Light Requirements of Seagrasses Along the West Coast of Peninsular Florida

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Title:
Light Requirements of Seagrasses Along the West Coast of Peninsular Florida
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1 online resource (66 p.)
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english
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Choice, Zanethia D
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University of Florida
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Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Interdisciplinary Ecology
Committee Chair:
Frazer, Tom K
Committee Members:
Jacoby, Charles A
Phlips, Edward J

Subjects

Subjects / Keywords:
light -- management -- quality -- seagrass -- thresholds -- water
Interdisciplinary Ecology -- Dissertations, Academic -- UF
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Interdisciplinary Ecology thesis, M.S.
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theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
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Electronic Thesis or Dissertation

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Abstract:
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. 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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In the series University of Florida Digital Collections.
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Includes vita.
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Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Zanethia D Choice.
Thesis:
Thesis (M.S.)--University of Florida, 2012.
Local:
Adviser: Frazer, Tom K.
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RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2013-06-30

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1 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 S CIENCE UNIVERSITY OF FLORIDA 2012

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2 2012 Zanethia Denise Choice

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3 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 hav ing 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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4 A CKNOWLEDGMENTS 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 hav e 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 Sai n don, and Sky No t estein, 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 than k 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 Floridas School of Natural Resources and Environment. I also give my special gratitude to Jake Ferg uson 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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5 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 Qualit y .......................................................................................................... 29 4 DISCUSSION ......................................................................................................... 48 Seagrass Distribution .............................................................................................. 48 Seagrass Thresholds .............................................................................................. 50 Conclusion .............................................................................................................. 57 LIST OF REFERENCES ............................................................................................... 58 BIOGRAPHICAL SKETCH ............................................................................................ 66

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6 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 19992011. ......................... 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 an d 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 st ations with suitable light but no seagrass ........................................................ 46 3 7 Values of water quality characteris tics 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 wit h seagrass and stations with suitable light but no seagrass ........................................................ 47

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7 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 fo r 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 irradianc e for percent covers of Halophila engelmannii for various light regimes ............................................................................................................... 44

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8 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 criteri a 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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9 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 S cience LIGHT REQUIREMENTS OF SEAGRASSES ALONG THE WEST COAST OF PENINSULAR FLORID A 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 c oast 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 estimate s 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 sp lit window L ight requirements calculated here differed among species of seagrass es and they also differed from requirements reported for seagrasses at other locations

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10 H alodule wrightii had the highest light requirement at 2427% of surface irradiance, a nd H. engelmannii exhibited the lowest light requirement at 810% of surface irradiance. Variation in seagrass light requirements is attributed to differences in morphology and physiology as well as light histories in specific locations W ater 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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11 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 c arbon 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 t he 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; An ton 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 fac tors, including suitable substrate for roots and rhizomes sufficient

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12 immersion, suitable salinities and adequate sub surface irradiance for photosynthesis (Hemminga and Duarte 2000). In general seagrasses usually are confined to i) sandy or muddy sedimen ts, 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 suffici ent 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, l ight 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 mat erials 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 av ailable 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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13 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 nit rogen 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 ef ficient 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 ( 199 5 ) 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 co astal systems, with Tomasko et al. (2001) demonstrating that a 45% increase in nitrogen in Lemon Bay, Florida resulted in a 2 9% increase in chlorophyll a a 9% increase in the light attenuation coefficient, and a consequent 24% decrease in the maximum dept h 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 cause d by a brown t ide 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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14 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 demo nstrating a minimum light requirement (Durako 2007; Dunton 1994; Dennison 1987; Dawes 1998). Averaged across species and locations, light limitat ion occurs at approximately 11% SI (Duarte 1991) but results indicate d that seagrass species have different li ght 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 T halassia testudinum req uired 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 No rthwest 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 import ance of estimating light requirements for multiple locations Although the percent of surface irradiance is important, seagrasses require light with wavelengths between 400700 nm f or maximum photosynthetic productivi ty. These wavelengths supply seagrasses with a certain q ual ity 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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15 by damaging chlorophyll, w hich 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 photosy nthetic photon flux density (PPFD) of about 350 2 s1 and a decline in photosynthetic production at irradiances 2 s1, which suggested photoinhibition (Thom et al. 2008). Similarly, Halophila engelmannii exhibited maximum pro 2 s1 and a decline in photosynthetic 2 s1 (Dawes et al. 1987). Durako (1993) found that T testudinum reached its light 2 s1, and Dunton (19 96) reported light 2 s1 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 4month 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 se veral species of seagrasses maintained high rates of photosynthesis from 12 pm until

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16 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 a vailable, 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 d ecrease in the abundance of seagrasses resulting in a loss of valuable habitat. However, with help from managers, plans can be implemented to preserve seagrasses. G iven the links between increased nutrient loads or concentrations, eutrophication, reduced l ight 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 Floridas 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 syst ems (Miller 2005). The resulting information can inform management actions, including the development of numeric nutrient criteria.

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17 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, Floridas second largest contiguous seagrass bed stretches from Tarpon Springs to St. Mar ks, 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). A verage depths at sampling sites are 1.8 m and averag e 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 in flows of freshwater and nutrient load s (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, s eagrass sampling took place at a subset of stations where water quality has been sampled si nce 1998 (Frazer et al. 1998). Water quality is sampled on a monthly basis at t en stations in each coastal system, ranging from 01 1 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 ; To uchette 2007; Lirman and Cropper 2003), and these stations

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18 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 L ight regimes were characterized with data collected between 1999 and 2011 as a part of the long term water quality monitoring program. D uring 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, E s1 m2). 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 inc ident 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 22 and 23): Iz = Io*e ( K d *z) ( 2 2) %SI = (Iz/Io)*100 ( 2 3 )

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19 Water Quality Sampling Field Sampling Data from the long term w ater 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 surveyors 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 L1. In addition, a t each station, a surface water sample was collected in an acidwashed 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 47mm 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 (T N) and total 1) were determined using the proc edures of Murphy and Riley (1962) with persulfate digestion (Menzel and Corwin 1965). 1) were determined by oxidizing w ater samples with persulfate and measuring nitratenitrogen concentrations with secondderivative spectroscopy (Bachmann and Canfield 1996).

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20 Filtered material and filtrates were analyzed to generate chlorophyll a 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 spectroph otometer (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 mark er was placed at each stations coordinates and four quadrants were established (northwest, southwest, southeast, and northeast; Figure 22 ). In each quadrant, SCUBA divers tossed three 0.5 m 0.5 m quadrats within 10 m of the center point to yield 12 unb iased samples of total percent cover of all seagrasses and percent cover by s pecies 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 m2.

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21 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 evaluat e 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 (90148 months, with 85% of stations having 120 or more months ). The seagrass data used in the boundary analysis comprised mean percent age 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 percent age 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, t he datapoint w as 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 perc entage 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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22 The perce ntage covers or shoot densities in each group were averaged, and an SED dissimilarity index was computed according to equation 24 : SED = [(Mean L1)] (Mean L2)]2 ( 2 4 ) Where, Mean L1 and Mean L2 represent the means of the three percent age 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 Welchs t tests. All mean percent age cover values and mean shoot densities equal to and below each threshold were compared to the mean percent age cover values and mean shoot densities above that threshold. Significant relationships between seagrasses and light regimes were indicated by pvalues 0.05. Water Quality Welchs 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 p arameter, separate t tests were performed using data parsed by species of seagrass. Welchs 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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23 sea grasses 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 wher e 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 becaus e 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 g L1 for TN and 37 g L1 for TP; Wazniak et al. 2007).

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24 Figure 21. Map of study area showing sampling stations (numbers) originally established by Frazer et al (1998).

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25 Figure 22. Schematic of quadrants and quadrats (squares) used to sample seagrass at a station (star). N W E S 10 m

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26 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 13year period of record (Table 31 ). Stations off the Suwannee River consistently received the least light, with an overall median of 3% SI during the period of record (Table 31 ). In contrast, stations off the Homos assa River consistently received the most light, with an overall median of 36% SI during the period of record (Table 31 ). Light regimes not only varied among systems, but median %SI values also varied among stations within each system (T able 32 ). 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 32 ). 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 32 ). 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 median s 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 betw een the median %SI b ased on

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27 90months 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 13year sampling period, with station s 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 34 ). 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 34) 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 34) Thalassia testudinum and Halodule wrightii were the most common and abundant seagr asses 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

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28 m2 and 303.2 938.7 shoot m2, as well as maximum mean percent age covers SDs of 19.7 22.6% and 7.3 19.4% (Table 34 ). Ruppia maritima was found in no more than 10% of the deployed q uadrats, with a maximum mean shoot density SD of 80.0 384.0 shoots m2 and a maximum mean percent age 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 peri ods. Mean shoot densities were then scaled to m2. 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 35; Figures 31 to 38). 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 1215% SI based on 6 months of light and a maximum range of 3133% SI based on 24 months of light. Thalassia testudinum exhibited the s mallest 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 2028% 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 6month period of record (15 months period of record (2427%). Due to

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29 the relatively small variation among light thresholds calculated for differ ent periods of record and the precision provided by shoot densities, thresholds based on shoot months of light records were used in discussion of threshold light requirements. Although threshold light requirements were relatively consist ent for a given species, these requirements did vary among species (Table 35; Figures 3 1 to 38). Halodule wrightii had the highest light requirement at 2427% S I and Halophila engelmannii exhibited the lowest light requirement at 810% SI Syringodium filiforme and Thalassia testudinum were intermediate; with thresholds of 816% 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 th e bottom met or exceeded the threshold and stations where light penetration was less (Table 35). Only results for shoot densities of H engelmannii were nonsignificant (Table 35). Water Quality Within this study, two types of water quality characteristi cs were identified. Characteristics that could directly affect the health of seagrasses included depth, temperature, salinity, DO, and pH. C haracteristics 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

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30 versus stations that had sufficient light penetration but lacked seagrasses (Table 36). Although statistically significant, these differences appeared unlikely to be biologically significant because they did not exceed the tolerances of seagrasses (Table 37). 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 36). In contrast, a consiste nt pattern of significant differences was observed for water quality characteristics that primarily affect seagrasses by affecting light availability (Table 38). In all cases, seagrasses were present at stations with significantly lower concentrations of TN, TP, chlorophyll a and color.

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31 Table 31. 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 6 12 18 24 36 60 6 12 18 24 36 60 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

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32 Table 31. 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 6 12 18 24 36 60 6 12 18 24 36 60 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

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33 Table 31. 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 6 12 18 24 36 60 6 12 18 24 36 60 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

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34 Table 32. Maximum variation in median percent surface irradiance reaching the bottom (median %SI) at stations in the eight systems from 19992011. 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

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35 Table 33. 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 1 1 36 2 3 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 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 3 7 6 10 6 27 22 6 49 Weeki Wachee 6 37 3 60 40 4 23 19

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36 Table 34. 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

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37 Figure 31. 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 = 2427% SI ; b = 12 months threshold = 1824% SI ; c = 18 months threshold = 1627% SI ; d = 24 months threshold = 2527% SI ; e = 36 months threshold = 2028% SI ; f = 60 months threshold = 2126% SI threshold = 2025% SI

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38 Figure 32. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Thalassia testudinum for various light regimes. a = 6 months threshold = 2324% SI ; b = 12 months threshold = 1824% SI ; c = 18 months threshold = 1627% SI ; d = 24 months threshold = 2527% SI ; e = 36 mon ths threshold = 2028% SI ; f = 60 months threshold = 2126% SI threshold = 1823% SI

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39 Figure 33. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Halodule wrightii for various light regimes. a = 6 months threshold = 1215% SI ; b = 12 months threshold = 23 27% SI ; c = 18 months threshold = 2931% SI ; d = 24 months threshold = 31 33% SI ; e = 36 months threshold = 2730% SI ; f = 60 months threshold = 2730% SI ; t hreshold = 2527% SI

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40 Figure 34. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Halodule wrightii for various light regimes. a = 6 months threshold = 1524% SI ; b = 12 months threshold = 1824% SI ; c = 18 months threshold = 2831% SI ; d = 24 months threshold = 3132% SI ; e = 36 months threshold 3132% SI ; f = 60 months threshold = 2730% SI threshold = 2527% SI

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41 Figure 35. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Syringodium filiforme for various light regimes. a = 6 months threshold = 68% SI ; b = 12 months threshold = 613% SI ; c = 18 months threshold = 816% SI ; d = 24 months threshold = 615% SI ; e = 36 months threshold = 616% SI ; f = 60 months threshold = 614% SI threshold = 816% SI

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42 Figure 36. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for percent covers of Syringodium filiforme for various light regimes. a = 6 months threshold = 68% SI ; b = 12 months threshold = 613% SI ; c = 18 months threshold = 914% SI ; d = 24 months threshold = 615% SI ; e = 36 months threshold = 716% ; f = 60 months threshold = 614% ; th reshold = 816%

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43 Figure 37. Squared Euclidean Distances plotted against mean percent surface irradiance (%SI) for shoot counts of Halophila engelmannii for various light regimes. a = 6 months threshold = 1521% SI ; b = 12 months threshold = 613% SI ; c = 18 months threshold = 914% SI ; d = 24 months threshold = 613% SI ; e = 36 months threshold = 1011% SI ; f = 60 months threshold = 810% SI threshold = 810% SI

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44 Figure 38. 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 = 613% SI ; c = 18 months threshold =914% SI ; d = 24 months threshold = 613% SI ; e = 36 mon ths threshold = 1011% SI ; f = 60 months threshold = 810% SI threshold = 810% SI

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45 Table 35. 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 = pvalue 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 6 12 18 24 36 60 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

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46 Table 36. Means for water quality parameters with direct effects on seagrasses and results of t tests comparing those parameters at stations with seagrass and ** significan 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 1.0** 23.2 19.1** 7.0* 7.97** H wrightii present 1.3 23.1 22.3** 7.1 8.03* absent 1.3 22.5 29.6** 7.0 8.07* S filiforme present 1.2** 22.8 25.2** 7.1 8.02 absent 1.3** 23.1 21.3** 7.0 8.01 H engelmannii present 1.5** 22.9 22.3 7.0 7.98** absent 1.2** 23.1 22.3 7.0 8.02** Table 37. Values of water quality characteristics that may stress seagrasses. DO = dissolved oxygen concentration Species C haracteristic Value s of concern Reference Thalassia testudinum salinity require s > 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 require s > 5 mg L 1 5 Halodule wrightii salinity require s > 3.5 optimum 30 1 d epth 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 require s > 5 mg L 1 5 Syringodium filiforme salinity require s > 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 require s > 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 require s > 5 mg L -1 5 1 Iverson and Bittaker 1986 ; 2 H emminga 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

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47 Table 38. Means for water quality parameters with indirect effects on seagrasses and results of t tests comparing those parameters at stations with seagrass and ** signific 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 = chlorophyl l a concentration Species Seagrass Mean TN (g L -1 ) TP (g L -1 ) Chl a (g 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**

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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 quadrat s containing seagrasses. Systems such as Suwannee, Wacasassa, and Withlacoochee, with an overall median %SI value of 3 % had little to no s eagrass. 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 system s, 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

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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 38) led to changes in kd ranging from 0 .04 to 0 .11 m1 and a consequent change in %SI of 2% to 7%. D ifferences in color values at stations that have seagrasses and stations that do not have seagrasses (Table 38) led to changes in kd ranging from 0.09 to 0.19 m1 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. S imilar 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,

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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 areas 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 90months 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 2125% was required by the most prominent seagrass in the region, Thalassia testudinum This result is v ery similar to previously reported values: 18.2% SI in Lemon Bay, F lorida 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) ; 2025% SI in Tampa Bay F lorida calculated i n the same manner (Tomasko and Hall 1999) ; and 20% SI in Corpus Christi Bay, T exas 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, F lorida based on the median % SI at the depth limit of beds (Steward et al 2005) ; 18% SI in San Antonio Bay, T exas based on a whole plant model that assumed light w as the dominant factor affecting seagras s production and growth (Dunton 1994) ; 14% SI in Perdido Bay, A labama based on the effects of in situ shading (Shafer 1999) ; and 24 27% SI in th is study. The variation between light thresholds for

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51 seagrasses in different locations may have reflected adaptation to light histor ies 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, F lorida by combining seagrass depth li mits and an optical model that calculated absorption and scattering coefficients (Gallegos and Kenworthy 1996) and 19.2% SI documented for n orthwest Cuba as the mean percent light at the maximum depth limit of S filiforme (Dennison et al 1993). Limited r esearch 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) show ed that H. engelmannii at Indian Bluff Island, Florida ha d a compensation point of 60 2 s1. 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, u sing average surface irradiance values for the stations where H. engelmannii was found in the study area the threshold estimated for this species, 810% SI, equates to a PAR flux of 104 to 131 2 s1, 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 n orthwest 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 gr owth rates for seagrass species due to different amino

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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 diff erent 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 respirato ry 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 turbi dity 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 condi tions 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 colonizati on. Larkum et al. (2006) also showed how longer

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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 complexi ty of interactions, knowledge of light requirements for seagrasses represents a valuable element in managing coastal systems sustainably. Seagrass Management Although threshold light requirement s for seagrasses vary, knowing those requirements remains important for managing and protecting seagrass habitats and the ecosystem services they deliver S eagrasses 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 densit ies 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 nations coastal system s. Due to the importance of aquatic environments, t he 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

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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 criteri a (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, m anagers currently are working to develop criteri a for estuarine systems Seagrass light requirements can serve as a valuable metric of a systems healt h 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 Fl orida 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 fr om 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

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55 Response based approach: M aximum allowable nutrient concentrations could be determined fro m rigorous causeeffect 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) demonstrate d how knowledge of seagrasses light requirements determine d water quality thresholds for Chesapeake Bay, V irginia and how improving water quality to meet these thresholds led to improvement in seagrasses and the system as a whole. Managers of F loridas 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 th at 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 se agrass light requirements. With knowledge of seagrass light

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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 nations waterbodies, protect ing and restor ing 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 concent rations 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, i ncreasing temperature could stress seagrasses (Iverson and Bittaker, 1986), with fatalities hav ing 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

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57 exacerbated by sea level rise (Short and Neckl es, 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, l ight 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, l ight 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 restor ing and preserv ing this important part of our coastal system s.

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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 r esilience to climate c hange. International Union for Conservation of Nature, Resilience Science Group, Working Paper Series No. 3. 48 p p Bricker, S.B., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J. Woerner. 2008. Effects of nutrient enrichment in the nations 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: 12551264. Campbell, S.J., S.P. Kerville, R.G. Coles, F. Short. 2008. Photosynthetic responses of subtid al seagrasses to a daily light cycle in Torres Strait: a comparative study. Continental Shelf Research 28: 22752281. Congdon, R.A., and A.J. McComb. 1979. Productivity Ruppia: seasonal changes and dependence on light in an Australian estuary. Aquatic Botany 6: 121132. Costanza, R., R. dArge, 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 worlds ecosystem services and natural capital. Nature 387: 253260. Czerny, A.B., and K.H. Dunton. 1995. The effects of in situ light reduction on the growth of two subtropical seagrasses. Estuaries 18: 418427.

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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: 195201. 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: 8996. Dennison, W. C. 1987. Effects of l ight on seagrass p hotosynthesis, g rowth, and d epth d istribution. Aquatic Botany 27: 1526. Dennison, W.C., and R. S. Alberte. 1985. Role of d aily light perio d s 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 w ater q uality with submerged a quatic v egetation. BioScience 43: 8694. 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: C RC 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 Laborat ory Technical Report No. 518. 84 p p. Doerin g, 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: 13431354. Duarte, C. M. 1991. Seagrass depth limits. A quatic Botany 40: 363 377. Duarte, C. M. 1995. Submerged a quatic v egetation in r elation to d ifferent n utrient r egimes. Ophelia 41: 87112. Duarte, C.M. 2007. Testing the predictive power of seagrass depth limit models. Estuaries and Coast 3: 652656. 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: 479489. Dunton, K. H. 1996. Photosynthetic production and biomass of subtropical seagrass Halodule wrightii along an estuarine gradient. Estuaries and Coasts 19: 436447.

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66 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 rec eived her bachelors degree in a gricultural e conomics from North Carolina A&T State University. This exper ience, as well as internships with an entomologist, economist, and ecologist, has pushed her to further her education in i nterdisciplinary e cology. Through her graduate experience, Zanethias interest in marine ecology and resource management has been stre ngthened. She hopes to use her expertise to help minimize the ongoing clash between economics and the environment.