it is also possible that the allometric equations used to estimate stem biomass from stem height were inconsistent across transects, causing bias in biomass estimates. For instance, at the far transects of Shired Island, the inland side had significantly higher stem density but shorter stem heights, which yielded no biomass effect. However, in a supplemental analysis, I found that J. roemerianus stems at the inland transect had a lower mass per unit height than other transects at Shired Island, meaning biomass was likely overestimated at this transect.

No significant differences in PSD were detected, and since sediment delivery likely conveys only fine particles (clay and silt, but not sand), this result does not suggest road effects on sedimentation. However, decreased snail abundances provide evidence for potential sedimentation impacts that were either not severe enough to cause significant differences in PSD, or not captured in the field methods. Specifically, since snail larvae are delivered to the marsh surface through tidal flooding, lower snail

densities to the inland sides provide evidence for interrupted flow that may also affect sediment transport. Additionally, the low number of sediment cores collected (three per transect) likely did not capture sediment conditions across the transect, and this is further suggested by wide variation often found within transects. Finally, even if the roads are reducing sedimentation to the inland sides, sediment delivery rates along the

Big Bend coastline are low (Hine et al. 1998), meaning effects of altered sedimentation may be difficult to detect in sediment properties.

At these sites, the effects of tidal restriction on marsh ability to build elevation with sea level rise through organic matter accretion and sedimentation are unclear. The lack of impacts to PSD suggests no sedimentation effects, though in contrast, lower snail densities support the possibility of reduced sedimentation from tidal restriction. No impacts of tidal restriction to vegetation biomass were detected, suggesting autochthonous organic matter input to sediment is unaffected. No impacts to soil organic matter content were detected, suggesting no road impacts to contributions from autochthonous or allochthonous organic matter, or to decomposition rates. However, despite poor evidence of changes to the mechanisms driving sediment elevation change, elevation differences at some sites in fact suggest an impact. Lower elevations on the inland side at Cabin Road are likely from decreased sediment input, considering the lower hydroperiod found here. It is less likely that these lower elevations are driven by reduced organic matter input or increased decomposition, given that soil organic matter percent was similar to the coastal side, plus shorter hydroperiod at Cabin Road is actually associated with higher biomass. At the Sand Ridge far transects, the inland side had considerably higher elevation than the coastal side, despite much lower

hydroperiods, which reduces sediment delivery, and similar organic matter content.

Higher elevations on the inland side may be caused by high belowground biomass found along this transect, which adds bulk to sediment.

Table 3-1. Mean salinity, tidal range, water level, and proportion of time inundated (C = coastal, I = inland) Sand Ridge Horseshoe Shired Island Cabin Road C I C I C I C I Salinity ± SD 26.4 23.8 25.5 25.8 18.5 19.1 17.3 14.9 (PPT) ± 0.9 ± 2.6 ± 3.2 ± 1.4 ± 3.2 ± 1.8 ± 1.6 ± 1.3 Tidal range 22.1 13.0 7.9 2.4 19.8 22.3 16.8 22.0 (cm) Mean water level, 5.4 -2.7 1.2 -0.7 4.0 -1.2 2.2 -4.4 all points (cm)* Mean portion of 0.63 0.48 0.79 0.47 0.60 0.35 0.55 0.40 time inundated, all points* Mean water level, 4.7 -0.7 -2.7 1.2 2.3 5.2 5.3 4.9 well point (cm) Portion of time 0.62 0.49 0.52 0.72 0.60 0.62 0.71 0.60 inundated, well point * Mean inundation frequency at all elevations measured, assuming that the water level measured by the well is representative of all transect points on one side of the road.

Table 3-2. Vegetation species occurrence at sites (proportion of transect points with species present) Sand Ridge Horseshoe Shired Island Cabin Road (n=26 points) (n=14) (n=50) (n=38) J. roemerianus only 0.31 0.93 0.82 0.97 S. alterniflora and 0.46 0.00 0.14 0.03 J. roemerianus mix S. alterniflora only 0.15 0.00 0.02 0.00 J. roemerianus and 0.08 0.07 0.02 0.00 high marsh species* * High marsh species were short-form S. alterniflora, Salicornia virginica (glasswort), Spartina patens (saltmeadow cordgass), Limonium carolinianum (sea lavender), and Batis maritima (salt wort).

Table 3-3. Porewater salinity (PPT) at sites (mean ± SD) Sand Ridge Horseshoe Shired Island Cabin Road (n=26 points) (n=14) (n=50) (n=38) June 2015 30.9 ± 5.2 -- 27.8 ± 5.1 -- July 2015 25.9 ± 7.5 32.1 ± 6.8 24.0 ± 6.0 11.1 ± 3.9 August 2015 19.6 ± 7.6 20.8 ± 5.2 12.6 ± 7.4 10.3 ± 3.3 May 2016 19.8 ± 3.5 20.6 ± 4.5 18.1 ± 4.6 8.5 ± 3.0

Table 3-4. Invertebrate occurrence at sites (proportion of transect points with species present). Sand Ridge Horseshoe Shired Island Cabin Road (n=18 points) (n=8) (n=28) (n=22) Snail, L. irrorata 0.83 0.25 0.82 0.32 Snail, M. coffeus 0.22 0.25 0.32 0.36 Snail, I. obsoleta 0.22 0.25 0.04 0.91 Uca spp. burrows 1.00 1.00 1.00 1.00 G. demissa 0.50 0.25 0.43 0.36

Figure 3-1. Overview map of study sites.

100

Figure 3-2. Map of Sand Ridge Road transect points, with proximity to tidal creek connection (near/mid/far) labeled.

Figure 3-3. Map of Horseshoe site transect points, with proximity to tidal creek connection (near/far) labeled. Near transect is a control.

101

Figure 3-4. Map of Shired Island transect points, with proximity to tidal creek connection (near/mid/far) labeled.

Figure 3-5. Map of Cabin Road transect points, with proximity to tidal creek connection (near/mid/far) labeled.

102

Figure 3-6. Daily mean water depth (0 = ground level) Sept. 2015 to July 2016, for coastal vs. inland at road sites. A) Sand Ridge, B) Horseshoe, C) Shired Island, D) Cabin Road.

103

Figure 3-7. Half-hourly groundwater salinity Sept. 2015 to July 2016, for coastal vs. inland at road sites. A) Sand Ridge, B) Horseshoe, C) Shired Island, D) Cabin Road.

104

Figure 3-8. Half-hourly groundwater temperature Sept. 2015 to July 2016, for coastal vs. inland at road sites. A) Sand Ridge, B) Horseshoe, C) Shired Island, D) Cabin Road. Note: dotted lines indicate temperature sensor malfunction; values were estimated using regression parameters from relating the series with missing data to the series from across the road.

105

Figure 3-9. Sand Ridge porewater salinity by transect from four sample events. A) June 2015 (inland-far not collected). B) July 2015. C) August 2015. D) May 2016.

Figure 3-10. Horseshoe porewater salinity by transect from three sample events. A) July 2015 (inland 11.7 PPT lower than coastal [p-value < 0.001]). B) August 2015. C) May 2016 (inland 5.3 PPT lower than coastal [p-value = 0.020]).

106

Figure 3-11. Shired Island porewater salinity by transect from three sample events; lowercase letters denote ANOVA significant pairwise differences. A) July 2015. B) August 2015. C) May 2016.

Figure 3-12. Cabin Road porewater salinity by transect from three sample events; lowercase letters denote ANOVA significant pairwise differences. A) July 2015. B) August 2015. C) May 2016.

107

Figure 3-13. Results from temporal test of porewater salinity variability over 36 hours (left side of each graph is coastal, right side is inland). A) Shired Island mid transects. B) Cabin Road mid transects.

Figure 3-14. Results from spatial test of porewater salinity variability across 1 square meter (left side of each graph is coastal, right side is inland). A) Shired Island mid transects. B) Cabin Road mid transects.

108

Figure 3-15. Porewater NH4 by transect from May 2016; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island (inland 0.13 mg/L < coastal [p-value = 0.034]). D) Cabin Road.

Figure 3-16. Porewater NO3 by transect from May 2016; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

109

Figure 3-17. Percent S. alterniflora biomass by transect. A) Sand Ridge (inland 29.3%- points higher, p-value = 0.038). B) Shired Island.

Figure 3-18. Total biomass by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road

110

Figure 3-19. Total J. roemerianus biomass by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

Figure 3-20. Percent live J. roemerianus biomass by transect; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road (inland 4.9%-points < coastal [p-value = 0.032]).

111

Figure 3-21. Total stem density by transect; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island (inland 196.6 > coastal [p-value = 0.021]). D) Cabin Road.

Figure 3-22. Total J. roemerianus density by transect; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island (inland 299.0 > coastal [p-value = 0.018]). D) Cabin Road

112

Figure 3-23. Average J. roemerianus height by transect; lowercase letters denote ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road

Figure 3-24. L. irrorata snail densities by transect; lowercase letters indicate ANOVA significant pairwise differences. A) Sand Ridge (inland 38.2 < coastal [p-value = 0.018]). B) Horseshoe. C) Shired Island (inland 26.3 < coastal [p-value = 0.003]). D) Cabin Road (inland 11.2 < coastal [p-value = 0.040]).

113

Figure 3-25. I. obsoleta densities by transect for Cabin Road; lowercase letters indicate ANOVA significant pairwise differences (inland 21.2 < coastal [p-value = 0.011]). All other sites had negligible I. obsoleta presence.

Figure 3-26. M. coffeus densities by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

114

Figure 3-27. Uca spp. burrow densities by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island (inland 21.6 < coastal [p-value = 0.038]). D) Cabin Road.

Figure 3-28. G. demissa counts by transect; lowercase letters indicate ANOVA significant pairwise differences. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

115

Figure 3-29. Bulk density by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

Figure 3-30. Percent soil organic matter by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

116

Figure 3-31. Percent sand by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

Figure 3-32. Percent silt by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

117

Figure 3-33. Percent clay by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

Figure 3-34. Belowground biomass by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

118

Figure 3-35. Belowground-to-aboveground biomass ratio by transect. A) Sand Ridge. B) Horseshoe. C) Shired Island. D) Cabin Road.

Figure 3-36. Elevation (meters > NAVD88) along Sand Ridge transects, with porewater and vegetation transect points indicated by solid circles.

119

Figure 3-37. Elevation (meters > NAVD88) along Horseshoe transects, with porewater and vegetation transect points indicated by solid circles.

Figure 3-38. Elevation (meters > NAVD88) along Shired Island transects, with porewater and vegetation transect points indicated by solid circles.

120

Figure 3-39. Elevation (meters > NAVD88) along Cabin Road transects, with porewater and vegetation transect points indicated by solid circles.

121

CHAPTER 4 GEOGRAPHIC DRIVERS OF LONG-TERM COASTAL FOREST DIE-OFF AND CHANGE IN THE LOWER SUWANNEE NATIONAL WILDLIFE REFUGE

Values of Coastal Forest Ecosystems

Coastal forests and other coastal wetlands are uniquely valuable ecosystems that provide numerous protective benefits for wildlife, coastal property, and neighboring ecosystems. Salt marshes and mangrove forests mitigate coastal eutrophication by reducing nutrient pollution from river systems before it reaches the ocean and support fisheries by providing habitat during juvenile stages (Berdalet et al. 2016, Mumby et al.

2004, Nelson & Zavaleta 2012). Coastal ecosystems shield coastal human populations from storm surges, reducing fatalities and structural damage to buildings; and coastal forests are especially effective at providing this barrier due to their vertical structure

(Barbier 2016, McIvor et al. 2012, Miura et al. 2015, Shepard et al. 2011). Globally, less than one percent of forest is identified as providing coastal stabilization, yet the benefit to human safety, property, and infrastructure is disproportionately large due to high population concentrations close to the coast (Miura et al. 2015).

While they are tremendously beneficial to human populations, coastal wetlands are also threatened by numerous anthropogenic threats, and few countries have prioritied coastal forest conservation (Miura et al. 2015). Although coastal development has so far been responsible for the greatest degree of coastal wetland loss, sea level rise is a growing pressure (Kirwin & Megonigal 2013). In some cases, sea level rise has caused salt marsh migration into ecosystems further inland, including coastal forest, but it is unclear whether coastal forest will similarly establish further inland (Raabe & Stumpf

2016). While the ability of salt marsh to migrate and accrete with sea level rise has been

122

extensively studied (e.g., Roman et al. 2016), the limits of coastal forest resilience has

received less attention. However, limited studies suggest their rates of accretion are

lower than sea level rise estimates, suggesting they are at risk of conversion to salt

marsh and open water (Craft 2012, Geselbracht et al. 2011, White & Kaplan 2017).

While conversion of coastal forest to salt marsh represents a shift from one

valuable coastal ecosystem to another, this displacement causes a reduction in

ecosystem heterogeneity, lowering the overall diversity of ecosystem services provided

(Loreau et al. 2001). As such, efforts to to study coastal forest susceptibility to sea level

rise and other impacts of climate change, such as increased drought frequency

(Groisman et al. 2005, Mann et al. 2017), are valuable for informing coastal

management activities and assessing whether restoration options are available.

Additionally, methods for evaluating future sea level rise impacts to coastal ecosystems

typically rely primarily on elevation data (e.g., Geselbracht et al. 2011), but observations

of heterogeneous changes across similar elevations suggest that methods that

incorporate other geographic variables would improve future predictions.

Coastal Forest Die-Off along Florida’s Big Bend Coastline

This study examines drivers of coastal forest die-off from salinity stress in a

coastal refuge along the Big Bend of Florida. The Big Bend region is a low-gradient and low-wave energy coastline extending from Apalachee Bay southeast to Anclote Key

north of Tampa Bay (Hine et al. 1988). The region has a subtropical climate with high

amounts of rainfall, averaging between 132 and 143 cm/year, with a June through

September wet season (see Chapter 2). Coastal waters receive freshwater discharge

from numerous rivers and spring systems (e.g., St. Marks, Suwannee, and

Withlacoochee), as well as diffuse groundwater seepage through sediments, though

123

estimates of groundwater contribution vary widely (Befus et al. 2017, Cable et al. 1997).

The coastline supports wide expanses of salt marsh alongside freshwater coastal forest

further inland and in elevated hammocks throughout the marsh. Trees in continuous

forest areas likely obtain freshwater both by retaining rainwater and from groundwater

discharge, while islands are thought to primarily rely on rainwater (Langston et al.

2017), although evidence exists that some islands also receive diffuse groundwater

seepage through the underlying bedrock (DeSantis et al. 2007, Williams et al. 2007).

The flat gradient of the Big Bend coastline makes it especially vulnerable to

impacts from sea level rise, given that even small increases can cause widespread

inundation of coastal habitats (Hine et al. 1988). A geologic study of sea level change

over the past 5000 years suggests that coastal forest islands in particular will have

limited resilience to sea level rise, since they are positioned on top of elevated bedrock

(Hine et al. 1988). It is estimated that over 82 km2 of coastal forest along the Big Bend

has already converted to salt marsh since the late 19th century, driven by increased

saltwater innundation (Raabe & Stumpf 2016). Wetland migration modeling in

Waccasassa Bay Preserve using one and two meter sea level rise scenarios by 2100

predicts 83% and 99% loss of coastal forest; while most becomes salt marsh under 1-

meter rise, virtually all becomes open water under 2-meter rise, since rise would

outpace marsh accretion (Geselbracht et al. 2011).

Field studies of coastal forest loss in Waccasassa Bay in recent decades have documented the changes that occur during die-off stages (Langston et al. 2017,

DeSantis et al. 2007, Williams et al. 1999). Islands in different stages of die-off were

found to have more frequent tidal flooding and higher groundwater salinity compared to

124

healthy stands. Changes in forest species assembly and reproductive success was

even observed in continuous forest areas not experiencing tidal flooding, suggesting

potential saltwater intrusion via groundwater (Langston et al. 2017). Because tree

seedlings are particularly sensitive to elevated salinity, one of the earliest indicators of

stress is the prevention of tree regeneration (Williams et al. 1999). In intermediate

stages of die-off, mature trees exhibit physiological signs of water stress and growth slows, species richness declines as trees with lower salinity tolerance die, and salt marsh shrubs encroach into the understory. Forests in advanced stages of die-off have

a high proportion of standing dead trees, and remaining live trees are likely Sabal

palmetto (cabbage palm) and Juniperus virginiana (red cedar), which have the highest

salinity tolerance of coastal forest trees (DeSantis et al. 2007, Langston et al. 2017).

Compounding threats from sea level rise is the decrease in discharge observed

in some Big Bend rivers and increased drought frequency that is expected to occur from

climate change, both of which have potential to increase coastal forest salinity

(Groisman et al. 2008, Seavey et al. 2011). Die-off patterns observed in Waccasassa

Bay Preserve also suggest the importance of freshwater influence in driving die-off. For

instance, during the 1992 through 2005 study period jointly examined by two studies, a

dramatic increase in tree mortality was observed in 2000 through 2005 and appeared

linked to an extreme drought associated with La Niña (DeSantis et al. 2007, Williams et

al. 1999). Additionally, while studies have documented forest replacement by salt marsh

in many areas, this process is greatly reduced in areas near freshwater discharge points

(Raabe et al. 2004). Finally, although low-elevation forest is typically more susceptible

to stress, one study observed a low-elevation, frequently flooded island supporting a

125

healthy tree stand, and the researchers speculated that subsurface discharge through limestone channels may deliver fresh water (Williams et al. 1999).

Sediment characteristics may also influence the resilience of a forest stand.

Coastal forests often have sandy surficial sediment with high pore space that likely retains freshwater from rain, though this layer is very thin, which likely limits surficial storage (often < 1 meter; Hine et al. 1988, Tihansky & Knochenms 2001). It is possible that certain stands have sediment qualities, such as high porosity or organic matter content, that enable them to better retain rainwater. Compared to stressed forest stands, groundwater in healthy forest stands have been shown to respond more strongly to rainfall, raising water levels and reducing salinity, even when salinity of nearby tidal creeks did not change (Williams et al. 1999). Elevated salinity may also induce changes to sediment biogeochemistry that could create feedbacks that accelerate die-off. Specifically, chemical constituents in seawater can accelerate organic carbon decomposition in the sediment, dropping sediment elevation (Chambers et al. 2011) and further increasing inundation.

Although it is relatively well-established that higher salinities are causing coastal forest die-off, interactions among elevation, distance to freshwater source, sediment characteristics, and other geographic and climatic factors make explaining current degradation pathways and predicting future die-off a non-trivial challenge. Specifically, although elevation is likely the major predictor of vulnerability, the spatial importance of other geographic conditions is unclear. Temporally, it is uncertain how sea level, local rainfall, river discharge, and groundwater seepage interact to drive salinity in a given area. Sea level rise modeling that expands beyond simple elevation (i.e., the “bathtub

126

model”) approaches has been identified as a major scientific and management need for the area, along with studies to untangle the effects of different hydrologic drivers (Katz &

Raabe 2005, Williams et al. 2007). Improved understanding of these interactions will improve planning and management of coastal refuges by helping managers to identify vulnerable areas and determine whether management interventions are feasible.

This study investigates how geographic variables in addition to elevation and different hydrologic variables influence coastal forest die-off in the Lower Suwannee

National Wildlife Refuge (LSNWR) along the Big Bend. Forest health was mapped by interpreting remote sensing data into the Normalized Difference Vegetation Index

(NDVI) and a geospatial model was built to assess how several geographic variables

(elevation; distance to nearest tidal creek, Gulf waters, and the Suwannee river mouth; island versus continuous; north of the Suwannee versus south; and perimeter-to-area

[P/A] ratio of islands) affect NDVI. I hypothesize that forests will be more susceptible to salinity exposure if they have lower elevation and are closer to tidal creeks and Gulf waters. Islands, compared to continuous forest, and islands with higher P/A ratios were also hypothesized to be more susceptible as edges are less protected from tidal flooding. Additionally, I hypothesize that forests will experience increased salinity stress if they are further from the Suwannee River, and to the north of the river, since the river’s freshwater plume flows dominantly south (Kaplan et al. 2016). Finally, the forest health map and aerial imagery was used to identify five sites of varying forest condition for monitoring of groundwater level and salinity to better understand whether groundwater signals varied even between two wells located close together and at similar elevations. The intent of these data was to support or refute the idea that

127

additional, non-geographic drivers (e.g., differences in groundwater flow) may be an

important driver of coastal forest health at the patch scale. The effect of Suwannee

River discharge, local rainfall, and tidal levels on these groundwater time series were

then analyzed, with the overarching hypothesis that that higher salinities will be

associated with periods of lower Suwannee River discharge and rainfall.

Methods

Study Site

The LSNWR encompasses 54,000 acres of coastal forest and wetland around

the mouth of the Suwannee River, which is the main source of freshwater to the NWR

(Figure 4-1). The Suwannee River has a mean annual flow rate of 225 m3/s (USGS

gage 02323500), with highest seasonal flows from February to May. Subsurface

freshwater discharge via spring vents and diffuse discharge contributes an unknown but

potentially substantial amount to the Suwannee Estuary (Burnett et al. 1990, Raabe et al. 2011, Williams et al. 2007). The mean tidal range is 0.86 m, measured at the Cedar

Key NOAA station (no. 8727520). Coastal forests in LSNWR are dominated by S.

palmetto and J. virginiana (red cedar), which are the most salinity-tolerant species, as

well as Quercus virginiana (live oak) and Pinus taeda (loblolly pine). Tree species

diversity is higher further inland and in areas of low salinity (DeSantis et al. 2007). Salt

marsh is dominated by Juncus roemerianus (black needlerush) and Spartina alterniflora

(smooth cordgrass).

Normalized Difference Vegetation Index Calculation

Maps of LSNWR coastal forest NDVI were created in Google Earth Engine

(GEE) and ArcGIS version 10.2. Orthorectified Landsat 8 top-of-atmosphere (TOA) Tier

1 images were used to create a 2016 NDVI image for the model of geographic drivers of

128

coastal forest health and stress. Landsat satellites are multi-spectral sensors that image

the earth every 16 days at a 30x30 meter resolution, collecting reflectance of visible

light, near infrared (NIR), short-wave infrared (SWIR), and thermal infrared. The Fmask

band (“Function of Mask” band) included with each image identifies pixels that are likely

clouds or cloud shadow, based on light reflectance, the thermal band, and sensor

viewing angle (Zhu & Woodcock, 2012; Zhu et al. 2015). TOA images with Fmask are

available through GEE for the periods March 1984 to May 2012 from Landsat 5 TM and

April 2013 to April 2017 from Landsat 8.

In GEE, all Landsat 5 image scenes and 2016 Landsat 8 image scenes taken

over the LSNWR (Worldwide Reference System path 17, row 40) were gathered into a

collection and images with greater than 80 percent cloud cover were excluded. Pixels

identified by the Fmask band as likely to be cloud or cloud shadow were masked out of

each image. Next, I calculated NDVI for each image pixel. NDVI is calculated as (NIR –

red)/(NIR + red) and indicates vegetation biomass density and health, given that healthy

vegetation absorbs most red light that reaches it and reflects NIR. Values can range

from -1 to 1, though values below 0 indicate non-vegetative surfaces, such as barren

land or impervious surface. In Landsat 5, NDVI is calculated as (Band 4 – Band

3)/(Band 4 + Band 3), while in Landsat 8, NDVI is calculated as (Band 5 – Band

4)/(Band 5 + Band 4). This difference represents a change in band numbering, not wavelengths. To construct annual datasets, I used the greenest pixel method, which selects the highest NDVI value in a given year for each pixel. This serves as a further step to eliminate pixels with cloud influence that may have not been identified in the

Fmask, and it standardizes pixels to be most likely taken during the growing season.

129

Spatial Model to Analyze Geographic Drivers

Identifying coastal forest pixels

The NDVI composite image from 2016 was imported into ArcGIS and coastal forest pixels were identified from National Wetland Inventory (NWI) data (U.S. Fish and

Wildlife Service 2016), with some modifications based on ground-truthing and cross- comparison with the Florida Land Cover Classification System Cooperative Land Cover

(CLC) Map v. 3.2. Although the CLC map is produced by Florida agencies and likely has greater categorization accuracy, NWI was chosen as the primary data source because of its fine spatial resolution. NWI categories assigned to coastal forest included most subclasses within estuarine forested, estuarine scrub shrub, and palustrine forested, based on the attribute’s primary classification. I also classified cells into transitional forest and salt marsh, to develop NDVI and elevation values for comparison to coastal forest. In cases where the NWI attribute had a secondary ecosystem type as emergent wetland, I considered the attribute transitional forest rather than coastal forest.

Because forest in the Suwannee River floodplain experiences a different dynamic between water level, elevation, and salinity, I eliminated pixels that fell within the

“Floodplain Swamp” category in the CLC dataset. Although NWI data generally excludes plantations and other agriculture, the CLC dataset was used to identify and exclude additional plantations, as well as forest surrounded by plantations (identified as

NWI attributes sharing > 25 percent of their border with plantation). These methods resulted in inclusion of some forest edge pixels that had greater salt marsh or water coverage than forest, which was determined by viewing aerial imagery. To address this issue, I excluded pixels below an NDVI of 0.44, which was calculated by Zonal Statistics

130

as the mean NDVI (0.71) minus three standard deviations (0.09) of coastal forest pixels,

since these pixels are very unlikely to be coastal forest; this calculation resulted in a

slight increase in the mean and decrease in the standard deviation of coastal forest

pixels.

Distance measurements to water features

The geographic model tests how forest health is affected by three distance

variables: Euclidean distance from the forest pixel to the nearest tidal creek (“creek

distance”), water flow distance from the nearest tidal creek to Gulf Coast waters (“Gulf

distance”), and water flow distance from the Gulf of Mexico connection point to the

Suwannee River mouth (“Suwannee distance”). Creek and Gulf distances are intended

to capture the degree of saltwater exposure via tidal inundation or saline groundwater,

and Suwannee distance is intended to capture freshwater influence in the general area,

since the Suwannee is the largest freshwater source in the LSNWR.

To identify water pixels, LiDAR Digital Elevation Model (DEM; Florida Division of

Emergency Management 2007) data was used in combination with the National

Hydrography Dataset (NHD; USGS 2013) and NWI. DEM elevation values are relative

to the North American Vertical Datum of 1988 (NAVD88). NHD and NWI indicate

boundaries of most water bodies, but they do not include tidal creeks with widths smaller than roughly 7 m, and these small tidal creeks are prevalent in LSNWR. Several elevation thresholds were tested, and an upper threshold of 0.16 meter (the mean

elevation of NWI estuarine water [0.03 m] plus one standard deviation [0.13 m]) was

very effective at identifying tidal creeks, while excluding salt marsh. The boundary

between tidal creek water and Gulf waters was assigned using the NHD Gulf of Mexico

polygon.

131

Distance variables were calculated over a grid of 1.524-m (nominally 5-foot)

cells, aligning with the DEM raster (Figure 4-2). First, a cost surface was created where

movement over a water pixel was close to 0 (specifically, 1.0 x 10-9), and movement

over coastal forest or salt marsh equaled 1. Creek distance was found by minimizing the

cost distance between each coastal forest pixel and water, while the cost allocation tool

recorded the location of each coastal forest pixel’s closest tidal creek pixel. Gulf

distance was then found by minimizing cost distance between this tidal creek pixel and

the Gulf, and this connection point was again recorded. Finally, distance to the

Suwannee was found by minimizing cost distance between this Gulf point and the

Suwannee River mouth. To test an additional indicator of influence from freshwater,

coastal forest cells were categorized as either north or south of the Suwannee River,

since the freshwater plume dominantly flows south from the river mouth (Kaplan et al.

2016).

Elevation and other land characteristics

All coastal forest cells were assigned values for elevation, assigned as either

islands or continuous forest, and (for islands), perimeter-to-area ratio was calculated.

Elevations were found using Zonal Statistics to calculate mean elevations from the

LiDAR DEM.1 Because islands likely experience greater saltwater exposure than

continuous forest, and likely receive freshwater from different sources (Langston et al.

2017), coastal forest was assigned as either island or continuous forest, with islands

1 The resampling technique in ArcGIS is a standard method to recalculate rasters to a different grid cell size. However, this was not used because its finest resampling method (cubic convolution) is calculated from only 16 overlapping grid cells, whereas the size difference between the NDVI grid and DEM grid means that each NDVI grid cell contains 387.5 DEM grid cells.

132

identified as forest surrounded by either salt marsh or water. Since irregularly shaped islands have more edge compared to interior area, I measured P/A of islands.

Statistical analysis

To test drivers of coastal forest health, two multiple linear regression models were created: one to predict NDVI by pixel, and another to predict mean island NDVI.

For the pixel model, a subset of pixels was selected using a minimum separation distance of 500 meters between pixels. The threshold of 500 m, used to minimize spatial autocorrelation in the model, was found using the Incremental Spatial

Autocorrelation tool in ArcGIS. Linear regression was used to determine significant predictors of NDVI using R version 3.2.2 (R Core Team 2016). The best models were found by running multiple candidate models with all combinations of explanatory variables and finding the model that explained the highest level of variability, as indicated by the coefficient of determination (R2). For the island model, pixels within islands were grouped into units and NDVI and geographic variables were averaged by island; multiple linear regression was again used to determine significant predictors of island NDVI. The effect of P/A was assessed only in the island model. Residuals from the pixel model were then mapped to determine whether there exists spatial variation in error distribution, which could be indicative of factors not assessed in the model, such as groundwater influence or sediment characteristics.

Water Level and Salinity Monitoring

Groundwater elevation and salinity were monitored at five sites south of the

Suwannee River from September 2015 through April 2017 to examine groundwater patterns in different areas of the refuge relative to observed vegetation. The “Cabin

Road” well (NDVI = 0.66, elevation = 0.40 m) was located in a low-brackish salinity

133

area, indicated by relatively high tree species diversity and the presence of Cladium jamaicense (sawgrass). To examine how groundwater signals could vary over a short distance, the other four wells were arranged into two paired sets of wells, with one well located in an island with relatively healthy forest (identified by mostly full canopy, low presence of dead trees, and presence of S. palmetto seedlings), and the other in a neighboring island with visible signs of stress (presence of dead trees, encroachment by salt marsh shrubs, and absence of S. palmetto seedlings). One set was located next to

Giger Creek in two islands roughly 200 meters apart (“Giger stressed,” NDVI = 0.55 and

“Giger healthy,” NDVI = 0.60). Vegetation at these islands was primarily S. palmetto and

J. virginiana. A tidal creek sensor was also installed nearby in Giger Creek. Two wells were located farther south near Dennis Creek in two islands roughly 410 meters apart

(“South stressed,” NDVI = 0.54, “South healthy,” NDVI = 0.65). Vegetation at these islands was primarily S. palmetto, P. taeda, and J. virginiana. A tidal creek sensor was installed nearby, but this went missing after a few months.

Wells were constructed of 10-cm diameter PVC, with a 20-cm well screen at roughly 0.4 to 0.6 meter depth at the Cabin Road well, and 0.8 to 1.0 meter depth at the other wells. The tidal creek sensor at Giger Creek was encased in a PVC capsule with

0.5-cm drilled holes to allow water flow and suspended from a buoy to roughly 0.5 meter below the surface, though actual depth varied with tidal velocities. Conductivity, temperature, and pressure were recorded every 30 minutes using a CTD-Diver

(vanEssen Instruments, ON, Canada) at roughly 45-cm depth. Atmospheric pressure was recorded at a central location with a Baro-Diver (vanEssen Instruments, ON,

Canada) and used to calculate water levels from well pressure data.

134

Statistical analysis

In addition to graphical comparison of salinity and groundwater level across the

different wells, cross correlation functions (CCFs) were used to assess how these

variables were affected by Suwannee River discharge, local rainfall, and tidal levels

from the previous 30 days. CCF results indicate the level of correlation between two

univariate time series. Specifically, they reveal significant correlations between a

dependent variable time series and lagged values of an explanatory time series. CCF

results were assessed both for significant correlations (p-value < 0.05), and to find

where correlation coefficients peaked. Daily Suwannee discharge for the 02323500 tide

gage was downloaded from NOAA’s WaterData website. Daily rainfall was downloaded

from the PRISM Climate Group. PRISM datasets are of 4 x 4 km spatial resolution

covering the continental United States and are created by interpolating meteorological

records from weather stations across topographical data. I downloaded data for a grid

cell central to the LSNWR. Hourly tidal water levels for the Cedar Key NOAA station

(no. 8727520) and averaged to daily values.

Results

Spatial Model to Analyze Geographic Drivers

The final model included 62385 pixels of coastal forest cells, comprising 5608

hectares (Figure 4-3). Coastal forest NDVI averaged 0.72 ± 0.07, higher than NDVI of

transitional forest (0.53 ± 0.12) and of salt marsh (0.40 ± 0.10) (Table 4-1, Figure 4-3).

Coastal forest elevation averaged 0.81 meter ± 0.47 above NAVD88, while transitional forest and salt marsh elevations were very similar (0.59 ± 0.25 and 0.58 ± 0.24, respectively). The total area of coastal forest north and south of the Suwannee River were relatively similar, and islands, though numerous (n=201) represented a much

135

smaller land area than continuous coastal forest (289 hectares compared to 5319;

Table 4-2, Figure 4-4). Overall, NDVI was lower in forest north of the Suwannee and on islands. Despite having lower mean NDVI, islands were on average higher in elevation than continuous areas, though they also had greater elevation variability, indicated by high standard deviations (Table 4-1). Pixel distance to water features ranged from 0 to

4.32 km for distance to creek (mean = 1.11 ± 1.00); 0 to 11.79 km for distance to Gulf waters (mean = 4.28 ± 1.32), and 0 to 19.70 km for distance to the Suwannee River mouth (mean = 7.92 ± 4.01).

Grouping island pixels together into individual patches yielded 201 islands.

Average NDVI by island ranged from 0.50 to 0.76 (mean = 0.63 ± 0.06), and average elevation ranged from 0.35 to 3.3 meters (mean = 1.07 ± 0.53). On islands, distance to tidal creeks (mean = 0.09 ± 0.10 km) and to Gulf waters (mean = 1.72 ± 1.47) were much lower than when considering all pixels, though distances to the Suwannee River mouth were similar. Values for P/A ranged from 0.16 to 1.01 km/ha (mean = 0.48 ±

0.18).

Linear regression of pixel NDVI was performed on the 332 pixels randomly selected to maintain the minimum sampling distance of 500 meters between points. The final regression model explained 41 percent of variation in NDVI and identified elevation, island versus continuous, interaction between elevation and island versus continuous, creek distance, and Gulf distance as significant predictors (Table 4-3,

Figure 4-5). Elevation, creek distance, and Gulf distance were positively related to

NDVI. Specifically, regression model parameters (Table 4-3) revealed that each additional meter of elevation increased NDVI, on average, by 0.02, and each additional

136

km of creek distance and Gulf distance each increased NDVI, on average, by 0.01. The positive effect of elevation was stronger for islands, and the 0.04 coefficient for the interaction term means indicates that an additional meter of elevation on islands increases NDVI by 0.06.

The regression model for mean island NDVI performed better than the model of all pixels, explaining 62 percent of variability (Table 4-4, Figure 4-6). Regression results indicated elevation, P/A, north versus south, and Gulf distance as significant predictors of island NDVI. Specifically, an additional meter of elevation was found to increase

NDVI by 0.05, and an additional km of distance from the Gulf increased NDVI by 0.01.

Greater P/A, indicating a higher portion of forest edge related to the interior, was strongly associated with lower NDVI. Finally, being located north of the Suwannee was associated with a decrease in NDVI of 0.03.

Graphing model residuals reveals a non-normal distribution for the pixel model, while the island model has a relatively normal distribution (Figure 4-7). In particular, the pixel model tended to overestimate NDVI at lower values, and the overall distribution of residuals is negatively skewed. For pixels within islands, the model also underestimated

NDVI at mid-to-high values of NDVI, though this effect was less consistent than the overestimation at low values. When applying the pixel model to all pixels in the study area (n=62,385), it greatly overestimated forest health by 0.15 to 0.25 NDVI for 5.4 percent of pixels, and by 0.10 to 0.15 for another 5.4 percent of pixels. In contrast, the model only underestimated by >0.10 NDVI for 0.4 percent of pixels.

Mapping these residuals revealed several distinct areas where forest health was considerably worse than predicted by the model (Figure 4-8). A large portion of this area

137

(indicated by dark red in Figure 4-8) was along the salt marsh-facing edge of continuous forest to the north of the Suwannee River. A second distinct area was in a patch of forest north of Shell Mound, directly to the east of a refuge road (Figure 4-9, Zoom 2).

The sharp demarcation between very low NDVI and negative residuals to the west of the road, and higher NDVI with positive residuals to the east of the road, suggests a likely road impact. A similar occurrence showing a potential road impact occurred directly north of the Suwannee River, where relatively healthy forest of high NDVI, with residuals close to zero, exists between the rivers floodplain and Southeast County Road

349, yet directly across the road, the forest transitions to low NDVI with negative residuals.

Water Level and Salinity Monitoring

Groundwater monitoring at the coastal forest sites revealed that water levels were highly dependent on well elevation, but salinity was only moderately associated with elevation (Table 4-5, Figures 4-10, 4-11). Additionally, groundwater signals were found to vary substantially across relatively short distances. The Cabin Road site was

0.40 m above NAVD88, and water levels averaged -3.0 ± 11.2 m, while salinity remained low, averaging 4.8 ± parts per thousand (PPT). At the Giger healthy and Giger stressed sites, groundwater salinity was typically higher than that of surface water in the adjacent tidal creek. Groundwater salinity at Giger stressed was on average 3.2 PPT higher than at Giger healthy, which is consistent with it having lower NDVI. Although elevation at Giger stressed was only 6 cm lower than Giger healthy, groundwater level was on average 15.3 cm closer to the ground surface. Values for other geographic variables assessed in the geospatial model were similar between these two islands. At the south healthy and south stressed islands, which had very similar elevations (south

138

healthy was 2 cm higher than south stressed), the expected negative relationship between salinity and NDVI was not apparent. Specifically, NDVI at the south stressed island was lower by 0.11, despite it being 2.2 PPT fresher, on average, than south healthy.

CCF values revealed that forest groundwater level at different sites respond relatively similarly to Suwannee River discharge, rainfall, and tidal level, while salinity responses vary (Figures 4-12, 4-13, 4-14). Water levels consistently showed little relation with Suwannee River discharge; although CCF values were in some cases significant, the magnitude of the correlation was minor. Water levels were positively associated with rainfall and tidal levels at all sites, particularly on the same day, with the effect dropping off after several days. In contrast, correlations with salinity were variable across the sites. Salinity at Cabin Road, which is closest to the Suwannee River, had a strong negative correlation with Suwannee discharge for the entire 30-day lag period examined. Other sites also had negative correlations, while Giger stressed salinity was positively correlated with discharge. Rainfall had significant negative correlations with salinity at four of the six sites, yet the magnitude of this effect was slight. Surprisingly, tidal levels had negative correlations with groundwater salinity at all sites. Giger Creek was the only monitoring location where tidal levels were positively associated with salinity, though this effect only lasted for three days.

Discussion

Spatial Model

The results of the regressions of NDVI supported my hypothesis that greater elevation and distance to saltwater features (tidal creeks and Gulf waters) increased

NDVI, while being an island (versus continuous forest) reduced NDVI, especially north

139

of the Suwannee River. The positive effect of elevation was stronger on islands, which

overall had a wider range of elevations than continuous forest. Although both elevation

and P/A were highly significant in predicting average island NDVI, the strongest

univariate correlation was with P/A (r = -0.69) rather than elevation (r = 0.59; Figure 4-

6). Although it is possible that a portion of the negative relationship with P/A resulted because islands with higher P/A have greater potential for surrounding salt marsh

(which has lower NDVI) to be included in pixels identified as coastal forest, comparison to aerial imagery suggests this effect is minimal. The moderately low explanatory power of the pixel model (R2 = 0.41) and the non-normal, negatively skewed residual

distribution, suggests important variables not included in the model are exacerbating

die-off. The island model explained a greater portion of variability (R2 = 0.62), and its

residuals are more normally distributed, indicating that any important variables lacking

in the model likely have a relatively even influence across forests of varying health (i.e.,

across the range of measured NDVI).

Interestingly, distance to the Suwannee River mouth was not significant for either

the pixel or island-based model. Additionally, for the pixel model, the hypothesized

increase in stress from being north of the Suwannee River was not significant. It is

possible that freshwater discharged from the Suwannee River only influences

background salinity of open water over large areas but has no consistent effect on

surface water salinity adjacent to forest pathces several km away. Prior studies have

revealed that close proximity to freshwater discharge points staves off die-off and

reduces the elevation at which forest becomes stressed (Raabe et al. 2004, Raabe &

Stumpf 2015). This effect could be driven by a threshold distance, whereby forests

140

beyond a certain distance from a freshwater discharge point remain unaffected. In other

words, Suwannee River discharge levels may only influence forest health close to the

river mouth, absent of a major and long-term decrease in discharge.

The highly non-normal distribution of residuals in the pixel model suggests that important drivers not included in the model are contributing to die-off; mapping these residuals revealed several areas where observed forest health is much higher or lower than predicted by the model. For example, negative residuals in the patch of forest north of Shell Mound (Figure 4-6) can very likely be attributed to a road effect, because of the abrupt demarcation between high and low NDVI between the two sides of the road.

Specifically, forest of very low NDVI, and negative residuals, are bounded by refuge roads to the north, east, and south sides. A likely explanation is that these roads cut off surface freshwater flow from this forest patch, an effect that has been documented in other studies (see Chapter 3).

In contrast, it is unclear what factors are causing very low NDVI values with highly negative residuals along the salt marsh-facing edge of continuous forest to the north of the Suwannee River. Consistent with results found by Langston et al. (2017), it is possible that forest in this area relies primarily on groundwater for freshwater supply, and sea level rise has resulted in saltwater intrusion. Alternatively, given the small portion of the study area that this fringe composes, it received few points in the randomly selected pixel sample, meaning conditions here would have little influence in the model. A potential solution for future modeling efforts is to use stratified random sampling to ensure adequate representation of different conditions throughout the study area.

141

The use of NDVI in this model carries certain limitations. NDVI is regarded as a valuable vegetation index for mapping vegetation health, and it has been widely used in ecological assessments for years (Li et al. 2014, Pettorelli et al. 2005). However, it indicates vegetation biomass as well as vegetation health. A drawback of my method is that I assumed lower NDVI to indicate poorer tree health, and higher NDVI to indicate good tree health. In reality, different forest types with different tree species and canopy structures exist throughout the refuge, and these categories have different NDVI. For instance, the six categories of the most common forest types in LSNWR (freshwater forested wetlands, dry flatwoods, hardwood forested upland, high pine and scrub, hydric hammock, and coastal uplands, in that order of prevalence) based on the CLC map ranged in NDVI from a mean of 0.67 in coastal uplands to 0.75 in hardwood forested upland. It is also noteworthy that averaging the value of residuals from the pixel model by CLC forest type revealed similar values across forest types. This suggests that the model performed equally well in the different categories, and therefore omission of forest type was likely not the cause of non-normal residual distribution. Nevertheless, inclusion of these forest types in future efforts would likely improve model performance.

Finally, the models developed here use current (2016), stationary NDVI as an indicator of salinity-induced forest stress. Extending this analysis to understand how coastal forest health is changing over time is a critical next step for further refining our understanding of the drivers of this change. One method for improving upon the model would be to predict NDVI change over time pixel-by-pixel. This could involve use of

Landsat 5 data since 1984 (when data collection began) and identifying the degree of change in NDVI over time, either by applying a Mann-Kendall trend test to values at

142

each pixel, or by subtracting recent NDVI from historical NDVI. By aggregating pixel-by-

pixel changes over time, this approach could identify specific places (hot spots) and

times (hot moments) of change; coupling this approach with time series modeling (e.g.,

Chapter 2) could provide additional insight to how the importance of different drivers of

coastal forest health decline vary in space and time.

Water Level and Salinity Monitoring

Groundwater monitoring results confirm the notion that elevation is only a partial

predictor of groundwater salinity. Specifically, monitoring data revealed considerably

different salinity patterns between four sites similar in elevation (ranging from 0.68 to

0.74 m). Furthermore, these monitoring data showed that even forest patches in close proximity to one another, and with similar geographic variables besides elevation, can have different groundwater salinity patterns. Although water level data generally showed expected positive correlations to rainfall and tidal levels, relationships of salinity with

Suwannee discharge, rainfall, and tidal level, were largely inconsistent across sites and in some cases, the direction of correlation was unexpected. One unexpected result is the negative correlation between Giger salinity and lagged Suwannee River discharge.

Additionally, it was unexpected that tidal levels were negatively associated with salinity.

143

Table 4-1. NDVI and elevation (mean and standard deviation) for wetland categories. Variable NDVI Elevation (m > NAVD88) Coastal forest 0.72 ± 0.07 0.81 ± 0.47 Transitional forest 0.53 ± 0.12 0.59 ± 0.25 Salt marsh 0.40 ± 0.10 0.58 ± 0.24

Table 4-2. NDVI and elevation (mean and standard deviation) for coastal forest geographic variables Category Pixel count Area NDVI Elevation (hectares) (m > NAVD88) North continuous 32,990 2,961 0.71 ± 0.08 0.72 ± 0.22 North island 563 51 0.63 ± 0.08 1.08 ± 0.58 South continuous 26,192 2,358 0.74 ± 0.06 0.85 ± 0.52 South island 2,640 238 0.68 ± 0.07 1.54 ± 1.10 Floodplain (excluded 15,144 1,363 0.73 ± 0.04 0.40 ± 0.18 from analysis)

Table 4-3. Regression results for subsample of NDVI pixels (n = 332, R2 = 0.41) Variable Coeff. p-value Description of results Elevation (m) 0.02 0.002 Additional meter elevation ↑ NDVI by 0.02 Island (vs. continuous) -0.10 <0.001 Island ↓ NDVI by 0.10 compared to continuous forest Island * elevation (m) 0.04 <0.001 On islands, additional meter elevation ↑ NDVI by another 0.04 (elevation effect is stronger than in continuous forest) North (vs. south) -0.01 0.115 Forest to the north ↓ NDVI by 0.01 compared to south Distance to creek (km) 0.01 <0.001 Additional km from tidal creek ↑ NDVI by 0.01 Distance to Gulf (km) 0.01 <0.001 Additional km from the Gulf ↑ NDVI by 0.01

144

Table 4-4. Regression results for islands, where values are averaged by island (n=201, R2 = 0.62) Variable Coeff. p-value Description of results Elevation (m) 0.05 <0.001 Additional meter elevation ↑ NDVI by 0.05 Perimeter-to-area -0.16 <0.001 More irregular shape ↓ NDVI (km/ha) North (vs. south) -0.03 <0.001 Forest to the north ↓ NDVI by 0.03 compared to south Distance to Gulf (km) 0.01 0.019 Additional km from Gulf waters ↑ NDVI by 0.01

Table 4-5. Mean water level and salinity levels at groundwater monitoring wells. Variable Salinity Water level NDVI* Elevation (m)* Cabin Road 4.8 ± 1.1 -3.0 ± 11.2 0.66 0.40 Giger Creek 11.9 ± 3.8 n/a n/a n/a Giger healthy 13.2 ± 2.5 -38.0 ± 16.3 0.60 0.74 Giger stressed 16.4 ± 1.0 -23.3 ± 22.1 0.55 0.68 South healthy 11.2 ± 2.0 -40.6 ± 13.9 0.65 0.71 South stressed 9.0 ± 1.4 -36.7 ± 16.2 0.54 0.69 * NDVI and elevation values are from the pixel overlapping with the well point, rather than from the values averaged by island.

145

Figure 4-1. Ecosystem categories of the study area, based on NWI data. A) Study Area. B) Ecosystem categories.

146

Figure 4-2. Example of the coastal forest grid cells and diagram of flow distance to the nearest tidal creek (yellow arrow), Gulf of Mexico (orange arrow), and Suwannee River mouth (red arrow)

147

Figure 4-3. NDVI results for all pixels.

148

Figure 4-4. Forest categories and areas excluded from analysis A) Forest included in analysis and excluded areas. B) Forest categories.

149

Figure 4-5. NDVI (by pixel) versus elevation, distance to Gulf, and distance to tidal creek. A) NDVI vs. elevation (blue = continuous, red = island). B) NDVI vs. distance to Gulf. C) NDVI vs. distance to tidal creek.

150

Figure 4-6. NDVI (by island) versus elevation, distance to Gulf, and perimeter-to-area ratio. A) NDVI vs. elevation. B) NDVI vs. distance to Gulf. C) NDVI vs. perimeter-to-area ratio (km/ha).

151

Figure 4-7. Predicted NDVI versus observed NDVI for pixel model and island mean model. A) Pixels. B) Islands.

152

Figure 4-8. NDVI residuals for all pixels, applying regression results from pixel sample (n=332) to all pixels (n=62,385). Inset images in next figure.

153

Figure 4-9. NDVI residuals in two areas showing potential road impact to forest health.

154

Figure 4-10. Groundwater and tidal creek monitoring sites in LSNWR.

155

Figure 4-11. Groundwater and tidal creek monitoring, salinity (black) and water level (grey). A) Cabin Road forest. B) Greek Creek. C) Giger Creek healthy forest. D) Giger Creek stressed forest. E) South healthy forest. F) South stressed forest.

156

Figure 4-12. Daily Suwannee River discharge, rainfall, and tidal levels. A) Suwannee River discharge. B) PRISM-derived rainfall at LSNWR. C) Average daily water level, Cedar Key tidal gage.

157

Figure 4-13. Cross correlations of monitoring well water level with Suwannee River discharge, rainfall, and tidal levels at different time lags (blue dotted lines indicate significant correlation at p-value < 0.05).

158

Figure 4-14. Cross correlations of site salinity with Suwannee River discharge, rainfall, and tidal levels at different time lags (blue dotted lines indicate significant correlation at p-value < 0.05).

159

CHAPTER 5 CONCLUSIONS

The Big Bend coastline is regarded as one of the most pristine coastlines in the

United States. However, major ecosystem changes are occurring in the region from drivers of a global scale (climate change), regional scale (groundwater extraction), and local scale (development). The overarching motivation of this dissertation was to explore how these various drivers interact to affect hydrologic conditions and saltwater intrusion into Big Bend coastal ecosystems, and the resulting ecological impacts. A primary goal of these efforts was to help guide efficient management of water resources and coastal ecosystems along the Big Bend by elucidating trends and drivers of water availability and ecosystem stress, and opportunities to mitigate threats to ecosystem stability. A second goal was to help advance methods for modeling regional hydrologic patterns and sea level rise impacts.

The statistical analysis of river discharge trends and drivers across the Big Bend presented in Chapter 2 revealed that observed decreases in river flow of several rivers can be partially attributed to changes in precipitation, though a portion of discharge variability was unexplained by climate data. Watershed precipitation and groundwater level were the strongest predictors of discharge, while potential evapotranspiration was not important. Given the likelihood of increased drought frequency in the southeastern

United States, the strong correlation with precipitation suggests further reductions in river discharge are likely to occur. However, the DFA also revealed a heavy reliance of regional discharge trends on groundwater levels, and this groundwater level dataset was only moderately correlated with rainfall. This suggests that other variables, such as groundwater extraction, land use change, or changes to evapotranspiration not

160

captured in the potential evapotranspiration datasets, are affecting river discharge levels throughout the region.

The assessment of road impacts to tidal flooding and salt marsh ecology presented in Chapter 3 revealed that even along a relatively pristine coastline, roads can modify hydrology, potentially affecting key processes related to salt marsh resilience to sea level rise. This chapter used field assessment and monitoring to measure the degree of tidal restriction created by roads at four sites, and resulting impacts to salt marsh vegetation, porewater chemistry, invertebrate densities, sediment characteristics, and elevation. Tidal flooding was found to be restricted on the inland side at all sites, and the strongest and most consistent impact was a dramatic reduction in invertebrate densities. Though the effects were less consistent across roads, reduced hydroperiods were also associated with lower cover of S. alterniflora compared to J. roemerianus, and lower sediment content of silt and clay compared to sand. Although no significant differences in sediment properties occurred between restricted and unrestricted sides of the road, decreased snail abundances provide evidence for potential sedimentation impacts that were either not severe enough to cause significant differences in PSD, or not captured in the field methods. Specifically, since snail larvae are delivered to the marsh surface through tidal flooding, lower snail densities to the inland sides provide evidence for interrupted flow that would likely also affect sediment transport.

Chapter 4 used remote sensing to examine geographic predictors of coastal forest stress, and field monitoring to assess drivers of coastal forest groundwater signals. Characteristics associated with greater forest stress included lower elevation,

161

being an island rather than continuous forest, and a higher perimeter-to-area ratio of

islands. Groundwater monitoring revealed that salinity response to Suwannee River

discharge, local rainfall, and tidal levels varied greatly across sites, even in cases where

wells were located in islands close together and at similar elevations. Sea level rise

impact modeling in coastal ecosystems typically relies primarily on elevation, and while

this is a very important predictor of stress, this analysis revealed the importance of other

geographic variables. Furthermore, groundwater salinity monitoring results suggested

that forest patch response to sea level rise might depend on different sediment and

bedrock characteristics that influence groundwater discharge and ability to retain

rainwater. These results are important findings for coastal land managers. Specifically,

given that Refuge staff are required to develop ten-year management plans, and

required activities vary for different ecosystems, it is valuable to have an understanding

of changes occurring throughout the area. For this purpose, the use of remote sensing data to assess forest health offers an efficient way to uncover changes on a large-scale,

without requiring field assessment.

Findings from Chapter 2 suggesting that decreased river discharge has resulted

at least partially from rainfall trends has important implications for coastal forest health.

Specifically, the DFA results corroborate the idea that further reductions in freshwater

discharge are likely in the future if climate change scenarios predicting lower rainfall are

accurate. Although distance to the Suwannee River mouth was not shown to affect

coastal forest health (Chapter 4), groundwater monitoring revealed a strong negative correlation between salinity and Suwannee discharge. It is possible that Suwannee

River discharge serves as a proxy for diffuse groundwater seepage into coastal forest

162

areas, meaning that decreased Suwannee discharge could be indirectly related to die- off throughout the refuge.

Although this dissertation helped elucidate several interacting drivers and impacts of saltwater intrusion and hydrologic change along the Big Bend, additional research is necessary to examine additional variables. An especially crucial research objective for ensuring a steady water supply for Florida’s growing population, without harming ecological resources, is to improve understanding of how groundwater extraction is affecting river discharge and groundwater levels. This study excluded effects of groundwater extraction because the temporal and spatial frequency of readily available data on groundwater extraction was inadequate for the selected modeling tool.

However, efforts should be made to assemble a database of groundwater extraction data throughout the region and to analyze impacts to groundwater levels. Additionally, although land use data was not included in this study because data on annual land use characteristics by watershed were not available, this could be an important driver of river discharge change. Interpretation of Landsat data could provide some coarse estimates of changes in land use over time.

The ability of roads to impact coastal ecosystems and influence saltwater intrusion and resilience to sea level rise was revealed in both Chapter 3, which directly assessed road impacts, and in Chapter 4, which revealed hot spots of coastal forest stress allegedly caused by roads. This represents a highly important finding for coastal land managers, as it presents an opportunity to improve ecosystem resilience by restoring hydrologic flow in these areas. Future efforts to understand road impacts in the

LSNWR and other areas of the Big Bend could include detailed flow modeling based on

163

elevation data, combined with remote sensing data to reveal forest health, revealing priority areas for flow restoration.

Natural flow regimes in coastal areas is a key predictor for resiliency to sea level rise, both in terms of tidal flow in salt marsh encouraging sedimentation, and freshwater flow mediating salinities. The Big Bend coastline is already predisposed to have relatively low resiliency to sea level rise impacts, owing to its flat gradient and low sediment supply. This coastline presents opportunities to study how climate change impacts manifest, while minimizing confounding variables from development present along other coastlines. Additionally, it is an opportunity to preserve and maintain an ecologically unique and relatively pristine coastline.

164

LIST OF REFERENCES

Anisfeld, S.C. and G. Benoit. 1997. Impacts of flow restrictions on salt marshes: An instance of acidification. Environmental Science and Technology 31(6): 1650- 1657. Anisfeld, S.C. 1999. Sedimentation rates in flow-restricted and restored salt marshes in Long Island Sound. Estuaries 22(2): 231-244. Apley, M.L. 1970. Field studies on life history, gonadal cycle and reproductive periodicity in Melampus bidentatus (Pulmonata: Ellobiidae). Malacologia 10: 381- 397. Barbier, E.B. 2016. The protective service of mangrove ecosystems: A review of valuation methods. Marine Pollution Bulletin 109(2): 676-681. Barlow, P.M. 2003. Ground water in freshwater-saltwater environments of the Atlantic Coast. U.S. Geological Survey Circular 1262. Befus, K.M., K.D. Kroeger, C.G. Smith, and P.W. Swarzenski. 2017. The magnitude and origin of groundwater discharge to eastern U.S. and Gulf of Mexico coastal waters. Geophysical Research Letters 44: 10,396-10,406. Berdalet, E., L.E. Fleming, R. Gowen, and K. Davidson. 2016. Marine harmful algal blooms, human health and wellbeing: challenges and opportunities in the 21st century. Oceans and Human Health 96(1): 61-91. Bergquist, D.C., J.A. Hale, P. Baker, and S.M. Baker. 2006. Development of ecosystem indicators for the Suwannee River estuary: Oyster reef habitat quality along a salinity gradient. Estuaries and Coasts 29: 353-360. Bertness, M.D. and A.M. Ellison. 1987. Determinants of pattern in a New England salt marsh plant community. Ecological Monographs 57(2): 129-147. Bertness, M.D., P.J. Ewanchuk, and B.R. Silliman. 2002. Anthropogenic modification of New England salt marsh landscapes. PNAS 99(3): 1395-1398. Blann, K.L., J.L. Anderson, G.R. Sands, and B. Vondracek. 2009. Effects of agricultural drainage on aquatic ecosystems: A review. Critical Reviews in Environmental Science and Technology 39(11): 909-1001. Bond, N. 2016. Package ‘hydrostats’: Hydrologic indeces for daily time series data. R package, version 0.2.5. Budd, David A. and H.L. Vacher. 2004. Matrix permeability of the confined Floridan Aquifer, Florida, USA. Hydrogeology Journal 12: 531-549. Burdick, D.M., M. Dionne, R.M. Boumans, and F.T. Short. 1997. Ecological responses to tidal restorations of two northern New England salt marshes. Wetlands Ecology and Management 4: 129-144.

165

Bush, P.W. and R.H. Johnston. 1988. Ground-water hydraulics, regional flow, and ground-water development of the Florida Aquifer System in Florida and in parts of Georgia, South Carolina, and Alabama. U.S. Geological Survey Proessional Paper 1403-C. Cable, J. E., W.C. Burnett, and J.P. Chanton. 1997. Magnitude and variations of groundwater seepage along a Florida marine shoreline. Biogeochemistry 38: 189-205. California Natural Resources Agency. 2016. California water action plan. California Natural Resources Agency. Campo-Bescós, M.A., R. Muñoz-Carpena, D.A. Kaplan, J. Southworth, L. Zhu, and P.R. Waylen. 2013. Beyond precipitation: Physiographic gradients dictate the relative importance of environmental drivers on savanna vegetation. PLOS One 8(8): e72348. Chambers, L.G., K.R. Reddy, and T.Z. Osborne. 2011. Short-term response of carbon cycling to salinity pulses in a freshwater wetland. Soil Science Society of America Journal 75(5): 2000-2007. Christiansen, T., P.L. Wiberg, and T.G. Milligan. 2000. Flow and sediment transport on a tidal salt marsh surface. Estuarine, Coastal and Shelf Science 50: 315-331. Chmura, G.L. 2013. What do we need to assess the sustainability of the tidal salt marsh carbon sink? Ocean & Coastal Management 83: 25-31. Chmura, G.L., S.C. Anisfeld, D.R. Cahoon, and J.C. Lynch. 2003. Global carbon sequestration in tidal, saline wetland soils. Global Biogeochemical Cycles 17(4): 1111. Cleveland, W.S. and S.J. Devlin. 1988. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83(403): 596-610. Craft, C. 2007. Freshwater input structures soil properties, vertical accretion, and nutrient accumulation of Georgia and U.S. tidal marshes. Limnology and Oceanography 52(3): 1220-1230. Craft, C. 2012. Tidal freshwater forest accretion does not keep pace with sea level rise. Global Change Biology 18: 3615-3623. Craft, C. 2013. Emergent macrophyte biomass production. In Methods in biogeochemistry of wetlands, eds. DeLaune, R.D., Reddy, K.R., Richardson, C.J., and Megonigal, J.P. Madison: Soil Science Society of America, Inc. Craft, C.B., E.D. Seneca, S.W. Broome. 1993. Vertical accretion in microtidal regularly and irregularly flooded estuarine marshes. Estuarine, Coastal and Shelf Science 37(4): 371-386.

166

Dahl, T.E. & S.M. Stedman. 2013. Status and trends of wetlands in the coastal watersheds of the conterminous United States 2004 to 2009. U.S. Department of the Interior, Fish and Wildlife Service, and National Oceanic and Atmospheric Administratrion, National Marine Fisheries Service. Davidson, N.C. 2014. How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research 65(10): 936-941. Dawes, C.J., R.C. Phillips, and G. Morrison. 2004. Seagrass communities of the Gulf Coast of Florida: status and ecology. Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute and the Tampa Bay Estuary Program, St. Petersburg, FL. Deegan, L.A., D.S. Johnson, R.S. Warren, B.J. Peterson, J.W. Fleeger, S. Fagherazzi, and W.M. Wolheim. 2012. Coastal eutrophication as a driver of salt marsh loss. Nature 490(7420): 388-392. DeSantis, L.R.G., S. Bhotika, K. Williams, and F.E. Putz. 2007. Sea-level rise and drought interactions accelerate declines of coastal forests on the Gulf Coast of Florida, USA. Global Change Biology 13: 2349-2360. Diffenbaugh, N.S., D.L. Swain, and D. Touma. 2016. Anthropogenic warming has increased drought risk in California. PNAS 112(13): 3931-3936. Doody, J.P. 2004. ‘Coastal squeeze’: An historical perspective. Journal of Coastal Conservation 10(1): 129-138. Duffie, J.A. and W.A. Beckman. 2013. Solar Engineering of Thermal Processes. Fourth edition. John Wiley & Sons, Inc., Hoboken, NJ. Eberhardt, A.L., D.M. Burdick, and M. Dionne. 2010. The effects of road culverts on nekton in New England salt marshes: Implications for tidal restoration. Restoration Ecology 19(6): 776-785. Eleuterius, L.N. 1984. Autoecology of the black needlerush Juncus roemerianus. Gulf Research Reports 7(4): 339-350. Enfield, D.B., A.M. Mestas-Nuñez, and P.J. Trimble. 2001. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophysical Research Letters 28(10): 2077-2080. Famiglietti, J.S. 2014. The global groundwater crisis. Nature Climate Change 4: 945- 948. Fish, J.D. and S. Fish. 1989. A student's guide to the seashore. Unwin Hyman Ltd., Boston. Flood, N. 2014. Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 Oli, for both top-of-atmosphere and surface reflectance: A study in the Australian landscape. Remote Sensing 6(9): 7952-7970.

167

Florea, L.J. and H.L. Vacher. 2006. Springflow hydrographs: Eogenetic vs. telogenetic karst. Groundwater 44(3): 352-361. Florida Division of Emergency Management. 2007. 2007 Florida Division of Emergency Management (FDEM) Lidar Project. Digital Elevation Model dataset. Florida Fish and Wildlife Commission and Florida Natural Areas Inventory. 2016. Cooperative Land Cover version 3.2 Raster. Tallahassee, FL. Florida Natural Areas Inventory. 2017. Florida Conservation Lands shapefile. Tallahassee, FL. Gee, G.W. and J.W. Bauder. 1986. Particle-size analysis. In Methods of soil analysis, eds. Klute, A. Madison: ASA and SSSA. Geselbracht, L., K. Freeman, E. Kelly, D.R. Gordon, and F.E. Putz. 2011. Retrospective and prospective model simulations of sea level rise impacts on Gulf of Mexico coastal marshes and forests in Waccasassa Bay, Florida. Climatic Change 107(1-2): 35-47. Geweke, J. 1977. The dynamic factor analysis of economic time series. In Latent variables in socio-economic models, eds. Aigner, D.J., Goldberger, A.S. Amsterdam: North Holland. Goto-Maeda, Y., D.W. Shin, and J.J. O’Brien. 2008. Freeze probability of Florida in a regional climate model and climate indeces. Geophysical Research Letters 35(11): L11703. Grabowski, J.H. and C.H. Peterson. 2007. Restoring oyster reefs to recover ecosystem services. In Ecosystem engineers: concepts, theory, and applications, eds. Cuddington, K., Byers, J.E., Wilson, W.G., and Hastings, A. Amsterdam: Elsevier-Academic Press. Groisman, P.Y., R.W. Knight, D.R. Easterling, T.R. Karl, G.C. Hegerl, and V.N. Razuvaev. 2005. Trends in intense precipitation in the climate record. Journal of Climate 18: 1326–1350 Grubbs, J.W. and C.A. Crandall. 2007. Exchanges of water between the Upper Floridan Aquifer and the Lower Suwannee and Lower Santa Fe rivers, Florida. U.S. Geological Survey Professional Paper 1656-C. Hagemeyer, B.C. 2006. ENSO, PNA and NAO scenarios for extreme storminess, rainfall, and temperature variability during the Florida dry season. 18th Conference on Climate Variability and Change. American Meteorological Society, Preprints, Atlanta, GA. Paper P2.4 Hagemeyer, B.C. 2007. The relationship between ENSO, PNA, and AO/NAO and extreme storminess, rainfall, and temperature variability during the Florida dry season: thoughts on predictability and attribution. 19th Conference on Climate Variability and Change. American Meteorological Society Preprints. San Antonio, TX. Paper JP2.16.

168

Hamilton, P.V. 1978. Intertidal distribution and long-term movements of Littorina irrorata (Mollusca: Gatropoda). Marine Biology 46: 49-58. Harley G. L., J.T. Maxwell, E. Larson, H.D. Grissino-Mayer, J. Henderson, and J. Huffman. 2017. Suwannee River flow variability 1550-2005 CE reconstructed from a multispecies tree-ring network. Journal of Hydrology 544: 438-451. He, Q. and B.R. Silliman. 2015. Biogeographic consequences of nutrient enrichment for plant-herbivore interactions in coastal wetlands. Ecology Letters 18: 462-471. Hernández, J.L., S. Hwwang, F. Escobedo, A.H. Davis, and J.W. Jones. 2012. Land use change in central Florida and sensitivity analysis based on agriculture to urban extreme conversion. Weather, Climate, and Society 4: 200-211. Hine, A.C., D.F. Belknap, J.G. Hutton, E.G. Hutton, E.B. Osking, and M.W. Evans. 1988. Recent geological history and modern sedimentary processes along an incipient, low-energy, epicontinental-sea coastline: Northwest Florida. Journal of Sedimentary Petrology 58(4): 567-579. Holmes, E., E. Ward, and K. Wills. 2012. MARSS: Multivariate autoregressive state- space models for analyzing time-series data. The R Journal 4(1): 11-19. Holmes, E., E. Ward, and K. Wills. 2013. MARSS: Multivariate autoregressive state- space modeling. R package version 3.9. Howard, R.J., J. Biagas, and L. Allain. 2016. Growth of common brackish marsh macrophytes under altered hydrologic and salinity regimes. Wetlands 36: 11-20. Howe, M.A. 1987. Wetlands and waterbird conservation. American Birds 41: 204–209. Howes, B.L., R.W. Howarth, J.M. Teal, and I. Valeila. 1981. Oxidation-reduction potentials in a salt marsh: spatial patterns and interactions with primary production. Limneology and Oceanography 26: 350-360. Hughes, A. 2012. A neighboring plant species creates associational refuge for consumer and host. Ecology 93(6): 1411-1420. Hutchison, J., A. Manica, R. Swetnam, A. Balmford, and M. Spalding. 2013. Predicting global patterns in mangrove forest biomass. Conservation Letters 7(3): 233-240. Jablonski, D. and R.A. Lutz. 1983. Larval ecology of marine benthic invertebrates: paleobiological implications. Biological Reviews 58: 21-89. Jackson, R.B., S.R. Carpenter, C.N. Dahm, D.M. McKnight, R.J. Naiman, S.L. Postel, and S.W. Running. 2001. Water in a changing world. Ecological Applications 11(4): 1027-2045. Kaplan, D.A., M. Olabarrieta, P. Frederick, and A. Valle-Levinson. 2016. Freshwater detention by oyster reefs: quantifying ecosystem service. PLOS One 11(12): e0167694. Kaplan, D.A. and R. Muñoz-Carpena. 2014. Groundwater salinity in a floodplain forest impacted by saltwater intrusion. Journal of Contaminant Hydrology 169: 19-36.

169

Kaplan, D., R. Muñoz-Carpena, and A. Ritter. 2010. Untangling complex shallow groundwater dynamics in the floodplain wetlands of a southeastern U.S. coastal river. Water Resources Research 46: W08528. Katz, B.G. and E.A. Raabe. Suwannee River Basin and estuary: An integrated watershed science program. U.S. Geological Survey Open-File Report 2005- 1210. Kettler, T.A., J.W. Doran, and T.L. Gilbert. 2001. Simplified method for soil particle-size determination to accompany soil-quality analyses. Publications from USDA- ARS/UNL Faculty 305: 849-852. Kirwan, M.L. and J.P. Megonigal. 2013. Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504:53-60. Konisky, R.A., D.M. Burdick, M. Dionne, and H.A. Neckles. 2006. A regional assessment of salt marsh restoration and monitoring in the Gulf of Maine. Restoration Ecology 14(4): 516-525. Kneib, R.T. 1984. Patterns of invertebrate distribution and abundance in the intertidal salt marsh: Causes and questions. Estuaries 7(4A): 392-412. Konrad II, C.E. and C.M. Fuhrmann. 2013. In Climate of the Southeast United States: Variability, change, impacts, and vulnerability, eds. Ingram, K., Dow, K., Carter, L., and Anderson. J. Washington: Island Press. Ladson, A.R., R. Brown, B. Neal, and R. Nathan. 2013. A standard approach to baseflow separation using the Lyne and Hollick filter. Australian Journal of Water Resources 17(1): 173- Langston, A.K., D.A. Kaplan, and F.E. Putz. 2017. A casualty of climate change? Loss of freshwater forest islands on Florida’s Gulf Coast. Global Change Biology 23(12): 5383-5397. Leonard, L.A., A.C. Hine, M.E. Luther, R.P. Stumpf, and E.E. Wright. 1995. Sediment transport processes in a west-central Florida open marine marsh tidal creek; the role of tides and extra-tropical storms. Estuarine, Coastal and Shelf Science 41: 225-248. Li, P., L. Jiang, and Z. Feng. 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing 6(1): 310-329. Li, X., R. Bellerby, C. Craft, and S.E. Widney. 2018. Coastal wetland loss, consequences, and challenges for restoration. Anthropocene Coasts 1: 1-15. Lima, M.A.L., D.A. Kaplan, and C. Doria. 2017. Hydrological controls of fisheries production in a major Amazonian tributary. Ecohydrology 10(8): e1899.

170

Livingston, R.J., F.G. Lewis, G.C. Woodsum, X.-F. Niu, B. Galperin, W. Huang, J.D. Christensen, M.E. Monaco, T.A. Battista, C.J. Klein, R.L. Howell IV, and G.L. Ray. 2000. Modelling oyster population response to variation in freshwater input. Estuarine, Coastal and Shelf Science 50: 655-672. Loreau, M., S. Naeem, P. Inchausti, J. Bengtsson, J.P. Grime, A. Hector, D.U. Hooper, M.A. Huston, D. Raffaelli, B. Schmid, D. Tilman, and D.A. Wardle. 2001. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294(5543): 804-808. Luk, Y.C. and R.N. Zajac. 2013. Spatial ecology of fiddler crabs, Uca pugnax, in southern New England salt marsh landscapes: Potential habitat expansion in relation to salt marsh change. Northeastern Naturalist 20(20): 255-274. Lyne, V. and M. Hollick. 1979. Stochastic time-variable rainfall-runoff modelling. Institute of Engineers Australia National Conference Publication 79(10): 89-93. Mann, M.E., S. Rahmstorf, K. Kornhuber, B.A. Steinman, S.K. Miller, and D. Coumou. 2017. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Scientific Reports 7: 45242. Marella, R.L. 2014. Water withdrawals, use, and trends in Florida, 2010. U.S. Geological Survey. Scientific Investigations Report 2014-5088. Marella, R.L. 2015. Water withdrawals in Florida, 2012. U.S. Geological Survey. Open- File Report 2015-1156. Marella, R.L. and M.P. Berndt. 2005. Water withdrawals and trends from the Floridan Aquifer System in the Southeastern United States, 1950-2000. U.S. Geological Survey Circular 1278. Marella, R.L., J.F. Dixon, and D.R. Berry. 2016. Agricultural irrigated land-use inventory for the counties in the Suwannee River Water Management District in Florida, 2015. U.S. Geological Survey. Open-File Report 2016-1111. Martin, J.B., and R.W. Dean. 2001. Exchange of water between conduits and matrix in the Floridan Aquifer. Chemical Geology 179(1-4): 145-165. Mattson, R.A. 2002. A resource-based framework for establishing freshwater inflow requiremetns for the Suwannee River Estuary. Estuaries 25(6B): 1333-1342. Mattson, R.A., T.K. Frazer, J.A. Hale, S.B. Blitch and L. Ahijevych. 2007. The Florida Big Bend. In Seagrass status and trends in the northern Gulf of Mexico: 1940– 2002, eds. Handley, L., Altsman, D. and DeMay, R. U.S. Geological Survey Scientific Investigations Report 2006–5287 and U.S. Environmental Protection Agency 855-R-04-003. McIvor, A.L., I. Möller, T. Spencer, and M. Spalding. 2012. Reduction of wind and swell waves by mangroves. Natural Coastal Protection Series: Report 1, Cambridge Coastal Research Unit Working Paper 40. Cambridge: The Nature Conservancy and Wetlands International.

171

Mcleod, E., G.L. Chmura, S. Bouillon, R. Salm, M. Björk, C.M. Duarte, C.E. Lovelock, W.H. Schlesinger, and B.R. Silliman. 2011. Frontiers in Ecology and the Environment 9(10): 552-560. Mekonnen, M.M. and A.Y. Hoekstra. 2016. Four billion people facing severe water scarcity. Science Advances 2(2): e1500323. Michaels, R.E. and J.C. Zieman. 2013. Fiddler crab (uca spp.) burrows have little effect on surrounding sediment oxygen concentrations. Journal of Experimental Marine Biology and Ecology 448: 104-113. Micklin, P. 2007. The Aral Sea disaster. Annual Review of Earth and Planetary Sciences 35: 47-72. Miller, James. 1990. Ground water atlas of the United States: Segment 6, Alabama, Florida, Georgia, and South Carolina. U.S. Geological Survey Hydrologic Investigation Atlas 730-G. Miura, S., M. Amacher, T. Hofer, J. San-Miguel-Ayanz, Ernawati, and R. Thackway. 2015. Protective functions and ecosystem services of global forests in the past quarter-century. Forest Ecology and Management 352: 35-46. Molenaar, P.C.M. 1985. A dynamic factor model for the analysis of multivariate time series. Psychometrika 50(20): 181-202. Mudd, S.M., S.M. Howell, and J.T. Morris. 2009. Impact of dynamic feedbacks between sedimentation, sea-level rise, and biomass production on near-surface marsh stratigraphy and carbon accumulation. Estuarine, Coastal and Shelf Science 82(3): 377-389. Mumby, P.J., A.J. Edwards, J.E. Arias-González, K.C. Lindeman, P.G. Blackwell, A. Gall, M.I. Gorczynska, A.R. Harborne, C.L. Pescod, H. Renken, C.C.C. Wabnitz and G. Llewellyn. 2004. Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature 427: 533-536. Nelson, J.L. and E.S. Zavaleta. 2012. Salt marsh as a coastal filter for the oceans: Changes in function with experimental increases in nitrogen loading and sea- level rise. PLOS One 7(8): e38558. NOAA Restoration Center & NOAA Coastal Services Center. 2010. Returning the tide: A tidal hydrology restoration guidance manual for the southeastern U.S. Silver Spring: NOAA. Nyman, J.A., R.J. Walters, R.D. Delaune, and W.H. Patrick Jr. 2006. Marsh vertical accretion via vegetation growth. Estuarine, Coastal and Shelf Science 69: 370- 380. O’Reilly, A.M., E.A. Roehl, Jr., P.A. Conrads, R.C. Daamen, and M.D. Petkewich. 2014. Simulation of the effects of rainfall and groundwater use on historical lake levels, groundwater levels, and spring flows in central Florida. U.S. Geological Survey Scientific Investigations Report 2014-5032.

172

Parida, A.K. and B. Jha. 2010. Salt tolerance mechanisms in mangroves: a review. Trees 24: 199-217. Pennings, S.C. and M.D. Bertness. 2001. Salt marsh communities. In Marine community ecology, eds. Bertness, M.D., Gaines, S.D., and Hay, M.E. Sunderland: Sinauer Associates. Pennings, Steven C., Mary-Bestor Grant, Mark D. Bertness. 2005. Plant zonation in low-latitude salt marshes: disentangling the roles of flooding, salinity and competition. Journal of Ecology 93: 159-167. Pettorelli, N., J. O. Vik, A. Mysterud, J-M Gaillard, C.J. Tucker, N.C. Stenseth. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution 20(9): 503-510. Portnoy, J.W. 1999. Salt marsh diking and restoration: biogeochemical implications of altered wetland hydrology. Environmental Management 24(1): 111-120. Portnoy, J.W. and A.E. Giblin. 1997. Effects of historic tidal restrictions on salt marsh sediment chemistry. Biogeochemistry 36: 275-303. Portnoy, J.W. and I. Valiela. 1997. Short-term effects of salinity reduction and drainage on salt-marsh biogeochemical cycling and Spartina (cordgrass) production. Estuaries 20(3): 569-578. R Core Team. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Raabe E.A, A.E. Streck, and R.P Stumpf. 2004. Historic topographic sheets to satellite imagery: A methodology for evaluating coastal change in Floridal’s Big Bend tidal marsh. U.S. Geological Survey Open-File Report 02-211. U.S. Geological Survey, Center for Coastal and Regional Marine Studies, St. Petersburg. Raabe, E., D. Stonehouse, K. Ebersol, K. Holland, and L. Robbins. 2011. Detection of coastal and submarine discharge on the Florida Gulf Coast with an airborne thermal-infrared mapping system. The Professional Geologist (TPG) 48(5): 42- 49. Raabe, E.A. and E. Bialkowska-Jelinska. 2007. Temperature anomalies in the Lower Suwannee River and tidal creeks, Florida, 2005. U.S. Geological Survey Open- File Report 2007-1311. Raabe, E.A. and E. Bialkowska-Jelinska. 2010. Thermal imaging of the Waccasassa Bay Preserve: Image acquisition and processing. U.S. Geological Survey Open- File Report 2010-1120 Raabe, E.A. and R.P. Stumpf. 2015. Expansion of tidal marsh in response to sea-level rise: Gulf Coast of Florida, USA. Estuaries and Coasts 39: 145-157. Ranjan, S.P., S. Kazama, M. Sawamoto. 2006. Effects of climate and land use changes on groundwater resources in coastal aquifers. Journal of Environtal Management 80: 25-35.

173

Rayer, S. and Y. Wang. 2017. Projections of Florida population by county, 2020-2045, with estimates for 2016. Bureau of Economic and Business Research, Florida Population Studies 50(177). Ritter, A., C.M. Regalado, and R. Muñoz-Carpena. 2009. Temporal common trends of topsoil water dynamics in a humid subtropical forest watershed. Vadose Zone Journal 8(2): 437-449. Roman, C.T. 2016. Salt marsh sustainability: Challenges during an uncertain future. Estuaries and Coasts 40(3): 711-716. Roman, C.T., K.B. Raposa, S.C. Adamowicz, M.-J. James-Pirri, and J.G. Catena. 2002. Quantifying vegetation and nekton response to tidal restoration of a New England salt marsh. Restoration Ecology 10(3): 450-460. Roman, C.T., W.A. Niering, and R.S. Warren. 1984. Salt marsh vegetation change in response to tidal restriction. Environmental Management 8(2): 141-149. Ryberg, K.R. and A.V. Vecchia. 2017. Package 'waterData': Retrieval, analysis, and anomaly calculation of daily hydrologic time series data. R package. Samani, Z. 2000. Estimating solar radiation and evapotranspiration using minimum climatological data. Journal of Irrigation and Drainage Engineering 126(4): 265- 267. Schmidt, N. and M.E. Luther. 2002. ENSO impacts on salinity in Tampa Bay, Florida. Estuaries 25(5): 976-984. Scott, T.M., G.H. Means, R.P. Meegan, R.C. Means, S.B. Upchurch, R.E. Copeland, J. Jones, T. Roberts, and A. Willet. 2004. Springs of Florida: Florida Geological Survey Bulletin Number 66. Scyphers, S.B., S.P. Powers, K.L. Heck Jr., and D. Byron. 2011. Oyster reefs as natural breakwaters mitigate shoreline loss and facilitate fisheries. PLoS ONE 6(8): e22396. Seavey, J.R., W.E. Pine, III, P. Frederick, L. Sturmer, and M. Berrigan. 2011. Decadal changes in oyster reefs in the Big Bend of Florida’s Gulf Coast. Ecosphere 2(10): 114. Shepard, C.C., C.M. Crain, and M.W. Beck. 2011. The protective role of coastal marshes: A systematic review and meta-analysis. PLOS One 6(11): e27374. Silliman, B.R., J. van de Koppel, M.D. Bertness, L.E. Stanton, and I.A. Mendelssohn. 2005. Drought, snails, and large-scale die-off of southern U.S. salt marshes. Science 310(5755): 1803-1806. Silvestri, S., A. Defina, and M. Marani. 2005. Tidal regime, salinity and salt marsh plant zonation. Estuarine, Coastal and Shelf Science 62(1-2): 119-130. Stanton, E.A. and F. Ackerman. 2007. Florida and climate change: the costs of inaction. Tufts University and Stockholm Environment Institute Report.

174

Teillet, P.M., J.L Barker, B.L Markham, R.R Irish, G. Fedosejevs, and J.C Storey. 2001. Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote Sensing of Environment 78(1–2): 39-54. Thom, T.A., K.J. Hunt, and J. Faustini. 2015. Water resource inventory and assessment (WRIA): Lower Suwannee National Wildlife Refuge, Dixie and Levy Counties, Florida. U.S. Fish and Wildlife Service, Southeast Region, Atlanta, Georgia. Tihansky, A.B. 1999. Sinkholes, West-Central Florida. In Land subsidence in the United States, eds. Galloway, D., Jones, D.R. and Ingebritsen, S.E. U.S. Geological Survey Circular 1182. Tihansky, A.B. and L.A. Knochenms. 2001. Karst features and hydrogeology in west- central Florida—a field perspective. In U.S. Geological Survey Karst Interest Group Proceedings, ed. Kuniansky, E.L. U.S. Geological Survey Water- Resources Investigations Report 01-4011. Touchette, B.W. 2006. Salt tolerance in a Juncus roemerianus brackish marsh: Spatial variations in plant water relations. Journal of Experimental Marine Biology and Ecology 337(1): 1-12. Touchette, B.W., G.A. Smith, K.L. Rhodes, and M. Poole. 2009. Tolerance and avoidance: Two contrasting physiological responses to salt stress in mature marsh halophytes Juncus roemerianus Scheele and Spartina alterniflora Loisel. Journal of Experimental Marine Biology and Ecology 380: 106-112. Tootle, G.A. and T.C. Piechota. 2004. Suwannee River long range streamflow forecasts based on seasonal climate predictors. Journal of the American Water Resources Association 40(2): 523-532. Trenberth, K.E. 2011. Changes in precipitation with climate change. Climate Research 47: 123-138. Trilla, G.G., M.M. Borro, N.S. Morandeira, F. Schivo, P. Kandus, and J. Marcovecchio. 2013. Allometric scaling of dry weight and leaf area for Spartina densiflora and Spartina alterniflora in two southwest Atlantic saltmarshes. Journal of Coastal Research 29(6): 1373-1381. United Nations Water. 2016. The United Nations world water development report 2016 Water and jobs facts and figures. United Nations World Water Assessment Programme. UNESCO, Paris. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Washington, D.C.: U.S. Department of the Interior and Fish and Wildlife Service. U.S. Geological Survey. 2013. National Hydrography Geodatabase. The National Map viewer. Accessed October 10, 2016. U.S. Geological Survey. 2016a. National Elevation Dataset 1/3 arc-second ArcGrid. The National Map viewer. Accessed October 10, 2016.

175

U.S. Geological Survey. 2016b. National Water Information System data. USGS Water Data for the Nation. Accessed May 10, 2017. Valiela, I. and M.L. Cole. 2002. Comparative evidence that salt marshes and mangroves may protect seagrass meadows from land-derived nitrogen loads. Ecosystems 5: 92-102. Vermeij, G.J. 1982. Environmental changea dn the evolutionary history of the periwinkle (Littorina Littorea) in North America. Evolution 36(3): 561-580. Wang, F.C., T. Lu, and W.B. Sikora. 1993. Intertidal marsh suspended sediment transport processes, Terrebonne Bay, Louisiana, U.S.A. Journal of Coastal Research 9(1): 209-220. Warren, R.S., P.E. Fell, R. Rozsa, A.H. Brawley, A.C. Orsted, E.T. Olson, V. Swamy, and W.A. Niering. Salt marsh restoration in Connecticut: 20 years of science and management. Restoration Ecology 10(3): 497-513. Weston, N.B., R.E. Dixon, and S.B. Joye. 2006. Ramifications of increased salinity in tidal freshwater sediments: Geochemistry and microbial pathways of organic matter mineralization. Journal of Geophysical Research 111(G1): G01009. White, E. and D. Kaplan. 2017. Restore or retreat? Saltwater intrusion and water management in coastal wetlands. Ecosystem Health and Sustainability 3(1): e01258. Williams, K., K.C. Ewel, R.P. Stumpf, F.E. Putz, and T.W. Workman. 1999. Sea-level rise and coastal forest retreat on the west coast of Florida, USA. Ecology 80: 2045-2063. Williams, K., M. MacDonald, K. McPherson, and T. H. Mirti. Chapter 10 – Ecology of the coastal edge of hydric hammocks of the Gulf Coast of Florida. In Ecology of Tidal Freshwater Forested Wetlands of the Southeastern United States, eds. Conner, W.H., Doyle, T.W., and Krauss, K.W. New York: Springer. Zhu, Z. and C.E. Woodcock, 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118: 83-94. Zhu, Z., S. Wang, C.E. Woodcock. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment 159: 269-277. Zuur, A.F., R.J. Fryer, I.T. Jolliffe, R. Dekker, and J.J. Beukema. 2003. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14(7): 665-685.

176

BIOGRAPHICAL SKETCH

Katie earned her Doctor of Philosophy degree through University of Florida's

School of Natural Resources and Environment and she was advised by Dr. David

Kaplan in the Environmental Engineering Sciences Department. She held a fellowship from the Water Institute, where she participated in an interdisciplinary cohort studying climate change impacts to coastal resources. She has a Master of Environmental

Management and a Certificate in Geospatial Analysis from Duke University (2013), with a concentration in ecosystem science and conservation. She has a Bachelor of Arts in economics and environmental policy and analysis from Boston University (2007).

177