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 FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010

1

© 2010 Nicole Dix Pangle

2

To my Peanut

3

ACKNOWLEDGEMENTS

Funding for this research was provided by the University of Florida and an award from the

Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National

Ocean Service, National Oceanic and Atmospheric Administration. Invaluable field and lab

assistance was generously provided by Don O’Steen, Loren Mathews, Lance Riley, Paula

Viveros, Heather Manley, Meredith Montgomery, Rio Throm, Shane Pangle, Debbie Dix, Jeff

Dix, Darlene Saindon, Joey Chait, and Dorota Roth. 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 Parkyn, 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 sincere 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.

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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 TROPHIC STATUS IN A HIGHLY FLUSHED ESTUARY..............................................................................................................................61

5

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

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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. Matanzas (FM) and San Sebastian (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 (SS) from January 2003 – December 2009.

m-2 -1

...............42

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......................43

2-5 Ambient nutrient and chlorophyll a concentrations (µg L ) during sample collection for nutrient addition bioassay and 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 biomass 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 predicted 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, zooplankton grazing rate, and phytoplankton growth rate estimates among estuaries in the subtropical southeastern United States. .....45

3-1 Data sources for estimating nutrient and carbon load........................................................76

3-2 Mean nutrient, particulate organic carbon (POC), and chlorophyll a (CHL) concentrations (µg L ) compared between regions (represented 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

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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) measured at the Ft. Matanzas site and total photosynthetically active radiation (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 monthly sampling event at Ft. Matanzas (gray line) and San Sebastian (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 productivity estimates (BZI) from 2003 – 2009........................................................................................................................53

2-10 Seasonal CHL variability from 2003 – 2009, excluding the October 2007 red tide event (20 µg 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 Sebastian (black line) sites from 2003 – 2009.................................................................................................................................57

2-14 Average phytoplankton biomass (estimated by fluorescence) in nutrient addition treatment groups during March and 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

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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) System-Wide Monitoring Program sites from January 2002 – December 2008. ...........................79

3-3 Seasonal trends 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

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, biomass, and condition index from high reef elevations in the Matanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer).. ..........................................................................................................................82

3-6 Mean oyster density, percent cover, biomass, and condition index from low reef elevations in the Matanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (winter) 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. Augustine (SA) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer)....................................84

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

NUTRIENT, PHYTOPLANKTON, AND OYSTER DYNAMICS IN A HIGHLY FLUSHED

SUBTROPICAL LAGOON, NORTHEAST FLORIDA

By

Nicole Dix Pangle

December 2010

Chair: Edward Phlips Major: Fisheries and Aquatic Sciences

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

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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.

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CHAPTER 1 INTRODUCTION

Half of the United States population now lives on the coast, and densities are expected to

increase in the future (NOAA, 2010). The prevalence of coastal development has prompted

numerous research studies investigating potential impacts on natural coastal environments. One

commonly documented impact has been anthropogenic increases in nutrients, leading to

eutrophication of nearshore waters. Nutrients, particularly nitrogen and phosphorus, are found

naturally in coastal waters, but concentrations are elevated near highly developed coastlines.

Atmospheric deposition of fossil fuel combustion products, storm water runoff, and wastewater

discharge artificially introduce nitrogen and phosphorus to coastal waters, which can accelerate

organic enrichment and negatively affect water quality and ecosystem health (NRC, 2000). In

fact, 65% of the major estuaries in the United States displayed problematic symptoms of

eutrophication in 2004 (Bricker et al., 2007).

The challenge of assessing the effects of excess nutrients in estuarine systems is a major

coastal management issue. Documented eutrophication impacts in estuaries include increases in

primary production, increases in oxygen demand and hypoxia/anoxia, changes in community

structure, increases in the frequency of harmful algal blooms, declines in submerged aquatic

vegetation and coral reef coverage, and declines in ecosystem function and/or resiliency (see

NRC, 2000 for a comprehensive review). The most studied estuaries with respect to

eutrophication are those that have 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 River (Rabalais et al., 2002). As research has continued, however, it has

become clear that not all systems respond predictably to nutrient enrichment (Cloern 1999,

2001). For example, the degree of hydrodynamic flushing in an estuary can affect the system’s

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sensitivity to nutrient enrichment, as documented 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 exchange are not typically characterized by large

phytoplankton standing stocks because biomass does not have a chance to accumulate before

being flushed out to the ocean. Eutrophication impacts 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 flushed 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 primary producers, and oysters, the dominant

primary consumers. Patterns in phytoplankton biomass were explored in relation to a suite of

potential regulating factors, 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 landscapes throughout the southeastern United States, were examined for

their potential as indicators of eutrophication in highly dynamic coastal environments.

This dissertation research investigated relationships between environmental conditions,

primary producers, and primary consumers in the GTMNERR using both experimental and

observational methods within the context of the following hypotheses:

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• Phytoplankton productivity and biomass in the 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 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.

• Due to long-term differences in nutrient regimes, oysters in the St. Augustine region of the GTMNERR were expected to be larger, more densely populated, and exhibit higher condition index scores than those in the Matanzas region.

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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. Factors that control gains in

phytoplankton biomass include those that promote phytoplankton growth, such as temperature

and the availability of light and nutrients. Allochthonous 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 temperate 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, and nutrients have been shown to play a major

role in the regulation of phytoplankton productivity and biomass in estuaries (Thayer, 1971; Cole

and Cloern, 1984; Cloern, 1999; Bledsoe and Phlips, 2000; Bouman et al., 2010). Patterns of

surface irradiance and light attenuation through the 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., 1989). In their review of 63

estuaries around the world, Boynton et al. (1982) found that productivity was higher in warm

months than cold months. Since subtropical and 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

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al., 2007; Winder and Cloern, 2010). Similarly, nutrient availability has also been shown to

control phytoplankton biomass. Phytoplankton growth can be nutrient-limited if nitrogen,

phosphorus, and/or silica concentrations are below that at which phytoplankton growth is

saturated (Day et al., 1989). Estuarine environments 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 primary production in the aquatic environment, although intrinsic

biotic factors may exert top-down control of phytoplankton biomass accumulation.

Even when temperature, light, and nutrient conditions favor optimal phytoplankton

growth; concomitant increases in phytoplankton biomass are not always observed. Differences

between phytoplankton production and biomass observed 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 estuaries (Mortazavi et al., 2000; Murrell et al., 2002;

Quinlan et al., 2009). The impact of suspension feeding bivalves on phytoplankton biomass in

estuaries has also been documented in the laboratory (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 survive periods of low food concentrations and take

advantage of phytoplankton growth more consistently 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, where phytoplankton production

and biomass patterns are similar (Sin et al., 1999).

Another factor that can limit phytoplankton biomass potential is the rate of hydrodynamic

transport out of a system. The effect of flushing on phytoplankton biomass has been documented

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in many systems around the world (Knoppers et al., 1991; Monbet, 1992; Phlips 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 Guana Tolomato Matanzas National

Estuarine Research Reserve (GTMNERR) in northeast Florida. Temporal variability in

phytoplankton biomass and productivity were compared to temporal patterns in light availability,

temperature, nutrient concentrations, nutrient limitation, flushing rates, and grazing pressure. It

was hypothesized that phytoplankton 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 enhance interannual variability

in phytoplankton biomass, but due to the subtropical 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

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 Atlantic Ocean is the main hydrodynamic force in the estuary, making it

well-mixed (see Chapter 3) and well-flushed (Phlips 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 water column is assumed to be dominated by phytoplankton.

Submerged aquatic vegetation is extremely sparse, and although macroalgal distributions have

not been quantified, personal observations suggest 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 GTMNERR. The study area has been

characterized as having low phytoplankton biomass compared to other sites along the Florida’s

northeastern coast (Phlips et al., 2004). The eastern oyster (Crassostrea virginica) is thought to

be the dominant suspension feeder in this and other estuaries of the Atlantic and Gulf of Mexico

coasts. Oyster reefs in northeast Florida are solely intertidal. According to preliminary oyster

maps, aerial coverage of live oyster reefs in the study area is approximately 3.5*105 m2 (St.

Johns River Water Management District, unpublished data). The average 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

large portion of the study area lies within the boundaries of the GTMNERR, which collects high-

frequency meteorological data from a weather station south of the Matanzas Inlet (Figure 2-1).

In this study, photosynthetically active radiation (PAR) and rainfall data for 2003 – 2009 were

downloaded from the NERR System Centralized 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 approximately 4 km north of the Matanzas Inlet

(Figure 2-1). FM has an average depth of approximately 3.6 m and a tidal range of about 1.4 m.

The San Sebastian (SS) site is located at the confluence of the San Sebastian and Matanzas

Rivers, approximately 4 km south of the St. Augustine Inlet (Figure 2-1). The average depth at

SS is approximately 4.4 m with a tidal range of about 1.7 m. The water 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 was 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 µm 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 Florida 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 quantification was completed on a Bran + Luebbe

Autoanalyzer 3 system. Concentrations of nitrate (NO3), total nitrogen (TN), and ammonium

(NH4) were first reduced to NO2 and then measured as described above. NO3 was reduced to

19

NO2 through a copperized cadmium redactor (APHA, 1998). TN was oxidized to NO3 via

alkaline potassium persulfate digestion and then reduced to NO2 through a copperized cadmium

redactor (APHA, 1998). NH4 was oxidized to NO2 with hypochlorite in an alkaline medium

using potassium bromide as a catalyst (Strickland and Parsons, 1972). Dissolved inorganic

nitrogen (DIN) was calculated 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 antimony 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 described above (APHA, 1998).

CDOM was measured spectrophotometrically against a platinum cobalt standard (APHA, 1998).

CHL was processed using the Sartory and Grobbelaar (1984) hot ethanol extraction method and

concentrations (not corrected for pheophytin) were determined spectrophotometrically according

to Standard Methods (APHA, 1998). All processing and analytical methods conformed to the

guidelines of the laboratory’s National Environmental 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

Lambert-Beer’s 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). Finally, 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 corrected 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 estuaries 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 the 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 (Oswald 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

Nutrient Limitation

The potential for nutrients to limit phytoplankton growth was explored in three ways.

First, nutrient concentrations were compared to published half-saturation constants (Reynolds,

2006). Concentrations at or below half-saturation constants indicate the potential for nutrient

uptake to limit phytoplankton growth (Day et al., 1989). Second, nutrient concentration ratios

were compared to Redfield proportions (Redfield et al., 1963). Nitrogen:phosphorus (N:P) ratios

below 7.2 (g:g) indicate nitrogen limitation potential, while N:P ratios above 7.2 (g:g) indicate

potential phosphorus limitation. Unfortunately, conclusions based on nutrient concentrations are

not sufficient by themselves because concentration values do not reflect supply, especially in

highly flushed systems (see Chapter 3). Therefore, limiting nutrient status was also explored

experimentally with two nutrient addition bioassays, one in March 2009 and one in June 2009,

representative of dry and wet climatic 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, and 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 tank and stirred continuously during experimental 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. Nutrients were added accordingly to obtain final concentrations of 400 µg

NO3-N L-1, 400 µg Si L-1, and 40 µg PO4-P L-1. Flasks were incubated in temperature-

controlled 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

light, 12 hours dark in March and 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 filtered through 0.7 µm glass fiber filters to determine

background fluorescence and all subsequent 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 (α = 0.05). Changes in

biomass were used to calculate instantaneous phytoplankton growth rates.

Zooplankton Grazing

The Landry and Hassett (1982) dilution method was used to measure zooplankton

community grazing rates and phytoplankton growth 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 portion of the water from each site was filtered

through 0.2 µm 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 µg NO3-N L-1, 400 µg Si L-1, and 40 µg PO4-P L-1) to ensure that nutrients did

not limit phytoplankton growth. Individual 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 were illuminated from above with photoperiods of

12 hours light, 12 hours dark in March and 14 hours light, 10 hours dark in June.

23

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 hours 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 between apparent growth

rate and fraction of unfiltered seawater. The percent standing 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 grazing on phytoplankton biomass was assessed on an

ecosystem scale by comparing bivalve clearance 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 the number of hours oysters filter

per day. FR was estimated following Doering 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) hours 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

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, and oyster reef aerial coverage. System-wide primary production

was estimated by multiplying average daily integrated productivity (BZI) by the area of the study

region. Filtration pressure estimated the reef-level impact of oysters on phytoplankton biomass,

and therefore, included biomass loss 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, making them wet years on

average. Also, 2007 and 2009 were comparatively wet 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 (Figure 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 photosynthetically active radiation (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 highest after the 2004 tropical storms, but did not

exhibit a clear seasonal pattern (Figure 2-6). Tripton was responsible for the majority of light

attenuation at both SWMP sites (Table 2-2). The average amount of light available in the water

25

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 for light limitation estimated by Geddes (1984) and

Phlips et al. (1995), but individual Im values often dropped within the threshold range. Im

reached potentially limiting levels more often 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 µg L-1) (Cloern and Jassby, 2008, 2010). Seasonal

variability was slightly higher than annual variability; standard deviations of monthly means

ranged from 1.5 – 5.0 µg 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 compared to the prior month (Figure 2-9). K. brevis cells

were found at a concentration of 3,630 ml-1 in one sample collected from FM on October 10,

2007 (Edward Phlips, unpublished data). The 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 pattern (Figures 2-9 and

2-10). CHL concentrations were generally elevated 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 concentrations did not appear to respond to

nutrient inputs after rainfall events and did not correlate well with bioavailable nutrient

26

concentrations (Table 2-4). However, peaks in CHL often corresponded to troughs in dissolved

inorganic nitrogen (DIN) and vice versa, indicating DIN uptake by phytoplankton (Figure 2-11).

Phytoplankton Productivity

Based on the BZI composite parameter, average 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 storm 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 October 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 were evident from the monthly nutrient

concentrations. The majority of total nitrogen (TN) was in the dissolved organic form (DON),

followed by particulate (PN) and dissolved inorganic (DIN) forms (Table 2-1). DIN was

dominated by NH4. Median NH4 (47 µg N L-1) and median NO3 (12 µg N L-1) were in the

range of published half-saturation levels for coastal diatoms (7 – 130 µg NH4-N L-1and 6 – 71

µg NO3-N L-1) (Reynolds, 2006). Soluble reactive phosphorus (SRP) was less than one third of

total phosphorus (TP). Median SRP (13 µg P L-1) was above the published half-saturation level

range (1.6 – 6.2 µg P L-1) for phytoplankton (Day et al., 1989). All nutrient concentrations were

negatively correlated with salinity and positively correlated with CDOM, light attenuation, and

temperature (Table 2-4). TN:TP (Figure 2-12) and DIN:SRP (Figure 2-13) ratios over the study

27

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 median 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 evidenced by the ambient salinity values at the time of

collection (34 and 26 ppt, respectively) (Figure 2-4). In all cases, maximum biomass in the

control group was significantly higher 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 other 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 presence 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 2-14), 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 various nutrient addition treatments was

remarkably similar over space and time (Figure 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

Zooplankton Grazing

Zooplankton grazing was measurable at FM during March 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-significant grazing rates were considered zero in

subsequent calculations. Phytoplankton 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 volume of the study area, was estimated as 17 – 25

days (using a range of 8 – 12 hours of oyster filtering time per day), 13 – 19 days in the wet

season and 26 – 39 days in the dry season. Oyster filtration pressure, or the proportion of

phytoplanktonic carbon produced that was removed through filtration and sedimentation on

oyster reefs, averaged 5 – 40 % annually depending on the published uptake rates and the

filtering times used in calculations.

Discussion

Productivity

Results from this study support the hypothesis 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). Estimated annual productivity in the GTMNERR was

29

lower than river-dominated, subtropical estuaries in the southeastern United States (Table 2-8),

likely due to less nutrient and organic carbon inputs in the GTMNERR. On a global scale,

annual productivity in the 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 world-

wide, 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 phytoplankton-

dominated, 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 patterns 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 biomass losses through processes such as grazing or

flushing. However, differences between patterns of phytoplankton production and biomass may

also occur if the BZI model did not accurately estimate productivity in the study area. The BZI

model assumes that phytoplankton are not nutrient-limited, and may overestimate primary

productivity when phytoplankton are limited by nutrients (Cole and Cloern, 1987). In addition,

BZI may not accurately predict productivity 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 attributed the discrepancy to differences in

photosynthetic efficiency, possibly caused by ammonium inhibition of nitrate uptake (Dugdale et

al., 2007). On the other hand, Cole and Cloern (1987) note that such physiological processes are

not relevant to the seasonal-scale observations that comprise the BZI parameter.

Temporal Variability in Phytoplankton Biomass

As expected, the seven-year mean CHL concentration (4.7 µg L-1) from the Ft. Matanzas

and San Sebastian sites in the GTMNERR was relatively low compared to the mean of 6.0 µg L-1

30

from 154 coastal sites (114 diverse ecosystems) examined by Cloern and Jassby (2008). While a

slightly small magnitude of phytoplankton biomass 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 variation (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 timeseries from around the world and found a

median annual CV of 30 %. In comparison, the GTMNERR exhibited fairly low interannual

variability in mean phytoplankton biomass. Interannual variability was hypothesized to be

relatively high since two active hurricane seasons occurred during the study period, but year-to-

year meteorological differences did 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 Jassby’s (2010) compilation, seasonal 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 slightly 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 characterized by short periods of low biomass from

December to March every year. Throughout the remainder of the year, there was no consistent

seasonal pattern in phytoplankton biomass, possibly indicative of the influence of the various

31

control mechanisms investigated in this study (see discussion below). The sampling

methodology may also be partly responsible for this pattern since discrete 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 hypotheses regarding the control of phytoplankton

biomass in the GTMNERR. Temperature and light 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 potential role of major abiotic and biotic factors

that may control phytoplankton biomass (Figure 2-16) as a means of justifying this conclusion.

Temperature and light

Temperature and photosynthetically active radiation (PAR) were both positively correlated

with phytoplankton biomass. However, since temperature and light covaried, simple correlation

analysis was not able to determine their relative individual 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 attenuation at FM and SS, and in some instances, light

attenuation appeared to be related 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 light availability that could have limited phytoplankton growth.

Winter lows in phytoplankton biomass 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

Based on our results, temperature likely influenced phytoplankton biomass primarily at the

seasonal level. Monthly water temperatures were related 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 consistently lower than growth rates predicted by

temperature alone (Eppley, 1978). Additionally, 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 increase 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 2-6). These findings imply

that phytoplankton growth potential was suppressed by other factors (e.g., micronutrient supply

or grazing) in the spring and summer. 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 experimentally 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 laboratory conditions cannot be ignored. In March,

algae may have received more light in the laboratory 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 explain low biomass in macrotidal (Monbet,

1992) and highly flushed coastal systems (Knoppers et al., 1991; Phlips et al., 2004). To explore

33

the effect of this physical factor in the GTMNERR, consider a hypothetical situation in which

only flushing limited the accumulation of phytoplankton biomass. The laboratory-derived

phytoplankton growth rates observed in our grazing 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 Garcés

(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 µg L-1, after two days,

exponential growth would result in a CHL concentration of 25 µg L-1. After four days, CHL

concentration would reach 120 µg L-1. The maximum observed CHL concentration from 2003 –

2009 was 12 µg 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 concentrations and colored dissolved organic matter,

suggest freshwater run-off as a principle source of nutrients to the 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 estuary during freshwater flushing

events. Nutrient concentrations were also significantly positively correlated with temperature,

which may reflect changes in decomposition and mineralization rates, a potentially indirect

effect of temperature on phytoplankton biomass. On the other hand, since higher temperatures in

Florida occur during the wet season, it is possible that the correlation between temperature and

nutrient concentrations is an artifact of the relationship between nutrients and freshwater inputs.

34

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-saturation 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 concomitant increases in chlorophyll a concentrations (Figure 2-11).

This lack of an ecosystem-level response to apparent changes in nitrogen load suggests that other

factors limited the system-wide accumulation of phytoplankton biomass. Further evidence that

nutrient limitation is not the main factor responsible for suppression of phytoplankton biomass in

this system can be illustrated by another hypothetical situation. Assuming nitrogen limitation

and Redfield proportions, a median DIN concentration of 60 µg L-1 should support an average of

9 µg L-1 CHL. The median CHL concentration over the study period, however, was half that

theoretical amount. In fact, the presence of theoretically unused bioavailable nitrogen was

consistent throughout all four seasons (data not shown), which further supports the hypothesis

that nutrient availability was not the primary factor limiting phytoplankton 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 Indian River Lagoon (IRL), a subtropical/tropical bar-

built lagoonal estuary (Table 2-8). However, the maximum grazing rates found in this study and

in the IRL were at least half the maximum rates found in subtropical, river-dominated systems

(Table 2-8). This finding suggests that the relative influence of planktonic grazers on

phytoplankton biomass may be greater in systems that receive more allochthonous inputs. In

35

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 due 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 developed watersheds in the

country, while the area around FM remains relatively 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 nutrients 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 zooplankton 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 grazing rates may be lower than those

measured without pressures of mesozooplankton grazers. Quinlan et al. (2009) conducted

grazing experiments on both whole water and water with mesozooplankton (> 202 µm) removed,

measuring net community and microzooplankton impacts, respectively. In the summer, higher

grazing rates were observed in microzooplankton 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

proportions of zooplankton size classes have yet to be explored in the GTMNERR, their relative

influences on phytoplankton dynamics is still unclear. However, from an ecosystem perspective,

the net impacts of micro- and mesozooplankton grazing are most important for explaining

observed patterns in phytoplankton abundance.

In general, zooplankton grazing rates in the southeastern 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 place in spring and summer,

so these grazing rates may overestimate the average 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 limited by light and/or nutrients in the natural environment (Day

et al., 1989).

Since phytoplankton differ in their sensitivity to grazing and zooplankton have a range of

prey preferences (Badylak and Phlips, 2008), detailed descriptions of the temporal and spatial

dynamics of plankton abundances in the GTMNERR are required to elucidate the mechanisms

behind the patterns observed in this study. While more information is needed to fully understand

the potential effect of planktonic grazers, the relatively low zooplankton grazing rates compared

to phytoplankton growth rates found in this study suggest that other loss processes such as

advection or benthic grazing are equally or more important drivers of phytoplankton biomass in

the GTMNERR.

Bivalve suspension feeders have the potential to play a significant role in controlling

phytoplankton biomass in the GTMNERR, as observed in other ecosystems (Dame et al., 1980;

Cloern, 1982; Officer et al., 1982). However, if estuary flushing time is less than bivalve

37

clearance time, then the grazing impact would be likely limited to the 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 times (17 – 25 days). There was also a relatively

low bivalve biomass:estuary 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 conservative. 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 Brockmyer, 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 Riisgård’s (1988) laboratory-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 oyster clearance times in North Inlet, SC,

which Small and Prins (1993) found to be slightly less than estuary residence time. A filtration

38

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 feeders are common on intertidal oyster reefs in this

region (Boudreaux et al., 2006) and contribute to losses of phytoplankton biomass. Reef

structure is also thought to enhance sedimentation of phytoplankton biomass (Cressman et al.,

2003). Therefore, the effect of benthic grazer communities 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 effect of individual oysters.

The comparative influence of planktonic and benthic grazers on phytoplankton 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 experiments. 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 grazers, bivalve grazing represents a more consistent top-down

pressure on phytoplankton biomass than zooplankton grazing. Typically, zooplankton

abundances are episodic and increases in numbers tend to lag behind phytoplankton blooms (Day

et al., 1989). Therefore, 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 phytoplankton biomass; however, Dame (1996, pg. 136)

suggested that when conditions favor benthic filter feeders, they will dominate planktonic

grazers because,

39

their food chains are shorter, they take advantage of tidal energy subsidies to receive their food, and their longer 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 production and biomass observed in this study

was fairly small compared to estuarine and coastal ecosystems around the world. While

interannual variability in phytoplankton biomass was also relatively small, seasonality was

inconsistent for most of the year in the Guana Tolomato Matanzas National Estuarine Research

Reserve (GTMNERR). The overall similarity of temporal patterns in phytoplankton biomass

and productivity observed seems to corroborate Cloern and Jassby’s (2010) assertion that mean

phytoplankton biomass is an indicator of the variability of ecosystem processes such as nutrient

cycling and energy transfer. Therefore, low chlorophyll a levels within the GTMNERR may

represent the system’s relative stability, indicating somewhat of a balance between production

and consumption.

The diverse array of factors controlling phytoplankton biomass in the study area probably

work together at different spatial and temporal scales to keep biomass low. For example, annual

cycles of temperature, irradiance, and photoperiod may drive seasonal changes in algal

abundance, while seemingly random events outside the estuary (red tides) can cause large

deviations in that pattern. At the scale of days to weeks, nutrients have the potential to stimulate

phytoplankton growth, but exchange with the Atlantic Ocean, the combination of benthic and

planktonic grazing, and occasional light limitation prevent the accumulation of phytoplankton

biomass. Low interannual variability over the seven years of extremely diverse climatic

conditions may reflect the importance of top-down control in the GTMNERR. Plus, the

persistence of a regular seasonal pattern without bloom events suggests that consistent benthic

40

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 temperate regions, the role of

grazers in controlling phytoplankton biomass may be relatively more important at lower latitudes

than farther north.

41

Table 2-1. Summary statistics for variables measured monthly at FM and SS from January 2003 – December 2009.

Variable N Mean Median Std dev Minimum Maximum SRP (µg P L-1) 168 14.8 13.0 9.8 1.0 67.0TP (µg P L-1) 165 55.4 52.0 18.9 19.0 121.0NH4 (µg N L-1) 167 62.1 47.3 44.8 3.2 244.2NO2 (µg N L-1) 163 3.6 2.5 3.3 0.0 19.8NO3 (µg N L-1) 154 15.1 10.3 15.1 0.0 86.6NO23 (µg N L-1) 156 18.6 11.8 17.5 0.9 91.7DIN (µg N L-1) 156 80.6 60.5 56.5 5.2 290.3DON (µg N L-1) 145 238.9 220.2 101.2 39.0 528.0PN (µg N L-1) 147 105.2 91.9 86.4 0.7 594.7TDN (µg N L-1) 154 310.3 282.4 121.9 108.4 814.3TN (µg N L-1) 162 404.6 375.5 158.4 159.9 1011.7DIN:SRP 155 7.3 4.9 9.5 1.7 88.5TN:TP 159 7.7 7.5 2.8 2.1 19.7Si (mg Si L-1) 165 1.4 1.3 0.8 0.1 4.6CHL (µg L-1) 168 4.7 4.1 2.9 0.6 23.4Temp_Surface (°C) 164 22.9 23.6 5.2 12.1 31.0Temp_Bottom (°C) 165 22.6 23.4 5.1 12.5 30.9Salinity_Surface 166 31.4 32.1 3.1 11.7 36.4Salinity_Bottom 162 31.6 32.3 2.8 14.6 36.4DO_Surface (mg L-1) 164 6.3 6.1 1.3 3.6 10.5DO_Bottom (mg L-1) 163 6.3 6.2 1.2 3.6 10.0CDOM (pcu) 167 16.8 12.0 16.6 0.4 147.3Kt (m-1) 162 1.6 1.5 0.7 0.6 4.4Im (mol photons m-2 d-1) 162 7.8 7.4 3.3 1.9 22.9PAR (millimoles m-2) 164 35775 35347 10612 13374 57018

Table 2-2. Mean light extinction coefficients (Kt, m-1) and percent contributions of water,

tripton, color, and phytoplankton at Ft. Matanzas (FM) and San Sebastian (SS) from January 2003 – December 2009.

Station Kt % water % tripton % CDOM % phytoplankton FM 1.6 2.7 75.5 17.0 4.8SS 1.7 2.7 79.8 11.7 5.8

Table 2-3. Mean light availability through the water column (Im; mol photons m-2 d-1) at Ft.

Matanzas (FM) and San Sebastian (SS) from January 2003 – December 2009. Season FM SS Spring (March – May) 8.7 7.3Summer (June – August) 10.5 9.1Fall (September – November) 7.8 6.2Winter (December – February) 7.5 5.5

42

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 (µg 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 <0.0001TP 0.36 0.39 - 0.32 - 0.18 0.30 <0.0001 <0.0001 <0.0001 <0.05 <0.0001DIN 0.16 0.26 - 0.21 - 0.40 0.43 0.05 0.001 0.01 <0.0001 <0.0001TN 0.28 0.45 - 0.07 - 0.18 0.24 <0.001 <0.0001 0.37 <0.05 <0.01Silica 0.16 0.43 - 0.06 - 0.24 0.46 0.05 <0.0001 0.46 <0.01 <0.0001CHL 0.51 0.13 0.13 0.09 <0.0001 0.10 0.09 0.26 Table 2-5. Ambient nutrient and chlorophyll a concentrations (µg 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 5.43FM 10 March 2009 23.9 9.50 8.00 984 6.16mid-point 10 March 2009 14.0 8.00 7.00 1410 8.00SS 9 June 2009 128 46.8 24.0 2460 8.09FM 9 June 2009 63.4 17.2 17.0 2560 10.7mid-point 9 June 2009 144 40.5 32.0 3420 9.10 Table 2-6. Nutrient-limited (change in biomass from initial to Day 1 in control treatment group)

and non-nutrient-limited (change in biomass 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 predicted growth rates based on temperature alone (Eppley, 1978).

Station Date

Nutrient-limited growth rate

Nutrient-limited doublings day-1

Non-nutrient-limited growth rate

Non-nutrient-limited doublings day-1

Predicted doublings day-1

SS March 09 0.84 1.21 1.16 1.67 2.66FM March 09 0.93 1.34 0.92 1.33 2.83Mid-point March 09 0.48 0.69 0.86 1.24 2.83

SS June 09 1.07 1.54 0.88 1.27 5.01FM June 09 0.80 1.15 0.58 0.84 4.70Mid-point June 09 0.91 1.31 0.50 0.72 5.01

43

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 (y-intercept)

y-intercept p-value

doublings d-1 Pi Pp

FM 10 March 09 0.48 0.0073 1.47 <0.0001 2.12 38 50SS 10 March 09 -0.03 0.8603 1.15 <0.0001 1.66 0 0FM 9 June 09 0.23 0.0372 1.36 <0.0001 1.95 21 28SS 9 June 09 0.10 0.2534 1.59 <0.0001 2.30 0 0

45

Table 2-8. Comparison of annual productivity, zooplankton grazing rate, and phytoplankton growth rate estimates among estuaries in the subtropical southeastern United States.

Location Estuarine Structure

Annual Productivity (g C m-2 yr-1)

Zooplankton Grazing Rates (day-1) c

Phytoplankton Growth Rates (day-1) c Source

255 g C m-2 yr-1 a Mortazavi et al.

(2000) Apalachicola Bay, Gulf of Mexico, FL

river-dominated, well-flushed, semi-enclosed 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, semi-

enclosed 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, well-mixed 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 b 0.00 – 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)

P

a 14C measurements; b BZI model; c Landry dilution technique

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

46

0

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rain

fall

tota

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Figure 2-2. Total annual precipitation at the GTMNERR weather station.

050

100150200250300350400450500

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ar-0

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ay-0

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ar-0

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ay-0

9Ju

l-09

Sep

-09

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-09

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fall

tota

l (m

m)

Figure 2-3. Total monthly precipitation at the GTMNERR weather station.

47

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Figure 2-4. Salinity at Ft. Matanzas (gray line) and San Sebastian (black line) measured

monthly.

48

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er T

empe

ratu

re (°

C)

0

10000

20000

30000

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50000

60000

PAR

(mill

imol

es m

-2)

Figure 2-5. Water temperature (black line) measured at the Ft. Matanzas site and total

photosynthetically active radiation (PAR, gray line) measured at the weather station on each sampling day.

49

0.0

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Ligh

t Atte

nuat

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)

Figure 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

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I m (m

ol p

hoto

ns m

-2 d

-1)

Figure 2-7. Mean light availability in the water column (Im) during each monthly sampling event at Ft. Matanzas (gray line) and San Sebastian (black line) from January 2003 – December 2009. The dashed line represents the upper limit to the estimated light limitation threshold.

51

2003 2004 2005 2006 2007 2008 20090

2

4

6

8

10

12C

hlor

ophy

ll a

(µg

L-1)

Figure 2-8. Mean annual chlorophyll a concentrations. Bars represent one standard deviation.

52

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roph

yll a

(µg/

L)BZI CHL

B Figure 2-9. Monthly chlorophyll a (CHL) concentrations and productivity estimates (BZI) from

2003 – 2009. A) from the Ft. Matanzas site, B) from the San Sebastian site.

53

0

2

4

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12

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

chlo

roph

yll a

(µg

L-1)

2003 2004 2005 2006 2007 2008 2009

Figure 2-10. Seasonal CHL variability from 2003 – 2009, excluding the October 2007 red tide

event (20 µg L-1). Values averaged from the Ft. Matanzas and San Sebastian sites.

54

0.00

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(mg

L-1)

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orop

hyll

a (µ

g L-1

)

FM CHL FM DIN

Figure 2-11. Monthly concentrations of chlorophyll a (CHL), ammonium (NH4), and

nitrate+nitrite (NO23) at the Ft. Matanzas (FM) monitoring site.

55

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TN:T

P (g

:g)

Bioassay

Bioassay

Figure 2-12. TN:TP at Ft. Matanzas (gray line) and San Sebastian (black line) sites from 2003 –

2009. Dotted lines represent the Redfield ratio of 7.2.

56

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:SR

P (g

:g)

88.5 61.3

Bioassay

Bioassay

Figure 2-13. DIN:SRP at Ft. Matanzas (gray line) and San Sebastian (black line) sites from 2003

– 2009. Dotted lines represent the Redfield ratio of 7.2.

57

Figure 2-14. Average phytoplankton biomass (estimated by fluorescence) in nutrient addition

treatment groups during March and June 2009 bioassay experiments.

58

Figure 2-15. Maximum biomass (estimated by fluorescence) of each treatment group in the

nutrient addition bioassay experiments. *significantly different from control (α = 0.05)

59

Figure 2-16. Schematic representing important factors controlling phytoplankton biomass in the

GTMNERR. The relative thickness of arrows approximates the relative influence of each factor.

60

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 effects 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 response 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 expressed as elevated phytoplankton biomass (Knoppers

et al., 1991; Phlips et al., 2002, 2004; Bledsoe et al., 2004). In contrast, increases in load to

estuaries with rapid tidal water exchange may not be associated with concomitant and

proportional increases in phytoplankton biomass for a number of reasons, such as rapid flushing

of nutrients and biomass before they accumulate (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 than plankton, when evaluating the impacts of

changes in nutrient load.

Benthic invertebrates have been used effectively 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 accumulations (Pearson and Rosenberg, 1978; Boesch

and Rosenberg, 1981; Graves et al., 2005). In estuaries along the southeast coast of America,

eastern oyster (Crassostrea virginica) populations are especially promising indicators of water

quality because they are a widely distributed key feature of the benthic landscape (Bahr and

61

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 retention 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 habitats of the Guana Tolomato Matanzas National Estuarine

Research Reserve (GTMNERR) in northeast Florida were used to investigate how well-mixed

estuaries with strong tidal influence respond to different 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 contrast, the watershed of the

Matanzas Inlet region of the estuary consists mainly of protected 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 properties and climatic

influences responded to varying degrees of load. Previous research has shown that the

magnitude of spatial differences in phytoplankton standing crops does not adequately reflect

differences in nutrient status between the St. Augustine and Matanzas regions (Phlips et al.,

2004). Alternatively, it was hypothesized that long-term differences in nutrient regimes between

the two regions would lead to differences in food availability for oyster populations, in terms of

62

allochthonous and autochthonous carbon, and yield differences in oyster density, relative size

distribution, and condition (Pearson and Rosenberg, 1978; Cederwall and Elmgren, 1980;

Lawrence and Scott, 1982; Volety and Savarese, 2001; Kirby and Miller, 2005). Specifically,

oysters in the St. Augustine region were expected to be larger, more densely populated, and

exhibit higher condition index scores than those in the Matanzas region. The results of this study

support this expectation and provide insights into the role of filter-feeding benthic invertebrates

as indicators of trophic status.

Methods

Site Description

The GTMNERR, as part of the System-Wide 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 Inlet (Figure 3-1). Data from this site have

revealed relatively high nutrient loads (Phlips et al., 2004). In contrast, 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 (Phlips 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 rates 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 various public agencies (Table 3-1).

63

Water Quality Sampling

YSI 6600 continuous monitoring data sondes measured temperature, salinity, dissolved

oxygen, and water depth at 30-minute intervals at the 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 from 2002 through 2008. Two replicate 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 filtered through glass-fiber filters (0.7 µm pore

size) for soluble inorganic nutrients. Samples for chlorophyll a (CHL) and particulate organic

carbon (POC) determination were filtered onto glass-fiber filters (0.7 µm 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), nitrate (NO3), nitrite (NO2),

dissolved inorganic nitrogen (DIN), total phosphorus (TP), soluble 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 oyster collection sites

(Krebs, 1999). Reefs at the edge of the Intracoastal Waterway with visible dead margins were

64

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 elevation (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 oyster 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 length 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) / Shell Cavity Volume (ml)] * 100,

where cavity volume was determined gravimetrically 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 drying the meat at 105 ºC 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

nutrient concentrations) were tested between wet and dry seasons and between St. Augustine and

Matanzas regions (represented by San Sebastian and Ft. Matanzas monitoring sites,

respectively). Wet season was defined as May through October, while dry season included

November through April. The non-parametric Kruskal-Wallis test was used since distributions

were non-normal (determined by the Shapiro-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, and 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 events, regions as St. Augustine and Matanzas,

and reef positions as high and low. A fourth independent variable, size class, 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 oyster length, biomass, and condition index.

Distributions for density and percent cover models were selected as Poisson and binomial,

respectively. All models were adjusted for autocorrelation between measurements on the same

reef during the two sampling events. Tukey’s test was used to determine differences between

means (α = 0.1).

Results

Water Quality

From 2002 – 2008, mean monthly temperature, salinity, and dissolved oxygen levels were

essentially identical at San Sebastian (SS) and Ft. Matanzas (FM) (Figure 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

the mean temperature difference was 0.08 °C. The 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 August (one month before summer oyster sampling).

Water temperatures at SS and FM ranged from around 14 °C at the end of January to 17 °C at the

beginning of February. Water temperatures in July and August were near 28 °C in both regions.

Monthly chlorophyll a (CHL) concentrations at the water quality sampling sites exhibited

a seasonal pattern, with the highest concentrations in the wet season (May – October) and the

lowest in the dry season (November – April) (Figure 3-3). The highest 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 consistent a seasonal pattern as CHL (Figure 3-

3). Immediately before the winter 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 generally less than 40% of POC (Figure 3-3), indicating the

presence of non-algal particulate organic 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 concentrations were higher at SS than at FM

(Table 3-2).

67

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). From February 2001 – September

2002 monthly estimates of total nitrogen (TN) and total phosphorus (TP) loads from the San

Sebastian River into the St. Augustine region were substantially higher than loads from Moses

Creek into the Matanzas region (Figure 3-4). Integrating the nutrient 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 estimated 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 density (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 significantly 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 the 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 (27.5 %; Figure 3-5). Overall, mean percent

cover was greater in the summer than in the winter (p < 0.001) and greater in the St. Augustine

68

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 (Figures 3-5 and 3-6).

When separated by size class, oyster density exhibited analogous seasonal and regional

patterns. At high reef elevations, spat, small, pre-fishery, and fishery-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, small, 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 oysters were significantly more abundant in the

summer than in the winter (p = 0.01 and < 0.0001, respectively). Spat were significantly more

abundant in the St. Augustine region than in the Matanzas region (p < 0.1). Overall, at both reef

elevations, oysters in the small size class (2.5 - 4.9 cm) were most abundant (Figure 3-7).

Oyster Length and Biomass

When both reef elevations were considered, the 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 elevations than at low elevations (p < 0.05). Oysters exhibited the

greatest mean length (5.0 cm) in the St. Augustine region at high reef elevations (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 elevations (Figures 3-5 and 3-6). Overall, mean biomass was

significantly greater in the 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 low reef elevations (p < 0.001).

69

Oyster Condition Index

The greatest mean condition index (CI) score (10.9) was observed during the winter in the

St. Augustine region at low reef elevations (Figures 3-5 and 3-6). When both reef elevations

were considered, mean CI was significantly greater in the St. Augustine region than in the

Matanzas region (p < 0.05). CI was inversely related 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 the St. Augustine region than in the Matanzas region (p < 0.05). Mean CI

of oysters in the pre-fishery and fishery size classes were significantly 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 biomass of benthic organisms

may be better indicators of trophic status in highly flushed estuaries than ambient concentrations

of nutrients, phytoplankton biomass, or particulate organic carbon (POC). Large nutrient inputs

from the watersheds in the St. Augustine region 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, CHL and POC concentrations were significantly

different between regions, but the magnitude of these regional disparities (15 % and 21 %

difference between regions, respectively) does not adequately reflect the large magnitude of

estimated differences in nitrogen, phosphorus, and 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 other 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 that the effects of relatively small differences in ambient food

70

concentrations are amplified by elevated levels of water exchange and flow in highly flushed

ecosystems, such as described for coral reefs (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 tidal range, salinity, temperature, dissolved oxygen,

current velocity, and substrate quality (Grizzle, 1990; 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 measured, but it is assumed to be similar since the St.

Augustine and Matanzas regions are equidistant from 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 distributions include disease and predation. High

salinities observed in both regions 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 salinities (Shumway, 1996; Chu and Volety, 1997; La Peyre et al.,

2003). Oyster predators (e.g., oyster drills, starfish, whelks, and crabs) 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, suggesting that this issue may not be responsible

for the observed regional differences in oyster biomass and density. Vulnerability to disease,

71

predation, siltation, and wave action may, however, explain why oysters at low reef elevations

were less densely populated and exhibited lower mean biomass than oysters at high reef

elevations (Kenny et al., 1990).

Another biological characteristic which may be associated with regional differences in

oyster populations is the character of the bacterial community (Scott 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 counts (Beadle, 2004). In

contrast, the Shellfish Harvesting Area in the Matanzas region has not required closure

(Browning, 2005). Higher concentrations of bacteria in the St. Augustine region might be

expected to be correlated with a lower mean condition index (CI) compared 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, are also potential driving factors for regional

differences in oyster populations. Recreational and commercial harvesting is permitted in the

Matanzas region of the GTMNERR but not in the St. Augustine region (FWC, 2008); however,

most commercial and recreational harvesting in the Matanzas region occurs outside of the reefs

sampled in this study (Mark Berrigan, Florida 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 Lagoon, 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

are present in the GTMNERR, but were explicitly removed from the sampling design of this

study to avoid confounding effects.

The impact of seasonal differences in food availability on oysters in this study could have

been confounded by the effects of the oyster spawning cycle. Oysters in the southeastern United

States spawn continuously from April – October/November (Kenny 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 summer 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, since 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 reproductive 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 conversion 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 class and CI further implies a correlation with

reproductive status since fecundity increases with size (Thompson et al., 1996).

Within the context of all the aforementioned factors which may contribute to spatial

differences in the character of oyster communities in the GTMNERR, disparities in nutrient and

particulate organic carbon loading remain high on the list of potential drivers.

73

From a broader perspective of ecosystem sustainability, another important question is the

resilience of highly flushed estuaries to increased nutrient and organic 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 oyster reefs (i.e., habitat value,

filtration capacity, etc.) make them sentinels for ecosystem function (Grabowski and Peterson,

2007).

The results of this study indicate 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 gradients of increasing organic enrichment up to the point where

detrimental effects of enrichment caused a decline in abundance. Organic matter inputs can

provide more food for benthic macrofauna, but, depending on rates of water renewal, bacterial

decomposition of organic material can lead to anoxic 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 levels, suggesting that oyster populations within

the GTMNERR have not reached the threshold for adverse effects. The high water turnover

rates that characterize this estuary may contribute to its resilience to variable nutrient load.

Hydrodynamic flushing, through dilution and removal processes, is thought to increase benthic

community resilience to the negative impacts of watershed inputs (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

typical molar Redfield ratio of 16:1 and impact phytoplankton community composition, leading

to less nutritious (and potentially more toxic) food species available to oysters (Cloern, 2001).

Conclusions

Given the similarity of water column conditions in the Matanzas and St. Augustine

regions of the Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR), it

is hypothesized that regional differences in oyster biomass and density were caused by

differences in nutrient inputs and carbon availability. Traditionally monitored water quality

parameters such as nutrient and chlorophyll a concentrations provide only modest indications of

regional differences in trophic status in the GTMNERR (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 inter-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 allow integration of environmental conditions over space and time

(Dame, 1996). Incorporation of oyster population metrics, especially those estimating density

and biomass, in estuarine monitoring programs will increase understanding of eutrophication

impacts in these systems.

75

Table 3-1. Data sources for estimating 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 (µg L-1) compared between regions (represented 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

Kruskal-Wallis p-value Wet season Dry season

Kruskal-Wallis 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

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*

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Figure 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).

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Figure 3-2. Mean monthly temperature, salinity, and dissolved oxygen measured every 30

minutes at the Fort Matanzas (solid line) and San Sebastian (dashed line) System-Wide Monitoring Program sites from January 2002 – December 2008.

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Figure 3-3. Seasonal trends 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 trends 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

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).

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Figure 3-5. Mean oyster density, percent cover, biomass, and condition index from high reef

elevations in the Matanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer). Different letters above bars represent statistically significant (α = 0.10) differences.

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Figure 3-6. Mean oyster density, percent cover, biomass, and condition index from low reef

elevations in the Matanzas (FM, black bars) and St. Augustine (SA, gray bars) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer). Different letters above bars represent statistically significant (α = 0.10) differences.

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0

100

200

300

400

500

600

FM winter FM summer SA winter SA summer

Den

sity

(# m

-2)

spat small pre-fishery fishery

Figure 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. Augustine (SA) regions during the February 2008 survey (winter) and the July/August 2008 survey (summer).

84

CHAPTER 4 SUMMARY

The focus of this study was to characterize important processes related to the effects of

nutrient enrichment in the Guana Tolomato Matanzas National Estuarine Research Reserve

(GTMNERR) in northeast Florida. A defining characteristic of the study area is its connection

with the Atlantic Ocean through two inlets. Flushing 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 primary producers in the GTMNERR, were

observed in major bloom concentrations only one 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 µg L-1 and interannual variability was relatively 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 were estimated to filter the entire volume of the

study region in two to three weeks, about the same timescale as hydrodynamic flushing. The

direct influences of temperature and light on phytoplankton biomass in this subtropical/warm

temperate region are apparently restricted to a narrow time period in the winter when estimated

primary productivity is consistently low. Light 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, primarily nitrogen, have the potential to

stimulate phytoplankton growth, but concomitant accumulation of phytoplankton biomass is

rarely observed due to the consistent top-down pressures of flushing and grazing.

Physical and biological forces appear to have influenced phytoplankton biomass more than

nutrient loads during the study period. Therefore, as expected in this highly flushed estuary,

85

effects of nutrient enrichment were difficult to discern by looking at traditionally measured

parameters like nutrient concentrations 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. Apparently, the well-flushed character of this estuary

promotes a certain level of resistance to the negative impacts of eutrophication that have been

observed in other hydrodynamically restricted systems. Of course, this conclusion must be taken

with caution since changes in nutrient loads can also affect phytoplankton species composition

and be associated with other harmful impacts such as toxic contaminants that were not

considered in this study. Significant alteration of any one of the various bottom-up and top-

down controls of phytoplankton biomass could upset the balance between production and

consumption in the GTMNERR.

The long-term monitoring program established by the GTMNERR provided a foundation

for studying temporal patterns in key physical, chemical, and biological 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 suggest that incorporation of oyster

population metrics in estuarine monitoring 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 estuaries respond to increases in nutrients, thereby creating more

effective management plans for the conservation of estuarine systems around the world in an

environment of ever increasing nutrient loads (Nixon, 1995).

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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 Environmental Services Department of a major planning

firm in Orlando. In the fall of 2004, she joined University 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. Nicole’s research

interests include estuarine ecology and management, nutrient and phytoplankton dynamics, and

the role of bivalves in aquatic ecosystems.

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