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LIGHT REQUIREMENTS OF SEAGRASSES ALONG THE WEST COAST OF PENINSULAR FLORIDA

By

ZANETHIA DENISE CHOICE

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2012

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©2012 Zanethia Denise Choice

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To my Mom and Dad for opening my eyes to the world and instilling in me the

importance of hard work and higher education To my grandparents, aunts, and uncles for your constant support throughout my

educational career To my church family for the constant prayers and high expectations that you all

have that pushes me to never quit To my siblings, cousins, and friends for always having a listening ear and

encouraging words But especially to the late GREAT Regenia Peterson for believing that I had what

it takes to do what you did not have the opportunity to do and providing the backbone that my family and I needed to succeed

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ACKNOWLEDGMENTS

I express my deepest appreciation to my advisor and committee; Tom Frazer,

Charles Jacoby, and Ed Phlips, who conveyed a spirit of adventure in regard to

research, and an excitement for teaching. With your guidance and persistent help, I

have become a knowledgeable and competent researcher. You all have opened my

eyes to a new aspect of the world that would have been impossible to gain on my own.

I thank the Frazer lab; especially Jessica Frost, Darlene Saindon, and Sky

Notestein, for making me feel right at home from the start. It was your willingness to

assist me with my project, allowing me to assist in your projects, and teaching me so

much through the sharing of your knowledge that has helped me grow as a person. I

would also like to thank Jessica Diller, Morgan Edwards, Stephanie Keller, Andrea

Krzystan, and everyone else who played a part, for your hard work and assistance in

data collection and lab analysis.

In addition, I give my gratitude to Stephen Humphrey for seeing my potential and

giving me the opportunity to show the passion that I have to make a difference in this

field; by granting me the financial support from the University of Florida’s School of

Natural Resources and Environment. I also give my special gratitude to Jake Ferguson

who took time out of his busy schedule to assist me with data analysis. Your patience

and constant help has broadened my knowledge and view of statistics.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 6

LIST OF FIGURES .......................................................................................................... 7

LIST OF ABBREVIATIONS ............................................................................................. 8

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 INTRODUCTION .................................................................................................... 11

2 METHODS .............................................................................................................. 17

Study Area .............................................................................................................. 17 Light Regimes ......................................................................................................... 18 Water Quality Sampling .......................................................................................... 19

Field Sampling .................................................................................................. 19 Laboratory Analyses ......................................................................................... 19

Seagrass Distribution .............................................................................................. 20 Statistical Analyses ................................................................................................. 21

Seagrass Light Requirements .......................................................................... 21 Water Quality .................................................................................................... 22

3 RESULTS ............................................................................................................... 26

Light Regimes ......................................................................................................... 26 Seagrass Distribution .............................................................................................. 27 Light Thresholds ..................................................................................................... 28 Water Quality .......................................................................................................... 29

4 DISCUSSION ......................................................................................................... 48

Seagrass Distribution .............................................................................................. 48 Seagrass Thresholds .............................................................................................. 50 Conclusion .............................................................................................................. 57

LIST OF REFERENCES ............................................................................................... 58

BIOGRAPHICAL SKETCH ............................................................................................ 66

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LIST OF TABLES

Table page 3-1 Light regimes for all stations and seven periods of record. ................................. 31

3-2 Maximum variation in median percent surface irradiance reaching the bottom (median %SI) at stations in the eight systems from 1999-2011. ......................... 34

3-3 Minimum and maximum variation in median percent surface irradiance reaching the bottom (%SI) at station within systems over differing periods of record. ................................................................................................................ 35

3-4 Distribution and abundance of seagrass species ............................................... 36

3-5 Estimated threshold light requirements for seagrasses and results of t-tests assessing differences in percentage covers (% cover) and shoot densities. ...... 45

3-6 Means for water quality parameters with direct effects on seagrasses and results of t-tests comparing those parameters at stations with seagrass and stations with suitable light but no seagrass ........................................................ 46

3-7 Values of water quality characteristics that may stress seagrasses ................... 46

3-8 Means for water quality parameters with indirect effects on seagrasses and results of t-tests comparing those parameters at stations with seagrass and stations with suitable light but no seagrass ........................................................ 47

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LIST OF FIGURES

Figure page 2-1 Map of study area showing sampling stations (numbers) originally

established by Frazer et al. (1998). .................................................................... 24

2-2 Schematic of quadrants and quadrats (squares) used to sample seagrass at a station (star). .................................................................................................... 25

3-1 Squared Euclidean Distances plotted against mean percent surface irradiance for shoot counts of Thalassia testudinum for various light regimes .... 37

3-2 Squared Euclidean Distances plotted against mean percent surface irradiance for percent covers of Thalassia testudinum for various light regimes ............................................................................................................... 38

3-3 Squared Euclidean Distances plotted against mean percent surface irradiance for shoot counts of Halodule wrightii for various light regimes ........... 39

3-4 Squared Euclidean Distances plotted against mean percent surface irradiance for percent covers of Halodule wrightii for various light regimes ........ 40

3-5 Squared Euclidean Distances plotted against mean percent surface irradiance for shoot counts of Syringodium filiforme for various light regimes .... 41

3-6 Squared Euclidean Distances plotted against mean percent surface irradiance for percent covers of Syringodium filiforme for various light regimes ............................................................................................................... 42

3-7 Squared Euclidean Distances plotted against mean percent surface irradiance for shoot counts of Halophila engelmannii for various light regimes .. 43

3-8 Squared Euclidean Distances plotted against mean percent surface irradiance for percent covers of Halophila engelmannii for various light regimes ............................................................................................................... 44

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LIST OF ABBREVIATIONS

CDOM chromophoric dissolved organic matter

CHL chlorophyll

CWA Clean Water Act

DO dissolved oxygen

EPA Environmental Protection Agency

FDEP Florida Department of Environmental Protection

Io incident irradiance at the water surface

Iz light intensity at depth

Kd light attenuation coefficient

NNC numeric nutrient criteria

SD standard deviation

SED squared Euclidean distances

SI surface irradiance

TMDL total maximum daily load

TN total nitrogen

TP total phosphorus

PAR photosynthetically active radiation

PPFD photosynthetic photon flux density

z depth

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

LIGHT REQUIREMENTS OF SEAGRASSES ALONG THE WEST COAST OF

PENINSULAR FLORIDA

By

Zanethia Denise Choice

December 2012

Chair: Thomas Frazer Major: Interdisciplinary Ecology

Healthy seagrasses generate ecosystem services worth more than $3.8 trillion.

Unfortunately, seagrasses around the world are threatened by human activities that

result in degraded water quality and reduced light availability in particular. Effective

management and conservation of seagrasses require detailed study of their light

requirements.

In this study, light thresholds were determined for the four most common and

abundant seagrass species along the Gulf coast of peninsular Florida; Thalassia

testudinum, Halodule wrightii, Syringodium filiforme, and Halophila engelmannii. Water

quality parameters measured monthly since 1999 were related to seagrass cover and

density estimates made in 2010 and 2011. The relationships between light availability

and seagrass abundance and distribution were evaluated by calculating squared

Euclidean distances between mean cover values or densities in either half of a moving

split-window.

Light requirements calculated here differed among species of seagrasses, and

they also differed from requirements reported for seagrasses at other locations.

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Halodule wrightii had the highest light requirement at 24-27% of surface irradiance, and

H. engelmannii exhibited the lowest light requirement at 8-10% of surface irradiance.

Variation in seagrass light requirements is attributed to differences in morphology and

physiology, as well as light histories in specific locations.

Water quality characteristics that directly affect light attenuation showed a

significant negative correlation with seagrass distribution and abundance. These results

confirm the need to address links between anthropogenic nutrient loads, eutrophication,

reduced light penetration, and loss of seagrasses and the services they provide.

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CHAPTER 1 INTRODUCTION

Seagrasses provide ecosystem services that are worth more than $3.8 trillion

(Costanza et al. 1997). Seagrasses serve as both refuge and foraging habitat for

numerous marine and estuarine animals including fish, shellfish, birds and other wildlife.

In fact, approximately 85% of the recreational and commercial fishery species in Florida

spend some portion of their life in vegetated habitats, and many are considered obligate

inhabitants of seagrass (Seaman 1985). In addition to providing an important habitat,

seagrasses stabilize bottom sediments, dampen wave action, decrease turbulence, and

reduce erosion of shorelines (Duarte 1995). Seagrass beds also serve as carbon sinks

by storing about 15% of the total carbon sequestered in marine ecosystems (Hemminga

and Duarte 2000); with the ability to store as much as 19.9 Pg organic carbon globally

(Fourqurean 2012). It is also estimated that present rates of seagrass loss could release

up to 299 Tg carbon per year (Fourqurean 2012). Additionally, oxygen released by

seagrass leaves as a byproduct of their photosynthesis supports aerobic organisms,

and oxygen translocated to and released from belowground tissue modifies the

biogeochemistry of sediments, including preventing sulfate reducing bacteria from

generating hydrogen sulfide that can become toxic to infauna and the seagrasses

themselves (Calleja et al. 2006; Greening and Janicki 2006; Anton et al. 2011).

Unfortunately, many seagrass beds throughout the United States and globally have

experienced declines in seagrass biomass and density since the middle 1900s (OSPAR

2009).

Due to their biology and physiology, survival and growth of seagrasses depend

on a number of factors, including suitable substrate for roots and rhizomes, sufficient

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immersion, suitable salinities, and adequate subsurface irradiance for photosynthesis

(Hemminga and Duarte 2000). In general, seagrasses usually are confined to i) sandy

or muddy sediments, being hindered in rocky areas by lack of root penetration; ii) low

intertidal and shallow subtidal areas where the entire plant regularly is submerged in

water with salinities between 10 and 45; and iii) areas where light reaching the plants is

sufficient to support photosynthesis that exceeds the metabolic demands of tissue

maintenance, growth and reproduction (Hemminga and Duarte 2000).

Although all of these factors are important, light has been shown to be a key

factor affecting seagrass growth because it drives photosynthesis, which generates the

energy needed for growth, respiration and reproduction. Incident sunlight is attenuated

at each of several steps: “For photosynthesis to occur in seagrasses, light must

penetrate the water column, enter the canopy of blades, pass through a layer of

epiphytes on the surfaces of blades and finally enter the leaf epidermis to reach the

photosynthetic apparatus” (Ralph et al. 2007). Thus, the degree of light attenuation will

vary with depth; concentrations of materials that absorb or scatter light, such as

chromophoric dissolved organic matter (CDOM), suspended solids and chlorophyll (a

measure of phytoplankton biomass); and the presence of macroalgae or epiphytic algae

that absorb light. Rainfall, droughts, high winds, dredging, aquaculture, fertilizer use,

sewage disposal, and other human activities can alter the amounts of CDOM,

suspended solids, phytoplankton and algae in the water that, in turn, affect the light

available to seagrasses (Ralph et al. 2007). In particular, increased inputs of nutrients

have generated increased growth of algae and phytoplankton causing many systems to

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become eutrophic, with light regimes that are unsuitable for seagrasses (Lee et al.

2007; Bricker et al. 2008).

Because seagrasses need considerable light, they usually dominate shallow

waters with low nutrient loads and concentrations. In such conditions, seagrasses

dominate because they can acquire substantial quantities of nutrients through their root

systems and they can store nitrogen and phosphorus in their leaves, stems and

rhizomes (Hocking et al. 1981; Valiela et al. 1997). As nutrient concentrations increase,

macroalgae, epiphytic algae and phytoplankton begin to outcompete seagrasses

because these photoautotrophs are more efficient at acquiring nutrients from the water

column and they require less light (Williams and Ruckelshaus 1993; Duarte 1995; Biber

et al. 2009). For example, Duarte (1995) showed that macroalgae required a mere 1%

of surface irradiance (SI); whereas, seagrasses required approximately 11% SI (Duarte

1991), with seagrasses colonizing turbid waters having higher light requirements than

those growing in clearer waters (Duarte 2007). Nutrients have been shown to have a

negative effect on water clarity in coastal systems, with Tomasko et al. (2001)

demonstrating that a 45% increase in nitrogen in Lemon Bay, Florida resulted in a 29%

increase in chlorophyll a, a 9% increase in the light attenuation coefficient, and a

consequent 24% decrease in the maximum depth of seagrass occurrence. Similarly,

Anton et al. (2011) showed that an increase in nutrients resulted in an increase in

chlorophyll a and a 30% decrease in light available to seagrasses, and Onuf (1996)

showed that a 50% decrease in SI caused by a brown tide event led to a 60% decrease

in seagrass biomass in beds deeper than 1.4 meters. Excess nutrients also promote

growth of macroalgae that can shade seagrasses (Valiela et al. 1997) and epiphytes

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that can absorb approximately 30% of the light reaching the seagrass canopy (Dixon

2000). Overall, increased nutrient delivery can generate negative effects on seagrasses

by altering light regimes.

Previous studies have evaluated light requirements for seagrass species from

different regions, with all studies demonstrating a minimum light requirement (Durako

2007; Dunton 1994; Dennison 1987; Dawes 1998). Averaged across species and

locations, light limitation occurs at approximately 11% SI (Duarte 1991), but results

indicated that seagrass species have different light requirements. It also has been

shown that the same species of seagrass can have different light requirements in

different systems. For example, in the Indian River Lagoon in Florida, Halodule wrightii,

Syringodium filiforme and Thalassia testudinum required approximately 33% of SI

(Steward et al. 2005). In contrast, T. testudinum in Tampa Bay, Florida required 20–

25% SI (Tomasko and Hall 1999), H. wrightii in Laguna Madre, Texas required 15–18%

SI (Williams and McRoy 1982), and S. filiforme along the Northwest coast of Cuba

required 19% SI (Dennison et al. 1993). These findings suggest that historical and

current light regimes interact with other environmental influences to determine the light

requirements of seagrasses; while also emphasizing the importance of estimating light

requirements for multiple locations.

Although the percent of surface irradiance is important, seagrasses require light

with wavelengths between 400-700 nm for maximum photosynthetic productivity. These

wavelengths supply seagrasses with a certain quality of photons from the portion of the

electromagnetic spectrum known as photosynthetically active radiation (PAR). It has

been shown that receiving wavelengths of light greater than PAR causes photoinhibition

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by damaging chlorophyll, which decreases the photosynthetic capacity of seagrasses.

For example, Durako (2007) found that Australian seagrasses obtained 80% of their

photosynthetic needs from PAR of 400–500 nm and 660–680 nm. In another study,

Thalassia testudinum in Puerto Morelos Coral, Mexico obtained its photosynthetic need

from PAR of 380-750 nm.

In addition to the quality of photons received by seagrasses the intensity of these

photons are also important. Zostera marina exhibited maximum productivity when it

received a photosynthetic photon flux density (PPFD) of about 350–550 μE m-2 s-1 and

a decline in photosynthetic production at irradiances greater than 700 μE m-2 s-1, which

suggested photoinhibition (Thom et al. 2008). Similarly, Halophila engelmannii exhibited

maximum productivity when it received 500 μE m-2 s-1 and a decline in photosynthetic

production at irradiances greater than 700 μE m-2 s-1 (Dawes et al. 1987). Durako

(1993) found that T. testudinum reached its light saturation point at 300 μE m-2 s-1, and

Dunton (1996) reported light saturation at 319 μE m-2 s-1 for H. wrightii.

Along with the %SI and the quality and intensity of photons, seagrasses respond

to exposure to light over certain durations, due to their ability to integrate light. At the

individual and population levels, Congdon and McComb (1979) showed that Ruppia

maritima needed at least 20% SI for up to 100 days during a 4-month period to maintain

50% of its maximum standing crop. Dennison and Alberte (1985) demonstrated that Z.

marina beds required at least 10% SI and a daily exposure to 8–10 hours of light at or

above the species’ compensation point, i.e., the irradiance at which photosynthetic

production of oxygen equaled respiratory demand. Campbell et al. (2008) found that

several species of seagrasses maintained high rates of photosynthesis from 12 pm until

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4 pm despite declining light availability, which allowed these species to cope with

varying, subtidal light regimes. In addition, when favorable light conditions are not

available, seagrasses can support metabolic needs by utilizing carbohydrates stored in

their rhizomes, reducing growth and limiting production of leaves (Hemminga and

Duarte 2000).

In summary, increasingly unfavorable light regimes ultimately will cause a

decrease in the abundance of seagrasses resulting in a loss of valuable habitat.

However, with help from managers, plans can be implemented to preserve seagrasses.

Given the links between increased nutrient loads or concentrations, eutrophication,

reduced light availability and detrimental impacts on seagrasses, managers of coastal

systems will benefit from an improved understanding of the light requirements of

seagrasses (Bricker et al. 2008). In this study, I will identify areas with and without

seagrasses in eight coastal systems along Florida’s Gulf coast; evaluate historical light

regimes in the eight systems to estimate the light requirements of local seagrasses; and

relate estimated light requirements and light availability to variations in chlorophyll a,

total nitrogen, total phosphorus, and color concentrations. Through these efforts, I aim

to gain insights into the likely consequences of increased nitrogen and phosphorus

loads, which often lead to eutrophication and altered light regimes in such systems

(Miller 2005). The resulting information can inform management actions, including the

development of numeric nutrient criteria.

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CHAPTER 2 METHODS

Study Area

Field sampling was conducted in eight coastal systems adjacent to the

Steinhatchee, Suwannee, Wacasassa, Withlacoochee, Crystal, Homosassa,

Chassahowitzka, and Weeki Wachee rivers along the Gulf coast of peninsular Florida

(Figure 2-1). Along this same coast, Florida’s second largest contiguous seagrass bed

stretches from Tarpon Springs to St. Marks, covering approximately 3000 km2 (Zieman

and Zieman 1989). The shallow estuarine waters in the region are tidally dominated;

tides are diurnal with a range of approximately one meter (Glancy et al. 2003). Average

depths at sampling sites are 1.8 m and average salinities are 18 psu (Frazer et al.

unpubl. data). A significant amount of groundwater also is transported to these areas

from springs and seeps; which generates relatively consistent inflows of freshwater and

nutrient loads (Jones et al. 1997; Frazer et al. 2001; Frazer et al. 2006). The sediments

in these systems are comprised largely of clay and silicious sand over limestone

(Iverson and Bittaker 1986).

Within the study area, seagrass sampling took place at a subset of stations

where water quality has been sampled since 1998 (Frazer et al. 1998). Water quality is

sampled on a monthly basis at ten stations in each coastal system, ranging from 0-11

km offshore (91% of stations were < 6 km offshore) to document dissolved oxygen

concentrations, salinities, temperatures, depths, light attenuation coefficients,

chlorophyll a concentrations, color, and concentrations of total nitrogen and total

phosphorus. Salinities were suitable for seagrasses at sixty-two of the 80 stations

(Doering et al. 2002; Touchette 2007; Lirman and Cropper 2003), and these stations

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were sampled to document seagrass cover and shoot density. Depths at selected

stations ranged from 0.1 to 5.6 m, and mean salinities at selected stations ranged from

10 to 30 between 1999 and 2011.

Light Regimes

Light regimes were characterized with data collected between 1999 and 2011 as

a part of the long-term water quality monitoring program. During daylight hours,

generally between 1000 and 1500 hours, two quantum light sensors (Li-Cor Instruments

Inc.) were used with a data logger to simultaneously measure surface and subsurface

flux of photosynthetically active radiation (PAR, µE s-1 m-2). For each sampling event at

each station, a light attenuation coefficient (Kd) was calculated using equation 2-1

derived from the Beer-Lambert law:

Kd = [ln(Io/Iz)]/z (2-1)

where Io is incident irradiance at the surface and Iz is light intensity at depth (z) in

meters (Kirk 1994). When feasible, light readings were recorded at three different

depths (all > 0.5 m) and an average Kd was calculated for use in subsequent analyses.

Light attenuation coefficients were not corrected for cloud cover or sun angle.

For each monthly sampling event at each of the selected stations, the

percentage of incident light reaching the bottom (%SI), where seagrasses would grow,

was calculated by rearranging the equation and entering the relevant values measured

in the field (equations 2-2 and 2-3):

Iz = Io*e (-Kd*z) (2-2)

%SI = (Iz/Io)*100 (2-3)

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Water Quality Sampling

Field Sampling

Data from the long-term water quality monitoring program for 1999–2011 were

used to characterize the key properties of the systems. Sampling took place during

daylight hours, generally between 1000 and 1500 hours. Maximum depth was

measured to the nearest 0.1 m at each station with a surveyor’s pole. Water

temperature, salinity, pH, and the concentration of dissolved oxygen (DO) were

recorded at each station with either a YSI model 85 or Y600QS meter. Temperature

was recorded to the nearest 0.1 °C, salinity to the nearest 0.1, pH to the nearest 0.01,

and dissolved oxygen to the nearest 0.1 mg L-1.

In addition, at each station, a surface water sample was collected in an acid-

washed Nalgene bottle that was first rinsed with ambient water. Water samples were

stored on ice, transported to the laboratory and frozen. A second water sample

collected at each station was filtered through a 47-mm Gelman type A/E glass fiber

filter. The filter and accompanying material were placed over silica gel desiccant, while

the filtrate was collected in acid cleaned Nalgene bottle that was first rinsed with the

filtrate then transported to the laboratory on ice and refrigerated.

Laboratory Analyses

All frozen water samples were analyzed for total nitrogen (TN) and total

phosphorus (TP). Total phosphorus concentrations (μg L-1) were determined using the

procedures of Murphy and Riley (1962) with persulfate digestion (Menzel and Corwin

1965). Total nitrogen concentrations (μg L-1) were determined by oxidizing water

samples with persulfate and measuring nitrate-nitrogen concentrations with second-

derivative spectroscopy (Bachmann and Canfield 1996).

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Filtered material and filtrates were analyzed to generate chlorophyll a

concentrations (μg L-1) and color (Pt-Co units), respectively. Chlorophyll a

concentrations were determined spectrophotometrically (Method 10200 H; American

Public Health Association 1989) following pigment extraction with ethanol (Sartory and

Grobbelaar 1984). Color was measured with a spectrophotometer (American Public

Health Association 1989).

Seagrass Distribution

In decreasing order, Thalassia testudinum, Halodule wrightii, Syringodium

filiforme, Halophila engelmannii, and Ruppia maritima are the five most common and

abundant seagrass species found in these eight systems. During 2010 and 2011, at the

beginning (May-June) and end (August-September) of each growing season,

seagrasses were sampled at the seven to eight stations selected within each coastal

system. To perform this sampling, a marker was placed at each station’s coordinates

and four quadrants were established (northwest, southwest, southeast, and northeast;

Figure 2-2). In each quadrant, SCUBA divers tossed three 0.5 m × 0.5 m quadrats

within 10 m of the center point to yield 12 unbiased samples of total percent cover of all

seagrasses and percent cover by species recorded to the nearest 5% (Fourqurean et al.

2002). Shoots of T. testudinum, H. wrightii, S. filiforme, H. engelmannii, and R. maritima

were counted in 0.25 m x 0.25 m subquadrats within each quadrat, and percent cover of

all seagrasses and each species also were estimated for subquadrats. Shoot counts

were scaled to number of shoots m-2.

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Statistical Analyses

Seagrass Light Requirements

A boundary analysis was performed to identify thresholds in light regimes linked

to percent cover or shoot density for seagrass species that were sufficiently common

and abundant. All thresholds were based on estimates of the median %SI reaching the

bottom at each seagrass station. To evaluate the effect of light histories with differing

durations, median %SI values were calculated using data from 6, 12, 18, 36, and 60

months prior to the beginning of seagrass sampling, as well as medians derived from all

Kd values recorded between 1999 and 2011 (90-148 months, with 85% of stations

having 120 or more months). The seagrass data used in the boundary analysis

comprised mean percentage covers or mean shoot densities for each species as

calculated from values for the 12 quadrats or subquadrats deployed at each station

during the two sampling periods in 2010 and 2011.

For each seagrass species, mean percentage covers and mean shoot densities

were associated with median %SI values from the appropriate stations after the latter

values were placed in ascending order. If a median %SI value was associated with a

percentage cover or shoot density of zero and that median %SI was greater than the

first median %SI associated with a percentage cover or shoot density greater than zero,

the datapoint was excluded because it was assumed that other factors besides light

limited establishment and survival of seagrasses. Once the data were organized, a

moving split-window was applied, with squared Euclidean distances (SED) calculated

from percentage covers or shoot densities associated with median %SI values falling in

the two halves of the split window (Ludwig and Tongway 1995). In other words, the

moving split-window of 6 median %SI values was split into 2 groups with 3 values each.

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The percentage covers or shoot densities in each group were averaged, and an SED

dissimilarity index was computed according to equation 2-4:

SED = [(Mean L1)] – (Mean L2)]2 (2-4)

Where, Mean L1 and Mean L2 represent the means of the three percentage covers or

shoot densities associated with the first and second groups of three median %SI values

within the window of six values. After each dissimilarity index was calculated, the split-

window was moved one position further along the ordered series of median %SI values,

and another dissimilarity index was calculated. This process was repeated until each

median %SI value was included within a split-window. Thresholds were identified as

occurring within the range of median %SI values associated with initial increases in

squared Euclidean distances. To illustrate thresholds, dissimilarity indices were plotted

against the third median %SI in each window of 6 values.

Relationships between seagrass metrics, i.e., percentage cover and shoot

densities, and these thresholds were each evaluated with Welch’s t-tests. All mean

percentage cover values and mean shoot densities equal to and below each threshold

were compared to the mean percentage cover values and mean shoot densities above

that threshold. Significant relationships between seagrasses and light regimes were

indicated by p-values ≤ 0.05.

Water Quality

Welch’s t-tests were performed on all water quality data, i.e., depth, temperature,

salinity, pH, color, and concentrations of DO, TP, TN, and chlorophyll a. For each

parameter, separate t-tests were performed using data parsed by species of seagrass.

Welch’s t-tests performed on depth, temperature, salinity, pH, and DO concentrations

compared data from stations where seagrasses were found to data from stations where

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seagrasses were not found only for stations with long-term median %SI values greater

than the estimated threshold for the relevant species of seagrass. T-tests performed on

color and concentrations of TP, TN, and chlorophyll a compared data from stations

where seagrasses were found to stations where seagrasses were not found, regardless

of the historical light regime. The different approaches reflected direct effects on

seagrass survival and productivity exerted by the first set of water quality parameters

and indirect effects mediated through light attenuation associated with the second set of

parameters. Although depth has an indirect affect on seagrasses by altering light

penetration, it is characterized as a parameter that directly affects seagrasses because

seagrasses in shallow water can be killed by prolonged exposure to air. Concentrations

of TN and TP also directly influence the health of seagrasses, but one of their principal

effects is indirect through stimulating the growth of phytoplankton, epiphytes and

macroalgae that shade seagrasses. In fact, 8% of TN records and 23% of TP records in

the study area exceeded criteria set for seagrasses in coastal bays of Maryland (640 µg

L-1 for TN and 37 µg L-1 for TP; Wazniak et al. 2007).

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Figure 2-1. Map of study area showing sampling stations (numbers) originally established by Frazer et al. (1998).

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Figure 2-2. Schematic of quadrants and quadrats (squares) used to sample seagrass at a station (star).

N

W E

S

10 m

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CHAPTER 3 RESULTS

Light Regimes

Calculations based on Kd values indicated that between 1999 and 2011 the

amount of light reaching the bottom at the 62 sampling stations varied from < 0.001% to

99% SI over the 13-year period of record (Table 3-1). Stations off the Suwannee River

consistently received the least light, with an overall median of 3% SI during the period of

record (Table 3-1). In contrast, stations off the Homosassa River consistently received

the most light, with an overall median of 36% SI during the period of record (Table 3-1).

Light regimes not only varied among systems, but median %SI values also varied

among stations within each system (Table 3-2). The systems with the largest range of

median %SI values over the thirteen years were the Crystal and Chassahowitzka rivers,

with ranges of 32% SI (Table 3-2). In contrast, the Suwannee River system had the

least variable values and the least amount of light reaching the bottom, with a range of

4% SI calculated from a maximum median of 5% SI at station 7 and a minimum median

of 1% SI at station 5 (Table 3-2). As anticipated, light penetration varied greatly among

systems and stations, and it likely reflected spatial variation in water chemistry.

Furthermore, this variation yields the basis for a natural experiment.

Short-term and long-term light regimes at stations within systems also varied

(Table 3-3). A maximum range of 23% SI was found at station 7 off the Wacasassa

River between medians derived from 6 and 36 months of records and at station 9 off

Crystal River between medians derived from 6 and 24 months of records. The smallest

ranges were found at station 8 off the Suwannee River and station 7 off the

Withlacoochee River, with less than 1% difference between the median %SI based on ≥

27

90-months and the median based on 36 months for Suwannee and between the

medians based on 12 and 24 months for Withlacoochee. These variations show that

light was not consistent at all stations throughout the 13-year sampling period, with

stations experiencing periods of both high and low light penetration. Through these

observations, insights into how seagrasses integrate light can be gained.

Seagrass Distribution

At least one species of seagrass was found at 60% of the 62 sampling stations

during at least one of the 4 sampling periods, and 84% of the stations where seagrass

was found supported more than one species (Table 3-4). Thalassia testudinum and

Halodule wrightii were the only species that occurred in monospecific beds, and a

mixture of these species occurred at 69% of the stations with 2 species. Three or more

species were present at 38% of the stations that supported seagrasses, with the most

common mixture comprising T. testudinum, H. wrightii, and S. filiforme.

The stations off the mouths of the Weeki Wachee and Homosassa rivers, two of

the southernmost systems, had the broadest distributions of seagrass, with at least one

species found at 100% and 89% of the sampling stations, respectively (Table 3-4). In

contrast the Suwannee, Wacasassa and Withlacoochee systems, three northern

systems, had less extensive coverage, with seagrass found at only 13%, 25% and 13%

of the stations, respectively (Table 3-4).

Thalassia testudinum and Halodule wrightii were the most common and

abundant seagrasses found in the study, and Ruppia maritima was the least common

and abundant. Thalassia testudinum and H. wrightii were found in up to 35% and 48%

of the deployed quadrats, respectively (Table 3-4). Thalassia testudinum and H. wrightii

had maximum mean shoot densities ± standard deviations (SD) of 463.0 ± 488.2 shoots

28

m-2 and 303.2 ± 938.7 shoot m-2, as well as maximum mean percentage covers ± SDs

of 19.7 ± 22.6% and 7.3 ± 19.4% (Table 3-4). Ruppia maritima was found in no more

than 10% of the deployed quadrats, with a maximum mean shoot density ± SD of 80.0 ±

384.0 shoots m-2 and a maximum mean percentage cover ± SD of 1.9 ± 7.8% (Table 3-

4). Mean shoot densities and percent covers were calculated by taking the mean of all

samples from each quadrat deployed within each system during the four sampling

periods. Mean shoot densities were then scaled to m-2.

Light Thresholds

Thresholds were estimated for four of the five seagrass species. Due to its

limited distribution and abundance, thresholds were not estimated for Ruppia maritima.

Thresholds varied by periods of record and species (Table 3-5; Figures 3-1 to 3-

8). The median %SI values calculated for the selected periods of record, varied by up to

23%. Halodule wrightii exhibited greatest variation in light requirements over the

selected periods of time, with thresholds based on shoot densities having a minimum

range of 12-15% SI based on 6 months of light and a maximum range of 31-33% SI

based on 24 months of light. Thalassia testudinum exhibited the smallest variation, with

thresholds based on percent cover and shoot densities having a minimum range of 16-

27% SI based on 18 months of light and a maximum range of 20-28% SI based on 36

months of light. Although there was variation among the periods of record, thresholds

for the different periods of record and two parameters (shoot count and percent cover)

tended to overlap. In addition, the range spanned by any given threshold tended to

decrease as periods of record and available data increased. For example, the

thresholds based on percent cover for H. wrightii spanned 9% SI for the 6-month period

of record (15-24% SI) and 3% SI for the ≥ 90-months period of record (24-27%). Due to

29

the relatively small variation among light thresholds calculated for different periods of

record and the precision provided by shoot densities, thresholds based on shoot

densities and ≥ 90-months of light records were used in discussion of threshold light

requirements.

Although threshold light requirements were relatively consistent for a given

species, these requirements did vary among species (Table 3-5; Figures 3-1 to 3-8).

Halodule wrightii had the highest light requirement at 24-27% SI, and Halophila

engelmannii exhibited the lowest light requirement at 8-10% SI. Syringodium filiforme

and Thalassia testudinum were intermediate; with thresholds of 8-16% SI and 20-25%

SI, respectively. These thresholds showed that although seagrass species may reside

in the same environment and be exposed to the same light regimes and water

chemistry, their physiologies and growth strategies generated differing light

requirements.

In general, t-tests confirmed that the abundance and distribution of seagrasses

differed among stations where the median percent of surface irradiance at the bottom

met or exceeded the threshold and stations where light penetration was less (Table 3-

5). Only results for shoot densities of H. engelmannii were non-significant (Table 3-5).

Water Quality

Within this study, two types of water quality characteristics were identified.

Characteristics that could directly affect the health of seagrasses included depth,

temperature, salinity, DO, and pH. Characteristics that affect seagrasses indirectly by

affecting light availability included TN, TP, chlorophyll a and color.

In ten cases, water quality parameters that directly affect seagrasses differed

significantly among stations that had seagrasses and sufficient light reaching the bottom

30

versus stations that had sufficient light penetration but lacked seagrasses (Table 3-6).

Although statistically significant, these differences appeared unlikely to be biologically

significant because they did not exceed the tolerances of seagrasses (Table 3-7). In

addition, the directionality of the differences was not consistent across species. For

example, T. testudinum and H. engelmannii were more common at deeper sites,

whereas S. filiforme was more common at shallow sites (Table 3-6).

In contrast, a consistent pattern of significant differences was observed for water

quality characteristics that primarily affect seagrasses by affecting light availability

(Table 3-8). In all cases, seagrasses were present at stations with significantly lower

concentrations of TN, TP, chlorophyll a and color.

31

Table 3-1. Light regimes for all stations and seven periods of record. %SI = percent of surface irradiance reaching the bottom; Sy = system; Stn = station; St = Steinhatchee; Su = Suwannee; Wa = Wacasassa; Wi = Withlacoochee; Cr = Crystal; Ho = Homosassa; Ch = Chassahowitzka; We = Weeki Wachee

Sy Median %SI for period of record (months) Minimum %SI for period of record (months) Maximum %SI for period of record (months) Stn 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 St

3 13.0 12.5 16.3 17.7 19.8 21.0 17.8 2.0 0.6 0.6 0.5 0.5 <0.1 <0.1 34.2 34.2 49.7 49.7 52.8 52.8 59.4 4 24.4 24.4 27.1 27.3 28.3 28.3 25.6 7.6 7.6 7.6 7.6 7.6 0.2 0.2 29.8 46.4 57.6 66.4 66.4 66.4 73.1 5 14.9 14.1 15.4 15.4 17.1 17.1 13.5 4.2 1.3 1.3 1.3 1.3 <0.1 <0.1 32.2 32.2 32.2 35.9 41.7 67.5 67.5 6 8.5 4.4 6.0 5.0 9.8 9.7 7.4 0.2 0.2 0.2 0.2 0.2 <0.1 <0.1 38.9 38.9 43.3 43.3 44.3 44.3 44.3 7 8.5 1.0 7.3 6.0 8.2 8.2 7.5 0.4 0.4 0.4 0.4 0.4 0.4 <0.1 33.5 33.5 33.5 33.5 33.5 33.5 54.6 8 6.0 3.6 4.4 4.4 5.4 5.4 4.9 0.5 0.2 0.2 0.2 0.2 0.2 <0.1 23.8 23.8 23.8 23.8 37.2 78.7 78.7 9 40.4 32.0 39.1 38.2 36.0 35.9 33.4 4.6 4.6 4.6 4.6 4.6 4.6 0.6 60.3 60.3 82.0 82.0 82.0 82.0 82.0

10 27.7 18.2 27.7 25.2 29.4 26.5 26.5 9.2 2.8 2.8 2.8 2.8 2.8 1.3 58.0 58.0 68.6 68.6 68.6 68.6 74.7 Su

3 10.1 5.7 7.6 5.7 5.6 5.1 4.5 0.2 0.2 <0.1 <0.1 <0.1 <0.1 <0.1 29.9 29.9 30.7 30.7 30.7 44.8 50.0 4 1.9 1.9 2.2 2.2 2.8 3.4 3.4 0.8 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 7.2 12.0 12.9 12.9 13.8 79.8 79.8 5 4.5 4.5 5.5 5.4 5.4 5.8 4.7 0.7 0.1 0.1 0.1 0.1 0.1 <0.1 10.2 10.2 19.6 19.6 38.6 60.1 60.1 6 0.8 0.8 0.8 0.8 1.1 1.4 1.7 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 21.9 21.9 21.9 21.9 21.9 25.4 42.7 7 1.1 0.6 1.6 0.8 0.7 0.6 0.5 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 3.3 3.3 5.1 9.4 9.4 9.4 32.2 8 1.4 1.2 1.1 1.3 1.4 1.2 1.1 0.1 0.1 <0.1 <0.1 <0.1 <0.1 <0.1 9.7 9.7 9.7 9.7 10.2 10.2 18.1 9 0.9 1.4 1.4 1.4 1.4 1.4 1.4 0.1 0.1 <0.1 <0.1 <0.1 <0.1 <0.1 5.1 5.1 10.4 10.4 10.4 32.0 68.2

10 2.1 3.2 2.5 2.5 3.1 3.2 3.2 0.3 0.3 <0.1 <0.1 <0.1 <0.1 <0.1 5.5 13.1 17.6 18.7 18.7 45.6 51.1 Wa

3 3.3 5.5 6.0 5.5 4.9 2.5 1.0 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 8.6 13.6 48.1 48.1 48.1 48.1 48.1 4 25.3 17.1 28.9 18.0 19.8 14.5 15.3 3.0 3.0 3.0 0.2 0.2 <0.1 <0.1 46.3 46.3 81.0 81.0 81.0 81.0 81.0 5 14.0 15.1 25.5 25.5 27.4 23.8 17.2 0.3 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 51.4 51.4 61.2 61.2 89.9 89.9 89.9 6 1.4 2.2 3.6 3.0 5.1 5.4 3.4 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 19.1 19.1 51.0 51.0 51.0 51.0 51.0 7 7.5 18.2 29.2 29.2 30.9 27.1 24.5 1.9 1.9 1.9 1.9 1.7 0.4 <0.1 45.5 45.5 49.0 49.0 51.9 54.3 65.0 8 2.1 2.8 4.0 3.7 3.8 3.7 3.8 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 6.1 8.5 49.0 49.0 49.0 51.5 51.5 9 8.5 8.7 11.4 9.7 11.4 9.8 9.0 0.4 0.4 0.4 0.4 0.4 0.1 <0.1 23.1 23.1 44.7 44.7 60.5 60.5 60.5

10 4.3 7.2 9.1 8.0 10.3 9.0 8.2 1.3 1.3 1.3 1.0 0.8 <0.1 <0.1 25.0 25.0 43.5 43.5 43.5 43.5 43.5

32

Table 3-1. continued

Sy Median %SI for period of record (months) Minimum %SI for period of record (months) Maximum %SI for period of record (months) Stn 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 Wi

1 13.6 3.8 9.1 3.9 10.1 7.6 8.1 1.6 0.1 0.1 <0.1 <0.1 <0.1 <0.1 13.9 35.9 71.9 71.9 71.9 71.9 71.9 4 2.4 2.8 3.6 3.2 3.2 2.2 2.9 0.6 0.2 0.2 <0.1 <0.1 <0.1 <0.1 5.7 5.7 6.8 7.1 7.1 13.5 28.8 5 6.1 6.1 8.3 6.1 6.6 6.3 8.2 1.5 2.2 0.8 <0.1 <0.1 <0.1 <0.1 26.5 27.8 30.6 30.6 51.8 51.8 67.1 6 11.0 11.0 13.9 11.2 18.2 14.4 13.3 6.5 2.4 2.4 0.9 <0.1 <0.1 <0.1 22.2 36.1 43.4 43.4 51.4 56.2 56.2 7 1.3 1.2 1.4 2.0 1.5 1.4 1.6 0.3 0.3 <0.1 <0.1 <0.1 <0.1 <0.1 5.7 5.7 5.7 5.7 13.4 43.4 43.4 8 3.2 3.1 4.2 3.7 3.4 2.6 2.4 0.5 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 12.0 12.0 71.4 71.4 71.4 71.4 71.4 9 17.3 10.8 17.5 16.6 16.6 12.3 11.0 3.5 2.9 2.9 0.2 0.2 <0.1 <0.1 51.2 51.2 63.1 63.1 63.1 66.1 66.1

10 4.5 3.4 4.8 3.8 3.3 2.8 3.1 0.4 0.1 0.4 0.1 <0.1 <0.1 <0.1 9.0 10.8 10.7 10.8 10.8 37.5 63.0 Cr

1 14.3 13.3 13.3 13.3 13.3 5.4 5.8 3.1 0.4 0.4 <0.1 <0.1 <0.1 <0.1 30.6 41.8 41.8 41.8 47.0 47.0 47.4 2 31.5 19.6 19.6 16.5 11.7 9.6 9.8 16.4 0.3 0.3 <0.1 <0.1 <0.1 <0.1 40.3 48.0 48.0 48.0 48.0 48.0 48.2 5 15.2 13.4 9.7 7.5 7.8 7.3 9.4 7.7 1.4 1.4 1.4 1.1 0.3 0.2 34.9 36.1 36.1 36.1 36.1 36.1 42.8 6 24.3 14.6 16.3 15.0 16.1 14.3 15.6 9.9 2.9 2.9 1.4 1.4 0.7 <0.1 38.1 47.3 47.3 47.3 55.4 55.4 55.4 7 42.4 34.6 37.2 33.9 33.9 32.8 31.4 24.9 17.9 17.9 6.9 6.8 1.6 1.3 90.0 90.0 90.0 90.0 90.0 90.0 90.0 8 43.0 37.6 36.2 35.0 35.7 33.7 34.8 26.3 16 16 5.7 5.3 3.2 1.1 80.1 80.1 80.1 80.1 80.1 80.1 85.4 9 23.3 36.5 36.5 45.7 36.5 37.3 37.7 2.9 2.9 2.9 2.9 2.2 2.2 1.7 62.5 66.1 66.1 66.1 66.1 87.0 87.0

10 26.2 23.2 23.2 22.9 23.7 24.2 18.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 78.3 78.3 78.3 78.3 78.3 78.3 78.3 Ho

1 49.4 44.3 47.5 47.5 47.1 47.0 46.5 31.5 12.4 12.4 12.4 12.4 12.4 12.4 61.2 99.6 99.6 99.6 99.6 99.6 99.6 2 46.8 51.4 51.4 49.3 51.4 43.4 48.8 18.5 18.4 18.4 18.4 16.1 8.6 8.6 75.6 75.6 75.6 75.6 79.9 95.2 95.2 3 37.7 37.7 43.8 43.5 44.4 43.1 37.2 8.9 8.9 8.9 0.2 0.2 0.2 0.2 61.5 61.5 82.3 82.3 82.3 82.3 82.3 6 37.1 32.8 35.7 32.8 31.3 28.0 28.6 27.6 6.8 6.8 0.4 0.4 0.4 0.4 53.6 53.6 53.6 53.6 53.6 79.0 79.0 7 33.4 32.8 33.4 30.5 30.5 26.5 30.5 26.5 14.2 14.2 0.1 0.1 0.1 0.1 69.7 69.7 69.7 69.7 69.7 69.7 71.7 8 32.9 28.7 33.4 32.3 31.2 22.3 20.0 14.5 3.6 3.6 3.0 3.0 3.0 0.6 49.6 49.6 58.1 58.1 58.1 58.1 70.4 9 45.4 36.4 36.4 31.1 36.4 41.6 39.8 26.4 11.6 11.6 1.5 1.5 1.5 1.5 54.5 54.5 60.8 60.8 70.6 70.6 78.1

10 27.2 27.2 31.8 33.6 33.6 35.6 35.0 6.5 4.9 4.9 1.8 1.8 1.8 1.8 71.2 71.2 71.2 71.2 71.2 71.2 88.0

33

Table 3-1. continued

Sy Median %SI for period of record (months) Minimum %SI for period of record (months) Maximum %SI for period of record (months) Stn 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90 Ch

4 9.7 6.7 8.1 7.2 6.7 4.9 3.0 4.9 0.4 0.4 0.4 <0.1 <0.1 <0.1 23.6 23.6 31.8 31.8 31.8 31.8 40.1 5 20.6 13.0 13.5 12.5 10.8 9.9 9.8 4.0 2.0 2.0 2.0 1.4 <0.1 <0.1 30.9 30.9 41.6 41.6 51.9 51.9 51.9 6 48.5 34.9 34.6 34.3 31.8 30.0 27.3 30.7 8.8 8.8 8.8 7.8 7.8 <0.1 60.2 60.2 60.2 60.2 72.3 72.3 72.3 7 35.5 26.7 29.1 28.0 23.0 25.3 23.1 25.3 2.6 2.6 2.6 0.6 0.6 0.2 41.7 53.3 62.0 62.0 62.0 62.0 62.0 8 32.6 24.4 25.1 23.1 18.8 19.9 20.8 13.0 7.6 3.0 3.0 0.5 0.5 <0.1 44.6 44.6 44.6 44.6 47.0 47.0 71.5 9 35.6 22.1 23.1 24.3 25.4 24.1 24.4 11.9 5.2 5.2 5.2 4.3 4.3 0.2 43.5 43.5 54.3 54.3 63.3 63.3 63.3

10 43.9 40.1 41.3 41.4 41.0 39.8 34.6 33.1 23.6 23.6 23.6 12.2 12.2 0.1 55.3 55.3 55.3 80.1 80.1 86.8 86.8 We

4 41.5 33.9 30.8 30.8 28.8 25.6 23.4 29.5 4.5 0.7 0.7 0.7 <0.1 <0.1 43.9 43.9 63.1 63.1 75.1 75.1 75.1 5 42.7 35.3 37.8 37.8 33.8 33.9 33.5 24.1 9.4 0.4 0.4 0.4 0.4 0.3 61.9 61.9 61.9 61.9 61.9 65.6 65.6 6 37.7 36.9 39.1 39.1 39.4 39.5 36.7 20.7 13.9 2.5 2.5 2.5 2.5 2.5 54.2 56.9 56.9 56.9 64.8 76.1 80.6 7 46.8 38.2 38.2 38.2 39.8 40.2 38.6 33.1 17.3 2.5 2.5 2.5 2.5 2.5 65.0 65.0 65.0 65.0 69.0 69.0 69.0 8 42.9 39.8 41.5 41.5 42.4 43.7 41.9 21.1 17.6 17.6 17.6 12.8 12.8 9.7 51.6 51.6 59.6 63.4 76.5 77.9 80.4 9 27.3 27.3 26.3 26.3 24.4 24.8 20.1 6.5 6.5 0.1 0.1 0.1 0.1 0.1 31.8 36.3 36.3 36.3 44.7 46.0 61.3

10 34.2 33.6 34.4 34.4 31.9 32.0 29.1 9.8 9.8 0.3 0.3 0.3 0.3 0.2 51.2 51.2 54.1 55.2 55.9 57.2 61.5

34

Table 3-2. Maximum variation in median percent surface irradiance reaching the bottom (median %SI) at stations in the eight systems from 1999-2011.

System Station Median %SI Range Steinhatchee 8 5 28 9 33 Suwannee 5 1 4 7 5 Wacasassa 3 1 24 7 25 Withlacoochee 7 2 11 6 13 Crystal 1 6 32 9 38 Homosassa 8 20 30 2 50 Chassahowitzka 4 3 32 10 35 Weeki Wachee 9 20 22 8 42

35

Table 3-3. Minimum and maximum variation in median percent surface irradiance reaching the bottom (%SI) at station within systems over differing periods of record.

System Station Period of record Median %SI Range for %SI Steinhatchee 8 12 4 2 6 6 10 12 18 11 36 29 Suwannee 8 ≥90 1 1 36 2 3 ≥90 5 5 6 10 Wacasassa 8 6 2 2 18 4 7 6 8 23 36 31 Withlacoochee 7 12 1 1 24 2 1 12 4 10 6 14 Crystal 10 ≥90 19 7 6 26 9 6 23 23 24 46 Homosassa 1 12 44 5 6 49 9 24 31 14 6 45 Chassahowitzka 4 ≥90 3 7 6 10 6 ≥90 27 22 6 49 Weeki Wachee 6 ≥90 37 3 60 40 4 ≥90 23 19

36

Table 3-4. Distribution and abundance of seagrass species. T. testudinum = Thalassia testudinum; H. wrightii = Halodule wrightii; S. filiforme = Syringodium filiforme; H. engelmannii = Halophila engelmannii; R. maritima = Ruppia maritima

System Species

Stations with seagrass/stations

sampled

Percent of quadrats with

seagrass

Mean % cover ± SD

Mean shoot count m-2 ± SD

Steinhatchee T. testudinum 6/8 35.0 18.5 ± 30.5 215.2 ± 372.0 H. wrightii 4/8 8.0 0.5 ± 3.0 18.6 ± 47.4 S. filiforme 3/8 21.0 5.9 ± 16.0 154.1 ± 419.0 H. engelmannii 4/8 3.0 0.3 ± 2.7 7.7 ± 90.2 R. maritima 1/8 2.0 0.1 ± 0.5 2.1 ± 30.7

Suwannee H. wrightii 1/8 0.3 0 ± 0.1 0 ± 0

Wacasassa T. testudinum 1/8 10.0 0.9 ± 3.8 6.1 ± 60.3 H. wrightii 2/8 7.0 0.5 ± 2.7 6.6 ± 34.4 S. filiforme 1/8 11.0 4.6 ± 15.2 123.4 ± 425.8 H. engelmannii 1/8 0.3 0 ± 0.2 0 ± 0

Withlacoochee H. wrightii 1/8 7.0 0.2 ± 1.0 5.0 ± 27.2 S. filiforme 1/8 4.0 0.1 ± 0.5 3.4 ± 21.3 H. engelmannii 2/8 1.0 0.0 ± 0.1 0.2 ± 1.8

Crystal T. testudinum 4/8 18.0 7.0 ± 19.2 101.4 ± 286.9 H. wrightii 6/8 24.0 3.4 ± 11.1 173.8 ± 746.2 S. filiforme 3/8 35.0 10.9 ± 19.5 300.2 ± 516.0 H. engelmannii 5/8 12.0 1.1 ± 4.1 30.2 ± 139.4 R. maritima 1/8 10.0 0 ± 0.2 0.5 ± 5.1

Homosassa T. testudinum 5/8 34.0 11.6 ± 20.8 157.4 ± 269.4 H. wrightii 8/8 32.0 7.3 ± 19.4 303.2 ± 938.7 S. filiforme 3/8 11.0 2.4 ± 8.4 75.7 ± 288.0 H. engelmannii 2/8 4.0 1.0 ± 6.1 37.0± 259.4 R. maritima 2/8 4.0 0.1 ± 0.9 2.2 ± 18.9

Chassahowitzka T. testudinum 1/7 1.0 0 ± 0.6 0 ± 0 H. wrightii 5/7 22.0 4.9± 14.3 286.2 ± 1046.9 S. filiforme 3/7 20.0 4.9 ± 12.8 182.9 ± 559.5 H. engelmannii 3/7 10.0 1.9 ± 7.8 80.0 ± 384.0

Weeki Wachee T. testudinum 7/7 9.0 19.7 ± 22.6 463.0 ± 488.2 H. wrightii 7/7 48.0 4.8 ± 9.9 301.1 ± 1174.4 R. maritima 1/7 5.0 0.8 ± 4.5 16.5 ± 113.9

37

Figure 3-1. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) irradiance for shoot counts of Thalassia testudinum for various light regimes. a = 6 months, threshold = 24-27% SI; b = 12 months, threshold = 18-24% SI; c = 18 months, threshold = 16-27% SI; d = 24 months, threshold = 25-27% SI; e = 36 months, threshold = 20-28% SI; f = 60 months, threshold = 21-26% SI; g = ≥ 90 months, threshold = 20-25% SI

38

Figure 3-2. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Thalassia testudinum for various light regimes. a = 6 months, threshold = 23-24% SI; b = 12 months, threshold = 18-24% SI; c = 18 months, threshold = 16-27% SI; d = 24 months, threshold = 25-27% SI; e = 36 months, threshold = 20-28% SI; f = 60 months, threshold = 21-26% SI; g = ≥ 90 months, threshold = 18-23% SI

39

Figure 3-3. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Halodule wrightii for various light regimes. a = 6 months, threshold = 12-15% SI; b = 12 months, threshold = 23-27% SI; c = 18 months, threshold = 29-31% SI; d = 24 months, threshold = 31-33% SI; e = 36 months, threshold = 27-30% SI; f = 60 months, threshold = 27-30% SI; g = ≥ 90 months, threshold = 25-27% SI

40

Figure 3-4. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Halodule wrightii for various light regimes. a = 6 months, threshold = 15-24% SI; b = 12 months, threshold = 18-24% SI; c = 18 months, threshold = 28-31% SI; d = 24 months, threshold = 31-32% SI; e = 36 months, threshold 31-32% SI; f = 60 months, threshold = 27-30% SI; g = ≥ 90 months, threshold = 25-27% SI

41

Figure 3-5. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Syringodium filiforme for various light regimes. a = 6 months, threshold = 6-8% SI; b = 12 months, threshold = 6-13% SI; c = 18 months, threshold = 8-16% SI; d = 24 months, threshold = 6-15% SI; e = 36 months, threshold = 6-16% SI; f = 60 months, threshold = 6-14% SI; g = ≥ 90 months, threshold = 8-16% SI

42

Figure 3-6. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Syringodium filiforme for various light regimes. a = 6 months, threshold = 6-8% SI; b = 12 months, threshold = 6-13% SI; c = 18 months, threshold = 9-14% SI; d = 24 months, threshold = 6-15% SI; e = 36 months, threshold = 7-16%; f = 60 months, threshold = 6-14%; g = ≥ 90 months, threshold = 8-16%

43

Figure 3-7. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Halophila engelmannii for various light regimes. a = 6 months, threshold = 15-21% SI; b = 12 months, threshold = 6-13% SI; c = 18 months, threshold = 9-14% SI; d = 24 months, threshold = 6-13% SI; e = 36 months, threshold = 10-11% SI; f = 60 months, threshold = 8-10% SI; g = ≥ 90 months, threshold = 8-10% SI

44

Figure 3-8. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Halophila engelmannii for various light regimes. a = 6 months, threshold = 14=15% SI; b = 12 months, threshold = 6-13% SI; c = 18 months, threshold =9-14% SI; d = 24 months, threshold = 6-13% SI; e = 36 months, threshold = 10-11% SI; f = 60 months, threshold = 8-10% SI; g = ≥ 90 months, threshold = 8-10% SI

45

Table 3-5. Estimated threshold light requirements for seagrasses and results of t-tests assessing differences in percentage covers (% cover) and shoot densities. Sp = species; Tt = Thalassia testudinum, Hw = Halodule wrightii; Sf = Syringodium filiforme; He = Halophila engelmannii; Th = threshold; p = p-value for t-test; df = degrees of freedom for t-test; MnB = mean for stations where light did not exceed the threshold; MnA = mean for stations where light exceeded the threshold; SE = standard error

Sp Months for % cover Months for shoot count m-2 Par 6 12 18 24 36 60 ≥90 6 12 18 24 36 60 ≥90

Tt Th 23-24% 18-24% 16-27% 25-27% 20-28% 21-26% 18-23% 24-27% 18-24% 16-27% 25-27% 20-28% 21-26% 20-25% p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0010 0.0001 0.0001 0.0001 df 18 18 19 18 19 19 19 16 17 18 17 18 18 17 MnB SE

0.3 0.32

0 0.02

0 0

0 0.02

0 0

0 0

0.2 0.02

11.4 8.82

0.3 0.28

0.1 0.06

0.4 3.23

0.1 0.85

0.1 0.09

0.4 0.36

MnA SE

23.0 4.59

23.2 4.36

22.0 4.25

23.2 4.36

22.0 4.25

22.0 4.25

22.0 4.25

402.6 68.88

393.0 65.20

372.7 64.93

393.0 65.20

372.7 64.93

372.6 85.48

393.0 65.20

Hw Th 15-24% 18-24% 28-31% 31-32% 31-32% 27-30% 25-27% 12-15% 23-27% 29-31% 31-33% 31-32% 27-30% 25-27% p 0.0093 0.0256 0.0302 0.0475 0.0361 0.0458 0.0525 0.0007 0.0015 0.0017 0.0022 0.0021 0.0026 0.0188 df 26 26 23 20 18 19 20 27 21 20 16 16 16 18 MnB SE

1.2 0.05

1.7 0.70

1.2 0.63

2.0 0.80

2.1 0.77

2.0 0.73

2.0 0.68

5.8 3.65

20.9 8.14

24.3 8.52

32.4 10.81

30.2 10.22

31.0 9.80

32.4 13.66

MnA SE

6.7 1.90

6.7 1.98

7.2 2.18

7.7 2.59

8.1 2.66

7.5 2.59

7.1 2.29

300.1 76.54

362.4 93.21

375.6 96.76

444.5 465.7

444.5 112.95

439.6 114.02

400.1 141.73

Sf Th 6-8% 6-13% 8-16% 6-15% 7-16% 6-14% 8-16% 6-8% 6-13% 8-16% 6-15% 6-16% 6-14% 8-16% p 0.0077 0.0069 0.0069 0.0069 0.0069 0.0069 0.0069 0.0056 0.0047 0.0047 0.0047 0.0047 0.0047 0.0047 df 11 10 10 10 10 10 10 10 9 9 9 9 9 9 MnB SE

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 4.00

0 0

0 0

MnA SE

15.9 4.91

17.3 5.13

17.3 5.13

17.3 5.13

17.3 5.13

17.3 5.13

17.3 5.13

477.8 135.95

525.5 140.72

525.5 140.72

525.5 140.72

525.5 140.72

525.5 140.72

525.5 140.72

He Th 14-15% 6-13% 9-14% 6-13% 10-11% 8-10% 8-10% 15-21% 6-13% 9-14% 6-13% 10-11% 8-10% 8-10% p 0.0425 0.0431 0.0429 0.0438 0.0407 0.0404 0.0421 0.0530 0.0546 0.0547 0.0551 0.0499 0.0498 0.0539 df 12 14 14 14 14 14 14 10 12 12 12 12 12 12 MnB SE

0.1 0.07

0.1 0.08

0.1 0.08

0.1 0.05

0.1 0.05

0 0.05

0.1 0.05

2.1 1.86

2.4 2.33

2.4 2.33

2.7 2.67

1.0 0.99

0.9 0.89

1.9 1.86

MnA SE

4.0 1.72

3.5 0.13

3.5 1.52

3.5 1.52

3.5 1.52

3.5 1.52

3.5 1.52

168.4 75.77

142.8 65.93

142.8 65.93

142.9 65.93

144.3 65.73

144.3 65.70

142.8 65.93

46

Table 3-6. Means for water quality parameters with direct effects on seagrasses and results of t-tests comparing those parameters at stations with seagrass and stations with suitable light but no seagrass. * = significant at 0.01 < p ≤ 0.05; ** significant at p ≤ 0.01; T. testudinum = Thalassia testudinum; H. wrightii = Halodule wrightii; S. filiforme = Syringodium filiforme; H. engelmannii = Halophila engelmannii; Temp = water temperature; Sal = salinity; DO = dissolved oxygen concentration

Species Seagrass Light Mean Depth

(m) Temp (°C)

Sal DO (mg L-1)

pH

T. testudinum present 1.4** 23.0 24.2** 7.1* 8.07** absent ≥ threshold 1.0** 23.2 19.1** 7.0* 7.97** H. wrightii present 1.3 23.1 22.3** 7.1 8.03* absent ≥ threshold 1.3 22.5 29.6** 7.0 8.07* S. filiforme present 1.2** 22.8 25.2** 7.1 8.02 absent ≥ threshold 1.3** 23.1 21.3** 7.0 8.01 H. engelmannii present 1.5** 22.9 22.3 7.0 7.98** absent ≥ threshold 1.2** 23.1 22.3 7.0 8.02**

Table 3-7. Values of water quality characteristics that may stress seagrasses. DO = dissolved oxygen concentration

Species Characteristic Values of concern Reference Thalassia testudinum salinity requires > 17, optimum 30 1

depth deeper than mean low tide (0.4 m here) 2; 3

pH 7.8–9.0 4

temperature optimum 30°C 1

DO requires > 5 mg L-1 5

Halodule wrightii salinity requires > 3.5, optimum 30 1

depth deeper than mean low tide (0.4 m here) 2; 3

can survive short periods of exposure 6

pH 7.8–9.0 4

temperature optimum 30°C 1

DO requires > 5 mg L-1 5

Syringodium filiforme salinity requires > 17, optimum 35 1; 7

depth deeper than mean low tide (0.4 m here) 2; 3

can survive short periods of exposure 8

pH 7.8–9.0 4

temperature optimum 30°C 1

DO requires > 5 mg L-1 5

Halophila engelmannii salinity survive 9–35, optimum 25 9

depth deeper than mean low tide (0.4 m here) 2; 3

pH 7.8–9.0 4

temperature optimum 30°C 1

DO requires > 5 mg L-1 5

1 Iverson and Bittaker 1986; 2 Hemminga and Duarte 2000; 3 NOAA 2012; 4 Invers et al. 1997; 5 Wazniak et al. 2007; 6 Zieman and Zieman 1989, 7 McMahan 1968 8 Moore 1963; 9 Dawes et al. 1987

47

Table 3-8. Means for water quality parameters with indirect effects on seagrasses and results of t-tests comparing those parameters at stations with seagrass and stations with suitable light but no seagrass. * = significant at 0.01 < p ≤ 0.05; ** significant at p ≤ 0.01; T. testudinum = Thalassia testudinum; H. wrightii = Halodule wrightii; S. filiforme = Syringodium filiforme; H. engelmannii = Halophila engelmannii; TN = total nitrogen concentration; TP = total phosphorus concentration; Chl a = chlorophyll a concentration

Species Seagrass Mean TN

(µg L-1) TP

(µg L-1) Chl a

(µg L-1) Color

(Pt-Co units) T. testudinum present 424.5** 13.5** 2.4** 14.8** absent 477.3** 34.3** 6.1** 26.5** H. wrightii present 439.5** 17.0** 3.1** 16.3** absent 480.1** 35.0** 6.6** 28.9** S. filiforme present 404.7** 18.6** 3.3** 17.0** absent 469.5** 28.0** 5.0** 23.1** H. engelmannii present 426.4** 17.8** 3.6** 16.8** absent 467.1** 28.8** 5.0** 23.5**

48

CHAPTER 4 DISCUSSION

Seagrass Distribution

Seagrass abundance and distribution in the study region showed a direct

relationship with the percent of surface irradiance reaching the seagrass beds. Systems

with stations where median light values for the 13-year duration were greater than 20%

SI (Homosassa and Weeki Wachee) had the most abundant and diverse seagrass

beds; with all species found and a maximum of 90% of quadrats containing seagrasses.

Systems such as Suwannee, Wacasassa, and Withlacoochee, with an overall median

%SI value of 3%, had little to no seagrass. At a maximum, only 11% of quadrats

contained seagrasses in these systems. Systems in which stations exhibited both low

and high median %SI values yielded seagrass abundances in between the two

extremes. In these systems, Steinhatchee, Crystal, and Chassahowitzka, a maximum of

35% of quadrats contained seagrasses. Water quality in systems with abundant

seagrasses and systems with less seagrass varied as expected. Systems with low

amounts of seagrass (Suwannee, Wacasassa, and Withlacoochee) had higher color,

nitrogen, phosphorus, and chlorophyll concentrations.

Although comparing light regimes within systems to the threshold %SI values

accurately predicted systems where seagrass were found, there were stations within

these systems with sufficient ambient light that did not have seagrasses. This

observation highlights the fact that although light is often the limiting factor in numerous

systems, it is not the only factor affecting seagrasses. Temperature, salinity, and DO

can have a profound influence on seagrass abundance and patterns of distribution (Lee

49

et al. 2007; Lee et al. 2007; Wazniak et al. 2007); however, analyses did not yield

consistent evidence for effects from these parameters.

In contrast, analyses of water quality characteristics that indirectly affect

seagrasses by altering light regimes did yield a consistent pattern. In all cases, results

highlighted the likelihood that increased nutrient concentrations were related to

increased chlorophyll a concentrations, which in combination with high levels of color,

led to shading of seagrasses. Jacoby et al. (2011) showed that chlorophyll

concentrations in these systems are positively correlated with concentrations of total

nitrogen and total phosphorus (r2 = 0.33 and 0.85, respectively, for log transformed

data). The effects of color and chlorophyll a on light absorbance can be estimated with

partial light extinction coefficients. In this case, changes in kd were estimated by

multiplying chlorophyll a concentrations by 0.03 (Wolfsy 1983) and color concentrations

by 0.015 (McPherson and Miller 1987). Using these estimates, chlorophyll a

concentrations at stations that have seagrasses and stations that do not have

seagrasses (Table 3-8) led to changes in kd ranging from 0.04 to 0.11 m-1 and a

consequent change in %SI of 2% to 7%. Differences in color values at stations that

have seagrasses and stations that do not have seagrasses (Table 3-8) led to changes

in kd ranging from 0.09 to 0.19 m-1 and a consequent change in %SI of 3% to 12%.

Furthermore, seagrasses in systems in the study area with both high color and

chlorophyll a concentrations could potentially receive up to 20% less SI. Similar results

have been reported previously, with Tomasko et al. (2001) documenting how nitrogen

and chlorophyll a concentrations increased, 45 and 29%, respectively, in Lemon Bay,

50

Florida, which caused the light attenuation coefficient to increase by 9% and decreased

the light available to seagrasses.

Seagrass Thresholds

Just as the systems’ water quality characteristics varied within the study region,

the study area’s light histories also showed some variability within the full period of

record. Nonetheless, due to the consistency of thresholds calculated from longer

periods of record and the precision provided by shoot densities, thresholds based on

shoot densities and ≥ 90-months of light guide subsequent discussion of species’ light

requirements.

Light thresholds varied among species and across the systems studied, and they

differed from previously reported thresholds for the same species in other locations. A

surface irradiance of 21-25% was required by the most prominent seagrass in the

region, Thalassia testudinum. This result is very similar to previously reported values:

18.2% SI in Lemon Bay, Florida found by taking the average of the SI reaching the

deepest parts of the study region where T. testudinum was found (Tomasko et al.

2001); 20-25% SI in Tampa Bay, Florida calculated in the same manner (Tomasko and

Hall 1999); and 20% SI in Corpus Christi Bay, Texas found by calculating the effects of

an in situ light reduction experiment (Czerny and Dutton 1995). In contrast, thresholds

reported for Halodule wrightii differed by up to 19% SI: 33% SI reported in the Indian

River Lagoon, Florida based on the median %SI at the depth limit of beds (Steward et

al. 2005); 18% SI in San Antonio Bay, Texas based on a whole plant model that

assumed light was the dominant factor affecting seagrass production and growth

(Dunton 1994); 14% SI in Perdido Bay, Alabama based on the effects of in situ shading

(Shafer 1999); and 24–27% SI in this study. The variation between light thresholds for

51

seagrasses in different locations may have reflected adaptation to light histories at their

specific locations (Duarte 1991). Furthermore, the light threshold for Syringodium

filiforme reported here, 8–15% SI, is lower than 20% SI calculated for the Indian River

Lagoon, Florida by combining seagrass depth limits and an optical model that

calculated absorption and scattering coefficients (Gallegos and Kenworthy 1996) and

19.2% SI documented for northwest Cuba as the mean percent light at the maximum

depth limit of S. filiforme (Dennison et al. 1993). Limited research on the light

requirements of Halophila engelmannii suggests that this species is an understory plant,

and due to its small rhizomes and lower respiratory demand, can survive under lower

irradiances (Duarte 1991). Dawes et al. (1987) showed that H. engelmannii at Indian

Bluff Island, Florida had a compensation point of 60 μE m-2 s-1. Given that the

compensation point represents a minimum amount of light required for survival, any

threshold meant to support growth of H. engelmannii must be higher. In fact, using

average surface irradiance values for the stations where H. engelmannii was found in

the study area the threshold estimated for this species, 8-10% SI, equates to a PAR flux

of 104 to 131 μE m-2 s-1, which is 1.75 to 2.00 times the estimated compensation point.

Furthermore, an even higher threshold, 23.7% SI, was reported for the species in

northwest Cuba (Dennison et al. 1993).

In addition to variation in threshold light requirements, other data suggest that

historical and current light regimes interact with seagrass physiology (especially growth

and respiration) and diverse environmental influences to determine the light

requirements of seagrasses. Through a fertilization experiment, Udy and Dennison

(1997) documented different growth rates for seagrass species due to different amino

52

acid compositions and tissue nutrient content. Duarte (1991) showed how growth

strategies and architecture of seagrass species differed and led to varying patterns in

abundance and distribution in different environments. Duarte (1991) distinguished two

main types of growth strategies, pioneer and climax species, describing pioneers as fast

growing species and climax species as long-lived species. He also explained how larger

rhizomes increased respiratory demands of seagrasses, and how increased demands

consequently increased requirements for light. Not only did Duarte (1991) identify the

differences among species, he also demonstrated how environmental factors can have

an effect on light requirements of seagrasses. In particular, he indicated that

seagrasses colonizing deeper areas in turbid waters do not grow at depths predicted by

equations derived from data collected in clearer waters (Duarte 2007). Thus, species

subjected to different levels of turbidity may receive not only less light, but also light that

is less suitable for photosynthesis (Duarte 2007). Additionally, salinity can affect light

requirements of seagrasses, with most species displaying optimum productivity in

oceanic salinity and reduced photosynthesis in suboptimal salinities (Torquemada et al.

2005; Lirman and Cropper 2003). For example, H. johnsonii displayed lower light

compensation and saturation points and elevated photosynthetic efficiency at optimal

salinities; but efficiency decreased significantly and light requirements rose at points

above and below this optimal level (Torquemada et al. 2005). In addition to water

quality, physical conditions also affect the abundance and distribution of seagrasses.

Fonseca and Bell (1998) showed how percent cover declined with increasing wave

exposure and current speed due to disturbance of sediment and negative effects on

seagrass rooting and colonization. Larkum et al. (2006) also showed how longer

53

residence times increased retention of nutrients, which made the affected systems more

susceptible to eutrophication and its subsequent effects on the light available to

seagrasses. Regardless of the complexity of interactions, knowledge of light

requirements for seagrasses represents a valuable element in managing coastal

systems sustainably.

Seagrass Management

Although threshold light requirements for seagrasses vary, knowing those

requirements remains important for managing and protecting seagrass habitats and the

ecosystem services they deliver. Seagrasses exhibit higher light requirements than

other photoautotrophs (Duarte 1995); therefore, managing habitats to meet their light

requirements will not only benefit seagrasses, but also ensure sufficient light for other

estuarine primary producers. Protecting seagrasses also will benefit numerous other

organisms. Seagrass habitats support high densities of fauna many of which are

dependent on the seagrasses for food and protection (Orth et al. 1984). In large part,

due to their sensitivity and ecological importance, seagrasses can and do play a central

role in current management approaches to protecting the nation’s coastal systems.

Due to the importance of aquatic environments, the United States Congress

responded to a request from the American people to protect their waterways by passing

the 1972 Federal Water Pollution Control Act and the 1977 amendment, which together

are known as the Clean Water Act (CWA; U.S. House of Representatives 2000). This

act is the origin of modern water management in the United States. The initial

implementation of this act required states to define designated uses for all waterbodies;

identify waterbodies not supporting their designated use, and apply Total Maximum

54

Daily Loads (TMDLs) to reduce inputs and reverse impairment (U.S. Environmental

Protection Agency 2008). Total maximum daily loads are the maximum loads of a

particular pollutant that can enter a waterbody and not lead to impairment of that

waterbody (FDEP 2006). Nonetheless, due to inefficiency of these procedures (U.S.

Environmental Protection Agency 2008) and the demand of American people for clean

waters, the Environmental Protection Agency (EPA) has asked every state to develop

numeric nutrient criteria (NNC) for its waterbodies. These criteria are designed to

protect healthy, well-balanced waterbodies from being polluted by excess nutrients and

provide measurable water quality targets expressed as in situ concentrations (U.S.

Environmental Protection Agency 2008).

In response to the EPA, managers currently are working to develop criteria for

estuarine systems. Seagrass light requirements can serve as a valuable metric of a

system’s health that reflects sustainable nutrient concentrations. For example, seagrass

light requirements can be used to identify non-detrimental chlorophyll levels and the

associated nutrient concentrations that can serve as numeric nutrient criteria. In fact,

the Florida Department of Environmental Protection (FDEP) can incorporate light

requirements of seagrasses into their two primary approaches to setting numeric

nutrient criteria:

Reference comparison approach: This approach would apply nutrient

concentrations from an area with healthy seagrass to an area in need of

restoration based on the fact that nutrients affect chlorophyll a concentrations

and alter water transparency.

55

Response based approach: Maximum allowable nutrient concentrations could be

determined from rigorous cause-effect relationships between nutrients and both

an initial biological response, chlorophyll concentrations, and a subsequent

biological response, healthy seagrasses.

In addition to guiding management of nutrient loadings and concentrations,

knowledge of seagrass light thresholds will help managers address other human

impacts on water clarity, such as dredging that increases turbidity by resuspending

sediment that generates higher concentrations of nutrients in the water column and

leads to higher chlorophyll concentrations (Morrison and Greening 2011; Nayer et al.

2007).

In fact, seagrass light requirements are being used in several locations to

manage the water quality of coastal systems. Wazniak et al. (2007) and Stevenson et

al. (1993) demonstrated how knowledge of seagrasses light requirements determined

water quality thresholds for Chesapeake Bay, Virginia and how improving water quality

to meet these thresholds led to improvement in seagrasses and the system as a whole.

Managers of Florida’s coastal systems have taken this same approach; using seagrass

light requirements as targets for improving water quality. In the Indian River Lagoon,

management targets for nutrient loads were based on data collected at reference sites

and times that supported robust amounts of seagrass (Steward et al. 2005). In Tampa

Bay, managers have used seagrass light requirements to develop numeric nutrient

criteria (Janicki Environmental 2011) by relating nutrient loading, chlorophyll a

concentrations, and seagrass light requirements. With knowledge of seagrass light

56

requirements and historical references where water quality conditions met the

requirements, water quality target were established (Janicki Environmental 2011).

Beyond improving the health of our nation’s waterbodies, protecting and restoring

seagrass also will serve as a buffer against climate change. It has been shown that

carbon dioxide (CO2) emissions are a major contributor to climate change (NRC 2010),

and healthy seagrass beds could help reduce CO2 levels in the atmosphere by serving

as important carbon sinks. In fact, Fourqurean et al. (2012) showed that healthy

seagrasses have the potential to store as much as 19.9 Pg organic carbon globally. On

the other hand, present rates of seagrass loss could release up to 299 Tg carbon per

year (Fourqurean et al. 2012). Furthermore, Smith (1981) states that “marine

macrophyte biomass production, burial, oxidation, calcium carbonate dissolution, and

metabolically accelerated diffusion of carbon dioxide across the air-sea interface may

combine to sequester at least 109 tons of carbon per year in the ocean.” Healthy,

growing seagrasses will mitigate CO2 levels more effectively.

The effects of increasing atmospheric CO2 concentrations on the oceans are a

major concern: pH levels will decrease, water temperatures will increase, sea level will

rise, ultraviolet radiation levels will increase, and cloud cover may reduce levels of PAR

(Bjork et al. 2008). Several of these changes can affect seagrasses detrimentally. For

instance, increasing temperature could stress seagrasses (Iverson and Bittaker, 1986),

with fatalities having been documented previously (Hall et al. 1999). Increased

temperatures also could increase growth of algae and epiphytes, which would in turn

shade seagrasses. Ultraviolet radiation can damage cells, and lowered PAR can reduce

the extent of suitable habitat for seagrasses (Bjork et al. 2008). Such an effect would be

57

exacerbated by sea level rise (Short and Neckles, 1999). Managers need to apply

existing knowledge of seagrass light requirements and promote research to document

the ability of seagrasses to adapt to climate change if they aim to protect and restore

this valuable resource.

Conclusion

In conclusion, light requirements have been shown to vary among species and

across regions, with light history playing an integral role in the expression of a

requirement for light. Furthermore, light requirements have been shown to be a key

determinant of the abundance and distribution of seagrasses; however, other factors

also play important roles, with higher concentrations of phosphorus, nitrogen,

chlorophyll a, and color being correlated with less seagrass. Applying and improving

knowledge of interactions among water quality, light attenuation and seagrass health

will be critical to restoring and preserving this important part of our coastal systems.

58

LIST OF REFERENCES

American Public Health Association. 1989. Standard methods for the examination of water and wastewater. 17th edition. American Public Health Association, Inc. New York.

Anton, A., J. Cebrian, K.L. Heck, C.M. Duarte, K.L. Sheehan, M.C. Miller, and C.D.

Foster. 2011. Decoupled effects (positive to negative) of nutrient enrichment on ecosystem services. Ecological Application 21: 991-1009.

Bachmann, R.W., and D.E. Canfield, Jr. 1996. Use of an alternative method for

monitoring total nitrogen concentrations in Florida lakes. Hydrobiologia 323: 1-8. Biber, P.D., W.J. Kenworthy, and H.W. Paerl. 2009. Experimental analysis of the

response and recover of Zostera marina (L.) and Halodule wrightii (Ascher.) to repeated light-limitation stress. Journal of Experimental Marine Biology and Ecology 369: 110-117.

Bjork, M., F. Short, E. Mcleod, and S. Beer. 2008. Managing seagrasses for resilience

to climate change. International Union for Conservation of Nature, Resilience Science Group, Working Paper Series No. 3. 48 pp.

Bricker, S.B., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J.

Woerner. 2008. Effects of nutrient enrichment in the nation’s estuaries: a decade of change. Harmful Algae 8: 21-32.

Calleja, M.L., C. Barron, J.A. Hale, T.K. Frazer, and C.M. Duarte. 2006. Light regulation

of benthic sulfate reduction rates mediated by seagrass (Thalassia testudinum) metabolism. Estuaries and Coasts 29: 1255-1264.

Campbell, S.J., S.P. Kerville, R.G. Coles, F. Short. 2008. Photosynthetic responses of

subtidal seagrasses to a daily light cycle in Torres Strait: a comparative study. Continental Shelf Research 28: 2275-2281.

Congdon, R.A., and A.J. McComb. 1979. Productivity Ruppia: seasonal changes and

dependence on light in an Australian estuary. Aquatic Botany 6: 121-132. Costanza, R., R. d’Arge, R. Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S.

Naeem, R.V. Oneil, J. Paruelo, R.G. Raskin, P. Sutton, and M. Belt. 1997. The value of the world’s ecosystem services and natural capital. Nature 387: 253-260.

Czerny, A.B., and K.H. Dunton. 1995. The effects of in situ light reduction on the growth

of two subtropical seagrasses. Estuaries 18: 418-427.

59

Dawes, C., M. Chan, R. Chinn, E.W. Koch, A. Lazar, and D. Tomasko. 1987. Proximate composition, photosynthetic and respiratory responses of the seagrass Halophila engelmannii from Florida. Aquatic Botany 27: 195-201.

Dawes, C.J. 1998. Biomass and photosynthetic responses to irradiance by a shallow

and a deep water population of Thalassia testudinum on the west coast of Florida. Bulletin of Marine Science 62: 89-96.

Dennison, W.C. 1987. Effects of light on seagrass photosynthesis, growth, and depth

distribution. Aquatic Botany 27: 15-26. Dennison, W.C., and R.S. Alberte. 1985. Role of daily light periods in the depth

distribution of Zostera marina (eelgrass). Marine Ecology Progress Series 25: 51-61.

Dennison, W.C., R.J. Orth, K.A. Moore, J.C. Stevenson, V. Carter, S. Kollar, P.W.

Bergstrom, and R.A. Batiuk. 1993. Assessing water quality with submerged aquatic vegetation. BioScience 43: 86-94.

Dixon, L.K. 2000. Establishing light requirements for the seagrass Thalassia

testudinum: an example from Tampa Bay, Florida. In Seagrasses: monitoring, ecology, physiology, and management, ed. S.A. Bortone, 9-31. Florida: CRC Press.

Dixon, L.K., and E.D. Estevez. 1997. Biogeochemical Indicators of Trophic Status in a

Relatively Undisturbed Shallow Water Estuary. U.S. Fish and Wildlife Service, Air Quality Branch, Mote Marine Laboratory Technical Report No. 518. 84 pp.

Doering, P.H, R.H. Chamberlain, and D.E. Haunert. 2002. Using submerged aquatic

vegetation to establish minimum and maximum freshwater inflows to the Caloosahatchee estuary, Florida. Estuaries and Coasts 25: 1343-1354.

Duarte, C.M. 1991. Seagrass depth limits. Aquatic Botany 40: 363-377. Duarte, C.M. 1995. Submerged aquatic vegetation in relation to different nutrient

regimes. Ophelia 41: 87-112. Duarte, C.M. 2007. Testing the predictive power of seagrass depth limit models.

Estuaries and Coast 3: 652-656. Dunton, K.H. 1994. Seasonal growth and biomass of the subtropical seagrass Halodule

wrightii in relation to continuous measurements of underwater irradiance. Marine Biology 120: 479-489.

Dunton, K.H. 1996. Photosynthetic production and biomass of subtropical seagrass

Halodule wrightii along an estuarine gradient. Estuaries and Coasts 19: 436-447.

60

Durako, M.J. 1993. Photosynthetic utilization of CO2(aq) and HCO3- in Thalassia

testudinum (Hydrocharitaceae). Marine Biology 115: 373-380. Durako, M.J. 2007. Leaf optical properties and photosynthetic leaf absorptances in

several Australian seagrasses. Aquatic Botany 87: 83-89. Florida Department of Environmental Protection (FDEP). 2006. Total maximum daily

load protocol. Florida Department of Environmental Protection. Task Assignment 003.03/05-003 Version 6.0. 216 pp.

Florida Department of Environmental Protection (FDEP). 2010. Overview of approaches

for nutrient criteria development in marine waters. Florida Department of Environmental Protection, Division of Environmental Assessment and Restoration Standards and Assessment Section. 60 pp.

Fonseca, M.S., and S.S. Bell. 1998. Influence of physical settings on seagrass

landscapes near Beaufort, North Carolina, USA. Marine Ecology Progress Series 171: 109-121.

Fourqurean, J.W., M.J. Durako, M.O. Hall, and L.N. Hefty. 2002. Seagrass distribution

in south Florida: a multi-agency coordinated monitoring program. In Linkages Between Ecosystems in the South Florida Hydroscape: The River of Grass Continues, eds. J.W. Porter and K.G. Porter, 489-514. Florida: CRC Press.

Fourqurean, J.W., C.M. Duarte, H. Kennedy, N. Marbà, M. Holmer, M.A. Mateo, E.T.

Apostolaki, G.A. Kendrick, D. Krause-Jensen, K.J. McGlathery, and O. Serrano. 2012. Seagrass ecosystems as a globally significant carbon stock. Nature Geoscience 5: 505-509.

Frazer, T.K., M.V. Hoyer, S.K. Notestein, D.E. Canfield, Jr, F.E. Vose, W.R. Leavens,

S.B. Blitch, and J. Conti. 1998. Nitrogen, phosphorus, and chlorophyll relations in selected rivers in nearshore coastal waters along the Big Bend region in Florida. Suwannee River Water Management District, Contract No. 96/97-156. 343 pp.

Frazer, T.K., S.K. Notestein, M.V. Hoyer, J.A. Hale, and D.E. Canfield, Jr. 2001.

Frequency and duration of pulsed salinity events in Kings Bay. Southwest Florida Water Management District, Contract No. 99CON000041. 17 pp.

Frazer, T.K., S.K. Notestein, and W.E. Pine, Jr. 2006. Changes in the physical,

chemical, and vegetative characteristics of the Homosassa, Chassahowitzka, and Weeki Wachee Rivers. Southwest Florida Water Management District, Contract No. 03CON000038. 174 pp.

Gallegos, C.L. and W.J. Kenworthy. 1996. Seagrass depth limits in the Indian River

Lagoon (Florida, U.S.A.): application of an optical water quality model. Estuarine, Coastal, and Shelf Science 42: 267-288.

61

Glancey, T.P., T.K. Frazer, C.E. Cichra, and W.J. Lindberg. 2003. Comparative patterns of occupancy by decapods crustaceans in seagrass, oyster, and marsh-edge habitats in a northeast Gulf of Mexico estuary. Estuaries 26: 1291-1301.

Greening, H., and A. Janicki. 2006. Toward reversal of eutrophic conditions in a

subtropical estuary: water quality and seagrass response to nitrogen loading reductions in Tampa Bay, Florida, USA. Environmental Management 38: 163-178.

Hall, M.O., M.J. Durako, J.W. Fourqurean, and J.C. Zieman. 1999. Decadal changes in

seagrass distribution and abundance in Florida Bay. Estuaries 22: 445-459. Hemminga, M.A., and C.M. Duarte. 2000. Seagrass ecology. New York: Cambridge

University Press. Hocking, P.J., M.L. Cambridge, and A.J. McComb. 1981. The nitrogen and phosphorus

nutrition of developing plants of 2 seagrasses, Posidonia australis and Posidonia sinuosa. Aquatic Botany 11: 245-261.

Jones, K.B., K.H. Ritters, J.D. Wickham, R.D. Tankersley, R.V. O’Neill, D.J. Chaloud,

E.R. Smith, and A.C. Neale. 1997. An ecological assessment of the United States mid-Atlantic Region: a landscape atlas. United States Environmental Protection Agency, EPA/600/R-97/130. 104 pp.

Invers, O., J. Romero, and M. Pérez. 1997. Effects of pH on seagrass photosynthesis: a

laboratory and field assessment. Aquatic Botany 59: 185-194. Iverson, R.L., and H.F. Bittaker. 1986. Seagrass distribution and abundance in Eastern

Gulf of Mexico coastal waters. Estuarine, Coastal and Shelf Science 22: 577-602.

Jacoby, C.A., T.K. Frazer, D.D. Saindon, and S.K. Notestein. 2011. Water quality

characteristics of the nearshore Gulf coast waters adjacent to Citrus, Hernando, and Levy counties: Project COAST 1997-2010. Southwest Florida Water Management District, Contract 09C00000052. 16 pp.

Janicki, A., and D. Wade. 1996. Estimating critical external nitrogen loads for the Tampa

Bay Estuary: an empirically based approach to setting management targets. Tampa Bay National Estuary Program. Technical Publication #06-96. 200 pp.

Janicki Environmental Incorporated. 2011. Development of numeric nutrient criteria for

Boca Ciega Bay, Terra Ceia Bay, and Manatee River Florida. Tampa Bay Estuary Program. Contract TBEP T-07-01. 12 pp.

Kirk, J.T.O. 1994. Light and photosynthesis in aquatic ecosystems. 2nd Edition.

Cambridge University Press. Great Britain.

62

Larkum, A.W.D., R. Orth, and C. Duarte. 2006. Seagrasses: biology, ecology and conservation. Netherlands: Springer.

Lee, K., S.R. Park, and Y.K. Kim. 2007. Effects of irradiance, temperature, and nutrients

on growth dynamics of seagrasses: a review. Journal of Experimental Marine Biology and Ecology 350: 144-175.

Lirman, D., and W.P. Cropper. 2003. The influence of salinity on seagrass growth,

survivorship, and distribution with Biscayne Bay, Florida; field, experiment, and modeling studies. Estuaries and Coasts 26: 131-141.

Ludwig, J.A., and D.J. Tongway. 1995. Spatial organization of landscapes and its

function in semi-arid woodlands. Australian Landscape Ecology 10: 51-63. McMahan, C.A. 1968. Biomass and salinity tolerance of shoalgrass and manateegrass

in lower Laguna Madre, Texas. Journal of Wildlife Management 32: 501-506. Menzel, D.W., and N. Corwin. 1965. The measurement of total phosphorus in seawater

based on the liberation of organically bound fractions by persulfate oxidation. Limnology and Oceanography 10: 280-282.

Miller, R. 2005. Water quality of the Homosassa and Halls rivers and Mason Creek.

Homosassa River Alliance. http://www.homosassariveralliance.org/Testreport.pdf. Accessed 3 March 2012.

McPherson, B.F., and R.L. Miller. 1987. The vertical attenuation of light in charlotte

Harbor, a shallow, subtropical estuary, south-western Florida. Estuarine, Coastal and Shelf Science 25: 721-737.

Moore, D.R. 1963. Distribution of the sea grass, Thalassia, in the United States. Bulletin

of Marine Science 13: 329-342. Morrison, G., and H. Greening. 2011. Seagrass. In Integrating science and resource

management in Tampa Bay, Florida, eds. K.K. Yates, H. Greening, and G. Morrison, 63-104. U.S. Geological Survey Circular 1348.

Murphy, J., and J.P. Riley. 1962. A modified single solution method for the

determination of phosphate in natural waters. Analytica Chimica Acta 27: 31-36. National Oceanic Atmospheric Administration (NOAA). 2012. NOAA tide predictions.

National Oceanic Atmospheric Administration Tides and Currents. http://tidesandcurrents.noaa.gov/tide_predictions.shtml?gid=198. Accessed 20 February 2012.

National Research Council (NRC). 2010. Advancing the science of climate change.

National Research Council. The National Academic Press, Washington, DC.

63

Nayer, S., D.J. Miller, A. Hunt, B.P.L. Goh, and L.M. Chou. 2007. Environmental effects of dredging on sediment nutrients, carbon, and granulometry in a tropical estuary. Environmental Monitoring and Assessment 127: 1-13.

Onuf, C.P. 1996. Seagrass responses to long-term light reduction by brown tide in

upper Laguna Madre, Texas: distribution and biomass patterns. Marine Ecology Progress Series 138: 219-231.

Orth, R.J., K.L. Heck, Jr., and J. Montfrans. 1984. Faunal communities in seagrass

beds: a review of the influence of plant structure and prey characteristics on Predator-Prey Relationships. Estuaries 7: 339-350.

OSPAR Commission. 2009. Background document for Zostera beds, seagrass beds.

Biodiversity Series. http://qsr2010.ospar.org/media/assessments/Species/P00426_Zostera_beds.pdf. Accessed 22 January 2011.

Ralph, P.J., M.J. Durako, S. Enriquez, C.J. Collier, and M.A. Doblin. 2007. Impact of

light limitation on seagrasses. Journal of Experimental Marine Biology and Ecology 350: 176-193.

Sartory, D.P., and J.U. Grobbelaar. 1984. Extraction of chlorophyll a from freshwater

phytoplankton for spectrophotometric analysis. Hydrobiologia 114: 177-187. Seaman Jr., W. 1985. Florida aquatic habitat and fishery resources. Florida: American

Fisheries Society, Florida Chapter. Shafer, D.J. 1999. The effects of dock shading on the seagrass Halodule wrightii in

Perdido Bay, Alabama. Estuaries 22: 936-943. Short, F.T., and H.A. Neckles. 1999. The effects of global climate change on

seagrasses. Aquatic Botany 63: 169-196. Smith, S.V. 1981. Marine macrophytes as a global carbon sink. Science 211: 838-840. Steward, J.S., R.W. Virnstein, L.J. Morris, and E.F. Lowe. 2005. Setting seagrass depth,

coverage, and light targets for the Indian River Lagoon system, Florida. Estuaries 28: 923-935.

Thom, R.M., S.L. Southard, A.B. Borde, and P. Stroltz. 2008. Light requirements for

growth and survival of eelgrass (Zostera marina L.) in Pacific northwest (USA) estuaries. Estuaries and Coasts 31: 969-980.

Tomasko, D.A., and M.O. Hall. 1999. Productivity and biomass of the seagrass

Thalassia testudinum along a gradient of freshwater influence in Charlotte Harbor, Florida. Estuaries 22: 592-602.

64

Tomasko, D.A., D.L. Bistrol, and J.A. Ott. 2001. Assessment of present and future nitrogen loads, water quality, and seagrass (Thalassia testudinum) depth distribution in Lemon Bay, Florida. Estuaries 24: 926-938.

Torquemada, Y.F., M.J. Durako, and J.L.S. Lizaso. 2005. Effects of salinity and possible

interactions with temperature and pH on growth and photosynthesis of Halophila johnsonii Eiseman. Marine Biology 148: 251-260.

Touchette, B.W. 2007. Seagrass-salinity interactions: Physiological mechanisms used

by submerged marine angiosperms for a life at sea. Journal of Experimental Marine Biology and Ecology 350: 194-215.

Udy, J.W., and W.C. Dennison. 1997. Growth and physiological responses of three

seagrass species to elevated sediment nutrients in Moreton Bay, Australia. Ecology 217: 253-277.

U.S. Environmental Protection Agency (EPA). 2008. An approach for developing

numeric nutrient criteria for a Gulf coast estuary. United States Environmental Protection Agency Report EPA-600/R-08/004. 35 pp.

U.S. Environmental Protection Agency (EPA). 2008. State adoption of numeric nutrient

standards (1998-2008). United States Environmental Protection Agency Report EPA-821-F-08-007. 96 pp.

U.S. House of Representatives. 2000. Unites States Code 2000 Edition. Office of the

Law Revision Counsel of the Unites States house of Representative, U.S. Government Printing Office, Title 33.

Valiela, I., J. McClelland, J. Hauxwell, P.J. Behr, D. Hersh, and K. Foreman. 1997.

Macroalgal blooms in shallow estuaries: controls and ecophysiological and ecosystem consequences. Limnology and Oceanography 42: 1105-1118.

Wazniak, C.E., M.R. Hall, T.J.B. Carruthers, B. Sturgis, W.C. Dennison, and R.J. Orth.

2007. Linking water quality to living resources in a mid-Atlantic lagoon system, USA. Ecological Applications 17: S64-S78.

Williams, S.L., and C.P. McRoy. 1982. Seagrass productivity: the effect of light on

carbon uptake. Aquatic Botany 12: 321-344. Williams, S.L., and M.H. Ruckelshaus. 1993. Effects of nitrogen availability and

herbivory on eelgrass (Zostera Marina) and epiphytes. Ecology 74: 904-918. Wolfsy, S.C. 1983. A simple model to predict extinction coefficients and phytoplankton

biomass in eutrophic waters. Limnology and Oceanography 28:114-155.

65

Zieman, J.C., and R.T. Zieman. 1989. The ecology of the seagrass meadows of the west coast of Florida: a community profile. United States Fish and Wildlife Service, Report No. BR-85(7.25). 155 pp.

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BIOGRAPHICAL SKETCH

Zanethia Choice grew up in Estill, SC, a small, rural town where she gained her

love for the environment. Throughout her life she has had extensive interaction with the

environment, with daily activities including fishing and hunting with her dad, climbing

trees, spending summers on the beach, and simply enjoying the wildlife and wilderness

around her small town. This love pushed her to further her education in both economics

and ecology; a background that would help her conserve the environment and the

valuable resources it provides. She received her bachelor’s degree in agricultural

economics from North Carolina A&T State University. This experience, as well as

internships with an entomologist, economist, and ecologist, has pushed her to further

her education in interdisciplinary ecology.

Through her graduate experience, Zanethia’s interest in marine ecology and

resource management has been strengthened. She hopes to use her expertise to help

minimize the ongoing clash between economics and the environment.