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Interactions among Phytoplankton, Microzooplankton, and Mesozooplankton in Riverine Coastal Systems along the West Coast...

Permanent Link: http://ufdc.ufl.edu/UFE0021440/00001

Material Information

Title: Interactions among Phytoplankton, Microzooplankton, and Mesozooplankton in Riverine Coastal Systems along the West Coast of Peninsular Florida
Physical Description: 1 online resource (73 p.)
Language: english
Creator: Robinson, Kelly L
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: coastal, mesozooplankton, microzooplankton, phytoplankton, suwannee, weeki, withlacoochee
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Rivers represent major conduits transporting nutrients to coastal oceans from natural and anthropogenic sources. The fates of these nutrients and their impacts on coastal systems are controlled not only by physical and chemical processes in nearshore and coastal environments but also by co-occurring biological interactions. One conceptual model predicts that discharges from large rivers create buoyant, freshwater plumes in which physical, chemical, and 'top-down' and 'bottom-up' biological controls combine to yield i) lower phytoplankton growth rates and biomasses in low light environments near river mouths; ii) higher phytoplankton growth rates and biomasses in zones where light climates improve and nutrients remain available; and iii) decreasing phytoplankton growth rates and biomasses in zones where grazing and depletion or dilution of nutrients become important. Grazing rates and abundances of microzooplankton and mesozooplankton are predicted to respond to this spatial pattern according to their grazing abilities and rates of reproduction. Microzooplankton grazers feed on smaller phytoplankton and reproduce more rapidly, so their abundances and the rates at which they remove phytoplankton standing crops more closely track increases in phytoplankton growth rates and biomasses. Mesozooplankton eventually respond, and their abundances and grazing rates become more important further offshore. The applicability of this conceptual model was tested in four salinity zones that delineated the influences of the Suwannee, Withlacoochee and Weeki Wachee Rivers along the west coast of peninsular Florida. Results from four sets of field sampling and 24-hour grazing experiments were not consistent with the model. In the Suwannee system, phytoplankton biomasses and growth rates were highest near the river mouth rather than peaking in the zone characterized by intermediate salinities. In the Withlacoochee and Weeki Wachee systems, phytoplankton biomasses and growth rates remained fairly uniform across the range of salinities. Microzooplankton grazing rates and abundances and mesozooplankton abundances were similar across the salinity gradients in the three systems. Microzooplankton grazing represented an important pressure on phytoplankton standing crops, because it removed an average (plus or minus standard deviation) of 99.5 plus or minus 46.8%, 81.3 plus or minus 31.6%, and 87.1 plus or minus 12.6% of primary production per day in the Suwannee, Withlacoochee, and Weeki Wachee systems. In comparison, mesozooplankton grazing impact was negligible, with less than or equal to 0.05% of phytoplankton production consumed per day. Interactions among phytoplankton, microzooplankton, and mesozooplankton were not consistent with the model; therefore, we hypothesized that river discharge was below the threshold required to induce the physical processes that establish the predicted gradients. For example, the light environment supported phytoplankton growth closer to shore than expected in all systems, and nutrients were quickly depleted in nearshore, coastal waters. The relatively large impact of grazing by microzooplankton suggests that the microbial loop plays a primary role in the transformation of nutrients delivered to coastal waters by the Suwannee, Withlacoochee, and Weeki Wachee Rivers, with consequences for cycling of elements, structure and function of food webs, and production of fisheries resources.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kelly L Robinson.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Frazer, Tom K.
Local: Co-adviser: Jacoby, Charles A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021440:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021440/00001

Material Information

Title: Interactions among Phytoplankton, Microzooplankton, and Mesozooplankton in Riverine Coastal Systems along the West Coast of Peninsular Florida
Physical Description: 1 online resource (73 p.)
Language: english
Creator: Robinson, Kelly L
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: coastal, mesozooplankton, microzooplankton, phytoplankton, suwannee, weeki, withlacoochee
Fisheries and Aquatic Sciences -- Dissertations, Academic -- UF
Genre: Fisheries and Aquatic Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Rivers represent major conduits transporting nutrients to coastal oceans from natural and anthropogenic sources. The fates of these nutrients and their impacts on coastal systems are controlled not only by physical and chemical processes in nearshore and coastal environments but also by co-occurring biological interactions. One conceptual model predicts that discharges from large rivers create buoyant, freshwater plumes in which physical, chemical, and 'top-down' and 'bottom-up' biological controls combine to yield i) lower phytoplankton growth rates and biomasses in low light environments near river mouths; ii) higher phytoplankton growth rates and biomasses in zones where light climates improve and nutrients remain available; and iii) decreasing phytoplankton growth rates and biomasses in zones where grazing and depletion or dilution of nutrients become important. Grazing rates and abundances of microzooplankton and mesozooplankton are predicted to respond to this spatial pattern according to their grazing abilities and rates of reproduction. Microzooplankton grazers feed on smaller phytoplankton and reproduce more rapidly, so their abundances and the rates at which they remove phytoplankton standing crops more closely track increases in phytoplankton growth rates and biomasses. Mesozooplankton eventually respond, and their abundances and grazing rates become more important further offshore. The applicability of this conceptual model was tested in four salinity zones that delineated the influences of the Suwannee, Withlacoochee and Weeki Wachee Rivers along the west coast of peninsular Florida. Results from four sets of field sampling and 24-hour grazing experiments were not consistent with the model. In the Suwannee system, phytoplankton biomasses and growth rates were highest near the river mouth rather than peaking in the zone characterized by intermediate salinities. In the Withlacoochee and Weeki Wachee systems, phytoplankton biomasses and growth rates remained fairly uniform across the range of salinities. Microzooplankton grazing rates and abundances and mesozooplankton abundances were similar across the salinity gradients in the three systems. Microzooplankton grazing represented an important pressure on phytoplankton standing crops, because it removed an average (plus or minus standard deviation) of 99.5 plus or minus 46.8%, 81.3 plus or minus 31.6%, and 87.1 plus or minus 12.6% of primary production per day in the Suwannee, Withlacoochee, and Weeki Wachee systems. In comparison, mesozooplankton grazing impact was negligible, with less than or equal to 0.05% of phytoplankton production consumed per day. Interactions among phytoplankton, microzooplankton, and mesozooplankton were not consistent with the model; therefore, we hypothesized that river discharge was below the threshold required to induce the physical processes that establish the predicted gradients. For example, the light environment supported phytoplankton growth closer to shore than expected in all systems, and nutrients were quickly depleted in nearshore, coastal waters. The relatively large impact of grazing by microzooplankton suggests that the microbial loop plays a primary role in the transformation of nutrients delivered to coastal waters by the Suwannee, Withlacoochee, and Weeki Wachee Rivers, with consequences for cycling of elements, structure and function of food webs, and production of fisheries resources.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kelly L Robinson.
Thesis: Thesis (M.S.)--University of Florida, 2007.
Local: Adviser: Frazer, Tom K.
Local: Co-adviser: Jacoby, Charles A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021440:00001


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INTERACTIONS AMONG PHYTOPLANKTON, MICROZOOPLANKTON, AND
MESOZOOPLANKTON IN RIVERINE COASTAL SYSTEMS ALONG THE WEST COAST
OF PENINSULAR FLORIDA



















By

KELLY L. ROBINSON


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2007

































O 2007 Kelly L. Robinson



































For my Dad.









ACKNOWLEDGMENTS

This research was supported in part by a grant from the Biological Oceanography program,

Division of Ocean Sciences, Directorate of Geosciences, National Science Foundation and by the

Department of Fisheries and Aquatic Sciences at the University of Florida.

I am thankful to Megan Brennan from the Institute of Food and Agriculture Sciences

Statistics Department (UF) and Dr. Mike Allen for statistical consultation. I also thank Dr. Ed

Phlips for kindly allowing me to use his microscopy and water collection equipment, and to

consult with his staff. In particular, I appreciate the courteous assistance of Mary Cichra. I thank

Dr. Karl Havens for his advice on zooplankton methodology and for allowing me to borrow his

equipment for processing zooplankton samples. I am indebt to Dr. Bill Pine for generously

sharing his technicians with me whenever I needed extra help in the laboratory or in the field.

Many other people assisted me over the course of this research. Without their help,

counsel, and support, completion of this proj ect would have been impossible. Foremost, I thank

my esteemed colleagues in the Frazer Lab. Stephanie Keller and Darlene Saindon were my gurus

for processing water chemistry samples and all the subtleties therein. I also thank them for

teaching me the practical side of science and for setting standards of excellence in laboratory and

field techniques that I will forever try to meet. Dr. Loreto de Brandabere, and Vincent and

Kristin Politano were tireless volunteers for field excursions and laboratory work. They never

begrudged me for the early morning start times or the long days. Kate Lazar was my gold

standard for making dilution treatments, and Emily Mitchem was my savior when the work went

deep into the night. I thank Matt Lauretta for his help in the field, and for being willing to go out

a second time when the tide left me high and dry at the first attempt. In addition to her assistance

in the field and in the lab, I thank Loreto for her friendship and for the frank and stimulating

discussions regarding ways to improve this research and future efforts. From Bill Pine's lab, I am










particularly grateful to Elissa Buttermore for working with me on numerous occasions. She

always had a great attitude and her help was instrumental in the completion of field and

laboratory work.

My committee, Tom Frazer, Chuck Jacoby, and Marsh Youngbluth have been

exceptionally positive and supportive of my work. Their guidance has improved my

understanding of nature, science, and life. I thank Marsh and Chuck for providing the

opportunity to be part of a submersible crew--a life-changing experience, and for their

incredibly helpful comments that surely improved this thesis. I also thank Chuck for his

friendship, for being a great mentor, and for his priceless insights regarding all aspects of this

research. I am particularly indebted to my maj or advisor, Tom. He has taught me more than I

ever expected to learn as graduate student about science and life. I will forever try to emulate his

ability to delve into the details of a proj ect and re-surface with a broader understanding of the

world. He has been an extraordinary mentor and sponsor, and I feel extremely lucky to have been

his student the past two and half years.

Finally, I thank my friends, my sister, and my parents, Adele and Harold, and my

stepmother, Kim Robinson, for their unswerving love, support, and enthusiasm. Their belief in

my abilities kept me working when the going got tough.












TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .........__... ......._. ...............4....


LIST OF TABLES ........._.___..... .__. ...............7....


LI ST OF FIGURE S .............. ...............9.....


AB S TRAC T ............._. .......... ..............._ 10...


CHAPTER


1 INTRODUCTION ................. ...............12.......... ......


2 MATERIALS AND METHODS .............. ...............16....


Study Sites .............. ...............16....
S ample Coll ection............... .............. 1
Laboratory Procedures ................. ...............20.................
Control s .............. ...............22....
Calculations .............. ........ .. .... ..... ... ... ........2

Phytoplankton Growth and Microzooplankton Grazing Rates .............. ...................24
Mesozooplankton Grazing Rates............... ...............25.
Data Analyses ................. ...............26.......... .....


3 RE SULT S .............. ...............29....


Field Parameters ............... ...............29....

Phytoplankton Growth Rates ................ ......__ ............... 1....
Microzooplankton Grazing Rates and Assemblages ................. ....._._. ............. .....31
Mesozooplankton Grazing Rates and Assemblages ................. ..............................33
Controls s................ ...............3.. 5......... ....


4 DI SCUS SSION ................. ...............56................


LIST OF REFERENCES ................. ...............67........... ....


BIOGRAPHICAL SKETCH .............. ...............73....










LIST OF TABLES


Table page

3-1 Mean values for the physical, chemical, and biological parameters measured in the
Suwannee, Withlacoochee, and Weeki Wachee systems.. ............ .....................3

3-2 Analyses of variance for transformed river discharge (m3 S-1), light attenuation (Kd m
1), in situ total chlorophyll concentrations (Cpg L^)~, water temperature (oC), and
dissolved oxygen concentrations (DO, mg L^)~............... ...............39....

3-3 Suwannee system: estimates of apparent growth rate (AGR) in controls (Co),
instantaneous maximum specific phytoplankton growth rates (k & SE), instantaneous
microzooplankton grazing rates (g & SE), percent of phytoplankton standing crop
removed daily (%PSC d- ), and percent of phytoplankton production lost daily (%PP
d-~ )............... ...............42....

3-4 Withlacoochee system: estimates of apparent growth rate (AGR) in controls (Co),
instantaneous maximum specific phytoplankton growth rates (k & SE), instantaneous
microzooplankton grazing rates (g & SE), percent of phytoplankton standing crop
removed daily (%PSC d- ), and percent of phytoplankton production lost daily (%PP
d-~ )............... ...............43....

3-5 Weeki Wachee system: estimates of apparent growth rate (AGR) in controls (CLo),
instantaneous maximum specific phytoplankton growth rates (k & SE), instantaneous
microzooplankton grazing rates (g & SE), percent of phytoplankton standing crop
removed daily (%PSC d- ). ............. ...............44.....

3-6 Analyses of variance for transformed phytoplankton growth rates (k),
microzooplankton grazing rates (g), microzooplankton total abundance, and
mesozooplankton total abundance. ............. ...............45.....

3-7 Suwannee system: microzooplankton total abundance (individuals L^1) and common
taxa during September and November ................. ...............47........... ...

3-8 Withlacoochee system: microzooplankton total abundance (individuals L^1) and
common taxa during November and December. ............. ...............48.....

3-9 Weeki Wachee system: total abundance (individuals L^1) and common taxa during
September and November. ................. ...............49...............

3-10 Suwannee system: mesozooplankton total abundance (individuals m-3) and common
taxa during March, May, September, and November. ................... ...............5

3-11 Withlacoochee system: mesozooplankton total abundance (individuals m-3) and
common taxa during June, July, November, and December. ............. .....................5










3-12 Weeki Wachee system: mesozooplankton total abundance (individuals m-3) and
common during April, June, September, and November ................. ................ ...._.52

4-1 Published values of instantaneous maximum specific phytoplankton growth rates (k
SSE) and instantaneous microzooplankton grazing rates (g & SE). ........._..._.._ .........._....66










LIST OF FIGURES


FiMr page

2-1 Location of study systems along the west coast of Florida. Filled circles denote
stations sampled. .............. ...............28....

3-1 Back transformed mean light attenuation (Kd m-1) & 95% confidence intervals (CI)
for zones ................. ...............40.................

3-2 Back transformed mean chlorophyll concentrations (Cpg L^1) & 95% confidence
intervals (CI) for zones.. ............ ...............41.....

3-3 Two-dimensional ordination (stress value = 0.11) based on microzooplankton
abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems.....................46

3-4 Three-dimensional ordination (stress = 0. 16) based on mesozooplankton abundances
in the Suwannee, Withlacoochee, and Weeki Wachee systems.. ............ ....................53

3-5 Three-dimensional ordination (stress = 0. 12) based on mesozooplankton abundances
in the Suwannee River plume during March, May, September, and November. ..............54

3-6 Three-dimensional ordination (stress = 0. 12) based on mesozooplankton abundances
in the Suwannee River plume.. ............ ...............55.....









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

INTERACTIONS AMONG PHYTOPLANKTON, MICROZOOPLANKTON, AND
MESOZOOPLANKTON IN RIVERINE COASTAL SYSTEMS ALONG WEST COAST OF
PENINSULAR FLORIDA

By

Kelly L. Robinson

August 2007

Chair: Thomas K. Frazer
Cochair: Charles A. Jacoby
Major: Fisheries and Aquatic Sciences

Rivers represent maj or conduits transporting nutrients to coastal oceans from natural and

anthropogenic sources. The fates of these nutrients and their impacts on coastal systems are

controlled not only by physical and chemical processes in nearshore and coastal environments

but also by co-occurring biological interactions. One conceptual model predicts that discharges

from large rivers create buoyant, freshwater plumes in which physical, chemical, and "top-

down" and "bottom-up" biological controls combine to yield i) lower phytoplankton growth rates

and biomasses in low light environments near river mouths; ii) higher phytoplankton growth

rates and biomasses in zones where light climates improve and nutrients remain available; and

iii) decreasing phytoplankton growth rates and biomasses in zones where grazing and depletion

or dilution of nutrients become important. Grazing rates and abundances of microzooplankton

and mesozooplankton are predicted to respond to this spatial pattern according to their grazing

abilities and rates of reproduction. Microzooplankton grazers feed on smaller phytoplankton and

reproduce more rapidly, so their abundances and the rates at which they remove phytoplankton

standing crops more closely track increases in phytoplankton growth rates and biomasses.

Mesozooplankton eventually respond, and their abundances and grazing rates become more










important further offshore. The applicability of this conceptual model was tested in four salinity

zones that delineated the influences of the Suwannee, Withlacoochee and Weeki Wachee Rivers

along the west coast of peninsular Florida. Results from four sets of field sampling and 24-hour

grazing experiments were not consistent with the model. In the Suwannee system, phytoplankton

biomasses and growth rates were highest near the river mouth rather than peaking in the zone

characterized by intermediate salinities. In the Withlacoochee and Weeki Wachee systems,

phytoplankton biomasses and growth rates remained fairly uniform across the range of salinities.

Microzooplankton grazing rates and abundances and mesozooplankton abundances were similar

across the salinity gradients in the three systems. Microzooplankton grazing represented an

important pressure on phytoplankton standing crops, because it removed an average (a standard

deviation) of 99.5 & 46.8%, 81.3 & 3 1.6%, and 87. 1 + 12.6% of primary production per day in

the Suwannee, Withlacoochee, and Weeki Wachee systems. In comparison, mesozooplankton

grazing impact was negligible, with I 0.05% of phytoplankton production consumed per day.

Interactions among phytoplankton, microzooplankton, and mesozooplankton were not consistent

with the model; therefore, we hypothesized that river discharge was below the threshold required

to induce the physical processes that establish the predicted gradients. For example, the light

environment supported phytoplankton growth closer to shore than expected in all systems, and

nutrients were quickly depleted in nearshore, coastal waters. The relatively large impact of

grazing by microzooplankton suggests that the microbial loop plays a primary role in the

transformation of nutrients delivered to coastal waters by the Suwannee, Withlacoochee, and

Weeki Wachee Rivers, with consequences for cycling of elements, structure and function of food

webs, and production of fisheries resources.









CHAPTER 1
INTTRODUCTION

Estuaries and adj acent coastal waters are highly productive systems, driven in large part by

the infusion of nutrient-rich waters from rivers. Excessive nutrient inputs increase the likelihood

of eutrophication or production of excess organic matter (Cloem 2001). In tumn, over-production

of organic matter in coastal waters can cause patches of hypoxia via settling and microbial

decomposition of phytoplankton (Cloem 2001), changes in the biogeochemistry of sediments as

hypoxic conditions alter chemical flux at the sediment-water interface (Jarrgensen (1996),

declines in the abundance of submerged macrophytes if high concentrations of phytoplankton

decrease light availability at depth (Duarte 1995), shifts in zooplankton community structure in

response to changes in algal communities (Paerl 1988), and mortalities of fish and shellfish from

algal toxins (Rosenberg & Loo 1988). The negative ecological consequences of nutrient over-

enrichment often have broad and far-reaching socio-economic implications. For this reason, it is

essential to more fully understand the factors and processes controlling phytoplankton and

zooplankton production in river-impacted coastal waters.

Due to the complex nature of coastal systems, the influence of river discharge on

interactions between nutrients, light, phytoplankton, and zooplankton is likely to vary markedly

among systems and times. Phytoplankton dynamics are dependent on complex interactions

between the availability of light, nutrients, and other factors that promote growth and factors like

sinking aggregation and grazing that result in loss. Empirical studies and modeling indicate that

in coastal systems dominated by river input, the spatial and temporal availability of nutrients and

light can be directly affected by mixing and the density fronts created by wind stress, tidal

cycles, and river discharge (Liu & Dagg 2003, Chen et al. 1997, Yin et al. 1997). These physical

forces and the gradients they create are highly variable in both space and time; therefore,










phytoplankton growth rates and production vary as well (Bowman et al. 1986). Variation in

phytoplankton production interacts with zooplankton production because of the tight coupling

between the zooplankton and phytoplankton communities (Cloern 2001, Kiarrboe 1993). In

riverine coastal systems along the Gulf of Mexico, zooplankton grazers are known to be an

important factor regulating phytoplankton biomass (Juhl & Murrell 2005, Liu & Dagg 2003,

Bledsoe 2003, Strom & Strom 1996, Fahnenstiel et al. 1995).

In coastal systems where large rivers dominate, phytoplankton production and zooplankton

grazing and abundance have been described with a conceptual model based on processes

occurring along gradients of nutrients and light (Dagg & Breed 2003). In the model, as water

moves offshore from the river mouth, phytoplankton biomass and production and zooplankton

abundance and grazing rates are low in or near the river, rise to maximum in the mid-field as

characterized by salinities between fresh and oceanic water, and decline again in the far field

characterized by oceanic salinities. Near the river mouth, algal growth rates are low, primarily

attributed to an unfavorable light regime caused by high concentrations of suspended particulate

matter. Algal growth rates are greater in the near field as a consequence of sedimentation of

lithogenic particles, which leads to a more favorable light environment. In the mid-field, where

nutrient concentrations remain sufficiently high and the light environment is more favorable,

algal growth rates are the highest. In the far field, as nutrients are diluted and depleted via

uptake, growth rates decline. The predicted grazing response of microzooplankton and

mesozooplankton to this distribution of algal biomass is thought to be dependent on their

respective rates of production. Because of the closer coupling between microzooplankton and

phytoplankton production (Azam et al. 1983, Thingstad et al .1999), microzooplankton grazers

are expected to respond more rapidly than mesozooplankton grazers to an increase in









phytoplankton biomass and remove a larger percentage of the phytoplankton production in the

mid-field. In the far field marked by oceanic salinities, mesozooplankton will have had enough

time to respond to phytoplankton production, and they will peak in abundance and remove a

greater percentage of phytoplankton than microzooplankton (Kiarrboe & Johansen 1986, Kahru et

al. 1984).

To date, the conceptual model has only been tested explicitly in the plume of the 6260 km

long Mississippi River (Liu & Dagg 2003). The drainage basin of this river encompasses greater

than 40% of the continental United States or an area of approximately 3 54,000 km2 (Berner &

Berner 1987). This drainage basin generates high annual riverine discharge (15,000 m3 S-1~

which results in a large region of interaction between the plume and the receiving waters of the

Gulf of Mexico (Dagg & Breed 2003). Light attenuation is attributed to high concentrations of

lithogenic particles (Dagg & Breed 2003). River nutrient concentrations are also high at 2.8 x 10s

to 2.8 x 109 Cpg TN L^1 and 9.29 x 106 to 1.55 x 10sCpg PO4 L^1 (Lohrenz et al. 1999). The findings

reported by Liu & Dagg (2003) were generally consistent with the model. Phytoplankton growth

and microzooplankton grazing rates were low in the near field, highest in the mid-field, and

decreasing in the far field. Mesozooplankton grazing impact was low at the near and mid-field,

and highest in the far field.

This study tested the generality of the conceptual model developed for riverine coastal

systems by describing interactions among phytoplankton growth and biomass and

microzooplankton and mesozooplankton grazing across salinity gradients in river-influenced

systems along the west coast of peninsular Florida. These river systems are ideal for examining

key interactions in the model because discharge rates, nutrient concentrations, and light

environments vary among the systems. If the interactions described by the model are observed in









these systems under a variety of flow regimes, then the model may apply to a wide range of

riverine coastal systems world wide.









CHAPTER 2
MATERIALS AND METHODS

To test the model's generality, biomass and growth of phytoplankton and abundance and

grazing rates of microzooplankton and mesozooplankton were estimated in riverine coastal

waters off the Suwannee, Withlacoochee, and Weeki Wachee rivers along the west coast of

peninsular Florida (Figure 2-1). The three rivers differ in the areal extent of their watersheds,

historical annual discharge rates, light attenuation coefficients, and nutrient concentrations

(Frazer et al. 1998). Intra-annual variability in discharge also was expected to yield high and low

discharge regimes for each river. Therefore, natural differences among the systems were

anticipated to provide a range of scenarios in which the model could be tested. Phytoplankton

biomasses were estimated by using chlorophyll concentrations as proxy measures. Phytoplankton

growth and microzooplankton grazing rates were estimated using the microzooplankton dilution

technique (Landry & Hassett 1982), and mesozooplankton grazing impact was estimated using

the mesozooplankton addition technique (Calbet & Landry 1999). Microzooplankton and

mesozooplankton abundances were determined with standard identification and enumeration

techniques (Omoi & Ikeda 1984).

Study Sites

The Suwannee River originates in the Okeefenokee Swamp, Georgia, and it drains

approximately 28,600 km2 Of southern Georgia and north central Florida (Wolfe & Wolfe 1985)

before discharging into the Gulf of Mexico (Figure 2-1). Surface water and groundwater

contribute to flow in this system (Bledsoe & Phlips 2000). Mean annual discharge is 280 m3 S-1,

with maximum and minimum rates typically occurring in the spring and fall months, respectively

(USGS Water Resources 2007). Light availability at depth is normally the lowest of the three

systems. Concentrations of lithogenic particles are low, with light attenuation attributed to









colored dissolved organic matter (particularly during high discharge), tripton, and algal particles

(Bledsoe & Phlips 2000). Nutrient concentrations are normally highest of the three systems, with

10-year (1996-2006) means (A SD) for total nitrogen (TN) and total phosphorus (TP) equal to

503.2 & 287.7 Cpg L^1 and 48.7 & 33.0 Cpg L^1, respectively (T. Frazer, University of Florida,

unpublished data).

The Withlacoochee River originates in the Green Swamp (Figure 2-1), and its drainage

basin covers 5,232 km2 (YObbi 1989). As in the Suwannee River, flows are generated by surface

water and groundwater, with a mean annual discharge of 23 m3 S-1 (USGS Water Resources

2007). Discharge rates vary intra-annually. Light availability at depth is typically intermediate

among the three systems; however, water clarity improves during periods of low rainfall because

spring waters comprise the bulk of the discharge. Among the three systems, Concentrations of

TN are the lowest and TP concentrations are intermediate among the three systems, with 10-year

means (A SD) of 420.8 & 188.9 Cpg L^1, and 39.4 & 26.8 Cpg L^1, respectively (T. Frazer, University

of Florida, unpublished data).

The Weeki Wachee River originates at a first magnitude spring and meanders 13 km

before discharging into the Gulf of Mexico (Figure 2-1). The river has a drainage basin of less

than 26 km2 (Medard et al. 1968). The annual mean discharge of the Weeki Wachee River is 4.5

m3 S-1 (USGS Water Resources 2007). High and low flows normally occur in the fall and spring,

respectively. Light availability at depth is the greatest for the three systems, with high water

clarity due to low suspended particle loads as is typical of spring-fed systems (Frazer et al.

2001). Concentrations of TN are intermediate among the three systems with a 10-year mean (A

SD) of 469. 1 & 203.8 Cpg L^1, while TP concentrations are the lowest, with a 10-year mean (A SD)

of 8.8 & 4.4 Cpg L^1 (T. Frazer, University of Florida, unpublished data).









Sample Collection

Environmental data, water for dilution and addition experiments, and mesozooplankton

samples were collected from each of the systems four times in 2006, with effort made to sample

twice during the low and high discharge periods for each river. Whole water for

microzooplankton enumeration was collected and preserved twice from each system in the fall

months. Distinct salinity ranges were used to select the four separate fields of interaction or

zones within each river plume, i.e. the river mouth (10-15 psu), near field (19-22 psu), mid-field

(28-30 psu), and far field (>30 psu). Samples of water from the three stations within each zone

were combined to yield physical means.

Environmental data were collected at each station during each sampling period. Water

temperature (oC), salinity (psu), dissolved oxygen (mg L^)~, and pH were measured 0.5 m below

the surface with a Yellow Springs, Inc. sonde coupled to an electronic datalogger (Models: 600R

& 650 MDS). Secchi depths (m) were determined. Photosynthetic ally active radiation was

measured with Li-Cor. Instruments, Inc. cosine-corrected submersible light sensors connected to

a Li-Cor (LI 1400) datalogger that simultaneously recorded surface and downwelling radiation.

At each station, underwater light levels were measured just below the water' s surface,

approximately at the mid-point of the water column, and 0.3 m above the bottom in water less

than 5.0 m deep. The attenuation coefficient (Kd) WaS calculated using Lambert-Beer' s Law

(Equation 2-1), where Io is surface irradiance (Cpmol photons m2 S-1) and Iz is light intensity at

depth (z):



K d (2-1)









In each of the target salinity zones, approximately 70 L of seawater was collected for

experiments and microzooplankton samples between the surface and 0. 1 m off the bottom using

an integrated sampling tube (Bledsoe & Phlips 2000). The submerged end of the tube was

covered with 1.0-mm mesh to filter out large zooplankton. When water depths exceeded the

length of the tube, only the top three meters of the water column were sampled. Pulled water was

filtered through 190-Cpm Nitex mesh prior to filling a 20-L plastic carboy at each station; this

water was used for the dilution and addition experiments. An additional 8 L of water was pulled

and filtered to serve as rinse water for the filtration system. Microzooplankton samples were

collected by filtering water through 190-Clm mesh to fill one-third of a 5-L carboy and then

preserving the sample with Lugol's solution. Five hundred to 2000 ml of water from each station

was filtered through Whatman GF/F filters for subsequent analysis of chlorophyll concentration.

Filters were stored in a container with desiccant that was placed on ice.

Mesozooplankton were collected at two stations within each zone using a 202-Cpm mesh,

0.5-m diameter plankton net with a filtering cod-end. Whenever possible, net tows undulated

between the surface and a depth of 3.0 m. At stations where the water depth was less than 3.0 m,

tows sampled the middle of the water column. Tows lasted for approximately 5 min, with

volumes determined from a General Oceanics mechanical flowmeter set off-center inside the net.

Each mesozooplankton sample was carefully poured into a 3.4-L plastic insulated container with

a 2000-Cpm mesh screen placed approximately 5.0 cm above the bottom. An OTAB@ was added

to supplement the oxygen supply in the water during transport to the laboratory.

Upon returning to the laboratory, water for the experiments and mesozooplankton samples

were stored for 16 to 22 hrs in a climate controlled environment prior to the start of the

experiments. Water temperatures during storage did not deviate more than 3.00C from the










temperature measured in situ, except in November when water temperatures in samples from the

Withlacoochee became 5.0-10.00C warmer. Air stones attached to aquarium air pumps were

inserted in each mesozooplankton container to reduce the potential for low dissolved oxygen

concentrations. The preserved microzooplankton samples were stored in a climate controlled

dark room prior to identification and enumeration.

Laboratory Procedures

Using water collected during each sampling period, phytoplankton growth rates and

microzooplankton grazing rates were estimated for each zone using the dilution technique first

developed by Landry & Hassett (1982). Seawater collected from a given zone and discharge

period was filtered through 190-Cpm Nitex mesh to exclude mesozooplankton from the

experimental medium (Bledsoe 2003). This water was designated as whole seawater, and it

included microzooplankton that is zooplankton smaller than 200 pm. Fifteen liters of whole

seawater were filtered through a step filtration system comprised of 10-Cpm, 5-Cpm, and 1-Cpm

sediment filters, as well as a 0.2-Cpm Gelman Microcapsule filter to create the dilution medium

that lacked particles larger than 0.2 pm. In order to maintain the appropriate salinity in each

batch of dilution medium, the filtration system was flushed by filtering approximately 5 L of

whole seawater prior to preparing the dilution medium. Duplicate 100, 75, 50, 25, and 10 percent

whole water treatments were created by combining whole seawater with dilution medium in 2.8-

L glass flasks with an experimental volume of2.5 L.

Mesozooplankton grazing rates were estimated using the addition method (Calbet &

Landry 1999). Aliquots of 100, 200, and 400 mL were removed from each mesozooplankton

sample using a 10-mL Hensen-Stimpel pipette. The mesozooplankton aliquots were added to

2.8-L glass flasks to create duplicate 100 percent whole seawater treatments with an

experimental volume of 2.5 L. Separate, but equal, aliquots of mesozooplankton were removed









and filtered through pre-weighed 20.0-Cpm polycarbonate filters that subsequently were dried at

600C for 48 hrs and weighed to obtain total mesozooplankton dry weights (mg). Only organisms

healthy enough to swim upward through a 2000-Cpm mesh screen set inside each insulated

container were added to treatment flasks or used to determine dry weights. Immediately

following the removal of aliquots for experiments and determination of dry weights, two

additional subsamples (50 to 100 mL) were taken and preserved in Lugol's solution for

subsequent identification and enumeration. Subsamples were taken from above and below the

2000-Cpm mesh in the sample container to determine if the mesozooplankton assemblage added to

treatments was representative of the mesozooplankton assemblage collected in situ.

Changes in chlorophyll concentrations were used as a proxy measure for changes in

phytoplankton density in the dilution and addition experiments. Initial and final chlorophyll

concentrations (Clg L 1) were determined from three subsamples of whole water taken at the start

of each experiment and two subsamples taken from each experimental flask at the end of each

experiment. Subsamples of 500, 1000, 1500, or 2000 mL were filtered onto Whatman GF/F

glass-fiber filters that were frozen until processing. Each filter was placed into a test tube with

8.0 mL of 90 percent ethanol and heated in a 780C water bath for 5 min. After 24 to 72 hrs of

passive extraction, filters were removed, and the sample was centrifuged to separate particulate

debris. Chlorophyll concentrations in the supernatant were determined using a Hitachi U200 dual

beam spectrophotometer, and the acidification method was used to correct for phaeophytin

(APHA 1998).

Experimental flasks were incubated for approximately 24 hrs in a climate controlled

laboratory with a 12/12 light/dark cycle in March, April, November, and December and a 14/10

light/dark cycle in May, June, July and September. Light was provided by cool white fluorescent









lights with an average intensity of 50 pLE m-2 S-1. Every six hours, flasks were gently swirled to

resuspend any settled material.

Mesozooplankton and microzooplankton in the preserved samples were identified and

enumerated. For mesozooplankton, three separate aliquots were taken from each preserved

subsample with a 1-mL Hensen-Stempel pipette, placed into a counting wheel, and then

processed using a dissecting microscope. Microzooplankton subsamples (minimum three) were

identified and enumerated using a Leica inverted-contrast microscope after being added to

settling chambers at least 30 min prior to processing. Both mesozooplankton and

microzooplankton counts were terminated after 100 individuals of any taxon were counted

(Utermohl 1958).

River discharge for each sampling event in the Suwannee, Withlacoochee, and Weeki

Wachee systems was taken as the daily mean calculated from hourly records at United States

Geological Survey gauging stations located at Wilcox (FL), Holder (FL), and Brooksville (FL),

respectively (USGS Water Resources 2007). Data used to calculate historical mean monthly

discharges were also taken from these gauging stations.

Controls

Assumptions underpin the microzooplankton dilution and mesozooplankton addition

techniques (Landry & Hassett 1982, Calbet & Landry 1999). The techniques are founded on four

assumptions: (1) phytoplankton growth is not density dependent, (2) phytoplankton growth is

exponential, (3) phytoplankton growth is not nutrient limited, and (4) the probability of a

phytoplankton cell being consumed is directly related to encounter rate of consumers. The

mesozooplankton addition technique also assumes that mesozooplankton added to treatments are

representative of the in situ assemblages.









To prevent nutrient limitation of phytoplankton growth, excess nutrients (KNO3, KPO4,

NaSiO4) were added to each microzooplankton and mesozooplankton treatment flask (10 mL of

400 Cpg N L^1, 40 Cpg P L^1 and 400 Cpg Si L^)~. To verify that excess nutrients were available for

phytoplankton uptake during the dilution and addition experiments, two sets of controls were

used. Firstly, two additional 100 percent whole seawater treatment flasks that were not spiked

with nutrients provided estimates of phytoplankton growth rates in situations where limitation

was possible during the incubation period (Jett 2004). These flasks and nutrient amended flasks

containing 100 percent whole seawater without mesozooplankton served as controls for the

mesozooplankton addition experiment. Secondly, soluble reactive phosphorus (SRP) was

measured in one 60 to 100 mL subsample from each 100 percent whole seawater treatment

before and after the incubation periods. These subsamples allowed the availability of phosphorus

to be compared between treatments with and without excess nutrients, and they provided

estimates of phosphorus availability within each flask at the beginning and end of experiments.

The latter information provided a means to evaluate if the nutrient-replete assumption of the

dilution and addition techniques was being met throughout the incubation period. All subsamples

were refrigerated prior to being analyzed within 72 hrs. Sub samples were filtered through

Millipore glass fiber pre-filters, a color reagent was added, and the solution was analyzed on a

Hitachi U2000 dual beam spectrophotometer after ten minutes of color development.

The representativeness of mesozooplankton added to treatment flasks was determined by

comparing the taxa and numbers found in subsamples taken from above and below the 2000-Cpm

mesh inserted in holding containers. If taxonomic compositions and abundances differed

substantially between the two subsamples, then estimates of in situ grazing rates could be

adjusted.









Calculations

Phytoplankton Growth and Microzooplankton Grazing Rates

Phytoplankton growth and mortality due to microzooplankton grazing was estimated

according to the methods of Landry & Hassett (1982). The relationship between the dilution

fraction (D) of unfiltered seawater and the net change in phytoplankton concentration over time

(i.e. apparent growth rate or AGR) was calculated using least squares regression based the linear

equation:


SIlnl = k gD (2-2)


where Po is the concentration of phytoplankton at the start of the experiment, Pt is the final

concentration of phytoplankton after time t, the y-intercept, k, is the instantaneous maximum

specific phytoplankton growth rate, and the negative slope, g, is the instantaneous

microzooplankton grazing rate. Values of Po measured in whole seawater were corrected for

dilution by multiplying by the appropriate dilution factor (i.e. 1.00, 0.75, 0.50, 0.25, and 0.10).

If the relationship of apparent growth rate to dilution fraction was found to be non-linear

for less dilute treatments, then grazing was assumed to be saturated and a piecewise linear

grazing model was fit to the data (Redden et al. 2002). The phytoplankton concentration at which

grazing becomes saturated, Ps, was calculated using Equation 2-3, and the variables k and g were

obtained from a least squares linear regression to data from treatments diluted below Ps where

equation 2-2 applied.

k[Pt Po exp(kt)]
Ps = (2-3)
gD[1- exp(kt)]

Microzooplankton impacts on phytoplankton were estimated in two ways (Landry &

Hassett 1982). The percent of phytoplankton biomass removed per day due to grazing (S) was









calculated using grazing coefficients, g, and Equation 2-4; and the percent of phytoplankton

production lost per day due to grazing (%PP) was calculated using grazing coefficients, g,

instantaneous maximum specific phytoplankton growth rates, k, and Equation 2-5.

S=(1-e *r100 (2-4)


%/PP =I' *100) (2-5)


Mesozooplankton Grazing Rates

Mesozooplankton grazing rates were estimated according to the methods of Calbet &

Landry (1999). The initial concentration of phytoplankton (Po), the final concentration of

phytoplankton (Pt), the duration of the experiment (t), the appropriate instantaneous maximum

specific phytoplankton growth rate (k), and the biomass of mesozooplankton added to the

treatment were used in a least squares linear regression based on Equation 2-6 to calculate an

instantaneous experimental grazing rate (z) that was scaled to in situ biomass of

mesozooplankton (Equation 2-7) to yield zo, an instantaneous grazing rate, if the regression was

significant.


fiIIntliP := k z mg drywtaddedL (2-6)


zo = z+ mg dry wt m-3 (2-7)

Instantaneous in situ grazing rates, zo, were used to estimate the impact of

mesozooplankton on phytoplankton. In situ phytoplankton growth (ko) was estimated with

equation 2-8 (Moigis & Gocke 2003), where puo is the apparent growth rate of phytoplankton

from experimental controls and g is the appropriate instantaneous microzooplankton grazing rate,









and this value was combined with the appropriate instantaneous in situ mesozooplankton grazing

rate (zo) to estimate the percent of phytoplankton growth consumed daily (Equation 2-9).

ko = puo + g (2-8)


Percent phytoplankton growth consumed =zo *100 (2-9)


Data Analyses

Regressions and analyses of variance (ANOVAs) were performed with the JMP software

package (v5.1, SAS). ANOVAs were used to test for differences in environmental parameters

and coefficients from dilution experiments among systems, zones, and discharge periods.

Tukey's HSD (a = 0.05) was employed as a follow-up test. Environmental data and coefficients

were tested for normality using Shapiro-Wilk goodness-of-fit tests and homoscedasticity using

Cochran's tests. Data were loglo, square root, or fourth-root transformed to improve normality

and homoscedasticity. Non-normal and heteroscedastic data were analyzed, and the results were

interpreted cautiously.

Multivariate analyses of microzooplankton and mesozooplankton abundances were

performed using the Plymouth Routines In Multivariate Ecological Research (v6.1.6; PRIMER-E

Ltd, Plymouth) software package. Only taxa contributing at least 3% to any given sample were

included in analyses and counts were loglo(x+1) transformed. Similarity indices were calculated

using the Bray-Curtis coefficient (Bray & Curtis 1975). Non-metric multi-dimensional scaling

(MDS) and analysis of similarity (ANOSIM) were used to discriminate differences in

microzooplankton and mesozooplankton assemblages among systems, sampling events, and

zones. Ordinations with the lowest dimensionality that yielded stress values below 0.20 were

considered acceptable. The test-specifie, R-value permutation distribution was used to determine

significance for ANOSIM (p < 0.001). The degree of dissimilarity between groups (pair-wise









comparisons) was assessed by examining the R-values for each pair, where large values are

indicative of complete separation and low values suggest little or no segregation (Clarke &

Warkwick 2001). The similarity percentages (SIMPER) routine was conducted when significant

dissimilarities were found to determine which taxa contributed to groupings.



























































83"30'O'W 83"20'O'W 83"10'O'W 83"0'O'W 82"50'O'W 82"40'O'W 82"30'O'W

Figure 2-1. Location of study systems along the west coast of Florida. Filled circles denote
stations sampled.













28









CHAPTER 3
RESULTS

Field Parameters

Mid-range to extremely dry conditions in the southeastern United States (standardized

precipitation index values -1.0 to below -2.0) and the second driest November-December

experienced in Florida over the 111-year record resulted in lower than expected discharge from

both the Suwannee and Withlacoochee Rivers (Guttman & Lawrimore 2007, Table 3-1).

Nevertheless, river discharge varied significantly among the systems (Table 3-2), with discharge

from the Suwannee River greater than discharge from the Withlacoochee and Weeki Wachee

rivers (Tukey's HSD; q = 2.79, p < 0.05). No significant differences in discharge were found

between the Withlacoochee and Weeki Wachee rivers.

Water temperatures, salinities, and dissolved oxygen concentrations measured in each of

three study systems were typical for those systems (T. Frazer, University of Florida, unpublished

data). Surface water temperatures ranged from 13.7 to 32.70C, with both the minimum and

maximum values recorded at Withlacoochee in December and July, respectively. Fourth-root

transformed water temperatures varied significantly among systems (Table 3-2), but the variation

was unlikely to be biologically significant. Salinities were normally within the targeted range for

a particular zone. However, deviations greater than 2.0 psu below the desired range occurred in

the river mouth of the Suwannee during November (Table 3-1). Loglo transformed dissolved

oxygen concentrations (mg L^1) varied significantly among systems (Table 3-2), with

concentrations in the Withlacoochee higher than those in the Weeki Wachee (Tukey's HSD; q =

2.79; p < 0.05). Nonetheless, water in all systems was normoxic whenever it was measured

(Table 3-1).









Light attenuation coefficients (Kd) meaSured in the three study systems were lower than

values normally observed in those systems. (T. Frazer, University of Florida, unpublished data).

Loglo transformed Kd ValUeS did not differ significantly among systems (Table 3-2). However,

light attenuation did vary significantly among zones within systems (Table 3-2), with light

attenuation in the Suwannee system near the river mouth and in the near field being greater than

light attenuation in the far field (Figure 3-1; Tukey's HSD; q = 3.97; p < 0.05). In the

Withlacoochee and Weeki Wachee systems, light attenuation did not vary significantly among

zones. Attenuation coefficients also varied significantly among discharge periods within systems

and zones (Table 3-2), with light attenuation being higher in the river mouth and near field in the

Suwannee system during the high discharge period (Table 3-2; Tukey's; q = 3.73; p < 0.05).

Loglo transformed chlorophyll concentrations (Cpg L^1) differed significantly among

systems, among zones, and among discharge periods (Table 3-2). Chlorophyll concentrations in

the Suwannee system were greater than those in the Weeki Wachee system (Tukey's HSD; q =

2.79; p < 0.05). In each system, chlorophyll concentrations were typically highest near the river

mouth, declining in the near and mid-fields, and lowest in the far field (Figure 3-2). This spatial

pattern was statistically significant in the Suwannee system (Table 3-2), with concentrations

being higher near the river mouth than in the far field (Tukey's HSD; q = 3.97; p < 0.05).

Chlorophyll concentrations also differed significantly between discharge periods (Table 3-2).

During the high discharge period, chlorophyll concentrations in the river mouth, near field, and

mid-field were greater than chlorophyll concentrations in the far field in the Suwannee system

(Tukey's HSD; q = 7. 15; p < 0.0001). A gradient in chlorophyll concentrations also was found in

the Withlacoochee system during high discharge, with concentrations near the river mouth being

significantly higher than in the mid-field and far field (Tukey's HSD; q = 4.85; p < 0.001).









During high discharge in the Weeki Wachee system, chlorophyll concentrations near the river

mouth were greater than those in the mid-Hield (Tukey's HSD; q = 3.72; p < 0.05). Similar

patterns in chlorophyll concentrations were observed during low discharge periods for the

Suwannee and Weeki Wachee systems (Tukey's HSD; q = 3.72; p < 0.05). There was no

significant variation in chlorophyll concentrations among the zones for the Withlacoochee

system during the low discharge period.

Phytoplankton Growth Rates

Twenty-eight of the 48 dilution experiments produced valid estimates of instantaneous

maximum specific phytoplankton growth rates (Tables 3-3 to 3-5). Phytoplankton growth rates

(k) ranged from -0.40 to 2.50 (mean & SD 0.99 & 0.68), 1.00 to 2.37 (mean & SD 1.55 & 0.58),

and 0.55 to 1.31 (mean & SD 0.89 & 0.27) in the Suwannee, Withlacoochee, and Weeki Wachee

systems, respectively. Square-root transformed growth rates did not vary significantly among

systems or among discharge periods (Table 3-6). Significant variability in k across salinity zones

was found only in the Suwannee system (Table 3-6), where growth rates near the river mouth

were higher than those in the far field (Tukey's HSD; q = 4.01; p < 0.05). In the Withlacoochee

and Weeki Wachee systems, k values were fairly uniform across zones.

Microzooplankton Grazing Rates and Assemblages

Microzooplankton grazing was determined to be a substantial loss factor for phytoplankton

communities in each of the three systems (Tables 3-3 to 3-5). Estimates of instantaneous grazing

rates (g) ranged from 0.38 to 2.04 (mean & SD = 0.92 & 0.53) in the Suwannee system, 0.39 to

1.67 (mean & SD 1.03 & 0.54) in the Withlacoochee system, and 0.34 to 1.17 (mean & SD = 0.72

& 0.25) in the Weeki Wachee system. These rates correspond to grazers removing an average of

approximately 50% of the phytoplankton standing crops on a daily basis (Tables 3-3 to 3-5).

Estimates of percent phytoplankton production lost daily were high in each system (Tables 3-3 to










3-5). Microzooplankton grazers accounted for, on average (A SD), 99.45 & 46.76%, 81.25 &

31.59%, and 87. 10 & 12.63% of primary production lost per day in the Suwannee,

Withlacoochee, and Weeki Wachee systems, respectively. Square-root transformed

microzooplankton grazing rates did not vary significantly among systems, salinity zones, or

discharge periods (Table 3-6).

Retrospective power analyses based on least square effect means of square-root

transformed g-values were conducted. For the system effect, a power analysis based on an

estimated weighted standard deviation (owt) of 0.27, 2 extra parameters in the model (Table 3-2),

a type I error rate (a) of 0.05, and power of 0.80 indicated that a minimum of 199 samples were

required to detect a difference of the magnitude observed. Thus, approximately 66 estimates of g

per system were needed. If owt= 0.27 and n = 15, the minimum detectable difference among

systems was 0.43, a value markedly larger than the observed maximum difference of 0. 14

between Withlacoochee (0.98) and Weeki Wachee (0.84). Among zones in the Suwannee

system, with n = 13 and G2wt = 0.16, 15 estimates of g (~ 4 per zone) are required to attain power

of 0.80. This value suggests the intended sample size (n = 16) would have been sufficient for

ANOVA to detect a significant difference if one existed. However, given the variability (owt =

0.16) and actual sample size (n = 6), the minimum detectable difference was 0.80, a value greater

than the observed difference of 0.44. In the Withlacoochee system, where n = 4 and G2wt = 0. 16,

10 estimates of g were needed to attain power = 0.80. Given n = 3 and awt = 0. 16, the minimum

detectable difference was 2.36, a value considerably greater than the observed maximum

difference of 0.50. In the Weeki Wachee system, the minimum sample size was 189 at n =11 and

awt = 0. 16. This large sample size (~ 50 estimates of g per zone) is likely a reflection of the

similarity observed for means of g among zones. The minimum detectable difference was 2.35 (n









= 3; Gwt = 0. 16), a value greater than the observed maximum difference of 0. 11. Overall, the

power analyses indicate that significant differences in microzooplankton grazing rates among

systems or zones would only be detected if they were considerably larger or if considerably more

samples were taken.

Total microzooplankton abundance (loglo individuals L^1) varied significantly among

systems in the September-December sampling events (Table 3-6), with total abundances in the

Suwannee and Withlacoochee systems greater than total abundance in the Weeki Wachee system

(Tukey's HSD; q = 3.84, p <0.01). Microzooplankton abundance did not vary significantly

among zones within any of the three systems (Table 3-6).

The microzooplankton taxa that occurred most frequently within the three systems were

copepod nauplii (100% occurrence in samples), tintinnids (100%), rotifers (92%), ciliated

protozoans (88%), Prorocentrum spp. (83%), and Protoperdinium spp. (83%). Ordination

indicated some dissimilarity among assemblages (Figure. 3-3); however, ANOSIM indicated that

assemblages were similar among salinity zones.

Mesozooplankton Grazing Rates and Assemblages

Six of the 48 addition experiments produced valid estimates of instantaneous rates of in

situ mesozooplankton grazing (zo). These estimates (range = 0.0003 to 0.0008) indicate that

mesozooplankton grazers had a negligible impact on phytoplankton biomass relative to

microzooplankton grazers in the Suwannee, Withlacoochee, and Weeki Wachee systems. Daily

phytoplankton growth consumed by mesozooplankton was <0.05% in all cases.

Total mesozooplankton abundance (loglo individuals m-3) did not vary significantly among

systems or among zones within a system (Table 3-6). There was, however, significant variation

in total abundance among discharge periods (Table 3-6). During the low discharge period in the

Suwannee system, total mesozooplankton abundance in the near field and mid-field was greater









than abundance near the river mouth (Tukey's HSD; q = 3.78; p < 0.05). No significant

differences in abundance were detected among zones in the Withlacoochee and Weeki Wachee

systems during either discharge period or among zones in the Suwannee during the high

discharge period.

When samples from all three systems were considered together, the five most frequently

occurring taxa were the calanoid copepods Acartia tons (occurrence in 98% of samples),

Paracalan2us sp. (76%), ParvocalanusP~~PP~~~PP~~PP sp. (74%), brachyuran crab zoea (72%), and the

harpacticoid copepod Euterpina acutifr~ons (69%). Acartia tons was numerically dominant in

each system and often in each zone (Tables 3-10 to 3-12). Fifty percent of the zones at

Suwannee, 63% at Withlacoochee, and 81% at Weeki Wachee were dominated by A. tonsa.

Despite the dominance ofA. tonsa across systems, ordination suggested dissimilarity among

assemblages in the three systems (Figure 3-4). This finding was supported by ANOSIM results

(global R = 0.82). Pair-wise comparisons indicated strong dissimilarity between

mesozooplankton assemblages in the Suwannee and Weeki Wachee systems (R = 0.92) and in

the Withlacoochee and Weeki Wachee systems (R = 0.95). SIMPER identified the higher

abundances of Balanzus nauplii and Parvocalan2us sp. and lower abundances of gastropod larvae

in the Suwannee as the primary causes of dissimilarity between the Suwannee and Weeki

Wachee systems. Dissimilarity between the Withlacoochee and Weeki Wachee systems was

driven by greater abundances of Parvocalan2us sp. and E. acutifr~ons and lower abundances of

gastropod larvae in the Withlacoochee system.

Ordination and ANOSIM for each system suggested potential dissimilarities in

mesozooplankton assemblages across time and space in the Suwannee system (Figure 3-5; global

R = 0.30). Pair-wise comparisons indicated that the assemblages in March (R = 0.50) and May










(R = 0.59) differed significantly from those present in November. This difference was driven by

large numbers of E acutifr~ons and the cladoceran, Penilia sp., in November. Significant spatial

variation also was found (global R = 0.35), with the river mouth (R = 0.61) and near Hield (R =

0.57) differing significantly from the far Hield (Figure 3-6). This gradient was due to the presence

of the copepods Temora sp. and Corycaeus sp. and absence of Balanzus nauplii in the far Hield.

In the Withlacoochee and Weeki Wachee systems, ordinations and ANOSIM indicated

dissimilarity in assemblages within each system in space but not time (Withlacoochee 3D stress

= 0. 11, global R = 0.88; Weeki Wachee 3D stress = 0. 14, global R = 0.75). Within both systems,

the river mouth assemblage differed significantly from the assemblages in the mid- and far fields

(pair-wise R-values: Withlacoochee RM, MD = 0.33, RM, FF = 0.56; Weeki Wachee RM, MD =

0.49, RM, FF = 0.77). Greater abundances of shrimp zoea, the copepods Labidocera sp. and

Parvocalan2us sp., gastropod larvae, and isopods in the higher salinity zones drove groups at

Weeki Wachee. At Withlacoochee, differences in assemblages were due to the greater abundance

of Corycaeus sp., brachyuran crab zoea, Temora sp., Parvocalan2us sp., E. acutifr~ons, and

Oithona spp. in the mid-field and far field relative to the two lower salinity zones.

Controls

Initial concentrations of soluble reactive phosphorus (SRP) in the nutrient amended

treatments were significantly greater than concentrations in the controls for each system (t-test, p

< 0.05). This result was also observed for the final SRP concentrations in experiments using

water taken from the Withlacoochee and Weeki Wachee systems, but not for experiments using

water from the Suwannee system (t-test, p > 0.05). The lack of a significant difference suggests

the nutrient amended treatments may have become phosphorus limited during the incubation

period. However, an analysis of nutrient-enrichment experiments found experiments lasting <1

day exhibited time lags in the numerical response of phytoplankton to nutrient addition









(Downing et al. 1999). Therefore, the decline in SRP concentrations during the 24-hr incubation

likely had a negligible impact on phytoplankton growth rates.

Final SRP concentrations (Cpg L^1) from the mesozooplankton treatments were significantly

greater than concentrations in treatments without mesozooplankton (t-test, p < 0.0001). In

addition, Einal SRP concentrations for each system often exhibited a positive linear relationship

with mesozooplankton biomass (mg dry wt added L^)~. Linear regressions were significant for

the Suwannee (r2 = 0.31, p < 0.0001), Withlacoochee (r2 = 0.34, p < 0.0001), and Weeki Wachee

(r = 0.23, p < 0.0001) systems, but they had low coefficients of determination.

To determine if the mesozooplankton added to treatments was a true reflection of the in

situ assemblage and not biased by taxon-specific mortality rates, the frequency that the dominant

taxon in the surface subsample was also the dominant or secondary dominant in the

corresponding bottom subsample was calculated. These frequencies are as follows: 93% of the

samples from Suwannee, 71% of Withlacoochee samples, and 84% of Weeki Wachee samples.

The high degree of agreement between sub samples from each system indicates that the

mesozooplankton added to treatments was normally dominated by same taxa that dominated the

in situ assemblage.












Table 3-1. Mean values for the physical, chemical, and biological parameters measured in the Suwannee, Withlacoochee, and Weeki
Wachee systems. Means are based on measurements from three stations within each zone. Historical river discharge rates
are the calculated monthly means (+ SD) for the 1994-2004 period of record at the same gauging stations used to estimate
daily mean river discharge rates. Notation "nd" indicates no data.
Suwannee Withlacoochee Weeki Wachee
RM NF IVF FF RM NF IVF FF RM NF IVF FF


Sampling Date 3/27
Daily Discharge m3 S-1 244.1
Historical Discharge m3 S-1 392.8 & 296.3
Depth m
TemperatureoC
Salinity psu
DO mg L '
Kdm-1
Chl pg L '
Tow Volume m3

Sampling Date 5/23
Daily Discharge m3 S-1 125.2
Historical Discharge m3 S-1 196.1 + 101.7
Depth m
TemperatureoC
Salinity psu
DO mg L '
Kdm-1
Chl pg L '
Tow Volume m3

Sampling Date 9/25
Daily Discharge m3 S-1 91.5
Historical Discharge m3 S-1 191.9 & 107.2
Depth m
TemperatureoC
Salinity psu
DO mg L '
Kd m-1
Chl pg L '
Tow Volume m3


6/27
9.7
10.7 & 9.1


4/3
4.6
4.1+ 1.0


1.17 2.67 4.63 11.73
18.15 17.98 17.16 17.73
13.41 21.67 31.57 33.63
7.84 9.22 6.08 4.30
3.34 1.53 0.80 0.57
3.69 3.98 1.82 0.35
27.63 29.59 32.71 22.65


3.37 2.70 >5.00 >5.00
29.97 29.36 29.34 29.27
13.35 21.61 28.35 30.87
7.18 6.69 5.73 5.73
0.72 0.67 0.99 0.65
10.87 6.74 3.69 2.18
29.77 19.33 29.81 35.86


1.60 1.77 2.43 3.43
24.06 23.73 22.45 21.77
13.00 18.95 27.21 30.85
9.08 8.58 7.76 8.00
1.18 0.93 0.81 0.65
1.36 0.54 0.24 0.73
27.65 29.74 29.74 29.74


8.1
16.6 & 20.7


4.1
3.8 & 0.9


2.70 2.50 1.83 >5.00
26.01 25.77 25.61 25.19
8.05 21.67 29.67 33.63
9.93 5.16 6.41 6.90
2.09 1.81 1.94 0.70
24.13 11.69 9.38 0.53
36.96 27.04 25.05 33.29


2.20 3.60 >4.00 >5.00
31.74 30.88 30.32 30.04
13.90 20.99 28.56 31.68
8.22 6.74 6.38 6.39
1.81 0.76 0.85 1.28
10.59 3.84 1.42 0.87
20.74 19.27 36.10 36.90


1.63 1.67 2.77 3.73
29.04 28.89 28.39 28.49
12.04 20.92 29.43 32.73
8.41 7.59 8.61 8.32
0.72 0.82 0.76 0.94
1.10 0.68 0.70 0.47
34.82 34.32 30.04 24.94


11/28
4.4
27.6 & 21.6


4.4
4.7 & 1.0


1.33 3.03 3.70 >6.00
19.40 17.66 18.97 18.02
6.06 22.39 29.64 32.72
8.06 6.60 5.75 6.31
1.39 1.48 0.62 0.49
3.61 1.56 1.79 0.91
31.25 34.14 21.63 19.30


3.43 3.23 4.30 >7.00
17.94 18.27 16.35 16.62
10.91 22.18 28.64 31.64
8.41 7.59 8.61 8.32
nd nd nd nd
1.75 2.09 2.50 1.79
33.40 30.37 12.53 32.71


2.03 3.50 3.97 >5.00
29.47 29.26 29.12 28.93
14.09 20.56 29.32 32.64
nd nd nd nd
0.95 0.45 0.51 0.46
1.64 0.86 0.48 0.59
43.01 38.92 43.27 38.31












Table 3-1. Continued


Suwannee
RM NF IVF FF


Withlacoochee
RM NF


Weeki Wachee
RM NF IVF


IVF FF


Sampling Date 11/13
Daily Discharge m3 S-1 58.9
Historical Discharge m3 S-1 212.5 124.5
Depth m
TemperatureoC
Salinity psu
DO mg L '
Kdm-1
Chl pg L '
Tow Volume m3


12/11
4.5
20.7 & 12.3


11/1
4.4
4.8 & 0.9


1.33 3.03 3.70 >6.00
19.40 17.66 18.97 18.02
6.06 22.39 29.64 32.72
6.49 5.40 4.35 7.58
1.23 1.48 0.62 0.49
3.61 1.56 1.79 0.91
31.25 34.14 21.63 19.30


4.53 3.57 4.23 >7.00
14.74 14.48 13.77 14.57
11.82 20.32 29.52 32.39
8.81 8.51 7.38 6.67
1.03 1.03 0.69 0.49
1.75 1.75 1.60 1.40
36.08 33.38 32.40 32.78


2.03 3.50 3.97 >5.00
29.47 29.26 29.12 28.93
14.09 20.56 29.32 32.64
6.74 7.19 5.75 7.51
1.61 1.09 0.76 0.71
1.64 0.86 0.48 0.59
43.01 38.92 43.27 38.31










Table 3-2. Analyses of variance for transformed river discharge (m3 S-1), light attenuation (Kd m
1), in situ total chlorophyll concentrations (Cpg L^)~, water temperature (oC), and
dissolved oxygen concentrations (DO, mg L ').
Variable Source df MS F p
Discharge Rate m3S-1 System 2 20635.5000 9.4748 0.0061
Error 9 2177.9000
Total 11

4 Temp"oC Model 23 0.0743 10.105 < 0.0001
System 2 0.0957 43.0250 < 0.0001
Zone(System) 9 0.0022 0.0178 1.0000
Discharge Period(System, Zone) 12 0.1248 16.9699 < 0.0001
Error 120 0.0074
Total 143


LogloDO mg L' Model 23 0.0519 4.1882 < 0.0001
System 2 0.1312 4.3698 0.0472
Zone(System) 9 0.0300 0.5453 0.8158
Discharge Period(System, Zone) 12 0.0551 4.4446 < 0.0001
Error 120
Total 143


LogloKd m-1 MOdel 23 0.1776 6.8752 < 0.0001
System 2 0.4851 1.7165 0.2335
Zone(System) 9 0.2866 5.5776 0.0037
Discharge Period(System, Zone) 12 0.0514 1.9894 0.0320
Error 107 0.0258
Total 130


LogoChl Cig L' Model 23 1.1647 23.8733 < 0.0001
System 2 6.4836 5.6779 0.0254
Zone(System) 9 1.1419 3.8665 0.0163
Discharge Period(System, Zone) 12 0.2953 6.0535 < 0.0001
Error 120 0.0488
Total 143













3.0-

2.0-

1.0 -

0.0-

-1.0-

-2.0
River Mouth Near Field Mid-Field Far Field


4.03.


River Mouth Near Field Mid-Field Far Field


2.0 -

1.0 -

0.0 -

-1.0 -


-2.0


4.0


3.0 -

2.0 -

1.0 -

0.0 -

-1.0 -

-2.0


River Mouth Near Field Mid-Field


Far Field


Figure 3-1. Back transformed mean light attenuation (Kd m-1) d_ 95% confidence intervals (CI)
for zones. A) Suwannee system. B) Withlacoochee system. C) Weeki Wachee
sy stem.










12.0

10.0

8.0

6.0

4.0

2.0

0.0

-2.0


12.0

10.0

8.0

6.0

4.0

2.0

0.0

-2.0


River Mouth Near Field Mid-Field Far Field


River Mouth Near Field Mid-Field


Far Field


12.0

; 10.0

8.0

6.0






0.0 -

-2.0 C
River Mouth Near Field Mid-Field Far Field

Figure 3-2. Back transformed mean chlorophyll concentrations (Cpg L 1) & 95% confidence
intervals (CI) for zones. A) Suwannee system. B) Withlacoochee system. C) Weeki
Wachee system.












Table 3-3. Suwannee system: estimates of apparent growth rate (AGR) in controls (Ro), instantaneous maximum specific
phytoplankton growth rates (k & SE), instantaneous microzooplankton grazing rates (g & SE), percent of phytoplankton
standing crop removed daily (%PSC d- ), and percent of phytoplankton production lost daily (%/PP d- ) during March, May,
September, and November. Upper (U) and lower (L) 95% confidence limits (CL) were calculated using the corresponding
confidence limits of k and g coefficients.
%PSC
Samping PSC Removed d %P %PP Lost d'
Date Zone uo k + SE L95% U95% g + SE L95% U95% r2 p Removed d' L95% U95% Lost d' L95% U95%
3/27 RM 0.37 1.12 +0.10 0.89 1.35 0.85 + 0.17 0.47 1.23 0.77 < 0.001 57.17 37.34 70.73 84. 82 63.45 95.35
NF 0.43 1.16 +0.13 0.85 1.46 0.82 + 0.21 0.33 1.31 0.65 < 0.01 55.91 27.78 73.08 81.59 48.38 95.25
MD 0.12 0.59 + 0.04 0.51 0.67 0.38 + 0.06 0.24 0.51 0.84 < 0.001 31.27 21.62 39.74 69.82 53.84 80.98
FF -1.57 -0.40 + 0.18 -0.80 0.00 0.92 + 0.00 0.92 0.93 0.62 < 0.05 60.26 60.01 60. 52 -122.23 -48.58 22036.89


5/23 RM 0.70 2.50 + 0.22 2.00 3.00 2.05 + 0.35 1.23 2.86 0.81 < 0.001 87.10 70.89 94.28 94. 86 81.94 99.20
NF 0.40 1.32 + 0.18 0.90 1.73 0.81 +0.29 0.14 1.49 0.49 < 0.05 55.60 12.75 77.41 75.98 21.46 94.07
MD 0.20 1.49 + 0.07 1.33 1.65 1.68 +0.11 1.42 1.94 0.96 < 0.0001 81.31 75.74 85.59 104.93 102.91 105.94
FF -0. 59 0.33 + 0.13 0.03 0.62 0.92 + 0.21 0.43 1.40 0.71 < 0.01 59.95 35.15 75.27 215.49 1157.61 162.67


9/25 RMt 0.62 1.49 + 0.17 1.09 1.90 1.63 + 0.53 0.17 3.10 0.70 < 0.05 80.49 15.49 95.49 103.78 23.30 112.38
NF* -0.15 1.20 +0.11 0.94 1.45 0.38 + 0.18 -0.04 0.80 0.34 ns
MD* 0.24 0.32 + 0.16 -0.05 0.68 0.06 + 0.24 -0.50 0.62 0.01 ns
FF 0.24 0.66 + 0.08 0.48 0.85 0.53 + 0.13 0.23 0.83 0.67 < 0.01 41.08 20.30 56.44 84.76 53.38 98.76


11/13 RM* -0.42 0.55 +0.09 0.33 0.76 0.29 + 0.15 -0.05 0.64 0.32 ns
NF 0.35 0.76 + 0.08 0.58 0.94 1.00 + 0.13 0.15 0.75 0.60 < 0.01 35.98 13.60 52.56 67. 53 30.96 86.08
MD 0.34 0.98 + 0.04 0.89 1.06 0.47 + 0.06 0.33 0.62 0.87 < 0.0001 37.75 28.02 46.17 60.61 47.70 70.46
FF 0.33 0.93 + 0.04 0.83 1.03 0.51 +0.07 0.34 0.68 0.86 < 0.001 40.07 29.08 49.36 66.23 51.67 76.74

fk and g calculated using a piecewise linear model.
*k and g from non-significant regressions not used in analyses.











Table 3-4. Withlacoochee system: estimates of apparent growth rate (AGR) in controls (Cto), instantaneous maximum specific
phytoplankton growth rates (k & SE), instantaneous microzooplankton grazing rates (g & SE), percent of phytoplankton
standing crop removed daily (%PSC d- ), and percent of phytoplankton production lost daily (%/PP d- ) during June and
July. No estimates of k and g from experiments conducted in November and December were used in analyses because the
regressions did not meet the assumptions of the dilution technique. Upper (U) and lower (L) 95% confidence limits (CL)
were calculated using the corresponding confidence limits of k and g: coefficients.
%/PSC
Removed d' %/PP Lost d'
Sampling %/PSC %/PP
Date Zone uo k f SE L95% U95% g f SE L95% U95% 12 p Removed d' L95% U95% Lost d' L95% U95%
6/27 RM 0.28 1.42 & 0.32 0.69 2.15 1.20 & 0.52 0.01 2.39 0.41 < 0.05 69.64 1.43 90.83 92.24 2.87 102.83
NF 0.05 1.00 + 0.16 0.63 1.36 0.85 & 0.26 0.25 1.44 0.57 < 0.05 57.09 22.02 76.38 90.52 47.15 102.66
IVD* 0.13 1.45 & 0.13 1.16 1.74 0.23 & 0.20 -0.24 0.70 0.14 ns
FF 1.72 2.37 & 0.10 2.15 2.60 0.39 & 0.16 0.02 0.75 0.43 < 0.05 31.95 2.04 52.73 35.24 2.31 56.98

7/4 RM* 0.35 1.13 & 0.14 0.82 1.45 0.37 & 0.22 -0.14 0.88 0.26 ns
NF 0.05 1.42 & 0.15 1.09 1.76 1.68 & 0.24 1.13 2.23 0.86 < 0.001 81.34 67.70 89.22 107.14 102.13 107.75
MD* 1.53 1.76 & 0.11 1.51 2.01 0.29 & 0.18 -0.11 0.70 0.26 ns
FF* 1.49 1.36 & 0.21 0.88 1.83 0.26 & 0.34 -0.52 1.04 0.06 ns
*k and g from non-significant regressions not used in analyses.












Table 3-5. Weeki Wachee system: estimates of apparent growth rate (AGR) in controls (Ro), instantaneous maximum specific
phytoplankton growth rates (k & SE), instantaneous microzooplankton grazing rates (g & SE), percent of phytoplankton
standing crop removed daily (%PSC d- ), and percent of phytoplankton production lost daily (%/PP d- ) during March, May,
September, and November. Upper (U) and lower (L) 95% confidence limits (CL) were calculated using the corresponding
confidence limits of k and g coefficients.
%PSC
Removed d' %PP Lost d'
Sampling %PSC %PP
Date Zone uo k f SE L95% U95% g f SE L95% U95% r2 p Removed d' L95% U95% Lost d' L95% U95%
4/3 RM -0.07 0.56 +0.10 0.33 0.80 0.59 +0.17 0.20 0.98 0.61 < 0.01 44.51 18.26 62.33 103.26 65.55 113.06
NF -0.01 0.71 +0.11 0.46 0.96 0.58 +0.18 0.18 0.99 0.58 < 0.05 44.12 16.15 62.76 87.05 43.96 101.96
MD 0.39 0.83 + 0.01 0.81 0.86 0.48 +0.16 0.10 0.85 0.52 < 0.05 37.81 9.65 57.20 66. 88 17.37 99.42
FF* 0.61 1.01 +0.08 0.82 1.20 0.21 +0.14 -0.10 0.53 0.23 ns


6/6 RM 0.45 1.31+0.21 0.84 1.78 0.87+0.33 0.10 1.64 0.46 <0.05 58.02 9.32 80. 57 79.49 16.44 96.88
NF* 0.05 0.37 +0.22 -0.15 0.89 0.31 +0.36 -0.53 1.15 0.08 ns
MD 0.49 1.27 + 0.19 0.84 1.70 1.17 +0.30 0.47 1.87 0.65 < 0.01 68.90 37.45 84. 54 95.92 66.00 103.53
FF 0.39 1.08 +0.09 0.88 1.29 0.79 + 0.15 0.45 1.13 0.78 < 0.001 54.66 36.37 67.70 82.60 62.30 93.35


9/18 RMt 0.64 0.87 +0.08 0.69 1.06 0.83 +0.24 0.16 1.51 0.74 < 0.05 56.53 14.65 77. 86 96.97 29.33 119.34
NF* 0.15 0.62 +0.13 0.32 0.92 0.27 +0.21 -0.22 0.76 0.17 ns
MD 0.17 0.55 +0.09 0.35 0.74 0.35 +0.14 0.03 0.67 0.44 < 0.05 29.39 2.93 48.64 69.85 9.93 92.84
FF* 0.00 0.55 +0.17 0.15 0.94 0.51 +0.28 -0.14 1.16 0.28 ns


11/1 RM 0.37 0.85 +0.14 0.52 1.19 0.59 +0.23 0.05 1.13 0.44 < 0.05 44.73 5.20 67.78 77.95 12.81 97.63
NF 0.08 0.63 +0.10 0.40 0.85 0.64 + 0.16 0.28 1.00 0.67 < 0.01 47.06 24.14 63.05 101.26 72.68 110.42
MD 0.09 1.15 +0.19 0.71 1.59 1.08 +0.31 0.37 1.80 0.60 < 0.01 66.07 30.66 83.40 96.69 60.20 104.82
FF* -0.03 0.51 +0.09 0.31 0.72 0.25 +0.15 -0.08 0.59 0.27 ns

tk and g calculated using a piecewise linear model.
*k and g from non-significant regressions not used in analyses.





J~ Model 17
System 2
Zone(System) 8
Discharge Period(System, Zone) 7
Error 9
Total 26

Model 17
System 2
Zone(System) 8
Discharge Period(System, Zone) 7
Error 10
Total 27


Table 3-6. Analyses of variance for transformed phytoplankton growth rates (k),
microzooplankton grazing rates (g), microzooplankton total abundance, and
mesozooplankton total abundance.


Variable


Source


MS
0.0558
0.1469
0.0710
0.0181
0.0474


0.0428
0.0189
0.0666
0.0218
0.0635


0.1203
0.4969
0.0400
0.1396


0.5452
0.1105
0.5701
0.5989
0.2096


F
1.1756
2.3175
3.4385
0.3807



0.6750
0.3067
2.5352
0.3436


p
0.4172
0.1507
0.0333
0.8918



0.7713
0.7422
0.0725
0.9153


Microzooplankton Total Abundance
(Loglolndividuals L')


Model
System
Zone(System)
Error
Total


0.8622
13.5590
0.2626



2.6010
0.1938
0.9519
2.8574


0.5936
0.0019
0.9737



0.0011
0.8272
0.5187
0.0029


Mesozooplankton Total Abundance
(Logolndividuals m-3)


Model
System
Zone(System)
Discharge Period(System, Zone)
Error












+ Suwannee River Mouth
a Suwannee Near Field
* Suwannee Mid Field
A Suwannee Far Field
o Withlacoochee River Mouth
o Withlacoochee Near Field
O Withlacoochee Mid Field 1
a Withlacoochee Far Field
* Weeki Wachee River Mouth
a Weeki Wachee Near Field
* Weeki Wachee Mid Field
A Weeki Wachee Far Field


O
5 e
e O A


Figure 3-3. Two-dimensional ordination (stress value = 0.11) based on microzooplankton
abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems. Distance
between points is indicative of similarity where points further apart are less similar
than those closer together.































Mid-Field









Far Field


Far Field


Table 3-7. Suwannee system: microzooplankton total abundance (individuals L 1) and common
taxa during September and November.


September
River Mouth 1502


November
River Mouth






Near Field


Copepod nauplii
Tintinnids
Rotifers
Tintinniopsis sp.


49.0%
24.1%
11.9%
3.8%

37.8%
18.0%
7.2%
6.9%
4.9%
3.9%
3.7%

29.8%
12.3%
10.9%
8.3%
3.3%
3.2%

43.0%
20.4%
7.8%
4.8%


274 Copepod nauplii
Tintinnids
Protozoa
Prorocentrum spp.
Rotifer

774 Copepod nauplii
Tintinnids
Rotifers
Larvacean
Nematode
Ceratium spp.

597 Copepod nauplii
Larvacean
Pryophacus spp.
Ceratium sp.
Protozoa
Prorocentrum sp.
Nematode

431 Copepod nauplii
Tintinnids
Parvocalanus sp.
Prorocentrum spp.
Protozoa


50.4%
15.1%
7.3%
6.9%
3.9%

27.3%
10.7%
7.0%
4. 1%
3.7%
3.1%

31.2%
15.6%
9. 1%
7.3%
7.3%
6.2%
3.4%

53.3%
7.7%
6.4%
5.3%
3.1%


Near Field 2939 Prorocentrum spp.
Copepod nauplii
Protoperidinium sp.
Pryophacus spp.
Tintinniopsis sp.
Rotifer
Tintinnids

Mid-Field 1476 Copepod nauplii
Prorocentrum spp.
Protoperidinium sp.
Ceratium sp.
Larvacean
Parvocalanus sp.


607 Copepod nauplii
Pryophacus spp.
Ceratium spp.
Protoperidinium sp.










Table 3-8.Withlacoochee system: microzooplankton total abundance (individuals L 1) and
common taxa during November and December.
November December
River Mouth 2277 Protozoa 43.9% River Mouth 396 Protozoa 78.6%


Tintinnids
Copepod nauplii


35.6%
14.8%

41.2%
41.0%
5.2%
4.3%

22.6%
15.8%
11.6%
8.8%
6.4%
6.2%
4.3%

24.0%
21.4%
18.6%
5.8%
5.5%


Copepod nauplii
Rotifers
Tintinnids


9.5%
4.5%
3.8%

76.2%
13.3%
4.7%
3.8%

27.7%
18.7%
10.4%
6.5%
6.5%
5.5%
4.6%

34.5%
11.1%
9.5%
9.3%
3.1%


Near Field 852 Tintinnids
Copepod nauplii
Tintinnopsis spp.
Protozoa

Mid-Field 1485 Tintinnids
Copepod nauplii
Prorocentrum sp.
Rotifers
Protoperidinium sp.
Pryophacus spp.
Ceratium sp.

Far Field 877 Copepod nauplii
Protoperidinium sp.
Pryophacus spp.
Ceratium sp.
Ceratium hircus


Near Field





Mid-Field









Far Field


369 Protozoa
Rotifers
Copepod nauplii
Tintinnids

294 Copepod nauplii
Tintinnids
Ceratium spp.
Protoperidinium sp.
Rotifers
Prorocentrum sp.
Pryophacus spp.

801 Copepod nauplii
Ceratium sp.
Protoperidinium sp.
Pryophacus spp.
Protozoa










Table 3-9.Weeki Wachee system: total abundance (individuals L 1) and common taxa during
September and November.


September


River Mouth


November
River Mouth






Near Field


731 Copepod nauplii
Prorocentrum sp.
Gastropod larvae
Protozoa
Bivalve veliger
Rotifers
Tintinnids


37.7%
16.9%
9.5%
5.8%
5.5%
3.4%
3.1%

30.9%
9.2%
8.8%
7.4%
4.7%
4. 1%
3.9%
3.7%
3.5%

16.2%
9. 1%
9.0%
8.5%
7.1%
4.1%

22.6%
15.6%
11.9%
7.2%
6.5%
4.7%
4.3%
4.2%


94 Copepod nauplii
Tintinnids
Nematode
Prorocentrum lima


55.4%
21.5%
4.1%
3.1%


Copepod nauplii
Prorocentrum sp.
Tintinnids
Nematode


42.5%
19.9%
9.0%
6.0%

30.9%
20.2%
11.2%
6.2%
3.7%

47.2%
8.3%
3.6%
3.6%


Near Field 347 Copepod nauplii
Gymnodinium sp.
Protozoa
Bivalve veliger
Prorocentrum sp.
Nematode
Gastropod larvae
Rotifers
Pryophacus spp.

Mid-Field 235 Copepod nauplii
Protozoa
Tintinnids
Ceratium hircus
Bivalve veliger
Pryophacus spp.


313 Ceratium hircus
Copepod nauplii
Tintinnids
Prorocentrum lima
Tintinniopsis sp.

197 Copepod nauplii
Ceratium hircus
Prorocentrum lima
Gymnodinium sp.


Far Field


Far Field


607 Copepod nauplii
Bivalve veliger
Ceratium hircus
Pryophacus spp.
Protoperidinium sp.
Tintinnids
Tintinnopsis sp.
Polychaete larvae


Mid-Field














































































































I I i I I


Stn 2 4589 Acar~a tosa
Bivalve vehlger
Lanvacean
Mid-Field


73 ()o
15 20 Stn 2 625 Acarla tonsa
5 10 Paracalanus spp
Co;3-caeusspp


ns


23 900 Stn 2 4()8 Balanus nauphi
21 500 Acarh~a tosa
14500 Pemlba spp
4 60 Eutevpina acuh# vons
4 60 Pan'ocalanus spp
4 30 Near Field
3 90 Stn 1 12544 Acar~a tosa
3 90
3 70 Stn 2 10)2(5 Acar~a tosa
Pemlbaspp
Pan'ocalanus spp
59 50 Eutevplna acuh# vons
36 ()o Mid-Field
9 10 Stn 1 10)595 Acar~a tosa
6 20 Pan'ocalanus spp
5 90 Eutevplna acuh~fons
5 40 Radiolarian
3 10 Pemlba spp
Teniora spp
13 0)o Balanus nauphi


9 10 Stn 2 17587 Pemlbaspp
6 80 Pan'ocalanus spp
Acarh~a tosa
6() 500 Eutevpina acuh# vons
12 60 Balanus nauphi
9 10 Far Field
5 30 Stn 1 21585 Pemlbaspp
4300 Pan'ocalanusspp
3 40 Eutevpina acuh~fons
Olthona spp
77 30 Co;? caeus spp
8 90 Teniora spp
5100
4 30 Stn 2 1925 Pan'ocalanus spp
Acar~a tosa
77 30 Eutevpina acuh~fons
20)700 Olthona spp
5 70 Teniora spp
4 70 Co;? caeus spp
3800


47 ()o
20)800
12800
6 90
6 70


91 60

72 10
11200
9 10
4 40

34 40
25 ()o
11 ()o
10)200
6 40
4 90
4 10


43 ()o
21 800
18 ()o
8 90
3 10


58 90
1()70
7 80
6 70
6 70
4 80


44 ()o
15600
12800
10) )o
6 20
4 10


Stn 1 21695 Acar~a tomsa 69 30 Olthona spp
Gastropod lanvae 13 800 Blvalve vehlger
Bivalve vehlger 4 90 Gastropod lan ae
Balanus nauphil 4 20 Pan'ocalanus spp


Stn 2 6248 Acar~a tomsa 83 600 Centropages spp
Balanus nauphil 5700 September
Far Field River Mouth
Stn 1 1959 Paracalanus spp 53 70 Stn 1 918 Balanus nauphi
Co;? caeus spp 12 40 Pan ocalanus spp
Olthona spp 7 10 Copepod nauphi
Pseudocalanus spp 5 80 Paracalanus spp
Pan'ocalanus spp 4 40 Barnacle cyprid
Acarh~a tomsa 4 20 Acarhla tonsa
Sagitta spp 3 60 Chthalantus nauphl


_


I


43 10 Stn 2 174 Pan'ocalanus spp
17 30 Copepod nauphi
12 800 Balanus nauphi
12 100 Acarhla tonsa
5 10 Near Field
3 70 Stn 1 2967() Acarla tonsa
Paracalanus spp
Balanus nauphi
97 800 Euterpina acuh fons


Stn 2 3374 Paracalanus spp
Co;? caeus spp
Olthona spp
Acarh~a tosa
Centropages spp
Pan'ocalanus spp


May
River Mouth
Stn 1 359


Brachyuran crab zoea


Pseudocalanus spp
Pan'ocalanus spp


901) 00
6 300


Stn 2 396 Brachyuran crab zoea
Balanus nauphi


Near Field
Stn 1 10)21


Brachyuran crab zoea
Pan'ocalanusspp
Acarh~a tosa
Balanus nauphi
Gastropod lavae
Marine mite


Stn 2 9763 Acarla tonsa
35 0)o Pan'ocalanus spp
16 50 Euterpina acuh fons
15 0)o Balanus nauphi
12900 Mid-Field
6700 Stn 1 6718 Acarhlatonsa
5 70 Euterpina acuh fons
Panocalanus spp
76 200 Teniora spp
8900 Balanusnauphi
3900
3 30 Stn 2 29227 Acarla tonsa
3 30 Euterplna acuh fons
Pan ocalanus spp
3() 900 Brachvuran crab zoea
24 90 Far Field
11 20 Stn 1 38()4 Tenioraspp
10 200 Euterpina acuh fons
9 60 Paracalanus spp
Brachvuran crab zoea
27 20
23 500 Stn 2 1991() Teniora spp
11 60 Euterpina acuh fons
9 60 Acarhla tonsa
8 90 Paracalanus spp
3900 Concaeus spp


Stn 2 261 Brachyuran crab zoea
Alarne mite


Acar~a tosa
Pan'ocalanus spp
Balanus nauphi

Acarh~a tosa
Brachvuran crab zoea
Pan'ocalanus spp
Paracalanusspp
Gastropod lan ae


63 60
27 80
4 20
3200


76 70
8600
4 ()o
3 30


66 ()o
14 80
3 90
3 50
3300


Mid-Field
Stn 1 5749


Stn 2 11()3 Acar~a tosa
Brachvuran crab zoea
Balanus nauphi
Gastropod lan ae
Pan'ocalanus spp
Paracalanusspp


Table 3-10. Suwannee system: mesozooplankton total abundance (individuals m-3) and common

taxa during March, May, September, and November.
March May cont November
River Mouth Far Field River Mouth
Stn 1 2925 Acar~a tomsa 97 200 Stn 1 535 Acarla tonsa 25 900 Stn 1 1341 Acarh~a tomsa 41 100
Paracalanus spp 25 800 Pan'ocalanus spp 21 200
Stn 2 18715 Acarh~atomsa 99 80 Covycaeus spp 160)o Balanus nauphil 18 )oo
Near Field Centronpges spp 5 10 0 Etevpina acuh# fons 5 40
Stn 1 64()9 Acar~a tomsa 96 700 Olthona spp 4 90 Pemlba spp 4 40
Panocalanus spp 3 40 Daphmla spp 4 20















individuals m-3) and


nt


30 0%
26 7%
11 4%
10 1%
9 5%
7 1%


Table 3-11. Withlacoochee system: mesozooplankton total abundance (i

common taxa during June, July, November, and December.
June July cont November col
River Mouth Near Field cont Far Field
Stn 1 9454 Acartla tonsa 78 1% Stn 2 6769 Acartla tonsa 58 4% Stn 1
Parvocalanus spp 3 9% Bivalve veliger 21 7%
Bivalve veliger 9 9% Parvocalanus spp 7 0%
Brachyuran crab zoea 5 0%


3545 Parvocalanus spp
Temora spp
Acarh~a tonsa
Olthona spp
Euterpina acuh~frons
Pentha spp


10085 Parvocalanus spp
Euterpina acuh~frons
Olthona spp
Temora spp
Pentha spp




863 Acar~a tonsa
Balanus naupln


378 Acar~a tonsa
Paracalanus spp
Ostracod


1318 Acar~a tonsa
Calanold copepod


2420 Acar~a tonsa


2431 Acar~a tonsa
Parvocalanus spp
Euterpina acuh~frons
Olthona spp
Barnacle cyprid


3298 Parvocalanus spp
Euterpina acuh~frons
Acarh~a tonsa
Olthona spp


1488 Euterpina acuh~frons
Parvocalanus spp
Acarh~a tonsa
Temora spp
Olthona spp


2070 Parvocalanus spp
Euterpina acuh~frons
Acarh~a tonsa
Temora spp
Olthona spp


Stn 2 12136 Bnvalve veliger
Acartla tonsa
Calanold copepod


45 4%
44 7% Mid-Field
4 2% Stn 1 1358 Acartla tonsa


46 0%
24 0% Stn 2
8 7%
5 9%
3 6%


28 4%
23 4% December
22 6% River Mouth
5 4% Stn 1
4 0%
3 2%
Stn 2
45 7%
25 6%
9 5% Near Field
5 7% Stn 1
3 3%


48 5% Stn 2
25 2% Mid-Field
11 4% Stn 1
4 4%



92 3%
3 1%
Stn 2
88 9%
5 0%


91 4% Far Field
Stn 1
89 8%


34 0%
12 5%
9 3%
8 6% Stn 2
7 8%
6 8%
4 3%


Near Field Parvocalanus spp
Stn 1 7529 Bnvalve veliger 69 1% Brachyuran crab zoea
Acartla tonsa 7 5% Euterpina acuh~frons
Parvocalanus spp 7 4% Olthona spp
Brachyuran crab zoea 6 1%
Euterpina acuh~frons 3 2% Stn 2 2364 Acartla tonsa
Parvocalanus spp
Stn 2 2159 Bnvalve veliger 42 4% Brachyuran crab zoea
Gastropod larvae 21 1% Etriaaufos
Brachyuran crab zoea 13 8% Shrimp zoea
Acartla tonsa 7 8% Labldocera spp
Eutrpnaacufrns 3 8% Far Field
Mid-Field Stn 1 1933 Acartla tonsa
Stn 1 3167 Acartla tonsa 45 6% Brachyuran crab zoea
Parvocalanus spp 18 0% Parvocalanus spp
Euerin auhros 12 3% Paracalanus spp
Brachyuran crab zoea 6 1% Centropages spp
Pseudodraptomus spp 5 7%
Stn 2 1649 Fish larvae


32 1%
31 1%
12 6%
9 3%
7 4%





91 6%
4 1%


83 8%
4 6%
3 7%


90 2%
4 3%


96 9%


36 4%
34 3%
114%
5 9%
3 7%


62 3%
15 7%
7 8%
6 6%


29 6%
21 7%
20 6%
15 2%
8 4%


30 7%
19 6%
18 5%
16 6%
5 1%


oea


Stn 2 3363 Acartla tonsa 40 3% Brachyuran crab ze
Euterpina acuh~frons 20 8% Acartla tonsa
Parvocalanus spp 12 2% Paracalanus spp
Pseudodraptomusspp 52% November
Paracalanus spp 4 4% River Mouth
Brachyuran crab zoea 3 5% Stn 1 1530 Acartla tonsa
Pseudocalanus spp 3 2% Calanold copepod
Far Field
Stn 1 1707 Acartla tonsa 40 9% Stn 2 1118 Acartla tonsa
Brachyuran crab zoea 27 9% Calanold copepod
Shrimp zoea 6 8% Near Field
Cenropgesspp 6 2% Stn 1 933 Acartla tonsa
Euterpina acuh~frons 3 9%
Paracalanus spp 3 2% Stn 2 1150 Acartla tonsa
Mid-Field
Stn 2 3188 Acartla tonsa 40 9% Stn 1 2836 Acartla tonsa
Brachyuran crab zoea 28 0% Copepod naupln
Centropages spp 9 1% Olthona spp
Euerin auros 4 4% Parvocalanus spp
Parvocalanus spp 3 2% Polychaete larvae
July Paracalanus spp
River Mouth Balanus nauphi
Stn 1 3041 Acartla tonsa 82 5%


Stn 2 3681 Acartla tonsa
Parvocalanus spp
Euterpina acuh~frons
Temora spp
Olthona spp
Ostracod
Balanus nauphi
Paracalanus spp
Calanold copepod


44 7%
21 9%
6 7%
5 6%
5 2%
5 1%
4 2%
3 6%
3 3%


Stn 2 4070 Acartla tonsa 46 7%
Bnvalveveliger 14 2%
Brachyuran crab zoea 12 2%
Balanus nauphil 4 8%
Parvocalanus spp 4 7%
Gastropod larvae 3 9%


Near Field
Stn 1 5673 Acartla tonsa
Bivalve vehlger
Parvocalanus spp
Gastropod larvae
Olthona son


56 1%
18 6%
6 5%
4 9%
4 2%












































































































I I


Olthona spp
Gastropod lan ae
Isopod larvae

Far Field
Stn 1 184() Acarha tonsa

Pan'ocalanus spp
Gastropod lan ae
Brachyuran crab zoea
Shnmp lan ae
Isopod lan ae

Sn2 1872 Acarha tonsa

Panocalanus spp
Olthona spp
Shnmp lan ae


8 30
5 30
4800



53 30
17 ()o
8 30
4 50
4 10
3 ()o


58 50
18 40
5 70
4 40


Stn 2 119() Gastropod lan ae
Acarh~a tonsa
Shimp larvae
Labldocera spp


61 500
19 900
8100
3 90



48 500
39 600
3 400


68 (oo
26 90


81 900
4 300


56 70
29 ()o
6 10


91 40
4 10


89 10
3300


56 20
7600
6 20
4 30
3 30


65 30
6 40
3 70
3 20
3200
3 20



84 30
4 50


84400
4 70
3 50


88 ()o

93 20


Stn 2 28648 .4car~a tonsa 87 300
Isopod larvae 5800
Brachyuran crab zoea 4 600


Mid-Field September
Stn 1 8662 .4car~a tonsa 79 70 River Mouth
Isopod lan ae 3 90 Stn 1 5499 Gastropod lan ae
Brachyuran crab zoea 3 50 Acarh~a tonsa
Pseudioaptonius spp
Stn 2 9474 .4car~a tonsa 73 400
Labldocera spp 7 50 Stn 2 1168 Acar~a tonsa
Brachruran crab zoea 3 70 Gastropod lan ae
Sagitta spp 3 30 Near Field
Far Field Stn 1 2551 Acar~a tonsa
Stn 1 61385 .4car~a tonsa 72 300 Gastropod lan ae
Brachruran crab zoea 6 300
Isopod 4 20 Stn 2 22()7 Acar~a tonsa
Labldocera spp 3 70 Gastropod lan ae
Pagurus crab zoea 3 20 Brachruran crab zoea
Mid-Field
Stn 2 2937() .car~a tonsa 68 500 Stn 1 2198 Acar~a tonsa
Gastropod lan ae 8 60 Brachruran crab zoea
Shrimp lanae 5600
Brachruran crab zoea 3 70 Stn 2 2241 Acar~a tonsa

June Isopod lavae


River Mouth
Stn 1 878


Far Field
93 700 Stn 1 2215 Acar~a tonsa

Shimp lavae
88 100 Pan'ocalanus spp
4 50 Paracalanus spp
3 10 Gastropod lan ae


.4carhoa tonsa


Stn 2 8() .carh~a tonsa
Eutevpina acuh fon
Calanold copepod


Near Field
Stn 1 987


Gastropod lanvae 26 )Oo Stn 2 2671 Acar~a tonsa
Acarh~a tonsa 17 60 Pan'ocalanus spp
Brachruran crab zoea 6 70 Paracalanus spp
Labldocera spp 5 70 Eutevpina acuh folm
Pagurus crab zoea 3 10 Shnimp lan ae
Barnacle cypnld


Stn 2 2983 .4car~a tonsa 64 10 November
Gastropod lan ae 59 70 River Mouth
Brachvuran crab zoea 23 10 Stn 1 835 Acar~a tonsa
Mid-Field Calanold copepod
Stn 1 3219 .4car~a tonsa 75 0)o
Brachruran crab zoea 8700 Stn 2 183 Acarh~atonsa
Gastropod lan ae 5 10 Eutevpina acuh folm

Harpacticold copepod
Stn 2 5()9 .4car~a tonsa 48 80 Near Field
Gastropod lan ae 26 )Oo Stn 1 2266 Acar~a tonsa
Pagurus crab zoea 8 50
Labldocera spp- 8 10 Stn 2 2998 Acar~a tonsa


Table 3-12. Weeki Wachee system: mesozooplankton total abundance (individuals m-3) and

common during April, June, September, and November.
Apnil June cont November cont
River Mouth Far Field Mid-Field
Stn 1 6453 Acar~a tonsa 98 300 Stn 1 917 Gastropod lan ae 57 600 Stn 1 13()8 Acarha tonsa 41 600
Acarh~a tonsa 13 0)o Gastropod lan ae 18 900
Stn 2 5536 .4car~a tonsa 93 70 Labldocera spp 8 80 Pan'ocalanus spp 16 60
Calanold copepod 4 60 Shnimp larvae 5 10 Isopod lan ae 5 80
Near Field Brachruran crab zoea 4 100 Brachvuran crab zoea 5 ()o
Stn 1 38467 .4car~a tonsa 87 0)o Pagurus crab zoea 4 ()o
Isopod lan ae 6 80 Isopod lan ae 3 20 Stn 2 2()72 Acarha tonsa 62 500
Brachyuran crab zoea 5 10 Pan'ocalanus sp 3












* Suwannee
O Withlacoochee
X Weeki Wachee


XX X

X
xxxx
x x x


S* o
O


. *.


XX XO
Xo


o **


a )


ex
1 xx x xox
xa ,Ixu &

X Xxxxo


o4..


Xx
X



ox o
xo


a d
* **


Figure 3-4. Three-dimensional ordination (stress = 0. 16) based on mesozooplankton abundances
in the Suwannee, Withlacoochee, and Weeki Wachee systems. Distance between
points is indicative of similarity where points further apart are less similar than those
closer together.











* March
O May
*September
X November O


O O


0 4
0 4


g *


0O O


*0*


O


x
?xx


~ O o~~


xx


Figure 3-5. Three-dimensional ordination (stress = 0. 12) based on mesozooplankton abundances
in the Suwannee River plume during March, May, September, and November.
Distance between points is indicative of similarity where points further apart are less
similar than those closer together.













*River Mouth
ONear Field
aMid-Field
X Far Field *


* 0


XX
4 x x


SO


XX


O

O


0 O



2 *


X


x
x
hi
X


Figure 3-6. Three-dimensional ordination (stress = 0. 12) based on mesozooplankton abundances
in the Suwannee River plume. Distance between points is indicative of similarity
where points further apart are less similar than those closer together.









CHAPTER 4
DISCUSSION

Overall, these results indicate the model proposed by Liu & Dagg (2003) and Dagg &

Breed (2003) to describe interactions between phytoplankton and zooplankton along nutrient and

light gradients in large, river-dominated, coastal systems cannot be generalized to these smaller,

river-influenced, coastal waters. Spatial patterns of phytoplankton biomass and growth rates,

microzooplankton grazing rates and abundance, and mesozooplankton grazing rates and

abundance in each of the three systems were not consistent with the conceptual model. In the

Suwannee system, where nutrient and light have been shown to vary inversely with salinity

(Frazer et al. 1998), phytoplankton crops and phytoplankton growth rates were normally the

highest at the river mouth, declining in the near field and mid-field, and lowest in the far field.

Furthermore, microzooplankton grazing rates and abundance and mesozooplankton grazing rates

and abundance did not peak in the two higher salinity zones as predicted. In the Withlacoochee

and Weeki Wachee systems, where nutrients inversely varied with salinity (Frazer et al. 1998)

and light availability was relatively consistent among zones (Figure 3-1), phytoplankton biomass

tended to peak in the lower salinity zones and phytoplankton growth and microzooplankton

grazing rates were similar among zones. Microzooplankton and mesozooplankton abundances

varied among zones, but they were not particularly high in the mid field and far fields. Although

spatial variability deviated from the model, estimates of phytoplankton biomass, phytoplankton

growth rates, microzooplankton grazing rates and mesozooplankton assemblage composition

were comparable to results of previous work in the three study systems and to studies of other

coastal waters in the northern Gulf of Mexico. To explain the deviations from the model's

predictions, we examined the hypothesis that river discharge rates were below the threshold









required to induce the physical processes that affect light availability on scales in time and space

at which phytoplankton could respond.

Phytoplankton biomass estimates (chl Cpg L 1) in the Suwannee, Withlacoochee, and Weeki

Wachee systems were within the range of previously reported values for those systems (Quinlan

& Phlips 2007, Frazer et al. 2007, Bledsoe 2003), and comparable to estimates from other coastal

areas in the northern Gulf of Mexico (Mississippi River plume: Wysocki et al. 2006, Liu & Dagg

2003; Mobile Bay: Lehrter et al. 1999; Pensacola Bay: Murrell et al. 2002). The spatial

distribution of phytoplankton biomass observed in this study, where biomass is highest near the

river mouth and lowest in the far field, corroborates findings of Quinlan & Phlips (2007),

Murrell et al. (2002), and Lehrter et al. (1999) indicating that mean chlorophyll concentrations

normally declined along salinity gradients in systems with freshwater flows lower than the

Mississippi River. Such a pattern is in stark contrast to that predicted by the model; chlorophyll

maxima are expected to occur at the mid-field.

Phytoplankton growth and microzooplankton grazing rates in the Suwannee,

Withlacoochee, and Weeki Wachee systems were typical of many coastal systems in the Gulf of

Mexico and other locations around the globe (Table 4-1). Prior estimates of phytoplankton

growth (range = -0.15 to 3.20) and grazing rates (range = 0.00 to 2.04) from the Suwannee River

estuary are similar to those reported here (Table 4-1), despite differences in river discharge and

the scale of sampling effort among studies. This similarity suggests rates of phytoplankton

growth and mortality due to microzooplankton grazing are remarkably constant in space and

time in the Suwannee system. Phytoplankton growth and microzooplankton grazing rates for the

Withlacoochee and Weeki Wachee systems have not been reported previously. The rates for

these systems were within the range of those reported from the Suwannee, indicating that










phytoplankton growth rates and microzooplankton grazing rates are likely to be uniform in

waters along the west coast of peninsular Florida. This finding is noteworthy as

microzooplankton grazing rates are generally assumed to be a function of prey concentration

(Landry & Hassett 1982). In this study, chlorophyll concentrations in the coastal waters adjacent

to the Suwannee were markedly higher than those in waters adj acent to the Weeki Wachee, and

one might have expected corresponding differences in microzooplankton grazing rates. The

similarity in grazing rates between these two systems suggests the need to consider other effects

on microzooplankton, such as predation by mesozooplankton and/or the physical environment.

The relative consistency in microzooplankton grazing rates among systems as compared to

the variability among results of dilution experiments was borne out by power analyses. These

analyses indicated that considerably larger sample sizes or differences among grazing rates were

needed to yield statistical significance. For example, power analyses indicated minimal

detectable differences among square-root transformed g-values of approximately 2.3, which is

greater than the square-root transformation of the largest g-value we found, i.e. -\2.5 = 1.6 (James

& Hall 1998). In general, power analyses indicated that detecting statistical differences in

microzooplankton grazing rates may require considerable effort.

Here, we report the first estimates of mesozooplankton total abundance and grazing impact

along salinity gradients in the Suwannee, Withlacoochee, and Weeki Wachee systems. Individual

estimates of total abundance among systems and zones varied widely, sometimes by an order of

magnitude (Tables 3-10 to 3-11). A potential response in mesozooplankton production related to

changes in phytoplankton biomass was found only in the Suwannee system during the low

discharge period, where higher total abundances in the near field and mid-field were coincident

with maximum chlorophyll concentrations near the river mouth. This pattern of spatial









variability supports the observation of Dagg (1995) that mesozooplankton exhibit a lag in

responding to accumulation of phytoplankton biomass accumulation.

In cases where mesozooplankton grazing rates could be calculated, the corresponding

percentages of phytoplankton consumed on a daily basis were quite low (<0.05%), suggesting

that mesozooplankton grazing impact is negligible in comparison to the impact of

microzooplankton grazing. The limited success of the mesozooplankton addition experiments is

indicative of the difficulties associated with collecting zooplankton and transferring them to a

land-based laboratory. The mesozooplankton addition method has been successfully

implemented on ships (Liu & Dagg 2003, Calbet & Landry 1999); however, its use may be

inappropriate for land-based experiments when the time between collection and incubation

exceeds a few hours. Qualitative observations during this study suggest that mesozooplankton

mortality rates remained low during the first 6 to 8 hrs after collection and increased greatly

thereafter. The few, low, estimates of mesozooplankton grazing rates may be due to moribund

zooplankton being used in the addition experiments. This issue may be resolved by simply

restricting the amount of time zooplankton are immersed in containers before being added to

treatments.

Mesozooplankton assemblages have seldom been characterized in the shallow coastal

waters along the central west coast of peninsular Florida. In fact, this is the first characterization

of mesozooplankton from the coastal waters adjacent to the Suwannee, Withlacoochee, and

Weeki Wachee rivers. The predominance of Acartia tons and Paracalan2us spp. in each of the

three study systems is consistent with findings from other coastal waters indicating the

dominance of these copepods in mesozooplankton assemblages in the northern Gulf of Mexico

(Apalachicola River estuary: Putland 2005; Mississippi River plume: Liu & Dagg 2003, Dagg









1995; Turkey Point, FL: Stalder & Marcus 1997). However, despite the overall dominance by A.

tonsa and ParacalanusPP~~PP~~~PP~~PP sp., mesozooplankton assemblages exhibited spatial heterogeneity along

salinity gradients in each of the three study systems. Brachyuran crab zoea, barnacle nauplii, and

euryhaline copepods like A. tons were abundant in nearshore assemblages while polyhaline

copepods like Temora sp. and Corycaeus sp. primarily comprised assemblages offshore (Tables

3-10 to 3-12). Relative abundances of A. tonsa and Paracalan2us spp. also varied with salinity,

where A. tonsa was the numerical dominant nearshore, but became secondary to ParacalanusPP~~PP~~~PP~~PP

spp. offshore. Lower abundance ofA. tons and higher numbers of Paracalanus spp. in the far

fields of each system may reflect the ability of Paracalan2us spp. to out-compete A tons when

phytoplankton concentrations are low (Paffenhoiffer & Stearns 1988). In environments with low

prey availability where it may be out-competed for food, Acartia tonsa likely exhibits facultative

omnivory (Gifford & Dagg 1991), switching between feeding on phytoplankton and

microzooplankton in response to their relative availability (Johnson & Allen 2005, Halvorsen et

al. 2001, Kiarrboe et al. 1996, Kleppel et al. 1992). Large numbers of adult A. tonsa and its

juvenile stages in nearshore coastal waters could restrict microzooplankton production where the

quantity of phytoplankton prey is relatively low (Batten et al. 2001). Furthermore, prey switching

by A. tons assemblages along the west coast of Florida is a distinct possibility given the marked

difference in chlorophyll concentrations between the Suwannee and Weeki Wachee systems.

Because the relative availability of autotrophic (chl Cpg L^1) to microzooplankton prey

(individuals L^1) is greater in the Suwannee system than it is in the Weeki Wachee system, A.

tonsa would be expected to ingest proportionately more phytoplankton in coastal waters adj acent

to the Suwannee River, and, conversely, more microzooplankton in waters adj acent to the Weeki

Wachee River. Acartia tonsa exhibiting shifts in prey selectivity and feeding behavior (Kiarrboe









et al. 1996) between the two systems would be especially likely during seasonal blooms of large

phytoplankton taxa in the Suwannee system (Quinlan & Phlips 2007) because A. tons

selectively feeds on particles larger thanl5Cpm (Rollwagen Bollens & Penry 2003).

In river-impacted, coastal systems, freshwater discharge, wind stress, and tide are the

primary forces affecting the physical structure of plume waters (Chen et al. 1997, Yin et al.

1997). In large river systems, discharge is a dominating physical force. For example, during a

period of high discharge, the Amazon's plume extended 162 km offshore (Smith & Demaster

1996). In contrast, low salinity water masses from rivers with low discharge rates persist at small

temporal (hours) and spatial scales (2 11 km), and have horizontal and vertical structure that is

proportionately more affected by wind stress and tidal forces (Gaston et al. 2006). The degree of

mixing that occurs between fresh and marine waters in nearshore areas of coastal system likely

depends on the momentum of river discharge. If the momentum of river discharge approximately

equals the force of tidal currents, then the water column is vertically mixed by the interaction

between riverine water and tidal forces. If the momentum of river discharge is less than the force

generated by tidal currents, then the spatial extent of low salinity riverine waters in nearshore

areas oscillates with the tide and turbulent mixing occurs. Furthermore, in shallow coastal

systems like the Suwannee, Withlacoochee, and Weeki Wachee, wind-driven vertical mixing is

also an important force to consider. Models of plume dispersion from the Ebro River (Spain)

indicate that a discharge rate of 400 m3 S-1 is the threshold at which the river' s momentum

overcomes the effect of wind on the hydrodynamics at the river mouth to allow the evolution of a

plume (Mestres et al. 2003). River discharge rates observed during this study were below this

threshold, likely resulting in a highly turbulent environment. This lack of vertical structure would

result in light conditions varying on spatial and temporal scales too finite to elicit a physiological










response from phytoplankton. Instead of light-shade acclimation, which occurs in stable light

conditions (Tillmann et al. 2000), phytoplankton in well-mixed estuaries adapt to a mean light

environment (MacIntyre & Cullen 1996). Phytoplankton growth rates would be similar in along

salinity gradients, except when the interaction of wind and tide entrain algal stocks in areas with

relatively higher nutrient availability (see Bledsoe 2003). Variability in the spatial distribution of

algal biomass in coastal waters would be driven primarily by physical processes like

conservative mixing rather than losses from grazing. Microzooplankton would not be able to

adapt to changes in prey availability on the time scale they occur and so grazing rates would be

relatively constant and reflect mean prey concentrations. Furthermore, a turbulent environment

would also affect the feeding ecology of mesozooplankton assemblages dominated by Acartia

tonsa because selection of ciliates increases with turbulence (Kiarrboe et al. 1996). This

preference would likely lead to greater regulation of microzooplankton production by A. tons in

riverine coastal systems where water column vertical structure persists at small spatial and

temporal scales.

Interactions between phytoplankton, microzooplankton and mesozooplankton in the

Suwannee, Withlacoochee, and Weeki Wachee coastal systems may be best described by the

Low Nutrient Model proposed by Putland (2005) for well-mixed or partially mixed river-

dominated estuaries. This model suggests that in coastal areas impacted by rivers with low

nutrient inputs, nutrient concentrations are low in the mid to high salinities and limiting

phytoplankton growth. The phytoplankton community in smaller, riverine, coastal systems is

dominated by picophytoplankton or nanophytoplankton due to the ability of small taxa to grow

faster than large taxa in a low nutrient environment (Kiarrboe 1993, Chisholm 1992).

Phytoplankton growth rates and biomass are expected to peak in the river mouth to mid-field,









where light and nutrient availability are intermediate. Application of this conceptual framework

to systems along the north central Gulf coast of peninsular Florida is likely appropriate because:

(1) riverine nutrient inputs into systems are much lower (10 to 620 TN Cpg L^1, 10 to 70 TP Cpg L-

1: Frazer et al. 1998, Frazer et al. 2007) than those from the Mississippi River (2.8 x 10s to 2.8 x

109 Cpg TN L^1, 9.29 x 106 to 1.55 x 10s Cpg PO4 L^1: Lohrenz et al.1999) and (2) spatial variability

in phytoplankton parameters observed in this study supports the predictions of high growth rates

and accumulation of phytoplankton biomass in the river mouth and near field.

The maj or ecological implication of this model for Big Bend coastal systems is the

expected dominance of the phytoplankton community by the picophytoplankton and

nanophytoplankton. Cyanobacteria, chlorophytes, cryptophytes, and small dinoflagellates (e.g.

Katodinium) are common and seasonally dominant in the river and nearshore regions of the

Suwannee during periods of low river discharge (Quinlan & Phlips 2007, Bledsoe 2003). While

the phytoplankton communities in the Withlacoochee and Weeki Wachee systems have yet to be

characterized, the relatively lower nutrient concentrations in these coastal waters would likely

lead to small taxa dominating the assemblage (Cloern & Dufford 2005). Because many

mesozooplankton cannot effectively feed on phytoplankton cells smaller than 20 Cpm, carbon

fixed by these primary producers must be first consumed by bacteria, heterotrophic

nanoflagellates, and microzooplankton (i.e. the microbial food web) before it is made available

to metazoan consumers (Azam et al. 1983). Estimates of microzooplankton grazing impact in the

three study systems support the hypothesis that the microbial food web plays a critical and maj or

role, as microzooplankton grazers removed at least 51% of phytoplankton standing crops and

87% of primary production on a daily basis (Table 3-3 to 3-5). The few estimates of

mesozooplankton grazing rates were very low, suggesting mesozooplankton grazing on










phytoplankton stocks was negligible in comparison to microzooplankton grazing. The

importance of the microbial food web in the Suwannee, Withlacoochee, and Weeki Wachee

systems has implications for carbon cycling, trophic ecology, and fisheries. Production by

bacteria and small phytoplankton is likely to be a maj or source of energy transferred to higher

trophic levels (Sherr & Sherr 1988). The apparent decoupling between grazing rates on primary

producers and the abundance of mesozooplankton could be explained by feeding on

microzooplankton (Olson et al. 2006, Batten et al. 2001, Kleppel et al. 1988). However,

elongation of trophic pathways via the microbial food web and a link to mesozooplankton

reduces the efficiency of carbon transport to higher consumers like juvenile fish (Ryther 1969).

Therefore, in systems with low phytoplankton concentrations and/or those periodically

dominated by small phytoplankton taxa, overall productivity is likely to be lower in comparison

to coastal systems with higher riverine nutrient inputs.

The management implications of this study are three-fold. Firstly, conceptual models for

large, river-dominated, coastal systems like the Mississippi River plume should not be

generalized for management purposes to river-impacted coastal waters along the Big Bend

Region without empirical validation. Secondly, because microzooplankton are maj or consumers

of phytoplankton standing stocks in the Suwannee, Withlacoochee, and Weeki Wachee systems,

managers need to incorporate microbial food web processes into models that predict how carbon

cycles in these systems. Thirdly, increases in riverine nutrient loads will lead to higher

concentrations of phytoplankton biomass at the river mouths. This has particular implications for

systems with extensive seagrass beds because increases in phytoplankton abundance can reduce

water clarity, shade seagrasses and result in their loss (Hale et al. 2004).









In conclusion, interactions between phytoplankton, microzooplankton and

mesozooplankton in the Suwannee, Withlacoochee, and Weeki Wachee systems were not

consistent with the conceptual model developed for coastal waters where large rivers dominate.

Instead of peaking at the intermediate salinities, phytoplankton biomass and phytoplankton

growth rates were highest near the river mouth in the Suwannee system. In the Withlacoochee

and Weeki Wachee systems, the presence of turbulent water columns across salinity zones likely

led to uniform phytoplankton growth rates across gradients of nutrients and light. Furthermore,

microzooplankton grazing rates and abundance, and mesozooplankton total abundance were

similar across the plumes in each of three systems. Although microzooplankton grazing rates did

not vary significantly in space, grazing was an important loss factor for phytoplankton in each

system. Nutrient over-enrichment of these coastal systems may disrupt the balance between algal

production and its consumption by zooplankton grazers. This sort of perturbation will likely lead

to increases in the concentration of particulate organic matter in the water column, with the

potential to change food web dynamics (Cloern 2001) and cause the accumulation of

phytoplankton biomass in the nearshore areas of the Suwannee, Withlacoochee, and Weeki

Wachee systems.





Environment
Suwannee


a = mean (A SE) calculated by averaging across size fractions for a station and then finding the study's grand mean.


Table 4-1. Published values of instantaneous maximum specific phytoplankton growth rates (k & SE) and instantaneous


microzooplankton grazing rates (g & SE).
:nt k & SE
River Plume, FL 0.99 & 0.19
1.56 & 0.26
1.63 & 0. 11


1.55 & 0.29
0.89 & 0.08
0.76 & 0.07


Range


g & SE
0.92 & 0.14
0.74 & 0.14
0.68 & 0.07
1.03 & 0.27
0.72 & 0.07
0.71 & 0.08

0.57 & 0.07
0.97 & 0.18
1.10 + 0.20
0.70 + 0.19
0.29 & 0.05

0.54 & 0.05
0.51 & 0.10
0.80 + 0.19

0.24 & 0.04

0.53 & 0.04
0.40 + 0.04
0.39 & 0.01


Range
0.38 2.05
0. 11 -1.41
0.00 2.04
0.39 1.67
0.34 1.16
0.00 1.95


Issues Reference
This Study
Bledsoe 2003
Jett 2004
This Study
This Study
Putland 2005
Lehrter et al. 1999


-0.40
0.41
-0.15
0.99
0.54
0.08

-0.09
0.25
0.01
0.46
0.53

0.68
0.33
0.50
<0.1
0.03


2.50
2.70
3.20
2.37
1.30
1.92

2.06
2.87
1.27
1.76
2.22

1.46
1.66
2.10
1.80
0.41


Withlacoochee River Plume, FL
Weeki Wachee River Plume, FL
Apalachicola River, FL
Mobile Bay, AL
Bay
Bay Mouth
Offshore
Mississippi River Plume
Mississippi River Plume
Pensacola Bay, FL
o\ Upper Bay
Lower Bay
Santa Rosa Sound, FL
Rhode River Estuary, MD
Chesapeake Bay Mid Bay


0.70 +
1.27 &
1.62 &
1.13 &
1.11 &


0.14
0.19
0.23
0.23
0.13


0.05
-0.03
-0.09
0.28
-0.10

0.26
0.08
0.00
0.00
0.00


0.96
2.44
2.93
1.39
0.67

0.81
1.25
1.50
1.50
1.60


Liu & Dagg 2003
Strom & Strom 1996
Murrell et al. 2002


Juhl & Murrell 2005
Gallegos & Jordan 1997
McManus &
Ederington-Cantrell 1992
Calbet & Landry 2004


1.02 & 0.07
1.00 + 0.12
1.50 + 0.16

0.23 & 0.06


Estuarine
Coastal
Oceanic


0.97 &
0.67 &
0.59 &


0.07
0.05
0.02









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BIOGRAPHICAL SKETCH

Kelly Lynn Robinson was born in 1982 in Tacoma, Washington to Harold

and Adele Robinson. She and her younger sister, Katie, grew up in Washington and Colorado.

Kelly attended Sweet Briar College, Virginia for her undergraduate education, and studied

abroad for a semester at James Cook University, Townsville, Queensland (Australia). She

graduated from Sweet Briar with a Bachelor of Science in biology (cum laude), and was the

recipient of the Judith Elkins Prize for a senior graduating with a degree in the sciences. After

graduating, she j oined the research apprentice program at Friday Harbor Laboratories, University

of Washington, where she was introduced by Dr. Jan Newton to the fields of biological

oceanography and zooplankton ecology. While at Friday Harbor, Kelly was accepted into the

Master of Science program at the Department of Fisheries and Aquatic Sciences, University of

Florida (UF) under the advisement of Dr. Tom K. Frazer. During her tenure at UF, Kelly

served on the executive committee of the department' s graduate student organization, and was

awarded "Outstanding Graduate Student of Year" for 2006. She graduated from UF with her MS

degree in summer 2007. In her final year, Kelly was accepted into the PhD program in the

Marine Sciences Department, University of South Alabama, and was awarded a Dauphin Island

Sea Lab Fellowship. She plans to conduct her doctoral research at the Dauphin Island Sea

Laboratory under the advisement of Dr. William M. Graham.





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INTERACTIONS AMONG PHYTOPLANKTON, MICROZOOPLANKTON, AND MESOZOOPLANKTON IN RIVERINE COASTAL SYSTEMS ALONG THE WEST COAST OF PENINSULAR FLORIDA By KELLY L. ROBINSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007 1

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2007 Kelly L. Robinson 2

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For my Dad. 3

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ACKNOWLEDGMENTS This research was supported in part by a grant from the Biological Oceanography program, Division of Ocean Sciences, Directorate of Geosciences, National Science Foundation and by the Department of Fisheries and Aquatic Sciences at the University of Florida. I am thankful to Megan Brennan from the Institute of Food and Agriculture Sciences Statistics Department (UF) and Dr. Mike Allen for statistical consultation. I also thank Dr. Ed Phlips for kindly allowing me to use his microscopy and water collection equipment, and to consult with his staff. In particular, I appreciate the courteous assistance of Mary Cichra. I thank Dr. Karl Havens for his advice on zooplankton methodology and for allowing me to borrow his equipment for processing zooplankton samples. I am indebt to Dr. Bill Pine for generously sharing his technicians with me whenever I needed extra help in the laboratory or in the field. Many other people assisted me over the course of this research. Without their help, counsel, and support, completion of this project would have been impossible. Foremost, I thank my esteemed colleagues in the Frazer Lab. Stephanie Keller and Darlene Saindon were my gurus for processing water chemistry samples and all the subtleties therein. I also thank them for teaching me the practical side of science and for setting standards of excellence in laboratory and field techniques that I will forever try to meet. Dr. Loreto de Brandabere, and Vincent and Kristin Politano were tireless volunteers for field excursions and laboratory work. They never begrudged me for the early morning start times or the long days. Kate Lazar was my gold standard for making dilution treatments, and Emily Mitchem was my savior when the work went deep into the night. I thank Matt Lauretta for his help in the field, and for being willing to go out a second time when the tide left me high and dry at the first attempt. In addition to her assistance in the field and in the lab, I thank Loreto for her friendship and for the frank and stimulating discussions regarding ways to improve this research and future efforts. From Bill Pines lab, I am 4

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particularly grateful to Elissa Buttermore for working with me on numerous occasions. She always had a great attitude and her help was instrumental in the completion of field and laboratory work. My committee, Tom Frazer, Chuck Jacoby, and Marsh Youngbluth have been exceptionally positive and supportive of my work. Their guidance has improved my understanding of nature, science, and life. I thank Marsh and Chuck for providing the opportunity to be part of a submersible crewa life-changing experience, and for their incredibly helpful comments that surely improved this thesis. I also thank Chuck for his friendship, for being a great mentor, and for his priceless insights regarding all aspects of this research. I am particularly indebted to my major advisor, Tom. He has taught me more than I ever expected to learn as graduate student about science and life. I will forever try to emulate his ability to delve into the details of a project and re-surface with a broader understanding of the world. He has been an extraordinary mentor and sponsor, and I feel extremely lucky to have been his student the past two and half years. Finally, I thank my friends, my sister, and my parents, Adele and Harold, and my stepmother, Kim Robinson, for their unswerving love, support, and enthusiasm. Their belief in my abilities kept me working when the going got tough. 5

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF TABLES ...........................................................................................................................7 LIST OF FIGURES .........................................................................................................................9 ABSTRACT ...................................................................................................................................10 CHAPTER 1 INTRODUCTION..................................................................................................................12 2 MATERIALS AND METHODS...........................................................................................16 Study Sites..............................................................................................................................16 Sample Collection...................................................................................................................18 Laboratory Procedures............................................................................................................20 Controls...........................................................................................................................22 Calculations............................................................................................................................24 Phytoplankton Growth and Microzooplankton Grazing Rates.......................................24 Mesozooplankton Grazing Rates.....................................................................................25 Data Analyses.........................................................................................................................26 3 RESULTS...............................................................................................................................29 Field Parameters.....................................................................................................................29 Phytoplankton Growth Rates..................................................................................................31 Microzooplankton Grazing Rates and Assemblages..............................................................31 Mesozooplankton Grazing Rates and Assemblages...............................................................33 Controls...................................................................................................................................35 4 DISCUSSION.........................................................................................................................56 LIST OF REFERENCES...............................................................................................................67 BIOGRAPHICAL SKETCH.........................................................................................................73 6

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LIST OF TABLES Table page 3-1 Mean values for the physical, chemical, and biological parameters measured in the Suwannee, Withlacoochee, and Weeki Wachee systems..................................................37 3-2 Analyses of variance for transformed river discharge (m 3 s -1 ), light attenuation (K d m -1 ), in situ total chlorophyll concentrations (g L -1 ), water temperature (C), and dissolved oxygen concentrations (DO, mg L -1 ).................................................................39 3-3 Suwannee system: estimates of apparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous microzooplankton grazing rates (g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ), and percent of phytoplankton production lost daily (%PP d -1 )......................................................................................................................................42 3-4 Withlacoochee system: estimates of apparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous microzooplankton grazing rates (g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ), and percent of phytoplankton production lost daily (%PP d -1 )......................................................................................................................................43 3-5 Weeki Wachee system: estimates of apparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous microzooplankton grazing rates (g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ).................................................................................................44 3-6 Analyses of variance for transformed phytoplankton growth rates (k), microzooplankton grazing rates (g), microzooplankton total abundance, and mesozooplankton total abundance.....................................................................................45 3-7 Suwannee system: microzooplankton total abundance (individuals L -1 ) and common taxa during September and November...............................................................................47 3-8 Withlacoochee system: microzooplankton total abundance (individuals L -1 ) and common taxa during November and December................................................................48 3-9 Weeki Wachee system: total abundance (individuals L -1 ) and common taxa during September and November..................................................................................................49 3-10 Suwannee system: mesozooplankton total abundance (individuals m -3 ) and common taxa during March, May, September, and November........................................................50 3-11 Withlacoochee system: mesozooplankton total abundance (individuals m -3 ) and common taxa during June, July, November, and December.............................................51 7

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3-12 Weeki Wachee system: mesozooplankton total abundance (individuals m -3 ) and common during April, June, September, and November...................................................52 4-1 Published values of instantaneous maximum specific phytoplankton growth rates (k SE) and instantaneous microzooplankton grazing rates (g SE)...................................66 8

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LIST OF FIGURES Figure page 2-1 Location of study systems along the west coast of Florida. Filled circles denote stations sampled.................................................................................................................28 3-1 Back transformed mean light attenuation (K d m -1 ) 95% confidence intervals (CI) for zones.............................................................................................................................40 3-2 Back transformed mean chlorophyll concentrations (g L -1 ) 95% confidence intervals (CI) for zones......................................................................................................41 3-3 Two-dimensional ordination (stress value = 0.11) based on microzooplankton abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems.....................46 3-4 Three-dimensional ordination (stress = 0.16) based on mesozooplankton abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems........................................53 3-5 Three-dimensional ordination (stress = 0.12) based on mesozooplankton abundances in the Suwannee River plume during March, May, September, and November...............54 3-6 Three-dimensional ordination (stress = 0.12) based on mesozooplankton abundances in the Suwannee River plume............................................................................................55 9

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science INTERACTIONS AMONG PHYTOPLANKTON, MICROZOOPLANKTON, AND MESOZOOPLANKTON IN RIVERINE COAS TAL SYSTEMS ALONG WEST COAST OF PENINSULAR FLORIDA By Kelly L. Robinson August 2007 Chair: Thomas K. Frazer Cochair: Charles A. Jacoby Major: Fisheries and Aquatic Sciences Rivers represent major conduits transporting nutrients to coastal oceans from natural and anthropogenic sources. The fates of these nutrients and their impacts on coastal systems are controlled not only by physical and chemical processes in nearshore and coastal environments but also by co-occurring biological interactions. One conceptual model predicts that discharges from large rivers create buoyant, freshwater plumes in which physical, chemical, and top-down and bottom-up biological controls combine to yield i) lower phytoplankton growth rates and biomasses in low light environments near river mouths; ii) higher phytoplankton growth rates and biomasses in zones where light climates improve and nutrients remain available; and iii) decreasing phytoplankton growth rates and biomasses in zones where grazing and depletion or dilution of nutrients become important. Grazing rates and abundances of microzooplankton and mesozooplankton are predicted to respond to this spatial pattern according to their grazing abilities and rates of reproduction. Microzooplankton grazers feed on smaller phytoplankton and reproduce more rapidly, so their abundances and the rates at which they remove phytoplankton standing crops more closely track increases in phytoplankton growth rates and biomasses. Mesozooplankton eventually respond, and their abundances and grazing rates become more 10

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important further offshore. The applicability of this conceptual model was tested in four salinity zones that delineated the influences of the Suwannee, Withlacoochee and Weeki Wachee Rivers along the west coast of peninsular Florida. Results from four sets of field sampling and 24-hour grazing experiments were not consistent with the model. In the Suwannee system, phytoplankton biomasses and growth rates were highest near the river mouth rather than peaking in the zone characterized by intermediate salinities. In the Withlacoochee and Weeki Wachee systems, phytoplankton biomasses and growth rates remained fairly uniform across the range of salinities. Microzooplankton grazing rates and abundances and mesozooplankton abundances were similar across the salinity gradients in the three systems. Microzooplankton grazing represented an important pressure on phytoplankton standing crops, because it removed an average ( standard deviation) of 99.5 46.8%, 81.3 31.6%, and 87.1 12.6% of primary production per day in the Suwannee, Withlacoochee, and Weeki Wachee systems. In comparison, mesozooplankton grazing impact was negligible, with 0.05% of phytoplankton production consumed per day. Interactions among phytoplankton, microzooplankton, and mesozooplankton were not consistent with the model; therefore, we hypothesized that river discharge was below the threshold required to induce the physical processes that establish the predicted gradients. For example, the light environment supported phytoplankton growth closer to shore than expected in all systems, and nutrients were quickly depleted in nearshore, coastal waters. The relatively large impact of grazing by microzooplankton suggests that the microbial loop plays a primary role in the transformation of nutrients delivered to coastal waters by the Suwannee, Withlacoochee, and Weeki Wachee Rivers, with consequences for cycling of elements, structure and function of food webs, and production of fisheries resources. 11

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CHAPTER 1 INTRODUCTION Estuaries and adjacent coastal waters are highly productive systems, driven in large part by the infusion of nutrient-rich waters from rivers. Excessive nutrient inputs increase the likelihood of eutrophication or production of excess organic matter (Cloern 2001). In turn, over-production of organic matter in coastal waters can cause patches of hypoxia via settling and microbial decomposition of phytoplankton (Cloern 2001), changes in the biogeochemistry of sediments as hypoxic conditions alter chemical flux at the sediment-water interface (Jrgensen (1996), declines in the abundance of submerged macrophytes if high concentrations of phytoplankton decrease light availability at depth (Duarte 1995), shifts in zooplankton community structure in response to changes in algal communities (Paerl 1988), and mortalities of fish and shellfish from algal toxins (Rosenberg & Loo 1988). The negative ecological consequences of nutrient over-enrichment often have broad and far-reaching socio-economic implications. For this reason, it is essential to more fully understand the factors and processes controlling phytoplankton and zooplankton production in river-impacted coastal waters. Due to the complex nature of coastal systems, the influence of river discharge on interactions between nutrients, light, phytoplankton, and zooplankton is likely to vary markedly among systems and times. Phytoplankton dynamics are dependent on complex interactions between the availability of light, nutrients, and other factors that promote growth and factors like sinking aggregation and grazing that result in loss. Empirical studies and modeling indicate that in coastal systems dominated by river input, the spatial and temporal availability of nutrients and light can be directly affected by mixing and the density fronts created by wind stress, tidal cycles, and river discharge (Liu & Dagg 2003, Chen et al. 1997, Yin et al. 1997). These physical forces and the gradients they create are highly variable in both space and time; therefore, 12

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phytoplankton growth rates and production vary as well (Bowman et al. 1986). Variation in phytoplankton production interacts with zooplankton production because of the tight coupling between the zooplankton and phytoplankton communities (Cloern 2001, Kirboe 1993). In riverine coastal systems along the Gulf of Mexico, zooplankton grazers are known to be an important factor regulating phytoplankton biomass (Juhl & Murrell 2005, Liu & Dagg 2003, Bledsoe 2003, Strom & Strom 1996, Fahnenstiel et al. 1995). In coastal systems where large rivers dominate, phytoplankton production and zooplankton grazing and abundance have been described with a conceptual model based on processes occurring along gradients of nutrients and light (Dagg & Breed 2003). In the model, as water moves offshore from the river mouth, phytoplankton biomass and production and zooplankton abundance and grazing rates are low in or near the river, rise to maximum in the mid-field as characterized by salinities between fresh and oceanic water, and decline again in the far field characterized by oceanic salinities. Near the river mouth, algal growth rates are low, primarily attributed to an unfavorable light regime caused by high concentrations of suspended particulate matter. Algal growth rates are greater in the near field as a consequence of sedimentation of lithogenic particles, which leads to a more favorable light environment. In the mid-field, where nutrient concentrations remain sufficiently high and the light environment is more favorable, algal growth rates are the highest. In the far field, as nutrients are diluted and depleted via uptake, growth rates decline. The predicted grazing response of microzooplankton and mesozooplankton to this distribution of algal biomass is thought to be dependent on their respective rates of production. Because of the closer coupling between microzooplankton and phytoplankton production (Azam et al. 1983, Thingstad et al .1999), microzooplankton grazers are expected to respond more rapidly than mesozooplankton grazers to an increase in 13

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phytoplankton biomass and remove a larger percentage of the phytoplankton production in the mid-field. In the far field marked by oceanic salinities, mesozooplankton will have had enough time to respond to phytoplankton production, and they will peak in abundance and remove a greater percentage of phytoplankton than microzooplankton (Kirboe & Johansen 1986, Kahru et al. 1984). To date, the conceptual model has only been tested explicitly in the plume of the 6260 km long Mississippi River (Liu & Dagg 2003). The drainage basin of this river encompasses greater than 40% of the continental United States or an area of approximately 354,000 km 2 (Berner & Berner 1987). This drainage basin generates high annual riverine discharge (15,000 m 3 s -1 ), which results in a large region of interaction between the plume and the receiving waters of the Gulf of Mexico (Dagg & Breed 2003). Light attenuation is attributed to high concentrations of lithogenic particles (Dagg & Breed 2003). River nutrient concentrations are also high at 2.8 x 10 8 to 2.8 x 10 9 g TN L -1 and 9.29 x 10 6 to 1.55 x 10 8 g PO4 L -1 (Lohrenz et al.1999). The findings reported by Liu & Dagg (2003) were generally consistent with the model. Phytoplankton growth and microzooplankton grazing rates were low in the near field, highest in the mid-field, and decreasing in the far field. Mesozooplankton grazing impact was low at the near and mid-field, and highest in the far field. This study tested the generality of the conceptual model developed for riverine coastal systems by describing interactions among phytoplankton growth and biomass and microzooplankton and mesozooplankton grazing across salinity gradients in river-influenced systems along the west coast of peninsular Florida. These river systems are ideal for examining key interactions in the model because discharge rates, nutrient concentrations, and light environments vary among the systems. If the interactions described by the model are observed in 14

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these systems under a variety of flow regimes, then the model may apply to a wide range of riverine coastal systems world wide. 15

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CHAPTER 2 MATERIALS AND METHODS To test the models generality, biomass and growth of phytoplankton and abundance and grazing rates of microzooplankton and mesozooplankton were estimated in riverine coastal waters off the Suwannee, Withlacoochee, and Weeki Wachee rivers along the west coast of peninsular Florida (Figure 2-1). The three rivers differ in the areal extent of their watersheds, historical annual discharge rates, light attenuation coefficients, and nutrient concentrations (Frazer et al. 1998). Intra-annual variability in discharge also was expected to yield high and low discharge regimes for each river. Therefore, natural differences among the systems were anticipated to provide a range of scenarios in which the model could be tested. Phytoplankton biomasses were estimated by using chlorophyll concentrations as proxy measures. Phytoplankton growth and microzooplankton grazing rates were estimated using the microzooplankton dilution technique (Landry & Hassett 1982), and mesozooplankton grazing impact was estimated using the mesozooplankton addition technique (Calbet & Landry 1999). Microzooplankton and mesozooplankton abundances were determined with standard identification and enumeration techniques (Omoi & Ikeda 1984). Study Sites The Suwannee River originates in the Okeefenokee Swamp, Georgia, and it drains approximately 28,600 km 2 of southern Georgia and north central Florida (Wolfe & Wolfe 1985) before discharging into the Gulf of Mexico (Figure 2-1). Surface water and groundwater contribute to flow in this system (Bledsoe & Phlips 2000). Mean annual discharge is 280 m 3 s -1 with maximum and minimum rates typically occurring in the spring and fall months, respectively (USGS Water Resources 2007). Light availability at depth is normally the lowest of the three systems. Concentrations of lithogenic particles are low, with light attenuation attributed to 16

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colored dissolved organic matter (particularly during high discharge), tripton, and algal particles (Bledsoe & Phlips 2000). Nutrient concentrations are normally highest of the three systems, with 10-year (1996-2006) means ( SD) for total nitrogen (TN) and total phosphorus (TP) equal to 503.2 287.7 g L -1 and 48.7 33.0 g L -1 respectively (T. Frazer, University of Florida, unpublished data). The Withlacoochee River originates in the Green Swamp (Figure 2-1), and its drainage basin covers 5,232 km 2 (Yobbi 1989). As in the Suwannee River, flows are generated by surface water and groundwater, with a mean annual discharge of 23 m 3 s -1 (USGS Water Resources 2007). Discharge rates vary intra-annually. Light availability at depth is typically intermediate among the three systems; however, water clarity improves during periods of low rainfall because spring waters comprise the bulk of the discharge. Among the three systems, Concentrations of TN are the lowest and TP concentrations are intermediate among the three systems, with 10-year means ( SD) of 420.8 188.9 g L -1 and 39.4 26.8 g L -1 respectively (T. Frazer, University of Florida, unpublished data). The Weeki Wachee River originates at a first magnitude spring and meanders 13 km before discharging into the Gulf of Mexico (Figure 2-1). The river has a drainage basin of less than 26 km 2 (Medard et al. 1968). The annual mean discharge of the Weeki Wachee River is 4.5 m 3 s -1 (USGS Water Resources 2007). High and low flows normally occur in the fall and spring, respectively. Light availability at depth is the greatest for the three systems, with high water clarity due to low suspended particle loads as is typical of spring-fed systems (Frazer et al. 2001). Concentrations of TN are intermediate among the three systems with a 10-year mean ( SD) of 469.1 203.8 g L -1 while TP concentrations are the lowest, with a 10-year mean ( SD) of 8.8 4.4 g L -1 (T. Frazer, University of Florida, unpublished data). 17

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Sample Collection Environmental data, water for dilution and addition experiments, and mesozooplankton samples were collected from each of the systems four times in 2006, with effort made to sample twice during the low and high discharge periods for each river. Whole water for microzooplankton enumeration was collected and preserved twice from each system in the fall months. Distinct salinity ranges were used to select the four separate fields of interaction or zones within each river plume, i.e. the river mouth (10-15 psu), near field (19-22 psu), mid-field (28-30 psu), and far field (>30 psu). Samples of water from the three stations within each zone were combined to yield physical means. Environmental data were collected at each station during each sampling period. Water temperature (C), salinity (psu), dissolved oxygen (mg L -1 ), and pH were measured 0.5 m below the surface with a Yellow Springs, Inc. sonde coupled to an electronic datalogger (Models: 600R & 650 MDS). Secchi depths (m) were determined. Photosynthetic ally active radiation was measured with Li-Cor. Instruments, Inc. cosine-corrected submersible light sensors connected to a Li-Cor (LI 1400) datalogger that simultaneously recorded surface and downwelling radiation. At each station, underwater light levels were measured just below the waters surface, approximately at the mid-point of the water column, and 0.3 m above the bottom in water less than 5.0 m deep. The attenuation coefficient (K d ) was calculated using Lambert-Beers Law (Equation 2-1), where I 0 is surface irradiance (mol photons m 2 s -1 ) and I z is light intensity at depth (z): zIIlnKz0d (2-1) 18

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In each of the target salinity zones, approximately 70 L of seawater was collected for experiments and microzooplankton samples between the surface and 0.1 m off the bottom using an integrated sampling tube (Bledsoe & Phlips 2000). The submerged end of the tube was covered with 1.0-mm mesh to filter out large zooplankton. When water depths exceeded the length of the tube, only the top three meters of the water column were sampled. Pulled water was filtered through 190-m Nitex mesh prior to filling a 20-L plastic carboy at each station; this water was used for the dilution and addition experiments. An additional 8 L of water was pulled and filtered to serve as rinse water for the filtration system. Microzooplankton samples were collected by filtering water through 190-m mesh to fill one-third of a 5-L carboy and then preserving the sample with Lugols solution. Five hundred to 2000 ml of water from each station was filtered through Whatman GF/F filters for subsequent analysis of chlorophyll concentration. Filters were stored in a container with desiccant that was placed on ice. Mesozooplankton were collected at two stations within each zone using a 202-m mesh, 0.5-m diameter plankton net with a filtering cod-end. Whenever possible, net tows undulated between the surface and a depth of 3.0 m. At stations where the water depth was less than 3.0 m, tows sampled the middle of the water column. Tows lasted for approximately 5 min, with volumes determined from a General Oceanics mechanical flowmeter set off-center inside the net. Each mesozooplankton sample was carefully poured into a 3.4-L plastic insulated container with a 2000-m mesh screen placed approximately 5.0 cm above the bottom. An OTAB was added to supplement the oxygen supply in the water during transport to the laboratory. Upon returning to the laboratory, water for the experiments and mesozooplankton samples were stored for 16 to 22 hrs in a climate controlled environment prior to the start of the experiments. Water temperatures during storage did not deviate more than 3.0C from the 19

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temperature measured in situ, except in November when water temperatures in samples from the Withlacoochee became 5.0-10.0C warmer. Air stones attached to aquarium air pumps were inserted in each mesozooplankton container to reduce the potential for low dissolved oxygen concentrations. The preserved microzooplankton samples were stored in a climate controlled dark room prior to identification and enumeration. Laboratory Procedures Using water collected during each sampling period, phytoplankton growth rates and microzooplankton grazing rates were estimated for each zone using the dilution technique first developed by Landry & Hassett (1982). Seawater collected from a given zone and discharge period was filtered through 190-m Nitex mesh to exclude mesozooplankton from the experimental medium (Bledsoe 2003). This water was designated as whole seawater, and it included microzooplankton that is zooplankton smaller than 200 m. Fifteen liters of whole seawater were filtered through a step filtration system comprised of 10-m, 5-m, and 1-m sediment filters, as well as a 0.2-m Gelman Microcapsule filter to create the dilution medium that lacked particles larger than 0.2 m. In order to maintain the appropriate salinity in each batch of dilution medium, the filtration system was flushed by filtering approximately 5 L of whole seawater prior to preparing the dilution medium. Duplicate 100, 75, 50, 25, and 10 percent whole water treatments were created by combining whole seawater with dilution medium in 2.8-L glass flasks with an experimental volume of 2.5 L. Mesozooplankton grazing rates were estimated using the addition method (Calbet & Landry 1999). Aliquots of 100, 200, and 400 mL were removed from each mesozooplankton sample using a 10-mL Hensen-Stimpel pipette. The mesozooplankton aliquots were added to 2.8-L glass flasks to create duplicate 100 percent whole seawater treatments with an experimental volume of 2.5 L. Separate, but equal, aliquots of mesozooplankton were removed 20

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and filtered through pre-weighed 20.0-m polycarbonate filters that subsequently were dried at 60C for 48 hrs and weighed to obtain total mesozooplankton dry weights (mg). Only organisms healthy enough to swim upward through a 2000-m mesh screen set inside each insulated container were added to treatment flasks or used to determine dry weights. Immediately following the removal of aliquots for experiments and determination of dry weights, two additional subsamples (50 to 100 mL) were taken and preserved in Lugols solution for subsequent identification and enumeration. Subsamples were taken from above and below the 2000-m mesh in the sample container to determine if the mesozooplankton assemblage added to treatments was representative of the mesozooplankton assemblage collected in situ. Changes in chlorophyll concentrations were used as a proxy measure for changes in phytoplankton density in the dilution and addition experiments. Initial and final chlorophyll concentrations (g L -1 ) were determined from three subsamples of whole water taken at the start of each experiment and two subsamples taken from each experimental flask at the end of each experiment. Subsamples of 500, 1000, 1500, or 2000 mL were filtered onto Whatman GF/F glass-fiber filters that were frozen until processing. Each filter was placed into a test tube with 8.0 mL of 90 percent ethanol and heated in a 78C water bath for 5 min. After 24 to 72 hrs of passive extraction, filters were removed, and the sample was centrifuged to separate particulate debris. Chlorophyll concentrations in the supernatant were determined using a Hitachi U200 dual beam spectrophotometer, and the acidification method was used to correct for phaeophytin (APHA 1998). Experimental flasks were incubated for approximately 24 hrs in a climate controlled laboratory with a 12/12 light/dark cycle in March, April, November, and December and a 14/10 light/dark cycle in May, June, July and September. Light was provided by cool white fluorescent 21

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lights with an average intensity of 50 E m -2 s -1 Every six hours, flasks were gently swirled to resuspend any settled material. Mesozooplankton and microzooplankton in the preserved samples were identified and enumerated. For mesozooplankton, three separate aliquots were taken from each preserved subsample with a 1-mL Hensen-Stempel pipette, placed into a counting wheel, and then processed using a dissecting microscope. Microzooplankton subsamples (minimum three) were identified and enumerated using a Leica inverted-contrast microscope after being added to settling chambers at least 30 min prior to processing. Both mesozooplankton and microzooplankton counts were terminated after 100 individuals of any taxon were counted (Utermohl 1958). River discharge for each sampling event in the Suwannee, Withlacoochee, and Weeki Wachee systems was taken as the daily mean calculated from hourly records at United States Geological Survey gauging stations located at Wilcox (FL), Holder (FL), and Brooksville (FL), respectively (USGS Water Resources 2007). Data used to calculate historical mean monthly discharges were also taken from these gauging stations. Controls Assumptions underpin the microzooplankton dilution and mesozooplankton addition techniques (Landry & Hassett 1982, Calbet & Landry 1999). The techniques are founded on four assumptions: (1) phytoplankton growth is not density dependent, (2) phytoplankton growth is exponential, (3) phytoplankton growth is not nutrient limited, and (4) the probability of a phytoplankton cell being consumed is directly related to encounter rate of consumers. The mesozooplankton addition technique also assumes that mesozooplankton added to treatments are representative of the in situ assemblages. 22

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To prevent nutrient limitation of phytoplankton growth, excess nutrients (KNO 3 KPO 4 NaSiO 4 ) were added to each microzooplankton and mesozooplankton treatment flask (10 mL of 400 g N L -1 40 g P L -1 and 400 g Si L -1 ). To verify that excess nutrients were available for phytoplankton uptake during the dilution and addition experiments, two sets of controls were used. Firstly, two additional 100 percent whole seawater treatment flasks that were not spiked with nutrients provided estimates of phytoplankton growth rates in situations where limitation was possible during the incubation period (Jett 2004). These flasks and nutrient amended flasks containing 100 percent whole seawater without mesozooplankton served as controls for the mesozooplankton addition experiment. Secondly, soluble reactive phosphorus (SRP) was measured in one 60 to 100 mL subsample from each 100 percent whole seawater treatment before and after the incubation periods. These subsamples allowed the availability of phosphorus to be compared between treatments with and without excess nutrients, and they provided estimates of phosphorus availability within each flask at the beginning and end of experiments. The latter information provided a means to evaluate if the nutrient-replete assumption of the dilution and addition techniques was being met throughout the incubation period. All subsamples were refrigerated prior to being analyzed within 72 hrs. Subsamples were filtered through Millipore glass fiber pre-filters, a color reagent was added, and the solution was analyzed on a Hitachi U2000 dual beam spectrophotometer after ten minutes of color development. The representativeness of mesozooplankton added to treatment flasks was determined by comparing the taxa and numbers found in subsamples taken from above and below the 2000-m mesh inserted in holding containers. If taxonomic compositions and abundances differed substantially between the two subsamples, then estimates of in situ grazing rates could be adjusted. 23

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Calculations Phytoplankton Growth and Microzooplankton Grazing Rates Phytoplankton growth and mortality due to microzooplankton grazing was estimated according to the methods of Landry & Hassett (1982). The relationship between the dilution fraction (D) of unfiltered seawater and the net change in phytoplankton concentration over time (i.e. apparent growth rate or AGR) was calculated using least squares regression based the linear equation: gDk0tPPlnt1 (2-2) where P 0 is the concentration of phytoplankton at the start of the experiment, P t is the final concentration of phytoplankton after time t, the y-intercept, k, is the instantaneous maximum specific phytoplankton growth rate, and the negative slope, g, is the instantaneous microzooplankton grazing rate. Values of P 0 measured in whole seawater were corrected for dilution by multiplying by the appropriate dilution factor (i.e. 1.00, 0.75, 0.50, 0.25, and 0.10). If the relationship of apparent growth rate to dilution fraction was found to be non-linear for less dilute treatments, then grazing was assumed to be saturated and a piecewise linear grazing model was fit to the data (Redden et al. 2002). The phytoplankton concentration at which grazing becomes saturated, P s was calculated using Equation 2-3, and the variables k and g were obtained from a least squares linear regression to data from treatments diluted below P s where equation 2-2 applied. texp1texpPPP0tskgDkk (2-3) Microzooplankton impacts on phytoplankton were estimated in two ways (Landry & Hassett 1982). The percent of phytoplankton biomass removed per day due to grazing (S) was 24

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calculated using grazing coefficients, g, and Equation 2-4; and the percent of phytoplankton production lost per day due to grazing (%PP) was calculated using grazing coefficients, g, instantaneous maximum specific phytoplankton growth rates, k, and Equation 2-5. 1001geS (2-4) 1001e1e1PP%kgk (2-5) Mesozooplankton Grazing Rates Mesozooplankton grazing rates were estimated according to the methods of Calbet & Landry (1999). The initial concentration of phytoplankton (P 0 ), the final concentration of phytoplankton (P t ), the duration of the experiment (t), the appropriate instantaneous maximum specific phytoplankton growth rate (k), and the biomass of mesozooplankton added to the treatment were used in a least squares linear regression based on Equation 2-6 to calculate an instantaneous experimental grazing rate (z) that was scaled to in situ biomass of mesozooplankton (Equation 2-7) to yield z 0 an instantaneous grazing rate, if the regression was significant. 0tPPlnt1 k z mg dry wt added L -1 (2-6) z 0 = z mg dry wt m -3 (2-7) Instantaneous in situ grazing rates, z 0 were used to estimate the impact of mesozooplankton on phytoplankton. In situ phytoplankton growth (k 0 ) was estimated with equation 2-8 (Moigis & Gocke 2003), where 0 is the apparent growth rate of phytoplankton from experimental controls and g is the appropriate instantaneous microzooplankton grazing rate, 25

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and this value was combined with the appropriate instantaneous in situ mesozooplankton grazing rate (z 0 ) to estimate the percent of phytoplankton growth consumed daily (Equation 2-9). 0k 0 + g (2-8) Percent phytoplankton growth consumed 10000kz (2-9) Data Analyses Regressions and analyses of variance (ANOVAs) were performed with the JMP software package (v5.1, SAS). ANOVAs were used to test for differences in environmental parameters and coefficients from dilution experiments among systems, zones, and discharge periods. Tukeys HSD ( = 0.05) was employed as a follow-up test. Environmental data and coefficients were tested for normality using Shapiro-Wilk goodness-of-fit tests and homoscedasticity using Cochrans tests. Data were log 10 square root, or fourth-root transformed to improve normality and homoscedasticity. Non-normal and heteroscedastic data were analyzed, and the results were interpreted cautiously. Multivariate analyses of microzooplankton and mesozooplankton abundances were performed using the Plymouth Routines In Multivariate Ecological Research (v6.1.6; PRIMER-E Ltd, Plymouth) software package. Only taxa contributing at least 3% to any given sample were included in analyses and counts were log 10 (x+1) transformed. Similarity indices were calculated using the Bray-Curtis coefficient (Bray & Curtis 1975). Non-metric multi-dimensional scaling (MDS) and analysis of similarity (ANOSIM) were used to discriminate differences in microzooplankton and mesozooplankton assemblages among systems, sampling events, and zones. Ordinations with the lowest dimensionality that yielded stress values below 0.20 were considered acceptable. The test-specific, R-value permutation distribution was used to determine significance for ANOSIM (p < 0.001). The degree of dissimilarity between groups (pair-wise 26

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comparisons) was assessed by examining the R-values for each pair, where large values are indicative of complete separation and low values suggest little or no segregation (Clarke & Warkwick 2001). The similarity percentages (SIMPER) routine was conducted when significant dissimilarities were found to determine which taxa contributed to groupings. 27

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Figure 2-1. Location of study systems along the west coast of Florida. Filled circles denote stations sampled. 28

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CHAPTER 3 RESULTS Field Parameters Mid-range to extremely dry conditions in the southeastern United States (standardized precipitation index values -1.0 to below -2.0) and the second driest November-December experienced in Florida over the 111-year record resulted in lower than expected discharge from both the Suwannee and Withlacoochee Rivers (Guttman & Lawrimore 2007, Table 3-1). Nevertheless, river discharge varied significantly among the systems (Table 3-2), with discharge from the Suwannee River greater than discharge from the Withlacoochee and Weeki Wachee rivers (Tukeys HSD; q = 2.79, p < 0.05). No significant differences in discharge were found between the Withlacoochee and Weeki Wachee rivers. Water temperatures, salinities, and dissolved oxygen concentrations measured in each of three study systems were typical for those systems (T. Frazer, University of Florida, unpublished data). Surface water temperatures ranged from 13.7 to 32.7C, with both the minimum and maximum values recorded at Withlacoochee in December and July, respectively. Fourth-root transformed water temperatures varied significantly among systems (Table 3-2), but the variation was unlikely to be biologically significant. Salinities were normally within the targeted range for a particular zone. However, deviations greater than 2.0 psu below the desired range occurred in the river mouth of the Suwannee during November (Table 3-1). Log 10 transformed dissolved oxygen concentrations (mg L -1 ) varied significantly among systems (Table 3-2), with concentrations in the Withlacoochee higher than those in the Weeki Wachee (Tukeys HSD; q = 2.79; p < 0.05). Nonetheless, water in all systems was normoxic whenever it was measured (Table 3-1). 29

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Light attenuation coefficients (K d ) measured in the three study systems were lower than values normally observed in those systems. (T. Frazer, University of Florida, unpublished data). Log 10 transformed K d values did not differ significantly among systems (Table 3-2). However, light attenuation did vary significantly among zones within systems (Table 3-2), with light attenuation in the Suwannee system near the river mouth and in the near field being greater than light attenuation in the far field (Figure 3-1; Tukeys HSD; q = 3.97; p < 0.05). In the Withlacoochee and Weeki Wachee systems, light attenuation did not vary significantly among zones. Attenuation coefficients also varied significantly among discharge periods within systems and zones (Table 3-2), with light attenuation being higher in the river mouth and near field in the Suwannee system during the high discharge period (Table 3-2; Tukeys; q = 3.73; p < 0.05). Log 10 transformed chlorophyll concentrations (g L -1 ) differed significantly among systems, among zones, and among discharge periods (Table 3-2). Chlorophyll concentrations in the Suwannee system were greater than those in the Weeki Wachee system (Tukeys HSD; q = 2.79; p < 0.05). In each system, chlorophyll concentrations were typically highest near the river mouth, declining in the near and mid-fields, and lowest in the far field (Figure 3-2). This spatial pattern was statistically significant in the Suwannee system (Table 3-2), with concentrations being higher near the river mouth than in the far field (Tukeys HSD; q = 3.97; p < 0.05). Chlorophyll concentrations also differed significantly between discharge periods (Table 3-2). During the high discharge period, chlorophyll concentrations in the river mouth, near field, and mid-field were greater than chlorophyll concentrations in the far field in the Suwannee system (Tukeys HSD; q = 7.15; p < 0.0001). A gradient in chlorophyll concentrations also was found in the Withlacoochee system during high discharge, with concentrations near the river mouth being significantly higher than in the mid-field and far field (Tukeys HSD; q = 4.85; p < 0.001). 30

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During high discharge in the Weeki Wachee system, chlorophyll concentrations near the river mouth were greater than those in the mid-field (Tukeys HSD; q = 3.72; p < 0.05). Similar patterns in chlorophyll concentrations were observed during low discharge periods for the Suwannee and Weeki Wachee systems (Tukeys HSD; q = 3.72; p < 0.05). There was no significant variation in chlorophyll concentrations among the zones for the Withlacoochee system during the low discharge period. Phytoplankton Growth Rates Twenty-eight of the 48 dilution experiments produced valid estimates of instantaneous maximum specific phytoplankton growth rates (Tables 3-3 to 3-5). Phytoplankton growth rates (k) ranged from -0.40 to 2.50 (mean SD 0.99 0.68), 1.00 to 2.37 (mean SD 1.55 0.58), and 0.55 to 1.31 (mean SD 0.89 0.27) in the Suwannee, Withlacoochee, and Weeki Wachee systems, respectively. Square-root transformed growth rates did not vary significantly among systems or among discharge periods (Table 3-6). Significant variability in k across salinity zones was found only in the Suwannee system (Table 3-6), where growth rates near the river mouth were higher than those in the far field (Tukeys HSD; q = 4.01; p < 0.05). In the Withlacoochee and Weeki Wachee systems, k values were fairly uniform across zones. Microzooplankton Grazing Rates and Assemblages Microzooplankton grazing was determined to be a substantial loss factor for phytoplankton communities in each of the three systems (Tables 3-3 to 3-5). Estimates of instantaneous grazing rates (g) ranged from 0.38 to 2.04 (mean SD = 0.92 0.53) in the Suwannee system, 0.39 to 1.67 (mean SD 1.03 0.54) in the Withlacoochee system, and 0.34 to 1.17 (mean SD = 0.72 0.25) in the Weeki Wachee system. These rates correspond to grazers removing an average of approximately 50% of the phytoplankton standing crops on a daily basis (Tables 3-3 to 3-5). Estimates of percent phytoplankton production lost daily were high in each system (Tables 3-3 to 31

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3-5). Microzooplankton grazers accounted for, on average ( SD), 99.45 46.76%, 81.25 31.59%, and 87.10 12.63% of primary production lost per day in the Suwannee, Withlacoochee, and Weeki Wachee systems, respectively. Square-root transformed microzooplankton grazing rates did not vary significantly among systems, salinity zones, or discharge periods (Table 3-6). Retrospective power analyses based on least square effect means of square-root transformed g-values were conducted. For the system effect, a power analysis based on an estimated weighted standard deviation ( wt ) of 0.27, 2 extra parameters in the model (Table 3-2), a type I error rate () of 0.05, and power of 0.80 indicated that a minimum of 199 samples were required to detect a difference of the magnitude observed. Thus, approximately 66 estimates of g per system were needed. If wt = 0.27 and n = 15, the minimum detectable difference among systems was 0.43, a value markedly larger than the observed maximum difference of 0.14 between Withlacoochee (0.98) and Weeki Wachee (0.84). Among zones in the Suwannee system, with n = 13 and 2 wt = 0.16, 15 estimates of g (~ 4 per zone) are required to attain power of 0.80. This value suggests the intended sample size (n = 16) would have been sufficient for ANOVA to detect a significant difference if one existed. However, given the variability ( wt = 0.16) and actual sample size (n = 6), the minimum detectable difference was 0.80, a value greater than the observed difference of 0.44. In the Withlacoochee system, where n = 4 and 2 wt = 0.16, 10 estimates of g were needed to attain power = 0.80. Given n = 3 and wt = 0.16, the minimum detectable difference was 2.36, a value considerably greater than the observed maximum difference of 0.50. In the Weeki Wachee system, the minimum sample size was 189 at n =11 and wt = 0.16. This large sample size (~ 50 estimates of g per zone) is likely a reflection of the similarity observed for means of g among zones. The minimum detectable difference was 2.35 (n 32

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= 3; wt = 0.16), a value greater than the observed maximum difference of 0.11. Overall, the power analyses indicate that significant differences in microzooplankton grazing rates among systems or zones would only be detected if they were considerably larger or if considerably more samples were taken. Total microzooplankton abundance (log 10 individuals L -1 ) varied significantly among systems in the September-December sampling events (Table 3-6), with total abundances in the Suwannee and Withlacoochee systems greater than total abundance in the Weeki Wachee system (Tukeys HSD; q = 3.84, p <0.01). Microzooplankton abundance did not vary significantly among zones within any of the three systems (Table 3-6). The microzooplankton taxa that occurred most frequently within the three systems were copepod nauplii (100% occurrence in samples), tintinnids (100%), rotifers (92%), ciliated protozoans (88%), Prorocentrum spp. (83%), and Protoperdinium spp. (83%). Ordination indicated some dissimilarity among assemblages (Figure. 3-3); however, ANOSIM indicated that assemblages were similar among salinity zones. Mesozooplankton Grazing Rates and Assemblages Six of the 48 addition experiments produced valid estimates of instantaneous rates of in situ mesozooplankton grazing (z 0 ). These estimates (range = 0.0003 to 0.0008) indicate that mesozooplankton grazers had a negligible impact on phytoplankton biomass relative to microzooplankton grazers in the Suwannee, Withlacoochee, and Weeki Wachee systems. Daily phytoplankton growth consumed by mesozooplankton was 0.05% in all cases. Total mesozooplankton abundance (log 10 individuals m -3 ) did not vary significantly among systems or among zones within a system (Table 3-6). There was, however, significant variation in total abundance among discharge periods (Table 3-6). During the low discharge period in the Suwannee system, total mesozooplankton abundance in the near field and mid-field was greater 33

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than abundance near the river mouth (Tukeys HSD; q = 3.78; p < 0.05). No significant differences in abundance were detected among zones in the Withlacoochee and Weeki Wachee systems during either discharge period or among zones in the Suwannee during the high discharge period. When samples from all three systems were considered together, the five most frequently occurring taxa were the calanoid copepods Acartia tonsa (occurrence in 98% of samples), Paracalanus sp. (76%), Parvocalanus sp. (74%), brachyuran crab zoea (72%), and the harpacticoid copepod Euterpina acutifrons (69%). Acartia tonsa was numerically dominant in each system and often in each zone (Tables 3-10 to 3-12). Fifty percent of the zones at Suwannee, 63% at Withlacoochee, and 81% at Weeki Wachee were dominated by A. tonsa. Despite the dominance of A. tonsa across systems, ordination suggested dissimilarity among assemblages in the three systems (Figure 3-4). This finding was supported by ANOSIM results (global R = 0.82). Pair-wise comparisons indicated strong dissimilarity between mesozooplankton assemblages in the Suwannee and Weeki Wachee systems (R = 0.92) and in the Withlacoochee and Weeki Wachee systems (R = 0.95). SIMPER identified the higher abundances of Balanus nauplii and Parvocalanus sp. and lower abundances of gastropod larvae in the Suwannee as the primary causes of dissimilarity between the Suwannee and Weeki Wachee systems. Dissimilarity between the Withlacoochee and Weeki Wachee systems was driven by greater abundances of Parvocalanus sp. and E. acutifrons and lower abundances of gastropod larvae in the Withlacoochee system. Ordination and ANOSIM for each system suggested potential dissimilarities in mesozooplankton assemblages across time and space in the Suwannee system (Figure 3-5; global R = 0.30). Pair-wise comparisons indicated that the assemblages in March (R = 0.50) and May 34

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(R = 0.59) differed significantly from those present in November. This difference was driven by large numbers of E. acutifrons and the cladoceran, Penilia sp., in November. Significant spatial variation also was found (global R = 0.35), with the river mouth (R = 0.61) and near field (R = 0.57) differing significantly from the far field (Figure 3-6). This gradient was due to the presence of the copepods Temora sp. and Corycaeus sp. and absence of Balanus nauplii in the far field. In the Withlacoochee and Weeki Wachee systems, ordinations and ANOSIM indicated dissimilarity in assemblages within each system in space but not time (Withlacoochee 3D stress = 0.11, global R = 0.88; Weeki Wachee 3D stress = 0.14, global R = 0.75). Within both systems, the river mouth assemblage differed significantly from the assemblages in the midand far fields (pair-wise R-values: Withlacoochee RM, MD = 0.33, RM, FF = 0.56; Weeki Wachee RM, MD = 0.49, RM, FF = 0.77). Greater abundances of shrimp zoea, the copepods Labidocera sp. and Parvocalanus sp., gastropod larvae, and isopods in the higher salinity zones drove groups at Weeki Wachee. At Withlacoochee, differences in assemblages were due to the greater abundance of Corycaeus sp., brachyuran crab zoea, Temora sp., Parvocalanus sp., E. acutifrons, and Oithona spp. in the mid-field and far field relative to the two lower salinity zones. Controls Initial concentrations of soluble reactive phosphorus (SRP) in the nutrient amended treatments were significantly greater than concentrations in the controls for each system (t-test, p < 0.05). This result was also observed for the final SRP concentrations in experiments using water taken from the Withlacoochee and Weeki Wachee systems, but not for experiments using water from the Suwannee system (t-test, p > 0.05). The lack of a significant difference suggests the nutrient amended treatments may have become phosphorus limited during the incubation period. However, an analysis of nutrient-enrichment experiments found experiments lasting 1 day exhibited time lags in the numerical response of phytoplankton to nutrient addition 35

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36 (Downing et al. 1999). Therefore, the decline in SRP concentrati ons during the 24-hr incubation likely had a negligible impact on phytoplankton growth rates. Final SRP concentrations (g L -1 ) from the mesozooplankton treatments were significantly greater than concentrations in treatments without mesozoopl ankton (t-test, p < 0.0001). In addition, final SRP concentrations for each system often exhibited a positive linear relationship with mesozooplankton biomass (mg dry wt added L -1 ). Linear regressions were significant for the Suwannee (r 2 = 0.31, p < 0.0001), Withlacoochee (r 2 = 0.34, p < 0.0001), and Weeki Wachee (r 2 = 0.23, p < 0.0001) systems, but they had low coefficients of determination. To determine if the mesozooplankton added to treatments was a true reflection of the in situ assemblage and not biased by taxon-specific mortality rates, the frequency that the dominant taxon in the surface subsample was also th e dominant or secondary dominant in the corresponding bottom subsample wa s calculated. These frequencies are as follows: 93% of the samples from Suwannee, 71% of Withlacoochee samples, and 84% of Weeki Wachee samples. The high degree of agreement between subsampl es from each system indicates that the mesozooplankton added to treatments was normally dominated by same taxa that dominated the in situ assemblage.

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37 Table 3-1. Mean values for the physical, chemical, and biological parameters measured in the Suwannee, Withlacoochee, and Weeki Wachee systems. Means are based on measurements from three stations within each zone. Historical river discharge rates are the calculated monthly means ( SD) for the 1994-2004 period of record at the same gauging stations used to estimate daily mean river discharge rates. Notation "nd" indicates no data. Suwannee Withlacoochee WeekiWachee RM NF MF FF RM NF MF FF RM NF MF FF Sampling Date 3/27 6/27 4/3 Daily Discharge m 3 s -1 244.1 9.7 4.6 Historical Discharge m 3 s -1 392.8 296.3 10.7 9.1 4.1 1.0 Depth m 1.17 2.67 4.63 11.73 3.37 2.70 >5.00 >5.00 1.60 1.77 2.43 3.43 TemperatureC 17.98 18.15 17.16 17.73 29.36 29.97 29.34 29.27 24.06 23.73 22.45 21.77 Salinity psu 13.41 21.67 31.57 33.63 13.35 21.61 28.35 30.87 13.00 18.95 27.21 30.85 DO mg L -1 7.84 9.22 6.08 4.30 7.18 6.69 5.73 5.73 9.08 8.58 7.76 8.00 K d m -1 3.34 1.53 0.80 0.57 0.72 0.67 0.99 0.65 1.18 0.93 0.81 0.65 Chl g L -1 3.69 3.98 1.82 0.35 10.87 6.74 3.69 2.18 1.36 0.54 0.24 0.73 Tow Volume m 3 27.63 29.59 32.71 22.65 29.77 19.33 29.81 35.86 27.65 29.74 29.74 29.74 Sampling Date 5/23 7/4 6/6 Daily Discharge m 3 s -1 125.2 8.1 4.1 Historical Discharge m 3 s -1 196.1 101.7 16.6 20.7 3.8 0.9 Depth m 2.70 2.50 1.83 >5.00 2.20 3.60 >4.00 >5.00 1.63 1.67 2.77 3.73 TemperatureC 25.77 26.01 25.61 25.19 30.88 31.74 30.32 30.04 29.04 28.89 28.39 28.49 Salinity psu 8.05 21.67 29.67 33.63 13.90 20.99 28.56 31.68 12.04 20.92 29.43 32.73 DO mg L -1 9.93 5.16 6.41 6.90 8.22 6.74 6.38 6.39 8.41 7.59 8.61 8.32 K d m -1 2.09 1.81 1.94 0.70 1.81 0.76 0.85 1.28 0.72 0.82 0.76 0.94 Chl g L -1 24.13 11.69 9.38 0.53 10.59 3.84 1.42 0.87 1.10 0.68 0.70 0.47 Tow Volume m 3 36.96 27.04 25.05 33.29 20.74 19.27 36.10 36.90 34.82 34.32 30.04 24.94 Sampling Date 9/25 11/28 9/18 Daily Discharge m 3 s -1 91.5 4.4 4.4 Historical Discharge m 3 s -1 191.9 107.2 27.6 21.6 4.7 1.0 Depth m 1.33 3.03 3.70 >6.00 3.43 3.23 4.30 >7.00 2.03 3.50 3.97 >5.00 TemperatureC 17.66 19.40 18.97 18.02 18.27 17.94 16.35 16.62 29.47 29.26 29.12 28.93 Salinity psu 6.06 22.39 29.64 32.72 10.91 22.18 28.64 31.64 14.09 20.56 29.32 32.64 DO mg L -1 8.06 6.60 5.75 6.31 8.41 7.59 8.61 8.32 nd nd nd nd K d m -1 1.39 1.48 0.62 0.49 nd nd nd nd 0.95 0.45 0.51 0.46 Chl g L -1 3.61 1.56 1.79 0.91 1.75 2.09 2.50 1.79 1.64 0.86 0.48 0.59 Tow Volume m 3 31.25 34.14 21.63 19.30 33.40 30.37 12.53 32.71 43.01 38.92 43.27 38.31

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38 Table 3-1. Continued Suwannee Withlacoochee WeekiWachee RM NF MF FF RM NF MF FF RM NF MF FF Sampling Date 11/13 12/11 11/1 Daily Discharge m 3 s -1 58.9 4.5 4.4 Historical Discharge m 3 s -1 212.5 .5 20.7 12.3 4.8 0.9 Depth m 1.33 3.03 3.70 >6.00 4.53 3.57 4.23 >7.00 2.03 3.50 3.97 >5.00 TemperatureC 19.40 17.66 18.97 18.02 14.74 14.48 13.77 14.57 29.47 29.26 29.12 28.93 Salinity psu 6.06 22.39 29.64 32.72 11. 82 20.32 29.52 32.39 14.09 20.56 29.32 32.64 DO mg L -1 6.49 5.40 4.35 7.58 8.81 8.51 7.38 6.67 6.74 7.19 5.75 7.51 K d m -1 1.23 1.48 0.62 0.49 1.03 1.03 0.69 0.49 1.61 1.09 0.76 0.71 Chl g L -1 3.61 1.56 1.79 0.91 1.75 1.75 1.60 1.40 1.64 0.86 0.48 0.59 Tow Volume m 3 31.25 34.14 21.63 19.30 36.08 33.38 32.40 32.78 43.01 38.92 43.27 38.31

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Table 3-2. Analyses of variance for transformed river discharge (m 3 s -1 ), light attenuation (K d m -1 ), in situ total chlorophyll concentrations (g L -1 ), water temperature (C), and dissolved oxygen concentrations (DO, mg L -1 ). Variable Source df MS F p Discharge Rate m 3 s -1 System 2 20635.5000 9.4748 0.0061 Error 9 2177.9000 Total 11 Model 23 0.0743 10.105 < 0.0001 System 2 0.0957 43.0250 < 0.0001 Zone(System) 9 0.0022 0.0178 1.0000 Discharge Period(System, Zone) 12 0.1248 16.9699 < 0.0001 Error 120 0.0074 Total 143 Log 10 DO mg L -1 Model 23 0.0519 4.1882 < 0.0001 System 2 0.1312 4.3698 0.0472 Zone(System) 9 0.0300 0.5453 0.8158 Discharge Period(System, Zone) 12 0.0551 4.4446 < 0.0001 Error 120 Total 143 Log 10 K d m -1 Model 23 0.1776 6.8752 < 0.0001 System 2 0.4851 1.7165 0.2335 Zone(System) 9 0.2866 5.5776 0.0037 Discharge Period(System, Zone) 12 0.0514 1.9894 0.0320 Error 107 0.0258 Total 130 Log 10 Chl g L -1 Model 23 1.1647 23.8733 < 0.0001 System 2 6.4836 5.6779 0.0254 Zone(System) 9 1.1419 3.8665 0.0163 Discharge Period(System, Zone) 12 0.2953 6.0535 < 0.0001 Error 120 0.0488 Total 143 4C. Temp 39

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40 -2.0-1.00.01.02.03.04.0River MouthNear FieldMid-FieldFar Field Mean K d m -1 95% A -2.0-1.00.01.02.03.04.0River MouthNear FieldMid-FieldFar Field Mean K d m -1 95% B -2.0-1.00.01.02.03.04.0River MouthNear FieldMid-FieldFar Field Mean K d m -1 95% CI C Figure 3-1. Back transformed mean light attenuation (K d m -1 ) 95% confidence intervals (CI) for zones. A) Suwannee system. B) Withlacoochee system. C) Weeki Wachee system.

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41 Figure 3-2. Back transformed mean chlorophyll concentrations (g L -1 ) 95% confidence intervals (CI) for zones. A) Suwannee system. B) Withlacoochee system. C) Weeki Wachee system. A -2.00.02.04.06.08.010.012.0River MouthNear FieldMid-FieldFar Field Mean Chl g L -1 95% CI B -2.00.02.04.06.08.0River MouthNear FieldMid-FieldFar Field 10.012.0 Mean Chl g L -1 95% CI C -2.00.02.04.06.08.010.012.0River MouthNear FieldMid-FieldFar Field Mean Chl g L -1 95%

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Table 3-3. Suwannee system: estimates of apparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous microzooplankton grazing rates (g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ), and percent of phytoplankton production lost daily (%PP d -1 ) during March, May, September, and November. Upper (U) and lower (L) 95% confidence limits (CL) were calculated using the corresponding confidence limits of k and g coefficients. %PSC Removed d -1 %PP Lost d -1 Sampling Date Zone u 0 k SE L95% U95% g SE L95% U95% r 2 p %PSC Removed d -1 L95% U95% %PP Lost d -1 L95% U95% 3/27 RM 0.37 1.12 0.10 0.89 1.35 0.85 0.17 0.47 1.23 0.77 < 0.001 57.17 37.34 70.73 84.82 63.45 95.35 NF 0.43 1.16 0.13 0.85 1.46 0.82 0.21 0.33 1.31 0.65 < 0.01 55.91 27.78 73.08 81.59 48.38 95.25 MD 0.12 0.59 0.04 0.51 0.67 0.38 0.06 0.24 0.51 0.84 < 0.001 31.27 21.62 39.74 69.82 53.84 80.98 FF -1.57 -0.40 0.18 -0.80 0.00 0.92 0.00 0.92 0.93 0.62 < 0.05 60.26 60.01 60.52 -122.23 -48.58 22036.89 5/23 RM 0.70 2.50 0.22 2.00 3.00 2.05 0.35 1.23 2.86 0.81 < 0.001 87.10 70.89 94.28 94.86 81.94 99.20 NF 0.40 1.32 0.18 0.90 1.73 0.81 0.29 0.14 1.49 0.49 < 0.05 55.60 12.75 77.41 75.98 21.46 94.07 MD 0.20 1.49 0.07 1.33 1.65 1.68 0.11 1.42 1.94 0.96 < 0.0001 81.31 75.74 85.59 104.93 102.91 105.94 FF -0.59 0.33 0.13 0.03 0.62 0.92 0.21 0.43 1.40 0.71 < 0.01 59.95 35.15 75.27 215.49 1157.61 162.67 9/25 RM 0.62 1.49 0.17 1.09 1.90 1.63 0.53 0.17 3.10 0.70 < 0.05 80.49 15.49 95.49 103.78 23.30 112.38 NF* -0.15 1.20 0.11 0.94 1.45 0.38 0.18 -0.04 0.80 0.34 ns . MD* 0.24 0.32 0.16 -0.05 0.68 0.06 0.24 -0.50 0.62 0.01 ns . FF 0.24 0.66 0.08 0.48 0.85 0.53 0.13 0.23 0.83 0.67 < 0.01 41.08 20.30 56.44 84.76 53.38 98.76 11/13 RM* -0.42 0.55 0.09 0.33 0.76 0.29 0.15 -0.05 0.64 0.32 ns . NF 0.35 0.76 0.08 0.58 0.94 1.00 0.13 0.15 0.75 0.60 < 0.01 35.98 13.60 52.56 67.53 30.96 86.08 MD 0.34 0.98 0.04 0.89 1.06 0.47 0.06 0.33 0.62 0.87 < 0.0001 37.75 28.02 46.17 60.61 47.70 70.46 FF 0.33 0.93 0.04 0.83 1.03 0.51 0.07 0.34 0.68 0.86 < 0.001 40.07 29.08 49.36 66.23 51.67 76.74 42 k and g calculated using a piecewise linear model. *k and g from non-significant regressions not used in analyses.

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Table 3-4. Withlacoochee system: estimates of apparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous microzooplankton grazing rates (g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ), and percent of phytoplankton production lost daily (%PP d -1 ) during June and July. No estimates of k and g from experiments conducted in November and December were used in analyses because the regressions did not meet the assumptions of the dilution technique. Upper (U) and lower (L) 95% confidence limits (CL) were calculated using the corresponding confidence limits of k and g coefficients. %PSC Removed d -1 %PP Lost d -1 Sampling Date Zone u 0 k SE L95% U95% g SE L95% U95% r 2 p %PSC Removed d -1 L95% U95% %PP Lost d -1 L95% U95% 6/27 RM 0.28 1.42 0.32 0.69 2.15 1.20 0.52 0.01 2.39 0.41 < 0.05 69.64 1.43 90.83 92.24 2.87 102.83 NF 0.05 1.00 0.16 0.63 1.36 0.85 0.26 0.25 1.44 0.57 < 0.05 57.09 22.02 76.38 90.52 47.15 102.66 MD* 0.13 1.45 0.13 1.16 1.74 0.23 0.20 -0.24 0.70 0.14 ns . FF 1.72 2.37 0.10 2.15 2.60 0.39 0.16 0.02 0.75 0.43 < 0.05 31.95 2.04 52.73 35.24 2.31 56.98 7/4 RM* 0.35 1.13 0.14 0.82 1.45 0.37 0.22 -0.14 0.88 0.26 ns . NF 0.05 1.42 0.15 1.09 1.76 1.68 0.24 1.13 2.23 0.86 < 0.001 81.34 67.70 89.22 107.14 102.13 107.75 MD* 1.53 1.76 0.11 1.51 2.01 0.29 0.18 -0.11 0.70 0.26 ns . FF* 1.49 1.36 0.21 0.88 1.83 0.26 0.34 -0.52 1.04 0.06 ns . 43 *k and g from non-significant regressions not used in analyses.

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44 Table 3-5. Weeki Wachee system: estimates of a pparent growth rate (AGR) in controls ( 0 ), instantaneous maximum specific phytoplankton growth rates (k SE), instantaneous micr ozooplankton grazing rates ( g SE), percent of phytoplankton standing crop removed daily (%PSC d -1 ), and percent of phytoplankton production lost daily (%PP d -1 ) during March, May, September, and November. Upper (U) and lower (L) 95% confidence li mits (CL) were calculat ed using the corresponding confidence limits of k and g coefficients. %PSC Removed d -1 %PP Lost d -1 Sampling Date Zone u 0 k SE L95% U95% g SE L95% U95% r 2 p %PSC Removed d -1 L95% U95% %PP Lost d -1 L95% U95% 4/3 RM -0.07 0.56 0.10 0.33 0.80 0.59 0.17 0.20 0.98 0.61 < 0.01 44.51 18.26 62.33 103.26 65.55 113.06 NF -0.01 0.71 0.11 0.46 0.96 0.58 0.18 0.18 0.99 0.58 < 0.05 44.12 16.15 62.76 87.05 43.96 101.96 MD 0.39 0.83 0.01 0.81 0.86 0.48 0.16 0.10 0.85 0.52 < 0.05 37.81 9.65 57.20 66.88 17.37 99.42 FF* 0.61 1.01 0.08 0.82 1.20 0.21 0.14 -0.10 0.53 0.23 ns . 6/6 RM 0.45 1.31 0.21 0.84 1.78 0.87 0.33 0.10 1.64 0.46 < 0.05 58.02 9.32 80.57 79.49 16.44 96.88 NF* 0.05 0.37 0.22 -0.15 0.89 0.31 0.36 -0.53 1.15 0.08 ns . MD 0.49 1.27 0.19 0.84 1.70 1.17 0.30 0.47 1.87 0.65 < 0.01 68.90 37.45 84.54 95.92 66.00 103.53 FF 0.39 1.08 0.09 0.88 1.29 0.79 0.15 0.45 1.13 0.78 < 0.001 54.66 36.37 67.70 82.60 62.30 93.35 9/18 RM 0.64 0.87 0.08 0.69 1.06 0.83 0.24 0.16 1.51 0.74 < 0.05 56.53 14.65 77.86 96.97 29.33 119.34 NF* 0.15 0.62 0.13 0.32 0.92 0.27 0.21 -0.22 0.76 0.17 ns . MD 0.17 0.55 0.09 0.35 0.74 0.35 0.14 0.03 0.67 0.44 < 0.05 29.39 2.93 48.64 69.85 9.93 92.84 FF* 0.00 0.55 0.17 0.15 0.94 0.51 0.28 -0.14 1.16 0.28 ns . 11/1 RM 0.37 0.85 0.14 0.52 1.19 0.59 0.23 0.05 1.13 0.44 < 0.05 44.73 5.20 67.78 77.95 12.81 97.63 NF 0.08 0.63 0.10 0.40 0.85 0.64 0.16 0.28 1.00 0.67 < 0.01 47.06 24.14 63.05 101.26 72.68 110.42 MD 0.09 1.15 0.19 0.71 1.59 1.08 0.31 0.37 1.80 0.60 < 0.01 66.07 30.66 83.40 96.69 60.20 104.82 FF* -0.03 0.51 0.09 0.31 0.72 0.25 0.15 -0.08 0.59 0.27 ns . k and g calculated using a piecewise linear model. *k and g from non-significant regressions not used in analyses.

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Table 3-6. Analyses of variance for transformed phytoplankton growth rates (k), microzooplankton grazing rates (g), microzooplankton total abundance, and mesozooplankton total abundance. k Variable Source df MS F p Model 17 0.0558 1.1756 0.4172 System 2 0.1469 2.3175 0.1507 Zone(System) 8 0.0710 3.4385 0.0333 Discharge Period(System, Zone) 7 0.0181 0.3807 0.8918 Error 9 0.0474 Total 26 Model 17 0.0428 0.6750 0.7713 System 2 0.0189 0.3067 0.7422 Zone(System) 8 0.0666 2.5352 0.0725 Discharge Period(System, Zone) 7 0.0218 0.3436 0.9153 Error 10 0.0635 Total 27 Microzooplankton Total Abundance Model 11 0.1203 0.8622 0.5936 (Log 10 Individuals L -1 ) System 2 0.4969 13.5590 0.0019 Zone(System) 9 0.0400 0.2626 0.9737 Error 12 0.1396 Total 23 Mesozooplankton Total Abundance Model 23 0.5452 2.6010 0.0011 (Log 10 Individuals m -3 ) System 2 0.1105 0.1938 0.8272 Zone(System) 9 0.5701 0.9519 0.5187 Discharge Period(System, Zone) 12 0.5989 2.8574 0.0029 Error 72 0.2096 g 45

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46 21 S u wannee River Mouth Suwannee Near Field Suwannee Mid Field Suwannee Far Field Withlacoochee River Mouth Withlacoochee Near Field Withlacoochee Mid Field Withlacoochee Far Field Weeki Wachee River Mouth Weeki Wachee Near Field Weeki Wachee Mid Field Weeki Wachee Far Field Figure 3-3. Two-dimensional ordination (stress value = 0.11) based on microzooplankton abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems. Distance between points is indicative of similarity where points further apart are less similar than those closer together.

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Table 3-7. Suwannee system: microzooplankton total abundance (individuals L -1 ) and common taxa during September and November. September November River Mouth 1502 Copepod nauplii 49.0% River Mouth 274 Copepod nauplii 50.4% Tintinnids 24.1% Tintinnids 15.1% Rotifers 11.9% Protozoa 7.3% Tintinniopsis sp. 3.8% Prorocentrum spp. 6.9% Rotifer 3.9% Near Field 2939 Prorocentrum spp. 37.8% Copepod nauplii 18.0% Near Field 774 Copepod nauplii 27.3% Protoperidinium sp. 7.2% Tintinnids 10.7% Pryophacus spp. 6.9% Rotifers 7.0% Tintinniopsis sp. 4.9% Larvacean 4.1% Rotifer 3.9% Nematode 3.7% Tintinnids 3.7% Ceratium spp. 3.1% Mid-Field 1476 Copepod nauplii 29.8% Mid-Field 597 Copepod nauplii 31.2% Prorocentrum spp. 12.3% Larvacean 15.6% Protoperidinium sp. 10.9% Pryophacus spp. 9.1% Ceratium sp. 8.3% Ceratium sp. 7.3% Larvacean 3.3% Protozoa 7.3% Parvocalanus sp. 3.2% Prorocentrum sp. 6.2% Nematode 3.4% Far Field 607 Copepod nauplii 43.0% Pryophacus spp. 20.4% Far Field 431 Copepod nauplii 53.3% Ceratium spp. 7.8% Tintinnids 7.7% Protoperidinium sp. 4.8% Parvocalanus sp. 6.4% Prorocentrum spp. 5.3% Protozoa 3.1% 47

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Table 3-8.Withlacoochee system: microzooplankton total abundance (individuals L -1 ) and common taxa during November and December. November December River Mouth 2277 Protozoa 43.9% River Mouth 396 Protozoa 78.6% Tintinnids 35.6% Copepod nauplii 9.5% Copepod nauplii 14.8% Rotifers 4.5% Tintinnids 3.8% Near Field 852 Tintinnids 41.2% Copepod nauplii 41.0% Near Field 369 Protozoa 76.2% Tintinnopsis spp. 5.2% Rotifers 13.3% Protozoa 4.3% Copepod nauplii 4.7% Tintinnids 3.8% Mid-Field 1485 Tintinnids 22.6% Copepod nauplii 15.8% Mid-Field 294 Copepod nauplii 27.7% Prorocentrum sp. 11.6% Tintinnids 18.7% Rotifers 8.8% Ceratium spp. 10.4% Protoperidinium sp. 6.4% Protoperidinium sp. 6.5% Pryophacus spp. 6.2% Rotifers 6.5% Ceratium sp. 4.3% Prorocentrum sp. 5.5% Pryophacus spp. 4.6% Far Field 877 Copepod nauplii 24.0% Protoperidinium sp. 21.4% Far Field 801 Copepod nauplii 34.5% Pryophacus spp. 18.6% Ceratium sp. 11.1% Ceratium sp. 5.8% Protoperidinium sp. 9.5% Ceratium hircus 5.5% Pryophacus spp. 9.3% Protozoa 3.1% 48

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Table 3-9.Weeki Wachee system: total abundance (individuals L -1 ) and common taxa during September and November. September November River Mouth 731 Copepod nauplii 37.7% River Mouth 94 Copepod nauplii 55.4% Prorocentrum sp. 16.9% Tintinnids 21.5% Gastropod larvae 9.5% Nematode 4.1% Protozoa 5.8% Prorocentrum lima 3.1% Bivalve veliger 5.5% Rotifers 3.4% Tintinnids 3.1% Near Field 241 Copepod nauplii 42.5% Near Field 347 Copepod nauplii 30.9% Prorocentrum sp. 19.9% Gymnodinium sp. 9.2% Tintinnids 9.0% Protozoa 8.8% Nematode 6.0% Bivalve veliger 7.4% Prorocentrum sp. 4.7% Mid-Field 313 Ceratium hircus 30.9% Nematode 4.1% Copepod nauplii 20.2% Gastropod larvae 3.9% Tintinnids 11.2% Rotifers 3.7% Prorocentrum lima 6.2% Pryophacus spp. 3.5% Tintinniopsis sp. 3.7% Mid-Field 235 Copepod nauplii 16.2% Far Field 197 Copepod nauplii 47.2% Protozoa 9.1% Ceratium hircus 8.3% Tintinnids 9.0% Prorocentrum lima 3.6% Ceratium hircus 8.5% Gymnodinium sp. 3.6% Bivalve veliger 7.1% Pryophacus spp. 4.1% Far Field 607 Copepod nauplii 22.6% Bivalve veliger 15.6% Ceratium hircus 11.9% Pryophacus spp. 7.2% Protoperidinium sp. 6.5% Tintinnids 4.7% Tintinnopsis sp. 4.3% Polychaete larvae 4.2% 49

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Table 3-10. Suwannee system: mesozooplankton total abundance (individuals m -3 ) and common taxa during March, May, September, and November. March May cont. November River Mouth Far Field River Mouth Stn 1 2925 Acartia tonsa 97.2% Stn 1 535 Acartia tonsa 25.9% Stn 1 1341 Acartia tonsa 41.1% Paracalanus spp. 25.8% Parvocalanus spp. 21.2% Stn 2 18715 Acartia tonsa 99.8% Corycaeus spp. 16.0% Balanus nauplii 18.0% Near Field Centropages spp. 5.1% Euterpina acutifrons 5.4% Stn 1 6409 Acartia tonsa 96.7% Oithona spp. 4.9% Penilia spp. 4.4% Parvocalanus spp. 3.4% Daphnia spp. 4.2% Stn 2 4589 Acartia tonsa 73.0% Bivalve veliger 15.2% Stn 2 625 Acartia tonsa 23.9% Stn 2 408 Balanus nauplii 47.0% Larvacean 5.1% Paracalanus spp. 21.5% Acartia tonsa 20.8% Mid-Field Corycaeus spp. 14.5% Penilia spp. 12.8% Stn 1 21695 Acartia tonsa 69.3% Oithona spp. 4.6% Euterpina acutifrons 6.9% Gastropod larvae 13.8% Bivalve veliger 4.6% Parvocalanus spp. 6.7% Bivalve veliger 4.9% Gastropod larvae 4.3% Near Field Balanus nauplii 4.2% Parvocalanus spp. 3.9% Stn 1 12544 Acartia tonsa 91.6% Euterpina acutifrons 3.9% Stn 2 6248 Acartia tonsa 83.6% Centropages spp. 3.7% Stn 2 10205 Acartia tonsa 72.1% Balanus nauplii 5.7% September Penilia spp. 11.2% Far Field River Mouth Parvocalanus spp. 9.1% Stn 1 1959 Paracalanus spp. 53.7% Stn 1 918 Balanus nauplii 59.5% Euterpina acutifrons 4.4% Corycaeus spp. 12.4% Parvocalanus spp. 36.0% Mid-Field Oithona spp. 7.1% Copepod nauplii 9.1% Stn 1 10595 Acartia tonsa 34.4% Pseudocalanus spp. 5.8% Paracalanus spp. 6.2% Parvocalanus spp. 25.0% Parvocalanus spp. 4.4% Barnacle cyprid 5.9% Euterpina acutifrons 11.0% Acartia tonsa 4.2% Acartia tonsa 5.4% Radiolarian 10.2% Sagitta spp. 3.6% Chthalamus nauplii 3.1% Penilia spp. 6.4% Temora spp. 4.9% Stn 2 3374 Paracalanus spp. 43.1% Stn 2 174 Parvocalanus spp. 13.0% Balanus nauplii 4.1% Corycaeus spp. 17.3% Copepod nauplii 9.1% Oithona spp. 12.8% Balanus nauplii 9.1% Stn 2 17587 Penilia spp. 43.0% Acartia tonsa 12.1% Acartia tonsa 6.8% Parvocalanus spp. 21.8% Centropages spp. 5.1% Near Field Acartia tonsa 18.0% Parvocalanus spp. 3.7% Stn 1 29670 Acartia tonsa 60.5% Euterpina acutifrons 8.9% May Paracalanus spp. 12.6% Balanus nauplii 3.1% River Mouth Balanus nauplii 9.1% Far Field Stn 1 359 Brachyuran crab zoea 97.8% Euterpina acutifrons 5.3% Stn 1 21585 Penilia spp. 58.9% Pseudocalanus spp. 4.3% Parvocalanus spp. 10.7% Stn 2 396 Brachyuran crab zoea 90.1% Parvocalanus spp. 3.4% Euterpina acutifrons 7.8% Balanus nauplii 6.3% Oithona spp. 6.7% Near Field Stn 2 9763 Acartia tonsa 77.3% Corycaeus spp. 6.7% Stn 1 1021 Brachyuran crab zoea 35.0% Parvocalanus spp. 8.9% Temora spp. 4.8% Parvocalanus spp. 16.5% Euterpina acutifrons 5.1% Acartia tonsa 15.0% Balanus nauplii 4.3% Stn 2 1925 Parvocalanus spp. 44.0% Balanus nauplii 12.9% Mid-Field Acartia tonsa 15.6% Gastropod larvae 6.7% Stn 1 6718 Acartia tonsa 77.3% Euterpina acutifrons 12.8% Marine mite 5.7% Euterpina acutifrons 20.7% Oithona spp. 10.0% Parvocalanus spp. 5.7% Temora spp. 6.2% Stn 2 261 Brachyuran crab zoea 76.2% Temora spp. 4.7% Corycaeus spp. 4.1% Marine mite 8.9% Balanus nauplii 3.8% Acartia tonsa 3.9% Parvocalanus spp. 3.3% Stn 2 29227 Acartia tonsa 63.6% Balanus nauplii 3.3% Euterpina acutifrons 27.8% Mid-Field Parvocalanus spp. 4.2% Stn 1 5749 Acartia tonsa 30.9% Brachyuran crab zoea 3.2% Brachyuran crab zoea 24.9% Far Field Parvocalanus spp. 11.2% Stn 1 3804 Temora spp. 76.7% Paracalanus spp. 10.2% Euterpina acutifrons 8.6% Gastropod larvae 9.6% Paracalanus spp. 4.0% Brachyuran crab zoea 3.3% Stn 2 1103 Acartia tonsa 27.2% Brachyuran crab zoea 23.5% Stn 2 19910 Temora spp. 66.0% Balanus nauplii 11.6% Euterpina acutifrons 14.8% Gastropod larvae 9.6% Acartia tonsa 3.9% Parvocalanus spp. 8.9% Paracalanus spp. 3.5% Paracalanus spp. 3.9% Corycaeus spp. 3.3% 50

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Table 3-11. Withlacoochee system: mesozooplankton total abundance (individuals m -3 ) and common taxa during June, July, November, and December. June July cont. November cont. River Mouth Near Field cont. Far Field Stn 1 9454 Acartia tonsa 78.1% Stn 2 6769 Acartia tonsa 58.4% Stn 1 3545 Parvocalanus spp. 30.0% Parvocalanus spp. 3.9% Bivalve veliger 21.7% Temora spp. 26.7% Bivalve veliger 9.9% Parvocalanus spp. 7.0% Acartia tonsa 11.4% Brachyuran crab zoea 5.0% Oithona spp. 10.1% Stn 2 12136 Bivalve veliger 45.4% Euterpina acutifrons 9.5% Acartia tonsa 44.7% Mid-Field Penilia spp. 7.1% Calanoid copepod 4.2% Stn 1 1358 Acartia tonsa 46.0% Near Field Parvocalanus spp. 24.0% Stn 2 10085 Parvocalanus spp. 32.1% Stn 1 7529 Bivalve veliger 69.1% Brachyuran crab zoea 8.7% Euterpina acutifrons 31.1% Acartia tonsa 7.5% Euterpina acutifrons 5.9% Oithona spp. 12.6% Parvocalanus spp. 7.4% Oithona spp. 3.6% Temora spp. 9.3% Brachyuran crab zoea 6.1% Penilia spp. 7.4% Euterpina acutifrons 3.2% Stn 2 2364 Acartia tonsa 28.4% Parvocalanus spp. 23.4% December Stn 2 2159 Bivalve veliger 42.4% Brachyuran crab zoea 22.6% River Mouth Gastropod larvae 21.1% Euterpina acutifrons 5.4% Stn 1 863 Acartia tonsa 91.6% Brachyuran crab zoea 13.8% Shrimp zoea 4.0% Balanus nauplii 4.1% Acartia tonsa 7.8% Labidocera spp. 3.2% Euterpina acutifrons 3.8% Far Field Stn 2 378 Acartia tonsa 83.8% Mid-Field Stn 1 1933 Acartia tonsa 45.7% Paracalanus spp. 4.6% Stn 1 3167 Acartia tonsa 45.6% Brachyuran crab zoea 25.6% Ostracod 3.7% Parvocalanus spp. 18.0% Parvocalanus spp. 9.5% Near Field Euterpina acutifrons 12.3% Paracalanus spp. 5.7% Stn 1 1318 Acartia tonsa 90.2% Brachyuran crab zoea 6.1% Centropages spp. 3.3% Calanoid copepod 4.3% Pseudodiaptomus spp. 5.7% Stn 2 1649 Fish larvae 48.5% Stn 2 2420 Acartia tonsa 96.9% Stn 2 3363 Acartia tonsa 40.3% Brachyuran crab zoea 25.2% Mid-Field Euterpina acutifrons 20.8% Acartia tonsa 11.4% Stn 1 2431 Acartia tonsa 36.4% Parvocalanus spp. 12.2% Paracalanus spp. 4.4% Parvocalanus spp. 34.3% Pseudodiaptomus spp. 5.2% November Euterpina acutifrons 11.4% Paracalanus spp. 4.4% River Mouth Oithona spp. 5.9% Brachyuran crab zoea 3.5% Stn 1 1530 Acartia tonsa 92.3% Barnacle cyprid 3.7% Pseudocalanus spp. 3.2% Calanoid copepod 3.1% Far Field Stn 2 3298 Parvocalanus spp. 62.3% Stn 1 1707 Acartia tonsa 40.9% Stn 2 1118 Acartia tonsa 88.9% Euterpina acutifrons 15.7% Brachyuran crab zoea 27.9% Calanoid copepod 5.0% Acartia tonsa 7.8% Shrimp zoea 6.8% Near Field Oithona spp. 6.6% Centropages spp. 6.2% Stn 1 933 Acartia tonsa 91.4% Far Field Euterpina acutifrons 3.9% Stn 1 1488 Euterpina acutifrons 29.6% Paracalanus spp. 3.2% Stn 2 1150 Acartia tonsa 89.8% Parvocalanus spp. 21.7% Mid-Field Acartia tonsa 20.6% Stn 2 3188 Acartia tonsa 40.9% Stn 1 2836 Acartia tonsa 34.0% Temora spp. 15.2% Brachyuran crab zoea 28.0% Copepod nauplii 12.5% Oithona spp. 8.4% Centropages spp. 9.1% Oithona spp. 9.3% Euterpina acutifrons 4.4% Parvocalanus spp. 8.6% Stn 2 2070 Parvocalanus spp. 30.7% Parvocalanus spp. 3.2% Polychaete larvae 7.8% Euterpina acutifrons 19.6% July Paracalanus spp. 6.8% Acartia tonsa 18.5% River Mouth Balanus nauplii 4.3% Temora spp. 16.6% Stn 1 3041 Acartia tonsa 82.5% Oithona spp. 5.1% Stn 2 3681 Acartia tonsa 44.7% Stn 2 4070 Acartia tonsa 46.7% Parvocalanus spp. 21.9% Bivalve veliger 14.2% Euterpina acutifrons 6.7% Brachyuran crab zoea 12.2% Temora spp. 5.6% Balanus nauplii 4.8% Oithona spp. 5.2% Parvocalanus spp. 4.7% Ostracod 5.1% Gastropod larvae 3.9% Balanus nauplii 4.2% Near Field Paracalanus spp. 3.6% Stn 1 5673 Acartia tonsa 56.1% Calanoid copepod 3.3% Bivalve veliger 18.6% Parvocalanus spp. 6.5% Gastropod larvae 4.9% Oithona spp. 4.2% 51

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Table 3-12. Weeki Wachee system: mesozooplankton total abundance (individuals m -3 ) and common during April, June, September, and November. April June cont. November cont. River Mouth Far Field Mid-Field Stn 1 6453 Acartia tonsa 98.3% Stn 1 917 Gastropod larvae 57.6% Stn 1 1308 Acartia tonsa 41.6% Acartia tonsa 13.0% Gastropod larvae 18.9% Stn 2 5536 Acartia tonsa 93.7% Labidocera spp. 8.8% Parvocalanus spp. 16.6% Calanoid copepod 4.6% Shrimp larvae 5.1% Isopod larvae 5.8% Near Field Brachyuran crab zoea 4.1% Brachyuran crab zoea 5.0% Stn 1 38467 Acartia tonsa 87.0% Pagurus crab zoea 4.0% Isopod larvae 6.8% Isopod larvae 3.2% Stn 2 2072 Acartia tonsa 62.5% Brachyuran crab zoea 5.1% Parvocalanus spp. 11.3% Stn 2 1190 Gastropod larvae 61.5% Oithona spp. 8.3% Stn 2 28648 Acartia tonsa 87.3% Acartia tonsa 19.9% Gastropod larvae 5.3% Isopod larvae 5.8% Shrimp larvae 8.1% Isopod larvae 4.8% Brachyuran crab zoea 4.6% Labidocera spp. 3.9% Mid-Field September Far Field Stn 1 8662 Acartia tonsa 79.7% River Mouth Stn 1 1840 Acartia tonsa 53.3% Isopod larvae 3.9% Stn 1 5499 Gastropod larvae 48.5% Parvocalanus spp. 17.0% Brachyuran crab zoea 3.5% Acartia tonsa 39.6% Gastropod larvae 8.3% Pseudioaptomus spp. 3.4% Brachyuran crab zoea 4.5% Stn 2 9474 Acartia tonsa 73.4% Shrimp larvae 4.1% Labidocera spp. 7.5% Stn 2 1168 Acartia tonsa 68.0% Isopod larvae 3.0% Brachyuran crab zoea 3.7% Gastropod larvae 26.9% Sagitta spp. 3.3% Near Field Stn 2 1872 Acartia tonsa 58.5% Far Field Stn 1 2551 Acartia tonsa 81.9% Parvocalanus spp. 18.4% Stn 1 61385 Acartia tonsa 72.3% Gastropod larvae 4.3% Oithona spp. 5.7% Brachyuran crab zoea 6.3% Shrimp larvae 4.4% Isopod 4.2% Stn 2 2207 Acartia tonsa 56.7% Labidocera spp. 3.7% Gastropod larvae 29.0% Pagurus crab zoea 3.2% Brachyuran crab zoea 6.1% Mid-Field Stn 2 29370 Acartia tonsa 68.5% Stn 1 2198 Acartia tonsa 91.4% Gastropod larvae 8.6% Brachyuran crab zoea 4.1% Shrimp larvae 5.6% Brachyuran crab zoea 3.7% Stn 2 2241 Acartia tonsa 89.1% June Isopod larvae 3.3% River Mouth Far Field Stn 1 878 Acartia tonsa 93.7% Stn 1 2215 Acartia tonsa 56.2% Shrimp larvae 7.6% Stn 2 480 Acartia tonsa 88.1% Parvocalanus spp. 6.2% Euterpina acutifrons 4.5% Paracalanus spp. 4.3% Calanoid copepod 3.1% Gastropod larvae 3.3% Near Field Stn 1 987 Gastropod larvae 26.0% Stn 2 2671 Acartia tonsa 65.3% Acartia tonsa 17.6% Parvocalanus spp. 6.4% Brachyuran crab zoea 6.7% Paracalanus spp. 3.7% Labidocera spp. 5.7% Euterpina acutifrons 3.2% Pagurus crab zoea 3.1% Shrimp larvae 3.2% Barnacle cyprid 3.2% Stn 2 2983 Acartia tonsa 64.1% November Gastropod larvae 59.7% River Mouth Brachyuran crab zoea 23.1% Stn 1 835 Acartia tonsa 84.3% Mid-Field Calanoid copepod 4.5% Stn 1 3219 Acartia tonsa 75.0% Brachyuran crab zoea 8.7% Stn 2 183 Acartia tonsa 84.4% Gastropod larvae 5.1% Euterpina acutifrons 4.7% Harpacticoid copepod 3.5% Stn 2 509 Acartia tonsa 48.8% Near Field Gastropod larvae 26.0% Stn 1 2266 Acartia tonsa 88.0% Pagurus crab zoea 8.5% Labidocera spp. 8.1% Stn 2 2998 Acartia tonsa 93.2% 52

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53 21 Suwannee Withlacoochee Weeki Wachee 31 32 Figure 3-4. Three-dimensional ordination (stress = 0.16) based on mesozooplankton abundances in the Suwannee, Withlacoochee, and Weeki Wachee systems. Distance between points is indicative of similarity where points further apart are less similar than those closer together.

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54 21 March May September November 31 32 Figure 3-5. Three-dimensional ordination (stress = 0.12) based on mesozooplankton abundances in the Suwannee River plume during March, May, September, and November. Distance between points is indicative of similarity where points further apart are less similar than those closer together.

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21 River Mouth Near Field Mid-Field Far Field 31 32 Figure 3-6. Three-dimensional ordination (stress = 0.12) based on mesozooplankton abundances in the Suwannee River plume. Distance between points is indicative of similarity where points further apart are less similar than those closer together. 55

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CHAPTER 4 DISCUSSION Overall, these results indicate the model proposed by Liu & Dagg (2003) and Dagg & Breed (2003) to describe interactions between phytoplankton and zooplankton along nutrient and light gradients in large, river-dominated, coastal systems cannot be generalized to these smaller, river-influenced, coastal waters. Spatial patterns of phytoplankton biomass and growth rates, microzooplankton grazing rates and abundance, and mesozooplankton grazing rates and abundance in each of the three systems were not consistent with the conceptual model. In the Suwannee system, where nutrient and light have been shown to vary inversely with salinity (Frazer et al. 1998), phytoplankton crops and phytoplankton growth rates were normally the highest at the river mouth, declining in the near field and mid-field, and lowest in the far field. Furthermore, microzooplankton grazing rates and abundance and mesozooplankton grazing rates and abundance did not peak in the two higher salinity zones as predicted. In the Withlacoochee and Weeki Wachee systems, where nutrients inversely varied with salinity (Frazer et al. 1998) and light availability was relatively consistent among zones (Figure 3-1), phytoplankton biomass tended to peak in the lower salinity zones and phytoplankton growth and microzooplankton grazing rates were similar among zones. Microzooplankton and mesozooplankton abundances varied among zones, but they were not particularly high in the mid field and far fields. Although spatial variability deviated from the model, estimates of phytoplankton biomass, phytoplankton growth rates, microzooplankton grazing rates and mesozooplankton assemblage composition were comparable to results of previous work in the three study systems and to studies of other coastal waters in the northern Gulf of Mexico. To explain the deviations from the models predictions, we examined the hypothesis that river discharge rates were below the threshold 56

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required to induce the physical processes that affect light availability on scales in time and space at which phytoplankton could respond. Phytoplankton biomass estimates (chl g L -1 ) in the Suwannee, Withlacoochee, and Weeki Wachee systems were within the range of previously reported values for those systems (Quinlan & Phlips 2007, Frazer et al. 2007, Bledsoe 2003), and comparable to estimates from other coastal areas in the northern Gulf of Mexico (Mississippi River plume: Wysocki et al. 2006, Liu & Dagg 2003; Mobile Bay: Lehrter et al. 1999; Pensacola Bay: Murrell et al. 2002). The spatial distribution of phytoplankton biomass observed in this study, where biomass is highest near the river mouth and lowest in the far field, corroborates findings of Quinlan & Phlips (2007), Murrell et al. (2002), and Lehrter et al. (1999) indicating that mean chlorophyll concentrations normally declined along salinity gradients in systems with freshwater flows lower than the Mississippi River. Such a pattern is in stark contrast to that predicted by the model; chlorophyll maxima are expected to occur at the mid-field. Phytoplankton growth and microzooplankton grazing rates in the Suwannee, Withlacoochee, and Weeki Wachee systems were typical of many coastal systems in the Gulf of Mexico and other locations around the globe (Table 4-1). Prior estimates of phytoplankton growth (range = -0.15 to 3.20) and grazing rates (range = 0.00 to 2.04) from the Suwannee River estuary are similar to those reported here (Table 4-1), despite differences in river discharge and the scale of sampling effort among studies. This similarity suggests rates of phytoplankton growth and mortality due to microzooplankton grazing are remarkably constant in space and time in the Suwannee system. Phytoplankton growth and microzooplankton grazing rates for the Withlacoochee and Weeki Wachee systems have not been reported previously. The rates for these systems were within the range of those reported from the Suwannee, indicating that 57

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phytoplankton growth rates and microzooplankton grazing rates are likely to be uniform in waters along the west coast of peninsular Florida. This finding is noteworthy as microzooplankton grazing rates are generally assumed to be a function of prey concentration (Landry & Hassett 1982). In this study, chlorophyll concentrations in the coastal waters adjacent to the Suwannee were markedly higher than those in waters adjacent to the Weeki Wachee, and one might have expected corresponding differences in microzooplankton grazing rates. The similarity in grazing rates between these two systems suggests the need to consider other effects on microzooplankton, such as predation by mesozooplankton and/or the physical environment. The relative consistency in microzooplankton grazing rates among systems as compared to the variability among results of dilution experiments was borne out by power analyses. These analyses indicated that considerably larger sample sizes or differences among grazing rates were needed to yield statistical significance. For example, power analyses indicated minimal detectable differences among square-root transformed g-values of approximately 2.3, which is greater than the square-root transformation of the largest g-value we found, i.e. 2.5 = 1.6 (James & Hall 1998). In general, power analyses indicated that detecting statistical differences in microzooplankton grazing rates may require considerable effort. Here, we report the first estimates of mesozooplankton total abundance and grazing impact along salinity gradients in the Suwannee, Withlacoochee, and Weeki Wachee systems. Individual estimates of total abundance among systems and zones varied widely, sometimes by an order of magnitude (Tables 3-10 to 3-11). A potential response in mesozooplankton production related to changes in phytoplankton biomass was found only in the Suwannee system during the low discharge period, where higher total abundances in the near field and mid-field were coincident with maximum chlorophyll concentrations near the river mouth. This pattern of spatial 58

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variability supports the observation of Dagg (1995) that mesozooplankton exhibit a lag in responding to accumulation of phytoplankton biomass accumulation. In cases where mesozooplankton grazing rates could be calculated, the corresponding percentages of phytoplankton consumed on a daily basis were quite low (0.05%), suggesting that mesozooplankton grazing impact is negligible in comparison to the impact of microzooplankton grazing. The limited success of the mesozooplankton addition experiments is indicative of the difficulties associated with collecting zooplankton and transferring them to a land-based laboratory. The mesozooplankton addition method has been successfully implemented on ships (Liu & Dagg 2003, Calbet & Landry 1999); however, its use may be inappropriate for land-based experiments when the time between collection and incubation exceeds a few hours. Qualitative observations during this study suggest that mesozooplankton mortality rates remained low during the first 6 to 8 hrs after collection and increased greatly thereafter. The few, low, estimates of mesozooplankton grazing rates may be due to moribund zooplankton being used in the addition experiments. This issue may be resolved by simply restricting the amount of time zooplankton are immersed in containers before being added to treatments. Mesozooplankton assemblages have seldom been characterized in the shallow coastal waters along the central west coast of peninsular Florida. In fact, this is the first characterization of mesozooplankton from the coastal waters adjacent to the Suwannee, Withlacoochee, and Weeki Wachee rivers. The predominance of Acartia tonsa and Paracalanus spp. in each of the three study systems is consistent with findings from other coastal waters indicating the dominance of these copepods in mesozooplankton assemblages in the northern Gulf of Mexico (Apalachicola River estuary: Putland 2005; Mississippi River plume: Liu & Dagg 2003, Dagg 59

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1995; Turkey Point, FL: Stalder & Marcus 1997). However, despite the overall dominance by A. tonsa and Paracalanus sp., mesozooplankton assemblages exhibited spatial heterogeneity along salinity gradients in each of the three study systems. Brachyuran crab zoea, barnacle nauplii, and euryhaline copepods like A. tonsa were abundant in nearshore assemblages while polyhaline copepods like Temora sp. and Corycaeus sp. primarily comprised assemblages offshore (Tables 3-10 to 3-12). Relative abundances of A. tonsa and Paracalanus spp. also varied with salinity, where A. tonsa was the numerical dominant nearshore, but became secondary to Paracalanus spp. offshore. Lower abundance of A. tonsa and higher numbers of Paracalanus spp. in the far fields of each system may reflect the ability of Paracalanus spp. to out-compete A. tonsa when phytoplankton concentrations are low (Paffenhffer & Stearns 1988). In environments with low prey availability where it may be out-competed for food, Acartia tonsa likely exhibits facultative omnivory (Gifford & Dagg 1991), switching between feeding on phytoplankton and microzooplankton in response to their relative availability (Johnson & Allen 2005, Halvorsen et al. 2001, Kirboe et al. 1996, Kleppel et al. 1992). Large numbers of adult A. tonsa and its juvenile stages in nearshore coastal waters could restrict microzooplankton production where the quantity of phytoplankton prey is relatively low (Batten et al. 2001). Furthermore, prey switching by A. tonsa assemblages along the west coast of Florida is a distinct possibility given the marked difference in chlorophyll concentrations between the Suwannee and Weeki Wachee systems. Because the relative availability of autotrophic (chl g L -1 ) to microzooplankton prey (individuals L -1 ) is greater in the Suwannee system than it is in the Weeki Wachee system, A. tonsa would be expected to ingest proportionately more phytoplankton in coastal waters adjacent to the Suwannee River, and, conversely, more microzooplankton in waters adjacent to the Weeki Wachee River. Acartia tonsa exhibiting shifts in prey selectivity and feeding behavior (Kirboe 60

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et al. 1996) between the two systems would be especially likely during seasonal blooms of large phytoplankton taxa in the Suwannee system (Quinlan & Phlips 2007) because A. tonsa selectively feeds on particles larger than15m (Rollwagen Bollens & Penry 2003). In river-impacted, coastal systems, freshwater discharge, wind stress, and tide are the primary forces affecting the physical structure of plume waters (Chen et al. 1997, Yin et al. 1997). In large river systems, discharge is a dominating physical force. For example, during a period of high discharge, the Amazons plume extended 162 km offshore (Smith & Demaster 1996). In contrast, low salinity water masses from rivers with low discharge rates persist at small temporal (hours) and spatial scales (2 11 km), and have horizontal and vertical structure that is proportionately more affected by wind stress and tidal forces (Gaston et al. 2006). The degree of mixing that occurs between fresh and marine waters in nearshore areas of coastal system likely depends on the momentum of river discharge. If the momentum of river discharge approximately equals the force of tidal currents, then the water column is vertically mixed by the interaction between riverine water and tidal forces. If the momentum of river discharge is less than the force generated by tidal currents, then the spatial extent of low salinity riverine waters in nearshore areas oscillates with the tide and turbulent mixing occurs. Furthermore, in shallow coastal systems like the Suwannee, Withlacoochee, and Weeki Wachee, wind-driven vertical mixing is also an important force to consider. Models of plume dispersion from the Ebro River (Spain) indicate that a discharge rate of 400 m 3 s -1 is the threshold at which the rivers momentum overcomes the effect of wind on the hydrodynamics at the river mouth to allow the evolution of a plume (Mestres et al. 2003). River discharge rates observed during this study were below this threshold, likely resulting in a highly turbulent environment. This lack of vertical structure would result in light conditions varying on spatial and temporal scales too finite to elicit a physiological 61

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response from phytoplankton. Instead of light-shade acclimation, which occurs in stable light conditions (Tillmann et al. 2000), phytoplankton in well-mixed estuaries adapt to a mean light environment (MacIntyre & Cullen 1996). Phytoplankton growth rates would be similar in along salinity gradients, except when the interaction of wind and tide entrain algal stocks in areas with relatively higher nutrient availability (see Bledsoe 2003). Variability in the spatial distribution of algal biomass in coastal waters would be driven primarily by physical processes like conservative mixing rather than losses from grazing. Microzooplankton would not be able to adapt to changes in prey availability on the time scale they occur and so grazing rates would be relatively constant and reflect mean prey concentrations. Furthermore, a turbulent environment would also affect the feeding ecology of mesozooplankton assemblages dominated by Acartia tonsa because selection of ciliates increases with turbulence (Kirboe et al. 1996). This preference would likely lead to greater regulation of microzooplankton production by A. tonsa in riverine coastal systems where water column vertical structure persists at small spatial and temporal scales. Interactions between phytoplankton, microzooplankton and mesozooplankton in the Suwannee, Withlacoochee, and Weeki Wachee coastal systems may be best described by the Low Nutrient Model proposed by Putland (2005) for well-mixed or partially mixed river-dominated estuaries. This model suggests that in coastal areas impacted by rivers with low nutrient inputs, nutrient concentrations are low in the mid to high salinities and limiting phytoplankton growth. The phytoplankton community in smaller, riverine, coastal systems is dominated by picophytoplankton or nanophytoplankton due to the ability of small taxa to grow faster than large taxa in a low nutrient environment (Kirboe 1993, Chisholm 1992). Phytoplankton growth rates and biomass are expected to peak in the river mouth to mid-field, 62

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where light and nutrient availability are intermediate. Application of this conceptual framework to systems along the north central Gulf coast of peninsular Florida is likely appropriate because: (1) riverine nutrient inputs into systems are much lower (10 to 620 TN g L -1 10 to 70 TP g L -1 : Frazer et al. 1998, Frazer et al. 2007) than those from the Mississippi River (2.8 x 10 8 to 2.8 x 10 9 g TN L -1 9.29 x 10 6 to 1.55 x 10 8 g P0 4 L -1 : Lohrenz et al.1999) and (2) spatial variability in phytoplankton parameters observed in this study supports the predictions of high growth rates and accumulation of phytoplankton biomass in the river mouth and near field. The major ecological implication of this model for Big Bend coastal systems is the expected dominance of the phytoplankton community by the picophytoplankton and nanophytoplankton. Cyanobacteria, chlorophytes, cryptophytes, and small dinoflagellates (e.g. Katodinium) are common and seasonally dominant in the river and nearshore regions of the Suwannee during periods of low river discharge (Quinlan & Phlips 2007, Bledsoe 2003). While the phytoplankton communities in the Withlacoochee and Weeki Wachee systems have yet to be characterized, the relatively lower nutrient concentrations in these coastal waters would likely lead to small taxa dominating the assemblage (Cloern & Dufford 2005). Because many mesozooplankton cannot effectively feed on phytoplankton cells smaller than 20 m, carbon fixed by these primary producers must be first consumed by bacteria, heterotrophic nanoflagellates, and microzooplankton (i.e. the microbial food web) before it is made available to metazoan consumers (Azam et al.1983). Estimates of microzooplankton grazing impact in the three study systems support the hypothesis that the microbial food web plays a critical and major role, as microzooplankton grazers removed at least 51% of phytoplankton standing crops and 87% of primary production on a daily basis (Table 3-3 to 3-5). The few estimates of mesozooplankton grazing rates were very low, suggesting mesozooplankton grazing on 63

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phytoplankton stocks was negligible in comparison to microzooplankton grazing. The importance of the microbial food web in the Suwannee, Withlacoochee, and Weeki Wachee systems has implications for carbon cycling, trophic ecology, and fisheries. Production by bacteria and small phytoplankton is likely to be a major source of energy transferred to higher trophic levels (Sherr & Sherr 1988). The apparent decoupling between grazing rates on primary producers and the abundance of mesozooplankton could be explained by feeding on microzooplankton (Olson et al. 2006, Batten et al. 2001, Kleppel et al. 1988). However, elongation of trophic pathways via the microbial food web and a link to mesozooplankton reduces the efficiency of carbon transport to higher consumers like juvenile fish (Ryther 1969). Therefore, in systems with low phytoplankton concentrations and/or those periodically dominated by small phytoplankton taxa, overall productivity is likely to be lower in comparison to coastal systems with higher riverine nutrient inputs. The management implications of this study are three-fold. Firstly, conceptual models for large, river-dominated, coastal systems like the Mississippi River plume should not be generalized for management purposes to river-impacted coastal waters along the Big Bend Region without empirical validation. Secondly, because microzooplankton are major consumers of phytoplankton standing stocks in the Suwannee, Withlacoochee, and Weeki Wachee systems, managers need to incorporate microbial food web processes into models that predict how carbon cycles in these systems. Thirdly, increases in riverine nutrient loads will lead to higher concentrations of phytoplankton biomass at the river mouths. This has particular implications for systems with extensive seagrass beds because increases in phytoplankton abundance can reduce water clarity, shade seagrasses and result in their loss (Hale et al. 2004). 64

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65 In conclusion, interactions betw een phytoplankton, microzooplankton and mesozooplankton in the Suwannee, Withlacoochee, and Weeki Wachee systems were not consistent with the conceptual model developed for coastal waters where large rivers dominate. Instead of peaking at the intermediate sa linities, phytoplankton bi omass and phytoplankton growth rates were highest near the river mouth in the Suwannee system. In the Withlacoochee and Weeki Wachee systems, the pr esence of turbulent water columns across salinity zones likely led to uniform phytoplankton growth rates across gradients of nutri ents and light. Furthermore, microzooplankton grazing rates and abundance, and mesozooplankton total abundance were similar across the plumes in each of three systems. Although microzooplankton grazing rates did not vary significantly in space, grazing was an important loss factor for phytoplankton in each system. Nutrient over-enrichment of these coasta l systems may disrupt the balance between algal production and its consumption by z ooplankton grazers. This sort of perturbation will likely lead to increases in the concentration of particulate organic matter in the water column, with the potential to change food web dynamics (C loern 2001) and cause the accumulation of phytoplankton biomass in the nearshore areas of the Suwannee, Withlacoochee, and Weeki Wachee systems.

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66 Table 4-1. Published values of instantaneous maximum specific phytoplankton growth rates ( k SE) and instantaneous microzooplankton grazing rates (g SE). Environment k SE Range g SE Range Issues Reference Suwannee River Plume, FL 0.99 0.19 -0.40 2.50 0.92 0.14 0.38 2.05 This Study 1.56 0.26 0.41 2.70 0.74 0.14 0.11 1.41 Bledsoe 2003 1.63 0.11 -0.15 3.20 0.68 0.07 0.00 2.04 Jett 2004 Withlacoochee River Plume, FL 1.55 0.29 0.99 2.37 1.03 0.27 0.39 1.67 This Study Weeki Wachee River Plume, FL 0.89 0.08 0.54 1.30 0.72 0.07 0.34 1.16 This Study Apalachicola River, FL 0.76 0.07 0.08 1.92 0.71 0.08 0.00 1.95 Putland 2005 Mobile Bay, AL Bay Bay Mouth Offshore 0.70 0.14 1.27 0.19 1.62 0.23 -0.09 2.06 0.25 2.87 0.01 1.27 0.57 0.07 0.97 0.18 1.10 0.20 0.05 0.96 -0.03 2.44 -0.09 2.93 Lehrter et al. 1999 Mississippi River Plume 1.13 0.23 0.46 1.76 0.70 0.19 0.28 1.39 a Liu & Dagg 2003 Mississippi River Plume 1.11 0.13 0.53 2.22 0.29 0.05 -0.10 0.67 Strom & Strom 1996 Pensacola Bay, FL Upper Bay Lower Bay 1.02 0.07 1.00 0.12 0.68 1.46 0.33 1.66 0.54 0.05 0.51 0.10 0.26 0.81 0.08 1.25 Murrell et al. 2002 Santa Rosa Sound, FL 1.50 0.16 0.50 2.10 0.80 0.19 0.00 1.50 Juhl & Murrell 2005 Rhode River Estuary, MD <0.1 1.80 0.00 1.50 Gallegos & Jordan 1997 Chesapeake Bay Mid Bay 0.23 0.06 0.03 0.41 0.24 0.04 0.00 1.60 McManus & Ederington-Cantrell 1992 Estuarine Coastal 0.97 0.07 0.67 0.05 0.53 0.04 0.40 0.04 Calbet & Landry 2004 Oceanic 0.59 0.02 0.39 0.01 a = mean ( SE) calculated by averaging across size fractions for a station and then find ing the studys grand mean.

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Lehrter JC, Pennock JR, McManus GB (1999) Microzooplankton grazing and nitrogen excretion across a surface estuarine-coastal interface. Estuaries 22:113-125 Liu H, Dagg M (2003) Interactions between nutrients, phytoplankton growth and microand mesozooplankton grazing in the plume of the Mississippi River. Mar Ecol Prog Ser 258:31-42 Lohrenz SE, Fahnenstiel GL, Redalje DG, Lang GA, Dagg MJ, Whitledge TE, Dortch Q (1999) Nutrients, irradiance, and mixing as factors regulating primary production in coastal waters impacted by the Mississippi River plume. Cont Shelf Res 19:1113-1141 MacIntyre HL, Cullen JJ (1996) Primary production by suspended and benthic microalgae in a turbid estuary: time-scales of variability in San Antonio Bay, Texas. Mar Ecol Prog Ser 145:245-268 McManus GB, Ederington-Cantrell MC (1992) Phytoplankton pigments and growth rates, and microzooplankton grazing in a large temperate estuary. Mar Ecol Prog Ser 87:77-85 Medard E, Cheezem C, Knowles CJ, Wysong RC, Larkin EB, McAteer D, Driver K, Connor JE, Pickard MA, Mazourek DAG, Allspaugh JP, Bronson TE (1968) Report of investigation of the Weeki Wachee River. Southwest Florida Water Management District Report 00089 Mestres M, Sierra JP, Sanchez-Arcilla A, del Rio JG, Wolf T, Rodriguez A, Ouillon S (2003) Modelling of the Ebro River plume: validation with field observations. Sci Mar 67:379-391 Moigis AG, Gocke K (2003) Primary production of phytoplankton estimated by the means of the dilution method in coastal waters. J Plankton Res 25:1291-1300 Murrell MC, Stanley RS, Lores EM, DiDonato GT, Flemer DA (2002) Linkage between microzooplankton grazing and phytoplankton growth in a Gulf of Mexico estuary. Estuaries 25:19-29 Olson MB, Lessard EJ, Wong CHJ, Bernhardt MJ (2006) Copepod feeding selectivity on microplankton including the toxigenic diatoms Pseudo-nitzschia spp., in the coastal Pacific Northwest. Mar Ecol Prog Ser 326:207-220 Omori M, Ikeda T (1984) Methods in Marine Zooplankton Ecology. John Wiley and Sons, New York, NY p 79 Paerl H (1988) Nuisance phytoplankton blooms in coastal, estuarine and inland waters. Limnol Oceanogr 33:823-847 Paffenhffer GA, Stearns DE (1988) Why is Acartia tonsa (Copepoda: Calanoida) restricted to nearshore environments? Mar Ecol Prog Ser 42:33-38 Putland JN (2005) Ecology of phytoplankton, Acartia tonsa, and microzooplankton in Apalachicola Bay, Florida. PhD dissertation, Florida State University, FL 70

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BIOGRAPHICAL SKETCH Kelly Lynn Robinson was born in 1982 in Tacoma, Washington to Harold and Adele Robinson. She and her younger sister, Katie, grew up in Washington and Colorado. Kelly attended Sweet Briar College, Virginia for her undergraduate education, and studied abroad for a semester at James Cook University, Townsville, Queensland (Australia). She graduated from Sweet Briar with a Bachelor of Science in biology (cum laude), and was the recipient of the Judith Elkins Prize for a senior graduating with a degree in the sciences. After graduating, she joined the research apprentice program at Friday Harbor Laboratories, University of Washington, where she was introduced by Dr Jan Newton to the fi elds of biological oceanography and zooplankton ecology. While at Friday Harbor, Kelly was accepted into the Master of Science program at the Department of Fisheries and Aquatic Sciences, University of Florida (UF) under the advisement of Dr. Tom K. Frazer. Duri ng her tenure at UF, Kelly served on the executive committee of the departments graduate student organization, and was awarded Outstanding Graduate Student of Year for 2006. She graduated from UF with her MS degree in summer 2007. In her final year, Kelly was accepted into the PhD program in the Marine Sciences Department, University of South Alabama, and was awarded a Dauphin Island Sea Lab Fellowship. She plans to conduct her doctoral research at the Dauphin Island Sea Laboratory under the advisement of Dr. William M. Graham. 73