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Nutrient, Phytoplankton, and Oyster Dynamics in a Highly Flushed Subtropical Lagoon, Northeast Florida

Permanent Link: http://ufdc.ufl.edu/UFE0042489/00001

Material Information

Title: Nutrient, Phytoplankton, and Oyster Dynamics in a Highly Flushed Subtropical Lagoon, Northeast Florida
Physical Description: 1 online resource (97 p.)
Language: english
Creator: Dix Pangle, Nicole
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: ecology, estuary, eutrophication, nutrients, oyster, phytoplankton
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: With ever-increasing coastal development, predicting the consequences of nutrient enrichment in coastal ecosystems has become a main focus of estuarine research. The goal of this research was to characterize important processes related to the effects of nutrient enrichment in a highly flushed subtropical estuary, the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in northeast Florida. Understanding the effects of nutrients on a system first requires knowledge of the structure and function of the base of the food web. In this study, patterns in phytoplankton biomass were explored in relation to a suite of potentially regulating factors, including nutrient availability, in context with other gain and loss processes in the GTMNERR. Monthly measurements of temperature, light, nutrient concentrations, and phytoplankton standing stock over seven years (2003-2009) were examined through correlation analysis. Laboratory experiments in the spring and summer of 2009 quantified phytoplankton growth rates, nutrient limitation potential, and zooplankton grazing rates. The potential influence of oyster grazing was also examined using population metrics and filtration rate estimates. All of the gain and loss factors were correlated to some degree with phytoplankton biomass in the GTMNERR, but results indicated a temporal shift in the primary controlling factors, from temperature, light, and flushing in the winter to grazing and flushing the remainder of the year. In contrast to temperate systems, or systems dominated by riverine inputs, phytoplankton biomass in this area exhibited a regular seasonal pattern characterized by short periods of low biomass rather than by bloom events. The magnitude and interannual variability of phytoplankton biomass observed in this study were fairly small compared to estuarine and coastal ecosystems around the world. Since traditionally monitored water quality parameters, such as nutrient and phytoplankton concentrations, often do not provide a clear indication of trophic status in estuaries with short water residence times, response to nutrient enrichment in this system was also measured at the level of benthic primary consumers. Oyster population structure was examined within two regions of the GTMNERR using measurements of oyster density, biomass, length, and condition. As expected, oysters exhibited greater population density, average biomass, and condition in the region with historically elevated nutrient loads and carbon availability than in the less urbanized region. Results suggest that oysters have the potential to be used as bioindicators of trophic state in highly flushed estuaries. Overall, this study demonstrated that well-flushed estuaries may be more resistant to some of the negative effects of nutrient enrichment than those systems with comparatively restricted hydrodynamics. Especially in subtropical systems without major riverine inputs, primary production can exist virtually in balance with consumption, resulting in relatively low primary producer biomass and food-limited benthic consumer populations. The threshold for maintaining such a balance is undefined, however, especially since changes in nutrient loads can also affect phytoplankton species composition and be associated with other harmful impacts such as toxic contaminants.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nicole Dix Pangle.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Phlips, Edward J.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042489:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042489/00001

Material Information

Title: Nutrient, Phytoplankton, and Oyster Dynamics in a Highly Flushed Subtropical Lagoon, Northeast Florida
Physical Description: 1 online resource (97 p.)
Language: english
Creator: Dix Pangle, Nicole
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: ecology, estuary, eutrophication, nutrients, oyster, phytoplankton
Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: With ever-increasing coastal development, predicting the consequences of nutrient enrichment in coastal ecosystems has become a main focus of estuarine research. The goal of this research was to characterize important processes related to the effects of nutrient enrichment in a highly flushed subtropical estuary, the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in northeast Florida. Understanding the effects of nutrients on a system first requires knowledge of the structure and function of the base of the food web. In this study, patterns in phytoplankton biomass were explored in relation to a suite of potentially regulating factors, including nutrient availability, in context with other gain and loss processes in the GTMNERR. Monthly measurements of temperature, light, nutrient concentrations, and phytoplankton standing stock over seven years (2003-2009) were examined through correlation analysis. Laboratory experiments in the spring and summer of 2009 quantified phytoplankton growth rates, nutrient limitation potential, and zooplankton grazing rates. The potential influence of oyster grazing was also examined using population metrics and filtration rate estimates. All of the gain and loss factors were correlated to some degree with phytoplankton biomass in the GTMNERR, but results indicated a temporal shift in the primary controlling factors, from temperature, light, and flushing in the winter to grazing and flushing the remainder of the year. In contrast to temperate systems, or systems dominated by riverine inputs, phytoplankton biomass in this area exhibited a regular seasonal pattern characterized by short periods of low biomass rather than by bloom events. The magnitude and interannual variability of phytoplankton biomass observed in this study were fairly small compared to estuarine and coastal ecosystems around the world. Since traditionally monitored water quality parameters, such as nutrient and phytoplankton concentrations, often do not provide a clear indication of trophic status in estuaries with short water residence times, response to nutrient enrichment in this system was also measured at the level of benthic primary consumers. Oyster population structure was examined within two regions of the GTMNERR using measurements of oyster density, biomass, length, and condition. As expected, oysters exhibited greater population density, average biomass, and condition in the region with historically elevated nutrient loads and carbon availability than in the less urbanized region. Results suggest that oysters have the potential to be used as bioindicators of trophic state in highly flushed estuaries. Overall, this study demonstrated that well-flushed estuaries may be more resistant to some of the negative effects of nutrient enrichment than those systems with comparatively restricted hydrodynamics. Especially in subtropical systems without major riverine inputs, primary production can exist virtually in balance with consumption, resulting in relatively low primary producer biomass and food-limited benthic consumer populations. The threshold for maintaining such a balance is undefined, however, especially since changes in nutrient loads can also affect phytoplankton species composition and be associated with other harmful impacts such as toxic contaminants.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nicole Dix Pangle.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Phlips, Edward J.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042489:00001


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NUTRIENT, PHYTOPLANKTON, AND OYSTER DYNAMICS IN A HIGHLY FLUSHED SUBTROPICAL LAGOON, NORTHEAST FLORIDA By NICOLE DIX PANGLE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 1

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2010 Nicole Dix Pangle 2

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To my Peanut 3

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ACKNOWLEDGEMENTS Funding for this research was provided by the Un iversity of Florida a nd an award from the Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National Ocean Service, National Oceanic and Atmospheri c Administration. Inva luable field and lab assistance was generously provided by Don OSteen, Loren Mathews, Lance Riley, Paula Viveros, Heather Manley, Meredith Montgomer y, Rio Throm, Shane Pangle, Debbie Dix, Jeff Dix, Darlene Saindon, Joey Chait, and Dorota Rot h. I thank you all for braving extreme weather conditions, waist-deep mud full of sharp oyster shells, and painfully tedious tasks with smiles and positive attitudes! I would also like to thank Daryl Park yn, Clay Montague, Rick Gleeson, and Shirley Baker for their feedback and support throughout my life as a graduate student. I give special thanks to Ed Phlips, my advisor and mentor for the past six years. He has helped shape my scientific career by teaching, challenging, and encouraging me, and I will be forever grateful. I also extend si ncere gratitude to my parents, parents-in-law, and entire family for their constant faith in me. Finally, I would like to thank my husband, Shane, for lending me his strength when I was weak and for making me laugh every day. 4

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TABLE OF CONTENTS page ACKNOWLEDGEMENTS .............................................................................................................4 LIST OF TABLES ...........................................................................................................................7 LIST OF FIGURES .........................................................................................................................8 ABSTRACT ...................................................................................................................................10 CHAPTER 1 INTRODUCTION................................................................................................................. .12 2 CONTROL OF PHYTOPLANKTON BIOMASS IN A HIGHLY FLUSHED SUBTROPICAL ESTUARY..................................................................................................15 Introduction .............................................................................................................................15 Methods ..................................................................................................................................17 Site Description ...............................................................................................................17 Weather and Water Quality .............................................................................................18 Light Availability ............................................................................................................20 Primary Production ..........................................................................................................21 Nutrient Limitation ..........................................................................................................22 Zooplankton Grazing .......................................................................................................23 Bivalve Grazing ...............................................................................................................24 Results .....................................................................................................................................25 Climatic and Physical Water Column Conditions ...........................................................25 Phytoplankton Biomass ...................................................................................................26 Phytoplankton Productivity .............................................................................................27 Nutrients ..........................................................................................................................27 Zooplankton Grazing .......................................................................................................29 Bivalve Grazing ...............................................................................................................29 Discussion ...............................................................................................................................29 Productivity .....................................................................................................................29 Temporal Variability in Phytoplankton Biomass ............................................................30 Control of Phytoplankton Biomass .................................................................................32 Temperature and light ..............................................................................................32 Flushing ....................................................................................................................33 Nutrients ...................................................................................................................34 Grazing .....................................................................................................................35 Conclusions .............................................................................................................................40 3 OYSTERS AS INDICATORS OF TROP HIC STATUS IN A HIGHLY FLUSHED ESTUARY..............................................................................................................................61 5

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Introduction .............................................................................................................................61 Methods ..................................................................................................................................63 Site Description ...............................................................................................................63 Water Quality Sampling ..................................................................................................64 Water Chemistry ..............................................................................................................64 Oyster Population Descriptions .......................................................................................64 Statistical Analyses ..........................................................................................................65 Results .....................................................................................................................................66 Water Quality ..................................................................................................................66 Nutrient Load ...................................................................................................................68 Oyster Density .................................................................................................................68 Oyster Length and Biomass .............................................................................................69 Oyster Condition Index ...................................................................................................70 Discussion ...............................................................................................................................70 Conclusions .............................................................................................................................75 4 SUMMARY............................................................................................................................85 LIST OF REFERENCES ...............................................................................................................87 BIOGRAPHICAL SKETCH .........................................................................................................97 6

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LIST OF TABLES Table page 2-1 Summary statistics for variables measured monthly at FM and SS from January 2003 December 2009. ..............................................................................................................42 2-2 Mean light extinction coefficients (K m ) and percent contributions of water, tripton, color, and phytoplankton at Ft. Mata nzas (FM) and San Se bastian (SS) from January 2003 December 2009.t -1........................................................................................42 2-3 Mean light availability through the water column (I ; mol photons m d ) at Ft. Matanzas (FM) and San Sebastian (S S) from January 2003 December 2009.m -2 -1...............42 2-4 Spearman rank correlation coefficients (top) and p-values (bo ttom) from monthly grab samples at the Ft. Matanzas and San Sebastian sites from 2003-2009. .....................43 2-5 Ambient nutrient and chlor ophyll a concentrations (g L ) during sample collection for nutrient addition bioassay a nd zooplankton grazing experiments.-1..............................43 2-6 Nutrient-limited (change in biomass from initial to Day 1 in control treatment group) and non-nutrient-limited (change in biom ass from Day 1 to Day 2 in P+N+Si treatment group) growth rate (day ) and doubling estimates from nutrient addition bioassay experiments and maximum predic ted growth rates based on temperature alone (Eppley, 1978).-1.........................................................................................................43 2-7 Apparent growth rates (k; day ), grazing rates (g; day ), percent biomass grazed per day (P), and percent potential production grazed per day (P ) observed during dilution experiments from water collected at the Ft. Matanzas (FM) and San Sebastian (SS) monitoring sites.-1 -1 i p........................................................................................44 2-8 Comparison of annual productivity, z ooplankton grazing rate and phytoplankton growth rate estimates among estuaries in th e subtropical southeas tern United States. .....45 3-1 Data sources for estima ting nutrient and carbon load. .......................................................76 3-2 Mean nutrient, particulate orga nic carbon (POC), and chlorophyll a (CHL) concentrations (g L ) compared between regions (re presented by the Ft. Matanzas and San Sebastian monitoring sites) and seasons. Results from non-parametric Kruskal-Wallis test for differences between means.-1..........................................................76 3-3 Mean annual discharge (m sec ; *calculated from incomplete data set).3 -1........................77 7

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LIST OF FIGURES Figure page 2-1 Site map.. ...........................................................................................................................46 2-2 Total annual precipitation at the GTMNERR weather station. ..........................................47 2-3 Total monthly precipitation at the GTMNERR weather station. .......................................47 2-4 Salinity at Ft. Matanzas (gray line) and San Sebastian (black line) measured monthly. .............................................................................................................................48 2-5 Water temperature (black line) measur ed at the Ft. Matanzas site and total photosynthetically active radia tion (PAR, gray line) measured at the weather station on each sampling day. ........................................................................................................49 2-6 Light attenuation measured during each monthly sampling event at Ft. Matanzas (gray line) and San Sebastian (black line) from January 2003 December 2009. ............50 2-7 Mean light availability in the water column (I ) during each mont hly sampling event at Ft. Matanzas (gray line) and San Se bastian (black line) from January 2003 December 2009..m................................................................................................................51 2-8 Mean annual chlorophyll a concentrations. Bars represent one standard deviation. ........52 2-9 Monthly chlorophyll a (CHL) concentrations and produc tivity estimates (BZI) from 2003 2009........................................................................................................................53 2-10 Seasonal CHL variability from 2003 2009, excluding the October 2007 red tide event (20 g L ).-1...............................................................................................................54 2-11 Monthly concentrations of chlorophyll a (CHL), ammonium (NH4), and nitrate+nitrite (NO23) at the Ft Matanzas (FM) monitoring site. .....................................55 2-12 TN:TP at Ft. Matanzas (gray line) and San Sebastian (black line) sites from 2003 2009....................................................................................................................................56 2-13 DIN:SRP at Ft. Matanzas (gray line) and San Sebastia n (black line) sites from 2003 2009.................................................................................................................................57 2-14 Average phytoplankton biomass (estimat ed by fluorescence) in nutrient addition treatment groups during March a nd June 2009 bioassay experiments. .............................58 2-15 Maximum biomass (estimated by fluorescence) of each treatment group in the nutrient addition bioassay experiments. .............................................................................59 2-16 Schematic representing important factors controlling phytoplankton biomass in the GTMNERR. .......................................................................................................................60 8

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3-1 Site map showing the San Sebastian ( SS) and Fort Matanzas (FM) System-Wide Monitoring Program sites (white triangles ) and oyster sampling locations (black dots). ...................................................................................................................................78 3-2 Mean monthly temperature, salinity, and dissolved oxygen measured every 30 minutes at the Fort Matanzas (solid line) and San Sebastian (dashed line) SystemWide Monitoring Program sites from January 2002 December 2008. ...........................79 3-3 Seasonal trends in chlorophyll a concentration (CHL), particulate organic carbon (POC) concentration, and phytoplanktoni c carbon (phyto C):POC, measured monthly at the Fort Matanzas (solid line) and San Sebastian (dashed line) SystemWide Monitoring Program sites from May 2002 December 2008. ................................80 3-4 Monthly mean total nitrogen (TN) and total phosphorus (TP) load estimates from February 2001 September 2002 for Moses Creek (dashed line) and San Sebastian River (solid line). ...............................................................................................................81 3-5 Mean oyster density, percent cover, bi omass, and condition index from high reef elevations in the Ma tanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (w inter) and the July /August 2008 survey (summer).. ..........................................................................................................................82 3-6 Mean oyster density, percent cover, bi omass, and condition index from low reef elevations in the Ma tanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (w inter) and the July /August 2008 survey (summer). ...........................................................................................................................83 3-7 Mean oyster density and standard error in the spat (< 2.5 cm), small (2.5 4.9 cm), pre-fishery (5.0 7.5 cm), and fishery (> 7.5 cm) size classes from high and low reef positions in the Matanzas (FM) and St. A ugustine (SA) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer). ...................................84 9

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Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NUTRIENT, PHYTOPLANKTON, AND OYSTER DYNAMICS IN A HIGHLY FLUSHED SUBTROPICAL LAGOON, NORTHEAST FLORIDA By Nicole Dix Pangle December 2010 Chair: Edward Phlips Major: Fisheries a nd Aquatic Sciences With ever-increasing coastal development, predicting the conse quences of nutrient enrichment in coastal ecosystems has become a main focus of estuarine research. The goal of this research was to characterize important proce sses related to the effects of nutrient enrichment in a highly flushed subtropical estuary, the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in northeast Florid a. Understanding the e ffects of nutrients on a system first requires knowledge of the structure a nd function of the base of the food web. In this study, patterns in phytoplankton biomass were expl ored in relation to a suite of potentially regulating factors, including nutrien t availability, in context with other gain and loss processes in the GTMNERR. Monthly measurements of temp erature, light, nutrient concentrations, and phytoplankton standing stock over seven years (2003-2009) were examined through correlation analysis. Laboratory experiments in the spring and summer of 2009 quantified phytoplankton growth rates, nutrient limitation potential, and zooplankton grazing rates. The potential influence of oyster grazing was also examined using population metric s and filtration rate estimates. All of the gain and loss factors we re correlated to some degree with phytoplankton biomass in the GTMNERR, but results indicated a temporal shift in the primary controlling factors, from temperature, light, and flushing in the winter to grazing and flushing the remainder 10

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of the year. In contrast to temperate syst ems, or systems dominated by riverine inputs, phytoplankton biomass in this area exhibited a regular seasonal pattern characterized by short periods of low biomass rather than by bloom ev ents. The magnitude and interannual variability of phytoplankton biomass observed in this study were fairly sma ll compared to estuarine and coastal ecosystems around the world. Since traditionally monitored water quality parameters, such as nutrient and phytoplankton concentrations, often do not provide a clear indication of trophic st atus in estuaries with short water residence times, response to nutrient enrichment in this sy stem was also measured at the level of benthic primary consumers. Oyster population structure was examined within two regions of the GTMNERR using measurements of oyster density, biomass, length, and condition. As expected, oysters exhibited greater population densit y, average biomass, and condition in the region with historically elevated nutrient loads and carbon availabil ity than in the less urbanized region. Results suggest that oysters have the potential to be used as bioindicators of trophic state in highly flushed estuaries. Overall, this study demonstrated that well-flus hed estuaries may be mo re resistant to some of the negative effects of nutri ent enrichment than those systems with comparatively restricted hydrodynamics. Especially in subtropical syst ems without major riverine inputs, primary production can exist virtually in balance with consumption, resulting in relatively low primary producer biomass and food-limited benthic consumer populations. The threshold for maintaining such a balance is undefined, however, especially si nce changes in nutrient loads can also affect phytoplankton species composition and be associated with other harmful impacts such as toxic contaminants. 11

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CHAPTER 1 INTRODUCTION Half of the United States population now lives on the coast, and densi ties are expected to increase in the future (NOAA, 2010). The prevalence of coastal development has prompted numerous research studies inve stigating potential impacts on natural coastal environments. One commonly documented impact has been anthrop ogenic increases in nutrients, leading to eutrophication of nearshore wate rs. Nutrients, particularly nitrogen and phosphorus, are found naturally in coastal waters, but concentrations are elevated near highly developed coastlines. Atmospheric deposition of fossil fuel combusti on products, storm water runoff, and wastewater discharge artificially introduce nitrogen and phos phorus to coastal waters, which can accelerate organic enrichment and negativel y affect water quality and ecosystem health (NRC, 2000). In fact, 65% of the major estuaries in the Unite d States displayed probl ematic symptoms of eutrophication in 2004 ( Bricker et al., 2007 ). The challenge of assessing the effects of exce ss nutrients in estuarine systems is a major coastal management issue. Documented eutrophi cation impacts in estuaries include increases in primary production, increases in oxygen demand and hypoxia/anoxia, changes in community structure, increases in the fre quency of harmful algal blooms, declines in submerged aquatic vegetation and coral reef covera ge, and declines in ecosystem function and/or resiliency (see NRC, 2000 for a comprehensive review). The most studied estuarie s with respect to eutrophication are those that ha ve exhibited obvious decline in function, such as the occurrence of major fish kills in Chesapeake Bay (Kemp et al., 2005) and the formation of a dead zone south of the Mississippi Ri ver (Rabalais et al., 2002). As rese arch has continued, however, it has become clear that not all systems respond pred ictably to nutrient enrichment (Cloern 1999, 2001). For example, the degree of hydrodynamic flus hing in an estuary can affect the systems 12

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sensitivity to nutrient enrichment, as documen ted in many estuaries around the world (Knoppers et al., 1991; Monbet, 1992; Phlips et al., 2004). Systems with the most severe negative impacts from eutrophication are those with hydrologic restriction (e.g., stratification of the water column in the Mississippi River Delta or restricted tidal exchange in Chesapeake Bay). In contrast, rapidly flushed systems with strong tidal exch ange are not typically characterized by large phytoplankton standing stocks because biomass doe s not have a chance to accumulate before being flushed out to the ocean. Eutrophication impact s in these systems are far less understood. The goal of this research was to define how spatial and temporal differences in nutrient load and concentration affect the structure and function of a highly flus hed subtropical estuary, using the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in northeast Florida as a model. The research focused on two key elements of the GTMNERR aquatic ecosystem; phytoplankton, the dominant pr imary producers, and oysters, the dominant primary consumers. Patterns in phytoplankton biom ass were explored in relation to a suite of potential regulating factor s, from both a gain (e.g., growth) and loss (e.g., grazing) perspective. Previous research suggests that concentrations of nutrients often do not accurately reflect phytoplankton biomass potential or trophic status (Phlips et al., 2004). Therefore, effects of nutrient enrichment in the GTMNERR were also measured at the level of benthic primary consumers. Specifically, properties of eastern oyster ( Crassostrea virginica ) populations, a key feature of estuarine la ndscapes throughout the southeastern United States, were examined for their potential as indicators of eutrophication in highly dynamic coastal environments. This dissertation research investigated re lationships between environmental conditions, primary producers, and primary consumers in the GTMNERR using both experimental and observational methods within the context of the following hypotheses: 13

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Phytoplankton productivity and biomass in th e GTMNERR were expected to be low or average relative to estuarine systems world-wide Seasonal variability was expected to be relatively small, with highs in the summer and lows in the winter, but annual variability was expected to be relatively large. Temperature and light availability were e xpected to act as primary controls of phytoplankton biomass in the winter. During th e remainder of the year, two factors were expected to play major roles in the regulat ion of phytoplankton biomass: 1) high, tidallydriven water exchange rates with the Atlant ic Ocean and 2) significant top-down control from the extensive oyster popul ations in the system. Due to long-term differences in nutrient regi mes, oysters in the St. Augustine region of the GTMNERR were expected to be larger, mo re densely populated, and exhibit higher condition index scores than thos e in the Matanzas region. 14

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CHAPTER 2 CONTROL OF PHYTOPLANKTON BIOMASS IN A HIGHLY FLUSHED SUBTROPICAL ESTUARY Introduction Patterns in phytoplankton biomass have been successfully correlated with metrics of ecosystem function in estuaries (i.e., production and metabolism), and represent key structural elements to understanding ecosystem dynamics (Cloern and Jassby, 2010). Phytoplankton biomass is a function of the influences of gain and loss processes. Factor s that control gains in phytoplankton biomass include those that promot e phytoplankton growth, such as temperature and the availability of light and nutrients. A llochthonous inputs of phytoplankton can also affect biomass levels. Common biomass loss processes include grazing, dilution, flushing, death, and sinking. The relative importance of factors controlling phytoplankton biomass has been synthesized for a number of te mperate estuaries (Cloern, 1996; Kemp et al., 2005; Smetacek and Cloern, 2008; Zingone et al., 2010 ), but comprehensive studies from subtropical systems are more limited (Murrell et al., 2007). Abiotic factors such as light, temperature, a nd nutrients have been shown to play a major role in the regulation of phytoplan kton productivity and biomass in estuaries (Thayer, 1971; Cole and Cloern, 1984; Cloern, 1999; Bl edsoe and Phlips, 2000; Bouman et al., 2010). Patterns of surface irradiance and light attenuation through th e water column influence the amount of energy available for phytoplankton growth (Day et al., 1989), while temperature can affect rates of phytoplankton growth and nutrient uptake (Eppley, 1972; Day et al., 198 9). In their review of 63 estuaries around the world, Boynt on et al. (1982) found that produc tivity was higher in warm months than cold months. Since subtropical a nd tropical ecosystems have extended periods of high temperature (i.e., > 10 C) and light flux (i.e., > 20 mole photons m-2 day-1), many exhibit less seasonal variability in primary production than those in temperate environments (Murrell et 15

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al., 2007; Winder and Cloern, 2010). Similarly, nutr ient availability has also been shown to control phytoplankton biomass. Phytoplankton gr owth can be nutrientlimited if nitrogen, phosphorus, and/or silica concen trations are below that at which phytoplankton growth is saturated (Day et al., 1989). Estuarine envi ronments tend toward nitrogen limitation (NRC, 2000), although phosphorus has been shown to be limiting in some areas at certain times of the year (Myers and Iverson, 1981; Phlips et al., 2002; Murrell et al., 2007). Together these abiotic factors help define maximum pr imary production in the aquatic environment, although intrinsic biotic factors may exert top-down control of phytoplankton biomass accumulation. Even when temperature, light, and nutrie nt conditions favor optimal phytoplankton growth; concomitant increases in phytoplankton biomass are not always observed. Differences between phytoplankton production and biomass ob served in some ecosystems have been attributed to high grazing rates (Malone et al., 1996). Zooplankton grazing has been shown to be an important control of algal biomass in estu aries (Mortazavi et al., 2000; Murrell et al., 2002; Quinlan et al., 2009). The impact of suspensi on feeding bivalves on phytoplankton biomass in estuaries has also been documented in the labora tory (Cloern, 1982; Officer et al., 1982), and in the field (Dame et al., 1984, 1991; Cressman et al ., 2003). Given sufficient habitat, the slower metabolism of bivalves allows them to surviv e periods of low food concentrations and take advantage of phytoplankton growth more consis tently over time than more dynamic populations of zooplankton (Dame, 1996; Prins et al., 1998). On the other hand, the impact of grazing might be minimal in some systems, such as the York River estuary, wher e phytoplankton production and biomass patterns are similar (Sin et al., 1999). Another factor that can limit phytoplankton bi omass potential is the rate of hydrodynamic transport out of a system. The effect of flus hing on phytoplankton biomass has been documented 16

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in many systems around the world (Knoppers et al., 1991; Monbet, 1992; Ph lips et al., 2004). Rapidly flushed systems may not typically be characterized by large standing stocks because phytoplankton biomass does not have a chance to accumulate. In this study, the relative influences of gains and loss processes on phytoplankton biomass were investigated in a highly flushed subtropical lagoon, the Guan a Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in nort heast Florida. Temporal variability in phytoplankton biomass and productivity were compared to temporal pa tterns in light availability, temperature, nutrient con centrations, nutrient limitation, flushi ng rates, and grazing pressure. It was hypothesized that phytoplankt on productivity and biomass in the GTMNERR would be low or average relative to estuarine systems world-wide due to high water exchange rates with the Atlantic Ocean and the lack of major riverine inputs, which limits nutrient loads to the estuary. Seasonal patterns in productivity and biomass were expected, with highs in the summer and lows in the winter, as observed in most estuaries (Boynton et al., 1982). Temperature and light availability were expected to act as primary controls of phytoplankton biomass in the winter. During the remainder of the year, two factors were expected to play major roles in the regulation of phytoplankton biomass: 1) high, tidally-driven water exchange rates with the Atlantic Ocean and 2) significant top-down control from the extensive oyster populations in the system. Climatic disturbances, such as tropical storms, were expected to enhan ce interannual variability in phytoplankton biomass, but due to the subtropi cal location of the system, seasonal variability was expected to be relatively low (Cloern and Jassbby, 2010). Methods Site Description The study area encompassed the Matanzas River estuary in northeast Florida from the St. Augustine Inlet to the Matanzas Inlet, including the San Sebastian River (Figure 2-1). The 17

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dimensions of the study area were estimated as 1.6*107 m2 and 6.17*107 m3 (Bilge Tutak, unpublished data). Land-use in the surrounding watersheds consists of approximately 40 % forests/rangeland, 30 % wetlands, 20 % urban areas, and 1 % agriculture (SJRWMD, 2006). Tidal exchange with the Atlant ic Ocean is the main hydrodynamic force in the estuary, making it well-mixed (see Chapter 3) and well-flushed (Phlip s et al., 2004; Sheng et al., 2008). The entire GTMNERR experiences flushing times of approximately 2 weeks, while flushing times near the inlets are shorter (i.e., 2 4 days) (Sheng et al., 2008). Primary production in the wate r column is assumed to be dominated by phytoplankton. Submerged aquatic vegetation is extremely sparse and although macroalgal distributions have not been quantified, personal observations sugge st only localized patches exist. Benthic microalgae, which can contribute significantly to primary production in estuaries (Pinckney and Zingmark, 1993), have also not been quantified in the GTMNE RR. The study area has been characterized as having low phyt oplankton biomass compared to other sites along the Floridas northeastern coast (Ph lips et al., 2004). The eastern oyster ( Crassostrea virginica ) is thought to be the dominant suspension feeder in this and other estuaries of the Atlant ic and Gulf of Mexico coasts. Oyster reefs in northeast Florida are so lely intertidal. According to preliminary oyster maps, aerial coverage of live oyster reefs in the st udy area is approximately 3.5*105 m2 (St. Johns River Water Management District, unpublished data). The av erage density of live oysters on reefs has previously been estimated as 540 individuals m-2, with an average length of 4.7 cm and an average tissue dry weight of 0.5 g (see Chapter 3). Weather and Water Quality In Florida, the climatic cycle consists of a cool, dry season and a warm, wet season; although, this region of the state typically experiences a bimodal rainfall pattern with a small peak in the late winter/early spring and a larger peak in the summer (Chen and Gerber, 1990). A 18

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large portion of the study area lies within the boundaries of the GTMNERR, which collects highfrequency meteorological data from a weather station south of the Matanzas Inlet (Figure 2-1). In this study, photosynthetically active radia tion (PAR) and rainfall data for 2003 2009 were downloaded from the NERR System Centraliz ed Data Management Office (NOAA, 2009). Water quality data were collected from two sites in the study area. The Fort Matanzas (FM) site is located in the Matanzas River a pproximately 4 km north of the Matanzas Inlet (Figure 2-1). FM has an average depth of appr oximately 3.6 m and a tidal range of about 1.4 m. The San Sebastian (SS) site is located at th e confluence of the San Sebastian and Matanzas Rivers, approximately 4 km south of the St. Augus tine Inlet (Figure 2-1). The average depth at SS is approximately 4.4 m with a tid al range of about 1.7 m. The wa ter quality sites were visited once per month from 2003 2009 on ebb tides. A Quanta Hydrolab multi-parameter probe was used to measure water temperature, salinity, and dissolved oxygen 0.5 m below the surface and 0.1 m above the bottom. Water wa s collected with a polyvinyl chloride (PVC ) integrated water column pole (Venrick, 1978) that captured the top 3 m of the water column. A portion of each sample was filtered through glass-fiber filters (0 .7 m pore size) for soluble inorganic nutrients, colored dissolved organic matter (CDOM), and chlorophyll a (CHL) determination. Samples were transported on ice to the University of Fl orida laboratory in Gainesville for subsequent processing. Nitrite (NO2) concentrations were determined by mixing the sample with color reagent (phosphoric acid, sulfanilalimide, and N-1-naphthylethylene diamine dihidrochloride) to form a purple azo-dye (APHA, 1998). Colorimetric quan tification was completed on a Bran + Luebbe Autoanalyzer 3 system. Concentrations of n itrate (NO3), total nitrogen (TN), and ammonium (NH4) were first reduced to NO2 and then meas ured as described above. NO3 was reduced to 19

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NO2 through a copperized cadmium redactor (A PHA, 1998). TN was oxidized to NO3 via alkaline potassium persulfate digestion and th en reduced to NO2 thr ough a copperized cadmium redactor (APHA, 1998). NH4 was oxidized to NO2 with hypochlorite in an alkaline medium using potassium bromide as a catalyst (Stric kland and Parsons, 1972). Dissolved inorganic nitrogen (DIN) was calculate d by summing NH4 + NO3 + NO2. Soluble reactive phosphorus (SRP) concentrations were determined by mixing the sample with color reagent (sulfuric acid, ammonium molybdate, ascorbic acid, and antim ony potassium tartrate) to form a blue dye (APHA, 1998). Colorimetric quantification was completed on a Hitachi U-2810 (Tokyo, Japan) dual-beam scanning spectrophotometer. Total phosphorus (TP) samples were digested with potassium persulfate to convert to SRP and measured as de scribed above (APHA, 1998). CDOM was measured spectrophotometrically against a platinum cobalt standard (APHA, 1998). CHL was processed using the Sartory and Grobbe laar (1984) hot ethanol extraction method and concentrations (not corrected for pheophytin) we re determined spectrophotometrically according to Standard Methods (APHA, 1998). All proce ssing and analytical methods conformed to the guidelines of the laboratorys National Envi ronmental Laboratory Accreditation Program certification (E72883). SAS software (Version 9.2, Cary, NC) was used to calculate summary statistics for all parameters. Distributions of most variables were non-normal (determined by the Shapiro-Wilk and Kolmogorov-Smirnov goodness-of-fit tests), necessitating the use of non-parametric Spearman rank correlation analysis to explore relationships between them. Light Availability Light attenuation (Kt) (m-1) was measured at the FM and SS sites each month by simultaneously measuring light intensity at the surface (Io) and at 1 m depth (Iz) with LiCor Instruments, Inc. LI-190SA (Lincoln, NE) quantum cosine corrected light probes. According to 20

PAGE 21

Lambert-Beers Law, at a depth of 1 m, Kt = ln (Io) ln (Iz). The relative contribution of seawater (Kw) to light attenuation was estimated as 0.0384 m-1 (Lorenzen, 1972). Light attenuation by phytoplankton (Kp) was calculated by multiplying CHL by 0.016 m2 mg-1 (Reynolds, 2006). The CDOM partial extinction coefficient (Kc) was calculated by multiplying CDOM by 0.014 pcu-1 m-1 (McPherson and Miller, 1987). Fina lly, light attenuation by tripton was estimated as Kt (Kw + Kp + Kc). The mean light intensity in the mixed layer (Im) (mole photons m-2 d-1) was estimated for each sampling day using the following equation from Stefan et al. (1976): Im = (Id (Kt Zm)-1) (1 (exp (Kt Zm))), where Zm = mixing depth and Id = mean daily PAR as estimated from the literature (Oswald and Gataas, 1957) and co rrected for 5 % surface reflectance. Zm was estimated as mean site depth since the water column was well mixed. Previous researchers have estimated that light limitation of phytoplankton occurs when Im is between 0.9 and 6 mole photons m-2 d-1 (Geddes, 1984; Phlips et al., 1995). Primary Production Studies from a number of estu aries have shown good agreement (r2 > 0.75) between primary productivity and a composite parameter of phytoplankton biomass and light availability (BZI) (Cole and Cloern, 1987; Murrell et al., 2007; Phlips and Mathews, 2009). Therefore, it was assumed that primary productivity in th e GTMNERR correlated well with BZI so that primary productivity could be estimated over the seven year study period. Monthly CHL concentrations (B) were multiplied by photic depth (Z, estimated as 4.61 Kt -1) and mean daily PAR (I), as estimated from the literature (O swald and Gataas, 1957) and corrected for 5 % surface reflectance, to obtain daily integrated production rates (mg C m-2 d-1). BZI was multiplied by 0.365 to obtain an annual productivity estimate (g C m-2 yr-1). 21

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Nutrient Limitation The potential for nutrients to limit phytoplan kton growth was explored in three ways. First, nutrient concentr ations were compared to published half-saturation constants (Reynolds, 2006). Concentrations at or below half-saturat ion constants indicate th e potential for nutrient uptake to limit phytoplankton growth (Day et al ., 1989). Second, nutrient concentration ratios were compared to Redfield proportions (Redfiel d et al., 1963). Nitroge n:phosphorus (N:P) ratios below 7.2 (g:g) indicate nitrogen limitation potential, while N:P ra tios above 7.2 (g:g) indicate potential phosphorus limitation. Unfortunately, conc lusions based on nutrient concentrations are not sufficient by themselves because concentratio n values do not reflect supply, especially in highly flushed systems (see Chapter 3). Therefor e, limiting nutrient status was also explored experimentally with two nutri ent addition bioassays, one in March 2009 and one in June 2009, representative of dry and wet c limatic conditions, respectively. For each nutrient limitation experiment, 5 L of water were collected using an integrated sampling pole at each of three sites: SS, FM, a nd a mid-point location. Water was transported to the lab in a large clear carboy covered with a white plastic bag (to best simulate natural light conditions) and closed with a foam stopper (to allow gas exchange). At the lab, water was transferred to a large mixing ta nk and stirred continuously during experiment al set-up. Water from each site was divided into 300 ml aliquots and poured into 15 500-ml Erlenmeyer flasks to create five treatment groups (control, N addition, P addition, N+P addition, and N+P+Si addition) in triplicate. Nutrie nts were added accordingly to obtai n final concentrations of 400 g NO3-N L-1, 400 g Si L-1, and 40 g PO4-P L-1. Flasks were incubated in temperaturecontrolled water baths illuminated from the bottom. Temperatures were set at ambient levels measured the day of sampling (18 C in March and 27 C in June). Light intensities were dampened with screens to approximate ambient levels and photoperiods were set at 12 hours 22

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light, 12 hours dark in March a nd 14 hours light, 10 hours dark in June to correspond to seasonal differences in day length. Flasks were swirled and sampled for algal biomass every 24 hours for five days. Throughout the experiment, in-vivo fluorescence measured on a Turner Designs TD-700 Fluorometer (Sunnyvale, CA) was used as a proxy for algal biomass (Lakowicz, 1983). Initial fluorescence was measured in triplicate for each sample prior to setting up the experiment. Water used for initial readings was also filtere d through 0.7 m glass fiber filters to determine background fluorescence and all s ubsequent fluorescence values were corrected for background levels. A t-test (SAS software, Version 9.2, Cary, NC) was used to test for differences between initial algal biomass and maximum biomass attained in the control, as well as between maximum biomass in the control and maximum biomass attained in each treatment ( = 0.05). Changes in biomass were used to calculate inst antaneous phytoplankton growth rates. Zooplankton Grazing The Landry and Hassett (1982) dilution me thod was used to measure zooplankton community grazing rates and phytoplankton grow th rates in the GTMNERR. Water was collected from SS and FM (25 L from each site) at the same time and in the same manner as for the nutrient addition bioassay experiments. A por tion of the water from each site was filtered through 0.2 m membrane filters and combined with whole water to create a dilution series of 100 %, 30 %, 20 %, and 10 % whole water treatments in triplicate. Nutrients were added to each 2 L sample (400 g NO3-N L-1, 400 g Si L-1, and 40 g PO4-P L-1) to ensure that nutrients did not limit phytoplankton growth. In dividual stir plates were used to continuously mix each beaker. Water temperatures were maintained at ambient levels measured the day of sampling (18 C in March and 27 C in June). Samples we re illuminated from above with photoperiods of 12 hours light, 12 hours dark in March and 14 hours light, 10 hours dark in June. 23

PAGE 24

Chlorophyll a corrected for phaeophytin (APHA, 1998) and extracted with hot ethanol (Sartory and Grobbelaar, 1984) was used as a proxy for algal biomass. Dilution mixtures were sampled for chlorophyll a at the time of set up and again 24 h ours later. Apparent growth rates were calculated as ln (Pt P0 -1), where Pt = final chlorophyll a concentration, and P0 = initial chlorophyll a concentration. Instantaneous growth (k ) and grazing (g) rates were estimated as the y-intercept and slope, respectively, of the regression relationship be tween apparent growth rate and fraction of unfiltered seawater. The percent st anding crop grazed per day (P i = 1 exp (-g)) and the percent potential production grazed per day (Pp = (exp k exp (k g)) (exp k 1)1) were determined as in Murrell et al. (2002) Bivalve Grazing The potential impact of benthic grazi ng on phytoplankton biomass was assessed on an ecosystem scale by comparing bivalve clearanc e time and estuary flushing time (Dame, 1996; Dame and Prins, 1998). Clearance time (CT), or the time it would take oysters to filter the entire volume of the estuary, was calculated as CT = estuary volume (FR FT total number of oysters)-1, where FR = filtration rate and FT = filtering time or th e number of hours oysters filter per day. FR was estimated following Do ering and Oviatt (1986), where FR = (L 0.96 T 0.95) 2.95-1, L = mean oyster length (see Chapter 3) and T = mean water temperature one month prior to oyster sampling (1/20/2008 2/20/2008 and 7/6/2008 8/6/2008). FT was estimated both as 8 (Borrero, 1987) and as 12 (Dame et al., 1980) ho urs per day. The total number of oysters was estimated by multiplying average density (see Chapter 3) by oyster reef aerial coverage (St. Johns River Water Management District, unpublished data). The filtration pressure of bivalve beds at an estuary scale was calculated as phytoplanktonic carbon uptake as a fraction of primary production (Smaal and Prins, 1993). A range of uptake estimates (3.2 16.7 mg CHL m-2 hr-1) previously obtained from Crassostrea 24

PAGE 25

virginica reefs in South Carolina (Dame et al., 1984) was multiplied by a C:CHL ratio of 40, the hours of filtering time per day, a nd oyster reef aerial coverage. System-wide primary production was estimated by multiplying average daily integr ated productivity (BZI) by the area of the study region. Filtration pressure estimated the reef-lev el impact of oysters on phytoplankton biomass, and therefore, included biomass lo ss through sedimentation on reefs. Results Climatic and Physical Water Column Conditions From 2003 2009, the study area received an average of 1.1 0.2 m of precipitation annually (Figure 2-2). While 2003, 2006, and 2008 were relatively dry years, a number of tropical storms affected rainfall patterns during 2004 and 2005, maki ng them wet years on average. Also, 2007 and 2009 were comparatively we t years. The largest monthly rainfall total of the seven-year time series occurred in May 2009 (Figure 2-3). In fact, the wet season of 2009 caused that year to be the wettest of the seven. Salinity at the Ft. Matanzas (FM) and San Sebastian (SS) sites was fairly high over the sampling period with the exception of occasional freshwater inflows after various rain events (Fig ure 2-4). The well-mixed nature of the Matanzas River estuary was illustrated by nearly identical surface and bottom salinity, temperature, and dissolved oxygen measurements (Table 2-1). Water temperatures ranged from 12 C to 31 C at both sites over the seven years (Figure 2-5). Temperature patterns lagged photosynthe tically active radiati on (PAR) by one to two months and the two parameters were weakly correlated with each other ( rs = 0.33, p < 0.001). PAR peaked in April or May every year, while temperatures peaked anywhere from May to August (Figure 2-5). Light attenuation was highe st after the 2004 tropica l storms, but did not exhibit a clear seasonal pattern (Figure 2-6). Tripton was respons ible for the majority of light attenuation at both SWMP sites (T able 2-2). The average amount of light available in the water 25

PAGE 26

column (Im) ranged from 2 23 mole photons m-2 d-1 (Figure 2-7). Mean Im over the seven years (Table 2-1) was above the threshold fo r light limitation estimated by Geddes (1984) and Phlips et al. (1995) but individual Im values often dropped within the threshold range. Imreached potentially limiting levels more ofte n at SS than at FM. At both sites, I st -3). m was lowe from December February and highest from June August (Table 2 Phytoplankton Biomass Interannual variability in phytoplankton biomass was relatively small, possibly due to the relatively small long-term mean (4.7 1.1 g L-1) (Cloern and Jassby, 2008, 2010). Seasonal variability was slightly higher than annual vari ability; standard deviat ions of monthly means ranged from 1.5 5.0 g L-1 over the seven years (Figure 2-8). Seasonal variability was highest in 2007 due to a red tide incursion in October. Karenia brevis entered the lagoon from offshore, which quadrupled CHL concentrations compar ed to the prior month (Figure 2-9). K. brevis cells were found at a conc entration of 3,630 ml-1 in one sample collected from FM on October 10, 2007 (Edward Phlips, unpublished data). Th e red tide was gone by the November 2007 sampling event, possibly suppressed by low salinity and/or temperature. As predicted, phytoplankton biomass followed a regular seasonal patt ern (Figures 2-9 and 2-10). CHL concentrations were generally elev ated from April through September, while the lowest concentrations usually occurred from December through March every year. Spring CHL maxima corresponded to elevated levels of PAR each year and occurred when temperatures reached optimum levels for phytoplankton growth (20 25 C; Goldman, 1979) (Figure 2-5). CHL was weakly correlated with PAR ( rs = 0.29, p < 0.001) and temperature, but not correlated with CDOM, Im, or salinity (Table 2-4). CHL concentr ations did not appear to respond to nutrient inputs after rainfall events and did not correlate well with bioavailable nutrient 26

PAGE 27

concentrations (Table 2-4). However, peaks in CHL often corresponded to troughs in dissolved inorganic nitrogen (DIN) and vice versa, indicating DIN uptake by phytopla nkton (Figure 2-11). Phytoplankton Productivity Based on the BZI composite parameter, averag e integrated productivity in the Matanzas River estuary was 0.42 g C m-2 d-1, or 153 g C m-2 yr-1. Productivity and CHL followed similar temporal patterns, especially at FM (Figure 2-9), but differences between productivity and biomass were observed on a number of occasions. For example, relatively high light levels and low light attenuation values resulted in BZI peaks during some spring and summer months, while CHL concentrations remained close to average. During the tropical st orm season of 2004, light attenuation was elevated, which caused low productivity estimates, but biomass did not show a concomitant decline. Finally, during the red tide event in Oc tober 2007, CHL levels were high due to the invasion of K. brevis but in situ production was not elevated. Nutrients Some general seasonal-scale patterns we re evident from th e monthly nutrient concentrations. The majority of total nitrogen (TN) was in th e dissolved organic form (DON), followed by particulate (PN) and dissolved inorganic (DIN) forms (Table 2-1). DIN was dominated by NH4. Median NH4 (47 g N L-1) and median NO3 (12 g N L-1) were in the range of published half-saturation levels for coastal diatoms (7 130 g NH4-N L-1and 6 71 g NO3-N L-1) (Reynolds, 2006). Soluble reactive phosphor us (SRP) was less than one third of total phosphorus (TP). Median SRP (13 g P L-1) was above the published half-saturation level range (1.6 6.2 g P L-1) for phytoplankton (Day et al., 1989). All nutrient concentrations were negatively correlated with salinity and positivel y correlated with CDOM light attenuation, and temperature (Table 2-4). TN:T P (Figure 2-12) and DIN:SRP (Fi gure 2-13) ratios over the study 27

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period oscillated around the Redfield ratio of 7.2 with very little predictability. Median TN:TP was close to the Redfield ratio (7.5), while medi an DIN:SRP was well below it (4.9) (Table 2-1). The nutrient addition bioassays performed in March and June 2009 were successful in representing dry and wet conditions as evidence d by the ambient salinity values at the time of collection (34 and 26 ppt, respectively) (Figure 2-4). In al l cases, maximum biomass in the control group was significantly highe r than the initial biomass. Xu et al. (2009) interpreted a similar response as an indication of light or hydraulic residence time limitation of phytoplankton growth in the natural environment. On the othe r hand, the initial biomass increase could also be attributed to phytoplankton adaptation to lower maximum light intensity in the laboratory compared to the natural environment or the pres ence of surplus nutrients. Maximum biomass in the control group was attained after the first day in the June experiments, but lasted through the second day in the March control group (Figure 214), possibly indicating a temperature effect or the presence of surplus nutrients in March. Evidence of surplus nutrients from ambient concentrations is unclear since both DIN and CHL concentrations were higher in June than in March (Table 2-5). The response of phytoplankton biomass to va rious nutrient addition treatments was remarkably similar over space and time (Fi gure 2-15). Typically, biomass responded to additions of N, N+P, or N+P+Si, but not P alone. In a few cases, the N+P treatment group attained higher biomass than the group with only N added, indicating potential co-limitation. Silica did not have a strong influence overall. Growth rates calculated from changes in biomass in these experiments averaged approximately 0.8 day-1 (1.2 doublings day-1), independent of whether they were calculated from the nutrient-limited control groups or from treatment groups with all nutrients added (Table 2-6). 28

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Zooplankton Grazing Zooplankton grazing was meas urable at FM during Marc h and June 2009, consuming 50 and 28 % of primary production, respectively (Table 2-7). Grazing rates from samples collected at SS were not statistically significant. Non-si gnificant grazing rates were considered zero in subsequent calculations. Phytopl ankton growth rates, consistently more than double zooplankton grazing rates, ranged from 1.15 1.59 (1.66 2.30 doublings day-1) and averaged 1.39 day-1 (2 doublings day-1). Bivalve Grazing Oyster filtration rate averaged 1.6 L hr-1 individual-1 (2.0 L hr-1 individual-1 in the winter and 1.3 L hr-1 individual-1 in the summer) and 3.2 L hr-1 g dry tissue-1. Clearance time, the time it would take for oysters to filter the entire vol ume of the study area, was estimated as 17 25 days (using a range of 8 12 hours of oyster filt ering time per day), 13 19 days in the wet season and 26 39 days in the dry season. Oy ster filtration pressure or the proportion of phytoplanktonic carbon produced that was rem oved through filtration and sedimentation on oyster reefs, averaged 5 40 % annually depending on the pub lished uptake rates and the filtering times used in calculations. Discussion Productivity Results from this study support the hypothesi s that aquatic primary productivity in the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) would be relatively low. The highest primary production estimates (BZI) were confined to March November when light availability and chlorophyll a were relatively high. While this relationship was driven by parameterization of the BZI model, the same general pattern has been observed in many estuaries (Boynton et al., 1982). Estimat ed annual productivity in the GTMNERR was 29

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lower than river-dominated, subtropical estuaries in the southeastern United States (Table 2-8), likely due to less nutrient and organic carbon inpu ts in the GTMNERR. On a global scale, annual productivity in th e GTMNERR was more comparable to other estuaries. For example, Boynton et al. (1982) reported a seasonal average of 190 g C m-2 yr-1 for 63 estuaries worldwide, and an average of 179 g C m-2 yr-1 for the five lagoons included. Knoppers (1994) reported a range of 50 500 g C m-2 yr-1 (with a median of 196 g C m-2 yr-1) for phytoplanktondominated, restricted and leaky coastal lagoons. More recently, Jassby et al. (2002) calculated a median of 200 g C m-2 yr-1 from 15 estuaries around the world. Some differences were observed between pa tterns in primary productivity (BZI) and phytoplankton biomass (CHL concentration) over the seven year time series in this study (Figure 2-9). Such differences may result from bioma ss losses through processes such as grazing or flushing. However, differences between patte rns of phytoplankton prod uction and biomass may also occur if the BZI model did not accurately estimate productiv ity in the study area. The BZI model assumes that phytoplankton are not nutr ient-limited, and may overestimate primary productivity when phytoplankton are limited by nutri ents (Cole and Cloern, 1987). In addition, BZI may not accurately predict pr oductivity if photosynthetic efficiency is compromised. For example, Bouman et al. (2010) found only 52 % of the variance in their productivity estimates was explained by the BZI model. They attr ibuted the discrepancy to differences in photosynthetic efficiency, possibly caused by ammonium inhibition of nitrat e uptake (Dugdale et al., 2007). On the other hand, Cole and Cloern (1987) note that su ch physiological processes are not relevant to the seasonal-scale observat ions that comprise the BZI parameter. Temporal Variability in Phytoplankton Biomass As expected, the seven-year m ean CHL concentration (4.7 g L-1) from the Ft. Matanzas and San Sebastian sites in the GTMNERR was rela tively low compared to the mean of 6.0 g L-1 30

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from 154 coastal sites (114 diverse ecosystems) ex amined by Cloern and Jassby (2008). While a slightly small magnitude of phytoplankton bioma ss was not surprising, there were unexpected observations regarding temporal variability. To compare temporal variability in phytoplankton biomass in the GTMNERR with other estuaries, methods described in Cloern and Jassby (2010) were followed to calculate the coefficient of va riation (CV) for CHL concentrations at annual and seasonal scales. CHL concentrations at the FM and SS sites exhibited an annual CV of 24 % and a seasonal CV of 44 %. Cloern and Jassby (2010) examined 84 CHL ti meseries from around the world and found a median annual CV of 30 %. In comparison, the GTMNERR exhibited fa irly low interannual variability in mean phytoplankton biomass. In terannual variability was hypothesized to be relatively high since two active hurricane seasons occurred during the study period, but year-toyear meteorological differences di d not correlate with differences in phytoplankton biomass. In fact, similar observations were made recently in the well-flushed central region of the Indian River Lagoon, FL (Phlips et al., 2010). In Cloern and Jassbys (2010) compilation, s easonal variability was generally higher (median CV = 39 %) than annual variability, whereas the GTMNERR exhibited an even greater difference in phytoplankton biomass within and among years. Unexpectedly, the seasonal CHL variability in the GTMNERR (CV = 44 %) was sli ghtly higher than the median of 84 sites around the world (CV = 39 %), which may be a result of the fairly short-term regularity of the seasonal pattern (Figure 2-9). In contrast to the regular spring bloom pattern of some temperate systems, regularity in the GTMNERR is charac terized by short periods of low biomass from December to March every year. Throughout the re mainder of the year, there was no consistent seasonal pattern in phytoplankton bi omass, possibly indicative of the influence of the various 31

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control mechanisms investigated in this study (see discussion be low). The sampling methodology may also be partly responsible for th is pattern since discre te monthly sampling is thought to underestimate signal strength of recurring cycles (Winder and Cloern, 2010). Control of Phytoplankton Biomass Results from this study support the hypothese s regarding the cont rol of phytoplankton biomass in the GTMNERR. Temperature and li ght availability limit phytoplankton growth in the winter and there is some potential for light limitation during episodic events, but hydrodynamic flushing and oyster grazing control the accumulation of phytoplankton biomass throughout the year. It is useful to examine the pot ential role of major ab iotic and biotic factors that may control phytoplankton biomass (Figure 216) as a means of jus tifying this conclusion. Temperature and light Temperature and photosynthetically active ra diation (PAR) were both positively correlated with phytoplankton biomass. However, since temp erature and light covari ed, simple correlation analysis was not able to determine their relative i ndividual influences. In fact, the interaction of temperature and light may be an important factor in itself since increases in temperature increase rates of photosynthesis at any given level of light intensity (Valiela, 1984) Non-algal suspended solids accounted for the majority of light attenua tion at FM and SS, and in some instances, light attenuation appeared to be re lated to flushing events. While the average amount of light available throughout the water column (Im) was not correlated with phytoplankton biomass, there were instances of decreased lig ht availability that could ha ve limited phytopl ankton growth. Winter lows in phytoplankton bioma ss appear to have been related to decreases in irradiance at the surface. Plus, phytoplankton growth may have occasionally been limited by declines in the amount of light available throughout the water column after freshwater flushing events. 32

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Based on our results, temperatur e likely influenced phytoplankt on biomass primarily at the seasonal level. Monthly wa ter temperatures were rela ted to monthly chlorophyll a concentrations. However, two separate lines of evidence suggest that temperature was not the primary limiting factor controlling algal growth in this region. First, experimentally measured phytoplankton growth rates (Table 2-6) were cons istently lower than gr owth rates predicted by temperature alone (Eppley, 1978). Additionall y, if phytoplankton growth were only limited by temperature, growth rates would be expected to double for every 10 C increase up to 30 C (Day et al., 1989). Coincidentally, a 10 C incr ease in ambient water temperatures occurred from March 2009 to June 2009, the months in which nutrient addition bioassay and zooplankton grazing experiments took place. To the contrary growth rates measured from N+P+Si bioassay treatments were higher in March than in June at all stations (Table 26). These findings imply that phytoplankton growth poten tial was suppressed by other fact ors (e.g., micronutrient supply or grazing) in the spring and su mmer. Since temperatures in this region were only suboptimum for phytoplankton growth from December to March, temperature may primarily be important for controlling phytoplankton biomass in the winter. The lack of a relationship between experime ntally measured growth rates and ambient water temperatures has been observed elsewhere (Lehrter et al., 1999). On the other hand, the possibility of phytoplankton adaptation to labora tory conditions cannot be ignored. In March, algae may have received more light in the labora tory than in the natural environment and/or, in June, algae may have received less light in the laboratory than in the natural environment, potentially creating artificially high and/ or low growth rates, respectively. Flushing Hydrologic conditions have been used to e xplain low biomass in macrotidal (Monbet, 1992) and highly flushed coastal systems (Knoppers et al., 1991; Phlips et al., 2004). To explore 33

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the effect of this physical fact or in the GTMNERR, consider a hypothetical situation in which only flushing limited the accumulation of phytopl ankton biomass. The laboratory-derived phytoplankton growth rates observed in our gr azing experiments ranged from 0.5 1.6 day-1 (0.7 2.3 doublings day-1). A review of taxon-specific in situ growth rates by Stolte and Garcs (2006) yielded an average of approximately 0.8 day-1 for diatoms, the dominate algal group in the GTMNERR (Edward Phlips, unpublished data). Since flushing times near the St. Augustine and Matanzas Inlets have been estimated as 2 4 days (Sheng et al., 2008), it can be assumed that flushing times at the FM and SS sites were in the same range. With an average growth rate of 0.8 day-1 and an average chlorophyll a (CHL) concentration of 5 g L-1, after two days, exponential growth would result in a CHL concentration of 25 g L-1. After four days, CHL concentration would reach 120 g L-1. The maximum observed CHL concentration from 2003 2009 was 12 g L-1, excluding the red tide incursion in October 2007, suggesting that one or more other factors played a role in defining phytoplankton biomass potential. Nutrients Significant negative correlations between nutrient concentrations and salinity, as well as positive correlations between nutrient concentra tions and colored dissolved organic matter, suggest freshwater run-off as a pr inciple source of nutrients to th e estuary. This conclusion is supported by the finding by Dix et al. (2008) that Pellicer Creek, a main tributary to the southern part of the Matanzas River, was a source of nitrogen to the es tuary during freshwater flushing events. Nutrient concentrations were also signi ficantly positively correlated with temperature, which may reflect changes in decomposition and mineralization rates, a potentially indirect effect of temperature on phytoplan kton biomass. On the other ha nd, since higher temperatures in Florida occur during the wet seas on, it is possible that the corre lation between temperature and nutrient concentrations is an artifact of the relationship between nutrients and freshwater inputs. 34

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Evidence from this study suggests that nitrogen was the primary limiting nutrient for phytoplankton growth in the GTMNERR. Algal populations in all of the nutrient addition bioassay experiments responded positively to the addition of nitrogen. Plus, the range of ambient DIN concentrations was similar to the range of published half-s aturation constants for phytoplankton nutrient uptake, which implies that phytoplankton growth was likely never saturated. On the other hand, nitrogen pulses within the GTMNERR (e.g., after rainfall events) were not accompanied by concom itant increases in chlorophyll a concentrations (Figure 2-11). This lack of an ecosystem-level response to appa rent changes in nitrogen load suggests that other factors limited the system-wide accumulation of phytoplankton biomass. Further evidence that nutrient limitation is not the ma in factor responsible for suppr ession of phytoplankton biomass in this system can be illustrated by another hypoth etical situation. Assu ming nitrogen limitation and Redfield proportions, a median DIN concentration of 60 g L-1 should support an average of 9 g L-1 CHL. The median CHL concentration over the study period, however, was half that theoretical amount. In fact, th e presence of theoretically unu sed bioavailable nitrogen was consistent throughout all four s easons (data not shown), which fu rther supports the hypothesis that nutrient availability was not the primary factor limiting p hytoplankton biomass within the GTMNERR. Grazing Zooplankton grazing rates found in this study were extremely variable but the range of rates was comparable to findings from the Indi an River Lagoon (IRL), a subtropical/tropical barbuilt lagoonal estuary (Table 2-8). However, the maximum grazing rates found in this study and in the IRL were at least half the maximum rate s found in subtropical, river-dominated systems (Table 2-8). This finding suggests that the relative infl uence of planktonic grazers on phytoplankton biomass may be greater in systems that receive more allochthonous inputs. In 35

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fact, phytoplankton growth and microzooplankton grazing have been found to covary along salinity and trophic gradients (Ruiz et al., 1998; Lehrter et al., 1999). Although the number of experiments in this study was limited, differences observed between zooplankton grazing rates at the SS and FM monitoring sites may be indicative of spatial variation du e to trophic state. Salinity was similar between the two sites, but the regions differed in their nutrient load regimes (see Chapter 3). The SS site is influenced by one of the oldest devel oped watersheds in the country, while the area around FM remains re latively undeveloped. Also, the lower Im values observed at SS compared to FM may be the result of an influx of colored dissolved organic matter (CDOM) and particulate material from the San Sebastian River after rainfall events. One would expect higher inputs of nutri ents and organic material to be related to higher grazing rates, but since the opposite pattern was observed, differences may be due to regional differences in plankton species composition. A caveat concerning comparisons of GTMNERR and IRL grazing rates with other studies is that the net impact of the entire zoopla nkton community was considered rather than only measuring microzooplankton grazing as most other studies have done. Since larger zooplankton directly consume microzooplankton and can be less important consumers of phytoplankton than microzooplankton (Liu and Dagg, 2003), net community graz ing rates may be lower than those measured without pressures of mesozooplankton grazers. Quinlan et al. (2009) conducted grazing experiments on both whole water and wate r with mesozooplankton (> 202 m) removed, measuring net community and microzooplankton impacts, respectively. In the summer, higher grazing rates were observed in microzooplankt on treatments than in whole community treatments. But, in the winter, the reverse pattern was observed, with equal or lower grazing rates in microzooplankton treatments compared to the whole water treatments. Since the 36

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proportions of zooplankton size classes have yet to be explored in the GTMNERR, their relative influences on phytoplankton dynamics is still unclea r. However, from an ecosystem perspective, the net impacts of microand mesozooplankt on grazing are most important for explaining observed patterns in phytoplankton abundance. In general, zooplankton grazing rates in the so utheastern United States are higher in the spring/summer than the fall/winter (Lehrter et al., 1999; Murrell et al., 2002; Putland and Iverson, 2007; Quinlan et al., 2009). Experiments in this study took pla ce in spring and summer, so these grazing rates may overestimate the aver age zooplankton influence. On the other hand, these experimental results were based on phytoplankton communities grown with surplus light and nutrients, so they may underestimate the relative impact of zooplankton grazing since phytoplankton growth is often limite d by light and/or nutrients in the natural environment (Day et al., 1989). Since phytoplankton differ in their sensitivity to grazing and zooplankt on have a range of prey preferences (Badylak and Phlips, 2008), detailed descriptions of the temporal and spatial dynamics of plankton abundances in the GTMNE RR are required to elucidate the mechanisms behind the patterns observed in this study. While mo re information is needed to fully understand the potential effect of planktoni c grazers, the relatively low zooplankton grazing rates compared to phytoplankton growth rates found in this study suggest that other lo ss processes such as advection or benthic grazing are equally or mo re important drivers of phytoplankton biomass in the GTMNERR. Bivalve suspension feeders have the potential to play a significan t role in controlling phytoplankton biomass in the GTMNERR, as observ ed in other ecosystems (Dame et al., 1980; Cloern, 1982; Officer et al., 1982) However, if estuary flus hing time is less than bivalve 37

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clearance time, then the grazing im pact would be likely limited to th e level of the bivalve bed or community, unless bivalve biomass:estuary volume is high (> 8 g m-3) (Dame, 1996). Estimations in this study suggest that the GTMNERR is a rapidly flushed system, with flushing times (2 14 days) less than oyster clearance time s (17 25 days). There was also a relatively low bivalve biomass:estu ary volume ratio (1.6 g m-3). These estimations suggest a bed-level influence of oysters on phytoplankton loss in the study area. However, the clearance time estimates used in these calculations may be too cons ervative. First, aerial coverage of oysters in the GTMNERR may have been underestimated due to difficulties in photographically documenting all oyster reefs at low tide (Ron Br ockmyer, St. Johns River Water Management District, personal communication). If aerial coverage was actually 0.70 km2 (double the original estimate), oyster biomass would double (from 6 to 12 g m-2), comparable to similar estuaries, such has North Inlet, SC (11 g m-2; Smaal and Prins, 1993). A doubling of oyster abundance estimates would decrease clearance time estimates to 8 12 days, in the same range as estuary flushing times. Clearance time estimates could have also been affected by low filtration rate estimates. Filtration rate (3.2 L hr-1 g dry weight-1) was estimated using a mesocosm-based equation developed by Doering and Oviatt (1986) for Mercenaria mercenaria If filtration rate was calculated with Riisgrds (1988) la boratory-based model developed for Crassostrea virginica (4.1 L hr-1 g dry weight -1) instead, clearance times would be 13 19 days. Interestingly, both filtration rate estimates were quite a bit lower than the 7 L hr-1 g dry weight -1 used by Dame et al. (1980) and Small and Prins ( 1993) to calculate oyste r clearance times in North Inlet, SC, which Small and Prins (1993) found to be slightly less than estuary residence time. A filtration 38

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rate of 7 L hr-1 g dry weight -1 would yield clearance times of 8 11 days for the area considered in this study. Finally, clearance time estimates developed in this study did not include community-level effects. Other species of benthic suspension feed ers are common on intertidal oyster reefs in this region (Boudreaux et al., 2006) and contribute to losses of ph ytoplankton biomass. Reef structure is also thought to enhance sediment ation of phytoplankton biomass (Cressman et al., 2003). Therefore, the effect of benthic grazer co mmunities relative to the effect of flushing time on losses of phytoplankton biomass may be greater than what has been implied here by only considering the cumulative e ffect of individual oysters. The comparative influence of planktonic a nd benthic grazers on phyt oplankton biomass in the GTMNERR is difficult to assess in the context of this study because of the small number of experiments and diverse methods for estimating impacts. Oysters were estimated to remove 5 40 % of annual phytoplankton production, while zooplankton removed 0 50 % of primary production in laboratory expe riments. These estimates suggest an almost equal influence of the two groups of grazers on phytoplankton biomass. However, due to differences in the life cycles of benthic and planktonic graze rs, bivalve grazing represents a more consistent top-down pressure on phytoplankton biomass than zoopl ankton grazing. T ypically, zooplankton abundances are episodic and increases in numbers tend to lag behind phyt oplankton blooms (Day et al., 1989). Theref ore, the lack of in situ phytoplankton blooms observed in the GTMNERR may be evidence of benthic grazer importance. More research is required to adequately assess the relative effects of grazing on phytopl ankton biomass; howev er, Dame (1996, pg. 136) suggested that when conditions favor benthic filter feeders, they w ill dominate planktonic grazers because, 39

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their food chains are shorter, they take advantage of tidal energy subsidies to receive their food, and their l onger life spans with greater stored biomass stabilizes a given ecosystem over longer time periods with a greater variety of environmental cycles. Conclusions The magnitude of phytoplankton primary produc tion and biomass observed in this study was fairly small compared to estuarine and coastal ecosystems around the world. While interannual variability in phytopl ankton biomass was also relatively small, seasonality was inconsistent for most of the year in the Guan a Tolomato Matanzas National Estuarine Research Reserve (GTMNERR). The overall similarity of temporal patterns in phytoplankton biomass and productivity observed seems to corroborate Cloe rn and Jassbys (2010) assertion that mean phytoplankton biomass is an indicator of the variability of ecosystem proc esses such as nutrient cycling and energy transfer. Therefore, low chlorophyll a levels within the GTMNERR may represent the systems relative stability, indi cating somewhat of a ba lance between production and consumption. The diverse array of factors controlling phytoplankton biomass in the study area probably work together at different spatia l and temporal scales to keep bi omass low. For example, annual cycles of temperature, irra diance, and photoperiod may driv e seasonal changes in algal abundance, while seemingly random events outs ide the estuary (red tides) can cause large deviations in that pattern. At th e scale of days to weeks, nutrients have the potential to stimulate phytoplankton growth, but exchange with the Atla ntic Ocean, the combination of benthic and planktonic grazing, and occasional light limita tion prevent the accumulation of phytoplankton biomass. Low interannual variability over th e seven years of extremely diverse climatic conditions may reflect the importance of topdown control in the GTMNERR. Plus, the persistence of a regular seasonal pattern without bloom events s uggests that consistent benthic 40

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grazing is responsible for the majority of phytoplankton biomass uptake. Since seasonal changes in light and temperature are less dramatic in the subtropics than in temperat e regions, the role of grazers in controlling phytoplankt on biomass may be relatively more important at lower latitudes than farther north. 41

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Table 2-1. Summary statistics fo r variables measured monthly at FM and SS from January 2003 December 2009. Variable N Mean Median Std dev Minimum Maximum SRP (g P L-1) 168 14.8 13.0 9.8 1.0 67.0 TP (g P L-1) 165 55.4 52.0 18.9 19.0 121.0 NH4 (g N L-1) 167 62.1 47.3 44.8 3.2 244.2 NO2 (g N L-1) 163 3.6 2.5 3.3 0.0 19.8 NO3 (g N L-1) 154 15.1 10.3 15.1 0.0 86.6 NO23 (g N L-1) 156 18.6 11.8 17.5 0.9 91.7 DIN (g N L-1) 156 80.6 60.5 56.5 5.2 290.3 DON (g N L-1) 145 238.9 220.2 101.2 39.0 528.0 PN (g N L-1) 147 105.2 91.9 86.4 0.7 594.7 TDN (g N L-1) 154 310.3 282.4 121.9 108.4 814.3 TN (g N L-1) 162 404.6 375.5 158.4 159.9 1011.7 DIN:SRP 155 7.3 4.9 9.5 1.7 88.5 TN:TP 159 7.7 7.5 2.8 2.1 19.7 Si (mg Si L-1) 165 1.4 1.3 0.8 0.1 4.6 CHL (g L-1) 168 4.7 4.1 2.9 0.6 23.4 Temp_Surface (C) 164 22.9 23.6 5.2 12.1 31.0 Temp_Bottom (C) 165 22.6 23.4 5.1 12.5 30.9 Salinity_Surface 166 31.4 32.1 3.1 11.7 36.4 Salinity_Bottom 162 31.6 32.3 2.8 14.6 36.4 DO_Surface (mg L-1) 164 6.3 6.1 1.3 3.6 10.5 DO_Bottom (mg L-1) 163 6.3 6.2 1.2 3.6 10.0 CDOM (pcu) 167 16.8 12.0 16.6 0.4 147.3 Kt (m-1) 162 1.6 1.5 0.7 0.6 4.4 Im (mol photons m-2 d-1) 162 7.8 7.4 3.3 1.9 22.9 PAR (millimoles m-2) 164 35775 35347 10612 13374 57018 Table 2-2. Mean light ex tinction coefficients (Kt, m-1) and percent contributions of water, tripton, color, and phytoplankton at Ft. Mata nzas (FM) and San Se bastian (SS) from January 2003 December 2009. Station Kt% water % tripton % CDOM % phytoplankton FM 1.6 2.7 75.5 17.0 4.8 SS 1.7 2.7 79.8 11.7 5.8 Table 2-3. Mean light availabi lity through the water column (Im; mol photons m-2 d-1) at Ft. Matanzas (FM) and San Sebastian (S S) from January 2003 December 2009. Season FM SS Spring (March May) 8.7 7.3 Summer (June August) 10.5 9.1 Fall (September November) 7.8 6.2 Winter (December February) 7.5 5.5 42

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Table 2-4. Spearman rank correlation coefficients (top) and p-values ( bottom) from monthly grab samples at the Ft. Matanzas and San Sebastian sites from 2003-2009. CHL (g L-1) Temperature (C) Im (mol photons -2-1m d ) Salinity (ppt) CDOM (pcu) SRP 0.06 0.34 0.22 0.23 0.41 0.41 <0.0001 <0.01 <0.01 <0.0001 TP 0.36 0.39 0.32 0.18 0.30 <0.0001 <0.0001 <0.0001 <0.05 <0.0001 DIN 0.16 0.26 0.21 0.40 0.43 0.05 0.001 0.01 <0.0001 <0.0001 TN 0.28 0.45 0.07 0.18 0.24 <0.001 <0.0001 0.37 <0.05 <0.01 Silica 0.16 0.43 0.06 0.24 0.46 0.05 <0.0001 0.46 <0.01 <0.0001 CHL 0.51 0.13 0.13 0.09 <0.0001 0.10 0.09 0.26 Table 2-5. Ambient nutrient and ch lorophyll a concentrations (g L-1) during sample collection for nutrient addition bioassay and zooplankton grazing experiments. Site Date NH4-N NO23-N SRP-P Silica-Si CHL SS 10 March 2009 23.4 17.6 9.00 1070 5.43 FM 10 March 2009 23.9 9.50 8.00 984 6.16 mid-point 10 March 2009 14.0 8.00 7.00 1410 8.00 SS 9 June 2009 128 46.8 24.0 2460 8.09 FM 9 June 2009 63.4 17.2 17.0 2560 10.7 mid-point 9 June 2009 144 40.5 32.0 3420 9.10 Table 2-6. Nutrient-limited (change in biomass fr om initial to Day 1 in control treatment group) and non-nutrient-limited (change in biom ass from Day 1 to Day 2 in P+N+Si treatment group) growth rate (day-1) and doubling estimates from nutrient addition bioassay experiments and maximum predic ted growth rates based on temperature alone (Eppley, 1978). Station Date Nutrientlimited growth rate Nutrientlimited doublings day-1 Nonnutrientlimited growth rate Nonnutrientlimited doublings day-1Predicted doublings day-1 SS March 09 0.84 1.21 1.16 1.67 2.66 FM March 09 0.93 1.34 0.92 1.33 2.83 Midpoint March 09 0.48 0.69 0.86 1.24 2.83 SS June 09 1.07 1.54 0.88 1.27 5.01 FM June 09 0.80 1.15 0.58 0.84 4.70 Midpoint June 09 0.91 1.31 0.50 0.72 5.01 43

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44 Table 2-7. Apparent growth rates (k; day-1), grazing rates (g; day-1), percent biomass grazed per day (Pi), and percent potential production grazed per day (Pp) observed during dilution experiments from water collected at the Ft. Matanzas (FM) and San Sebastian (SS) monitoring sites. Station Sampling Date g (-slope) slope p-value k (yintercept) yintercept p-value doublings d-1PiPp FM 10 March 09 0.48 0.0073 1.47 <0.0001 2.12 38 50 SS 10 March 09 -0.03 0.8603 1.15 <0.0001 1.66 0 0 FM 9 June 09 0.23 0.0372 1.36 <0.0001 1.95 21 28 SS 9 June 09 0.10 0.2534 1.59 <0.0001 2.30 0 0

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45 Table 2-8. Comparison of annual productiv ity, zooplankton grazing rate, and phytoplankton growth rate estimates among estuarie s in the subtropical southeastern United States. Location Estuarine Structure Annual Productivity (g C m-2 yr-1) Zooplankton Grazing Rates (day-1) cPhytoplankton Growth Rates (day-1) cSource 255 g C m-2 yr-1 a Mortazavi et al. (2000) Apalachicola Bay, Gulf of Mexico, FL river-dominated, wellflushed, semienclosed bay 0.00 1.95 0.08 1.92 Putland and Iverson (2007) river-dominated bay 0.05 0.96 -0.09 2.06 bay mouth -0.03 2.44 0.25 2.87 Mobile Bay, Gulf of Mexico, AL offshore -0.09 2.93 0.01 3.45 Lehrter et al. (1999) 0.08 1.25 0.33 1.66 Murrell et al. (2002) Pensacola Bay, Gulf of Mexico, FL river-dominated, semienclosed bay 291 g C m-2 yr-1 a; 288 g C m-2 yr-1 b Murrell et al. (2007) Suwannee River Estuary, Gulf of Mexico, FL river-dominated, wellmixed delta 0.12 1.45 0.41 2.76 Quinlan et al. (2009) Matanzas River Estuary, Atlantic coast, FL well-flushed, bar-built lagoon 153 g C m-2 yr-1 b0.00 0.48 0.48 1.59 present study Indian River Lagoon, Atlantic coast, FL long, narrow, bar-built lagoon with varying degrees of hydraulic flushing 0.24 0.33 -0.035 0.436 Phlips et al. (2002) Pa 14C measurements; b BZI model; c Landry dilution technique

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Figure 2-1. Site map. The ticked line marks th e estuary boundary used for oyster filtration calculations. The black circles represent locations of the San Sebastian and Ft. Matanzas water quality SWMP stations. 46

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0 200 400 600 800 1000 1200 1400 1600 2003200420052006200720082009rainfall total (mm) Figure 2-2. Total annual precipitation at the GTMN ERR weather station. 0 50 100 150 200 250 300 350 400 450 500Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Sep-04 Nov-04 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 rainfall total (mm) Figure 2-3. Total monthly precipitati on at the GTMNERR weather station. 47

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10 15 20 25 30 35 40Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 Salinity Figure 2-4. Salinity at Ft. Mata nzas (gray line) and San Seba stian (black line) measured monthly. 48

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5 10 15 20 25 30 35Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09Water Temperature (C)0 10000 20000 30000 40000 50000 60000PAR (millimoles m-2) Figure 2-5. Water temperature (black line) m easured at the Ft. Matanzas site and total photosynthetically active radia tion (PAR, gray line) measured at the weather station on each sampling day. 49

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0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 Light Attenuation (m-1) Figure 2-6. Light attenuation m easured during each monthly sampling event at Ft. Matanzas (gray line) and San Sebastian (black line) from January 2003 December 2009. 50

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0 5 10 15 20 25Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09Im (mol photons m-2 d-1) Figure 2-7. Mean light availabi lity in the water column (Im) during each mont hly sampling event at Ft. Matanzas (gray line) and San Se bastian (black line) from January 2003 December 2009. The dashed line represents the upper limit to the estimated light limitation threshold. 51

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2003 2004 2005 2006 2007 2008 2009 0 2 4 6 8 10 12Chlorophyll a (g L-1) Figure 2-8. Mean annual chlorophyll a concentrations. Bars represent one standard deviation. 52

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0 500 1000 1500 2000 2500 3000Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 BZI (mg C m-2 d-1)0 5 10 15 20 25chlorophyll a (g/L) BZI CHL A 0 500 1000 1500 2000 2500 3000Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 BZI (mg C m-2 d-1)0 5 10 15 20chlorophyll a (g/L) BZI CHL B Figure 2-9. Monthly chlorophyll a (CHL) concentrations and produc tivity estimates (BZI) from 2003 2009. A) from the Ft. Matanzas site, B) from the San Sebastian site. 53

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0 2 4 6 8 10 12 JanFebMarAprMayJunJulAugSepOctNovDecchlorophyll a (g L-1) 2003 2004 2005 2006 2007 2008 2009 Figure 2-10. Seasonal CHL variability from 2003 2009, excluding the Oc tober 2007 red tide event (20 g L-1). Values averaged from the Ft. Matanzas and San Sebastian sites. 54

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0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09DIN (mg L-1)0 5 10 15 20 25Chlorophyll a (g L-1) FM CHL FM DIN Figure 2-11. Monthly concen trations of chlorophyll a (CHL), ammonium (NH4), and nitrate+nitrite (NO23) at the Ft Matanzas (FM) monitoring site. 55

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0 5 10 15 20Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09TN:TP (g:g) Bioassay Bioassay Figure 2-12. TN:TP at Ft. Mata nzas (gray line) and San Sebas tian (black line) sites from 2003 2009. Dotted lines represent th e Redfield ratio of 7.2. 56

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0 5 10 15 20 25 30 35 40Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09DIN:SRP (g:g) 88.5 61.3 Bioassay Bioassay Figure 2-13. DIN:SRP at Ft. Mata nzas (gray line) and San Sebastian (black line) sites from 2003 2009. Dotted lines represent the Redfield ratio of 7.2. 57

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Figure 2-14. Average phytoplankton biomass (e stimated by fluorescence) in nutrient addition treatment groups during March and June 2009 bioassay experiments. 58

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Figure 2-15. Maximum biomass (estimated by fluorescence) of each treatment group in the nutrient addition bioassay experiments. *s ignificantly different from control ( = 0.05) 59

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Figure 2-16. Schematic representing important factors controlling phytopl ankton biomass in the GTMNERR. The relative thickness of arro ws approximates the relative influence of each factor. 60

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CHAPTER 3 OYSTERS AS INDICATORS OF TROPHIC STATUS IN A HIGHLY FLUSHED ESTUARY Introduction Predicting the consequences of eutrophication in coastal ecosystems has become a major priority for marine researchers (Hobbie, 2000). Understanding the e ffects of increases in nutrient load to estuaries has been particularly challenging due to interactions between multiple limiting factors, such as tidal mixing and freshwater discharges from rivers (Meeuwig, 1999; Cloern, 2001). In addition, structural differences between estuaries impact the resp onse to nutrient loads. For example, estuaries with high water turnover rates function differently than estuaries with long water residence times (Monbet, 1992; NRC, 2000) In estuaries with long water residence times, increases in nutrient load can be expr essed as elevated phytoplankton biomass (Knoppers et al., 1991; Phlips et al., 2002, 2004; Bledsoe et al., 2004). In cont rast, increases in load to estuaries with rapid tidal water exchange ma y not be associated with concomitant and proportional increases in phytoplan kton biomass for a number of reasons, such as rapid flushing of nutrients and biomass before they accumula te (Josefson and Rasmussen, 2000; NRC, 2000). Given these considerations, in some ecosystems it may be more suitable to focus on other components of the estuarine community, rather th an plankton, when evaluating the impacts of changes in nutrient load. Benthic invertebrates have been used effectiv ely as indicators of nutrient enrichment in estuaries because they integrate environmental conditions over greater periods of time than do the rapidly changing plankton and nutrient accu mulations (Pearson and Rosenberg, 1978; Boesch and Rosenberg, 1981; Graves et al., 2005). In estuaries along the southeas t coast of America, eastern oyster ( Crassostrea virginica ) populations are especially pr omising indicators of water quality because they are a wide ly distributed key feature of the benthic landscape (Bahr and 61

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Lanier, 1981). Crassostrea virginica could be considered a keystone species because they support an entire community of organisms (Tolley and Volety, 2005; Boudreaux et al., 2006) and filter large quantities of estuarine water (Dame et al., 1980). Since oysters are thought to play a role in phytoplankton removal and nutrient reten tion in shallow estuaries (Dame et al., 1989; Dame and Libes, 1993; Smaal and Prins, 1993), they offer an indirect measure of eutrophication. In our study, the lagoonal habi tats of the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) in northeast Florid a were used to inves tigate how well-mixed estuaries with strong tidal influence respond to di fferent nutrient load scenarios. Most of the GTMNERR is subject to high tidal flushing due to the proximity of two inlets to the Atlantic Ocean (Sheng et al., 2008; Figure 3-1); however, nutrient loads differ between regions of the estuary. At one extreme, near the city of St. Augustine, the estuary receives nutrient-enriched inputs from the oldest urbanized watershed in the United States. Three wastewater treatment plants discharge into the estuary near the city. Untreated stormwater runoff, septic systems, and numerous marinas also release nutrients into the lagoon. In c ontrast, the watershed of the Matanzas Inlet region of the estu ary consists mainly of protec ted salt marshes, where marinas and major wastewater treatment discharges are absent. Differences in nutrient inputs between the two regions of the GTMNERR provide an opportunity to examine how estuarine environments with similar tidal pr operties and climatic influences responded to varying degrees of lo ad. Previous research has shown that the magnitude of spatial differences in phytoplankt on standing crops does no t adequately reflect differences in nutrient status between the St. Augustine and Matanzas re gions (Phlips et al., 2004). Alternatively, it wa s hypothesized that long-term differen ces in nutrient regimes between the two regions would lead to differences in food availability for oyster po pulations, in terms of 62

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allochthonous and autochthonous carbon, and yiel d differences in oyster density, relative size distribution, and condition (P earson and Rosenberg, 1978; Ce derwall and Elmgren, 1980; Lawrence and Scott, 1982; Volety and Savarese 2001; Kirby and Miller 2005). Specifically, oysters in the St. Augustine region were expect ed to be larger, more densely populated, and exhibit higher condition index scores than those in the Matanzas re gion. The results of this study support this expectation and provide insights into the ro le of filter-feeding benthic invertebrates as indicators of trophic status. Methods Site Description The GTMNERR, as part of the System-W ide Monitoring Program (SWMP; Kennish, 2004), maintains water quality monitoring sites in the St. Augustine and Matanzas regions (Figure 3-1). The San Sebastian site is located at the mouth of the San Sebastian River, a tidal creek which drains the urbanized watershed of St. Augustine and feeds into the estuary approximately 4 km south of the St. Augustine Inle t (Figure 3-1). Data from this site have revealed relatively high nutrient lo ads (Phlips et al., 2004). In c ontrast, water samples collected at the Fort Matanzas site, which is located approximately 4 km north of the Matanzas Inlet (Figure 3-1), have revealed relatively low nutrient loads (Phlips et al., 2004). Relatively low phytoplankton standing crops (chlorophyll a concentrations) and high salinities have been observed at both sites (Phlip s et al., 2004). Tidal ranges are 1.7 m and 1.4 m for the San Sebastian and Ft. Matanzas sites, respectively (NOAA, 2008). To explore potential differences in nutrient and carbon loading rate s between the St. Augustine and Matanzas regions, loads were estimated for three local freshwater sources by combining historical water discharge and nutrient/carbon concentration data from va rious public agencies (Table 3-1). 63

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Water Quality Sampling YSI 6600 continuous monitoring data sondes me asured temperature, salinity, dissolved oxygen, and water depth at 30-minute intervals at th e San Sebastian (SS) and Ft. Matanzas (FM) SWMP sites (NOAA, 2008). Data from 2002 to 2008 were downloaded from the Centralized Data Management Office website (http://cdmo.baruch.sc.edu; NOAA, 2008). Water was also collected monthly at the SS and FM sites fr om 2002 through 2008. Two re plicate whole water samples were collected from a depth of 1.5 m using a polyvinyl chloride (PVC) pole sampler (Venrick, 1978). Whole water samples were fi ltered through glass-fiber filters (0.7 m pore size) for soluble inorganic nutri ents. Samples for chlorophyll a (CHL) and particulate organic carbon (POC) determination were filtered onto gla ss-fiber filters (0.7 m pore size). Samples were stored on ice and transported to the University of Florida laboratory in Gainesville, FL for processing. To test for stratification, salinity and temperature at the bottom and surface of the water column were measured with an environmental multi-parameter probe (i.e., Hydrolab Quanta multi-probe). Water Chemistry Methods to determine total nitrogen (TN), ammonium (NH4), n itrate (NO3), nitrite (NO2), dissolved inorganic nitrogen (DIN ), total phosphorus (TP), sol uble reactive phosphorus (SRP), and CHL concentrations are described in Chapter 2. POC concentrations were determined against a dextrose standard using a coulometer (APHA, 1998). Phytoplanktonic carbon concentration was estimated by assuming a Redfield ratio of 40 g carbon:1 g TP (Redfield et al., 1963), and a 1:1 ratio of TP:CHL (Reynolds, 2006). Oyster Population Descriptions A stratified random sampling design was employed to determine oys ter collection sites (Krebs, 1999). Reefs at the edge of the Intracoastal Waterway w ith visible dead margins were 64

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not included in the collections (Walters et al., 2007). Two oyster-sampling events were completed in 2008. During the winter (middle of February), four reefs in the Matanzas region and four reefs in the St. Augustine region were sampled. In the summer (end of July and beginning of August 2008), the eight reefs were re -sampled and two additional reefs were added in each region (Figure 3-1). Following methods of Bergquist et al. (2006) two transects were traversed on each reef: one along the highest el evation (the top ridge) and another along the lowest (the edge of the reef). Percent cover within a 1.0 m2 grid was estimated at six random points along each transect. The grid contained 100 intersecting points of nylon string. Each intersection over a living oyste r was counted, and that number was divided by 100 to determine the percent cover. Oysters from one 0.25 m2 quadrat per transect were collected, rinsed, and counted for direct density measurements. The le ngth of each oyster was measured with calipers. A subsample of oysters (n 52) was frozen for biomass and condition index (CI) analyses. CI was determined using the following formula: CI = [Tissue Dry Weight (g) / Sh ell Cavity Volume (ml)] 100, where cavity volume was determined gravimetri cally as the difference between the weight of the whole oyster and the weight of the shells measured immediately after shucking (Galtsoff, 1964). The wet weight (biomass) of the material inside the whole oyster was converted to shell cavity volume by assuming a density of 1 g ml-1 (Lawrence and Scott, 1982; Abbe and Albright, 2003). Dry weight was determined after dryi ng the meat at 105 C for 24 hours. Mean individual biomass for each sample was multiplied by four times the number of oysters in the 0.25 m2 quadrat to obtain a biomass estimate per square meter. Statistical Analyses SAS Software, Version 9.1.3 (SAS Institute, Cary, NC), was used for statistical computations. Differences in monthly mean trophic state parameters (i.e., CHL, POC, and 65

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nutrient concentrations) were te sted between wet and dry seasons and between St. Augustine and Matanzas regions (represented by San Seba stian and Ft. Matanzas monitoring sites, respectively). Wet season was defined as Ma y through October, while dry season included November through April. The non-parametric Krus kal-Wallis test was used since distributions were non-normal (determined by the Shapir o-Wilk and Kolmogorov-Smirnov goodness-of-fit tests). Generalized linear mixed models were used to test for significant relationships between fixed environmental conditions (region, season, a nd oyster reef position) and measured response variables (oyster length, density, percent cover, biomass, and condition index). Seasons were defined as winter and summer oyster sampling ev ents, regions as St. Augustine and Matanzas, and reef positions as high and low. A fourth i ndependent variable, size cl ass, was included in the model for oyster condition index. Size classes were defined as spat (< 2.5 cm), small (2.5 4.9 cm), pre-fishery (5.0 7.5 cm), and fishery (> 7.5 cm; Bergquist et al., 2006). The normal distribution was used in the models for oys ter length, biomass, and condition index. Distributions for density and percent cover mode ls were selected as Poisson and binomial, respectively. All models were adjusted for autocorrelation between measurements on the same reef during the two sampling even ts. Tukeys test was used to determine differences between means ( = 0.1). Results Water Quality From 2002 2008, mean monthly temperature, sa linity, and dissolved oxygen levels were essentially identical at San Sebastian (SS) a nd Ft. Matanzas (FM) (F igure 3-2). The water columns at each of the two water quality sampling sites were well-mixed. The mean difference in salinity between the surface and bottom at SS from August 2007 August 2008 was 0.09 and 66

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the mean temperature difference was 0.08 C. Th e mean difference in salinity between the surface and bottom was also 0.09 at FM, and the mean temperature difference was 0.14 C. Salinity and temperature differences between the surface and bottom were always less than one unit of measure at both sites from August 2007 August 2008. Salinities at SS and FM ranged from 31 to 36 from mid-January to mid-February (one month prior to winter oyster sampling). Salinities ranged from 33 to 38 from July to Augu st (one month before summer oyster sampling). Water temperatures at SS and FM ranged from ar ound 14 C at the end of January to 17 C at the beginning of February. Water temp eratures in July and August we re near 28 C in both regions. Monthly chlorophyll a (CHL) concentrations at the water quality sampling sites exhibited a seasonal pattern, with the highe st concentrations in the wet season (May October) and the lowest in the dry season (November April) (Figure 3-3). The hi ghest peak in CHL occurred in October 2007 due to the incursion of a red tide from offshore. Monthly particulate organic carbon (POC) concentrations did not exhibit as c onsistent a seasonal pattern as CHL (Figure 33). Immediately before the wint er oyster sampling event, CHL and POC concentrations at the SS and FM sites were relatively low. Before the summer oyster sampling event, CHL concentrations were relatively high and POC values were near average at both sites. Estimated phytoplanktonic carbon was genera lly less than 40% of POC (F igure 3-3), indicating the presence of non-algal particulate orga nic carbon, such as detritus. Mean wet season concentrations of CHL, POC, and nutrients were higher than mean dry season concentrations (Table 3-2). On average, nutrient concentrations were not significantly different between regions, but POC and CHL co ncentrations were highe r at SS than at FM (Table 3-2). 67

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Nutrient Load The San Sebastian River, a major source of watershed inputs to the St. Augustine region, exhibited a mean annual discharge up to two orders of magnitude higher than Moses Creek in the Matanzas region from 2000 2002 (Figure 3-1, Table 3-3). Fr om February 2001 September 2002 monthly estimates of total nitrogen (TN) and total phosphorus (TP) loads from the San Sebastian River into the St. A ugustine region were substantially higher than loads from Moses Creek into the Matanzas region (F igure 3-4). Integrating the nut rient loads over the period from February 2001 to September 2002 yielded rough estimates of 59,200 and 1,500 kg of TP for the San Sebastian River and Moses Creek, respectively. Integrating TN loads over the period from February 2001 to September 2002 yielded rough estimates of 352,100 and 9,700 kg of TN for the San Sebastian River and Moses Creek, respectively. Although no POC values were available for Moses Creek, a comparison of average esti mated loads during December 2002 March 2003 and July 2003 showed higher loads from the San Sebastian River (12,977 mg C sec-1) than from another tidal creek south of the Matanzas Inlet; i.e., Pellicer Creek (5,500 mg C sec-1). Oyster Density The highest mean oyster de nsity (1,131 individuals m-2) was observed at high reef elevations during the summer in the St. Augustine region (Figure 3-5). At both high and low reef elevations, mean oyster densities were significantl y higher in the summer than in the winter (p < 0.0001). When only high reef elevations were considered, mean oyster density was significantly higher in th e St. Augustine region than in the Matanzas region (p < 0.01). The highest mean percent cover estimates were observed at high reef positions in the St. Augustine region during the summer (27.4 %) and winter (2 7.5 %; Figure 3-5). Overall, mean percent cover was greater in the summer than in the wi nter (p < 0.001) and greater in the St. Augustine 68

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region than in the Matanzas region (p < 0.1). Mean oyster density and percent cover were consistently less at low reef elevations than at high elevations (Fi gures 3-5 and 3-6). When separated by size class, oyster density exhibited analogous s easonal and regional patterns. At high reef elevations, spat, sma ll, pre-fishery, and fish ery-size oysters were significantly more abundant in the summer than in the winter (p < 0.0001, < 0.0001, < 0.01, and < 0.01, respectively) and spat, smal l, pre-fishery, and fishery-size oysters were significantly more abundant in the St. Augustine region than in the Matanzas region (p < 0.05, < 0.11, < 0.01, and < 0.01, respectively). At low reef elevations, spat and small oyste rs were significantly more abundant in the summer than in the winter (p = 0.01 and < 0.0001, re spectively). Spat were significantly more abundant in the St. Augustine region than in the Mata nzas region (p < 0.1). Overall, at both reef elevations, oysters in the sma ll size class (2.5 4.9 cm) were most abundant (Figure 3-7). Oyster Length and Biomass When both reef elevations were considered, th e mean lengths of oysters were significantly greater in the winter than in the summer (p < 0.05) In addition, the mean lengths of oysters were significantly greater at high eleva tions than at low elevations (p < 0.05). Oysters exhibited the greatest mean length (5.0 cm) in the St. Augustine region at high reef eleva tions (Figures 3-5 and 3-6). However, no significant differences were observed in mean oyster length between regions, whether high and low reef positions were considered together or separately. The greatest mean biomass (9.9 kg m-2) was observed in the St Augustine region during the summer at high reef eleva tions (Figures 3-5 and 3-6). Overall, mean biomass was significantly greater in th e summer than in the winter (p < 0.01), and greater in the St. Augustine region than in the Matanzas region (p < 0.05). Mean biomass was also significantly greater at high reef elevations than at lo w reef elevations (p < 0.001). 69

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Oyster Condition Index The greatest mean condition index (CI) score (1 0.9) was observed during the winter in the St. Augustine region at low reef el evations (Figures 3-5 and 3-6) When both reef elevations were considered, mean CI was significantly gr eater in the St. Augustine region than in the Matanzas region (p < 0.05). CI was inversely re lated to size class. When each size class was modeled independently and both reef positions were considered, mean CI of small oysters was significantly greater in th e St. Augustine region than in the Ma tanzas region (p < 0.05). Mean CI of oysters in the pre-fishery and fishery size classes were signifi cantly greater in the winter than in the summer (p < 0.1). Discussion The results of this study in the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) support our hypothesis that density and bi omass of benthic organisms may be better indicators of trophic status in highly flushed estuarie s than ambient concentrations of nutrients, phytoplankton biomass, or particulate organic carbon (POC). Large nutrient inputs from the watersheds in the St. Augustine regi on of the GTMNERR, compared to the Matanzas region, were reflected in higher average concentrations of nutrients, chlorophyll a (CHL), and POC in the St. Augustine region. Statistically, CH L and POC concentrations were significantly different between regions, but the magnitude of these regional dispar ities (15 % and 21 % difference between regions, respectively) does no t adequately reflect the large magnitude of estimated differences in nitrogen, phosphorus, an d carbon load to the two regions. By contrast, regional differences observed for oyster biomass (109 %), density (64 %), and percent cover (41 %) were considerably more pronounced. In othe r words, differences in concentrations of phytoplankton and other forms of POC did not accurately reflect the availability of carbon to the oyster populations. This suggests th at the effects of relatively sm all differences in ambient food 70

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concentrations are amplified by elevated levels of water exchange and flow in highly flushed ecosystems, such as described for coral re efs (Adey and Steneck, 1985). In coral reef ecosystems high productivity is achieved in a low-nutrient environment, in part, through enhanced nutrient flux produced by water motion (Hearn et al., 2001) and the same influences may be present on oyster reefs in the GTMNERR. It is, of course, important to consider other abiotic and biotic factors that could have contributed to regional differences in oyster abundance and biomass. Abiotic factors that have been shown to influence distribution include tid al range, salinity, temperature, dissolved oxygen, current velocity, and substrat e quality (Grizzle, 1 990; Shumway, 1996; Livingston et al., 2000). Tidal range, salinity, temperature, and dissolved oxygen were similar in both regions of the study. Current velocity was not directly measure d, but it is assumed to be similar since the St. Augustine and Matanzas regions are equidistant fr om inlets and are subject to similar hydrologic regimes (Sheng et al., 2008). Preliminary sediment studies indicate that both regions are primarily characterized by sand bottoms of similar particle size (Nicole Dix, personal observation). Potential biotic factors controlling oyster dist ributions include disease and predation. High salinities observed in both regi ons of the GTMNERR make local oysters susceptible to disease and predation. For example, Perkinsus marinus, a common oyster parasite, is often found at high temperatures and high salin ities (Shumway, 1996; Chu and Vole ty, 1997; La Peyre et al., 2003). Oyster predators (e.g., oyster drills, starfish, whelks, and crab s) are also more abundant at high salinities (Wells, 1961; Shumway, 1996). No obvious regional disparities in disease or predation were observed over the study period, sugge sting that this issue may not be responsible for the observed regional differen ces in oyster biomass and densit y. Vulnerability to disease, 71

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predation, siltation, and wave acti on may, however, explain why oyste rs at low reef elevations were less densely populated and exhibited lowe r mean biomass than oysters at high reef elevations (Kenny et al., 1990). Another biological characterist ic which may be associated with regional differences in oyster populations is the character of the bacterial community (S cott and Lawrence, 1982). The waters just north of the St. Augustine sampling region included in this study are closed to shellfish harvesting due to historically high fecal coliform bacteria count s (Beadle, 2004). In contrast, the Shellfish Harvesting Area in th e Matanzas region has not required closure (Browning, 2005). Higher concentrations of b acteria in the St. Augustine region might be expected to be correlated with a lower mean condition index (CI) comp ared to the Matanzas region if bacteria were harmful or indicate the presence of other harmful pollutants (i.e.; Scott and Lawrence, 1982). However, since mean CI was higher in the St. Augustine region, oysters may be able to use bacteria as a food source. Direct human influences, such as harvesting, ar e also potential driving factors for regional differences in oyster populations. Recreational and commercial harvesting is permitted in the Matanzas region of the GTMNE RR but not in the St. Augusti ne region (FWC, 2008); however, most commercial and recreational harvesting in the Matanzas regi on occurs outside of the reefs sampled in this study (Mark Berrigan, Florid a Department of Agriculture and Consumer Services, personal communication). Therefore, regional differences in oyster harvesting, although not directly measured, are not expected to have influenced oyster metrics. Boat wakes have been shown to cause oyster mortality along the margins of the Intracoastal Waterway in the Indian River La goon, pushing living reef away from the channel over time (Grizzle et al., 2002; Wall et al., 2005; Walters et al., 2007). The same dead margins 72

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are present in the GTMNERR, but were explici tly removed from the sampling design of this study to avoid confounding effects. The impact of seasonal differences in food avai lability on oysters in this study could have been confounded by the effects of the oyster spawning cycle. Oyst ers in the southeastern United States spawn continuously from April October/November (K enny et al., 1990; Thompson et al., 1996; Volety and Savarese, 2001; Volety et al., 2009). Although oyster recruitment patterns were not specifically addressed in this study, mean spat density observed at both reef elevations was significantly higher in the summer than in the winter, supporting the assumption of summer spawning. Although spat settlement in the summe r likely affected the relationship between season and oyster density in our model, oysters in all size classes (not just new recruits) at high reef elevations were denser in the summer than in the winter. Also, sinc e spat were not included in biomass or percent cover estimations, those metrics were less sensitive to effects of the reproduction cycle. Although some regional differences were apparent in CI scores of oysters, the metric was likely influenced by seasonal differences in re productive effort. Mean CI was higher in the winter than in the summer for oysters larger than 5.0 cm. Past studies have shown declining CI from winter to summer in response to the c onversion of glycogen to glucose and a loss of gametes during the spawning season (Shumway, 1996; Thompson et al., 1996; Volety et al., 2009). The negative relationship between size cla ss and CI further implies a correlation with reproductive status since f ecundity increases with size (Thompson et al., 1996). Within the context of all th e aforementioned factors whic h may contribute to spatial differences in the character of oyster communities in the GTMNERR, disparities in nutrient and particulate organic carbon loading remain hi gh on the list of potential drivers. 73

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From a broader perspective of ecosystem sustainability, another important question is the resilience of highly flushed estuaries to incr eased nutrient and orga nic carbon load often associated with human development (Nixon, 1995; Cloern, 2001). As a potential keystone species in the GTMNERR, the sustainability of oyster populations is a major management concern. The important ecosystem services provided by living oys ter reefs (i.e., habitat value, filtration capacity, etc.) make them sentinels for ecosystem function (Grabowski and Peterson, 2007). The results of this study indica te that the elevated nutrient and organic carbon loads in the St. Augustine region have a stimulatory effect on oyster biomass and density. Similarly, Pearson and Rosenberg (1978) described a general pattern of increasing biomass and abundance of benthic invertebrates along grad ients of increasing or ganic enrichment up to the point where detrimental effects of enrichment caused a de cline in abundance. Organic matter inputs can provide more food for benthic macrofauna, but, de pending on rates of water renewal, bacterial decomposition of organic material can lead to an oxic conditions and reduced habitat. In this study, regional and seasonal differences in oyster biomass and abundance were positively correlated to nutrient, CHL, and POC loads and le vels, suggesting that oy ster populations within the GTMNERR have not reached the threshold fo r adverse effects. The high water turnover rates that characterize this estuary may contribut e to its resilience to variable nutrient load. Hydrodynamic flushing, through dilution and removal pr ocesses, is thought to increase benthic community resilience to the ne gative impacts of watershed in puts (Nordby and Zedler, 1991; Fabricius, 2005). However, further increases in nutrient load could shift the response from positive effects on oyster biomass and abundance to negative effects on the health of the populations. For example, anthropogenic loading of nutrients can alter elemental ratios from the 74

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typical molar Redfield ratio of 16:1 and impact phytoplankton community composition, leading to less nutritious (and potentiall y more toxic) food species ava ilable to oysters (Cloern, 2001). Conclusions Given the similarity of water column c onditions in the Matanzas and St. Augustine regions of the Guana Tolomato Matanzas Natio nal Estuarine Research Reserve (GTMNERR), it is hypothesized that regional differences in oyster biomass and density were caused by differences in nutrient inputs and carbon availa bility. Traditionally monitored water quality parameters such as nutrient and chlorophyll a concentrations provide on ly modest indications of regional differences in trophic status in the GT MNERR (Phlips et al., 2004). Rapid biological uptake and tidal flushing create high temporal variability in the latter parameters, further obscuring long-term trends, a common problem in interpreting changes in highly dynamic coastal ecosystems (Zingone et al., 2010). Oyster populations, on the other hand, appear to be promising bioindicators of water quality, in part due to their wide distribution among estuaries throughout the world, making them ideal for inte r-system comparisons regardless of current, tide, salinity, or flow conditions (Bortone, 2005; Volety et al., 2009) Also, their sessile nature and relatively long life span allo w integration of environmenta l conditions over space and time (Dame, 1996). Incorporation of oyster population metrics, especially those estimating density and biomass, in estuarine monitoring program s will increase understa nding of eutrophication impacts in these systems. 75

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Table 3-1. Data sources for es timating nutrient and carbon load. Data Set Agency Source San Sebastian River Discharge (Feb 2001 July 2003) United States Geological Survey http://waterdata.usgs.gov/nwis (Site # 02246895) Moses Creek Discharge (Feb 2001 Sept 2002) United States Geological Survey http://waterdata.usgs.gov/nwis (Site # 02247027) Pellicer Creek Discharge (Dec 2002 July 2003) United States Geological Survey http://waterdata.usgs.gov/nwis (Site # 02247222) San Sebastian River Nutrients (Feb 2001 July 2003) St. Johns River Water Management District http://www.epa.gov/storet (Watershed ID: 03080201; Site ID: SSB) San Sebastian River Carbon (Dec 2002 July 2003) Guana Tolomato Matanzas National Estuarine Research Reserve http://cdmo.baruch.sc.edu (Station Code: gtmssnut) Moses Creek Nutrients (Feb 2001 Sept 2002) St. Johns River Water Management District http://www.epa.gov/storet (Watershed ID: 03080201; Site ID: JXTR21) Pellicer Creek Carbon (Dec 2002 July 2003) Guana Tolomato Matanzas National Estuarine Research Reserve http://cdmo.baruch.sc.edu (Station Code: gtmpcnut) Table 3-2. Mean nutrient, particulate organic carbon (POC), and chlorophyll a (CHL) concentrations (g L-1) compared between regions (re presented by the Ft. Matanzas and San Sebastian monitoring sites) and seasons. Results from non-parametric Kruskal-Wallis test for differences between means. San Sebastian Ft. Matanzas KruskalWallis p-value Wet season Dry season KruskalWallis p-value SRP 15 14 0.6625 17 12 <.0001 TP 54 52 0.4544 60 46 <.0001 NH4 585 561 0.4672 729 420 <.0001 NO2+3 184 136 0.6328 196 120 0.0002 TN 378 375 0.8652 436 317 <.0001 POC (mg L-1) 1.56 1.30 0.0016 1.56 1.30 <.0001 CHL 4.82 4.20 0.0001 5.76 3.27 <.0001 76

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Table 3-3. Mean annual discharge (m3 sec-1; *calculated from incomplete data set). Year San Sebastian River Moses Creek Pellicer Creek 1999 0.08 2000 7.87* 0.10 2001 12.94 0.38 2002 10.53 0.23 1.49* 2003 9.51* 2.66* 77

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Figure 3-1. Site map showing the San Sebastia n (SS) and Fort Matanz as (FM) System-Wide Monitoring Program sites (white triangles ) and oyster sampling locations (black dots). 78

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10 15 20 25 30 35Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08Temperature (C) 20 25 30 35 40Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08Salinity 4 5 6 7 8 9Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08DO (mg L-1) Figure 3-2. Mean monthly temperature, sali nity, and dissolved oxygen measured every 30 minutes at the Fort Matanzas (solid line) and San Sebastian (dashed line) SystemWide Monitoring Program sites from January 2002 December 2008. 79

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0 5 10 15 20 25 30Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08CHL (g L-1) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08POC (mg L-1) 0.0 0.1 0.2 0.3 0.4 0.5Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08Phyto C:PO C Figure 3-3. Seasonal tr ends in chlorophyll a concentration (CHL), particulate organic carbon (POC) concentration, and phytoplanktonic carbon (phyto C):POC, measured monthly at the Fort Matanzas (solid line) and San Sebastian (dashed line) System-Wide Monitoring Program sites from May 2002 December 2008. Figure 3-3. Seasonal tr ends in chlorophyll a concentration (CHL), particulate organic carbon (POC) concentration, and phytoplanktonic carbon (phyto C):POC, measured monthly at the Fort Matanzas (solid line) and San Sebastian (dashed line) System-Wide Monitoring Program sites from May 2002 December 2008. 80

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Figure 3-4. Monthly mean total nitrogen (TN) and total phosphorus (TP) load estimates from February 2001 September 2002 for Moses Creek (dashed line) and San Sebastian River (solid line). 81

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Figure 3-5. Mean oyster density, percent cover, biomass, and condition index from high reef elevations in the Ma tanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (w inter) and the July /August 2008 survey (summer). Different letters above bars represent statistically significant ( = 0.10) differences. 82

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Figure 3-6. Mean oyster density, percent cover, biomass, and condition index from low reef elevations in the Ma tanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (w inter) and the July /August 2008 survey (summer). Different letters above bars represent statistically significant ( = 0.10) differences. 83

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0 100 200 300 400 500 600 FM winterFM summerSA winterSA summerDensity (# m-2) spat small pre-fishery fisher y Figure 3-7. Mean oyster density a nd standard error in the spat (< 2.5 cm), small (2.5 4.9 cm), pre-fishery (5.0 7.5 cm), and fishery (> 7.5 cm) size classes from high and low reef positions in the Matanzas (FM) and St. A ugustine (SA) regions during the February 2008 survey (winter) and the Ju ly/August 2008 survey (summer). 84

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CHAPTER 4 SUMMARY The focus of this study was to characterize im portant processes relate d to the effects of nutrient enrichment in the Guana Tolomato Ma tanzas National Estuarine Research Reserve (GTMNERR) in northeast Florida. A defining char acteristic of the study area is its connection with the Atlantic Ocean through two inlets. Flus hing times are on the scale of days to weeks, salinities are consistently above 20, and the water column is well-mixed. In the seven years from 2003 2009, phytoplankton, the main aquatic prim ary producers in the GTMNERR, were observed in major bloom concentrations only on e time, a red tide event in which algae was transported into the lagoon from offshore. In situ production resulted in a maximum chlorophyll a concentration of 12 g L-1 and interannual variability was relati vely small. Results from this study indicate that hydrodynamic flushing is a major factor limiting algal biomass accumulation throughout the year. Grazing is another important control of algal biomass in the GTMNERR. In particular, the extensive oyster populations we re estimated to filter the entire volume of the study region in two to three weeks, about th e same timescale as hydrodynamic flushing. The direct influences of temperat ure and light on phytoplankton bioma ss in this subtropical/warm temperate region are apparently re stricted to a narrow time period in the winter when estimated primary productivity is consistently low. Li ght also has the potential to limit phytoplankton growth during episodic flushing events, especially near tributaries that transport colored and particulate material into the estuary. Inputs of nutrients, prim arily nitrogen, have the potential to stimulate phytoplankton growth, but concomita nt accumulation of phytoplankton biomass is rarely observed due to the consistent t op-down pressures of flushing and grazing. Physical and biological forces appear to ha ve influenced phytoplankton biomass more than nutrient loads during the study period. Therefore, as expect ed in this highly flushed estuary, 85

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effects of nutrient enrichment were difficult to discern by look ing at traditionally measured parameters like nutrient con centrations and phytoplankton biomass alone. On the other hand, regional and seasonal differences in nutrient loads and organic matter were positively correlated to oyster density and biomass in 2008. Apparentl y, the well-flushed char acter of this estuary promotes a certain level of resi stance to the negative impacts of eutrophication that have been observed in other hydrodynamically restricted system s. Of course, this conclusion must be taken with caution since changes in nutrient loads can also affect phytoplan kton species composition and be associated with other harmful impacts such as toxic contaminants that were not considered in this study. Significant alterati on of any one of the various bottom-up and topdown controls of phytoplankton biomass could upset the balance between production and consumption in the GTMNERR. The long-term monitoring program establ ished by the GTMNERR provided a foundation for studying temporal patterns in key physical, chemical, and biologi cal parameters related to the effects of nutrient enrichment. However, rapid biological uptake and tidal flushing create large variability in traditionally monitored parameters, making long-term trends and correlative relationships difficult to detect. Conclusions from this study sugge st that incorporation of oyster population metrics in estuarine monitori ng programs will increase understanding of eutrophication impacts in these systems. Overall, results from this study can be used to enhance the ability to predict how estu aries respond to increases in nut rients, thereby creating more effective management plans for the conservati on of estuarine systems around the world in an environment of ever increasi ng nutrient loads (Nixon, 1995). 86

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BIOGRAPHICAL SKETCH Nicole Dix Pangle grew up in Longwood, FL about 2 hours south of Gainesville. She graduated from Florida State University in 2002 with a B.S. in biology and a B.S. in science education. After that, she worked in the Environm ental Services Department of a major planning firm in Orlando. In the fall of 2004, she joined Un iversity of Florida's Department of Fisheries and Aquatic Sciences under Dr. Ed Phlips, earning her M.S. in December 2006. Her master's research examined temporal water quality variations within a tidal creek associated with the passage of the 2004 hurricanes. In 2006, Nikki received NOAA's National Estuarine Research Reserve Graduate Research Fellowship to continue working toward her Ph.D. Nicoles research interests include estuarine ecology and management, nutrien t and phytoplankton dynamics, and the role of bivalves in aquatic ecosystems. 97